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1 EBRAINS offers an extensive range of data and also provides compute resources (High-performance computing and Neuromorphic hardware). SLU can guide your journey on EBRAINS, show you how you find tools/data/related research projects appropriate for your research, and help you translate your work from the structured formalization process of science to technical requirements on EBRAINS.
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3 EBRAINS support teams will help you connect to the community and give you tips for creating your simulations and models. If you are a neuroscientist, professor, or student, or are you interested in brain-related research, simulation, AI, robotics, and brain-inspired hardware check out what EBRAINS has for you.
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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.
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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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422
423 <body lang=EN-US link="#0563C1" vlink="#954F72">
424
425 <div class=WordSection1>
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>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.</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>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.</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></p>
1431
1432 <h2><a name="_Toc138932265"><span lang=en-DE>Brion</span></a></h2>
1433
1434 <p class=MsoNormal><span lang=en-DE>Brion is a C++ project for read and write
1435 access to Blue Brain data structures, including BlueConfig/CircuitConfig,
1436 Circuit, CompartmentReport, Mesh, Morphology, Synapse and Target files. It also
1437 offers an interface in Python.</span></p>
1438
1439 <h2></h2>
1440
1441 <h2><a name="_Toc138932266"><span lang=en-DE>BSB</span></a></h2>
1442
1443 <p class=MsoNormal><span lang=en-DE>The BSB reconstructs realistic neural
1444 circuits by placing and connecting fibres and neurons with detailed
1445 morphologies or only simplified geometrical features. Configure your model the
1446 way you need. Interfaces with several simulators (CoreNEURON, Arbor, NEST)
1447 allow simulation of the reconstructed network and investigation of the
1448 structure-function-dynamics relationships at different levels of resolution.
1449 The 'scaffold' design allows an easy model reconfiguration reflecting variants
1450 across brain regions, animal species and physio-pathological conditions without
1451 dismounting the basic network structure. The BSB provides effortless parallel
1452 computing both for the reconstruction and simulation phase.</span></p>
1453
1454 <h2></h2>
1455
1456 <h2><a name="_Toc138932267"><span lang=en-DE>BSP Service Account</span></a></h2>
1457
1458 <p class=MsoNormal><span lang=en-DE>The BSP Service Account is a rest API
1459 service that allows developers to submit user's jobs on HPC systems and
1460 retrieve results using the EBRAINS authentication, even if users don't have a
1461 personal account on the available HPC facilities.</span></p>
1462
1463 <h2></h2>
1464
1465 <h2><a name="_Toc138932268"><span lang=en-DE>bsp-usecase-wizard</span></a></h2>
1466
1467 <p class=MsoNormal><span lang=en-DE>The CLS interactive workflows and use cases
1468 application guides the users through the resolution of realistic scientific
1469 problems. They are implemented as either front-end or full stack web
1470 applications or Python-based Jupyter Notebooks that allow the user to
1471 interactively build, reconstruct or simulate data-driven brain models and
1472 perform data analysis visualisation. Web applications are freely accessible and
1473 only require authentication to EBRAINS when specific actions are required
1474 (e.g., submitting a simulation job to an HBP HPC system). Jupyter Notebooks are
1475 cloned to the lab.ebrains.eu platform and require authentication via an EBRAINS
1476 account.</span></p>
1477
1478 <h2></h2>
1479
1480 <h2><a name="_Toc138932269"><span lang=en-DE>CGMD Platform</span></a></h2>
1481
1482 <p class=MsoNormal><span lang=en-DE>Recent advances in CGMD simulations have
1483 allowed longer and larger molecular dynamics simulations of biological
1484 macromolecules and their interactions. The CGMD platform is dedicated to the
1485 preparation, running, and analysis of CGMD simulations, and built on a
1486 completely revisited version of the Martini coarsE gRained MembrAne proteIn
1487 Dynamics (MERMAID) web server. In its current version, the platform expands the
1488 existing implementation of the Martini force field for membrane proteins to
1489 also allow the simulation of soluble proteins using the Martini and SIRAH force
1490 fields. Moreover, it offers an automated protocol for carrying out the
1491 backmapping of the coarse-grained description of the system into an atomistic
1492 one.</span></p>
1493
1494 <h2></h2>
1495
1496 <h2><a name="_Toc138932270"><span lang=en-DE>CNS-ligands</span></a></h2>
1497
1498 <p class=MsoNormal><span lang=en-DE>The project is part of the Parameter
1499 generation and mechanistic studies of neuronal cascades using multi-scale
1500 molecular simulations of the HBP. CNS conformers are generated using a powerful
1501 multilevel strategy that combines a low-level (LL) method for sampling the
1502 conformational minima and high-level (HL) ab initio calculations for estimating
1503 their relative stability. CNS database presents the results in a graphical user
1504 interface, displaying small molecule properties, analyses and generated 3D
1505 conformers. All data produced by the project is available to download.</span></p>
1506
1507 <h2></h2>
1508
1509 <h2><a name="_Toc138932271"><span lang=en-DE>Cobrawap</span></a></h2>
1510
1511 <p class=MsoNormal><span lang=en-DE>Cobrawap is an adaptable and reusable
1512 software tool to study wave-like activity propagation in the cortex. It allows for
1513 the integration of heterogeneous data from different measurement techniques and
1514 simulations through alignment to common wave descriptions. Cobrawap provides an
1515 extendable collection of processing and analysis methods that can be combined
1516 and adapted to specific input data and research applications. It enables broad
1517 and rigorous comparisons of wave characteristics across multiple datasets,
1518 model calibration and validation applications, and its modular building blocks
1519 may serve to construct related analysis pipelines.</span></p>
1520
1521 <h2></h2>
1522
1523 <h2><a name="_Toc138932272"><span lang=en-DE>Collaboratory Bucket service</span></a></h2>
1524
1525 <p class=MsoNormal><span lang=en-DE>The Bucket service provides object storage
1526 to EBRAINS users without them having to request an account on Fenix (the
1527 EBRAINS infrastructure provider) and storage resources there. This is the
1528 recommended storage for datasets that are shared by data providers, on the
1529 condition that these do not contain sensitive personal data. For sharing
1530 datasets with personal data, users should refer to the Health Data Cloud. The
1531 Bucket service is better suited for larger files that are usually not edited,
1532 such as datasets and videos. For Docker images, users should refer to the
1533 EBRAINS Docker registry. For smaller files and files which are more likely to
1534 be edited, users should consider the Collaboratory Drive service.</span></p>
1535
1536 <h2></h2>
1537
1538 <h2><a name="_Toc138932273"><span lang=en-DE>Collaboratory Drive</span></a></h2>
1539
1540 <p class=MsoNormal><span lang=en-DE>The Drive service offers users cloud
1541 storage space for their files in each collab (workspace). The Drive storage is
1542 mounted in the Collaboratory Lab to provide persistent storage (as opposed to
1543 the Lab containers which are deleted after a few hours of inactivity). All
1544 files are under version control. The Drive is intended for smaller files
1545 (currently limited to 1 GB) that change more often. Users must not save files
1546 containing personal information in the Drive (i.e. data of living human subjects).
1547 The Drive is also integrated with the Collaboratory Office service to offer
1548 easy collaborative editing of Office files online.</span></p>
1549
1550 <h2></h2>
1551
1552 <h2><a name="_Toc138932274"><span lang=en-DE>Collaboratory IAM</span></a></h2>
1553
1554 <p class=MsoNormal><span lang=en-DE>The EBRAINS Collaboratory IAM allows the
1555 developers of different EBRAINS services to benefit from a single sign-on
1556 solution. End users will benefit from a seamless experience, whereby they can
1557 access a specific service and have direct access from it to resources in other
1558 EBRAINS services without re-authentication. For the developer, it is a good way
1559 for separating concerns and offloading much of the identification and
1560 authentication to a central service. The EBRAINS IAM is recognised as an
1561 identity provider at Fenix supercomputing sites. The IAM service also provides
1562 three ways of managing groups of users: Units, Groups and Teams.</span></p>
1563
1564 <h2></h2>
1565
1566 <h2><a name="_Toc138932275"><span lang=en-DE>Collaboratory Lab</span></a></h2>
1567
1568 <p class=MsoNormal><span lang=en-DE>The Collaboratory Lab provides EBRAINS
1569 users with a user-friendly programming environment for reproducible science.
1570 EBRAINS tools are pre-installed for the user. The latest release is selected by
1571 default, but users can choose to run an older release to reuse an older
1572 notebook, or try out the very latest features in the weekly experimental
1573 deployment. Official releases are produced by EBRAINS every few months. End
1574 users do not need to build and install the tools, and, more importantly, they
1575 do not need to resolve dependency conflicts among tools as this has been
1576 handled for them.</span></p>
1577
1578 <h2></h2>
1579
1580 <h2><a name="_Toc138932276"><span lang=en-DE>Collaboratory Office</span></a></h2>
1581
1582 <p class=MsoNormal><span lang=en-DE>With the Office service, EBRAINS users can
1583 collaboratively edit Office documents (Word, PowerPoint or Excel) with most of
1584 the key features of the MS Office tools. It uses the open standard formats
1585 .docx, .pptx and .xlsx so that files can alternately be edited in the
1586 Collaboratory Office service and in other compatible tools including the MS
1587 Office suite.</span></p>
1588
1589 <h2></h2>
1590
1591 <h2><a name="_Toc138932277"><span lang=en-DE>Collaboratory Wiki</span></a></h2>
1592
1593 <p class=MsoNormal><span lang=en-DE>The Wiki service offers the user-friendly
1594 wiki functionality for publishing web content. It acts as central user
1595 interface and API to access the other Collaboratory services. EBRAINS
1596 developers can integrate their services as app which can be instantiated by
1597 users in their collabs. The Wiki is a good place to create tutorials and
1598 documentation and it is also the place to publish your work on the internet if
1599 you choose to do so.</span></p>
1600
1601 <h2></h2>
1602
1603 <h2><a name="_Toc138932278"><span lang=en-DE>CoreNEURON</span></a></h2>
1604
1605 <p class=MsoNormal><span lang=en-DE>In order to adapt NEURON to evolving
1606 computer architectures, the compute engine of the NEURON simulator was
1607 extracted and optimised as a library called CoreNEURON. CoreNEURON is a compute
1608 engine library for the NEURON simulator optimised for both memory usage and
1609 computational speed on modern CPU/GPU architectures. Some of its key goals are
1610 to: 1) Efficiently simulate large network models, 2) Support execution on
1611 accelerators such as GPU, 3) Support optimisations such as vectorisation and
1612 cache-efficient memory layout.</span></p>
1613
1614 <h2></h2>
1615
1616 <h2><a name="_Toc138932279"><span lang=en-DE>CxSystem2</span></a></h2>
1617
1618 <p class=MsoNormal><span lang=en-DE>CxSystem is a cerebral cortex simulation
1619 framework, which operates on personal computers. The CxSystem enables easy
1620 testing and build-up of diverse models at single-cell resolution and it is
1621 implemented on the top of the Python-based Brain2 simulator. The CxSystem
1622 interface comprises two csv files - one for anatomy and technical details, the
1623 other for physiological parameters.</span></p>
1624
1625 <h2></h2>
1626
1627 <h2><a name="_Toc138932280"><span lang=en-DE>DeepSlice</span></a></h2>
1628
1629 <p class=MsoNormal><span lang=en-DE>DeepSlice is a deep neural network that
1630 aligns histological sections of mouse brain to the Allen Mouse Brain Common
1631 Coordinate Framework, adjusting for anterior-posterior position, angle,
1632 rotation and scale. At present, DeepSlice only works with tissue cut in the
1633 coronal plane, although future versions will be compatible with sagittal and
1634 horizontal sections.</span></p>
1635
1636 <h2></h2>
1637
1638 <h2><a name="_Toc138932281"><span lang=en-DE>EBRAINS Ethics &amp; Society
1639 Toolkit</span></a></h2>
1640
1641 <p class=MsoNormal><span lang=en-DE>The aim of the toolkit is to offer
1642 researchers who carry out cross-disciplinary brain research a possibility to
1643 engage with ethical and societal issues within brain health and brain disease.
1644 The user is presented with short introductory texts, scenario-based dilemmas,
1645 animations and quizzes, all tailored to specific areas of ethics and society in
1646 a setting of brain research. All exercises are reflection-oriented, with an
1647 interactive approach to inspire users to incorporate these reflections into
1648 their own research practices. Moreover, it is possible to gain further
1649 knowledge by utilising the links for relevant publications, teaching modules
1650 and the EBRAINS Community Space.</span></p>
1651
1652 <h2></h2>
1653
1654 <h2><a name="_Toc138932282"><span lang=en-DE>EBRAINS Image Service</span></a></h2>
1655
1656 <p class=MsoNormal><span lang=en-DE>The Image Service takes large 2D (and 3D)
1657 images and preprocesses them to generate small 2D tiles (or 3D chunks).
