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145 results for “nudging”
User study data: Nudges to Mitigate Confirmation Bias during Web Search for Opinion Formation, automatic vs. reflective study
<p>Data of two user studies (282 and 307 participants), investigating the risks and benefits of warning labels with and without obfuscations to mitigate confirmation bias during web search on debated topics.</p> <p> </p> <p>Study Variables (study 1 and study 2)</p> <p> </p> <p> display_con: Search result display<br> - Study 1<br> - 1: targeted warning label with obfuscation<br> - 2: random warning label with obfuscation<br> - 3: regular (no intervention)<br> - Study 2<br> - 1: targeted warning label with obfuscation<br> - 2: targeted warning label without obfuscation<br> - 3: random warning label with obfuscation<br> - 4: random warning label without obfuscation<br> - 5: regular (no intervention)<br>- CRT_cat: Cognitive reflection<br> - 1: intuitive<br> - 2: analytic<br>- topic: Assigned debated topic<br> - 1: Is drinking milk healthy for humans? <br> - 2: Is homework beneficial?<br> - 3: Should people become vegetarian?<br> - 4: Should students have to wear school uniforms?<br>- clicksup_prop: Clicks on attitude-confirming (AC) search results (proportion of all clicks)<br>- clickwarn_prop: Clicks on warning label (WL) search results (proportion of all clicks)<br>- show_clicked: Clicks on show-button (number of clicks, only in conditions with obfuscation)<br>- accuracy_bias: Accuracy bias estimation (Difference between a) observed bias (as the proportion of attitude-confirming clicks) and b) perceived bias (reported in the post-interaction questionnaire and re-coded into values from 0 to 1), positive values indicate an overestimation of bias)<br>- att_change: Attitude change (Difference between attitude reported in the pre-interaction questionnaire and the post-interaction questionnaire. Negative values indicate an attitude change in the attitude-opposing direction, while positive values indicate an attitude strengthening in the attitude-supporting direction.)<br>- knowledge_1: Self-reported prior knowledge (Reported on a seven-point Likert scale ranging from non-existent to excellent as a response to how they would describe their knowledge on the topic they were assigned to)<br>- N_clicks: Cumulative clicks (Number of all clicks on search results)<br>- NFC: Need for Cognition (Mean response to 4-item subset of the NFC questionnaire)<br>- UX_usability: Usability (Mean of responses on a seven-point Likert scale to the module "usability"from the meCUE 2.0 questionnaire)<br>- UX_usefulness: Usefulness (Mean of responses on a seven-point Likert scale to the module "usefulness"from the meCUE 2.0 questionnaire)</p>
ECHAM6-wiso nudged simulation water isotopes and precipitation for the period 1990-2020 at the EastGRIP drilling location, Greenland
<p>This model dataset contains output produced with the isotope-enabled atmosphere GCM ECHAM6-wiso at T127L95 spatial resolution, nudged to the ERA-5 reanalysis product. The 6-hourly model output is provided for the period 01/1990-12/2020 for the grid cell containing EastGRIP drilling location in Greenland, centered at 75.27N, -36.57 E.</p> <p>The complete description of the simulation can be found in:</p> <p><em>Cauquoin, A. and Werner, M., 2021. High‐Resolution Nudged Isotope Modeling With ECHAM6‐Wiso: Impacts of Updated Model Physics and ERA5 Reanalysis Data. Journal of Advances in Modeling Earth Systems, </em><a href="https://doi.org/10.1029/2021MS002532">https://doi.org/10.1029/2021MS002532</a></p> <p>The provided files (netCDF) contain the ECHAM6-wiso model data used as input for the SNOWISO snow pack model in:</p> <p><em>Dietrich, L.J., Steen-Larsen, H.C., Wahl, S., Jones, T.R., Town, M.S. and Werner, M., 2023. Snow-atmosphere humidity exchange at the ice sheet surface alters annual mean climate signals in ice core records. Geophysical Research Letters, </em><a href="https://doi.org/10.1029/2023GL104249">https://doi.org/10.1029/2023GL104249</a><em>.</em></p> <p>The provided variables are:</p> <p> d18O_vapor: delta value for <sup>18</sup>O (‰) in the vapor of the lowest atmospheric layer (ECHAM level 95).<br> dD_vapor: delta value for H<sub>2</sub> (D) (‰) in the vapor of the lowest atmospheric layer (ECHAM level 95).<br> aprt: total precipitation (mm water equivalent per month)<br> d18O_precip: delta value for <sup>18</sup>O (‰) in the precipitation<br> dD_precip: delta value for H<sub>2</sub> (D) (‰) in the precipitation</p> <p><strong>Data usage notice:</strong></p> <p>If you use<strong> any of these data</strong> you should refer to:</p> <p><em>Cauquoin, A. and Werner, M., 2021. High‐Resolution Nudged Isotope Modeling With ECHAM6‐Wiso: Impacts of Updated Model Physics and ERA5 Reanalysis Data. Journal of Advances in Modeling Earth Systems, </em><a href="https://doi.org/10.1029/2021MS002532">https://doi.org/10.1029/2021MS002532</a></p>
Simulated Sea Ice and Snow Thickness along the MOSAIC drift trajectory, from AWI-CM-1 and AWI-CM-3 nudged simulations.
