The presentation explains in the simplest possible way what you need to know about open source licenses when starting from scratch. It also sums up the course "Open Source Licensing Basics for Software Developers (LFC191)" (Linux Foundation)

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.

Recent advances in Computer Science and the spread of internet connection have allowed specialists to virtualize complex environments on the web and offer further information with realistic exploration experiences. At the same time, the fruition of complex geospatial datasets (point clouds, Building Information Modelling (BIM) models, 2D and 3D models) on the web is still a challenge, because usually it involves the usage of different proprietary software solutions, and the input data need further simplification for computational effort reduction. Moreover, integrating geospatial datasets acquired in different ways with various sensors remains a challenge. An interesting question, in that respect, is how to integrate 3D information in a 3D GIS (Geographic Information System) environment and manage different scales of information in the same application. Integrating a multiscale level of information is currently the first step when it comes to digital twinning. It is needed to properly manage complex urban datasets in digital twins related to the management of the buildings (cadastral management, prevention of natural and anthropogenic hazards, structure monitoring, etc.). Therefore, the current research shows the development of a freely accessible 3D Web navigation model based on open-source technology that allows the visualization of heterogeneous complex geospatial datasets in the same virtual environment. This solution employs JavaScript libraries based on WebGL technology. The model is accessible through web browsers and does not need software installation from the user side. The case study is the new building of the University of Twente-Faculty of Geo-Information (ITC), located in Enschede (the Netherlands). The developed solution allows switching between heterogeneous datasets (point clouds, BIM, 2D and 3D models) at different scales and visualization (indoor first-person navigation, outdoor navigation, urban navigation). This solution could be employed by governmental stakeholders or the private sector to remotely visualize complex datasets on the web in a unique visualization, and take decisions only based on open-source solutions. Furthermore, this system can incorporate underground data or real-time sensor data from the IoT (Internet of Things) for digital twinning tasks.

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory Protected Areas Database of the United States (PAD-US) areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

136 views (3 recent) Wind data set of ENSPRESO - an open source, EU-28 wide, transparent and coherent database of wind, solar and biomass energy potentials

The World Database on Protected Areas (WDPA) is the most comprehensive global database of marine and terrestrial protected areas, updated on a monthly basis, and is one of the key global biodiversity data sets being widely used by scientists, businesses, governments, International secretariats and others to inform planning, policy decisions and management.The WDPA is a joint project between UN Environment and the International Union for Conservation of Nature (IUCN). The compilation and management of the WDPA is carried out by UN Environment World Conservation Monitoring Centre (UNEP-WCMC), in collaboration with governments, non-governmental organisations, academia and industry. There are monthly updates of the data which are made available online through the Protected Planet website where the data is both viewable and downloadable.Data and information on the world's protected areas compiled in the WDPA are used for reporting to the Convention on Biological Diversity on progress towards reaching the Aichi Biodiversity Targets (particularly Target 11), to the UN to track progress towards the 2030 Sustainable Development Goals, to some of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) core indicators, and other international assessments and reports including the Global Biodiversity Outlook, as well as for the publication of the United Nations List of Protected Areas. Every two years, UNEP-WCMC releases the Protected Planet Report on the status of the world's protected areas and recommendations on how to meet international goals and targets.Many platforms are incorporating the WDPA to provide integrated information to diverse users, including businesses and governments, in a range of sectors including mining, oil and gas, and finance. For example, the WDPA is included in the Integrated Biodiversity Assessment Tool, an innovative decision support tool that gives users easy access to up-to-date information that allows them to identify biodiversity risks and opportunities within a project boundary.The reach of the WDPA is further enhanced in services developed by other parties, such as the Global Forest Watch and the Digital Observatory for Protected Areas, which provide decision makers with access to monitoring and alert systems that allow whole landscapes to be managed better. Together, these applications of the WDPA demonstrate the growing value and significance of the Protected Planet initiative.Community Protected Areas query:All of these expressions must be true:Status is 'Inscribed' orStatus is 'Established' orStatus is 'Designated'andGovernace Type is 'Local communities' orGovernace Type is 'Indigenous peoples'

A public and anonymous survey aimed towards soliciting direct feedback from users of DEQ's Envionmental Data Hub and related geospatial online offerings.

