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370 results for “GEO”
LAGOS-US GEO v1.0: Data module of lake geospatial ecological context at multiple spatial and temporal scales in the conterminous U.S.
The LAGOS-US GEO data package is one of the core data modules of LAGOS-US, an extensible research-ready platform designed to study the 479,950 lakes and reservoirs larger than or equal to 1 ha in the conterminous US (48 states plus the District of Columbia). The GEO module contains data on the geospatial and temporal ecological setting (e.g., land use, terrain, soils, climate, hydrology, atmospheric deposition, and human influence) quantified at multiple spatial divisions (e.g., equidistant buffers around lakes, watersheds, hydrologic basins, political boundaries, and ecoregions) relevant to the LAGOS-US lake population defined in the LAGOS-US LOCUS module. The database design that supports the LAGOS-US research platform was created based on several important design features: lakes are the fundamental unit of consideration, all lakes in the spatial extent above the minimum size must be represented, and most information is connected to individual lakes. The design is modular, interoperable (the modules can be used with each other), and extensible (future database modules can be developed and used in the LAGOS-US research platform by others). Users are encouraged to use the other two core data modules that are part of the LAGOS-US platform: LOCUS (location, identifiers, and physical characteristics of lakes and their watersheds) and LIMNO (in situ lake physical, chemical, and biological measurements through time) that are each found in their own data packages.
Bicycle trips collected using Cyclists Geo-C geo-game
<p>This is an experimental dataset for the bicycle trips recorded using and geo-game called "Cyclist Geo-C". It contains the geometry of the trips recorded by 60 participants from three European Cities: Münster, Germany; Castelló, Spain; Valletta, Malta. This dataset was collected and analysed for the PhD Thesis "Mobile Services for Green Living" part of the European Joint Doctorate in Geoinformatics and the <a href="http://geo-c.eu/">Geo-C </a>Project. </p> <p>The dataset is composed of three subsets.</p> <ol> <li>There is a point dataset called "<em><strong>trips_od.geojson</strong></em>" which contained the point geometries where each trip started and ended with attributes for latitude, longitude, altitude, and precision coordinates. Each point also had the timestamp which indicates the time when the user started or ended the trip.</li> <li>There is a line dataset called "<em><strong>segments.geojson</strong></em>" which contained the geometries of the straight lines connecting two locations of the participant. Each segment started from an initial point "p<sub>i</sub>" recorded at a "t<sub>i</sub>” and ended at the next point recorded by the user "p<sub>f</sub>” at time “t<sub>f</sub>”. The time difference between "t<sub>i</sub>” and “t<sub>f</sub>” was at most five minutes while the length of the segment was at most one kilometre. Each segment also had the participant and trip identifier, and the segment's sequence number within the trip For each of the trip segments, we calculated the distance and speed using the recorded coordinates and timestamps from "p<sub>i</sub>" and "p<sub>f</sub>" points. <span class="math-tex">\(trip\_segment = f(p_i,p_f)\)</span> and <span class="math-tex">\(segment\_speed = \frac{distance(p_i,p_f)}{\Delta time(p_i,p_f)}\)</span>. Then we classified the segments according to the calculated distance as: “<em>walking segment</em>” when the calculated speed was less than 5 km/h; “<em>cycling segment</em>” when the calculated speed was between 5 and 50 km/h; or “<em>non-cycling segment</em>” when the calculated speed was more than 50 Km/h.</li> <li>There was another line dataset called “<em><strong>trips_tags.geojson </strong></em>” which contained the geometries of each of the trip paths. A trip was a line (also called polyline by GIS users) defined by the ordered sequence of trip segments. It started from origin point "p<sub>i</sub>" of the trip’s first segment and ended at the destination point "p<sub>f</sub>" of the trip's last segment. Each trip also had the participant's identification, trip's identification, the number of segments, start and end times.</li> </ol> <p>In addition to the experimental dataset recorded by participants, our analysis used a secondary dataset to define a comparable framework for the three cities. The secondary dataset consisted of the existing bicycle paths in the cities of Münster and Castelló as well as the planned bicycle paths around Valletta. For the city of Münster, the source of the bicycle paths was the <a href="http://www.openstreetmap.org">OpenStreetMap</a> (we downloaded the line elements with the tags “<em>bicycle=yes</em>” and "<em>cycleway=yes</em>”). For the city of Castelló, we obtained the bicycle paths from the city transport authority, including the city of Valletta, we created a digital version of the national bicycle network plan.