Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
26,784
datasets available to search
ShareScore release 0.9.0
Dataset results
26,784 results for “M”
iSDAsoil: soil pH for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil pH (1:1 Soil-Water Suspension) predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_ph_h2o_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil pH mean value,</li> <li>sol_ph_h2o_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil pH model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: ph_h2o R-square: 0.818 Fitted values sd: 0.972 RMSE: 0.459 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -5.5939 -0.2328 -0.0066 0.2222 4.7477 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.113440 1.164473 0.956 0.338986 regr.ranger 1.032918 0.003138 329.116 < 2e-16 *** regr.xgboost -0.014201 0.004185 -3.393 0.000691 *** regr.cubist 0.049667 0.003709 13.392 < 2e-16 *** regr.nnet -0.188570 0.188214 -1.002 0.316398 regr.cvglmnet -0.059763 0.003636 -16.438 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.4591 on 133378 degrees of freedom Multiple R-squared: 0.8176, Adjusted R-squared: 0.8176 F-statistic: 1.195e+05 on 5 and 133378 DF, p-value: < 2.2e-16</code></pre> <p>To back-transform values (y) to index use:</p> <pre><code>index = y / 10</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
DataSet of "No renal dysfunction or salt and water retention in acute mountain sickness at 4,559 m among young resting males after passive ascent"
<p><strong>Abstract</strong></p> <p><strong>Purpose</strong>: This study examined the role and function of the kidney at high altitude in relation to fluid balance and the development of acute mountain sickness (AMS), avoiding confounders that have contributed to conflicting results in previous studies.</p> <p><strong>Methods</strong>: We examined 18 healthy male volunteers (18 - 40 years) not acclimatized to high altitude while on a controlled diet and resting recumbently for 24 h at Lausanne (altitude: 560 m) followed by a period of 44 hours after reaching the Regina Margherita hut (4,559 m) by helicopter.</p> <p><strong>Results</strong>: AMS scores peaked after 20 h at 4,559 m. AMS was defined as functional Lake Louise score <span class="math-tex">\({\ge}\)</span> 2.There were no significant differences between 10 subjects with and 8 subjects without AMS for urinary flow, fluid balance and weight change. Sodium excretion rate was lower in those with AMS after 24 h at altitude. Microalbuminuria increased at altitude but not differently between the groups. Creatinine clearance was not affected by altitude or AMS, while sinistrin and PAH clearances decreased slightly, more markedly in those without AMS. Plasma concentrations of epinephrine, norepinephrine, atrial natriuretic factor and vasopressin increased while renin activity, angiotensin and aldosterone decreased at altitude. Hormones levels did not differ between those with and without AMS.</p> <p><strong>Conclusions</strong>: 1) Renal function is not affected by hypoxia at 4,559 m in resting subjects except for minor microalbuminuria, 2) high altitude diuresis does not occur and 3) AMS is not associated with salt and water retention or renal dysfunction.</p>
A fading radius valley towards M-dwarfs, a persistent density valley across stellar types -- data
Open the record for dataset details and reuse information.
Data belonging to: Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.
