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Fig. 5 in Double alignments of ammonoid aptychi from the Lower Cretaceous of Southeast France: Result of a post-mortem transport or bromalites?
Fig. 5. Mid−Valanginian aptychi in alignments. A. FSL 710903, Vergol section, layer 17 (Fig. 2B), Saynoceras verrucosum Zone, Subzone, and Horizon. B. FSL 710904, Vergol section, layer 16 (Fig. 2B), Busnardoites campylotoxus Zone, Karakaschiceras biassalense Subzone, and Neocomites platycostatus Horizon. C. FSL 710905, Vergol section, layer 41 (Fig. 2B), S. verrucosum Zone, Subzone, and Horizon. D. FSL 710906, Vergol section, layer 43 (Fig. 2B), S. verrucosum Zone, Subzone, and Horizon. Scale bars 10 mm.
Fig. 3 in Double alignments of ammonoid aptychi from the Lower Cretaceous of Southeast France: Result of a post-mortem transport or bromalites?
Fig. 3. Morphological nomenclature of aptychi. Redrawn and partially modified from Arkell (1957) and Farinacci et al. (1976).
Enhancing multi-mode transport emission inventories: combining open-source data with traditional approaches
<p>The primary goal of this dataset is to enhance the spatial and temporal distribution of emissions from civil aviation (NFR1.A.3.a), road transport (NFR1.A.3.b), railways (NFR1.A.3.c), and military aviation (NFR1.A.5), using Portugal as case study. For more information, please refer to the published article “Enhancing multi-mode transport emission inventories: combining open-source data with traditional approaches” (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.uclim.2024.102097" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.uclim.2024.102097</span></span></a>). This dataset contains the following folders and files:</p> <p><strong>1. Spatial_Location</strong></p> <p> 1.1. NFR1_A_3_a.gdb: Geodatabase containing the locations of Portuguese airports and aerodromes.</p> <p> 1.2. NFR1_A_3_b.gdb: Geodatabase containing the locations of Portuguese roads.</p> <p> 1.3 NFR1_A_3_c.gdb: Geodatabase containing non-electrified Portuguese railways and train station locations.</p> <p> 1.4 NFR1_A_5.gdb: Geodatabase containing the locations of Portuguese military airport facilities.</p> <p><strong>2. Temporal_Profiles</strong></p> <p><em> 2.1. Daily</em></p> <p> 2.1.1. Daily_NFR1_A_3_a.csv: This csv file contains the daily movements profiles of civil aviation sites in Portugal.</p> <p><em> 2.2. Hourly</em></p> <p> 2.2.1. Hourly_NFR1_A_3_b.txt: This txt file contains the hourly road traffic volume profiles for the road transport activities in Portugal at different locations (BigAir column).</p> <p> 2.2.2. Hourly_NFR1_A_3_c.txt: This text file contains the hourly railway profile in Portugal, categorized by line and train station.</p> <p><strong>3. Emission_Factors</strong></p> <p> 3.1. EF_NFR1_A_3_a.xlsx: This Excel file contains emission factors for civil aviation activities, categorized by technology, flight phase, fuel, and pollutant. Additionally, it includes information about engines and aircraft.</p> <p> 3.2. EF_NFR1_A_3_b.xlsx: This Excel file contains emission factors for road transport activities, categorized by vehicle type, technology, fuel, abatement, and pollutant. Emission factors for road resuspension are not provided because the papers using this dataset are still under review.</p> <p> 3.3. EF_NFR1_A_3_c.xlsx: This Excel file contains emission factors for railways activities, categorized by technology, fuel, and pollutant.</p> <p> 3.4. EF_NFR1_A_5.xlsx: This Excel file contains emission factors for military aviation activities, categorized by fuel, and pollutant.</p> <p><strong>4. Other_Info</strong></p> <p> 4.1 NFR1_A_3_b: This folder contains information organized by road segments, including fuel consumption (in the “FuelConsumption” folder), hourly meteorology (in the “Meteorology” folder), population data (in the “Population” folder), daily traffic volume (in the “TrafficVolume” folder), vehicle categories (in the “VehicleCategory” folder), and vehicle classes (in the “VehicleClasses” folder). Additionally, it includes the link between road traffic volume measurement points and the Portuguese road network (in the “sensorsVSroads” folder)</p>
FIGURE 1 in Characterization and expression of the gene glucose transporter 2 (GLUT2) in embryonic, larval and adult Bay snook Petenia splendida (Cichliformes: Cichlidae)
FIGURE 1 | Partial sequence of nucleotides and amino acids (AA) encoding glut2 (glucose transporter 2) from Petenia splendida taken from Gen Bank to design specific oligonucleotides for qPCR.
