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6,410 results for “Results”
Inter-Chemical Correlation results for the study: HHEARx2017-1729 (Air Pollution, Placenta Function, and Birth Outcomes in Los Angeles)
Title: Air Pollution, Placenta Function, and Birth Outcomes in Los Angeles <br>Species: Homo sapiens <br>Number of samples: 450 <br>Number of named analytes: 14 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=48 <br>
Inter-Chemical Correlation results for the study: HHEARx2017-1740 (Mitochondrial DNA biomarkers of prenatal metal mixture exposure: intergenerational inheritance and infant growth)
Title: Mitochondrial DNA biomarkers of prenatal metal mixture exposure: intergenerational inheritance and infant growth <br>Species: Homo sapiens <br>Number of samples: 1423 <br>Number of named analytes: 20 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=19 <br>
Inter-Chemical Correlation results for the study: HHEARx2017-1598 (Evaluation of Environmental Exposures in TEDDY)
Title: Evaluation of Environmental Exposures in TEDDY <br>Species: Homo sapiens <br>Number of samples: 1025 <br>Number of named analytes: 45 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=37 <br>
Inter-Chemical Correlation results for the study: HHEARx2017-1593 (Role of environmental toxicants in modulating disease severity in children with NAFLD)
Title: Role of environmental toxicants in modulating disease severity in children with NAFLD <br>Species: Homo sapiens <br>Number of samples: 436 <br>Number of named analytes: 7 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=30 <br>
Inter-Chemical Correlation results for the study: HHEARx2016-1534 (A Nested Case-Control Study of Prenatal Exposure to Phthalates and Psychosocial Stress: Adverse Pregnancy Outcomes and the Mediating Role of Placental Function)
Title: A Nested Case-Control Study of Prenatal Exposure to Phthalates and Psychosocial Stress: Adverse Pregnancy Outcomes and the Mediating Role of Placental Function <br>Species: Homo sapiens <br>Number of samples: 5789 <br>Number of named analytes: 17 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=14 <br>
Inter-Chemical Correlation results for the study: HHEARx2016-1450 (Denver Asthma Panel Study-CHEAR Ancillary Study)
Title: Denver Asthma Panel Study-CHEAR Ancillary Study <br>Species: Homo sapiens <br>Number of samples: 911 <br>Number of named analytes: 33 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=12 <br>
Inter-Chemical Correlation results for the study: HHEARx2016-1461 (ECHO ReCHARGE Study - Environmental Exposures)
Title: ECHO ReCHARGE Study - Environmental Exposures <br>Species: Homo sapiens <br>Number of samples: 1231 <br>Number of named analytes: 75 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=17 <br>
Inter-Chemical Correlation results for the study: HHEARx2016-1432 (Micronutrient deficiencies, environmental exposures and severe malaria: Risk factors for adverse neurodevelopmental outcomes in Ugandan children)
Title: Micronutrient deficiencies, environmental exposures and severe malaria: Risk factors for adverse neurodevelopmental outcomes in Ugandan children <br>Species: Homo sapiens <br>Number of samples: 1256 <br>Number of named analytes: 51 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=5 <br>
Input data and results of the RECC-ODYM model for Greater Oslo study (v1.0)
<p>This repository contains the input data and results of the modified RECC-ODYM model for Greater Oslo study (v1.0) used in "Reducing material use and their greenhouse gas emissions in Greater Oslo" by Lola Rousseau, Jan Sandstad Næss, Fabio Carrer, Sara Amini, Helge Brattebø, and Edgar Hertwich.</p> <p>The publication and its supplementary information are available at: <a href="https://doi.org/10.1111/jiec.13611">https://doi.org/10.1111/jiec.13611</a></p> <p>The following data are included in this repository:</p> <ul> <li>A description (<strong>How_to_use_RECCODYM_Greater_Oslo.pdf</strong>) how to run the code with the database to generate the results </li> <li>The database (<strong>CURRENT_VN1_0.zip</strong>) with the parameters, the master classification file RECC_Classifications_Master_V2.0.xlsx, the model config file RECC_Config.xlsx and the list of scenario configurations RECC_ModelConfig_List.xlsx</li> <li>The results organized (<strong>results_organized.zip</strong>) by folder depending on the model run (results_organized) </li> </ul> <p>The code used with this database and generating these results is archived as v1.0 (<a href="https://github.com/LolaRousseau/RECC-ODYM/releases" target="_blank" rel="noopener">https://github.com/LolaRousseau/RECC-ODYM/releases</a>). The latest version is available on GitHub: <a href="https://github.com/LolaRousseau/RECC-ODYM" target="_blank" rel="noopener">https://github.com/LolaRousseau/RECC-ODYM</a></p> <div> <p>Please note that this is a modified version of RECC-ODYM with changes made for this study specifically. More general information about RECC-ODYM can be found on: <a href="https://www.industrialecology.uni-freiburg.de/odym-recc">https://www.industrialecology.uni-freiburg.de/odym-recc</a> and the original framework is also described here: <a href="https://doi.org/10.1111/jiec.13023">https://doi.org/10.1111/jiec.13023</a></p> </div>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Croatia
