Research Data Management Practices Research Articles

A Comprehensive Review of Technological Pedagogical Content Knowledge (TPACK)

This review of 36 studies investigates the Technological Pedagogical Content Knowledge (TPACK) framework and instructional practices in various educational contexts. The majority of the reviewed studies focus on pre-service and in-service teachers, highlighting the multifaceted nature of TPACK research. Various demographic factors, technology-related variables, and research methodologies were considered, revealing diverse approaches, characteristics, and trends. A majority of the reviewed studies utilized non-probability sampling techniques, indicating a need for more consistency in sampling procedures to facilitate the comparison and synthesis of findings. The findings suggest that future research should prioritize developing well-defined research questions and data management practices to ensure the accuracy and reliability of the findings. Furthermore, future studies should continue to explore the complex relationships between TPACK and various aspects of instructional practices, as well as the potential impact of TPACK on student learning outcomes. By building on these findings and employing rigorous research methods, future studies can continue to advance our understanding of the role of TPACK in shaping effective instructional practices, ultimately contributing to the improvement of teaching and learning in the digital age. However, the study's focus on articles published in journals hosted by ULAKBIM and conducted in Turkey, as well as the exclusion of non-empirical research, may have limited the generalizability of the findings to other contexts and populations. Additionally, the lack of explicit information on data cleaning procedures and missing data from the studies reviewed might affect the accuracy and reliability of the findings.

A research data management (RDM) community for ELIXIR

Research data management (RDM) is central to the implementation of the FAIR (Findable Accessible, Interoperable, Reusable) and Open Science principles. Recognising the importance of RDM, ELIXIR Platforms and Nodes have invested in RDM and launched various projects and initiatives to ensure good data management practices for scientific excellence. These projects have resulted in a rich set of tools and resources highly valuable for FAIR data management. However, these resources remain scattered across projects and ELIXIR structures, making their dissemination and application challenging. Therefore, it becomes imminent to coordinate these efforts for sustainable and harmonised RDM practices with dedicated forums for RDM professionals to exchange knowledge and share resources. The proposed ELIXIR RDM Community will bring together RDM experts to develop ELIXIR’s vision and coordinate its activities, taking advantage of the available assets. It aims to coordinate RDM best practices and illustrate how to use the existing ELIXIR RDM services. The Community will be built around three integral pillars, namely, a network of RDM professionals, RDM knowledge management and RDM training expertise and resources. It will also engage with external stakeholders to leverage benefits and provide a forum to RDM professionals for regular knowledge exchange, capacity building and development of harmonised RDM practices, keeping in line with the overall scope of the RDM Community. In the short term, the Community aims to build upon the existing resources and ensure that the content of these remain up to date and fit for purpose. In the long run, the Community will aim to strengthen the skills and knowledge of its RDM professionals to support the emerging needs of the scientific community. The Community will also devise an effective strategy to engage with other ELIXIR structures and international stakeholders to influence and align with developments and solutions in the RDM field.

Research data management practices of faculty members in Ghanaian universities

PurposeThe purpose of this study is to investigate the current research data management practices among researchers in Ghana and their impact on data reuse and collaborative research. The study aims to identify the methods used by researchers to store and preserve their research data, as well as to determine the extent to which researchers share their data with others.Design/methodology/approachThe study uses a mixed-method research strategy to blend qualitative and quantitative data and is conducted at two public and two private universities in Ghana.FindingsThe study revealed that researchers in Ghana currently store and preserve their research data using personal devices, such as laptops, CDs and external flash drives, rather than keeping the data in university data repositories. They also do not share their research data with others, which negatively affects collaborative research. The current practice of storing data on personal devices and not sharing data with others hinders collaborative research. The study recommends that universities in Ghana revise their research policy documents to address RDM-related issues such as data storage, data preservation, data sharing and data reuse.Research limitations/implicationsThe study was conducted at two public and two private universities in Ghana, but the findings were placed in a wider context through appropriate references.Practical implicationsThis study emphasises the need for sound research data management procedures to support research collaboration and data reuse in Ghana. Universities should provide incentives to academics to disclose their data to encourage data sharing and collaboration.Social implicationsThe government and management of universities should consciously invest in the needed technologies and equipment to implement research data management in their universities.Originality/valueThis study looks at how researchers in Ghana manage their research data and how it affects data reuse and collaborative research.

