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 Research Data Management Initiatives at University of Edinburgh

2011 ◽  
Vol 6 (2) ◽  
pp. 232-244 ◽  
Author(s):  
Robin Rice ◽  
Jeff Haywood
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Academic Staff ◽  
Research Data ◽  
Lessons Learned ◽  
The Public ◽  
Research Data Management ◽  
Online Tools ◽  
University Of Edinburgh ◽  
Future Direction

During the last decade, national and international attention has been increasingly focused on issues of research data management and access to publicly funded research data. The pressure brought to bear on researchers to improve their data management and data sharing practice has come from research funders seeking to add value to expensive research and solve cross-disciplinary grand challenges; publishers seeking to be responsive to calls for transparency and reproducibility of the scientific record; and the public seeking to gain and re-use knowledge for their own purposes using new online tools. Meanwhile higher education institutions have been rather reluctant to assert their role in either incentivising or supporting their academic staff in meeting these more demanding requirements for research practice, partly due to lack of knowledge as to how to provide suitable assistance or facilities for data storage and curation/preservation. This paper discusses the activities and drivers behind one institution’s recent attempts to address this gap, with reflection on lessons learned and future direction.

scholarly journals Disciplinary differences in faculty research data management practices and perspectives

2013 ◽  
Vol 8 (2) ◽  
pp. 5-26 ◽  
Author(s):  
Katherine G. Akers ◽  
Jennifer Doty
Keyword(s):  
Data Management ◽  
Research University ◽  
Management Practices ◽  
Research Data ◽  
Data Curation ◽  
Academic Librarians ◽  
Disciplinary Differences ◽  
Research Data Management ◽  
Management Actions ◽  
Research Domains

Academic librarians are increasingly engaging in data curation by providing infrastructure (e.g., institutional repositories) and offering services (e.g., data management plan consultations) to support the management of research data on their campuses. Efforts to develop these resources may benefit from a greater understanding of disciplinary differences in research data management needs. After conducting a survey of data management practices and perspectives at our research university, we categorized faculty members into four research domains—arts and humanities, social sciences, medical sciences, and basic sciences—and analyzed variations in their patterns of survey responses. We found statistically significant differences among the four research domains for nearly every survey item, revealing important disciplinary distinctions in data management actions, attitudes, and interest in support services. Serious consideration of both the similarities and dissimilarities among disciplines will help guide academic librarians and other data curation professionals in developing a range of data-management services that can be tailored to the unique needs of different scholarly researchers.

scholarly journals Gathering Evidence of Benefits: A Structured Approach from the Jisc Managing Research Data Programme

2013 ◽  
Vol 8 (2) ◽  
pp. 123-133
Author(s):  
Laura Molloy ◽  
Simon Hodson ◽  
Meik Poschen ◽  
Jonathan Tedds
Keyword(s):  
Higher Education ◽  
Data Management ◽  
Research Funding ◽  
Research Data ◽  
Lessons Learned ◽  
Education Sector ◽  
Research Data Management ◽  
The Uk ◽  
Uk Higher Education ◽  
Structured Approach

The work of the Jisc Managing Research Data programme is – along with the rest of the UK higher education sector – taking place in an environment of increasing pressure on research funding. In order to justify the investment made by Jisc in this activity – and to help make the case more widely for the value of investing time and money in research data management – individual projects and the programme as a whole must be able to clearly express the resultant benefits to the host institutions and to the broader sector. This paper describes a structured approach to the measurement and description of benefits provided by the work of these projects for the benefit of funders, institutions and researchers. We outline the context of the programme and its work; discuss the drivers and challenges of gathering evidence of benefits; specify benefits as distinct from aims and outputs; present emerging findings and the types of metrics and other evidence which projects have provided; explain the value of gathering evidence in a structured way to demonstrate benefits generated by work in this field; and share lessons learned from progress to date.

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 Delivering Innovative RDM Training: The immersiveInformatics Pilot Programme

2014 ◽  
Vol 9 (1) ◽  
pp. 313-323 ◽  
Author(s):  
Anna Shadbolt ◽  
Leo Konstantelos ◽  
Liz Lyon ◽  
Marieke Guy
Keyword(s):  
Data Management ◽  
Training Programme ◽  
Professional Staff ◽  
Research Data ◽  
Lessons Learned ◽  
Single Institution ◽  
Pilot Programme ◽  
Research Data Management ◽  
Capability Building ◽  
The University

This paper presents the findings, lessons learned and next steps associated with the implementation of the immersiveInformatics pilot: a distinctive research data management (RDM) training programme designed in collaboration between UKOLN Informatics and the Library at the University of Melbourne, Australia. The pilot aimed to equip a broad range of academic and professional staff roles with RDM skills as a key element of capacity and capability building within a single institution.

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 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.

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