Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data repositories”

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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

HRP Data Management Plan

The purpose of Human Research Program Data Management Plan (DMP) is to define the processes and activities required for the overall management of the research data collected and managed by HRP throughout their life cycle. New updates to the Data Management Plan in 2023 include 1. CAPABILITIES AND SERVICES Data Repositories. Principal Investigators (PIs) funded by HRP may be asked to submit data to one of several NASA data repositories. HRP archives data in the NASA Life Sciences Portal (NLSP) that it considers to be unique and high value. This includes data from human subjects in space flight (ISS and commercial flights) and ground analogs to spaceflight; spaceflight tech demos involving humans; human omics data including the microbiome; parabolic flight studies; and the NASA Space Radiation Laboratory (NSRL). The Open Science Data Repository (OSDR) includes The Ames Life Sciences Data Archive (ALSDA), used to archive non-human biological data (e.g., animal) generated by the Human Research program, and GeneLab, available to HRP PIs to archive non-human omics data. Catalog for search and retrieval. A catalog of non-human HRP life science experiments, with all associated descriptions (mission, payload, hardware, and personnel related information), and biospecimens is provided on the NLSP public web site for search and retrieval. 2. IRB ROLE IN RETURN OF INDIVIDUAL RESEARCH RESULTS The NASA IRB manages the process for incidental findings and for returning results to subjects for studies for which NASA IRB is the IRB of record. Omics data, especially genomics data, may generate information significant to the health of or risk to a research subject. These data potentially hold the keys to understand lifetime risks of chronic diseases, such as cancer, as well as risks associated with exposures common in space flight. 3. UPDATE OF TERMS – IDENTIFIABLE AND ATTRIBUTABLE DATA HRP now follows Federal and NASA policy by using “identifiable” instead of “attributable” for Personally Identifiable Information (PII). 4. POLICY ABOUT INTERNAL NON-RESEARCH USE OF DATA The HRP Chief Scientist grants access to data from HRP-funded research for non-research internal use that includes program management, customer facilitation, strategic planning, and risk research planning. Typical HRP personnel granted access to HRP research data for internal use include the Element Scientist, Subject Matter Experts (SME), and Data/bioinformatics Scientists. If data accessed for Internal Use is provided to an intramural or extramural scientist for hypothesis driven research, all Federal and NASA regulations (e.g., IRB review) regarding human subject research apply.

Data Management Plan↗
DOE OSTI2022

Data from: “Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats”

This dataset contains supplementary information for a manuscript describing the ESS-DIVE (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) data repository's community data and metadata reporting formats. The purpose of creating the ESS-DIVE reporting formats was to provide guidelines for formatting some of the diverse data types that can be found in the ESS-DIVE repository. The 6 teams of community partners who developed the reporting formats included scientists and engineers from across the Department of Energy National Lab network. Additionally, during the development process, 247 individuals representing 128 institutions provided input on the formats. The primary files in this dataset are 10 data and metadata crosswalk for ESS-DIVE’s reporting formats (all files ending in _crosswalk.csv). The crosswalks compare elements used in each of the reporting formats to other related standards and data resources (e.g., repositories, datasets, data systems). This dataset also contains additional files recommended by ESS-DIVE’s file-level metadata reporting format. Each data file has an associated dictionary (files ending in _dd.csv) which provide a brief description of each standard or data resource consulted in the data reporting format development process. The flmd.csv file describes each file contained within the dataset.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2006

A Predictive Approach to Eliminating Errors in Software Code

NASA s Metrics Data Program Data Repository is a database that stores problem, product, and metrics data. The primary goal of this data repository is to provide project data to the software community. In doing so, the Metrics Data Program collects artifacts from a large NASA dataset, generates metrics on the artifacts, and then generates reports that are made available to the public at no cost. The data that are made available to general users have been sanitized and authorized for publication through the Metrics Data Program Web site by officials representing the projects from which the data originated. The data repository is operated by NASA s Independent Verification and Validation (IV&V) Facility, which is located in Fairmont, West Virginia, a high-tech hub for emerging innovation in the Mountain State. The IV&V Facility was founded in 1993, under the NASA Office of Safety and Mission Assurance, as a direct result of recommendations made by the National Research Council and the Report of the Presidential Commission on the Space Shuttle Challenger Accident. Today, under the direction of Goddard Space Flight Center, the IV&V Facility continues its mission to provide the highest achievable levels of safety and cost-effectiveness for mission-critical software. By extending its data to public users, the facility has helped improve the safety, reliability, and quality of complex software systems throughout private industry and other government agencies. Integrated Software Metrics, Inc., is one of the organizations that has benefited from studying the metrics data. As a result, the company has evolved into a leading developer of innovative software-error prediction tools that help organizations deliver better software, on time and on budget.

