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NASA NTRS2017

Exposing the Strategies that Can Reduce the Obstacles: Improving the Science User Experience

It is now well established that pursuing generic solutions to what seem are common problems in Earth science data access and use can often lead to disappointing results for both system developers and the intended users. This presentation focuses on real-world experience of managing a large and complex data system, NASAs Earth Science Data and Information Science System (EOSDIS), whose mission is to serve both broad user communities and those in smaller niche applications of Earth science data and services. In the talk, we focus on our experiences with known data user obstacles characterizing EOSDIS approaches, including various technological techniques, for engaging and bolstering, where possible, user experiences with EOSDIS. For improving how existing and prospective users discover and access NASA data from EOSDIS we introduce our cross-archive tool: Earthdata Search. This new search and order tool further empowers users to quickly access data sets using clever and intuitive features. The Worldview data visualization tool is also discussed highlighting how many users are now performing extensive data exploration without necessarily downloading data. Also, we explore our EOSDIS data discovery and access webinars, data recipes and short tutorials, targeted technical and data publications, user profiles and social media as additional tools and methods used for improving our outreach and communications to a diverse user community. These efforts have paid substantial dividends for our user communities by allowing us to target discipline specific community needs. The desired take-away from this presentation will be an improved understanding of how EOSDIS has approached, and in several instances achieved, removing or lowering the barriers to data access and use. As we look ahead to more complex Earth science missions, EOSDIS will continue to focus on our user communities, both broad and specialized, so that our overall data system can continue to serve the needs of science and applications users.

communications↗

NASA NTRS2020

Tuning a variational autoencoder for data accountability problem in the Mars Science Laboratory ground data system

The Mars Curiosity rover is frequently sending back engineering and science data that goes through a pipeline of systems before reaching its final destination at the mission operations center making it prone to volume loss and data corruption. A ground data system analysis (GDSA) team is charged with the monitoring of this flow of information and the detection of anomalies in that data in order to request a re-transmission when necessary. This work presents ∆-MADS, a derivative-free optimization method applied for tuning the architecture and hyperparameters of a variational autoencoder trained to detect the data with missing patches in order to assist the GDSA team in their mission.

Lakhmiri, Dounia↗

NASA NTRSDate not supplied

Biomass Harmonization and SAR Analysis with the Multi-mission Algorithm and Analysis Platform (MAAP)

