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THD technique applied on Vp data of Long Valley Caldera
<p>We considered the V<sub>p</sub> tomographic model of Seccia et al. (2011) for Long Valley Caldera on which we applied the Horizontal Gradient technique for retrieving geometrical information about the deep structures characterizing the first 10 km of crust. Specifically, after the regularization through spatial based kriging interpolator, we compute the horizontal derivatives on single z-layers with sampling step of 500 m. The results are presented in Gola G., Barone A., Castaldo R., Chiodini G., D'Auria L., Garcia-Hernandez R., Pepe S., Solaro G. & Tizzani P. "A novel multidisciplinary approach for the thermo-rheological study of volcanic areas: The case study of Long Valley Caldera", which has been submitted for possible publication in Journal of Geophysical Research - Solid Earth.</p> <p>Coordinates: North America NAD27 UTM Zone 11N<br> Easting: 308000 - 353000 m.; Northing: 4155000 - 4185000 m; XY resolution: 500 m.</p>
Quantifying hail and lightning risk factors using long-term observations around Australia - Hail and Lightning Datasets
<p>These data accompany a paper referenced as doi: 10.1029/2020JD033101. Two spreadsheets are provided as used to derive the hourly and monthly occurrence of hail or lightning events within the domains of the ten radars sites (Melbourne, Wollongong, Gympie, Grafton, Canberra, Marburg, Adelaide, Namoi, Perth, Hobart). Lightning events as defined in this paper (derived from post-processed lightning information) were provided for 2005-2018 and hail data from 1997-2018 (noting that the start date of hail is dependent on when the respective radar site was established). A value of 1 indicates a lightning/hail event occurred during that hour (in UTC +0). Information on processing methods and data are provided in the published paper for the doi listed above.</p>
96 Long Term Solar System Integrations
<p>We share 96 long term Solar System integrations. The simulations are in the REBOUND SimulationArchive format. Please see the companion paper for details.</p>
Trained convolutional neural network for the identification of long-duration mixed precipitation in Montréal (Canada)
<p>In this dataset the trained convolutional neural network is published that accompanies the paper "A deep learning approach for the identification of long-duration mixed precipitation in Montréal (Canada)" submitted to the special issue on "Machine-Learning Applications in the Atmospheric and Oceanic Sciences" by the journal Atmosphere&Ocean.</p> <p>The files were created using tensorflow in python. The trained network is available in .h5-format the history as numpy-file (npy).</p>
SGD-SM: Generating Seamless Global Daily AMSR2 Soil Moisture Long-term Products (2013-2019)
<p><strong>If you used our dataset, please cite our reference:</strong></p> <p><strong>Zhang, Q., Yuan, Q., Li, J., Wang, Y., Sun, F., and Zhang, L.: Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019, Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, 2021.</strong></p> <p><strong>Description:</strong></p> <ul> <li>A <strong>seamless global daily</strong> (<strong>SGD</strong>) AMSR2 soil moisture long-term (2013-2019) dataset is generated through the proposed model. This daily products include <strong>2553</strong> global soil moisture NetCDF4 files, starting from Jan 01, 2013 to Dec 31, 2019 (about <strong>20GB</strong> memory after uncompressing this zip file).</li> <li>To further validate the effectiveness of these products, three verification ways are employed as follow: 1) In-situ validation; 2) Time-series validation; And 3) simulated missing regions validation. More validation results can be viewed at <strong><a href="https://qzhang95.github.io/Projects/Global-Daily-Seamless-AMSR2">SGD-SM</a></strong>.</li> <li>An example Python code of extracting this dataset is also available at <strong><a href="https://github.com/qzhang95/SGD-SM">https://github.com/qzhang95/SGD-SM</a></strong>.