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86 results for “open access data”
Survey Data on Current Open Access Terms and Future Trends (2024)
<p><strong>Description:</strong><br>This dataset contains the analysis, codebook, and raw survey data from the 2024 survey <em>"Open Access – Current Terms and Future Areas of Focus"</em>. The survey aimed to gather perspectives from Open Access experts in the German-speaking region, focusing on the evaluation of current Open Access terminology, concepts, and emerging trends.</p> <p>The survey highlights how Open Access terminology has evolved over the past two decades and explores current perceptions regarding key terms in the Open Access discourse, as well as the anticipated future developments in this field. A total of 131 complete responses (<em>N=131</em>) were collected, providing valuable insights into the views of professionals working in Open Access publishing, information infrastructures, and scientific publishing houses.</p> <p><strong>Contents:</strong></p> <ol> <li><strong>codebook_oa_2024_2024-11-21.xlsx</strong>: The codebook, including detailed explanations of the variables, codes, and definitions used in the survey.</li> <li><strong>survey_results_oa_2024_2024-11-21.xlsx</strong>: Anonymized raw data from the survey, including both quantitative and qualitative responses from the participants.</li> <li><strong>values_oa_2024_2024-11-21.csv</strong>: CSV file containing the key terms and concepts identified by participants in response to the question on Open Access terminology.</li> <li><strong>values_oa_2024_2024-11-21.csv</strong>: An additional CSV file with detailed classification and analysis of the terms related to Open Access, including their frequency and significance based on participant responses.</li> </ol> <p><strong>Methodology:</strong><br>The survey was conducted via an online questionnaire distributed from September 7 to October 15, 2024, to professionals working in Open Access, both within information infrastructures (e.g., libraries) and in academic publishing houses. The survey gathered both qualitative and quantitative data, focusing on how Open Access terminology is understood and its future developments. The data were cleaned, anonymized, and analyzed using appropriate statistical and content analysis methods.</p> <p><strong>Purpose and Use:</strong><br>This dataset is valuable for researchers and professionals studying Open Access terminology, trends, and future developments. It provides insights into the current understanding of Open Access within the academic community and can be used for comparative studies, policy analysis, and future Open Access research.</p>
Data of European University Association (EUA) Open Access Survey 2017-2018
<p>This database refers to the data collected by the European University Association (EUA) for its Open Access Survey 2017-2018, which gathered responses from universities and higher education institutions across Europe. The full report published by the association is available at <a href="https://eua.eu/resources/publications/826:2017-2018-eua-open-access-survey-results.html">https://eua.eu/resources/publications/826:2017-2018-eua-open-access-survey-results.html</a>.</p> <p>The data included in this database refers only to those universities and higher education institutions that accepted their data to be available in open access (n=266). All information that could lead to the identification of individual universities and higher education institutions was removed from the database. The following files are available:</p> <ul> <li>Questionnaire</li> <li>Database in the following formats: .sav (IBM SPSS Statistics), .xlsx (Microsoft Excel) and .csv</li> <li>Codebook: includes information on all the variables and their coding.</li> </ul>
Global Naturalized Alien Flora (GloNAF). Open access data to support research on understanding global plant invasions.
