Wiki source code of Tools description

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1 **Human Brain Project Tools Description**
2
3 On this page you will find a list with a brief description of the tools we have for the HBP tools Book, if you have a suggestion or detect an omission please send your feedback to [[slu@ebrains.eu>>mailto:slu@ebrains.eu]].
4
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417 </head>
418
419 <body lang=EN-US link="#0563C1" vlink="#954F72">
420
421 <div class=WordSection1>
422
423 <p class=MsoTocHeading>HBP Tools list</p>
424
425 <p class=MsoToc2><span lang=en-DE><span class=MsoHyperlink><a
426 href="#_Toc138932248">AngoraPy<span style='color:windowtext;display:none;
427 text-decoration:none'>. </span><span
428 style='color:windowtext;display:none;text-decoration:none'>5</span></a></span></span></p>
429
430 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
431 href="#_Toc138932249">AnonyMI<span style='color:windowtext;display:none;
432 text-decoration:none'> </span><span
433 style='color:windowtext;display:none;text-decoration:none'>5</span></a></span></span></p>
434
435 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
436 href="#_Toc138932250">Arbor<span style='color:windowtext;display:none;
437 text-decoration:none'> </span><span
438 style='color:windowtext;display:none;text-decoration:none'>6</span></a></span></span></p>
439
440 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
441 href="#_Toc138932251">Arbor GUI<span style='color:windowtext;display:none;
442 text-decoration:none'> </span><span
443 style='color:windowtext;display:none;text-decoration:none'>6</span></a></span></span></p>
444
445 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
446 href="#_Toc138932252">Bayesian Virtual Epileptic Patient (BVEP)<span
447 style='color:windowtext;display:none;text-decoration:none'> </span><span
448 style='color:windowtext;display:none;text-decoration:none'>6</span></a></span></span></p>
449
450 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
451 href="#_Toc138932253">BIDS Extension Proposal Computational Model
452 Specifications<span style='color:windowtext;display:none;text-decoration:none'>. </span><span
453 style='color:windowtext;display:none;text-decoration:none'>6</span></a></span></span></p>
454
455 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
456 href="#_Toc138932254">BioBB<span style='color:windowtext;display:none;
457 text-decoration:none'>. </span><span
458 style='color:windowtext;display:none;text-decoration:none'>6</span></a></span></span></p>
459
460 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
461 href="#_Toc138932255">BioExcel-CV19<span style='color:windowtext;display:none;
462 text-decoration:none'>. </span><span
463 style='color:windowtext;display:none;text-decoration:none'>7</span></a></span></span></p>
464
465 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
466 href="#_Toc138932256">BioNAR<span style='color:windowtext;display:none;
467 text-decoration:none'>. </span><span
468 style='color:windowtext;display:none;text-decoration:none'>7</span></a></span></span></p>
469
470 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
471 href="#_Toc138932257">BlueNaaS-single cell<span style='color:windowtext;
472 display:none;text-decoration:none'> </span><span
473 style='color:windowtext;display:none;text-decoration:none'>7</span></a></span></span></p>
474
475 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
476 href="#_Toc138932258">BlueNaaS-subcellular<span style='color:windowtext;
477 display:none;text-decoration:none'> </span><span
478 style='color:windowtext;display:none;text-decoration:none'>7</span></a></span></span></p>
479
480 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
481 href="#_Toc138932259">BluePyEfe<span style='color:windowtext;display:none;
482 text-decoration:none'>. </span><span
483 style='color:windowtext;display:none;text-decoration:none'>7</span></a></span></span></p>
484
485 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
486 href="#_Toc138932260">BluePyMM<span style='color:windowtext;display:none;
487 text-decoration:none'>... </span><span
488 style='color:windowtext;display:none;text-decoration:none'>8</span></a></span></span></p>
489
490 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
491 href="#_Toc138932261">BluePyOpt<span style='color:windowtext;display:none;
492 text-decoration:none'> </span><span
493 style='color:windowtext;display:none;text-decoration:none'>8</span></a></span></span></p>
494
495 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
496 href="#_Toc138932262">Brain Cockpit<span style='color:windowtext;display:none;
497 text-decoration:none'> </span><span
498 style='color:windowtext;display:none;text-decoration:none'>8</span></a></span></span></p>
499
500 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
501 href="#_Toc138932263">BrainScaleS<span style='color:windowtext;display:none;
502 text-decoration:none'>. </span><span
503 style='color:windowtext;display:none;text-decoration:none'>8</span></a></span></span></p>
504
505 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
506 href="#_Toc138932264">Brayns<span style='color:windowtext;display:none;
507 text-decoration:none'>. </span><span
508 style='color:windowtext;display:none;text-decoration:none'>8</span></a></span></span></p>
509
510 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
511 href="#_Toc138932265">Brion<span style='color:windowtext;display:none;
512 text-decoration:none'>. </span><span
513 style='color:windowtext;display:none;text-decoration:none'>9</span></a></span></span></p>
514
515 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
516 href="#_Toc138932266">BSB<span style='color:windowtext;display:none;text-decoration:
517 none'>. </span><span
518 style='color:windowtext;display:none;text-decoration:none'>9</span></a></span></span></p>
519
520 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
521 href="#_Toc138932267">BSP Service Account<span style='color:windowtext;
522 display:none;text-decoration:none'> </span><span
523 style='color:windowtext;display:none;text-decoration:none'>9</span></a></span></span></p>
524
525 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
526 href="#_Toc138932268">bsp-usecase-wizard<span style='color:windowtext;
527 display:none;text-decoration:none'>. </span><span
528 style='color:windowtext;display:none;text-decoration:none'>9</span></a></span></span></p>
529
530 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
531 href="#_Toc138932269">CGMD Platform<span style='color:windowtext;display:none;
532 text-decoration:none'>.. </span><span
533 style='color:windowtext;display:none;text-decoration:none'>9</span></a></span></span></p>
534
535 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
536 href="#_Toc138932270">CNS-ligands<span style='color:windowtext;display:none;
537 text-decoration:none'>. </span><span
538 style='color:windowtext;display:none;text-decoration:none'>9</span></a></span></span></p>
539
540 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
541 href="#_Toc138932271">Cobrawap<span style='color:windowtext;display:none;
542 text-decoration:none'>. </span><span
543 style='color:windowtext;display:none;text-decoration:none'>10</span></a></span></span></p>
544
545 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
546 href="#_Toc138932272">Collaboratory Bucket service<span style='color:windowtext;
547 display:none;text-decoration:none'>. </span><span
548 style='color:windowtext;display:none;text-decoration:none'>10</span></a></span></span></p>
549
550 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
551 href="#_Toc138932273">Collaboratory Drive<span style='color:windowtext;
552 display:none;text-decoration:none'>. </span><span
553 style='color:windowtext;display:none;text-decoration:none'>10</span></a></span></span></p>
554
555 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
556 href="#_Toc138932274">Collaboratory IAM<span style='color:windowtext;
557 display:none;text-decoration:none'>... </span><span
558 style='color:windowtext;display:none;text-decoration:none'>10</span></a></span></span></p>
559
560 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
561 href="#_Toc138932275">Collaboratory Lab<span style='color:windowtext;
562 display:none;text-decoration:none'>. </span><span
563 style='color:windowtext;display:none;text-decoration:none'>11</span></a></span></span></p>
564
565 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
566 href="#_Toc138932276">Collaboratory Office<span style='color:windowtext;
567 display:none;text-decoration:none'>. </span><span
568 style='color:windowtext;display:none;text-decoration:none'>11</span></a></span></span></p>
569
570 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
571 href="#_Toc138932277">Collaboratory Wiki<span style='color:windowtext;
572 display:none;text-decoration:none'> </span><span
573 style='color:windowtext;display:none;text-decoration:none'>11</span></a></span></span></p>
574
575 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
576 href="#_Toc138932278">CoreNEURON<span style='color:windowtext;display:none;
577 text-decoration:none'>.. </span><span
578 style='color:windowtext;display:none;text-decoration:none'>11</span></a></span></span></p>
579
580 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
581 href="#_Toc138932279">CxSystem2<span style='color:windowtext;display:none;
582 text-decoration:none'>. </span><span
583 style='color:windowtext;display:none;text-decoration:none'>11</span></a></span></span></p>
584
585 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
586 href="#_Toc138932280">DeepSlice<span style='color:windowtext;display:none;
587 text-decoration:none'>. </span><span
588 style='color:windowtext;display:none;text-decoration:none'>11</span></a></span></span></p>
589
590 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
591 href="#_Toc138932281">EBRAINS Ethics &amp; Society Toolkit<span
592 style='color:windowtext;display:none;text-decoration:none'> </span><span
593 style='color:windowtext;display:none;text-decoration:none'>12</span></a></span></span></p>
594
595 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
596 href="#_Toc138932282">EBRAINS Image Service<span style='color:windowtext;
597 display:none;text-decoration:none'>. </span><span
598 style='color:windowtext;display:none;text-decoration:none'>12</span></a></span></span></p>
599
600 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
601 href="#_Toc138932283">EBRAINS Knowledge Graph<span style='color:windowtext;
602 display:none;text-decoration:none'>. </span><span
603 style='color:windowtext;display:none;text-decoration:none'>12</span></a></span></span></p>
604
605 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
606 href="#_Toc138932284">EDI Toolkit<span style='color:windowtext;display:none;
607 text-decoration:none'> </span><span
608 style='color:windowtext;display:none;text-decoration:none'>12</span></a></span></span></p>
609
610 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
611 href="#_Toc138932285">eFEL<span style='color:windowtext;display:none;
612 text-decoration:none'>. </span><span
613 style='color:windowtext;display:none;text-decoration:none'>12</span></a></span></span></p>
614
615 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
616 href="#_Toc138932286">Electrophysiology Analysis Toolkit<span style='color:
617 windowtext;display:none;text-decoration:none'> </span><span
618 style='color:windowtext;display:none;text-decoration:none'>13</span></a></span></span></p>
619
620 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
621 href="#_Toc138932287">FAConstructor<span style='color:windowtext;display:none;
622 text-decoration:none'> </span><span
623 style='color:windowtext;display:none;text-decoration:none'>13</span></a></span></span></p>
624
625 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
626 href="#_Toc138932288">fairgraph<span style='color:windowtext;display:none;
627 text-decoration:none'>. </span><span
628 style='color:windowtext;display:none;text-decoration:none'>13</span></a></span></span></p>
629
630 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
631 href="#_Toc138932289">Fast sampling with neuromorphic hardware<span
632 style='color:windowtext;display:none;text-decoration:none'>. </span><span
633 style='color:windowtext;display:none;text-decoration:none'>13</span></a></span></span></p>
634
635 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
636 href="#_Toc138932290">fastPLI<span style='color:windowtext;display:none;
637 text-decoration:none'> </span><span
638 style='color:windowtext;display:none;text-decoration:none'>13</span></a></span></span></p>
639
640 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
641 href="#_Toc138932291">Feed-forward LFP-MEG estimator from mean-field models<span
642 style='color:windowtext;display:none;text-decoration:none'>. </span><span
643 style='color:windowtext;display:none;text-decoration:none'>13</span></a></span></span></p>
644
645 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
646 href="#_Toc138932292">FIL<span style='color:windowtext;display:none;text-decoration:
647 none'>. </span><span
648 style='color:windowtext;display:none;text-decoration:none'>14</span></a></span></span></p>
649
650 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
651 href="#_Toc138932293">FMRALIGN<span style='color:windowtext;display:none;
652 text-decoration:none'>.. </span><span
653 style='color:windowtext;display:none;text-decoration:none'>14</span></a></span></span></p>
654
655 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
656 href="#_Toc138932294">Foa3D<span style='color:windowtext;display:none;
657 text-decoration:none'>.. </span><span
658 style='color:windowtext;display:none;text-decoration:none'>14</span></a></span></span></p>
659
660 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
661 href="#_Toc138932295">Frites<span style='color:windowtext;display:none;
662 text-decoration:none'>. </span><span
663 style='color:windowtext;display:none;text-decoration:none'>14</span></a></span></span></p>
664
665 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
666 href="#_Toc138932296">gridspeccer<span style='color:windowtext;display:none;
667 text-decoration:none'> </span><span
668 style='color:windowtext;display:none;text-decoration:none'>14</span></a></span></span></p>
669
670 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
671 href="#_Toc138932297">Hal-Cgp<span style='color:windowtext;display:none;
672 text-decoration:none'>. </span><span
673 style='color:windowtext;display:none;text-decoration:none'>14</span></a></span></span></p>
674
675 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
676 href="#_Toc138932298">Health Data Cloud<span style='color:windowtext;
677 display:none;text-decoration:none'>. </span><span
678 style='color:windowtext;display:none;text-decoration:none'>15</span></a></span></span></p>
679
680 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
681 href="#_Toc138932299">Hodgkin-Huxley Neuron Builder<span style='color:windowtext;
682 display:none;text-decoration:none'> </span><span
683 style='color:windowtext;display:none;text-decoration:none'>15</span></a></span></span></p>
684
685 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
686 href="#_Toc138932300">HPC Job Proxy<span style='color:windowtext;display:none;
687 text-decoration:none'>. </span><span
688 style='color:windowtext;display:none;text-decoration:none'>15</span></a></span></span></p>
689
690 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
691 href="#_Toc138932301">HPC Status Monitor<span style='color:windowtext;
692 display:none;text-decoration:none'> </span><span
693 style='color:windowtext;display:none;text-decoration:none'>15</span></a></span></span></p>
694
695 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
696 href="#_Toc138932302">Human Intracerebral EEG Platform<span style='color:windowtext;
697 display:none;text-decoration:none'>.. </span><span
698 style='color:windowtext;display:none;text-decoration:none'>15</span></a></span></span></p>
699
700 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
701 href="#_Toc138932303">Hybrid MM/CG Webserver<span style='color:windowtext;
702 display:none;text-decoration:none'> </span><span
703 style='color:windowtext;display:none;text-decoration:none'>16</span></a></span></span></p>
704
705 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
706 href="#_Toc138932304">Insite<span style='color:windowtext;display:none;
707 text-decoration:none'>. </span><span
708 style='color:windowtext;display:none;text-decoration:none'>16</span></a></span></span></p>
709
710 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
711 href="#_Toc138932305">Interactive Brain Atlas Viewer<span style='color:windowtext;
712 display:none;text-decoration:none'> </span><span
713 style='color:windowtext;display:none;text-decoration:none'>16</span></a></span></span></p>
714
715 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
716 href="#_Toc138932306">JuGEx<span style='color:windowtext;display:none;
717 text-decoration:none'>. </span><span
718 style='color:windowtext;display:none;text-decoration:none'>16</span></a></span></span></p>
719
720 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
721 href="#_Toc138932307">KnowledgeSpace<span style='color:windowtext;display:none;
722 text-decoration:none'>. </span><span
723 style='color:windowtext;display:none;text-decoration:none'>16</span></a></span></span></p>
724
725 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
726 href="#_Toc138932308">L2L<span style='color:windowtext;display:none;text-decoration:
727 none'>. </span><span
728 style='color:windowtext;display:none;text-decoration:none'>17</span></a></span></span></p>
729
730 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
731 href="#_Toc138932309">Leveltlab/SpectralSegmentation<span style='color:windowtext;
732 display:none;text-decoration:none'>. </span><span
733 style='color:windowtext;display:none;text-decoration:none'>17</span></a></span></span></p>
734
735 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
736 href="#_Toc138932310">LFPy<span style='color:windowtext;display:none;
737 text-decoration:none'>. </span><span
738 style='color:windowtext;display:none;text-decoration:none'>17</span></a></span></span></p>
739
740 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
741 href="#_Toc138932311">libsonata<span style='color:windowtext;display:none;
742 text-decoration:none'>. </span><span
743 style='color:windowtext;display:none;text-decoration:none'>17</span></a></span></span></p>
744
745 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
746 href="#_Toc138932312">Live Papers<span style='color:windowtext;display:none;
747 text-decoration:none'>. </span><span
748 style='color:windowtext;display:none;text-decoration:none'>17</span></a></span></span></p>
749
750 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
751 href="#_Toc138932313">Livre<span style='color:windowtext;display:none;
752 text-decoration:none'>. </span><span
753 style='color:windowtext;display:none;text-decoration:none'>18</span></a></span></span></p>
754
755 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
756 href="#_Toc138932314">LocaliZoom<span style='color:windowtext;display:none;
757 text-decoration:none'>.. </span><span
758 style='color:windowtext;display:none;text-decoration:none'>18</span></a></span></span></p>
759
760 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
761 href="#_Toc138932315">MD-IFP<span style='color:windowtext;display:none;
762 text-decoration:none'>. </span><span
763 style='color:windowtext;display:none;text-decoration:none'>18</span></a></span></span></p>
764
765 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
766 href="#_Toc138932316">MEDUSA<span style='color:windowtext;display:none;
767 text-decoration:none'>. </span><span
768 style='color:windowtext;display:none;text-decoration:none'>18</span></a></span></span></p>
769
770 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
771 href="#_Toc138932317">MeshView<span style='color:windowtext;display:none;
772 text-decoration:none'>.. </span><span
773 style='color:windowtext;display:none;text-decoration:none'>18</span></a></span></span></p>
774
775 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
776 href="#_Toc138932318">MIP<span style='color:windowtext;display:none;text-decoration:
777 none'>. </span><span
778 style='color:windowtext;display:none;text-decoration:none'>19</span></a></span></span></p>
779
780 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
781 href="#_Toc138932319">Model Validation Service<span style='color:windowtext;
782 display:none;text-decoration:none'>. </span><span
783 style='color:windowtext;display:none;text-decoration:none'>19</span></a></span></span></p>
784
785 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
786 href="#_Toc138932320">Model Validation Test Suites<span style='color:windowtext;
787 display:none;text-decoration:none'>. </span><span
788 style='color:windowtext;display:none;text-decoration:none'>19</span></a></span></span></p>
789
790 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
791 href="#_Toc138932321">MoDEL-CNS<span style='color:windowtext;display:none;
792 text-decoration:none'>. </span><span
793 style='color:windowtext;display:none;text-decoration:none'>19</span></a></span></span></p>
794
795 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
796 href="#_Toc138932322">Modular Science<span style='color:windowtext;display:
797 none;text-decoration:none'>. </span><span
798 style='color:windowtext;display:none;text-decoration:none'>19</span></a></span></span></p>
799
800 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
801 href="#_Toc138932323">Monsteer<span style='color:windowtext;display:none;
802 text-decoration:none'> </span><span
803 style='color:windowtext;display:none;text-decoration:none'>20</span></a></span></span></p>
804
805 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
806 href="#_Toc138932324">MorphIO<span style='color:windowtext;display:none;
807 text-decoration:none'>.. </span><span
808 style='color:windowtext;display:none;text-decoration:none'>20</span></a></span></span></p>
809
810 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
811 href="#_Toc138932325">Morphology alignment tool<span style='color:windowtext;
812 display:none;text-decoration:none'> </span><span
813 style='color:windowtext;display:none;text-decoration:none'>20</span></a></span></span></p>
814
815 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
816 href="#_Toc138932326">MorphTool<span style='color:windowtext;display:none;
817 text-decoration:none'> </span><span
818 style='color:windowtext;display:none;text-decoration:none'>20</span></a></span></span></p>
819
820 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
821 href="#_Toc138932327">Multi-Brain<span style='color:windowtext;display:none;
822 text-decoration:none'>. </span><span
823 style='color:windowtext;display:none;text-decoration:none'>20</span></a></span></span></p>
824
825 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
826 href="#_Toc138932328">Multi-Image-OSD<span style='color:windowtext;display:
827 none;text-decoration:none'>.. </span><span
828 style='color:windowtext;display:none;text-decoration:none'>21</span></a></span></span></p>
829
830 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
831 href="#_Toc138932329">MUSIC<span style='color:windowtext;display:none;
832 text-decoration:none'>. </span><span
833 style='color:windowtext;display:none;text-decoration:none'>21</span></a></span></span></p>
834
835 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
836 href="#_Toc138932330">NEAT<span style='color:windowtext;display:none;
837 text-decoration:none'>. </span><span
838 style='color:windowtext;display:none;text-decoration:none'>21</span></a></span></span></p>
839
840 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
841 href="#_Toc138932331">Neo<span style='color:windowtext;display:none;text-decoration:
842 none'>. </span><span
843 style='color:windowtext;display:none;text-decoration:none'>21</span></a></span></span></p>
844
845 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
846 href="#_Toc138932332">Neo Viewer<span style='color:windowtext;display:none;
847 text-decoration:none'> </span><span
848 style='color:windowtext;display:none;text-decoration:none'>21</span></a></span></span></p>
849
850 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
851 href="#_Toc138932333">NEST Desktop<span style='color:windowtext;display:none;
852 text-decoration:none'>. </span><span
853 style='color:windowtext;display:none;text-decoration:none'>22</span></a></span></span></p>
854
855 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
856 href="#_Toc138932334">NEST Simulator<span style='color:windowtext;display:none;
857 text-decoration:none'> </span><span
858 style='color:windowtext;display:none;text-decoration:none'>22</span></a></span></span></p>
859
860 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
861 href="#_Toc138932335">NESTML<span style='color:windowtext;display:none;
862 text-decoration:none'>. </span><span
863 style='color:windowtext;display:none;text-decoration:none'>22</span></a></span></span></p>
864
865 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
866 href="#_Toc138932336">NetPyNE<span style='color:windowtext;display:none;
867 text-decoration:none'>. </span><span
868 style='color:windowtext;display:none;text-decoration:none'>22</span></a></span></span></p>
869
870 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
871 href="#_Toc138932337">NEURO-CONNECT<span style='color:windowtext;display:none;
872 text-decoration:none'>. </span><span
873 style='color:windowtext;display:none;text-decoration:none'>22</span></a></span></span></p>
874
875 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
876 href="#_Toc138932338">NeuroFeatureExtract<span style='color:windowtext;
877 display:none;text-decoration:none'> </span><span
878 style='color:windowtext;display:none;text-decoration:none'>23</span></a></span></span></p>
879
880 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
881 href="#_Toc138932339">NeurogenPy<span style='color:windowtext;display:none;
882 text-decoration:none'>. </span><span
883 style='color:windowtext;display:none;text-decoration:none'>23</span></a></span></span></p>
884
885 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
886 href="#_Toc138932340">NeuroM<span style='color:windowtext;display:none;
887 text-decoration:none'>... </span><span
888 style='color:windowtext;display:none;text-decoration:none'>23</span></a></span></span></p>
889
890 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
891 href="#_Toc138932341">Neuromorphic Computing Job Queue<span style='color:windowtext;
892 display:none;text-decoration:none'>. </span><span
893 style='color:windowtext;display:none;text-decoration:none'>23</span></a></span></span></p>
894
895 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
896 href="#_Toc138932342">Neuronize v2<span style='color:windowtext;display:none;
897 text-decoration:none'>. </span><span
898 style='color:windowtext;display:none;text-decoration:none'>23</span></a></span></span></p>
899
900 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
901 href="#_Toc138932343">NeuroR<span style='color:windowtext;display:none;
902 text-decoration:none'>. </span><span
903 style='color:windowtext;display:none;text-decoration:none'>24</span></a></span></span></p>
904
905 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
906 href="#_Toc138932344">Neurorobotics Platform<span style='color:windowtext;
907 display:none;text-decoration:none'>.. </span><span
908 style='color:windowtext;display:none;text-decoration:none'>24</span></a></span></span></p>
909
910 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
911 href="#_Toc138932345">Neurorobotics Platform Robot Designer<span
912 style='color:windowtext;display:none;text-decoration:none'> </span><span
913 style='color:windowtext;display:none;text-decoration:none'>24</span></a></span></span></p>
914
915 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
916 href="#_Toc138932346">NeuroScheme<span style='color:windowtext;display:none;
917 text-decoration:none'>. </span><span
918 style='color:windowtext;display:none;text-decoration:none'>24</span></a></span></span></p>
919
920 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
921 href="#_Toc138932347">NeuroSuites<span style='color:windowtext;display:none;
922 text-decoration:none'>. </span><span
923 style='color:windowtext;display:none;text-decoration:none'>24</span></a></span></span></p>
924
925 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
926 href="#_Toc138932348">NeuroTessMesh<span style='color:windowtext;display:none;
927 text-decoration:none'>. </span><span
928 style='color:windowtext;display:none;text-decoration:none'>25</span></a></span></span></p>
929
930 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
931 href="#_Toc138932349">NMODL Framework<span style='color:windowtext;display:
932 none;text-decoration:none'>. </span><span
933 style='color:windowtext;display:none;text-decoration:none'>25</span></a></span></span></p>
934
935 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
936 href="#_Toc138932350">NSuite<span style='color:windowtext;display:none;
937 text-decoration:none'>. </span><span
938 style='color:windowtext;display:none;text-decoration:none'>25</span></a></span></span></p>
939
940 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
941 href="#_Toc138932351">ODE-toolbox<span style='color:windowtext;display:none;
942 text-decoration:none'>. </span><span
943 style='color:windowtext;display:none;text-decoration:none'>26</span></a></span></span></p>
944
945 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
946 href="#_Toc138932352">openMINDS<span style='color:windowtext;display:none;
947 text-decoration:none'>. </span><span
948 style='color:windowtext;display:none;text-decoration:none'>26</span></a></span></span></p>
949
950 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
951 href="#_Toc138932353">openMINDS metadata for TVB-ready data<span
952 style='color:windowtext;display:none;text-decoration:none'>. </span><span
953 style='color:windowtext;display:none;text-decoration:none'>26</span></a></span></span></p>
954
955 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
956 href="#_Toc138932354">PCI<span style='color:windowtext;display:none;text-decoration:
957 none'> </span><span
958 style='color:windowtext;display:none;text-decoration:none'>26</span></a></span></span></p>
959
960 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
961 href="#_Toc138932355">PIPSA<span style='color:windowtext;display:none;
962 text-decoration:none'>. </span><span
963 style='color:windowtext;display:none;text-decoration:none'>26</span></a></span></span></p>
964
965 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
966 href="#_Toc138932356">PoSCE<span style='color:windowtext;display:none;
967 text-decoration:none'>. </span><span
968 style='color:windowtext;display:none;text-decoration:none'>26</span></a></span></span></p>
969
970 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
971 href="#_Toc138932357">Provenance API<span style='color:windowtext;display:none;
972 text-decoration:none'> </span><span
973 style='color:windowtext;display:none;text-decoration:none'>26</span></a></span></span></p>
974
975 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
976 href="#_Toc138932358">PyNN<span style='color:windowtext;display:none;
977 text-decoration:none'>.. </span><span
978 style='color:windowtext;display:none;text-decoration:none'>27</span></a></span></span></p>
979
980 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
981 href="#_Toc138932359">Pyramidal Explorer<span style='color:windowtext;
982 display:none;text-decoration:none'> </span><span
983 style='color:windowtext;display:none;text-decoration:none'>27</span></a></span></span></p>
984
985 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
986 href="#_Toc138932360">QCAlign software<span style='color:windowtext;display:
987 none;text-decoration:none'>. </span><span
988 style='color:windowtext;display:none;text-decoration:none'>27</span></a></span></span></p>
989
990 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
991 href="#_Toc138932361">QuickNII<span style='color:windowtext;display:none;
992 text-decoration:none'> </span><span
993 style='color:windowtext;display:none;text-decoration:none'>27</span></a></span></span></p>
994
995 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
996 href="#_Toc138932362">Quota Manager<span style='color:windowtext;display:none;
997 text-decoration:none'> </span><span
998 style='color:windowtext;display:none;text-decoration:none'>27</span></a></span></span></p>
999
1000 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1001 href="#_Toc138932363">RateML<span style='color:windowtext;display:none;
1002 text-decoration:none'>. </span><span
1003 style='color:windowtext;display:none;text-decoration:none'>28</span></a></span></span></p>
1004
1005 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1006 href="#_Toc138932364">Region-wise CBPP using the Julich BrainÊCytoarchitectonic
1007 Atlas<span style='color:windowtext;display:none;text-decoration:none'>. </span><span
1008 style='color:windowtext;display:none;text-decoration:none'>28</span></a></span></span></p>
1009
1010 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1011 href="#_Toc138932365">RRI Capacity Development Resources<span style='color:
1012 windowtext;display:none;text-decoration:none'>. </span><span
1013 style='color:windowtext;display:none;text-decoration:none'>28</span></a></span></span></p>
1014
1015 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1016 href="#_Toc138932366">rsHRF<span style='color:windowtext;display:none;
1017 text-decoration:none'>. </span><span
1018 style='color:windowtext;display:none;text-decoration:none'>28</span></a></span></span></p>
1019
1020 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1021 href="#_Toc138932367">RTNeuron<span style='color:windowtext;display:none;
1022 text-decoration:none'>. </span><span
1023 style='color:windowtext;display:none;text-decoration:none'>29</span></a></span></span></p>
1024
1025 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1026 href="#_Toc138932368">sbs: Spike-based Sampling<span style='color:windowtext;
1027 display:none;text-decoration:none'>. </span><span
1028 style='color:windowtext;display:none;text-decoration:none'>29</span></a></span></span></p>
1029
1030 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1031 href="#_Toc138932369">SDA 7<span style='color:windowtext;display:none;
1032 text-decoration:none'>. </span><span
1033 style='color:windowtext;display:none;text-decoration:none'>29</span></a></span></span></p>
1034
1035 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1036 href="#_Toc138932370">Shape &amp; Appearance Modelling<span style='color:windowtext;
1037 display:none;text-decoration:none'>. </span><span
1038 style='color:windowtext;display:none;text-decoration:none'>29</span></a></span></span></p>
1039
1040 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1041 href="#_Toc138932371">siibra-api<span style='color:windowtext;display:none;
1042 text-decoration:none'> </span><span
1043 style='color:windowtext;display:none;text-decoration:none'>29</span></a></span></span></p>
1044
1045 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1046 href="#_Toc138932372">siibra-explorer<span style='color:windowtext;display:
1047 none;text-decoration:none'> </span><span
1048 style='color:windowtext;display:none;text-decoration:none'>29</span></a></span></span></p>
1049
1050 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1051 href="#_Toc138932373">siibra-python<span style='color:windowtext;display:none;
1052 text-decoration:none'>. </span><span
1053 style='color:windowtext;display:none;text-decoration:none'>30</span></a></span></span></p>
1054
1055 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1056 href="#_Toc138932374">Single Cell Model (Re)builder Notebook<span
1057 style='color:windowtext;display:none;text-decoration:none'>. </span><span
1058 style='color:windowtext;display:none;text-decoration:none'>30</span></a></span></span></p>
1059
1060 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1061 href="#_Toc138932375">Slurm Plugin for Co-allocation of Compute and Data
1062 Resources<span style='color:windowtext;display:none;text-decoration:none'>. </span><span
1063 style='color:windowtext;display:none;text-decoration:none'>30</span></a></span></span></p>
1064
1065 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1066 href="#_Toc138932376">Snudda<span style='color:windowtext;display:none;
1067 text-decoration:none'>. </span><span
1068 style='color:windowtext;display:none;text-decoration:none'>30</span></a></span></span></p>
1069
1070 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1071 href="#_Toc138932377">SomaSegmenter<span style='color:windowtext;display:none;
1072 text-decoration:none'> </span><span
1073 style='color:windowtext;display:none;text-decoration:none'>30</span></a></span></span></p>
1074
1075 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1076 href="#_Toc138932378">SpiNNaker<span style='color:windowtext;display:none;
1077 text-decoration:none'> </span><span
1078 style='color:windowtext;display:none;text-decoration:none'>31</span></a></span></span></p>
1079
1080 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1081 href="#_Toc138932379">SSB toolkit<span style='color:windowtext;display:none;
1082 text-decoration:none'> </span><span
1083 style='color:windowtext;display:none;text-decoration:none'>31</span></a></span></span></p>
1084
1085 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1086 href="#_Toc138932380">Subcellular model building and calibration tool set<span
1087 style='color:windowtext;display:none;text-decoration:none'> </span><span
1088 style='color:windowtext;display:none;text-decoration:none'>31</span></a></span></span></p>
1089
1090 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1091 href="#_Toc138932381">Synaptic Events Fitting<span style='color:windowtext;
1092 display:none;text-decoration:none'>. </span><span
1093 style='color:windowtext;display:none;text-decoration:none'>31</span></a></span></span></p>
1094
1095 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1096 href="#_Toc138932382">Synaptic Plasticity Explorer<span style='color:windowtext;
1097 display:none;text-decoration:none'> </span><span
1098 style='color:windowtext;display:none;text-decoration:none'>32</span></a></span></span></p>
1099
1100 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1101 href="#_Toc138932383">Synaptic proteome database (SQLite)<span
1102 style='color:windowtext;display:none;text-decoration:none'> </span><span
1103 style='color:windowtext;display:none;text-decoration:none'>32</span></a></span></span></p>
1104
1105 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1106 href="#_Toc138932384">Synaptome.db<span style='color:windowtext;display:none;
1107 text-decoration:none'>. </span><span
1108 style='color:windowtext;display:none;text-decoration:none'>32</span></a></span></span></p>
1109
1110 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1111 href="#_Toc138932385">Tide<span style='color:windowtext;display:none;
1112 text-decoration:none'>. </span><span
1113 style='color:windowtext;display:none;text-decoration:none'>32</span></a></span></span></p>
1114
1115 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1116 href="#_Toc138932386">TVB EBRAINS<span style='color:windowtext;display:none;
1117 text-decoration:none'>. </span><span
1118 style='color:windowtext;display:none;text-decoration:none'>32</span></a></span></span></p>
1119
1120 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1121 href="#_Toc138932387">TVB Image Processing Pipeline<span style='color:windowtext;
1122 display:none;text-decoration:none'>. </span><span
1123 style='color:windowtext;display:none;text-decoration:none'>33</span></a></span></span></p>
1124
1125 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1126 href="#_Toc138932388">TVB Inversion<span style='color:windowtext;display:none;
1127 text-decoration:none'>. </span><span
1128 style='color:windowtext;display:none;text-decoration:none'>33</span></a></span></span></p>
1129
1130 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1131 href="#_Toc138932389">TVB Web App<span style='color:windowtext;display:none;
1132 text-decoration:none'>. </span><span
1133 style='color:windowtext;display:none;text-decoration:none'>33</span></a></span></span></p>
1134
1135 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1136 href="#_Toc138932390">TVB Widgets<span style='color:windowtext;display:none;
1137 text-decoration:none'>. </span><span
1138 style='color:windowtext;display:none;text-decoration:none'>33</span></a></span></span></p>
1139
1140 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1141 href="#_Toc138932391">TVB-Multiscale<span style='color:windowtext;display:none;
1142 text-decoration:none'>. </span><span
1143 style='color:windowtext;display:none;text-decoration:none'>33</span></a></span></span></p>
1144
1145 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1146 href="#_Toc138932392">VIOLA<span style='color:windowtext;display:none;
1147 text-decoration:none'>. </span><span
1148 style='color:windowtext;display:none;text-decoration:none'>34</span></a></span></span></p>
1149
1150 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1151 href="#_Toc138932393">Vishnu 1.0<span style='color:windowtext;display:none;
1152 text-decoration:none'>. </span><span
1153 style='color:windowtext;display:none;text-decoration:none'>34</span></a></span></span></p>
1154
1155 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1156 href="#_Toc138932394">ViSimpl<span style='color:windowtext;display:none;
1157 text-decoration:none'> </span><span
1158 style='color:windowtext;display:none;text-decoration:none'>34</span></a></span></span></p>
1159
1160 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1161 href="#_Toc138932395">VisuAlign<span style='color:windowtext;display:none;
1162 text-decoration:none'>. </span><span
1163 style='color:windowtext;display:none;text-decoration:none'>34</span></a></span></span></p>
1164
1165 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1166 href="#_Toc138932396">VMetaFlow<span style='color:windowtext;display:none;
1167 text-decoration:none'>.. </span><span
1168 style='color:windowtext;display:none;text-decoration:none'>34</span></a></span></span></p>
1169
1170 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1171 href="#_Toc138932397">Voluba<span style='color:windowtext;display:none;
1172 text-decoration:none'>. </span><span
1173 style='color:windowtext;display:none;text-decoration:none'>35</span></a></span></span></p>
1174
1175 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1176 href="#_Toc138932398">WebAlign<span style='color:windowtext;display:none;
1177 text-decoration:none'>. </span><span
1178 style='color:windowtext;display:none;text-decoration:none'>35</span></a></span></span></p>
1179
1180 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1181 href="#_Toc138932399">Webilastik<span style='color:windowtext;display:none;
1182 text-decoration:none'>. </span><span
1183 style='color:windowtext;display:none;text-decoration:none'>35</span></a></span></span></p>
1184
1185 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1186 href="#_Toc138932400">WebWarp<span style='color:windowtext;display:none;
1187 text-decoration:none'>. </span><span
1188 style='color:windowtext;display:none;text-decoration:none'>35</span></a></span></span></p>
1189
1190 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1191 href="#_Toc138932401">ZetaStitcher<span style='color:windowtext;display:none;
1192 text-decoration:none'> </span><span
1193 style='color:windowtext;display:none;text-decoration:none'>35</span></a></span></span></p>
1194
1195 <p class=MsoToc2><span class=MsoHyperlink><span lang=en-DE><a
1196 href="#_Toc138932402">TauRAMD<span style='color:windowtext;display:none;
1197 text-decoration:none'>.. </span><span
1198 style='color:windowtext;display:none;text-decoration:none'>36</span></a></span></span></p>
1199
1200 <p class=MsoNormal><span lang=en-DE>&nbsp;</span></p>
1201
1202 <h2><a name="_Toc138932248"><span lang=en-DE>AngoraPy</span></a></h2>
1203
1204 <p class=MsoNormal><span lang=en-DE>AngoraPy is an open-source Python library
1205 that helps neuroscientists build and train goal-driven models of the sensorimotor
1206 system. The toolbox comprises state-of-the-art machine learning techniques
1207 under the hood of an easy-to-use API. With the help of deep reinforcement
1208 learning, the connectivity required for solving complex, ecologically valid
1209 tasks, can be learned autonomously, obviating the need for hand-engineered or
1210 hypothesis-driven connectivity patterns. With AngoraPy, neuroscientists can
1211 train custom deep neural networks on custom sensorimotor tasks.</span></p>
1212
1213 <h2></h2>
1214
1215 <h2><a name="_Toc138932249"><span lang=en-DE>AnonyMI</span></a></h2>
1216
1217 <p class=MsoNormal><span lang=en-DE>AnonyMI is an MRI de-identification tool
1218 that uses 3D surface modelling in order to de-identify MRIs while retaining as
1219 much geometrical information as possible. It can be run automatically or
1220 manually, which allows precise tailoring for specific needs. AnonyMI is
1221 distributed as a plug-in of 3D Slicer, a widely used, open-source, stable and
1222 reliable image-processing software. It leverages the power of this platform for
1223 reading and saving images, which makes it applicable on almost any MRI file
1224 type, including all the most commonly used formats (e.g., DICOM, Nifti, Analyze
1225 etc.).</span></p>
1226
1227 <h2></h2>
1228
1229 <h2><a name="_Toc138932250"><span lang=en-DE>Arbor</span></a></h2>
1230
1231 <p class=MsoNormal><span lang=en-DE>Arbor is a simulation software library for
1232 neuron models with complex morphologies Ñ from single cells to large
1233 distributed networks. Developed entirely inside HBP, it enables running
1234 large-scale simulations on any HPC, including those available through EBRAINS.
