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Summary statistics accompanying the article "Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency" in Scientific Reports (2022)
<p>Summary statistics for genome-wide association studies reported in:</p> <p>Bell, S., Tozer, D.J., & Markus H.S. (2022). Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency. <em>Scientific Reports</em>, DOI: <a href="https://dx.doi.org/10.1038/s41598-022-19106-7">10.1038/s41598-022-19106-7</a>. </p> <p><strong>Abstract</strong></p> <p>Complex brain networks play a central role in integrating activity across the human brain, and such networks can be identified in the absence of any external stimulus. We performed 10 genome-wide association studies of resting state network measures of intrinsic brain activity in up to 36,150 participants of European ancestry in the UK Biobank. We found that the heritability of global network efficiency was largely explained by blood oxygen level-dependent (BOLD) resting state fluctuation amplitudes (RSFA), which are thought to reflect the vascular component of the BOLD signal. RSFA itself had a significant genetic component and we identified 24 genomic loci associated with RSFA, 157 genes whose predicted expression correlated with it, and 3 proteins in the dorsolateral prefrontal cortex and 4 in plasma. We observed correlations with cardiovascular traits, and single-cell RNA specificity analyses revealed enrichment of vascular related cells. Our analyses also revealed a potential role of lipid transport, store-operated calcium channel activity, and inositol 1,4,5-trisphosphate binding in resting-state BOLD fluctuations. We conclude that that the heritability of global network efficiency is largely explained by the vascular component of the BOLD response as ascertained by RSFA, which itself has a significant genetic component.</p> <p> </p> <p>Further information on the files uploaded here can be found in the README. Users interested in bulk downloading these summary statistics may find <a href="https://github.com/dvolgyes/zenodo_get">zenodo_get</a> helpful.</p>
Survey on policies for innovation in the Global South, 2022.
<p>In July-August 2022, the <a href="acceleratorlabs.undp.org">UNDP Accelerator Labs</a> launched a survey to get a big picture view on the work that its global network of 91 labs was doing to support innovation ecosystems. The results were surprisingly clear-cut and coherent.</p> <p>First, <strong>we learned that a solid majority of Labs had partnered with governments to deploy interventions in support of national innovation ecosystems, or was planning to do so in the near future</strong>. We were looking at a surge of government investment in innovation across the Global South. Furthermore, these Global South governments were looking beyond the usual Global North example of policies to support innovation and learning from each other.</p> <p>Second, <strong>we learned that UNDP was widely recognized as the leading organization in supporting Global South governments in this journey</strong>. And third, <strong>we learned that collaboration with governments that have invested in innovation becomes smoother and more impactful</strong>.</p> <p>This Zenodo entry contains a file aggregating all the responses to that survey. </p>
Global database of Coastal Characteristics (GCC)
<p>This dataset present a Global database of Coastal Characteristics (GCC) with 80 indicators spanning the</p> <ul> <li>geophysical,</li> <li>hydrometeorological and</li> <li>socioeconomic</li> </ul> <p>environment, at a high alongshore resolution of 1 km and provided at ~730,000 points along the global ice-free coastline. The latest freely available global datasets and a global high-resolution transect system are used to derive these indicators.</p> <p>The geophysical indicators include coastal slopes and elevation maxima, land-use, presence of vegetation or sandy beaches.The hydro-meteorological indicators involve water level, wave conditions and meteorological conditions (rain and temperature). Additionally, the socioeconomic indices are related to population, GDP and presence of critical infrastructure (roads, railways, ports and airports).</p> <p>The indicators are provided in three comma-separated values (CSV) files, one for each group:</p> <ul> <li>GCC_geophysical.csv</li> <li>GCC_hydrometeorological.csv</li> <li>GCC_socioeconomic.csv</li> </ul> <p>Information for each individual indicator, including its name, long name (description), units and type, are provided in the meta_data.yml file.</p>
Global Dataset of Cyber Incidents V.1.2
<p>The dataset contains data on 2889 cyber incidents between 01.01.2000 and 02.05.2024 using 60 variables, including the start date, names and categories of receivers along with names and categories of initiators. The database was compiled as part of the <strong><a href="https://eurepoc.eu">European Repository of Cyber Incidents (EuRepoC)</a> </strong>project.</p> <p><br>EuRepoC gathers, codes, and analyses publicly available information from over 200 sources and 600 Twitter accounts daily to report on dynamic trends in the global, and particularly the European, cyber threat environment.<br><br>For more information on the scope and data collection methodology see: <a href="https://eurepoc.eu/methodology">https://eurepoc.eu/methodology</a><br><br><strong>Codebook available <a href="https://eurepoc.eu/wp-content/uploads/2023/07/EuRepoC_Codebook_1_2.pdf">here</a><br><br>Information about each file:</strong></p> <p><strong>Global Database (csv or xlsx):<br></strong>This file includes all variables coded for each incident, organised such that one row corresponds to one incident - our main unit of investigation. Where multiple codes are present for a single variable for a single incident, these are separated with semi-colons within the same cell.</p> <p><strong>Receiver Dataset (csv):<br></strong>In this file, the data of affected entities and individuals (receivers) is restructured to facilitate analysis. Each cell contains only a single code, with the data "unpacked" across multiple rows. Thus, a single incident can span several rows, identifiable through the unique identifier assigned to each incident (incident_id). </p> <p><strong>Attribution Dataset (csv):</strong><br>This file follows a similar approach to the receiver dataset. The attribution data is "unpacked" over several rows, allowing each cell to contain only one code. Here too, a single incident may occupy several rows, with the unique identifier enabling easy tracking of each incident (incident_id). In addition, some attributions may also have multiple possible codes for one variable, these are also "unpacked" over several rows, with the attribution_id enabling to track each attribution.<br><br><strong>eurepoc_global_database_1.2 (json):</strong><br>This file contains the whole database in JSON format. </p>