The Multi‐mission Algorithm and Analysis Platform (MAAP) is a collaborative effort between NASA and the European Space Agency (ESA) to support above ground biomass (AGB) research in an open science framework. MAAP brings together relevant data, algorithms, and computing capabilities in a common cloud environment to address the challenges of sharing and processing data from field, airborne and satellite measurements. MAAP was publicly released in October 2021, providing computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support the estimation and visualization of data. MAAP has allowed scientists from both North America and Europe to collaborate on the generation and analysis/visualization of data derived from multiple, discipline-adjacent missions in an open, collaborative environment that has reached beyond traditional scientific investigation. MAAP has been used to support multiple scientific activities. To date, existing LiDAR data from multiple platforms has been calibrated with field measurements and combined for more comprehensive and accurate estimates of above ground biomass AGB; these LiDAR platforms include airborne (e.g. LVIS), the International Space Station (NASA’s Global Ecosystem Dynamics Investigation (GEDI), and satellites (e.g. ICESat-2). The current challenge is to effectively and seamlessly combine the aforementioned LiDAR-based data with new data sources such as P-band RADAR from ESA’s upcoming BIOMASS mission, existing ESA Sentinel-1 C-band SAR, and the 30 PB/yr of high cadence global coverage L-band SAR data from the upcoming NASA-ISRO SAR (NISAR) mission. Recent analysis using MAAP merged ICESat-2 and optical data (Harmonized Landsat Sentinel) produced the most comprehensively precise estimate of boreal-wide AGB to date. Another effort using MAAP is the production and open distribution of global comparisons of AGB map estimates, including from ICESat-2 and GEDI, to bolster stakeholder uptake for policy applications. These map estimates will feed into the Intergovernmental Panel on Climate Change (IPCC) database, likely aiding the next Global Carbon Stocktake of the UNFCCC. Furthermore, the biomass retrieval intercomparison exercise BRIX-2 could benefit from the MAAP providing standardized test cases (based on airborne campaign and spaceborne data) allowing the community to develop and apply retrieval algorithms based on these test cases, while forthcoming SAR data training curricula could also use the MAAP as a teaching and learning platform. The MAAP is meeting the challenges inherent in international, open science collaboration and large scale computing with a platform that is entirely open source and cloud native, using open standards for data access, manipulation, protocols, and formats. The MAAP data system consists of a dedicated data store whose data is indexed in an online catalog conforming to established metadata, application programmatic interfaces (APIs), and service interface standards, using an implementation of the open sourced NASA Common Metadata Repository. Federation of user identities allows users from either NASA or ESA to access and consume services from the other using a unified metadata catalog for the data utilized across the ESA and NASA MAAP platforms. Similarly, we are exploring how to increase interoperability to achieve a common approach to packaging, orchestrating and executing algorithms, with interoperable access to data for subsetting, fast browse, and cloud-optimized access, all using interoperable standards such as those from the Open Geospatial Consortium (OGC). Designed for interoperability, ESA and NASA utilize a common architecture for the software platform. It provides a cloud-based algorithm development environment (ADE) that enables scientists to develop algorithms collaboratively with access to the MAAP data catalog as well as other data archives. MAAP provides an Eclipse Che-based ADE supporting both Python and R languages, popular in this biomass community. Algorithms developed and containerized within the ADE can be deployed to run to thousands of computational nodes in the MAAP’s data processing system (DPS), dramatically speeding up processing and giving scientists a rapid, iterative turnaround of results. NASA’s implementation of the DPS is based on the Hybrid Science Data System (HySDS) framework, used by NASA flight projects to produce Earth science standard products.

cloud computing↗

NASA NTRS2019

Standards and Best Practices - Two NASA Examples

Formal international standards as well as promotion of community or recommended practices have their place in ensuring "FAIRness" of data. Data management in NASA's Earth Observation System Data and Information System (EOSDIS) has benefited from both of these avenues to a significant extent. The purpose of this paper is to present one example of each of these, which promote (re)usability. The first is an ISO standard for specifying preservation content from Earth observation missions. The work on this started in 2011, informally within the Earth Science Information Partners (ESIP) in the US, while the European Space Agency (ESA) was leading an effort on Long-Term Data Preservation (LTDP). Resulting from the ESIP discussions was NASA's Preservation Content Specification, which was applied in 2012 as a requirement for NASA's new missions. ESA's Preserved Data Set Content (PDSC) document was codified into a document adopted by the Committee on Earth Observation Satellites (CEOS). It was recognized that it would be useful to combine PCS and PDSC into an ISO standard to ensure consistency in data preservation on a broader international scale. This standard, numbered ISO 19165-2 has been under development since mid-2017. The second is an example of developing recommendations for "best practices" within more limited (still fairly broad) communities. A Data Product Developers' Guide (DPDG) is currently being developed by one of NASA's Earth Science Data System Working Groups (ESDSWGs). It is for use by developers of products to be derived from Earth observation data to improve product (re)usability. One of the challenges in developing the guide is the fact that there are already many applicable standards and guides. The relevant information needs to be selected and expressed in a succinct manner, with appropriate pointers to references. The DPDG aims to compile the most applicable parts of earlier guides into a single document outlining the typical development process for Earth Science data products. Standards and best practices formally endorsed by the Earth Science Data and Information System (ESDIS) Standards Office (ESO), outputs from ESDSWGs (e.g., Dataset Interoperability Working Group, and Data Quality Working Group), and recommendations from Distributed Active Archive Centers and data producers are emphasized.

