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8,537 results for “OpenITI”
Density Layers of selected Points of Interest from Open Street Map
<p>This dataset contains a raster layer of 100*100m resolution showing the density of selected amenities from Open Street Map. The amenities are selected as points of interest, where electric vehicles owners are likely to stop for a moment and recharge their vehicles. This dataset covers Europe and was obtained with the Overpass API. This dataset can be used to identify possible charging location for electric vehicles or any other purpose requiring to quantify the number of amenities in an area.</p> <p> </p> <p>This dataset shows densities of a selection of Points of Interest in Europe from Open Street Map [1].</p> <p>Included countries are:</p> <p> </p> <p><em><strong>country_codes</strong> = ['AT', 'BE', 'BG', 'HR', 'CY', 'CZ', 'DK', 'EE', 'FI', 'FR', 'DE', 'GR', 'HU', 'IE', 'IT','LV', 'LT', 'LU', 'MT', 'NL', 'PL', 'PT', 'RO', 'SK', 'SI', 'ES', 'SE', 'AL', 'AD', 'AM', 'BY', 'BA', 'FO', 'GE', 'GI', 'IS', 'IM', 'XK', 'LI', 'MK', 'MD', 'MC', 'ME', 'NO', 'SM', 'RS', 'CH', 'TR', 'UA', 'GB', 'VA']</em></p> <p> </p> <p>The requests of Points of Interest have been performed with the Overpass API [2] (free of charge).</p> <p> </p> <p>The codes included in each density are listed below :</p> <p> </p> <p><strong><em>'highway'</em></strong><em> = ['"highway"="motorway"', '"highway"="rest_area"'];</em></p> <p><strong><em>'parkings'</em></strong><em> = ['"parking"="surface"', '"parking"="multi-storey"', '"parking"="street_side"', '"parking"="underground"' , '"park_ride"' ];</em></p> <p><strong><em>'school'</em></strong><em> = ['"amenity"="college"', '"building"="college"', '"building"="university"', '"amenity"="university"', '"amenity"="school"' , '"amenity"="school"', '"amenity"="kindergarten"', '"amenity"="library"'];</em></p> <p><strong><em>'health'</em></strong><em>= ['"amenity"="clinic"', '"amenity"="dentist"', '"amenity"="school"' , '"amenity"="doctors"', '"amenity"="hospital"', '"amenity"="pharmacy"','"amenity"="veterinary"']; </em></p> <p><strong><em>'cafe'</em></strong><em>= ['"amenity"="cafe"','"amenity"="ice_cream"', '"amenity"="internet_cafe"']; </em></p> <p><strong><em>'supermarket'</em></strong><em> = ['"shop"="supermarket"', '"shop"="mall"', '"shop"= "department_store"', '"shop"= "convenience"'];</em></p> <p><strong><em>'restaurant'</em></strong><em>= ['"amenity"="restaurant"'];</em></p> <p><strong><em>'fastfood'</em></strong><em> = ['"amenity"="fast_food"'];</em></p> <p><strong><em>'sport'</em></strong><em>= ['"sport"']; </em></p> <p><strong><em>'hotel'</em></strong><em> = ['"tourism"="hotel"', '"building"="hotel"', '"tourism"="guest_house"','"tourism"="apartment"','"tourism"="hostel"','"tourism"="motel"','"tourism"="camp_site"']; </em></p> <p><strong><em>'pubs'</em></strong><em> = ['"amenity"="bar"','"amenity"="pub"', '"amenity"="biergarten"'];</em></p> <p><em>'theatre'= ['"amenity"="theatre"', '"amenity"="cinema"', '"amenity"="music_venue"', '"leisure"="stadium"' ]; </em></p> <p><strong><em>'night'</em></strong><em> = ['"amenity"="nightclub"', '"amenity"="casino"','"amenity"="gambling"','"amenity"="stripclub"']; </em></p> <p><strong><em>'socio'</em></strong><em>= ['"amenity"="arts_centre"', '"amenity"="community_centre"', '"amenity"="social_centre"', '"amenity"="music_school"', '"amenity"="language_school"']; </em></p> <p><strong><em>'shop'</em></strong><em> = ['"shop"'];</em></p> <p><strong><em>'tourism'</em></strong><em> = ['"amenity"="exhibition_centre"', '"tourism"="attraction"','"tourism"="viewpoint"','"tourism"="aquarium "','"leisure"="beach_resort "','"tourism"="gallery"','"tourism"="museum"','"tourism"="theme_park"','"tourism"="zoo"','"tourism"="artwork"'];</em></p> <p> </p> <p>The pixel values are the sum of the number of POIs of each type located in the pixel.</p> <p> </p> <p><em>Limitations of the dataset</em></p> <p>- The dataset provides densities of only a selection of points of interests, regardless of its type. The complete list of amenity codes can be found on the OSM Wiki [3].</p> <p>- Ways are only considered through their centre points.</p> <p> </p> <p>[1] “Open Street Map.” <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a> (accessed Sep. 05, 2023).</p> <p>[2] “Overpass API.” <a href="https://wiki.openstreetmap.org/wiki/Overpass_API">https://wiki.openstreetmap.org/wiki/Overpass_API</a> (accessed Sep. 05, 2023).</p> <p>[3] “Open Street Map Wiki.” <a href="https://wiki.openstreetmap.org/wiki/Key:amenity#Sustenance">https://wiki.openstreetmap.org/wiki/Key:amenity#Sustenance</a> (accessed Sep. 05, 2023).</p>
