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13,117 results for “Maps”
GIS70 Konza Prairie Woody Plant Mapping in Core Watersheds (1D, 20B, and 4B) in 2019
This dataset contains the point and polygon boundaries of shrubs and trees mapped in watersheds 1D, 4B, and 20B from May to August 2019. Datatype one (GIS700) defines the point locations of all trees mapped in these watersheds. Datatype two (GIS701) defines the point locations of all shrubs less than one meter wide in these watersheds. Datatype three (GIS702) defines the boundaries of select shrub species greater than one meter wide in these watersheds. These data are available to download as zipped shapefiles (.zip), and compressed Google Earth KML layers (.kmz).
Tree Map for Census at the Luquillo Forest Dynamics Plot (LFDP), Puerto Rico
This data set shows the Tag number, Quadrat location, Species code, diameter and XY coordinates of stems >=10 cm D130 present at the time of Hurricane Hugo and in the first census. The data set is composed of two files both with the same file structure. In LFDP_C1treemap.txt the diameters (Fdiam) are as recorded in the field data. In LFDP_C1TREEMAPa.txt the stem diameters (Fdiam) were calculated to allocate "missed" stems (stems >=10 cm D130) that were found in survey 2, 3 or Census 2 to Census 1 survey 1. We calculated the diameter the stem would have had, if it had been recorded at the same time the quadrat it was located in was assessed, in the appropriate survey for that stem size. To extrapolate the stem size back in time, we used the actual growth rate of that individual stem if more than one measurement was available. If only one diameter measurement was available we used the median growth rate for that species in the appropriate size class stems >=10, <30 cm D130). In our publications we will combine data sets LFDP_C1treemap.txt and LFDP_C1TREEMAPa.txt to make Census 1 and to reconstruct the forest for stems >= 10 cm D130 at the time of Hurricane Hugo. We have divided the data into two separate files to ensure that when stem diameters are compared to future censuses the diameter data in LFDP_C1TREEMAPa.txt are not used to calculate growth rates. The last corrections to the Census 1 data were made in May 2001. The National Science Foundation requires that data from projects it funds are posted on the web two years after any data set has been organized and "cleaned". The data from each census of the LFDP will be updated at intervals as each survey of the LFDP shows errors in the previous data collection. After posting on the web, researchers who are not part of the project are then welcome to use the data. Given the enormous amount of time, effort and resources required to manage the LFDP, obtain these data, and ensure data accuracy, LFDP Princi
MCR LTER: Coral Reef: 2018 land cover map of Moorea, French Polynesia
Using a collection of imagery from June-September 2018 taken by the Worldview-3 satellite, land cover was classified for the island of Mo’orea, French Polynesia. A deep learning pixel classification model was trained for each of four separate dates of image collection when clouds were sparse over the island. The model was trained at the native resolution of the imagery (<2m pixels). Training data included the multispectral WV-3 bands in addition to derived bands that index vegetation productivity (NDVI), vegetation texture (NDVI IDM), and water cover (NDWI). A consensus land cover map was generated from model predictions across the four sets of imagery.
Surface temperature mapped from thermal infrared survey from UAV campaign at Niwot Ridge, 2017.
Data collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. Investigating snow depth variability and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment. Surface temperature of Niwot Ridge saddle was mapped from thermal infrared survey on June 21, July 11, 18, 25, and August 14, 2017.
Robust functional mapping of layer-selective responses in human lateral geniculate nucleus with high-resolution 7T fMRI
Open the record for dataset details and reuse information.
BIDS Data for "A Whole-Brain Map and Assay Parameter Analysis of Mouse VTA Dopaminergic Activation"
<p>Base data package for the “"A Whole-Brain Map and Assay Parameter Analysis of Mouse VTA Dopaminergic Activation” article, formatted corresponding to the Brain Imaging Data Structure.</p>
Augmented emission maps: 1968 cc 55 kW Euro 6 diesel engine
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 1968 cc 55 kW Euro 6 diesel engine that has been applied in the Volkswagen Caddy.</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI 10.5281/zenodo refers to a meta-data document that provides the full description of the standardized emission map.</p>
NDVI Raster maps of Scotland for 2013-2016 used to analyse correlations between greenness, mortality and mental health.
