Active Data Management Plans - Concept and Potential
Creators: name: Cardoso, João
name: Borbinha, José
Publisher: name: figshare
Year: 2018
Subjects: subject: 150301 Business Information Management (incl. Records, Knowledge and Information Management, and Intelligence)
subject: FOS: Economics and business
subject: FOS: Economics and business
subject: 80708 Records and Information Management (excl. Business Records and Information Management)
subject: FOS: Media and communications
subject: FOS: Media and communications

description: <b>Scientific Data</b><br>Scientific data is information collected using scpecific methods for the purpse of scientific research. Scientific data can be perceived to have to main origins: Hypothesis-driven science, where hypothesis are presented and data is collected to counter the theory; Data-driven science, where collected data is studied to establish patterns either within itself or other datasets. Scientific data has two main proveniences: Observation and measurement, where raw measures are recorded either automatically (by sensors, or instruments) or manually by scientists; Calculation, where data is generated based on computational analysis, generaly run by computers.<br><b>Scientific Data Management</b><b><br></b>With the large sets of Scientific Data that are produced nowadays, having clear methods policies and techniques to manage data is fundamental. The overall goal of Scientific Data Management , is to ease knowledge discovery and innovation, and to facilitate the integration and reuse of data and knowledge by the community after publication. <br>As there is a need to have infrastructure capable of supporting the reuse of scientific data, the FAIR data principles were introduced to act as guidelines to enhance reusability. FAIR stands for:<br>- Findable, easy to find for both humans and computer, with metadata that facilitates searching for specific datasets;<br>- Accessible, stored for long ter so that it can easily be accessed and/or downloaded with well-defined license and access conditions (open access when pssible), whether at the level of metadata, or at the level of the actual data;<br>- Interoperable, ready to be combined with other datasets by humans or computers;<br>- Reusable, ready to be used for future research and to be further processed using computational methods.<br><b>What is a DMP?</b><b><br></b>A Data Management Plan (DMP) is a document detailing how the scientific data from a research project is to be managed throughout its lifecycle. This implies describing the techniques, methods and policies, on how data is to be created, accessed, preserved, documented and dissiminated.<br><b>Issues with DMPs</b><b><br></b>Nowadays several funding agents require funding applications to be accompanied by a DMP. Usually funding agents provide their dedicated DMP templates for applicants, in which they specify requirements in accordance to their needs. The European Comission (EC), for example, clearly establishes in their H2020 DMP guidelines that projects should produce FAIR data. Requirements such as this are not always shared between different funding agents. Thus emphasizing the lack of a common DMP model.<br>The DMP is supposed to be a living document in which information is described with increasing detail as the project progresses. However the DMP is frequently a text document holding a static structure, that once created is rarely revisited. As a result, the DMP is seen more as a bureaucratic obligation, than as a valuable asset for data management.<br><b>Why an Active DMP?</b><b><br></b>The concept of an Active DMP (sometimes referred as "machine-actionable", "dynamic", "or "machine-readable" DMP) was brn from an initiative to introduce dynamic features to a DMP. Active DMPs were brought to light in conferences such as the International Digital Curation Conference 2017 (IDCC17), or through the recently created Research Data Alliance (RDA) work groups on Active DMP and DMP Common Standards.<br>There is great potential to be explored in DMPs. If seen as living documents, they could, for instance, support the application of the FAIR Data Principles. The concept of Active DMP could aid in this process by making the DMP machine-readable without compromising its human-readability. The adoption of an open, shared and interoperable concept of Active DMP could bring multiple benefits in other areas, such as: Easier data discovery, reuse, evaluation, monitoring and exchange. It could also facilitate the exchange of information between research tools and systems by embedding DMPs in existing workflows.<br><b>References</b><br>Petermr's Blog. (2010, July 25) PP1_0.1: What is Scientific Data? [Blog post]. Retrieved from https://blogs.ch.cam.ac.uk/pmr/2010/07/25/pp01-what-is-scientific-data/<br>Dutch Techcentre for Life Sciences, About FAIR Data, https://www.dtls.nl/fair-data/fair-data/<br>Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., ... &amp; Bouwman, J. (2016). The FAIR Guiding Principles for scientific data management and stewardship. <i>Scientific data</i>, <i>3</i>.<br><br>International Digital Curation Conference 2017 (IDCC17): International digital curation conference 2017 website, http://www.dcc.ac.uk/drupal/events/idcc17<br>Michener, W.K.: Ten simple rules for creating a good data management plan. PLoS computational biology 11(10), e1004525 (2015)<br>Miksa, T., Simms, S., Mietchen, D., Jones, S.: Ten simple rules for machine-actionable data management plans (preprint) (Feb 2018). https://doi.org/10.5281/zenodo.1172673<br>Research Data Alliance (RDA: Active dmp workgroup, https://www.rd-alliance.org/groups/active-data-management-plans.html<br>Research Data Alliance (RDA: Dmp common standards workgroup, https://www.rd-alliance.org/groups/dmp-common-standards-wg<br>Research Data Alliance (RDA: Rda website, https://www.rdalliance.org<br>Simms, S., Jones, S., Mietchen, D., Miksa, T.: Machine-actionable data management plans (madmps). Research Ideas and Outcomes 3, e13086 (2017). https://doi.org/10.3897/rio.3.e13086<br><br>