The National Environmental Data Centre (NEDC) online directory http://nedc.nz is a website resource that provides information on environmental datasets for Aotearoa New Zealand and in some places wider coverage. The datasets are each hosted by one of New Zealand's Crown Research Institutes; AgResearch, ESR, GNS Science, Manaaki Whenua Landcare Research, NIWA, Scion and Plant & Food Research. The datasets are categorised in terms of Atmosphere, Biodiversity, Climate, Freshwater, Geology, Land and Ocean themes.

As per data from a recent report, in 2020, 42 percent of respondents mentioned that they refreshed their data center servers every two to three years, whilst 26 percent stated that they did so every year.

According to Cognitive Market Research, the global Data Center Market size is USD 342514.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 6.00% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 137005.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.2% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 102754.26 million.
 Asia Pacific held the market of around 23% of the global revenue with a market size of USD 78778.27 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.0% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 17125.71 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.4% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 6850.28 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.7% from 2024 to 2031.
The BFSI held the highest Data Center Market revenue share in 2024.

Market Dynamics of Data Center Market

Key Drivers for Data Center Market

Growing Environmental concerns are Pushing Data Center Operators to Adopt Renewable Energy Sources to Power their Facilities

Growing environmental concerns are compelling data center operators to embrace renewable energy sources for powering their facilities. This shift towards sustainability not only addresses carbon footprint concerns but also aligns with corporate social responsibility initiatives. By harnessing renewable energy such as solar, wind, or hydroelectric power, data centers can reduce their environmental impact while also potentially benefiting from cost savings in the long run. This commitment to renewable energy reflects a broader trend toward greener practices in the data center industry.

Exponential Growth in Cloud Services is Propelling Demand for Data Center Infrastructure Worldwide

The exponential growth of cloud services is a major driver propelling the demand for data center infrastructure worldwide. As businesses increasingly adopt cloud-based solutions for storage, computing, and networking needs, the demand for data centers to support these services surges. This trend is fueled by the scalability, flexibility, and cost-effectiveness offered by cloud computing, driving businesses to invest in robust data center infrastructure to meet the growing demands of digital transformation and data storage requirements.

Restraint Factor for the Data Center Market

Limited Availability of Skilled Workforce and Specialized Talent in the Data Center Industry Acts as a Restraint

Limited availability of skilled workforce and specialized talent in the data center industry presents a significant challenge for companies operating in this sector. The complex nature of data center operations requires personnel with specific expertise in areas such as network engineering, systems administration, and cybersecurity. However, the demand for such professionals often outstrips the available supply, leading to difficulties in recruiting and retaining qualified staff. This scarcity of talent can hinder companies' ability to manage and optimize their data center operations effectively, impacting overall performance and competitiveness.

Impact of Covid-19 on the Data Center Market

The COVID-19 pandemic has exerted a notable impact on the data center market. With the acceleration of remote work and digitalization initiatives, there has been a surge in demand for data center services to support increased online activities. However, supply chain disruptions and construction delays have impeded the expansion and deployment of new data center facilities. Additionally, economic uncertainty has led some organizations to reevaluate their IT spending, potentially affecting investment in data center infrastructure projects. Introduction of the Data Center Market

The data center market refers to the industry segment encompassing facilities designed to house and manage computer systems and associated components, such as telecommunications and storage systems. These centers are crucial for organizations to store, process, and distribute large volumes of data securely and efficiently. Data centers vary in size and complexity, ranging from small server rooms to mass...

No description is available. Visit https://dataone.org/datasets/%7BEA9ACC6C-1FA2-4829-89F9-15C87B0D2FEB%7D for complete metadata about this dataset.

As per data from a recent report, in 2019, 56 percent of respondents claimed that overall performance or utilization was their primary method of measuring success in relation to data center infrastructure, whilst a return on investment (ROI) was cited by 38 percent. These measures do not have a focus on reducing energy usage or lowering the environmental impact - measurements like total cost to the environment (TCE) or IT assets lifecycle, which would contribute toward this, were cited less often by respondents. Just 11 percent stated IT asset lifecycles as being a method of measuring the success of data center infrastructure.

