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10,006 results for “randomness”
Photographic dataset of random peppercorns
<p>This is an open access photographic dataset collected for testing image processing algorithms. The idea is to have sets of different but statistically similar images. The images show randomly distributed peppercorns.</p> <p>The dataset consists of ten high-resolution black and white images of peppercorn in TIFF file format. Documentation of the dataset is available at <a href="https://arxiv.org/abs/1603.01046">arxiv.org/abs/1603.01046</a>. See also <a href="https://www.fips.fi/photographic_dataset.php">www.fips.fi/photographic_dataset.php</a>.</p> <p>Example usage in Matlab:</p> <pre><code>im = imread('Peppercorn_1.tif'); im = double(im); % Converting to floating point data </code></pre> <p>The data is collected at the Industrial Mathematics Laboratory of the Department of Mathematics and Statistics of University of Helsinki.</p>
Simulated dMRI images and ground truth of random fiber phantoms in various configurations
<p>This archive contains simulated dMRI images of random fiber phantoms in various configurations created with Fiberfox and other tools available in MITK Diffusion (<a href="http://mitk.org/wiki/DiffusionImaging">http://mitk.org/wiki/DiffusionImaging</a>). RandomFibers_Example.png illustrates one of the random fiber configurations used for these phantoms.</p> <p>If you are using any of these datasets or the tools used to generate them, please don't forget to cite the dataset itself as well as other relevant publications.</p> <p>Each subfolder contains the following elements:<br> The simulated dMRI image with b-values and gradient directions: dwi.nii.gz, dwi.bvals, dwi.bvecs<br> The fibers used for simulation: AllBundles.fib (binary vtk format)<br> parameters.ffp: Fiberfox simulation parameters<br> parameters.ffp.bvals: b-value file for Fiberfox simulation<br> parameters.ffp.bvecs: gradient vector file for Fiberfox simulation<br> parameters.ffp_VOLUME1.nii.gz: fiber compartment volume fraction map for Fiberfox simulation<br> The logfile detailing all steps of the generation process of the respective phantom: LOGFILE.json</p> <p>bundles: folder containing the individual fiber bundles (binary vtk format .fib)<br> centroids: folder containing the centerlines of each bundle<br> masks: folder containing the binary envelope of each bundle<br> peaks: folder containing the principal fiber direction image (peaks) of each bundle</p> <p>Each subfolder contains the fibers and dMRI simulations with the following fiber specifications:<br> Phantom 1:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 2:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 3:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 4:<br> - Number of bundles: 25<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 5:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 6:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 7:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 8:<br> - Number of bundles: 25<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 9:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 10:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 11:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 12:<br> - Number of bundles: 50<br> - Fiber density: 250 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 13:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 14:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 0-30 in degree<br> - Bundle start radius: 15-30 in mm</p> <p>Phantom 15:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 5-15 in mm</p> <p>Phantom 16:<br> - Number of bundles: 50<br> - Fiber density: 50-500 streamlines per cm²<br> - Bundle curvature: 30-60 in degree<br> - Bundle start radius: 15-30 in mm</p>
Supporting Jupyter Python notebook for "A new class of efficient randomized benchmarking protocols"
<p>Python notebook containing the code used to generate the data for figure 2 in the appendix of "A new class of efficient randomized benchmarking protocols" (arXiv:1806.02048).</p>
Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields (data sets)
<p>Supplementary dataset for Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields. Output functionals of interest from finite element simulations together with postprocessing source code. </p>
