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QuaLiKiz-v2.6.2 turbulent transport model evaluations based on JET experimental plasma profiles
<p>This dataset was used to train the QuaLiKiz-neural-network (QLKNN) model, QLKNN-jetexp-15D, described within the following published article: <a href="https://doi.org/10.1063/5.0038290">https://doi.org/10.1063/5.0038290</a>. It was generated with approximately 33 million standalone evaluations of QuaLiKiz-v2.6.2, each performed with a standard vector of 18 wavenumbers. Only approximately 21 million of these are kept for training due to various consistency checks applied to the code outputs. More information about the QuaLiKiz code can be found at <a href="https://www.qualikiz.com">www.qualikiz.com</a>.</p> <p>The data is saved under 3 keys in HDF5 format: "/input", "/output", and "/label". The inputs to the QuaLiKiz evaluations are provided under "/input", representing the local plasma parameters extracted from experimental measurements from the JET plasma device in Culham, UK, along with variations of select parameters according to propagated experimental uncertainties. Selected relevant outputs of the QuaLiKiz evaluations are provided under "/output", namely the local turbulent transport coefficients after applying a semi-empirical turbulent fluctuation saturation rule. Some useful metadata is provided under "/label", giving some degree of provenance tracking back to the JET experimental database, as well as describing the applied parameter variations and explaining why certain output rows were removed from the output structure.</p>
A Global Review of Long-range Transported Lead Concentration and Isotopic Ratio Records in Snow and Ice (Supplementary Data)
<p><strong>This is the supplemental material for:</strong></p> <p>Brooks, H.L., Miner, K.R., Kreutz, K.J., Winski, D.A., (in review). A Global Review of Long-range Transported Lead Concentration and Isotopic Ratio Records in Snow and Ice. </p> <p><strong>Purpose:</strong></p> <p>This systematic literature review contextualizes current data availability and examines spatial and temporal gaps in the long-range transported Pb analyses (concentration and isotope ratios) in ice and snow samples. Additionally, we note areas of needed community improvement. It is our hope that researchers will also benefit from a queryable set of references, allowing for quick access to the records appropriate to address multiple research questions. </p> <p><strong>Available Files:</strong></p> <p><em><strong>Table A1:</strong></em> Metadata for Pb records -- Individual sample sites</p> <p><em><strong>Table A2:</strong></em> Metadata for Pb records -- Transect sample sites</p> <p><em><strong>Table A3:</strong></em> Records grouped into 23 regions</p> <p><em><strong>Supplement_fig_25Aug2024: </strong></em>Additional figures supporting main manuscript</p> <p><em><strong>Supplement_method_25Aug2024: </strong></em>Methodology used for the systematic literature review</p> <p><em><strong>Supplement_citations_25Aug2024:</strong></em> Citations for all records included in the systematic literature review</p> <p><em><strong>citations_export.bib:</strong></em> Export of all systematic literature review citation data as bibtex format. Easy import to citation managers (Zotero, Mendley, Endnote, etc)</p> <p><em><strong>indexedReferences.csv:</strong></em> CSV dump of citations_export.bib indexed with citation keys used in TableA.3</p> <p><em><strong>tables.RDS: </strong></em>TableA.1, TableA.2, and indexed References formatted for easy import into R</p> <p><em><strong>tables.sqlite: </strong></em>TableA.1, TableA.2, and indexed References formatted for SQL queries in SQLite</p> <p><em><strong>readme_tables_sqlite.md:</strong></em> Examples of SQLite queries</p> <p> </p> <p><strong>Systematic Literature Review Methodology:</strong></p> <p>To address the current spatial and temporal distribution of long-range transported Pb deposited in the cryosphere (snow-pits and ice cores), we completed a systematic literature review, following the methodology outlined by Booth et al (2016). We completed an “exhaustive coverage [search], citing all relevant literature" (Booth et al., 2016), using the search terms “Lead (Pb) isotopes and concentration in surface snow, snow pits, and ice cores”. We performed an initial comprehensive literature search on these search terms on Web of Science Collection databases in September 2020 and May 2023. Records evaluated for relevance using the title and abstract. Removal of clearly off-topic papers (e.g., the chemistry of penguin feces) gathered in the search due to the dual meaning of “lead” reduced the paper count to 326 titles. The full text of the remaining publications was evaluated with clear explicit criteria for inclusion and exclusion, based on the following criteria.</p> <ol> <li> <ol> <li>Only studies examining long-traveled background atmospheric lead signals were considered. All point source pollution studies examining the localized effects of traffic, road salt, mines, industry, power plants, human activity at base camp stations, etc, were excluded. An exception was made for samples which were taken at sufficient depths in the analyzed record to predate the pollution source or where wind trajectory did not transport pollution to the collection site regardless of close geographic proximity.</li> <li> <p>Only studies of natural, undisturbed snowpacks and ice cores were examined. Studies which sampled snow from urban structures were excluded. Point source studies of emissions detail the localized effects of traffic, road salt, mines, industry, power plants, and human activity at base camp stations. While meaningful for understanding the direct emissions from various sources and developing new technology aimed at reducing source emissions, point source emission studies do not contribute to the understanding of regional and global signals. Additionally, studies examining the volcanic signal in snow following major modern eruptions were excluded, as this was classified as disturbed snow.