Research Data Management Research Articles

Research data services (RDS) in Spanish academic libraries

Research data services (RDS) are actions undertaken to provide researchers with support, advising, training, information, a technological infrastructure and a regulatory framework with respect to research data management (RDM) throughout the life cycle of the data. The implementation of RDS is an activity that is becoming increasingly important within the functions carried out by academic and research libraries. The aim of this study is to provide a snapshot of the research data services implemented in academic libraries in public universities in Spain, identifying the number and types of services deployed. We conducted a Website Content Analysis of the libraries of the 48 Spanish public universities to discover which RDS they are implementing and promoting. The results show that, despite a lack of institutional policies regarding research data, libraries are deploying these new services, albeit unevenly in terms of both the number of services and the type of support provided. Services related to development of the data management plan (DMP) and preservation of the data in repositories predominate. This inventory is presented as a first step towards learning the level of RDS implementation in Spanish academic libraries, providing a partial perspective and leading the way to exploring other aspects in greater depth in future studies.

Fostering Research Data Management in Collaborative Research Contexts: Lessons learnt from an ‘Embedded’ Evaluation of ‘Data Story’

Fostering Research Data Management in Collaborative Research Contexts: Lessons learnt from an ‘Embedded’ Evaluation of ‘Data Story’

FAIRness through automation: development of an automated medical data integration infrastructure for FAIR health data in a maximum care university hospital

BackgroundSecondary use of routine medical data is key to large-scale clinical and health services research. In a maximum care hospital, the volume of data generated exceeds the limits of big data on a daily basis. This so-called “real world data” are essential to complement knowledge and results from clinical trials. Furthermore, big data may help in establishing precision medicine. However, manual data extraction and annotation workflows to transfer routine data into research data would be complex and inefficient. Generally, best practices for managing research data focus on data output rather than the entire data journey from primary sources to analysis. To eventually make routinely collected data usable and available for research, many hurdles have to be overcome. In this work, we present the implementation of an automated framework for timely processing of clinical care data including free texts and genetic data (non-structured data) and centralized storage as Findable, Accessible, Interoperable, Reusable (FAIR) research data in a maximum care university hospital.MethodsWe identify data processing workflows necessary to operate a medical research data service unit in a maximum care hospital. We decompose structurally equal tasks into elementary sub-processes and propose a framework for general data processing. We base our processes on open-source software-components and, where necessary, custom-built generic tools.ResultsWe demonstrate the application of our proposed framework in practice by describing its use in our Medical Data Integration Center (MeDIC). Our microservices-based and fully open-source data processing automation framework incorporates a complete recording of data management and manipulation activities. The prototype implementation also includes a metadata schema for data provenance and a process validation concept. All requirements of a MeDIC are orchestrated within the proposed framework: Data input from many heterogeneous sources, pseudonymization and harmonization, integration in a data warehouse and finally possibilities for extraction or aggregation of data for research purposes according to data protection requirements.ConclusionThough the framework is not a panacea for bringing routine-based research data into compliance with FAIR principles, it provides a much-needed possibility to process data in a fully automated, traceable, and reproducible manner.

REDbox: a comprehensive semantic framework for data collection and management in tuberculosis research

Clinical research outcomes depend on the correct definition of the research protocol, the data collection strategy, and the data management plan. Furthermore, researchers often need to work within challenging contexts, as is the case in tuberculosis services, where human and technological resources for research may be scarce. Electronic Data Capture Systems mitigate such risks and enable a reliable environment to conduct health research and promote result dissemination and data reusability. The proposed solution is based on needs pinpointed by researchers, considering the need for an accommodating solution to conduct research in low-resource environments. The REDbox framework was developed to facilitate data collection, management, sharing, and availability in tuberculosis research and improve the user experience through user-friendly, web-based tools. REDbox combines elements of the REDCap and KoBoToolbox electronic data capture systems and semantics to deliver new valuable tools that meet the needs of tuberculosis researchers in Brazil. The framework was implemented in five cross-institutional, nationwide projects to evaluate the users' perceptions of the system's usefulness and the information and user experience. Seventeen responses (representing 40% of active users) to an anonymous survey distributed to active users indicated that REDbox was perceived to be helpful for the particular audience of researchers and health professionals. The relevance of this article lies in the innovative approach to supporting tuberculosis research by combining existing technologies and tailoring supporting features.

