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Automated experimentation methods are unlocking a new data-rich research paradigm in materials science that promises to accelerate the pace of materials discovery. However, if our data management practices do not keep pace with progress in automation, this revolution threatens to drown us in unusable data. In this perspective, we highlight the need to update data management practices to track, organize, process, and share data collected from laboratories with deeply integrated automation equipment. We argue that a holistic approach to data management that integrates multiple scales (experiment, group and community scales) is needed. We propose a vision for what this integrated data future could look like and compare existing work against this vision to find gaps in currently available data management tools. To realize this vision, we believe that development of standard protocols for communicating with equipment and data sharing, the development of new open-source software tools for managing data in research groups, and leadership and direction from funding agencies and other organizations are needed.
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The Value of a Data and Digital Object Management Plan (D(DO)MP) in Fostering Sharing Practices in a Multidisciplinary Multinational Project
Data Management Plans (DMP) are now a routine part of research proposals but are generally not referred to after funding is granted. The Belmont Forum requires an extensive document, a ‘Data and Digital Object Management Plan’ (D(DO)MP) for its awarded projects that is expected to be kept current over the life of the project. The D(DO)MP is intended to record team decisions about major tools and practices to be used over the life of the project for data and software stewardship, and for preservation of data and software products, aligned with the desired Open Science outcomes relevant to the project. Here we present one of the first instances of the use of Belmont’s D(DO)MP through a case study of the PARSEC project, a multinational and multidisciplinary investigation of the socioeconomic impacts of protected areas. We describe the development and revision of our interpretation of the D(DO)MP and discuss its adoption and acceptance by our research group. We periodically assessed the data management sophistication of team members and their use of the various nominated tools and practices. As a result, for example, we included summaries to enable the key components of the D(DO)MP to be readily viewed by the researcher. To meet the Open Science outcomes in a complex project like PARSEC, a comprehensive and appropriately structured D(DO)MP helps project leaders (a) ensure that team members are committed to the collaboration goals of the project, (b) that there is regular and effective feedback within the team, (c) training in new tools is provided as and when needed, and (d) there is easy access to a short reference to the tools and descriptions of the nominated practices.
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- PAR ID:
- 10525546
- Publisher / Repository:
- CODATA
- Date Published:
- Journal Name:
- Data Science Journal
- Volume:
- 22
- ISSN:
- 1683-1470
- Subject(s) / Keyword(s):
- Digital object management DMP collaboration transnational inter/multidisciplinary Open Science
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5334/dsj-2023-038
