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1,255 results for “High-resolution”
Data for "Detection of metabolite-protein interactions in complex biological samples by high-resolution relaxometry: towards interactomics by NMR"
<p>Raw NMR data for relaxometry experiments, divided by donor sample. For every donor sample 2 or 3 different samples were used in order to record data at 19 different magnetic fields.</p> <p>Data from fast field-cycling relaxometry. All the data is in one xlsx file, divided by donor sample.</p> <p>Relaxometry results for alanine, lactate, creatinine and glutamine, obtained from the fitting of their relaxation decays recorded at 19 different fields, divided by donor sample.</p>
Outputs of the Jupyter Notebook - Met Office UKV high-resolution atmosphere model data
<p>The dataset contains the outputs of the notebook "Met Office UKV high-resolution atmosphere model data" published in the urban sensors section of The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Samantha V. Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>Met Office Informatics Lab (creator)</p> </li> <li> <p>Microsoft (support)</p> </li> <li> <p>European Regional Development Fund (support)</p> </li> </ul> <p><em>Dataset authors</em></p> <ul> <li> <p>Met Office</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>Theo McCaie. Met office and partners offer data and compute platform for covid-19 researchers. URL: <a href="https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f">https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f</a>.</p> </li> </ul> <p> </p>
High-resolution, mixed layer NCP estimates and ancillary data from the Central and Eastern North American Arctic: 2015, 2018, 2019
<p><strong>Dataset overview</strong></p> <p>This dataset contain ship-based, high-resolution (underway) estimates of mixed layer net community production (NCP) and ancillary data from three summertime cruises in the Central and Eastern North American Arctic in 2015, 2018 and 2019. NCP estimates were derived from underway O2/Ar observations, obtained using ship-based membrane inlet mass spectrometry. Ancillary data include geospatial information (time, location), surface and depth-resolved hydrography and biogeochemical observations, and select output from a simulation of an oceanographic circulation model, based on the NEMO framework.</p> <p>Please cite as:</p> <p>Izett, R. and Tortell, P. 2021. High-resolution, mixed layer NCP estimates and ancillary data from the Central and Eastern North American Arctic: 2015, 2018, 2019 (Dataset). Zenodo. https://doi.org/https://doi.org/10.5281/zenodo.5593381.</p> <p>This dataset is supplement to:</p> <p>Izett, R. W., Castro de la Guardia, L., Chanona, M., Myers, P. G., Waterman, S, and Tortell, P. D. Impact of vertical mixing on summertime net community production in Canadian Arctic and Subarctic waters: Insights from in situ measurements and numerical simulations. </p> <p> </p> <p><strong>Abstract</strong></p> <p>We present ΔO<sub>2</sub>/Ar-based estimates of mixed layer net community production (NCP) from three summer cruises in the North American Arctic and Subarctic oceans. Coupling shipboard underway and discrete observations with output from an ocean circulation model, we correct the NCP estimates for vertical mixing fluxes impacting the surface O<sub>2</sub> budget. Large positive mixing fluxes, exceeding 100 mmol O<sub>2</sub> m<sup>-2</sup> d<sup>-1</sup>, were derived in regions of strong wind-driven mixing, such as the Labrador Sea, and in the physically-dynamic Canadian Arctic Archipelago. In contrast, flux corrections were small (<10 mmol O<sub>2</sub> m<sup>-2 </sup>d<sup>-1</sup>, on average) in the density-stratified Baffin Bay, where mixing was low, and parts of the well-mixed Hudson Strait, where vertical O<sub>2</sub> gradients were weak. The distribution of corrected NCP was highly heterogenous across the study region, reflecting varying contributions of nutrient supply, freshwater input and sea ice dynamics. Elevated NCP was apparent in the Labrador Sea, Hudson Strait, and nearshore regions influenced by glacial meltwater and recent ice retreat. Low