NMRduino: A modular, open-source, low-field magnetic resonance platform
<p>The NMRduino is a compact, cost-effective, sub-MHz NMR spectrometer that utilizes readily available open-source hardware and software components. One of its aims is to simplify the processes of instrument setup and data acquisition control to make experimental NMR spectroscopy accessible to a broader audience. In this introductory paper, the key features and potential applications of NMRduino are described to highlight its versatility both for research and education.</p>
An Integrated Usability Framework for Evaluating Open Government Data Portals and Analysis of EU and GCC OGD Portals
<p><span>This dataset contains data collected during a study (<em><strong>"<a href="https://arxiv.org/ftp/arxiv/papers/2403/2403.08451.pdf">An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries</a>"</strong></em>) conducted by Fillip Molodtsov and Anastasija Nikiforova (University of Tartu).</span></p> <p><span> </span><span>It being made public both to act as supplementary data for the paper and in order for other researchers to use these data in their own work potentially contributing to the improvement of current data ecosystems and develop user-friendly, collaborative, robust, and sustainable open data portals.</span></p> <p><span>***Purpose of the study***</span></p> <p><span>This paper develops an integrated framework for evaluating OGD portal effectiveness that accommodates user diversity (regardless of their data literacy and language), evaluates collaboration and participation, and the ability of users to explore and understand the data provided through them. </span></p> <p><span>The framework is validated by applying it to 33 national portals across European Union (EU) and Gulf Cooperation Council (GCC) countries, as a result of which we rank OGD portals, identify some good practices that lower-performing portals can learn from, and common shortcomings.</span></p> <p><span>***Methodology***</span></p> <p><span>(1) systematic literature review to establish a knowledge base and identify frameworks have been used to evaluate OGD portals, we conducted a systematic literature review - Dataset_ Usability_Framework_SLR;</span></p> <p><span>(2) development of the Integrated Usability Framework for Evaluating Open Government Data Portals, which content is based on the outputs of the first step, along with selected articles of experts in portal design, and an exploratory assessment of the French, Irish, Estonian and Spanish portals - Dataset_Integrated_Usability_Framework;</span></p> <p><span>(3) data collection, that is a completion of the protocol developed in the previous step by analysing 34 national OGD portals of the EU and GCC countries. When all individual protocols were collected, the total score are calculated using the weighting system. The average scores are calculated for the EU and GCC. The portals are ranked. The top portals (best performers) are determined for each dimension - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p><span>(4) identification of relationships and patterns among different portals based on their performance metrics as a result of the cluster analysis. By calculating the average dimensional scores of portals from both types of clusters, their performance across multiple dimensions is evaluated - Dataset_EU_GCC_OGDportal_Usability_results_clustering.</span></p> <p> </p> <p><strong><em><span>For more details see Molodtsov, F., Nikiforova, A. (2024). “An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries”. In Proceedings of the 25th Annual International Conference on Digital Government Research (DGO 2024), June 11--14, 2024, Taipei, Taiwan, 10.1145/3657054.3657159</span></em></strong></p> <p><span>***Format of the file***</span></p> <p><span>.xls, .csv</span></p> <p><span>***Licenses or restrictions***</span></p> <p><span>CC-BY</span></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>
Quality-Assurance Package for the "Automated, Open-Source, Vendor-Independent Quality Assurance Protocol Based on the Pulseq Framework" Manuscript
