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358 results for “dataset generation”
Experimental dataset on basal stresses and seismic signals generated by granular flows moving on a 3D-printed bumpy substrate
<p>This dataset provides the data supporting the experimental study of granular flows moving on a 3D-printed bumpy substrate considering the response of basal stresses and seismic signatures.</p> <p>S1_video clips_the side-view of the kinematic behaviors of the granular flows tracked by a high-speed camera.</p> <p>S2_data_stress and seismic signals measured at the instrumented plate</p> <p>S3_data_example of velocity fields downstream and normal to the base<br>S4_data_propagation features of the flow characterized by basal stresses and seismic signals<br>S5_data_variations of the depth-averaged velocity of the granular flows and corresponding nondimensionalized downslope velocity profiles<br>S6_data_profiles of the velocity of the granular flows normal to the base along with their depth-averaged velocities in the basal layers<br>S7_data_ nondimensionalized shear rates of the granular flows for various particle diameters <br>S8_data_depth-averaged shear rates and corresponding inertial numbers for granular flows with different particle sizes<br>S9_data_the mean normal stress and shear stress and stress fluctuations normal and tangential to the base<br>S10_data_characteristics of seismic signals in terms of peak amplitude, mean envelope, seismic deviation factor, and the signal mean frequency<br>S11_data_effective basal friction coefficient and equivalent friction coefficient as functions of particle diameter<br>S12_data_relationships between nondimensionalized normal stress fluctuations and nondimensionalized basal vertical velocity, inertial number, effective basal friction coefficient and equivalent friction coefficient of the flows.<br>S13_data_seismic deviation factor as functions of nondimensionalized normal stress fluctuations, inertial number, effective basal friction coefficient and equivalent friction coefficient</p> <p>S14_data_Comparison of effective basal friction coefficient μ_b and that scaled by the nondimensionalized basal vertical velocity.</p>
Establishing a Dual Generational Modality Dataset: Comparing the Ride-Sharing Adoption Trends and Perspectives of Consumers from two Generational Cohorts, Millennials and Gen-Xers - E2
<p>Ride-hailing services such as Uber or Lyft are the latest tool in sustainable transportation strategies to come under scrutiny. Originally thought to be a way to reduce congestion, these services have actually been shown to increase it in some cases. Although the number of individuals driving around urban centers to find parking appears to decrease with the adoption of ride-hailing, Uber or Lyft drivers are instead circling around waiting for riders. Additionally, ride-hailing services have not led to the abandonment of personal vehicles, but rather to the abandonment of public transit in some cases.</p> <p>The purpose of this study is to evaluate the use of ride-hailing services in the two largest age cohorts in the United States: Millennials and Generation X-ers, focusing on the Southeastern states of Florida and North Carolina. This study seeks to determine how each generation has adopted these methods to help planners learn how to incorporate these strategies in transportation planning.</p> <p>The data provides information about the mode of transportation, trip details, and important socio-economic indicators of 1903 respondents from Florida and North Carolina.</p>
Supporting dataset for paper"Simultaneous generation of ultrahigh pressure and temperature to 50 GPa and 3300 K in multi-anvil apparatus "
<p>Supporting dataset for paper"Simultaneous generation of ultrahigh pressure and temperature to 50 GPa and 3300 K in multi-anvil apparatus "</p>
dataset of test trial with random pattern generator
<p>test session of a mems based random caustic-pattern generator. In this session we collected the experimental patterns that have helped us understand the physics and functioning of the device that we used, moreover these patterns are being used to train a neural network that will aid us to perform computational ghost imaging experiments with electrons.</p>
Assembly of human HG002 (GM24385) ONT Q20+ Simplex dataset generated by phasebook
<p>This is assembly of human HG002 (GM24385) dataset generated by phasebook. The raw sequencing data is from : October 2021 GM24385 Q20+ Simplex Dataset Release. Reads from 4 flowcells were used. https://labs.epi2me.io/gm24385_q20_2021.10/?</p>
Multi-contrast MRI and histology datasets used to train and validate MRH networks to generate virtual mouse brain histology
<p><span>H MRI maps brain structure and function non-invasively through versatile contrasts that exploit inhomogeneity in tissue micro-environments. Inferring histopathological information from MRI findings, however, remains challenging due to absence of direct links between MRI signals and cellular structures. Here, we provided deep convolutional neural networks, called MRH-Nets, developed using co-registered multi-contrast MRI and histological data of the mouse brain, can estimate histological staining intensity directly from MRI signals at each voxel. The results provide three-dimensional maps of axons and myelin with tissue contrasts that closely mimics target histology and enhanced sensitivity and specificity compared to conventional MRI markers. </span><span> </span>The dataset contains multi-contrast MRI and histology used for the training and testing and the acquisition parameters. The datasets have been carefully registered to mouse brain images from the Allen Mouse Brain Atlas (https://mouse.brain-map.org). The source codes for MRH-Nets can be found at <a href="https://github.com/liangzifei/MRH-Net">https://github.com/liangzifei/MRH-Net</a>.</p>
Metagenome-assembled genomes(MAGs) generated from soil dataset.