NCP and localized net heterotrophy occurred in Baffin Bay, and near strong freshwater and organic matter sources in Hudson Bay and the Queen Maud Gulf. A multiple linear regression model developed using available oceanographic data explained ~58 % of the observed NCP variability. Our work demonstrates the spatially explicit influence of vertical mixing on ΔO<sub>2</sub>/Ar-based NCP calculations across varied hydrographic conditions, and presents a novel approach to account for this process. This study contributes new knowledge of biological productivity distributions in under-sampled, rapidly changing, high-latitude waters.</p> <p> </p> <p><strong>Lay summary</strong></p> <p>Net community production (NCP; i.e., net organic matter production) constrains the ocean’s ability to support marine ecosystems and remove carbon dioxide from the atmosphere. A common approach to estimating NCP involves measurements of upper ocean oxygen (O<sub>2</sub>) concentrations. However, while vertical mixing may be a significant component of the surface water O<sub>2</sub> budget in some regions, applications of this approach typically do not quantify the magnitude of this flux, which can lead to potentially inaccurate NCP estimates. In this paper, we introduce a method combining ship-based measurements and the output from an ocean circulation model to refine NCP calculations for vertical mixing effects in North American Arctic and Subarctic oceans. The dataset reveals high NCP in the Labrador Sea (Inuktitut: <em>Lâbradorip Imappinga</em>), North Atlantic, Hudson Strait (<em>Ikirasarjuaq</em>) and northern Canadian Arctic Archipelago (CAA), and low values in Baffin Bay (<em>Saknirutiak Imanga</em>) and southern CAA. Riverine freshwater input to Hudson Bay (<em>Tasiujarjuar</em>) and the Queen Maud Gulf (<em>Ugjulik</em>) can reduce local NCP, while glacial meltwater may stimulate NCP elsewhere. Overall, this work provides a new NCP dataset in an under-sampled region. Similar studies will be necessary to document changes in biological productivity in response to changing environmental conditions in polar waters.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This work was supported by the ArcticNet and MEOPAR Networks of Centres of Excellence Canada, Polar Knowledge Canada, the Natural Sciences and Engineering Research Council of Canada (NSERC) and Compute Canada.</p> <p>Hydrography and ancillary oceanographic data were provided by the Amundsen Science group of Université Laval.</p> <p>The model simulation was run by P. Myers (University of Alberta).</p> <p>The underway gas data were collected by R. Izett & P. Tortell (University of British Columbia)</p> <p>All data were archived by R. Izett. </p> <p> </p> <p><strong>Files and variables:</strong></p> <p>data_yyyy (data provided in NetCDF and Matlab format; "yyyy" denotes sampling year): Ship-board observations and derived quantities.</p> <table> <tbody> <tr> <td><em>Variable</em></td> <td><em>Description </em></td> <td><em>Unit</em></td> </tr> <tr> <td>time</td> <td>UTC YYYY Julian Day (year-day since YYYY-01-01)</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Decimal degrees E</td> </tr> <tr> <td>dist</td> <td>Along-track distance</td> <td>km</td> </tr> <tr> <td>reg_index</td> <td>Regional index</td> <td> </td> </tr> <tr> <td>region_mask_lat</td> <td>Latitude for region indices mask</td> <td>Decimal degrees N</td> </tr> <tr> <td>region_mask_long</td> <td>Longitude for region indices mask</td> <td>Decimal degrees E</td> </tr> <tr> <td>region_mask</td> <td>Regional masks</td> <td> </td> </tr> <tr> <td>sst</td> <td>Sea surface temperature measured in the instrument laboratory</td> <td>deg. C</td> </tr> <tr> <td>sal</td> <td>Sea surface salinity measured in the instrument laboratory</td> <td> </td> </tr> <tr> <td>chl_fluor</td> <td>Calibrated mixed layer Chl a fluorescence in the instrument laboratory</td> <td>(mg Chl a)/m3</td> </tr> <tr> <td>do2ar</td> <td>Biological O2 saturation anomaly, deltaO2/Ar</td> <td>%</td> </tr> <tr> <td>kwo2</td> <td>Weighted O2 gas transfer