Results for “data citations” — Page 8

DOE OSTI2016

TH-C-204-02: Improving Manuscript Quality Via Structured Reviews, Enhanced Scientific Category Taxonomy, and Outreach

In this presentation, the Editors will outline our vision for the future of Medical Physics and review recent work-in-progress initiatives to implement this vision. Finally, we will close with guidance to authors on how to write a good Medical Physics paper. A major focus will be the transition to a new publisher in 2017 following a more than 40-year association with American Institute of Physics Publishing (AIPP). Vision for Medical Physics and status of current initiatives: Jeff Williamson, Editor-in-Chief The broad vision of Medical Physics is “to continue the Journal’s tradition of publishing the very best science that propels our discipline forward and improves our contribution to patient care.” More concretely, the Journal should be the preeminent forum for exchange of cutting edge medical physics science. We seek to identify the best contributions in (a) high impact clinical physics innovations; (b) clinical translation and validation of basic science innovations; and (c) cutting edge basic science developments with potential for patient care improvements. Among the challenges and opportunities, we face are: electronic-only and open access publishing; competition from new radiological science journals; trends towards more interactive, social-media based scientific communities; and diversification of the medical physics research, authorship, and readership domains, including clinical applications quite foreign to core ABR clinical competencies. Recently implemented and ongoing initiatives include: Revised Table of Contents (TOC) and more contemporary topical submission categories Structured review template in HTML format Comprehensive hierarchical taxonomy for identifying reviewer expertise Formal process for soliciting high quality and impact Review and Vision 20/20 Articles We have recruited four Review Article Co-editors: John Rowlands and Ingrid Reiser (imaging physics) and Joao Seco and Tim Zhu (therapy physics). The Co-Editors will identify timely topics and solicit high profile authors to submit review manuscripts. To submit an article, authors will need to work with an assigned Co-Editor to develop a mutually acceptable outline and abstract. 5) A new and exciting class of articles: Medical Physics Dataset Articles (MPDAs) MPDAs describe scientifically or clinically valuable open-access datasets with high potential for contributing to the research of medical physicists working on related problems. In contrast to Research Articles, MPDAs should not include hypothesis testing; or data analyses supporting generalizable conclusions. The publically accessible dataset must be permanently archived before the MPDA can be published. This initiative is being led by Joe Deasy. Update on new publisher transition: The transition of AAPM scientific publishing operations to a major publishing house is a major opportunity to expand Medical Physics readership and its scholarly impact. The advantages include: (a) common manuscript management and web hosting platforms for Medical Physics and its sister journal, JACMP; (b) greater than 4-fold expansion of subscribing institutions; and (c) resources to mount data-driven, highly targeted marketing campaigns to enhance citation and download rates. A transition update of this epochal development, which has only begun as of this writing (3/31/16), will be given. Improving manuscript quality via structured reviews, enhanced scientific category