Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6,410
datasets available to search
ShareScore release 0.9.0
Dataset results
6,410 results for “results”
Input data for ismip6-gris-results-processing
<p>This archive provides the input data used for scalar processing of ISMIP6 Greenland ice sheet output data. </p><p>Processing scripts are available on github (https://github.com/ismip/ismip6-gris-results-processing) and have been additionally archived on zenodo (https://zenodo.org/records/3939115).</p><p>Results are related to publication "The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6" , Goelzer et al., 2020</p><p> </p>
Data and results of the example used in the SI-Hg D1 protocol for the SI-traceable calibration of elemental mercury (Hg0) gas generators used in the field
<p>During the SI-Hg project a metrological traceable protocol for the calibration of mercury gas generators used in the field was developed and validated. The SI-Hg calibration protocol specifies the procedures for establishing traceability to the SI units for the quantitative output of elemental mercury generators that are employed in regulatory applications for emission monitoring or testing. This protocol provides methods for</p><ul><li>the experimental procedures to compare the output of elemental mercury gas generators</li><li>the data processing for determination of mercury concentration and the expanded uncertainty of the mercury concentration obtained from the elemental mercury gas generator.</li></ul><p>In the protocol examples are given to explain the data processing, determining the mercury concentration and corresponding uncertainty. In this repository the raw data and results used for the example calculated with the data processing script can be found.</p>
Dataset and supplementary files - Behavioral response of chub (Squalius cephalus), barbel (Barbus barbus) and brown trout (Salmo trutta) to pulsed direct current electric fields and resulting optimal waveform for use at electrified bar racks
<p><strong>Behavior Library.zip: </strong>For each species and behavior observed during the experiments an exemplary video is provided. </p><p><strong>Behavior_all.pdf: </strong>Additional plots showing the thresholds for the first time each individual behavior was observed for all fish species and tested waveforms</p><p><strong>Species.pdf: </strong>Additional plot allowing direct comparison of observed thresholds for the tested species when subjected to different waveforms. </p><p><strong>data.csv:</strong> All data necessary to reevaluate the conducted experiments. The dataset consists of</p><ul><li>Experiment ID</li><li>waveform - indicating the set of electrical parameters used</li><li>fish species and fish id </li><li>behavior - observed behavior</li><li>time from and time to - time in s after the start of the experiment that a behavior was started and ended respectively</li><li>type - point or interval referring to whether a behavior is considered instantaneous or continuous</li><li>voltage - applied voltage at the start of the given behavior</li><li>experiment_timestamp - date and time of the start of the experiment</li><li>breathing rate start - breathing rate at the start of the experiment</li><li>water conductivity - water conductivity at a reference temperature of 25°C [muS/cm]</li><li>water temperature [°C]</li><li>breathing rate end - breathing rate at the end of the experiment</li><li>meta behavior - assigned category of meta behavior based on the observe behavior category</li><li>standard length, total length and height - standard length, total length and height of the tested fish in [mm]</li><li>volume - calculated fish volume based on the measured length and height and an assumed elliptical form of the fish</li><li>Fangdatum - Date of catch</li><li>t.Pulse - pulse length of the tested waveform [ms]</li><li>Frequency - Frequency of the tested waveform</li><li>N.Pulses.Group - Number of pulses per group of pulses for the waveform pattern</li><li>t.Gap - time between two pulses within a group of pulses [ms]</li><li>DutyCycle - Percentage of time current is flowing for a given waveform. Calculated based on the waveform parameters</li><li>usage - first, second or third time a fish was used in the experiments. </li><li>field strength - field strength at the time of this behavior calculated based on the applied voltage</li><li>c_w ambient water conductivity [muS/cm]</li><li>p_d - power density calculated based on the field strength and the ambient water conductivity</li><li>p_t - power transferred to the fish calculated based on the field strength, the ambient water conductivity and an assumed conductivity of the fish of 115 muS/cm</li></ul><p> </p><p> </p>
