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2,271 results for “transport”
French domestic bilateral transport costs between districts c. 1789
<p>Downloading the data will provide you with a .zip file.</p> <p>The data include bilateral transport costs between French districts around 1789, as computed for the paper See Daudin, G. (2010). Domestic Trade and Market Size in Late-Eighteenth-Century France. <em>The Journal of Economic History,</em> 70(3), 716-743. doi:10.1017/S0022050710000598</p>
Inventory of Policy Interventions related to Energy and Transport Behaviours
<p>User behaviour is a complex issue, as it is influenced by a multitude of factors from economic status, age, education, political environment etc. The WHY project aims to identify the possible user reactions and their effect on the energy consumption due to external stimuli. In context of the WHY project these external stimuli are called “interventions”. In this chapter different approaches for policy driven interventions are discussed and presented. </p>
Sensitivity experiment data using the CHASER chemical transport model for investigation of lower-tropospheric spring ozone enhancement over Hanoi
<p>This is the data from the numerical model experiment for investigating the relative importance of different emission source regions on the spring ozone enhancement in the lower troposphere over Hanoi, Vietnam. The details of the investigation are written in the paper by Ogino et al. (2022, Journal of Geophysical Research, Atmosphere, in revision).</p> <p><strong>Experiment description</strong></p> <p>We performed sensitivity experiments using the global chemical-transport model, CHASER (Sudo et al., 2002) with T42 horizontal resolution (approximately 2.8 degrees longitude × 2.8 degrees latitude) and 32 vertical layers from the surface up to 10 hPa in sigma coordinate. The two-hourly model outputs interpolated onto the constant pressure levels at 1000, 990, 970, 930, 870, 790, 700, 610, 530, 460, 400, 350, 300, 260, 230, 200, 176, 153, 133, 116, and 100 hPa were used in this study. Note that the updated model, MIROC-Chem (Miyazaki et al., 2017; Watanabe et al., 2011), includes more detailed chemical processes for both troposphere and stratosphere. Nevertheless, CHASER already includes the most important chemical processes in the NOx-CO-Ozone reactions and can be used to evaluate the impact of NOx emissions on ozone productions. In addition, the simulated ozone performance, as well as ozone response to NOx emissions, are comparable between CHASER and MIROC-Chem (Miyazaki et al., 2020). Thus, the results should not be sensitive to the choice of model.</p> <p>The surface emissions of major ozone precursors, such as carbon monoxide (CO), nitrogen oxide (NOx), and nonmethane hydrocarbons, were included in the model based on the published emission inventories (the Emission Database for Global Atmospheric Research (EDGAR) version 4.2 (EC-JRC/PBL, 2011), the monthly Global Fire Emissions Database (GFED) version 3.1 (van der Werf et al., 2010), and monthly mean Global Emissions Inventory Activity (GEIA) (Graedel et al., 1993)). We employed daily NOx and CO emissions that were optimized using the assimilation of satellite NO2 and CO measurements, where the a priori emissions were constructed based upon bottom-up emission inventories (Miyazaki et al., 2015; 2017). These emissions, including both anthropogenic and biomass burning components, used were obtained from the Tropospheric Chemistry Reanalysis version 1 (TCR-1, Miyazaki et al., 2015) and enabled us to evaluate the emission impacts for individual sources.</p> <p>In the sensitivity experiments, we eliminated the emissions of ozone precursors from the following three source regions: the Indian subcontinent, the northern Indochina Peninsula, and southern China. We conducted spin-up calculations with the optimized emissions for all regions (i.e., standard emissions) from January 1st to the end of February in each year for 10 years from 2005 to 2014. Then, we performed four types of experiments from March 1st to 21st: the control experiment with the standard emissions, and the three sensitivity experiments with the elimination of emission from the above-mentioned three regions, namely the Indian subcontinent, the northern Indochina, the southern China experiments. Because of the non-linear chemistry, the cumulative response from the sensitivity calculations can be different from the total ozone response in the control simulation to some extent as shown by the HTAP modeling works (Turnock et al., 2018; Wild et al., 2012). Nevertheless, they provided important information on the relative contributions of emission sources from different regions. The results of the sensitivity experiments will be compared with the control experiment to investigate the relative contributions of individual emission sources to the ozone enhancement over Hanoi.