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1,267 results for “Data paper”
Data underlying the paper titled "Positron unveiling high mobility graphene stack interfaces in Li-ion cathodes"
<p>The folder includes data regarding 4 figures shown in this paper. </p> <p>FIG_1: Simulation structure of 6 layers of graphene bulk and slab (6C_Bulk.vasp, 6C_Slab.vasp), LiCoO2(LCO_336.vasp), ABA Graphite coating LiCoO2(G@LCO.vasp). </p> <p>FIG_2: Raw data of band structure of Graphite coating LCO (Band_G@LCO_EIGENVAL), Density of States (DOS_G@LCO_DOSCAR)</p> <p>FIG_3: Calculated plane-averaged charge density difference of SW-G@LCO perpendicular to (001) plane at the equilibrium distance. G@LCO_CHGCAR, LCO_CHGCAR, C_CHGCAR are the CHGCAR for Graphitte coating LCO, LCO, graphite, respectively. </p> <p>d24_diff.vasp is the charge difference</p> <p>d24_PACD.dat is the plane-averaged charge density difference. </p> <p>FIG_4: posden indicates the positron density, while posvtot means positron potential. Data are named by their structure. </p>
Succession in abandoned fields: Chronosequence data verified by monitoring of semi-permanent plots - Dataset underlying paper
<p>In task 3.1 of the WILDCARD project (www.wildcard-project.eu), we performed the analyses to identify the similarity between successional patterns in permanent plots and chronosequences on abandoned fields. This data is needed to know the accuracy of a chronosequence approach to reveal general successional vegetation pattens compared to the results obtained from semi-permanent plots. Raw data was provided by external collaborators. Results can be applied to other types of successional studies and restoratin projects, they verified the reliability of chronosequence approach. Rewiew process (3 reviewers plus coordinationg editor)</p>
Data from systematic audit for paper: Insights into the quantification and reporting of model-related uncertainty across different disciplines
<p>This upload contains 7 data files (each contains cleaned and compiled data for a given scientific field) and 2 R scripts. These files support the paper: Insights into the quantification and reporting of model-related uncertainty across different disciplines.</p> <p> </p> <p><strong>Description of the data</strong></p> <p>Compiled data files for each field contain all reviewers audit answers for eligible papers. All papers that met exclusion criteria have been removed.</p> <p>Data checks have been performed and formatting errors corrected either in R or manually, following steps detailed in the STAR methods.</p> <p>Column names and description:</p> <ul> <li>Number: number of question from 1 to 9</li> <li>Questions: question text – question to be answered by the reviewer</li> <li>QuestionCode: shortened code for each question</li> <li>Paper: paper code - first author surname/initial and surname and year</li> <li>Initials: initials of reviewer</li> <li>Answer: answer to the question</li> <li>Details: extra details to support the answer</li> <li>Location: where in the text the uncertainty was presented</li> <li>Presentation: how the uncertainty was presented</li> <li>ModelType: type of model (focal model)</li> <li>Comments: any other comments from the reviewer</li> <li>Checks: checks of whether NA or no have been included in correct places e.g. if answers to questions 1:4 are no then question 9 is NA, if question 7 is no then 8 is NA</li> <li>Check 1 = when Answer = No, Location is NA</li> <li>Check 2 = when Answer to Number 1-4, 6 or 8-9 is Yes that Details are not NA</li> <li>Check 3 = when Answer = No, Presentation = NA</li> <li>Check 4 = when Location is not NA, presentation is not NA</li> <li>Check 5 = if the Answer to 5 or 7 is "No" then Answer to 6 and 8 = "NA"</li> <li>Check 6 = if Answer for 1-4 is "No", then Answer for 9 = "NA"</li> </ul> <p><strong>Code description</strong></p> <p>Two scripts are included, the first is theme_script.R, this includes code to set up a ggplot theme for the figures. The second is Figure_code.R, this script contains all code to plot and save the three figures from the paper.</p>
Data and code related to the paper: "Integrated stretchable pneumatic strain gauges for electronics-free soft robots"
