scholarly journals Doctoral Students' Educational Needs in Research Data Management: Perceived Importance and Current Competencies

2021 ◽  
Vol 16 (1) ◽  
pp. 36
Author(s):  
Jukka Rantasaari
Keyword(s):  
Data Management ◽  
Doctoral Students ◽  
Educational Needs ◽  
Research Data ◽  
Structured Interviews ◽  
High Quality Research ◽  
Management Planning ◽  
Data Preservation ◽  
Research Data Management ◽  
Data Lifecycle

Sound research data management (RDM) competencies are elementary tools used by researchers to ensure integrated, reliable, and re-usable data, and to produce high quality research results. In this study, 35 doctoral students and faculty members were asked to self-rate or rate doctoral students’ current RDM competencies and rate the importance of these competencies. Structured interviews were conducted, using close-ended and open-ended questions, covering research data lifecycle phases such as collection, storing, organization, documentation, processing, analysis, preservation, and data sharing. The quantitative analysis of the respondents’ answers indicated a wide gap between doctoral students’ rated/self-rated current competencies and the rated importance of these competencies. In conclusion, two major educational needs were identified in the qualitative analysis of the interviews: to improve and standardize data management planning, including awareness of the intellectual property and agreements issues affecting data processing and sharing; and to improve and standardize data documenting and describing, not only for the researcher themself but especially for data preservation, sharing, and re-using. Hence the study informs the development of RDM education for doctoral students.

scholarly journals Automating Research Data Management Using Machine-Actionable Data Management Plans

2022 ◽  
Vol 13 (2) ◽  
pp. 1-22
Author(s):  
Tomasz Miksa ◽  
Simon Oblasser ◽  
Andreas Rauber
Keyword(s):  
Data Management ◽  
Research Data ◽  
Use Cases ◽  
Full Potential ◽  
Institutional Research ◽  
Reference Architecture ◽  
Management Planning ◽  
Research Data Management ◽  
Management Plans ◽  
New Generation

Many research funders mandate researchers to create and maintain data management plans (DMPs) for research projects that describe how research data is managed to ensure its reusability. A DMP, being a static textual document, is difficult to act upon and can quickly become obsolete and impractical to maintain. A new generation of machine-actionable DMPs (maDMPs) was therefore proposed by the Research Data Alliance to enable automated integration of information and updates. maDMPs open up a variety of use cases enabling interoperability of research systems and automation of data management tasks. In this article, we describe a system for machine-actionable data management planning in an institutional context. We identify common use cases within research that can be automated to benefit from machine-actionability of DMPs. We propose a reference architecture of an maDMP support system that can be embedded into an institutional research data management infrastructure. The system semi-automates creation and maintenance of DMPs, and thus eases the burden for the stakeholders responsible for various DMP elements. We evaluate the proposed system in a case study conducted at the largest technical university in Austria and quantify to what extent the DMP templates provided by the European Commission and a national funding body can be pre-filled. The proof-of-concept implementation shows that maDMP workflows can be semi-automated, thus workload on involved parties can be reduced and quality of information increased. The results are especially relevant to decision makers and infrastructure operators who want to design information systems in a systematic way that can utilize the full potential of maDMPs.

scholarly journals PROVIDING RESEARCH DATA MANAGEMENT SERVICES AND PRACTICES AT PDDI-LIPI: PREPAREDNESS, ROLES, CHALLENGES, AND TRAINING

2020 ◽  
Vol 41 (2) ◽  
pp. 169
Author(s):  
Hermin Triasih ◽  
Rahmi Rahmi ◽  
Katrin Setio Devi
Keyword(s):  
Human Resources ◽  
Data Management ◽  
Online Survey ◽  
Training Needs ◽  
Research Data ◽  
Management Planning ◽  
Data Archiving ◽  
Research Data Management ◽  
And Training ◽  
Management Services

This study aims to analyse the implementation of RDM at PDDI-LIPI and to assess its staff’s understanding about RDM services. This article also discusses the challenges and obstacles PDDI faces in providing RDM services. The data was collected via an online survey from 28 July to 7 August 2020. The survey consisted of 35 questions and was shared with 36 respondents via social media. The results identified categories such as research data management services, data management planning services, data archiving services, funding, and staff competency and training needs. In addition, this article also discusses the approach and assessment of RDM services, challenges in providing RDM services, and plans for further developing RDM services at PDDI-LIPI. The results showed that the PDDI staff's understanding of RDM services is adequate. As a new service, the implementation of RDM at PDDI-LIPI continues to develop toward optimisation. RIN is a platform used by PDDI to support this goal. The three biggest obstacles faced by PDDI-LIPI in developing RDM services are limited human resources, competence and budget.  Various trainings related to RDM, both sending staff off campus and inviting trainers to campus, were carried out by PDDI to overcome these obstacles. It is recommended to conduct further research on the mapping and upskilling of staff in charge of RDM services.

