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275 results for “Spatial resolution”
SIRV: Spatial inference of RNA velocity at the single-cell resolution
<p>Spatial transcriptomics and scRNA-seq datasets used for integration and prediction of un/spliced expression for spatially measured genes using SIRV, used to infer the RNA velocity in the spatial context</p>
SESMG scenario-files of the study "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution"
<p>This dataset contains model scenario-files belonging to the publication "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution".</p> <p>The individual scenarios can be executed and evaluated with the "Spreadsheet Energy System Model Generator" (<a href="https://github.com/chrklemm/SESMG">SESMG</a>) <a href="https://github.com/chrklemm/SESMG/tree/v0.4.0rc1">v0.4.0rc1</a></p> <p>The respective file names indicate to which model run mentioned in the main study the scenario-files belong. For model runs for which no sepparate scenario file exists, the scenario "reference.xlsx" with adjusted SESMG settings was used.</p> <p> </p>
Daily satellite and gauge observed rainfall dataset (1980-2019) for Oman at 1km spatial resolution in GeoTIFFs
<p>This is a re-gridded daily TRMM datasets. It has been resampled to 1km spatial resolution. The datasets have also been projected to UTM 40N</p>
Spatial multiomic landscape of the human placenta at molecular resolution
<p>Successful pregnancy and healthy human embryo development rely directly on the placenta’s complex, dynamic gene regulatory networks, both within placental subtypes and at the maternal-fetal interface (MFI), that underlie stemness, proliferation, differentiation, invasion, immune tolerance, and communication. These cellular and molecular mechanisms are notoriously challenging to elucidate and make this organ arguably the least understood in the human body. Additionally, disruption of this vast collection of intercellular and intracellular programs and pathways leads to pregnancy complications and developmental defects. Here, we generated a comprehensive spatially resolved multi-modal cell census elucidating the molecular architecture of the first trimester human placenta. We utilized paired single-cell ATAC and RNA sequencing, spatial single-cell ATAC and RNA sequencing (Slide-tags), and in situ sequencing and hybridization mapping of transcriptomes at molecular resolution (STARmap-ISS and STARmap-ISH) using 922,961 cells to construct a spatial single-cell atlas outlining joint epigenomic and transcriptomic regulatory dynamics. Paired analyses unraveled intricate tumor-like gene expression and transcription factor motif programs sustaining the placenta in a hostile uterine environment; further investigation of gene-linked cis-regulatory elements revealed heightened regulatory complexity governing trophoblast differentiation and placental disease risk. Complementary spatial mapping techniques decoded these programs within the placental villous core and extravillous trophoblast (EVT) cell column architecture while simultaneously revealing niche-establishing transcriptional elements and cell-cell communication. To unify our datasets, we computationally imputed 33,357-gene multiomic single-cell profiles and spatially characterized the placental chromatin accessibility landscape. This spatially resolved single-cell multiomic framework of the first trimester human placenta at molecular resolution serves as a blueprint for future studies investigating cellular and molecular programs regulating early placental development and pregnancy.</p>
Implications of Lateral Groundwater Flow Across Varying Spatial Resolutions in Global Land Surface Modeling
<p>This folder contains the data used for plotting and analysis in the manuscript.</p>
Dakar population estimates at 100x100m spatial resolution - grid layer - Dasymetric mapping
<p>This dataset contains the a raster layer with the population estimates obtained using a dasymetric mapping procedure (top-down approach). For a detailed description of the methodology, please refer to the following paper:</p> <p>Grippa, Taïs, Catherine Linard, Moritz Lennert, Stefanos Georganos, Nicholus Mboga, Sabine Vanhuysse, Assane Gadiaga, and Eléonore Wolff. 2019. “Improving Urban Population Distribution Models with Very-High Resolution Satellite Information.” <em>Data</em> 4 (1): 13. <a href="https://doi.org/10.3390/data4010013">https://doi.org/10.3390/data4010013</a>.</p> <p>Funding and aknowledgement: </p> <p>This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be/">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be/">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p> <p>The authors gratefully thanks the \href{http://assess-sn.org/}{ASSESS project}, funded by the \href{https://www.ares-ac.be}{ARES-CDD}, that provided the access to the census data.</p>
Horizontal-plane HRTFs of KEMAR mannequin spatially interpolated with a resolution of 0.5°
