Find research datasets worth reusing

openCC0Nov 2021View details →

Concentration of dissolved organic carbon in water samples taken from the Upper Clark Fork River (Montana, USA) during water year 2019 (1 Oct 2018 - 30 Sep 2019)

These data were collected to support monitoring of the Upper Clark Fork River restoration, and data collection was funded by the US NSF Long Term Research in Environmental Biology (LTREB) program and the US NSF EPSCoR funded Montana Consortium for Research on Environmental Water Systems. The LTREB monitoring project consists of monthly or bi-weekly water quality monitoring across a 200-km restoration gradient contaminated by historic mining practices to monitor inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and heavy metal contamination. The original analytical intent for these data was to assess the response of river dissolved organic carbon to the floodplain restoration. Data are Aurora Total Organic Carbon combustion analyses of the concentration of organic carbon dissolved in filtered samples of well-mixed river thalweg water. Data are from the 2019 water year (1 Oct 2018 to 30 Sep 2019). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA.

openCC (other)Jan 2024View details →

Marcell Experimental Forest 30-minute water table elevation and temperature from transects of wells in the S2 and S6 peatlands, 2018-ongoing

This data publication contains 30-minute water table elevation and temperature data collected along bog to lagg transects within two watersheds at the Marcell Experimental Forest (MEF) in Itasca County, Minnesota. The bog to lagg transects are located on the north and south sides of S2 and S6 peatlands and contain three surface water wells each. The water table elevations provide information to calculate the hydraulic gradients that drive flow to and from the bogs. The collection of these data was funded by the US Department of Energy. The research program at Marcell Experimental Forest is managed by the USDA Forest Service Northern Research Station.

openCC0Mar 2023View details →

Concentration of nutrients in water samples collected from the Upper Clark Fork River (Montana, USA) during water year 2019 (1 Oct 2018 - 30 Sept 2019)

The umbrella LTREB monitoring project generating these data is conducted separately and complementarily to the $200 million-dollar (USD) superfund project for ecological restoration of the Upper Clark Fork River (UCFR), associated tributaries, and head water streams including Silver Bow and Warm Springs Creeks. Restoration along the Upper Clark Fork River includes removal of metal-laden floodplain soils, lowering of the floodplain to its original elevation, and re-vegetation of over 70 km of the river's floodplain closest to contaminant sources. The UCFR Long Term Research in Environmental Biology (LTREB) project includes bi-weekly water quality monitoring across a 200-km gradient of heavy metal contamination associated with historic mining. Monitoring includes inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and dissolved and whole-water heavy metal concentrations. The UCFR LTREB monitoring project is conducted within the first 200 km of the Upper Clark Fork River and associated tributaries located in western Montana. The current monitoring program began in 2017 and will be completed in the year 2023, with likely funding extension to 2028. Surface water samples represented in this data product are collected from fourteen sites along the mainstem of the UCFR, and one site representing a major tributary to the UCFR. Water samples are collected at each monitoring site in triplicate and filtered with a 0.7-µm glass fiber filter. Nutrient samples are analyzed using a spectrophotometric flow injection analyzer (AP2) for nitrate (NO3-N), soluble reactive phosphorus (SRP, as representative of PO4-P), and ammonium (NH4-N) concentrations reported in mg/L. The analysis-ready data of this dataset therefore represent Quality Assurance and Quality Control (QAQC) processed NH4-N, SRP, and NO3-N concentrations from fourteen sites along the mainstem of the UCFR and one tributary, collected in water year 2019 (1 Oct 2018 - 30 Sept 2019).

openCC (other)Dec 2025View details →

Marcell Experimental Forest 30-minute resolution meteorological data, 2006 - ongoing

This data publication contains 30-minute meteorological data collected from 2006 - ongoing at the Marcell Experimental Forest (MEF) in Itasca County, Minnesota, which is operated and maintained by the USDA Forest Service, Northern Research Station. Air temperature, relative humidity, wind speed and direction, photosynthetic photon flux density, soil temperature, and soil volumetric water content were measured at three meteorological monitoring stations. One station is in an upland clearing, one is under an upland forest canopy, and the third is in a peatland.

openCC (other)Aug 2024View details →

Marcell Experimental Forest 30-minute air temperature, relative humidity, and barometric pressure, 2015 - ongoing

This data publication contains air temperature, relative humidity, and atmospheric pressure data collected at 30-minute resolution from 2015-ongoing at three long term meteorological monitoring stations at the Marcell Experimental Forest (MEF). The MEF is located in Itasca County, Minnesota and is operated and maintained by the USDA Forest Service, Northern Research Station.

openCC (other)Apr 2021View details →

Eddy covariance 30-minute CO2 fluxes with accompanying biophysical variables from the Grand Bay, Mississippi flux tower site from March 2018 to January 2019

