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465 results for “academics”
Academics for Land Protection in New England (ALPINE) GIS Data 2015-2018
Academics for Land Protection in New England (ALPINE) is a network of academic institutions committed to increasing the pace of land protection in New England to address the region’s environmental challenges and to support nature and people. ALPINE seeks to expand the role that academic institutions play in conserving the New England landscape by sharing experiences and resources among faculty and staff, students, administrations, and alumni. This dataset contains point locations of colleges that are participants in ALPINE and parcels of natural land owned by participating schools who submitted data to the ALPINE coordinator.
Academic Family Tree Data Export
<p>The Academic Family Tree is a live, crowdsourced project, that documents academic mentoring relationships across many fields. Data are updated continually. This snapshot was taken on 2024-10-18.</p> <p>We welcome inquiries about this dataset and are interested in learning about any new results you uncover. Contact: <a href="mailto:davids@ohsu.edu?subject=Academic%20Family%20Tree%20%2F%20Zenodo%20dataset">davids@ohsu.edu</a>.</p> <p>This dataset contains key tables from the Academic Family Tree, including information on names/institutions of academic mentoring relationships and semi-automated links of authors to publications and US grants (NSF, NIH only). A subset of publication and grant links have been validated by human users. To save space, only unique identifiers are included for publications (PMID, DOI) and grants (federal project number), without other metadata (author, title, journal, principle investigator, etc). These identifiers should be adequate to link to other databases. Also note, author-publication links are broken into several separate files. These files should be concatenated into a single table to generate a complete dataset. Some additional information is available here: <a href="https://academictree.org/export.php">https://academictree.org/export.php</a>.</p> <p>These data are associated with Liénard, J.F., Achakulvisut, T., Acuna, D.E. <em>et al.</em> Intellectual synthesis in mentorship determines success in academic careers. <em>Nature Communications</em> <strong>9, </strong>4840 (2018) (<a href="https://doi.org/10.1038/s41467-018-07034-y">https://doi.org/10.1038/s41467-018-07034-y</a>). Please cite this publication in work that uses this dataset.</p> <p>Funded by NSF Award 1933675.</p>
Research data supporting "Considerations for Implementing Electronic Laboratory Notebooks in an Academic Research Environment"
<p><em>Research data supporting the publication:</em></p> <p><em>Higgins SG, Nogiwa-Valdez AA, Stevens MM, Considerations for Implementing Electronic Laboratory Notebooks in an Academic Research Environment, Nature Protocols, 2021.</em></p> <p>This repository contains the raw survey data of 172 current and historic electronic laboratory notebook (ELN) software packages.</p> <p>Main files:</p> <ul> <li>"ELN_Review_Higgins_2021_Survey.csv" = raw survey data in 'tidy' data format</li> <li>"ELN_Review_Higgins_2021.Rmd" = an R Markdown File (R Notebook) that takes the survey data as input and produces summary statistics and plots. This file was written using R Studio as the IDE.</li> </ul> <p>Derived files, generated from those above:</p> <ul> <li>"ELN_Review_Higgins_2021.nb.html" = a self-contained HTML file that is automatically generated by R Studio, based on the markdown file. This can be opened in any web browser to allow manual inspection of the code and comments without the need for specialist software. Embedded within this file is also the original markdown script (i.e. a copy of the code in "ELN_Review_Higgins_2021.Rmd")</li> <li>"ELN_Review_Higgins_2021_Lifetimes_Interactive_Figure1.html" = an HTML file generated by the script above via the plotly package. It contains an interactive version of the ELN survey data, allowing the user to hover over the timeline and explore the data.</li> <li>"ELN_Review_Higgins_2021_Timeline.pdf" = static version of ELN timeline, used to generate figure in main manuscript.</li> <li>"ELN_Review_Higgins_2021_Releases-Per-Year.pdf" = static version of number of new ELNs per year, used to generate figure in main manuscript.</li> </ul> <p>This survey was generated from a mixture of primary and secondary sources (see references for secondary sources).</p>