1235 Arbor provides performance portability for native execution on all HPC
1236 architectures. Optimized vectorized code is generated for Intel, AMD and ARM
1237 CPUs, NVIDIA and AMD GPUs, and support will be added for new architectures as
1238 they become available. Model portability is easier due to an interface for
1239 model description independent of how Arbor represents models internally. 
1240 Interoperability with other simulation engines is enabled via API for spike
1241 exchange and the output of voltages, currents and model state.</span></p>
1242
1243 <h2></h2>
1244
1245 <h2><a name="_Toc138932251"><span lang=en-DE>Arbor GUI</span></a></h2>
1246
1247 <p class=MsoNormal><span lang=en-DE>Arbor GUI strives to be self-contained,
1248 fast and easy to use. Design morphologically detailed cells for simulation in
1249 Arbor. Load morphologies from SWC .swc, NeuroML .nml, NeuroLucida .asc. Define
1250 and highlight Arbor regions and locsets. Paint ion dynamics and bio-physical
1251 properties onto morphologies. Place spike detectors and probes. Export cable
1252 cells to Arbor's internal format (ACC) for direct simulation. Import cable
1253 cells in ACC format. This project is under active development and welcomes
1254 early feedback.</span></p>
1255
1256 <h2></h2>
1257
1258 <h2><a name="_Toc138932252"><span lang=en-DE>Bayesian Virtual Epileptic Patient
1259 (BVEP)</span></a></h2>
1260
1261 <p class=MsoNormal><span lang=en-DE>BVEP relies on the fusion of structural
1262 data of individuals, a generative model of epileptiform discharges, and the
1263 state-of-the-art probabilistic machine learning algorithms. It uses self-tuning
1264 Monte Carlo sampling algorithm, and the deep neural density estimators for
1265 reliable and efficient model-based inference at source and sensor levels data.
1266 The Bayesian framework provides an appropriate patient-specific strategy for
1267 estimating the extent of epileptogenic and propagation zones of the brain
1268 regions to improve outcome after epilepsy surgery.</span></p>
1269
1270 <h2></h2>
1271
1272 <h2><a name="_Toc138932253"><span lang=en-DE>BIDS Extension Proposal
1273 Computational Model Specifications</span></a></h2>
1274
1275 <p class=MsoNormal><span lang=en-DE>A data structure schema for neural network
1276 computational models that aims to be generically applicable to all kinds of
1277 neural network simulation software, mathematical models, computational models,
1278 and data models, but with a focus on dynamic circuit models of brain activity. </span></p>
1279
1280 <h2></h2>
1281
1282 <h2><a name="_Toc138932254"><span lang=en-DE>BioBB</span></a></h2>
1283
1284 <p class=MsoNormal><span lang=en-DE>The BioExcel Building Blocks (BioBB)
1285 software library is a collection of Python wrappers on top of popular
1286 biomolecular simulation tools. The library offers a layer of interoperability
1287 between the wrapped tools, which make them compatible and prepared to be
1288 directly interconnected to build complex biomolecular workflows. Building and
1289 sharing complex biomolecular simulation workflows just requires joining and
1290 connecting BioExcelBuilding Blocks together. Biomolecular simulation workflows
1291 built using the BioBB library are integrated in the Collaboratory Jupyter lab
1292 infrastructure, allowing the exploration of dynamics and flexibility of
1293 proteins related to the Central Nervous Systems.</span></p>
1294
1295 <h2></h2>
1296
1297 <h2><a name="_Toc138932255"><span lang=en-DE>BioExcel-CV19</span></a></h2>
1298
1299 <p class=MsoNormal><span lang=en-DE>BioExcel-CV19 is a platform designed to
1300 provide web access to atomistic-MD trajectories for macromolecules involved in
1301 the COVID-19 disease. The project is part of the open access initiatives
1302 promoted by the world-wide scientific community to share information about
1303 COVID-19 research. BioExcel-CV19 web server interface presents the resulting
1304 trajectories, with a set of quality control analyses and system information.
1305 All data produced by the project is available to download from an associated
1306 programmatic access API.</span></p>
1307
1308 <h2></h2>
1309
1310 <h2><a name="_Toc138932256"><span lang=en-DE>BioNAR</span></a></h2>
1311
1312 <p class=MsoNormal><span lang=en-DE>BioNAR combines a selection of
1313 existing R protocols for network analysis with newly designed original
1314 methodological features to support step-by-step analysis of
1315 biological/biomedical networks. BioNAR supports a pipeline approach where many
1316 networks and iterative analyses can be performed. BioNAR helps to achieve a
1317 number of network analysis goals that are difficult to achieve anywhere else,
1318 e.g., choose the optimal clustering algorithm from a range of options based on
1319 independent annotation enrichment</span><span lang=en-DE style='font-family:
1320 "Times New Roman",serif'> </span><span lang=en-DE>predict a proteins influence
1321 within and across multiple sub-complexes in the network and estimate the
1322 co-occurrence or linkage between meta-data at the network level.</span></p>
1323
1324 <h2></h2>
1325
1326 <h2><a name="_Toc138932257"><span lang=en-DE>BlueNaaS-single cell</span></a></h2>
1327
1328 <p class=MsoNormal><span lang=en-DE>BlueNaaS-SingleCell is an open-source web
1329 application. It enables users to quickly visualize single cell model
1330 morphologies in 3D or as a dendrogram. Using a simple web user interface, single
1331 cell simulations can be easily configured and launched, producing voltage
1332 traces from selected compartments.</span></p>
1333
1334 <h2></h2>
1335
1336 <h2><a name="_Toc138932258"><span lang=en-DE>BlueNaaS-subcellular</span></a></h2>
1337
1338 <p class=MsoNormal><span lang=en-DE>BlueNaaS-Subcellular is a web-based
1339 environment for creation and simulation of reaction-diffusion models. It allows
1340 the user to import, combine and simulate existing models derived from other
1341 parts of the pipeline. It is integrated with a number of solvers for
1342 reaction-diffusion systems of equations and can represent rule-based systems
1343 using BioNetGen. Additionally, it supports simulation of spatially distributed
1344 systems using STEPS (stochastic engine for pathway simulation), providing
1345 spatial stochastic and deterministic solvers for simulation of reactions and
1346 diffusion on tetrahedral meshes. It includes some visualisation tools such as a
1347 geometry viewer, a contact map and a reactivity network graph.</span></p>
1348
1349 <h2></h2>
1350
1351 <h2><a name="_Toc138932259"><span lang=en-DE>BluePyEfe</span></a></h2>
1352
1353 <p class=MsoNormal><span lang=en-DE>BluePyEfe eases the process of reading
1354 experimental recordings and extracting batches of electrical features from
1355 these recordings. It combines trace reading functions and features extraction
1356 functions from the eFel library. BluePyEfe outputs protocols and features files
1357 in the format used by BluePyOpt for neuron electrical model building.</span></p>
1358
1359 <h2></h2>
1360
1361 <h2><a name="_Toc138932260"><span lang=en-DE>BluePyMM</span></a></h2>
1362
1363 <p class=MsoNormal><span lang=en-DE>When building a network simulation,
1364 biophysically detailed electrical models (e-models) need to be tested for every
1365 morphology that is possibly used in the circuit.  With current resources,
1366 e-models are not re-optimised for every morphology in the network. In a process
1367 called Cell Model Management (MM), we test if an existing e-model matches a
1368 particular morphology 'well enough'. It takes as input a morphology release, a
1369 circuit recipe and a set of e-models, then finds all possible (morphology,
1370 e-model)-combinations (me-combos) based on e-type, m-type, and layer as
1371 described by the circuit recipe, then calculates the scores for every
1372 combination. Finally, it writes out the resulting accepted me-combos to a
1373 database and produces a report with information on the number of matches.</span></p>
1374
1375 <h2></h2>
1376
1377 <h2><a name="_Toc138932261"><span lang=en-DE>BluePyOpt</span></a></h2>
1378
1379 <p class=MsoNormal><span lang=en-DE>BluePyOpt simplifies the task of creating
1380 and sharing these optimisations, and the associated techniques and knowledge.
1381 This is achieved by abstracting the optimisation and evaluation tasks into
1382 various reusable and flexible discrete elements according to established best
1383 practices. Further, BluePyOpt provides methods for setting up both small- and
1384 large-scale optimisations on a variety of platforms, ranging from laptops to
1385 Linux clusters and cloud-based computer infrastructures.</span></p>
1386
1387 <h2></h2>
1388
1389 <h2><a name="_Toc138932262"><span lang=en-DE>Brain Cockpit</span></a></h2>
1390
1391 <p class=MsoNormal><span lang=en-DE>Brain Cockpit is a web app comprising a
1392 Typescript front-end and a Python back-end. It is meant to help explore large
1393 surface fMRI datasets projected on surface meshes and alignments computed
1394 between brains, such as those computed with Fused Unbalanced Gromov-Wasserstein
1395 (fugw) for Python.</span></p>
1396
1397 <h2></h2>
1398
1399 <h2><a name="_Toc138932263"><span lang=en-DE>BrainScaleS</span></a></h2>
1400
1401 <p class=MsoNormal><span lang=en-DE>Emulate spiking neural networks in
1402 continuous time on the BrainScaleS analog neuromorphic computing system. Models
1403 and experiments can be described in Python using the PyNN modelling language,
1404 or in hxtorch, a PyTorch-based machine-learning-friendly API.  The platform can
1405 be used interactively via the EBRAINS JupyterLab service or EBRAINS HPC</span><span
1406 lang=en-DE style='font-family:"Times New Roman",serif'> </span><span
1407 lang=en-DE>in addition, the NMPI web service provides batch-style access. The
1408 modelling APIs employ common data formats for input and output data, e.g.,
1409 neo.</span></p>
1410
1411 <h2></h2>
1412
1413 <h2><a name="_Toc138932264"><span lang=en-DE>Brayns</span></a></h2>
1414
1415 <p class=MsoNormal><span lang=en-DE>Brayns is a large-scale scientific
1416 visualization platform based on Intel OSPRAY to perform CPU Ray-tracing and
1417 uses an extension-plugin architecture. The core provides basic functionalities
1418 that can be reused and/or extended on plugins, which are independent and can be
1419 loaded or disabled at start-up. This simplifies the process of adding support
1420 for new scientific visualization use cases, without compromising the
1421 reliability of the rest of the software. Brayns counts with braynsService, a
1422 rendering backend which can be accessed over the internet and streams images to
1423 connected clients. Already-made plugins include CircuitExplorer, DTI,
1424 AtlasExplorer, CylindricCamera and MoleculeExplorer.</span></p>
1425
1426 <p class=MsoNormal></p>
1427
1428 <h2><a name="_Toc138932265"><span lang=en-DE>Brion</span></a></h2>
1429
1430 <p class=MsoNormal><span lang=en-DE>Brion is a C++ project for read and write
1431 access to Blue Brain data structures, including BlueConfig/CircuitConfig,
1432 Circuit, CompartmentReport, Mesh, Morphology, Synapse and Target files. It also
1433 offers an interface in Python.</span></p>
1434
1435 <h2></h2>
1436
1437 <h2><a name="_Toc138932266"><span lang=en-DE>BSB</span></a></h2>
1438
1439 <p class=MsoNormal><span lang=en-DE>The BSB reconstructs realistic neural
1440 circuits by placing and connecting fibres and neurons with detailed
1441 morphologies or only simplified geometrical features. Configure your model the
1442 way you need. Interfaces with several simulators (CoreNEURON, Arbor, NEST)
1443 allow simulation of the reconstructed network and investigation of the
1444 structure-function-dynamics relationships at different levels of resolution.
1445 The 'scaffold' design allows an easy model reconfiguration reflecting variants
1446 across brain regions, animal species and physio-pathological conditions without
1447 dismounting the basic network structure. The BSB provides effortless parallel
1448 computing both for the reconstruction and simulation phase.</span></p>
1449
1450 <h2></h2>
1451
1452 <h2><a name="_Toc138932267"><span lang=en-DE>BSP Service Account</span></a></h2>
1453
1454 <p class=MsoNormal><span lang=en-DE>The BSP Service Account is a rest API
1455 service that allows developers to submit user's jobs on HPC systems and
1456 retrieve results using the EBRAINS authentication, even if users don't have a
1457 personal account on the available HPC facilities.</span></p>
1458
1459 <h2></h2>
1460
1461 <h2><a name="_Toc138932268"><span lang=en-DE>bsp-usecase-wizard</span></a></h2>
1462
1463 <p class=MsoNormal><span lang=en-DE>The CLS interactive workflows and use cases
1464 application guides the users through the resolution of realistic scientific
1465 problems. They are implemented as either front-end or full stack web
1466 applications or Python-based Jupyter Notebooks that allow the user to
1467 interactively build, reconstruct or simulate data-driven brain models and
1468 perform data analysis visualisation. Web applications are freely accessible and
1469 only require authentication to EBRAINS when specific actions are required
1470 (e.g., submitting a simulation job to an HBP HPC system). Jupyter Notebooks are
1471 cloned to the lab.ebrains.eu platform and require authentication via an EBRAINS
1472 account.</span></p>
1473
1474 <h2></h2>
1475
1476 <h2><a name="_Toc138932269"><span lang=en-DE>CGMD Platform</span></a></h2>
1477
1478 <p class=MsoNormal><span lang=en-DE>Recent advances in CGMD simulations have
1479 allowed longer and larger molecular dynamics simulations of biological
1480 macromolecules and their interactions. The CGMD platform is dedicated to the
1481 preparation, running, and analysis of CGMD simulations, and built on a
1482 completely revisited version of the Martini coarsE gRained MembrAne proteIn
1483 Dynamics (MERMAID) web server. In its current version, the platform expands the
1484 existing implementation of the Martini force field for membrane proteins to
1485 also allow the simulation of soluble proteins using the Martini and SIRAH force
1486 fields. Moreover, it offers an automated protocol for carrying out the
1487 backmapping of the coarse-grained description of the system into an atomistic
1488 one.</span></p>
1489
1490 <h2></h2>
1491
1492 <h2><a name="_Toc138932270"><span lang=en-DE>CNS-ligands</span></a></h2>
1493
1494 <p class=MsoNormal><span lang=en-DE>The project is part of the Parameter
1495 generation and mechanistic studies of neuronal cascades using multi-scale
1496 molecular simulations of the HBP. CNS conformers are generated using a powerful
1497 multilevel strategy that combines a low-level (LL) method for sampling the
1498 conformational minima and high-level (HL) ab initio calculations for estimating
1499 their relative stability. CNS database presents the results in a graphical user
1500 interface, displaying small molecule properties, analyses and generated 3D
1501 conformers. All data produced by the project is available to download.</span></p>
1502
1503 <h2></h2>
1504
1505 <h2><a name="_Toc138932271"><span lang=en-DE>Cobrawap</span></a></h2>
1506
1507 <p class=MsoNormal><span lang=en-DE>Cobrawap is an adaptable and reusable
1508 software tool to study wave-like activity propagation in the cortex. It allows for
1509 the integration of heterogeneous data from different measurement techniques and
1510 simulations through alignment to common wave descriptions. Cobrawap provides an
1511 extendable collection of processing and analysis methods that can be combined
1512 and adapted to specific input data and research applications. It enables broad
1513 and rigorous comparisons of wave characteristics across multiple datasets,
1514 model calibration and validation applications, and its modular building blocks
1515 may serve to construct related analysis pipelines.</span></p>
1516
1517 <h2></h2>
1518
1519 <h2><a name="_Toc138932272"><span lang=en-DE>Collaboratory Bucket service</span></a></h2>
1520
1521 <p class=MsoNormal><span lang=en-DE>The Bucket service provides object storage
1522 to EBRAINS users without them having to request an account on Fenix (the
1523 EBRAINS infrastructure provider) and storage resources there. This is the
1524 recommended storage for datasets that are shared by data providers, on the
1525 condition that these do not contain sensitive personal data. For sharing
1526 datasets with personal data, users should refer to the Health Data Cloud. The
1527 Bucket service is better suited for larger files that are usually not edited,
1528 such as datasets and videos. For Docker images, users should refer to the
1529 EBRAINS Docker registry. For smaller files and files which are more likely to
1530 be edited, users should consider the Collaboratory Drive service.</span></p>
1531
1532 <h2></h2>
1533
1534 <h2><a name="_Toc138932273"><span lang=en-DE>Collaboratory Drive</span></a></h2>
1535
1536 <p class=MsoNormal><span lang=en-DE>The Drive service offers users cloud
1537 storage space for their files in each collab (workspace). The Drive storage is
1538 mounted in the Collaboratory Lab to provide persistent storage (as opposed to
1539 the Lab containers which are deleted after a few hours of inactivity). All
1540 files are under version control. The Drive is intended for smaller files
1541 (currently limited to 1 GB) that change more often. Users must not save files
1542 containing personal information in the Drive (i.e. data of living human subjects).