taxonomy, and outreach: Shiva Das, Therapy Physics Editor Medical Physics is committed to continuous improvement with the ultimate goal of improving the potential impact of accepted manuscripts. In order to do so, Medical Physics must be able to tap into important/emerging areas and be able to select high quality contributions consistently via discerning reviews. Improving the quality of reviews is crucial to selecting high quality manuscripts and also to improving manuscript impact via feedback in the review process. With this in mind, Medical Physics is in the process of: (a) fostering outreach to important areas that are currently underrepresented in Medical Physics; (b) implementing a structured template review form; and (c) implementing a comprehensive scientific category taxonomy to identify reviewers who are best suited to an article. Outreach efforts have begun to various scientific areas. Strategies to increase submissions from these areas will be discussed. As a consequence of this effort, a special issue on particle therapy is under development. A review template was implemented in late 2014 on a limited test basis. Based on reviewer feedback, the template was restructured and shortened to capture essential review elements. The restructured template is due to be released shortly. The new scientific category taxonomy is in the process of being deployed to reviewers and associate editors. Salient aspects of the structured review template and scientific category taxonomy will be discussed in this talk. Writing good scientific papers and responding to critiques: Mitch Goodsitt, Imaging Physics Editor The essential components of the abstract, introduction, methods, discussion and conclusion sections of manuscripts, as well as the desired writing style and style of the figures and tables will be reviewed. Publishable Medical Physics manuscripts must include clear and concise statements of the novelty and clinical and/or scientific importance of the authors’ work. Examples of novelty include: new technical solution to an important clinical problem; new generalizable knowledge; and first demonstration that an existing engineering solution solves a clinical problem. Please note that we encourage authors of recently published conference proceedings (e.g., SPIE, IEEE) papers on novel medical physics related work to submit more substantial versions of that work to our journal. All submissions must include: sufficient background information and rationale; enough detail for others to reproduce the authors’ work; sufficient statistical analysis to refute or validate the authors’ hypotheses; a description of how the present work compares to, is distinct from, and improves upon others’ work; and sections devoted to the limitations of the study and future directions. Writing should be polished. Poor wording, grammar and composition frustrate the review process. Our journal does not have copyeditors for revising manuscripts. When authors receive critiques from the referees and associate editor, the authors should provide a detailed point-by-point response to each comment. The authors’ rebuttal should include the text of the original criticism, the authors’ response, and a pasted copy of the modified text along with the line numbers in the revised article. The new text should be highlighted in a different font color in the revised submission. Following these recommendations will improve submissions and facilitate the review process.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-C-204-03: Writing Good Scientific Papers and Responding to Critiques

