Data Curation, Open Data, and Research Data Management – Page 2

"Do Disappearing Data Repositories Pose a Threat to Open Science and the Scholarly Record? "

Only little more than half of the research data repositories in the sample have detailed strategies they use to mitigate data loss. It is important to note that none of the strategies analysed offers a permanent solution; instead, infrastructure maintenance requires continuous efforts. The burden of infrastructure maintenance and data preservation is currently placed on individual repositories alone; preservation systems comparable to those for scholarly texts, such as CLOCKSS, are not widely spread and can be difficult to realise.

http://tinyurl.com/3snrhxpk

"DataCite Launches First Release of the Data Citation Corpus"

DataCite, in partnership with the Chan Zuckerberg Initiative (CZI), is delighted to announce the first release of the Data Citation Corpus. A major milestone in the Make Data Count initiative, the release makes eight million data citations openly available and usable for the first time via an interactive dashboard and public data file.

https://makedatacount.org/first-release-of-the-open-global-data-citation-corpus/

"Agile Research Data Management with Open Source: LinkAhead"

Research data management (RDM) in academic scientific environments increasingly enters the focus as an important part of good scientific practice and as a topic with big potentials for saving time and money. Nevertheless, there is a shortage of appropriate tools, which fulfill the specific requirements in scientific research. We identified where the requirements in science deviate from other fields and proposed a list of requirements which RDM software should answer to become a viable option. We analyzed a number of currently available technologies and tool categories for matching these requirements and identified areas where no tools can satisfy researchers’ needs. Finally we assessed the open-source RDMS (research data management system) LinkAhead for compatibility with the proposed features and found that it fulfills the requirements in the area of semantic, flexible data handling in which other tools show weaknesses.

https://doi.org/10.48694/inggrid.3866

"RDA Professionalising Data Stewardship — What Does a Career Track for Data Stewards Look Like?"

The report "What does a career track for data stewards look like?" provides an initial discussion of the results of the RDA IG Professionalising Data Stewardship career tracks survey completed in 2022. The survey asked respondents who self-identified as performing data stewardship roles about their job titles, educational background, match between educational background and area of professional activity, contract types, as well as future career perspectives. The report is of value to international and national projects and initiatives seeking to define and develop the professional role of data stewards as well as to organizations employing data stewards that seek to define career progression pathways for data stewards and better job satisfaction and job security of data stewards. The report is also of value to the emerging communities of data stewards because it provides evidence base for understanding which professionals already fulfill data stewardship roles and how these professionals perceive their career paths.

https://zenodo.org/records/10571388

"Towards a Shared Framework: A Classificatory Matrix for Teaching Data Standards"

Standards for research data can be a mystifying topic for both researchers and data professionals. A common source of confusion is that they are multipurpose: standards can (and should) be applied to both primary data and metadata, enabling a wide range of functions from the search features in a repository to the integration of disparate data sources. This paper reviews examples of classificatory approaches used by both librarians and researchers to describe data standards. This literature is synthesized into a classificatory matrix that can be used to map different types of standards. The matrix is constructed around two organizing principles: purpose (finding or using data) and type of information controlled (meaning or syntax). The objective of this classificatory exercise is to encourage further discussion about the misunderstandings between researchers and data support professionals and to spur further development of the educational resources needed to improve understanding and use of data standards.

https://doi.org/10.7191/jeslib.758

"Decades of Transformation: Evolution of the NASA Astrophysics Data System’s Infrastructure"

The NASA Astrophysics Data System (ADS) is the primary Digital Library portal for researchers in astronomy and astrophysics. Over the past 30 years, the ADS has gone from being an astronomy-focused bibliographic database to an open digital library system supporting research in space and (soon) earth sciences. This paper describes the evolution of the ADS system, its capabilities, and the technological infrastructure underpinning it.

https://arxiv.org/abs/2401.09685

"On the Readiness of Scientific Data for a Fair and Transparent Use in Machine Learning"

To ensure the fairness and trustworthiness of machine learning (ML) systems, recent legislative initiatives and relevant research in the ML community have pointed out the need to document the data used to train ML models. Besides, data-sharing practices in many scientific domains have evolved in recent years for reproducibility purposes. In this sense, the adoption of these practices by academic institutions has encouraged researchers to publish their data and technical documentation in peer-reviewed publications such as data papers. In this study, we analyze how this scientific data documentation meets the needs of the ML community and regulatory bodies for its use in ML technologies. We examine a sample of 4041 data papers of different domains, assessing their completeness and coverage of the requested dimensions, and trends in recent years, putting special emphasis on the most and least documented dimensions. As a result, we propose a set of recommendation guidelines for data creators and scientific data publishers to increase their data’s preparedness for its transparent and fairer use in ML technologies.