Source record↗
NASA NTRSDate not supplied

Development of Machine Learning Algorithms to Segment and Study Images of Astromaterial Samples

Introduction: Micrometer-scale chemical analyses of chondritic meteorites and mission-returned asteroid samples can reveal details of the physical and chemical processes operating in the early solar system, including processes that gave rise to planets, moons, and minor bodies. These primitive astromaterials are comprised of chondrules, calcium- and aluminum-rich inclusions (CAI), and many other silicates, oxides, metals, sulfides, and fine-grained materials. The chemical and mineralogical complexity of these samples, vast populations of different components, and heterogeneity across mm to km scales, all limit our understanding of the origin and evolution of these materials. Here, we describe recent efforts to use machine learning techniques to automate the segmentation of chemical maps of chondritic meteorites, designed to aid studies of asteroid samples returned by spacecraft. By automating the task of segmentation it will become possible to rapidly analyze and interpret the sizes, shapes, mineralogy, chemistry, and other properties of every chondrule, calcium- and aluminum-rich inclusion (CAI) and other clast within and between asteroid samples. Sample return missions significantly accelerate and heighten the need to develop such new data analysis techniques, and associated data repositories. Techniques: Neural networks require abundant training data, i.e. images which have been segmented by a human user. We have manually segmented data available from previous petrologic and chemical work at NASA Johnson Space Center and the American Museum of Natural History [1-4]. These data were derived from energy- and wavelength-dispersive X-ray spectroscopy (EDS, WDS) mapping of samples from many chondrite groups. The Deeplabv3+ [5] neural network architecture was trained on human-labeled masks and used to create machine-labeled masks. Several different algorithms were investigated, with inputs ranging from common RGB image formats through to hyperspectral datasets, with raw data comprising greyscale maps of Mg, Ca, and Al, with or without Si, Fe, Ti for both EDS and WDS data, and extending to other elements in EDS only. Each greyscale image was paired with a binary mask for each labelled particle type. Results: The trained algorithms can segment (Fig 1), classify, and measure the dimensions of thousands of particles in chemical maps of a standard 1-inch round petrographic section in seconds to minutes, rather than many hours needed by a human. Accuracy of the algorithms varied from chondrite to chondrite and across particle types. Further results and details of the algorithms will be presented at the workshop. Future directions: Machine learning has the potential to revolutionize our understanding of complex particle populations contained within primitive astromaterial, with segmentation being a critical first step. Example applications include better understanding of particle transport, nebular reservoirs, parent body accretion, and a deeper understanding of the relationships between particle populations and bulk rock elemental and isotopic compositions. In addition to benefits that machine learning can bring to individual researchers, building a community data repository of thousands to millions of particles across hundreds of samples will open up many other possibilities. For example, with a large enough dataset it will be possible to search for exceptionally closely matching particles across disparate samples. Such a capability would enable a single CAI from OSIRISREx or Hayabusa/II samples to be matched to chondritic CAIs that exhibit near-identical size, texture, and mineralogy, down to the level of similar core phenocrysts, zonation, and rim sequences. Such comparative analyses will help to disentangle precursor chemistry, chronology, gas/dust reservoirs during heating, and accretion. Such an endeavor would be impossible without machine learning and a large community data repository of astromaterial chemical/mineralogic maps.

Machine Learning↗
DOE OSTI2025

Opening doors to physical sample tracking and attribution in Earth and environmental sciences

Physical samples and their associated data and metadata underpin scientific discoveries across disciplines and can enable new science when appropriately archived. However, there are significant gaps in current practices and infrastructure that prevent accurate provenance tracking, reproducibility, and attribution. For most samples, descriptive metadata are often sparse, inaccessible, or absent. Samples and associated data and metadata may also be scattered across numerous physical collections, data repositories, laboratories, data files, and papers with no clear linkage or provenance tracking as new information is generated over time. The Earth Science Information Partners (ESIP) Physical Samples Curation Cluster has therefore developed guidance for scientific authors on ‘Publishing Open Research Using Physical Samples.’ This involved synthesizing existing practices, gathering community feedback, and assessing real-world examples. We identified improvements needed to enable authors to efficiently cite and link Earth science samples and related data, and track their use. Our goal is to help improve discoverability, interoperability, and reuse of physical samples, and associated data and metadata. Though primarily focused on the needs of Earth and environmental sciences, these guidelines are broadly applicable.