</p> <p>We estimated the number of trips "<em><strong>bikepaths_trips.geojson</strong></em>" and the number of segments "<em><strong>bikepaths_segments</strong></em><em><strong>.</strong></em><em><strong>geojson</strong></em>" at each bike path. Also, we provide the areas where participants faced frictions during the experiment which corresponded to low cycling speeds "frictions.geojson".</p> <p>Finally, we provide a visual reference of the dataset in "<em><strong>frictions_cities.pdf</strong></em>".</p>
LAGOS-NE-GEO v1.05: A module for LAGOS-NE, a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of U.S. Lakes: 1925-2013
This data package, LAGOS-NE-GEO v1.05, is 1 of 5 data packages associated with the LAGOS-NE database-- the LAke multi-scaled GeOSpatial and temporal database. Three of the data packages each contain different types of data for 51,101 lakes and reservoirs larger than 4 ha in 17 lake-rich U.S. states to support research on thousands of lakes. These three package are: (1) LAGOS-NE-LOCUS: lake location and physical characteristics for all lakes. (2) LAGOS-NE-GEO: ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes. These geospatial data were created by processing national-scale and publicly-accessible datasets to quantify numerous metrics at multiple spatial resolutions. And, (3) LAGOS-NE-LIMNO: in-situ measurements of lake water quality from the past three decades for approximately 2,600-12,000 lakes, depending on the variable. This module was created by harmonizing 87 water quality datasets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. The other two data packages contain supporting data for the LAGOS-NE database: (4) LAGOS-NE-GIS v1.0: the GIS data layers for lakes, wetlands, and streams, as well as the spatial resolutions that were used to create the LAGOS-NEGEO module. (5) LAGOS-NE-RAWDATA: the original 87 datasets of lake water quality prior to processing, the R code that converts the original data formats into LAGOS-NE data format, and the log file from this procedure to create LAGOS-NE. This latter data package supports the reproducibility of LAGOS-NE-LIMNO. The LAGOS-NE-GEO v1.05 module includes information on the ecological context of the census lakes, all lakes > 4 ha in the study extent, their watersheds, and their regions. The information provided in the data tables for this module is organized into three main themes: CHAG - climate, hydrology, atmospheric deposition of nitrogen and sulfur, and surficial geology; LULC - land use/cover, impervious co
Database of geo-hydrological hazards in Apulia (Italy)
<p>Geospatial database containing data on geo-hydrological processes (Landslides, Floods, Sinkholes) and/or related damage, occurred between 2008 and 2019 in the Apulia Region (Italy).</p> <p>We provide a GPKG file containing multiple layers for the different types of geometries (point, line, polygon).<br> Data are extracted from a complex relational database structure described originally here https://doi.org/10.1016/j.jenvman.2017.11.022 but recently updated and improved.<br> For the different damage and phenomena we provide information about type, data and time of occurrence, temporal and spatial accuracy, main predisposing factor, etc..<br> For floods we provides codes and information compliant with the EC Flood Directive.</p> <p>The different phenomena and damages are grouped based on the meteorological event responsible for their occurrence.</p>
PEATCLSM_Trop: Integrating peat-specific land surface hydrology of natural and drained tropical peatlands in the GEOS CLSM framework
<p>The datasets archived here include simulation results shown in the peer-reviewed article “Tropical peatland hydrology simulated with a global land surface model“, published in the open access AGU Journal of Advances in Modeling Earth Systems (JAMES; Apers et al., 2022). The output was produced using the Catchment land surface model (CLSM), the land model component of the NASA Goddard Earth Observing System (GEOS) modeling framework, and various versions of peatland-specific adaptations of CLSM, i.e. PEATCLSM. Here, we provide netCDF files (*.nc or *.nc4c) for CLSM, the natural (PEATCLSM<sub>Trop,Nat</sub>), and drained (PEATCLSM<sub>Trop,Drain</sub>) tropical versions of PEATCLSM. The simulations are at a 9-km spatial resolution (EASEv2 grid) for the three major tropical peatland regions in Central and South America, the Congo Basin, and Southeast Asia, using a peat grid cell distribution