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51 results for “Public Transport”
Data for paper publication "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3"
<p>The dataset presented here is related to the article by Leon-Marcos et al. 2025: "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3" accepted for publication in GMD. It comprises global fields of the FESOM2.1-REcoM3 biogeochemistry model tracers employed to calculate the ocean biomolecule concentration that serve as input data for the aerosol model. Additionally, the ECHAM6.3–HAM2.3 code of the marine aerosol implementation and the required scripts to run the model experiments are provided here. The aerosol-climate model simulation results of the marine aerosol emission, as well as the evaluation of the model results compared to observations, are also included. For further information, please refer to the attached data description. </p> <p> </p> <h2> </h2>
Bus Violence: a large-scale benchmark for video violence detection in public transport
<p><strong>Dataset</strong></p> <p>The <em>Bus Violence </em>dataset<em> </em>is a large-scale collection of videos depicting violent and non-violent situations in public transport environments. This benchmark was gathered from multiple cameras located inside a moving bus where several people simulated violent actions, such as stealing an object from another person, fighting between passengers, etc. It contains 1,400 video clips manually annotated as having or not violent scenes, making it one of the biggest benchmarks for video violence detection in the literature.</p> <p>Specifically, videos are recorded from three cameras at 25 Frames Per Second (FPS) --- two cameras located in the corners of the bus (with resolution 960x540 px) and one fisheye in the middle (1280x960 px). The clips have a minimum length of 16 frames and a maximum of 48 frames, capturing a very precise action (either violence or non-violence). The dataset is perfectly balanced, containing 700 videos of violence and 700 videos of non-violence.</p> <p>The <em>Bus Violence</em> dataset is intended as a test data benchmark. However, for researchers interested in using our data also for training purposes, we provide training and test splits.</p> <p>In this repository, we provide</p> <ul> <li> <p>the 1,400 video clips divided into two folders named Violence /NoViolence, containing clips of violent situations and non-violent situations, respectively;</p> </li> <li> <p>two txt files containing the names of the videos belonging to the training and test splits, respectively.</p> </li> </ul> <p> </p> <p><strong>Citing our work</strong></p> <p>If you found this dataset useful, please cite the following paper</p> <blockquote> <pre>@inproceedings{bus_violence_dataset_2022, title = {Bus Violence: An Open Benchmark for Video Violence Detection on Public Transport}, doi = {10.3390/s22218345}, url = {https://doi.org/10.3390%2Fs22218345}, year = 2022, month = {oct}, publisher = {{MDPI} {AG}}, volume = {22}, number = {21}, pages = {8345}, author = {Luca Ciampi and Pawe{\l} Foszner and Nicola Messina and Micha{\l} Staniszewski and Claudio Gennaro and Fabrizio Falchi and Gianluca Serao and Micha{\l} Cogiel and Dominik Golba and Agnieszka Szcz{\k{e}}sna and Giuseppe Amato}, journal = {Sensors} } </pre> </blockquote> <p>and this Zenodo Dataset</p> <blockquote> <pre>@dataset{pawel_bus_violence_zenodo, author = {Paweł Foszner, Michał Staniszewski, Agnieszka Szczęsna, Michał Cogiel, Dominik Golba, Luca Ciampi, Nicola Messina, Claudio Gennaro, Fabrizio Falchi, Giuseppe Amato, Gianluca Serao}, title = {{Bus Violence: a large-scale benchmark for video violence detection in public transport}}, month = sep, year = 2022, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.7044203}, url = {https://doi.org/10.5281/zenodo.7044203} } </pre> </blockquote> <p> </p> <p><strong>Contact Information</strong></p> <p>Blees Sp. z o.o., Gliwice, Poland<br> mstaniszewski@blees.co</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>The presented dataset was supported by: European Union funds awarded to Blees Sp. z o.o. under grant POIR.01.01.01-00-0952/20-00 “Development of a system for analysing vision data captured by public transport vehicles interior monitoring, aimed at detecting undesirable situations/behaviours and passenger counting (including their classification by age group) and the objects they carry”); EC H2020 project "AI4media: a Centre of Excellence delivering next generation AI Research and Training at the service of Media, Society and Democracy" under GA 951911; research project INAROS (INtelligenza ARtificiale per il mOnitoraggio e Supporto agli anziani), Tuscany POR FSE CUP B53D21008060008.</p> <p> </p> <p><strong>License</strong></p> <p>The <em>Bus Violence </em>dataset was acquired by Blees Sp. z o.o. and is released under a Creative Commons Attribution license for non-commercial use.</p>