scholarly journals Incorporating Software Curation into Research Data Management Services: Lessons Learned

2018 ◽  
Vol 13 (1) ◽  
pp. 235-247
Author(s):  
Fernando Rios
Keyword(s):  
Data Management ◽  
Research University ◽  
Service Level ◽  
Research Data ◽  
Lessons Learned ◽  
Related Services ◽  
Management Planning ◽  
Research Data Management ◽  
Management Context ◽  
Large Research

Many large research universities provide research data management (RDM) support services for researchers. These may include support for data management planning, best practices (e.g., organization, support, and storage), archiving, sharing, and publication. However, these data-focused services may under-emphasize the importance of the software that is created to analyse said data. This is problematic for several reasons. First, because software is an integral part of research across all disciplines, it undermines the ability of said research to be understood, verified, and reused by others (and perhaps even the researcher themselves). Second, it may result in less visibility and credit for those involved in creating the software. A third reason is related to stewardship: if there is no clear process for how, when, and where the software associated with research can be accessed and who will be responsible for maintaining such access, important details of the research may be lost over time. This article presents the process by which the RDM services unit of a large research university addressed the lack of emphasis on software and source code in their existing service offerings. The greatest challenges were related to the need to incorporate software into existing data-oriented service workflows while minimizing additional resources required, and the nascent state of software curation and archiving in a data management context. The problem was addressed from four directions: building an understanding of software curation and preservation from various viewpoints (e.g., video games, software engineering), building a conceptual model of software preservation to guide service decisions, implementing software-related services, and documenting and evaluating the work to build expertise and establish a standard service level.

scholarly journals Education Needs in Research Data Management for Science-Based Disciplines

2019 ◽  
Author(s):  
Judith E Pasek ◽  
Jennifer Mayer
Keyword(s):  
Graduate Students ◽  
Data Management ◽  
Data Visualization ◽  
Management Training ◽  
Research Data ◽  
Academic Community ◽  
Data Conversion ◽  
Data Preservation ◽  
Research Data Management ◽  
Doctoral Universities

Research data management is a prominent and evolving consideration for the academic community, especially in scientific disciplines. This research study surveyed 131 graduate students and 79 faculty members in the sciences at two public doctoral universities to determine the importance, knowledge, and interest levels around research data management training and education. The authors adapted 12 competencies for measurement in the study. Graduate students and faculty ranked the following areas most important among the 12 competencies: ethics and attribution, data visualization, and quality assurance. Graduate students indicated they were least knowledgeable and skilled in data curation and re-use, metadata and data description, data conversion and interoperability, and data preservation. Their responses generally matched the perceptions of faculty. The study also examined how graduate students learn research data management, and how faculty perceive that their students learn research data management. Results showed that graduate students utilize self-learning most often and that faculty may be less influential in research data management education than they perceive. Responses for graduate students between the two institutions were not statistically different, except in the area of perceived deficiencies in data visualization competency.

scholarly journals Research Data Management Practices: Synergies and Discords between Researchers and Institutions

2018 ◽  
Vol 13 (1) ◽  
pp. 73-90
Author(s):  
Sally Vanden-Hehir ◽  
Helena Cousijn ◽  
Hesham Attalla
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Qualitative Interviews ◽  
Research Data ◽  
Research Data Management ◽  
Early Stages ◽  
Lifecycle Model ◽  
Data Lifecycle

The aim of this study was to explore the synergies and discords in attitudes towards research data management (RDM) drivers and barriers for both researchers and institutions. Previous work has studied RDM from a single perspective, but not compared researchers’ and institutions’ perspectives. We carried out qualitative interviews with researchers as well as institutional representatives to identify drivers and barriers, and to explore synergies and discords of both towards RDM. We mapped these to a data lifecycle model and found that the contradictions occur at early stages in the lifecycle of data and the synergies occur at the later stages. This means that for future successful RDM, the points of discord at the start of the data lifecycle must be overcome. Finally, we conclude by proposing key recommendations that could help institutions when addressing both researcher and institutional RDM needs.

scholarly journals Exploring research data management planning challenges in practice

2020 ◽  
Vol 62 (1) ◽  
pp. 29-37
Author(s):  
Armel Lefebvre ◽  
Baharak Bakhtiari ◽  
Marco Spruit
Keyword(s):  
Data Management ◽  
Scientific Information ◽  
Public Funding ◽  
Research Data ◽  
High Quality ◽  
Management Planning ◽  
Research Data Management ◽  
Barriers To Access ◽  
Funding Agencies ◽  
Management Plans

AbstractResearch data management planning (RDMP) is the process through which researchers first get acquainted with research data management (RDM) matters. In recent years, public funding agencies have implemented governmental policies for removing barriers to access to scientific information. Researchers applying for funding at public funding agencies need to define a strategy for guaranteeing that the acquired funds also yield high-quality and reusable research data. To achieve that, funding bodies ask researchers to elaborate on data management needs in documents called data management plans (DMP). In this study, we explore several organizational and technological challenges occurring during the planning phase of research data management, more precisely during the grant submission process. By doing so, we deepen our understanding of a crucial process within research data management and broaden our understanding of the current stakeholders, practices, and challenges in RDMP.