1658 Applications consuming image data (viewers or other) can then access regions of
1659 interest by downloading a few tiles rather than the entire large image. Tiles
1660 are also generated at coarser resolutions to support zooming out of large
1661 images. The service supports multiple input image formats. The serving of tiles
1662 to apps is provided by the Collaboratory Bucket (based on OpenStack Swift
1663 object storage), which provides significantly higher network bandwidth than
1664 could be provided by any VM.</span></p>
1665
1666 <h2></h2>
1667
1668 <h2><a name="_Toc138932283"><span lang=en-DE>EBRAINS Knowledge Graph</span></a></h2>
1669
1670 <p class=MsoNormal><span lang=en-DE>The EBRAINS Knowledge Graph (KG) is the
1671 metadata management system of the EBRAINS Data and Knowledge services. It
1672 provides fundamental services and tools to make neuroscientific data, models
1673 and related software FAIR. The KG Editor and API (incl. Python SDKs) allow to
1674 annotate scientific resources in a semantically correct way. The KG Search
1675 exposes the research information via an intuitive user interface and makes the
1676 information publicly available to any user. For advanced users, the KG Query
1677 Builder and KG Core API provide the necessary means to execute detailed queries
1678 on the graph database whilst enforcing fine-grained permission control.</span></p>
1679
1680 <h2></h2>
1681
1682 <h2><a name="_Toc138932284"><span lang=en-DE>EDI Toolkit</span></a></h2>
1683
1684 <p class=MsoNormal><span lang=en-DE>The EDI Toolkit supports projects in
1685 integrating EDI in their research content and as guiding principles for team
1686 collaboration. It is designed for everyday usage by offering: Basic information
1687 Guiding questions, templates and tools to design responsible research Quick
1688 checklists, guidance for suitable structures and standard procedures Measures
1689 to support EDI-based leadership, fair teams and events</span></p>
1690
1691 <h2></h2>
1692
1693 <h2><a name="_Toc138932285"><span lang=en-DE>eFEL</span></a></h2>
1694
1695 <p class=MsoNormal><span lang=en-DE>eFEL allows neuroscientists to
1696 automatically extract features from time series data recorded from neurons
1697 (both in vitro and in silico). Examples include  action potential width and
1698 amplitude in voltage traces recorded during whole-cell patch clamp experiments.
1699 Users can provide a set of traces and select which  features to calculate. The
1700 library will then extract the requested features and return the values.</span></p>
1701
1702 <h2></h2>
1703
1704 <h2><a name="_Toc138932286"><span lang=en-DE>Electrophysiology Analysis Toolkit</span></a></h2>
1705
1706 <p class=MsoNormal><span lang=en-DE>The Electrophysiology Analysis Toolkit
1707 (Elephant) is a Python library that provides a modular framework for the
1708 analysis of experimental and simulated neuronal activity data, such as spike
1709 trains, local field potentials, and intracellular data. Elephant builds on the
1710 Neo data model to facilitate usability, enable interoperability, and support
1711 data from dozens of file formats and network simulation tools. Its analysis
1712 functions are continuously validated against reference implementations and
1713 reports in the literature. Visualisations of analysis results are made
1714 available via the Viziphant companion library. Elephant aims to act as a
1715 platform for sharing analysis methods across the field.</span></p>
1716
1717 <h2></h2>
1718
1719 <h2><a name="_Toc138932287"><span lang=en-DE>FAConstructor</span></a></h2>
1720
1721 <p class=MsoNormal><span lang=en-DE>FAConstructor allows a simple and effective
1722 creation of fibre models based on mathematical functions or the manual input of
1723 data points. Models are visualised during creation and can be interacted with
1724 by translating them in 3D space.</span></p>
1725
1726 <h2></h2>
1727
1728 <h2><a name="_Toc138932288"><span lang=en-DE>fairgraph</span></a></h2>
1729
1730 <p class=MsoNormal><span lang=en-DE>fairgraph is a Python library for working
1731 with metadata in the EBRAINS Knowledge Graph (KG), with a particular focus on
1732 data reuse, although it is also useful in registering and curating metadata.
1733 The library represents metadata nodes (also known as openMINDS instances) from
1734 the KG as Python objects. fairgraph supports querying the KG, following links
1735 in the graph, downloading data and metadata, and creating new nodes in the KG.
1736 It builds on openMINDS and on the KG Core Python library.</span></p>
1737
1738 <h2></h2>
1739
1740 <h2><a name="_Toc138932289"><span lang=en-DE>Fast sampling with neuromorphic
1741 hardware</span></a></h2>
1742
1743 <p class=MsoNormal><span lang=en-DE>Compared to conventional neural networks,
1744 physical model devices offer a fast, efficient, and inherently parallel
1745 substrate capable of related forms of Markov chain Monte Carlo sampling. This
1746 software suite enables the use of a neuromorphic chip to replicate the
1747 properties of quantum systems through spike-based sampling.</span></p>
1748
1749 <h2></h2>
1750
1751 <h2><a name="_Toc138932290"><span lang=en-DE>fastPLI</span></a></h2>
1752
1753 <p class=MsoNormal><span lang=en-DE>fastPLI is an open-source toolbox based on
1754 Python and C++ for modelling myelinated axons, i.e., nerve fibres, and
1755 simulating the results of measurement of fibre orientations with a polarisation
1756 microscope using 3D-PLI. The fastPLI package includes the following modules:
1757 nerve fibre modelling, simulation, and analysis. All computationally intensive
1758 calculations are optimised either with Numba on the Python side or with
1759 multithreading C++ algorithms, which can be accessed via pybind11 inside the
1760 Python package. Additionally, the simulation module supports the Message
1761 Passing Interface (MPI) to facilitate the simulation of very large volumes on
1762 multiple computer nodes.</span></p>
1763
1764 <h2></h2>
1765
1766 <h2><a name="_Toc138932291"><span lang=en-DE>Feed-forward LFP-MEG estimator
1767 from mean-field models</span></a></h2>
1768
1769 <p class=MsoNormal><span lang=en-DE>This tool was developed to calculate the
1770 local field potentials (LFP) and magnetoencephalogram (MEG) signals generated
1771 by a population of neurons described by a mean-field model. The calculation of
1772 LFP is done via a kernel method based on unitary LFP's (the LFP generated by a
1773 single axon) which was recently introduced for spiking-networks simulations and
1774 that we adapt here for mean-field models. The calculation of the magnetic field
1775 is based on current-dipole and volume-conductor models, where the secondary
1776 currents (due to the conducting extracellular medium) are estimated using the
1777 LFP calculated via the kernel method and where the effects of
1778 medium-inhomogeneities are incorporated.</span></p>
1779
1780 <h2></h2>
1781
1782 <h2><a name="_Toc138932292"><span lang=en-DE>FIL</span></a></h2>
1783
1784 <p class=MsoNormal><span lang=en-DE>This is a scheme for training and applying
1785 the FIL framework. Some functionality from SPM12 is required for handling
1786 images. After training, labelling a new image is relatively fast because
1787 optimising the latent variables can be formulated within a scheme similar to a recurrent
1788 Residual Network (ResNet).</span></p>
1789
1790 <h2></h2>
1791
1792 <h2><a name="_Toc138932293"><span lang=en-DE>FMRALIGN</span></a></h2>
1793
1794 <p class=MsoNormal><span lang=en-DE>This library is meant to be a light-weight
1795 Python library that handles functional alignment tasks (also known as
1796 hyperalignment). It is compatible with and inspired by Nilearn. Alternative
1797 implementations of these ideas can be found in the pymvpa or brainiak packages.</span></p>
1798
1799 <h2></h2>
1800
1801 <h2><a name="_Toc138932294"><span lang=en-DE>Foa3D</span></a></h2>
1802
1803 <p class=MsoNormal><span lang=en-DE>Foa3D is a tool for multiscale nerve fibre
1804 enhancement and orientation analysis in high-resolution volume images acquired
1805 by two-photon scanning or light-sheet fluorescence microscopy, exploiting the
1806 brain tissue autofluorescence or exogenous myelin stains. Its image processing
1807 pipeline is built around a 3D Frangi filter that enables the enhancement of
1808 fibre structures of varying diameters, and the generation of accurate 3D
1809 orientation maps in both grey and white matter. Foa3D features the computation
1810 of multiscale orientation distribution functions that facilitate the comparison
1811 with orientations assessed via 3D-PLI or 3D PS-OCT, and the validation of
1812 mesoscale dMRI-based connectivity information.</span></p>
1813
1814 <h2></h2>
1815
1816 <h2><a name="_Toc138932295"><span lang=en-DE>Frites</span></a></h2>
1817
1818 <p class=MsoNormal><span lang=en-DE>Frites allows the characterisation of
1819 task-related cognitive brain networks. Neural correlates of cognitive functions
1820 can be extracted both at the single brain area (or channel) and network level.
1821 The toolbox includes time-resolved directed (e.g., Granger causality) and
1822 undirected (e.g., Mutual Information) functional connectivity metrics. In
1823 addition, it includes cluster-based and permutation-based statistical methods
1824 for single-subject and group-level inference.</span></p>
1825
1826 <h2></h2>
1827
1828 <h2><a name="_Toc138932296"><span lang=en-DE>gridspeccer</span></a></h2>
1829
1830 <p class=MsoNormal><span lang=en-DE>Plotting tool to make plotting with many
1831 subfigures easier, especially for publications. After installation, gridspeccer
1832 can be used from the command line to create plots.</span></p>
1833
1834 <h2></h2>
1835
1836 <h2><a name="_Toc138932297"><span lang=en-DE>Hal-Cgp</span></a></h2>
1837
1838 <p class=MsoNormal><span lang=en-DE>Hal-Cgp is an extensible pure Python
1839 library implementing Cgp to represent, mutate and evaluate populations of
1840 individuals encoding symbolic expressions targeting applications with
1841 computationally expensive fitness evaluations. It supports the translation from
1842 a CGP genotype, a two-dimensional Cartesian graph, into the corresponding
1843 phenotype, a computational graph implementing a particular mathematical expression.
1844 These computational graphs can be exported as pure Python functions, in a
1845 NumPy-compatible format, SymPy expressions or PyTorch modules. The library
1846 implements a mu + lambda evolution strategy to evolve a population of
1847 individuals to optimise an objective function.</span></p>
1848
1849 <h2></h2>
1850
1851 <h2><a name="_Toc138932298"><span lang=en-DE>Health Data Cloud</span></a></h2>
1852
1853 <p class=MsoNormal><span lang=en-DE>The Health Data Cloud (HDC) provides
1854 EBRAINS services for sensitive data as a federated research data ecosystem that
1855 enables scientists across Europe and beyond to collect, process and share
1856 sensitive data in compliance with EU General Data Protection Regulations
1857 (GDPR). The HDC is a federation of interoperable nodes. Nodes share a common
1858 system architecture based on CharitŽ Virtual Research Environment (VRE),
1859 enabling research consortia to manage and process data, and making data
1860 discoverable and sharable via the EBRAINS Knowledge Graph.</span></p>
1861
1862 <p class=MsoNormal></p>
1863
1864 <p class=MsoNormal><a name="_Toc138932299"><span class=Heading2Char><span
1865 lang=en-DE style='font-size:14.0pt;line-height:120%'>Hodgkin-Huxley Neuron
1866 Builder</span></span></a></p>
1867
1868 <p class=MsoNormal><span lang=en-DE>The Hodgkin-Huxley Neuron Builder is a
1869 web-application that allows users to interactively go through an entire NEURON
1870 model building pipeline of individual biophysically detailed cells. 2. Model
1871 parameter optimisation via HPC systems. 3. In silico experiments using the
1872 optimised model cell.  </span></p>
1873
1874 <h2></h2>
1875
1876 <h2><a name="_Toc138932300"><span lang=en-DE>HPC Job Proxy</span></a></h2>
1877
1878 <p class=MsoNormal><span lang=en-DE>The HPC Job Proxy provides a simplified way
1879 for EBRAINS service providers to launch jobs on Fenix supercomputers on behalf
1880 of EBRAINS end users. The proxy offers a wrapper over the Unicore service which
1881 adds logging, access to stdout/stderr/status, verification of user quota, and
1882 updating of user quota at the end of the job.</span></p>
1883
1884 <h2></h2>
1885
1886 <h2><a name="_Toc138932301"><span lang=en-DE>HPC Status Monitor</span></a></h2>
1887
1888 <p class=MsoNormal><span lang=en-DE>The HPC Status Monitor allows a real-time
1889 check of the availability status of the HPC Systems accessible from HBP tools
1890 and services and provides an instant snapshot of the resource quotas available
1891 to individual users on each system.</span></p>
1892
1893 <h2></h2>
1894
1895 <h2><a name="_Toc138932302"><span lang=en-DE>Human Intracerebral EEG Platform</span></a></h2>
1896
1897 <p class=MsoNormal><span lang=en-DE>The HIP is an open-source platform designed
1898 for collecting, managing, analysing and sharing multi-scale iEEG data at an
1899 international level. Its mission is to assist clinicians and researchers in
1900 improving research capabilities by simplifying iEEG data analysis and
1901 interpretation. The HIP integrates different software, modules and services
1902 necessary for investigating spatio-temporal dynamics of neural processes in a
1903 secure and optimised fashion. The interface is browser-based and allows
1904 selecting sets of tools according to specific research needs.</span></p>
1905
1906 <h2></h2>
1907
1908 <h2><a name="_Toc138932303"><span lang=en-DE>Hybrid MM/CG Webserver</span></a></h2>
1909
1910 <p class=MsoNormal><span lang=en-DE>MM/CG simulations help predict ligand poses
1911 in hGPCRs  for pharmacological applications. This approach allows for the
1912 description of the ligand, the binding cavity and the surrounding water
1913 molecules at atomistic resolution, while coarse-graining the rest of the
1914 receptor. The webserver automatises and speeds up the simulation set-up of
1915 hGPCR/ligand complexes. It also allows for equilibration of the systems, either
1916 fully automatically or interactively. The results are visualised online,
1917 helping the user identify possible issues and modify the set-up parameters.
