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1,754 results for “synthetic”
Synthetic JWST MIRI-MRS Observations of Mid-IR Noble Gas Emission from T Cha
<p>Synthetic detctor images for the continuum + line emission from [Ne II], [Ne III], [Ar II], [Ar III] for T Cha made with MIRISim (Klaassen et al. 2021) for the overall best fitting model with r_in = 0.1 rG (both with and without a cavity). Also provided are backgrounds for the source observation (applicable to both with and without a cavity) and models for a synthetic standard star and its background. </p> <p>The .fits files are the underlying data cubes for the [Ne II] and [Ar II] lines which were provided as inputs to the simulator. </p> <p>For a full description, see Sellek et al. (2024a).</p>
Datasets of synthetic task graphs for evaluating a reliability and latency multi-objective task allocation framework
<p>These datasets of synthetic task graphs were generated to evaluate the performance and scalability of a multi-objective task allocation approach for workflow applications of various structures and sizes in a system based on the edge-hub-cloud paradigm. The targeted architecture comprised an edge device (e.g., a single-board computer attached to an unmanned aerial vehicle (UAV)) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. The objectives were the maximization of the overall reliability and the minimization of the overall latency of the application, under memory, storage, energy, and task precedence constraints. We considered that a percentage of the tasks required fixed allocation on the edge or hub device. Each task had a different vulnerability factor (i.e., probability of failure) on each device.</p> <p>We generated nine task graphs of serial, parallel, and mixed (a combination of serial and parallel) structure with 10, 100, and 1000 nodes, utilizing the Task Graphs For Free (TGFF) random task graph generator [1]. Additional task parameters (e.g., execution time, power consumption, vulnerability factor, memory, storage, output data size) were included post-generation, using representative random values. More details are provided in README.txt.</p> <p>Note: These datasets are released under a Creative Commons Attribution license. If you utilize these datasets in your work, please cite us using the corresponding Zenodo DOI https://doi.org/10.5281/zenodo.10357101.</p> <p>References:</p> <p>[1] R. P. Dick, D. L. Rhodes and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE'98), Seattle, WA, USA, 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.</p>
An updated modular set of synthetic spectral energy distributions for young stellar objects
<p>These are the models released with the following publication:</p> <p><strong><em>An updated modular set of synthetic spectral energy distributions for young stellar objects</em></strong> (<a href="https://ui.adsabs.harvard.edu/abs/2024ApJ...961..188R/abstract" target="_blank" rel="noopener">Richardson et al. 2024</a>).</p> <p>This is a set of young stellar object (YSO) models with associated spectral energy distributions (SEDs) calculated through radiative transfer. It is a significant update to the data published alongside Robitaille (2017, R17). It contains the parameters shaping each model and adds the newly calculated parameters of envelope mass, average dust temperature, disk stability, and line-of-sight extinction. It also makes explicit quantities, such as source luminosity, that were left implicit in the previous release. This set also convolves the SEDs with several new filters, primarily those on the James Webb Space Telescope, and adds a script to facilitate convolution of these models with additional filters as desired by users. All data included in Version 1.1 of the R17 set (the most recent) are included here.</p> <p>Like their predecessors, these models are versioned. Updates will be released as more models are completed or other changes are made.</p> <p>Files unzip to r+24_models-{version}/{geometry}. "files.tar.gz" contains scripts for SED convolution and main sequence comparison, the opacity to absorption of dust used in the radiative transfer calculations, main sequence T/L values used for results in the accompanying work, and reference material for the contents of the dataset and latest version.</p> <p>The primary use of these models is as templates for SED fitting. The R17 models were structured for use with the <a href="https://sedfitter.readthedocs.io/en/stable/" target="_blank" rel="noopener">sedfitter</a> python package, which enables fitting and analysis of the fit results. For a version of sedfitter which accommodates the new additions, use <a href="https://github.com/richardson-t/sedfitter/tree/dev" target="_blank" rel="noopener">this fork</a>.</p>
TERMINET eHealth post-operation complications synthetic dataset
