cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/dea.carbon.10000022
title: Dynamic vegetation model 2100

description: The biomes of South Africa are simulated using a dynamic vegetation model – the aDGVM- designed specifically for tropical and subtropical African ecosystems. Biomes distribution is simulated using climate simulations. Simulations were forced with projected changes in climate given by the Max Planck Institute for Meteorology's (Hamburg) ECHAM5 IPCC projections with atmospheric CO2 from IPCC (2007) SRES A1B projections. Biomes distribution are output for 2100.
DOI: 10.15493/nccis.data.10000045
title: Projected changes in annual mean temperature (°C) under RCP 4.5 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/saeon.fynbos.lr1120
title: Langrivier stream flow and stage height August 2011 - August 2020

description: Hourly average and halfhourly sample stream flow in cumec estimated from stage height measured with an OTT Orpheus Mini in the stilling well at a V-notch weir in Langrivier catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.48331/scielo.b9oavw/0lcfmx
title: 1 Jézio Hernani Bomfim.pptx

description: :unav
DOI: 10.15493/sarva.csag.10000400
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: 10.48331/scielo.b9oavw
title: Livros: Os livros acadêmicos na ciência aberta - Comunicação acadêmica em um mundo em transformação

description: Apresentação de Jézio Hernani Bomfim Gutierre 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.10000055
title: Minimum 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/saeon.fynbos.21022022
title: Deelpan Farm rainfall data

description: Monthly rainfall data collected at Deelpan Farm, Northern Cape.This record forms part of a collection of historical rainfall data submitted to SAEON. The data were generated by farmers on a voluntary basis and have been published as submitted.
DOI: 10.15493/sarva.beeh.10000043
title: Climatically Suitable Growth Areas for Eucalyptus GxU (grandis x urophylla)

description: Eucalyptus GxU (grandis x urophylla) is, like the GxC hybrid, one which thrives in warm climates with MATs > 17 degree Celsius, but unlike GxC grows optimally at high rainfalls with MAPs in excess of 950 mm. Below MAT = 17 degree Celsius frost damage can occur and below 16 degree Celsius snow can also damage this hybrid (Kunz, 2004).
DOI: 10.15493/saeon.fynbos.26022022
title: Louiestraat 12, Paarl, rainfall data

description: Daily and monthly rainfall data recorded in Paarl. The data was collected using a Snowden standard rain gauge.This record forms part of a collection of historical rainfall data submitted to SAEON. The data were generated by farmers on a voluntary basis and have been published as submitted.
DOI: 10.15493/dea.carbon.10000075
title: South African Land Cover 2014 Class I Pineapple 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 22 of the 2013-14 South African National land cover dataset was used to derive South African land cover 2014 Class I Pineapple 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.dws.10000020
title: River profile classification Catchment T

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/bea.data.250320-13
title: Dataset of roads in South Africa used in the modelling of biomass transportation for the BioEnergy Atlas of South Africa

description: A spatial dataset of roads in South Africa used in the modelling of biomass transportation for the BioEnergy Atlas of South Africa. This data was derived from the National Geo-spatial Information (NGI) roads dataset for the entire country and processed to remove errors within the roads network.
DOI: 10.25345/c5d21rv51
title: MassIVE MSV000093082 - 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. Keywords LC-MS, Lipidomics, Glycerophospholipids, Hyperuricemia, Gout, Urate-lowering treatment
DOI: 10.15493/sarva.csag.10000404
title: Number of days for Minimum 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.egagasini.10000101
title: The ASCA July 2019 (SEAmester IV) raw Scientific Data System 5 minute averaged data, SA Agulhas Voyage 037

description: The Agulhas System Climate Array (ASCA) is a mooring array and is designed to provide long-term observations of the Agulhas Current volume, heat and salt transport of the and it`s 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. This raw data was collected by the underway Scientific Data System (SDS) averaged to 5-minute bins. The SDS is designed to collect, integrate, log and display various scientific, navigational and meterological sensors, instruments and systems onboard the ship.
DOI: 10.15493/sarva.beeh.10000246
title: Standard Deviation Of Daily Solar Radiation (Mj/M2/Day) August

description: These daily estimates of solar radiation were derived using modifications for South African conditions of the Bristow and Campbell (1984) equation. The daily values can be used as input in other equations such as the Penman-Monteith equation for potential crop evaporation.