velocity</td> <td>m/d</td> </tr> <tr> <td>bioflux_ncp</td> <td>Bioflux-NCP</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>cor_ncp</td> <td>corrected-NCP</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>uw_kz</td> <td>Underway model-based eddy diffusivity at the base of the mixed layer</td> <td>m2/s</td> </tr> <tr> <td>bling_ncp</td> <td>BLING model-based mixed layer NCP, matched to underway cruise time/position</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>prof_time</td> <td>UTC 2015 Julian Day (year-day since 2015-01-01) at CTD profile stations</td> <td>UTC Days</td> </tr> <tr> <td>prof_lat</td> <td>Latitude N at CTD profile stations</td> <td>Decimal degrees N</td> </tr> <tr> <td>prof_long</td> <td>Longitude E at CTD profile stations</td> <td>Decimal degrees E</td> </tr> <tr> <td>prof_dist</td> <td>Along-track distance at CTD profile stations</td> <td>km</td> </tr> <tr> <td>prof_reg_index</td> <td>Regional index at CTD profile stations</td> <td> </td> </tr> <tr> <td>prof_do2bdz</td> <td>Subsurface O2b gradient, dO2B/dZ at CTD profile stations</td> <td>mmol O2/m4</td> </tr> <tr> <td>prof_mld</td> <td>Mixed layer depth, calculated at CTD profile stations</td> <td>m</td> </tr> <tr> <td>prof_pycnocline_dep</td> <td>Pycnocline depth, calculated at CTD profile stations</td> <td>m</td> </tr> </tbody> </table> <p> </p> <p>nemo_yyyy (data provided in NetCDF format only; "yyyy" denotes sampling year): 4-dimensional gridded NEMO model output. </p> <table> <tbody> <tr> <td>Varaible</td> <td>Description </td> <td>Unit</td> </tr> <tr> <td>time</td> <td>Model time, UTC YYYY Julian Day (year-day since YYYY-01-01)</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Model Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Model Decimal degrees W</td> </tr> <tr> <td>depth_grid_kz</td> <td>kz depth</td> <td>m</td> </tr> <tr> <td>depth_grid</td> <td>depth</td> <td>m</td> </tr> <tr> <td>kz</td> <td>Eddy diffusivity coefficient</td> <td>m2/s</td> </tr> <tr> <td>T</td> <td>Temperature</td> <td>deg-C</td> </tr> <tr> <td>sal</td> <td>Salinity</td> <td> </td> </tr> <tr> <td>oxy</td> <td>Oxygen concentration</td> <td>mol O2/m3</td> </tr> </tbody> </table> <p> </p> <p>ArcticNet2003-2014_derived_quantities (data provided in NetCDF format only): Derived quantities from ArcticNet sampling.</p> <table> <tbody> <tr> <td>Varaible</td> <td>Description </td> <td>Unit</td> </tr> <tr> <td>time</td> <td>UTC; Days since 2010-01-01</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Decimal degrees E</td> </tr> <tr> <td>reg_index</td> <td>Regional index</td> <td> </td> </tr> <tr> <td>prof_do2bdz</td> <td>Subsurface O2b gradient, dO2B/dZ at CTD profile stations</td> <td>mmol O2/m4</td> </tr> <tr> <td>prof_mld</td> <td>Mixed layer depth, calculated at CTD profile stations</td> <td>m</td> </tr> <tr> <td>prof_pycnocline_dep</td> <td>Pycnocline depth, calculated at CTD profile stations</td> <td>m</td> </tr> </tbody> </table> <p> </p> <p> </p>
5D-NP-MATER_MDO - Open Dataset for "Novel, High-Resolution, Subtractive Photoresist Formulations for 3D Direct Laser Writing Based on Cyclic Ketene Acetals"
<p>This is the open dataset for the paper: "Marco Carlotti*, Omar Tricinci, Virgilio Mattoli*, Novel, High-Resolution, Subtractive Photoresist Formulations for 3D Direct Laser Writing based on Cyclic Ketene Acetals, Advanced Materials Technologies, On line (2022) [DOI: 10.1002/admt.202101590] "</p> <p>This include the Supplementary Information file ("SI.pdf") , all the source material used for the paper preparation and more. </p> <p>For each folder (sub-dataset) there is a corresponding readme file describing the content and including metadata.</p> <p> </p>
CRIRES high-resolution near-infrared spectroscopy of diffuse interstellar band profiles
<p>This archive contains data used for the paper:</p> <p>CRIRES high-resolution near-infrared spectroscopy of diffuse interstellar band profiles. Detection of 12 new DIBs in the YJ band and the introduction of a combined ISM sight line and stellar analysis approach</p> <p>Paper-DOI: 10.1051/0004-6361/202142990</p> <p>It contains reduced oCRIRES spectra. For more details on the reduction see the paper.</p>
High-resolution maps of material stock and population in Germany from 1985 to 2018