Data and model for detecting spam activity on academic articles
<p>With the remarkable capability to reach the public instantly, social media has become integral in sharing scholarly articles to measure public response. This paper analyzes how Twitter bots interact with scholarly articles on the platform. Spamming by bots on social media can steer the conversation and present a false public interest in given research, affecting policies impacting the public's lives in the real world. In this paper, we determined whether bots are disseminating a given scholarly article based on analyzing the relationship between Twitter bots and several research factors. We developed and tested several supervised machine-learning classification models to tackle this problem. Through our analysis, we also identified that scholarly articles in health and human science are more prone to bot activity than other research areas.</p>
Typology of academic disciplines in the Modern China Biographical Database
<p>This table presents the bilingual typology of academic disciplines used in the Modern China Biographical Database. It is based mainly on the typology created by Yuan T'ung-li in his three bibliographical volumes about the doctoral dissertations by Chinese students in the United States, the United Kingdom, and continental Europe. We adapted this typology to include other disciplines that were present in historical sources.</p> <p><strong>Dataset Description: Typology of Disciplines (Level 2)</strong></p> <p><strong>Overview:</strong> This dataset provides a bilingual typology of academic disciplines, specifically focusing on Level 2 classifications. The terms are extracted from various Chinese sources, with English translations provided. It is structured hierarchically, connecting each Level 2 discipline to broader categories (Level 1 and Level 0), facilitating multilingual academic classification.</p> <p><strong>Structure:</strong> The dataset consists of the following key columns:</p> <ul> <li> <p><strong>Level 2 Discipline (English & Chinese):</strong> The specific sub-discipline classification.</p> </li> <li> <p><strong>Level 1 Discipline (English & Chinese):</strong> A broader category that groups multiple Level 2 disciplines.</p> </li> <li> <p><strong>Level 0 Discipline (English & Chinese):</strong> The highest-level classification representing major academic domains.</p> </li> <li> <p><strong>Level 1 Code:</strong> A numerical or coded identifier for Level 1 disciplines, supporting structured data processing.</p> </li> </ul> <p><strong>Purpose & Applications:</strong></p> <ul> <li> <p><strong>Hierarchical Classification:</strong> Enables structured categorization of academic fields across multiple levels.</p> </li> <li> <p><strong>Multilingual Standardization:</strong> Supports bilingual terminology consistency in academic and research contexts.</p> </li> </ul> <p>Main sources:</p> <p> </p> <p>Yuan, T’ung-li. <em>A Guide to Doctoral Dissertations by Chinese Students in America, 1905-1960</em>. Washington, D.C.: Published under the auspices of the Sino-American Cultural Society, 1961.</p> <p>———. <em>A Guide to Doctoral Dissertations by Chinese Students in Continental Europe, 1907-1962</em>. S.l., 1964.</p> <p>———. <em>Doctoral dissertations by Chinese students in Great Britain and Northern Ireland, 1916-1961.</em> Uden sted og forlag, 1963.</p>
VABB-SHW: Dataset of Flemish Academic Bibliography for the Social Sciences and Humanities (edition 11)
<p>This dataset contains the eleventh edition of the <em>Flemish Academic Bibliography for the Social Sciences and Humanities (VABB-SHW)</em>, a database of academic publications from the social sciences and humanities authored by researchers affiliated to Flemish universities (<a href="https://www.ecoom.be/en/data-collections/vabb-shw">more information</a>). Publications in the database are used as one of the parameters of the Flemish performance-based research funding system. Only approved publications are included in this dataset.</p>
Datasets and results of the paper titled "Are citation networks relevant to explain academic promotions? An empirical analysis of the Italian national scientific qualification"
