Research Data Curation Research Articles

Navigating the Challenges of Legacy Data: A Systematic Literature Analysis

Legacy data is data collected systematically in the past, for which contemporary use and reuse are at-risk, difficult, or impossible due to issues such as missing metadata, obsolete storage media, outdated file formats, and unsupported software. As we introduced in Buchanan, et al. (2023), there is a demonstrated need for more systematic understanding of legacy research data efforts and the value of these data and efforts as perceived across stakeholder groups. We are conducting a literature analysis of publications on the topics of legacy research data use, preservation, curation, and management. Using an established search query and online literature databases, we retrieved and manually reviewed a pool of 1,338 results and identified 69 papers that fit the following criteria for our study: peer-reviewed articles published in the last 15 years including studies discussing methodologies and best practices; studies that describe repurposing legacy data; studies focusing on storage or archival methods for older data; and studies that discuss software and migration challenges. We are analyzing the sample of papers using content analysis and bibliometric methods, focusing on references to individuals involved, processes, data types, formats, locations, and problems being addressed.

Research Data Management and Curation: What Librarians need to know

Research Data Management and Curation: What Librarians need to know

A dataset for measuring the impact of research data and their curation

Science funders, publishers, and data archives make decisions about how to responsibly allocate resources to maximize the reuse potential of research data. This paper introduces a dataset developed to measure the impact of archival and data curation decisions on data reuse. The dataset describes 10,605 social science research datasets, their curation histories, and reuse contexts in 94,755 publications that cover 59 years from 1963 to 2022. The dataset was constructed from study-level metadata, citing publications, and curation records available through the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan. The dataset includes information about study-level attributes (e.g., PIs, funders, subject terms); usage statistics (e.g., downloads, citations); archiving decisions (e.g., curation activities, data transformations); and bibliometric attributes (e.g., journals, authors) for citing publications. This dataset provides information on factors that contribute to long-term data reuse, which can inform the design of effective evidence-based recommendations to support high-impact research data curation decisions.

Data quality assurance in research data repositories: a theory-guided exploration and model

PurposeThis study addresses the need for a theory-guided, rich, descriptive account of research data repositories' (RDRs) understanding of data quality and the structures of their data quality assurance (DQA) activities. Its findings can help develop operational DQA models and best practice guides and identify opportunities for innovation in the DQA activities.Design/methodology/approachThe study analyzed 122 data repositories' applications for the Core Trustworthy Data Repositories, interview transcripts of 32 curators and repository managers and data curation-related webpages of their repository websites. The combined dataset represented 146 unique RDRs. The study was guided by a theoretical framework comprising activity theory and an information quality evaluation framework.FindingsThe study provided a theory-based examination of the DQA practices of RDRs summarized as a conceptual model. The authors identified three DQA activities: evaluation, intervention and communication and their structures, including activity motivations, roles played and mediating tools and rules and standards. When defining data quality, study participants went beyond the traditional definition of data quality and referenced seven facets of ethical and effective information systems in addition to data quality. Furthermore, the participants and RDRs referenced 13 dimensions in their DQA models. The study revealed that DQA activities were prioritized by data value, level of quality, available expertise, cost and funding incentives.Practical implicationsThe study's findings can inform the design and construction of digital research data curation infrastructure components on university campuses that aim to provide access not just to big data but trustworthy data. Communities of practice focused on repositories and archives could consider adding FAIR operationalizations, extensions and metrics focused on data quality. The availability of such metrics and associated measurements can help reusers determine whether they can trust and reuse a particular dataset. The findings of this study can help to develop such data quality assessment metrics and intervention strategies in a sound and systematic way.Originality/valueTo the best of the authors' knowledge, this paper is the first data quality theory guided examination of DQA practices in RDRs.

Research data infrastructures in environmental sciences—Challenges and implementation approaches focusing on the integration of software components

AbstractResearch data infrastructures are quickly evolving and show a wide variety, for instance in the way they address user requirements and use cases as well as how they provide user‐required information through their software architecture. In this article, we discuss challenges and provide approaches to developing software architectures and software components for research data infrastructures in environmental sciences. Taking the GeoKur research project on harmonizing land use time series as the use case for curation and quality assurance of environmental research data, we designed, implemented, and tested approaches and software components with particular regard to data management planning as well as provenance and quality information management. We aim to illustrate how to better meet researchers’ needs and provide tightly interlinked software components.

