Software
DataLad
DataLad is ongoing work funded by NSF and German BMBF, to adapt the model of open-source software (OSS) distributions to address the technical limitations of today's data-sharing and provides a versatile data management platform. It uses software for data tracking and deployment logistics specialized for large data (git-annex) built atop Git, the most capable distributed version control system (dVCS) available today. DataLad provides access to data available from various sources (e.g. lab or consortium web-sites such as humanconnectome.org; data sharing portals such as openneuro.org and crcns.org) through a single interface. It enables students and scientists to operate on data using familiar concepts, such as files and directories, while transparently managing data access and authorization with underlying hosting providers.
- M. Hanke, M. Visconti di Oleggio Castello, K. Meyer, B. Poldrack, and Y.O. Halchenko (2018). YODA: YODA's organigram on data analysis. OHBM 2018, Singapore.
- JOSS paper: providing a succinct overview of DataLad
- DataLad Handbook: everything you need to know about DataLad.
- Funding support
- datalad-container: a DataLad extension for working with containerized computational environments. It enables version-controlled execution of containerized pipelines (Singularity, Docker, and others) with full provenance capture as DataLad run records.
- datalad-fuse: a DataLad extension to read annexed files from their remote URLs without a full download — sparse fetching via fsspec and optional FUSE mount give immediate access to petabyte-scale datasets.
8 components
- datalad-crawler: DataLad extension for tracking web resources as datasets
- datalad-deprecated: Retired DataLad functionality, kept available
- DataLad Handbook: A hands-on course in data management with DataLad, written to be worked through rather than looked up.
- datalad-installer: Installation script for DataLad and its dependencies
- datalad-metalad: Next generation metadata handling for DataLad
- datalad-neuroimaging: DataLad extension for neuroimaging data
- datalad/git-annex: Build and distribution infrastructure that packages git-annex for DataLad's platforms. Not upstream git-annex, which Joey Hess develops.
- datasets.datalad.org: Registry of public datasets provided by the DataLad project
NeuroDebian
NeuroDebian is a turnkey research software platform for all aspects of the neuroscientific research process. It takes the ideas of the software hosting portals such as NITRC on maximizing research transparency and methods sharing, one step further, by providing a comprehensive suite of readily usable and fully integrated software with a robust testing and deployment infrastructure. Consequently, it improves interoperability among the tools and frees researchers from the burden of tedious installation or upgrade procedures. That, in turn, positively affects their availability for actual research activities, as well as their motivation to test new analysis tools and stay connected with the latest methodological developments in the field.
- Y.O. Halchenko & M. Hanke (2012). Open is not enough. Let's take the next step: An integrated, community-driven computing platform for neuroscience. Frontiers in Neuroinformatics, 6:22. [PDF] DOI: 10.3389/fninf.2012.00022
NeuroDebian grew out of pkg-exppsy, the Debian team packaging experimental psychology software -- FSL, PyEPL, PsychoPy -- from 2005 onwards. Same people, same work, wider scope.
PyMVPA
PyMVPA is a Python-based framework for neural decoding using multivariate pattern analysis. It affords both volume- and surface-based analyses using a wide variety of supervised and unsupervised machine learning methods, representational similarity analyses, searchlight analyses, hyperalignment of representational spaces, and model-based decoding and encoding. The software also can be used for neural data other than fMRI, including analysis of MEG and EEG data through spatio-temporo-frequency band searchlights and cross-modal EEG to fMRI trans-fusion. It also has been used for analyses on data unrelated to neuroscience, demonstrating its general utility. PyMVPA also serves as a repository for sample data sets (e.g., Haxby et al. 2001) that has found wide applicability for education, development of new algorithms, or new analyses and independent research reports.
