Find research datasets worth reusing

openmit-licenseMar 2023View details →

Celluloepidemiology: a novel paradigm for quantifying infectious disease dynamics through T-cell modelling on a population level

T-cell receptor sequencing (TCR-seq) was performed on enriched CD8+ T-cells. TCR clonotype annotation was performed using MiXCR v.3.0.13 with the default input parameters. Full origin and method description available in: Celluloepidemiology: a novel paradigm for quantifying infectious disease dynamics through T-cell modelling on a population level

DynamicRead: Eye Movement Data of Reading on Handheld Mobile Devices under Dynamic Conditions

The DynamicRead dataset contains eye movement data from 20 participants engaged in reading tasks on a handheld mobile device, while sitting and walking. Participants read 10 texts using four gaze interaction methods based on scrolling techniques, as well as one touch-based interaction method. The dataset captures the impact of motion on eye movement and features an unstable eye-movement sampling frequency ranging from 8-12fps/hz, which poses a challenge for traditional eye-movement toolkits. This dataset can be valuable for academic research on the impacts of motion on eye movement and the development of robust gaze interaction methods for handheld mobile devices under dynamic conditions. ### Folder Structure: -'eye_data'         -- contains eye movement of reading data -'text_screenshot'     -- contains different reading material screenshot -'heatmap_img'        -- contains heat map of each reading page  -'scanpath_img'        -- contains scan path of each reading page -'text'            -- contains reading material -'code'         -- contains R code for reading eye movement visualisation Name Method - GAP1_GazeA_log.txt -- Group A, Participant No1, Scrolling technique: GazeA - Gaze A: Auto-scrolling - Gaze B: Heatbox - Gaze C: Eye-Swipe - Gaze D: Moving-bar

openapache2.0Dec 2022View details →

Quantum critical dynamics in a 5000-qubit programmable spin glass: data repository

Supporting data for "Quantum critical dynamics in a 5000-qubit programmable spin glass", Nature, 2023.

Dataset of the Article "Reconstruction of the unbinding pathways of new inhibitors of the SARS-CoV-2 Papain-like protease using molecular dynamics simulation"

This dataset contains concatenated trajectory files of the SuMD simulation of the unbinding pathways of the new inhibitors for SARS-CoV-2 papain-like protease. This data will be published in an article titled: "Reconstruction of the unbinding pathways of new inhibitors of the SARS-CoV-2 Papain-like protease using molecular dynamics simulation".

opencc-by-4.0May 2022View details →

Comparison of dynamical cores in simulating SSWs

These files are the result or the initial field of my simulation with CESM. Detail of each file follows :    1) Files, begin with EUL, FV, SE, WeakDD or PF70, are 10 hPa zonal-mean zonal wind, 10 hPa zonal-mean temperature difference between 60ºN and pole. 2) Archived file "initial.zip" contains 3 files for 3 dynamical cores.

Spatio-temporal dynamics of the proton motive force on single bacteria - dataset

Data set used in our manuscript "Spatio-temporal dynamics of the proton motive force on single bacteria" [<a href="https://www.biorxiv.org/content/10.1101/2023.04.03.535353v1">Biorxiv</a>].   To produce fig1 and fig2, unzip file in bash: <pre><code class="language-bash">$ 7z e data_fig1_fig2.7z</code></pre> Open fig1 data in python: <pre><code class="language-python">> b = pickle.load(open('fig1.p', 'rb')) > b {'speed_Hz': array([-34.01139986, 13.07744575, 79.66060694, ..., -4.79440373, -4.88539275, -0.1366687 ]), 'speed_Hz_f': array([-11.11722186, 15.74953016, 32.24685265, ..., 20.11726694, -3.54263861, -36.89432049]), 'laser': array([0., 0., 0., ..., 0., 0., 0.]), 'FramesPerSecond': 5000.0}</code></pre> where b['speed_Hz'] : speed trace in Hz b['speed_Hz_f'] : speed trace in Hz, savgol filtered (5th order, 41 points) b['laser'] : laser trace in arbitrary units b['FramesPerSecond'] : camera frame acquisition rate   Open fig2 data: <pre><code class="language-python">> a = pickle.load(open('fig2.p','rb')) > a {11: {'speed_Hz': array([ 16.2828179 , 38.42508915, 94.68772452, ..., -12.24519659, 160.68335892, 114.39589274]), 'speed_Hz_f': array([ 25.8833788 , 31.87792607, 37.33062434, ..., 66.97264585, 91.38908923, 122.73732123]), 'laser': array([555950., 554818., 555193., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}, 12: {'speed_Hz': array([ 57.41541418, 24.4936895 , 248.68571544, ..., 86.08667522, -46.11733599, 18.16993041]), 'speed_Hz_f': array([102.04557049, 91.82376088, 84.51288552, ..., 33.63965888, 16.31075159, -5.36834171]), 'laser': array([555950., 554818., 555193., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}, 21: {'speed_Hz': array([ -57.50587524, 74.55546084, 65.87878605, ..., -197.26554361, 140.95754077, 52.72452236]), 'speed_Hz_f': array([-24.36465769, 22.9146126 , 49.48957346, ..., 47.67536653, 43.44702708, 36.80127664]), 'laser': array([0., 0., 0., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}, 22: {'speed_Hz': array([ 56.78413066, -147.32742392, -14.28783413, ..., -29.51455248, 15.66125098, 41.38001828]), 'speed_Hz_f': array([-23.57073686, -18.61154574, -15.85582681, ..., 7.05157621, 18.45809539, 37.52083946]), 'laser': array([0., 0., 0., ..., 0., 0., 0.]), 'FramesPerSecond': 10000.0}} </code></pre> where a[11] : dictionary for motor 1 trace, laser on motor 1, composed as above. a[12] : dictionary for motor 1 trace, laser on motor 2. a[21] : dictionary for motor 2 trace, laser on motor 1. a[22] : dictionary for motor 2 trace, laser on motor 2.

