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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Center for Astrophysics Optical Infrared Science Archive. I. FAST Spectrograph

We announce the public release of 141,531 moderate-dispersion optical spectra of 72,247 objects acquired over the past 25 yr with the FAST Spectrograph on the Fred L. Whipple Observatory 1.5 m Tillinghast telescope. We describe the data acquisition and processing so that scientists can understand the spectra. We highlight some of the largest FAst Spectrograph for the Tillinghast Telescope (FAST) survey programs, and make recommendations for use. The spectra have been placed in a Virtual Observatory–accessible archive and ready for download.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

EMISSA (Exploring Millimeter Indicators of Solar-Stellar Activity): I. The initial millimeter–centimeter main-sequence star sample

Due to their wide wavelength coverage across the millimeter to centimeter (mm–cm) range and their increased sensitivity, modern interferometric arrays facilitate observations of the thermal and non-thermal radiation that is emitted from different layers in the outer atmospheres of stars. We study the spectral energy distribution (S obs (ν)) of main-sequence stars based on archival observations in the mm–cm range with the aim to study their atmospheric stratification as a function of stellar type. The main-sequence stars with significant detection in mm bands were identified in the ALMA Science Archive. These data were then complemented with spectral flux data in the extreme ultraviolet to cm range as compiled from various catalogues and observatory archives. We compared the resultant S obs (ν) of each star with a photospheric emission model (S mod (ν)) calculated with the PHOENIX code. The departures of S obs (ν) from S mod (ν) were quantified in terms of a spectral flux excess parameter (ΔS/S mod ) and studied as a function of stellar type. The initial sample consists of 12 main-sequence stars across a broad range of spectral types from A1 to M3.5 and the Sun-as-a-star as reference. The stars with T eff = 3000–7000 K (F–M type) showed a systematically higher S obs (ν) than S mod (ν) in the mm–cm range. Their ΔS/S mod exhibits a monotonic rise with decreasing frequency. The steepness of this rise is higher for cooler stars in the T eff = 3000–7000 K range, although the single fully convective star (T eff ~ 3000 K) in the sample deviates from this trend. Meanwhile, Sobs(ν) of the A-type stars agrees with S mod (ν) within errors. The systematically high ΔS/S mod in F–M stars indicates hotter upper atmospheric layers, that is, a chromosphere and corona in these stars, like for the Sun. The mm–cm ΔS/S mod spectrum offers a way to estimate the efficiency of the heating mechanisms across various outer atmospheric layers in main-sequence stars, and thereby to understand their structure and activity. We emphasise the need for dedicated surveys of main-sequence stars in the mm–cm range.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

The CatWISE Preliminary Catalog: Motions from WISE and NEOWISE Data

CatWISE is a program to catalog sources selected from combined WISE and NEOWISE all-sky survey data at 3.4 and 4.6 μm (W1 and W2). The CatWISE Preliminary Catalog consists of 900,849,014 sources measured in data collected from 2010 to 2016. This data set represents four times as many exposures and spans over 10 times as large a time baseline as that used for the AllWISE Catalog. CatWISE adapts AllWISE software to measure the sources in coadded images created from six-month subsets of these data, each representing one coverage of the inertial sky, or epoch. The catalog includes the measured motion of sources in eight epochs over the 6.5 yr span of the data. From comparison to Spitzer, signal-to-noise ratio = 5 limits in magnitudes in the Vega system are W1 = 17.67 and W2 = 16.47, compared to W1 = 16.96 and W2 = 16.02 for AllWISE. From comparison to Gaia, CatWISE positions have typical accuracies of 50 mas for stars at W1 = 10 mag and 275 mas for stars at W1 = 15.5 mag. Proper motions have typical accuracies of 10 mas yr -1 and 30 mas yr -1 for stars with these brightnesses, an order of magnitude better than from AllWISE. The catalog is available in the WISE/NEOWISE Enhanced and Contributed Products area of the NASA/IPAC Infrared Science Archive.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

