International Journal of Digital Curation
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Reusable, FAIR Humanities Data: Creating Practical Guidance for Authors at Routledge Open Research
Full text linkWhile 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
Putting the R into PlatfoRms
Full text linkThis paper looks at the question of how and why to bring about greater reusability of Research Platforms (variously called Virtual Laboratories, Virtual Research Environments, or Science Gateways). It begins with some context for the Australian Research Data Commons, where the authors are based. It then examines the infrastructure concerns that are driving the need for platforms to be created and remain sustainable, and the connection from this to reusability. The paper then proceeds to discuss the ways in which FAIR is being extended to a range of research objects and infrastructure elements, before reviewing the work of the FAIR4VREs WG. The core of the paper is an examination, with examples or case studies, of four different paradigms for platform reusability: accessing, adopting, adapting, and abstracting. The paper concludes by examining actions undertaken by the ARDC to increase the likelihood of reusability.
 
OpenCitations: an Open e-Infrastructure to Foster Maximum Reuse of Citation Data
Full text linkOpenCitations is an independent not-for-profit infrastructure organization for open scholarship dedicated to the publication of open bibliographic and citation data by the use of Semantic Web (Linked Data) technologies. OpenCitations collaborates with projects that are part of the Open Science ecosystem and complies with the UNESCO founding principles of Open Science, the I4OC recommendations, and the FAIR data principles that data should be Findable, Accessible, Interoperable and Reusable. Since its data satisfies all the Reuse guidelines provided by FAIR in terms of richness, provenance, usage licenses and domain-relevant community standards, OpenCitations provides an example of a successful open e-infrastructure in which the reusability of data is integral to its mission
The Data Life Aquatic: Oceanographers\u27 Experience with Interoperability and Re-usability
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This paper assesses data consumers’ perspectives on the interoperable and re-usable aspects of the FAIR Data Principles. Taking a domain-specific informatics approach, ten oceanographers were asked to think of a recent search for data and describe their process of discovery, evaluation, and use. The interview schedule, derived from the FAIR Data Principles, included questions about the interoperability and re-usability of data. Through this critical incident technique, findings on data interoperability and re-usability give data curators valuable insights into how real-world users access, evaluate, and use data. Results from this study show that oceanographers utilize tools that make re-use simple, with interoperability seamless within the systems used. The processes employed by oceanographers present a good baseline for other domains adopting the FAIR Data Principles. 
Data Curation Strategies to Support Responsible Big Social Research and Big Social Data Reuse
Full text linkBig social research repurposes existing data from online sources such as social media, blogs, or online forums, with a goal of advancing knowledge of human behavior and social phenomena. Big social research also presents an array of challenges that can prevent data sharing and reuse.
This brief report presents an overview of a larger study that aims to understand the data curation implications of big social research to support use and reuse of big social data. The study, which is based in the United States, identifies six key issues relating to big social research and big social data curation through a review of the literature. It then further investigates perceptions and practices relating to these six key issues through semi-structured interviews with big social researchers and data curators.
This report concludes with implications for data curation practice: metadata and documentation, connecting with researchers throughout the research process, data repository services, and advocating for community standards. Supporting responsible practices for using big social data can help scale up social science research, thus enhancing our understanding of human behavior and social phenomena
Who Writes Scholarly Code?
Full text linkThis paper presents original research about the behaviours, histories, demographics, and motivations of scholars who code, specifically how they interact with version control systems locally and on the Web. By understanding patrons through multiple lenses – daily productivity habits, motivations, and scholarly needs – librarians and archivists can tailor services for software management, curation, and long-term reuse, raising the possibility for long-term reproducibility of a multitude of scholarship
The Role of Data in an Emerging Research Community:: Environmental Health Research as an Exemplar
Full text linkOpen 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
First Line Research Data Management for Life Sciences: a Case Study
Full text linkModern life sciences studies depend on the collection, management and analysis of comprehensive datasets in what has become data-intensive research. Life science research is also characterised by having relatively small groups of researchers. This combination of data-intensive research performed by a few people has led to an increasing bottleneck in research data management (RDM). Parallel to this, there has been an urgent call by initiatives like FAIR and Open Science to openly publish research data which has put additional pressure on improving the quality of RDM. Here, we reflect on the lessons learnt by DataHub Maastricht, a RDM support group of the Maastricht University Medical Centre (MUMC+) in Maastricht, the Netherlands, in providing first-line RDM support for life sciences. DataHub Maastricht operates with a small core team, and is complemented with disciplinary data stewards, many of whom have joint positions with DataHub and a research group. This organisational model helps creating shared knowledge between DataHub and the data stewards, including insights how to focus support on the most reusable datasets. This model has shown to be very beneficial given limited time and personnel. We found that co-hosting tailored platforms for specific domains, reducing storage costs by implementing tiered storage and promoting cross-institutional collaboration through federated authentication were all effective features to stimulate researchers to initiate RDM. Overall, utilising the expertise and communication channel of the embedded data stewards was also instrumental in our RDM success. Looking into the future, we foresee the need to further embed the role of data stewards into the lifeblood of the research organisation, along with policies on how to finance long-term storage of research data. The latter, to remain feasible, needs to be combined with a further formalising of appraisal and reappraisal of archived research data
From Siloed to Reusable: The Opening of Digital Collections at Johns Hopkins University
Full text linkIn the past twenty-five years, cross-institutional communities have come together in the creation and use of open source software and open data standards to build digital collections (Madden, 2012). These librarians, developers, archivists, artists, and researchers recognize that the custom-built architectures and bespoke data structures of earlier digital collections development are unsustainable. Their collaborations have produced now-standard technologies such as Samvera, Fedora, GeoBlacklight, Islandora 8, as well as RDF, and JSON-LD among other open schemas. A core principle animating these efforts is reusability: data, schemas, and technologies in the open era must be coherent and flexible enough to be reused across multiple digital contexts. The authors of this paper show how reuse guided the migration of the Hopkins Digital Library from an outdated isolated system to a sustainable interconnected environment in GeoBlacklight, Islandora, with metadata based in Linked Open Data. Three areas of reuse focus this paper: the creation of robust interoperable metadata; the expansion of IIIF functionality to integrate the needs of the Hopkins Geoportal’s users; the development of a broadly re/usable data migration module focused on expanding a diverse community of invested users. In focusing on reusability as an organising principle of digital collections development, this case study shows how one digital curation team produced a platform that meets the changing and specific needs of an individual institution, on the one hand, and participated in and furthered the creative coherence of the open communities supporting the team’s work, on the other
On the Reusability of Data Cleaning Workflows
Full text linkThe goal of data cleaning is to make data fit for purpose, i.e., to improve data quality, through updates and data transformations, such that downstream analyses can be conducted and lead to trustworthy results. A transparent and reusable data cleaning workflow can save time and effort through automation, and make subsequent data cleaning on new data less errorprone. However, reusability of data cleaning workflows has received little to no attention in the research community. We identify some challenges and opportunities for reusing data cleaning workflows. We present a high-level conceptual model to clarify what we mean by reusability and propose ways to improve reusability along different dimensions. We use the opportunity of presenting at IDCC to invite the community to share their uses cases, experiences, and desiderata for the reuse of data cleaning workflows and recipes in order to foster new collaborations and guide future work