<p>Global societal material stocks such as buildings and infrastructure accumulated rapidly within recent decades, along with population growth. Material stocks constitute the physical basis of most socio-economic activities and services, such as mobility, housing, health, or education. The dynamics of stock growth, and its relation to the population that demands those services, is an essential indicator for long-term societal resource use and patterns of emissions. The creation of societal material stock creates path dependencies for future resource use, with an important impact on how the transformation towards sustainable societies can succeed.</p> <p>This dataset features detailed maps of material stock and population for Germany on a 30m grid. The data is based on recent maps of material stock and building volume (compare to Haberl et al. 2021, doi: 10.1021/acs.est.0c05642), recent and historic census data, and a time series of Landsat TM, ETM+, and OLI Earth Observation data.</p> <p><strong>Temporal extent</strong></p> <p>The data contains annual maps from 1985 to 2018.</p> <p><strong>Data format and units</strong></p> <p>Per German federal state, the data come in tiles of 30x30km. The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Please consider the generation of image pyramids before using *.vrt files.</p> <p>All image data has 34 bands, where band 1 is data for 1985, and band 34 is data for 2018.</p> <p>The dataset features</p> <ul> <li>population (Scaled by 100 to reduce data storage size. Divide by 100 to get people per cell)</li> <li>mass (in tons) of … <ul> <li>total material stock <ul> <li>… material stock in buildings <ul> <li>… in commercial and industrial buildings</li> <li>… in multi-family residential buildings</li> <li>… in single-family residential buildings</li> <li>… in high-rise buildings</li> <li>… in lightweight buildings</li> </ul> </li> <li>… material stock in road infrastructure</li> <li>… material stock in rail infrastructure</li> <li>… material stock in other infrastructure</li> </ul> </li> </ul> </li> </ul> <p>Material stock in high-rise and lightweight buildings is not featured in the corresponding publication due to its overall negligible amount. It is, however, included here for completeness.</p> <p><strong>Further information</strong></p> <p>For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p> <p><strong>Corresponding publication</strong></p> <p>Schug, F., Frantz, D., Wiedenhofer, D., Virág, D., Haberl, H., van der Linden, S., Hostert, P. (in rev.): High-resolution mapping of 33 years of material stock and population growth in Germany. Journal of Industrial Ecology</p> <p><strong>Funding</strong></p> <p>This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).</p> <p> </p>
Dataset of "An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification"
<p>Dataset of the article "An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification" (https://doi.org/10.1016/j.expthermflusci.2022.110756). Local similarity between non-time-resolved snapshots is enforced by KNN to extract high-resolution velocity fields and estimate the uncertainty of the measurements.</p> <p>The codes processing data here are on https://github.com/erc-nextflow/KNN-PTV.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085, NEXTFLOW).</p>
Inputs of the Jupyter Notebook - Met Office UKV high-resolution atmosphere model data
<p>The dataset contains the inputs of the notebook "Met Office UKV high-resolution atmosphere model data" published in The Environmental Data Science Book.</p> <p>The input data refer to a subset of single sample data file for 1.5 m temperature as part of the Met Office contribution to the COVID 19 modelling effort.</p> <p>The full dataset was available for download from the Met Office Azure (https://metdatasa.blob.core.windows.net/covid19-response-non-commercial/). The full dataset was available for download under the terms of non-commercial purposes.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Samantha V. Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>Met Office Informatics Lab (creator)</p> </li> <li> <p>Microsoft (support)</p> </li> <li> <p>European Regional Development Fund (support)</p> </li> </ul> <p><em>Dataset authors</em></p> <ul> <li> <p>Met Office</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>Theo McCaie. Met office and partners offer data and compute platform for covid-19 researchers. URL: <a href="https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f">https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f</a>.</p> </li> </ul> <p><strong>Note this data should be used only for non-commercial purposes.</strong></p>