</p> </li> <li>Studies must specify the sampling localities by providing a minimum of latitude and longitude. Where sampling locations are only referenced by colloquial names, the distance from point source pollution cannot be verified. Therefore, such studies were excluded.</li> <li> <p>Records of <sup>210</sup>Pb in snow and ice were excluded. <sup>210</sup>Pb is useful for establishing chronology in young snow and ice due to its small half life (~ 22.3 years). But it is not useful for consideration of old records and the source constraint of <sup>210</sup>Pb into the atmosphere is poorly constrained over time (Nijampurkar & Clausen, 1990). Therefore, it cannot be considered in conjunction with Pb isotopes and concentrations. Records of <sup>210</sup>Pb in snow and ice were excluded.</p> </li> <li> <p>Pb isotopes and concentrations taken from cryoconites (soil-like composites of dust, industrial soot, and microbial mats of photosynthetic bacteria) were excluded from this literature review. Cryoconites are important to glacial systems as they alter the albedo of the glacier surface, and therefore affect the glacier melt rate (Fountain et al., 2004). However, they must be considered separately from surface snow, snow pits, and ice cores due to the drastic differences in formation and biologic nature.</p> </li> <li> <p>The publication must be available to the author (<em>e.g.,</em> through the University Library, from collaborators)</p> </li> </ol> </li> </ol> <p>To ensure that the literature search conducted on the Web of Science was robust and complete, citations were checked to ensure inclusion in the literature search results and included when missing. Publications were indexed into Table A.1 and Table A.2. Following the completion of publication indexing, Table A.1 and Table A.2 were evaluated against the 23 regions (Table A.3) -- 20 from RGI 7.0 (RGI 7.0 Consortium, 2023) and 3 author defined regions -- to identify areas/papers that may have been missed in the initial search. Areas with few or no results were searched again using Google Scholar and Web of Science.</p> <p>Based on these searches, we sought to understand the current spatial and temporal coverage of these records, shed light on gaps in the previous research and make recommendations on mitigating these gaps going forward. We used tables and graphics, included in the main text and the supplement, to summarize the characteristics of the compiled records. In the main text, we discuss the limitations and gaps within the current long-range transported Pb literature, and recommend paths to mitigate these gaps. Finally, in the main text, we illustrate an example of how researchers can query this record compilation, allowing for quick access to the records appropriate to address their research questions.</p> <p><strong>Methodology Bibliography:</strong></p> <p>Booth, A., Sutton, A., & Papaioannou, D. (2016). Systematic approaches to a successful literature review (Second edition). Sage.</p> <p>Fountain, A. G., Tranter, M., Nylen, T. H., Lewis, K. J., & Mueller, D. R. (2004). Evolution of cryoconite holes and their contribution to meltwater runoff from glaciers in the McMurdo dry valleys, Antarctica. Journal of Glaciology, 50(168), 35–45. https://doi.org/10.3189/172756504781830312</p> <p>Nijampurkar, V. N., & Clausen, H. B. (1990). A century old record of lead-210 fallout on the greenland ice sheet. Tellus Series B Chemical and Physical Meteorology, 42(1), 29–38. https://doi.org/10.1034/j.1600-0889.1990.00005.</p> <p>RGI 7.0 Consortium. (2023). Randolph glacier inventory—A dataset of global glacier outlines, version 7.0. (Version 7.0) [Dataset]. NSIDC: National Snow and Ice Data Center. https://doi.org/doi:10.5067/f6jmovy5navz</p>
Larval transport pathways from three prominent sand lance habitats in the Gulf of Maine: otolith data, model data, and post-processed model data products
This dataset includes hatch and larval period for sand lance collected in 2019 and results from particle tracking runs of simulated sand lance larvae throughout the Northeast U.S. Shelf as part of Long-Term Ecological Research (NES-LTER). Release dates vary by region, corresponding to hatch and settlement dates of settling sand lance collected in 2019. Particles were depth-keeping throughout the upper 40 m to best replicate our understanding of the vertical distribution of sand lance larvae. Data were used to determine the average particle transport pathways from these sand lance habitats, including connectivity among the three hotspots, and spatial variability of connectivity within each hotspot. Further information can be found within the manuscript: Suca, J. J., Ji, R., Baumann, H., Pham, K., Silva, T. L., Wiley, D. N., Feng, Z., & Llopiz, J. K. (2022). Larval transport pathways from three prominent sand lance habitats in the Gulf of Maine. Fisheries Oceanography, 31( 3), 333-352. https://doi.org/10.1111/fog.12580
Genome-Wide DNA Methylation in Peripheral Blood and Long-Term Exposure to Source-Specific Transportation Noise and Air Pollution: The SAPALDIA Study (Supplementary Data)
<p>The zip file contains supplementary data for the publication - Genome-Wide DNA Methylation in Peripheral Blood and Long-Term Exposure to Source-Specific Transportation Noise and Air Pollution: The SAPALDIA Study, accepted for publication in Environmental Health Perspectives (DOI: 10.1289/EHP6174).</p> <p>The description of the files are noted below:</p> <p><strong>1. Readme File for SAPALDIA Noise and Air Pollution EWAS Single Exposure.zip </strong></p> <p>This zip file contains all the results of the association between source-specific transportation noise (aircraft, railway and road traffic), air pollution (NO<sub>2</sub> and PM<sub>2.5</sub>), and genome-wide DNA methylation, derived from multi-exposure models.