Category: Data Curation, Open Data, and Research Data Management

"FAIR Principles: Interpretations and Implementation Considerations"

Annika Jacobsen, et al. have published "FAIR Principles: Interpretations and Implementation Considerations" in Data Intelligence.

Here's an excerpt:

The FAIR principles have been widely cited, endorsed and adopted by a broad range of stakeholders since their publication in 2016. By intention, the 15 FAIR guiding principles do not dictate specific technological implementations, but provide guidance for improving Findability, Accessibility, Interoperability and Reusability of digital resources. This has likely contributed to the broad adoption of the FAIR principles, because individual stakeholder communities can implement their own FAIR solutions. However, it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible implementations. Thus, while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways, for true interoperability we need to support convergence in implementation choices that are widely accessible and (re)-usable. We introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible, robust, widespread and consistent FAIR implementations. Any self-identified stakeholder community may either choose to reuse solutions from existing implementations, or when they spot a gap, accept the challenge to create the needed solution, which, ideally, can be used again by other communities in the future. Here, we provide interpretations and implementation considerations (choices and challenges) for each FAIR principle.

Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

"The History and Future of Data Citation in Practice"

Mark A. Parsons et al. have published "The History and Future of Data Citation in Practice" in Data Science Journal.

Here's an excerpt:

In this review, we adopt the definition that 'Data citation is a reference to data for the purpose of credit attribution and facilitation of access to the data' (TGDCSP 2013: CIDCR6). Furthermore, access should be enabled for both humans and machines (DCSG 2014). We use this to discuss how data citation has evolved over the last couple of decades and to highlight issues that need more research and attention.

Data citation is not a new concept, but it has changed and evolved considerably since the beginning of the digital age. Basic practice is now established and slowly but increasingly being implemented. Nonetheless, critical issues remain. These issues are primarily because we try to address multiple human and computational concerns with a system originally designed in a non-digital world for more limited use cases. The community is beginning to challenge past assumptions, separate the multiple concerns (credit, access, reference, provenance, impact, etc.), and apply different approaches for different use cases.

Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

"Practice Meets Principle: Tracking Software and Data Citations to Zenodo DOIs"

Stephanie van de Sandt et al. have self-archived "Practice Meets Principle: Tracking Software and Data Citations to Zenodo DOIs."

Here's an excerpt:

Data and software citations are crucial for the transparency of research results and for the transmission of credit. But they are hard to track, because of the absence of a common citation standard. As a consequence, the FORCE11 recently proposed data and software citation principles as guidance for authors. Zenodo is recognized for the implementation of DOIs for software on a large scale. The minting of complementary DOIs for the version and concept allows measuring the impact of dynamic software. This article investigates characteristics of 5,456 citations to Zenodo data and software that were captured by the Asclepias Broker in January 2019. We analyzed the current state of data and software citation practices and the quality of software citation recommendations with regard to the impact of recent standardization efforts. Our findings prove that current citation practices and recommendations do not match proposed citation standards. We consequently suggest practical first steps towards the implementation of the software citation principles.

Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

"Different Preservation Levels: The Case of Scholarly Digital Editions"

Elias Oltmanns et al. have published "Different Preservation Levels: The Case of Scholarly Digital Editions" in Data Science Journal.

Here's an excerpt:

Ensuring the long-term availability of research data forms an integral part of data management services. Where OAIS compliant digital preservation has been established in recent years, in almost all cases the services aim at the preservation of file-based objects. In the Digital Humanities, research data is often represented in highly structured aggregations, such as Scholarly Digital Editions. Naturally, scholars would like their editions to remain functionally complete as long as possible. Besides standard components like webservers, the presentation typically relies on project specific code interacting with client software like webbrowsers. Especially the latter being subject to rapid change over time invariably makes such environments awkward to maintain once funding has ended. Pragmatic approaches have to be found in order to balance the curation effort and the maintainability of access to research data over time.

A sketch of four potential service levels aiming at the long-term availability of research data in the humanities is outlined: (1) Continuous Maintenance, (2) Application Conservation, (3) Application Data Preservation, and (4) Bitstream Preservation. The first being too costly and the last hardly satisfactory in general, we suggest that the implementation of services by an infrastructure provider should concentrate on service levels 2 and 3. We explain their strengths and limitations considering the example of two Scholarly Digital Editions.

Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

"Cultural Obstacles to Research Data Management and Sharing at TU Delft"

Esther Plomp et al. have published "Cultural Obstacles to Research Data Management and Sharing at TU Delft" in Insights.

