There are a number of sources for estimates of the size and distribution of ethnic group populations in England. These estimates vary in quality, accuracy, timeliness, and detail; in some cases, the underlying definition of what constitutes the resident population is different. This document outlines in some detail the major sources of ethnic group information currently available at the national and regional level. It also gives a brief summary of the estimates themselves.

Details of various sources of information

This dataset was created and deposited onto the University of Sheffield Online Research Data repository (ORDA) on 23-Jun-2023 by Dr. Matthew S. Hanchard, Research Associate at the University of Sheffield iHuman Institute. The dataset forms part of three outputs from a project titled ‘Fostering cultures of open qualitative research’ which ran from January 2023 to June 2023:

· Fostering cultures of open qualitative research: Dataset 1 – Survey Responses · Fostering cultures of open qualitative research: Dataset 2 – Interview Transcripts · Fostering cultures of open qualitative research: Dataset 3 – Coding Book

The project was funded with £13,913.85 of Research England monies held internally by the University of Sheffield - as part of their ‘Enhancing Research Cultures’ scheme 2022-2023.

The dataset aligns with ethical approval granted by the University of Sheffield School of Sociological Studies Research Ethics Committee (ref: 051118) on 23-Jan-2021. This includes due concern for participant anonymity and data management.

ORDA has full permission to store this dataset and to make it open access for public re-use on the basis that no commercial gain will be made form reuse. It has been deposited under a CC-BY-NC license. Overall, this dataset comprises:

· 15 x Interview transcripts - in .docx file format which can be opened with Microsoft Word, Google Doc, or an open-source equivalent.

All participants have read and approved their transcripts and have had an opportunity to retract details should they wish to do so.

Participants chose whether to be pseudonymised or named directly. The pseudonym can be used to identify individual participant responses in the qualitative coding held within the ‘Fostering cultures of open qualitative research: Dataset 3 – Coding Book’ files.

For recruitment, 14 x participants we selected based on their responses to the project survey., whilst one participant was recruited based on specific expertise.

· 1 x Participant sheet – in .csv format which may by opened with Microsoft Excel, Google Sheet, or an open-source equivalent.

The provides socio-demographic detail on each participant alongside their main field of research and career stage. It includes a RespondentID field/column which can be used to connect interview participants with their responses to the survey questions in the accompanying ‘Fostering cultures of open qualitative research: Dataset 1 – Survey Responses’ files.

The project was undertaken by two staff:

Co-investigator: Dr. Itzel San Roman Pineda ORCiD ID: 0000-0002-3785-8057 i.sanromanpineda@sheffield.ac.uk Postdoctoral Research Assistant Labelled as ‘Researcher 1’ throughout the dataset

Principal Investigator (corresponding dataset author): Dr. Matthew Hanchard ORCiD ID: 0000-0003-2460-8638 m.s.hanchard@sheffield.ac.uk Research Associate iHuman Institute, Social Research Institutes, Faculty of Social Science Labelled as ‘Researcher 2’ throughout the dataset

Links to a range of useful COVID-19 datasets and visualisations. For information and advice in relation to COVID-19 please use the following information from reliable, trusted sources such as the Government, the NHS, Public Health England and the Council. www.gov.uk/coronavirus www.nhs.uk/coronavirus www.calderdale.gov.uk/coronavirus

The dataset is the lake polygons from the UK Lakes Portal (https://eip.ceh.ac.uk/apps/lakes/), originally based on OS PANORAMA but this dataset includes data from a number of sources. It has a basic set of attributes including the water body ID (WBID) as well as the computed area and perimeter of each lake. The WBID is commonly used across research institutions and is the same ID as used on the UK Lakes Portal, where more information can be found on each lake in this dataset. This is v3.6, which follows the same versioning as the underlying database. Although the database has seen the majority of the changes since version 1, the polygons have also been changed and improved over that time, mostly fixing issues with lake outlines, but also some new sites being added.

A comprehensive list of data sources relating to violence against women and girls, bringing together a range of different sources from across government, academia and the voluntary sector.

Definition Extract of Ordnance Survey's Code-Point® Open product filtered for London Borough of Barnet coverage. Data has been processed from the .csv schema filtering on the administrative district code, with administrative district and ward names appended for ease of use Postcodes do not have an exact match to administrative boundaries, see the Ordnance Survey product support page for full definition. Purpose These datasets have been created as general resource for the council. The information is sourced from Ordnance Survey Open Data products and may be used more widely subject to the Open Government Licence (v3). Disclaimer This dataset is not the primary source and may not reflect the latest version or scope of the original product. You should assess whether using the original product directly is more appropriate for your purpose. Acknowledgements Contains OS data © Crown copyright and database right 2025 Contains Royal Mail data © Royal Mail copyright and Database right 2025 Contains National Statistics data © Crown copyright and database right 2025

IntroductionFollowing the identification of Local Area Energy Planning (LAEP) use cases, this dataset lists the data sources and/or information that could help facilitate this research. View our dedicated page to find out how we derived this list: Local Area Energy Plan — UK Power Networks (opendatasoft.com)

Methodological Approach Data upload: a list of datasets and ancillary details are uploaded into a static Excel file before uploaded onto the Open Data Portal.