<p><strong>1. Introduction</strong></p> <p>Older adults with cancer often need to undergo operations. Post-surgery complications may arise, and Real-World Data (RWD) collected from such patients during a pre-operation monitoring period of two weeks can help identify risk for post-surgery complications. The involved RWD span behavioral data (measured or reported) as well as clinical data (collected during clinical tests). This dataset is synthesized by <a href="https://innovationsprint.eu/">Innovation Sprint</a>, using actual data collected from eligible <a href="https://www.policlinicogemelli.it/en/">Fondazione Policlinico Gemelli</a> patients participating to the SUPERO study. The clinical data is collected by the hospital, while the behavioral is collected using <a href="https://innovationsprint.eu/healthentia/">Healthentia</a>, a medical decision support software developed by Innovation Sprint, facilitating the collection, analysis and presentation of behavioral data.</p> <p><strong>2. Dataset description</strong></p> <p>The provided TERMINET eHealth post-operation complications synthetic dataset contains 10,000 synthetic patients, provided in an equal number of rows in the CSV file containing the dataset. The different attributes of the dataset are organized in columns.</p> <p>The attributes are summarized as follows:</p> <ul> <li>6 columns of step data statistics</li> <li>20 columns of clinical attributes</li> <li>2 columns of demographics attributes</li> <li>12 columns of questionnaire attributes</li> <li>1 column of outcome attribute</li> </ul> <p><em>2.1. Step statistics</em></p> <p>Step data is collected per day of the pre-hospitalization period. The final two weeks of that period are used to derive the step statistics. For each of the weeks, the mean, standard deviation and slope of the linear regression of the step data is reported, 3 attributes per week, 6 attributes in total.</p> <p><em>2.2. Clinical attributes</em></p> <p>The 20 clinical attributes collected at the hospital are ALT, Hematocrit (%), AST, Lymphocytes, Hepatitis B, Neutrophils (%), Hepatitis C, Neutrophils, INR (%), INR, White Blood Cells, INR (seconds), Platelets, Sodium, Hemoglobin, Potassium, Lymphocytes (%), Creatinine, Bilirubin and Urea Nitrogen.</p> <p><em>2.3. Demographic attributes</em></p> <p>The sex and age are the two demographic attributes collected.</p> <p><em>2.4. Questionnaire attributes</em></p> <p>Three questionnaires are involved in the SUPERO study are:</p> <ul> <li>G8, spanning the categories of food intake, weight loss, movement, neuropsychological, BMI, multiple medication, health and age.</li> <li>SPPB, spanning the categories of balance, speed and strength</li> <li>MiniCog, where only the clock drawing capabilities are assessed</li> </ul> <p>2.5. Outcome attribute</p> <p>The single outcome attribute is the existence of any post-surgery complications. Please note that the dataset is quite imbalanced, since complications are very rare.</p> <p><strong>3. Data synthesis</strong></p> <p>This dataset is synthesized from the early data of the SUPERO study. Currently there are 21 patients registered, with the decision to operate them being reached for 20 of them. 16 of the patients have already been operated, 2 of them having exhibited post-surgery complications. More vectors have been generated by adding Gaussian noise to the original 16 vectors, resulting to 128 vectors. The resulting vectors have been clustered into 16 clusters using Agglomerative clustering. Every cluster has been modelled via Gaussian Mixture Models. The resulting set of GMMs has been used to generate the 10,000 synthetic vectors of the dataset.</p> <p><strong>4. Acknowledgement</strong></p> <p>The development of this dataset has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 957406 (TERMINET).</p>
Datasets of synthetic workflows for evaluating a multi-objective and multi-constrained scheduling approach for cyber-physical applications
<p>These datasets of synthetic workflows (task graphs) were generated to evaluate the performance and scalability of a multi-objective and multi-constrained scheduling approach for workflow applications of various structures, sizes, and sensing/actuating requirements in a cyber-physical system (CPS) based on the edge-hub-cloud paradigm. The examined CPS comprised four edge devices (i.e., single-board computers, each attached to an unmanned aerial vehicle (UAV) equipped with sensors/actuators) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. All system devices featured heterogeneous multicore processors with different processing core failure rates and varied sensing/actuating or other specialized capabilities. Our objectives were the minimization of the overall latency, the minimization of the overall energy consumption, and the maximization of the overall reliability of the workflow application in the specific CPS, under deadline, reliability, memory, storage, energy, capability, and task precedence constraints.</p> <p>We generated 25 random task graphs with 10, 20, 30, 40, and 50 nodes (5 task graphs for each size), utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size, capability, reliability threshold) were included post-generation, using appropriate values. More details are provided in README.txt.<br><br>References:<br>[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.<br>[2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/.</p>
Synthetic Multimodal Drone Delivery Dataset
