Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
104
datasets available to search
ShareScore release 0.7.1
Dataset results
104 results for “earth observation”
Sentinel-3A OLCI Level-3M Global Mapped Earth-observation Reduced Resolution (ERR) Remote-Sensing Reflectance (RRS) - Near Real-time (NRT) Data, version R2022.0
The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.
Learning to Improve Earth Observation Flight Planning
This paper describes a method and system for integrating machine learning with planning and data visualization for the management of mobile sensors for Earth science investigations. Data mining identifies discrepancies between previous observations and predictions made by Earth science models. Locations of these discrepancies become interesting targets for future observations. Such targets become goals used by a flight planner to generate the observation activities. The cycle of observation, data analysis and planning is repeated continuously throughout a multi-week Earth science investigation.
Earth Resources Observation and Science (EROS) Center's Landsat State Mosaics Gallery
The Earth Resources Observation and Science (EROS) Center manages the this gallery of images of the 50 U.S. states plus Puerto Rico as derived by Landsat data.
Sentinel-3B OLCI Level-3M Global Mapped Earth-observation Reduced Resolution (ERR) Chlorophyll (CHL) - Near Real-time (NRT) Data, version R2022.0
The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.
Machine Learning for Earth Observation Flight Planning Optimization
This paper is a progress report of an effort whose goal is to demonstrate the effectiveness of automated data mining and planning for the daily management of Earth Science missions. Currently, data mining and machine learning technologies are being used by scientists at research labs for validating Earth science models. However, few if any of these advancedtechniques are currently being integrated into daily mission operations. Consequently, there are significant gaps in the knowledge that can be derived from the models and data that are used each day for guiding mission activities. The result can be sub-optimal observation plans, lack of useful data, and wasteful use of resources. Recent advances in data mining, machine learning, and planning make it feasible to migrate these technologies into the daily mission planning cycle. This paper describes the design of a closed loop system for data acquisition, processing, and flight planning that integrates the results of machine learning into the flight planning process.
Sentinel-3A OLCI Level-2 Earth-observation Reduced-Resolution (ERR) Inherent Optical Properties (IOP), Near Real-time (NRT) Data, version R2022.0
The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.
Sentinel-3A OLCI Level-1B Earth-observation Reduced Resolution (ERR) Data, version 1
The Ocean and Land Colour Instrument (OLCI) is the successor to ENVISAT's Medium Resolution Imaging Spectrometer (MERIS) having additional spectral channels, different camera arrangements and simplified on-board processing. The OLCI is a push-broom instrument with five camera modules sharing the field of view. The field of view of the five cameras is arranged in a fan-shaped configuration in the vertical plane, perpendicular to the platform velocity. Each camera has an individual field of view of 14.2° and a 0.6° overlap with its neighbors. The whole field of view is shifted across track by 12.6 degrees away from the sun to minimize the impact of sun glint. OLCI is equipped with on-board calibration hardware based on sun diffusers. There are three sun diffusers--two 'white' diffusers dedicated to radiometric calibration and one dedicated to spectral calibration, with spectral reflectance features. The native resolution is approximately 300m, referred to as Full Resolution (FR). A Reduced Resolution (RR) processing mode provides Level-1B data at sampling rates decreased by a factor of four in both spatial dimensions resulting to resolution of approximately 1.2 km.
Earth Resources Observation and Science (EROS) Center's Journey of Lewis and Clark Gallery
The Earth Resources Observation and Science (EROS) Center manages the this gallery of Landsat-derived images of one of the most remarkable and productive scientific explorations in American history. The Corps of Discovery expedition crossed the territory of the newly acquired but uncharted Louisiana Purchase.
Earth Resources Observation and Science (EROS) Center's Image of the Week Gallery
The Earth Resources Observation and Science (EROS) Center manages the this image of the week gallery.
Sentinel-3B OLCI Level-2 Earth-observation Reduced Resolution (ERR) Ocean Color (OC) - Near Real-time (NRT) Data, version R2022.0
The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.
Sentinel-3A OLCI Level-2 Earth-observation Reduced Resolution (ERR) Ocean Color (OC) - Near Real-time (NRT) Data, version R2022.0
The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.
Sentinel-3B OLCI Level-3B Global Binned Earth-observation Reduced Resolution (ERR) Diffuse Attenuation Coefficient for Downwelling Irradiance (KD) - Near Real-time (NRT) Data, version R2022.0
The Ocean Biology DAAC produces near real-time (quicklook) products using the best-available combination of ancillary data from meteorological and ozone data. As such, the inputs and the calibration used are less than optimal. Quicklook products provide a snapshot of the data during a short time period within a single orbit.
Radial dependence of SEP peak fluxes and fluences: Multi-spacecraft observations based on Parker Solar Probe, Solar Orbiter, and near-Earth particle detectors
Open the record for dataset details and reuse information.