Land Use coding for points within approximately 10 acre grid cell analysis zones from 1961-1970-1975 with other land use related attributes also listed.

These layers are polar climatological and other summary environmental layers that may be useful for purposes such as general modelling, regionalisation, and exploratory analyses. All of the layers in this collection are provided on a consistent 0.1-degree grid, which covers -180 to 180E, 80S to 30S (Antarctic) and 45N to 90N (Arctic). As far as practicable, each layer is provided for both the Arctic and Antarctic regions. Where possible, these have been derived from the same source data; otherwise, source data have been chosen to be as compatible as possible between the two regions. Some layers are provided for only one of the two regions.

Each data layer is provided in netCDF and ArcInfo ASCII grid format. A png preview map of each is also provided.

Processing details for each layer:

Bathymetry File: bathymetry Measured and estimated seafloor topography from satellite altimetry and ship depth soundings. Antarctic: Source data: Smith and Sandwell V13.1 (Sep 4, 2010) Processing steps: Depth data subsampled from original 1-minute resolution to 0.05-degree resolution and interpolated to 0.1-degree grid using bilinear interpolation. Reference: Smith, W. H. F., and D. T. Sandwell (1997) Global seafloor topography from satellite altimetry and ship depth soundings. Science 277:1957-1962. http://topex.ucsd.edu/WWW_html/mar_topo.html Arctic: Source data: ETOPO1 Processing steps: Depth data subsampled to 0.05-degree resolution and interpolated to 0.1-degree grid using bilinear interpolation on polar stereographic projection. Reference: Amante, C. and B. W. Eakins, ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24, 19 pp, March 2009. http://www.ngdc.noaa.gov/mgg/global/global.html

Bathymetry slope File: bathymetry_slope Slope of sea floor, derived from Smith and Sandwell V13.1 and ETOPO1 bathymetry data (above). Processing steps: Slope calculated on 0.1-degree gridded depth data (above). Calculated using the equation given by Burrough, P. A. and McDonell, R.A. (1998) Principles of Geographical Information Systems (Oxford University Press, New York), p. 190 (see http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=How%20Slope%20works)

CAISOM model-derived variables Variables derived from the CAISOM ocean model. This model has been developed by Ben Galton-Fenzi (AAD and ACE-CRC), and is based on the Regional Ocean Modelling System (ROMS). It has circum-Antarctic coverage out to 50S, with a spatial resolution of approximately 5km. The values here are averaged over 12 snapshots from the model, each separated by 2 months. These parameters should be treated as experimental.

Reference: Galton-Fenzi BK, Hunter JR, Coleman R, Marsland SJ, Warner RC (2012) Modeling the basal melting and marine ice accretion of the Amery Ice Shelf. Journal of Geophysical Research: Oceans, 117, C09031. http://dx.doi.org/10.1029/2012jc008214

Floor current speed File: caisom_floor_current_speed Current speed near the sea floor.

Floor temperature File: caisom_floor_temperature Potential temperature near the sea floor.

Floor vertical velocity File: caisom_floor_vertical_velocity Vertical water velocity near the sea floor.

Surface current speed File: caisom_surface_current_speed Near-surface current speed (at approximately 2.5m depth)

Chlorophyll summer File: chl_summer_climatology Source data: Near-surface chl-a summer climatology from MODIS Aqua Antarctic: Climatology spans the 2002/03 to 2009/10 austral summer seasons. Data interpolated from original 9km resolution to 0.1-degree grid using bilinear interpolation. Arctic: Climatology spans the 2002 to 2009 boreal summer seasons. Data interpolated from original 9km resolution to 0.1-degree grid using bilinear interpolation. Reference: Feldman GC, McClain CR (2010) Ocean Color Web, MODIS Aqua Reprocessing, NASA Goddard Space Flight Center. Eds. Kuring, N., Bailey, S.W. https://oceancolor.gsfc.nasa.gov/

Distance to Antarctica File: distance_antarctica Distance to nearest part of Antarctic continent (Antarctic only) Source data: A modified version of ESRI's world map shapefile Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km.

