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242 results for “Spatial Dataset”
Spatial variability in water chemistry of four Wisconsin aquatic ecosystems - High speed limnology Environmental Science and Technology datasets
Advanced sensor technology is widely used in aquatic monitoring and research. Most applications focus on temporal variability, whereas spatial variability has been challenging to document. We assess the capability of water chemistry sensors embedded in a high-speed water intake system to document spatial variability. We developed a new sensor platform to continuously samples surface water at a range of speeds (0 to > 45 km hr-1) resulting in high-density, meso-scale spatial data. Here, we archive data associated with an Environmental Science and Technology publication. Data include a single spatial survey of the following aquatic ecosystems: Lake Mendota, Allequash Creek, Pool 8 of the Upper Mississippi River, and Trout Bog. Data have been provided in three formats (raw, hydraulic-corrected, and tau-corrected).
Mississippi River spatial water chemistry Environmental Research Letters datasets
We mapped surface water chemistry along the entire length of the Upper Mississippi River (UMR) to understand spatial patterns in nitrate sources and processing. We used a sensor-based and boat-mounted sensing platform to continuously measure underway water chemistry. Measurements were linked with global positioning systems (GPS) to create maps of surface water chemistry. Here, we archive data associated with an Environmental Research Letters publication (Loken et al. 2018). Data include a single spatial survey of the entire length of the UMR (Minneapolis, Minnesota to Cairo, Illinois) in August 2015 and repeat surveys in Navigation Pool 8 (located near La Crosse, WI). Data have been provided in three formats (raw, hydraulic-corrected, and tau-corrected). Additionally, we archive laboratory chemistry data from water samples collected during the project. Sites include a range of main channel, backwaters, and tributaries. Water chemistry samples were analyzed at the North Temperate Lakes - Long Term Ecological Research facility and linked with underway sensor measurements.
Dataset for the study Multisensory spatial perception in visually impaired infants
<p>Data from the study "Multisensory spatial perception in visually impaired infants". Data are in textual tab-delimited format.</p> <p> </p> <p>Summary</p> <p>Congenitally blind infants are not only deprived of visual input but also of visual influences on the intact senses. The important role that vision plays in the early development of multisensory spatial perception<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib1">1</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib2">2</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib3">3</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib4">4</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib5">5</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib6">6</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib7">7</a> (e.g., in crossmodal calibration<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib8">8</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib9">9</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib10">10</a> and in the formation of multisensory spatial representations of the body and the world<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib1"><sup>1</sup></a><sup>,</sup><a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib2"><sup>2</sup></a>) raises the possibility that impairments in spatial perception are at the heart of the wide range of difficulties that visually impaired infants show across spatial,<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib8">8</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib9">9</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib10">10</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib11">11</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib12">12</a> motor,<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib13">13</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib14">14</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib15">15</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib16">16</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib17">17</a> and social domains.