<p>This folder contains the raw data and Matlab scripts to reproduce the plots and supplementary movies for the paper:</p> <p>Anastasia Koivikko, Vilma Lampinen, Mika Pihlajamäki, Kyriacos Yiannacou, Vipul Sharma & Veikko Sariola, "Integrated Stretchable Pneumatic Strain Gauges for Electronics-Free Soft Robots", Communications Engineering, 1, 14 (2022).</p> <p><a href="https://doi.org/10.1038/s44172-022-00015-6">Link to the paper</a>.</p> <p>The scripts were tested on Matlab R2021a on Windows.</p> <p>Generally speaking, there is a folder containing the plotting scripts for each figure. In most cases, the folder contains scripts named <strong>plot<...>.m</strong> that recreate the actual plots. Some folders also have a scripts <strong>analyze<...>.m</strong> to analyze the data; these need to be run before the actual plotting.</p> <p>For more details, please see the paper.</p>
Data and code related to the paper: "Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation"
<p>This archive contains the raw data and Matlab scripts to reproduce the plots and supplementary movies for the paper:</p> <p>Kyriacos Yiannacou, Vipul Sharma and Veikko Sariola, "Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation", <em>Langmuir</em> 2022, 38, 38, 11557–11564.</p> <p><a href="https://doi.org/10.1021/acs.langmuir.2c01061">Link to the paper</a>.</p> <p>The scripts were tested on Matlab R2021a on Windows.</p> <p>The acoustofluidic controller software is the same as in our previous paper and is archived <a href="https://doi.org/10.5281/zenodo.4593021">here</a>.</p> <p>Generally speaking, there is a folder containing the plotting scripts for each figure(s) and/or movie(s). Within each folder, the raw data files are under the folder `data/`. Once ran, the scripts produce another folder called `output/`, to which they place the created plots and movies. Most folder contain a script name `plot_*.m` that makes the figure(s) and `video_*.m` that generates the video(s). You will need `ffmpeg` installed to convert the serial images into a video.<br> </p>
MITgcm Dataset for paper: Sensitivity analysis of a data-driven model of ocean temperature
<p>MITgcm dataset used in paper, Sensitivity analysis of a data-driven model of ocean temperature, made available here. The dataset comes from running a sector config of the MITgcm model, briefly described in the paper. This dataset is used to train the regression model described in the paper.</p> <p>Updated to include ncra_cat_tave.nc file which was accidentally missed on first version.</p>
Data for paper "Magnetohydrodynamic Equilibrium Reconstruction with Consistent Uncertainties"
<p>Data and scripts for the conference paper "Magnetohydrodynamic Equilibrium Reconstruction with Consistent Uncertainties" for the 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering.</p> <p><strong>Abstract</strong>: We report on progress towards a probabilistic framework for consistent uncertainty quantification and propagation in analysis and numerical modeling of physics in magnetically confined plasmas in the stellarator configuration. A frequent starting point in this process is the calculation of a magnetohydrodynamic equilibrium from plasma profiles. Profiles and therefore the equilibrium are typically reconstructed from experimental data. What sets equilibrium reconstruction apart from usual inverse problems is that profiles are given as functions over a magnetic flux derived from the magnetic field, rather than spatial coordinates. This makes it a fixed-point problem that is traditionally left inconsistent or solved iteratively in a least-squares sense[1–3]. The aim here is towards a straightforward and transparent process to quantify and propagate uncertainties and their correlations for function-valued fields and profiles in this setting. We propose a framework that utilizes a low dimensional prior distribution of equilibria, constructed with principal component analysis. A surrogate of the forward model[4] is trained to enable faster sampling.</p> <p><strong>Funding</strong>: The present contribution is supported by the Helmholtz Association of German Research Centers under the joint research school HIDSS-0006 'Munich School for Data Science - MUDS'. This work has been carried out within the framework of the EUROfusion Consortium, funded by the European Union via the Euratom Research and Training Programme (Grant Agreement No 101052200 - EUROfusion). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them.</p>
