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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Orbital Debris Ontology, Terminology, and Knowledge Modeling

The looming threat orbital debris poses to assets in orbit demands solutions. As the orbital population grows, so does this hazard, but so does the sea of data. The problem is also an opportunity for interdisciplinary innovation and cooperation. This paper focuses on the data and information management aspect of developing solutions for a sustainable and safe orbital space environment. The corresponding author’s in-progress work to develop an orbital debris domain ontology is summarized in order to discuss knowledge modeling for this domain. Methodological approaches of this effort can also contribute to standards efforts and address terminological and policy questions. Leveraging the growing volumes of orbital debris and space situational awareness (SSA) data will create a more complete picture of the orbital space environment. Part of the solution will be: consistent and correct data interpretation, sharing orbital debris and SSA data in one form or another, terminology development & harmonization, and knowledge or domain modeling. To facilitate this, [Rovetto, 2015/16] discussed ontology development for the orbital debris domain. This paper lists concepts from that paper, and subsequently developed concepts [2-9]. Ontology engineering is an interdisciplinary field related to knowledge representation and reasoning in artificial intelligence, semantic technologies and the so-called semantic web. An ontology is effectively a computable and semantically rich terminology that presents a knowledge or domain model for a topic area. Expressions of knowledge or assertions are stored using formally defined term. This knowledge base is reasoned over to yield answers to queries, among other things. Ontologies have been developed in knowledge-based projects across various disciplines, and used for such things as search engines, chatbots, enterprise knowledge graphs, etc. Ontologies support: interoperability, automated reasoning, data sharing and integration, data search and retrieval, and communicating the meaning of data. The Orbital Debris Ontology (ODO), and related ontologies [Rovetto & Kelso 2016] [Rovetto 2016, 2017], were proposed to help achieve this. ODO, for instance, is intended as a domain ontology that can be used across federated databases, offering an explicitly specified set of concepts describing the orbital debris domain. Its meaning-rich taxonomy will provide a sharable semantics for orbital debris data to, in part, consistently communicate the meaning of data to both humans and machines, and tag data elements in space object catalogs to help afford inference tasks, decision support, knowledge discovery, and information integration. ODO and the SSA ontology (SSAO) is part of the overall Orbital Space Domain Ontology concept, which is conceived as a broader domain reference ontology. It aims to provide a knowledge representation structure of the orbital space environment, a common semantic model, and develop a sharable terminology. Collectively this will provide common meaning for datasets, a high-level taxonomy or classification for orbital space objects, and thus means to characterize space objects. Ongoing efforts have included using visualizations, R, JSON-LD, and contemporary semantic technologies. Potential applications and interdisciplinary partnerships include web-based platforms, web apps, visualizations, and academia projects. Community input and participation may yield a more widely understood domain model as well as facilitate terminological standards. For example, the proposed conceptual, terminological and ontological analysis may contribute to such efforts as the Space Debris Mitigation Requirements in the International Standards Organization by developing more precise, consistent and coherent terms and definitions. Projects that seek to develop in-house ontologies can use ODO and related ontologies as domain reference ontologies. This paper was developed independent of author affiliations. Readers are encouraged to contact corresponding author(1) with general interest and potential opportunities to support or realize the described project.

Robert J. Rovetto↗
NASA NTRS2020

Customer Avionics Interface Development and Analysis Development Activity Tracking System

The Customer Avionics Interface Development and Analysis (CAIDA) Development Activity (DA)Tracking System is a Microsoft Access Database that tracks, organizes, and analyzes data about DAs from the work management tool. The CAIDA DA Tracking System takes data imported from the work management system. Once in the system, the data is filtered to generate each DA’s Asset. From there, many different queries are run on the data and their results are imported into forms to create graphs to get and display a wide range of metrics. These graphs are automatically updated with each new data import and over time. They can be easily exported for use in presentations, documents, etc. Additionally, the system is highly customizable and can be added upon to include more data members, generate new graphs, and much more.

