cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.beeh.10000079
title: Heat Units (October)

description: The concept of the heat unit (or degree day), known since the mid-18th century already, revolves around the development of a plants or organism`s being dependent upon the accumulated heat to which it was subjected during its lifetime, or else during a certain developmental stage. This measure of accumulated heat is known as physiological time. In general it holds that the lower the temperature, the slower the rate of growth and development of plants and invertebrate animals and the amount of heat required to complete a given organism's development does not vary. Physiological time is usually expressed and approximated in units of degree days, also popularly called heat units, where these are an accumulation of mean temperatures above a certain lower threshold value and below an upper developmental threshold (above which growth is considered to remain static or even decline), over a period of time. The degree day concept is not without its limitations. These include that, a linear relationship is assumed between growth and temperature, threshold temperatures may change during the life cycle of a crop or Pest, temperatures exceeding the upper threshold may, in fact, have a detrimental/negative effect on development.

Derivation over South Africa of heat units were done by calculating degree days for the daily temperature fluctuations that occur in nature. These range from degree hours, sine and triangular methods based on hourly temperatures, as well as so-called cut-off methods when the upper threshold of temperature is reached. To estimate heat units (HUs) over South Africa, the 50 year time series of daily maximum and minimum temperatures (Tmxd, Tmnd), generated at each of the 429 700 raster points covering the area at 1` x 1` latitude/longitude, was used (Schulze and Maharaj, 2004). The generation of this time series is described in Section 2a. HUs was computed for each day for a threshold temperature of 10 degree Celsius, i.e. HU = [(Tmxd + Tmnd) / 2 - 10] for HU ≥ 0.0 This method is a considerable advance on the computation of HUs in the previous version of this Atlas (Schulze, 1997), in which HUs were calculated from regression derived monthly means of Tmx and Tmn. From these daily values, monthly as well as seasonal and annual HUs and their standard deviations, could be calculated and mapped.
DOI: 10.15493/sarva.csag.10000359
title: Maximum 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/sarva.csag.10000353
title: Number of days for Maximum Temperature over 90th 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/sarva3.data.10000007
title: Percentage of Households Using Alternatives to Electricity for Cooking

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

The number of individuals using alternatives sources to electricity for cooking were obtained from the census small area layer data.

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 energy use for cooking.
DOI: 10.25345/c51r6nb20
title: MassIVE MSV000093080 - Alterations in lipidome profiles distinguish early-onset hyperuricemia, gout, and the effect of urate-lowering treatment
title: Background: Currently, it is not possible to predict whether patients with hyperuricemia (HUA) will develop gout and how this progression may be affected by urate-lowering treatment (ULT). Our study aimed to evaluate differences in plasma lipidome between patients with asymptomatic HUA detected &lt; 40 years (HUA&lt;40) and &gt; 40 years, gout patients with disease onset &lt; 40 years (Gout&lt;40) and &gt; 40 years, and normouricemic healthy controls (HC). Methods: Plasma samples were collected from 94 asymptomatic HUA (77% HUA&lt;40) subjects, 196 gout patients (59% Gout&lt;40), and 53 HC. A comprehensive targeted lipidomic analysis was performed to semi-quantify 608 lipids in plasma. Univariate and multivariate statistics and advanced visualizations were applied. Results: Both HUA and gout patients showed alterations in lipid profiles with the most significant upregulation of phosphatidylethanolamines and downregulation of lysophosphatidylcholine plasmalogens/plasmanyls. More profound changes were observed in HUA&lt;40 and Gout&lt;40 without ULT. Multivariate statistics differentiated HUA&lt;40 and Gout&lt;40 groups from HC with an overall accuracy of &gt; 95%. Conclusion: Alterations in the lipidome of HUA and Gout patients show a significant impact on lipid metabolism. The most significant glycerophospholipid dysregulation was found in HUA&lt;40 and Gout&lt;40 patients, together with a correction of this imbalance with ULT.
DOI: 10.15493/sarva.csag.10000432
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/nccis.data.10000048
title: Projected changes in annual mean temperature (°C) under RCP 8.5 for the 2015-2035 time period based on the Max Planck Institute Coupled Earth System Model (MPI-ESM-LR)