1918 This framework allows for the automatic preparation and running of hybrid
1919 molecular dynamics simulations of molecules and their cognate receptors.</span></p>
1920
1921 <h2></h2>
1922
1923 <h2><a name="_Toc138932304"><span lang=en-DE>Insite</span></a></h2>
1924
1925 <p class=MsoNormal><span lang=en-DE>Insite enables users to access data via the
1926 in transit paradigm for NEST, TVB and Arbor simulations. Compared to the
1927 traditional approach of offline processing, in transit paradigms allow
1928 accessing of data while the simulation runs. This is especially useful for
1929 simulations that produce large amounts of data and are running for a long time.
1930 In transit allows the user to access only parts of the data and prevents the
1931 need for storing all data. It also allows the user early insights into the data
1932 even before the simulation finishes. Insite provides an easy-to-use and
1933 easy-to-integrate architecture to enable in transit features in other tools.</span></p>
1934
1935 <h2></h2>
1936
1937 <h2><a name="_Toc138932305"><span lang=en-DE>Interactive Brain Atlas Viewer</span></a></h2>
1938
1939 <p class=MsoNormal><span lang=en-DE>The Interactive Brain Atlas Viewer provides
1940 various kinds of interactive visualisations for multi-modal brain and head
1941 image data: different parcellations, degrees of transparency and overlays. The
1942 Viewer provides the following functions and supports data from the following
1943 sources: EEG, white matter tracts, MRI and PET 3D volumes, 2D slices,
1944 intracranial electrodes, brain activity, multiscale brain network models,
1945 supplementary information for brain regions and functional brain networks in
1946 multiple languages. It comes as a web app, mobile app and desktop app.</span></p>
1947
1948 <h2></h2>
1949
1950 <h2><a name="_Toc138932306"><span lang=en-DE>JuGEx</span></a></h2>
1951
1952 <p class=MsoNormal><span lang=en-DE>Decoding the chain from genes to cognition
1953 requires detailed insights into how areas with specific gene activities and
1954 microanatomical architectures contribute to brain function and dysfunction. The
1955 Allen Human Brain Atlas contains regional gene expression data, while the
1956 Julich Brain Atlas, which can be accessed via siibra, offers 3D
1957 cytoarchitectonic maps reflecting the interindividual variability. JuGEx offers
1958 an integrated framework that combines the analytical benefits of both
1959 repositories towards a multilevel brain atlas of adult humans. JuGEx is a new
1960 method for integrating tissue transcriptome and cytoarchitectonic segregation.</span></p>
1961
1962 <h2></h2>
1963
1964 <h2><a name="_Toc138932307"><span lang=en-DE>KnowledgeSpace</span></a></h2>
1965
1966 <p class=MsoNormal><span lang=en-DE>KnowledgeSpace (KS) is a globally-used,
1967 data-driven encyclopaedia and search engine for the neuroscience community. As
1968 an encyclopaedia, KS provides curated definitions of brain research concepts
1969 found in different neuroscience community ontologies, Wikipedia and
1970 dictionaries. The dataset discovery in KS makes research datasets across many
1971 large-scale brain initiatives universally accessible and useful. It also
1972 promotes FAIR data principles that will help data publishers to follow best
1973 practices for data storage and publication. As more and more data publishers
1974 follow data standards like OpenMINDS or DATS, the quality of data discovery
1975 through KS will improve. The related publications are also curated from PubMed
1976 and linked to the concepts in KS to provide an improved search capability.</span></p>
1977
1978 <h2></h2>
1979
1980 <h2><a name="_Toc138932308"><span lang=en-DE>L2L</span></a></h2>
1981
1982 <p class=MsoNormal><span lang=en-DE>L2L is an easy-to-use and flexible
1983 framework to perform parameter and hyper-parameter space exploration of
1984 mathematical models on HPC infrastructure. L2L is an implementation of the
1985 learning-to-learn concept written in Python. This open-source software allows
1986 several instances of an optimisation target to be executed with different
1987 parameters in an massively parallel fashion on HPC. L2L provides a set of
1988 built-in optimiser algorithms, which make adaptive and efficient exploration of
1989 parameter spaces possible. Different from other optimisation toolboxes, L2L
1990 provides maximum flexibility for the way the optimisation target can be
1991 executed.</span></p>
1992
1993 <h2></h2>
1994
1995 <h2><a name="_Toc138932309"><span lang=en-DE>Leveltlab/SpectralSegmentation</span></a></h2>
1996
1997 <p class=MsoNormal><span lang=en-DE>SpecSeg is a toolbox that segments neurons
1998 and neurites in chronic calcium imaging datasets based on low-frequency
1999 cross-spectral power. The pipeline includes a graphical user interface to edit
2000 the automatically extracted ROIs, to add new ones or delete ROIs by further
2001 constraining their properties.</span></p>
2002
2003 <h2></h2>
2004
2005 <h2><a name="_Toc138932310"><span lang=en-DE>LFPy</span></a></h2>
2006
2007 <p class=MsoNormal><span lang=en-DE>LFPy is an open-source Python module linking
2008 simulated neural activity with measurable brain signals. This is done by
2009 enabling calculation of brain signals from neural activity simulated with
2010 multi-compartment neuron models (single cells or networks). LFPy can be used to
2011 simulate brain signals like extracellular action potentials, local field
2012 potentials (LFP), and in vitro MEA recordings, as well as ECoG, EEG, and MEG
2013 signals. LFPy is well-integrated with the NEURON simulator and can, through
2014 LFPykit, also be used with other simulators like Arbor. Through the recently
2015 developed extensions hybridLFPy and LFPykernels, LFPy can also be used to
2016 calculate brain signals directly from point-neuron network models or
2017 population-based models.</span></p>
2018
2019 <h2></h2>
2020
2021 <h2><a name="_Toc138932311"><span lang=en-DE>libsonata</span></a></h2>
2022
2023 <p class=MsoNormal><span lang=en-DE>libsonata allows circuit and simulation
2024 config loading, node set materialisation, and access to node and edge
2025 populations in an efficient manner. It is generally a read-only library, but
2026 support for writing edge indices has been added.</span></p>
2027
2028 <h2></h2>
2029
2030 <h2><a name="_Toc138932312"><span lang=en-DE>Live Papers</span></a></h2>
2031
2032 <p class=MsoNormal><span lang=en-DE>EBRAINS Live Papers are structured and
2033 interactive documents that complement published scientific articles. Live
2034 Papers feature integrated tools and services that allow users to download,
2035 visualise or simulate data, models and results presented in the corresponding
2036 publications: Build interactive documents to showcase your data and the
2037 simulation or data analysis code used in your research. Easily link to
2038 resources in community databases such as EBRAINS, NeuroMorpho.org, ModelDB, and
2039 Allen Brain Atlas. Embedded, interactive visualisation of electrophysiology
2040 data and neuronal reconstructions. Launch EBRAINS simulation tools to explore
2041 single neuron models in your browser. Share live papers pre-publication with
2042 anonymous reviewers during peer review of your manuscript. Explore already
2043 published live papers, or develop your own live paper with our authoring tool.</span></p>
2044
2045 <h2></h2>
2046
2047 <h2><a name="_Toc138932313"><span lang=en-DE>Livre</span></a></h2>
2048
2049 <p class=MsoNormal><span lang=en-DE>Livre is an out-of-core, multi-node,
2050 multi-GPU, OpenGL volume rendering engine to visualise large volumetric
2051 datasets. It provides the following major features to facilitate rendering of
2052 large volumetric datasets: Visualisation of pre-processed UVF format volume
2053 datasets. Real-time voxelisation of different data sources (surface meshes, BBP
2054 morphologies, local field potentials, etc.) through the use of plugins.
2055 Multi-node, multi-GPU rendering (only sort-first rendering).</span></p>
2056
2057 <h2></h2>
2058
2059 <h2><a name="_Toc138932314"><span lang=en-DE>LocaliZoom</span></a></h2>
2060
2061 <p class=MsoNormal><span lang=en-DE>Pan-and-zoom type viewer displaying image
2062 series with overlaid atlas delineations. LocaliZoom is a pan-and-zoom type
2063 viewer displaying high-resolution image series coupled with overlaid atlas
2064 delineations. It has three operating modes: Display series with atlas overlay.
