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635 results for “attribution”
Attribution of 2022 August Heavy Precipitation Event in South Korea Using High-resolution Pseudo Global Warming Simulations: Sensitivity to Vertical Temperature Changes
<p>Post-processed CPM simulation datasets used for the paper "Attribution of 2022 August Heavy Precipitation Event in South Korea Using High-resolution Pseudo Global Warming Simulations: Sensitivity to Vertical Temperature Changes".</p>
A geospatial dataset of lichen key attributes in the Earth's three poles
<p>To develop the geospatial dataset, we initially defined two lichen attributes: color type and growth form. These attributes were chosen due to their significant correlation with lichen physiological and biochemical characteristics, as well as their association with reflection spectra. Each record of this geospatial dataset consists of information such as scientific name, longitude, latitude, ecoregion name, biome name, color type, growth form, and the occurranceID belongs to the GBIF original dataset.</p> <p>This dataset serves as a foundational resource for extensive investigations into the intricate interplay between lichen physiology and the environment, addressing a significant knowledge gap in the field. Furthermore, our dataset holds the potential to address challenges associated with remote sensing monitoring of lichens, a longstanding issue in vegetation remote sensing. Precise in situ observation records, as provided by our dataset, can facilitate the development of remote sensing techniques tailored for lichen monitoring.</p> <p>"Program" is the code used in the process of establishing the geospatial dataset of lichen key attributes in the Earth’s three poles.</p> <p> </p>
CAMELS-ES: Catchment Attributes and Meteorology for Large-Sample Studies – Spain
<p>CAMELS-ES is a hydrometeorological dataset covering 269 catchments in Spain and the time period from 1991 to 2020. It is a contribution to the Caravan initiative, a global community that collects open hydrometeorological data to support global hydrological modelling. As other datasets in Caravan, CAMELS-ES includes both catchment attributes extracted from HydroATLAS and ERA5-Land, meteorological time series from ERA5-Land and discharge records from the Spanish Ministry of the Environment. In addition, CAMELS-ES includes information from the European Flood Awareness System (EFASv5): catchment attributes extracted from the input static maps used in the hydrological model LISFLOOD, and the simulated discharge from EFASv5 long run.</p>
Recognized Fault Locations and Attributes, Getaberget, Åland Islands
<p>We mapped faults using their secondary indicators such as damage zones and secondary fracturing at Getaberget shoreline, Åland Islands, Finland.</p> <p>Coordinates are in x and y -columns in<em> EPSG:3067 ETRS-TM35FIN</em> coordinate system.</p> <p>The field mapping and photos were taken as part of a Geological Survey of<br> Finland project, KYT KARIKKO, with funding from Finnish National Nuclear Waste<br> Management Fund (KYT) during the summers of 2020 and 2021.</p>
Combining Horizontal Strain DAS and Local Seismic Stations in a Full Waveform Attribute Stacking Detector/Locator Algorithm: Verification Test for the Thorbjörn, Iceland, 2020 Unrest Episode
<p>We present a waveform stacking-based earthquake catalog of the seismicity unrest episode in the Svartsengi fissure swarm close to Mt. Thorbjörn, SW Iceland, which started in January 2020 and was still ongoing in January 2021. The magmatic unrest produced more than 5 earthquake swarms comprising thousands of individual events each. We were able to combine local and regional seismic networks with 6 months recording of a 17 km long distributed acoustic sensing (DAS) fibre optical cable with a channel resolution of 4 m. The kHz DAS data were downsampled to 200 Hz and stacked every 64 m. The catalog is based on a migration-based detector / locator technique as for instance implemented in Lassie (Pyrocko). In the accompanying we demonstrate the robustness in a wide variety of applications in seismology. For this dataset, we have extended Lassie to efficiently combine linear ultra-dense sensor arrays with sparse seismological networks.</p>
Reproducible and Attributable Materials Science Workflows
<p>This set includes the deidentified data, reproducible analysis and research report of the project on Reproducible and Attributable Materials Science Workflows.</p>