<p>Sea ice thickness and snow (on sea ice) thickness from nudged simulations performed using the coupled climate models AWI-CM-1 (zonal wavenumber truncated at 20) and AWI-CM-3 (T20 truncation; Pithan et al., 2023) with a 1h relaxation time. The model data is collocated to the drift trajectory of the Multidisciplinary Drifting Observatory for the Study of the Arctic Climate (MOSAIC) across the Arctic Ocean, from 01 September 2019 until 31 August 2020. The collocation is done daily, by finding all model grid cells within the area covered by the distributed network of snow and sea ice measuring instruments deployed and maintained during MOSAIC. The sea ice and snow thickness is then spatially averaged for each day. </p><p>Data is provided in three .nc files for each model and variable (m_ice for sea ice thickness, m_snow for snow thickness) representing ensemble members 1 to 3.</p>
ECHAM6-wiso and ECHAM5-wiso nudged simulation data for the period 1979-2018
<p>This data set contains model values from 4 simulations produced with the isotope-enabled atmosphere GCMs ECHAM5-wiso and ECHAM6-wiso for the period 1979-2018. These simulations been performed at different spatial resolutions (T63 and T127) or with different reanalyses for the nudging (ERA5 and ERA-Interim). A complete description can be found in Cauquoin, A. and Werner, M. (2021). High-resolution nudged isotope modeling with ECHAM6-wiso: Impacts of updated model physics and ERA5 reanalysis data. <em>J. Adv. Model. Earth Syst.</em>, <strong>13</strong>, e2021MS002532, <a href="https://doi.org/10.1029/2021MS002532">https://doi.org/10.1029/2021MS002532</a>.</p> <p> </p> <p>The 4 performed simulations, described in Cauquoin and Werner (JAMES, 2021), are: </p> <p>- E6_LR_ERA5: ECHAM6-wiso at T63L47 spatial resolution, nudged to ERA5.</p> <p>- E6_LR_ERAI: ECHAM6-wiso at T63L47 spatial resolution, nudged to ERA-Interim.</p> <p>- E5_LR_ERA5 : ECHAM5-wiso at T63L47 spatial resolution, nudged to ERA5.</p> <p>- E6_HR_ERA5: ECHAM6-wiso at T127L95 spatial resolution, nudged to ERA5.</p> <p> </p> <p>The files are in netcdf or excel format:</p> <p>- *.temp2_timmean.nc: annual mean 2m air temperature (°C)</p> <p>- *.d18Op_timmean.nc: annual mean d18O of precipitation (permil)</p> <p>- *.dexp_timmean.nc: annual mean d-excess of precipitation (permil)</p> <p>- E6_LR_ERA5.precip_timmean.nc and E6_LR_ERA5.precip_timmean.nc: annual mean precipitation from ECHAM6-wiso T63L47 (mm/month)</p> <p>- E6_LR_ERA5.d18Oqvi_timmean.nc and E6_LR_ERA5.d18Oqvi_timmean.nc: annual mean d18O of vertically integrated water vapor from ECHAM6-wiso T63L47 (permil)</p> <p>- E6_LR_ERA*.qtot_*_timmean.nc: u and v components of annual mean water vapor transport from ECHAM6-wiso T63L47 (kg/m/s)</p> <p>- E6_LR_ERA*_tropics_mermean.q.nc: meridional mean between 15°S and 15°N of the modeled annual mean specific humidity from ECHAM6-wiso T63L47 (kg/kg)</p> <p>- E6_LR_ERA*_tropics_mermean.d18Oq.nc: meridional mean between 15°S and 15°N of the modeled annual mean d18O of water vapor from ECHAM6-wiso T63L47 (permil)</p> <p>- echam_wiso_seasonal_signals_*.xlsx: modeled monthly mean variations of 2m air temperature, precipitation, d18O of precipitation and d-excess of precipitation according to the 4 simulations at Ankara, Belem, Halley Bay, Reykjavik, Valentia and Vienna for the period 1979-2018.</p> <p>- echam_wiso_subdaily_signals_*.xlsx: modeled (sub-)daily variations surface specific humidity, d18O of surface water vapor and d-excess of surface water vapor according to the 4 simulations at Ankara, Mase, Niwot Ridge and Summit.</p>
ECHAM6-HAM2 nudged simulation daily data using SMOGv1 India inventory