2065 Both linear and nonlinear alignments are supported (created with QuickNII or
2066 VisuAlign). Create or edit nonlinear alignments. Create markup which can be
2067 exported as MeshView point clouds or to Excel for further numerical analysis.</span></p>
2068
2069 <h2></h2>
2070
2071 <h2><a name="_Toc138932315"><span lang=en-DE>MD-IFP</span></a></h2>
2072
2073 <p class=MsoNormal><span lang=en-DE>MD-IFP is a python workflow for the
2074 generation and analysis of protein-ligand interaction fingerprints from
2075 molecular dynamics trajectories. If used for the analysis of Random
2076 Acceleration Molecular Dynamics (RAMD) trajectories, it can help to investigate
2077 dissociation mechanisms by characterising transition states as well as the
2078 determinants and hot-spots for dissociation. As such, the combined use of
2079 RAMD and MD-IFP may assist the early stages of drug discovery campaigns for the
2080 design of new molecules or ligand optimisation.</span></p>
2081
2082 <h2></h2>
2083
2084 <h2><a name="_Toc138932316"><span lang=en-DE>MEDUSA</span></a></h2>
2085
2086 <p class=MsoNormal><span lang=en-DE>Using a spherical meshing technique that
2087 decomposes each microstructural item into a set of overlapping spheres, the
2088 phantom construction is made very fast while reliably avoiding the collisions
2089 between items in the scene. This novel method is applied to the construction of
2090 human brain white matter microstructural components, namely axonal fibers,
2091 oligodendrocytes and astrocytes. The algorithm reaches high values of packing
2092 density and angular dispersion for the axonal fibers, even in the case of
2093 multiple white matter fiber populations and enables the construction of complex
2094 biomimicking geometries including myelinated axons, beaded axons and glial
2095 cells.</span></p>
2096
2097 <h2></h2>
2098
2099 <h2><a name="_Toc138932317"><span lang=en-DE>MeshView</span></a></h2>
2100
2101 <p class=MsoNormal><span lang=en-DE>MeshView is a web application for real-time
2102 3D display of surface mesh data representing structural parcellations from
2103 volumetric atlases, such as the Waxholm Space atlas of the Sprague Dawley rat
2104 brain. Key features: orbiting view with toggleable opaque/transparent/hidden
2105 parcellation meshes, rendering user-defined cut surface as if meshes were solid
2106 objects, rendering point-clouds (simple type-in, or loaded from JSON). The
2107 coordinate system is compatible with QuickNII.</span></p>
2108
2109 <h2></h2>
2110
2111 <h2><a name="_Toc138932318"><span lang=en-DE>MIP</span></a></h2>
2112
2113 <p class=MsoNormal><span lang=en-DE>MIP is an open-source platform enabling
2114 federated data analysis in a secure environment for centres involved in
2115 collaborative initiatives. It allows users to initiate or join disease-oriented
2116 federations with the aim of analysing large-scale distributed clinical
2117 datasets. For each federation, users can create specific data models based on
2118 well-accepted common data elements, approved by all participating centres. MIP
2119 experts assist in creating the data models and facilitate coordination and
2120 communication among centres. They provide advice and support for data curation,
2121 harmonisation, and anonymisation, as well as data governance, especially with
2122 regards to Data Sharing Agreements and general ethical considerations.</span></p>
2123
2124 <h2></h2>
2125
2126 <h2><a name="_Toc138932319"><span lang=en-DE>Model Validation Service</span></a></h2>
2127
2128 <p class=MsoNormal><span lang=en-DE>The HBP/EBRAINS Model Validation Service is
2129 a set of tools for performing and tracking validation of models with respect to
2130 experimental data. It consists of a web API, a GUI client (the Model Catalog
2131 app) and a Python client. The service enables users to store, query, view and
2132 download: (i) model descriptions/scripts, (ii) validation test definitions and
2133 (iii) validation results. In a typical workflow, users will find models and
2134 validation tests by searching the Model Catalog (or upload their own), run the
2135 tests using the Python client in a Jupyter notebook, with simulations running
2136 locally or on HPC, and then upload the results.</span></p>
2137
2138 <h2></h2>
2139
2140 <h2><a name="_Toc138932320"><span lang=en-DE>Model Validation Test Suites</span></a></h2>
2141
2142 <p class=MsoNormal><span lang=en-DE>As part of the HBP/EBRAINS model validation
2143 framework, we provide a Python Software Development Kit (SDK) for model
2144 validation, which provides: (i) validation test definitions and (ii) interface
2145 definitions intended to decouple model validation from the details of model
2146 implementation. This more formal approach to model validation aims to make it
2147 quicker and easier to compare models, to provide validation test suites for
2148 models and to develop new validations of existing models. The SDK consists of a
2149 collection of Python packages all using the sciunit framework: HippoUnit,
2150 MorphoUnit, NetworkUnit, BasalUnit, CerebUnit, eFELUnit, HippoNetworkUnit.</span></p>
2151
2152 <h2></h2>
2153
2154 <h2><a name="_Toc138932321"><span lang=en-DE>MoDEL-CNS</span></a></h2>
2155
2156 <p class=MsoNormal><span lang=en-DE>MoDEL-CNS is a database and server platform
2157 designed to provide web access to atomistic MD trajectories for relevant signal
2158 transduction proteins. The project is part of the service for providing
2159 molecular simulation-based predictions for systems neurobiology of the HBP.
2160 MoDEL-CNS expands the MD Extended Library database of atomistic MD trajectories
2161 with proteins involved in CNS processes, including membrane proteins. MoDEL-CNS
2162 web server interface presents the resulting trajectories, analyses and protein
2163 properties. All data produced are available to download.</span></p>
2164
2165 <h2></h2>
2166
2167 <h2><a name="_Toc138932322"><span lang=en-DE>Modular Science</span></a></h2>
2168
2169 <p class=MsoNormal><span lang=en-DE>Modular Science is a middleware that
2170 provides robust deployment of complex multi-application workflows. It contains
2171 protocols and interfaces for multi-scale co-simulation workloads on
2172 high-performance computers and local hardware. It allows for synchronisation
2173 and coordination of individual components and contains dedicated and
2174 parallelised modules for data transformations between scales. Modular Science
2175 offers insight into both the system level and the individual subsystems to
2176 steer the execution, to monitor resource usage, and system health &amp; status
2177 with small overheads on performance. Modular Science comes with a number of
2178 neuroscience co-simulation use cases including NEST-TVB, NEST-Arbor, LFPy and neurorobotics.</span></p>
2179
2180 <h2></h2>
2181
2182 <h2><a name="_Toc138932323"><span lang=en-DE>Monsteer</span></a></h2>
2183
2184 <p class=MsoNormal><span lang=en-DE>Monsteer is a library for interactive
2185 supercomputing in the neuroscience domain. It facilitates the coupling of
2186 running simulations (currently NEST) with interactive visualization and
2187 analysis applications. Monsteer supports streaming of simulation data to
2188 clients (currently limited to spikes) as well as control of the simulator from
2189 the clients (also known as computational steering). Monsteer's main components
2190 are a C++ library, a MUSIC-based application and Python helpers.</span></p>
2191
2192 <h2></h2>
2193
2194 <h2><a name="_Toc138932324"><span lang=en-DE>MorphIO</span></a></h2>
2195
2196 <p class=MsoNormal><span lang=en-DE>MorphIO is a library for reading and
2197 writing neuron morphology files. It supports the following formats: SWC, ASC
2198 (also known as neurolucida), H5. There are two APIs: mutable, for creating or
2199 editing morphologies, and immutable, for read-only operations. Both are
2200 represented in C++ and Python. Extended formats include glia, mitochondria and
2201 endoplasmic reticulum.</span></p>
2202
2203 <h2></h2>
2204
2205 <h2><a name="_Toc138932325"><span lang=en-DE>Morphology alignment tool</span></a></h2>
2206
2207 <p class=MsoNormal><span lang=en-DE>Starting with serial sections of a brain in
2208 which a complete single morphology has been labelled, the pieces of neurite
2209 (axons/dendrites) in each section are traced with Neurolucida or similar
2210 microscope-attached software. The slices are then aligned, first using an
2211 automated algorithm that tries to find matching pieces in adjacent sections
2212 (Python script), and second using a GUI-driven tool (web-based, JavaScript).
2213 Finally, the pieces are stitched into a complete neuron (Python script). The
2214 neuron and tissue volume are then registered to one of the EBRAINS-supported
2215 reference templates (Python script). The web-based tool can also be used to align
2216 slices without a neuron being present.</span></p>
2217
2218 <h2></h2>
2219
2220 <h2><a name="_Toc138932326"><span lang=en-DE>MorphTool</span></a></h2>
2221
2222 <p class=MsoNormal><span lang=en-DE>MorphTool is a python toolkit designed for
2223 editing morphological skeletons of cell reconstructions. It has been developed
2224 to provide helper programmes that perform simple tasks such as morphology
2225 diffing, file conversion, soma area calculation, skeleton simplification,
2226 process resampling, morphology repair and spatial transformations. It allows
2227 neuroscientists to curate and manipulate morphological reconstruction and
2228 correct morphological artifacts due to the manual reconstruction process.</span></p>
2229
2230 <h2></h2>
2231
2232 <h2><a name="_Toc138932327"><span lang=en-DE>Multi-Brain</span></a></h2>
2233
2234 <p class=MsoNormal><span lang=en-DE>The Multi-Brain (MB) model has the
2235 general aim of integrating a number of disparate image analysis components
2236 within a single unified generative modelling framework. Its objective is to
2237 achieve diffeomorphic alignment of a wide variety of medical image modalities
2238 into a common anatomical space. This involves the ability to construct a
2239 &quot;tissue probability template&quot; from a population of scans
2240 through group-wise alignment. The MB model has been shown to provide accurate
2241 modelling of the intensity distributions of different imaging modalities.</span></p>
2242
2243 <h2></h2>
2244
2245 <h2><a name="_Toc138932328"><span lang=en-DE>Multi-Image-OSD</span></a></h2>
2246
2247 <p class=MsoNormal><span lang=en-DE>It has browser-based classic pan and zoom
2248 capabilities. A collection of images can be displayed as a filmstrip (Filmstrip
2249 Mode) or as a table (Collection Mode) with adjustable number of rows and
2250 columns. The tool supports keyboard or/and mouse navigation options, as well as
2251 touch devices. Utilising the open standard Deep Zoom Image (DZI) format, it is
2252 able to efficiently visualise very large brain images in the gigapixel range,
2253 allowing to zoom from common, display-sized overview resolutions down to the
2254 microscopic resolution without downloading the underlying, very large image
2255 dataset.</span></p>
2256
2257 <h2></h2>
2258
2259 <h2><a name="_Toc138932329"><span lang=en-DE>MUSIC</span></a></h2>
2260
2261 <p class=MsoNormal><span lang=en-DE>MUSIC is a communication framework in the
2262 domain of computational neuroscience and neuromorphic computing which enables
2263 co-simulations, where components of a model are simulated by different
2264 simulators or hardware. It consists of an API and C++ library which can be
2265 linked into existing software with minor modifications. MUSIC enables the
2266 communication of neuronal spike events, continuous values and text messages
2267 while hiding the complexity of data distribution over ranks, as well as
2268 scheduling of communication in the face of loops. MUSIC is light-weight with a
2269 simple API.</span></p>
2270
2271 <h2></h2>
2272
2273 <h2><a name="_Toc138932330"><span lang=en-DE>NEAT</span></a></h2>
2274
2275 <p class=MsoNormal><span lang=en-DE>NEAT allows for the convenient definition
2276 of morphological neuron models. These models can be simulated through an
2277 interface with the NEURON simulator or analysed with two classical methods: (i)
2278 the separation-of-variables method to obtain impedance kernels as a
2279 superposition of exponentials and (ii) Koch's method to compute impedances with
2280 linearised ion channels analytically in the frequency domain. NEAT also
2281 implements the neural evaluation tree framework and an associated C++ simulator
2282 to analyse sub-unit independence. Finally, NEAT implements a new method to
2283 simplify morphological neuron models into models with few compartments, which
2284 can also be simulated with NEURON.</span></p>
2285
2286 <h2></h2>
2287
2288 <h2><a name="_Toc138932331"><span lang=en-DE>Neo</span></a></h2>
2289
2290 <p class=MsoNormal><span lang=en-DE>Neo implements a hierarchical data model
2291 well adapted to intracellular and extracellular electrophysiology and EEG data.