</li> <li>Official LPRM AMSR2 Descending L3 soil moisture products indeed only have 28 daily files in May 2013 (missing data files in date May 11, May 12, and May 13).</li> <li>This soil moisture dataset is comprised of netCDF4 (*.nc) files. Therefore, users need to install <strong>netCDF4</strong> toolkit before reading the data: <pre><code class="language-python">pip install netCDF4 pip install numpy</code></pre> <p> </p> </li> <li>It should be noted that the original and reconstructed soil moisture data are both recorded in one NC file. User can read the original data, reconstructed data, and mask data as follows:</li> <li> <pre><code class="language-python">Data = nc.Dataset(NC_file_position) Ori_data = Data.variables['original_sm_c1'] Rec_data = Data.variables['reconstructed_sm_c1'] Ori = Ori_data[0:720, 0:1440] Rec = Rec_data[0:720, 0:1440] Mask_ori = np.ma.getmask(Ori)</code></pre> <p> </p> </li> </ul>
Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis - Image data
<p>The dataset contains raw imaging data from the work:</p> <p>"Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis"</p> <p>The dataset is organized as the following: the "FigureX_" or SupplementaryFigure_X" suffix in the filename refers to the figure in the paper in which the raw data is analyzed and/or visualized. The data is "raw", i.e. not processed. However, in many cases, maximum projections of the original 3D image stacks have been uploaded due to size limitations. The total size of the image stacks approaches 0.5TB. To access the full 3D image stacks please contact the corresponding author (Francesco Pampaloni, fpampalo@bio.uni-frankfurt.de).</p> <p><strong>Authors</strong></p> <p>Lotta Hof<sup>1</sup>*, Till Moreth<sup>1</sup>*, Michael Koch<sup>1</sup>, Tim Liebisch<sup>2</sup>, Marina Kurtz<sup>3</sup>, Julia Tarnick<sup>4</sup>, Susanna M. Lissek<sup>5</sup>, Monique M.A. Verstegen<sup>6</sup>, Luc J.W. van der Laan<sup>6</sup>, Meritxell Huch<sup>7</sup>, Franziska Matthäus<sup>2</sup>, Ernst H.K. Stelzer<sup>1</sup>, Francesco Pampaloni<sup>1§</sup></p> <p><sup>1</sup>Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>2</sup>Faculty of Biological Sciences, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>3</sup>Department of Physics, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>4</sup>Deanery of Biomedical Science, University of Edinburgh, Edinburgh, United Kingdom</p> <p><sup>5</sup>Experimental Medicine and Therapy Research, University of Regensburg, Regensburg, Germany</p> <p><sup>6</sup>Department of Surgery, Erasmus MC – University Medical Center, Rotterdam, The Netherlands</p> <p><sup>7</sup>The Wellcome Trust/CRUK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom. Present address: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany</p> <p>*contributed equally</p> <p><sup>§</sup>corresponding author: fpampalo@bio.uni-frankfurt.de</p> <p><strong>Abstract</strong></p> <p><em>Background</em></p> <p>Organoids are morphologically heterogeneous three-dimensional cell culture systems and serve as an ideal model for understanding the principles of collective cell behaviour in mammalian organs during development, homeostasis, regeneration and pathogenesis. To investigate the underlying cell organisation principles of organoids, we imaged hundreds of pancreas and cholangio carcinoma organoids in parallel using light sheet and bright field microscopy for up to seven days.</p> <p><em>Results</em></p> <p>We quantified organoid behaviour at single-cell (microscale), individual-organoid (mesoscale), and entire-culture (macroscale) levels. At single-cell resolution, we monitored formation, monolayer polarisation and degeneration, and identified diverse behaviours, including lumen expansion and decline (size oscillation), migration, rotation and multi-organoid fusion. Detailed individual organoid quantifications lead to a mechanical 3D agent-based model. A derived scaling law and simulations support the hypotheses that size oscillations depend on organoid properties and cell division dynamics, which is confirmed by bright field microscopy analysis of entire cultures.</p> <p><em>Conclusion</em></p> <p>Our multiscale analysis provides a systematic picture of the diversity of cell organisation in organoids by identifying and quantifying the core regulatory principles of organoid morphogenesis.</p>