Synthetic bus validation of Rennes public transportation network focus on Beaulieu campus
<p>Synthetic data based on the STAR/Keolis open data version GTFS_2021.1.0.3_20210830_20211017 for regular weeks and GTFS_2020.10.1_20210705_20210829 for holidays. This dataset contains the generation of bus validations for 3500 individuals during 14 weeks with an average of 15 travels per week. It also contains GPS information of the bus stops associated to the validation.</p>
Data of the publication "Transport and entanglement growth in long-range random Clifford circuits"
<p>Conservation laws can constrain entanglement dynamics in isolated quantum systems, manifest in a slowdown of higher Rényi entropies. Here, we explore this phenomenon in a class of long-range random Clifford circuits with U(1) symmetry where transport can be tuned from diffusive to superdiffusive. We unveil that the different<br> hydrodynamic regimes reflect themselves in the asymptotic entanglement growth according to <span class="math-tex">\(S(t) \propto t^{1/z}\)</span> where<br> the dynamical transport exponent z depends on the probability <span class="math-tex">\(\propto r^{-\alpha}\)</span> of gates spanning a distance r. For<br> sufficiently small <span class="math-tex">\(\alpha\)</span>, we show that the presence of hydrodynamic modes becomes irrelevant such that S(t) behaves<br> similarly in circuits with and without conservation law. We explain our findings in terms of the inhibited operator<br> spreading in U(1)-symmetric Clifford circuits where the emerging light cones can be understood in the context<br> of classical Lévy flights. Our Letter sheds light on the connections between Clifford circuits and more generic<br> many-body quantum dynamics.</p>
Result data related to "Tröndle et al (2023): Public preferences for phasing-out fossil fuels in the German building and transport sectors"
<p>Parameter estimations from the conjoint experiments performed in "Tröndle et al (2023): Public preferences for phasing-out fossil fuels in the German building and transport sectors". Parameter estimations are given for different:</p> <p>* estimands: average marginal component effects (amce) or marginal means,</p> <p>* variables: choice and rating,</p> <p>* sectors: buildings (heat) and transport sector,</p> <p>* subgroups: by-<subgroupname>.</p> <p>Filenames accordingly are: <estimand>-<variable>-<sector>.csv or <estimand>-<variable>-<sector>-by-<subgroup>.csv</p>
Data and code for the publication "Tracing the horizontal transport of microplastics on rough surfaces"
<p><strong>Background</strong></p> <p>The data set contains images of fluorescent PMMA (Polymethyl methacrylate) particles that are moved by water on rough surfaces in an irrigation experiment. The experiments were done in the laboratory at the Institute of Geography, University of Cologne, Germany, in Septembre 2020. The images were taken with an sCMOS (advanced scientific complementary metal-oxide-semiconductor) high resolution pco.panda 4.2 camera (PCO AG, Kehlheim, Germany).</p> <p>The data set was analysed in the publication: Laermanns, H., Lehmann, M., Klee, M., Löder, M.G.J., Gekle, S. and Bogner, C., 2021, “Tracing the horizontal transport of microplastics on rough surfaces,” Microplastics and Nanoplastics, <a href="https://doi.org/10.1186/s43591-021-00010-2">https://doi.org/10.1186/s43591-021-00010-2</a></p> <p>Additionally to the data, this collection of files contains the Python and R scripts/notebooks used to analyse the images and create graphics for the publication. The code for the simulation of flow patterns can be obtained from the authors upon request.</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>The data and code are provided as is without any warranty.</p> <p>Experimental parameters</p> <ul> <li> <p>Surface roughness: two levels, fine and course</p> </li> <li> <p>Inclination: 6 levels, 2.5°, 5°, 7.5°, 10°, 12.5° and 15°</p> </li> <li> <p>Irrigation: three levels, 4.8, 7.2 and 10.44 L/h</p> </li> <li> <p>Repetitions: three</p> </li> </ul> <p>More details on the experimental setup are given in the publication.</p> <p> </p> <p><strong>Description of the dataset</strong></p> <p>The folder <strong>images.zip</strong> contains the images. They are organized as follows:</p> <ul> <li><strong>Feinsand_10_Partikel</strong>: images of PMMA particles on the fine surface</li> <li><strong>Grobsand_10_Partikel</strong>: images of PMMA particles on the rough surface <ul> <li> <p>Both folders contain six subfolders <strong>_XX_Grad_Gefaelle</strong>, XX being 2_5, 5, 7_5, 10, 12_5, 15. These folders refer to inclinations of 2.5°, 5°, 7.5°, 10°, 12.5° and 15° of the rough surfaces, respectively.