cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/bea.data.10000092
title: Population Growth and Distribution

description: * The dataset shows population growth estimates for South Africa allocated to district municipalities. * A time series analysis conducted by the CSIR (utilising an innovative Temporal Analyses Tool to compare the StatsSA data of 1996 with that of 2001 and 2011 for the recently updated settlement typology) enabled an analysis of growth and decline per town and settlement, as well as for less developed areas, across the country. * For the South African population as a whole growth was estimated as 1.85% (based on linear calculation of average growth for periods 1996-2001/2001-2011/1996-2011). For cities and larger towns the average growth rate was calculated as 2.87%, pointing to on-going trends of population concentration. Even though this is a relative slow rate, a trend of concentration is clearly evident
DOI: 10.15493/sarva.csag.10000377
title: Precipitation 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: 10.4121/19758436.v5
title: Data and analysis underlying the research into the Artificial-Social-Agent Questionnaire: Establishing the long and short questionnaire versions

description: We present a technical report, data and analysis underlying the study on establishing the Artificial Social Agent (ASA) Questionnaire. The ASA Questionnaire is an instrument for evaluating human interaction with an ASA, resulted from multi-year efforts involving more than 100 Intelligent Virtual Agent (IVA) researchers worldwide. It has 19 measurement constructs constituted by 90 items, which capture more than 80% of the constructs identified in empirical studies published in the IVA conference 2013-2018. This paper reports on construct validity analysis, specifically convergent and discriminant validity of initial 131 instrument items that involved 532 crowd-workers who were asked to rate human interaction with 14 different ASAs. The analysis included several factor analysis models, and resulted in the selection of 90 items for inclusion of the long version of the ASA questionnaire. In addition, a representative item of each construct or dimension was select to create a 24-item short version of the ASA questionnaire. Whereas the long version is suitable for a comprehensive evaluation of human-ASA interaction, the short version allows quick analysis and description of the interaction with the ASA. To support reporting ASA questionnaire results, we also put forward an ASA chart. The chart provides a quick overview of agent profile.
DOI: 10.15493/sarva.beeh.10000151
title: Standard Deviations Of Daily Minimum Relative Humidity (%) February

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.fynbos.20b1820
title: 20B event rainfall February 2018 - August 2020

description: Event rainfall data collected by Texas Electronics tipping bucket gauge installed at 1.2m above ground at 409 masl at the Uitkyk station in 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 0.254 mm increments.
DOI: 10.15493/sarva.beeh.10000100
title: Koppen Climatic Zonation

description: When hydrological landscapes, or climatic drivers of hydrological processes, are in a state of flux, baselines of climate and climatic zones are important, because these baselines are the references against which hydrological response comparisons may be made, for example, of possible changes in the climate regime. The hydrological responses to perturbations of climate include changes in total streamflows and their seasonal distributions. Baselines against which climate impacts may be assessed are all the more important in water resources management because hydrological responses, especially to changes in rainfall, are non-linear.

In order determination of Köppen climate zones over South Africa, Lesotho and Swaziland, the 1` x 1` latitude/longitude raster (i.e.1.7 x 1.7 km) of median monthly precipitation from Lynch (2004) was used, while for monthly means of daily average temperatures the 1` x 1` gridded values, derived from Schulze and Maharaj (2004), were used. A computer program was written to identify the Koppen class at each of the 429 700 grid points covering southern Africa.
DOI: 10.15493/sarva.251120-14
title: 2018 Sub-Antarctic Marine ecosystem threat status and protection level

description: This layer includes the threat status, protection level and condition of the Sub-Antarctic Marine Threat Status and Protection level assessed for the National Biodiversity Assessment 2018
DOI: 10.15493/sarva.beeh.10000323
title: Standard Deviations (C) Of Daily Mean Temperature November

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.saws.10000162
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal mean near-surface (2m) temperature for the 10% percentile for 2036 - 2065 relative to 1976-2005, for the JJA season, under the RCP 8.5 pathway

description: Root Mean Square Difference for Seasonal (JJA) 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.10000005
title: Langrivier 700m fog station, event rainfall, 05/2014-07/2019

description: Hourly rainfall data collected by a Texas Electronics tipping bucket gauge installed at 1.2m above ground at 700 masl in the Langriver catchment of the Jonkershoek valley in the Western Cape of South Africa. Data recorded by CR1000 logger (Campbell Scientific, Utah, U.S.A).  Tipping bucket calibrated to a 0.254 mm per tip event. (Collection continuing, contact SAEON for updated datasets)
DOI: 10.15493/sarva.csag.10000076
title: Precipitation 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.csag.10000120
title: Number of days for Minimum Temperature over 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.energy.10000015
title: AOW-NOT: All Domestic Organic Waste - Biogas Digesters (MW)

description: Biomass feasibility assesment dataset extracted from the BioEnergy Atlas for South Africa.

* Assessments are made of impacts of economy of scale, logistics, and placement on techno-economic feasibility of feedstock and processing combinations, leading to identification of a portfolio of feasible project options. These options are illustrative of areas where bioenergy is likely to be a competitive, sustainable alternative.