In this presentation, the Editors will outline our vision for the future of Medical Physics and review recent work-in-progress initiatives to implement this vision. Finally, we will close with guidance to authors on how to write a good Medical Physics paper. A major focus will be the transition to a new publisher in 2017 following a more than 40-year association with American Institute of Physics Publishing (AIPP). Vision for Medical Physics and status of current initiatives: Jeff Williamson, Editor-in-Chief The broad vision of Medical Physics is “to continue the Journal’s tradition of publishing the very best science that propels our discipline forward and improves our contribution to patient care.” More concretely, the Journal should be the preeminent forum for exchange of cutting edge medical physics science. We seek to identify the best contributions in (a) high impact clinical physics innovations; (b) clinical translation and validation of basic science innovations; and (c) cutting edge basic science developments with potential for patient care improvements. Among the challenges and opportunities, we face are: electronic-only and open access publishing; competition from new radiological science journals; trends towards more interactive, social-media based scientific communities; and diversification of the medical physics research, authorship, and readership domains, including clinical applications quite foreign to core ABR clinical competencies. Recently implemented and ongoing initiatives include: Revised Table of Contents (TOC) and more contemporary topical submission categories Structured review template in HTML format Comprehensive hierarchical taxonomy for identifying reviewer expertise Formal process for soliciting high quality and impact Review and Vision 20/20 Articles We have recruited four Review Article Co-editors: John Rowlands and Ingrid Reiser (imaging physics) and Joao Seco and Tim Zhu (therapy physics). The Co-Editors will identify timely topics and solicit high profile authors to submit review manuscripts. To submit an article, authors will need to work with an assigned Co-Editor to develop a mutually acceptable outline and abstract. 5) A new and exciting class of articles: Medical Physics Dataset Articles (MPDAs) MPDAs describe scientifically or clinically valuable open-access datasets with high potential for contributing to the research of medical physicists working on related problems. In contrast to Research Articles, MPDAs should not include hypothesis testing; or data analyses supporting generalizable conclusions. The publically accessible dataset must be permanently archived before the MPDA can be published. This initiative is being led by Joe Deasy. Update on new publisher transition: The transition of AAPM scientific publishing operations to a major publishing house is a major opportunity to expand Medical Physics readership and its scholarly impact. The advantages include: (a) common manuscript management and web hosting platforms for Medical Physics and its sister journal, JACMP; (b) greater than 4-fold expansion of subscribing institutions; and (c) resources to mount data-driven, highly targeted marketing campaigns to enhance citation and download rates. A transition update of this epochal development, which has only begun as of this writing (3/31/16), will be given. Improving manuscript quality via structured reviews, enhanced scientific category taxonomy, and outreach: Shiva Das, Therapy Physics Editor Medical Physics is committed to continuous improvement with the ultimate goal of improving the potential impact of accepted manuscripts. In order to do so, Medical Physics must be able to tap into important/emerging areas and be able to select high quality contributions consistently via discerning reviews. Improving the quality of reviews is crucial to selecting high quality manuscripts and also to improving manuscript impact via feedback in the review process. With this in mind, Medical Physics is in the process of: (a) fostering outreach to important areas that are currently underrepresented in Medical Physics; (b) implementing a structured template review form; and (c) implementing a comprehensive scientific category taxonomy to identify reviewers who are best suited to an article. Outreach efforts have begun to various scientific areas. Strategies to increase submissions from these areas will be discussed. As a consequence of this effort, a special issue on particle therapy is under development. A review template was implemented in late 2014 on a limited test basis. Based on reviewer feedback, the template was restructured and shortened to capture essential review elements. The restructured template is due to be released shortly. The new scientific category taxonomy is in the process of being deployed to reviewers and associate editors. Salient aspects of the structured review template and scientific category taxonomy will be discussed in this talk. Writing good scientific papers and responding to critiques: Mitch Goodsitt, Imaging Physics Editor The essential components of the abstract, introduction, methods, discussion and conclusion sections of manuscripts, as well as the desired writing style and style of the figures and tables will be reviewed. Publishable Medical Physics manuscripts must include clear and concise statements of the novelty and clinical and/or scientific importance of the authors’ work. Examples of novelty include: new technical solution to an important clinical problem; new generalizable knowledge; and first demonstration that an existing engineering solution solves a clinical problem. Please note that we encourage authors of recently published conference proceedings (e.g., SPIE, IEEE) papers on novel medical physics related work to submit more substantial versions of that work to our journal. All submissions must include: sufficient background information and rationale; enough detail for others to reproduce the authors’ work; sufficient statistical analysis to refute or validate the authors’ hypotheses; a description of how the present work compares to, is distinct from, and improves upon others’ work; and sections devoted to the limitations of the study and future directions. Writing should be polished. Poor wording, grammar and composition frustrate the review process. Our journal does not have copyeditors for revising manuscripts. When authors receive critiques from the referees and associate editor, the authors should provide a detailed point-by-point response to each comment. The authors’ rebuttal should include the text of the original criticism, the authors’ response, and a pasted copy of the modified text along with the line numbers in the revised article. The new text should be highlighted in a different font color in the revised submission. Following these recommendations will improve submissions and facilitate the review process.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1986