1543 The Drive is also integrated with the Collaboratory Office service to offer
1544 easy collaborative editing of Office files online.</span></p>
1545
1546 <h2></h2>
1547
1548 <h2><a name="_Toc138932274"><span lang=en-DE>Collaboratory IAM</span></a></h2>
1549
1550 <p class=MsoNormal><span lang=en-DE>The EBRAINS Collaboratory IAM allows the
1551 developers of different EBRAINS services to benefit from a single sign-on
1552 solution. End users will benefit from a seamless experience, whereby they can
1553 access a specific service and have direct access from it to resources in other
1554 EBRAINS services without re-authentication. For the developer, it is a good way
1555 for separating concerns and offloading much of the identification and
1556 authentication to a central service. The EBRAINS IAM is recognised as an
1557 identity provider at Fenix supercomputing sites. The IAM service also provides
1558 three ways of managing groups of users: Units, Groups and Teams.</span></p>
1559
1560 <h2></h2>
1561
1562 <h2><a name="_Toc138932275"><span lang=en-DE>Collaboratory Lab</span></a></h2>
1563
1564 <p class=MsoNormal><span lang=en-DE>The Collaboratory Lab provides EBRAINS
1565 users with a user-friendly programming environment for reproducible science.
1566 EBRAINS tools are pre-installed for the user. The latest release is selected by
1567 default, but users can choose to run an older release to reuse an older
1568 notebook, or try out the very latest features in the weekly experimental
1569 deployment. Official releases are produced by EBRAINS every few months. End
1570 users do not need to build and install the tools, and, more importantly, they
1571 do not need to resolve dependency conflicts among tools as this has been
1572 handled for them.</span></p>
1573
1574 <h2></h2>
1575
1576 <h2><a name="_Toc138932276"><span lang=en-DE>Collaboratory Office</span></a></h2>
1577
1578 <p class=MsoNormal><span lang=en-DE>With the Office service, EBRAINS users can
1579 collaboratively edit Office documents (Word, PowerPoint or Excel) with most of
1580 the key features of the MS Office tools. It uses the open standard formats
1581 .docx, .pptx and .xlsx so that files can alternately be edited in the
1582 Collaboratory Office service and in other compatible tools including the MS
1583 Office suite.</span></p>
1584
1585 <h2></h2>
1586
1587 <h2><a name="_Toc138932277"><span lang=en-DE>Collaboratory Wiki</span></a></h2>
1588
1589 <p class=MsoNormal><span lang=en-DE>The Wiki service offers the user-friendly
1590 wiki functionality for publishing web content. It acts as central user
1591 interface and API to access the other Collaboratory services. EBRAINS
1592 developers can integrate their services as app which can be instantiated by
1593 users in their collabs. The Wiki is a good place to create tutorials and
1594 documentation and it is also the place to publish your work on the internet if
1595 you choose to do so.</span></p>
1596
1597 <h2></h2>
1598
1599 <h2><a name="_Toc138932278"><span lang=en-DE>CoreNEURON</span></a></h2>
1600
1601 <p class=MsoNormal><span lang=en-DE>In order to adapt NEURON to evolving
1602 computer architectures, the compute engine of the NEURON simulator was
1603 extracted and optimised as a library called CoreNEURON. CoreNEURON is a compute
1604 engine library for the NEURON simulator optimised for both memory usage and
1605 computational speed on modern CPU/GPU architectures. Some of its key goals are
1606 to: 1) Efficiently simulate large network models, 2) Support execution on
1607 accelerators such as GPU, 3) Support optimisations such as vectorisation and
1608 cache-efficient memory layout.</span></p>
1609
1610 <h2></h2>
1611
1612 <h2><a name="_Toc138932279"><span lang=en-DE>CxSystem2</span></a></h2>
1613
1614 <p class=MsoNormal><span lang=en-DE>CxSystem is a cerebral cortex simulation
1615 framework, which operates on personal computers. The CxSystem enables easy
1616 testing and build-up of diverse models at single-cell resolution and it is
1617 implemented on the top of the Python-based Brain2 simulator. The CxSystem
1618 interface comprises two csv files - one for anatomy and technical details, the
1619 other for physiological parameters.</span></p>
1620
1621 <h2></h2>
1622
1623 <h2><a name="_Toc138932280"><span lang=en-DE>DeepSlice</span></a></h2>
1624
1625 <p class=MsoNormal><span lang=en-DE>DeepSlice is a deep neural network that
1626 aligns histological sections of mouse brain to the Allen Mouse Brain Common
1627 Coordinate Framework, adjusting for anterior-posterior position, angle,
1628 rotation and scale. At present, DeepSlice only works with tissue cut in the
1629 coronal plane, although future versions will be compatible with sagittal and
1630 horizontal sections.</span></p>
1631
1632 <h2></h2>
1633
1634 <h2><a name="_Toc138932281"><span lang=en-DE>EBRAINS Ethics &amp; Society
1635 Toolkit</span></a></h2>
1636
1637 <p class=MsoNormal><span lang=en-DE>The aim of the toolkit is to offer
1638 researchers who carry out cross-disciplinary brain research a possibility to
1639 engage with ethical and societal issues within brain health and brain disease.
1640 The user is presented with short introductory texts, scenario-based dilemmas,
1641 animations and quizzes, all tailored to specific areas of ethics and society in
1642 a setting of brain research. All exercises are reflection-oriented, with an
1643 interactive approach to inspire users to incorporate these reflections into
1644 their own research practices. Moreover, it is possible to gain further
1645 knowledge by utilising the links for relevant publications, teaching modules
1646 and the EBRAINS Community Space.</span></p>
1647
1648 <h2></h2>
1649
1650 <h2><a name="_Toc138932282"><span lang=en-DE>EBRAINS Image Service</span></a></h2>
1651
1652 <p class=MsoNormal><span lang=en-DE>The Image Service takes large 2D (and 3D)
1653 images and preprocesses them to generate small 2D tiles (or 3D chunks).
1654 Applications consuming image data (viewers or other) can then access regions of
1655 interest by downloading a few tiles rather than the entire large image. Tiles
1656 are also generated at coarser resolutions to support zooming out of large
1657 images. The service supports multiple input image formats. The serving of tiles
1658 to apps is provided by the Collaboratory Bucket (based on OpenStack Swift
1659 object storage), which provides significantly higher network bandwidth than
1660 could be provided by any VM.</span></p>
1661
1662 <h2></h2>
1663
1664 <h2><a name="_Toc138932283"><span lang=en-DE>EBRAINS Knowledge Graph</span></a></h2>
1665
1666 <p class=MsoNormal><span lang=en-DE>The EBRAINS Knowledge Graph (KG) is the
1667 metadata management system of the EBRAINS Data and Knowledge services. It
1668 provides fundamental services and tools to make neuroscientific data, models
1669 and related software FAIR. The KG Editor and API (incl. Python SDKs) allow to
1670 annotate scientific resources in a semantically correct way. The KG Search
1671 exposes the research information via an intuitive user interface and makes the
1672 information publicly available to any user. For advanced users, the KG Query
1673 Builder and KG Core API provide the necessary means to execute detailed queries
1674 on the graph database whilst enforcing fine-grained permission control.</span></p>
1675
1676 <h2></h2>
1677
1678 <h2><a name="_Toc138932284"><span lang=en-DE>EDI Toolkit</span></a></h2>
1679
1680 <p class=MsoNormal><span lang=en-DE>The EDI Toolkit supports projects in
1681 integrating EDI in their research content and as guiding principles for team
1682 collaboration. It is designed for everyday usage by offering: Basic information
1683 Guiding questions, templates and tools to design responsible research Quick
1684 checklists, guidance for suitable structures and standard procedures Measures
1685 to support EDI-based leadership, fair teams and events</span></p>
1686
1687 <h2></h2>
1688
1689 <h2><a name="_Toc138932285"><span lang=en-DE>eFEL</span></a></h2>
1690
1691 <p class=MsoNormal><span lang=en-DE>eFEL allows neuroscientists to
1692 automatically extract features from time series data recorded from neurons
1693 (both in vitro and in silico). Examples include  action potential width and
1694 amplitude in voltage traces recorded during whole-cell patch clamp experiments.
1695 Users can provide a set of traces and select which  features to calculate. The
1696 library will then extract the requested features and return the values.</span></p>
1697
1698 <h2></h2>
1699
1700 <h2><a name="_Toc138932286"><span lang=en-DE>Electrophysiology Analysis Toolkit</span></a></h2>
1701
1702 <p class=MsoNormal><span lang=en-DE>The Electrophysiology Analysis Toolkit
1703 (Elephant) is a Python library that provides a modular framework for the
1704 analysis of experimental and simulated neuronal activity data, such as spike
1705 trains, local field potentials, and intracellular data. Elephant builds on the
1706 Neo data model to facilitate usability, enable interoperability, and support
1707 data from dozens of file formats and network simulation tools. Its analysis
1708 functions are continuously validated against reference implementations and
1709 reports in the literature. Visualisations of analysis results are made
1710 available via the Viziphant companion library. Elephant aims to act as a
1711 platform for sharing analysis methods across the field.</span></p>
1712
1713 <h2></h2>
1714
1715 <h2><a name="_Toc138932287"><span lang=en-DE>FAConstructor</span></a></h2>
1716
1717 <p class=MsoNormal><span lang=en-DE>FAConstructor allows a simple and effective
1718 creation of fibre models based on mathematical functions or the manual input of
1719 data points. Models are visualised during creation and can be interacted with
1720 by translating them in 3D space.</span></p>
1721
1722 <h2></h2>
1723
1724 <h2><a name="_Toc138932288"><span lang=en-DE>fairgraph</span></a></h2>
1725
1726 <p class=MsoNormal><span lang=en-DE>fairgraph is a Python library for working
1727 with metadata in the EBRAINS Knowledge Graph (KG), with a particular focus on
1728 data reuse, although it is also useful in registering and curating metadata.
1729 The library represents metadata nodes (also known as openMINDS instances) from
1730 the KG as Python objects. fairgraph supports querying the KG, following links
1731 in the graph, downloading data and metadata, and creating new nodes in the KG.
1732 It builds on openMINDS and on the KG Core Python library.</span></p>
1733
1734 <h2></h2>
1735
1736 <h2><a name="_Toc138932289"><span lang=en-DE>Fast sampling with neuromorphic
1737 hardware</span></a></h2>
1738
1739 <p class=MsoNormal><span lang=en-DE>Compared to conventional neural networks,
1740 physical model devices offer a fast, efficient, and inherently parallel
1741 substrate capable of related forms of Markov chain Monte Carlo sampling. This
1742 software suite enables the use of a neuromorphic chip to replicate the
1743 properties of quantum systems through spike-based sampling.</span></p>
1744
1745 <h2></h2>
1746
1747 <h2><a name="_Toc138932290"><span lang=en-DE>fastPLI</span></a></h2>
1748
1749 <p class=MsoNormal><span lang=en-DE>fastPLI is an open-source toolbox based on
1750 Python and C++ for modelling myelinated axons, i.e., nerve fibres, and
1751 simulating the results of measurement of fibre orientations with a polarisation
1752 microscope using 3D-PLI. The fastPLI package includes the following modules:
1753 nerve fibre modelling, simulation, and analysis. All computationally intensive
1754 calculations are optimised either with Numba on the Python side or with
1755 multithreading C++ algorithms, which can be accessed via pybind11 inside the
1756 Python package. Additionally, the simulation module supports the Message
1757 Passing Interface (MPI) to facilitate the simulation of very large volumes on
1758 multiple computer nodes.</span></p>
1759
1760 <h2></h2>
1761
1762 <h2><a name="_Toc138932291"><span lang=en-DE>Feed-forward LFP-MEG estimator
1763 from mean-field models</span></a></h2>
1764
1765 <p class=MsoNormal><span lang=en-DE>This tool was developed to calculate the
1766 local field potentials (LFP) and magnetoencephalogram (MEG) signals generated
1767 by a population of neurons described by a mean-field model. The calculation of
1768 LFP is done via a kernel method based on unitary LFP's (the LFP generated by a
1769 single axon) which was recently introduced for spiking-networks simulations and
1770 that we adapt here for mean-field models. The calculation of the magnetic field
1771 is based on current-dipole and volume-conductor models, where the secondary
1772 currents (due to the conducting extracellular medium) are estimated using the
1773 LFP calculated via the kernel method and where the effects of
1774 medium-inhomogeneities are incorporated.</span></p>
1775
1776 <h2></h2>
1777
1778 <h2><a name="_Toc138932292"><span lang=en-DE>FIL</span></a></h2>
1779
1780 <p class=MsoNormal><span lang=en-DE>This is a scheme for training and applying
1781 the FIL framework. Some functionality from SPM12 is required for handling
1782 images. After training, labelling a new image is relatively fast because
1783 optimising the latent variables can be formulated within a scheme similar to a recurrent
1784 Residual Network (ResNet).</span></p>
1785
1786 <h2></h2>
1787
1788 <h2><a name="_Toc138932293"><span lang=en-DE>FMRALIGN</span></a></h2>
1789
1790 <p class=MsoNormal><span lang=en-DE>This library is meant to be a light-weight
1791 Python library that handles functional alignment tasks (also known as
1792 hyperalignment). It is compatible with and inspired by Nilearn. Alternative
1793 implementations of these ideas can be found in the pymvpa or brainiak packages.</span></p>
1794
1795 <h2></h2>
1796
1797 <h2><a name="_Toc138932294"><span lang=en-DE>Foa3D</span></a></h2>
1798
1799 <p class=MsoNormal><span lang=en-DE>Foa3D is a tool for multiscale nerve fibre
1800 enhancement and orientation analysis in high-resolution volume images acquired
1801 by two-photon scanning or light-sheet fluorescence microscopy, exploiting the
1802 brain tissue autofluorescence or exogenous myelin stains. Its image processing
1803 pipeline is built around a 3D Frangi filter that enables the enhancement of
1804 fibre structures of varying diameters, and the generation of accurate 3D
1805 orientation maps in both grey and white matter. Foa3D features the computation
1806 of multiscale orientation distribution functions that facilitate the comparison
1807 with orientations assessed via 3D-PLI or 3D PS-OCT, and the validation of
1808 mesoscale dMRI-based connectivity information.</span></p>
1809
1810 <h2></h2>
1811
1812 <h2><a name="_Toc138932295"><span lang=en-DE>Frites</span></a></h2>
1813
1814 <p class=MsoNormal><span lang=en-DE>Frites allows the characterisation of
1815 task-related cognitive brain networks. Neural correlates of cognitive functions
1816 can be extracted both at the single brain area (or channel) and network level.
1817 The toolbox includes time-resolved directed (e.g., Granger causality) and
1818 undirected (e.g., Mutual Information) functional connectivity metrics. In
1819 addition, it includes cluster-based and permutation-based statistical methods
1820 for single-subject and group-level inference.</span></p>
1821
1822 <h2></h2>
1823
1824 <h2><a name="_Toc138932296"><span lang=en-DE>gridspeccer</span></a></h2>
1825
1826 <p class=MsoNormal><span lang=en-DE>Plotting tool to make plotting with many
1827 subfigures easier, especially for publications. After installation, gridspeccer
1828 can be used from the command line to create plots.</span></p>
1829
1830 <h2></h2>
1831
1832 <h2><a name="_Toc138932297"><span lang=en-DE>Hal-Cgp</span></a></h2>
1833
1834 <p class=MsoNormal><span lang=en-DE>Hal-Cgp is an extensible pure Python
1835 library implementing Cgp to represent, mutate and evaluate populations of
1836 individuals encoding symbolic expressions targeting applications with
1837 computationally expensive fitness evaluations. It supports the translation from
1838 a CGP genotype, a two-dimensional Cartesian graph, into the corresponding
1839 phenotype, a computational graph implementing a particular mathematical expression.
1840 These computational graphs can be exported as pure Python functions, in a
1841 NumPy-compatible format, SymPy expressions or PyTorch modules. The library
1842 implements a mu + lambda evolution strategy to evolve a population of
1843 individuals to optimise an objective function.</span></p>
1844
1845 <h2></h2>
1846
1847 <h2><a name="_Toc138932298"><span lang=en-DE>Health Data Cloud</span></a></h2>
1848
1849 <p class=MsoNormal><span lang=en-DE>The Health Data Cloud (HDC) provides
1850 EBRAINS services for sensitive data as a federated research data ecosystem that
1851 enables scientists across Europe and beyond to collect, process and share
1852 sensitive data in compliance with EU General Data Protection Regulations
1853 (GDPR). The HDC is a federation of interoperable nodes. Nodes share a common
1854 system architecture based on CharitŽ Virtual Research Environment (VRE),
1855 enabling research consortia to manage and process data, and making data
1856 discoverable and sharable via the EBRAINS Knowledge Graph.</span></p>
1857
1858 <p class=MsoNormal></p>
1859
1860 <p class=MsoNormal><a name="_Toc138932299"><span class=Heading2Char><span
1861 lang=en-DE style='font-size:14.0pt;line-height:120%'>Hodgkin-Huxley Neuron
1862 Builder</span></span></a></p>
1863
1864 <p class=MsoNormal><span lang=en-DE>The Hodgkin-Huxley Neuron Builder is a
1865 web-application that allows users to interactively go through an entire NEURON
1866 model building pipeline of individual biophysically detailed cells. 2. Model
1867 parameter optimisation via HPC systems. 3. In silico experiments using the
1868 optimised model cell.  </span></p>
1869
1870 <h2></h2>
1871
1872 <h2><a name="_Toc138932300"><span lang=en-DE>HPC Job Proxy</span></a></h2>
1873
1874 <p class=MsoNormal><span lang=en-DE>The HPC Job Proxy provides a simplified way
1875 for EBRAINS service providers to launch jobs on Fenix supercomputers on behalf
1876 of EBRAINS end users. The proxy offers a wrapper over the Unicore service which
1877 adds logging, access to stdout/stderr/status, verification of user quota, and
1878 updating of user quota at the end of the job.</span></p>
1879
1880 <h2></h2>
1881
1882 <h2><a name="_Toc138932301"><span lang=en-DE>HPC Status Monitor</span></a></h2>
1883
1884 <p class=MsoNormal><span lang=en-DE>The HPC Status Monitor allows a real-time
1885 check of the availability status of the HPC Systems accessible from HBP tools
1886 and services and provides an instant snapshot of the resource quotas available
1887 to individual users on each system.</span></p>
1888
1889 <h2></h2>
1890
1891 <h2><a name="_Toc138932302"><span lang=en-DE>Human Intracerebral EEG Platform</span></a></h2>
1892
1893 <p class=MsoNormal><span lang=en-DE>The HIP is an open-source platform designed
1894 for collecting, managing, analysing and sharing multi-scale iEEG data at an
1895 international level. Its mission is to assist clinicians and researchers in
1896 improving research capabilities by simplifying iEEG data analysis and
1897 interpretation. The HIP integrates different software, modules and services
1898 necessary for investigating spatio-temporal dynamics of neural processes in a
1899 secure and optimised fashion. The interface is browser-based and allows
1900 selecting sets of tools according to specific research needs.</span></p>
1901
1902 <h2></h2>
1903
1904 <h2><a name="_Toc138932303"><span lang=en-DE>Hybrid MM/CG Webserver</span></a></h2>
1905
1906 <p class=MsoNormal><span lang=en-DE>MM/CG simulations help predict ligand poses
1907 in hGPCRs  for pharmacological applications. This approach allows for the
1908 description of the ligand, the binding cavity and the surrounding water
1909 molecules at atomistic resolution, while coarse-graining the rest of the
1910 receptor. The webserver automatises and speeds up the simulation set-up of
1911 hGPCR/ligand complexes. It also allows for equilibration of the systems, either
1912 fully automatically or interactively. The results are visualised online,
1913 helping the user identify possible issues and modify the set-up parameters.