opencc-by-4.0Mar 2023View details →

Leveraging preserved specimens of Nerodia to infer the spatiotemporal dynamics of Ophidiomyces ophidiicola via quantitative polymerase chain reaction

Ophidiomyces ophidiicola (Oo) is a fungal pathogen and the causative agent of ophidiomycosis that has affected multiple snake taxa across the United States, Europe, and Asia. Ophidiomycosis has often been referred to as an emerging infectious disease (EID), however its status as an EID has recently come under debate. Oo infections have been confirmed in wild snake populations in Texas; however, it is unknown if the pathogen is novel (i.e., invasive) or endemic to the state. To address this knowledge gap, we conducted surveys for Oo among preserved Nerodia deposited at three university museums in Texas. First, we visually assessed snakes for signs of infection (SOI), and if SOI were present, we sampled the affected area. We then used quantitative polymerase chain reaction to diagnose the presence of Oo DNA on areas with SOI and used these data to evaluate spatiotemporal patterns of Oo prevalence. We also tested for significant spatial clusters of Oo infection using a Bernoulli probability model as implemented in the program SatScan. We found that the proportion of snakes exhibiting SOI was constant over time while the prevalence of Oo DNA among those SOI increased across space and time. Within these data, we detected an incidence pattern consistent with an introduction and then spread. We detected six spatial clusters of Oo infection, although only one was significant. Our results support the hypothesis that Oo was an emerging, novel pathogen to Texas snakes. These data narrow the knowledge gap regarding the history of Oo infections in Texas and establish a historical record of confirmed Oo detections in several counties across the state. Thus, our results will guide future research to those areas with evidence of past Oo infections but lacking confirmation in contemporary hosts.

opencc-zeroApr 2023View details →

Protein Structure Files and Galaxy Workflows for Conducting Molecular Dynamics Simulations of Coronavirus Helicases -- Output Files

These are the output files generated using the input files and Galaxy workflows for coronavirus helicase simulations, from:  <pre>https://doi.org/10.5281/zenodo.7492987</pre>

dryad40/100

Data for: Ornaments indicate parasite load only if they are dynamic, or parasites are contagious

Choosing to mate with an infected partner has several potential fitness costs, including disease transmission and infection-induced reductions in fecundity and parental care. By instead choosing a mate with no, or few, parasites, animals avoid these costs and may also obtain resistance genes for offspring. Within a population, then, the quality of sexually selected ornaments on which mate choice is based should correlate negatively with the number of parasites with which a host is infected ('parasite load'). However, the hundreds of tests of this prediction yield positive, negative, or no correlation between parasite load and ornament quality. Here, we use phylogenetically controlled meta-analysis of 424 correlations from 142 studies on a wide range of host and parasite taxa to evaluate explanations for this ambiguity. We found that ornament quality is weakly negatively correlated with parasite load overall, but the relationship is more strongly negative among ornaments that can dynamically change in quality, such as behavioral displays and skin pigmentation, and thus can accurately reflect current parasite load. The relationship was also more strongly negative among parasites that can transmit during sex. Thus, the direct benefit of avoiding parasite transmission may be a key driver of parasite-mediated sexual selection. No other moderators, including methodological details and whether males exhibit parental care, explained the substantial heterogeneity in our dataset. We hope to stimulate research that more inclusively considers the many and varied ways in which parasites, sexual selection, and epidemiology intersect.

opencc-zeroApr 2023View details →

Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction - sample treated_213nm