<p>This dataset is a snapshot of the Global Naturalized Alien Flora (GloNAF) database, version 2.02. GloNAF is a continuously updated, curated compilation of alien naturalized vascular plant inventories for geographic regions from around the world. The dataset has 16,429 unique taxa reported as naturalized or invasive and covers 1,343 regions (including 427 islands) from 336 data sources. For each region, the status (invasive, naturalized) is provided as listed in the original source. We provide the scientific names included with the original data source, and the matching accepted name or synonym of the taxon as given in the World Checklist of Vascular Plants (WCVP) Version 12. In addition, we provide an ESRI shapefile of polygons for each region. We also provide several variables that can be used to filter the data according to quality and completeness of alien taxon lists, which vary among the combinations of regions and data sources.</p> <p>The 'glonaf_flora2.csv' file lists the IDs ('taxon_wcvp_id') of all naturalized taxa contained in GloNAF and the regions they occur in. The 'glonaf_taxon_wcvp.csv' lists the original taxon names provided in the source data along with the corresponding accepted taxon name from the WCVP (version 12) for all alien taxa in GloNAF, regardless of their naturalization status. To link taxon names with naturalization records, join the 'id' column of the 'glonaf_taxon_wcvp.csv' file to the 'taxon_wcvp_id' column in 'glonaf_flora2.csv' . Additional information regarding the original source of the data ('glonaf_reference.csv'), specific attributes of the taxon lists ('glonaf_list.csv') and the region ('glonaf_region.csv') can also be joined similarly to 'glonaf_flora2.csv '. </p> <p> </p>
Survey Data and Analysis on Open Access Strategies (2022)
<p><strong>Description:</strong><br>This dataset includes the analysis, codebook, and raw survey data from a 2022 survey titled <em>"Which Open Access Strategies Are Relevant?"</em>. The survey targeted professionals in library and information sciences specializing in open access and scholarly publishing.</p> <p>The dataset is based on 100 adjusted responses (<em>N=100</em>) and aims to provide insights into the strategies and challenges associated with open access implementation in academic and professional environments.</p> <p><strong>Contents:</strong></p> <ol> <li><strong>analysis_oa-strategies-2024-01-14.xlsx</strong>: Processed data and key analyses, including summary tables and graphs.</li> <li><strong>codebook_oa-strategies_2024-01-14.xlsx</strong>: Comprehensive documentation of variables, codes, and their definitions for interpretation of the raw data.</li> <li><strong>survey_results_oa-strategies_2024-01-14.xlsx</strong>: Anonymized raw data from the survey, suitable for further analysis.</li> </ol> <p><strong>Methodology:</strong><br>The survey employed a structured questionnaire distributed in 2022 to professionals in library and information sciences. It focused on identifying key strategies, institutional policies, and perceived barriers to open access. The collected data were cleaned and anonymized to ensure privacy and compliance with ethical standards.</p> <p><strong>Purpose and Use:</strong><br>This dataset is designed for researchers, policymakers, and information science professionals. It is particularly valuable for studying open access adoption strategies, evaluating institutional policies, and conducting comparative research.</p>
Figures in Scientific Open Access Publications - Underlying Data
<p>This publication contains data for a statistical analysis of an OA article corpus. The underlying dataset consists of over 1 million open access articles from different publishers (Copernicus: 9592; Springer:78418; Hindawi: 147848; Frontiers: 57621; PMC (aggregator): 747839)</p>
Research Data of the 2014 Census of Open Access Repositories in Germany, Austria and Switzerland
<p>The "2014 Census of Open Access Repositories in Germany, Austria and Switzerland” (2014 Census) is a study on the green open access landscape conducted in the course of a project seminar at the Berlin School of Library and Information Science (BSLIS) at Humboldt-Universität zu Berlin. The 2014 Census not only succeeds the "2012 Census of Open Access Repositories in Germany"[1] but enhances it by adding an online survey to the qualitative analysis of the open access repository websites and the automatic validation of its metadata. Like in 2012 the 2014 Census gives insights into the development of open access repositories and current trends in repository design being of substantial use to open access repository operators.