Wind tunnel wall interference in V/STOL and high lift testing: A selected, annotated bibliography

This bibliography, with abstracts, consists of 260 citations of interest to persons involved in correcting aerodynamic data, from high lift or V/STOL type configurations, for the interference arising from the wind tunnel test section walls. It provides references which may be useful in correcting high lift data from wind tunnel to free air conditions. References are included which deal with the simulation of ground effect, since it could be viewed as having interference from three tunnel walls. The references could be used to design tests from the standpoint of model size and ground effect simulation, or to determine the available testing envelope with consideration of the problem of flow breakdown. The arrangement of the citations is chronological by date of publication in the case of reports or books, and by date of presentation in the case of papers. Included are some documents of historical interest in the development of high lift testing techniques and wall interference correction methods. Subject, corporate source, and author indices, by citation numbers, have been provided to assist the users. The appendix includes citations of some books and documents which may not deal directly with high lift or V/STOL wall interference, but include additional information which may be helpful.

Tuttle, M. H.↗

NASA NTRSDate not supplied

An Automated Approach to Labelling Datasets in Earth Science Publications

NASA Data Active Archive Centers, orDAACs, ingest, store, and distribute dataacquired from satellites, ground systems as well asreanalysis models. Many authors use this datain their research. However, most of the datasets usedin Earth Science Publications are not citedcorrectly or not cited at all. Thus, there is no directlink between the datasets used and thescientific publications which reference them. Thisleads to issues with reproducibility of theresults, attribution of the research results, anddiscovery of new datasets. This project began byexploring various methods of automatically labellingGoddard Earth Sciences Data andInformation Services Center (GES DISC) datasets usingSupervised Machine Learning and EarthData Search Common Metadata Repository (CMR) queries.The ultimate goal was to create alibrary of citations that utilized automated citationlabeling to directly link the researchpublications to the data they use. Supervised MachineLearning approaches struggled due to thelimited amount of labelled training data to learnfrom. Increasing the volume of training data isdifficult as it requires subject matter experts todevote time to manually reviewing journalarticles and determining the datasets used. The CMRqueries were inconsistent because theunderlying metadata is continuously being updated.Thus, it is hard to generalize theeffectiveness of the CMR results as they are dependenton the internal state of CMR. Theseapproaches helped inform the decision to transitionthe project into using a Knowledge Graph.Another key aspect of this project focused on theautomated extraction of features (platform,instrument, variables, etc) and explicit citationsfrom within Earth Science Publications. Theseautomated extractions were used to classify researchpapers based on their platform/instrumentcouples. This information was input into the CitationManagement System for GES DISC. Theseplatform/instrument couples also provide an additionalfacet that can be searched on the GESDISC website.

Edward Jahoda↗

DOE OSTI2024

Data and scripts associated with a manuscript on a meta-analysis synthesizing stream biogeochemical response to wildfires across space and time (v2)

This data package is associated with the publication “Catchment characteristics modulate the influence of wildfires on nitrate and dissolved organic carbon in lotic systems across space and time: A meta-analysis” submitted to Global Biogeochemical Cycles (Cavaiani et al. 2025). This study uses meta-analytical techniques to evaluate the effect of wildfire on in-stream responses in burned and unburned watersheds. The study aims to provide additional insight into the range of responses and net influences that wildfires have on hydro-biogeochemistry across broad spatial scales, burn extents, and the persistence of water-quality change. This study compiles data and metadata from 18 total publications that includes 1) surface water geochemistry data (dissolved organic carbon; nitrate), 2) climate classifications, 3) year of the wildfire, 4) the time lag between when the fire occurred and when the sampling occurred, and 5) study design of the publication. In total, this meta-analysis draws data that spans 8 climate guilds, 3 biomes, 62 watersheds, and 20 unique wildfires. See Sites_meta_data.csv for citations of the papers used in this meta-analysis. All R scripts and the associated data can also be found on GitHub at This data package was originally published in March 2024. It was updated in April 2025 (v2; new and modified files). See the change history section in the readme for more details. This data package contains five primary folders that include the following: (1) inputs; (2) output for analysis; (3) initial plots; (4) R scripts; and (5) GIS data. The data package also contains a data dictionary (dd) that provides column header definitions and a file-level metadata (flmd) file that describes every file. The “inputs” folder contains a list of all publications identified during the formal web search and an indication of whether each publication was included in the final analysis. Additionally, it includes site-level metadata, catchment characteristics, and GIS data for all publications included in the final analysis. The “Output_for_analysis” folder contains all data frames and figures generated from each R script used for additional data analysis. The “initial_plots” folder includes all exploratory figures that will be included in a supplemental and figures that will be submitted with the manuscript for publication. The “R_scripts” folder contains the scripts that perform all the data manipulations, statistical analyses, and plots. The “gis_data” folder includes shape files for each fire included in this meta-analysis. This data package contains the following file types: csv, pdf, jpeg, cpg, dbf, prj, shp, shp.ea.iso.xml, shp.iso.xml, shx.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

A novel approach for characterizing the variability in mass–dimension relationships: results from MC3E