Elongation of very long chain fatty acids protein 7 (ELOVL7); A Target Enabling Package
<p>The long-chain fatty acid elongases (ELOVL) catalyse the first rate-limiting step in the two carbon elongation of the acyl chains of fatty acids (FAs) greater than 12 carbons in length. Defects in these ELOVL elongases cause severe genetic diseases, such as Stargardt disease-3 and several ataxias, and knockout studies suggest roles in insulin resistance and hepatic steatosis. This TEP provides the first structural information for this family of enzymes which, coupled with mutagenesis and biophysical studies, demonstrates how substrates and products bind within the active site.</p>
Data from: Advancement in long-distance bird migration through individual plasticity in departure
<p>Research summary: Globally, bird migration is occurring earlier, consistent with climate-related changes in breeding resources. Although often attributed to phenotypic plasticity, there is no clear demonstration of long-term population advancement in avian migration through individual plasticity. Using direct observations of bar-tailed godwits (<em>Limosa lapponica</em>) departing New Zealand on a 16,000-km journey to Alaska, we show that migration advanced by six days during 2008–2020, and that within-individual advancement was sufficient to explain this population-level change. However, in individuals tracked for the entire migration, earlier departure did not lead to earlier arrival or breeding in Alaska, due to prolonged stopovers in Asia. Moreover, changes in breeding-site phenology varied across Alaska, but were not reflected in within-population differences in advancement of migratory departure. We demonstrate that plastic responses can drive population-level changes in timing of long-distance migration, but also that behavioral and environmental constraints <em>en route</em> may yet limit adaptive responses to global change.</p> <p>The collection of long-term departure data was supported by Chris & Neville Hopkins, David & Lucile Packard Foundation, Dobberke Foundation for Comparative Psychology, Manawatu Estuary Trust, Marsden Fund (Royal Society of New Zealand), Massey University Doctoral Scholarship, New Zealand Department of Conservation, Ornithological Society of New Zealand, Pacific Shorebird Migration Project, Pūkorokoro Miranda Naturalist’s Trust, and Royal Netherlands Academy of Arts & Sciences.</p>
CLRD-GLPS: A Long-term Seasonal Dataset of Ruminant Livestock Distribution in China's Grazing Production Systems (2000-2021) Using Stacking-based Interpretable Machine Learning
<p>Advanced computational methods integrating ensemble learning with interpretable machine learning are essential for precision livestock management under increasing environmental constraints and food security pressures. This study develops a novel stacking-based interpretable machine learning (IML) framework that combines multiple algorithms with SHAP analysis techniques to generate the China's Long-term Ruminant Livestock Distribution in Grazing Livestock Production Systems (CLRD-GLPS) dataset. Our computational approach addresses critical challenges in livestock distribution modelling: livestock segmentation and spatial prediction accuracy. The framework integrates Random Forest, XGBoost, CatBoost, LightGBM, and Extra Trees through a two-layer stacking architecture, enhanced with SHAP (Shapley Additive Explanations) analysis for model interpretability. We also implemented interpretable machine learning for livestock production system segmentation to distinguish grazing from total livestock populations. The stacking ensemble demonstrated superior performance over individual algorithms, achieving R² values of 0.954-0.961 for cattle and 0.896-0.901 for sheep and goats, with improvements