58 GEOSCIENCES↗
DOE OSTI2022

MultiProBE Software Platform

MultiProBE Software Platform is cloud based multi-omic data repository and data processing compute platform.

Wright, Devin↗
DOE OSTI2024

Data for publication: "A fresh take: Seasonal changes in terrestrial freshwater inputs impact salt marsh hydrology and vegetation dynamics"

This data repository contains data associated with the manuscript "A fresh take: Seasonal changes in terrestrial freshwater inputs impact salt marsh hydrology and vegetation dynamics". This study was conducted at the Elkhorn Slough National Estuarine Research Reserve in Watsonville, California from October 2019 - June 2022. We sought to understand the role of shallow freshwater inputs from adjacent uplands on salt marsh hydrologic behavior and vegetation productivity. This dataset contains CSV files of the following: daily salt marsh subsurface water level and pore water conductivity, monthly vegetation survey measurements, soil core data. Estuary surface water level, conductivity, and local precipitation were downloaded from the National Estuarine Research Reserve System (Centralized Data Management Office) at https://cdmo.baruch.sc.edu/.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Seismology in the Cloud: A New Streaming Workflow

Data-intensive research in seismology is experiencing a recent boom, driven in part by large volumes of available data and advances in the growing field of data science. However, there are significant barriers to processing large data volumes, such as long retrieval times from data repositories, complex data management, and limited computational resources. New tools and platforms have reduced the barriers to entry for scientific cluster computing, including the maturation of the commercial cloud as an accessible instrument for research. Here, we build a customized research cluster in the cloud to test a new workflow for large-scale seismic analysis, in which data are processed as a stream (retrieved on-the-fly and acted upon without storing), with data from the Incorporated Research Institutions for Seismology Data Management Center. We use this workflow to deploy a spectral peak detection algorithm over 5.6 TB of compressed continuous seismic data from 2074 stations of the USArray Transportable Array EarthScope network. Using a 50-node cluster in the cloud, we completed the noise survey in 80 hr, with an average data throughput of 1.7 GB per minute. By varying cluster sizes, we find the scaling of our analysis to be sublinear, due to a combination of algorithmic limitations and data center response times. The cloud-based streaming workflow represents an order-of-magnitude increase in acquisition and processing speed compared to a traditional download-store-process workflow, and offers the additional benefits of employing a flexible, accessible, and widely used computing architecture. It is limited, however, due to its reliance on Internet transfer speeds and data center service capacity, and may not work well for repeated analyses or those for which even higher data throughputs are needed. These research applications will require a new class of cloud-native approaches in which both data and analysis are in the cloud.