https://arxiv.org/abs/2401.10304

Scaling Up: How Data Curation Can Help Address Key Issues in Qualitative Data Reuse and Big Social Research

This book explores the connections between qualitative data reuse, big social research, and data curation. A review of existing literature identifies the key issues of context, data quality and trustworthiness, data comparability, informed consent, privacy and confidentiality, and intellectual property and data ownership. Through interviews of qualitative researchers, big social researchers, and data curators, the author further examines each key issue and produces new insights about how domain differences affect each community of practice’s viewpoints, different strategies that researchers and curators use to ensure responsible practice, and different perspectives on data curation.

https://doi.org/10.1007/978-3-031-49222-8

"Towards a Quality Indicator for Research Data Publications and Research Software publications — A Vision from the Helmholtz Association"

Research data and software are widely accepted as an outcome of scientific work. However, in comparison to text-based publications, there is not yet an established process to assess and evaluate quality of research data and research software publications. This paper presents an attempt to fill this gap. Initiated by the Working Group Open Science of the Helmholtz Association the Task Group Helmholtz Quality Indicators for Data and Software Publications currently develops a quality indicator for research data and research software publications to be used within the Association. This report summarizes the vision of the group of what all contributes to such an indicator. The proposed approach relies on generic well-established concepts for quality criteria, such as the FAIR Principles and the COBIT Maturity Model. It does — on purpose — not limit itself to technical implementation possibilities to avoid using an existing metric for a new purpose. The intention of this paper is to share the current state for further discussion with all stakeholders, particularly with other groups also working on similar metrics but also with entities that use the metrics.

https://arxiv.org/abs/2401.08804

"Data Stewardship: Case Studies from North-American, Dutch, and Finnish Universities"

This work seeks to elaborate the picture of different data stewardship programs running in different institutional arrangements and research environments. Design/methodology/approach – Drawing from autoethnography and case study methods, this study described three distinct data stewardship programs from Purdue University (United States), Delft Technical University (Netherlands) and Aalto University (Finland). In addition, this work investigated the institutional arrangements and national research environments of the programs. The focus was on initiatives led by academic libraries or similar services. Findings – This work demonstrates that data stewardship may be understood differently within different national and institutional contexts. The data stewardship programs differed in terms of roles, organization and funding structures. Moreover, the mesh of policies and legislation, organizational structures, and national infrastructures differed.

https://arxiv.org/abs/2312.04092

"Fair Sharing of Health Data: A Systematic Review of Applicable Solutions"

Health science researchers face additional specific challenges. Firstly, ethical and legal issues are barriers regarding the sharing of IPD. Legislation, like the General Data Protection Regulation (GDPR [16]) in Europe or the Health Insurance Portability and Accountability Act (HIPAA [17]) in the USA, prevents research data from being openly shared. IPD can only be shared publicly after the removal of all information allowing the identification of the individual participants, unless explicit consent has been obtained from the individual participants. Furthermore, the legislation has been growing stronger over the years. State laws have emerged in the USA, like the CCPA in California [18], as well as European legislation such as the Convention 108 [19] or the proposal for a reform of ePrivacy legislation [20].

Secondly, health data are diverse and heterogeneous and can be of very different types and formats, depending on the field they belong to, e.g., imaging, genomics, and mass spectrometry. Handling these data requires specific expertise and tools which can usually only be found in the specialized, dedicated communities.

The objective of this paper is to identify and evaluate technical solutions to implement systematic data sharing in an academic context, in order to help researchers making their data FAIR. We will evaluate various software programs and online platforms used in academic projects to manage and store data through a systematic literature review focusing on the implementation of the FAIR principles and the ability to support sharing of Individual Participant Data (IPD).

https://doi.org/10.1007/s12553-023-00789-5

Paywall: "Value of Open Research Data: A Systematic Evaluation Framework Based on Multi-Stakeholder Survey"

Stakeholders such as funders, data centers and data curators need specific and detailed evidence to support and justify the value of their existing or proposed open data policies and practices. A questionnaire-based evaluation framework was designed through systematic review and expert interviews, focusing on the scientific, economic, and societal values of open research data. . . . On average, participants (strongly) agreed that open research data has scientific (83.97%), economic (76.94%), and societal (86.89%) value, all 37 statements of the framework were supported and confirmed. This framework is one of the most comprehensive and systematic tools for measuring data value, which can offer support to diverse stakeholders, especially funders, data centers, and data curators in managing and promoting open data systems.

https://doi.org/10.1016/j.lisr.2023.101269

Paywall: "Data Curation Education: Cross-Disciplinary Analysis of Master’s Programs "

The main goal of this study is to analyze the course content from the syllabi of various programs to understand what is being taught in LIS schools throughout graduate-level education. Further, because the need for data curation is apparent across different disciplines, and thus not only LIS but also other disciplines have been offering data curation courses, this study also analyzed syllabi from other disciplines. . . . Our findings suggest a notable growth in LIS education in data curation since 2012, but LIS education still provides less training in technical skills. There was also a distinctive difference in educational approach to teach data curation between LIS (user- and service-oriented) and other disciplines (technical skills-focused), which brought different strengths and weaknesses in curriculum.

https://tinyurl.com/bdfjwjbh

Scholarly Communication Librarianship and Open Knowledge

Scholarly Communication Librarianship and Open Knowledge is an open textbook and practitioner’s guide that collects theory, practice, and case studies from nearly 80 experts in scholarly communication and open education. Divided into three parts:

What is Scholarly Communication?

