2022Health Information & Libraries JournalRequires access

Characterizing the research data management practices of NIH biomedical researchers indicates the need for better support at laboratory level

Sue Yeon Syn, Soojung Kim

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Abstract

BACKGROUND: The study investigated the research data management (RDM) practices of biomedical researchers at the National Institutes of Health (NIH) representing various biomedical disciplines. OBJECTIVES: This study aimed to analyse the state of biomedical researchers' RDM practices based on RDM practice levels (individual, laboratory, institution and external). The findings of the study are expected to provide directions to information professionals for effective RDM services. METHODS: Semi-structured interviews with 11 researchers were conducted. The interviews were analysed by levels of RDM practices. RESULTS: The findings revealed that biomedical researchers focus on storing and sharing data and that RDM is performed mainly at the individual level. There seems to be a lack of laboratory level RDM system that allows consistent RDM practices among researchers. External RDM practice is often challenged by not having one responsible for RDM. DISCUSSION: Findings suggested a need for an agreed RDM system and customized support, particularly at the laboratory level. Also, institutional support can help researchers prepare for long term data preservation. CONCLUSION: Our suggestions emphasize the importance of RDM training and support for long term data preservation, especially at the laboratory level.

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What this paper is about

BACKGROUND: The study investigated the research data management (RDM) practices of biomedical researchers at the National Institutes of Health (NIH) representing various biomedical disciplines. OBJECTIVES: This study aimed to analyse the state of biomedical researchers' RDM practices based on RDM practice levels (individual, laboratory, institution and external). The findings of the study are expected to provide directions to information professionals for effective RDM services. METHODS: Semi-structured interviews with 11 researchers were conducted. The interviews were analysed by levels of RDM practices. RESULTS: The findings revealed that biomedical researchers focus on storing and sharing data and that RDM is performed mainly at the individual level. There seems to be a lack of laboratory level RDM system that allows consistent RDM practices among researchers. External RDM practice is often challenged by not having one responsible for RDM. DISCUSSION: Findings suggested a need for an agreed RDM system and customized support, particularly at the laboratory level. Also, institutional support can help researchers prepare for long term data preservation. CONCLUSION: Our suggestions emphasize the importance of RDM training and support for long term data preservation, especially at the laboratory level.

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Available abstract

BACKGROUND: The study investigated the research data management (RDM) practices of biomedical researchers at the National Institutes of Health (NIH) representing various biomedical disciplines. OBJECTIVES: This study aimed to analyse the state of biomedical researchers' RDM practices based on RDM practice levels (individual, laboratory, institution and external). The findings of the study are expected to provide directions to information professionals for effective RDM services. METHODS: Semi-structured interviews with 11 researchers were conducted. The interviews were analysed by levels of RDM practices. RESULTS: The findings revealed that biomedical researchers focus on storing and sharing data and that RDM is performed mainly at the individual level. There seems to be a lack of laboratory level RDM system that allows consistent RDM practices among researchers. External RDM practice is often challenged by not having one responsible for RDM. DISCUSSION: Findings suggested a need for an agreed RDM system and customized support, particularly at the laboratory level. Also, institutional support can help researchers prepare for long term data preservation. CONCLUSION: Our suggestions emphasize the importance of RDM training and support for long term data preservation, especially at the laboratory level.

Key concepts: RDM, Computer science, Knowledge management, Data science, Quality (philosophy), Medicine, Epistemology, Philosophy

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