<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_HR: Ministry of Agriculture (MPS)</li> <li>TSE_2022_HR: Ministry of Agriculture (MPS)</li> <li>TSE_2021_HR: Ministry of Agriculture (MPS)</li> <li>TSE_2020_HR: Ministry of Agriculture (MPS)</li> <li>TSE_2019_HR: Ministry of Agriculture (MPS)</li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Austria
<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_AT: Austrian Agency for Health and Food Safety (AGES)</li> <li>TSE_2022_AT: Austrian Agency for Health and Food Safety (AGES)</li> <li>TSE_2021_AT: Austrian Agency for Health and Food Safety (AGES)</li> <li>TSE_2020_AT: Austrian Agency for Health and Food Safety (AGES)</li> <li>TSE_2019_AT: Austrian Agency for Health and Food Safety (AGES)</li> </ul>
Results of "Storm Time Data Assimilation in the Thermosphere Ionosphere with TIDA" CHAMP, GRACE-A, and GRACE-B neutral density data assimilation into CTIPe for 2003 Halloween Storms
<p># README</p> <p>Results for the article "Storm Time Neutral Density Assimilation in the Thermosphere Ionosphere with<br> TIDA".</p> <p>There are three storms presented here:</p> <p>1. 2003 storm: October 26-30, 2003<br> 2. 2004 storm: July 26-30, 2004<br> 3. 2002 storm: September 27 - October 2, 2002.</p> <p>For each of these three storms, there are four runs. For each storm, we've done a run<br> assimilating all satellites, and then three more assimilating each satellite individually and<br> comparing against the others.</p> <p>Each directory name before underscore identifies the date the run was<br> started. After the underscore identifies the date assimilated.</p> <p>This readme uses the notation that in curly brackets the satellites assimilated are given.</p> <p>- a stands for GRACE-A<br> - b stands for GRACE-B<br> - c stands for CHAMP</p> <p>The runs are summarized below:</p> <p>1. 2003 storm: {a, b, c}: 2021-12-29T1259_...<br> 3. 2003 storm: {a}: 2022-01-01T1654_...<br> 4. 2003 storm: {b}: 2022-01-02T1423_...<br> 2. 2003 storm: {c}: 2021-12-29T2342_...</p> <p>5. 2004 storm: {a, b, c}: 2022-01-03T1614_...<br> 6. 2004 storm: {a}: 2022-01-04T1027_...<br> 7. 2004 storm: {b}: 2022-01-04T2144_...<br> 8. 2004 storm: {c}: 2022-01-05T0502_...</p> <p>9. 2002 storm: {a, b, c}: 2022-01-02T2025_...<br> 10. 2002 storm: {a}: 2022-01-05T1056_...<br> 11. 2002 storm: {b}: 2022-01-05T1854_...<br> 12. 2002 storm: {c}: 2022-01-06T1017_...</p> <p>## Example result directory</p> <p>2021-12-29T1259_d2003-10-27<br> ├── density_champ_density.csv<br> ├── density_grace-a_density.csv<br> ├── density_grace-b_density.csv<br> └── inputs<br> ├── reference_2003-10-27_input.txt<br> └── special_2003-10-27_input.txt<br> <br> 1 directory, 5 files</p> <p>## Zenodo doesn't support directories</p> <p>So, the file structure has been flattened in the following way:</p> <p>Before: ./aaa/bbb/ccc.png</p> <p>After: ./aaa-bbb-ccc.png</p> <p>https://unix.stackexchange.com/~/45659</p> <p> </p>
Data: Weak Cation Selectivity in HCN Channels Results from K+-mediated release of Na+ from selectivity filter binding sites
<p>Complementary data for the paper: Weak Cation Selectivity in HCN Channels Results from K+-mediated release of Na+ from selectivity filter binding sites.</p>
Matching results between landmark in different sources and landmark in a referenced dataset (BDTOPO)
