| packages |
[{'author': None, 'author_email': None, 'creator_user_id': '64e47517-dce0-4a13-9ab7-ef1cfcd1d892', 'doi': '10.15493/SARVA.BEEH.10000104', 'featured': 'false', 'id': '84eba355-8f57-484b-b365-b008e6769b37', 'isopen': False, 'license_id': None, 'license_title': None, 'lineage_statement': 'Litchis, Litchi chinensis, are indigenous to subtropical southern China and were first imported to South Africa from Mauritius in the 1870s (ARC, 2005). Of the 7 000 tons of litchis produced annually in South Africa, with a gross value exceeding R25 million, about 60 percent come from Mpumalanga, 38 percent from Limpopo and 2 percent from KwaZulu-Natal (Statistics SA, 2002; NDA, 2005).', 'maintainer': None, 'maintainer_email': None, 'metadata_created': '2025-10-17T10:52:41.204356', 'metadata_date': '2025-10-17T00:00', 'metadata_modified': '2025-10-17T11:03:29.674696', 'metadata_thumbnail': '', 'name': 'climatically_optimum_growth_areas_litchis_composite_of_all_criteria', 'notes': 'Litchis, Litchi chinensis, are indigenous to subtropical southern China and were first imported to South Africa from Mauritius in the 1870s (ARC, 2005). Of the 7 000 tons of litchis produced annually in South Africa, with a gross value exceeding R25 million, about 60 percent come from Mpumalanga, 38 percent from Limpopo and 2 percent from KwaZulu-Natal (Statistics SA, 2002; NDA, 2005).', 'num_resources': 1, 'num_tags': 9, 'organization': {'id': 'c4a18544-cded-4488-9f43-c02a1094ad39', 'name': 'saeon', 'title': 'NRF-SAEON', 'type': 'organization', 'description': 'The South African Environmental Observation Network (SAEON) is a long-term environmental observation and research facility of the National Research Foundation (NRF). SAEON’s three focus areas are environmental observation, data management and education outreach. The Department of Science, Technology and Innovation (DSTI) provides core funding for these activities. ', 'image_url': '2025-08-01-083136.289314LogoSAEON.png', 'created': '2025-05-26T07:57:06.135422', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'c4a18544-cded-4488-9f43-c02a1094ad39', 'private': False, 'reference_system_additional_info': '', 'spatial': '{"type": "Polygon", "coordinates": [[[16.458333, -34.841667], [32.908333, -34.841667], [32.908333, -22.141667], [16.458333, -22.141667], [16.458333, -34.841667]]]}', 'state': 'active', 'title': 'Climatically Optimum Growth Areas Litchis Composite Of All Criteria', 'type': 'dataset', 'url': None, 'version': None, 'contact': [{'electronic_mail_address': 'curation@saeon.nrf.ac.za', 'individual_name': 'SAEON', 'position_name': '', 'role': 'point_of_contact'}], 'contact_information': [{'facsimile': '', 'voice': ''}], 'dataset_reference_date': [{'reference': '2007-01-01T00:00', 'reference_date_type': 1}], 'distribution_format': [{'name': 'Electronic metadata record', 'version': '1.1'}], 'metadata_language_and_character_set': [{'dataset_character_set': 'utf-8', 'dataset_language': 'English', 'metadata_character_set': 'utf-8', 'metadata_language': 'English'}], 'metadata_standard': [{'name': 'SANS 1878-1:2011', 'version': '1.1'}], 'online_resource': [{'application_profile': '', 'description': 2, 'linkage': 'https://catalogue.saeon.ac.za/records/10.15493/SARVA.BEEH.10000104', 'name': 'Climatically Optimum Growth Areas Litchis Composite Of All Criteria'}], 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2025-10-17T10:57:28.000498', 'description': '', 'format': '', 'format_version': '', 'hash': '', 'id': '0d6d7010-b440-48b4-b1f1-23f9e48ae3e9', 'last_modified': None, 'metadata_modified': '2025-10-17T10:57:27.994148', 'mimetype': None, 'mimetype_inner': None, 'name': 'Climatically Optimum Growth Areas Litchis Composite Of All Criteria', 'package_id': '84eba355-8f57-484b-b365-b008e6769b37', 'position': 0, 'resource_type': 'other', 'size': None, 'state': 'active', 'upload_mode': 'single', 'url': 'https://repository.saeon.ac.za/index.php/s/3E4cCsAMaj92xjw?opendetails=', 'url_type': None}], 'responsible_party': [{'electronic_mail_address': '', 'individual_name': 'R.E. 