ZIRFs: zero-inflated random forests for estimating gene regulatory networks from single cell RNA-seq data (assessment of predictive accuracy and VIM stability)
<p>We developed a zero-inflated random forests (ZIRFs) algorithm to produce a metric of connection strength between regulator genes and target genes. This file contains SCENIC results for the aorta and diaphragm tissue data sets from the Tabula Muris Consortium results. SCENIC is a genetic regulatory network analysis published by Aibar et al. (2017). The purpose of the data sets and R source code are described by README files in each directory.</p>
A computational intelligence approach to predict energy demand using Random Forest in a Cloudera cluster
<p>Society’s energy consumption has shot up in recent years, making the prediction of its demand a current challenge to ensure an efficient and responsible use. Artificial intelligence techniques have proven to be potential tools in handling tedious tasks and making sense of large-scale data to make better business decisions in different areas of knowledge. In this article, the use of random forests algorithms in a Big Data environment is proposed for households energy demand forecasting. The predictions are based on the use of information from different sources, confirming a fundamental role of socioeconomic data in consumer’s behaviours. On the other hand, the use of Big Data architectures is proposed to perform horizontal and vertical scaling of the solution to be used in real environments. Finally, a tool for high-resolution predictions with great efficiency is introduced, which enables energy management in a very accurate way.</p> <p>Raw data is incuded in data.csv. This file contains half hourly home electricity consumption registers for 4404 households with fix tariffs (not subject to dynamic time of use) for a period between November 2011 and February 2014. Original information was acquired from the Low Carbon London project led by UK Power Networks (https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households)</p> <p>RFResults.zip contains the energy predictions for each ACORN group using the generated Random Forest algorithm. For this purpose, the first 613 days of a total of 818 observations of each group were considered for training and the last 205 days for testing.</p> <p>Meteorological data was adquired from the darksky app (https://darksky.net). These data are included in the weather_hourly_darksky.csv</p> <p>uk_bank_holidays. xlsx contains the dated of UK bank holidays for the studied period, used as additional variable related to occupancy</p>
Surfalex HF formability study - Workflow 6 - Generate random volume element
<p>This MatFlow workflow is the sixth in a set of eight workflows developed during our formability study of the Surfalex HF (AA6016A) material. In this workflow, we generate a comparison volume element from a random texture and equiaxed microstructure. This RVE is used in a comparison of the simulated Lankford coefficients between the Surfalex model RVE and this "random" RVE.</p> <p>This workflow can be downloaded and explored in a Jupyter notebook, as explained in the <a href="https://github.com/LightForm-group/surfalex_data_explorer">GitHub repository here</a>.</p>
Global gross primary production (GPP) product generated by data fusion based on random forest
<p>Improving the ability of gross primary production (GPP) estimates to capture extreme climate perturbations and reduce the uncertainty of GPP response processes to extreme climate is a new challenge. Based on the random forest algorithm, we integrated the multimodel GPP simulation results published by the Multiscale Synthesis and Terrestrial Model Intercomparison Project, the FLUXNET flux-site-observed GPP, the standardized precipitation index (SPI) and the standardized temperature index (STI) to generate a set of global GPP time-series data products from 2001 to 2010. The new GPP product was named DFRF-GPP, referring to the GPP generated by data fusion based on random forest. DFRF-GPP is highly reliable and can be used as a valuable data source for various applications, especially in high-temperature and drought-related studies.</p>