<h1><strong>README: Synthetic Logistics Dataset Structure and Components</strong></h1> <p>This dataset provides a structured representation of logistics data designed to evaluate and optimize hybrid truck-and-drone delivery networks. It captures a comprehensive set of parameters essential for modeling real-world logistics scenarios, including spatial coordinates, environmental conditions, and operational constraints. The data is meticulously organized into distinct keys, each representing a critical aspect of the delivery network, enabling researchers and practitioners to conduct flexible and in-depth analyses. </p> <p>The dataset is a curated subset derived from the research presented in the paper "Synthetic Dataset Generation for Optimizing Multimodal Drone Delivery Systems" by Altinsel et al. (2024), published in Drones. It serves as a practical resource for studying the interplay between ground-based and aerial delivery systems, with a focus on efficiency, environmental impact, and operational feasibility. </p> <p><strong>Altinses, D., Torres, D. O. S., Gobachew, A. M., Lier, S., & Schwung, A. (2024). Synthetic Dataset Generation for Optimizing Multimodal Drone Delivery Systems. <em>Drones (2504-446X)</em>, <em>8</em>(12).</strong></p> <p>Each data file contains information on ten customer locations, specified by their x and y coordinates, which facilitate the modeling of delivery routes and service areas. Additionally, the dataset includes communication data represented as a two-dimensional grid, which can be used to assess signal strength, connectivity, or other network-related factors that influence drone operations. </p> <p>A key feature of this dataset is the inclusion of wind data, structured as a two-dimensional grid with four distinct features per grid point. These features likely represent wind velocity components (such as horizontal and vertical directions) along with auxiliary parameters like turbulence intensity or wind shear, which are crucial for drone path planning and energy consumption estimation. The wind data enables researchers to simulate realistic environmental conditions and evaluate their impact on drone performance, stability, and battery life. </p> <p>By integrating geospatial, environmental, and operational data, this dataset supports a wide range of applications, from route optimization and energy efficiency studies to risk assessment and resilience planning in multimodal delivery systems. Its synthetic nature ensures reproducibility while maintaining relevance to real-world logistics challenges, making it a valuable tool for advancing research in drone-assisted delivery networks. </p> <p> </p> <h3><strong>The 4 wind channels represent:</strong></h3> <ol> <li> <p><strong><code>X</code> and <code>Y</code> (Grid Positions)</strong></p> <ul> <li> <p>These define <strong>where the arrows start</strong> (usually a meshgrid).</p> </li> </ul> </li> <li> <p><strong><code>U</code> and <code>V</code> (Arrow Directions)</strong></p> <ul> <li> <p><code>U</code> = Horizontal component (e.g., gradient in <code>x</code>).</p> </li> <li> <p><code>V</code> = Vertical component (e.g., gradient in <code>y</code>).</p> </li> </ul> </li> </ol> <p> </p> <h3><strong>How to load the files using Python:</strong></h3> <p>data = np.loadtxt('data.txt')</p> <p>#### Just for Wind data:</p> <p>data = data.reshape((4,16,16)) </p>
ASVspoof2019LA-Sim: Augmented Dataset for An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems
<p>This is the dataset we augmented to study the channel effects for anti-spoofing. For more details, please refer to our Interspeech 2021 paper: "An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems".</p> <p>Proceeding: <a href="https://www.isca-speech.org/archive/interspeech_2021/zhang21ea_interspeech.html">https://www.isca-speech.org/archive/interspeech_2021/zhang21ea_interspeech.html</a></p> <p>Arxiv: <a href="https://arxiv.org/pdf/2104.01320.pdf">https://arxiv.org/pdf/2104.01320.pdf</a></p> <p>Code: <a href="https://github.com/yzyouzhang/Empirical-Channel-CM">https://github.com/yzyouzhang/Empirical-Channel-CM</a></p> <p>Contact: you.zhang@rochester.edu</p> <p><strong>Version 1.0</strong> contains the <strong>training</strong> and the <strong>development</strong> set. We have added the <strong>evaluation</strong> set in <strong>version 1.1 </strong>but deleted the training set due to the size limitation, but you can still access the training set in version 1.0.</p> <p>Please check it out.</p> <p>To extract the files, please use the following commands:</p> <pre><code class="language-bash">cat eval.tar.gz-part* > eval.tar.gz tar -xvzf *.tar.gz</code></pre> <p>After concatenation, to make sure the download is complete, you can check with the following:</p> <pre><code>md5sum *.tar.gz 15dea7d28b126994bb6b159778f706af dev.tar.gz 0615052b34ca6c7f58505eaa8647844f eval.tar.gz 3058dd9d407f3c9ae697acca8c34a6c3 train.tar.gz</code></pre> <p>Thanks.</p>
Synthetic Data for Uplift Modeling and Heterogenous Treatment Effect with Known Counterfactuals and ITE
<p>This dataset is designed and simulated for evaluating uplift modeling. The data generation process is based on a logistic regression model - no real data is included or used for generating this dataset.</p> <p>This dataset has several signatures:</p> <ul> <li>It generates features with various patterns associated with the outcome variable and the causal effect (or treatment effect). Thus it is suitable for evaluating feature importance and model interpretation for uplift modeling.</li> <li>The true counterfactual outcomes under control and treatment are known for each user, as well as the true ITE (Individual treatment effect).</li> </ul> <p>This dataset consists of 50 trials (replicates with different random seeds), each trial with 20,000 samples and 36 features. The outcome variable is binary, which makes this dataset for classification problems. The samples are equally split for the control and treatment groups (10,000 samples in each group in each trial).</p> <p>The generated data has three types of features: (1) uplift features influencing the treatment effect on the conversion probability; (2) classification features affecting the conversion probability but independent of the treatment effect; and (3) irrelevant features that are independent of both conversion probability and the treatment effect.