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Changes for page Tools description

Last modified by marissadiazpier on 2023/06/29 13:09
From version 1.1
edited by marissadiazpier
on 2023/06/25 23:08
Change comment: There is no comment for this version
To version 2.1
edited by marissadiazpier
on 2023/06/29 12:03
Change comment: There is no comment for this version

Summary

Details

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Content
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5 5  As a researcher, you might find yourself immersed in a continuous flow of information, data, environments, and platforms which offer different tools and aids to fulfill an investigation and publish your results. This new way to doing science helps us advance at a fast pace and reduces efforts on searching data and tests, also allows us to deploy state-of-the-art simulations that can improve the quality of our work, and increase the capacity of neuroscientists for multiscale neural activity modeling of the human brain network.
6 6  
7 7  On this page you will find a quick overview of the different tools and services available in EBRAINS. It will address, in an interactive way, how to use EBRAINS for specific use cases from the participants and focus on exploring all the potential that EBRAINS, as a digital research infrastructure, provides to its users. Researchers will have the opportunity to get creative and combine the different EBRAINS components to respond to existing questions and formulate new avenues based on collaboration, sharing, co-design, and innovation.
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426 +
427 +<p class=MsoTocHeading>HBP Tools list</p>
428 +
429 +<p class=MsoToc2><span lang=en-DE><span class=MsoHyperlink><a
430 +href="#_Toc138932248">AngoraPy<span style='color:windowtext;display:none;
431 +text-decoration:none'>. </span><span
432 +style='color:windowtext;display:none;text-decoration:none'>5</span></a></span></span></p>
433 +
434 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
435 +href="#_Toc138932249">AnonyMI<span style='color:windowtext;display:none;
436 +text-decoration:none'> </span><span
437 +style='color:windowtext;display:none;text-decoration:none'>5</span></a></span></span></p>
438 +
439 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
440 +href="#_Toc138932250">Arbor<span style='color:windowtext;display:none;
441 +text-decoration:none'> </span><span
442 +style='color:windowtext;display:none;text-decoration:none'>6</span></a></span></span></p>
443 +
444 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
445 +href="#_Toc138932251">Arbor GUI<span style='color:windowtext;display:none;
446 +text-decoration:none'> </span><span
447 +style='color:windowtext;display:none;text-decoration:none'>6</span></a></span></span></p>
448 +
449 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
450 +href="#_Toc138932252">Bayesian Virtual Epileptic Patient (BVEP)<span
451 +style='color:windowtext;display:none;text-decoration:none'> </span><span
452 +style='color:windowtext;display:none;text-decoration:none'>6</span></a></span></span></p>
453 +
454 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
455 +href="#_Toc138932253">BIDS Extension Proposal Computational Model
456 +Specifications<span style='color:windowtext;display:none;text-decoration:none'>. </span><span
457 +style='color:windowtext;display:none;text-decoration:none'>6</span></a></span></span></p>
458 +
459 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
460 +href="#_Toc138932254">BioBB<span style='color:windowtext;display:none;
461 +text-decoration:none'>. </span><span
462 +style='color:windowtext;display:none;text-decoration:none'>6</span></a></span></span></p>
463 +
464 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
465 +href="#_Toc138932255">BioExcel-CV19<span style='color:windowtext;display:none;
466 +text-decoration:none'>. </span><span
467 +style='color:windowtext;display:none;text-decoration:none'>7</span></a></span></span></p>
468 +
469 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
470 +href="#_Toc138932256">BioNAR<span style='color:windowtext;display:none;
471 +text-decoration:none'>. </span><span
472 +style='color:windowtext;display:none;text-decoration:none'>7</span></a></span></span></p>
473 +
474 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
475 +href="#_Toc138932257">BlueNaaS-single cell<span style='color:windowtext;
476 +display:none;text-decoration:none'> </span><span
477 +style='color:windowtext;display:none;text-decoration:none'>7</span></a></span></span></p>
478 +
479 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
480 +href="#_Toc138932258">BlueNaaS-subcellular<span style='color:windowtext;
481 +display:none;text-decoration:none'> </span><span
482 +style='color:windowtext;display:none;text-decoration:none'>7</span></a></span></span></p>
483 +
484 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
485 +href="#_Toc138932259">BluePyEfe<span style='color:windowtext;display:none;
486 +text-decoration:none'>. </span><span
487 +style='color:windowtext;display:none;text-decoration:none'>7</span></a></span></span></p>
488 +
489 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
490 +href="#_Toc138932260">BluePyMM<span style='color:windowtext;display:none;
491 +text-decoration:none'>... </span><span
492 +style='color:windowtext;display:none;text-decoration:none'>8</span></a></span></span></p>
493 +
494 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
495 +href="#_Toc138932261">BluePyOpt<span style='color:windowtext;display:none;
496 +text-decoration:none'> </span><span
497 +style='color:windowtext;display:none;text-decoration:none'>8</span></a></span></span></p>
498 +
499 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
500 +href="#_Toc138932262">Brain Cockpit<span style='color:windowtext;display:none;
501 +text-decoration:none'> </span><span
502 +style='color:windowtext;display:none;text-decoration:none'>8</span></a></span></span></p>
503 +
504 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
505 +href="#_Toc138932263">BrainScaleS<span style='color:windowtext;display:none;
506 +text-decoration:none'>. </span><span
507 +style='color:windowtext;display:none;text-decoration:none'>8</span></a></span></span></p>
508 +
509 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
510 +href="#_Toc138932264">Brayns<span style='color:windowtext;display:none;
511 +text-decoration:none'>. </span><span
512 +style='color:windowtext;display:none;text-decoration:none'>8</span></a></span></span></p>
513 +
514 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
515 +href="#_Toc138932265">Brion<span style='color:windowtext;display:none;
516 +text-decoration:none'>. </span><span
517 +style='color:windowtext;display:none;text-decoration:none'>9</span></a></span></span></p>
518 +
519 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
520 +href="#_Toc138932266">BSB<span style='color:windowtext;display:none;text-decoration:
521 +none'>. </span><span
522 +style='color:windowtext;display:none;text-decoration:none'>9</span></a></span></span></p>
523 +
524 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
525 +href="#_Toc138932267">BSP Service Account<span style='color:windowtext;
526 +display:none;text-decoration:none'> </span><span
527 +style='color:windowtext;display:none;text-decoration:none'>9</span></a></span></span></p>
528 +
529 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
530 +href="#_Toc138932268">bsp-usecase-wizard<span style='color:windowtext;
531 +display:none;text-decoration:none'>. </span><span
532 +style='color:windowtext;display:none;text-decoration:none'>9</span></a></span></span></p>
533 +
534 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
535 +href="#_Toc138932269">CGMD Platform<span style='color:windowtext;display:none;
536 +text-decoration:none'>.. </span><span
537 +style='color:windowtext;display:none;text-decoration:none'>9</span></a></span></span></p>
538 +
539 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
540 +href="#_Toc138932270">CNS-ligands<span style='color:windowtext;display:none;
541 +text-decoration:none'>. </span><span
542 +style='color:windowtext;display:none;text-decoration:none'>9</span></a></span></span></p>
543 +
544 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
545 +href="#_Toc138932271">Cobrawap<span style='color:windowtext;display:none;
546 +text-decoration:none'>. </span><span
547 +style='color:windowtext;display:none;text-decoration:none'>10</span></a></span></span></p>
548 +
549 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
550 +href="#_Toc138932272">Collaboratory Bucket service<span style='color:windowtext;
551 +display:none;text-decoration:none'>. </span><span
552 +style='color:windowtext;display:none;text-decoration:none'>10</span></a></span></span></p>
553 +
554 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
555 +href="#_Toc138932273">Collaboratory Drive<span style='color:windowtext;
556 +display:none;text-decoration:none'>. </span><span
557 +style='color:windowtext;display:none;text-decoration:none'>10</span></a></span></span></p>
558 +
559 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
560 +href="#_Toc138932274">Collaboratory IAM<span style='color:windowtext;
561 +display:none;text-decoration:none'>... </span><span
562 +style='color:windowtext;display:none;text-decoration:none'>10</span></a></span></span></p>
563 +
564 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
565 +href="#_Toc138932275">Collaboratory Lab<span style='color:windowtext;
566 +display:none;text-decoration:none'>. </span><span
567 +style='color:windowtext;display:none;text-decoration:none'>11</span></a></span></span></p>
568 +
569 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
570 +href="#_Toc138932276">Collaboratory Office<span style='color:windowtext;
571 +display:none;text-decoration:none'>. </span><span
572 +style='color:windowtext;display:none;text-decoration:none'>11</span></a></span></span></p>
573 +
574 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
575 +href="#_Toc138932277">Collaboratory Wiki<span style='color:windowtext;
576 +display:none;text-decoration:none'> </span><span
577 +style='color:windowtext;display:none;text-decoration:none'>11</span></a></span></span></p>
578 +
579 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
580 +href="#_Toc138932278">CoreNEURON<span style='color:windowtext;display:none;
581 +text-decoration:none'>.. </span><span
582 +style='color:windowtext;display:none;text-decoration:none'>11</span></a></span></span></p>
583 +
584 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
585 +href="#_Toc138932279">CxSystem2<span style='color:windowtext;display:none;
586 +text-decoration:none'>. </span><span
587 +style='color:windowtext;display:none;text-decoration:none'>11</span></a></span></span></p>
588 +
589 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
590 +href="#_Toc138932280">DeepSlice<span style='color:windowtext;display:none;
591 +text-decoration:none'>. </span><span
592 +style='color:windowtext;display:none;text-decoration:none'>11</span></a></span></span></p>
593 +
594 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
595 +href="#_Toc138932281">EBRAINS Ethics &amp; Society Toolkit<span
596 +style='color:windowtext;display:none;text-decoration:none'> </span><span
597 +style='color:windowtext;display:none;text-decoration:none'>12</span></a></span></span></p>
598 +
599 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
600 +href="#_Toc138932282">EBRAINS Image Service<span style='color:windowtext;
601 +display:none;text-decoration:none'>. </span><span
602 +style='color:windowtext;display:none;text-decoration:none'>12</span></a></span></span></p>
603 +
604 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
605 +href="#_Toc138932283">EBRAINS Knowledge Graph<span style='color:windowtext;
606 +display:none;text-decoration:none'>. </span><span
607 +style='color:windowtext;display:none;text-decoration:none'>12</span></a></span></span></p>
608 +
609 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
610 +href="#_Toc138932284">EDI Toolkit<span style='color:windowtext;display:none;
611 +text-decoration:none'> </span><span
612 +style='color:windowtext;display:none;text-decoration:none'>12</span></a></span></span></p>
613 +
614 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
615 +href="#_Toc138932285">eFEL<span style='color:windowtext;display:none;
616 +text-decoration:none'>. </span><span
617 +style='color:windowtext;display:none;text-decoration:none'>12</span></a></span></span></p>
618 +
619 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
620 +href="#_Toc138932286">Electrophysiology Analysis Toolkit<span style='color:
621 +windowtext;display:none;text-decoration:none'> </span><span
622 +style='color:windowtext;display:none;text-decoration:none'>13</span></a></span></span></p>
623 +
624 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
625 +href="#_Toc138932287">FAConstructor<span style='color:windowtext;display:none;
626 +text-decoration:none'> </span><span
627 +style='color:windowtext;display:none;text-decoration:none'>13</span></a></span></span></p>
628 +
629 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
630 +href="#_Toc138932288">fairgraph<span style='color:windowtext;display:none;
631 +text-decoration:none'>. </span><span
632 +style='color:windowtext;display:none;text-decoration:none'>13</span></a></span></span></p>
633 +
634 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
635 +href="#_Toc138932289">Fast sampling with neuromorphic hardware<span
636 +style='color:windowtext;display:none;text-decoration:none'>. </span><span
637 +style='color:windowtext;display:none;text-decoration:none'>13</span></a></span></span></p>
638 +
639 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
640 +href="#_Toc138932290">fastPLI<span style='color:windowtext;display:none;
641 +text-decoration:none'> </span><span
642 +style='color:windowtext;display:none;text-decoration:none'>13</span></a></span></span></p>
643 +
644 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
645 +href="#_Toc138932291">Feed-forward LFP-MEG estimator from mean-field models<span
646 +style='color:windowtext;display:none;text-decoration:none'>. </span><span
647 +style='color:windowtext;display:none;text-decoration:none'>13</span></a></span></span></p>
648 +
649 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
650 +href="#_Toc138932292">FIL<span style='color:windowtext;display:none;text-decoration:
651 +none'>. </span><span
652 +style='color:windowtext;display:none;text-decoration:none'>14</span></a></span></span></p>
653 +
654 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
655 +href="#_Toc138932293">FMRALIGN<span style='color:windowtext;display:none;
656 +text-decoration:none'>.. </span><span
657 +style='color:windowtext;display:none;text-decoration:none'>14</span></a></span></span></p>
658 +
659 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
660 +href="#_Toc138932294">Foa3D<span style='color:windowtext;display:none;
661 +text-decoration:none'>.. </span><span
662 +style='color:windowtext;display:none;text-decoration:none'>14</span></a></span></span></p>
663 +
664 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
665 +href="#_Toc138932295">Frites<span style='color:windowtext;display:none;
666 +text-decoration:none'>. </span><span
667 +style='color:windowtext;display:none;text-decoration:none'>14</span></a></span></span></p>
668 +
669 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
670 +href="#_Toc138932296">gridspeccer<span style='color:windowtext;display:none;
671 +text-decoration:none'> </span><span
672 +style='color:windowtext;display:none;text-decoration:none'>14</span></a></span></span></p>
673 +
674 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
675 +href="#_Toc138932297">Hal-Cgp<span style='color:windowtext;display:none;
676 +text-decoration:none'>. </span><span
677 +style='color:windowtext;display:none;text-decoration:none'>14</span></a></span></span></p>
678 +
679 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
680 +href="#_Toc138932298">Health Data Cloud<span style='color:windowtext;
681 +display:none;text-decoration:none'>. </span><span
682 +style='color:windowtext;display:none;text-decoration:none'>15</span></a></span></span></p>
683 +
684 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
685 +href="#_Toc138932299">Hodgkin-Huxley Neuron Builder<span style='color:windowtext;
686 +display:none;text-decoration:none'> </span><span
687 +style='color:windowtext;display:none;text-decoration:none'>15</span></a></span></span></p>
688 +
689 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
690 +href="#_Toc138932300">HPC Job Proxy<span style='color:windowtext;display:none;
691 +text-decoration:none'>. </span><span
692 +style='color:windowtext;display:none;text-decoration:none'>15</span></a></span></span></p>
693 +
694 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
695 +href="#_Toc138932301">HPC Status Monitor<span style='color:windowtext;
696 +display:none;text-decoration:none'> </span><span
697 +style='color:windowtext;display:none;text-decoration:none'>15</span></a></span></span></p>
698 +
699 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
700 +href="#_Toc138932302">Human Intracerebral EEG Platform<span style='color:windowtext;
701 +display:none;text-decoration:none'>.. </span><span
702 +style='color:windowtext;display:none;text-decoration:none'>15</span></a></span></span></p>
703 +
704 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
705 +href="#_Toc138932303">Hybrid MM/CG Webserver<span style='color:windowtext;
706 +display:none;text-decoration:none'> </span><span
707 +style='color:windowtext;display:none;text-decoration:none'>16</span></a></span></span></p>
708 +
709 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
710 +href="#_Toc138932304">Insite<span style='color:windowtext;display:none;
711 +text-decoration:none'>. </span><span
712 +style='color:windowtext;display:none;text-decoration:none'>16</span></a></span></span></p>
713 +
714 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
715 +href="#_Toc138932305">Interactive Brain Atlas Viewer<span style='color:windowtext;
716 +display:none;text-decoration:none'> </span><span
717 +style='color:windowtext;display:none;text-decoration:none'>16</span></a></span></span></p>
718 +
719 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
720 +href="#_Toc138932306">JuGEx<span style='color:windowtext;display:none;
721 +text-decoration:none'>. </span><span
722 +style='color:windowtext;display:none;text-decoration:none'>16</span></a></span></span></p>
723 +
724 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
725 +href="#_Toc138932307">KnowledgeSpace<span style='color:windowtext;display:none;
726 +text-decoration:none'>. </span><span
727 +style='color:windowtext;display:none;text-decoration:none'>16</span></a></span></span></p>
728 +
729 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
730 +href="#_Toc138932308">L2L<span style='color:windowtext;display:none;text-decoration:
731 +none'>. </span><span
732 +style='color:windowtext;display:none;text-decoration:none'>17</span></a></span></span></p>
733 +
734 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
735 +href="#_Toc138932309">Leveltlab/SpectralSegmentation<span style='color:windowtext;
736 +display:none;text-decoration:none'>. </span><span
737 +style='color:windowtext;display:none;text-decoration:none'>17</span></a></span></span></p>
738 +
739 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
740 +href="#_Toc138932310">LFPy<span style='color:windowtext;display:none;
741 +text-decoration:none'>. </span><span
742 +style='color:windowtext;display:none;text-decoration:none'>17</span></a></span></span></p>
743 +
744 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
745 +href="#_Toc138932311">libsonata<span style='color:windowtext;display:none;
746 +text-decoration:none'>. </span><span
747 +style='color:windowtext;display:none;text-decoration:none'>17</span></a></span></span></p>
748 +
749 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
750 +href="#_Toc138932312">Live Papers<span style='color:windowtext;display:none;
751 +text-decoration:none'>. </span><span
752 +style='color:windowtext;display:none;text-decoration:none'>17</span></a></span></span></p>
753 +
754 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
755 +href="#_Toc138932313">Livre<span style='color:windowtext;display:none;
756 +text-decoration:none'>. </span><span
757 +style='color:windowtext;display:none;text-decoration:none'>18</span></a></span></span></p>
758 +
759 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
760 +href="#_Toc138932314">LocaliZoom<span style='color:windowtext;display:none;
761 +text-decoration:none'>.. </span><span
762 +style='color:windowtext;display:none;text-decoration:none'>18</span></a></span></span></p>
763 +
764 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
765 +href="#_Toc138932315">MD-IFP<span style='color:windowtext;display:none;
766 +text-decoration:none'>. </span><span
767 +style='color:windowtext;display:none;text-decoration:none'>18</span></a></span></span></p>
768 +
769 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
770 +href="#_Toc138932316">MEDUSA<span style='color:windowtext;display:none;
771 +text-decoration:none'>. </span><span
772 +style='color:windowtext;display:none;text-decoration:none'>18</span></a></span></span></p>
773 +
774 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
775 +href="#_Toc138932317">MeshView<span style='color:windowtext;display:none;
776 +text-decoration:none'>.. </span><span
777 +style='color:windowtext;display:none;text-decoration:none'>18</span></a></span></span></p>
778 +
779 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
780 +href="#_Toc138932318">MIP<span style='color:windowtext;display:none;text-decoration:
781 +none'>. </span><span
782 +style='color:windowtext;display:none;text-decoration:none'>19</span></a></span></span></p>
783 +
784 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
785 +href="#_Toc138932319">Model Validation Service<span style='color:windowtext;
786 +display:none;text-decoration:none'>. </span><span
787 +style='color:windowtext;display:none;text-decoration:none'>19</span></a></span></span></p>
788 +
789 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
790 +href="#_Toc138932320">Model Validation Test Suites<span style='color:windowtext;
791 +display:none;text-decoration:none'>. </span><span
792 +style='color:windowtext;display:none;text-decoration:none'>19</span></a></span></span></p>
793 +
794 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
795 +href="#_Toc138932321">MoDEL-CNS<span style='color:windowtext;display:none;
796 +text-decoration:none'>. </span><span
797 +style='color:windowtext;display:none;text-decoration:none'>19</span></a></span></span></p>
798 +
799 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
800 +href="#_Toc138932322">Modular Science<span style='color:windowtext;display:
801 +none;text-decoration:none'>. </span><span
802 +style='color:windowtext;display:none;text-decoration:none'>19</span></a></span></span></p>
803 +
804 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
805 +href="#_Toc138932323">Monsteer<span style='color:windowtext;display:none;
806 +text-decoration:none'> </span><span
807 +style='color:windowtext;display:none;text-decoration:none'>20</span></a></span></span></p>
808 +
809 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
810 +href="#_Toc138932324">MorphIO<span style='color:windowtext;display:none;
811 +text-decoration:none'>.. </span><span
812 +style='color:windowtext;display:none;text-decoration:none'>20</span></a></span></span></p>
813 +
814 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
815 +href="#_Toc138932325">Morphology alignment tool<span style='color:windowtext;
816 +display:none;text-decoration:none'> </span><span
817 +style='color:windowtext;display:none;text-decoration:none'>20</span></a></span></span></p>
818 +
819 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
820 +href="#_Toc138932326">MorphTool<span style='color:windowtext;display:none;
