Search NASA⌕ Search

SEARCH · Search NASA

Results for “metadata recommendations”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3

Long-Lasting Science Returns from the Apollo Heat Flow Experiments

The Apollo astronauts deployed geothermal heat flow instruments at landing sites 15 and 17 as part of the Apollo Lunar Surface Experiments Packages (ALSEP) in July 1971 and December 1972, respectively. These instruments continuously transmitted data to the Earth until September 1977. Four decades later, the data from the two Apollo sites remain the only set of in-situ heat flow measurements obtained on an extra-terrestrial body. Researchers continue to extract additional knowledge from this dataset by utilizing new analytical techniques and by synthesizing it with data from more recent lunar orbital missions such as the Lunar Reconnaissance Orbiter. In addition, lessons learned from the Apollo experiments help contemporary researchers in designing heat flow instruments for future missions to the Moon and other planetary bodies. For example, the data from both Apollo sites showed gradual warming trends in the subsurface from 1971 to 1977. The cause of this warming has been debated in recent years. It may have resulted from fluctuation in insolation associated with the 18.6-year-cycle precession of the Moon, or sudden changes in surface thermal environment/properties resulting from the installation of the instruments and the astronauts' activities. These types of reanalyses of the Apollo data have lead a panel of scientists to recommend that a heat flow probe carried on a future lunar mission reach 3 m into the subsurface, approx 0.6 m deeper than the depths reached by the Apollo 17 experiment. This presentation describes the authors current efforts for (1) restoring a part of the Apollo heat flow data that were left unprocessed by the original investigators and (2) designing a compact heat flow instrument for future robotic missions to the Moon. First, at the conclusion of the ALSEP program in 1977, heat flow data obtained at the two Apollo sites after December 1974 were left unprocessed and not properly archived through NASA. In the following decades, heat flow data from January 1975 through February 1976, as well as the metadata necessary for processing the data (the data reduction algorithm, instrument calibration data, etc.), were somehow lost. In 2010, we located 450 original master archival tapes of unprocessed data from all the ALSEP instruments for a period of April through June 1975 at the Washington National Records Center. We are currently extracting the heat flow data packets from these tapes and processing them. Second, on future lunar missions, heat flow probes will likely be deployed by a network of small robotic landers, as recommended by the latest Decadal Survey of the National Academy of Science. In such a scenario, the heat flow probe must be a compact system, and that precludes use of heavy excavation equipment such as a rotary drill for reaching the 3-m target depth. The new heat flow system under development uses a pneumatically driven penetrator. It utilizes a stem that winds out of a reel and pushes its conical tip into the regolith. Simultaneously, gas jets, emitted from the cone tip, loosen and blow away the soil. Lab experiments have demonstrated its effectiveness in lunar vacuum.

Nagihara, S.↗

Outcomes of a NASA Workshop to Develop a Portfolio of Low Latency Datasets for Time-Sensitive Applications

It is widely accepted that time-sensitive remote sensing data serve the needs of decision makers in the applications communities and yet to date, a comprehensive portfolio of NASA low latency datasets has not been available. This paper will describe the NASA low latency, or Near-Real Time (NRT), portfolio, how it was developed and plans to make it available online through a portal that leverages the existing EOSDIS capabilities such as the Earthdata Search Client (https:search.earthdata.nasa.gov), the Common Metadata Repository (CMR) and the Global Imagery Browse Service (GIBS). This paper will report on the outcomes of a NASA Workshop to Develop a Portfolio of Low Latency Datasets for Time-Sensitive Applications (27-29 September 2016 at NASA Langley Research Center, Hampton VA). The paper will also summarize findings and recommendations from the meeting outlining perceived shortfalls and opportunities for low latency research and application science.

remote sensing↗

Challenges and Vision for Standardization of Biopolymer Data Sets for Machine Learning

Machine learning (ML) is transforming materials research, yet potential for biopolymer discovery remains constrained by fragmented data and nonstandardized reporting. Biopolymers differ significantly from synthetic polymers, requiring specialized approaches to represent their biosynthetic origins, hierarchical structures, and application-specific metrics. In this Perspective, we identify three core challenges limiting biopolymer representation: information encoding, data quality, and data sharing. We describe the most pressing issues and propose commensurate approaches to address each key challenge. Recommendations include the design and adoption of biopolymer-specific fingerprinting and representation frameworks, development of hybrid human-large language model (LLM) data extraction strategies, and expanding Findable, Accessible, Interoperable, Reusable (FAIR)-compliant repositories. We propose a robust foundation to define interoperable, high-quality data sets that capture the full context of biopolymer materials. Standardized metadata, shared ontologies, and community-driven infrastructure would enable scalable, reproducible workflows and accelerate the ML-driven development of biopolymers.