FIGURE 3 in Characterization and expression of the gene glucose transporter 2 (GLUT2) in embryonic, larval and adult Bay snook Petenia splendida (Cichliformes: Cichlidae)
FIGURE 3 | Phylogenetic tree based on the sequence of glut2 (glucose transporter 2) from Petenia splendida and other teleosts using the neighbor-joining (NJ) method. Values at branch points represent percentage frequencies for tree topology after 1,000 interations.
FIGURE 4 in Characterization and expression of the gene glucose transporter 2 (GLUT2) in embryonic, larval and adult Bay snook Petenia splendida (Cichliformes: Cichlidae)
FIGURE 4 | Relative expression of glut2 in kidney (K), liver (L), muscle (M), brain (B), pancreas (P), gill (G), heart (H), intestine (I), stomach (S) and testis (T) of adult Petenia splendida (mean ± SEM; n = 3). Lowercase letters indicate significant differences between the expression level in the tissues (p <0.05).
FIGURE 2 in Characterization and expression of the gene glucose transporter 2 (GLUT2) in embryonic, larval and adult Bay snook Petenia splendida (Cichliformes: Cichlidae)
FIGURE 2 | Amino acid sequence of glut2 in Petenia splendida aligned with other species other species of teleost fish. Identical amino acids are presented in black, and the high and less conserved amino acids are presented in gray and period, respectively.
FIGURE 5 in Characterization and expression of the gene glucose transporter 2 (GLUT2) in embryonic, larval and adult Bay snook Petenia splendida (Cichliformes: Cichlidae)
FIGURE 5 | Relative expression of glut2 during the early ontogeny of Petenia splendida. Lowercase letters indicate significant differences in the expression of glut2 as a function of developmental time (p <0.05). AN: Artemia nauplii (3–10 dph), CF: cofeeding (Artemia nauplii and tilapia feed) (11–13 dph), TF: trout feed (14–30 dph) (mean ± SEM; n = 3).
Seismic modeling of bedload transport in a gravel-bed alluvial channel
<p>This repository publishes data for 4 flow events at the Arroyo de Los Pinos on the paper "Seismic modeling of bedload transport in a sandy gravel-bed alluvial channel".</p>
Efficiency and heat transport processes of low-temperature aquifer thermal energy storage systems: new insights from global sensitivity analyses - Supporting Dataset
<p>This dataset contains the files used to substantiate the outcomes of the publication <em>"Efficiency and heat transport processes of low-temperature aquifer thermal energy storage systems: new insights from global sensitivity analyses"</em>. </p> <p>It includes the output of 250 random model realizations of an aquifer thermal energy storage system in a thick productive aquifer (Case 1). It also includes the output of 500 random model realizations of an aquifer thermal energy storage system in a shallow alluvial aquifer (Case 2 part 1 and part 2).</p> <p>If there is interest in generating new output, the datset also includes the model input files for both cases.</p> <p>(Scripts to process the output data or to generate new output data can be found in the corresponding GitHub repository: https://github.com/lukatas/ATES_SensitivityAnalyses.git )</p>
Planetary Boundary Layer Height Retrievals from the Cloud-Aerosol Transport System (CATS) around the US Southern Great Plains and the Eastern North Atlantic
<p>Planetary Boundary Layer Height (PBLH) retrievals in kilometers from the Cloud-Aerosol Transport System (CATS) around the DOE ARM US Southern Great Plains (SGP) and the Eastern North Atlantic (ENA), using a modified version of the Different Thermo-Dynamics Stability (DTDS) algorithm. Quality control Flags are included as follows:</p> <ul> <li>0 = 'Good Quality'</li> <li>1 = 'Mediate Quality'</li> <li>2 = 'Bad Quality'</li> </ul> <p>In addition, -999 values in the dataset represent no data. <br>The PBLH for daytime denoised CATS photon counts at SGP is named: "daytime-denoised-dtds-pblh-sgp.csv"<br>The PBLH for the original daytime and nighttime data at SGP and ENA, without denoising the data, are named: "original-cats-dtds-pblh-daytime-nighttime-sgp.csv" and "original-cats-dtds-pblh-daytime-nighttime-ena.csv"</p> <p>References: </p> <p>Roldán-Henao, N., Yorks, J., Su, T., Selmer, P., & Li, Z. (2024). Statistically Resolved Planetary Boundary Layer Height Diurnal Variability Using Spaceborne Lidar Data. <em>Remote Sensing. </em></p> <p>Su, T., Li, Z., & Kahn, R. (2020). A new method to retrieve the diurnal variability of planetary boundary layer height from lidar under different thermodynamic stability conditions. <em>Remote Sensing of Environment</em>, <em>237</em>, 111519.</p>