<p>The variables mentioned below are taken from present-day and pre-industrial simulations. Hence the computations are applicable for both of them<br> All the input variables are extracted to the Indian region during the south Asian monsoon season (JJAS) </p> <p>1. Precipitation daily data is prepared by adding the variables like 'aprl' and 'aprc' from echam.nc model output <br> 2. cloud droplet effective radiyus (CDER) at 850hPa is taken from model output 'filename_activ.nc'<br> 3. Cloud liquid water path (CLWP) is computed as the sum of cloud liquid water from the surface to top of the atmosphere and their units (kg. Kg-1) converted into (Kg/m3) <br> 4. Cloud condensation nuclei at 850hPa is extracted from the file 'file_activ.nc'<br> 5. Lower tropospheric stability computed as the difference of potential temperature from the pressure levels 1000-700hPa<br> 6. The variables like specific humidity, u wind and v wind is used for computing vertically integrated moisture flux<br> 7. Convective available potential energy (CAPE) is computed using specific humidity (q), surface temperature, geopotential height, surface pressure <br> 8. Black carbon concentration is computed as below<br> step.1: Add BC_KS+BC_AS+BC_CS+BC_KI<br> Step.2: Converting (kg. Kg-1) into (Mg/m3)<br> Step.3: Integrate the above values for the pressure levels 1000 to 850hPa<br> 9. Same as followed for SO4</p>
Nudging the tropical Indian Ocean Applied to the IPSL-CM6A-LR model - Control Experiments
<p>This experiment applies monthly nudging of -2, -1, +1, and +2 degree C to the tropical Indian Ocean region in the IPSL-CM6A-LR pre-industrial control run. The methods of nudging are defined in Ferster et al. (2021), where we apply a constant nudging and refer to the experiments as TIOxC, where "x" is the monthly nudging value (as in Ferster et al., 2021). Additional experiments were simulated for a paper within the submission process (to be included and updated within future versions) including the "TIOc" and "C0C" experiments, where the TIOC nudges towards towards the time-mean climatology and the C0C nudges at each monthly time step towards the pre-industrial control's corresponding monthly value (similar to adding white noise at each time step).</p> <p>The datasets are included from the experiments to help share our results and reproducibility of the figures within our manuscripts, the original model output is stored on the TGCC computing machine Irene. All datasets are saved on this data repository are saved as annual means! These datasets represent the C0C (TIO=0C) and TIOc control experiments.</p> <p>Please use the additional thredds link to access the full IPSL output for the experiments:</p> <p>https://thredds-su.ipsl.fr/thredds/catalog/tgcc_thredds/store/fersterb/IPSLCM6/DEVT/TIO-Experiment/Control-0C-TIO/1950-01-01/Control-0C-TIO1950-01/catalog.html</p> <p>https://thredds-su.ipsl.fr/thredds/catalog/tgcc_thredds/store/fersterb/IPSLCM6/DEVT/TIO-Experiment/TIOc/1950-01-01/TIOc1950-01/catalog.html</p> <p>The pre-industrial control from IPSL can be found on the CMIP6-ESGF servers and the TGCC computing machine Irene.</p> <p>Please contact us for additional explanations, datasets, or collaboration.</p> <p>*The datasets are created from the IPSL-CM6A-LR output files</p> <p>**This version serves as a preliminary version of the dataset for publication purposes, additional output is available and the experiment output exceeds the 50gb of storage offered through this platform (TGCC Irene).</p> <p>***Contact: brady.ferster@locean.ipsl.fr or brady.ferster@yale.edu</p>
Nudging the tropical Indian Ocean Applied to the IPSL-CM6A-LR model - Cooling Experiments
<p>This experiment applies monthly nudging of -2, -1, +1, and +2 degree C to the tropical Indian Ocean region in the IPSL-CM6A-LR pre-industrial control run. The methods of nudging are defined in Ferster et al. (2021), where we apply a constant nudging and refer to the experiments as TIOxC, where "x" is the monthly nudging value (as in Ferster et al., 2021). Additional experiments were simulated for a paper within the submission process (to be included and updated within future versions) including the "TIOc" and "C0C" experiments, where the TIOC nudges towards towards the time-mean climatology and the C0C nudges at each monthly time step towards the pre-industrial control's corresponding monthly value (similar to adding white noise at each time step).</p> <p>The datasets are included from the experiments to help share our results and reproducibility of the figures within our manuscripts, the original model output is stored on the TGCC computing machine Irene. All datasets are saved on this data repository are saved as annual means! These datasets represent the -2 and -1 degree C experiments.