Earthdata Search is a web application developed by NASA EOSDIS to enable data discovery, search, comparison, visualization, and access across EOSDIS' Earth Science data holdings.

SMAP Level 1B Sigma Naught Low Res Product Metadata Version 2

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem — (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual disk access for only less than 5% of the observations. To the best of our knowledge, this is the first flexible MTS search algorithm capable of subsequence search on any subset of variables. Moreover, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.

NED Cone Search service (search for objects Near Position). This service searches NED's master list of extragalactic objects for entries near a given position.

KEYWORD SEARCH IN TEXT CUBE: FINDING TOP-K RELEVANT CELLS BOLIN DING, YINTAO YU, BO ZHAO, CINDY XIDE LIN, JIAWEI HAN, AND CHENGXIANG ZHAI Abstract. We study the problem of keyword search in a data cube with text-rich dimension(s) (so-called text cube). The text cube is built on a multidimensional text database, where each row is associated with some text data (e.g., a document) and other structural dimensions (attributes). A cell in the text cube aggregates a set of documents with matching attribute values in a subset of dimensions. A cell document is the concatenation of all documents in a cell. Given a keyword query, our goal is to find the top-k most relevant cells (ranked according to the relevance scores of cell documents w.r.t. the given query) in the text cube. We define a keyword-based query language and apply IR-style relevance model for scoring and ranking cell documents in the text cube. We propose two efficient approaches to find the top-k answers. The proposed approaches support a general class of IR-style relevance scoring formulas that satisfy certain basic and common properties. One of them uses more time for pre-processing and less time for answering online queries; and the other one is more efficient in pre-processing and consumes more time for online queries. Experimental studies on the ASRS dataset are conducted to verify the efficiency and effectiveness of the proposed approaches.

Use this search engine to generate custom tables of orbital and/or physical parameters for all asteroids and comets (or a specified sub-set) in our small-body database. If this is your first time here, you may find it helpful to read our tutorial. Otherwise, simply follow the steps in each section: 'Search Constraints', 'Output Fields', and finally 'Format Options'. If you want details for a single object, use the Small Body Browser instead.

The Cloud Radar System (CRS) IMPACTS dataset consists of calibrated radar reflectivity, Doppler velocity, linear depolarization ratio, and normalized radar cross-section estimates collected by the Cloud Radar System (CRS) onboard the NASA ER-2 high-altitude research aircraft. These data were gathered during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The CRS IMPACTS dataset files are available from January 25, 2020, through February 28, 2023, in HDF-5 format.

We contend that Earth science metadata assets are dark resources, information resources that organizations collect, process, and store for regular business or operational activities but fail to utilize for other purposes. The challenge for any organization is to recognize, identify and effectively utilize the dark data stores in their institutional repositories to better serve their stakeholders. These metadata hold rich textual descriptions and browse imagery that allow users to review search results and preview data, but have not been fully exploited by information systems to serve the research and education communities. This proposed work looks at these metadata assets in a completely new and innovative light; it will result in a search tool built on semantic technologies to create new knowledge discovery pathways in Earth Science.
This proposal brings together a strong team of informatics experts with a long history of research in data systems, scientific search and semantics, as well as a proven track record of previous collaborations: PI Dr. Rahul Ramachandran (NASA/MSFC), geoinformatics specialist and Manager of GHRC DAAC; Co-I Dr. Christopher Lynnes (NASA/GSFC), Information Systems Architect at the GES DISC; Co-I Dr. Peter Fox (Rensselaer Polytechnic Institute; RPI), Tetherless Constellation World Chair; and Manil Maskey (University of Alabama in Huntsville; UAH), lead designer and developer for multiple projects in Earth science information systems. The proposed work addresses the core AIST topic of Data-Centric Technologies, with a particular focus on utilizing semantic technologies to explore, visualize, and analyze representations of semantically identified information in order to discover new useful information ' directly addressing the subtopic, Alternative Approaches / Disruptive Technologies for Earth Science Data System. This project will develop a Semantic Middle Layer (SML) consisting of a content based image retrieval service to provide for visual search for events or phenomena in Earth science imagery; an ontology based data curation service which uses structured metadata and descriptive text to find data relevant to that event, phenomenon, or thematic topic; and a semantic rule based processing service to create curated data albums consisting of data bundles and exploratory plots generated on the fly. Together these components will allow users to identify events of interest in images and assemble a collection of pre-processed data to support scientific investigations focused on these events. We will design the SML and a demonstration Event Nexus Discovery Client using three science use cases developed in collaboration with Dr. Sundar Christopher, an expert in satellite remote sensing at UAH.

The NCAR Particle Probes IMPACTS dataset consists of data collected from six instruments on the NASA P-3 aircraft, the SPEC Hawkeye Cloud Particle Imager (CPI), the Hawkeye Fast Cloud Droplet Probe (FastCDP), the Hawkeye Two-Dimensional Stereo Probe (Hawkeye2D-S), the SPEC Two-Dimensional Stereo probe (2D-S), and two SPEC High Volume Precipitation Spectrometers (HVPS3). The 2D-S and HVPS3 are two-dimensional optical array probes that record images of particles that travel through their sampling area. The recorded images are then analyzed to produce particle size distributions from 20 microns to 3 centimeters in diameter. The FastCDP is a forward scattering instrument designed to measure the size and concentration of cloud droplets between 2 and 50 microns in diameter. The CPI is a high-resolution imager with a 256-level color depth. No particle concentration estimates have been attempted with the CPI. These data were collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign, a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic coast. IMPACTS aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. Data files are available in netCDF-4 format, as well as browse imagery available in PNG format, from January 18, 2020, through February 26, 2020, and January 14, 2022 through February 28, 2023.

