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Version 2.1 by marissadiazpier on 2023/06/29 12:03
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marissadiazpier 1.1 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.
2
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.
6
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.
marissadiazpier 2.1 8
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420
421 </head>
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>&quot;BioNAR combines a selection of
1317 existing R protocols for network analysis with newly designed original
1318 methodological features to support step-by-step analysis of
1319 biological/biomedical networks. BioNAR supports a pipeline approach where many
1320 networks and iterative analyses can be performed. BioNAR helps to achieve a
1321 number of network analysis goals that are difficult to achieve anywhere else,
1322 e.g., choose the optimal clustering algorithm from a range of options based on
1323 independent annotation enrichment</span><span lang=en-DE style='font-family:
1324 "Times New Roman",serif'> </span><span lang=en-DE>predict a proteins influence
1325 within and across multiple sub-complexes in the network and estimate the
1326 co-occurrence or linkage between meta-data at the network level.&quot;</span></p>
1327
1328 <h2></h2>
1329
1330 <h2><a name="_Toc138932257"><span lang=en-DE>BlueNaaS-single cell</span></a></h2>
1331
1332 <p class=MsoNormal><span lang=en-DE>BlueNaaS-SingleCell is an open-source web
1333 application. It enables users to quickly visualize single cell model
1334 morphologies in 3D or as a dendrogram. Using a simple web user interface, single
1335 cell simulations can be easily configured and launched, producing voltage
1336 traces from selected compartments.</span></p>
1337
1338 <h2></h2>
1339
1340 <h2><a name="_Toc138932258"><span lang=en-DE>BlueNaaS-subcellular</span></a></h2>
1341
1342 <p class=MsoNormal><span lang=en-DE>BlueNaaS-Subcellular is a web-based
1343 environment for creation and simulation of reaction-diffusion models. It allows
1344 the user to import, combine and simulate existing models derived from other
1345 parts of the pipeline. It is integrated with a number of solvers for
1346 reaction-diffusion systems of equations and can represent rule-based systems
1347 using BioNetGen. Additionally, it supports simulation of spatially distributed
1348 systems using STEPS (stochastic engine for pathway simulation), providing
1349 spatial stochastic and deterministic solvers for simulation of reactions and
1350 diffusion on tetrahedral meshes. It includes some visualisation tools such as a
1351 geometry viewer, a contact map and a reactivity network graph.</span></p>
1352
1353 <h2></h2>
1354
1355 <h2><a name="_Toc138932259"><span lang=en-DE>BluePyEfe</span></a></h2>
1356
1357 <p class=MsoNormal><span lang=en-DE>BluePyEfe eases the process of reading
1358 experimental recordings and extracting batches of electrical features from
1359 these recordings. It combines trace reading functions and features extraction
1360 functions from the eFel library. BluePyEfe outputs protocols and features files
1361 in the format used by BluePyOpt for neuron electrical model building.</span></p>
1362
1363 <h2></h2>
1364
1365 <h2><a name="_Toc138932260"><span lang=en-DE>BluePyMM</span></a></h2>
1366
1367 <p class=MsoNormal><span lang=en-DE>When building a network simulation,
1368 biophysically detailed electrical models (e-models) need to be tested for every
1369 morphology that is possibly used in the circuit.  With current resources,
1370 e-models are not re-optimised for every morphology in the network. In a process
1371 called Cell Model Management (MM), we test if an existing e-model matches a
1372 particular morphology 'well enough'. It takes as input a morphology release, a
1373 circuit recipe and a set of e-models, then finds all possible (morphology,
1374 e-model)-combinations (me-combos) based on e-type, m-type, and layer as
1375 described by the circuit recipe, then calculates the scores for every
1376 combination. Finally, it writes out the resulting accepted me-combos to a
1377 database and produces a report with information on the number of matches.</span></p>
1378
1379 <h2></h2>
1380
1381 <h2><a name="_Toc138932261"><span lang=en-DE>BluePyOpt</span></a></h2>
1382
1383 <p class=MsoNormal><span lang=en-DE>BluePyOpt simplifies the task of creating
1384 and sharing these optimisations, and the associated techniques and knowledge.
1385 This is achieved by abstracting the optimisation and evaluation tasks into
1386 various reusable and flexible discrete elements according to established best
1387 practices. Further, BluePyOpt provides methods for setting up both small- and
1388 large-scale optimisations on a variety of platforms, ranging from laptops to
1389 Linux clusters and cloud-based computer infrastructures.</span></p>
1390
1391 <h2></h2>
1392
1393 <h2><a name="_Toc138932262"><span lang=en-DE>Brain Cockpit</span></a></h2>
1394
1395 <p class=MsoNormal><span lang=en-DE>Brain Cockpit is a web app comprising a
1396 Typescript front-end and a Python back-end. It is meant to help explore large
1397 surface fMRI datasets projected on surface meshes and alignments computed
1398 between brains, such as those computed with Fused Unbalanced Gromov-Wasserstein
1399 (fugw) for Python.</span></p>
1400
1401 <h2></h2>
1402
1403 <h2><a name="_Toc138932263"><span lang=en-DE>BrainScaleS</span></a></h2>
1404
1405 <p class=MsoNormal><span lang=en-DE>&quot;Emulate spiking neural networks in
1406 continuous time on the BrainScaleS analog neuromorphic computing system. Models
1407 and experiments can be described in Python using the PyNN modelling language,
1408 or in hxtorch, a PyTorch-based machine-learning-friendly API.  The platform can
1409 be used interactively via the EBRAINS JupyterLab service or EBRAINS HPC</span><span
1410 lang=en-DE style='font-family:"Times New Roman",serif'> </span><span
1411 lang=en-DE>in addition, the NMPI web service provides batch-style access. The
1412 modelling APIs employ common data formats for input and output data, e.g.,
1413 neo.&quot;</span></p>
1414
1415 <h2></h2>
1416
1417 <h2><a name="_Toc138932264"><span lang=en-DE>Brayns</span></a></h2>
1418
1419 <p class=MsoNormal><span lang=en-DE>Brayns is a large-scale scientific
1420 visualization platform based on Intel OSPRAY to perform CPU Ray-tracing and
1421 uses an extension-plugin architecture. The core provides basic functionalities
1422 that can be reused and/or extended on plugins, which are independent and can be
1423 loaded or disabled at start-up. This simplifies the process of adding support
1424 for new scientific visualization use cases, without compromising the
1425 reliability of the rest of the software. Brayns counts with braynsService, a
1426 rendering backend which can be accessed over the internet and streams images to
1427 connected clients. Already-made plugins include CircuitExplorer, DTI,
1428 AtlasExplorer, CylindricCamera and MoleculeExplorer.</span></p>
1429
1430 <p class=MsoNormal><span lang=en-DE>https://github.com/BlueBrain/Brayns/ </span></p>
1431
1432 <p class=MsoNormal></p>
1433
1434 <h2><a name="_Toc138932265"><span lang=en-DE>Brion</span></a></h2>
1435
1436 <p class=MsoNormal><span lang=en-DE>Brion is a C++ project for read and write
1437 access to Blue Brain data structures, including BlueConfig/CircuitConfig,
1438 Circuit, CompartmentReport, Mesh, Morphology, Synapse and Target files. It also
1439 offers an interface in Python.</span></p>
1440
1441 <h2></h2>
1442
1443 <h2><a name="_Toc138932266"><span lang=en-DE>BSB</span></a></h2>
1444
1445 <p class=MsoNormal><span lang=en-DE>The BSB reconstructs realistic neural
1446 circuits by placing and connecting fibres and neurons with detailed
1447 morphologies or only simplified geometrical features. Configure your model the
1448 way you need. Interfaces with several simulators (?CoreNEURON, ?Arbor, ?NEST)
1449 allow simulation of the reconstructed network and investigation of the
1450 structure-function-dynamics relationships at different levels of resolution.
1451 The ÒscaffoldÓ design allows an easy model reconfiguration reflecting variants
1452 across brain regions, animal species and physio-pathological conditions without
1453 dismounting the basic network structure. The BSB provides effortless parallel
1454 computing both for the reconstruction and simulation phase.</span></p>
1455
1456 <h2></h2>
1457
1458 <h2><a name="_Toc138932267"><span lang=en-DE>BSP Service Account</span></a></h2>
1459
1460 <p class=MsoNormal><span lang=en-DE>The BSP Service Account is a rest API
1461 service that allows developers to submit userÕs jobs on HPC systems and
1462 retrieve results using the EBRAINS authentication, even if users donÕt have a
1463 personal account on the available HPC facilities.</span></p>
1464
1465 <h2></h2>
1466
1467 <h2><a name="_Toc138932268"><span lang=en-DE>bsp-usecase-wizard</span></a></h2>
1468
1469 <p class=MsoNormal><span lang=en-DE>The CLS interactive workflows and use cases
1470 application guides the users through the resolution of realistic scientific
1471 problems. They are implemented as either front-end or full stack web
1472 applications or Python-based Jupyter Notebooks that allow the user to
1473 interactively build, reconstruct or simulate data-driven brain models and
1474 perform data analysis visualisation. Web applications are freely accessible and
1475 only require authentication to EBRAINS when specific actions are required
1476 (e.g., submitting a simulation job to an HBP HPC system). Jupyter Notebooks are
1477 cloned to the lab.ebrains.eu platform and require authentication via an EBRAINS
1478 account.</span></p>
1479
1480 <h2></h2>
1481
1482 <h2><a name="_Toc138932269"><span lang=en-DE>CGMD Platform</span></a></h2>
1483
1484 <p class=MsoNormal><span lang=en-DE>Recent advances in CGMD simulations have
1485 allowed longer and larger molecular dynamics simulations of biological
1486 macromolecules and their interactions. The CGMD platform is dedicated to the
1487 preparation, running, and analysis of CGMD simulations, and built on a
1488 completely revisited version of the Martini coarsE gRained MembrAne proteIn
1489 Dynamics (MERMAID) web server. In its current version, the platform expands the
1490 existing implementation of the Martini force field for membrane proteins to
1491 also allow the simulation of soluble proteins using the Martini and SIRAH force
1492 fields. Moreover, it offers an automated protocol for carrying out the
1493 backmapping of the coarse-grained description of the system into an atomistic
1494 one.</span></p>
1495
1496 <h2></h2>
1497
1498 <h2><a name="_Toc138932270"><span lang=en-DE>CNS-ligands</span></a></h2>
1499
1500 <p class=MsoNormal><span lang=en-DE>The project is part of the Parameter
1501 generation and mechanistic studies of neuronal cascades using multi-scale
1502 molecular simulations of the HBP. CNS conformers are generated using a powerful
1503 multilevel strategy that combines a low-level (LL) method for sampling the
1504 conformational minima and high-level (HL) ab initio calculations for estimating
1505 their relative stability. CNS database presents the results in a graphical user
1506 interface, displaying small molecule properties, analyses and generated 3D
1507 conformers. All data produced by the project is available to download.</span></p>
1508
1509 <h2></h2>
1510
1511 <h2><a name="_Toc138932271"><span lang=en-DE>Cobrawap</span></a></h2>
1512
1513 <p class=MsoNormal><span lang=en-DE>Cobrawap is an adaptable and reusable
1514 software tool to study wave-like activity propagation in the cortex. It allows for
1515 the integration of heterogeneous data from different measurement techniques and
1516 simulations through alignment to common wave descriptions. Cobrawap provides an
1517 extendable collection of processing and analysis methods that can be combined
1518 and adapted to specific input data and research applications. It enables broad
1519 and rigorous comparisons of wave characteristics across multiple datasets,
1520 model calibration and validation applications, and its modular building blocks
1521 may serve to construct related analysis pipelines.</span></p>
1522
1523 <h2></h2>
1524
1525 <h2><a name="_Toc138932272"><span lang=en-DE>Collaboratory Bucket service</span></a></h2>
1526
1527 <p class=MsoNormal><span lang=en-DE>The Bucket service provides object storage
1528 to EBRAINS users without them having to request an account on Fenix (the
1529 EBRAINS infrastructure provider) and storage resources there. This is the
1530 recommended storage for datasets that are shared by data providers, on the
1531 condition that these do not contain sensitive personal data. For sharing
1532 datasets with personal data, users should refer to the ?Health Data Cloud. The
1533 Bucket service is better suited for larger files that are usually not edited,
1534 such as datasets and videos. For Docker images, users should refer to the
1535 EBRAINS Docker registry. For smaller files and files which are more likely to
1536 be edited, users should consider the ?Collaboratory Drive service.</span></p>
1537
1538 <h2></h2>
1539
1540 <h2><a name="_Toc138932273"><span lang=en-DE>Collaboratory Drive</span></a></h2>
1541
1542 <p class=MsoNormal><span lang=en-DE>The Drive service offers users cloud
1543 storage space for their files in each collab (workspace). The Drive storage is
1544 mounted in the ?Collaboratory Lab to provide persistent storage (as opposed to
1545 the Lab containers which are deleted after a few hours of inactivity). All
1546 files are under version control. The Drive is intended for smaller files
1547 (currently limited to 1 GB) that change more often. Users must not save files
1548 containing personal information in the Drive (i.e. data of living human subjects).