A bridge between trust and control: computational workflows meet automated battery cycling.

Compliance with good research data management practices means trust in the integrity of the data, and it is achievable by full control of the data gathering process. In this work, we demonstrate tooling which bridges these two aspects, and illustrate its use in a case study of automated battery cycling. We successfully interface off-the-shelf battery cycling hardware with the computational workflow management software AiiDA, allowing us to control experiments, while ensuring trust in the data by tracking its provenance. We design user interfaces compatible with this tooling, which span the inventory, experiment design, and result analysis stages. Other features, including monitoring of workflows and import of externally generated and legacy data are also implemented. Finally, the full software stack required for this work is made available in a set of open-source packages.

DataPLAN: A Web-Based Data Management Plan Generator for the Plant Sciences

Research data management (RDM) combines a set of practices for the organization, storage and preservation of data from research projects. The RDM strategy of a project is usually formalized as a data management plan (DMP)—a document that sets out procedures to ensure data findability, accessibility, interoperability and reusability (FAIR-ness). Many aspects of RDM are standardized across disciplines so that data and metadata are reusable, but the components of DMPs in the plant sciences are often disconnected. The inability to reuse plant-specific DMP content across projects and funding sources requires additional time and effort to write unique DMPs for different settings. To address this issue, we developed DataPLAN—an open-source tool incorporating prewritten DMP content for the plant sciences that can be used online or offline to prepare multiple DMPs. The current version of DataPLAN supports Horizon 2020 and Horizon Europe projects, as well as projects funded by the German Research Foundation (DFG). Furthermore, DataPLAN offers the option for users to customize their own templates. Additional templates to accommodate other funding schemes will be added in the future. DataPLAN reduces the workload needed to create or update DMPs in the plant sciences by presenting standardized RDM practices optimized for different funding contexts.

DMPFrame: A Conceptual Metadata Framework for Data Management Plans

Research Data Management (RDM) is a complex process and to ensure good RDM practice, one of the ways is to associate or create Data Management Plans (DMPs) with every research project. Interestingly this opens up other challenges such as finding the corresponding DMP for a research dataset. It is sometimes difficult or impossible to find a particular DMP associated with a dataset, which undermines the actual value of DMPs. This is the result of poor metadata management of DMPs throughout their creation using a DMP tool to their archiving. Implementing adequate metadata management for the description and organization of DMPs might solve this issue. We have examined 12 open-source DMP tools, in particular, to evaluate the metadata adopted by these tools. The current study spots and highlights the gaps in the DMP metadata management in DMP tools and suggests DMPFrame as a conceptual framework addressing such gaps to improve the existing tools’ DMP metadata management practices. Based on the examined DMP tool’s metadata elements analysis and mapping, DMPFrame manages DMP metadata under 6 categories, namely, contributors, project, funding, organization, DMP, and output. The current study also suggests a systematic workflow that DMP tools could incorporate for metadata creation for DMPs.

