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NASA NTRSDate not supplied

NASA Life Sciences Portal (NLSP): Supporting Scientific Transparency and Reproducibility

NASA’s Life Sciences Ports (NLSP) serves the scientific community by providing curated data from space life science experiment. The Human Research Program (HRP) with the help of NLSP is currently transforming their life sciences data archive systems and processes to improve compliance with the FAIR principles [1]. Some of these improvements will at the same time support the twin pillars of Open Science [2]: transparency of methods and reproducibility of results. Scientific transparency is marked by the easily intelligible communication of what has been investigated: what were the procedures for collecting sample and the characteristics of samples collected? what kinds of measurements were made, what were the environmental conditions of the measurements? What were the analysis techniques of the collected data? Reproducibility of the results and findings from the investigation requires a high level of transparency for all but the simplest investigations; the slightest deviation in communicating and replicating complex experimental procedures or data analyses can often yield quite different data and even findings, thwarting their validation. One of the ways the NLSP is aiming to improve the communication of scientific information is through the use of ontology-driven metadata. Ontologies are powerful, graph-based knowledge representation structures, which can be leveraged to increase data interoperability, the area of the FAIR principles in which many data systems most lack compliance. Over the past decade, there has been a concerted effort in the biomedical community to develop modular and narrowly focused domain and application-specific ontologies in a common, open-source framework, the Open Biological and Biomedical Ontology (OBO) Foundry [3]. The open sharing and modular nature of this effort promises huge increases in harmonized data sharing for systems that leverage these models. Which is in line with the FAIR Data Principles of Findability, Accessibility, Interoperability, and Reuse for scientific data management and stewardship.

Life Sciences data↗

NASA NTRS1990

NSSDC data listing

In a highly summarized way, data available from the National Space Science Data Center (NSSDC) is identified. Most data are offline data sets (on magnetic tape or as film/print products of various sizes) from individual instruments carried on spacecraft; these compose the Satellite Data Listing. Descriptive names, time spans, data form, and quantity of these data sets are identified in the listing, which is sorted alphabetically-first by spacecraft name and then by the principal investigator's or team leader's last name. Several data sets held at NSSDC, not associated with individual spaceflight instruments, are identified in separate listings following the Satellite Data Listing. These data sets make up the Supplementary Data Listings and include composite spacecraft data sets, ground-based data, models, and computer routines. The identifiers used in the Supplementary Data Listings were created by NSSDC and are explained in the pages preceding the listings. Data set form codes are listed. NSSDC offers primarily archival, retrieval, replication, and dissemination services associated with the data sets discussed in the two major listings identified above. NSSDC also provides documentation which enables the data recipient to use the data received. NSSDC is working toward expanding presently limited capabilities for data subsetting and for promotion of data files to online residence for user downloading. NSSDC data holdings span the range of scientific disciplines in which NASA is involved, and include astrophysics, lunar and planetary science, solar physics, space plasma physics, and Earth science. In addition to the functions mentioned above, NSSDC offers data via special services and systems in a number of areas, including Astronomical Data Center (ADC), Coordinated Data Analysis Workshops (CDAWs), NASA Climate Data System (NCDS), Pilot Land Data System (PLDS), and Crustal Dynamics Data Information System (CDDIS). Furthermore, NSSDC has a no-password account on its SPAN/Telenet-accessible VAX through which the NASA Master Directory and selected online data bases are accessible and through which any data described here may be ordered. Astrophysics data support by NSSDC is not limited to the ADC. Each of these special services/systems is described briefly.

Horowitz, Richard↗

NASA NTRSDate not supplied

Comparison of Dynamic Stability Testing Techniques with Magnetic Suspension Wind Tunnel

Dynamic stability testing techniques currently utilized at NASA Langley Research Center (LaRC) are conducted in multiple facilities and consists of free flight, forced oscillation, and free-to-oscillate tests. The MIT/NASA/ODU Magnetic Suspension and Balance System (MSBS) has been recommissioned to explore its utility as an additional facility to expand the dynamic stability test capabilities currently available at NASA LaRC. Simulations were created to replicate each current test facility and method as closely as possible. Data collected from the simulated environments was corrupted with replicated noise sources of the different testing environments and then compared to real data collected during tests when such data was available. The corrupted data was then passed through data reduction and System Identification (SID) to estimate the accuracy of the results with the known aerodynamic model that was utilized within the simulation to generate the original data. Magnitudes of noise were varied utilizing Monte Carlo analysis to perform sensitivity analysis of each noise source on the extracted dynamic stability coefficients. Some preliminary results will be presented.