3D S-wave velocity profiles with random fields
<p>This dataset contains 100,000 3D matrices representing S-wave velocity profiles with random fields. The spatial resolution corresponds to a grid of 9.6 x 9.6 x 9.6km with a spatial step of 300m (matrices are of size 33 x 33 x 33). Velocities go from 1,071 to 4,500m/s.</p> <p>Each profile contains a 1.8km-thick bottom layer at constant velocity 4500m/s. Above, the profile contains between 1 and 6 layers of random thickness. In each layer, the mean velocity is chosen uniformly in [1,785; 3,214m/s] and the coefficient of variation follows a Gaussian distribution N(0.2; 0.1). Then, a correlation length is chosen randomly among 1.5, 3, 4.5, 6 km in all three directions for each layer.</p> <p>The random fields' kernel has a von Karman correlation with a Hurst exponent of 0.1 and log-normal marginal distributions.</p>
Data for Project 'Feasibility, Usability and Acceptance of a Newly Developed Exergame-Based Training Concept for Older Adults with Mild Neurocognitive Disorder - A Pilot Randomized Controlled Trial'
<p>Data for Project 'Feasibility, Usability and Acceptance of a Newly Developed Exergame-Based Training Concept for Older Adults with Mild Neurocognitive Disorder - A Pilot Randomized Controlled Trial' (trial registered at clinicaltrials.gov (<a href="https://clinicaltrials.gov/ct2/show/NCT04996654">NCT04996654</a>; date of registration: 11 July 2021), consisting of:</p> <p>(1) the original and complete data set for all primary outcomes ('Data_Primary-Outcomes_Brain-IT-Pilot-Feasibility-RCT_for-publication.xlsx');</p> <p>(2) the original and complete data set for all secondary outcomes ('Data_Secondary-Outcomes_Brain-IT-Pilot-Feasibility-RCT_for-publication.xlsx');</p> <p>(3) the original and complete data set for all other outcomes (i.e. baseline factors (demographic data, type of usual care interventions) and training heart rate; 'Data_Other-Outcomes_Brain-IT-Pilot-Feasibility-RCT_for-publication.xlsx');</p> <p>(4) folder including the raw and processed heart rate variability (HRV) and electroencephalography (EEG) data for all participants and measurements (HRV-and-EEG_raw-and-processed-data.zip);</p> <p>(5) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.</p>
Raw and post-processed data for the microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception
<p>The current dataset consists of three main folders:</p> <ul> <li><strong>01-Stimuli/</strong>: Contains the three sets of noises (white noise, bump noise, MPS noise) for the 12 study participants (S01 to S12).</li> <li><strong>02-Raw-data/fastACI/</strong>: Contains the raw data as obtained for each participant, which are also available within the GitHub repository of the fastACI toolbox, using the same directory tree. The results for each (anonymised) participant (under: <strong>publ_osses2022b/data_SXX/1-experimental_results/</strong>) include their audiometric thresholds (folder: <strong>audiometry</strong>), the results for the Intellitest speech test (folder: <strong>intellitest</strong>), and for the phoneme-in-noise test /aba/-/ada/ for the three noises (savegame files in MAT format).</li> <li><strong>02-Raw-data/ACI_sim/</strong>: Contains the raw data as obtained for the artificial listener, i.e., the model osses2022a.m (available within the fastACI toolbox). Twelve sets of simulations (using the waveforms of participants S01 to S12) were run for the three types of test noises. The results of the simulations of the phoneme-in-noise test are stored in the savegame MAT files. The template derived from 100 repetitions of /aba/ and /aba/ at an SNR=-6 dB in white noise is also included (template-osses2022a-speechACI_Logatome-abda-S43M-trial-1-v1-white-2022-7-15-N-0100.mat). The same template was used in all simulations.</li> <li><strong>03-Post-proc-data/ACI_exp/</strong>: Auditory classification images (ACIs) derived from the participants' data (folder: <strong>ACI_exp</strong>) and from the simulations (folder: <strong>ACI_sim</strong>). For each participant (or artificial listener) there are three ACIs (MAT files) for each of the corresponding noises. Cross predictions are also included with performance predictions across 'participants' (Crosspred.mat, 12 cross predictions for each noise) or across 'noises' (Crosspred-noise.mat, 3 cross predictions for each participant). The cross predictions all have the same names but are stored in dedicated directories.