<h2>Background</h2> <p>Neuroimaging research requires consistent image quality and temporal signal stability, especially for functional magnetic resonance imaging (MRI) studies that rely on detecting subtle blood-oxygen-level-dependent (BOLD) signal changes. Regular MR system performance monitoring is essential, especially for longitudinal and multi-site studies. This study aims to establish a robust quality assurance (QA) protocol to promote data comparability across scanner models, vendors, and sites, as well as over a prolonged period.</p> <p>The manuscript titled "<em>Automated, Open-Source, Vendor-Independent Quality Assurance Protocol Based on the Pulseq Framework</em>" was submitted to the Special Issue <a href="https://link.springer.com/journal/10334/updates/26638300">Reproducibility and Quality Assurance</a> of the Magnetic Resonance Materials in Physics, Biology and Medicine (MAGMA) journal.</p> <p>This QA package proposed by the manuscript hosts materials for</p> <ul> <li>all reconstructed images,</li> <li>instruction for data acquisition,</li> <li>instruction for image reconstruction,</li> <li>instruction for post-processing,</li> <li>example raw data and DICOM images, and</li> <li>images and scripts for T1/T2 fitting.</li> </ul> <p>The detailed information is listed below.</p> <h2>All reconstructed images</h2> <p>This directory contains all reconstructed images from the fBIRN phantom on three Siemens 3T scanners (Trio, Prisma.Fit, and Cima.X) and one GE (UHP) 3T scanner. It contains four sub-folders for each scanner. And each sub-folder contains (some of) the following sub-folders:</p> <ul> <li><code>product_epi_ice</code>: ICE-reconstructed product EPI images.</li> <li><code>product_epi_gt</code>: Gadgetron-reconstructed product EPI images.</li> <li><code>pulseq_epi_ice</code>: ICE-reconstructed Pulseq EPI images.</li> <li><code>pulseq_epi_gt</code>: Gadgetron-reconstructed Pulseq EPI images.</li> <li><code>product_se_ice</code>: ICE-reconstructed product spin-echo (SE) images.</li> <li><code>product_se_gt</code>: Gadgetron-reconstructed product SE images.</li> <li><code>pulseq_se_ice</code>: ICE-reconstructed Pulseq SE images.</li> <li><code>pulseq_se_gt</code>: Gadgetron-reconstructed Pulseq SE images.</li> </ul> <h2>Instruction for data acquisition</h2> <p>This directory includes the following documents:</p> <ul> <li><code>write_QA_Tran_EPIrs.m</code> to generate the <code>QA_epi.seq</code> file for EPI scans.</li> <li><code>write_QA_Tran_T1.m</code>: to generate the <code>QA_T1.seq</code> file for SE scans.</li> <li><code>20241122_QA_protocol_instruction_siemens.docx</code>: standard operating procedure for QA measurements.</li> <li><code>QA_record.xlsx</code>: Excel sheet for the record of QA measurements.</li> </ul> <h2>Instruction for image reconstruction</h2> <h3><em>Documents</em></h3> <ul> <li><code>pulseq2mrd_epi.m</code>: convert GE Pulseq EPI raw data (<code>.mat</code>) to MRD raw data (<code>.h5</code>) using the LABEL information in the <code>QA_epi.seq</code> file.</li> <li><code>pulseq2mrd_se.m</code>: convert GE Pulseq SE raw data (<code>.mat</code>) to MRD raw data (<code>.h5</code>) using the LABEL information in the <code>QA_T1.seq</code> file.</li> <li><code>siemens2mrd_epi.m</code>: convert Siemens Pulseq EPI raw data (<code>.dat</code>) to MRD raw data (<code>.h5</code>) using the information in the <code>.dat</code> raw data.</li> </ul> <ul> <li><code>default.xml</code>: Gadgetron configuration file for SE image reconstruction. This document is already in the Gadgetron container: <code>/opt/conda/envs/gadgetron/share/gadgetron/config/default.xml</code>.</li> <li><code>qc_epi.xml</code>: Gadgetron configuration file for EPI image reconstruction, which is modified from the <code>default epi.xml</code> located in the Gadgetron container: <code>/opt/conda/envs/gadgetron/share/gadgetron/config/</code>.</li> </ul> <ul> <li><code>specialCard_ICE.png</code>: Special card setting for ICE online reconstruction.</li> </ul> <h3><em>Procedures for Gadgetron offline reconstruction</em></h3> <p><strong>Step 1: Gadgetron installation (for more details, visit <a href="https://gadgetron.github.io/tutorial/">here</a>)</strong></p> <ul> <li>Download and install <a href="https://www.docker.com/">Docker</a> software. You may need to install/update the Windows Sub Linux (WSL) system for the Docker installation.</li> <li>Open your terminal (Power shell with administrative privilege in Windows) and navigate to the folder you would like to map to the Gadgetron Docker container.</li> <li>Run: <code>docker run -t --name gt_latest --detach --volume ${pwd}:/opt/data ghcr.io/gadgetron/gadgetron/gadgetron_ubuntu_rt_nocuda:latest</code>. If docker is not recognized, set <code>docker</code> to connect to <code>C:\Program Files\Docker\Docker\resources\bin</code> in the Environment Path in Windows. This will download and then launch the <a href="https://gadgetron.readthedocs.io/en/latest/building.html">latest Gadgetron version</a> in a Docker container. It will also mount your current folder as a data folder inside the container.</li> <li>Run this command: <code>docker exec -ti gt_latest /bin/bash</code>. This will execute your Gadgetron container.