Computational results data for the assoziated publication "Network Interdiction Problems in Urban Transportation: Theoretical Insights and Computational Characteristics"
<p>This repository contains two Excel tables with computational results for our paper "Network Interdiction Problems in Urban Transportation: Theoretical Insights and Computational Characteristics". Each Excel table includes multiple worksheets, each representing different scenarios and models evaluated in our study.</p> <p><strong>Worksheets Overview</strong></p> <p>Each Excel table contains the following worksheets:<br>1. <strong>ML</strong>: Results for the "ML" big M values.<br>2. <strong>MH</strong>: Results for the "MH" big M values.<br>3. <strong>MF</strong>: Results for the "MF" big M values.<br>4. <strong>Path</strong>: Results for the path model.<br>5. <strong>FMInstances</strong>: Results from applying our models on the original Fontaine and Minner (2018) instances.</p> <p><strong>Columns Description</strong></p> <p>Each worksheet contains the following columns:</p> <p>- <strong>Name of Instance</strong>: A complex string with the identifier of the used instance. The relevant part is "_RXXX_", where XXX is the random seed used to generate the instance.<br>- <strong>Number users</strong>: The number of users/commodities in the network.<br>- <strong>B:</strong> The budget (always set to infinity in our instances).<br>- <strong>GUROBI_RUNTIME</strong>: The time limit set for the computations.<br>- <strong>Modelkind</strong>: The type of model used. Possible values are:<br> - INDICATOR: Compact model.<br> - FMbenders: Benders model from Fontaine and Minner (2018).<br> - ICM: Benders-like cuts.<br> - PathModel: Path enumeration model.<br>- <strong>BigM computation</strong>: Time required to compute all the big M values used (not included in the time limit).<br>- <strong>runtime</strong>: Runtime of the selected model.<br>- <strong>BBnodes</strong>: Number of nodes in the Branch & Bound tree.<br>- <strong>gap</strong>: Gap reported by Gurobi after reaching the time limit.<br>- <strong>Cuts BLC</strong>: Number of Benders-like cuts included.<br>- <strong>Time BLC</strong>: Time required for separating Benders-like cuts.<br>- <strong>M improve BLC</strong>: Frequency of improvements to a big M when using the improved big M term in Benders-like cuts.<br>- <strong>Mcutoff_AVE</strong>: Average (non-zero) improvement of a big M when using the improved big M term in Benders-like cuts.<br>- <strong>Cuts FMBenders</strong>: Number of Benders cuts generated in the Fontaine and Minner (2018) model.<br>- <strong>Time FMBenders</strong>: Time required to generate the Benders cuts in the Fontaine and Minner (2018) model.<br>- <strong>Runtime path enum</strong>: Time required to enumerate all paths for the path-based model (not included in the time limit).<br>- <strong>Average Num Path</strong>: Average number of paths generated for a single commodity/user. Multiply this value by the number of users to obtain the absolute number of paths generated.</p> <p><strong>Note on FCP</strong></p> <p>All the results found for the instances already had integer flow solutions. Additionally, we conducted experiments where we explicitly forced the solutions to be integer for the Benders-like cuts model. We observed that the runtimes remained the same, with only some natural insignificant hardware-induced fluctuations. Therefore, we omit reporting these results again.</p> <p><br>For further information or questions, please refer to our paper "Network Interdiction Problems in Urban Transportation: Theoretical Insights and Computational Characteristics" or contact the authors.</p>
