A conceptual virtual research environment model for the management of research data, a South African perspective

See video of the presentation.On 17th July 2015 the Ministry of Science, Research and the Arts for Baden-Wuerttemberg, Germany, invited national experts to the presentation of the final report of the ‘bwFDM communities’ project. This 18 month project was launched at the beginning of 2014 to evaluate the needs of services and the support that libraries and IT service centres should offer researchers in the area of research data management. Full-time key project staff had been established at all 9 universities in the state of Baden-Wuerttemberg to conduct semi-structured personal interviews of all research groups working with research data (in a broad sense including all areas of science, social science and humanities) and to document them in the form of user stories. 627 interviews have been conducted and more than 2,500 user stories could be extracted, showing the wide range of needs and wishes articulated by researchers. On this basis issues of importance and requirements had be identified, categorised in 18 different groups and finalised into an analysis of the status quo and recommendations for concrete action plans. The results cover the areas ‘general requirements and policy framework’, ‘data collection and data sharing’, ‘technical framework and virtual research environments’, ‘preservation’, ‘IT infrastructure and IT support’, ‘licencing’ and ‘Open Science’.The presentation will give an overview of the project results and will highlight the roles libraries and IT service centres are expected to play from the researcher´s point of view.As the final report to the Ministry contributes to a comprehensive research data management strategy for the State of Baden-Wuerttemberg, the presentation will also point out the status of the federal strategy in RDM.

Science is changing: the volume and complexity of data are increasing, the number of studies is growing and the goal of achieving reproducible results requires new solutions for scientific data management. In the field of neuroscience, the German National Research Data Infrastructure (NFDI-Neuro) initiative aims to develop sustainable solutions for research data management (RDM). To obtain an understanding of the present RDM situation in the neuroscience community, NFDI-Neuro conducted a comprehensive survey among the neuroscience community. Here, we report and analyze the results of the survey. We focused the survey and our analysis on current needs, challenges, and opinions about RDM. The German neuroscience community perceives barriers with respect to RDM and data sharing mainly linked to (1) lack of data and metadata standards, (2) lack of community adopted provenance tracking methods, (3) lack of secure and privacy preserving research infrastructure for sensitive data, (4) lack of RDM literacy, and (5) lack of resources (time, personnel, money) for proper RDM. However, an overwhelming majority of community members (91%) indicated that they would be willing to share their data with other researchers and are interested to increase their RDM skills. Taking advantage of this willingness and overcoming the existing barriers requires the systematic development of standards, tools, and infrastructure, the provision of training, education, and support, as well as additional resources for RDM to the research community and a constant dialogue with relevant stakeholders including policy makers to leverage of a culture change through adapted incentivization and regulation.

The main aim of this study was to improve the quality of nursing care for older acutely ill hospitalized medical patients through developing, implementing and evaluating a new model of care using a participatory action research process. One of the challenges of nursing today is to meet the health-care needs of the growing older population. It is important to consider what quality of nursing care means to older patients if nurses are to address gaps between their own perceptions and those of older patients themselves and to consider conceptual models of care appropriate for older patients care in order to improve the quality of care provided. This study is a mixed method triangulated study, involving the use of both quantitative and qualitative methods through participatory action research methodology to establish an evidence-base for an evolving model of care. The model was tested on 60 acutely ill patients aged at least 65 years. The medical ward nurses selected a key reference group including the researcher to facilitate the participatory action research process to develop, implement and evaluate a new model of care based on Orem's self-care model incorporating the Nurses Improving Care to Health System Elders Faculty (Am J Nurs 1994; 94:21) medication protocol to improve the nursing care provided for acutely ill older patients. The participatory action research process resulted in improved heath-care outcomes for the patients, such as significant improvements in activities of daily living capabilities between admission to discharge, significant improvements in knowledge levels regarding their medication regimes, as well as increased satisfaction with nursing care activities as perceived by older patients and nursing staff. The implementation of educational sessions during the model of care improved the older patient's functional activities and knowledge levels of their medication regime prior to discharge. In addition, by repeatedly explaining procedures, nurses became more involved with their individual patient's care, developing a patient-centred care relationship based on Orem's self-care model. This study demonstrates the efficacy of a new model of nursing care in improving the quality of nursing care for older patients in the acute medical ward setting. This study is significant because of its evidence-base and demonstrates how the participatory action research process empowered nurses to make sustainable changes to their practice. The nurses in the study wanted to affect change. The planned change was not dictated by management, but was driven by the clinical nursing staff at the 'grass roots' level. Therefore, being involved in the decision-making process provided an incentive to actively implement change.