Data inputs and results from AI-supported title and abstract screening "Lack of evidence regarding markers identifying acute heart failure in patients with COPD: an AI-supported systematic review"
<p>These comma-separated data files were used to conduct the AI supported screening of [Lack of Evidence Regarding Markers Identifying Acute Heart Failure in Patients with COPD: An AI-supported Systematic Review (working title)], following the methodology described in the publication (URL/doi to be uploaded).</p> <p>These files provide insight into the AI-supported screening process and the choices made by the human reviewer.</p>
Results from the OnStove Nepal model "Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis"
<p>This repository includes all result datasets and figures from the <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">OnStove Nepal</a> model presented in the paper "<strong>Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis</strong>" DOI: <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>.</p> <p>The code and automated workflow to run the model can be found in the Github repository <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal</a>. All model input data can be downloaded from the permanent repository at<em> </em><a href="https://doi.org/10.5281/zenodo.10641858">10.5281/zenodo.10641858</a>.</p> <h2>Folder structure</h2> <p>The folder structure consists of a <strong>Procedded GIS Data </strong>folder containing all GIS processed data. These are the outputs from the <strong>DataProcessor.ipynb </strong>script and the raw GIS input data files found in the input data repository.</p> <p>A folder for <strong>each scenario</strong> results. Within each scenario folder, there are:</p> <ul> <li>A <strong>model.pkl </strong>and a <strong>results.pkl </strong>files. These are a calibrated OnStove model with the scenario inputs and a complete results model file of the scenario respectively. Both of these files can be read and explored using the OnStove tool. </li> <li>A <strong>summary.csv </strong>file with the summary results of the scenario for each technology.</li> <li>A <strong>Subsidies_scenario_name.csv </strong>file showing the required total subsidies per technology of the scenario.</li> <li>Image files in pdf format for: <ul> <li>The baseline technologies used in the country (<strong>current_shares.pdf</strong>),</li> <li>The spatial mix of technologies providing the maximum net-benefits throughout the country (<strong>max_benefit_tech.pdf</strong>), </li> <li>The total costs and benefits of the transition per technology (<strong>costs_benefits.pdf</strong>),</li> <li>The bar plot of max benefit technology shares (<strong>tech_split.pdf</strong>),</li> <li>The max benefit technologies distribution over relative wealth in the country (<strong>tech_histogram.pdf</strong>),</li> </ul> </li> <li>A <strong>Rasters </strong>folder with raster files of different result maps in .tif format.</li> </ul> <p>Inside the <strong>MCA </strong>folder, all results from the prioritization analysis are found, including:</p> <ul> <li>The prioritized spatial technology mix to achieve the goals of the country (<strong>Prioritized_hh.pdf</strong>),</li> <li>The biogas cookstoves relative wealth distribution index (<strong>Biogas_index.pdf</strong>),</li> <li>The biomass ICS T3 cookstoves relative wealth distribution index (<strong>Biomass_ICS_T3_index.pdf</strong>),</li> <li>The electrical cookstoves relative wealth distribution index (<strong>Electricity_index.pdf</strong>),</li> <li>The biogas cookstoves priority map (<strong>Biogas_priority_areas.pdf</strong>),</li> <li>The biomass ICS T3 cookstoves priority map (<strong>Biomass_ICS_T3_priority_areas.pdf</strong>),</li> <li>The electrical cookstoves priority map (<strong>Electricity_priority_areas.pdf</strong>),</li> <li>The total costs and benefits of the transition per technology (<strong>costs_benefits.pdf</strong>),</li> <li>The prioritized technology shares distribution over relative wealth in the country (<strong>tech_histogram_prioritized.pdf</strong>),</li> <li>A <strong>Subsidies_prioritized.csv </strong>file showing the required total subsidies per technology,</li> <li>A <strong>mca.pkl </strong>file with the MCA model that can be manipulated using the OnStove tool,</li> <li>A <strong>access_results.txt </strong>file with the current and after prioritization clean cooking access shares in the country.