</p> <p>To simulate the relationship between uplift features and the treatment effect and classification features and outcome probability, we implement six types of association patterns in the data generation process: linear, quadratic, cubic, ReLU (Rectified Linear Unit), trigonometric function sine, and cosine.</p> <p>In this data set, there are 36 features in total, including 10 classification features, 6 uplift features, and 20 irrelevant features.</p> <p>Column names:</p> <p> Trial ID: 'trial_id'<br> Experiment group label: 'treatment_group_key'<br> Outcome variable (classification label): 'conversion'<br> Feature names: ['x1_informative',<br> 'x2_informative',<br> 'x3_informative',<br> 'x4_informative',<br> 'x5_informative',<br> 'x6_informative',<br> 'x7_informative',<br> 'x8_informative',<br> 'x9_informative',<br> 'x10_informative',<br> 'x11_irrelevant',<br> 'x12_irrelevant',<br> 'x13_irrelevant',<br> 'x14_irrelevant',<br> 'x15_irrelevant',<br> 'x16_irrelevant',<br> 'x17_irrelevant',<br> 'x18_irrelevant',<br> 'x19_irrelevant',<br> 'x20_irrelevant',<br> 'x21_irrelevant',<br> 'x22_irrelevant',<br> 'x23_irrelevant',<br> 'x24_irrelevant',<br> 'x25_irrelevant',<br> 'x26_irrelevant',<br> 'x27_irrelevant',<br> 'x28_irrelevant',<br> 'x29_irrelevant',<br> 'x30_irrelevant',<br> 'x31_uplift_increase',<br> 'x32_uplift_increase',<br> 'x33_uplift_increase',<br> 'x34_uplift_increase',<br> 'x35_uplift_increase',<br> 'x36_uplift_increase']<br> True underlying control conversion probability: 'control_conversion_prob'<br> True underlying treatment conversion probability: 'treatment1_conversion_prob'<br> True treatment effect: 'treatment1_true_effect'</p>
[DCASE2022 Task 3] Synthetic SELD mixtures for baseline training
<p><strong>DESCRIPTION:</strong><br> <br> This audio dataset serves serves as supplementary material for the <a href="http://Sound Event Localization and Detection Evaluated in Real Spatial Sound Scenes">DCASE2022 Challenge Task 3: Sound Event Localization and Detection Evaluated in Real Spatial Sound Scenes</a>. The dataset consists of synthetic spatial audio mixtures of sound events spatialized for two different spatial formats using real measured room impulse responses (RIRs) measured in various spaces of Tampere University (TAU). The mixtures are generated using the same process as the one used to generate the recordings of the <a href="https://zenodo.org/record/5476980">TAU-NIGENS Spatial Sound Scenes 2021</a> dataset for the <a href="https://dcase.community/challenge2021/task-sound-event-localization-and-detection-results">DCASE2021 Challenge Task 3</a>. </p> <p>The SELD task setup in DCASE2022 is based on spatial recordings of real scenes, captured in the <a href="https://zenodo.org/record/6387880">STARS22</a> dataset. Since the task setup allows use of external data, these synthetic mixtures serve as additional training material for the <a href="https://github.com/sharathadavanne/seld-dcase2022">baseline model</a>, and they are shared for reasons of reproducibility. For more details on the task setup, please refer to the <a href="http://Sound Event Localization and Detection Evaluated in Real Spatial Sound Scenes">task description</a>.</p> <p>Note that the generator code and the collection of room responses used to spatialize sound samples will be also be made available soon. For more details on the recording of RIRs, spatialization, and generation, see:</p> <ul> <li>Archontis Politis, Sharath Adavanne, Daniel Krause, Antoine Deleforge, Prerak Srivastava, Tuomas Virtanen (2021). A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection. In <em>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2021)</em>, Barcelona, Spain.</li> </ul> <p>available <a href="https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Politis_43.pdf">here</a>.</p> <p><strong>SPECIFICATIONS:</strong></p> <ul> <li><strong>13 target sound classes</strong> (see task description for details)</li> <li>The sound event samples are sources from the <strong><a href="https://zenodo.org/record/4060432">FSD50K</a></strong> dataset, based on affinity of the labels in that dataset to the target classes. The selection on distinguishing which labels in FSD50K corresponded to the target ones, then selecting samples that were tagged with only those labels, and additionally that they had annotator rating of Present and Predominant (see FSD50K for more details). The list of the selected files is included here.</li> <li><strong>1200</strong> 1-minute long spatial recordings</li> <li>Sampling rate of<strong> 24kHz</strong></li> <li>Two 4-channel recording formats, first-order Ambisonics (<strong>FOA</strong>) and tetrahedral microphone array (<strong>MIC</strong>)</li> <li>Spatial events spatialized in <strong>9 unique rooms</strong>, using measured RIRs for the two formats</li> <li>Maximum <strong>polyphony of 2</strong> (with possible same-class events overlapping)</li> <li>Even though the whole set is used for training of the baseline without distinction between the mixtures, we have included a <strong>separation into a training and testing split</strong>, in case on one needs to test the performance purely on those synthetic conditions (for example for comparisons with training on mixed synthetic-real data, fine-tuning on real data, or training on real data only).