AndreaBozzo/ceres-open-data-index huggingface dataset:
CCE Index 2025 — All Countries Data

Comprehensive country-level dataset accompanying the publication "The Circular Carbon Economy Index 2025: Results." Contains data for approximately 125 countries, with each country's income group, region, and changes in CCE Performance and CCE Enablers sub-indices between 2021 and 2025. Includes detailed breakdowns across key sub-dimensions: Energy efficiency, Fuel switching, Clean energy, Electrification, Natural sinks, and Material efficiency and waste management.
CCE Index 2025 — Top 25 Improvers

The 25 countries that showed the largest improvement in their Total CCE Index score between 2021 and 2025, accompanying the publication "The Circular Carbon Economy Index 2025: Results." The dataset includes each country's income group, region, and the breakdown of improvement across the CCE Performance and CCE Enablers sub-indices, along with detailed changes in key sub-dimensions: Energy efficiency, Fuel switching, Clean energy, Electrification, Natural sinks, and Material efficiency and waste management. This dataset highlights which countries have made the most progress on the road to net-zero over the past five years.
CCE Index 2025 — Top 25 Decliners

The 25 countries that experienced the largest decline in their Total CCE Index score between 2021 and 2025, accompanying the publication "The Circular Carbon Economy Index 2025: Results." The dataset includes each country's income group, region, and the breakdown of decline across the CCE Performance and CCE Enablers sub-indices, along with detailed changes in key sub-dimensions: Energy efficiency, Fuel switching, Clean energy, Electrification, Natural sinks, and Material efficiency and waste management. This dataset highlights which countries have fallen behind on the road to net-zero over the past five years and where renewed policy attention may be needed.
SP ENW - DFES - Primary Data

Dataset Classification - Open SP ENW - DFES – Primary Substation Data - New 2026 Release The Primary Substation Data section within our Distributed Future Electricity Scenarios (DFES) content, details our view of the North West’s future electricity requirements and represents forecasts based on the Primary Substations within our operational boundary. The collection of DFES datasets envisages a range of possible views of the future, which indicate how different influences can change electrical demand and generation on our network. This dataset is an accompaniment to our latest DFES report. It contains detailed datasets and interactive tools which allow our customers to understand what our Distribution Future Electricity Scenarios mean to them. Please refer to our main DFES report for a full description of our forecasts and scenarios. Our forecasts are not precise predictions of what the future is likely to be, but instead frame the uncertainty within our planning as explained in the main report. While we use reasonable endeavours to ensure that the data contained within this dataset is accurate, we do not accept any responsibility or liability for the accuracy or the completeness of the content held, or for any loss which may arise from reliance on this dataset and/or its related information. If you have any query related to this DFES Primary Dataset, please contact us [ HERE] *Actual dataset content is available to registered users only – If you have not already done so, log-in or create new account below* New User? - Create your new account [ HERE] Already registered as a SP ENWL Portal User? - Log-In [ HERE] Have you an ODS Portal account created elsewhere? - Log-In [ HERE]
SP ENW - DFES - Bulk Supply Point Data

Dataset Classification - Open SP ENW - DFES – Bulk Supply Point Data - New 2026 Release The Bulk Supply Point (BSP) Data section within our Distributed Future Electricity Scenarios (DFES) content, details our view of the North West’s future electricity requirements and represents forecasts based on the BSPs within our operational boundary. The collection of DFES datasets envisages a range of possible views of the future, which indicate how different influences can change electrical demand and generation on our network. This dataset is an accompaniment to our latest DFES report. It contains detailed datasets and interactive tools which allow our customers to understand what our Distribution Future Electricity Scenarios mean to them. Please refer to our main DFES report for a full description of our forecasts and scenarios. Our forecasts are not precise predictions of what the future is likely to be, but instead frame the uncertainty within our planning as explained in the main report. While we use reasonable endeavours to ensure that the data contained within this dataset is accurate, we do not accept any responsibility or liability for the accuracy or the completeness of the content held, or for any loss which may arise from reliance on this dataset and/or its related information. If you have any query related to this DFES BSP Dataset, please contact us [ HERE] *Actual dataset content is available to registered users only – If you have not already done so, log-in or create new account below* New User? - Create your new account [ HERE] Already registered as a SP ENW Portal User? - Log-In [ HERE] Have you an ODS Portal account created elsewhere? - Log-In [ HERE]
Funnel Web Spider Collection for Australian Reptile Park Antivenom Program