<p>These are the <strong>input datasets</strong> and the <strong>results of the analyses</strong> reported on the paper titled <strong>"Are citation networks relevant to explain academic promotions? An empirical analysis of the Italian national scientific qualification"</strong>.</p> <p><strong>Abstract:</strong> </p> <p>The aim of this paper is to study the role of citation network measures in the assessment of scientific maturity. Referring to the case of the Italian national scientific qualification (ASN), we investigate if there is a relationship between citation network indices and the results of the researchers’ evaluation procedures. In particular, we want to understand if network measures can enhance the prediction accuracy of the results of the evaluation procedures beyond basic performance indices. Moreover, we want to highlight which citation network indices prove to be more relevant in explaining the ASN results, and if quantitative indices used in the citation-based disciplines assessment can replace the citation network measures in non-citation-based disciplines. Data concerning Statistics and Computer Science disciplines are collected from different sources (ASN, Italian Ministry of University and Research, and Scopus) and processed in order to calculate the citation-based measures used in this study. Following, we apply classification models to estimate the effects of network variables. We find that network measures are strongly related to the results of the ASN and significantly improve the explanatory power of the models, especially for the research fields of Statistics. Additionally, citation networks in the specific sub-disciplines are far more relevant than those in the general disciplines. Finally, results show that the citation network measures are not a substitute of the citation-based bibliometric indices.</p> <p><strong>Code</strong></p> <p>The code to collect and process the data used in this paper is available on GitHub at <a href="https://github.com/DigitalDataLab/ASN16-18_CitationNetwork">https://github.com/DigitalDataLab/ASN16-18_CitationNetwork</a><strong>.</strong> </p> <p><strong>Dataset description</strong></p> <p>The files <strong>AdjacencyMatrix_01B1.csv</strong>, <strong>AdjacencyMatrix_09H1.csv</strong>, <strong>AdjacencyMatrix_13D1.csv</strong>, <strong>AdjacencyMatrix_13D2.csv</strong> and <strong>AdjacencyMatrix_13D3.csv</strong> are the citation matrices for Italian academics (i.e. ASN candidates and permanent positions in the Italian academic system) in the Recruitment Fields (RFs) 01/B1, 09/H1, 13/D1, 13/D2 and 13/D3, respectively.</p> <p>The files <strong>AdjacencyMatrix_CS.csv</strong> and <strong>AdjacencyMatrix_ST.csv</strong> are the citation matrices for the Italian academics in the Computer Science disciplines (i.e. RFs 01/B1 and 09/H1) and the Statistical disciplines (i.e. RFs 13/D1, 13/D2 and 13/D3), respectively.</p> <p>The files <strong>CS_01B1_1.csv, CS_09H1_1.csv, ST_13D1_1.csv, ST_13D2_1.csv</strong> and <strong>ST_13D3_1.csv</strong> contain the data used to build the logistic regression models presented in the paper for the Italian academics at the Full Professor (FP) level.</p> <p>The files <strong>CS_01B1_2.csv, CS_09H1_2.csv, ST_13D1_2.csv, ST_13D2_2.csv</strong> and <strong>ST_13D3_2.csv</strong> contain the data used to build the logistic regression models presented in the paper for the Italian academics at the Associate Professor (AP) level.</p> <p>The file <strong>Codebook.pdf</strong> is the codebook of the previous ten files.</p> <p>The file <strong>Appendix.pdf</strong> contains the final results of the stepwise logistic regressions computed for each level (i.e. Full Professor and Associate Professor) and Recruitment Field in the Computer Science and Statistics disciplines.</p> <p>The file <strong>NormalityAssessment.pdf</strong> contains the normality assessment of citation network indices. </p>
UK HESA 2020 Academic women: Changing the Academic Gender Narrative through Open Access
<p>This Zenodo entry includes the full data files (.csv and .xlsx) for Figure 6: 'Percentages of women academic staff (headcount) in a subset of 165 United Kingdom universities by grouping, 2020', included in the manuscript "Changing the Academic Gender Narrative through Open Access", authored by members of the Curtin Open Knowledge Initiative (COKI). The analysis is of publicly available data sourced from the United Kingdom Higher Education Statistics Agency (HESA).</p>