Data for paper "Stratocumulus adjustments to aerosol perturbations disentangled with a causal approach"
<p>Timeseries data used for the causal effect estimation of the paper "Stratocumulus adjustments to aerosol perturbations disentangled with a causal approach".</p> <p>This dataset contains several cloud parameters and meteorological co-variates corresponding to the evolution of the South-East Atlantic stratocumulus deck for the time period January 2016 to December 2017 and the spatial domain [lon1,lon2,lat1,lat2]=[0, 10, -20, -10]. </p> <p>The processing code used to generate the timeseries data, as well as the analysis code are uploaded separately on Zenodo. The input raw satellite and reanalysis data for the processing code are from EUMETSAT (Copyright (c) (2020) EUMETSAT), NASA and COPERNICUS data (generated using Copernicus Climate Change Service information [2022]). </p> <p> </p> <p>Citations for the raw data sources: </p> <p>Finkensieper, S., Meirink, J.-F., van Zadelhoff, G.-J., Hanschmann, T., Benas, N., Stengel, M., Fuchs, P., Hollmann, R., Kaiser, J., Werscheck, M.: CLAAS-2.1: CM SAF CLoud property dAtAset using SEVIRI - Edition 2.1. Satellite Application Facility on Climate Monitoring (2020). <a href="https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002_01">https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002_01</a></p> <p>Huffman, G.J., Stocker, E.F., Bolvin, D.T., Nelkin, E.J., Tan, J.: GPMIMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06. MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) (2019). <a href="https://doi.org/10.5067/GPM/IMERG/3B-HH/06">https://doi.org/10.5067/GPM/IMERG/3B-HH/06</a>.</p> <p>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horanyi, A., Munoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Th ́ebaut, J.-N.: ERA5 hourly data on single levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (2018). <a href="https://doi.org/10.24381/cds.adbb2d47">https://doi.org/10.24381/cds.adbb2d47</a></p> <p>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horanyi, A., Munoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Th ́ebaut, J.-N.: ERA5 hourly data on pressure levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (2018). <a href="https://doi.org/10.24381/cds.bd0915c6">https://doi.org/10.24381/cds.bd0915c6</a></p>
Soybean data for paper: "Increase of simultaneous soybean failures due to climate change"
<p>Input and Output data used in the paper: ""Increase of simultaneous soybean failures due to climate change""</p> <p>Upon use of part of this dataset, please cite authors and paper related.</p> <p>Input:</p> <p>Observed soybean data obtained from official authorities pre-processed and regularised at 0.5 x 0.5 spatial resolution:</p> soy_yield_1975_2016_05x05_1prc.nc 43.6 MB soy_yield_arg_1974_2019_05x05.nc 44.6 MB soy_yields_US_all_1975_2020_05x05.nc 47.7 MB soybean_harvest_area_calculated_americas_hg.nc soybean_yields_america_detrended_1978_2016.nc 78.8 MB <p> </p> <p>Outputs:</p> <p>Hybrid model outputs for soybean yield from 2015-2100l with trends at 0.5 x 0.5 spatial resolution:</p> hybrid_trend_gfdl-esm4_ssp126_default_yield_soybea ... 14.6 MB hybrid_trend_gfdl-esm4_ssp585_default_yield_soybea ... 14.6 MB hybrid_trend_ipsl-cm6a-lr_ssp126_default_yield_soy ... 14.6 MB hybrid_trend_ipsl-cm6a-lr_ssp585_default_yield_soy ... 14.6 MB hybrid_trend_ukesm1-0-ll_ssp126_default_yield_soyb ... 14.6 MB hybrid_trend_ukesm1-0-ll_ssp585_default_yield_soyb ... 14.6 MB <p>Hybrid model outputs for soybean yield from 2015-2100 without trends at 0.5 x 0.5 spatial resolution:</p> hybrid_gfdl-esm4_ssp126_default_yield_soybean_2015 ... 7.3 MB hybrid_gfdl-esm4_ssp585_default_yield_soybean_2015 ... 7.3 MB hybrid_ipsl-cm6a-lr_ssp126_default_yield_soybean_2 ... 7.3 MB hybrid_ipsl-cm6a-lr_ssp585_default_yield_soybean_2 ... 7.3 MB hybrid_ukesm1-0-ll_ssp126_default_yield_soybean_20 ... 7.3 MB hybrid_ukesm1-0-ll_ssp585_default_yield_soybean_20 ... 7.3 MB
