cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4231/8gbw-9p30
title: Influence of Ions on Hydrophobic Self-Assembly

description: &lt;p&gt;This archive contains Raman spectra of 1,2-hexanediol and 1-hexanol dissolved in water and aqueous salt solutions, as well as analyses of the spectra performed using the SMCR (Raman-MCR) and MCPS strategies described in the associated publication.  The files whose names begin with Fig1... Fig2... and Fig3... contain the the data and graphs in the corresponding figures in the publication.  The files that begin with 12HD... contain measured Raman spectra and SMCR/MCPS analysis results. These and other archived files are further described in the publication and the ReadMe_Influence_of_Ions file). The combined experimental and theoretical strategy is used to quantify the influence of H&lt;sup&gt;+&lt;/sup&gt; and OH&lt;sup&gt;&ndash;&lt;/sup&gt;, as well as salts containing Li&lt;sup&gt;+&lt;/sup&gt;, Na&lt;sup&gt;+&lt;/sup&gt;, Cl&lt;sup&gt;&ndash;&lt;/sup&gt; and Br&lt;sup&gt;&ndash;&lt;/sup&gt;, on the hydrophobic self-assembly of micelles composed of neutral oily 1,2-hexanediol surfactants. The distributions of aggregate sizes, both below and above the critical micelle concentration (CMC), are determined using Raman multivariate curve resolution (Raman-MCR) spectroscopy to quantify the multi-aggregation chemical potential surface (MCPS) that drives self-assembly. &lt;/p&gt;

&lt;p&gt; &lt;/p&gt;
description: Combined experimental and theoretical study of the influence of ions, including H+ and OH-, on the self-assembly of micelles composed of 1,2-hexanediol.
DOI: 10.4231/v1zh-st79
title: Inferred information (distance information) repository of Industry Foundation Classes (IFC)-based Building Information Modeling (BIM)

description: &lt;p&gt;BIM design information can be classified as explicit information and inferred information in the context of code compliance checking. Explicit information, such as dimensions, materials, and geometric shapes, is directly represented in a BIM model and is easily accessible by users. Inferred information such as distance information, on the other hand, is not directly represented in the BIM model but can be derived or inferred from explicit information. While representing explicit design information from BIM models is relatively straightforward, the extraction and processing of inferred information require a more substantial effort. We developed an automated method aimed at efficiently processing and gathering explicit information, specifically position and distance data, from three IFC-based BIM models. To validate the effectiveness of our approach, we compared its results with those obtained through manual methods, and we subsequently compiled this repository.&lt;/p&gt;

&lt;p&gt;The BIM models included in this repository consist of a hotel model, a convenience store, and a fast-food restaurant, all provided by our industry partner, located in Texas. Within this repository, we offer access to the 3D global coordinates of the central points of 242 building components, including windows, plates, and doors, along with their corresponding minimal distance information. Additionally, we included a comprehensive comparison of the two approaches - automated and manual - within the repository.&lt;/p&gt;

&lt;p&gt;This dataset contains vital location information for each component and can prove instrumental in supporting various automation tasks within the architecture, engineering, and construction (AEC) domain, including but not limited to automated compliance checking and streamlining site scheduling processes.&lt;/p&gt;
description: A collection of position and distance information for 242 components in building information modeling using two methods (manual and automatic).
DOI: 10.4231/r8n7-hx28
title: A Rotated Staggered Grid (RSG) finite-difference elastic wave solver based on Devito

description: &lt;p&gt;Built upon the open-source Python finite-difference framework, Devito, this Rotated Staggered Grid elastic wave solver is designed to model seismic wave propagation in intricate anisotropic media.&lt;/p&gt;

&lt;p&gt;Key Components:&lt;/p&gt;

