cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.csag.10000151
title: Mean of Maximum Temperature for Current Climate (1950-1999)

description: Model Run: Control - Current Climate (Observed/current climate as modelled by climate model). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.csag.10000148
title: Number of days for Maximum Temperature under 10th percentile for Current Climate (1950-1999)

description: Model Run: Control - Current Climate (Observed/current climate as modelled by climate model). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.20230928
title: Hazard Data Sources

description: This dataset comprises a compilation of databases and sources related to various natural hazard events. The dataset is organised as an Excel spreadsheet, with data sources categorised according to their geographical coverage, which is delineated into distinct demarcations including local municipality, district municipality, provincial, national, and continental levels. Within the dataset, users will find links to the respective databases and sources, facilitating their ability to further explore the data holdings of the individual sites. It is essential to emphasize that certain data sources may appear duplicated across multiple sheets, as they provide data spanning different spatial extents. Moreover, in instances where a data source reports on the occurrence of multiple types of hazardous events, the Hazard Type category is designated as Natural hazard under the corresponding tab. Conversely, databases exclusively addressing a single category of hazardous events, such as floods, wildfires, droughts, and others, are appropriately labeled to reflect their specialised focus.
DOI: 10.15493/sarva.csag.10000113
title: Total precipitation for the month (mm) for Near Future (2046-2065)

description: Model Run: Near future (2046 - 2065) (Near future (2046 - 2065)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.saws.10000103
title: Seasonal total rainfall (mm per 3-month season) change from the 90% percentile projected for 2066-2095, relative to present (1976 - 2005), for MAM season under the RCP 4.5 pathway

description: Seasonal (MAM) mean rainfall (mm per month) from the 90% percentile projected for 2066-2095, relative to present (1976 - 2005), under the RCP 4.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily rainfall averages, which are used to generate projections of seasonal change. The projections are generated using the medium to low (RCP4.5) pathway which associates CO2 concentrations of approximately 560ppm by the year 2100. The associated RMSD it calculated and shows the uncertainty range of the projected model simulated residual values, and gives a relative perspective of spatial areas associated with higher and lower projection uncertainties.
DOI: 10.15493/dea.carbon.10000117
title: Total carbon per land unit for 1990 in t/ha

description: Total carbon per land unit includes the soil organic carbon as calculated from ISRIC soil data as well as the carbon computed from  biomass for the herbaceous, woody and litter vegetation components. NLC1990 was used to allocated the total carbon proportionally to different land cover classes.
DOI: 10.15493/sarva.dws.10000011
title: River profile classification Catchment H

description: Longitudinal slope profiles derived for all 1:500 000 rivers of South Africa, including cross-border catchments extending into neighbouring countries. River main stems are identified by a unique reach number (REACHNUM) within each primary catchment.
DOI: 10.15493/sarva.beeh.10000391
title: Vapour Pressure Deficit (kPa) June

description: Daily values of vapour pressure deficit (VPD) are, however, required far more frequently, particularly when modelling crop potential evaporation using the Penman- Monteith equation (Penman, 1948). In this section a method used to estimate VPD is outlined, the method is verified and then maps and statistics of VPD are presented.

The hypothesis in estimating daily values of VPD at any specified location in South Africa is that actual VP, actual vapour pressure, which is considered to be a conservative climate element, and which may be derived from the monthly regression equations be held constant at that location for a given month. The fluctuating day-to-day daily temperature values at that location are then used with the Tetens (1930) formula given above to calculate a daily saturated VP, saturated vapour pressure. From actual vapour pressure and saturated vapour pressure daily values of VPD (and RH, if desired) can then be computed. This approach is known as the Schulze-Chapman model.
DOI: 10.15493/sarva.csag.10000407
title: Minimum of Minimum Temperature for Current Climate (1950-1999)

description: Model Run: Control - Current Climate (Observed/current climate as modelled by climate model). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/dea.carbon.10000068
title: South African Land Cover 2014 Class B Wetlands Resampled to a 1km Grid

description: The 2013-14 South African National land cover dataset produced by (© GEOTERRAIMAGE - 2014) as a commercial data product was generated from digital, multi-seasonal Landsat 8 multispectral imagery, acquired between April 2013 and March 2014. In excess of 600 Landsat images were used to generate the land cover information, based on an average of 8 different seasonal image acquisition dates, within each of the 76 x image frames required to cover South Africa. The land cover dataset, which covers the whole of South Africa, is presented in a map-corrected, raster format, based on 30x30m cells equivalent to the image resolution of the source Landsat 8 multi-spectral imagery. The dataset contains 72 x land cover / use information classes, covering a wide range of natural and man-made landscape characteristics. The original land cover dataset was processed in UTM (north) / WGS84 map projection format based on the Landsat 8 standard map projection format as provided by the USGS. Class 3 of the 2013-14 South African National land cover dataset was used to derive South African land cover 2014 Class B Wetlands for the current product and resampled to a 1000 x 1000m grid resolution Albers Equal Area map projection +proj=aea +lat_1=-22 +lat_2=-38 +lat_0=-30 +lon_0=25 +x_0=1400000 +y_0=1300000 +datum=WGS84 +units=m +no_defs
DOI: 10.15493/dea.carbon.10000080
title: South African Land Cover 2014 Class N Mines Resampled to a 1km Grid