</p> </li> <li> <p>every folder _XX_Grad_Gefaelle contains three subfolders <strong>Fliessgeschwindigkeit_YY</strong>, with YY being 20mlx4, 30mlx4 and 43_5mlx4, the parameters of the peristaltic pump, corresponding to irrigation rates of 4.8, 7.2 or 10.44 L/h, respectively.</p> </li> <li> <p>every folder Fliessgeschwindigkeit_YY contains three subfolders <strong>Z_Durchgang</strong> with Z being 1, 2 or 3 corresponding to the tree repetitions of the experiment.</p> </li> </ul> </li> <li><strong>stained_flow_patterns</strong>: images of flow patterns of the fluorescent dye Nile Red (in methanol), an mp4 video and a text file with parameters to produce the video based on the images. The images were produced for the following experimental parameters: <ul> <li> <p><strong>Feinsand_2_5_Grad_20_ml</strong>: fine surface, inclined by 2.5° and irrigated with 7.2 L/h</p> </li> <li> <p><strong>Grobsand_7_5_Grad_20_ml</strong>: coarse surface, inclined by 7.5° and irrigated with 7.2 L/h</p> </li> </ul> </li> </ul> <p>The file <strong>experimental_data.csv</strong> links the concatenated folder names to experimental parameters.</p> <p> </p> <p><strong>Description of the code</strong></p> <p>The images were first processed in Python to locate the PMMA particles and calculate particle sizes. The Python code is located in the <strong>py_scripts.zip</strong> folder. It contains the following files:</p> <ul> <li> <p><strong>find_XYZ</strong>: locates PMMA particles. XYZ stands for different experimental parameters (see above). Scripts containing the string <strong>_problems</strong> locate PMMA particles for images with possible artefacts (smeared particles, residual light etc.). You need to uncomment the appropriate lines in the files to rerun the code because it was run piece by piece.</p> </li> <li> <p><strong>pickle_to_csv.py</strong>: converts pickle files to csv files</p> </li> <li> <p><strong>calculate_sizes.py</strong>: calculates the sizes of PMMA particles from the first image of each experiment</p> </li> <li> <p><strong>py_functions_new.py</strong>: contains custom functions</p> </li> </ul> <p>Further analysis run in a mixture of R and Pyhton in one working document (R Notebook):</p> <ul> <li> <p><strong>Analysis_with_loops.Rmd</strong>: tracking of the PMMA particles by PtrakPy version 0.4.2 (Allan et al. 2019). Python 3.8 (Python Software Foundation, <a href="https://www.python.org/">https://www.python.org/</a>) was called directly from R using the R package reticulate (<a href="https://rstudio.github.io/reticulate/">https://rstudio.github.io/reticulate/</a>) in RStudio (<a href="https://www.rstudio.com/">https://www.rstudio.com/</a>).</p> </li> <li> <p><strong>Analysis_for_paper.Rmd</strong>: R code for analysis of tracking, statistical analysis, plotting. We used the R version 4.0.3 (R Core Team 2020).</p> </li> <li> <p><strong>helper_function.R</strong>: contains custom R functions for the analysis</p> </li> </ul> <p> </p> <p><strong>Results</strong></p> <p>The file <strong>results.zip</strong> contains the folders:</p> <ul> <li> <p><strong>data</strong>: *.pickle files produced by Python containing the trajectories of PMMA particles</p> </li> <li> <p><strong>data_csv</strong>: *.pickle files converted to *.csv files</p> </li> <li> <p><strong>figures</strong>: figures produced by the code during the analysis, organized in different subfolders</p> </li> <li> <p><strong>RData</strong>: large computational results produced and saved during analysis</p> </li> <li> <p><strong>sizes_csv</strong>: *.csv files containing PMMA particle sizes and further morphological characteristics; produced during analysis</p> </li> </ul> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The authors thank Julia Horn for support in the laboratory and Florian Steininger for technical assistance.</p> <p> </p> <p><strong>Funding</strong></p> <p>This project was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project Number 391977956, SFB 1357, subprojects B04 and B06.</p> <p> </p> <p><strong>References</strong></p> <p>Allan, Dan, Casper van der Wel, Nathan Keim, Thomas A Caswell, Devin Wieker, Ruben Verweij, Chaz Reid, et al. 2019. <em>Soft-Matter/Trackpy: Trackpy V0.4.2</em> (version v0.4.2). Zenodo. <a href="https://doi.org/10.5281/zenodo.3492186">https://doi.org/10.5281/zenodo.3492186</a>.</p> <p>Laermanns, Hannes, Moritz Lehmann, Marcel Klee, Martin GJ Löder, Stephan Gekle, and Christina Bogner. 2021. “Tracing the Horizontal Transport of Microplastics on Rough Surfaces.” <em>Microplastics and Nanoplastics</em>. <a href="https://doi.org/10.1186/s43591-021-00010-2">https://doi.org/10.1186/s43591-021-00010-2</a>.</p> <p>R Core Team. 2020. <em>R: A Language and Environment for Statistical Computing</em>. Vienna, Austria: R Foundation for Statistical Computing. <a href="https://www.R-project.org/">https://www.R-project.org/</a>.</p>