1914 This framework allows for the automatic preparation and running of hybrid
1915 molecular dynamics simulations of molecules and their cognate receptors.</span></p>
1916
1917 <h2></h2>
1918
1919 <h2><a name="_Toc138932304"><span lang=en-DE>Insite</span></a></h2>
1920
1921 <p class=MsoNormal><span lang=en-DE>Insite enables users to access data via the
1922 in transit paradigm for NEST, TVB and Arbor simulations. Compared to the
1923 traditional approach of offline processing, in transit paradigms allow
1924 accessing of data while the simulation runs. This is especially useful for
1925 simulations that produce large amounts of data and are running for a long time.
1926 In transit allows the user to access only parts of the data and prevents the
1927 need for storing all data. It also allows the user early insights into the data
1928 even before the simulation finishes. Insite provides an easy-to-use and
1929 easy-to-integrate architecture to enable in transit features in other tools.</span></p>
1930
1931 <h2></h2>
1932
1933 <h2><a name="_Toc138932305"><span lang=en-DE>Interactive Brain Atlas Viewer</span></a></h2>
1934
1935 <p class=MsoNormal><span lang=en-DE>The Interactive Brain Atlas Viewer provides
1936 various kinds of interactive visualisations for multi-modal brain and head
1937 image data: different parcellations, degrees of transparency and overlays. The
1938 Viewer provides the following functions and supports data from the following
1939 sources: EEG, white matter tracts, MRI and PET 3D volumes, 2D slices,
1940 intracranial electrodes, brain activity, multiscale brain network models,
1941 supplementary information for brain regions and functional brain networks in
1942 multiple languages. It comes as a web app, mobile app and desktop app.</span></p>
1943
1944 <h2></h2>
1945
1946 <h2><a name="_Toc138932306"><span lang=en-DE>JuGEx</span></a></h2>
1947
1948 <p class=MsoNormal><span lang=en-DE>Decoding the chain from genes to cognition
1949 requires detailed insights into how areas with specific gene activities and
1950 microanatomical architectures contribute to brain function and dysfunction. The
1951 Allen Human Brain Atlas contains regional gene expression data, while the
1952 Julich Brain Atlas, which can be accessed via siibra, offers 3D
1953 cytoarchitectonic maps reflecting the interindividual variability. JuGEx offers
1954 an integrated framework that combines the analytical benefits of both
1955 repositories towards a multilevel brain atlas of adult humans. JuGEx is a new
1956 method for integrating tissue transcriptome and cytoarchitectonic segregation.</span></p>
1957
1958 <h2></h2>
1959
1960 <h2><a name="_Toc138932307"><span lang=en-DE>KnowledgeSpace</span></a></h2>
1961
1962 <p class=MsoNormal><span lang=en-DE>KnowledgeSpace (KS) is a globally-used,
1963 data-driven encyclopaedia and search engine for the neuroscience community. As
1964 an encyclopaedia, KS provides curated definitions of brain research concepts
1965 found in different neuroscience community ontologies, Wikipedia and
1966 dictionaries. The dataset discovery in KS makes research datasets across many
1967 large-scale brain initiatives universally accessible and useful. It also
1968 promotes FAIR data principles that will help data publishers to follow best
1969 practices for data storage and publication. As more and more data publishers
1970 follow data standards like OpenMINDS or DATS, the quality of data discovery
1971 through KS will improve. The related publications are also curated from PubMed
1972 and linked to the concepts in KS to provide an improved search capability.</span></p>
1973
1974 <h2></h2>
1975
1976 <h2><a name="_Toc138932308"><span lang=en-DE>L2L</span></a></h2>
1977
1978 <p class=MsoNormal><span lang=en-DE>L2L is an easy-to-use and flexible
1979 framework to perform parameter and hyper-parameter space exploration of
1980 mathematical models on HPC infrastructure. L2L is an implementation of the
1981 learning-to-learn concept written in Python. This open-source software allows
1982 several instances of an optimisation target to be executed with different
1983 parameters in an massively parallel fashion on HPC. L2L provides a set of
1984 built-in optimiser algorithms, which make adaptive and efficient exploration of
1985 parameter spaces possible. Different from other optimisation toolboxes, L2L
1986 provides maximum flexibility for the way the optimisation target can be
1987 executed.</span></p>
1988
1989 <h2></h2>
1990
1991 <h2><a name="_Toc138932309"><span lang=en-DE>Leveltlab/SpectralSegmentation</span></a></h2>
1992
1993 <p class=MsoNormal><span lang=en-DE>SpecSeg is a toolbox that segments neurons
1994 and neurites in chronic calcium imaging datasets based on low-frequency
1995 cross-spectral power. The pipeline includes a graphical user interface to edit
1996 the automatically extracted ROIs, to add new ones or delete ROIs by further
1997 constraining their properties.</span></p>
1998
1999 <h2></h2>
2000
2001 <h2><a name="_Toc138932310"><span lang=en-DE>LFPy</span></a></h2>
2002
2003 <p class=MsoNormal><span lang=en-DE>LFPy is an open-source Python module linking
2004 simulated neural activity with measurable brain signals. This is done by
2005 enabling calculation of brain signals from neural activity simulated with
2006 multi-compartment neuron models (single cells or networks). LFPy can be used to
2007 simulate brain signals like extracellular action potentials, local field
2008 potentials (LFP), and in vitro MEA recordings, as well as ECoG, EEG, and MEG
2009 signals. LFPy is well-integrated with the NEURON simulator and can, through
2010 LFPykit, also be used with other simulators like Arbor. Through the recently
2011 developed extensions hybridLFPy and LFPykernels, LFPy can also be used to
2012 calculate brain signals directly from point-neuron network models or
2013 population-based models.</span></p>
2014
2015 <h2></h2>
2016
2017 <h2><a name="_Toc138932311"><span lang=en-DE>libsonata</span></a></h2>
2018
2019 <p class=MsoNormal><span lang=en-DE>libsonata allows circuit and simulation
2020 config loading, node set materialisation, and access to node and edge
2021 populations in an efficient manner. It is generally a read-only library, but
2022 support for writing edge indices has been added.</span></p>
2023
2024 <h2></h2>
2025
2026 <h2><a name="_Toc138932312"><span lang=en-DE>Live Papers</span></a></h2>
2027
2028 <p class=MsoNormal><span lang=en-DE>EBRAINS Live Papers are structured and
2029 interactive documents that complement published scientific articles. Live
2030 Papers feature integrated tools and services that allow users to download,
2031 visualise or simulate data, models and results presented in the corresponding
2032 publications: Build interactive documents to showcase your data and the
2033 simulation or data analysis code used in your research. Easily link to
2034 resources in community databases such as EBRAINS, NeuroMorpho.org, ModelDB, and
2035 Allen Brain Atlas. Embedded, interactive visualisation of electrophysiology
2036 data and neuronal reconstructions. Launch EBRAINS simulation tools to explore
2037 single neuron models in your browser. Share live papers pre-publication with
2038 anonymous reviewers during peer review of your manuscript. Explore already
2039 published live papers, or develop your own live paper with our authoring tool.</span></p>
2040
2041 <h2></h2>
2042
2043 <h2><a name="_Toc138932313"><span lang=en-DE>Livre</span></a></h2>
2044
2045 <p class=MsoNormal><span lang=en-DE>Livre is an out-of-core, multi-node,
2046 multi-GPU, OpenGL volume rendering engine to visualise large volumetric
2047 datasets. It provides the following major features to facilitate rendering of
2048 large volumetric datasets: Visualisation of pre-processed UVF format volume
2049 datasets. Real-time voxelisation of different data sources (surface meshes, BBP
2050 morphologies, local field potentials, etc.) through the use of plugins.
2051 Multi-node, multi-GPU rendering (only sort-first rendering).</span></p>
2052
2053 <h2></h2>
2054
2055 <h2><a name="_Toc138932314"><span lang=en-DE>LocaliZoom</span></a></h2>
2056
2057 <p class=MsoNormal><span lang=en-DE>Pan-and-zoom type viewer displaying image
2058 series with overlaid atlas delineations. LocaliZoom is a pan-and-zoom type
2059 viewer displaying high-resolution image series coupled with overlaid atlas
2060 delineations. It has three operating modes: Display series with atlas overlay.
2061 Both linear and nonlinear alignments are supported (created with QuickNII or
2062 VisuAlign). Create or edit nonlinear alignments. Create markup which can be
2063 exported as MeshView point clouds or to Excel for further numerical analysis.</span></p>
2064
2065 <h2></h2>
2066
2067 <h2><a name="_Toc138932315"><span lang=en-DE>MD-IFP</span></a></h2>
2068
2069 <p class=MsoNormal><span lang=en-DE>MD-IFP is a python workflow for the
2070 generation and analysis of protein-ligand interaction fingerprints from
2071 molecular dynamics trajectories. If used for the analysis of Random
2072 Acceleration Molecular Dynamics (RAMD) trajectories, it can help to investigate
2073 dissociation mechanisms by characterising transition states as well as the
2074 determinants and hot-spots for dissociation. As such, the combined use of
2075 RAMD and MD-IFP may assist the early stages of drug discovery campaigns for the
2076 design of new molecules or ligand optimisation.</span></p>
2077
2078 <h2></h2>
2079
2080 <h2><a name="_Toc138932316"><span lang=en-DE>MEDUSA</span></a></h2>
2081
2082 <p class=MsoNormal><span lang=en-DE>Using a spherical meshing technique that
2083 decomposes each microstructural item into a set of overlapping spheres, the
2084 phantom construction is made very fast while reliably avoiding the collisions
2085 between items in the scene. This novel method is applied to the construction of
2086 human brain white matter microstructural components, namely axonal fibers,
2087 oligodendrocytes and astrocytes. The algorithm reaches high values of packing
2088 density and angular dispersion for the axonal fibers, even in the case of
2089 multiple white matter fiber populations and enables the construction of complex
2090 biomimicking geometries including myelinated axons, beaded axons and glial
2091 cells.</span></p>
2092
2093 <h2></h2>
2094
2095 <h2><a name="_Toc138932317"><span lang=en-DE>MeshView</span></a></h2>
2096
2097 <p class=MsoNormal><span lang=en-DE>MeshView is a web application for real-time
2098 3D display of surface mesh data representing structural parcellations from
2099 volumetric atlases, such as the Waxholm Space atlas of the Sprague Dawley rat
2100 brain. Key features: orbiting view with toggleable opaque/transparent/hidden
2101 parcellation meshes, rendering user-defined cut surface as if meshes were solid
2102 objects, rendering point-clouds (simple type-in, or loaded from JSON). The
2103 coordinate system is compatible with QuickNII.</span></p>
2104
2105 <h2></h2>
2106
2107 <h2><a name="_Toc138932318"><span lang=en-DE>MIP</span></a></h2>
2108
2109 <p class=MsoNormal><span lang=en-DE>MIP is an open-source platform enabling
2110 federated data analysis in a secure environment for centres involved in
2111 collaborative initiatives. It allows users to initiate or join disease-oriented
2112 federations with the aim of analysing large-scale distributed clinical
2113 datasets. For each federation, users can create specific data models based on
2114 well-accepted common data elements, approved by all participating centres. MIP
2115 experts assist in creating the data models and facilitate coordination and
2116 communication among centres. They provide advice and support for data curation,
2117 harmonisation, and anonymisation, as well as data governance, especially with
2118 regards to Data Sharing Agreements and general ethical considerations.</span></p>
2119
2120 <h2></h2>
2121
2122 <h2><a name="_Toc138932319"><span lang=en-DE>Model Validation Service</span></a></h2>
2123
2124 <p class=MsoNormal><span lang=en-DE>The HBP/EBRAINS Model Validation Service is
2125 a set of tools for performing and tracking validation of models with respect to
2126 experimental data. It consists of a web API, a GUI client (the Model Catalog
2127 app) and a Python client. The service enables users to store, query, view and
2128 download: (i) model descriptions/scripts, (ii) validation test definitions and
2129 (iii) validation results. In a typical workflow, users will find models and
2130 validation tests by searching the Model Catalog (or upload their own), run the
2131 tests using the Python client in a Jupyter notebook, with simulations running
2132 locally or on HPC, and then upload the results.</span></p>
2133
2134 <h2></h2>
2135
2136 <h2><a name="_Toc138932320"><span lang=en-DE>Model Validation Test Suites</span></a></h2>
2137
2138 <p class=MsoNormal><span lang=en-DE>As part of the HBP/EBRAINS model validation
2139 framework, we provide a Python Software Development Kit (SDK) for model
2140 validation, which provides: (i) validation test definitions and (ii) interface
2141 definitions intended to decouple model validation from the details of model
2142 implementation. This more formal approach to model validation aims to make it
2143 quicker and easier to compare models, to provide validation test suites for
2144 models and to develop new validations of existing models. The SDK consists of a
2145 collection of Python packages all using the sciunit framework: HippoUnit,
2146 MorphoUnit, NetworkUnit, BasalUnit, CerebUnit, eFELUnit, HippoNetworkUnit.</span></p>
2147
2148 <h2></h2>
2149
2150 <h2><a name="_Toc138932321"><span lang=en-DE>MoDEL-CNS</span></a></h2>
2151
2152 <p class=MsoNormal><span lang=en-DE>MoDEL-CNS is a database and server platform
2153 designed to provide web access to atomistic MD trajectories for relevant signal
2154 transduction proteins. The project is part of the service for providing
2155 molecular simulation-based predictions for systems neurobiology of the HBP.
2156 MoDEL-CNS expands the MD Extended Library database of atomistic MD trajectories
2157 with proteins involved in CNS processes, including membrane proteins. MoDEL-CNS
2158 web server interface presents the resulting trajectories, analyses and protein
2159 properties. All data produced are available to download.</span></p>
2160
2161 <h2></h2>
2162
2163 <h2><a name="_Toc138932322"><span lang=en-DE>Modular Science</span></a></h2>
2164
2165 <p class=MsoNormal><span lang=en-DE>Modular Science is a middleware that
2166 provides robust deployment of complex multi-application workflows. It contains
2167 protocols and interfaces for multi-scale co-simulation workloads on
2168 high-performance computers and local hardware. It allows for synchronisation
2169 and coordination of individual components and contains dedicated and
2170 parallelised modules for data transformations between scales. Modular Science
2171 offers insight into both the system level and the individual subsystems to
2172 steer the execution, to monitor resource usage, and system health &amp; status
2173 with small overheads on performance. Modular Science comes with a number of
2174 neuroscience co-simulation use cases including NEST-TVB, NEST-Arbor, LFPy and neurorobotics.</span></p>
2175
2176 <h2></h2>
2177
2178 <h2><a name="_Toc138932323"><span lang=en-DE>Monsteer</span></a></h2>
2179
2180 <p class=MsoNormal><span lang=en-DE>Monsteer is a library for interactive
2181 supercomputing in the neuroscience domain. It facilitates the coupling of
2182 running simulations (currently NEST) with interactive visualization and
2183 analysis applications. Monsteer supports streaming of simulation data to
2184 clients (currently limited to spikes) as well as control of the simulator from
2185 the clients (also known as computational steering). Monsteer's main components
2186 are a C++ library, a MUSIC-based application and Python helpers.</span></p>
2187
2188 <h2></h2>
2189
2190 <h2><a name="_Toc138932324"><span lang=en-DE>MorphIO</span></a></h2>
2191
2192 <p class=MsoNormal><span lang=en-DE>MorphIO is a library for reading and
2193 writing neuron morphology files. It supports the following formats: SWC, ASC
2194 (also known as neurolucida), H5. There are two APIs: mutable, for creating or
2195 editing morphologies, and immutable, for read-only operations. Both are
2196 represented in C++ and Python. Extended formats include glia, mitochondria and
2197 endoplasmic reticulum.</span></p>
2198
2199 <h2></h2>
2200
2201 <h2><a name="_Toc138932325"><span lang=en-DE>Morphology alignment tool</span></a></h2>
2202
2203 <p class=MsoNormal><span lang=en-DE>Starting with serial sections of a brain in
2204 which a complete single morphology has been labelled, the pieces of neurite
2205 (axons/dendrites) in each section are traced with Neurolucida or similar
2206 microscope-attached software. The slices are then aligned, first using an
2207 automated algorithm that tries to find matching pieces in adjacent sections
2208 (Python script), and second using a GUI-driven tool (web-based, JavaScript).