Usage and Attribution of Stack Overflow Code Snippets in GitHub Projects — Supplementary Material
<p><em>Background:</em> Stack Overflow (SO) is the largest Q&A website for software developers, providing a huge amount of copyable code snippets. Using those snippets raises various maintenance and legal issues. SO’s license (CC BY-SA 3.0) requires attribution, i.e., referencing the original question or answer, and requires derived work to adopt a compatible license. While there is a heated debate on SO’s license model for code snippets and the required attribution, little is known about the extent to which snippets are copied from SO without proper attribution.</p> <p><em>Aim:</em> Our main goal was to analyze how often code from SO posts is used in public GitHub projects, but not attributed as required by the license. Further, we wanted to investigate if developers are aware of SO’s license and its implications, and to what degree they adhere to the attribution requirements defined in SO’s terms of service.</p> <p><em>Method:</em> We present results of a large-scale empirical study analyzing the usage and attribution of non-trivial Java code snippets from SO answers in public GitHub projects. We followed three different approaches to triangulate an estimate for the ratio of unattributed usages and conducted two online surveys with software developers to complement our results.</p> <p><em>Results:</em> For the different sets of projects that we analyzed, the amount of projects containing files with a reference to SO varied between 3.3% and 11.9%. We found that at most 1.8% of all analyzed repositories containing code from SO used the code in a way compatible with CC BY-SA 3.0. Moreover, we estimate that at most a quarter of the copied code snippets from SO are attributed as required, i.e., using a link in a source code comment. About half of the surveyed developers admitted copying code from SO without attribution. Furthermore, about two thirds of them were not aware of the license of SO code snippets and its implications.</p>
Ancillary data for article "The general formulation for runoff components estimation and attribution at mean annual time scale"
<p>The mean annual (1960-1990) precipitation and estimated model parameters wetting potential (Wp), vaporization potential (Vp) and upper limit of <span>the portion remaining after precipitation (</span>Up) of 312 catchments over China.</p>
Paired Vegetation and Soil Burn Severity Metrics and Associated Climate, Weather, Topographical, and Land Cover Attributes
<p>This dataset pairs differenced Normalized Burn Ratio (dNBR) and soil burn severity (SBS) for 254 large (>400 ha in size) fires across the western US. Dataset also includes climate, weather, topography, physical and chemical soil characteristics, and land cover attributes of each burned pixel at the time of fire. This effort provided a table of 16.3 million burned pixels and their associated characteristics including dNBR, SBS, and 94 biological and physical covariates. After removing correlated features, the final data includes 18 fire covariates namely: dNBR, elevation, slope, aspect, land cover type, wind speed, energy release component, vapor pressure deficit, annual precipitation, and annual average daily max temperature, as well as the clay, sand and silt content of the soil and volumetric fraction of coarse fragments and soil organic carbon content. We also included spatial coherence metrices for dNBR, including DVAR, SHADE and SAVG. This data is provided as CSV files in Xtrain, Xvalidation, Xtest, as well as Ytrain, Yvalidation, and Ytest; in which X files (model input) provide all features except for SBS and Y files (model output) include SBS.</p><p>We also provided this data for an additional 16 large fires across the western US ("Extra Test" folder, including Dataset – X file – and Label – Y file).</p><p>Finally, the trained XGBoost model to translate dNBR to SBS using the associated features is also provided in this folder.</p>
Structural, ecological and biogeographical attributes of European vegetation alliances
