cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.beeh.10000178
title: Papaya: Optimum Growth Areas - Criterion 4: Optimum growth areas should have a low frequency of 4 consecutive days with minimum temperatures < 17 degree Celsius

description: Papaya, or pawpaw (Carica papaya) is a tropical fruit grown widely between latitudes 32 degree N and S. High in Vitamin C, papaya is indigenous to tropical America (ARC, 2005). This fruit, initially brought to the attention of Europe around 1520, was introduced to South Africa in seed form by Jan van Riebeeck in 1652, but only grown commercially in the Lowveld of Mpumalanga for the first time in the early 20th century by a Captain Elphick (ARC, 2005). Papaya needs relatively little water in the rainy summer season in South Africa, but if irrigated every 2 weeks in the dry season it is well adapted to hot, dry areas as the fruit then matures early and is then highly palatable owing to its high sugar content (Smith, 1998). Papaya has high heat requirements, with average daily temperatures for optimum growth between 20 and 30 degree Celsius.

Determination of climatically optimum growth areas for Papaya in South Africa is based on the expert knowledge of Bower (2005) and Moll (2005), climatically optimum growth areas were determined according to the four basic criteria: Criterion 1: Heat units (base 12 degree Celsius) should exceed 2 000 days per annum, Criterion 2: Optimum areas should have a low frequency of 4 consecutive days with maximum temperatures > 36 degree Celsius, Criterion 3: Monthly means of daily average temperatures in December and January should be 23 degree Celsius - 30 degree Celsius and Criterion 4: Optimum growth areas should have a low frequency of 4 consecutive days with minimum temperatures < 17 degree Celsius. Using the 50 year time series of quality controlled daily maximum and minimum temperatures generated by Schulze and Maharaj (2004) at a spatial resolution of 1 arc minute (i.e. 1` x 1` of a degree latitude/longitude), the above four temperature based criteria were first mapped individually and then superimposed to determine the climatically optimum growth areas of papaya in South Africa.
DOI: 10.15493/saeon.ndlovu.10000006
title: SAEON Letaba Excl. Campbell Scientific AWS 5 minutes (AirTemp, Rain, WindSpeed, WindDir, SolRad, RH) 2018-06-22 to 2019-12-05

description: This is a data set collected by a SAEON Ndlovu Node automated weather stations (AWS), located at the Kruger National Park`s Letaba exclosures, specifically inside the full large mammal exclosure (-23.754756 Latitude; 31.432049 Longitude). The station is made up of mostly Campbell Scientific (CS) equipment incl.: a CR1000 data logger, Young wind sentry, CS RH and AirTemp sensor, Texas T-525I Rain Gauge, Vaisala Barometric pressure sensor, Knipp and Zonnen solar radiation sensor.
DOI: 10.15493/sarva3.data.10000041
title: Soils Susceptible to Water Erosion
DOI: 10.15493/dea.carbon.10000111
title: Total Soil Organic Carbon for 2014 in t/ha

description: ISRIC Africa SoiGrids data were used to calculate a reference layer of soil organic carbon (SOC). It was done by using the organic carbon content and bulk density layers available at 250m resolution and for 6 different depths. The data was aggregated to a depth of 30cm for topsoil and 100cm for subsoil. The impact of land use on the SOC is included by incorportating the 2014 National Land Cover data set for South Africa.
DOI: 10.15493/sarva.csag.10000255
title: Number of days for Minimum 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.beeh.10000344
title: Extremes (1950 - 2000) Of Minimum Temperatures (C) August

description: When days with very low or very high temperatures are experienced, the question is invariably asked as to whether that `extreme` minimum or maximum temperature was a record, either for the country or province, or for a specific location or month of the year. It is for that reason that `extremes` of temperatures, at both ends of the spectrum, were extracted from the daily temperature database generated by Schulze and Maharaj (2004) for the 51 year period (in this case) from 1950 to 2000, and analysed in tabular and mapped form. While these maps/tables are informative and interesting, there are nevertheless some limitations as to their accuracy, for reasons which are discussed in the shaded blocks containing some scientific background to the analyses.Limitations over South Africa to mapping extremes of temperatures, the generation of daily temperature time series at one arc minute spatial resolution at the 429 700 grid points covering South Africa depends on the selection of "best" control stations, extrapolating to altitudes above that of the highest-lying station in a particular region, as well as computational procedures regarding initial and final daily temperature values (Schulze and Maharaj, 2004). With respect to extreme minimum temperatures, no account has been taken of cold air drainage into valleys, which may aggravate minimum extremes.