</li> </ul> <p><strong>Step 2: Data preparation</strong></p> <ul> <li>Place your SE/EPI <code>.dat</code>/<code>.h5</code> data in the mounted folder.</li> <li>Run the command in Terminal: <code>cd /opt/data</code> to enter the mounted folder.</li> </ul> <p><strong>Step 3: MRD conversion</strong></p> <ul> <li>For Siemens data, you can convert the <code>.dat</code> data to MRD data by using Gadgetron. If Gsdgetron doesn't work (e.g. for XA EPI data), you can then use the Matlab script <code>siemens2mrd_epi.m</code>.</li> <li>The command for Siemens SE data conversion: <code>siemens_to_ismrmrd -f meas_MID*.dat -z 2 -o se_data.h5</code>.</li> <li>The command for Siemens EPI data conversion: <code>siemens_to_ismrmrd -f meas_MID*.dat -z 2 -m IsmrmrdParameterMap_Siemens.xml -x IsmrmrdParameterMap_Siemens_EPI.xsl -o epi_data.h5</code>.</li> <li>For GE data, you can convert the <code>.mat</code> raw data to MRD data by using the Matlab scripts with the corresponding <code>.seq</code> files. For SE conversion: use <code>pulseq2mrd_se.m</code> with <code>QA_T1.seq</code>. For EPI conversion: use <code>pulseq2mrd_epi.m</code> with <code>QA_epi.seq</code>.</li> </ul> <p><strong>Step 4: Gadgetron reconstruction</strong></p> <ul> <li>SE reconstruction: <code>gadgetron_ismrmrd_client -f se_data.h5 -c default.xml -o se_out.h5</code>.</li> <li>EPI reconstruction: first, put <code>qc_epi.xml</code> to the mounted folder and then copy it to the Gadgetron container: <code>cp /opt/data/qc_epi.xml /opt/conda/envs/gadgetron/share/gadgetron/config/</code>. Then, run the reconstruction: <code>gadgetron_ismrmrd_client -f epi_data.h5 -c qc_epi.xml -o epi_out.h5</code>.</li> </ul> <p><strong>Step 5: Load Gadgetron-reconstructed images (<code>.h5</code>)</strong></p> <ul> <li>Load SE <code>.h5</code> images in Matlab:</li> </ul> <blockquote> <p>filename = 'pulseq_se_out.h5' ;</p> <p>info = hdf5info(filename) ;</p> <p>address_data_1 = info.GroupHierarchy.Groups(1).Groups.Datasets(2).Name ;</p> <p>pulseq_se_im = squeeze(double( hdf5read(filename, address_data_1) ) ) ;</p> <p>pulseq_se_im = reshape(pulseq_se_im, [256, 256, 11, 2]) ;</p> </blockquote> <ul> <li>Load EPI <code>.h5</code> images in Matlab:</li> </ul> <blockquote> <p>filename = 'pulseq_epi_out.h5';</p> <p>info = hdf5info(filename) ;</p> <p>address_data_1 = info.GroupHierarchy.Groups(1).Groups.Datasets(2).Name ;</p> <p>pulseq_epi_im = squeeze(double( hdf5read(filename, address_data_1) ) ) ;</p> <p>pulseq_epi_im = reshape(pulseq_epi_im, [64, 64, 27, 200]) ;</p> </blockquote> <h3><em>Procedures for ICE online reconstruction</em></h3> <p>Before executing the Pulseq-based sequences, you can enable ICE online Reconstruction following the procedures below:</p> <ul> <li>Navigate to the Special Card (<code>specialCard_ICE.png</code>), set <code>Data handling</code> to <code>ICE STD</code> for NUMARIS/X (e.g. XA60A and XA61A), and <code>ICE 2D</code> for NUMARIS/4 (e.g. VB, VD, and VE).</li> <li>Select <code>Sum-of-Square</code> for coil combination.</li> <li>Be sure that the maximal pixel intensity does not violate the intensity threshold of <strong>4096</strong>.</li> </ul> <h2>Instruction for post-processing</h2> <p>The example post-processing is based on the reconstructed images from Cima.X over five days.</p> <h3><em>Reconstructed images from Cima.X</em></h3> <p><strong>Note</strong>: All <code>se</code> folders contain a <code>structuralQuality_main.m</code> to call the <code>structuralQuality.m</code> function for structural quality analysis. All <code>epi</code> folders contain a <code>temporalQuality_main.m</code> to call the <code>temporalQuality.m</code> function for temporal quality analysis.</p> <ul> <li><code>product_epi_ice</code>: ICE-reconstructed product EPI images.</li> <li><code>product_epi_gt</code>: Gadgetron-reconstructed product EPI images.</li> <li><code>pulseq_epi_ice</code>: ICE-reconstructed Pulseq EPI images.</li> <li><code>pulseq_epi_gt</code>: Gadgetron-reconstructed Pulseq EPI images.</li> <li><code>product_se_ice</code>: ICE-reconstructed product SE images.</li> <li><code>product_se_gt</code>: Gadgetron-reconstructed product SE images.</li> <li><code>pulseq_se_ice</code>: ICE-reconstructed Pulseq SE images.</li> <li><code>pulseq_se_gt</code>: Gadgetron-reconstructed Pulseq SE images.</li> </ul> <h3><em>QA analysis Matlab package: </em><code><em>QA_functions</em></code></h3> <ul> <li><code>circfit.m</code>: to find the center point and radius of the phantom.</li> <li><code>makeCircleMask.m</code>: to make a circular mask based on the center point and radius.</li> <li><code>structuralQuality.m</code>: to analyze the structural quality of the SE images.