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All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Advanced Curation of Current and Future Extraterrestrial Samples

Curation of extraterrestrial samples is the critical interface between sample return missions and the international research community. Curation includes documentation, preservation, preparation, and distribution of samples. The current collections of extraterrestrial samples include: Lunar rocks / soils collected by the Apollo astronauts Meteorites, including samples of asteroids, the Moon, and Mars "Cosmic dust" (asteroid and comet particles) collected by high-altitude aircraft Solar wind atoms collected by the Genesis spacecraft Comet particles collected by the Stardust spacecraft Interstellar dust collected by the Stardust spacecraft Asteroid particles collected by the Hayabusa spacecraft These samples were formed in environments strikingly different from that on Earth. Terrestrial contamination can destroy much of the scientific significance of many extraterrestrial materials. In order to preserve the research value of these precious samples, contamination must be minimized, understood, and documented. In addition the samples must be preserved - as far as possible - from physical and chemical alteration. In 2011 NASA selected the OSIRIS-REx mission, designed to return samples from the primitive asteroid 1999 RQ36 (Bennu). JAXA will sample C-class asteroid 1999 JU3 with the Hayabusa-2 mission. ESA is considering the near-Earth asteroid sample return mission Marco Polo-R. The Decadal Survey listed the first lander in a Mars sample return campaign as its highest priority flagship-class mission, with sample return from the South Pole-Aitken basin and the surface of a comet among additional top priorities. The latest NASA budget proposal includes a mission to capture a 5-10 m asteroid and return it to the vicinity of the Moon as a target for future sampling. Samples, tools, containers, and contamination witness materials from any of these missions carry unique requirements for acquisition and curation. Some of these requirements represent significant advances over methods currently used. New analytical and screening techniques will increase the value of current sample collections. Improved web-based tools will make information on all samples more accessible to researchers and the public. Advanced curation of current and future extraterrestrial samples includes: Contamination Control - inorganic / organic Temperature of preservation - subfreezing / cryogenic Non-destructive preliminary examination - X-ray tomography / XRF mapping / Raman mapping Microscopic samples - handling / sectioning / transport Special samples - unopened lunar cores Informatics - online catalogs / community-based characterization.

Allen, Carlton C.↗
NASA NTRS2015

Curation and Analysis of Samples from Comet Wild-2 Returned by NASA's Stardust Mission

The NASA Stardust mission returned the first direct samples of a cometary coma from comet 81P/Wild‐2 in 2006. Intact capture of samples encountered at 6 km/s was enabled by the use of aerogel, an ultralow dense silica polymer. Approximately 1000 particles were captured, with micron and submicron materials distributed along ~mm scale length tracks. This sample collection method and the fine scale of the samples posed new challenges to the curation and cosmochemistry communities. Sample curation involved extensive, detailed photo‐documentation and delicate micro‐surgery to remove particles without loss from the aerogel tracks. This work had to be performed in highly clean facility to minimize the potential of contamination. JSC Curation provided samples ranging from entire tracks to micrometer‐sized particles to external investigators. From the analysis perspective, distinguishing cometary materials from aerogel and identifying the potential alteration from the capture process were essential. Here, transmission electron microscopy (TEM) proved to be the key technique that would make this possible. Based on TEM work by ourselves and others, a variety of surprising findings were reported, such as the observation of high temperature phases resembling those found in meteorites, rarely intact presolar grains and scarce organic grains and submicrometer silicates. An important lesson from this experience is that curation and analysis teams must work closely together to understand the requirements and challenges of each task. The Stardust Mission also has laid important foundation to future sample returns including OSIRIS‐REx and Hayabusa II and future cometary nucleus sample return missions.