<p>These files were used in the analysis for "Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study" Hyam. Submitted to RIO 2020.</p> <p><strong>Extract on construction of data</strong></p> <p>NDVI data was downloaded from the United States Geological Survey (USGS) Land Satellites Data System (LSDS) Science Research and Development (LSRD) (United States Geological Survey 2018). Which produces Level 2 and Level 3 data products from the Level 1 data of instruments aboard Landsat Satellites. For this study Surface Reflectance data generated by the Landsat Surface Reflectance Code (LaSRC) from the Operational Land Imager (OLI) instrument aboard the Landsat 8 satellite was used (United States Geological Survey 2018). The Surface Reflectance NDVI (sr_ndvi) product and Level-2 Pixel Quality Assessment band (pixel_qa) were downloaded for Landsat scenes 204/21, 205/21, 206/21, 204/20, 205/20, 206/20 WRS-2 (NASA 2018) for the calendar years 2013 to 2016. These scenes cover most of Scotland and include all the major urban areas. A full list of the 333 products is given in supplemental material. Suppl. material 2</p> <p>All of Scotland is over 54° North and so for many satellite images the sun is at too low an angle to give reliable surface reflectance data especially in the winter months. Scotland also has an oceanic climate so the ground is often obscured by cloud or mist. To build a detailed, contiguous NDVI map of the whole country therefore requires combining images taken on many satellite passes especially if points are to be sampled multiple times to overcome measurement errors. The images downloaded from USGS were therefore combined. A cloud free version of each NDVI image was created by setting the pixels that corresponded to cloud, snow or water in the Quality Assurance Assessment band to NA. These cloud free images were then combined into a single, mosaic stack of images to cover all of the study area and then averaged down to a single layer as a tiff image. This was done for two seasonal periods, Winter (October, November, December of 2013, 2014, 2015 and 2016 combined with January, February, March of 2014, 2015, 2016) and summer (April, May, June, July, August, September of 2014, 2015, and 2016). The resulting two images covering most of Scotland for winters and summers between 2013 and 2016 and formed the basis of subsequent analysis.</p> <p>These two files are included here along with a list of the Landsat products used to produce them.</p>
Solotvyno hazard&risk maps_ImProDiReT-783232_UA
<p>As a result of the analysis of geological natural environment of Solotvyno, several natural and anthropogenic processes those are potentially dangerous for the population have been identified: karst and suffosion (subsidence, sinkholes, collapses), seasonal and flash floods, flooding, slope erosion, landslides.</p> <p>A set of hazards / risk maps has been elaborated based on expert complex assessment of the natural and anthropogenic hazardous processes manifestations:</p> <p>1. Inventory map of hazardous technogenic-geological and engineering-geological processes manifestations and phenomena for Solotvyno</p> <p>2. Zonation of the hazardous technogenic-geological and engineering-geological processes manifestations;</p> <p>3. Specific land use for Solotvyno;</p> <p>4. Category of land for Solotvyno (according to StateGeoCadastre data);</p> <p>5. Risk Map of Natural and Natural-Antropogenic Hazards for Solotvyno;</p> <p>6. Risk Map of Natural and Natural-Antropogenic Hazards for Solotvyno (with critical infrastructure objects).</p>
Morpho-stratigraphic map of Jezero crater landing area
<p>The rover Perseverance of the Mars2020 mission will depart to Mars in July 2020 and land on Mars in February 2021. Its landing site, Jezero crater, has been selected due to the presence of two fan deltas, inlet and outlet valleys and a huge number of aqueous landforms (fluvial and lacustrine sediments).<br> This morpho-stratigraphic map has been produced from orbital visible imagery and its interpretations take into account the orbital facies (layers, massive, etc.) and their stratigraphic relationships, the texture and albedo of terrains, without taking into account mineralogical data. The area studied here is centered around the landing area comprising the east of the fan delta and the west of the crater floor. The map has been done at 1:10,000 scale.</p>
Review of the evidence for Oceans and Human Health relationships in Europe: A systematic map.