Global continental and ocean basin reconstructions since 200 Ma
<div>Description of Resources - Seton et al. (2012)</div> <div> </div> <div>This file provides a detailed description of all of the files that make up the data collection associated with the publication: Seton, M., Müller, R. D., Zahirovic, S., Gaina, C., Torsvik, T., Shephard, G., Talsma, A., Gurnis, M., Turner, M., Maus, S., Chandler, M. (2012). Global continental and ocean basin reconstructions since 200 Ma. Earth-Science Reviews, 113(3), 212-270. doi:<a href="https://doi.org/10.1016/j.earscirev.2012.03.002" target="_blank" rel="noopener">10.1016/j.earscirev.2012.03.002</a></div> <div> </div> <div>Note: For information on file formats and what programs to use to interact with various file formats, see "File Formats and Recommended Programs”.</div> <div> </div> <div> </div> <div>The files associated with this data collection allow for the visualisation and/or manipulation of the global plate motion model presented by Seton et al. (2012), they include:</div> <div>• <strong>Rotations </strong>- Global rotation model that contains the reconstruction poles that describe the motions of the continents and oceans.</div> <div>* Seton_etal_ESR2012_2012.1.rot (410 KB)</div> <div> </div> <div>• <strong>Plate IDs </strong>- A list of all the plate IDs used in the rotation and geometry files and their corresponding plate names.</div> <div>* Seton_etal_ESR2012_PlateIDs.pdf (102 KB) </div> <div> </div> <div>• <strong>Coastlines </strong>- Geometries of the present-day coastlines.</div> <div>* Seton_etal_ESR2012_Coastline_2012.1.gpml (17.5 MB)</div> <div>* Seton_etal_ESR2012_Coastline_2012.1_polyline.shp (3.3 MB inc. auxiliary files, datum-WGS 1984)</div> <div>* Seton_etal_ESR2012_Coastline_2012.1_polyline.txt (2.7 MB)</div> <div>* Seton_etal_ESR2012_Coastline_2012.1_polyline.kml (4.4 MB)</div> <div> </div> <div>• <strong>Continent-ocean boundaries </strong>(COBs) - Locations of the boundaries between oceanic and continental crust for plates involved in this study.</div> <div>* Seton_etal_ESR2012_COB_2012.1.gpml (410 KB)</div> <div>* Seton_etal_ESR2012_COB_2012.1.shp (152 KB inc. auxiliary files, datum-WGS 1984)</div> <div>* Seton_etal_ESR2012_COB_2012.1.txt (86 KB)</div> <div>* Seton_etal_ESR2012_COB_2012.1.kml (176 KB)</div> <div> </div> <div>• <strong>Plate polygons and boundary geometries</strong> - Topologically closed plate polygons are constructed from the intersection of ridges, transforms, subduction zones and other plate boundary geometries. These 'resolved topologies' are valid at 1 Myr intervals (0-200 Ma). The plate boundary geometries and plate polygons have been assigned plate reconstruction IDs to allow them to be reconstructed using the supplied rotation file. </div> <div>* Seton_etal_ESR2012_PP_2012.1.gpml (29.5 MB)</div> <div> </div> <div>Note:</div> <div>Paleo age grids used by Seton et al. (2012) are released in Müller et al. (2013) [Müller, R. D., Dutkiewicz, A., Seton, M., & Gaina, C. (2013). Seawater chemistry driven by supercontinent assembly, breakup, and dispersal. Geology, 41(8), 907-910. doi: <a href="https://doi.org/10.1130/G34405.1" target="_blank" rel="noopener">10.1130/g34405.1</a>]</div> <div> </div> <div>The paleo age grids associated with this model can be accessed at: <a href="https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Seton_etal_2012_ESR/" target="_blank" rel="noopener">https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Seton_etal_2012_ESR/</a></div>
Global kinematics of tectonic plates and subduction zones since the late Paleozoic Era
<div>Global kinematics of tectonic plates and subduction zones since the late Paleozoic Era</div> <div> </div> <div>Alexander Young(1), Nicolas Flament(1), Kayla Maloney(2), Simon Williams(2), Kara Matthews(2), Sabin Zahirovic(2), Dietmar Müller(2,3)</div> <div> </div> <div>1. The University of Wollongong, NSW 2522, Australia </div> <div> </div> <div>2. EarthByte Group, School of Geosciences, The University of Sydney, NSW 2006, Australia</div> <div> </div> <div>3. Sydney Informatics Hub, The University of Sydney, NSW 2006, Australia </div> <div> </div> <div>Contact: ajy321@uowmail.edu.au</div> <div> </div> <div> </div> <div>Supplementary Material</div> <div> </div> <div>We provide the digital plate model files (including rotations and geometries). These files allow for the visualisation and/or manipulation of the late Paleozoic to present-day (410-0 Ma) global plate motion model presented in this study. </div> <div> </div> <div>#########################################</div> <div>The digital plate model files are compatible with the open-source GPlates plate reconstruction software (<a href="https://www.gplates.org" target="_blank" rel="noopener">www.gplates.org</a>):</div> <div> </div> <div>(1) Rotations - Global rotation model that contains the reconstruction poles that describe the motions of the continents and oceans.</div> <div>- <strong>Global_250-0Ma_Young_et_al.rot</strong> (455 KB)</div> <div>-<strong> Global_410-250Ma_Young_et_al.rot</strong> (154 KB)</div> <div> </div> <div>(2) Plate polygons and boundary geometries - Topologically closed plate polygons are constructed from the intersection of ridges, transforms, subduction zones and other plate boundary geometries. These 'resolved topologies' are defined at 1 Myr intervals (410-0 Ma). The plate boundary geometries and plate polygons have been assigned plate reconstruction IDs to allow them to be reconstructed using the supplied rotation file.</div> <div>- <strong>Global_Mesozoic-Cenozoic_plate_bounds_Young_etal.gpml</strong> (36.5 MB)</div> <div>- <strong>Global_Paleozoic_plate_bounds_Young_etal.gpml</strong> (6.7 MB)</div> <div>- <strong>TopologyBuildingBlocks_Young_etal.gpml</strong> (2 MB) - this file is identical to Müller et al. (2016)</div> <div> </div> <div>(3) Coastlines - Geometries of the present-day coastlines.</div> <div>- <strong>Global_coastlines_Young_et_al_low_res.shp</strong> (1.2 MB including auxiliary files, datum-WGS 1984)</div> <div> </div> <div>(4) Static polygons (optional) - Includes ocean isochron and terrane polygon geometries.</div> <div>- <strong>GlobalPresentDay_SPP_Young_etal.shp</strong> (1.4 MB inc. auxillary files, datum-WGS 1984)</div> <div> </div> <div>(5) Continental polygons (optional) - Includes continental terrane polygon geometries and excludes oceanic lithosphere.