Nakamura-Messenger, Keiko↗
NASA NTRS2015

Curating NASA's Astromaterials Collections: Past, Present, and Future

Planning for the curation of samples from future sample return missions must begin during the initial planning stages of a mission. Waiting until the samples have been returned to Earth, or even when you begin to physically build the spacecraft is too late. A lack of proper planning could lead to irreversible contamination of the samples, which in turn would compromise the scientific integrity of the mission. For example, even though the Apollo missions first returned samples in 1969, planning for the curation facility began in the early 1960s, and construction of the Lunar Receiving Laboratory was completed in 1967. In addition to designing the receiving facility and laboratory that the samples will be characterized and stored in, there are many aspects of contamination that must be addressed during the planning and building of the spacecraft: planetary protection (both outbound and inbound); cataloging, documenting, and preserving the materials used to build spacecraft (also known as coupons); near real-time monitoring of the environment in which the spacecraft is being built using witness plates for critical aspects of contamination (known as contamination control); and long term monitoring and preservation of the environment in which the spacecraft is being built for most aspects of potential contamination through the use of witness plates (known as contamination knowledge). The OSIRIS REx asteroid sample return mission, currently being built, is dealing with all of these aspects of contamination in order to ensure they return the best preserved sample possible. Coupons and witness plates from OSIRIS REx are currently being studied and stored (for future studies) at the Johnson Space Center. Similarly, planning for the clean room facility at Johnson Space Center to house the OSIRIS-REx samples is well advanced, and construction of the facility should begin in early 2017 (despite a nominal 2023 return date for OSIRIS-REx samples). Similar development is being done, in concert with JAXA, for the return of Hayabusa 2 samples (nominally in 2020). We are also actively developing advanced techniques like cold curation and organically clean curation in anticipation of future sample return missions such as comet nucleus sample return and Mars sample return.

Zeigler, Ryan↗
NASA NTRS2016

An Interdisciplinary Method for the Visualization of Novel High-Resolution Precision Photography and Micro-XCT Data Sets of NASA's Apollo Lunar Samples and Antarctic Meteorite Samples to Create Combined Research-Grade 3D Virtual Samples for the Benefit of Astromaterials Collections Conservation, Curation, Scientific Research and Education

New technologies make possible the advancement of documentation and visualization practices that can enhance conservation and curation protocols for NASA's Astromaterials Collections. With increasing demands for accessibility to updated comprehensive data, and with new sample return missions on the horizon, it is of primary importance to develop new standards for contemporary documentation and visualization methodologies. Our interdisciplinary team has expertise in the fields of heritage conservation practices, professional photography, photogrammetry, imaging science, application engineering, data curation, geoscience, and astromaterials curation. Our objective is to create virtual 3D reconstructions of Apollo Lunar and Antarctic Meteorite samples that are a fusion of two state-of-the-art data sets: the interior view of the sample by collecting Micro-XCT data and the exterior view of the sample by collecting high-resolution precision photography data. These new data provide researchers an information-rich visualization of both compositional and textural information prior to any physical sub-sampling. Since January 2013 we have developed a process that resulted in the successful creation of the first image-based 3D reconstruction of an Apollo Lunar Sample correlated to a 3D reconstruction of the same sample's Micro- XCT data, illustrating that this technique is both operationally possible and functionally beneficial. In May of 2016 we began a 3-year research period during which we aim to produce Virtual Astromaterials Samples for 60 high-priority Apollo Lunar and Antarctic Meteorite samples and serve them on NASA's Astromaterials Acquisition and Curation website. Our research demonstrates that research-grade Virtual Astromaterials Samples are beneficial in preserving for posterity a precise 3D reconstruction of the sample prior to sub-sampling, which greatly improves documentation practices, provides unique and novel visualization of the sample's interior and exterior features, offers scientists a preliminary research tool for targeted sub-sample requests, and additionally is a visually engaging interactive tool for bringing astromaterials science to the public.

Blumenfeld, E. H.↗
NASA NTRS2017

Astromaterials Curation Online Resources for Principal Investigators

The Astromaterials Acquisition and Curation office at NASA Johnson Space Center curates all of NASA's extraterrestrial samples, the most extensive set of astromaterials samples available to the research community worldwide. The office allocates ~1500 individual samples to researchers and students each year and has served the planetary research community for 45+ years. The Astromaterials Curation office provides access to its sample data repository and digital resources to support the research needs of sample investigators and to aid in the selection and request of samples for scientific study. These resources can be found on the Astromaterials Acquisition and Curation website at https://curator.jsc.nasa.gov. To better serve our users, we have engaged in several activities to enhance the data available for astromaterials samples, to improve the accessibility and performance of the website, and to address user feedback. We havealso put plans in place for continuing improvements to our existing data products.