&lt;ul&gt;
 &lt;li&gt;
 &lt;p&gt;&lt;code&gt;model.py&lt;/code&gt;: A refined file derived from Devito&#39;s original &lt;code&gt;model.py&lt;/code&gt;. It introduces a new object, &lt;code&gt;AnisoSeismicModel&lt;/code&gt;, tailored to support the input of 21 distinct elastic parameters for the grid.&lt;/p&gt;
 &lt;/li&gt;
 &lt;li&gt;
 &lt;p&gt;&lt;code&gt;wavesolver.py&lt;/code&gt;: Solver object provides the operators for forward modeling computations. It encapsulates both temporal and spatial discretizations pertinent to a specified problem setup.&lt;/p&gt;
 &lt;/li&gt;
 &lt;li&gt;
 &lt;p&gt;&lt;code&gt;operators.py&lt;/code&gt;: Construct Method for the forward modeling operator handling source excitation and monitoring component recorded by receivers.&lt;/p&gt;
 &lt;/li&gt;
 &lt;li&gt;
 &lt;p&gt;&lt;code&gt;stiffnessoperator.py&lt;/code&gt;: Serving as an operator for the stiffness tensor, this object accommodates isotropy, Vertical Transverse Isotropy (VTI), and 3D rotations. Users can either opt for manual input of the 21 elastic parameters or utilize the automated setup. When paired with a mask, it aids in crafting the &lt;code&gt;AnisoSeismicModel&lt;/code&gt;.&lt;/p&gt;
 &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Getting Started:&lt;/p&gt;

&lt;p&gt;For a practical introduction and hands-on experience, refer to the tutorial Jupyter notebook &lt;code&gt;Tutorial.ipynb&lt;/code&gt;. This guide walks users through the application of the package, showcasing wave propagation modeling within a two-layered structure (comprising an upper water layer and a lower TTI media) in 2D.&lt;/p&gt;

&lt;p&gt;Open Research Archive (&lt;code&gt;OpenResearchArchive.zip&lt;/code&gt;) contains the result and Python code to conduct the numerical tests in terms of stability, numerical dispersion and performance benchmark described in the manuscript: Zhang, O. and Schmitt, D. R. (2023) An optimized 2D/3D Finite-difference Seismic Wave Propagator Using Rotated Staggered Grid for Complex Elastic Anisotropic Structures. &lt;i&gt;ESS Open Archive.&lt;/i&gt; November, 2023. File &lt;code&gt;Readme.md&lt;/code&gt; details the content in each subdirectory.&lt;/p&gt;
description: An Optimized Rotated Staggered Grid (RSG) finite-difference elastic wave solver for modeling 2D/3D seismic wave propagation in complex anisotropic media, based on the open-source Python finite-difference package, Devito.
DOI: 10.4231/sw73-d037
title: MATLAB scripts and SimVascular case files for "Oscillatory flows in compliant conduits at arbitrary Womersley number"

description: &lt;p&gt;The Zip archive contains two directories, &quot;2D Channel&quot; and &quot;3D Axisymmetric Tube&quot;. Each subfolder within these directories contains a &quot;readme.txt&quot; file to further explain what is contained therein.&lt;/p&gt;

&lt;p&gt;Specifically, in &quot;2D Channel&quot;, the MATLAB scripts generate the reduced-model solutions and make the plots from Sec. II of the manuscript &quot;Oscillatory flows in compliant conduits at arbitrary Womersley number&quot; by Pande, Wang &amp; Christov (2023). In &quot;3D Axisymmetric Tube&quot;, the subfolder &quot;Theory&quot; contains the post-processed SimVascular data and the MATLAB scripts to generate the reduced-model solutions and to make the plots from Sec. III of the manuscript.&lt;/p&gt;

&lt;p&gt;Further, in &quot;3D Axisymmetric Tube&quot;, the subfolder &quot;3D Simulation (svFSI)&quot; contains folders and files to run 3D simulation via SimVascular&#39;s svFSI.  The output data is not provided. A &lt;a href="http://simvascular.github.io/index.html" rel="nofollow noopener noreferrer" target="_blank"&gt;SimVascular&lt;/a&gt; installation with working svFSI is required to run the simulations and generate the raw data. The post-processed data is in the subfolder &quot;Theory&quot;.&lt;/p&gt;

&lt;p&gt;Some plots are based on analytical formulas derived in the manuscript, while others require the numerical solution of differential or algebraic/transcendental equations as described in the manuscript. Inferences based on these results/plots and a discussion of their meaning/implications can be found in the manuscript.&lt;/p&gt;
description: Zip file with codes and data to make the plots in the manuscript "Oscillatory flows in compliant conduits at arbitrary Womersley number" by Pande, Wang & Christov (2023).
DOI: 10.4231/aq5k-nr86
title: Applications of Production Vehicle Road Roughness Data Before and After Pavement Construction