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.

The vision of NFDI4Chem is the digitalisation of all key steps in chemical research to support scientists in their efforts to collect, store, process, analyse, disclose and re-use research data. Measures to promote Open Science and Research Data Management (RDM) in agreement with the FAIR data principles are fundamental aims of NFDI4Chem to serve the chemistry community with a holistic concept for access to research data. To this end, the overarching objective is the development and maintenance of a national research data infrastructure for the research domain of chemistry in Germany, and to enable innovative and easy to use services and novel scientific approaches based on re-use of research data. NFDI4Chem intends to represent all disciplines of chemistry in academia. We aim to collaborate closely with thematically related consortia. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation. This overarching goal is achieved by working towards a number of key objectives: Key Objective 1: Establish a virtual environment of federated repositories for storing, disclosing, searching and re-using research data across distributed data sources. Connect existing data repositories and, based on a requirements analysis, establish domain-specific research data repositories for the national research community, and link them to international repositories. Key Objective 2: Initiate international community processes to establish minimum information (MI) standards for data and machine-readable metadata as well as open data standards in key areas of chemistry. Identify and recommend open data standards in key areas of chemistry, in order to support the FAIR principles for research data. Finally, develop standards, if there is a lack. Key Objective 3: Foster cultural and digital change towards Smart Laboratory Environments by promoting the use of digital tools in all stages of research and promote subsequent Research Data Management (RDM) at all levels of academia, beginning in undergraduate studies curricula. Key Objective 4: Engage with the chemistry community in Germany through a wide range of measures to create awareness for and foster the adoption of FAIR data management. Initiate processes to integrate RDM and data science into curricula. Offer a wide range of training opportunities for researchers. Key Objective 5: Explore synergies with other consortia and promote cross-cutting development within the NFDI. Key Objective 6: Provide a legally reliable framework of policies and guidelines for FAIR and open RDM.

As significant players in the production of scholarly work, academic researchers are increasingly gathering and producing data rapidly, exceeding the development of the knowledge and skills required for proper data storage and management. However, the focus has typically been on research publication outputs rather than the research data determining the academic's research output. This study conducted an audit survey on research data management in a University of Technology faculty context to gain insight into their research data management practices. The study was guided by the Data Audit Framework (DAF), the Community Capability Model Framework (CCMF), and the User-Centered Research Data Management Framework (UCRDMF). The study utilized an explanatory sequential mixed method research design incorporating quantitative and qualitative components. In the quantitative phase, an online survey was administered to postgraduate students pursuing Master's and Doctoral degrees between 2015 and 2020. On the other hand, the qualitative phase involved conducting a meta-analysis of research repositories across global Higher Education Institutions (HEIs) and conducting online interviews with postgraduate supervisors who play a crucial role in postgraduate study and research administration. The quantitative data were analyzed using the Statistical Package for the Social Sciences (SPSS), while the qualitative data were analyzed using NVivo software. The collected data were then analyzed respectively with theoretical frameworks and existing literature. The findings revealed that managing research data was primarily a personal matter. The main reason for the difficulties was that the faculty lacked a research data management investment. The use of emails, external hard drives, and personal laptops are additional examples that showcased that the faculty had not established centralized systems for managing research. However, participants did recognize the benefits of managing research data, such as scientific advances, enhanced data repurpose, and simplicity for data reusers. Findings also showed that the lack of platforms permitting data sharing and reuse was one of the main reasons that most researchers had not shared or reused other researchers' research data. Researchers, however, wanted to share their data without restrictions. The study's conclusions were also used to create and document a conceptual framework for the faculty research data repository platform. The proposed model aims to guide the conception of a repository that will secure the storage of faculty digital information that can be easily retrieved.