High-resolution topography and layer attitude measurements over Juventae Chasma (Valles Marineris, Mars)
<p>Stereo-derived topography over mounds in Juventae Chasma (Valles Marineris, Mars), including derived measurements (ASCII .csv and .xls). File naming in measurements follows the numbering of NASA MRO HiRiSE stereo pairs.</p> <p>Please note for all DTMs</p> <p>Format: GeoTiff<br> Projection: Equirectangular<br> Datum: Mars 2000 Sphere</p> <p>Bit depth: 32bit</p> <p>Spatial resolution: 1m/pixel</p> <p>HiRise images avalable at:https://hirise.lpl.arizona.edu/<br> (use one of the image numbers listed below in the search box)</p> <p>Stereo pairs:</p> <p>HiRISE 1 (PSP_002590_1765_RED-PSP_002946_1765_RED-DEM_cyli.tif)<br> PSP_002590_1765<br> PSP_002946_1765</p> <p><br> HiRISE 2 (PSP_006915_1760_RED-PSP_007060_1760_RED-DEM_cyli.tif)<br> PSP_006915_1760<br> PSP_007060_1760</p> <p>HiRISE 3 (ESP_015934_1760_RED-ESP_016646_1760_RED-DEM_cyli.tif)<br> ESP_016646_1760<br> ESP_015934_1760</p> <p>HiRISE 4 (ESP_020470_1755_RED-ESP_014378_1755_RED-DEM_cyli.tif)<br> ESP_020470_1755<br> ESP_014378_1755</p> <p>HiRISE 5 (PSP_002379_1755_RED-PSP_002023_1755_RED-DEM_cyli.tif)<br> PSP_002379_1755<br> PSP_002023_1755</p> <p>HiRISE 6 (ESP_016567_1755_RED-ESP_017279_1755_RED-DEM_cyli.tif)<br> ESP_016567_1755<br> ESP_017279_1755</p> <p>HiRISE 7 (PSP_003790_1755_RED-PSP_004291_1755_RED-DEM_cyli.tif)<br> PSP_003790_1755<br> PSP_004291_1755</p> <p>HiRISE 8 (ESP_016145_1775_RED-ESP_017424_1775_RED-DEM_cyli.tif)<br> ESP_016145_1775<br> ESP_017424_1775</p> <p>HiRISE 9 (PSP_008708_1780_RED-PSP_008998_1780_RED-DEM_cyli.tif)<br> PSP_008708_1780<br> PSP_008998_1780</p> <p>HiRISE 10 (ESP_011688_1760_RED-ESP_019613_1760_RED-DEM_cyli.tif)<br> ESP_019613_1760<br> ESP_011688_1760</p>
High-resolution digital topography and layer attitude measurements over Juventae Chasma (Valles Marineris, Mars)
<p>Stereo-derived topography over mounds in Juventae Chasma (Valles Marineris, Mars), including derived measurements (ASCII .csv and .xls). File naming in measurements follows the numbering of NASA MRO HiRiSE stereo pairs.</p> <p>Please note for all DTMs</p> <p>Format: GeoTiff<br> Projection: Equirectangular<br> Datum: Mars 2000 Sphere</p> <p>Bit depth: 32bit</p> <p>Spatial resolution: 1m/pixel</p> <p>HiRise images avalable at:https://hirise.lpl.arizona.edu/<br> (use one of the image numbers listed below in the search box)</p> <p>Stereo pairs:</p> <p>HiRISE 1 (PSP_002590_1765_RED-PSP_002946_1765_RED-DEM_cyli.tif)<br> PSP_002590_1765<br> PSP_002946_1765</p> <p><br> HiRISE 2 (PSP_006915_1760_RED-PSP_007060_1760_RED-DEM_cyli.tif)<br> PSP_006915_1760<br> PSP_007060_1760</p> <p>HiRISE 3 (ESP_015934_1760_RED-ESP_016646_1760_RED-DEM_cyli.tif)<br> ESP_016646_1760<br> ESP_015934_1760</p> <p>HiRISE 4 (ESP_020470_1755_RED-ESP_014378_1755_RED-DEM_cyli.tif)<br> ESP_020470_1755<br> ESP_014378_1755</p> <p>HiRISE 5 (PSP_002379_1755_RED-PSP_002023_1755_RED-DEM_cyli.tif)<br> PSP_002379_1755<br> PSP_002023_1755</p> <p>HiRISE 6 (ESP_016567_1755_RED-ESP_017279_1755_RED-DEM_cyli.tif)<br> ESP_016567_1755<br> ESP_017279_1755</p> <p>HiRISE 7 (PSP_003790_1755_RED-PSP_004291_1755_RED-DEM_cyli.tif)<br> PSP_003790_1755<br> PSP_004291_1755</p> <p>HiRISE 8 (ESP_016145_1775_RED-ESP_017424_1775_RED-DEM_cyli.tif)<br> ESP_016145_1775<br> ESP_017424_1775</p> <p>HiRISE 9 (PSP_008708_1780_RED-PSP_008998_1780_RED-DEM_cyli.tif)<br> PSP_008708_1780<br> PSP_008998_1780</p> <p>HiRISE 10 (ESP_011688_1760_RED-ESP_019613_1760_RED-DEM_cyli.tif)<br> ESP_019613_1760<br> ESP_011688_1760</p>
Global high-resolution growth projections dataset for rooftop area consistent with the shared socioeconomic pathways, 2020-2050.