</p> <p><strong>Files</strong></p> <ul> <li>O3_Fullyear_[YYYY].nc: The 2-hourly data of ozone mixing ratio obtained in the control experiment from January 1 to December 31 in year [YYYY] from 2005 to 2014.</li> <li>[Param]_March_[YYYY].nc: The 2-hourly data obtained in the sensitivity experiment from Mar 1 to 21 in every year [YYYY] from 2005 to 2014. [Param] is one of the following: <ul> <li>O3_Control: Ozone mixing ratio in the control experiment</li> <li>O3_IndianSubcontinent: Ozone mixing ratio in the Indian Subcontinent experiment</li> <li>O3_NorthernIndochina: Ozone mixing ratio in the northern Indochina experiment</li> <li>O3_SouthernChina: Ozone mixing ratio in the southern China experiment</li> <li>CO: Carbon monoxide</li> <li>T: Temperature</li> <li>U: Zonal wind</li> </ul> </li> <li>CO_Emission.nc and NOx_Emission.nc: The monthly mean CO and NOx emissions from the surface used in the model experiments.</li> </ul> <p><strong>Contact</strong></p> <p>Shin-Ya Ogino<br> Japan Agency for Marine-Earth Science and Technology (JAMSTEC)<br> E-mail: ogino-sy@jamstec.go.jp</p>
The Tracing Convective Momentum Transport in Complex Cloudy Atmospheres Experiment - Level 1
<p>The first field campaign from the Tracing Convective Momentum Transport in Complex Cloudy Atmospheres experiment project (CMTRACE) took place in Cabauw, the Netherlands, between September 13th and October 3rd 2021. During this field campaign, two cloud radars and one wind lidar were operated with a similar scanning strategy for deriving wind speed and direction profiles from near the surface up to cloud tops. Here we provide the daily Level 1 data from each instrument. At this level, several processing steps were applied to the raw data to minimize offsets, reduce the number of spurious data and derive wind speed and direction profiles; however, the data from each instrument is kept on its original spatial and temporal resolution. The raw data is available for the users on request from the corresponding author.</p> <p><strong>Prefix identificaiton:</strong></p> <p>Lidar data: cmtrace_cabauw_wls200-218<br> Scanning radar data: cmtrace_cabauw_rpg_radar_35-94<br> Vertically pointing radar data: cmtrace_cabauw_rpg_radar_94</p> <p> </p>
Data used in: 'Atmospheric impacts of chlorinated very short-lived substances over the recent past – Part 1: Stratospheric chlorine budget and the role of transport' by Bednarz et al. (2022)
<p>Data used in: 'Atmospheric impacts of chlorinated very short-lived substances over the recent past – Part 1: Stratospheric chlorine budget and the role of transport' by Bednarz et al. (2022), which has been accepted for publication in Atmospheric Chemistry and Physics.</p> <p> </p>
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>
Dataset Cumulative CO2 Emissions of International Transport
<p>This dataset considers the year 1783, when the first steamship was built, as the first year of the international transport CO2 emissions.</p> <p>The global cumulative CO2 emissions including international transport are converted to the 1875 baseline, similar to the Global Warming baseline (1850-1900). </p>
X-Ray Diffraction data from Membrane transport protein AcrB, V612F mutant with bound minocycline, source of 9FHC structure
<p>Crystals were grown of the membrane transport protein AcrB, V612F mutant, with bound minocycline. </p> <p>X-ray diffraction data of this upload: 400 frames of 0.5° width were collected on 2007-04-30 at the X06SA beamline of Swiss Light Source at Paul-Scherrer-Institute (Switzerland).</p> <p>The data can be processed with XDS; XDS.INP is provided as part of the upload.</p> <p>The data are the basis of the PDB 9FHC structure.</p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part IV: Post-processing)
<p>This is Part IV of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files for the post-processing of all model results.</p> <p>To be able to run the scripts as is, the folder structure should be as follows:</p> <p>Runs (includes all model run folders from Part II and Part III)<br>Post/Basic/Channels<br>Post/Basic/Cross_sections<br>Post/Basic/Integrals<br>Post/Basic/Median_neighborhood_analysis (includes all unzipped MNA_TIGER_XX.zip folders)<br>Post/Basic/Skeleton_clean<br>Post/Basic/Skeleton_final<br>Post/Basic/Skeleton_raw<br>Post/Basic/Unchanneled_path_length<br>Post/Basic/Watersheds<br>Post/Basic/Scenarios.txt<br>Post/Basic/TIGER_2km_5m.slf<br>Post/Paper_1/Erosion-deposition<br>Post/Paper_1/Fluxes<br>Post/Paper_1/Profiles<br>Post/Paper_1/Std</p>
BRAIN Journal-About the Design of QUIC Firefox Transport Protocol-Figure 2. QUIC vs TLS handshake protocol
<p>Figure 2 describes a sequence diagram using QUIC VS TLS handshake protocol. The right<br> side of Figure 2 shows the seven steps of calls and returns until a HTTP Get() method is<br> successfully implemented using a TLS handshake. In contrast, on the left side of Figure 2, we see<br> the implementation of HTTP Get() method using a single call of QUIC handshake.<br> Firefox is a completely open source browser with a tremendous community support. The<br> latest version of Firefox supports TLS 1.3 protocol in an experimental stage. The primary purpose<br> of this study was integrating the QUIC protocol in the Firefox web browser. The source code of this<br> software product is as large as 650MB. </p> <p> </p>