Todd, Nancy S.↗
NASA NTRS2017

Sample Curation in Support of the OSIRIS-REx Asteroid Sample Return Mission

The OSIRIS-REx asteroid sample return mission launched to asteroid Bennu Sept. 8, 2016. The spacecraft will arrive at Bennu in late 2019, orbit and map the asteroid, and perform a touch and go (TAG) sampling maneuver in July 2020. After sample is stowed and confirmed the spacecraft will return to Earth, and the sample return capsule (SRC) will land in Utah in September 2023. Samples will be recovered from Utah [2] and then transported and stored in a new sample cleanroom at NASA Johnson Space Center in Houston [3]. The materials curated for the mission are described here. a) Materials Archive and Witness Plate Collection: The SRC and TAGSAM were built between March 2014 and Summer of 2015, and instruments (OTES,OVIRS, OLA, OCAMS, REXIS) were integrated from Summer 2015 until May 2016. A total of 395 items were received for the materials archive at NASA-JSC, with archiving finishing ~30 days after launch (with the final archived items being related to launch operations)[4]. The materials fall into several general categories including metals (stainless steel, aluminum, titanium alloys, brass and BeCu alloy), epoxies, paints, polymers, lubricants, non-volatile-residue samples (NVR), sapphire, and various miscellaneous materials. All through the ATLO process (from March 2015 until late August 2016) contamination knowledge witness plates (Si wafer and Al foil) were deployed in the various cleanrooms in Denver and KSC to provide an additional record of particle counts and volatiles that is archived for current and future scientific studies. These plates were deployed in roughly monthly increments with each unit containing 4 Si wafers and 4 Al foils. We archived 128 individual witness plates (64 Si wafers and 64 Al foils); one of each witness plate (Si and Al) was analyzed immediately by the science team after archiving, while the remaining 3 of each are archived indefinitely. Information about each material archived is stored in an extensive database at NASA-JSC, and key summary information for each will be presented in an online catalog. b) Bulk Asteroid sample: The Touch and Go Sampling Mechanism (TAGSAM) head will contain up to 1.5 kg of asteroid material. Upon return to Earth, the TAGSAM head with the sample canister will be subjected to a nitrogen purge and then opened in a nitrogen cabinet in Houston. Once the TAGSAM head is removed from the canister, it will be dis-assembled slowly and carefully under nitrogen until the sample can be removed for processing in a dedicated nitrogen glovebox. Bennu surface samples are expected to be sub-cm sized, based on thermal infrared and radar polarization ratio measurements [1]. The upper limit on material collected by the TAGSAM head is ~2 cm. Therefore, we will be prepared to handle, subdivide, and characterize materials of a wide grain size (from ~10 m to 2 cm), and for both organic (UV fluorescence) and inorganic (SEM, FTIR, optical) properties. Representative portions of the bulk sample will be prepared for JAXA (0.5 %; see also [5]) and Canadian Space Agency (4%), with the remaining divided between the science team (<25%) and archived for future studies (NASA) (>75%). c) Contact Pad samples: The base of the TAGSAM head contains 24 contact pads that are designed to trap the upper surface layer of material and thus offer an opportunity to study asteroid samples that have resided at the very top surface of the regolith. Asteroid material is trapped on the pads in spring steel Velcro hooks, and material will have to be removed from these pads by curation specialists in the lab. d) Hardware: Some canister and SRC hardware items will contain information that will be important to understanding the collected samples, including the canister gas filter, temperature strips, flight witness plates, and the TAGSAM and canister parts that might have adhering dust grains. Some challenges remaining for both bulk sample and contact pad samples include: i) working with intermediate size range (200 to 500 micron) samples - a size range NASA has not previously worked in such detail; ii) techniques for removal of contact pad material from the spring steel hooks, iii) static electrical effects of dust sized particles during sample handling and curation is likely to be significant, and iv) the TAGSAM head and associated canister hardware will undoubtedly be coated with fine adhering dust grains from Bennu. In the case of collection of a large bulk sample mass, the adhering dust grains may be of lower priority. If a small sample mass is returned, the adhering dust may attain a higher priority, so recovery of adhering dust grains is an additional challenge to consider. In the year leading up to sample return we plan a variety of sample handling rehearsals that will enables the curation team to be prepared for many new aspects posed by this sample suite.

