Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6,498
datasets available to search
ShareScore release 0.9.0
Dataset results
6,498 results for “Physicality”
A biogeographical approach to characterising the climatic, physical and geomorphic niche of the most widely distributed mangrove species, Avicennia marina
<p><strong>Aim:</strong> Mangroves are coastal ecosystems exposed to terrestrial, marine, geomorphic and climatic forcings operating in concert, making the mangrove niche hard to define, as evidenced by extremely poor restoration outcomes. We have developed a set of high-resolution species distribution models and interpreted the variables that have the largest impact on the niche of <em>Avicennia</em> <em>marina</em>, the most globally widespread mangrove species, to comprehensively detail the forcings driving habitat suitability.</p> <p><strong>Location</strong>: Australia.</p> <p><strong>Time period</strong>: 1970–2020.</p> <p><strong>Major taxa studied</strong>: <em>Avicennia</em> <em>marina</em> (Forssk.) Vierh.</p> <p><strong>Methods</strong>: We modelled the suitable habitat for <em>A</em>. <em>marina</em> in Australia using the maxent method incorporating 38 environmental variables and the Global Mangrove Watch baseline for the presence records. Using k-means grouping we identified sub-regions where similar suites of environmental variables influence habitat suitability, while also identifying biogeographical commonalities among the sub-regions. To better understand the low realisation of the fundamental niche we analysed the other land covers occupying the niche.</p> <p><strong>Results</strong>: <em>A</em>. <em>marina</em> in Australia occupies six different environmental sub-regions. Maxent distribution models accurately predicted the presence of <em>A</em>. <em>marina</em> in each subpopulation (AUC > 0.9). <em>A</em>. <em>marina</em>'s presence in all sub-regions was strongly determined by its proximity to freshwater. Precipitation and temperature extreme values were more important than average values in predicting the species' presence. The species requires low-energy coastlines with high solar radiation. The suitable areas are primarily shared with salt marshes, seagrass and buildings or cleared land.</p> <p><strong>Main conclusions</strong>: Our results offer a baseline for the suitable area of A. marina's presence that includes a range of environmental conditions, <em>A</em>. <em>marina</em> currently occupies <50% of its suitable habitat and there is scope for restoration with significant ecosystem service gains. The six different sub-regions in Australia map to known phylogenetically distinct populations indicating genetic plasticity in response to region-specific climatic conditions.</p>
Impact of a pulmonary rehabilitation program on social disadvantage and physical activity data of postCOVID19 patients: A North-African pilot study
<p><strong>WHAT IS ALREADY KNOWN</strong></p> <ul> <li>In post-acute COVID19, many patients still manifest symptoms for weeks and months.</li> <li>Psychological manifestations are prevalent and persisting in recovered COVID19 patients.</li> <li>Indeed, COVID19 was reported to possibly lead to symptoms of depression and anxiety, and to alter health-related quality of life (HRQoL).</li> <li>Physical activity has been proven to be effective in improving both mental and physical health.</li> <li>Thus, COVID19 patients could benefit from pulmonary rehabilitation program (PRP).</li> <li>Many studies related the effects of a standard PRP on physical activity and HRQoL in post COVID19 patients. However, to the best of the authors’ knowledge, no study evaluated the physical activity and social disadvantage of patients who were hospitalized for COVID19 in a low income country.</li> <li>This paper is the second part of a cross sectional study that aimed to evaluate the impact of a PRP on social disadvantage (physical activity, hospital anxiety and depression (HAD), HRQoL) data of Tunisian patients with postCOVID19.