</div> <div>- <strong>PresentDay_ContPolygons_Young_etal.shp</strong> (451 KB inc. auxillary files, datum-WGS 1984)</div> <div> </div> <div>GPlates: </div> <div>To view the model, load all files in GPlates (either drag and drop files onto the globe OR from the navigation bar at the top of the screen click File -> Open Feature Collection and select files). Both rotation files (1) and each of the three plate geometry files (2) need to be loaded for the model to work properly. It is recommended that coastlines (3) are loaded to see how the continents move, however only one coastline file is necessary (.gpml or .shp). The static polygons (4) and continental polygons (5) are optional. </div> <div> </div> <div>The two rotation files need to be 'connected' in order for the model to run continuously from 410 to 0 Ma. In the GPlates 'Layers' window (opened from the main navigation bar, click 'Window' -> 'Show Layers') the rotation files will be highlighted yellow, yet only one will have a yellow tick next to it to signify it is being used. Click the small black triangle to the left the ticked rotation file. Under 'Inputs' -> 'Reconstruction features' click 'Add new connection' and then select the other rotation file from the list of files that will appear. This will ensure that both rotation files are active. </div> <div> </div> <div>Finally, it is recommended to experiment with geometry visibility in order to make the globe less cluttered. For instance, from the navigation bar click View -> Geometry Visibility and untick 'Show Line Geometries'. Alternatively, files can be toggled on and off using the tick boxes in the Layers window. For more information about using GPlates, a set of user tutorials can be accessed from the GPlates website - http://www.gplates.org/docs.html.</div> <div> </div> <div> </div> <div>#########################################</div> <div>MODEL REFERENCING:</div> <div>When using our model, in addition to citing this publication, please consider citing the studies of Domeier and Torsvik (2014), Matthews et al. (2016) and Müller et al. (2016) which served as the basis for this model in the late Paleozoic and Mesozoic-Cenozoic, respectively, and citing any other study that describes refinements to the plate reconstructions in your region of interest as appropriate. </div> <div> </div> <div>- Domeier, M., & Torsvik, T. H. (2014). Plate tectonics in the late Paleozoic. Geoscience Frontiers, 5(3), 303-350. DOI: <a href="https://doi.org/10.1016/j.gsf.2014.01.002" target="_blank" rel="noopener">10.1016/j.gsf.2014.01.002</a></div> <div>- Müller, R. D., Seton, M., Zahirovic, S., Williams, S. E., Matthews, K. J., Wright, N. M., Shephard, G. E., Maloney, K., Barnett-Moore, N., Hosseinpour, M., Bower, D. J., & Cannon, J. (2016). Ocean Basin Evolution and Global-Scale Plate Reorganization Events Since Pangea Breakup. Annual Review of Earth and Planetary Sciences, 44(1). DOI: <a href="https://doi.org/10.1146/annurev-earth-060115-012211" target="_blank" rel="noopener">10.1146/annurev-earth-060115-012211</a></div> <div>-Matthews, K. J., Maloney, K. T., Zahirovic, S., Williams, S. E., Seton, M., & Mueller, R. D. (2016). Global plate boundary evolution and kinematics since the late Paleozoic. Global and Planetary Change, 146, 226-250.</div> <div>DOI: <a href="https://doi.org/10.1016/j.gloplacha.2016.10.002" target="_blank" rel="noopener">10.1016/j.gloplacha.2016.10.002</a></div>
Global plate boundary evolution and kinematics since the late Paleozoic
<h3>Global plate boundary evolution and kinematics since the late Paleozoic </h3> <p>Kara J. Matthews*^, Kayla T. Maloney*, Sabin Zahirovic*, Simon E. Williams*, Maria Seton*, R. Dietmar Müller*</p> <p>* EarthByte Group, School of Geosciences, The University of Sydney, Sydney, NSW 2006, Australia<br>^ Present address: Department of Earth Sciences, University of Oxford, South Parks Road, Oxford OX1 3AN, UK</p> <p>Contact: karajmatthews@gmail.com</p> <p>CORRECTION applied for the Pacific plate prior to 83 Ma based on Torsvik et al. (2019)</p> <h3><br>Supplementary Material</h3> <p>We provide a digital plate model files (including rotations and geometries) with this publication. These files allow for the visualisation and/or manipulation of the late Paleozoic to present-day (410-0 Ma) global plate motion model presented in this study. </p> <p>#########################################<br>The digital plate model files are compatible with the open-source GPlates plate reconstruction software (<a href="https://www.gplates.org" target="_blank" rel="noopener">www.gplates.org</a>):</p> <p>(1) Rotations - Global rotation model that contains the reconstruction poles that describe the motions of the continents and oceans.<br>- <strong>Global_EB_250-0Ma_GK07_Matthews_etal.rot</strong> (455 KB)<br>- <strong>Global_EB_410-250Ma_GK07_Matthews_etal.rot</strong> (115 KB) - in the comments 'POLE_RECALCULATED' means that we recalculated that finite pole of rotation such that the moving plate moves relative to a neighbouring plate rather than directly to the absolute reference frame (see Section 2.2.1 of the main text for more details). This process should have a minimal effect on the absolute motion of the plate.</p> <p>(2) Plate polygons and boundary geometries - Topologically closed plate polygons are constructed from the intersection of ridges, transforms, subduction zones and other plate boundary geometries. These 'resolved topologies' are valid at 1 Myr intervals (410-0 Ma). The plate boundary geometries and plate polygons have been assigned plate reconstruction IDs to allow them to be reconstructed using the supplied rotation file.<br>- <strong>Global_Mesozoic-Cenozoic_plate_bounds_Matthews_etal.gpml</strong> (36 MB)<br>- <strong>Global_Paleozoic_plate_bounds_Matthews_etal.gpml</strong> (8.7 MB)<br>- <strong>TopologyBuildingBlocks_Matthews_etal.gpml</strong> (2 MB) - this file has not been modified from Müller et al. (2016)</p> <p>(3) Coastlines - Geometries of the present-day coastlines.<br>- <strong>Global_coastlines_low_res_Matthews_etal.gpml</strong> (25.4 MB)<br>- <strong>Global_coastlines_low_res_Matthews_etal.shp</strong> (2.9 MB inc. auxillary files, datum-WGS 1984)<br>NOTE: From 410 to 320-310 Ma Kazakhstania is represented as one or two ('Internal' and 'External' Kazakhstania - Domeier and Torsvik, 2014) ovate polygons. Kazakhstania is highly deformed following a long and complicated history, and so for simplicity we avoid using their present-day outlines in the earlier part of the model.</p> <p>(4) Static polygons (optional) - Includes ocean isochron and terrane polygon geometries.<br>- <strong>Global_EarthByte_GPlates_PresentDay_StaticPlatePolygons_Matthews_etal.shp</strong> (2.7 MB inc. auxillary files, datum-WGS 1984)</p> <p>(5) Continenal polygons (optional) - Includes continental terrane polygon geometries and excludes oceanic lithosphere.