<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib8"><sup>8</sup></a><sup>,</sup><a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib18"><sup>18</sup></a><sup>,</sup><a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib19"><sup>19</sup></a> But investigations of early development are needed to clarify how visually impaired infants’ spatial hearing and touch support their emerging ability to make sense of their body and the outside world. We compared sighted (S) and severely visually impaired (SVI) infants’ responses to auditory and tactile stimuli presented on their hands. No statistically reliable differences in the direction or latency of responses to <a href="https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/auditory-stimulation">auditory stimuli</a> emerged, but significant group differences emerged in responses to tactile and audiotactile stimuli. The visually impaired infants showed attenuated audiotactile spatial integration and interference, weighted more tactile than auditory cues when the two were presented in conflict, and showed a more limited influence of representations of the external layout of the body on tactile spatial perception.<a href="https://www.sciencedirect.com/science/article/pii/S0960982221012513#bib20"><sup>20</sup></a> These findings uncover a distinct phenotype of multisensory spatial perception in early postnatal visual deprivation. Importantly, evidence of audiotactile spatial integration in visually impaired infants, albeit to a lesser degree than in sighted infants, signals the potential of multisensory rehabilitation methods in early development.</p> <p>Orienting responses and reaction times (RT) are reported, based on the scoring of two independent naive raters, for each trial of each subject, group (SVI/S), posture (Uncrossed/Crossed), and sensory condition (Tactile only, Auditory only, Audiotactile congruent, Audiotactile incongruent).</p> <p>Trial is the trial number, condition is the sensory condition, audio and tactile respectively refer to the side of the stimulated hand, response_status reports if the response is defined or undefined, response modality reports if the modality used by subjects to respond/not to respond to stimuli (hand, eye, both hands, no motion), group is if the subject was a sighted (S) or a severely visually impaired (SVI) infant, age_mounth is the age expressed in months, RT_rater1, RT_rater 2 and RT are respectively the RT assigned by the two raters and the merge of the two estimations (for RTs, the mean), the same organization for response_side, and for response_modality (for those variables, when the estimation of the two raters did not agree, the merged classification was set to unknown, that is uncertain/undefined).</p>
Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain – Dataset
<p><strong>Dataset of <a href="https://doi.org/10.1109/jstars.2022.3188922">Hugonnet et al. (2022), Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain</a>.</strong></p> <p>The data is composed of:</p> <ul> <li><strong>For the Mont-Blanc case study: </strong>the Pléiades reference DEM, the SPOT-6 DEM, the Pléiades–SPOT-6 elevation difference, and the forest mask generated from the ESA CCI landcover (delainey polygonization);</li> <li><strong>For the Northern Patagonian Icefield case study: </strong>the ASTER reference DEM, the SPOT-5 DEM, the ASTER–SPOT-5 elevation difference, and the quality of stereo-correlation of the ASTER DEM from MicMac.</li> </ul> <p>The filenames correspond to those used in the <strong>associated GitHub repository</strong>: <a href="https://github.com/rhugonnet/dem_error_study">https://github.com/rhugonnet/dem_error_study</a>. The shapefiles used for masking glaciers are available directly from the <strong>Randolph Glacier Inventory 6.0</strong> at <a href="https://www.glims.org/RGI/">https://www.glims.org/RGI/</a>.</p> <p>The date of the DEMs is in their original format: <strong>year-month-day for all but ASTER</strong> that has the original naming of <a href="https://lpdaac.usgs.gov/products/ast_l1av003/">AST L1A products</a>. <strong>Units are meters</strong> for the DEMs and elevation differences, <strong>and percentages</strong> for the quality of stereo-correlation.</p>
2007 Environmental Protection Agency (EPA) National Lakes Assessment dataset plus derived data and additional spatially explicit ancillary environmental data.
Lake water quality is known to be affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology conceptual framework using a random forest algorithm on large, national-scale, spatially explicit dataset, the United States Environmental Protection Agency 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity).
MeteoSerbia1km: the first daily gridded meteorological dataset at a 1-km spatial resolution across Serbia for the 2000–2019 period