Otoniel Ramirez↗

NASA NTRSDate not supplied

Comparison of Dynamic Stability Testing Techniques with Magnetic Suspension Wind Tunnel

Otoniel Ramirez↗

NASA NTRS2001

Pilot-Induced Oscillation Prediction With Three Levels of Simulation Motion Displacement

Simulator motion platform characteristics were examined to determine if the amount of motion affects pilot-induced oscillation (PIO) prediction. Five test pilots evaluated how susceptible 18 different sets of pitch dynamics were to PIOs with three different levels of simulation motion platform displacement: large, small, and none. The pitch dynamics were those of a previous in-flight experiment, some of which elicited PIOs These in-flight results served as truth data for the simulation. As such, the in-flight experiment was replicated as much as possible. Objective and subjective data were collected and analyzed With large motion, PIO and handling qualities ratings matched the flight data more closely than did small motion or no motion. Also, regardless of the aircraft dynamics, large motion increased pilot confidence in assigning handling qualifies ratings, reduced safety pilot trips, and lowered touchdown velocities. While both large and small motion provided a pitch rate cue of high fidelity, only large motion presented the pilot with a high fidelity vertical acceleration cue.

Schroeder, Jeffery A.↗

NASA NTRS2005

Evaluation of Decision Trees for Cloud Detection from AVHRR Data

Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.

AVHRR (ADVANCED VERY HIGH RESOLUTION RADIOMETER)↗

NASA NTRS1972

The application of remote sensing techniques to inter and intra urban analysis

This is an effort to assess the applicability of air and spaceborne photography toward providing data inputs to urban and regional planning, management, and research. Through evaluation of remote sensing inputs to urban change detection systems, analyzing an effort to replicate an existing urban land use data file using remotely sensed data, estimating population and dwelling units from imagery, and by identifying and evaluating a system of urban places ultilizing space photography, it was determined that remote sensing can provide data concerning land use, changes in commercial structure, data for transportation planning, housing quality, residential dynamics, and population density.

Horton, F. E.↗

NASA NTRSDate not supplied

Lessons learned from replicating services for flood prediction and monitoring in Asia to the assessment of hurricane impacts in Central America

In October and November 2020, two dangerous back-to-back hurricanes, Eta and Iota, made landfall in Central America. The SERVIR program - a joint effort of NASA and the U.S. Agency for International Development, and whose motto is “connecting space to village” was able to leverage two tools originally developed for use in other regions for predicting and assessing the flood impacts of the hurricanes. The GEOGLoWS Streamflow Prediction tool - originally implemented in the Hindu Kush Himalayan region - was used for predicting potential flooding ahead of landfall by Eta and Iota. In conjunction, the Hydrologic Remote Sensing Analysis for Floods (HYDRAFloods) framework - originally developed along with SERVIR-Mekong - was used for post-event flood mapping, leveraging its ability to map floods in cloud-covered areas using synthetic aperture radar (SAR) imagery from the Copernicus program. Both tools were used in support of disaster coordination efforts being led by the Central American Regional Disaster Prevention Center (CEPREDENAC), in conjunction with its sister agency, the Regional Water Resources Committee (CRRH). The support provided to regional entities - and to their stakeholder national governments - served as an example of rapid generation of Earth observation products for disaster response. Feedback on those products was also provided, especially in terms of the implications of (i) calibration of predicted river volumes, and (ii) the latency of the input Earth observation imagery and attempts to map the floods’ maximum extents. An upcoming NASA DEVELOP project will also seek to strengthen the capability of CEPREDENAC and CRRH to apply HYDRAFloods for future extreme events. The application of the tools also provides a useful case study on capacity building, in terms of how Earth observation data and models can be replicated across regions.