<p>Data belonging to the paper Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.</p> <p>Data includes analysis scripts (R Language) and all used data files. Data is composed of location information of the different sites, environmental conditions on site and vegetation composition.</p>
Global GFED-based monthly burned area time series (1996-2016) at 1 km and ESA CCI MODIS-based long-term monthly P90 burned area occurrence at 500 m
<p>Contains two separate datasets:</p> <ol> <li>Global <a href="https://www.globalfiredata.org/data.html">GFED-based monthly burned area</a> (in ha) <a href="https://youtu.be/kBJcP8mL2Qs">time series (1996-2016)</a> at 1 km (downscaled using cubic-splines from 25 km);</li> <li>Global burned area long term (2000-2012) P90 (quantile probability = 0.9) based on the <a href="http://maps.elie.ucl.ac.be/CCI/viewer/index.php">ESA CCI burned area accumulated weekly product</a>;</li> </ol> <p>Original GFED monthly data is provided as HDF4 files (ftp.fuoco.geog.umd.edu/data/GFED/GFED4). Dataset is described in detail in <a href="https://doi.org/10.1002/jgrg.20042">Giglio et al. (2013)</a>. Processing steps are available <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/GFED"><strong>here</strong></a>. Antarctica is not included.</p> <p>To access and visualize global datasets use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a> or watch <a href="https://youtu.be/kBJcP8mL2Qs"><strong>this video</strong></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> </ul> <p>All files provided as Cloud-Optimized GeoTIFFs / internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>nhz = theme: natural hazards,</li> <li>monthly.burned.ha = variable: estimated monthly burned area in ha,</li> <li>gfed = data source GFED data,</li> <li>m = mean value,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000.02 = time reference aggregated: month Feb of year 2000,</li> <li>v4 = version number: GFEDv4,</li> </ul>
Global MODIS-based snow cover monthly long-term (2000-2012) at 500 m, and aggregated monthly values (2000-2020) at 1 km
<p>The Global monthly snow cover repository contains multiple products (based on the MODIS/Terra MOD10A2):</p> <ol> <li>Global snow cover monthly long-term (2000–2012) P90 and standard deviation derived from the <a href="http://maps.elie.ucl.ac.be/CCI/viewer/index.php">ESA CCI snow cover weekly product</a>;</li> <li>Global snow cover monthly values P05, P50 and P95 for the period 2000–2020 derived using <a href="https://climate.esa.int/en/odp/#/project/snow">ESA snow cover fraction daily 1-km values</a>;</li> <li>Min and max geometric temperatures for the mid-month (dtm_temp.max_geom.*_m_1km_s0..0cm_xxxx_epsg4326_v1.tif);</li> </ol> <p>Quantiles (probability either 0.05, 0.5, 0.9 and/or 0.95) have been derived by matching dates in the filenames (daily or weekly values). After deriving quantiles, gaps were filled using temporal neighbors (e.g. missing values for year 2002 were filled using average of values between year 2001 and 2003). The gaps were especially large for months of November, December, January and February, northern Hemisphere. Important note: maps still contain some artifacts due to high reflections of white-sands e.g. Salar de Uyuni desert in Bolivia and similar. Processing steps are available <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/snow.cover"><strong>here</strong></a>. Antarctica is not included.</p> <p>To access and visualize global datasets use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></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> </ul> <p>All files provided as Cloud-Optimized GeoTIFFs / internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>clm = theme: climate,</li> <li>snow.cover = variable: snow cover fractions,</li> <li>esa.modis = data source ESA snow product,</li> <li>p.90 = upper 90% quantile,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000..2012 = time reference aggregated: from 2000 to 2012,</li> <li>v1 = version number: 1,</li> </ul>
An hourly ground temperature dataset for 16 high-elevation sites (3493–4377 m a.s.l.) in the Bale Mountains, Ethiopia (2017–2020)