</p> <p><strong>SAPALDIA_EWAS_SingleExposure_AircraftLden.txt</strong> contains the results for aircraft noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_RailwayLden.txt</strong> contains the results for railway noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_RoadtrafficLden.txt</strong> contains the results for road traffic noise</p> <p><strong>SAPALDIA_EWAS_SingleExposure_NO2.txt</strong> contains the results for nitrogen dioxide</p> <p><strong>SAPALDIA_EWAS_SingleExposure_PM25.txt</strong> contains the results for fine particulate matter</p> <p> </p> <p><strong>General footnote for all files:</strong>SAPALDIA: Swiss cohort study on air pollution and lung and heart diseases in adults. CpG: Cytosine-phosphate-Guanine. CHR: chromosome. SE: standard error. Lden: day-evening-night noise level. NO<sub>2</sub>: nitrogen dioxide. PM<sub>2.5</sub>: particulate matter with aerodynamic diameter <2.5 µm. Beta coefficients represent increase or decrease in DNA methylation per 10 dB increase in aircraft, railway or road traffic Lden or 10 µg/m<sup>3</sup> increase in NO<sub>2</sub> or PM<sub>2.5</sub>. All estimates were from single exposure epigenome-wide linear mixed models, with random intercept at the level of participant. Each model was adjusted for age, sex, educational level, area, and neighborhood socio-economic status, greenness index, smoking status and pack years, exposure to passive smoke, consumption of fruits, vegetables and alcohol, nested study, asthma status, survey, source-specific noise truncation indicator (for Lden models) and leukocyte composition. In a preliminary step, DNA methylation β-values were regressed on the Illumina control probe-derived first 30 principal components to correct for correlation structures and technical bias, and residuals of these regressions covering 430,477 CpGs were used as the technical bias-corrected methylation level at the CpG sites.</p> <p>Extreme values of the residuals (lying beyond three times the interquartile range below the first quartile and above the third quartile at each CpG site) were replaced with their corresponding detection threshold value (“modified winsorization”). The “winsorized” data were then used as the dependent variables in the epigenome-wide association study.</p> <p> </p> <p><strong>2. Readme File for SAPALDIA Noise and Air Pollution EWAS Multi Exposure.zip </strong></p> <p>This zip file contains all the results of the association between source-specific transportation noise (aircraft, railway and road traffic), air pollution (NO<sub>2</sub> and PM<sub>2.5</sub>), and genome-wide DNA methylation, derived from multi-exposure models.</p> <p><strong>SAPALDIA_EWAS_MultiExposure_AircraftLden.txt</strong> contains the results for aircraft noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_RailwayLden.txt</strong> contains the results for railway noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_RoadtrafficLden.txt</strong> contains the results for road traffic noise</p> <p><strong>SAPALDIA_EWAS_MultiExposure_NO2.txt</strong> contains the results for nitrogen dioxide</p> <p><strong>SAPALDIA_EWAS_MultiExposure_PM25.txt</strong> contains the results for fine particulate matter</p> <p><strong>General table footnotes: </strong>SAPALDIA: Swiss cohort study on air pollution and lung and heart diseases in adults. CpG: Cytosine-phosphate-Guanine. CHR: chromosome. SE: standard error. Lden: day-evening-night noise level. NO<sub>2</sub>: nitrogen dioxide. PM<sub>2.5</sub>: particulate matter with aerodynamic diameter <2.5 µm. Beta coefficients represent increase or decrease in DNA methylation per 10 dB increase in aircraft, railway or road traffic Lden or 10 µg/m<sup>3</sup> increase in NO<sub>2</sub> or PM<sub>2.5</sub>. All estimates were from multi-exposure epigenome-wide linear mixed models, with random intercept at the level of participant, and were adjusted for age, sex, educational level, area, and neighborhood socio-economic status, greenness index, smoking status and pack years, exposure to passive smoke, consumption of fruits, vegetables and alcohol, nested study, asthma status, survey, source-specific noise truncation indicator and leukocyte composition. Multi-exposure models included all five exposures (Aircraft, railway, road traffic Lden and respective truncation indicators, NO<sub>2</sub> and PM<sub>2.5</sub>) at the same time. In a preliminary step, DNA methylation β-values were regressed on the Illumina control probe-derived first 30 principal components to correct for correlation structures and technical bias, and residuals of these regressions covering 430,477 CpGs were used as the technical bias-corrected methylation level at the CpG sites. Extreme values of the residuals (lying beyond three times the interquartile range below the first quartile and above the third quartile at each CpG site) were replaced with their corresponding detection threshold value (“modified winsorization”). The “winsorized” data were then used as the dependent variables in the epigenome-wide association study.</p>
Extracted patterns about transport from the French Great National Debate (Grand Débat National)
<p>This data set is composed by 5 geojson files, that can be used to generate maps of mainland France :</p> <ul> <li>motifs_all.geojson : pattern about transport extracted from contributions of the French Great National Debate (Grand Débat National). Original dataset : https://granddebat.fr/pages/donnees-ouvertes</li> <li>bikeway_fr.geojson and railroad_fr.geojson : cycleways and railways of mainland France, from Open Street Map. Original dataset : https://download.geofabrik.de/europe/france.html</li> <li>trainstations.geojson : train stations and halts of mainland France, from Open Street Map. Original dataset : https://download.geofabrik.de/europe/france.html</li> <li>au2010_carto.geojson : categorized urban areas of mainland France. Original dataset : https://www.insee.fr/fr/information/2115011</li> <li>communesimportantes.geojson : the main cities of mainland France</li> </ul> <p>The data set is in French.</p> <p><em>Ce jeu de données est composé de 5 fichiers geojson qui peuvent être utilisés pour générer des cartes en France métropolitaine :</em></p> <ul> <li><em>motifs_all.geojson : motifs à propos du transport extraient des contributions en ligne au Grand Débat National. Jeu de données d'origine : https://granddebat.fr/pages/donnees-ouvertes</em></li> <li><em>bikeway_fr.geojson and railroad_fr.geojson : pistes cyclables et voies ferrées en France métropolitaine, venant d'Open Street Map. Jeu de données d'origine : https://download.geofabrik.de/europe/france.html</em></li> <li><em>trainstations.geojson : gares et petites gares en France métropolitaine, from Open Street Map. Original dataset : https://download.geofabrik.de/europe/france.html</em></li> <li><em>au2010_carto.geojson : aires urbaines catégorisées en France métropolitaine, définies par l'INSEE. Jeu de données d'origine : https://www.insee.fr/fr/information/2115011</em></li> <li><em>communesimportantes.geojson : principales villes de France métropolitaine</em></li> </ul>
Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 2: Aviation" (Righi et al., Atmos. Chem. Phys., 2016)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2016). For details see the README.md file.</p>
Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 1: Land transport and shipping" (Righi et al., Atmos. Chem. Phys., 2015)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2015). For details see the README.md file.</p>
Lagrangian Decomposition of the Meridional Heat Transport at 26.5N - Water Parcel Crossings of the RAPID 26.5N Array
<p>This dataset contains the initial and final positions and properties of Lagrangian trajectories evaluated using 5-day mean velocity and tracer fields output from the ORCA0083-N06 ocean sea-ice model hindcast (1958-2015). Numerical water parcels are initialised to sample the full-depth southward transport across the RAPID 26.5N array every month during 2004-2015. Water parcels are advected backwards-in-time using a bespoke version of TRACMASS v7.1 Lagrangian particle tracking tool which enables users to specify a custom domain using a mask netCDF file.</p><p>Particles are initialised on the first-available day of each month (based on the centre of the model 5-day mean field windows) between 2004 and 2015 (inclusive) before being advected backwards-in-time within the North Atlantic Ocean until any one of four termination conditions are met: (1) water parcels reach the RAPID 26.5N array, (2) water parcels reach the OSNAP (West or East) arrays in the subpolar North Atlantic, (3) water parcels reach either the English Channel or Gibraltar Strait, or (4) particles reach the maximum advection time of 25-years. The 25-year maximum advection time ensures that we adequately resolve the subtropical gyre circulation north to the RAPID 26.5N array. The pathway transporting dense North Atlantic Deep Water from the OSNAP arrays to RAPID at 26.5N is not fully resolved in this Lagrangian experiment since these water parcels transit on multi-decadal timescales.</p><p>The number of water parcels initialised in each model-grid cell scales with the total northward transport through that cell, such that the maximum possible transport conveyed by any single particle is 5.0 mSv (mSv == 10-3 Sv), enabling the calculation of robust Lagrangian statistics. In reality, the average. water parcel has an associated volume transport of 3.3 mSv which is conserved throughout its circulation.</p><p>Water parcel locations (converted to geographical coordinates) and properties (conservative temperature, absolute salinity, potential density [TEOS-10]) are output on every model-grid cell crossing. TRACMASS determines particle properties on grid-cell crossings by taking the average of the properties stored at the nearest two T-grid points. Here, we provide the initial and final locations and properties of all water parcels initialised from RAPID 26.5N.</p><p>All Lagrangian experiments were completed using the JASMIN High-Performance Computing facility (<a href="https://jasmin.ac.uk">https://jasmin.ac.uk</a>).</p><p><strong>For a complete description of the ORCA0083-N06 hindcast configuration see:</strong> Moat et al. (2016).</p><p><strong>For a complete description of TRACMASS v7.1 see</strong>: <a href="https://www.tracmass.org">https://www.tracmass.org</a></p>
In-depth insights on multi-ionic transport in Electrodialysis with bipolar membrane systems
<p>Electrodialysis with Bipolar Membranes (EDBM) has become a key technology for valorising waste brine streams as a new chemical production route. Even though its application has been widely studied using single electrolyte solutions (e.g., NaCl or Na2SO4), there is still a lack of knowledge about using multi-ionic mixtures. For the first time, this work aims to evaluate the EDBM performance when treating synthetic solutions mimicking the waste brines produced in a integrated process for the valorisation of solar saltworks bitterns. The behaviour of a lab-scale EDBM unit was assessed using SUEZ ion exchange membranes (IEMs), operating at 300 A m− 2, and the ion transport through IEMs was investigated, based on the calculation of apparent transport numbers and selectivities. The results highlighted that multi-ionic solutions barely affected the production of hydroxide ions. Chlorides were transported up to 7 times faster than sulphates across the anion-exchange membranes, while the cation-exchange membranes exhibited slightly higher selectivity for potassium than for sodium (~1.2). The current efficiencies ranged between 70 % and 80 %, while a minimum specific energy consumption of 1.60 kWh kg-1 NaOH was obtained for the most concentrated brine at 1 mol L-1 OH–. These results provide novel and valuable information to support the development and implementation of EDBM as a sustainable technology for supporting a resource-efficient and competitive economy through on-site and delocalized chemicals production routes.</p>
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>
Mean current velocity sections along 11°S, 5°S, 35°W, and 23°W from shipboard measurements used in "Transports and pathways of the tropical AMOC return flow from Argo data and shipboard velocity measurements"