36 MATERIALS SCIENCE↗

Perspectives on Data Reproducibility and Replicability in Paleoclimate and Climate Science

This paper summarizes the current state of reproducibility and replicability in the fields of climate and paleoclimate science, including brief histories of their development and applications in climate science, new and recent approaches towards improvement of reproducibility and replicability, and challenges. Recommendations for addressing those challenges include: development of searchable, auto-updated, interlinked, multi-archive public paleoclimate repositories for raw and processed digital datasets; cross-center standardized code base cases, improved data storage techniques, and a focus on replicability for climate simulation storage and access; and support of the development and community awareness of findable, accessible, interoperable and reusable (FAIR) principles by funding agencies and publishers. This paper is largely based on the May 2018 presentations of a panel of researchers to the Committee on Reproducibility and Replicability in Science, part of the National Academies of Science, Engineering, and Medicine. The commentary and recommendations made here are in alignment with those of its Consensus Study Report on Reproducibility and Replicability in Science (2019).

data repositories↗

Metadata Standards for the NSE: Core Fields

This standard presents a core set of metadata fields required for each managed digital object within the Nuclear Security Enterprise (NSE). Metadata standardization is a critical enabler for two primary objectives: 1) effectively sharing data, documents, and other digital objects between NSE sites; and 2) supporting digital engineering through the digital thread at the object level. Standardization is necessary for both schemas and vocabularies, meaning that both field standards and value standards must be specified. This document serves as a foundational field standard, recommending a core set of fields that should be uniformly required for all managed digital objects within the NSE.

99 GENERAL AND MISCELLANEOUS↗

EGIS - An Environmental GIS Developed for NASA Field Center Applications

As the principal center for Environmental Geographic Information Systems (EGIS), the John C. Stennis Space Center (SSC), located in Hancock County, Mississippi, has been assigned technical support requirements to design and to implement a basic EGIS data base for all NASA Field Centers. The intent of this Phase I effort is to produce a baseline EGIS data base incorporating newly available remotely sensed data as well as existing environmental data. A example application of the use of the data base at Stennis Space Center will be to illustrate baseline environmental conditions for consideration with proposed propulsion test stand development and operation. To effectively answer questions related to environmental issues at each center, organization of the data layers and sources will include the following categories: Cadastral/Geodetic; Geopolitical; Hydrography; Infrastructure; Physical Geography; Socioeconomic; Remote Sensing Imagery; Associated Metadata. As part of a Phase II effort, site-specific data and applications will be implemented and added to the data base at each Field Center. This poster illustrates the framework of the design and implementation of a basic EGIS data base. Shown are example data sources, hardware and software, and data base delivery and installation. The poster also depicts future recommendations for a centrally located server to house each of the NASA Field Center data bases. The server will allow real-time data base updates with additional layers and models for each center. Expansion of the EGIS data base will continue to grow as site-specific applications are developed addressing the ongoing evolution of environmental concerns at all NASA Field Centers.

Smoot, James↗

Commercial Smallsat Data Acquisition Program On-ramp #2 Airbus U.S. Synthetic Aperture Radar (SAR) Evaluation Report