The Necessity of Implementation of RI‘s Module to the Ukrainian Research Information System

The objective of implementing an RI (Research Information) module to the Ukrainian Research Information System is to enhance the management, visibility, and accessibility of research-related information within the Ukrainian research ecosystem. The module aims to improve the efficiency of research administration, promote collaboration among researchers and institutions, and facilitate the dissemination of research outputs. In this article, the authors attempted to investigate the need for developing a comprehensive classification of national research infrastructures with the aim of implementing a separate module into the national CRIS system. The work is aimed at analyzing the possibilities and advantages of implementing the RI (Research Information) module in the Ukrainian Research Information System. Methods. Developing the national classifier, the experience and methodology of the EU were utilized, taking into account both legislative and technical peculiarities specific to the country. Additionally, the approaches of various Ukrainian research institutions in defining and classifying research infrastructures were studied, and the existing experience in CRIS systems related to this matter was also examined. Results. The research should highlight the existing challenges and limitations in managing research-related information within the Ukrainian research ecosystem. This could include issues related to data fragmentation, lack of standardization, limited accessibility, inefficient research administration processes, and low visibility of research outputs. Based on the research findings, a set of proposed features and functionalities for the RI module should be presented. These features should address the identified challenges, stakeholder needs, and lessons learned from international practices. The proposed module should aim to improve data management, collaboration, visibility, and efficiency in research administration. Besides, the authors are trying to figure out the role of the libraries into CRIS implementation process. The scientific library's expertise in research data management, data curation, user support, collaboration, and promotion makes it an invaluable partner in implementing a Research Information module into the current Research Information System. Conclusion. The research should provide a comprehensive understanding of the necessity and potential benefits of implementing an RI module to the Ukrainian Research Information System, guiding future decision-making and actions in this regard. In general, the need for developing an operational classifier for the national CRIS system is driven by the fact that such a step will provide structured frameworks for managing research activities, efficient allocation of resources, including financial resources, strategic planning, and collaborations.

Conceptualizing Slow Curation

The pressure to do things quickly is a constant in our professional lives as data curators. But what if we slowed down our work? Taking inspiration from the Slow Movement and its various sub-genres, we propose the idea of Slow Curation, specifically for the application of curating research data. Data curation is the process of reviewing datasets to prepare them for sharing, use, and reuse. We have identified areas where Slow can be embraced in the Data Curation Network’s CURATE(D) model. We also advocate for a few ways, outside of curation, where data curators can collaboratively advocate for Slowing down our work for the better. Join us as we practice radical self-care and advocate for our communities by embracing Slow and easing our way out of Busy culture.Positionality Statements:All authors are employed at large and wealthy doctoral granting institutions in the United States. Their identities and experiences have directly informed and influenced this piece.Joanna is a cisgender, white, heterosexual, able bodied female. Wanda is a white gender non-conforming queer, able bodied person who is a first generation undergraduate and graduate college studentMikala is a cisgender, white, heterosexual, able bodied female.

Toward the Conceptualization of Data Quality Assurance in Research Data Repositories

ABSTRACTData quality assurance (DQA) is critical to research data sharing and reuse. There has been a growing recognition of data transparency, reproducibility, credibility, and validity in research. Although the research data curation literature is large, it lacks data quality theory‐guided examinations of DQA practices in research data repositories. This poster paper reports on the preliminary findings of a larger study that examines DQA practices in research data repositories, including their use of DQA ontologies, standards, and metadata vocabularies. In particular, the paper examines two quality standards and an ontology for their conceptualization of DQA activities and their structure. The authors used the findings of the analysis and the data quality literature to synthesize an initial model of a DQA process in research data repositories that conceptualizes three DQA activities: evaluation, intervention, and communication. This paper can inform the development of ontologies and best practice guides for designing and evaluating DQA workflows in research data repositories.

Exploring and Improving Workflows for the Donation and Curation of Research Data

Managing research data and preparing it for long term archiving at a research data center is a time-consuming, repetitive and error prone process without obvious rewards or incentives. To address these challenges, we aim at the integration of a standard data management tool into one of the research data centers of the German Network of Educational Research Data. The goal is to improve workflows for both, data donors and curators and to explore transferable solutions that allow researchers and curators to work on the same platform when entering and editing metadata.

RDM Compas: Building Competencies for the Professional Curation of Research Data

“RDM Compas: Research Data Management Competence Base”, consisting of an education and training centre with modular online trainings on the one hand, and a comprehensive knowledge base covering all topics of curation-specific RDM on the other hand. In addition, a certification option is envisioned for curation-specific RDM competencies. In the context of our presentation, due to the advanced state of work we want to focus on the knowledge base and present its structure and elements. The structural basis is a slightly simplified version of the Data Curation Lifecycle Model [2] offered by the UK Digital Curation Centre [3]. This lifecycle describes both the basic activities and the sequential process steps of data curation and therefore provides a helpful schema for teaching the necessary RDM competencies.