</li> </ul> <p>A <strong>main_plot.pdf </strong>and a <strong>prioritized_plot.pdf </strong>files showing the compiled results for all scenarios and prioritized scenario respectively.</p> <h2>License</h2> <p>All datasets are released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a> (CC BY 4.0).</p>
HEATDesalination - Case-study lowest-cost optimisation results
<p>Optimisation results for the lowest lifetime cost system consisting of solar photovoltaic (PV), hybrid photovoltaic-thermal (PV-T) and solar-thermal collectors alongside battery and hot-water storage systems for meeting the electrical and thermal (hot-water) needs of three multi-effect distillation (MED) plants.</p> <p>The updated results are from optimisations runs carried out in response to peer-review comments.</p>
Full Results from Simulations for Static and Dynamic Workloads Using BSC Slurm Simulator
<p>This dataset contains two CSV files. One is for the static runs, done by emulating LUMI's supercomputer environment, and dynamic, using CEA-Curie workload taken from Feitelson's repository.</p>
Dataset: Results of the CRAFT-OA requirement survey for OJS installation and update toolkit
<p>This dataset is the result of a CRAFT-OA survey that collected requirements for an installation and update toolkit which aims at facilitating a state-of-the-art implementation and operation of the journal software OJS. </p>
Tables of results for lower mantle grain size and viscosity estimates
<p>Data on diffusivity, grain size, and viscosity calculations are shown in Figs. 7-10 and Figs. S4-S5 in Okamoto and Hiraga's "A Common Diffusional Mechanism for Creep and Grain Growth in Polycrystalline Rocks: Application to Lower Mantle Viscosity Estimates".</p>
Results of KROWN: Knowledge Graph Construction Benchmark
<p>In this Zenodo repository we present the results of using KROWN to benchmark popular RDF Graph Materialization systems such as RMLMapper, RMLStreamer, Morph-KGC, SDM-RDFizer, and Ontop (in materialization mode). </p> <h1>What is KROWN 👑?</h1> <p>KROWN 👑 is a benchmark for materialization systems to construct Knowledge Graphs from (semi-)heterogeneous data sources using declarative mappings such as<a href="http://w3id.org/rml/portal"> RML</a>.</p> <p>Many benchmarks already exist for virtualization systems e.g.<a href="https://github.com/oeg-upm/gtfs-bench"> GTFS-Madrid-Bench</a>,<a href="https://ontop-vkg.org/npd-benchmark/"> NPD</a>,<a href="http://wbsg.informatik.uni-mannheim.de/bizer/berlinsparqlbenchmark/"> BSBM</a> which focus on complex queries with a single declarative mapping. However, materialization systems are unaffected by complex queries since their input is the dataset and the mappings to generate a Knowledge Graph. Some specialized datasets exist to benchmark specific limitations of materialization systems such as duplicated or empty values in datasets e.g.<a href="https://doi.org/10.57702/4c9ivpgs"> GENOMICS</a>, but they do not cover all aspects of materialization systems. Therefore, it is hard to compare materialization systems among each other in general which is where KROWN 👑 comes in! </p> <h1>Results</h1> <p>The raw results are available as ZIP archives, the analysis of the results are available in the spreadsheet <em>results.ods</em>.</p> <h2>Evaluation setup</h2> <p>We generated several scenarios using <a href="https://github.com/kg-construct/KROWN/tree/main/data-generator">KROWN’s data generator</a> and executed them 5 times with <a href="https://github.com/kg-construct/KROWN/tree/main/execution-framework">KROWN’s execution framework</a>. All experiments were performed on Ubuntu 22.04 LTS machines (Linux 5.15.0, x86_64) with each Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz, 48 GB RAM memory, and 2 GB swap memory. The output of each materialization system was set to N-Triples.