</li> <li><code>temporalQuality.m</code>: to analyze the temporal quality of the EPI images.</li> </ul> <h3><em>Post-processing procedures</em></h3> <ul> <li>Step 1: Add the <code>QA_functions</code> folder to your Matlab Path.</li> <li>Step 2: Run the <code>temporalQuality_main.m</code> or <code>structuralQuality_main.m</code> script in each folder to produce the QA results of all reconstructed images inside the folder.</li> <li>Step 3: Run the <code>make_figure_epi.m</code> and <code>make_figure_se.m</code> to produce some of the tables and figures used in the manuscript.</li> </ul> <h2>Example raw data and DICOM images</h2> <p>The data and DICOM images were acquired from Cima.X on the fBIRN phantom on 06.08.2024.</p> <ul> <li>DICOM folder: contains the DICOM images for four EPI scans (the first two scans for warm-up) and two SE scans.</li> <li><code>meas*.dat</code>: Siemens raw data of two EPI scans for temporal quality analysis and two SE scans for structural quality analysis.</li> <li><code>*data.h5</code> files: the ISMRMRD data of the four raw datasets.</li> <li><code>*out.h5</code> files: the images reconstructed by Gadgetron.</li> <li><code>*.nii</code>: the NIFTI-format reconstructed images.</li> <li><code>siemens2mrd_epi.m</code>: to convert the Siemens EPI raw data to ISMRMRD data.</li> <li><code>read_image.m</code>: to convert the Gadgetron-reconstructed h5-format images to NIFTI-format images.</li> </ul> <h2>Images and scripts for T1/T2 fitting</h2> <p>This package includes DICOM images and T1/T2 fitting scripts for the fBIRN phantom. Images for T1 fitting were acquired using a product turbo spin echo sequence with an inversion recovery pulse (repetition time = 4000 ms, echo train length = 4). Images for T2 fitting were obtained using a product SE sequence (repetition time = 3500 ms). Both measurements were conducted on the Siemens Prisma.Fit 3T scanner on 05.06.2024.</p> <ul> <li><code>T1 sub-folder</code>: contains all DICOM images for T1 fitting with inversion recovery times of {50, 150, 300, 450, 600, 750, 900, 1050, 1200, 1350, 1500, 2200, 3000} ms.</li> <li><code>T2 sub-folder</code>: contains all DICOM images for T2 fitting with echo times of {7.5, 15, 30, 45, 60, 75, 90, 130, 200, 250} ms.</li> <li><code>Do_T1fit.m</code>: Matlab script for T1 fitting.</li> <li><code>Do_T2fit.m</code>: Matlab script for T2 fitting.</li> </ul> <p>For more information regarding Pulseq and the workflow for data acquisition and image reconstruction, please visit our GitHub repositories: <a href="https://github.com/pulseq/pulseq">Pulseq Matlab software</a>, <a href="https://github.com/pulseq/tutorials">Pulseq Tutorials</a>, and <a href="https://github.com/pulseq/Pulseq-Rocks-2023-24-ISMRM-Reproducibility-Challenge">Pulseq Rocks for the 2024 ISMRM Reproducibility Team Challenge</a>.</p> <p>If you need any further information or have any questions, please feel free to contact our Pulseq email address: pulseq.mr@uniklinik-freiburg.de.</p>
Data of Survey on National Contributions to EOSC and Open Science 2023
<p>This is the data set of the annual survey on National Contributions to EOSC and Open Science 2023 for the EOSC Steering Board</p> <p>The annual survey on National Contributions to EOSC and Open Science was developed by the EOSC Future project and EOSC Steering Board to monitor policies, practices, and impacts related to EOSC and Open Science at national and institutional levels in Europe</p> <p>The annual survey for 2023 was published in the EOSC Open Science Observatory on 17 January 2024 and ran until 01 July 2024 whereby 32 European member states, associated countries, and other countries responded to the survey</p> <p>The data of the annual survey for 2023 is available and exploitable in the online dashboard of the EOSC Open Science Observatory developed by Technopolis Group and OpenAIRE in the EOSC Future project and continued in the EOSC Track project: [<a href="https://eoscobservatory.eosc-portal.eu">https://eoscobservatory.eosc-portal.eu</a>]</p> <p>Disclaimer 1: The annual survey on National Contributions to EOSC and Open Science is in an initial stage of implementation and will be improved in future iterations whereby the data should for now be taken as a best-effort attempt by participating countries</p> <p>Disclaimer 2: V1 of the data set included an error in the data set and has thus been restricted and replaced by an updated V2 of the data set</p>
Open Soil Atlas Motivation Survey
