Data Curation, Open Data, and Research Data Management

"Data Reuse among Digital Humanities Scholars: A Qualitative Study of Practices, Challenges and Opportunities"

The study investigates the challenges and opportunities in reusing research data among digital humanities (DH) scholars. Its findings may serve as a case study for how disciplinary practices influence the ways in which scholars reuse data. . . .

The study found that lack of time and resources, inconsistent data practices, technical training gaps, labour intensity and difficulties in finding data were the most challenging. Participants revealed a number of enabling factors in data reuse as well, and chief among them were collaboration and autodidacticism as a feature of DH. The results indicate a gap between data reusers and data sharers — low rates of sharing reduce the amount of findable and accessible data available for reuse. Both data reusers and data sharers must begin to see themselves as embedded into the research data lifecycle within the research infrastructure.

https://tinyurl.com/4hy77dsz

"ACME-FAIR: a Guide for Research Performing Organisations (RPO)"

The overall purpose of ACME-FAIR is to help those managing and delivering relevant professional services to self-assess how they are enabling researchers and their colleagues to do just that. Each part deals with one of the key issues that Research Performing Organisations (RPO) face in establishing the capabilities to put the FAIR principles into practice. . . . Each of the 7 guides has a thematic introduction, an overview of the relevant capabilities, and a rubric for assessing the levels of maturity and community engagement for each capability.

https://tinyurl.com/yckfdjtd

"An Approach to Assess the Quality of Jupyter Projects Published by GLAM Institutions"

Jupyter Notebooks have become a powerful tool to foster use of these collections by digital humanities researchers. Based on previous approaches for quality assessment, which have been adapted for cultural heritage collections, this paper proposes a methodology for assessing the quality of projects based on Jupyter Notebooks published by relevant GLAM institutions. A list of projects based on Jupyter Notebooks using cultural heritage data has been evaluated. Common features and best practices have been identified. A detailed analysis, that can be useful for organizations interested in creating their own Jupyter Notebooks projects, has been provided. Open issues requiring further work and additional avenues for exploration are outlined.

https://doi.org/10.1002/asi.24835

"Umbrella Data Management Plans to Integrate FAIR Data: Lessons From the ISIDORe and BY-COVID Consortia for Pandemic Preparedness"

The Horizon Europe project ISIDORe is dedicated to pandemic preparedness and responsiveness research. It brings together 17 research infrastructures (RIs) and networks to provide a broad range of services to infectious disease researchers. An efficient and structured treatment of data is central to ISIDORe’s aim to furnish seamless access to its multidisciplinary catalogue of services, and to ensure that users’ results are treated FAIRly. ISIDORe therefore requires a data management plan (DMP) covering both access management and research outputs, applicable over a broad range of disciplines, and compatible with the constraints and existing practices of its diverse partners.

Here, we describe how, to achieve that aim, we undertook an iterative, step-by-step, process to build a community-approved living document, identifying good practices and processes, on the basis of use cases, presented as proof of concepts. International fora such as the RDA and EOSC, and primarily the BY-COVID project, furnished registries, tools and online data platforms, as well as standards, and the support of data scientists. Together, these elements provide a path for building an umbrella, FAIR-compliant DMP, aligned as fully as possible with FAIR principles, which could also be applied as a framework for data management harmonisation in other large-scale, challenge-driven projects. Finally, we discuss how data management and reuse can be further improved through the use of knowledge models when writing DMPs and, how, in the future, an inter-RI network of data stewards could contribute to the establishment of a community of practice, to be integrated subsequently into planned trans-RI competence centres.

https://doi.org/10.5334/dsj-2023-035

"Data Management Plan Tools: Overview and Evaluation"