821 +text-decoration:none'> </span><span
822 +style='color:windowtext;display:none;text-decoration:none'>20</span></a></span></span></p>
823 +
824 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
825 +href="#_Toc138932327">Multi-Brain<span style='color:windowtext;display:none;
826 +text-decoration:none'>. </span><span
827 +style='color:windowtext;display:none;text-decoration:none'>20</span></a></span></span></p>
828 +
829 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
830 +href="#_Toc138932328">Multi-Image-OSD<span style='color:windowtext;display:
831 +none;text-decoration:none'>.. </span><span
832 +style='color:windowtext;display:none;text-decoration:none'>21</span></a></span></span></p>
833 +
834 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
835 +href="#_Toc138932329">MUSIC<span style='color:windowtext;display:none;
836 +text-decoration:none'>. </span><span
837 +style='color:windowtext;display:none;text-decoration:none'>21</span></a></span></span></p>
838 +
839 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
840 +href="#_Toc138932330">NEAT<span style='color:windowtext;display:none;
841 +text-decoration:none'>. </span><span
842 +style='color:windowtext;display:none;text-decoration:none'>21</span></a></span></span></p>
843 +
844 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
845 +href="#_Toc138932331">Neo<span style='color:windowtext;display:none;text-decoration:
846 +none'>. </span><span
847 +style='color:windowtext;display:none;text-decoration:none'>21</span></a></span></span></p>
848 +
849 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
850 +href="#_Toc138932332">Neo Viewer<span style='color:windowtext;display:none;
851 +text-decoration:none'> </span><span
852 +style='color:windowtext;display:none;text-decoration:none'>21</span></a></span></span></p>
853 +
854 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
855 +href="#_Toc138932333">NEST Desktop<span style='color:windowtext;display:none;
856 +text-decoration:none'>. </span><span
857 +style='color:windowtext;display:none;text-decoration:none'>22</span></a></span></span></p>
858 +
859 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
860 +href="#_Toc138932334">NEST Simulator<span style='color:windowtext;display:none;
861 +text-decoration:none'> </span><span
862 +style='color:windowtext;display:none;text-decoration:none'>22</span></a></span></span></p>
863 +
864 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
865 +href="#_Toc138932335">NESTML<span style='color:windowtext;display:none;
866 +text-decoration:none'>. </span><span
867 +style='color:windowtext;display:none;text-decoration:none'>22</span></a></span></span></p>
868 +
869 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
870 +href="#_Toc138932336">NetPyNE<span style='color:windowtext;display:none;
871 +text-decoration:none'>. </span><span
872 +style='color:windowtext;display:none;text-decoration:none'>22</span></a></span></span></p>
873 +
874 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
875 +href="#_Toc138932337">NEURO-CONNECT<span style='color:windowtext;display:none;
876 +text-decoration:none'>. </span><span
877 +style='color:windowtext;display:none;text-decoration:none'>22</span></a></span></span></p>
878 +
879 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
880 +href="#_Toc138932338">NeuroFeatureExtract<span style='color:windowtext;
881 +display:none;text-decoration:none'> </span><span
882 +style='color:windowtext;display:none;text-decoration:none'>23</span></a></span></span></p>
883 +
884 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
885 +href="#_Toc138932339">NeurogenPy<span style='color:windowtext;display:none;
886 +text-decoration:none'>. </span><span
887 +style='color:windowtext;display:none;text-decoration:none'>23</span></a></span></span></p>
888 +
889 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
890 +href="#_Toc138932340">NeuroM<span style='color:windowtext;display:none;
891 +text-decoration:none'>... </span><span
892 +style='color:windowtext;display:none;text-decoration:none'>23</span></a></span></span></p>
893 +
894 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
895 +href="#_Toc138932341">Neuromorphic Computing Job Queue<span style='color:windowtext;
896 +display:none;text-decoration:none'>. </span><span
897 +style='color:windowtext;display:none;text-decoration:none'>23</span></a></span></span></p>
898 +
899 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
900 +href="#_Toc138932342">Neuronize v2<span style='color:windowtext;display:none;
901 +text-decoration:none'>. </span><span
902 +style='color:windowtext;display:none;text-decoration:none'>23</span></a></span></span></p>
903 +
904 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
905 +href="#_Toc138932343">NeuroR<span style='color:windowtext;display:none;
906 +text-decoration:none'>. </span><span
907 +style='color:windowtext;display:none;text-decoration:none'>24</span></a></span></span></p>
908 +
909 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
910 +href="#_Toc138932344">Neurorobotics Platform<span style='color:windowtext;
911 +display:none;text-decoration:none'>.. </span><span
912 +style='color:windowtext;display:none;text-decoration:none'>24</span></a></span></span></p>
913 +
914 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
915 +href="#_Toc138932345">Neurorobotics Platform Robot Designer<span
916 +style='color:windowtext;display:none;text-decoration:none'> </span><span
917 +style='color:windowtext;display:none;text-decoration:none'>24</span></a></span></span></p>
918 +
919 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
920 +href="#_Toc138932346">NeuroScheme<span style='color:windowtext;display:none;
921 +text-decoration:none'>. </span><span
922 +style='color:windowtext;display:none;text-decoration:none'>24</span></a></span></span></p>
923 +
924 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
925 +href="#_Toc138932347">NeuroSuites<span style='color:windowtext;display:none;
926 +text-decoration:none'>. </span><span
927 +style='color:windowtext;display:none;text-decoration:none'>24</span></a></span></span></p>
928 +
929 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
930 +href="#_Toc138932348">NeuroTessMesh<span style='color:windowtext;display:none;
931 +text-decoration:none'>. </span><span
932 +style='color:windowtext;display:none;text-decoration:none'>25</span></a></span></span></p>
933 +
934 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
935 +href="#_Toc138932349">NMODL Framework<span style='color:windowtext;display:
936 +none;text-decoration:none'>. </span><span
937 +style='color:windowtext;display:none;text-decoration:none'>25</span></a></span></span></p>
938 +
939 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
940 +href="#_Toc138932350">NSuite<span style='color:windowtext;display:none;
941 +text-decoration:none'>. </span><span
942 +style='color:windowtext;display:none;text-decoration:none'>25</span></a></span></span></p>
943 +
944 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
945 +href="#_Toc138932351">ODE-toolbox<span style='color:windowtext;display:none;
946 +text-decoration:none'>. </span><span
947 +style='color:windowtext;display:none;text-decoration:none'>26</span></a></span></span></p>
948 +
949 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
950 +href="#_Toc138932352">openMINDS<span style='color:windowtext;display:none;
951 +text-decoration:none'>. </span><span
952 +style='color:windowtext;display:none;text-decoration:none'>26</span></a></span></span></p>
953 +
954 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
955 +href="#_Toc138932353">openMINDS metadata for TVB-ready data<span
956 +style='color:windowtext;display:none;text-decoration:none'>. </span><span
957 +style='color:windowtext;display:none;text-decoration:none'>26</span></a></span></span></p>
958 +
959 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
960 +href="#_Toc138932354">PCI<span style='color:windowtext;display:none;text-decoration:
961 +none'> </span><span
962 +style='color:windowtext;display:none;text-decoration:none'>26</span></a></span></span></p>
963 +
964 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
965 +href="#_Toc138932355">PIPSA<span style='color:windowtext;display:none;
966 +text-decoration:none'>. </span><span
967 +style='color:windowtext;display:none;text-decoration:none'>26</span></a></span></span></p>
968 +
969 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
970 +href="#_Toc138932356">PoSCE<span style='color:windowtext;display:none;
971 +text-decoration:none'>. </span><span
972 +style='color:windowtext;display:none;text-decoration:none'>26</span></a></span></span></p>
973 +
974 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
975 +href="#_Toc138932357">Provenance API<span style='color:windowtext;display:none;
976 +text-decoration:none'> </span><span
977 +style='color:windowtext;display:none;text-decoration:none'>26</span></a></span></span></p>
978 +
979 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
980 +href="#_Toc138932358">PyNN<span style='color:windowtext;display:none;
981 +text-decoration:none'>.. </span><span
982 +style='color:windowtext;display:none;text-decoration:none'>27</span></a></span></span></p>
983 +
984 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
985 +href="#_Toc138932359">Pyramidal Explorer<span style='color:windowtext;
986 +display:none;text-decoration:none'> </span><span
987 +style='color:windowtext;display:none;text-decoration:none'>27</span></a></span></span></p>
988 +
989 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
990 +href="#_Toc138932360">QCAlign software<span style='color:windowtext;display:
991 +none;text-decoration:none'>. </span><span
992 +style='color:windowtext;display:none;text-decoration:none'>27</span></a></span></span></p>
993 +
994 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
995 +href="#_Toc138932361">QuickNII<span style='color:windowtext;display:none;
996 +text-decoration:none'> </span><span
997 +style='color:windowtext;display:none;text-decoration:none'>27</span></a></span></span></p>
998 +
999 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1000 +href="#_Toc138932362">Quota Manager<span style='color:windowtext;display:none;
1001 +text-decoration:none'> </span><span
1002 +style='color:windowtext;display:none;text-decoration:none'>27</span></a></span></span></p>
1003 +
1004 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1005 +href="#_Toc138932363">RateML<span style='color:windowtext;display:none;
1006 +text-decoration:none'>. </span><span
1007 +style='color:windowtext;display:none;text-decoration:none'>28</span></a></span></span></p>
1008 +
1009 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1010 +href="#_Toc138932364">Region-wise CBPP using the Julich BrainÊCytoarchitectonic
1011 +Atlas<span style='color:windowtext;display:none;text-decoration:none'>. </span><span
1012 +style='color:windowtext;display:none;text-decoration:none'>28</span></a></span></span></p>
1013 +
1014 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1015 +href="#_Toc138932365">RRI Capacity Development Resources<span style='color:
1016 +windowtext;display:none;text-decoration:none'>. </span><span
1017 +style='color:windowtext;display:none;text-decoration:none'>28</span></a></span></span></p>
1018 +
1019 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1020 +href="#_Toc138932366">rsHRF<span style='color:windowtext;display:none;
1021 +text-decoration:none'>. </span><span
1022 +style='color:windowtext;display:none;text-decoration:none'>28</span></a></span></span></p>
1023 +
1024 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1025 +href="#_Toc138932367">RTNeuron<span style='color:windowtext;display:none;
1026 +text-decoration:none'>. </span><span
1027 +style='color:windowtext;display:none;text-decoration:none'>29</span></a></span></span></p>
1028 +
1029 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1030 +href="#_Toc138932368">sbs: Spike-based Sampling<span style='color:windowtext;
1031 +display:none;text-decoration:none'>. </span><span
1032 +style='color:windowtext;display:none;text-decoration:none'>29</span></a></span></span></p>
1033 +
1034 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1035 +href="#_Toc138932369">SDA 7<span style='color:windowtext;display:none;
1036 +text-decoration:none'>. </span><span
1037 +style='color:windowtext;display:none;text-decoration:none'>29</span></a></span></span></p>
1038 +
1039 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1040 +href="#_Toc138932370">Shape &amp; Appearance Modelling<span style='color:windowtext;
1041 +display:none;text-decoration:none'>. </span><span
1042 +style='color:windowtext;display:none;text-decoration:none'>29</span></a></span></span></p>
1043 +
1044 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1045 +href="#_Toc138932371">siibra-api<span style='color:windowtext;display:none;
1046 +text-decoration:none'> </span><span
1047 +style='color:windowtext;display:none;text-decoration:none'>29</span></a></span></span></p>
1048 +
1049 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1050 +href="#_Toc138932372">siibra-explorer<span style='color:windowtext;display:
1051 +none;text-decoration:none'> </span><span
1052 +style='color:windowtext;display:none;text-decoration:none'>29</span></a></span></span></p>
1053 +
1054 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1055 +href="#_Toc138932373">siibra-python<span style='color:windowtext;display:none;
1056 +text-decoration:none'>. </span><span
1057 +style='color:windowtext;display:none;text-decoration:none'>30</span></a></span></span></p>
1058 +
1059 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1060 +href="#_Toc138932374">Single Cell Model (Re)builder Notebook<span
1061 +style='color:windowtext;display:none;text-decoration:none'>. </span><span
1062 +style='color:windowtext;display:none;text-decoration:none'>30</span></a></span></span></p>
1063 +
1064 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1065 +href="#_Toc138932375">Slurm Plugin for Co-allocation of Compute and Data
1066 +Resources<span style='color:windowtext;display:none;text-decoration:none'>. </span><span
1067 +style='color:windowtext;display:none;text-decoration:none'>30</span></a></span></span></p>
1068 +
1069 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1070 +href="#_Toc138932376">Snudda<span style='color:windowtext;display:none;
1071 +text-decoration:none'>. </span><span
1072 +style='color:windowtext;display:none;text-decoration:none'>30</span></a></span></span></p>
1073 +
1074 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1075 +href="#_Toc138932377">SomaSegmenter<span style='color:windowtext;display:none;
1076 +text-decoration:none'> </span><span
1077 +style='color:windowtext;display:none;text-decoration:none'>30</span></a></span></span></p>
1078 +
1079 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1080 +href="#_Toc138932378">SpiNNaker<span style='color:windowtext;display:none;
1081 +text-decoration:none'> </span><span
1082 +style='color:windowtext;display:none;text-decoration:none'>31</span></a></span></span></p>
1083 +
1084 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1085 +href="#_Toc138932379">SSB toolkit<span style='color:windowtext;display:none;
1086 +text-decoration:none'> </span><span
1087 +style='color:windowtext;display:none;text-decoration:none'>31</span></a></span></span></p>
1088 +
1089 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1090 +href="#_Toc138932380">Subcellular model building and calibration tool set<span
1091 +style='color:windowtext;display:none;text-decoration:none'> </span><span
1092 +style='color:windowtext;display:none;text-decoration:none'>31</span></a></span></span></p>
1093 +
1094 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1095 +href="#_Toc138932381">Synaptic Events Fitting<span style='color:windowtext;
1096 +display:none;text-decoration:none'>. </span><span
1097 +style='color:windowtext;display:none;text-decoration:none'>31</span></a></span></span></p>
1098 +
1099 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1100 +href="#_Toc138932382">Synaptic Plasticity Explorer<span style='color:windowtext;
1101 +display:none;text-decoration:none'> </span><span
1102 +style='color:windowtext;display:none;text-decoration:none'>32</span></a></span></span></p>
1103 +
1104 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1105 +href="#_Toc138932383">Synaptic proteome database (SQLite)<span
1106 +style='color:windowtext;display:none;text-decoration:none'> </span><span
1107 +style='color:windowtext;display:none;text-decoration:none'>32</span></a></span></span></p>
1108 +
1109 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1110 +href="#_Toc138932384">Synaptome.db<span style='color:windowtext;display:none;
1111 +text-decoration:none'>. </span><span
1112 +style='color:windowtext;display:none;text-decoration:none'>32</span></a></span></span></p>
1113 +
1114 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1115 +href="#_Toc138932385">Tide<span style='color:windowtext;display:none;
1116 +text-decoration:none'>. </span><span
1117 +style='color:windowtext;display:none;text-decoration:none'>32</span></a></span></span></p>
1118 +
1119 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1120 +href="#_Toc138932386">TVB EBRAINS<span style='color:windowtext;display:none;
1121 +text-decoration:none'>. </span><span
1122 +style='color:windowtext;display:none;text-decoration:none'>32</span></a></span></span></p>
1123 +
1124 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1125 +href="#_Toc138932387">TVB Image Processing Pipeline<span style='color:windowtext;
1126 +display:none;text-decoration:none'>. </span><span
1127 +style='color:windowtext;display:none;text-decoration:none'>33</span></a></span></span></p>
1128 +
1129 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1130 +href="#_Toc138932388">TVB Inversion<span style='color:windowtext;display:none;
1131 +text-decoration:none'>. </span><span
1132 +style='color:windowtext;display:none;text-decoration:none'>33</span></a></span></span></p>
1133 +
1134 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1135 +href="#_Toc138932389">TVB Web App<span style='color:windowtext;display:none;
1136 +text-decoration:none'>. </span><span
1137 +style='color:windowtext;display:none;text-decoration:none'>33</span></a></span></span></p>
1138 +
1139 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1140 +href="#_Toc138932390">TVB Widgets<span style='color:windowtext;display:none;
1141 +text-decoration:none'>. </span><span
1142 +style='color:windowtext;display:none;text-decoration:none'>33</span></a></span></span></p>
1143 +
1144 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1145 +href="#_Toc138932391">TVB-Multiscale<span style='color:windowtext;display:none;
1146 +text-decoration:none'>. </span><span
1147 +style='color:windowtext;display:none;text-decoration:none'>33</span></a></span></span></p>
1148 +
1149 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1150 +href="#_Toc138932392">VIOLA<span style='color:windowtext;display:none;
1151 +text-decoration:none'>. </span><span
1152 +style='color:windowtext;display:none;text-decoration:none'>34</span></a></span></span></p>
1153 +
1154 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1155 +href="#_Toc138932393">Vishnu 1.0<span style='color:windowtext;display:none;
1156 +text-decoration:none'>. </span><span
1157 +style='color:windowtext;display:none;text-decoration:none'>34</span></a></span></span></p>
1158 +
1159 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1160 +href="#_Toc138932394">ViSimpl<span style='color:windowtext;display:none;
1161 +text-decoration:none'> </span><span
1162 +style='color:windowtext;display:none;text-decoration:none'>34</span></a></span></span></p>
1163 +
1164 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1165 +href="#_Toc138932395">VisuAlign<span style='color:windowtext;display:none;
1166 +text-decoration:none'>. </span><span
1167 +style='color:windowtext;display:none;text-decoration:none'>34</span></a></span></span></p>
1168 +
1169 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1170 +href="#_Toc138932396">VMetaFlow<span style='color:windowtext;display:none;
1171 +text-decoration:none'>.. </span><span
1172 +style='color:windowtext;display:none;text-decoration:none'>34</span></a></span></span></p>
1173 +
1174 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1175 +href="#_Toc138932397">Voluba<span style='color:windowtext;display:none;
1176 +text-decoration:none'>. </span><span
1177 +style='color:windowtext;display:none;text-decoration:none'>35</span></a></span></span></p>
1178 +
1179 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1180 +href="#_Toc138932398">WebAlign<span style='color:windowtext;display:none;
1181 +text-decoration:none'>. </span><span
1182 +style='color:windowtext;display:none;text-decoration:none'>35</span></a></span></span></p>
1183 +
1184 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1185 +href="#_Toc138932399">Webilastik<span style='color:windowtext;display:none;
1186 +text-decoration:none'>. </span><span
1187 +style='color:windowtext;display:none;text-decoration:none'>35</span></a></span></span></p>
1188 +
1189 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1190 +href="#_Toc138932400">WebWarp<span style='color:windowtext;display:none;
1191 +text-decoration:none'>. </span><span
1192 +style='color:windowtext;display:none;text-decoration:none'>35</span></a></span></span></p>
1193 +
1194 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1195 +href="#_Toc138932401">ZetaStitcher<span style='color:windowtext;display:none;
1196 +text-decoration:none'> </span><span
1197 +style='color:windowtext;display:none;text-decoration:none'>35</span></a></span></span></p>
1198 +
1199 +<p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1200 +href="#_Toc138932402">TauRAMD<span style='color:windowtext;display:none;
1201 +text-decoration:none'>.. </span><span
1202 +style='color:windowtext;display:none;text-decoration:none'>36</span></a></span></span></p>
1203 +
1204 +<p class=MsoNormal><span lang=en-DE>&nbsp;</span></p>
1205 +
1206 +<h2><a name="_Toc138932248"><span lang=en-DE>AngoraPy</span></a></h2>
1207 +
1208 +<p class=MsoNormal><span lang=en-DE>AngoraPy is an open-source Python library
1209 +that helps neuroscientists build and train goal-driven models of the sensorimotor
1210 +system. The toolbox comprises state-of-the-art machine learning techniques
1211 +under the hood of an easy-to-use API. With the help of deep reinforcement
1212 +learning, the connectivity required for solving complex, ecologically valid
1213 +tasks, can be learned autonomously, obviating the need for hand-engineered or
1214 +hypothesis-driven connectivity patterns. With AngoraPy, neuroscientists can
1215 +train custom deep neural networks on custom sensorimotor tasks.</span></p>
1216 +
1217 +<h2></h2>
1218 +
1219 +<h2><a name="_Toc138932249"><span lang=en-DE>AnonyMI</span></a></h2>
1220 +
1221 +<p class=MsoNormal><span lang=en-DE>AnonyMI is an MRI de-identification tool
1222 +that uses 3D surface modelling in order to de-identify MRIs while retaining as
1223 +much geometrical information as possible. It can be run automatically or
1224 +manually, which allows precise tailoring for specific needs. AnonyMI is
1225 +distributed as a plug-in of 3D Slicer, a widely used, open-source, stable and
1226 +reliable image-processing software. It leverages the power of this platform for
1227 +reading and saving images, which makes it applicable on almost any MRI file
1228 +type, including all the most commonly used formats (e.g., DICOM, Nifti, Analyze
1229 +etc.).</span></p>
1230 +
1231 +<h2></h2>
1232 +
1233 +<h2><a name="_Toc138932250"><span lang=en-DE>Arbor</span></a></h2>
1234 +
1235 +<p class=MsoNormal><span lang=en-DE>Arbor is a simulation software library for
1236 +neuron models with complex morphologies Ñ from single cells to large
1237 +distributed networks. Developed entirely inside HBP, it enables running
1238 +large-scale simulations on any HPC, including those available through EBRAINS.
