RDMkit: A research data management toolkit for life sciences

Pinar Alper; Flora D'Anna; Bert Droesbeke; Munazah Andrabi; Rafael Andrade Buono; Federico Bianchini; Korbinian Bösl; Ishwar Chandramouliswaran; Martin Cook; Daniel Faria; Nazeefa Fatima; Rob Hooft; Niclas Jareborg; Mijke Jetten; Diana Pilvar; Gil Poires-Oliveira; Marina Popleteeva; Laura Portell-Silva; Jan Slifka; Marek Suchánek; Celia van Gelder; Danielle Welter; Ulrike Wittig; Frederik Coppens; Carole Goble

doi:10.1016/j.patter.2025.101345

RDMkit: A research data management toolkit for life sciences

Pinar Alper
, Flora D'Anna
, Bert Droesbeke
, Munazah Andrabi
, Rafael Andrade Buono
, Federico Bianchini
, Korbinian Bösl
, Ishwar Chandramouliswaran
, Martin Cook
, Daniel Faria
, Nazeefa Fatima
, Rob Hooft
, Niclas Jareborg
, Mijke Jetten
, Diana Pilvar
, Gil Poires-Oliveira
, Marina Popleteeva
, Laura Portell-Silva
, Jan Slifka
, Marek Suchánek

Celia van Gelder, Danielle Welter, Ulrike Wittig, Frederik Coppens, Carole Goble

Data Integration Center

Research output: Contribution to journal › Article › Research › peer-review

Abstract

The rise of data-driven scientific investigations has made research data management (RDM) essential for good scientific practice. Implementing RDM is a complex challenge for research communities, infrastructures, and host organizations. Generic RDM guidelines often do not address practical questions, and disciplinary best practices can be overwhelming without proper context. Once guidelines are established, expanding their reach and keeping them up to date is challenging. The RDMkit is an open community-led resource designed as a gateway to reach the wealth of RDM knowledge, tools, training, and resources in life sciences. The RDMkit provides best-practice guidelines on common RDM tasks expected of data stewards and researchers, specific data management challenges and solutions from life science domains, and tool assemblies showcasing holistic solutions to support the research data life cycle. Built on a reusable open infrastructure, the RDMkit allows organizations to create their own guidelines using it as a blueprint.

Original language	English
Number of pages	15
Journal	Patterns
Volume	6
Issue number	9
DOIs	https://doi.org/10.1016/j.patter.2025.101345
Publication status	Published - 22 Aug 2025

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Alper, P., D'Anna, F., Droesbeke, B., Andrabi, M., Andrade Buono, R., Bianchini, F., Bösl, K., Chandramouliswaran, I., Cook, M., Faria, D., Fatima, N., Hooft, R., Jareborg, N., Jetten, M., Pilvar, D., Poires-Oliveira, G., Popleteeva, M., Portell-Silva, L., Slifka, J., ... Goble, C. (2025). RDMkit: A research data management toolkit for life sciences. Patterns, 6(9). https://doi.org/10.1016/j.patter.2025.101345

@article{be7e0263aed140a79200b061cdbfe96e,

title = "RDMkit: A research data management toolkit for life sciences",

abstract = "The rise of data-driven scientific investigations has made research data management (RDM) essential for good scientific practice. Implementing RDM is a complex challenge for research communities, infrastructures, and host organizations. Generic RDM guidelines often do not address practical questions, and disciplinary best practices can be overwhelming without proper context. Once guidelines are established, expanding their reach and keeping them up to date is challenging. The RDMkit is an open community-led resource designed as a gateway to reach the wealth of RDM knowledge, tools, training, and resources in life sciences. The RDMkit provides best-practice guidelines on common RDM tasks expected of data stewards and researchers, specific data management challenges and solutions from life science domains, and tool assemblies showcasing holistic solutions to support the research data life cycle. Built on a reusable open infrastructure, the RDMkit allows organizations to create their own guidelines using it as a blueprint.",