Eddy covariance (EC) CO2 fluxes from March 2018 to January 2019 collected over a Juncus roemerianus marsh located in the Grand Bay National Estuarine Research Reserve (NERR) in Mississippi. EC fluxes were processed in EddyPro 7. Additional biophysical variables included are air temperature, relative humidity, vapor pressure deficit, soil temperature, and water table height within the marsh.

openCC (other)Apr 2021View details →

Eddy covariance 30-minute CO2 fluxes with accompanying biophysical variables from the GCE-LTER flux tower site from December 2018 to January 2020

Eddy covariance (EC) CO2 fluxes from December 2018 to January 2020 collected over a Spartina alterniflora marsh located on the western side of Sapelo Island bounded by the Duplin River and Barn Creek. EC fluxes were processed in EddyPro 7. Additional biophysical variables included are air temperature, relative humidity, vapor pressure deficit, soil temperature, and water table height from a nearby tidal creek.

iSDAsoil: soil clay content (USDA system) for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

iSDAsoil dataset soil clay content (USDA system) in % 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>, <a href="https://landpotential.org/data-portal/">LandPKS</a>, and other national and regional soil datasets). Cite as: Hengl, T., Miller, M.A.E., Križan, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a> 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">Cloud-Optimized GeoTIFF version</a>. Layer description: <ul> <li>sol_clay_tot_psa_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil clay content mean value,</li> <li>sol_clay_tot_psa_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil clay content (prediction) errors,</li> </ul> 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: <pre><code>Variable: clay_tot_psa R-square: 0.746 Fitted values sd: 16.5 RMSE: 9.63 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -75.803 -4.512 -0.178 3.748 82.146 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.494652 8.914671 0.504 0.61413 regr.ranger 1.076957 0.003611 298.210 < 2e-16 *** regr.xgboost -0.012617 0.004678 -2.697 0.00699 ** regr.cubist 0.030730 0.003930 7.820 5.32e-15 *** regr.nnet -0.238376 0.365390 -0.652 0.51415 regr.cvglmnet -0.044547 0.004379 -10.174 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 9.629 on 122269 degrees of freedom Multiple R-squared: 0.7458, Adjusted R-squared: 0.7458 F-statistic: 7.175e+04 on 5 and 122269 DF, p-value: < 2.2e-16</code></pre> To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil">https://isda-africa.com/isdasoil</a>

iSDAsoil: soil extractable Iron for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

iSDAsoil dataset soil extractable Iron (Fe) log-transformed 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: Hengl, T., Miller, M.A.E., Križan, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a> 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">Cloud-Optimized GeoTIFF version</a>. Layer description: <ul> <li>sol_log.fe_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Iron mean value,</li> <li>sol_log.fe_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Iron model (prediction) errors,</li> </ul> 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: <pre><code>Variable: log.fe_mehlich3 R-square: 0.817 Fitted values sd: 0.497 RMSE: 0.235 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -4.0165 -0.1312 -0.0082 0.1238 2.5077 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.913522 1.869721 2.093 0.036344 * regr.ranger 0.856893 0.007912 108.306 < 2e-16 *** regr.xgboost 0.027856 0.007738 3.600 0.000318 *** regr.cubist 0.146095 0.007230 20.207 < 2e-16 *** regr.nnet -0.879348 0.402810 -2.183 0.029037 * regr.cvglmnet 0.005610 0.004470 1.255 0.209415 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2349 on 57526 degrees of freedom Multiple R-squared: 0.8173, Adjusted R-squared: 0.8173 F-statistic: 5.148e+04 on 5 and 57526 DF, p-value: < 2.2e-16</code></pre> To back-transform values (y) to ppm use the following formula: <pre><code>ppm = expm1( y / 10 )</code></pre> To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil">https://isda-africa.com/isdasoil</a>

iSDAsoil: soil extractable Calcium for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

iSDAsoil dataset soil extractable Calcium log-transformed 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: Hengl, T., Miller, M.A.E., Križan, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a> 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">Cloud-Optimized GeoTIFF version</a>. Layer description: <ul> <li>sol_log.ca_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Calcium mean value,</li> <li>sol_log.ca_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Calcium model (prediction) errors,</li> </ul> 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: <pre><code>Variable: log.ca_mehlich3 R-square: 0.84 Fitted values sd: 1.24 RMSE: 0.543 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -6.0376 -0.2577 0.0076 0.2756 5.3825 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.737959 3.850998 1.490 0.136 regr.ranger 1.054018 0.003175 331.978 < 2e-16 *** regr.xgboost -0.030930 0.003939 -7.853 4.1e-15 *** regr.cubist 0.061829 0.003561 17.364 < 2e-16 *** regr.nnet -0.855297 0.561006 -1.525 0.127 regr.cvglmnet -0.065040 0.003225 -20.166 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.5428 on 144593 degrees of freedom Multiple R-squared: 0.8403, Adjusted R-squared: 0.8402 F-statistic: 1.521e+05 on 5 and 144593 DF, p-value: < 2.2e-16 </code></pre> To back-transform values (y) to ppm use the following formula: <pre><code>ppm = expm1( y / 10 )</code></pre> To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil">https://isda-africa.com/isdasoil</a>

