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4,276 results for “Estimation”
A NICER View of the Massive Pulsar PSR J0740+6620 Informed by Radio Timing and XMM-Newton Spectroscopy: Nested Samples for Millisecond Pulsar Parameter Estimation
<p>Posterior sample files associated with the preprint "A <em>NICER</em> View of the Massive Pulsar PSR J0740+6620 Informed by Radio-Timing and <em>XMM-Newton</em> Spectroscopy" by Riley et al. (2021; <a href="https://arxiv.org/abs/2105.06980">arXiv:2105.06980 [astro-ph.HE]</a>; submitted to ApJL).</p> <p>Also included are: the data products; the numeric model files including the telescope calibration products; model modules in the Python language using the X-PSI framework; and Jupyter analysis notebooks.</p> <p>Please refer to the README for detailed information.</p> <p> </p> <p> </p>
A NICER View of PSR J0030+0451: Nested Samples for Millisecond Pulsar Parameter Estimation
<p>Posterior sample files associated with "A <em>NICER</em> View of PSR J0030+0451: Millisecond Pulsar Parameter Estimation" by Riley et al. (2019; <a href="https://iopscience.iop.org/article/10.3847/2041-8213/ab481c">DOI: 10.3847/2041-8213/ab481c</a>).</p> <p>Also included are model modules and scripts in the Python language using the X-PSI framework.</p> <p>Please refer to the README for detailed information.</p>
Soil Organic Carbon Content estimations over the Lithuanian pilot area (2022)
<p>In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Lithuania (National Paying Agency - NPA) to mine meaningful information about overall soil health and the effects applied agricultural practices.<br> The dataset is delivered in a single-banded GeoTIFF file (DIONE_SOC_estimations_LT_2022.tif- EPSG:4326) containing the SOC content (SOC %) labelled as Band 1.</p>
Soil Organic Carbon Content estimations over the Cypriot pilot area (2022)
<p>In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Cyprus (Cyprus Agricultural Payments Organisation - CAPO) to mine meaningful information about overall soil health and the effects applied agricultural practices.<br> The dataset is delivered in a single-banded GeoTIFF file (DIONE_SOC_estimations_CY_2022.tif- EPSG:4326) containing the SOC content (SOC %) labelled as Band 1.<br> </p>
Soil properties as point estimations over the Lithuanian pilot area (2022)
<p>In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Lithuania and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties:</p> <ul> <li>Sand %</li> <li>Clay %</li> <li>Silt %</li> <li>Electrical Conductivity (mS/m)</li> <li>pH</li> <li>Calcium carbonate %</li> <li>Soil Organic Carbon %</li> </ul> <p>The dataset is delivered in a shapefile format (DIONE_LT_point_estimations_2022_WP4.shp - EPSG:4326 - WGS 84) containing the following fields:</p> <table> <caption><strong>Description of the information contained in the corresponding "LT points estimations" dataset</strong></caption> <thead> <tr> <th scope="col">Field </th> <th scope="col">Type </th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>Sample ID</td> <td>String </td> <td>Unique ID</td> </tr> <tr> <td>Lat</td> <td>Real </td> <td>Latitude </td> </tr> <tr> <td>Lon</td> <td>Real </td> <td>Longitude</td> </tr> <tr> <td>Sand </td> <td>Real </td> <td>Sand fraction</td> </tr> <tr> <td>Clay</td> <td>Real </td> <td>Clay fraction</td> </tr> <tr> <td>Silt</td> <td>Real </td> <td>Silt fraction</td> </tr> <tr> <td>EC </td> <td>Real </td> <td>Electrical Conductivity</td> </tr> <tr> <td>ph_H<sub>2</sub>0</td> <td>Real </td> <td>ph</td> </tr> <tr> <td>CaCO<sub>3</sub></td> <td>Real </td> <td>Calcium Carbonate</td> </tr> <tr> <td>SOC </td> <td>Real </td> <td>Soil Organic Carbon</td> </tr> </tbody> </table>
Soil properties as point estimations over the Cypriot pilot area (2022)
