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

79.3 Exabytes Capacity Sold in 2022: "Magnetic Tape Storage Is Seeing Cloud Go Back to the Future for Its Archival Data Needs"


Even then [in 1981], says Goodwin, people were saying tape was not long for this world. Those critics appear to have been silenced by recent sales figures, which show year-on-year shipments of hard disk drives (HDDs) sink by 34% in 2022, while consignments of magnetic tape drives rose by 14% — a total of 79.3 exabytes, or roughly equivalent to the entirety of data created on the internet every 32 days.

bit.ly/3ky5Trv

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"Research Data Management Needs Assessment for Social Sciences Graduate Students: A Mixed Methods Study"


The complexity and privacy issues inherent in social science research data makes research data management (RDM) an essential skill for future researchers. Data management training has not fully addressed the needs of graduate students in the social sciences. To address this gap, this study used a mixed methods design to investigate the RDM awareness, preparation, confidence, and challenges of social science graduate students. A survey measuring RDM preparedness and training needs was completed by 98 graduate students in a school of education at a research university in the southern United States. Then, interviews exploring data awareness, knowledge of RDM, and challenges related to RDM were conducted with 10 randomly selected graduate students. All participants had low confidence in using RDM, but United States citizens had higher confidence than international graduate students. Most participants were not aware of on-campus RDM services, and were not familiar with data repositories or data sharing. Training needs identified for social science graduate students included support with data documentation and organization when collaborating, using naming procedures to track versions, data analysis using open access software, and data preservation and security. These findings are significant in highlighting the topics to cover in RDM training for social science graduate students. Additionally, RDM confidence and preparation differ between populations so being aware of the backgrounds of students taking the training will be essential for designing student-centered instruction.

https://doi.org/10.1371/journal.pone.0282152

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"How and Why Do Researchers Reference Data? A Study of Rhetorical Features and Functions of Data References in Academic Articles"


Data reuse is a common practice in the social sciences. While published data play an essential role in the production of social science research, they are not consistently cited, which makes it difficult to assess their full scholarly impact and give credit to the original data producers. Furthermore, it can be challenging to understand researchers’ motivations for referencing data. Like references to academic literature, data references perform various rhetorical functions, such as paying homage, signaling disagreement, or drawing comparisons. This paper studies how and why researchers reference social science data in their academic writing. We develop a typology to model relationships between the entities that anchor data references, along with their features (access, actions, locations, styles, types) and functions (critique, describe, illustrate, interact, legitimize). We illustrate the use of the typology by coding multidisciplinary research articles (n=30) referencing social science data archived at the Inter-university Consortium for Political and Social Research (ICPSR). We show how our typology captures researchers’ interactions with data and purposes for referencing data. Our typology provides a systematic way to document and analyze researchers’ narratives about data use, extending our ability to give credit to data that support research.

https://arxiv.org/abs/2302.08477

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"Data Management Librarians Role in a Large Interdisciplinary Scientific Grant for PFAS Remediation: Considerations and Recommendations"


This article explores the conflicts, disparities, and inequalities experienced by two librarians when collaborating on a federal grant proposal. The authors discuss concerns related to time and salary expectations and the inequities that can occur during faculty and staff collaborations on research grants. The bureaucratic structure and the job classifications of staff at academic institutions in addition to the contract limitations of non-faculty status librarian positions can hinder successful collaborations. The authors also describe data management needs that may occur when working with interdisciplinary research teams and detail the type of work that is included in writing a data management grant. This article concludes with considerations and recommendations for other data librarians who may undertake similar projects with a focus on ways to create parity between faculty and staff collaborators.

https://doi.org/10.7191/jeslib.616

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"There’s No “I” in Research Data Management: Reshaping RDM Services Toward a Collaborative Multi-Stakeholder Model"


Objective: This article examines a reshaped service model for research data management (RDM) founded on centralized and cohesive collaboration between multiple stakeholders at a large research university in Canada. This initiative, along with a newly formed team dedicated to RDM service provision, is a joint effort by the institution’s Vice-Principal Research and Innovation (VPRI), Library, IT Services, and Research Ethics units.