<br>- <strong>Global_EarthByte_GPlates_PresentDay_ContinentalPolygons_Matthews_etal.shp</strong> (804 KB inc. auxillary files, datum-WGS 1984)</p> <p>GPLATES: <br>To view the model load all files in GPlates (either drag and drop files onto the globe OR from the navigation bar at the top of the screen click File -> Open Feature Collection and select files). Both rotation files (1) and each of the three plate geometry files (2) need to be loaded for the model to work properly. It is recommended that coastlines (3) are loaded to see how the continents move, however only one coastline file is necessary (.gpml or .shp). The static polygons (4) and continental polygons (5) are optional. </p> <p>The two rotation files need to be 'connected' in order for the model to run continuously from 410 to 0 Ma. In the GPlates 'Layers' window (opened from the main navigation bar, click 'Window' -> 'Show Layers') the rotation files will be highlighted yellow, yet only one will have a yellow tick next to it to signify it is being used. Click the small black triangle to the left the ticked rotation file. Under 'Inputs' -> 'Reconstruction features' click 'Add new connection' and then select the other rotation file from the list of files that will appear. This will ensure that both rotation files are active. </p> <p>Finally, it is recommended to experiment with geometry visibility in order to make the globe less cluttered. For instance, from the navigation bar click View -> Geometry Visibility and untick 'Show Line Geometries'. Alternatively, files can be toggled on and off using the tick boxes in the Layers window. For more information about using GPlates, a set of user tutorials can be accessed from the GPlates website - http://www.gplates.org/docs.html.</p> <p><br>#########################################<br>We also provide a list of the plate reconstruction IDs used in the model:</p> <p>Plate IDs - A list of all the plate IDs used in the rotation and geometry files and their corresponding plate names.<br>- <strong>EarthByte_Plate_ID_Table_Matthews_etal.txt</strong> (33 KB)</p> <p>#########################################<br>MODEL REFERENCING:<br>When using our model, in addition to citing this publication:</p> <p>Matthews, K.J., Maloney, K.T., Zahirovic, S., Williams, S.E., Seton, M. and Müller, R.D., 2016, Global plate boundary evolution and kinematics since the late Paleozoic, Global and Planetary Change, in press, accepted 3 October 2016.</p> <p>please also consider citing the studies of Domeier and Torsvik (2014) and Müller et al. (2016) which served as the basis for this model in the late Paleozoic and Mesozoic-Cenozoic, respectively, and cite any other study that describes refinements to the plate reconstructions in your region of interest. See Section 2 and Section 3 of the main text for more information on how the present model was constructed.</p> <p>- Domeier, M., & Torsvik, T. H. (2014). Plate tectonics in the late Paleozoic. Geoscience Frontiers, 5(3), 303-350. DOI:<a href="https://doi.org/10.1016/j.gsf.2014.01.002" target="_blank" rel="noopener">10.1016/j.gsf.2014.01.002</a><br>- Müller, R. D., Seton, M., Zahirovic, S., Williams, S. E., Matthews, K. J., Wright, N. M., Shephard, G. E., Maloney, K., Barnett-Moore, N., Hosseinpour, M., Bower, D. J., & Cannon, J. (2016). Ocean Basin Evolution and Global-Scale Plate Reorganization Events Since Pangea Breakup. Annual Review of Earth and Planetary Sciences, 44(1). DOI:<a href="https://doi.org/10.1146/annurev-earth-060115-012211" target="_blank" rel="noopener">10.1146/annurev-earth-060115-012211</a></p> <p>Note: We have recently fixed some issues in this model, namely the motion of the Pacific plate (following Torsvik et al., 2019), and some MOR topologies in the Arctic. The fixes are in the model files included in this folder, but the old (published) version of the model is included in a sub-folder called "_OLD_MODEL_DO_NOT_USE". </p> <p>Torsvik, T. H., B. Steinberger, G. E. Shephard, P. V. Doubrovine, C. Gaina, M. Domeier, C. P. Conrad, and W. W. Sager (2019), Pacific‐Panthalassic reconstructions: Overview, errata and the way forward, Geochemistry, Geophysics, Geosystems, 20(7), 3659-3689.</p> <p> </p>
S1S2-Water: A global dataset for semantic segmentation of water bodies from Sentinel-1 and Sentinel-2 satellite images
<p>The S1S2-Water dataset is a global reference dataset for training, validation and testing of convolutional neural networks for semantic segmentation of surface water bodies in publicly available Sentinel-1 and Sentinel-2 satellite images. The dataset consists of 65 triplets of Sentinel-1 and Sentinel-2 images with quality checked binary water mask. Samples are drawn globally on the basis of the Sentinel-2 tile-grid (100 x 100 km) under consideration of pre-dominant landcover and availability of water bodies. Each sample is complemented with metadata and Digital Elevation Model (DEM) raster from the Copernicus DEM.</p><p>This work was supported by the German Federal Ministry of Education and Research (BMBF) through the project "Künstliche Intelligenz zur Analyse von Erdbeobachtungs- und Internetdaten zur Entscheidungsunterstützung im Katastrophenfall" (AIFER) under Grant 13N15525, and by the Helmholtz Artificial Intelligence Cooperation Unit through the project "AI for Near Real Time Satellite-based Flood Response" (AI4FLOOD) under Grant ZT-IPF-5-39. </p>
GLOBAL SNAPSHOT Physician Distribution and Density of Physicians per 1000 population - Worldwide 2021
<p>The chart presents the most up-to-date data (2021) available for 49 of the world’s 195 countries, focusing on the total number of physicians and the number of physicians per 1000 population(1). The countries are categorized into four income groups based on World Bank classifications, which are updated annually on July 1st each year(2).</p> <p>Only 25% of the countries present current data. This information is critical for decision-making for healthcare planning and policy development. Equally crucial, is for researchers to have comparable data to propose initiatives, to establish benchmarks and for crafting holistic strategies to gauge and advance progress in healthcare systems globally.</p> <p>Data sources: UnData <a href="https://data.un.org/">https://data.un.org/</a></p> <p>Visualization tools used: RAWGraphs <a href="https://www.rawgraphs.io/">https://www.rawgraphs.io/</a>, MS PowerPoint and Microsoft Excel</p> <p>Intended Audience: Academics and Researchers; Students and Educators; Healthcare Administrators and Policy Makers; Non-Governmental Organizations</p> <p>The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</p> <p>The NNLM Data Visualization Challenge happens through work funded by the National Institutes of Health's National Library of Medicine, grant number U24LM013751</p> <p> </p> <p>References:</p> <p>1. United Nations, Department of Economic and Social Affairs. 10 Health Personnel. In: Statistical Yearbook. 66th issue (2023). New York: United Nations; 2023. (ST/ESA/STAT/SER.S/42). [Dataset available at UnData] <a href="https://data.un.org/_Docs/SYB/CSV/SYB66_154_202310_Health%20Personnel.csv">https://data.un.org/_Docs/SYB/CSV/SYB66_154_202310_Health%20Personnel.csv</a></p> <p>2 World Bank. World Bank Country and Lending Groups. World Bank Data Help Desk [Internet]. [cited 2024 Apr 5]. Available from:<a href="https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups"> https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups</a></p>
Ocean Basin Evolution and Global-Scale Plate Reorganization Events Since Pangea Breakup