<p>MeteoSerbia1km is the first daily gridded meteorological dataset at a 1-km spatial resolution across Serbia for the 2000–2019 period. The dataset consists of five daily variables: maximum, minimum and mean temperature, mean sea level pressure, and total precipitation. Besides daily summaries, it contains monthly and annual summaries, daily, monthly, and annual long term means (LTM). Daily gridded data were interpolated using the Random Forest Spatial Interpolation methodology based on Random Forest and using nearest observations and distances to them as spatial covariates, together with environmental covariates.</p> <p>Complete script in R and datasets used for modelling, tuning, validation, and prediction of daily meteorological variables are available <a href="https://github.com/AleksandarSekulic/MeteoSerbia1km">here</a>.</p> <p>If you discover a bug, artifact or inconsistency in the MeteoSerbia1km maps, or if you have a question please use <a href="https://github.com/AleksandarSekulic/MeteoSerbia1km/issues">this channel</a>.</p> <p>File naming convention of .zip files and containing MeteoSerbia1km files:</p> <ul> <li>Daily summaries per year: day_<em>yyyy</em>_<em>proj</em>.zip <ul> <li><em>var</em>_day_<em>yyyymmdd</em>_<em>proj</em>.tif</li> </ul> </li> <li>Monthly summaries: mon_<em>proj</em>.zip <ul> <li><em>var</em>_mon_<em>yyyymm</em>_<em>proj</em>.tif</li> </ul> </li> <li>Annual summaries: ann_<em>proj</em>.zip <ul> <li><em>var</em>_ann_<em>yyyy</em>_<em>proj</em>.tif</li> </ul> </li> <li>Daily, monthly and annual LTM: ltm_<em>proj</em>.zip <ul> <li>daily LTM: <em>var</em>_ltm_day_mmdd_<em>proj</em>.tif</li> <li>monthly LTM: <em>var</em>_ltm_mon_mm_<em>proj</em>.tif</li> <li>annual LTM: <em>var</em>_ltm_ann_<em>proj</em>.tif</li> </ul> </li> </ul> <p>where:</p> <ul> <li><em>var</em> is a daily meteorological variable name - tmax, tmin, tmean, slp, or prcp</li> <li><em>proj</em> is a dataset projection - wgs84 or utm34</li> </ul> <p>Units of the dataset values are</p> <ul> <li>temperature (Tmean, Tmax, and Tmin) - tenths of a degree in the Celsius scale (℃)</li> <li>SLP - tenths of a mbar</li> <li>PRCP - tenths of a mm</li> </ul> <p>All dataset values are stored as integers (INT32 data type) in order to reduce the size of the GeoTIFF files, i.e., temperature values should be divided by 10 to obtain degrees Celsius, and the same for SLP and PRCP to obtain millibars and millimeters.<br> </p>
Five spatial landmark datasets
<p>This release contains different open data, multi-source and heterogeneous landmark datasets. They were used to study their heterogeneity, complementarity, and quality on the one hand, and to define a data warehouse of landmarks for mountain rescue (See related <a href="https://dx.doi.org/10.1080/23729333.2019.1615730">article</a> for further details), on the other hand. This archive is released for transparency and reproducibility purposes.</p> <p>The landmark datasets, except BDTOPO dataset that are coming from a shapefile, are collected from APIs in a JSON format. Then, the datasets are transformed into a tabular format to store the location of a landmark (point geometry), the type related to the classification of landmark in sources, and a name if the landmark has one. The extraction was build at the end of the year 2021.</p> <p>Sources of dataset offer a provenance diversity: authoritative or crowdsourced sources, specialized on a thematic domain or general-interest usage.</p> <p>Here after, a short description of landmark datasets:</p> <ul> <li>« Dataset_POI_BDTopo.csv »: points of interest or activity from the French National Mapping Agency. Extracted from the national topographic data (BDTOPO) that is opendata since January 2021. The licence of this dataset is Etalab 2.0.</li> <li> <p>« Dataset_RandoEcrinsParcnational_RandoParcDuVercors »: protected area led by French public institutions publish touristic data dedicated to a recreational use. The licence of this dataset is also Etalab 2.0.</p> </li> <li> <p>« Dataset_Camptocamp_org.csv »: Camptocamp (C2C) is a website dedicated to more or less experienced mountaineers. Landmark concern topographical guidelines for leisure activities (running, cycling, climbing, etc.). The licence is CC-by-nc-nd.</p> </li> <li> <p>« Dataset_RefugesInfo.csv »: Refuges.info, as the name suggests, provides detailed information concerning shelters and others landmarks such as water points, summits, etc. The licence is CC-By-Sa 2.0.</p> </li> <li> <p>« Dataset_Openstreetmap_org.csv »: OpenStreetMap (OSM) is the very well known collaborative project proposing many types of topographic and thematic spatial data. All data have been downloaded from an API endpoint. The centroid calculation was applied on the polygonal and linear geometries in order to build the point geometries. The licence is Open Data Commons Open Database License (ODbL).</p> </li> </ul>
Spatial transcriptome analysis defines heme as a hemopexin-targetable inflammatoxin in the brain - Datasets and Python notebooks
<p>This dataset and the associated Python notebooks and R-code are related to the publication "Spatial transcriptome analysis defines heme as a hemopexin-targetable inflammatoxin in the brain".</p>
Dataset used for the analysis described in "Spatial patterns and controls on wind erosion in the Great Basin"
<p>This data set contains AERO model outputs and associated Bureau of Land Management Assessment, Inventory, and Monitoring calculated values for functional plant group cover estimates for monitoring plots across the Great Basin. Versrion 2 (V2) includes MLRA number and sampling year column ("sample_yr") that were omitted in previous version.</p>
