scholarly journals Outside the R1: Equitable data management at the undergraduate level

2021 ◽  
Vol 45 (2) ◽  
Author(s):  
Elizabeth Blackwood
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Undergraduate Curriculum ◽  
Research Data ◽  
Management Skills ◽  
State University System ◽  
Final Project ◽  
Research Data Management ◽  
Management Plans ◽  
Regional Universities

Universities within the California State University System are given the mandate to teach the students of the state, as is the case with many regional, public universities. This mandate places teaching first; however, research and scholarship are still required activities for reaching retention, tenure, and promotion, as well as important skills for students to practice. Data management instruction for both faculty and undergraduates is often omitted at these institutions, which fall outside of the R1 designation. This happens for a variety of reasons, including personnel and resource limitations. Such limitations disproportionately burden students from underrepresented populations, who are more heavily represented at these institutions. These students have pathways to graduate school and the digital economy, like their counterparts at R1s; thus, they are also in need of research data management skills. This paper describes and provides a scalable, low-resource model for data management instruction from the university library and integrated into a department’s capstone or final project curriculum. In the case study, students and their instructors participated in workshops and submitted data management plans as a requirement of their final project. The analysis will analyze the results of the project and focus on the broader implications of integrating research data management into undergraduate curriculum at public, regional universities. By working with faculty to integrate data management practices into their curricula, librarians reach both students and faculty members with best practices for research data management. This work also contributes to a more equitable and sustainable research landscape.

scholarly journals Research data management and services: Resources for novice data librarians

2017 ◽  
Vol 78 (5) ◽  
pp. 274 ◽  
Author(s):  
Sarah Barbrow ◽  
Denise Brush ◽  
Julie Goldman
Keyword(s):  
Life Cycle ◽  
Data Management ◽  
Academic Libraries ◽  
Research Data ◽  
Research Data Management ◽  
Academic Fields ◽  
Data Life Cycle ◽  
Management Plans ◽  
Share Data

Research in many academic fields today generates large amounts of data. These data not only must be processed and analyzed by the researchers, but also managed throughout the data life cycle. Recently, some academic libraries have begun to offer research data management (RDM) services to their communities. Often, this service starts with helping faculty write data management plans, now required by many federal granting agencies. Libraries with more developed services may work with researchers as they decide how to archive and share data once the grant work is complete.

scholarly journals VARIABILITY IN CLINICAL RESEARCH DATA MANAGEMENT PRACTICES: LESSONS FROM THE MALARIA COMMUNITY

2017 ◽  
Vol 2 (Suppl 2) ◽  
pp. A19.1-A19
Author(s):  
Amélie Julé ◽  
Hazel Ashurst ◽  
Laura Merson ◽  
Piero Olliaro ◽  
Vicki Marsh ◽  
...  
Keyword(s):  
Clinical Research ◽  
Data Management ◽  
Management Practices ◽  
Research Data ◽  
Research Data Management ◽  
Clinical Research Data

scholarly journals Plano de gestão de dados fair: uma proposta para a Fiocruz | Fair data management plan: a proposal for Fiocruz

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Viviane Santos de Oliveira Veiga ◽  
Patricia Henning ◽  
Simone Dib ◽  
Erick Penedo ◽  
Jefferson Da Costa Lima ◽  
...  
Keyword(s):  
Life Cycle ◽  
Data Management ◽  
Open Science ◽  
Management Plan ◽  
Research Data ◽  
Research Institutions ◽  
Research Data Management ◽  
Management Plans ◽  
Fair Principles

RESUMO Este artigo trás para discussão o papel dos planos de gestão de dados como instrumento facilitador da gestão dos dados durante todo o ciclo de vida da pesquisa. A abertura de dados de pesquisa é pauta prioritária nas agendas científicas, por ampliar tanto a visibilidade e transparência das investigações, como a capacidade de reprodutibilidade e reuso dos dados em novas pesquisas. Nesse contexto, os princípios FAIR, um acrônimo para ‘Findable’, ‘Accessible’, ‘Interoperable’ e ‘Reusable’ é fundamental por estabelecerem orientações basilares e norteadoras na gestão, curadoria e preservação dos dados de pesquisa direcionados para o compartilhamento e o reuso. O presente trabalho tem por objetivo apresentar uma proposta de template de Plano de Gestão de Dados, alinhado aos princípios FAIR, para a Fundação Oswaldo Cruz. A metodologia utilizada é de natureza bibliográfica e de análise documental de diversos planos de gestão de dados europeus. Concluímos que a adoção de um plano de gestão nas práticas cientificas de universidades e instituições de pesquisa é fundamental. No entanto, para tirar maior proveito dessa atividade é necessário contar com a participação de todos os atores envolvidos no processo, além disso, esse plano de gestão deve ser machine-actionable, ou seja, acionável por máquina.Palavras-chave: Plano de Gestão de Dados; Dado de Pesquisa; Princípios FAIR; PGD Acionável por Máquina; Ciência Aberta.ABSTRACT This article proposes to discuss the role of data management plans as a tool to facilitate data management during researches life cycle. Today, research data opening is a primary agenda at scientific agencies as it may boost investigations’ visibility and transparency as well as the ability to reproduce and reuse its data on new researches. Within this context, FAIR principles, an acronym for Findable, Accessible, Interoperable and Reusable, is paramount, as it establishes basic and guiding orientations for research data management, curatorship and preservation with an intent on its sharing and reuse. The current work intends to present to the Fundação Oswaldo Cruz a new Data Management Plan template proposal, aligned with FAIR principles. The methodology used is bibliographical research and documental analysis of several European data management plans. We conclude that the adoption of a management plan on universities and research institutions scientific activities is paramount. However, to be fully benefited from this activity, all actors involved in the process must participate, and, on top of that, this plan must be machine-actionable.Keywords: Data Management Plan; Research Data; FAIR Principles; DMP Machine-Actionable; Open Science.