description: The analyses of future climate change over South Africa as described in the Third National Communication, are from the projections of the Coupled Global Climate Models (CGCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) and Assessment Report (AR) 5 of the Intergovernmental Panel on Climate Change (IPCC). These projections are used to inform on the uncertainty range of the large-scale climate change futures over the southern African region. At the Council for Scientific Industrial Research (CSIR), a dynamic regional climate model CCAM (conformal-cubic atmospheric model) of the Commonwealth Scientific and Industrial Research Organisation (CSIRO) was used to downscale CMIP5 CGCM projections to 50 km resolution over Africa. These downscalings were for both Representative Concentration Pathway (RCP) 8.5 and Representative Concentration Pathway (RCP) 4.5 of AR5 of the IPCC. RCP 4.5 describes a future with relatively ambitious emission reductions whereas RCP 8.5 describes a future with no reductions in emissions. Emissions in RCP 4.5 peak around 2040, then decline and in RCP 8.5 emissions continue to rise throughout the 21st century. The change in temperature is expressed as an anomaly, the difference between the average climate over a period of the last several decades (1971-2000), and the projected climate (short to medium term 2021 to 2050). The simulations were performed on supercomputers of the CSIRO and on the Centre for High Performance Computing (CHPC) of the Meraka Institute of the CSIR in South Africa.
DOI: 10.15493/sarva.beeh.10000076
title: Heat Units (July)

description: The concept of the heat unit (or degree day), known since the mid-18th century already, revolves around the development of a plants or organism`s being dependent upon the accumulated heat to which it was subjected during its lifetime, or else during a certain developmental stage. This measure of accumulated heat is known as physiological time. In general it holds that the lower the temperature, the slower the rate of growth and development of plants and invertebrate animals and the amount of heat required to complete a given organism's development does not vary. Physiological time is usually expressed and approximated in units of degree days, also popularly called heat units, where these are an accumulation of mean temperatures above a certain lower threshold value and below an upper developmental threshold (above which growth is considered to remain static or even decline), over a period of time. The degree day concept is not without its limitations. These include that, a linear relationship is assumed between growth and temperature, threshold temperatures may change during the life cycle of a crop or Pest, temperatures exceeding the upper threshold may, in fact, have a detrimental/negative effect on development.

Derivation over South Africa of heat units were done by calculating degree days for the daily temperature fluctuations that occur in nature. These range from degree hours, sine and triangular methods based on hourly temperatures, as well as so-called cut-off methods when the upper threshold of temperature is reached. To estimate heat units (HUs) over South Africa, the 50 year time series of daily maximum and minimum temperatures (Tmxd, Tmnd), generated at each of the 429 700 raster points covering the area at 1` x 1` latitude/longitude, was used (Schulze and Maharaj, 2004). The generation of this time series is described in Section 2a. HUs was computed for each day for a threshold temperature of 10 degree Celsius, i.e. HU = [(Tmxd + Tmnd) / 2 - 10] for HU ≥ 0.0 This method is a considerable advance on the computation of HUs in the previous version of this Atlas (Schulze, 1997), in which HUs were calculated from regression derived monthly means of Tmx and Tmn. From these daily values, monthly as well as seasonal and annual HUs and their standard deviations, could be calculated and mapped.
DOI: 10.15493/sarva.csag.10000251
title: Number of days for Maximum Temperature over 90th 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.141220-5
title: Jonkershoek forestry stock map 1957

description: Map of forestry plantation compartments for the Jonkershoek valley, Western Cape, South Africa as of 1957.
DOI: 10.15493/sarva.csag.10000143
title: Rainfall days per month (> 2mm) 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.10000036
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.saws.10000201
title: Seasonal total rainfall (mm per 3-month season) change from the median projected for 2036-2065, relative to present (1976 - 2005), for MAM season under the RCP 8.5 pathway

description: Seasonal (MAM) mean rainfall (mm per month) from the median 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 rainfall 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/sarva.dws.10000014
title: River profile classification Catchment M