iSDAsoil: soil fine-earth bulk density for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

iSDAsoil dataset soil fine-earth bulk density in 10×kg/m3 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>, <a href="https://landpotential.org/data-portal/">LandPKS</a>, and other national and regional soil datasets). Cite as: Hengl, T., Miller, M.A.E., Križan, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a> 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">Cloud-Optimized GeoTIFF version</a>. Layer description: <ul> <li>sol_db_od_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil bulk density mean value,</li> <li>sol_db_od_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil bulk density model (prediction) errors,</li> </ul> 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: <pre><code>Variable: db_od R-square: 0.819 Fitted values sd: 0.269 RMSE: 0.126 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -1.06778 -0.06450 0.00215 0.06585 0.90016 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.05538 0.04860 -1.140 0.25451 regr.ranger 0.86305 0.01577 54.733 < 2e-16 *** regr.xgboost 0.15383 0.01651 9.315 < 2e-16 *** regr.cubist 0.02039 0.01113 1.832 0.06695 . regr.nnet 0.03465 0.03710 0.934 0.35036 regr.cvglmnet -0.03021 0.01032 -2.927 0.00343 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.1263 on 13565 degrees of freedom Multiple R-squared: 0.8194, Adjusted R-squared: 0.8193 F-statistic: 1.231e+04 on 5 and 13565 DF, p-value: < 2.2e-16</code></pre> To back-transform values (y) to kg/m-cubic use: <pre><code>kg/m3 = y * 10</code></pre> To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil">https://isda-africa.com/isdasoil</a>

iSDAsoil: soil extractable Phosphorus for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

iSDAsoil dataset soil extractable Phosphorus (P) log-transformed 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: Hengl, T., Miller, M.A.E., Križan, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a> 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">Cloud-Optimized GeoTIFF version</a>. Layer description: <ul> <li>sol_log.p_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Phosphorus mean value,</li> <li>sol_log.p_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Phosphorus model (prediction) errors,</li> </ul> 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: <pre><code>Variable: log.p_mehlich3 R-square: 0.486 Fitted values sd: 0.687 RMSE: 0.707 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -3.2892 -0.3942 -0.0637 0.2614 4.9466 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.378801 3.143200 1.075 0.282 regr.ranger 0.861655 0.011099 77.631 < 2e-16 *** regr.xgboost 0.066139 0.013091 5.052 4.38e-07 *** regr.cubist 0.157674 0.008886 17.744 < 2e-16 *** regr.nnet -1.649621 1.442240 -1.144 0.253 regr.cvglmnet 0.013628 0.010407 1.310 0.190 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.7066 on 53493 degrees of freedom Multiple R-squared: 0.486, Adjusted R-squared: 0.486 F-statistic: 1.012e+04 on 5 and 53493 DF, p-value: < 2.2e-16 </code></pre> To back-transform values (y) to ppm use the following formula: <pre><code>ppm = expm1( y / 10 )</code></pre> To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil">https://isda-africa.com/isdasoil</a>

iSDAsoil: soil extractable Magnesium for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

iSDAsoil dataset soil extractable Magnesium (Mg) log-transformed 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: Hengl, T., Miller, M.A.E., Križan, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a> 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">Cloud-Optimized GeoTIFF version</a>. Layer description: <ul> <li>sol_log.mg_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Magnesium mean value,</li> <li>sol_log.mg_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Magnesium model (prediction) errors,</li> </ul> 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: <pre><code>Variable: log.mg_mehlich3 R-square: 0.815 Fitted values sd: 1.05 RMSE: 0.498 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -5.8775 -0.2312 0.0028 0.2465 3.7400 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.034349 0.051219 -0.671 0.5025 regr.ranger 1.034217 0.003263 316.950 <2e-16 *** regr.xgboost -0.008057 0.003854 -2.091 0.0366 * regr.cubist 0.073223 0.003649 20.067 <2e-16 *** regr.nnet -0.017388 0.009528 -1.825 0.0680 . regr.cvglmnet -0.075566 0.003402 -22.213 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.4979 on 136681 degrees of freedom Multiple R-squared: 0.8152, Adjusted R-squared: 0.8152 F-statistic: 1.206e+05 on 5 and 136681 DF, p-value: < 2.2e-16 </code></pre> To back-transform values (y) to ppm use the following formula: <pre><code>ppm = expm1( y / 10 )</code></pre> To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil">https://isda-africa.com/isdasoil</a>