1549 The Drive is also integrated with the ?Collaboratory Office service to offer
1550 easy collaborative editing of Office files online.</span></p>
1551
1552 <h2></h2>
1553
1554 <h2><a name="_Toc138932274"><span lang=en-DE>Collaboratory IAM</span></a></h2>
1555
1556 <p class=MsoNormal><span lang=en-DE>The EBRAINS Collaboratory IAM allows the
1557 developers of different EBRAINS services to benefit from a single sign-on
1558 solution. End users will benefit from a seamless experience, whereby they can
1559 access a specific service and have direct access from it to resources in other
1560 EBRAINS services without re-authentication. For the developer, it is a good way
1561 for separating concerns and offloading much of the identification and
1562 authentication to a central service. The EBRAINS IAM is recognised as an
1563 identity provider at Fenix supercomputing sites. The IAM service also provides
1564 three ways of managing groups of users: Units, Groups and Teams.</span></p>
1565
1566 <h2></h2>
1567
1568 <h2><a name="_Toc138932275"><span lang=en-DE>Collaboratory Lab</span></a></h2>
1569
1570 <p class=MsoNormal><span lang=en-DE>The Collaboratory Lab provides EBRAINS
1571 users with a user-friendly programming environment for reproducible science.
1572 EBRAINS tools are pre-installed for the user. The latest release is selected by
1573 default, but users can choose to run an older release to reuse an older
1574 notebook, or try out the very latest features in the weekly experimental
1575 deployment. Official releases are produced by EBRAINS every few months. End
1576 users do not need to build and install the tools, and, more importantly, they
1577 do not need to resolve dependency conflicts among tools as this has been
1578 handled for them.</span></p>
1579
1580 <h2></h2>
1581
1582 <h2><a name="_Toc138932276"><span lang=en-DE>Collaboratory Office</span></a></h2>
1583
1584 <p class=MsoNormal><span lang=en-DE>With the Office service, EBRAINS users can
1585 collaboratively edit Office documents (Word, PowerPoint or Excel) with most of
1586 the key features of the MS Office tools. It uses the open standard formats
1587 .docx, .pptx and .xlsx so that files can alternately be edited in the
1588 Collaboratory Office service and in other compatible tools including the MS
1589 Office suite.</span></p>
1590
1591 <h2></h2>
1592
1593 <h2><a name="_Toc138932277"><span lang=en-DE>Collaboratory Wiki</span></a></h2>
1594
1595 <p class=MsoNormal><span lang=en-DE>The Wiki service offers the user-friendly
1596 wiki functionality for publishing web content. It acts as central user
1597 interface and API to access the other Collaboratory services. EBRAINS
1598 developers can integrate their services as app which can be instantiated by
1599 users in their collabs. The Wiki is a good place to create tutorials and
1600 documentation and it is also the place to publish your work on the internet if
1601 you choose to do so.</span></p>
1602
1603 <h2></h2>
1604
1605 <h2><a name="_Toc138932278"><span lang=en-DE>CoreNEURON</span></a></h2>
1606
1607 <p class=MsoNormal><span lang=en-DE>In order to adapt NEURON to evolving
1608 computer architectures, the compute engine of the NEURON simulator was
1609 extracted and optimised as a library called CoreNEURON. CoreNEURON is a compute
1610 engine library for the NEURON simulator optimised for both memory usage and
1611 computational speed on modern CPU/GPU architectures. Some of its key goals are
1612 to: 1) Efficiently simulate large network models, 2) Support execution on
1613 accelerators such as GPU, 3) Support optimisations such as vectorisation and
1614 cache-efficient memory layout.</span></p>
1615
1616 <h2></h2>
1617
1618 <h2><a name="_Toc138932279"><span lang=en-DE>CxSystem2</span></a></h2>
1619
1620 <p class=MsoNormal><span lang=en-DE>CxSystem is a cerebral cortex simulation
1621 framework, which operates on personal computers. The CxSystem enables easy
1622 testing and build-up of diverse models at single-cell resolution and it is
1623 implemented on the top of the Python-based Brain2 simulator. The CxSystem
1624 interface comprises two csv files - one for anatomy and technical details, the
1625 other for physiological parameters.</span></p>
1626
1627 <h2></h2>
1628
1629 <h2><a name="_Toc138932280"><span lang=en-DE>DeepSlice</span></a></h2>
1630
1631 <p class=MsoNormal><span lang=en-DE>DeepSlice is a deep neural network that
1632 aligns histological sections of mouse brain to the Allen Mouse Brain Common
1633 Coordinate Framework, adjusting for anterior-posterior position, angle,
1634 rotation and scale. At present, DeepSlice only works with tissue cut in the
1635 coronal plane, although future versions will be compatible with sagittal and
1636 horizontal sections.</span></p>
1637
1638 <h2></h2>
1639
1640 <h2><a name="_Toc138932281"><span lang=en-DE>EBRAINS Ethics &amp; Society
1641 Toolkit</span></a></h2>
1642
1643 <p class=MsoNormal><span lang=en-DE>The aim of the toolkit is to offer
1644 researchers who carry out cross-disciplinary brain research a possibility to
1645 engage with ethical and societal issues within brain health and brain disease.
1646 The user is presented with short introductory texts, scenario-based dilemmas,
1647 animations and quizzes, all tailored to specific areas of ethics and society in
1648 a setting of brain research. All exercises are reflection-oriented, with an
1649 interactive approach to inspire users to incorporate these reflections into
1650 their own research practices. Moreover, it is possible to gain further
1651 knowledge by utilising the links for relevant publications, teaching modules
1652 and the EBRAINS Community Space.</span></p>
1653
1654 <h2></h2>
1655
1656 <h2><a name="_Toc138932282"><span lang=en-DE>EBRAINS Image Service</span></a></h2>
1657
1658 <p class=MsoNormal><span lang=en-DE>The Image Service takes large 2D (and 3D)
1659 images and preprocesses them to generate small 2D tiles (or 3D chunks).
1660 Applications consuming image data (viewers or other) can then access regions of
1661 interest by downloading a few tiles rather than the entire large image. Tiles
1662 are also generated at coarser resolutions to support zooming out of large
1663 images. The service supports multiple input image formats. The serving of tiles
1664 to apps is provided by the ?Collaboratory Bucket (based on OpenStack Swift
1665 object storage), which provides significantly higher network bandwidth than
1666 could be provided by any VM.</span></p>
1667
1668 <h2></h2>
1669
1670 <h2><a name="_Toc138932283"><span lang=en-DE>EBRAINS Knowledge Graph</span></a></h2>
1671
1672 <p class=MsoNormal><span lang=en-DE>The EBRAINS Knowledge Graph (KG) is the
1673 metadata management system of the EBRAINS Data and Knowledge services. It
1674 provides fundamental services and tools to make neuroscientific data, models
1675 and related software FAIR. The KG Editor and API (incl. Python SDKs) allow to
1676 annotate scientific resources in a semantically correct way. The KG Search
1677 exposes the research information via an intuitive user interface and makes the
1678 information publicly available to any user. For advanced users, the KG Query
1679 Builder and KG Core API provide the necessary means to execute detailed queries
1680 on the graph database whilst enforcing fine-grained permission control.</span></p>
1681
1682 <h2></h2>
1683
1684 <h2><a name="_Toc138932284"><span lang=en-DE>EDI Toolkit</span></a></h2>
1685
1686 <p class=MsoNormal><span lang=en-DE>The EDI Toolkit supports projects in
1687 integrating EDI in their research content and as guiding principles for team
1688 collaboration. It is designed for everyday usage by offering: Basic information
1689 Guiding questions, templates and tools to design responsible research Quick
1690 checklists, guidance for suitable structures and standard procedures Measures
1691 to support EDI-based leadership, fair teams and events</span></p>
1692
1693 <h2></h2>
1694
1695 <h2><a name="_Toc138932285"><span lang=en-DE>eFEL</span></a></h2>
1696
1697 <p class=MsoNormal><span lang=en-DE>eFEL allows neuroscientists to
1698 automatically extract features from time series data recorded from neurons
1699 (both in vitro and in silico). Examples include  action potential width and
1700 amplitude in voltage traces recorded during whole-cell patch clamp experiments.
1701 Users can provide a set of traces and select which  features to calculate. The
1702 library will then extract the requested features and return the values.</span></p>
1703
1704 <h2></h2>
1705
1706 <h2><a name="_Toc138932286"><span lang=en-DE>Electrophysiology Analysis Toolkit</span></a></h2>
1707
1708 <p class=MsoNormal><span lang=en-DE>The Electrophysiology Analysis Toolkit
1709 (Elephant) is a Python library that provides a modular framework for the
1710 analysis of experimental and simulated neuronal activity data, such as spike
1711 trains, local field potentials, and intracellular data. Elephant builds on the
1712 Neo data model to facilitate usability, enable interoperability, and support
1713 data from dozens of file formats and network simulation tools. Its analysis
1714 functions are continuously validated against reference implementations and
1715 reports in the literature. Visualisations of analysis results are made
1716 available via the Viziphant companion library. Elephant aims to act as a
1717 platform for sharing analysis methods across the field.</span></p>
1718
1719 <h2></h2>
1720
1721 <h2><a name="_Toc138932287"><span lang=en-DE>FAConstructor</span></a></h2>
1722
1723 <p class=MsoNormal><span lang=en-DE>FAConstructor allows a simple and effective
1724 creation of fibre models based on mathematical functions or the manual input of
1725 data points. Models are visualised during creation and can be interacted with
1726 by translating them in 3D space.</span></p>
1727
1728 <h2></h2>
1729
1730 <h2><a name="_Toc138932288"><span lang=en-DE>fairgraph</span></a></h2>
1731
1732 <p class=MsoNormal><span lang=en-DE>fairgraph is a Python library for working
1733 with metadata in the ?EBRAINS Knowledge Graph (KG), with a particular focus on
1734 data reuse, although it is also useful in registering and curating metadata.
1735 The library represents metadata nodes (also known as openMINDS instances) from
1736 the KG as Python objects. fairgraph supports querying the KG, following links
1737 in the graph, downloading data and metadata, and creating new nodes in the KG.
1738 It builds on ?openMINDS and on the KG Core Python library.</span></p>
1739
1740 <h2></h2>
1741
1742 <h2><a name="_Toc138932289"><span lang=en-DE>Fast sampling with neuromorphic
1743 hardware</span></a></h2>
1744
1745 <p class=MsoNormal><span lang=en-DE>Compared to conventional neural networks,
1746 physical model devices offer a fast, efficient, and inherently parallel
1747 substrate capable of related forms of Markov chain Monte Carlo sampling. This
1748 software suite enables the use of a neuromorphic chip to replicate the
1749 properties of quantum systems through spike-based sampling.</span></p>
1750
1751 <h2></h2>
1752
1753 <h2><a name="_Toc138932290"><span lang=en-DE>fastPLI</span></a></h2>
1754
1755 <p class=MsoNormal><span lang=en-DE>fastPLI is an open-source toolbox based on
1756 Python and C++ for modelling myelinated axons, i.e., nerve fibres, and
1757 simulating the results of measurement of fibre orientations with a polarisation
1758 microscope using 3D-PLI. The fastPLI package includes the following modules:
1759 nerve fibre modelling, simulation, and analysis. All computationally intensive
1760 calculations are optimised either with Numba on the Python side or with
1761 multithreading C++ algorithms, which can be accessed via pybind11 inside the
1762 Python package. Additionally, the simulation module supports the Message
1763 Passing Interface (MPI) to facilitate the simulation of very large volumes on
1764 multiple computer nodes.</span></p>
1765
1766 <h2></h2>
1767
1768 <h2><a name="_Toc138932291"><span lang=en-DE>Feed-forward LFP-MEG estimator
1769 from mean-field models</span></a></h2>
1770
1771 <p class=MsoNormal><span lang=en-DE>This tool was developed to calculate the
1772 local field potentials (LFP) and magnetoencephalogram (MEG) signals generated
1773 by a population of neurons described by a mean-field model. The calculation of
1774 LFP is done via a kernel method based on unitary LFPÕs (the LFP generated by a
1775 single axon) which was recently introduced for spiking-networks simulations and
1776 that we adapt here for mean-field models. The calculation of the magnetic field
1777 is based on current-dipole and volume-conductor models, where the secondary
1778 currents (due to the conducting extracellular medium) are estimated using the
1779 LFP calculated via the kernel method and where the effects of
1780 medium-inhomogeneities are incorporated.</span></p>
1781
1782 <h2></h2>
1783
1784 <h2><a name="_Toc138932292"><span lang=en-DE>FIL</span></a></h2>
1785
1786 <p class=MsoNormal><span lang=en-DE>This is a scheme for training and applying
1787 the FIL framework. Some functionality from SPM12 is required for handling
1788 images. After training, labelling a new image is relatively fast because
1789 optimising the latent variables can be formulated within a scheme similar to a recurrent
1790 Residual Network (ResNet).</span></p>
1791
1792 <h2></h2>
1793
1794 <h2><a name="_Toc138932293"><span lang=en-DE>FMRALIGN</span></a></h2>
1795
1796 <p class=MsoNormal><span lang=en-DE>This library is meant to be a light-weight
1797 Python library that handles functional alignment tasks (also known as
1798 hyperalignment). It is compatible with and inspired by Nilearn. Alternative
1799 implementations of these ideas can be found in the pymvpa or brainiak packages.</span></p>
1800
1801 <h2></h2>
1802
1803 <h2><a name="_Toc138932294"><span lang=en-DE>Foa3D</span></a></h2>
1804
1805 <p class=MsoNormal><span lang=en-DE>Foa3D is a tool for multiscale nerve fibre
1806 enhancement and orientation analysis in high-resolution volume images acquired
1807 by two-photon scanning or light-sheet fluorescence microscopy, exploiting the
1808 brain tissue autofluorescence or exogenous myelin stains. Its image processing
1809 pipeline is built around a 3D Frangi filter that enables the enhancement of
1810 fibre structures of varying diameters, and the generation of accurate 3D
1811 orientation maps in both grey and white matter. Foa3D features the computation
1812 of multiscale orientation distribution functions that facilitate the comparison
1813 with orientations assessed via 3D-PLI or 3D PS-OCT, and the validation of
1814 mesoscale dMRI-based connectivity information.</span></p>
1815
1816 <h2></h2>
1817
1818 <h2><a name="_Toc138932295"><span lang=en-DE>Frites</span></a></h2>
1819
1820 <p class=MsoNormal><span lang=en-DE>Frites allows the characterisation of
1821 task-related cognitive brain networks. Neural correlates of cognitive functions
1822 can be extracted both at the single brain area (or channel) and network level.