<p>This is a database of structural, ecological and biogeographical attributes of 1115 European phytosociological alliances. The original version was published by Preislerová et al. (2024). This article also contained definitions and descriptions of individual attributes.</p> <p>Version 2 of this dataset has been updated to match Version 3 of EuroVegChecklist (Mucina et al. 2016) published on https://floraveg.eu/download/. This version contains syntaxonomic changes in the vegetation of coastal dunes (classes <em>Ammophiletea arundinaceae</em>, <em>Helichryso-Crucianelletea maritimae</em> and <em>Honckenyo peploidis-Leymetea arenarii</em>), Mediterranean pine forests (order <em>Pinetalia halepensis</em>) and bogs (class <em>Oxycocco-Sphagnetea</em>) approved by the European Vegetation Classification Committee in January 2024 following the proposals published by Marcenò et al. (2018, 2024), Bonari et al. (2021) and Jiroušek et al. (2022), respectively.</p> <p>The data are provided in two files with identical contents, one in the XLSX format, and the other in the TXT format with columns separated by tabs.</p> <p><strong>References</strong></p> <div> <div> <div> <ul> <li>Bonari G., Fernández‐González F., Çoban S., Monteiro‐Henriques T., Bergmeier E., Didukh Ya. P. … Chytrý, M. (2021). Classification of the Mediterranean lowland to submontane pine forest vegetation. <em>Applied Vegetation Science</em>, 24, e12544. <a href="https://doi.org/10.1111/avsc.12544">https://doi.org/10.1111/avsc.12544</a></li> <li>Jiroušek, M., Peterka, T., Chytrý, M., Jiménez-Alfaro, B., Kuznetsov, O.L., Pérez-Haase, A. … Hájek, M. (2022). Classification of European bog vegetation of the Oxycocco-Sphagnetea class. <em>Applied Vegetation Science</em>, 25, e12646. <a href="https://doi.org/10.1111/avsc.12646">https://doi.org/10.1111/avsc.12646</a></li> <li>Marcenò, C., Guarino, R., Loidi, J., Herrera, M., Isermann, M., Knollová, I. … Chytrý, M. (2018). Classification of European and Mediterranean coastal dune vegetation. <em>Applied Vegetation Science</em>, 21, 533–559. <a href="https://doi.org/10.1111/avsc.12379">https://doi.org/10.1111/avsc.12379</a></li> <li>Marcenò, C., Danihelka, J., Dziuba, T., Willner, W. & Chytrý, M. (2024). Nomenclatural revision of the syntaxa of European coastal dune vegetation. <em>Vegetation Classification and Survey</em>, 5, 27–37. <a href="https://doi.org/10.3897/VCS.108560">https://doi.org/10.3897/VCS.108560</a></li> <li>Mucina, L., Bültmann, H., Dierßen, K., Theurillat, J.-P., Raus, T., Čarni, A. … Tichý, L. (2016). Vegetation of Europe: Hierarchical floristic classification system of vascular plant, bryophyte, lichen, and algal communities. <em>Applied Vegetation Science</em>, 19(Suppl. 1.), 3–264. <a href="https://doi.org/10.1111/avsc.12257">https://doi.org/10.1111/avsc.12257</a></li> <li>Preislerová Z., Marcenò C., Loidi J., Bonari G., Borovyk D., Gavilán R.G., Golub V., Terzi M., Theurillat J.-P., Argagnon O., Bioret F., Biurrun I., Campos J.A., Capelo J., Čarni A., Çoban S., Csiky J., Ćuk M., Ćušterevska R., Dengler J., Didukh Ya., Dítě D., Fanelli G., Fernández-González F., Guarino R., Hájek O., Iakushenko D., Iemelianova S., Jansen F., Jašková A., Jiroušek M., Kalníková V., Kavgacı A., Kuzemko A., Landucci F., Lososová Z., Milanović Đ., Molina J.A., Monteiro-Henriques T., Mucina L., Novák P., Nowak A., Pätsch R., Perrin G., Peterka T., Rašomavičius V., Reczyńska K., Rūsiņa S., Sánchez Mata D., Santos Guerra A., Šibík J., Škvorc Ž., Stešević D., Stupar V., Świerkosz K., Tzonev R., Vassilev K., Vynokurov D., Willner W. & Chytrý M. (2024) Structural, ecological and biogeographical attributes of European vegetation alliances. <em>Applied Vegetation Science</em>, 27, e12766. <a href="https://doi.org/10.1111/avsc.12766">https://doi.org/10.1111/avsc.12766</a></li> </ul> </div> </div> </div>
Self-attribution of distorted reaching movements in immersive virtual reality
<p>This dataset and Unity 3D code scripts are associated to the following paper : H. Debarba, R. Boulic, R. Solomon, O. Blanke, B. Herbelin (Computers & Graphics, Vol 76, November 2018, pp 142-152, <a href="https://www.sciencedirect.com/science/article/pii/S0097849318301353?utm_campaign=STMJ_75273_AUTH_SERV_PPUB&utm_medium=email&utm_dgroup=&utm_acid=810891&SIS_ID=0&dgcid=STMJ_75273_AUTH_SERV_PPUB&CMX_ID=&utm_in=DM377782&utm_source=AC_30">in Open Access</a>) : “Self-attribution of distorted reaching movements in immersive virtual reality”. <a href="https://doi.org/10.1016/j.cag.2018.09.001">https://doi.org/10.1016/j.cag.2018.09.001</a></p> <p> </p>
Attribution of intentional agency towards robots reduces one's own sense of agency.
