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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 NTRS2007

Accident/Mishap Investigation System

InvestigationOrganizer (IO) is a Web-based collaborative information system that integrates the generic functionality of a database, a document repository, a semantic hypermedia browser, and a rule-based inference system with specialized modeling and visualization functionality to support accident/mishap investigation teams. This accessible, online structure is designed to support investigators by allowing them to make explicit, shared, and meaningful links among evidence, causal models, findings, and recommendations.

Keller, Richard↗

NASA NTRSDate not supplied

Ramdb: The NASA Raman Spectral Database (version 1.00).

Given that, in most instances, minimal sample preparation is required and due to its contactless instrument design, Raman spectroscopy is one of the most versatile vibrational spectroscopic techniques for the chemical analysis of environmental and biological specimens. The diversity of applications of Raman spectroscopy ranges anywhere from art [1] to planetary science missions [2]. The advancement in the use of Raman spectroscopy in Solar System missions, notably in post-mission sample return analysis, requires a spectral library holding the broad range of specimens that could be found in Solar System sources. For this purpose, we have initiated the development of a Raman spectral database (Ramdb) at NASA Ames Research Center. Currently, the database includes experimental and theoretical Raman spectra of PAHs [3, 4], as well as laboratory Raman spectra of amino acids, carbon allotropes, minerals, and analogs relevance to Earth Sciences [5], Exobiology [6], Planetary [7], and Astrochemistry [8] to name just a few examples. Ramdb can be found on the web at www.astrochemistry.org/ramdb, where raw and processed Raman spectra can be downloaded in CSV format. The laboratory Raman spectra are measured using a laser Raman spectrometer (JASCO NRS-5500-532QRI). The Raman instrument is equipped with three excitation lasers, with wavelengths of 405, 532, and 785 nm. A clean silicon substrate is used as the internal standard for wavenumber calibration. Powdered samples were prepared (microscopic >10 um, grounded microscopic < 10 um) on glass slides. Some raw data exhibited a background signal arising as a combination of laser-induced fluorescence from the sample. To correct this background, we developed a Python pipeline that uses open-source Python libraries. Ramdb provides both raw and processed (using Python pipeline) data, which includes tabulated Raman shift transitions and other measurement details. The theoretical Raman band positions of PAHs (pyrene monomers and tetramer clusters) were computed using density functional theory (DFT) with the help of the Gaussian 16 suite of programs [9]. In the near future, Ramdb will serve as a repository of Raman spectral data from Laboratory Astrophysics and Planetary Science experiments involving the irradiation of organic compounds under simulated space and planetary conditions. In addition, online and offline tools will be developed for utilising the database for comparison to the user’s sample.

N Punnakayathil↗

NASA NTRS2003

Mars Data Visualization and E/PO with Marsoweb

Marsoweb is a collaborative web environment that has been developed for the Mars research community to better visualize and analyze Mars orbiter data. Its goal is to enable online data discovery by providing an intuitive, interactive interface to data from the Mars Global Surveyor and other orbiters. Recently Marsoweb has served a prominent role as a resource center for the site selection process for the Mars Explorer Rover 2003 missions. In addition to hosting a repository of landing site memoranda and workshop talks, it includes a Java-based interface to a variety of datamaps and images. This interface enables the display and numerical querying of data, and allows data profiles to be rendered from user-drawn cross-sections. High-resolution Mars Orbiter Camera (MOC) images (currently, over 100,000) can be graphically perused; browser-based image processing tools can be used on MOC images of potential landing sites. An automated VRML atlas allows users to construct 'flyovers' of their own regions-of-interest in 3D. These capabilities enable Marsoweb to be used for general global data studies, in addition to those specific to landing site selection. As of September 2002, over 70,000 distinct users from NASA, USGS, academia, and the general public have accessed Marsoweb.

