Find research datasets worth reusing

Appendix 4 in Morphometry and DNA barcoding reveal cryptic diversity in the genus Enteromius (Cypriniformes: Cyprinidae) from the Congo basin, Africa

Appendix 4. PC loadings of the PCA in Fig. 5. Most important loadings indicated in bold.

Appendix 3 in Morphometry and DNA barcoding reveal cryptic diversity in the genus Enteromius (Cypriniformes: Cyprinidae) from the Congo basin, Africa

Appendix 3. PC loadings of the PCA in Fig. 4. Most important loadings indicated in bold.

Fig. 27 in A new genus and eight new species of tail-wagger snails from eastern South Africa, with a key to genera within Sheldonia s.l. (Gastropoda: Urocyclidae)

Fig. 27. Selatodryas luteosoma gen. et sp. nov., holotype, diameter 10.5 mm (NMSA W3925/T3873).

Fig. 22 in A new genus and eight new species of tail-wagger snails from eastern South Africa, with a key to genera within Sheldonia s.l. (Gastropoda: Urocyclidae)

Fig. 22. Microkerkus sibaya sp. nov., distal genitalia (paratype, NMSA P0658/T4170).

Fig. 23 in A new genus and eight new species of tail-wagger snails from eastern South Africa, with a key to genera within Sheldonia s.l. (Gastropoda: Urocyclidae)

Fig. 23. Selatodryas roseosoma gen. et sp. nov., holotype, diameter 14.9 mm (NMSA W5379/T3865).

opencc-by-3.0Oct 2016View details →

Fig. 14 in New narrow-range endemic land snails from the sky islands of northern South Africa (Gastropoda: Streptaxidae and Urocyclidae)

Fig. 14. Sheldonia wolkbergensis sp. nov., holotype, shell diameter 7.7 mm (NMSA P0156/T4074).

opencc-by-4.0Jun 2020View details →

Open Repositories 2020: Botswana, Ethiopia, Nigeria, South Africa, Tanzania

Recordings of the following presentations and discussion: The National Academic Digital Repository of Ethiopia – Technical, Policy and Governance Aspects – Roberto Barbera, Department of Physics and Astronomy “E. Majorana” of the University of Catania https://doi.org/10.20372/nadre/6373 Overcoming Language Barrier in Open Repositories: A case of Sokoine University of Agriculture – Gilbert Mushi http://doi.org/10.5281/zenodo.3873721 Skills needs assessment for South African open access repository practitioners – Ansie van der Westhuizen, UNISA http://doi.org/10.5281/zenodo.3873723 The NREN and Open Repositories (an ORCID use case in South Africa) – Wesley Barry, TENET | Tertiary Education & Research Network of South Africa Documenting the Ephemeral: Performance Archive and the Showcase Repository – Sanjin Muftic and Jayne Batzofin, The Reimagining Tragedy in Africa and the Global South (RETAGS) project at the University of Cape Town https://doi.org/10.25375/uct.12403937.v1 Evaluation of institutional digital repository contents by postgraduate students of some selected tertiary institutions in Nigeria – Dr. Michael Esew, Kashim Ibrahim Library, Ahmadu Bello University, Okeoghene Mayowa-Adebara, National Open University of Nigeria, Ibadan Study Centre http://doi.org/10.5281/zenodo.3873730 Institutional repository adoption among Nigerian private universities: Institutional factors and willingness to use – Adetomiwa Basiru, Tekena Tamuno Library, Redeemer's University http://doi.org/10.5281/zenodo.3873734 Working towards knowledge accessibility and visibility through collaboration efforts: the case of Botswana International University of Science and Technology (BIUST) library - Ayanda Lebele and Tuelo Ntlotlang http://repository.biust.ac.bw/handle/123456789/133

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Signals interpreted as archaic introgression are driven primarily by accelerated evolution in Africa

Non-African humans appear to carry a few percent archaic DNA due to ancient inter-breeding. This modest legacy and its likely recent timing imply that most introgressed fragments will be rare and hence will occur mainly in the heterozygous state. I tested this prediction by calculating D statistics, a measure of legacy size, for pairs of humans where one of the pair was conditioned always to be either homozygous or heterozygous. Using coalescent simulations, I confirmed that conditioning the non-African to be heterozygous increased D while conditioning the non-African to be homozygous reduced D to zero. Repeating with real data reveals the exact opposite pattern. In African – non-African comparisons, D is near-zero if the African individual is held homozygous. Conditioning one of two Africans to be either homozygous or heterozygous invariably generates large values of D, even when both individuals are drawn from the same population. Invariably, the African with more heterozygous sites (conditioned heterozygous > unconditioned > conditioned homozygous) appears less related to the archaic. In contrast, the same analysis applied to pairs of non-Africans always yields near-zero D, showing that conditioning does not create large D without an underlying signal to expose. Large D values in humans are therefore driven almost entirely by heterozygous sites in Africans acting to increase divergence from related taxa such as Neanderthals. In comparison with heterozygous Africans, individuals that lack African heterozygous sites, whether non-African or conditioned homozygous African, always appear more similar to archaic outgroups, a signal previously interpreted as evidence for introgression. I hope these analyses will encourage others to consider increased divergence as well as increased similarity to archaics as mechanisms capable of driving asymmetrical base-sharing.

opencc-zeroJun 2020View details →

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

iSDAsoil dataset soil total carbon in permilles (g/kg) 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.c_tot_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil total Carbon mean value,</li> <li>sol_log.c_tot_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil total Carbon 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.c_tot R-square: 0.794 Fitted values sd: 0.571 RMSE: 0.291 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -2.70312 -0.16714 -0.00549 0.15691 3.01116 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.025841 0.032713 0.790 0.429570 regr.ranger 0.902240 0.008462 106.619 < 2e-16 *** regr.xgboost 0.066535 0.008145 8.169 3.18e-16 *** regr.cubist 0.145730 0.006927 21.039 < 2e-16 *** regr.nnet -0.048957 0.013466 -3.636 0.000278 *** regr.cvglmnet -0.075212 0.005556 -13.537 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.291 on 50140 degrees of freedom Multiple R-squared: 0.7938, Adjusted R-squared: 0.7938 F-statistic: 3.861e+04 on 5 and 50140 DF, p-value: < 2.2e-16 </code></pre> To back-transform values (y) to g/kg use the following formula: <pre><code>g/kg = 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 Aluminium for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

iSDAsoil dataset soil extractable Aluminium (Al) 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>, <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_log.al_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Aluminium mean value,</li> <li>sol_log.al_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Aluminium 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.al_mehlich3 R-square: 0.881 Fitted values sd: 0.872 RMSE: 0.321 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -5.7042 -0.1036 0.0059 0.1189 3.3777 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.675492 2.771906 -0.244 0.807 regr.ranger 0.879567 0.005464 160.969 <2e-16 *** regr.xgboost 0.071537 0.005813 12.306 <2e-16 *** regr.cubist 0.150157 0.004553 32.979 <2e-16 *** regr.nnet 0.087603 0.431261 0.203 0.839 regr.cvglmnet -0.084440 0.003182 -26.534 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3208 on 63551 degrees of freedom Multiple R-squared: 0.8808, Adjusted R-squared: 0.8808 F-statistic: 9.391e+04 on 5 and 63551 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 Potassium for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

iSDAsoil dataset soil extractable Potassium (K) 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.k_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Potassium mean value,</li> <li>sol_log.k_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Potassium 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.k_mehlich3 R-square: 0.773 Fitted values sd: 0.938 RMSE: 0.509 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -4.3088 -0.2648 -0.0037 0.2639 6.8136 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 10.907726 6.134422 1.778 0.0754 . regr.ranger 1.004487 0.003878 259.026 <2e-16 *** regr.xgboost -0.004081 0.004739 -0.861 0.3892 regr.cubist 0.084556 0.004346 19.454 <2e-16 *** regr.nnet -2.205286 1.228586 -1.795 0.0727 . regr.cvglmnet -0.064510 0.003933 -16.401 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.5092 on 139122 degrees of freedom Multiple R-squared: 0.7725, Adjusted R-squared: 0.7725 F-statistic: 9.451e+04 on 5 and 139122 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 effective Cation Exchange Capacity (eCEC) for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

iSDAsoil dataset soil effective Cation Exchange Capacity (eCEC) 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.ecec.f_tot_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil eCEC mean value,</li> <li>sol_log.ecec.f_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil eCEC 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.ecec.f R-square: 0.754 Fitted values sd: 0.729 RMSE: 0.417 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -3.2877 -0.1888 0.0097 0.2023 3.1494 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.807991 1.806781 1.554 0.1202 regr.ranger 1.046105 0.004845 215.911 < 2e-16 *** regr.xgboost -0.016558 0.005912 -2.801 0.0051 ** regr.cubist 0.031843 0.005063 6.289 3.21e-10 *** regr.nnet -1.142820 0.713071 -1.603 0.1090 regr.cvglmnet -0.027630 0.005607 -4.928 8.34e-07 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.4166 on 66380 degrees of freedom Multiple R-squared: 0.7538, Adjusted R-squared: 0.7538 F-statistic: 4.065e+04 on 5 and 66380 DF, p-value: < 2.2e-16 </code></pre> To back-transform values (y) to cmol(+)/kg use the following formula: <pre><code>cmol(+)/kg = 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>

opencc-zeroOct 2020View details →

dryad36/100

Opportunities and challenges in achieving co-management in marine protected areas in East Africa: a comparative case study

As marine ecosystems decline globally, scientists recommend increasing the coverage of marine protected areas (MPAs), but many are not effectively managed to deliver benefits. Community integration into MPA decision-making can increase management effectiveness by supporting behaviour change, but this poses implementation challenges. We examine differences in adaptive capacity, community engagement, and perceived MPA benefits using interviews and focal group meetings with two fishing communities from MPAs with different management strategies and geographic settings: a centrally managed MPA in Kenya and a co-managed MPA in Tanzania. We hypothesized that perceptions of MPA benefits and MPA support would be higher among fishers and fish vendors in the more collaboratively designed system in Tanzania, and supported by greater adaptive capacity. Indeed, far fewer Kenyan participants (37%) felt they benefited from the MPA compared to Tanzanian participants (95%). However, agency and trust were largely similar and challenges existed in both systems that reduced collaborative action including low interaction/communication between staff and community, internal leadership challenges, and social conflict. We identified pathways towards improved co-management that transcend systems: institutional prioritization of community integration, investment in community leadership, mapping social networks during MPA establishment, and adequate staffing and operational budgets.

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

iSDAsoil dataset soil organic carbon (C) 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.oc_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil organic Carbon mean value,</li> <li>sol_log.oc_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil organic Carbon 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.oc R-square: 0.791 Fitted values sd: 0.716 RMSE: 0.369 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -3.1517 -0.1900 -0.0060 0.1793 4.2621 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.821657 0.794000 2.294 0.0218 * regr.ranger 1.047507 0.005146 203.571 <2e-16 *** regr.xgboost -0.005943 0.005340 -1.113 0.2657 regr.cubist 0.052084 0.004884 10.664 <2e-16 *** regr.nnet -0.867384 0.359213 -2.415 0.0158 * regr.cvglmnet -0.050157 0.003863 -12.984 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3687 on 122457 degrees of freedom Multiple R-squared: 0.7906, Adjusted R-squared: 0.7906 F-statistic: 9.248e+04 on 5 and 122457 DF, p-value: < 2.2e-16 </code></pre> To back-transform values (y) to g/kg use the following formula: <pre><code>g/kg = 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 Zinc for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

iSDAsoil dataset soil extractable Zinc (Zn) 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.zn_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Zinc mean value,</li> <li>sol_log.zn_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil extractable Zinc 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.zn_mehlich3 R-square: 0.711 Fitted values sd: 0.588 RMSE: 0.375 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -2.1382 -0.2038 -0.0274 0.1632 3.6353 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.836394 1.555108 1.181 0.23766 regr.ranger 0.823144 0.013982 58.871 < 2e-16 *** regr.xgboost 0.037861 0.013644 2.775 0.00552 ** regr.cubist 0.169653 0.010091 16.813 < 2e-16 *** regr.nnet -1.511745 1.244933 -1.214 0.22463 regr.cvglmnet 0.009770 0.008046 1.214 0.22467 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3747 on 39344 degrees of freedom Multiple R-squared: 0.7109, Adjusted R-squared: 0.7109 F-statistic: 1.935e+04 on 5 and 39344 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 texture class (USDA system) for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

iSDAsoil dataset soil texture classes derived from sand, silt and clay fractions 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_texture.class_c_30m_*..*cm_2001..2017_v0.13_wgs84.tif = soil texture class,</li> </ul> Classes: <pre><code>Code,Name,Value,Color Cl,clay,1,#d5c36b SiCl,silty clay,2,#b96947 SaCl,sandy clay,3,#9d3706 ClLo,clay loam,4,#ae868f SiClLo,silty clay loam,5,#f86714 SaClLo,sandy clay loam,6,#46d143 Lo,loam,7,#368f20 SiLo,silt loam,8,#3e5a14 SaLo,sandy loam,9,#ffd557 Si,silt,10,#fff72e LoSa,loamy sand,11,#ff5a9d Sa,sand,12,#ff005b NODATA,,255,#ffffff </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 sand content (USDA system) for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

iSDAsoil dataset soil sand content 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_sand_tot_psa_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil sand content mean value,</li> <li>sol_sand_tot_psa_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil sand 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: sand_tot_psa R-square: 0.736 Fitted values sd: 22.8 RMSE: 13.7 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -80.626 -5.321 0.221 6.071 88.686 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.687471 24.022001 0.278 0.780714 regr.ranger 1.060521 0.003503 302.742 < 2e-16 *** regr.xgboost -0.018718 0.004910 -3.812 0.000138 *** regr.cubist 0.031749 0.003922 8.096 5.73e-16 *** regr.nnet -0.161127 0.422746 -0.381 0.703098 regr.cvglmnet -0.028217 0.004462 -6.323 2.57e-10 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 13.65 on 122261 degrees of freedom Multiple R-squared: 0.736, Adjusted R-squared: 0.736 F-statistic: 6.818e+04 on 5 and 122261 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 total organic Nitrogen for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

iSDAsoil dataset soil total organic Nitrogen (N) 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.n_tot_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil total N mean value,</li> <li>sol_log.n_tot_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil total N 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.n_tot_ncs R-square: 0.732 Fitted values sd: 0.326 RMSE: 0.197 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -1.87298 -0.09584 -0.00985 0.07613 3.14728 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.267429 0.493235 0.542 0.588 regr.ranger 1.128208 0.005766 195.669 < 2e-16 *** regr.xgboost -0.048780 0.006108 -7.987 1.4e-15 *** regr.cubist 0.143954 0.004424 32.539 < 2e-16 *** regr.nnet -0.482261 0.797938 -0.604 0.546 regr.cvglmnet -0.170889 0.004955 -34.489 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.1972 on 99249 degrees of freedom Multiple R-squared: 0.7319, Adjusted R-squared: 0.7319 F-statistic: 5.419e+04 on 5 and 99249 DF, p-value: < 2.2e-16</code></pre> To back-transform values (y) to g/kg use the following formula: <pre><code>g/kg = expm1( y / 100 )</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 silt content (USDA system) for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths

iSDAsoil dataset soil silt content 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_silt_tot_psa_mehlich3_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil silt content mean value,</li> <li>sol_silt_tot_psa_mehlich3_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil silt 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: silt_tot_psa R-square: 0.64 Fitted values sd: 11.9 RMSE: 8.92 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -63.746 -3.631 -0.526 2.630 72.486 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -35.876865 36.887592 -0.973 0.331 regr.ranger 0.948111 0.003874 244.733 < 2e-16 *** regr.xgboost 0.062717 0.005506 11.391 < 2e-16 *** regr.cubist 0.025705 0.004747 5.415 6.14e-08 *** regr.nnet 1.902142 1.968248 0.966 0.334 regr.cvglmnet -0.028579 0.005799 -4.928 8.32e-07 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 8.915 on 122223 degrees of freedom Multiple R-squared: 0.6399, Adjusted R-squared: 0.6399 F-statistic: 4.344e+04 on 5 and 122223 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>

Fig. 8 in Deep-water Photinae (Gastropoda: Nassariidae) from eastern Africa, with descriptions of five new species

Fig. 8. Distribution of Phos ganii sp. nov. and P. geminus sp. nov.