2209 Finally, the pieces are stitched into a complete neuron (Python script). The
2210 neuron and tissue volume are then registered to one of the EBRAINS-supported
2211 reference templates (Python script). The web-based tool can also be used to align
2212 slices without a neuron being present.</span></p>
2213
2214 <h2></h2>
2215
2216 <h2><a name="_Toc138932326"><span lang=en-DE>MorphTool</span></a></h2>
2217
2218 <p class=MsoNormal><span lang=en-DE>MorphTool is a python toolkit designed for
2219 editing morphological skeletons of cell reconstructions. It has been developed
2220 to provide helper programmes that perform simple tasks such as morphology
2221 diffing, file conversion, soma area calculation, skeleton simplification,
2222 process resampling, morphology repair and spatial transformations. It allows
2223 neuroscientists to curate and manipulate morphological reconstruction and
2224 correct morphological artifacts due to the manual reconstruction process.</span></p>
2225
2226 <h2></h2>
2227
2228 <h2><a name="_Toc138932327"><span lang=en-DE>Multi-Brain</span></a></h2>
2229
2230 <p class=MsoNormal><span lang=en-DE>The Multi-Brain (MB) model has the
2231 general aim of integrating a number of disparate image analysis components
2232 within a single unified generative modelling framework. Its objective is to
2233 achieve diffeomorphic alignment of a wide variety of medical image modalities
2234 into a common anatomical space. This involves the ability to construct a
2235 &quot;tissue probability template&quot; from a population of scans
2236 through group-wise alignment. The MB model has been shown to provide accurate
2237 modelling of the intensity distributions of different imaging modalities.</span></p>
2238
2239 <h2></h2>
2240
2241 <h2><a name="_Toc138932328"><span lang=en-DE>Multi-Image-OSD</span></a></h2>
2242
2243 <p class=MsoNormal><span lang=en-DE>It has browser-based classic pan and zoom
2244 capabilities. A collection of images can be displayed as a filmstrip (Filmstrip
2245 Mode) or as a table (Collection Mode) with adjustable number of rows and
2246 columns. The tool supports keyboard or/and mouse navigation options, as well as
2247 touch devices. Utilising the open standard Deep Zoom Image (DZI) format, it is
2248 able to efficiently visualise very large brain images in the gigapixel range,
2249 allowing to zoom from common, display-sized overview resolutions down to the
2250 microscopic resolution without downloading the underlying, very large image
2251 dataset.</span></p>
2252
2253 <h2></h2>
2254
2255 <h2><a name="_Toc138932329"><span lang=en-DE>MUSIC</span></a></h2>
2256
2257 <p class=MsoNormal><span lang=en-DE>MUSIC is a communication framework in the
2258 domain of computational neuroscience and neuromorphic computing which enables
2259 co-simulations, where components of a model are simulated by different
2260 simulators or hardware. It consists of an API and C++ library which can be
2261 linked into existing software with minor modifications. MUSIC enables the
2262 communication of neuronal spike events, continuous values and text messages
2263 while hiding the complexity of data distribution over ranks, as well as
2264 scheduling of communication in the face of loops. MUSIC is light-weight with a
2265 simple API.</span></p>
2266
2267 <h2></h2>
2268
2269 <h2><a name="_Toc138932330"><span lang=en-DE>NEAT</span></a></h2>
2270
2271 <p class=MsoNormal><span lang=en-DE>NEAT allows for the convenient definition
2272 of morphological neuron models. These models can be simulated through an
2273 interface with the NEURON simulator or analysed with two classical methods: (i)
2274 the separation-of-variables method to obtain impedance kernels as a
2275 superposition of exponentials and (ii) Koch's method to compute impedances with
2276 linearised ion channels analytically in the frequency domain. NEAT also
2277 implements the neural evaluation tree framework and an associated C++ simulator
2278 to analyse sub-unit independence. Finally, NEAT implements a new method to
2279 simplify morphological neuron models into models with few compartments, which
2280 can also be simulated with NEURON.</span></p>
2281
2282 <h2></h2>
2283
2284 <h2><a name="_Toc138932331"><span lang=en-DE>Neo</span></a></h2>
2285
2286 <p class=MsoNormal><span lang=en-DE>Neo implements a hierarchical data model
2287 well adapted to intracellular and extracellular electrophysiology and EEG data.
2288 It improves interoperability between Python tools for analysing, visualising
2289 and generating electrophysiology data by providing a common, shared object
2290 model. It reads a wide range of neurophysiology file formats, including Spike2,
2291 NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt and Igor Pro and writes
2292 to open formats such as NWB and NIX. Neo objects behave just like normal NumPy
2293 arrays, but with additional metadata, checks for dimensional consistency and
2294 automatic unit conversion. Neo has been endorsed as a community standard by the
2295 International Neuroinformatics Coordinating Facility (INCF).</span></p>
2296
2297 <h2></h2>
2298
2299 <h2><a name="_Toc138932332"><span lang=en-DE>Neo Viewer</span></a></h2>
2300
2301 <p class=MsoNormal><span lang=en-DE>Neo Viewer consists of a REST-API and a
2302 Javascript component that can be embedded in any web page. Electrophysiology
2303 traces can be zoomed, scrolled and saved as images. Individual points can be
2304 measured off the graphs. Neo Viewer can visualise data from most of the
2305 widely-used file formats in neurophysiology, including community standards such
2306 as NWB.</span></p>
2307
2308 <h2></h2>
2309
2310 <h2><a name="_Toc138932333"><span lang=en-DE>NEST Desktop</span></a></h2>
2311
2312 <p class=MsoNormal><span lang=en-DE>NEST Desktop comprises of GUI components
2313 for creating and configuring network models, running simulations, and
2314 visualising and analysing simulation results. NEST Desktop allows students to
2315 explore important concepts in computational neuroscience without the need to
2316 first learn a simulator control language. This is done by offering a
2317 server-side NEST simulator, which can also be installed as a package together
2318 with a web server providing NEST Desktop as visual front-end. Besides local
2319 installations, distributed setups can be installed, and direct use through
2320 EBRAINS is possible. NEST Desktop has also been used as a modelling front-end
2321 of the Neurorobotics Platform.</span></p>
2322
2323 <h2></h2>
2324
2325 <h2><a name="_Toc138932334"><span lang=en-DE>NEST Simulator</span></a></h2>
2326
2327 <p class=MsoNormal><span lang=en-DE>NEST is used in computational neuroscience
2328 to model and study behaviour of large networks of neurons. The models describe
2329 single neuron and synapse behaviour and their connections. Different mechanisms
2330 of plasticity can be used to investigate artificial learning and help to shed
2331 light on the fundamental principles of how the brain works. NEST offers
2332 convenient and efficient commands to define and connect large networks, ranging
2333 from algorithmically determined connections to data-driven connectivity. Create
2334 connections between neurons using numerous synapse models from STDP to gap
2335 junctions.</span></p>
2336
2337 <h2></h2>
2338
2339 <h2><a name="_Toc138932335"><span lang=en-DE>NESTML</span></a></h2>
2340
2341 <p class=MsoNormal><span lang=en-DE>NESTML is a domain-specific language for
2342 neuron and synapse models. These dynamical models can be used in simulations of
2343 brain activity on several platforms, in particular NEST Simulator. NESTML
2344 combines an easy to understand, yet powerful syntax with good simulation
2345 performance by means of code generation (C++ for NEST Simulator), but flexibly
2346 supports other simulation engines including neuromorphic hardware.</span></p>
2347
2348 <h2></h2>
2349
2350 <h2><a name="_Toc138932336"><span lang=en-DE>NetPyNE</span></a></h2>
2351
2352 <p class=MsoNormal><span lang=en-DE>NetPyNE provides programmatic and graphical
2353 interfaces to develop data-driven multiscale brain neural circuit models using
2354 Python and NEURON. Users can define models using a standardised
2355 JSON-compatible, rule-based, declarative format. Based on these specifications,
2356 NetPyNE will generate the network in CoreNEURON, enabling users to run
2357 parallel simulations, optimise and explore network parameters through automated
2358 batch runs, and use built-in functions for visualisation and analysis (e.g.,
2359 generate connectivity matrices, voltage traces, spike raster plots, local field
2360 potentials and information theoretic measures). NetPyNE also facilitates model
2361 sharing by exporting and importing standardised formats: NeuroML and SONATA.</span></p>
2362
2363 <h2></h2>
2364
2365 <h2><a name="_Toc138932337"><span lang=en-DE>NEURO-CONNECT</span></a></h2>
2366
2367 <p class=MsoNormal><span lang=en-DE>The NEURO-CONNECT platform provides
2368 functions to integrate multimodal brain imaging information in a unifying
2369 feature space. Thus, Surface Based Morphometry (SBM), Functional Magnetic
2370 Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI) can be combined and
2371 visualised at the whole-brain scale. Moreover, multiple brain atlases are
2372 aligned to match research outcomes to neuroanatomical entities. The datasets
2373 are appended with openMINDS metadata and thus enable integrative data analysis
2374 and machine learning.</span></p>
2375
2376 <h2></h2>
2377
2378 <h2><a name="_Toc138932338"><span lang=en-DE>NeuroFeatureExtract</span></a></h2>
2379
2380 <p class=MsoNormal><span lang=en-DE>The NeuroFeatureExtract is a web
2381 application that allows the users to extract an ensemble of
2382 electrophysiological properties from voltage traces recorded upon electrical
2383 stimulation of neuronal cells. The main outcome of the application is the
2384 generation of two files Ð features.json and protocol.json Ð that can be used
2385 for later analysis and model parameter optimisations via the Hodgkin-Huxley
2386 Neuron Builder application.</span></p>
2387
2388 <h2></h2>
2389
2390 <h2><a name="_Toc138932339"><span lang=en-DE>NeurogenPy</span></a></h2>
2391
2392 <p class=MsoNormal><span lang=en-DE>NeurogenPy is a Python package for working
2393 with Bayesian networks. It is focused on the analysis of gene expression data
2394 and learning of gene regulatory networks, modelled as Bayesian networks. For
2395 that reason, at the moment, only the Gaussian and fully discrete cases are
2396 supported. The package provides different structure learning algorithms,
2397 parameters estimation and input/output formats. For some of them, already
2398 existing implementations have been used, with bnlearn, pgmpy, networkx and
2399 igraph being the most relevant used packages. This project has been conceived
2400 to be included as a plugin in the EBRAINS interactive atlas viewer, but it may
2401 be used for other purposes.</span></p>
2402
2403 <h2></h2>
2404
2405 <h2><a name="_Toc138932340"><span lang=en-DE>NeuroM</span></a></h2>
2406
2407 <p class=MsoNormal><span lang=en-DE>NeuroM is a Python toolkit for the analysis
2408 and processing of neuron morphologies. It allows the extraction of various
2409 information about morphologies, e.g., the segment lengths of a morphology via
2410 the segment_lengths feature. More than 50 features that can be extracted.</span></p>
2411
2412 <h2></h2>
2413
2414 <h2><a name="_Toc138932341"><span lang=en-DE>Neuromorphic Computing Job Queue</span></a></h2>
2415
2416 <p class=MsoNormal><span lang=en-DE>The Neuromorphic Computing Job Queue allows
2417 users to run simulations/emulations on the SpiNNaker and BrainScaleS systems by
2418 submitting a PyNN script and associated job configuration information to a
2419 central queue. The system consists of a web API, a GUI client (the Job Manager
2420 app) and a Python client. Users can submit scripts stored locally on their own
2421 machine, in a Git repository, in the KG, or in EBRAINS Collaboratory storage
2422 (Drive/Bucket). Users can track the progress of their job, and view and/or
2423 download the results, log files, and provenance information.</span></p>
2424
2425 <h2></h2>
2426
2427 <h2><a name="_Toc138932342"><span lang=en-DE>Neuronize v2</span></a></h2>
2428
2429 <p class=MsoNormal><span lang=en-DE>Neuronize v2 has been developed to generate
2430 a connected neural 3D mesh. If the input is a neuron tracing, it generates a 3D
2431 mesh from it, including the shape of the soma. If the input is data extracted
2432 with Imaris Filament Tracer (a set of unconnected meshes of a neuron),
2433 Neuronize v2 generates a single connected 3D mesh of the whole neuron (also
2434 generating the soma) and provides its neural tracing, which can then be
2435 imported into tools such as Neurolucida, facilitating the interoperability of
2436 two of the most widely used proprietary tools.</span></p>
2437
2438 <h2></h2>
2439
2440 <h2><a name="_Toc138932343"><span lang=en-DE>NeuroR</span></a></h2>
2441
2442 <p class=MsoNormal><span lang=en-DE>NeuroR is a collection of tools to repair
2443 morphologies.  This includes cut plane detection, sanitisation (removing
2444 unifurcations, invalid soma counts, short segments) and 'unravelling': the
2445 action of 'stretching' the cell that has been shrunk due to  the dehydratation
2446 caused by the slicing.</span></p>
2447
2448 <h2></h2>
2449
2450 <h2><a name="_Toc138932344"><span lang=en-DE>Neurorobotics Platform</span></a></h2>
2451
2452 <p class=MsoNormal><span lang=en-DE>The Neurorobotics Platform (NRP) is an
2453 integrative simulation framework that enables in silico experimentation and
2454 embodiment of brain models inside virtual agents interacting with realistic
2455 simulated environments. Entirely Open Source, it offers a browser-based
2456 graphical user interface for online access. It can be installed locally (Docker
2457 or source install). It can be interfaced with multiple spike-based neuromorphic
2458 chips (SpiNNaker, Intel Loihi). You can download and install the NRP locally
2459 for maximum experimental convenience or access it online in order to leverage
2460 the HBP High Performance Computing infrastructure for large-scale experiments.</span></p>
2461
2462 <h2></h2>
2463
2464 <h2><a name="_Toc138932345"><span lang=en-DE>Neurorobotics Platform Robot
2465 Designer</span></a></h2>
2466
2467 <p class=MsoNormal><span lang=en-DE>The Robot Designer is a plugin for the 3D
2468 modeling suite Blender that enables researchers to design morphologies for
2469 simulation experiments in, particularly but not restricted to, the
2470 Neurorobotics Platform. This plugin helps researchers design and parameterize
2471 models with a Graphical User Interface, simplifying and speeding up the design
2472 process.cess. It includes design capabilities for musculoskeletal bodies as
2473 well as robotic systems, fostering not only the understanding of biological
2474 motions and enabling better robot designs, but also enabling true Neurorobotic
2475 experiments that consist of biomimetic models such as tendon-driven robots or a
2476 transition between biology and technology.</span></p>
2477
2478 <h2></h2>
2479
2480 <h2><a name="_Toc138932346"><span lang=en-DE>NeuroScheme</span></a></h2>
2481
2482 <p class=MsoNormal><span lang=en-DE>NeuroScheme uses schematic
2483 representations, such as icons and glyphs, to encode attributes of neural
2484 structures (neurons, columns, layers, populations, etc.), alleviating problems
2485 with displaying, navigating and analysing large datasets. It manages
2486 hierarchically organised neural structures</span><span lang=en-DE
2487 style='font-family:"Times New Roman",serif'> </span><span lang=en-DE>users can
2488 navigate through the levels of the hierarchy and hone in on and explore the
2489 data at their desired level of detail. NeuroScheme has currently two built-in
2490 &quot;domains&quot;, which specify entities, attributes and
2491 relationships used for specific use cases: the 'cortex' domain, designed for
2492 navigating and analysing cerebral cortex structures</span><span lang=en-DE
2493 style='font-family:"Times New Roman",serif'> </span><span lang=en-DE>and the
2494 'congen' domain, used to define the properties of cells and connections, create
2495 circuits of neurons and build populations.</span></p>
2496
2497 <h2></h2>
2498
2499 <h2><a name="_Toc138932347"><span lang=en-DE>NeuroSuites</span></a></h2>
2500
2501 <p class=MsoNormal><span lang=en-DE>NeuroSuites is a web-based platform
2502 designed to handle large-scale, high-dimensional data in the field of
2503 neuroscience. It offers neuroscience-oriented applications and tools for data
2504 analysis, machine learning and visualisation, while also providing
2505 general-purpose tools for data scientists in other research fields. NeuroSuites
2506 requires no software installation and runs on the backend of a server, making
2507 it accessible from various devices. The platform's main strengths include its
2508 defined architecture, ability to handle complex neuroscience data and the
2509 variety of available tools.</span></p>
2510
2511 <h2></h2>
2512
2513 <h2><a name="_Toc138932348"><span lang=en-DE>NeuroTessMesh</span></a></h2>
2514
2515 <p class=MsoNormal><span lang=en-DE>NeuroTessMesh takes morphological tracings
2516 of cells acquired by neuroscientists and generates 3D models that approximate
2517 the neuronal membrane. The resolution of the models can be adapted at the time
2518 of visualisation. You can colour-code different parts of a morphology,
2519 differentiating relevant morphological variables or even neuronal activity.
2520 NeuroTessMesh copes with many of the problems associated with the visualisation
2521 of neural circuits consisting of large numbers of cells. It facilitates the
2522 recovery and visualisation of the 3D geometry of cells included in databases,
2523 such as NeuroMorpho, and allows to approximate missing information such as the
2524 soma's morphology.</span></p>
2525
2526 <h2></h2>
2527
2528 <h2><a name="_Toc138932349"><span lang=en-DE>NMODL Framework</span></a></h2>
2529
2530 <p class=MsoNormal><span lang=en-DE>NMODL Framework is designed with
2531 modern compiler and code generation techniques. It provides modular tools for
2532 parsing, analysing and transforming NMODL it provides an easy to use, high
2533 level Python API</span><span lang=en-DE style='font-family:"Times New Roman",serif'>
2534 </span><span lang=en-DE> it generates optimised code for modern compute architectures
2535 including CPUs and GPUs</span><span lang=en-DE style='font-family:"Times New Roman",serif'>
2536 </span><span lang=en-DE> it provides flexibility to implement new simulator
2537 backends and it supports full NMODL specification.</span></p>
2538
2539 <h2></h2>
2540
2541 <h2><a name="_Toc138932350"><span lang=en-DE>NSuite</span></a></h2>
2542
2543 <p class=MsoNormal><span lang=en-DE>NSuite is a framework for maintaining and
2544 running benchmarks and validation tests for multi-compartment neural network
2545 simulations on HPC systems. NSuite automates the process of building simulation
2546 engines, and running benchmarks and validation tests. NSuite is specifically
2547 designed to allow easy deployment on HPC systems in testing workflows, such as
2548 benchmark-driven development or continuous integration. The development of
2549 NSuite has been driven by the need (1) for a definitive resource for comparing
2550 performance and correctness of simulation engines on HPC systems, (2) to verify
2551 the performance and correctness of individual simulation engines as they change
2552 over time and (3) to test that changes to an HPC system do not cause
2553 performance or correctness regressions in simulation engines. The framework
2554 currently supports the simulation engines Arbor, NEURON, and CoreNeuron, while
2555 allowing other simulation engines to be added.</span></p>
2556
2557 <p class=MsoNormal></p>
2558
2559 <p class=MsoNormal><span lang=en-DE>Nutil</span></p>
2560
2561 <p class=MsoNormal><span lang=en-DE>Nutil is a pre- and post-processing toolbox
2562 that enables analysis of large collections of histological images of rodent
2563 brain sections. The software is open source and has both a graphical user
2564 interface for specifying the input and output parameters and a command-line
2565 execution option for batch processing. Nutil includes a transformation tool for
2566 automated scaling, rotation, mirroring and renaming of image files, a file
2567 format converter, a simple resize tool and a post-processing method for
2568 quantifying and localising labelled features based on a reference atlas of the
2569 brain (mouse or rat). The quantification method requires input from customised
2570 brain atlas maps generated with the QuickNII software, and segmentations
2571 generated with ilastik or another image analysis tool. The output from Nutil
2572 include csv reports, 3D point cloud coordinate files and atlas map images
2573 superimposed with colour-coded objects.</span></p>
2574
2575 <h2></h2>
2576
2577 <h2><a name="_Toc138932351"><span lang=en-DE>ODE-toolbox</span></a></h2>
2578
2579 <p class=MsoNormal><span lang=en-DE>ODE-toolbox is a Python package that
2580 assists in solver benchmarking, and recommends solvers on the basis of a set of
2581 user-configurable heuristics. For all dynamical equations that admit an
2582 analytic solution, ODE-toolbox generates propagator matrices that allow the
2583 solution to be calculated at machine precision. For all others, first-order
2584 update expressions are returned based on the Jacobian matrix. In addition to
2585 continuous dynamics, discrete events can be used to model instantaneous changes
2586 in system state, such as a neuronal action potential. These can be generated by
2587 the system under test as well as applied as external stimuli, making
2588 ODE-toolbox particularly well-suited for applications in computational
2589 neuroscience.</span></p>
2590
2591 <h2></h2>
2592
2593 <h2><a name="_Toc138932352"><span lang=en-DE>openMINDS</span></a></h2>
2594
2595 <p class=MsoNormal><span lang=en-DE>openMINDS is composed of: (i) integrated
2596 metadata models adoptable by any graph database system (GDBS), (ii) a set of
2597 libraries of serviceable metadata instances with external resource references
2598 for local and global knowledge integration, and (iii) supportive tooling for
2599 handling the metadata models and instances. Moreover, the framework provides
2600 machine-readable mappings to other standardisation efforts (e.g., schema.org).