that is a combination of the PEATMAP distribution from Xu et al. (2018) and the peat distribution from De Lannoy et al. (2014). Simulations with the northern version of PEATCLSM (PEATCLSM<sub>North,Nat</sub>) are not included in the archived dataset but can be obtained upon request. We provide three types of netCDF files:<br> • daily_images_*.nc4c: daily land states and fluxes for variables discussed in Apers et al., (2022; Table 1), provided as netCDF image-chunked image stack;<br> • daily_mean_*.nc: 20-year mean of the land states and fluxes (Table 1), provided as a single netCDF image;<br> • daily_std_*.nc: 20-year standard deviation of the land states and fluxes (Table 1), provided as a single netCDF image.</p> <p>The file content is described in the file PEATCLSM_Trop-Simulations.pdf.</p> <p>Please contact Sebastian Apers (sebastian.apers@kuleuven.be) or Michel Bechtold (michel.bechtold@kuleuven.be) for any questions.<br> <br> References:<br> Apers, S., De Lannoy, G. J. M., Baird, A. J., Cobb, A. R., Dargie, G. C., del Aguila Pasquel, J., … others (2022). Tropical peatland hydrology simulated with a global land surface model. <em>Journal of Advances in Modeling Earth Systems</em>. https://doi.org/10.1029/2021MS002784<br> Bechtold, M., De Lannoy, G. J. M., Koster, R. D., Reichle, R. H., Mahanama, S. P., Bleuten, W., ... others (2019). PEAT-CLSM: A specific treatment of peatland hydrology in the NASA Catchment Land Surface Model. <em>Journal of Advances in Modeling Earth Systems, 11</em>(7), 2130–2162. https://doi.org/10.1029/2018MS001574<br> De Lannoy, G. J. M., Koster, R. D., Reichle, R. H., Mahanama, S. P. P., & Liu, Q. (2014). An updated treatment of soil texture and associated hydraulic properties in a global land modeling system. <em>Journal of Advances in Modeling Earth Systems, 6</em>(4), 957– 979. https://doi.org/10.1002/2014MS000330<br> Xu, J., Morris, P. J., Liu, J., & Holden, J. (2018). PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis. <em>Catena, 160</em>, 134–140. https://doi.org/10.1016/j.catena.2017.09.010</p>
Geo-referencing of journal articles and platform design for spatial query capabilities
<p>We analyzed the corpus of three geoscientific journals to investigate if there are enough locational references in research articles to apply a geographical search method, on the example of New Zealand. We counted place name occurrences that match records from the official Land Information New Zealand (LINZ) gazetteer in the titles, abstracts and full texts of freely available papers of the New Zealand Journal of Geology and Geophysics, the New Zealand Journal of Marine and Freshwater Research, and the Journal of Hydrology, New Zealand, for the years 1958 to 2015. We generated ISO standard compliant metadata records for each article including the spatial references and make them available in a public catalogue service.</p> <ol> <li><em>articles_georef_count_data.xlsx</em>: The counts and evaluation tracking of the place name occurrences in the journal articles.</li> <li><em>summary_final.xlsx</em>: Summary statistics for evaluation based on the counts data.</li> <li><em>article_template.xml</em>: XML template for ISO 19139 compliant metadata record filled for each article.</li> <li><em>full_article.xm</em>l: Exemplary fully filled ISO 19139 compliant metadata record.<br> </li> </ol>
A new Geo-Lithological Map (Geo-LiM) for Central Europe (Germany, France, Switzerland, Austria, Slovenia, and Northern Italy)
<p><strong>We introduce a new geo-lithological map of Central Europe (Geo-LiM) elaborated adopting a lithological classification compliant to the methods more used in the litterature for estimating the consumption of atmospheric CO2 due by chemical weathering. <br> Geo-LiM represents a novelty if compared with published global geo-lithological maps. The first novelty is due by the attention paid in discriminating metamorphic rocks that were classified according to the chemistry of protoliths. The second novelty is that the procedure used for the definition of the map is made available on the web to allow the replicability and reproducibility of the product.</strong></p>
Geo-referenced Harmonized Financial Data on Soil Defense Public Works in Italy