2292 It improves interoperability between Python tools for analysing, visualising
2293 and generating electrophysiology data by providing a common, shared object
2294 model. It reads a wide range of neurophysiology file formats, including Spike2,
2295 NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt and Igor Pro and writes
2296 to open formats such as NWB and NIX. Neo objects behave just like normal NumPy
2297 arrays, but with additional metadata, checks for dimensional consistency and
2298 automatic unit conversion. Neo has been endorsed as a community standard by the
2299 International Neuroinformatics Coordinating Facility (INCF).</span></p>
2300
2301 <h2></h2>
2302
2303 <h2><a name="_Toc138932332"><span lang=en-DE>Neo Viewer</span></a></h2>
2304
2305 <p class=MsoNormal><span lang=en-DE>Neo Viewer consists of a REST-API and a
2306 Javascript component that can be embedded in any web page. Electrophysiology
2307 traces can be zoomed, scrolled and saved as images. Individual points can be
2308 measured off the graphs. Neo Viewer can visualise data from most of the
2309 widely-used file formats in neurophysiology, including community standards such
2310 as NWB.</span></p>
2311
2312 <h2></h2>
2313
2314 <h2><a name="_Toc138932333"><span lang=en-DE>NEST Desktop</span></a></h2>
2315
2316 <p class=MsoNormal><span lang=en-DE>NEST Desktop comprises of GUI components
2317 for creating and configuring network models, running simulations, and
2318 visualising and analysing simulation results. NEST Desktop allows students to
2319 explore important concepts in computational neuroscience without the need to
2320 first learn a simulator control language. This is done by offering a
2321 server-side NEST simulator, which can also be installed as a package together
2322 with a web server providing NEST Desktop as visual front-end. Besides local
2323 installations, distributed setups can be installed, and direct use through
2324 EBRAINS is possible. NEST Desktop has also been used as a modelling front-end
2325 of the Neurorobotics Platform.</span></p>
2326
2327 <h2></h2>
2328
2329 <h2><a name="_Toc138932334"><span lang=en-DE>NEST Simulator</span></a></h2>
2330
2331 <p class=MsoNormal><span lang=en-DE>NEST is used in computational neuroscience
2332 to model and study behaviour of large networks of neurons. The models describe
2333 single neuron and synapse behaviour and their connections. Different mechanisms
2334 of plasticity can be used to investigate artificial learning and help to shed
2335 light on the fundamental principles of how the brain works. NEST offers
2336 convenient and efficient commands to define and connect large networks, ranging
2337 from algorithmically determined connections to data-driven connectivity. Create
2338 connections between neurons using numerous synapse models from STDP to gap
2339 junctions.</span></p>
2340
2341 <h2></h2>
2342
2343 <h2><a name="_Toc138932335"><span lang=en-DE>NESTML</span></a></h2>
2344
2345 <p class=MsoNormal><span lang=en-DE>NESTML is a domain-specific language for
2346 neuron and synapse models. These dynamical models can be used in simulations of
2347 brain activity on several platforms, in particular NEST Simulator. NESTML
2348 combines an easy to understand, yet powerful syntax with good simulation
2349 performance by means of code generation (C++ for NEST Simulator), but flexibly
2350 supports other simulation engines including neuromorphic hardware.</span></p>
2351
2352 <h2></h2>
2353
2354 <h2><a name="_Toc138932336"><span lang=en-DE>NetPyNE</span></a></h2>
2355
2356 <p class=MsoNormal><span lang=en-DE>NetPyNE provides programmatic and graphical
2357 interfaces to develop data-driven multiscale brain neural circuit models using
2358 Python and NEURON. Users can define models using a standardised
2359 JSON-compatible, rule-based, declarative format. Based on these specifications,
2360 NetPyNE will generate the network in CoreNEURON, enabling users to run
2361 parallel simulations, optimise and explore network parameters through automated
2362 batch runs, and use built-in functions for visualisation and analysis (e.g.,
2363 generate connectivity matrices, voltage traces, spike raster plots, local field
2364 potentials and information theoretic measures). NetPyNE also facilitates model
2365 sharing by exporting and importing standardised formats: NeuroML and SONATA.</span></p>
2366
2367 <h2></h2>
2368
2369 <h2><a name="_Toc138932337"><span lang=en-DE>NEURO-CONNECT</span></a></h2>
2370
2371 <p class=MsoNormal><span lang=en-DE>The NEURO-CONNECT platform provides
2372 functions to integrate multimodal brain imaging information in a unifying
2373 feature space. Thus, Surface Based Morphometry (SBM), Functional Magnetic
2374 Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI) can be combined and
2375 visualised at the whole-brain scale. Moreover, multiple brain atlases are
2376 aligned to match research outcomes to neuroanatomical entities. The datasets
2377 are appended with openMINDS metadata and thus enable integrative data analysis
2378 and machine learning.</span></p>
2379
2380 <h2></h2>
2381
2382 <h2><a name="_Toc138932338"><span lang=en-DE>NeuroFeatureExtract</span></a></h2>
2383
2384 <p class=MsoNormal><span lang=en-DE>The NeuroFeatureExtract is a web
2385 application that allows the users to extract an ensemble of
2386 electrophysiological properties from voltage traces recorded upon electrical
2387 stimulation of neuronal cells. The main outcome of the application is the
2388 generation of two files Ð features.json and protocol.json Ð that can be used
2389 for later analysis and model parameter optimisations via the Hodgkin-Huxley
2390 Neuron Builder application.</span></p>
2391
2392 <h2></h2>
2393
2394 <h2><a name="_Toc138932339"><span lang=en-DE>NeurogenPy</span></a></h2>
2395
2396 <p class=MsoNormal><span lang=en-DE>NeurogenPy is a Python package for working
2397 with Bayesian networks. It is focused on the analysis of gene expression data
2398 and learning of gene regulatory networks, modelled as Bayesian networks. For
2399 that reason, at the moment, only the Gaussian and fully discrete cases are
2400 supported. The package provides different structure learning algorithms,
2401 parameters estimation and input/output formats. For some of them, already
2402 existing implementations have been used, with bnlearn, pgmpy, networkx and
2403 igraph being the most relevant used packages. This project has been conceived
2404 to be included as a plugin in the EBRAINS interactive atlas viewer, but it may
2405 be used for other purposes.</span></p>
2406
2407 <h2></h2>
2408
2409 <h2><a name="_Toc138932340"><span lang=en-DE>NeuroM</span></a></h2>
2410
2411 <p class=MsoNormal><span lang=en-DE>NeuroM is a Python toolkit for the analysis
2412 and processing of neuron morphologies. It allows the extraction of various
2413 information about morphologies, e.g., the segment lengths of a morphology via
2414 the segment_lengths feature. More than 50 features that can be extracted.</span></p>
2415
2416 <h2></h2>
2417
2418 <h2><a name="_Toc138932341"><span lang=en-DE>Neuromorphic Computing Job Queue</span></a></h2>
2419
2420 <p class=MsoNormal><span lang=en-DE>The Neuromorphic Computing Job Queue allows
2421 users to run simulations/emulations on the SpiNNaker and BrainScaleS systems by
2422 submitting a PyNN script and associated job configuration information to a
2423 central queue. The system consists of a web API, a GUI client (the Job Manager
2424 app) and a Python client. Users can submit scripts stored locally on their own
2425 machine, in a Git repository, in the KG, or in EBRAINS Collaboratory storage
2426 (Drive/Bucket). Users can track the progress of their job, and view and/or
2427 download the results, log files, and provenance information.</span></p>
2428
2429 <h2></h2>
2430
2431 <h2><a name="_Toc138932342"><span lang=en-DE>Neuronize v2</span></a></h2>
2432
2433 <p class=MsoNormal><span lang=en-DE>Neuronize v2 has been developed to generate
2434 a connected neural 3D mesh. If the input is a neuron tracing, it generates a 3D
2435 mesh from it, including the shape of the soma. If the input is data extracted
2436 with Imaris Filament Tracer (a set of unconnected meshes of a neuron),
2437 Neuronize v2 generates a single connected 3D mesh of the whole neuron (also
2438 generating the soma) and provides its neural tracing, which can then be
2439 imported into tools such as Neurolucida, facilitating the interoperability of
2440 two of the most widely used proprietary tools.</span></p>
2441
2442 <h2></h2>
2443
2444 <h2><a name="_Toc138932343"><span lang=en-DE>NeuroR</span></a></h2>
2445
2446 <p class=MsoNormal><span lang=en-DE>NeuroR is a collection of tools to repair
2447 morphologies.  This includes cut plane detection, sanitisation (removing
2448 unifurcations, invalid soma counts, short segments) and 'unravelling': the
2449 action of 'stretching' the cell that has been shrunk due to  the dehydratation
2450 caused by the slicing.</span></p>
2451
2452 <h2></h2>
2453
2454 <h2><a name="_Toc138932344"><span lang=en-DE>Neurorobotics Platform</span></a></h2>
2455
2456 <p class=MsoNormal><span lang=en-DE>The Neurorobotics Platform (NRP) is an
2457 integrative simulation framework that enables in silico experimentation and
2458 embodiment of brain models inside virtual agents interacting with realistic
2459 simulated environments. Entirely Open Source, it offers a browser-based
2460 graphical user interface for online access. It can be installed locally (Docker
2461 or source install). It can be interfaced with multiple spike-based neuromorphic
2462 chips (SpiNNaker, Intel Loihi). You can download and install the NRP locally
2463 for maximum experimental convenience or access it online in order to leverage
2464 the HBP High Performance Computing infrastructure for large-scale experiments.</span></p>
2465
2466 <h2></h2>
2467
2468 <h2><a name="_Toc138932345"><span lang=en-DE>Neurorobotics Platform Robot
2469 Designer</span></a></h2>
2470
2471 <p class=MsoNormal><span lang=en-DE>The Robot Designer is a plugin for the 3D
2472 modeling suite Blender that enables researchers to design morphologies for
2473 simulation experiments in, particularly but not restricted to, the
2474 Neurorobotics Platform. This plugin helps researchers design and parameterize
2475 models with a Graphical User Interface, simplifying and speeding up the design
2476 process.cess. It includes design capabilities for musculoskeletal bodies as
2477 well as robotic systems, fostering not only the understanding of biological
2478 motions and enabling better robot designs, but also enabling true Neurorobotic
2479 experiments that consist of biomimetic models such as tendon-driven robots or a
2480 transition between biology and technology.</span></p>
2481
2482 <h2></h2>
2483
2484 <h2><a name="_Toc138932346"><span lang=en-DE>NeuroScheme</span></a></h2>
2485
2486 <p class=MsoNormal><span lang=en-DE>NeuroScheme uses schematic
2487 representations, such as icons and glyphs, to encode attributes of neural
2488 structures (neurons, columns, layers, populations, etc.), alleviating problems
2489 with displaying, navigating and analysing large datasets. It manages
2490 hierarchically organised neural structures</span><span lang=en-DE
2491 style='font-family:"Times New Roman",serif'> </span><span lang=en-DE>users can
2492 navigate through the levels of the hierarchy and hone in on and explore the
2493 data at their desired level of detail. NeuroScheme has currently two built-in
2494 &quot;domains&quot;, which specify entities, attributes and
2495 relationships used for specific use cases: the 'cortex' domain, designed for
2496 navigating and analysing cerebral cortex structures</span><span lang=en-DE
2497 style='font-family:"Times New Roman",serif'> </span><span lang=en-DE>and the
2498 'congen' domain, used to define the properties of cells and connections, create
2499 circuits of neurons and build populations.</span></p>
2500
2501 <h2></h2>
2502
2503 <h2><a name="_Toc138932347"><span lang=en-DE>NeuroSuites</span></a></h2>
2504
2505 <p class=MsoNormal><span lang=en-DE>NeuroSuites is a web-based platform
2506 designed to handle large-scale, high-dimensional data in the field of
2507 neuroscience. It offers neuroscience-oriented applications and tools for data
2508 analysis, machine learning and visualisation, while also providing
2509 general-purpose tools for data scientists in other research fields. NeuroSuites
2510 requires no software installation and runs on the backend of a server, making
2511 it accessible from various devices. The platform's main strengths include its
2512 defined architecture, ability to handle complex neuroscience data and the
2513 variety of available tools.</span></p>
2514
2515 <h2></h2>
2516
2517 <h2><a name="_Toc138932348"><span lang=en-DE>NeuroTessMesh</span></a></h2>
2518
2519 <p class=MsoNormal><span lang=en-DE>NeuroTessMesh takes morphological tracings
2520 of cells acquired by neuroscientists and generates 3D models that approximate
2521 the neuronal membrane. The resolution of the models can be adapted at the time
2522 of visualisation. You can colour-code different parts of a morphology,
2523 differentiating relevant morphological variables or even neuronal activity.
2524 NeuroTessMesh copes with many of the problems associated with the visualisation
2525 of neural circuits consisting of large numbers of cells. It facilitates the
2526 recovery and visualisation of the 3D geometry of cells included in databases,
2527 such as NeuroMorpho, and allows to approximate missing information such as the
2528 soma's morphology.</span></p>
2529
2530 <h2></h2>
2531
2532 <h2><a name="_Toc138932349"><span lang=en-DE>NMODL Framework</span></a></h2>
2533
2534 <p class=MsoNormal><span lang=en-DE>NMODL Framework is designed with
2535 modern compiler and code generation techniques. It provides modular tools for
2536 parsing, analysing and transforming NMODL it provides an easy to use, high
2537 level Python API</span><span lang=en-DE style='font-family:"Times New Roman",serif'>
2538 </span><span lang=en-DE> it generates optimised code for modern compute architectures
2539 including CPUs and GPUs</span><span lang=en-DE style='font-family:"Times New Roman",serif'>
2540 </span><span lang=en-DE> it provides flexibility to implement new simulator
2541 backends and it supports full NMODL specification.</span></p>
2542
2543 <h2></h2>
2544
2545 <h2><a name="_Toc138932350"><span lang=en-DE>NSuite</span></a></h2>
2546
2547 <p class=MsoNormal><span lang=en-DE>NSuite is a framework for maintaining and
2548 running benchmarks and validation tests for multi-compartment neural network
2549 simulations on HPC systems. NSuite automates the process of building simulation
2550 engines, and running benchmarks and validation tests. NSuite is specifically
2551 designed to allow easy deployment on HPC systems in testing workflows, such as
2552 benchmark-driven development or continuous integration. The development of
2553 NSuite has been driven by the need (1) for a definitive resource for comparing
2554 performance and correctness of simulation engines on HPC systems, (2) to verify
2555 the performance and correctness of individual simulation engines as they change
2556 over time and (3) to test that changes to an HPC system do not cause
2557 performance or correctness regressions in simulation engines. The framework
2558 currently supports the simulation engines Arbor, NEURON, and CoreNeuron, while
2559 allowing other simulation engines to be added.</span></p>
2560
2561 <p class=MsoNormal></p>
2562
2563 <p class=MsoNormal><span lang=en-DE>Nutil</span></p>
2564
2565 <p class=MsoNormal><span lang=en-DE>Nutil is a pre- and post-processing toolbox
2566 that enables analysis of large collections of histological images of rodent
2567 brain sections. The software is open source and has both a graphical user
2568 interface for specifying the input and output parameters and a command-line
2569 execution option for batch processing. Nutil includes a transformation tool for
2570 automated scaling, rotation, mirroring and renaming of image files, a file
2571 format converter, a simple resize tool and a post-processing method for
2572 quantifying and localising labelled features based on a reference atlas of the
2573 brain (mouse or rat). The quantification method requires input from customised
2574 brain atlas maps generated with the QuickNII software, and segmentations
2575 generated with ilastik or another image analysis tool. The output from Nutil
2576 include csv reports, 3D point cloud coordinate files and atlas map images
2577 superimposed with colour-coded objects.</span></p>
2578
2579 <h2></h2>
2580
2581 <h2><a name="_Toc138932351"><span lang=en-DE>ODE-toolbox</span></a></h2>
2582
2583 <p class=MsoNormal><span lang=en-DE>ODE-toolbox is a Python package that
2584 assists in solver benchmarking, and recommends solvers on the basis of a set of
2585 user-configurable heuristics. For all dynamical equations that admit an
2586 analytic solution, ODE-toolbox generates propagator matrices that allow the
2587 solution to be calculated at machine precision. For all others, first-order
2588 update expressions are returned based on the Jacobian matrix. In addition to
2589 continuous dynamics, discrete events can be used to model instantaneous changes
2590 in system state, such as a neuronal action potential. These can be generated by
2591 the system under test as well as applied as external stimuli, making
2592 ODE-toolbox particularly well-suited for applications in computational
2593 neuroscience.</span></p>
2594
2595 <h2></h2>
2596
2597 <h2><a name="_Toc138932352"><span lang=en-DE>openMINDS</span></a></h2>
2598
2599 <p class=MsoNormal><span lang=en-DE>openMINDS is composed of: (i) integrated
2600 metadata models adoptable by any graph database system (GDBS), (ii) a set of
2601 libraries of serviceable metadata instances with external resource references
2602 for local and global knowledge integration, and (iii) supportive tooling for
2603 handling the metadata models and instances. Moreover, the framework provides
2604 machine-readable mappings to other standardisation efforts (e.g., schema.org).