description: &lt;p&gt;Current quantitative methods to evaluate pavement conditions in the United States are most commonly focused on construction acceptance using the International Roughness Index (IRI).  However, qualitative visual inspection techniques are still the most prevalent for routine asset management.  The evolution of modern vehicles with factory-equipped sensors drive these roadways daily and can passively assess the condition of infrastructure at an accuracy level more definitively and ubiquitously than visual qualitative assessment. This video monitors 11 miles of roadway before and after a construction project with representative images to demonstrate the quality and accuracy of the data. This data was collected on March 28&lt;sup&gt;th&lt;/sup&gt;, 2022 and December 8&lt;sup&gt;th&lt;/sup&gt;, 2022. The provided dataset shows the mile marker, before-construction IRI value and after-construction IRI value from a production vehicle.&lt;/p&gt;

&lt;p&gt; &lt;/p&gt;

&lt;p&gt;&lt;iframe allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen="" frameborder="0" height="315" src="https://www.youtube.com/embed/u22oGdQn794?si=NUYxOLv_glCHIUXl" title="YouTube video player" width="560"&gt;&lt;/iframe&gt;&lt;/p&gt;
description: This visualization shows a synced view of a road improvement project before the project began and after. The video also displays the international roughness index value for each respective tenth mile before and after construction.
DOI: 10.4231/e38n-jm26
title: Salt Stockpile Inventory – 100 Bucket Salt Movement Experiment September 12 to 13, 2023 – INDOT Lebanon Salt Facility

description: &lt;p&gt;Indiana Department of Transportation is evaluating the use of LiDAR technology to monitor the salt stockpile volumes. This timelapse shows the experimental test using a front-end loader moving salt from the barn to the dome and then vice versa. Scans were performed about every 10 loader buckets with multiple LiDAR equipment. The provided dataset shows the volume amounts in each facility, which helps validate the value outputs. &lt;/p&gt;

&lt;p&gt; &lt;/p&gt;

&lt;p&gt;&lt;iframe allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen="" frameborder="0" height="315" src="https://www.youtube.com/embed/9PHxqGgivtk?si=VLqX7ufdbzCVe7Il" title="YouTube video player" width="560"&gt;&lt;/iframe&gt;&lt;/p&gt;
description: This visualization shows a timelapse of a salt pile being moved from a barn to a dome and then back using a front-end loader on September 12 and 13, 2023.
DOI: 10.4231/t6j3-h649
title: Water vapor stable isotope memory effects of common tubing materials

description: &lt;p&gt;Water molecules in vapor can exchange with water molecules sticking to surfaces of sampling tubing, and exchange rates are unique for each isotopologue and tubing material. Therefore, water molecules on tubing walls take some time to reach isotopic equilibrium with a new vapor isotopic signal. This creates a memory effect observed as attenuation time for signal propagation in continuous laser-based stable water vapor isotope measurement systems. Tubing memory effects in &delta;D and &delta;&lt;sup&gt;18&lt;/sup&gt;O measurements can limit the ability to observe fast changes, and because &delta;D and &delta;&lt;sup&gt;18&lt;/sup&gt;O memory are not identical, this introduces transient deuterium excess (D-excess, defined as &delta;D &ndash; 8* &delta;&lt;sup&gt;18&lt;/sup&gt;O) artifacts in time-varying observations. A comprehensive performance comparison of commonly used tubing material water exchange properties has not been published to our knowledge. We compared how a large isotopic step change propagated through five commonly used tubing materials, PFA, FEP, PTFE, HDPE, and copper, at two different temperatures and an air flow rate of 0.635 L min&lt;sup&gt;-1&lt;/sup&gt; through approximately 100 feet (~30.5 m) of &frac14; in. (6.35 mm) outer diameter (OD) tubing. All tubing materials performed similarly to each other in terms of attenuation times, reaching 95 % completion in less than 45 seconds in all but 2 experiments with slight variations based on temperature. Bev-A-Line XX was also tested, unheated, but it did not reach isotopic equilibrium after an hour, and we cannot recommend its use in water vapor applications. While shorter tubing length and smaller inner diameters shortens the delay of signal propagation through the tubing, they don&rsquo;t greatly change the shape of the attenuation curve or the delay-adjusted attenuation times under these conditions. Our results show that these commonly-used plastic tubing materials are not inferior to copper in terms of isotopic memory under these conditions, and they are easier to work with and are less expensive than copper. Our experience and results from other published studies indicate that maximizing air flow rates through the analyzer is the most effective way to minimize memory effects when accurate high-frequency deuterium-excess measurements are desired.&lt;/p&gt;
description: A laboratory experiment testing the water vapor isotopic memory of a few common air intake tubing materials (PFA, FEP, PTFE, HDPE, and copper, plus Bev-A-Line XX). Memory metrics are quantified at 2 different temperatures.
DOI: 10.4231/tjk1-hs57
title: Chavas and Peters (2023, BAMS): Static energy deserves greater emphasis in the meteorology community.