- M. Hanke, Y.O. Halchenko, et al. (2009). PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7, 37-53. DOI: 10.1007/s12021-008-9041-y
ORINOCO Lite
Content-neutral engine and deterministic runtime for metadata-driven sites. A site's content lives as records in a store; the engine projects them into the pages that get served, so the records stay the single source of truth. This website is built that way. Derived from ORINOCO, the Psychoinformatics group's research information infrastructure, and lighter by design.
Historia
Backs up a record of GitHub activity and turns it into a work-history dashboard. CON uses it to see what its members have actually been doing, across the several organizations the work is spread over.
mechababs
mechababs runs BIDS Apps over many studies on an HPC cluster, with BABS running the jobs. It works on a BIDS study, adding derivatives to it, and records how each one was made inside the study itself: the exact mechababs and BABS versions pinned in a lock, and every orchestration step as a DataLad run record. A superstudy groups many studies so one campaign sweeps them all, which is how mechababs feeds OpenNeuroStudies.
con/annextube
con/annextube is a YouTube-channel archiving system that uses git-annex for efficient, incremental storage of videos, metadata, comments, and captions. Archives are browsable offline through a client-side web interface generated by annextube itself. Actively maintained archives include:
MyKrok
MyKrok (Ukrainian: "мій крок" — "my step") is a CLI tool to incrementally back up fitness activities from Strava, including GPS tracks, photos, comments, and kudos. Backups are browsable offline through a built-in web frontend with interactive maps and activity statistics — applying the same git-annex / open-data philosophy of CON's archival tools to personal fitness data. Try the live demo with synthetic data.
con/duct
con/duct (or just duct) is a lightweight wrapper that collects execution data for an arbitrary command. Execution data includes execution time, system information, and resource usage statistics of the command and all its child processes. duct is intended to simplify the problem of recording the resources necessary to execute a command, particularly in an HPC environment. Additionally duct includes optional helpers that interact with logs produced by duct exectutions.
con/solidation
con/solidation is a command-line tool that produces a Markdown activity report of recent issue and pull-request events across a configured set of GitHub repositories and organizations. It is used by CON to generate regular summaries of activity across the projects it maintains and contributes to, enabling lightweight project-health monitoring without leaving the terminal.
con/tinuous
con/tinuous is a command for downloading build logs and (for GitHub only) artifacts and release assets for a GitHub repository from GitHub Actions, Travis-CI.com, and/or Appveyor. By downloading them all, and optionally placing them under DataLad control you can establish the backup, distribution, and convenient harmonious access to all those artifacts.
DataLad-Registry
DataLad-Registry is a service that maintains up-to-date information on an expanding collection of datasets, currently numbering over ten thousand. It automatically registers datasets from various online sources, extracts metadata, and keeps both the datasets and their metadata current. DataLad-Registry offers search functionalities, including metadata-based searches, accessible through both a web interface and a RESTful API. Registry is regularly updated with DataLad datasets discovered across multiple hosting portals and listed on our DataLad Usage Dashboard.
con/tributors
con/tributors is a Python library and GitHub Action to automate maintenance of contributor attribution files. It updates all-contributors, Zenodo metadata (.zenodo.json), and CodeMeta (codemeta.json) files in one go, using a shared .tributors cache that maps GitHub logins to ORCID iDs and other persistent identifiers — so that contributor records stay consistent and up-to-date across all metadata files with a single command. For example, DataLad uses a dedicated GitHub Actions workflow powered by con/tributors to automatically keep its contributor files current.
con/fscacher
con/fscacher is a Python library providing a persistent, file-content-aware memoization cache. Functions decorated with @cache.memoize_path are re-executed only when the fingerprint (timestamps + size) of their file arguments changes — making it easy to cache expensive operations on large file trees without stale results. It is used internally by the DANDI client and other CON tools.
ReproStim
ReproStim captures the audio-visual stimuli actually presented during an experiment, into BIDS datasets -- so what the participant saw and heard is recorded rather than inferred from the script that was supposed to show it. ReproFlow is the setup around it, from the screen and the event relay through to the dataset.
- The ReproFlow setup, and a talk walking through it.
- ReproEvents: the event relay that timestamps a button press in hardware rather than in whatever the stimulus computer was doing.
ReproNim/containers
ReproNim/containers is a DataLad dataset providing a ready-to-use "distribution" of containerized neuroimaging environments (Singularity images) for reproducible analysis. It collects a curated set of containers — including those used by HeuDiConv and the broader ReproNim ecosystem — and exposes them via the ///repronim/containers DataLad dataset path, making it trivial to pin and share exact software environments alongside data and workflows.
pyout
pyout is a Python package that defines an interface for writing structured records as a table in a terminal. It is being developed to replace custom code for displaying tabular data in in DANDI client and others.
ReproMan
ReproMan (Reproducible computational environments Manager; formerly known as REPROMAN) is also a part of the NIH supported ReproNim effort. It aims to facilitate reproducible computation via collection of detailed information about origin of the used components (Debian and/or Conda packages, VCS repositories, etc), so that computational environments could be analyzed, and re-created.
- M. Travers, R. Buccigrossi, C. Haselgrove, K. Meyer, and Y.O. Halchenko NICEMAN: NeuroImaging Computational Environments Manager, OHBM 2018, Singapore.
HeuDiConv / ReproIn
HeuDiConv is a flexible DICOM converter for organizing brain imaging data into structured directory layouts. As a part of the larger, NIH supported ReproNim effort, we are developing a HeuDiConv-based ReproIn solution for turnkey automatic conversion of all collected MR data to a collection of the BIDS DataLad datasets. It includes a flexible BIDS-like specification how to name scanning sequences in the scanner, and a HeuDiConv reproin.py heuristic to automate layout and conversion of the datasets. This solution is deployed at DBIC (Dartmouth Brain Imaging Center), it already facilitates reproducible research, data sharing, and uploads to central archives such as NDA.
- Halchenko et al., (2024). HeuDiConv — flexible DICOM conversion into structured directory layouts. Journal of Open Source Software, 9(99), 5839 DOI: 10.21105/joss.05839.
- M. Visconti di Oleggio Castello, James E. Dobson, Terry Sackett, Chandana Kodiweera, J.V. Haxby, M. Goncalves, S. Ghosh, Y.O. Halchenko ReproIn: automatic generation of shareable, version-controlled BIDS datasets from MR scanners, OHBM 2018, Singapore.
DueCredit
DueCredit provides solution for the problem of inadequate citation and referencing of scientific software and methods. It provides a simple framework (at the moment for Python only) to embed publication or other references in the original code so they are automatically collected and reported to the user at the necessary level of reference detail, i.e. only references for actually used functionality will be presented back if software provides multiple citeable implementations.
As a side-effect, we hope that DueCredit also will reduce demand in "prima-ballerina" projects, will encourage contributions to existing open-source codebases, and as a result would solidify scientific software ecosystem.
- Y.O. Halchenko and M. Visconti di Oleggio Castello (2016). DueCredit - automagically collect citations for software, methods, and data you use. OHBM 2016, Geneva, Switzerland
svgtune
svgtune generates a set of SVG files from a single (Inkscape) drawing by toggling the visibility, style or placement of its layers and groups, as directed by a short script -- the way to build incremental slides, or to extract a panel of a poster that is regenerated whenever the poster changes. Written in 2009 and packaged for Debian, it was revived in 2026 with %only, %prune and %crop directives and a test suite.
Archives
DANDI
Distributed Archives for Neurophysiology Data Integration (DANDI) is a platform for publishing, sharing, and processing neurophysiology data funded by the BRAIN Initiative. The platform is now available for data upload and distribution, and provides supplementary client tools to assist with introspection and organization of data following NWB standard.
- NIH (#1R24MH117295-01A1). PIs: S. Ghosh and Y.O. Halchenko
- dandisets: a DataLad superdataset providing unified access to all public DANDI datasets, with individual dandisets also mirrored as DataLad datasets in the dandisets GitHub organization and their Zarr stores in dandizarrs.