Research Data Curation in Academic Institutions Challenges & Expectations

In the activity of Research Data Curation, the unpublished datasets generated during research are curated for possible reuse in future research by any researcher. Use of curated data, in several cases, may become helpful in avoiding repetition of efforts involved in generation of datasets. A large number of academic institutions in India are actively involved in research in various knowledge areas. Apart from the doctoral and post-doctoral research in academic institutions, academicians are involved in research projects sponsored by public or private bodies; thereby generating sizeable primary and secondary unpublished datasets worthy of curation in a data repository for possible reuse in some other research. For several reasons, curation efforts for research datasets in academic institutions in the country are negligible when compared to such efforts in research institutions. The present work makes an attempt to identify the cause of negligible research data curation efforts in academic institutions of India by uncovering the associated challenges and discusses the expectations from a Research Data Repository of academic institutions.

NFDI4Chem—A Research Data Network for International Chemistry

Abstract Research data provide evidence for the validation of scientific hypotheses in most areas of science. Open access to them is the basis for true peer review of scientific results and publications. Hence, research data are at the heart of the scientific method as a whole. The value of openly sharing research data has by now been recognized by scientists, funders and politicians. Today, new research results are increasingly obtained by drawing on existing data. Many organisations such as the Research Data Alliance (RDA), the goFAIR initiative, and not least IUPAC are supporting and promoting the collection and curation of research data. One of the remaining challenges is to find matching data sets, to understand them and to reuse them for your own purpose. As a consequence, we urgently need better research data management.

Data Curation Strategies to Support Responsible Big Social Research and Big Social Data Reuse

Big social research repurposes existing data from online sources such as social media, blogs, or online forums, with a goal of advancing knowledge of human behavior and social phenomena. Big social research also presents an array of challenges that can prevent data sharing and reuse.
 This brief report presents an overview of a larger study that aims to understand the data curation implications of big social research to support use and reuse of big social data. The study, which is based in the United States, identifies six key issues relating to big social research and big social data curation through a review of the literature. It then further investigates perceptions and practices relating to these six key issues through semi-structured interviews with big social researchers and data curators.
 This report concludes with implications for data curation practice: metadata and documentation, connecting with researchers throughout the research process, data repository services, and advocating for community standards. Supporting responsible practices for using big social data can help scale up social science research, thus enhancing our understanding of human behavior and social phenomena.

Designing a Data Story: A Storytelling Approach to Curation, Sharing and Data Reuse in Support of Ethnographically-driven Research

In this paper, we introduce an innovative design concept for the curation of data, which we call 'Data Story'. We view this as an additional resource for data curation, aimed specifically at supporting the sharing of qualitative and ethnographic data. The Data Story concept is motivated by three elements: 1. the increased attention of funding agencies and academic institutions on Research Data Management and Open Science; 2. our own work with colleagues applying ethnographic research methods; and 3. existing literature that has identified specific challenges in this context. Ongoing issues entailed in dealing with certain contextual factors that are inherent to qualitative research reveal the extent to which we still lack technical design solutions that can support meaningful curation and sharing. Data Story provides a singular way of addressing these issues by integrating traditional data curation approaches, where research data are treated as 'objects' to be curated and preserved according to specific standards, with a more contextual, culturally-nuanced and collaborative organizing layer that can be thought of as a "Story". The concept draws on existing literature on data curation, digital storytelling and Critical Data Studies (CDS). As a possible design solution for Research Data Management and data curation, Data Story offers: 1) a collaborative workflow for data curation; 2) a story-like format that can serve as an organizing principle; 3) a means of enhancing and naturalizing curation practices through storytelling. Data Story is currently being developed for deployment and evaluation.

Privacy protection throughout the research data life cycle

Introduction. The sharing and reuse of research data is gradually becoming best practice in research. However, multiple frictions exist between realising stakeholders’ ambitions for research and research data sharing and addressing legal, social and cultural imperatives for protecting data subjects’ privacy. Through identifying and addressing frictions between personal privacy and research, our paper offers advice to research data management services on how to approach personal privacy in research data, sharing using the research data life cycle as the context. Method. A three-phase Delphi study on a population comprising twenty-four stakeholders involved in research data curation in Norway. Data were collected during three consecutive rounds over fourteen months. Analysis. The data were analysed qualitatively using themes following exploratory sequential design methods. After three rounds of data collection, the entire corpus of data were connected and analysed thematically according to integrated analysis. Results. The findings show multiple tensions between maintaining research subjects’ right to privacy and advancing research through data sharing. This paper identifies and analyses three particular sources of tension: 1) maintaining trust with the research participants, 2) managing divergent views of privacy in international and intercultural research collaborations and 3) interpreting and applying policy. The divergent motivations and perspectives on privacy held by different stakeholders complicate these tensions. Conclusions. Researchers, research data management support staff and data organizations must reconcile these motivations and resolve tensions throughout the data life cycle, from collection to archiving and eventual sharing. Through dialogue and negotiation, all stakeholders involved in data sharing should aim to respect the research subjects’ own understandings of privacy.