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Independent validation data revealed overall predictive performance of the interpolation to have R2 values of 0.90, 0.85, and 0.59 for maximum temperature, minimum temperature, and precipitation, respectively. A suite of ecologically-relevant climate metrics that include the uncertainty introduced by the interpolation were then generated. By providing the high resolution climate metric surfaces and uncertainties, this work facilitates richer and more robust predictive modeling in ecology and bio- geography. These data can be incorporated into ecological models to propagate the uncertainties through to the final predictions', 'maintainer': None, 'maintainer_email': None, 'metadata_created': '2025-10-16T11:32:14.831108', 'metadata_date': '2016-01-01T00:00', 'metadata_modified': '2025-10-17T09:38:59.712167', 'metadata_thumbnail': '', 'name': 'interpolated_observed_weather_data_for_the_cape_floristic_region', 'notes': 'Conservation of biodiversity demands comprehension of evolutionary and ecological patterns and processes that occur over vast spatial and temporal scales. A central goal of ecology is to understand the climatic factors that control ecological processes and this has become even more important in the face of climate change. Especially at global scales, there can be enormous uncertainty in underlying environmental data used to explain ecological processes, but that uncertainty is rarely quantified or incorporated into ecological models. In this study a climate-aided Bayesian kriging approach is used to interpolate 20 years of daily meteorological observations (maximum and minimum temperature and precipitation) to a 1 arc-minute grid for the Cape Floristic Region of South Africa. Independent validation data revealed overall predictive performance of the interpolation to have R2 values of 0.90, 0.85, and 0.59 for maximum temperature, minimum temperature, and precipitation, respectively. A suite of ecologically-relevant climate metrics that include the uncertainty introduced by the interpolation were then generated. By providing the high resolution climate metric surfaces and uncertainties, this work facilitates richer and more robust predictive modeling in ecology and bio- geography. These data can be incorporated into ecological models to propagate the uncertainties through to the final predictions.', 'num_resources': 1, 'num_tags': 6, 'organization': {'id': 'c4a18544-cded-4488-9f43-c02a1094ad39', 'name': 'saeon', 'title': 'NRF-SAEON', 'type': 'organization', 'description': 'The South African Environmental Observation Network (SAEON) is a long-term environmental observation and research facility of the National Research Foundation (NRF). SAEON’s three focus areas are environmental observation, data management and education outreach. The Department of Science, Technology and Innovation (DSTI) provides core funding for these activities. 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This data was only available at provincial aggregate level, and derives from statistics published by the Department of Water and Sanitation, or recent studies funded by them. * Data from StatsSA (Census 2011) enabled the calculation of number of households within each planning zone that were serviced at the time, with the balance unserviced. * SAEON developed a model from national and international statistics linking waste water production and composition to household income. This model was used, based on StatsSA Census Data, to estimate the organic component produced by each household per planning zone (mesozone) annually. * These factors were used to disaggregate provincial production data, resulting in a value for unserviced and serviced organic wastewater from household sources to be calculated for each mesozone.', 'maintainer': None, 'maintainer_email': None, 'metadata_created': '2025-10-17T07:12:59.317624', 'metadata_date': '2025-10-17T00:00', 'metadata_modified': '2025-10-17T09:37:29.037486', 'metadata_thumbnail': '', 'name': 'serviced_and_unserviced_waste_water_organic_component', 'notes': 'Data was derived from the following sources: * CSIR was commissioned by the BioEnergy Atlas to assemble known data on solid waste production from household and commercial sources in South Africa. 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This model was used, based on StatsSA Census Data, to estimate the organic component produced by each household per planning zone (mesozone) annually. * These factors were used to disaggregate provincial production data, resulting in a value for unserviced and serviced organic wastewater from household sources to be calculated for each mesozone.', 'num_resources': 1, 'num_tags': 8, 'organization': {'id': 'c4a18544-cded-4488-9f43-c02a1094ad39', 'name': 'saeon', 'title': 'NRF-SAEON', 'type': 'organization', 'description': 'The South African Environmental Observation Network (SAEON) is a long-term environmental observation and research facility of the National Research Foundation (NRF). SAEON’s three focus areas are environmental observation, data management and education outreach. The Department of Science, Technology and Innovation (DSTI) provides core funding for these activities. 