In 2017, NASA’s Earth Science Division (ESD) launched the Private-Sector Small Constellation Satellite Data Product Pilot, now referred to as the Commercial Smallsat Data Acquisition (CSDA) program. The objective of CSDA is to identify, evaluate, and acquire commercial remote sensing data that support NASA’s Earth science research and application activities. The Pilot successfully concluded in early 2020, when CSDA transitioned into a sustained program with on-ramping opportunities for new vendors as the industry emerges with new candidates and capabilities. In October 2019, a Request for Information (RFI) seeking capability statements from parties interested in providing data from spaceborne platforms was released for the CSDA on-ramp #2 evaluations. To be responsive to the RFI, the commercial satellite constellations had to consist of three or more operating spacecraft actively collecting data in a non-geostationary orbit with full latitudinal coverage and be U.S. companies. Two vendors responded to the RFI and were evaluated by a committee composed of NASA ESD leadership, program managers, and scientists. Both vendors satisfied the RFI requirements and were asked to respond to a Request for Proposal (RFP). After review of the proposals, NASA entered into a Blanket Purchase Agreement (BPA) with Airbus Defense and Space GEO, Inc. (Airbus) U.S. in September 2021 and with BlackSky Geospatial Solutions, Inc. (BlackSky) in November 2021. In this report, CSDA provides an evaluation of the usefulness of data provided by the Airbus U.S. Synthetic Aperture Radar (SAR) satellite constellation, consisting of TerraSAR-X (launched in 2007), TanDEM-X (launched in 2010), and PAZ (launched in 2018), for advancing NASA’s Earth system science research and applications. The evaluation of the BlackSky commercial data will be provided in a separate report. To conduct the Airbus evaluation, NASA’s ESD augmented 13 existing research projects that could potentially benefit from, and had the expertise to evaluate, the commercial data being considered for longer-term purchase. Investigators from NASA’s Research and Analysis Program science focus areas and from NASA’s Applied Sciences Program elements participated in the evaluation. A summary of the research areas evaluated by the Principal Investigator (PI) teams is presented in Figure 3. CSDA also funded a dedicated activity to evaluate the satellite data quality (calibration and geolocation) independently by assessing the accuracy of data from Airbus. Evaluation activities were carried out by the selected PIs from December 7, 2022, to December 7, 2023. Delivery of datasets requested by the researchers began in January 2023. The vendors were evaluated on the accessibility of data, accuracy and completeness of metadata, and promptness and quality of user support services. Datasets purchased during the evaluation have been archived by NASA and will be made available to current and future government-funded researchers in accordance with the End User License Agreement (EULA). This synthesis report distills and integrates the findings of research reports commissioned by NASA for the Airbus evaluation. This report also includes recommendations that inform the way ahead for the program. The scientific results from the evaluations demonstrated that the commercial data from Airbus were able to advance NASA research and applications. However, the PIs encountered limitations that diminished the usefulness of the data due to the amount of effort that was required to access, preprocess, and analyze these data. One significant issue encountered was the limited spatial and temporal coverage of the data in the Airbus archive that could be used to conduct time series analyses or assessments over large spatial scales. Overall, however, the utility and the quality of the evaluated data outweighed the difficulties encountered, and NASA has concluded that the Airbus SAR data would complement NASA’s existing Earth observation capabilities and Airbus U.S. would qualify to participate in the sustained phase of the program.

Batuhan Osmanoglu↗

Search Enhancements using Natural Language Processing Techniques

NASA Goddard Earth Sciences Data and Information Services Center (GESDISC) is one of the 12 NASA Science Mission Directorate Data Centers. The main goal of GESDISC is to provide earth science data, information, and services to the earth science data community. Consequently, data discovery is at the center of our mission and our search engine is the primary tool for our users to interact, find, and access our data. Existing search approaches are largely focused on hard-matching of keywords in the search query with dataset metadata. Here we propose to expand the search by introducing a complementary natural language processing (NLP) search. At the heart of our proposed NLP search, we trained a joint embedding using scientific text corpus and a curated set of dataset metadata. The embedding learns the association between words in our dataset metadata and those of the scientific text corpus. This enables us to go beyond simple hard-matching of a query and data set metadata and have a notion of “similarity” between the search query and the datasets. We further integrated our NLP search into the Elastic Search (ES) framework leveraging similarity search capabilities offered through the “dense_vector” field type. Our preliminary evaluations show that our proposed NLP search has the potential to be utilized to complement the existing search engine and serve as a base for a dataset recommendation system.