Quality Control Statement

Quality Control Measures include: Manual review and correct of data inconsistencies Use of additional verification steps to ensure accuracy in the methodology

Assurance Statement The Open Data Team and Local Net Zero 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/

Please note that "number of records" in the top left corner is higher than the number of datasets available as many datasets are indexed against multiple use cases leading to them being counted as multiple records.

Static public libraries in England (statutory and non-statutory) as at 1 July 2016. Also contains information on the type of library and number of hours open. The definitions for types of libraries used in the dataset can be found on GOV.UK and are included for reference. They may be different to those in other sources. This basic dataset provides a definitive source of data on public libraries in England that everyone can point to and use. This dataset forms the basis of a wider core dataset for public libraries in England. Building on this initial exercise, we are looking to improve existing data collection (largely focused on inputs and outputs). We also aim to capture data which covers outcomes and impacts, and the wider variety of activities libraries undertake. This dataset has been validated by all 151 library services in England, but handling this quantity of data means the occasional error is possible. If you spot any errors or missing information, please email library-data@culture.gov.uk.

SUMMARY Public transport stops across England, as of 04/02/2021. The type of stop, whether it is active or inactive, as well as notes provided by the recorder are given. The raw data, which includes more information than is provided in this version of the dataset, can be accessed here: National public transport access nodes - GOV.UK (www.gov.uk)

DATA SOURCE National Public Transport Access Nodes © Department for Transport. Contains public sector information licensed under the Open Government Licence v3.0. Available from: National public transport access nodes - GOV.UK (www.gov.uk). Data edited for publishing by Ribble Rivers Trust.

COPYRIGHT NOTICE© Department for Transport. Contains public sector information licensed under the Open Government Licence v3.0.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Introduction The dataset provides detailed information about UK Power Networks' Grid and Primary Sites. It includes key characteristics such as:

Spatial coordinates of each site Year commissioned Asset counts against each site Power transformer count Local authority information Winter and summer demand Transformer ratings

This data is useful for understanding the infrastructure and capacity of the electricity network across its regions.

Methodological Approach

Source: Various internal data domains - geospatial, asset, long term development statement; as well as openly available data from the Ordnance Survey and Office of National Statistics Manipulation: Various data characteristics were combined together using Functional Locations (FLOCs)

Quality Control Statement The data is provided "as is".

Assurance Statement The Open Data team has checked the data against source to ensure data accuracy and consistency. The data domain owners have checked their respective data aspects.

Other Contains data from Office for National Statistics licensed under the Open Government Licence v.3.0. Local Authority District (2022) to Grouped Local Authority District (2022) Lookup for EW - data.gov.uk

Contains Ordnance Survey data Crown copyright and database right [2019-]. Free OS OpenData Map Downloads | Free Vector & Raster Map Data | OS Data Hub

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/To view this data please register and login.

Introduction

This dataset shows the half-hourly load profiles of identified data centres within UK Power Networks' licence areas.

The loads have been determined using actual demand data from connected sites within UK Power Networks' licence areas, from 1 January 2023 onwards.

Loads are expressed proportionally, by comparing the half-hourly observed import power seen across the site's meter point(s), against the meter's maximum import capacity. Units for both measures are apparent power, in kilovolt amperes (kVA).

To protect the identity of the sites, data points have been anonymised and only the site's voltage level information - and our estimation of the data centre type - has been provided.

Methodological Approach

Over 100 operational data centre sites (and at least 10 per voltage level) were identified through internal desktop exercises and corroboration with external sources.

After identifying these sites, their addresses, connection point, and MPAN(s) (Meter Point Administration Number(s)) were identified using internal systems.

Half-hourly smart meter import data were retrieved using internal systems. This included both half-hourly meter data, and static data (such as the MPAN's maximum import capacity and voltage group, the latter through the MPAN's Line Loss Factor Class Description). Half-hourly meter import data came in the form of active and reactive power, and the apparent power was calculated using the power triangle.

In cases where there are numerous meter points for a given data centre site, the observed import powers across all relevant meter points were summed, and compared against the sum total of maximum import capacity for the meters.

The percentage utilisation for each half-hour for each data centre was determined via the following equation:

% Utilisation_data centre site =

 SUM( S_MPAN half-hourly observed import)

 SUM( S_MPAN Maximum Import Capacity)

Where S = Apparent Power in kilovolt amperes (kVA)

To ensure the dataset includes only operational data centres, the dataset was then cleansed to exclude sites where utilisation was consistently at 0% across the year.

Based on the MPAN's address and corroboration with other open data sources, a data centre type was derived: either enterprise (i.e. company-owned and operated), or co-located (i.e. one company owns the data centre, but other customers operate IT load in the premises as tenants).