<p>In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Cyprus and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties:</p> <ul> <li>Sand %</li> <li>Clay %</li> <li>Silt %</li> <li>Electrical Conductivity (mS/m)</li> <li>pH</li> <li>Calcium carbonate %</li> <li>Soil Organic Carbon %</li> </ul> <p>The dataset is delivered in a shapefile format (DIONE_CY_point_estimations_2022_WP4.shp - EPSG:4326 - WGS 84) containing the following fields:</p> <table> <caption><strong>Description of the information contained in the corresponding "CY points estimations" dataset</strong></caption> <thead> <tr> <th scope="col">Field </th> <th scope="col">Type </th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>Sample_ID</td> <td>String</td> <td>Unique ID</td> </tr> <tr> <td>Lat</td> <td>Real</td> <td>Latitude </td> </tr> <tr> <td>Lon</td> <td>Real</td> <td>Longitude</td> </tr> <tr> <td>Sand</td> <td>Real</td> <td>Sand fraction</td> </tr> <tr> <td>Clay</td> <td>Real</td> <td>Clay fraction</td> </tr> <tr> <td>Silt</td> <td>Real</td> <td>Silt fraction</td> </tr> <tr> <td>EC</td> <td>Real</td> <td>Electrical Conductivity</td> </tr> <tr> <td>ph_H<sub>2</sub>0</td> <td>Real</td> <td>pH</td> </tr> <tr> <td>CaCO<sub>3</sub></td> <td>Real</td> <td>Calcium Carbonate</td> </tr> <tr> <td>SOC</td> <td>Real</td> <td>Soil Organic Carbon</td> </tr> </tbody> </table> <p> </p>
Soil Organic Carbon Content estimations over the Cypriot pilot area (2021)
<p>In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Cyprus (Cyprus Agricultural Payments Organisation - CAPO) to mine meaningful information about overall soil health and the effects applied agricultural practices at a parcel level.</p> <p>The dataset is delivered in a shapefile format (DIONE_SOC_estimations_CY_2021.shp - EPSG: 4326) containing the SOC content (SOC %) labeled as SOC.</p>
Soil properties as point estimations over the Lithuanian pilot area (2021)
<p>In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Lithuania and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties:</p> <ul> <li>Sand %</li> <li>Clay %</li> <li>Silt %</li> <li>Electrical Conductivity (mS/m)</li> <li>pH</li> <li>Calcium carbonate %</li> <li>Soil Organic Carbon %</li> </ul> <p>The dataset is delivered in a shapefile format (DIONE_LT_point_estimations_2021.shp - EPSG:4326 - WGS 84) containing the following fields:</p> <table> <caption><br> <strong>Description of the information contained in the corresponding "LTH points estimations" dataset</strong></caption> <thead> <tr> <th scope="col">Field</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>Sample_ID</td> <td>String</td> <td>Unique ID</td> </tr> <tr> <td>lat</td> <td>Real</td> <td>Latitude </td> </tr> <tr> <td>lon</td> <td>Real</td> <td>Longitude</td> </tr> <tr> <td>Sand</td> <td>Real</td> <td>Sand fraction</td> </tr> <tr> <td>Clay</td> <td>Real</td> <td>Clay fraction</td> </tr> <tr> <td>Silt</td> <td>Real</td> <td>Silt fraction</td> </tr> <tr> <td>EC</td> <td>Real</td> <td>Electrical Conductivity</td> </tr> <tr> <td>pH_H20</td> <td>Real</td> <td>pH</td> </tr> <tr> <td>CaCO3</td> <td>Real</td> <td>Calcium Carbonate </td> </tr> <tr> <td>SOC</td> <td>Real</td> <td>Soil Organic Carbon</td> </tr> </tbody> </table> <p><br> </p>
Soil properties as point estimations over the Cypriot pilot area (2021)
<p>In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Cyprus and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties:</p> <ul> <li>Sand %</li> <li>Clay %</li> <li>Silt %</li> <li>Electrical Conductivity (mS/m)</li> <li>pH</li> <li>Calcium carbonate %</li> <li>Soil Organic Carbon %</li> </ul> <p>The dataset is delivered in a shapefile format (DIONE_CY_point_estimations_2021.shp - EPSG:4326 - WGS 84) containing the following fields:</p> <table> <caption><strong>Description of the information contained in the corresponding "CY points estimations" dataset</strong></caption> <thead> <tr> <th scope="col">Field</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>Sample_ID</td> <td>String</td> <td>Unique ID</td> </tr> <tr> <td>lat</td> <td>Real</td> <td>Latitude </td> </tr> <tr> <td>lon</td> <td>Real </td> <td>Longitude</td> </tr> <tr> <td>Sand</td> <td>Real</td> <td>Sand fraction</td> </tr> <tr> <td>Clay</td> <td>Real</td> <td>Clay fraction</td> </tr> <tr> <td>Silt</td> <td>Real</td> <td>Silt fraction</td> </tr> <tr> <td>EC</td> <td>Real</td> <td>Electrical Conductivity</td> </tr> <tr> <td>pH_H20</td> <td>Real</td> <td>pH</td> </tr> <tr> <td>CaCO3</td> <td>Real</td> <td>Calcium Carbonate </td> </tr> <tr> <td>SOC</td> <td>Real</td> <td>Soil Organic Carbon</td> </tr> </tbody> </table> <p> </p>
