Archives des mots-clés : data reuse

Reusable, FAIR Humanities Data : Creating Practical Guidance for Authors at Routledge Open Research

Posté le par

Author : Rebecca Grant

While stakeholders including funding agencies and academic publishers implement more stringent data sharing policies, challenges remain for researchers in the humanities who are increasingly prompted to share their research data.

This paper outlines some key challenges of research data sharing in the humanities, and identifies existing work which has been undertaken to explore these challenges. It describes the current landscape regarding publishers’ research data sharing policies, and the impact which strong data policies can have, regardless of discipline.

Using Routledge Open Research as a case study, the development of a set of humanities-inclusive Open Data publisher data guidelines is then described. These include practical guidance in relation to data sharing for humanities authors, and a close alignment with the FAIR Data Principles.

URL : Reusable, FAIR Humanities Data : Creating Practical Guidance for Authors at Routledge Open Research

DOI : https://doi.org/10.2218/ijdc.v17i1.820

Increasing the Reuse of Data through FAIR-enabling the Certification of Trustworthy Digital Repositories

Posté le par

Authors : Benjamin Jacob Mathers, Hervé L’Hours

The long-term preservation of digital objects, and the means by which they can be reused, are addressed by both the FAIR Data Principles (Findable, Accessible, Interoperable, Reusable) and a number of standards bodies providing Trustworthy Digital Repository (TDR) certification, such as the CoreTrustSeal.

Though many of the requirements listed in the Core Trustworthy Data Repositories Requirements 2020–2022 Extended Guidance address the FAIR Data Principles indirectly, there is currently no formal ‘FAIR Certification’ offered by the CoreTrustSeal or other TDR standards bodies. To address this gap the FAIRsFAIR project developed a number of tools and resources that facilitate the assessment of FAIR-enabling practices at the repository level as well as the FAIRness of datasets within them.

These include the CoreTrustSeal+FAIRenabling Capability Maturity model (CTS+FAIR CapMat), a FAIR-Enabling Trustworthy Digital Repositories-Capability Maturity Self-Assessment template, and F-UJI ,  a web-based tool designed to assess the FAIRness of research data objects.

The success of such tools and resources ultimately depends upon community uptake. This requires a community-wide commitment to develop best practices to increase the reuse of data and to reach consensus on what these practices are.

One possible way of achieving community consensus would be through the creation of a network of FAIR-enabling TDRs, as proposed by FAIRsFAIR.

URL : Increasing the Reuse of Data through FAIR-enabling the Certification of Trustworthy Digital Repositories

DOI : https://doi.org/10.2218/ijdc.v17i1.852

Seeing oneself as a data reuser: How subjectification activates the drivers of data reuse in science

Posté le par

Authors : Marcel LaFlamme, Marion Poetz, Daniel Spichtinger

Considerable resources are being invested in strategies to facilitate the sharing of data across domains, with the aim of addressing inefficiencies and biases in scientific research and unlocking potential for science-based innovation.

Still, we know too little about what determines whether scientific researchers actually make use of the unprecedented volume of data being shared. This study characterizes the factors influencing researcher data reuse in terms of their relationship to a specific research project, and introduces subjectification as the mechanism by which these influencing factors are activated.

Based on our analysis of semi-structured interviews with a purposive sample of 24 data reusers and intermediaries, we find that while both project-independent and project-dependent factors may have a direct effect on a single instance of data reuse, they have an indirect effect on recurring data reuse as mediated by subjectification.

We integrate our findings into a model of recurring data reuse behavior that presents subjectification as the mechanism by which influencing factors are activated in a propensity to engage in data reuse.

Our findings hold scientific implications for the theorization of researcher data reuse, as well as practical implications around the role of settings for subjectification in bringing about and sustaining changes in researcher behavior.

URL : Seeing oneself as a data reuser: How subjectification activates the drivers of data reuse in science

DOI : https://doi.org/10.1371/journal.pone.0272153

Open research data: A case study into institutional and infrastructural arrangements to stimulate open research data sharing and reuse

Posté le par

Authors : Thijmen van Gend, Anneke Zuiderwijk

This study investigates which combination of institutional and infrastructural arrangements positively impact research data sharing and reuse in a specific case. We conducted a qualitative case study of the institutional and infrastructural arrangements implemented at Delft University of Technology in the Netherlands.

