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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) was conceived to address the existing ground testing data management of the NASA Ames arc jet facilities (e.g., manually entered Excel files and USB drive data transfers). These data management practices were seen as a choke point for future thermal protection system (TPS) development as they limit statistical tracking, resolution of diagnostics, coordination between video/time series, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗
NASA NTRSDate not supplied

ICARTT File Format Enhancements: Supporting FAIRness of Airborne and Field Campaign Data

The ICARTT (International Consortium for Atmospheric Research on Transport and Transformation) standards were developed to fulfill data management needs for the ICARTT campaign in 2004. The ICARTT file format is text-based and composed of a header with important data description information and the data section. The ICARTT format, built on the NASA Ames and GTE data formats, was created to facilitate data exchange and promote collaborations among the science teams for achieving the ICARTT campaign goals. Due to the success of the ICARTT campaign, the ICARTT file format was exposed to a broad range of airborne researchers and was adopted for use in many other field campaigns sponsored by NASA and other partner agencies. The ICARTT format standards became a NASA standard in 2010 and was amended in January 2017 providing many enhancements, including the requirement for variable standard names. Primarily designed for airborne field studies, ICARTT has been further utilized for ground-based studies. The ICARTT format can host metadata that is critical for proper use of the data, especially for in-situ measurements. However, the information that needs to be included is often in free text, meaning the information are human readable, but not machine interpretable. Furthermore, the amount and type of information provided can vary substantially between principal investigators and campaigns. To support interoperability and FAIR principles, further enhancements to the ICARTT standards are recommended. Possible recommendations include standardizing timestamps for easier data comparisons and analysis; potential use of controlled and consistent vocabulary for variable short name and certain common metadata elements; and providing guidance on variable measurement units and how they are reported.

Megan Buzanowicz↗
NASA NTRSDate not supplied

The Use of Atmospheric Composition Variable Standard Names in Airborne and Field Data Products

The number of variables measured during airborne field campaigns has increased more than tenfold over the last thirty years. With this increase in measurements, the complexity for distributed active archive centers (DAACs) to distribute the data and for data users to search for and find measurements of interest has also increased. Part of this complexity arises from the unique variable names in suborbital atmospheric composition field studies. With limited guidelines related to variable naming, variable names and structures can vary significantly, even for the same type of variable. It is common for instrument scientists to use their intended measurable quantity as the data variable name. This can make it difficult for users to locate and interact with a particular variable across multiple data sets. One effective solution to this problem, identified by the Earth Science Data System (ESDS) ICARTT Refresh Working Group [1], was to introduce variable standard names that can be used as tags for each data variable. This allows similar measurements (e.g., dew point) to be categorized and located across field campaigns, regardless of what variable name the instrument scientist has used. From this the atmospheric composition variable standard names were developed with the goal to use Findable, Accessible, Interoperable, and Reusable (FAIR) principles [2] and provide context for all users, while remaining connected to those in the subject area. These standard names have been successfully implemented in FIREX-AQ, CAMP 2EX, ACTIVATE, and DCOTSS field campaigns.

metadata↗
NASA NTRSDate not supplied

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) is a facility data management application developed for the NASA Ames arc jet facilities. The current decentralized data management practices limit statistical tracking, synchronization between video/time series, search capability, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗
NASA NTRSDate not supplied

Big-data Efficient Automated Science Transfer (BEAST): an open-source software architecture for arc jet data management, modeling, and automation

Big-data Efficient and Automated Science Transfer (BEAST) was conceived to address the existing ground testing data management of the NASA Ames arc jet facilities (e.g., manually entered Excel files and USB drive data transfers). These data management practices were seen as a choke point for future thermal protection system (TPS) development as they limit statistical tracking, resolution of diagnostics, coordination between video/time series, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗
NASA NTRSDate not supplied

ICARTT File Format Enhancements: Supporting FAIRness and Data Discovery of Suborbital Campaign Data

