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Reinforcement Learning

Offline RL trajectories, game data, robot demonstrations, RLHF, multi-agent interaction

11,136 datasets

Hemispherically-Symmetric Strategies for Stratospheric Aerosol Injection

Four hemispherically-symmetric injection strategies designed to maintain the same global mean surface temperature, plus a more complex strategy. The data supports research comparing how injection location affects climate responses, such as aerosol optical depth and temperature gradients. Yan Zhang from Cornell University produced this data.

TabularGeoengineeringStratospheric Aerosol InjectionClimate ResponseLarge ScaleClimate Modeling+1

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Reinforcement Learning

GEOMAT: Australian Continental Shelf Sediment Mobility Models

Australian continental shelf sediment mobility is modelled using tidal and swell-wave energy. The project predicts that swell waves mobilized fine sand over 63.5% of the shelf during a one-year period, while tidal currents affected about 56.4%. This work was conducted by a team from AGSO, the University of Tasmania, the Australian Bureau of Meteorology, and a Danish geodetic agency.

Geospatial🇦🇺 AustraliaOceanographyMarine PlanningGeological ModellingSediment Mobility+1

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Reinforcement Learning

Impact of runoff on nutrient patterns in northern Port Phillip Bay, Victoria

The Port Phillip Bay Environmental Study measured bay-wide nutrient and chlorophyll a distributions monthly for two years. This dataset focuses on nutrient variation in northern Port Phillip Bay during a high-runoff period in September 1993 and a low-runoff period in January 1995, collected via continuous profiling from a ship. Concentrations of ammonium, oxidised nitrogen, phosphate, and silicate are reported, with highest levels and variability observed on the northern and western perimeters during high runoff.

TabularGeospatialRunoff ImpactPort Phillip BayMarine ChemistryNutrient PollutionCoastal Monitoring+1

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Reinforcement Learning

Financial Allocations Between Linz and Other Authorities, 2019

Cash flows without benefit between the federal, state, EU, other municipalities and the city of Linz for the 2019 fiscal year. The dataset is structured according to the Preliminary and Financial Statements Ordinance (VRV), which defines supporting documents for budget estimates. It was aggregated by Cooperation OGD Österreich and Wikimedia Österreich from the eu_open_data platform.

TabularCSVMunicipal AccountsFinanceCash FlowsLocal GovernmentAustriaPublic Finance+1

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Reinforcement Learning

Clearance of Accounts 2019: Staff and Pension Expenditure for Linz

The dataset contains the 2019 clearance of accounts for the city of Linz, Austria, detailing total staff expenditure and pensions. It is structured according to the Preliminary and Financial Statements Ordinance (VRV), which defines the supporting documents for budget estimates. The data is provided by the Cooperation OGD Österreich and Wikimedia Österreich under a CC-BY-4.0 license.

TabularCSVGovernment ExpenditurePensionsFinanceAustriaStaff CostsPublic Finance+1

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Reinforcement Learning

Bicheno Region Urchin Barrens Scored from AUV Imagery, 2011-2016

Scored time-series data from Autonomous Underwater Vehicle surveys in the Bicheno region of Tasmania between 2011 and 2016. The dataset, associated with a 2020 PLOS ONE article, tracks the presence of Centrostephanus rodgersii urchin barrens inside and outside the Governor Island Marine Reserve. It includes depth and rugosity measures for each image, subsetted to every fifth image to avoid overlap.

TabularTime SeriesGeospatialCSVAuv ImageryComputer VisionMarine BiologyEcological Monitoring+1

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Reinforcement Learning

Analysis of NICS Recruitment Competitions

A report summarising applications and appointments from Northern Ireland Civil Service recruitment competitions across various equality categories. The data covers competitions with a closing date for applications within a calendar year, produced by the Department of Finance and Personnel as Official Statistics. The report includes analysis of various stages between application and appointment.