58 GEOSCIENCES↗
DOE OSTI2025

Integrated GW Farm ABM

This Data Repository includes data used for the integrated groundwater- farm ABM model, raw model output from scenario ensemble, and processed outputs that isolate the groundwater storage depletion outcomes for the 35,000 farm cells. Model Inputs: Farm ABM Inputs: This folder contains the input data used by the integrated groundwater - farm ABM modelling script (Python file) used for the high performance computing (HPC) experiments. The sub-folder "data inputs" contains all of the farm attribute data, while the three files in the folder have the hydrogeological data lookup table (NLDAS Cost Curve Attributes.csv), a lookup table (Theis well function table.csv) for the groundwater cost curve function, and the farm indexes and corresponding NLDAS ids for all of the cells run in this experiment (nldas farms subset final.csv). NLDAS Cost curve hydrogeological data: Hydrogeological data aggregated to 1/8 degree resolution and aligned with the NLDAS grid. Parameters include: water depth below ground surface [meters], subsurface porosity [unitless], aquifer depth from ground surface to aquifer bottom [meters], annual average recharge (USGS: mm, Doll: meters), and three different hydraulic conductivity (K) values (meters/day). The three K values represent the mean value from Gleeson et al. (2018), one standard deviation above the mean from Gleeson et al. (2018), and the de Graaf et al. 2020 modifications to certain lithologies. Additional information about these datasets and their processing are documented in the supplement to Yoon et al. 2025 (in review). Output: Raw outputs: This folder contains a .zip file that has model outputs for the entire scenario ensemble. There is one csv for each farm id, using the format "farm farmid cases.csv". The relationship between the farm id and NLDAS id is defined by the "nldas farms subset final.csv" located in the Farm ABM Inputs folder. Each csv has 625 rows, corresponding to 625 combinations of different scenario parameter values. Each row (scenario) represents the outcome of a 100 year simulation. Columns define scenario settings and summary statistics for each scenario. The first four columns define the scenario settings: "hydro ratio," "econ ratio," "K scenario," and "gamma scenario." The hydro and econ ratios are values passed to the modeling script that influence multipliers for other model parameters, as documented in the supplement to Yoon et al. 2025 (in review). The gamma multiplier is a coefficient multiplier applied to the baseline gamma values (values below 1 represent lower unobserved costs compared to baseline, values above 1 represent higher costs). The K scenario names represent K values of: "low": 0.5 m/d, "int 1": 2.5 m/d, "int 2": 10 m/d, "high": 50 m/d, and "gleeson": mean Gleeson K value. "Perc vol depleted" is the fraction of groundwater depleted at the end of the 100 simulation. Processed Output: Derived depletion outcomes from raw outputs: All of the individual csv files from the Raw outputs were aggregated into a single file that has the scenario settings and fraction depletion "Perc vol depleted" for every farm cell, for every scenario. The other two files define relationships between the farm id, NLDAS id, and local and major aquifer units, used for aquifer-level depletion analysis.

Agent based modeling↗
NASA NTRS2020

GeneLab

GeneLab collects and enables analysis of spaceflight and ground-based spaceflight simulation genomic data, RNA and protein expression, and metabolic profiles. It interfaces with other existing databases containing spaceflight omic data. The 2011 National Research Council (NRC) Decadal Survey on NASA Life and Physical Sciences called for increased opportunities for multi-investigator spaceflight opportunities and greater use of genomic approaches to meet the needs of NASA researchers. To address these recommendations of the NRC Decadal Survey, the Space Life and Physical Sciences Research and Applications Division of NASA's Human Exploration and Operations Mission Directorate has initiated a transition to an Open Science architecture to increase research opportunities, and has developed the GeneLab Platform based on highly leveraged and integrated bioinformatics analytics. GeneLab is an interactive, open-access resource where scientists can upload, download, store, search, share, transfer, and analyze omics data from spaceflight and corresponding analogue experiments. Users can explore GeneLab datasets in the Data Repository, analyze data using the Analysis Platform, visualize high-order data and create collaborative projects using the Collaborative Workspace. Our primary goal is to maximize the utilization of the valuable biological research conducted aboard the International Space Station (ISS) by collecting genomic, transcriptomic, proteomic, and metabolomics data known as “omics”. By providing a portal linking processed data to flight parameters, GeneLab enables exploration of the molecular network responses of terrestrial biology to the space environment. This allows researchers to understand the complex responses of biological systems to the space environment. This technology development activity was transferred from the Human Exploration and Operations Mission Directorate to the Science Mission Directorate Division of Biological and Physical Sciences (BPS) in October 2020.

GeneLab↗
NASA NTRS2017

Benefits of International Collaboration on the International Space Station

The International Space Station is a valuable platform for research in space, but the benefits are limited if research is only conducted by individual countries. Through the efforts of the ISS Program Science Forum, international science working groups, and interagency cooperation, international collaboration on the ISS has expanded as ISS utilization has matured. Members of science teams benefit from working with counterparts in other countries. Scientists and institutions bring years of experience and specialized expertise to collaborative investigations, leading to new perspectives and approaches to scientific challenges. Combining new ideas and historical results brings synergy and improved peer-reviewed scientific methods and results. World-class research facilities can be expensive and logistically complicated, jeopardizing their full utilization. Experiments that would be prohibitively expensive for a single country can be achieved through contributions of resources from two or more countries, such as crew time, up- and down mass, and experiment hardware. Cooperation also avoids duplication of experiments and hardware among agencies. Biomedical experiments can be completed earlier if astronauts or cosmonauts from multiple agencies participate. Countries responding to natural disasters benefit from ISS imagery assets, even if the country has no space agency of its own. Students around the world participate in ISS educational opportunities, and work with students in other countries, through open curriculum packages and through international competitions. Even experiments conducted by a single country can benefit scientists around the world, through specimen sharing programs and publicly accessible "open data" repositories. For ISS data, these repositories include GeneLab, the Physical Science Informatics System, and different Earth data systems. Scientists can conduct new research using ISS data without having to launch and execute their own experiments. Multilateral collections of research results publications, maintained by the ISS international partnership and accessible via nasa.gov, make ISS results available worldwide, and encourage new users, ideas and research.