BRAIN Journal-About the Design of QUIC Firefox Transport Protocol-Figure 1. TLS vs QUIC protocol stack
<p>QUIC addresses many network problems such as the Head Of Line (HOL) blocking as well as the TCP reconnection over a subnet/network change. In addition, QUIC has many features such as connection IDs, which can overcome the challenge of changing networks. In this way, if someone switches from a WIFI network to a cellular network, the connection to the server will not be broken or lost. Paper (Langley and Chang, 2016) described the QUIC crypto protocol, representing the part of QUIC that provides transport security to a connection. The QUIC crypto protocol is now replaced by TLS 1.3. Currently, QUIC provides security of TLS 1.3 (in an experimental stage), considered the highest security standards for the communication protocols (Valsorda; 2016). </p> <p>Figure 1 describes the TLS vs. QUIC protocol user level stack in the context of application layer and transport layer.</p>
INTEND D 2.1 Transport projects & future technologies synopses database
<p>This excel file provides the database that contains all the project synopses that were carried out in the INTEND D 2.1 Transport projects & future technologies handbook deliverable. The reviews are divided into transport modes and contain the technology themes that were identified and brief summaries of what each project that was reviewed had researched. The database contains a total of 354 transport projects that have carried out hard technology research, predominantly funded under FP7 (2010-2014), all H2020 projects that have been funded as well as other international projects.</p>
Construction, validation and application of nocturnal pollen transport networks in an agro-ecosystem: datasets collected using light microscopy and DNA metabarcoding
<p>This dataset contains all data required to reproduce the analyses conducted in Macgregor <em>et al. </em>(2018), using the R Notebook archived at doi: <a href="https://dx.doi.org/10.5281/zenodo.1322712">10.5281/zenodo.1322712</a>.</p> <p>Specifically, the dataset contains details of pollen transport detected on two matched samples, each containing 311 moths of 41 species, using two methods: a traditional light microscopy approach and a novel DNA metabarcoding approach. Both raw and manually-curated versions of each dataset are archived for full clarity. The dataset additionally contains all metadata required to fully interpret these data, including the RGB tables used to prepare Fig 4 in Macgregor <em>et al. </em>(2018).</p> <p>Macgregor <em>et al. </em>(2018) Construction, validation and application of nocturnal pollen transport networks in an agro-ecosystem: a comparison using light microscopy and DNA metabarcoding. <em>Ecological Entomology</em>, doi: <a href="https://dx.doi.org/10.1111/een.12674">10.1111/een.12674</a>.</p>
RTI Experiment Simulation assuming Convective and Diffusive Interstitial Transport in the Brain: Concentration over time (and space)
<p>Simulation of interstitial transport in the brain assuming convective and diffusive transport with perivascular efflux routes. The movie shows the transient concentration of TMA ions in a real-time iontophoresis (RTI) experiment, where a small molecular probe is applied to brain tissue at a known rate and its concentration measured over time a a point 100-200um away, here 150 um. RTI experiments are used to characterize the properties of interstitial tissue to determine its void volume and tortuosity, 0.18 and 1.85 for the condition shown here. In this simulation, a model of combined diffusion and convection (superficial velocity=50 um/min) is applied to fit experimental data and range. (Convection assumes Darcy's Law with a hydraulic conductivity of 2x10<sup>-6</sup> cm<sup>2</sup> mmHg s<sup>-1</sup> and pressure difference of 2.15 mmHg). The model domain is a cube 750 um on a side with 8 penetrating arterioles and 8 penetrating venules. The first and third columns from the left are venules and the second and fourth are arterioles, with convective flow from arteriole to venule. As transport of molecules in the perivascular space is known to be faster than in the interstitium, the concentration is assumed to be c=0 at the vascular walls. The solute (TMA) must pass through a perivascular wall with lower diffusivity than the interstitium to leave the domain through a vascular wall (D<sub>wall</sub>=5%D<sub>interstitium</sub>). Although it is difficult to see in the movie, both the presence of convection and the perivascular efflux routes cause range(variability) in the measured concentration curves for different source and detection point combinations that is consistent with experimental data--see additional posted data. Computations performed using FEniCS, movie made using Paraview. </p>