description: The 2013-14 South African National land cover dataset produced by (© GEOTERRAIMAGE - 2014) as a commercial data product was generated from digital, multi-seasonal Landsat 8 multispectral imagery, acquired between April 2013 and March 2014. In excess of 600 Landsat images were used to generate the land cover information, based on an average of 8 different seasonal image acquisition dates, within each of the 76 x image frames required to cover South Africa. The land cover dataset, which covers the whole of South Africa, is presented in a map-corrected, raster format, based on 30x30m cells equivalent to the image resolution of the source Landsat 8 multi-spectral imagery. The dataset contains 72 x land cover / use information classes, covering a wide range of natural and man-made landscape characteristics. The original land cover dataset was processed in UTM (north) / WGS84 map projection format based on the Landsat 8 standard map projection format as provided by the USGS. Class 35–39 of the 2013-14 South African National land cover dataset was clustered to derive South African land cover 2014 Class N Mines for the current product and resampled to a 1000 x 1000m grid resolution Albers Equal Area map projection +proj=aea +lat_1=-22 +lat_2=-38 +lat_0=-30 +lon_0=25 +x_0=1400000 +y_0=1300000 +datum=WGS84 +units=m +no_defs
DOI: 10.15493/sarva.quartets.10000001
title: NBA 2011 Terrestrial Ecosystem Protection Level

description: Ecosystem protection level tells us whether ecosystems are adequately protected or under-protected. Ecosystem types are categorized as not protected, poorly protected, moderately protected or well protected, based on the proportion of each ecosystem type that occurs within a not protected, poorly protected, moderately protected or well protected, based on the proportion of each ecosystem type that occurs within a protected area recognized in the Protected Areas Act.

The assessment of ecosystem level is then evaluated as the proportion of each vegetation type protected relative to the biodiversity target.

The ecosystem protection level status was assigned using the following:
i) If an ecosystem type has more than 100% of its biodiversity target protected in a formal protected area either A or B we classify it as Well Protected
ii) When less than 100% of the biodiversity target is met in formal A or B protected areas it is classified it as Moderately Protected,
iii) If less than 50% of the biodiversity target is met, it is classified it as Poorly Protected,
iv) If less than 5% it is Hardly Protected.
This shapefile uses the South African Vegetation Map as a base file. Most of the original attributes of the original vegetation map are retained. For the purpose of the NBA all vegetation types are classified as ecosystems. The “map code” column and vegmap avl can be used to view the South African vegetation map. To view the ecosystem protection level the “PROTEC_LEV” field together with the “vegmap_ecosystem_protection_level” layer file to display ecosystem protection level. The “NBA_NAME” field shows the name of the ecosystem used for this analysis. Names for the protection level analysis will come from either the South African vegetation map or the DAFF forest patches. The “VEG_SOURCE” columns will indicate where the ecosystem name and description is from.

See and refer to the following reports for more information:
National Biodiversity Assessment 2011 Synthesis Report. Driver et al 2012. (available on BGIS website)
National Biodiversity Assessment 2011 Terrestrial Report. (available on BGIS website)
DOI: 10.15493/sarva.csag.10000242
title: Minimum of Minimum Temperature for Far Future (2080 - 2100)

description: Model Run: Far future (2080 - 2100) (Far future (2080 - 2100)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.saws.10000263
title: Seasonal percentage (%) change in rainfall from the median projected for 2066-2095, relative to present (1976 - 2005), for SON season under the RCP 4.5 pathway

description: Seasonal percentage (%) change in rainfall from the median projected for 2066-2095, relative to present (1976 - 2005), for SON season under the RCP 4.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily rainfall averages, which are used to generate projections of seasonal change. The projections are generated using the medium to low (RCP4.5) pathway which associates CO2 concentrations of approximately 560ppm by the year 2100. The associated RMSD it calculated and shows the uncertainty range of the projected model simulated residual values, and gives a relative perspective of spatial areas associated with higher and lower projection uncertainties.
DOI: 10.15493/sarva.saws.10000009
title: Annual mean near-surface (2m) temperature (°C) change from the median projected for 2066-2095, relative to present (1976 - 2005), under the RCP 4.5 pathway

description: Annual mean near-surface (2m) temperature (°C) change from the median projected for 2066-2095, relative to present (1976 - 2005), under the RCP 4.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily temperature averages, which are used to generate projections of annual change. The projections are generated using the medium to low (RCP4.5) pathway which associates CO2 concentrations of approximately 560ppm by the year 2100. The associated RMSD it calculated and shows the uncertainty range of the projected model simulated residual values, and gives a relative perspective of spatial areas associated with higher and lower projection uncertainties.
DOI: 10.15493/sarva.csag.10000051
title: Number of days for Maximum Temperature under 10th percentile for Current Climate (1950-1999)