- DANDI Compute: an experimental reproducible-computing initiative running processing pipelines directly on DANDI assets. The first campaign applies the AIND ephys pipeline to electrophysiology dandisets, with orchestration code and a public job queue hosted in the dandi-compute GitHub organization.
15 components
- DANDI Archive: Software stack behind the DANDI archive: API, web client and validation, deployable as an independent instance.
- access-page: Visualizations of data usage for the DANDI Archive
- dandi-api-webshots-tools: Tooling for the DANDI API webshots
- dandi-cli: DANDI command line client
- DANDI Compute: Runs processing pipelines directly on DANDI assets, rather than asking everyone to download them first. Orchestration, a submission client and a public queue; the first campaign puts the AIND ephys pipeline over electrophysiology dandisets.
- dandi-hub: Infrastructure and code for the dandihub
- dandi-schema: Schemata for the DANDI archive project
- dandidav: WebDAV view to DANDI Archive
- dandisets-healthstatus: Healthchecks of dandisets and their tooling
- dandisets-linkml-status-tools: Reporting the status of the DANDI metadata translation
- nwb2bids: Reorganizes NWB files into a BIDS directory layout, so a dandiset can be read by the tooling that expects BIDS without being converted twice.
- pydantic2linkml: Translating models expressed in Pydantic into LinkML
- s3-log-extraction: Fast extraction of access summaries from S3 logs
- s3-log-parser: S3 log parsing for the DANDI Archive
- STAMPED Principles: STAMPED is a set of seven principles, Self-containment, Tracking, Actionability, Modularity, Portability, Ephemerality, and Distributability, for building research objects that others can trust, rerun, and build upon. Each principle is a spectrum from a practical minimum to an aspirational ideal, so adoption can be incremental and start from existing practice. STAMPED grew out of YODA and comes with an interactive checklist and worked examples.
EMBER
Ecosystem for Multi-modal Brain-behavior Experimentation and Research (EMBER) is the BRAIN Initiative archive for multi-modal neurophysiological and behavioral data, supporting the Brain Behavior Quantification and Synchronization (BBQS) Program. The platform is being kick-started based on DANDI to provide data upload and distribution. CON is working on integration with DANDI and involved standards.
HEARTH is EMBER's companion suite of data management tools, providing apps for preparing media prior to submission and publication on EMBER.
Clip Extractor
for excerpting and preparing behavioral video clips for submission.
Encoding Helper
for analyzing and transcoding media prior to publication on EMBER.
Components:
- BBQS uploader: A drag-and-drop web app for BBQS teams to put files into an existing dandiset on the EMBER Archive, so contributing data does not require learning the command line first.
- study-template: A template for an EMBER study laid out as a BIDS study, so that data ingest starts from a structure the archive already understands rather than from a conversion afterwards.
- Pozu: Pose labeling of EMBER video data in the browser, by whoever is willing to label it. Inspired by Galaxy Zoo: the bottleneck on pose estimation is annotated video, and a web page anyone can open is a cheaper way to get it than a pipeline only the lab can run.
- STAMPED Principles: STAMPED is a set of seven principles, Self-containment, Tracking, Actionability, Modularity, Portability, Ephemerality, and Distributability, for building research objects that others can trust, rerun, and build upon. Each principle is a spectrum from a practical minimum to an aspirational ideal, so adoption can be incremental and start from existing practice. STAMPED grew out of YODA and comes with an interactive checklist and worked examples.
OpenNeuro
OpenNeuro is likely the oldest and the most well known BRAIN Initiative archive for neuroimaging data. One of the exciting features of the project is that being inspired by our early DataLad crawling of its OpenfMRI.org precursor, it is now using git-annex directly as its core technology. CON is contributing in ensuring availability of recent versions of git-annex and DataLad, involved standardization efforts, and inter-archive collaborations.