scholarly journals Research Data Management within the 4TU Research Centres

2018 ◽  
Author(s):  
Maria J. Cruz ◽  
Alastair Dunning
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Qualitative Interviews ◽  
Research Data ◽  
Research Data Management ◽  
Research Centres

Survey of research data management practices and attitudes within the 4TU Research Centres based on qualitative interviews with the Centres' Scientific Directors.

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 The Data Management Skills Support Initiative: Synthesising Postgraduate Training in Research Data Management

2012 ◽  
Vol 7 (2) ◽  
pp. 101-109 ◽  
Author(s):  
Laura Molloy ◽  
Kellie Snow
Keyword(s):  
Data Management ◽  
Management Training ◽  
Skills Training ◽  
Research Data ◽  
Management Skills ◽  
Research Information ◽  
Research Data Management ◽  
Researcher Development ◽  
Development Framework ◽  
Wide Range

This paper will describe the efforts and findings of the JISC Data Management Skills Support Initiative (‘DaMSSI’). DaMSSI was co-funded by the JISC Managing Research Data programme and the Research Information Network (RIN), in partnership with the Digital Curation Centre, to review, synthesise and augment the training offerings of the JISC Research Data Management Training Materials (‘RDMTrain’) projects.DaMSSI tested the effectiveness of the Society of College, National and University Libraries’ Seven Pillars of Information Literacy model (SCONUL, 2011), and Vitae’s Researcher Development Framework (‘Vitae RDF’) for consistently describing research data management (‘RDM’) skills and skills development paths in UK HEI postgraduate courses.With the collaboration of the RDMTrain projects, we mapped individual course modules to these two models and identified basic generic data management skills alongside discipline-specific requirements. A synthesis of the training outputs of the projects was then carried out, which further investigated the generic versus discipline-specific considerations and other successful approaches to training that had been identified as a result of the projects’ work. In addition we produced a series of career profiles to help illustrate the fact that data management is an essential component – in obvious and not-so-obvious ways – of a wide range of professions.We found that both models had potential for consistently and coherently describing data management skills training and embedding this within broader institutional postgraduate curricula. However, we feel that additional discipline-specific references to data management skills could also be beneficial for effective use of these models. Our synthesis work identified that the majority of core skills were generic across disciplines at the postgraduate level, with the discipline-specific approach showing its value in engaging the audience and providing context for the generic principles.Findings were fed back to SCONUL and Vitae to help in the refinement of their respective models, and we are working with a number of other projects, such as the DCC and the EC-funded Digital Curator Vocational Education Europe (DigCurV2) initiative, to investigate ways to take forward the training profiling work we have begun.

scholarly journals Student Research Data Management Practices at UC Data Day 2017

2017 ◽  
Author(s):  
Amy E Koshoffer ◽  
Keloni Parks
Keyword(s):  
Student Engagement ◽  
Data Management ◽  
Poster Session ◽  
Management Practices ◽  
Research Data ◽  
Student Research ◽  
Research Data Management ◽  
University Of Cincinnati ◽  
The University

This article discusses increasing student engagement surrounding data management and how the University of Cincinnati Libraries tried to engage students with a poster session for its Data Day event in 2017.

A systematic literature review on research data management practices and services

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Murtaza Ashiq ◽  
Muhammad Haroon Usmani ◽  
Muhammad Naeem
Keyword(s):  
Literature Review ◽  
Data Management ◽  
Systematic Literature Review ◽  
Academic Libraries ◽  
Management Practices ◽  
Developed Countries ◽  
Research Data ◽  
Open Access Publishing ◽  
Content Type ◽  
Research Data Management

Purpose Research data management (RDM) has been called a “ground-breaking” area for research libraries and it is among the top future trends for academic libraries. Hence, this study aims to systematically review RDM practices and services primarily focusing on the challenges, services and skills along with motivational factors associated with it. Design/methodology/approach A systematic literature review method was used focusing on literature produced between 2016–2020 to understand the latest trends. An extensive research strategy was framed and 15,206 results appeared. Finally, 19 studies have fulfilled the criteria to be included in the study following preferred reporting items for systematic reviews and meta-analysis. Findings RDM is gradually gaining importance among researchers and academic libraries; however, it is still poorly practiced by researchers and academic libraries. Albeit, it is better observed in developed countries over developing countries, however, there are lots of challenges associated with RDM practices by researchers and services by libraries. These challenges demand certain sets of skills to be developed for better practices and services. An active collaboration is required among stakeholders and university services departments to figure out the challenges and issues. Research limitations/implications The implications of policy and practical point-of-view present how research data can be better managed in the future by researchers and library professionals. The expected/desired role of key stockholders in this regard is also highlighted. Originality/value RDM is an important and emerging area. Researchers and Library and Information Science professionals are not comprehensively managing research data as it involves complex cooperation among various stakeholders. A combination of measures is required to better manage research data that would ultimately move forward for open access publishing.

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