Here's an excerpt:

Research data management (RDM) is increasingly important in scholarship. Many researchers are, however, unaware of the benefits of good RDM and unsure about the practical steps they can take to improve their RDM practices. Delft University of Technology (TU Delft) addresses this cultural barrier by appointing Data Stewards at every faculty. By providing expert advice and increasing awareness, the Data Stewardship project focuses on incremental improvements in current data and software management and sharing practices. This cultural change is accelerated by the Data Champions who share best practices in data management with their peers. The Data Stewards and Data Champions build a community that allows a discipline-specific approach to RDM. Nevertheless, cultural change also requires appropriate rewards and incentives. While local initiatives are important, and we discuss several examples in this paper, systemic changes to the academic rewards system are needed. This will require collaborative efforts of a broad coalition of stakeholders and we will mention several such initiatives. This article demonstrates that community building is essential in changing the code and data management culture at TU Delft.

Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

Engaging Researchers with Data Management: The Cookbook

Connie Clare, et al. have published "Engaging Researchers with Data Management: The Cookbook".

Here's an excerpt:

Engaging Researchers with Data Management is an invaluable collection of 24 case studies, drawn from institutions across the globe, that demonstrate clearly and practically how to engage the research community with RDM. These case studies together illustrate the variety of innovative strategies research institutions have developed to engage with their researchers about managing research data.

Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

"Evaluating Fair Maturity Through a Scalable, Automated, Community-Governed Framework"

Mark D. Wilkinson et al. have published "Evaluating Fair Maturity Through a Scalable, Automated, Community-Governed Framework" in Scientific Data.

Here's an excerpt:

We propose a scalable, automatable framework to evaluate digital resources that encompasses measurable indicators, open source tools, and participation guidelines, which come together to accommodate domain relevant community-defined FAIR assessments. The components of the framework are: (1) Maturity Indicators—community-authored specifications that delimit a specific automatically-measurable FAIR behavior; (2) Compliance Tests—small Web apps that test digital resources against individual Maturity Indicators; and (3) the Evaluator, a Web application that registers, assembles, and applies community-relevant sets of Compliance Tests against a digital resource, and provides a detailed report about what a machine "sees" when it visits that resource. We discuss the technical and social considerations of FAIR assessments, and how this translates to our community-driven infrastructure. We then illustrate how the output of the Evaluator tool can serve as a roadmap to assist data stewards to incrementally and realistically improve the FAIRness of their resources.

Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

Research Data Rights Management Guide

The Australian Data Research Commons has released the "Research Data Rights Management Guide."

Here's an excerpt:

When taken together, data management, copyright and licensing issues relating to data can be complicated. Data is complicated and can take many forms. It can be a seemingly random compilation of numbers, or it could be a complex dataset containing recorded interviews or creative works. Combined data is often unable to be separated into component parts, unlike chapters in a book, so, unlike a book, it is difficult to separate different copyright conditions that might apply to certain sections of a dataset. Apart from legal ownership, other factors such as policy and business requirements, and relationships and norms can impact on data licensing decisions. For example, grant funding agreements may require a certain licence to be applied to research data outputs, or, in some cases, expectations or norms in a particular field of study will impact on licensing decisions.

Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

"From Persistent Identifiers to Digital Objects to Make Data Science More Efficient "

Peter Wittenburg has published "From Persistent Identifiers to Digital Objects to Make Data Science More Efficient" in Data Intelligence.

Here's an excerpt:

In fact after 20 years of experience we can claim that there are trustworthy PID systems already in broad use. It is argued, however, that assigning PIDs is just the first step. If we agree to assign PIDs and also use the PID to store important relationships such as pointing to locations where the bit sequences or different metadata can be accessed, we are close to defining Digital Objects (DO) which could indeed indicate a solution to solve some of the basic problems in data management and processing. In addition to standardizing the way we assign PIDs, metadata and other state information we could also define a Digital Object Access Protocol as a universal exchange protocol for DOs stored in repositories using different data models and data organizations. We could also associate a type with each DO and a set of operations allowed working on its content which would facilitate the way to automatic processing which has been identified as the major step for scalability in data science and data industry. A globally connected group of experts is now working on establishing testbeds for a DO-based data infrastructure.

Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

Research Data Platform: "New Dryad Is Here"

Dryad has released "New Dryad Is Here."