Suborbital campaigns aim to accomplish a wide variety of goals and can include a variety of platforms, instruments, and parameters measured. In 2004, the ICARTT (International Consortium for Atmospheric Research on Transport and Transformation) standards were developed to fulfill data management needs for the ICARTT campaign. The ICARTT file format is text-based and composed of a header with important data description information and the data section. Built on the NASA Ames and GTE data formats, the ICARTT format was created to facilitate data exchange and promote collaborations among the science teams for achieving the ICARTT campaign goals. Due to its success and adaptation for use in many other field campaigns, the ICARTT file format became a NASA standard in 2010 and was amended in January 2017. These changes provided many enhancements, including the requirement for variable standard names. Primarily designed for airborne field studies, ICARTT has been further utilized for ground-based studies. NASA has made a commitment to build an inclusive open science community over the next decade. Open-source science strives to make publicly funded scientific research transparent, inclusive, accessible, and reproducible. The ICARTT format can host metadata that is critical for proper use of the data, particularly for in-situ measurements, and can enhance data discovery and accessibility. However, the required fields are often free text, meaning that the information is human readable, but not machine interpretable. Furthermore, the amount and type of information provided can vary significantly between principal investigators and campaigns. To support FAIR principles and interoperability, enhancements to the ICARTT standards are recommended. Possible recommendations include potential use of controlled and consistent vocabulary for variable standard name and certain common metadata elements; standardizing timestamps for easier data comparisons and analysis; and providing guidance on variable measurement units and how they are reported. Enhancing ICARTT metadata can further streamline the process to make suborbital data more readily available to the data user and improve variable-level metadata. Providing more variable-level metadata can enhance data searching and discovery, supporting NASA’s Open-Source Science Initiative (OSSI).

Megan Buzanowicz↗
NASA NTRS2023

NLSP: NASA Life Sciences Portal

NASA’s Life Sciences Ports (NLSP) serves the scientific community by providing curated data from space life science experiment. The Human Research Program (HRP) with the help of NLSP is currently transforming their life sciences data archive systems and processes to improve compliance with the FAIR principles. Some of these improvements will at the same time support the twin pillars of Open Science: transparency of methods and reproducibility of results. This video is a high level overview of the NLSP for existing and new users.

Life Sciences data↗
NASA NTRSDate not supplied

Developing a Vision for Heliophysics Infrastructure: The LIKED Resource and the DIARieS Ecosystem

Heliophysics data and computational infrastracture are not equipped for 21st science, suffering from holes in the know-how to build better systems. Without a clear vision, efforts to improve the infrastructure have been incremental and incoherent. This poster presents both the vision and the technology required: an online LIbrary KnowledgE and Discovery (LIKED) resource for discovering and implementing knowledge, data, and infrastructure resources; and an online analysis ecosystem to simplify Discovery, Implementation, Analysis, Reproducibility, and Sharing (DIARieS) of scientific results and environments. The LIKED and DIARieS solutions adopt FAIR data principles and the best practices from the budding field of open science. The proposed new infrastructure components will close many of the current gaps in heliophysics’ infrastructure, such as the ability to search for data and knowledge by phenomenon across domains, and to find software and examples relevant to the desired data set (including model data). Further, these components will enable community members to more efficiently use the resources already present and improve upon the content via a community-curated and trusted library. Combining these solutions lowers the barriers to heliophysics resources for all, increasing the return on our investments. Finally, the structure behind these ideas are topic-agnostic, so they are fully extensible to other fields, leading to invaluable connections to other disciplines. Just as with the development and construction of a long-term satellite mission, we must work together as a community to build a vision of the infrastructure that will most benefit the community, and then collaborate to construct, assemble, and test all the necessary pieces individually and as a unit. Our purpose in presenting this work is to not only describe the proposed vision, but also to gather feedback from the community on this topic.

infrastructure↗
NASA NTRS2022

Data Needs to be…

Findable, Accessible, Interoperable, and Reusable (FAIR) data are essential to heliophysics, indeed all scientific research. We make recommendations intended to prioritize resources needed to satisfy FAIR data principles, treating them as a fundamental research infrastructure, rather than a simple research product.

A Halford↗
NASA NTRSDate not supplied

Laying the Foundations for FAIR-er Science: ISA and the LSDA Data Submission Process in NASA's Evolving Data Management Environment

The Life Sciences Data Archive (LSDA) archives data resulting from research on the effects of spaceflight on humans and the development of countermeasures to mitigate spaceflight hazards. Archivists work with researchers to ensure that unique and high value data products and their metadata are preserved and managed to support current and future research. Currently, LSDA is updating its procedures and data submission requirements in response to the evolving data preservation environment at NASA. LSDA is implementing best practices for research data management through the establishment of clear data submission guidelines, integration of the FAIR (Findability, Accessibility, Interoperability, Reusability) principles, and use of the ISA (Investigation, Study, Assay) research metadata framework for data discoverability and transparency into the data management processes. These changes directly impact LSDA’s requirements for research data submissions. The newly revised Research Data Submission Agreement (RDSA), formerly the Data Submission Agreement (DSA), introduces ISA-compatible metadata collection standards to LSDA’s process. Adherence to LSDA’s data submission guidelines enhances the FAIR-ness of the repository’s collections for future users. This presentation will discuss (1) how submission of research data and associated metadata are impacted by current data management policies, (2) benefits of the adoption of FAIR principles and the ISA metadata framework for retrospective studies utilizing existing LSDA datasets and historic data collections, and (3) the support LSDA will provide to researchers during this transition.