Tara Conti↗
NASA NTRS1993

EnviroNET: On-line information for LDEF

EnviroNET is an on-line, free-form database intended to provide a centralized repository for a wide range of technical information on environmentally induced interactions of use to Space Shuttle customers and spacecraft designers. It provides a user-friendly, menu-driven format on networks that are connected globally and is available twenty-four hours a day - every day. The information, updated regularly, includes expository text, tabular numerical data, charts and graphs, and models. The system pools space data collected over the years by NASA, USAF, other government research facilities, industry, universities, and the European Space Agency. The models accept parameter input from the user, then calculate and display the derived values corresponding to that input. In addition to the archive, interactive graphics programs are also available on space debris, the neutral atmosphere, radiation, magnetic fields, and the ionosphere. A user-friendly, informative interface is standard for all the models and includes a pop-up help window with information on inputs, outputs, and caveats. The system will eventually simplify mission analysis with analytical tools and deliver solutions for computationally intense graphical applications to do 'What if...' scenarios. A proposed plan for developing a repository of information from the Long Duration Exposure Facility (LDEF) for a user group is presented.

Lauriente, Michael↗
NASA NTRS2011

Telemetry and Science Data Software System

The Telemetry and Science Data Software System (TSDSS) was designed to validate the operational health of a spacecraft, ease test verification, assist in debugging system anomalies, and provide trending data and advanced science analysis. In doing so, the system parses, processes, and organizes raw data from the Aquarius instrument both on the ground and while in space. In addition, it provides a user-friendly telemetry viewer, and an instant pushbutton test report generator. Existing ground data systems can parse and provide simple data processing, but have limitations in advanced science analysis and instant report generation. The TSDSS functions as an offline data analysis system during I&T (integration and test) and mission operations phases. After raw data are downloaded from an instrument, TSDSS ingests the data files, parses, converts telemetry to engineering units, and applies advanced algorithms to produce science level 0, 1, and 2 data products. Meanwhile, it automatically schedules upload of the raw data to a remote server and archives all intermediate and final values in a MySQL database in time order. All data saved in the system can be straightforwardly retrieved, exported, and migrated. Using TSDSS s interactive data visualization tool, a user can conveniently choose any combination and mathematical computation of interesting telemetry points from a large range of time periods (life cycle of mission ground data and mission operations testing), and display a graphical and statistical view of the data. With this graphical user interface (GUI), the data queried graphs can be exported and saved in multiple formats. This GUI is especially useful in trending data analysis, debugging anomalies, and advanced data analysis. At the request of the user, mission-specific instrument performance assessment reports can be generated with a simple click of a button on the GUI. From instrument level to observatory level, the TSDSS has been operating supporting functional and performance tests and refining system calibration algorithms and coefficients, in sync with the Aquarius/SAC-D spacecraft. At the time of this reporting, it was prepared and set up to perform anomaly investigation for mission operations preceding the Aquarius/SAC-D spacecraft launch on June 10, 2011.

Bates, Lakesha↗
NASA NTRS2015

The Open Data Repositorys Data Publisher

Data management and data publication are becoming increasingly important components of researcher's workflows. The complexity of managing data, publishing data online, and archiving data has not decreased significantly even as computing access and power has greatly increased. The Open Data Repository's Data Publisher software strives to make data archiving, management, and publication a standard part of a researcher's workflow using simple, web-based tools and commodity server hardware. The publication engine allows for uploading, searching, and display of data with graphing capabilities and downloadable files. Access is controlled through a robust permissions system that can control publication at the field level and can be granted to the general public or protected so that only registered users at various permission levels receive access. Data Publisher also allows researchers to subscribe to meta-data standards through a plugin system, embargo data publication at their discretion, and collaborate with other researchers through various levels of data sharing. As the software matures, semantic data standards will be implemented to facilitate machine reading of data and each database will provide a REST application programming interface for programmatic access. Additionally, a citation system will allow snapshots of any data set to be archived and cited for publication while the data itself can remain living and continuously evolve beyond the snapshot date. The software runs on a traditional LAMP (Linux, Apache, MySQL, PHP) server and is available on GitHub (http://github.com/opendatarepository) under a GPLv2 open source license. The goal of the Open Data Repository is to lower the cost and training barrier to entry so that any researcher can easily publish their data and ensure it is archived for posterity.

