Joining Together to Build More: The New England Software Carpentry Library Consortium

Atwood, Thea; Creamer, Andrew; Dull, Joshua; Goldman, Julie; Lee, Kristin; Leligdon, Lora; Oelker, Sarah

doi:10.7191/jeslib.2019.1161

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…Libraries interested in hosting a workshop pay a small fee to Software Carpentry to recruit instructors and provide the workshop materials to their local communities; those interested in hosting regular workshops can become sustaining members and pay an annual fee [14]. The University of Oklahoma library became a Software Carpentry member in 2014 as a way of teaching “modern research workflows” [15], and the New England Software Carpentry Library Consortium was formed in 2017 to bring together libraries from across the region so that they could offer programming workshops and train instructors from the library community [16].…”

Section: Introductionmentioning

confidence: 99%

Why do biomedical researchers learn to program? An exploratory investigation

Deardorff

2020

jmla

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ObjectiveAs computer programming becomes increasingly important in the biomedical sciences and more libraries offer programming classes, it is crucial for librarians to understand how researchers use programming in their work. The goal of this study was to understand why biomedical researchers who enrolled in a library-sponsored workshop wanted to learn to program in R and Python.MethodsSemi-structured in-depth interviews were performed with fourteen researchers registered for beginning R and Python programming workshops at the University of California, San Francisco Library. A thematic analysis approach was used to extract the top reasons that researchers learned to program.ResultsFour major themes emerged from the interviews. Researchers wanted to learn R and Python programming in order to perform their data analysis independently, to be an informed collaborator, to engage with new forms of big data research, and to have more flexibility in the tools that they used for their research.ConclusionsLibrarians designing programming workshops should remember that most researchers are hoping to apply their new skills to a specific research task such as data cleaning, data analysis, and statistics and that language preferences can vary based on research community as well as personal preferences. Understanding the programming goals of researchers will make it easier for librarians to partner effectively and offer services that are critically needed in the biomedical community.

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Section: Introductionmentioning

confidence: 99%

Why do biomedical researchers learn to program? An exploratory investigation

Deardorff

2020

jmla

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Library Carpentry: Towards a New Professional Dimension (Part I – Concepts and Case Studies)

Mukhopadhyay¹,

Mitra²,

Mukhopadhyay³

2021

srels

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The domain of library and information science is always on the move and LIS professionals are ardent users of emerging technologies. This research work discusses an emerging possibility in the LIS domain, which applies data science principles and techniques in the bibliographic world. The concept is known as library carpentry and involves different data wrangling techniques to get insight of bibliographic datasets. The discussion starts with the basic concepts of library carpentry and systematically reveals the components and methods of library carpentry with the help of three case studies. The case studies represent a variety of actual problem solving projects by using open datasets and open source data wrangling software called Openrefine. The case study (I) deals with the application of library carpentry in e-book selection by taking into consideration socio-academic web space data, the case study (II) shows how is it possible to quickly get an overview of institutional contributions to open access domain by applying library carpentry methods and the case study (III) demonstrates the process of gender analysis with the help of a name-to-gender inference service and by applying data wrangling techniques. Each case study is supported by a comprehensive and representative dataset to support and promote real-life problem solving in processional sphere by applying library carpentry methods.

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Data Services Librarians’ Responsibilities and Perspectives on Research Data Management

Bishop

Orehek

Eaker

et al. 2022

JeSLIB

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This study of data services librarians is part of a series of studies examining the current roles and perspectives on Research Data Management (RDM) services in higher education. Reviewing current best practices provides insights into the role-based responsibilities for RDM services that data services librarians perform, as well as ways to improve and create new services to meet the needs of their respective university communities. Objectives: The objectives of this article are to provide the context of research data services through a review of past studies, explain how they informed this qualitative study, and provide the methods and results of the current study. This study provides an in-depth overview of the overall job responsibilities of data services librarians and as well as their perspectives on RDM through job analyses. Methods: Job analysis interviews provide insight and context to the tasks employees do as described in their own words. Interviews with 10 data services librarians recruited from the top 10 public and top 10 private universities according to the 2020 Best National University Rankings in the US News and World Reports were asked 30 questions concerning their overall job tasks and perspectives on RDM. Five public and five private data services librarians were interviewed. The interviews were recorded and transcribed. The transcriptions were analyzed in NVivo using a grounded theory application of open, axial, and selective coding to generate categories and broad themes based on the responses using synonymous meanings. Results: The results presented here provide the typical job tasks of data services librarians that include locating secondary data, reviewing data management plans (DMPs), conducting outreach, collaborating, and offering RDM training. Fewer data services librarians assisted with data curation or manage an institutional repository. Discussion: The results indicate that there may be different types of data services librarians depending on the mix of responsibilities. Academic librarianship will benefit from further delineation of job titles using tasks while planning, advertising, hiring, and evaluating workers in this emerging area. There remain many other explorations needed to understand the challenges and opportunities for data services librarians related to RDM. Conclusions: This article concludes with a proposed matrix of job tasks that indicates different types of data services librarians to inform further study. Future job descriptions, training, and education will all benefit from differentiating between the many associated research data services roles and with increased focus on research data greater specializations will emerge.

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Joining Together to Build More: The New England Software Carpentry Library Consortium

Cited by 3 publications

References 3 publications

Why do biomedical researchers learn to program? An exploratory investigation

Why do biomedical researchers learn to program? An exploratory investigation

Library Carpentry: Towards a New Professional Dimension (Part I – Concepts and Case Studies)

Data Services Librarians’ Responsibilities and Perspectives on Research Data Management

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