<p>The four datasets represent the results of a two sequentials processus. The first processus consists on a automatic matching between landmark in different sources and landmark in a referenced dataset (french national topographic data: BDTOPO). Then the links 1:1 are manually validated by experts in the second processus.</p> <p>The four different datasets and the BDTOPO dataset are archived <a href="https://doi.org/10.5281/zenodo.6480986">here</a>.</p> <p>The data matching algorithm is described in this <a href="http://dx.doi.org/10.5311/JOSIS.2015.10.194">paper</a>.</p> <p>Each file represents the result matching for features belonging to a data source with:</p> <p>- the name of file depending on the data source</p> <p>- the column "id_source" corresponds to the identifier of the landmark in data source</p> <p>- the column "types_of_matching_results" describes the type of matching result:</p> <ul> <li>« 1:0 »: means that a landmark from a data source (e.g. Camptocamp) has no homologue landmark in BDTOPO</li> <li>« 1:1 validated »: means that a homologous feature exist in BDTOPO and the link was validated</li> <li>« 1:1 non validated »: means that the matching link was not validated</li> <li>« without candidates »: represents the non-matched landmarks because there are no candidates in BDTOPO or because the landmark in data source is far away from its homologous in BDTOPO</li> <li>« uncertain »: uncertainty cases are complex cases where any decision is taken by the data matching algorithm</li> </ul> <p>- the column "id_candidat" corresponds to the identifier of the landmark in BDTOPO if and only if there is a validated matching link</p> <p>- the column "samal" corresponds to the <a href="https://doi.org/10.1080/13658810410001658076">Samal distance</a></p> <p>The matching results are obtained using an ontology application named <a href="http://choucas.ign.fr/doc/ontologies/index-fr.html">OOR</a>. These specific results are obtained using the version of OOR V1.0.1 which is an improved version and contains new concepts compared to the first release 1.0.0. The new version of OOR (i.e. 1.0.1) will be released by the end of May 31 2022. The new link will be added here.</p> <p>This archive is released for transparency and reproducibility purposes.</p>
Source code and simulation results for nanoantennas supporting an enhanced Purcell factor due to interfering resonances
<p><strong>Summary</strong></p> <p>Data and source code relate to the article "<a href="https://doi.org/10.1103/PhysRevResearch.4.023189">Enhanced Purcell factor for nanoantennas supporting interfering resonances</a>" [1], whose subject are the effects of coupled resonances and quasibound states in the continuum on the Purcell factor in dielectric resonant nanoantennas. The provided scripts reproduce the analysis of interfering resonances in a nanodisk coupled to an enclosed emitter and can be easily adapted for further investigations. </p> <p><strong>Structure</strong></p> <p>The cases refer to different aspect ratios of the nanodisk with (a and b) and without (c and d) substrate. The scans reproduce the data used to find the aspect ratios (a and c) supporting the maximal Purcell enhancement. </p> <p><a href="https://doi.org/10.1016/j.softx.2021.100763">RPExpand</a> [2] is used for Riesz projection expansions, which quantify the interactions of the resonances.</p> <p>The directories <strong>resonance</strong> and <strong>scattering </strong>contain input files for the commercial software JCMsuite, which rigorously solves Maxwell's equations with the finite-element method (FEM). In order to switch to a custom setup, you must adapt these input files. If you want to recalculate all results, make sure that you remove the directories containing resultbags. These are stored in the directory <strong>results</strong>, e.g., results/case_a/resultbags.</p> <p><strong>Requirements</strong></p> <ul> <li>JCMsuite (tested with version: 4.6.3)</li> <li>Matlab (tested with version: R2019b)</li> </ul> <p>In order to run the scripts you must replace the corresponding place holders in the files by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>. </p> <p>[1] Rémi Colom, Felix Binkowski, Fridtjof Betz, Yuri Kivshar, Sven Burger, Enhanced Purcell factor for nanoantennas supporting interfering resonances, Physical Review Research <strong>4</strong>, 023189 (2022), https://doi.org/10.1103/PhysRevResearch.4.023189</p> <p>[2] Fridtjof Betz, Felix Binkowski, Sven Burger, RPExpand: Software for Riesz projection expansion of resonance phenomena, SoftwareX <strong>15</strong>, 100763 (2021), https://doi.org/10.1016/j.softx.2021.100763</p>
deGeco genomic compartments model fit results on whole genome at resolution of 50kb
