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20 results for “academic libraries”
Dataset for: Exploring the experiences of academic libraries with research data management: a meta-ethnographic analysis of qualitative studies
<p><strong>Overview</strong></p> <p>This dataset contains the raw data for the manusript:<br> Perrier L, Blondal E, MacDonald H. Exploring the experiences of academic libraries with research data management: a meta-ethnographic analysis of qualitative studies. 2018; 40(3-4): 173-183. doi: 10.1016/j.lisr.2018.08.002</p> <p>Full-text available at: <a href="https://doi.org/10.1016/j.lisr.2018.08.002">https://doi.org/10.1016/j.lisr.2018.08.002</a> </p> <p><strong>Data and Documentation Files</strong></p> <p>Five files make up the dataset:</p> <ol> <li>Data Dictionary: RDMMetaEthnography_DataDictionary_v1.pdf</li> <li>Data Abstraction Sheet: RDMMetaEthnography_StudyCharacteristics.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_ParticipantCharacteristics.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_Outcomes.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_COREQ,csv</li> </ol> <p>Contact: Laure Perrier: <a href="https://orcid.org/0000-0001-9941-7129">orcid.org/0000-0001-9941-7129</a></p>
Figure 1. OER faculty support for academic libraries
<p>The deriving out of the answers given to questions about the extent to which Academic Libraries have supported and guided them to a. OER adoption/use, b. OER creation and c. OER intellectual property (copyright), it is revealed that the faculty of the research sample does not consider academic libraries much supportive of them in any of these three areas [1]. More precisely, only 22% of the survey’ s participants believe that academic libraries have guided them on OER intellectual property (copyright), another 26% consider that academic libraries have supported them in the OER creation, and less than one in three (30%) are of the opinion that the academic libraries have contributed to their OER adoption/use (see figure 1). </p>
The Pan-Canadian Chemical Library: A Mechanism to Open Academic Chemistry to High-Throughput Virtual Screening
<h1>Pan-Canadian Chemical Library</h1> <p>This Zenodo repository contains the cheap and druglike subset of the Pan-Canadian Chemical Library (PCCL) project. For more information, visit <a href="https://pccl.thesgc.org/" rel="nofollow">https://pccl.thesgc.org</a>.</p> <h2>PCCL library</h2> <p>The PCCL library is splitted by reaction, then by number of heavy atoms. Two types of files are available in zip archives:</p> <ul> <li>The SMILES format files, with the SMILES string and their product name,</li> <li>The CSV format file, with all the information generated during their enumeration: reagents, druglike properties, etc.</li> </ul> <p>Note: Purchasability is defined according to two integers: 1 for products only composed of BB-50 reagents, and 2 for products composed of BB-40 or BB-50 reagents. Read more about the meaning of these reagents groups in the article below.</p> <h2>Citation</h2> <p>If you find the PCCL useful or if you use it, please cite our paper:</p> <p>Bedart, C. <em>et al.</em> The Pan-Canadian Chemical Library: A mechanism to open academic chemistry to high-throughput virtual screening. Scientific Data 11, (2024).<br>doi: <a title="10.1038/s41597-024-03443-5" href="https://www.nature.com/articles/s41597-024-03443-5">10.1038/s41597-024-03443-5</a></p> <p> </p> <p> </p>
LIBER 2019 Workshop. Open Access books in academic libraries – how can we adapt workflows and cost management to an open scholarly communications landscape?