Soil Organic Carbon Content estimations over the Lithuanian pilot area (2021)
<p>In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Lithuania (National Paying Agency - NPA) to mine meaningful information about overall soil health and the effects applied agricultural practices at a parcel level.</p> <p>The dataset is delivered in a shapefile format (DIONE_SOC_estimations_LT_2021.shp - EPSG:3346) containing the SOC content (SOC %) labeled as SOC.</p>
LOFAR Observation (MS file) from the Boötes Field and the Toothbrush cluster used in the paper: "Looking beyond pixels with continuous-space EstimAtion of Point sources"
<p>The dataset contains the measurement sets (MS file) of the LOFAR observations from the Boötes field and the Toothbrush cluster. The dataset was used in the experiments of the paper: </p> <blockquote> <p>LEAP: Looking beyond pixels with continuous-spaceEstimAtion of Point sources</p> <p>Pan, H., Simeoni, M., Hurley, P., Blu, T. & Vetterli, M. In: Astronomy & Astrophysics, in press, 2017</p> </blockquote> <p>The data was provided as a collaboration between ASTRON and IBM within the DOME project. The data was acquired for a LOFAR sky survey of the Boötes field:</p> <blockquote> <p>LOFAR 150-MHz observations of the Boötes field: Catalogue and Source Counts</p> <p>Williams, W. L. , Hardcastle, M. J. & 33 others In: Monthly Notices of the Royal Astronomical Society. 460, 3, p. 2385–2412</p> </blockquote> <p>and the Toothbrush cluster (RX J0603.3+4214):</p> <blockquote> <p>Simulating the toothbrush: evidence for a triple merger of galaxy clusters</p> <p>Brüggen, M., van Weeren, R. J., Röttgering, H. J. A. In: Monthly Notices of the Royal Astronomical Society: Letters. 425, 1, p. L76--L80</p> </blockquote> <p>In case of questions concerning the measurement set, please contact the original authors for details.</p> <p> </p> <p>We have also included the three catalogs used in the experiments, which are converted from their original FITS table to Numpy arrays:</p> <ul> <li>skycatalog.npz is the catalog of the Boötes field: https://academic.oup.com/mnras/article-lookup/doi/10.1093/mnras/stw1056</li> <li>TGSSADR1_7sigma_catalog.npz is the TGSS ADR1 source catalog: http://tgssadr.strw.leidenuniv.nl/catalogs/TGSSADR1_7sigma_catalog.fits</li> <li>NVSS_CATALOG.npz is the NRAO/VLA Sky Survey: ftp://nvss.cv.nrao.edu/pub/nvss/CATALOG/</li> </ul>
Long-term biomass estimates for the central stock of northern anchovy
<p>Long-term biomass estimates for the central stock of northern anchovy (CSNA; <em>Engraulis mordax</em>) in the California Current Ecosystem are estimated from geospatial weighting of egg and larval data from winter/spring CalCOFI surveys (1951–2021). Estimates include the entire range of the CSNA, from northwestern Baja California, Mexico, to north of Point Reyes, California, and nearshore waters.</p>
Geothermal heat source estimations through ice flow modelling at Mýrdalsjökull, Iceland - Datasets
<p>This repository contains data used in the study "<em>Geothermal heat source estimations through ice flow modelling at</em><br><em>Mýrdalsjökull, Iceland", </em>to be published in <strong>The Cryosphere. </strong>A detailed reference will be added after publication.</p> <p>Details on processing of the data and the creation of the simulated data can be found in the aforementioned publication.</p> <p><strong>Data Specifications:</strong></p> <ul> <li>Cartographic projection: ISN93 / Lambert 1993 (EPSG:3057, <a href="http://https/epsg.io/3057">https://epsg.io/3057</a>)</li> <li>Origin of Elevation: meters above GRS80 ellipsoid (WGS84)</li> <li>Raster data format: GeoTIFF</li> <li>Pléiades dataset includes only DEMs because the Pléiades ortho imagery is for licensed use only. Please contact the authors for further information on this.</li> </ul> <p><strong>File descriptions:</strong></p> <ul> <li><em><strong>bedrock_Magnusson_etal_2021.tif: </strong></em>contains bedrock data published by Magnússon et al. 2021 for the simulation domain used in the paper. See reference below.</li> <li><em><strong>surface_27092016_pleiades.tif: </strong></em>contains glacier surface data from September 27th, 2016 which is used as a starting geometry for the simulations described in the paper. This data is based on Pléiades satellite images.