<p>MAGs generated from soil dataset with Maxbin2, VAMB, Metabat2, SemiBin(single-sample binning) and VAMB, SemiBin(multi-sample binning).</p> <p>Single-sample binning: Maxbin2.tar.gz, Metabat2.tar.gz, VAMB.tar.gz and SemiBin(pretrain).tar.gz. </p> <p>Multi-sample binning: VAMB_multi.tar.gz and SemiBin_multi.tar.gz.</p>
TRAVEL: A Dataset with Toolchains for Test Generation and Regression Testing of Self-driving Cars Software
<p><strong>Introduction</strong></p> <p>This repository hosts the Testing Roads for Autonomous VEhicLes (TRAVEL) dataset. TRAVEL is an extensive collection of virtual roads that have been used for testing lane assist/keeping systems (i.e., driving agents) and data from their execution in state of the art, physically accurate driving simulator, called <a href="https://www.beamng.tech/">BeamNG.tech</a>. Virtual roads consist of sequences of <strong>road points</strong> interpolated using Cubic splines.</p> <p>Along with the data, this repository contains instructions on how to install the tooling necessary to generate new data (i.e., test cases) and analyze them in the context of test regression. We focus on test selection and test prioritization, given their importance for developing high-quality software following the DevOps paradigms.</p> <p>This dataset builds on top of our previous work in this area, including work on</p> <ul> <li>test generation (e.g., <a href="https://doi.org/10.1145/3293882.3330566">AsFault</a>, <a href="https://doi.org/10.1145/3368089.3409730">DeepJanus</a>, and <a href="https://doi.org/10.1145/3460319.3464811">DeepHyperion</a>) and the SBST CPS tool competition (<a href="https://doi.org/10.1109/SBST52555.2021.00011">SBST2021</a>),</li> <li>test selection: <a href="https://arxiv.org/abs/2111.04666">SDC-Scissor</a> and related <a href="https://github.com/ChristianBirchler/sdc-scissor">tool</a></li> <li>test prioritization: <a href="https://arxiv.org/abs/2107.09614">automated test cases prioritization work for SDCs</a>.</li> </ul> <p><strong>Dataset Overview</strong></p> <p>The TRAVEL dataset is available under the <code>data</code> folder and is organized as a set of <em>experiments</em> folders. Each of these folders is generated by running the <code>test-generator</code> (see below) and contains the configuration used for generating the data (<code>experiment_description.csv</code>), various statistics on generated tests (<code>generation_stats.csv</code>) and found faults (<code>oob_stats.csv</code>). Additionally, the folders contain the raw test cases generated and executed during each experiment (<code>test.<TEST_ID>.json</code>).</p> <p>The following sections describe what each of those files contains.</p> <p>Experiment Description</p> <p>The <code>experiment_description.csv</code> contains the settings used to generate the data, including:</p> <ul> <li><strong>Time budget</strong>. The overall generation budget in hours. This budget includes both the time to generate and execute the tests as driving simulations.</li> <li><strong>The size of the map</strong>. The size of the squared map defines the boundaries inside which the virtual roads develop in meters.</li> <li><strong>The test subject</strong>. The driving agent that implements the lane-keeping system under test. The TRAVEL dataset contains data generated testing the BeamNG.AI and the end-to-end Dave2 systems.</li> <li><strong>The test generator</strong>. The algorithm that generated the test cases. The TRAVEL dataset contains data obtained using various algorithms, ranging from naive and advanced random generators to complex evolutionary algorithms, for generating tests.</li> <li><strong>The speed limit</strong>. The maximum speed at which the driving agent under test can travel.</li> <li><strong>Out of Bound (OOB) tolerance</strong>. The test cases' oracle that defines the tolerable amount of the ego-car that can lie outside the lane boundaries. This parameter ranges between 0.0 and 1.0. In the former case, a test failure triggers as soon as any part of the ego-vehicle goes out of the lane boundary; in the latter case, a test failure triggers only if the entire body of the ego-car falls outside the lane.</li> </ul> <p>Experiment Statistics</p> <p>The <code>generation_stats.csv</code> contains statistics about the test generation, including:</p> <ul> <li><strong>Total number of generated tests</strong>. The number of tests generated during an experiment. This number is broken down into the number of <strong>valid tests</strong> and <strong>invalid tests</strong>. Valid tests contain virtual roads that do not self-intersect and contain turns that are not too sharp.