1239 +Arbor provides performance portability for native execution on all HPC
1240 +architectures. Optimized vectorized code is generated for Intel, AMD and ARM
1241 +CPUs, NVIDIA and AMD GPUs, and support will be added for new architectures as
1242 +they become available. Model portability is easier due to an interface for
1243 +model description independent of how Arbor represents models internally. 
1244 +Interoperability with other simulation engines is enabled via API for spike
1245 +exchange and the output of voltages, currents and model state.</span></p>
1246 +
1247 +<h2></h2>
1248 +
1249 +<h2><a name="_Toc138932251"><span lang=en-DE>Arbor GUI</span></a></h2>
1250 +
1251 +<p class=MsoNormal><span lang=en-DE>Arbor GUI strives to be self-contained,
1252 +fast and easy to use. Design morphologically detailed cells for simulation in
1253 +Arbor. Load morphologies from SWC .swc, NeuroML .nml, NeuroLucida .asc. Define
1254 +and highlight Arbor regions and locsets. Paint ion dynamics and bio-physical
1255 +properties onto morphologies. Place spike detectors and probes. Export cable
1256 +cells to Arbor's internal format (ACC) for direct simulation. Import cable
1257 +cells in ACC format. This project is under active development and welcomes
1258 +early feedback.</span></p>
1259 +
1260 +<h2></h2>
1261 +
1262 +<h2><a name="_Toc138932252"><span lang=en-DE>Bayesian Virtual Epileptic Patient
1263 +(BVEP)</span></a></h2>
1264 +
1265 +<p class=MsoNormal><span lang=en-DE>BVEP relies on the fusion of structural
1266 +data of individuals, a generative model of epileptiform discharges, and the
1267 +state-of-the-art probabilistic machine learning algorithms. It uses self-tuning
1268 +Monte Carlo sampling algorithm, and the deep neural density estimators for
1269 +reliable and efficient model-based inference at source and sensor levels data.
1270 +The Bayesian framework provides an appropriate patient-specific strategy for
1271 +estimating the extent of epileptogenic and propagation zones of the brain
1272 +regions to improve outcome after epilepsy surgery.</span></p>
1273 +
1274 +<h2></h2>
1275 +
1276 +<h2><a name="_Toc138932253"><span lang=en-DE>BIDS Extension Proposal
1277 +Computational Model Specifications</span></a></h2>
1278 +
1279 +<p class=MsoNormal><span lang=en-DE>A data structure schema for neural network
1280 +computational models that aims to be generically applicable to all kinds of
1281 +neural network simulation software, mathematical models, computational models,
1282 +and data models, but with a focus on dynamic circuit models of brain activity. </span></p>
1283 +
1284 +<h2></h2>
1285 +
1286 +<h2><a name="_Toc138932254"><span lang=en-DE>BioBB</span></a></h2>
1287 +
1288 +<p class=MsoNormal><span lang=en-DE>The BioExcel Building Blocks (BioBB)
1289 +software library is a collection of Python wrappers on top of popular
1290 +biomolecular simulation tools. The library offers a layer of interoperability
1291 +between the wrapped tools, which make them compatible and prepared to be
1292 +directly interconnected to build complex biomolecular workflows. Building and
1293 +sharing complex biomolecular simulation workflows just requires joining and
1294 +connecting BioExcelBuilding Blocks together. Biomolecular simulation workflows
1295 +built using the BioBB library are integrated in the Collaboratory Jupyter lab
1296 +infrastructure, allowing the exploration of dynamics and flexibility of
1297 +proteins related to the Central Nervous Systems.</span></p>
1298 +
1299 +<h2></h2>
1300 +
1301 +<h2><a name="_Toc138932255"><span lang=en-DE>BioExcel-CV19</span></a></h2>
1302 +
1303 +<p class=MsoNormal><span lang=en-DE>BioExcel-CV19 is a platform designed to
1304 +provide web access to atomistic-MD trajectories for macromolecules involved in
1305 +the COVID-19 disease. The project is part of the open access initiatives
1306 +promoted by the world-wide scientific community to share information about
1307 +COVID-19 research. BioExcel-CV19 web server interface presents the resulting
1308 +trajectories, with a set of quality control analyses and system information.
1309 +All data produced by the project is available to download from an associated
1310 +programmatic access API.</span></p>
1311 +
1312 +<h2></h2>
1313 +
1314 +<h2><a name="_Toc138932256"><span lang=en-DE>BioNAR</span></a></h2>
1315 +
1316 +<p class=MsoNormal><span lang=en-DE>&quot;BioNAR combines a selection of
1317 +existing R protocols for network analysis with newly designed original
1318 +methodological features to support step-by-step analysis of
1319 +biological/biomedical networks. BioNAR supports a pipeline approach where many
1320 +networks and iterative analyses can be performed. BioNAR helps to achieve a
1321 +number of network analysis goals that are difficult to achieve anywhere else,
1322 +e.g., choose the optimal clustering algorithm from a range of options based on
1323 +independent annotation enrichment</span><span lang=en-DE style='font-family:
1324 +"Times New Roman",serif'> </span><span lang=en-DE>predict a proteins influence
1325 +within and across multiple sub-complexes in the network and estimate the
1326 +co-occurrence or linkage between meta-data at the network level.&quot;</span></p>
1327 +
1328 +<h2></h2>
1329 +
1330 +<h2><a name="_Toc138932257"><span lang=en-DE>BlueNaaS-single cell</span></a></h2>
1331 +
1332 +<p class=MsoNormal><span lang=en-DE>BlueNaaS-SingleCell is an open-source web
1333 +application. It enables users to quickly visualize single cell model
1334 +morphologies in 3D or as a dendrogram. Using a simple web user interface, single
1335 +cell simulations can be easily configured and launched, producing voltage
1336 +traces from selected compartments.</span></p>
1337 +
1338 +<h2></h2>
1339 +
1340 +<h2><a name="_Toc138932258"><span lang=en-DE>BlueNaaS-subcellular</span></a></h2>
1341 +
1342 +<p class=MsoNormal><span lang=en-DE>BlueNaaS-Subcellular is a web-based
1343 +environment for creation and simulation of reaction-diffusion models. It allows
1344 +the user to import, combine and simulate existing models derived from other
1345 +parts of the pipeline. It is integrated with a number of solvers for
1346 +reaction-diffusion systems of equations and can represent rule-based systems
1347 +using BioNetGen. Additionally, it supports simulation of spatially distributed
1348 +systems using STEPS (stochastic engine for pathway simulation), providing
1349 +spatial stochastic and deterministic solvers for simulation of reactions and
1350 +diffusion on tetrahedral meshes. It includes some visualisation tools such as a
1351 +geometry viewer, a contact map and a reactivity network graph.</span></p>
1352 +
1353 +<h2></h2>
1354 +
1355 +<h2><a name="_Toc138932259"><span lang=en-DE>BluePyEfe</span></a></h2>
1356 +
1357 +<p class=MsoNormal><span lang=en-DE>BluePyEfe eases the process of reading
1358 +experimental recordings and extracting batches of electrical features from
1359 +these recordings. It combines trace reading functions and features extraction
1360 +functions from the eFel library. BluePyEfe outputs protocols and features files
1361 +in the format used by BluePyOpt for neuron electrical model building.</span></p>
1362 +
1363 +<h2></h2>
1364 +
1365 +<h2><a name="_Toc138932260"><span lang=en-DE>BluePyMM</span></a></h2>
1366 +
1367 +<p class=MsoNormal><span lang=en-DE>When building a network simulation,
1368 +biophysically detailed electrical models (e-models) need to be tested for every
1369 +morphology that is possibly used in the circuit.  With current resources,
1370 +e-models are not re-optimised for every morphology in the network. In a process
1371 +called Cell Model Management (MM), we test if an existing e-model matches a
1372 +particular morphology 'well enough'. It takes as input a morphology release, a
1373 +circuit recipe and a set of e-models, then finds all possible (morphology,
1374 +e-model)-combinations (me-combos) based on e-type, m-type, and layer as
1375 +described by the circuit recipe, then calculates the scores for every
1376 +combination. Finally, it writes out the resulting accepted me-combos to a
1377 +database and produces a report with information on the number of matches.</span></p>
1378 +
1379 +<h2></h2>
1380 +
1381 +<h2><a name="_Toc138932261"><span lang=en-DE>BluePyOpt</span></a></h2>
1382 +
1383 +<p class=MsoNormal><span lang=en-DE>BluePyOpt simplifies the task of creating
1384 +and sharing these optimisations, and the associated techniques and knowledge.
1385 +This is achieved by abstracting the optimisation and evaluation tasks into
1386 +various reusable and flexible discrete elements according to established best
1387 +practices. Further, BluePyOpt provides methods for setting up both small- and
1388 +large-scale optimisations on a variety of platforms, ranging from laptops to
1389 +Linux clusters and cloud-based computer infrastructures.</span></p>
1390 +
1391 +<h2></h2>
1392 +
1393 +<h2><a name="_Toc138932262"><span lang=en-DE>Brain Cockpit</span></a></h2>
1394 +
1395 +<p class=MsoNormal><span lang=en-DE>Brain Cockpit is a web app comprising a
1396 +Typescript front-end and a Python back-end. It is meant to help explore large
1397 +surface fMRI datasets projected on surface meshes and alignments computed
1398 +between brains, such as those computed with Fused Unbalanced Gromov-Wasserstein
1399 +(fugw) for Python.</span></p>
1400 +
1401 +<h2></h2>
1402 +
1403 +<h2><a name="_Toc138932263"><span lang=en-DE>BrainScaleS</span></a></h2>
1404 +
1405 +<p class=MsoNormal><span lang=en-DE>&quot;Emulate spiking neural networks in
1406 +continuous time on the BrainScaleS analog neuromorphic computing system. Models
1407 +and experiments can be described in Python using the PyNN modelling language,
1408 +or in hxtorch, a PyTorch-based machine-learning-friendly API.  The platform can
1409 +be used interactively via the EBRAINS JupyterLab service or EBRAINS HPC</span><span
1410 +lang=en-DE style='font-family:"Times New Roman",serif'> </span><span
1411 +lang=en-DE>in addition, the NMPI web service provides batch-style access. The
1412 +modelling APIs employ common data formats for input and output data, e.g.,
1413 +neo.&quot;</span></p>
1414 +
1415 +<h2></h2>
1416 +
1417 +<h2><a name="_Toc138932264"><span lang=en-DE>Brayns</span></a></h2>
1418 +
1419 +<p class=MsoNormal><span lang=en-DE>Brayns is a large-scale scientific
1420 +visualization platform based on Intel OSPRAY to perform CPU Ray-tracing and
1421 +uses an extension-plugin architecture. The core provides basic functionalities
1422 +that can be reused and/or extended on plugins, which are independent and can be
1423 +loaded or disabled at start-up. This simplifies the process of adding support
1424 +for new scientific visualization use cases, without compromising the
1425 +reliability of the rest of the software. Brayns counts with braynsService, a
1426 +rendering backend which can be accessed over the internet and streams images to
1427 +connected clients. Already-made plugins include CircuitExplorer, DTI,
1428 +AtlasExplorer, CylindricCamera and MoleculeExplorer.</span></p>
1429 +
1430 +<p class=MsoNormal><span lang=en-DE>https://github.com/BlueBrain/Brayns/ </span></p>
1431 +
1432 +<p class=MsoNormal></p>
1433 +
1434 +<h2><a name="_Toc138932265"><span lang=en-DE>Brion</span></a></h2>
1435 +
1436 +<p class=MsoNormal><span lang=en-DE>Brion is a C++ project for read and write
1437 +access to Blue Brain data structures, including BlueConfig/CircuitConfig,
1438 +Circuit, CompartmentReport, Mesh, Morphology, Synapse and Target files. It also
1439 +offers an interface in Python.</span></p>
1440 +
1441 +<h2></h2>
1442 +
1443 +<h2><a name="_Toc138932266"><span lang=en-DE>BSB</span></a></h2>
1444 +
1445 +<p class=MsoNormal><span lang=en-DE>The BSB reconstructs realistic neural
1446 +circuits by placing and connecting fibres and neurons with detailed
1447 +morphologies or only simplified geometrical features. Configure your model the
1448 +way you need. Interfaces with several simulators (?CoreNEURON, ?Arbor, ?NEST)
1449 +allow simulation of the reconstructed network and investigation of the
1450 +structure-function-dynamics relationships at different levels of resolution.
1451 +The ÒscaffoldÓ design allows an easy model reconfiguration reflecting variants
1452 +across brain regions, animal species and physio-pathological conditions without
1453 +dismounting the basic network structure. The BSB provides effortless parallel
1454 +computing both for the reconstruction and simulation phase.</span></p>
1455 +
1456 +<h2></h2>
1457 +
1458 +<h2><a name="_Toc138932267"><span lang=en-DE>BSP Service Account</span></a></h2>
1459 +
1460 +<p class=MsoNormal><span lang=en-DE>The BSP Service Account is a rest API
1461 +service that allows developers to submit userÕs jobs on HPC systems and
1462 +retrieve results using the EBRAINS authentication, even if users donÕt have a
1463 +personal account on the available HPC facilities.</span></p>
1464 +
1465 +<h2></h2>
1466 +
1467 +<h2><a name="_Toc138932268"><span lang=en-DE>bsp-usecase-wizard</span></a></h2>
1468 +
1469 +<p class=MsoNormal><span lang=en-DE>The CLS interactive workflows and use cases
1470 +application guides the users through the resolution of realistic scientific
1471 +problems. They are implemented as either front-end or full stack web
1472 +applications or Python-based Jupyter Notebooks that allow the user to
1473 +interactively build, reconstruct or simulate data-driven brain models and
1474 +perform data analysis visualisation. Web applications are freely accessible and
1475 +only require authentication to EBRAINS when specific actions are required
1476 +(e.g., submitting a simulation job to an HBP HPC system). Jupyter Notebooks are
1477 +cloned to the lab.ebrains.eu platform and require authentication via an EBRAINS
1478 +account.</span></p>
1479 +
1480 +<h2></h2>
1481 +
1482 +<h2><a name="_Toc138932269"><span lang=en-DE>CGMD Platform</span></a></h2>
1483 +
1484 +<p class=MsoNormal><span lang=en-DE>Recent advances in CGMD simulations have
1485 +allowed longer and larger molecular dynamics simulations of biological
1486 +macromolecules and their interactions. The CGMD platform is dedicated to the
1487 +preparation, running, and analysis of CGMD simulations, and built on a
1488 +completely revisited version of the Martini coarsE gRained MembrAne proteIn
1489 +Dynamics (MERMAID) web server. In its current version, the platform expands the
1490 +existing implementation of the Martini force field for membrane proteins to
1491 +also allow the simulation of soluble proteins using the Martini and SIRAH force
1492 +fields. Moreover, it offers an automated protocol for carrying out the
1493 +backmapping of the coarse-grained description of the system into an atomistic
1494 +one.</span></p>
1495 +
1496 +<h2></h2>
1497 +
1498 +<h2><a name="_Toc138932270"><span lang=en-DE>CNS-ligands</span></a></h2>
1499 +
1500 +<p class=MsoNormal><span lang=en-DE>The project is part of the Parameter
1501 +generation and mechanistic studies of neuronal cascades using multi-scale
1502 +molecular simulations of the HBP. CNS conformers are generated using a powerful
1503 +multilevel strategy that combines a low-level (LL) method for sampling the
1504 +conformational minima and high-level (HL) ab initio calculations for estimating
1505 +their relative stability. CNS database presents the results in a graphical user
1506 +interface, displaying small molecule properties, analyses and generated 3D
1507 +conformers. All data produced by the project is available to download.</span></p>
1508 +
1509 +<h2></h2>
1510 +
1511 +<h2><a name="_Toc138932271"><span lang=en-DE>Cobrawap</span></a></h2>
1512 +
1513 +<p class=MsoNormal><span lang=en-DE>Cobrawap is an adaptable and reusable
1514 +software tool to study wave-like activity propagation in the cortex. It allows for
1515 +the integration of heterogeneous data from different measurement techniques and
1516 +simulations through alignment to common wave descriptions. Cobrawap provides an
1517 +extendable collection of processing and analysis methods that can be combined
1518 +and adapted to specific input data and research applications. It enables broad
1519 +and rigorous comparisons of wave characteristics across multiple datasets,
1520 +model calibration and validation applications, and its modular building blocks
1521 +may serve to construct related analysis pipelines.</span></p>
1522 +
1523 +<h2></h2>
1524 +
1525 +<h2><a name="_Toc138932272"><span lang=en-DE>Collaboratory Bucket service</span></a></h2>
1526 +
1527 +<p class=MsoNormal><span lang=en-DE>The Bucket service provides object storage
1528 +to EBRAINS users without them having to request an account on Fenix (the
1529 +EBRAINS infrastructure provider) and storage resources there. This is the
1530 +recommended storage for datasets that are shared by data providers, on the
1531 +condition that these do not contain sensitive personal data. For sharing
1532 +datasets with personal data, users should refer to the ?Health Data Cloud. The
1533 +Bucket service is better suited for larger files that are usually not edited,
1534 +such as datasets and videos. For Docker images, users should refer to the
1535 +EBRAINS Docker registry. For smaller files and files which are more likely to
1536 +be edited, users should consider the ?Collaboratory Drive service.</span></p>
1537 +
1538 +<h2></h2>
1539 +
1540 +<h2><a name="_Toc138932273"><span lang=en-DE>Collaboratory Drive</span></a></h2>
1541 +
1542 +<p class=MsoNormal><span lang=en-DE>The Drive service offers users cloud
1543 +storage space for their files in each collab (workspace). The Drive storage is
1544 +mounted in the ?Collaboratory Lab to provide persistent storage (as opposed to
1545 +the Lab containers which are deleted after a few hours of inactivity). All
1546 +files are under version control. The Drive is intended for smaller files
1547 +(currently limited to 1 GB) that change more often. Users must not save files
1548 +containing personal information in the Drive (i.e. data of living human subjects).