PurposeThe purpose of this paper is to investigate the differences between scientific disciplines (SDs) in Slovenia in research data literacy (RDL) and research data management (RDM) to form recommendations regarding how to move things forward on the institutional and national level.Design/methodology/approachPurposive sample of active researchers was used from widest possible range of SD. Data were collected from April 21 to August 7, 2017, using 24-question online survey (5 demographic, 19 content questions (single/multiple choice and Likert scale type). Bivariate (ANOVA) and multivariate methods (clustering) were used.FindingsThe authors identified three perception-related and four behavior-related connections; this gave three clusters per area. First, perceptions – skeptical group, mainly social (SocS) and natural sciences (NatS): no clear RDM and ethical issues standpoints, do not agree that every university needs a data management plan (DMP). Careful group, again including mainly SocS and NatS: RDM is problematic and linked to ethical dilemmas, positive toward institutional DMPs. Convinced group, mainly from humanities (HUM), NatS, engineering (ENG) and medicine and health sciences (MedHeS): no problems regarding RDM, agrees this is an ethical question, is positive toward institutional DMP’s. Second, behaviors – sparse group, mainly from MedHeS, NatS and HUM, some agricultural scientists (AgS), and some SocS and ENG: do not tag data sets with metadata, do not use file-naming conventions/standards. Frequent group – many ENG, SocS, moderate numbers of NatS, very few AgS and only a few MedHeS and HUM: often use file-naming conventions/standards, version-control systems, have experience with public-domain data, are reluctant to use metadata with their RD. Slender group, mainly from AgS and NatS, moderate numbers of ENG, SocS and HUM, but no MedHeS: often use public-domain data, other three activities are rare.Research limitations/implicationsResearch could be expanded to a wider population, include other stakeholders and use qualitative methods.Practical implicationsResults are useful for international comparisons but also give foundations and recommendations on institutional and national RDM and RDL policies, implementations, and how to bring academic libraries into the picture. Identified differences suggest that different educational, awareness-raising and participatory approaches are needed for each group.Originality/valueThe findings offer valuable insight into RDM and RDL of Slovenian scientists, which have not yet been investigated in Slovenia.

Despite increasing recommendations for health professionals to participate in intervention design and implementation to effect changes in clinical practice, little is known about this strategy's effectiveness. This study analyses the effectiveness of interventions designed and implemented through participatory action research (PAR) processes in healthcare networks of Brazil, Chile, Colombia, Mexico and Uruguay to improve clinical coordination across care levels, and offers recommendations for future research. The study was quasi-experimental. Two comparable networks, one intervention (IN) and one control (CN), were selected in each country. Baseline (2015) and evaluation (2017) surveys of a sample of primary and secondary care doctors (174 doctors/network/year) were conducted using the COORDENA® questionnaire. Most of the interventions chosen were based on joint meetings, promoting cross-level clinical agreement and communication for patient follow-up. Outcome variables were: a) intermediate: interactional and organizational factors; b) distal: experience of cross-level clinical information coordination, of clinical management coordination and general perception of coordination between levels. Poisson regression models were estimated. A statistically significant increase in some of the interactional factors (intermediate outcomes) -knowing each other personally and mutual trust- was observed in Brazil and Chile INs; and in some organizational factors -institutional support- in Colombia and Mexico. Compared to CNs in 2017, INs of Brazil, Chile, Colombia and Mexico showed significant differences in some factors. In distal outcomes, care consistency items improved in Brazil, Colombia and Uruguay INs; and patient follow-up improved in Chile and Mexico. General perception of clinical coordination increased in Brazil, Colombia and Mexico INs. Compared to CNs in 2017, only Brazil showed significant differences. Although more research is needed, results show that PAR-based interventions improved some outcomes regarding clinical coordination at network level, with differences between countries. However, a PAR process is, by definition, slow and gradual, and longer implementation periods are needed to achieve greater penetration and quantifiable changes. The participatory and flexible nature of interventions developed through PAR processes poses methodological challenges (such as defining outcomes or allocating individuals to different groups in advance), and requires a comprehensive mixed-methods approach that simultaneously evaluates effectiveness and the implementation process to better understand its outcomes.