</p> <p>This 2014 Census data set represents the data collected in three different ways:</p> <ul> <li>qualitative analysis of the open access repository websites</li> <li>automatic validation of the metadata via OAI-PMH using the DINI-Validator [2] </li> <li>online survey of repository operators</li> </ul> <p>As in 2012 [3] the data set is provided in XLSX as well as in CSV format. The columns represent the criteria and the rows represent the analyzed open access repositories. In the XLSX file the header row gives the definition of each criterion in English and German. In the CSV "content" file the header row is in English short terms. The respective English and German definition can be found in the CSV "readme" file.</p> <p> </p> <p>[1] Vierkant, P. (2013). 2012 Census of Open Access Repositories in Germany: Turning Perceived Knowledge Into Sound Understanding. <em>D-Lib Magazine</em>, 19. http://dx.doi.org/10.1045/november2013-vierkant </p> <p>[2] http://oanet.cms.hu-berlin.de/validator/pages/validation_dini.xhtml</p> <p>[3] Vierkant, Paul; Voigt, Michaela; Dupski, Jens; David, Sammy; Lösch, Mathias (2013): 2012 Census of Open Access Repositories in Germany. fig<strong>share</strong>. <br /> http://dx.doi.org/10.6084/m9.figshare.677099</p>
ROARMAP Open Access Policy data - Country list
<p>This data is a sub set of a dump from ROARMAP [http://roarmap.eprints.org/] taken on 24th August 2014.</p> <p>ROARMAP is the Registry of Open Access Repository Mandates and Policies, a searchable international registry charting the growth of open access mandates and policies adopted by universities, research institutions and research funders that require or request their researchers to provide open access to their peer-reviewed research article output by depositing it in an open access repository.</p> <p>Number of Open Access policies and a ranking is shown for each country.</p> <p>The data is being used for a series of data visualisations [http://pasteur4oa-dataviz.okfn.org/] for the PATEUR4OA Project [http://pasteur4oa.eu/].</p> <p> </p> <p>PASTEUR4OA (Open Access Policy Alignment Strategies for European Union Research) aims to support the European Commission’s Recommendation to Member States of July 2012 that they develop and implement policies to ensure Open Access to all outputs from publicly-funded research. </p> <p>PASTEUR4OA will help develop and/or reinforce open access strategies and policies at the national level and facilitate their coordination among all Member States. It will build a network of centres of expertise in Member States that will develop a coordinated and collaborative programme of activities in support of policymaking at the national level under the direction of project partners.</p>
Data of the Open Access Repository Ranking 2015
<p>The Open Access Repository Ranking 2015 ranks open access repositories from Germany, Austria and Switzerland. Data for the 2015 ranking was partly submitted by the respective repository managers, and partly automatically validated via the OAI interfaces. The OARR team reviewed all submissions assuring the quality and validity.<br> The ranking is based on an open and transparent metric that was developed in accordance with the open access community. This metric is a synthesis of different schemes and studies that surveyed and describe open access repositories.<br> Data includes the 2015 scores and descriptive information on the repositories as well as the 2015 metric.</p>
Data for publication "Benefits of open access to researchers from lower-income countries: A global analysis of reference patterns in 1980–2020"
<p>Data to reproduce figures for the publication "Benefits of open access to researchers from lower-income countries: A global analysis of reference patterns in 1980–2020" (DOI: 10.1177/01655515241245952). Each file contains the data underlying the figure corresponding to the file name.</p>
Modular control of human movement during running: an open access data set
<p>The human body is an outstandingly complex machine including around 1000 muscles and joints acting synergistically. Yet, the coordination of the enormous amount of degrees of freedom needed for movement is mastered by our one brain and spinal cord. The idea that some synergistic neural components of movement exist was already suggested at the beginning of the XX century. Since then, it has been widely accepted that the central nervous system might simplify the production of movement by avoiding the control of each muscle individually. Instead, it might be controlling muscles in common patterns that have been called muscle synergies. Only with the advent of modern computational methods and hardware it has been possible to numerically extract synergies from electromyography (EMG) signals. However, typical experimental setups do not include a big number of individuals, with common sample sizes of five to 20 participants. With this study, we make publicly available a set of EMG activities recorded during treadmill running from the right lower limb of 135 healthy and young adults (78 males, 57 females). Moreover, we include in this open access data set the code used to extract synergies from EMG data using non-negative matrix factorization and the relative outcomes. Muscle synergies, containing the time-invariant