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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). The map of climatically optimum growth areas shows the main potential areas for A. mearnsii to be in a strip along the coast of the Eastern Cape, parts of the midlands and northwest of KwaZulu-Natal and a strip in Mpumalanga, with smaller patches of climatically optimum areas elsewhere.', 'maintainer': None, 'maintainer_email': None, 'metadata_created': '2025-10-17T09:32:50.896907', 'metadata_date': '2025-10-17T00:00', 'metadata_modified': '2025-10-17T09:35:51.349560', 'metadata_thumbnail': '', 'name': 'acacia_mearnsii_bark_yield_estimates_per_mesozone', 'notes': 'The dataset shows Acacia mearnsii - bark 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). The map of climatically optimum growth areas shows the main potential areas for A. mearnsii to be in a strip along the coast of the Eastern Cape, parts of the midlands and northwest of KwaZulu-Natal and a strip in Mpumalanga, with smaller patches of climatically optimum areas elsewhere.', 'num_resources': 1, 'num_tags': 11, 'organization': {'id': 'c4a18544-cded-4488-9f43-c02a1094ad39', 'name': 'saeon', 'title': 'NRF-SAEON', 'type': 'organization', 'description': 'The South African Environmental Observation Network (SAEON) is a long-term environmental observation and research facility of the National Research Foundation (NRF). SAEON’s three focus areas are environmental observation, data management and education outreach. The Department of Science, Technology and Innovation (DSTI) provides core funding for these activities. 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', 'image_url': '2025-08-01-083136.289314LogoSAEON.png', 'created': '2025-05-26T07:57:06.135422', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'c4a18544-cded-4488-9f43-c02a1094ad39', 'private': False, 'reference_system_additional_info': '', 'spatial': '{"type": "Polygon", "coordinates": [[[16.452, -34.8341698957], [32.8925317467, -34.8341698957], [32.8925317467, -22.12503], [16.452, -22.12503], [16.452, -34.8341698957]]]}', 'state': 'active', 'title': 'Infrastructure Assessments', 'type': 'dataset', 'url': None, 'version': None, 'contact': [{'electronic_mail_address': 'curation@saeon.nrf.ac.za', 'individual_name': 'Wim Hugo', 'position_name': '', 'role': 'point_of_contact'}], 'contact_information': [{'facsimile': '', 'voice': ''}], 'dataset_reference_date': [{'reference': '2015-01-01T00:00', 'reference_date_type': 1}], 'distribution_format': [{'name': 'Electronic metadata record', 'version': '1.1'}], 'metadata_language_and_character_set': [{'dataset_character_set': 'utf-8', 'dataset_language': 'English', 'metadata_character_set': 'utf-8', 'metadata_language': 'English'}], 'metadata_standard': [{'name': 'SANS 1878-1:2011', 'version': '1.1'}], 'online_resource': [{'application_profile': '', 'description': 2, 'linkage': 'https://catalogue.saeon.ac.za/records/10.15493/BEA.DATA.10000060', 'name': 'Infrastructure Assessments'}], 'resources': [{'cache_last_updated': None, 'cache_url': None, 'created': '2025-10-16T14:43:34.437295', 'description': '', 'format': 'GEOMAP', 'format_version': '', 'hash': '', 'id': '3cd55fec-5252-49d0-9681-0f30991fd80b', 'last_modified': None, 'metadata_modified': '2025-10-16T14:43:34.427997', 'mimetype': None, 'mimetype_inner': None, 'name': 'Infrastructure Assessments', 'package_id': '3754a61d-e1e2-4cf6-a830-b8481b888581', 'position': 0, 'resource_type': 'other', 'size': None, 'state': 'active', 'upload_mode': 'single', 'url': 'https://catalogue.saeon.ac.za/records/10.15493/BEA.DATA.10000060', 'url_type': None}], 'responsible_party': [{'electronic_mail_address': '', 'individual_name': 'South African Environmental Observation Network', 'position_name': 'Organisation', 'role': 'resource_provider', 'website': 'catalogue.saeon.ac.za'}], 'responsible_party_contact_address': [{'administrative_area': '', 'city': '', 'delivery_point': '', 'postal_code': ''}], 'responsible_party_contact_info': [{'facsimile': '', 'voice': ''}], 'spatial_parameters': [{'equivalent_scale': 'N/A', 'spatial_reference_system': 'EPSG: 4326', 'spatial_representation_type': '001'}], 'tags': [{'display_name': 'Demography', 'id': 'ccd1fbaf-5fe7-4784-a643-b6f498578432', 'name': 'Demography', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Economic vulnerability', 