Hasbrook, Pete↗
NASA NTRS2017

Benefits of International Collaboration on the International Space Station

The International Space Station is a valuable platform for research in space, but the benefits are limited if research is only conducted by individual countries. Through the e orts of the ISS Program Science Forum, international science working groups, and interagency cooperation, international collaboration on the ISS has expanded as ISS utilization has matured. Members of science teams benefit from working with counterparts in other countries. Scientists and institutions bring years of experience and specialized expertise to collaborative investigations, leading to new perspectives and approaches to scientific challenges. Combining new ideas and historical results brings synergy and improved peer-reviewed scientific methods and results. World-class research facilities can be expensive and logistically complicated, jeopardizing their full utilization. Experiments that would be prohibitively expensive for a single country can be achieved through contributions of resources from two or more countries, such as crew time, up- and downmass, and experiment hardware. Cooperation also avoids duplication of experiments and hardware among agencies. Biomedical experiments can be completed earlier if astronauts or cosmonauts from multiple agencies participate. Countries responding to natural disasters benefit from ISS imagery assets, even if the country has no space agency of its own. Students around the world participate in ISS educational opportunities, and work with students in other countries, through open curriculum packages and through international competitions. Even experiments conducted by a single country can benefit scientists around the world, through specimen sharing programs and publicly accessible \open data" repositories. For ISS data, these repositories include GeneLab, the Physical Science Informatics System, and different Earth science data systems. Scientists can conduct new research using ISS data without having to launch and execute their own experiments. Multilateral collections of research results publications, maintained by the ISS international partnership and accessible via nasa.gov, make ISS results available worldwide, and encourage new users, ideas and research. The paper explores effectiveness of international collaboration in the course of the ISS Program execution. The collaboration history, its evolution and maturation, change of focus during its different phases, and growth of its effectiveness (in accordance with the especially established criteria) are also considered in the paper in the light of benefits for the entire ISS community. With the International Space Station extended through at least 2024, more crew time becoming available and new facilities arriving on board the ISS, these benefits of international scientific collaboration on the ISS can only increase.

Robinson, Julie A.↗
NASA NTRSDate not supplied

The TOLNet 2.0 Website: How an API Can Promote Open Science and FAIR Principles

The Tropospheric Ozone Lidar Network (TOLNet) has generated over a decade of ozone vertical profile data products over North America. The science value of the TOLNet data has been demonstrated in numerous peer-reviewed publications on air quality and other ozone relevant research. To support the broad spectrum of data use, the TOLNet team launched a major effort to upgrade the web-based data repository aiming to enhance the data discoverability and to enable machine-to-machine data upload and download processes. Specifically, the TOLNet website included an application programming interface (API), which supports machine-to-machine data search and data download. The API also extracts selected variables from the files, which can be retrieved as JSON objects and used to create data displays without having to download or open the underlying files. The TOLNet science team members can also use the API for automated data upload, including a data file scanning feature to ensure data product integrity. To be presented will include a summary of key features of data repositories, an actual use case of machine-to-machine data access/use, as well as our journey to make TOLNet data more FAIR, i.e., more findable, accessible, interoperable, and (re)usable.