This page contains freight forecasts and associated documentation from the Strategic Freight Model 2022. This includes: The Excel flat file for the Strategic Freight Model 2022, known as SFM22. This contains NSW freight commodity volume movement forecasts for the 40 year period from 2021 to 2061. The flat file shows forecasts in kilo tonnes on an origin-destination basis for 20 commodity groups moved by road and rail across NSW; Data Dictionary. This document describes the data attributes of the SFM22 Excel flat file; Fact sheet. This document provides a high-level overview of the key features of the SFM22 forecasts. This includes inputs, assumptions, methods, drivers and outputs. More information on the drivers and rationale used to produce the SFM22 forecasts is outlined in the ‘NSW Freight Commodity Demand Forecast Report 2021-2061 Summary’ report. This report will be released on the Open Data hub in July 2025. A visualisation of the SFM22 is available on the Transport for NSW Website under Freight data. Performance Data This page contains freight performance and other related statistics. The data includes: Freight performance dashboard – Strategic Targets from NSW Freight and Ports Plan 2018-2023 including Use of rail freight Road safety Rail freight access Rail freight capability Port Botany Efficiency Additional information on above Strategic Targets is available in the NSW Freight and Ports Plan 2018-2023. Visualisations of the Strategic Targets are available on the Transport for NSW Website under Freight data.

The Connecticut Housing Data Hub is a public resource developed by the Office of Policy and Management (OPM), Department of Housing (DOH), and Department of Economic and Community Development (DECD). Data available on this site includes permitting and demolition data from DECD, housing stock from the American Community Survey, data on affordable housing collected by DOH, and housing market indicator data from RedFin.The dashboard centralizes various types of housing data into one comprehensive platform. It is not all encompassing, as there are still many of pieces of data, analyses, and perspectives that aren’t included here. The goal is to provide a starting point to understand an overview of housing in the state, and we offer resources and data sources for users to further explore the data.The dashboard is updated throughout the year. The data is processed and spatialized using Python and regularly updated in alignment with the underlying datasets. The feature layers and web maps used to create the dashboard are publicly available on the CT Geodata Portal.Key DetailsData Sources Referenced: demolition data from DECD, housing stock data from ACS 5-year, affordable housing data from DOH, housing market indicator data from RedFinData Owner: CT GIS OfficeMap Creator/Data Steward: Sarah Hurley (GIS Office)Update Frequency: Annually (last updated: v2.1 July 2025) For more information and full access to the Housing Dashboard, visit: https://geodata.ct.gov/pages/housing

The Global Power Plant Database is a comprehensive, open source database of power plants around the world. It centralizes power plant data to make it easier to navigate, compare and draw insights for one’s own analysis. The database covers approximately 35,000 power plants from 167 countries and includes thermal plants (e.g. coal, gas, oil, nuclear, biomass, waste, geothermal) and renewables (e.g. hydro, wind, solar). Each power plant is geolocated and entries contain information on plant capacity, generation, ownership, and fuel type. It will be continuously updated as data becomes available.

The H-Mat DataHUB is the submission point for data collected from research conducted by the Hydrogen Materials Compatibility Consortium, or H-Mat. H-Mat, composed of Sandia National Laboratories (SNL, lead), Pacific Northwest National Laboratory (PNNL, lead), Oak Ridge National Laboratory (ORNL), Savannah River National Laboratory (SRNL), and Argonne National Laboratory (ANL), is a framework for cross-cutting early-stage research and development (R&D) on hydrogen materials compatibility. Working in collaboration with partners in industry and academia, H-Mat R&D focuses on the effects of hydrogen on performance of polymers and metals used in hydrogen infrastructure and storage. H-Mat's ultimate goals are to improve the reliability of materials, reduce the costs of materials, and inform codes and standards that guide development and use of hydrogen technologies. H-Mat was launched in 2018 by the U.S. Department of Energy's Fuel Cell Technologies Office within the Office of Energy Efficiency and Renewable Energy in support of the H2@Scale initiative. The H-Mat DataHUB is built upon the CKAN open source data portal. CKAN is used by governments and user groups worldwide and powers a variety of official and community data portals including portals for local, national and international government, such as data.gov