1549 +The Drive is also integrated with the ?Collaboratory Office service to offer
1550 +easy collaborative editing of Office files online.</span></p>
1551 +
1552 +<h2></h2>
1553 +
1554 +<h2><a name="_Toc138932274"><span lang=en-DE>Collaboratory IAM</span></a></h2>
1555 +
1556 +<p class=MsoNormal><span lang=en-DE>The EBRAINS Collaboratory IAM allows the
1557 +developers of different EBRAINS services to benefit from a single sign-on
1558 +solution. End users will benefit from a seamless experience, whereby they can
1559 +access a specific service and have direct access from it to resources in other
1560 +EBRAINS services without re-authentication. For the developer, it is a good way
1561 +for separating concerns and offloading much of the identification and
1562 +authentication to a central service. The EBRAINS IAM is recognised as an
1563 +identity provider at Fenix supercomputing sites. The IAM service also provides
1564 +three ways of managing groups of users: Units, Groups and Teams.</span></p>
1565 +
1566 +<h2></h2>
1567 +
1568 +<h2><a name="_Toc138932275"><span lang=en-DE>Collaboratory Lab</span></a></h2>
1569 +
1570 +<p class=MsoNormal><span lang=en-DE>The Collaboratory Lab provides EBRAINS
1571 +users with a user-friendly programming environment for reproducible science.
1572 +EBRAINS tools are pre-installed for the user. The latest release is selected by
1573 +default, but users can choose to run an older release to reuse an older
1574 +notebook, or try out the very latest features in the weekly experimental
1575 +deployment. Official releases are produced by EBRAINS every few months. End
1576 +users do not need to build and install the tools, and, more importantly, they
1577 +do not need to resolve dependency conflicts among tools as this has been
1578 +handled for them.</span></p>
1579 +
1580 +<h2></h2>
1581 +
1582 +<h2><a name="_Toc138932276"><span lang=en-DE>Collaboratory Office</span></a></h2>
1583 +
1584 +<p class=MsoNormal><span lang=en-DE>With the Office service, EBRAINS users can
1585 +collaboratively edit Office documents (Word, PowerPoint or Excel) with most of
1586 +the key features of the MS Office tools. It uses the open standard formats
1587 +.docx, .pptx and .xlsx so that files can alternately be edited in the
1588 +Collaboratory Office service and in other compatible tools including the MS
1589 +Office suite.</span></p>
1590 +
1591 +<h2></h2>
1592 +
1593 +<h2><a name="_Toc138932277"><span lang=en-DE>Collaboratory Wiki</span></a></h2>
1594 +
1595 +<p class=MsoNormal><span lang=en-DE>The Wiki service offers the user-friendly
1596 +wiki functionality for publishing web content. It acts as central user
1597 +interface and API to access the other Collaboratory services. EBRAINS
1598 +developers can integrate their services as app which can be instantiated by
1599 +users in their collabs. The Wiki is a good place to create tutorials and
1600 +documentation and it is also the place to publish your work on the internet if
1601 +you choose to do so.</span></p>
1602 +
1603 +<h2></h2>
1604 +
1605 +<h2><a name="_Toc138932278"><span lang=en-DE>CoreNEURON</span></a></h2>
1606 +
1607 +<p class=MsoNormal><span lang=en-DE>In order to adapt NEURON to evolving
1608 +computer architectures, the compute engine of the NEURON simulator was
1609 +extracted and optimised as a library called CoreNEURON. CoreNEURON is a compute
1610 +engine library for the NEURON simulator optimised for both memory usage and
1611 +computational speed on modern CPU/GPU architectures. Some of its key goals are
1612 +to: 1) Efficiently simulate large network models, 2) Support execution on
1613 +accelerators such as GPU, 3) Support optimisations such as vectorisation and
1614 +cache-efficient memory layout.</span></p>
1615 +
1616 +<h2></h2>
1617 +
1618 +<h2><a name="_Toc138932279"><span lang=en-DE>CxSystem2</span></a></h2>
1619 +
1620 +<p class=MsoNormal><span lang=en-DE>CxSystem is a cerebral cortex simulation
1621 +framework, which operates on personal computers. The CxSystem enables easy
1622 +testing and build-up of diverse models at single-cell resolution and it is
1623 +implemented on the top of the Python-based Brain2 simulator. The CxSystem
1624 +interface comprises two csv files - one for anatomy and technical details, the
1625 +other for physiological parameters.</span></p>
1626 +
1627 +<h2></h2>
1628 +
1629 +<h2><a name="_Toc138932280"><span lang=en-DE>DeepSlice</span></a></h2>
1630 +
1631 +<p class=MsoNormal><span lang=en-DE>DeepSlice is a deep neural network that
1632 +aligns histological sections of mouse brain to the Allen Mouse Brain Common
1633 +Coordinate Framework, adjusting for anterior-posterior position, angle,
1634 +rotation and scale. At present, DeepSlice only works with tissue cut in the
1635 +coronal plane, although future versions will be compatible with sagittal and
1636 +horizontal sections.</span></p>
1637 +
1638 +<h2></h2>
1639 +
1640 +<h2><a name="_Toc138932281"><span lang=en-DE>EBRAINS Ethics &amp; Society
1641 +Toolkit</span></a></h2>
1642 +
1643 +<p class=MsoNormal><span lang=en-DE>The aim of the toolkit is to offer
1644 +researchers who carry out cross-disciplinary brain research a possibility to
1645 +engage with ethical and societal issues within brain health and brain disease.
1646 +The user is presented with short introductory texts, scenario-based dilemmas,
1647 +animations and quizzes, all tailored to specific areas of ethics and society in
1648 +a setting of brain research. All exercises are reflection-oriented, with an
1649 +interactive approach to inspire users to incorporate these reflections into
1650 +their own research practices. Moreover, it is possible to gain further
1651 +knowledge by utilising the links for relevant publications, teaching modules
1652 +and the EBRAINS Community Space.</span></p>
1653 +
1654 +<h2></h2>
1655 +
1656 +<h2><a name="_Toc138932282"><span lang=en-DE>EBRAINS Image Service</span></a></h2>
1657 +
1658 +<p class=MsoNormal><span lang=en-DE>The Image Service takes large 2D (and 3D)
1659 +images and preprocesses them to generate small 2D tiles (or 3D chunks).
1660 +Applications consuming image data (viewers or other) can then access regions of
1661 +interest by downloading a few tiles rather than the entire large image. Tiles
1662 +are also generated at coarser resolutions to support zooming out of large
1663 +images. The service supports multiple input image formats. The serving of tiles
1664 +to apps is provided by the ?Collaboratory Bucket (based on OpenStack Swift
1665 +object storage), which provides significantly higher network bandwidth than
1666 +could be provided by any VM.</span></p>
1667 +
1668 +<h2></h2>
1669 +
1670 +<h2><a name="_Toc138932283"><span lang=en-DE>EBRAINS Knowledge Graph</span></a></h2>
1671 +
1672 +<p class=MsoNormal><span lang=en-DE>The EBRAINS Knowledge Graph (KG) is the
1673 +metadata management system of the EBRAINS Data and Knowledge services. It
1674 +provides fundamental services and tools to make neuroscientific data, models
1675 +and related software FAIR. The KG Editor and API (incl. Python SDKs) allow to
1676 +annotate scientific resources in a semantically correct way. The KG Search
1677 +exposes the research information via an intuitive user interface and makes the
1678 +information publicly available to any user. For advanced users, the KG Query
1679 +Builder and KG Core API provide the necessary means to execute detailed queries
1680 +on the graph database whilst enforcing fine-grained permission control.</span></p>
1681 +
1682 +<h2></h2>
1683 +
1684 +<h2><a name="_Toc138932284"><span lang=en-DE>EDI Toolkit</span></a></h2>
1685 +
1686 +<p class=MsoNormal><span lang=en-DE>The EDI Toolkit supports projects in
1687 +integrating EDI in their research content and as guiding principles for team
1688 +collaboration. It is designed for everyday usage by offering: Basic information
1689 +Guiding questions, templates and tools to design responsible research Quick
1690 +checklists, guidance for suitable structures and standard procedures Measures
1691 +to support EDI-based leadership, fair teams and events</span></p>
1692 +
1693 +<h2></h2>
1694 +
1695 +<h2><a name="_Toc138932285"><span lang=en-DE>eFEL</span></a></h2>
1696 +
1697 +<p class=MsoNormal><span lang=en-DE>eFEL allows neuroscientists to
1698 +automatically extract features from time series data recorded from neurons
1699 +(both in vitro and in silico). Examples include  action potential width and
1700 +amplitude in voltage traces recorded during whole-cell patch clamp experiments.
1701 +Users can provide a set of traces and select which  features to calculate. The
1702 +library will then extract the requested features and return the values.</span></p>
1703 +
1704 +<h2></h2>
1705 +
1706 +<h2><a name="_Toc138932286"><span lang=en-DE>Electrophysiology Analysis Toolkit</span></a></h2>
1707 +
1708 +<p class=MsoNormal><span lang=en-DE>The Electrophysiology Analysis Toolkit
1709 +(Elephant) is a Python library that provides a modular framework for the
1710 +analysis of experimental and simulated neuronal activity data, such as spike
1711 +trains, local field potentials, and intracellular data. Elephant builds on the
1712 +Neo data model to facilitate usability, enable interoperability, and support
1713 +data from dozens of file formats and network simulation tools. Its analysis
1714 +functions are continuously validated against reference implementations and
1715 +reports in the literature. Visualisations of analysis results are made
1716 +available via the Viziphant companion library. Elephant aims to act as a
1717 +platform for sharing analysis methods across the field.</span></p>
1718 +
1719 +<h2></h2>
1720 +
1721 +<h2><a name="_Toc138932287"><span lang=en-DE>FAConstructor</span></a></h2>
1722 +
1723 +<p class=MsoNormal><span lang=en-DE>FAConstructor allows a simple and effective
1724 +creation of fibre models based on mathematical functions or the manual input of
1725 +data points. Models are visualised during creation and can be interacted with
1726 +by translating them in 3D space.</span></p>
1727 +
1728 +<h2></h2>
1729 +
1730 +<h2><a name="_Toc138932288"><span lang=en-DE>fairgraph</span></a></h2>
1731 +
1732 +<p class=MsoNormal><span lang=en-DE>fairgraph is a Python library for working
1733 +with metadata in the ?EBRAINS Knowledge Graph (KG), with a particular focus on
1734 +data reuse, although it is also useful in registering and curating metadata.
1735 +The library represents metadata nodes (also known as openMINDS instances) from
1736 +the KG as Python objects. fairgraph supports querying the KG, following links
1737 +in the graph, downloading data and metadata, and creating new nodes in the KG.
1738 +It builds on ?openMINDS and on the KG Core Python library.</span></p>
1739 +
1740 +<h2></h2>
1741 +
1742 +<h2><a name="_Toc138932289"><span lang=en-DE>Fast sampling with neuromorphic
1743 +hardware</span></a></h2>
1744 +
1745 +<p class=MsoNormal><span lang=en-DE>Compared to conventional neural networks,
1746 +physical model devices offer a fast, efficient, and inherently parallel
1747 +substrate capable of related forms of Markov chain Monte Carlo sampling. This
1748 +software suite enables the use of a neuromorphic chip to replicate the
1749 +properties of quantum systems through spike-based sampling.</span></p>
1750 +
1751 +<h2></h2>
1752 +
1753 +<h2><a name="_Toc138932290"><span lang=en-DE>fastPLI</span></a></h2>
1754 +
1755 +<p class=MsoNormal><span lang=en-DE>fastPLI is an open-source toolbox based on
1756 +Python and C++ for modelling myelinated axons, i.e., nerve fibres, and
1757 +simulating the results of measurement of fibre orientations with a polarisation
1758 +microscope using 3D-PLI. The fastPLI package includes the following modules:
1759 +nerve fibre modelling, simulation, and analysis. All computationally intensive
1760 +calculations are optimised either with Numba on the Python side or with
1761 +multithreading C++ algorithms, which can be accessed via pybind11 inside the
1762 +Python package. Additionally, the simulation module supports the Message
1763 +Passing Interface (MPI) to facilitate the simulation of very large volumes on
1764 +multiple computer nodes.</span></p>
1765 +
1766 +<h2></h2>
1767 +
1768 +<h2><a name="_Toc138932291"><span lang=en-DE>Feed-forward LFP-MEG estimator
1769 +from mean-field models</span></a></h2>
1770 +
1771 +<p class=MsoNormal><span lang=en-DE>This tool was developed to calculate the
1772 +local field potentials (LFP) and magnetoencephalogram (MEG) signals generated
1773 +by a population of neurons described by a mean-field model. The calculation of
1774 +LFP is done via a kernel method based on unitary LFPÕs (the LFP generated by a
1775 +single axon) which was recently introduced for spiking-networks simulations and
1776 +that we adapt here for mean-field models. The calculation of the magnetic field
1777 +is based on current-dipole and volume-conductor models, where the secondary
1778 +currents (due to the conducting extracellular medium) are estimated using the
1779 +LFP calculated via the kernel method and where the effects of
1780 +medium-inhomogeneities are incorporated.</span></p>
1781 +
1782 +<h2></h2>
1783 +
1784 +<h2><a name="_Toc138932292"><span lang=en-DE>FIL</span></a></h2>
1785 +
1786 +<p class=MsoNormal><span lang=en-DE>This is a scheme for training and applying
1787 +the FIL framework. Some functionality from SPM12 is required for handling
1788 +images. After training, labelling a new image is relatively fast because
1789 +optimising the latent variables can be formulated within a scheme similar to a recurrent
1790 +Residual Network (ResNet).</span></p>
1791 +
1792 +<h2></h2>
1793 +
1794 +<h2><a name="_Toc138932293"><span lang=en-DE>FMRALIGN</span></a></h2>
1795 +
1796 +<p class=MsoNormal><span lang=en-DE>This library is meant to be a light-weight
1797 +Python library that handles functional alignment tasks (also known as
1798 +hyperalignment). It is compatible with and inspired by Nilearn. Alternative
1799 +implementations of these ideas can be found in the pymvpa or brainiak packages.</span></p>
1800 +
1801 +<h2></h2>
1802 +
1803 +<h2><a name="_Toc138932294"><span lang=en-DE>Foa3D</span></a></h2>
1804 +
1805 +<p class=MsoNormal><span lang=en-DE>Foa3D is a tool for multiscale nerve fibre
1806 +enhancement and orientation analysis in high-resolution volume images acquired
1807 +by two-photon scanning or light-sheet fluorescence microscopy, exploiting the
1808 +brain tissue autofluorescence or exogenous myelin stains. Its image processing
1809 +pipeline is built around a 3D Frangi filter that enables the enhancement of
1810 +fibre structures of varying diameters, and the generation of accurate 3D
1811 +orientation maps in both grey and white matter. Foa3D features the computation
1812 +of multiscale orientation distribution functions that facilitate the comparison
1813 +with orientations assessed via 3D-PLI or 3D PS-OCT, and the validation of
1814 +mesoscale dMRI-based connectivity information.</span></p>
1815 +
1816 +<h2></h2>
1817 +
1818 +<h2><a name="_Toc138932295"><span lang=en-DE>Frites</span></a></h2>
1819 +
1820 +<p class=MsoNormal><span lang=en-DE>Frites allows the characterisation of
1821 +task-related cognitive brain networks. Neural correlates of cognitive functions
1822 +can be extracted both at the single brain area (or channel) and network level.
1823 +The toolbox includes time-resolved directed (e.g., Granger causality) and
1824 +undirected (e.g., Mutual Information) functional connectivity metrics. In
1825 +addition, it includes cluster-based and permutation-based statistical methods
1826 +for single-subject and group-level inference.</span></p>
1827 +
1828 +<h2></h2>
1829 +
1830 +<h2><a name="_Toc138932296"><span lang=en-DE>gridspeccer</span></a></h2>
1831 +
1832 +<p class=MsoNormal><span lang=en-DE>Plotting tool to make plotting with many
1833 +subfigures easier, especially for publications. After installation, gridspeccer
1834 +can be used from the command line to create plots.</span></p>
1835 +
1836 +<h2></h2>
1837 +
1838 +<h2><a name="_Toc138932297"><span lang=en-DE>Hal-Cgp</span></a></h2>
1839 +
1840 +<p class=MsoNormal><span lang=en-DE>Hal-Cgp is an extensible pure Python
1841 +library implementing Cgp to represent, mutate and evaluate populations of
1842 +individuals encoding symbolic expressions targeting applications with
1843 +computationally expensive fitness evaluations. It supports the translation from
1844 +a CGP genotype, a two-dimensional Cartesian graph, into the corresponding
1845 +phenotype, a computational graph implementing a particular mathematical expression.
1846 +These computational graphs can be exported as pure Python functions, in a
1847 +NumPy-compatible format, SymPy expressions or PyTorch modules. The library
1848 +implements a mu + lambda evolution strategy to evolve a population of
1849 +individuals to optimise an objective function.</span></p>
1850 +
1851 +<h2></h2>
1852 +
1853 +<h2><a name="_Toc138932298"><span lang=en-DE>Health Data Cloud</span></a></h2>
1854 +
1855 +<p class=MsoNormal><span lang=en-DE>The Health Data Cloud (HDC) provides
1856 +EBRAINS services for sensitive data as a federated research data ecosystem that
1857 +enables scientists across Europe and beyond to collect, process and share
1858 +sensitive data in compliance with EU General Data Protection Regulations
1859 +(GDPR). The HDC is a federation of interoperable nodes. Nodes share a common
1860 +system architecture based on CharitŽ Virtual Research Environment (VRE),
1861 +enabling research consortia to manage and process data, and making data
1862 +discoverable and sharable via the ?EBRAINS Knowledge Graph.</span></p>
1863 +
1864 +<p class=MsoNormal></p>
1865 +
1866 +<p class=MsoNormal><a name="_Toc138932299"><span class=Heading2Char><span
1867 +lang=en-DE style='font-size:14.0pt;line-height:120%'>Hodgkin-Huxley Neuron
1868 +Builder</span></span></a></p>
1869 +
1870 +<p class=MsoNormal><span lang=en-DE>The Hodgkin-Huxley Neuron Builder is a
1871 +web-application that allows users to interactively go through an entire NEURON
1872 +model building pipeline of individual biophysically detailed cells. 2. Model
1873 +parameter optimisation via HPC systems. 3. In silico experiments using the
1874 +optimised model cell.  </span></p>
1875 +
1876 +<h2></h2>
1877 +
1878 +<h2><a name="_Toc138932300"><span lang=en-DE>HPC Job Proxy</span></a></h2>
1879 +
1880 +<p class=MsoNormal><span lang=en-DE>The HPC Job Proxy provides a simplified way
1881 +for EBRAINS service providers to launch jobs on Fenix supercomputers on behalf
1882 +of EBRAINS end users. The proxy offers a wrapper over the Unicore service which
1883 +adds logging, access to stdout/stderr/status, verification of user quota, and
1884 +updating of user quota at the end of the job.</span></p>
1885 +
1886 +<h2></h2>
1887 +
1888 +<h2><a name="_Toc138932301"><span lang=en-DE>HPC Status Monitor</span></a></h2>
1889 +
1890 +<p class=MsoNormal><span lang=en-DE>The HPC Status Monitor allows a real-time
1891 +check of the availability status of the HPC Systems accessible from HBP tools
1892 +and services and provides an instant snapshot of the resource quotas available
1893 +to individual users on each system.</span></p>
1894 +
1895 +<h2></h2>
1896 +
1897 +<h2><a name="_Toc138932302"><span lang=en-DE>Human Intracerebral EEG Platform</span></a></h2>
1898 +
1899 +<p class=MsoNormal><span lang=en-DE>The HIP is an open-source platform designed
1900 +for collecting, managing, analysing and sharing multi-scale iEEG data at an
1901 +international level. Its mission is to assist clinicians and researchers in
1902 +improving research capabilities by simplifying iEEG data analysis and
1903 +interpretation. The HIP integrates different software, modules and services
1904 +necessary for investigating spatio-temporal dynamics of neural processes in a
1905 +secure and optimised fashion. The interface is browser-based and allows
1906 +selecting sets of tools according to specific research needs.</span></p>
1907 +
1908 +<h2></h2>
1909 +
1910 +<h2><a name="_Toc138932303"><span lang=en-DE>Hybrid MM/CG Webserver</span></a></h2>
1911 +
1912 +<p class=MsoNormal><span lang=en-DE>MM/CG simulations help predict ligand poses
1913 +in hGPCRs  for pharmacological applications. This approach allows for the
1914 +description of the ligand, the binding cavity and the surrounding water
1915 +molecules at atomistic resolution, while coarse-graining the rest of the
1916 +receptor. The webserver automatises and speeds up the simulation set-up of
1917 +hGPCR/ligand complexes. It also allows for equilibration of the systems, either
1918 +fully automatically or interactively. The results are visualised online,
1919 +helping the user identify possible issues and modify the set-up parameters.