</li> <li><strong>Test outcome</strong>. The test outcome contains the number of <strong>passed tests</strong>, <strong>failed tests</strong>, and <strong>test in error</strong>. Passed and failed tests are defined by the OOB Tolerance and an additional (implicit) oracle that checks whether the ego-car is moving or standing. Tests that did not pass because of other errors (e.g., the simulator crashed) are reported in a separated category.</li> </ul> <p>The TRAVEL dataset also contains statistics about the failed tests, including the overall number of failed tests (<strong>total oob</strong>) and its breakdown into OOB that happened while driving <strong>left</strong> or <strong>right</strong>. Further statistics about the diversity (i.e., <strong>sparseness</strong>) of the failures are also reported.</p> <p>Test Cases and Executions</p> <p>Each <code>test.<TEST_ID>.json</code> contains information about a test case and, if the test case is valid, the data observed during its execution as driving simulation.</p> <p>The data about the test case definition include:</p> <ul> <li><strong>The road points</strong>. The list of points in a 2D space that identifies the center of the virtual road, and their interpolation using cubic splines (<strong>interpolated_points</strong>)</li> <li><strong>The test ID</strong>. The unique identifier of the test in the experiment.</li> <li><strong>Validity flag and explanation</strong>. A flag that indicates whether the test is valid or not, and a brief message describing why the test is not considered valid (e.g., <code>the road contains sharp turns</code> or <code>the road self intersects</code>)</li> </ul> <p>The test data are organized according to the following JSON Schema and can be interpreted as <code>RoadTest</code> objects provided by the <a href="https://github.com/se2p/tool-competition-av/blob/main/code_pipeline/tests_generation.py">tests_generation.py</a> module.</p> <pre><code>{ "type": "object", "properties": { "id": { "type": "integer" }, "is_valid": { "type": "boolean" }, "validation_message": { "type": "string" }, "road_points": { §\label{line:road-points}§ "type": "array", "items": { "$ref": "schemas/pair" }, }, "interpolated_points": { §\label{line:interpolated-points}§ "type": "array", "items": { "$ref": "schemas/pair" }, }, "test_outcome": { "type": "string" }, §\label{line:test-outcome}§ "description": { "type": "string" }, "execution_data": { "type": "array", "items": { "$ref" : "schemas/simulationdata" } } }, "required": [ "id", "is_valid", "validation_message", "road_points", "interpolated_points" ] } </code></pre> <p>Finally, the <strong>execution data</strong> contain a list of timestamped state information recorded by the driving simulation. State information is collected at constant frequency and includes absolute position, rotation, and velocity of the ego-car, its speed in Km/h, and control inputs from the driving agent (steering, throttle, and braking). Additionally, execution data contain OOB-related data, such as the lateral distance between the car and the lane center and the OOB percentage (i.e., how much the car is outside the lane).</p> <p>The simulation data adhere to the following (simplified) JSON Schema and can be interpreted as Python objects using the <a href="https://github.com/se2p/tool-competition-av/blob/main/self_driving/simulation_data.py">simulation_data.py</a> module.</p> <pre><code>{ "$id": "schemas/simulationdata", "type": "object", "properties": { "timer" : { "type": "number" }, "pos" : { "type": "array", "items":{ "$ref" : "schemas/triple" } } "vel" : { "type": "array", "items":{ "$ref" : "schemas/triple" } } "vel_kmh" : { "type": "number" }, "steering" : { "type": "number" }, "brake" : { "type": "number" }, "throttle" : { "type": "number" }, "is_oob" : { "type": "number" }, "oob_percentage" : { "type": "number" } §\label{line:oob-percentage}§ }, "required": [ "timer", "pos", "vel", "vel_kmh", "steering", "brake", "throttle", "is_oob", "oob_percentage" ] } </code></pre> <p><strong>Dataset Content</strong></p> <p>The TRAVEL dataset is a lively initiative so the content of the dataset is subject to change. Currently, the dataset contains the data collected during the SBST CPS tool competition, and data collected in the context of our recent work on test selection (<a href="https://arxiv.org/abs/2111.04666">SDC-Scissor work</a> and <a href="https://github.com/ChristianBirchler/sdc-scissor">tool</a>) and test prioritization (<a href="https://arxiv.org/abs/2107.09614">automated test cases prioritization work for SDCs</a>).