</li> <li><em><strong>surface_01092017_pleiades.tif: </strong></em>contains glacier surface data from September 1st, 2017 which is used as a reference target geometry for the simulations described in the paper. This data is based on Pléiades satellite images.</li> <li><em><strong>HM_run04.tif:</strong></em> contains the best fitting simulation based surface which was compared to <em><strong>surface_01092017_pleiades.tif </strong></em>in the paper.</li> <li><em><strong>HM_run04_hillshade.png: </strong></em>a simple hillshade image for preview purposes.</li> </ul> <p> </p>
An estimate of fitness reduction from mutation accumulation in a mammal allows assessment of the consequences of relaxed selection: Dataset
<p>Supplementary files (data and analysis) for "An estimate of fitness reduction from mutation accumulation in a mammal allows assessment of the consequences of relaxed selection"</p> <p>Supplementary File 1: C3H_pheno_fix_Jun7_2023_nolowmut.csv</p> <p>Data for all mice in MA experiment including: mouse ID, sire, dam, generation, mating ID, sex, weight at 3 weeks, weight at 6 weeks, tail length, litter size, litter ID, line ID</p> <p> </p> <p>Supplementary File 2: C3H_pheno_Kontrol_June2023.csv</p> <p>Data for all control mice including: mouse ID, sire, dam, generation, mating ID, sex, weight at 3 weeks, weight at 6 weeks, tail length, litter size, litter ID, line ID</p> <p> </p> <p>Supplementary File 3: C3H_birthdates.csv</p> <p>Data for all C3H mice including: mouse ID, birthdate</p> <p> </p> <p>Supplementary File 4: MA_pheno.R</p> <p>R code for visualising trait data, running linear regressions, and comparing control and MA experiment data</p> <p> </p> <p>Supplementary File 5: C3H_pheno_burnin20_Jun7_2023_nolowmut.csv</p> <p>Data for all mice in MA experiment including a 20 generation burn-in to simulate mutation-drift balance for Animal model analyses: mouse ID, sire, dam, generation, mating ID, sex, weight at 3 weeks, weight at 6 weeks, tail length, litter size, litter ID, line ID</p> <p> </p> <p>Supplementary File 6: asreml_C3H_ALL.R</p> <p>R code for estimating mutational heritabilities using mixed model analysis</p> <p> </p> <p>Supplementary File 7: C3H_ped_rekey_Jun2023.csv</p> <p>Pedigree data for all mice in MA experiment</p> <p> </p> <p>Supplementary File 8: C3H_ped_rekey_KEY.csv</p> <p>Key for pedigree data file</p> <p> </p> <p>Supplementary File 9: plot_pedigree_tree_MS_final.R</p> <p>R code for visualising pedigree of mice in MA experiment</p>
SMDP: SARS-CoV-2 Mutation Distribution Profiler for rapid estimation of mutational histories of unusual lineages
<p>Supplementary information relating to the manuscript titled "SMDP: SARS-CoV-2 Mutation Distribution Profiler for rapid estimation of mutational histories of unusual lineages" that has been published on the preprint server arXiv.</p> <ul> <li>PersistentInfectionScore.nb: Mathematica code used to process the data and generate Figure 2</li> <li>PersistentInfectionScore.pdf: pdf version of the above file</li> <li>Supplementary_tables_Harari_et_al_2022.xlsx: raw data from (<a href="https://www.nature.com/articles/s41591-022-01882-4#Sec19">Harari et al. 2022</a>) that was used to generate mutation distributions</li> </ul>
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>
Grand Mesa 2017-02-01 snow depth estimate
<p>Elevation difference (snow depth estimate for exposed ground surfaces) between co-registered WorldView-3 optical stereo DSM products from 2016-09-25 (snow-off) and 2017-02-01 (snow-on). These are preliminary products from the Stereo2SWE workflow, used to derive snow depth estimates from time series of very-high-resolution commercial stereo imagery. More formal releases of data products will be available in the coming years.<br> <br> Analysis of this dataset is presented in the following publication:</p> <ul> <li>McGrath, D., Webb, R., Shean, D., Bonnell, R., Marshall, H-P., Painter, T., Molotch, N., Elder, K., Hiemstra, C., Brucker, L., (2019), Spatially Extensive Ground-Penetrating Radar Snow Depth Observations During NASA's 2017 SnowEx Campaign: Comparison With In Situ, Airborne, and Satellite Observations, Water Resources Research, 55 (19), 10026-10036, doi:<a href="https://doi.org/10.1029/2019WR024907">10.1029/2019WR024907</a>.</li> </ul> <p>If you use these data, please cite the above publication.</p>