2605 With this, openMINDS is a unique and powerful metadata framework for flexible
2606 knowledge integration within and beyond any GDBS.</span></p>
2607
2608 <h2></h2>
2609
2610 <h2><a name="_Toc138932353"><span lang=en-DE>openMINDS metadata for TVB-ready
2611 data</span></a></h2>
2612
2613 <p class=MsoNormal><span lang=en-DE>Jupyter Python notebook with code and
2614 commentaries for creating openMINDS metadata version 1.0 in JSON-LD format for
2615 ingestion of TVB-ready data in EBRAINS Knowledge Graph.</span></p>
2616
2617 <h2></h2>
2618
2619 <h2><a name="_Toc138932354"><span lang=en-DE>PCI</span></a></h2>
2620
2621 <p class=MsoNormal><span lang=en-DE>The notebook allows the computation of the
2622 PCI Lempel-Ziv and PCI state transitions. In order to run the examples, a wake
2623 and sleep data set needs to be provided in the Python-MNE format.</span></p>
2624
2625 <h2></h2>
2626
2627 <h2><a name="_Toc138932355"><span lang=en-DE>PIPSA</span></a></h2>
2628
2629 <p class=MsoNormal><span lang=en-DE>PIPSA enables the comparison of the
2630 electrostatic interaction properties of proteins. It permits the classification
2631 of proteins according to their interaction properties. PIPSA may assist in
2632 function assignment, the estimation of binding properties and enzyme kinetic
2633 parameters.</span></p>
2634
2635 <h2></h2>
2636
2637 <h2><a name="_Toc138932356"><span lang=en-DE>PoSCE</span></a></h2>
2638
2639 <p class=MsoNormal><span lang=en-DE>PoSCE is a functional connectivity
2640 estimator of fMRI time-series. It relies on the Riemannian geometry of
2641 covariances and integrates prior knowledge of covariance distribution over a
2642 population.</span></p>
2643
2644 <h2></h2>
2645
2646 <h2><a name="_Toc138932357"><span lang=en-DE>Provenance API</span></a></h2>
2647
2648 <p class=MsoNormal><span lang=en-DE>The EBRAINS Provenance API is a web service
2649 to facilitate working with computational provenance metadata. Metadata are
2650 stored in the EBRAINS Knowledge Graph (KG) using openMINDS schemas. The
2651 Provenance API provides a somewhat simplified interface compared to accessing
2652 the KG directly and performs checks of metadata consistency. The service covers
2653 workflows involving simulation, data analysis, visualisation, optimisation,
2654 data movement and model validation.</span></p>
2655
2656 <h2></h2>
2657
2658 <h2><a name="_Toc138932358"><span lang=en-DE>PyNN</span></a></h2>
2659
2660 <p class=MsoNormal><span lang=en-DE>A model description written with the PyNN
2661 API and the Python programming language runs on any simulator that PyNN
2662 supports (currently NEURON, NEST and Brian 2) as well as on the BrainScaleS
2663 and SpiNNaker neuromorphic hardware systems. PyNN provides a library of
2664 standard neuron, synapse and synaptic plasticity models, verified to work the
2665 same on different simulators. PyNN also provides commonly used connectivity
2666 algorithms (e.g. all-to-all, random, distance-dependent, small-world) but makes
2667 it easy to provide your own connectivity in a simulator-independent way. PyNN
2668 transparently supports distributed simulations using MPI.</span></p>
2669
2670 <h2></h2>
2671
2672 <h2><a name="_Toc138932359"><span lang=en-DE>Pyramidal Explorer</span></a></h2>
2673
2674 <p class=MsoNormal><span lang=en-DE>PyramidalExplorer is a tool to
2675 interactively explore and reveal the detailed organisation of the microanatomy
2676 of pyramidal neurons with functionally related models. Possible regional
2677 differences in the pyramidal cell architecture can be interactively discovered
2678 by combining quantitative morphological information about the structure of the
2679 cell with implemented functional models. The key contribution of this tool is the
2680 morpho-functional oriented design,  allowing the user to navigate within the 3D
2681 dataset, filter and perform content-based retrieval operations to find the 
2682 spines that are alike and dissimilar within the neuron, according to particular
2683 morphological or functional variables.</span></p>
2684
2685 <h2></h2>
2686
2687 <h2><a name="_Toc138932360"><span lang=en-DE>QCAlign software</span></a></h2>
2688
2689 <p class=MsoNormal><span lang=en-DE>The QUINT workflow enables spatial analysis
2690 of labelling in series of brain sections from mouse and rat based on
2691 registration to a reference brain atlas. The QCAlign software supports the use
2692 of QUINT for high-throughput studies by providing information about: 1. The
2693 quality of the section images used as input to the QUINT workflow. 2. The
2694 quality of the atlas registration performed in the QUINT workflow. 3. QCAlign
2695 also makes it easier for the user to explore the atlas hierarchy and decide on
2696 a customised hierarchy level to use for the investigation</span></p>
2697
2698 <h2></h2>
2699
2700 <h2><a name="_Toc138932361"><span lang=en-DE>QuickNII</span></a></h2>
2701
2702 <p class=MsoNormal><span lang=en-DE>QuickNII is a tool for user-guided affine
2703 registration (anchoring) of 2D experimental image data, typically high
2704 resolution microscopic images, to 3D atlas reference space, facilitating data
2705 integration through standardised coordinate systems. Key features: Generate
2706 user-defined cut planes through the atlas templates, matching the orientation
2707 of the cut plane of the 2D experimental image data, as a first step towards
2708 anchoring of images to the relevant atlas template. Propagate spatial
2709 transformations across series of sections following anchoring of selected
2710 images.</span></p>
2711
2712 <h2></h2>
2713
2714 <h2><a name="_Toc138932362"><span lang=en-DE>Quota Manager</span></a></h2>
2715
2716 <p class=MsoNormal><span lang=en-DE>The Quota Manager enables each EBRAINS
2717 service to manage user quotas for resources EBRAINS users consume in their
2718 respective services. The goal is to encourage the responsible use of resources.
2719 It is recommended that all users (except possibly guest accounts) are provided
2720 with a default quota, and that specific users have the option of receiving
2721 larger quotas based on their affiliation, role or motivated requests.</span></p>
2722
2723 <h2></h2>
2724
2725 <h2><a name="_Toc138932363"><span lang=en-DE>RateML</span></a></h2>
2726
2727 <p class=MsoNormal><span lang=en-DE>RateML enables users to generate
2728 whole-brain network models from a succinct declarative description, in which
2729 the mathematics of the model are described without specifying how their
2730 simulation should be implemented. RateML builds on NeuroML's Low Entropy Model
2731 Specification (LEMS), an XML-based language for specifying models of dynamical systems,
2732 allowing descriptions of neural mass and discretized neural field models, as
2733 implemented by the TVB simulator. The end user describes their model's
2734 mathematics once and generates and runs code for different languages, targeting
2735 both CPUs for fast single simulations and GPUs for parallel ensemble
2736 simulations.</span></p>
2737
2738 <h2></h2>
2739
2740 <h2><a name="_Toc138932364"><span lang=en-DE>Region-wise CBPP using the Julich
2741 BrainÊCytoarchitectonic Atlas</span></a></h2>
2742
2743 <p class=MsoNormal><span lang=en-DE>Many studies have been investigating the
2744 relationships between interindividual variability in brain regions'
2745 connectivity and behavioural phenotypes, by utilising connectivity-based
2746 prediction models. Recently, we demonstrated that an approach based on the
2747 combination of whole-brain and region-wise CBPP can provide important insight
2748 into the predictive model, and hence in brain-behaviour relationships, by
2749 offering interpretable patterns. Here, we applied this approach using the
2750 Julich Brain Cytoarchitectonic Atlas with the resting-state functional
2751 connectivity and psychometric variables from the Human Connectome Project
2752 dataset, illustrating each brain region's predictive power for a range of
2753 psychometric variables. As a result, a psychometric prediction profile was
2754 established for each brain region, which can be validated against brain mapping
2755 literature.</span></p>
2756
2757 <h2></h2>
2758
2759 <h2><a name="_Toc138932365"><span lang=en-DE>RRI Capacity Development Resources</span></a></h2>
2760
2761 <p class=MsoNormal><span lang=en-DE>A series of training resources developed to
2762 enable anticipation, critical reflection and public engagement/deliberation of
2763 societal consequences of brain research and innovation activities. These
2764 resources were designed primarily for HBP researchers and EBRAINS leadership
2765 and management, involving EBRAINS data and infrastructure providers. However,
2766 they are also useful for engaging the wider public with RRI. The resources are
2767 based on the legacy of over 10 years of research and activities of the ethics
2768 and society-team in the HBP. They cover important RRI-related topics on
2769 neuroethics, data governance, dual-use, public engagement and foresight,
2770 diversity, search integrity etc.</span></p>
2771
2772 <h2></h2>
2773
2774 <h2><a name="_Toc138932366"><span lang=en-DE>rsHRF</span></a></h2>
2775
2776 <p class=MsoNormal><span lang=en-DE>This toolbox is aimed to retrieve the
2777 onsets of pseudo-events triggering an hemodynamic response from resting state
2778 fMRI BOLD signals. It is based on point process theory and fits a model to
2779 retrieve the optimal lag between the events and the HRF onset, as well as the
2780 HRF shape, using different shape parameters or combinations of basis functions.
2781 Once the HRF has been retrieved for each voxel/vertex, it can be deconvolved
2782 from the time series (for example, to improve lag-based connectivity
2783 estimates), or one can map the shape parameters everywhere in the brain
2784 (including white matter) and use it as a pathophysiological indicator.</span></p>
2785
2786 <h2></h2>
2787
2788 <h2><a name="_Toc138932367"><span lang=en-DE>RTNeuron</span></a></h2>
2789
2790 <p class=MsoNormal><span lang=en-DE>The main utility of RTNeuron is twofold:
2791 (i) the interactive visual inspection of structural and functional features of
2792 the cortical column model and (ii) the generation of high-quality movies and
2793 images for presentations and publications.RTNeuron provides a C++ library with
2794 an OpenGL-based rendering backend, a Python wrapping and a Python application
2795 called rtneuron. RTNeuron is only supported in GNU/Linux systems. However, it
2796 should also be possible to build it on Windows systems. For OS/X it may be
2797 quite challenging and require changes in OpenGL-related code to get it working.</span></p>
2798
2799 <h2></h2>
2800
2801 <h2><a name="_Toc138932368"><span lang=en-DE>sbs: Spike-based Sampling</span></a></h2>
2802
2803 <p class=MsoNormal><span lang=en-DE>Spike-based sampling, sbs, is a software
2804 suite that takes care of calibrating spiking neurons for given target
2805 distributions and allows the evaluation of these distributions as they are
2806 produced by stochastic spiking networks.</span></p>
2807
2808 <h2></h2>
2809
2810 <h2><a name="_Toc138932369"><span lang=en-DE>SDA 7</span></a></h2>
2811
2812 <p class=MsoNormal><span lang=en-DE>SDA 7 can be used to carry out Brownian
2813 dynamics simulations of the diffusional association in a continuum aqueous
2814 solvent of two solute molecules, e.g., proteins, or of a solute molecule to an
2815 inorganic surface. SDA 7 can also be used to simulate the diffusion of multiple
2816 proteins, in dilute or concentrated solutions, e.g., to study the effects of
2817 macromolecular crowding.</span></p>
2818
2819 <h2></h2>
2820
2821 <h2><a name="_Toc138932370"><span lang=en-DE>Shape &amp; Appearance Modelling</span></a></h2>
2822
2823 <p class=MsoNormal><span lang=en-DE>A framework for automatically learning
2824 shape and appearance models for medical (and certain other) images. The
2825 algorithm was developed with the aim of eventually enabling distributed
2826 privacy-preserving analysis of brain image data, such that shared information
2827 (shape and appearance basis functions) may be passed across sites, whereas
2828 latent variables that encode individual images remain secure within each site.