Data Management Plans (DMPs) are crucial for a structured research data management and often a mandatory part of research proposals. DMP tools support the development of DMPs. Among the variety of tools available, it can be difficult for researchers, data stewards and institutions to choose the one that is most appropriate for their specific needs and context. We evaluated 18 DMP tools according to 31 requirement parameters covering aspects relating to basic functions, technical aspects and user-friendliness. The highest total evaluation scores were reached by Data Stewardship Wizard (703.5), DMPTool (615.5) and RDMO NFDI4Ing (549.5). The tools evaluated satisfied between 10 % and 87 % of the requirement parameters. 11 tools cover at least half of the parameters. In terms of correlation among the tools, which indicates to which degree their scores in the different requirement parameters are alike, we found the highest correlation for ezDMP and GFBio DMPT. Regarding the relatedness between the tools, 85 % of the DMP tools were positively and 16 % negatively correlated. Accounting for the recent developments in the area of DMP tools, this study provides an up-to-date evaluation that can support tool developers in identifying potential improvements, and hosting institutions to select a tool suited to their specific needs.

https://tinyurl.com/yewhv8rn

"Understanding Barriers Affecting the Adoption and Usage of Open Access Data in the Context of Organizations"

Although the benefits of organizational adoption are significant, most OAD-related projects fail because of organizational barriers and resistance to adoption. This study first aims to find these organizational barriers to adopting OAD to raise awareness of the obstacles organizations must overcome. Towards this aim, after conducting a systematic literature review (SLR) and an expert panel, a research model based on the Technology – Organization – Environment (TOE) framework is proposed in this study. As a result of SLR, 97 barriers were identified from ten primary studies. After critically examining these barriers, a research model classifying 22 crucial barriers to organizational OAD adoption based on the TOE framework is proposed.

https://doi.org/10.1016/j.dim.2023.100049

Paywall: "Images as Metadata: A New Perspective for Describing Research Data"

Abstract Through studies and work developed over the last few years, we propose a new approach to description, where images can have a preponderant role in the description of data, assuming the role of metadata. We present several pieces of evidence, point out their challenges and determine the opportunities this new perspective can have in the research. Images have specific characteristics that can be leveraged in improving data description. Historical evidence establish that images have always been used and produced in research, yet their representational ability has never been harnessed to describe data and give more context to the scientific process.

https://doi.org/10.1080/19386389.2023.2252722

"Tracing Data: A Survey Investigating Disciplinary Differences in Data Citation"

Data citations, or citations in reference lists to data, are increasingly seen as an important means to trace data reuse and incentivize data sharing. Although disciplinary differences in data citation practices have been well documented via scientometric approaches, we do not yet know how representative these practices are within disciplines. Nor do we yet have insight into researchers’ motivations for citing — or not citing — data in their academic work. Here, we present the results of the largest known survey (n = 2,492) to explicitly investigate data citation practices, preferences, and motivations, using a representative sample of academic authors by discipline, as represented in the Web of Science (WoS). We present findings about researchers’ current practices and motivations for reusing and citing data and also examine their preferences for how they would like their own data to be cited. We conclude by discussing disciplinary patterns in two broad clusters, focusing on patterns in the social sciences and humanities, and consider the implications of our results for tracing and rewarding data sharing and reuse.

https://doi.org/10.1162/qss_a_00264

"Expanding the Data Ark: An Attempt to Make the Data from Highly Cited Social Science Papers Publicly Available"

Access to scientific data can enable independent reuse and verification; however, most data are not available and become increasingly irrecoverable over time. This study aimed to retrieve and preserve important datasets from 160 of the most highly-cited social science articles published between 2008-2013 and 2015-2018. We asked authors if they would share data in a public repository — the Data Ark — or provide reasons if data could not be shared. Of the 160 articles, data for 117 (73%, 95% CI [67% – 80%]) were not available and data for 7 (4%, 95% CI [0% – 12%]) were available with restrictions. Data for 36 (22%, 95% CI [16% – 30%]) articles were available in unrestricted form: 29 of these datasets were already available and 7 datasets were made available in the Data Ark. Most authors did not respond to our data requests and a minority shared reasons for not sharing, such as legal or ethical constraints. These findings highlight an unresolved need to preserve important scientific datasets and increase their accessibility to the scientific community.