Each data centre site was then anonymised by removing any identifiers other than voltage level and UK Power Networks' view of the data centre type.

Quality Control Statement

The dataset is primarily built upon customer smart meter data for connected customer sites within the UK Power Networks' licence areas.

The smart meter data that is used is sourced from external providers. While UK Power Networks does not control the quality of this data directly, these data have been incorporated into our models with careful validation and alignment.

Any missing or bad data has been addressed though robust data cleaning methods, such as omission.

Assurance Statement

The dataset is generated through a manual process, conducted by the Distribution System Operator's Regional Development Team.

The dataset will be reviewed quarterly - both in terms of the operational data centre sites identified, their maximum observed demands and their maximum import capacities - to assess any changes and determine if updates of demand specific profiles are necessary.

Deriving the data centre type is a desktop-based process based on the MPAN's address and through corroboration with external, online sources.

This process ensures that the dataset remains relevant and reflective of real-world data centre usage over time.

There are sufficient data centre sites per voltage level to assure anonymity of data centre sites.

Other Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/Download dataset information: Metadata (JSON)To view this data please register and login.

Crime Survey for England and Wales (CSEW) estimates, by each combination of offence group, age, sex, and important demographic characteristics.

A variety of multiple Data sets showing Transport Statistics in Plymouth. This DATA is taken from both internal sources in Plymouth City Council Departments and The Local Government data source and the Census of 2011. Please see information on each data item to ascertain individual source and accreditation. This material is Crown Copyright. You may re-use this information (not including logos) free of charge in any format or medium, under the terms of the Open Government Licence. To view this licence, visit www.nationalarchives.gov.uk/doc/open-government-licence

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Government Open Data Management Platform Market Size 2025-2029

The government open data management platform market size is valued to increase by USD 189.4 million, at a CAGR of 12.5% from 2024 to 2029. Rising demand for digitalization in government operations will drive the government open data management platform market.

Market Insights

North America dominated the market and accounted for a 38% growth during the 2025-2029.
By End-user - Large enterprises segment was valued at USD 108.50 million in 2023
By Deployment - On-premises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 138.56 million 
Market Future Opportunities 2024: USD 189.40 million
CAGR from 2024 to 2029 : 12.5%

Market Summary

The market witnesses significant growth due to the increasing demand for digitalization in government operations. Open data management platforms enable governments to make large volumes of data available to the public in a machine-readable format, fostering transparency and accountability. The adoption of advanced technologies such as artificial intelligence (AI) and machine learning (ML) in these platforms enhances data analysis capabilities, leading to more informed decision-making. However, data privacy concerns remain a major challenge in the open data management market. Governments must ensure the protection of sensitive information while making data publicly available. A real-world business scenario illustrating the importance of open data management platforms is supply chain optimization in the public sector.
By sharing data related to procurement, logistics, and inventory management, governments can streamline their operations and improve efficiency. For instance, a city government could share real-time traffic data to optimize public transportation routes, reducing travel time and improving overall service delivery. Despite these benefits, it is crucial for governments to address data security concerns and establish robust data management policies to ensure the safe and effective use of open data platforms.

What will be the size of the Government Open Data Management Platform Market during the forecast period?

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The market continues to evolve, with recent research indicating a significant increase in data reuse initiatives among government agencies. The use of open data platforms in the public sector has grown by over 25% in the last two years, driven by a need for transparency and improved data-driven decision making. This trend is particularly notable in areas such as compliance and budgeting, where accurate and accessible data is essential. Data replication strategies, data visualization libraries, and data portal design are key considerations for government agencies looking to optimize their open data management platforms.
Effective data discovery tools and metadata schema design are crucial for ensuring data silos are minimized and data usage patterns are easily understood. Data privacy regulations, such as GDPR and HIPAA, also require robust data governance frameworks and data security audits to maintain data privacy and protect against breaches. Data access logs, data consistency checks, and data quality dashboards are essential components of any open data management platform, ensuring data accuracy and reliability. Data integration services and data sharing platforms enable seamless data exchange between different agencies and departments, while data federation techniques allow for data to be accessed in its original source without the need for data replication.
Ultimately, these strategies contribute to a more efficient and effective data lifecycle, allowing government agencies to make informed decisions and deliver better services to their constituents.

Unpacking the Government Open Data Management Platform Market Landscape

The market encompasses a range of solutions designed to facilitate the efficient and secure handling of data throughout its lifecycle. According to recent studies, organizations adopting data lifecycle management practices experience a 30% reduction in data processing costs and a 25% improvement in ROI. Performance benchmarking is crucial for ensuring optimal system scalability, with leading platforms delivering up to 50% faster query response times than traditional systems. Data anonymization techniques and data modeling methods enable compliance with data protection regulations, while open data standards streamline data access and sharing. Data lineage tracking and metadata management are essential for maintaining data quality and ensuring data interoperability. API integration strategies and data transformation methods enable seamless data enrichment processes and knowledge graph implementation. Data access control, data versioning, and data security protocols