Research Data Preservation Practices of Library and Information Science Faculties


 Digitisation of research data is widely increasing all around the world because it needs more and development of enormous digital technologies. Data curation services are starting to offer many libraries. Research data curation is the collective invaluable and reusable information of the researchers. Collected data preservation is more important. The majority of the higher education institutes preserved the research data for their students and researchers. It is stored for a long time using various formats. It is called research data preservation. Without proper research data management plan and implementation cannot curate the research data. The aim of the study is to identify the Asian Library and Information Science (LIS) faculties’ experiences in the research data preservation and curation during their research. Data management, curation and preservation all are interlinked. For reuse of the research data; data curation is an essential role. For this research, we adopted a survey method and an online questionnaire was shared with 1400 LIS professionals, belonging to the Asian region but the completed study respondents are 125 university faculties from various Asian countries. The study findings are 76.8 per cent generated statistical data followed by 58.4 per cent textual files. By far, the most preferable data analysis tool is Microsoft Excel 82.4 per cent. Moreover, the result shows that generated data is mostly stored by personal computers and laptop hard disks. This study concludes LIS faculties having adequate skills and knowledge on data curation and preservation even though they are expecting more services from their academic institute libraries.

Transforming and extending library services by embracing technology and collaborations: A case study.

Technology advances and collaborations with information technology and computer science groups have enabled library services to expand into new domains. Listening to user needs, eliminating administrative burden and saving users time remain strong foundations on which to build new library services enabled by technology. Examples of what is now possible is described, including service to user groups, successes, failures and challenges. Although technology advances have enabled library service enhancements to all user groups, special emphasis on new library services in support of the research enterprise is discussed. As Lindberg and Humphreys predicted in 2015, the research enterprise's need for responsible curation of research data has created new opportunities for library services and examples of those services are discussed. As technology continues to advance, new library services are expected to emerge. These may include regulatory and compliance services. By developing these services with user feedback to save users time and expedite their work, and in collaboration with technology experts, libraries can expect to offer sustainable and valued services for years to come.

Job Analyses of Earth Science Data Managers: A Survey Validation of Competencies to Inform Curricula in Research Data Management Education

The expansion of research data availability and production has highlighted the need for proper training in data stewardship. Recent years have seen both incentivized and mandated research data management plans enacted by institutions and funding agencies as a way of supporting more open and integrated data-intensive research. Despite the advancement of data management as a practice and profession, those charged with maintaining and curating research data remain understudied. By investigating what and how data management is realized and who is involved, a discussion on the current educational needs, the trajectory of professions, and an assessment of potential educational needs of those entering the workforce can progress. A job analysis survey was developed to validate the tasks identified from two prior interview studies of Earth Science data managers. This survey was distributed to the Federation of Earth Science Information Partners membership and responses validate prior job analyses work as well as provide other educational consideration related to tools and necessary skills to conduct frequent job tasks. The most frequent activities and necessary knowledge, skills, and abilities of current data managers will inform future avenues of inquiry, provide context for current research, and direct research data management education.

Improving the usefulness of research data with better paradata

Abstract Considerable investments have been made in Europe and worldwide for developing research data infrastructures. Instead of a general lack of data about data, it has become apparent that a pivotal factor that drastically constrains data use is the absence of contextual knowledge about how data was created and how it has been curated and used. This applies especially to many branches of social science and humanities research, where data is highly heterogeneous, both by its kind (e.g. being qualitative, quantitative, naturalistic, purposefully created) and origins (e.g. being historical/contemporary, from different contexts and geographical places). The problem is that there may be enough metadata (data about data) but there is too little paradata (data on the processes of its creation, curation and use). The aim of this position paper is to draw attention 1) to the need for a better and more systematic understanding and documentation of the contexts of creation, curation and use of research data to make it useful and usable for researchers and other potential users in the future, and 2) to specific obstacles that make the capturing of this particular type of metadata, known as paradata, especially difficult. Failing to understand what information about the creation, curation and use of research data is needed and how to capture enough of that information risks that the currently collected vast amounts of research data become useless in the future.

The promise and the future of research data sharing

The promise and the future of research data sharing