Righter, Kevin↗
NASA NTRSDate not supplied

Complications of Metadata Curation for NASA Airborne and Field Campaigns, Platforms, and Instruments

The Airborne Data Management Group (ADMG) curates metadata that describe NASA's airborne and field campaigns, platforms and instruments. This activity is vital to building a useful inventory of sub-orbital Earth science data that improves data discovery and access. During the curation process, many metadata issues were identified that required improvement to campaign and data product metadata. In some cases, locating the needed metadata to add to the inventory was a simple process. For other cases, the information was hard to find. In addition, identifying accurate investigation instrument details to add to the inventory was especially complicated because of the variety of definitions used in the Earth science community for the same concepts. One example of this is the concept of instruments' spatial and temporal resolution. The spatial resolution is one of the more difficult elements to curate given the variations in meaning across various disciplines. Clarified definitions are needed to enable consistency of information across campaigns and instruments. In this presentation, we introduce results from a survey of scientists from various fields in which we asked for definitions of spatial and temporal resolution. Our survey results highlight the importance of creating more universally acceptable definitions for certain metadata elements. By curating sub-orbital field campaign and instrument metadata, ADMG is enabling more efficient discovery and access to NASA observations by allowing science data users to search for certain clearly defined criteria and metadata values.

Ashlyn Shirey↗
NASA NTRSDate not supplied

Insights From Routine Microbiological Monitoring of Air in Astromaterials Curation Cleanrooms

NASA maintains nine separate cleanrooms at the Johnson Space Center to curate and preserve astromaterials samples. Routine microbial monitoring of the surfaces in these cleanrooms began in 20181. Until recently, materials compatibility requirements prevented monitoring airborne biological particles in all but one of these cleanrooms. New astromaterials collections from carbon rich asteroids are more susceptible to biological degradation than previous collections. Therefore, it is important to monitor the bioburden in the air and on surfaces in these labs. Establishing a comprehensive microbial monitoring program will help inform the monitoring and curation plans for Mars sample return which will include samples that are extremely biologically sensitive. In April of 2022 we began routinely collecting air samples in seven of the nine curation cleanrooms (Meteorite ISO 7 equivalent, Lunar ISO 6 equivalent, Stardust, Hayabusa2, OSIRIS-REx ISO 5 equivalent, and Genesis ISO 4 equivalent) using a sampling device that collects airborne biological particles on an electret filter instead conventional sampler that collect cells in a liquid media or onto organic rich Petri dishes. Electret is a generic term for electrostatically charged media. The charge on these materials increases particle trapping when compared to non-charged filters of similar thickness. N95 respirators also use electret filters. This dry sample collection method allows us to meet materials requirements for all the curation labs and reduces the risk of inadvertently introducing contamination as part of our monitoring effort. This method also allows us to preserve a portion of each sample for DNA extraction and next generation sequencing. DNA sequencing helps us to characterize the portion of the cleanroom microbiome that we cannot culture. We will present the results of our first 8 months of monitoring, compare these results to particle counts in the labs and to measurements made directly onto Petri dishes when possible. We will also make recommendations for modifications to the sampling method to improve sampling efficiency and preserve diversity.

A. B. Regberg↗
DOE OSTI2022

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.

access↗
DOE OSTI2024

G2Aero Database of Airfoils - Curated Airfoils

This dataset contains a curated set of 19,164 airfoil shapes from various applications and the data-driven design space of separable shape tensors (PGA space), which can be used as a parameter space for machine-learning applications focused on airfoil shapes. We constructed the airfoil dataset in two main stages. First, we identified 13 baseline airfoils from the NREL 5MW and IEA 15MW reference wind turbines. We reparameterized these shapes using least-squares fits of 8-order CST parametrizations, which involve 18 coefficients. By uniformly perturbing all 18 CST coefficients by +/-20% around each baseline airfoil, we generated 1,000 unique airfoils. Each airfoil was sampled with 1,001 shape landmarks whose x-coordinates followed a cosine distribution along the chord. This process resulted in a total of 13,000 airfoil shapes, each with 1,001 landmarks. In the second phase, we gathered additional airfoils from the extensive BigFoil database, which consolidates data from sources such as the University of Illinois Urbana-Champaign (UIUC) airfoil database, the JavaFoil database, the NACA-TR-824 database, and others. We undertook a thorough pre-processing step to filter out shapes with sparse, noisy, or incomplete data. We also removed airfoils with sharp leading edge and those exceeding our threshold for trailing edge thickness. Additionally, we thinned out the collection of NACA airfoils-- parametric sweeps of NACA airfoils with increasing thickness and camber present in BigFoil database-- by selecting every fourth step in the parameter sweeps. Finally, we regularized the airfoils by reparametrizing them with an 8-order CST parametrization (with 1,001 shape landmarks with x coordinated following cosine distribution along the chord) and removing airfoils with high reconstruction errors. This data pre-processing resulted in a set of 6,164 airfoils. In total, our curated airfoil dataset comprises 19,164 airfoils, each with 1,001 landmarks, and is stored in the curated_airfoils.npz file. Using this curated airfoil dataset, we utilized the separable shape tensors framework to develop a data-driven parameterization of airfoils based on principal geodesic analysis (PGA) of separable shape tensors. This PGA space is provided in PGAspace.npz file.