Extremes of temperatures were derived from over 970 qualifying stations` records for the 51 year (1950 - 2000) time series of quality controlled daily maximum and minimum values generated at each of the 429 700 one arc minute (i.e. 1.7 x 1.7 km) raster points covering South Africa, using the regional/seasonal lapse rate and infilling techniques developed by Schulze and Maharaj (2004). In order to avoid analysing and mapping anomalous temperature extremes resulting from incorrect recordings, the extremes which were derived are therefore taken as the means of the two highest or lowest values found in the temperature time series at each grid point.
DOI: 10.15493/sarva.csag.10000320
title: Maximum of Maximum 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/
DOI: 10.15493/sarva.csag.10000034
title: Number of days for Maximum Temperature under 10th percentile 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.10000046
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal mean near-surface (2m) temperature for the median for 2036 - 2065 relative to 1976-2005, for the SON season, under the RCP 4.5 pathway

description: Root Mean Square Difference for seasonal (SON) mean near-surface (2m) temperature (°C) change from the median projected for 2036-2065, 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 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.231120-4
title: 2018 National Senior Certificate (NSC) matric results per municipal district

description: The National Senior Certificate (NSC) matric results is a measure of performance of all the secondary public schools that wrote the 2018 NSC Examination. This indicator was calculated for each municipal district using the average results obtained by the total number of secondary schools in a municipal district. The master list of schools dataset was linked to the National Senior Certificate (NSC) dataset using the Natemis (a unique number assigned to each school) to calculate the total number of secondary schools in a municipal district.
DOI: 10.15493/sarva.csag.10000014
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/bea.data.10000089
title: Field Crop Boundaries

description: "Data on field crop extent and type of cultivation was obtained from DAFF for the whole of South Africa. This was reviewed for consistency of description and corrected as required, and provincial data sets combined into a national data set. Original data was generated in 2006 using 2006 SPOT 5, colour merge, 2.5 meters imagery. On screen digitising with photo interpretation from SPOT 5 colour merge at 2.5 meter resolution at a scale of 1:50000. Fields were added to categorise type of cultivation into `Formal` and `Informal`, and whether it would be available for additional agricultural production."
DOI: 10.15493/nccis.data.10000018
title: Linear trend in annual mean amount of daily rainfall, indicating mean daily rainfall intensity (1921-2015)

description: An updated analysis of trends in South African rainfall is presented in The Third National Communication (TNC) to UNFCCC published in 2018. The analysis builds on the studies of Kruger & Sekele (2012) and Mackellar et al. (2014). 

A total of 60 weather stations were used for the rainfall trend analysis spanning the period 1921-2015. Apart from the trends in the annual rainfall totals, long-term changes in rainfall can manifest in changes in rainfall extremes. An extreme rainfall trend analysis is also performed, based on rainfall extreme indices developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) (Wang and Feng, 2013).  The base period, from which the annual index values of all indices are determined (except the annual maxima and minima) was selected as 1981 – 2010, which can be considered to be the present general norm for similar trend studies. The trends were tested for significance at the 95% confidence level.

Based on the data, there is strong evidence of statistically significant increases in rainfall occurring over the southern interior regions, extending from the western interior of the Eastern Cape and eastern interior of the Western Cape northwards into the central interior region of the Northern Cape, over the period 1921-2015. Extreme daily rainfall events have increased over these same areas, with these increases also being statistically significant and extending northwards into North West, the Free State and Gauteng. Over Limpopo there is strong evidence of statistically significant decreases in annual rainfall totals.
DOI: 10.15493/sarva.energy.10000012
title: Wind Speed (m/s) - Eastern Cape

description: Purpose: 
This data set was created for the WASA project and the Department of Energy, South Africa. The wind resource maps were designed specifically for inclusion in GIS-based strategic environmental assessments (SEA) for wind power in Western Cape and parts of Northern and Eastern Cape.