<p>This data set contains current velocity measurements used in the study "Transports and pathways of the tropical AMOC return flow from Argo data and shipboard velocity measurements“ by <em>Tuchen et al. (2022)</em> published at <em>Journal of Geophysical Research: Oceans</em>.</p> <p>For the meridional mean sections along 35°W and 23°W, and for the quasi-zonal sections along 11°S and 5°S, one ".mat" file is provided for each of the sections. Please note that the section along 11°S consists of a zonal part (east of 34.2°W) and a cross-shore part closer to the coast. The meridional velocities along the cross-shore part of the 11°S-section are rotated clockwise by 36° in order to derive along-shore velocities.</p> <ul> <li>11°S: meridional velocity / alongshore velocity (V), neutral density (gamma_n), longitude (LON), depth (Z)</li> <li>5°S: meridional velocity (V), neutral density (gamma_n), longitude (LON), depth (Z)</li> <li>35°W: zonal velocity (U), neutral density (gamma_n), latitude (LAT), depth (Z)</li> <li>23°W: zonal velocity (U), neutral density (gamma_n), latitude (LAT), depth (Z)</li> </ul> <p>Mean velocity data in the upper 10 m are replaced by the gridded mean surface current velocities at 1/4° horizontal resolution derived from satellite-tracked surface drifting buoys (<em>Laurindo et al. 2017</em>) that were horizontally interpolated to the resolution of the individual ship sections.</p>
Comparison among transport layer protocols
<p>Data provide a comparison of transport layer protocols (TCP/MPTCP, UDP, SCTP, DCCP, QUIC, RTP) for railway applications in terms of "Congestion control", "Flow control", "Error control", "Connection establishment", "Addressing", "Checksum (for misdelivery)", "Multiplexing", "Connection-oriented", "Casting", "Stream/message-oriented", "Reliable", "Data bundling".</p> <p> </p>
Calix[6]arenes with halogen bond donor groups as selective and efficient anion transporters
<p>Dataset for the publication: <strong>Calix[6]arenes with halogen bond donor groups as selective and efficient anion transporters</strong> by A. Singh, A. Torres-Huerta, T. Vanderlinden, N. Renier, L. Martínez-Crespo, N. Tumanov, J. Wouters, K. Bartik, I. Jabin, H. Valkenier, <em>Chem. Commun.</em> <strong>2022</strong>, doi:10.1039/D2CC008472E,</p> <p>containing:</p> <ul> <li>A file with the structures of compounds <strong>1</strong>-<strong>5</strong> (PDF)</li> <li>NMR spectra for the characterisation of compounds <strong>1a</strong>, <strong>1b</strong>, <strong>1c</strong>, <strong>2</strong>, and <strong>3</strong> (Mestrenova files)</li> <li>NMR spectra for the titration experiments with compounds <strong>1</strong><strong>-5</strong> in different solvents (Mestrenova files)</li> <li>Concentrations of Host and Guests in the various titration experiments (Excel file)</li> <li>Transport data in the lucigenin assay (Excel file)</li> <li>Transport data in the HPTS assay (Excel file)</li> </ul> <p> </p> <p> </p> <div> </div>
Dataset for simulations of a beamline that controls longitudinal phase space whilst transporting LWFA electrons to an undulator
<p>This dataset relates to a design for a particle accelerator beamline. The beamline transports particles (electrons) from a laser wakefield accelerator (LWFA) source to an undulator. The unique design allows the 'chirp' or 'longitudinal phase space' of the electron distribution to be sheared during transport.<br> The dataset contains: 1) Initial bunch distributions created by the ASTRA generator program and conversion to MAD8 program format; 2) a working MAD8 batch file; 3) Three set-ups of the beamline to provide positive, negative or no shear; 4) Tracking simulation input files to track the initial distributions through each beamline set-up; 5) Output electron distributions that result from each tracking simulation.</p>
Integrated Agent-based Modelling and Simulation of Transportation Demand and Mobility Patterns in Sweden
<h2>About</h2> <p><span>The Synthetic Sweden Mobility (SySMo) model provides a simplified yet statistically realistic microscopic representation of the real population of Sweden. The agents in this synthetic population contain socioeconomic attributes, household characteristics, and corresponding activity plans for an average weekday. This agent-based modelling approach derives the transportation demand from the agents’ planned activities using various transport modes (e.g., car, public transport, bike, and walking).</span></p> <div> <p>This open data repository contains four datasets: </p> <p>(1) Synthetic Agents, </p> </div> <div> <p>(2) Activity Plans of the Agents, </p> </div> <div> <p>(3) Travel Trajectories of the Agents, and </p> </div> <div> <p>(4) Road Network (EPSG: 3006)</p> <p><span>(OpenStreetMap data were retrieved on August 28, 2023, from https://download.geofabrik.de/europe.html, and GTFS data were retrieved on September 6, 2023 from https://samtrafiken.se/)</span></p> <p><span>The database can serve as input to assess the potential impacts of new transportation technologies, infrastructure changes, and policy interventions on the mobility patterns of the Swedish population.</span></p> </div> <h2>Methodology</h2> <p>This dataset contains statistically simulated 10.2 million agents representing the population of Sweden, their socio-economic characteristics and the activity plan for an average weekday. For preparing data for the MATSim simulation, we randomly divided all the agents into 10 batches. Each batch's agents are then simulated in MATSim using the multi-modal network combining road networks and public transit data in Sweden using the package pt2matsim (https://github.com/matsim-org/pt2matsim). </p> <p>The agents' daily activity plans along with the road network serve as the primary inputs in the MATSim environment which ensures iterative replanning while aiming for a convergence on optimal activity plans for all the agents. Subsequently, the individual mobility trajectories of the agents from the MATSim simulation are retrieved.