Source data for "Synthetic gauge fields for phonon transport in a nano-optomechanical system"
<ul> <li>Experimental raw data for density plots in Fig 2. Each .csv contains an array, where 1st row corresponds to x_axis (mechanical frequency in MHz for panels 1,2,3,4) and first column the y_axis (optical frequency in THz for panel 1, modulation frequency in MHz for panels 2,3,4). First nonzero component is the 2nd for each array. Remaining array elements contain the z values (Thermomechanical noise spectral for panel 1, Amplitude of driven responses for panels 2,3,4). An illustrative example of plotting in an ipython notebook follows:</li> </ul> <p> %pylab inline</p> <p> A= genfromtxt('Fig2_data_modVolt=0mV_experiment.csv', delimiter=',') </p> <p> x = A[0,1:]<br> y = A[1:,0]<br> z = A[1:,1:]<br> imshow(z,aspect='auto',vmin=z.min(),vmax=z.max(),extent=[x.min(),x.max(),y.min(),y.max()],cmap='magma') </p> <ul> <li> Theoretical data for panel 4 in Fig 2, stored in a .csv with the same structure as previous.</li> <li> Raw experimental data for upper panels in Fig 3. Each .csv contains an array where 1st row corresponds to x_axis (modulation phase) and first column the y_axis (optical frequency in THz). Z values contain the experimental signal proportional to the Y optical quadrature of the transferred mode.</li> <li>Theoretical data for lower panels in Fig 3, stored in a .csv with the same structure as previous.</li> <li>Jupyter notebook to produce and plot typical data for Fig 4: phononic amplitude averaged over 100 disorder realizations, normalized to the maximum value (*extra_dependencies: Kwant Python library: <a href="https://kwant-project.org/">https://kwant-project.org/</a>).</li> </ul>
The International Transport Energy Modeling (iTEM) Open Data & Harmonized Transport Database
<p>This dataset and documentation contains detailed information of the iTEM Open Database, a harmonized transport data set of historical values, 1970 - present. It aims to create transparency through two key features:</p> <ul> <li>Open-Data: Assembling a comprehensive collection of publicly-available transportation data</li> <li>Open-Code: All code and documentation will be publicly accessible and open for modification and extension. <a href="https://github.com/transportenergy">https://github.com/transportenergy</a></li> </ul> <p>The iTEM Open Database is comprised of individual datasets collected from public sources. Each dataset is downloaded, cleaned, and harmonised to the common region and technology definitions defined by the iTEM consortium https://transportenergy.org. For each dataset, we describe the name of the dataset, the web link to the original source, the web link to the cleaning script (in python), variables, and explain the data cleaning steps (which explains the data cleaning script in plain English).</p> <p>Shall you find any problems with the dataset, please report the issues here <a href="https://github.com/transportenergy/database/issues">https://github.com/transportenergy/database/issues</a>. </p> <p> </p>
Data set for risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm
<p>The purpose of this dataset is to enable the replication of the research results presented in the article: Izdebski, M. (2023). Risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm. Archives of Transport, 67(3), 139-153. https://doi.org/10.5604/01.3001.0053.7463 - published online: 2023-09-30, which discusses the allocation problem of vehicles to tasks, taking into account risk issues.</p> <p>Dataset contains:</p> <ul> <li>Readme.txt: description of the dataset</li> <li>InputData.xlsx: Contains the input data used in the model</li> <li>DistributionFit.xlsx: Compliance testing and distribution parameters for road accidents of any type and collision-type</li> <li>OutputAssignment.xlsx: Results of assignment and alghoritm tests</li> </ul> <p>The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).<br>Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.<br> E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).<br>- The Chinese part of the project is funded by National Natural Science Foundation of China.<br>- The Swedish part of the project is funded by Swedish Energy Agency.<br>- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).</p>
Dataset of a multiphase flow and reactive transport benchmark for radioactive waste disposal
<p>The files include the full dataset (tables and figures) of the comparion the results of a multiphase flow and reactive transport<br>benchmark for radioactive waste disposal. The codes INVERSE-FADES-CORE V2, DuMuX , TOUGHREACT and<br>iCP were benchmarked with 6 test cases of increasing complexity, starting with conservative tracer transport under variably<br>unsaturated conditions and ending with water flow, gas diffusion, minerals and cation exchange.</p>