description: Model Run: Control - Current Climate (Observed/current climate as modelled by climate model). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/saeon.fynbos.ti6108
title: Tierkloof Belfort stream flow volume March 1961 - December 2008

description: Hourly stream flow in cubic meters estimated from stage height measured with Belfort 5-FW liquid level recorder at a V-notch weir in Tierkloof catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/saeon.fynbos.141220-8
title: Jonkershoek forestry stock map 1991

description: Map of forestry plantation compartments for the Jonkershoek valley, Western Cape, South Africa as of 1991.
DOI: 10.25345/c5ng4h309
title: MassIVE MSV000093081 - PHF6 cooperates with SWI/SNF complexes to facilitate transcriptional progression
title: Genes encoding subunits of SWI/SNF (BAF) chromatin remodeling complexes are mutated in nearly 25% of cancers. To gain insight into the mechanisms by which SWI/SNF mutations drive cancer, we contributed ten rhabdoid tumor (RT) cell lines mutant for SWI/SNF subunit SMARCB1 to a genome-scale CRISPR Cas9 depletion screen performed across 896 cell lines. We identify PHF6 as specifically essential for RT cell survival and demonstrate that dependency on Phf6 extends to Smarcb1-deficient cancers in vivo. As mutations in either SWI/SNF or PHF6 can cause the neurodevelopmental disorder Coffin-Siris syndrome, our findings of a dependency suggest a previously unrecognized functional link. We demonstrate that PHF6 co-localizes with SWI/SNF complexes at promoters, where it is essential for maintenance of an active chromatin state. We show that in the absence of SMARCB1, PHF6 loss disrupts the recruitment and stability of residual SWI/SNF complex members, collectively resulting in the loss of active chromatin at promoters and stalling of RNA Polymerase II progression. Our work establishes a mechanistic basis for the shared syndromic features of SWI/SNF and PHF6 mutations in CSS and the basis for selective dependency on PHF6 in SMARCB1-mutant cancers.
DOI: 10.15493/sarva.csag.10000084
title: Minimum of Maximum Temperature for Far Future (2080 - 2100)

description: Model Run: Far future (2080 - 2100) (Far future (2080 - 2100)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.csag.10000090
title: Rainfall days per month for Current Climate (1950-1999)

description: Model Run: Control - Current Climate (Observed/current climate as modelled by climate model). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.dws.10000001
title: Dams and Lakes

description: Data from the Department of Water Affairs and Forestry, showing dams and some lakes of South Africa.
DOI: 10.15493/dea.carbon.10000039
title: Rain A2 Change between 2000 and 2050 10th Percentile

description: % change in the long term mean annual total rainfall between 1986-2015 and 2036 - 2065. The mean annual rainfall for each downscaled global circulation model (GCM), i.e. csiro, gfdla20, gfdl21, miroc, mpi and ukmo using the the Cubic Conformal Atmospheric Model (CCAM) developed by CSIRO (Australia) and updated and modified by the CSIR (South Africa) was calculated for each of the 30 year periods. The percentage difference in the 30-year mean annual rainfall  between the  two periods was calculated for each of the six models as follows: ((rain2050 - rain2000)/rain2000)x100. The 10th percentile is based on these differences. Data are in raster format with a 0.5º resolution using a projected latitude / longitude coordinate reference system with `+proj=utm +zone=48 +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0`.
DOI: 10.15493/sarva3.data.10000011
title: Household Sources of Water

description: The data was derived from the 2011 national census. 

Datasets for household sources of water were obtained from the census small area layer data and grouped into the following categories: (i) Municipal, (ii) Dam/Spring/Stream and (iii) Households that do not source water from piped water schemes.

A 500m grid was used to map the dwelling frame distribution for South Africa. The resulting dataset was then intersected with the small area layer for the purpose of esmating the area percentages of dwellings within each grid cell. The area percentages for the grid cells were multiplied with the corresponding census count for each small area in order to map household sources of water.
DOI: 10.15493/bea.data.10000082
title: Serviced and Unserviced Waste Water - Organic Component

description: Data was derived from the following sources: * CSIR was commissioned by the BioEnergy Atlas to assemble known data on solid waste production from household and commercial sources in South Africa. This data was only available at provincial aggregate level, and derives from statistics published by the Department of Water and Sanitation, or recent studies funded by them. * Data from StatsSA (Census 2011) enabled the calculation of number of households within each planning zone that were serviced at the time, with the balance unserviced. * SAEON developed a model from national and international statistics linking waste water production and composition to household income. This model was used, based on StatsSA Census Data, to estimate the organic component produced by each household per planning zone (mesozone) annually. * These factors were used to disaggregate provincial production data, resulting in a value for unserviced and serviced organic wastewater from household sources to be calculated for each mesozone.
DOI: 10.15493/sarva.csag.10000276
title: Minimum of Minimum Temperature for Near Future (2046-2065)

description: Model Run: Near future (2046 - 2065) (Near future (2046 - 2065)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/