airfoils↗
NASA NTRS2017

A Relevancy Algorithm for Curating Earth Science Data Around Phenomenon

Earth science data are being collected for various science needs and applications, processed using different algorithms at multiple resolutions and coverages, and then archived at different archiving centers for distribution and stewardship causing difficulty in data discovery. Curation, which typically occurs in museums, art galleries, and libraries, is traditionally defined as the process of collecting and organizing information around a common subject matter or a topic of interest. Curating data sets around topics or areas of interest addresses some of the data discovery needs in the field of Earth science, especially for unanticipated users of data. This paper describes a methodology to automate search and selection of data around specific phenomena. Different components of the methodology including the assumptions, the process, and the relevancy ranking algorithm are described. The paper makes two unique contributions to improving data search and discovery capabilities. First, the paper describes a novel methodology developed for automatically curating data around a topic using Earthscience metadata records. Second, the methodology has been implemented as a standalone web service that is utilized to augment search and usability of data in a variety of tools.

earth science phenomena↗
DOE OSTI2021

Curating a comprehensive set of enzymatic reaction rules for efficient novel biosynthetic pathway design

Enzyme substrate promiscuity has significant implications for metabolic engineering. The ability to predict the space of possible enzymatic side reactions is crucial for elucidating underground metabolic networks in microorganisms, as well as harnessing novel biosynthetic capabilities of enzymes to produce desired chemicals. Reaction rule-based cheminformatics platforms have been implemented to computationally enumerate possible promiscuous reactions, relying on existing knowledge of enzymatic transformations to inform novel reactions. However, past versions of curated reaction rules have been limited by a lack of comprehensiveness in representing all possible transformations, as well as the need to prune rules to enhance computational efficiency in pathway expansion. To this end, we curated a set of 1224 most generalized reaction rules, automatically abstracted from atom-mapped MetaCyc reactions and verified to uniquely cover all common enzymatic transformations. Additionally, we developed a framework to systematically identify and correct redundancies and errors in the curation process, resulting in a minimal, yet comprehensive, rule set. These reaction rules were capable of reproducing more than 85% of all reactions in the KEGG and BRENDA databases, for which a large fraction of reactions is not present in MetaCyc. Our rules exceed all previously published rule sets for which reproduction was possible in this coverage analysis, which allows for the exploration of a larger space of known enzymatic transformations. By leveraging the entire knowledge of possible metabolic reactions through generalized enzymatic reaction rules, we are able to better utilize underground metabolic pathways and accelerate novel biosynthetic pathway design to enable bioproduction towards a wider range of new molecules.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The Gaia EDR3 view of Johnson-Kron-Cousins standard stars: the curated Landolt and Stetson collections