TabularOfficial StatisticsNorthern IrelandPublic Sector RecruitmentEquality Analysis+1

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Reinforcement Learning

Strategic Opportunity Areas for Great Crested Newt Habitat in North East England

This dataset identifies areas where new ponds would benefit Great Crested Newt populations in Northumberland, Durham, Tyne & Wear, and Tees Valley. It classifies areas as 'Core' (2+ ponds per 25x25m square) or 'Fringe' (1 pond or within 250m of a Core Area) based on predicted newt presence. The data was produced by Natural England in 2021, incorporating land cover, soil, and environmental records from multiple sources.

GeospatialGreat Crested NewtConservation PlanningPond DensityGeospatial AnalysisHabitat Suitability+1

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Reinforcement Learning

Strategic Opportunity Areas for Great Crested Newt Habitat in North and East Yorkshire

Natural England's dataset identifies areas where new ponds would benefit Great Crested Newt populations in North and East Yorkshire. It classifies areas as 'Core' or 'Fringe' based on pond density and predicted newt presence, scoring habitat suitability using features like soil pH, land cover, and distance to water. The data is derived from multiple sources including Ordnance Survey, the Centre for Ecology & Hydrology, and local authorities, with attribution dated 2021.

GeospatialGreat Crested NewtConservation PlanningPond DensityGeospatial AnalysisHabitat Suitability+1

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Reinforcement Learning

GCN - Strategic Opportunity Areas (Leicestershire, Rutland, Rushcliffe and South Kesteven)

Leicestershire, Rutland, Rushcliffe, and South Kesteven areas are mapped to identify locations where new ponds would benefit Great Crested Newt populations. The dataset classifies core and fringe areas based on pond density and predicted newt presence, scoring habitat suitability using features like grassland, woodland, and arable density. It was created by Natural England in 2020, incorporating data from multiple environmental and governmental sources.

GeospatialGreat Crested NewtConservation PlanningPond DensityGeospatial AnalysisHabitat Suitability+1

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Reinforcement Learning

GCN Strategic Opportunity Areas: Great Crested Newt Habitat Suitability in Lancashire

Natural England's 2020 dataset identifies 25x25m grid squares in Lancashire where new ponds would benefit Great Crested Newt populations. It classifies areas as 'Core' or 'Fringe' based on predicted newt presence and pond density, scoring habitat suitability against eight specific environmental features. The data is derived from multiple sources including Ordnance Survey, the Centre for Ecology & Hydrology, and local council records.

GeospatialGreat Crested NewtConservation PlanningPond DensityGeospatial AnalysisHabitat Suitability+1

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Reinforcement Learning

GCN - Strategic Opportunity Areas for Great Crested Newt Pond Creation in Dorset

Areas in Dorset where new ponds would benefit Great Crested Newt populations, classified into Core and Fringe zones based on pond density and predicted newt presence. The dataset scores habitat suitability based on features like pH, distance to grassland and woodland, and excludes urban areas, roads, rivers, and flood zones. It was created by Natural England in 2020 using land cover data from 2015 and 2017.

GeospatialGreat Crested NewtConservation PlanningPond DensityGeospatial AnalysisHabitat Suitability+1

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Reinforcement Learning

GCN Strategic Opportunity Areas: Pond Suitability for Great Crested Newts in Derbyshire

A geospatial dataset identifying areas where new ponds would benefit Great Crested Newt populations in Derbyshire, UK. It classifies areas as 'Core' or 'Fringe' based on pond density and predicted newt presence, scoring habitat suitability using features like pH, distance to grassland and woodland, and land cover diversity. The dataset was created by Natural England and is based on multiple environmental data sources from 2007 to 2021.

GeospatialGreat Crested NewtConservation PlanningPond DensityGeospatial AnalysisHabitat Suitability+1

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Reinforcement Learning

Strategic Opportunity Areas for Great Crested Newt Habitat in Cambridgeshire

Natural England's dataset identifies strategic opportunity areas for pond creation to benefit Great Crested Newt populations in Cambridgeshire. It classifies areas as 'Core' or 'Fringe' based on predicted newt presence and pond density, and scores habitat suitability using features like land cover and water proximity. The data is derived from sources including Natural England, the Centre for Ecology & Hydrology, and Ordnance Survey, with attribution spanning 2007 to 2021.