author = "Pinar Alper and Flora D'Anna and Bert Droesbeke and Munazah Andrabi and \{Andrade Buono\}, Rafael and Federico Bianchini and Korbinian B{\"o}sl and Ishwar Chandramouliswaran and Martin Cook and Daniel Faria and Nazeefa Fatima and Rob Hooft and Niclas Jareborg and Mijke Jetten and Diana Pilvar and Gil Poires-Oliveira and Marina Popleteeva and Laura Portell-Silva and Jan Slifka and Marek Such{\'a}nek and \{van Gelder\}, Celia and Danielle Welter and Ulrike Wittig and Frederik Coppens and Carole Goble",

note = "doi: 10.1016/j.patter.2025.101345 {\textcopyright} 2025 The Authors.",

year = "2025",

month = aug,

day = "22",

doi = "10.1016/j.patter.2025.101345",

language = "English",

volume = "6",

journal = "Patterns",

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Alper, P, D'Anna, F, Droesbeke, B, Andrabi, M, Andrade Buono, R, Bianchini, F, Bösl, K, Chandramouliswaran, I, Cook, M, Faria, D, Fatima, N, Hooft, R, Jareborg, N, Jetten, M, Pilvar, D, Poires-Oliveira, G, Popleteeva, M, Portell-Silva, L, Slifka, J, Suchánek, M, van Gelder, C, Welter, D, Wittig, U, Coppens, F & Goble, C 2025, 'RDMkit: A research data management toolkit for life sciences', Patterns, vol. 6, no. 9. https://doi.org/10.1016/j.patter.2025.101345

TY - JOUR

T1 - RDMkit: A research data management toolkit for life sciences

AU - Alper, Pinar

AU - D'Anna, Flora

AU - Droesbeke, Bert

AU - Andrabi, Munazah

AU - Andrade Buono, Rafael

AU - Bianchini, Federico

AU - Bösl, Korbinian

AU - Chandramouliswaran, Ishwar

AU - Cook, Martin

AU - Faria, Daniel

AU - Fatima, Nazeefa

AU - Hooft, Rob

AU - Jareborg, Niclas

AU - Jetten, Mijke

AU - Pilvar, Diana

AU - Poires-Oliveira, Gil

AU - Popleteeva, Marina

AU - Portell-Silva, Laura

AU - Slifka, Jan

AU - Suchánek, Marek

AU - van Gelder, Celia

AU - Welter, Danielle

AU - Wittig, Ulrike

AU - Coppens, Frederik

AU - Goble, Carole

PY - 2025/8/22

Y1 - 2025/8/22

N2 - The rise of data-driven scientific investigations has made research data management (RDM) essential for good scientific practice. Implementing RDM is a complex challenge for research communities, infrastructures, and host organizations. Generic RDM guidelines often do not address practical questions, and disciplinary best practices can be overwhelming without proper context. Once guidelines are established, expanding their reach and keeping them up to date is challenging. The RDMkit is an open community-led resource designed as a gateway to reach the wealth of RDM knowledge, tools, training, and resources in life sciences. The RDMkit provides best-practice guidelines on common RDM tasks expected of data stewards and researchers, specific data management challenges and solutions from life science domains, and tool assemblies showcasing holistic solutions to support the research data life cycle. Built on a reusable open infrastructure, the RDMkit allows organizations to create their own guidelines using it as a blueprint.

AB - The rise of data-driven scientific investigations has made research data management (RDM) essential for good scientific practice. Implementing RDM is a complex challenge for research communities, infrastructures, and host organizations. Generic RDM guidelines often do not address practical questions, and disciplinary best practices can be overwhelming without proper context. Once guidelines are established, expanding their reach and keeping them up to date is challenging. The RDMkit is an open community-led resource designed as a gateway to reach the wealth of RDM knowledge, tools, training, and resources in life sciences. The RDMkit provides best-practice guidelines on common RDM tasks expected of data stewards and researchers, specific data management challenges and solutions from life science domains, and tool assemblies showcasing holistic solutions to support the research data life cycle. Built on a reusable open infrastructure, the RDMkit allows organizations to create their own guidelines using it as a blueprint.

UR - https://pubmed.ncbi.nlm.nih.gov/41040964/

U2 - 10.1016/j.patter.2025.101345

DO - 10.1016/j.patter.2025.101345

M3 - Article

C2 - 41040964

SN - 2666-3899

VL - 6

JO - Patterns

JF - Patterns

IS - 9

ER -

RDMkit: A research data management toolkit for life sciences

Abstract

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