- NIH (#5R24MH117179-07). PI: R. Poldrack, Co-I: Y.O. Halchenko
- OpenNeuroStudies: a DataLad superdataset organising OpenNeuro datasets into BIDS Study structure together with their derivatives.
Standards
STAMPED Principles
STAMPED is a set of seven principles, Self-containment, Tracking, Actionability, Modularity, Portability, Ephemerality, and Distributability, for building research objects that others can trust, rerun, and build upon. Each principle is a spectrum from a practical minimum to an aspirational ideal, so adoption can be incremental and start from existing practice. STAMPED grew out of YODA and comes with an interactive checklist and worked examples.
- Austin Macdonald, Cody C. Baker, Isaac To, John A. Lee, Yaroslav O. Halchenko. STAMPED principles for reproducible research objects (preprint). DOI: 10.31222/osf.io/f3h82_v1
- 🎬 Talk by Cody C. Baker introducing STAMPED — archived on CON/tube.
- 🎬 YODA: Structure your studies, observable and reproducible they become — webinar recording archived on CON/tube.
8 components
- stamped-agent-skills
- stamped-branding: Collection of formal logos for the STAMPED principles.
- stamped-checklist: An interactive checklist for compliance to STAMPED principles.
- stamped-checklist-schema: The formal LinkML schema and instance for the STAMPED checklist.
- stamped-examples: Examples addendum for STAMPED principles paper
- stamped-paper
- stamped-principles-schema: The formal LinkML schema and instance for the STAMPED principles.
- stamped-skills-assessment: A store of assessments for skills supporting STAMPED
Brain Imaging Data Structure (BIDS)
BIDS is a project lead by a steering group elected by the BIDS community to provide a simple and intuitive way to organize and describe your neuroimaging and behavioral data.
- Gorgolewski, K. J., et many, Y.O. Halchenko et many more (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3. DOI: 10.1038/sdata.2016.44
- Example (test) datasets
- Historical OpenfMRI and up-to-date OpenNeuro Datasets in DataLad distribution.
- bids-dandisets: a GitHub organization hosting individual DANDI dandisets converted to BIDS format using con/nwb2bids, with a conversion dashboard tracking the status across all dandisets.
Components:
- BEP020: Extends BIDS to eye-tracking data: gaze position, pupil size and the events detected from them. Merged into the specification in January 2026. Leads: Dejan Draschkow, Martin Szinte.
- BEP028: Extends BIDS to record how a derivative was produced -- the provenance layer, carrying forward NIDM-PROV. Leads: Satrajit Ghosh, Camille Maumet, Yaroslav Halchenko.
- BEP032: Extends BIDS to microelectrode electrophysiology. Leads: Sylvain Takerkart, Benjamin Dichter, Yaroslav Halchenko, Lyuba Zehl, Andrew Davison.
- BEP044: Extends BIDS to describe the stimuli presented during an experiment. Leads: Seyed Yahya Shirazi, Dora Hermes, Yaroslav Halchenko, Kay Robbins, Scott Makeig.
- BIDS 2.0: The effort to work out what BIDS should change when backwards compatibility is no longer a constraint -- the breaking changes that a decade of 1.x could not make.
BEP028
Extends BIDS to record how a derivative was produced -- the provenance layer, carrying forward NIDM-PROV. Leads: Satrajit Ghosh, Camille Maumet, Yaroslav Halchenko.
BIDS 2.0
The effort to work out what BIDS should change when backwards compatibility is no longer a constraint -- the breaking changes that a decade of 1.x could not make.
BEP020
Extends BIDS to eye-tracking data: gaze position, pupil size and the events detected from them. Merged into the specification in January 2026. Leads: Dejan Draschkow, Martin Szinte.
BEP032
Extends BIDS to microelectrode electrophysiology. Leads: Sylvain Takerkart, Benjamin Dichter, Yaroslav Halchenko, Lyuba Zehl, Andrew Davison.