of up to 8.3% compared to best performance single-model approaches. Multi-scale validation confirmed computational robustness: livestock segmentation achieved R² = 0.80 at county level, while independent city-level validation of CLRD-GLPS datasets yielded R² = 0.76-0.80. SHAP interpretability analysis revealed distinct environmental drivers, with vegetation indices and topography primarily influencing cattle distribution, while snow conditions and elevation dominated sheep and goat patterns. This computational framework advances livestock distribution modelling through enhanced prediction accuracy, model stability, and interpretability, while the CLRD-GLPS dataset provides essential spatial-temporal information for rangeland sustainability assessments and evidence-based livestock management policies. This dataset is supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP, grant no. 2019QZKK0906).</p>
Exploring the critical zone heterogeneity and the hydrological diversity using an integrated ecohydrological model in three contrasted long-term observatories
<p>These files provide useful data and supplementary material associated with the publication 'Exploring the critical zone heterogeneity and the hydrological diversity using an integrated ecohydrological model in three contrasted long-term observatories' (MNT information, atmospheric forcings, R scripts used to process and draw the graphs from the EcH2O-iso simulations, and observed water discharges).</p>
Long-Term Net Primary Productivity Dataset of the Tibetan Plateau from 1982 to 2013
<ul><li>This dataset is generated by the advanced CASA model, encompassing vegetation net primary productivity (NPP) raster data for the Tibetan Plateau from 1982 to 2013. The model's input parameters comprise NDVI time series, monthly average temperature, monthly total precipitation, monthly total solar radiation, and vegetation type. The data is provided at an 8 km × 8 km spatial resolution and is formatted in ENVI format (.dat).</li><li>Remarkably, during cross-validation with the MODIS 500m resolution product (MOD17A3HGF.061), it exhibited significantly strong correlations, with the correlation coefficients (R) ranging from 0.74 to 0.82.</li><li>Please cite this dataset as<br>Tan, Q., Sun, G., & Pang, Y. (2023). Long-Term Net Primary Productivity Dataset of the Tibetan Plateau from 1982 to 2013 (V1.0) [Data set]. Zenodo. https://doi.org/<a href="https://doi.org/10.5281/zenodo.10040818">10.5281/zenodo.10040818</a></li></ul>
Logic-Induced-Long-Tail (LINT)
<p>Logic-Induced-Long-Tail (LINT) dataset for arxiv paper "IN SEARCH OF THE LONG-TAIL: SYSTEMATIC GENERATION OF LONG-TAIL KNOWLEDGE VIA LOGICAL RULE GUIDED SEARCH."</p>
An Annotated Corpus of Tonal Piano Music from the Long 19th Century
<p>This corpus has been created within the <a href="https://github.com/DCMLab/dcml_corpora">DCML corpus initiative</a> and employs the <a href="https://github.com/DCMLab/standards">DCML harmony annotation standard</a>.</p> <p><strong>Version 1</strong> has been released for submitting it as part of the data report <code>Hentschel, J., Rammos, Y., Neuwirth, M., Rohrmeier, M. (forthcoming). An Annotated Corpus of Tonal Piano Music from the Long 19th Century</code> that accompanies nine corpora grouped under the DOI <a href="https://doi.org/10.5281/zenodo.7483349">10.5281/zenodo.7483349</a>.</p> <p><strong>Version 1.1</strong> comes with a complete set of metadata and score headers. Among more accurate composition dates, the metadata now include URIs that identify the compositions in terms of the <a href="https://viaf.org/">Virtual International Authority File (VIAF)</a>, <a href="https://www.wikidata.org/">Wikidata</a>, <a href="https://imslp.org/">IMSLP</a> and <a href="https://musicbrainz.org/">MusicBrainz</a>. The data has been re-extracted from the scores using <a href="https://pypi.org/project/ms3/">ms3 1.1.1</a>.