cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.csag.10000449
title: Number of days for Maximum Temperature under 10th 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/sarva.csag.10000126
title: Rainfall days per month (> 2mm) 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: 10.48331/scielo.zivv17/lizfoo
title: 1 Alicia J Kowaltowski.pptx

description: :unav
DOI: 10.48331/scielo.zivv17
title: Equity in Open Access: Equity in Open Science - Scielo 25 years: Way ahead of its time!

description: Presentation by Alicia J. Kowaltowski 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/sarva.saws.10000134
title: Root Mean Square Difference between the nine ensemble member change anomalies of the annual total rainfall for the 90% percentile for 2036-2065 relative to 1976-2005, under the RCP 8.5 pathway

description: Root Mean Square Difference for annual total rainfall (mm per year) 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 rainfall averages, which are used to generate projections of annual 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: 10.6084/m9.figshare.24231019
title: Weekly cholera cases by governorate and district in Yemen between 2016 and 2020

description: These datasets have been digitised from publicly available official WHO data on cholera cases in Yemen. They are provided in xlsx and csv formats.More information on cholera surveillance in Yemen is available in the report Cholera in Yemen:<br>a case study of epidemic preparedness and response by the Johns Hopkins Center for Humanitarian Health.EOC dashboard dataThe dataset "YEM-CHOLERA-EOC-DIS-WEEK-20170424-20200621" contains weekly cholera case counts by district (administrative level 2) in Yemen from 2017-04-24 to 2020-06-21.These data were automatically extracted from the cholera dashboard developed by the emergency operations center (EOC) led by WHO. The EOC brought the efforts of all UN agencies, Humanitarian partners, government ministries and other stakeholders together in one place. The EOC concept worked at national and governorate levels led by the incident management cell of WHO. This unified platform provided Operations, technical, logistics and Information Management support during the cholera epidemic in Yemen.WHO Epi Bulletin dataThe dataset "YEM-CHOLERA-WHO-GOV-WEEK-20160926-20170423" contains weekly cholera case counts by governorate (administrative level 1) in Yemen from 2016-09-26 to 2017-04-23.The EOC dashboard does not contain data on the first wave of the cholera epidemic in Yemen, which occurred in 2016. For this period, the only publicly available data comes from the WHO Epidemiological Bulletin, which was published regularly on the WHO website as well as on Relief-Web. The report for the week 26 of 2016 (see file "WHO-YEM-CHOLERA-EPI-BULLETIN-2018-W26.pdf") contains time series graphs of weekly cholera cases by governorate, which have been digitised using PlotDigitizer. Due to the limited resolution of the graphs, we have decided to round the value obtained to the nearest 50.Combined dataThe dataset "YEM-CHOLERA-WHOEOC-GOV-WEEK-20160926-20200621" contains weekly cholera case counts by governorate (administrative level 1) in Yemen from 2016-09-26 to 2020-06-21.This dataset was created by aggregating district-level EOC dashboard data at governorate level and combining it to data from the first wave of the WHO Epi Bulletin.Dictionary of variablesgovernorate_en (text) : name of governorate in Englishgovernorate_ar (text) : name of governorate in Arabicdistrict_en (text) : name of district in Englishdistrict_ar (text) : name of district in Arabicdate_monday (date) : start date of the week (Monday)date_sunday (date) : end date of the week (Sunday)cases (integer) : number of cholera cases reported on that week<br><br>
DOI: 10.15493/sarva.csag.10000140
title: Minimum 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: 10.6084/m9.figshare.24231019.v1
title: Weekly cholera cases and rainfall by governorate and district in Yemen between 2016 and 2020

description: These datasets have been digitalised from publicly available official data on cholera cases in Yemen.<br><br>
DOI: 10.15493/sarva.csag.10000371
title: Maximum 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: 10.15493/sarva.csag.10000041
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/sarva.beeh.10000183
title: Passion Fruit: Optimum Growth Areas - Criterion 4: In total, there should be < 25 days per annum with frost