Crystal Gummo↗
NASA NTRS2017

Benefits of International Collaboration on the International Space Station

The International Space Station is a valuable platform for research in space, but the benefits are limited if research is only conducted by individual countries. Through the efforts of the ISS Program Science Forum, international science working groups, and interagency cooperation, international collaboration on the ISS has expanded as ISS utilization has matured. Members of science teams benefit from working with counterparts in other countries. Scientists and institutions bring years of experience and specialized expertise to collaborative investigations, leading to new perspectives and approaches to scientific challenges. Combining new ideas and historical results brings synergy and improved peer-reviewed scientific methods and results. World-class research facilities can be expensive and logistically complicated, jeopardizing their full utilization. Experiments that would be prohibitively expensive for a single country can be achieved through contributions of resources from two or more countries, such as crew time, up- and downmass, and experiment hardware. Cooperation also avoids duplication of experiments and hardware among agencies. Biomedical experiments can be completed earlier if astronauts or cosmonauts from multiple agencies participate. Countries responding to natural disasters benefit from ISS imagery assets, even if the country has no space agency of its own. Students around the world participate in ISS educational opportunities, and work with students in other countries, through open curriculum packages and through international competitions. Even experiments conducted by a single country can benefit scientists around the world, through specimen sharing programs and publicly accessible "open data" repositories. For ISS data, these repositories include GeneLab and the Physical Science Informatics System. Scientists can conduct new research using ISS data without having to launch and execute their own experiments. Multilateral collections of research results publications, maintained by the ISS international partnership and accessible via nasa.gov, make ISS results available worldwide, and encourage new users, ideas and research. The paper explores international collaboration history, its evolution and maturation, change of focus during its different phases, and growth of its effectiveness (in accordance with the especially established criteria) in the light of benefits for the entire ISS community. With the International Space Station extended through at least 2024, more crew time becoming available and new facilities arriving on board the ISS, these benefits of international scientific collaboration on the ISS can only increase.

Hasbrook, Pete↗
DOE OSTI2026

MSD CoP Webinar: "Advances in MSD-LIVE to Support the MSD Community of Practice"

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Advances in MSD-LIVE to Support the MSD Community of Practice Presenters: Casey Burleyson and Zoe Guillen (Pacific Northwest National Laboratory) Abstract: The MultiSector Dynamics Living, Intuitive, Value-adding, Environment (MSD-LIVE; msdlive.org) is a cloud-based data management system and advanced computing platform that enables MSD researchers to document and archive their data, run their models and analysis tools, and share their data, software, and workflows within the MSD Community of Practice. Recently, several high-profile datasets have attracted many new users to MSD-LIVE. This webinar has two goals: 1) To refamiliarize the MSD community and new users with the components of the platform (e.g., the data repository, model training notebooks, and data dashboards) and to highlight examples of how these components are advancing MSD science and 2) To demonstrate new features in v3 of the platform, released in late 2025. The main new feature in v3 is the ability to interactively explore data in MSD-LIVE without downloading it. MSD-LIVE users can now click a button in our data repository and launch a blank Jupyter notebook with access to the underlying data on AWS. Users can use the notebook to write analysis, visualization, or subsetting routines that process the data directly on the AWS cloud. We also added a GitHub integration feature that allows users to share analysis or visualization code they develop with the community of MSD-LIVE users. The webinar will wrap up with a look at what's coming next for MSD-LIVE in 2026. Moderator: Patrick M. Reed (MSD CoP Facilitation Team) This webinar was held on: May 12th, 2026 from 1-2 PM EST.

Open Science↗
DOE OSTI2023

Complete β-decay patterns of 142 Cs, 142 Ba, and 142 La determined using total absorption spectroscopy