<h2>Description (V2 - Latest):</h2> <p>To enable easy integration in the workflows, we have provided the main datasets in the following formats:</p> <p> </p> <ul> <li><strong><em>Vector dataset:</em><code> Folder - Vector</code> </strong>The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code><em>Geopackage (.gpkg)</em></code> file <strong>(</strong><strong><em>Results_Vis.gpkg</em></strong><strong>)</strong> with polygon geometries at 1/8-degree spatial resolution in an <strong>EPSG:4326 </strong>coordinate system. The <em>attribute table</em> of this file contains <em>FN_ID</em> column representing the FN grid cell ID, and other columns representing the FN_ID specific assessed rooftop area. The assessed gross rooftop area columns are sequenced as <em>BF_X_Y</em> with <em>X</em> having values as <em>1, 2, 3, 4, and 5</em> for <em>SSP1, SSP2, SSP3, SSP4, SSP5</em><strong><em> </em></strong>narratives with <em>Y</em><strong> </strong>representing the assessment year having values as <em>20, 30, 40, and 50</em> for years <em>2020, 2030, 2040, and 2050</em> and with <strong><em>km<sup>2</sup></em></strong> units. In addition, a CF column is added for each FN_ID entry that documents the Capacity Factor for rooftop solar PV based on the World Bank solar atlas.</li> </ul> <p> </p> <ul> <li><strong><em>Raster datasets:</em></strong><strong> <code> Folder - Raster</code> </strong>The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code><em>geotiff (.tif)</em></code> files with <strong>LZW</strong> compression in an <strong>EPSG:4326</strong> coordinate system. The assessed gross rooftop area datasets are sequenced as <em>BF_X_Y</em> with <em>X</em> having values as <em>1, 2, 3, 4, and 5 </em>for <em>SSP1, SSP2, SSP3, SSP4, SSP5</em><strong><em> </em></strong>narratives with<strong> </strong><em>Y</em> representing the assessment year having values as <em>20, 30, 40, and 50</em> for years <em>2020, 2030, 2040, and 2050</em><strong> </strong>and with <strong><em>km<sup>2</sup></em></strong> units.</li> </ul> <p> </p> <ul> <li><strong><em>Numerical dataset:</em></strong> <strong><code> Folder - Numerical</code> </strong>The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code><em>parquet (.parquet)</em></code> file <strong><em>(Results.parquet).</em></strong> This file contains <em>FN_ID</em> column representing the FN grid cell ID, and other columns representing the FN_ID specific assessed rooftop area. The assessed gross rooftop area columns are sequenced as <em>BF_X_Y</em> with <em>X</em> having values as <em>1, 2, 3, 4, and 5</em> for <em>SSP1, SSP2, SSP3, SSP4, SSP5</em> narratives with <em>Y </em>representing the assessment year having values as<strong> </strong><em>20, 30, 40, and 50</em><strong> </strong>for years <em>2020, 2030, 2040, and 2050</em><strong> </strong>and with <strong><em>km<sup>2</sup></em></strong> units. In addition, a <em>CF</em> column is added for each FN_ID entry that documents the Capacity Factor for rooftop solar PV based on the World Bank solar atlas.</li> </ul> <p> </p> <p>In addition to the main datasets, we have provided additional files to enable generating the vector and numerical datasets from this study: <strong><code> Folder - Models</code></strong></p> <ul> <li><strong><em>M2_Model.json:</em></strong><strong> </strong>This file contains the frozen parameters of the M2 model in <code><em>.json</em></code> format generated from <code>XGBoost version 2.0.3</code></li> <li><strong><em>SSP_drivers.parquet:</em><em> </em></strong>This file contains the driver data used for generating the main dataset in our study</li> <li><strong><em>FN_MAP.parquet:</em></strong><strong> </strong>This file contains the boundary information for each fishnet grid tile in a Well Known Text <em>(WKT)</em> format.</li> <li><strong><em>Prediction.ipynb:</em></strong><strong> </strong>This file provides a python notebook interface to generate inferencing from <em><code>M2_Model.json</code> </em>using <code><em>SSP_drivers.parquet</em></code> file. In addition, this file also generates the numerical dataset and converts it into vector dataset using <code><em>FN_MAP.parquet</em></code><code> </code>file.</li> <li><strong><em>environment.yaml:</em></strong><strong> </strong>This file contains the frozen configuration of python virtual environment used to generate the results presented in this study.</li> </ul> <p> </p> <h2><strong>Version history:</strong></h2> <p><strong>This version corresponds to the revised journal submission (Round 1). <em>The version will be updated upon the completion of the review of the main manuscript.</em></strong></p> <ul> <li><em>This version <strong>V2</strong> is supersedes <strong>V1</strong> to correspond with round 1 of review.</em></li> <li>The database(s) in this version is associated with a Data Descriptor paper manuscript entitled " <em>Global high-resolution growth projections for rooftop area consistent with the shared socioeconomic pathways, 2020-2050 </em>", submitted to <em>Scientific Reports</em> Journal (<a href="https://www.nature.com/srep/">https://www.nature.com/srep/</a>)</li> </ul> <p> </p> <h2>Changelog:</h2> <p>The following files from version <strong>V1</strong> of this dataset are now <strong><em>archived</em></strong> based on the reviews (Round 1).</p> <ol> <li> <blockquote><em><strong>1_Geospatial_Dataset_V1.gpkg</strong></em></blockquote> </li> <li> <blockquote><em><strong>2_Countrylevel_gross_rooftop_area_V1.parquet</strong></em></blockquote> </li> <li> <blockquote><em><strong>3_Analytics_Scripts_V1.ipynb</strong></em></blockquote> </li> </ol>
