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GPC/m: Global Precipitation Climatology by Machine Learning; Quasi-global, Daily, and One Degree Spatial Resolution
<p>A precipitation dataset, Global Precipitation Climatology by Machine Learning (ML), GPC/m, is released.</p> <p>This new precipitation dataset has been produced by machine learning, which is daily from 1979 to 2020 (will be to present), 1° × 1° spatial resolution. Three ML methods are used. Data is produced from outgoing longwave radiation (OLR) and atmospheric circulation from reanalysis. You can download this with DOI.</p> <p>This daily precipitation dataset has been produced by machine learning (ML) methods using satellite observations and atmospheric circulations from reanalysis. The quasi-global daily precipitation dataset has been around for 42 years from 1979 to 2020, which will be updated to the present. The spatial resolution is 1° × 1° zonally global and from 40°S to 50°N. The ML methods are supervised learning, and the reference data are estimated precipitation datasets from 2001 to the present. The input data are somewhat modified based on knowledge of the climatological background. Using the trained statistical models, we predict back to 1979, when daily precipitation data was almost unavailable globally. For now, this GPC/m precipitation dataset version is GPC/m-v1-2024. This data will be updated in the future with added value. The purpose of this dataset is a challenge to produce a climatological dataset by reducing artificial gaps as much as possible for discussion of climatology, climate variability, and climate change. This dataset is very useful for statistical analysis, such as composite analysis and correlation analysis. Disadvantages should also be understood in the description paper (Takahashi, 2024c). Also, I hope that this dataset can contribute to improving the current precipitation datasets, which are based on physical or researcher-explaining algorithms.<br><br>To facilitate analysis of the dataset, it is distributed in Network Common Data Form (netCDF) format and the Grid Analysis and Display System (GrADS) format (with control file). If you would like recently updated data, please contact the creator. If it has already been created, it can be distributed.<br><br><em>Added on September 18, 2024.</em><br>More details are in the preprint paper at this link (<a href="https://doi.org/10.48550/arXiv.2409.09639">Takahashi, 2024, https://doi.org/10.48550/arXiv.2409.09639</a>).</p> <p><em>Added on March 4, 2025.</em><br><strong>Alternative Download Options</strong><br>If you experience slow download speeds from Zenodo, alternative mirrors are available for the dataset files.<br><em><span>However, we kindly request you to download the .ctl file from Zenodo for tracking purposes.</span></em><br>Download NetCDF (.nc) or Binary (.bin) from:<br><a href="https://camo.fpark.tmu.ac.jp/gpcm.html">https://camo.fpark.tmu.ac.jp/gpcm.html</a></p>
QMC Raw Data for "Friedel oscillations in one-dimensional 4He"
<p>Raw path integral quantum Monte Carlo data associted with "Friedel oscillations in one-dimensional 4He". Connected github repository with associated analysis scripts can be found at <a title="Github repo" href="https://github.com/DelMaestroGroup/papers-code-HourglassNanopores">https://github.com/DelMaestroGroup/papers-code-HourglassNanopores.</a> Path integral software is available: <a title="PIMC" href="https://github.com/DelMaestroGroup/pimc">https://github.com/DelMaestroGroup/pimc</a></p> <h2><strong>Contents</strong></h2> <p><code>dR_eq_{δR}.tar.gz</code> includes raw QMC data where <code>{δR} = {0.0,2.0,3.0.4.0} Å</code> is the perturbed radius of the hourglass. All simulations include a perturbation with width <code>w = 3.0 Å</code>. Temperature is fixed at <code>T = 2.0 K</code>. </p> <p>Each <code>.tar.gz</code> contains <code>log</code> and <code>estimator</code> files for the simulations described in the associated paper. </p> <p><code>U_hourglass.npz</code>: generated xy mesh for potential plotting. </p> <p><code>gce-position-dR_eq_{δR}.npz</code>: 3D local densities inside the nanopore</p> <p> </p> <h3>Notes</h3> <p>v3: This version contains <code>gce-position-dR_eq_{δR}.npz</code> which used to have to be regenerated from raw QMC data.</p> <p>v2: This version contains an updated <code>dR_eq_2.0.tar.gz</code> (the one in v1 was corrupted).</p> <p> </p>