</li> </ul> <p><strong>Use these data:</strong></p> <ol> <li>Download all these data, place them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="http://github.com/aosses-tue/fastACI">GitHub</a>) you can recreate the figures of our paper.</li> <li>After initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB) and then type either of the following commands, to recreate the figure you want. To recreate the figures in the main text:</li> </ol> <pre><code class="language-javascript">publ_osses2022b_JASA_figs('fig1','zenodo'); publ_osses2022b_JASA_figs('fig2a','zenodo'); publ_osses2022b_JASA_figs('fig2b','zenodo'); publ_osses2022b_JASA_figs('fig3','zenodo'); publ_osses2022b_JASA_figs('fig4','zenodo'); publ_osses2022b_JASA_figs('fig5','zenodo'); publ_osses2022b_JASA_figs('fig6','zenodo'); publ_osses2022b_JASA_figs('fig7','zenodo'); publ_osses2022b_JASA_figs('fig8','zenodo'); publ_osses2022b_JASA_figs('fig8b','zenodo'); publ_osses2022b_JASA_figs('fig9','zenodo'); publ_osses2022b_JASA_figs('fig9b','zenodo'); publ_osses2022b_JASA_figs('fig10','zenodo');</code></pre> <p>To generate the figures of the supplementary materials (Appendix in the BioRxiv preprint):</p> <pre><code class="language-javascript">publ_osses2022b_JASA_figs('fig1_suppl','zenodo'); publ_osses2022b_JASA_figs('fig2_suppl','zenodo'); publ_osses2022b_JASA_figs('fig3_suppl','zenodo'); publ_osses2022b_JASA_figs('fig3b_suppl','zenodo'); publ_osses2022b_JASA_figs('fig4_suppl','zenodo'); publ_osses2022b_JASA_figs('fig4b_suppl','zenodo'); publ_osses2022b_JASA_figs('fig5_suppl','zenodo'); publ_osses2022b_JASA_figs('fig5b_suppl','zenodo');</code></pre> <p><strong>References:</strong></p> <ul> <li><strong>Preprint</strong>: Alejandro Osses, Léo Varnet. "A microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception." BioRxiv.</li> <li><strong>fastACI toolbox</strong>: Alejandro Osses, Léo Varnet. fastACI toolbox: the MATLAB toolbox for investigating auditory perception using reverse correlation (v1.2). Zenodo. doi:<a href="https://doi.org/10.5281/zenodo.7314014">10.5281/zenodo.7314014</a>. Supplement to: <a href="http://github.com/aosses-tue/fastACI/tree/v1.2">https://github.com/aosses-tue/fastACI/tree/v1.2</a></li> </ul>
Dataset and codebook for the article by Gaume J, Bertholet N, McCambridge J, et al. Effect of a Novel Brief Motivational Intervention for Alcohol-Intoxicated Young Adults in the Emergency Department: A Randomized Clinical Trial. JAMA Netw Open. 2022;5(10):e2237563. doi: 10.1001/jamanetworkopen.2022.37563
<p>Dataset and codebook for the article Gaume J, Bertholet N, McCambridge J, et al. <strong>Effect of a Novel Brief Motivational Intervention for Alcohol-Intoxicated Young Adults in the Emergency Department: A Randomized Clinical Trial</strong>. JAMA Netw Open. 2022;5(10):e2237563. doi: <a href="http://jamanetwork.com/article.aspx?doi=10.1001/jamanetworkopen.2022.37563">10.1001/jamanetworkopen.2022.37563</a></p> <p>The dataset contains all data needed to reproduce the results in the above cited article.</p> <p>Variable description and labels can be found in the codebook.</p> <p>Please refer to the published article and supplemental online content for further information about the data and the study procedures.</p>
ESM Atlas v0 representative random sample of predicted protein structures
<p>A representative random sample of the ESM Atlas v0 dataset introduced in "Evolutionary-scale prediction of atomic level protein structure with a language model.".<br> All predictions can be accessed in the ESM Metagenomic Atlas (<a href="https://esmatlas.com/">https://esmatlas.com</a>) open science resource, released on 2022-11-01.<br> Sample size: 997,405.</p>
ESM Atlas v0 random sample of high confidence predicted protein structures
<p>A random sample out of the 225M high confidence predictions in the ESM Atlas v0 dataset introduced in "Evolutionary-scale prediction of atomic level protein structure with a language model.".<br> All predictions can be accessed in the ESM Metagenomic Atlas (<a href="https://esmatlas.com/">https://esmatlas.com</a>) open science resource, released on 2022-11-01.<br> High confidence is defined as mean pLDDT > 0.7 and pTM > 0.7 and corresponds to ∼36% of the total 617M proteins folded.<br> This is the random sample used for analysis in the paper as well as visualization on the <a href="http://esmatlas.com/">esmatlas.com</a> Explore page.<br> Sample size: 999,520 based on 999,996 unique randomly sampled IDs and 0.05% missing data in the processing pipeline.</p>