</p> <p>SBST CPS Tool Competition Data</p> <p>The data collected during the SBST CPS tool competition are stored inside <code>data/competition.tar.gz</code>. The file contains the test cases generated by <a href="https://github.com/mahshidhelali/tool-competition-av">Deeper</a>, <a href="https://github.com/ERATOMMSD/frenetic-sbst21">Frenetic</a>, <a href="https://github.com/TayYim/adafrenetic-sbst22">AdaFrenetic</a>, and <a href="https://github.com/alessiogambi/swat-sbst2021">Swat</a>, the <strong>open-source</strong> test generators submitted to the competition and executed against BeamNG.AI with an aggression factor of 0.7 (i.e., conservative driver).</p> <table> <thead> <tr> <th>Name</th> <th>Map Size (m x m)</th> <th>Max Speed (Km/h)</th> <th>Budget (h)</th> <th>OOB Tolerance (%)</th> <th>Test Subject</th> </tr> </thead> <tbody> <tr> <td>DEFAULT</td> <td>200 × 200</td> <td>120</td> <td>5 (real time)</td> <td>0.95</td> <td>BeamNG.AI - 0.7</td> </tr> <tr> <td>SBST</td> <td>200 × 200</td> <td>70</td> <td>2 (real time)</td> <td>0.5</td> <td>BeamNG.AI - 0.7</td> </tr> </tbody> </table> <p>Specifically, the TRAVEL dataset contains 8 repetitions for each of the above configurations for each test generator totaling 64 experiments.</p> <p>SDC Scissor</p> <p>With SDC-Scissor we collected data based on the <a href="https://github.com/ERATOMMSD/frenetic-sbst21">Frenetic</a> test generator. The data is stored inside <code>data/sdc-scissor.tar.gz</code>. The following table summarizes the used parameters.</p> <table> <thead> <tr> <th>Name</th> <th>Map Size (m x m)</th> <th>Max Speed (Km/h)</th> <th>Budget (h)</th> <th>OOB Tolerance (%)</th> <th>Test Subject</th> </tr> </thead> <tbody> <tr> <td>SDC-SCISSOR</td> <td>200 × 200</td> <td>120</td> <td>16 (real time)</td> <td>0.5</td> <td>BeamNG.AI - 1.5</td> </tr> </tbody> </table> <p>The dataset contains 9 experiments with the above configuration. For generating your own data with SDC-Scissor follow the instructions in its <a href="https://github.com/ChristianBirchler/sdc-scissor">repository</a>.</p> <p><strong>Dataset Statistics</strong></p> <p>Here is an overview of the TRAVEL dataset: generated tests, executed tests, and faults found by all the test generators grouped by experiment configuration. Some 25,845 test cases are generated by running 4 test generators 8 times in 2 configurations using the SBST CPS Tool Competition code pipeline (SBST in the table). We ran the test generators for 5 hours, allowing the ego-car a generous speed limit (120 Km/h) and defining a high OOB tolerance (i.e., 0.95), and we also ran the test generators using a smaller generation budget (i.e., 2 hours) and speed limit (i.e., 70 Km/h) while setting the OOB tolerance to a lower value (i.e., 0.85). We also collected some 5, 971 additional tests with SDC-Scissor (SDC-Scissor in the table) by running it 9 times for 16 hours using Frenetic as a test generator and defining a more realistic OOB tolerance (i.e., 0.50).</p> <p><strong>Generating new Data</strong></p> <p>Generating new data, i.e., test cases, can be done using the <a href="https://github.com/se2p/tool-competition-av">SBST CPS Tool Competition pipeline</a> and the driving simulator <a href="https://www.beamng.tech/">BeamNG.tech</a>.</p> <p>Extensive instructions on how to install both software are reported inside the SBST CPS Tool Competition pipeline <a href="https://github.com/se2p/tool-competition-av/blob/main/documentation/README.md">Documentation</a>; therefore, below we only summarize the overall installation process.</p> <p>Installation</p> <ol> <li> <p>Install Python 3.7. (We tested the code using 3.7.9, so we suggest to install that version of Python)</p> </li> <li> <p>Clone the <a href="https://github.com/se2p/tool-competition-av">SBST CPS Tool Competition pipeline</a></p> </li> <li> <p>Request a (free) copy of the driving simulator at <a href="https://register.beamng.tech/">https://register.beamng.tech</a> and, after receiving the registration key file (<code>tech.key</code>) and the download link, download and install the latest distribution of the software (<code>0.24</code>).</p> </li> <li> <p>Create a python virtual environment (<code>python -m venv .venv</code>) inside the root of the SBST CPS Tool Competition pipeline and activate it. Please, be sure to call the virtual environment exactly <code>.venv</code> and place it in the correct position as the scripts to collect new data assume this setup.</p> </li> <li> <p>Install inside the virtual environment all the SBST CPS Tool Competition pipeline requirements (<code>pip install -r requirements.txt</code>).</p> </li> </ol> <p>If you have successfully setup the python environment, activate the virtual environment, and run</p> <pre><code>python.exe competition.py --help </code></pre> <p>and check that the command outputs the <a href="https://github.com/se2p/tool-competition-av/blob/main/documentation/GUIDELINES.md#technical-considerations">usage</a> of the SBST CPS Tool Competition pipeline.