Dataset and code: One-tenth of EU's biomethane potential combined with carbon capture and storage can shift the region's ammonia production to net-zero
<h2>Overview</h2> <p>Repository to share the data and code associated with the scientific article <strong>Istrate et al. One-tenth of EU’s biomethane potential combined with carbon capture and storage can shift the region’s ammonia production to net-zero. One Earth (2024)</strong>. The repository contains data files and code to import the life cycle inventories (LCIs), reproduce the results, and generate the figures presented in the article.</p> <div> <h2>Repository structure</h2> </div> <p>The data folder includes:</p> <ul> <li><code>inventories.xlsx</code> contains the LCI datasets for biomethane and ammonia production formatted for use with <a href="https://github.com/brightway-lca">Brightway</a>.</li> <li><code>sustainable_biomethane_potential_Europe.xlsx</code> contains data on the sustainable biomethane potential in Europe disaggregated by feedstock and country.</li> <li><code>ammonia_production_europe.xlsx</code> contains ammonia production levels in the EU in 2021.</li> <li><code>SA_methane leakage_for presample.xlsx</code> contains data to perform sensitivity analysis on the methane leakage with <a href="https://github.com/PascalLesage/presamples">presamples</a></li> <li><code>SA_upgrading technology_presamples.xlsx</code> contains data to perform sensitivity analysis on upgrading technologies with <a href="https://github.com/PascalLesage/presamples">presamples</a></li> <li><code>results</code> folder within data contains csv files with the results, which are used in <code>05_visualization.ipynb</code> for analysis and visualization purposes.</li> </ul> <p>The notebooks folder includes:</p> <ul> <li><code>01_project_setup.ipynb</code> sets up a new Brightway project and imports the ecoinvent database.</li> <li><code>02_lci.ipynb</code> imports the LCIs and regionalize some datasets (e.g., biomethane supply based on the bimethane potential).</li> <li><code>03_lcia.ipynb</code> calculates life cycle impacts and all the additional results presented in the paper (e.g., calculation of blending ratios).</li> <li><code>04_sensitivity_analysis.ipynb</code> performs the sensitivity analysis.</li> <li><code>05_visualization.ipynb</code> imports all results and generates the figures presented in the scientific article.</li> </ul> <p>The src folder contains supporting functions required to regionalize LCIs and perform the calculations.</p> <div> <h2>How to get propertary data</h2> </div> <p>Some of the LCI datasets in the <code>inventories.xlsx</code> file are partially based on data from the ecoinvent LCI database. To comply with licensing requirements, the file shared in this repository does not include these data points. If you hold a valid ecoinvent license, please contact me directly to receive the full input files containing all ecoinvent data points.</p> <h2>Contact</h2> <p>Robert Istrate: i.r.istrate@cml.leidenuniv.nl</p>
Rational polygons with exactly one interior lattice point
<p>This record contains</p> <ul> <li>maximal \(k\)-rational polygons with exactly one interior lattice point for \(1 \leq k \leq 10\),</li> <li>all \(k\)-rational polygons with exactly one interior lattice point for \(1 \leq k \leq 4\),</li> <li>all Ehrhart quasipolynomials of these polygons for \(1 \leq k \leq 5\).</li> <li>all \(k\)-rational LDP polygons (=almost \(k\)-hollow LDP polygons) for \(1 \leq k \leq 5\).</li> </ul> <p>The maximal polygons are stored as text files with one polygon per line. Each line contains the vertices of the polygon multiplied by \(k\) (so that they are integral). The other polygons as well as the Ehrhart quasipolynomials are stored using the <a href="https://www.hdfgroup.org/solutions/hdf5/">HDF5</a> file format. All polygons and Ehrhart quasipolynomials have been obtained using <a href="https://github.com/justus-springer/RationalPolygons.jl">RationalPolygons.jl</a>.