This dataset is a compilation of address point data for the City of Tempe. The dataset contains a point location, the official address (as defined by The Building Safety Division of Community Development) for all occupiable units and any other official addresses in the City. There are several additional attributes that may be populated for an address, but they may not be populated for every address. Contact: Lynn Flaaen-Hanna, Development Services Specialist Contact E-mail Link: Map that Lets You Explore and Export Address Data Data Source: The initial dataset was created by combining several datasets and then reviewing the information to remove duplicates and identify errors. This published dataset is the system of record for Tempe addresses going forward, with the address information being created and maintained by The Building Safety Division of Community Development.Data Source Type: ESRI ArcGIS Enterprise GeodatabasePreparation Method: N/APublish Frequency: WeeklyPublish Method: AutomaticData Dictionary

Due to the cost of developing and training deep learning models from scratch, machine learning engineers have begun to reuse pre-trained models (PTMs) and fine-tune them for downstream tasks. PTM registries known as “model hubs” support engineers in distributing and reusing deep learning models. PTM packages include pre-trained weights, documentation, model architectures, datasets, and metadata. Mining the information in PTM packages will enable the discovery of engineering phenomena and tools to support software engineers. However, accessing this information is difficult — there are many PTM registries, and both the registries and the individual packages may have rate limiting for accessing the data.

We present an open-source dataset, PTMTorrent, to facilitate the evaluation and understanding of PTM packages. This paper describes the creation, structure, usage, and limitations of the dataset. The dataset includes a snapshot of 5 model hubs and a total of 15,913 PTM packages. These packages are represented in a uniform data schema for cross-hub mining. We describe prior uses of this data and suggest research opportunities for mining using our dataset.

We provide links to the PTM Dataset and PTM Torrent Source Code.

Habitat Areas of Particular ConcernThis feature layer, utilizing data from the National Oceanic and Atmospheric Administration (NOAA), displays habitat types and/or geographic areas identified by the eight regional fishery management councils and NOAA Fisheries as priorities for habitat conservation, management, and research in the United States. Per NOAA, "HAPCs are now defined as subsets of EFH that exhibit one or more of the following traits: rare, stressed by development, provide important ecological functions for federally managed species, or are especially vulnerable to anthropogenic (or human impact) degradation. They can cover a specific location (a bank or ledge, spawning location) or cover habitat that is found at many locations (e.g., coral, nearshore nursery areas, or pupping grounds)."Data Disclaimer for Nationwide HAPC:This GIS data layer is composite of regional designations for HAPC and a generalized interpretation of the textual definition, it does not fully represent the complexity of the habitats described in the designation. This GIS data should be used as a general indicator of the extents of the HAPC but should not be relied upon to completely or accurately define this HAPC.Rocky Reefs HAPCData downloaded: 03/18/2025Data source: Essential Fish Habitat - Data InventoryData modification: NoneFor more information: Essential Fish Habitat; Essential Fish Habitat MapperFor feedback please contact: ArcGIScomNationalMaps@esri.comNational Oceanic and Atmospheric AdministrationPer NOAA, its mission is "To understand and predict changes in climate, weather, ocean, and coasts, to share that knowledge and information with others, and to conserve and manage coastal and marine ecosystems and resources."

Rodriguez Aseretto, D., Di Leo, M., de Rigo, D., Corti, P., McInerney, D., Camia, A., San Miguel-Ayanz, J., 2013. Free and Open Source Software underpinning the European Forest Data Centre. Geophysical Research Abstracts 15, 12101+. ISSN 1607-7962, European Geosciences Union (EGU).

This is the authors’ version of the work. The definitive version is published in the Vol. 15 of Geophysical Research Abstracts (ISSN 1607-7962) and presented at the European Geosciences Union (EGU) General Assembly 2013, Vienna, Austria, 07-12 April 2013http://www.egu2013.eu/

Free and Open Source Software underpinning the European Forest Data Centre

Dario Rodriguez Aseretto ¹, Margherita Di Leo ¹, Daniele de Rigo ¹ ², Paolo Corti ¹ ³, Daniel McInerney ¹, Andrea Camia ¹, Jes ús San-Miguel-Ayanz ¹ ¹ European Commission, Joint Research Centre, Institute for Environment and Sustainability,Via E. Fermi 2749, I-21027 Ispra (VA), Italy ² Politecnico di Milano, Dipartimento di Elettronica e Informazione,Via Ponzio 34/5, I-20133 Milano, Italy ³ United Nations World Food Programme,Via C.G.Viola 68 Parco dei Medici, I-00148 Rome, Italy