<p>The dataset collects financial data about public works in Italy, specifically, it focuses on soil defense investments. The data is sourced from three distinct platforms: the OpenCoesione website, the OpenBDAP database, the Ministry of Economy and Finance's open data platform, and the ReNDiS database, provided by ISPRA, that exclusively gathers information about interventions in soil defense. The data obtained is interconnected using unique project codes (CUP) to prevent duplication.</p> <p>Georeferencing involves integrating geographic references into the three datasets. It enhances the accuracy of spatial analyses of spatial defense investments and provides valuable context for understanding the geographical distribution of available financial data. By incorporating geographic references such as regions, provinces, and municipalities analysts can gain insights into the spatial patterns and relationships within the datasets. This step is crucial for effective decision-making and policy formulation in the field of soil defense investments.</p> <p>Geographical references for each project were integrated using codes and names of regions, provinces, and municipalities from the ISPRA database. This database retrieves information directly from ISTAT websites, ensuring constant updates to names and codes, thus enhancing the accuracy of spatial analyses.</p> <p>Furthermore, geographical codes facilitated the association of centroids coordinates and polygon shapes for each financial observation, enhancing spatial visualization and analysis of soil defense investments, empowering decision-makers with a deeper understanding of the geographic distribution and impact of these initiatives. This comprehensive approach allows for a deeper exploration of the geographical factors influencing soil defense investments, including identifying hotspots of activity, assessing spatial trends, and understanding the localized impact of interventions on environmental sustainability and community resilience.</p> <p>The zip folder comprises four subfolders and two files. Among the files, one is a text file containing metadata, while the other is a CSV file consolidating merged data at the national level from three repositories. The subfolders contain data categorized by region and data categorized by region sourced from the three distinct repositories.</p> <p> Datasets present 28 variables: </p> <ul> <li>Columns 1-2: descriptive variables;</li> <li>Column 3: total amount financed for each intervention;</li> <li>Columns 4-9: geo-reference variables;</li> <li>Columnn 10:25: key dates of the public works process;</li> <li>Column 26: source of the data;</li> <li>Columns 27-28: geo-referencing (centroids and areal shape).</li> </ul> <p>An additional dataset has been added comprising all Italian municipalities, including thos that lack information on soil defense investments. In such a way, there are geographical information regarding all the peninsula. </p>
Meshing strategies for 3D geo-electromagnetic modeling in the presence of metallic infrastructure
<p>Accompanying data to journal article</p> <blockquote> <p>Castillo-Reyes, O., Rulff, P., Schankee Um, E., Amor-Martin, A. (2023) Meshing strategies for 3D geo-electromagnetic modeling in the presence of metallic infrastructure. Accepted for publication in Computational Geosciences.</p> </blockquote>
Subset of nucleosomal DNA sequences from mouse brain nucleus accumbens tissue (GEO dataset GSE54263)
<p>This dataset contains a subset of nucleosomal DNA sequences of +1 nucleosomes from mouse brain nucleus accumbens cells (NAC) used to analyze nucleosome positioning sequence (NPS) patterns in <a href="https://doi.org/10.1371/journal.pcbi.1007365">Pranckeviciene, Erinija and Hosid, Sergey and Liang, Nathan and Ioshikhes, Ilya (2020). Nucleosome positioning sequence patterns as packing or regulatory. In PLoS computational biology, 16 (1), pp. e1007365.</a></p> <ul> <li>controlm.fa.gz contains sequences of <strong>control</strong> mice (GSE54263 subset Con_H3 GSM1311267)</li> <li> resilientm.fa.gz contains sequences of mice <strong>resilient to social stress</strong> (GSE54263 subset Res_H3 GSM1311268)</li> <li> susceptiblem.fa.gz contains sequences of<strong> </strong>mice <strong>susceptible to social stress</strong> (GSE54263 subset Sus_H3 GSM1311269)</li> </ul> <p>This dataset originates from the GEO accession GSE54263 data from <a href="https://www.nature.com/articles/nm.3939">Sun H, Damez-Werno DM, Scobie KN, Shao NY et al. ACF chromatin-remodeling complex mediates stress-induced depressive-like behavior. <em>Nat Med</em> 2015 Oct;21(10):1146-53.</a></p>
Planmap's Deliverable 6.2- 3D geo-models based on multiple datasets of the Moon (implicit or explicit modelling)
<p>Outputs of the 3D geomodelling of the shallow-surface layered deposits on the Chang'e 3 landing site. This is based on the Yutu rover GPR channel 2B data gathered along its traverse in Sinus Iridum on the Moon.</p> <p> </p> <p>Notebooks at https://doi.org/10.5281/zenodo.4055213</p>
Geo-referenced crop-nutrient response function dataset for Tropical Africa
