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1,457 results for “feedback”
Supplementary material of Investigating User Feedback from a Crowd in Requirements Management in Software Ecosystems
<p>Supplementary material of Investigating User Feedback from a Crowd in Requirements Management in Software Ecosystems.</p>
On the increase of climate sensitivity and cloud feedback with warming in the Community Atmosphere Models
<p><strong>Citation:</strong> Zhu, J., & Poulsen, C. J. (2020). On the Increase of Climate Sensitivity and Cloud Feedback With Warming in the Community Atmosphere Models. <em>Geophysical Research Letters</em>, <em>47</em>(18), e2020GL089143. <a href="https://doi.org/10.1029/2020GL089143">https://doi.org/10.1029/2020GL089143</a></p> <p> </p> <p><strong>Upadated on July 3, 2024</strong>: add more timeseries of precipitation and energy fluxes for analysis in Bonan, Schneider, & Zhu (2024).</p> <p>David Bonan, Tapio Schneider, Jiang Zhu. Precipitation over a wide range of climates simulated with comprehensive GCMs. <em>ESS Open Archive .</em> April 25, 2024.<br><span>DOI: <a href="https://doi.org/10.22541/essoar.171405864.45942692/v1" target="_blank" rel="noopener noreferrer">10.22541/essoar.171405864.45942692/v1</a></span> </p>
Data for "Elevated urban energy risks due to climate-driven biophysical feedbacks"
<p>This dataset contains the global multi-model urban climate and energy projections from Li et al. (2024), "Elevated urban energy risks due to climate-driven biophysical feedbacks", published in <em>Nature Climate Change</em>. It contains global monthly mean projections of urban 2-meter air temperature, and urban cooling and heating energy fluxes derived from 25 Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Details about how this dataset was generated are described in the article. This dataset may be useful for multiple communities interested in future energy risks, climate change impacts and vulnerability, and climate-sensitive adaptation and energy planning.</p> <p>For more details, please refer to the README.md file included in the dataset.</p>
Figure data for Prolonging a discrete time crystal by quantum-classical feedback
<p>The dataset corresponds to the original work developed by the authors.</p>
VizRob's Inital Feedback: Case Study
<p>All the necessary information to run the experiment on VizRob and the data to analyze the conducted case study.</p> <ul> <li>Questions and answers from the semi-structured survey</li> <li>Pharo Smalltalk image of the case study</li> <li>ROS Bag files used in the case study</li> <li>Audios and Videos (in Spanish) of the case study</li> <li>Transcriptions (in English, translated from Spanish) of the pre and postquestionnaire of the case study</li> </ul>
THE INFLUENCE OF PITCH FEEDBACK ON LEARNING OF MOTOR-TIMING AND SEQUENCING: A PIANO STUDY WITH NOVICES
<p>These files contain values and conditions for each subject in csv format.</p>
User Study Data from "HaptiGlow: Helping Users Position their Hands for Better Mid-Air Gestures and Ultrasound Haptic Feedback"
<p>This dataset contains user study data from the experiment reported in our IEEE World Haptics Conference 2019 paper.</p>
CESM1.2 simulation data for "Quantifying the cloud particle-size feedback in an Earth system model"
<p>CESM1.2-CAM5 simulation data for "Quantifying the cloud particle-size feedback in an Earth system model"</p> <p><strong>Citation: </strong>Zhu, J., & Poulsen, C. J. (2019). Quantifying the cloud particle-size feedback in an Earth system model. <em>Geophysical Research Letters</em>, <em>46</em>, 10910–10917. <a href="https://doi.org/10.1029/2019GL083829">https://doi.org/10.1029/2019GL083829</a></p> <p>Data include:</p> <p>(1) cloud liquid particle size for liquid (AREL) and ice (AREI), grid box averaged cloud liquid (CLDLIQ) and ice (CLDICE), fractional occurrence of liquid (FREQL) and ice (FREQI), and surface temperature (TS) in the preindustrial and 2xCO2 experiments; and<br> (2) the cloud feedback (lam_CLDTOT) and cloud particle-size feedback (lam_CLDEFR3L) from our PRP-based method.</p>
Replication package for Journal paper title "Supporting the identification of prevalent quality issues in code changes by analyzing reviewers' feedback"
<p>Replication package for the study "Supporting the identification of prevalent quality issues in code changes by analyzing reviewers' feedback"</p> <p> </p>
Sea ice feedbacks cause more greenhouse cooling than greenhouse warming at high northern latitudes on multi-century timescales
<p>Figure and data files for Kay et al. (revised) to Environmental Research Letters, September 6, 2024</p>
Data for paper "Diagnosing the factors that contribute to the intermodel spread of climate feedback in CMIP6"