</p> <h2>Materialization systems</h2> <p>We selected the most popular maintained materialization systems for constructing RDF graphs for performing our experiments with KROWN:</p> <ul> <li> <p>RMLMapper</p> </li> <li> <p>RMLStreamer</p> </li> <li> <p>Morph-KGC</p> </li> <li> <p>SDM-RDFizer</p> </li> <li> <p>OntopM (Ontop in materialization mode)</p> </li> </ul> <p><strong>Note</strong>: KROWN is flexible and allows adding any other materialization system, see <a href="https://github.com/kg-construct/KROWN/tree/main/execution-framework">KROWN’s execution framework</a> documentation for more information.</p> <h2>Scenarios</h2> <p>We consider the following scenarios:</p> <ul> <li> <p>Raw data: number of rows, columns and cell size</p> </li> <li> <p>Duplicates & empty values: percentage of the data containing duplicates or empty values</p> </li> <li> <p>Mappings: Triples Maps (TM), Predicate Object Maps (POM), Named Graph Maps (NG).</p> </li> <li> <p>Joins: relations (1-N, N-1, N-M), conditions, and duplicates during joins</p> </li> </ul> <p><strong>Note</strong>: KROWN is flexible and allows adding any other scenario, see <a href="https://github.com/kg-construct/KROWN/tree/main/data-generator">KROWN’s data generator documentation</a> for more information.</p> <p>In the table below we list all parameter values we used to configure our scenarios:</p> <div> <table> <tbody> <tr> <td> <p><strong>Scenario</strong></p> </td> <td> <p><strong>Parameter values</strong></p> </td> </tr> <tr> <td> <p>Raw data: rows</p> </td> <td> <p>10K, 100K, 1M, 10M</p> </td> </tr> <tr> <td> <p>Raw data: columns</p> </td> <td> <p>1, 10, 20, 30</p> </td> </tr> <tr> <td> <p>Raw data: cell size</p> </td> <td> <p>500, 1K, 5K, 10K </p> </td> </tr> <tr> <td> <p>Duplicates: percentage</p> </td> <td> <p>0%, 25%, 50%, 75%, 100%</p> </td> </tr> <tr> <td> <p>Empty values: percentage</p> </td> <td> <p>0%, 25%, 50%, 75%, 100%</p> </td> </tr> <tr> <td> <p>Mappings: TMs + 5POMs</p> </td> <td> <p>1, 10, 20, 30 TMs</p> </td> </tr> <tr> <td> <p>Mappings: 20TMs + POMs</p> </td> <td> <p>1, 3, 5, 10 POMs</p> </td> </tr> <tr> <td> <p>Mappings: NG in SM</p> </td> <td> <p>1, 5, 10, 15 NGs</p> </td> </tr> <tr> <td> <p>Mappings: NG in POM</p> </td> <td> <p>1, 5, 10, 15 NGs</p> </td> </tr> <tr> <td> <p>Mappings: NG in SM/POM</p> </td> <td> <p>1/1, 5/5, 10/10, 15/15 NGs</p> </td> </tr> <tr> <td> <p>Joins: 1-N relations</p> </td> <td> <p>1-1, 1-5, 1-10, 1-15</p> </td> </tr> <tr> <td> <p>Joins: N-1 relations</p> </td> <td> <p>1-1, 5-1, 10-1, 15-1</p> </td> </tr> <tr> <td> <p>Joins: N-M relations </p> </td> <td> <p>3-3, 3-5, 5-3, 10-5, 5-10</p> </td> </tr> <tr> <td> <p>Joins: join conditions</p> </td> <td> <p>1, 5, 10, 15</p> </td> </tr> <tr> <td> <p>Joins: join duplicates</p> </td> <td> <p>0, 5, 10, 15</p> </td> </tr> </tbody> </table> </div> <h1> </h1>
On CNF Conversion for SAT and SMT Enumeration: Benchmarks, Results and Plots
<p>Experimental results for the paper:<br><br><a title="Arxiv Link" href="https://arxiv.org/abs/2303.14971" target="_blank" rel="noopener">On CNF encoding for SAT and SMT enumeration</a>, Gabriele Masina, Giuseppe Spallitta and Roberto Sebastiani. ArXiv, 2024.</p> <p>Content:</p> <ul> <li><code>aig-bench.zip</code> <code>iscas85-bench.zip</code> <code>syn-bool-bench.zip</code> contain the inputs and results for the Boolean benchmarks. Each zip contains: <ul> <li><code>data/</code> that contains the input data</li> <li><code>results-<tool>/</code> for each tested tool.</li> </ul> </li> <li><code>syn-lra-bench.zip</code> <code>wmi-bench.zip</code> contain the inputs and results for the Boolean benchmarks. Each zip contains: <ul> <li><code>data/</code> that contains the input data</li> <li><code>results-<tool>/</code> for each tested tool.</li> </ul> </li> <li><code>plot-d4</code>, <code>plot-msat</code>, <code>plot-tabularallsat</code>, <code>plot-tabularallsmt</code> contain the plots for enumeration with different tools. </li> <li><code>plot-msat-sat</code> contains the plot for plain satisfiability using MathSAT.</li> </ul> <p>Results are stored in JSON files, where the field <code>"mode"</code> indicates the CNF transformation used to preprocess the input:</p> <ul> <li><code>LAB</code> for Tseitin CNF</li> <li><code>LABELNEG_POL</code> for Plaisted&Greenbaum CNF, using negative labels for subformulas occurring negatively only.</li> <li><code>NNF_MUTEX_POL</code> for NNF+Plaisted&Greenbaum CNF+mutex clauses, as described in the paper</li> </ul> <p>The source code used to run the experiments is available at <a href="https://doi.org/10.5281/zenodo.14033422" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14033422</a>.</p> <p> </p>