BAM Data Store

As a partner in several NFDI consortia, the Bundesanstalt für Materialforschung und -prüfung (BAM, German federal institute for materials science and testing) contributes to research data standardization efforts in various domains of materials science and engineering (MSE). To implement a central research data management (RDM) infrastructure that meets the requirements of MSE groups at BAM, we initiated the Data Store pilot project in 2021. The resulting infrastructure should enable researchers to digitally document research processes and store related data in a standardized and interoperable manner. As a software solution, we chose openBIS, an open-source framework that is increasingly being used for RDM in MSE communities. The pilot project was conducted for one year with five research groups across different organizational units and MSE disciplines. The main results are presented for the use case “nanoPlattform”. The group registered experimental steps and linked associated instruments and chemicals in the Data Store to ensure full traceability of data related to the synthesis of ~400 nanomaterials. The system also supported researchers in implementing RDM practices in their workflows, e.g., by automating data import and documentation and by integrating infrastructure for data analysis. Based on the promising results of the pilot phase, we will roll out the Data Store as the central RDM infrastructure of BAM starting in 2023. We further aim to develop openBIS plugins, metadata standards, and RDM workflows to contribute to the openBIS community and to foster RDM in MSE.

Towards FAIR Research Data in Metrology

Good data management is necessary to maintain the trustworthiness and reliability of data. This is particularly important in metrology, the science of measurement, which ensures stable, comparable, coherent, and traceable measurement results. The digitalization of metrology has increased the demand for structured and harmonised research data management (RDM).
 To meet this demand, the project TC-IM 1449 "Research data management in European metrology" was established in 2018. The project aims to promote good RDM practices underpinned by the FAIR principles, supporting traceability and reproducibility of measurement results. For that purpose, the project is providing researchers with the knowledge, competency, awareness, and tools to implement good RDM practices.
 The project has formulated a vision for RDM in metrology for the support of scientists by developing and disseminating recommendations and in the organisation of training. As part of this vision, the project has produced several deliverables, including a template research data management policy, guidelines for data documentation, creation of metadata, and quality assurance for data publication. The project is also creating a comprehensive guide to RDM, a checklist for project coordinators, and providing training modules.
 The project's activities reflect the needs of metrologists that are collated and communicated by the technical experts from the relevant Technical Committees and European Metrology Networks. Furthermore, the project's deliverables will be an invaluable resource for researchers seeking to effectively manage and share their research data.

Introducing Undergraduates to Research Data Management: A Marine Science Case Study-Based Asynchronous Express Course

ABSTRACT Experiential learning can connect undergraduate students with research projects where they create and analyze data. Research Data Management (RDM) competencies are needed to ensure that research data are effectively organized, described, stored, and curated for future use. In recent years, undergraduates have been taught RDM in credit classes and workshops, but opportunities to learn RDM competencies are not widespread in undergraduate education. This study describes a one-credit asynchronous express RDM course that was developed and taught by an academic librarian as a 100-level undergraduate class between 2021 and 2023. Students applied RDM theory and best practices to marine science case study-based assignments to experience organizing and describing research data firsthand. The course culminated with students writing proposals to implement RDM practices at a research institution. Evaluation of the course is described from the librarian-instructor perspective and using enrollment, grade, and student course evaluation data. Teaching asynchronously at an express pace presented challenges in keeping some students engaged and progressing throughout the seven-week course, requiring proactive outreach and instructor attentiveness. While the asynchronous mode adds flexibility to student schedules, a reduced scope or slower pace is recommended for providing undergraduates a meaningful experiential introduction to data management.

Teaching the Best Research Data Management Practices to PhD Students

Good Research Data Management (RDM) practices make it easier for researchers to conduct their research and ensure their data is preserved and stored securely for the long-term. Archiving is one of the last stages of Research Data Lifecycle and deals with depositing and preserving datasets at the end of a project in a suitable storage location (repositories, local server, or hard drive). Students may not find data management topic as interesting as their core course topics. This can result in lack of students’ knowledge of the importance and benefits of good data management practices for their studies. At Warwick, I design and deliver Research Data Management & Planning (RDM&P) case study sessions to PhD students. Students learn different elements of RDM and importance of RDM for long-term preservation. Students work in groups to develop and present Data Management Plan (DMP) and put RDM&P learning into practice. In this presentation, I briefly introduce the above case study, feedbacks received from students across different disciplines and further steps.

Partnering with health sciences libraries to address challenges in bioimaging data management and sharing.