Methods: This article presents a single case study methodology. The authors reflect on services such as "query the panel" sessions where researchers across all disciplines bring their questions to representatives from the Library, IT, Research Ethics, and VPRI. This case study also highlights the use of Jira’s service desk software as a user management system. The authors also present descriptive statistics representing engagement with this new unit and our services.

Results: Support for RDM requires expertise from multiple domains. With a collaborative approach as a guiding principle and a focus on establishing a small, but agile team comprised of a librarian along with stakeholders from IT and VPRI, it is possible to leverage resources and support for RDM from a broad range of units across an institution.

Conclusions: At many institutions, RDM services are siloed within the library or an adjacent campus unit. New digital technologies have profoundly transformed academic research across all disciplines, necessitating the evolution of corresponding research data-related services. The authors will conclude by outlining specific lessons learned in reshaping digital research infrastructure-related services at their institution.

https://doi.org/10.7191/jeslib.624

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"Are Institutional Research Data Policies in the US Supporting the FAIR Principles? A Content Analysis"


Objective: The FAIR principles were created with the goal of enhancing the reusability of research data and to give guidance on how to make data Findable, Accessible, Interoperable and Reusable. In this article we explore the role of institutional research data policies in enabling and encouraging researchers at their institutions to generate FAIR data.

Methods: We identified the research data policies in place for “very high research activity” institutions (as defined by Carnegie classification) in the United States. We created a list of 31 criteria, based on previous work by Davidson et al. (2019) and Briney et al. (2015), and evaluated the 40 policies using a content analysis methodology.

Results: The guiding principles and the definitions for research data in the policies support the idea that institutional policies are a potential tool for the implementation of the FAIR principles. However, our analysis indicates that they are not generally used for that purpose. Only one policy mentions FAIR. Data sharing is mentioned in half of the policies, but 11 of these only note this concept in the context of funder requirements. Access and retention sections are mostly written without considering publicly available data. Twenty-nine policies do not mention data documentation.

Conclusions: We discuss ways in which these institutional policies represent a missed opportunity to implement the FAIR principles and suggest ways policies could be modified to encourage researchers to follow them. We also discuss future research opportunities to examine how policy implementation may affect what institutional support researchers receive.

https://doi.org/10.7191/jeslib.614

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Rethinking Data and Rebalancing Digital Power


This report highlights and contextualises four cross-cutting interventions with a strong potential to reshape the digital ecosystem:

  • Transforming infrastructure into open and interoperable ecosystems.
  • Reclaiming control of data from dominant companies.
  • Rebalancing the centres of power with new (non-commercial) institutions.
  • Ensuring public participation as an essential component of technology policymaking.

http://bit.ly/40WNbKA

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"The Role of Open Data in Digital Society: The Analysis of Scientific Trending Topics through a Bibliometric Approach"


The analysis of contemporary society, characterized by technological, economic, political, social, and cultural changes, has become more challenging due to the development of the internet and information and communication technologies, which provide a vast and increasingly valuable source of information, knowledge, and data. Within this context, so-called open data—that is, data that are made public, especially by public administrations, through an open governance model (transparent and accessible to citizens) are assuming a significant role. This is a topic of growing importance that scientific research is addressing in an attempt to discern the multiplicity of social, educational, legal, technological, statistical, and methodological issues that underlie the creation and use of such data. This article aims to provide insights into understanding scientific trends on the topic of open data through a bibliometric approach. Specifically, a total of 3,110 publications related to the disciplinary fields of the social sciences and humanities published from 2013 to 2022 were collected. The data was then analyzed using network and factorial analysis techniques to detect the conceptual structure to identify the trends of topics and perspectives of research that characterize open data studies.

bit.ly/40Xgahi

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"Data Archive for the BRAIN Initiative (DABI)"