<p>This is a multiannual ground temperature dataset covering sixteen high elevation sites (3493-4377 m a.s.l.) in the Bale Mountains, southern Ethiopian Highlands</p> <p>The dataset is described in detail in the corresponding data paper by Groos et al. 2021 (https://doi.org/10.5194/essd-2021-268)</p> <p>The repository contains a readme file ("readme.txt"), a GeoPackage ("Data_Logger_Location.gpkg"), a thermal infrared time-lapse video ("thermal_infrared_time-lapse_video.mp4"), a metadata file for the video ("video_metadata.txt"), and two sub-folders: "raw_data" and "processed_data"</p> <p>The GeoPackage provides information on the location and environmental setting of each logger and can be easily opened and displayed in a Geographic Information System. The coordinate reference system is WGS84 / Geographic (EPSG code: 4326).</p> <p>The thermal infrared time-lapse video (<a href="https://vimeo.com/676294827">https://vimeo.com/676294827</a>) visualises the phenomenon of nocturnal cold air drainage and ponding in the Bale Mountains (for more information see the metadata file and Appendix C in the corresponding data paper).</p> <p>The folder "raw_data" contains the original logfiles of all GT and TM data loggers (see Table 1) in a tab-delimited text format with the logger ID and download date encoded in the file name. The date format of the GT data loggers is YYYY.MM.DD hh:mm:ss East Africa Time (EAT). The date format of the TM data loggers is DD.MM.YYYY hh:mm:ss EAT.</p> <p>The folder "processed_data" contains the followings two files:</p> <p>"Information_Sheet_Data_Gap-Filling.ods": An overview table with relevant information regarding the filling of (longer) data gaps in the ground temperature time series. The gap-filling procedure based on simple linear regression models is described individually for each logger.</p> <p>"Hourly_Ground_Temperatures.csv": Compilation of hourly ground temperature data from all GT and TM data loggers installed in the Bale Mountains (see Table 1 in the data paper). The dataset covers the period from 1 January 2017 to 31 January 2020, but individual time series may be shorter or contain data gaps (see Fig. 3 in the data paper). We use the international date format (ISO 8601): YYYY-MM-DD hh:mm:ss EAT. The following numerical indices (or a combination of them) in the columns starting with "Flag_*" are used to provide additional information on the post-processing of each hourly measurement of each time series:</p> <p>0 no data available<br> 1 original data (no post-processing)<br> 2 data interpolated to full hour<br> 3 erroneous data corrected<br> 4 erroneous data removed<br> 5 data gap-filled</p> <p>The meteorological data from the ten automatic weather stations in the Bale Mountains, which are operated since 2017, are currently post-processed and analysed in the framework of the DFG Research Unit 2358 "The Mountain Exile Hypothesis". The data will be made publicly available at some point in the future. However, individual access to the weather station data may be granted before on request to the coordination board of the research unit (bale@staff.uni-marburg.de).</p>
Minimal dataset to test multiplexed DNA imaging (Hi-M) software pipelines
<p>This is a dataset of nuclei (DAPI), and 3 multiplexed DNA imaging cycles to test and validate processing software packages, such as pyHiM (https://github.com/marcnol/pyHiM). This dataset was acquired in a nc14 Drosophila embryo.</p> <p>File contents:</p> <p>scan_001_RT27_001_ROI_converted_decon_ch00.tif barcode 27, fiducial <br> scan_001_RT27_001_ROI_converted_decon_ch01.tif barcode 27<br> scan_001_RT29_001_ROI_converted_decon_ch00.tif barcode 29, fiducial <br> scan_001_RT29_001_ROI_converted_decon_ch01.tif barcode 29 <br> scan_001_RT37_001_ROI_converted_decon_ch00.tif barcode 37, fiducial <br> scan_001_RT37_001_ROI_converted_decon_ch01.tif barcode 37 <br> scan_006_DAPI_001_ROI_converted_decon_ch00.tif DAPI <br> scan_006_DAPI_001_ROI_converted_decon_ch01.tif DAPI, fiducial <br> scan_006_DAPI_001_ROI_converted_decon_ch02.tif RNA</p> <p> </p> <p>To test this dataset please refer to <a href="https://github.com/marcnol/pyHiM">pyHiM documentation page</a>.</p>
30-m HRSC DTM Mosaic of Gale Crater, Mars
<p>Digital terrain model (DTM) mosaic of Gale crater, Mars, processed from High-Resolution Stereo Camera (HRSC) stereo images using the modification of DLR-VICAR described by Kim and Muller (2009).</p> <p>Format: GeoTiff<br> Projection: Equidistant cylindrical<br> Datum: Spheroid (r = 3396.190 km)<br> Bit depth: Float32<br> Grid-spacing: 30 m/pixel<br> Terrain reference: 200-m MOLA and HRSC blended global DTM (Fergason et al. 2018)</p> <p>HRSC source images: H1938_0000, H1927_0000, and H1916_0000</p>
Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets
<p><strong>Sydney morphology and land surface dataset</strong></p> <p>This dataset for Sydney, Australia, represents land cover, building morphology, vegetation morphology and other parameters appropriate for input into local or mesoscale urban climate models.</p> <p>The dataset is provided in netCDF4 and GeoTiff formats.</p> <p>Associated manuscript:</p> <blockquote> <p><a href="https://doi.org/10.3389/fenvs.2022.866398">A transformation in city-descriptive input data for urban climate models</a></p> </blockquote> <p>Citation for the open dataset:<br> - Lipson, M., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets (v1.01), <a href="https://doi.org/10.5281/zenodo.6579061">https://doi.org/10.5281/zenodo.6579061</a>, 2022.</p> <p>Citation for the associated manuscript:<br> - Lipson, M. J., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: A Transformation in City-Descriptive Input Data for Urban Climate Models, Frontiers in Environmental Science, 10, <a href="https://doi.org/10.3389/fenvs.2022.866398">https://doi.org/10.3389/fenvs.2022.866398</a>, 2022.</p> <p>Location of associated processing code:<br> - <a href="https://github.com/matlipson/geoscape_processing_public.git">https://github.com/matlipson/geoscape_processing_public.git</a></p> <p><strong>Acknowledgments</strong></p> <p>We gratefully acknowledge the Australian Urban Research Infrastructure Network (AURIN) and Geoscape Australia for <br> providing the datasets necessary for this study, drawing on Geoscape Buildings, Surface Cover and Trees datasets, <br> © Geoscape Australia, 2020: https://geoscape.com.au/legal/data-copyright-and-disclaimer/. <br> This research was supported by the Australian Research Council (ARC) Centre of Excellence for Climate System Science <br> (grant CE110001028), the ARC Centre of Excellence for Climate Extremes (grant CE170100023). </p> <p> </p>
Soil bulk density [10x kg/m3] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: db_od = bulk density over dry [kg/m3 ⨉ 10];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>The bulk density maps are also provided in 10 kg / m-cubic to reduce total data size; to convert values to kg / m-cubic multiply by 10 e.g. 120 = 1200 kg / m-cubic = 1.2 t / m-cubic.</p>
Soil pH in H2O [-] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: soil pH in H2O;</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Soil organic carbon content [g/kg] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: log organic carbon [g/kg] to back-transform use exp(x/10)-1;</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>To back-transform the log.oc maps use formula: exp(x/10)-1. These are examples of back-transformed values:</p> <p> log.oc = 15 → 0.3% SOC;<br> log.oc = 20 → 0.6% SOC;<br> log.oc = 25 → 1.1% SOC;<br> log.oc = 30 → 1.9% SOC;<br> log.oc = 35 → 3.2% SOC;<br> log.oc = 40 → 5.3% SOC;<br> log.oc = 50 → 14.8% SOC;</p>
Soil clay content [%] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: clay.tot = clay content [percent];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Soil sand content [%] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: sand.tot = sand content [percent];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Soil water content (volumetric %) for 33kPa and 1500kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Training points are based on a global compilation of soil profiles (<a href="https://ncsslabdatamart.sc.egov.usda.gov/">USDA NCSS</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, <a href="https://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/a351682c-330a-4995-a5a1-57ad160e621c">ISRIC WISE</a>, <a href="http://egrpr.esoil.ru/">EGRPR</a>, <a href="https://esdac.jrc.ec.europa.eu/content/soil-profile-analytical-database-2">SPADE</a>, <a href="https://open.canada.ca/data/en/dataset/6457fad6-b6f5-47a3-9bd1-ad14aea4b9e0">CanNPDB</a>, <a href="https://data.nal.usda.gov/dataset/unsoda-20-unsaturated-soil-hydraulic-database-database-and-program-indirect-methods-estimating-unsaturated-hydraulic-properties">UNSODA</a>, <a href="https://doi.pangaea.de/10.1594/PANGAEA.885492">SWIG</a>, <a href="http://www.cprm.gov.br/en/Hydrology/Research-and-Innovation/HYBRAS-4208.html">HYBRAS</a> and <a href="http://dx.doi.org/10.4228/ZALF.2003.273">HydroS</a>). Data import steps are available <a href="https://gitlab.com/openlandmap/compiled-ess-point-data-sets/-/tree/master/themes/sol/SoilHydroDB"><strong>here</strong></a>. Spatial prediction steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/soil_water">here</a></strong>. Note: these are actually measured and mapped soil content values; no Pedo-Transfer-Functions have been used (except to fill-in the missing NCSS bulk densities). Available water capacity in mm (derived as a difference between field capacity and wilting point multiplied by layer thickness) per layer is available <strong><a href="https://doi.org/10.5281/zenodo.2629148">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize some of the 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>sol = theme: soil,</li> <li>watercontent.33kPa = water content (volumetric percent) under field capacity (33 kPa suction),</li> <li>usda.4b1c = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Perhydrobenzyltoluene dehydrogenation using monometallic M/Al2O3 and bimetallic Pt-M/Al2O3 catalysts (M = Co, Ni): Effect of metal content.