Federal mandates, publishing requirements, and an interest in open science have all generated renewed attention on research data management and, in particular, data sharing practices. Due to the size and types of data they produce, bioimaging researchers confront specific challenges in aligning their data with FAIR principles, ensuring that it is findable, accessible, interoperable, and reusable. Although not always recognized by researchers, libraries can, and have been, offering support for data throughout its lifecycle by assisting with data management planning, acquisition, processing and analysis, and sharing and reuse of data. Libraries can educate researchers on best practices for research data management and sharing, facilitate connections to experts by coordinating sessions using peer educators and appropriate vendors, help assess the needs of different researcher groups to identify challenges or gaps, recommend appropriate repositories to make data as accessible as possible, and comply with funder and publisher requirements. As a centralized service within an institution, health sciences libraries have the capability to bridge silos and connect bioimaging researchers with specialized data support across campus and beyond.

Data sharing attitudes and practices of researchers in Korean government research institutes: a survey-based descriptive study

Purpose: This study explored to what extent and how researchers in five Korean government research institutes that implement research data management practices share their research data and investigated the challenges they perceive regarding data sharing.Methods: The study collected survey data from 224 respondents by posting a link to a SurveyMonkey questionnaire on the homepage of each of the five research institutes from June 15 to 29, 2022. Descriptive statistical analyses were conducted.Results: Among 148 respondents with data sharing experience, the majority had shared some or most of their data. Restricted data sharing within a project was more common than sharing data with outside researchers on request or making data publicly available. Sharing data directly with researchers who asked was the most common method of data sharing, while sharing data via institutional repositories was the second most common method. The most frequently cited factors impeding data sharing included the time and effort required to organize data, concerns about copyright or ownership of data, lack of recognition and reward, and concerns about data containing sensitive information.Conclusion: Researchers need ongoing training and support on making decisions about access to data, which are nuanced rather than binary. Research institutes’ commitment to developing and maintaining institutional data repositories is also important to facilitate data sharing. To address barriers to data sharing, it is necessary to implement research data management services that help reduce effort and mitigate concerns about legal issues. Possible incentives for researchers who share data should also continue to be explored.

State of Research Data Management Practices in the Top-ranked Higher Education Institutions in India

Due to the vast amounts of data generated during research activities and mandates obtained from funding agencies, higher education institutions put a high priority on research data management (RDM). RDM is prioritized by global organizations, and India is no exception. The current work aims to address the current state of RDM in the top-ranked higher education institutions (HEIs) of India and attempts to answer the research questions posed on various aspects of RDM, such as RDM services, policy implementation, responsible stakeholders, funding support for RDM strategy and development, and so on. Furthermore, unlike most of the existing literature, the development of RDM capacity is viewed as an institutional concern in this study rather than a library concern. The survey and website analysis approaches were used to conduct this study. The outcomes of the study show that RDM is still in its early stage among India’s leading institutions.

Research Data Management Practices and Challenges in Academic Libraries: A Comprehensive Review

Research Data Management Practices and Challenges in Academic Libraries: A Comprehensive Review

RDM in a Decentralised University Ecosystem—A Case Study of the University of Cologne

The University of Cologne (UoC) has historically grown in highly decentralised structures. This is reflected by a two-layered library structure as well as by a number of decentralised research data management (RDM) activities established on the faculty and research consortium level. With the aim to foster networking, cooperation, and synergies between existing activities, a university-wide RDM will be established. A one-year feasibility study was commissioned by the Rectorate in 2016 and carried out by the department research management, library and computing centre. One study outcome was the adoption of a university-wide research data guideline. Based on a comprehensive RDM service portfolio, measures were developed to put a central RDM into practice. The challenges have been to find the right level of integration and adaptation of existing and established decentralised structures and to develop additional new structures and services. We will report on first steps to map out central RDM practices at the UoC and to develop a structure of cooperation between loosely coupled information infrastructure actors. Central elements of this structure are a competence center, an RDM expert network, a forum for exchange about RDM and associated topics as well as the faculties with their decentralized, domain-specific RDM services. The Cologne Competence Center for Research Data Management (C³RDM) was founded at the end of 2018 and is still in its development phase. It provides a one-stop entry point for all questions regarding RDM. The center itself provides basic and generic RDM services, such as training, consulting, and data publication support, and acts as a hub to the decentral experts, information infrastructure actors, and resources.