Gulick, V. C.↗

NASA NTRS2017

Astromaterials Curation Online Resources for Principal Investigators

The Astromaterials Acquisition and Curation office at NASA Johnson Space Center curates all of NASA's extraterrestrial samples, the most extensive set of astromaterials samples available to the research community worldwide. The office allocates ~1500 individual samples to researchers and students each year and has served the planetary research community for 45+ years. The Astromaterials Curation office provides access to its sample data repository and digital resources to support the research needs of sample investigators and to aid in the selection and request of samples for scientific study. These resources can be found on the Astromaterials Acquisition and Curation website at https://curator.jsc.nasa.gov. To better serve our users, we have engaged in several activities to enhance the data available for astromaterials samples, to improve the accessibility and performance of the website, and to address user feedback. We havealso put plans in place for continuing improvements to our existing data products.

Todd, Nancy S.↗

NASA NTRS2018

Planetary Spectrum Generator: An Accurate Online Radiative Transfer Suite for Atmospheres, Comets, Small Bodies and Exoplanets

We have developed an online radiative-transfer suite (https://psg.gsfc.nasa.gov) applicable to a broad range of planetary objects (e.g., planets, moons, comets, asteroids, TNOs, KBOs, exoplanets). The Planetary Spectrum Generator (PSG) can synthesize planetary spectra (atmospheres and surfaces) for a broad range of wavelengths (UV/Vis/near-IR/IR/far-IR/THz/sub-mm/Radio) from any observatory (e.g., JWST, ALMA, Keck, SOFIA), any orbiter (e.g., ExoMars, Juno), or any lander (e.g., MSL). This is achieved by combining several state-of-the-art radiative transfer models, spectroscopic databases and planetary databases (i.e., climatological and orbital). PSG has a 3D (three-dimensional) orbital calculator for most bodies in the solar system, and all confirmed exoplanets, while the radiative-transfer models can ingest billions of spectral signatures for hundreds of species from several spectroscopic repositories. It integrates the latest radiative-transfer and scattering methods in order to compute high resolution spectra via line-by-line calculations, and utilizes the efficient correlated-k method at moderate resolutions, while for computing cometary spectra, PSG handles non-LTE and LTE excitation processes. PSG includes a realistic noise calculator that integrates several telescope/instrument configurations (e.g., interferometry, coronagraphs) and detector technologies (e.g., CCD, heterodyne detectors, bolometers). Such an integration of advanced spectroscopic methods into an online tool can greatly serve the planetary community, ultimately enabling the retrieval of planetary parameters from remote sensing data, efficient mission planning strategies, interpretation of current and future planetary data, calibration of spectroscopic data, and development of new instrument/spacecraft concepts.

Villanueva, G. L.↗

NASA NTRS2016

Using Cloud-Based Storage Technologies for Earth Science Data

Cloud based infrastructure may offer several key benefits of scalability, built in redundancy and reduced total cost of ownership as compared with a traditional data center approach. However, most of the tools and software systems developed for NASA data repositories were not developed with a cloud based infrastructure in mind and do not fully take advantage of commonly available cloud-based technologies. Object storage services are provided through all the leading public (Amazon Web Service, Microsoft Azure, Google Cloud, etc.) and private (Open Stack) clouds, and may provide a more cost-effective means of storing large data collections online. We describe a system that utilizes object storage rather than traditional file system based storage to vend earth science data. The system described is not only cost effective, but shows superior performance for running many different analytics tasks in the cloud. To enable compatibility with existing tools and applications, we outline client libraries that are API compatible with existing libraries for HDF5 and NetCDF4. Performance of the system is demonstrated using clouds services running on Amazon Web Services.

Data↗

NASA NTRS2019

Integrated support of NASA satellite and in situ oceanographic data via the PO.DAAC

The NASA Physical Oceanography DAAC (PO.DAAC) serves as one of the premier repositories for oceanographic satellite data. More recently, however, it is also increasingly archiving and distributing complementary in situ datasets from NASA-sponsored field campaigns. Here we present an overview of these projects, the complex multivariate data they produce, and some of the data interoperability challenges faced when dealing with such a heterogeneous suite of observations. We summarize the range of online tools and services currently available via the PODAAC, including data discovery services, web-services for subsetting/extraction, compliance checking and visualization. We also preview some new capabilities under development that may feature in future. These efforts are indicative of an evolution of DAAC services in pursuit of our broader vision: a more integrated approach to multi-sensor oceanographic data access and delivery spanning NASA satellite missions and field campaigns in support of science and applications for societal benefit.