Hawkesbury City Council has been participating in the Australian Reptile Park funnel web spider antivenom program for many years and Council began collecting data in March 2023. Their antivenom program started in 1981. Since then, there have been zero deaths recorded due to funnel-web spider bites. Funnel web spiders can be dropped off at our Service Centre. We encourage residents to [follow the Australian Reptile Park funnel web spider and sac collection guidelines]. Learn more about [how they milk spiders and lifesaving antivenom program].
Cardiovascular deaths

This dataset reports age-standardised death rates (per 100,000 population) for a range of cardiovascular disease (CVD) categories across NSW Local Government Areas from 2013–14 to 2021–22. It includes total cardiovascular diseases as well as specific conditions such as coronary heart disease, stroke, heart failure, peripheral vascular disease, and other CVDs. Each estimate is accompanied by 95% confidence intervals (LL 95% CI and UL 95% CI), allowing users to assess statistical uncertainty.
SP ENW - Substation - DSO BSP Polygons

Dataset Classification - Open SP ENW - Substation - DSO BSP Polygons Individual Polygons are created using the Voronoi methodology, each one containing a single distribution substation, these individual polygons are first grouped together (based on common attribution) then merged, to create a new series of interlocking polygons forming a high-level representation of Primary Substation feeding areas. The larger polygons representing the feeding areas of Bulk Supply Points (BSP's) are generated by grouping, then merging, the Primary polygons in the same manner (a similar process creates GSP polygons from the BSP Voronoi). Document explaining the Voronoi process and creation of the feeding area polygons is available to download from the 'Alternative Exports' section of the Export tab (above) While we use reasonable endeavours to ensure that the data contained within this dataset is accurate, we do not accept any responsibility or liability for the accuracy or the completeness of the content held, or for any loss which may arise from reliance on this dataset and/or its related information. If you have any query related to this SP ENW Substation - DSO BSP Polygons dataset, please contact us [ HERE] *Actual dataset content is available to registered users only – If you have not already done so, log-in or create new account below* New User? - Create your new account [ HERE] Already registered as a SP ENW Portal User? - Log-In [ HERE] Have you an ODS Portal account created elsewhere? - Log-In [ HERE]
SP ENW - Substation - DSO GSP Polygons

Dataset Classification - Open SP ENW - Substation - DSO GSP Polygons Individual Polygons are created using the Voronoi methodology, each one containing a single distribution substation, these individual polygons are first grouped together (based on common attribution) then merged, to create a new series of interlocking polygons forming a high-level representation of Primary Substation feeding areas. The larger polygons representing the feeding areas of Bulk Supply Points (BSP's) are generated by grouping, then merging, the Primary polygons in the same manner (a similar process creates GSP polygons from the BSP Voronoi). Document explaining the Voronoi process and creation of the feeding area polygons is available to download from the 'Alternative Exports' section of the Export tab (above) While we use reasonable endeavours to ensure that the data contained within this dataset is accurate, we do not accept any responsibility or liability for the accuracy or the completeness of the content held, or for any loss which may arise from reliance on this dataset and/or its related information. If you have any query related to this SP ENW Substation - DSO GSP Polygons dataset, please contact us [ HERE] *Actual dataset content is available to registered users only – If you have not already done so, log-in or create new account below* New User? - Create your new account [ HERE] Already registered as a SP ENW Portal User? - Log-In [ HERE] Have you an ODS Portal account created elsewhere? - Log-In [ HERE]
SP ENW - DFES - Entire Network Data