</p> <p>The activity plans of the individual agents extracted from the MATSim simulation output data are then further processed. All agents with negative utility score and negative activity time corresponding to at least one activity are filtered out as the ‘infeasible’ agents. The dataset ‘<strong>Synthetic Agents</strong>’ contains all synthetic agents regardless of their <span>‘<em>feasibility</em>’ (0=excluded & 1=included in plans and trajectories). In the other datasets, only agents with feasible activity plans are included. </span></p> <p>The simulation setup adheres to the MATSim 13.0 benchmark scenario, with slight adjustments. The strategy for replanning integrates BestScore (60%), TimeAllocationMutator (30%), and ReRoute (10%)— the percentages denote the proportion of agents utilizing these strategies. In each iteration of the simulation, the agents adopt these strategies to adjust their activity plans. The "BestScore" strategy retains the plan with the highest score from the previous iteration, selecting the most successful strategy an agent has employed up until that point. The "TimeAllocationMutator" modifies the end times of activities by introducing random shifts within a specified range, allowing for the exploration of different schedules. The "ReRoute" strategy enables agents to alter their current routes, potentially optimizing travel based on updated information or preferences. These strategies are detailed further in W. Axhausen et al. (2016) work, which provides comprehensive insights into their implementation and impact within the context of transport simulation modeling. </p> <h2>Data Description</h2> <h3>(1) Synthetic Agents</h3> <p>This dataset contains all agents in Sweden and their socioeconomic characteristics. </p> <p>The attribute ‘<span><em>feasibility</em></span>’ has two categories: <em>feasible</em><em> agents </em>(73%), and <em>infeasible agents</em> (27%). <span>Infeasible agents are agents with negative utility score and negative activity time corresponding to at least one activity.</span> </p> <p>File name: 1_syn_pop_all.parquet</p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>PId</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td>Deso</td> <td>Zone code of Demographic statistical areas (DeSO)<sup>1</sup></td> <td>String</td> <td>-</td> </tr> <tr> <td> <pre>kommun</pre> </td> <td>Municipality code</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>marital </pre> </td> <td>Marital Status (single/ couple/ child)</td> <td>String</td> <td>-</td> </tr> <tr> <td> <pre>sex </pre> </td> <td>Gender (0 = Male, 1 = Female)</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>age</pre> </td> <td>Age</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>HId</pre> </td> <td>A unique identifier for households</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>HHtype </pre> </td> <td>Type of households (single/ couple/ other)</td> <td>String</td> <td>-</td> </tr> <tr> <td> <pre>HHsize </pre> </td> <td>Number of people living in the households</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>num_babies</pre> </td> <td>Number of children less than six years old in the household</td> <td>Integer</td> <td>-</td> </tr> <tr> <td>employment</td> <td>Employment Status (0 = Not Employed, 1 = Employed)</td> <td>Integer</td> <td>-</td> </tr> <tr> <td>studenthood</td> <td>Studenthood Status (0 = Not Student, 1 = Student)</td> <td>Integer</td> <td>-</td> </tr> <tr> <td>income_class</td> <td>Income Class (0 = No Income, 1 = Low Income, 2 = Lower-middle Income, 3 = Upper-middle Income, 4 = High Income)</td> <td>Integer</td> <td>-</td> </tr> <tr> <td>num_cars</td> <td>Number of cars owned by an individual </td> <td>Integer</td> <td>-</td> </tr> <tr> <td>HHcars</td> <td>Number of cars in the household</td> <td>Integer</td> <td>-</td> </tr> <tr> <td> <pre>feasibility</pre> </td> <td>Status of the individual (1=feasible, 0=infeasible)</td> <td>Integer</td> <td>-</td> </tr> </tbody> </table> <p>1 <a href="https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/">https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/</a></p> <h3>(2) Activity Plans of the Agents</h3> <p>The dataset contains the car agents’ (agents that use cars on the simulated day) activity plans for a simulated average weekday. </p> <p>File name: <span>2_plans_i.parquet, i = 0, 1, 2, ..., 8, 9. (10 files in total)</span></p> <table> <tbody> <tr> <td> <p><strong>Column</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> </tr> <tr> <td> <p>act_purpose</p> </td> <td> <p>Activity purpose (work/ home/ school/ other)</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>PId</p> </td> <td> <p>Agent ID</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>act_end </p> </td> <td> <p>End time of activity (0:00:00 – 23:59:59)</p> </td> <td> <p>String</p> </td> <td> <p>hour:minute:seco</p> <p>nd</p> </td> </tr> <tr> <td> <p>act_id</p> </td> <td> <p>Activity index of each agent</p> </td> <td> <p>Integer</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>mode</p> </td> <td> <p>Transport mode to reach the activity location</p> </td> <td> <p>String</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>POINT_X </p> </td> <td> <p>Coordinate X of activity location (SWEREF99TM)</p> </td> <td> <p>Float</p> </td> <td> <p>metre</p> </td> </tr> <tr> <td> <p>POINT_Y</p> </td> <td> <p>Coordinate Y of activity location (SWEREF99TM)</p> </td> <td> <p>Float</p> </td> <td> <p>metre</p> </td> </tr> <tr> <td> <p>dep_time </p> </td> <td> <p>Departure time (0:00:00 – 23:59:59)</p> </td> <td> <p>String</p> </td> <td> <p>hour:minute:seco</p> <p>nd</p> </td> </tr> <tr> <td> <p>score</p> </td> <td> <p>Utility score of the simulation day as obtained from MATSim</p> </td> <td> <p>Float</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>trav_time </p> </td> <td> <p>Travel time to reach the activity location</p> </td> <td> <p>String</p> </td> <td> <p>hour:minute:seco</p> <p>nd</p> </td> </tr> <tr> <td> <p>trav_time_min </p> </td> <td> <p>Travel time in decimal minute</p> </td> <td> <p>Float</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>act_time </p> </td> <td> <p>Activity duration in decimal minute</p> </td> <td> <p>Float</p> </td> <td> <p>minute</p> </td> </tr> <tr> <td> <p>distance</p> </td> <td> <p>Travel distance between the origin and the destination</p> </td> <td> <p>Float</p> </td> <td> <p>km</p> </td> </tr> <tr> <td> <p>speed</p> </td> <td> <p>Travel speed to reach the activity location</p> </td> <td> <p>Float</p> </td> <td> <p>km/h</p> </td> </tr> </tbody> </table> <h3>(3) Travel Trajectories of the Agents</h3> <p>This dataset contains the driving trajectories of all the agents on the road network, <span>and the public transit vehicles used by these agents, including buses, ferries, trams etc. The files are produced by MATSim simulations and organised into 10 *.parquet’ files (representing different batches of simulation) corresponding to each plan file.