Vannan, Suresh↗

NASA NTRSDate not supplied

Enhancing NASA Earth Science Data Discovery from Scientific Publications

Earth observations from space borne instruments have evolved explosively in the past decades. Following closely are reanalysis systems assimilating model and observational data, yielding even longer records and larger number of variables. Thanks to advances in internet technology, it is now easier than ever to visualize and analyze these data using web interfaces. On the other hand, it also becomes an increasingly daunting task to build upon the existing knowledge published in various peer reviewed sources, and navigate toward the most relevant data, analysis, and visualization. We present an analysis of a subset of publications that utilized a popular visualization web interface at the NASA Goddard Earth Science Data and Information Services Center. Known as "Giovanni", it allows researchers from wide backgrounds to work with hundreds of variables from space observations and assimilation systems. Since coming online more than a decade ago, Giovanni has been credited in more than 100 papers per year, and the total count now is estimated to be nearly 1,500. Many of these papers contain valuable information about when, where and how Giovanni has been used, and hence forge an opportunity to learn and share the knowledge of which variables were used for what research projects. The purpose of our work is to retrieve the information from the papers and organize it as a knowledge repository which links together datasets, variables, places, dates and phenomena all of which reflect the essence of the published research. Since the publications are unstructured texts, we use natural language processing along with machine learning methods in the retrieval process. One of the challenges is deciphering the dataset names, because in many cases researchers refer to variables, rather than the datasets containing them. To constrain the number of terms, we deploy Earth Science ontologies as dictionaries for the term extraction. We demonstrate that storing these terms and underlying ontologies, along with datasets, variables and papers in the knowledge graph database, enables various linkages between all these entities facilitating the data discovery. Thus, we are setting a qualitatively new stage in improvements of web data interfaces, where machine learning techniques are used to establish and optimize usage-based discovery of data.

Irina V Gerasimov↗

NASA NTRSDate not supplied

Beyond Fair: Engagement, Data Usability, and Open Community Productivity through the NASA Open Science Data Repository

The FAIR principle (findable, accessible, interoperable, and reusable) governs the storage and sharing of NASA space biology and health data[1]. These guiding principles maximize reuse of data and the reproducibility of scientific findings. The NASA Open Science Data Repository (OSDR; an expansion of NASA GeneLab) was built on the FAIR principles and houses over 500 studies and close to 1000 datasets from decades of space life sciences experiments. OSDR embodies the FAIR principles through data governance that includes mediated, embargoed, and fully open access data. The FAIR data governance principles were recently proposed to be expanded to encompass a FAIREST framework for assessing research data repositories (FAIR + Engagement, Social connections, and Trust)[2]. FAIREST emphasizes the importance of data repositories engaging with the scientific community and gaining the trust of researchers regarding data quality. Trust also refers to the TRUST principles developed for assessment of digital repositories: Transparency, Responsibility, User Focus, Sustainability, Technology[3]. We present the “Open Science for Life in Space” Analysis Working Groups (AWGs) as evidence regarding the power of engagement, social connections, and trust which has enhanced OSDR’s capabilities and productivity. AWG members engage in two main activities. One, members provide feedback on OSDR scientific standards for data ingestion, curation, and reuse (study, subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability). Two, AWG members collaborate to mine-reuse OSDR data to conduct scientific analysis. With nearly 800 active members, the AWGs have resulted in 32 publications re-using OSDR data and contributed many papers in two major special issues in Cell (2020) and Nature (2024). AWGs also serve as networking groups, facilitate social connections between researchers at all levels of experience, and also have a social online ‘Forum’ used to keep members informed on projects and opportunities. This community-centric, productive, and trustworthy data culture has resulted in a broader effect with international space agencies, academics, and the commercial space sector wanting to submit their data to OSDR. Ten studies of Inspiration 4 data were recently publicly released by OSDR, as were some JAXA human data. Coming up soon in OSDR are data submissions from the European Space Agency, Virgin Galactic PIs, and SpaceX Polaris Dawn. A major benefit of OSDR is the array of standardized and uniformly formatted data (which was developed through AWG member consensus), from which visualization tools, analysis tools, and machine learning models can be built or trained. This talk will cover the Multi-Study Visualization Tool, the Environmental Data Application, RadLab, and a UCSF-NSF funded knowledge graph biomedical health discovery tool ‘SPOKE’ currently being integrated with OSDR. OSDR also provides training programs in bioinformatics and machine learning to improve the scientific community’s awareness of data availability and to boost their ability to perform data analysis. The increasing engagement of the scientific community and the public with technologies powered by artificial intelligence (AI) heightens the need for data analysis to be transparent. The AI for Life in Space initiative leverages the data products provided in OSDR to train AI models, with an emphasis on explainable and trustworthy AI, which would not be possible without FAIR data and metadata. Overall, here we will demonstrate the importance for NASA life sciences data repositories to adhere to the FAIREST framework, by providing examples and success stories from different aspects of OSDR.