Data sharing is becoming ubiquitous and can be advantageous for most biomedical research. However, some data are inherently more amenable to sharing than others. For example, human intracranial neurophysiology recordings and associated multimodal data have unique features that warrant special considerations. The associated data are heterogeneous, difficult to compare, highly specific, and collected from small cohorts with treatment resistant conditions, posing additional complications when attempting to perform generalizable analyses across projects. We present the Data Archive for the BRAIN Initiative (DABI) and describe features of the platform that are designed to overcome these and other challenges. DABI is a data repository and portal for BRAIN Initiative projects that collect human and animal intracranial recordings, and it allows users to search, visualize, and analyze multimodal data from these projects. The data providers maintain full control of data sharing privileges and can organize and manage their data with a user-friendly and intuitive interface. We discuss data privacy and security concerns, example analyses from two DABI datasets, and future goals for DABI.

https://doi.org/10.1038/s41597-023-01972-z

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Data Preservation in High Energy Physics: DPHEP Global Report 2022


This document summarizes the status of data preservation in high energy physics. The paradigms and the methodological advances are discussed from a perspective of more than ten years of experience with a structured effort at international level. The status and the scientific return related to the preservation of data accumulated at large collider experiments are presented, together with an account of ongoing efforts to ensure long-term analysis capabilities for ongoing and future experiments. Transverse projects aimed at generic solutions, most of which are specifically inspired by open science and FAIR principles, are presented as well. A prospective and an action plan are also indicated.

https://arxiv.org/abs/2302.03583

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"What are Researchers’ Needs in Data Discovery? Analysis and Ranking of a Large-Scale Collection of Crowdsourced Use Cases"


Data discovery is important to facilitate data re-use. In order to help frame the development and improvement of data discovery tools, we collected a list of requirements and users’ wishes. This paper presents the analysis of these 101 use cases to examine data discovery requirements; these cases were collected between 2019 and 2020. We categorized the information across 12 "topics" and eight types of users. While the availability of metadata was an expected topic of importance, users were also keen on receiving more information on data citation and a better overview of their field. We conducted and analysed a survey among data infrastructure specialists in a first attempt at ranking the requirements. Between these data professionals, these rankings were very different, excepting the availability of metadata and data quality assessment.

http://doi.org/10.5334/dsj-2023-003

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"Data Sharing and Reuse Practices: Disciplinary Differences and Improvements Needed"


This study investigates differences and commonalities in data production, sharing and reuse across the widest range of disciplines yet and identifies types of improvements needed to promote data sharing and reuse. . . .From the 3,257 survey responses, data sharing and reuse are still increasing but not ubiquitous in any subject area and are more common among experienced researchers.

https://doi.org/10.1108/OIR-08-2021-0423

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"Analysis of U.S. Federal Funding Agency Data Sharing Policies: 2020 Highlights and Key Observations "


Federal funding agencies in the United States (U.S.) continue to work towards implementing their plans to increase public access to funded research and comply with the 2013 Office of Science and Technology memo Increasing Access to the Results of Federally Funded Scientific Research. In this article we report on an analysis of research data sharing policy documents from 17 U.S. federal funding agencies as of February 2021. Our analysis is guided by two questions: 1.) What do the findings suggest about the current state of and trends in U.S. federal funding agency data sharing requirements? 2.) In what ways are universities, institutions, associations, and researchers affected by and responding to these policies? Over the past five years, policy updates were common among these agencies and several themes have been thoroughly developed in that time; however, uncertainty remains around how funded researchers are expected to satisfy these policy requirements.

http://www.ijdc.net/article/view/791

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"Licensing Challenges Associated With Text and Data Mining: How Do We Get Our Patrons What They Need?"


Today’s researchers expect to be able to complete text and data mining (TDM) work on many types of textual data. But they are often blocked more by contractual limitations on what data they can use, and how they can use it, than they are by what data may be available to them. This article lays out the different types of TDM processes currently in use, the issues that may block researchers from being able to do the work they would like, and some possible solutions.

https://doi.org/10.31274/jlsc.15530

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"Cluster Analysis of Open Research Data: A Case for Replication Metadata"


