All notable changes to this project between releases will be documented in this file.
Handbook release matching the release of DataLad v0.13.0 With contributions from Dorian Pustina, Sarah Oliveira, Tristan Glatard, Hamzah Hamid Baagil, Giulia Ippoliti, Yaroslav Halchenko, Alex Waite, and Michael Hanke -- thank you!
- RF: Replace
datalad publish
withdatalad push
(#412) - RF: The Basics part was split into a Basics and Advanced part (#450). The chapters "Advanced Options" and "Go big or go home" have been moved/added there.
- Installation instructions for Windows subsystem for linux have been removed (#397)
- Installation instructions for rpm-based Linux distributions were added (#435)
- A "user-type" overview now serves as a guide through the handbook (#403)
- A stand-alone section
on
datalad push
summarizes all previous publishing-related information (#417) - A section for collecting gists (nifty code snippets for various tasks) is added to the chapter on help(#445)
datalad drop
is introduced in the first chapter (#463)- Gin's new feature of anonymous read-only access to datasets is now mentioned in the chapter on using third party infrastructure(#456)
- The section on getting help started to collect and explain common warnings and error messages (#418)
- A new chapter on scaling up with DataLad was added (#414)
- A section on configuring custom data access was added to the chapter "Advanced Options"(#440)
- The extension overview has been updated to a complete overview (#477)
- A new Usecase Scaling Up: Managing 80TB and 15 Million files from the HCP release was added (#225)
- Giulia Ippoliti contributed the Usecase Using Globus as a data store for the Canadian Open Neuroscience Portal (opened in #421, merged as #479)
- Introduction of a system to improve intersphinx linkage between the handbook and the technical docs & docstrings of DataLad (#377)
- Various improvements to the PDF version of the handbook (#367)
- Major toctree restructuring: Chapter-wise toctrees (#367), robustified URLs (#457)
- Addition of short, README-ready explanations of DataLad datasets for published projects (#370)
- Redirections are now possible, using a
?<label>
element afterhandbook.datalad.org/r.html
(#518) - (Almost) complete correspondence between HTML and PDF part, chapter, and section labeling (#500)
Beta stage release matching the release of datalad v0.12.0.
- RF: Replace
datalad install
withdatalad clone
(#326)
- High-level, one page description "What you really need to know" about DataLad (#295)
-
The DataLad Cheatsheet (#157)
-
Chapter "One step further" with content on advanced dataset nesting (#226) and computational reproducibility with the
datalad-containers
extension (#242) -
Chapter "Further options" with content on DataLad's result hooks (#304), an overview on DataLad's extensions (#242), and how to keep clean datasets despite untracked contents (#84)
-
Chapter "Third party infrastructure" on how to use various hosting services to share DataLad datasets, with concrete demonstrations/step-by-step instructions of sharing via Dropbox and GIN (#111)
-
Section "Frequently Asked Questions" (#239)
-
Section "Back and forth in time" on interacting with dataset history with Git tools/commands (#106)
-
Section "YODA-compliant data analysis project" with an example data science project (including Python API) (#226)
-
Include
datalad download-url
in first chapter to emphasize provenance capture abilities of DataLad (#294)
-
Use case "An automatically reproducible analysis of public neuroimaging data" (#205)
-
Use case "Building a scalable data storage for scientific computing" (#223)
-
Adjust contents to autorunrecord update to record a flexible set of code snippets in "casts" for live demonstrations. Add cast associations for existing contents with speakernotes (#219)
-
Additional book segment "Code lists from chapters" with code lists used for workshops (#273
-
Tagged "showroom" repositories with branches reflecting dataset states at different book sections (#341)
Alpha stage release with handbook content covering most of the core commands.
-
Chapter "DataLad datasets" on local version control (create, save, status, install)
-
Chapter "DataLad, Run!" on reproducible execution with
datalad run
anddatalad rerun
-
Chapter "Under the hood: git-annex" on the dataset annex and the
text2git
procedure -
Chapter "Collaboration" on sharing datasets (on the same computational infrastructure), siblings, and updating.
-
Chapter "Tuning datasets to your needs" on various configurations
-
Chapter "Help yourself" on common file system operations and on help.
-
Chapter "Make the most out of datasets" about the YODA principles
-
Use case "A typical collaborative data management workflow"
-
Use case "Basic provenance tracking"
-
Use case "Basic provenance tracking"
-
Use case "Student supervision in a research project"