data↗

NASA NTRS2018

Analysis and Review of NASA Earth Science Metadata: How Automation Plays a Role

The Analysis and Review of the Common Metadata Repository (CMR ARC) Team reviews all EOSDIS metadata. The team’s objective is to achieve consistency, correctness, and completeness for all metadata records in the CMR, as well as improve the discoverability of NASA's Earth Science data within the CMR framework. This work is currently being completed at Marshall Space Flight Center. CMR makes a single discovery point possible for NASA's Earth Science data users. The CMR team, in collaboration with three other core metadata teams, contributes to the stewardship of NASA's Earth Science data through a process of continual curation and the ongoing development of the Unified Metadata Model (UMM). A key tool now used in the curation process, referred to as the NASA CMR Dashboard, is an online curation dashboard developed in collaboration with software development company, Element 84. This tool facilitates the review of Earth Science metadata records and subsequent stakeholder collaboration on the resolution of identified issues. A key capability of the new tool is a suite of automated compliance checks written in Python 3.6 that verify the integrity of various metadata elements across multiple standards.

Staton, Patrick↗

NASA NTRS2002

Ocean Color Data at the Goddard Earth Sciences (GES) DAAC: CZCS, SeaWiFS, OCTS, MODIS-Terra, MODIS-Aqua

The Goddard Earth Sciences Distributed Active Archive Center (DAAC) is the designated archive for all of the ocean color data produced by NASA satellite missions. The DAAC is a long-term, high volume, secure repository for many different kinds of environmental data. With respect to ocean color, the Goddard DAAC holds all the data obtained during the eight-year mission of the Coastal Zone Color Scanner (CZCS). The DAAC is currently receiving data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and the MODIS-Terra instrument. The DAAC recently received reformatted data from the Ocean Color and Temperature Scanner (OCTS) and will also archive MODIS-Aqua Ocean products. In addition to its archive and distribution services, the Goddard DAAC strives to improve data access, ease-of-use, and data applicability for a broad spectrum of customers. The DAAC's data support teams practice dual roles, both insuring the integrity of the DAAC data archive and serving the user community with answers to user inquiries, online and print documentation, and customized data services.

Source record↗

NASA NTRSDate not supplied

Application of ML/AI for Identifying Earth Science Datasets in Research Publications