</li> <li>The training split is indicated as <strong>fold1</strong> in the dataset, contains 900 recordings spatialized on 6 rooms (150 recordings/room) and it is based on samples from the development set of FSD50K.</li> <li>The testing split is indicated as <strong>fold2</strong> in the dataset, contains 300 recordings spatialized on 3 rooms (100 recordings/room) and it is based on samples from the evaluation set of FSD50K.</li> <li>Common metadata files for both formats are provided. For the file naming and the metadata format, refer to the task setup.</li> </ul> <p><strong>FSD50K SELECTION:</strong></p> <p>The list of selected sound event recordings is included along the recordings and metadata, as <strong>FSD50K_selected.txt</strong>. Each line in the text file has the following structure:</p> <pre><code>[target_label]/[train/test]/[FSD50K_label]/filename.wav</code></pre> <p>with an example:</p> <pre><code>domesticSounds/train/Boiling/16584.wav</code></pre> <p>meaning that the file 16584.wav from FSD50K, with the <em>Boiling</em> label of FSD50K, is included in the samples for the training split of those synthetic recordings, and it is mapped to the target class of <em>domestic sounds. </em>Note that there can be multiple FSD50K labels mapped the same target class. Also note that if these are downloaded from FSD50K, and a folder structure is created that replicates the structure in the list, the resulting folder can be used out-of-the-box with the scene generator to generate new mixtures with the same or different parameters.</p> <p>Note that no sounds form FSD50K have been selected for the <em>Music</em> target class. Background and pop music tracks from the public domain have been cropped and used instead.</p> <p><strong>DOWNLOAD INSTRUCTIONS:</strong></p> <p>Download the zip files and use your preferred compression tool to unzip these split zip files. To extract a split zip archive (named as zip, z01, z02, ...), you could use, for example, the following syntax in Linux or OSX terminal:</p> <ol> <li>Combine the split archive to a single archive: <pre>zip -s 0 split.zip --out single.zip</pre> </li> <li>Extract the single archive using unzip: <pre>unzip single.zip</pre> </li> </ol>
Zellige example dataset: synthetic image dataset
<p><strong>Phantom 3D image containing three distinct and superimposed synthetic surfaces. </strong></p> <p>It models a typical stack of confocal images of epithelial and non-epithelial structures.The surfaces generated are of two types: “solid” surfaces, presenting a homogeneous signal over the entire surface, or surfaces presenting a signal restricted to a polygonal mesh mimicking the mesh of apical cellular junctions of an epithelium observed at its surface. This dataset contains both the ground-truth height maps and the height maps generated with Zellige. The Zellige parameters used are:</p> <p><span class="math-tex">\(T_{A}=16, T_{otsu}=12, S_{min}=5, \sigma_{xy}=4, \sigma_{z}=2, T_{OSE1}=0.9, R_{1}=5, C_{1}=0.1, T_{OSE2}=0.1, R_{2}=10, C_{2}=0.8.\)</span></p> <p>Nota: the ground-truth height maps can be directly compared to Zellige height maps by subtraction.</p> <p>See the accompanying paper: Extracting multiple surfaces from 3D microscopy images in complex biological tissues with the Zellige software tool. Trébeau <em>et al.</em> 2022: <a href="https://doi.org/10.1101/2022.04.05.485876">https://doi.org/10.1101/2022.04.05.485876</a></p>
HIKARI-2021: Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic
<p>Available datasets from the paper Generating Encrypted Network Traffic for Intrusion Detection Datasets.</p> <p>To produce the dataset follow the technical detail in <a href="https://github.com/andreysfc/generating-encrypted-network">github</a></p>
Synthetic population for KEN
<p><strong>Synthetic populations for regions of the World (SPW) | Kenya</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Kenya</td> </tr> <tr> <td>Region ID</td> <td>ken</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0_adm1</td> </tr> </tbody> </table> <p><strong>Statistics</strong></p><table> <thead><tr> <th>Name</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Population</td> <td>39129968.0</td> </tr> <tr> <td>Average age</td> <td>21.6</td> </tr> <tr> <td>Households</td> <td>10938486.0</td> </tr> <tr> <td>Average household size</td> <td>3.6</td> </tr> <tr> <td>Residence locations</td> <td>10938486.0</td> </tr> <tr> <td>Activity locations</td> <td>838758.0</td> </tr> <tr> <td>Average number of activities</td> <td>5.4</td> </tr> <tr> <td>Average travel distance</td> <td>134.7</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>DHS</td> <td></td> <td><a href="https://dhsprogram.com">https://dhsprogram.com</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (ken_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ken_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>ken_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>ken_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>ken_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>ken_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ken_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ken_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>ken_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>ken_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>ken_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>ken_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>ken_ken_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>ken_ken_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>ken_ken_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>ken_ken_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>ken_ken_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>ken_ken_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>