<p>### Attribution of intentional agency towards robots reduces one’s own sense of agency ###</p> <p>The data presented here are reported in Ciardo, Beyer, De Tommaso & Wykowska (accepted). Attribution of intentional agency towards robots reduces one’s own sense of agency. Cognition.</p> <p>Please refer to that paper for context and method. </p> <p>Files descriptions:<br> Raw Data.csv: Raw data of the three experiments. Please read the .txt file for variables definition.<br> Exp3_Data_Goodspeed.csv: Goodspeed questionnaire data of Experimet 3.<br> Listof Variables:..txt file with definition of variables and labels.</p>
Stand structure and tree population dynamic attribute dataset of long abandoned strict forest reserves
<p>We provide an integrated dataset of two consecutive forest inventories, both containing plot-level, and individual tree-level data. The first provides the descriptions and measuring units (or categories) of plot-level variables (Table 1). The plot level table contains 233 records (rows), one for each selected permanent plot of six strict forest reserves located in Hungary. This dataset is georeferenced and contains information on inventories and basic stand structure attributes (Table_1_Plots ESRI shape format). </p> <p>The individual tree-level datasets were acquired by the sampling procedure, detailed in section 2.2. Species, dendrometric attributes, relative crown position, health, and decay status were documented for each tree belonging to the samples. Table 2 provides the descriptions and measuring units (or categories) of tree-level datasets in detail. Furthermore, it provides a tree history classification based on the interpretation of tree status changes. According to a simple scheme of the life and dead history of a tree, it could be classified into four main phases: establishment/regeneration phase; developmental phase; death and gradual decay of the tree trunk; terminated in decomposed/disintegrated state. The main events along these phases are ingrowth regeneration; death of the tree (mortality); disaggregation and decomposition of deadwood. We classify each sampled tree individuals into tree history categories (events and phases, Table 3) that can provide population dynamic aspects at stand level by appropriate tree aggregation functions.</p> <p>Relational link can be set between the plot-level and tree-level datasets based on the unique identification code of the site and sampling plots.</p>
Appendices of the work "On the perceived relevance of critical internal quality attributes when evolving software features"
<p>Several refactorings performed while evolving software features aim to improve internal quality attributes like cohesion and complexity. Studies show that non-assisted refactorings might worsen, not improve, internal attributes. Current knowledge is scarce on how developers perceive the relevance of critical internal attributes while evolving features. Internal attributes are critical if their measurement assumes anomalous values. This qualitative study investigates the developer's perception on the relevance of critical internal attributes when evolving features. We target six class-level critical attributes: low cohesion, high complexity, high coupling, large hierarchy depth, large hierarchy breadth, and large size. We performed two industry case studies based on online focus group sessions. Developers discussed how much (and why) critical attributes are relevant for adding or enhancing features. We assessed the relevance of critical attributes individually and relatively, reasons behind the relevance of each critical attribute, and interrelations of critical attributes. Low cohesion and high complexity were perceived as very relevant because they often make evolving features hard while tracking failures and adding features. The other critical attributes were perceived as less relevant when reusing code or adopting design patterns. An example of perceived interrelation is high complexity leading to high coupling.</p>
Appendices of the work "On the perceived relevance of critical internal quality attributes when evolving software features"
<p>Several refactorings performed while evolving software features aim to improve internal quality attributes like cohesion and complexity. Studies show that non-assisted refactorings might worsen, not improve, internal attributes. Current knowledge is scarce on how developers perceive the relevance of critical internal attributes while evolving features. Internal attributes are critical if their measurement assumes anomalous values. This qualitative study investigates the developer's perception on the relevance of critical internal attributes when evolving features. We target six class-level critical attributes: low cohesion, high complexity, high coupling, large hierarchy depth, large hierarchy breadth, and large size. We performed two industry case studies based on online focus group sessions. Developers discussed how much (and why) critical attributes are relevant for adding or enhancing features. We assessed the relevance of critical attributes individually and relatively, reasons behind the relevance of each critical attribute, and interrelations of critical attributes. Low cohesion and high complexity were perceived as very relevant because they often make evolving features hard while tracking failures and adding features. The other critical attributes were perceived as less relevant when reusing code or adopting design patterns. An example of perceived interrelation is high complexity leading to high coupling.</p>
The global water resources and use model WaterGAP v2.2e: location and attributes of reservoirs and regulated lakes
<p>This dataset contain the location and attributes of the reservoirs and regulated lakes in WaterGAP v2.2e. This dataset is provided to be transparent how the reservoirs are included in this WaterGAP version and e.g. to check deviations from the locations as provided by ISIMIP (www.isimip.org).</p> <p>Please see the readme.md for furhter details and please consider the license terms from the data sources listed in the readme.md.</p>
CoastSeg: 30-m atlas of the coastal shoreline attributes of California, in geoJSON format.