Data physicalization papers analysis (2019) V1
<p>Dataset containing a "report" of the reading of articles listed on <a href="http://dataphys.org/wiki/Bibliography">http://dataphys.org/wiki/Bibliography</a> with classification by human sense used in prototype/proposal.</p>
Data for the paper Lemoine, Gmel, Foster, Marmet, Studer (2020). Multiple trajectories of alcohol use and the development of alcohol use disorder: do Swiss men mature-out of problematic alcohol use during emerging adulthood? Plos One. https://doi.org/10.1371/journal.pone.0220232
<p>Data for the paper Lemoine, Gmel, Foster, Marmet, Studer (2020). Multiple trajectories of alcohol use and the development of alcohol use disorder: do Swiss men mature-out of problematic alcohol use during emerging adulthood?</p> <p>Plos One. <a href="https://doi.org/10.1371/journal.pone.0220232">https://doi.org/10.1371/journal.pone.0220232</a></p> <p>Please refer to the paper for further information about the data.</p> <p> </p> <p>The dataset contains all data needed to reproduce the results in the above cited paper. Variable description and labels can be found in the codebook. For further information on the instruments used please refer to the paper.</p> <p>The dataset contains data for three waves that was collected between September 2010 and March 2018 in Switzerland by the C-SURF study (<a href="http://www.c-surf.ch/">www.c-surf.ch</a>). Participants were on average 20 years old at wave 1, 21 at wave 2 and 25 at wave 3 when they answered the questionnaires. The final sample size used in the paper is 4746 after excluding those that did not reply to a questionnaire or to a variable of interest for the main analysis.</p> <p>The research protocol for this study was approved by the Human Research Ethics Committee of the Canton Vaud (Protocol No. 15/07). Data collection was funded by the Swiss National Science Foundation (FN 33CSC0-122679, FN 33CS30_139467, FN 33CS30_148493).</p>
Data for resonator behavior in the paper "Self-sensing, tunable monolayer MoS2 nanoelectromechanical resonators"
<p>Data for resonator behavior in the paper "Self-sensing, tunable monolayer MoS2<br> nanoelectromechanical resonators", <em>Nat. Commun.</em> 10, 4831 (2019); DOI:<a href="https://doi.org/10.1038/s41467-019-12795-1">10.1038/s41467-019-12795-1</a>.</p>
Experimental data of the paper "Trial-based Heuristic Tree Search for MDPs with Factored Action Spaces"
<p>This data set contains the code of our planner and of the planner that was used as baseline, the benchmark set that was used to perform experiments as well as the parsed values and basic reports that are reported in the paper. More information can be found in the README that is also included.</p>
Model data from GRL paper: "Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone"
<p>This folder includes monthly model data (experiments using SC-WACCM4 and E3SMv1) of temperature (TEMP) and sea level pressure (SLP) that were used in the Geophysical Research Letters paper "<strong>Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone</strong>", # 2020GL088583. See also for additional information/data: <a href="https://zenodo.org/record/3066448">https://zenodo.org/record/3066448</a></p> <p>Labe, Z., Peings, Y., & Magnusdottir, G. (2020). Warm Arctic , cold Siberia pattern : role of full Arctic amplification versus sea ice loss alone. <em>Geophysical Research Letters</em>, 1–26. <a href="https://doi.org/10.1029/2020GL088583">https://doi.org/10.1029/2020GL088583</a></p> <p><a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020GL088583">[Paper]</a><a href="https://sites.uci.edu/zlabe/arctic-amplification/">[Plain Language Summary]</a><a href="https://github.com/zmlabe/AA">[GitHub]</a></p>
Supplementary material (aggregated data set): Egeler, G.-A. & Baur, P. (2020). Menüwahl in der Hochschulmensa: Fleisch oder Vegi? Ergebnisse eines 12-wöchigen Feldexperiments (NOVANIMAL Working Paper No. 5). ZHAW. https://doi.org/10.21256/zhaw-1405
