Amazon Web Service. A commercial cloud service approved for use by NASA’s Office of the Chief Information Officer.

The Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) was developed by NASA's LP DAAC and offers a simple and efficient way to access and transform geospatial data from a variety of federal data archives. AppEEARS enables users to subset geospatial datasets using spatial, temporal, and band/layer parameters. Two types of sample requests are available: point samples for geographic coordinates and area samples for spatial areas via vector polygons.

An application programming interface (API) is a set of defined rules that explain how computers or applications communicate with one another. APIs sit between an application and the web server and act as an intermediary layer that processes data transfer between systems.

Artificial Intelligence (AI) refers to the simulation of human decision-making capabilities in machines.  When applied to Big Data collections, such as NASA Earth observing data, AI can be used to sift through years of data and imagery rapidly and efficiently to find relationships that would be impossible for a human to detect. NASA's Earth Science Data Systems (ESDS) Program is committed to the responsible use of AI and recognizes its potential to significantly advance existing data systems capabilities, improve operations, and maximize the use of NASA Earth observing data. 

Calibration and validation (often shortened to cal/val) are processes for ensuring the validity of remotely sensed data. Measurements acquired locally are compared with data from remote sensing satellites. 

The web-based Catalog of Archived Suborbital Earth Science Investigations (CASEI) was developed by NASA’s Airborne Data Management Group (ADMG). CASEI facilitates quick access to detailed information about NASA’s airborne and field investigations along with links to associated data products.

Cloud computing is the ability to access and work with data virtually in a cloud-based environment. Analyses and data manipulations can be accomplished directly with data in the cloud with only the analysis results needing to be downloaded.

A Cloud Optimized GeoTIFF (COG) is a GeoTIFF that contains internal organization, which enables it to be efficiently delivered and processed on the cloud. 

Commercial data refers to data created and provided by commercial entities rather than government agencies. NASA’s Commercial Smallsat Data Acquisition (CSDA) program identifies, evaluates, and acquires remote sensing imagery and data from commercial sources that support NASA’s Earth science research and application activities. 

The Common Metadata Repository (CMR) is the definitive management system for EOSDIS Earth science metadata. As a single, shared, scalable metadata repository, CMR merges all current capabilities and metadata from the existing NASA Earth science metadata systems.

Cumulus is an ESDIS Project effort to prototype and test how EOSDIS data collections can be archived collectively and disseminated in the commercial cloud. A primary feature of Cumulus is a cloud-based framework for data ingest, archive, distribution, and management, which are the primary activities of the discipline-specific EOSDIS Distributed Active Archive Centers (DAACs).

A data access protocol (DAP) is a system that allows outsiders to be granted access to databases without overloading either system. One common example is the Open-source Project for a Network Data Access Protocol (OPeNDAP), which is a data transmission protocol designed specifically for science data. 

Tools and resources which assist in the retrieval, manipulation, and display of Earth science data. 

A data collection is an assemblage of data grouped according to a standardized hierarchy or organizing system.

The organization and integration of Earth science observation data collected from sensors in orbit, from airborne sources, or in-situ. It can involve collection, processing, archiving and publication of data so that the value of the data is maintained over time, and the data remains available for reuse and preservation.

Standardized ways that Earth science information is encoded for storage in a computer file. Different data formats describe the structure and compression of data, metadata requirements and the scope of the variables included to meet the varying needs of the data users. 

Data provenance is metadata that details the origin, changes to, and details supporting the confidence or validity of data. Data provenance includes information on how data sets were generated, providing a historical record of the data and its origins.

Daymet provides long-term, continuous, gridded estimates of daily weather and climatology variables from ground-based observations through statistical modeling techniques. The Daymet data products provide driver data for biogeochemical terrestrial modeling and have myriad applications in many Earth science, natural resource, biodiversity, and agricultural research areas. Daymet weather variables include daily minimum and maximum temperature, precipitation, vapor pressure, shortwave radiation, snow water equivalent, and day length over continental North America and Hawaii and Puerto Rico.

Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition.

Directory Interchange Format (DIF) content metadata is a specific set of attributes for describing Earth science data at the collection level. It serves as a way to document and exchange information on scientific data to its implementation in the International Directory Network (IDN), and the DIF has evolved to serve the user community in the discovery, access and use of Earth science and related data. 

In the Earthdata Forum, subject matter experts from several NASA Distributed Active Archive Centers (DAAC) discuss general questions, research needs, and data applications. Users can query how to access, view, and interpret data.

Earthdata Search provides easy-to-use access to EOSDIS services for Earth science data discovery, filtering, visualization, and access. It also serves as a platform to feature planned EOSDIS services as they become available.