</p> <p> </p> <p>Please use the additional thredds link to access the full IPSL output for the experiments:</p> <p>https://thredds-su.ipsl.fr/thredds/catalog/tgcc_thredds/store/fersterb/IPSLCM6/DEVT/TIO-Experiment/M1C-TIO/1950-01-01/M1C-TIO1950-01/catalog.html</p> <p>https://thredds-su.ipsl.fr/thredds/catalog/tgcc_thredds/store/fersterb/IPSLCM6/DEVT/TIO-Experiment/M2C-TIO/1950-01-01/M2C-TIO1950-01/catalog.html</p> <p>The pre-industrial control from IPSL can be found on the CMIP6-ESGF servers and the TGCC computing machine Irene.</p> <p>Please contact us for additional explanations, datasets, or collaboration.</p> <p>*The datasets are created from the IPSL-CM6A-LR output files</p> <p>**This version serves as a preliminary version of the dataset for publication purposes, additional output is available and the experiment output exceeds the 50gb of storage offered through this platform (TGCC Irene).</p> <p>***Contact: brady.ferster@locean.ipsl.fr or brady.ferster@yale.edu</p>
Nudging Repository – Set of Nudges (DyMoN Project)
<p>This file provides the DyMoN nudging repository that is a set of digital behaviour change techniques (i.e., nudges) which are suitable to be used as push notifications for a mobile application in order to motivate sustainable mobility (walking, bicycling, public transport).</p>
Implementation of Nudges to Promote Utilization of Low Tidal Volume Ventilation (INPUT) Study
ClinicalTrials.gov study NCT04663802. IPD Sharing: YES. Countries: 1. Publications: 1.
Personalized Patient Data and Behavioral Nudges to Improve Adherence to Chronic Cardiovascular Medications
ClinicalTrials.gov study NCT03973931. IPD Sharing: YES. Countries: 1. Publications: 18.
Storyline data used in the paper "The July 2019 European heatwave in a warmer climate: Storyline scenarios with a coupled model using spectral nudging"
<p>We provide the storyline data (in NetCDF format) used in the paper: “The July 2019 European heatwave in a warmer climate: Storyline scenarios with a coupled model using spectral nudging” published in Journal of Climate. The data is structured in four .tar.gz files (Preindustrial, Present, 2 and 4 K warmer climates) containing all variables used in this each climate. The data from the five ensemble members (E1 to E5) have been included separately in 3-months files.</p> <p>Atmospheric variables (Files are named as: {variable}_E{ensemble member}_{starting month}{year}.nc:</p> <ul> <li> <p>Latent heat flux (ahfl)</p> </li> <li> <p>Sensible heat flux (ahfs)</p> </li> <li> <p>Monthly Global Mean 2m Temperature (GMTT2mMonthly)</p> </li> <li> <p>Maximum 2m Temperature (t2max)</p> </li> <li> <p>Mean 2m Temperature (t2mean)</p> </li> <li> <p>Minimum 2m Temperature (t2max)</p> </li> <li> <p>Soil Wetness (ws)</p> </li> </ul> <p> Only for present climate:</p> <ul> <li> <p>850 hPa Temperature (T850)</p> </li> <li> <p>Total Cloud Cover (TCC)</p> </li> <li> <p>500 hPa Geopotential Height (Z500)</p> </li> </ul> <p>Five layers soil moisture (Only for present climate, Files are named as: From20172019in2017Climatessp370{ensemble member}_{year}{starting month}.01_jsbid.nc) </p> <p>Oceanic variables (from FESOM, Files are named as: {variable}_E{ensemble member}_{year}{starting month}01.nc:</p> <ul> <li> <p>Sea Ice Concentration (SIC)</p> </li> <li> <p>Sea Surface Temperature (SST)</p> </li> </ul> <p><strong>Please, note that FESOM uses an unstructured mesh.</strong></p>
Unified Model GA 7.0 Nudging Experiments used in JGR-A publication "Investigating Tropical versus Extratropical Influences on the Southern Hemisphere Tropical Edge in the Unified Model", Freisen et al. (2022)
<p><strong>UK Met Office Unified Model (UM) Global Atmosphere version 7.0 (GA7.0) Nudging Experiments </strong><strong>used in </strong><strong>"Investigating Tropical versus Extratropical Influences on the Southern Hemisphere Tropical Edge in the Unified Model" published in Journal of Geophysical Research: Atmospheres (doi.org/10.1029/2021JD036106).</strong></p> <p>Please refer to UM_nudging_experiments_readme.txt.</p>
Monthly-Mean Model Output for Paper Titled "Do Nudging Tendencies Depend on the Nudging Timescale Chosen in Atmospheric Models?"