LSDA↗
NASA NTRSDate not supplied

Laying The Foundations for FAIR-ER Science: ISA And LSDA Data Submission Process in NASA’s Evolving Data Management Environment

The Life Sciences Data Archive (LSDA) archives data resulting from research on the effects of spaceflight on humans and the development of countermeasures to mitigate spaceflight hazards. Archivists work with researchers to ensure that unique and high value data products and their metadata are preserved and managed to support current and future research. Currently, LSDA is updating its procedures and data submission requirements in response to the evolving data preservation environment at NASA. LSDA is implementing best practices for research data management through the establishment of clear data submission guidelines, integration of the FAIR (Findability, Accessibility, Interoperability, Reusability) principles, and use of the ISA (Investigation, Study, Assay) research metadata framework for data discoverability and transparency into the data management processes. These changes directly impact LSDA’s requirements for research data submissions. The newly revised Research Data Submission Agreement (RDSA), formerly the Data Submission Agreement (DSA), introduces ISA-compatible metadata collection standards to LSDA’s process. Adherence to LSDA’s data submission guidelines enhances the FAIR-ness of the repository’s collections for future users. This presentation will discuss (1) how submission of research data and associated metadata are impacted by current data management policies, (2) benefits of the adoption of FAIR principles and the ISA metadata framework for retrospective studies utilizing existing LSDA datasets and historic data collections, and (3) the support LSDA will provide to researchers during this transition.

Data submission↗
NASA NTRS2018

Design Principles for Smallsat SARs

Synthetic Aperture Radar (SAR) is by now a mature remote sensing technique to obtain spatially-resolved radar measurements of terrain. Currently, SAR image data are readily available from an ever-expanding multitude of SAR satellites in Earth orbit. Many spaceborne SAR systems currently in use or planned for the near-term are multifunctional: their designs tend to maximize the menu of image modes available to the end user. They also follow fairly conventional design principles, laid down decades ago, that lead almost inevitably to large antennas and even larger spacecraft. This raises the question: how does one go about designing a SAR system that fits in a Smallsat (<200 kg) form factor? The design principles for Smallsat SARs outlined in this paper have been developed over a twenty-year period in architecting Earth-orbiting SARs such as NASA/JPL’s NISAR and ESA’s Biomass mission, as well as planetary SAR mission concepts. Example mission concepts following this approach will be presented at the end of the paper. These include an S-Band Smallsat geodetic constellation to measure surface deformation, as called for by the 2018 National Academy Decadal Survey for Earth Observation from Space. Another example is a Ka-band cubesat-sized system designed to detect changes on Earth’s surface.

Freeman, Anthony↗
NASA NTRSDate not supplied

Automated Collection of Scientific Publications Linked to NASA Earth Science Datasets

NASA's Earth Observing System Data and Information System (EOSDIS) began dataset Digital Object Identifier (DOI) registration in 2012. The number of dataset DOIs registered as of January of 2023 exceeds 11,000. As the research community becomes aware of the importance of sharing data through Open Science and optimizing data reuse through Findability, Accessibility, Interoperability, and Reuse (FAIR) data management principles, datasets are increasingly being cited in scientific publications. When datasets are cited explicitly by DOI within published works, automated methods can be developed for collecting these published works from a variety of bibliometric sources. The coverage of the sources varies, so each source can collect citations that are only available within it. Using major citation databases such as Scopus and Web of Science, the Google Scholar search engine, the CrossRef Open Citation Index, and the dataset DOI registry DataCite, we present an automated workflow for dataset citation collection. By harvesting citations automatically, a citation library is created explicitly linking EOSDIS datasets to publications that cite them. Using Zotero, a free and open-source citation manager, we demonstrate how to access and browse this library by the tags indicating bibliometric sources, dataset DOI, and the dataset archive center. We also demonstrate temporary trends in the number of publications harvested from bibliometric sources.