The Sloan Digital Sky Survey Quasar Catalog: Sixteenth Data Release

We present the final Sloan Digital Sky Survey IV (SDSS-IV) quasar catalog from Data Release 16 of the extended Baryon Oscillation Spectroscopic Survey (eBOSS). This catalog comprises the largest selection of spectroscopically confirmed quasars to date. The full catalog includes two subcatalogs (the current versions are DR16Q_v4 and DR16Q_Superset_v3 at https://data.sdss.org/sas/dr16/eboss/qso/DR16Q/): a "superset" of all SDSS-IV/eBOSS objects targeted as quasars containing 1,440,615 observations and a quasar-only catalog containing 750,414 quasars, including 225,082 new quasars appearing in an SDSS data release for the first time, as well as known quasars from SDSS-I/II/III. We present automated identification and redshift information for these quasars alongside data from visual inspections for 320,161 spectra. Here, the quasar-only catalog is estimated to be 99.8% complete with 0.3%-1.3% contamination. Automated and visual inspection redshifts are supplemented by redshifts derived via principal component analysis and emission lines. We include emission-line redshifts for Hα, Hβ, Mg II, C III], C IV, and Lyα. Identification and key characteristics generated by automated algorithms are presented for 99,856 broad absorption-line quasars and 35,686 damped Lyman alpha quasars. In addition to SDSS photometric data, we also present multiwavelength data for quasars from the Galaxy Evolution Explorer, UKIDSS, the Wide-field Infrared Survey Explorer, FIRST, ROSAT/2RXS, XMM-Newton, and Gaia. Calibrated digital optical spectra for these quasars can be obtained from the SDSS Science Archive Server.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

The CatWISE2020 Catalog

The CatWISE2020 Catalog consists of 1,890,715,640 sources over the entire sky selected from Wide-field Infrared Survey Explorer (WISE) and NEOWISE survey data at 3.4 and 4.6 μm (W1 and W2) collected from 2010 January 7 to 2018 December 13. This data set adds two years to that used for the CatWISE Preliminary Catalog, bringing the total to six times as many exposures spanning over 16 times as large a time baseline as the AllWISE catalog. The other major change from the CatWISE Preliminary Catalog is that the detection list for the CatWISE2020 Catalog was generated using crowdsource from Schlafly et al., while the CatWISE Preliminary Catalog used the detection software used for AllWISE. These two factors result in roughly twice as many sources in the CatWISE2020 Catalog. The scatter with respect to Spitzer photometry at faint magnitudes in the COSMOS field, which is out of the Galactic Plane and at low ecliptic latitude (corresponding to lower WISE coverage depth) is similar to that for the CatWISE Preliminary Catalog. The 90% completeness depth for the CatWISE2020 Catalog is at W1 = 17.7 mag and W2 = 17.5 mag, 1.7 mag deeper than in the CatWISE Preliminary Catalog. In comparison to Gaia, CatWISE2020 motions are accurate at the 20 mas yr-1 level for W1~15 mag sources and at the ~100 mas yr-1 level for W1~17 mag sources. This level of accuracy represents a 12 improvement over AllWISE. Finally, the CatWISE catalogs are available in the WISE/NEOWISE Enhanced and Contributed Products area of the NASA/IPAC Infrared Science Archive.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