https://doi.org/10.31222/osf.io/w9crz

"Towards a Toolbox for Automated Assessment of Machine-Actionable Data Management Plans"

Most research funders require Data Management Plans (DMPs). The review process can be time consuming, since reviewers read text documents submitted by researchers and provide their feedback. Moreover, it requires specific expert knowledge in data stewardship, which is scarce. Machine-actionable Data Management Plans (maDMPs) and semantic technologies increase the potential for automatic assessment of information contained in DMPs. However, the level of automation and new possibilities are still not well-explored and leveraged. This paper discusses methods for the automation of DMP assessment. It goes beyond generating human-readable reports. It explores how the information contained in maDMPs can be used to provide automated pre-assessment or to fetch further information, allowing reviewers to better judge the content. We map the identified methods to various reviewer goals.

https://doi.org/10.5334/dsj-2023-028

"Engaging with Researchers and Raising Awareness of FAIR and Open Science through the FAIR+ Implementation Survey Tool (FAIRIST)"

Seven years after the seminal paper on FAIR was published, that introduced the concept of making research outputs Findable, Accessible, Interoperable, and Reusable, researchers still struggle to understand how to implement the principles. For many researchers, FAIR promises long-term benefits for near-term effort, requires skills not yet acquired, and is one more thing in a long list of unfunded mandates and onerous requirements for scientists. Even for those required to, or who are convinced that they must make time for FAIR research practices, their preference is for just-in-time advice properly sized to the scientific artifacts and process. Because of the generality of most FAIR implementation guidance, it is difficult for a researcher to adjust to the advice according to their situation. Technological advances, especially in the area of artificial intelligence (AI) and machine learning (ML), complicate FAIR adoption, as researchers and data stewards ponder how to make software, workflows, and models FAIR and reproducible. The FAIR+ Implementation Survey Tool (FAIRIST) mitigates the problem by integrating research requirements with research proposals in a systematic way. FAIRIST factors in new scholarly outputs, such as nanopublications and notebooks, and the various research artifacts related to AI research (data, models, workflows, and benchmarks). Researchers step through a self-serve survey process and receive a table ready for use in their data management plan (DMP) and/or work plan. while gaining awareness of the FAIR Principles and Open Science concepts. FAIRIST is a model that uses part of the proposal process as a way to do outreach, raise awareness of FAIR dimensions and considerations, while providing timely assistance for competitive proposals.

https://doi.org/10.5334/dsj-2023-032

"The Effects of Research Data Management Services: Associating the Data Curation Lifecycle with Open Research Output"

This study seeks to understand the relationship between research data management (RDM) services framed in the data curation life cycle and the production of open data. An electronic questionnaire was distributed to US researchers and RDM specialists, and the results were analyzed using Chi-Square tests for association. The data curation life cycle does associate with the production of open data and shareable research, but tasks like data management plans have stronger associations with the production of open data. The findings analyze the intersection of these concepts and provide insight into RDM services that facilitate the production of open data and shareable research.

https://doi.org/10.5860/crl.84.5.751

"re3data — Indexing the Global Research Data Repository Landscape Since 2012"

For more than ten years, re3data, a global registry of research data repositories (RDRs), has been helping scientists, funding agencies, libraries, and data centers with finding, identifying, and referencing RDRs. As the world’s largest directory of RDRs, re3data currently describes over 3,000 RDRs on the basis of a comprehensive metadata schema. The service allows searching for RDRs of any type and from all disciplines, and users can filter results based on a wide range of characteristics. The re3data RDR descriptions are available as Open Data accessible through an API and are utilized by numerous Open Science services. re3data is engaged in various initiatives and projects concerning data management and is mentioned in the policies of many scientific institutions, funding organizations, and publishers. This article reflects on the ten-year experience of running re3data and discusses ten key issues related to the management of an Open Science service that caters to RDRs worldwide.