Scholarly Communication and Open Culture

Voices from the Field: Perspectives, Intersections, and Case Studies

The book delves into the economic, social, policy, and legal aspects of scholarly communication as well as open access, open data, open education, and open science and infrastructure. Practitioners provide insight into the relationship between university presses and academic libraries, defining collection development as operational scholarly communication, and promotion and tenure and the challenge for open access.

https://bit.ly/SCLAOK

The State of Open Data 2023

Support is not making its way to those who need it

Almost three-quarters of respondents had never received support with making their data openly available.

One size does not fit all

Variations in responses from different subject expertise and geographies highlight a need for a more nuanced approach to research data management support globally.

Challenging stereotypes

Are later career academics really opposed to progress? The results of the 2023 survey indicate that career stage is not a significant factor in open data awareness or support levels.

Credit is an ongoing issue

For eight years running, our survey has revealed a recurring concern among researchers: the perception that they don’t receive sufficient recognition for openly sharing their data.

AI awareness hasn’t translated to action

For the first time, this year we asked survey respondents to indicate if they were using ChatGPT or similar AI tools for data collection, processing and metadata creation.

https://tinyurl.com/mr28u6tt

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

Paywall: "Cutting the Gordian (Workload) Knot? Adding Data Services to Academic Library Public Services"

The library and information science literature wants two irreconcilable things out of its workload data: 1) aggregate comparable data to document and measure use of libraries and its value; and 2) accurate descriptions to document and measure the individual work done by librarians. . . . We propose here to change the question asked: how can we achieve a reasonable balance of workload within a group of librarians? . . . The goal was to answer a common and longstanding question: we are in continual process of assessing what needs to be done and how/where to shift workloads, but how do we know we’re doing it in a reasonable and fair way beyond anecdotes and intuitions? We developed a weighted measure of public services workload in order to assess and track and assign a) areas of declining workload, b) areas of increasing workload (data services), and c) a balance between library divisions contributing to public services.

https://doi.org/10.1016/j.acalib.2023.102801

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

"Structural Elements and Spheres of Expertise: Creating a Healthy Ecosystem for Cultural Data Initiatives"

While technology affords creation of digital collections, and promises access to all, the reality is that many cultural data collections exist in a precarious ecosystem, where erratic funding, fragmented support, and disconnected expertise threaten their continued existence. As a significant branch of the broader information ecosystem, cultural data collections range in size and scope, from national institutions to bespoke local collections supported by individuals. This exploratory, qualitative study engaged cultural data experts in Australia, Canada, and the United Kingdom to map the broad cultural data ecosystem and to identify opportunities for healthier growth. The development and maintenance of cultural data collections requires integration across the spheres of expertise of creators, curators, subject matter experts, information science, and computing and technology. The foundational structural elements of the ecosystem include funding, policies, access to existing data, community context, and technological infrastructure. The key elements of a healthy data ecosystem are clarity of purpose, user-focused design, sustainability, allied coproduction, and reciprocal interconnection. A healthier cultural data ecosystem means more collections and initiatives will have positive impacts for research, knowledge, and diverse communities, contributing positively to the broader information ecosystem and to society, at large.

https://doi.org/10.1002/asi.24849

"Training to Act FAIR: A Pre-post Study on Teaching FAIR Guiding Principles to (Future) Researchers in Higher Education."

Before FAIR training, 81.1% of students suggest scientific actions not in line with the FAIR guiding principles. However, after the training, there is a 3.75-fold increase in scientific actions that adhere to these principles. Interestingly, the training does not significantly impact how students justify FAIR actions. The study observes a positive correlation between the presence of university legal frameworks on FAIR guiding principles and students’ inclination towards FAIR training. It explicates safe space, participation, motivation, usefulness, and satisfaction as the five highest-rated learning factors in FAIR training.

https://doi.org/10.21203/rs.3.rs-3409769/v1

"Dissemination Effect of Data Papers on Scientific Datasets"

This study aims to investigate the citation practices associated with data papers and to explore the role of data papers in disseminating scientific datasets. . . . The findings indicate a consistent growth in the number of biomedical data journals published in recent years, with data papers gaining attention and recognition as both publications and data sources. Although the use of data papers as citation sources for data remains relatively rare, there has been a steady increase in data paper citations for data utilization through formal data citations.