Astrobiology data↗
NASA NTRS1989

Noiseless compression using non-Markov models

Adaptive data compression techniques can be viewed as consisting of a model specified by a database common to the encoder and decoder, an encoding rule and a rule for updating the model to ensure that the encoder and decoder always agree on the interpretation of the next transmission. The techniques which fit this framework range from run-length coding, to adaptive Huffman and arithmetic coding, to the string-matching techniques of Lempel and Ziv. The compression obtained by arithmetic coding is dependent on the generality of the source model. For many sources, an independent-letter model is clearly insufficient. Unfortunately, a straightforward implementation of a Markov model requires an amount of space exponential in the number of letters remembered. The Directed Acyclic Word Graph (DAWG) can be constructed in time and space proportional to the text encoded, and can be used to estimate the probabilities required for arithmetic coding based on an amount of memory which varies naturally depending on the encoded text. The tail of that portion of the text which was encoded is the longest suffix that has occurred previously. The frequencies of letters following these previous occurrences can be used to estimate the probability distribution of the next letter. Experimental results indicate that compression is often far better than that obtained using independent-letter models, and sometimes also significantly better than other non-independent techniques.

Blumer, Anselm↗
NASA NTRSDate not supplied

Influence of Thermophysical Property Variability on Thermal Predictions of Ti-6Al-4V

The additive manufacturing (AM) industry has experienced rapid growth in recent decades as industrial interest in the process has grown. Because of this, there are many groups interested in simulations of the AM process. However, the influence of thermophysical property variability on the melt pool geometry during processing of various AM alloys is unclear. The goal of my work at NASA Langley Research Center (LaRC) was to characterize this influence on Ti-6Al-4V during AM processing alongside developing a tool to enable equivalent studies on other AM materials. To facilitate this process, a database tool was developed to contain and organize thermophysical data previously reported by primary sources. The specific thermophysical properties of interest for this study were density, specific heat capacity, and conductivity. Thermal diffusivity was calculated using the other three properties. This data was then fit with a Gaussian distribution and then randomly sampled from using Monte Carlo random value sampling. Using the Rosenthal equation, this sample data was used to simulate the temperature field during additive manufacturing and extract the melt pool geometry. Specifically, the melt pool’s length, width, and depth. This process was repeated an arbitrary number of times, with the default being 1000. Once this information was obtained, histograms were made showing the distributions of the sizes of the melt pool’s length, width, and depth. Representative statistical metrics of the distributions were calculated (mean, standard deviation, and coefficient of variance). This work found that at the simulated processing values, the melt pool’s width and depth had a 5.7% variation while the length had a 3.2% variation. Additionally, a tool was constructed to graph a thermal color map representation of the melt pool for specific, arbitrary values of density, specific heat, and conductivity. This tool allowed for more efficient plotting of individual queries of the thermophysical properties. The results that were observed in the research were that values reported in literature vary and this variance can have a significant impact on the simulation. Predictions of the melt pool’s dimensions show that length has a standard deviation of approximately 6 μm, width has a standard deviation of approximately 5 μm, and depth can vary by approximately 3 μm. Considering the mean sizes of length, width, and depth are 185 μm, 88 μm, and 44 μm respectively, such a deviation is significant. This shows that depth, for example, could be more than 10% larger or smaller than expected. This demonstrates that variability in the reported thermophysical properties are not negligible and should be expected to have an influence on the results of the laser powder bed fusion additive manufacturing process. When simulating this process in the future, measures should be taken to account for this discrepancy and the uncertainty involved

Justin Martin↗
NASA NTRS2004

Advanced Technology Lifecycle Analysis System (ATLAS)