Excerpt: Worldwide, governments are growingly focusing on free and open source software (FOSS) as a move toward transparency and the freedom to run, copy, study, change and improve the software. The European Commission (EC) is also supporting the development of FOSS [...]. In addition to the financial savings, FOSS contributes to scientific knowledge freedom in computational science (CS) and is increasingly rewarded in the science-policy interface within the emerging paradigm of open science. Since complex computational science applications may be affected by software uncertainty, FOSS may help to mitigate part of the impact of software errors by CS community- driven open review, correction and evolution of scientific code. The continental scale of EC science-based policy support implies wide networks of scientific collaboration. Thematic information systems also may benefit from this approach within reproducible integrated modelling. This is supported by the EC strategy on FOSS: "for the development of new information systems, where deployment is foreseen by parties outside of the EC infrastructure, [F]OSS will be the preferred choice and in any case used whenever possible". The aim of this contribution is to highlight how a continental scale information system may exploit and integrate FOSS technologies within the transdisciplinary research underpinning such a complex system. A European example is discussed where FOSS innervates both the structure of the information system itself and the inherent transdisciplinary research for modelling the data and information which constitute the system content. [...]

This dataset contains selected train stations that have been mapped in accordance with GTFS Pathways format: https://developers.google.com/transit/gtfs/reference#pathwaystxt This data - in combination with other TfNSW GTFS datasets - can be used to give users step by step navigational guidance between station entrances and platforms. This includes estimated traversal time, signposts, and pathway accessibility.

ITS JPO's Connected Vehicle Pilot Deployment Program integrates connected vehicle research concepts into practical and effective elements to enhance existing operational capabilities. Data were collected throughout each pilot to facilitate independent evaluations of the use of connected vehicle technology on real roadways. To encourage additional study and reuse of these data, ITS DataHub has partnered with each pilot site to make sanitized and anonymized tabular and non-tabular data from these projects available to the public. This article gives you a brief overview of what each pilot focused on and what types of CV Pilot data and tools are available on ITS DataHub.