Dataset Classification - Open SP ENW - DFES – Entire Network Data - New 2026 Release The Entire Network Data section within our Distributed Future Electricity Scenarios (DFES) content, details our view of the North West’s future electricity requirements and represents forecasts based on the entire network within our operational boundary. The collection of DFES datasets envisages a range of possible views of the future, which indicate how different influences can change electrical demand and generation on our network. This dataset is an accompaniment to our latest DFES report. It contains detailed datasets and interactive tools which allow our customers to understand what our Distribution Future Electricity Scenarios mean to them. Please refer to our main DFES report for a full description of our forecasts and scenarios. Our forecasts are not precise predictions of what the future is likely to be, but instead frame the uncertainty within our planning as explained in the main report. While we use reasonable endeavours to ensure that the data contained within this dataset is accurate, we do not accept any responsibility or liability for the accuracy or the completeness of the content held, or for any loss which may arise from reliance on this dataset and/or its related information. If you have any query related to this DFES Entire Network dataset, please contact us [ HERE] *Actual dataset content is available to registered users only – If you have not already done so, log-in or create new account below* New User? - Create your new account [ HERE] Already registered as a SP ENW Portal User? - Log-In [ HERE] Have you an ODS Portal account created elsewhere? - Log-In [ HERE]
SP ENW - Substation - DSO Primary Polygons

Dataset Classification - Open SP ENW - Substation - DSO Primary Polygons Individual Polygons are created using the Voronoi methodology, each one containing a single distribution substation, these individual polygons are first grouped together (based on common attribution) then merged, to create a new series of interlocking polygons forming a high-level representation of Primary Substation feeding areas. The larger polygons representing the feeding areas of Bulk Supply Points (BSP's) are generated by grouping, then merging, the Primary polygons in the same manner (a similar process creates GSP polygons from the BSP Voronoi). Document explaining the Voronoi process and creation of the feeding area polygons is available to download from the 'Alternative Exports' section of the Export tab (above) While we use reasonable endeavours to ensure that the data contained within this dataset is accurate, we do not accept any responsibility or liability for the accuracy or the completeness of the content held, or for any loss which may arise from reliance on this dataset and/or its related information. If you have any query related to this SP ENW Substation - DSO Primary Polygons dataset, please contact us [ HERE] *Actual dataset content is available to registered users only – If you have not already done so, log-in or create new account below* New User? - Create your new account [ HERE] Already registered as a SP ENWL Portal User? - Log-In [ HERE] Have you an ODS Portal account created elsewhere? - Log-In [ HERE]
SPM LTDS Appendix 2 Transformer Data (Table 2) 132 to 33kV

The "SPM LTDS Appendix 2 Transformer Data (Table 2) 132 to 33kV" data table provides the parameters for each group of transformers on the SP Manweb (SPM) system. Click [here] to access our full Long Term Development Statements for both SP Distribution (SPD) & SP Manweb (SPM). The table gives the following information: 1. Node 1 & 2 per substation group 2. Positive sequence impedance R & X per substation group 3. Zero sequence reactance per substation group 4. Minimum and maximum tap percentage per substation group 5. Transformer rating 6. Reverse power capability For additional information on column definitions, please click on the Dataset schema link below. ### Disclaimer Whilst all reasonable care has been taken in the preparation of this data, SP Energy Networks does not accept any responsibility or liability for the accuracy or completeness of this data, and is not liable for any loss that may be attributed to the use of this data. For the avoidance of doubt, this data should not be used for safety critical purposes without the use of appropriate safety checks and services e.g. LineSearchBeforeUDig etc. Please raise any potential issues with the data which you have received via the feedback form available at the Feedback tab above (must be logged in to see this). ### Data Triage As part of our commitment to enhancing the transparency, and accessibility of the data we share, we publish the results of our Data Triage process. Our Data Triage documentation includes our Risk Assessments; detailing any controls we have implemented to prevent exposure of sensitive information. Click [here]to access the Data Triage documentation for the Long Term Development Statement dataset. To access our full suite of Data Triage documentation, visit [the SP Energy Networks Data & Information page]. [Download dataset metadata (JSON)]
Global dataset of cotton pest weekly counting in Benin

[Lien vers la fiche source] This dataset reports, for each plot and cropping season, the cumulative counts of six cotton bollworm species (Diparopsis watersi, Earias sp., Helicoverpa armigera, Pectinophora gossypiella, Spodoptera littoralis, Spodoptera frugiperda) monitored weekly across cotton-producing areas of Benin from 2018 to 2023, on a network of geolocated smallholder and experimental plots.
Montant trimestriel des mises en chantier, dès 1984

Ce jeu de données contient une série temporelle des montants des mises en chantier, exprimés en milliers de francs, pour chaque trimestre depuis 1984, dans le canton de Fribourg. Chaque enregistrement comprend la période concernée, l'état des données (provisoires, définitives) ainsi que le montant. Données selon la dernière version du catalogue des caractères du Registre fédéral des bâtiments et des logements (RegBL).
Montant trimestriel des constructions terminées, dès 1978