2829 These latent variables are proposed as features for privacy-preserving data
2830 mining applications.</span></p>
2831
2832 <h2></h2>
2833
2834 <h2><a name="_Toc138932371"><span lang=en-DE>siibra-api</span></a></h2>
2835
2836 <p class=MsoNormal><span lang=en-DE>siibra-api provides an HTTP wrapper around
2837 siibra-python, allowing developers to access atlas (meta)data over HTTP
2838 protocol. Deployed on the EBRAINS infrastructure, developers can access the
2839 centralised (meta)data on atlases, as provided by siibra-python, regardless of
2840 the programming language.</span></p>
2841
2842 <h2></h2>
2843
2844 <h2><a name="_Toc138932372"><span lang=en-DE>siibra-explorer</span></a></h2>
2845
2846 <p class=MsoNormal><span lang=en-DE>The interactive atlas viewer
2847 siibra-explorer allows exploring the different EBRAINS atlases for the human,
2848 monkey and rodent brains together with a comprehensive set of linked multimodal
2849 data features. It provides a 3-planar view of a parcellated reference volume
2850 combined with a rotatable overview of the 3D surface. Several templates can be
2851 selected to navigate through the brain from MRI-scale to microscopic
2852 resolution, allowing inspection of terabyte-size image data. Anatomically
2853 anchored datasets reflecting aspects of cellular and molecular organisation,
2854 fibres, function and connectivity can be discovered by selecting brain regions
2855 from parcellations, or zooming and panning the reference brain. siibra-explorer
2856 also allows annotation of brain locations as points and polygons and is
2857 extensible via interactive plugins.</span></p>
2858
2859 <h2></h2>
2860
2861 <h2><a name="_Toc138932373"><span lang=en-DE>siibra-python</span></a></h2>
2862
2863 <p class=MsoNormal><span lang=en-DE>siibra-python is a Python client to a brain
2864 atlas framework that integrates brain parcellations and reference spaces at
2865 different spatial scales and connects them with a broad range of multimodal
2866 regional data features. It aims to facilitate programmatic and reproducible
2867 incorporation of brain parcellations and brain region features from different
2868 sources into neuroscience workflows. Also, siibra-python provides an easy
2869 access to data features on the EBRAINS Knowledge Graph in a well-structured
2870 manner. Users can preconfigure their own data to use within siibra-python.</span></p>
2871
2872 <h2></h2>
2873
2874 <h2><a name="_Toc138932374"><span lang=en-DE>Single Cell Model (Re)builder
2875 Notebook</span></a></h2>
2876
2877 <p class=MsoNormal><span lang=en-DE>The Single Cell Model (Re)builder Notebook
2878 is a web application, implemented via a Jupyter Notebook on EBRAINS, which
2879 allows users to configure the BluePyOpt to re-run an optimisation with their
2880 own choices for the parameters range. The optimisation jobs are submitted
2881 through Neuroscience Gateway.</span></p>
2882
2883 <h2></h2>
2884
2885 <h2><a name="_Toc138932375"><span lang=en-DE>Slurm Plugin for Co-allocation of
2886 Compute and Data Resources</span></a></h2>
2887
2888 <p class=MsoNormal><span lang=en-DE>This Simple linux utility for resource
2889 management (Slurm) plugin enables the co-allocation of compute and data resources
2890 on a shared multi-tiered storage cluster by estimating waiting times when the
2891 high-performance storage (burst buffers) will become available to submitted
2892 jobs. Based on the current job queue and the estimated waiting time, the plugin
2893 decides whether scheduling the high-performance or lower-performance storage
2894 system (parallel file system) benefits the job's turnaround time. The
2895 estimation depends on additional information the user provides at submission
2896 time.</span></p>
2897
2898 <h2></h2>
2899
2900 <h2><a name="_Toc138932376"><span lang=en-DE>Snudda</span></a></h2>
2901
2902 <p class=MsoNormal><span lang=en-DE>Snudda ('touch' in Swedish) allows the user
2903 to set up and generate microcircuits where the connectivity between neurons is
2904 based on reconstructed neuron morphologies. The touch detection algorithm looks
2905 for overlaps of axons and dendrites, and places putative synapses where they
2906 touch. The putative synapses are pruned, removing a fraction to match
2907 statistics from pairwise connectivity experiments. If needed, Snudda can also
2908 use probability functions to create realistic microcircuits. The Snudda
2909 software is written in Python and includes support for supercomputers. It uses
2910 ipyparallel to parallelise network creation, and NEURON as the backend for
2911 simulations. Install using pip or by directly downloading.</span></p>
2912
2913 <h2></h2>
2914
2915 <h2><a name="_Toc138932377"><span lang=en-DE>SomaSegmenter</span></a></h2>
2916
2917 <p class=MsoNormal><span lang=en-DE>SomaSegmenter allows neuronal soma
2918 segmentation in fluorescence microscopy imaging datasets with the use of a
2919 parametrised version of the U-Net segmentation model, including additional
2920 features such as residual links and tile-based frame reconstruction.</span></p>
2921
2922 <h2></h2>
2923
2924 <h2><a name="_Toc138932378"><span lang=en-DE>SpiNNaker</span></a></h2>
2925
2926 <p class=MsoNormal><span lang=en-DE>SpiNNaker is a neuromorphic computer with
2927 over a million low power, small memory ARM cores arranged in chips, connected
2928 together with a unique brain-like mesh network, and designed to simulate
2929 networks of spiking point neurons. Software is provided to compile networks
2930 described with PyNN into running simulations, and to extract and convert
2931 results into the neo data format, as well as providing support for live
2932 interaction with running simulations. This allows integration with external
2933 devices such as real or virtual robotics as well as live simulation
2934 visualisation. Scripts can be written and executed using Jupyter for
2935 interactive access.</span></p>
2936
2937 <h2></h2>
2938
2939 <h2><a name="_Toc138932379"><span lang=en-DE>SSB toolkit</span></a></h2>
2940
2941 <p class=MsoNormal><span lang=en-DE>The SSB toolkit is an open-source Python
2942 library to simulate mathematical models of the signal transduction pathways of
2943 G-protein coupled receptors (GPCRs). By merging structural macromolecular data
2944 with systems biology simulations, the framework allows simulation of the signal
2945 transduction kinetics induced by ligand-GPCR interactions, as well as the consequent
2946 change of concentration of signalling molecular species, as a function of time
2947 and ligand concentration. Therefore, this tool allows the possibility to
2948 investigate the subcellular effects of ligand binding upon receptor activation,
2949 deepening the understanding of the relationship between the molecular level of
2950 ligand-target interactions and higher-level cellular and physiological or
2951 pathological response mechanisms.</span></p>
2952
2953 <h2></h2>
2954
2955 <h2><a name="_Toc138932380"><span lang=en-DE>Subcellular model building and
2956 calibration tool set</span></a></h2>
2957
2958 <p class=MsoNormal><span lang=en-DE>The toolset includes interoperable modules
2959 for: model building, calibration (parameter estimation) and model analysis. All
2960 information needed to perform these tasks (models, experimental calibration
2961 data and prior assumptions on parameter distributions) are stored in a
2962 structured, human- and machine-readable file format based on SBtab. The toolset
2963 enables simulations of the same model in simulators with different
2964 characteristics, e.g., STEPS, NEURON, MATLAB's Simbiology and R via automatic
2965 code generation. The parameter estimation can include uncertainty
2966 quantification and is done by optimisation or Bayesian approaches. Model
2967 analysis includes global sensitivity analysis and functionality for analysing
2968 thermodynamic constraints and conserved moieties.</span></p>
2969
2970 <h2></h2>
2971
2972 <h2><a name="_Toc138932381"><span lang=en-DE>Synaptic Events Fitting</span></a></h2>
2973
2974 <p class=MsoNormal><span lang=en-DE>The Synaptic Events Fitting is a web
2975 application, implemented in a Jupyter Notebook on EBRAINS that allows users to
2976 fit synaptic events using data and models from the EBRAINS Knowledge Graph
2977 (KG). Select, download and visualise experimental data from the KG and then choose
2978 the data to be fitted. A mod file is then selected (local or default) together
2979 with the corresponding configuration file (including protocol and the name of
2980 the parameters to be fitted, their initial values and allowed variation range,
2981 exclusion rules and an optional set of dependencies). The fitting procedure can
2982 run on Neuroscience Gateway. Fetch the fitting results from the storage of the
2983 HPC system to the storage of the Collab or to analyse the optimised parameters.</span></p>
2984
2985 <h2></h2>
2986
2987 <h2><a name="_Toc138932382"><span lang=en-DE>Synaptic Plasticity Explorer</span></a></h2>
2988
2989 <p class=MsoNormal><span lang=en-DE>The Synaptic Plasticity Explorer is a web
2990 application, implemented via a Jupyter Notebook on EBRAINS, which allows to
2991 configure and test, through an intuitive GUI, different synaptic plasticity
2992 models and protocols on single cell optimised models, available in the EBRAINS
2993 Model Catalog. It consists of two tabs: 'Config', where the user can specify
2994 the plasticity model to use and the synaptic parameters, and 'Sim', where the
2995 recording location, weight's evolution and number of simulations to run are
2996 defined. The results are plotted at the end of the simulation and the traces
2997 are available for download.</span></p>
2998
2999 <h2></h2>
3000
3001 <h2><a name="_Toc138932383"><span lang=en-DE>Synaptic proteome database
3002 (SQLite)</span></a></h2>
3003
3004 <p class=MsoNormal><span lang=en-DE>Integration of 57 published synaptic
3005 proteomic datasets reveals a stunningly complex picture involving over 7000
3006 proteins. Molecular complexes were reconstructed using evidence-based
3007 protein-protein interaction data available from public databases. The
3008 constructed molecular interaction network model is embedded into an SQLite
3009 implementation, allowing queries that generate custom network models based on
3010 meta-data including species, synaptic compartment, brain region, and method of
3011 extraction.</span></p>
3012
3013 <h2></h2>
3014
3015 <h2><a name="_Toc138932384"><span lang=en-DE>Synaptome.db</span></a></h2>
3016
3017 <p class=MsoNormal><span lang=en-DE>The Synaptome.db bioconductor package
3018 contains a local copy of the Synaptic proteome database. On top of this it
3019 provides a set of utility R functions to query and analyse its content. It
3020 allows for extraction of information for specific genes and building the
3021 protein-protein interaction graph for gene sets, synaptic compartments and
3022 brain regions.</span></p>
3023
3024 <h2></h2>
3025
3026 <h2><a name="_Toc138932385"><span lang=en-DE>Tide</span></a></h2>
3027
3028 <p class=MsoNormal><span lang=en-DE>BlueBrain's Tide provides multi-window,
3029 multi-user touch interaction on large surfaces Ð think of a giant collaborative
3030 wall-mounted tablet. Tide is a distributed application that can run on multiple
3031 machines to power display walls or projection systems of any size. Its user interface
3032 is designed to offer an intuitive experience on touch walls. It works just as
3033 well on non-touch-capable installations by using its web interface from any web
3034 browser.</span></p>
3035
3036 <h2></h2>
3037
3038 <h2><a name="_Toc138932386"><span lang=en-DE>TVB EBRAINS</span></a></h2>
3039
3040 <p class=MsoNormal><span lang=en-DE>TVB EBRAINS is the principal full brain
3041 network simulation engine in EBRAINS and covers every aspect of realising
3042 personalised whole-brain simulations on the EBRAINS platform. It consists of
3043 the simulation tools and adaptors connecting the data, atlas and computing
3044 services to the rest of the TVB ecosystem and Cloud services available in
3045 EBRAINS. As such it allows the user to find and fetch relevant datasets through
3046 the EBRAINS Knowledge Graph and Atlas services, construct the personalised TVB
3047 models and use the HPC systems to perform parameter exploration, optimisation and
3048 inference studies. The user can orchestrate the workflow from the Jupyterlab
3049 interactive computing environment of the EBRAINS Collaboratory or use the
3050 dedicated web application of TVB.</span></p>
3051
3052 <h2></h2>
3053
3054 <h2><a name="_Toc138932387"><span lang=en-DE>TVB Image Processing Pipeline</span></a></h2>
3055
3056 <p class=MsoNormal><span lang=en-DE>TVB Image Processing Pipeline takes multimodal
3057 MRI data sets (anatomical, functional and diffusion-weighted MRI) as input and
3058 generates structural connectomes, region-average fMRI time series, functional
3059 connectomes, brain surfaces, electrode positions, lead field matrices and atlas
3060 parcellations as output. The pipeline performs preprocessing and
3061 distortion-correction on MRI data as well as white matter fibre bundle
3062 tractography on diffusion data. Outputs are formatted according to two data
3063 standards: a TVB-ready data set that can be directly used to simulate brain
3064 network models and the same output in BIDS format.</span></p>
3065
3066 <h2></h2>
3067
3068 <h2><a name="_Toc138932388"><span lang=en-DE>TVB Inversion</span></a></h2>
3069
3070 <p class=MsoNormal><span lang=en-DE>The TVB Inversion package implements the
3071 machinery required to perform parameter exploration and  inference over
3072 parameters of The Virtual Brain simulator. It implements Simulation Based
3073 Inference (SBI) which is a Bayesian inference method for complex models, where
3074 calculation of the likelihood function is either analytically or
3075 computationally intractable. As such, it allows the user to formulate with
3076 great expressive power both the model and the inference scenario in terms of
3077 observed data features and model parameters. Part of the integration with TVB
3078 entails the option to perform numerous simulations in parallel, which can be
3079 used for parameter space exploration.</span></p>
3080
3081 <h2></h2>
3082
3083 <h2><a name="_Toc138932389"><span lang=en-DE>TVB Web App</span></a></h2>
3084
3085 <p class=MsoNormal><span lang=en-DE>TVB Web App provides The Virtual Brain
3086 Simulator as an EBRAINS Cloud Service with an HPC back-end. Scientists can run
3087 intense personalised brain simulations without having to deploy software. Users
3088 can access the service with their EBRAINS credentials (single sign on). TVB Web
3089 App uses private/public key cryptography, sandboxing, and access control to
3090 protect personalised health information contained in digital human brain twins