ECCO Iter22 Global Ocean State Estimate - 1 January 2004 to 30 April 2005
<p>Time series of global ocean temperature, salinity, and sound speed derived from the “Estimating the Circulation and Climate of the Ocean" (ECCO) program Iter22 state estimates. The sound speed fields were computed for simulation of acoustic propagation over basin scales or longer in a realistic oceanic environment. These estimates were computed in 2010 by the JPL-MIT-SIO ECCO program. <br>Original link: http://ecco2.jpl.nasa.gov/data9/cube/iter22/lat_lon/quart_80S_80N/THETA/ , now defunct.</p> <p>The solution is mesoscale permitting. The solution was obtained on a cube sphere grid between 80S and 80N with 18-km horizontal grid spacing and 50 vertical levels (Menemenlis et al., 2005, NASA supercomputer improves prospects for ocean <br>climate research, Eos Trans., AGU 86, 89, 95–96.). State estimates were averaged over a 3-day interval. File 003 is averaged over 2004/1/1 -- 2004/1/3. Three-day-mean temperature and salinity profiles from the iter22 solution were provided on 1/4 degree <br>grid for the period 1 January 2004 to 30 April 2005. There are 162 snapshots at 3-day intervals. </p> <p>Depths were decimated to the standard 33 depths of the World Ocean Atlas to 5500 m. YearDay 1 is 1 January 1992. The number of the filename indicates the yearday in 2004. In situ temperature was computed from model potential temperature. Sound speed was computed using the Del Grosso sound speed equation. Original model profiles descended only to the model sea floor. Temperature, salinity and sound speed were filled in on a uniform grid using nearest neighbor to 5500 m depth. Values on a regular grid make life easier. Product documented in Dushaw and Menemenlis, 2014, Antipodal acoustic thermometry: 1960, 2004, Deep Sea Research Part I: Oceanographic Research Papers, 86, 1–20, https://doi.org/10.1016/j.dsr.824 2013.12.008.</p> <p>Each snapshot is stored as a netcdf 4 file. Latitude, Longitude, Depth, and YearDay variables given separately in sspgrid.nc . <br>N.B.: Values in the files are stored as 32-bit or 16-bit integers to save disk space:</p> <p>Sound Speed: saved as "round( (c-1000)*1000 )", so to get actual c: c=1000. + double(c)/1000. <br>Sound speed is stored to 3 decimal places as a 32-bit integer.</p> <p>Temperature: saved as "round( (T-10)*1000 )", so to get actual T: T=10. + double(T)/1000. <br>Temperature is stored to 3 decimal places as a 16-bit integer. Note that abyssal temperature can sometimes be negative.</p> <p>Salinity: saved as "round( (S-10)*1000 )", so to get actual S: S=10. + double(S)/1000. <br>Salinity is stored to 3 decimal places as a 16-bit integer.</p> <p>Data directory also has two matlab routines: get_section.m and dist.m. get_section.m shows how to load the files, compute the physical variable from the stored value, and compute a section of ssp, T, or S. dist.m is a utility for computing geodesics; it relies on R. Pawlowitz's m_map package which can be downloaded freely from his University of Vancouver web page.</p> <p>$ md5sum *tgz <br>53ca3621f422b89c599c92fbab71d2fc S_ecco_iter22.tgz (2.99 GB)<br>a9e9109b6dae7355bf2fa0926d436d9a ssp_ecco_iter22.tgz (6.09 GB)<br>0cb8d1c2cdee4c7377353dff722ece34 T_ecco_iter22.tgz (4.33 GB)</p>
Data for estimating spruce tree health using drone-based RGB and multispectral imagery