</p> <p>Data Collection</p> <p>Collecting new data is as simple as starting the SBST CPS Tool Competition pipeline with suitable parameters for test generation (<code>--time-budget</code>, <code>--module-name</code>, <code>--module-path</code> and <code>--class-name</code>), test executor (<code>--executor</code>), and test definition (<code>--map-size</code>, <code>--speed-limit</code> and <code>--oob-tolerance</code>).</p> <p>To ease the collection of new data, we include the (PowerShell) scripts we used to create the current TRAVEL dataset. Those scripts can be found inside the <code>data-collection</code> folder and are named after the test generator and configuration they implement. For instance, <code>Frenetic-DEFAULT.ps1</code> will execute the Frenetic test generator in the DEFAULT configuration, whereas <code>Deeper-SBST.ps1</code> will execute the Deeper test generation in the SBST configuration. The description of these configurations is reported in the <a href="https://github.com/se2p/msr-travel-dataset#sbst-cps-tool-competition-data">table above</a>).</p> <p>To run those scripts, open a PowerShell, <code>cd</code> to the folder containing the script (<code>data-collection</code>), and invoke the script you want. Assuming you have configured the SBST CPS Tool Competition pipeline as described above, the scripts will start the code pipeline and the BeamNG.tech driving simulator.</p> <p>Once the run is finished, you will find the results under the <code>results</code> folder inside the SBST CPS Tool Competition pipeline project. The results of each experiment are stored in a uniquely named folder that clearly indicates the test generator used to produce them (see <a href="https://github.com/se2p/msr-travel-dataset#dataset-overview">above</a> for the detailed description of the produced results).</p> <p><strong>Data Usage</strong></p> <p>So far, the data contained in the TRAVEL dataset have been used for benchmarking test generators as well as for optimizing regression testing in the context of Self-driving car software.</p> <p>We used the TRAVEL to study the problem of test generation from different perspectives.</p> <p>In the last edition of the <a href="https://doi.org/10.1109/SBST52555.2021.00011">SBST Tool Challenge</a>, we answer the question <em>"How efficient and effective are the existing test generators for SDCs?"</em>. We assess test generation efficiency by counting how many tests are produced by each test generator within a given time budget. To assess test generation effectiveness, we count how many of the generated tests are valid and invalid.</p> <p>In a <a href="https://doi.org/10.1145/3460319.3464811">recent study</a>, we propose (along the DeepHyperion test generator) a novel approach to assess SDCs test suites adequacy by measuring the coverage of feature maps that they achieved. To do so, we extract structural and behavioral test case features from the test case descriptions and simulation data by looking at the geometrical properties describing the roads and the physical quantities describing the ego-car behavior (e.g., position, speed).</p> <p>We also answer the question <em>"How effective are the resulting tests in finding faults in SDCs?"</em>, by counting how many failures are triggered by the generated tests and how different those failures are (e.g., Left or Right OOBs). Specifically, we propose an approach to measure OOBs similarity by computing the edit distance of the road segments relevant to the corresponding failure.</p> <p><a href="https://github.com/ERATOMMSD/roadsearch">Castellano et al.</a> demonstrated that a dataset such as TRAVEL can be used to answer other interesting questions, such as <em>"Which road representation is the most suitable for generating SDCs test cases?"</em>.</p> <p>In the context of regression testing the TRAVEL dataset is also used to develop tools to select test cases that are likely to fail before executing them simulation (e.g., <a href="https://github.com/ChristianBirchler/sdc-scissor">SDC-Scissor</a>) and also to prioritize them in a single-objective and multi-objective approach by <a href="https://arxiv.org/abs/2107.09614">SDC-Prioritizer</a>.</p> <p>With SDC-Scissor we can select test cases that are more likely to fail. This allows us to answer the following research questions like "To what extent is it possible to identify safe and unsafe test scenarios for SDCs before executing them?" or "Does SDC-Scissor improve the cost-effectiveness of simulation-based testing of SDCs?" <a href="https://arxiv.org/abs/2111.04666">Khatiri et al.