<p>This dataset includes all the results from a workshop held at the LIBER Annual Conference 2019 on June 26, 2019, in Dublin, Ireland. The workshop aimed at collecting and discussing current library practices related to open access books.</p> <p>The dataset includes information from a survey made in preparation for the conference, where 67 European libraries responded to a questionnaire based on activities or workflows in libraries related to open access books. Both the survey questionnaire and the results from the survey are uploaded as separate files.</p> <p>The dataset also includes the presentation made by keynote speaker Eelco Ferwerda from the OAPEN Foundation. He presented results based on the 2017 landscape study report on open access monographs with some new results from more recent studies by Springer Nature and a follow-up report by Knowledge Exchange.</p> <p>Olaf Siegert from ZBW - Leibniz Information Centre for Economics presented a brief overview of what libraries can do to promote OA books in terms of collection management, publication services and development of staff and organisation. The conclusion is that it is not necessarily big changes that are needed.</p> <p>Sofie Wennström presented results from a survey aimed at European research libraries on behalf of the LIBER Open Access Working Group. The survey reveals that many libraries are already working with processes to promote OA books. This is done by libraries organising publishing services or inhouse publishing, by including OA books in discovery services and repositories and by supporting authors to learn more about open access and open licensing.</p> <p>Finally, the LIBER Open Access Working Group shares a report from the workshop providing some quick takeaways and some good examples brought up during the breakout session with the workshop participants.</p>
Research Data Services (RDS) in Spanish Academic Libraries
<p>Files in csv format to analyze Research Data Services implemented in Spanish university libraries (2022).</p>
Research Data Services in Academic Libraries: A Survey of North American Academic Libraries in 2019
<p>To determine the extent to which research data services (RDS) are supported in academic libraries and how that has changed over a decade, in 2019 a research team led by Carol Tenopir at the University of Tennessee Center for Information and Communication Studies, in collaboration with ACRL-Choice, surveyed academic library directors in the United States and Canada. This survey allowed us to compare results with a similar survey conducted in 2012. The goal of both studies was to discover the types of data services offered, the staffing deployed or anticipated for such services, the training necessary to support RDS, and RDS plans for the future.</p> <p>The associated white paper can be accessed at <a href="http://www.choice360.org/librarianship/whitepaper">http://www.choice360.org/librarianship/whitepaper</a> and downloaded at <a href="https://www.research.net/r/CHOICERDSWP">https://www.research.net/r/CHOICERDSWP</a></p>
Research Data Services in Academic Libraries: A Survey of North American Academic Libraries in 2019
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Data from: User experience methods and maturity in academic libraries
<p>This article presents a mixed-methods study of the methods and maturity of user experience (UX) practice in academic libraries. The authors apply qualitative content analysis and quantitative statistical analysis to a research dataset derived from a survey of UX practitioners. Results reveal the type and extent of UX methods currently in use by practitioners in academic libraries. Themes extracted from the survey responses also reveal a set of factors that influence the development of UX maturity. Analysis and discussion focuses on organizational characteristics that influence UX methods and maturity. The authors conclude by offering a library-focused maturity scale with recommended practices for advancing UX maturity in academic libraries. </p>
Is There an Academic Elegancy in the Field of Library Science and Information Science? Are the Higher Impact Studies Citing the Studies Published in the Higher Impact Journals?
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Dataset - Instagram Posts from Academic Libraries Profiles
<p>The current dataset comprises a total of 1681 instances (rows) of Instagram posts that originate from 120 academic library profiles across the globe. Each post instance comes with a range of features, including the publication date, text caption, profile name, post interaction rate, post link, post type (image, carousel, reel), and the thematic category to which it belongs out of 14 proposed categories. This dataset provides a valuable resource for researchers and practitioners interested in investigating the social media practices and content structure strategies of academic libraries worldwide. </p> <p><strong>Note: </strong>This dataset was developed by the authors and has been utilized in the publication of the paper titled "Unveiling the Feed: Academic Libraries' Instagram Unpacked" in <em>The Journal of Academic Librarianship. </em>Find more for this publication at: <a href="https://doi.org/10.1016/j.acalib.2024.102924" target="_blank" rel="noopener">https://doi.org/10.1016/j.acalib.2024.102924.</a></p>
Academic Libraries Follow-Up dataset
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Data from: User experience methods and maturity in academic libraries
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Data from: Book club programs in United States academic libraries: A survey
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Figure 3 from: Murray M, O'Donnell M, Laufersweiler MJ, Novak J, Rozum B, Thompson S (2019) A survey of the state of research data services in 35 U.S. academic libraries, or "Wow, what a sweeping question". Research Ideas and Outcomes 5: e48809. https://doi.org/10.3897/rio.5.e48809
Figure 3 Types of campus groups that provide RDS (n=103). Type codes are defined as follows: Admin: a campus administrative unit that does not fall into any other category; Center: research centers or institutes excluding HPC groups; Dept = Departments or colleges; HPC: High Performance Computing and research computing units including HPC run by IT units; Individuals: Individual staff, faculty, students, etc.; IT: Information Technology associated with the entire campus, colleges, or departments excluding HPC groups; Lab: Various labs on campus that do not fall into any other category; Research Office: Groups that oversee university research; Other: Groups that cannot be categorized under any other code.