Ce jeu de données présente un historique détaillé des montants des constructions terminées pour chaque trimestre, dans le canton de Fribourg, dès 1978. Les données sont exprimées en milliers de francs et sont classées comme données définitives ou provisoires. Données selon la dernière version du catalogue des caractères du Registre fédéral des bâtiments et des logements (RegBL).
Real Gross Domestic Product By Main Economic Activities (2023=100)

Explore the Real Gross Domestic Product By Main Economic Activities for Saudi Arabia. Find information on Oil activities, non-oil activities, government activities, and more in this quarterly dataset. Follow data.kapsarc.org for timely data to advance energy economics research. 2024-2025: Preliminary Data The methodology of chain-linking represents a non-additive model, thus the subcomponents do not correspond to the aggregates.
Real Gross Domestic Product By Kind of Economic Activity (2023=100)

Explore the Gross Domestic Product data by kind of economic activity at constant prices for Saudi Arabia. Find information on Mining & Quarrying, Manufacturing, Construction, Agriculture, Finance, Insurance, Real Estate, and more in this quarterly dataset. Follow data.kapsarc.org for timely data to advance energy economics research. 2024-2025: Preliminary Data. The methodology of chain-linking represents a non-additive model, thus the subcomponents do not correspond to the aggregates.
Real Gross Domestic Product By Institutional Sectors (2023=100)

Explore Saudi Arabia's Real Gross Domestic Product By Institutional Sectors with this comprehensive dataset. Includes data on the Non-Oil Sector, Government Sector, Private Sector, Oil Sector, Quarterly GDP information, and more. Follow [data.kapsarc.org] for timely data to advance energy economics research. 2024-2025: Preliminary Data. The methodology of chain-linking represents a non-additive model, thus the subcomponents do not correspond to the aggregates.
Real Gross Domestic Product By Expenditure Components (2023=100)

Explore the Real Gross Domestic Product By Expenditure Components, including components such as Gross Fixed Capital Formation, Government, Non-Government, Exports of Goods and Services, and more in Saudi Arabia. Access comprehensive GDP data and National Accounts information. Follow [data.kapsarc.org] for timely data to advance energy economics research. 2024-2025: Preliminary Data. The methodology of chain-linking represents a non-additive model, thus the subcomponents do not correspond to the aggregates.
Seasonally Adjusted Real Gross Domestic Product By Main Economic Activities (2023=100)

Explore the Seasonally Adjusted Real Gross Domestic Product By Main Economic Activities (2023=100) for Saudi Arabia. Find information on Oil activities, non-oil activities, government activities, and more in this quarterly dataset. Follow data.kapsarc.org for timely data to advance energy economics research. 2024-2025: Preliminary Data. The methodology of chain-linking represents a non-additive model, thus the subcomponents do not correspond to the aggregates.
Seasonally Adjusted Real Gross Domestic Product By Kind of Economic Activity  (2023=100)

Explore the Seasonally Adjusted Real Gross Domestic Product By Kind of Economic Activity for Saudi Arabia. Find information on Mining & Quarrying, Manufacturing, Construction, Agriculture, Finance, Insurance, Real Estate, and more in this quarterly dataset. Follow data.kapsarc.org for timely data to advance energy economics research. 2024-2025: Preliminary Data. The methodology of chain-linking represents a non-additive model, thus the subcomponents do not correspond to the aggregates.
Seasonally Adjusted Real Gross Domestic Product By Institutional Sectors (2023=100)

Explore Seasonally Adjusted Real Gross Domestic Product By Institutional Sectors. Analyze data on GDP, oil and non-oil sectors, private and government sectors, and more. Follow [data.kapsarc.org] for timely data to advance energy economics research. 2024-2025: Preliminary Data. The methodology of chain-linking represents a non-additive model, thus the subcomponents do not correspond to the aggregates.
Seasonally Adjusted Real Gross Domestic Product By Expenditure Components (2023=100)