<p>The Open Soil Atlas motivation study was conducted within the ongoing H2020 project named <a href="https://actionproject.eu/">ACTION</a> (pArticipatory sCience Toolkit agaInst pollutiON) on citizen science. Volunteers participate to citizen science initiatives for multiple reasons: personal enjoyment, desire for improvement or achievement, establishment of personal relationships, care for the environment, etc.<br> Studying motivation and investigating the factors influencing people participation to citizen science projects is an essential aspect in the analysis of citizen science communities. Understanding the reasons that foster people to engage can support the successful design and implementation of effective participant involvement tasks, as well as pave the way for long-term engagement.<br> The goal of the study is to analyse the motivation to participate of a specific citizen science community focused on fighting soil pollution in the Open Soil Atlas pilot supported by the ACTION project. More info on the pilot available at <a href="https://actionproject.eu/citizen-science-pilots/open-soil-atlas/">https://actionproject.eu/citizen-science-pilots/open-soil-atlas/</a>.</p> <p>The Open Soil Atlas motivation study is part of the study about motivation in citizen science projects conducted within the ACTION project (<a href="https://doi.org/10.5281/zenodo.5753092">https://doi.org/10.5281/zenodo.5753092</a>). The survey was designed and administered using the <a href="https://coney.cefriel.com/">Coney</a> toolkit.</p> <p>The research object adopts the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification. Files made available within the research object are:</p> <ul> <li><em>*-procedure.ttl</em> contains the RDF representation of the structure of the conversational survey (questions, answers, etc.) using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li><em>*-results.ttl </em>contains the RDF representation of the answers collected using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li><em>*-survey.tll </em>contains a comprehensive RDF representation of the survey data using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li><em>*-results.csv </em>contains the CSV of the collected answers</li> <li>*-<em>script.R</em> is the R script developed to analyse the collected answers</li> <li>*-<em>mean-var-motivating-questions.csv </em>contains the computed mean and average for each question considered (observable variables)</li> <li>*-<em>mean-var-motivating-factor.csv </em>contains the computed mean and average for each motivation factor considered (latent variables)</li> <li>*-<em>correlation-factors-global-motivation.csv </em>contains the correlation analysis between each motivation factor and the global motivation </li> </ul>
Dataset supporting the paper "Inducing open-shell character in porphyrins through surface-assisted phenalenyl π-extension. J. Am. Chem. Soc 142, 18109 (2020)"
<p>Dataset corresponding to theoretical calculations in the paper "<em>Inducing open-shell character in porphyrins through surface-assisted phenalenyl π-extension. J. Am. Chem. Soc 142, 18109 (2020)</em>" DOI: <a href="https://doi.org/10.1021/jacs.0c07781">10.1021/jacs.0c07781</a>.</p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain:</p> <ul> <li>.siesta files: STM images in WsXM format (<a href="http://www.wsxm.eu/">http://www.wsxm.eu/</a>) simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>).</li> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>).</li> <li>.agr: grace files (<a href="https://plasma-gate.weizmann.ac.il/Grace/">https://plasma-gate.weizmann.ac.il/Grace/</a>).</li> </ul>
Soil bulk density [10x kg/m3] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: db_od = bulk density over dry [kg/m3 ⨉ 10];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>The bulk density maps are also provided in 10 kg / m-cubic to reduce total data size; to convert values to kg / m-cubic multiply by 10 e.g. 120 = 1200 kg / m-cubic = 1.2 t / m-cubic.</p>
Soil pH in H2O [-] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: soil pH in H2O;</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Soil organic carbon content [g/kg] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: log organic carbon [g/kg] to back-transform use exp(x/10)-1;</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>To back-transform the log.oc maps use formula: exp(x/10)-1. These are examples of back-transformed values:</p> <p> log.oc = 15 → 0.3% SOC;<br> log.oc = 20 → 0.6% SOC;<br> log.oc = 25 → 1.1% SOC;<br> log.oc = 30 → 1.9% SOC;<br> log.oc = 35 → 3.2% SOC;<br> log.oc = 40 → 5.3% SOC;<br> log.oc = 50 → 14.8% SOC;</p>
Soil clay content [%] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: clay.tot = clay content [percent];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Soil sand content [%] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: sand.tot = sand content [percent];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Number of Attendees of Hungarian Open Science Forum Events, and Number of Visitors of KIFÜ Open Science Newsfeed