Capacity building↗

NASA NTRS2001

The First SeaWiFS HPLC Analysis Round-Robin Experiment (SeaHARRE-1)

Four laboratories, which had contributed to various aspects of SeaWiFS calibration and validation activities, participated in the first SeaWiFS HPLC Analysis Round-Robin Experiment (SeaHARRE-1): Horn Point Laboratory (USA), the Joint Research Centre (Italy), the Laboratoire de Physique et Chimie Marines (France), and the Marine and Coastal Management group (South Africa). The analyses of the data are presented in Chapter 1 and the individual methods of the four groups are presented in Chapters 2-5. The average (or overall) conclusions of the round-robin are derived from 12 in situ stations occupied during a cruise in the Mediterranean Sea, although, only 11 stations are used in the analyses. The data set is composed of 12 replicates taken during each sampling opportunity with 3 replicates going to each of the 4 laboratories. The average (or overall) results from the intercomparison of 15 pigments or pigment associations are as follows (in some cases, data subsets that exclude pigments which were not analyzed by all the laboratories, or that had unusually large variances, are used to exclude a variety of problematic pigments): a) the accuracy of the four methods in determining the concentration of total chlorophyll a is 7.9%, (one method did not separate mono- and divinyl chlorophyll a, and if the samples containing significant divinyl chlorophyll a concentrations are ignored, the four methods have an accuracy of 6.7%); b) the accuracy in determining the full set of pigments is 19.1%; c) there is a reduction in accuracy of approximately - 12.2% for every decade (factor of 10) decrease in concentration (based on a data subset); d) the precision of the four methods using a subset data is 8.617( 6.2% for an edited subset); e) the repeatability of the four methods using the subset data is 9.2% (7.2%; for an edited subset, and f) the reproducibility of the four methods using the subset data is 21.31% (15.0% for an edited subset).

Hooker, Stanford B.↗

NASA NTRS1988

Mathematical algorithms for approximate reasoning

Most state of the art expert system environments contain a single and often ad hoc strategy for approximate reasoning. Some environments provide facilities to program the approximate reasoning algorithms. However, the next generation of expert systems should have an environment which contain a choice of several mathematical algorithms for approximate reasoning. To meet the need for validatable and verifiable coding, the expert system environment must no longer depend upon ad hoc reasoning techniques but instead must include mathematically rigorous techniques for approximate reasoning. Popular approximate reasoning techniques are reviewed, including: certainty factors, belief measures, Bayesian probabilities, fuzzy logic, and Shafer-Dempster techniques for reasoning. A group of mathematically rigorous algorithms for approximate reasoning are focused on that could form the basis of a next generation expert system environment. These algorithms are based upon the axioms of set theory and probability theory. To separate these algorithms for approximate reasoning various conditions of mutual exclusivity and independence are imposed upon the assertions. Approximate reasoning algorithms presented include: reasoning with statistically independent assertions, reasoning with mutually exclusive assertions, reasoning with assertions that exhibit minimum overlay within the state space, reasoning with assertions that exhibit maximum overlay within the state space (i.e. fuzzy logic), pessimistic reasoning (i.e. worst case analysis), optimistic reasoning (i.e. best case analysis), and reasoning with assertions with absolutely no knowledge of the possible dependency among the assertions. A robust environment for expert system construction should include the two modes of inference: modus ponens and modus tollens. Modus ponens inference is based upon reasoning towards the conclusion in a statement of logical implication, whereas modus tollens inference is based upon reasoning away from the conclusion. These algorithms allow one to reason accurately with uncertain data. The above environment can replicate state-f-the-art expert system environments which provides a continuity between the current expert systems which cannot be validated or verified and future expert systems which should be both validated and verified

Murphy, John H.↗

NASA NTRS1986

Standard data systems architecture for the Space Station

Attention is given to an end-to-end Space Station Data System (SSDS) architecture which is based on internationally-recommended standards developed by the Consultative Committee for Space Data Systems (CCSDS). The proposed system uses simple modular building blocks that are recursively replicated and linked to construct essentially any desired data system configuration. The SSDS concept provides for a user-transparent data transport system which is entirely independent of the characteristics of the user data being transported, and in addition, has the flexibility to accommodate mission-induced changes in data traffic. SSDS physical elements include the following: (1) on-orbit local area networks, (2) space-to-ground, ground-to-space, and space-to-space data links, and (3) ground mission support facilities containing telemetry and telecommand data handling termini and preprocessing services.