Synthetic population for URY
<p><strong>Synthetic populations for regions of the World (SPW) | Uruguay</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Uruguay</td> </tr> <tr> <td>Region ID</td> <td>ury</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0</td> </tr> </tbody> </table> <p><strong>Statistics</strong></p><table> <thead><tr> <th>Name</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Population</td> <td>3208260.0</td> </tr> <tr> <td>Average age</td> <td>36.3</td> </tr> <tr> <td>Households</td> <td>1161045.0</td> </tr> <tr> <td>Average household size</td> <td>2.8</td> </tr> <tr> <td>Residence locations</td> <td>1161045.0</td> </tr> <tr> <td>Activity locations</td> <td>65127.0</td> </tr> <tr> <td>Average number of activities</td> <td>6.1</td> </tr> <tr> <td>Average travel distance</td> <td>18.9</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>IPUMS</td> <td></td> <td><a href="https://international.ipums.org/international">https://international.ipums.org/international</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (ury_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ury_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>ury_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>ury_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>ury_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>ury_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ury_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ury_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>ury_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>ury_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>ury_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>ury_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>ury_ury_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>ury_ury_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>ury_ury_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>ury_ury_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>ury_ury_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>ury_ury_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>
Synthetic population for JOR
<p><strong>Synthetic populations for regions of the World (SPW) | Jordan</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Jordan</td> </tr> <tr> <td>Region ID</td> <td>jor</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0</td> </tr> </tbody> </table> <p><strong>Statistics</strong></p><table> <thead><tr> <th>Name</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Population</td> <td>5723567.0</td> </tr> <tr> <td>Average age</td> <td>23.5</td> </tr> <tr> <td>Households</td> <td>1235755.0</td> </tr> <tr> <td>Average household size</td> <td>4.6</td> </tr> <tr> <td>Residence locations</td> <td>1235755.0</td> </tr> <tr> <td>Activity locations</td> <td>131978.0</td> </tr> <tr> <td>Average number of activities</td> <td>6.4</td> </tr> <tr> <td>Average travel distance</td> <td>44.5</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>DHS</td> <td></td> <td><a href="https://dhsprogram.com">https://dhsprogram.com</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (jor_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>jor_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>jor_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>jor_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>jor_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>jor_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>jor_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>jor_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>jor_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>jor_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>jor_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>jor_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>jor_jor_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>jor_jor_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>jor_jor_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>jor_jor_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>jor_jor_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>jor_jor_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>
Synthetic population for ITA
<p><strong>Synthetic populations for regions of the World (SPW) | Italy</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Italy</td> </tr> <tr> <td>Region ID</td> <td>ita</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>IPUMS</td> <td></td> <td><a href="https://international.ipums.org/international">https://international.ipums.org/international</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (ita_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ita_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>ita_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>ita_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>ita_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>ita_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ita_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>ita_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>ita_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>ita_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>ita_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>ita_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>ita_ita_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>ita_ita_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>ita_ita_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>ita_ita_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>ita_ita_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>ita_ita_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>