</li> </ul> <p><strong>WHAT THIS PAPER ADDS</strong></p> <ul> <li>The present North African study demonstrated that a PRP improved HRQoL, HAD, and physical activity data of Tunisian COVID19 patients.</li> <li>For instance, the means of the functional, psychological and relation dimensions, and the VQ11 total score (ie; main outcome) decreased significantly by 1.79, 2.00, 1.57, and 5.36 points. In addition, the means of the HAD anxiety and depression scores decreased significantly by 2.07, and 2.57 points, respectively, and the means of the physical activity and total scores improved significantly by 1.75 and 1.78 points, respectively.</li> <li>To the best of the authors’ knowledge, this is the first African and Arab study investigating the impact of a PRP on postcovid19 patients in terms of social disadvantages and physical activity.</li> <li> </li> </ul> <ul> <li>This study confirms that an outpatient PRP program improves physical activity, psychological status and HRQoL in the management of postCOVID19 patients.</li> </ul>
Confs N_f=2+1 physical point fullQCD T=230 MeV, lattice = 40^3 x 10
<p>README (Written by Claudio Bonanno: claudio.bonanno@fi.infn.it)</p> <p>Archives of thermalized and well-decorrelated gauge configurations</p> <p>Discretization: Symanzik-improved gauge action & N_f = 2+1 flavors of rooted stout staggered fermions with physical quark masses<br> and physical pion mass</p> <p>Theory details: T=230 MeV, 40^3 x 10 lattice with a lattice spacing a = 0.0857 fm.</p> <p>Algorithm: standard RHMC.</p> <p>Conf name: stored_conf.${conf_ID} where conf_ID is equal to the RHMC step the conf has been saved.</p> <p>Conf have been saved every 30 RHMC seps in binary files according to the ILDG format for standard C programs.</p> <p>For more details about the ILDG format see, e.g., https://www-zeuthen.desy.de/~pleiter/ildg/ildg-file-format-1.1.pdf</p>
Supporting Data of Bonadonna et al. "Physical Characterization of Long-Lasting Hybrid Eruptions: the Tajogaite Eruption of Cumbre Vieja (La Palma, Canary Islands)"
<p>Data on plume height, volcanic tremor, deposit thinning and lava emissions of Bonadonna et al. "Physical Characterization of Long-Lasting Hybrid Eruptions: the Tajogaite Eruption of Cumbre Vieja (La Palma, Canary Islands)"</p>
CLEVRER-Humans: Describing physical and causal events the human way
<p>Building machines that can reason about physical events and their causal relationships is crucial for flexible interaction with the physical world. However, most existing physical and causal reasoning benchmarks are exclusively based on synthetically generated events and synthetic natural language descriptions of causal relationships. This design brings up two issues. First, there is a lack of diversity in both event types and natural language descriptions; second, causal relationships based on manually-defined heuristics are different from human judgments. To address both shortcomings, we present the CLEVRER-Humans benchmark, a video reasoning dataset for causal judgment of physical events with human labels. We employ two techniques to improve data collection efficiency: first, a novel iterative event cloze task to elicit a new representation of events in videos, which we term Causal Event Graphs (CEGs); second, a data augmentation technique based on neural language generative models. We convert the collected CEGs into questions and answers to be consistent with prior work. Finally, we study a collection of baseline approaches for CLEVRER-Humans question-answering, highlighting the great challenges set forth by our benchmark.</p>
Supplementary material 1 from: Scacchetti P, Pansonato-Alves J, Utsunomia R, Oliveira C, Foresti F (2011) Karyotypic diversity in four species of the genus Gymnotus Linnaeus, 1758 (Teleostei, Gymnotiformes, Gymnotidae): physical mapping of ribosomal genes and telomeric sequences. Comparative Cytogenetics 5(3): 223-235. https://doi.org/10.3897/compcytogen.v5i3.1375
Nexus file of aligned COI and COII nucleotide sequences.
Effect Of Pulsed Electromagnetic Field And Microwave Therapy On Pain And Physical Function In Older Adults With Knee Osteoarthritis: A Randomized Clinical Trial.