</p> <h3>Structure of the HDF5 files for polygons</h3> <p>In the HDF5 files "all_k<k>.h5", the polygons are split into different datasets according to their normalized area and number of vertices. We use "a" to denote the normalized area (twice the euclidian area) and "n" to denote the number of vertices. For example, the \(3\)-rational polygons with normalized area \(30\) having \(5\) vertices are located in "k3_all.h5" under the dataset "/a30/n5". Each dataset of polygons is one-dimensional with one entry per polygon. A polygon is stored using a compound datatype with \(2 \cdot n\) fields of integers. These integers are the vertices of the polygon multiplied by \(k\), stored in column major layout. For example, a triangle with vertices \((x_1,y_1), (x_2,y_2)\) and \((x_3,y_3)\) is stored as the tuple \((k\cdot x_1,k\cdot y_1,k \cdot x_2,k\cdot y_2,k \cdot x_3,k \cdot y_3)\). Moreover, each file contains the special two-dimensional dataset "numbers_of_polygons" that stores the numbers of polygons for a given normalized volume and number of vertices.</p> <h3>Structure of the HDF5 files for Ehrhart quasipolynomials</h3> <p>In the HDF5 files "ehrhart_k<k>.h5", the Ehrhart quasipolynomials are split into different datasets according to the normalized area of the associated polygon. We use "a" for the normalized area (twice the euclidian area). For example, the Ehrhart quasipolynomials of all \(3\)-rational polygons with normalized area \(30\) are located in "ehrhart_k3.h5" under the dataset "/a30". Each Ehrhart quasipolynomial is stored as a \(3 \times k\) integral matrix. Hence each dataset is three-dimensional, with the first dimension to enumerate the quasipolynomials and the latter two dimensions to store each quasipolynomial itself. For the way Ehrhart quasipolynomials are encoded as \(3 \times k\) integral matrices, we refer to the <a href="https://justus-springer.github.io/RationalPolygons.jl/dev/polygons/#Ehrhart-Theory">relevant documentation of RationalPolygons.jl</a>.</p>
AI-related patents (WIPO, category G06N) and market capitalisation by companies registering at least 2 new ones in 2019, sorted into four global regions (China, USA, EEA, rest of the world)
<p>NOTE: for some reason the pptx and previews keep getting munged on this supposedly permanent arxiv, but the data is still there, unchanged, and you can see how the pptx should look in either the jpg, or the the article.</p> <p>Datasets and presentations concerning the strength of the EU and "the rest of the world" relative to China and the USA, for the purpose of illustrating and counteracting / better informing narratives concerning a "new AI cold war". The materials authored by us may be freely used under the terms of the MIT License, which appears in its entirety in both the dataset and the presentation. The other materials are only curated by us, taken from Twitter as examples of misinformation pertaining to this concern.</p> <p>As of 28 June, this work now also appears in a formal publication: Joanna J. Bryson, Helena Malikova; Is There an AI Cold War?. <em><em>Global Perspectives</em></em> 2021; 2 (1): 24803. doi: <a href="https://doi.org/10.1525/gp.2021.24803">https://doi.org/10.1525/gp.2021.24803</a></p> <p>Authors: The original analysis was conducted primarily by Malikova in collaboration with Bryson. An associated publication is anticipated where Bryson is the lead author.</p> <p>Contributors: independently followed Malikova's procedures to check her work. Inconsistencies were triple checked and resolved.</p>
One qubit as a universal approximant
<p>In this repository one can find all the code used to generate the figures in the paper <em>One qubit as a Universal Approximant</em>, <a href="https://arxiv.org/abs/2102.04032">arXiv:2102.04032</a> including the original data sets.</p>
Emission line models for the lowest mass core-collapse supernovae - I. Case study of a 9 M⊙ one-dimensional neutrino-driven explosion
<p>Model spectra of the 9 Msun iron-core model, and the pure H toy model, 200-600d. Distance 10 Mpc assumed.</p>
A computer program to calculate discrete wavelet transform for one-dimensional signals
<p>This is the most recent version of the True Basic program 'NDHAAR.TRU', which was part of the supplementary materials for the following publication: X. Dong, P. Nyren, B. Patton, A. Nyren, J. Richardson and T. Maresca, 2008. Wavelets for agriculture and biology: A tutorial with applications and outlook. BioScience 58: 445-453.</p> <p>The original version (1.0, April 8, 2008) accepts a one-dimensional signal with 1024 data points. It was previously posted at http://www.ag.ndsu.edu/CentralGrasslandsREC/wavelets-for-agriculture-and-biology</p> <p>Version 1.1 (June 1, 2010) accepts signals with a length of 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384,<br> 32768, or 65536. This version with documentation was initially posted at www.infoclearinghouse.com. Later the website was closed. Now the documentation can still be accessed at https://www.scss.tcd.ie/Khurshid.Ahmad/Research/Wavelets/wva.pdf.