NASA Data Active Archive Centers, or DAACs, ingest, store and distribute data acquired from satellites, ground systems as well as modelling data. These data are organized by the datasets, each presenting collection of files usually associated with the certain mission, instrument, processing level, parameter(s), algorithm and/or model. The number of datasets offered by a single DAAC to the public varies. GES DISC, for example, currently offers for public use approximately ~1,300 datasets. While each publicly offered dataset comes with supporting documentation, it is challenging for novice and even experienced scientists to navigate among the datasets that offer similar parameters to find the datasets for their particular research application. Supplying dataset documentation with the scientific paper citations that refer to that dataset provides means for the dataset users to educate themselves with the application research that dataset is being used in. Collecting citations of the papers that use the datasets for their research yield valuable insights into application areas of those datasets, information about usage of the dataset groups for specific applications and those application topics. It also gives insights into the “deep metrics” of the dataset usage, as opposed to the common metrics of the dataset usage such as number of users who downloaded the dataset files and volumes of downloaded data. Association of a certain scientific paper with the dataset(s) presents a challenge because most of the paper authors do not properly cite the datasets, datasets usually have cryptic names and Digital Object Identifiers (DOIs) that are used for dataset identification were assigned to the datasets only few years ago. Simple Google or online library search do not provide even meaningful fraction of the results when performed by the dataset name or DOI, however they provide too many results when the search is done by more broader terms such as mission and instrument names. Attempts to create an AI system capable to identify dataset in the scientific papers have already been made using neural networks classifiers on the basis of the dataset mission, instrument and variable name. This method was applied to NASA SEDAC, which has 41 datasets in total. In GES DISC there can be as many as ~100 datasets per mission/instrument with some of the datasets consisting of multiple variables so there is a need for more differentiating parameters for dataset identification in the paper. The approach we are currently investigating is creating AI classifiers that are based on multiple dataset features, or keywords, extracted from the NASA Earthdata Common Dataset Repository (CMR). The features are weighted based on how precisely they can identify a dataset. The classifier uses preprocessed paper text as input and searches for the CMR datasets whose feature sets are the closest to the feature sets contained in the paper. The challenges of dataset identification include variety of ways the paper authors describe the datasets in their papers and incomplete tagging of the CMR dataset description (DIFs).

Irina Gerasimov↗

NASA NTRS2002

A Virtual Mission Operations Center: Collaborative Environment

The Virtual Mission Operations Center - Collaborative Environment (VMOC-CE) intent is to have a central access point for all the resources used in a collaborative mission operations environment to assist mission operators in communicating on-site and off-site in the investigation and resolution of anomalies. It is a framework that as a minimum incorporates online chat, realtime file sharing and remote application sharing components in one central location. The use of a collaborative environment in mission operations opens up the possibilities for a central framework for other project members to access and interact with mission operations staff remotely. The goal of the Virtual Mission Operations Center (VMOC) Project is to identify, develop, and infuse technology to enable mission control by on-call personnel in geographically dispersed locations. In order to achieve this goal, the following capabilities are needed: Autonomous mission control systems Automated systems to contact on-call personnel Synthesis and presentation of mission control status and history information Desktop tools for data and situation analysis Secure mechanism for remote collaboration commanding Collaborative environment for remote cooperative work The VMOC-CE is a collaborative environment that facilitates remote cooperative work. It is an application instance of the Virtual System Design Environment (VSDE), developed by NASA Goddard Space Flight Center's (GSFC) Systems Engineering Services & Advanced Concepts (SESAC) Branch. The VSDE is a web-based portal that includes a knowledge repository and collaborative environment to serve science and engineering teams in product development. It is a "one stop shop" for product design, providing users real-time access to product development data, engineering and management tools, and relevant design specifications and resources through the Internet. The initial focus of the VSDE has been to serve teams working in the early portion of the system/product lifecycle - concept development, proposal preparation, and formulation. The VMOC-CE expands the application of the VSDE into the operations portion of the system lifecycle. It will enable meaningful and real-time collaboration regardless of the geographical distribution of project team members. Team members will be able to interact in satellite operations, specifically for resolving anomalies, through access to a desktop computer and the Internet. Mission Operations Management will be able to participate and monitor up to the minute status of anomalies or other mission operations issues. In this paper we present the VMOC-CE project, system capabilities, and technologies.