https://doi.org/10.1038/s41597-023-02462-y

"Data Management Plan Implementation, Assessments, and Evaluations: Implications and Recommendations"

Data management plans (DMPs) have become nearly a worldwide requirement for research funding. To meet these new funding agency expectations, information professionals across domains and the world have worked to create resources and services to successfully implement and sometimes assess DMPs. This essay presents a series of case studies from different institutions across the globe to highlight current practices and share recommendations for future work. A summary of various projects related to DMP implementation, assessment, and evaluation in different contexts provides a useful overview of current practices. The essay concludes with recommendations for practical oversight and scoring to improve DMPs’ utility in enabling the sharing of data.

https://doi.org/10.5334/dsj-2023-027

"Computational Reproducibility of Jupyter Notebooks from Biomedical Publications"

Jupyter notebooks facilitate the bundling of executable code with its documentation and output in one interactive environment, and they represent a popular mechanism to document and share computational workflows. The reproducibility of computational aspects of research is a key component of scientific reproducibility but has not yet been assessed at scale for Jupyter notebooks associated with biomedical publications. We address computational reproducibility at two levels: First, using fully automated workflows, we analyzed the computational reproducibility of Jupyter notebooks related to publications indexed in PubMed Central. We identified such notebooks by mining the articles full text, locating them on GitHub and re-running them in an environment as close to the original as possible. We documented reproduction success and exceptions and explored relationships between notebook reproducibility and variables related to the notebooks or publications. Second, this study represents a reproducibility attempt in and of itself, using essentially the same methodology twice on PubMed Central over two years. Out of 27271 notebooks from 2660 GitHub repositories associated with 3467 articles, 22578 notebooks were written in Python, including 15817 that had their dependencies declared in standard requirement files and that we attempted to re-run automatically. For 10388 of these, all declared dependencies could be installed successfully, and we re-ran them to assess reproducibility. Of these, 1203 notebooks ran through without any errors, including 879 that produced results identical to those reported in the original notebook and 324 for which our results differed from the originally reported ones. Running the other notebooks resulted in exceptions. We zoom in on common problems, highlight trends and discuss potential improvements to Jupyter-related workflows associated with biomedical publications.

https://arxiv.org/abs/2308.07333

More about Jupyter notebooks.

The Jupyter Notebook is an interactive computing environment that enables users to author notebook documents that include code, interactive widgets, plots, narrative text, equations, images and even video! The Jupyter name comes from 3 programming languages: Julia, Python, and R. It is a popular tool for literate programming. Donald Knuth first defined literate programming as a script, notebook, or computational document that contains an explanation of the program logic in a natural language (e.g. English or Mandarin), interspersed with snippets of macros and source code, which can be compiled and rerun. You can think of it as an executable paper!

"Actually Accessible Data: An Update and a Call to Action"

As funder, journal, and disciplinary norms and mandates have foregrounded obligations of data sharing and opportunities for data reuse, the need to plan for and curate data sets that can reach researchers and end-users with disabilities has become even more urgent. We begin by exploring the disability studies literature, describing the need for advocacy and representation of disabled scholars as data creators, subjects, and users. We then survey the landscape of data repositories, curation guidelines, and research-data-related standards, finding little consideration of accessibility for people with disabilities. We suggest three sets of minimal good practices for moving toward truly accessible research data: 1) ensuring Web accessibility for data repositories; 2) ensuring accessibility of common text formats, including those used in documentation; and 3) enhancement of visual and audiovisual materials. We point to some signs of progress in regard to truly accessible data by highlighting exemplary practices by repositories, standards, and data professionals. Accessibility needs to become a mainstream component of curation practice included in every training, manual, and primer.

https://tinyurl.com/2p8p4dau

| Research Data Publication and Citation Bibliography | Research Data Sharing and Reuse Bibliography | Research Data Curation and Management Bibliography | Digital Scholarship |

"A Decade of Surveys on Attitudes to Data Sharing Highlights Three Factors for Achieving Open Science"

Over a 10 year period Carol Tenopir of DataONE and her team conducted a global survey of scientists, managers and government workers involved in broad environmental science activities about their willingness to share data and their opinion of the resources available to do so. . . .