2601 With this, openMINDS is a unique and powerful metadata framework for flexible
2602 knowledge integration within and beyond any GDBS.</span></p>
2603
2604 <h2></h2>
2605
2606 <h2><a name="_Toc138932353"><span lang=en-DE>openMINDS metadata for TVB-ready
2607 data</span></a></h2>
2608
2609 <p class=MsoNormal><span lang=en-DE>Jupyter Python notebook with code and
2610 commentaries for creating openMINDS metadata version 1.0 in JSON-LD format for
2611 ingestion of TVB-ready data in EBRAINS Knowledge Graph.</span></p>
2612
2613 <h2></h2>
2614
2615 <h2><a name="_Toc138932354"><span lang=en-DE>PCI</span></a></h2>
2616
2617 <p class=MsoNormal><span lang=en-DE>The notebook allows the computation of the
2618 PCI Lempel-Ziv and PCI state transitions. In order to run the examples, a wake
2619 and sleep data set needs to be provided in the Python-MNE format.</span></p>
2620
2621 <h2></h2>
2622
2623 <h2><a name="_Toc138932355"><span lang=en-DE>PIPSA</span></a></h2>
2624
2625 <p class=MsoNormal><span lang=en-DE>PIPSA enables the comparison of the
2626 electrostatic interaction properties of proteins. It permits the classification
2627 of proteins according to their interaction properties. PIPSA may assist in
2628 function assignment, the estimation of binding properties and enzyme kinetic
2629 parameters.</span></p>
2630
2631 <h2></h2>
2632
2633 <h2><a name="_Toc138932356"><span lang=en-DE>PoSCE</span></a></h2>
2634
2635 <p class=MsoNormal><span lang=en-DE>PoSCE is a functional connectivity
2636 estimator of fMRI time-series. It relies on the Riemannian geometry of
2637 covariances and integrates prior knowledge of covariance distribution over a
2638 population.</span></p>
2639
2640 <h2></h2>
2641
2642 <h2><a name="_Toc138932357"><span lang=en-DE>Provenance API</span></a></h2>
2643
2644 <p class=MsoNormal><span lang=en-DE>The EBRAINS Provenance API is a web service
2645 to facilitate working with computational provenance metadata. Metadata are
2646 stored in the EBRAINS Knowledge Graph (KG) using openMINDS schemas. The
2647 Provenance API provides a somewhat simplified interface compared to accessing
2648 the KG directly and performs checks of metadata consistency. The service covers
2649 workflows involving simulation, data analysis, visualisation, optimisation,
2650 data movement and model validation.</span></p>
2651
2652 <h2></h2>
2653
2654 <h2><a name="_Toc138932358"><span lang=en-DE>PyNN</span></a></h2>
2655
2656 <p class=MsoNormal><span lang=en-DE>A model description written with the PyNN
2657 API and the Python programming language runs on any simulator that PyNN
2658 supports (currently NEURON, NEST and Brian 2) as well as on the BrainScaleS
2659 and SpiNNaker neuromorphic hardware systems. PyNN provides a library of
2660 standard neuron, synapse and synaptic plasticity models, verified to work the
2661 same on different simulators. PyNN also provides commonly used connectivity
2662 algorithms (e.g. all-to-all, random, distance-dependent, small-world) but makes
2663 it easy to provide your own connectivity in a simulator-independent way. PyNN
2664 transparently supports distributed simulations using MPI.</span></p>
2665
2666 <h2></h2>
2667
2668 <h2><a name="_Toc138932359"><span lang=en-DE>Pyramidal Explorer</span></a></h2>
2669
2670 <p class=MsoNormal><span lang=en-DE>PyramidalExplorer is a tool to
2671 interactively explore and reveal the detailed organisation of the microanatomy
2672 of pyramidal neurons with functionally related models. Possible regional
2673 differences in the pyramidal cell architecture can be interactively discovered
2674 by combining quantitative morphological information about the structure of the
2675 cell with implemented functional models. The key contribution of this tool is the
2676 morpho-functional oriented design,  allowing the user to navigate within the 3D
2677 dataset, filter and perform content-based retrieval operations to find the 
2678 spines that are alike and dissimilar within the neuron, according to particular
2679 morphological or functional variables.</span></p>
2680
2681 <h2></h2>
2682
2683 <h2><a name="_Toc138932360"><span lang=en-DE>QCAlign software</span></a></h2>
2684
2685 <p class=MsoNormal><span lang=en-DE>The QUINT workflow enables spatial analysis
2686 of labelling in series of brain sections from mouse and rat based on
2687 registration to a reference brain atlas. The QCAlign software supports the use
2688 of QUINT for high-throughput studies by providing information about: 1. The
2689 quality of the section images used as input to the QUINT workflow. 2. The
2690 quality of the atlas registration performed in the QUINT workflow. 3. QCAlign
2691 also makes it easier for the user to explore the atlas hierarchy and decide on
2692 a customised hierarchy level to use for the investigation</span></p>
2693
2694 <h2></h2>
2695
2696 <h2><a name="_Toc138932361"><span lang=en-DE>QuickNII</span></a></h2>
2697
2698 <p class=MsoNormal><span lang=en-DE>QuickNII is a tool for user-guided affine
2699 registration (anchoring) of 2D experimental image data, typically high
2700 resolution microscopic images, to 3D atlas reference space, facilitating data
2701 integration through standardised coordinate systems. Key features: Generate
2702 user-defined cut planes through the atlas templates, matching the orientation
2703 of the cut plane of the 2D experimental image data, as a first step towards
2704 anchoring of images to the relevant atlas template. Propagate spatial
2705 transformations across series of sections following anchoring of selected
2706 images.</span></p>
2707
2708 <h2></h2>
2709
2710 <h2><a name="_Toc138932362"><span lang=en-DE>Quota Manager</span></a></h2>
2711
2712 <p class=MsoNormal><span lang=en-DE>The Quota Manager enables each EBRAINS
2713 service to manage user quotas for resources EBRAINS users consume in their
2714 respective services. The goal is to encourage the responsible use of resources.
2715 It is recommended that all users (except possibly guest accounts) are provided
2716 with a default quota, and that specific users have the option of receiving
2717 larger quotas based on their affiliation, role or motivated requests.</span></p>
2718
2719 <h2></h2>
2720
2721 <h2><a name="_Toc138932363"><span lang=en-DE>RateML</span></a></h2>
2722
2723 <p class=MsoNormal><span lang=en-DE>RateML enables users to generate
2724 whole-brain network models from a succinct declarative description, in which
2725 the mathematics of the model are described without specifying how their
2726 simulation should be implemented. RateML builds on NeuroML's Low Entropy Model
2727 Specification (LEMS), an XML-based language for specifying models of dynamical systems,
2728 allowing descriptions of neural mass and discretized neural field models, as
2729 implemented by the TVB simulator. The end user describes their model's
2730 mathematics once and generates and runs code for different languages, targeting
2731 both CPUs for fast single simulations and GPUs for parallel ensemble
2732 simulations.</span></p>
2733
2734 <h2></h2>
2735
2736 <h2><a name="_Toc138932364"><span lang=en-DE>Region-wise CBPP using the Julich
2737 BrainÊCytoarchitectonic Atlas</span></a></h2>
2738
2739 <p class=MsoNormal><span lang=en-DE>Many studies have been investigating the
2740 relationships between interindividual variability in brain regions'
2741 connectivity and behavioural phenotypes, by utilising connectivity-based
2742 prediction models. Recently, we demonstrated that an approach based on the
2743 combination of whole-brain and region-wise CBPP can provide important insight
2744 into the predictive model, and hence in brain-behaviour relationships, by
2745 offering interpretable patterns. Here, we applied this approach using the
2746 Julich Brain Cytoarchitectonic Atlas with the resting-state functional
2747 connectivity and psychometric variables from the Human Connectome Project
2748 dataset, illustrating each brain region's predictive power for a range of
2749 psychometric variables. As a result, a psychometric prediction profile was
2750 established for each brain region, which can be validated against brain mapping
2751 literature.</span></p>
2752
2753 <h2></h2>
2754
2755 <h2><a name="_Toc138932365"><span lang=en-DE>RRI Capacity Development Resources</span></a></h2>
2756
2757 <p class=MsoNormal><span lang=en-DE>A series of training resources developed to
2758 enable anticipation, critical reflection and public engagement/deliberation of
2759 societal consequences of brain research and innovation activities. These
2760 resources were designed primarily for HBP researchers and EBRAINS leadership
2761 and management, involving EBRAINS data and infrastructure providers. However,
2762 they are also useful for engaging the wider public with RRI. The resources are
2763 based on the legacy of over 10 years of research and activities of the ethics
2764 and society-team in the HBP. They cover important RRI-related topics on
2765 neuroethics, data governance, dual-use, public engagement and foresight,
2766 diversity, search integrity etc.</span></p>
2767
2768 <h2></h2>
2769
2770 <h2><a name="_Toc138932366"><span lang=en-DE>rsHRF</span></a></h2>
2771
2772 <p class=MsoNormal><span lang=en-DE>This toolbox is aimed to retrieve the
2773 onsets of pseudo-events triggering an hemodynamic response from resting state
2774 fMRI BOLD signals. It is based on point process theory and fits a model to
2775 retrieve the optimal lag between the events and the HRF onset, as well as the
2776 HRF shape, using different shape parameters or combinations of basis functions.
2777 Once the HRF has been retrieved for each voxel/vertex, it can be deconvolved
2778 from the time series (for example, to improve lag-based connectivity
2779 estimates), or one can map the shape parameters everywhere in the brain
2780 (including white matter) and use it as a pathophysiological indicator.</span></p>
2781
2782 <h2></h2>
2783
2784 <h2><a name="_Toc138932367"><span lang=en-DE>RTNeuron</span></a></h2>
2785
2786 <p class=MsoNormal><span lang=en-DE>The main utility of RTNeuron is twofold:
2787 (i) the interactive visual inspection of structural and functional features of
2788 the cortical column model and (ii) the generation of high-quality movies and
2789 images for presentations and publications.RTNeuron provides a C++ library with
2790 an OpenGL-based rendering backend, a Python wrapping and a Python application
2791 called rtneuron. RTNeuron is only supported in GNU/Linux systems. However, it
2792 should also be possible to build it on Windows systems. For OS/X it may be
2793 quite challenging and require changes in OpenGL-related code to get it working.</span></p>
2794
2795 <h2></h2>
2796
2797 <h2><a name="_Toc138932368"><span lang=en-DE>sbs: Spike-based Sampling</span></a></h2>
2798
2799 <p class=MsoNormal><span lang=en-DE>Spike-based sampling, sbs, is a software
2800 suite that takes care of calibrating spiking neurons for given target
2801 distributions and allows the evaluation of these distributions as they are
2802 produced by stochastic spiking networks.</span></p>
2803
2804 <h2></h2>
2805
2806 <h2><a name="_Toc138932369"><span lang=en-DE>SDA 7</span></a></h2>
2807
2808 <p class=MsoNormal><span lang=en-DE>SDA 7 can be used to carry out Brownian
2809 dynamics simulations of the diffusional association in a continuum aqueous
2810 solvent of two solute molecules, e.g., proteins, or of a solute molecule to an
2811 inorganic surface. SDA 7 can also be used to simulate the diffusion of multiple
2812 proteins, in dilute or concentrated solutions, e.g., to study the effects of
2813 macromolecular crowding.</span></p>
2814
2815 <h2></h2>
2816
2817 <h2><a name="_Toc138932370"><span lang=en-DE>Shape &amp; Appearance Modelling</span></a></h2>
2818
2819 <p class=MsoNormal><span lang=en-DE>A framework for automatically learning
2820 shape and appearance models for medical (and certain other) images. The
2821 algorithm was developed with the aim of eventually enabling distributed
2822 privacy-preserving analysis of brain image data, such that shared information
2823 (shape and appearance basis functions) may be passed across sites, whereas
2824 latent variables that encode individual images remain secure within each site.