Research data management practices of chemistry researchers in federal universities of technology in Nigeria

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fatimah Jibril Abduldayan ◽  
Fasola Petunola Abifarin ◽  
Georgina Uchey Oyedum ◽  
Jibril Attahiru Alhassan
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Management Practice ◽  
Research Process ◽  
Management Plan ◽  
Research Data ◽  
Content Type ◽  
Data Preservation ◽  
Research Data Management ◽  
Research Findings

Purpose The purpose of this study was to understand the research data management practices of chemistry researchers in the five specialized federal universities of technology in Nigeria. Appropriate research data management practice ensures that research data are available for reuse by secondary users, and research findings can be verified and replicated within the scientific community. A poor research data management practice can lead to irrecoverable data loss, unavailability of data to support research findings and lack of trust in the research process. Design/methodology/approach An exploratory research technique involving semi-structured, oral and face-to-face interview is used to gather data on research data management practices of chemistry researchers in Nigeria. Interview questions were divided into four major sections covering chemistry researchers’ understanding of research data, experience with data loss, data storage method and backup techniques, data protection, data preservation and availability of data management plan. Braun and Clarke thematic analysis approach was adapted, and the Provalis Qualitative Data Miner (version 5) software was used for generating themes and subthemes from the coding framework and for presenting the findings. Findings Findings revealed that chemistry researchers in Nigeria have a good understanding of the concept of research data and its importance to research findings. Chemistry researchers have had several experiences of irrecoverable loss of data because of poor choice of storage devices, back-up methods and weak data protection systems. Even though the library was agreed as the most preferred place for long-term data preservation, there is the issue of trust and fear of loss of ownership of data to unauthorized persons or party. No formal data management plan is used while conducting their scientific research. Research limitations/implications The research focused on research data management practices of chemistry researchers in the five specialized federal universities of technology in Nigeria. Although the findings of the study are similar to perceptions and practices of researchers around the world, it cannot be used as a basis for generalization across other scientific disciplines. Practical implications This study concluded that chemistry researchers need further orientation and continuous education on the importance and benefits of appropriate research data management practice. The library should also roll out research data management programs to guide researchers and improve their confidence throughout the research process. Social implications Appropriate research data management practice not only ensures that the underlying research data are true and available for reuse and re-validation, but it also encourages data sharing among researchers. Data sharing will help to ensure better collaboration among researchers and increased visibility of the datasets and data owners through the use of standard data citations and acknowledgements. Originality/value This is a qualitative and in-depth study of research data management practices and perceptions among researchers in a particular scientific field of study.

scholarly journals Practical considerations for a library’s research data management services: the case of the National Institutes of Health Library

2021 ◽  
Vol 109 (3) ◽  
Author(s):  
Soojung Kim ◽  
Sue Yeon Syn
Keyword(s):  
Data Management ◽  
Research Data ◽  
National Institutes Of Health ◽  
User Characteristics ◽  
Data Services ◽  
Research Data Management ◽  
Semistructured Interviews ◽  
Computer Library ◽  
Data Lifecycle

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.

scholarly journals Playing Well on the Data FAIRground: Initiatives and Infrastructure in Research Data Management

2019 ◽  
Vol 1 (4) ◽  
pp. 350-367 ◽  
Author(s):  
Danielle Descoteaux ◽  
Chiara Farinelli ◽  
Marina Soares e Silva ◽  
Anita de Waard
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Research Data ◽  
Current Status ◽  
Data Repositories ◽  
Data Preservation ◽  
Management Platform ◽  
Research Data Management ◽  
Future Goals ◽  
External Data

Over the past five years, Elsevier has focused on implementing FAIR and best practices in data management, from data preservation through reuse. In this paper we describe a series of efforts undertaken in this time to support proper data management practices. In particular, we discuss our journal data policies and their implementation, the current status and future goals for the research data management platform Mendeley Data, and clear and persistent linkages to individual data sets stored on external data repositories from corresponding published papers through partnership with Scholix. Early analysis of our data policies implementation confirms significant disparities at the subject level regarding data sharing practices, with most uptake within disciplines of Physical Sciences. Future directions at Elsevier include implementing better discoverability of linked data within an article and incorporating research data usage metrics.

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