Abstract. Mass–dimension (m–D) relationships determining bulkmicrophysical properties such as total water content (TWC) and radarreflectivity factor (Z) from particle size distributions are used in bothnumerical models and remote sensing retrievals. The a and b coefficientsrepresenting m=aD b relationships, however, can vary significantlydepending on meteorological conditions, particle habits, the definition ofparticle maximum dimension, the probes used to obtain the data, techniquesused to process the cloud probe data, and other unknown reasons. Thus,considering a range of a,b coefficients may be more applicable for use innumerical models and remote sensing retrievals. Microphysical data collectedby two-dimensional optical array probes (OAPs) installed on the University ofNorth Dakota (UND) Citation aircraft during the Mid-latitude Continental ConvectiveClouds Experiment (MC3E) were used in conjunction with TWC data from aNevzorov probe and ground-based S-band radar data to determine a and busing a technique that minimizes the chi-square difference between the TWC andZ derived from the OAPs and those directly measured by a TWC probe andradar. All a and b values within a specified tolerance were regarded as equallyplausible solutions. Of the 16 near-constant-temperature flight legs analyzedduring the 25 April, 20 May, and 23 May 2011 events, the derived surfaces ofsolutions on the first 2 days where the aircraft-sampled stratiform cloudhad a larger range in a and b for lower temperature environments thatcorrespond to less variability in N(D), TWC, and Z for a flight leg.Because different regions of the storm were sampled on 23 May, differences inthe variability in N(D), TWC, and Z influenced the distribution ofchi-square values in the (a,b) phase space and the specified tolerance in away that yielded 2.8 times fewer plausible solutions compared to the flightlegs on the other dates. These findings show the importance of representingthe variability in a,b coefficients for numerical modeling and remotesensing studies, rather than assuming fixed values, as well as the need tofurther explore how these surfaces depend on environmental conditions inclouds containing ice hydrometeors.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Temperature, Humidity, and Time-Lapse Video Data from the East River Watershed, Water Year 2024

A new version of this dataset is available at doi:10.15485/3001338 and is the first citation in the 'Related References' section. It is expands on this dataset by appending another water year of data collection and additional logger sites.This dataset contains time-lapse imagery and distributed measurements of air temperature, relative humidity, dew point, and soil temperature across the East River basin from 3 October 2023 to 12 August 2024. Instruments were deployed at 14 sites as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by Jessica Lundquist (University of Washington), Rosemary Carroll (Desert Research Institute), and Ethan Gutmann (National Center for Atmospheric Research). The data are published to support studies of surface climate or hydrologic processes in complex terrain. Measurements were collected with low-cost data loggers installed 2 m high on evergreen trees or buried just below the soil surface. Time-lapse cameras were deployed at three sites. Imagery from sites AP BONUS and AP5 provides insight into large-scale seasonal snow cover variability. Imagery from site EL2 shows smaller-scale snow patterns across a nearby meadow.Dataset files are organized by site and variable (air measurements, ground measurements, or time-lapse video). Air and ground measurements are packaged in LoggerData.zip, and time-lapse imagery is compiled into short videos stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. The locations metadata file describes site characteristics, locations, and associated GPS methods.Dataset update 2025-03-03: Resolved header and datetime formatting inconsistencies within LoggerData.zip files KP1_Air, KP3_Air, KP3_Ground, KP6_Ground, AP3_Ground, AP4_Air, and AP6_Air.Dataset update 2025-11-18: Modified abstract and related references sections to include new version of dataset.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Deep-learning methods for contrast enhancement and artifact reduction in cryo-electron tomography: a systematic analysis of the state of the art and proposed improvements

Cryo-electron tomography (cryo-ET) has emerged as the preferred technique for visualizing the organization of macromolecular complexes in situ and resolving their structures at subnanometre resolution [Tegunov et al. (2021)View full citation, Nat. Methods, 18, 186–193]. Despite improvements in data quality as a result of advances in detector technology, microscope stability and stage precision, the analysis and interpretation of tomograms remains challenging due to a low signal-to-noise ratio and reconstruction artifacts stemming from experimental constraints in specimen tilt during data collection resulting in a missing wedge in the Fourier space. Recently, self-supervised deep-learning methods have been proposed for contrast enhancement and reduction of resolution anisotropy in reconstructed tomograms. Here, we evaluate several state-of-the-art deep-learning methods which aim to improve the interpretability of cryo-ET reconstructions, with a focus on their performance on downstream tasks of template matching, subtomogram averaging and segmentation. We propose new training architectures and a loss function based on Fourier shell correlation that show improved performance over the standard U-Net with L1/L2 losses. We demonstrate our analysis on four diverse experimental datasets: purified 80S ribosomes, in situ Chlamydomonas reinhardtii, immature HIV-1 virus-like particles and INS-1E cells.