Metadata implementation and data discoverability: A survey on university libraries' Dataverse portals

The purpose of this practical case study is to examine the development of Dataverse, a global research data management consortium. This paper is the second in a project focusing on data discoverability and current metadata implementation on the Dataverse portals established by 27 university libraries worldwide. Five research questions were proposed to identify the most popular metadata standards and elements, search interface options, and result display formats by those portals. The data were collected from 27 university libraries worldwide between December 1, 2020 and January 31, 2021. According to the results of the descriptive analyses, the most popular metadata elements for the dataset overview were Subject and Description, while Dataset persistent ID, Publication Date, Title, Author, Contact, Deposit Date, Depositor, Description, and Subject were the most popular elements for the metadata record of each dataset. Publication Year, Author Names, and Subject were found to be the most common search facets used by the portals. English was the most common language used for the search interfaces and metadata descriptions. Based on their findings from this evidence-based study, the authors recommend future research on the development of institutional data portal infrastructure, on stakeholder outreach and training, and on user studies on dataset retrieval.

Research data management services in academic libraries: a comparative study of South Asia and Southeast Asia

Purpose This study aims to understand the research data management (RDM) services offered by academic libraries in South Asian and Southeast Asian countries. This study aims to evaluate the library and information science professionals’ required RDM skills and the challenges faced with providing RDM services. Design/methodology/approach The research methodology for this study used a survey method with purposive sampling. Data were collected through online structured questionnaires, which were used to examine the current state of RDM services offered in academic libraries in South Asia and Southeast Asia. Findings South Asian and Southeast Asian region major types of RDM services provided were data repository, data management training, maintaining Web resources, data study and analysis, and promoting awareness of reusable data sources. Little attention was given to advisory services on data analysis/mining/visualization and supporting reproducibility and workflow transparency. The results indicated that most respondents agreed that metadata standards and data management planning skills were required for RDM services in South Asia and Southeast Asia. Originality/value This study is significant because it offers a comprehensive assessment of ongoing RDM services in academic libraries of South Asia and Southeast Asia. Most current literature focuses on best practices in developed nations. This study highlights the need for more competent and dedicated academic staff for effective RDM services. Library professionals can use this study to identify the gaps in RDM services and suggest formative measures to overcome such challenges.

SP-0677 FAIR data management in radiation biology and radiation oncology research

SP-0677 FAIR data management in radiation biology and radiation oncology research

Assessing the publishing priorities and preferences among STEM researchers at a large R1 institution

The cost of academic publishing has increased substantially despite the ease with which information can be shared on the web. Open Access publishing is a key mechanism for amplifying research access, inclusivity, and impact. Despite this, shifting to a free-to-read publishing environment requires navigating complex barriers that vary by career status and publishing expectations. In this article, we investigate the motivations and preferences of researchers situated within our large research institution as a case study for publishing attitudes at similar institutions. We surveyed the publishing priorities and preferences of researchers at various career stages in STEM fields as they relate to openness, data practices, and assessment of research impact. Our results indicate that publishing preferences, data management experience and research impact assessment vary by career status and departmental approaches to promotion. We find that open access publishing is widely appreciated regardless of career status, but financial limitations and publishing expectations were common barriers to publishing in Open Access journals. Our findings shed light on publishing attitudes and preferences among researchers at a major R1 research institution, and offer insight into advocacy strategies that incentivize open access publishing.

The overcome of public health research data management chaos: exploratory study on research infrastructure implementation

The overcome of public health research data management chaos: exploratory study on research infrastructure implementation

A pilot study on social science graduate students' data core competency

In the field of research, teaching graduate students how to manage data on a regular basis is becoming increasingly crucial. However, there is a lack of adequate evidence in the literature to determine the necessity of Research Data Management (RDM) training from the perspectives of social science student researchers. To address this gap, a pilot study was conducted to develop a data core competency measure to gauge the perceived importance of data competency among social science graduate students and assess their data core competency. Forty graduate students in a college of education at an US research university filled out a researcher-designed data core competency survey. This pilot study found that graduate students in the social sciences do not generally appreciate RDM. Consequently, the results underscore the need to foster an environment in graduate social science programs that value RDM best practices. The study suggested that future studies build on the preliminary findings by developing standardized data core competency measures.

A workflow model for holistic data management and semantic interoperability in quantitative archival research

Abstract Archival research is a complicated task that involves several diverse activities for the extraction of evidence and knowledge from a set of archival documents. The involved activities are usually unconnected, in terms of data connection and flow, making difficult their recursive revision and execution, as well as the inspection of provenance information at data element level. This article proposes a workflow model for holistic data management in archival research: from transcribing and documenting a set of archival documents, to curating the transcribed data, integrating it to a rich semantic network (knowledge graph), and then exploring the integrated data quantitatively. The workflow is provenance-aware, highly recursive and focuses on semantic interoperability, aiming at the production of sustainable data of high value and long-term validity. We provide implementation details for each step of the workflow and present its application in maritime history research. We also discuss relevant quality aspects and lessons learned from its application in a real context.

The engagement of academic libraries in open science: A systematic review

Open science is reshaping the environment of scholarly communication. In this context, academic libraries are beginning to redefine or expand their functions and role. Using a systematic review and meta-synthesis approach, this study analyses 65 literature related to the engagement of academic libraries in open science. The results show that the existing research mainly focuses on four aspects of open science: open access, research data management, open educational resources, and citizen science. Based on these themes, academic libraries have implemented a range of practical activities, playing the roles of open science service providers, advocates or educators, policy makers, publishers or knowledge producers, etc. At the same time, however, academic libraries are facing a number of challenges caused by inadequate security of internal resources and insufficient support from the external environment.

RESEARCH DATA MANAGEMENT AND A SYSTEM DESIGN TO SEMI-AUTOMATICALLY COMPLETE INTEGRATED DATA MANAGEMENT PLANS [POSITION PAPER

Data is an integral part of modern scientific work. Good research data management (RDM) and the communication of the related information is extremely an important matter. It is not only crucial for the ongoing research and its claims but also for the future uses of data. In recent years some guiding principles, e.g. FAIR principles and initiatives at the national and international level, e.g. NFDI, NFDI4Ing have also been founded to improve RDM. The data and its metadata are often handled in file system like structures which are versioned and logged. The information relating to the data handling are documented in data management plan (DMP). DMPs are also usually managed in similar file structures. These are made available in editable document formats as well as online free-text editable forms to which users are required to keep updating manually. These are isolated documents which have neither direct relation to data for verification nor are common to understand with consistency. In this paper, research data management of large-scale interdisciplinary projects is presented. On one hand it introduces, contemporary practices of RDM and on the other hand it helps researchers to determine the features of RDM system in the situations when it comes to select or develop a system for the same purpose. It further introduces a system design for semi-automatic completion of DMP functions in collaborative environment a.k.a. virtual research environment (VRE). It is assumed that the proposed system will assist and enable users to update semi-automatically integrated DMP during all phases of data life cycle. Direct relation to the data for verification, common understanding and consistency will also be maintainable.

Progressive Legal Approach to Modern Community Law Enforcement in Indonesia

Law is the rule of norms and sanctions made to regulate human behavior, maintain order and justice, and prevent chaos. Law functions to guarantee the existence of legal law in society. Influence does not mean identical. The Indonesian legal system is not the same as the Anglo-American. Law in Indonesia refers to the development of law in Indonesia. The Indonesian legal system is a system itself. A system built from the process of discovery, development, adaptation and even compromise of several existing systems or it can be said the Pancasila legal system. Usually, laws change due to changing elements in life. However, this does not mean that the law is always in a dependent position. Social changes caused by technological developments lead to modern life because modernization has penetrated traditional rural communities due to these technological advances. Law enforcement in Indonesia through social analysis and progressive law is categorized as a new paradigm in law enforcement in Indonesia that is relevant to progressive law as the fulfillment of justice. This study uses library research methods and data management techniques. However, because the scope of this research is only in legal disciplines, this research was conducted by examining literature, laws and other written materials or known as normative legal research which is descriptive in nature and researched with legal socio-legal. The research results show that the assumption used is to make the law a social phenomenon. Social analysis is needed to examine various law enforcement problems because the law is part of social phenomena. The use of progressive law is a legal thought introduced and developed by Satjipto Rahardjo by viewing law as a social phenomenon.

Leveraging socio-technical processes to support researchers

Purpose The purpose of this project was to develop research support services that address local and external research data management (RDM) support drivers within the existing organizational culture at the University of Florida. The goal was to prompt organization change to support a campus-wide electronic lab notebook. Design/methodology/approach This project used a mixed-methods research approach to cultivate an organizational change program that support technological infrastructure to benefit researchers. The mixed-methods research involved participation action research integrated with a stakeholder approach. Findings The development of the grant proposal which was unfunded led to development of continued project goals. This project confirmed the development for support for an institution-wide electronic research notebook (ERN) solution requires adherence to the summary of five key actions for developing RDM services. Failure to complete all of the key actions engenders fragmentation culture. Research limitations/implications This project includes implications for institutions to develop grant proposals with integrated budgets for research support services of funded projects; and to use the summary of key actions for developing RDM services articulated by Jones et al. (2013) in “How to Develop RDM Services – a guide for HEIs.” Both are need to support findable, accessible, interoperable and reusable data for researchers. Practical implications This project has practical implications for higher education institutions interested in leveraging socio-technical processes to advance the role of libraries as collaborator, partner and stakeholder in developing institution-wide adoption, support and training for ERN as a research support service to RDM. Social implications This paper contributes to the body of developing literature on ERN as support services to RDM lead by academic research libraries. Originality/value This project contributed to the change in organization culture resulting in the successful collaboration between the Research Office and College of Medicine to support an institution-wide ERN technological infrastructure for one year as a pilot at a large academic research institution in the southeast USA.