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.gb.10000004
title: Sectoral Gross Value Added (GVA) for electricity, gas and water supply (SIC4) as a driver from 1995 to 2016

description: The South African Gross Geographic Value Added (GVA) data for each economic sector per year since 1995 to 2016 was supplied by Quantec to the CSIR built environment. This data was disaggregated to a 500m grid using a dasymetric mapping algorithm (https://gap.csir.co.za/gap/images/documents/developing-a-geo-data-frame-using-dasymetric-mapping-principles-to-facilitate-data-integration).  A linear regression was then fitted to the GVA dataset and the Slope of the regression was used as an indicator for exposure, the R2 value was converted to a percentage to provide a measure of probability. An Average GVA value was calculated as was the sample standard deviation as a measure of economic uncertainty.
DOI: 10.15493/sarva.beeh.10000319
title: Standard Deviations (C) Of Daily Mean Temperature July

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: 10.48331/scielo.fwbgbe/sstbtg
title: 3 Rafael Pezzuto Damaceno.pdf

description: :unav
DOI: 10.48331/scielo.fwbgbe
title: Visibilidade dos periódicos SciELO - Métricas de acesso aos periódicos SciELO - SciELO SUSHI API

description: Apresentação de Rafael J Pezzuto Damaceno no painel Visibilidade dos periódicos SciELO da Semana SciELO 25 Anos. Página do evento: https://25.scielo.org/
DOI: 10.15493/sarva.csag.10000338
title: Mean 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.10000193
title: Rainfall days per month (> 10mm) 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.10000054
title: South African Land Cover 1990 Class D Natural Vegetation Resampled to a 1km Grid

description: The 1990 South African National land cover dataset produced by (© GEOTERRAIMAGE - 2014) as a commercial data product was generated from digital, multi-seasonal Landsat 4/5 multispectral imagery, acquired between April 1989 and October 1993. 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 4/5 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 4/5 standard map projection format as provided by the USGS. Class 5–9 of the 1990 South African National land cover dataset was clustered to derive South African land cover 1990 Class D Natural Vegetation 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/saeon.egagasini.10000052
title: The ASCA June 2018 raw Current Meter data collected at Mooring D for all depths

description: The Agulhas System Climate Array (ACSA) is a mooring array and is designed to provide long-term observations of the Agulhas Current`s volume, heat and salt transport and its variability from mesoscale (eddies), through seasonal to interannual timescales. The ASCA shelf and tall moorings extend 200 km offshore along decending TOPEX/Jason satellite ground track # 96, through the core of the Agulhas Current, with CPIES measurements extending the array to 300 km offshore of Port Elizabeth. The data collected during this project have been obtained from various oceanographic instrumentation fixed to each mooring. This raw data was collected by a Current Meters at Mooring D for the following depths: 1000m, 1500m, 2000m, 2500m and 3000m.
DOI: 10.15493/saeon.egagasini.10000016
title: The ASCA July 2018 (SEAmester III) raw oblique Bongo data for unit 6412

description: The Agulhas System Climate Array (ACSA) is a mooring array and is designed to provide long-term observations of the Agulhas Current`s volume, heat and salt transport and its variability from mesoscale (eddies), through seasonal to interannual timescales. The ASCA shelf and tall moorings extend 200 km offshore along decending TOPEX/Jason satellite ground track # 96, through the core of the Agulhas Current, with CPIES measurements extending the array to 300 km offshore of Port Elizabeth. The data was collected during the ASCA project have been obtained from various oceanographic instrumentation. This specific raw data was collected using an oblique Bongo net for unit 6412.
DOI: 10.48331/scielo.rmgcx2/kyhrki
title: 2 Washington Segundo.pdf

description: :unav
DOI: 10.48331/scielo.rmgcx2
title: Visibilidade dos periódicos SciELO - Visibilidade das revistas SciELO na rede LA Referencia

description: Apresentação de Washington Segundo no painel Visibilidade dos periódicos SciELO da Semana SciELO 25 Anos. Página do evento: https://25.scielo.org/
DOI: 10.48331/scielo.dscaxf/jggzze
title: 1-Gimena_del_Rio_Riande.pptx

description: :unav
DOI: 10.48331/scielo.dscaxf
title: Visibilidad de las revistas SciELO - Las revistas SciELO en DOAJ

description: Presentación de Gimena del Río Riande en el panel Visibilidad de las revistas SciELO de la Semana SciELO 25 Años. Página del evento: https://25.scielo.org/es