In the era of large surveys and space missions, it is necessary to rely on large samples of well-characterized stars for inter-calibrating and comparing measurements from different surveys and catalogues. Among the most employed photometric systems, the Johnson-Kron-Cousins has been used for decades and for a large amount of important datasets. Our goal is to profit from the Gaia EDR3 data, Gaia official cross-match algorithm, and Gaia -derived literature catalogues, to provide a well-characterized and clean sample of secondary standards in the Johnson-Kron-Cousins system, as well as a set of transformations between the main photometric systems and the Johnson-Kron-Cousins one. Using Gaia as a reference, as well as data from reddening maps, spectroscopic surveys, and variable stars monitoring surveys, we curated and characterized the widely used Landolt and Stetson collections of more than 200 000 secondary standards, employing classical as well as machine learning techniques. In particular, our atmospheric parameters agree significantly better with spectroscopic ones, compared to other machine learning catalogues. We also cross-matched the curated collections with the major photometric surveys to provide a comprehensive set of reliable measurements in the most widely adopted photometric systems. We provide a curated catalogue of secondary standards in the Johnson-Kron-Cousins system that are well-measured and as free as possible from variable and multiple sources. We characterize the collection in terms of astrophysical parameters, distance, reddening, and radial velocity. We provide a table with the magnitudes of the secondary standards in the most widely used photometric systems (ugriz, grizy, Gaia , HIPPARCOS, Tycho , 2MASS). We finally provide a set of 167 polynomial transformations, valid for dwarfs and giants, metal-poor and metal-rich stars, to transform UBVRI magnitudes in the above photometric systems and vice-versa.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Meta2DB: Curated Shotgun Metagenomic Feature Sets and Metadata for Health State Prediction

Meta2DB is a curated metagenomic and metadata database that provides structurally consistent microbiome taxonomy feature count tables for 13 897 samples across 84 studies, 23 disease states, and 34 geographical locations. All samples were uniformly processed using a streamlined metagenomic classification pipeline that employs a unique and comprehensive reference database indexed to contain all sequences across all kingdoms of life that were present in the NCBI Nucleotide (nt) database retrieved on 4 January 2023. This pipeline leverages high-performance computing (HPC) resources at Lawrence Livermore National Laboratory and was used to process 50TB of publicly available raw metagenomic sequence data. Extensive metadata curation was carried out through a combination of manual curation and automated parsing, producing a consistent inter-study metadata table specifically structured to facilitate training of ML models for prediction of human health.

Kok, C [Lawrence Livermore National Laboratory (LL↗
DOE OSTI2025

Scientific Open-Source Software Is Less Likely to Become Abandoned Than One Might Think! Lessons from Curating a Catalog of Maintained Scientific Software

Scientific software is essential to scientific innovation and in many ways it is distinct from other types of software. Abandoned (or unmaintained), buggy, and hard to use software, a perception often associated with scientific software can hinder scientific progress, yet, in contrast to other types of software, its longevity is poorly understood. Existing data curation efforts are fragmented by science domain and/or are small in scale and lack key attributes. We use large language models to classify public software repositories in World of Code into distinct scientific domains and layers of the software stack, curating a large and diverse collection of over 18,000 scientific software projects. Using this data, we estimate survival models to understand how the domain, infrastructural layer, and other attributes of scientific software affect its longevity. We further obtain a matched sample of non-scientific software repositories and investigate the differences. We find that infrastructural layers, downstream dependencies, mentions of publications, and participants from government are associated with a longer lifespan, while newer projects with participants from academia had shorter lifespan. Against common expectations, scientific projects have a longer lifetime than matched non-scientific open-source software projects. We expect our curated attribute-rich collection to support future research on scientific software and provide insights that may help extend longevity of both scientific and other projects.

