Research Data Management Support Research Articles

NFDI4Earth: Improving Research Data Management in the Earth System Sciences

The increasing availability of digital research products – typically encompassing data and software – in the Earth System Sciences (ESS) calls for new approaches to facilitate the management and reuse of these digital resources. NFDI4Earth addresses this gap and targets the harmonization of services related to digital research products in the ESS. In our initial phase, we aim to support researchers in 1) discovering and exploring relevant data and software sources, 2) exploratory spatial data analysis, 3) solving research data management problems and 4) creating and publishing information products. This abstract briefly presents concepts and the first results of the harmonization efforts within NFDI4Earth. It touches upon four points: the harmonization of resource descriptions, the harmonization of ESS-specific support for research data management (RDM), joint training activities for researchers in the ESS, and the harmonization of discovery and publishing workflows.

The Impact of Key Stakeholders and the Computer Skills of Librarians on Research Data Management Support Services (Id so-21-1893.r2)

This study aims to investigate key stakeholders’ impact and to assess its effect on Research Data Management (RDM), thereby setting the agenda for sound local government Research Data Management (RDM) policy for maximum profit. This study relies mainly on the stakeholder’ theory. Our target population is stakeholders in RDM. The survey used the Partial Least Squares and Structural Equation Modeling tool with a sample of 295 received from respondents to find the effect among the stakeholders. The Statistical results confirmed there is a positive effect among stakeholders. The Funders agencies criteria influence researchers’ work. The effect of government support policies on librarians RDM support services was positive but not significant. The impact of computer literacy of librarians on the RDM support service delivered by librarians was insignificant. The finding is mainly due to overreliance on manual approach to work in most libraries in Ghana as a result inadequate fund support. Librarians need to take a leading role in RDM to implement RDM policy fully. This study contributes to the discourse by helping to strengthen the existing relationship and also to understand the role of various stakeholders in RDM. Stakeholders in Ghana and developing countries need to take greater responsibility to achieve success. Government can rely on it for policy formulation. The research can be used to expand the existing stakeholders’ effective support and interactions as well as in teaching. The survey results can also help identify best practices, improve current observation, and compare practices across different countries.

PANGAEA - Data Publisher for Earth & Environmental Science

The information system PANGAEA provides targeted support for research data management as well as long-term data archiving and publication. PANGAEA is operated as an open access library for archiving, publishing, and distributing georeferenced data from earth and environmental sciences. It focuses on observational and experimental data. Citability, comprehensive metadata descriptions, interoperability of data and metadata, a high degree of structural and semantic harmonization of the data inventory as well as the commitment of the hosting institutions ensures the long-term usability of archived data. PANGAEA is a pioneer of FAIR and open data infrastructures to enable data intensive science and an integral component of national and international science and technology activities. This paper provides an overview of the recent organisational, structural, and technological advancements in developing and operating the information system.

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.

Peran Pustakawan Data Dalam Meningkatkan Literasi dan Kapasitas Pengelolaan Data Penelitian bagi Komunitas Penelitian dan Profesional Informasi di Indonesia

Data librarians are responsible for managing research data, who play an active role in improving the literacy and capacity of the research community and information professionals. Some Indonesian programmes have emerged, but their role and readiness have not been studied. The aspects of personnel readiness to support research data management (RDM) are further discussed in this research, as obligated on the National System for Science and Technology Act No. 11 of 2019. This study was conducted to investigate the readiness of librarians, academic communities, and information professionals in managing research data. A mixed method was used in this research. Firstly, a poll survey was digitally distributed in thematic seminar discussions on data management to librarians and scientists performing RDM tasks. Then, more detailed information about the role of librarians in managing research data was gathered from selected informants on FGD. The study found several points about the role of data librarians in improving data literacy and capacity through advocacy, workshops, seminars, training, and apprenticeship development. We then called for the involvement of all stakeholders, the scientific community, and information professionals to encourage them to continue working together in building a good data management ecosystem through innovative and collaborative programs. The results of this study could be a policy recommendation for the development of the status of library functions by the supervisory authority, the National Library of the Republic of Indonesia. The scientific community could use them to raise awareness and capacity for research data management from all institutions as data owners (scientific community) and data managers (data librarians).

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.

