cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/dea.carbon.10000018
title: LADA Degradation Index

description: Land degradation is a complex set of processes of impoverishment of terrestrial ecosystems mainly under the impact of human activities. Land degradation can be understood as the gradual or permanent loss of productivity of the land to produce ecosystem goods and services. During the Land Degradation Assessment in Drylands (LADA) project, the South African National Assessment of Land Degradation and Conservation was done between 2008 and 2011. 728 Contributing specialists throughout the country contribute their knowledge and experience on land degradation and sustainable land management during a series of 34 Participatory Expert Assessment Workshops. It is important to notice that the results of these national assessments are qualitative in nature and based on the perceptions of contributing specialists based on the assumption that they all know their respective assessment areas well enough to report on land degradation and conservation attributes. Variables on the state of land degradation (the extend, degree and rate of degradation processes) were combined with the level of impact of these degradation processes on ecosystem services to provide an unique Degradation Index (DI) for each type of degradation identified by the contributing specialist for each mapping unit as defined by the LADA Land Use Map for South Africa. The DI values range from 0 to 100, for all degradation types identified. The higher the DI value, the more degraded the area are.
DOI: 10.15493/sarva3.data.10000040
title: Seismic Intensity Lines

description: Data processed by Council for GeoScience to show seismic intensity lines for South Africa.
DOI: 10.15493/sarva.beeh.10000365
title: Monthly mean of diurnal (daily) temperature ranges for the 50 year (1950 - 1999) period (C) - April

description: The diurnal temperature range is the difference between the maximum and minimum temperatures on a given day. Some plants respond to the rhythmic variations brought about by shifts from high temperatures during the day to low temperatures during the night. The main long term factors affecting diurnal temperature ranges over South Africa are continentality and altitude (Schulze, 1997). It nevertheless needs emphasising here that short term, irregular diurnal conditions are often controlled by the passage of cold fronts or other air masses which may be associated with strongly contrasting temperature regimes.

"Monthly means of diurnal (daily) temperature ranges were derived from the 50 year (1950 - 1999) time series of quality controlled daily  maximum and minimum temperature 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). From theoretical considerations, experience and analyses of high quality daily temperature datasets, the diurnal temperature range is very seldom < 2 degree Celsius. When, therefore, on a given day the temperature range value was computed to be < 1.5 degree Celsius, this value was `forced` to be 1.5 degree Celsius, with equal compensatory adjustments being made to that day's maximum and minimum temperature values."
DOI: 10.15493/sarva.saws.10000058
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal mean near-surface (2m) temperature for the median for 2066 - 2095 relative to 1976-2005, for the MAM season, under the RCP 4.5 pathway

description: Root Mean Square Difference for seasonal (MAM) mean near-surface (2m) temperature (°C) change from the median projected for 2066-2095, relative to present (1976 - 2005), under the RCP 4.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily 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.csag.10000231
title: Median monthly precipitation (mm) for Far Future (2080 - 2100)

description: Model Run: Far future (2080 - 2100) (Far future (2080 - 2100)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.csag.10000181
title: Total precipitation for the month (mm) for Far Future (2080 - 2100)

description: Model Run: Far future (2080 - 2100) (Far future (2080 - 2100)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.saws.10000210
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal total rainfall 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) rainfall (mm per month) 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 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.csag.10000122
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.saws.10000164
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal mean near-surface (2m) temperature for the 90% percentile for 2036 - 2065 relative to 1976-2005, for the SON season, under the RCP 8.5 pathway

description: Root Mean Square Difference for seasonal (SON) mean near-surface (2m) temperature (°C) change from the 90% 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/sarva.csag.10000380
title: Total precipitation for the month (mm) for Far Future (2080 - 2100)

description: Model Run: Far future (2080 - 2100) (Far future (2080 - 2100)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.dws.10000016
title: River profile classification Catchment P

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.beeh.10000082
title: Heat Units (Annual)

