cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.beeh.10000296
title: Standard Deviation (C) Of Daily Maximum Temperature September

description: High daytime maximum temperatures, up to 30 – 35 degree Celsius, generally increase rates of photosynthesis (i.e. the process by which `food` is manufactured in a plant), and thereby the rate of organic matter production. High maxima can be critical for some plants in both a negative context (e.g. the potential for grain production is reduced in maize when temperatures are too high during the flowering period) and a positive context (e.g. strawberries need high daytime temperatures to develop flavour). However, the rate of respiration (i.e. the processes by which `food` is used by a plant) also increases with temperature. As maximum temperatures reach the upper limits for a given crop, the rate of respiration (`food` used) may exceed that of photosynthesis (`food` manufactured). Thus, for example, for tomatoes growth peaks at 36 degree Celsius (Whiting et al., 2004). 

Time series of daily maximum temperatures from over 970 qualifying temperature stations were generated from quality controlled data for common 50 year period 1950 - 1999 using infilling and record extension techniques developed by Schulze and Maharaj (2004). From these time series at point locations, 50 years of daily maximum temperature values were generated at each of 429 700 one arc minute (1.7 km x 1.7 km) raster points covering South Africa, using regionally and seasonally determined lapse rates and other physically appropriate spatial interpolation approaches. These are given in detail in Schulze and Maharaj (2004). From these daily values, monthly means of daily minimum temperatures, as well as standard deviations of daily values for each month of the year, could be computed for purposes of mapping and production of tables of statistics for Swaziland, Lesotho and for each of the nine provinces of the RSA.
DOI: 10.15493/sarva.beeh.10000211
title: June Standard Deviations Of Daily Reference Crop Evaporation (mm) Fao Penman-Monteith Equivalent

description: The first detailed maps for South Africa of Penman-Monteith equivalent reference crop, Erpm, were derived from monthly ratios of Erpm: A-pan using data at 174 sites (Crosby and Crosby, 1996). From these ratios, Schulze (1997) identified 14 `regions` of similar ratio patterns which were then used to map Erpm from gridded A-pan values. The maps in the present Atlas are based on a much more deterministic and more fundamental approach with daily maximum and minimum temperatures, and hence daily saturated vapour pressures, generated over South Africa on a 1` x 1` (1.7 x 1.7 km) raster for 50 years, based on research by Schulze and Maharaj (2004). Empirically determined month-by-month gridded values of actual vapour pressure for South Africa.

The FAO (1992) definition of Penman-Monteith derived reference crop evaporation (Erpm) is `The rate of evapotranspiration from a hypothetical crop with an assumed crop height of 0.12 m, a fixed canopy resistance of 70 s.m-1 and albedo of 0.23, which would closely resemble evapotranspiration from an extensive surface of green grass cover of uniform height, actively growing, completely shading the ground and not short of water`.
DOI: 10.15493/sarva.beeh.10000333
title: Monthly Means Of Daily Minimum Temperature (C) September

description: Minimum (Night-Time) temperatures, there is abundant evidence for the climatic limitation of crop distributions to be described by minimum temperatures, from a perspective of crop survival (on Frost for tolerance levels of crops). Many subtropical crops may be killed at 5 degree Celsius already through chilling injury, even in the absence of frost (Chang, 1968). High minima increase respiration loss, thereby decreasing net photosynthesis.

Daily minimum temperature time series were derived from over 970 qualifying temperature stations` quality controlled daily records for the 50 year period 1950 - 1999. From these time series, 50 years of daily values of minimum temperatures were generated at each of 429 700 one arc minute (i.e. 1.7 km x 1.7 km) raster points covering South Africa, using regional and seasonal lapse rate and infilling/data extension techniques developed by Schulze and Maharaj (2004). Note that in the generation of daily minimum temperatures no account could be taken of cold air drainage into hollows or valleys, which can take place from surrounding higher-lying areas through radiative cooling during calm, stable clear night conditions. Reasons for this are provided in Schulze and Maharaj (2004). From these daily values, monthly means of daily minimum temperatures, as well as standard deviation of daily minimum temperatures for each month of the year, could be computed for purposes of mapping and production of statistics which are tabulated for Swaziland, Lesotho and for each of the nine provinces of the RSA. This approach to mapping minimum temperatures is thus fundamentally different to that used in the previous `South African Atlas of Agrohydrology and Climatology` (Schulze, 1997), in being much more physically based.
DOI: 10.58119/ulg/miruu5
title: Dataset Code for paper: "Multispecies detection and identification of African mammals in aerial imagery using convolutional neural networks"

