pISA-tree - a data management framework for life science research projects using a standardised directory tree

        We have developed pISA-tree, a straightforward and flexible data management solution for organisation of life science project-associated research data and metadata. It enables on the fly creation of enriched directory tree structure (
        ssay), via a series of sequential batch files in a standardised manner based upon the ISA metadata framework. Metadata, according to the system-provided metadata templates, is generated in parallel at each level. The system supports reproducible research and is in accordance with the Open Science initiative and FAIR principles. Compared with similar frameworks, it does not require any systems administration and maintenance as it can be run on a personal computer or network drive. It is complemented with two R packages,
        , where the former facilitates integration of the pISA-tree datasets into bioinformatic pipelines and the latter enables synchronisation with the FAIRDOMHub public repository using the SEEK API. Source code and detailed documentation of pISA-tree and its supporting R packages are available from
        . We demonstrate the usability of pISA-tree with two examples of medium sized life science projects. Accordingly, it is suitable and also currently used to manage larger projects including several partners from different countries. Since pISA-tree was initiated by end user requirements with an emphasis on practicality, it will facilitate adoption of FAIR data management practices and open science principles.

SEEK ID: https://fairdomhub.org/publications/651

DOI: 10.1101/2021.11.18.468977

Projects: pISA-tree

Publication type: Journal

Citation: biorxiv;2021.11.18.468977v2,[Preprint]

Date Published: 21st Nov 2021

Registered Mode: by DOI

help Submitter
Petek, M., Zagorščak, M., Blejec, A., Ramšak, Ž., Coll, A., Baebler, Š., & Gruden, K. (2021). pISA-tree - a data management framework for life science research projects using a standardised directory tree. In []. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2021.11.18.468977

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Created: 5th Sep 2022 at 13:53

Last updated: 5th Sep 2022 at 13:55

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