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The operation of the Earth Resources Research Data Facility (ERRDF) in Clear Lake City, Texas, which contains both aircraft and spacecraft gathered information on remote sensing of earth resources, including geology, geography, agriculture, forestry, hydrology, and oceanography, is described in detail. Possible uses of ERRDF data for further studies and applications are discussed.
A fundamental research data base was created on a single 9-track 1600 BPI tape containing ground truth, image, and Badhwar profile feature data for 17 North Dakota, South Dakota, and Minnesota agricultural sites. Each site is 5x6 nm in area. Image data has been provided for a minimum of four acquisition dates for each site. All four images have been registered to one another. A list of the order of the files on tape and the dates of acquisition is provided.
A fundamental research data base containing ground truth, image, and Badhwar profile feature data for 17 North Dakota, South Dakota, and Minnesota agricultural sites is described. Image data was provided for a minimum of four acquisition dates for each site and all four images were registered to one another.
Earth Resources Research Data Facility Index
Understanding of the relationships between the optical, spectral characteristics and important biological-physical parameters of earth-surface features can best be obtained by carefully controlled studies over fields and plots where complete data describing the condition of targets are attainable and where frequent, timely spectral measurement can be obtained. Development of a vegetation and soils field research data base was initiated in 1972 at Purdue University's Laboratory for Applications of Remote Sensing and expanded in the fall of 1974 by NASA as part of LACIE. Since then, over 250,000 truck-mounted and helicopter-borne spectrometer/multiband radiometer observations have been obtained of more than 50 soil series and 20 species of crops, grasses, and trees. These data are supplemented by an extensive set of biophysical and meteorological data acquired during each mission. The field research data form one of the most complete and best-documented data sets acquired for agricultural remote sensing research. Thus, they are well-suited to serve as a data base for research to: (1) quantiatively determine the relationships of spectral and biophysical characteristics of vegetation, (2) define future sensor systems, and (3) develop advanced data analysis techniques.
The Research Data Alliance (RDA) enables data to be shared across barriers through focused working groups and interest groups, formed of experts from around the world - from academia, industry and government. Its Big Data Analytics (BDA) interest groups seeks to develop community based recommendations on feasible data analytics approaches to address scientific community needs of utilizing large quantities of data. BDA seeks to analyze different scientific domain applications (e.g. earth science use cases) and their potential use of various big data analytics techniques. These techniques reach from hardware deployment models up to various different algorithms (e.g. machine learning algorithms such as support vector machines for classification). A systematic classification of feasible combinations of analysis algorithms, analytical tools, data and resource characteristics and scientific queries will be covered in these recommendations. This contribution will outline initial parts of such a classification and recommendations in the specific context of the field of Earth Sciences. Given lessons learned and experiences are based on a survey of use cases and also providing insights in a few use cases in detail.
Researchers at widely separated locations able to participate in tests and analyze data immediately. Basic data-handling needs common: Communicates with vehicle, pilot, and test team; Acquires, computes, and displays data; knows exact location of research vehicle at all times. Continuing challenge for designers and operators of ground support facilities to perform tasks in real time and present integrated results to research team in real time. Paper presents several approaches to satisfaction of requirements of representative types of aircraft research programs at NASA Western Aeronautical Test Range of Ames Research Center.
The National Transonic Facility is a complex cryogenic wind tunnel facility. This report briefly describes the facility, the data systems, and the instrumentation used to acquire research data. The computational methods and equations are discussed in detail and many references are listed for those who need additional technical information. This report is intended to be a user's guide, not a programmer's guide; therefore, the data reduction code itself is not documented. The purpose of this report is to assist personnel involved in conducting a test in the National Transonic Facility.
Jackson State University recently established the Center for Spatial Data Research and Applications, a Geographical Information System (GIS) and remote sensing laboratory. Taking advantage of new technologies and new directions in the spatial (geographic) sciences, JSU is building a Center of Excellence in Spatial Data Management. New opportunities for research, applications, and employment are emerging. GIS requires fundamental shifts and new demands in traditional computer science and geographic training. The Center is not merely another computer lab but is one setting the pace in a new applied frontier. GIS and its associated technologies are discussed. The Center's facilities are described. An ARC/INFO GIS runs on a Vax mainframe, with numerous workstations. Image processing packages include ELAS, LIPS, VICAR, and ERDAS. A host of hardware and software peripheral are used in support. Numerous projects are underway, such as the construction of a Gulf of Mexico environmental data base, development of AI in image processing, a land use dynamics study of metropolitan Jackson, and others. A new academic interdisciplinary program in Spatial Data Management is under development, combining courses in Geography and Computer Science. The broad range of JSU's GIS and remote sensing activities is addressed. The impacts on changing paradigms in the university and in the professional world conclude the discussion.
NASA Lewis Research Center's new, world-class, 60-atmosphere (atm) combustor research facility, the Advanced Subsonic Combustion Rig (ASCR), is in operation and producing highly unique research data. Specifically, data were acquired at high pressures and temperatures representative of future subsonic engines from a fundamental flametube configuration with an advanced fuel injector. The data acquired include exhaust emissions as well as pressure and temperature distributions. Results to date represent an improved understanding of nitrous oxide (NOx) formation at high pressures and temperatures and include an NOx emissions reduction greater than 70 percent with an advanced fuel injector at operating pressures to 800 pounds per square inch absolute (psia). ASCR research is an integral part of the Advanced Subsonic Technology (AST) Propulsion Program. This program is developing critical low-emission combustion technology that will result in the next generation of gas turbine engines producing 50 to 70 percent less NOx emissions in comparison to 1996 International Civil Aviation Organization (ICAO) limits. The results to date indicate that the AST low-emission combustor goals of reducing NOx emissions by 50 to 70 percent are feasible. U.S. gas turbine manufacturers have started testing the low-emissions combustors at the ASCR. This collaborative testing will enable the industry to develop low-emission combustors at the high pressure and temperature conditions of future subsonic engines. The first stage of the flametube testing has been implemented. Four GE Aircraft Engines low-emissions fuel injector concepts, three Pratt & Whitney concepts, and two Allison concepts have been tested at Lewis ASCR facility. Subsequently, the flametube was removed from the test stand, and the sector combustor was installed. The testing of low emissions sector has begun. Low-emission combustors developed as a result of ASCR research will enable U.S. engine manufacturers to compete on a worldwide basis by producing environmentally acceptable commercial engines.