The most surprising result was that a higher willingness to share data corresponded with a decrease in satisfaction with data sharing resources across nations (e.g., skills, tools, training) (Fig.1). That is, researchers who did not want to share data were satisfied with the available resources, and those that did want to share data were dissatisfied. Researchers appear to only discover that the tools are insufficient when they begin the hard work of engaging in open science practices. This indicates that a cultural shift in the attitudes of researchers needs to precede the development of support and tools for data management.

https://tinyurl.com/4sx54c6d

Data Sharing for Research: A Compendium of Case Studies, Analysis, and Recommendations

This report contains eight case studies that look at specific corporate/academic data-sharing partnerships in depth, from initiation through the publication of research findings. These case studies illuminate practical challenges for implementing corporate data sharing with researchers. Some common themes that emerged from the case studies include:

Successful data-sharing partnerships use Data-Sharing Agreements that require both the company and researchers to take steps to protect privacy.

Some of the challenges of data sharing include technical knowledge and infrastructure gaps between companies and researchers, and the continuing need for ethics and privacy review for industry-based research.

Promising practices for data sharing include the use of Privacy Enhancing Technologies and company-created, public-facing data-sharing menus to facilitate new partnerships.

While data sharing has significant costs and inherent risks, the risks can be managed, and the benefits to researchers, companies, and society make data sharing worth the effort.

https://tinyurl.com/a9axcscp

"Research Reproducibility Activities in Health Sciences Libraries"

Within medical and health sciences libraries, research reproducibility work and services are seldom described in those terms, and are often hidden within other data services. RR work is highly dependent on institutional context, such as availability of partners and institutional needs. Most of the RR work is handled by individuals or teams who tend to focus on data services broadly. Meaningful assessment of the work is not done well at present. Getting administrators, researchers, and other stakeholders to associate the library with RR is a particular challenge. Librarians who are interested in RR could learn from others who are doing the work, understand their institutional context, identify relevant institutional partners, and model RR practices in their own work.

https://doi.org/10.7191/jeslib.650

"Actually Accessible Data: An Update and a Call to Action"

As funder, journal, and disciplinary norms and mandates have foregrounded obligations of data sharing and opportunities for data reuse, the need to plan for and curate data sets that can reach researchers and end-users with disabilities has become even more urgent. We begin by exploring the disability studies literature, describing the need for advocacy and representation of disabled scholars as data creators, subjects, and users. We then survey the landscape of data repositories, curation guidelines, and research-data-related standards, finding little consideration of accessibility for people with disabilities. We suggest three sets of minimal good practices for moving toward truly accessible research data: 1) ensuring Web accessibility for data repositories; 2) ensuring accessibility of common text formats, including those used in documentation; and 3) enhancement of visual and audiovisual materials. We point to some signs of progress in regard to truly accessible data by highlighting exemplary practices by repositories, standards, and data professionals. Accessibility needs to become a mainstream component of curation practice included in every training, manual, and primer.

https://tinyurl.com/2p4au2ar

"Data Journals: Where Data Sharing Policy Meets Practice"

Data journals incorporate elements of traditional scholarly communications practices—reviewing for quality and rigor through editorial and peer-review—and the data sharing / open data movement—prioritizing broad dissemination through repositories, sometimes with curation or technical checks. Their goals for dataset review and sharing are recorded in journal-based data policies and operationalized through workflows. In this qualitative, small cohort semi-structured interview study of eight different journals that review and publish research data, we explored (1) journal data policy requirements, (2) data review standards, and (3) implementation of standardized data evaluation workflows. Differences among the journals can be understood by considering editors’ approaches to balancing the interests of varied stakeholders. Assessing data quality for reusability is primarily conditional on fitness for use which points to an important distinction between disciplinary and discipline-agnostic data journals.