High-resolution basin-wide correlations with dynamic time-warping: code and data for a case study from the Usseln Limestone (Late Devonian, Rhenish Massif, Germany)
<p>This dataset accompanies the manuscript of Wichern et al. (GRL, 2024), entitled "Decoding Deep-Time Rhythms: Probing the limit of Stratigraphic Correlation in the Usseln Limestone's (Late Devonian) Time-Specific Facies". It contains both datasets and code. </p> <p>The dataset concerns samples collected from the Usseln Limestone, a rock unit that underlies the Late Devonian Kellwasser Crisis deposits in the Rhenish Massif, western Germany. The data consists of high-resolution thin-section composite photos, as well as micro-XRF scanning data (both maps and depth records) for three localities. The code contains the workflow to structure and plot the micro-XRF maps and convert them to depth records, as well as the workflow to analyse these depth records using dynamic time warping. Further analytical details can be found in the supporting material of the associated manuscript.</p>
A global synthesis of high-resolution stable isotope data from benthic foraminifera of the last deglaciation
<p>In paleoceanography, carbon and oxygen stable isotope ratios from benthic foraminifera are used as tracers of physical and biogeochemical properties of the deep ocean. We present the first version of the Ocean Carbon Cycling working group database, of stable isotope ratios of oxygen and carbon from benthic foraminifera from deep ocean sediment cores from the Last Glacial Maximum (LGM, 23-20 ky before present (BP)) to the Holocene (<10 ky BP) with a particular focus on the early last deglaciation (20-15 ky BP). It includes 287 globally distributed coring sites, with metadata, isotopic and chronostratigraphic information, and age models. A quality check was performed for all data and age models. Sites with at least millennial resolution were preferred, because the main goal is to resolve ocean changes associated with the last deglaciation on at least millennial timescales. Software tools were produced to access and analyze the data, and are included with this publication. Deep water mass structure as well as differences between the early deglaciation and LGM are captured by the data in the compilation, even though its coverage is still sparse in many ocean regions. We find high correlations among time series calculated with different age models at sites that allow such analysis. The database provides a useful dynamical approach to map physical and biogeochemical changes of the ocean throughout the last deglaciation.</p>
Data from: Possible provenance of IRD by tracing late Eocene Antarctic iceberg melting using a high-resolution ocean model
<p>This repository contains the data supplemented to <a href="https://doi.org/10.5194/cp-21-441-2025">Elbertsen et al. (2025)</a> based on Mark Elbertsen's MSc project in which he performed depth-integrated Lagrangian iceberg tracing around Antarctica during the late Eocene using high-resolution ocean model data. Using the OceanParcels framework, iceberg melting (or growth) was simulated using several kernels, including for the dominant iceberg melt terms: basal melt, buoyant convection and wave erosion. By defining kernels for five different order-of-magnitude iceberg size classes, the model was be used to determine the minimum iceberg size required for icebergs to survive the late Eocene warmth. The model output of these simulations can be found here.</p> <p> </p> <p>This research is funded by ERC Starting Grant 802835 (OceaNice) to Peter K. Bijl.</p>
A multiple model high-resolution head-related impulse response database for aided and unaided ears (HDF5 format)
<p>The Multiple-Model High Resolution HRTF database is a collection of HRTFs measured using four different Head-and-Torso Simulators at high spatial resolution (2 degree azimuth and elevation). The data here is stored in HDF5 files, the SOFA files are published in a separate dataset <a href="https://zenodo.org/record/2582553">doi:10.5281/zenodo.2582553</a>.</p>
High-Resolution Quantitative Phase Imaging of Plasmonic Metasurfaces with Sensitivity down to a Single Nanoantenna_experimental dataset
<p>This dataset shares the data presented in the paper "Geometric-phase microscopy for high-resolution quantitative phase imaging of plasmonic metasurfaces with sensitivity down to a single nanoantenna" available in open access under <a href="https://doi.org/10.5281/zenodo.3355170">10.5281/zenodo.3355170</a>. The archive contains experimental files titled with references to the figures as they appear in the paper. </p>
