AndreaBozzo/ceres-open-data-index huggingface dataset:
SP ENW - Substation - GSP Polygons

Dataset Classification - Open SP ENW - Substation - 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 ENWL Substation - 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 - Primary Polygons

Dataset Classification - Open SP ENW - DFES - Primary Polygons Specifically formatted to align with SP ENW DFES data, this Polygon dataset is still created using Voronoi methodology (described below), but includes the polygonal representation of the sites joined together or split through busbars as defined in SP ENW's DFES submission. 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's DFES - 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 ENW Portal User? - Log-In [ HERE] Have you an ODS Portal account created elsewhere? - Log-In [ HERE]
SP ENW - Substation - BSP Polygons

Dataset Classification - Open SP ENW - Substation - 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 ENWL Substation - 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 - DFES - LV Peak Demand (Monitored)

Dataset Classification - Open SP ENW - DFES - LV Peak Demand (Monitored) Data - New 2024 Dataset SP ENW is currently using PreSense units to monitor the power of secondary substations, particularly those with a large number of customers. The data is processed in real-time and aggregated into half-hourly intervals. In its RIIO-ED2 Business Plan, SP ENW outlined its plans to achieve 95% network visibility coverage during the price control period using distribution monitoring equipment (PreSense), with the remaining 5% covered by smart meters. As of March 2025, 60% of customers are covered by PreSense. The substations being monitored have been identified because they have been monitored for over a year, providing sufficient data points throughout the period, especially during winter. This real-time monitoring data is used to identify the maximum demand of secondary substations. There is an ongoing programme to identify new sites for monitoring to expand coverage and improve network visibility. This table contains the maximum demand (kVA) forecast for PreSense-monitored secondary substations up to 2051. The forecast is based on historical peak demand data identified from PreSense monitoring and considers the demand increase from electric vehicles and heat pumps. 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 2024 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 SPENW - DFES - LV Peak Demand (Monitored) Data, 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 - LV Headroom (Unmonitored)

Dataset Classification - Open SP ENW - DFES - LV Headroom (Unmonitored) Data - New 2024 Dataset SP ENW is currently using PreSense units to monitor the power of secondary substations, particularly those with a large number of customers. The data is processed in real-time and aggregated into half-hourly intervals. In its RIIO-ED2 Business Plan, SP ENW outlined its plans to achieve 95% network visibility coverage during the price control period using distribution monitoring equipment (PreSense), with the remaining 5% covered by smart meters. As of March 2025, 60% of customers are covered by PreSense. The substations being monitored have been identified because they have been monitored for over a year, providing sufficient data points throughout the period, especially during winter. This real-time monitoring data is used to identify the maximum demand of secondary substations. There is an ongoing programme to identify new sites for monitoring to expand coverage and improve network visibility. This table contains the headroom (kVA) forecast for unmonitored secondary substations, based on the maximum demand forecast and substation transformer ratings. 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 2024 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 SP ENW - DFES - LV Headroom (Unmonitored) Data, 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 - LV Peak Demand (Unmonitored)

Dataset Classification - Open SP ENW - DFES - LV Peak Demand (Unmonitored) Data - New 2024 Dataset SP ENW is currently using PreSense units to monitor the power of secondary substations, particularly those with a large number of customers. The data is processed in real-time and aggregated into half-hourly intervals. In its RIIO-ED2 Business Plan, SP ENW outlined its plans to achieve 95% network visibility coverage during the price control period using distribution monitoring equipment (PreSense), with the remaining 5% covered by smart meters. As of March 2025, 60% of customers are covered by PreSense. The substations being monitored have been identified because they have been monitored for over a year, providing sufficient data points throughout the period, especially during winter. This real-time monitoring data is used to identify the maximum demand of secondary substations. There is an ongoing programme to identify new sites for monitoring to expand coverage and improve network visibility. This table contains the maximum demand (kVA) forecast for un-monitored secondary substations up to 2051. The forecast is based on historical peak demand data allocated from the head of High Voltage level feeders using customer count ratios and considers the demand increase from electric vehicles and heat pumps. 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 2024 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 SP ENW - DFES - LV Peak Demand (Unmonitored) Data, 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 - GIS - 11kV Overhead Conductors

Dataset Classification - Open SP ENW - 11kV Overhead Conductors. Dataset depicting the sections of Electricity North West Ltd owned, 11kV overhead conductors. At this present moment in time, 11kV network underground assets are currently not available for sharing For reference, individual datasets for the overhead conductor sections of 132kV, 33kV, 11kV, 6.6kV and LV networks are available to access as separate datasets in the data section of this Portal - or, can be viewed together in a single map [ HERE] 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 other query related to this 11kV Overhead Conductor 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]
Riolering drukriolering Den Haag 2016

Beschrijving: Locatie leidingen drukriolering 2016 Bron: beheersysteem gemeente Den Haag, Pijnacker/Nootdorp Doel registratie: bepaling locaties ter publicatie Beperkingen: Deze dataset is niet geschikt voor juridische of landmeetkundige doeleinden Mogelijkheden: Deze dataset is geschikt voor het inzichtelijk maken van de locatie op de kaart Coördinatenstelsel: RDnew
Assessing the effects of age-at-death assignment and allocation on the interpretation of past sheep/goat management practices.

[Lien vers la fiche source] [EN] Through the course of his career, Jean-Denis Vigne emphasised the use of standardised age-at-death methodologies to investigate slaughter management and its relationship to subsistence practices focused on animal products, such as milk and meat. Payne’s (1973, 1987) system, involving teeth allocation to age classes, is the most widespread age-at-death methodology for sheep and goats. Fragmentation of archaeological mandibles, especially in early Neolithic contexts and in areas of poor preservation, means that specimens often cannot be placed into a single age class. A lack of standardisation between researchers in how fragmentary mandibles are assigned to age class, and if necessary ‘allocated’ across multiple classes, may hamper interpretation and comparison between sites and time periods. Moreover, some have argued (Timpson et al. 2018) that sharing teeth between age classes can create inaccurate representation of the data, resulting in incorrect age-at-death distribution. Here, we review the often-unspoken decisions researchers must make when applying the Payne method, discuss current approaches to stage assignment/allocation, and assess what impact these choices have on age-at-death results and inter-site comparisons in light of the development of recent statistical approaches. [FR] Tout au long de sa carrière, Jean-Denis Vigne a défendu l’utilisation de méthodes standardisées pour la détermination de l’âge de mort dans le cadre de la construction de profils d’abattage et leur association avec des pratiques de subsistances spécialisées, comme par exemple l’exploitation laitière ou la production de viande. Payne (1973, 1987) développa un système très communément utilisé aujourd’hui parmi les méthodes de détermination de l’âge de mort chez les moutons et les chèvres, impliquant l’attribution des dents à certaines classes d’âge. La fragmentation des mâchoires en contexte archéologique, et particulièrement pour le Néolithique ancien et dans certaines zones où la conservation du matériel archéologique est faible, impliquent souvent que les spécimens ne peuvent pas être placés dans une seule classe d’âge. Un manque de standardisation entre chercheurs dans la façon d’attribuer ces mâchoires fragmentées à une – ou plusieurs – classes d’âge, peut limiter les interprétations et les comparaisons entre sites et périodes. De plus, certains auteurs (Timpson et al. 2018) ont avancé que l’assignation d’une dent à plusieurs classes d’âge générerait une fausse représentation des données, résultant en une distribution biaisée/incorrecte des âges de mort. Nous passons en revue ici les décisions non-explicites que les chercheurs doivent prendre en appliquant la méthode de Payne ; nous discutons ensuite les approches actuelles d’attribution des classes d’âge ; et enfin, à l’aide d’approches statistiques récentes, nous tentons d’évaluer l’effet de ces décisions sur les visualisations des données d’âge de mort et sur les comparaisons entre sites.
SP ENW - GIS - 132kV Overhead Conductors

Dataset Classification - Open SP ENW - 132kV Overhead Conductors Dataset depicting the sections of SP Electricity North West owned, 132kV overhead conductors. At this present moment in time, 132kV network underground assets are currently not available for sharing For reference, individual datasets for the overhead conductor sections of 132kV, 33kV, 11kV, 6.6kV and LV networks are available to access as separate datasets in the data section of this Portal - or, can be viewed together in a single map [ HERE] 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 other query related to this 132kV Overhead Conductor 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 - GIS - 6.6kV Overhead Conductors

Dataset Classification - Open SP ENW - 6.6kV Overhead Conductors Dataset depicting the sections of SP Electricity North West owned, 6.6kV overhead conductors. At this present moment in time, 6.6kV network underground assets are currently not available for sharing For reference, individual datasets for the overhead conductor sections of 132kV, 33kV, 11kV, 6.6kV and LV networks are available to access as separate datasets in the data section of this Portal - or, can be viewed together in a single map [ HERE] 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 other query related to this 6.6kV Overhead Conductor 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 - Capacity - BSP Heatmap

Dataset Classification - Open SP ENW - Capacity - Bulk Supply Point (BSP) Heatmap Dataset depicting all Bulk Supply Points represented as interlocking Voronoi polygons, The information contained in the dataset will enable developers to assess the level of capacity that might be available for new connections to our network. This capacity dataset contains all the available capacity information on the Bulk Supply Points connected to our network. The information held in this dataset, along with the three further datasets listed below Grid Supply Point Connection Queue Grid Supply Point Capacity Primary Capacity Have been combined in a map page [ HERE] 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 the BSP Capacity Heatmap data, 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 create elsewhere? - Log-In [ HERE]
SP ENW - Capacity - PRY Heatmap

Dataset Classification - Open SP ENW - Capacity - Primary Heatmap Dataset depicting all Primary Substations represented as interlocking Voronoi polygons, The information contained in the dataset will enable developers to assess the level of capacity that might be available for new connections to our network. This capacity dataset contains all the available capacity information on the Primary Substations connected to our network. The information held in this dataset, along with the three further datasets listed below Grid Supply Point Connection Queue Grid Supply Point Capacity Bulk Supply Point Capacity Have been combined in a map page [ HERE] 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 the Primary Capacity Heatmap data, 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 an SP ENW Portal User? - Log-In [ HERE] Have you an ODS Portal account created elsewhere? - Log-In [ HERE]
Vergunningen Den Haag

Beschrijving: Historische data tot eind 2020. Actuele data wordt niet meer geleverd. Locaties van aangevraagde en verleende vergunningen door de gemeente Den Haag met een eind 2020 nog niet verstreken of niet ingevulde einddatum. Het gaat om de volgende vergunningen: "Speelautomaten aanwezigheids vergunning" "Nachtontheffing Sluitingstijden" "APV vergunning art. 2.28" "Wijziging Apv vergunning art. 57" "Drank- en horeca vergunning" "Wijziging Drank- en Horecavergunning" Doel registratie: Beheer openbare ruimte Beperkingen: Deze dataset is niet geschikt voor juridische of landmeetkundige doeleinden Mogelijkheden: Deze dataset is geschikt voor het inzichtelijk maken van de locatie op de kaart Coördinatenstelsel: RDnew. De KML is WGS84