</p> <p>The publication covers the following corpora (the DOI links always point at the latest version respectively):</p> <ul> <li><a href="https://doi.org/10.5281/zenodo.7473560">Ludwig van Beethoven - Piano Sonatas</a></li> <li><a href="https://doi.org/10.5281/zenodo.7473566">Frédéric Chopin - Mazurkas</a></li> <li><a href="https://doi.org/10.5281/zenodo.7473568">Claude Debussy - Suite Bergamasque</a></li> <li><a href="https://doi.org/10.5281/zenodo.7473576">Antonín Dvořák - Silhouettes</a></li> <li><a href="https://doi.org/10.5281/zenodo.7473580">Franz Liszt - Années de Pèlerinage</a></li> <li><a href="https://doi.org/10.5281/zenodo.7473528">Nikolai Medtner - Tales</a></li> <li><a href="https://doi.org/10.5281/zenodo.7473582">Robert Schumann - Kinderszenen</a></li> <li><a href="https://doi.org/10.5281/zenodo.7473586">Pyotr Tchaikovsky - The Seasons</a></li> <li><a href="https://doi.org/10.5281/zenodo.7473578">Edvard Grieg - Lyric Pieces</a></li> </ul> <p> </p>
Long-term monitoring of the fish community in the Minho Estuary (NW Iberian Peninsula)
<p>The dataset contains data from fyke nets deployed in the Minho Estuary (Portugal) from 2010 to 2019. The fyke nets were used for fish sampling and data collection. The sampling frequency varied but, on average, data was collected weekly using five different fyke nets. However, due to technical issues (e.g. lost or damaged fyke nets), the sampling pattern is not constant, with some fyke nets staying underwater for shorter or longer periods, and occasionally having fewer than five fyke nets per parentEventID. The dataset includes various terms such as parentEventID, eventID, eventDate, year, startDayOfYear, endDayOfYear, country, countryCode, geodeticDatum, decimalLatitude, decimalLongitude, coordinateUncertaintyInMeters, DEIMS.iD, habitat, basisOfRecord, samplingProtocol, sampleSizeValue, sampleSizeUnit, samplingEffort, occurrenceStatus, occurrenceID, organismQuantity, organismQuantityType, degreeOfEstablishment, vernacularName, scientificName, acceptedNameUsageID, taxonRank, kingdom, phylum, order, family, genus, and scientificNameAuthorship.</p>
Shapefiles showing the locations of long-term climate change refugia and hotspots identified in the FairSeas report "A Climate Resilient Path for Ireland's Marine Protected Areas Network"
<p>Shapefiles created for the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network", an addendum chapter to "Revitalising Our Seas report: Identifying<br>Areas of Interest for Marine Protected Area Designation in Irish Waters"</p> <p>These shapefiles summarise long-term patterns that emerge from the spatial-meta analysis of physical-biogeochemical and species distribution modelling data, providing an overview of the distribution of climate change refugia and climate change hotspots across Ireland's National Marine Planning Framework between 2026 - 2069, and across the two emissions scenarios considered in the report (RCP4.5 and RCP8.5). </p> <p>Filenames refer to the specific analysis each set of shapefiles belong to: Benthic habitats, benthic megafauna, pelagic habitats, pelagic megafauna and forage fish. Details of the modelling datasets used in each of these analyses, the meta-analysis method and shapefile creation can be found in Annex A1 in the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network".</p>
Coefficients for a Proxy Long-Wavelength Correction for the SWOT 1-Day Repeat Mission
<p>This archive contains NetCDF-formatted files containing the weighting coefficients which define the proxy long-wavelength correction (LWC) described in a manuscript submitted to AGU Earth and Space Science, "The Significance of the Long-Wavelength Correction for Studies of Baroclinic Tides with SWOT".</p>
HALOC Dataset | WiFi CSI-based Long-Range Person Localization Using Directional Antennas