Armin Mehrabian↗

Videos, photos, and AI-derived grain size data associated with “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization” under review. This data package includes five data types: 1) raw photos and videos from drone survey and walking smartphone surveys; 2) images derived from raw videos; 3) manual labeling of reference scales; 4) metadata for all images and photo resolution derived from artificial intelligence (AI) models or manual labels, 5) grain size data obtained from AI models for all photos, 6) metadata and grain size data after quality control, 7) summaries of sample efficiency for all data, and 8) computational fluid dynamics (CFD) data used to support hydro-biogeochemical (HBGC) parameter estimation. Such data is used to 1) demonstrate significant improvements in accuracy, efficiency, and quality control for grain size data collection with the help of AI models, 2) study the spatial heterogeneity of grain size and observation reproducibility based on tens of thousands of data points generated by the AI models, and 3) evaluate the impacts of grain size heterogeneity on key HBGC parameters across sediment-to-reach and hourly-to-yearly scales. In particular, the data package contains 116 folders and 179696 files. The files include 41 videos in .mov format, 64047 photos in .jpg format, 13541 video-derived photos in .png format, 12747 segmentation mask data in .tif format, 12747 segmentation data in .json format, 24771 .csv files that with metadata and grain size for each individual photo as well as water depth and velocity data from CFD and observation, 51791 .txt files of raw AI predicted labels, and 11 flight record data in .srt format. The summary for all metadata and grain size statistics information is included in “Scales_V3_NG.csv” and “Statistics_V3_NG.csv”. The summary for data that pass data quality control (QC) level 0-2 is included in “QCStatistics_V3_NG.csv”. The QC level 0 represents photos whose photo resolution is positive, excluding photos that miss reference scale. The QC level 1 means reference scale circularity uncertainty is less than 5% for smartphone images while representing photo resolution is larger than 0.44 mm/pixel for drone images. The QC level 2 means excluding photos whose grain number is less than 100, a minimum number of grains recommended by classic literature. The summary for each video’s name, length, frame rates, survey area, grain number, survey efficiency, etc. can be found in “QCSummary_V3_NG.csv”. The summary for site name, GPS coordinates, and number of images at each site can be found in “SitesSummary_V3_*.csv” files. Overall computational efficiency summary is reported in Table 4 of accompanying manuscript. Additionally, the nitrate concentration data used in this work was downloaded from an existing dataset published on ESS-DIVE (Boat-Dragged Sensor Hanford Reach.csv; Conner A. et al., 2020). We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Port of Benton, and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the data were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate data collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗

Storage of Physical Sample Metadata in the Astrobiology Habitable Environments Database (AHED)

The National Aeronautics and Space Administration has begun an effort to store, curate, and publish information about physical samples collected and analyzed in conjunction with NASA-funded astrobiology research. Astrobiology is a multidisciplinary area of scientific research being conducted by collaborating teams of biologists, chemists, geologists, atmospheric scientists, oceanographers, astrophysicists, astronomers, and other specialists. Astrobiology studies the origin, evolution, and distribution of life in the Universe. NASA uses the results of astrobiology research to focus its future missions on targets of opportunity for the discovery of life off Earth. Astrobiology researchers conduct both field-based and laboratory-based research, during which physical samples are collected, processed, and catalogued. The cataloguing practices employed by different teams of astrobiologists vary widely, and there are no specific standards available to guide the collection and recording of astrobiology sample data. The disparity in data collection approaches and the lack of a centralized sample repository makes it difficult for astrobiology teams to share data and benefit from resultant synergies.To facilitate data sharing within the astrobiology community, NASA is developing a prototype database the Astrobiology Habitable Environments Database (AHED) and an associated set of data collection templates. The database will store information about samples, along with associated measurements and analyses, including information about biological cultures enriched or isolated from samples, and the results of analyses performed on the samples (e.g., via spectrography, microscopy, etc.). In addition, the system will store contextual information about field sites where samples were collected, the instruments or equipment used for analysis, and people and institutions involved in their collection. AHED is being implemented on top of Open Data Repository's Data Publisher [1], an open source software platform for the publication of scientific datasets. The data collection templates under development represent an initial attempt to propose a set of metadata for capture and storage within AHED. The design of these templates is being conducted by a consolidated group of astrobiologists from active research teams at NASA Ames Research Center, assisted by data science and software engineering specialists. These initial templates must be vetted with the broader astrobiology community through a defined process to ensure that they meet community needs. Each template captures a different type of data collection record. For each template, we are developing a list of fields to be captured, including a set of required entry fields, a set of recommended but optional fields, and a set of discretionary fields. A datatype selected from a variety of text and numeric types is specified for each field. Included is a 'choice' type that restricts user input to an enumerated list of values. Many of the fields and field values capture information of particular interest to the astrobiology community, and are intended to facilitate search and retrieval of relevant data across multiple datasets.