2825 These latent variables are proposed as features for privacy-preserving data
2826 mining applications.</span></p>
2827
2828 <h2></h2>
2829
2830 <h2><a name="_Toc138932371"><span lang=en-DE>siibra-api</span></a></h2>
2831
2832 <p class=MsoNormal><span lang=en-DE>siibra-api provides an HTTP wrapper around
2833 siibra-python, allowing developers to access atlas (meta)data over HTTP
2834 protocol. Deployed on the EBRAINS infrastructure, developers can access the
2835 centralised (meta)data on atlases, as provided by siibra-python, regardless of
2836 the programming language.</span></p>
2837
2838 <h2></h2>
2839
2840 <h2><a name="_Toc138932372"><span lang=en-DE>siibra-explorer</span></a></h2>
2841
2842 <p class=MsoNormal><span lang=en-DE>The interactive atlas viewer
2843 siibra-explorer allows exploring the different EBRAINS atlases for the human,
2844 monkey and rodent brains together with a comprehensive set of linked multimodal
2845 data features. It provides a 3-planar view of a parcellated reference volume
2846 combined with a rotatable overview of the 3D surface. Several templates can be
2847 selected to navigate through the brain from MRI-scale to microscopic
2848 resolution, allowing inspection of terabyte-size image data. Anatomically
2849 anchored datasets reflecting aspects of cellular and molecular organisation,
2850 fibres, function and connectivity can be discovered by selecting brain regions
2851 from parcellations, or zooming and panning the reference brain. siibra-explorer
2852 also allows annotation of brain locations as points and polygons and is
2853 extensible via interactive plugins.</span></p>
2854
2855 <h2></h2>
2856
2857 <h2><a name="_Toc138932373"><span lang=en-DE>siibra-python</span></a></h2>
2858
2859 <p class=MsoNormal><span lang=en-DE>siibra-python is a Python client to a brain
2860 atlas framework that integrates brain parcellations and reference spaces at
2861 different spatial scales and connects them with a broad range of multimodal
2862 regional data features. It aims to facilitate programmatic and reproducible
2863 incorporation of brain parcellations and brain region features from different
2864 sources into neuroscience workflows. Also, siibra-python provides an easy
2865 access to data features on the EBRAINS Knowledge Graph in a well-structured
2866 manner. Users can preconfigure their own data to use within siibra-python.</span></p>
2867
2868 <h2></h2>
2869
2870 <h2><a name="_Toc138932374"><span lang=en-DE>Single Cell Model (Re)builder
2871 Notebook</span></a></h2>
2872
2873 <p class=MsoNormal><span lang=en-DE>The Single Cell Model (Re)builder Notebook
2874 is a web application, implemented via a Jupyter Notebook on EBRAINS, which
2875 allows users to configure the BluePyOpt to re-run an optimisation with their
2876 own choices for the parameters range. The optimisation jobs are submitted
2877 through Neuroscience Gateway.</span></p>
2878
2879 <h2></h2>
2880
2881 <h2><a name="_Toc138932375"><span lang=en-DE>Slurm Plugin for Co-allocation of
2882 Compute and Data Resources</span></a></h2>
2883
2884 <p class=MsoNormal><span lang=en-DE>This Simple linux utility for resource
2885 management (Slurm) plugin enables the co-allocation of compute and data resources
2886 on a shared multi-tiered storage cluster by estimating waiting times when the
2887 high-performance storage (burst buffers) will become available to submitted
2888 jobs. Based on the current job queue and the estimated waiting time, the plugin
2889 decides whether scheduling the high-performance or lower-performance storage
2890 system (parallel file system) benefits the job's turnaround time. The
2891 estimation depends on additional information the user provides at submission
2892 time.</span></p>
2893
2894 <h2></h2>
2895
2896 <h2><a name="_Toc138932376"><span lang=en-DE>Snudda</span></a></h2>
2897
2898 <p class=MsoNormal><span lang=en-DE>Snudda ('touch' in Swedish) allows the user
2899 to set up and generate microcircuits where the connectivity between neurons is
2900 based on reconstructed neuron morphologies. The touch detection algorithm looks
2901 for overlaps of axons and dendrites, and places putative synapses where they
2902 touch. The putative synapses are pruned, removing a fraction to match
2903 statistics from pairwise connectivity experiments. If needed, Snudda can also
2904 use probability functions to create realistic microcircuits. The Snudda
2905 software is written in Python and includes support for supercomputers. It uses
2906 ipyparallel to parallelise network creation, and NEURON as the backend for
2907 simulations. Install using pip or by directly downloading.</span></p>
2908
2909 <h2></h2>
2910
2911 <h2><a name="_Toc138932377"><span lang=en-DE>SomaSegmenter</span></a></h2>
2912
2913 <p class=MsoNormal><span lang=en-DE>SomaSegmenter allows neuronal soma
2914 segmentation in fluorescence microscopy imaging datasets with the use of a
2915 parametrised version of the U-Net segmentation model, including additional
2916 features such as residual links and tile-based frame reconstruction.</span></p>
2917
2918 <h2></h2>
2919
2920 <h2><a name="_Toc138932378"><span lang=en-DE>SpiNNaker</span></a></h2>
2921
2922 <p class=MsoNormal><span lang=en-DE>SpiNNaker is a neuromorphic computer with
2923 over a million low power, small memory ARM cores arranged in chips, connected
2924 together with a unique brain-like mesh network, and designed to simulate
2925 networks of spiking point neurons. Software is provided to compile networks
2926 described with PyNN into running simulations, and to extract and convert
2927 results into the neo data format, as well as providing support for live
2928 interaction with running simulations. This allows integration with external
2929 devices such as real or virtual robotics as well as live simulation
2930 visualisation. Scripts can be written and executed using Jupyter for
2931 interactive access.</span></p>
2932
2933 <h2></h2>
2934
2935 <h2><a name="_Toc138932379"><span lang=en-DE>SSB toolkit</span></a></h2>
2936
2937 <p class=MsoNormal><span lang=en-DE>The SSB toolkit is an open-source Python
2938 library to simulate mathematical models of the signal transduction pathways of
2939 G-protein coupled receptors (GPCRs). By merging structural macromolecular data
2940 with systems biology simulations, the framework allows simulation of the signal
2941 transduction kinetics induced by ligand-GPCR interactions, as well as the consequent
2942 change of concentration of signalling molecular species, as a function of time
2943 and ligand concentration. Therefore, this tool allows the possibility to
2944 investigate the subcellular effects of ligand binding upon receptor activation,
2945 deepening the understanding of the relationship between the molecular level of
2946 ligand-target interactions and higher-level cellular and physiological or
2947 pathological response mechanisms.</span></p>
2948
2949 <h2></h2>
2950
2951 <h2><a name="_Toc138932380"><span lang=en-DE>Subcellular model building and
2952 calibration tool set</span></a></h2>
2953
2954 <p class=MsoNormal><span lang=en-DE>The toolset includes interoperable modules
2955 for: model building, calibration (parameter estimation) and model analysis. All
2956 information needed to perform these tasks (models, experimental calibration
2957 data and prior assumptions on parameter distributions) are stored in a
2958 structured, human- and machine-readable file format based on SBtab. The toolset
2959 enables simulations of the same model in simulators with different
2960 characteristics, e.g., STEPS, NEURON, MATLAB's Simbiology and R via automatic
2961 code generation. The parameter estimation can include uncertainty
2962 quantification and is done by optimisation or Bayesian approaches. Model
2963 analysis includes global sensitivity analysis and functionality for analysing
2964 thermodynamic constraints and conserved moieties.</span></p>
2965
2966 <h2></h2>
2967
2968 <h2><a name="_Toc138932381"><span lang=en-DE>Synaptic Events Fitting</span></a></h2>
2969
2970 <p class=MsoNormal><span lang=en-DE>The Synaptic Events Fitting is a web
2971 application, implemented in a Jupyter Notebook on EBRAINS that allows users to
2972 fit synaptic events using data and models from the EBRAINS Knowledge Graph
2973 (KG). Select, download and visualise experimental data from the KG and then choose
2974 the data to be fitted. A mod file is then selected (local or default) together
2975 with the corresponding configuration file (including protocol and the name of
2976 the parameters to be fitted, their initial values and allowed variation range,
2977 exclusion rules and an optional set of dependencies). The fitting procedure can
2978 run on Neuroscience Gateway. Fetch the fitting results from the storage of the
2979 HPC system to the storage of the Collab or to analyse the optimised parameters.</span></p>
2980
2981 <h2></h2>
2982
2983 <h2><a name="_Toc138932382"><span lang=en-DE>Synaptic Plasticity Explorer</span></a></h2>
2984
2985 <p class=MsoNormal><span lang=en-DE>The Synaptic Plasticity Explorer is a web
2986 application, implemented via a Jupyter Notebook on EBRAINS, which allows to
2987 configure and test, through an intuitive GUI, different synaptic plasticity
2988 models and protocols on single cell optimised models, available in the EBRAINS
2989 Model Catalog. It consists of two tabs: 'Config', where the user can specify
2990 the plasticity model to use and the synaptic parameters, and 'Sim', where the
2991 recording location, weight's evolution and number of simulations to run are
2992 defined. The results are plotted at the end of the simulation and the traces
2993 are available for download.</span></p>
2994
2995 <h2></h2>
2996
2997 <h2><a name="_Toc138932383"><span lang=en-DE>Synaptic proteome database
2998 (SQLite)</span></a></h2>
2999
3000 <p class=MsoNormal><span lang=en-DE>Integration of 57 published synaptic
3001 proteomic datasets reveals a stunningly complex picture involving over 7000
3002 proteins. Molecular complexes were reconstructed using evidence-based
3003 protein-protein interaction data available from public databases. The
3004 constructed molecular interaction network model is embedded into an SQLite
3005 implementation, allowing queries that generate custom network models based on
3006 meta-data including species, synaptic compartment, brain region, and method of
3007 extraction.</span></p>
3008
3009 <h2></h2>
3010
3011 <h2><a name="_Toc138932384"><span lang=en-DE>Synaptome.db</span></a></h2>
3012
3013 <p class=MsoNormal><span lang=en-DE>The Synaptome.db bioconductor package
3014 contains a local copy of the Synaptic proteome database. On top of this it
3015 provides a set of utility R functions to query and analyse its content. It
3016 allows for extraction of information for specific genes and building the
3017 protein-protein interaction graph for gene sets, synaptic compartments and
3018 brain regions.</span></p>
3019
3020 <h2></h2>
3021
3022 <h2><a name="_Toc138932385"><span lang=en-DE>Tide</span></a></h2>
3023
3024 <p class=MsoNormal><span lang=en-DE>BlueBrain's Tide provides multi-window,
3025 multi-user touch interaction on large surfaces Ð think of a giant collaborative
3026 wall-mounted tablet. Tide is a distributed application that can run on multiple
3027 machines to power display walls or projection systems of any size. Its user interface
3028 is designed to offer an intuitive experience on touch walls. It works just as
3029 well on non-touch-capable installations by using its web interface from any web
3030 browser.</span></p>
3031
3032 <h2></h2>
3033
3034 <h2><a name="_Toc138932386"><span lang=en-DE>TVB EBRAINS</span></a></h2>
3035
3036 <p class=MsoNormal><span lang=en-DE>TVB EBRAINS is the principal full brain
3037 network simulation engine in EBRAINS and covers every aspect of realising
3038 personalised whole-brain simulations on the EBRAINS platform. It consists of
3039 the simulation tools and adaptors connecting the data, atlas and computing
3040 services to the rest of the TVB ecosystem and Cloud services available in
3041 EBRAINS. As such it allows the user to find and fetch relevant datasets through
3042 the EBRAINS Knowledge Graph and Atlas services, construct the personalised TVB
3043 models and use the HPC systems to perform parameter exploration, optimisation and
3044 inference studies. The user can orchestrate the workflow from the Jupyterlab
3045 interactive computing environment of the EBRAINS Collaboratory or use the
3046 dedicated web application of TVB.</span></p>
3047
3048 <h2></h2>
3049
3050 <h2><a name="_Toc138932387"><span lang=en-DE>TVB Image Processing Pipeline</span></a></h2>
3051
3052 <p class=MsoNormal><span lang=en-DE>TVB Image Processing Pipeline takes multimodal
3053 MRI data sets (anatomical, functional and diffusion-weighted MRI) as input and
3054 generates structural connectomes, region-average fMRI time series, functional
3055 connectomes, brain surfaces, electrode positions, lead field matrices and atlas
3056 parcellations as output. The pipeline performs preprocessing and
3057 distortion-correction on MRI data as well as white matter fibre bundle
3058 tractography on diffusion data. Outputs are formatted according to two data
3059 standards: a TVB-ready data set that can be directly used to simulate brain
3060 network models and the same output in BIDS format.</span></p>
3061
3062 <h2></h2>
3063
3064 <h2><a name="_Toc138932388"><span lang=en-DE>TVB Inversion</span></a></h2>
3065
3066 <p class=MsoNormal><span lang=en-DE>The TVB Inversion package implements the
3067 machinery required to perform parameter exploration and  inference over
3068 parameters of The Virtual Brain simulator. It implements Simulation Based
3069 Inference (SBI) which is a Bayesian inference method for complex models, where
3070 calculation of the likelihood function is either analytically or
3071 computationally intractable. As such, it allows the user to formulate with
3072 great expressive power both the model and the inference scenario in terms of
3073 observed data features and model parameters. Part of the integration with TVB
3074 entails the option to perform numerous simulations in parallel, which can be
3075 used for parameter space exploration.</span></p>
3076
3077 <h2></h2>
3078
3079 <h2><a name="_Toc138932389"><span lang=en-DE>TVB Web App</span></a></h2>
3080
3081 <p class=MsoNormal><span lang=en-DE>TVB Web App provides The Virtual Brain
3082 Simulator as an EBRAINS Cloud Service with an HPC back-end. Scientists can run
3083 intense personalised brain simulations without having to deploy software. Users
3084 can access the service with their EBRAINS credentials (single sign on). TVB Web
3085 App uses private/public key cryptography, sandboxing, and access control to
3086 protect personalised health information contained in digital human brain twins
3087 while being processed on HPC. Users can upload their connectomes or use
3088 TVB-ready image-derived data discoverable via the EBRAINS Knowledge Graph.
3089 Users can also use containerised processing workflows available on EBRAINS to
3090 render image raw data into simulation-ready formats.</span></p>
3091
3092 <h2></h2>
3093
3094 <h2><a name="_Toc138932390"><span lang=en-DE>TVB Widgets</span></a></h2>
3095
3096 <p class=MsoNormal><span lang=en-DE>In order to support the usability of
3097 EBRAINS workflows, TVB-widgets has been developed as a set of modular graphic
3098 components and software solutions, easy to use in the Collaboratory within the
3099 JupyterLab. These GUI components are based on and under open source licence,
3100 supporting open neuroscience and support features like: Setup of models and
3101 region-specific or cohort simulations. Selection of Data sources and their
3102 links to models. Querying data from siibra and the EBRAINS Knowledge Graph.
3103 Deployment and monitoring jobs on HPC resources. Analysis and visualisation.
3104 Visual workflow builder for configuring and launching TVB simulations.</span></p>
3105
3106 <h2></h2>
3107
3108 <h2><a name="_Toc138932391"><span lang=en-DE>TVB-Multiscale</span></a></h2>
3109
3110 <p class=MsoNormal><span lang=en-DE>TVB-Multiscale is a Python toolbox aimed at
3111 facilitating the configuration of multiscale brain models and their
3112 co-simulation with TVB and spiking network simulators (currently NEST,
3113 NetPyNE (NEURON) and ANNarchy). A multiscale brain model consists of a full
3114 brain model formulated at the coarse scale of networks of tens up to thousands
3115 of brain regions, and an additional model of networks of spiking neurons
3116 describing selected brain regions at a finer scale. The toolbox has a
3117 user-friendly interface for configuring different kinds of models for
3118 transforming and exchanging data between the two scales during co-simulation.</span></p>
3119
3120 <h2></h2>
3121
3122 <h2><a name="_Toc138932392"><span lang=en-DE>VIOLA</span></a></h2>
3123
3124 <p class=MsoNormal><span lang=en-DE>VIOLA is an interactive, web-based tool to
3125 visualise activity data in multiple 2D layers such as the simulation output of
3126 neuronal networks with 2D geometry. As a reference implementation for a
3127 developed set of interactive visualisation concepts, the tool combines and
3128 adapts modern interactive visualisation paradigms, such as coordinated multiple
3129 views, for massively parallel neurophysiological data. The software allows for
3130 an explorative and qualitative assessment of the spatiotemporal features of
3131 neuronal activity, which can be performed prior to a detailed quantitative data
3132 analysis of specific aspects of the data.</span></p>
3133
3134 <h2></h2>
3135
3136 <h2><a name="_Toc138932393"><span lang=en-DE>Vishnu 1.0</span></a></h2>
3137
3138 <p class=MsoNormal><span lang=en-DE>DC Explorer, Pyramidal Explorer and Clint
3139 Explorer are the core of an application suite designed to help scientists to
3140 explore their data. Vishnu 1.0 is a communication framework that allows them to
3141 interchange information and cooperate in real time. It provides a unique access
3142 point to the three applications and manages a database with the users'
3143 datasets. Vishnu was originally designed to integrate data for
3144 Espina.Whole-brain-scale tools.</span></p>
3145
3146 <h2></h2>
3147
3148 <h2><a name="_Toc138932394"><span lang=en-DE>ViSimpl</span></a></h2>
3149
3150 <p class=MsoNormal><span lang=en-DE>ViSimpl integrates a set of visualisation
3151 and interaction components that provide a semantic view of brain data with the
3152 aim of improving its analysis procedures. ViSimpl provides 3D particle-based
3153 rendering that visualises simulation data with their associated spatial and
3154 temporal information, enhancing the knowledge extraction process. It also
3155 provides abstract representations of the time-varying magnitudes, supporting
3156 different data aggregation and disaggregation operations and giving focus and
3157 context clues. In addition, ViSimpl provides synchronised playback control of
3158 the simulation being analysed.</span></p>
3159
3160 <h2></h2>
3161
3162 <h2><a name="_Toc138932395"><span lang=en-DE>VisuAlign</span></a></h2>
3163
3164 <p class=MsoNormal><span lang=en-DE>VisuAlign is a tool for user-guided
3165 nonlinear registration after QuickNII of 2D experimental image data, typically
3166 high resolution microscopic images, to 3D atlas reference space, facilitating
3167 data integration through standardised coordinate systems. Key features:
3168 Generate user-defined cut planes through the atlas templates, matching the
3169 orientation of the cut plane of the 2D experimental image data, as a first step
3170 towards anchoring of images to the relevant atlas template. Propagate spatial
3171 transformations across series of sections following anchoring of selected
3172 images.</span></p>
3173
3174 <h2></h2>
3175
3176 <h2><a name="_Toc138932396"><span lang=en-DE>VMetaFlow</span></a></h2>
3177
3178 <p class=MsoNormal><span lang=en-DE>VMetaFlow is an abstraction layer placed
3179 over existing visual grammars and visualisation declarative languages,
3180 providing them with interoperability mechanisms. The main contribution of this
3181 research is to provide a user-friendly system to design visualisation and data
3182 processing operations that can be interconnected to form data analysis
3183 workflows. Visualisations and data processes can be saved as cards. Cards and
3184 workflows can be saved, distributed and reused between users.</span></p>
3185
3186 <h2></h2>
3187
3188 <h2><a name="_Toc138932397"><span lang=en-DE>Voluba</span></a></h2>
3189
3190 <p class=MsoNormal><span lang=en-DE>A common problem in high-resolution brain
3191 atlasing is spatial anchoring of volumes of interest from imaging experiments
3192 into the detailed anatomical context of an ultrahigh-resolution reference model
3193 like BigBrain. The interactive volumetric alignment tool voluba is implemented
3194 as a web service and allows anchoring of volumetric image data to reference
3195 volumes at microscopical spatial resolutions. It enables interactive
3196 manipulation of image position, scale, and orientation, flipping of coordinate
3197 axes, and entering of anatomical point landmarks in 3D. The resulting
3198 transformation parameters can, amongst others, be downloaded or used to view
3199 the anchored image volume in the interactive atlas viewer siibra-explorer.</span></p>
3200
3201 <h2></h2>
3202
3203 <h2><a name="_Toc138932398"><span lang=en-DE>WebAlign</span></a></h2>
3204
3205 <p class=MsoNormal><span lang=en-DE>WebAlign is the web version of QuickNII.
3206 Presently, it is available as a community app in the Collaboratory. Features
3207 include: Spatial registration of sectional image data. Generation of customised
3208 atlas maps for your sectional image data.</span></p>
3209
3210 <h2></h2>
3211
3212 <h2><a name="_Toc138932399"><span lang=en-DE>Webilastik</span></a></h2>
3213
3214 <p class=MsoNormal><span lang=en-DE>webilastik brings the popular machine
3215 learning-based image analysis tool ilastik from the desktop into the browser.
3216 Users can perform semantic segmentation tasks on their data in the cloud.
3217 webilastik runs computations on federated EBRAINS HPC resources and uses
3218 EBRAINS infrastructure for data access and storage. webilastik makes machine
3219 learning-based image analysis workflows accessible to users without deep
3220 knowledge of image analysis and machine learning. webilastik is part of the
3221 QUINT workflow for extraction, quantification and analysis of features from
3222 rodent histological images.</span></p>
3223
3224 <h2></h2>
3225
3226 <h2><a name="_Toc138932400"><span lang=en-DE>WebWarp</span></a></h2>
3227
3228 <p class=MsoNormal><span lang=en-DE>WebWarp is the web version of VisuAlign.
3229 Presently, it is available as a community app in the Collaboratory. Features
3230 include: Nonlinear refinements of atlas registration by WebAlign of sectional
3231 image data. Generation of customised atlas maps for your sectional image data.</span></p>
3232
3233 <h2></h2>
3234
3235 <h2><a name="_Toc138932401"><span lang=en-DE>ZetaStitcher</span></a></h2>
3236
3237 <p class=MsoNormal><span lang=en-DE>ZetaStitcher is a Python package designed
3238 to stitch large volumetric images, such as those produced by Light-Sheet
3239 Fluorescence Microscopes. It is able to quickly compute the optimal alignment
3240 of large mosaics of tiles thanks to its ability to perform a sampling along the
3241 tile depth, i.e., pairwise alignment is computed only at certain depths along
3242 the thickness of the tile. This greatly reduces the amount of data that needs
3243 to be read and transferred, thus, making the process much faster. ZetaStitcher
3244 comes with an API that can be used to programmatically access the aligned
3245 volume in a virtual fashion as if it were a big NumPy array, without having to
3246 produce the fused 3D image of the whole sample.Cellular- and subcellular-scale
3247 tools.</span></p>
3248
3249 <h2></h2>
3250
3251 <h2><a name="_Toc138932402"><span lang=en-DE>TauRAMD</span></a></h2>
3252
3253 <p class=MsoNormal><span lang=en-DE>The TauRAMD technique makes use of RAMD
3254 simulations to compute relative residence times (or dissociation rates) of
3255 protein-ligand complexes. In the RAMD method, the egress of a molecule from a
3256 target receptor is accelerated by the application of an adaptive randomly
3257 oriented force on the ligand. This enables ligand egress events to be observed
3258 in short, nanosecond timescale simulations without imposing any bias regarding
3259 the ligand egress route taken. If coupled to the MD-IFP tool, the TauRAMD
3260 method can be used to investigate dissociation mechanisms and characterize
3261 transition states.</span></p>
3262
3263 </div>
3264
3265 </body>
3266
3267 </html>
3268
3269 {{/html}}
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