<p>HRTFs of the KEMAR manikin (Gardner and Martin, 1995):</p> <ul> <li>"hrtf_M_normal pinna.sofa": the original HRTF dataset from the KEMAR manikin at a lateral resolution of 5° stored as a SOFA file (Majdak et al., 2013)</li> <li>"hrtf_M_normal pinna resolution 0.5 deg.sofa": A super-resolution HRTF set with a directional up-sampling of the original HRTF set to the lateral resolution of 0.5°. To this end, for each ear's HRTF set: <ul> <li>The broadband timing was removed by replacing the HRTF's phase spectrum by the minimum-phase spectrum (Oppenheim et al. 1999) corresponding to HRTF's amplitude spectrum.</li> <li>For the interpolation of the amplitude spectra, the complex spectra of the minimum-phase HRTFs for two adjacent available directions were averaged according to a weighting that corresponded to the interpolated target direction. </li> <li>For the interpolation of the timing, a continuous-direction model of the time-of-arrival (TOA) was applied (Ziegelwanger & Majdak, 2014). TOA is the broadband delay arising from the propagation paths from the sound source to the listener's ear. For a given direction of a sound, the interaural difference of TOAs corresponds to the ITD. The TOA model parameters describe listener's geometry (head and ears) and configure a continuous-direction function of broadband TOA. We used this function to calculate TOAs for directions in steps of 0.5°. To this end, for each ear, the model was fit to an HRTF set as described by Majdak & Ziegelwanger (2013) using the implementation from the Auditory Modeling Toolbox (Søndergaard & Majdak, 2013). Then, each minimum-phase HRTF was temporally up-sampled by a factor of 64, circularly shifted by the TOA obtained from the continuous-direction TOA model for the target direction, and then down-sampled to the sampling rate of 44.1 kHz. Note that the temporal oversampling was required to achieve an interaural resolution of 0.35 µs. </li> </ul> </li> </ul> <p> </p>
Groundwater Potential Zones of Khyber Pakhtunkhwa 2024 at 10 m Spatial Resolution
<p>This study presents the <strong>Groundwater Potential Zones (GWPZ)</strong> map for the Khyber Pakhtunkhwa province of Pakistan, developed at a high spatial resolution of <strong>10 meters</strong>. The mapping was performed using a <strong>Multi-Criteria Decision Analysis (MCDA)</strong> approach, specifically the <strong>Analytic Hierarchy Process (AHP)</strong>, integrating seven key spatial layers to identify potential groundwater zones. These layers include <strong>Rainfall, </strong><strong>Landcover, </strong><strong>Slope, </strong><strong>Drainage Density</strong>, <strong>Geology</strong>, <strong>Soil Texture</strong>, and <strong>Lineament Density.</strong></p> <p>The GWPZ map was validated with field data collected from <strong>300 random sample points</strong> measuring the <strong>depth to the water table</strong>. An <strong>Area Under the Curve (AUC)</strong> analysis of the validation results showed an accuracy of <strong>70%</strong>, indicating a reliable identification of potential groundwater zones. This GWPZ map provides valuable insights for water resource management, enabling targeted exploration and sustainable utilization of groundwater resources in the study area.</p>
ASM-SS: The First Quasi-Global High Spatial Resolution Coastal Storm Surge Dataset Reconstructed from Tide Gauge Records
<p>The ASM-SS dataset is a high spatial resolution (every 10 km per node along the coastline), long-term (over 80 years from 1940 to 2020), quasi-global (within 45°S-45°N), hourly data-driven storm surge dataset. Each NetCDF file includes five parameters: longitude, latitude, nodes, time, and surge level. Longitude and latitude are the location information of nodes in degree; the unit of time is accumulated hours since 1900-01-01 00:00:00; surge levels are given in meters. Users can use longitude, latitude, and time as keywords to select surge levels at nodes of interest within a target period. </p>
The species richness-productivity relationship varies among regions and productivity estimates, but not with spatial resolution
<p>The relationship between species richness and productivity (SRPR) has been a long-studied and hotly debated topic in ecology. Different studies have reported different results with variable shapes (i.e. unimodal, linear) and directions (i.e. positive, negative) of SRPRs depending on spatial grain (i.e. size of sampling unit for species richness), productivity estimates, and study extent. In this study, we quantified the effect of multiple estimates of productivity (aboveground, belowground and total biomass, and various measures of soil fertility) on species richness across three spatial grains (0.04 m<sup>2</sup>, 1 m<sup>2</sup>, and 25 m<sup>2</sup>) across temperate grasslands from two regions in Central Europe. We analyzed SRPR in each of the two regional datasets separately, as well as the two datasets pooled together. Our results have revealed that differences caused by spatial grain were unexpectedly small, and the direction of the SRPR was consistent within each