MeteoEurope1km - TMAX (1991–2000): daily gridded meteorological dataset for Europe at a 1-km spatial resolution for the 1991–2020 period
<p>MeteoEurope1km is the daily gridded meteorological dataset for Europe at a 1-km spatial resolution for the 1991–2020 period. The dataset consists of five daily variables:</p> <ul> <li><strong>TMAX - maximum temperature</strong> (<strong>1991–2005 period</strong>, 2006–2020 period)</li> <li>TMIN - minimum temperature (1991–2005 period, 2006–2020 period)</li> <li>TMEAN - mean temperature (1991–2005 period, 2006–2020 period)</li> <li>SLP - mean sea level pressure</li> <li>PRCP - total precipitation</li> </ul> <p>Daily gridded temperature data were interpolated using the Regression Kriging, with digital elevation model (DEM) and topographic wetness index (TWI) as covariates.<br> Daily gridded sea level pressure data were interpolated using Ordinary Kriging.<br> Daily gridded precipitation data were interpolated using Indicator and Ordinary Kriging methodology in two steps:</p> <ol> <li>Indicator Kriging - prediction of precipitation occurence</li> <li>Ordinary Kriging - prediction of total daily precipitation for locations where precipitation occurs (1. step).</li> </ol> <p>File naming convention of the MeteoEurope1km files is <em>var_day_yyyymmdd_proj.tif</em> (e.g. <em>tmax_day_20201231_3035.tif</em>), where:</p> <ul> <li><em>var</em> is a daily meteorological variable name - tmax, tmin, tmean, slp, or prcp</li> <li><em>proj</em> is a dataset projection EPSG code - 3035</li> </ul> <p>Units of the dataset values are</p> <ul> <li>temperature (Tmean, Tmax, and Tmin) - tenths of a degree in the Celsius scale (℃)</li> <li>SLP - tenths of a mbar</li> <li>PRCP - tenths of a mm</li> </ul> <p>All dataset values are stored as integers (INT32 data type) in order to reduce the size of the GeoTIFF files, i.e., temperature values should be divided by 10 to obtain degrees Celsius, and the same for SLP and PRCP to obtain millibars and millimeters.<br> All dataset files are available as Cloud-Optimized GeoTIFFs (COGs).<br> Use the R <a href="https://github.com/AleksandarSekulic/Rmeteo">meteo</a> package, <em>europe1km</em> function to make a point query and obtain the values for a specific location and a specific period.</p>
The S&M-HSTPM2d5 dataset: High Spatial-Temporal Resolution PM 2.5 Measures in Multiple Cities Sensed by Static & Mobile Devices
<p>This S&M-HSTPM2d5 dataset contains the high spatial and temporal resolution of the particulates (PM2.5) measures with the corresponding timestamp and GPS location of mobile and static devices in the three Chinese cities: Foshan, Cangzhou, and Tianjin. Different numbers of static and mobile devices were set up in each city. The sampling rate was set up as one minute in Cangzhou, and three seconds in Foshan and Tianjin. For the specific detail of the setup, please refer to the Device_Setup_Description.txt file in this repository and the data descriptor paper.</p> <p>After the data collection process, the data cleaning process was performed to remove and adjust the abnormal and drifting data. The script of the data cleaning algorithm is provided in this repository. The data cleaning algorithm only adjusts or removes individual data points. The removal of the entire device's data was done after the data cleaning algorithm with empirical judgment and graphic visualization. For specific detail of the data cleaning process, please refer to the script (Data_cleaning_algorithm.ipynb) in this repository and the data descriptor paper.</p> <p>The dataset in this repository is the processed version. The raw dataset and removed devices are not included in this repository.</p> <p>The data is stored as a CSV file. Each CSV file which is named by the device ID represents the data that was collected by the corresponding device. Each CSV file has three types of data: timestamp as the China Standard Time (GMT+8), geographic location as latitude and longitude, and PM2.5 concentration with the unit of microgram per cubic meter. The CSV files are stored in either Static or Mobile folder which represents the devices' type. The Static and Mobile folder are stored in the corresponding city's folder.</p> <p>To access the dataset, any programming language that can access CSV files is appropriate. Users can also open the CSV file directly. The get_dataset.ipynb file in this repository also provides an option of accessing the dataset. To successfully execute ipynb file, Jupyter Notebook with Python 3.0 is required. The following python library is also required:</p> <p>get_dataset.ipynb:<br> 1. os library<br> 2. pandas library</p> <p>Data_cleaning_algorithm.ipynb:<br> 1. os library<br> 2. pandas library<br> 3. datetime library<br> 4. math library</p> <p>The instruction of installing the libraries above can be found online. After installing the Jupyter Notebook with Python 3.0 and the required libraries, users can try to open the ipynb file with Jupyter Notebook and follow the instruction inside the file. </p> <p>For questions or suggestions please e-mail Xinlei Chen <xinlei.chen@sv.cmu.edu></p>