Ramapriyan, Hampapuram↗

NASA NTRS2013

Science Data Preservation: Implementation and Why It Is Important

Remote Sensing data generation by NASA to study Earth s geophysical processes was initiated in 1960 with the launch of the first Television Infrared Observation Satellite Program (TIROS), to develop a meteorological satellite information system. What would be deemed as a primitive data set by today s standards, early Earth science missions were the foundation upon which today s remote sensing instruments have built their scientific success, and tomorrow s instruments will yield science not yet imagined. NASA Scientific Data Stewardship requirements have been documented to ensure the long term preservation and usability of remote sensing science data. In recent years, the Federation of Earth Science Information Partners and NASA s Earth Science Data System Working Groups have organized committees that specifically examine standards, processes, and ontologies that can best be employed for the preservation of remote sensing data, supporting documentation, and data provenance information. This presentation describes the activities, issues, and implementations, guided by the NASA Earth Science Data Preservation Content Specification (423-SPEC-001), for preserving instrument characteristics, and data processing and science information generated for 20 Earth science instruments, spanning 40 years of geophysical measurements, at the NASA s Goddard Earth Sciences Data and Information Services Center (GES DISC). In addition, unanticipated preservation/implementation questions and issues in the implementation process are presented.

Kempler, Steven J.↗

NASA NTRS2014

Data Albums: An Event Driven Search, Aggregation and Curation Tool for Earth Science

Approaches used in Earth science research such as case study analysis and climatology studies involve discovering and gathering diverse data sets and information to support the research goals. To gather relevant data and information for case studies and climatology analysis is both tedious and time consuming. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. In cases where researchers are interested in studying a significant event, they have to manually assemble a variety of datasets relevant to it by searching the different distributed data systems. This paper presents a specialized search, aggregation and curation tool for Earth science to address these challenges. The search rool automatically creates curated 'Data Albums', aggregated collections of information related to a specific event, containing links to relevant data files [granules] from different instruments, tools and services for visualization and analysis, and information about the event contained in news reports, images or videos to supplement research analysis. Curation in the tool is driven via an ontology based relevancy ranking algorithm to filter out non relevant information and data.

Ramachandran, Rahul↗

DOE OSTI2024

Data and scripts associated with “Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damköhler numbers in nested watersheds” (v2)

This data package is associated with the publication “Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damköhler numbers in nested watersheds” submitted to Biogeochemistry by Ryan et al., 2024 (DOI: https://doi.org/10.1007/s10533-024-01169-5). This study aims to investigate fundamental and transferable drivers of dissolved organic matter (DOM) diversity across five nested watersheds within the contiguous United States. DOM diversity was explored using ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The samples and the unprocessed FTICR-MS data used in this study are publicly available on the Environmental System Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) data repository (see DOIs below). The data for the Willamette, Gunnison, Connecticut, and Deschutes basins were collected as part of a collaboration between the Watershed Rules of Life (WROL) project and Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS). The data for the Yakima River basin (YRB) was collected by the PNNL River Corridor SFA. The raw, unprocessed FTICR-MS data with additional (meta)data can be found at doi:10.15485/1895159 for WROL samples and doi:10.15485/1898912 for YRB samples. This data package contains the processed data used in the associated manuscript. This package also contains ancillary geospatial, hydrological, and geochemical information that supports the interpretation of the FTICR-MS data within Ryan et al., 2024. This data package is associated with the GitHub repository found at https://github.com/WHONDRS-Hub/rcsfa-RC4-WROL-YRB_DOM_Diversity. This data package was originally published August 2024. It was updated January 2025 (modified files). See the change history in the readme more details. At the directory level, the data package is comprised of three folders: (1) data, (2) output, and (3) src; and five additional files including the data dictionary (file ending in "_dd.csv”) and file-level metadata (file ending in “_flmd.csv”). The “src” folder contains the scripts used to process the FTICR data, conduct the analyses, and produce the manuscript figures. The inputs for these scripts are in the “data” folder and the returned outputs in the “output” folder. Inputs include temporal and spatial metadata associated with the sampling efforts, processed FTICR data, and total and normalized putative biochemical transformations per sample. Outputs include cleaned and combined data presented as tables, descriptive statistics, and plots. The file-level metadata file lists all files contained in this data package and descriptions for each. The data dictionary describes the units and definitions for each tabular data column or row header.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2010

Progress Towards a NASA Earth Science Reuse Enablement System (RES)

A Reuse Enablement System (RES) allows developers of Earth science software to contribute software for reuse by others and.for users to find, select, and obtain software for reuse in their own systems. This paper describes work that the X4S,4 Earth Science Data Systems (ESDS) Software Reuse Working Group has completed to date in the development of an RES for NASA.