1823 The toolbox includes time-resolved directed (e.g., Granger causality) and
1824 undirected (e.g., Mutual Information) functional connectivity metrics. In
1825 addition, it includes cluster-based and permutation-based statistical methods
1826 for single-subject and group-level inference.</span></p>
1827
1828 <h2></h2>
1829
1830 <h2><a name="_Toc138932296"><span lang=en-DE>gridspeccer</span></a></h2>
1831
1832 <p class=MsoNormal><span lang=en-DE>Plotting tool to make plotting with many
1833 subfigures easier, especially for publications. After installation, gridspeccer
1834 can be used from the command line to create plots.</span></p>
1835
1836 <h2></h2>
1837
1838 <h2><a name="_Toc138932297"><span lang=en-DE>Hal-Cgp</span></a></h2>
1839
1840 <p class=MsoNormal><span lang=en-DE>Hal-Cgp is an extensible pure Python
1841 library implementing Cgp to represent, mutate and evaluate populations of
1842 individuals encoding symbolic expressions targeting applications with
1843 computationally expensive fitness evaluations. It supports the translation from
1844 a CGP genotype, a two-dimensional Cartesian graph, into the corresponding
1845 phenotype, a computational graph implementing a particular mathematical expression.
1846 These computational graphs can be exported as pure Python functions, in a
1847 NumPy-compatible format, SymPy expressions or PyTorch modules. The library
1848 implements a mu + lambda evolution strategy to evolve a population of
1849 individuals to optimise an objective function.</span></p>
1850
1851 <h2></h2>
1852
1853 <h2><a name="_Toc138932298"><span lang=en-DE>Health Data Cloud</span></a></h2>
1854
1855 <p class=MsoNormal><span lang=en-DE>The Health Data Cloud (HDC) provides
1856 EBRAINS services for sensitive data as a federated research data ecosystem that
1857 enables scientists across Europe and beyond to collect, process and share
1858 sensitive data in compliance with EU General Data Protection Regulations
1859 (GDPR). The HDC is a federation of interoperable nodes. Nodes share a common
1860 system architecture based on CharitŽ Virtual Research Environment (VRE),
1861 enabling research consortia to manage and process data, and making data
1862 discoverable and sharable via the ?EBRAINS Knowledge Graph.</span></p>
1863
1864 <p class=MsoNormal></p>
1865
1866 <p class=MsoNormal><a name="_Toc138932299"><span class=Heading2Char><span
1867 lang=en-DE style='font-size:14.0pt;line-height:120%'>Hodgkin-Huxley Neuron
1868 Builder</span></span></a></p>
1869
1870 <p class=MsoNormal><span lang=en-DE>The Hodgkin-Huxley Neuron Builder is a
1871 web-application that allows users to interactively go through an entire NEURON
1872 model building pipeline of individual biophysically detailed cells. 2. Model
1873 parameter optimisation via HPC systems. 3. In silico experiments using the
1874 optimised model cell.  </span></p>
1875
1876 <h2></h2>
1877
1878 <h2><a name="_Toc138932300"><span lang=en-DE>HPC Job Proxy</span></a></h2>
1879
1880 <p class=MsoNormal><span lang=en-DE>The HPC Job Proxy provides a simplified way
1881 for EBRAINS service providers to launch jobs on Fenix supercomputers on behalf
1882 of EBRAINS end users. The proxy offers a wrapper over the Unicore service which
1883 adds logging, access to stdout/stderr/status, verification of user quota, and
1884 updating of user quota at the end of the job.</span></p>
1885
1886 <h2></h2>
1887
1888 <h2><a name="_Toc138932301"><span lang=en-DE>HPC Status Monitor</span></a></h2>
1889
1890 <p class=MsoNormal><span lang=en-DE>The HPC Status Monitor allows a real-time
1891 check of the availability status of the HPC Systems accessible from HBP tools
1892 and services and provides an instant snapshot of the resource quotas available
1893 to individual users on each system.</span></p>
1894
1895 <h2></h2>
1896
1897 <h2><a name="_Toc138932302"><span lang=en-DE>Human Intracerebral EEG Platform</span></a></h2>
1898
1899 <p class=MsoNormal><span lang=en-DE>The HIP is an open-source platform designed
1900 for collecting, managing, analysing and sharing multi-scale iEEG data at an
1901 international level. Its mission is to assist clinicians and researchers in
1902 improving research capabilities by simplifying iEEG data analysis and
1903 interpretation. The HIP integrates different software, modules and services
1904 necessary for investigating spatio-temporal dynamics of neural processes in a
1905 secure and optimised fashion. The interface is browser-based and allows
1906 selecting sets of tools according to specific research needs.</span></p>
1907
1908 <h2></h2>
1909
1910 <h2><a name="_Toc138932303"><span lang=en-DE>Hybrid MM/CG Webserver</span></a></h2>
1911
1912 <p class=MsoNormal><span lang=en-DE>MM/CG simulations help predict ligand poses
1913 in hGPCRs  for pharmacological applications. This approach allows for the
1914 description of the ligand, the binding cavity and the surrounding water
1915 molecules at atomistic resolution, while coarse-graining the rest of the
1916 receptor. The webserver automatises and speeds up the simulation set-up of
1917 hGPCR/ligand complexes. It also allows for equilibration of the systems, either
1918 fully automatically or interactively. The results are visualised online,
1919 helping the user identify possible issues and modify the set-up parameters.
1920 This framework allows for the automatic preparation and running of hybrid
1921 molecular dynamics simulations of molecules and their cognate receptors.</span></p>
1922
1923 <h2></h2>
1924
1925 <h2><a name="_Toc138932304"><span lang=en-DE>Insite</span></a></h2>
1926
1927 <p class=MsoNormal><span lang=en-DE>Insite enables users to access data via the
1928 in transit paradigm for ?NEST, ?TVB and ?Arbor simulations. Compared to the
1929 traditional approach of offline processing, in transit paradigms allow
1930 accessing of data while the simulation runs. This is especially useful for
1931 simulations that produce large amounts of data and are running for a long time.
1932 In transit allows the user to access only parts of the data and prevents the
1933 need for storing all data. It also allows the user early insights into the data
1934 even before the simulation finishes. Insite provides an easy-to-use and
1935 easy-to-integrate architecture to enable in transit features in other tools.</span></p>
1936
1937 <h2></h2>
1938
1939 <h2><a name="_Toc138932305"><span lang=en-DE>Interactive Brain Atlas Viewer</span></a></h2>
1940
1941 <p class=MsoNormal><span lang=en-DE>The Interactive Brain Atlas Viewer provides
1942 various kinds of interactive visualisations for multi-modal brain and head
1943 image data: different parcellations, degrees of transparency and overlays. The
1944 Viewer provides the following functions and supports data from the following
1945 sources: EEG, white matter tracts, MRI and PET 3D volumes, 2D slices,
1946 intracranial electrodes, brain activity, multiscale brain network models,
1947 supplementary information for brain regions and functional brain networks in
1948 multiple languages. It comes as a web app, mobile app and desktop app.</span></p>
1949
1950 <h2></h2>
1951
1952 <h2><a name="_Toc138932306"><span lang=en-DE>JuGEx</span></a></h2>
1953
1954 <p class=MsoNormal><span lang=en-DE>Decoding the chain from genes to cognition
1955 requires detailed insights into how areas with specific gene activities and
1956 microanatomical architectures contribute to brain function and dysfunction. The
1957 Allen Human Brain Atlas contains regional gene expression data, while the
1958 Julich Brain Atlas, which can be accessed via ?siibra, offers 3D
1959 cytoarchitectonic maps reflecting the interindividual variability. JuGEx offers
1960 an integrated framework that combines the analytical benefits of both
1961 repositories towards a multilevel brain atlas of adult humans. JuGEx is a new
1962 method for integrating tissue transcriptome and cytoarchitectonic segregation.</span></p>
1963
1964 <h2></h2>
1965
1966 <h2><a name="_Toc138932307"><span lang=en-DE>KnowledgeSpace</span></a></h2>
1967
1968 <p class=MsoNormal><span lang=en-DE>KnowledgeSpace (KS) is a globally-used,
1969 data-driven encyclopaedia and search engine for the neuroscience community. As
1970 an encyclopaedia, KS provides curated definitions of brain research concepts
1971 found in different neuroscience community ontologies, Wikipedia and
1972 dictionaries. The dataset discovery in KS makes research datasets across many
1973 large-scale brain initiatives universally accessible and useful. It also
1974 promotes FAIR data principles that will help data publishers to follow best
1975 practices for data storage and publication. As more and more data publishers
1976 follow data standards like ?OpenMINDS or DATS, the quality of data discovery
1977 through KS will improve. The related publications are also curated from PubMed
1978 and linked to the concepts in KS to provide an improved search capability.</span></p>
1979
1980 <h2></h2>
1981
1982 <h2><a name="_Toc138932308"><span lang=en-DE>L2L</span></a></h2>
1983
1984 <p class=MsoNormal><span lang=en-DE>L2L is an easy-to-use and flexible
1985 framework to perform parameter and hyper-parameter space exploration of
1986 mathematical models on HPC infrastructure. L2L is an implementation of the
1987 learning-to-learn concept written in Python. This open-source software allows
1988 several instances of an optimisation target to be executed with different
1989 parameters in an massively parallel fashion on HPC. L2L provides a set of
1990 built-in optimiser algorithms, which make adaptive and efficient exploration of
1991 parameter spaces possible. Different from other optimisation toolboxes, L2L
1992 provides maximum flexibility for the way the optimisation target can be
1993 executed.</span></p>
1994
1995 <h2></h2>
1996
1997 <h2><a name="_Toc138932309"><span lang=en-DE>Leveltlab/SpectralSegmentation</span></a></h2>
1998
1999 <p class=MsoNormal><span lang=en-DE>SpecSeg is a toolbox that segments neurons
2000 and neurites in chronic calcium imaging datasets based on low-frequency
2001 cross-spectral power. The pipeline includes a graphical user interface to edit
2002 the automatically extracted ROIs, to add new ones or delete ROIs by further
2003 constraining their properties.</span></p>
2004
2005 <h2></h2>
2006
2007 <h2><a name="_Toc138932310"><span lang=en-DE>LFPy</span></a></h2>
2008
2009 <p class=MsoNormal><span lang=en-DE>LFPy is an open-source Python module linking
2010 simulated neural activity with measurable brain signals. This is done by
2011 enabling calculation of brain signals from neural activity simulated with
2012 multi-compartment neuron models (single cells or networks). LFPy can be used to
2013 simulate brain signals like extracellular action potentials, local field
2014 potentials (LFP), and in vitro MEA recordings, as well as ECoG, EEG, and MEG
2015 signals. LFPy is well-integrated with the NEURON simulator and can, through
2016 LFPykit, also be used with other simulators like Arbor. Through the recently
2017 developed extensions hybridLFPy and LFPykernels, LFPy can also be used to
2018 calculate brain signals directly from point-neuron network models or
2019 population-based models.</span></p>
2020
2021 <h2></h2>
2022
2023 <h2><a name="_Toc138932311"><span lang=en-DE>libsonata</span></a></h2>
2024
2025 <p class=MsoNormal><span lang=en-DE>libsonata allows circuit and simulation
2026 config loading, node set materialisation, and access to node and edge
2027 populations in an efficient manner. It is generally a read-only library, but
2028 support for writing edge indices has been added.</span></p>
2029
2030 <h2></h2>
2031
2032 <h2><a name="_Toc138932312"><span lang=en-DE>Live Papers</span></a></h2>
2033
2034 <p class=MsoNormal><span lang=en-DE>EBRAINS Live Papers are structured and
2035 interactive documents that complement published scientific articles. Live
2036 Papers feature integrated tools and services that allow users to download,
2037 visualise or simulate data, models and results presented in the corresponding
2038 publications: Build interactive documents to showcase your data and the
2039 simulation or data analysis code used in your research. Easily link to
2040 resources in community databases such as EBRAINS, NeuroMorpho.org, ModelDB, and
2041 Allen Brain Atlas. Embedded, interactive visualisation of electrophysiology
2042 data and neuronal reconstructions. Launch EBRAINS simulation tools to explore
2043 single neuron models in your browser. Share live papers pre-publication with
2044 anonymous reviewers during peer review of your manuscript. Explore already
2045 published live papers, or develop your own live paper with our authoring tool.</span></p>
2046
2047 <h2></h2>
2048
2049 <h2><a name="_Toc138932313"><span lang=en-DE>Livre</span></a></h2>
2050
2051 <p class=MsoNormal><span lang=en-DE>Livre is an out-of-core, multi-node,
2052 multi-GPU, OpenGL volume rendering engine to visualise large volumetric
2053 datasets. It provides the following major features to facilitate rendering of
2054 large volumetric datasets: Visualisation of pre-processed UVF format volume
2055 datasets. Real-time voxelisation of different data sources (surface meshes, BBP
2056 morphologies, local field potentials, etc.) through the use of plugins.