Mars PCM temperature results from MY 36 LS = 0° to MY 37 LS = 60°
<p>We present here the numerical simulation results of the temperature in the Martian atmosphere from Martian Year (MY) 36 solar longitude (LS) 0° to MY 37 LS 60°. The data is based on the Mars PCM (version 6). A description of the model setting is presented in Fan et al. (2024), and a general description of the model architecture is given in Forget et al. (1999). The model has a 64×48×73 grid in longitude, latitude, and pressure levels, which corresponds to a horizontal resolution of 5.625° and 3.75° in longitude and latitude, respectively, and 73 hybrid terrain-following vertical pressure levels (σ-grid) from surface to ~2×10^-3 Pa. The run has 960 dynamical timesteps in each Martian day, and the physical timestep is 7.5 Martian minutes.The dust injection is semi-interactive regulated by the MY 36 and MY 37 dust scenarios.<br><br>The data is seven NetCDF files, each including two Martian months. They are self-explanatory with the meanings of variables included in their headers, which contain atmosphere temperature, surface temperature, and surface pressure in the simulation, together with information about the four-dimensional grid in longitude, latitude, pressure level, and time, and also the corresponding LS at each timestep.</p> <p>The variable names of the longitude, latitude, pressure level, and time of grid points and their units are <em>latitude</em> [degree], <em>longitude</em> [degree], <em>altitude</em> [Pa], and <em>Time</em> [sol], respectively. The names of the variables and their corresponding units and dimensions are below.<br>Temperature [K]: <em>temp</em>[<em>Time</em>, <em>altitude</em>, <em>latitude</em>, <em>longitude</em>]<br>Surface temperature [K]: <em>tsurf</em>[<em>Time</em>, <em>latitude</em>, <em>longitude</em>]<br>Surface pressure [Pa]: <em>ps</em>[<em>Time</em>, <em>latitude</em>, <em>longitude</em>]<br>Solar longitude [degree]: <em>Ls</em>[<em>Time</em>]</p> <p><br>Reference: (1) Forget et al. (1999) Improved general circulation models of the Martian atmosphere from the surface to above 80 km. Journal of Geophysical Research, 104(E10), 24155-24176. (2) Fan et al. (2025) Diurnal temperature variations and migrating thermal tides in the Martian lower atmosphere observed by the Emirates Mars InfraRed Spectrometer. Journal of Geophysical Research: Planets. <span>130</span>, e2025JE009092.</p>
Local risks from Arctic permafrost thaw – Results from a transdisciplinary, comparative analysis
<p>This dataset underpins the findings of a transdisciplinary and comparative assessment of permafrost thaw risks across four distinct Arctic regions: Longyearbyen (Svalbard, Norway), the Avannaata Municipality (Greenland), the Beaufort Sea region and Mackenzie River Delta (Canada), and the Bulunskiy District of the Sakha Republic (Yakutiya, Russia). Information on permafrost thaw risks was gathered from multiple disciplines and stakeholders over a five-year period from 2019 to 2023, and classified via thematic network analysis (Attride-Stirling, 2001). The identified risks were subsequently verified and ranked by scientists and local experts through an iterative process and a series of workshops (see Ingeman-Nielsen et al., 2024 in Related Works).</p> <p>The dataset contains the results of the thematic network analysis and ranking of permafrost thaw risks specific to each Arctic region. Provided as an .xlsx file, it consists of six main sheets comprising the following information:</p> <ul> <li>Global theme - Physical Processes</li> <li>Ranking - Physical Processes</li> <li>Global theme - Key Hazards</li> <li>Global theme - Societal Consequences</li> <li>Ranking - Consequences</li> <li>List of Actions Needed</li> </ul> <p>Additional details about the methodological approach and dataset can be found in the accompanying README document.</p> <p> </p>
