This paper discusses the quality of Data Management Plans(DMPs)in the health sector and assesses remove the researchers’perceptions of DMPs.We applied qualitative methods to examine publicly available DMPs in healthc...This paper discusses the quality of Data Management Plans(DMPs)in the health sector and assesses remove the researchers’perceptions of DMPs.We applied qualitative methods to examine publicly available DMPs in healthcare,analyzing researchers’views and practices for creating these plans.The study combines three research methods:analysis of DMPs in the health sector,semi-structured questionnaires,and interviews.Our findings reveal that researchers are generally unaware of the importance and usefulness of DMPs,and acknowledge various inconsistencies and challenges in their development.In this paper,we identified that data management practices need to be improved and advocate for automating them and making DMPs machine-actionable.We also recommend more educational programs,such as workshops and courses,in data management especially for researchers.Finally,we recommend defining clear,accessible guidelines for researchers to effectively elaborate DMPs,and institutionalizing data management within organizations by establishing data(or digital)competence centers.展开更多
A growing number of research funding organizations(RFOs)are taking responsibility to increase the scientific and social impact of research output.Also reusable research data are recognized as relevant output for gaini...A growing number of research funding organizations(RFOs)are taking responsibility to increase the scientific and social impact of research output.Also reusable research data are recognized as relevant output for gaining impact.RFOs are therefore promoting FAIR research data management and stewardship(RDM)in their research funding cycle.However,the implementation of FAIR RDM still faces important obstacles and challenges.To solve these,stakeholders work together to develop innovative tools and practices.Here we elaborate on the role of RFOs in developing a FAIR funding model to support the FAIR RDM in the funding cycle,integrated with research community specific guidance,criteria and metadata,and enabling automatic assessments of progress and output from RDM.The model facilitates to create research data with a high level of FAIRness that are meaningful for a research community.To fully benefit from the model,RFOs,research institutions and service providers need to implement machine actionability in their FAIR RDM tools and procedures.As many stakeholders still need to get familiar with“human actionable”FAIR data practices,the introduction of the model will be stepwise,with an active role of the RFOs in driving FAIR RDM processes as effectively as possible.展开更多
文摘This paper discusses the quality of Data Management Plans(DMPs)in the health sector and assesses remove the researchers’perceptions of DMPs.We applied qualitative methods to examine publicly available DMPs in healthcare,analyzing researchers’views and practices for creating these plans.The study combines three research methods:analysis of DMPs in the health sector,semi-structured questionnaires,and interviews.Our findings reveal that researchers are generally unaware of the importance and usefulness of DMPs,and acknowledge various inconsistencies and challenges in their development.In this paper,we identified that data management practices need to be improved and advocate for automating them and making DMPs machine-actionable.We also recommend more educational programs,such as workshops and courses,in data management especially for researchers.Finally,we recommend defining clear,accessible guidelines for researchers to effectively elaborate DMPs,and institutionalizing data management within organizations by establishing data(or digital)competence centers.
文摘A growing number of research funding organizations(RFOs)are taking responsibility to increase the scientific and social impact of research output.Also reusable research data are recognized as relevant output for gaining impact.RFOs are therefore promoting FAIR research data management and stewardship(RDM)in their research funding cycle.However,the implementation of FAIR RDM still faces important obstacles and challenges.To solve these,stakeholders work together to develop innovative tools and practices.Here we elaborate on the role of RFOs in developing a FAIR funding model to support the FAIR RDM in the funding cycle,integrated with research community specific guidance,criteria and metadata,and enabling automatic assessments of progress and output from RDM.The model facilitates to create research data with a high level of FAIRness that are meaningful for a research community.To fully benefit from the model,RFOs,research institutions and service providers need to implement machine actionability in their FAIR RDM tools and procedures.As many stakeholders still need to get familiar with“human actionable”FAIR data practices,the introduction of the model will be stepwise,with an active role of the RFOs in driving FAIR RDM processes as effectively as possible.