2057 Multi-node, multi-GPU rendering (only sort-first rendering).</span></p>
2058
2059 <h2></h2>
2060
2061 <h2><a name="_Toc138932314"><span lang=en-DE>LocaliZoom</span></a></h2>
2062
2063 <p class=MsoNormal><span lang=en-DE>Pan-and-zoom type viewer displaying image
2064 series with overlaid atlas delineations. LocaliZoom is a pan-and-zoom type
2065 viewer displaying high-resolution image series coupled with overlaid atlas
2066 delineations. It has three operating modes: Display series with atlas overlay.
2067 Both linear and nonlinear alignments are supported (created with QuickNII or
2068 VisuAlign). Create or edit nonlinear alignments. Create markup which can be
2069 exported as MeshView point clouds or to Excel for further numerical analysis.</span></p>
2070
2071 <h2></h2>
2072
2073 <h2><a name="_Toc138932315"><span lang=en-DE>MD-IFP</span></a></h2>
2074
2075 <p class=MsoNormal><span lang=en-DE>MD-IFP is a python workflow for the
2076 generation and analysis of protein-ligand interaction fingerprints from
2077 molecular dynamics trajectories. If used for the analysis of Random
2078 Acceleration Molecular Dynamics (RAMD) trajectories, it can help to investigate
2079 dissociation mechanisms by characterising transition states as well as the
2080 determinants and hot-spots for dissociation. As such, the combined use of
2081 ??RAMD and MD-IFP may assist the early stages of drug discovery campaigns for the
2082 design of new molecules or ligand optimisation.</span></p>
2083
2084 <h2></h2>
2085
2086 <h2><a name="_Toc138932316"><span lang=en-DE>MEDUSA</span></a></h2>
2087
2088 <p class=MsoNormal><span lang=en-DE>Using a spherical meshing technique that
2089 decomposes each microstructural item into a set of overlapping spheres, the
2090 phantom construction is made very fast while reliably avoiding the collisions
2091 between items in the scene. This novel method is applied to the construction of
2092 human brain white matter microstructural components, namely axonal fibers,
2093 oligodendrocytes and astrocytes. The algorithm reaches high values of packing
2094 density and angular dispersion for the axonal fibers, even in the case of
2095 multiple white matter fiber populations and enables the construction of complex
2096 biomimicking geometries including myelinated axons, beaded axons and glial
2097 cells.</span></p>
2098
2099 <h2></h2>
2100
2101 <h2><a name="_Toc138932317"><span lang=en-DE>MeshView</span></a></h2>
2102
2103 <p class=MsoNormal><span lang=en-DE>MeshView is a web application for real-time
2104 3D display of surface mesh data representing structural parcellations from
2105 volumetric atlases, such as the Waxholm Space atlas of the Sprague Dawley rat
2106 brain. Key features: orbiting view with toggleable opaque/transparent/hidden
2107 parcellation meshes, rendering user-defined cut surface as if meshes were solid
2108 objects, rendering point-clouds (simple type-in, or loaded from JSON). The
2109 coordinate system is compatible with QuickNII.</span></p>
2110
2111 <h2></h2>
2112
2113 <h2><a name="_Toc138932318"><span lang=en-DE>MIP</span></a></h2>
2114
2115 <p class=MsoNormal><span lang=en-DE>MIP is an open-source platform enabling
2116 federated data analysis in a secure environment for centres involved in
2117 collaborative initiatives. It allows users to initiate or join disease-oriented
2118 federations with the aim of analysing large-scale distributed clinical
2119 datasets. For each federation, users can create specific data models based on
2120 well-accepted common data elements, approved by all participating centres. MIP
2121 experts assist in creating the data models and facilitate coordination and
2122 communication among centres. They provide advice and support for data curation,
2123 harmonisation, and anonymisation, as well as data governance, especially with
2124 regards to Data Sharing Agreements and general ethical considerations.</span></p>
2125
2126 <h2></h2>
2127
2128 <h2><a name="_Toc138932319"><span lang=en-DE>Model Validation Service</span></a></h2>
2129
2130 <p class=MsoNormal><span lang=en-DE>The HBP/EBRAINS Model Validation Service is
2131 a set of tools for performing and tracking validation of models with respect to
2132 experimental data. It consists of a web API, a GUI client (the Model Catalog
2133 app) and a Python client. The service enables users to store, query, view and
2134 download: (i) model descriptions/scripts, (ii) validation test definitions and
2135 (iii) validation results. In a typical workflow, users will find models and
2136 validation tests by searching the Model Catalog (or upload their own), run the
2137 tests using the Python client in a Jupyter notebook, with simulations running
2138 locally or on HPC, and then upload the results.</span></p>
2139
2140 <h2></h2>
2141
2142 <h2><a name="_Toc138932320"><span lang=en-DE>Model Validation Test Suites</span></a></h2>
2143
2144 <p class=MsoNormal><span lang=en-DE>As part of the HBP/EBRAINS model validation
2145 framework, we provide a Python Software Development Kit (SDK) for model
2146 validation, which provides: (i) validation test definitions and (ii) interface
2147 definitions intended to decouple model validation from the details of model
2148 implementation. This more formal approach to model validation aims to make it
2149 quicker and easier to compare models, to provide validation test suites for
2150 models and to develop new validations of existing models. The SDK consists of a
2151 collection of Python packages all using the sciunit framework: HippoUnit,
2152 MorphoUnit, NetworkUnit, BasalUnit, CerebUnit, eFELUnit, HippoNetworkUnit.</span></p>
2153
2154 <h2></h2>
2155
2156 <h2><a name="_Toc138932321"><span lang=en-DE>MoDEL-CNS</span></a></h2>
2157
2158 <p class=MsoNormal><span lang=en-DE>MoDEL-CNS is a database and server platform
2159 designed to provide web access to atomistic MD trajectories for relevant signal
2160 transduction proteins. The project is part of the service for providing
2161 molecular simulation-based predictions for systems neurobiology of the HBP.
2162 MoDEL-CNS expands the MD Extended Library database of atomistic MD trajectories
2163 with proteins involved in CNS processes, including membrane proteins. MoDEL-CNS
2164 web server interface presents the resulting trajectories, analyses and protein
2165 properties. All data produced are available to download.</span></p>
2166
2167 <h2></h2>
2168
2169 <h2><a name="_Toc138932322"><span lang=en-DE>Modular Science</span></a></h2>
2170
2171 <p class=MsoNormal><span lang=en-DE>Modular Science is a middleware that
2172 provides robust deployment of complex multi-application workflows. It contains
2173 protocols and interfaces for multi-scale co-simulation workloads on
2174 high-performance computers and local hardware. It allows for synchronisation
2175 and coordination of individual components and contains dedicated and
2176 parallelised modules for data transformations between scales. Modular Science
2177 offers insight into both the system level and the individual subsystems to
2178 steer the execution, to monitor resource usage, and system health &amp; status
2179 with small overheads on performance. Modular Science comes with a number of
2180 neuroscience co-simulation use cases including NEST-TVB, NEST-Arbor, LFPy and neurorobotics.</span></p>
2181
2182 <h2></h2>
2183
2184 <h2><a name="_Toc138932323"><span lang=en-DE>Monsteer</span></a></h2>
2185
2186 <p class=MsoNormal><span lang=en-DE>Monsteer is a library for interactive
2187 supercomputing in the neuroscience domain. It facilitates the coupling of
2188 running simulations (currently NEST) with interactive visualization and
2189 analysis applications. Monsteer supports streaming of simulation data to
2190 clients (currently limited to spikes) as well as control of the simulator from
2191 the clients (also known as computational steering). Monsteer's main components
2192 are a C++ library, a MUSIC-based application and Python helpers.</span></p>
2193
2194 <h2></h2>
2195
2196 <h2><a name="_Toc138932324"><span lang=en-DE>MorphIO</span></a></h2>
2197
2198 <p class=MsoNormal><span lang=en-DE>MorphIO is a library for reading and
2199 writing neuron morphology files. It supports the following formats: SWC, ASC
2200 (also known as neurolucida), H5. There are two APIs: mutable, for creating or
2201 editing morphologies, and immutable, for read-only operations. Both are
2202 represented in C++ and Python. Extended formats include glia, mitochondria and
2203 endoplasmic reticulum.</span></p>
2204
2205 <h2></h2>
2206
2207 <h2><a name="_Toc138932325"><span lang=en-DE>Morphology alignment tool</span></a></h2>
2208
2209 <p class=MsoNormal><span lang=en-DE>Starting with serial sections of a brain in
2210 which a complete single morphology has been labelled, the pieces of neurite
2211 (axons/dendrites) in each section are traced with Neurolucida or similar
2212 microscope-attached software. The slices are then aligned, first using an
2213 automated algorithm that tries to find matching pieces in adjacent sections
2214 (Python script), and second using a GUI-driven tool (web-based, JavaScript).
2215 Finally, the pieces are stitched into a complete neuron (Python script). The
2216 neuron and tissue volume are then registered to one of the EBRAINS-supported
2217 reference templates (Python script). The web-based tool can also be used to align
2218 slices without a neuron being present.</span></p>
2219
2220 <h2></h2>
2221
2222 <h2><a name="_Toc138932326"><span lang=en-DE>MorphTool</span></a></h2>
2223
2224 <p class=MsoNormal><span lang=en-DE>MorphTool is a python toolkit designed for
2225 editing morphological skeletons of cell reconstructions. It has been developed
2226 to provide helper programmes that perform simple tasks such as morphology
2227 diffing, file conversion, soma area calculation, skeleton simplification,
2228 process resampling, morphology repair and spatial transformations. It allows
2229 neuroscientists to curate and manipulate morphological reconstruction and
2230 correct morphological artifacts due to the manual reconstruction process.</span></p>
2231
2232 <h2></h2>
2233
2234 <h2><a name="_Toc138932327"><span lang=en-DE>Multi-Brain</span></a></h2>
2235
2236 <p class=MsoNormal><span lang=en-DE>&quot;The Multi-Brain (MB) model has the
2237 general aim of integrating a number of disparate image analysis components
2238 within a single unified generative modelling framework. Its objective is to
2239 achieve diffeomorphic alignment of a wide variety of medical image modalities
2240 into a common anatomical space. This involves the ability to construct a
2241 &quot;&quot;tissue probability template&quot;&quot; from a population of scans
2242 through group-wise alignment. The MB model has been shown to provide accurate
2243 modelling of the intensity distributions of different imaging modalities.&quot;</span></p>
2244
2245 <h2></h2>
2246
2247 <h2><a name="_Toc138932328"><span lang=en-DE>Multi-Image-OSD</span></a></h2>
2248
2249 <p class=MsoNormal><span lang=en-DE>It has browser-based classic pan and zoom
2250 capabilities. A collection of images can be displayed as a filmstrip (Filmstrip
2251 Mode) or as a table (Collection Mode) with adjustable number of rows and
2252 columns. The tool supports keyboard or/and mouse navigation options, as well as
2253 touch devices. Utilising the open standard Deep Zoom Image (DZI) format, it is
2254 able to efficiently visualise very large brain images in the gigapixel range,
2255 allowing to zoom from common, display-sized overview resolutions down to the
2256 microscopic resolution without downloading the underlying, very large image
2257 dataset.</span></p>
2258
2259 <h2></h2>
2260
2261 <h2><a name="_Toc138932329"><span lang=en-DE>MUSIC</span></a></h2>
2262
2263 <p class=MsoNormal><span lang=en-DE>MUSIC is a communication framework in the
2264 domain of computational neuroscience and neuromorphic computing which enables
2265 co-simulations, where components of a model are simulated by different
2266 simulators or hardware. It consists of an API and C++ library which can be
2267 linked into existing software with minor modifications. MUSIC enables the
2268 communication of neuronal spike events, continuous values and text messages
2269 while hiding the complexity of data distribution over ranks, as well as
2270 scheduling of communication in the face of loops. MUSIC is light-weight with a
2271 simple API.</span></p>
2272
2273 <h2></h2>
2274
2275 <h2><a name="_Toc138932330"><span lang=en-DE>NEAT</span></a></h2>
2276
2277 <p class=MsoNormal><span lang=en-DE>NEAT allows for the convenient definition
2278 of morphological neuron models. These models can be simulated through an
2279 interface with the NEURON simulator or analysed with two classical methods: (i)
2280 the separation-of-variables method to obtain impedance kernels as a
2281 superposition of exponentials and (ii) KochÕs method to compute impedances with
2282 linearised ion channels analytically in the frequency domain. NEAT also
2283 implements the neural evaluation tree framework and an associated C++ simulator
2284 to analyse sub-unit independence. Finally, NEAT implements a new method to
2285 simplify morphological neuron models into models with few compartments, which
2286 can also be simulated with NEURON.</span></p>
2287
2288 <h2></h2>
2289
2290 <h2><a name="_Toc138932331"><span lang=en-DE>Neo</span></a></h2>
2291
2292 <p class=MsoNormal><span lang=en-DE>Neo implements a hierarchical data model
2293 well adapted to intracellular and extracellular electrophysiology and EEG data.