<p><strong>Abstract </strong></p> <p>We present a revised global plate motion model with continuously closing plate boundaries ranging from the Triassic at 230 Ma to the present day, assess differences between alternative absolute plate motion models, and review global tectonic events. Relatively high mean absolute plate motion rates around 9–10 cm yr-1 between 140 and 120 Ma may be related to transient plate motion accelerations driven by the successive emplacement of a sequence of large igneous provinces during that time. A ~100 Ma event is most clearly expressed in the Indian Ocean and may reflect the initiation of Andean-style subduction along southern continental Eurasia, while an ~80 Ma acceleration of mean rates from 6 to 8 cm yr-1 reflects the initial northward acceleration of India and simultaneous speedups of plates in the Pacific. An event at ~50 Ma expressed in relative, and some absolute plate motion changes around the globe and in a reduction of global mean velocities from about 6 to 4–5 cm yr-1, indicates that an increase in collisional forces (such as the India-Eurasia collision) and ridge subduction events in the Pacific (such as the Izanagi-Pacific Ridge) play a significant role in modulating plate velocities.</p> <p><strong>Muller et al. (2016) AREPS model file versions</strong></p> <p>This model has been maintained for some time after initial publication. There are six versions of the model that we provide, including:</p> <ul> <li>v1.10 – Some minor fixes were made to plate topologies, and so conforms to the originally-published model.</li> <li>v1.11 – A back-arc basin north of Arabia was introduced in the Cretaceous (see note below), and hence slightly diverges from the original model in plate topologies, velocities, and seafloor age-grids for this region.</li> <li>v1.14 – The latest version of the model that has duplicated topology segments cleaned from the evolving polygons, which helps with quantifying plate boundary lengths in the resolved topology output.</li> <li>v1.15 – The correction to the pre-83 Ma Pacific rotations according to Torsvik et al. (2019) has been applied.</li> <li>v1.16 – Some fixes to topologies</li> <li>v1.17 – Major update to the seafloor age-grids and topologies. Age-grids are consistent with v1.15 and 1.16 as well. We strongly recommend you use this version of the model.</li> </ul> <p>Note about the evolution of the western Tethys in this model: The Western Tethys, north of Arabia, is punctuated by ophiolite formation and obduction in Cretaceous times. The first end-member involves applying the central and eastern Tethys analogues of back-arc opening and closure following ophiolite obduction, much like is usually implied in the Kohistan-Ladakh and Greater India collision zone. This scenario makes the Western Tethys north of Arabia consistent with the model of the eastern Tethys. However, a second end-member interpretation for the formation of many of the ophiolites in the region is that they develop when a mid-oceanic ridge inverts to become a subduction zone. Both options are plausible, but we implemented a change in this plate model after it was published to reflect the first end-member scenario in order to link the region to the eastern Tethys in a plausible way. This scenario is based on back-arc opening from ~125 Ma (Jolivet et al., 2016), with subduction of back-arc initiating in Albian times from ~110 Ma (Ghazi et at., 2003; Aygul et al., 2015). Obduction and Arabia collision with an arc occurs at 85 Ma (Jolivet et al., 2016; Jagoutz et al., 2016). The scenario is also consistent with the recent work of Morris et al. (2016) on the Oman Ophiolite.</p> <p> </p> <p>The agegrids associated with this model can be accessed at: <a href="https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Muller_etal_2016_AREPS/" target="_blank" rel="noopener">https://repo.gplates.org/webdav/PlateModel_Age_SR_Grids/Muller_etal_2016_AREPS/</a></p>
The FORCIS database: A global census of planktonic Foraminifera from ocean waters
<p>The FORCIS (Foraminifera Response to Climatic Stress) database is a synthesis grouping datasets on living planktonic foraminifera. We assembled foraminiferal diversity and distribution data in the global oceans from 1910 until 2018, curating published and unpublished datasets. This database includes data collected using plankton tows, continuous plankton recorder, sediment traps and plankton pump from the global ocean.</p> <p>The FORCIS database version 01 is composed of 5 files (“.csv” format). All data coming from different sampling devices were put into separate “.csv” files. Only the data of the CPR from the Southern Hemisphere have been separated from the Northern Hemisphere CPR data as the data structure is not the same (species counts resolved vs. binned total counts, respectively). </p> <p>Apart from the file of CPR data from the Northern Hemisphere that contains only metadata and binned total counts, all the remaining four files contain 4 blocks:</p> <ul> <li> <p>Block 1: metadata (from column 1 to 71)</p> </li> <li> <p>Block 2: original counts (from column 72 to 274)</p> </li> <li> <p>Block 3: generated counts based on the validated taxonomy (from column 275 to 331). We added “_VT” to each species name to distinguish it from other taxonomy levels. E.g. “g_bulloides” became “g_bulloides_VT”. The number of species counted per subsample is also reported in the column “number_of_species_counted_VT”</p> </li> <li> <p>Block 4: generated counts based on the lumped taxonomy (from column 332 to 379). In this case, we added “_LT” to each species name. E.g. “n_dutertrei” became “n_dutertrei_VT”. We also calculated the number of species counted per subsample and reported it in the column “number_of_species_counted_LT”</p> </li> </ul> <p>Foraminifera abundance data counts are reported in different categories in the blocks 1,2 and 3 and described in the table below:</p> <table> <tbody> <tr> <td> <p><strong>count_type</strong></p> </td> <td> <p><strong>unit</strong></p> </td> </tr> <tr> <td> <p>Absolute</p> </td> <td> <p>ind/m3</p> </td> </tr> <tr> <td> <p>Relative</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>Raw</p> </td> <td> <p>number of individuals</p> </td> </tr> <tr> <td> <p>Fluxes</p> </td> <td> <p>ind/m2/day</p> </td> </tr> <tr> <td> <p>Bin_Absolute</p> </td> <td> <p>ind/m3</p> </td> </tr> <tr> <td> <p>Bin_Relative</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>Bin_Raw</p> </td> <td> <p>number of individuals</p> </td> </tr> <tr> <td> <p>Bin_Fluxes</p> </td> <td> <p>ind/m2/day</p> </td> </tr> </tbody> </table> <p> </p> <p>For more details about the FORCIS database column description, please check the data descriptor paper <strong>Chaabane et al. (2023) (https://doi.org/10.1038/s41597-023-02264-2).</strong></p> <p>The database is kept open for any new entries and the updated version will be released in csv format. The labels of updated versions of the released “.csv” files will contain the date of their publication and versioning number.</p>