Distance to nearest seabird breeding colony (Antarctic only) File: distance_colony Antarctic source data: Inventory of Antarctic seabird breeding sites, collated by Eric Woehler. http://data.aad.gov.au/aadc/biodiversity/display_collection.cfm?collection_id=61. Processing steps: The closest distance of each grid point to the colonies was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km.

Distance to maximum winter sea ice extent File: distance_max_ice_edge Source data: SMMR-SSM/I passive microwave estimates of daily sea ice concentration from the National Snow and Ice Data Center (NSIDC). Processing steps: Antarctic: Mean maximum winter sea ice extent was derived from daily estimates of sea ice concentration as described at https://data.aad.gov.au/metadata/records/sea_ice_extent_winter. The closest distance of each grid point to this extent line was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Arctic: The median March winter sea ice extent was obtained from the NSIDC at http://nsidc.org/data/g02135.html. The closest distance of each grid point to this extent line was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Reference: Cavalieri, D., C. Parkinson, P. Gloersen, and H. J. Zwally. 1996, updated 2008. Sea ice concentrations from Nimbus-7 SMMR and DMSP SSM/I passive microwave data. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media. tp://nsidc.org/data/nsidc-0051.html

Distance to shelf break File: distance_shelf Distance to nearest area of sea floor of depth 500m or less. Derived from Smith and Sandwell V13.1 and ETOPO1 bathymetry data (above). Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Points in less than 500m of water (i.e. over the shelf) were assigned negative distances. See also distance to upper slope

Distance to subantarctic islands (Antarctic only) File: distance_subantarctic_islands Distance to nearest land mass north of 65S (includes land masses of e.g. South America, Africa, Australia, and New Zealand). Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km.

Distance to canyon File: distance_to_canyon Distance to the axis of the nearest canyon (Antarctic only) Source data: O'Brien and Post (2010) seafloor geomorphic feature dataset, expanded from O'Brien et al. (2009). Mapping based on GEBCO contours, ETOPO2, seismic lines. Processing steps: Distances to nearest canyon axis calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. NOTE: source data extend only as far north as 45S. Do not rely on this layer near or north of 45S. Reference: O'Brien, P.E., Post, A.L., and Romeyn, R. (2009) Antarctic-wide geomorphology as an aid to habitat mapping and locating vulnerable marine ecosystems. CCAMLR VME Workshop 2009. Document WS-VME-09/10

Distance to polynya File: distance_to_polynya Distance to the nearest polynya area (Antarctic only) Source data: AMSR-E satellite estimates of daily sea ice concentration at 6.25km resolution Processing steps: The seaice_gt_85 layer (see below) was used. Pixels which were (on average) covered by sea ice for less than 35% of the year were identified. The distance from each grid point on the 0.1-degree grid to the nearest such polynya pixel was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. (NB the threshold of 35% was chosen to give a good empirical match to the polynya locations identified by Arrigo and van Dijken (2003), although the results were not particularly sensitive to the choice of threshold. Reference: Arrigo KR, van Dijken GL (2003) Phytoplankton dynamics within 37 Antarctic coastal polynya systems. Journal of Geophysical Research, 108, 3271. http://dx.doi.org/10.1029/2002JC001739

Distance to upper slope (Antarctic only) File: distance_upper_slope Distance to the "upper slope" geomorphic feature from the Geoscience Australia geomorphology data set. This is probably a better indication of the distance to the Antarctic continental shelf break than the "distance to shelf break" data (above). Source data: O'Brien and Post (2010) seafloor geomorphic feature dataset, expanded from O'Brien et al. (2009). Mapping based on GEBCO contours, ETOPO2, seismic lines. Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Points inside of an "upper slope" polygon were assigned negative distances. Reference: O'Brien, P.E., Post, A.L., and Romeyn, R. (2009) Antarctic-wide geomorphology as an aid to habitat mapping and locating vulnerable marine ecosystems. CCAMLR VME Workshop 2009. Document WS-VME-09/10

Fast ice File: fast_ice The average proportion of the year for which landfast sea ice is present in a location Source data: 20-day composite records of East Antarctic landfast sea-ice, derived from MODIS imagery (Fraser at al. 2012) Processing steps: The average proportion of the year for which each pixel was covered by landfast sea ice was calculated as an average across 2001--2008. Data were regridded to the 0.1-degree grid using bilinear interpolation.

Distance to fast ice File: distance_to_fast_ice Distance to the nearest location where fast ice is typically present. Source data: 20-day composite records of East Antarctic landfast sea ice, derived from MODIS imagery (Fraser at al. 2012) Processing steps: Pixels in the landfast sea ice data that were associated with fast ice presence for more than half of the

The National Oceanic and Atmospheric Administration (NOAA) monitors the geospace and solar environments using a variety of space weather sensors aboard its fleet of operational satellites.

The Data Center Construction market is experiencing robust growth, driven by the escalating demand for digital services, cloud computing adoption, and the proliferation of big data. The market's Compound Annual Growth Rate (CAGR) of 9% from 2019 to 2024 indicates a significant expansion, projected to continue through 2033. Several factors contribute to this growth. The increasing reliance on data-intensive applications across various industries, including finance, healthcare, and e-commerce, fuels the need for more sophisticated and expansive data center infrastructure. Furthermore, the rise of edge computing, aiming to reduce latency by placing data centers closer to end-users, is creating new construction opportunities. Technological advancements, such as advancements in cooling systems and increased energy efficiency, are also driving market expansion. However, the market faces challenges including high initial investment costs, the complexity of building and managing data centers, and concerns regarding energy consumption and environmental impact. Geographic expansion varies; North America and Europe currently hold significant market shares but Asia-Pacific is predicted to experience faster growth due to its burgeoning digital economy. Competition within the market is intense, with established players like ABB, Cisco, and Schneider Electric employing strategies focused on innovation, partnerships, and service offerings to maintain and expand their market presence. The focus on sustainable and efficient data center designs is growing to address environmental concerns and long-term operational costs. The segmentation of the Data Center Construction market by type and application reveals diverse growth trajectories. For example, the hyperscale data center segment may be growing faster than traditional data centers due to the scale of investment from major cloud providers. Similarly, applications within specific verticals such as finance or healthcare could show higher growth rates based on their unique data requirements. Regional differences will also influence growth – regions with more stringent regulatory environments or higher energy costs might see different construction trends compared to regions with more supportive government policies. Companies are increasingly focusing on consumer engagement by offering tailored solutions and emphasizing sustainability to align with the evolving needs of clients and growing environmental consciousness. The forecast period of 2025-2033 suggests continued market expansion, although the rate of growth may fluctuate depending on macroeconomic factors, technological breakthroughs, and regulatory changes.

Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries. The online data files begin with 1929 and are at the time of this writing at the Version 8 software level. Over 9000 stations' data are typically available. The daily elements included in the dataset (as available from each station) are: Mean temperature (.1 Fahrenheit) Mean dew point (.1 Fahrenheit) Mean sea level pressure (.1 mb) Mean station pressure (.1 mb) Mean visibility (.1 miles) Mean wind speed (.1 knots) Maximum sustained wind speed (.1 knots) Maximum wind gust (.1 knots) Maximum temperature (.1 Fahrenheit) Minimum temperature (.1 Fahrenheit) Precipitation amount (.01 inches) Snow depth (.1 inches) Indicator for occurrence of: Fog, Rain or Drizzle, Snow or Ice Pellets, Hail, Thunder, Tornado/Funnel Cloud Global summary of day data for 18 surface meteorological elements are derived from the synoptic/hourly observations contained in USAF DATSAV3 Surface data and Federal Climate Complex Integrated Surface Hourly (ISH). Historical data are generally available for 1929 to the present, with data from 1973 to the present being the most complete. For some periods, one or more countries' data may not be available due to data restrictions or communications problems. In deriving the summary of day data, a minimum of 4 observations for the day must be present (allows for stations which report 4 synoptic observations/day). Since the data are converted to constant units (e.g, knots), slight rounding error from the originally reported values may occur (e.g, 9.9 instead of 10.0). The mean daily values described below are based on the hours of operation for the station. For some stations/countries, the visibility will sometimes 'cluster' around a value (such as 10 miles) due to the practice of not reporting visibilities greater than certain distances. The daily extremes and totals--maximum wind gust, precipitation amount, and snow depth--will only appear if the station reports the data sufficiently to provide a valid value. Therefore, these three elements will appear less frequently than other values. Also, these elements are derived from the stations' reports during the day, and may comprise a 24-hour period which includes a portion of the previous day. The data are reported and summarized based on Greenwich Mean Time (GMT, 0000Z - 2359Z) since the original synoptic/hourly data are reported and based on GMT.