Replication Data for: Determination of Intrinsic Effective Fields and Microwave Polarizations by High-Resolution Spectroscopy of Single NV Center Spins
<p>Data repository for: <strong>Determination of Intrinsic Effective Fields and Microwave Polarizations by High-Resolution Spectroscopy of Single NV Center Spins</strong></p> <p><em>Data description.pdf</em> describes the uploaded data.<br> <em>Data.xlsx</em> is the data represented in the paper.<br> <em>Esrfit_Npeak.m</em>, <em>Esrfit_xN.m</em>, <em>GaussianFunc.m</em>, <em>Gaussian_xN_Func.m</em>, <em>Lorentz_Func.m</em>, <em>Lorentz_xN_Func.m</em>, <em>Rabifit_xN.m</em>, <em>Rabi_xN_Func.m</em>, <em>FourierTransformRabi.m</em> are Matlab code files to transform and fit the data.</p>
D-PLACE dataset derived from Wessel and Smith 2015 'Global Self-consistent, Hierarchical, High-resolution Geography Database'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Wessel, P., and W. H. F. Smith (1996), A global, self-consistent, hierarchical, high-resolution shoreline database, J. Geophys. Res., 101(B4), 8741–8743, doi:10.1029/96JB00104. Wessel P, Smith, W. H. F. Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHS) v2.3.4 [Internet]. 2015. Available: https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html</p> </blockquote>
High-resolution water budget estimates over the Po basin: progress towards digital replicas (OL*, DAg(*), DAs(*), DAgs(*))
<p>NASA LIS output with water budget variables at 0.7 km^2 resolution over the Po river basin (Italy) for 2015-2023. Netcdf files for 8 + 2 experiments, described in De Lannoy et al. (2024, JAMES). Because of storage limitations, this upload contains 7 of the 8 experiments with ERA5. The baseline OL experiment without irrigation is on a separate zenodo link (see below).</p> <p><strong>8 experiments forced with ERA5 meteorology</strong></p> <p>po_ol_hymap_noirr: (OL) open loop simulation, no irrigation modeling --> 10.5281/zenodo.13768739<br>po_ol_hymap_irr: (OL*) open loop simulation, with irrigation modeling</p> <p>po_da_hymap_gamma_noirr: (DAg) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, no irrigation modeling<br>po_da_hymap_gamma_irr: (DAg*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, with irrigation modeling</p> <p>po_da_hymap_snd_noirr: (DAs) data assimilation of Sentinel-1 snow depth retrievals, no irrigation modeling<br>po_da_hymap_snd_irr: (DAs*) data assimilation of Sentinel-1 snow depth retrievals, with irrigation modeling</p> <p>po_da_hymap_gamma_snd_noirr: (DAgs) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, no irrigation modeling<br>po_da_hymap_gamma_snd_irr: (DAgs*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, with irrigation modeling</p> <p><strong>2 experiments forced with MERRA2 meteorology </strong></p> <p><strong>--> These are not provided on Zenodo, because we hit the maximum storage limit. Feel free to reach out to the authors and ask for these data.<br></strong></p> <p>po_ol_hymap_noirr_M2: open loop simulation, no irrigation modeling<br>po_ol_hymap_irr_M2: open loop simulation, with irrigation modeling </p>
High-resolution water budget estimates over the Po basin: progress towards digital replicas (OL): open loop without irrigation
<p>NASA LIS output with water budget variables at 0.7 km^2 resolution over the Po river basin (Italy) for 2015-2023. Netcdf files for 8 + 2 experiments, described in De Lannoy et al. (2024, JAMES). Because of storage limitations, this upload only contains the baseline open loop without irrigation (OL), i.e. 1 of the 8 experiments with ERA5. The other 7 experiments are on a separate zenodo link (see below).</p> <p><strong>8 experiments forced with ERA5 meteorology</strong></p> <p>po_ol_hymap_noirr: (OL) open loop simulation, no irrigation modeling <br>po_ol_hymap_irr: (OL*) open loop simulation, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p>po_da_hymap_gamma_noirr: (DAg) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, no irrigation modeling --> 10.5281/zenodo.13754454<br>po_da_hymap_gamma_irr: (DAg*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p>po_da_hymap_snd_noirr: (DAs) data assimilation of Sentinel-1 snow depth retrievals, no irrigation modeling --> 10.5281/zenodo.13754454<br>po_da_hymap_snd_irr: (DAs*) data assimilation of Sentinel-1 snow depth retrievals, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p>po_da_hymap_gamma_snd_noirr: (DAgs) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, no irrigation modeling --> 10.5281/zenodo.13754454<br>po_da_hymap_gamma_snd_irr: (DAgs*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p><strong>2 experiments forced with MERRA2 meteorology </strong></p> <p><strong>--> These are not provided on Zenodo, because we hit the maximum storage limit. Feel free to reach out to the authors and ask for these data.<br></strong></p> <p>po_ol_hymap_noirr_M2: open loop simulation, no irrigation modeling<br>po_ol_hymap_irr_M2: open loop simulation, with irrigation modeling</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.