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-
Git Based & Version Control & Joint Management
for code, data, model and their relationship
> Delivers distributed data management system that keeps track of your data from code to PB scale dataset and ensures reproducibility.
@@ -58,7 +57,9 @@
JZFS 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.
-
+
+ 
+
Compared with code in software development, data tend not to be as precisely
identified because data versioning is rarely or only coarsely practiced. Scientific computation
@@ -93,7 +94,7 @@ all experimental metadata about the complete timeline of longitudinal and multim
including MRI, histology, electrophysiology, and behavior.
-Project planning and experimental details are recorded in an in-house relational cloud-based database.
+Project planning and experimental details are recorded in an in-house relational cloud-based database of GitData.AI.
A key element for both the database and the data storage is the
identifier, the study ID for each animal, used in a standardized fle name structure to make the data findable.
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+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 (Wilkinson et al.,
+2016). 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) (Hanke,
+Pestilli, et al., 2021). It features a Python and a command-line 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.
+
+
+Statement of Need
+Code, data and computing environments are core components of scientific projects. While
+the collaborative development and use of research software and code is streamlined with established procedures and infrastructures, such as software distributions, distributed version
+control systems, and social coding portals like GitHub, other components of scientific projects
+are not as transparently managed or accessible. Data consumption is complicated by disconnected data portals that require a large variety of different data access and authentication
+methods. Compared with code in software development, data tend not to be as precisely
+identified because data versioning is rarely or only coarsely practiced. Scientific computation
+is not reproducible enough, because data provenance, the information of how a digital file
+came to be, is often incomplete and rarely automatically captured. Last but not least, in
+the absence of standardized data packages, there is no uniform way to declare actionable
+data dependencies and derivative relationships between inputs and outputs of a computation. DataLad aims to solve these issues by providing streamlined, transparent management
+of code, data, computing environments, and their relationship. It provides targeted interfaces
+and interoperability adapters to established scientific and commercial tools and services to
+set up unobstructed, unified access to all elements of scientific projects. This unique set of
+features enables workflows that are particularly suited for reproducible science, such as actionable process provenance capture for arbitrary command execution that affords automatic
+re-execution. To this end, it builds on and extends two established tools for version control
+and transport logistics, Git and git-annex.
+
+
+Why Git and git-annex?
+Git is the most popular version control system for software development1
+. It is a distributed
+content management system, specifically tuned towards managing and collaborating on text
+files, and excels at making all committed content reliably and efficiently available to all clones
+of a repository. At the same time, Git is not designed to efficiently handle large (e.g., over
+a gigabyte) or binary files (see, e.g., Kenlon, 2016). This makes it hard or impossible to
+use Git directly for distributed data storage with tailored access to individual files. Gitannex takes advantage of Git’s ability to efficiently manage textual information to overcome
+this limitation. File content handled by git-annex is placed into a managed repository annex,
+which avoids committing the file content directly to Git. Instead, git-annex commits a compact
+reference, typically derived from the checksum of a file’s content, that enables identification
+and association of a file name with the content. Using these identifiers, git-annex tracks
+content availability across all repository clones and external resources such as URLs pointing
+to individual files on the web. Upon user request, git-annex automatically manages data
+transport to and from a local repository annex at a granularity of individual files. With this
+simple approach, git-annex enables separate and optimized implementations for identification
+and transport of arbitrarily large files, using an extensible set of protocols, while retaining the
+distributed nature and compatibility with versatile workflows for versioning and collaboration
+provided by Git.
+
+
+What does DataLad add to Git and git-annex?
+Easy to use modularization. Research workflows impose additional demands for an efficient research data management platform besides version control and data transport. Many
+research datasets contain millions of files, but a large number of files precludes managing
+such a dataset in a single Git repository, even if the total storage demand is not huge. Partitioning such datasets into smaller, linked components (e.g., one subdataset per sample in
+a dataset comprising thousands) allows for scalable management. Research datasets and
+projects can also be heterogeneous, comprising different data sources or evolving data across
+different processing stages, and with different pace. Beyond scalability, modularization into
+homogeneous components also enables efficient reuse of a selected subset of datasets and for
+recording a derivative relationship between datasets. Git’s submodule mechanism provides a
+way to nest individual repositories via unambiguously versioned linkage, but Git operations
+must still be performed within each individual repository. To achieve modularity without impeding usability, DataLad simplifies working with the resulting hierarchies of Git repositories
+via recursive operations across dataset boundaries. With this, DataLad provides a “monorepo”-like user experience in datasets with arbitrarily deep nesting, and makes it trivial to
+operate on individual files deep in the hierarchy or entire trees of datasets. A testament of
+this is datasets.datalad.org, created as the project’s initial goal to provide a data distribution
+with unified access to already available public data archives in neuroscience, such as crcns.org
+and openfmri.org. It is curated by the DataLad team and provides, at the time of publication,
+streamlined access to over 260 TBs of data across over 5,000 subdatasets from a wide range
+of projects and dozens of archives in a fully modularized way.
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