This dataset includes data on policy and environmental supports for physical activity, diet, and breastfeeding. This data is used for DNPAO's Data, Trends, and Maps database, which provides national and state specific data on obesity, nutrition, physical activity, and breastfeeding.

This hosted feature layer has been published in RI State Plane Feet NAD 83. Aquifers designated as sole source water supplies meeting federal government criteria as accepted by the U.S. Environmental Protection Agency Data intended for water resource management and protection. USGS-RIWRB contributed these data to the Rhode Island Geographic Information System Database

Marine species records from around Northern Ireland and the Republic of Ireland stored by CEDaR. Predominantly collected through CEDaR field work and recording initiatives e.g. BioBlitz events and miscallaenous records from local recorders. CEDaR submit marine data to NBN periodically (subject to requirement), therefore this may not represent the full dataset. For additional metadata and/or the most complete dataset, contact CEDaR.

This bar chart displays books by publication date and is filtered where the book publisher is Centre for Environmental Data and Recording. The data is about books.

This hosted feature layer has been published in RI State Plane Feet NAD 83. A wellhead protection area (WHPA) is the portion of an aquifer through which groundwater moves to a well. Under the Rhode Island Department of Environmental Management (DEM) Wellhead Protection (WHP) Program approved by the US Environmental Protection Agency in 1990, DEM is responsible for delineating a WHPA for each of the public wells in the state. DEM contracted with the GZA Consulting to delineate select non-community stratified drift wells using analytical modeling and hydrogeologic mapping.As of August 2013, the Rhode Island Department of Health Office of Drinking Water Quality (HEALTH) is responsible for delineating the Calculated Fixed Radius WHPA's for bedrock wells, based on the pump rate of the well. Non-Transient Non-Community Well - regularly serves at least 25 of the same persons (not residents) over 6 months of the year. Examples include wells serving schools and places of employment. Transient Non-Community Well - does not regularly serve the same persons, but does serve at least 25 people at least 60 days of hte year. DEM relied on technical input from the Wellhead Protection Program Advisory Committee in developing the delineation methodology. A mapping approach was required that was scientifically defensible, could be applied consistently across the state, and could be applied with the resources available to DEM. The delineations are based on reasonably available information regarding the hydrogeologic environment and the well characteristics. The WHPAs were delineated using the US Geological Survey quadrangle maps at a scale of 1:24000. WHPA maps are available for review at the DEM Office of Water Resources, on the DEM web page at www.dem.ri.gov/maps, and on the Rhode Island Geographic Information System webpage at www.rigis.org.

The information system PANGAEA is operated as an Open Access library aimed at archiving, publishing and distributing georeferenced data from earth system research. The system guarantees long-term …Show full descriptionThe information system PANGAEA is operated as an Open Access library aimed at archiving, publishing and distributing georeferenced data from earth system research. The system guarantees long-term availability of its content through a commitment of the operating institutions. The data and any associated material in PANGAEA is made available under the Creative Commons Attribution license if not otherwise specified in the description of the dataset.

Tropical Ocean Global Atmosphere (TOGA) blended Sea Surface Temperatures (SST) data for July-December 1988 was provided by Mr. John J. Kundrat Jr. from the Ocean Products Center. SST were collected from ships and buoys (in situ) and from satellites (POLAR AVHRR). Blended SST's eliminate biases or large scale trends in the satellite data by using in situ data as anchor points.