ESS-DIVE guidelines for archiving terrestrial model data

This dataset contains supporting documents and images for ESS-DIVE terrestrial model data archiving guidelines.Terrestrial models are broadly defined as numerical models that couple both land dynamics and energy, water, carbon, or nutrient fluxes. We created these guidelines based on input from the U.S. Department of Energy’s Biological and Environmental Research land modeling community. The guidelines are intended to help modelers determine which components of their terrestrial model data associated with publication should be archived. Based on input from the land modeling community, the guidelines recommend archiving both model input and testing data, as well as code, script, and metadata. The guidelines also recommend archiving model data output, depending on the limitations set by data repositories. Lastly, we provide recommendations for bundling data files for publication as well as a discussion about tools that can facilitate model data archiving and reuse.This dataset is an archive of the associated GitHub repository for our model archiving guidelines (https://github.com/ess-dive-community/essdive-model-data-archiving-guidelines). The ‘README.pdf’ file gives a general introduction to the guidelines, and the ‘instructions.pdf’ file provides more detailed steps for following the guidelines. We also provide 2 figures in this data package: 1) a decision tree (model_data_guidelines_decision_tree.png) that can help users determine which components of their model data to archive. and 2) the ‘model_data_guidelines_flmd.png’ file depicts the different files that can be archived in addition to the model data itself. Lastly, we include 3 digitized tables from our associated manuscript and 3 CSV files with anonymized input from DOE scientists about the importance of different aspects of model data archiving from which we developed the guidelines.Dataset updates for v1.1.0: We updated this data package on 2021-11-22 in response to review comments on our related manuscript. In this update we removed one figure so that the model archiving guidelines are conveyed in text rather than an image. We updated the file-level metadata (FLMD) figure to be in accord with the most recent FLMD recommendations. We made minor edits to the README file to update the recommended citation and added two co-authors. We also added 6 new data files (3 are anonymized input from DOE scientists that helped to inform guidelines, and 3 are digitized tables from our manuscript.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

BinaryCIF and CIFTools—Lightweight, efficient and extensible macromolecular data management

3D macromolecular structural data is growing ever more complex and plentiful in the wake of substantive advances in experimental and computational structure determination methods including macromolecular crystallography, cryo-electron microscopy, and integrative methods. Efficient means of working with 3D macromolecular structural data for archiving, analyses, and visualization are central to facilitating interoperability and reusability in compliance with the FAIR Principles. We address two challenges posed by growth in data size and complexity. First, data size is reduced by bespoke compression techniques. Second, complexity is managed through improved software tooling and fully leveraging available data dictionary schemas. To this end, we introduce BinaryCIF, a serialization of Crystallographic Information File (CIF) format files that maintains full compatibility to related data schemas, such as PDBx/mmCIF, while reducing file sizes by more than a factor of two versus gzip compressed CIF files. Moreover, for the largest structures, BinaryCIF provides even better compression—factor ten and four versus CIF files and gzipped CIF files, respectively. Herein, we describe CIFTools, a set of libraries in Java and TypeScript for generic and typed handling of CIF and BinaryCIF files. Together, BinaryCIF and CIFTools enable lightweight, efficient, and extensible handling of 3D macromolecular structural data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Why don't we share data and code? Perceived barriers and benefits to public archiving practices

The biological sciences community is increasingly recognizing the value of open, reproducible and transparent research practices for science and society at large. Despite this recognition, many researchers fail to share their data and code publicly. This pattern may arise from knowledge barriers about how to archive data and code, concerns about its reuse, and misaligned career incentives. Here, we define, categorize and discuss barriers to data and code sharing that are relevant to many research fields. We explore how real and perceived barriers might be overcome or reframed in the light of the benefits relative to costs. By elucidating these barriers and the contexts in which they arise, we can take steps to mitigate them and align our actions with the goals of open science, both as individual scientists and as a scientific community.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Hashes are not suitable to verify fixity of the public archived web