Global atmospheric simulation using the Super-Parameterized Community Atmosphere Model
<p>A 1-month subset from a global atmospheric simulation using the Super-Parameterized Community Atmosphere Model (SP-CAM), which implements the Multi-scale Modeling Framework (MMF) described in Khairoutdinov and Randall (2001) and Khairoutdinov et al. (2005). The version of the SP-CAM used here is described in Marchand et al. (2009) and Ovtchinnikov et al (2006), and was run at Pacific Northwest National Laboratory with DOE support. It is based on CAM 3.0 for the global atmospheric component, and uses the System for Atmospheric Modeling (SAM; Khairoutdinov and Randall 2003). This simulation is configured with CAM running the finite volume dynamical core on a 2x2.5 degree latitude-longitude grid with 26 vertical levels. The embedded CRM (SAM) is configured with 64 horizontal columns at 4 km grid spacing with 24 vertical levels (sharing the bottom 24 levels with the CAM grid), and single-moment microphysics. The simulation was initialized on 1 September 1997 and runs through June 2002, forced with observed monthly-mean sea surface temperatures. Only the month of July 2000 is uploaded here, which is what is required to reproduce the results in Hillman et al. (2018).</p>
SM2RAIN-CCI (1 Jan 1998 – 31 December 2015) global daily rainfall dataset
<p>A NEW GLOBAL SCALE RAINFALL PRODUCT obtained from satellite soil moisture data through the SM2RAIN algorithm (<em>Brocca et al., 2014</em>), at 0.25 degree/daily spatial-temporal resolution, has been delivered (Ciabatta et al., 2018). The SM2RAIN method was applied to the ESA CCI soil moisture Active and Passive products (<em>Liu et al., 2011, 2012; Wagner et al., 2012</em>) for the period from January 1998 to December 2015 (18 years).</p> <p>The CCI-derived rainfall datasets (in mm/day) is gridded over a 0.25-degree grid on a global scale. The number of dates is 6574 (1998/01/01 – 2015/12/31). The product represents the cumulated rainfall between the 00:00 and the 23:59 UTC of the indicated day. A climatological correction has been applied to the data at monthly scale.</p> <p>The rainfall dataset is provided in netCDF format. A total of 18 netCDF files, one per year, are provided.</p> <p>The rainfall dataset is obtained by applying the SM2RAIN algorithm to the ESA CCI soil moisture Active and Passive products at version 03.1 separately. Then, an integration procedure based on a weighted average is applied in order to obtain the rainfall estimate. The algorithm has been calibrated during three different periods (1998-2001, 2002-2006 and 2007-2013) against the Global Precipitation Climatology Centre Full-Data daily dataset (GPCC-FDD, Schamm et al., 2015). The quality flag provided within the raw soil moisture observations has been used to mask out low quality data, as well as the areas characterized by high topographic complexity, high frozen soil and snow probability and presence of tropical forests.</p> <p><strong>References</strong></p> <p>Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., Levizzani, V. (2014). Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data. <em>Journal of Geophysical Research</em>, 119(9), 5128-5141, doi:10.1002/2014JD021489.</p> <p>Ciabatta, L., Massari, C., Brocca, L., Gruber, A., Reimer, C., Hahn, S., Paulik, C., Dorigo, W., Kidd, R., and Wagner, W.: SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture, Earth Syst. Sci. Data, 10, 267-280, https://doi.org/10.5194/essd-10-267-2018, 2018.</p> <p>Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. (2011). Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrology and Earth System Sciences, 15, 425-436, doi:10.5194/hess-15-425-2011.</p> <p>Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M., Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014.</p> <p>Schamm, K., Ziese, M., Raykova, K., Becker, A., Finger, P., Meyer-Christoffer, A., Schneider, U. (2015). GPCC Full Data Daily Version 1.0 at 1.0°: Daily Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data. DOI: 10.5676/DWD_GPCC/FD_D_V1_100.</p> <p>Wagner, W., Dorigo, W., de Jeu, R., Fernandez, D., Benveniste, J., Haas, E., Ertl, M. (2012). Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals), Volume I-7, XXII ISPRS Congress, Melbourne, Australia, 25 August-1 September 2012, 315-321.</p>
MOD17A2H version 6 Gross Primary Productivity (GPP) global mosaics at 500 m resolution and difference 2000-2017
<p>MOD17A2H version 6 Gross Primary Productivity (GPP) global mosaics at 500 m resolution and difference in GPP for the period 2000-2017. Changes in GPP could be used to estimate land degradation or similar. Derived using <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/MOD17A2H">the data.table package and quantile function in R</a>. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod17a2h_v006. Antartica is not included.</p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>veg = theme: vegetation,</li> <li>gpp = variable: gross primary productivity in kg C m<sup>2</sup>,</li> <li>mod17a2h.oct = determination method: MOD17A2H product, GPP values for October,</li> <li>d = median value / difference = difference between periods / u.975 = aggregation/statistics method: 97.5% probability upper quantile,</li> <li>500m = spatial resolution / block support: 500 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000..2017 = time reference: from 2000 to 2017,</li> <li>v1.0 = version number: 1.0,</li> </ul>
Global DEM derivatives at 250 m, 1 km and 2 km based on the MERIT DEM
<p>Layers include: various DEM derivatives computed using SAGA GIS at 250 m and using MERIT DEM (Yamazaki et al., 2017) as input. Antartica is not included. MERIT DEM was first reprojected to 6 global tiles based on the Equi7 grid system (Bauer-Marschallinger et al. 2014) and then these were used to derive all DEM derivatives. To access original DEM tiles please refer to MERIT DEM <a href="http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/">download page</a>.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>dtm = theme: digital terrain models,</li> <li>twi = variable: SAGA GIS Topographic Wetness Index,</li> <li>merit.dem = determination method: MERIT DEM,</li> <li>m = mean value,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2017 = time reference: year 2017,</li> <li>v1.0 = version number: 1.0,</li> </ul>
Global landform and lithology class at 250 m based on the USGS global ecosystem map
<p>Layers include: lithology (15) and landform (7) indicator maps (0-100%). Derived from the <a href="https://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/">USGS Global Ecosystem Map</a>, i.e. the EcoTapestry map. Water bodies masked out. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>dtm = theme: digital terrain models / relief and soil,</li> <li>lithology = variable: lithological class,</li> <li>usgs.ecotapestry = determination method: USGS Global Ecosystem Map,</li> <li>p = probability 0-100%,</li> <li>250m = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2014 = time reference: year 2014,</li> <li>v1.0 = version number: 1.0,</li> </ul>