description: Passion fruit are believed to have originated from southern Brazil (Kenyaweb, 2005).The most commonly commercially grown of 55 edible species of passion fruit is Passiflora edulis, which consists of perennial woody vines with each producing about 100 fruits per year (RBGK, 2005). In South Africa passion fruit ideally requires a cool subtropical climate for optimum production (Smith, 1998). The vines prefer moderate temperatures throughout the year, with monthly means of daily maxima < 29 degree Celsius. The plants are sensitive to severe frosts, and monthly means of minimum temperatures should be > 6 degree Celsius (NDA, 2005). For commercial production under rainfed conditions MAP should ideally exceed 1 200 mm and be well distributed throughout the year (NDA, 2005), as it is important to maintain the soil moist throughout the growing season to keep the vines flowering and fruiting for longer periods and prevent the fruit from shriveling and falling prematurely (Kenyaweb, 2005). Total commercial production of passion fruit in South Africa is 1 300t per season, with a range (between 1998/9 - 2003/4) in more recent from 900 - 1 700 t (NDA, 2005).The gross value is R65 million/year (NDA, 2005).

Criteria for climatically optimum growth areas for passion fruit were refined by Bower (2005). Six criteria were identified and using the 1` x 1` gridded information on temperature related parameters derived by Schulze and Maharaj (2004), the following criteria were mapped: Criterion 1: > 330 days per year with a minimum temperature exceeding 2 degree Celsius; Criterion 2: Fewer than 2 consecutive days per annum with minimum temperatures < -2 degree Celsius; Criterion 3: There should not be > 3 consecutive frost days per annum; Criterion 4: In total, there should be < 25 days per annum with frost; Criterion 5: Maximum temperatures should be < 28 degree Celsius on at least 25 days of each month; Criterion 6: Maximum temperatures should not exceed 35 degree Celsius on any two consecutive days per month. Mapped criteria were given either categorical or probabilistic weightings and when the six maps were superimposed, suitable and less suitable climatically optimum growth areas could be identified. The assumption was made that for commercial production the passion
DOI: 10.15493/sarva.beeh.10000014
title: A-Pan Equivalent Reference Potential Evaporation (November)

description: Reasons for having selected the A-Pan as a reference for potential evaporation is that the US Weather Bureau Class A evaporimeter is, universally, the most common evaporation pan in usage, having been adopted as the standard evaporation pan since the International Geophysical Year 1957/8. Also when operated properly it is accepted as a reasonably reliable, simple and inexpensive local integrator of atmospheric evaporative demand, particularly for time periods longer than one day. Problems associated with the A-Pan as a reference for potential evaporation is that they are found generally near dams, existing irrigation projects or in areas of low relative relief where commercial agriculture is practiced. These sites are not always representative of higher altitudes where water is generated.

The data from over 570 South African stations at which a minimum of three years` concurrent monthly temperature, rainfall and A-pan evaporation observations are available, were used to develop temperature based equations of A-pan equivalent evaporation for each of the 12 evaporation regions identified in South Africa. The equations, unique for each month and for each region, were used to develop the 12 rasterised images of mean monthly A-pan equivalent evaporation on a 1` x 1` of a degree latitude/longitude grid.
DOI: 10.48331/scielo.cmbxl6/r7jpkh
title: 3-Leonardo-Cabral.pptx

description: :unav
DOI: 10.48331/scielo.cmbxl6
title: Padrões, métodos, técnicas, práticas: Interoperabilidade e Acessibilidade - Acessibilidade atitudinal na produção e difusão do conhecimento científico

description: Apresentação de Leonardo Santos Amâncio Cabral no painel Padrões, métodos, técnicas, práticas: Interoperabilidade e Acessibilidade da Semana SciELO 25 Anos. Página do evento: https://25.scielo.org/
DOI: 10.15493/sarva.dws.10000006
title: River profile classification Catchment B

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/saeon.egagasini.10000023
title: The ASCA July 2018 (SEAmester III) 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. This specific raw environental data was collected by a shipborne underway Thermosalinograph (TSG).
DOI: 10.15493/sarva.beeh.10000406
title: Groundnut Yield Estimation (T.Ha-1.Season-1; Shelled) Based On SmithS (1994) Climatic Criteria

description: Rich in protein, groundnuts (Arachis hypogaea) can be eaten raw, cooked or roasted.  For optimum yields, groundnuts require a MAP > 700 mm of which > 500 mm should fall over their 5 - 6 month summer growing season (Smith, 1998), although yields can be attained with only 300 mm over the growing season (NDA, 2005). Mean daily temperatures should be in the range of 22 – 28 degree Celsius for optimum growth, with yield reductions occurring above 33 degree Celsius and below 18 degree Celsius.