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/saeon.tierberg.10000002
title: Bird Counts

description: Data on the species and numbers of birds counted along transects through shrubland and an adjacent drainage line.
DOI: 10.15493/sarva.csag.10000219
title: Mean 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: 10.25345/c5x05xp79
title: MassIVE MSV000093081 - PHF6 cooperates with SWI/SNF complexes to facilitate transcriptional progression
title: Genes encoding subunits of SWI/SNF (BAF) chromatin remodeling complexes are mutated in nearly 25% of cancers. To gain insight into the mechanisms by which SWI/SNF mutations drive cancer, we contributed ten rhabdoid tumor (RT) cell lines mutant for SWI/SNF subunit SMARCB1 to a genome-scale CRISPR Cas9 depletion screen performed across 896 cell lines. We identify PHF6 as specifically essential for RT cell survival and demonstrate that dependency on Phf6 extends to Smarcb1-deficient cancers in vivo. As mutations in either SWI/SNF or PHF6 can cause the neurodevelopmental disorder Coffin-Siris syndrome, our findings of a dependency suggest a previously unrecognized functional link. We demonstrate that PHF6 co-localizes with SWI/SNF complexes at promoters, where it is essential for maintenance of an active chromatin state. We show that in the absence of SMARCB1, PHF6 loss disrupts the recruitment and stability of residual SWI/SNF complex members, collectively resulting in the loss of active chromatin at promoters and stalling of RNA Polymerase II progression. Our work establishes a mechanistic basis for the shared syndromic features of SWI/SNF and PHF6 mutations in CSS and the basis for selective dependency on PHF6 in SMARCB1-mutant cancers.
DOI: 10.15493/saeon.ndlovu.10000005
title: SAEON OWNR & PHALA & SAWC Davis Vantage Pro2 AWS 30 minutes (AirTemp, Rain) 2019-09-01 to 2020-02-29 DATASET

description: This is a data set collected by three (3) of SAEON Ndlovu Node automated weather stations (AWS), located at the following locations: SAEON/Scientific Services, Kruger National Park, Phalaborwa, Limpopo Province, South Africa (PHALA; -23.945075 latitude, 31.165242 longitude);  Olifants West Nature Reserve, Balule Nature Reserve, Limpopo Province, South Africa (OWNR; -24.227122 latitude, 30.86838 longitude); South African Wildlife College, Mpumalanga Province, South Africa (SAWC; -24.542253 latitude, 31.334914 longitude). The make and model of the stations is Davis Vantage Pro 2.
DOI: 10.15493/saeon.egagasini.24000002
title: The ASCA April 2015 raw Thermosalinograph data

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 Thermosalinograph.
DOI: 10.15493/sarva.beeh.10000220
title: Pineapples: Optimum Growth Areas (Temperature Requirements)

description: The pineapple, i.e. Ananas comosus is a frost sensitive tropical and subtropical plant. Pineapples can grow well under a wide range of rainfall from MAP 600 - 1 200 mm (Smith, 1998). However, irrigation is necessary when MAP is < 500 mm or when consecutive months of low rainfall occur. Daily mean temperatures around 23 – 24 degree Celsius are considered optimum for both acid and sugar content of pineapples. South Africa accounts for 6 percent of the world`s pineapple production in a market dominated by Thailand, Indonesia, the Phillipines and Kenya (SA Yearbook, 2005). Of the 6 350 ha planted to pineapple in South Africa, 69 percent is in the Eastern Cape and 30 percent in KwaZulu-Natal (Statistics SA, 2002). Two varieties are grown, viz. the smoother, larger and juicier Cayenne and the smaller, but sweeter Queen variety, with production shifting more and more towards the Cayenne variety. In South Africa pineapples flower within 14 - 20 months of planting, with summer fruit in the Eastern Cape taking 5 months to mature and winter fruit up to 7 months, implying that the plant crop is harvested at 18 - 24 months (Bathurst, 2005).

For pineapple production potential, precipitation is evaluated as follows: Monthly precipitation is divided by 10, with any monthly amount in excess of 100 mm scoring 10. The largest possible annual precipitation score would thus be 120, areas with MAP less than 500 mm are considered unsuitable for pineapple production, except under irrigation, areas with more than 3 consecutive months less than 15 mm or 4 consecutive months less than 25 mm or 5 consecutive months less than 40 mm precipitation per month are also considered unsuitable without supplementary irrigation.
DOI: 10.15493/saeon.tierberg.10000013
title: Shrub demography 1989-1996