muscle weightings (motor modules) and the time-dependent activation coefficients (motor primitives), were extracted from 13 ipsilateral EMG activities using non-negative matrix factorization. Four synergies were enough to describe as many gait cycle phases during running: weight acceptance, propulsion, early swing and late swing. We foresee many possible applications of our data, that we can summarize in three key points. First, it can be a prime source for broadening the representation of human motor control due to the big sample size. Second, it could serve as a benchmark for scientists from multiple disciplines such as musculoskeletal modelling, robotics, clinical neuroscience, sport science, etc. Third, the data set could be used both to train students or to support established scientists in the perfection of current muscle synergies extraction methods.</p> <p>The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus.</p> <p>The file "dataset.rar" contains data in older format, not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a>.</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>
Monitoring open access publishing of NWO funded research (2015-2021) data set
<p>This is the dataset underlying the report "Monitoring open access publishing of NWO funded research" (<a href="https://doi.org/10.5281/zenodo.7041897">https://doi.org/10.5281/zenodo.7041897</a>)</p> <p>The report presents statistics on the extent to which publications from the period 2015–2021 funded by NWO are available in Open Access. The analyses presented in this report also cover publications funded by the Netherlands Organisation for Health Research and Development ZonMw. This report builds on two earlier reports, published in <a href="https://zenodo.org/record/4446042">2020</a> and <a href="https://zenodo.org/record/5056043">2021</a>, covering publications from the period 2015–2018 and 2015-2020, respectively.</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>
Data and Statistical analysis for: "Predator in the pool? A quantitative evaluation of non-indexed open access journals in aquaculture research"
<p>Data and Statistical analysis for: "Predator in the pool? A quantitative evaluation of non-indexed open access journals in aquaculture research" published in <em>Frontiers in Marine Science</em></p>
Method Classification of Open Access INTACT Molecular Interaction data.
<p>Simple classification data derived from open access papers indexed in the INTACT database (https://www.ebi.ac.uk/intact/downloads) based on PSI-MI25 codes for interaction detection methods or participant detection methods based on the subfigure caption text. <br> <br> intact_records_and_captions_complete.tsv - This file links available text of subfigure captions to PSI-MI25 codes for the interaction detection method and participant detection method. </p> <p>evidx_run_file.txt - This file provides execution codes for the 'EvidX' machine learning text classifier (https://github.com/SciKnowEngine/evidX/releases/tag/v0.1.0)</p> <p> </p> <p> </p> <p> </p>
Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values - Dataset
<p>This dataset accompanies the report <em>"Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values"</em>, which is available via Zenodo.<br><br>It provides record-level data of UKRI-funded and UK-affiliated research output (limited to journal articles with Crossref DOIs) published between 2012 and 2022 - including bibliographic metadata as well as data on open access availability, publisher, national and international collaborations, citations, views and downloads, altmetrics and subjects (fields). All variables are documented in the data dictionary included in this Zenodo record.</p> <p>The code used to generate the dataset from open data sources is available on GitHub. </p> <p>The following data sources were used:</p> <ul> <li> <p>Gateway to Research (records downloaded between 2023-11-05 and 2023-11-13)</p> </li> <li> <p>Crossref (Metadata Plus snaphot 2023-10-31, Crossref member route API 2024-01-23)</p> </li> <li> <p>OpenAlex (data snapshot 2023-10-18)</p> </li> <li> <p>Unpaywall (data snapshot 2023-11-27)</p> </li> <li> <p>IRUS UK (2024-04-03)</p> </li> <li> <p>Crossref Event Data (2023-04-01)</p> </li> </ul> <p><strong></strong><br><br>The project made use of Curtin Open Knowledge Initiative (COKI) infrastructure, which is documented on GitHub: <a href="https://github.com/The-Academic-Observatory">https://github.com/The-Academic-Observatory</a>. </p>
Open-Access Data for "Received SignalStrength Measurements with BLE Signals for Contact Tracing and Proximity Detection"