Greenberg, E.↗

NASA NTRS1984

A study of environmental characterization of conventional and advanced aluminum alloys for selection and design. Phase 2: The breaking load test method

A technique is demonstrated for accelerated stress corrosion testing of high strength aluminum alloys. The method offers better precision and shorter exposure times than traditional pass fail procedures. The approach uses data from tension tests performed on replicate groups of smooth specimens after various lengths of exposure to static stress. The breaking strength measures degradation in the test specimen load carrying ability due to the environmental attack. Analysis of breaking load data by extreme value statistics enables the calculation of survival probabilities and a statistically defined threshold stress applicable to the specific test conditions. A fracture mechanics model is given which quantifies depth of attack in the stress corroded specimen by an effective flaw size calculated from the breaking stress and the material strength and fracture toughness properties. Comparisons are made with experimental results from three tempers of 7075 alloy plate tested by the breaking load method and by traditional tests of statistically loaded smooth tension bars and conventional precracked specimens.

Sprowls, D. O.↗

NASA NTRSDate not supplied

Crowd Sourcing Medical Data Collection Using Medical Students

OBJECTIVE We undertook an upgrade of the Evidence Library database of NASA HRP’s Integrated Medical Model, assessing 120 medical conditions which integrate with a novel probabilistic risk assessment (IMPACT) tool of medical risk and resource utilization for long duration exploration human spaceflight. This data collection process included a selection of these conditions crowd sourced over one year via three 4-week medical student electives at the University of Colorado School of Medicine (IDPT 8059 Space Medicine: Human Spaceflight Factors & Medical Risk Assessment). Students undertook a rapid systematic review of each medical condition, under close preceptors with backgrounds in clinical medicine, library science, epidemiology, biostatistics, and evidence-based medicine. As part of the elective, students also received instruction in core space medicine concepts, evidence based medicine and problem based learning sessions as a flight surgeon supporting a simulated Mars mission. METHODS The list of 120 medical conditions includes both common, terrestrial illness/injury (epistaxis, diverticulitis) as well as spaceflight-specific ones (space adaptation conditions, EVA-related injuries). A rapid systematic review process was developed that would allow students to find the data for determining disease incidence/prevalence, return to definitive care (often a surrogate such as hospitalization rates), loss of crew life, and treatment duration. Each data point required a tailored, specialized search process using different databases and corresponding specialized search filters. Databases were selected on their ability to provide high quality literature in an efficient manner and prioritized by their ability to provide graded evidence via a set rubrics specific to human spaceflight. Students were responsible for performing all literature searches and identifying the highest quality available evidence for each data point. Completed student data sheets underwent initial review by faculty preceptors followed by a secondary editing review by the ExMC Clinical Science Team. RESULTS Over the course of three electives, approximately 105 medical conditions were researched by students using spreadsheets with pre-crafted search strategies. Overall, this process was successful in allowing students to perform the preponderance of work to update incidence, treatment duration, return to definitive care, and loss of crew life data points. Students were successful in running searches, identifying the necessary data points within the literature, and determining the types of terrestrial data that most aligns with the astronaut population for successful completion of their tasks. Limitations included variable student experience with search methodologies [PubMed], differing values of evidence grading [best practice evidence based medicine vs. relevant to spaceflight], and students’ unfamiliarity with spaceflight specific conditions. CONCLUSION Finding the relevant literature for medical conditions in spaceflight within terrestrial databases in a systematic method is time consuming and not intuitive. However, the stepwise process that balanced sensitivity with specificity allowed for students to be highly successful in a short amount of time. Additionally, as the process was refined over the course of three electives, preceptors were better able to anticipate where students were likely to encounter barriers, which allowed the course to be adjusted to account for certain data points needing more time for completion. This replicable process may be an efficient way to accomplish rapid systematic reviews for a large volume of data in a short amount of time.

J Lemery↗

NASA NTRSDate not supplied

The Jörmungandr Dark Flight Model for Meteorite and Orbital Debris Recovery

Jörmungandr is a model that replicates the flight paths of falling meteorites to facilitate rapid meteorite collection [e.g. 1-3]. By iteratively entering a range of meteorite masses, a polygonal landing field (or “strewn field”) is generated to simplify any search for meteorites. Jörmungandr uses winds-aloft data collected by weather balloons to accurately replicate lateral movement of falling meteorites. Jörmungandr is recently upgraded to v.2 with slightly more accurate fall modelling than v.1, and the math and operations of the model are described here. Jörmungandr data show that strewn field shapes are dominated by local wind conditions which can produce banana-shaped strewn fields in the event of strong crosswinds.