Marshall, James J.↗

NASA NTRS2015

Data Recipes: Toward Creating How-To Knowledge Base for Earth Science Data

Both the diversity and volume of Earth science data from satellites and numerical models are growing dramatically, due to an increasing population of measured physical parameters, and also an increasing variety of spatial and temporal resolutions for many data products. To further complicate matters, Earth science data delivered to data archive centers are commonly found in different formats and structures. NASA data centers, managed by the Earth Observing System Data and Information System (EOSDIS), have developed a rich and diverse set of data services and tools with features intended to simplify finding, downloading, and working with these data. Although most data services and tools have user guides, many users still experience difficulties with accessing or reading data due to varying levels of familiarity with data services, tools, and or formats. The data recipe project at Goddard Earth Science Data and Information Services Center (GES DISC) was initiated in late 2012 for enhancing user support. A data recipe is a How-To online explanatory document, with step-by-step instructions and examples of accessing and working with real data (http:disc.sci.gsfc.nasa.govrecipes). The current suite of recipes has been found to be very helpful, especially to first-time-users of particular data services, tools, or data products. Online traffic to the data recipe pages is significant, even though the data recipe topics are still limited. An Earth Science Data System Working Group (ESDSWG) for data recipes was established in the spring of 2014, aimed to initiate an EOSDIS-wide campaign for leveraging the distributed knowledge within EOSDIS and its user communities regarding their respective services and tools. The ESDSWG data recipe group is working on an inventory and analysis of existing data recipes and tutorials, and will provide guidelines and recommendation for writing and grouping data recipes, and for cross linking recipes to data products. This presentation gives an overview of the data recipe activites at GES DISC and ESDSWG. We are seeking requirements and input from a broader data user community to establish a strong knowledge base for Earth science data research and application implementations.

data recipe↗

NASA NTRS2009

MODIS Science Algorithms and Data Systems Lessons Learned

For almost 10 years, standard global products from NASA's Earth Observing System s (EOS) two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors are being used world-wide for earth science research and applications. This paper discusses the lessons learned in developing the science algorithms and the data systems needed to produce these high quality data products for the earth sciences community. Strong science team leadership and communication, an evolvable and scalable data system, and central coordination of QA and validation activities enabled the data system to grow by two orders of magnitude from the initial at-launch system to the current system able to reprocess data from both the Terra and Aqua missions in less than a year. Many of the lessons learned from MODIS are already being applied to follow-on missions.

Wolfe, Robert E.↗

NASA NTRS1991

Workshop proceedings: Information Systems for Space Astrophysics in the 21st Century, volume 1

The Astrophysical Information Systems Workshop was one of the three Integrated Technology Planning workshops. Its objectives were to develop an understanding of future mission requirements for information systems, the potential role of technology in meeting these requirements, and the areas in which NASA investment might have the greatest impact. Workshop participants were briefed on the astrophysical mission set with an emphasis on those missions that drive information systems technology, the existing NASA space-science operations infrastructure, and the ongoing and planned NASA information systems technology programs. Program plans and recommendations were prepared in five technical areas: Mission Planning and Operations; Space-Borne Data Processing; Space-to-Earth Communications; Science Data Systems; and Data Analysis, Integration, and Visualization.

Cutts, James↗

NASA NTRS2012

Preservation of Data for Earth System Science- Towards a Content Standard

Various remote sensing agencies of the world have created a data rich environment for research and applications over the last three decades. Especially over the last decade, the volume and variety of data useful for Earth system science have increased quite rapidly. One of the key purposes of collecting these data and generating useful digital products containing derived geophysical parameters is to study the long-term trends in the Earth s behavior. Long-term observational data and derived products are essential for validating results from models that predict the future behavior of the Earth system. Given the significant resources expended in gathering the observational data and developing the derived products, it is important to preserve them for the benefit of future generations of users. Preservation involves maintaining the bits with no loss (or loss within scientifically acceptable bounds) as they move across systems as well as over time, ensuring readability over time, and providing for long-term understandability and repeatability of previously obtained results. In order to ensure long-term understandability and repeatability, it is necessary to identify all items of content that must be preserved and plan for such preservation. This paper discusses the need for a standard enumerating and describing such content items and reports on the progress made by NASA and the Federation of Earth Science Information Partners (ESIP Federation) in the U.S. towards such a standard.