</span></p> <p>File name: <span>3_events_i.parquet, i = 0, 1, 2, ..., 8, 9. (10 files in total)</span></p> <p> </p> <table> <tbody> <tr> <td> <div> <div> <p><strong>Column </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Description </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Data type </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Unit </strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>time </p> </div> </div> </td> <td> <div> <div> <p>Time in second in a simulation day (0-86399) </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>second </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>type </p> </div> </div> </td> <td> <div> <div> <p>Event type defined by MATSim simulation* </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>person </p> </div> </div> </td> <td> <div> <div> <p>Agent ID </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>link </p> </div> </div> </td> <td> <div> <div> <p>Nearest road link consistent with the road network </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>vehicle </p> </div> </div> </td> <td> <div> <div> <p>Vehicle ID identical to person </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>from_node </p> </div> </div> </td> <td> <div> <div> <p>Start node of the link </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>to_node </p> </div> </div> </td> <td> <div> <div> <p>End node of the link </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> </tbody> </table> <p>* One typical episode of MATSim simulation events: Activity ends (actend) -> Agent’s vehicle enters traffic (vehicle enters traffic) -> Agent’s vehicle moves from previous road segment to its next connected one (left link) -> Agent’s vehicle leaves traffic for activity (vehicle leaves traffic) -> Activity starts (actstart) </p> <h3>(4) Road Network</h3> <p>This dataset contains the road network.</p> <p>File name: 4_network.shp</p> <table> <tbody> <tr> <td> <div> <div> <p><strong>Column </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Description </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Data type </strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Unit </strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>length </p> </div> </div> </td> <td> <div> <div> <p>The length of road link </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>metre </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>freespeed </p> </div> </div> </td> <td> <div> <div> <p>Free speed </p> </div> </div> </td> <td> <div> <div> <p>Float </p> </div> </div> </td> <td> <div> <div> <p>km/h </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>capacity </p> </div> </div> </td> <td> <div> <div> <p>Number of vehicles </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>permlanes </p> </div> </div> </td> <td> <div> <div> <p>Number of lanes </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>oneway </p> </div> </div> </td> <td> <div> <div> <p>Whether the segment is one-way (0=no, 1=yes) </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>modes </p> </div> </div> </td> <td> <div> <div> <p>Transport mode </p> </div> </div> </td> <td> <div> <div> <p>String </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>from_node </p> </div> </div> </td> <td> <div> <div> <p>Start node of the link </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>to_node </p> </div> </div> </td> <td> <div> <div> <p>End node of the link </p> </div> </div> </td> <td> <div> <div> <p>Integer </p> </div> </div> </td> <td> <div> <div> <p>- </p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>geometry </p> </div> </div> </td> <td> <div> <div> <p>LINESTRING (SWEREF99TM) </p> </div> </div> </td> <td> <div> <div> <p>geometry </p> </div> </div> </td> <td> <div> <div> <p>metre </p> </div> </div> </td> </tr> </tbody> </table> <p> </p> <p><strong><span>Additional Notes</span></strong></p> <p><span>This research is funded by the RISE Research Institutes of Sweden, the Swedish Research Council for Sustainable Development (Formas, project number 2018-01768), and Transport Area of Advance, Chalmers.</span></p> <p><strong><span>Contributions</span></strong></p> <p><span>YL designed the simulation, analyzed the simulation data, and, along with CT, executed the simulation. CT, SD, FS, and SY conceptualized the model (SySMo), with CT and SD further developing the model to produce agents and their activity plans. KG wrote the data document. All authors reviewed, edited, and approved the final document.</span></p>
Crowdsourcing vibration data stemming from different transportation usages
<p> </p> <p>Crowdsourcing vibration data stemming from different activities and transportation usages (by trains, by buses, by bicycles by walking). We present a comprehensive dataset that provides the pattern of five activities walking, cycling, taking a train, a bus or a taxi. The measurements are carried out by embedded sensor accelerometer in smartphones. The dataset offers dynamic responses of subjects carrying smartphones in varied styles as they performing the five activities through vibrations acquired by accelerometers. The dataset contains corresponding time stamps and vibrations in three directions longitudinal, horizontal, and vertical stored in an Excel Macro-enabled Workbook (xlsm) format can be used to train an AI model in a smartphone which has potentials to collect people’s vibration data and decides what movement is being conducted. Besides, with more data are received, the database can be updated and it can be fed to train the model with a larger dataset. The prevalent of the smartphone opens the door of crowdsensing which leads to the pattern of people talking public transports can be understood. Furthermore, the time consumed in each activity is available in the dataset. Therefore, with a better understanding of people using public transports, the service and schedule can be planned perceptively. Activities to obtain the dataset are jointly funded by H2020 and Hitachi Europe.</p>