<p>These tarballs contains monthly-mean model output, which was primarily what was presented in the AGU JAMES paper titled "Do nudging tendencies depend on the nudging timescale chosen in atmospheric models?". Also included are the scripts used to set up these simulations, allowing reproducibility of the portion of the paper that used 3-hourly output. The 3-hourly output was not included, as it totaled ~7TB.</p>
Oservational data for sfdda nudging analysis in WRF model over China during 2017
<p>Oservational data for sfdda nudging analysis in WRF model over China during 2017.</p>
Webis-Nudged-Questions-23
<p>Accompanying data for the paper "Guiding Oral Conversations: How to Nudge Users Towards Asking Questions?" as a result of the crowdsourcing study.</p>
Using nudges to promote clinical decision making of healthcare professionals: A scoping review
<p>Nudging has been discussed in the context of policy and public health, but not so much within healthcare and how it can be applied to change the behavior of healthcare professionals (HCPs). This scoping review aimed to assess the empirical evidence on how nudging techniques can be used to affect the behavior of HCPs in clinical settings.</p> <p>A systematic database search was conducted for the period January 2010-December 2020 using the PRISMA extension for Scoping Review checklist. Two reviewers independently screened each article for inclusion. Included articles were reviewed to extract key information about each intervention, including purpose, target behavior, measured outcomes, key findings, nudging strategies used, and their theoretical underpinnings. Two independent dimensions were used to describe nudging strategies according to action and timing where each of the identified nudging strategies and objectives was characterized as active, passive, synchronous, or asynchronous. </p> <p>Of the included 51 articles, 40 reported statistically positive results, six reported no success and two reported mixed results. Thirteen different nudging strategies were identified aimed at modifying four types of HPCs behavior:prescriptions/orders, procedure, hand hygiene, and vaccination. The most common nudging strategy used involved default/pre-order nudges, followed by alerts/reminders and active choice. Both passive and active nudges showed positive results, highlighting the importance of long-term studies that evaluate if the desired outcome is maintained. Careful ethical consideration should however be given to passive strategies where the user might not be aware of the nudge. </p>
QBO nudging simulation
<p>Zonal wind, meridional wind and temperature derived from the CESM1.0.6 simulation with a 28-month fixed cycle zonal wind as a Quasi-Biennial Oscillation (QBO) forcing.</p>
Code and Data for Sturm and Silva (2024) A nudge to the truth: atom conservation as a hard constraint in models of atmospheric composition using an uncertainty-weighted correction
<p>This record contains the Julia photochemical model (https://doi.org/10.5281/zenodo.13385541) output in csv format used for training XGBoost in ProjectionConservationRF.py to emulate ozone photochemical formation. Nonphysical predictions that violate conservation of atoms are corrected using a closed-form, constrained least-squares approach that factors in uncertainty and scale using species-level weights. The file ozoneNOx_visualization.py contains an example and visualization for a smaller system, the primary photolytic cycle from which the Leighton relationship can be derived.</p> <p>The corresponding preprint is available here: <a href="https://doi.org/10.48550/arXiv.2408.16109">https://doi.org/10.48550/arXiv.2408.16109</a></p>
Nudging healthcare professionals to improve treatment of COVID-19: a narrative review
<p>The dataset is a complete description of all included and excluded studies in a narrative review synthesizing the available literature 2010-2020 on how nudging techniques can be used to affect the behavior of HCPs in clinical settings in order to see if these can be useful in the prevention and treatment of COVID-19. </p>
Data and code info for: Reducing single-use cutlery with green nudges: Evidence from China's food delivery industry
<p>The rising consumer demand for online food delivery has significantly increased the consumption of disposable cutlery, leading to much greater plastic pollution worldwide. This study investigates the impact of green nudges on single-use cutlery consumption in China. Collaborating with Alibaba's food delivery platform Eleme (similar to Uber Eats and DoorDash), we analyzed detailed customer-level data and found that the green nudges — changing the default to "No Cutlery" and rewarding consumers with "green points" — increased the share of "No Cutlery" orders by 648%. The aggregate environmental benefits are significant: if the green nudges were applied to all of China, more than 21.75 billion sets of single-use cutlery could be saved every year, equivalent to a 20.4% of plastic waste reduction in the food delivery industry or a 6.12% reduction in China's total municipal plastic waste.</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
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.