<p><strong>WiFi CSI-based Long-Range Person Localization Using Directional Antennas</strong></p> <p>This repository contains the HAllway LOCalization (HALOC) dataset and WiFi system CAD files as proposed in <a href="https://openreview.net/forum?id=AOJFcEh5Eb" target="_blank" rel="noopener">[1]</a>.</p> <p><strong>PyTroch Dataloader</strong></p> <p>A minimal PyTorch dataloader for the HALOC dataset is provided at: <a href="https://github.com/StrohmayerJ/HALOC" target="_blank" rel="noopener">https://github.com/StrohmayerJ/HALOC</a></p> <p><strong>Dataset Description</strong></p> <p>The HALOC dataset comprises six sequences (in .csv format) of synchronized WiFi Channel State Information (CSI) and 3D position labels. Each row in a given .csv file represents a single WiFi packet captured via ESP-IDF, with CSI and 3D coordinates stored in the "data" and ("x", "y", "z") fields, respectively.</p> <p>The sequences are divided into training, validation, and test subsets as follows:</p> <table> <tbody> <tr> <td><strong>Subset</strong></td> <td><strong>Sequences</strong></td> </tr> <tr> <td>Training</td> <td>0.csv, 1.csv, 2.csv and 3.csv</td> </tr> <tr> <td>Validation</td> <td>4.csv</td> </tr> <tr> <td>Test</td> <td>5.csv</td> </tr> </tbody> </table> <p> </p> <p><strong>WiFi System CAD files</strong></p> <p>We provide CAD files for the 3D printable parts of the proposed WiFi system consisting of the main housing (housing.stl), the lid (lid.stl), and the carrier board (carrier.stl) featuring mounting points for the Nvidia Jetson Orin Nano and the ESP32-S3-DevKitC-1 module. </p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].</p> <p>[1] Strohmayer, J., and Kampel, M. (2024). “WiFi CSI-based Long-Range Person Localization Using Directional Antennas”, <em>The Second Tiny Papers Track at ICLR 2024</em>, May 2024, Vienna, Austria. <a href="https://openreview.net/forum?id=AOJFcEh5Eb" target="_blank" rel="noopener">https://openreview.net/forum?id=AOJFcEh5Eb</a></p> <p>BibTeX citation:</p> <pre>@inproceedings{<br>strohmayer2024wifi,<br>title={WiFi {CSI}-based Long-Range Person Localization Using Directional Antennas},<br>author={Julian Strohmayer and Martin Kampel},<br>booktitle={The Second Tiny Papers Track at ICLR 2024},<br>year={2024},<br>url={https://openreview.net/forum?id=AOJFcEh5Eb}<br>}</pre>
Change detection technique comparison in long-term wetland monitoring: datasets and maps of the Poitevin Marsh (France)
<h3>For a full description of the methodology and results, please see the following article:</h3> <div> <div>Demarquet, Q., Rapinel, S., Gore, O., Dufour, S., Hubert-Moy, L., 2024. Continuous change detection outperforms traditional post-classification change detection for long term monitoring of wetlands. <em>International Journal of Applied Earth Observation and Geoinformation </em>133, 104142. <a href="https://doi.org/10.1016/j.jag.2024.104142">https://doi.org/10.1016/j.jag.2024.104142</a></div> <div> </div> <div>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> </div> <h3># Datasets</h3> <p>Points datasets are projected in WGS84 (EPSG:4326), and are provided in the open source GeoPackage format.</p> <p>The first dataset (<strong>Dataset_1.gpkg</strong>) contains training and validation points for random forest classification of EUNIS habitats in the Poitevin Marsh. This dataset consists of 3360 training and 840 validation points (total: 4200).<br>Fields description:</p> <ul> <li>"<em>ID</em>": unique identifier</li> <li>"<em>CLASS</em>": EUNIS first level habitat type, classified as following:<br> <ul> <li>1: EUNIS habitat A</li> <li>2: EUNIS habitat B</li> <li>3: EUNIS habitat C1J5</li> <li>4: EUNIS habitat C3</li> <li>5: EUNIS habitat E</li> <li>6: EUNIS habitat G</li> <li>7: EUNIS habitat I</li> <li>8: EUNIS habitat J</li> </ul> </li> <li>"<em>DATE</em>": Date associated with EUNIS habitat sample</li> <li>"<em>LON</em>": Point longitude in decimal degrees</li> <li>"<em>LAT</em>": Point latitude in decimal degrees</li> <li>"<em>TYPE</em>": Either training ("<em>train</em>") or validation ("<em>test</em>") sample</li> </ul> <p>The second dataset (<strong>Dataset_2.gpkg</strong>) contains points for the Olofsson correction method. This dataset consists of 326 points where the change classes are classified as following: -10 (wetland loss), 10 (wetland gain), 100 (stable existing wetland), and 200 (stable damaged wetland).