<p><strong><span>Background and purpose:</span></strong><span> </span><span>Pulsed electromagnetic field (PEMF) therapy and microwaves (MW) are two electrotherapy modalities that have shown to improve pain and function in patients with knee osteoarthritis (KOA). Nevertheless, the effectiveness of these therapies is controversial due to diversity in the application parameters and treatment protocols in these patients. The objective is to compare the effectiveness of active PEMF versus MW, as well as sham PEMF, in addressing pain and improving functionality for treating KOA.</span></p> <p><strong><span>Methods:</span></strong><span> </span><span>Double-blind, placebo-controlled, randomized clinical trial. Participants diagnosed with KOA were assigned to an intervention combining an exercise program with active PEMF, MW, or sham PEMF, delivered in three weekly sessions over four weeks. The main outcomes were pain, reported on a 0–10 cm visual analogue scale (VAS), and functionality, as evaluated with the Western Ontario and McMaster Universities Arthritis (WOMAC) questionnaire, and the Timed Up and Go test. The outcomes were measured at pre-intervention, immediately post-intervention, and one and four months after the intervention.</span></p>
Substorm Dataset (SI Dataset 1 - Substorm Identification With The WINDMI Magnetosphere - Ionosphere Nonlinear Physics Model)
<p>Dataset information on substorm onset times, magnetic local times, magnetic latitudes, geographic latitudes, and longitudes, along with detection techniques and their abbreviations (utilized by authors Forsyth, Frey, Liou, Newell, and Ohtani). Columns denote the presence (1) or absence (0) of substorms detected by each technique. Time differences between substorms are recorded. Additionally, it includes counts of techniques detecting substorm onsets, WINDMI model trigger occurrences based on field-aligned current crossing a critical threshold, Geotail current with median and 70th percentile Ic, where Ic doubles as the threshold for WINDMI substorm detection, and maximum field-aligned current during substorm onsets.</p>
Data for "A benchmarking method to rank the performance of physics-based earthquake simulations"
<p>This repository contains the datasets and codes supplementary to the article "<strong>A benchmarking method to rank the performance of physics-based earthquake simulations</strong>" submitted to <em>Seismological Research Letters</em>.</p> <p>The datasets include the codes to run the ranking analyses, inputs and outputs for the RSQSim earthquake simulation cases explained in the paper: a single fault and the fault system of the Eastern Betics Shear Zone (simulations from Herrero-Barbero et al. 2021). The results and data are stored in a separate folder for each case study presented in the paper: "Single fault" and "EBSZ". Each folder contains a series of subfolders and a Python script to run the ranking analysis for that specific case study. The script contains the default path references to read all necessary input files for the analysis and automatically save all the outputs. The subfolders are:</p> <p><strong>./Inputs: </strong>This folder contains the input files required for the RSQSim simulations. This includes:</p> <p>a. The fault model ("Nodes_RSQSim.flt" and "EBSZ_model.csv" for the single fault and EBSZ cases, respectively), which specifies the coordinate nodes of the fault triangular meshes and fault properties such as rake (º) and slip rate (m/yr).</p> <p>b. Neighbor file ("neighbors.dat"/"neighbors.12") that contains lists of triangular patches of the fault model that are neighboring. This file is used in RSQSim.</p> <p>c. Input parameter file ("Input_Parameters.txt"): this file specifies the parameters that are variable in each catalogue. This file is just for information purposes and is not used for the calculations.</p> <p>d. Parameter file(s) to run the RSQSim calculations.</p> <p>*For the single fault, this file is common ("test_normal.in") and is updated during the calculation when executing the "Run.sh" file in the terminal when running RSQSim. This file contains a script that loops through the input parameters a, b and normal stress explored in the study and changes the input parameter file accordingly in each iteration.</p> <p>*For the EBSZ, this file is specific for each simulation ("param_EBSZ_(n).in"), as each simulation was run separately.