<p><strong>CoastSeg: 30-m atlas of the coastal shoreline attributes of California, in geoJSON format.</strong></p> <p>This is a shoreline atlas of California at 30m resolution, to support analysis of CoastSat/CoastSeg-derived shoreline time-series and other shoreline data, and miscellaneous analyses of coastal shoreline data. The dataset consists of a GeoJSON files containing a 30-m shoreline estimate for California, based on an analysis of 2014 Landsat imagery (Sayre et al., 2019). This shoreline vector has been attributed with the following fields that may be useful in analyses of shoreline patterns and regional variability:</p> <ol> <li>MEAN_SIG_WAVEHEIGHT (m)</li> <li>TIDAL_RANGE (m)</li> <li>CHLOROPHYLL (mg/L)</li> <li>TURBIDITY</li> <li>TEMP_MOISTURE (descriptive)</li> <li>EMU_PHYSICAL (descriptive)</li> <li>REGIONAL_SINUOSITY</li> <li>GHM</li> <li>MAX_SLOPE (%)</li> <li>OUTFLOW_DENSITY</li> <li>ERODIBILITY (descriptive)</li> <li>LENGTH_GEO</li> <li>ch_label (descriptive)</li> <li>river_label (descriptive)</li> <li>sinuosity_label (descriptive)</li> <li>slope_label (descriptive)</li> <li>tidal_label (descriptive)</li> <li>turbid_label (descriptive)</li> <li>wave_label (descriptive)</li> <li>CSU_Descriptor (descriptive)</li> <li>CSU_ID</li> <li>elevation (m)</li> <li>aspect (degrees N)</li> <li>slope (degrees)</li> </ol> <p>Fields 1 to 21 inclusive originally come from raw data https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/USGSEsriGlobalCoastalSegmentsv1.mpk, which is described in Sayre et al (2019)</p> <p>Fields 22 and 24 come from raw data originally in the U.S. Geological Survey Elevation Derivatives for National Applications (EDNA) database (https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-elevation-derivatives-national), accessed through Earth Explorer and processed in QGIS.</p> <p>The figure shows distributions of selected quantities. A python script to reproduce this plot is provided</p> <p>A subset of numeric-only variables and descriptive-only variables has also been prepared and made available. A CSV version of the full dataset is also provided</p> <p> </p> <p><strong>References</strong></p> <ol> <li>Roger Sayre, Suzanne Noble, Sharon Hamann, Rebecca Smith, Dawn Wright, Sean Breyer, Kevin Butler, Keith Van Graafeiland, Charlie Frye, Deniz Karagulle, Dabney Hopkins, Drew Stephens, Kevin Kelly, Zeenatul Basher, Devon Burton, Jill Cress, Karina Atkins, D. Paco Van Sistine, Beverly Friesen, Rebecca Allee, Tom Allen, Peter Aniello, Irawan Asaad, Mark John Costello, Kathy Goodin, Peter Harris, Maria Kavanaugh, Helen Lillis, Eleonora Manca, Frank Muller-Karger, Bjorn Nyberg, Rost Parsons, Justin Saarinen, Jac Steiner & Adam Reed (2019) A new 30 meter resolution global shoreline vector and associated global islands database for the development of standardized ecological coastal units, Journal of Operational Oceanography, 12:sup2, S47-S56, DOI: <a href="https://doi.org/10.1080/1755876X.2018.1529714">10.1080/1755876X.2018.1529714</a></li> <li><a href="https://doi.org/10.5066/F7TD9VTQ">Elevation Derivatives for National Applications (EDNA) Seamless Three-Dimensional Hydrologic Database Digital Object Identifier (DOI) number: /10.5066/F7TD9VTQ</a></li> </ol> <p> </p>
Input data for: Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution
<p>This Zenodo archive contains essential input datasets utilized in our <a href="https://doi.org/10.5194/essd-2023-112">research study</a> titled "Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution". </p><p>This archive contains only input data. The Hourly Coastal water levels with Counterfactual (HCC) dataset is published in the <a href="https://doi.org/10.48364/ISIMIP.749905">ISIMIP repository</a>.</p><p><strong>Datasets Included</strong>:</p><p><strong>CoDEC (Coastal Dataset for the Evaluation of Climate Impact)</strong>:</p><ul><li>This dataset is described in<a href="https://doi.org/10.3389/fmars.2020.00263"> Muis et al. (2020)</a></li><li><strong>cf_esl folder</strong>: Contains data representing total CoDEC water levels. Individual NetCDF files store data for each grid point.</li><li><strong>cf_tides folder</strong>: This folder holds data related to tidal elevation.</li><li><strong>coor_coastal.nc</strong>: A NetCDF file featuring the spatial grid utilized in CoDEC. This dataset comprises only coastal grid points.