Research Data Management Commitment Drivers: An Analysis of Practices, Training, Policies, Infrastructure, and Motivation in Global Agricultural Science

Scientists largely acknowledge the value of research data management (RDM) to enable reproducibility and reuse. But, RDM practices are not sufficiently rewarded within the traditional academic reputation economy. Recent work showed that emerging RDM tools can offer new incentives and rewards. But, the design of such platforms and scientists' commitment to RDM is contingent on additional factors, including policies, training, and several types of personal motivation. To date, studies focused on investigating single or few of those RDM components within a given environment. In contrast, we conducted three studies within a global agricultural science organization, to provide a more complete account of RDM commitment drivers: one survey study (n = 23) and two qualitative explorations of regulatory frameworks (n = 17), as well as motivation, infrastructure, and training components (n = 13). Based on the sum of findings, we contribute to the triangulation of a recent RDM commitment evolution model. In particular, we find that strong support and suitable tools help develop RDM commitment, while policy conflicts, unclear data standards, and multi-platform sharing, lead to unexpected negotiation processes. We expect that these findings will help to better understand RDM commitment drivers, refine the RDM commitment evolution model, and benefit its application in science.

Challenges of promoting open science within the NI4OS-Europe project in Hungary

National Initiatives for Open Science in Europe (NI4OS-Europe) is a Horizon 2020 project, one of whose objectives is to promote the European Open Science Cloud (EOSC) and open science in 15 Central and East European EU states and EU associated countries.
 This paper describes the variety of NI4OS-Europe promoting activities carried out in Hungary by the Governmental Agency for IT Development (KIFÜ). All these activities aim at different, and sometimes overlapping groups and involve: 
 ▸ publishing researcher interviews on research data management and open science practices for early career researchers;▸ open science news feed for open science practitioners;▸ publishing an e-learning course on EOSC and open science for graduate and PhD students;▸ testing research data management (RDM) tools for the Hungarian research community;▸ organising various events, including the Hungarian Open Science Forum targeting senior researchers and stakeholders;▸ an EOSC Champion programme at three major Hungarian universities. 
 Identifying good practices will give us the chance to find the best communication channels andmethods to promote open science and to manage expectations of funders, researchers, and librarians. 
 We will analyse audience diversity at six NI4OS events organised as part of the project. The anonymized dataset based on registration forms will be filtered by affiliation and profession. We will attempt to identify the main event features that mostly attract librarians or researchers. We will test the hypothesis that open science is generally more welcomed by research institutions’ librarians than, researchers, whose interests are more attached to funds and applications.
 We will also study usage data of open science news feeds. Having around 200 posts and 5000 visitors from May 2021 to April 2022 gives us the chance to learn more about the characteristics of the most visited, event-related posts, followed by posts related to national policies, which are visited more frequently than international ones.
 The paper will also argue that bottom-up and top-down approaches of promoting EOSC need to be used in a delicate balance to reach out to the widest audience possible.

Research data management in agricultural sciences in Germany: We are not yet where we want to be.