<p>The dataset contains multispectral and RGB orthomosaics (.tif), and photogrammetric point clouds (.laz) of four study areas (about 25 ha each), where bark beetle-related decline of Norway spruce has been observed in Helsinki, Finland. The filenames refer to Area 1 (Männikkötie), Area 2 (Maunulanmaja), Area 3 (Hakuninmaa), and Area 4 (Paloheinä), described in detail in Junttila et al. 2022. Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season, Remote Sensing 14(4), 909: <a href="https://doi.org/10.3390/rs14040909">https://doi.org/10.3390/rs14040909</a> </p> <p>The image data was acquired between 11th and 14th September 2020.</p> <p>RE = Red-Edge M multispectral data<br>RGB = RGB data (Phantom 4 Pro)<br>Altum = Altum multispectral data</p> <p>The ground sampling distances (GSD) were approximately 3 cm, 5 cm, and 8 cm for RGB, Altum, and RedEdge, respectively.</p> <p>The field reference data file contains 556 geolocated trees assessed in the field (between 11.9. and 17.9.2020), of which 203 were dead and 353 were alive. The data is in polygon format, representing the crown delineation done during the data processing. The file includes tree heights estimated from airborne laser scanning data, dbh (for a subset of trees), discoloration, defoliation, resin flow, bark structural damage, and canopy size estimates. More details are in the journal article mentioned above.</p> <p>Key for Field Reference:</p> <p>Z = tree height<br>dbh = diameter-at-breast-height (cm)<br>vari = Discoloration (score 0-5)<br>harsu = Defoliation (score 0-4)<br>pihka = Resin flows (score 0-2)<br>runko = Stem/bark structural damage (score 0-2)<br>latvus = Significantly decreased canopy size (score 0-1)</p>
Datasets for phylogenetic analyses and phylogenetic trees for: Genetic barcodes for species identification and phylogenetic estimation in ghost spiders (Araneae: Anyphaenidae: Amaurobioidinae). Invertebrate Systematics, 2024
<p>We combined the COI sequence data with legacy multigene sequence data to create a new, taxon-rich phylogeny for the Amaurobioidinae. We used sequences for four loci that have been used in previous studies on the subfamily: two mitochondrial loci, COI (658bp) and ribosomal subunit 16S (16S, 410bp); and two nuclear loci, Histone H3 (H3, 327bp) and ribosomal subunit 28S (28S, 839bp). We complemented the Amaurobioidinae data with sequences from several non-amaurobioidine anyphaenids and two clubionids as outgroups. Sequence alignment was performed using the MAFFT (ver. 7.308) plugin in Geneious, allowing MAFFT to automatically select an appropriate alignment strategy based on the properties of each locus, or with the online MAFFT server (https://mafft.cbrc.jp), which consistently selected the L-INS-i algorithm. Finally, alignments of the four loci were concatenated to construct a 2234 bp multigene sequence matrix containing 692 taxa, with about 55% missing/gap data (“full” matrix henceforth). To ensure that excessive missing data did not affect the resulting topology, we also constructed a reduced matrix by removing additional COI-only specimens so that each species and morphotype was represented by just one or two specimens for which all loci were available (where possible). After realignment, this reduced matrix was 2235 bp long, included 167 taxa, and had about 22% missing/gap data (“reduced” matrix henceforth). Phylogenetic analyses under maximum likelihood, including model selection, were then conducted with IQ-TREE 2. We performed phylogenetic analyses on both concatenated matrices (the full matrix and the reduced matrix) and on each individual locus. For model selection, we provided an initial scheme that partitioned the matrix by locus, and further partitioned the protein-coding loci (COI and H3) by codon position. We used ModelFinder and searched for the best partition scheme, all in IQ-TREE. The best models (partitions) for the full dataset were: GTR+F+I+G4 (16S), GTR+F+I+I+R4 (28S), TVM+F+I+I+R2 (COI-1), TIM2+F+R4 (COI-2), GTR+F+R5 (COI-3), TVMe+G4 (H3-1-H3-2), SYM+G4 (H3-3); and for the reduced dataset: GTR+F+I+G4 (16S), GTR+F+I+G4: (28S), GTR+F+I+G4: (COI-2), GTR+F+I+G4: (COI-3), TVM+F+I+G4: (COI-1, H3-2), GTR+F+I+G4: (H3-1), GTR+F+I+G4: (H3-3). For each dataset, once the best models and partitions were defined, we executed 10 independent replicates of tree calculations followed by 1000 ultrafast bootstrap replicates, and the replicate reaching the maximum likelihood was chosen. Phylogenetic analyses under parsimony were made with TNT, under equal weights, using the “new technology” search with default values, asking for 10 independent hits to the minimal length, and submitting the resulting trees to a round of TBR branch swapping. </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.