<p>These files contain the fitted parameters for the <a href="https://www.biorxiv.org/content/10.1101/2022.10.01.510432v1.article-info">deGeco</a> model for genomic compartments. Fits were done at 50kb on four cell lines: GM12878 (from Rao, et al., 2014), H1, HFF (both from Krietenstein, et al., 2020) and mESC (from Bonev, et al., 2017). Each Hi-C file was zoomified again using cooler, to prevent duplicate entries in the pixel table.</p> <p>The file format is NumPy's npz object that has two main keys:</p> <ol> <li>Metadata - an object containing various information on the run: command line parameters, duration of run, etc</li> <li>Parameters - an object containing the actual fitted parameters: <ol> <li>state_probabilities - an NxS matrix of state probabilities, where N is the number of bins and S the number of states the model was run with</li> <li>cis_weights - an SxS matrix of cis state affinities</li> <li>trans_weights - an SxS matrix of trans state affinities</li> <li>cis_dd_power - the exponent of the power law decay of interaction intensity in cis (also denoted as alpha)</li> <li>trans_dd - the constant background level of trans interaction (also denoted as beta)</li> <li>cis_lengths - Number of bins for each chromosome. Sum of cis_lengths is N, the total number of bins.</li> </ol> </li> </ol> <p>To read using numpy:</p> <pre><code class="language-python">import numpy as np fit = np.load(filename, allow_pickle=True) metadata = fit['metadata'][()] parameters = fit['parameters'][()]</code></pre> <p>or use the gc_datafile module from the deGeco <a href="https://github.com/KaplanLab/deGeco">repository</a>:</p> <pre><code class="language-python">import gc_datafile parameters = gc_datafile.load_params(filename)</code></pre> <p> </p>
Results of "Ensemble Kalman Filter for the Thermosphere Ionosphere", CHAMP neutral density assimilation into CTIPe for March 20, 2007
<p>These data are the result of assimilating neutral density measurements from the CHAMP satellite on March 20, 2007 into the CTIPe model and and comparison of results with observations made by the GRACE satellite. Data assimilation is performed in three configurations: Configuration (i) is ds, state correction. Configuration (ii) is dfds, both input estimatation and state correction. Configuration (iii) is df, estimation of model inputs only.</p> <p>This data is associated with the following publication:</p> <blockquote> <p>Codrescu S., M.V. Codrescu, and M. Fedrizzi (2018), An Ensemble Kalman Filter for the Thermosphere-Ionosphere, Space Weather, 16, doi:<a href="http://dx.doi.org/10.1002/2017SW001752" title="Link to external resource: 10.1002/2017SW001752">10.1002/2017SW001752</a>.</p> </blockquote> <p> </p>
Measurement of the bound-state beta decay of 205Tl(81+): intermediate and result data
<p>The data presented here is the intermediate and result data from the measurement of the bound-state beta decay of 205Tl(81+), experiment G-20-0E121, which was performed at the Experimental Storage Ring (ESR) at the GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt (Germany) in the frame of FAIR Phase-0. The experimental measurement was done from the 26th March 2020 to 6th April 2020.</p> <p><strong>Intermediate Data:</strong> During the experiment, the ESR monitored the beam via three main detectors: the non-destructive 245 MHz Schottky resonator, the DC Current Transformer (DCCT), and a Multi-Wire Proportional Chamber (MWPC). In particular:</p> <ol> <li>Schottky data: the integrated Schottky noise power density for the 205Tl(81+) peak and the 205Pb(82+) peak is provided for each storage measurement. The full Schottky spectrum can be made available on request. As described in the related works below, the Schottky data became saturated above a certain threshold due to a mismatched amplifier in the NTCAP DAQ. This results in non-exponential decay of Schottky peaks for high intensities. </li> <li>DCCT data: the entire beam current in the ring was monitored using the DCCT. The DCCT was recorded using a scalar counter and thus has a non-zero offset value, which we determined to be 28.199 µA from a period with no beam. The DCCT is intended to be used as a diagnostic tool, and is not as precise as other, purpose-built detectors.</li> <li>MWPC data: for most storage measurements, a MWPC detector was placed downstream of the gas target on the outside of the ring to detect electron recombination products. The provided data is the detection rate on the anode.</li> <li>The gas target density, as recorder by a scaler counter, is also included.</li> </ol> <p>The intermediate data on all three of these detectors plus the gas target density is provided in the tar.gz repository, with individual ROOT files for each storage time. The ROOT files have the naming format "Storage time_MMDD_HH.root". Each file contains 5 TGraph objects.</p> <p><strong>Result Data: </strong>The result data provides all the necessary individual measurement and correction values to extract a bound-state beta-decay rate from the corrected ratios. It is provided in two forms:</p> <ol> <li>BSBD_205Tl-result_data.ods is an ODS table for easy visualisation.</li> <li>BSBD_205Tl-final_vals.txt is a text file used by the Monte Carlo analysis script provided in <a href="https://doi.org/10.5281/zenodo.11560338" target="_blank" rel="noopener">DOI 10.5281/zenodo.11560338</a>.</li> </ol> <p>The "Ratio" column is the corrected 205Pb/205Tl decay ratio for each storage, following Equation (1) of <a href="https://www.nature.com/articles/s41586-024-08130-4" target="_blank" rel="noopener">Leckenby et al. (2024) Nature 635:321–326</a>. 1 sigma error bars, both including and not-including the estimated contamination variation, are provided.</p> <p>Please refer to the related works below or contact the authors for more details.</p>