Infometrics↗
NASA NTRS2025

Why We Do What We Do: Data Reuse, Open Access, and Privacy in Data Management at the Life Sciences Data Archive

As custodian of the unique and irreplaceable collections of human subject research data generated by the Human Research Program and its predecessors throughout the agency’s history, the Life Sciences Data Archive (LSDA) is charged with protecting participants’ privacy and implementing their consent decisions as it provides retrospective data for use in new studies. This active, stewardship-focused approach to data management and preservation shapes the products that LSDA provides to researchers and the responsibilities of researchers in using the data and publishing their results. This presentation reviews how federal and agency mandates shape LSDA’s data management procedures and expectations for researchers. Topics covered will include LSDA’s movement towards implementation of the FAIR (Findable, Accessible, Interoperable, Reusable) principles and how the archive’s evolving data management practices support FAIR-ness; collaboration between LSDA and the Lifetime Surveillance of Astronaut Health (LSAH) project (the repository of astronaut medical data); LSDA’s response to the challenges of performing its stewardship role and maintaining trust given the public profiles of the subjects whose data it preserves; and the ever-increasing challenges to expectations of subject privacy stemming from the growing power and ubiquity of of data analysis and aggregation tools.

Data↗
NASA NTRSDate not supplied

Breaking Barriers: Integrating Geo-Leo Aerosol Data with an Open-Source Approach

The scientific community is still examining the novel data from geostationary satellite observations and evaluating methods for effectively fusing the polar observations with various spatial and temporal resolutions. However, the merged data will present a significant ""Big Data"" challenge, including processing, storage, data discoverability, accessibility, and migration within cloud computing environments. We have developed an open-source package to fuse aerosol optical depths (AOD) products from six satellite sensors in the past four years (2019~2023), and this presentation will update our recent progress. Using this Python-based package, we produced a level 3 global (AOD) product in a quarter-degree spatial resolution every half-hour, fusing the Level 2 AOD data with the Dark Target aerosol retrieval algorithm from six satellites: three geostationary (GOES-16/17 and Himawari-8) with high temporal resolution, and three polar orbiting (TERRA/MODIS, AQUA/MODIS, and SNPP-VIIRS) with global coverage. By integrating these observations, the diurnal cycle of global AOD in this fused product can be characterized at local, regional, and global scales. Furthermore, we are committed to openness and transparency by providing our package and its associated functionalities as open-source. Our dedication to adhering to the FAIR, CARE, and TRUST principles ensures that our users can rely on the integrity and ethical standards of our work. For instance of Interoperability, this package fuses remote sensing products on demand into desired temporal and spatial domains. It can be run in a central processing unit (CPU) or a Graphics processing unit (GPU) mode. This package will empower researchers and practitioners to use satellite and sensor data efficiently in various applications and research.

Xiaohua Pan↗
NASA NTRSDate not supplied

Why We Do What We Do: Data Reuse, Open Access and Privacy in Data Management at the Life Sciences Data Archive

As custodians of the unique and irreplaceable collections of human subject research data generated by the Human Research Program and its predecessors throughout the agency’s history, the Life Sciences Data Archive (LSDA) is charged with protecting participants’ privacy and implementing their consent decisions as it provides retrospective data for use in new studies. This active, stewardship-focused approach to data management and preservation shapes the products that LSDA provides to researchers and the responsibilities of researchers in using the data and publishing their results. This presentation reviews how federal and agency mandates shape LSDA’s data management procedures and expectations for researchers. Topics covered will include LSDA’s movement towards implementation of the FAIR (Findable, Accessible, Interoperable, Reusable) principles and the archive’s evolving data management practices; collaboration between LSDA and the Lifetime Surveillance of Astronaut Health (LSAH) project (the repository of astronaut medical data); LSDA’s response to the challenges of performing its stewardship role and maintaining trust given the public profiles of the subjects whose data it preserves; and the ever-increasing challenges to expectations of subject privacy stemming from the growing power and ubiquity of data analysis and aggregation tools.

data management↗
NASA NTRSDate not supplied

Open Science Practices at the Community Coordinated Modeling Center

Open Science is defined as “the principle and practice of making research products and processes available to all, while respecting diverse cultures, maintaining security and privacy, and fostering collaborations, reproducibility, and equity” by Federal Agencies. The CCMC has been practicing open science based on FAIR (Findable, Accessible, Interoperable and Reusable) principle by providing access to the state-of -the art space science and space weather models to users around the world through various simulation services such as Runs-on-Request, Instant Runs, Real time runs on iSWA system. The CCMC also provides a wide range of tools and framework to help users easily utilize modeled data. One of the tools is the official NASA open-sourced software called Kamodo. Kamodo allows users to work with complex space weather models and data with little or no coding experience. Additionally, to support transparent model validation efforts, the CCMC is providing an integrated and flexible framework called CAMEL. CAMEL allows users to seamlessly compare model outputs with observational data sets. Currently, we are working on a user-friendly database of the papers and research that used CCMC services, so that the future users will have open access to previously performed research by other users and its details. In this presentation, we will show the open tools and resources provided by the CCMC. Furthermore, we will share our new efforts to support open data and open science results.

Ja Soon Shim↗