2294 It improves interoperability between Python tools for analysing, visualising
2295 and generating electrophysiology data by providing a common, shared object
2296 model. It reads a wide range of neurophysiology file formats, including Spike2,
2297 NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt and Igor Pro and writes
2298 to open formats such as NWB and NIX. Neo objects behave just like normal NumPy
2299 arrays, but with additional metadata, checks for dimensional consistency and
2300 automatic unit conversion. Neo has been endorsed as a community standard by the
2301 International Neuroinformatics Coordinating Facility (INCF).</span></p>
2302
2303 <h2></h2>
2304
2305 <h2><a name="_Toc138932332"><span lang=en-DE>Neo Viewer</span></a></h2>
2306
2307 <p class=MsoNormal><span lang=en-DE>Neo Viewer consists of a REST-API and a
2308 Javascript component that can be embedded in any web page. Electrophysiology
2309 traces can be zoomed, scrolled and saved as images. Individual points can be
2310 measured off the graphs. Neo Viewer can visualise data from most of the
2311 widely-used file formats in neurophysiology, including community standards such
2312 as NWB.</span></p>
2313
2314 <h2></h2>
2315
2316 <h2><a name="_Toc138932333"><span lang=en-DE>NEST Desktop</span></a></h2>
2317
2318 <p class=MsoNormal><span lang=en-DE>NEST Desktop comprises of GUI components
2319 for creating and configuring network models, running simulations, and
2320 visualising and analysing simulation results. NEST Desktop allows students to
2321 explore important concepts in computational neuroscience without the need to
2322 first learn a simulator control language. This is done by offering a
2323 server-side ?NEST simulator, which can also be installed as a package together
2324 with a web server providing NEST Desktop as visual front-end. Besides local
2325 installations, distributed setups can be installed, and direct use through
2326 EBRAINS is possible. NEST Desktop has also been used as a modelling front-end
2327 of the Neurorobotics Platform.</span></p>
2328
2329 <h2></h2>
2330
2331 <h2><a name="_Toc138932334"><span lang=en-DE>NEST Simulator</span></a></h2>
2332
2333 <p class=MsoNormal><span lang=en-DE>NEST is used in computational neuroscience
2334 to model and study behaviour of large networks of neurons. The models describe
2335 single neuron and synapse behaviour and their connections. Different mechanisms
2336 of plasticity can be used to investigate artificial learning and help to shed
2337 light on the fundamental principles of how the brain works. NEST offers
2338 convenient and efficient commands to define and connect large networks, ranging
2339 from algorithmically determined connections to data-driven connectivity. Create
2340 connections between neurons using numerous synapse models from STDP to gap
2341 junctions.</span></p>
2342
2343 <h2></h2>
2344
2345 <h2><a name="_Toc138932335"><span lang=en-DE>NESTML</span></a></h2>
2346
2347 <p class=MsoNormal><span lang=en-DE>NESTML is a domain-specific language for
2348 neuron and synapse models. These dynamical models can be used in simulations of
2349 brain activity on several platforms, in particular ?NEST Simulator. NESTML
2350 combines an easy to understand, yet powerful syntax with good simulation
2351 performance by means of code generation (C++ for NEST Simulator), but flexibly
2352 supports other simulation engines including neuromorphic hardware.</span></p>
2353
2354 <h2></h2>
2355
2356 <h2><a name="_Toc138932336"><span lang=en-DE>NetPyNE</span></a></h2>
2357
2358 <p class=MsoNormal><span lang=en-DE>NetPyNE provides programmatic and graphical
2359 interfaces to develop data-driven multiscale brain neural circuit models using
2360 Python and NEURON. Users can define models using a standardised
2361 JSON-compatible, rule-based, declarative format. Based on these specifications,
2362 NetPyNE will generate the network in ?CoreNEURON, enabling users to run
2363 parallel simulations, optimise and explore network parameters through automated
2364 batch runs, and use built-in functions for visualisation and analysis (e.g.,
2365 generate connectivity matrices, voltage traces, spike raster plots, local field
2366 potentials and information theoretic measures). NetPyNE also facilitates model
2367 sharing by exporting and importing standardised formats: NeuroML and SONATA.</span></p>
2368
2369 <h2></h2>
2370
2371 <h2><a name="_Toc138932337"><span lang=en-DE>NEURO-CONNECT</span></a></h2>
2372
2373 <p class=MsoNormal><span lang=en-DE>The NEURO-CONNECT platform provides
2374 functions to integrate multimodal brain imaging information in a unifying
2375 feature space. Thus, Surface Based Morphometry (SBM), Functional Magnetic
2376 Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI) can be combined and
2377 visualised at the whole-brain scale. Moreover, multiple brain atlases are
2378 aligned to match research outcomes to neuroanatomical entities. The datasets
2379 are appended with openMINDS metadata and thus enable integrative data analysis
2380 and machine learning.</span></p>
2381
2382 <h2></h2>
2383
2384 <h2><a name="_Toc138932338"><span lang=en-DE>NeuroFeatureExtract</span></a></h2>
2385
2386 <p class=MsoNormal><span lang=en-DE>The NeuroFeatureExtract is a web
2387 application that allows the users to extract an ensemble of
2388 electrophysiological properties from voltage traces recorded upon electrical
2389 stimulation of neuronal cells. The main outcome of the application is the
2390 generation of two files Ð features.json and protocol.json Ð that can be used
2391 for later analysis and model parameter optimisations via the Hodgkin-Huxley
2392 Neuron Builder application.</span></p>
2393
2394 <h2></h2>
2395
2396 <h2><a name="_Toc138932339"><span lang=en-DE>NeurogenPy</span></a></h2>
2397
2398 <p class=MsoNormal><span lang=en-DE>NeurogenPy is a Python package for working
2399 with Bayesian networks. It is focused on the analysis of gene expression data
2400 and learning of gene regulatory networks, modelled as Bayesian networks. For
2401 that reason, at the moment, only the Gaussian and fully discrete cases are
2402 supported. The package provides different structure learning algorithms,
2403 parameters estimation and input/output formats. For some of them, already
2404 existing implementations have been used, with bnlearn, pgmpy, networkx and
2405 igraph being the most relevant used packages. This project has been conceived
2406 to be included as a plugin in the EBRAINS interactive atlas viewer, but it may
2407 be used for other purposes.</span></p>
2408
2409 <h2></h2>
2410
2411 <h2><a name="_Toc138932340"><span lang=en-DE>NeuroM</span></a></h2>
2412
2413 <p class=MsoNormal><span lang=en-DE>NeuroM is a Python toolkit for the analysis
2414 and processing of neuron morphologies. It allows the extraction of various
2415 information about morphologies, e.g., the segment lengths of a morphology via
2416 the segment_lengths feature. More than 50 features that can be extracted.</span></p>
2417
2418 <h2></h2>
2419
2420 <h2><a name="_Toc138932341"><span lang=en-DE>Neuromorphic Computing Job Queue</span></a></h2>
2421
2422 <p class=MsoNormal><span lang=en-DE>The Neuromorphic Computing Job Queue allows
2423 users to run simulations/emulations on the SpiNNaker and BrainScaleS systems by
2424 submitting a PyNN script and associated job configuration information to a
2425 central queue. The system consists of a web API, a GUI client (the Job Manager
2426 app) and a Python client. Users can submit scripts stored locally on their own
2427 machine, in a Git repository, in the KG, or in EBRAINS Collaboratory storage
2428 (Drive/Bucket). Users can track the progress of their job, and view and/or
2429 download the results, log files, and provenance information.</span></p>
2430
2431 <h2></h2>
2432
2433 <h2><a name="_Toc138932342"><span lang=en-DE>Neuronize v2</span></a></h2>
2434
2435 <p class=MsoNormal><span lang=en-DE>Neuronize v2 has been developed to generate
2436 a connected neural 3D mesh. If the input is a neuron tracing, it generates a 3D
2437 mesh from it, including the shape of the soma. If the input is data extracted
2438 with Imaris Filament Tracer (a set of unconnected meshes of a neuron),
2439 Neuronize v2 generates a single connected 3D mesh of the whole neuron (also
2440 generating the soma) and provides its neural tracing, which can then be
2441 imported into tools such as Neurolucida, facilitating the interoperability of
2442 two of the most widely used proprietary tools.</span></p>
2443
2444 <h2></h2>
2445
2446 <h2><a name="_Toc138932343"><span lang=en-DE>NeuroR</span></a></h2>
2447
2448 <p class=MsoNormal><span lang=en-DE>NeuroR is a collection of tools to repair
2449 morphologies.  This includes cut plane detection, sanitisation (removing
2450 unifurcations, invalid soma counts, short segments) and ÒunravellingÓ: the
2451 action of ÒstretchingÓ the cell that has been shrunk due to  the dehydratation
2452 caused by the slicing.</span></p>
2453
2454 <h2></h2>
2455
2456 <h2><a name="_Toc138932344"><span lang=en-DE>Neurorobotics Platform</span></a></h2>
2457
2458 <p class=MsoNormal><span lang=en-DE>The Neurorobotics Platform (NRP) is an
2459 integrative simulation framework that enables in silico experimentation and
2460 embodiment of brain models inside virtual agents interacting with realistic
2461 simulated environments. Entirely Open Source, it offers a browser-based
2462 graphical user interface for online access. It can be installed locally (Docker
2463 or source install). It can be interfaced with multiple spike-based neuromorphic
2464 chips (SpiNNaker, Intel Loihi). You can download and install the NRP locally
2465 for maximum experimental convenience or access it online in order to leverage
2466 the HBP High Performance Computing infrastructure for large-scale experiments.</span></p>
2467
2468 <h2></h2>
2469
2470 <h2><a name="_Toc138932345"><span lang=en-DE>Neurorobotics Platform Robot
2471 Designer</span></a></h2>
2472
2473 <p class=MsoNormal><span lang=en-DE>The Robot Designer is a plugin for the 3D
2474 modeling suite Blender that enables researchers to design morphologies for
2475 simulation experiments in, particularly but not restricted to, the
2476 Neurorobotics Platform. This plugin helps researchers design and parameterize
2477 models with a Graphical User Interface, simplifying and speeding up the design
2478 process.cess. It includes design capabilities for musculoskeletal bodies as
2479 well as robotic systems, fostering not only the understanding of biological
2480 motions and enabling better robot designs, but also enabling true Neurorobotic
2481 experiments that consist of biomimetic models such as tendon-driven robots or a
2482 transition between biology and technology.</span></p>
2483
2484 <h2></h2>
2485
2486 <h2><a name="_Toc138932346"><span lang=en-DE>NeuroScheme</span></a></h2>
2487
2488 <p class=MsoNormal><span lang=en-DE>&quot;NeuroScheme uses schematic
2489 representations, such as icons and glyphs, to encode attributes of neural
2490 structures (neurons, columns, layers, populations, etc.), alleviating problems
2491 with displaying, navigating and analysing large datasets. It manages
2492 hierarchically organised neural structures</span><span lang=en-DE
2493 style='font-family:"Times New Roman",serif'> </span><span lang=en-DE>users can
2494 navigate through the levels of the hierarchy and hone in on and explore the
2495 data at their desired level of detail. NeuroScheme has currently two built-in
2496 &quot;&quot;domains&quot;&quot;, which specify entities, attributes and
2497 relationships used for specific use cases: the ÒcortexÓ domain, designed for
2498 navigating and analysing cerebral cortex structures</span><span lang=en-DE
2499 style='font-family:"Times New Roman",serif'> </span><span lang=en-DE>and the
2500 ÒcongenÓ domain, used to define the properties of cells and connections, create
2501 circuits of neurons and build populations.&quot;</span></p>
2502
2503 <h2></h2>
2504
2505 <h2><a name="_Toc138932347"><span lang=en-DE>NeuroSuites</span></a></h2>
2506
2507 <p class=MsoNormal><span lang=en-DE>NeuroSuites is a web-based platform
2508 designed to handle large-scale, high-dimensional data in the field of
2509 neuroscience. It offers neuroscience-oriented applications and tools for data
2510 analysis, machine learning and visualisation, while also providing
2511 general-purpose tools for data scientists in other research fields. NeuroSuites
2512 requires no software installation and runs on the backend of a server, making
2513 it accessible from various devices. The platform's main strengths include its
2514 defined architecture, ability to handle complex neuroscience data and the
2515 variety of available tools.</span></p>
2516
2517 <h2></h2>
2518
2519 <h2><a name="_Toc138932348"><span lang=en-DE>NeuroTessMesh</span></a></h2>
2520
2521 <p class=MsoNormal><span lang=en-DE>NeuroTessMesh takes morphological tracings
2522 of cells acquired by neuroscientists and generates 3D models that approximate
2523 the neuronal membrane. The resolution of the models can be adapted at the time
2524 of visualisation. You can colour-code different parts of a morphology,
2525 differentiating relevant morphological variables or even neuronal activity.