contrast enhancement↗

DOE OSTI2021

Pantropical Tree Sapwood Area Data, 1977 - 2015

This data package containsata synthesis of pantropical sapwood area as a function of tree size (height or DBH) for trees ranging in sizes 1 - 140 cm DBH and 6 - 44 m tall. Data span both natural tropical forests and plantations and rainfall gradients from deciduous dry tropical forests to everwet tropical forests, and come from the following countries: Queensland, Australia; Costa Rica; Panama; Sulawesi, Indonesia; Venezuela; Ecuador; Brazil; French Guiana; Bolivia. Three CSV files are included: SA_headers.csv: description of variables in the two files below SA.csv: tree size (DBH or height) sapwood area SA_references.csv: citation, bibliographic reference, and DOI for the published references from which data were extracted

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1989

Performance of Lempel-Ziv compressors with deferred innovation

The noiseless data-compression algorithms introduced by Lempel and Ziv (LZ) parse an input data string into successive substrings each consisting of two parts: The citation, which is the longest prefix that has appeared earlier in the input, and the innovation, which is the symbol immediately following the citation. In extremal versions of the LZ algorithm the citation may have begun anywhere in the input; in incremental versions it must have begun at a previous parse position. Originally the citation and the innovation were encoded, either individually or jointly, into an output word to be transmitted or stored. Subsequently, it was speculated that the cost of this encoding may be excessively high because the innovation contributes roughly 1g(A) bits, where A is the size of the input alphabet, regardless of the compressibility of the source. To remedy this excess, it was suggested to store the parsed substring as usual, but encoding for output only the citation, leaving the innovation to be encoded as the first symbol of the next substring. Being thus included in the next substring, the innovation can participate in whatever compression that substring enjoys. This strategy is called deferred innovation. It is exemplified in the algorithm described by Welch and implemented in the C program compress that has widely displaced adaptive Huffman coding (compact) as a UNIX system utility. The excessive expansion is explained, an implicit warning is given against using the deferred innovation compressors on nearly incompressible data.

Cohn, Martin↗

NASA NTRS1999

Analysis of Cirrus Cloud Microphysical Data

The First International Satellite Cloud Climatology Regional Experiment (FIRE) program has the goal of improving our capabilities to understand, model and detect the properties of climatically-important clouds. This is being undertaken through a three-pronged effort of modeling, long-term observations and short-term intensive field studies. Through examination of satellite and other data it is apparent that stratus and cirrus cloud types have the greatest impact on climate due to their radiative effects and ubiquitous nature. As a result, the FIRE program has developed two paths of investigation, each having its own subset of research objectives and measurement programs. The work conducted under this grant was directed toward furthering our understanding of cirrus cloud systems. While it is known that cirrus are climatically important, the magnitude and even sign of the impact is unclear. Cirrus clouds affect the transfer of radiation according to their physical depth and location in the atmosphere and their microphysical composition. However, significant uncertainties still exist in how cirrus clouds form and how they are maintained, what their physical properties are and how they can be parameterized in numerical models. Better remote sensing techniques for monitoring cirrus cloud systems and improved modeling of radiative transfer through ice particles are also needed. A critical element in resolving these issues is a better understanding of cirrus cloud microphysical properties and how they vary. The focus of the research to be conducted under this grant was th data collected in situ by the University of North Dakota Citation aircraft. The goals of this research were to add to the body of knowledge of cirrus cloud microphysics, particularly at the small end of the size spectrum; and analyze the spatial variation of cirrus clouds.