To meet the future challenges and foster integrated and holistic research approaches in agricultural sciences, new and sustainable methods in research data management (RDM) are needed. The involvement of scientific users is a critical success factor for their development. We conducted an online survey in 2020 among different user groups in agricultural sciences about their RDM practices and needs. In total, the questionnaire contained 52 questions on information about produced and (re-)used data, data quality aspects, information about the use of standards, publication practices and legal aspects of agricultural research data, the current situation in RDM in regards to awareness, consulting and curricula as well as needs of the agricultural community in respect to future developments. We received 196 (partially) completed questionnaires from data providers, data users, infrastructure and information service providers. In addition to the diversity in the research data landscape of agricultural sciences in Germany, the study reveals challenges, deficits and uncertainties in handling research data in agricultural sciences standing in the way of access and efficient reuse of valuable research data. However, the study also suggests and discusses potential solutions to enhance data publications, facilitate and secure data re-use, ensure data quality and develop services (i.e. training, support and bundling services). Therefore, our research article provides the basis for the development of common RDM, future infrastructures and services needed to foster the cultural change in handling research data across agricultural sciences in Germany and beyond.

Multi-Stakeholder Research Data Management Training as a Tool to Improve the Quality, Integrity, Reliability and Reproducibility of Research

To ensure the quality and integrity of data and the reliability of research, data must be well documented, organised, and described. This calls for research data management (RDM) education for researchers. In light of 3 ECTS Basics of Research Data Management (BRDM) courses held between 2019 and 2021, we aim to find how a generic level multi-stakeholder training can improve STEM and HSS disciplines’ doctoral students’ and postdoc researchers’ competencies in RDM. The study uses quantitative, descriptive and inferential statistics to analyse respondents’ self-ratings of their competencies, and a qualitative grounded theory-inspired approach to code and analyse course participants’ feedback. Results: On average, based on the post-course surveys, respondents’ (n = 123) competencies improved one point on a four-level scale, from “little competence” (2) to “somewhat competent” (3). Participants also reported that the training would change their current practices in planning research projects, data management and documentation, acknowledging legal and data privacy viewpoints, and data collecting and organising. Participants indicated that it would be helpful to see legal and data privacy principles and regulations presented as concrete instructions, cases, and examples. The most requested continuing education topics were metadata and description, discipline specific cultures, and backup, version management, and storage. Conclusions: Regarding to the widely used criteria for successful training containing 1) active participation during training; 2) demand for RDM training; 3) increased participants’ knowledge and understanding of RDM and confidence in enacting RDM practices; and 4) positive post-training feedback, BRDM meets the criteria. This study shows that although reaching excellent competence in a RDM basics training is improbable, participants become aware of RDM and its contents and gain the elementary tools and basic skills to begin applying sound RDM practices in their research. Furthermore, participants are introduced to the academic and research support professionals and vice versa: Stakeholders will get to know the challenges that young researchers and research students encounter when applying RDM. The study reveals valuable information on doctoral students’ and postdoc researchers’ competencies, the impact of education on competencies, and further learning needs in RDM.

Research Data Management Training in Academic Libraries: A Scoping Review

Purpose: The present scoping review examines research data management (RDM) best practices and empirical studies in academic libraries between 2010 and 2021. Method: The current study developed systematic database searches to locate potential articles for inclusion and designed a detailed and systematic coding scheme to examine the substantive features of RDM and characteristics of RDM practice, with an emphasis on RDM instruction. Results and Discussion: The results from the current study demonstrated that there is great demand for RDM training after 2011. Furthermore, research about RDM training spread across North America, Europe, Asia Pacific, and elsewhere. The findings also proved that RDM training is essential for both STEM and non- STEM subjects but simultaneously indicated that non-STEM subjects such as the social sciences in particular lack RDM training. Results from the current literature also found that a large number of RDM training programs focused on the introduction of RDM or an RDM overview, without in-depth and discipline-based curriculum for researchers across domains. Additionally, this study identified a lack of quantitative research, especially statistical analysis, on the effect of RDM interventions. Conclusion: This study contributes to our comprehensive understanding of some essential elements associated with RDM training, with the primary finding that future practitioners in the RDM field would benefit from stronger collaboration with faculty or researchers to develop more discipline-based curriculums for RDM and more application-based approaches for teaching RDM.