The method of estimation of daily solar radiation loadings over South Africa and its verification are as follows: based on the premise that clear sky solar radiation is determined essentially by extraterrestrial solar radiation modulated by an atmospheric extinction function, and dependent on high maximum temperatures associated with a high diurnal range in temperature, while cloudy/rainy conditions are associated with a low diurnal temperature range, the Bristow and Campbell (1984) equation was modified for South African conditions by regionally optimised expressions of clear sky depletion for atmospheric water vapour content, and by month-by-month regionalised expressions for solar radiation extinction by cloudy/rainy conditions. Other than the optimised regional/monthly coefficients and the computation of extraterrestrial radiation, which is undertaken with trigonometrical geometry functions, the only other variables used were daily maximum and minimum temperatures. The latter were determined for the 50 year period 1950 - 1999 at each of 429 700 one arc minute (1.7 x 1.7 km) raster points over South Africa by techniques outlined in Schulze and Maharaj (2004).
DOI: 10.15493/saeon.fynbos.35022022
title: Glen Cumming Farm Rainfall data

description: Monthly rainfall records collected on Glen Cumming Farm, near Somerset East, Eastern Cape. The data was collected using a Snowden standard rain gauge. This record forms part of a collection of historical rainfall data submitted to SAEON. The data were generated by farmers on a voluntary basis and have been published as submitted.
DOI: 10.15493/sarva.csag.10000025
title: Mean 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/
DOI: 10.15493/sarva.csag.10000205
title: Number of days for Minimum 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/bea.data.10000012
title: Pinus patula growth areas allocated to mesozones

description: *The dataset shows climatically optimum growth areas and yield estimates of Pinus patula allocated to mesozones. Yield estimates were derived from Schulze R.E. and Maharaj M. (2007) and then allocated to mesozones by combining with a base mesozone layer obtained from the CSIR Geospatial Analysis Platform (GAP). *Climatically optimum growth areas of P. patula occur in an arc inland of the coast from the northeast areas of the Eastern Cape, through KwaZulu-Natal and including a strip along this province's border with Lesotho and the Free State, the western third of Swaziland and into Mpumalanga. The major constraint on the inland side of this arc is a lack of rainfall, while on the coastal side of the arc temperatures are too high. *Mean Annual Increments of P. patula, at> 20 t/ha/annum, are highest in an arc coastwards of the climatically optimum areas, where the trees tend to be vulnerable to heat related diseases. Away from the coast MAIs drop off into the 16 - 20 t/ha/annum range.
DOI: 10.15493/saeon.gfw.10000008
title: Two Streams fog data, KwaZulu-Natal

description: Two Streams fog data together with associated climatological variables recorded at the Automatic Weather station, Seven Oaks, KwaZulu-Natal, South Africa. The published data is for the 10 minute intervals for the period 1 July 2015 to 31 May 2018.
DOI: 10.15493/sarva.csag.10000039
title: Number of days for Minimum 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/saeon.fynbos.14b1118
title: 14B event rainfall June 2011 - February 2018

description: Event rainfall data collected by Davis Instruments tipping bucket gauge installed at 1.2m above ground at 472 masl in the Langrivier catchment of the Jonkershoek valley in the Western Cape of South Africa. Data recorded by a Pendant Event logger (UA-003-64, Onset, Massachusetts, U.S.A). Tipping bucket calibrated to 0.2 mm increments.
DOI: 10.15493/bea.data.10000003
title: Maize high yield estimates per mesozone

description: *The data shows maize yield estimates allocated to mesozones. Yield estimates were derived from Schulze R.E. and Walker N.J. (2007) and then allocated to mesozones by combining with a base mesozone layer obtained from the CSIR Geospatial Analysis Platform (GAP). *This dataset contains Maize, Zea mays L. in South Africa and is the country`s most important field and grain crop. Objectives of the study was to simulate maize yields, and their inter-annual, at a spatial resolution of Quaternary Catchments for 12 different combinations of three plant dates, viz. 15 October, 15 November and 15 December. This was done to evaluate which hybrid lengths and plant dates give the highest yields irrespective of plant dates and hybrid lengths respectively and also which hybrid lengths and plant dates give the lowest coefficients of variation (%) irrespective of plant dates and hybrid lengths respectively.
DOI: 10.15493/saeon.egagasini.10000040
title: The ASCA June 2018 processed CTD Autosal salinity calculations for stations 001 - 011

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 the ASCA project have been obtained from various oceanographic instrumentation. This raw data was collected by the shipborne CTD (Conductivity, Temperature, Depth) sensor, from which water samples were collected analysed further using an Autosal Salinometer for stations 001, 001B, 002, 003, 004, 005, 006, 007, 008, 009, 010, 011. The Autosal logsheet is also included.
DOI: 10.15493/sarva.beeh.10000167
title: Monthly Means Of Daily Minimum Relative Humidity (%) June

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/saeon.egagasini.10000070
title: The ASCA April 2016 raw CTD profile data collected for station 001 - 004

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 the ASCA project have been obtained from various oceanographic instrumentation. This raw data was collected by the shipborne CTD (Conductivity, Temperature, Depth) sensor for the following stations: 001, 002, 003, 004 and 005.
DOI: 10.15493/sarva.saws.10000065
title: Seasonal mean near-surface (2m) temperature (°C) change from the 10% percentile projected for 2066 - 2095, relative to present (1976 - 2005), for JJA season under the RCP 4.5 pathway

description: Seasonal (JJA) mean near-surface (2m) temperature (°C) change from the 10% 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 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.