<br>Fields description:</p> <ul> <li>"<em>ID</em>": unique identifier</li> <li>"<em>LON</em>": Point longitude in decimal degrees</li> <li>"<em>LAT</em>": Point latitude in decimal degrees</li> <li>"<em>REFERENCE</em>": Change class reference</li> <li>"<em>CCDC</em>": Change class obtained from the Continuous Change Detection and Classification approach</li> <li>"<em>PCCD</em>": Change class obtained from the Post-Classification Change Detection approach</li> </ul> <p>Supplementary layout files (<strong>Dataset_1.qml</strong> and <strong>Dataset_2.qml</strong>) support formatting of the points in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># EUNIS habitat</h3> <p>Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. </p> <p>Habitat maps are given for the two approaches in years 1984 and 2022:</p> <ul> <li>CCDC: Continuous Change Detection and Classification (<strong>CCDC_HABITAT_1984.tif</strong> and <strong>CCDC_HABITAT_2022.tif</strong>)</li> <li>PCCD: Traditional post-classification approach (<strong>PCCD_HABITAT_1984.tif </strong>and <strong>PCCD_HABITAT_2022.tif</strong>)</li> </ul> <p>Supplementary layout files (<strong>CCDC_HABITAT_1984.qml, CCDC_HABITAT_2022.qml, PCCD_HABITAT_1984.qml, PCCD_HABITAT_2022.qml</strong>) support formatting of raster layers in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># Change detection during the 1984-2022 period</h3> <p>Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. Raster values follow the classification scheme used in Dataset_2.</p> <p>Change detection maps are given for the two approaches:</p> <ul> <li>CCDC: Continuous Change Detection and Classification (<strong>CCDC_CHANGE_1984_2022.tif</strong>)</li> <li>PCCD: Traditional post-classification approach (<strong>PCCD_CHANGE_1984_2022.tif</strong>)</li> </ul> <p>Supplementary layer files (<strong>CCDC_CHANGE_1984_2022.qml</strong> and<strong> PCCD_CHANGE_1984_2022.qml</strong>) support formatting of the raster layers in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># GEE repository</h3> <p>To get direct access to GEE scripts and assets, please follow those two links:</p> <p>https://code.earthengine.google.com/?accept_repo=users/demarquetquentin/CCDC_Poitevin</p> <p>https://code.earthengine.google.com/?asset=projects/ee-quen-dem/assets/CCDC_Poitevin</p>
DELTA: Dense Electromyography for Long-Term Adaptive control
<p>The <em><strong>DELTA </strong></em>dataset, namely ”<strong>D</strong>ense <strong>E</strong>lectromyography for <strong>L</strong>ong-<strong>T</strong>erm <strong>A</strong>daptive control”, holds significance in the realm of prosthetic applications, featuring High Density surface Electromyography data collected over an extended duration. It serves as a valuable resource for training data-driven models and testing them under conditions that closely emulate real-world prosthetic applications. Recognizing the temporal variability in EMG data, constructing models that are agnostic to such fluctuations could significantly enhance the efficacy of machine learning models in prosthetic applications</p>
Data for recreation (with adjustments) of Wheatley's 1996 long barrow viewshed analysis
<p>Following Wheatley's (1996) viewshed analysis of real and simulated long barrows in two regions of Wiltshire, this study aimed to replicate the analysis (investigating regional variation in barrow viewsheds) with the additional factor of elevation included to limit the random generation of long barrows to elevations where they have been observed, to avoid potential skewing of viewshed areas.</p> <p>This dataset contains 20x20km squares surrouding Avebury and Stonehenge which match Wheatley's demarcated 'subregions;' as well as polygons matching the elevation ranges within which long barrows were found in each subregion; random points generated in these subregions; the calculated viewshed areas for real and simulated barrow points, and a complete dataset for long barrows recorded on the Wiltshire HER, both certain and potential. To carry out a viewshed analysis with this data, a DTM is also required.</p>
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.