M D Fries↗

NASA NTRS1990

Bioclimatic distribution of vegetation for general circulation model studies

Four global bioclimatic schemes which qualify climates on the basis of the distribution of vegetation, including the Holdridge (1947), Thornthwaite (1948), Koeppen (1936), and Troll and Paffen (1964) schemes, were applied to two global climate data sets to produce maps of global vegetation distribution: the Rand set described by Schutz and Gates (1971, 1973, and 1974) and the Shea (1986) data set. The results show that only 38 to 40 percent of the observed land surface, mapped as 31 vegetation types, could be replicated by applying the four schemes to these data sets. The simulations were significantly improved by further subdividing and regrouping the climates defined by the schemes and by regrouping the observed vegetation types. With these alterations, 77 percent of the predicted vegetative landscape coresponded with the observed distribution of vegetation.

Prentice, Katharine Culbertson↗

NASA NTRS1988

Long decoding runs for Galileo's convolutional codes

Decoding results are described for long decoding runs of Galileo's convolutional codes. A 1 k-bit/sec hardware Viterbi decoder is used for the (15, 1/4) convolutional code, and a software Viterbi decoder is used for the (7, 1/2) convolutional code. The output data of these long runs are stored in data files using a data compression format which can reduce file size by a factor of 100 to 1 typically. These data files can be used to replicate the long, time-consuming runs exactly and are useful to anyone who wants to analyze the burst statistics of the Viterbi decoders. The 1 k-bit/sec hardware Viterbi decoder was developed in order to demonstrate the correctness of certain algorithmic concepts for decoding Galileo's experimental (15, 1/4) code, and for the long-constraint-length codes in general. The hardware decoder can be used both to search for good codes and to measure accurately the performance of known codes.

Lahmeyer, C. R.↗

NASA NTRS1994

Human-Computer Interaction with Medical Decisions Support Systems

Decision Support Systems (DSSs) have been available to medical diagnosticians for some time, yet their acceptance and use have not increased with advances in technology and availability of DSS tools. Medical DSSs will be necessary on future long duration space missions, because access to medical resources and personnel will be limited. Human-Computer Interaction (HCI) experts at NASA's Human Factors and Ergonomics Laboratory (HFEL) have been working toward understanding how humans use DSSs, with the goal of being able to identify and solve the problems associated with these systems. Work to date consists of identification of HCI research areas, development of a decision making model, and completion of two experiments dealing with 'anchoring'. Anchoring is a phenomenon in which the decision maker latches on to a starting point and does not make sufficient adjustments when new data are presented. HFEL personnel have replicated a well-known anchoring experiment and have investigated the effects of user level of knowledge. Future work includes further experimentation on level of knowledge, confidence in the source of information and sequential decision making.

Adolf, Jurine A.↗

NASA NTRS2017

CME Velocity and Acceleration Error Estimates Using the Bootstrap Method

The bootstrap method is used to determine errors of basic attributes of coronal mass ejections (CMEs) visually identified in images obtained by the Solar and Heliospheric Observatory (SOHO) mission's Large Angle and Spectrometric Coronagraph (LASCO) instruments. The basic parameters of CMEs are stored, among others, in a database known as the SOHO/LASCO CME catalog and are widely employed for many research studies. The basic attributes of CMEs (e.g. velocity and acceleration) are obtained from manually generated height-time plots. The subjective nature of manual measurements introduces random errors that are difficult to quantify. In many studies the impact of such measurement errors is overlooked. In this study we present a new possibility to estimate measurements errors in the basic attributes of CMEs. This approach is a computer-intensive method because it requires repeating the original data analysis procedure several times using replicate datasets. This is also commonly called the bootstrap method in the literature. We show that the bootstrap approach can be used to estimate the errors of the basic attributes of CMEs having moderately large numbers of height-time measurements. The velocity errors are in the vast majority small and depend mostly on the number of height-time points measured for a particular event. In the case of acceleration, the errors are significant, and for more than half of all CMEs, they are larger than the acceleration itself.

Michalek, Grzegorz↗