<p>This database details the results of a systematic mapping exercise linking marine exposures to measured human health outcomes for the Seas, Oceans and Public Health in Europe Project.</p>
Mapping the intertidal microphytobenthos Gross Primary Production
<p>Dataset used in the papers "Mapping the intertidal microphytobenthos Gross Primary Production. PartI & PartII" published in Frontiers Marine Science Research Topic "Advances and Challenges in Microphytobenthos Research: From Cell Biology to Coastal Ecosystem Function", and maps (geotiff) resulting of the use of the GPP-algorithm with Platt or Eilers and Peeters models.</p>
ESA-WOC North Atlantic Sea Surface Salinity maps from a multivariate combination of satellite and in situ surface measurements (2010-2018)
<p>We deliver here the daily sea surface salinity level 4 (SSS L4) product developed in the framework of the European Space Agency World Ocean Circulation project (ESA-WOC), covering the period 2010-2018. This product was obtained by adapting to a 1/10° North Atlantic grid the multidimensional optimal interpolation algorithm used within the Copernicus Marine Environment Monitoring Service to retrieve the global SSS multi-year dataset (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: MULTIOBS_GLO_PHY_REP_015_002, dataset_id: dataset-sss-ssd-rep-weekly). This algorithm interpolates SMOS observations and in situ SSS observations considering a space-time-thermal decorrelation function, estimated by including information from high-pass filtered daily SST data (Droghei et al., 2016; Buongiorno Nardelli, 2012). Here, we ingested the L3OS 2Q debiased daily valid ocean salinity values product from SMOS satellite, produced and disseminated by the Centre Aval de Traitement des Données SMOS (CATDS, 2017), OSTIA SST data (CMEMS, <a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id=SST_GLO_SST_L4_REP_OBSERVATIONS_010_011) and CORA5.2 surface salinity values (<a href="http://marine.copernicus.eu/services-portfolio/access-to-products/">http://marine.copernicus.eu/services-portfolio/access-to-products/</a>, product_id: INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, doi: 10.17882/46219TS1, Szekely et al., 2019) as input data, and used CMEMS weekly SSS dataset to build our background field (linearly interpolating it in time between the two closest analysis dates, and upsizing to the 1/10° grid through a cubic spline). All other interpolation parameters were set as in Droghei et al. (2018). </p> <p> </p> <p><em>References:</em></p> <p>Buongiorno Nardelli, B.: A Novel Approach for the High-Resolution Interpolation of In Situ Sea Surface Salinity, J. Atmos. Ocean. Technol., 29(6), 867–879, doi:10.1175/JTECH-D-11-00099.1, 2012.</p> <p>CATDS (2017). CATDS-PDC L3OS 2Q - Debiased daily valid ocean salinity values product from SMOS satellite. CATDS (CNES, IFREMER, LOCEAN, ACRI). http://dx.doi.org/10.12770/12dba510-cd71-4d4f-9fc1-9cc027d128b0</p> <p>Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: Combining in-situ and satellite observations to retrieve salinity and density at the ocean surface, J. Atmos. Ocean. Technol., 33, 1211–1223, doi:10.1175/JTECH-D-15-0194.1, 2016.</p> <p>Droghei, R., Buongiorno Nardelli, B. and Santoleri, R.: A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016), Front. Mar. Sci., 5(March), 1–13, doi:10.3389/fmars.2018.00084, 2018.</p> <p>Szekely, T., Gourrion, J., Pouliquen, S. and Reverdin, G.: The CORA 5.2 dataset for global in situ temperature and salinity measurements: Data description and validation, Ocean Sci., 15(6), 1601–1614, doi:10.5194/os-15-1601-2019, 2019.</p>
Scored protein-protein interactions accompanying "A pan-plant protein complex map reveals deep conservation and novel assemblies"