* Original dataset: https://dx.doi.org/10.15493/BEA.DATA.10000020

* BioEnergy Atlas Synopsis Report: https://dx.doi.org/10.15493/SAEON.BEA.DOCS.10000001
DOI: 10.15493/sarva.energy.10000022
title: IAP-NOT: Invasive Alien Plants - Pelleting (MW)

description: Biomass feasibility assesment dataset extracted from the BioEnergy Atlas for South Africa.

* Assessments are made of impacts of economy of scale, logistics, and placement on techno-economic feasibility of feedstock and processing combinations, leading to identification of a portfolio of feasible project options. These options are illustrative of areas where bioenergy is likely to be a competitive, sustainable alternative.

* Original dataset: https://dx.doi.org/10.15493/BEA.DATA.10000034

* BioEnergy Atlas Synopsis Report: https://dx.doi.org/10.15493/SAEON.BEA.DOCS.10000001
DOI: 10.15493/saeon.fynbos.28022022
title: Ruigtevlei Farm rainfall data

description: Daily rainfall data collected at Ruigtevlei Farm near Montagu, Western 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.10000431
title: Lowest Inter-Annual Cv (%) Of Maize Yield Irrespective Of Date and Hybrid

description: This data set 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/nccis.data.10000007
title: Linear trend in annual nighttime coldest temperature (1931-2015) defined as the annual minimum value of minimum temperature (TN)

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: 10.5281/zenodo.8431072
title: Vegetation dataset Kalahari 2021

description: Woody and herbaceous vegetation survey data were collected on three wildlife and three cattle farms in areas that were either selectively bush controlled or bush encroached. Surveys were conducted along transects at four distances from waterpoints. Herbaceous plants were divided into four plant functional types (PFT): annual and perennial, forbs and grasses. Data were collected in April 2021 in the Kalahari Molopo region, South Africa.
DOI: 10.15493/sarva.csag.10000354
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: 10.5281/zenodo.8431071
title: Vegetation dataset Kalahari 2021

description: Woody and herbaceous vegetation survey data were collected on three wildlife and three cattle farms in areas that were either selectively bush controlled or bush encroached. Surveys were conducted along transects at four distances from waterpoints. Herbaceous plants were divided into four plant functional types (PFT): annual and perennial, forbs and grasses. Data were collected in April 2021 in the Kalahari Molopo region, South Africa.
DOI: 10.15493/sarva.saws.10000076
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal total rainfall for the median for 2036 - 2065 relative to 1976-2005, for the DJF season, under the RCP 4.5 pathway

description: Root Mean Square Difference for seasonal (DJF) rainfall (mm per month) 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 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/nrf.saiab.20232501
title: Bathymetry data for Seascapes, December 2022

description: African Coelacanth Ecosystem Programme (ACEP) Smart Zones MPA
description: This dataset forms part of the the South African Institute for Aquatic Biodiversity (SAIAB)'s collection of Bathymetry data for the African Coelacanth Ecosystem Programme (ACEP) Smart Zones MPA. This hydrographic survey covers 18 square km and is part of the SeaScapes 2022 project. The dataset consists of locations below the surface of the water. Habitat degradation and loss of structural complexity are major threats to coastal ecosystems, with impacts associated with climate change placing additional pressure on these valuable ecosystems. Seascape ecology (which focuses on mosaics of spatially discrete and recognisable patches of habitat) is an emerging science that strives to understand spatial pattern-process linkages in marine environments. Understanding multi-scale linkages between seascape structure, function and change can better support sustainable ocean development, biodiversity protection and help to understand the consequences of human activity. A multi-disciplinary approach will be undertaken within shallow coastal waters of the south coast of South Africa in two different coastal seascapes: 1) Swartkops Estuary to Maitlands Estuary (incorporating the lower reaches of the Swartkops Estuary and nearshore from 1-30 m) and 2) Aston Bay to Oyster Bay (incorporating the lower reaches of the Kromme Estuary and nearshore from 1-30 m). This project aims to 1) map and model seascape spatial structure through the collection of high-resolution georeferenced imagery; 2) study the impact of climate change in seascapes (seascape change), with a focus on macrophytes (seagrass and seaweed) and associated organisms as well as the role that macrophytes may have in mitigating the effects of ocean acidification; and 3) assess connectivity of mobile organisms (fish) within seascapes and between shallow nursery seascapes (<7 m) and deeper adult habitat (reef deeper than 7 m). These data are combined from MB11_2022 and MB12_2022 surveys. This dataset is used to map, delineate and classify the seabed for the purpose of classifying habitats, ecosystems, understanding spatial pattern-process linkages in marine environments (biological and geological).
description: See metadata file for additional information.
DOI: 10.15493/saeon.fynbos.24022022
title: Kamferskraal Farm rainfall data

description: Monthly rainfall data collected on Kamferskraal Farm in the Western Cape near Beaufort West. 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.beeh.10000294
title: Standard Deviation (C) Of Daily Maximum Temperature July

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.