<p>This data deposit includes the model simulation data and intermediate data. All data is annunal mean to reduce the size.</p> <p>The globalmean.zip includes the global mean radiative repsonse data. </p> <p>The annualmean.zip includes the annual mean data of all model simulations done and have been regridded to 2x2.5 degree resolution. </p>
Data From: ChatGPT versus expert feedback on clinical reasoning questions and their effect on learning: a randomized controlled trial
<p>Dataset Info</p> <p><strong>1) Immediate Test</strong><br>- The first row of the dataset identifies the columns.<br>- Column A represents the participants’ iDs.<br>- Column B represents the participants’ assigned group [0: Control (ExpertFeedback) 1: Intervention (ChatGPTFeedback)].<br>- Column C represents the genders of the participants (1: Female, 2: Male).<br>- Column D represents the first-year repetition status of the participants. (0: No, 1: Yes)<br>- Column E to H represent the scores in four different uncomplicated urinary tract infection (UTI) Key-Features Questions Items separately. <br>- Column I represents the total scores in uncomplicated UTI Key-Features Questions Items. <br>- Column J to M represent the scores in four different complicated UTI Key-Features Questions Items separately. <br>- Column N represents the total scores in complicated UTI Key-Features Questions Items. <br>- Column O to R represent the scores in four different pyelonephritis Key-Features Questions Items separately. <br>- Column S represents the total scores in pyelonephritis Key-Features Questions Items. <br>- Column T represents the total scores in immediate test. </p> <p><strong>2) Delayed Test</strong><br>- The first row of the dataset identifies the columns.<br>- Column A represents the participants iDs.<br>- Column B represents the participants’ assigned group [0: Control (ExpertFeedback) 1: Intervention (ChatGPTFeedback)].<br>- Column C represents the genders of the participants (1: Female, 2: Male).<br>- Column D represents the first-year repetition status of the participants. (0: No, 1: Yes)<br>- Column E to H represent the scores in four different uncomplicated urinary tract infection (UTI) Key-Features Questions Items separately. <br>- Column I represents the total scores in uncomplicated UTI Key-Features Questions Items. <br>- Column J to M represent the scores in four different complicated UTI Key-Features Questions Items separately. <br>- Column N represents the total scores in complicated UTI Key-Features Questions Items. <br>- Column O to R represent the scores in four different pyelonephritis Key-Features Questions Items separately. <br>- Column S represents the total scores in pyelonephritis Key-Features Questions Items. <br>- Column T represents the total scores in delayed test. </p> <p><strong>3) Pre-Intervention Survey on Critical Approach to AI</strong><br>- The first row of the dataset identifies the columns.<br>- Column A represents the participants iDs.<br>- Column B represents the participants’ assigned group [0: Control (ExpertFeedback) 1: Intervention (ChatGPTFeedback)].<br>- Column C represents the genders of the participants (1: Female, 2: Male).<br>- Column D represents the first-year repetition status of the participants (0: No, 1: Yes).<br>- Column E to J represent the responses of the participants to survey questions before the intervention. Each column is evaluated on a scale from 1 to 7. As it progresses from 1 to 7, the agreement status of participants to survey questions increases. (1: No agreement at all, 7: completely agree)</p> <p><strong>4) Post-Intervention Survey on Critical Approach to AI</strong><br>- The first row of the dataset identifies the columns.<br>- Column A represents the participants iDs.<br>- Column B represents the participants’ assigned group [0: Control (ExpertFeedback) 1: Intervention (ChatGPTFeedback)].<br>- Column C represents the genders of the participants (1: Female, 2: Male).<br>- Column D represents the first-year repetition status of the participants (0: No, 1: Yes).<br>- Column E to J represent the evaluation of the participants to survey questions after intervention. Each column is evaluated on a scale from 1 to 7. As it progresses from 1 to 7, the agreement status of participants to survey questions increases. (1: No agreement at all, 7: completely agree)</p>
Bulgarian dataset SafeFood4EU Feedback on Project and Course Developed
<p>These are the Bulgarian language results from the feedback of the SafeFood4EU project and course.</p>
Influence of Feedback Phase on Time Delay Signature and Chaos Bandwidth in a Laser subject to Dual Optical Feedback
<p>This archive provides all the data and information needed to reproduce the simulation results from the paper titled "Influence of Feedback Phase on Time Delay Signature and Chaos Bandwidth in a Laser subject to Dual Optical Feedback". You can access the paper on ArXiv: https://arxiv.org/abs/2311.14449</p> <p> </p> <p> </p>
Learning Feedback
Open the record for dataset details and reuse information.