description: &lt;p&gt;Excel file for Figure 1 and MATLAB code + data for Figures 2 and 3 of the manuscript Chavas and Peters (2023) submitted to Bulletin of the American Meteorological Society (BAMS). Project abstract: Potential temperature and static energy are useful quantities for understanding the properties of air in our atmosphere. While both concepts are used widely in the climate community, static energy is used relatively little in the meteorology community, a contrast that is evident in both American Meteorological Society journal publications and in introductory textbooks. Here we highlight this contrast and emphasize key conceptual benefits of static energy. Static energy is an intuitive and tangible quantity that describes changes in temperature in terms of conversions among different forms of energy. This intuition can help explain basic atmospheric science concepts, such as the dry adiabatic lapse rate, to the introductory student and the established scientist alike. Bridging this gap between the weather and climate science communities is important as these communities must work together to understand and predict changing weather patterns in a warming world.&lt;/p&gt;
description: Excel file for Figure 1 and MATLAB code + data for Figures 2 and 3 of the manuscript Chavas and Peters (2023) accepted for publication in Bulletin of the American Meteorological Society
DOI: 10.15468/dl.n6gc6q
title: Occurrence Download

description: A dataset containing 3048 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Material sample, Material citation, Specimen, Living Specimen, Occurrence evidence)",
 "Continent is Europe",
 "OccurrenceStatus is Present",
 "TaxonKey is Carex hartmanii",
 "Year 2000-2024"
 ]
}

 The dataset includes 3048 records from 87 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0059547-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4231/7zk6-eg87
title: Climate-influenced phenology of larval fish transport in a large lake

description: &lt;p&gt;In large lake ecosystems, e.g., Lake Michigan, USA, larval fish environmental experiences and recruitment dynamics are directly influenced by climate-driven physical transport processes that advect passive larvae through heterogeneous habitats. Integrating a series of climate, hydrodynamic, biogeochemical, and Lagrangian particle dispersion models, we simulate the hatch and transport of fish larvae throughout Lake Michigan and evaluate patterns of historic and potential future climate-induced larval transport. Results demonstrate that prior to summer stratification relatively strong alongshore currents, generated by the nearshore thermal bar, limit offshore transport of nearshore hatched fish larvae. Shift towards greater offshore transport became more frequent with the onset of summer stratification, which occurred earlier during relatively warm historic and future years. Nearshore retention may have important implications for larval fish growth and/or survival.&lt;/p&gt;
description: Here, we integrate a series of climate, hydrodynamic, biogeochemical, and Lagrangian particle dispersion models to investigate larval fish transport phenology within a large lake.
DOI: 10.4231/qexr-7w10
title: Interstate Vehicle Miles Travelled and Miles Operating Below 45 mph Dataset

description: &lt;p&gt;This dataset provides the total interstate vehicle miles traveled and interstate miles operating below 45 mph (congested conditions) across the contiguous United States categorized by 9 US Geographic Divisions (as defined by the US Census Bureau at https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf) for every minute in the month of December 2022. Approximately 500 billion commercially available connected vehicle records were aggregated by geographic division and minute to develop this dataset. In total, there were approximately 3.071 billion vehicle miles travelled by connected vehicles on US Interstates during this period.&lt;/p&gt;

&lt;p&gt;The dataset (geographic_divisions_vmt_mileslt45_dec2022.csv) consists of the following attributes:&lt;/p&gt;

&lt;p&gt;1. division (one of 9 US Geographic Divisions as defined by the US Census Bureau)&lt;/p&gt;

&lt;p&gt;2. timestamp_est (timestamp corresponding to a minute in Eastern Standard Time)&lt;/p&gt;

&lt;p&gt;3. mileslt45 (miles of interstate operating below 45 mph)&lt;/p&gt;

&lt;p&gt;4. vmt (interstate vehicle miles traveled)&lt;/p&gt;