Medina, Barbara↗

NASA NTRSDate not supplied

Metadata Entry Optimization for NASA's Biological Institutional Scientific Collection (NBISC)

The NASA Biological Institutional Sample Collection (NBISC) at NASA’s Ames Research Center is a critical resource housing non-human samples collected from spaceflight missions and ground analog studies, primarily consisting of specimens from rats, mice, and select microbes. The primary objective of NBISC is to systematically receive, document, preserve, and facilitate access to these samples for the global scientific community. NBISC promotes international collaboration and maximizes the return on investment for precious tissues from spaceflight and analog experiments. Researchers can request physical samples through an online request form and subsequent written proposal review process. This study addresses two core research objectives: streamlining the NBISC sample lifecycle processes and strategizing for managing an influx of 50,000 tissue samples from a series of cosmic radiation analog experiments carried out at the NASA Space Radiation Laboratory (NSRL) by Drs. Eleanor Chang (Lawrence Berkeley Laboratory) and Polly Blakely (SRI). The Chang/Blakely studies investigated Harderian gland (HG) tumorigenesis in mice exposed to low dose and LET radiation comprising 8 different exposure protocols in over 4000 mice. NBISC sample metadata is stored in a Laboratory Information Management System (SLIMS). To streamline sample data entry, we customize python scripts using information extracted from the individual experimental protocols. The scripts automate entry into multiple SLIMS data fields including protocol name, unique sample barcode, tissue and sub-tissue information, freezer location, sample preservation method, etc. The semi-automated procedure significantly decreases the time spent on data entry by several orders of magnitude. Automation and data organization are essential, as they free up time for curation and promotion of the collection which, in turn, increase the accessibility of samples to the broader research community. NBISC benefits from streamlined data ingestion, and the methodologies developed here are applicable to other projects which use SLIMS including the NASA Biospecimen Sharing Program and GeneLab. As of Fall 2023, plans include transferring sample data from SLIMS to public facing repositories (OSDR and NLSP), expanding the reach of the Chang/Blakely sample collection. The Human Research Program Space Radiation Element plans to transfer non-human tissues from many more investigations to NBISC in the coming year.

Sample Repository↗

NASA NTRSDate not supplied

Metadata Entry Optimization For NASA's Biological Institutional Scientific Collection (NBISC)

Biospecimen↗

NASA NTRSDate not supplied

Preserving NASA Historic and Current Mission Data and Adding Value to These for Future Researchers

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has been actively involved in many aspects of ensuring the long-term preservation of NASA earth science data and knowledge. This involves both the recovery and preservation of early NASA meteorological and other earth observation data, as well as preserving the more recent Earth Observation System (EOS) mission data sets which continue or have reached their end of lifetime. The GES DISC adds value to these preserved data by adding metadata and making the data available online to future researchers. The early NASA meteorological and earth observation data sets from the 1960s and 70s were originally archived on magnetic tapes, and visualizations of these data were preserved on 70-mm film. As these media have aged, their contents have been at risk of permanent loss. NASA has given the task of preserving these early data sets to the GES DISC and making these data sets easily available to the public. The data from these early missions are potentially useful to climate researchers as these are some of the only global measurements made at their time. These old data on magnetic tapes and film strips do not contain easily readable metadata, and so to add value the GES DISC has added digital metadata to them so that the data are searchable and findable. The GES DISC is also involved in preserving the data and knowledge from the EOS era missions. The GES DISC follows the guidelines developed for the preservation of data as specified in the NASA EOS Data and Information System (EOSDIS) Earth Science Data Preservation Content Specification (423-SPEC-001) document. To date, the GES DISC has consulted with the data science teams from the following missions: UARS, Earth Probe TOMS, Aura HIRDLS, and SORCE, in order to properly preserve their data and accompanying documentation. The GES DISC is also currently working with the EOS science teams from TRMM, AIRS, MLS, OMI and additional missions to ensure that the relevant documents and data sets are properly archived for future researchers. A standardized procedure for mission data preservation following 423-SPEC-001 makes preservation among the many NASA EOSDIS data centers uniform, so that these could be transitioned easily to a common EOSDIS preservation repository. This presentation will give an overview of the preservation and recovery of the old NASA historical data sets archived at the GES DISC, as well as the data and documentation preservation efforts of the EOS era missions.

James Johnson↗