<p><a href="http://plants.proteincomplexes.org/static/data/panplant_cfms_scores_annot.txt.gz">All scored pairwise protein-protein interactions with CF-MS scores (3,076,999 unique pairwise interactions)</a></p> <ul> <li>Description: Scores between Orthogroups with the corresponding CF-MS score and eggNOG generated orthogroup descriptions.</li> <li>Note: Only the highest scoring pairs are considered significant. A CF-MS score >= 0.509 corresponds to 10% FDR, >= 0.207 corresponds to 50% FDR</li> <li>Format: OrthogroupID1 [tab] OrthogroupID2 [tab] Score [tab] Annotation1 [tab] Annotation2</li> </ul>
Imaging spectroscopy and elemental mapping of Haughton impact melt rock: Datasets
<p>Description of archived data for manuscript Greenberger et al. (accepted, JGR Planets)<br> </p> <p>This archive contains the data underlying the results reported in the following paper:<br> Greenberger, R. N., Ehlmann, B. L., Osinski, G. R., Tornabene, L. L., & Green, R. O. Compositional Heterogeneity of Impact Melt Rocks at the Haughton Impact Structure, Canada: Implications for Planetary Processes and Remote Sensing. Journal of Geophysical Research: Planets, accepted.<br> 1. ImageList.txt: Contains information required to connect sample names from paper with images, which often contain multiple samples.<br> 2. FieldImages.tar.gz: Imaging spectroscopy files from images of outcrops in the field<br> 3. LabImages.tar.gz: Imaging spectroscopy files from samples imaged in the laboratory<br> 4. XRF_Data.tar.gz: Elemental mapping of cut samples via mapping x-ray fluorescence</p> <p>Imaging spectroscopy files (for all below, * is image name from ImageList.txt):<br> 1. *_SWIRcalib.img files: Laboratory images of samples processed to reflectance, including a dark current subtraction, line-by-line ratio to an image of Spectralon acquired with identical lighting, and correction for the reflectance properties of Spectralon. These files are stored with BIL interleave.<br> 2. *_SWIRcalib.hdr files: Header files for (1).<br> 3. *_SWIRcalib_atmcorr.img: Images of outcrops acquired in the field processed to reflectance, including instrument level corrections (dark current subtraction and flat field correction) and atmospheric correction (dark object subtraction and correction to in-scene Spectralon calibration target).<br> 4. *_SWIRcalib_atmcorr.hdr: Header files for (3).<br> 5. mask# and mask#.hdr: Masks and associated header files where the sample or region of interest has a value of 1 and outside of the sample or region of interest has a value of 0. Imaging spectroscopy measurements of multiple samples were sometimes acquired within the same image, and each sample has its own mask. ImageList.txt shows conversions from image and mask names to sample numbers. For files with no # after mask, only one sample or region of interest is present within the image.<br> 6. *_SWIRcalibmask#_MAP, *_SWIRcalib_atmcorrmask_MAP, and corresponding .hdr files: These are image files with 12 bands, one for each lithologic classification in the paper, and associated header files. Values of 1 indicate that the lithology is present, and values of 0 indicate that it is absent. The mask files (5) were used to ignore areas outside of the sample or outcrop. The band named "Mixed 2.2 and 2.3 micron features" is Mixed Carbonate + Si-OH, and "Illite-y" is Illite-like. These files are stored with BSQ interleave.</p> <p>X-ray fluorescence (XRF) data:<br> Each folder corresponds with measurements of a single sample. These are measurements of the same surfaces of some cut samples analyzed by imaging spectroscopy. ImageList.txt gives the corresponding image cubes. All exported elements are .tsv files and are in quantized relative counts as exported by the instrument software. Data for Mg are unreliable due to its low atomic number, as is typical for XRF.</p>
Dataset: Mapping intrinsic and scattering attenuation in the southern Aegean crust using S-wave envelope inversion and sensitivity kernels derived from perturbation theory