Research data are often released upon journal publication to enable result verification and reproducibility. For that reason, research dissemination infrastructures typically support diverse datasets coming from numerous disciplines, from tabular data and program code to audio-visual files. Metadata, or data about data, is critical to making research outputs adequately documented and FAIR. Aiming to contribute to the discussions on the development of metadata for research outputs, I conducted an exploratory analysis to determine how research datasets cluster based on what researchers organically deposit together. I use the content of over 40,000 datasets from the Harvard Dataverse research data repository as my sample for the cluster analysis. I find that the majority of the clusters are formed by single-type datasets, while in the rest of the sample, no meaningful clusters can be identified. For the result interpretation, I use the metadata standard employed by DataCite, a leading organization for documenting a scholarly record, and map existing resource types to my results. About 65% of the sample can be described with a single-type metadata (such as Dataset, Software orReport), while the rest would require aggregate metadata types. Though DataCite supports an aggregate type such as a Collection, I argue that a significant number of datasets, in particular those containing both data and code files (about 20% of the sample), would be more accurately described as a Replication resource metadata type. Such resource type would be particularly useful in facilitating research reproducibility.

http://www.ijdc.net/article/view/833

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Paywall: "LABDRIVE, a Petabyte Scalable, OAIS/ISO 16363 Conformant, for Scientific Research Organisations to Preserve Documents, Processed Data, and Software"


Before LABDRIVE no system could adequately preserve such information, especially in such gigantic volume and variety. In this paper we describe the development of LABDRIVE and its ability to preserve and to scale up to tens or hundreds of Petabytes in a way which is conformant to the OAIS Reference Model and capable of being ISO 16363 certified.

https://doi.org/10.1109/BigData55660.2022.10020648

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"Data Management Plans: Implications for Automated Analyses"


Data management plans (DMPs) are an essential part of planning data-driven research projects and ensuring long-term access and use of research data and digital objects; however, as text-based documents, DMPs must be analyzed manually for conformance to funder requirements. This study presents a comparison of DMPs evaluations for 21 funded projects using 1) an automated means of analysis to identify elements that align with best practices in support of open research initiatives and 2) a manually-applied scorecard measuring these same elements. The automated analysis revealed that terms related to availability (90% of DMPs), metadata (86% of DMPs), and sharing (81% of DMPs) were reliably supplied. Manual analysis revealed 86% (n = 18) of funded DMPs were adequate, with strong discussions of data management personnel (average score: 2 out of 2), data sharing (average score 1.83 out of 2), and limitations to data sharing (average score: 1.65 out of 2). This study reveals that the automated approach to DMP assessment yields less granular yet similar results to manual assessments of the DMPs that are more efficiently produced. Additional observations and recommendations are also presented to make data management planning exercises and automated analysis even more useful going forward.

http://doi.org/10.5334/dsj-2023-002

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"Canadian Policy: Data Management Requirement Takes Effect in March"


Canadian institutions are preparing for a research data management policy developed by three major federal granting agencies to go into effect this March. The policy of the Tri-Agency Council, comprising the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Social Sciences and Humanities Research Council of Canada (SSHRC), asserts that "research data collected through the use of public funds should be responsibly and securely managed and be, where ethical, legal and commercial obligations allow, available for reuse by others."

https://cutt.ly/N9vGKLh

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"Community Consensus on Core Open Science Practices to Monitor in Biomedicine"


The state of open science needs to be monitored to track changes over time and identify areas to create interventions to drive improvements. In order to monitor open science practices, they first need to be well defined and operationalized. To reach consensus on what open science practices to monitor at biomedical research institutions, we conducted a modified 3-round Delphi study. Participants were research administrators, researchers, specialists in dedicated open science roles, and librarians. In rounds 1 and 2, participants completed an online survey evaluating a set of potential open science practices, and for round 3, we hosted two half-day virtual meetings to discuss and vote on items that had not reached consensus. Ultimately, participants reached consensus on 19 open science practices. This core set of open science practices will form the foundation for institutional dashboards and may also be of value for the development of policy, education, and interventions.

https://doi.org/10.1371/journal.pbio.3001949

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"An Iterative and Interdisciplinary Categorisation Process towards FAIRer Digital Resources for Sensitive Life-Sciences Data"


