Introduction The Long Term Development Statements (LTDS) report on a 0-5 year period, describing a forecast of load on the network and envisioned network developments. The LTDS is published at the end of May and November each year.

Long Term Development Statement Table 8 indicates any Fault Level restrictions or mitigations in place at our Grid and Primary substations. Published 30 May 2025.

More information and full reports are available from the landing page below: Long Term Development Statement and Network Development Plan Landing Page

Methodological Approach

Site Functional Locations (FLOCs) are used to associate the Substation to Key characteristics of active Grid and Primary sites — UK Power Networks 
ID field added to identify row number for reference purposes

Quality Control Statement Quality Control Measures include:

Verification steps to match features only with confirmed functional locations.
Manual review and correction of data inconsistencies.
Use of additional verification steps to ensure accuracy in the methodology.

Assurance Statement The Open Data Team and Network Insights Team worked together to ensure data accuracy and consistency.

Other Download dataset information: Metadata (JSON)

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/

Intelligent Hybrid model to Enhance Time Series Models for Predicting Network Traffic

Regional Road Network for 31 local authorities. Extracted from MapRoad Asset Management System. The Road Management Office and Local Authorities provide this information with the understanding that it is not guaranteed to be accurate, correct or complete. The Road Management Office and Local Authorities accept no liability for any loss or damage suffered by those using this data for any purpose.The road infrastructure is the largest asset managed by local authorities in Ireland. It’s efficient management (both day to day and in the long term) is essential to economic activity as the majority of commuting and haulage occurs using it. The 31 local authorities operate, maintain and improve the network of regional and local roads.

A compilation of experimental forage data from 108 unique locations across the United States, with harvest dates ranging from 1958 to 2022. This dataset contains a subset of the data compiled in the initial stages of development of the Forage Data Hub. In particular, these are the 37,970 data entries used for the forage system resiliency analysis presented in the primary article. Resources in this dataset: Resource Title: FDH Data Dictionary File Name: FDH_Data_Dictionary.csv Resource Description: Data dictionary for the data compiled as a result of the efforts described in Ashworth et al. (2023) - Framework to Develop an Open-Source Forage Data Network to Improve Primary Productivity and Enhance System Resiliency (in review). Includes descriptions for the data fields in the FDH Data data file. Resource Title: FDH Data File Name: FDH_Data_03-04-2023.csv Resource Description: Data compiled as a result of the efforts described in Ashworth et al. (2023) - Framework to Develop an Open-Source Forage Data Network to Improve Primary Productivity and Enhance System Resiliency (in review). Includes a lightly preprocessed version of the data housed in the Forage Data Hub as of March 4th, 2023.

Network Address Translation (NAT)

This dataset comprises of two .csv format files used within workstream 2 of the Wellcome Trust funded ‘Orphan drugs: High prices, access to medicines and the transformation of biopharmaceutical innovation’ project (219875/Z/19/Z). They appear in various outputs, e.g. publications and presentations.

The deposited data were gathered using the University of Amsterdam Digital Methods Institute’s ‘Twitter Capture and Analysis Toolset’ (DMI-TCAT) before being processed and extracted from Gephi. DMI-TCAT queries Twitter’s STREAM Application Programming Interface (API) using SQL and retrieves data on a pre-set text query. It then sends the returned data for storage on a MySQL database. The tool allows for output of that data in various formats. This process aligns fully with Twitter’s service user terms and conditions. The query for the deposited dataset gathered a 1% random sample of all public tweets posted between 10-Feb-2021 and 10-Mar-2021 containing the text ‘Rare Diseases’ and/or ‘Rare Disease Day’, storing it on a local MySQL database managed by the University of Sheffield School of Sociological Studies (http://dmi-tcat.shef.ac.uk/analysis/index.php), accessible only via a valid VPN such as FortiClient and through a permitted active directory user profile. The dataset was output from the MySQL database raw as a .gexf format file, suitable for social network analysis (SNA). It was then opened using Gephi (0.9.2) data visualisation software and anonymised/pseudonymised in Gephi as per the ethical approval granted by the University of Sheffield School of Sociological Studies Research Ethics Committee on 02-Jun-201 (reference: 039187). The deposited dataset comprises of two anonymised/pseudonymised social network analysis .csv files extracted from Gephi, one containing node data (Issue-networks as excluded publics – Nodes.csv) and another containing edge data (Issue-networks as excluded publics – Edges.csv). Where participants explicitly provided consent, their original username has been provided. Where they have provided consent on the basis that they not be identifiable, their username has been replaced with an appropriate pseudonym. All other usernames have been anonymised with a randomly generated 16-digit key. The level of anonymity for each Twitter user is provided in column C of deposited file ‘Issue-networks as excluded publics – Nodes.csv’.

This dataset was created and deposited onto the University of Sheffield Online Research Data repository (ORDA) on 26-Aug-2021 by Dr. Matthew S. Hanchard, Research Associate at the University of Sheffield iHuman institute/School of Sociological Studies. ORDA has full permission to store this dataset and to make it open access for public re-use without restriction under a CC BY license, in line with the Wellcome Trust commitment to making all research data Open Access.

The University of Sheffield are the designated data controller for this dataset.

PlayStation 4.

This data table provides the detailed data quality assessment scores for the Network Development Plan dataset. The quality assessment was carried out on 31st March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality; to demonstrate our progress we conduct annual assessments of our data quality in line with the dataset refresh rate. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks.We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.DisclaimerThe data quality assessment may not represent the quality of the current dataset that is published on the Open Data Portal. Please check the date of the latest quality assessment and compare to the 'Modified' date of the corresponding dataset. The data quality assessments will be updated on either a quarterly or annual basis, dependent on the update frequency of the dataset. This information can be found in the dataset metadata, within the Information tab. If you require a more up to date quality assessment, please contact the Open Data Team at opendata@spenergynetworks.co.uk and a member of the team will be in contact.

Data from the SPHERE House referenced in the paper "TSCH Networks for Health IoT: Design, Evaluation and Trials In The Wild". This subset is published as open data.

Network traffic datasets created by Single Flow Time Series Analysis

Datasets were created for the paper: Network Traffic Classification based on Single Flow Time Series Analysis -- Josef Koumar, Karel Hynek, Tomáš Čejka -- which was published at The 19th International Conference on Network and Service Management (CNSM) 2023. Please cite usage of our datasets as:

J. Koumar, K. Hynek and T. Čejka, "Network Traffic Classification Based on Single Flow Time Series Analysis," 2023 19th International Conference on Network and Service Management (CNSM), Niagara Falls, ON, Canada, 2023, pp. 1-7, doi: 10.23919/CNSM59352.2023.10327876.

This Zenodo repository contains 23 datasets created from 15 well-known published datasets which are cited in the table below. Each dataset contains 69 features created by Time Series Analysis of Single Flow Time Series. The detailed description of features from datasets is in the file: feature_description.pdf

In the following table is a description of each dataset file:

This data table provides the detailed data quality assessment scores for the SPD DG Connections Network Info dataset. The quality assessment was carried out on the 31st of March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refresehed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

Water networks length

The North American Rail Network (NARN) Rail Nodes dataset was created in 2016 and was updated on July 18, 2025 from the Federal Railroad Administration (FRA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The NARN Rail Nodes dataset is a database of North America's railway system at 1:24,000 or better within the United States. The data set covers all 50 States, the District of Columbia, Mexico, and Canada. The dataset holds topology of the network and provides geographic location information. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529070

AADL and Error Annex models of SAFEbus, SPIDER, TTP and BRAIN

Numbers of Active knowledge stations Connected on optical fiber network and their coordinates, in line with the Royal vision and upon the approval of the prime Ministers on 25 June 2003, the establishment of the national fiber-optic network started by Ministry of Digital Economy and Entrepreneurship.

In 2020, with generous funding from the National Lottery Heritage Fund, Ulster Wildlife, National Trust NI, RSPB NI and Woodland Trust NI came together to start building capacity to deliver Nature Recovery Networks in Northern Ireland. As part of the project, habitat networks maps were produced for all terrestrial and intertidal priority habitats, based on the Natural England (Edwards et al., 2020) methodology. The habitat networks comprise vector datasets that map areas of land into different network categories, based on how favourable the land is for restoration to, or creation of the priority habitat, and how effective actions in each area would be at enhancing connectivity of the priority habitat, based on proximity to existing habitat patches. A description of these network categories is provided in Table 1 in the methodology report, available at https://www.ulsterwildlife.org/sites/default/files/2022-10/EnvSys%20NI%20NRN%20mapping%20report.pdf. The habitat network maps do not represent a fully comprehensive depiction of land cover, nor do they provide specific land management options and do not therefore replace the need for an on-site ecological surveys/appraisals. The maps are intended to function as a decision-support tool alongside other pieces of information, both from on-site surveys and data from other sources.

The Road Network dataset includes traffic directions, turning restrictions at road junctions, stopping restrictions, on-street parking spaces and other road traffic data for supporting the development of intelligent transport system, fleet management system and car navigation etc. by the public. There are 3 kinds of spatial data file format available: File GeoDatabase(FGDB, provided in ZIP): Users can read this consolidated list to enquire the data resource and file names. KML: Users can read this consolidated list to enquire the data resource and file names. GML + GFS:Users can read this consolidated list to enquire the data resource and file names. Delta Change provided in CSV format, users can read this consolidated list to enquire the data resource and file names.

Peer-to-peer (P2P) networks are gaining popularity in many applications such as file sharing, e-commerce, and social networking, many of which deal with rich, distributed data sources that can benefit from data mining. P2P networks are, in fact,well-suited to distributed data mining (DDM), which deals with the problem of data analysis in environments with distributed data,computing nodes,and users. This article offers an overview of DDM applications and algorithms for P2P environments,focusing particularly on local algorithms that perform data analysis by using computing primitives with limited communication overhead. The authors describe both exact and approximate local P2P data mining algorithms that work in a decentralized and communication-efficient manner.

Notice - Replacement of the English and French Web services (WMS and ESRI REST) with a bilingual one. The NRN product is distributed in the form of thirteen provincial or territorial datasets and consists of two linear entities (Road Segment and Ferry Connection Segment) and three punctual entities (Junction, Blocked Passage, Toll Point) with which is associated a series of descriptive attributes such as, among others: First House Number, Last House Number, Street Name Body, Place Name, Functional Road Class, Pavement Status, Number Of Lanes, Structure Type, Route Number, Route Name, Exit Number. The development of the NRN was realized by means of individual meetings and national workshops with interested data providers from the federal, provincial, territorial and municipal governments. In 2005, the NRN edition 2.0 was alternately adopted by members from the Inter-Agency Committee on Geomatics (IACG) and the Canadian Council on Geomatics (CCOG). The NRN content largely conforms to the ISO 14825 from ISO/TC 204.

Sewer network length