BEP044
Extends BIDS to describe the stimuli presented during an experiment. Leads: Seyed Yahya Shirazi, Dora Hermes, Yaroslav Halchenko, Kay Robbins, Scott Makeig.
Neurodata Without Borders: Neurophysiology (NWB:N)
NWB:N is a data standard for neurophysiology, providing neuroscientists with a common standard to share, archive, use, and build analysis tools for neurophysiology data. It is a standard supported by BIDS and the DANDI archive.
- con/nwb2bids: a CON tool to reorganize NWB files into a BIDS directory layout, enabling NWB-format datasets to be shared and validated using BIDS conventions.
DICOM
The standard for medical imaging data and its exchange, and the format every MRI scanner writes. Everything downstream -- BIDS layouts, HeuDiConv, ReproIn -- starts from what the scanner put in the DICOM header.
Components:
- DICOM WG-16: The DICOM working group responsible for MR. Where the conventions that make a scanner's output machine-readable are argued out, which is upstream of every BIDS conversion CON does.
- DICOM WG-32: The DICOM working group on imaging compression. CON's interest is empirical: what lossy compression actually costs downstream analyses, which is what the compression-comparisons studies measure.
DICOM WG-16
The DICOM working group responsible for MR. Where the conventions that make a scanner's output machine-readable are argued out, which is upstream of every BIDS conversion CON does.
DICOM WG-32
The DICOM working group on imaging compression. CON's interest is empirical: what lossy compression actually costs downstream analyses, which is what the compression-comparisons studies measure.
Components:
- compression-comparisons: Reproducing and extending the DICOM WG-32 compression work: what lossy compression costs, measured rather than assumed.
- compression-comparisons-t261-study: Reproduction of the compression-effects-on-spike-sorting paper, extended to the T.261 codec.
NIDM
A family of specifications for describing neuroimaging experiments, their data and their provenance, built on W3C PROV. NIDM-PROV is its provenance layer, and the ancestor of the provenance work now carried into BIDS as BEP028.
Initiatives
Highly specific region localization from naturally rich data
Localizing a functional region usually means running a dedicated localizer. This asks whether the same regions can be found in data that was rich to begin with -- a feature film, watched once -- and reports that a GLM is the wrong tool for it. Dormant; worth returning to.
- A. Wagner, S. Nastase, Y.O. Halchenko and M. Hanke (2019). Functional ROI localization in a rich stimuli dataset requires non-GLM style approach. OHBM 2019, Rome, Italy.
- S.A. Nastase, J.S. Guntupalli, J.V. Haxby and Y.O. Halchenko (2016). Localizing functional regions of interest based on responses to dynamic naturalistic stimuli. SfN 2016, San Diego.
- M. Visconti di Oleggio Castello, S.A. Nastase, M.I. Gobbini, J.V. Haxby and Y.O. Halchenko (2015). Finding Cortical Patches of Shared Representations. SfN 2015, Chicago, Poster 451.11.
Open Brain Consent
Open Brain Consent initiative aims to facilitate neuroimaging data sharing by providing an "out of the box" solution addressing aforementioned human subjects concerns and consisting of
- widely acceptable consent form allowing deposition of anonymized data to public data archives
- collection of tools/pipelines to help anonymization of neuroimaging data making it ready for sharing
Initiated and managed by CON, OBC is now an international effort with materials for GDPR compliance and multiple internationalization efforts to make sure materials are available in all applicable languages. Contribute if you see your language is missing!
- Bannier E, Barker G, Borghesani V, Broeckx N, Clement P, Emblem KE, Ghosh S, Glerean E, Gorgolewski KJ, Havu M, Halchenko YO, Herholz P, Hespel A, Heunis S, Hu Y, Hu CP, Huijser D, de la Iglesia Vayá M, Jancalek R, Katsaros VK, Kieseler ML, Maumet C, Moreau CA, Mutsaerts HJ, Oostenveld R, Ozturk-Isik E, Pascual Leone Espinosa N, Pellman J, Pernet CR, Pizzini FB, Trbalić AŠ, Toussaint PJ, Visconti di Oleggio Castello M, Wang F, Wang C, Zhu H.