30-m Spatial Resolution Bioclimatic Dataset of 1991-2020 Climate Normals for Hubei Province, the Yangtze River Middle Reaches
<p><strong>Brief Introduction of the Dataset</strong></p> <p>This bioclimatic dataset is the product of research article "Mapping 30-m Resolution Bioclimatic Variables During 1991-2020 Climate Normals for Hubei Province, the Yangtze River Middle Reaches." published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.</p> <p>The dataset contains 19 30-m resolution average bioclimatic variables during 1991-2020 Climate Normals for Hubei Province (108°21′42″—116°07′50″ E, 29°01′53″—33°6′47″ N), the core region of the Yangtze River middle reaches. The dataset was constructed by statistically downscaling the Climatic Research Unit (CRU) 1-km monthly climate variables (1440 in total), cablirating with ground observation data with 82 weather stations and aggregating based on the defination of 19 bioclimatic variables. The downscaling of four 1-km Climatic Research Unit monthly climate variables including monthly maximum, mean, minimum temperature and precipitation was firstly achieved by random forest model with 30-m resolution terrain and spatial data. Then the interpolation-based geographical differential analysis (GDA) was applied to improve the accuracy of downscaled products based on ground observation data. Finally, the bioclimatic variables were aggregated based on their definitions and averaged for the 30 years. The Yangtze River middle reaches is abundant of forestry, agriculture, biodiversity resources that requires finer bioclimatic data for better understands of these aspects. This dataset will provide higher spatial accuracy, more information and applicability in finer regional studies in the Yangtze River middle reaches.</p> <p> </p> <p><strong>Description of the 19 Bioclimatic Variables</strong></p> <p>The dataset contains 19 geotiff files in total. File names and the corresponding full name of bioclimatic variables are described as follows:</p> <p>Bio01 Mean annual air temperature (℃)<br>Bio02 Mean diurnal air temperature range (℃)<br>Bio03 Isothermality (%)<br>Bio04 Temperature seasonality (℃)<br>Bio05 Mean daily maximum air temperature of the warmest month (℃)<br>Bio06 Mean daily minimum air temperature of the coldest month (℃)<br>Bio07 Annual range of air temperature (℃)<br>Bio08 Mean daily mean air temperatures of the wettest quarter (℃)<br>Bio09 Mean daily mean air temperatures of the driest quarter (℃)<br>Bio10 Mean daily mean air temperatures of the warmest quarter (℃)<br>Bio11 Mean daily mean air temperatures of the coldest quarter (℃)<br>Bio12 Annual precipitation amount (mm)<br>Bio13 Precipitation amount of the wettest month (mm)<br>Bio14 Precipitation amount of the driest month (mm)<br>Bio15 Precipitation seasonality (%)<br>Bio16 Precipitation amount of the wettest quarter (mm)<br>Bio17 Precipitation amount of the driest quarter (mm)<br>Bio18 Precipitation amount of the warmest quarter (mm)<br>Bio19 Precipitation amount of the coldest quarter (mm)</p> <p> </p> <p><strong>Others</strong></p> <p>More information related to bioclimatic variables can be found on https://chelsa-climate.org/bioclim/</p>
Investigation of spatial and temporal variability in lower tropospheric ozone from RAL Space UV-Vis satellite products - Dataset
<p>This data set represents a long-term (1996-2017) harmonised record of lower tropospheric ozone (surface - 450 hPa or surface - approximately 6 km) from satellite instruments. These instruments include the Global Ozone Monitoring Experiment (GOME-1, 1996–2002), the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY, 2003–2004) and the Ozone Monitoring Instrument (OMI, 2005–2017). These original products were produced by the Rutherford Appleton Laboratory (RAL) Space using the retrieval scheme described by Miles et al., (2015 - doi:10.5194/amt-8-385-2015). Pre-print of accepted manuscript can be found at https://doi.org/10.5194/egusphere-2023-1172.</p>
Dataset from: Spatially heterogeneous shifts in vegetation phenology induced by climate change threaten the integrity of the avian migration network