Methodology:
Reference is made to the information and documentation available from www.wasaproject.info and www.wasa.csir.co.za.

Limitations:
The data set is limited by the operational envelopes of the wind atlas methodology and the WAsP models. The accuracy depends on a) the accuracy of the VNWA, which has been verified against the data from 10 WASA measurement masts, b) the WAsP microscale modelling and c) the input topographical data.

In complex terrain (RIX > 5%), the wind resources may be significantly over-estimated by the WAsP microscale modelling. Above and close to built-up areas like cities, towns and villages, the results are less reliable. Close to and above forested areas, the results are also less reliable and should be interpreted and used accordingly.

The data set was designed specifically for planning purposes and should be used with utmost care for design, development and detailed assessments of actual wind farms; where local, on-site measurements are strongly recommended. The wind resource maps are subject to change without notice if and when more accurate and reliable data, models and procedures become available.

Available documentation:
The wind atlas methodology is described in the European Wind Atlas (1989); the application of WAsP in the program documentation, see www.wasp.dk. The Verified Numerical Wind Atlas for South Africa is a product of the Wind Atlas for South Africa project and is described further on the WASA download pages http://wasadata.csir.co.za/wasa1/WASAData.

Acknowledgements:
WASA project for Frogfoot application and provision of wind-climatological and topographical data. WAsP development teams at DTU Wind Energy and World in a Box Oy for Frogfoot development.
DOI: 10.15493/saeon.egagasini.05152022
title: TOTAL Sediment Data 2019

description: Sediment data collected from 4 stations off the south coast of South Africa during the TOTAL Brulpadda-1AX cruise in January 2019. Sediment analysis reports are available on request.
DOI: 10.48331/scielo.bhuuk6/vmp0nx
title: 2-Lilian_Caló.pptx

description: :unav
DOI: 10.48331/scielo.bhuuk6
title: Apresentações das Coordenações Nacionais - A Coleção SciELO Saúde Pública e seu posicionamento sobre Ciência Aberta

description: Apresentação de Lilian Calò no painel Apresentações das Coordenações Nacionais da Semana SciELO 25 Anos. Página do evento: https://25.scielo.org/
DOI: 10.15493/saeon.pub.10000002
title: Climate change: from challenges to opportunities - High school learner booklet

description: An educational booklet on climate change for high school learners
DOI: 10.15493/nccis.data.10000002
title: Linear trend in annual number of cool days (1931-2015) defined as the number of days when maximum temperature (TX) < 90th percentile of the baseline average.

description: An updated analysis of trends in South African temperatures is presented in The Third National Communication (TNC) to UNFCCC published in 2018. The analysis builds on the studies of Kruger & Sekele (2012) and Mackellar et al. (2014). 

A total of 27 weather stations was used for for temperature trends analysis spanning the period 1931-2015. Apart from the trends in the annual mean minimum, maximum and average temperatures, and average diurnal temperature range, the analyses also includes the determination of trends in extreme temperatures, of which the indices considered are based on those developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) (Wang and Feng, 2013).  The base period, from which the annual index values of all indices are determined (except the annual maxima and minima) was selected as 1981 – 2010, which can be considered to be the present general norm for similar trend studies. The trends were tested for significance at the 95% confidence level.

The analysis demonstrates that, South Africa has been warming significantly over the period 1931-2015. Over the western parts of the country, including much of the Western and Northern Cape, and also in the east over Gauteng, Limpopo and the east coast of KwaZulu-Natal, the observed rate of warming has been 2 °C/century or even higher – in the order of twice the global rate of temperature increase.  Associated increases in the annual number of hot days have also occurred, but there have been decreases in the annual number of cold nights over most of the country.
DOI: 10.15493/sarva.energy.10000004
title: Terrain Elevation (m) - Northern Cape

description: Purpose: 
This data set was created for the WASA project and the Department of Energy, South Africa. The wind resource maps were designed specifically for inclusion in GIS-based strategic environmental assessments (SEA) for wind power in Western Cape and parts of Northern and Eastern Cape.

