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1,457 results for “feedback”
UKESM1 Data accompanying "Chemistry-albedo feedbacks offset up to a third of forestation's CO2 removal benefit"
<p>UKESM1 Data accompanying "Chemistry-albedo feedbacks from reforestation reduce climate benefits and crop yields".</p> <p>The UKESM1_README provides an explanation of the diagnostics.</p> <p>These simulations were performed by Dr James Weber, University of Sheffield.</p> <p> </p>
CESM2 Data accompanying "Chemistry-albedo feedbacks offset up to a third of forestation's CO2 removal benefits."
<p>CESM2 Data accompanying "Chemistry-albedo feedbacks from reforestation reduce climate benefits and crop yields".</p> <p>The CESM2_README provides an explanation of the diagnostics.</p> <p>These simulations were performed by Dr James King, University of Sheffield.</p>
D6.6 - White goods pilot report - Test user feedback of 9 users
<p>During the pilot phase, usage and consumption data is generated during the use of the appliance and sent to the ConnectLife app. The logged data consists of, among others, store time stamps, program choice, program duration, washing temperature, and error codes, this provided a continuous stream of raw data. As a result of these insights, individual user consumption data can be compared to the average user consumption data of users (reference data). This allows for providing details feedback to users regarding their program choices and the timing of conducting washing cycles. In addition, average consumption values are included (in grey) in order to create a reference for evaluation and recommendation. The green and red smileys indicate if the test user is below or above the average value, resulting in a note on how to improve the environmental burden.</p>
Anthropogenic change decouples a freshwater predator's density feedback
<p>The datasets here were used to determine annual changes in anthropogenic environmental stressors, the prey-fish community, and the walleye population in Lake Erie and its western basin during 1969–2018. Here, we provide three datasets, which are used in the paper by Sinclair et al. titled: "Anthropogenic change decouples a freshwater predator's density feedback". Each dataset is provided as a separate tab in a single Excel worksheet. The first dataset ("Environmental stressors") provides the annual values for nutrient inputs, water transparency, temperature, and commercial walleye harvest. The second dataset ("Prey-fish community") provides the annual catch-per-unit-effort (CPUE; individuals per trawl minute) for all fish species that tend to be found in walleye diets caught in Lake Erie's western basin during 1969–2018 in fall (September-October) trawl surveys conducted by the Ohio Department of Natural Resources - Division of Wildlife. The third dataset ("Walleye") provides the annual estimated size of the age-3+ walleye population, the number of walleye growing degree days, and the average lengths of age-1 and age-2 walleye caught in gill net surveys conducted in the western basin by the Ohio Department of Natural Resources - Division of Wildlife. A summary and explanation of each variable are also provided in the "Info" tab. Further information on how values were calculated is provided in the methods and supporting information of the associated article.</p>
Simulations for manuscript Unravelling the forcing and feedbacks contributing to Pliocene Arctic warming in EC-Earth simulations
<p>Nine PlioMIP2 experiments were conducted to investigate the mid-Pliocene Arctic climate. These experiments considered three CO2 levels (280, 400 and 560 ppm), modern or Pliocene ice sheet conditions in Greenland and Antarctica and either prescribed or dynamic vegetation. Simulations were performed by the EC-Earth3-LR Veg climate model with a horizontal resolution of ~1.125°. The dataset contains selected output data from the simulations. Simulations were run for 1000 years. The last 200 years were used for analysis after the model reached an equilibrium as measured by global surface air temperature trend less than 0.05 K per century. </p> <p>The dataset contains Earth system model results from EC-Earth3 presented in the study by Power et al. (2023).</p>
Simulation results from WRF-Chem without aerosol feedbacks in eastern China for April-June 2017
<p>Simulation results from WRF-Chem without aerosol feedbacks in eastern China for April-June 2017 including meteorology and air quality,which are in wrfout_YYYY-MM-DD_00_00_00 file on each day for April-June 2017. The file contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB, PM2_5_DRY, o3, no2, so2, co, nh3, EXTCOF55 and ALT.</p>
Simulation results from WRF-Chem without aerosol feedbacks in eastern China for January-March 2017