First Line Research Data Management for Life Sciences: a Case Study


 Modern life sciences studies depend on the collection, management and analysis of comprehensive datasets in what has become data-intensive research. Life science research is also characterised by having relatively small groups of researchers. This combination of data-intensive research performed by a few people has led to an increasing bottleneck in research data management (RDM). Parallel to this, there has been an urgent call by initiatives like FAIR and Open Science to openly publish research data which has put additional pressure on improving the quality of RDM. Here, we reflect on the lessons learnt by DataHub Maastricht, a RDM support group of the Maastricht University Medical Centre (MUMC+) in Maastricht, the Netherlands, in providing first-line RDM support for life sciences. DataHub Maastricht operates with a small core team, and is complemented with disciplinary data stewards, many of whom have joint positions with DataHub and a research group. This organisational model helps creating shared knowledge between DataHub and the data stewards, including insights how to focus support on the most reusable datasets. This model has shown to be very beneficial given limited time and personnel. We found that co-hosting tailored platforms for specific domains, reducing storage costs by implementing tiered storage and promoting cross-institutional collaboration through federated authentication were all effective features to stimulate researchers to initiate RDM. Overall, utilising the expertise and communication channel of the embedded data stewards was also instrumental in our RDM success. Looking into the future, we foresee the need to further embed the role of data stewards into the lifeblood of the research organisation, along with policies on how to finance long-term storage of research data. The latter, to remain feasible, needs to be combined with a further formalising of appraisal and reappraisal of archived research data.

Characterizing the research data management practices of NIH biomedical researchers indicates the need for better support at laboratory level.

The study investigated the research data management (RDM) practices of biomedical researchers at the National Institutes of Health (NIH) representing various biomedical disciplines. This study aimed to analyse the state of biomedical researchers' RDM practices based on RDM practice levels (individual, laboratory, institution and external). The findings of the study are expected to provide directions to information professionals for effective RDM services. Semi-structured interviews with 11 researchers were conducted. The interviews were analysed by levels of RDM practices. The findings revealed that biomedical researchers focus on storing and sharing data and that RDM is performed mainly at the individual level. There seems to be a lack of laboratory level RDM system that allows consistent RDM practices among researchers. External RDM practice is often challenged by not having one responsible for RDM. Findings suggested a need for an agreed RDM system and customized support, particularly at the laboratory level. Also, institutional support can help researchers prepare for long term data preservation. Our suggestions emphasize the importance of RDM training and support for long term data preservation, especially at the laboratory level.

Supporting Research Data Management and Publication by Kyoto University Library Network

Supporting Research Data Management and Publication by Kyoto University Library Network

BEXIS2: A FAIR-aligned data management system for biodiversity, ecology and environmental data.

BackgroundObtaining fit-to-use data associated with diverse aspects of biodiversity, ecology and environment is challenging since often it is fragmented, sub-optimally managed and available in heterogeneous formats. Recently, with the universal acceptance of the FAIR data principles, the requirements and standards of data publications have changed substantially. Researchers are encouraged to manage the data as per the FAIR data principles and ensure that the raw data, metadata, processed data, software, codes and associated material are securely stored and the data be made available with the completion of the research.New informationWe have developed BEXIS2 as an open-source community-driven web-based research data management system to support research data management needs of mid to large-scale research projects with multiple sub-projects and up to several hundred researchers. BEXIS2 is a modular and extensible system providing a range of functions to realise the complete data lifecycle from data structure design to data collection, data discovery, dissemination, integration, quality assurance and research planning. It is an extensible and customisable system that allows for the development of new functions and customisation of its various components from database schemas to the user interface layout, elements and look and feel.During the development of BEXIS2, we aimed to incorporate key aspects of what is encoded in FAIR data principles. To investigate the extent to which BEXIS2 conforms to these principles, we conducted the self-assessment using the FAIR indicators, definitions and criteria provided in the FAIR Data Maturity Model. Even though the FAIR data maturity model is developed initially to judge the conformance of datasets, the self-assessment results indicated that BEXIS2 remarkably conforms and supports FAIR indicators. BEXIS2 strongly conforms to the indicators Findability and Accessibility. The indicator Interoperability is moderately supported as of now; however, for many of the lesssupported facets, we have concrete plans for improvement. Reusability (as defined by the FAIR data principles) is partially achieved.This paper also illustrates community deployment examples of the BEXIS2 instances as success stories to exemplify its capacity to meet the biodiversity and ecological data management needs of differently sized projects and serve as an organisational research data management system.

Potential Roles for Science Librarians in Research Data Management: A Gap Analysis

As many sciences move to be more data-intensive, some science librarians are offering more research data services and perform research data management roles. Job analyses provide insight and context to the tasks employees actually do versus what their job descriptions depict or employers assume. Two separate job analyses studies investigated the roles and responsibilities of data services librarians and research integrity officers among the top 10 private and top 10 public higher education institutions. The focus of these interviews was research data management support roles. Comparing these two groups’ responses indicates that the role-based responsibilities for research data services are not always clear within institutions and are predominantly placed on individual researchers or research teams, but science librarians may provide some solutions to address this gap. This paper presents a model for the potential roles of science librarians in research data management.