2526 NeuroTessMesh copes with many of the problems associated with the visualisation
2527 of neural circuits consisting of large numbers of cells. It facilitates the
2528 recovery and visualisation of the 3D geometry of cells included in databases,
2529 such as NeuroMorpho, and allows to approximate missing information such as the
2530 somaÕs morphology.</span></p>
2531
2532 <h2></h2>
2533
2534 <h2><a name="_Toc138932349"><span lang=en-DE>NMODL Framework</span></a></h2>
2535
2536 <p class=MsoNormal><span lang=en-DE>&quot;NMODL Framework is designed with
2537 modern compiler and code generation techniques. It provides modular tools for
2538 parsing, analysing and transforming NMODL it provides an easy to use, high
2539 level Python API</span><span lang=en-DE style='font-family:"Times New Roman",serif'>
2540 </span><span lang=en-DE> it generates optimised code for modern compute architectures
2541 including CPUs and GPUs</span><span lang=en-DE style='font-family:"Times New Roman",serif'>
2542 </span><span lang=en-DE> it provides flexibility to implement new simulator
2543 backends and it supports full NMODL specification.&quot;</span></p>
2544
2545 <h2></h2>
2546
2547 <h2><a name="_Toc138932350"><span lang=en-DE>NSuite</span></a></h2>
2548
2549 <p class=MsoNormal><span lang=en-DE>NSuite is a framework for maintaining and
2550 running benchmarks and validation tests for multi-compartment neural network
2551 simulations on HPC systems. NSuite automates the process of building simulation
2552 engines, and running benchmarks and validation tests. NSuite is specifically
2553 designed to allow easy deployment on HPC systems in testing workflows, such as
2554 benchmark-driven development or continuous integration. The development of
2555 NSuite has been driven by the need (1) for a definitive resource for comparing
2556 performance and correctness of simulation engines on HPC systems, (2) to verify
2557 the performance and correctness of individual simulation engines as they change
2558 over time and (3) to test that changes to an HPC system do not cause
2559 performance or correctness regressions in simulation engines. The framework
2560 currently supports the simulation engines Arbor, NEURON, and CoreNeuron, while
2561 allowing other simulation engines to be added.</span></p>
2562
2563 <p class=MsoNormal></p>
2564
2565 <p class=MsoNormal><span lang=en-DE>Nutil</span></p>
2566
2567 <p class=MsoNormal><span lang=en-DE>Nutil is a pre- and post-processing toolbox
2568 that enables analysis of large collections of histological images of rodent
2569 brain sections. The software is open source and has both a graphical user
2570 interface for specifying the input and output parameters and a command-line
2571 execution option for batch processing. Nutil includes a transformation tool for
2572 automated scaling, rotation, mirroring and renaming of image files, a file
2573 format converter, a simple resize tool and a post-processing method for
2574 quantifying and localising labelled features based on a reference atlas of the
2575 brain (mouse or rat). The quantification method requires input from customised
2576 brain atlas maps generated with the QuickNII software, and segmentations
2577 generated with ilastik or another image analysis tool. The output from Nutil
2578 include csv reports, 3D point cloud coordinate files and atlas map images
2579 superimposed with colour-coded objects.</span></p>
2580
2581 <h2></h2>
2582
2583 <h2><a name="_Toc138932351"><span lang=en-DE>ODE-toolbox</span></a></h2>
2584
2585 <p class=MsoNormal><span lang=en-DE>ODE-toolbox is a Python package that
2586 assists in solver benchmarking, and recommends solvers on the basis of a set of
2587 user-configurable heuristics. For all dynamical equations that admit an
2588 analytic solution, ODE-toolbox generates propagator matrices that allow the
2589 solution to be calculated at machine precision. For all others, first-order
2590 update expressions are returned based on the Jacobian matrix. In addition to
2591 continuous dynamics, discrete events can be used to model instantaneous changes
2592 in system state, such as a neuronal action potential. These can be generated by
2593 the system under test as well as applied as external stimuli, making
2594 ODE-toolbox particularly well-suited for applications in computational
2595 neuroscience.</span></p>
2596
2597 <h2></h2>
2598
2599 <h2><a name="_Toc138932352"><span lang=en-DE>openMINDS</span></a></h2>
2600
2601 <p class=MsoNormal><span lang=en-DE>openMINDS is composed of: (i) integrated
2602 metadata models adoptable by any graph database system (GDBS), (ii) a set of
2603 libraries of serviceable metadata instances with external resource references
2604 for local and global knowledge integration, and (iii) supportive tooling for
2605 handling the metadata models and instances. Moreover, the framework provides
2606 machine-readable mappings to other standardisation efforts (e.g., schema.org).
2607 With this, openMINDS is a unique and powerful metadata framework for flexible
2608 knowledge integration within and beyond any GDBS.</span></p>
2609
2610 <h2></h2>
2611
2612 <h2><a name="_Toc138932353"><span lang=en-DE>openMINDS metadata for TVB-ready
2613 data</span></a></h2>
2614
2615 <p class=MsoNormal><span lang=en-DE>Jupyter Python notebook with code and
2616 commentaries for creating openMINDS metadata version 1.0 in JSON-LD format for
2617 ingestion of TVB-ready data in EBRAINS Knowledge Graph.</span></p>
2618
2619 <h2></h2>
2620
2621 <h2><a name="_Toc138932354"><span lang=en-DE>PCI</span></a></h2>
2622
2623 <p class=MsoNormal><span lang=en-DE>The notebook allows the computation of the
2624 PCI Lempel-Ziv and PCI state transitions. In order to run the examples, a wake
2625 and sleep data set needs to be provided in the Python-MNE format.</span></p>
2626
2627 <h2></h2>
2628
2629 <h2><a name="_Toc138932355"><span lang=en-DE>PIPSA</span></a></h2>
2630
2631 <p class=MsoNormal><span lang=en-DE>PIPSA enables the comparison of the
2632 electrostatic interaction properties of proteins. It permits the classification
2633 of proteins according to their interaction properties. PIPSA may assist in
2634 function assignment, the estimation of binding properties and enzyme kinetic
2635 parameters.</span></p>
2636
2637 <h2></h2>
2638
2639 <h2><a name="_Toc138932356"><span lang=en-DE>PoSCE</span></a></h2>
2640
2641 <p class=MsoNormal><span lang=en-DE>PoSCE is a functional connectivity
2642 estimator of fMRI time-series. It relies on the Riemannian geometry of
2643 covariances and integrates prior knowledge of covariance distribution over a
2644 population.</span></p>
2645
2646 <h2></h2>
2647
2648 <h2><a name="_Toc138932357"><span lang=en-DE>Provenance API</span></a></h2>
2649
2650 <p class=MsoNormal><span lang=en-DE>The EBRAINS Provenance API is a web service
2651 to facilitate working with computational provenance metadata. Metadata are
2652 stored in the ?EBRAINS Knowledge Graph (KG) using openMINDS schemas. The
2653 Provenance API provides a somewhat simplified interface compared to accessing
2654 the KG directly and performs checks of metadata consistency. The service covers
2655 workflows involving simulation, data analysis, visualisation, optimisation,
2656 data movement and model validation.</span></p>
2657
2658 <h2></h2>
2659
2660 <h2><a name="_Toc138932358"><span lang=en-DE>PyNN</span></a></h2>
2661
2662 <p class=MsoNormal><span lang=en-DE>A model description written with the PyNN
2663 API and the Python programming language runs on any simulator that PyNN
2664 supports (currently NEURON, ?NEST and Brian 2) as well as on the ?BrainScaleS
2665 and ?SpiNNaker neuromorphic hardware systems. PyNN provides a library of
2666 standard neuron, synapse and synaptic plasticity models, verified to work the
2667 same on different simulators. PyNN also provides commonly used connectivity
2668 algorithms (e.g. all-to-all, random, distance-dependent, small-world) but makes
2669 it easy to provide your own connectivity in a simulator-independent way. PyNN
2670 transparently supports distributed simulations using MPI.</span></p>
2671
2672 <h2></h2>
2673
2674 <h2><a name="_Toc138932359"><span lang=en-DE>Pyramidal Explorer</span></a></h2>
2675
2676 <p class=MsoNormal><span lang=en-DE>PyramidalExplorer is a tool to
2677 interactively explore and reveal the detailed organisation of the microanatomy
2678 of pyramidal neurons with functionally related models. Possible regional
2679 differences in the pyramidal cell architecture can be interactively discovered
2680 by combining quantitative morphological information about the structure of the
2681 cell with implemented functional models. The key contribution of this tool is the
2682 morpho-functional oriented design,  allowing the user to navigate within the 3D
2683 dataset, filter and perform content-based retrieval operations to find the 
2684 spines that are alike and dissimilar within the neuron, according to particular
2685 morphological or functional variables.</span></p>
2686
2687 <h2></h2>
2688
2689 <h2><a name="_Toc138932360"><span lang=en-DE>QCAlign software</span></a></h2>
2690
2691 <p class=MsoNormal><span lang=en-DE>The QUINT workflow enables spatial analysis
2692 of labelling in series of brain sections from mouse and rat based on
2693 registration to a reference brain atlas. The QCAlign software supports the use
2694 of QUINT for high-throughput studies by providing information about: 1. The
2695 quality of the section images used as input to the QUINT workflow. 2. The
2696 quality of the atlas registration performed in the QUINT workflow. 3. QCAlign
2697 also makes it easier for the user to explore the atlas hierarchy and decide on
2698 a customised hierarchy level to use for the investigation</span></p>
2699
2700 <h2></h2>
2701
2702 <h2><a name="_Toc138932361"><span lang=en-DE>QuickNII</span></a></h2>
2703
2704 <p class=MsoNormal><span lang=en-DE>QuickNII is a tool for user-guided affine
2705 registration (anchoring) of 2D experimental image data, typically high
2706 resolution microscopic images, to 3D atlas reference space, facilitating data
2707 integration through standardised coordinate systems. Key features: Generate
2708 user-defined cut planes through the atlas templates, matching the orientation
2709 of the cut plane of the 2D experimental image data, as a first step towards
2710 anchoring of images to the relevant atlas template. Propagate spatial
2711 transformations across series of sections following anchoring of selected
2712 images.</span></p>
2713
2714 <h2></h2>
2715
2716 <h2><a name="_Toc138932362"><span lang=en-DE>Quota Manager</span></a></h2>
2717
2718 <p class=MsoNormal><span lang=en-DE>The Quota Manager enables each EBRAINS
2719 service to manage user quotas for resources EBRAINS users consume in their
2720 respective services. The goal is to encourage the responsible use of resources.
2721 It is recommended that all users (except possibly guest accounts) are provided
2722 with a default quota, and that specific users have the option of receiving
2723 larger quotas based on their affiliation, role or motivated requests.</span></p>
2724
2725 <h2></h2>
2726
2727 <h2><a name="_Toc138932363"><span lang=en-DE>RateML</span></a></h2>
2728
2729 <p class=MsoNormal><span lang=en-DE>RateML enables users to generate
2730 whole-brain network models from a succinct declarative description, in which
2731 the mathematics of the model are described without specifying how their
2732 simulation should be implemented. RateML builds on NeuroMLÕs Low Entropy Model
2733 Specification (LEMS), an XML-based language for specifying models of dynamical systems,
2734 allowing descriptions of neural mass and discretized neural field models, as
2735 implemented by the TVB simulator. The end user describes their modelÕs
2736 mathematics once and generates and runs code for different languages, targeting
2737 both CPUs for fast single simulations and GPUs for parallel ensemble
2738 simulations.</span></p>
2739
2740 <h2></h2>
2741
2742 <h2><a name="_Toc138932364"><span lang=en-DE>Region-wise CBPP using the Julich
2743 BrainÊCytoarchitectonic Atlas</span></a></h2>
2744
2745 <p class=MsoNormal><span lang=en-DE>Many studies have been investigating the
2746 relationships between interindividual variability in brain regionsÕ
2747 connectivity and behavioural phenotypes, by utilising connectivity-based
2748 prediction models. Recently, we demonstrated that an approach based on the
2749 combination of whole-brain and region-wise CBPP can provide important insight
2750 into the predictive model, and hence in brain-behaviour relationships, by
2751 offering interpretable patterns. Here, we applied this approach using the
2752 Julich Brain Cytoarchitectonic Atlas with the resting-state functional
2753 connectivity and psychometric variables from the Human Connectome Project
2754 dataset, illustrating each brain regionÕs predictive power for a range of
2755 psychometric variables. As a result, a psychometric prediction profile was
2756 established for each brain region, which can be validated against brain mapping
2757 literature.</span></p>
2758
2759 <h2></h2>
2760
2761 <h2><a name="_Toc138932365"><span lang=en-DE>RRI Capacity Development Resources</span></a></h2>
2762
2763 <p class=MsoNormal><span lang=en-DE>A series of training resources developed to
2764 enable anticipation, critical reflection and public engagement/deliberation of
2765 societal consequences of brain research and innovation activities. These
2766 resources were designed primarily for HBP researchers and EBRAINS leadership
2767 and management, involving EBRAINS data and infrastructure providers. However,
2768 they are also useful for engaging the wider public with RRI. The resources are
2769 based on the legacy of over 10 years of research and activities of the ethics
2770 and society-team in the HBP. They cover important RRI-related topics on
2771 neuroethics, data governance, dual-use, public engagement and foresight,
2772 diversity, search integrity etc.</span></p>
2773
2774 <h2></h2>
2775
2776 <h2><a name="_Toc138932366"><span lang=en-DE>rsHRF</span></a></h2>
2777
2778 <p class=MsoNormal><span lang=en-DE>This toolbox is aimed to retrieve the
2779 onsets of pseudo-events triggering an hemodynamic response from resting state
2780 fMRI BOLD signals. It is based on point process theory and fits a model to
2781 retrieve the optimal lag between the events and the HRF onset, as well as the