<p>Simulation results from WRF-Chem without aerosol feedbacks in eastern China for January-March 2017 including meteorology and air quality,which are in wrfout_YYYY-MM-DD_00_00_00 file on each day for January-March 2017. The file contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB, PM2_5_DRY, o3, no2, so2, co, nh3, EXTCOF55 and ALT.</p>
Simulation results from WRF-Chem without aerosol feedbacks in eastern China for October-December 2017
<p>Simulation results from WRF-Chem without aerosol feedbacks in eastern China for October-December 2017 including meteorology and air quality,which are in wrfout_YYYY-MM-DD_00_00_00 file on each day for October-December 2017. The file contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB, PM2_5_DRY, o3, no2, so2, co, nh3, EXTCOF55 and ALT.</p>
Simulation results from WRF-Chem without aerosol feedbacks in eastern China for July-September 2017
<p>Simulation results from WRF-Chem without aerosol feedbacks in eastern China for July-September 2017 including meteorology and air quality,which are in wrfout_YYYY-MM-DD_00_00_00 file on each day for July-September 2017. The file contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB, PM2_5_DRY, o3, no2, so2, co, nh3, EXTCOF55 and ALT.</p>
Simulation results from WRF-CMAQ without aerosol feedbacks in eastern China for May-August 2017
<p>Simulation results from WRF-CMAQ without aerosol feedbacks in eastern China for May-August 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for May-August 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_CCTM_ACONC.zip:</p><p>Air quality file including CCTM_ACONC_v3852_YYYYMMDD.nc on each day for May-August 2017, which contains these variables:</p><p>ASO4I, ANO3I, ANH4I, ANAI, ACLI, AECI, ALVPO1I, ASVPO1I, ASVPO2I, ALVOO1I, ALVOO2I, ASVOO1I, ASVOO2I, AOTHRI, ASO4J, ANO3J, ANH4J, ANAJ, ACLJ, AECJ, AOTHRJ, AFEJ, ASIJ, ATIJ, ACAJ, AMGJ, AMNJ, AALJ, AKJ, ALVPO1J, ASVPO1J, ASVPO2J, AXYL1J, AXYL2J, AXYL3J, ATOL1J, ATOL2J, AXYL2J, AXYL3J, ATOL1J, ABNZ3J, AISO1J, AISO2J, AISO3J, ATRP1J, ATRP2J, ASQTJ, AALK1J, AALK2J, APAH1J, APAH2J, APAH3J, AORGCJ, AOLGBJ, AOLGAJ, ALVOO1J, ALVOO2J, ASVOO1J, ASVOO2J, ASVOO3J, APCSOJ, ASOIL, ACORS, ACLK, ASO4K, ANO3K, ANH4K, O3, SO2, NO2, CO, and NH3.</p><p>YYYYMM_CCTM_APMDIAG.zip:</p><p>Air quality file including CCTM_APMDIAG_v3852_YYYYMMDD.nc on each day for May-August 2017, which contains these variables:</p><p>PM25AT, PM25AC and PM25CO.</p><p>YYYYMM_CCTM_PHOTDIAG1.zip:</p><p>Air quality file including CCTM_PHOTDIAG1_v3852_YYYYMMDD.nc on each day for May-August 2017, which contains these variables:</p><p>OZONE_COLUMN, NO2_COLUMN, CO_COLUMN, SO2_COLUMN, HCHO_COLUMN, TROPO_O3_COLUMN and AOD_W550_ANGST.</p>
Allnighter pseudokinase-mediated feedback links proteostasis and sleep in Drosophila
<p>Data from sleep study of Drosophila Allnighter mutation. Relevant to Figure 7 of publication Shashank et al. Nature Communication (in press). Ethoscopes were used for video tracking of Drosophila activity. Resulting activity records are contained in the current datasets.</p>
Simulation results from WRF-CMAQ without aerosol feedbacks in eastern China for January-April 2017
<p>Simulation results from WRF-CMAQ without aerosol feedbacks in eastern China for January-April 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for January-April 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_CCTM_ACONC.zip:</p><p>Air quality file including CCTM_ACONC_v3852_YYYYMMDD.nc on each day for January-April 2017, which contains these variables:</p><p>ASO4I, ANO3I, ANH4I, ANAI, ACLI, AECI, ALVPO1I, ASVPO1I, ASVPO2I, ALVOO1I, ALVOO2I, ASVOO1I, ASVOO2I, AOTHRI, ASO4J, ANO3J, ANH4J, ANAJ, ACLJ, AECJ, AOTHRJ, AFEJ, ASIJ, ATIJ, ACAJ, AMGJ, AMNJ, AALJ, AKJ, ALVPO1J, ASVPO1J, ASVPO2J, AXYL1J, AXYL2J, AXYL3J, ATOL1J, ATOL2J, AXYL2J, AXYL3J, ATOL1J, ABNZ3J, AISO1J, AISO2J, AISO3J, ATRP1J, ATRP2J, ASQTJ, AALK1J, AALK2J, APAH1J, APAH2J, APAH3J, AORGCJ, AOLGBJ, AOLGAJ, ALVOO1J, ALVOO2J, ASVOO1J, ASVOO2J, ASVOO3J, APCSOJ, ASOIL, ACORS, ACLK, ASO4K, ANO3K, ANH4K, O3, SO2, NO2, CO, and NH3.</p><p>YYYYMM_CCTM_APMDIAG.zip:</p><p>Air quality file including CCTM_APMDIAG_v3852_YYYYMMDD.nc on each day for January-April 2017, which contains these variables:</p><p>PM25AT, PM25AC and PM25CO.</p><p>YYYYMM_CCTM_PHOTDIAG1.zip:</p><p>Air quality file including CCTM_PHOTDIAG1_v3852_YYYYMMDD.nc on each day for January-April 2017, which contains these variables:</p><p>OZONE_COLUMN, NO2_COLUMN, CO_COLUMN, SO2_COLUMN, HCHO_COLUMN, TROPO_O3_COLUMN and AOD_W550_ANGST.</p>
Simulation results from WRF-CMAQ without aerosol feedbacks in eastern China for September-December 2017
