Abstract:Data management plans are free-form text documents describing the data used and produced in scientific experiments. The complexity of data-driven experiments requires precise descriptions of tools and datasets used in computations to enable their reproducibility and reuse. Data management plans fall short of these requirements. In this paper, we propose machine-actionable data management plans that cover the same themes as standard data management plans, but particular sections are filled with information obta… Show more
“…Finally, there will be still questions that can only be answered by humans, e.g., about ethical issues [15]. In such cases, an informed guess can cause more problems than solve.…”
Section: Principle 7: Make Dmps Available For Human and Machine Consumentioning
Data management plans (DMPs) are documents accompanying research proposals and project outputs. DMPs are created as free-form text and describe the data and tools employed in scientific investigations. They are often seen as an administrative exercise and not as an integral part of research practice.
There is now widespread recognition that the DMP can have more thematic, machine-actionable richness with added value for all stakeholders: researchers, funders, repository managers, research administrators, data librarians, and others. The research community is moving toward a shared goal of making DMPs machine-actionable to improve the experience for all involved by exchanging information across research tools and systems and embedding DMPs in existing workflows. This will enable parts of the DMP to be automatically generated and shared, thus reducing administrative burdens and improving the quality of information within a DMP.
This paper presents 10 principles to put machine-actionable DMPs (maDMPs) into practice and realize their benefits. The principles contain specific actions that various stakeholders are already undertaking or should undertake in order to work together across research communities to achieve the larger aims of the principles themselves. We describe existing initiatives to highlight how much progress has already been made toward achieving the goals of maDMPs as well as a call to action for those who wish to get involved.
“…Finally, there will be still questions that can only be answered by humans, e.g., about ethical issues [15]. In such cases, an informed guess can cause more problems than solve.…”
Section: Principle 7: Make Dmps Available For Human and Machine Consumentioning
Data management plans (DMPs) are documents accompanying research proposals and project outputs. DMPs are created as free-form text and describe the data and tools employed in scientific investigations. They are often seen as an administrative exercise and not as an integral part of research practice.
There is now widespread recognition that the DMP can have more thematic, machine-actionable richness with added value for all stakeholders: researchers, funders, repository managers, research administrators, data librarians, and others. The research community is moving toward a shared goal of making DMPs machine-actionable to improve the experience for all involved by exchanging information across research tools and systems and embedding DMPs in existing workflows. This will enable parts of the DMP to be automatically generated and shared, thus reducing administrative burdens and improving the quality of information within a DMP.
This paper presents 10 principles to put machine-actionable DMPs (maDMPs) into practice and realize their benefits. The principles contain specific actions that various stakeholders are already undertaking or should undertake in order to work together across research communities to achieve the larger aims of the principles themselves. We describe existing initiatives to highlight how much progress has already been made toward achieving the goals of maDMPs as well as a call to action for those who wish to get involved.
“…While the processes help individual organisations to streamline the discussion on machineactionable DMPs, we also developed 10 principles for machine-actionable DMPs that call for coordinated effort within the broad research data management community (Miksa, Simms, Mietchen and Jones [2019]). The principles "contain specific actions that various stakeholders are already undertaking or should undertake in order to work together across research communities to achieve the larger aims of the principles themselves" (Miksa, Simms, Mietchen and Jones [2019]). The principles also "describe existing initiatives to highlight how much progress has already been made toward achieving the goals of maDMPs as well as a call to action for those who wish to get involved" (Miksa, Simms, Mietchen and Jones [2019]).…”
Section: Processes and Guidelinesmentioning
confidence: 99%
“…Data Management Plans (DMPs) are documents that accompany research proposals and project outputs. "They describe the data that is used and produced during the course of research activities, where the data will be archived, which licenses and constraints apply, and to whom credit should be given" (Miksa, Simms, Mietchen and Jones [2019]). The existing practice of writing DMPs is primarily driven by research funders who consider DMPs not only to be planning, but also a steering and evaluation tool.…”
Section: Introductionmentioning
confidence: 99%
“…Community needs that led to establishing this working group are described in the paper by Simms et al [2017]. An initial model for maDMPs and its mapping to tools and standards was presented in Miksa et al [2017] and was used to kick-off developments by the group.…”
This paper presents the application profile for machine-actionable data management plans that allows information from traditional data management plans to be expressed in a machine-actionable way. We describe the methodology and research conducted to define the application profile. We also discuss design decisions made during its development and present systems which have adopted it. The application profile was developed in an open and consensus-driven manner within the DMP Common Standards Working Group of the Research Data Alliance and is its official recommendation.
“…Los PGDs son requeridos por una gran parte de las agencias de financiamiento y cada vez hay más iniciativas para mejorar su legibilidad por máquinas, de forma que los investigadores solo deban completar un mínimo de preguntas y el resto de la información sea recuperada de otras infraestructuras (p.ej. repositorio, bancos de publicaciones o ORCID) (Miksa, Simms, Mietchen y Jones, 2019;Miksa, Rauber, Ganguly y Budroni, 2017).…”
Section: La Gestión De Los Datos De Investigaciónunclassified
Los datos se han convertido en la base de la infraestructura de la ciencia. La implementación de políticas y servicios de gestión de datos de investigación (GDI) en contextos académicos deviene en factor crítico del éxito organizacional, lo que presenta nuevas oportunidades para el desarrollo de las bibliotecas universitarias y de investigación. A partir de un análisis bibliográfico sobre GDI y bibliotecas y otro exploratorio sobre los servicios de 34 bibliotecas universitarias y de investigación en Europa y América Latina, se extraen los principales puntos de actuación de estas en los procesos de GDI. Para completar el análisis exploratorio, se incluyen los resultados sobre los servicios de identificadores persistentes (PIDs). En muchos casos, los servicios de GDI se convierten en modelos de cooperación con otras unidades institucionales y los investigadores. Asimismo, se discuten las competencias de dos perfiles profesionales necesarios en este contexto: el bibliotecario de datos y el administrador de datos.
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