description: This dataset contains aerial images, model result files and the code used in the paper: Multispecies detection and identification of African mammals in aerial imagery using convolutional neural networks. It is divided into 3 main parts: 1) The code, which contains the mmdetection v1.0.0 package as well as the adapted version and Jupyter notebooks; 2) The data (general_dataset.zip), which contains all the images and annotations of the general dataset used for training the deep learning models in the paper; 3) The results (results_files.zip), which contain the models outputs on the general dataset.
DOI: 10.15493/sarva.csag.10000252
title: Maximum 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.10000212
title: Precipitation 90th percentile 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.10000155
title: Seasonal mean near-surface (2m) temperature (°C) change from the 10% percentile projected for 2036-2065, relative to present (1976 - 2005), for MAM season under the RCP 8.5 pathway

description: Seasonal (MAM) mean near-surface (2m) temperature (°C) change from the 10% percentile projected for 2036-2065, relative to present (1976 - 2005), under the RCP 8.5 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 seasonal change. The projections are generated using the high (RCP8.5) pathway which associates CO2 concentrations of approximately 950ppm 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/saeon.fynbos.20b0912
title: 20B manual weekly rainfall January 2009 - September 2012

description: Weekly rainfall data collected by a Snowdon gauge fitted with a Nipher shield. Gauge orifice parallel to the slope, with orifice at 1.2 meters above ground at Uitkyk in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.beeh.10000166
title: Monthly Means Of Daily Minimum Relative Humidity (%) May

description: Daily minimum and maximum relative humidity is necessary as relative humidity, defined for practical purposes as the ratio of actual to saturated vapour pressure, expressed as a percentage generally varies considerably in the course of a day because, despite actual vapour pressure, ed, remaining relatively constant, saturated vapour pressure, ea, varies curvilinearly with temperature. The minimum RH of a day is thus experienced during the hottest part of the day when ea is high and the ratio of ed : ea is therefore small. Conversely, the maximum RH of a day is experienced around sunrise when temperatures are low, thus ea is low and the ratio of ed : ea therefore approaches unity.

In computations of daily RHmin and RHmax, the uncorrected actual vapour pressure, ed , is assumed not to change significantly in the course of a day. Because uncorrected ed at any specific location has been shown to be predictable over South Africa on a month-by-month basis with a high degree of confidence when using predominantly geographical factors in multiple regression equations for each month the ed at any location is therefore held at a relatively constant value for any given month of the year, varying only slightly on a day-by-day basis within that month because temperature range is a variable in the regression equation. However, at that specific location the saturated vapour pressure, ea , can vary considerably from one day to the next, as well as within a given day between morning and midday, because it is related to temperature. Therefore, RHmin and RHmax can range considerably within a given day and vary markedly from one day to the next.
DOI: 10.15493/sarva.beeh.10000392
title: Vapour Pressure Deficit (kPa) July

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: 10.5683/sp2/brb00z
title: Neural correlates of manual action language: a comparative review, ALE meta-analysis and ROI-analysis

description: Neural correlates of manual action language: a comparative review and ALE meta-analysis
DOI: 10.15493/sarva.csag.10000253
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.10000261
title: Rainfall days per month (> 10mm) 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/bea.data.10000024
title: AWB-COF: All Woody Biomass - Co-Generation