productivity estimate, but differed between regions. Productivity estimates (across all spatial scales) had different, sometimes contrasting effects on SRPR (together with predictive power) within a region, and this pattern was more pronounced when compared between regions. The combination of different datasets led to very different results than when these were analyzed separately. We did not find any evidence for a unimodal response. This study points to the necessity of careful assessing when combining datasets from different regions, even if the plant communities belong to the same vegetation type. The dataset combination may blur the role of different drivers, which likely determine the shape and strength of SRPR. We suggest that data and study comparability may be enhanced by consistently using the same productivity estimates, which would allow for more robust interpretation of possible ecological drivers underlying the SRPR.</p>
High spatial resolution thickness changes of Pyrenean glaciers from 2011 to 2020
<p>Pyrenean glaciers are the largest in southern Europe. Because their very survival is threatened by climate change in the coming decades, it is urgently necessary to monitor their evolution. This dataset presents high spatial resolution observation of Pyrenean glaciers during the period 2011–2020. In 2011, the glacier surface was retrieved with an airborne lidar and in 2020 data were obtained with three different UAVs (fixed-wing or quad copters). Comparing these observations, glacier surface changes were computed for 17 out of the 24 remaining glaciers. Using high-resolution optical satellite imagery and field observations, glacier outline changes between 2011 and 2020 were also obtained. On average the glacierized area has shrunk by 23.2% and thickness has decreased on average by 6.3 m.</p>
Model data for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions and burned area as part of the publication:</p> <p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p> <p>Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br><sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br><sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br><sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br><sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p> <p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p> <p> </p> <p><strong>UPDATE OF DATASET TO 2023:</strong></p> <p>This dataset has now been extended to 2023. Since the first release of this dataset, multiple updates to the model input data have been made:</p> <p>- Update from MODIS C6 to MODIS C6.1 for all MODIS input data, including MCD12Q1 land cover types, MCD14ML active fires, MCD15A2H fPAR, MOD44B VCF, MOD44W land-water mask, and MCD64A1 burned area.<br>- Update of Hansen forest loss data from v1.9 to v1.11.<br>- Update of GLEAM evaporative stress data from v3.6b to v3.7b.<br>- Extension of ERA5-land data to 2023.<br>- Addition of land cover type layers to the 500-m resolution data files.</p> <p> </p> <p>Files contain 500-m (per MODIS tile) and 0.25 degree aggregated (global grid) carbon emissions and burned area from biomass burning for 2002-2022, as part of the paper "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-15-8411-2022). 500-m resolution files include land cover type grids. 0.25 degree global grid files also include biome partitioning and accompanying biome fractional cover grids.</p> <p>Zip archives with filenames "500m_YYYY.zip" contain annual files named "Model500m_2002-2023yr_h##v##_YYYY.nc", which are the 500-meter resolution model results per MODIS tile using the MODIS sinusoidal projection. Carbon emission data layers are:</p> <p>- Total biomass burning carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_TOT)</p> <p>- Total biomass burning carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_TOT)</p> <p>- Fire-related forest loss carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_FL)</p> <p>- Fire-related forest loss carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_FL)</p> <p>Total emissions are calculated as: C_AG_TOT + C_BG_TOT. Total fire-related forest loss emissions are calculated as: C_AG_FL + C_BG_FL.</p> <p>Burned area data layers are:</p> <p>- Total burned area; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_TOT)</p> <p>- Burned area from fire-related forest loss; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_FL)</p> <p>The Zip archive with filename "025d_2002_2023.zip" contains annual files named "Model500m_2002-2023yr_025d_YYYY.nc", which are the 500-m model results aggregated to a 0.25 degree global lat-lon grid. These files contain the same variables as the 500-m files, but aggregated to 0.25 degree resolution (MOD_CMG025). Furthermore, these files include biome partitioning of emissions and burned area (MOD_CMG025BIOME) and provide accompanying biome fractional cover grids for all 20 biomes (variable 'biomes'). Biomes are listed in detail in Table S1 of the van Wees et al. (2022) paper. The biomes 'water', 'snow/ice' and 'barren' were excluded from Table S1 because of their negligible share, but are included in the files provided here for completeness.</p>