</p> <p>e. (Only for the EBSZ case) Input paleoseismic data for the paleorate benchmark. One file ("coord_sites_EBSZ.csv") contains a list of UTM coordinates of each paleoseismic site in the EBSZ and another ("paleo_rates_EBSZ.csv") contains the mean recurrence intervals and annual paleoearthquake rates in those sites (data from Herrero-Barbero et al., 2021).</p> <p><strong>./Simulation_models:</strong> contains several subfolders, one for each simulated catalogue (96 for the single fault case and 11 for the EBSZ). Each subfolder contains data that is read by the ranking code to perform the analysis. </p> <p>*For the single fault, the folder names follow the structure "model_(normal stress)<em>(a)</em>(b)". </p> <p>*For the EBSZ, the folder names are "cat-(n)".</p> <p><strong>./Ranking_results: </strong>contains the outputs of the ranking analysis, which are two figures and one text file.</p> <p>*Figure 1 ("Final_ranking.pdf"): visualization of the final ranking analysis for all models against the analyzed benchmarks.</p> <p>*Figure 2 ("Parameter_sensitivity.pdf"): visualization of the final and benchmark performance versus the input parameter of the models.</p> <p>*Text file ("Ranking_results.txt"): contains the final and benchmark scores of each simulation model. This file is outputted so the user can reproduce and customize their own figures with the ranking results.</p> <p>To use the ranking codes in you own datasets, please replicate the folder structure explained above. Use the code that best suits your data: use the one for the single fault if you wish not to use the paleorate benchmarks, and use the EBSZ one if you wish to include these data in your analysis. At the beginning of the respective codes (before the "Start" block comment) you will find the variables where the file names of the fault model and paleoseismic data are indicated. Change them to adapt it to your data. There you can also assign weights to the respective benchmarks in the analysis (default is set at equal weight for all benchmarks).</p> <p>For updates of the code please visit our GitHub: https://github.com/octavigomez/Ranking-physics-based-EQ-simulations</p>
Replication data for: "Effectiveness of iso-inertial resistance training on eccentric and concentric power, physical performance, and risk of falls in physically active middle-older adults: a randomised controlled trial"
<p>Replication data for: "Effectiveness of iso-inertial resistance training on eccentric and concentric power, physical performance, and risk of falls in physically active middle-older adults: a randomised controlled trial"</p> <p>This folder contains 4 files:</p> <p>1) Database that contains the values for concentric and eccentric power measured with both iso-inertial and gravitational systems (Dataset_power.xlsx)</p> <p>2) Database that contains the values for the Short Physical Performance Battery (SPPB) and Get Up and Go (GUG) test (Dataset_SPPB_GUG.xlsx)</p> <p>3) R Software script used to analyse file 1 (Iso-inertial analysis_power.R)<br> <br>4) R Software script used to analyse file 2 (Iso-inertial analysis_SPPB_GUG.R)</p>
The Aliased Complex Oscillator as a Paradigm for Analog Physical Modeling Sound Synthesis --- Audio Samples
<p>Additional material to the paper with the title: "The Aliased Complex Oscillator as a Paradigm for Analog Physical Modeling Sound Synthesis"</p>
Results from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>This repository contains results from the paper, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied Statistics</em>. The storage of these results helps facilitate access to and replication of the analysis in the paper. The results include output from the:</p> <ol> <li>Sulfate analysis <ul> <li>tx-2016-100k-int-mx.RDS</li> </ul> </li> <li>Asthma analysis <ul> <li>asthma-pois-cut.RDS</li> <li>asthma-pois-plugin.RDS</li> <li>asthma-bart-cut.RDS</li> <li>asthma-bart-plugin.RDS</li> </ul> </li> <li>Medicare analysis <ul> <li>medicare-pois-cut.RDS</li> <li>medicare-pois-plugin.RDS</li> <li>medicare-bart-cut.RDS</li> <li>medicare-bart-plugin.RDS</li> </ul> </li> <li>Simulation study <ul> <li>simstudy-cm1-lm.RDS</li> <li>simstudy-cm1-bart.RDS</li> <li>simstudy-cm2-lm.RDS</li> <li>simstudy-cm2-bart.RDS</li> <li>simstudy-cm3-lm.RDS</li> <li>simstudy-cm3-bart.RDS</li> <li>simstudy-pm1-pois.RDS</li> <li>simstudy-pm1-bart.RDS</li> <li>simstudy-pm2-pois.RDS</li> <li>simstudy-pm2-bart.RDS</li> <li>simstudy-pm3-pois.RDS</li> <li>simstudy-pm3-bart.RDS</li> </ul> </li> <li>Log-linear BART sensitivity analysis <ul> <li>sensitivity-m100.RDS</li> <li>sensitivity-m200.RDS</li> <li>sensitivity-m300.RDS</li> <li>sensitivity-m400.RDS</li> <li>sensitivity-power05.RDS</li> <li>sensitivity-power1.RDS</li> <li>sensitivity-power15.RDS</li> <li>sensitivity-power2.RDS</li> <li>sensitivity-power25.RDS</li> <li>sensitivity-power3.RDS</li> <li>sensitivity-power4.RDS</li> <li>sensitivity-power5.RDS</li> </ul> </li> </ol> <p>A description of these results can be found at <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a>, along with the R code used to generate these (and other results, such as figures) found in the manuscript and supplementary material.</p>