<p>Number of attendees of Hungarian Open Science Forum events, which took place on 28/5/2021, 24/9/2021, 19/1/2022, and 28/4/2022.</p> <p>Number of visitors of KIFÜ open science newsfeed till 30/6/2022. Altogether 133 posts that were published between 5 May 2021 and 7 April 2022.</p>
5D-NP-FABTECH_ALD - Open Dataset for: "Shedding light on the initial growth of ZnO during plasma-enhanced atomic layer deposition on vapor-deposited polymer thin films"
<p>This is the open dataset for the paper: "Demelius, L. <em>et al.</em> Shedding light on the initial growth of ZnO during plasma-enhanced atomic layer deposition on vapor-deposited polymer thin films. <em>Applied Surface Science</em> <strong>604</strong>, (2022)."</p> <p>This includes the supplementary information and all the source material that was used for the paper preparation.</p> <p>For each folder (sub-dataset), there exists a corresponding readme file describing the content and including material.</p>
Annual Article Processing Charges (APCs) and number of gold and hybrid open access articles in Web of Science indexed journals published by Elsevier, Sage, Springer-Nature, Taylor & Francis and Wiley 2015-2018
<p><strong>Dataset of annual Article Processing Charges (APCs) for 6,252 journals from 2015 to 2018. </strong>The dataset contains annual APCs for journals indexed in the Web of Science (WoS) and published by the oligopoly of academic publishers (Elsevier, Sage, Springer-Nature, Taylor & Francis, Wiley). It also includes an estimate of the total APCs paid by the academic community based on the number of gold and hybrid articles published between 2015 and 2018. The dataset was created using publication data from WoS, OA status from Unpaywall and annual APC prices from open datasets (<a href="https://doi.org/10.5281/ZENODO.3841568">Matthias, 2020</a>; <a href="https://doi.org/10.5683/SP2/84PNSG">Morrison, 2021</a>) and historical fees retrieved via the Internet Archive Wayback Machine. </p> <p>Detailed methods and findings are reported in the following journal article</p> <p>Butler, L.-A., Matthias, L., Simard, M.-A., Mongeon, P., & Haustein, S. (2023). The Oligopoly's Shift to Open Access. How the Big Five Academic Publishers Profit from Article Processing Charges. <em>Quantitative Science Studies</em>. Preprint: <a href="https://doi.org/10.5281/zenodo.8322555">https://doi.org/10.5281/zenodo.8322555</a></p> <p><strong>Description of included files (v1):</strong></p> <p><em>APCs.csv: </em>contains the annual APCs for gold and hybrid OA journals indexed in Web of Science published by the oligopoly of academic publishers (Elsevier, Sage, Springer-Nature, Taylor & Francis, Wiley) between 2015 and 2018 including the total estimate of APCs paid per journal per year. It contains APC data for 18,846 journal-year-OA status combinations.</p> <p><em>countries.csv</em>: contains the fractionalized number of annual gold and hybrid OA articles by oligopoly publishers between 2015 and 2018 and the total estimate of fractionalized APCs paid per country per journal per year.</p> <p><em>oecd.csv</em>: contains the fractionalized number of annual gold and hybrid OA articles by oligopoly publishers between 2015 and 2018 and the total estimate of fractionalized APCs per discipline per journal per year.</p> <p><em>ReadMe.csv</em>: contains a description of the variables used in <em>APCs.csv</em>, <em>countries.csv</em> and <em>oecd.csv</em>.</p> <p> </p>
COCO, LVIS, Open Images V4 classes mapping
<p>This repository contains a mapping between the classes of COCO, LVIS, and Open Images V4 datasets into a unique set of 1460 classes.</p> <p>COCO [Lin et al 2014] contains 80 classes, LVIS [gupta2019lvis] contains 1460 classes, Open Images V4 [Kuznetsova et al. 2020] contains 601 classes.</p> <p>We built a mapping of these classes using a semi-automatic procedure in order to have a unique final list of 1460 classes. We also generated a hierarchy for each class, using <a href="https://wordnet.princeton.edu/">wordnet</a></p> <p>This repository contains the following files:</p> <ul> <li><em>coco_classes_map.txt</em>, contains the mapping for the 80 coco classes</li> <li><em>lvis_classes_map.txt</em>, contains the mapping for the 1460 coco classes</li> <li><em>openimages_classes_map.txt</em>, contains the mapping for the 601 coco classes</li> <li><em>classname_hyperset_definition.csv</em>, contains the final set of 1460 classes, their definition and hierarchy</li> <li><em>all-classnames.xlsx</em>, contains a side-by-side view of all classes considered</li> </ul> <p>This mapping was used in VISIONE [Amato et al. 2021, Amato et al. 2022] that is a content-based retrieval system that supports various search functionalities (text search, object/color-based search, semantic and visual similarity search, temporal search). For the object detection VISIONE uses three pre-trained models: VfNet [Zhang et al. 2021] (trained on COCO dataset), Mask R-CNN [He et al. 2017] (trained on LVIS), and a <a href="http://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1">Faster R-CNN+Inception ResNet</a> (trained on the Open Images V4).