Model code for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions model code as part of the publication:</p> <p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p> <p>Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br> <sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br> <sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br> <sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br> <sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p> <p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p> <p> </p> <p>Developed in Python version 2.7.16. Please note, this code is meant to give a general overview of the model structure and not to fully reproduce the model results with the push of one button. The full model code is much more complex to account for various simulation scenarios and relies on numerous large input datasets that all require extensive preprocessing. By omitting these complexities, we tried to make this script as understandable as possible. In case your goal is to reproduce the model in detail, please contact the first author to discuss the possibilities.</p>
High-resolution spatial multi-omics datasets
<p>Supplementary raw data. The raw microscopy data are not uploaded owing to their large size (6.8 Tb), but are available upon reasonable request (Long Cai: lcai@caltech.edu, Yodai Takei: ytakei@caltech.edu).</p> <p>These supplementary data contain additional files for RNA seqFISH+, DNA seqFISH+, and sequential immunofluorescence from cell culture and adult mouse cerebellum experiments.</p> <p>DNA seqFISH+ datasets (provided as a tar.gz folder for each replicate): Super-resolved DNA spot locations by DNA seqFISH+ along with sequential immunofluorescence intensity at the rounded voxel location.</p> <p>RNA seqFISH datasets (provided as a zip folder): Super-resolved mRNA or intron spot locations.</p> <p>Sequential immunofluorescence table (provided as a csv file): Mean voxel intensity of each immunofluorescence marker per nucleus for the adult mouse cerebellum datasets.</p> <p>Note that voxel sizes are 103 nm for x and y, and 250 nm for z in our experimental setting.</p> <p>Please find the uploaded readme.txt file for more details.</p>
High spatial resolution satellite images for glacier outlines
<p>Two RapidEye satellite images acquired on Sep 13, 2013 and Sep 17, 2018. The two satellite images have a high spatial resolution of 5 m × 5 m and were used to derive the outlines of the Parlung No. 94 Glacier in years 2013 and 2018, respectively. The GaoFen-7 (GF-7) satellite image with a high spatial resolution of 3 m × 3 m acquired on Feb 26, 2021 was used to derive the outlines of the Dongkemadi Glacier in 2021</p>
Spatial soil properties maps for Switzerland at 30 m resolution
<p>The Swiss Soil Property Map (SSPM) was developed using the quantile random forest machine learning algorithm and remotely sensed surrogate information such as terrain, climate, vegetation, and soil covariates. The SSPM dataset provides maps at 30 m resolution for different soil depths (0, 30, 60, and 100 cm) in GeoTIFF format. The mean and respective uncertainty information is provided for each map. Please note that the phosphorus spatial map is only available for the topsoil (0-20 cm) due to the unavailability of the dataset at deeper depths.</p> <table> <caption>Description of soil properties (SP) and their units</caption> <tbody> <tr> <td>SP</td> <td>Description</td> <td>units</td> </tr> <tr> <td>Sand </td> <td>Sand content</td> <td>%</td> </tr> <tr> <td>Clay</td> <td>Clay content</td> <td>%</td> </tr> <tr> <td>OC</td> <td>Organic carbon content </td> <td>%</td> </tr> <tr> <td>N</td> <td>Nitrogen content</td> <td>% </td> </tr> <tr> <td>P</td> <td>Phosphorus content</td> <td>mg/kg</td> </tr> </tbody> </table> <p>For more details / to cite this dataset please use:</p> <ul> <li><strong>Gupta, S. </strong>, Hasler, K. J., Alewell, C.: Mapping soil properties of Switzerland using remote sensing datasets and machine learning approach. Manuscript <strong>submitted</strong>, <strong>Geoderma Regional</strong>, 2023</li> </ul> <p> </p>
Preprocessed dataset of the Spatially Resolved Single-cell Translatomics at Molecular Resolution
