scholarly journals Dinner and Data Management: Engaging undergraduates in research data management topics outside of the curriculum

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Cameron Cook ◽  
Tobin Magle ◽  
Heather Shimon ◽  
Trisha Adamus

Researchers are faced with unprecedented challenges due to the size and complexity of data, and libraries are stepping in to help by providing guidance on research data management primarily to graduate students and faculty. Currently, many universities are encouraging an undergraduate research experience where students engage in research projects in the classroom and in research labs, yet research data management is often not included as part of these opportunities. At UW-Madison, we piloted researchERS (Emerging Research Scholars), a program for undergraduates from all disciplines to learn data management skills. Focusing on core concepts as well as data ethics, reproducibility, and research workflows, the format of the program included seven evening workshops, two networking events, and one field trip. Each workshop invited campus and community speakers relevant to the workshop’s theme as a way to introduce the students to the network of available resources and data expertise and provided food for attendees. The workshops also built in customized activities to show students how to incorporate best practices into their work. Local businesses provided a tour of their facilities as well as a talk on how they leverage data. This paper will describe this program as well as the benefits and drawbacks of tailoring a research data management program toward undergraduates.

2014 ◽  
Vol 15 (2) ◽  
pp. 368-389 ◽  
Author(s):  
Hailey Mooney ◽  
W. Aaron Collie ◽  
Shawn Nicholson ◽  
Marya R. Sosulski

The undergraduate research experience (URE) provides an opportunity for students to engage in meaningful work with faculty mentors on research projects. An increasingly important component of scholarly research is the application of research data management best practices, yet this often falls out of the scope of URE programs. This article presents a case study of faculty and librarian collaboration in the integration of a library and research data management curriculum into a social work URE research team. Discussion includes reflections on the content and learning outcomes, benefits of a holistic approach to introducing undergraduate students to research practice, and challenges of scale.


10.29173/iq12 ◽  
2017 ◽  
Vol 41 (1-4) ◽  
pp. 12
Author(s):  
Bhojaraju Gunjal ◽  
Panorea Gaitanou

This paper attempts to present a brief overview of several Research Data Management (RDM) issues and a detailed literature review regarding the RDM aspects adopted in libraries globally. Furthermore, it will describe several tendencies concerning the management of repository tools for research data, as well as the challenges in implementing the RDM. The proper planned training and skill development for all stakeholders by mentors to train both staff and users are some of the issues that need to be considered to enhance the RDM process. An effort will be also made to present the suitable policies and workflows along with the adoption of best practices in RDM, so as to boost the research process in an organisation. This study will showcase the implementation of RDM processes in the Higher Educational Institute of India, referring particularly to the Central Library @ NIT Rourkela in Odisha, India with a proposed framework. Finally, this study will also propose an area of opportunities that can boost research activities in the Institute.


2012 ◽  
Vol 7 (2) ◽  
pp. 101-109 ◽  
Author(s):  
Laura Molloy ◽  
Kellie Snow

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.


2021 ◽  
Vol 45 (2) ◽  
Author(s):  
Elizabeth Blackwood

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.


2021 ◽  
Author(s):  
Kai Fay ◽  
Julie Goldman

The Harvard Medical School Countway Library’s Massive Open Online Course (MOOC) Best Practices for Biomedical Research Data Management launched on Canvas in January 2018. This report analyzes student reported data and course generated analytics from January 2018, through July 8, 2020, for the course Best Practices for Biomedical Research Data Management. By comparing the findings from the enrollment period through March 8, 2020 (pre-pandemic) to the period through July 8, 2020 (during-pandemic), the main goal is to investigate potential shifts due to the COVID-19 pandemic.


2021 ◽  
Vol 45 (3-4) ◽  
Author(s):  
Tom Kwanya

Research data management is an umbrella term used to describe activities related to the creation, organisation, structuring, naming, backing up, storage, conservation, and sharing of research data as well as all actions that guarantee security of research data. As is often the case, researchers from Sub-Saharan Africa are lagging behind their counterparts in developed countries in embracing the best practices of research data management. One of the factors to which this slow pace of adoption of research data management could be attributed, is inadequate research on the subject. The purpose of this paper is to analyse the quantity, quality, visibility and authorship of publications on research data management in Sub-Saharan Africa. Bibliometrics approaches were used to analyse publications on research data management from, and on, Sub-Saharan Africa which are currently indexed in Google Scholar. The index was chosen because it is free and is reputed to have liberal selection criteria which do not favour, or discriminate, any discipline or geographic regions. Data was retrieved from Google Scholar using Harzing’s “Publish or Perish” software and analysed using VOSviewer. The findings of the study revealed that the quantity, quality, visibility and authorship collaboration of scholarly publications on research data management in Sub-Saharan Africa is low. The findings may be used by libraries and research institutions in Sub-Saharan Africa to develop and promote best practices in research data management as a means of enhancing their research output and impact.


2020 ◽  
Vol 6 ◽  
Author(s):  
Kristin Briney ◽  
Heather Coates ◽  
Abigail Goben

The importance of research data has grown as researchers across disciplines seek to ensure reproducibility, facilitate data reuse, and acknowledge data as a valuable scholarly commodity. Researchers are under increasing pressure to share their data for validation and reuse. Adopting good data management practices allows researchers to efficiently locate their data, understand it, and use it throughout all of the stages of a project and in the future. Additionally, good data management can streamline data analysis, visualization, and reporting, thus making publication less stressful and time-consuming. By implementing foundational practices of data management, researchers set themselves up for success by formalizing processes and reducing common errors in data handling, which can free up more time for research. This paper provides an introduction to best practices for managing all types of data.


2017 ◽  
Author(s):  
Chealsye Bowley

Role playing exercise for student workshops on data management and sharing best practices.


IFLA Journal ◽  
2017 ◽  
Vol 43 (1) ◽  
pp. 105-118 ◽  
Author(s):  
Ryan Clement ◽  
Amy Blau ◽  
Parvaneh Abbaspour ◽  
Eli Gandour-Rood

This paper describes a collaborative approach taken by librarians at five small, regional liberal arts colleges to developing/enhancing research data management services on their campuses. The five colleges collectively belong to a consortium known as the Northwest Five Consortium. Over 10 months, librarians from the five schools collaborated to plan a data management and curation workshop with the goals of developing relationships with researchers working with data, developing their own research data management skills and services, and building a model for future training and outreach around institutional research data management services. This workshop brought together research teams including faculty, students, and librarians, and incorporated active learning modules as well as in-depth pre-workshop discussion. This article will discuss the context and background for this workshop, the model itself, and the outcomes and possibilities for future developments.


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