DockM8_Benchmarking_results
<p>The repository contains the benchmarking data obtained alongside the first version of DockM8.</p> <p>The file structure is explained in DockM8_v1_file_structure_explanation.txt</p> <p>We hope this data is useful for benchmarking scoring functions and machine learning models, as well as being a large repository of pre-docked poses using a variety of algorithms.</p>
Audio Commons Estimation Results Data for deliverables D4.4, D4.10 and D4.12
<p>This dataset contains the results of running the automatic audio annotation algorithms for <strong>pitch</strong>, <strong>tempo</strong> and <strong>key </strong>used for the evaluation of algorithms developed during the AudioCommons H2020 EU project and which are part of the <a href="https://www.audiocommons.org/2018/07/15/audio-commons-audio-extractor.html">Audio Commons Audio Extractor tool</a>. It also includes estimation results information for the <strong>single-event<em>ness</em> </strong>audio descriptor also developed for the same tool.</p> <p>These estimation results data has been used to generate the following documents:</p> <ul> <li><strong>Deliverable D4.4</strong>: Evaluation report on the first prototype tool for the automatic semantic description of music samples</li> <li><strong>Deliverable D4.10</strong>: Evaluation report on the second prototype tool for the automatic semantic description of music samples</li> <li><strong>Deliverable D4.12</strong>: Release of tool for the automatic semantic description of music samples</li> </ul> <p>All these documents are available in the <a href="https://www.audiocommons.org/materials/">materials section </a>of the AudioCommons website.</p> <p>All data in this repository is provided in the form of CSV files. Each CSV file corresponds to the analysis results of one musical task and one of the individual datasets used in the aforementioned deliverables. This repository <strong>does not include the audio files </strong>of each individual dataset, but includes references to the audio files. The following paragraphs describe the structure of the CSV files and give some notes about how to obtain the audio files in case these would be needed.</p> <p><br> <strong>Structure of the CSV files</strong></p> <p>All the CSV files in this repository (with the sole exception of <em>SINGLE EVENT - Estimation Results Truth.csv</em>) are named according to the following convention: "<em>DATASET_NAME</em> - <em>ESTIMATION_TASK</em> Estimation Results.csv". Therefore, estimation results for pitch, tempo and tonality music tasks are separated in different files. All these files share the same structure for the first 2 CSV columns:</p> <ol> <li><strong>Audio reference</strong>: reference to the corresponding audio file. This will either be a string withe the <strong>filename</strong>, or the <strong>Freesound ID </strong>(for one dataset based on Freesound content). See below for details about how to obtain those files. </li> <li><strong>Audio reference type</strong>: will be one of <em>Filename</em> or <em>Freesound ID</em>, and specifies how the previous column should be interpreted. </li> </ol> <p>The rest of the columns include the estimation results for each one of the algorithms included in the evaluation of each music facet. For <strong>each algorithms two columns</strong> are reserved, the first one containing the actual <strong>estimation</strong> and the second one the <strong>confidence</strong> of this estimation (see CSV file previews below). The format of actual estimations depends on the musical task, check the description of the <a href="https://zenodo.org/deposit/2545728">corresponding ground truth dataset</a> for more information on that. The confidence value is a float number, typically in the range from 0.0 to 1.0. It can happen that one or both columns are empty for a given analysis algorithm and CSV row. This will be the case if the algorithm could not successfully produce an estimation for the audio file row corresponding to the CSV row.