Developing credible mass and cost estimates for space exploration and development architectures require multidisciplinary analysis based on physics calculations, and parametric estimates derived from historical systems. Within the National Aeronautics and Space Administration (NASA), concurrent engineering environment (CEE) activities integrate discipline oriented analysis tools through a computer network and accumulate the results of a multidisciplinary analysis team via a centralized database or spreadsheet Each minute of a design and analysis study within a concurrent engineering environment is expensive due the size of the team and supporting equipment The Advanced Technology Lifecycle Analysis System (ATLAS) reduces the cost of architecture analysis by capturing the knowledge of discipline experts into system oriented spreadsheet models. A framework with a user interface presents a library of system models to an architecture analyst. The analyst selects models of launchers, in-space transportation systems, and excursion vehicles, as well as space and surface infrastructure such as propellant depots, habitats, and solar power satellites. After assembling the architecture from the selected models, the analyst can create a campaign comprised of missions spanning several years. The ATLAS controller passes analyst specified parameters to the models and data among the models. An integrator workbook calls a history based parametric analysis cost model to determine the costs. Also, the integrator estimates the flight rates, launched masses, and architecture benefits over the years of the campaign. An accumulator workbook presents the analytical results in a series of bar graphs. In no way does ATLAS compete with a CEE; instead, ATLAS complements a CEE by ensuring that the time of the experts is well spent Using ATLAS, an architecture analyst can perform technology sensitivity analysis, study many scenarios, and see the impact of design decisions. When the analyst is satisfied with the system configurations, technology portfolios, and deployment strategies, he or she can present the concepts to a team, which will conduct a detailed, discipline-oriented analysis within a CEE. An analog to this approach is the music industry where a songwriter creates the lyrics and music before entering a recording studio.

O'Neil, Daniel A.↗
NASA NTRS2004

Automated Processing of ISIS Topside Ionograms into Electron Density Profiles

Modeling of the topside ionosphere has for the most part relied on just a few years of data from topside sounder satellites. The widely used Bent et al. (1972) model, for example, is based on only 50,000 Alouette 1 profiles. The International Reference Ionosphere (IRI) (Bilitza, 1990, 2001) uses an analytical description of the graphs and tables provided by Bent et al. (1972). The Alouette 1, 2 and ISIS 1, 2 topside sounder satellites of the sixties and seventies were ahead of their times in terms of the sheer volume of data obtained and in terms of the computer and software requirements for data analysis. As a result, only a small percentage of the collected topside ionograms was converted into electron density profiles. Recently, a NASA-funded data restoration project has undertaken and is continuing the process of digitizing the Alouette/ISIS ionograms from the analog 7-track tapes. Our project involves the automated processing of these digital ionograms into electron density profiles. The project accomplished a set of important goals that will have a major impact on understanding and modeling of the topside ionosphere: (1) The TOPside Ionogram Scaling and True height inversion (TOPIST) software was developed for the automated scaling and inversion of topside ionograms. (2) The TOPIST software was applied to the over 300,000 ISIS-2 topside ionograms that had been digitized in the fkamework of a separate AISRP project (PI: R.F. Benson). (3) The new TOPIST-produced database of global electron density profiles for the topside ionosphere were made publicly available through NASA s National Space Science Data Center (NSSDC) ftp archive at . (4) Earlier Alouette 1,2 and ISIS 1, 2 data sets of electron density profiles from manual scaling of selected sets of ionograms were converted fiom a highly-compressed binary format into a user-friendly ASCII format and made publicly available through nssdcftp.gsfc.nasa.gov. The new database for the topside ionosphere established as a result of this project, has stimulated a multitude of new studies directed towards a better description and prediction of the topside ionosphere. Marinov et al. (2004) developed a new model for the upper ion transition height (Oxygen to Hydrogen and Helium) and Bilitza (2004) deduced a correction term for the I N topside electron density model. Kutiev et al. (2005) used this data to develop a new model for the topside ionosphere scale height (TISH) as a function of month, local time, latitude, longitude and solar flux F10.7. Comparisons by Belehaki et al. (2005) show that TISH is in general agreement with scale heights deduced from ground ionosondes but the model predicts post-midnight and afternoon maxima whereas the ionosonde data show a noon maximum. Webb and Benson (2005) reported on their effort to deduce changes in the plasma temperature and ion composition from changes in the topside electron density profile as recorded by topside sounders. Limitations and possible improvements of the IRI topside model were discussed by Coisson et al. (2005) including also the possible use of the NeQuick model, Our project progressed in close collaboration and coordination with the GSFC team involved in the ISIS digitization effort. The digitization project was highly successful producing a large amount of digital topside ionograms. Several no-cost extensions of the TOPIST project were necessary to keep up with the pace and volume of the digitization effort.

