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NASA NTRS2019

Designing the User Experience for Earth Observation Data Services in the Cloud

NASA's Earth Observation (EO) inventory is projected to grow by an order of magnitude over the next 5-6 years. The current mode for working with EO data of downloading the data to a local machine (laptop, desktop or server) will be difficult to sustain for these upcoming volumes. Therefore, NASA is in the process of developing a capability to host large volume data in commercial cloud, with an eye toward encouraging data analysis in the cloud. However, in order for the user community to take advantage of this new mode of data interaction, the user experience must be redesigned. Cloud-hosted data brings new challenges, such as managing the costs of data egress and working with data in Web Object Storage instead of a Posix filesystem. However, it also brings new opportunities. Scaling data transformation processes may permit more synchronous data services with near-immediate response vs. cumbersome ordering systems with latencies of hours or days. Data co-location in the cloud can facilitate data integration and fusion. Highly scalable filesystems and databases in the cloud support data reorganization to facilitate analysis at scale. In the course of NASA's reimagining of the User Experience for EO data usage relies on end user input gathered through surveys, workshops and meetings (such as this). At the same time, we have embarked on a course of pedagogy and capacity building to help the user community evolve to cloud-based analysis.

Lynnes, Christopher↗

NASA NTRS2006

Human Factors Lessons Learned on the International Space Station

Experience on International Space Station (ISS) provides many important lessons for future space flight. NASA human factors engineers have been systematically collecting lessons learned from crew debriefs, as well as working with ground support teams to continuously improve crew operations. This paper describes the methods for collecting data from debriefs, lessons learned through that process, and an example of a technology development task funded through the Space Human Factors Engineering (SHFE) program element in response to an identified operational need. Each ISS increment crew spends many hours after the flight answering questions from the various subsystem leads. The Flight Crew Integration subsystem lead asks questions specific to human factors and habitability issues. In addition, crew comments on many other subsystems provide insight into interface designs, operability and maintainability. The debrief comments are unique to each crew, and must be categorized to provide operational lessons learned. Personal identifiers are removed and comments aggregated to separate consistent issues from personal preferences. Examples will be given, and the procedure for incorporating the lessons into requirements and guidelines for the next human space vehicle will be described. In flight, very few astronauts are medical doctors. Written medical procedures during flight need to be easy to follow and quick to understand. The problem was analyzed as part of a SHFE task. Organization was analyzed and reorganizations were created and tested. Results will be reported. The ISS is a very important analog for planning future long-term missions. Collection of data from debriefs, studying the lessons learned and focusing on requirements for future missions are examples of the accomplishments through the SHFE program.

Woolford, Barbara↗

NASA NTRS1999

A New Compendium of Unsteady Aerodynamic Test Cases for CFD: Summary of AVT WG-003 Activities

With the continuous progress in hardware and numerical schemes, Computational Unsteady Aerodynamics (CUA), that is, the application of Computational Fluid Dynamics (CFD) to unsteady flowfields, is slowly finding its way as a useful and reliable tool (turbulence and transition modeling permitting) in the aircraft, helicopter, engine and missile design and development process. Before a specific code may be used with confidence it is essential to validate its capability to describe the physics of the flow correctly, or at least to the level of approximation required, for which purpose a comparison with accurate experimental data is needed. Unsteady wind tunnel testing is difficult and expensive; two factors which dramatically limit the number of organizations with the capability and/or resources to perform it. Thus, unsteady experimental data is scarce, often classified and scattered in diverse documents. Additionally, access to the reports does not necessarily assure access to the data itself. The collaborative effort described in this paper was conceived with the aim of collecting into a single easily accessible document as much quality data as possible. The idea is not new. In the early 80's NATO's AGARD (Advisory Group for Aerospace Research & Development) Structures and Material Panel (SMP) produced AGARD Report No. 702 "Compendium of Unsteady Aerodynamic Measurements", which has found and continues to find extensive use within the CUA Community. In 1995 AGARD's Fluid Dynamics Panel (FDP) decided to update and expand the former database with new geometries and physical phenomena, and launched Working Group WG-22 on "Validation Data for Computational Unsteady Aerodynamic Codes". Shortly afterwards AGARD was reorganized as the RTO (Research and Technology Organization) and the WG was renamed as AVT (Applied Vehicle Technolology) WG-003. Contributions were received from AEDC, BAe, DLR, DERA, Glasgow University, IAR, NAL, NASA, NLR, and ONERA. The final publication with the results of the exercise is expected in the second part of 1999. The aim of the present paper is to announce and present the new database to the Aeroelasticity community. It is also intended to identify, together with one of the groups of end users it targets, deficiencies in the compendium that should be addressed by means of new wind tunnel tests or by obtaining access to additionally existing data.

Ruiz-Calavera, Luis P.↗

NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, whole organism, behavior; tabular, imagery). Open Science is the concept that the more people have access to scientifically curated data, the more knowledge will be gained. This led NASA to start the development of GeneLab in 2015. GeneLab houses spaceflight and space-analog multi-omics datasets from plant, rodent, small animal, and microbial experiments. The success and knowledge gained from GeneLab led to a new alliance of NASA “Open Science Data Repositories” (OSDR), which include the Ames Life Sciences Data Archive (ALSDA) and the NASA Biological Institutional Scientific Collection (NBISC). Both are adopting the GeneLab data system, so data are more findable, accessible, interoperable, and reusable (FAIR). OSDR systems provide users the ability to upload, download, search, share, analyze, and visualize. Open Science also needs strong confidence in the data, which is gained through building science communities. With ~400 current members, GeneLab and ALSDA formed Analysis Working Groups (AWGs) to provide feedback on processing pipelines, metadata curation standards (for ‘omics and phenotypic-physiological-behavioral assays), and to collaborate in effectively reusing data. The AWG also led to the development of the Radiation Biology Ontology (RBO), ensuring radiation metadata are efficiently captured, connected, and interoperable. Feedback from the AWG provided design input toward the new single point-of-entry data submission portal for all investigators to submit, curate, and share their research data. Space biological data is now maximally open access, collected-curated with rich metadata, and formatted for interoperability to enable systems biology, meta-analysis, knowledge graphs, machine learning, modeling, and other reuse approaches. With potential for further federation of OSDR for data mining with traditional biological and medical databases (NIH, NCI, EBI, etc.), a new era for space biology has begun to support the knowledge discovery necessary for Lunar and Martian missions.

Ryan T Scott↗