Metadata and annotation data for the XSample corpus on German academic language
<p>The XSample corpus war created in the project <em>XSample</em> (https://www.izus.uni-stuttgart.de/fokus/fdm-projekte/xsample/) at Universität Stuttgart in 2021 by Melanie Andresen and Axel Pichler. It contains 135 German academic journal articles, 45 each from the disciplines linguistics, literary studies and philosophy. The texts themselves cannot be made public for copyright reasons. However, metadata and some annotation data are published here.</p> <p><strong>xsample-metadata.csv</strong><br> This file contains metadata on the texts in the corpus, like journal, title, authors, text length, and the URL to the original paper. It also contains two analytical metrics, 'past-ratio' and 'temp-expr-ratio', that are based on the annotations in the other two files. The variable 'past-ratio' expresses the proportion of verbs in past tense relative to all finite verbs in the text. The variable 'temp-expr-ratio' gives the number of temporal expressions per 1000 token.</p> <p><strong>xsample-heidel.csv</strong><br> This file contains all temporal expressions found and classified by the annotation tool <em>HeidelTime</em> (https://github.com/HeidelTime/heideltime, V. 2.2.1, Strötgen & Gertz 2013 ). The variable 'position' expresses the position of the first character of the temporal expression in the text in characters.</p> <p><strong>xsample-sticker2.csv</strong><br> This file contains all finite verbs found and classified by the annotation tool <em>sticker2</em> (https://github.com/stickeritis/sticker2). The variable 'position' expresses the position of the first character of the finite verb in the text in characters.</p> <p><strong>References</strong><br> Strötgen, Jannik & Michael Gertz. 2013. Multilingual and cross-domain temporal tagging. <em>Language Resources and Evaluation</em>. Springer 47(2). 269–298. <a href="https://doi.org/10.1007/s10579-012-9179-y">https://doi.org/10.1007/s10579-012-9179-y</a>.</p> <p> </p>
Data and codes: Changing the Academic Gender Narrative through Open Access
<p>This Zenodo entry includes data and R codes used to generate the figures included in the manuscript "Changing the Academic Gender Narrative through Open Access", authored by members of the Curtin Open Knowledge Initiative (COKI). These include data that are either publicly available or derived through the COKI data infrastructure.</p> <p>The R file includes codes used to generate Figures 1, 2, 3, 4, 1A, 2A and 3A. It uses data contained in the files "au_data_all.csv", "au_groupings.csv", "uk_data_all.csv" and "uk_groupings.csv".</p> <p>This entry also includes the full data files (.csv and .xlsx) for Figures 5 and 6 included in the manuscript:</p> <ul> <li>Figure 5: ‘Percentages of women academic staff (headcount) compared to the total number of academics in the institution for 43 Australian universities by grouping, 2020’. The analysis is of publicly available data sourced from the Australian Department of Education, Skills and Employment.</li> <li>Figure 6: ‘Percentages of women academic staff (headcount) compared to the total number of academics in the institution for a subset of 165 United Kingdom higher education institutions by grouping, 2020’. The analysis is of publicly available data sourced from the United Kingdom Higher Education Statistics Agency (HESA).</li> </ul>
Typology of academic degrees in the Modern China Biographical Database
<p>This table presents the bilingual typology of academic degrees used in the Modern China Biographical Database. It is based mainly on the data collected in historical sources.</p> <p>There are two levels:</p> <p>- Degree name: full name of the academic degree in English</p> <p>- Degree_Level_Eng: first level of classification and clustering of academic degrees in English</p> <p>- Degree_Level_ZhT: first level of classification and clustering of academic degrees in Chinese</p>
Supplementary data to `Do science maps from open access literature capture the overall topic structure of an academic field?`
