A multithreaded package to validate, curate, and transform large heterogeneous datasets using reproducible recipes, which can be created both in TOML human readable format, or in Julia.
A key aim of this package is that recipes can be read/written by any researcher without the need for being able to write code, making data sharing/validation faster, more accurate, and reproducible.
DataCurator is a Swiss army knife that ensures:
- pipelines can focus on the algorithm/problem solving
- human readable "recipes" for future reproducibility
- validation of huge datasets at high speed
- out-of-the-box operation without the need for code or dependencies
DataCurator requires a command-line interface and is supported on Linux, Windows Subsystem for Linux (WSL2), and MacOS. See Quickstart and Installation for detail.
- Quickstart via Singularity
- Status
- Documentation (including installation)
- What to Find Where
- Publication/Cite
- Troubleshooting
The recommended way to use DataCurator is via the Singularity container. Note this is only supported in Linux, Windows Subsystem for Linux (WSL2), and MacOS (x86). For ARM-based Macs (e.g. from early 2021 onward), use the Docker container or source codes - see installation for detail.
wget https://github.com/apptainer/singularity/releases/download/v3.8.7/singularity-container_3.8.7_amd64.deb
sudo apt-get install ./singularity-container_3.8.7_amd64.debPlease refer to the Singularity docs.
After installation, test by typing in a terminal singularity --version. This will return singularity version 3.8.7
singularity pull datacurator.sif library://bcvcsert/datacurator/datacurator:latestThe container image can be also found at Sylabs.
chmod u+x ./datacurator.sifDepending on the directory you're in, you may need to grant Singularity read/write access. By default Singularity has read/write access to $HOME, no other directory.
export SINGULARITY_BINDPATH=${PWD}
wget https://raw.githubusercontent.com/bencardoen/DataCurator.jl/main/example_recipes/count.tomlmkdir testdir
touch testdir/text.txt./datacurator.sif -r count.tomlThat should show output similar to
The recipe used can be found here.
What next? Check out two simple examples of use cases and TOML recipes, and follow that with the large collection of well commented example recipes or the complete walkthrough of DataCurator. Please see the documentation.
The outcome of automated tests (including building on Mac OS & Debian docker image) :
Code coverage (which parts of the source code are tested) :
For full documentation, click here >> 
repository
├── example_recipes ## Start here for easy to copy example recipes
├── docs
│ ├── src ## Documentation in markdown format (viewable online as well)
│ │ ├── make.jl ## `cd docs && julia --project=.. make.jl` to rebuild docs
├── singularity ## Singularity image instructions
├── src ## source code of the package itself
├── scripts ## Utility scripts to run DC, generate test data, ...
├── test ## test suite and related files
└── runjulia.sh ## Required for Singularity image
└── buildimage.sh ## Rebuilds singularity image for you (Needs root !!)
@article{10.1093/bioadv/vbad068,
author = {Cardoen, Ben and Ben Yedder, Hanene and Lee, Sieun and Nabi, Ivan Robert and Hamarneh, Ghassan},
title = "{DataCurator.jl: efficient, portable and reproducible validation, curation and transformation of large heterogeneous datasets using human-readable recipes compiled into machine-verifiable templates}",
journal = {Bioinformatics Advances},
volume = {3},
number = {1},
pages = {vbad068},
year = {2023},
month = {06},
abstract = "{Large-scale processing of heterogeneous datasets in interdisciplinary research often requires time-consuming manual data curation. Ambiguity in the data layout and preprocessing conventions can easily compromise reproducibility and scientific discovery, and even when detected, it requires time and effort to be corrected by domain experts. Poor data curation can also interrupt processing jobs on large computing clusters, causing frustration and delays. We introduce DataCurator, a portable software package that verifies arbitrarily complex datasets of mixed formats, working equally well on clusters as on local systems. Human-readable TOML recipes are converted into executable, machine-verifiable templates, enabling users to easily verify datasets using custom rules without writing code. Recipes can be used to transform and validate data, for pre- or post-processing, selection of data subsets, sampling and aggregation, such as summary statistics. Processing pipelines no longer need to be burdened by laborious data validation, with data curation and validation replaced by human and machine-verifiable recipes specifying rules and actions. Multithreaded execution ensures scalability on clusters, and existing Julia, R and Python libraries can be reused. DataCurator enables efficient remote workflows, offering integration with Slack and the ability to transfer curated data to clusters using OwnCloud and SCP. Code available at: https://github.com/bencardoen/DataCurator.jl.}",
issn = {2635-0041},
doi = {10.1093/bioadv/vbad068},
url = {https://doi.org/10.1093/bioadv/vbad068},
eprint = {https://academic.oup.com/bioinformaticsadvances/article-pdf/3/1/vbad068/50693195/vbad068.pdf},
}If you have any issue, please search the issues to see if your problem has been encountered before. If not, please create a new issue, and follow the templates for bugs and / or features you wish to be added.
If you have a workflow that DataCurator right now does not support, or not the way you'd like it to, you can mention this too. In that case, do share a minimum example of your data so we can add, upon completion of the feature, a new testcase.
DataCurator relies heavily on existing Julia packages for specialized functionality: