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11,982 results for “africa”
Anyskop Blowout Prehistoric Dataset, Western Cape, South Africa
<p>These Stone Age archaeological datasets were collected in 2001 and 2002 by a team from the Department of Early Prehistory and Quaternary Ecology of the University of Tübingen (Germany) headed by Nicholas J. Conard. Many South African researchers collaborated on this project, with Pippa Haarhoff, John Compton, Dave Roberts, and Stephan Woodborne deserving special mention.</p> <p>The field work took place at the Anyskop Blowout (ANY1) located within the West Coast Fossil Park near Langebaanweg, Western Cape, South Africa. The field work was conducted with the help of students from the universities of Tübingen and Cape Town. The datasets are predominantly in English (with some German as well) and include field data in the MAIN table. Further analytical data for many classes of artifacts include: LITHICS, FAUNA, POTTERY, MODERN, BUCKETS, REFITS.</p> <p>All collected materials are curated by the Iziko South African Museums in Cape Town under accession numbers SAM-AA-8903 (finds collected by other teams before 2001) and SAM-AA-9007 (finds from this study, 2001-2002). Some of the finds are exhibited in the museum at the West Coast Fossil Park.</p> <p>Funding for this research project came mainly from the German Research Foundation (DFG - CO 226/5-1, 5-2, 5-5 and 5-6) and the University of Tübingen. Significant support was provided by the Iziko South African Museums, the West Coast Fossil Park, and the University of Cape Town.</p>
Woody vegetation composition and structure at long-term monitoring plots on the Stevenson-Hamilton Research Supersite, Kruger National Park, South Africa (2012)
This dataset contains measurements of woody vegetation composition and structural attributes collected in 2012 from long-term ecological monitoring plots located on the Stevenson-Hamilton Research Supersite in the Kruger National Park, South Africa. The study region is characterized by granitic soils, broad-leaved savanna vegetation, and a long history of fire, herbivory, and climate-driven ecological dynamics. Vegetation surveys were conducted in sixteen 0.25-ha sampling plots to quantify woody species composition, stem density, and size structure. Additional measurements of vegetation structure were collected, including grass biomass, canopy cover, canopy height, and canopy diversity, providing a broader assessment of both woody and herbaceous layers. These data establish an important baseline for monitoring ecological change, evaluating woody vegetation dynamics under variable fire and herbivore regimes, and supporting ongoing research on savanna ecosystem functioning within the Kruger National Park.
Hoedjiespunt Middle Stone Age Dataset, Western Cape, South Africa
<p>This Middle Stone Age archaeological dataset from Hoedjiespunt 1 was collected in 2011 by a team from the Department of Early Prehistory and Quaternary Ecology of the University of Tübingen (Germany) headed by Nicholas J. Conard. South African and European researchers collaborated on this project, with John E. Parkington, Katherine Kyriacou, Deano Stynder, Graham Avery, and Chantal Tribolo making substantial contributions. The site is located within the property of Transnet National Ports Authority in the municipality of Saldanha, Western Cape, South Africa.</p> <p>The locality of Hoedjiespunt 1 was well known as a paleontological site since at least the 1990s, when the site yielded several important Middle Pleistocene hominin remains dated between 200,000 and 350,000 years. The paleontological site also yielded a well preserved assemblage of fauna, including terrestrial and marine mammals, shellfish and ostrich eggshell. The excavators interpreted the accumulation of these finds as the remains of a hyena den. Cultural remains such as lithic artifacts were absent from the paleontological site, which is situated immediately below the archaeological site.</p> <p>The 2011 field work at the archaeological site of Hoedjiespunt 1 took place with the help of students from the universities of Tübingen and Cape Town. The datasets are predominantly in English (with some parts in German) and include field data in the MAIN table. Further analytical data for several classes of artifacts include: LITHICS, FAUNA, OCHRE, and BUCKETS.</p> <p>All of the archaeological materials collected in 2011 are curated by the Department of Archaeology of the University of Cape Town in Rondebosch, South Africa. Funding for this research came mainly from the Heidelberg Academy of Sciences and Humanities and the University of Tübingen. Significant support was provided by the Department of Archaeology of the University of Cape Town and the Iziko South African Museums.</p> <p> </p> <p>Importnat references for the paleontological excavations are listed here, while the main publications associated with the 2011 excavations are presented below in the reference section: </p> <p>Berger, L.R. & Parkington, J.E. (1995). A new Pleistocene hominid-bearing locality at Hoedjiespunt, South Africa. American Journal of Physical Anthropology 98: 601-609. <a href="https://doi.org/10.1002/ajpa.1330980415">https://doi.org/10.1002/ajpa.1330980415</a></p> <p>Churchill, S.E., Berger, L.E. & Parkington, J.E. (2000). A Middle Pleistocene human tibia from Hoedjiespunt, Western Cape, South Africa. South African Journal of Science 96: 367-368. <a href="https://hdl.handle.net/10520/AJA00382353_8943">https://hdl.handle.net/10520/AJA00382353_8943</a> </p> <p>Stynder, D.D., Moggi-Cecchi, J. Berger, R.L. & Parkington, J.E. (2001). Human mandibular incisors from the late Middle Pleistocene locality of Hoedjiespunt 1, South Africa. Journal of Human Evolution 41: 369-383. <a href="https://doi.org/10.1006/jhev.2001.0488">https://doi.org/10.1006/jhev.2001.0488</a></p>
Data for SARS-CoV-2 Reinfection Trends in South Africa: Monthly Report (2022-12-07)
<p>This version contains a single file, with time series data for the most recent <a href="https://www.nicd.ac.za/diseases-a-z-index/disease-index-covid-19/surveillance-reports/sarscov2-reinfection-trends-in-south-africa-monthly-report/">monthly report on SARS­-CoV-­2 Reinfection Trends in South Africa</a>:</p> <ul> <li><code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>)</li> </ul> <p>Note: There may be some inconsistencies with the numbers of infections through time in earlier versions of this data set due to back-filling of late-arriving data.</p> <p> </p> <p>Note: Earlier versions of this data set included data files for Pulliam, JRC, C van Schalkwyk, B Lombard, N Govender, A von Gottberg, C Cohen, MJ Groome, J Dushoff, K Mlisana, and H Moultrie. <a href="https://www.science.org/doi/10.1126/science.abn4947">Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa</a>. DOI: 0.1126/science.abn4947</p> <p>For code and more details see: <a href="https://github.com/jrcpulliam/reinfections/releases/tag/v3.0">https://github.com/jrcpulliam/reinfections/releases/tag/v3.0</a> or <a href="https://zenodo.org/record/6108448">10.5281/zenodo.6108448</a></p> <p>The version of this data set associated with the publication (available via the links above) included the following files:</p> <ul> <li><code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>)</li> <li><code>demog_data.csv</code> - counts of individuals eligible for reinfection (<code>total</code>), who have 0 suspected reinfections (<code>no_reinf</code>) or >0 suspected reinfections (<code>reinf</code>) by province (<code>province</code>), age group (5-year bands, <code>agegrp5</code>), and sex (M = Male, F = Female, U = Unknown, <code>sex</code>)</li> <li><code>posterior_90_null.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript)</li> <li><code>sim_90_null.RDS</code> - simulation results (as used in the manuscript)</li> <li><code>emp_haz_sens_an.RDS</code> - output of sensitivity analysis of relative empirical hazard estimation to assumed observation probabilities (as used in the manuscript)</li> </ul>
iSDAsoil: soil clay content (USDA system) for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>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:</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_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> <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: 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> <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>
iSDAsoil: soil extractable Iron for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>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:</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_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> <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: 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> <p>To back-transform values (y) to ppm use the following formula:</p> <pre><code>ppm = expm1( 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>
iSDAsoil: soil extractable Calcium for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>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:</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_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> <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: 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> <p>To back-transform values (y) to ppm use the following formula:</p> <pre><code>ppm = expm1( 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>
iSDAsoil: soil fine-earth bulk density for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>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:</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_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> <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: 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> <p>To back-transform values (y) to kg/m-cubic use:</p> <pre><code>kg/m3 = 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>
iSDAsoil: soil extractable Phosphorus for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>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:</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_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> <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: 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> <p>To back-transform values (y) to ppm use the following formula:</p> <pre><code>ppm = expm1( 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>
iSDAsoil: soil extractable Magnesium for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>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:</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_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> <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: 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> <p>To back-transform values (y) to ppm use the following formula:</p> <pre><code>ppm = expm1( 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>
iSDAsoil: soil stone content for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>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:</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_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> <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: 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> <p>To back-transform values (y) to % use the following formula:</p> <pre><code>% = expm1( 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>
iSDAsoil: soil extractable Sulphur for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>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:</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_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> <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: 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> <p>To back-transform values (y) to ppm use the following formula:</p> <pre><code>ppm = expm1( 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>
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>
Supplementary Table S27.1: Animal species native to South Africa that have invasive populations elsewhere.
<p>Animal species native to South Africa that have invasive populations elsewhere. Sorted by expected chronological appearance in the first place they were recorded as alien species. Notes are made on whether the introduction is known to be (Y) or not (N) from South Africa (or unknown U). Pathways are according to the CBD pathway classification scheme (Harrower et al. 2017), along with an indication of whether the introduction was intentional or accidental. Species that have multi-continental distributions, and which may in addition have some introduced populations are shown at the end of the table.</p>
Dataset of nitrogen in rivers and streams in sub-Saharan Africa
<p>This is a dataset on concentrations and export of all nitrogen compounds in rivers and streams in sub-Saharan Africa reported in scientific literature (<em>n</em>=254) until July 2024. Data are aggregated by site and, where possible, data are reported for the dry and wet season separately. In addition to concentrations and export of nitrogen compounds, data on ancillary parameters, such as pH, electrical conductivity and dissolved oxygen area also included. Each site for which (approximate) coordinates could be extracted from the original study was assigned to a land cover class based on open source data on tree cover and the extent of cropland, settlement and wetlands across Africa (see second tab in the file 'Dataset.xlsx' for land cover classes and corresponding classification conditions as well as data sources). The third tab in the file 'Dataset.xlsx' contains links to the individual studies and full references are provided in the file 'Reference list.pdf'.</p>
Water quality data (River sediment, Nitrogen and Phosphorus loads) for Africa
<p>Output data on African water quality and scripts for preprint - "One third of African rivers fail to meet the 'good ambient water quality' nutrient targets" at <a href="https://dx.doi.org/10.2139/ssrn.4829742">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4829742</a> . Please check the readme file for data description. The data includes river flow, sediment load, nitrogen and Phosphorus loads for Africa at daily and yearly time scale. This work is currently under review in Ecological Indicators journal. </p>
Raw Data for Mapping Repositories and their Institutional Open Science Policies in the Middle East and North Africa (MENA)
<div> <p>Persistent Identifiers (PIDs), particularly Digital Object Identifiers (DOIs), are crucial for establishing a robust and globally accessible research infrastructure. In the Middle East and North Africa (MENA) region, a diverse array of research outputs and resources are produced and published in repositories. However, a significant number of these repositories, and outputs remain undiscoverable in global registries and aggregators. <br><br>These three datasets provides comprehensive information on the adoption of repositories, Open Access mandates, and DOIs adoption in MENA countries. It includes detailed records from different registry sources and repository platforms.<br><br>You can read the full report titled 'Mapping Repositories and their Institutional Open Science Policies in MENA' at <a href="https://doi.org/10.5281/zenodo.11370031">https://doi.org/10.5281/zenodo.11370031</a></p> </div>
MSG SEVIRI NDVI dataset for the Horn of Africa 2005-2023
<p>Dataset related to the paper "A high temporal resolution NDVI time series to monitor drought events in the Horn of Africa". The dataset does not contain the bias correction explained in the paper but be can easily applied using the provided formula. The dataset is for the countries Kenya, Ethiopia, Djibouti and Somalia.</p>
Datasets for greenhouse gasses emissions and removals from inventories and global models over Africa
<p>This file includes the data from Mostefaoui et al. (ESSD, under submission), for 54 countries African countries</p> <p> The data includes: </p> <p>(1) CO2 fluxes from global models - satellite inversions and Dynamic Global Vegetation Models (DGVM) -, and from a collection of national inventories for LULUCF, GFEDv4 and FAO data.</p> <p> DGVM values are the median of 14 models, consistent with the Global Carbon Budget 2020 (https://essd.copernicus.org/articles/12/3269/2020/) LULUCF UNFCCC corrected values are from Grassi <a href="https://priv-bx-myremote.tech.ec.europa.eu/preprints/essd-2022-104/,DanaInfo=.aetugDhuwm0xto76O48y,SSL+">https://essd.copernicus.org/preprints/essd-2022-104/</a> </p> <p>(2) CH4 fluxes from global models consistent with the Global Methane Budget 2020 (https://essd.copernicus.org/articles/12/1561/2020/)</p> <p>(3 N2O fluxes from global models (three inversions)</p> <p>For further methodological details, see Mostefaoui et al. (ESSD, under submission):</p> <p>Mounia Mostefaoui, Philippe Ciais, Matthew J. McGrath, Philippe Peylin, Prabir Patra. Greenhouse gasses emissions and their trends over the last three decades across Africa, ESSD (under submission)</p>
Qualitative Data on 60 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain)
<h2><span lang="EN-US">Dataset name</span></h2> <p><span lang="EN-US">Small_Scale_Fishery_Data_2023_v2 </span><span lang="EN-US"> </span></p> <h2><span lang="EN-US">Title</span></h2> <p><span lang="EN-US">Qualitative Data on 60 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain). </span><span lang="EN-US"> </span></p> <h2><span lang="EN-US">Description </span></h2> <p><span lang="EN-US"> This dataset was created for the Fish2Sustainability research project, which aims to evaluate how small-scale fisheries (SSF) contribute to Sustainable Development Goals (SDGs). The dataset includes 60 case studies across eight countries and was developed using a rapid appraisal framework. The framework includes a four-step process: </span></p> <p><span lang="EN-US"> 1. Identifying specific SDG targets influenced by SSF;</span></p> <p><span lang="EN-US"> 2. Extracting relevant variables from UN indicators;</span></p> <p><span lang="EN-US"> 3. Gathering expert input via a questionnaire to score these variables;</span></p> <p><span lang="EN-US"> 4. Creating composite indicators to measure SSF performance against SDGs.</span></p> <p><span lang="EN-US"> The dataset contains raw data from step 3, case study details, variable scores, and comments from data collectors (contributing authors). The dataset is valuable for researchers interested in small-scale fisheries and socio-ecological systems. By incorporating expert judgments from individuals with expertise in SSF, particularly in data-poor contexts, the dataset offers a wealth of knowledge for conducting comparative analyses across different contexts.</span><span lang="EN-US"> </span></p> <h2><span lang="EN-US">Authors </span></h2> <p><span lang="EN-US">Léopold, M.1, Bitoun, R.E.2, Beckensteiner, J.3, Chuenpagdee, R.4, Fondo, E.N.5, Akintola, S.L.6, Bach, P.7, Frangoudes, K.8, Gaibor, N.9, Gutierrez-Cala, L.10, Massey, Y.7, Randrianandrasana, R.11, Razanakoto, T.11, Saavedra-Díaz, L.M.10, Schreiber Arias, M.12,13, Salas, S.14, Devillers, R.2,4 </span></p> <h3><span lang="EN-US">Affiliations </span></h3> <p><span lang="EN-US">1 ENTROPIE (IRD, University of La Reunion, CNRS, University of New Caledonia, Ifremer), c/o IUEM, Plouzané, France </span></p> <p><span lang="EN-US">2 Espace-Dev (IRD, Univ. </span>Montpellier, Univ. Guyane, Univ. La Réunion, Univ. Antilles, Univ. Nouvelle Calédonie), Montpellier, France</p> <p>3 AMURE (Ifremer, UBO, CNRS), Plouzané, France</p> <p><span lang="EN-US">4 Department of Geography, Memorial University of Newfoundland, St. John’s, NL, Canada</span></p> <p><span lang="EN-US">5 Kenya Marine and Fisheries Research Institute, Mombasa, Kenya</span></p> <p><span lang="EN-US">6 Department of Fisheries, Faculty of Science, Lagos State University, Nigeria</span></p> <p><span lang="EN-US">7 MARBEC, University of Montpellier, CNRS, Ifremer, IRD, Sète, France</span></p> <p>8 Université de Bretagne Occidentale: Brest, France</p> <p>9 Instituto Público de Investigación de Acuicultura y Pesca (IPIAP), Universidad del Pacifico (UPAC), Guayaquil, Ecuador</p> <p>10 Grupo de Investigación en Sistemas Socioecológicos para el Bienestar Humano (GISSBH), Programa de Biología, Universidad del Magdalena, Colombia</p> <p>11 Centre d’Etudes et de Recherches Economiques pour le Développement (CERED), Université d’Antananarivo, Madagascar</p> <p><span lang="EN-US">12 EqualSea Lab, Universidad Santiago de Compostela, A Coruña, Spain</span></p> <p><span lang="EN-US">13 School of Global Studies, University of Gothenburg, Gothenburg, Sweden</span></p> <p>14 Centro de Investigación y de Estudios