Synthetic population for GTM
<p><strong>Synthetic populations for regions of the World (SPW) | Guatemala</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Guatemala</td> </tr> <tr> <td>Region ID</td> <td>gtm</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0</td> </tr> </tbody> </table> <p><strong>Statistics</strong></p><table> <thead><tr> <th>Name</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Population</td> <td>14137968.0</td> </tr> <tr> <td>Average age</td> <td>22.8</td> </tr> <tr> <td>Households</td> <td>3107786.0</td> </tr> <tr> <td>Average household size</td> <td>4.6</td> </tr> <tr> <td>Residence locations</td> <td>3107786.0</td> </tr> <tr> <td>Activity locations</td> <td>650298.0</td> </tr> <tr> <td>Average number of activities</td> <td>7.2</td> </tr> <tr> <td>Average travel distance</td> <td>53.2</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>DHS</td> <td></td> <td><a href="https://dhsprogram.com">https://dhsprogram.com</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (gtm_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>gtm_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>gtm_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>gtm_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>gtm_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>gtm_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>gtm_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>gtm_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>gtm_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>gtm_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>gtm_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>gtm_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>gtm_gtm_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>gtm_gtm_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>gtm_gtm_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>gtm_gtm_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>gtm_gtm_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>gtm_gtm_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>
Synthetic population for FIN
<p><strong>Synthetic populations for regions of the World (SPW) | Finland</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Finland</td> </tr> <tr> <td>Region ID</td> <td>fin</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>DYB</td> <td></td> <td><a href="https://unstats.un.org/unsd/demographic/products/dyb/dyb_Household/dyb_household.htm">https://unstats.un.org/unsd/demographic/products/dyb/dyb_Household/dyb_household.htm</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (fin_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>fin_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>fin_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>fin_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>fin_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>fin_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>fin_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>fin_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>fin_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>fin_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>fin_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>fin_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>fin_fin_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>fin_fin_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>fin_fin_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>fin_fin_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>fin_fin_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>fin_fin_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>
Synthetic population for NOR
<p><strong>Synthetic populations for regions of the World (SPW) | Norway</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Norway</td> </tr> <tr> <td>Region ID</td> <td>nor</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0_adm1</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Household data</td> <td>DYB</td> <td></td> <td><a href="https://unstats.un.org/unsd/demographic/products/dyb/dyb_Household/dyb_household.htm">https://unstats.un.org/unsd/demographic/products/dyb/dyb_Household/dyb_household.htm</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (nor_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>nor_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>nor_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>nor_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>nor_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>nor_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>nor_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>nor_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>nor_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>nor_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>nor_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>nor_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>nor_nor_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>nor_nor_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>nor_nor_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>nor_nor_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>nor_nor_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>nor_nor_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>