<p>Original data and code for the study:</p> <p>Wei, J., Xu, F., Cole, E. F., Sheldon, B. C., de Boer, W. F., Wielstra, B., Fu, H., Gong, P., & Si, Y. (2024, Accepted). Spatially heterogeneous shifts in vegetation phenology induced by climate change threaten the integrity of the avian migration network. Global Change Biology.</p> <p>The dataset mainly contains data showing the climate change-induced heterogeneous shifts in vegetation phenology and the migration integrity change from 2000 to 2020 for 16 Asian herbivorous waterfowl species. These data were derived from the following resources available in the public domain.</p> <p>The Global Lakes and Wetlands Database is available from “https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database”. The global land cover datasets are available from European Space Agency (ESA) Climate Change Initiative (CCI) products, “https://maps.elie.ucl.ac.be/CCI/viewer/download.php”. The Global Multi-resolution Terrain Elevation Data are available from “https://www.usgs.gov/centers/eros/science/terrain-monitoring-and-modeling”. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance product is available from “https://modis.gsfc.nasa.gov/data/dataprod/mod09.php”. The bird distribution maps are available from Birdlife International, “https://www.birdlife.org/”. The bird foraging attribute data are available from EltonTraits 1.0, “https://figshare.com”. The bird occurrence data are available from eBird Basic Dataset (EBD), “https://science.ebird.org/en/use-ebird-data/download-ebird-data-products”. The Hackett backbone phylogenetic trees are available from “https://birdtree.org/”.</p> <p>The code contains the R scripts and MATLAB scripts that we used for this study.</p> <p>For details please see the file “Readme.txt”, and the research paper.</p>
[Dataset & scripts] to "Spatial scales of kinetic energy in the Arctic Ocean", dataset from Caili Liu
<p>## "Spatial scales of kinetic energy in the Arctic Ocean"</p> <p>Available dataset for each figure (1~9) and figure10 in the main text, including Jupyter notebook scripts (Fig1, Fig2, Fig5, Fig10) and Matlab scripts (Fig3, Fig4, Fig6, Fig7, Fig8, Fig9).</p> <p>## Description</p> <p>This dataset is as the supplementary to the manuscript "Spatial scales of kinetic energy in the Arctic Ocean", including jupyter notebook scripts and matlab scripts of visualization directly for figures1~9.</p> <p>1) Jupyter notebook scripts for visualization<br>the MESH and BG are used for visualization, and *.mat are the dataset for Fig1/2/5/10. The load path in the script should be changed to your files accordingly.</p> <p>2) Matlab scripts for plots<br>All figures/panels are directly produced, but it is composed of panels for Fig7/8/9 additionally.</p>
Global monthly sectoral water withdrawal and allocation datasets (QUAlloc, water use and allocation model) at 10 km spatial resolution
<p>Output data of water withdrawals and water allocation per water source from the sectoral water use and allocation model (QUAlloc).</p> <p>Dataset properties:</p> <ul> <li>spatial resolution: 10 km (global-scale)</li> <li>temporal resolution: monthly time-step</li> <li>period: 1980 - 2019</li> <li>units: m3/month</li> </ul> <p>Output datasets:<br> <data_type>_<sector_name>_allocated_to_<source_type>_monthlyTot_1980_2019.nc</p> <ul> <li><data_type><br> <ul> <li>"withdrawal": refers to the water that is withdrawn at a water source level to satisfy the demands within an allocation zone</li> <li>"demand": refers to the withdrawn water that is supplied to each location (cell) where there are demands to satisfy</li> </ul> </li> <li><sector_name> <ul> <li>"domestic"</li> <li>"irrigation"</li> <li>"livestock"</li> <li>"manufacture"</li> <li>"thermoelectric"</li> </ul> </li> <li><source_type> <ul> <li>"renewable_surfacewater": refers to water obtained from the surface water system components (e.g., direct runoff, base flow, interflow, etc.)</li> <li>"renewable_groundwater": refers to water obtained from aquifers that are recharged by percolation from the upper soil layers</li> <li>"nonrenewable_groundwater": refers to water obtained from aquifers not replenished on a human time scale</li> </ul> </li> </ul> <p>The sectoral water use and allocation model used, QUAlloc, can be found at: https://github.com/SustainableWaterSystems/QUAlloc.</p>
[ITU AI/ML Challenge 2021] Dataset IEEE 802.11ax Spatial Reuse
<p>This dataset has been created for the problem statement ITU-ML5G-PS-004 of the ITU AI/ML Challenge (2021 edition). More information can be found here: <a href="https://challenge.aiforgood.itu.int/">https://challenge.aiforgood.itu.int/</a> and <a href="https://www.upf.edu/web/wnrg/2021-edition">https://www.upf.edu/web/wnrg/2021-edition</a>. </p> <p>The dataset contains the information of 3.000 IEEE 802.11ax deployments (divided into two different scenarios) at which the Basic Service Set (BSS) of interest applies different possible OBSS/PD thresholds in the context of the Spatial Reuse (SR) operation. In total, 21 OBSS/PD values are considered for each deployment, and some of the deployments include data from different STA locations. The provided files are expected to be used for training Machine Learning (ML) and Federated Learning (FL) algorithms.