<p>The dataset contains the 8,528 academic articles records related to Sustainable Food research sourced with the query `TS=("sustainab*" NEAR/2 "food*")` .</p> <p>They are the records present in the largest component of the citation network, as specified in the manuscript. </p> <p>The dataset was sourced from OpenAlex based on the original data used in the manuscript and it is composed of the following columns:</p> <table> <tbody> <tr> <td><em><strong>Column</strong></em></td> <td><em><strong>Description</strong></em></td> </tr> <tr> <td>Id</td> <td>OpenAlex ID</td> </tr> <tr> <td>DOI</td> <td>Document Object Identifier</td> </tr> <tr> <td>display_name</td> <td>The article title</td> </tr> <tr> <td>publication_year</td> <td>The publication year of the article</td> </tr> <tr> <td>open_access</td> <td>An object with details of the open access status of the article</td> </tr> </tbody> </table> <p>We choose the `.rdata` format for easy loading in R. Use the function `load()` to add the data frame to the enviroment. </p>
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>
Research institutions clustering based on the intensity of academic collaboration
<p>The clustering of research institutions has been conducted using the Louvain modularity algorithm. The Louvain modularity is a state-of-the-art method of identifying communities (clusters) in large networks. Modularity is a value between -1 and 1 that measures the density of edges inside communities to edges outside communities. Optimizing this value results in the best possible grouping of the nodes of a given network.</p> <p>In our exercise, the Louvain methods were applied to identify clusters of institutions within ACM and SSRN networks. In the network, nodes are constituted of institutions, and edges are represented by the intensity of research collaboration measured by number of papers co-authored by authors affiliated with the institutions.</p> <p>As an example, including the paper: <em>Fast unfolding of communities in large networks</em>, written by V. D. Blondel (Universite Catholique de Louvain), J-L. Guillaume (Universite Pierre et Marie Curie), R. Lambiotte (Imperial College London) and Etienne Lefebvre (Universite Catholique de Louvain) would impact the number of edges in our analysis in the following way:</p> <p>“Universite catholique de Louvain” ⇔ “Imperial College London” =+1</p> <p>“Universite catholique de Louvain” ⇔ “Universite Pierre et Marie Curie” =+1</p> <p>“Imperial College London” ⇔ “Universite Pierre et Marie Curie” =+1</p> <p>In our largest network we analyse 5362 institution nodes with 147 482 edges. The number of identified clusters highly depends on the resolution parameter. Resolution is a parameter for the Louvain community detection algorithm that affects the size of the recovered clusters. Smaller resolutions recover smaller, and therefore a larger number of clusters, and conversely, larger values recover clusters containing more data points. In all clusterizations, we have used a default resolution (1.0) tuned in the popular Gephi software for network analysis. Resolutions equal to one result in a moderate number of clusters, characterised by satisfactory statistical distribution. </p> <p><strong>Source:</strong></p> <p>- Association for Computing Machinery (ACM)</p> <p>Characteristics of the ACM Data Set following geographical classification</p> <p>Number of institutions: 5477</p> <p>Number of papers: 674684</p> <p>Number of countries: 122</p> <p>Years: 2011-2018</p> <p>As ACM contains publications across various areas of computer science, a more in-depth analysis requires the classification of papers into fields of interests. During the analysis, we looked at 3 wide areas:</p> <ul> <li> <p>Artificial intelligence and machine learning</p> </li> <li> <p>Technology (hardware, emerging technologies, infrastructure)</p> </li> <li> <p>Social issues</p> </li> </ul> <p>The 3 categories were set following expert analysis of the 1000 most frequent keywords in the dataset. If a term from the following list appeared among the paper’s keywords, the paper was assigned to that group, allowing a paper to assign to more than one group.</p> <p><strong>Files:</strong></p> <p>mod_ai.csv (based on keywords related to artificial intelligence)</p> <p>mod_tech.csv (based on keywords related to technologies)</p> <p>mod_soc.csv (based on keywords related to social issues)</p> <p>mod_all.csv (based on all papers)</p> <p> </p>
A census of research software in 171 academic institutional repositories.