3091 while being processed on HPC. Users can upload their connectomes or use
3092 TVB-ready image-derived data discoverable via the EBRAINS Knowledge Graph.
3093 Users can also use containerised processing workflows available on EBRAINS to
3094 render image raw data into simulation-ready formats.</span></p>
3095
3096 <h2></h2>
3097
3098 <h2><a name="_Toc138932390"><span lang=en-DE>TVB Widgets</span></a></h2>
3099
3100 <p class=MsoNormal><span lang=en-DE>In order to support the usability of
3101 EBRAINS workflows, TVB-widgets has been developed as a set of modular graphic
3102 components and software solutions, easy to use in the Collaboratory within the
3103 JupyterLab. These GUI components are based on and under open source licence,
3104 supporting open neuroscience and support features like: Setup of models and
3105 region-specific or cohort simulations. Selection of Data sources and their
3106 links to models. Querying data from siibra and the EBRAINS Knowledge Graph.
3107 Deployment and monitoring jobs on HPC resources. Analysis and visualisation.
3108 Visual workflow builder for configuring and launching TVB simulations.</span></p>
3109
3110 <h2></h2>
3111
3112 <h2><a name="_Toc138932391"><span lang=en-DE>TVB-Multiscale</span></a></h2>
3113
3114 <p class=MsoNormal><span lang=en-DE>TVB-Multiscale is a Python toolbox aimed at
3115 facilitating the configuration of multiscale brain models and their
3116 co-simulation with TVB and spiking network simulators (currently NEST,
3117 NetPyNE (NEURON) and ANNarchy). A multiscale brain model consists of a full
3118 brain model formulated at the coarse scale of networks of tens up to thousands
3119 of brain regions, and an additional model of networks of spiking neurons
3120 describing selected brain regions at a finer scale. The toolbox has a
3121 user-friendly interface for configuring different kinds of models for
3122 transforming and exchanging data between the two scales during co-simulation.</span></p>
3123
3124 <h2></h2>
3125
3126 <h2><a name="_Toc138932392"><span lang=en-DE>VIOLA</span></a></h2>
3127
3128 <p class=MsoNormal><span lang=en-DE>VIOLA is an interactive, web-based tool to
3129 visualise activity data in multiple 2D layers such as the simulation output of
3130 neuronal networks with 2D geometry. As a reference implementation for a
3131 developed set of interactive visualisation concepts, the tool combines and
3132 adapts modern interactive visualisation paradigms, such as coordinated multiple
3133 views, for massively parallel neurophysiological data. The software allows for
3134 an explorative and qualitative assessment of the spatiotemporal features of
3135 neuronal activity, which can be performed prior to a detailed quantitative data
3136 analysis of specific aspects of the data.</span></p>
3137
3138 <h2></h2>
3139
3140 <h2><a name="_Toc138932393"><span lang=en-DE>Vishnu 1.0</span></a></h2>
3141
3142 <p class=MsoNormal><span lang=en-DE>DC Explorer, Pyramidal Explorer and Clint
3143 Explorer are the core of an application suite designed to help scientists to
3144 explore their data. Vishnu 1.0 is a communication framework that allows them to
3145 interchange information and cooperate in real time. It provides a unique access
3146 point to the three applications and manages a database with the users'
3147 datasets. Vishnu was originally designed to integrate data for
3148 Espina.Whole-brain-scale tools.</span></p>
3149
3150 <h2></h2>
3151
3152 <h2><a name="_Toc138932394"><span lang=en-DE>ViSimpl</span></a></h2>
3153
3154 <p class=MsoNormal><span lang=en-DE>ViSimpl integrates a set of visualisation
3155 and interaction components that provide a semantic view of brain data with the
3156 aim of improving its analysis procedures. ViSimpl provides 3D particle-based
3157 rendering that visualises simulation data with their associated spatial and
3158 temporal information, enhancing the knowledge extraction process. It also
3159 provides abstract representations of the time-varying magnitudes, supporting
3160 different data aggregation and disaggregation operations and giving focus and
3161 context clues. In addition, ViSimpl provides synchronised playback control of
3162 the simulation being analysed.</span></p>
3163
3164 <h2></h2>
3165
3166 <h2><a name="_Toc138932395"><span lang=en-DE>VisuAlign</span></a></h2>
3167
3168 <p class=MsoNormal><span lang=en-DE>VisuAlign is a tool for user-guided
3169 nonlinear registration after QuickNII of 2D experimental image data, typically
3170 high resolution microscopic images, to 3D atlas reference space, facilitating
3171 data integration through standardised coordinate systems. Key features:
3172 Generate user-defined cut planes through the atlas templates, matching the
3173 orientation of the cut plane of the 2D experimental image data, as a first step
3174 towards anchoring of images to the relevant atlas template. Propagate spatial
3175 transformations across series of sections following anchoring of selected
3176 images.</span></p>
3177
3178 <h2></h2>
3179
3180 <h2><a name="_Toc138932396"><span lang=en-DE>VMetaFlow</span></a></h2>
3181
3182 <p class=MsoNormal><span lang=en-DE>VMetaFlow is an abstraction layer placed
3183 over existing visual grammars and visualisation declarative languages,
3184 providing them with interoperability mechanisms. The main contribution of this
3185 research is to provide a user-friendly system to design visualisation and data
3186 processing operations that can be interconnected to form data analysis
3187 workflows. Visualisations and data processes can be saved as cards. Cards and
3188 workflows can be saved, distributed and reused between users.</span></p>
3189
3190 <h2></h2>
3191
3192 <h2><a name="_Toc138932397"><span lang=en-DE>Voluba</span></a></h2>
3193
3194 <p class=MsoNormal><span lang=en-DE>A common problem in high-resolution brain
3195 atlasing is spatial anchoring of volumes of interest from imaging experiments
3196 into the detailed anatomical context of an ultrahigh-resolution reference model
3197 like BigBrain. The interactive volumetric alignment tool voluba is implemented
3198 as a web service and allows anchoring of volumetric image data to reference
3199 volumes at microscopical spatial resolutions. It enables interactive
3200 manipulation of image position, scale, and orientation, flipping of coordinate
3201 axes, and entering of anatomical point landmarks in 3D. The resulting
3202 transformation parameters can, amongst others, be downloaded or used to view
3203 the anchored image volume in the interactive atlas viewer siibra-explorer.</span></p>
3204
3205 <h2></h2>
3206
3207 <h2><a name="_Toc138932398"><span lang=en-DE>WebAlign</span></a></h2>
3208
3209 <p class=MsoNormal><span lang=en-DE>WebAlign is the web version of QuickNII.
3210 Presently, it is available as a community app in the Collaboratory. Features
3211 include: Spatial registration of sectional image data. Generation of customised
3212 atlas maps for your sectional image data.</span></p>
3213
3214 <h2></h2>
3215
3216 <h2><a name="_Toc138932399"><span lang=en-DE>Webilastik</span></a></h2>
3217
3218 <p class=MsoNormal><span lang=en-DE>webilastik brings the popular machine
3219 learning-based image analysis tool ilastik from the desktop into the browser.
3220 Users can perform semantic segmentation tasks on their data in the cloud.
3221 webilastik runs computations on federated EBRAINS HPC resources and uses
3222 EBRAINS infrastructure for data access and storage. webilastik makes machine
3223 learning-based image analysis workflows accessible to users without deep
3224 knowledge of image analysis and machine learning. webilastik is part of the
3225 QUINT workflow for extraction, quantification and analysis of features from
3226 rodent histological images.</span></p>
3227
3228 <h2></h2>
3229
3230 <h2><a name="_Toc138932400"><span lang=en-DE>WebWarp</span></a></h2>
3231
3232 <p class=MsoNormal><span lang=en-DE>WebWarp is the web version of VisuAlign.
3233 Presently, it is available as a community app in the Collaboratory. Features
3234 include: Nonlinear refinements of atlas registration by WebAlign of sectional
3235 image data. Generation of customised atlas maps for your sectional image data.</span></p>
3236
3237 <h2></h2>
3238
3239 <h2><a name="_Toc138932401"><span lang=en-DE>ZetaStitcher</span></a></h2>
3240
3241 <p class=MsoNormal><span lang=en-DE>ZetaStitcher is a Python package designed
3242 to stitch large volumetric images, such as those produced by Light-Sheet
3243 Fluorescence Microscopes. It is able to quickly compute the optimal alignment
3244 of large mosaics of tiles thanks to its ability to perform a sampling along the
3245 tile depth, i.e., pairwise alignment is computed only at certain depths along
3246 the thickness of the tile. This greatly reduces the amount of data that needs
3247 to be read and transferred, thus, making the process much faster. ZetaStitcher
3248 comes with an API that can be used to programmatically access the aligned
3249 volume in a virtual fashion as if it were a big NumPy array, without having to
3250 produce the fused 3D image of the whole sample.Cellular- and subcellular-scale
3251 tools.</span></p>
3252
3253 <h2></h2>
3254
3255 <h2><a name="_Toc138932402"><span lang=en-DE>TauRAMD</span></a></h2>
3256
3257 <p class=MsoNormal><span lang=en-DE>The TauRAMD technique makes use of RAMD
3258 simulations to compute relative residence times (or dissociation rates) of
3259 protein-ligand complexes. In the RAMD method, the egress of a molecule from a
3260 target receptor is accelerated by the application of an adaptive randomly
3261 oriented force on the ligand. This enables ligand egress events to be observed
3262 in short, nanosecond timescale simulations without imposing any bias regarding
3263 the ligand egress route taken. If coupled to the MD-IFP tool, the TauRAMD
3264 method can be used to investigate dissociation mechanisms and characterize
3265 transition states.</span></p>
3266
3267 </div>
3268
3269 </body>
3270
3271 </html>
3272
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