description: This package includes two worksheets “seedlings wild” and “seedlings sown”. The data in “ssedlings wild” are counts of seedlings that emerged in situ from the soil seedbank in 150 mm diameter rings pegged to the soil surface in a variety of microsites sites from 1989 to 1996. Seedling monitoring rings were placed in 55 permanent (5 m x 5 m) plots in indigenous Prince Albert Succulent Karoo vegetation on Tierberg Karoo Research Centre (TKRC) and on the adjacent farms Argentina (ARG) and Tierberg (TBG), as well microsites influenced by insects (ant mounds, termite mounds) or deposition of organic matter (litter trains). These data were originally collected to understand the influence of seed availability, weather, microsite, vegetation clearing and grazing on emergence and survival of seedlings in arid (<200ÿmm p.a.) Karoo shrubland.  In undisturbed vegetation 20-800 seedlings emerged per square metre in the austral autumn when conditions were relatively cool and humid. Densities of emerging seedlings were greater in microsites influenced by grazing, insects, litter and shade than in undisturbed or exposed sites. The species composition of seedling assemblages resembled that of the surrounding vegetation. Survival of seedlings varied from 5% in 1989 and 1990 to 25% in 1991 and appeared to be limited by the quantity of rain in winter and spring.

Data in “seedlings sown” are repeated counts of seedlings that emerged from sown seed of the shrubs Pteronia pallens, P. empetrifolia, Tripteris sinuata and the annual Tetragonia echinata. Seeds were sown in May 1990 and monitored until March 1992 on permanent plots in grazed and protected shrubland where neighboring plants were removed or left intact. The objective of the experiment was to understand how competition, grazing and rainfall influenced the emergence and subsequent survival of seeds of perennial and annual plants. Emergence of planted seeds was similar in all treatments, but survival of both annual and perennial species were better on cleared sites than in undisturbed vegetation. Seeds of a common annual Tetragonia echinata (Aizoaceae) maintain a seedbank but those of three perennial shrubs Pteronia empetrifolia, P. pallens and Tripteris sinuata (all Asteraceae) did not.

Acknowledge as follows: Data are the property of SAEON and were collected by SJ Milton, students and assistants, funded by the National Research Foundation, the Percy FitzPatrick Institute, University of Cape Town, and the Department of Science and Technology (DST) through the Biota-DST “Tierberg Reborn project”
DOI: 10.15493/nrf.saiab.20232509
title: Bathymetry data collected from Ballito Birthday, November 2021

description: African Coelacanth Ecosystem Programme (ACEP) Smart Zones MPA
description: This dataset forms part of the South African Institute for Aquatic Biodiversity (SAIAB)'s collection of Bathymetry data for the African Coelacanth Ecosystem Programme (ACEP) Smart Zones MPA. This multi-disciplinary study will begin at the start of implementation of two new MPAs (uThukela MPA and Protea Banks MPA, both proclaimed in 2019), and will generate the relevant data needed to underpin an evidence-based approach to adaptive management of the MPAs, within a “SMART” framework. This framework defines Specific objectives for the different MPA zones; develops measurable indicators of success for each zone; sets achievable goals given the limited resources available for planning and management; ensures that the research findings are relevant (to both biodiversity and human stakeholders). This is a rare time-sensitive opportunity to conduct baseline surveys of the geology, benthic biodiversity (sessile and mobile species), reef-associated ichthyoplankton and mero-zooplankton and fish communities of mesophotic (40-150m) soft-coral and sponge-dominated reefs and submarine habitats located in three different management zones at the initiation of management of two brand new MPAs, against which long-term monitoring data can be compared to assess the effectiveness of the MPAs in achieving their objectives.  The balanced sampling design is such that it will also initiate a “natural experiment” that over time will investigate the effect of no-take zones on fish diversity and abundance, benthic community structure, and trophic complexity. This will also enable the determination of the value of the MPA for fisheries sustainability support and resilience to disturbances caused by extrinsic factors such as pollution or climate change. Additionally, this study will assess whether the zonation plan provides for representation of all reef and canyon types within each zone or if there is unbalanced coverage for certain reefs and in certain zones. This is important to assess at the initiation of the MPA, especially to ensure that the interpretation of monitoring results in future, which compare ecosystems and fish on reefs and canyons in the mesophotic zone, are not flawed by natural differences from the outset due to the selection of zone boundaries. This dataset consists of XYZ points below the sea surface and relative to the mean sea level. This data was collected under the ACEP SMART ZONES MPA project (2021 – 2023) using the ACEP Geophysics and Mapping Platform.
description: See metadata file for additional information.