Physical Features for my study on Automatic Seismic Event Classification System in Pacific Northwest (Origin time - 50, +100)
<p>These features used the revised version of the feature extraction code. The revision involves changing the envelope filtering options and some minor modifications. </p>
Multi-physic analysis of power electronic control parameters in a simulation framework
<p>Dataset for conference paper "Multi-physic analysis of power electronic control parameters in a simulation framework"</p>
Global Urban Activity Changes from COVID-19 Physical Distancing Restrictions Dataset: TRacking Anomalous COVID-19 induced changEs in NightTime Lights (TRACE-NTL)
<p>We use satellite-derived (NASA Black Marble) nighttime lights to identify, quantify, and map daily changes in human activity that are atypical for each urban area, globally, from the beginning of the pandemic until two years after its onset. The dataset TRACE-NTL consists of global daily urban disruption and recovery metrics as a response to COVID-19.</p> <p>TRACE-NTL:</p> <p>├───ancillary<br>├───data<br>└───metrics<br> ├───disruption<br> │ ├───change_segment<br> │ ├───city_uncertainty<br> │ ├───daily_change<br> │ └───daily_qa_flags<br> └───recovery</p> <p><a title="Dataset description" href="https://github.com/srijac/covid-19_Nightlights">https://github.com/srijac/covid-19_Nightlights</a></p> <p>Accompanying paper: Accompanying paper: Chakraborty, S., Stokes, E.C. & Alexander, O. Global urban activity changes from COVID-19 physical distancing restrictions. <em>Sci Data</em> <strong>12</strong>, 98 (2025). https://doi.org/10.1038/s41597-025-04398-x </p>
Physical and Mechanical Properties of Chir Pine (Pinus roxburghii) after vacuum drying.
<p>The study was conducted on Pinus roxburghii (Chir pine) wood under a convective vacuum dryer. Wooden planks were seasoned at 45℃ above 20% moisture content and at 50℃ below 20% moisture content to final moisture content of 8 % to 12 %. It was found that all the physical and mechanical properties under study were increased. This study will encourage convective vacuum seasoning of timber for fast and defect-free drying.</p>
Raw data for publication: Weathering of Wood Modified with Acetic Anhydride – Physical, Chemical, and Aesthetical Evaluation
<p>Color.xlsx</p> <p>This file contains CIE Lab* color coordinates measured on the surface of wood samples.</p> <p>Weather data.xlsx</p> <p>This file contains daily local weather conditions in San Michelle, Italy during the natural weathering test.</p> <p>Gloss.xlsx</p> <p>This file contains the gloss value measured on the surface of wood samples.</p> <p>contact angle and surface energy.xlsx</p> <p> This file contains dynamic contact angle and surface energy measured on the surface of wood samples.</p> <p>TGA.xlsx</p> <p>This file contains the Thermogravimetric (TG) and derivative thermogravimetric (DTG) data of the wood samples.</p> <p>Surface roughness.xlsx</p> <p>This file contains the roughness value of wood samples.</p>
Effect of long-term physical disability and aging on extracellular matrix biogenesis in human skeletal muscle
<p>Supplemental Table for a study <strong>Effect of long-term physical disability and aging on extracellular matrix biogenesis in human skeletal muscle</strong></p>
Global Physically-Constrained Deep Learning Water Cycle Model with Vegetation: Model Simulations
<p>Welcome to our repository, which features simulations from the Hybrid Hydrological Model with Vegetation (H2MV). This collection includes 11 NetCDF files, representing temporal model simulations on a monthly scale and the static output of maximum soil moisture capacity (also known as plant rooting water storage) derived from a 10-fold cross-validation (CV) setup:</p> <ul> <li><strong>Temporal Simulations</strong>: The files named "fold1.nc" through "fold10.nc" contain the temporal model simulations, aggregated to a monthly scale, from 10 fold cross-validation (CV) setup.