The profit potential for a given investment in fertilizer use can be estimated using representative crop nutrient response functions. Where response data is scarce, determination of representative response functions can be strengthened by using results from homologous crop growing conditions. Maize (Zea mays L.) nutrient response functions were selected from the Optimization of Fertilizer Recommendations in Africa (OFRA) database of 5500 georeferenced response functions determined from field research conducted in Sub-Saharan Africa. Three methods for defining inference domains for selection of response functions were compared. Use of the OFRA Inference Tool (OFRA-IT; http://agronomy.unl.edu/OFRA) resulted in greater specificity of maize N, P, and K response functions with higher R2 values indicating superiority compared with using the Harvest Choice Agroecological Zones (HC-AEZ) and the recommendation domains of the Global Yield Gap Atlas project (GYGA-RD). The OFRA-IT queries three soil properties in addition to climate-related properties while the latter two options use climate properties only. The OFRA-IT was generally insensitive to changes in criteria ranges of 20–25% used in queries suggesting value in using wider criteria ranges compared with the default for information scarce crop nutrient response functions.
A Geo-Tagged COVID-19 Twitter Dataset for 10 North American Metropolitan Areas
<p>The dataset comprises of 10 JSON files, each containing geographic metadata and a sentiment score collected from tweets between March 20, 2020 and December 1, 2020 pertaining to the COVID-19 global pandemic for ten of the most populous cities in the United States and Canada. </p>
The Literary Geographies of Christine de Pizan (geo-data)
<p>This geodata set gives the place names found in the corpus of writings of Christine de Pizan, woman writer of the late 14th and early 15th centuries. It accompanies an eponymous essay forthcoming in the Modern Language Association's Approaches to Teaching Christine de Pizan volume. </p> <p> </p>
geo command-line bootcamp data
<p>Data used in the geo command-line bootcamp https://github.com/joeyklee/geo-commandline-bootcamp</p>
Geo-tagged Tweets in Paris during Nov 2015
<p><strong>Abstract</strong></p> <p>The data sets released here have been used in our study on quantitatively evaluating the impact of disasters in the city. The study of disaster events and their impact in the urban space has been traditionally conducted through manual collections and analysis of surveys, questionnaires and authority documents. While there have been increasingly rich troves of human behavioral data related to the events of interest, the ability to obtain hindsight following a disaster event has not been scaled up. In this study, we propose a novel approach for analyzing events called PairFac. PairFac utilizes discriminant tensor analysis to automatically discover the impact of a major event from rich human behavioral data. Our method aims to (i) uncover the persistent patterns across multiple interrelated aspects of urban behavior (e.g., when, where and what citizens do in a city) and at the same time (ii) identify the salient changes following a potentially impactful event. We show the effectiveness of PairFac in comparison with previous methods through extensive experiments. We also demonstrate the advantages of our approach through case studies with real-world traffic sensor data and social media streams surrounding the 2015 terrorist attacks in Paris. Our work has both methodological contributions in studying the impact of an external stimulus on a system as well as practical implications in the area of disaster event analysis and assessment.</p> <p><strong>Dataset</strong></p> <p>There are two datasets used in this study, traffic sensor dataset and social media dataset. </p> <p>Traffic Sensor dataset was collected from open data Paris. This dataset includes all the hourly data for the flow and the occupancy rate assembled by the permanent traffic sensors installed on the Paris City network (urban network and peripheral boulevard). For interested readers, please direct to the link as: https://opendata.paris.fr/explore/dataset/comptages-routiers-permanents/</p> <p>Social media dataset was collected from Twitter API. The dataset contains geo-tagged tweets from Paris collected through Twitter API between the period of Oct 16th, 2015 and Nov 20, 2015. 75,982 geo-located tweets were extracted during the period covered.</p> <p>Duration: 2015-10-16 to 2015-11-20.</p> <p>Total number of tweets: 75,982</p> <p><strong>Publication</strong></p> <p>If you make use of this data set, please kindly cite:</p> <p>Xidao Wen, Yu-Ru Lin, and Konstantinos Pelechrinis. 2016. PairFac: Event Analytics through Discriminant Tensor Factorization. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (CIKM '16). ACM, New York, NY, USA, 519-528. DOI: https://doi.org/10.1145/2983323.2983837</p> <p> </p>
Land-Sea mask files for GEO satellites.