Ramapriyan, Hampapuram↗

NASA NTRS1995

Satellite Situation Center data system for magnetospheric science planning

Critical problems in planning coordinated observation campaigns for magnetospheric science include the need to predict time intervals when one or more observing satellites or ground stations will be connected along magnetic field lines to other observation sites, or when such sites will be located within magnetospheric regions of common interest. The Satellite Situation Center (SSC) was created at the National Space Science Data Center (NSSDC) during the International Magnetospheric Study in the 1970s to address these problems. The SSC Data System has evolved since that era to support potentially complex queries by SSC staff and has now been opened to NASA Science Internet access via the NSSDC On-line Data Information System (NODIS). The SSC software, ephemeris data base, and access modes are described for the Version 2.1 release in 1993.

Aist-Sagara, L.↗

NASA NTRS2016

Advancing User Supports with a Structured How-To Knowledge Base for Earth Science Data

It is a challenge to access and process fast growing Earth science data from satellites and numerical models, which may be archived in very different data format and structures. NASA data centers, managed by the Earth Observing System Data and Information System (EOSDIS), have developed a rich and diverse set of data services and tools with features intended to simplify finding, downloading, and working with these data. Although most data services and tools have user guides, many users still experience difficulties with accessing or reading data due to varying levels of familiarity with data services, tools, and/or formats. A type of structured online document, data recipe, were created in beginning 2013 by Goddard Earth Science Data and Information Services Center (GES DISC). A data recipe is the How-To document created by using the fixed template, containing step-by-step instructions with screenshots and examples of accessing and working with real data. The recipes has been found to be very helpful, especially to first-time-users of particular data services, tools, or data products. Online traffic to the data recipe pages is significant to some recipes. In 2014, the NASA Earth Science Data System Working Group (ESDSWG) for data recipes was established, aimed to initiate an EOSDIS-wide campaign for leveraging the distributed knowledge within EOSDIS and its user communities regarding their respective services and tools. The ESDSWG data recipe group started with inventory and analysis of existing EOSDIS-wide online help documents, and provided recommendations and guidelines and for writing and grouping data recipes. This presentation will overview activities of creating How-To documents at GES DISC and ESDSWG. We encourage feedback and contribution from users for improving the data How-To knowledge base.

how-to↗

NASA NTRS1993

Cost effective data system design approach for EOS AM-1

The question of how to design a cost-effective data system for a space-based application with a high aggregate data rate is addressed. The steps used by the EOS AM-1 design team are defined. A summary of the design as of January 1993 is outlined. The AM-1 Science Data system is comprised of one subsystem that ingests, formats, and routes data - the Science Formatting Equipment (SFE), and another that records the data. Key interfaces are the interfaces to the high rate science instruments, the real-time transmitters, and the internal interface between the SFR and the Solid State Recorder. A cost-effective design approach for the AM-1 data system must take into account impacts on ground systems, data processing facilities, availability of data for real-time users, and future spacecraft designs.

Westmeyer, Paul A.↗

NASA NTRS2014

Data Albums: An Event Driven Search, Aggregation and Curation Tool for Earth Science