Avanzados (CINVESTAV), IPN, Unidad Mérida, Mexico </p> <h2><span lang="EN-US">Method </span></h2> <p><span lang="EN-US">Case studies were selected in eight countries by national SSF experts, based on specific criteria and research priorities. Case studies were not selected to represent the full diversity of SSF globally or even nationally. Instead, they were chosen to capture a range of fisheries that could showcase different contributions to SDGs. SSF were defined based on various characteristics, such as resources harvested, gear used, and location of the fishery. </span><span lang="EN-US"> </span></p> <h3><span lang="EN-US">Geographical Coverage </span></h3> <p><span lang="EN-US">60 small-scale fisheries located in seven countries are documented in the data:</span></p> <ul> <li><span lang="EN-US">Colombia (4 case studies) – Pacifico: La Guajira, San Andrés y Providencia; Caribe: Chocó, Cauca, Valle del Cauca, Nariño.</span></li> <li><span lang="EN-US">Ecuador (3) – Region: Esmeraldas, Manabi, Guayas, El Oro.</span></li> <li><span lang="EN-US">France (2) – Region: Bretagne, Occitanie.</span></li> <li><span lang="EN-US">Kenya (22) – County: Kilifi, Kwale, Lamu, Mombasa, Tana River.</span></li> <li><span lang="EN-US">Madagascar (20) – Region: Analanjirofo, Anosy, Atsimo Andrefana, Boeny, Diana, Menabe, Vatovavy Fitovinany.</span></li> <li><span lang="EN-US">Mexico (2) – State: Baja California Sur, Campeche, Yucatan.</span></li> <li><span lang="EN-US">Nigeria (6) – State: Bayelsa, Cross River, Lagos, Ondo, Ogun. </span></li> <li><span lang="EN-US">Spain (1) – State: Galicia.</span> </li> </ul> <h3><span lang="EN-US">Data Collection </span></h3> <p><span lang="EN-US">Data collection took place from November 30, 2022, to July 3, 2023, spanning approximately seven months. The data presented serve as a snapshot of the conditions within a specific small-scale fishery during the assessment period. To consider the evolution of trends such as exports, economic growth, and income, we considered any relevant variables over the past decade. </span></p> <p><span lang="EN-US">Data collection approaches varied depending on the context, and data collectors received training to ensure survey consistency. We used primary data sources such as interviews, observations, and measurements whenever possible. In cases where resources were limited, we preferred secondary sources such as existing datasets and literature. Our methods were standardized, but data collectors could adjust them based on their resources. We primarily used direct observation, focus groups, and interviews to collect data. Scoring in interviews and focus groups was done directly or through group analysis by interviewers. Disagreements were resolved through additional interviews or group discussions, with secondary data used if needed. Please refer to the methods in : </span></p> <p><strong><span lang="EN-US">Bitoun et al., (2024). A methodological framework for capturing marine small-scale fisheries’ contributions to the sustainable development goals. Sustainability Science, 19(4), 1119–1137. https://doi.org/10.1007/s11625-024-01470-0. </span></strong><span lang="EN-US"><strong> </strong> </span></p> <h3><span lang="EN-US">Ethics </span></h3> <p><span lang="EN-US">Participants had the option to join of their own accord, were fully briefed on the research goals, and were given the opportunity to review interview guidelines before proceeding. Depending on the circumstances, interviews could last 45 minutes to 4.5 hours. Participants were guaranteed confidentiality and anonymity in the handling and reporting of their data.</span><span lang="EN-US"> </span></p> <h3><span lang="EN-US">Suggested citation</span></h3> <p><span lang="EN-US">Léopold, M., Bitoun, R., & Devillers, R. (2023). Qualitative Data on 61 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain) (Version 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.16077739</span></p> <h2><span lang="EN-US">Data Files</span></h2> <p><span lang="EN-US">The dataset includes the following:</span></p> <ul> <li><span lang="EN-US">The raw dataset (.xls format).</span></li> <li><span lang="EN-US">A data dictionary describing and defining each dataset column (.xls format).</span></li> </ul>
ScienceDex guides
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