</p> <p>Version 1.2 is posted in this current upload. A major change in this version is the correction of a few typos existing in Version 1.1, so that the program can correctly process signals longer than 4096 (that is, with signal length as either of 8192, 16384, 32768, or 65536). Note that Version 1.1 is fine in correctly processing signals with a length at or shorter than 4096.</p> <p>Two sample input data files are included. Also included is the original supplemental material Suppl_dong_2008.pdf. The first input data file 'pdsi.txt' has a length of 1024, and the related output files are OO1.txt, OO2.txt, OO3.txt, OO4.txt and OO5.txt. These data files are discussed in the original BioScience paper as well as in Suppl_dong_2008.pdf. </p> <p>The second sample input file 'warm.txt' has a length of 65536 and the related output files are OUT_1.txt, OUT_2.txt, OUT_3.txt, OUT_4.txt, and OUT_5.txt. The sample input file warm.txt contains NDVI values of winter wheat measured at Uvalde, TX, USA, from about 8 am to 10 am on April 12, 2018. The measurement was made using an ACS-430 Crop Circle sensor mounted to a push-wheel cart. This file and the associated output files are part of the intermediate results for Supplementary Figure S2 to the article entitled "Leaf water potential of field crops estimated using NDVI in ground-based remote sensing - opportunities to increase prediction precision" (<em>PeerJ</em>. 9:e12005 DOI 10.7717/peerj.12005), which can be accessed at https://zenodo.org/record/4574674#.YD7kI2hKiUk</p>
Data for One Health and Veterinary Systems in Africa
<p>One Health Database Africa<br> The “One Health and Veterinary Systems in Africa: Taking stock of current coverage, needs, and opportunities to meet present and changing threats and optimize collaboration” is reviewing current capacity and programmatic status, gaps, and operations in each country and by sub-regions of Africa.</p> <p>This datasets combines multiple sources to produce a single One Health database containing selected indicators. The database encompasses 54 countries across the continent of Africa (according the UN).</p>
[Dataset] One year of high-precision operational data including measurement uncertainties from a large-scale solar thermal collector array with flat plate collectors, located in Graz, Austria
<p><strong>Highlights:</strong></p> <ul> <li>High-precision measurement data acquired within a scientific research project, using high-quality measurement equipment and implementing extensive data quality assurance measures.</li> <li>The dataset includes data from one full operational year in a 1-minute sampling rate, covering all seasons.</li> <li>Measured data channels include global, beam and diffuse irradiances in horizontal and collector plane. Heat transfer fluid properties were determined in a dedicated laboratory test.</li> <li>In addition to the measured data channels, calculated data channels, such as thermal power output, mass flow, fluid properties, solar incidence angle and shadowing masks are provided to facilitate further analysis.</li> <li>Uncertainties of data channels are provided based on data sheet specifications and GUM error propagation.</li> <li>The dataset refers to a real-scale application which is representative of typical large-scale solar thermal plant designs (flat plate collectors, common hydraulic layout).</li> <li>Additional information is provided in a "Data in Brief" journal article: <a href="https://doi.org/10.1016/j.dib.2023.109224">https://doi.org/10.1016/j.dib.2023.109224</a></li> </ul> <p> </p> <p><strong>Collector array description: </strong>The data is from a flat plate collector array with a total gross collector area of 516 m<sup>2</sup> (361 kW nominal thermal power). The array consists of four parallel collector rows with a common inlet and outlet manifold. Large-area flat-plate collectors from Arcon-Sunmark A/S are used in the plant. Collectors are all oriented towards the south (180°), have a tilt angle of 30° and a row spacing of 3.1 m. The collector array is part of a large-scale solar thermal plant located at Fernheizwerk Graz, Austria (latitude: 47.047294 N, longitude: 15.436366 E). The plant feeds into the local district heating network and is one of the largest Solar District Heating installations in Central Europe.