</a>. SDC-Scissor achieves a F1-score (between 47% and 90%) compared to a baseline in identifying failing tests and reduced the time spent running uninformative tests by 107% and 170%. <a href="https://christianbirchler.github.io/sdc_scissor_demonstration_paper.pdf">(Birchler et al.)</a></p> <p>Regarding test prioritization, the TRAVEL dataset allows us to answer the following questions: "To what extent is it possible to apply test prioritization strategies to prioritize the execution of safe and unsafe test scenarios for SDCs?", “What is the cost-effectiveness of SDC-Prioritizer compared to baseline approaches?” and “What is the overhead introduced by it?”. To address the former question, we compared the test prioritization approaches in terms of fault detection rate (i.e., how fast faults are detected during the test execution process) using Cost cognizant Average Percentage of Fault Detection (APFD𝑐) (<a href="https://doi.org/10.1145/2771783.2771788">Epitropakis et al.</a>, <a href="https://doi.org/10.1109/ICSM.1999.792604">Rothermel et al.</a>) and concluded that SDC-Prioritizer (with 82.5% APFD𝑐) significantly outperformed the baseline random and greedy test prioritization approaches <a href="https://arxiv.org/abs/2107.09614">Birchler et al.</a>. For the latter research question, we compared the time required by SDC-Prioritizer to sort tests against the time needed to run all of the tests and showed that, on average, SDC-Prioritizer needs less than 13 minutes to perform the test prioritization while running the SDC test suites takes between 16 to 106 hours. As explained in Section 3.2.3, SDC-Prioritizer uses the road features shared with TRAVEL to guide the search process towards generating test orders with the high diversity in the road shapes. The selection of best features for test prioritization was performed with the Principal component analysis (PCA) <a href="https://arxiv.org/abs/2107.09614">Birchler et al.</a>.</p>
Second dataset on diffraction patterns of electron vortex beams generated by the electrostatic chopstic/MINEON device- CL340
<p>Franhofer diffraction dataset of electron vortex beams (EVBs) generated by the chopstic/MINEON device observed with a Camera Length of 340m on the K2 camera of the Titan Holo present in the Ernst-Ruska Centre at FZ-Julich. In this dataset, which can be accessed through Digital Micrograph and STEM_Cell it is possible to observe how by changing the potential difference between the two tips (main electrodes of the device) the radius of the EVB increases. A striking feature is that the radius increases almost linearly with the Orbital Angular Momentum. The analisys of this dataset can be found.at <strong>https://arxiv2203.00477.org/abs/</strong></p>
Third dataset on diffraction patterns of electron vortex beams generated by the electrostatic chopstic/MINEON device- CL23
<p>Franhofer diffraction dataset of electron vortex beams (EVBs) generated by the chopstic/MINEON device observed with a Camera Length of 23m on the K2 camera of the Titan Holo present in the Ernst-Ruska Centre at FZ-Julich. In this dataset, which can be accessed through Digital Micrograph and STEM_Cell it is possible to observe how by changing the potential difference between the two tips (main electrodes of the device) the radius of the EVB increases. A striking feature is that the radius increases almost linearly with the Orbital Angular Momentum. The analisys of this dataset can be found.at https://arxiv.org/abs/2203.00477</p>
Pathway2Text: Dataset for Biomedical Pathway Description Generation
<p>This is the dataset of the NAACL 2022 paper:</p> <p>Pathway2Text: Dataset and Method for Biomedical Pathway Description Generation.</p> <p>This dataset contains 2,367 pairs of biomedical pathways and textual descriptions. It can be used for automatic pathway description generation. In our paper, we showed it is also appropriate for Text2Graph and BioNER.</p> <p>Read readme.pdf for detaild information.</p>
Pixel-based forest classification of Sentinel-2 images using automatically generated datasets
<p>Contains six training datasets, composed of 800, 1600 and 3200 images. Each training dataset made up of OSM (<em>OpenStreetMap</em>) masks or HRL (<em>Copernicus pan-European High Resolution Layers</em>).</p> <p>Additional 2 evaluation datasets based on OSM and HRL. Composed of 200 evaluation images.</p> <p>For study area <em>lithuania_2018_06.tiff</em> is provided. This contains a fully preprocessed study area (removed clouds, composed mosaic).</p> <p>We provide additionally a merged mosaic of Lithuanian HRL in <em>lithuania_HRL.tiff </em>file.</p> <p><em>OpenStreetMap</em> database is not provided, it can be found at https://planet.openstreetmap.org.</p>