iSDAsoil: soil stone content for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

iSDAsoil dataset soil stone content / coarse fragments log-transformed 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: Hengl, T., Miller, M.A.E., Križan, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a> 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">Cloud-Optimized GeoTIFF version</a>. Layer description: <ul> <li>sol_log.wpg2_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil stone content mean value,</li> <li>sol_log.wpg2_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil stone content model (prediction) errors,</li> </ul> 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: <pre><code>Variable: log.wpg2 R-square: 0.709 Fitted values sd: 1.25 RMSE: 0.803 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -4.0555 -0.3113 -0.0222 0.2378 4.5794 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.008606 1.361982 -0.006 0.995 regr.ranger 0.972265 0.004443 218.854 < 2e-16 *** regr.xgboost 0.034649 0.006404 5.411 6.3e-08 *** regr.cubist 0.069589 0.005229 13.308 < 2e-16 *** regr.nnet -0.012756 0.796535 -0.016 0.987 regr.cvglmnet -0.056645 0.005509 -10.283 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.8032 on 92785 degrees of freedom Multiple R-squared: 0.7092, Adjusted R-squared: 0.7092 F-statistic: 4.525e+04 on 5 and 92785 DF, p-value: < 2.2e-16 </code></pre> To back-transform values (y) to % use the following formula: <pre><code>% = expm1( y / 10 )</code></pre> To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil">https://isda-africa.com/isdasoil</a>

iSDAsoil: soil extractable Sulphur for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

iSDAsoil dataset soil extractable Sulphur (S) log-transformed 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: Hengl, T., Miller, M.A.E., Križan, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a> 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">Cloud-Optimized GeoTIFF version</a>. Layer description: <ul> <li>sol_log.s_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Sulphur mean value,</li> <li>sol_log.s_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Sulphur model (prediction) errors,</li> </ul> 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: <pre><code>Variable: log.s_mehlich3 R-square: 0.548 Fitted values sd: 0.423 RMSE: 0.384 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -2.5729 -0.2102 -0.0264 0.1694 5.0049 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.459208 4.154229 0.351 0.725 regr.ranger 0.937179 0.016167 57.967 < 2e-16 *** regr.xgboost 0.002587 0.016252 0.159 0.874 regr.cubist 0.145396 0.010890 13.351 < 2e-16 *** regr.nnet -0.672062 1.796642 -0.374 0.708 regr.cvglmnet -0.045157 0.011256 -4.012 6.04e-05 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3841 on 37530 degrees of freedom Multiple R-squared: 0.5481, Adjusted R-squared: 0.548 F-statistic: 9103 on 5 and 37530 DF, p-value: < 2.2e-16</code></pre> To back-transform values (y) to ppm use the following formula: <pre><code>ppm = expm1( y / 10 )</code></pre> To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil">https://isda-africa.com/isdasoil</a>

iSDAsoil: soil pH for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

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: Hengl, T., Miller, M.A.E., Križan, J. et al. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Sci Rep 11, 6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a> 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">Cloud-Optimized GeoTIFF version</a>. Layer description: <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> 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: <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> To back-transform values (y) to index use: <pre><code>index = y / 10</code></pre> To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil">https://isda-africa.com/isdasoil</a>

opencc-by-4.0Dec 2023View details →

Coding table for chapter 30 'Valency change and causation' in Bowern (ed. 2023)

This is the coding table used for chapter 30 'Valency change and causation' (pp. 344-359) in the Oxford Guide on Australian Languages (Bowern, ed. 2023).

opencc-by-4.0Dec 2021View details →

30-m HRSC DTM Mosaic of Gale Crater, Mars

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). Format: GeoTiff Projection: Equidistant cylindrical Datum: Spheroid (r = 3396.190 km) Bit depth: Float32 Grid-spacing: 30 m/pixel Terrain reference: 200-m MOLA and HRSC blended global DTM (Fergason et al. 2018) HRSC source images: H1938_0000, H1927_0000, and H1916_0000

opencc-by-sa-4.0May 2022View details →

Soil bulk density [10x kg/m3] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe

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. The short description of currently available soil properties: db_od = bulk density over dry [kg/m3 ⨉ 10]; Soil properties were predicted at fixed depths:     Surface soil = s0..0cm,     Subsoil 1 = s30..30cm,     Subsoil 2 = s60..60cm,     Subsoil 3 = s100..100cm. To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula. Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020; 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.