Keller, Rich↗

HAPI: An API Standard for Accessing Heliophysics Time Series Data

Heliophysics data analysis often involves combining diverse science measurements, many of them captured as time series. Although there are now only a few commonly used data file formats, the diversity in mechanisms for automated access to and aggregation of such data holdings can make analysis that requires intercomparison of data from multiple data providers difficult. The Heliophysics Application Programmer's Interface (HAPI) is a recently developed standard for accessing distributed time series data to increase interoperability. The HAPI specification is based on the common elements of existing data services, and it standardizes the two main parts of a data service: the request interface and the response data structures. The interface is based on the REpresentational State Transfer (REST) or RESTful architecture style, and the HAPI specification defines five required REST endpoints. Data are returned via a streaming format that hides file boundaries; the metadata is detailed enough for the content to be scientifically useful, e.g., plotted with appropriate axes layout, units, and labels. Multiple mature HAPI-related open-source projects offer server-side implementation tools and client-side libraries for reading HAPI data in multiple languages (IDL, Java, MATLAB, and Python). Multiple data providers in the US and Europe have added HAPI access alongside their existing interfaces. Based on this experience, data can be served via HAPI with little or no information loss compared to similar existing web interfaces. Finally, HAPI has been recommended as a COSPAR standard for time series data delivery.

Robert S. Weigel↗

Governing Data Findability, Accessibility, Interoperability and Reusability (FAIR) Compliance

The most recent data strategy documents at both the federal and NASA levels stipulate that systems should strive for the data they manage to be Findable, Accessible, Interoperable, and Reusable (FAIR). The NASA Life Sciences Portal (NLSP) has already begun leading efforts in this area for HRP, initiating efforts to comply with the FAIR principles. The broad interpretation of the FAIR principles has led to a plethora of tools that use a splay of metrics specifically but variably developed to judge how compliant data and systems are with the principles. A recent review [3] identified and studied 1,180 metrics across 20 publicly available tools for checking FAIR compliance of data and systems. Because of their very recent development, many organizations and data systems managers and developers have not yet had adequate time or resources to understand these FAIR compliance tools and metrics, their variations in design, accuracy or ease of application to their specific data sets and systems. Thus, it would be best for larger organizations like NASA to approach formulating a strategy for governance of FAIR compliance that can be flexibly applied and is adaptable to an evolving awareness knowledge of FAIR compliance methods and tools. In September 2024, the NASA Science Mission Directorate(SMD) organized a workshop on NASA science data repositories, including the topics of implementing FAIR and governing FAIR compliance across SMD. The initial part of these FAIR discussions focused on developing consensus around required science metadata fields. This is challenging given the diverse nature of NASA’s scientific data portfolio, the variety of metadata models and vocabularies used, and variable level of resources available to curate these data. Later discussion focused on three possible approaches to governing FAIR compliance: distributed, in which various programs, projects or systems define their own methods for assessing FAIR compliance, reporting results up appropriate management lines; centralized, in which higher-level organization(s) specify compliance tools or methods for the various data systems; and multi-level, in which a group comprised of individuals with expertise from multiple levels with organizations is formed to provide guidance and/or specifications for governing FAIR compliance. We report on the recommendations this session yielded, and how these might be shaped specifically to help implement and govern the compliance with FAIR of Human Research Program data and systems.

governance↗

Enabling Model Organism and Commercial Astronaut Data Access Through the NASA Open Science Data Repository

