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
{"title":"RDMkit: A research data management toolkit for life sciences.","authors":"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","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"6 9","pages":"101345"},"PeriodicalIF":7.4000,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12485516/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Patterns","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.patter.2025.101345","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/9/12 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
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.
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