<p><strong>Data Set S1: </strong>File “ds01.csv” contains the catalogue of relocated events used in this study. The columns in the file represent origin time (in year-month-day’H’hour’M’minute’S’seconds format), event longitude, event latitude, event depth in a sequential manner.</p> <p><strong>Data Set S2: </strong>File “ds02.zip” contains four ASCII data files (ray_prmtrs12.txt, ray_prmtrs24.txt, ray_prmtrs48.txt and ray_prmtrs816.txt). The data files contain scattering coefficient (<em>g<sup>*</sup></em>) and intrinsic coefficient (<em>b</em>) values in 1-2, 2-4 Hz, 4-8 Hz and 8-16 Hz bands respectively. The columns in the text files represent event latitude, event longitude, event depth, station latitude, station longitude, station velocity, envelope duration, <em>g<sup>*</sup></em>, <em>b</em>, early-S window length, percentage error in early-S window, percentage error for full envelope, and event origin time in a sequential manner.</p> <p><strong>Data Set S3: </strong>File “ds03.zip” contains four data files (envnodes15g_3_3_1-2.txt, envnodes15g_3_3_2-4.txt, envnodes15g_3_3_4-8.txt, and envnodes15g_3_3_8-16.txt), one BASH script containing GMT and Octave commands (Qs_envg.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of log<sub>10</sub>(<em>Q<sub>sc</sub></em><sup>-1</sup>) using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above.</p> <p><strong>Data Set S4: </strong>File “ds04.zip” contains four data files (envnodes15b_3_3_1-2.txt, envnodes15b_3_3_2-4.txt, envnodes15b_3_3_4-8.txt, and envnodes15b_3_3_8-16.txt), one BASH script containing GMT and Octave commands (Qi_env.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of log<sub>10</sub>(<em>Q<sub>i</sub></em><sup>-1</sup>) using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above.</p> <p><strong>Data Set S5: </strong>File “ds05.zip” contains four data files (envnodes15a_3_3_1-2.txt, envnodes15a_3_3_2-4.txt, envnodes15a_3_3_4-8.txt, and envnodes15a_3_3_8-16.txt), one BASH script containing GMT and Octave commands (albd_env.gmt), and a lat-long coordinate file (SAegean_poly_coord_extnd.txt) to mask the area outside the seismic network. The data files contain Albedo (<em>B<sub>o</sub></em>) as % values in 1-2, 2-4, 4-8 and 8-16 Hz bands respectively. The columns in the text files represent node latitude, node longitude and <em>B<sub>o</sub></em> value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script by changing the value of variable GDIR at the beginning of the script. The BASH script file can be run to see the spatial distribution of <em>B<sub>o</sub></em> using GMT-6 (Wessel et al., 2019) and Octave version 5.1 and above. </p>
Building height map of Germany
<p>Urban areas have a manifold and far-reaching impact on our environment, and the three-dimensional structure is a key aspect for characterizing the urban environment. </p> <p>This dataset features a map of building height predictions for entire Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. We utilized machine learning regression to extrapolate building height reference information to the entire country. The reference data were obtained from several freely and openly available 3D Building Models originating from official data sources (building footprint: cadaster, building height: airborne laser scanning), and represent the average building height within a radius of 50m relative to each pixel. Building height was only estimated for built-up areas (European Settlement Mask), and building height predictions <2m were set to 0m.</p> <p><strong>Temporal extent</strong><br> The acquisition dates of the different data sources vary to some degree:<br> - Independent variables: Sentinel-2 data are from 2018; Sentinel-1 data are from 2017.<br> - Dependent variables: the 3D building models are from 2012-2020 depending on data provider.<br> - Settlement mask: the ESM is based on a mosaic of imagery from 2014-2016.<br> Considering that net change of building stock is positive in Germany, the building height map is representative for ca. 2015. </p> <p><strong>Data format</strong><br> The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). Metadata are located within the Tiff, partly in the FORCE domain. There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Building height values are in meters, scaled by 10, i.e. a pixel value of 69 = 6.9m.