AI Generated Summary: The Open Data catalog of South Tyrol provides freely accessible and reusable public data from various institutional actors in the region. It aims to foster innovation and economic growth by enabling the creative reuse of public information for solving common problems and developing new services. The catalog is managed by a project group and utilizes the CKAN open-source software for data cataloging, encouraging collaboration and feedback from the public. About: Public administrations share a common characteristic: they produce, manage, and accumulate data as a result of their normal operations. Some data are subject to specific constraints, such as privacy protection, national security, or intellectual property. Others, however, can be freely disseminated and reused by everyone. Information relating to cycle paths or the location of local products can stimulate the development of innovative services for tourists, families, and citizens. Information relating to public transport and the road network can allow for the optimization of services from the bottom up through feedback sent by the users themselves. Data relating to public events and the weather allow the creation of applications capable of further increasing the visibility of events, as well as allowing better organization by participants. This information has great economic value and strong potential for innovative growth: opening public data means creating new opportunities for the territory, based on their creative reuse, proposing and inventing new solutions to common problems, without burdening the activities of the public administration. The Open Data catalog of South Tyrol makes this data available, promoting its reuse both in a technological and legal sense. It contains datasets provided by all the institutional actors of the South Tyrol system, the Autonomous Province of Bolzano, local authorities, system companies and, more generally, by all actors interested in participating in this process. The catalog is managed and implemented by a project group composed of experts in the various thematic areas involved (such as data collection, analysis and modeling, the semantics and legal correctness of the opening process, the organizational impact). The working group is coordinated by the Managerial Position in Information Technology of the Autonomous Province of Bolzano, which is the provincial structure indicated by the Provincial Council as competent for the Open Data Project in South Tyrol. The site is based on open-source data cataloging software called CKAN, developed by the Open Knowledge Foundation: a non-profit organization that promotes free knowledge. Each entry contains a description of the data (metadata) and other useful information, such as the available formats, the holder, the freedom of access and reuse, and the topics that the data address. CKAN, used for various data catalogs on the internet, converges in The Data Hub: a centrally modifiable and reusable collector, in the style of Wikipedia. The level of innovation that the liberation of data achieves is remarkable: already the opening of a simple data point by a public structure is a great result in terms of organizational change towards the culture of data and the knowledge economy. For this reason, the catalog is constantly evolving: starting from the opening of data, connections are created that will lead to a change in the way of thinking and working, in the public and private sectors alike: from data as a private resource for management purposes to data as a "public good" for public and social purposes. Public sector information is seen as a basic platform whose applications are yet to be written. This enormous change, in order to be realized, also needs you. Send us suggestions and feedback via email (opendata@siag.it). For more detailed information, see the F.A.Q. page. Translated from Italian Original Text: Le pubbliche amministrazioni presentano una caratteristica comune: producono, gestiscono ed accumulano dati come risultato del loro normale funzionamento. Alcuni dati sono soggetti a precisi vincoli, quali ad esempio la tutela della privacy, la sicurezza nazionale o la proprietà intellettuale. Altri invece possono essere liberamente diffusi e riutilizzati da tutti. Le informazioni relative a piste ciclabili, la localizzazione dei prodotti locali possono stimolare lo sviluppo di servizi innovativi per il turista, le famiglie, il cittadino. Le informazioni relative ai trasporti pubblici e al grafo stradale possono consentire una ottimizzazione dei servizi dal basso mediante un feedback inviato dagli stessi utenti. I dati relativi alle manifestazioni pubbliche e al meteo consentono la creazione di applicazioni capaci di aumentare ancora più visibilità agli eventi, oltre a consentirne una migliore organizzazione da parte dei partecipanti. Queste informazioni possiedono un grande valore economico e un forte potenziale di crescita innovativa: aprire i dati pubblici significa creare nuove opportunità per il territorio, basate sul loro riutilizzo creativo, proponendo e inventando nuove soluzioni a problemi comuni, senza aggravio per le attività della pubblica amministrazione. Il catalogo Open Data dell'Alto Adige mette a disposizione questi dati, favorendone il riutilizzo sia in senso tecnologico che giuridico. Contiene dataset forniti da tutti gli attori istituzionali del sistema Alto Adige, Provincia autonoma di Bolzano, gli Enti locali, le società di sistema e, più in generale, da tutti gli attori interessati a partecipare a questo processo. Il catalogo è gestito e implementato da un gruppo di progetto composto da esperti nelle diverse aree tematiche coinvolte (quali la raccolta, l'analisi e la modellazione dei dati, la semantica e correttezza giuridica del processo di apertura, l’impatto organizzativo). Il gruppo di lavoro è coordinato dall'Incarico dirigenziale in materia di Informatica della Provincia autonoma di Bolzano che è la struttura provinciale indicata dalla Giunta Provinciale come competente per il Progetto Open data in Alto Adige. Il sito è basato su un software open-source di catalogazione dei dati, chiamato CKAN, sviluppato dalla Open Knowledge Foundation: un'organizzazione no-profit che promuove il sapere libero. Ogni voce contiene una descrizione dei dati (metadati) e altre informazioni utili, come i formati disponibili, il detentore, la libertà di accesso e riuso, e gli argomenti che i dati affrontano. CKAN, utilizzato per diversi cataloghi di dati su internet, converge in The Data Hub: un raccoglitore centrale liberamente modificabile e riutilizzabile, nello stile di Wikipedia. Il livello di innovazione che realizza la liberazione dei dati è notevole: già solo l'apertura di un semplice dato da parte di una struttura pubblica è un grande risultato in termini di cambiamento organizzativo in direzione della cultura del dato e dell’economia della conoscenza. Per questo, il catalogo è in continua evoluzione: partendo dall'apertura dei dati si creano connessioni che porteranno ad un cambiamento nel modo di pensare e di lavorare, nel settore pubblico come in quello privato: dal dato come risorsa privata per scopi gestionali al dato come "bene pubblico" per scopi pubblici e sociali. L'informazione del settore pubblico è vista come una piattaforma di base le cui applicazioni sono ancora da scrivere. Questo enorme cambiamento, per potersi realizzare, ha bisogno anche di voi. Inviateci suggerimenti e feedback attraverso l'email (opendata@siag.it). Per informazioni più dettagliate consulta la pagina delle F.A.Q.

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.