FAIR Data by Design : A Case of the DiVA Portal

FAIR Data Principles is a guideline for making data and other digital objects findable, accessible, interoperable, and reusable. Thus far, the traction and uptake of the principle are primarily in the domain of bio and natural sciences. The knowledge gap is the application of the FAIR Data Principles in designing data repositories for FAIR data in the academic data ecosystem. This paper provides a critical insight into how the principle can be utilised as a paradigm to design data that embodies the tenets of FAIR Data Principles. We conducted a case study of the DiVA portal, an information repository and finding tool in Sweden, to explicate FAIR data by design. The portal scored high in a qualitative assessment against the 15 facets of FAIR DataPrinciples, as illustrated by the high density of green cells in the traffic light rating matrix (see Table 1). It indicates the robustness of data in the portal that is easy to share, find, and reuse. This study suggests practitioners operationalise FAIR Data Principles in their data repositories by design through systems and policies underpinned by the principle. It would enrich data governance and management for the back office and data experiences for end users. The study also advances the knowledge base on data management through a granular exposition of FAIR data by design

FAIRifying STEM Data Ecosystem to Enhance Data Reuse

In the science, technology, engineering, and mathematics (STEM) community, academic and research workflows and work practices are increasingly mediated and informed by data. However, making digital materials and resources findable, accessible, interoperable, and reusable (FAIR) for teaching, learning, and research is an under-researched area. Thus, it is vital to examine the current data practices of STEM students and faculties and acquaint them with the FAIR data concept. FAIR Data Principles is a set of guidelines that underscore metadata, vocabularies, licences, and standards to enhance data reuse. A study was conducted among students and faculties in the STEM community of the Royal University of Bhutan (RUB) to unpack their current data practices and explore areas for improvement using the FAIR Data Principles. The STEM students and faculties of the RUB share and reuse digital materials and resources for teaching, learning, and research. Nevertheless, their data practice is not as widespread or desired in the literature on optimum data reuse. Moreover, the compliance of current data practices to the tenets of FAIR Data Principles is not satisfactory. A pragmatic solution is complementing data practices with policies and infrastructural systems that underscore FAIR Data Principles. A sensitisation programme such as seminars and hands-on exercises on data FAIRification is crucial to familiarise people with the essentialness of FAIR data, and doing so will provide a platform to develop their repertoire for FAIRifying data and encourage systematic sharing and reuse of data. An in-depth account of the FAIRifying STEM data ecosystem in the study contributes to the growing knowledge base on adopting FAIR Data Principles in other areas of data-informed work and life

FAIRifying STEM Data Ecosystem to Enhance Data Reuse

In the science, technology, engineering, and mathematics (STEM) community, academic and research workflows and work practices are increasingly mediated and informed by data. However, making digital materials and resources findable, accessible, interoperable, and reusable (FAIR) for teaching, learning, and research is an under-researched area. Thus, it is vital to examine the current data practices of STEM students and faculties and acquaint them with the FAIR data concept. FAIR Data Principles is a set of guidelines that underscore metadata, vocabularies, licences, and standards to enhance data reuse. A study was conducted among students and faculties in the STEM community of the Royal University of Bhutan (RUB) to unpack their current data practices and explore areas for improvement using the FAIR Data Principles. The STEM students and faculties of the RUB share and reuse digital materials and resources for teaching, learning, and research. Nevertheless, their data practice is not as widespread or desired in the literature on optimum data reuse. Moreover, the compliance of current data practices to the tenets of FAIR Data Principles is not satisfactory. A pragmatic solution is complementing data practices with policies and infrastructural systems that underscore FAIR Data Principles. A sensitisation programme such as seminars and hands-on exercises on data FAIRification is crucial to familiarise people with the essentialness of FAIR data, and doing so will provide a platform to develop their repertoire for FAIRifying data and encourage systematic sharing and reuse of data. An in-depth account of the FAIRifying STEM data ecosystem in the study contributes to the growing knowledge base on adopting FAIR Data Principles in other areas of data-informed work and life

Conservation threats to the endangered golden langur (Trachypithecus geei, Khajuria 1956) in Bhutan

Threat assessment is critical to species conservation and management planning, because prior identification and assessment of key threats to conservation planning can assist in developing appropriate interventions or strategies. Comprehensive threat assessments are currently lacking for many threatened primates. In this paper, we classify and rank all direct threats to the endangered golden langur (Trachypithecus geei) in Bhutan in order to provide a practical guide to future conservation of the species. Information on threats was based on interviews with local people, discussion with field forestry staff, and social media interaction. We classified threats to golden langur habitats and populations, and ranked them using Miradi™, an analytical software for the adaptive management of conservation projects. We identified five habitat threats: (1) hydropower development, (2) road development, (3) housing development, (4) resource extraction, and (5) agricultural expansion. We also identified seven population threats: (1) electrocution, (2) road kill, (3) road injury, (4) dog kill, (5) retaliatory killing, (6) illegal pet keeping, and (7) hybridization with capped langurs. We rated the overall threat to golden langurs in Bhutan as 'medium'. Hydropower, road, and housing development constituted 'high' impact, while agricultural expansion, resource extraction, electrocution, and road kill had 'medium' impact; the remaining threats had 'low' impact. To immediately mitigate threats to golden langurs, we recommend: (a) installing speed limit signage and speed breakers with strict enforcement of speed limits; (b) installing insulated electric cables and fencing around power transformers; and (c) reducing and restraining domestic dog populations