</p> <p>This is repository is released under a Creative Commons Attribution license, please cite the following paper if you use it in your work in any form:</p> <blockquote> <pre>@inproceedings{amato2021visione, title={The visione video search system: exploiting off-the-shelf text search engines for large-scale video retrieval}, author={Amato, Giuseppe and Bolettieri, Paolo and Carrara, Fabio and Debole, Franca and Falchi, Fabrizio and Gennaro, Claudio and Vadicamo, Lucia and Vairo, Claudio}, journal={Journal of Imaging}, volume={7}, number={5}, pages={76}, year={2021}, publisher={Multidisciplinary Digital Publishing Institute} } </pre> </blockquote> <p> </p> <p> </p> <p><em><strong>References:</strong></em></p> <p>[Amato et al. 2022] Amato, G. et al. (2022). VISIONE at Video Browser Showdown 2022. In: , et al. MultiMedia Modeling. <em>MMM 2022. Lecture Notes in Computer Science</em>, vol 13142. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-98355-0_52">https://doi.org/10.1007/978-3-030-98355-0_52</a></p> <p>[Amato et al. 2021] Amato, G., Bolettieri, P., Carrara, F., Debole, F., Falchi, F., Gennaro, C., Vadicamo, L. and Vairo, C., 2021. The visione video search system: exploiting off-the-shelf text search engines for large-scale video retrieval. <em>Journal of Imaging</em>, <em>7</em>(5), p.76.</p> <p>[Gupta et al.2019] Gupta, A., Dollar, P. and Girshick, R., 2019. Lvis: A dataset for large vocabulary instance segmentation. In <em>Proceedings of the IEEE/CVF conference on computer vision and pattern recognition</em> (pp. 5356-5364).</p> <p>[He et al. 2017] He, K., Gkioxari, G., Dollár, P. and Girshick, R., 2017. Mask r-cnn. In <em>Proceedings of the IEEE international conference on computer vision</em> (pp. 2961-2969).</p> <p>[Kuznetsova et al. 2020] Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A. and Duerig, T., 2020. The open images dataset v4. <em>International Journal of Computer Vision</em>, <em>128</em>(7), pp.1956-1981.</p> <p>[Lin et al. 2014] Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. and Zitnick, C.L., 2014, September. Microsoft coco: Common objects in context. In <em>European conference on computer vision</em> (pp. 740-755). Springer, Cham.</p> <p>[Zhang et al. 2021] Zhang, H., Wang, Y., Dayoub, F. and Sunderhauf, N., 2021. Varifocalnet: An iou-aware dense object detector. In <em>Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition</em> (pp. 8514-8523).</p> <p> </p>
QST - open data of the article Vanwindekens & Hardy (2022)
<p>This is the opendata repository linked to the paper "The QuantiSlakeTest, dynamic weighting of soil under water to measure soil structural stability" submitted to the SOIL journal by Vanwindekens & Hardy (2022).</p>
Dataset and framework for the Open Potential Ecological Network of Tuscany
<p>This repository contains the main output of the related research, specifically: two Open Potential Ecological Networks (OPEN) of Tuscany in the Cytoscape format: "tuscany_potential_directed.cys" and "tuscany_potential_undirected.cys".</p> <p>Please see the "annotations.txt" file to more detailed explanation regarding the content in this repository.</p>
The potential of low-cost UAVs and open-source photogrammetry software for high-resolution monitoring of alpine glaciers: A case study from the Kanderfirn (Swiss Alps)
<p>This dataset contains high-resolution orthophotos (5 x 5 cm) and digital surface models (25 x 25 cm) of the Kandernfirn Glacier located in the Swiss Alps. Aerial images were aquired with a self-developed fixed-wing Unmanned Aerial Vehicle during ten surveys on five different days in 2017 and 2018. The open-source photogrammetry software OpenDroneMap (version 0.4.1) was used for image processing.</p> <p>The orthophotos and digital surface models were validated through dGNSS point measurements of ground control points. Please refer to the corresponding paper for information on the horizontal and vertical accuracy of the files.</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.