&lt;p&gt;Acknowledgements &ndash; Connected vehicle trajectory data for December 2022 used in this dataset was provided by Wejo Data Services Inc.&lt;/p&gt;
description: Total interstate vehicle miles travelled and miles operating below 45 mph for every minute in December 2022 across the contiguous United States categorized by 9 US Geographic Divisions (New England, Middle Atlantic, East North Central, West North Central,
DOI: 10.4231/10sf-ph62
title: Timelapse of weather, road speeds, INDOT trucks, and salt trails during a winter storm from October 30 to November 1, 2023

description: &lt;p&gt;Indiana Department of Transportation has installed telematics on approximately 500 snowplow trucks to monitor truck deployment.  This timelapse shows the location of those trucks during a winter storm and integrates doppler weather and highway speed data.  The blue circles are the active trucks. The dataset contains sample data of a route of one truck.&lt;/p&gt;

&lt;p&gt;&lt;iframe allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen="" frameborder="0" height="315" src="https://www.youtube.com/embed/ePxFjbaT8Yw?si=8B9kTwN430KJ0obs" title="YouTube video player" width="560"&gt;&lt;/iframe&gt;&lt;/p&gt;
description: This timelapse shows interstate traffic speeds, doppler weather data, telematics data, and salt trail data from Indiana Department of Transportation trucks during a winter storm beginning on October 30 to November 2, 2023.
DOI: 10.4231/4q4v-mb96
title: Wildfires shift a cumulative carbon sink to a source for boreal forest ecosystems in North America from 1986 to 2020

description: &lt;p&gt;This study uses a process-based biogeochemistry model to simulate the carbon budget in North American boreal forests during 1986-2020 with satellite-derived burn severity data. We find that fires remove ecosystem carbon through combustion emissions of 2.4 Pg C and reduce net ecosystem production (NEP) from 32.6 Tg C/year to 0.8 Tg C/year, resulting in 3.5 Pg C loss to the atmosphere during the period, changing the boreal forests from a carbon sink to a source. When canopy&rsquo;s cooling effects are considered, model estimates lower soil temperatures in summer, inducing lower net primary production due to inhibited nitrogen uptake and mineralization and lower heterotrophic respiration, reducing NEP from 1.6 Tg C/year to 0.8 Tg C/year. This reduction accounts for 50% of the simulated post-fire NEP without considering canopy influences. Our study highlights the importance of fire and its induced canopy change in boreal forest soil thermal dynamics and carbon balance.&lt;/p&gt;
description: TEM model simulation results
DOI: 10.4231/y7kv-tt50
title: How has the concept of resilience been applied in research across forest regions

description: &lt;p&gt;We conducted a systematic literature review of empirical studies of forest resilience in the U.S. from 2010&ndash;2020 to determine how researchers conceptualized, measured, and reflected the concept of resilience in reporting and interpreting their results. We determined that most studies defined resilience as recovery to the original state post-disturbance, focused on the state of individual species within the ecosystem rather than the state of the ecosystem, and measured the impacts of one disturbance rather than multiple disturbances. &lt;/p&gt;
description: These data are from a quantitative literature review that was conducted to understand how the concept of resilience has been applied in forest research across the United States and Territories.
DOI: 10.4231/18m6-gc48
title: 3D Mapping of Trough Migration Paths

description: &lt;p&gt;The dataset contains the data products developed and analyzed in the mapping of Mars&#39; northern polar spiral troughs. The spiral troughs have related subsurface features in radar referred to as Trough Migration Paths (TMPs). We have mapped these TMPs in a 3D radar dataset (Shallow Radar, SHARAD) across the entire northern polar ice cap. As a result, we have produced ~30 TMPs files, provided here as depth mapped in distance from the surface above.  This repository also contains the slope, azimuth, and wavelength measurements for surface troughs from MOLA elevation data. This data provides insight into Mars&#39; paleoclimate, and reveals regional patterns of accumulation of ice and trough retreat across the northern polar ice cap. The relevant manuscript is Laferriere, Bramson and Smith (2023) in JGR: Planets.&lt;/p&gt;

&lt;p&gt; &lt;/p&gt;

&lt;p&gt; &lt;/p&gt;
description: The dataset contains the data products developed in the mapping of Mars' northern polar spiral troughs. This included 3D depth maps of trough migration paths from radar data, and slope, azimuth, and wavelength measurements for surface troughs.