2782 HRF shape, using different shape parameters or combinations of basis functions.
2783 Once the HRF has been retrieved for each voxel/vertex, it can be deconvolved
2784 from the time series (for example, to improve lag-based connectivity
2785 estimates), or one can map the shape parameters everywhere in the brain
2786 (including white matter) and use it as a pathophysiological indicator.</span></p>
2787
2788 <h2></h2>
2789
2790 <h2><a name="_Toc138932367"><span lang=en-DE>RTNeuron</span></a></h2>
2791
2792 <p class=MsoNormal><span lang=en-DE>The main utility of RTNeuron is twofold:
2793 (i) the interactive visual inspection of structural and functional features of
2794 the cortical column model and (ii) the generation of high-quality movies and
2795 images for presentations and publications.RTNeuron provides a C++ library with
2796 an OpenGL-based rendering backend, a Python wrapping and a Python application
2797 called rtneuron. RTNeuron is only supported in GNU/Linux systems. However, it
2798 should also be possible to build it on Windows systems. For OS/X it may be
2799 quite challenging and require changes in OpenGL-related code to get it working.</span></p>
2800
2801 <h2></h2>
2802
2803 <h2><a name="_Toc138932368"><span lang=en-DE>sbs: Spike-based Sampling</span></a></h2>
2804
2805 <p class=MsoNormal><span lang=en-DE>Spike-based sampling, sbs, is a software
2806 suite that takes care of calibrating spiking neurons for given target
2807 distributions and allows the evaluation of these distributions as they are
2808 produced by stochastic spiking networks.</span></p>
2809
2810 <h2></h2>
2811
2812 <h2><a name="_Toc138932369"><span lang=en-DE>SDA 7</span></a></h2>
2813
2814 <p class=MsoNormal><span lang=en-DE>SDA 7 can be used to carry out Brownian
2815 dynamics simulations of the diffusional association in a continuum aqueous
2816 solvent of two solute molecules, e.g., proteins, or of a solute molecule to an
2817 inorganic surface. SDA 7 can also be used to simulate the diffusion of multiple
2818 proteins, in dilute or concentrated solutions, e.g., to study the effects of
2819 macromolecular crowding.</span></p>
2820
2821 <h2></h2>
2822
2823 <h2><a name="_Toc138932370"><span lang=en-DE>Shape &amp; Appearance Modelling</span></a></h2>
2824
2825 <p class=MsoNormal><span lang=en-DE>A framework for automatically learning
2826 shape and appearance models for medical (and certain other) images. The
2827 algorithm was developed with the aim of eventually enabling distributed
2828 privacy-preserving analysis of brain image data, such that shared information
2829 (shape and appearance basis functions) may be passed across sites, whereas
2830 latent variables that encode individual images remain secure within each site.
2831 These latent variables are proposed as features for privacy-preserving data
2832 mining applications.</span></p>
2833
2834 <h2></h2>
2835
2836 <h2><a name="_Toc138932371"><span lang=en-DE>siibra-api</span></a></h2>
2837
2838 <p class=MsoNormal><span lang=en-DE>siibra-api provides an HTTP wrapper around
2839 ?siibra-python, allowing developers to access atlas (meta)data over HTTP
2840 protocol. Deployed on the EBRAINS infrastructure, developers can access the
2841 centralised (meta)data on atlases, as provided by siibra-python, regardless of
2842 the programming language.</span></p>
2843
2844 <h2></h2>
2845
2846 <h2><a name="_Toc138932372"><span lang=en-DE>siibra-explorer</span></a></h2>
2847
2848 <p class=MsoNormal><span lang=en-DE>The interactive atlas viewer
2849 siibra-explorer allows exploring the different EBRAINS atlases for the human,
2850 monkey and rodent brains together with a comprehensive set of linked multimodal
2851 data features. It provides a 3-planar view of a parcellated reference volume
2852 combined with a rotatable overview of the 3D surface. Several templates can be
2853 selected to navigate through the brain from MRI-scale to microscopic
2854 resolution, allowing inspection of terabyte-size image data. Anatomically
2855 anchored datasets reflecting aspects of cellular and molecular organisation,
2856 fibres, function and connectivity can be discovered by selecting brain regions
2857 from parcellations, or zooming and panning the reference brain. siibra-explorer
2858 also allows annotation of brain locations as points and polygons and is
2859 extensible via interactive plugins.</span></p>
2860
2861 <h2></h2>
2862
2863 <h2><a name="_Toc138932373"><span lang=en-DE>siibra-python</span></a></h2>
2864
2865 <p class=MsoNormal><span lang=en-DE>siibra-python is a Python client to a brain
2866 atlas framework that integrates brain parcellations and reference spaces at
2867 different spatial scales and connects them with a broad range of multimodal
2868 regional data features. It aims to facilitate programmatic and reproducible
2869 incorporation of brain parcellations and brain region features from different
2870 sources into neuroscience workflows. Also, siibra-python provides an easy
2871 access to data features on the ?EBRAINS Knowledge Graph in a well-structured
2872 manner. Users can preconfigure their own data to use within siibra-python.</span></p>
2873
2874 <h2></h2>
2875
2876 <h2><a name="_Toc138932374"><span lang=en-DE>Single Cell Model (Re)builder
2877 Notebook</span></a></h2>
2878
2879 <p class=MsoNormal><span lang=en-DE>The Single Cell Model (Re)builder Notebook
2880 is a web application, implemented via a Jupyter Notebook on EBRAINS, which
2881 allows users to configure the BluePyOpt to re-run an optimisation with their
2882 own choices for the parameters range. The optimisation jobs are submitted
2883 through Neuroscience Gateway.</span></p>
2884
2885 <h2></h2>
2886
2887 <h2><a name="_Toc138932375"><span lang=en-DE>Slurm Plugin for Co-allocation of
2888 Compute and Data Resources</span></a></h2>
2889
2890 <p class=MsoNormal><span lang=en-DE>This Simple linux utility for resource
2891 management (Slurm) plugin enables the co-allocation of compute and data resources
2892 on a shared multi-tiered storage cluster by estimating waiting times when the
2893 high-performance storage (burst buffers) will become available to submitted
2894 jobs. Based on the current job queue and the estimated waiting time, the plugin
2895 decides whether scheduling the high-performance or lower-performance storage
2896 system (parallel file system) benefits the job's turnaround time. The
2897 estimation depends on additional information the user provides at submission
2898 time.</span></p>
2899
2900 <h2></h2>
2901
2902 <h2><a name="_Toc138932376"><span lang=en-DE>Snudda</span></a></h2>
2903
2904 <p class=MsoNormal><span lang=en-DE>Snudda (ÔtouchÕ in Swedish) allows the user
2905 to set up and generate microcircuits where the connectivity between neurons is
2906 based on reconstructed neuron morphologies. The touch detection algorithm looks
2907 for overlaps of axons and dendrites, and places putative synapses where they
2908 touch. The putative synapses are pruned, removing a fraction to match
2909 statistics from pairwise connectivity experiments. If needed, Snudda can also
2910 use probability functions to create realistic microcircuits. The Snudda
2911 software is written in Python and includes support for supercomputers. It uses
2912 ipyparallel to parallelise network creation, and NEURON as the backend for
2913 simulations. Install using pip or by directly downloading.</span></p>
2914
2915 <h2></h2>
2916
2917 <h2><a name="_Toc138932377"><span lang=en-DE>SomaSegmenter</span></a></h2>
2918
2919 <p class=MsoNormal><span lang=en-DE>SomaSegmenter allows neuronal soma
2920 segmentation in fluorescence microscopy imaging datasets with the use of a
2921 parametrised version of the U-Net segmentation model, including additional
2922 features such as residual links and tile-based frame reconstruction.</span></p>
2923
2924 <h2></h2>
2925
2926 <h2><a name="_Toc138932378"><span lang=en-DE>SpiNNaker</span></a></h2>
2927
2928 <p class=MsoNormal><span lang=en-DE>SpiNNaker is a neuromorphic computer with
2929 over a million low power, small memory ARM cores arranged in chips, connected
2930 together with a unique brain-like mesh network, and designed to simulate
2931 networks of spiking point neurons. Software is provided to compile networks
2932 described with PyNN into running simulations, and to extract and convert
2933 results into the neo data format, as well as providing support for live
2934 interaction with running simulations. This allows integration with external
2935 devices such as real or virtual robotics as well as live simulation
2936 visualisation. Scripts can be written and executed using Jupyter for
2937 interactive access.</span></p>
2938
2939 <h2></h2>
2940
2941 <h2><a name="_Toc138932379"><span lang=en-DE>SSB toolkit</span></a></h2>
2942
2943 <p class=MsoNormal><span lang=en-DE>The SSB toolkit is an open-source Python
2944 library to simulate mathematical models of the signal transduction pathways of
2945 G-protein coupled receptors (GPCRs). By merging structural macromolecular data
2946 with systems biology simulations, the framework allows simulation of the signal
2947 transduction kinetics induced by ligand-GPCR interactions, as well as the consequent
2948 change of concentration of signalling molecular species, as a function of time
2949 and ligand concentration. Therefore, this tool allows the possibility to
2950 investigate the subcellular effects of ligand binding upon receptor activation,
2951 deepening the understanding of the relationship between the molecular level of
2952 ligand-target interactions and higher-level cellular and physiological or
2953 pathological response mechanisms.</span></p>
2954
2955 <h2></h2>
2956
2957 <h2><a name="_Toc138932380"><span lang=en-DE>Subcellular model building and
2958 calibration tool set</span></a></h2>
2959
2960 <p class=MsoNormal><span lang=en-DE>The toolset includes interoperable modules
2961 for: model building, calibration (parameter estimation) and model analysis. All
2962 information needed to perform these tasks (models, experimental calibration
2963 data and prior assumptions on parameter distributions) are stored in a
2964 structured, human- and machine-readable file format based on SBtab. The toolset
2965 enables simulations of the same model in simulators with different
2966 characteristics, e.g., STEPS, NEURON, MATLABÕs Simbiology and R via automatic
2967 code generation. The parameter estimation can include uncertainty
2968 quantification and is done by optimisation or Bayesian approaches. Model
2969 analysis includes global sensitivity analysis and functionality for analysing
2970 thermodynamic constraints and conserved moieties.</span></p>
2971
2972 <h2></h2>
2973
2974 <h2><a name="_Toc138932381"><span lang=en-DE>Synaptic Events Fitting</span></a></h2>
2975
2976 <p class=MsoNormal><span lang=en-DE>The Synaptic Events Fitting is a web
2977 application, implemented in a Jupyter Notebook on EBRAINS that allows users to
2978 fit synaptic events using data and models from the ?EBRAINS Knowledge Graph
2979 (KG). Select, download and visualise experimental data from the KG and then choose
2980 the data to be fitted. A mod file is then selected (local or default) together
2981 with the corresponding configuration file (including protocol and the name of
2982 the parameters to be fitted, their initial values and allowed variation range,
2983 exclusion rules and an optional set of dependencies). The fitting procedure can
2984 run on Neuroscience Gateway. Fetch the fitting results from the storage of the
2985 HPC system to the storage of the Collab or to analyse the optimised parameters.</span></p>
2986
2987 <h2></h2>
2988
2989 <h2><a name="_Toc138932382"><span lang=en-DE>Synaptic Plasticity Explorer</span></a></h2>
2990
2991 <p class=MsoNormal><span lang=en-DE>The Synaptic Plasticity Explorer is a web
2992 application, implemented via a Jupyter Notebook on EBRAINS, which allows to
2993 configure and test, through an intuitive GUI, different synaptic plasticity
2994 models and protocols on single cell optimised models, available in the EBRAINS
2995 Model Catalog. It consists of two tabs: ÒConfigÓ, where the user can specify
2996 the plasticity model to use and the synaptic parameters, and ÒSimÓ, where the
2997 recording location, weightÕs evolution and number of simulations to run are
2998 defined. The results are plotted at the end of the simulation and the traces
2999 are available for download.</span></p>
3000
3001 <h2></h2>
3002
3003 <h2><a name="_Toc138932383"><span lang=en-DE>Synaptic proteome database
3004 (SQLite)</span></a></h2>
3005
3006 <p class=MsoNormal><span lang=en-DE>Integration of 57 published synaptic
3007 proteomic datasets reveals a stunningly complex picture involving over 7000
3008 proteins. Molecular complexes were reconstructed using evidence-based
3009 protein-protein interaction data available from public databases. The
3010 constructed molecular interaction network model is embedded into an SQLite
3011 implementation, allowing queries that generate custom network models based on
3012 meta-data including species, synaptic compartment, brain region, and method of
3013 extraction.</span></p>
3014
3015 <h2></h2>
3016
3017 <h2><a name="_Toc138932384"><span lang=en-DE>Synaptome.db</span></a></h2>
3018
3019 <p class=MsoNormal><span lang=en-DE>The Synaptome.db bioconductor package
3020 contains a local copy of the Synaptic proteome database. On top of this it
3021 provides a set of utility R functions to query and analyse its content. It
3022 allows for extraction of information for specific genes and building the
3023 protein-protein interaction graph for gene sets, synaptic compartments and
3024 brain regions.</span></p>
3025
3026 <h2></h2>
3027
3028 <h2><a name="_Toc138932385"><span lang=en-DE>Tide</span></a></h2>
3029
3030 <p class=MsoNormal><span lang=en-DE>BlueBrain's Tide provides multi-window,
3031 multi-user touch interaction on large surfaces Ð think of a giant collaborative
3032 wall-mounted tablet. Tide is a distributed application that can run on multiple
3033 machines to power display walls or projection systems of any size. Its user interface
3034 is designed to offer an intuitive experience on touch walls. It works just as
3035 well on non-touch-capable installations by using its web interface from any web
3036 browser.</span></p>
3037
3038 <h2></h2>
3039
3040 <h2><a name="_Toc138932386"><span lang=en-DE>TVB EBRAINS</span></a></h2>
3041
3042 <p class=MsoNormal><span lang=en-DE>TVB EBRAINS is the principal full brain
3043 network simulation engine in EBRAINS and covers every aspect of realising
3044 personalised whole-brain simulations on the EBRAINS platform. It consists of
3045 the simulation tools and adaptors connecting the data, atlas and computing
3046 services to the rest of the TVB ecosystem and Cloud services available in
3047 EBRAINS. As such it allows the user to find and fetch relevant datasets through
3048 the ?EBRAINS Knowledge Graph and Atlas services, construct the personalised TVB
3049 models and use the HPC systems to perform parameter exploration, optimisation and
3050 inference studies. The user can orchestrate the workflow from the Jupyterlab
3051 interactive computing environment of the EBRAINS Collaboratory or use the
3052 dedicated web application of TVB.</span></p>
3053
3054 <h2></h2>
3055
3056 <h2><a name="_Toc138932387"><span lang=en-DE>TVB Image Processing Pipeline</span></a></h2>
3057
3058 <p class=MsoNormal><span lang=en-DE>TVB Image Processing Pipeline takes multimodal
3059 MRI data sets (anatomical, functional and diffusion-weighted MRI) as input and
3060 generates structural connectomes, region-average fMRI time series, functional
3061 connectomes, brain surfaces, electrode positions, lead field matrices and atlas
3062 parcellations as output. The pipeline performs preprocessing and
3063 distortion-correction on MRI data as well as white matter fibre bundle
3064 tractography on diffusion data. Outputs are formatted according to two data
3065 standards: a TVB-ready data set that can be directly used to simulate brain
3066 network models and the same output in BIDS format.</span></p>
3067
3068 <h2></h2>
3069
3070 <h2><a name="_Toc138932388"><span lang=en-DE>TVB Inversion</span></a></h2>
3071
3072 <p class=MsoNormal><span lang=en-DE>The TVB Inversion package implements the
3073 machinery required to perform parameter exploration and  inference over
3074 parameters of ?The Virtual Brain simulator. It implements Simulation Based
3075 Inference (SBI) which is a Bayesian inference method for complex models, where
3076 calculation of the likelihood function is either analytically or
3077 computationally intractable. As such, it allows the user to formulate with
3078 great expressive power both the model and the inference scenario in terms of
3079 observed data features and model parameters. Part of the integration with TVB
3080 entails the option to perform numerous simulations in parallel, which can be
3081 used for parameter space exploration.</span></p>
3082
3083 <h2></h2>
3084
3085 <h2><a name="_Toc138932389"><span lang=en-DE>TVB Web App</span></a></h2>
3086
3087 <p class=MsoNormal><span lang=en-DE>TVB Web App provides The Virtual Brain
3088 Simulator as an EBRAINS Cloud Service with an HPC back-end. Scientists can run
3089 intense personalised brain simulations without having to deploy software. Users
3090 can access the service with their EBRAINS credentials (single sign on). TVB Web
3091 App uses private/public key cryptography, sandboxing, and access control to
3092 protect personalised health information contained in digital human brain twins