NASA’s Open Science Data Repository (OSDR) brings together omics data from NASA’s GeneLab project and non-omics data, including physiological, phenotypic, imaging, and behavioral data from NASA’s Ames Life Sciences Data Archive (ALSDA) collected from decades of space biology research, providing open and FAIR (findable, accessible, interoperable, and reusable) access of these precious data to scientists world-wide. This rich source of meticulously curated metadata and data from spaceflight and analog studies has been mined by the scientific community resulting in dozens of high impact scientific publications that reveals a complex network of molecular and physiological effects of spaceflight across living systems, from microbes to plants, to mammals. Understanding how these effects translate to the human condition is critical as we move deeper into the era of commercial space travel. However, the integration of data, specifically omics data, from astronauts is particularly challenging due to their sensitive nature. OSDR has risen to this challenge by developing a mechanism to control access to identifiable levels of omics data, such as raw sequence data, while enabling public access to processed, unidentifiable, data and associated metadata that will allow the scientific community to interrogate human astronaut data alongside data from model organisms to begin answering these critical questions. The 2021 SpaceX Inspiration4 (I4) mission collected a comprehensive atlas of biological measurements from four civilian astronauts, providing a wealth of data to characterize the effects of spaceflight on the human body. These data include both non-omics and omics assays such as direct RNA sequencing (RNA-seq), single nuclei ATAC-seq and RNA-seq, metagenomics, proteomics, and comprehensive metabolic and cytokine panels, all of which have been integrated into the OSDR system across no less than 9 studies. Each study has been carefully curated using community-backed OSDR standards for sample and assay level metadata ensuring these data are findable and accessible. In addition to hosting both raw and processed data from the principal investigator team for each assay type, the GeneLab team plans to re-process the I4 omics data using GeneLab’s standard processing pipelines. The GeneLab processed data outputs will allow for comparisons across studies on OSDR and enable visualization of these data through the OSDR data visualization platform thereby enabling data reusability and interoperability. Here we describe the robust privacy and security protocols implemented by OSDR to safeguard sensitive health data from astronauts while facilitating metadata and processed data sharing for research purposes. We further provide a road map for navigating the vast amount of data provided for each I4 study on the OSDR, including experimental design, associated experiments, payloads, and missions, data generation and analysis protocols, and associated scientific articles. Additionally, we illustrate how to interrogate the standardized metadata provided in the sample and assay tables as well as various means to download and access the data including programmatically through the GeneLab Open API (GLOpenAPI). The open access of datasets in NASA’s OSDR provides a unique opportunity for the scientific community, as well as citizen scientists and students, to continue using OSDR resources to further unlock profound insights into the consequences of space travel on the human body. Through implementation of security measures to protect sensitive human data, the OSDR seeks to strengthen the science exchange between the Biological and Physical Sciences Program and the Human Research Program, per recommendation 4-1 of the 2023-2032 Decadal Survey, and encourage further sharing and dissemination of astronaut data to provide the scientific community with the resources needed to lay the groundwork for developing targeted mitigation strategies to help withstand the rigors of long-duration spaceflight.

Amanda Marie Saravia-butler↗

Evolving a NASA Digital Object Identifiers System with Community Engagement

To demonstrate how the ESDIS (Earth Science Data and Information System) DOI (Digital Object Identifier) system and its processes have evolved over these years based on the recommendations provided by the user community (whether the community members create and manage DOI information or use DOIs in the data citations). The user community is comprised of people with common interests and needs for data identifiers who are actively involved in the creation and usage process. Engagement describes the interactive context wherein the community provides information, evaluates the proposed processes, and provides guidance in the area of identifiers.