Methodology:
Reference is made to the information and documentation available from www.wasaproject.info and www.wasa.csir.co.za.

Limitations:
The data set is limited by the operational envelopes of the wind atlas methodology and the WAsP models. The accuracy depends on a) the accuracy of the VNWA, which has been verified against the data from 10 WASA measurement masts, b) the WAsP microscale modelling and c) the input topographical data.

In complex terrain (RIX > 5%), the wind resources may be significantly over-estimated by the WAsP microscale modelling. Above and close to built-up areas like cities, towns and villages, the results are less reliable. Close to and above forested areas, the results are also less reliable and should be interpreted and used accordingly.

The data set was designed specifically for planning purposes and should be used with utmost care for design, development and detailed assessments of actual wind farms; where local, on-site measurements are strongly recommended. The wind resource maps are subject to change without notice if and when more accurate and reliable data, models and procedures become available.

Available documentation:
The wind atlas methodology is described in the European Wind Atlas (1989); the application of WAsP in the program documentation, see www.wasp.dk. The Verified Numerical Wind Atlas for South Africa is a product of the Wind Atlas for South Africa project and is described further on the WASA download pages http://wasadata.csir.co.za/wasa1/WASAData.

Acknowledgements:
WASA project for Frogfoot application and provision of wind-climatological and topographical data. WAsP development teams at DTU Wind Energy and World in a Box Oy for Frogfoot development.
DOI: 10.15493/bea.data.10000077
title: Production of Groundnuts on Subsistence and Underutilised Farmland

description: Data was derived from the following sources: * Extent of underutilised and subsistence farmland, data obtained from Department of Agriculture, Forestry, and Fisheries. * On such land, groundnut potential was calculated from data published by Schulze and Maharaj (2007) on groundnut-growing potential. * Grain, Oil, Oilcake, and Residue production was calculated based on seed yields, and aggregated to meso-zones for planning and feasibility analysis. * Grain, Oil and Residue ratios were derived from literature
DOI: 10.48331/scielo.yvj54a/3udime
title: 2 Édgar Garcia.pptx

description: :unav
DOI: 10.48331/scielo.yvj54a
title: Livros: Os livros acadêmicos na ciência aberta - Os desafios dos livros acadêmicos em ciência aberta

description: Apresentação de Édgar García Valencia no painel Livros: Os livros acadêmicos na ciência aberta da Semana SciELO 25 Anos. Página do evento: https://25.scielo.org/
DOI: 10.15493/sarva.csag.10000179
title: Consecutive Dry Days 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.10000137
title: Number of days for Minimum Temperature over 90th percentile 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.beeh.10000457
title: Inter-Annual Coefficient Of Variation (%) Dryland Sugarcane Yield

description: South Africa is ranked 13th in the world (SA Yearbook, 2005) as a producer of sugarcane, Saccharum officinarum. Average sucrose content of the South African crop is approx 13.5%, varying from 11.9 - 13.8% and inversely related to the year`s rainfall. It takes approximately 8.5 tonne sugarcane to produce 1 t sugar, varying from 8.3 - 10.0 t (SASA, 2005). Of the cane under irrigation, 72% is in KwaZulu-Natal (mainly Pongola and Mfolozi flats) and the remaining 28% in Mpumalanga (Statistics SA, 2002). 95% of the sugarcane crop is fertilized and this accounts for 18% of the fertilizer use in South Africa (FAO, 2005). Estimation of Sugarcane Yield was with the ACRU-Thompson Model equation which related sugarcane water use (total evaporation) to yield as Ysc = 9.53(Ean / 100) - 2.36 and where Ysc = annual sugarcane yield (t/ha), and Ean = annual total evaporation (mm).