Dataset of the publication: Probing the spin dimensionality in single-layer CrSBr van der Waals heterostructures by magneto-transport measurements
<p>Dataset of the publication: Probing the spin dimensionality in single-layer CrSBr van der Waals heterostructures by magneto-transport measurements</p> <p>DOI: 10.1002/adma.202204940</p> <p>C. Boix-Constant, S. Mañas-Valero, A. M. Ruiz, A. Rybakov, K. A. Konieczny, S. Pillet, J. J. Baldoví, E. Coronado</p> <p>Adv. Mater., 34, 2204940 (2022)</p>
Supporting Data: Do spatial climate messages increase pro-environmental engagement? Evidence from a survey experiment on public transport
<p>Additional material for the paper:</p> <p>Victor von Loessl, Eva Weingärtner & Sonja Zitzelsberger (2022) Do spatial climate messages increase pro-environmental engagement? Evidence from a survey experiment on public transport, Journal of Environmental Economics and Policy, DOI: 10.1080/21606544.2022.2097960</p>
Can Public Transport Improve Accessibility for the Poor Over the Long Term? Empirical Evidence in Paris, 1968-2010
<p>This dataset allows to reproduce the results of the paper "Can Public Transport Improve Accessibility for the Poor Over the Long Term? Empirical Evidence in Paris, 1968-2010", by Vincent Viguié, Charlotte Liotta, Basile Pfeiffer, and Nicolas Coulombel, submitted to the Journal of Transport Geography and available at SSRN: <a href="https://ssrn.com/abstract=4049765">https://ssrn.com/abstract=4049765</a> or <a href="https://dx.doi.org/10.2139/ssrn.4049765">http://dx.doi.org/10.2139/ssrn.4049765</a>.</p> <p>Files MODUS_private_cars.csv and MODUS_public_transport.csv correspond to the average transport time between municipalities by public transit for the Census years between 1968 and 20109, and by car for the year 2010. MODUS is a model calibrated by the public regional road and transport authority (DRIEAT Île de France) to simulate road traffic and transit in the region. More details can be found in the research paper.</p> <p>Files COM_RES_csp_1968_2010.csv and COM_EMP_csp_1968_2010.csv correspond to the number of employed workers, by place of residence or employment, at the municipality (communes) level, with stable geographical boundaries over time. They are derived from the detailed census of the French population carried out by the INSEE, the French national statistical institute. More details can be found in the research paper.</p>
Analysis of public transport in Vienna with Co2 emissions in Austria's households
<p>This repository serves as a backup and longterm storage for the datasets and plots resulting from the analysis on means of transport in Vienna in the time period 2010 - 2021 together with the Co2 emissions of Austria's households.</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>
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>
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>
Data from: Unveiling the paths of COVID-19 in a large city based on public transportation data
<p>Human mobility plays a key role in the dissemination of infectious diseases around the world. However, the complexity introduced by commuting patterns in the daily life of cities makes such a role unclear, especially at the intracity scale. Here, we propose a multiplex network fed with 9 months of mobility data with more than 107 million public bus validations in order to understand the relation between urban mobility and the spreading of COVID-19 within a large city, namely, Fortaleza in the northeast of Brazil. Our results suggest that the shortest bus rides in Fortaleza changed ≈ 25% more than the longest ones after an epidemic wave. Such a result is the opposite of what has been observed at the intercity scale. We also find that mobility changes among the neighborhoods are synchronous and geographically homogeneous. Furthermore, we find that the most central neighborhoods in mobility are the primary targets for infectious disease outbreaks, which is quantified here in terms of the linear relation between the disease arrival time and the average of the closeness centrality ranking. These central neighborhoods are also the top neighborhoods in the number of reported cases at the end of an epidemic wave as indicated by the exponential decay behavior of the disease arrival time in relation to the number of accumulated reported cases with decay constant λ ≈ 33 days. We believe that these results can help in the development of new strategies to impose restriction measures in the cities guiding decision-makers with smart actions in public health policies, as well as supporting future research on urban mobility and epidemiology.</p>