Molecular insights into substrate translocation in an elevator-type metal transporter
<p>This dataset contains biased simulation trajectories for the paper entitled "Molecular insights into substrate translocation in an elevator-type metal transporter." The trajectories are named based on the information provided in the paper. The deposited trajectories contain only the protein and zinc metal ions; due to size constraints, we retained only these components and removed the rest. The entire systems and additional input files are available upon request to the authors. The dataset comprises four folders named as follows: </p> <p>First_scenario, Second_scenario, Third_scenario, and Last_scenario.</p> <p>To extract the contents of the compressed file (.tar.gz), use the following command line:</p> <p>tar -xzvf zip_transporter.tar.gz</p> <p>Each folder contains topology files and their corresponding trajectory files. The term "Rep" in the file names denotes the simulation replicates. For instance, "rep1" indicates the first simulation replicate mentioned in the paper. All analyses were performed using cpptraj and VMD software. Custom codes for performing the analyses are available at https://gitlab.msu.edu/jafarima/zip-transporter.git.</p>
Data for device simulation in the article "Analysing the impact of the hole transport layer on the space charge distribution and hysteresis in perovskite solar cells using capacitance-voltage profiling"
<p>This repository contains the data used to perform device simulation in the article "Analysing the impact of the hole transport layer on the space charge distribution and hysteresis in perovskite solar cells using capacitance-voltage profiling", submitted in September 2024 to the journal Sustainable Energy and Fuels.</p> <p><br>The authors of this data and the article are E. Regalado-Pérez, Evelyn B. Díaz-Cruz, and J. Villanueva-Cab </p> <p><br>The scripts (.m) and input files (.csv) hosted here are based on the files created by the authors of the Driftfusion code, which can be found in the GitHub repository "barnesgroupICL/Driftfusion" at https://github.com/barnesgroupICL/Driftfusion.</p> <p> </p>
Neural-network-based molecular dynamics simulations reveal that proton transport in water is doubly gated by sequential hydrogen-bond exchange: Neural network potentials training data
<h1>Neural network potentials of an excess proton in bulk water, training data</h1> <p>This dataset contains 2188 configurations labeled at two hybrid DFT levels (revPBE0-D3 and B3LYP-D3).</p> <p>The configurations are given as a single XYZ file: configurations.xyz</p> <p>The box dimensions are written in box.txt</p> <p>The energies for all configurations at a given level of theory are written in energies_LEVEL.txt (one configuration per line)</p> <p>The atomic forces for each configuration at a given level of theory are gathered in a XYZ file: forces_LEVEL.xyz</p> <p>The relative displacements of the Wannier centroids, with respect to the closest oxygen atom, for each configuration at a given level of theory, are in the following XYZ file: wannier-centroids-displacements_LEVEL.xyz</p>
Dataset for publication: "Magnesium and Aluminum in Contact with Liquid Battery Electrolytes: Ion Transport through Interphases and in the Bulk"
<div> </div> <div> <p>This is the experimental raw data set associated with the following publication: M. Löw, J. Grill, MM May, and J. Popovic-Neuber, Magnesium and Aluminium in Contact with Liquid Battery Electrolyte: Ion Transport through Interphases and in the Bulk, ACS Material Letters (2024). DOI:10.1021/acsmaterialslett.4c01589</p> <p>The data set is organized according to the publication's figures. </p> </div>
A High-Performance Code for Analyzing Loss Transport Equations in High-Fidelity Simulations - velocity field data