The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data . Hum Brain Mapp. 2021 May;42(7):1945-1951. doi: 10.1002/hbm.25351. Epub 2021 Feb 1. PMID: 33522661; PMCID: PMC8046140.
MRI Phantoms Afterlife
Scanner QA data is acquired to watch the scanner, and then it is filed away. This asks what else it is good for -- whether the variance in routine phantom scans explains variance in the human data acquired around them, which would make years of accumulated QA scans a covariate rather than a formality. It runs on dbic/QA, the phantom, human and LEGO scans collected at DBIC.
- C.P. Cheng and Y.O. Halchenko (2020). A new virtue of phantom MRI data: explaining variance in human participant data. F1000Research, 9:1131.
ReproNim TR&D3
The technology research and development project within ReproNim that YOH leads at Dartmouth. In practice it is the tooling that makes a study reproducible end to end: capturing what the scanner and the stimulus computer actually did, converting it into standard layouts without a human in the loop, and re-running the analysis in a recorded environment.
Components:
- ReproEvents: An event relay device: a single-board microcontroller running micropython that sits between the input peripherals a participant uses and the event recording system, so button presses are timestamped by hardware rather than by whatever the stimulus computer was doing.
- ReproIn: A convention and a setup for naming sequences at the scanner so that conversion to a shareable, version-controlled BIDS dataset needs no human in the loop. Runs on HeuDiConv; deployed at DBIC.
- ReproMan: Tool for creating and managing reproducible computation environments and executing analyses in them.
- con/duct: Lightweight wrapper that records execution time, CPU and memory usage of a command, for resource-aware reproducible runs.
- ReproNim/containers: ReproNim/containers is a DataLad dataset providing a ready-to-use "distribution" of containerized neuroimaging environments (Singularity images) for reproducible analysis. It collects a curated set of containers — including those used by HeuDiConv and the broader ReproNim ecosystem — and exposes them via the ///repronim/containers DataLad dataset path, making it trivial to pin and share exact software environments alongside data and workflows.
Meetings
For over a decade, CON and its projects are presented annually with exhibits at the annual meetings of the Organization for Human Brain Mapping (OHBM) Society and the Society for Neuroscience. In collaboration with Soichi Hayashi and other wonderful people we proudly saved the first ever virtual OHBM 2020 by providing a virtual posters venue.distribits
distribits is a community for enthusiasts of tools and workflows in the domain of distributed data. Take a look at the topics of past meeting contributions, and YouTube videos from distribits 2024 to learn what is being talked about at distribits.
OHBM 2020 posters
OHBM 2020 posters was a centralized registry of posters and their Jitsi rooms for OHBM 2020, the first edition held entirely online. The conference had no working way for an attendee to find a poster and walk up to its author; this gave it one, as a browsable catalogue backed by a DataLad dataset. Twenty-three people contributed to it in the weeks around the meeting. Archived since 2022: it did its job for the meeting it was built for.
Education
DBIC Handbook
The working handbook for the Dartmouth Brain Imaging Center -- how to get scanned, how data leaves the scanner, and the conventions everything downstream assumes.
ReproNim: Reproducible Basics
Reproducible Basics training module of the ReproNim training curriculum presents daily core tools (shell, version control, etc) and explains how you could make your research more reproducible having gained improved knowledge of them.
- Y.O. Halchenko et al.
Infrastructure
SingularityHub (after-life)
To provide archival and uninterrupted access to over 9TBs of singularity-hub.org Singularity containers, we have established ///shub DataLad dataset and a service to serve all the shub:// URLs.
https://ror.org/04tfhh831