giaIndoorLoc – Auto-labeled WLAN + IMU dataset generated via VI-SLAM2tag
<p>This repository holds the data that belongs to the publication:</p> <p>M. Laska, T. Schulz, J. Grottke, C. Blut and J. Blankenbach, "VI-SLAM2tag: Low-Effort Labeled Dataset Collection for Fingerprinting-Based Indoor Localization", [arXiv:2207.02668]</p> <p>which is to appear at the 2022 IPIN conference. </p> <p>It is split into the following sub-parts:<br> - giaIndoorLoc_raw: Raw data recorded via the VI-SLAM2tag android app (https://github.com/laskama/VI-SLAM2tag_app)<br> - giaIndoorLoc: Annotated dataset (generated from giaIndoorLoc_raw)<br> - evaluation_data: Raw trajectory data that is used during evaluation of labeling accuracy of VI-SLAM2tag (Control-Point + Total Station (Tachymeter))<br> - model_evaluation: Model weights of fitted models used during baseline performance section (VII-B) of paper. Required for reproducing experiments with repo (https://github.com/laskama/mCELindoorLoc)</p> <p> </p> <p>For a detailed description, please refer to the given paper and the additional github repositories that host the implementations:</p> <p>- https://github.com/laskama/VI-SLAM2tag_post</p> <p>- https://github.com/laskama/VI-SLAM2tag_app</p> <p>- https://github.com/laskama/mCELindoorLoc</p>
Highly efficient generation of isogenic pluripotent stem cell models using prime editing - Datasets
<p>This collection contains the following datasets:</p> <p>1. AAVS1 knock-in genotyping</p> <p>2. aCGH karyotyping</p> <p>3. Tabular datasets for associated graphical figures </p> <p> </p>
The raw data and datasets generated to plot the Figure 2 to Figure 8
<p>(1) The Terrestrial Hybrid Repeated Gravity Observation data is applied from Data Sharing Infrastructure of National Earthquake Data Center(http://data.earthquake.cn).Only Chinese language link(https://data.earthquake.cn/datashare/report.shtml?PAGEID=datasourcelist&dt=40280d0453e5add30153e5ee3dc1001f; https://data.earthquake.cn/datashare/report.shtml?PAGEID=datasourcelist&dt=40280d0453e5add30153e5f03dd10022). Data can be requested through the email application form or the offline application form. <strong>We provide the raw data (a constant value is removed for reading).</strong> </p> <p>(2) Earthquake catalog data comes from the end of China Seismic Experimental Site webpage (http://www.cses.ac.cn/sjcp/ggmx/2021/132.shtml). Click on "cata2019".<br> (3) <sup>3</sup>He/<sup>4</sup>He release data is downloaded from the supplementary data of published article. (https://github.com/mzhangrocks/Plateau-Growth) We have cited this article in this paper.<br> (4) The 3-D P- and S-wave community velocity model of the crust and uppermost mantle in southwest China is downloaded from the supplementary data of published article. (https://github.com/liuyingustc/SWChinaCVM),(SWChinaCVM-1.0, DOI:10.12093/02md.02.2019.01.v1). We have cited this article in this paper.</p>
Dataset for High-resolution mantle flow models reveal importance of plate boundary geometry and slab pull forces on generating tectonic plate motions
<p>This repository contains the plugin and dataset used to setup models in the manuscript: "High-resolution mantle flow models reveal importance of plate boundary geometry and slab pull forces on generating tectonic plate motions".</p> <p>The material model plugin used in our models is in the "plugins" folder. The "models" folder contains the reference input parameter file described in the paper. All other model configurations shown in the paper can be obtained by modifying this parameter file. The input files used to set up the models are in the respective folder. Additionally, the Jupyter notebook used to compute residuals of our models is provided in the "scripts" folder.</p> <p> </p>
Dataset for interface calculations as BSON mongodump and JSON formats for the publication: "High-throughput generation of potential energy surfaces for solid interfaces"
<p>This dataset that contains the results presented in the journal article entitled "High-throughput generation of potential energy surfaces for solid interfaces" that was published in Volume 207 of the Elsevier journal Computational Materials Science.</p> <p>The dataset consists of a dump of a MongoDB database with a single collection that contains data on 6 solid interfaces including the generalized stacking fault energies, corrugation, interface distances and adhesion sites for the film and the substrate at minimum and maximum adhesion energy configurations, and images of the full potential energy surface (PES).</p> <p>The data is served in two different formats; a BSON mongodump folder that can be restored to a MongoDB instance using the mongorestore tool, and additionally as simple .json files. The contents are identical and the users are encouraged to choose the format that is convenient for them.</p>
