DataLad is a tool for the joint management of code, data, and their relationship, built on top of the version control systems Git & git-annex. It adapts principles of open-source software development & distribution to address challenges of data management, data sharing, & digital provenance capture.
DataLad is a Python-based tool for the joint management of code, data, and their relationship, built on top of a versatile system for data logistics (git-annex) and the most popular distributed version control system (Git). It adapts principles of open-source software development and distribution to address the technical challenges of data management, data sharing, and digital provenance collection across the life cycle of digital objects.
DataLad aims to make data management as easy as managing code. It streamlines procedures to consume, publish, and update data, for data of any size or type, and to link them as precisely versioned, lightweight dependencies. DataLad helps to make science more reproducible and FAIR. It can capture complete and actionable process provenance of data transformations to enable automatic re-computation. The DataLad project (datalad.org) delivers a completely open, pioneering platform for flexible decentralized research data management (RDM). It features a Python and a command-line interface as well as a dedicated graphical user interface, an extensible architecture, and does not depend on any centralized services but facilitates interoperability with a plurality of existing tools and services. In order to maximize its utility and target audience, DataLad is available for all major operating systems, and can be integrated into established workflows and environments with minimal friction.
Datalad. Tracks your data just like git tracks your code.
Doing a PhD equals working your way through many tutorials that can be quite boring. The @datalad handbook, however, is really enjoyable! Keeps my motivation for reproducible research up.
With the upcoming requirement of funding agencies for FAIR data management in Canada, we’ve started helping other neuroimaging centres in Montreal to transition to @datalad. Thank you, Datalad
Yesterday was the 1st time I used @datalad and I feel ashamed for not having looked into it earlier! Very beautiful, sophisticated and absolutely necessary piece of software!
The @datalad folks are doing God's work!
One of my favorite tools! Thank you, @datalad :)
@datalad is such a terrific tool for managing the evolution of your research project (code, data and beyond) in a transparent, reproducible and shareable way!
Likely one of the most impactful data sharing tools in the past few years. Go @datalad !
This is a fantastic tool for reproducible research which solves several issues and has IMO not enough attention so far.
Datalad can really help you simplify research data management and provides access to many data resources. like many tools it does have a learning curve to get comfortable. so spend the time using it and spend the time reporting issues.
Great tool to help you become the Marie Kondo of data and digital life!
The @datalad project doesn't receive nearly enough kudos, so here is an official endorsement tweet from yours truly \o/ #fromtartodatalad #justuseit
Datalad is awesome. The ability to easily maintain shared file trees with optional data-files across machines (though just the tip the iceberg of datalad's functionality) makes life SO much better :)
Creating @datalad datasets and analyzing them with "datalad run"... I'm going mad with power!!!
I want to talk about one of the best tools we use here at TIES: @datalad [...] It has allowed us to centralize data management in ways that previously have been difficult in academia.
This DataLad extension package equips DataLad's run/rerun functionality with the ability to transparently execute commands in containerized computational environments. On re-run, DataLad will automatically obtain any required container at the correct version prior execution.
This DataLad extension is a staging area for add-ons, for performance upgrades, and user experience improvements. Unlike other topical extensions, the focus is on functionality with broad applicability.
JTrack is a software(s) and mobile application ecosystem for digital biomarkers collection and remote assessment. JTrack is designed to collect health-related information from participants' smartphones. It also has a clinicians and administration dashboard for study, user, and data management.