Data of the publication "Transport and entanglement growth in long-range random Clifford circuits"
<p>Conservation laws can constrain entanglement dynamics in isolated quantum systems, manifest in a slowdown of higher Rényi entropies. Here, we explore this phenomenon in a class of long-range random Clifford circuits with U(1) symmetry where transport can be tuned from diffusive to superdiffusive. We unveil that the different<br> hydrodynamic regimes reflect themselves in the asymptotic entanglement growth according to <span class="math-tex">\(S(t) \propto t^{1/z}\)</span> where<br> the dynamical transport exponent z depends on the probability <span class="math-tex">\(\propto r^{-\alpha}\)</span> of gates spanning a distance r. For<br> sufficiently small <span class="math-tex">\(\alpha\)</span>, we show that the presence of hydrodynamic modes becomes irrelevant such that S(t) behaves<br> similarly in circuits with and without conservation law. We explain our findings in terms of the inhibited operator<br> spreading in U(1)-symmetric Clifford circuits where the emerging light cones can be understood in the context<br> of classical Lévy flights. Our Letter sheds light on the connections between Clifford circuits and more generic<br> many-body quantum dynamics.</p>
Data for "Randomizing the Growth of Silica Nanofibers for Whiteness"
<p>This dataset contains the raw data used for the publication "Randomizing the Growth of Silica Nanofibers for Whiteness".</p>
Geographic Scope of Randomized Clinical Trials from Africa
<p><strong>Overview</strong></p> <p>This map reports the geographic scope of randomized controlled trials (RCTs) conducted in Africa, as found in PubMed. The map highlights a discrepancy between actual trial location and how trials are reported: although reports of research from Africa are often labeled as being "African" in scope, RCTs are rarely continent-wide and many countries are not represented in even a single study. The intent of the map is to visualize actual trial locations, hopefully leading to a more accurate portrayal of RCT study sites on the African continent.</p> <p><strong>Data Source and Tools</strong></p> <p>Data for the map was extracted from PubMed and the map was created in ArcGIS Online.</p> <p><strong>Audience</strong></p> <p>The map was created for an original research poster at the 2022 International Congress on Peer Review and Scientific Publication (see "Attribution" below). Its audience is researchers, clinicians, policy makers, librarians, scholarly communications stakeholders, and editors in chief interested in improving the accuracy of the reported scope of research in Africa to better inform health care research, policy, and resource distribution.</p> <p><strong>Design</strong></p> <p>A PubMed search using Medical Subject Headings and keywords representing Africa, African, and RCTs was run to identify citations published between 1968 and February 2022. The citation titles and abstracts were screened against established inclusion/exclusion criteria, with included studies continuing through a full text review and data extraction process. Total RCT representation per country was then mapped in ArcGIS with higher RCT counts represented by darker shading. The map highlights the variance in RCT representation across the continent, with some countries represented in up to 80 RCTs and many countries represented in none.</p> <p><strong>Attribution</strong></p> <p>Folafoluwa Olutobi Odetola and Marisa L. Conte conceived the research question, crafted the PubMed search, and analyzed the citation data; Tyler Nix created the map. Special thanks to Caroline Kayko (University of Michigan) for her input in the creation of the map. </p> <p>See the original research poster at:</p> <p>Odetola, FO; Conte, ML. Geographical Scope of Randomized Clinical Trials from Africa. [Poster]. 9th International Congress on Peer Review and Scientific Publication, September 8-10, 2022, Chicago, IL. Available from: <a href="https://peerreviewcongress.org/abstract/geographical-scope-of-randomized-clinical-trials-from-africa/">https://peerreviewcongress.org/abstract/geographical-scope-of-randomized-clinical-trials-from-africa/</a></p> <p>The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</p>