</p> <p><strong>Further information</strong><br> For further information, please see the publication or contact David Frantz (david.frantz@geo.hu-berlin.de).<br> A web-visualization of this dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/building-height/">here</a>.</p> <p><strong>Publication</strong><br> Frantz, D., Schug, F., Okujeni, A., Navacchi, C., Wagner, W., van der Linden, S., & Hostert, P. (2021). National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment, 252, 112128. DOI: <a href="https://doi.org/10.1016/j.rse.2020.112128">https://doi.org/10.1016/j.rse.2020.112128</a></p> <p><strong>Acknowledgements</strong><br> The dataset was generated by FORCE v. 3.1 (<a href="https://doi.org/10.3390/rs11091124">paper</a>, <a href="https://github.com/davidfrantz/force">code</a>), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the <a href="https://scihub.copernicus.eu/">European Space Agency and the European Commission</a>. The European Settlement Mask was obtained from the <a href="https://data.jrc.ec.europa.eu/dataset/8bd2b792-cc33-4c11-afd1-b8dd60b44f3b">European Commission</a>. 3D building models were obtained from <a href="https://www.businesslocationcenter.de/en/economic-atlas/download-portal/">Berlin Partner für Wirtschaft und Technologie GmbH</a>, <a href="http://suche.transparenz.hamburg.de/dataset/3d-stadtmodell-lod2-de-hamburg4?forceWeb=true">Freie und Hansestadt Hamburg / Landesbetrieb Geoinformation und Vermessung</a>, <a href="https://opendata.potsdam.de/explore/dataset/3d-gebaudemodell-lod2-citygml/information">Landeshauptstadt Potsdam</a>, <a href="https://www.bezreg-koeln.nrw.de/brk_internet/geobasis/3d_gebaeudemodelle/index.html">Bezirksregierung Köln / Geobasis NRW</a>, and <a href="https://www.geoportal-th.de/de-de/Downloadbereiche/Download-Offene-Geodaten-Th%C3%BCringen/Download-3D-Geb%C3%A4ude">Kompetenzzentrum Geodateninfrastruktur Thüringen</a>. This dataset was partly produced on <a href="https://eodc.eu">EODC</a> - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.</p> <p><strong>Funding</strong><br> This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (<a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">MAT_STOCKS</a>, grant agreement No 741950).</p>
Estimation and mapping of the material stocks of buildings of Europe
<p>This data repository includes the results of the paper:<strong> Estimation and mapping of the material stocks of buildings of Europe: a novel nighttime lights-based approach</strong></p> <p>Link to the paper: <a href="https://doi.org/10.1016/j.resconrec.2021.105509">https://doi.org/10.1016/j.resconrec.2021.105509</a></p> <p>The data layers are in the high resolution of individual NLCs (Nighttime Light Cells) and aggregated to the standardized spatial units of NUTS2, NUTS3, and 47 countries and territories in Europe.</p> <p>Refer to SI Codebook.xls for details of the different fields within each layer.</p>
Oligonucleotide probes used for comparative mapping in lupin species
<p>The supplementary list of oligonucleotide probes generated in Chorus software. Filtered probes were used in oligo-FISH comparative mapping of lupin species. Detailed description provided in the article: 'The puzzling fate of a lupin chromosome revealed by reciprocal oligo-FISH and BAC-FISH mapping'.</p>
Augmented emission maps: 998 cc 74 kW Euro 6dT petrol engine
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 998 cc 74 kW Euro 6dT petrol engine that has been applied in the Ford Fiesta, Ecosport, Transit Courier.</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI <a href="http://doi.org/10.5281/zenodo.4268034">http://doi.org/10.5281/zenodo.4268034</a> refers to a updated meta-data document that provides the full description of the standardized emission map.</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.