GeospatialGreat Crested NewtConservation PlanningPond DensityLand CoverHabitat Suitability+1

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Reinforcement Learning

Radiocarbon and Paleomagnetic Data from 5 North Atlantic Sediment Cores, 0-15 ka

Five sediment cores from the northern North Atlantic provide data supporting research on paleomagnetic secular variation over the last 15,000 years. The dataset includes radiocarbon dates, tephra data, paleomagnetic measurements, and independent age models for each core, compiled by Brendan T. Reilly of Columbia University for a 2023 publication. It also contains a synthesized paleomagnetic stack (GREENICE15) and Dynamic Time Warping solutions aligning three cores to a target curve.

TabularTime SeriesPaleomagnetismGeochronologySediment CoresNorth AtlanticRadiocarbon Dating+1

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Reinforcement Learning

Greenland Ice Slab Radar and Surface Hydrology Data

Data from 2000 to 2016 supports a study of shallow fractures buffering high-elevation runoff in Northwest Greenland. It includes ice-penetrating radar mappings of transient firn aquifers and buried ice complexes, surface water feature locations, and derived metrics like crevasse density and fracture toughness. The dataset integrates radar, optical satellite imagery, and derived indices for a multi-faceted analysis of ice slab hydrology.

TabularTime SeriesGeospatialSurface FeaturesRadar ImageryGreenland Ice SheetsGreenland Ice SheetHydrologySurface HydrologyGeophysical ResearchComputer VisionIce Penetrating RadarFirn Aquifers+1

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Reinforcement Learning

PSSdb: Global Pelagic Particle Size Distributions from Imaging Systems

A global database of pelagic particle size distributions derived from multiple imaging systems, including Imaging Flow Cytobot, ZooScan, and Underwater Vision Profiler. The dataset, created by Mathilde Dugenne at CNRS, provides processed data products like Normalized Biomass Size Spectra and Particle Size Distribution parameters. Data are aggregated by year, month, and 1-degree longitude/latitude grid cells.

TabularTime SeriesGeospatialOceanographySize SpectraComputer VisionBiogeochemistryPlanktonMarine Biology+1

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Reinforcement Learning

Halpin et al. (2026) Supporting Geological Data for the Charcot Province, East Antarctica

Supplementary geological datasets accompany the paper 'Ediacaran–Cambrian high-strain melt-present deformation of the Archean Charcot Province, East Antarctica' in review with AGU Tectonics. The data includes tables of sample details, field observations, whole rock geochemical data, zircon U–Pb data, zircon Hf-isotope data, and feldspar Pb–Pb data. These tables are provided in XLSX format and were last updated on 2026-07-09.

TabularTime SeriesZIPAntarcticaGeochronologyTectonicsGeologyAntarctica GeologyZircon DataGeochemistry+1

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Reinforcement Learning

Property Location Service: Queensland Address Validation and Geocoding

A service for validating Queensland addresses and property descriptions, and supplying location data from a geocode. The service is provided by the Queensland Government's Department of Natural Resources and Mines, Manufacturing and Regional and Rural Development. It was last updated on 2026-06-26.

GeospatialXMLGeocodingAddress ValidationProperty LocationGovernment ServiceAustralia Queensland+1

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Reinforcement Learning

NSTA and Lloyd's Register UKCS Merged Regional Geological Maps

Public domain geological maps for the UK Continental Shelf, including depth structure, isochore, and facies maps for intervals like the Paleocene and Upper Jurassic. The project integrates data from sources like the 21CXRM Palaeozoic project, the Southern Permian Basin Atlas, and university research. Products are available via ARCGIS GEOSERVICES REST API and HTML formats.

GeospatialNorth SeaOil Gas ExplorationBenchmarkGeological MapsGeospatial DataOffshore Geology+1

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