<p><strong>Meal choice at two university canteens in a field experiment during 12 weeks: aggregated menu sales data</strong></p> <p>How do canteen visitors respond to a revised offer of meat-based and plant-based meals? Selected innovations were simultaneously implemented and tested in a trans­disciplinary field experiment in two university canteens over a 12-week period in the autumn semester 2017. Throughout this time, the meat dishes and ‘veg-meals’ (ovo-lacto-vegetarian and vegan meals) were randomly distributed among the three menu lines, the veg-meals were not marketed and advertised as such and the previous vegetarian menu line was abolished. Weeks where the usual number of meat dishes were on offer (the ‘base weeks’) alternated with weeks where the share of veg-meals was increased (the ‘intervention weeks’). <br> The field experiment did not have a negative impact on the number of meals sold or the turnover compared to the two previous years. Women choose meat dishes less often than men. This connection applies in the base weeks and intervention weeks, in all age groups, among both students and among staff. Remarkably, the share of (non-labelled) vegan dishes is comparable for women and men over all age groups, independent of university affiliation (student, staff). Authentic vegan dishes were particularly welcome. Veg-meals could also be sold on the more expensive menu line. There was a better correlation between meal choice, eating habits and attitudes (health, environment, animal welfare, social aspects) than expected. <br> One quarter of canteen visitors show ‘veg-oriented’ eating habits and three quarters thereof ‘meat-oriented’ eating habits. Only a minority of potential visitors eat regularly at the canteen, and those who do exhibit meat-oriented eating habits more often. We conclude, therefore, that the canteen’s usual menu offer is primarily aimed at visitors with meat-oriented eating habits at lunchtime. The most typical visitors to the canteen are male students who select meat dishes.<br> It has been shown, therefore, that the simultaneous changes in supply have worked. Veg-meals are preferred, particularly by women and those prone to flexitarian eating habits; however, also the canteen visitors with meat-oriented eating habits chose veg-meals during the intervention weeks. Catering in canteens has the great potential to expand the range of veg-meals at the expense of meat dishes, provided that the culinary quality is of a high enough standard and meals are not offered as vegetarian or vegan. The question arises as to whether canteens are not missing an economic opportunity if they only offer traditional meat dishes? Canteens are perfectly suited as real-world laboratories in which innovations for sustainable catering can be tried out. The field experiment in the two university canteens is a start; further experiments are needed.</p> <p><strong>The data set contains more than <em>26'000</em> aggregated menu sales. The analyses and results are summarized in the working paper No. 5 <a href="https://doi.org/10.21256/zhaw-1405">https://doi.org/10.21256/zhaw-1405</a></strong></p> <p>The corresponding scripts are: </p> <p>- <a href="http://doi.org/10.5281/zenodo.4034686">10.5281/zenodo.4034686</a></p> <p>- <a href="http://doi.org/10.5281/zenodo.4034698">10.5281/zenodo.4034698</a></p> <p>- <a href="http://doi.org/10.5281/zenodo.4244258">10.5281/zenodo.4244258</a> (newer Version)</p> <p>For more information visit the <a href="http://novanimal.ch">novanimal.ch</a> website.</p>
Data set to Conference Paper "The Effect of Queuing Technology on Customer Experience in Physical Retail Environments"
<p>Following an open data policy as supported by the European Union (https://www.openaire.eu/), this is the data set used for the following conference paper: Obermeier, G., Zimmermann, R., & Auinger, A. (2020, July). The Effect of Queuing Technology on Customer Experience in Physical Retail Environments. In <em>International Conference on Human-Computer Interaction</em> (pp. 141-157). Springer, Cham.</p> <p>The present work was conducted within the Innovative Training Network project PERFORM funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 765395. The EU Research Executive Agency is not responsible for any use that may be made of the information it contains.</p>