Synthetic population for ISL
<p><strong>Synthetic populations for regions of the World (SPW) | Iceland</strong></p><p><strong>Dataset information</strong></p><p>A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).</p> <p><strong>License</strong></p><p><a href="https://creativecommons.org/licenses/by/4.0/">CC-BY-4.0</a></p> <p><strong>Acknowledgment</strong></p><p>This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).</p> <p><strong>Contact information</strong></p><p>Henning.Mortveit@virginia.edu</p> <p><strong>Identifiers</strong></p><table> <thead><tr> <th></th> <th></th> </tr> </thead> <tbody> <tr> <td>Region name</td> <td>Iceland</td> </tr> <tr> <td>Region ID</td> <td>isl</td> </tr> <tr> <td>Model</td> <td>coarse</td> </tr> <tr> <td>Version</td> <td>0_9_0</td> </tr> </tbody> </table> <p><strong>Statistics</strong></p><table> <thead><tr> <th>Name</th> <th>Value</th> </tr> </thead> <tbody> <tr> <td>Population</td> <td>293477.0</td> </tr> <tr> <td>Average age</td> <td>36.8</td> </tr> <tr> <td>Households</td> <td>115570.0</td> </tr> <tr> <td>Average household size</td> <td>2.5</td> </tr> <tr> <td>Residence locations</td> <td>115570.0</td> </tr> <tr> <td>Activity locations</td> <td>48082.0</td> </tr> <tr> <td>Average number of activities</td> <td>5.8</td> </tr> <tr> <td>Average travel distance</td> <td>53.2</td> </tr> </tbody> </table> <p><strong>Sources</strong></p><table> <thead><tr> <th>Description</th> <th>Name</th> <th>Version</th> <th>Url</th> </tr> </thead> <tbody> <tr> <td>Activity template data</td> <td>World Bank</td> <td>2021</td> <td><a href="https://data.worldbank.org">https://data.worldbank.org</a></td> </tr> <tr> <td>Administrative boundaries</td> <td>ADCW</td> <td>7.6</td> <td><a href="https://www.adci.com/adc-worldmap">https://www.adci.com/adc-worldmap</a></td> </tr> <tr> <td>Curated POIs based on OSM</td> <td>SLIPO/OSM POIs</td> <td></td> <td><a href="http://slipo.eu/?p=1551">http://slipo.eu/?p=1551</a> <a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></td> </tr> <tr> <td>Population count with demographic attributes</td> <td>GPW</td> <td>v4.11</td> <td><a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11">https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11</a></td> </tr> </tbody> </table> <p><strong>Files description</strong></p><p><strong>Base data files (isl_data_v_0_9.zip)</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>isl_person_v_0_9.csv</code></td> <td>Data for each person including attributes such as age, gender, and household ID.</td> </tr> <tr> <td><code>isl_household_v_0_9.csv</code></td> <td>Data at household level.</td> </tr> <tr> <td><code>isl_residence_locations_v_0_9.csv</code></td> <td>Data about residence locations</td> </tr> <tr> <td><code>isl_activity_locations_v_0_9.csv</code></td> <td>Data about activity locations, including what activity types are supported at these locations</td> </tr> <tr> <td><code>isl_activity_location_assignment_v_0_9.csv</code></td> <td>For each person and for each of their activities, this file specifies the location where the activity takes place</td> </tr> </tbody> </table> <p><strong>Derived data files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>isl_contact_matrix_v_0_9.csv</code></td> <td>A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.</td> </tr> </tbody> </table> <p><strong>Validation and measures files</strong></p><table> <thead><tr> <th>Filename</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>isl_household_grouping_validation_v_0_9.pdf</code></td> <td>Validation plots for household construction</td> </tr> <tr> <td><code>isl_activity_durations_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of time spent on generated activities with survey data</td> </tr> <tr> <td><code>isl_activity_patterns_{adult,child}_v_0_9.pdf</code></td> <td>Comparison of generated activity patterns by the time of day with survey data</td> </tr> <tr> <td><code>isl_location_construction_0_9.pdf</code></td> <td>Validation plots for location construction</td> </tr> <tr> <td><code>isl_location_assignement_0_9.pdf</code></td> <td>Validation plots for location assignment, including travel distribution plots</td> </tr> <tr> <td><code>isl_isl_ver_0_9_0_avg_travel_distance.pdf</code></td> <td>Choropleth map visualizing average travel distance</td> </tr> <tr> <td><code>isl_isl_ver_0_9_0_travel_distr_combined.pdf</code></td> <td>Travel distance distribution</td> </tr> <tr> <td><code>isl_isl_ver_0_9_0_num_activity_loc.pdf</code></td> <td>Choropleth map visualizing number of activity locations</td> </tr> <tr> <td><code>isl_isl_ver_0_9_0_avg_age.pdf</code></td> <td>Choropleth map visualizing average age</td> </tr> <tr> <td><code>isl_isl_ver_0_9_0_pop_density_per_sqkm.pdf</code></td> <td>Choropleth map visualizing population density</td> </tr> <tr> <td><code>isl_isl_ver_0_9_0_pop_size.pdf</code></td> <td>Choropleth map visualizing population size</td> </tr> </tbody> </table>
Data for manuscript "Creating boundaries along a synthetic frequency dimension" in Nature Communications
<p>Data for manuscript "Creating boundaries along a synthetic frequency dimension"</p> <p>https://www.nature.com/articles/s41467-022-31140-7</p> <p>https://arxiv.org/abs/2203.11296</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.