</p> <p>More specifically, the dataset is divided as follows:</p> <ul> <li><strong>Scenario 1:</strong> 1,000 different deployments with 2-6 APs and 1 STA per AP. A minimum distance limitation is applied, so that each AP different from AP_A is located at a minimum distance of 10 meters from that one. Each deployment is simulated for each of the 21 possible OBSS/PD thresholds in 11ax. Two files are considered: <ol> <li><a href="https://zenodo.org/api/files/a153e748-7ff5-419d-a7a0-ab7d962d9ccb/output_11ax_sr_simulations_sce1.txt">output_11ax_sr_simulations_sce1.txt</a>: contains the output generated by the simulator for the deployments in Scenario 1.</li> <li><a href="https://zenodo.org/api/files/a153e748-7ff5-419d-a7a0-ab7d962d9ccb/simulator_input_files_sce1.zip">simulator_input_files_sce1.zip</a>: contains the input files used by the simulator to simulate the deployments in Scenario 1.</li> </ol> </li> <li><strong>Scenario 2: </strong>1,000 different deployments with 2-6 APs and 1-4 STAs per AP. No distance limitation is applied. Each deployment is simulated for each of the 21 possible OBSS/PD thresholds in 11ax. Two files are considered: <ol> <li><a href="https://zenodo.org/api/files/a153e748-7ff5-419d-a7a0-ab7d962d9ccb/output_11ax_sr_simulations_sce2.txt">output_11ax_sr_simulations_sce2.txt</a>: contains the output generated by the simulator for the deployments in Scenario 2.</li> <li><a href="https://zenodo.org/api/files/a153e748-7ff5-419d-a7a0-ab7d962d9ccb/simulator_input_files_sce2.zip">simulator_input_files_sce2.zip</a>: contains the input files used by the simulator to simulate the deployments in Scenario 2.</li> </ol> </li> <li><strong>Scenario 3: </strong>1,000 different deployments with 2-6 APs and 1-4 STAs per AP. No distance limitation is applied. Each deployment is simulated for each of the 21 possible OBSS/PD thresholds in 11ax, and for up to 20 different locations of different STAs of the BSS of interest ("BSS_A"). Two files are considered: <ol> <li><a href="https://zenodo.org/api/files/8057ea42-dd8c-4a02-9fe2-0e3bde8b3db0/output_11ax_sr_simulations_sce3.txt">output_11ax_sr_simulations_sce3.txt</a>: contains the output generated by the simulator for the deployments in Scenario 3.</li> <li><a href="https://zenodo.org/api/files/8057ea42-dd8c-4a02-9fe2-0e3bde8b3db0/simulator_input_files_sce3.zip">simulator_input_files_sce3.zip</a>: contains the input files used by the simulator to simulate the deployments in Scenario 3.</li> </ol> </li> <li><strong>Test:</strong> 1,000 different deployments with 2-6 APs and 1-4 STAs per AP. No distance limitation is applied. A random OBSS/PD threshold is applied. Two files are considered: <ol> <li><a href="https://zenodo.org/api/files/22d1265c-0dea-4bf8-a223-e61c45deb076/output_11ax_sr_simulations_test.txt?versionId=91ccbe70-a821-4601-8db7-1763883dc984">output_11ax_sr_simulations_test.txt</a>: contains the output generated by the simulator for the evaluation deployments. The label (throughput) has been replaced with "0s".</li> <li><a href="https://zenodo.org/api/files/8057ea42-dd8c-4a02-9fe2-0e3bde8b3db0/simulator_input_files_test.zip?versionId=b1581c0b-419c-422b-bb2d-4dbf77f05dd2">simulator_input_files_test.zip</a>: contains the input files used by the simulator to simulate the evaluation deployments.</li> </ol> </li> </ul>
Dataset from Holding et al. (2019)––Seasonal and spatial patterns of primary production in a high latitude fjord
<p>Unprecedented melting of the Greenland Ice Sheet (GrIS) is impacting the coastal ocean, and its effects on fjord ecology remain understudied. It has been suggested that as glaciers retreat, primary production regimes may be altered, rendering fjords less productive. Here we present data from the paper Holding et al. (2019). Seasonal and spatial patterns of primary production in a high-latitude fjord affected by Greenland Ice Sheet run-off. <em>Biogeosciences</em>, <em>16</em>(19), 3777-3792, /doi.org/10.5194/bg-16-3777-2019. This paper investigates patterns of primary productivity in a northeast Greenland fjord (Young Sound, 74°N), which receives run-off from the GrIS via land-terminating glaciers. This dataset includes measures of size fractioned primary production and chlorophyll <em>a </em>biomass, as well as CTD data and biochemical parameters. Furthermore, primary production was measured using photosynthesis v. irradiance (PI) curves, thus PI curve parameters are also available. The data were taken during the ice-free season along a spatial gradient of meltwater influence. </p> <p>We thank Egon Frandsen, Kunuk Lennert, and Ivali Lennert for excellent assistance during fieldwork. This research has beensupported by the Danish Environmental Protection Agency’s programme for Arctic research (DANCEA) (grant no. MST-112-0023), The Carlsberg Foundation (grant no. 2013_01_0532), the Norwegian Research Council (Mi- croPolar) (grant no. RCN 225956), and the European Commission, H2020 Research Infrastructures (GrIS-Melt (grant no. 752325) and INTAROS (grant no. 727890)). </p>
Milan (ITALY) - Urban Agriculture spatial dataset (years 2007 and 2014)