Background: The β decays of fission products produced in nuclear fuel are important for nuclear energy applications and fundamental science of reactor antineutrinos. In particular, nuclear reactor safety is related to the decay modes of radioactive neutron-rich nuclei, primarily via the emission of γ rays, neutrons, and electrons. Additionally nuclear reactors are the most powerful man-made source of antineutrinos emitted during the β decay of fission products. These antineutrinos are used to inspect fundamental properties of leptons as well as informing reactor operation. However, the majority of data on complex decays of fission products collected in the evaluated nuclear data repositories like Evaluated Nuclear Structure Data File (ENSDF) and Evaluated Nuclear Data Files (ENDF) are based on low-efficiency and often incomplete measurements resulting in questionable reference reactor antineutrino flux predictions, see the analysis by [Nichols, J. Nucl. Sci. Technol. 52, 17 (2015)]. Various assessments like the one done under the auspices of the [Yoshida et al., Assessment of Fission Product Decay Data for Decay Heat Calculations: A report by the Working Party on International Evaluation Co-operation of the Nuclear Energy Agency Nuclear Science Committee (Nuclear Energy Agency, Organization for Economic Co-operation and Development, Paris, France, 2007), Vol. 25], as well as by [Sonzogni, Johnson, and McCutchan, Phys. Rev. C 91, 011301(R) (2015)] and [Dwyer and Langford, Phys. Rev. Lett. 114, 012502 (2015)], list the A = 142 isobars with high cumulative fission yield among the important nuclei where data for reactor decay heat and/or antineutrino production should be verified and/or improved. Purpose: Here, our goal is to improve the quality of β -decay measurements and evaluate the impact of modified decay schemes on reactor decay heat and antineutrino energy spectra, for fission products along the A = 142 isobaric chain. This work is an in depth follow-up on [Rasco et al., Phys. Rev. Lett. 117, 092501 (2016)]. which presented briefly the impact of the corrected decay scheme of 142 Cs . Here, we extend the data to full isobaric decay chain including the daughter nuclei, 142 Ba and 142 La, and present more details on the 142 Cs results. Method: The decays of neutron-rich isobars of mass A = 142 produced by means of proton-induced fission of 238 U were measured using the Modular Total Absorption Spectrometer (MTAS) array on-line at the mass separator and Tandem accelerator at Oak Ridge National Laboratory. Results: The β -decay schemes for 142 Cs and 142 La were modified with respect to the nuclear data repositories. A small β-delayed neutron branching ratio for 142 Cs emitter was remeasured as $0.10^{+5}_{–3}% %. Improved precision on the measured half-lives is reported. Small corrections to the low-energy decay of 142 Ba are made. The β-decay patterns for 142 La and 142 Cs are presented. The decay heat release and cross section for the detection of reactor antineutrinos are deduced and compared to earlier results. Conclusions: The β-feeding pattern for 142 Cs having decay energy value $Q_β$ of over 7 MeV was substantially modified with respect to the current ENSDF entry. Smaller changes were encountered for 142 La, but since this A = 142 isobar also has a large cumulative yield in fission, the changes influence both decay heat and the antineutrino spectra. The previously known β intensities for 142 Ba decay ($Q_β$ value of 2.2 MeV) were verified and slightly modified. Overall, increased decay heat values and lower flux of antineutrinos interacting with matter are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2011

The Challenges of Releasing Human Data for Analysis

The NASA Johnson Space Center s (NASA JSC) Committee for the Protection of Human Subjects (CPHS) recently approved the formation of two human data repositories: the Lifetime Surveillance of Astronaut Health Repository (LSAH-R) for clinical data and the Life Sciences Data Archive Repository (LSDA-R) for research data. The establishment of these repositories forms the foundation for the release of data and information beyond the scope for which the data was originally collected. The release of clinical and research data and information is primarily managed by two NASA groups: the Evidence Base Working Group (EBWG), consisting of members of both repositories, and the LSAH Policy Board. The goal of unifying these repositories and their processes is to provide a mutually supportive approach to handling medical and research data, to enhance the use of medical and research data to reduce risk, and to promote the understanding of space physiology, countermeasures and other mitigation strategies. Over the past year, both repositories have received over 100 data and information requests from a wide variety of requesters. The disposition of these requests has highlighted the challenges faced when attempting to make data collected on a unique set of subjects available beyond the original intent for which the data were collected. As the EBWG works through each request, many considerations must be factored into account when deciding what data can be shared and how - from the Privacy Act of 1974 and the Health Insurance Portability and Accountability Act (HIPAA), to NASA s Health Information Management System (10HIMS) and Human Experimental and Research Data Records (10HERD) access requirements. Additional considerations include the presence of the data in the repositories and vetting requesters for legitimacy of their use of the data. Additionally, fair access must be ensured for intramural, as well as extramural investigators. All of this must be considered in the formulation of the charters, policies and workflows for the human data repositories at NASA.

Fitts, Mary↗
DOE OSTI2025

A cost and community perspective on the barriers to microbiome data reuse

Microbiome research is becoming a mature field with a wealth of data amassed from diverse ecosystems, yet the ability to fully leverage multi-omics data for reuse remains challenging. To provide a view into researchers’ behavior and attitudes towards data reuse, we surveyed over 700 microbiome researchers to evaluate data sharing and reuse challenges. We found that many researchers are impeded by difficulties with metadata records, challenges with processing and bioinformatics, and problems with data repository submissions. We also explored the cost constraints of data reuse at each step of the data reuse process to better understand “pain points” and to provide a more quantitative perspective from sixteen active researchers. The bioinformatics and data processing step was estimated to be the most time consuming, which aligns with some of the most frequently reported challenges from the community survey. From these two approaches, we present evidence-based recommendations for how to address data sharing and reuse challenges with concrete actions for future work.

59 BASIC BIOLOGICAL SCIENCES↗