<p> A dataset of metadata for 171 UK academic institutional repositories, including a census of research software contained.</p> <table> <tbody> <tr> <td><strong>URL</strong></td> <td>The OAI url</td> </tr> <tr> <td><strong>id</strong></td> <td>CORE Identifier</td> </tr> <tr> <td><strong>openDoarId</strong></td> <td>Open DOAR identifier</td> </tr> <tr> <td><strong>name</strong></td> <td>Name of repository</td> </tr> <tr> <td><strong>Russell_member</strong></td> <td>If the university is a member of the Russell Group of research intensive universities</td> </tr> <tr> <td><strong>RSE_group</strong></td> <td>If an RSE group is present (based on Soc of RSE data)</td> </tr> <tr> <td><strong>email</strong></td> <td>Redacted</td> </tr> <tr> <td><strong>uri</strong></td> <td>Not used</td> </tr> <tr> <td><strong>uni_sld</strong></td> <td>Second level domain (the part of the url between . And .ac.uk</td> </tr> <tr> <td><strong>homepageUrl</strong></td> <td>University website</td> </tr> <tr> <td><strong>source</strong></td> <td>Not used</td> </tr> <tr> <td><strong>ris_software</strong></td> <td>the Research Information System software used</td> </tr> <tr> <td><strong>ris_software_enum</strong></td> <td>Resolve ris_software into similar types (e.g. Eprints 3, EPrints3.3.16 both equal eprints)</td> </tr> <tr> <td><strong>metadataFormat</strong></td> <td>the protocol used for metadata</td> </tr> <tr> <td><strong>createdDate</strong></td> <td>Repository creation date</td> </tr> <tr> <td><strong>location</strong></td> <td>location of university</td> </tr> <tr> <td><strong>logo</strong></td> <td>University logo (resolves in error)</td> </tr> <tr> <td><strong>type</strong></td> <td>Only = Repository for this dataset. Can be = journal etc.</td> </tr> <tr> <td><strong>stats</strong></td> <td>Not used</td> </tr> <tr> <td><strong>contains_software_set</strong></td> <td>Whether the OAI-PMH software set is present in the repository.</td> </tr> <tr> <td><strong>Num_sw_records</strong></td> <td>The response of the OAI-PMH query for software (erroneous as discussed in paper)</td> </tr> <tr> <td><strong>Error</strong></td> <td>The category of error returned by the experiment’s OAI-PMH queries (see paper)</td> </tr> <tr> <td><strong>Manual_Num_sw_records</strong></td> <td>The true amount of software contained in the repository as found by a manual exhaustive search of each university website</td> </tr> <tr> <td><strong>Category</strong></td> <td>Whether the repository (a) contains software; (b) can contain software, but doesn’t yet; (c) has no separate type of research output called software or similar</td> </tr> </tbody> </table>
VABB-SHW: Dataset of Flemish Academic Bibliography for the Social Sciences and Humanities (edition 12)
<p>This dataset contains the twelfth edition of the <em>Flemish Academic Bibliography for the Social Sciences and Humanities (VABB-SHW)</em>, a database of academic publications from the social sciences and humanities authored by researchers affiliated to Flemish universities (<a href="https://www.ecoom.be/en/data-collections/vabb-shw">more information</a>). Publications in the database are used as one of the parameters of the Flemish performance-based research funding system. Only approved publications are included in this dataset.</p>
A dataset of metadata for UK academic institutional repositories, including a census of research software contained.
