Filter
Reset all

Subjects

Content Types

Countries

API

Certificates

Data access

Data access restrictions

Database access

Data licenses

Data upload

Data upload restrictions

Enhanced publication

Institution responsibility type

Institution type

Keywords

Metadata standards

PID systems

Provider types

Quality management

Repository languages

Software

Syndications

Repository types

Versioning

  • * at the end of a keyword allows wildcard searches
  • " quotes can be used for searching phrases
  • + represents an AND search (default)
  • | represents an OR search
  • - represents a NOT operation
  • ( and ) implies priority
  • ~N after a word specifies the desired edit distance (fuzziness)
  • ~N after a phrase specifies the desired slop amount
  • 1 (current)
Found 6 result(s)
The National Earth Observation Science Data Center, whose predecessor was the National Integrated Earth Observation Data Sharing Platform, has formed a sustainable, cross-agency, one-stop data sharing service capability after years of construction, and it is also the main channel for international exchange of remote sensing data in China. In the future, it will manage and coordinate scientific data resources in the field of earth observation on behalf of the country, and build a national-level earth observation big data infrastructure. Coordinate various industry data centers, scientific research institutions and enterprises in the field of Earth observation in China to cooperate in building a national strategic, fundamental, scientific, internationalized, and independent and controllable scientific big data environment in the field of Earth observation. On the basis of the already formed data ecology and cooperation mechanism, data sharing services, and international data cooperation, we will actively expand to the whole life cycle management of data and carry out data management work such as the collection, management, analysis and mining, and sharing services of national scientific data resources for Earth observation. Form a unified technical support system and data sharing service environment for Earth observation data in China. Maintain and enhance its international influence and become a domestic and international first-class scientific data center for Earth observation!
The Centre for Environmental Data Analysis (CEDA) serves the environmental science community through managing data centres, data analysis environments, and participation in a host of relevant research projects. We aim to support environmental science, further environmental data archival practices, and develop and deploy new technologies to enhance access to data. Additionally we provide services to aid large scale data analysis. The CEDA Archive operates the atmospheric and earth observation data centre functions on behalf of NERC for the UK atmospheric science and earth observation communities. It covers climate, composition, observations and NWP data as well as various earth observation datasets, including airborne and satellite data and imagery. Prior to November 2016 these functions were operted by CEDA under the titles of the British Atmospheric Data Centre (BADC) and the NERC Earth Observation Data Centre (NEODC). CEDA also operates the UK Solar System Data Centre (UKSSDC), which curates and provides access to archives of data from the upper atmosphere, ionosphere and Earth's solar environment.
The USGS currently houses the institute at the Center for Earth Resources Observation and Science (EROS) in Sioux Falls, South Dakota. The LCI will address land cover topics from local to global scales, and in both domestic and international settings. The USGS through the Land Cover Institute serves as a facilitator for land cover and land use science, applications, and production functions. The institute assists in the availability and technical support of land cover data sets through increasing public and scientific awareness of the importance of land cover science. LCI continues, after the reorganization of the World Data Centers in 2009, serving as the World Data Center (WDC) for land cover data for access to, or information about, land cover data of the world
The Copernicus Marine Environment Monitoring Service (CMEMS) provides regular and systematic reference information on the physical and biogeochemical state, variability and dynamics of the ocean and marine ecosystems for the global ocean and the European regional seas. The observations and forecasts produced by the service support all marine applications, including: Marine safety; Marine resources; Coastal and marine environment; Weather, seasonal forecasting and climate. For instance, the provision of data on currents, winds and sea ice help to improve ship routing services, offshore operations or search and rescue operations, thus contributing to marine safety. The service also contributes to the protection and the sustainable management of living marine resources in particular for aquaculture, sustainable fisheries management or regional fishery organisations decision-making process. Physical and marine biogeochemical components are useful for water quality monitoring and pollution control. Sea level rise is a key indicator of climate change and helps to assess coastal erosion. Sea surface temperature elevation has direct consequences on marine ecosystems and appearance of tropical cyclones. As a result of this, the service supports a wide range of coastal and marine environment applications. Many of the data delivered by the service (e.g. temperature, salinity, sea level, currents, wind and sea ice) also play a crucial role in the domain of weather, climate and seasonal forecasting.
The range of CIRAD's research has given rise to numerous datasets and databases associating various types of data: primary (collected), secondary (analysed, aggregated, used for scientific articles, etc), qualitative and quantitative. These "collections" of research data are used for comparisons, to study processes and analyse change. They include: genetics and genomics data, data generated by trials and measurements (using laboratory instruments), data generated by modelling (interpolations, predictive models), long-term observation data (remote sensing, observatories, etc), data from surveys, cohorts, interviews with players.