<p>Here are the pre-processed image datasets of RIBOmap included in "<strong>Spatially Resolved Single-cell Translatomics at Molecular Resolution</strong>" from Zeng et al. Please refer to the README file for more detailed information. </p> <p> </p> <p><strong>Abstract</strong></p> <p>The precise control of mRNA translation is a crucial step in post-transcriptional gene regulation of cellular physiology. However, it remains a major challenge to systematically study mRNA translation at the transcriptomic scale with spatial and single-cell resolution. Here, we report the development of RIBOmap, a three-dimensional (3D) in situ profiling method to detect mRNA translation of thousands of genes simultaneously in intact cells and tissues. By applying RIBOmap to 981 genes in HeLa cells, we revealed a remarkable dependency of translation on cell-cycle stages and subcellular localization. Furthermore, we profiled single-cell translatomes of 5,413 genes in adult mouse brain tissues yielding a spatial cell atlas of 119,173 cells. The pairwise spatial mapping of single-cell translatome and transcriptome in two adjacent mouse brain slices revealed cell-type and brain-region-dependent translational regulation and suggested a translation remodeling during oligodendrocyte lineage maturation. The spatial translatome profiling detected widespread patterns of localized translation in neuronal and glial cells in intact brain tissue networks. Together, RIBOmap presents the first spatially resolved single-cell translatomics technology, accelerating our understanding of protein synthesis in the context of subcellular architecture, cell types, and tissue anatomy.</p>
In silico spatial transcriptomic editing at single-cell resolution
<p>The data for training the GAN (Inversion) model and reproduce the results reported in the paper </p>
Data from "Understanding and controlling spatial resolution, sensitivity, and surface selectivity in resonant-mode photothermal-induced resonance spectroscopy"
<p>Dataset of PTIR spectra used for the article "Understanding and controlling spatial resolution, sensitivity, and surface selectivity in resonant-mode photothermal-induced resonance spectroscopy", L. Quaroni, Ana. Chem. 2020, 92, 5, 3544-3554</p>
GPRChinaSPEI1km: High spatial resolution and century-long SPEI datasets for China from 1901 to 2020 generated by machine learning
<p>The high spatial resolution and century-long Standardized Precipitation Evapotranspiration Index (SPEI) dataset with a spatial resolution of 0.0083 degrees (~1 km) was spatially downscaled from the global SPEI data with a 0.5 degrees spatial resolution (https://spei.csic.es/database.html) based on machine learning integrated with high spatial resolution climatic and topographic variables. The 1-km SPEI datasets are across the land areas of China from January 1901 to December 2020, including 1-month, 3-month, 6-month and 12-month SPEIs. The unit of the data is 0.01. The dataset was evaluated using the root zone soil moisture and the historical drought events, and the evaluation indicated that the high spatial resolution SPEI dataset is reliable.</p> <p>Data Information: </p> <p>GPRChinaSPEI1km: High spatial resolution and century-long SPEI datasets over China from 1901 to 2020 generated by machine learning</p> <p>Publication: </p> <p><span>He, Q., Wang, M., Liu, K., & Wang, B. (2025). High-resolution Standardized Precipitation Evapotranspiration Index (SPEI) reveals trends in drought and vegetation water availability in China. <em>Geography and Sustainability</em>, <em>6</em>(2), 100228. https://doi.org/10.1016/j.geosus.2024.08.007</span></p> <p></p> <p>----------------------------------------------------data description---------------------------------------------</p> <p>This is a gridded dataset for the Standardized Precipitation Evapotranspiration Index (SPEI) at a spatial resolution of 1 km over the main terrestrial lands of China for each month during 1901-2020, which is generated using the Gaussian process regression (GPR) based on the Global SPEI database (https://spei.csic.es/database.html) integrated with high spatial resolution climatic and topographic variables. Four timescales of SPEI were generated: 1-month (SPEI-1), 3-month (SPEI-3), 6-month (SPEI-6) and 12-month (SPEI-12). The details are as follows:</p> <p>Region: China</p> <p>Temporal Extent: January 1901 to December 2020</p> <p>Spatial resolution: 0.0083° (~1 km)</p> <p>Temporal resolution: month</p> <p>Timescales: 1-month, 3-month, 6-month and 12-month</p> <p>Data format: GeoTIFF</p> <p>Unit: unitless (0.01)</p> <p>Geographic coordinate system: WGS 1984</p> <p>---------------------------------------------------dataset filename---------------------------------------------</p> <p>The file name specifically shows the data information.</p> <p>For example,</p> <p>“SPEI_1_2020_1.tif” means “1-month SPEI of January 2020”.</p> <p>“SPEI_3_2020_1.tif” means “3-month SPEI of January 2020”.</p> <p>All the file names are formatted in “SPEI_timescale_year_month”</p> <p>timescale: 1, 3, 6 and 12 indicate 1-month, 3-month, 6-month and 12-month, respectively</p> <p>year: from 1901 to 2020</p> <p>month: from 1 to 12</p> <p>--------------------------------------------------storage information-------------------------------------------</p> <p>The high-resolution SPEI dataset is stored in TIFF format using WGS 1984 coordinate system. The data type is int16 with a scale factor of 0.01. The nodata value is -32768. The dataset requires multiplication by 0.01 during application to obtain the actual value ranges.</p> <p>The data were compressed into .rar format every 10 years for each timescale SPEI.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.