Supplementary Table 8. Full Description of the Feedback Loops
Open the record for dataset details and reuse information.
Soil Moisture Dominates the Land Surface Feedback in the Development of Compound Drought-Heat Extremes in Tropical South America (Part 3)
<p>This dataset is for a <em>JHM</em> (<em>Journal of Hydrometeorology</em>) paper, titled "<strong>Soil Moisture Dominates the Land Surface Feedback in the Development of Compound Drought-Heat Extremes in Tropical South America</strong>".</p>
Soil Moisture Dominates the Land Surface Feedback in the Development of Compound Drought-Heat Extremes in Tropical South America (Part 2)
<p>This dataset is for a <em>JHM</em> (<em>Journal of Hydrometeorology</em>) paper, titled "<strong>Soil Moisture Dominates the Land Surface Feedback in the Development of Compound Drought-Heat Extremes in Tropical South America</strong>".</p>
Soil Moisture Dominates the Land Surface Feedback in the Development of Compound Drought-Heat Extremes in Tropical South America (Part 1)
<p>This dataset is for a <em>JHM</em> (<em>Journal of Hydrometeorology</em>) paper, titled "<strong>Soil Moisture Dominates the Land Surface Feedback in the Development of Compound Drought-Heat Extremes in Tropical South America</strong>".</p>
Koster et al. - Balance training in older adults enhances feedback control after perturbations - data & code
<p>Data & code for: </p> <p><strong><u>Balance training in older adults enhances feedback control after perturbations</u></strong></p> <p><em><u>R A J Koster, L Alizadehsaravi, W Muijres, S M Bruijn, N Dominici, J H van Dieen</u></em></p> <p> </p> <p>This folder contains two subfolders: <em>Code</em> & <em>Data</em>. The folder <em>Code</em> contains the MATLAB scripts used to analyse the data contained within the folder <em>Data</em>.</p> <p><strong>Scripts</strong>/</p> <p>· <strong>RK_Analysis_Kinematics.m</strong>: This is the main script used to analyse the kinematics. Running it will compute the kinematic parameters investigated in the paper and contrast them between conditions. Visualisations of these as well as the statistical results will be stored in the newly created folder <em>/Data/Processed/Figures/Kinematics/</em></p> <p>· <strong>RK_Analysis_Synergies.m</strong>: This is the main script used to analyse the EMG activity. Running it will compute the synergies from the EMGs and contrast them between conditions. Visualisations of these as well as the statistical results will be stored in the newly created folder <em>/Data/Processed/Figures/Synergies/</em></p> <p>· <strong>Subfunctions/</strong>: This folder contains all scripts used within the two main scripts. These scripts are subdivided into 3 folders (<em>General/, Kinematics analysis/, Synergy analysis/</em>) based on which part of the analysis they belong to.</p> <p>o <strong>General/spmi1d/</strong>: The external toolbox used to perform the statistics.</p> <p>o <strong>Kinematics analysis/=VU 3D model=/</strong>: the VU 3D model used to compute kinematic parameters from the trajectories of individual body segments.</p> <p><strong>Data/</strong></p> <p>· <strong>EMG/</strong>: Contains the raw EMG data for each participant & recording.</p> <p>· <strong>Excel sheets/</strong>: Contains participant & recording information sheets. This is used to determine on which leg the participant was standing.</p> <p>· <strong>PT/</strong>: Contains information about the rotating platform the participants were standing on for every recording. This is used to determine perturbation onset and direction.</p> <p>· <strong>Trajectory/</strong>: Contains the trajectories of the participant’s body segments during the recordings.</p> <p>· <strong>Processed/</strong>: (This folder is not there initially, but it is added by the 2 main codes) Will contain the kinematic parameters and synergy weights & activation patterns for every condition and participant after they are computed. This folder is also where the results from the analysis will be stored.</p> <p>o <strong>Figures/</strong>: For kinematics & synergies separately, will contain figures displaying the parameters and the statistical results. The ‘<em>Stats – X – Y.tiff</em>’ figure files display the ANOVA and post hoc test results. If statistical differences are found, their p-values are reported in the title of the individual plots (Note: for subthreshold results p-values are undefined in such analyses). Additional individual muscle analysis is included in the synergy analysis.</p>
ScienceDex guides
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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.