Reinisch, bodo W.↗
NASA NTRS2016

Automated ISS Flight Utilities

During my internship at NASA Johnson Space Center, I worked in the Space Radiation Analysis Group (SRAG), where I was tasked with a number of projects focused on the automation of tasks and activities related to the operation of the International Space Station (ISS). As I worked on a number of projects, I have written short sections below to give a description for each, followed by more general remarks on the internship experience. My first project is titled "General Exposure Representation EVADOSE", also known as "GEnEVADOSE". This project involved the design and development of a C++/ ROOT framework focused on radiation exposure for extravehicular activity (EVA) planning for the ISS. The utility helps mission managers plan EVAs by displaying information on the cumulative radiation doses that crew will receive during an EVA as a function of the egress time and duration of the activity. SRAG uses a utility called EVADOSE, employing a model of the space radiation environment in low Earth orbit to predict these doses, as while outside the ISS the astronauts will have less shielding from charged particles such as electrons and protons. However, EVADOSE output is cumbersome to work with, and prior to GEnEVADOSE, querying data and producing graphs of ISS trajectories and cumulative doses versus egress time required manual work in Microsoft Excel. GEnEVADOSE automates all this work, reading in EVADOSE output file(s) along with a plaintext file input by the user providing input parameters. GEnEVADOSE will output a text file containing all the necessary dosimetry for each proposed EVA egress time, for each specified EVADOSE file. It also plots cumulative dose versus egress time and the ISS trajectory, and displays all of this information in an auto-generated presentation made in LaTeX. New features have also been added, such as best-case scenarios (egress times corresponding to the least dose), interpolated curves for trajectories, and the ability to query any time in the EVADES output. As mentioned above, GEnEVADOSE makes extensive use of ROOT version 6, the data analysis framework developed at the European Organization for Nuclear Research (CERN), and the code is written to the C++11 standard (as are the other projects). My second project is the Automated Mission Reference Exposure Utility (AMREU).Unlike GEnEVADOSE, AMREU is a combination of three frameworks written in both Python and C++, also making use of ROOT (and PyROOT). Run as a combination of daily and weekly cron jobs, these macros query the SRAG database system to determine the active ISS missions, and query minute-by-minute radiation dose information from ISS-TEPC (Tissue Equivalent Proportional Counter), one of the radiation detectors onboard the ISS. Using this information, AMREU creates a corrected data set of daily radiation doses, addressing situations where TEPC may be offline or locked up by correcting doses for days with less than 95% live time (the total amount time the instrument acquires data) by averaging the past 7 days. As not all errors may be automatically detectable, AMREU also allows for manual corrections, checking an updated plaintext file each time it runs. With the corrected data, AMREU generates cumulative dose plots for each mission, and uses a Python script to generate a flight note file (.docx format) containing these plots, as well as information sections to be filled in and modified by the space weather environment officers with information specific to the week. AMREU is set up to run without requiring any user input, and it automatically archives old flight notes and information files for missions that are no longer active. My other projects involve cleaning up a large data set from the Charged Particle Directional Spectrometer (CPDS), joining together many different data sets in order to clean up information in SRAG SQL databases, and developing other automated utilities for displaying information on active solar regions, that may be used by the space weather environment officers to monitor solar activity. I consulted my mentor Dr. Ryan Rios and Dr. Kerry Lee for project requirements and added features, and ROOT developer Edmond Offermann for advice on using the ROOT library. I also received advice and feedback from Dr. Janet Barzilla of SRAG, who tested my code. Besides these inputs, I worked independently, writing all of the code by myself. The code for all these projects is documented throughout, and I have attempted to write it in a modular format. Assuming that ROOT is updated accordingly, these codes are also Y2038-compliant (and Y10K-compliant). This allows the code to be easily referenced, modified and possibly repurposed for non-ISS missions in the future, should the necessary inputs exist. These projects have taught me a lot about coding and software design - I have become a much more skilled C++ programmer and ROOT user, and I also learned to code in Python and PyROOT (and its advantages and disadvantages compared to C++/ ROOT). Furthermore, I have learned about space radiation and radiation modeling, topics that greatly interest me as I pursue a degree in physics. Working alongside experimental physicists like Dr. Rios, I have developed a greater understanding and appreciation for experimental science, something I have always leaned towards but to which I lacked significant exposure. My work in SRAG has also given me the invaluable opportunity to witness the work environment for physicists at NASA, and what a career in academia may look like at a government laboratory such as NASA Johnson Space Center. As I continue my studies and look forward to graduate school and a future career, this experience at NASA has given me a meaningful and enjoyable opportunity to put my skills to use and see what my future career path might hold.