ETimeline: An Extensive Timeline Generation Dataset based on Large Language Model
<div> <div>Timeline generation is of great significance for a comprehensive understanding of the development of events over time. Its goal is to organize news chronologically, which helps to identify patterns and trends that may be obscured when viewing news in isolation, making it easier to track the development of stories and understand the interrelationships between key events. Timelines have appeared in many commercial products, but there is a noticeable lack of research in this field in academia, and existing datasets need improvement in terms of effectiveness and scale. We propose the ETimeline dataset, which contains over 13,000 news articles, covering 600 bilingual timelines across 23 news domains. We collected more than 120,000 news articles as a candidate news pool and used the large language model (LLM) Pipeline to enhance performance, ultimately obtaining the ETimeline, and the news pool data will also be provided. This work contributes to the advancement of timeline generation research and supports a wide range of tasks, including topic generation and event relationships. We believe that this dataset will serve as a catalyst for innovative research and bridge the gap between academia and industry in understanding the practical application of technology services.</div> </div>
Unlocking LLM Insights: A Dataset for Automatic Model Card Generation
<p>Language models (LMs) are no longer restricted to the ML community, and instruction-following LMs have led to a rise in autonomous AI agents. As the accessibility of LMs grows, it is imperative that an understanding of their capabilities, intended usage, and development cycle also improves. Model cards are a widespread practice for documenting detailed information about an ML model. To automate model card generation, we introduce a dataset of 500 question-answer pairs for 25 LMs that cover crucial aspects of the model, such as its training configurations, datasets, biases, architecture details, and training resources. We employ annotators to extract the answers from the original paper. Further, we explore the capabilities of LMs in generating model cards by answering questions. We experiment with three configurations: zero-shot generation, retrieval-augmented generation, and fine-tuning on our dataset. The fine-tuned Llama 3 model shows an improvement of 7 points over the retrieval-augmented generation setup. This indicates that our dataset can be used to train models to automatically generate model cards from paper text and reduce the human effort in the model card curation process.</p>
Dataset related to article 'The Collaborative Seismic Earth Model: Generation 2'
<p>This repository holds 23 earthquake simulations conducted using the second generation of the Collaborative Seismic Earth Model (CSEM2, Noe et al., submitted), serving as benchmark simulations for reproducibility. The simulations are performed with the spectral-element wave propagation solver Salvus (Afanasiev et al., 2019) on event-adapted meshes (Thrastarson et al., 2020). Accompanying waveform data from the stations can be downloaded from public FDSN services and accessed via tools such as the ObsPy mass downloader (Krischer et al., 2015). Information about the earthquakes and receivers is provided in the event files. The source-time function, a filtered heaviside for 50 - 160 s, is included in 'stf.h5' for global simulations and for 20 - 160 s for regional simulations, respectively.</p> <p>The event and waveform information to create Figure 4 in the manuscript are the following:</p> <p>a) <a href="../api/records/11047927/draft/files/GCMT_event_LAKE_TANGANYIKA_REGION_Mag_6.0_2017-2-24-0.h5/content" target="_blank" rel="noopener noreferrer">GCMT_event_LAKE_TANGANYIKA_REGION_Mag_6.0_2017-2-24-0.h5</a>, stf 50 - 160 s</p> <p>b) <a href="../api/records/11047927/draft/files/GCMT_event_CENTRAL_CALIFORNIA_Mag_5.6_2020-6-4-1.h5/content" target="_blank" rel="noopener noreferrer">GCMT_event_CENTRAL_CALIFORNIA_Mag_5.6_2020-6-4-1.h5</a>, stf 20 - 160 s</p> <p>c) <a href="../api/records/11047927/draft/files/GCMT_event_CHILE-ARGENTINA_BORDER_REGION_Mag_5.7_2018-6-21-16.h5/content" target="_blank" rel="noopener noreferrer">GCMT_event_CHILE-ARGENTINA_BORDER_REGION_Mag_5.7_2018-6-21-16.h5</a>, stf 20 - 160 s</p> <p>Simulations for b&c are performed on smaller domains and on regular cubed-sphere meshes.</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.