Emissions Database for Global Atmospheric Research, version v4.3.2 part I Greenhouse gases
<p>The Emissions Database for Global Atmospheric Research (EDGAR) v4.3.2, partim Greenhouse gases compiles anthropogenic emissions data for CO2, CH4 and N2O based on international statistics and emission factors. The version v4.3.2 of the EDGAR emission inventory provides global estimates, broken down to IPCC-relevant source-sector levels, from 1970 (the year of EU’s first Air Quality Directive) to 2012 (the end year of the first commitment period of the Kyoto Protocol (KP)). Strengths of EDGAR v4.3.2 include global geo-coverage (226 countries), continuity in time, and comprehensiveness in activities. Emission sources of the multiple gases include all human activities except the land-use, land-use change and forestry sector and are compiled following a bottom-up and IPCC-compliant approach. The dataset provides in addition to the complete timeseries 1970-2012 also annual and global gridmaps of 0.1 degree by 0.1 degree resolution for each source-sector and each year. For 2010 also 12 monthly gridmaps per source-sector are provided.</p>
Global restoration opportunities in tropical rainforest landscapes - Supplementary Materials - Spatial Data Layers
<p><strong>Global restoration opportunities in tropical rainforest landscapes</strong></p> <p><strong>Sci Adv 5 (7), eaav3223</strong></p> <p><strong>DOI: 10.1126/sciadv.aav3223</strong></p> <p><strong><a href="https://advances.sciencemag.org/content/5/7/eaav3223">https://advances.sciencemag.org/content/5/7/eaav3223</a></strong></p> <p><strong>Supplementary Materials</strong></p> <p><strong><a href="https://advances.sciencemag.org/content/suppl/2019/07/01/5.7.eaav3223.DC1">https://advances.sciencemag.org/content/suppl/2019/07/01/5.7.eaav3223.DC1</a></strong></p> <p><strong>Spatial Data layers:</strong></p> <p><strong><a href="https://doi.org/10.5281/zenodo.3233495">https://doi.org/10.5281/zenodo.3233495</a></strong></p> <p><strong>_OutR10:</strong></p> <p><strong>r_10.img → Global restoration opportunity score (ROS)</strong></p> <p><strong>r_10_sc.img → Global restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_nt_sc.img → Neo Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_aa_sc.img → Australiasia restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_at_sc.img → Afro Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_im_sc.img → Indo Malay restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p><strong>r_10_nt_sc.img → Neo Tropic restoration opportunity score (ROS) – rescaled 0-1</strong></p> <p> </p> <p><strong>_OutBasics:</strong></p> <p><strong>r_1.img → Study Area</strong></p> <p><strong>r_2.img → Restorable Area</strong></p> <p><strong>r_3.img → Restoration Benefits</strong></p> <p><strong>r_4.img → Restoration feasibility</strong></p> <p><br> <strong>_OutCountry:</strong></p> <p><strong>r_10_XXX_sc.tif → restoration opportunity score (ROS) for country XXX – rescaled 0-1</strong></p> <p><br> <strong>_OutHotspots:</strong></p> <p><strong>r_10_hotspot_XXX_hotspot_area_sc.tif → restoration opportunity score (ROS) for conservation hotspot area XXX – rescaled 0-1</strong></p> <p><strong>r_10_hotspots_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in conservation hotspots</strong></p> <p><br> <strong>_OutKBA:</strong></p> <p><strong>r_10_XXX_sc.tif → restoration opportunity score (ROS) for Key Biodiversity Area XXX – rescaled 0-1</strong></p> <p><strong>r_10_kba_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in Key Biodiversity Areas</strong></p> <p><br> <strong>_OutAichi:</strong></p> <p><strong>r_10_aichi_XXX.tif → Top 15% area of with highest restoration opportunity score (ROS) in country XXX</strong></p> <p><strong>r_10_aichi.img → Top 15% area of with highest restoration opportunity score (ROS) global</strong></p> <p><br> <strong>_OutBonn:</strong></p> <p><strong>r_10_XXX_Bonn.img → Area with highest restoration opportunity score (ROS) in country XXX according to their Bonn Challenge commitments</strong></p> <p> </p> <p><strong>_OutParis:</strong></p> <p><strong>r_10_at_paris.img → Area with highest restoration opportunity score (ROS) in Afro Tropic Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><strong>r_10_im_paris.img → Area with highest restoration opportunity score (ROS) in Indo Malay Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><strong>r_10_nt_paris.img → Area with highest restoration opportunity score (ROS) in Neo Tropic Nationally Determined Contributions to the Paris Climate Agreement</strong></p> <p><br> <strong>_OutTEOW:</strong></p> <p><strong>r_10_ECOREGION_XXX_sc.tif → restoration opportunity score (ROS) for Ecoregion XXX – rescaled 0-1</strong></p> <p><strong>r_10_ECOREGION_upper60.img → Areas with restoration opportunity score (ROS) above 0.6 in Ecoregions</strong></p> <p> </p> <p><strong>_OutAll</strong></p> <p><strong>alltargets.img → Area with highest restoration opportunity score (ROS) according to all targets (excluded from the paper)</strong></p> <p> </p>
Data for the 'Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation'
<p>All numerical data used in the manuscript <strong>“Evaluation of global simulations of aerosol particle number and cloud condensation nuclei, and implications for cloud droplet formation” </strong>by G. S. Fanourgakis et al. ACP (2019) are categorized and provided in a number of files. All files are in the hdf format. A readme file is also provided.</p> <p>These data files have been created by G. S. Fanourgakis (fanourg@uoc.gr)</p> <p>Details on the data are provided in Fanourgakis et al. Atmos. Chem. Phys. 2019 https://doi.org/10.5194/acp-2018-1340 (e-mail to <a href="mailto:mariak@uoc.gr">mariak@uoc.gr</a> ; <a href="mailto:athanasios.nenes@epfl.ch">athanasios.nenes@epfl.ch</a> )</p> <p>For an in-depth understanding of the description below, a study of the above mentioned manuscript is required.</p> <p>(A) Station model results</p> <p>The station results can be found in files with filenames of the form:</p> <p>station $MODEL.nc</p> <p>The “$MODEL” (as well as all names starting with “$”) indicates a variable, and more specifically one of the models participated in the present study. The values of this variable are tabulated in Table 1 in the readme file.</p> <p>In each file a number of computational results are provided by the specified model for all nine (9) stations that provided observational data. The name of the variable is formed as:</p> <p>st $STATION $FIELDhour st $STATION $FIELD month</p> <p>where all possible values of the variables $STATION and $FIELD are tabulated in Tables 2 and 3 in the readme file, respectively. The extension _hour denotes that hourly values for the field are provided, while the extension _month the monthly average of this quantity. For example, the variable</p> <p>st Finokalia CCN02 hour</p> <p>found in the file station_TM4-ECPL.nc, contains the hourly values of the CCN<sub>0<em>.