</p> <p> </p> <p><strong>Data files:</strong></p> <ul> <li><strong>FHW_ArcS__main__2017.csv</strong> – This is the main dataset. It is advised to use this file for further analysis. The file contains the full time series of all measured and all calculated data channels and their (propagated) measurement uncertainty (53 data channels in total). Calculated data channels are derived from measured channels (see script make_data.py below) and have the suffix __calc in their channel names. Uncertainty information is given in terms of standard deviation of a normal distribution (suffix __std); some data channels are assumed to have no uncertainty (e.g., sun azimuth or shadowing).</li> <li><strong>FHW_ArcS__main__2017.parquet</strong> – Same as FHW_ArcS__main__2017.csv, but in parquet file format for smaller file size and improved performance when loading the dataset in software.</li> <li><strong>FHW_ArcS__parameters.json</strong> – Contains various metadata about the dataset, in both human and machine-readable format. Includes plant parameters, data channel descriptions, physical units, etc.</li> <li><strong>FHW_ArcS__raw__2017.csv </strong>– Dataset with time series of all measured data channels and their measurement uncertainty. The main dataset FHW_ArcS__main__2017.csv, which includes all calculated data channels, is a superset of this file.</li> </ul> <p> </p> <p><strong>Scripts: </strong></p> <ul> <li><strong>make_data.py</strong> – This Python script exposes the calculation process of the calculated data channels (suffix __calc), including error propagation. The main calculations are defined as functions in the module utils_data.py.</li> <li><strong>make_plots.py</strong> – This Python script, together with utils_plots.py, generates several figures based on the main dataset.</li> </ul> <p> </p> <p><strong>Data collection and preparation</strong>: AEE — Institute for Sustainable Technologies (AEE INTEC), Feldgasse 19, 8200 Gleisdorf, Austria; and SOLID Solar Energy Systems GmbH (SOLID), Am Pfangberg 117, 8045 Graz, Austria</p> <p> </p> <p><strong>Data owner</strong>: solar.nahwaerme.at Energiecontracting GmbH, Puchstrasse 85, 8020 Graz, Austria</p> <p> </p> <p><strong>Additional information</strong> is provided in a journal article in "Data in Brief", titled <a href="https://doi.org/10.1016/j.dib.2023.109224">"One year of high-precision operational data including measurement uncertainties from a large-scale solar thermal collector array with flat plate collectors in Graz, Austria"</a>.</p> <p> </p> <p><strong>Note: </strong>A Gitlab repository is associated with this dataset, intended as a companion to facilitate maintenance of the Python code that is provided along with the data. If you want to use or contribute to the code, please do so using the Gitlab project: <a href="https://gitlab.com/sunpeek/zenodo-fhw-arconsouth-dataset-2017">https://gitlab.com/sunpeek/zenodo-fhw-arconsouth-dataset-2017</a></p> <p> </p>
One meter integrated depth velocity (total, ageostrophy, Ekman and Stokes components) fields for the Mediterranean Sea each 6 hours
<p>One meter integrated depth surface velocity fields for the Mediterranean Sea calculated following the methodology in Morales-Marquez et al.(2020). This dataset provides the total velocity field, the ageostrophic, Ekman and Stokes components.</p>
One-particle and excitonic band structure in cubic Boron Arsenide
<p><strong>Data for "One-particle and excitonic band structure in cubic Boron Arsenide"</strong></p> <p>The two directories are for calculations with (soc) and without (nosoc) SOC. Launching the supplied scripts in either will generate the BSE optics data (with opt.sh) and projected bands/DOS (with spd.sh) respectively.</p> <p>File description ([^1-2]):</p> <ul> <li> init.bas: initial structure definition</li> <li> ctrl.bas: main input file</li> <li> site.bas: structure input</li> <li> basp.bas: basis definition</li> <li> rst.bas: density/restart file</li> <li> sigm.bas: self-energy</li> <li> switches-for-lm: command line switches used across the scripts</li> </ul> <p>--</p> <p>[^1]: https://www.questaal.org/docs/input/inputfile/</p> <p>[^2]: https://www.questaal.org/docs/input/data_format/</p>
All three types of otoliths of Cyprinus carpio (total length=30 cm); left/right asterisci and lapilli from top and bottom view, and one sagittus.
