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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.

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NASA NTRS2023

A Community Convention for Ecological Forecasting: Output Files and Metadata Version 1.0

This paper summarizes the open community conventions developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. Such open standards are intended to promote interoperability and facilitate forecast communication, distribution, validation, and synthesis. For output files, we first describe the convention conceptually in terms of global attributes, forecast dimensions, forecasted variables, and ancillary indicator variables. We then illustrate the application of this convention to the two file formats that are currently preferred by the EFI, netCDF (network common data form), and comma-separated values (CSV), but note that the convention is extensible to future formats. For metadata, EFI's convention identifies a subset of conventional metadata variables that are required (e.g., temporal resolution and output variables) but focuses on developing a framework for storing information about forecast uncertainty propagation, data assimilation, and model complexity, which aims to facilitate cross-forecast synthesis. The initial application of this convention expands upon the Ecological Metadata Language (EML), a commonly used metadata standard in ecology. To facilitate community adoption, we also provide a Github repository containing a metadata validator tool and several vignettes in R and Python on how to both write and read in the EFI standard. Lastly, we provide guidance on forecast archiving, making an important distinction between short-term dissemination and long-term forecast archiving, while also touching on the archiving of code and workflows. Overall, the EFI convention is a living document that can continue to evolve over time through an open community process.

Michael C. Dietze↗
NASA NTRSDate not supplied

Enabling Cloud Services and Enhanced Data Discovery With Earthdata-Varinfo

NASA’s Earth Observing System Data and Information System (EOSDIS) contains thousands of Earth science datasets from satellites, models, and field campaigns. Each of these collections can contain hundreds of variables that describe each measurement within the dataset, therefore an automated method for generating UMM-Var records is necessary. The Unified Metadata Model for Variables (UMM-Var) provides a framework for variable metadata records in NASA’s Common Metadata Repository (CMR). The Python tool, earthdata-varinfo, was developed to solve this problem of automating the curation of UMM-Var records. Given either a collection DMR file or a netCDF-4 file, earthdata-varinfo can scrape variable metadata and return a CMR compliant UMM-Var record. Earthdata-varinfo can generate thousands of UMM-Var records in a matter of seconds, thus enabling subsetting capabilities and enhancing data discovery.

Eni Awowale↗
NASA NTRSDate not supplied

Blast from the Past: ASDC Curation for NASA Suborbital Legacy Missions to Promote Data Discovery and Accessibility

NASA has an extensive history of conducting suborbital field campaigns to further advances in atmospheric sciences. Beginning with the Chemical Instrument Test and Evaluation (CITE) conducted in 1983-1984, NASA has completed many suborbital campaigns over the past three decades. Since the early 2010s, suborbital missions are typically assigned to a NASA Distributed Active Archive Center (DAAC) prior to the mission for long-term archival and distribution. Efforts are being made by NASA’s Earth Science Data and Information System (ESDIS) Project and the Airborne Data Management Group (ADMG) to assign legacy missions to DAACs for permanent archival and distribution, so that these valuable datasets remain to be available to the scientific community. NASA’s Atmospheric Science Data Center (ASDC) has been named the assigned DAAC for nearly 20 atmospheric composition legacy missions, including missions conducted as part of the Global Tropospheric Experiment (GTE) and expects to be named the assigned DAAC for more of these missions over the next few years. The primary goal of the ASDC is to provide access to the datasets as they are currently formatted to the broad user community and enhance their findability and accessibility. However, data reporting standards have evolved significantly since 1983 and the datasets span a wide variety of file formats, including text, Ames, GTE, and ICARTT (International Consortium for Atmospheric Research on Transport and Transformation), and the amount of metadata and relevant information included in the files also varies greatly and can not be readily extracted without subject matter knowledge. This has caused challenges for the ASDC’s suborbital metadata extraction pipeline in ensuring that accurate and necessary metadata is being provided for the missions by all the ASDC’s existing search mechanisms. To make the data more findable and accessible, the ASDC has begun researching ways to further enhance the datasets, including distributing value-added products (i.e. consistent file format such as ICARTT or netCDF), adding standard names from the ESDIS Standards Coordination Office (ESCO)-approved Atmospheric Composition Variable Standard Names Convention (ACVSNC), and creating outreach materials such as ArcGIS StoryMaps, User Guides, and Micro Articles, providing overviews of the missions and what type of data was collected during the missions. These efforts also help support NASA’s Open-Source Science by enhancing the FAIRness of the legacy data products. This presentation will review the ASDC’s ongoing efforts, progress made, and future plans for legacy missions.

Megan Buzanowicz↗
NASA NTRSDate not supplied

ASDC’s Python-Based Metadata Extraction Pipeline for Suborbital Campaigns

The FAIRness of data products, especially findability and accessibility depend on rich metadata which, when extracted, can allow for proper curation. Over the past few years, the Atmospheric Science Data Center (ASDC) suborbital science support team has developed a metadata extraction pipeline to ensure the required metadata can be retrieved systematically, effectively, and efficiently to ensure the data can be used by a broad community. The development of a pipeline has presented many, but necessary, challenges to support archival and distribution of ASDC’s 30+ suborbital missions. Though sufficient metadata is provided by instrument scientists, the metadata may not be readily machine actionable due to different formats and templates. Further complicating metadata extraction, our team has found that the nature of metadata can be quite diverse given the difference in measurement types, instruments, and measurement platforms. A metadata extraction pipeline has been developed to provide an efficient, plugin-in based, method for adding new parsers, a configuration system that lets non-developers customize how files are processed, and a system for identifying and logging metadata quality issues to ensure they are readily found and addressed. The metadata extraction pipeline identifies critical pieces of metadata that are needed to promote data FAIRness, including location, file revision, measurement start/end datetime and can be easily modified to extract further information (such as variables). Given the wide-ranging datasets, the pipeline has been modified to accommodate multiple file formats, including multiple versions of ICARTT (International Consortium for Atmospheric Research on Transport and Transformation), HDF (Hierarchical Data Format), netCDF (network Common Data Form), and multiple versions of the Ames File Format. The pipeline also supports building metadata for file formats that cannot have metadata easily extracted from them, such as PDF (Portable Document Format) and GIF (Graphics Interchange Format). The pipeline has allowed our team to maintain a consistent flow of data and metadata to archival and distribution services, ensuring the ASDC meets the needs of the suborbital science community. This presentation will highlight the ASDC’s suborbital metadata extraction pipeline, its development, how it’s been modified to support data FAIRness, and plans for maintaining the pipeline and adding new features.