USGS Group on Earth Observations (GEO) Global Agricultural Monitoring (GLAM) Algeria
The objective of GEO is to fulfil a vision of a world where decisions and actions are informed by coordinated, comprehensive and sustained Earth Observation (EO). This is being pursued mainly through the added value of co-ordinating existing institutions, organised communities, space agencies, in-situ monitoring agencies, scientific institutions, research centres, universities, modelling centres, technology developers and other groups that deal with one or more aspects of EO. To reach this overarching goal, GEO focuses on capacity development in three dimensions: infrastructure, individuals and institutions. In the field of agriculture, the general goal is to promote the utilization of Earth observations for advancing sustainable agriculture, aquaculture and fisheries. Key issues include early warning, risk assessment, food security, market efficiency and combating desertification. (Source: http://www.research-europe.com/index.php/2011/08/joao-soares-secretariat-expert-for-agriculture-group-on-earth-observations/)
USGS Group on Earth Observations (GEO) Global Agricultural Monitoring (GLAM) Russia
The objective of GEO is to fulfil a vision of a world where decisions and actions are informed by coordinated, comprehensive and sustained Earth Observation (EO). This is being pursued mainly through the added value of co-ordinating existing institutions, organized communities, space agencies, in-situ monitoring agencies, scientific institutions, research centres, universities, modelling centres, technology developers and other groups that deal with one or more aspects of EO. To reach this over arching goal, GEO focuses on capacity development in three dimensions: infrastructure, individuals and institutions. In the field of agriculture, the general goal is to promote the utilisation of Earth observations for advancing sustainable agriculture, aquaculture and fisheries. Key issues include early warning, risk assessment, food security, market efficiency and combating desertification. (Source: http://www.research-europe.com/index.php/2011/08/joao-soares-secretariat-expert-for-agriculture-group-on-earth-observations/)
EARTH BASED CCD OBSERVATIONS V1.0
This data set presents images of 26P/Grigg-Skjellerup obtained by various observers at several ground-based observatories using CCD instruments. These data have not been through the full PDS review process.
USGS Group on Earth Observations (GEO) Global Agricultural Monitoring (GLAM) Ethiopia
The objective of GEO is to fulfil a vision of a world where decisions and actions are informed by coordinated, comprehensive and sustained Earth Observation (EO). This is being pursued mainly through the added value of co-ordinating existing institutions, organised communities, space agencies, in-situ monitoring agencies, scientific institutions, research centres, universities, modelling centres, technology developers and other groups that deal with one or more aspects of EO. To reach this overarching goal, GEO focuses on capacity development in three dimensions: infrastructure, individuals and institutions. In the field of agriculture, the general goal is to promote the utilization of Earth observations for advancing sustainable agriculture, aquaculture and fisheries. Key issues include early warning, risk assessment, food security, market efficiency and combating desertification. (Source: http://www.research-europe.com/index.php/2011/08/joao-soares-secretariat-expert-for-agriculture-group-on-earth-observations/)
USGS Group on Earth Observations (GEO) Global Agricultural Monitoring (GLAM) Uganda
The objective of GEO is to fulfil a vision of a world where decisions and actions are informed by coordinated, comprehensive and sustained Earth Observation (EO). This is being pursued mainly through the added value of co-ordinating existing institutions, organised communities, space agencies, in-situ monitoring agencies, scientific institutions, research centres, universities, modelling centres, technology developers and other groups that deal with one or more aspects of EO. To reach this over arching goal, GEO focuses on capacity development in three dimensions: infrastructure, individuals and institutions. In the field of agriculture, the general goal is to promote the utilisation of Earth observations for advancing sustainable agriculture, aquaculture and fisheries. Key issues include early warning, risk assessment, food security, market efficiency and combating desertification. (Source: http://www.research-europe.com/index.php/2011/08/joao-soares-secretariat-expert-for-agriculture-group-on-earth-observations/)
NASA Earth Observations (NEO)
Our mission is to help you picture climate change and environmental changes happening on our home planet. Here you can search for and retrieve satellite images of Earth. Download them; export them to GoogleEarth; perform basic analysis. Tracking regional and global changes around the world just got easier.
USGS Group on Earth Observations (GEO) Global Agricultural Monitoring (GLAM) Argentina
The objective of GEO is to fulfil a vision of a world where decisions and actions are informed by coordinated, comprehensive and sustained Earth Observation (EO). This is being pursued mainly through the added value of co-ordinating existing institutions, organised communities, space agencies, in-situ monitoring agencies, scientific institutions, research centres, universities, modelling centres, technology developers and other groups that deal with one or more aspects of EO. To reach this overarching goal, GEO focuses on capacity development in three dimensions: infrastructure, individuals and institutions. In the field of agriculture, the general goal is to promote the utilization of Earth observations for advancing sustainable agriculture, aquaculture and fisheries. Key issues include early warning, risk assessment, food security, market efficiency and combating desertification. (Source: http://www.research-europe.com/index.php/2011/08/joao-soares-secretariat-expert-for-agriculture-group-on-earth-observations/)
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.