cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/saeon.egagasini.10000033
title: ASCA April 2015 analysed chemistry bottle data for stations C11201a - C11218

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. This raw data was collected by the shipborne CTD (Conductivity, Temperature, Depth) sensor, from which water samples were collected and put through chemistry analysis for the following stations: C11201a, C11202a, C11203a, C11204a, C11205a, C11206a, C11207a, C11208a, C11209a, C112010a, C112011a, C11212a, C11213a, C11214a, C11215a, C11216a, C11217 and C11218.
DOI: 10.48331/scielo.q4pffu/ikdoqi
title: 1 Barend Mons.pptx

description: :unav
DOI: 10.48331/scielo.q4pffu
title: Visions of the Future: SciELO 25+ Roundtable 1 - Musings about the future

description: Presentation by Barend Mons in the Visions of the Future: SciELO 25+ Roundtable 1 panel of the SciELO 25 Years Week. Event page: https://25.scielo.org/en
DOI: 10.15493/saeon.fynbos.141220-3
title: Protocol for taking readings and routine servicing and maintenance of Belfort waterlevel gauges

description: Protocol used for taking readings as well as performing routine servicing and maintenance of Belfort waterlevel gauges at V-notch weirs in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.csag.10000131
title: Number of days for Maximum Temperature under 10th percentile for Far Future (2080 - 2100)

description: Model Run: Far future (2080 - 2100) (Far future (2080 - 2100)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.beeh.10000022
title: Actual Vapour Pressure (June)

description: Actual vapour pressure is the variable expressing atmospheric moisture content through the partial pressure exerted by the water vapour present in the atmosphere. 

The monthly uncorrected vapour pressure, ed, at any given location in South Africa was subsequently held constant for that entire month and derivatives of ed, such as daily maximum and minimum relative humidity or daily vapour pressure deficit which depend on ratios of uncorrected actual to saturated vapour pressure, were then computed using daily maximum/minimum temperatures as inputs to daily saturated vapour pressure calculations, which were in turn used with that location's monthly uncorrected actual vapour pressure.
DOI: 10.15493/sarva.beeh.10000335
title: Monthly Means Of Daily Minimum Temperature (C) November

description: Minimum (Night-Time) temperatures, there is abundant evidence for the climatic limitation of crop distributions to be described by minimum temperatures, from a perspective of crop survival (on Frost for tolerance levels of crops). Many subtropical crops may be killed at 5 degree Celsius already through chilling injury, even in the absence of frost (Chang, 1968). High minima increase respiration loss, thereby decreasing net photosynthesis.

Daily minimum temperature time series were derived from over 970 qualifying temperature stations` quality controlled daily records for the 50 year period 1950 - 1999. From these time series, 50 years of daily values of minimum temperatures were generated at each of 429 700 one arc minute (i.e. 1.7 km x 1.7 km) raster points covering South Africa, using regional and seasonal lapse rate and infilling/data extension techniques developed by Schulze and Maharaj (2004). Note that in the generation of daily minimum temperatures no account could be taken of cold air drainage into hollows or valleys, which can take place from surrounding higher-lying areas through radiative cooling during calm, stable clear night conditions. Reasons for this are provided in Schulze and Maharaj (2004). From these daily values, monthly means of daily minimum temperatures, as well as standard deviation of daily minimum temperatures for each month of the year, could be computed for purposes of mapping and production of statistics which are tabulated for Swaziland, Lesotho and for each of the nine provinces of the RSA. This approach to mapping minimum temperatures is thus fundamentally different to that used in the previous `South African Atlas of Agrohydrology and Climatology` (Schulze, 1997), in being much more physically based.
DOI: 10.15493/sarva.csag.10000042
title: Mean of Minimum 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.10000117
title: Seasonal total rainfall (mm per 3-month season) change from the median projected for 2066-2095, relative to present (1976 - 2005), for SON season under the RCP 4.5 pathway

description: Seasonal (SON)rainfall (mm per month) 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 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/sarva.beeh.10000089
title: Heat Units (Standard Deviation) - 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/bea.data.10000002
title: Sorghum Yield Estimates per mesozone