<p>The image shows all three types of otoliths of <em>Cyprinus carpio</em> (total length=30 cm): left/right <em>asterisci</em> and <em>lapilli </em>from top and bottom view, and one sagittus. </p>
PLAE web app enables powerful searching and multiple visualizations across one million unified single-cell ocular transcriptomes
<p>Supplementary Data for "PLAE web app enables powerful searching and multiple visualizations across one million unified single-cell ocular transcriptomes"</p> <p> </p>
Cipher Machine Siemens & Halske Mixer Unit One Time Tape_surface meshes (Inv. 2017-449)
<p>Surface Meshes created from reconstructed CT-image stacks.<br> The inital CT dataset is linked as a related identifier.</p>
Baltimore Ecosystem Study: Household Telephone Survey in support of Locke et al 2019 in PLoS One
This is a subset of the data found in Grove and Locke (2018), to be included with: Locke, D.H., Polsky, C., Grove, J. M., Groffman, P. M., Nelson, K.C., Larson, K. L., Cavender-Bares, J., Heffernan, J. B., Roy Chowdhury, R., Hobbie, S. E., Bettez, N., Neill, C., Ogden, L.A., O’Neil-Dunne, J. P. M.. [accepted]. Heterogeneity of practice underlies the homogeneity of ecological outcomes of United States yard care in metropolitan regions, neighborhoods and households. PLoS ONE doi:10.1371/journal.pone.0222630 These data contain answers 2011 survey questions: In the past year, which of the following has been applied to any part of your yard: Water for irrigating grass, plants, or trees? Fertilizers? Pesticides to get rid of weeds or pests? The total household annual income (8 ordinal categories), age of respondent (5 ordinal categories), and the answer to: About how many neighbors do you know by name? (recorded in 5 ordinal categories). Two additional columns are provided to indicate the metropolitan region of the respondent (one of the following six: Phoenix, Los Angeles, Minneapolis - St. Paul, Baltimore, Boston, or Miami) and the degree of urbanicity in that region (Urban, Suburban, or Exurban). See Grove and Locke 2018 for additional details. This research is supported by the Macro- Systems Biology Program (US NSF) under Grants EF-1065548, -1065737, -1065740, -1065741, -1065772, -1065785, -1065831, and -121238320 and the NIFA McIntire-Stennis 1000343 MIN-42-051. The work arose from research funded by grants from the NSF LTER program for Baltimore (DEB- 0423476, DEB-1027188); Phoenix (BCS-1026865, DEB-0423704, DEB-9714833, DEB-1637590, DEB-1832016); Plum Island, Boston (OCE-1058747 and 1238212); Cedar Creek, Minneapolis–St. Paul (DEB- 0620652); and Florida Coastal Everglades, Miami (DBI-0620409). Edna Bailey Sussman Foundation, Libby Fund Enhancement Award and the Marion I. Wright ‘46 Travel Grant at Clark University, The Warnock Foundation, the USDA Forest Service No
Monitoring permafrost thaw in Denali National Park and Preserve: year one of data collection 2013
Transects were established at six sites in the Toklat river basin, Denali National Park and Preserve. This was the fist year of what will be a long-term monitoring project of permafrost and ecosystem characteristics. Transects were co-located with known thaw subsidence (thermokarst) features and run from outside (relatively undisturbed tundra) through the feature.