Data in: Aging power spectrum of membrane protein transport and other subordinated random walks
<p>Datasets generated in the report "Aging power spectrum of membrane protein transport and other subordinated random walks". Included data are:</p> <p><strong>Numerical simulations </strong><br> RWdata1.mat: 10,000 realizations, subordinated random walk with Hurst exponent, <em>H</em>=0.3 and <span class="math-tex">\(\alpha\)</span>=0.4.<br> RWdata3.mat: 10,000 realizations, subordinated random walk with Hurst exponent, <em>H</em>=0.7 and <span class="math-tex">\(\alpha\)</span>=0.4.<br> RWdata8.mat: 5,000 realizations, subordinated random walk with Hurst exponent, <em>H</em>=0.75 and <span class="math-tex">\(\alpha\)</span>=0.8.<br> RWdataCTRW.mat: 10,000 realizations, continuous time random walk (CTRW), <span class="math-tex">\(\alpha\)</span>=0.7.</p> <p><strong>Spectra of simulations</strong><br> PSDdata1.mat: Power spectral density (PSD) of a subordinated random walk with Hurst exponent, <em>H</em>=0.3 and <span class="math-tex">\(\alpha\)</span>=0.4. Five different realization times are used to compute the PDS: 2^8, 2^10, 2^12, 2^14, and 2^16.<br> PSDdata3.mat: PSD of a subordinated random walk with Hurst exponent, <em>H</em>=0.7 and <span class="math-tex">\(\alpha\)</span>=0.4. Five different realization times are used to compute the PDS: 2^8, 2^10, 2^12, 2^14, and 2^16.<br> PSDdata8.mat: PSD of a subordinated random walk with Hurst exponent, <em>H</em>=0.75 and <span class="math-tex">\(\alpha\)</span>=0.8. Four different realization times are used to compute the PDS: 2^15, 2^16, 2^17, and 2^18.<br> PSDs_CTRW.mat: PSD of a continuous-time random walk (CTRW), <span class="math-tex">\(\alpha\)</span>=0.7. Five different realization times are used to compute the PDS: 2^8, 2^10, 2^12, 2^14, and 2^16.</p> <p><strong>Experimental data of Nav1.6 channels in the soma of hippocampal neurons</strong><br> NavMSDtimes.csv: ensemble-averaged (EA) MSD and time-averaged (TA) MSD. The TA-MSD is measured for three observation times, 64, 128, and 256 frames (3.2, 6.4, and 12.8 s).<br> NavPSD.csv: Power spectral density (PSD) measured for three observation times, 64, 128, and 256 frames.</p>
Application of two-step clustering algorithm to QuaLiKiz-v2.6.2 turbulent transport simulation data
<p>QuaLiKiz simulation data in support of the two-step clustering algorithm, developed by Bart J. J. Kremers.</p> <p>The NETCDF file, generated via NETCDF4, contains the raw QuaLiKiz output for the 3-dimensional (2-input, 1-output) toy case used to develop the algorithm. Within the NETCDF file, the coordinates represent the code inputs and various vector indices and the data variables represent the code outputs.</p> <p>There are also 4 HDF5 files, containing the results from the two-step clustering reduction algorithm, where the data is saved under 2 keys: "/input" and "/flattened". The file names indicate the reduction algorithm settings used to produce the results within.</p> <p>The algorithm is available open-source at <a href="https://gitlab.com/BartKremers/two-step-clustering">https://gitlab.com/BartKremers/two-step-clustering</a>.</p>
Planetary boundaries analysis of Fischer-Tropsch Diesel for decarbonizing heavy-duty transport
<p>Dataset associated with the publication "Planetary boundaries analysis of Fischer-Tropsch Diesel for decarbonizing heavy-duty transport" by Margarita A. Charalambous, Juan D. Medrano-Garcia, and Gonzalo Guillén-Gosálbez, available at <a href="https://doi.org/10.1016/B978-0-323-85159-6.50328-6">https://doi.org/10.1016/B978-0-323-85159-6.50328-6</a>. The dataset includes the numeric data required to plot all the figures embedded in the manuscript.</p> <p>The structure of the dataset is here elucidated sheet by sheet:</p> <ul> <li><strong>LCA-Inventories:</strong> Inventory datasets used for life cycle assessment. Includes the inventory for the production of FT-diesel from CO<sub>2</sub> and H<sub>2</sub> sources investigated in this work, carbon dioxide from direct air capture (DAC), and point source coal power plant, as well as, the production of hydrogen from biomass and polymer electrolyte water electrolysis. Moreover, required adjustments to accommodation FT-diesel fuel in the truck transport activity are summarized.</li> <li><strong>LCA-Total</strong>: numerical values associated with the total share of safe operating space for all the assessed control variables of the seven planetary boundaries quantified in the study, for all the considered scenarios. These values represent the data used to create Figure 2.</li> <li><strong>LCA-Breakdown</strong>: numerical values associated with the breakdown of the environmental impacts for the studied scenarios, for three control variables (CO<sub>2</sub> concentration, and biosphere integrity). These values represent the data used to create Figure 3. </li> </ul>
The role of hydrogen in heavy transport to operate within planetary boundaries
<p>Dataset associated with the publication "The role of hydrogen in heavy transport to operate within planetary boundaries" by Antonio Valente, Victor Tulus, Galán-Martín, Mark A. J. Huijbregts, and Gonzalo Guillén-Gosálbez, available at <a href="https://doi.org/10.1039/D1SE00790D">https://doi.org/10.1039/D1SE00790D</a>. The dataset includes the numeric data associated with the plots described in the main manuscript, as well as the tables presented in the main manuscript converted in a machine-readable format.</p> <p>The structure of the dataset is here elucidated sheet by sheet:</p> <ul> <li><strong>Tables</strong>: tables 3 and 4, as reported in the main manuscript, with evolution considered for the main technical parameters and the values of the parameters used in the baseline, best and worst scenario.</li> <li><strong>Plots</strong>: numerical values associated with figures 2, 3, and 4, as reported in the main manuscript.</li> </ul>
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.