</li> <li><strong>Static Output</strong>: The "sm_max.nc" file presents the H2MV's estimation of the maximum soil moisture capacity</li> </ul> <h3>Contents of the Temporal Simulation Files</h3> <p>Each of the "fold" files ("fold1.nc" to "fold10.nc") contains the following variables:</p> <ul> <li><strong>Snow Dynamics</strong> <ul> <li><span><code>snow_acc</code></span>: Snow accumulation (mm/day)</li> <li><span><code>snow_melt</code></span>: Snow melt (mm/day)</li> <li><span><code>swe</code></span>: Snow water equivalent (mm)</li> </ul> </li> <li><strong>Evapotranspiration and its components</strong> <ul> <li><span><code>Ei</code></span>: Interception evaporation (mm/day)</li> <li><span><code>Es</code></span>: Soil evaporation (mm/day)</li> <li><span><code>T</code></span>: Transpiration (mm/day)</li> <li><span><code>ET</code></span>: Evapotranspiration (mm/day)</li> </ul> </li> <li><strong>Recharge</strong> <ul> <li><span><code>r_soil</code></span>: Soil recharge (mm/day)</li> <li><span><code>r_gw</code></span>: Groundwater recharge (mm/day)</li> </ul> </li> <li><strong>Runoff</strong> <ul> <li><span><code>runoff_surface</code></span>: Surface runoff (mm/day)</li> <li><span><code>baseflow</code></span>: Baseflow (mm/day)</li> <li><span><code>runoff_total</code></span>: Total runoff (mm/day)</li> </ul> </li> <li><strong>Water Storages </strong> <ul> <li><span><code>GW</code></span>: Groundwater (mm)</li> <li><span><code>SM</code></span>: Soil moisture (mm)</li> <li><span><code>tws</code></span>: Terrestrial water storage (mm)</li> <li><span><code>tws_anomaly</code></span>: Anomalies of terrestrial water storage (mm)</li> </ul> </li> <li><strong>Vegetation</strong> <ul> <li><span><code>fapar</code></span>: Fraction of absorbed photosynthetically active radiation (-)</li> </ul> </li> </ul> <h3>Contents of the Static Output File</h3> <p>The "sm_max.nc" file contains 10 variables corresponding to the 10 folds of CV, with each variable (e.g., "fold1") referring to the respective fold.</p> <h3>Additional Information</h3> <p>It's important to note that the original model simulations were conducted with a daily temporal resolution, but the data shared here have been aggregated to a monthly scale. We are open to sharing the original daily simulations and additional variables not included in this repository upon request. Please feel free to reach out to us for more information or data requests.</p>
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification
<p><strong>Introduction</strong></p> <p>The Robot Inverse Dynamics Dataset is a collection of trajectories and joint torque measurements of two robotic manipulators, a 7 DoF Franka Emika Panda, and a 6 DOF MELFA RV4FL. Additionally, the dataset contains the inverse dynamical models and other useful quantities learned to reproduce the results reported on our reference paper "A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification". The proposed model relies on a novel multidimensional kernel, called Lagrangian Inspired Polynomial (LIP) kernel.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped dataset is ~700MB.</li> <li>The dataset contains</li> <ul> <li>collections of joint trajectories and joint torque measurements of two robot manipulators: a 7 DoF Franka Emika Panda, and a 6 DOF MELFA RV4FL.</li> <li>models of the inverse dynamics of the two manipulators learned on the datasets</li> </ul> <li>The main directories are</li> <ul> <li>Simulated_PANDA/ contains the trajectories, models and results obtained on different configurations of a Franka Emika PANDA robot, simulated in sympybotics.</li> <li>Robots/ contains the data, models and results obtained on two real robots, a Franka Emika PANDA and a Mitsubishi Electric MELFA RV4FRL</li> </ul> <li>See the README.md file for a detailed description of the directories.</li> </ul> <p><strong>Other Resources</strong></p> <p>Python code to train the models and reproduce the results in the paper are available <a href="https://github.com/merlresearch/LIP4RobotInverseDynamics">here</a>.</p> <p><strong>Citation</strong></p> <p>If you use the Robot Inverse Dynamics dataset in your research, please cite our contribution:</p> <pre><code>@InProceedings{ title={A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification}, author={Giacomuzzo, G., Dalla Libera, A., Romeres, D.,}, booktitle={IEEE Transaction on Robotics}, year={2024} } </code></pre> <p><strong>License</strong></p> <p>The Robot Inverse Dynamics dataset is released under <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA-4.0 license</a>.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2024 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre>
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.