<p>Velocity field to be processed by the methodology described in </p> <p>Biassoni, D, Russo, M, Viviani, P, Vitali, G, & Lengani, D. "A High-Performance Code for Analyzing Loss Transport Equations in High-Fidelity Simulations." <em>Proceedings of the ASME Turbo Expo 2024: Turbomachinery Technical Conference and Exposition</em>. <em>Volume 12C: Turbomachinery — Design Methods and CFD Modeling for Turbomachinery; Ducts, Noise, and Component Interactions</em>. London, United Kingdom. June 24–28, 2024. V12CT32A039. ASME. <a href="https://doi.org/10.1115/GT2024-127953" target="_blank" rel="noopener">https://doi.org/10.1115/GT2024-127953</a></p> <p>and with the code provided at </p> <p><a href="https://gitlab.linksfoundation.com/across-public/aeronautics-workflows/asme-turboexpo-2024">https://gitlab.linksfoundation.com/across-public/aeronautics-workflows/asme-turboexpo-2024</a></p> <h3> </h3>
Transport effects of twist-angle disorder in mesoscopic twisted bilayer graphene
<p>Magic-angle twisted bilayer graphene is a tunable material with remarkably flat energy bands near the Fermi level, leading to fascinating transport properties and correlated states at low temperatures. However, grown pristine samples of this material tend to break up into landscapes of twist-angle domains, strongly influencing the physical properties of each individual sample. This poses a significant problem to the interpretation and comparison between measurements obtained from different samples. In this work, we study numerically the effects of twist-angle disorder on quantum electron transport in mesoscopic samples of magic-angle twisted bilayer graphene. We find a significant property of twist-angle disorder that distinguishes it from onsite-energy disorder: it leads to an asymmetric broadening of the energy-resolved conductance. The magnitude of the twist-angle variation has a strong effect on conductance, while the number of twist-angle domains is of much lesser significance. We further establish a relationship between the asymmetric broadening and the asymmetric density of states of twisted bilayer graphene at angles smaller than the first magic angle. Our results show that the qualitative differences between the types of disorder in the energy-resolved conductance of twisted bilayer graphene samples can be used to characterize them at temperatures above the critical temperatures of the correlated phases, enabling systematic experimental studies of the effects of the different types of disorders also on the other properties such as the competition of the different types of correlated states appearing at lower temperatures.</p> <p>The provided repository contains all data and scripts to reproduce the figures of the manuscript. </p>
Supplemental data of publication "Reactive Transport Model of Kinetically Controlled Celestite to Barite Replacement".
<p>Supplemental data of publication "Reactive Transport Model of Kinetically Controlled Celestite to Barite Replacement".</p> <p>Authors: Morgan Tranter, Maria Wetzel, Marco De Lucia, Michael Kühn</p> <p>Contact: mtranter@gfz-potsdam.de</p> <p>Submitted to Advances in Geosciences (31.06.2021).<br> Special Issue: European Geosciences Union General Assembly 2021, EGU Division Energy, Resources & Environment (ERE)</p> <p>EGU21 Abstract:<br> https://doi.org/10.5194/egusphere-egu21-9832</p> <p>Licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)</p>
Dopant Engineering for Spiro-OMeTAD Hole-Transporting Materials towards Efficient Perovskite Solar Cells
<p>Optoelectronic, photovoltaic, and supplementary characterization data for “Dopant Engineering for Spiro-OMeTAD Hole-Transporting Materials towards Efficient Perovskite Solar Cells”, DOI:10.1002/adfm.202102124</p> <ul> <li>CV.zip: Data (cyclic voltammograms) described in Figures S3–S5 in Origin (*opj) file format.</li> <li>EPR.zip: Data (EPR spectra) described in Figure 5, Figure S2, and Table S2 in Origin (*opj) file format.</li> <li>Optical.zip: Data (UV-vis, PL, and TRPL spectra) described in Figure 4 and Table S1 in Origin (*opj) file format.</li> <li>PV.zip: Data (photovoltaic characteristics) described in the Figures 2–3, Table 1, Figure S1, and Figure S6 in Origin (*opj) file format.</li> </ul>
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