1920 +This framework allows for the automatic preparation and running of hybrid
1921 +molecular dynamics simulations of molecules and their cognate receptors.</span></p>
1922 +
1923 +<h2></h2>
1924 +
1925 +<h2><a name="_Toc138932304"><span lang=en-DE>Insite</span></a></h2>
1926 +
1927 +<p class=MsoNormal><span lang=en-DE>Insite enables users to access data via the
1928 +in transit paradigm for ?NEST, ?TVB and ?Arbor simulations. Compared to the
1929 +traditional approach of offline processing, in transit paradigms allow
1930 +accessing of data while the simulation runs. This is especially useful for
1931 +simulations that produce large amounts of data and are running for a long time.
1932 +In transit allows the user to access only parts of the data and prevents the
1933 +need for storing all data. It also allows the user early insights into the data
1934 +even before the simulation finishes. Insite provides an easy-to-use and
1935 +easy-to-integrate architecture to enable in transit features in other tools.</span></p>
1936 +
1937 +<h2></h2>
1938 +
1939 +<h2><a name="_Toc138932305"><span lang=en-DE>Interactive Brain Atlas Viewer</span></a></h2>
1940 +
1941 +<p class=MsoNormal><span lang=en-DE>The Interactive Brain Atlas Viewer provides
1942 +various kinds of interactive visualisations for multi-modal brain and head
1943 +image data: different parcellations, degrees of transparency and overlays. The
1944 +Viewer provides the following functions and supports data from the following
1945 +sources: EEG, white matter tracts, MRI and PET 3D volumes, 2D slices,
1946 +intracranial electrodes, brain activity, multiscale brain network models,
1947 +supplementary information for brain regions and functional brain networks in
1948 +multiple languages. It comes as a web app, mobile app and desktop app.</span></p>
1949 +
1950 +<h2></h2>
1951 +
1952 +<h2><a name="_Toc138932306"><span lang=en-DE>JuGEx</span></a></h2>
1953 +
1954 +<p class=MsoNormal><span lang=en-DE>Decoding the chain from genes to cognition
1955 +requires detailed insights into how areas with specific gene activities and
1956 +microanatomical architectures contribute to brain function and dysfunction. The
1957 +Allen Human Brain Atlas contains regional gene expression data, while the
1958 +Julich Brain Atlas, which can be accessed via ?siibra, offers 3D
1959 +cytoarchitectonic maps reflecting the interindividual variability. JuGEx offers
1960 +an integrated framework that combines the analytical benefits of both
1961 +repositories towards a multilevel brain atlas of adult humans. JuGEx is a new
1962 +method for integrating tissue transcriptome and cytoarchitectonic segregation.</span></p>
1963 +
1964 +<h2></h2>
1965 +
1966 +<h2><a name="_Toc138932307"><span lang=en-DE>KnowledgeSpace</span></a></h2>
1967 +
1968 +<p class=MsoNormal><span lang=en-DE>KnowledgeSpace (KS) is a globally-used,
1969 +data-driven encyclopaedia and search engine for the neuroscience community. As
1970 +an encyclopaedia, KS provides curated definitions of brain research concepts
1971 +found in different neuroscience community ontologies, Wikipedia and
1972 +dictionaries. The dataset discovery in KS makes research datasets across many
1973 +large-scale brain initiatives universally accessible and useful. It also
1974 +promotes FAIR data principles that will help data publishers to follow best
1975 +practices for data storage and publication. As more and more data publishers
1976 +follow data standards like ?OpenMINDS or DATS, the quality of data discovery
1977 +through KS will improve. The related publications are also curated from PubMed
1978 +and linked to the concepts in KS to provide an improved search capability.</span></p>
1979 +
1980 +<h2></h2>
1981 +
1982 +<h2><a name="_Toc138932308"><span lang=en-DE>L2L</span></a></h2>
1983 +
1984 +<p class=MsoNormal><span lang=en-DE>L2L is an easy-to-use and flexible
1985 +framework to perform parameter and hyper-parameter space exploration of
1986 +mathematical models on HPC infrastructure. L2L is an implementation of the
1987 +learning-to-learn concept written in Python. This open-source software allows
1988 +several instances of an optimisation target to be executed with different
1989 +parameters in an massively parallel fashion on HPC. L2L provides a set of
1990 +built-in optimiser algorithms, which make adaptive and efficient exploration of
1991 +parameter spaces possible. Different from other optimisation toolboxes, L2L
1992 +provides maximum flexibility for the way the optimisation target can be
1993 +executed.</span></p>
1994 +
1995 +<h2></h2>
1996 +
1997 +<h2><a name="_Toc138932309"><span lang=en-DE>Leveltlab/SpectralSegmentation</span></a></h2>
1998 +
1999 +<p class=MsoNormal><span lang=en-DE>SpecSeg is a toolbox that segments neurons
2000 +and neurites in chronic calcium imaging datasets based on low-frequency
2001 +cross-spectral power. The pipeline includes a graphical user interface to edit
2002 +the automatically extracted ROIs, to add new ones or delete ROIs by further
2003 +constraining their properties.</span></p>
2004 +
2005 +<h2></h2>
2006 +
2007 +<h2><a name="_Toc138932310"><span lang=en-DE>LFPy</span></a></h2>
2008 +
2009 +<p class=MsoNormal><span lang=en-DE>LFPy is an open-source Python module linking
2010 +simulated neural activity with measurable brain signals. This is done by
2011 +enabling calculation of brain signals from neural activity simulated with
2012 +multi-compartment neuron models (single cells or networks). LFPy can be used to
2013 +simulate brain signals like extracellular action potentials, local field
2014 +potentials (LFP), and in vitro MEA recordings, as well as ECoG, EEG, and MEG
2015 +signals. LFPy is well-integrated with the NEURON simulator and can, through
2016 +LFPykit, also be used with other simulators like Arbor. Through the recently
2017 +developed extensions hybridLFPy and LFPykernels, LFPy can also be used to
2018 +calculate brain signals directly from point-neuron network models or
2019 +population-based models.</span></p>
2020 +
2021 +<h2></h2>
2022 +
2023 +<h2><a name="_Toc138932311"><span lang=en-DE>libsonata</span></a></h2>
2024 +
2025 +<p class=MsoNormal><span lang=en-DE>libsonata allows circuit and simulation
2026 +config loading, node set materialisation, and access to node and edge
2027 +populations in an efficient manner. It is generally a read-only library, but
2028 +support for writing edge indices has been added.</span></p>
2029 +
2030 +<h2></h2>
2031 +
2032 +<h2><a name="_Toc138932312"><span lang=en-DE>Live Papers</span></a></h2>
2033 +
2034 +<p class=MsoNormal><span lang=en-DE>EBRAINS Live Papers are structured and
2035 +interactive documents that complement published scientific articles. Live
2036 +Papers feature integrated tools and services that allow users to download,
2037 +visualise or simulate data, models and results presented in the corresponding
2038 +publications: Build interactive documents to showcase your data and the
2039 +simulation or data analysis code used in your research. Easily link to
2040 +resources in community databases such as EBRAINS, NeuroMorpho.org, ModelDB, and
2041 +Allen Brain Atlas. Embedded, interactive visualisation of electrophysiology
2042 +data and neuronal reconstructions. Launch EBRAINS simulation tools to explore
2043 +single neuron models in your browser. Share live papers pre-publication with
2044 +anonymous reviewers during peer review of your manuscript. Explore already
2045 +published live papers, or develop your own live paper with our authoring tool.</span></p>
2046 +
2047 +<h2></h2>
2048 +
2049 +<h2><a name="_Toc138932313"><span lang=en-DE>Livre</span></a></h2>
2050 +
2051 +<p class=MsoNormal><span lang=en-DE>Livre is an out-of-core, multi-node,
2052 +multi-GPU, OpenGL volume rendering engine to visualise large volumetric
2053 +datasets. It provides the following major features to facilitate rendering of
2054 +large volumetric datasets: Visualisation of pre-processed UVF format volume
2055 +datasets. Real-time voxelisation of different data sources (surface meshes, BBP
2056 +morphologies, local field potentials, etc.) through the use of plugins.
2057 +Multi-node, multi-GPU rendering (only sort-first rendering).</span></p>
2058 +
2059 +<h2></h2>
2060 +
2061 +<h2><a name="_Toc138932314"><span lang=en-DE>LocaliZoom</span></a></h2>
2062 +
2063 +<p class=MsoNormal><span lang=en-DE>Pan-and-zoom type viewer displaying image
2064 +series with overlaid atlas delineations. LocaliZoom is a pan-and-zoom type
2065 +viewer displaying high-resolution image series coupled with overlaid atlas
2066 +delineations. It has three operating modes: Display series with atlas overlay.
2067 +Both linear and nonlinear alignments are supported (created with QuickNII or
2068 +VisuAlign). Create or edit nonlinear alignments. Create markup which can be
2069 +exported as MeshView point clouds or to Excel for further numerical analysis.</span></p>
2070 +
2071 +<h2></h2>
2072 +
2073 +<h2><a name="_Toc138932315"><span lang=en-DE>MD-IFP</span></a></h2>
2074 +
2075 +<p class=MsoNormal><span lang=en-DE>MD-IFP is a python workflow for the
2076 +generation and analysis of protein-ligand interaction fingerprints from
2077 +molecular dynamics trajectories. If used for the analysis of Random
2078 +Acceleration Molecular Dynamics (RAMD) trajectories, it can help to investigate
2079 +dissociation mechanisms by characterising transition states as well as the
2080 +determinants and hot-spots for dissociation. As such, the combined use of
2081 +??RAMD and MD-IFP may assist the early stages of drug discovery campaigns for the
2082 +design of new molecules or ligand optimisation.</span></p>
2083 +
2084 +<h2></h2>
2085 +
2086 +<h2><a name="_Toc138932316"><span lang=en-DE>MEDUSA</span></a></h2>
2087 +
2088 +<p class=MsoNormal><span lang=en-DE>Using a spherical meshing technique that
2089 +decomposes each microstructural item into a set of overlapping spheres, the
2090 +phantom construction is made very fast while reliably avoiding the collisions
2091 +between items in the scene. This novel method is applied to the construction of
2092 +human brain white matter microstructural components, namely axonal fibers,
2093 +oligodendrocytes and astrocytes. The algorithm reaches high values of packing
2094 +density and angular dispersion for the axonal fibers, even in the case of
2095 +multiple white matter fiber populations and enables the construction of complex
2096 +biomimicking geometries including myelinated axons, beaded axons and glial
2097 +cells.</span></p>
2098 +
2099 +<h2></h2>
2100 +
2101 +<h2><a name="_Toc138932317"><span lang=en-DE>MeshView</span></a></h2>
2102 +
2103 +<p class=MsoNormal><span lang=en-DE>MeshView is a web application for real-time
2104 +3D display of surface mesh data representing structural parcellations from
2105 +volumetric atlases, such as the Waxholm Space atlas of the Sprague Dawley rat
2106 +brain. Key features: orbiting view with toggleable opaque/transparent/hidden
2107 +parcellation meshes, rendering user-defined cut surface as if meshes were solid
2108 +objects, rendering point-clouds (simple type-in, or loaded from JSON). The
2109 +coordinate system is compatible with QuickNII.</span></p>
2110 +
2111 +<h2></h2>
2112 +
2113 +<h2><a name="_Toc138932318"><span lang=en-DE>MIP</span></a></h2>
2114 +
2115 +<p class=MsoNormal><span lang=en-DE>MIP is an open-source platform enabling
2116 +federated data analysis in a secure environment for centres involved in
2117 +collaborative initiatives. It allows users to initiate or join disease-oriented
2118 +federations with the aim of analysing large-scale distributed clinical
2119 +datasets. For each federation, users can create specific data models based on
2120 +well-accepted common data elements, approved by all participating centres. MIP
2121 +experts assist in creating the data models and facilitate coordination and
2122 +communication among centres. They provide advice and support for data curation,
2123 +harmonisation, and anonymisation, as well as data governance, especially with
2124 +regards to Data Sharing Agreements and general ethical considerations.</span></p>
2125 +
2126 +<h2></h2>
2127 +
2128 +<h2><a name="_Toc138932319"><span lang=en-DE>Model Validation Service</span></a></h2>
2129 +
2130 +<p class=MsoNormal><span lang=en-DE>The HBP/EBRAINS Model Validation Service is
2131 +a set of tools for performing and tracking validation of models with respect to
2132 +experimental data. It consists of a web API, a GUI client (the Model Catalog
2133 +app) and a Python client. The service enables users to store, query, view and
2134 +download: (i) model descriptions/scripts, (ii) validation test definitions and
2135 +(iii) validation results. In a typical workflow, users will find models and
2136 +validation tests by searching the Model Catalog (or upload their own), run the
2137 +tests using the Python client in a Jupyter notebook, with simulations running
2138 +locally or on HPC, and then upload the results.</span></p>
2139 +
2140 +<h2></h2>
2141 +
2142 +<h2><a name="_Toc138932320"><span lang=en-DE>Model Validation Test Suites</span></a></h2>
2143 +
2144 +<p class=MsoNormal><span lang=en-DE>As part of the HBP/EBRAINS model validation
2145 +framework, we provide a Python Software Development Kit (SDK) for model
2146 +validation, which provides: (i) validation test definitions and (ii) interface
2147 +definitions intended to decouple model validation from the details of model
2148 +implementation. This more formal approach to model validation aims to make it
2149 +quicker and easier to compare models, to provide validation test suites for
2150 +models and to develop new validations of existing models. The SDK consists of a
2151 +collection of Python packages all using the sciunit framework: HippoUnit,
2152 +MorphoUnit, NetworkUnit, BasalUnit, CerebUnit, eFELUnit, HippoNetworkUnit.</span></p>
2153 +
2154 +<h2></h2>
2155 +
2156 +<h2><a name="_Toc138932321"><span lang=en-DE>MoDEL-CNS</span></a></h2>
2157 +
2158 +<p class=MsoNormal><span lang=en-DE>MoDEL-CNS is a database and server platform
2159 +designed to provide web access to atomistic MD trajectories for relevant signal
2160 +transduction proteins. The project is part of the service for providing
2161 +molecular simulation-based predictions for systems neurobiology of the HBP.
2162 +MoDEL-CNS expands the MD Extended Library database of atomistic MD trajectories
2163 +with proteins involved in CNS processes, including membrane proteins. MoDEL-CNS
2164 +web server interface presents the resulting trajectories, analyses and protein
2165 +properties. All data produced are available to download.</span></p>
2166 +
2167 +<h2></h2>
2168 +
2169 +<h2><a name="_Toc138932322"><span lang=en-DE>Modular Science</span></a></h2>
2170 +
2171 +<p class=MsoNormal><span lang=en-DE>Modular Science is a middleware that
2172 +provides robust deployment of complex multi-application workflows. It contains
2173 +protocols and interfaces for multi-scale co-simulation workloads on
2174 +high-performance computers and local hardware. It allows for synchronisation
2175 +and coordination of individual components and contains dedicated and
2176 +parallelised modules for data transformations between scales. Modular Science
2177 +offers insight into both the system level and the individual subsystems to
2178 +steer the execution, to monitor resource usage, and system health &amp; status
2179 +with small overheads on performance. Modular Science comes with a number of
2180 +neuroscience co-simulation use cases including NEST-TVB, NEST-Arbor, LFPy and neurorobotics.</span></p>
2181 +
2182 +<h2></h2>
2183 +
2184 +<h2><a name="_Toc138932323"><span lang=en-DE>Monsteer</span></a></h2>
2185 +
2186 +<p class=MsoNormal><span lang=en-DE>Monsteer is a library for interactive
2187 +supercomputing in the neuroscience domain. It facilitates the coupling of
2188 +running simulations (currently NEST) with interactive visualization and
2189 +analysis applications. Monsteer supports streaming of simulation data to
2190 +clients (currently limited to spikes) as well as control of the simulator from
2191 +the clients (also known as computational steering). Monsteer's main components
2192 +are a C++ library, a MUSIC-based application and Python helpers.</span></p>
2193 +
2194 +<h2></h2>
2195 +
2196 +<h2><a name="_Toc138932324"><span lang=en-DE>MorphIO</span></a></h2>
2197 +
2198 +<p class=MsoNormal><span lang=en-DE>MorphIO is a library for reading and
2199 +writing neuron morphology files. It supports the following formats: SWC, ASC
2200 +(also known as neurolucida), H5. There are two APIs: mutable, for creating or
2201 +editing morphologies, and immutable, for read-only operations. Both are
2202 +represented in C++ and Python. Extended formats include glia, mitochondria and
2203 +endoplasmic reticulum.</span></p>
2204 +
2205 +<h2></h2>
2206 +
2207 +<h2><a name="_Toc138932325"><span lang=en-DE>Morphology alignment tool</span></a></h2>
2208 +
2209 +<p class=MsoNormal><span lang=en-DE>Starting with serial sections of a brain in
2210 +which a complete single morphology has been labelled, the pieces of neurite
2211 +(axons/dendrites) in each section are traced with Neurolucida or similar
2212 +microscope-attached software. The slices are then aligned, first using an
2213 +automated algorithm that tries to find matching pieces in adjacent sections
2214 +(Python script), and second using a GUI-driven tool (web-based, JavaScript).