MCR LTER: Coral Reef: Fish Counts versus Coral Diversity for Holbrook, et al. PLoS One 2015
To test the impact of the regional species pool on the coral-fish diversity relationship, the same experiment was conducted in lagoons of Schumann Island in Kimbe Bay, Papua New Guinea (5°31’S, 150°5’E), Lizard Island on the Great Barrier Reef, Australia (14º 41’S, 145º 27’E), and Moorea in French Polynesia (17° 30’S, 149° 50’W). Divers counted individuals of all species observed on or interacting with 45 patch reefs were constructed using six abundant, co-occurring coral species that were major habitat providers for fish. Fish were allowed to naturally colonize over 8-12 months and the patch reefs were surveyed by scuba divers four times during the period. The species of corals used in the experiment were selected to include species that exhibit a broad range of structural morphologies and potential sensitivities to climate change and other environmental stressors. These data support the manuscript: Holbrook, Sally J., Russel J. Schmitt, Vanessa Messmer, Andrew J. Brooks, Maya Srinivasan, Philip L. Munday and Geoffrey P. Jones, Reef Fishes in Biodiversity Hotspots are at Greatest Risk from Loss of Coral Species, accepted 2015, PLoS One.
Livorno, Highway pilot, one automated car, two connected cars, smart highway
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>1. AD cars with C-eHorizon and V2X OBU devices on board travels on the highway. The highway is equipped with IoT G5 RSUs. All the devices publish and share the information by the oneM2M platform in the cloud.</p> <p>Actions or events: </p> <p>1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform.</p> <p>2 The RSU (subscribed to the OneM2M platform) receives the information and it broadcasts to the vehicles the DENM message containing information about available lanes, speed limits, geometry, alternative routes etc.</p> <p>3 At the same time the CONTI cloud is subscribed to the oneM2M platform; it receives and share with the FCA cloud the information of the road works, updating dynamically the maps of the Connected e-Horizon installed onboard the CRF AD car</p> <p>4 The in-vehicle application fusing the information from the OBU, the C-eHorizon and on-board sensors, performs speed adaptation and lane change maneuvers</p> <p>Relevant situations: How the AD function interacts with different IoT input: from I2V (DENM, Roadwork position and extension); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>Test session with one AD+connected car and two connected cars, lap of 11,6 km on the highway.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</p>
Livorno, Highway pilot, one automated and connected car and one connected car
<p><strong>Scenario description</strong>:</p> <p>Precondition:</p> <p>1. AD cars with C-eHorizon and V2X OBU devices on board travels on the highway. The highway is equipped with IoT G5 RSUs. All the devices publish and share the information by the oneM2M platform in the cloud.</p> <p>Actions or events:</p> <p>1 The Traffic Control Center publishes the presence of roadway works to the OneM2M platform.</p> <p>2 The RSU (subscribed to the OneM2M platform) receives the information and it broadcasts to the vehicles the DENM message containing information about available lanes, speed limits, geometry, alternative routes etc.</p> <p>3 At the same time the CONTI cloud is subscribed to the oneM2M platform; it receives and share with the FCA cloud the information of the road works, updating dynamically the maps of the Connected e-Horizon installed onboard the CRF AD car</p> <p>4 The in-vehicle application fusing the information from the OBU, the C-eHorizon and on-board sensors, performs speed adaptation and lane change maneuvers</p> <p>Relevant situations: How the AD function interacts with different IoT input: from I2V (DENM, Roadwork position and extension); from V2V (CAM with info from other vehicles).</p> <p><strong>Session description</strong>:</p> <p>Test session with one AD+connected car and one connected car, lap of 11,6 km on the highway.</p> <p><strong>Datasets description</strong>:</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Highway Piloting in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Highway Piloting in Livorno.</p> <p><strong>AUTOPILOT_Livorno_HighwayPilot_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by HighwayPilot devices, applications and services across the oneM2M platform.</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.