For life science infrastructures, sensitive data generate an additional layer of complexity. Cross-domain categorisation and discovery of digital resources related to sensitive data presents major interoperability challenges. To support this FAIRification process, a toolbox demonstrator aiming at support for discovery of digital objects related to sensitive data (e.g., regulations, guidelines, best practice, tools) has been developed. The toolbox is based upon a categorisation system developed and harmonised across a cluster of 6 life science research infrastructures. Three different versions were built, tested by subsequent pilot studies, finally leading to a system with 7 main categories (sensitive data type, resource type, research field, data type, stage in data sharing life cycle, geographical scope, specific topics). 109 resources attached with the tags in pilot study 3 were used as the initial content for the toolbox demonstrator, a software tool allowing searching of digital objects linked to sensitive data with filtering based upon the categorisation system. Important next steps are a broad evaluation of the usability and user-friendliness of the toolbox, extension to more resources, broader adoption by different life-science communities, and a long-term vision for maintenance and sustainability.

https://doi.org/10.1038/s41598-022-25278-z

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"Challenges of Qualitative Data Sharing in Social Sciences"


Open science offers hope for new accountability and transparency in social sciences. Nevertheless, it still fails to fully consider the complexities of qualitative research, as exemplified by a reflection on sensitive qualitative data sharing. As a result, the developing patterns of rewards and sanctions promoting open science raise concern that quantitative research, whose "replication crisis" brought the open science movement to life, will benefit from "good science" re-evaluations at the expense of other research epistemologies, despite the necessity to define accountability and transparency in social sciences more widely and not to conflate those with either reproducibility or data sharing.

bit.ly/3j6NTTV

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iPres 2022 International Conference on Digital Preservation Conference Proceedings


The proceedings are the official record of all the peer reviewed submissions presented at iPres 2022, ensuring visibility and promotion of both academic research work and the projects and initiatives of institutions involved in digital preservation practices.

bit.ly/3hlkFAx

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"Non-Fungible Token (NFT) in the Academic and Open Access Publishing Environment: Considerations towards Science-Friendly Scenarios"


The article describes the use and possible value creation of Non-Fungible Tokens (NFT) in the academic and open access publishing environment. It defines NFTs, describes disadvantages and possible solutions, especially in the intended scientific environment. An overview of existing NFT service providers from the publishing environment illustrates that there is not yet a suitable one for researchers. Accordingly, three possible scenarios are shown where NFT services could be located in a science-friendly way. One would be with library- or scholarly-led university presses, repositories, and other publication infrastructures (such as OJS or OMP). Another would be to use centralizing and channelling article submission platforms with which universities have contracts, such asChronosHub. The third and broadest approach would be through Digital ObjectIdentifier (DOI) registration agencies such as ChronosHub and DataCite, although complexities come into play here due to the triangular relationship with publishers registering DOIs (some of them having exclusive usage rights transferred to themselves). This complexity could be reduced by registeringNFTs only for open access publications with a Creative Commons Attribution license. A summary and outlook provide an overview of open questions and initial starting points to get started.

https://doi.org/10.3998/jep.2574

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Wolters Kluwer: The Path to Open Medicine: Driving Global Health Equity through Medical Research


The paper is divided into three parts. Part 1 traces the historical events that led to the modern system of scientific research, funding, knowledge dissemination, and recognition, which largely confines health and medical knowledge production to those in HICs [high income countries]. By understanding our shared past and the rise of structural barriers to global health equity, we can better inform our shared path to dismantle them. Part 2 takes a clear-eyed look at where the scientific community is now. Are the ideals of Open Medicine playing out as envisioned? Are the benefits of Open Medicine shared amongst all of humanity, or with only a select few? Lastly, Part 3 offers ideas and recommendations for all stakeholders to chart a path to bring Open Medicine into alignment with its goals and aspirations.

https://cutt.ly/E15vETj

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"Access to Research Data and EU Copyright"


The article seeks to contribute to this aim by exploring the legal framework in which research data can be accessed and used in EU copyright law. First, it delineates the authors’ understanding of research data. It then examines the protection research data currently receives under EU and Member State law via copyright and related rights, as well as the ownership of these rights by different stakeholders in the scientific community. After clarifying relevant conflict-of-laws issues that surround research data, it maps ways to legally access and use them, including statutory exceptions, the open science movement and current developments in law and practice.

bit.ly/3VVx7pg

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