In the examined case, it was fundamental to change the mindset of researchers and to make them aware of the benefits of sharing data. Therefore, arrangements should be designed bottom-up and used as a “carrot” rather than as a “stick.” Moreover, support offered to researchers should cover at least legal, financial, administrative, and practical issues of research data management and should be informal in nature.

Previous research describes generic institutional and infrastructural instruments that can stimulate open research data sharing and reuse. This study is among the first to analyze what and how infrastructural and institutional arrangements work in a particular context. It provides the basis for other scholars to study such arrangements in different contexts.

Open data policymakers, universities, and open data infrastructure providers can use our findings to stimulate data sharing and reuse in practice, adapted to the contextual situation. Our study focused on a single case and a particular part of the university.

We recommend repeating this research in other contexts, that is, at other universities, faculties, and involving other research data infrastructure providers.

URL : Open research data: A case study into institutional and infrastructural arrangements to stimulate open research data sharing and reuse

DOI : https://doi.org/10.1177/09610006221101200

The Role of Data in an Emerging Research Community : Environmental Health Research as an Exemplar

Posté le par

Authors : Danielle Polloc, An Yan, Michelle Parker, Suzie Allard

Open science data benefit society by facilitating convergence across domains that are examining the same scientific problem. While cross-disciplinary data sharing and reuse is essential to the research done by convergent communities, so far little is known about the role data play in how these communities interact.

An understanding of the role of data in these collaborations can help us identify and meet the needs of emerging research communities which may predict the next challenges faced by science. This paper represents an exploratory study of one emerging community, the environmental health community, examining how environmental health research groups form, collaborate, and share data.

Five key insights about the role of data in emerging research communities are identified and suggestions are made for further research.

URL : The Role of Data in an Emerging Research Community : Environmental Health Research as an Exemplar

DOI : https://doi.org/10.2218/ijdc.v16i1.653

Integrative data reuse at scientifically significant sites: Case studies at Yellowstone National Park and the La Brea Tar Pits

Posté le par

Author : Andrea K. Thomer

Scientifically significant sites are the source of, and long-term repository for, considerable amounts of data—particularly in the natural sciences. However, the unique data practices of the researchers and resource managers at these sites have been relatively understudied.

Through case studies of two scientifically significant sites (the hot springs at Yellowstone National Park and the fossil deposits at the La Brea Tar Pits), I developed rich descriptions of site-based research and data curation, and high-level data models of information classes needed to support integrative data reuse.

Each framework treats the geospatial site and its changing natural characteristics as a distinct class of information; more commonly considered information classes such as observational and sampling data, and project metadata, are defined in relation to the site itself.

This work contributes (a) case studies of the values and data needs for researchers and resource managers at scientifically significant sites, (b) an information framework to support integrative reuse at these sites, and (c) a discussion of data practices at scientifically significant sites.

URL : Integrative data reuse at scientifically significant sites: Case studies at Yellowstone National Park and the La Brea Tar Pits

DOI : https://doi.org/10.1002/asi.24620

An overview of biomedical platforms for managing research data

Posté le par

Authors : Vivek Navale, Denis von Kaeppler, Matthew McAuliffe

Biomedical platforms provide the hardware and software to securely ingest, process, validate, curate, store, and share data. Many large-scale biomedical platforms use secure cloud computing technology for analyzing, integrating, and storing phenotypic, clinical, and genomic data. Several web-based platforms are available for researchers to access services and tools for biomedical research.

The use of bio-containers can facilitate the integration of bioinformatics software with various data analysis pipelines. Adoption of Common Data Models, Common Data Elements, and Ontologies can increase the likelihood of data reuse. Managing biomedical Big Data will require the development of strategies that can efficiently leverage public cloud computing resources.

The use of the research community developed standards for data collection can foster the development of machine learning methods for data processing and analysis. Increasingly platforms will need to support the integration of data from multiple disease area research.

URL : An overview of biomedical platforms for managing research data

DOI : https://doi.org/10.1007/s42488-020-00040-0