Offermann, Jan Tuzlic↗
NASA NTRS1993

Long Duration Exposure Facility (LDEF) experiment M0003 meteoroid and debris survey

A survey of the meteoroid and space debris impacts on LDEF experiment M0003 was performed. The purpose of this survey was to document significant impact phenomenology and to obtain impact crater data for comparison to current space debris and micrometeoroid models. The survey consists of the following: photomicrographs of significant impacts in a variety of material types; accurate measurements of impact crater coordinates and dimensions for selected experiment surfaces; and databasing of the crater data for reduction, manipulation, and comparison to models. Large area surfaces that were studied include the experiment power and data system (EPDS) sunshields, environment exposure control canister (EECC) sunshields, and the M0003 signal conditioning unit (SCU) covers. Crater diameters down to 25 microns were measured and cataloged. Both leading (D8) and trailing (D4) edge surfaces were studied and compared. The EPDS sunshields are aluminum panels painted with Chemglaze A-276 white thermal control paint, the EECC sunshields are chromic acid-anodized aluminum, and the SCU covers are aluminum painted with S13GLO white thermal control paint. Typical materials that have documented impacts are metals, glasses and ceramics, composites, polymers, electronic materials, and paints. The results of this survey demonstrate the different response of materials to hypervelocity impacts. Comparison of the survey data to curves derived from the Kessler debris model and the Cour-Palais micrometeoroid model indicates that these models overpredict small impacts (less than 100 micron) and may underpredict large impacts (greater than 1000 micron) while having fair to good agreement for the intermediate impacts. Comparison of the impact distributions among the various surfaces indicates significant variations, which may be a function of material response effects, or in some cases surface roughness. Representative photographs and summary graphs of the impact data are presented.

Meshishnek, M. J.↗
NASA NTRS1993

An experimental study of the sources of fluctuating pressure loads beneath swept shock/boundary-layer interactions

An experimental research program providing basic knowledge and establishing a database on the fluctuating pressure loads produced on aerodynamic surfaces beneath three dimensional shock wave/boundary layer interactions is described. Such loads constitute a fundamental problem of critical concern to future supersonic and hypersonic flight vehicles. A turbulent boundary layer on a flat plate is subjected to interactions with swept planar shock waves generated by sharp fins at angle of attack. Fin angles from 10 to 20 deg at freestream Mach numbers of 3 and 4 produce a variety of interaction strengths from weak to very strong. Miniature Kulite pressure transducers flush-mounted in the flat plate are used to measure interaction-induced wall pressure fluctuations. The distributions of properties of the pressure fluctuations, such as their ring levels, amplitude distributions, and power spectra, are also determined. Measurements were made for the first time in the aft regions of these interactions, revealing fluctuating pressure levels as high as 160 dB. These fluctuations are dominated by low frequency (0-5 kHz) signals. The maximum ring levels in the interactions show an increasing trend with increasing interaction strength. On the other hand, the maximum ring levels in the forward portion of the interactions decrease linearly with increasing interaction sweep back. These ring pressure distributions and spectra are correlated with the features of the interaction flowfield. The unsteadiness of the off-surface flowfield is studied using a new, non-intrusive technique based on the shadow graph method. The results indicate that the entire lambda-shock structure generated by the interaction undergoes relatively low-frequency oscillations. Some regions where particularly strong fluctuations are generated were identified. Fluctuating pressure measurements are also made along the line of symmetry of an axisymmetric jet impinging upon a flat plate at an angle. This flow was chosen as a simple analog to the impinging jet region found in the rear portion of the shock wave/boundary layer interactions under study. It is found that a sharp peak in ring pressure level exists at or near the mean stagnation point. It is suggested that the phenomena responsible for this peak may be active in the swept interactions as well, and may cause the extremely high fluctuating pressures observed in the impinging jet region in the present experimental program.