</em>2 </sub>at the Finokalia station as computed by the TM4-ECPL model. In a similar way, in the file station_EMAC.nc, the variable below gives the monthly values of dust at Vavihill as computed with the EMAC model.</p> <p>st Vavihill DU month</p> <p>Notice also that in all files hourly and monthly data are provided for the time period from 1-1-2011 up to 31-12-2015 (60 months and 43,824 hours)</p> <p>(B) Station observational results</p> <p>There is one file that contains all observational data from Schmale et al., SCIENTIFIC DATA | 4:170003 | DOI: 10.1038/sdata.2017.3, 2017 (<a href="mailto:julia.schmale@psi.ch">julia.schmale@psi.ch</a>) and the data that were computed based on the observations (i.e. number of cloud droplets) (contact person: athanasios.nenes@epfl.ch). The file is</p> <p>station observations.nc</p> <p>while the following fields are contained in there:</p> <p>st $STATION $FIELDhour</p> <p>st $STATION $FIELD month</p> <p>The values of variables are given in the Tables 2 and 3 in the readme file. The time period covered is from 1-1-2011 up to 31-12-2015. Notice that due to the lack of observations a lot of data are missing. For missing observational data the value -9999.999 is given. Contact person for the observational data is Julia Schmale (julia.schmale@psi.ch).</p> <p>(C) Station Multi-model Median</p> <p>Monthly averages of the models can be found in the file</p> <p>station MMM.nc</p> <p>The following fields can be found in the file</p> <p>st $STATION $FIELD month median</p> <p>st $STATION$FIELD month quart25</p> <p>st $STATION$FIELD month quart75</p> <p>where the values of the variables $STATION and $FIELD can be found in Tables 2 and 3, respectively. The extension median corresponds to the multi-model median, while the quart25 and quart75 to the 25 % and 75 % quartiles, respectively.</p> <p>(D) Global model results</p> <p>In the following single file can be found for each of the models the surface distribution of various fields.</p> <p>results global models year2011.nc</p> <p>They correspond to the annual mean of the year 2011. The resolution of the grid is 1<sup>◦ </sup>× 1<sup>◦</sup>. The file contains the following variables:</p> <p>$FIELD $MODEL</p> <p>The $FIELD and $MODEL can be found in Tables 3 and 1, respectively.</p> <p>(E) Global average results</p> <p>In the file</p> <p>surface_ global_average_year2011.nc</p> <p>can be found in 5<sup>◦</sup>×5<sup>◦ </sup>resolution, the Multi-model median of surface distribution of the various fields denoted in Table 3 and their corresponding diversity. The names of the variables are formed as:</p> <p>med $FIELD</p> <p>div $FIELD</p> <p>where, ‘med’ stands for median and ‘div’ for diversity calculated as standard deviation divided by the mean of the model results.</p> <p>Tables and details on the fields provided are given in the readme file.</p>
Supplementary data: Impact of a global temperature rise of 1.5 degrees Celsius on Asia's glaciers
<p>Supplementary data to <a href="http://doi.org/10.1038/nature23878"><em>Kraaijenbrink, Bierkens, Lutz and Immerzeel, 2017. Impact of a global temperature rise of 1.5 degrees Celsius on Asia’s glaciers, Nature.</em></a> Model code can be found <a href="https://doi.org/10.5281/zenodo.2548689">here</a>.</p> <p>Please note that all data is provided in 7z-archives. To extract the data use the open source software <a href="http://www.7-zip.org/">7zip</a>.</p> <p> </p> <p><strong>Model input: </strong>Raster data</p> <p>The raster data that is required to run the model is available for the entire High Mountain Asia (<em>complete-hma.7z</em>) and for each <a href="https://www.glims.org/RGI/">RGI v5.0</a> sub-region (<<em>region-name>.7z</em>). The 7z-archives hold separate folders for each glacier, which are named by RGI glacier ID. The rasters for each glacier are in GeoTIFF format, have a 30 m resolution, are in local UTM projection (WGS84 datum), and are clipped to the RGI glacier extent.</p> <p>Rasters present for each glacier are:</p> <pre>classification.tif The debris classification made in google earth engine. debris-thickness-50cm.tif Debris thickness estimation based on Landsat 8 surface temperature. ice-thickness.tif Ice thickness determined using the Glabtop2 model ls8-composite-b456.tif Landsat 8 warmest-pixel optical composite (bands RED, NIR, SWIR1) ls8-composite-tsurf.tif Landsat 8 warmest-pixel surface temperature composite srtm-elevation.tif SRTM 1 arc second elevation data srtm-slope.tif Slope of the SRTM 1 arc second data</pre> <p> </p> <p><strong>Model input: </strong>RDS data</p> <p>The general model input data (<em>mbg-model-rds-data.7z)</em> is stored in R’s binary RDS format and <em><a href="https://www.r-project.org/">R</a></em> is required to open and read the data.</p> <p>Files present in the 7z-archive are:</p> <pre>dP_factors_2006-2100.rds Precipitation changes (delta factors) up to 2100 dT_degrees_2006-2100.rds Temperature changes (Kelvin) up to 2100 glacier-data.rds Glacier centroids with current climate and mass balance input ostrem_meancurve.rds The Östrem curve used by the model rgi-subregions.rds RGI sub-region polygons for Asia</pre> <p> </p> <p><strong>Output data</strong></p> <p>Region-aggregated output is available in ESRI Shapefile format for the RGI sub-regions, major river basins, and for a 1×1 degree grid (<em>output-shapefiles.7z</em>). The attribute tables of all shapefiles hold data on the occurrence of debris as well as current glacier area and volume, and volume projections for the end of century.</p> <p>The available shapefile attributes are:</p> <pre>count number of glaciers a_total total glacier area (m2) a_debris glacier area covered by debris (m2) a_ela glacier area below modelled ELA (m2) a_ela_deb glacier area below modelled ELA covered by debris (m2) v_total total glacier volume (m3) v_debris glacier volume covered by debris (m3) v_ela glacier volume below modelled ELA (m3) v_ela_deb glacier volume below modelled ELA covered by debris (m3) m_total_gt total glacier mass (gigaton) volST_EOC volume remaining in end of century under a stable current temperature vol15_EOC volume remaining in end of century under 1.5 degree scenario vol26_EOC volume remaining in end of century for the RCP2.6 model ensemble vol45_EOC volume remaining in end of century for the RCP4.5 model ensemble vol60_EOC volume remaining in end of century for the RCP6.0 model ensemble vol85_EOC volume remaining in end of century for the RCP8.5 model ensemble</pre>
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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)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.