Model data and code for "Freeze-thaw effects on daily sediment transport in an Alpine river"
<p>Supporting information for the research article "Freeze-thaw effects on daily sediment transport in an Alpine river" by Skålevåg et al., submitted to Water Resources Research.</p> <p>This data repository contains the processed data, model code, and results presented in the research article. Please refer to the article and its supplementary information for details on primary data.</p> <p> </p> <p><strong>Contents:</strong></p> <ul> <li>processed data: <ul> <li>Standardised target and predictor variables, in addition to non-standardised data used for freeze-thaw state classification <a href="https://zenodo.org/api/records/13928999/draft/files/model_variables.csv/content" target="_blank" rel="noopener noreferrer">model_variables.csv</a></li> <li>Means and standard deviations of standardised variables <a href="https://zenodo.org/api/records/13928999/draft/files/regression_variables_mean_std.csv/content" target="_blank" rel="noopener noreferrer">regression_variables_mean_std.csv</a></li> </ul> </li> <li>model code: <ul> <li>final model presented in research article: <a href="https://zenodo.org/api/records/13928999/draft/files/model.py/content" target="_blank" rel="noopener noreferrer">model.py</a></li> <li>model comparison performed as part of model development: <a href="https://zenodo.org/api/records/13928999/draft/files/model_comparison_predictors_and_segmentation.html/content" target="_blank" rel="noopener noreferrer">model_comparison_predictors_and_segmentation.html</a></li> </ul> </li> <li>results: <ul> <li>final model: <ul> <li>Inference trace from the pymc model <a href="https://zenodo.org/api/records/13928999/draft/files/inference.nc/content" target="_blank" rel="noopener noreferrer">inference.nc</a></li> <li>Summary table of the inference trace <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary.csv/content" target="_blank" rel="noopener noreferrer">inference_summary.csv</a></li> <li>Visualisation of the inference trace <a href="https://zenodo.org/api/records/13928999/draft/files/inference_trace.png/content" target="_blank" rel="noopener noreferrer">inference_trace.png</a></li> </ul> </li> <li>other models: <ul> <li>non-segmented sediment rating curve: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_SRC.nc/content" target="_blank" rel="noopener noreferrer">inference_SRC.nc</a> and <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_SRC.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_SRC.csv</a></li> <li>non-segmented "pooled" model with all predictors: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_full_nonsegmented.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_full_nonsegmented.csv</a></li> <li>freeze-thaw-state-segmented sediment rating curve: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_segm_SRC.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_segm_SRC.csv</a></li> <li>freeze-thaw-state-segmented "unpooled" model with all predictors: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_full_unpooled.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_full_unpooled.csv</a></li> </ul> </li> <li>model comparison: <ul> <li><a href="https://zenodo.org/api/records/13928999/draft/files/model_comparison_waic.csv/content" target="_blank" rel="noopener noreferrer">model_comparison_waic.csv</a></li> <li> <div><a href="https://zenodo.org/api/records/13928999/draft/files/model_comparison_loo.csv/content" target="_blank" rel="noopener noreferrer">model_comparison_loo.csv</a></div> </li> </ul> </li> </ul> </li> </ul>
Urea Transporter B (UT-B; SLC14A1); A Target Enabling Package
<p>UT-B is a member of urea transporter family, which consists of two members (UT-A and UT-B). UT-B is primarily a urea channel transporting urea molecules across plasma membrane by concentration gradient. While UT-B is widely expressed, it is particularly important in red blood cells (RBCs). UT-B is associated with improved response to hydroxyurea treatment in sickle cell diseases due to its transport of hydroxyurea into RBCs, and it is the primary antigen for Kidd (Jk) blood group as well as an accessory protein for ABO blood group. UT-B is responsible for urea transport in the vasa recta of kidney, with secondary effect on controlling the urine volume. This makes UT-B a potential target for the development of a new class of diuretics. This TEP presents the structure of UT-B in apo and inhibitor-bound states, determined by crystallography and cryo-electron microscopy, respectively. We have also confirmed the binding of an inhibitor molecule to UT-B <em>in vitro</em> using a biophysical assay. These enable the molecular characterisation of UT-B as an important blood antigen and will also aid further improvement of the inhibitors as a novel therapy for diuretics and potentially neurodegenerative disorders.</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.