https://doi.org/10.17615/nqtz-b568

"Who Re-Uses Data? A Bibliometric Analysis of Dataset Citations"

Open data is receiving increased attention and support in academic environments, with one justification being that shared data may be re-used in further research. But what evidence exists for such re-use, and what is the relationship between the producers of shared datasets and researchers who use them? Using a sample of data citations from OpenAlex, this study investigates the relationship between creators and citers of datasets at the individual, institutional, and national levels. We find that the vast majority of datasets have no recorded citations, and that most cited datasets only have a single citation. Rates of self-citation by individuals and institutions tend towards the low end of previous findings and vary widely across disciplines. At the country level, the United States is by far the most prominent exporter of re-used datasets, while importation is more evenly distributed. Understanding where and how the sharing of data between researchers, institutions, and countries takes place is essential to developing open research practices.

https://arxiv.org/abs/2308.04379

"Metadata Standard for Continuous Preservation, Discovery, and Reuse of Research Data in Repositories by Higher Education Institutions: A Systematic Review"

This systematic review synthesised existing research papers that explore the available metadata standards to enable researchers to preserve, discover, and reuse research data in repositories. This review provides a broad overview of certain aspects that must be taken into consideration when creating and assessing metadata standards to enhance research data preservation discoverability and reusability strategies. Research papers on metadata standards, research data preservation, discovery and reuse, and repositories published between January 2003 and April 2023 were reviewed from a total of five databases. The review retrieved 1597 papers, and 13 papers were selected in this review. We revealed 13 research articles that explained the creation and application of metadata standards to enhance preservation, discovery, and reuse of research data in repositories. Among them, eight presented the three main types of metadata, descriptive, structural, and administrative, to enable the preservation of research data in data repositories. We noted limited evidence on how these metadata standards can be used to enhance the discovery and reuse of research data in repositories to enable the preservation, discovery, and reuse of research data in repositories. No reviews indicated specific higher education institutions employing metadata standards for the research data created by their researchers. Repository designs and a lack of expertise and technology know-how were among the challenges identified from the reviewed papers. The review has the potential to influence professional practice and decision-making by stakeholders, including researchers, students, librarians, information communication technologists, data managers, private and public organisations, intermediaries, research institutions, and non-profit organizations.

https://doi.org/10.3390/info14080427

"ARL Awarded Grant to Continue Research on Institutional Expenses for Public Access to Research Data"

The US Institute of Museum and Library Services (IMLS) has awarded the Association of Research Libraries (ARL), in collaboration with Duke University, the University of Minnesota, and Washington University in St. Louis, all of whom are members of the Data Curation Network (DCN), a $741,921 National Leadership Grant to examine institutional expenses for public access to research data. This research builds upon ARL’s existing Realities of Academic Data Sharing initiative.

https://tinyurl.com/378dzab6

"Association of Research Libraries and California Digital Library Receive Grant to Advance Data Management and Sharing"

The Association of Research Libraries (ARL) and the California Digital Library (CDL) have received a $668,048 National Leadership Grant from the US Institute of Museum and Library Services (IMLS) to assist institutions in managing and sharing federally funded research data. This project will build a machine-actionable data-management plan (maDMP) tool by enhancing and developing new DMPTool features utilizing persistent identifiers (PIDs). CDL and ARL will work together to further strengthen institutional capacity for tracking research outputs by piloting the institutional integration of maDMPs across an academic campus and building community across institutions for maDMPs.

https://tinyurl.com/35x9d45z

| Research Data Publication and Citation Bibliography | Research Data Sharing and Reuse Bibliography | Research Data Curation and Management Bibliography | Digital Scholarship |