<p>A collection of land-sea mask images, resampled and georeferenced to match the projection of various geostationary satellite sensors.</p> <p>This is derived from Todd Karin's `global-land-mask` package: https://zenodo.org/records/4066722</p> <p>In this version pixel values are as follows:</p> <p> - 0: Water</p> <p> - 1: Coastline</p> <p> - 2: Land</p> <p> - 255: Fill value</p>
Dataset related to article "How Academics and the Public Experienced Immersive Virtual Reality for Geo-education"
<p>The dataset is associated with the paper entitled: How Academics and the Public Experienced Immersive Virtual Reality for Geo-education.</p> <p>It contains feedback regarding users’ experience with Immersive Virtual Reality for geological exploration, through a tailored approach developed by Tibaldi et al. (2020) where the Virtual Landscape is based on 3D photogrammetry-based high-resolution models.</p> <p>Such feedback has been acquired through anonymous questionnaires during nine dissemination events held in 2018 and 2019 in various locations (Vienna in Austria, Milan and Catania in Italy and Santorini in Greece), in the framework of the following projects: i) the MIUR project ACPR15T4_00098–Argo3D (http://argo3d.unimib.it/); ii) 3DTeLC Erasmus+Project 2017-1-UK01-KA203-036719 (<a href="http://www.3dtelc.com">http://www.3dtelc.com</a>); iii) EGU 2018 Public Engagement Grant (https://www.egu.eu/outreach/peg/) .</p> <p>In the dataset, feedback has been grouped into categories, based on users age and background:</p> <p>i) Middle and High School Students (Schools students, results in Sheet 1);</p> <p>ii) MSc Students in Earth Sciences (MSc, results in Sheet 2);</p> <p>iii) Academics/Researchers in Earth Sciences, that include PhD students and postdocs (Academics, results in Sheet 3);</p> <p>iv) Lay Public (i.e. participants that do not belong to the other groups, results in Sheet 4).</p> <p>It lists a total of 459 records; further details are available in the manuscript.</p> <p>If you use this dataset, please do cite the following papers:</p> <p>Bonali et al., How Academics and the Public Experienced Immersive Virtual Reality for Geo-education. Geosciences.</p> <p>Tibaldi, A.; Bonali, F.L.; Vitello, F.; Delage, E.; Nomikou, P.; Antoniou, V.; Becciani, U.; Van Wyk de Vries, B.; Krokos, M.; Whitworth, M. Real world–based immersive Virtual Reality for research, teaching and communication in volcanology. Bull. Volcanol. 2020, 82, 1–12.</p> <p> </p> <p> </p> <p> </p>
GEOS CCM free-running simulation data of the Pacific-Northwest pyrocumulonimbus Event-like aerosol injection, SWIRL selection
<p>Data from GEOS CCM free running simulation of the Pacific-Northwest pyrocumulonimbus Event. </p> <p>The simulations have been designed and performed by Sampa Das and Peter R. Colarco at the NASA Center for Climate Simulations; the computing resources supporting the simulations shown in this work were provided by the NASA High-End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at the Goddard Space Flight Center.</p> <p>The datasets stored here have been generated by Giorgio Doglioni starting from the whole results of the simulations.</p> <p>The files are in hdf5 format and their structure and content is described in the README file. </p> <p> </p>
Geo-Harmonized PM2.5 maps over Europe for the years 2018-2020
<p>A space-time extremely randomised trees model was used to estimate daily (between 10 a.m. and 2 p.m.) PM<sub>2.5</sub> concentrations with 1 km spatial resolution for a three-year period 2018–2020 over Europe.<br> Satellite remote sensing, meteorological data, and land variables were used as the independent variables, PM<sub>2.5 </sub>ground-observations were used as the dependent variable while building the model.</p> <p> </p> <p> </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.