Identifiers↗

The Re-Analysis of Ozone Profile Data from a 41-Year Series of SBUV Instruments

In this study we present the validation of ozone profiles from a number of Solar Back Scattered Ultra Violet (SBUV) and SBUV/2 instruments that were recently reprocessed using an updated (Version 8.6) algorithm. The SBUV dataset provides the longest available record of global ozone profiles, spanning a 41-year period from 1970 to 2011 (except a 5-year gap in the 1970s) and includes ozone profile records obtained from the Nimbus-4 BUV and Nimbus-7 SBUV instruments, and a series of SBUV(/2) instruments launched on NOAA operational satellites (NOAA 09, 11, 14, 16, 17, 18, 19). Although modifications in instrument design were made in the evolution from the BUV instrument to the modern SBUV(/2) model, the basic principles of the measurement technique and retrieval algorithm remain the same. The long term SBUV data record allows us to create a consistent, calibrated dataset of ozone profiles that can be used for climate studies and trend analyses. In particular, we focus on estimating the various sources of error in the SBUV profile ozone retrievals using independent observations and analysis of the algorithm itself. For the first time we include in the metadata a quantitative estimate of the smoothing error, defined as the error due to profile variability that the SBUV observing system cannot inherently measure. The magnitude of the smoothing error varies with altitude, latitude, season and solar zenith angle. Between 10 and 1 hPa the smoothing errors for the SBUV monthly zonal mean retrievals are of the order of 1 %, but start to increase above and below this layer. The largest smoothing errors, as large as 15-20%, were detected in in the troposphere. The SBUV averaging kernels, provided with the ozone profiles in version 8.6, help to eliminate the smoothing effect when comparing the SBUV profiles with high vertical resolution measurements, and make it convenient to use the SBUV ozone profiles for data assimilation and model validation purposes. The smoothing error can also be minimized by combining layers of data, and we will discuss recommendations for this approach as well. The SBUV ozone profiles have been intensively validated against satellite profile measurements obtained from the Microwave Limb Sounders (MLS) (on board the UARS and AURA satellites), Stratospheric Aerosol and Gas Experiment (SAGE) and Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). Also, we compare coincident and collocated SBUV ozone retrievals with observations made by ground-based instruments, such as microwave spectrometers, lidars, Umkehr instruments and balloon-borne ozonosondes. Finally, we compare the SBUV ozone profiles with output from the NASA GSFC GEOS-CCM model. In the stratosphere between 25 and 1 hPa the mean biases and standard deviations are within 5% for monthly mean ozone profiles. Above and below this layer the vertical resolution of the SBUV algorithm decreases and the effects of vertical smoothing should be taken into account. Though the SBUV algorithm has a coarser vertical resolution in the lower stratosphere and troposphere, it is capable of precisely estimating the integrated ozone column between the surface and 25 hPa. The time series of the tropospheric - lower stratospheric ozone column derived from SBUV agrees within 5% with the corresponding values observed by an ensemble of ozone sonde stations in North Hemisphere. Drift of the ozone time series obtained from each SBUV(/2) instrument relative to ground based and satellite measurements are evaluated and some features of individual SBUV(l2) instruments are discussed. In addition to evaluating individual instruments against independent observations, we also focus on the instrument to instrument consistency in the series. Overall, Version 8.6 ozone profiles obtained from two different SBUV(l2) instruments compare within a couple of percent during overlap periods and are consistently varying in time, with some exceptions. Some of the noted discrepancies might bssociated with ozone diurnal variations, since the difference in the local time of the observations for a pair of SBUV(l2) instruments could be several hours. Other issues include the potential short-term drift in measurements as the instrument orbit drifts, and measurements are obtained at high solar zenith angles (>85 ). Based on the results of the validation, a consistent, calibrated dataset of SBUV ozone profiles has been created based on internal calibration only.