Data example and code used in the publication "Is transport of microplastics different from that of mineral dust? Results from idealized wind tunnel studies"
<p>Background</p> <p>The code labels microspheres and counts them. Further, the code determines which microspheres are independent of microsphere-microsphere collisions by their relative position to the other microspheres in an image. Images were taken with a full-frame visual camera (Sony Alpha 7RII) with a long-distance-microscopy lens (K2 DistaMax).</p> <p>Description of the dataset</p> <ul> <li>image_data_all.zip contains 228 tif-format images taken in a single experiment <ul> <li>the images show borosilicate microspheres with diameters from 63 to 75 µm</li> <li>during the experiment, the microspheres are detached from the substrate and are transported out of the image</li> </ul> </li> <li>functions_particle_labeling.jl contains all necessary functions for particle labeling</li> <li>analysis_protocol.jl is an example, that first determines a color threshold, and then labels all microspheres in all images stored in "image_data_all/substrate_a/image_data_single_experiment"</li> <li>post_processing_visualisation.R is an r-script, that reads the output of analysis_protocol.jl and demonstrates how logistic functions were fitted to the data</li> </ul> <p> </p> <p>We used julia 1.8.5 and R 4.3.0.</p> <p> </p>
Dataset accompanying the publication "Transport and retention of micro-Polystyrene in coarse riverbed sediments: Effects of flow velocity, particle and sediment sizes"
<p>The dataset in this repository is accompanying the publication "Transport and retention of micro-Polystyrene in coarse riverbed sediments: Effects of flow velocity, particle and sediment sizes" (in Microplastics and Nanoplastics, 2023, submitted 09.06.2023)</p> <p>The repository contains the raw image files of all sample filters which were scanned using the fluorescence imaging system ChemiDoc and used to analyse the infiltration behaviour of microplastic polystyrene in the manuscript. In addition, we provide the resulting data from the particle identification and geometric analysis which were derived from the raw data using ImageJ in tabular excel format. The data is structured in folders following the naming of the columns from the manuscript.</p>
MarTREC Publication forBio-Inspired Stabilization of Levee Slope on Expansive Yazoo Clay at the Maritime and Multimodal Transportation Infrastructure in Mississippi
<p>The link considers the data file for the MarTREC Project: Bio-Inspired Stabilization of Levee Slope on Expansive Yazoo Clay at the Maritime and Multimodal Transportation Infrastructure in Mississippi.</p> <p>PI: Dr. Sadik Khan</p> <p>Funding Agency: This material is based upon work supported by the U.S. Department of Transportation under Grant Award Number DTRT13-G-UTC50. The work was conducted through the Maritime Transportation Research and Education Center at the University of Arkansas.</p>
Project F2 - Discovering Potential Market for the Integration of Public Transportation and Emerging Shared-Mobility Services
<p>This dataset compiles the results of ridesharing trajectory data aggregation, the analysis result of the transit and ridesharing trip data, and raw data of transit station coordinates and schedules in Chengdu, China. This is part of STRIDE project F2 titled "Discovering Potential Market for the Integration of Public Transportation and Emerging Shared-Mobility Services."</p>
Public transport occupancy capture
<p>This repository contains a capture of the occupancy of a campus bus at ESPOL, in Ecuador.</p><p>The entry and exit events are recorded in the `measurements` array, `counts` with an `id` of 8 are entry events,</p><p>and `counts` with an `id` of 9 are exit events.</p><p>The timestamps are incorrect, as the sensor used was not connected to the internet. However, the relative time between events is correct.</p><p>The experiment was carried out on July 19, 2023 and lasted 1 hour.</p>
Data and code for the publication "Tracking microplastics across the streambed interface: Using laser-induced fluorescence to quantitatively analyze microplastic transport in an experimental flume"
<p>This archive contains datasets and codes that were used in the publication "Tracking microplastics across the streambed interface: Using laser-induced fluorescence to quantitatively analyse microplastic transport in an experimental flume".</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.