Introduction: ‘Long-tail’ research is performed by individual PIs and small research teams, producing what are often highly diverse, but relatively small da-tasets that span a variety of traditional scientific disci-plines. ‘Long-tail’ research is a fundamental part of realizing NASA’s goals in planetary sciences and a key input feeding into mission life cycles. The ‘long-tail’ has traditionally lacked the resources (available to larg-er groups and missions) to overcome barriers inhibit-ing the adoption of open science practices, including lack of acknowledgment, time, money, guidance, ex-pertise, and trust in available platforms1. Here we show how the Open Data Repository’s (ODR) data publish-ing platform could help lower some of these barriers and accelerate the adoption of open-science practices in planetary science.
Computer controlled digital display techniques for direct editing of research data
Solid state data storage digital telemetry system which makes it possible to store and retransmit after reentry blackout
This presentation is an attempt to clarify several aspects of the current procedures, tools, and challenges of human data sharing for ISS flight activities. There are several binary variables to consider with respect to human spaceflight data sharing: Medical vs. Research, Active Flight vs. Non-Flight, Tactical vs. Supplemental, Prospective vs. Retrospective. This presentation will address each of these variables and how they determine which processes and mechanisms are used both to document and facilitate human data sharing. Some of these variables will likely be so obvious that they induce eye rolls. Please bear with us. We're trying to make these slides fairly rudimentary for a wide, (eventually) international audience. Other distinctions are made if data originated from a NASA vs. IP crewmember. Those distinctions will be made apparent when needed.
Established in 2002 to demonstrate the weather and forecasting application of real-time EOS measurements, the NASA Short-term Prediction Research and Transition (SPoRT) program has grown to be an end-to-end research to operations activity focused on the use of advanced NASA modeling and data assimilation approaches, nowcasting techniques, and unique high-resolution multispectral data from EOS satellites to improve short-term weather forecasts on a regional and local scale. With the ever-broadening application of real-time high resolution satellite data from current EOS and planned NPP, JPSS, and GOES-R sensors to weather forecast problems, significant challenges arise in the acquisition, delivery, and integration of the new capabilities into the decision making process of the operational weather community. For polar orbiting sensors such as MODIS, AIRS, VIIRS, and CRiS, the use of direct broadcast ground stations is key to the real-time delivery of the data and derived products in a timely fashion. With the ABI on the geostationary GOES-R satellite, the data volume will likely increase by a factor of 5- 10 from current data streams. However, the high data volume and limited bandwidth of end user facilities presents a formidable obstacle to timely access to the data. This challenge can be addressed through the use of subsetting techniques, innovative web services, and the judicious selection of data formats. Many of these approaches have been implemented by SPoRT for the delivery of real-time products to NWS forecast offices and other weather entities. Once available in decision support systems like AWIPS II, these new data and products must be integrated into existing and new displays that allow for the integration of the data with existing operational products in these systems. SPoRT is leading the way in demonstrating this enhanced capability. This paper will highlight the ways SPoRT is overcoming many of the challenges presented by the enormous data volumes of current and future satellite systems to get unique high quality research data into the operational weather environment.
Space related scientific research has passed through a natural evolutionary process. The task of extracting the meaningful information from the raw data is highly involved and will require data processing capabilities that do not exist today. The results are presented of a three year examination of this subject, using an earlier report as a starting point. The general conclusion is that there are areas in which NASA's data management practices can be improved and recommends specific actions. These actions will enhance NASA's ability to extract more of the potential data and to capitalize on future opportunities.
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For the last three decades, Earth science remote sensing technologies have been providing an enormous amount of useful data and information serving to broaden our understanding of the home planet as a system. NASA's Earth science program has deployed about 18 complex satellites and is in the process of defining and launching multiple observing systems in this decade. At the same time, the European Community and many other countries such as Russia, France, India, Japan, and China have also significantly contributed to Earth science research. To date, the majority of such efforts have concentrated on expanding our scientific understanding of the multiple nonlinear and chaotic processes of Earth's behavior. In recent years, legislators and stakeholders have put serious pressure on the science community to devote more attention to making use of scientific results for societal benefit. For instance, there are a number of areas such as energy forecasting, aviation safety, agricultural efficiency, disaster management, air quality and public health that can directly take advantage of Earth science results to analyze and predict large scale problems and conditions. This is becoming even more important now that we live in a global economy interconnected via the internet and transportation systems; regional environmental conditions can have far reaching impact across continental boundaries. These factors dictate requirements for global data that can help us assess and control the devastating problems of famine, water resources, wildfires, human health and more. To do this requires a serious, organized, and systematic approach that transfers fundamental research products to the applied sciences domain. This paper presents a systems engineering and management process that can effectively make such transfer of data to the user community. Examples are presented on how the above decision making framework can help in solving critical problems such as the spread of vector borne diseases, forecasts of harmful algal blooms as well as forest fires and wildfires, and the intercontinental transport of dust storms and pollution.