</p> <p>The remaining CSV file, <em>SINGLE EVENT - Estimation Results.csv</em>, has the following 4 columns:</p> <ul> <li><strong>Freesound ID</strong>: sound ID used in Freesound to identify the audio clip.</li> <li><strong>ACExtractorV2</strong>: single-event<em>ness</em> estimation of the algorithm included in the second version of the Audio Commons Audio Extractor tool (bool).</li> <li><strong>ACExtractorV2-opt</strong>: single-event<em>ness</em> estimation of the algorithm included in the second version of the Audio Commons Audio Extractor tool with optimized parameters (bool).</li> <li><strong>ACExtractorV3</strong>: single-event<em>ness</em> estimation of the algorithm included in the third version of the Audio Commons Audio Extractor tool (bool).</li> </ul> <p> </p> <p><strong>How to get the audio data</strong></p> <p>In this section we provide some notes about how to obtain the audio files corresponding to the estimation results provided here. Note that due to licensing restrictions we are not allowed to re-distribute the audio data corresponding to most of these automatic annotations.</p> <ul> <li><strong>Apple Loops (APPL)</strong>: This dataset includes some of the music loops included in Apple's music software such as Logic or GarageBand. Access to these loops requires owning a license for the software. Detailed instructions about how to set up this dataset are <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#appl">provided here</a>.</li> <li><strong>Carlos Vaquero Instruments Dataset (CVAQ)</strong>: This dataset includes single instrument recordings carried out by <a href="https://www.linkedin.com/in/carlosvaquero/">Carlos Vaquero</a>as part of this <a href="http://mtg.upf.edu/node/2609">master thesis</a>. Sounds are available as Freesound packs and can be downloaded at this page: https://freesound.org/people/Carlos_Vaquero/packs</li> <li><strong>Freesound Loops 4k (FSL4)</strong>: This dataset set includes a selection of music loops taken from Freesound. Detailed instructions about how to set up this dataset are <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#instructions-for-setting-up-datasets">provided here</a>.</li> <li><strong>Giant Steps Key Dataset (GSKY)</strong>: This dataset includes a selection of previews from Beatport annotated by key. Audio and original annotations <a href="https://github.com/GiantSteps/giantsteps-key-dataset">available here</a>.</li> <li><strong>Good-sounds Dataset (GSND)</strong>: This dataset contains monophonic recordings of instrument samples. Full description, original annotations and audio are <a href="https://zenodo.org/record/820937#.XEYMiy2ZN25">available here</a>.</li> <li><strong>University of IOWA Musical Instrument Samples (IOWA)</strong>: This dataset was created by the Electronic Music Studios of the University of IOWA and contains recordings of instrument samples. The dataset is available upon request by <a href="http://theremin.music.uiowa.edu/MIS.html">visiting this website</a>.</li> <li><strong>Mixcraft Loops (MIXL)</strong>: This dataset includes some of the music loops included in Acoustica's Mixcraft music software. Access to these loops requires owning a license for the software. Detailed instructions about how to set up this dataset are <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#mixl">provided here</a>.</li> <li><strong>NSynth Dataset Test and Validation sets (NSYT and NSYV)</strong>: NSynth is a large-scale and high-quality dataset of annotated musical notes built with synthesized sounds by Google's Magenta team. Full dataset description including original annotations and audio files is <a href="https://magenta.tensorflow.org/datasets/nsynth">available here</a>.</li> <li><strong>Philarmonia Orchestra Sound Samples Dataset (PHIL)</strong>: This includes thousands of free, downloadable sound samples specially recorded by Philharmonia Orchestra players. Audio files are freely downloadable from the <a href="http://www.philharmonia.co.uk/explore/sound_samples">philarmonia orchestra website</a>.</li> <li><strong>Freesound Single Events Dataset (SINGLE EVENT)</strong>: This includes a selection of Freesound audio clips representing audio signals containing either a single audio <em>event</em>or multiple ones. Original audio files can be retrieved by downloading individual audio clips from Freesound using the ID identifier provided in the CSV file. A similar procedure to that described <a href="https://github.com/ffont/ismir2016/blob/master/docs/create_dataset.md#getting-fsl4-by-downloading-content-from-freesound">here</a> could be followed.</li> </ul>
ScienceDex guides
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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.