</li></ul><ol><li><strong>HR (Hybrid Reconstructions)</strong>:<ul><li><strong>HybridRec_Upd0422.mat</strong>: This file contains data from the Hybrid Reconstructions dataset (<a href="https://doi.org/10.1038/s41558-019-0531-8">Dangendorf et al 2019</a>), aligned to the CoDEC grid, and includes satellite altimetry integral to producing the Hybrid Reconstructions dataset. Each row corresponds to one grid point on the CoDEC grid. For ease of use in our applications, we offer a preprocessing script in our <a href="https://doi.org/10.5281/zenodo.7771501">source code</a> named split_hr_dataset_to_stations.py.</li></ul></li></ol><p>We here provide the specific versions of HR and CoDEC that are used in our study to ensure accurate replication.</p>
GAMMA: Galactic Attributes of Mass, Metallicity, and Age Dataset
<p>We introduce the <strong>GAMMA</strong> (<strong>G</strong>alactic <strong>A</strong>ttributes of <strong>M</strong>ass, <strong>M</strong>etallicity, and <strong>A</strong>ge) dataset, a comprehensive collection of galaxy data tailored for Machine Learning applications. This dataset offers detailed 2D maps and 3D cubes of 11 727 galaxies, capturing essential attributes: stellar age, metallicity, and mass.</p><p>Together with the dataset we publish our code to extract any other stellar or gaseous property from the raw simulation suite to extend the dataset beyond these initial properties, ensuring versatility for various computational tasks. Ideal for feature extraction, clustering, and regression tasks, <strong>GAMMA</strong> offers a unique lens for exploring galactic structures through computational methods and is a bridge between astrophysical simulations and the field of scientific machine learning (ML).</p><p>As a first benchmark, we apply Principal Component Analysis (PCA) on this dataset. We find that PCA effectively captures the key morphological features of galaxies with a small number of components. We achieve a dimensionality reduction by a factor of ∼200 (∼3650) for 2D images (3D cubes) with a reconstruction accuracy below 5%.</p><p>We calculate UMAP (Uniform Manifold Approximation and Projection for Dimension Reduction) on the lower dimensional PCA scores of the 2D images to visualize the image space. An interactive version of this plot can be accessed using an online <a href="https://umap-dashboard.onrender.com/">Dashboard</a> (hover over a point to see the galaxy image and the IllustrisTNG Subhalo ID).</p><p>All the code to generate this dataset and load the data structure is publicly available on <a href="https://github.com/ufuk-cakir/GAMMA">GitHub</a>, with an additional documentation page hosted on <a href="https://gamma-dataset.readthedocs.io/en/latest/">ReadTheDocs</a>.</p><p> </p>
Physical, Social, and Biological Attributes for Improved Understanding and Prediction of Wildfires: FPA FOD-Attributes Dataset
<p><strong>Wildfires are increasingly impacting social and environmental systems in the United States. The ability to mitigate the undesirable effects of wildfires increases with the understanding of the social, physical, and biological conditions that co-occurred with or caused the wildfire ignitions and contributed to the wildfire impacts. To this end, we developed the FPA FOD-Attributes dataset, which augments the sixth version of the Fire Program Analysis-Fire Occurrence Database (FPA FOD v6) with nearly 270 attributes that coincide with the date and location of each wildfire ignition in the contiguous United States (CONUS). FPA FOD v6 contains information on the location, jurisdiction, discovery time, cause, and final size of >2.2 million wildfires from 1992-2020 in CONUS. For each wildfire, we added physical (e.g., weather, climate, topography, infrastructure), biological (e.g., land cover, normalized difference vegetation index), social (e.g., population density, social vulnerability index), and administrative (e.g., national and regional preparedness level, jurisdiction) attributes. This publicly available dataset can be used to answer numerous questions about the covariates associated with human- and lightning-caused wildfires. Furthermore, the FPA FOD-Attributes dataset can support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models.</strong></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.