3093 while being processed on HPC. Users can upload their connectomes or use
3094 TVB-ready image-derived data discoverable via the ?EBRAINS Knowledge Graph.
3095 Users can also use containerised processing workflows available on EBRAINS to
3096 render image raw data into simulation-ready formats.</span></p>
3097
3098 <h2></h2>
3099
3100 <h2><a name="_Toc138932390"><span lang=en-DE>TVB Widgets</span></a></h2>
3101
3102 <p class=MsoNormal><span lang=en-DE>In order to support the usability of
3103 EBRAINS workflows, TVB-widgets has been developed as a set of modular graphic
3104 components and software solutions, easy to use in the ?Collaboratory within the
3105 JupyterLab. These GUI components are based on and under open source licence,
3106 supporting open neuroscience and support features like: Setup of models and
3107 region-specific or cohort simulations. Selection of Data sources and their
3108 links to models. Querying data from ?siibra and the ?EBRAINS Knowledge Graph.
3109 Deployment and monitoring jobs on HPC resources. Analysis and visualisation.
3110 Visual workflow builder for configuring and launching TVB simulations.</span></p>
3111
3112 <h2></h2>
3113
3114 <h2><a name="_Toc138932391"><span lang=en-DE>TVB-Multiscale</span></a></h2>
3115
3116 <p class=MsoNormal><span lang=en-DE>TVB-Multiscale is a Python toolbox aimed at
3117 facilitating the configuration of multiscale brain models and their
3118 co-simulation with TVB and spiking network simulators (currently ?NEST,
3119 ?NetPyNE (NEURON) and ANNarchy). A multiscale brain model consists of a full
3120 brain model formulated at the coarse scale of networks of tens up to thousands
3121 of brain regions, and an additional model of networks of spiking neurons
3122 describing selected brain regions at a finer scale. The toolbox has a
3123 user-friendly interface for configuring different kinds of models for
3124 transforming and exchanging data between the two scales during co-simulation.</span></p>
3125
3126 <h2></h2>
3127
3128 <h2><a name="_Toc138932392"><span lang=en-DE>VIOLA</span></a></h2>
3129
3130 <p class=MsoNormal><span lang=en-DE>VIOLA is an interactive, web-based tool to
3131 visualise activity data in multiple 2D layers such as the simulation output of
3132 neuronal networks with 2D geometry. As a reference implementation for a
3133 developed set of interactive visualisation concepts, the tool combines and
3134 adapts modern interactive visualisation paradigms, such as coordinated multiple
3135 views, for massively parallel neurophysiological data. The software allows for
3136 an explorative and qualitative assessment of the spatiotemporal features of
3137 neuronal activity, which can be performed prior to a detailed quantitative data
3138 analysis of specific aspects of the data.</span></p>
3139
3140 <h2></h2>
3141
3142 <h2><a name="_Toc138932393"><span lang=en-DE>Vishnu 1.0</span></a></h2>
3143
3144 <p class=MsoNormal><span lang=en-DE>DC Explorer, Pyramidal Explorer and Clint
3145 Explorer are the core of an application suite designed to help scientists to
3146 explore their data. Vishnu 1.0 is a communication framework that allows them to
3147 interchange information and cooperate in real time. It provides a unique access
3148 point to the three applications and manages a database with the usersÕ
3149 datasets. Vishnu was originally designed to integrate data for
3150 Espina.Whole-brain-scale tools.</span></p>
3151
3152 <h2></h2>
3153
3154 <h2><a name="_Toc138932394"><span lang=en-DE>ViSimpl</span></a></h2>
3155
3156 <p class=MsoNormal><span lang=en-DE>ViSimpl integrates a set of visualisation
3157 and interaction components that provide a semantic view of brain data with the
3158 aim of improving its analysis procedures. ViSimpl provides 3D particle-based
3159 rendering that visualises simulation data with their associated spatial and
3160 temporal information, enhancing the knowledge extraction process. It also
3161 provides abstract representations of the time-varying magnitudes, supporting
3162 different data aggregation and disaggregation operations and giving focus and
3163 context clues. In addition, ViSimpl provides synchronised playback control of
3164 the simulation being analysed.</span></p>
3165
3166 <h2></h2>
3167
3168 <h2><a name="_Toc138932395"><span lang=en-DE>VisuAlign</span></a></h2>
3169
3170 <p class=MsoNormal><span lang=en-DE>VisuAlign is a tool for user-guided
3171 nonlinear registration after ?QuickNII of 2D experimental image data, typically
3172 high resolution microscopic images, to 3D atlas reference space, facilitating
3173 data integration through standardised coordinate systems. Key features:
3174 Generate user-defined cut planes through the atlas templates, matching the
3175 orientation of the cut plane of the 2D experimental image data, as a first step
3176 towards anchoring of images to the relevant atlas template. Propagate spatial
3177 transformations across series of sections following anchoring of selected
3178 images.</span></p>
3179
3180 <h2></h2>
3181
3182 <h2><a name="_Toc138932396"><span lang=en-DE>VMetaFlow</span></a></h2>
3183
3184 <p class=MsoNormal><span lang=en-DE>VMetaFlow is an abstraction layer placed
3185 over existing visual grammars and visualisation declarative languages,
3186 providing them with interoperability mechanisms. The main contribution of this
3187 research is to provide a user-friendly system to design visualisation and data
3188 processing operations that can be interconnected to form data analysis
3189 workflows. Visualisations and data processes can be saved as cards. Cards and
3190 workflows can be saved, distributed and reused between users.</span></p>
3191
3192 <h2></h2>
3193
3194 <h2><a name="_Toc138932397"><span lang=en-DE>Voluba</span></a></h2>
3195
3196 <p class=MsoNormal><span lang=en-DE>A common problem in high-resolution brain
3197 atlasing is spatial anchoring of volumes of interest from imaging experiments
3198 into the detailed anatomical context of an ultrahigh-resolution reference model
3199 like BigBrain. The interactive volumetric alignment tool voluba is implemented
3200 as a web service and allows anchoring of volumetric image data to reference
3201 volumes at microscopical spatial resolutions. It enables interactive
3202 manipulation of image position, scale, and orientation, flipping of coordinate
3203 axes, and entering of anatomical point landmarks in 3D. The resulting
3204 transformation parameters can, amongst others, be downloaded or used to view
3205 the anchored image volume in the interactive atlas viewer siibra-explorer.</span></p>
3206
3207 <h2></h2>
3208
3209 <h2><a name="_Toc138932398"><span lang=en-DE>WebAlign</span></a></h2>
3210
3211 <p class=MsoNormal><span lang=en-DE>WebAlign is the web version of ?QuickNII.
3212 Presently, it is available as a community app in the ?Collaboratory. Features
3213 include: Spatial registration of sectional image data. Generation of customised
3214 atlas maps for your sectional image data.</span></p>
3215
3216 <h2></h2>
3217
3218 <h2><a name="_Toc138932399"><span lang=en-DE>Webilastik</span></a></h2>
3219
3220 <p class=MsoNormal><span lang=en-DE>webilastik brings the popular machine
3221 learning-based image analysis tool ilastik from the desktop into the browser.
3222 Users can perform semantic segmentation tasks on their data in the cloud.
3223 webilastik runs computations on federated EBRAINS HPC resources and uses
3224 EBRAINS infrastructure for data access and storage. webilastik makes machine
3225 learning-based image analysis workflows accessible to users without deep
3226 knowledge of image analysis and machine learning. webilastik is part of the
3227 QUINT workflow for extraction, quantification and analysis of features from
3228 rodent histological images.</span></p>
3229
3230 <h2></h2>
3231
3232 <h2><a name="_Toc138932400"><span lang=en-DE>WebWarp</span></a></h2>
3233
3234 <p class=MsoNormal><span lang=en-DE>WebWarp is the web version of ?VisuAlign.
3235 Presently, it is available as a community app in the ?Collaboratory. Features
3236 include: Nonlinear refinements of atlas registration by WebAlign of sectional
3237 image data. Generation of customised atlas maps for your sectional image data.</span></p>
3238
3239 <h2></h2>
3240
3241 <h2><a name="_Toc138932401"><span lang=en-DE>ZetaStitcher</span></a></h2>
3242
3243 <p class=MsoNormal><span lang=en-DE>ZetaStitcher is a Python package designed
3244 to stitch large volumetric images, such as those produced by Light-Sheet
3245 Fluorescence Microscopes. It is able to quickly compute the optimal alignment
3246 of large mosaics of tiles thanks to its ability to perform a sampling along the
3247 tile depth, i.e., pairwise alignment is computed only at certain depths along
3248 the thickness of the tile. This greatly reduces the amount of data that needs
3249 to be read and transferred, thus, making the process much faster. ZetaStitcher
3250 comes with an API that can be used to programmatically access the aligned
3251 volume in a virtual fashion as if it were a big NumPy array, without having to
3252 produce the fused 3D image of the whole sample.Cellular- and subcellular-scale
3253 tools.</span></p>
3254
3255 <h2></h2>
3256
3257 <h2><a name="_Toc138932402"><span lang=en-DE>TauRAMD</span></a></h2>
3258
3259 <p class=MsoNormal><span lang=en-DE>The TauRAMD technique makes use of RAMD
3260 simulations to compute relative residence times (or dissociation rates) of
3261 protein-ligand complexes. In the RAMD method, the egress of a molecule from a
3262 target receptor is accelerated by the application of an adaptive randomly
3263 oriented force on the ligand. This enables ligand egress events to be observed
3264 in short, nanosecond timescale simulations without imposing any bias regarding
3265 the ligand egress route taken. If coupled to the MD-IFP tool, the TauRAMD
3266 method can be used to investigate dissociation mechanisms and characterize
3267 transition states.</span></p>
3268
3269 </div>
3270
3271 </body>
3272
3273 </html>
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3275 {{/html}}
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SLU_HBPB