Explore the Seasonally Adjusted Real Gross Domestic Product By Expenditure Components, including components such as Gross Fixed Capital Formation, Government, Non-Government, Exports of Goods and Services, and more in Saudi Arabia. Access comprehensive GDP data and National Accounts information. Follow [data.kapsarc.org] for timely data to advance energy economics research. 2024-2025: Preliminary Data. The methodology of chain-linking represents a non-additive model, thus the subcomponents do not correspond to the aggregates.
NOURMED beam trawl survey catch data : Coastal benthic assemblages of the Gulf of Lion (North-western Mediterranean) in 2018-2019

[Lien vers la fiche source] As part of the monitoring program of the Marine Strategy Framework Directive (MSFD), the monitoring of demersal fish and cephalopods in coastal soft sediments has been deemed a priority. In order to do this, annual fish surveys have been deployed to cover coastal nurseries, which constitute essential habitats for many marine species. The characterization of the ecological state of these populations and the study of the functionality of nursery habitats is required for the calculation of the indicators of Good Ecological Status (Delaunay et al., 2019) and is essential for the implementation of ecosystem management measures guaranteeing sustainable uses. On the French Mediterranean coast, the NOURMED survey exploring the feasibility of such monitoring was successfully carried out in the Gulf of Lion in September 2018 and 2019 (Vaz et al., 2019, Vaz et al. 2020). They provide detailed quantitative data on benthic and demersal species composition in coastal nursery grounds of the Gulf of Lion.
Passantenzählung - Tagesdaten

Vor dem Hintergrund von Digitalisierung, dem Online-Handel und den Nachwirkungen von globalen Pandemien, stellen Passantenfrequenzen einen wichtigen Indikator für die Attraktivität eines Standorts dar. Dieser Messwert gibt die Anzahl der Personen an, die einen abgegrenzten Ort innerhalb eines bestimmten Zeitraums betreten haben. Als eine von vielen Smart City Lösungen befinden sich auch in Würzburg Sensoren, die mithilfe von Lasertechnik in der Lage sind, das Passantenaufkommen vollkommen datenschutzkonform und anonym zu bemessen. Als einer der ersten Standorte Deutschlands veröffentlicht die Stadt Würzburg seit April 2019 mithilfe der Lasertechnik transparent und offen die stündliche Passantenfrequenz in der [Schönbornstraße]. Im Oktober 2022 kamen die [Kaiserstraße ]und die [Spiegelstraße ]als weitere Standorte hinzu. Ziel dieser Messungen ist es, den Pulsschlag der Innenstadt zu erfassen, um ein realistisches Lagebild und differenzierte Analysen der Passantenaufkommen zu schaffen. In diesem Datensatz können Sie das seit 2020 täglich gemessene Tagesaufkommen an Passanten an den drei Messstationen verfolgen. Zudem finden Sie in unserem Open Data Portal einen äquivalenten Datensatz zum [stündlichen Passantenaufkommen] seit 2024, sowie eine [Monatsübersicht ]der Passanten unserer drei Standorte.
Passantenzählung - Stundendaten

Vor dem Hintergrund von Digitalisierung, dem Online-Handel und den Nachwirkungen von globalen Pandemien, stellen Passantenfrequenzen einen wichtigen Indikator für die Attraktivität eines Standorts dar. Dieser Messwert gibt die Anzahl der Personen an, die einen abgegrenzten Ort innerhalb eines bestimmten Zeitraums betreten haben. Als eine von vielen Smart City Lösungen befinden sich auch in Würzburg Sensoren, die mithilfe von Lasertechnik in der Lage sind, das Passantenaufkommen vollkommen datenschutzkonform und anonym zu bemessen. Als einer der ersten Standorte Deutschlands veröffentlicht die Stadt Würzburg seit April 2019 mithilfe der Lasertechnik transparent und offen die stündliche Passantenfrequenz in der [Schönbornstraße]. Im Oktober 2022 kamen die [Kaiserstraße ]und die [Spiegelstraße ]als weitere Standorte hinzu. Ziel dieser Messungen ist es, den Pulsschlag der Innenstadt zu erfassen, um ein realistisches Lagebild und differenzierte Analysen der Passantenaufkommen zu schaffen. In diesem Datensatz können Sie das stündliche Aufkommen an Passanten an den drei Messstellen im Jahr 2024 verfolgen. Zudem finden Sie in unserem Open Data Portal einen äquivalenten Datensatz zum [täglichen Passantenaufkommen] seit 2020, sowie eine [Monatsübersicht ]der Passanten unserer drei Standorte.