SeaDataNet is the Pan-European infrastructure for marine and ocean data management and delivery services. It is supported by the EU under its Research Infrastructures programme. It connects 40 National Oceanographic Data Centres (NODC's) and 50 other data centres from 35 countries, bordering the European seas and Atlantic Ocean. The centres are mostly part of major marine management and research organisations that are acquiring and managing a large collection of marine and ocean data from various disciplines. This includes major international organisations, ICES and IOC-IODE. The overall objective is provide overview and access to marine and oceanographic data and data-products from government and research institutes in Europe. SeaDataNet contributes to the implementation of the EU INSPIRE and Marine Strategy Framework Directives. It also plays a key role in the development and operation of the EU EMODNet initiative. The SeaDataNet infrastructure is fully operational and INSPIRE compliant. It includes a versatile SeaDataNet portal (https://www.seadatanet.org) that provides users with a range of metadata, data and data product access services as well as standards, tools and guides for good marine data management. The Common Data Index (CDI) data discovery and access service provides harmonised access to the large volumes of datasets that are managed by the connected data centres. The CDI service contains already references and gives access to more than 1,5 milllion marine and oceanographic datasets as managed by 90 data centres. These numbers are increasing regularly because of further data population and more connected data centres as part of SeaDataNet II, EMODnet and other EU projects. For inclusion in the SeaDataNet INSPIRE compliant CSW service, the CDI records (at granule level) have been aggregated into CDI collections by a combination of Discipline, Data Centre, and geometric type. Each CSW XML record therefore represents a large collection of individual metadata records and associated datasets. By following the specified URL to the SeaDataNet portal users can evaluate these metadata in detail and request access by downloading of interesting datasets via the shopping cart transaction system that is integrated in the SeaDataNet portal.

The European Environment Agency (EEA) is an agency of the European Union (EU) tasked with providing independent information on the environment. Established in 1990 and headquartered in Copenhagen, Denmark, the EEA serves as a key source of environmental data, assessments, and reports for policymakers and the public across Europe. With a focus on improving environmental decision-making and promoting sustainable development, the agency plays a vital role in monitoring, analyzing, and communicating environmental trends and challenges.

At its core, the EEA aims to support the development, implementation, and evaluation of EU environmental policies. It achieves this mission through a range of activities, including data collection, analysis, and reporting. The agency collaborates with national environmental agencies in EU member states, as well as other international organizations, to gather and harmonize environmental data from across Europe. This wealth of information is then used to produce high-quality assessments and reports on various environmental topics.

One of the primary functions of the EEA is to provide regular assessments of the state of Europe's environment. These assessments cover a wide range of issues, including air and water quality, biodiversity, climate change, and resource use. By compiling and analyzing data from different sources, the agency produces comprehensive reports that highlight key environmental trends, identify emerging challenges, and assess progress towards environmental objectives. These assessments are invaluable tools for policymakers, helping them to make informed decisions and prioritize actions to protect and improve the environment.

In addition to assessing the state of the environment, the EEA also plays a crucial role in monitoring the effectiveness of environmental policies and measures. The agency tracks the implementation of EU environmental legislation and policies, assessing their impact on the ground. By evaluating the success or shortcomings of these policies, the EEA provides valuable feedback to policymakers, helping them to refine and strengthen environmental governance at the European level.

Furthermore, the EEA acts as a hub for environmental information and knowledge exchange. The agency maintains several databases and online platforms, such as the European Environmental Data Centre (EEDC) and the European Environment Information and Observation Network (EIONET), which provide access to a wealth of environmental data, maps, and indicators. These resources are freely available to policymakers, researchers, NGOs, and the public, supporting evidence-based decision-making and fostering greater transparency and accountability in environmental governance.

In this page you can find the Web Services of the European Environment Agency

The Rhode Island Division of Statewide Planning, in partnership with 13 other Rhode Island state agencies, developed a platform to better align social equity policies, decisions, and outcomes into our planning process. The platform is intended to increase social equity data transparency and to overlay the unique justice issues communities across the state face.Visit RI Division of Statewide Planning for more details.The RI Social Equity Data Platform pulls together more than 37 spatial data indicators on public health, environmental justice, socioeconomics, and transportation into one easy to use, publicly accessible platform. The Platform is intended to help with the initial stage of incorporating equity into policies, plans, and practices – identifying how certain quantifiable indicators are distributed across population groups across the state. The Platform does not designate any specific areas as “equity areas,” but rather displays the extent of individual indicators for every census tract in the state.Access the RI Social Equity Data Platform