<p>Hydrogen production from renewable sources emerges as a key strategy for decarbonizing the energy system. However, the advancement of the hydrogen-based energy economy is delayed by limitations in storage and transportation systems. Over the past decade, H<sub>2 </sub>chemical storage, particularly systems based on liquid organic hydrogen carriers (LOHCs), have emerged as a promising solution, employing reversible catalytic reactions for hydrogen storage within organic compounds [1-3].</p> <p>Platinum-Group-Metal (PGM)-based catalysts have been identified as optimal for LOHC technology [4-7]. However, their high cost and environmental impact set significant barriers for large scale applications. To mitigate this challenge, we investigated the perhydrobenzyltoluene dehydrogenation process to benzyltoluene, focusing on minimizing PGM usage.</p> <p>In this work, we synthesized bimetallic catalysts (Pt-M/Al<sub>2</sub>O<sub>3</sub>) with low Pt-content (0.5 wt.%) and varying second metal loads (M = Co, Ni). Catalysts were prepared using the incipient wetness impregnation method, wincorporating Co and Ni first, followed by a second impregnation of Pt, as described elsewhere [6]. Additionally, monometallic Co/Al<sub>2</sub>O<sub>3</sub> and Ni/Al<sub>2</sub>O<sub>3</sub> catalysts were prepared for comparison. Dehydrogenation tests were conducted in a laboratory-scale batch reactor, with hydrogen release quantified using a flow indicator (Brooks SLA5800).</p> <p>The activity results, summarized in the following figure and attached as a dataset, reveal that monometallic Co/Al<sub>2</sub>O<sub>3</sub> and Ni/Al<sub>2</sub>O<sub>3</sub> exhibited poor dehydrogenation performance, with a Degree of Dehydrogenation (DoD) lower than 10%. Conversely, bimetallic catalysts, particularly those with reduced Ni and Co contents, demonstrated enhanced dehydrogenation activity, achieving optimal rates with a 0.5 wt.% metal load. Upon comparing the activity results, it was observed that Co exhibited greater activity than Ni when considering the same metal content. These findings highlight the superior dehydrogenation promotion capability of Co.</p> <p>In summary, this study underscores the potential of low-metal content and sustainable catalysts as initial steps toward optimizing metal content for LOHC technology. These findings offer promising possibilities for enhancing the efficiency and sustainability of hydrogen storage and release systems, crucial for realizing the full potential of hydrogen as a clean energy carrier.</p>
Landsat bands (cloud free), tree cover (2000, 2010), bare-ground and surface water occurrence at 250 m based on GlobalForestWatch and USGS
<p>Landsat bands (cloud free) and tree cover (2000) based on Hansen et al. (2013), global surface water occurrence based on Pekel at al. (2016), and tree cover and bare-ground cover (2010) based the USGS land cover mapping projects (University of Maryland, Department of Geographical Sciences and USGS). All layers resampled to spatial resolution 1/480 d.d. (about 250 m) using gdalwarp with "average" resampling. Antarctica is not included. Original layers are available at 30 m resolution.</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>lcv = theme: land cover,</li> <li>bareground = variable: occurrence of bareground,</li> <li>landsat.usgs = determination method: Landsat landcover at 30 m resolution project (https://landcover.usgs.gov/glc/),</li> <li>p = probability or fraction,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2010..2010 = time reference: year 2010,</li> <li>v1.0 = version number: 1.0,</li> </ul>
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>
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.