FAIR and scalable management of small-angle X-ray scattering data.

A modular research data management toolbox based on the programming language Python, the widely used computing platform Jupyter Notebook, the standardized data exchange format for analytical data (AnIML) and the generic repository Dataverse has been established and applied to analyze small-angle X-ray scattering (SAXS) data according to the FAIR data principles (findable, accessible, interoperable and reusable). The SAS-tools library is a community-driven effort to develop tools for data acquisition, analysis, visualization and publishing of SAXS data. Metadata from the experiment and the results of data analysis are stored as an AnIML document using the novel Python-native pyAnIML API. The AnIML document, measured raw data and plots resulting from the analysis are combined into an archive in OMEX format and uploaded to Dataverse using the novel easyDataverse API, which makes each data set accessible via a unique DOI and searchable via a structured metadata block. SAS-tools is applied to study the effects of alkyl chain length and counterions on the phase diagrams of alkyltrimethyl-ammonium surfactants in order to demonstrate the feasibility and usefulness of a scalable data management workflow for experiments in physical chemistry.

What is data stewardship? Towards a comprehensive understanding

Data stewardship is a term that is understood in heterogenous ways. In recent organisational developments and efforts to build infrastructures and hire professional staff for research data management in various scientific fields in Europe, data stewardship is understood as mainly aiming at optimising data management in line with the FAIR principles (findability, accessibility, interoperability, reusability) forpurposes of reuse in the interests of the scientific community and the public. In addition, especially in the health and biomedical sciences some understandings of data stewardship mainly focus on the responsibility to respect the informational rights of data subjects. Following on from these different understandings and from recent developments to include ever more stakeholders in data stewardship, we propose a comprehensive understanding of data stewardship. According to this comprehensive understanding, data stewardship includes responsibilities towards all pertinent stakeholders and to equally consider and respect their legitimate rights and interests in order to build and maintain an efficient, trusted and fair data ecosystem. We also point out some of the practical challenges implied in such a comprehensive understanding.

Evolution of research data management in academic libraries: A review of the literature

This study provides insights into the evolution and conceptual framework of research data management (RDM). It also investigates the role of libraries and librarians in offering data management services and the challenges they face in this regard. The study is qualitative in nature and based on an extensive literature review survey. The analysis of the reviewed literature reveals that the idea of RDM has emerged as a new addition to library research support services. The more recent literature clearly established the pivotal role of libraries and librarians in developing and managing RDM services. However, data sharing practices and the development of RDM services in libraries are more prevalent in developed countries. While these trends are still lacking among researchers and libraries in developing countries. Creating awareness among researchers about the benefits of data sharing is a challenging task for libraries. Furthermore, institutional commitment, collaboration, academic engagement, technological infrastructure development, lack of policies, funding, and storage, skills, and competencies required for librarians to offer RDM-based services are some of the other significant challenges highlighted in the literature. Certainly, RDM services are difficult and complicated; therefore, librarians need to master the skills of research data to offer library-based RDM services.

Research data management needs assessment for social sciences graduate students: A mixed methods study.

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

Mobile researchers, immobile data: Managing data (producers).

Scientific institutions have increasingly embraced formalized research data management strategies, which involve complex social practices of codifying the tacit dimensions of data practices. Several guidelines to facilitate these practices have been introduced in recent years, for example, the FAIR guiding principles. The aim of these practices is to foster transparency and reproducibility through 'data sharing,' the public release of data for unbounded reuse. However, a closer look suggests that many scientists' practices of data release might be better described as what I call data handovers. These practices are not rooted in the lofty ideals of good scientific practice and global data reuse but in the more mundane necessities of research continuity, which have become more urgent in light of increasing academic mobility. The Austrian scientists interviewed for this study reinterpreted defining features of research data management - such as ensuring findability - as techniques for managing the effects of researcher mobility. This suggests that the adoption of Open Science practices might be dissociated from its stated epistemic goals, and explains why many Open Science initiatives at present are administratively strong but normatively weak.