Local 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.

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.

About this layerNetwork contribution area scores were exported from the SciMAP application outputs within SAGA GIS. The scores were based on inputs from CEH Land cover 2007, Met Office average rainfall and 5 meter DTM. The High contribution category boundary was determined by selecting +1 standard deviation of the distribution of the scores.What can you do with the layer?Visualisation: This layer can be used for visualisation online in web maps.Analysis: This layer can be used in dashboards.Download: The data is downloadable.

IntroductionUK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is distributing this electricity across our regions through circuits. Electricity enters our network through Super Grid Transformers at substations shared with National Grid we call Grid Supply Points. It is then sent at across our 132 kV Circuits towards our grid substations and primary substations. These circuits can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables.

This dataset provides half-hourly current and power flow data across these named circuits from 2021 through to the previous month across our license areas. The data are aligned with the same naming convention as the LTDS for improved interoperability.

Care is taken to protect the private affairs of companies connected to the 132 kV network, resulting in the redaction of certain circuits. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted.

To find which circuit you are looking for, use the ‘ltds_line_name’ that can be cross-referenced in the 132kV Circuits Monthly Data, which describes by month what circuits were triaged, if they could be made public, and what the monthly statistics are of that site.

If you want to download all this data, it is perhaps more convenient from our public sharepoint: Sharepoint

This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.

Methodological Approach

The dataset is not derived, it is the measurements from our network stored in our historian.

The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps.

We developed a data redactions process to protect the privacy of companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer.

The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation.

Quality Control Statement

The data is provided "as is".

In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these measurements are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that.

Assurance Statement

Creating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS circuit from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same circuit in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets.

There is potential for human error during the manual data processing. These issues can include missing circuits, incorrectly labelled circuits, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible.

Additional Information

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary.

Download dataset information: Metadata (JSON)

The Global Roads Open Access Data Set, Version 1 (gROADSv1) was developed under the auspices of the CODATA Global Roads Data Development Task Group. The data set combines the best available roads data by country into a global roads coverage, using the UN Spatial Data Infrastructure Transport (UNSDI-T) version 2 as a common data model. All country road networks have been joined topologically at the borders, and many countries have been edited for internal topology. Source data for each country are provided in the documentation, and users are encouraged to refer to the readme file for use constraints that apply to a small number of countries. Because the data are compiled from multiple sources, the date range for road network representations ranges from the 1980s to 2010 depending on the country (most countries have no confirmed date), and spatial accuracy varies. The baseline global data set was compiled by the Information Technology Outreach Services (ITOS) of the University of Georgia. Updated data for 27 countries and 6 smaller geographic entities were assembled by Columbia University's Center for International Earth Science Information Network (CIESIN), with a focus largely on developing countries with the poorest data coverage.

For Performance.Seattle.Gov

A log of dataset alerts open, monitored or resolved on the open data portal. Alerts can include issues as well as deprecation or discontinuation notices.

This report provides the status of each agency’s progress in meeting milestones within the Open Data Program. These include appointing a data coordinator; creating an agency open data inventory; completing an open data plan; and publishing open data.

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

Air Quality Monitoring

The National Broadband Data represents coverage information across Canada for existing broadband service providers with their associated technology types. The coverage information is aggregated and deployed over a grid of hexagons, which cover areas of roughly 25 square km each. Broadband Internet service availability is provided for download/upload speed markers (5/1, 10/2, 25/5 and 50/10 Mbps) where more than 75% of total dwellings covered within the hexagon have access to broadband service offerings meeting these markers. In order to improve the granularity of the broadband data, ISED and the CRTC are providing aggregated and anonymous broadband services data based on the pseudo-household statistical model, hence achieving higher precision in depicting the broadband Internet service availability. This information is available below under the "NBD PHH Speeds" resource. For more information on the pseudo-household statistical model, refer to the Pseudo-Household Demographic Distribution dataset. A representation of broadband services per 250m road segments is now available for download under the “NBD Roads” resource. To generate this dataset, the NBD PHH Speeds information was projected over the nearest road arc from Statistics Canada’s Road Network File, and those roads were spliced in approximately 250m segments. NEW: The data has been augmented to include new presentation layers as published on the National Broadband Map.

Open Data Catalog

Global fixed broadband and mobile (cellular) network performance, allocated to zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Data is provided in both Shapefile format as well as Apache Parquet with geometries represented in Well Known Text (WKT) projected in EPSG:4326. Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile. Measurements are filtered to results containing GPS-quality location accuracy.

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. This file groups together the individual wetland priority habitats, which are also available for individual download. 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.

This data set includes rainfall data from 25 sites in Iowa, centered on the Iowa City Municipal Airport.

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.

damien-johnston/open-data-project dataset hosted on Hugging Face and contributed by the HF Datasets community

An incomplete collection of open data domains throughout the U.S. (intended for comparison with King County open data)

Building a comprehensive data inventory as required by section 6.3 of the Directive on Open Government: “Establishing and maintaining comprehensive inventories of data and information resources of business value held by the department to determine their eligibility and priority, and to plan for their effective release.” Creating a data inventory is among the first steps in identifying federal data that is eligible for release. Departmental data inventories has been published on the Open Government portal, Open.Canada.ca, so that Canadians can see what federal data is collected and have the opportunity to indicate what data is of most interest to them, helping departments to prioritize data releases based on both external demand and internal capacity. The objective of the inventory is to provide a landscape of all federal data. While it is recognized that not all data is eligible for release due to the nature of the content, departments are responsible for identifying and including all datasets of business values as part of the inventory exercise with the exception of datasets whose title contains information that should not be released to be released to the public due to security or privacy concerns. These titles have been excluded from the inventory. Departments were provided with an open data inventory template with standardized elements to populate, and upload in the metadata catalogue, the Open Government Registry. These elements are described in the data dictionary file. Departments are responsible for maintaining up-to-date data inventories that reflect significant additions to their data holdings. For purposes of this open data inventory exercise, a dataset is defined as: “An organized collection of data used to carry out the business of a department or agency, that can be understood alone or in conjunction with other datasets”. Please note that the Open Data Inventory is no longer being maintained by Government of Canada organizations and is therefore not being updated. However, we will continue to provide access to the dataset for review and analysis.

About this layerNetwork contribution area scores were exported from the SciMAP application outputs within SAGA GIS. The scores were based on inputs from CEH Land cover 2007, Met Office average rainfall and 5 meter DTM. The High contribution category boundary was determined by selecting +1 standard deviation of the distribution of the scores.What can you do with the layer?Visualisation: This layer can be used for visualisation online in web maps.Analysis: This layer can be used in dashboards.Download: The data is downloadable.