Web archives, such as the Internet Archive, preserve the web and allow access to prior states of web pages. We implicitly trust their versions of archived pages, but as their role moves from preserving curios of the past to facilitating present day adjudication, we are concerned with verifying the fixity of archived web pages, or mementos, to ensure they have always remained unaltered. A widely used technique in digital preservation to verify the fixity of an archived resource is to periodically compute a cryptographic hash value on a resource and then compare it with a previous hash value. If the hash values generated on the same resource are identical, then the fixity of the resource is verified. We tested this process by conducting a study on 16,627 mementos from 17 public web archives. We replayed and downloaded the mementos 39 times using a headless browser over a period of 442 days and generated a hash for each memento after each download, resulting in 39 hashes per memento. The hash is calculated by including not only the content of the base HTML of a memento but also all embedded resources, such as images and style sheets. We expected to always observe the same hash for a memento regardless of the number of downloads. However, our results indicate that 88.45% of mementos produce more than one unique hash value, and about 16% (or one in six) of those mementos always produce different hash values. We identify and quantify the types of changes that cause the same memento to produce different hashes. These results point to the need for defining an archive-aware hashing function, as conventional hashing functions are not suitable for replayed archived web pages.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
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 OSTI2024

Prediction of the SYM-H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification

We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short-term forecasts of the SYM-H index based on 1- and 5-min resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM-H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM-H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1- and 5-min resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM-H indices (1 hr in advance) in a large storm (SYM-H = -393 nT) using 5-min resolution data. When predicting the SYM-H indices (2 hr in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Plant science history manuscript GitHub repo

This archive contains codes, notebooks, and other information for reproducing the study that links to this file. The original files are in the plant_sci_hist Github repository which will likely be updated when the code base is adopted for other projects. Thus, this archive is provided so a frozen version used for publishing the plant science history manuscript that references this file.

Botany/trend/methods↗
DOE OSTI2021

Developing Bloom Filters for Web Archives’ Holdings (Final Project Report)

The main goal of the project was to develop a framework for web archives to create Bloom filters based on their holdings of archived web resources. A Bloom filter (BF) is a data structure that, in our scenario, contains hash values of all (or a subset of) URLs, of which an archive has one or more archival copies. Two main use cases fall in scope for this project and are supported by a BF implementation: 1) Sharing of an archive’s holdings (URLs) and 2) Querying the holdings of one or more archives. Since URL strings are hashed before ingested into the BF, the index of an archive is not shared in plain text when the BF is shared with trusted parties. On the other hand, a BF implementation does allow queries for a URL to confirm if an archive indeed has one or more archival copies of that URL. This collaborative project between Los Alamos National Laboratory (LANL) and the Croatian Web Archive (HAW), from the National and University Library in Zagreb (NSK), developed by NSK and University of Zagreb University Computing Center SRCE), aimed at developing software to create BFs, evaluate the scalability of the approach, pilot a search service based on BFs, and design a framework for archives to share their filters with trusted parties. In the remainder of this document, we will report on the work completed with respect to the individual deliverables, outline aspects of future work, and conclude with our recommendations for the use of BFs for the web archiving community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Hyper Suprime-Cam Legacy Archive

Abstract We present the launch of the Hyper Suprime-Cam Legacy Archive (HSCLA), a public archive of processed, science-ready data from the Hyper Suprime-Cam (HSC). HSC is an optical wide-field imager installed at the prime focus of the Subaru Telescope and has been in operation since 2014. While ∼1/3 of the total observing time of HSC has been used for the Subaru Strategic Program (SSP), the remainder of the time is used for Principal Investigator (PI)-based programs. We have processed the data from these PI-based programs and make the processed, high-quality data available to the community through HSCLA. The current version of HSCLA includes data taken in the first year of science operation, 2014. We provide both individual and coadd images as well as photometric catalogs. The photometric catalog from the coadd is loaded to the database, which offers a fast access to the large catalog. There are other online tools such as an image browser and an image cutout tool and they will be useful for science analyses. The coadd images reach 24–27th magnitudes at 5σ for point sources and cover approximately 580 square degrees in at least one filter with 150 million objects in total. We perform extensive quality assurance tests and verify that the photometric and astrometric quality of the data is good enough for most scientific explorations. However, the data are not without problems and users are referred to the list of known issues before exploiting the data for science. All the data and documentations can be found at the data release site, 〈https://hscla.mtk.nao.ac.jp/〉.

Tanaka, Masayuki↗