Participatory Community-based Agrotourism: A Case Study of Bangplakod Community, Nakhonnayok Province, Thailand

BackgroundPublic engagement in health and biomedical research is being influenced by the paradigm of citizen science. However, conventional health and biomedical research relies on sophisticated research data management tools and methods. Considering these, what contribution can citizen science make in this field of research? How can it follow research protocols and produce reliable results?ObjectiveThe aim of this article is to analyze research data management practices in existing biomedical citizen science studies, so as to provide insights for members of the public and of the research community considering this approach to research.MethodsA scoping review was conducted on this topic to determine data management characteristics of health and bio medical citizen science research. From this review and related web searching, we chose five online platforms and a specific research project associated with each, to understand their research data management approaches and enablers.ResultsHealth and biomedical citizen science platforms and projects are diverse in terms of types of work with data and data management activities that in themselves may have scientific merit. However, consistent approaches in the use of research data management models or practices seem lacking, or at least are not prevalent in the review.ConclusionsThere is potential for important data collection and analysis activities to be opaque or irreproducible in health and biomedical citizen science initiatives without the implementation of a research data management model that is transparent and accessible to team members and to external audiences. This situation might be improved with participatory development of standards that can be applied to diverse projects and platforms, across the research data life cycle.

Evaluation of Research Data Management (RDM) services in academic libraries of India: A triangulation approach

PurposeThe purpose of this study was to analyse the skills required by lecturers to be able to support research data management effectively; assess the research data management literacy levels amongst lecturers at Strathmore University; and suggest how research data management capacity can be strengthened to mitigate the knowledge gaps identified.Design/methodology/approachThis study was conducted as a mixed methods research. Explanatory sequential mixed methods approach was used to collect, analyse and interpret quantitative and qualitative data from lecturers at Strathmore University in Nairobi, Kenya. Quantitative data was collected using questionnaires while qualitative data was collected through focus group discussions. Quantitative data was analysed using SPSS while qualitative data was analysed thematically.FindingsThe findings of this study indicate varied levels of research data management literacy amongst lecturers at Strathmore University. Lecturers understand the need of having literacy skills in managing research data. They also participate in data creation, collection, processing, validation, dissemination, sharing and archiving. This is a clear indication of good research data management. However, the study also revealed gaps in research data management skills amongst the lecturers in areas such as sharing of research data on open access journals, data legislation and securing research data.Research limitations/implicationsThe study has been conducted in one university in Kenya. However, the findings have been contextualised in the global landscape through suitable references.Practical implicationsThe findings of this study may be used to attract the attention of lecturers and librarians to research data management. The findings may also be used to develop institutional policies on research data management at Strathmore University and beyond. The suggested ways of research data capacity strengthening can be adopted or adapted by other universities to enhance research data management.Originality/valueThis is an original study.

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).

Research data management has become a critical issue for campus researchers, funding agencies, and libraries, who have made substantial investments of time, energy, and resources into support for managing and sharing data. As data management programs proliferate, however, assessment of research data services has become a notorious challenge for libraries. How can we know—and demonstrate—that our efforts are having an impact, and how can we learn to make them even more effective? In this session, we will present a survey of several frameworks for assessing research data management services. We will lead a discussion about the application of different frameworks for assessing or auditing existing skill sets, external facing services, and capacity to support an array of research data services. This discussion will be grounded in a demonstration of how we applied one framework to audit the North Carolina State University (NCSU) Libraries’s “training ground” model, which serves the dual purpose of developing competencies within our librarians and supporting researchers in their needs to manage, preserve, and share research assets. Through an active discussion of our efforts, and the efforts of libraries around the world, we can chart a course for effective research data management that can help guide libraries already deep into the process as well as those just getting their feet wet. Note: This presentation and conference paper is derived in part from the following publication: Davis, H. M., & Cross, W. M. (2015).Using a data management plan review service as a training ground for librarians. Journal of Librarianship and Scholarly Communication, 3(2), eP1243.

Research data is an asset. Researchers may be required to provide access to their data by scientific funders or academic journals and deposit their data in archives. Managers of archives are guided by principles, policies, and the law when curating and providing access to data. Practices around data storage and access, however, are not always cut and dry; research data managers sometimes need to interpret policies. This paper presents findings from qualitative interviews with 15 data managers from 8 repositories in the U.S. These repositories were all affiliated with universities but served varied constituents and provided a range of services. Differences revealed opportunities and challenges in managing data repositories regarding, for instance, who can access data and the level of protection data requires. We also found that data‐related policy challenges may stem from any stage of the research lifecycle.