https://doi.org/10.1002/asi.24843

"Repository Staff Perspectives on the Benefits Of Trustworthy Digital Repository Certification"

This paper reports on the results from a qualitative study that asks whether and how staff members from TRAC certified repositories find value in the audit and certification process. While some interviewees found certification valuable, others argued that the costs outweighed the benefits or expressed ambivalence towards certification. Findings indicate that TRAC certification offered both internal and external benefits, such as improved documentation, accountability, transparency, communication, and standards, but there were concerns about high costs, implementation problems, and lack of objective evaluation criteria.

https://tinyurl.com/bddmuwjy

"FAIR EVA: Bringing Institutional Multidisciplinary Repositories into the FAIR Picture"

The FAIR Principles are a set of good practices to improve the reproducibility and quality of data in an Open Science context. Different sets of indicators have been proposed to evaluate the FAIRness of digital objects, including datasets that are usually stored in repositories or data portals. However, indicators like those proposed by the Research Data Alliance are provided from a high-level perspective that can be interpreted and they are not always realistic to particular environments like multidisciplinary repositories. This paper describes FAIR EVA, a new tool developed within the European Open Science Cloud context that is oriented to particular data management systems like open repositories, which can be customized to a specific case in a scalable and automatic environment. It aims to be adaptive enough to work for different environments, repository software and disciplines, taking into account the flexibility of the FAIR Principles. As an example, we present DIGITAL.CSIC repository as the first target of the tool, gathering the particular needs of a multidisciplinary institution as well as its institutional repository.

https://doi.org/10.1038/s41597-023-02652-8

"Understanding the Value of Curation: A Survey of Researcher Perspectives of Data Curation Services from Six Us Institutions"

Data curation encompasses a range of actions undertaken to ensure that research data are fit for purpose and available for discovery and reuse, and can help to improve the likelihood that data is more FAIR (Findable, Accessible, Interoperable, and Reusable). The Data Curation Network (DCN) has taken a collaborative approach to data curation, sharing curation expertise across a network of partner institutions and data repositories, and enabling those member institutions to provide expert curation for a wide variety of data types and discipline-specific datasets. This study sought to assess the satisfaction of researchers who had received data curation services, and to learn more about what curation actions were most valued by researchers. By surveying researchers who had deposited data into one of six academic generalist data repositories between 2019–2021, this study set out to collect feedback on the value of curation from the researchers themselves. A total of 568 researchers were surveyed; 42% (238) responded. Respondents were positive in their evaluation of the importance and value of curation, indicating that the participants not only value curation services, but are largely satisfied with the services provided. An overwhelming majority 97% of researchers agreed that data curation adds value to the data sharing process, 96% agreed it was worth the effort, and 90% felt more confident sharing their data due to the curation process. We share these results to provide insights into researchers’ perceptions and experience of data curation, and to contribute evidence of the positive impact of curation on repository depositors. From the perspective of researchers we surveyed, curation is worth the effort, increases their comfort with data sharing, and makes data more findable, accessible, interoperable, and reusable.

https://doi.org/10.1371/journal.pone.0293534

Digital Preservation Coalition: The Global ‘Bit List’ of Endangered Digital Species

A free-to-access and open resource for digital preservation advocacy, the DPC’s Global Bit List of Endangered Digital Species (or Bit List for short) is a community-sourced list of at-risk digital materials which is revised every two years. Entries to the list are nominated by the community, who are at the forefront of digital preservation efforts, and reviewed by international organizations which represent global expertise in the preservation of the listed digital species.

https://tinyurl.com/jevyn9mj

"The Impacts of Changes in Journal Data Policies: A Cross-disciplinary Survey"

This discipline-specific survey of journal DSP and SMP highlighted the increasing adoption rates and rankings of DSP over time. Furthermore, the findings suggest that DSP adoption may have a notable impact on the increase in JIF. The adoption of DSP by journals may be associated with the increased attention and credibility of the articles.

https://doi.org/10.1002/pra2.924

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

"How Can Open Data Sharing Policies Be More Attentive to Qualitative Researchers?"

The expected and prescriptive ways of preparing data are a key part of the problem. These are governed largely by quantitative data management strategies. Qualitative data is the outcome of personal interactions between researchers and participants. Yet, data sharing guidance is seldom attentive to the co-constructed nature of qualitative material. "The identities of researchers and what they reflexively reveal of themselves, how they interact with participants, their techniques and approaches and the messiness of qualitative work are laid bare within the artefacts of qualitative data" (Weller 2023: 9). This can make researchers especially vulnerable to personal and professional scrutiny in a way that survey and other quantitative researchers are not.

https://tinyurl.com/2fpr82vr