Practical considerations for a library's research data management services: the case of the National Institutes of Health Library.

Objective:This study investigates research data management (RDM) services using a crosstab framework with the National Institutes of Health (NIH) Library as a case study to provide practical considerations for libraries seeking to improve their RDM services.Methods:We conducted semistructured interviews with four librarians who provide data services at the NIH Library regarding library user characteristics, RDM services provided, RDM infrastructure, and collaboration experiences. Through the analysis of interview transcripts, we identified and analyzed the NIH Library's RDM services according to Online Computer Library Center (OCLC)'s three categories of RDM services and the six stages of the data lifecycle.Results:The findings show that the two models' crosstab framework can provide an overview of an institution's current RDM services and identify service gaps. The NIH Library tends to take more responsibility in providing education and expertise services while relying more on information technology departments for curation services. The library provides significant support for data creation, analysis, and sharing stages to meet biomedical researchers' needs, suggesting areas for potential expansion of RDM services in the less supported stages of data description, storage, and preservation. Based on these findings, we recommend three key considerations for libraries: identify gaps in current services, identify services that can be supported via partnerships, and get regular feedback from users.Conclusion:These findings provide a deeper understanding of RDM support on the basis of RDM service categories and the data lifecycle and promote discussion of issues to be considered for future improvements in RDM services.

Toward Best Practices for Unstructured Descriptions of Research Data

AbstractAchieving the potential of widespread sharing of open research data requires that sharing data is straightforward, supported, and well‐understood; and that data is discoverable by researchers. Our literature review and environment scan suggest that while substantial effort is dedicated to structured descriptions of research data, unstructured fields are commonly available (title, description) yet poorly understood. There is no clear description of what information should be included, in what level of detail, and in what order. These human‐readable fields, routinely used in indexing and search features and reliably federated, are essential to the research data user experience. We propose a set of high‐level best practices for unstructured description of datasets, to serve as the essential starting point for more granular, discipline‐specific guidance. We based these practices on extensive review of literature on research article abstracts; archival practice; experience in supporting research data management; and grey literature on data documentation. They were iteratively refined based on comments received in a webinar series with researchers, data curators, data repository managers, and librarians in Canada. We demonstrate the need for information research to more closely examine these unstructured fields and provide a foundation for a more detailed conversation.

MetaFAIR: A Metadata Application Profile for Managing Research Data

AbstractThis paper reports on the development of a metadata application profile (AP), MetaFAIR, designed to support research data management (RDM) to make research data findable, accessible, interoperable, and reusable. The development of MetaFAIR followed a three‐step process that included learning about the characteristics of datasets from researchers to establish their context and requirements, as well as iterative design and testing with researchers' feedback. Guided by the FAIR principles (Findability, Accessibility, Interoperability, and Reusability), MetaFAIR focuses on accommodating description needs particular to computational social science datasets while seeking to provide general enough elements to describe data collections across many different domains. In this paper, MetaFAIR is placed in the context of historical and recent developments in the areas of RDM and application profile creation; following this contextualization, the paper describes the central considerations and challenges of the MetaFAIR development process and discusses its significance for future work in RDM.

Creating Guidance for Canadian Dataverse Curators: Portage Network’s Dataverse Curation Guide

Purpose: This paper introduces the Portage Network’s Dataverse Curation Guide and the new bilingual curation framework developed to support it. Brief Description: Canadian academic institutions and national organizations have been building infrastructure, staffing, and programming to support research data management. Amidst this work, a notable gap emerged between requirements for data curation in general repositories like Dataverse and the requisite workflows and guidance materials needed by curators to meet them. In response, Portage, a national network of data experts, organized a working group to develop a Dataverse curation guide built upon the Data Curation Network’s CURATED workflow. To create a bilingual resource, the original CURATE(D) acronym was modified to CURATION—which has the same meaning in both French and English—and steps were augmented with Dataverse-specific guidance and mapped to three conceptualized levels of curation to assist curators in prioritizing curation actions. Methods: An environmental scan of relevant deposit and curation guidance materials from Canadian and international institutions identified the need for a comprehensive Dataverse Curation Guide, as most existing resources were either depositor-focused or contained only partial workflows. The resulting Guide synthesized these guidance materials into the CURATION steps and mapped actions to various theoretical levels of data repository services and levels of curation. Resources: The following documents are supplemental to the Dataverse Curation Guide: the Portage Dataverse North Metadata Best Practices Guide, the Scholars Portal Dataverse Guide, and the Data Curation Network CURATED Workflow and Data Curation Primers.