description: * Technical Challenges - ESKOM may have some risks associated with introduction of co-firing at power stations, and may elect to do so only at selected sites. The most feasible location is in Mpumalanga Highveld, but the availability of biomass is somewhat dependent on maize residues which may be highly variable. Western Cape option may not be feasible since power stations have been decommissioned. * Cost Challenges - Costs are acceptable comparable to current electricity from coal costs. * Policy Challenges - The projects are feasible, and well aligned with existing expertise and infrastructure in respect of `Working for Water` programmes. Integration with DEA `Working for Energy` required and incorporation into IPP programmes needed. * Environmental Challenges - The net impact on greenhouse gas emissions is sizable, despite land use change effects, given the significant reduction in GHG as CO2 equivalents in comparison to coal. If natural vegetation replaces invasives at more or less the same annual increment, LUC effects are near zero.
DOI: 10.15493/sarva.csag.10000016
title: Rainfall days per month (> 10mm) 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/saeon.arid.10000001
title: Environmental baseline review of the core area and surrounds of the Square Kilometre Array (SKA)

description: An in-depth environmental baseline review of the core area of the Square Kilometre Array and surrounds compiled from numerous information sources, scientific and grey literature.
DOI: 10.15493/sarva.beeh.10000324
title: Standard Deviations (C) Of Daily Mean Temperature December

description: Under conditions of natural vegetation, mean temperatures are used to distinguish between three broad thermal divisions of plants, viz. mega-thermal plants, which require mean monthly temperatures above 20 degree Celsius for at least 4 months of the year; micro-thermal plants, which grow where 8 months or longer have means below 10 degree Celsius; and the meso-thermal plants of the mid-latitudes, which cover most of South Africa, and whose physiology is adapted to the strong seasonal rhythms which are experienced over most of the southern African subcontinent. Month-by-month variations of mean temperature influence the geographic range and the optimum growth areas of certain vegetation and crop species, especially those with life-cycles extending one year or less. 

Assuming a daily average temperature to be represented by the mean of that days` minimum and maximum temperatures, these averages were derived from the 50 year (1950 – 1999) time series of quality controlled daily minimum and maximum temperatures which were generated from over 970 qualifying temperature stations using infilling and record extension techniques developed by Schulze and Maharaj (2004). From these point location time series, 50 years of daily mean temperature values were derived at each of 429 700 one arc minute (1.7 km x 1.7 km) grid points covering South Africa, using regionally and seasonally determined lapse rates and other physically appropriate spatial interpolation approaches (Schulze and Maharaj, 2004). From the daily values, monthly means and standard deviations of daily values for each month of the year could be computed for purposes of mapping and production of tables of statistics for each of the nine provinces of the RSA as well as for Swaziland and Lesotho.
DOI: 10.15493/sarva.csag.10000235
title: Maximum 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.10000160
title: Rainfall days per month (> 2mm) 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.10000106
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal total rainfall for the median for 2066-2095 relative to 1976-2005, for the MAM season, under the RCP 4.5 pathway

description: Root Mean Square Difference for seasonal (MAM) rainfall (mm per month) 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 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.181121-2
title: Health facilities managed by the public health sector in South Africa

description: This dataset was clipped from a comprehensive spatial inventory of 98,745 public health facilities provided by government and non-government sources in 50 countries and islands in sub-Saharan Africa, this subset represents the locations of health care facilites in South Africa. The original dataset was a national master health facility list assembled from a variety of government and non-government sources from 50 countries and islands in sub Saharan Africa. The dataset, compromising of 98,745 public health facilities, was generated utilising multiple geocoding methods to provide this comprehensive spatial inventory. This dataset was originally published in a spreadsheet (William Snow, Robert; Maina, Joseph; Ouma, Paul Onyango; Macharia, Peter Mwangi; Alegana, Victor Adagi; Mitto, Benard; et al. (2019): Public health facilities in sub Saharan Africa. figshare. Dataset. https://doi.org/10.6084/m9.figshare.7725374.v1) with a data descriptor (Maina, J., Ouma, P.O., Macharia, P.M. et al. A spatial database of health facilities managed by the public health sector in sub Saharan Africa. Sci Data 6, 134 (2019). https://doi.org/10.1038/s41597-019-0142-2) and this subset of it has now been republished by SAEON as a shapefile.
DOI: 10.15493/sarva.csag.10000282
title: Median monthly precipitation (mm) 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/