Abraham Porter↗
NASA NTRSDate not supplied

Simplifying Analysis of Hierarchical HDF5 and NetCDF4 Files with Xarray-Datatree

NASA’s Earth Observing System Data and Information System (EOSDIS) contains thousands of Earth science datasets from satellites, models, and field campaigns. EOSDIS data are stored in formats that are well supported by the Earth Science community. These formats include the Hierarchical Data Format (HDF), with derivative flavors such as HDF-5 and the Network Common Data Format (NetCDF-4). The HDF specification allows for a directory-like hierarchy within a single file, known as "groups". Observational data and associated metadata within a single file can be distributed amongst multiple internal groups, which can also be nested to multiple levels. Working with datasets that have a group hierarchical structure can be difficult because of the nested structure of groups. Widely used packages, such as xarray, have data models that do not accommodate the hierarchical structure within HDF files, requiring users to traverse the file and open different HDF groups as separate, unrelated objects. Xarray-datatree is a Python package developed to solve the difficulty of traversing HDFs with a hierarchical group structure by creating a tree-like hierarchical data structure in xarray. The tree-like structure allows each group to be accessed once a DataTree object is instantiated. The migration of xarray-datatree into the xarray core library will reduce barriers to accessing Earth science data by eliminating the need to understand and traverse the specific hierarchy of a grouped HDF file.

Eni Awowale↗
NASA NTRSDate not supplied

Data Democratization: Challenges and Opportunities

Democratizing Earth data is one of the challenges many organizations around the world face in order to maximize the use of their Earth data for research, applications, education, and societal benefits. For example, at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC), over 1600 global and regional datasets in several NASA Earth science focus areas, including atmospheric composition, water and energy cycles, and climate variability, are archived and distributed to the public. Giovanni, the Geospatial Interactive Online Visualization and Analysis Infrastructure, was developed by GES DISC to facilitate data access and exploration, especially for novice users of Earth science. With Giovanni, users can analyze and visualize over 2000 Earth science variables (e.g., precipitation, aerosol, surface wind) without downloading data, software, the expert understanding of data formats and structures, and coding skills, lowering the barrier to data analysis/comparison by preprocessing and accessing to the data. Results of data analysis and visualization can be accessed in several popular formats (e.g., NetCDF, CSV). As a result of Giovanni's efforts, more than 3000 referral papers have been published in various fields. In spite of this, Giovanni is still difficult to use for some users. For instance, if one searches for "precipitation," it will return over 150 related variables. The question is, which one to use? Furthermore, variables from different data providers (e.g., satellites and models) are named differently with different units, further confusing users, especially those outside the communities. Data democratization is complex and multifaceted. Challenges include service and data discovery, user experiences, visualization, data quality, trustworthiness, and more. In this presentation, we will examine Giovanni as an example of challenges and opportunities in developing data democratization services.

data democratization↗
NASA NTRS2017

Data are from Mars, Tools are from Venus

Although during the data production phase, the data producers will usually ensure the products to be easily used by the specific power users the products serve. However, most data products are also posted for general public to use. It is not straightforward for data producers to anticipate what tools that these general end-data users are likely to use. In this talk, we will try to help fill in the gap by going over various tools related to Earth Science and how they work with the existing NASA HDF (Hierarchical Data Format) data products and the reasons why some products cannot be visualized or analyzed by existing tools. One goal is for to give insights for data producers on how to make their data product more interoperable. On the other hand, we also provide some hints for end users on how to make tools work with existing HDF data products. (tool category list: check the comments) HDF-EOS tools: HDFView HDF-EOS Plugin, HEG, h4tonccf, hdf-eos2 dumper, NCL, MATLAB, IDL, etc.net; CDF-Java tools: Panoply, IDV, toosUI, NcML, etc.net; CDF-C tools: ArcGIS Desktop, GrADS, NCL, NCO, etc.; GDAL tools: ArcGIS Desktop, QGIS, Google Earth, etc.; CSV tools: ArcGIS Online, MS Excel, Tableau, etc.

hdf↗
NASA NTRS2018

Hierarchical Data Format for Earth Observing System Data Product Developer's Guide

The "Hierarchical Data Format for Earth Observing System" talk will address the best practices for creating ESDIS data products. The work presented is done in support of Data Product Developers Guide Working Group with mission "to help data product developers make data usable for end users". During the presentation, we will use some examples of NASA data products and show how to modify them to make data more usable.

Data usability↗
NASA NTRSDate not supplied

NASA Briefing to Unidata

The NASA report to Unidata for October, 2021 covers several topics of mutual interest to Unidata. These include plans for hosting NASA data in the cloud, data analysis-in-place, and NASA plans for Open Source Science. Major collaborations with other organizations are also discussed.

NetCDF↗