Using Smith (1998) climatic criteria, yields of groundnuts are estimated using the effective rainfall for October to March and heat units (base 10 degree Celsius) for the same period, with modifications to yields made for soil properties and levels of management. 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/saeon.ndlovu.10000004
title: SAEON OWNR Davis Vantage Pro2 AWS 30 minutes (AirTemp, Rain, RainRate) 2011-07-06 to 2020-01-31

description: Olifants-West Nature Reserve, Balule, Limpopo Province, South Africa (ownr; -24.227078 latitude, 30.868392 longitude). The make and model of the stations is Davis Vantage Pro 2.
DOI: 10.15493/sarva.csag.10000220
title: Minimum 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: 10.15493/saeon.fynbos.ab6692
title: Abdolskloof Belfort stream flow volume March 1966 - July 1992

description: Hourly stream flow in cubic meters estimated from stage height measured with Belfort 5-FW liquid level recorder at a V-notch weir in Abdolskloof catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.48331/scielo.xui6z9/rqukaq
title: 2 Erik Schultes.pdf

description: :unav
DOI: 10.48331/scielo.xui6z9
title: Standards, methods, techniques, practices: Interoperability and Accessibility - Academic publishing using FAIR Digital Objects

description: Presentation by Erik Schultes in the Standards, methods, techniques, practices: Interoperability and Accessibility panel of the SciELO 25 Years Week. Event page: https://25.scielo.org/en
DOI: 10.15493/sarva.csag.10000215
title: Total precipitation for the month (mm) 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.10000101
title: Seasonal total rainfall (mm per 3-month season) change from the 10% percentile projected for 2066-2095, relative to present (1976 - 2005), for DJF season under the RCP 4.5 pathway

description: Seasonal (DJF) rainfall (mm per month) 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 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.saws.10000260
title: Root Mean Square Difference in percentage (%) between the nine ensemble member change anomalies of the seasonal percentage rainfall for the median for 2066 - 2095 relative to 1976-2005, for the JJA season, under the RCP 4.5 pathway

description: Root Mean Square Difference in percentage (%) between the nine ensemble member change anomalies of the seasonal percentage rainfall for the median for 2066 - 2095 relative to 1976-2005, for the JJA season, 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: 10.48331/scielo.p9hc9d/sknqgf
title: 4 Shelley Stall.pptx

description: :unav
DOI: 10.48331/scielo.p9hc9d
title: Standards, methods, techniques, practices: Interoperability and Accessibility - Publisher Guidelines: Artificial Intelligence Tools for Research and Authorship

description: Presentation by Shelley Stall in the Standards, methods, techniques, practices: Interoperability and Accessibility panel of the SciELO 25 Years Week. Event page: https://25.scielo.org/en
DOI: 10.15493/sarva.csag.10000172
title: Maximum 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/dea.carbon.10000032
title: Tmax 1986-2015 Median

description: The current long term annual climate conditions for the 30-year period 1986-2015. The maximum daily temperature values simulated by six different global circulation models (GCMs), i.e. csiro, gfdl20, gfdl21, miroc, mpi, and ukmo were downscaled with the Cubic Conformal Atmospheric Model (CCAM) developed by CSIRO (Australia) and updated and modified by the CSIR (South Africa). The mean maximum daily temperature was calculated per year for each of the six downscaled GCMs for the 30-year period. The median (50th percentile) values are based on the mean 30-year maximum daily temperature per year from the the six downscaled GCMs. Data are in raster format with a 0.5º resolution using a projected latitude / longitude coordinate reference system with `+proj=utm +zone=48 +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0`.
DOI: 10.15493/dea.carbon.10000038
title: Rain A2 Change between 2000 and 2050 Median

description: % change in the long term mean annual total rainfall between 1986-2015 and 2036 - 2065. The mean annual rainfall for each downscaled global circulation model (GCM), i.e. csiro, gfdla20, gfdl21, miroc, mpi and ukmo using the the Cubic Conformal Atmospheric Model (CCAM) developed by CSIRO (Australia) and updated and modified by the CSIR (South Africa) was calculated for each of the 30 year periods. The percentage difference in the 30-year mean annual rainfall  between the  two periods was calculated for each of the six models as follows: ((rain2050 - rain2000)/rain2000)x100. The 50th percentile (median value) is based on these differences. Data are in raster format with a 0.5º resolution using a projected latitude / longitude coordinate reference system with `+proj=utm +zone=48 +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0`.
DOI: 10.15493/sarva.dws.10000013
title: River profile classification Catchment K

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.