2215 +Finally, the pieces are stitched into a complete neuron (Python script). The
2216 +neuron and tissue volume are then registered to one of the EBRAINS-supported
2217 +reference templates (Python script). The web-based tool can also be used to align
2218 +slices without a neuron being present.</span></p>
2219 +
2220 +<h2></h2>
2221 +
2222 +<h2><a name="_Toc138932326"><span lang=en-DE>MorphTool</span></a></h2>
2223 +
2224 +<p class=MsoNormal><span lang=en-DE>MorphTool is a python toolkit designed for
2225 +editing morphological skeletons of cell reconstructions. It has been developed
2226 +to provide helper programmes that perform simple tasks such as morphology
2227 +diffing, file conversion, soma area calculation, skeleton simplification,
2228 +process resampling, morphology repair and spatial transformations. It allows
2229 +neuroscientists to curate and manipulate morphological reconstruction and
2230 +correct morphological artifacts due to the manual reconstruction process.</span></p>
2231 +
2232 +<h2></h2>
2233 +
2234 +<h2><a name="_Toc138932327"><span lang=en-DE>Multi-Brain</span></a></h2>
2235 +
2236 +<p class=MsoNormal><span lang=en-DE>&quot;The Multi-Brain (MB) model has the
2237 +general aim of integrating a number of disparate image analysis components
2238 +within a single unified generative modelling framework. Its objective is to
2239 +achieve diffeomorphic alignment of a wide variety of medical image modalities
2240 +into a common anatomical space. This involves the ability to construct a
2241 +&quot;&quot;tissue probability template&quot;&quot; from a population of scans
2242 +through group-wise alignment. The MB model has been shown to provide accurate
2243 +modelling of the intensity distributions of different imaging modalities.&quot;</span></p>
2244 +
2245 +<h2></h2>
2246 +
2247 +<h2><a name="_Toc138932328"><span lang=en-DE>Multi-Image-OSD</span></a></h2>
2248 +
2249 +<p class=MsoNormal><span lang=en-DE>It has browser-based classic pan and zoom
2250 +capabilities. A collection of images can be displayed as a filmstrip (Filmstrip
2251 +Mode) or as a table (Collection Mode) with adjustable number of rows and
2252 +columns. The tool supports keyboard or/and mouse navigation options, as well as
2253 +touch devices. Utilising the open standard Deep Zoom Image (DZI) format, it is
2254 +able to efficiently visualise very large brain images in the gigapixel range,
2255 +allowing to zoom from common, display-sized overview resolutions down to the
2256 +microscopic resolution without downloading the underlying, very large image
2257 +dataset.</span></p>
2258 +
2259 +<h2></h2>
2260 +
2261 +<h2><a name="_Toc138932329"><span lang=en-DE>MUSIC</span></a></h2>
2262 +
2263 +<p class=MsoNormal><span lang=en-DE>MUSIC is a communication framework in the
2264 +domain of computational neuroscience and neuromorphic computing which enables
2265 +co-simulations, where components of a model are simulated by different
2266 +simulators or hardware. It consists of an API and C++ library which can be
2267 +linked into existing software with minor modifications. MUSIC enables the
2268 +communication of neuronal spike events, continuous values and text messages
2269 +while hiding the complexity of data distribution over ranks, as well as
2270 +scheduling of communication in the face of loops. MUSIC is light-weight with a
2271 +simple API.</span></p>
2272 +
2273 +<h2></h2>
2274 +
2275 +<h2><a name="_Toc138932330"><span lang=en-DE>NEAT</span></a></h2>
2276 +
2277 +<p class=MsoNormal><span lang=en-DE>NEAT allows for the convenient definition
2278 +of morphological neuron models. These models can be simulated through an
2279 +interface with the NEURON simulator or analysed with two classical methods: (i)
2280 +the separation-of-variables method to obtain impedance kernels as a
2281 +superposition of exponentials and (ii) KochÕs method to compute impedances with
2282 +linearised ion channels analytically in the frequency domain. NEAT also
2283 +implements the neural evaluation tree framework and an associated C++ simulator
2284 +to analyse sub-unit independence. Finally, NEAT implements a new method to
2285 +simplify morphological neuron models into models with few compartments, which
2286 +can also be simulated with NEURON.</span></p>
2287 +
2288 +<h2></h2>
2289 +
2290 +<h2><a name="_Toc138932331"><span lang=en-DE>Neo</span></a></h2>
2291 +
2292 +<p class=MsoNormal><span lang=en-DE>Neo implements a hierarchical data model
2293 +well adapted to intracellular and extracellular electrophysiology and EEG data.
2294 +It improves interoperability between Python tools for analysing, visualising
2295 +and generating electrophysiology data by providing a common, shared object
2296 +model. It reads a wide range of neurophysiology file formats, including Spike2,
2297 +NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt and Igor Pro and writes
2298 +to open formats such as NWB and NIX. Neo objects behave just like normal NumPy
2299 +arrays, but with additional metadata, checks for dimensional consistency and
2300 +automatic unit conversion. Neo has been endorsed as a community standard by the
2301 +International Neuroinformatics Coordinating Facility (INCF).</span></p>
2302 +
2303 +<h2></h2>
2304 +
2305 +<h2><a name="_Toc138932332"><span lang=en-DE>Neo Viewer</span></a></h2>
2306 +
2307 +<p class=MsoNormal><span lang=en-DE>Neo Viewer consists of a REST-API and a
2308 +Javascript component that can be embedded in any web page. Electrophysiology
2309 +traces can be zoomed, scrolled and saved as images. Individual points can be
2310 +measured off the graphs. Neo Viewer can visualise data from most of the
2311 +widely-used file formats in neurophysiology, including community standards such
2312 +as NWB.</span></p>
2313 +
2314 +<h2></h2>
2315 +
2316 +<h2><a name="_Toc138932333"><span lang=en-DE>NEST Desktop</span></a></h2>
2317 +
2318 +<p class=MsoNormal><span lang=en-DE>NEST Desktop comprises of GUI components
2319 +for creating and configuring network models, running simulations, and
2320 +visualising and analysing simulation results. NEST Desktop allows students to
2321 +explore important concepts in computational neuroscience without the need to
2322 +first learn a simulator control language. This is done by offering a
2323 +server-side ?NEST simulator, which can also be installed as a package together
2324 +with a web server providing NEST Desktop as visual front-end. Besides local
2325 +installations, distributed setups can be installed, and direct use through
2326 +EBRAINS is possible. NEST Desktop has also been used as a modelling front-end
2327 +of the Neurorobotics Platform.</span></p>
2328 +
2329 +<h2></h2>
2330 +
2331 +<h2><a name="_Toc138932334"><span lang=en-DE>NEST Simulator</span></a></h2>
2332 +
2333 +<p class=MsoNormal><span lang=en-DE>NEST is used in computational neuroscience
2334 +to model and study behaviour of large networks of neurons. The models describe
2335 +single neuron and synapse behaviour and their connections. Different mechanisms
2336 +of plasticity can be used to investigate artificial learning and help to shed
2337 +light on the fundamental principles of how the brain works. NEST offers
2338 +convenient and efficient commands to define and connect large networks, ranging
2339 +from algorithmically determined connections to data-driven connectivity. Create
2340 +connections between neurons using numerous synapse models from STDP to gap
2341 +junctions.</span></p>
2342 +
2343 +<h2></h2>
2344 +
2345 +<h2><a name="_Toc138932335"><span lang=en-DE>NESTML</span></a></h2>
2346 +
2347 +<p class=MsoNormal><span lang=en-DE>NESTML is a domain-specific language for
2348 +neuron and synapse models. These dynamical models can be used in simulations of
2349 +brain activity on several platforms, in particular ?NEST Simulator. NESTML
2350 +combines an easy to understand, yet powerful syntax with good simulation
2351 +performance by means of code generation (C++ for NEST Simulator), but flexibly
2352 +supports other simulation engines including neuromorphic hardware.</span></p>
2353 +
2354 +<h2></h2>
2355 +
2356 +<h2><a name="_Toc138932336"><span lang=en-DE>NetPyNE</span></a></h2>
2357 +
2358 +<p class=MsoNormal><span lang=en-DE>NetPyNE provides programmatic and graphical
2359 +interfaces to develop data-driven multiscale brain neural circuit models using
2360 +Python and NEURON. Users can define models using a standardised
2361 +JSON-compatible, rule-based, declarative format. Based on these specifications,
2362 +NetPyNE will generate the network in ?CoreNEURON, enabling users to run
2363 +parallel simulations, optimise and explore network parameters through automated
2364 +batch runs, and use built-in functions for visualisation and analysis (e.g.,
2365 +generate connectivity matrices, voltage traces, spike raster plots, local field
2366 +potentials and information theoretic measures). NetPyNE also facilitates model
2367 +sharing by exporting and importing standardised formats: NeuroML and SONATA.</span></p>
2368 +
2369 +<h2></h2>
2370 +
2371 +<h2><a name="_Toc138932337"><span lang=en-DE>NEURO-CONNECT</span></a></h2>
2372 +
2373 +<p class=MsoNormal><span lang=en-DE>The NEURO-CONNECT platform provides
2374 +functions to integrate multimodal brain imaging information in a unifying
2375 +feature space. Thus, Surface Based Morphometry (SBM), Functional Magnetic
2376 +Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI) can be combined and
2377 +visualised at the whole-brain scale. Moreover, multiple brain atlases are
2378 +aligned to match research outcomes to neuroanatomical entities. The datasets
2379 +are appended with openMINDS metadata and thus enable integrative data analysis
2380 +and machine learning.</span></p>
2381 +
2382 +<h2></h2>
2383 +
2384 +<h2><a name="_Toc138932338"><span lang=en-DE>NeuroFeatureExtract</span></a></h2>
2385 +
2386 +<p class=MsoNormal><span lang=en-DE>The NeuroFeatureExtract is a web
2387 +application that allows the users to extract an ensemble of
2388 +electrophysiological properties from voltage traces recorded upon electrical
2389 +stimulation of neuronal cells. The main outcome of the application is the
2390 +generation of two files Ð features.json and protocol.json Ð that can be used
2391 +for later analysis and model parameter optimisations via the Hodgkin-Huxley
2392 +Neuron Builder application.</span></p>
2393 +
2394 +<h2></h2>
2395 +
2396 +<h2><a name="_Toc138932339"><span lang=en-DE>NeurogenPy</span></a></h2>
2397 +
2398 +<p class=MsoNormal><span lang=en-DE>NeurogenPy is a Python package for working
2399 +with Bayesian networks. It is focused on the analysis of gene expression data
2400 +and learning of gene regulatory networks, modelled as Bayesian networks. For
2401 +that reason, at the moment, only the Gaussian and fully discrete cases are
2402 +supported. The package provides different structure learning algorithms,
2403 +parameters estimation and input/output formats. For some of them, already
2404 +existing implementations have been used, with bnlearn, pgmpy, networkx and
2405 +igraph being the most relevant used packages. This project has been conceived
2406 +to be included as a plugin in the EBRAINS interactive atlas viewer, but it may
2407 +be used for other purposes.</span></p>
2408 +
2409 +<h2></h2>
2410 +
2411 +<h2><a name="_Toc138932340"><span lang=en-DE>NeuroM</span></a></h2>
2412 +
2413 +<p class=MsoNormal><span lang=en-DE>NeuroM is a Python toolkit for the analysis
2414 +and processing of neuron morphologies. It allows the extraction of various
2415 +information about morphologies, e.g., the segment lengths of a morphology via
2416 +the segment_lengths feature. More than 50 features that can be extracted.</span></p>
2417 +
2418 +<h2></h2>
2419 +
2420 +<h2><a name="_Toc138932341"><span lang=en-DE>Neuromorphic Computing Job Queue</span></a></h2>
2421 +
2422 +<p class=MsoNormal><span lang=en-DE>The Neuromorphic Computing Job Queue allows
2423 +users to run simulations/emulations on the SpiNNaker and BrainScaleS systems by
2424 +submitting a PyNN script and associated job configuration information to a
2425 +central queue. The system consists of a web API, a GUI client (the Job Manager
2426 +app) and a Python client. Users can submit scripts stored locally on their own
2427 +machine, in a Git repository, in the KG, or in EBRAINS Collaboratory storage
2428 +(Drive/Bucket). Users can track the progress of their job, and view and/or
2429 +download the results, log files, and provenance information.</span></p>
2430 +
2431 +<h2></h2>
2432 +
2433 +<h2><a name="_Toc138932342"><span lang=en-DE>Neuronize v2</span></a></h2>
2434 +
2435 +<p class=MsoNormal><span lang=en-DE>Neuronize v2 has been developed to generate
2436 +a connected neural 3D mesh. If the input is a neuron tracing, it generates a 3D
2437 +mesh from it, including the shape of the soma. If the input is data extracted
2438 +with Imaris Filament Tracer (a set of unconnected meshes of a neuron),
2439 +Neuronize v2 generates a single connected 3D mesh of the whole neuron (also
2440 +generating the soma) and provides its neural tracing, which can then be
2441 +imported into tools such as Neurolucida, facilitating the interoperability of
2442 +two of the most widely used proprietary tools.</span></p>
2443 +
2444 +<h2></h2>
2445 +
2446 +<h2><a name="_Toc138932343"><span lang=en-DE>NeuroR</span></a></h2>
2447 +
2448 +<p class=MsoNormal><span lang=en-DE>NeuroR is a collection of tools to repair
2449 +morphologies.  This includes cut plane detection, sanitisation (removing
2450 +unifurcations, invalid soma counts, short segments) and ÒunravellingÓ: the
2451 +action of ÒstretchingÓ the cell that has been shrunk due to  the dehydratation
2452 +caused by the slicing.</span></p>
2453 +
2454 +<h2></h2>
2455 +
2456 +<h2><a name="_Toc138932344"><span lang=en-DE>Neurorobotics Platform</span></a></h2>
2457 +
2458 +<p class=MsoNormal><span lang=en-DE>The Neurorobotics Platform (NRP) is an
2459 +integrative simulation framework that enables in silico experimentation and
2460 +embodiment of brain models inside virtual agents interacting with realistic
2461 +simulated environments. Entirely Open Source, it offers a browser-based
2462 +graphical user interface for online access. It can be installed locally (Docker
2463 +or source install). It can be interfaced with multiple spike-based neuromorphic
2464 +chips (SpiNNaker, Intel Loihi). You can download and install the NRP locally
2465 +for maximum experimental convenience or access it online in order to leverage
2466 +the HBP High Performance Computing infrastructure for large-scale experiments.</span></p>
2467 +
2468 +<h2></h2>
2469 +
2470 +<h2><a name="_Toc138932345"><span lang=en-DE>Neurorobotics Platform Robot
2471 +Designer</span></a></h2>
2472 +
2473 +<p class=MsoNormal><span lang=en-DE>The Robot Designer is a plugin for the 3D
2474 +modeling suite Blender that enables researchers to design morphologies for
2475 +simulation experiments in, particularly but not restricted to, the
2476 +Neurorobotics Platform. This plugin helps researchers design and parameterize
2477 +models with a Graphical User Interface, simplifying and speeding up the design
2478 +process.cess. It includes design capabilities for musculoskeletal bodies as
2479 +well as robotic systems, fostering not only the understanding of biological
2480 +motions and enabling better robot designs, but also enabling true Neurorobotic
2481 +experiments that consist of biomimetic models such as tendon-driven robots or a
2482 +transition between biology and technology.</span></p>
2483 +
2484 +<h2></h2>
2485 +
2486 +<h2><a name="_Toc138932346"><span lang=en-DE>NeuroScheme</span></a></h2>
2487 +
2488 +<p class=MsoNormal><span lang=en-DE>&quot;NeuroScheme uses schematic
2489 +representations, such as icons and glyphs, to encode attributes of neural
2490 +structures (neurons, columns, layers, populations, etc.), alleviating problems
2491 +with displaying, navigating and analysing large datasets. It manages
2492 +hierarchically organised neural structures</span><span lang=en-DE
2493 +style='font-family:"Times New Roman",serif'> </span><span lang=en-DE>users can
2494 +navigate through the levels of the hierarchy and hone in on and explore the
2495 +data at their desired level of detail. NeuroScheme has currently two built-in
2496 +&quot;&quot;domains&quot;&quot;, which specify entities, attributes and
2497 +relationships used for specific use cases: the ÒcortexÓ domain, designed for
2498 +navigating and analysing cerebral cortex structures</span><span lang=en-DE
2499 +style='font-family:"Times New Roman",serif'> </span><span lang=en-DE>and the
2500 +ÒcongenÓ domain, used to define the properties of cells and connections, create
2501 +circuits of neurons and build populations.&quot;</span></p>
2502 +
2503 +<h2></h2>
2504 +
2505 +<h2><a name="_Toc138932347"><span lang=en-DE>NeuroSuites</span></a></h2>
2506 +
2507 +<p class=MsoNormal><span lang=en-DE>NeuroSuites is a web-based platform
2508 +designed to handle large-scale, high-dimensional data in the field of
2509 +neuroscience. It offers neuroscience-oriented applications and tools for data
2510 +analysis, machine learning and visualisation, while also providing
2511 +general-purpose tools for data scientists in other research fields. NeuroSuites
2512 +requires no software installation and runs on the backend of a server, making
2513 +it accessible from various devices. The platform's main strengths include its
2514 +defined architecture, ability to handle complex neuroscience data and the
2515 +variety of available tools.</span></p>
2516 +
2517 +<h2></h2>
2518 +
2519 +<h2><a name="_Toc138932348"><span lang=en-DE>NeuroTessMesh</span></a></h2>
2520 +
2521 +<p class=MsoNormal><span lang=en-DE>NeuroTessMesh takes morphological tracings
2522 +of cells acquired by neuroscientists and generates 3D models that approximate
2523 +the neuronal membrane. The resolution of the models can be adapted at the time
2524 +of visualisation. You can colour-code different parts of a morphology,
2525 +differentiating relevant morphological variables or even neuronal activity.
2526 +NeuroTessMesh copes with many of the problems associated with the visualisation
2527 +of neural circuits consisting of large numbers of cells. It facilitates the
2528 +recovery and visualisation of the 3D geometry of cells included in databases,
2529 +such as NeuroMorpho, and allows to approximate missing information such as the
2530 +somaÕs morphology.</span></p>
2531 +
2532 +<h2></h2>
2533 +
2534 +<h2><a name="_Toc138932349"><span lang=en-DE>NMODL Framework</span></a></h2>
2535 +
2536 +<p class=MsoNormal><span lang=en-DE>&quot;NMODL Framework is designed with
2537 +modern compiler and code generation techniques. It provides modular tools for
2538 +parsing, analysing and transforming NMODL it provides an easy to use, high
2539 +level Python API</span><span lang=en-DE style='font-family:"Times New Roman",serif'>
2540 +</span><span lang=en-DE> it generates optimised code for modern compute architectures
2541 +including CPUs and GPUs</span><span lang=en-DE style='font-family:"Times New Roman",serif'>
2542 +</span><span lang=en-DE> it provides flexibility to implement new simulator
2543 +backends and it supports full NMODL specification.&quot;</span></p>
2544 +
2545 +<h2></h2>
2546 +
2547 +<h2><a name="_Toc138932350"><span lang=en-DE>NSuite</span></a></h2>
2548 +
2549 +<p class=MsoNormal><span lang=en-DE>NSuite is a framework for maintaining and
2550 +running benchmarks and validation tests for multi-compartment neural network
2551 +simulations on HPC systems. NSuite automates the process of building simulation
2552 +engines, and running benchmarks and validation tests. NSuite is specifically
2553 +designed to allow easy deployment on HPC systems in testing workflows, such as
2554 +benchmark-driven development or continuous integration. The development of
2555 +NSuite has been driven by the need (1) for a definitive resource for comparing
2556 +performance and correctness of simulation engines on HPC systems, (2) to verify
2557 +the performance and correctness of individual simulation engines as they change
2558 +over time and (3) to test that changes to an HPC system do not cause
2559 +performance or correctness regressions in simulation engines. The framework
2560 +currently supports the simulation engines Arbor, NEURON, and CoreNeuron, while
2561 +allowing other simulation engines to be added.</span></p>
2562 +
2563 +<p class=MsoNormal></p>
2564 +
2565 +<p class=MsoNormal><span lang=en-DE>Nutil</span></p>
2566 +
2567 +<p class=MsoNormal><span lang=en-DE>Nutil is a pre- and post-processing toolbox
2568 +that enables analysis of large collections of histological images of rodent
2569 +brain sections. The software is open source and has both a graphical user
2570 +interface for specifying the input and output parameters and a command-line
2571 +execution option for batch processing. Nutil includes a transformation tool for
2572 +automated scaling, rotation, mirroring and renaming of image files, a file
2573 +format converter, a simple resize tool and a post-processing method for
2574 +quantifying and localising labelled features based on a reference atlas of the
2575 +brain (mouse or rat). The quantification method requires input from customised
2576 +brain atlas maps generated with the QuickNII software, and segmentations
2577 +generated with ilastik or another image analysis tool. The output from Nutil
2578 +include csv reports, 3D point cloud coordinate files and atlas map images
2579 +superimposed with colour-coded objects.</span></p>
2580 +
2581 +<h2></h2>
2582 +
2583 +<h2><a name="_Toc138932351"><span lang=en-DE>ODE-toolbox</span></a></h2>
2584 +
2585 +<p class=MsoNormal><span lang=en-DE>ODE-toolbox is a Python package that
2586 +assists in solver benchmarking, and recommends solvers on the basis of a set of
2587 +user-configurable heuristics. For all dynamical equations that admit an
2588 +analytic solution, ODE-toolbox generates propagator matrices that allow the
2589 +solution to be calculated at machine precision. For all others, first-order
2590 +update expressions are returned based on the Jacobian matrix. In addition to
2591 +continuous dynamics, discrete events can be used to model instantaneous changes
2592 +in system state, such as a neuronal action potential. These can be generated by
2593 +the system under test as well as applied as external stimuli, making
2594 +ODE-toolbox particularly well-suited for applications in computational
2595 +neuroscience.</span></p>
2596 +
2597 +<h2></h2>
2598 +
2599 +<h2><a name="_Toc138932352"><span lang=en-DE>openMINDS</span></a></h2>
2600 +
2601 +<p class=MsoNormal><span lang=en-DE>openMINDS is composed of: (i) integrated
2602 +metadata models adoptable by any graph database system (GDBS), (ii) a set of
2603 +libraries of serviceable metadata instances with external resource references
2604 +for local and global knowledge integration, and (iii) supportive tooling for
2605 +handling the metadata models and instances. Moreover, the framework provides
2606 +machine-readable mappings to other standardisation efforts (e.g., schema.org).