Figure 2 from: Murray M, O'Donnell M, Laufersweiler MJ, Novak J, Rozum B, Thompson S (2019) A survey of the state of research data services in 35 U.S. academic libraries, or "Wow, what a sweeping question". Research Ideas and Outcomes 5: e48809. https://doi.org/10.3897/rio.5.e48809
Figure 2 Breakdown of the workshops or topics with a tool or programming language code applied (n=47). Only tool codes that have a frequency >1 are shown. Tool code names are self-explanatory (i.e. the name of tool).
Figure 1 from: Murray M, O'Donnell M, Laufersweiler MJ, Novak J, Rozum B, Thompson S (2019) A survey of the state of research data services in 35 U.S. academic libraries, or "Wow, what a sweeping question". Research Ideas and Outcomes 5: e48809. https://doi.org/10.3897/rio.5.e48809
Figure 1 Workshop topic code frequencies. Up to two topic codes were applied to each workshop (n=160). Topic codes are defined as follows: Carpentry: a data or software Carpentry workshop; Cleaning: data cleaning and related techniques; Coding: how to work with data via command line or in a specific language; General: the basics of data management; GIS: geographic information system or spatial data/tools; Grants: the word "grants" or the name of a funding agency was explicitly mentioned in the workshop's title or description; HPC: high performance computing; Locate: focused on how to search and locate datasets; Metadata: metadata and data documentation; Mining: focused on text and data mining; Org: data organization; Other: misc. topics or unclassifiable; Plans: data management plans; Repository: addresses a specific repository, how to use a repository, or data repositories in general; Reproducibility: focused on research reproducibility; StorageSec: data storage and/or security tools and topics; Tool: focused on how to use tools related to data and data management (see Fig. 2); Visualization: data visualization.
Supplementary material 1 from: Murray M, O'Donnell M, Laufersweiler MJ, Novak J, Rozum B, Thompson S (2019) A survey of the state of research data services in 35 U.S. academic libraries, or "Wow, what a sweeping question". Research Ideas and Outcomes 5: e48809. https://doi.org/10.3897/rio.5.e48809
Links to library and university/college research data management policies.
Figure 4 from: Murray M, O'Donnell M, Laufersweiler MJ, Novak J, Rozum B, Thompson S (2019) A survey of the state of research data services in 35 U.S. academic libraries, or "Wow, what a sweeping question". Research Ideas and Outcomes 5: e48809. https://doi.org/10.3897/rio.5.e48809
Figure 4 Disciplinary categorization of campus groups that provide RDS (n=34). Discipline codes are defined as follows: Bio: Groups that specialize in biology, including health and medicine; Bio/Stats: Groups that specialize in biology and statistics; Data: no specific discipline but has the word 'data' in the name; GIS: Groups that specialize in spatial and GIS (Geographic Information Systems) data; Humanities: Groups specializing in humanities; Social/Stats: Groups that specialize in statistics and social science; SocialSci: Groups specializing in social science; Stats: Groups specializing in statistics.
Academic Libraries and Research Data Services dataset
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Academic Library Checkout Data
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