'id': '2174dd69-d0cf-4f56-8a32-f527bfb3eb68', 'name': 'Economic vulnerability', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Environment', 'id': 'ad6bb22c-77f7-4d69-81bb-a19bc4bceb95', 'name': 'Environment', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Settlement', 'id': '76b43ead-57a0-4dd5-91ab-fcde5a799ad4', 'name': 'Settlement', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'Socio-economic', 'id': '1ae6c2fa-2640-4c76-a1e7-a68f7a113248', 'name': 'Socio-economic', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'biomass potential', 'id': 'c536ff24-f315-44b0-a86c-ceeb3d7ce9ce', 'name': 'biomass potential', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'economic activity', 'id': '100a8a2a-70eb-498d-9c1b-9fa723baf80b', 'name': 'economic activity', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'infrastructure', 'id': '280c04f7-fe83-4294-8167-f2526f7c4a1f', 'name': 'infrastructure', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'population', 'id': 'f3244691-7bd3-4de1-a346-92f132d043ee', 'name': 'population', 'state': 'active', 'vocabulary_id': None}, {'display_name': 'pressure', 'id': '4036c4e1-0924-4266-899c-fef8fdc97411', 'name': 'pressure', 'state': 'active', 'vocabulary_id': None}], 'topic_and_saeoss_themes': [{'iso_topic_category': 'environment'}], 'groups': [], 'relationships_as_subject': [], 'relationships_as_object': []}, {'author': None, 'author_email': None, 'creator_user_id': '64e47517-dce0-4a13-9ab7-ef1cfcd1d892', 'doi': '', 'featured': 'true', 'id': 'eeac54f3-4f1d-4486-9c28-02aa24fe5298', 'isopen': False, 'license_id': None, 'license_title': None, 'lineage_statement': 'This database was originally developed from a DFFE internal database containing environmental applications for renewable energy developments based on the SEA project for Renewable Energy updated to December 2013. From December 2013 to 30 May 2025 the information extracted from these databases were verified and supplemented with official environment application documentation where available, such as application forms, EIA reports, Environmental Authorisations, and lapse letters. Such documentation was primarily provided by DFFE. In cases where documentation was not available from DFFE it was sourced from project websites. In some instances where official documentation was not available from DFFE applicants or their consultants were also contacted to obtain or verify information. There are 0 amendments added; 24 new applications captured and 0 corrections made, see ANNEXURE A with details of records.', 'maintainer': None, 'maintainer_email': None, 'metadata_created': '2025-07-29T13:12:22.439409', 'metadata_date': '2025-07-29T00:00', 'metadata_modified': '2025-07-29T16:15:55.992836', 'metadata_thumbnail': '', 'name': 'south_african_renewable_energy_eia_application_database', 'notes': 'The South African Renewable Energy EIA Application Database contains spatial data for renewable energy applications for environmental authorisation. It includes spatial and attribute information for both active (in process and with valid authorisations) and non-active (lapsed or replaced by amendments) applications. Data is captured and managed on a parcels level as well as aggregated to the project level. Only outer boundaries are provided in this release.', 'num_resources': 3, 'num_tags': 2, 'organization': {'id': 'a08da558-64c6-4214-a930-c678f7300334', 'name': 'dffe', 'title': 'DFFE', 'type': 'organization', 'description': 'Department of Forestry, Fisheries and the Environment', 'image_url': '2025-07-28-071113.285804DFFE.png', 'created': '2025-07-28T07:11:13.295821', 'is_organization': True, 'approval_status': 'approved', 'state': 'active'}, 'owner_org': 'a08da558-64c6-4214-a930-c678f7300334', 'private': False, 'reference_system_additional_info': 'N/A', 'spatial': '{"type": "Polygon", "coordinates": [[[16.452278, -46.98034], [38.011607, -46.98034], [38.011607, -22.329087], [16.452278, -22.329087], [16.452278, -46.98034]]]}', 'state': 'active', 'title': 'South African Renewable Energy EIA Application Database', 'type': 'dataset', 'url': None, 'version': None, 'contact': [{'electronic_mail_address': 'Reapplicationmap@dffe.gov.za', 'individual_name': 'Acting Director: Environmental Spatial Information', 'position_name': 'Acting Director', 'role': 'point_of_contact'}], 'contact_information': [{'facsimile': '', 'voice': ''}], 'dataset_reference_date': [{'reference': '2025-04-30T00:00', 'reference_date_type': 2}], 'distribution_format': [{'name': 'ArcGIS Pro 3.2.0. 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