Poellot, Michael R.↗

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 OSTI2024

COMPASS-FME Synoptic Sites Level 1 Sensor Data v1-2

This is the version 1-2 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our synoptic field sites. COMPASS-FME is studying sites in two distinct regions, the Chesapeake Bay and the Western Lake Erie Basin. We established the network at seven "synoptic" (observational) sites along the Chesapeake Bay and Lake Erie coastlines, collectively generating over three million observations per month, to track and comprehend environmental changes where land and water intersect. Additionally, the two regions provide an interesting contrast of saltwater and freshwater coasts that allow us to differentiate the impacts of inundation and coastal water chemistries in two nationally important coastal systems.L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds and out-of-service flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project**This dataset includes:- An overall dataset README file that describes the current version, gives citation and contact information, etc.- Site- and year-specific folders, each holding up to 12 CSV (comma separated value) data files for each site and plot in that year.- Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, as well as a general description of the site.- Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see v1-2 Synoptic L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 1 Sensor Data v1-2

This is the version 1-2 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments.L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds and out-of-service flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project**This dataset includes:- An overall dataset README file that describes the current version, gives citation and contact information, etc.- Site- and year-specific folders, each holding up to 12 CSV (comma separated value) data files for each site and plot in that year.- Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site.- Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes.Please see v1-2 TEMPEST L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning.The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods.* Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021* TEMPEST 1: June 22, 2022* TEMPEST 2: June 6-7, 2023* TEMPEST 3: June 11-13, 2024

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

COMPASS-FME Synoptic Sites Level 1 Sensor Data v2-1

This is the version 2-1 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our synoptic field sites. COMPASS-FME is studying sites in two distinct regions, the Chesapeake Bay and the Western Lake Erie Basin. We established the network at seven "synoptic" (observational) sites along the Chesapeake Bay and Lake Erie coastlines, collectively generating over three million observations per month, to track and comprehend environmental changes where land and water intersect. Additionally, the two regions provide an interesting contrast of saltwater and freshwater coasts that allow us to differentiate the impacts of inundation and coastal water chemistries in two nationally important coastal systems. L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds, out-of-service, and outlier flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project** This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding up to 12 CSV (comma separated value) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see v2-0 Synoptic L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. This dataset was updated 2026-03-12: (i) data now go through 2025-12-31 (previous end was 2025-06-30) and (ii) dataset and file names updated to “…v2-1” (previously was “v2-0”).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 1 Sensor Data v2-1

This is the version 2-1 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds, out-of-service, and outlier flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project** This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific CSV (comma separated value) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see v2-1 TEMPEST L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods. * Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021 * TEMPEST 1: June 22, 2022 * TEMPEST 2: June 6-7, 2023 * TEMPEST 3: June 11-13, 2024 This dataset was updated 2026-03-12: (i) data now go through 2025-12-31 (previous end was 2025-06-30) and (ii) dataset and file names updated to “…v2-1” (previously was “v2-0”).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Ten simple rules for getting and giving credit for data

This article attempts to summarize current best practices that support the movement towards enabling researchers to cite and receive credit for their data. The authors are a small representation of the people and organizations trying to make this happen, and we acknowledge that it is not possible to capture all efforts behind this endeavor in 10 Simple Rules. We encourage interested readers to dive deeper by providing related resources along the way.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

De-starring Challenging Cygnus Data: A Comparison of Three Methods

This report addresses recent questions concerning the RadPro de-starring process. Ultimately, the issues that gave rise to the questions proved to be unrelated to the de-starring process. However, the nature of the questions exposed some misunderstanding on how to evaluate de-starring results. A fundamental of the de-starring process is that not every data point that is extracted as a star is actually a star. This work applies and compares three different methods to very challenging data to illustrate how to evaluate de-starring results. These three methods include (1) the procedure in RadPro, (2) a version of StarStats8, and (3) a method found in the literature by Abel Diaz (L.A. Cosmic), that was developed for astrophysical applications by Pieter G. van Dokkum. In L.A. Cosmic the radiation stars are generated by cosmic-ray hits and the data of interest are true physical stars and galaxies. Dokkum’s work is heavily referenced and appears in over 1,000 citations; a scholarly article on the topic describes it. The term de-starring will include removing cosmic-ray hits from such data. Another de-starring application, developed by Archambault et al. at the M. D. Anderson Cancer Center at the University of Texas in Houston is reviewed in this work but no comparison is made, as the algorithm is very similar to that of RadPro. This process was not named in their paper, and we will refer to it as MDACC. The focus of this report is on identifying stars. Computing replacement values, a different topic, is not covered in this report.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