RHEED intensity image dataset of sample "treated_213nm" in work "Predicting Pulsed Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."

opencc-zeroApr 2023View details →

dryad40/100

Data and software for: Temporal novelty detection and multiple timescale integration drive Drosophila orientation dynamics in temporally diverse olfactory environments

To survive, insects must effectively navigate odors plumes to their source. In natural plumes, turbulent winds break up smooth odor regions into disconnected patches, so navigators encounter brief bursts of odor interrupted by bouts of clean air. The timing of these encounters plays a critical role in navigation, determining the direction, rate, and magnitude of insects' orientation and speed dynamics. Disambiguating the specific role of odor timing from other cues, such as spatial structure, is challenging due to natural correlations between plumes' temporal and spatial features. Here, we use optogenetics to isolate temporal features of odor signals, examining how the frequency and duration of odor encounters shape the navigational decisions of freely-walking Drosophila. We find that fly angular velocity depends on signal frequency and intermittency – fraction of time signal can be detected – but not directly on durations. Rather than switching strategies when signal statistics change, flies smoothly transition between signal regimes, by combining an odor offset response with a frequency-dependent novelty-like response. In the latter, flies are more likely to turn in response to each odor hit only when the hits are sparse. Finally, the upwind bias of individual turns relies on a filtering scheme with two distinct timescales, allowing rapid and sustained responses in a variety of signal statistics. A quantitative model incorporating these ingredients recapitulates fly orientation dynamics across a wide range of environments and shows that temporal novelty detection, when combined with odor motion detection, enhances odor plume navigation.

Dataset article "Demand-Response Control of Electric Storage Water Heaters Based on Dynamic Electricity Pricing and Comfort Optimization"

"README-SupplementaryMaterial.txt" explains the information gathered in each csv files, and including the DHW consumption profiles generated, the hourly electricity pricing for 2022 (Spain), and the experimental data utilized for the validation of the model.

NAIADES Dynamical Treatments Suggestion Tool Modelling Dataset

This data was generated within the European NAIADES project (grant agreement No 820985) for the development of the Dynamical Treatments Suggestion (DTS) Service. The DTS purpose is the estimation of the most efficient treatments (dosage of coagulant and chlorine) for drinking Water Treatment Plants (dWTPs) whose configuration is: coagulation/sedimentation, filtration and chlorination. The inlet water quality (pH, conductivity and turbidity) of the dWTP is used as input of the service. Then, it suggests the most efficient treatments that guarantee good drinking water quality at the outlet of the dWTP, based on WHO recommendations (turbidity below 5 NTU and chlorine concentration between 0.5 and 1 mg/L). The dataset includes data generated with Stimela, an open source water treatments processes simulation SW; and data collected in a scaled down replica of a real dWTP.

Fast prediction in marmoset reach-to-grasp movements for dynamic prey - Reach Data and Supplemental Video

Supplemental video and data corresponding to Shaw, L., Wang, K.H., Mitchell, J. (2023) Fast prediction in marmoset reach-to-grasp movements for dynamic prey. 1. Video Files MarmoReach 1 is an illustrative example. MarmoReach 2 illustrates the reaching trial shown in Figure 3D. MarmoReach 3-5 are example reach to grasps from grasp clusters found in Figure 2.  2. Data marmo_reach_model.mat is a Matlab struct. 2D position data of hand and cricket used for analyses related to Figure 3 and Figure 4.  x.hand,y.hand = position data of the central hand marker for each trial. x.cricket,y.cricket = position data of the cricket marker for each trial. x.cricketexfull,y.cricketexfull = position data of the cricket marker preceding reach onset for delay analyses.  To reconstruct cricket position from beginning to end with the inclusion of the exfull data (prior to reach to reach end) for the second reach, for example, [model.x.cricketexfull{2}' model.x.cricket{2}'].

NsCircle datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"

Datasets with the simulations of the incompressible flow around an elliptical as described by the incompressible Navier-Stokes equations. These simulations were used to train and test the MuS-GNN models in the paper:     Multi-scale rotation-equivariant graph neural networks for     unsteady Eulerian fluid dynamics (https://doi.org/10.1063/5.0097679) The datasets are:   - train/NsEllipse   - test/NsEllipseLowRe   - test/NsEllipseHighRe   - test/NsEllipseThin   - test/NsEllipseThick   - test/NsEllipseNarrow   - test/NsEllipseWide   - test/NsEllipseAoA     To cite these datasets, use the following reference: Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022). @article{lino2022multi,     author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris},     title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}},     journal = {Physics of Fluids},     volume = {34},     year = {2022},     url = {https://doi.org/10.1063/5.0097679}, }