<p>A dataset of metadata for UK academic institutional repositories, including a census of research software contained.</p> <table> <tbody> <tr> <td><strong>URL</strong></td> <td>The OAI url</td> </tr> <tr> <td><strong>id</strong></td> <td>CORE Identifier</td> </tr> <tr> <td><strong>openDoarId</strong></td> <td>Open DOAR identifier</td> </tr> <tr> <td><strong>name</strong></td> <td>Name of repository</td> </tr> <tr> <td><strong>Russell_member</strong></td> <td>If the university is a member of the Russell Group of research intensive universities</td> </tr> <tr> <td><strong>RSE_group</strong></td> <td>If an RSE group is present (based on Soc of RSE data)</td> </tr> <tr> <td><strong>email</strong></td> <td>Redacted</td> </tr> <tr> <td><strong>uri</strong></td> <td>Not used</td> </tr> <tr> <td><strong>uni_sld</strong></td> <td>Second level domain (the part of the url between . And .ac.uk</td> </tr> <tr> <td><strong>homepageUrl</strong></td> <td>University website</td> </tr> <tr> <td><strong>source</strong></td> <td>Not used</td> </tr> <tr> <td><strong>ris_software</strong></td> <td>the Research Information System software used</td> </tr> <tr> <td><strong>ris_software_enum</strong></td> <td>Resolve ris_software into similar types (e.g. Eprints 3, EPrints3.3.16 both equal eprints)</td> </tr> <tr> <td><strong>metadataFormat</strong></td> <td>the protocol used for metadata</td> </tr> <tr> <td><strong>createdDate</strong></td> <td>Repository creation date</td> </tr> <tr> <td><strong>location</strong></td> <td>location of university</td> </tr> <tr> <td><strong>logo</strong></td> <td>University logo (resolves in error)</td> </tr> <tr> <td><strong>type</strong></td> <td>Only = Repository for this dataset. Can be = journal etc.</td> </tr> <tr> <td><strong>stats</strong></td> <td>Not used</td> </tr> <tr> <td><strong>contains_software_set</strong></td> <td>Whether the OAI-PMH software set is present in the repository.</td> </tr> <tr> <td><strong>Num_sw_records</strong></td> <td>The response of the OAI-PMH query for software (erroneous as discussed in paper)</td> </tr> <tr> <td><strong>Error</strong></td> <td>The category of error returned by the experiment’s OAI-PMH queries (see paper)</td> </tr> <tr> <td><strong>Manual_Num_sw_records</strong></td> <td>The true amount of software contained in the repository as found by a manual exhaustive search of each university website</td> </tr> <tr> <td><strong>Category</strong></td> <td>Whether the repository (a) contains software; (b) can contain software, but doesn’t yet; (c) has no separate type of research output called software or similar</td> </tr> </tbody> </table> <p> </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>
Model Collection of the Business Process Management Academic Initiative
<p>This model collection contains 29,810 models. The collection originates from the BPM Academic Initiative (BPMAI). In 2012, the initial members of this initiative were the following institutions represented by the corresponding professors: Mathias Weske (HPI, University of Potsdam), Marlon Dumas (University of Tartu), Marcello La Rosa (University of Melbourne), Jan Mendling (WU Vienna), Hajo A. Reijers (Utrecht University), Michael Rosemann (Queensland University of Technology, Australia), Jan Recker (University of Cologne, Germany), Wil van der Aalst (RWTH Aachen, Germany), Michael zur Mühlen (Stevens Institute of Technology, Hoboken, NJ) and Frank Leymann (University of Stuttgart), respectively, complemented by Dr. Gero Decker (Signavio GmbH, Berlin). The vendor Signavio provides a <a href="http://www.signavio.com/en/academic.html">free workspace</a> to the members of the BPM Academic Initiative and the models being created are made available for reseach purposes on the Creative Commons licence.</p> <p>The BPM Academic Initiative collection comprises tens of thousands of models, of various process modeling languages, and size. Models of the collection are available in several revisions, which opens a new perspective in researching the way people model.<br> The BPMAI collection is shared in a JSON format. Tools have been developed at HPI, University of Potsdam for efficiently processing these files. See the <a href="https://github.com/tobiashoppe/promnicat">PromniCat project</a>. Note that this project is no longer further developed, so you have to work with the information available there. Various analysis techniques can be applied for these models using the <a href="https://code.google.com/archive/p/jbpt/">jBPT library</a> or <a href="https://code.google.com/archive/p/apromore/">Apromore</a>.</p>
Secondary studies in the academic context: A systematic mapping and survey
<p>An online version of the survey executed in the paper "Secondary studies in the academic context: A systematic mapping and survey".</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.