Shifting Horizons: A Literature Review of Research Data Management Train-the-Trainer Models for Library and Campus-Wide Research Support Staff in Canadian Institutions

Objective – In consideration of emerging national Research Data Management (RDM) policy and infrastructure, this literature review seeks answers to the following questions: 1) What is the most effective way for a Canadian research university to build capacity among library and campus-wide research support staff, with a view towards providing coordinated RDM support services for our researcher community?2) What international training models and course offerings are available and appropriate for a local context?3) What national guidelines and best practices for pedagogical design and delivery can be adapted for a local context? Methods – This literature review synthesizes a total of 13 sources: 9 articles, 2 book chapters, and 2 whitepapers. The whitepapers were selected for a narrative literature review because of their focus on case studies detailing train-the-trainer models. Within the 13 sources we found 14 key case studies. This review serves as a supplement to the 2017 CARL Portage Training Expert Group white paper, “Research Data Management Training Landscape in Canada,” the focus of which was to identify RDM training gaps in order to recommend a coordinated approach to RDM training in a national environment. Results – The narrative review of case studies revealed three thematic areas. Firstly, pedagogical challenges were identified, including the need to target training to RDM support staff such as librarians and researchers, as they comprise distinct groups of trainees with divergent disciplinary vocabularies and incentives for training. Secondly, the case studies cover a broad range of pedagogical models including single or multiple sessions, self-directed or instructor-led, in-person or online instruction, and a hybrid of the two. Finally, RDM training also emerged as a key factor in community building within library staff units, among service units on campus, and with campus research communities. Conclusion – RDM training programs at local institutions should be guided by a set of principles aligned with the training methods, modes of assessment, and infrastructure development timeline outlined in a national training strategy. When adapting principles and training strategies to a local context, the following trends in the literature should be considered: librarians and researchers must have meaningful incentives to undertake training in RDM or to join a community of practice; disciplinary-specific instruction is preferable to general instruction; a librarian’s own training opportunities will influence their ability to provide discipline-specific RDM instruction to researchers; in-person training opportunities improve learning retention and produce beneficial secondary effects, whereas online instruction is most effective when paired with an in-person component; generalized third-party RDM training should be adapted to local context to be meaningful. Future directions for RDM training will integrate into open access and digital scholarship training, and into cross-disciplinary, open science communities of practice.

Structuring better services for unstructured data: Academic libraries are key to an ethical research data future with big data

Academic libraries have been supporting research data management for decades, but are rarely their institutions central research support unit. As more researchers look to leverage big datasets for new insights, research data management is going to get ever more complex. For institutions of higher education to face these new challenges, they need to make libraries institutional leaders in this area. Doing so will reduce waste and risk while providing clearer guidance and fuller support through the entire research cycle.

Research Data Management Policy and Practice in China


 On April 2, 2018, the State Council of China formally released a national research data management (RDM) policy “Measures for Managing Scientific Data”. Literature review shows that university libraries have played an important role in supporting Research Data Management at an institutional level in countries in North America, Europe and Australasia. The aim of this paper is to capture the current status of RDM in Chinese universities, in particular how university libraries have involved in taking the agenda forward.
 This paper uses mixed methods: a website analysis of university policies and services; a questionnaire for university librarians; and semi-structured interviews. Findings from website analysis and questionnaires indicate that RDS at a local level in Chinese Universities are in their infancy. On the whole there is more evidence of activity in developing data repositories than support services. Despite the existence of a national policy there remain significant barriers to further service development, such as the lag in the creation of local policy, insufficient funding for technical infrastructure, shortages of staff skills in data curation, and language barriers to international data sharing and open science. RDS in Chinese university libraries are still lagging behind the English-speaking countries and Europe.

Plasma-MDS, a metadata schema for plasma science with examples from plasma technology

A metadata schema, named Plasma-MDS, is introduced to support research data management in plasma science. Plasma-MDS is suitable to facilitate the publication of research data following the FAIR principles in domain-specific repositories and with this the reuse of research data for data driven plasma science. In accordance with common features in plasma science and technology, the metadata schema bases on the concept to separately describe the source generating the plasma, the medium in which the plasma is operated in, the target the plasma is acting on, and the diagnostics used for investigation of the process under consideration. These four basic schema elements are supplemented by a schema element with various attributes for description of the resources, i.e. the digital data obtained by the applied diagnostic procedures. The metadata schema is first applied for the annotation of datasets published in INPTDAT—the interdisciplinary data platform for plasma technology.

Service, consulting and training: Portfolio expansion to support research data management

Service, consulting and training: Portfolio expansion to support research data management