Raw data acquired necessary to produce the plots introduced in the scientific paper: "Upper-limb kinematic reconstruction during stroke robot-aided therapy" (Medical & Biological Engineering & Computing)
<p>These files contain the raw data acquired necessary to produce the plots introduced the Figure 6 of the scientific paper: “Upper-limb kinematic reconstruction during stroke robot-aided therapy” (Medical & Biological Engineering & Computing).</p> <p>Fig. 6 shows the data recorded from two patients performing five forward/backward movements at InMotion2 robot before and after rehabilitation treatment. Mean values of the five execution have been reported in Fig. 6.</p>
Raw data employed to perform the algorithm used in the scientific paper: "Kinematic reconstruction of the upper limb joints in planar robot-aided therapies
<p>These files contain the raw data (acquired from different users) necessary to perform the algorithm introduced in the scientific paper:</p> <p>PAPER: Kinematic reconstruction of the upper limb joints in planar robot-aided therapies</p> <p>Authors: Arturo Bertomeu-Motos, Ricardo Morales, Jorge A. Díez, Luis D. Lledó, Francisco J. Badesa, Nicolas Garcia-Aracil</p> <p>Conference: ICORR 2015, IEEE 14th International Conference on Rehabilitation Robotics, August 2015</p> <p><br> All the orientations are expressed regarding the origin of the robot.</p> <p>a) Robot Joints: Planar robot joints acquired during the experiment, in radians (j1-j3 columns). This robot is referenced in the paper.<br> b) Quaternion IMU shoulder: Unit quatenion acquired through a 9DoFs Inertial Measurement Unit (IMU) developed by Shimmer (qw1-qz columns).<br> c) Upper arm acceleration: Acceleration acquired from a 3-axial accelerometer developed by Shimmer (X-Z columns). It is normalized regarding the gravity (9.81m/s^2).<br> d) Quaternion Tracker onto Shoulder: unit quaternion of the tracker placed onto the shoulder acquired from the tracking camera V120:trio developed by Optitrack (qw1-qz columns).<br> e) Quaternion Tracker onto Upper Arm: unit quaternion of the tracker placed onto the upper arm acquired from the tracking camera V120:trio developed by Optitrack (qw1-qz columns).</p>
Raw data employed to perform the algorithm used in the scientific paper: "Kinematic reconstruction of the human arm joints in robot-aided therapies with Hermes robot"
<p>This file contains the raw data necessary to perform the algorithm introduced in the scientific paper:</p> <p>PAPER: Kinematic reconstruction of the human arm joints in robot-aided therapies with Hermes robot</p> <p>Authors: Arturo Bertomeu-Motos, Ricardo Morales, Luis D. Lledó, Jorge A. Díez, Jose M. Catalan, Nicolas Garcia-Aracil.</p> <p>Conference: EMBC 2015, IEEE 37th International Conference in Medicine and Biology Society, August 2015.</p> <p>Raw data acquired necessary to perform thee algorithm introduced in this paper.</p> <p>a) Robot Joints: Robot joints generated to develop the simulation, in radians (j1-j7 colums). This robot is referenced in the paper.<br> b) Direct Upper Limb Joints: Upper limb joints generated to develop the simulation, in radians (q1-q7 columns). This data is used to simulate the accelerometer value.</p>
Raw data corresponding to the scientific paper: "A modular telerehabilitation architecture for upper limb robotic therapy" (Advances in Mechanical Engineering 2017, Vol. 9(1) 1-13)
<p>Acquired raw data necessary to implement the adaptive control strategy grounded on multimodal information.<br> In addition, raw data for the computation of the communication parameters needed for the assessment of the implemented telerehabilitation architecture are provided.</p> <p>a) End-effector positions and velocities (x, y, vx, vy) in three conditions: healthy (Fig 9) and constraint simulated stroke behaviour (Fig 10) without robotic assistance and simulated stroke behavior with robotic assistance (Fig 11)</p> <p>b) Performance indicators and control parameters for all the recruited subjects in both conditions healthy behaviour and simulated stroke behaviour (Fig 12a and Fig 12b)</p> <p>c) Computational values for evaluating telerehabilitation performance (Table 1)</p> <p> </p> <p> </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.