<p>The data in this dataset is a spatial inventory of <strong>urban agriculture</strong> (UA) carried out in the city of Milan (Italy). UA areas where identified with a multi-step and iterative procedure by using different web-mapping tools, especially multitemporal Google Earth images, and ancillary data such as Google Street View and Bing Maps.</p> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p> <p><strong>Disclaimer</strong></p> <p>Despite our best efforts to validate the data, some information may be incorrect.</p> <p><strong>Description of the dataset</strong></p> <p><em><strong>Typologies of UA</strong></em></p> <ul> <li><strong>Residential garden: </strong>Private parcel near single houses (e.g. backyard), villas, buildings, industrial and commercial activities, generally managed by property owners. Cultivation is diversified ranging from leafy vegetables to herbs and fruit trees. Production is intended for self-consumption and/or for hobby purposes.</li> <li><strong>Community garden: </strong>A large area subdivided into multipleplots managed individually (i.e. allotment) or collectively by a group of people. Crop production is intended for self-consumption. Land is assigned by the Municipality; several cases of land cultivated without authorization are also common.</li> <li><strong>Urban farm: </strong>Parcel managed by professional farmers with an intensive and an advanced cropping system. The cultivation can be specialized or oriented to high diversity vegetables. The production is intended for market. The mapping procedure focus on arable crops, horticulture, vineyard, olive groves and orchard.</li> <li><strong>Institutional garden: </strong>Parcel managed by institutions or organizations like schools, religious center, prisons and non-profit organizations. The production is generally intended for self-consumption and less frequently for trade. Several gardens in this category are intended for social purposes (e.g. recreation,education, etc.).</li> <li><strong>Illegal garden: </strong>Parcel isolated, cultivated without authorization organized and managed individually or by a few people. Localization occurs on unused or abandoned areas owned by public bodies or private subjects. The production is intended for self-consumption.</li> <li><strong>Nurseries: </strong>A large area subdivided into multiple plots managed for growing ornamental plants and flowers.</li> </ul> <p><em><strong>Land use typologies</strong></em></p> <ul> <li><strong>Horticulture: </strong>annual crops generally seed sown in spring or summer (tomatoes, lettuce, zucchini, cucumbers, peppers).</li> <li><strong>Vineyard: </strong>grape vines grown in order to produce wine or table grape.</li> <li><strong>Olive groves: </strong>olive trees grown in order to produce olive oil or table olives.</li> <li><strong>Orchards: </strong>mixed trees such as orange, stone fruit, pome fruit, olive trees.</li> <li><strong>Mixed crops: </strong>an area grown with a mix of horticulture crops and fruit trees, not divisible.</li> <li><strong>Nurseries: </strong>ornamental plants, trees, flowers.</li> </ul> <p><strong>Credit</strong></p> <p>Pulighe G., Lupia F. (2019) <em>Multitemporal Geospatial Evaluation of Urban Agriculture and (Non)-Sustainable Food Self-Provisioning in Milan, Italy. </em><strong>Sustainability </strong>2019, <em>11</em>(7), 1846</p> <p>https://www.mdpi.com/2071-1050/11/7/1846</p>
Dataset of Spatial Room Impulse Responses in a Variable Acoustics Room for Six Degrees-of-Freedom Rendering and Analysis
<p>Room acoustics measurements are used in many areas of audio research, from physical acoustics modelling and speech enhancement to virtual reality applications. This paper documents the technical specifications and choices made in the measurement of a dataset of spatial room impulse responses (SRIRs) in a variable acoustics room. Two spherical microphone arrays are used: the mh Acoustics Eigenmike em32 and the Zylia ZM-1, capable of up to fourth- and third-order Ambisonic capture, respectively. The dataset consists of three source and seven receiver positions, repeated with five configurations of the room's acoustics with varying levels of reverberation. Possible applications of the dataset include six degrees-of-freedom (6DoF) analysis and rendering, SRIR interpolation methods, and spatial dereverberation techniques. </p> <p>Accompanying paper on details of the dataset measurement: https://arxiv.org/abs/2111.11882</p> <p>Changelog:</p> <p>V 1.0 - Initial version.<br> V 1.1 - SOFA files updated to latest Matlab API (1.1.3), 'SingleRoomDRIR' convention, with SourcePosition and ListenerPosition z data corrected. Changed ListenerPosition and SourcePosition x data so that it follows the convention of origin in bottom left corner (rather than the previous bottom right). Fixed the swapped x and y labels in 6dof_source_and_receiver_positions.pdf.</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.