<p>This archive contains three folders which are supplementary material for the paper accepted for publishing in IEEE Sensors Journal.</p> <p><strong>Contents:</strong></p> <ul> <li> The folder `open-access-data/upb/` contains the measurements acquired at UPB. The subfolders are named as `upb_ble_*`, where an asterisk masks the directory number. Whenever UPB is specified, use the data sets from the corresponding directory.</li> <li>The folder `open-access-data/tau/` contains the measurements acquired at TAU. The subfolders are named as `tau_ble_*`, where an asterisk masks the directory number. Whenever TAU is specified, use the data sets from the corresponding directory.</li> <li>The folder `open-access-data/wifi-on-off/` contains a sample code to read the files and plot the data from Fig. 14 in `open-access-data/wifi-on-off/wifi_on_off_read_plot.py` and Fig. 15 in `open-access-data/wifi-on-off/wifi_on_off_read_plot.ipynb`.</li> </ul> <p><strong>Results based on the data have been presented in the paper:</strong><br> Flueratoru, L., Shubina, V., Niculescu, D., Lohan, E.S. (2021). On the High Fluctuations of Received Signal Strength Measurements with BLE Signals for Contact Tracing and Proximity Detection, IEEE Sensors, Special Issue on Advanced Sensors and Sensing Technologies for Indoor Positioning and Navigation</p> <p><strong>To cite these data sets please use the following:</strong><br> Laura Flueratoru, Viktoriia Shubina, Dragoș Niculescu, & Elena Simona Lohan. (2021). Open Access Data for "Received SignalStrength Measurements with BLE Signals for Contact Tracing and Proximity Detection" [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4643668</p>
Data from the OPERAS business models survey on open access books
<p>OPERAS (the European Research Infrastructure for the development of open scholarly communication in the social sciences and humanities) has conducted a survey of publishing organisations throughout Europe to identify and better understand existing and potential business models to support the Open Access publication of research monographs. The results of the survey are used to inform the formulation of recommendations about how to create a sustainable open access book publishing ecosystem within Europe.</p> <p>The survey was designed to serve two core aims: <br> 1. To further, better or improve our understanding of the scholarly publishing landscape and of the challenges that publishers face in the context of publishing OA monographs;<br> 2. To identify main trends (including opportunities and challenges) and the knowledge of collaborative funding and infrastructure models in OA publishing in SSH. </p> <p>The survey was open between 16 February and 14 April 2021.</p> <p>The results are presented in two versions of the white paper of the Open Access Business Models Special Interest Group: Stone, Graham, Błaszczyńska, Marta, Lebon, Chloé, Morka, Agata, Mosterd, Tom, Mounier, Pierre, Proudman, Vanessa, Speicher, Lara, & Melinščak Zlodi, Iva. (2021). Collaborative models for OA book publishers (1.0). Zenodo. https://doi.org/10.5281/zenodo.5494731 and the second version to be published in Spring 2023.</p>
Data for Research Assessment in the Transition to Open Science. 2019 EUA Open Science and Access Survey Results
<p>This database refers to the data collected by the European University Association (EUA) for its Open Science and Access Survey 2019, which gathered responses from universities and higher education institutions across Europe. The full report published by the association is available at <a href="https://eua.eu/resources/publications/888:research-assessment-in-the-transition-to-open-science.html">https://eua.eu/resources/publications/888:research-assessment-in-the-transition-to-open-science.html</a>.</p> <p>The data included in this database refers only to those universities and higher education institutions that accepted their data to be available in open access (n=174). All information that could lead to the identification of individual universities and higher education institutions was removed from the database (cf. cells highlighted in red). The following files are available:</p> <ul> <li>2019 EUA Open Science and Access Survey</li> <li>Database in the following formats: .xlsx (Microsoft Excel)</li> <li>Survey Codebook: includes information on all the variables and their coding.</li> </ul>
Ancient Greek Literature for Advanced Data Processing: A Text Fabric Representation of Open Access Texts in TEI XML
<p>This data set contains a full conversion of Greek texts available in the Perseus Digital Library and the Open Greek and Latin Project to the Text Fabric data format. The main advantage of the Text Fabric datatype over the original TEI XML format is that it utilizes a strict separation of text and annotation in a flat data structure. At the same time, it permits multiple distinct formats of the same text as well as an unlimited depth of (embedded) annotations. Because of its flat data structure, it facilitates easy and clean procedures to analyze, transform, and enrich the available data. Many of these processes are very difficult to conduct while departing from the hierarchically organized XML tree representation.</p>
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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.