Kramarova, Natalya↗

Package Data for CERF-Data Centers

This dataset contains sample input 100m resolution raster files for running the CERF-DC python package (see https://github.com/IMMM-SFA/cerf_data_centers) at the state level across the CONUS. Due to data availability constraints, some of the items included in this dataset are proxies or assumptions for siting factors used in the model. These are individually noted in the item descriptions and can be exchanged with more detailed information upon availability. Data Descriptions The following raster files are included in the data download: state_siting_region.tif — State areas identified by state FIPS code composite_siting_suitability.tif — Value of 1 indicates suitable siting location, 0 otherwise. The following areas are excluded from siting: Areas within 300m of a federal airport runway Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory Protected Areas Database of the United States (PAD-US) areas Railroads, major roadways, and minor roadways Military areas and training grounds Developed lands Areas >0.8 km (0.5 miles) from developed lands land_value_dollar_per_sqft.tif — USD per square foot (sqft) derived from USDA $/acre land cost personal_property_tax_rate.tif — Personal property tax rate by state. Uses an assumed 0.0125 personal property tax rate for states with personal property tax, 0 for states without personal property tax. real_property_tax_rate.tif — Real property tax rate. Based on county level residential real estate property tax rates. sales_tax_rate.tif — Sales tax rate by state. mechanical_cooling_fraction.tif — Fraction of year (values between 0 and 1, inclusive) that the data center would be cooled through mechanical processes based on local water stress and humidity levels. water_cooling_fraction.tif — Fraction of year (values between 0 and 1, inclusive) that the data center would be cooled through evaporative (water cooled) processes based on local water stress and humidity levels. distance_to_substation.tif — Distance to nearest substation in hundreds of meters (i.e., value of 1 equals a distance of 100m). Offshore areas have a value of 0. industrial_electricity_rates_dollar_per_kwh.tif — USD/kWh industrial electricity rates. Represents the average industrial rate across all utilities that operate within a given county. Values are derived from the US Utility Rate Database. commercial_electricity_rates_dollar_per_kwh.tif — USD/kWh commercial electricity rates. Represents the average commercial rate across all utilities that operate within a given county. Values are derived from the US Utility Rate Database. data_center_market_locations.tif — Grid cells with positive values represent the centroid of existing data center market clusters. The value of non-zero grid cells represents the number of data centers in the market cluster. All other grid cells have a value of 0. Geospatial Metadata CRS: Albers Equal Area Conic (ESRI:102003) Extent: -2415585.0000000023283064,-1441981.2605773280374706 : 2384414.9999999976716936,1708018.7394226719625294 Dimensions: X: 48000 Y: 31500 Bands: 1 Origin: -2415585.0000000023283064,1708018.7394226719625294 Pixel Size: 100,-100 Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall↗

Package Data for CERF-Data Centers

This dataset contains sample input 100m resolution raster files for running the CERF-DC python package (see https://github.com/IMMM-SFA/cerf_data_centers) at the state level across the CONUS. Due to data availability constraints, some of the items included in this dataset are proxies or assumptions for siting factors used in the model. These are individually noted in the item descriptions and can be exchanged with more detailed information upon availability. Data Descriptions The following raster files are included in the data download: state_siting_region.tif — State areas identified by state FIPS code composite_siting_suitability.tif — Value of 1 indicates suitable siting location, 0 otherwise. The following areas are excluded from siting: Areas within 300 m of a federal airport runway or within an airport area boundary Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory USGS Protected Areas Database of the United States (PAD-US) GAP status 1, 2, or 3 areas US National Parks Wetlands USFWS critical habitats BIA land areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands land_value_dollar_per_sqft.tif — USD per square foot (sqft) derived from USDA $/acre land cost personal_property_tax_rate.tif — Personal property tax rate by state. Uses an assumed 0.0125 personal property tax rate for states with personal property tax, 0 for states without personal property tax. real_property_tax_rate.tif — Real property tax rate. Based on county level residential real estate property tax rates. sales_tax_rate.tif — Sales tax rate by state. mechanical_cooling_fraction.tif — Fraction of year (values between 0 and 1, inclusive) that the data center would be cooled through mechanical processes based on local water stress and humidity levels. water_cooling_fraction.tif — Fraction of year (values between 0 and 1, inclusive) that the data center would be cooled through evaporative (water cooled) processes based on local water stress and humidity levels. distance_to_substation.tif — Distance to nearest substation in hundreds of meters (i.e., value of 1 equals a distance of 100m). Offshore areas have a value of 0. industrial_electricity_rates_dollar_per_kwh.tif — USD/kWh industrial electricity rates. Represents the average industrial rate across all utilities that operate within a given county. Values are derived from the US Utility Rate Database. commercial_electricity_rates_dollar_per_kwh.tif — USD/kWh commercial electricity rates. Represents the average commercial rate across all utilities that operate within a given county. Values are derived from the US Utility Rate Database. data_center_market_locations.tif — Grid cells with positive values represent the centroid of existing data center market clusters. The value of non-zero grid cells represents the number of data centers in the market cluster. All other grid cells have a value of 0. Geospatial Metadata CRS: Albers Equal Area Conic (ESRI:102003) Extent: -2415585.0000000023283064,-1441981.2605773280374706 : 2384414.9999999976716936,1708018.7394226719625294 Dimensions: X: 48000 Y: 31500 Bands: 1 Origin: -2415585.0000000023283064,1708018.7394226719625294 Pixel Size: 100,-100 Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall↗

The NASA Open Science Data Repository: Biomedical Fair Data, Analysis Tools, User Communities, Publications, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

space biology↗