Hardware-biased quantum random numbers
<p>Bitstring from a hardware-biased quantum random number generator. The data is used in the numerical experiments for the publication "On the effects of biased quantum random numbers on the initialization of artificial neural networks" (<a href="http://arxiv.org/abs/2108.13329">arXiv:2108.13329</a>).</p> <p> </p>
Random forest regression for fuzzy grades
<p>The used dataset contained information about:</p> <ul> <li>students' gender (1 = male, 2 = female);</li> <li>schools' type (1 = scientific lyceums, 2 = other lyceums, 3 = technical schools, 4 = vocational schools);</li> <li>schools' macreregion (1 = Northwester Italy, 2 = Northeastern Italy, 3 = Central Italy, 4 = Southern Italy, 5 = Southern Italy and Isles);</li> <li>students' origin (1 = native Italian student, 2 = first-generation immigrant student, 3 = second-generation immigrant student);</li> <li>students' ESCS (continuous data);</li> <li>teacher-given grades in mathematics (from 1 to 10);</li> <li>students' achievements on the INVALSI mathematics test (continuous data);</li> <li>students' fuzzy grade, obtained as a combination of teacher-given grades and achievements in mathematics (from 1 to 10).</li> </ul>
EXTREMA: Random autonomous interplanetary mission scenarios for EXTREMA Simulation Hub (ESH) hardware-in-the-loop simulations
<p>EXTREMA (short for Engineering Extremely Rare Events in Astrodynamics for Deep-Space Missions in Autonomy) enables self-driving spacecraft, challenging the current paradigm under which spacecraft are piloted in the interplanetary space [1]. Deep-space guidance, navigation, and control applied in a complex scenario is the subject of EXTREMA, which wants to engineer ballistic capture in a totally autonomous fashion. EXTREMA is erected on three pillars. Pillar 1 is on autonomous navigation. Pillar 2 involves autonomous guidance and control. Pillar 3 deals with autonomous ballistic capture. The outcomes from each one of the three pillars is validated with a tailored experiment featuring the model of the associated CubeSat subsystems. Integrated experiments involving all the components of a CubeSat GNC system are carried on in the EXTREMA Simulation Hub, an integrated facility simulating an interplanetary transfer with a hardware-in-the-loop setup. The project has been awarded a European Research Council (ERC) Consolidator Grant in 2019.</p> <p>The data set is made of 1000 (even more in some releases) random interplanetary mission scenarios used for hardware-in-the-loop simulations performed in the EXTREMA simulation Hub (ESH). Each scenario comes with the following information: generation seed, initial epoch, initial state (Keplerian elements and Cartesian coordinates), initial attitude (quaternion and direct cosine matrix), initial mass, number of revolution, time of flight, target final epoch, target final state (Keplerian elements and Cartesian Coordinates), and other supplementary data. Scenarios are organized in directories each containing at least the following files:</p> <ul> <li>'esh_ic.json': random scenario information saved in .json format;</li> <li>'esh_ic.mat': random scenario information saved in .mat format.</li> </ul> <p>Some releases provide only a limited set of data in the 'esh_ic.json' file. For those, the full set of information is found in the 'esh_ic_full.json' file.</p> <p>For additional information about the EXTREMA project visit the page <a href="http://extrema.polimi.it/">extrema.polimi.it</a>.</p> <p><strong>References</strong><br> [1] Di Domenico G., et al. "The ERC-Funded EXTREMA Project: Achieving Self-Driving Interplanetary CubeSats." <em>Modeling and Optimization in Space Engineering: New Concepts and Approaches</em>. Cham: Springer International Publishing, 2022. 167-199. DOI: <a href="https://doi.org/10.1007/978-3-031-24812-2_6">10.1007/978-3-031-24812-2_6</a>.</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.