Settles, G. S.↗
NASA NTRSDate not supplied

LSKnowledge: Nexus for Transformative Scientific Discoveries and Enhanced Information Retrieval in NASA Life Sciences Portal

We stand at the brink of an extraordinary transformation in the field of AI, driven by the convergence of generative AI and semantic technologies (e.g., knowledge graphs). This fusion holds immense potential and could redefine the future of scientific exploration, particularly in the realm of life sciences research. In this context, we shed light on the pivotal roles that Large Language Models (LLMs) and semantic technologies will play in advancing research, unearthing and comprehending life sciences information through innovative approaches, and empowering researchers to extract insights from NASA's extensive Life Sciences Data Archive. Within the NASA Life Sciences Portal (NLSP), the integration of LLMs and semantic technologies unlocks several advanced capabilities. First and foremost, it equips scientists with sophisticated tools to manage the ever-expanding wealth of scientific literature and data. Furthermore, it facilitates the creation of knowledge graphs that visually represent intricate relationships among biological entities, enabling comprehensive systems-level analysis. Additionally, the fusion of generative AI (including LLMs) and semantic technology can significantly benefit NASA's life sciences research by enhancing information retrieval and hypothesis generation. These tools enhance natural language understanding, facilitating knowledge discovery within NLSP. The overarching vision is to establish a cohesive knowledge ecosystem within NLSP, harnessing the power of LLMs and semantic technologies to synthesize and cross-reference data from diverse missions, disciplines, and research domains. This holistic approach ultimately deepens our understanding of how space environments impact life sciences data. To advance this initiative, we have launched LSKnowledge, aimed at enhancing the information retrieval capabilities of NLSP. In the short term, our primary goal is to develop a robust semantic search system. This system will empower HRP (Human Research Program) researchers to navigate NLSP data repositories more efficiently and precisely, catalyzing the process of hypothesis formation and scientific breakthroughs. To achieve this, we have employed pre-trained LLMs as part of a semantic search tool that can rank and highlight the most relevant records for user queries. To assess the tool's performance, we have curated a set of approximately 200 queries from subject matter experts (SMEs) and manually ranked the top records retrieved by both the current search system and the new semantic search, using SME judgments as the gold standard for relevancy. Herein, we present the results of our comparative analysis and illustrate how these findings have informed the fine-tuning of the system for enhanced performance. In the long term, our objectives include 1) retrieving publicly available information and integrating it with NLSP data to provide more precise answers to user queries, and 2) incorporating non-textual information from the NLSP database into our approach. In conclusion, the fusion of LLMs and semantic technologies within NLSP represents a pioneering stride towards reshaping the landscape of scientific discovery. This synergy not only equips researchers with powerful tools to navigate the burgeoning sea of information but also facilitates a deeper understanding of complex biological relationships, all while accelerating hypothesis generation and knowledge discovery. Through our initiative, LSKnowledge, we are committed to continually refining and expanding these capabilities, with the aim of not only enhancing information retrieval but also integrating diverse data sources to provide more precise insights. In the grand vision, NLSP strives to become the cornerstone of a comprehensive knowledge ecosystem, unraveling the enigmatic intricacies of life sciences phenomena in the context of space environments.

Life Sciences↗