Malviya Thakur, Addi [ORNL] (ORCID:000000022681999↗
DOE OSTI2020

COVID-19 Data Curation Effort: An Initial Analysis of the Data

During the COVID-19 pandemic of 2020, major case reporting outlets quickly coalesced around two or three primary vendors. Johns Hopkins University and The New York Times were among the more prominent, and all were of great value to the nation, particularly during the uncertain early stages of the pandemic. They primarily focused on three major attributes: number of new cases, deaths, and recovery. Recognizing that many states were reporting very detailed data sets (e.g., hospital beds) at a county level or finer, the ORNL Pandemic Modeling team embarked on a major data curation effort from March to June 2020 for the purpose of capturing this wealth of detailed data. The challenge of curating this data was daunting. The number of attributes reported by the states grew on almost on a weekly basis. States were routinely shifting their web tool strategies away from easily parsable HTML-based formatting to new Tableau and ArcGIS content. This growth in the sheer number of attributes, combined with the unpredictable shifts in data format, meant an aggressive and agile combination of automated scripting and manual scraping was required to capture new daily streams. Further, the team had to scale up staff and widen its approach for capture and storage. As a result, the team collected more than 11 million data points. Following the close of this data collection effort on June 30 th , 2020, the team embarked on a major effort to appraise what had been collected, including an inventory list, spatial completeness, temporal completeness, scale and geographic characteristics, and a determination. A report on this matter was submitted on September 15 th , 2020, titled “DOE COVID-19 Data Curation Effort: Overview of Data Collection Coverage”. Over 2000 unique attributes had been netted over a wide range of spatial scales, including state, county, zip codes, health regions, and census blocks. Over 11 million individual data points were collected across these attributes, and spatial coverage (in total) included all 50 states and multiple territories. What became apparent in the process is that in the absence of any data standards, many states reported a wide variety of unique attributes that were not always compatible with attributes reported in other states. As time continued, states began adding new attributes and offering finer grain detail in some older attributes. This meant that not all data streams existed for the entire time period; in fact, the number tended to increase dramatically towards the end. Often, states would begin an attribute series and then stop altogether. These highly variable and uncertain conditions illuminated the need for harmonization approaches that would reconcile and conflate changing attribute names and detail over time. For example, grouping racial data reported as either Black or African American, depending on the state, into a single harmonized attribute. These choices would make a within-state analysis possible during the time period and lead to potential between-state analytics later on. This was almost entirely a manual decision process, requiring some subjective decision-making at times, to prevent a fragmented, short-lived collection of time series fragments that would offer few insights into trends, patterns, and correlates. This report imports harmonized data for state and county into the World Spatio-Temporal Analytics and Mapping Project (WSTAMP). WSTAMP is a major space-time analysis and visualization tool developed at ORNL for the National Geospatial-Intelligence Agency specifically for this kind of exploratory analysis. WSTAMP offers a rich analytical and graphical environment consisting of a wide range of analytics. These include time series plots, statistical summaries, data mining techniques, trend and pattern detection, and hypothesis generation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

The disCO2ver Platform: Curating Data and Tools for Geologic Carbon Sequestration and Deep Subsurface Research Systems

The U.S. DOE National Energy Technology Laboratory has invested 12+ years of development into the data repository and digital laboratory, the Energy Data eXchange (EDX, edx.netl.doe.gov). Supporting a variety of research areas across the DOE Office of Fossil Energy and Carbon Management, the platform has successfully curated and preserved thousands of data products from DOE research. The Carbon Storage Program has successfully supported data curation, upload, and publishing of data products on EDX for many years, demonstrating a success story of how resources like EDX can effectively help with long term preservation and publishing of DOE data products. EDX continues to shift towards cloud-supported infrastructure, taking a hybrid approach combining on-premises compute and storage integrated with cloud-hosted services. The integration of cloud compute and hybrid architecture enables the development of EDX-hosted platforms that tailor the data and tools hosted on them to a specific community, enables implementation of machine learning tools for data discovery and filtering, and enables the hosting of virtual (online user interface) tools. Geologic carbon sequestration (GCS) research continues to scale up in response to the current administration goals to reduce greenhouse gas emissions and transition the energy economy. Over the last year, EDX’s disCO2ver platform has been developed in response to the need for access to data products and tools to support the scaling up of GCS research. disCO2ver provides access to data resources and tools, produced by DOE and outside authoritative external resources. The platform also provides a user-access control component for the virtualization and cloud hosting of tools. Tools that need to be virtualized, to eliminate the need for users to download the tool and use local compute resources, is essential to supporting big-data analysis and machine learning that is becoming common place in carbon storage modeling, risk analysis, and data publishing practices. This talk will review the EDX’s disCO2ver platform and the current work ongoing to curate data and tools to support GCS and deep subsurface systems research.

Morkner, Paige↗
NASA NTRS2008

Curation, Spacecraft Recovery and Preliminary Examination for the Stardust Mission: A Perspective from the Curatorial Facility

We briefly describe some of the challenges to the Stardust mission, curation and sample preliminary analysis, from the perspective of the Curation Office at the Johnson Space Center. Our goal is to inform persons planning future sample returns, so that they may learn from both our successes and challenges (and avoid some of our mistakes). The Curation office played a role in the mission from its inception, most critically assisting in the design and implementation of the spacecraft contamination control plan, and in planning and documenting the recovery of the spacecraft reentry capsule in Utah. A unique class 100 cleanroom was built to maintain the returned comet and interstellar samples in clean comfort, and to permit dissection and allocation of samples for analysis.

Zolensky, Michael↗