2607 +With this, openMINDS is a unique and powerful metadata framework for flexible
2608 +knowledge integration within and beyond any GDBS.</span></p>
2609 +
2610 +<h2></h2>
2611 +
2612 +<h2><a name="_Toc138932353"><span lang=en-DE>openMINDS metadata for TVB-ready
2613 +data</span></a></h2>
2614 +
2615 +<p class=MsoNormal><span lang=en-DE>Jupyter Python notebook with code and
2616 +commentaries for creating openMINDS metadata version 1.0 in JSON-LD format for
2617 +ingestion of TVB-ready data in EBRAINS Knowledge Graph.</span></p>
2618 +
2619 +<h2></h2>
2620 +
2621 +<h2><a name="_Toc138932354"><span lang=en-DE>PCI</span></a></h2>
2622 +
2623 +<p class=MsoNormal><span lang=en-DE>The notebook allows the computation of the
2624 +PCI Lempel-Ziv and PCI state transitions. In order to run the examples, a wake
2625 +and sleep data set needs to be provided in the Python-MNE format.</span></p>
2626 +
2627 +<h2></h2>
2628 +
2629 +<h2><a name="_Toc138932355"><span lang=en-DE>PIPSA</span></a></h2>
2630 +
2631 +<p class=MsoNormal><span lang=en-DE>PIPSA enables the comparison of the
2632 +electrostatic interaction properties of proteins. It permits the classification
2633 +of proteins according to their interaction properties. PIPSA may assist in
2634 +function assignment, the estimation of binding properties and enzyme kinetic
2635 +parameters.</span></p>
2636 +
2637 +<h2></h2>
2638 +
2639 +<h2><a name="_Toc138932356"><span lang=en-DE>PoSCE</span></a></h2>
2640 +
2641 +<p class=MsoNormal><span lang=en-DE>PoSCE is a functional connectivity
2642 +estimator of fMRI time-series. It relies on the Riemannian geometry of
2643 +covariances and integrates prior knowledge of covariance distribution over a
2644 +population.</span></p>
2645 +
2646 +<h2></h2>
2647 +
2648 +<h2><a name="_Toc138932357"><span lang=en-DE>Provenance API</span></a></h2>
2649 +
2650 +<p class=MsoNormal><span lang=en-DE>The EBRAINS Provenance API is a web service
2651 +to facilitate working with computational provenance metadata. Metadata are
2652 +stored in the ?EBRAINS Knowledge Graph (KG) using openMINDS schemas. The
2653 +Provenance API provides a somewhat simplified interface compared to accessing
2654 +the KG directly and performs checks of metadata consistency. The service covers
2655 +workflows involving simulation, data analysis, visualisation, optimisation,
2656 +data movement and model validation.</span></p>
2657 +
2658 +<h2></h2>
2659 +
2660 +<h2><a name="_Toc138932358"><span lang=en-DE>PyNN</span></a></h2>
2661 +
2662 +<p class=MsoNormal><span lang=en-DE>A model description written with the PyNN
2663 +API and the Python programming language runs on any simulator that PyNN
2664 +supports (currently NEURON, ?NEST and Brian 2) as well as on the ?BrainScaleS
2665 +and ?SpiNNaker neuromorphic hardware systems. PyNN provides a library of
2666 +standard neuron, synapse and synaptic plasticity models, verified to work the
2667 +same on different simulators. PyNN also provides commonly used connectivity
2668 +algorithms (e.g. all-to-all, random, distance-dependent, small-world) but makes
2669 +it easy to provide your own connectivity in a simulator-independent way. PyNN
2670 +transparently supports distributed simulations using MPI.</span></p>
2671 +
2672 +<h2></h2>
2673 +
2674 +<h2><a name="_Toc138932359"><span lang=en-DE>Pyramidal Explorer</span></a></h2>
2675 +
2676 +<p class=MsoNormal><span lang=en-DE>PyramidalExplorer is a tool to
2677 +interactively explore and reveal the detailed organisation of the microanatomy
2678 +of pyramidal neurons with functionally related models. Possible regional
2679 +differences in the pyramidal cell architecture can be interactively discovered
2680 +by combining quantitative morphological information about the structure of the
2681 +cell with implemented functional models. The key contribution of this tool is the
2682 +morpho-functional oriented design,  allowing the user to navigate within the 3D
2683 +dataset, filter and perform content-based retrieval operations to find the 
2684 +spines that are alike and dissimilar within the neuron, according to particular
2685 +morphological or functional variables.</span></p>
2686 +
2687 +<h2></h2>
2688 +
2689 +<h2><a name="_Toc138932360"><span lang=en-DE>QCAlign software</span></a></h2>
2690 +
2691 +<p class=MsoNormal><span lang=en-DE>The QUINT workflow enables spatial analysis
2692 +of labelling in series of brain sections from mouse and rat based on
2693 +registration to a reference brain atlas. The QCAlign software supports the use
2694 +of QUINT for high-throughput studies by providing information about: 1. The
2695 +quality of the section images used as input to the QUINT workflow. 2. The
2696 +quality of the atlas registration performed in the QUINT workflow. 3. QCAlign
2697 +also makes it easier for the user to explore the atlas hierarchy and decide on
2698 +a customised hierarchy level to use for the investigation</span></p>
2699 +
2700 +<h2></h2>
2701 +
2702 +<h2><a name="_Toc138932361"><span lang=en-DE>QuickNII</span></a></h2>
2703 +
2704 +<p class=MsoNormal><span lang=en-DE>QuickNII is a tool for user-guided affine
2705 +registration (anchoring) of 2D experimental image data, typically high
2706 +resolution microscopic images, to 3D atlas reference space, facilitating data
2707 +integration through standardised coordinate systems. Key features: Generate
2708 +user-defined cut planes through the atlas templates, matching the orientation
2709 +of the cut plane of the 2D experimental image data, as a first step towards
2710 +anchoring of images to the relevant atlas template. Propagate spatial
2711 +transformations across series of sections following anchoring of selected
2712 +images.</span></p>
2713 +
2714 +<h2></h2>
2715 +
2716 +<h2><a name="_Toc138932362"><span lang=en-DE>Quota Manager</span></a></h2>
2717 +
2718 +<p class=MsoNormal><span lang=en-DE>The Quota Manager enables each EBRAINS
2719 +service to manage user quotas for resources EBRAINS users consume in their
2720 +respective services. The goal is to encourage the responsible use of resources.
2721 +It is recommended that all users (except possibly guest accounts) are provided
2722 +with a default quota, and that specific users have the option of receiving
2723 +larger quotas based on their affiliation, role or motivated requests.</span></p>
2724 +
2725 +<h2></h2>
2726 +
2727 +<h2><a name="_Toc138932363"><span lang=en-DE>RateML</span></a></h2>
2728 +
2729 +<p class=MsoNormal><span lang=en-DE>RateML enables users to generate
2730 +whole-brain network models from a succinct declarative description, in which
2731 +the mathematics of the model are described without specifying how their
2732 +simulation should be implemented. RateML builds on NeuroMLÕs Low Entropy Model
2733 +Specification (LEMS), an XML-based language for specifying models of dynamical systems,
2734 +allowing descriptions of neural mass and discretized neural field models, as
2735 +implemented by the TVB simulator. The end user describes their modelÕs
2736 +mathematics once and generates and runs code for different languages, targeting
2737 +both CPUs for fast single simulations and GPUs for parallel ensemble
2738 +simulations.</span></p>
2739 +
2740 +<h2></h2>
2741 +
2742 +<h2><a name="_Toc138932364"><span lang=en-DE>Region-wise CBPP using the Julich
2743 +BrainÊCytoarchitectonic Atlas</span></a></h2>
2744 +
2745 +<p class=MsoNormal><span lang=en-DE>Many studies have been investigating the
2746 +relationships between interindividual variability in brain regionsÕ
2747 +connectivity and behavioural phenotypes, by utilising connectivity-based
2748 +prediction models. Recently, we demonstrated that an approach based on the
2749 +combination of whole-brain and region-wise CBPP can provide important insight
2750 +into the predictive model, and hence in brain-behaviour relationships, by
2751 +offering interpretable patterns. Here, we applied this approach using the
2752 +Julich Brain Cytoarchitectonic Atlas with the resting-state functional
2753 +connectivity and psychometric variables from the Human Connectome Project
2754 +dataset, illustrating each brain regionÕs predictive power for a range of
2755 +psychometric variables. As a result, a psychometric prediction profile was
2756 +established for each brain region, which can be validated against brain mapping
2757 +literature.</span></p>
2758 +
2759 +<h2></h2>
2760 +
2761 +<h2><a name="_Toc138932365"><span lang=en-DE>RRI Capacity Development Resources</span></a></h2>
2762 +
2763 +<p class=MsoNormal><span lang=en-DE>A series of training resources developed to
2764 +enable anticipation, critical reflection and public engagement/deliberation of
2765 +societal consequences of brain research and innovation activities. These
2766 +resources were designed primarily for HBP researchers and EBRAINS leadership
2767 +and management, involving EBRAINS data and infrastructure providers. However,
2768 +they are also useful for engaging the wider public with RRI. The resources are
2769 +based on the legacy of over 10 years of research and activities of the ethics
2770 +and society-team in the HBP. They cover important RRI-related topics on
2771 +neuroethics, data governance, dual-use, public engagement and foresight,
2772 +diversity, search integrity etc.</span></p>
2773 +
2774 +<h2></h2>
2775 +
2776 +<h2><a name="_Toc138932366"><span lang=en-DE>rsHRF</span></a></h2>
2777 +
2778 +<p class=MsoNormal><span lang=en-DE>This toolbox is aimed to retrieve the
2779 +onsets of pseudo-events triggering an hemodynamic response from resting state
2780 +fMRI BOLD signals. It is based on point process theory and fits a model to
2781 +retrieve the optimal lag between the events and the HRF onset, as well as the
2782 +HRF shape, using different shape parameters or combinations of basis functions.
2783 +Once the HRF has been retrieved for each voxel/vertex, it can be deconvolved
2784 +from the time series (for example, to improve lag-based connectivity
2785 +estimates), or one can map the shape parameters everywhere in the brain
2786 +(including white matter) and use it as a pathophysiological indicator.</span></p>
2787 +
2788 +<h2></h2>
2789 +
2790 +<h2><a name="_Toc138932367"><span lang=en-DE>RTNeuron</span></a></h2>
2791 +
2792 +<p class=MsoNormal><span lang=en-DE>The main utility of RTNeuron is twofold:
2793 +(i) the interactive visual inspection of structural and functional features of
2794 +the cortical column model and (ii) the generation of high-quality movies and
2795 +images for presentations and publications.RTNeuron provides a C++ library with
2796 +an OpenGL-based rendering backend, a Python wrapping and a Python application
2797 +called rtneuron. RTNeuron is only supported in GNU/Linux systems. However, it
2798 +should also be possible to build it on Windows systems. For OS/X it may be
2799 +quite challenging and require changes in OpenGL-related code to get it working.</span></p>
2800 +
2801 +<h2></h2>
2802 +
2803 +<h2><a name="_Toc138932368"><span lang=en-DE>sbs: Spike-based Sampling</span></a></h2>
2804 +
2805 +<p class=MsoNormal><span lang=en-DE>Spike-based sampling, sbs, is a software
2806 +suite that takes care of calibrating spiking neurons for given target
2807 +distributions and allows the evaluation of these distributions as they are
2808 +produced by stochastic spiking networks.</span></p>
2809 +
2810 +<h2></h2>
2811 +
2812 +<h2><a name="_Toc138932369"><span lang=en-DE>SDA 7</span></a></h2>
2813 +
2814 +<p class=MsoNormal><span lang=en-DE>SDA 7 can be used to carry out Brownian
2815 +dynamics simulations of the diffusional association in a continuum aqueous
2816 +solvent of two solute molecules, e.g., proteins, or of a solute molecule to an
2817 +inorganic surface. SDA 7 can also be used to simulate the diffusion of multiple
2818 +proteins, in dilute or concentrated solutions, e.g., to study the effects of
2819 +macromolecular crowding.</span></p>
2820 +
2821 +<h2></h2>
2822 +
2823 +<h2><a name="_Toc138932370"><span lang=en-DE>Shape &amp; Appearance Modelling</span></a></h2>
2824 +
2825 +<p class=MsoNormal><span lang=en-DE>A framework for automatically learning
2826 +shape and appearance models for medical (and certain other) images. The
2827 +algorithm was developed with the aim of eventually enabling distributed
2828 +privacy-preserving analysis of brain image data, such that shared information
2829 +(shape and appearance basis functions) may be passed across sites, whereas
2830 +latent variables that encode individual images remain secure within each site.