opencc-by-4.0Mar 2020View details →

Gel phantom data for dynamic X-ray tomography

The gel phantom was constructed to simulate diffusion of liquids inside plant stems, namely the flow of iodine-based contrast agents used in high resolution tomographic X-ray imaging of plants. In order to test different reconstruction methods, this radiation resistant phantom with similar diffusion properties was constructed. A more detailed documentation can be found on arXiv: <a href="https://arxiv.org/abs/2003.02841">https://arxiv.org/abs/2003.02841</a> The phantom consists of a 50 ml Falcon test tube filled with agarose gel. After the agarose solidified, five cavities were made into the gel and filled with 20% sucrose solution to guarantee the diffusion by directing osmosis to the gel body. In addition densely punctured plastic straws were placed in the cavities to simulate cellular passages such as phloem plasmodesmata and to slow down the lateral diffusion. The primary measurements consisted of 17 consecutive time frames, with initial stage of no contrast agent followed by steady increase and diffusion into the gel body over time. Each round of measurements consist of 360 projections with a fanbeam microCT-scanner, but we used only the central plane of the cone beam, resulting in 2D fan beam geometry. Data is given in two different resolutions corresponding to reconstructions of size 256 x 256 or 512 x 512, (GelPhantomData_b4.mat and GelPhantomData_b2.mat respectively). In addition to the primary measurements, a more densely sampled measurements from the first time step and an additional 18th time step are provided in GelPhantom_extra_frames.mat. The measurements are stored in special data structures containing all the necessary metadata. In combination with the MATLAB toolboxes this allows for easy application of forward operators and reconstruction algorithms. This is demonstrated using the included example codes. These require ASTRA Toolbox, Spot Linear Operator Toolbox and HelTomo Toolbox (v1) for MATLAB. NOTE: Some of the metadata field names are different in HelTomo v2 and higher. Use of the data requires renaming (or adding) two fields in to the parameters: numDetectorsPost = numDetectors; effectivePixelSizePost = effectivePixelSize; Update: in v1.2 the data and example codes have been updated to match HelTomo v2.1 convention. For backwards compatibility (with Heltomo 1.0) the old medatadata field names remain. The full cone-beam measurements and corresponding documentation are available in <a href="https://doi.org/10.5281/zenodo.7739984">Zenodo</a>.

NsEllipse datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"

Datasets with simulations of the incompressible flow around an elliptical cylinder as described by the incompressible Navier-Stokes equations. These simulations were used to train and test the MuS-GNN models in the paper:     "Multi-scale rotation-equivariant graph neural networks for     unsteady Eulerian fluid dynamics" (https://doi.org/10.1063/5.0097679) The datasets are:   - train/NsEllipse   - test/NsEllipseLowRe   - test/NsEllipseHighRe   - test/NsEllipseThin   - test/NsEllipseThick   - test/NsEllipseNarrow   - test/NsEllipseWide   - test/NsEllipseAoA   To cite these datasets, use the following reference: Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022). @article{lino2022multi,     author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris},     title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}},     journal = {Physics of Fluids},     volume = {34},     year = {2022},     url = {https://doi.org/10.1063/5.0097679}, }

opencc-zeroDec 2022View details →

dryad40/100

Steering herds away from dangers in dynamic environments

Shepherding, the task of guiding a herd of autonomous individuals in a desired direction, is an essential skill to herd animals, enable crowd control, and rescue from danger. Equipping robots with the capability of shepherding would allow performing such tasks with increased efficiency and reduced labor costs. So far, only single-robot or centralized multi-robot solutions have been proposed. The former is unable to observe dangers at any place surrounding the herd, and the latter does not generalize to unconstrained environments. Therefore, we propose a decentralized control algorithm for multi-robot shepherding, where the robots maintain a caging pattern around the herd to detect potential nearby dangers. When danger is detected, part of the robot swarm positions itself in order to repel the herd towards a safer region. We study the performance of our algorithm for different collective motion models of the herd. We task the robots to shepherd a herd to safety in two dynamic scenarios: (i) to avoid dangerous patches appearing over time, and (ii) to remain inside a safe circular enclosure. Simulations show that the robots are always successful in shepherding when the herd remains cohesive, and enough robots are deployed.

Datasets for "Dynamics and deposits of pyroclastic density currents in magmatic and phreatomagmatic eruptions revealed by a two-layer depth-averaged model"

Dataset for the manuscript entitled "Dynamics and Deposits of Pyroclastic Density Currents in Magmatic and Phreatomagmatic Eruptions Revealed by a Two-Layer Depth-Averaged Model" by H. A. Shimizu, T. Koyaguchi, and Y. J. Suzuki for submission in Geophysical Research Letter. This contains datasets for each run.

Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"

Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics" (https://doi.org/10.1063/5.0097679). The training code is available at https://github.com/mario-linov/graphs4cfd.