One of the largest continuing challenges in any Earth science investigation is the discovery and access of useful science content from the increasingly large volumes of Earth science data and related information available. Approaches used in Earth science research such as case study analysis and climatology studies involve gathering discovering and gathering diverse data sets and information to support the research goals. Research based on case studies involves a detailed description of specific weather events using data from different sources, to characterize physical processes in play for a specific event. Climatology-based research tends to focus on the representativeness of a given event, by studying the characteristics and distribution of a large number of events. This allows researchers to generalize characteristics such as spatio-temporal distribution, intensity, annual cycle, duration, etc. To gather relevant data and information for case studies and climatology analysis is both tedious and time consuming. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. Those who know exactly the datasets of interest can obtain the specific files they need using these systems. However, in cases where researchers are interested in studying a significant event, they have to manually assemble a variety of datasets relevant to it by searching the different distributed data systems. In these cases, a search process needs to be organized around the event rather than observing instruments. In addition, the existing data systems assume users have sufficient knowledge regarding the domain vocabulary to be able to effectively utilize their catalogs. These systems do not support new or interdisciplinary researchers who may be unfamiliar with the domain terminology. This paper presents a specialized search, aggregation and curation tool for Earth science to address these existing challenges. The search tool automatically creates curated "Data Albums", aggregated collections of information related to a specific science topic or event, containing links to relevant data files (granules) from different instruments; tools and services for visualization and analysis; and information about the event contained in news reports, images or videos to supplement research analysis. Curation in the tool is driven via an ontology based relevancy ranking algorithm to filter out non-relevant information and data.

Ramachandran, Rahul↗

NASA NTRS2001

Autonomy technology at JPL

This paper and exhibit describes the on-going research activities, plans and products of the Autonomy Technology program at NASA Jet Propulsion Laboratory. In this paper, we briefly describe the areas of Mission Planning and Execution, Distributed Autonomous Systems, Science Data Understanding, and Autonomous Guidance and Control.

autonomous rovers distributed systems planning con↗

NASA NTRS2011

Validating Phasing and Geometry of Large Focal Plane Arrays

The Kepler Mission is designed to survey our region of the Milky Way galaxy to discover hundreds of Earth-sized and smaller planets in or near the habitable zone. The Kepler photometer is an array of 42 CCDs (charge-coupled devices) in the focal plane of a 95-cm Schmidt camera onboard the Kepler spacecraft. Each 50x25-mm CCD has 2,200 x 1,024 pixels. The CCDs accumulate photons and are read out every six seconds to prevent saturation. The data is integrated for 30 minutes, and then the pixel data is transferred to onboard storage. The data is subsequently encoded and transmitted to the ground. During End-to-End Information System (EEIS) testing of the Kepler Mission System (KMS), there was a need to verify that the pixels requested by the science team operationally were correctly collected, encoded, compressed, stored, and transmitted by the FS, and subsequently received, decoded, uncompressed, and displayed by the Ground Segment (GS) without the outputs of any CCD modules being flipped, mirrored, or otherwise corrupted during the extensive FS and GS processing. This would normally be done by projecting an image on the focal plane array (FPA), collecting the data in a flight-like way, and making a comparison between the original data and the data reconstructed by the science data system. Projecting a focused image onto the FPA through the telescope would normally involve using a collimator suspended over the telescope opening. There were several problems with this approach: the collimation equipment is elaborate and expensive; as conceived, it could only illuminate a limited section of the FPA (.25 percent) during a given test; the telescope cover would have to be deployed during testing to allow the image to be projected into the telescope; the equipment was bulky and difficult to situate in temperature-controlled environments; and given all the above, test setup, execution, and repeatability were significant concerns. Instead of using this complicated approach of projecting an optical image on the FPA, the Kepler project developed a method using known defect features in the CCDs to verify proper collection and reassembly of the pixels, thereby avoiding the costs and risks of the optical projection approach. The CCDs composing the Kepler FPA, as all CCDs, had minor defects. At ambient temperature, some pixels look far brighter than they should. These ghot h pixels have a higher rate of charge leakage than the others due to manufacturing variations. They are usually stable over time, and appear at temperatures above 5 oC. The hot pixels on the Kepler FPA were mapped before photometer assembly during module testing. Selected hot pixels were used as target gstars h for the purposes of EEIS testing. gDead h pixels are permanently off, producing a permanently black pixel. These can also be used if there is some illumination of the FPA. During EEIS testing, Dark Current Full Frame Images (FFIs) taken at room temperature were used to create the hot pixel maps for all 84 Kepler photometer CCD channels. Data from two separate nights were used to create two hot pixel maps per channel, which were cross-correlated to remove cosmic ray events which appear to be hot pixels. These hot pixel maps obtained during EEIS testing were compared to the maps made during module testing to verify that the end-to-end data flow was correct.

Standley, Shaun P.↗