2831 +These latent variables are proposed as features for privacy-preserving data
2832 +mining applications.</span></p>
2833 +
2834 +<h2></h2>
2835 +
2836 +<h2><a name="_Toc138932371"><span lang=en-DE>siibra-api</span></a></h2>
2837 +
2838 +<p class=MsoNormal><span lang=en-DE>siibra-api provides an HTTP wrapper around
2839 +?siibra-python, allowing developers to access atlas (meta)data over HTTP
2840 +protocol. Deployed on the EBRAINS infrastructure, developers can access the
2841 +centralised (meta)data on atlases, as provided by siibra-python, regardless of
2842 +the programming language.</span></p>
2843 +
2844 +<h2></h2>
2845 +
2846 +<h2><a name="_Toc138932372"><span lang=en-DE>siibra-explorer</span></a></h2>
2847 +
2848 +<p class=MsoNormal><span lang=en-DE>The interactive atlas viewer
2849 +siibra-explorer allows exploring the different EBRAINS atlases for the human,
2850 +monkey and rodent brains together with a comprehensive set of linked multimodal
2851 +data features. It provides a 3-planar view of a parcellated reference volume
2852 +combined with a rotatable overview of the 3D surface. Several templates can be
2853 +selected to navigate through the brain from MRI-scale to microscopic
2854 +resolution, allowing inspection of terabyte-size image data. Anatomically
2855 +anchored datasets reflecting aspects of cellular and molecular organisation,
2856 +fibres, function and connectivity can be discovered by selecting brain regions
2857 +from parcellations, or zooming and panning the reference brain. siibra-explorer
2858 +also allows annotation of brain locations as points and polygons and is
2859 +extensible via interactive plugins.</span></p>
2860 +
2861 +<h2></h2>
2862 +
2863 +<h2><a name="_Toc138932373"><span lang=en-DE>siibra-python</span></a></h2>
2864 +
2865 +<p class=MsoNormal><span lang=en-DE>siibra-python is a Python client to a brain
2866 +atlas framework that integrates brain parcellations and reference spaces at
2867 +different spatial scales and connects them with a broad range of multimodal
2868 +regional data features. It aims to facilitate programmatic and reproducible
2869 +incorporation of brain parcellations and brain region features from different
2870 +sources into neuroscience workflows. Also, siibra-python provides an easy
2871 +access to data features on the ?EBRAINS Knowledge Graph in a well-structured
2872 +manner. Users can preconfigure their own data to use within siibra-python.</span></p>
2873 +
2874 +<h2></h2>
2875 +
2876 +<h2><a name="_Toc138932374"><span lang=en-DE>Single Cell Model (Re)builder
2877 +Notebook</span></a></h2>
2878 +
2879 +<p class=MsoNormal><span lang=en-DE>The Single Cell Model (Re)builder Notebook
2880 +is a web application, implemented via a Jupyter Notebook on EBRAINS, which
2881 +allows users to configure the BluePyOpt to re-run an optimisation with their
2882 +own choices for the parameters range. The optimisation jobs are submitted
2883 +through Neuroscience Gateway.</span></p>
2884 +
2885 +<h2></h2>
2886 +
2887 +<h2><a name="_Toc138932375"><span lang=en-DE>Slurm Plugin for Co-allocation of
2888 +Compute and Data Resources</span></a></h2>
2889 +
2890 +<p class=MsoNormal><span lang=en-DE>This Simple linux utility for resource
2891 +management (Slurm) plugin enables the co-allocation of compute and data resources
2892 +on a shared multi-tiered storage cluster by estimating waiting times when the
2893 +high-performance storage (burst buffers) will become available to submitted
2894 +jobs. Based on the current job queue and the estimated waiting time, the plugin
2895 +decides whether scheduling the high-performance or lower-performance storage
2896 +system (parallel file system) benefits the job's turnaround time. The
2897 +estimation depends on additional information the user provides at submission
2898 +time.</span></p>
2899 +
2900 +<h2></h2>
2901 +
2902 +<h2><a name="_Toc138932376"><span lang=en-DE>Snudda</span></a></h2>
2903 +
2904 +<p class=MsoNormal><span lang=en-DE>Snudda (ÔtouchÕ in Swedish) allows the user
2905 +to set up and generate microcircuits where the connectivity between neurons is
2906 +based on reconstructed neuron morphologies. The touch detection algorithm looks
2907 +for overlaps of axons and dendrites, and places putative synapses where they
2908 +touch. The putative synapses are pruned, removing a fraction to match
2909 +statistics from pairwise connectivity experiments. If needed, Snudda can also
2910 +use probability functions to create realistic microcircuits. The Snudda
2911 +software is written in Python and includes support for supercomputers. It uses
2912 +ipyparallel to parallelise network creation, and NEURON as the backend for
2913 +simulations. Install using pip or by directly downloading.</span></p>
2914 +
2915 +<h2></h2>
2916 +
2917 +<h2><a name="_Toc138932377"><span lang=en-DE>SomaSegmenter</span></a></h2>
2918 +
2919 +<p class=MsoNormal><span lang=en-DE>SomaSegmenter allows neuronal soma
2920 +segmentation in fluorescence microscopy imaging datasets with the use of a
2921 +parametrised version of the U-Net segmentation model, including additional
2922 +features such as residual links and tile-based frame reconstruction.</span></p>
2923 +
2924 +<h2></h2>
2925 +
2926 +<h2><a name="_Toc138932378"><span lang=en-DE>SpiNNaker</span></a></h2>
2927 +
2928 +<p class=MsoNormal><span lang=en-DE>SpiNNaker is a neuromorphic computer with
2929 +over a million low power, small memory ARM cores arranged in chips, connected
2930 +together with a unique brain-like mesh network, and designed to simulate
2931 +networks of spiking point neurons. Software is provided to compile networks
2932 +described with PyNN into running simulations, and to extract and convert
2933 +results into the neo data format, as well as providing support for live
2934 +interaction with running simulations. This allows integration with external
2935 +devices such as real or virtual robotics as well as live simulation
2936 +visualisation. Scripts can be written and executed using Jupyter for
2937 +interactive access.</span></p>
2938 +
2939 +<h2></h2>
2940 +
2941 +<h2><a name="_Toc138932379"><span lang=en-DE>SSB toolkit</span></a></h2>
2942 +
2943 +<p class=MsoNormal><span lang=en-DE>The SSB toolkit is an open-source Python
2944 +library to simulate mathematical models of the signal transduction pathways of
2945 +G-protein coupled receptors (GPCRs). By merging structural macromolecular data
2946 +with systems biology simulations, the framework allows simulation of the signal
2947 +transduction kinetics induced by ligand-GPCR interactions, as well as the consequent
2948 +change of concentration of signalling molecular species, as a function of time
2949 +and ligand concentration. Therefore, this tool allows the possibility to
2950 +investigate the subcellular effects of ligand binding upon receptor activation,
2951 +deepening the understanding of the relationship between the molecular level of
2952 +ligand-target interactions and higher-level cellular and physiological or
2953 +pathological response mechanisms.</span></p>
2954 +
2955 +<h2></h2>
2956 +
2957 +<h2><a name="_Toc138932380"><span lang=en-DE>Subcellular model building and
2958 +calibration tool set</span></a></h2>
2959 +
2960 +<p class=MsoNormal><span lang=en-DE>The toolset includes interoperable modules
2961 +for: model building, calibration (parameter estimation) and model analysis. All
2962 +information needed to perform these tasks (models, experimental calibration
2963 +data and prior assumptions on parameter distributions) are stored in a
2964 +structured, human- and machine-readable file format based on SBtab. The toolset
2965 +enables simulations of the same model in simulators with different
2966 +characteristics, e.g., STEPS, NEURON, MATLABÕs Simbiology and R via automatic
2967 +code generation. The parameter estimation can include uncertainty
2968 +quantification and is done by optimisation or Bayesian approaches. Model
2969 +analysis includes global sensitivity analysis and functionality for analysing
2970 +thermodynamic constraints and conserved moieties.</span></p>
2971 +
2972 +<h2></h2>
2973 +
2974 +<h2><a name="_Toc138932381"><span lang=en-DE>Synaptic Events Fitting</span></a></h2>
2975 +
2976 +<p class=MsoNormal><span lang=en-DE>The Synaptic Events Fitting is a web
2977 +application, implemented in a Jupyter Notebook on EBRAINS that allows users to
2978 +fit synaptic events using data and models from the ?EBRAINS Knowledge Graph
2979 +(KG). Select, download and visualise experimental data from the KG and then choose
2980 +the data to be fitted. A mod file is then selected (local or default) together
2981 +with the corresponding configuration file (including protocol and the name of
2982 +the parameters to be fitted, their initial values and allowed variation range,
2983 +exclusion rules and an optional set of dependencies). The fitting procedure can
2984 +run on Neuroscience Gateway. Fetch the fitting results from the storage of the
2985 +HPC system to the storage of the Collab or to analyse the optimised parameters.</span></p>
2986 +
2987 +<h2></h2>
2988 +
2989 +<h2><a name="_Toc138932382"><span lang=en-DE>Synaptic Plasticity Explorer</span></a></h2>
2990 +
2991 +<p class=MsoNormal><span lang=en-DE>The Synaptic Plasticity Explorer is a web
2992 +application, implemented via a Jupyter Notebook on EBRAINS, which allows to
2993 +configure and test, through an intuitive GUI, different synaptic plasticity
2994 +models and protocols on single cell optimised models, available in the EBRAINS
2995 +Model Catalog. It consists of two tabs: ÒConfigÓ, where the user can specify
2996 +the plasticity model to use and the synaptic parameters, and ÒSimÓ, where the
2997 +recording location, weightÕs evolution and number of simulations to run are
2998 +defined. The results are plotted at the end of the simulation and the traces
2999 +are available for download.</span></p>
3000 +
3001 +<h2></h2>
3002 +
3003 +<h2><a name="_Toc138932383"><span lang=en-DE>Synaptic proteome database
3004 +(SQLite)</span></a></h2>
3005 +
3006 +<p class=MsoNormal><span lang=en-DE>Integration of 57 published synaptic
3007 +proteomic datasets reveals a stunningly complex picture involving over 7000
3008 +proteins. Molecular complexes were reconstructed using evidence-based
3009 +protein-protein interaction data available from public databases. The
3010 +constructed molecular interaction network model is embedded into an SQLite
3011 +implementation, allowing queries that generate custom network models based on
3012 +meta-data including species, synaptic compartment, brain region, and method of
3013 +extraction.</span></p>
3014 +
3015 +<h2></h2>
3016 +
3017 +<h2><a name="_Toc138932384"><span lang=en-DE>Synaptome.db</span></a></h2>
3018 +
3019 +<p class=MsoNormal><span lang=en-DE>The Synaptome.db bioconductor package
3020 +contains a local copy of the Synaptic proteome database. On top of this it
3021 +provides a set of utility R functions to query and analyse its content. It
3022 +allows for extraction of information for specific genes and building the
3023 +protein-protein interaction graph for gene sets, synaptic compartments and
3024 +brain regions.</span></p>
3025 +
3026 +<h2></h2>
3027 +
3028 +<h2><a name="_Toc138932385"><span lang=en-DE>Tide</span></a></h2>
3029 +
3030 +<p class=MsoNormal><span lang=en-DE>BlueBrain's Tide provides multi-window,
3031 +multi-user touch interaction on large surfaces Ð think of a giant collaborative
3032 +wall-mounted tablet. Tide is a distributed application that can run on multiple
3033 +machines to power display walls or projection systems of any size. Its user interface
3034 +is designed to offer an intuitive experience on touch walls. It works just as
3035 +well on non-touch-capable installations by using its web interface from any web
3036 +browser.</span></p>
3037 +
3038 +<h2></h2>
3039 +
3040 +<h2><a name="_Toc138932386"><span lang=en-DE>TVB EBRAINS</span></a></h2>
3041 +
3042 +<p class=MsoNormal><span lang=en-DE>TVB EBRAINS is the principal full brain
3043 +network simulation engine in EBRAINS and covers every aspect of realising
3044 +personalised whole-brain simulations on the EBRAINS platform. It consists of
3045 +the simulation tools and adaptors connecting the data, atlas and computing
3046 +services to the rest of the TVB ecosystem and Cloud services available in
3047 +EBRAINS. As such it allows the user to find and fetch relevant datasets through
3048 +the ?EBRAINS Knowledge Graph and Atlas services, construct the personalised TVB
3049 +models and use the HPC systems to perform parameter exploration, optimisation and
3050 +inference studies. The user can orchestrate the workflow from the Jupyterlab
3051 +interactive computing environment of the EBRAINS Collaboratory or use the
3052 +dedicated web application of TVB.</span></p>
3053 +
3054 +<h2></h2>
3055 +
3056 +<h2><a name="_Toc138932387"><span lang=en-DE>TVB Image Processing Pipeline</span></a></h2>
3057 +
3058 +<p class=MsoNormal><span lang=en-DE>TVB Image Processing Pipeline takes multimodal
3059 +MRI data sets (anatomical, functional and diffusion-weighted MRI) as input and
3060 +generates structural connectomes, region-average fMRI time series, functional
3061 +connectomes, brain surfaces, electrode positions, lead field matrices and atlas
3062 +parcellations as output. The pipeline performs preprocessing and
3063 +distortion-correction on MRI data as well as white matter fibre bundle
3064 +tractography on diffusion data. Outputs are formatted according to two data
3065 +standards: a TVB-ready data set that can be directly used to simulate brain
3066 +network models and the same output in BIDS format.</span></p>
3067 +
3068 +<h2></h2>
3069 +
3070 +<h2><a name="_Toc138932388"><span lang=en-DE>TVB Inversion</span></a></h2>
3071 +
3072 +<p class=MsoNormal><span lang=en-DE>The TVB Inversion package implements the
3073 +machinery required to perform parameter exploration and  inference over
3074 +parameters of ?The Virtual Brain simulator. It implements Simulation Based
3075 +Inference (SBI) which is a Bayesian inference method for complex models, where
3076 +calculation of the likelihood function is either analytically or
3077 +computationally intractable. As such, it allows the user to formulate with
3078 +great expressive power both the model and the inference scenario in terms of
3079 +observed data features and model parameters. Part of the integration with TVB
3080 +entails the option to perform numerous simulations in parallel, which can be
3081 +used for parameter space exploration.</span></p>
3082 +
3083 +<h2></h2>
3084 +
3085 +<h2><a name="_Toc138932389"><span lang=en-DE>TVB Web App</span></a></h2>
3086 +
3087 +<p class=MsoNormal><span lang=en-DE>TVB Web App provides The Virtual Brain
3088 +Simulator as an EBRAINS Cloud Service with an HPC back-end. Scientists can run
3089 +intense personalised brain simulations without having to deploy software. Users
3090 +can access the service with their EBRAINS credentials (single sign on). TVB Web
3091 +App uses private/public key cryptography, sandboxing, and access control to
3092 +protect personalised health information contained in digital human brain twins
3093 +while being processed on HPC. Users can upload their connectomes or use
3094 +TVB-ready image-derived data discoverable via the ?EBRAINS Knowledge Graph.
3095 +Users can also use containerised processing workflows available on EBRAINS to
3096 +render image raw data into simulation-ready formats.</span></p>
3097 +
3098 +<h2></h2>
3099 +
3100 +<h2><a name="_Toc138932390"><span lang=en-DE>TVB Widgets</span></a></h2>
3101 +
3102 +<p class=MsoNormal><span lang=en-DE>In order to support the usability of
3103 +EBRAINS workflows, TVB-widgets has been developed as a set of modular graphic
3104 +components and software solutions, easy to use in the ?Collaboratory within the
3105 +JupyterLab. These GUI components are based on and under open source licence,
3106 +supporting open neuroscience and support features like: Setup of models and
3107 +region-specific or cohort simulations. Selection of Data sources and their
3108 +links to models. Querying data from ?siibra and the ?EBRAINS Knowledge Graph.
3109 +Deployment and monitoring jobs on HPC resources. Analysis and visualisation.
3110 +Visual workflow builder for configuring and launching TVB simulations.</span></p>
3111 +
3112 +<h2></h2>
3113 +
3114 +<h2><a name="_Toc138932391"><span lang=en-DE>TVB-Multiscale</span></a></h2>
3115 +
3116 +<p class=MsoNormal><span lang=en-DE>TVB-Multiscale is a Python toolbox aimed at
3117 +facilitating the configuration of multiscale brain models and their
3118 +co-simulation with TVB and spiking network simulators (currently ?NEST,
3119 +?NetPyNE (NEURON) and ANNarchy). A multiscale brain model consists of a full
3120 +brain model formulated at the coarse scale of networks of tens up to thousands
3121 +of brain regions, and an additional model of networks of spiking neurons
3122 +describing selected brain regions at a finer scale. The toolbox has a
3123 +user-friendly interface for configuring different kinds of models for
3124 +transforming and exchanging data between the two scales during co-simulation.</span></p>
3125 +
3126 +<h2></h2>
3127 +
3128 +<h2><a name="_Toc138932392"><span lang=en-DE>VIOLA</span></a></h2>
3129 +
3130 +<p class=MsoNormal><span lang=en-DE>VIOLA is an interactive, web-based tool to
3131 +visualise activity data in multiple 2D layers such as the simulation output of
3132 +neuronal networks with 2D geometry. As a reference implementation for a
3133 +developed set of interactive visualisation concepts, the tool combines and
3134 +adapts modern interactive visualisation paradigms, such as coordinated multiple
3135 +views, for massively parallel neurophysiological data. The software allows for
3136 +an explorative and qualitative assessment of the spatiotemporal features of
3137 +neuronal activity, which can be performed prior to a detailed quantitative data
3138 +analysis of specific aspects of the data.</span></p>
3139 +
3140 +<h2></h2>
3141 +
3142 +<h2><a name="_Toc138932393"><span lang=en-DE>Vishnu 1.0</span></a></h2>
3143 +
3144 +<p class=MsoNormal><span lang=en-DE>DC Explorer, Pyramidal Explorer and Clint
3145 +Explorer are the core of an application suite designed to help scientists to
3146 +explore their data. Vishnu 1.0 is a communication framework that allows them to
3147 +interchange information and cooperate in real time. It provides a unique access
3148 +point to the three applications and manages a database with the usersÕ
3149 +datasets. Vishnu was originally designed to integrate data for
3150 +Espina.Whole-brain-scale tools.</span></p>
3151 +
3152 +<h2></h2>
3153 +
3154 +<h2><a name="_Toc138932394"><span lang=en-DE>ViSimpl</span></a></h2>
3155 +
3156 +<p class=MsoNormal><span lang=en-DE>ViSimpl integrates a set of visualisation
3157 +and interaction components that provide a semantic view of brain data with the
3158 +aim of improving its analysis procedures. ViSimpl provides 3D particle-based
3159 +rendering that visualises simulation data with their associated spatial and
3160 +temporal information, enhancing the knowledge extraction process. It also
3161 +provides abstract representations of the time-varying magnitudes, supporting
3162 +different data aggregation and disaggregation operations and giving focus and
3163 +context clues. In addition, ViSimpl provides synchronised playback control of
3164 +the simulation being analysed.</span></p>
3165 +
3166 +<h2></h2>
3167 +
3168 +<h2><a name="_Toc138932395"><span lang=en-DE>VisuAlign</span></a></h2>
3169 +
3170 +<p class=MsoNormal><span lang=en-DE>VisuAlign is a tool for user-guided
3171 +nonlinear registration after ?QuickNII of 2D experimental image data, typically
3172 +high resolution microscopic images, to 3D atlas reference space, facilitating
3173 +data integration through standardised coordinate systems. Key features:
3174 +Generate user-defined cut planes through the atlas templates, matching the
3175 +orientation of the cut plane of the 2D experimental image data, as a first step
3176 +towards anchoring of images to the relevant atlas template. Propagate spatial
3177 +transformations across series of sections following anchoring of selected
3178 +images.</span></p>
3179 +
3180 +<h2></h2>
3181 +
3182 +<h2><a name="_Toc138932396"><span lang=en-DE>VMetaFlow</span></a></h2>
3183 +
3184 +<p class=MsoNormal><span lang=en-DE>VMetaFlow is an abstraction layer placed
3185 +over existing visual grammars and visualisation declarative languages,
3186 +providing them with interoperability mechanisms. The main contribution of this
3187 +research is to provide a user-friendly system to design visualisation and data
3188 +processing operations that can be interconnected to form data analysis
3189 +workflows. Visualisations and data processes can be saved as cards. Cards and
3190 +workflows can be saved, distributed and reused between users.</span></p>
3191 +
3192 +<h2></h2>
3193 +
3194 +<h2><a name="_Toc138932397"><span lang=en-DE>Voluba</span></a></h2>
3195 +
3196 +<p class=MsoNormal><span lang=en-DE>A common problem in high-resolution brain
3197 +atlasing is spatial anchoring of volumes of interest from imaging experiments
3198 +into the detailed anatomical context of an ultrahigh-resolution reference model
3199 +like BigBrain. The interactive volumetric alignment tool voluba is implemented
3200 +as a web service and allows anchoring of volumetric image data to reference
3201 +volumes at microscopical spatial resolutions. It enables interactive
3202 +manipulation of image position, scale, and orientation, flipping of coordinate
3203 +axes, and entering of anatomical point landmarks in 3D. The resulting
3204 +transformation parameters can, amongst others, be downloaded or used to view
3205 +the anchored image volume in the interactive atlas viewer siibra-explorer.</span></p>
3206 +
3207 +<h2></h2>
3208 +
3209 +<h2><a name="_Toc138932398"><span lang=en-DE>WebAlign</span></a></h2>
3210 +
3211 +<p class=MsoNormal><span lang=en-DE>WebAlign is the web version of ?QuickNII.
3212 +Presently, it is available as a community app in the ?Collaboratory. Features
3213 +include: Spatial registration of sectional image data. Generation of customised
3214 +atlas maps for your sectional image data.</span></p>
3215 +
3216 +<h2></h2>
3217 +
3218 +<h2><a name="_Toc138932399"><span lang=en-DE>Webilastik</span></a></h2>
3219 +
3220 +<p class=MsoNormal><span lang=en-DE>webilastik brings the popular machine
3221 +learning-based image analysis tool ilastik from the desktop into the browser.
3222 +Users can perform semantic segmentation tasks on their data in the cloud.
3223 +webilastik runs computations on federated EBRAINS HPC resources and uses
3224 +EBRAINS infrastructure for data access and storage. webilastik makes machine
3225 +learning-based image analysis workflows accessible to users without deep
3226 +knowledge of image analysis and machine learning. webilastik is part of the
3227 +QUINT workflow for extraction, quantification and analysis of features from
3228 +rodent histological images.</span></p>
3229 +
3230 +<h2></h2>
3231 +
3232 +<h2><a name="_Toc138932400"><span lang=en-DE>WebWarp</span></a></h2>
3233 +
3234 +<p class=MsoNormal><span lang=en-DE>WebWarp is the web version of ?VisuAlign.
3235 +Presently, it is available as a community app in the ?Collaboratory. Features
3236 +include: Nonlinear refinements of atlas registration by WebAlign of sectional
3237 +image data. Generation of customised atlas maps for your sectional image data.</span></p>
3238 +
3239 +<h2></h2>
3240 +
3241 +<h2><a name="_Toc138932401"><span lang=en-DE>ZetaStitcher</span></a></h2>
3242 +
3243 +<p class=MsoNormal><span lang=en-DE>ZetaStitcher is a Python package designed
3244 +to stitch large volumetric images, such as those produced by Light-Sheet
3245 +Fluorescence Microscopes. It is able to quickly compute the optimal alignment
3246 +of large mosaics of tiles thanks to its ability to perform a sampling along the
3247 +tile depth, i.e., pairwise alignment is computed only at certain depths along
3248 +the thickness of the tile. This greatly reduces the amount of data that needs
3249 +to be read and transferred, thus, making the process much faster. ZetaStitcher
3250 +comes with an API that can be used to programmatically access the aligned
3251 +volume in a virtual fashion as if it were a big NumPy array, without having to
3252 +produce the fused 3D image of the whole sample.Cellular- and subcellular-scale
3253 +tools.</span></p>
3254 +
3255 +<h2></h2>
3256 +
3257 +<h2><a name="_Toc138932402"><span lang=en-DE>TauRAMD</span></a></h2>
3258 +
3259 +<p class=MsoNormal><span lang=en-DE>The TauRAMD technique makes use of RAMD
3260 +simulations to compute relative residence times (or dissociation rates) of
3261 +protein-ligand complexes. In the RAMD method, the egress of a molecule from a
3262 +target receptor is accelerated by the application of an adaptive randomly
3263 +oriented force on the ligand. This enables ligand egress events to be observed
3264 +in short, nanosecond timescale simulations without imposing any bias regarding
3265 +the ligand egress route taken. If coupled to the MD-IFP tool, the TauRAMD
3266 +method can be used to investigate dissociation mechanisms and characterize
3267 +transition states.</span></p>
3268 +
3269 +</div>
3270 +
3271 +</body>
3272 +
3273 +</html>
3274 +
3275 +{{/html}}
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