Here's an excerpt:

Dryad’s newest features are centered around making data publishing as easy as possible for researchers:

  • In addition to supporting datasets as part of a journal submission, Dryad now also supports datasets being submitted independently
  • Data can be uploaded from cloud storage or lab servers
  • Datasets can be as large as 300GB
  • Datasets can easily be updated or versioned at any time in our process
  • Standardized data usage and citation statistics are updated and displayed for each published dataset
  • Data can be submitted and downloaded through our new REST APIs

Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

"Teaching Practical Research Data Management Skills through Online Training and Data Management Plan Creation"

Beth Montague-Hellen and Holly Ranger have self-archived "Teaching Practical Research Data Management Skills through Online Training and Data Management Plan Creation."

Here's an excerpt:

Introduction: Research Data Management is growing in importance as a field as the amount of data which researchers must manage increases. It is important to ensure that postgraduate researchers are trained through engaging courses which practically prepare them to fulfil the data management requirements of funders and Universities, and to carry out their research in a transparent and effective manner. Description of program: We present a case study of the development and delivery of a new Research Data Management (RDM) online course for postgraduates and early career researchers. The course implements pedagogical theory and a reverse design paradigm in the development of library training provision enabling the creation of a course vastly more relevant to academic research practice than our previous offering. The course uses a simplified Data Management Plan to introduce students to Research Data Management Concepts, and by asking them to apply this knowledge, lifts the course from one which simply asks students to remember knowledge to one which shows them how to apply this knowledge in a way that is applicable to their own research. The course has been evaluated for effectiveness and student engagement at 3 months. Next steps: Although some analysis of the effectiveness of the new course has been undertaken, the course will continue to be evaluated. Although the course was developed for PGRs it has been popular with ECRs and Professional support staff and we will investigate how we can further meet the needs of these groups. The platform used will allow for the topics most often accessed to be identified and the course, and the University’s training provision will be adjusted based on this evidence. We hope that other institutions will be able to learn from our experience and implement similar courses.

Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

"Policy Needs to Go Hand in Hand with Practice: The Learning and Listening Approach to Data Management"

Maria Cruz et al. have published "Policy Needs to Go Hand in Hand with Practice: The Learning and Listening Approach to Data Management" in Data Science Journal.

Here's an excerpt:

In this paper, we explain our strategy for developing research data management policies at TU Delft. Policies can be important drivers for research institutions in the implementation of good data management practices. As Rans and Jones note (Rans and Jones 2013), "Policies provide clarity of purpose and may help in the framing of roles, responsibilities and requisite actions. They also legitimise making the case for investment". However, policy development often tends to place the researchers in a passive position, while they are the ones managing research data on a daily basis. Therefore, at TU Delft, we have taken an alternative approach: a policy needs to go hand in hand with practice. The policy development was initiated by the Research Data Services at TU Delft Library, but as the process continued, other stakeholders, such as legal and IT departments, got involved. Finally, the faculty-based Data Stewards have played a key role in leading the consultations with the research community that led to the development of the faculty-specific policies. This allows for disciplinary differences to be reflected in the policies and to create a closer connection between policies and day-to-day research practice. Our primary intention was to keep researchers and research practices at the centre of our strategy for data management. We did not want to introduce and mandate requirements before adequate infrastructure and professional support were available to our research community and before our researchers were themselves willing to discuss formalisation of data management practices.

This paper describes the key steps taken and the most important decisions made during the development of RDM policies at TU Delft.

Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap

"Decommissioning a Large Data Archive: Lessons Learned from Cleaning out the Attic"

Richard L. Moore et al. have self-archived "Decommissioning a Large Data Archive: Lessons Learned from Cleaning out the Attic"

Here's an excerpt:

This paper describes key elements of the decommissioning of a large tape-based data archive that the San Diego Supercomputer Center (SDSC) operated for its users from the center's inception in 1985 until ~2010. . . . Over the archive's last decade, data volume grew exponentially with a doubling period of ~16 months to a maximum size of ~10 PB. In ~2010, the National Science Foundation terminated funding for SDSC's tape archive and SDSC proceeded with decommissioning the archive over a ~2-year period. This paper briefly describes the principles and process by which we decommissioned this large archive, key issues that arose during this process, and implications for institutions that operate data archival systems and suggestions for operating archival systems in the FAIR data environment.

Research Data Curation Bibliography, Version 10 | Digital Curation and Digital Preservation Works | Open Access Works | Digital Scholarship | Digital Scholarship Sitemap