Quantitative results of the analysis of human bioengineered tissues corresponding to the work "Development of novel squid gladius biomaterials for cornea tissue engineering"
<p>This dataset corresponds to the quantitative data generated in the work entitled "Development of novel squid gladius biomaterials for cornea tissue engineering".</p> <p>Cornea tissue engineering is strictly dependent on the development of biomaterials fulfilling the strict biocompatibility, biomechanical and optical requirements of this organ. In this work, we have generated novel biomaterials from the squid gladius (SG) and their application in cornea tissue engineering was evaluated. Results revealed that the native SG (N-SG) was biocompatible in laboratory animals, although a local inflammatory reaction was driven by the material. Cellularized biomaterials (C-SG) demonstrated that the SG provides an adequate substrate for cell attachment and growth, and corneal epithelial cells cultured on this biomaterial were able to express crystallin alpha, a marker for this type of cells. Biomechanical analyses showed that N-SG biomaterials have higher Young modulus and lower traction deformation than control native corneas (CTR), and C-SG showed similar Young modulus than CTR. Analysis of the optical properties of these samples revealed that the diffuse transmittance of N-SG and C-SG were higher than CTR, with the diffuse reflectance showing the opposite behavior. These results confirm the putative usefulness of this abundant marine-derived biomaterial that can be obtained as a byproduct of the fishing industry.</p>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Slovakia
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_SK: State Veterinary & Food Institute (SVPU)</li> <li>TSE_2022_SK: State Veterinary & Food Institute (SVPU)</li> <li>TSE_2021_SK: State Veterinary & Food Institute (SVPU)</li> <li>TSE_2020_SK: State Veterinary & Food Institute (SVPU)</li> <li>TSE_2019_SK: State Veterinary & Food Institute (SVPU)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Denmark
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_DK: Danish Veterinary and Food Administration (DVFA)</li> <li>TSE_2022_DK: Danish Veterinary and Food Administration (DVFA)</li> <li>TSE_2021_DK: Danish Veterinary and Food Administration (DVFA)</li> <li>TSE_2020_DK: Danish Veterinary and Food Administration (DVFA)</li> <li>TSE_2019_DK: Danish Veterinary and Food Administration (DVFA)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Sweden
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_SE: Swedish Board of Agriculture</li> <li>TSE_2022_SE: Swedish Board of Agriculture</li> <li>TSE_2021_SE: Swedish Board of Agriculture</li> <li>TSE_2020_SE: Swedish Board of Agriculture</li> <li>TSE_2019_SE: Swedish Board of Agriculture</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Turkey
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_TR - General Directorate of Food and Control)</li> <li>TSE_2022_TR - General Directorate of Food and Control)</li> <li>TSE_2021_TR - General Directorate of Food and Control)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Belgium
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_BE: Federal Agency for the Safety of the Food Chain (FASFC)</li> <li>TSE_2022_BE: Federal Agency for the Safety of the Food Chain (FASFC)</li> <li>TSE_2021_BE: Federal Agency for the Safety of the Food Chain (FASFC)</li> <li>TSE_2020_BE: Federal Agency for the Safety of the Food Chain (FASFC)</li> <li>TSE_2019_BE: Federal Agency for the Safety of the Food Chain (FASFC)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Spain
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_ES: Ministerio de Agricultura, Pesca y Alimentación (MAPA)</li> <li>TSE_2022_ES: Ministerio de Agricultura, Pesca y Alimentación (MAPA)</li> <li>TSE_2021_ES: Ministerio de Agricultura, Pesca y Alimentación (MAPA)</li> <li>TSE_2020_ES: Ministerio de Agricultura, Pesca y Alimentación (MAPA)</li> <li>TSE_2019_ES: Ministerio de Agricultura, Pesca y Alimentación (MAPA)</li> </ul>
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.