<p>Simulation results from WRF-CMAQ without aerosol feedbacks in eastern China for September-December 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for September-December 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_CCTM_ACONC.zip:</p><p>Air quality file including CCTM_ACONC_v3852_YYYYMMDD.nc on each day for September-December 2017, which contains these variables:</p><p>ASO4I, ANO3I, ANH4I, ANAI, ACLI, AECI, ALVPO1I, ASVPO1I, ASVPO2I, ALVOO1I, ALVOO2I, ASVOO1I, ASVOO2I, AOTHRI, ASO4J, ANO3J, ANH4J, ANAJ, ACLJ, AECJ, AOTHRJ, AFEJ, ASIJ, ATIJ, ACAJ, AMGJ, AMNJ, AALJ, AKJ, ALVPO1J, ASVPO1J, ASVPO2J, AXYL1J, AXYL2J, AXYL3J, ATOL1J, ATOL2J, AXYL2J, AXYL3J, ATOL1J, ABNZ3J, AISO1J, AISO2J, AISO3J, ATRP1J, ATRP2J, ASQTJ, AALK1J, AALK2J, APAH1J, APAH2J, APAH3J, AORGCJ, AOLGBJ, AOLGAJ, ALVOO1J, ALVOO2J, ASVOO1J, ASVOO2J, ASVOO3J, APCSOJ, ASOIL, ACORS, ACLK, ASO4K, ANO3K, ANH4K, O3, SO2, NO2, CO, and NH3.</p><p>YYYYMM_CCTM_APMDIAG.zip:</p><p>Air quality file including CCTM_APMDIAG_v3852_YYYYMMDD.nc on each day for September-December 2017, which contains these variables:</p><p>PM25AT, PM25AC and PM25CO.</p><p>YYYYMM_CCTM_PHOTDIAG1.zip:</p><p>Air quality file including CCTM_PHOTDIAG1_v3852_YYYYMMDD.nc on each day for September-December 2017, which contains these variables:</p><p>OZONE_COLUMN, NO2_COLUMN, CO_COLUMN, SO2_COLUMN, HCHO_COLUMN, TROPO_O3_COLUMN and AOD_W550_ANGST.</p>
Simulation results from WRF-CHIMERE without aerosol feedbacks in eastern China for October-December 2017
<p>Simulation results from WRF-CHIMERE without aerosol feedbacks in eastern China for October-December 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for October-December 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for October-December 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
Simulation results from WRF-CHIMERE without aerosol feedbacks in eastern China for July-September 2017
<p>Simulation results from WRF-CHIMERE without aerosol feedbacks in eastern China for July-September 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for July-September 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for July-September 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
Simulation results from WRF-CHIMERE without aerosol feedbacks in eastern China for January-March 2017
<p>Simulation results from WRF-CHIMERE without aerosol feedbacks in eastern China for January-March 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for January-March 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for January-March 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
Simulation results from WRF-CHIMERE without aerosol feedbacks in eastern China for April-June 2017
<p>Simulation results from WRF-CHIMERE without aerosol feedbacks in eastern China for April-June 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for April-June 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for April-June 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
Using Formative Assessment and Feedback from Student Response Systems (SRS) to Equitably Revise Statistics Instruction
<p>Instructors are increasingly using interactive student response systems (SRS) to foster active learning and deepen student understanding in statistics education. Yet most studies focus on either the benefits of SRS or on how <em>students</em> can receive and use feedback, rather than on how <em>instructors</em> can use formative assessment data to inform instructional revisions in the pursuit of reaching more learners and creating a more equitable and inclusive classroom. This dataset is being publicly shared to accompany a manuscript submission that draws from a case study of an introductory statistics course to present a framework for how an instructor can engage in what I call a <em>reciprocal formative assessment and feedback cycle </em>using SRS. This cycle includes adopting a structured approach of reflecting on student misconceptions through guided questions like who to reteach, what to reteach, when to reteach, and how to reteach course content – all with the goal of increasing learning for all students.</p>
SRL data feedback in higher education and validation of microlearning proposal
<p>This dataset corresponds to those used for the SRL in the category Feedback. It also presents the data collected from a process of expert validation of a training proposal based on microlearning for the development of digital teacher competence related to feedback. </p>
Dataset: What can be concluded from User Feedback? - An Empirical Study
<p>In 2022, we conducted a study on the awareness of users concerning existing software in order to compare users' description and their feedback.</p> <p>This data set contain 100 participants' description and feedback about the Komoot hiking app. The aspects were manually coded in the data set.</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.