description: *Data shows sorghum yield estimates 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). *Sorghum is indigenous to Africa. In comparison with maize, it is grown in relatively warm areas. *Using Smith`s (1998) climatic criteria, yields of sorghum are estimated using the effective rainfall for October to March and heat units (base 10 degree Celsius) for the same period, with modifications to yield made for soil properties and management levels. Rainfall values were derived from the 1 arc minute (1` x 1` latitude x longitude) median monthly rainfalls generated for South Africa by Lynch (2004).
DOI: 10.15493/sarva.csag.10000012
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/bea.data.250320-24
title: BioEnergy Atlas Model Outputs - Used Cooking Oil

description: Optimised Facility Locations, Transport Routes and Feedstock Catchment Areas, and modelled cost of Transport for the production of BioDiesel from Used Cooking Oil. These datasets were compiled in support of the Women in BioDiesel Project case study published in the second edition of the BioEnergy Atlas for South Africa.
DOI: 10.15493/dea.carbon.10000021
title: Dynamic vegetation model 1990

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 1990.
DOI: 10.48331/scielo.whr71h/3jksya
title: 3 Juan Pablo Alperin.pptx

description: :unav
DOI: 10.48331/scielo.whr71h
title: Equity in Open Access: Equity in Open Science - Journals using PKP’s Open Journal System

description: Presentation by Juan Pablo Alperín in the Equity in Open Access: Equity in Open Science panel of the SciELO 25 Years Week. Event page: https://25.scielo.org/en
DOI: 10.15493/nrf.saiab.20232502
title: Bathymetry data collected from Blood Reef, December 2021

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 is from the Blood Reef CGS area. This dataset consists of locations below the surface of the water where the depth of water is 7 to 100m deep. This dataset is used for biological and geological research purposes.
description: See metadata file for additional information.
DOI: 10.15493/sarva.beeh.10000269
title: Monthly Means Of Daily Maximum Temperature (C) 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.
DOI: 10.15493/saeon.ndlovu.250423
title: South African Wildlife College automated weather station [60 minutes] 2022-10-01 to 2023-03-31

description: This is a dataset collected by one of SAEON Ndlovu Node automated weather stations (AWS), located at the following locations: South African Wildlife College (-24.54092, latitude, 31.33022 longitude). The station contains a mixture of sensor manufacturers including Campbell Scientific, Texas and Kipp and Zonen. For more information regarding these contact the creators.
DOI: 10.15493/sarva.saws.10000062
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 2066 - 2095 relative to 1976-2005, for the JJA season, under the RCP 4.5 pathway

description: Root Mean Square Difference for seasonal (JJA) mean near-surface (2m) temperature (°C) change from the 90% 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.
DOI: 10.15493/sarva.beeh.10000173
title: Monthly Means Of Daily Minimum Relative Humidity (%) December

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.10000006
title: Langrivier gauging weir hourly stage and estimated flow, 08/2011 - 09/2019

description: Stream water stage measured at a V-notch weir in the Langrivier stream in the Jonkershoek Valley. Water levels at the weir measured and logged half hourly by an OTT Orpheus Mini (Hach, Germany) vented pressure transducer. Hourly averages produced from half hourly readings. Streamflow estimated from stage using attached rating curve. (Collection continuing, contact SAEON for updated datasets)
DOI: 10.15493/sarva.beeh.10000144
title: Monthly Means Of Daily Average Relative Humidity (%) October

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/sarva.csag.10000138
title: Maximum of Minimum 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/saeon.egagasini.10000100
title: The ASCA July 2019 (SEAmester IV) raw Scientific Data System 1 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 1-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: 10.6092/unibo/amsacta/7377
title: Georeferenced 3D model of the rock pinnacle "Campanile di Val Montanaia" from Structure from Motion

description: We produced a high-quality 3D model of a rocky pinnacle in the Dolomites (Italy), the "Campanile di Val Montanaia". The survey was performed by means of a lightweight Unmanned Aerial Vehicle (UAV) and by climbing the pinnacle to collect Ground Control Points (GCPs) coordinates. All data has been processed using Structure from Motion (SfM) technique. We share a georeferenced 3D mesh of the pinnacle aligned to ITRF2014 and delivered using projected coordinates UTM zone 33N (EPSG:32633). The dataset is associated with the following publication submitted to Remote Sensing: A Laboratory for the Integration of Geomatic and Geomechanical Data: The Rock Pinnacle "Campanile di Val Montanaia". Remote Sens. 2023, 15, 4854. https://doi.org/10.3390/rs15194854