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Text classification, translation, QA, summarization, dialogue, sentiment analysis, language modeling, text corpora
49,416 datasets
Quarterly in-year time series data from the UK's Combined Online Information System (COINS), a database of public sector financial expenditure. The data was collected by HM Treasury and used for official reports like the Budget and Public Expenditure Statistical Analyses until the system was replaced in 2013. It was released in June 2010 to make key parts of government spending data accessible to the public.
A 2021 spatial dataset contains a register of private water supplies in Northern Ireland, created under the Private Water Supplies Regulations (Northern Ireland) 2017. It consists of 100m by 100m square polygons randomly placed around registered supply locations, covering supplies to public, commercial, or multiple domestic premises. The data supports monitoring of water sources not provided by the public utility NI Water Ltd.
A spatial dataset of 100m by 100m squares randomly placed around registered private water supplies in Northern Ireland. The register, required by the Private Water Supplies Regulations (Northern Ireland) 2017, includes supplies to public or commercial premises or two or more private dwellings. Both currently and historically monitored supplies are identifiable, with the dataset created on 24th April 2020 and superseded on 6th July 2020.
A 2020 geospatial register maps private water supplies for human consumption not provided by the public utility NI Water Ltd. Each registered supply is represented by a randomly placed 100m by 100m square polygon, covering supplies to public, commercial, or multi-dwelling premises. The dataset, mandated by the Private Water Supplies Regulations (Northern Ireland) 2017, includes both current and historically monitored supplies.
The Drinking Water Inspectorate's register contains a spatial dataset of private water supplies in Northern Ireland. It includes supplies to public, commercial, or multiple domestic premises used for human consumption. The data is represented as 100m by 100m squares randomly placed around registered supply locations.
A 2018 spatial dataset contains 100m x 100m squares randomly placed around registered private water supplies in Northern Ireland. This geospatial layer, mandated by the 2017 Private Water Supplies Regulations, specifically identifies supplies to public/commercial premises or multiple dwellings used for domestic purposes. Its primary purpose is to support the Drinking Water Inspectorate's monitoring register for supplies not managed by the public utility NI Water Ltd.
A spatial dataset of 100m by 100m squares randomly placed around registered private water supplies in Northern Ireland. The register includes supplies to public or commercial premises or two or more private dwellings, as required by the Private Water Supplies Regulations (Northern Ireland) 2017. Both currently and historically monitored supplies are identifiable from the dataset created on 7th October 2020.
A 2021 spatial dataset contains a register of private water supplies in Northern Ireland, required by the Private Water Supplies Regulations (Northern Ireland) 2017. It consists of 100m by 100m square polygons randomly placed around each registered supply location, covering supplies to public, commercial, or multiple domestic premises. The data includes both currently and historically monitored supplies, intended for regulatory oversight and public health mapping.
A 2021 spatial dataset contains a register of private water supplies in Northern Ireland, as required by the Private Water Supplies Regulations (Northern Ireland) 2017. The data consists of 100m by 100m square polygons randomly placed around each registered supply location, covering supplies to public, commercial, or multiple private dwellings. It includes supplies that are currently or were historically monitored by the Drinking Water Inspectorate.
A 2018-2019 spatial dataset contains a register of private water supplies in Northern Ireland, as required by the Private Water Supplies Regulations (Northern Ireland) 2017. It consists of 100m by 100m square polygons randomly placed around registered supply locations serving public, commercial, or multiple domestic premises. The data identifies only supplies being monitored by the Drinking Water Inspectorate at the time of its creation.
Northern Ireland's register of private water supplies, mandated by the Private Water Supplies Regulations (Northern Ireland) 2017. This geospatial dataset represents supplies to public, commercial, or multiple domestic premises as 100m by 100m squares randomly placed around each registered location. It includes supplies currently or historically monitored by the Drinking Water Inspectorate.
A 12-year dataset from 2002 onward analyzes coastal turbidity in the Great Barrier Reef. The project, managed by the Australian Ocean Data Network, processes MODIS/Aqua remote sensing data to calculate water clarity and analyzes it against environmental factors like tides, waves, wind, rain, and river flow. It aims to determine quantitative relationships between river discharges and seasonal/annual variations in inshore water clarity.
Over 7,000 days of wave measurements collected between 2016 and 2023 from 20 shallow water locations in New South Wales, Australia. The dataset includes time series of spectral and time-domain parameters describing wave height, period, and direction at half-hourly resolution, along with buoy displacement and wave spectra. Data were collected and processed by the NSW Government Environment Coastal and Marine Science team and a snapshot is maintained by the Australian Ocean Data Network.
Around 6,000 regulated waste management facilities in England report annual data on waste quantities and types received and sent on from site. The dataset, collected since 2006, is used for compliance monitoring, planning new facilities, and tracking statutory targets. It is provided in multiple formats including an MS Access interrogator, Excel extracts, and regional summary tables, though site details are withheld for operators claiming commercial confidentiality.
2,500 manually selected images of the Great Barrier Reef, each scored for aesthetic value by at least 10 participants in an online survey launched in October 2017. The dataset includes the original and resized images, survey results in an Excel file, and a full deep learning framework setup for training and testing aesthetic prediction models. It was prepared by the Griffith Institute for Tourism Research as part of the NESP TWQ 3.2.3 project.
Reef Restoration and Adaptation Program experiments on three coral species assessed survival, growth, and photosynthetic efficiency under heat stress. Data covers adult, larval, and juvenile life stages from intra-region and inter-region crosses along a Great Barrier Reef thermal gradient. Experiments ran from November 2020 to April 2022, investigating the potential of selective breeding for enhanced thermal resilience.
Environment Agency's WFD Cycle 2 morphology classification dataset contains summary data for the morphology element used in assessing Ecological Status under the Water Framework Directive. The dataset classifies water bodies on a High, Supports Good, or Does Not Support Good basis, considering features like river continuity, lake bed substrate, and coastal wave exposure. This record was retired and superseded by a new version in 2026.
United Kingdom coastal water bodies contain classification data for phytoplankton, microscopic primary producers used as indicators of nutrient conditions. The data is used to produce Ecological Quality Ratios (EQRs) and a Water Framework Directive classification (High, Good, Moderate, Poor, Bad). This subset of the WFD Classification Status Cycle 2 dataset was produced by the Environment Agency and was last updated on 2026-07-17.
Environment Agency data classifies UK river water bodies based on pH levels for the Water Framework Directive. Classifications are derived from spot sampling monitoring data, assessed against environmental quality standards for acidity and alkalinity. The dataset assigns categories of High, Good, Moderate, Poor, and Bad for ecological status.
Since January 2010, the Never-Ending Language Learner (NELL) has been autonomously reading the web to build a knowledge base. It has acquired over 80 million confidence-weighted beliefs, learned millions of features for reading, and can synthesize new relational predicates. The project is led by Tom M. Mitchell at Carnegie Mellon University.