The World Climate Research Program (WCRP) Coupled Model Intercomparison Project, Phase 6 (CMIP6) data from the NASA Goddard Institute for Space Studies (NASA GISS) GISS-E2-1-G model output for the "AMIP SSTs with pre-industrial anthropogenic and natural forcing" (amip-piForcing) experiment. These are available at the following frequency: Amon. The runs included the ensemble member: r1i1p1f1. CMIP6 was a global climate model intercomparison project, coordinated by PCMDI (Program For Climate Model Diagnosis and Intercomparison) on behalf of the WCRP and provided input for the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6). The official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.

The World Climate Research Program (WCRP) Coupled Model Intercomparison Project, Phase 6 (CMIP6) data from the NASA Goddard Institute for Space Studies (NASA GISS) GISS-E2-1-G model output for the "update of RCP2.6 based on SSP1" (ssp126) experiment. These are available at the following frequencies: Amon and day. The runs included the ensemble member: r1i1p3f1. CMIP6 was a global climate model intercomparison project, coordinated by PCMDI (Program For Climate Model Diagnosis and Intercomparison) on behalf of the WCRP and provided input for the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6). The official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.

PO.DAAC provides several ways to discover and access physical oceanography data, from the PO.DAAC Web Portal to FTP access to front-end user interfaces (see http://podaac.jpl.nasa.gov). That same data can also be discovered and accessed through PO.DAAC Web Services, enabling efficient machine-to-machine communication and data transfers. Dataset Search service searches PO.DAAC's dataset catalog.

The NIMBUS7_ERB_Ch10C_TSI_NAT data set is the Nimbus-7 Channel 10C (Ch10C) Total Solar Irradiance (TSI) aboard the Earth Radiation Budget (ERB) satellite Data in Native (NAT) format.The Nimbus 7 research-and-development satellite served as a stabilized, earth-oriented platform for the testing of advanced systems for sensing and collecting data in the pollution, oceanographic and meteorological disciplines. The polar-orbiting spacecraft consisted of three major structures: (1) a hollow torus-shaped sensor mount, (2) solar paddles, and (3) a control housing unit that was connected to the sensor mount by a tripod truss structure.

These data are a copy of MODIS data from the NASA Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC). The copy is potentially only a subset. Below is the description from https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD09A1 Shortname: MOD09A1 , Platform: Terra , Instrument: MODIS , Processing Level: Level-3 , Spatial Resolution: 500 m , Temporal Resolution: 8 day , ArchiveSets: 61, 6 , Collection: MODIS Collection 6.1 - Level 1, Atmosphere, Land (ArchiveSet 61) , PGE Number: PGE21

This global Level-3 (L3) data set provides monthly mean snow cover extent within 0.05° (approx. 5 km) MODIS Climate Modeling Grid (CMG) cells. This data set is derived from snow cover observations in the 'MODIS/Terra Snow Cover Daily L3 Global 0.05Deg CMG’ data set (DOI:10.5067/MODIS/MOD10C1.061).

The terms "Version 61" and "Collection 6.1" are used interchangeably in reference to this release of MODIS data.

This online data repository is managed by the Goddard Space Flight Centre of NASA. The site enables a user to browse the entire global ocean color data set for many parameters and time periods and …Show full descriptionThis online data repository is managed by the Goddard Space Flight Centre of NASA. The site enables a user to browse the entire global ocean color data set for many parameters and time periods and download PNG images or digital data in HDF format.

The Airborne Arctic Stratospheric Expedition (AASE) which was based in Stavanger, Norway during January and February, 1989, was designed to study the production and loss mechanisms of ozone in the north polar stratospheric environment, and the effect on ozone distribution of the Arctic polar vortex and of the cold temperatures associated with the formation of Polar Stratospheric Clouds (PSC). This dataset contains measurements from the meteorological meteorological measurement system on the NASA ER-2 Aircraft.

The VIIRS Near Real Time (NRT) Vegetation Indices 8-Day L3 Global 500m SIN Grid data, short-name VNP13A4N are provided everyday at 500-meter spatial resolution as a gridded level-3 product in the Sinusoidal projection. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes and global and regional climate. These data also may be used for characterizing land surface biophysical properties and processes including primary production and land cover conversion.

Note: This is a near real-time product only. Standard historical data and imagery for VNP13A4N (8-Day 500m) are not available. The only 500m standard Vegetation Indices product available is a 16-Day composite (VNP13A1). So, users can either use VNP13A1, use the NDVI standard products from LAADS web (https://ladsweb.modaps.eosdis.nasa.gov/search/), or access the science quality VNP09A1 data and create the VI product of their own.

Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.

This data set consists of a sampling of each type of Hierarchical Data Format version 4 (HDF4) data that are archived at the eight National Aeronautic and Space Administration (NASA) Earth Science Data Centers (ESDCs). The data were sampled for a collaborative study between The HDF Group, the Goddard Earth Sciences Data and Information Services Center (GES-DISC), and the National Snow and Ice Data Center (NSIDC) in order to assess the complex internal byte layout of HDF files. Based on the results of this assessment, methods for producing a map of the layout of the HDF4 files held by NASA were prototyped using a markup-language-based HDF tool. The resulting maps allow a separate program to read the file without recourse to the HDF application programming interface (API). Data products selected for the study, and a table summarizing the results, are available via HTTPS.

SMAP Level 1A Radar Product Metadata