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1,457 results for “feedbacks”
Dataset for "Low-cloud feedback in CAM5-CLUBB: physical mechanisms and parameter sensitivity analysis"
<p>This repository contains the data of 512 perturbed-parameter ensemble experiments and CAM5-CLUBB default experiments for the paper "Low-cloud feedback in CAM5-CLUBB: physical mechanisms and parameter sensitivity analysis".</p> <p>In this paper, the quasi-Monte Carlo (QMC) sampling approach is applied to explore the high-dimensional space. 512 samples are generated with the 18 perturbed parameters. For each parameter sample, a pair of experiments is performed: the control one is based on the climatological sea surface temperature (SST), and the 4K experiment applies a uniform +4K SST perturbation to the control experiment. The total of 1024 simulations are then performed. In addition, CAM5-CLUBB default experiments that adopt the default values of the 18 selected parameters as in Bogenschutz et al. (2013) are performed to provide detailed model diagnostics for analyzing physical mechanisms of the cloud feedback, and they include both control and +4K simulations. Each simulation is run for 5 years and 4 months, forced by climatological SSTs. Monthly mean results from the last 5 years are analysed in this study.</p> <p>Note: data uploaded here is annual-mean and the dimension name 'time' in the files (CAM5-CLUBB_PPE_512*.nc) is the number of 512 PPE member.</p>
Dataset: Feedback contribution to surface motion perception in the human early visual cortex
<p><strong>Dataset</strong></p> <p>Dataset accompanying the manuscript "Feedback contribution to surface motion perception in the human early visual cortex" (<a href="https://doi.org/10.1101/653626">biorxiv</a>).</p> <p><strong>Description</strong></p> <p>fMRI data are arrange by subject (following BIDS convention). For each subject, there are subfolders for anatomical and functional MRI data.</p> <p>├── sub-01<br> │ ├── anat<br> │ │ └── ...<br> │ ├── func<br> │ │ └── ...<br> │ ├── func_se<br> │ │ └── ...<br> │ └── func_se_op<br> │ └── ...</p> <p>The subfolder 'anat' contains four images from the MP2RAGE sequence (among these, T1 and proton-density weighted images). The subfolder 'func' contains the functional data (GE EPI, T2* weighted) from the main experiment (i.e. the data from which the haemodynamic response was estimated, and on which statistical analysis was performed). The subfolders 'func_se' and 'func_se_op' contain SE EPI images with opposite phase encode polarity that were used for distortion correction. Moreover, for each image/timeseries there is a json file with metadata.</p> <p>Anatomical images have been masked anteriorly (defaced). Functional images are in coronal oblique orientation, covering early visual cortex.</p> <p>The folder 'stimuli' contains information on the stimuli used for retinotopic mapping, including timecourse models used for population receptive field mapping. (These files are included here because of their relatively large file size, which would make distribution via a git repository impractical.) The software used for the presentation of retinotopic mapping stimuli (and for the corresponding analysis) is available on <a href="https://github.com/ingo-m/pyprf">github</a>.</p> <p>For example videos of the main experimental stimuli, see <a href="https://doi.org/10.5281/zenodo.2583017">zenodo.2583017</a>. If you would like to reproduce the experimental stimuli, the respective PsychoPy code can be found on <a href="https://github.com/ingo-m/PacMan/tree/master/stimuli/experiment">github</a>.</p> <p>The exact timing of events during the experiments (rest & stimulus blocks, target events) can be found in FSL-style design matrices ("3 column format") on <a href="https://github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata">github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata</a>.</p> <p><strong>Analysis</strong></p> <p>The analysis pipeline makes use of several MRI software packages (such as SPM and FSL for preprocessing, and CBS tools for cortical depth sampling). In order to facilitate reproducibility, the entire analysis was containerised using docker. Because of licensing issues, the docker images with the third-party software cannot be directly made available. However, the docker files and detailed instructions for the creation of the docker images are available on <a href="https://github.com/ingo-m/PacMan/tree/master/docker">github</a>.</p> <p>If you would like to reproduce the analysis, the first step will be to create the docker images (which provide an exact copy of the system environment that was used to conduct the published analysis). There are two docker images, one for the main analysis (motion correction, distortion correction, GLM fitting; named "dockerimage_pacman_jessie"), and another one for the depth sampling (named "dockerimage_cbs"). Detailed instructions on how to create the docker images can be found <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_PacMan_Image_Jessie.txt">here</a> and <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_CBS_Image.txt">here</a>.</p> <p>Once you set up the docker images, the analysis can be run automatically. For each subject, there is one parent script for the main analysis (e.g. <a href="http://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_01.sh">~/analysis/20180118/metascript_01.sh</a> for subject 20180118) and a separate script for the depth sampling (e.g. <a href="https://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_03.sh">~/analysis/20180118/metascript_03.sh</a>). The only manual adjustments you should have to perform to reproduce the analysis is to change the file paths in the first section of these scripts ('pacman_anly_path' is the parent directory containing the analysis code, i.e. the git repository, and 'pacman_data_path' is the parent directory containing the MRI data). The main analysis (metascript_01.sh) should take about 24 h per subject on a workstation with 12 cores, and the depth sampling (metascript_02.sh) about 2 h. The analysis can be run on consumer-grade hardware, but some parts of the analysis may not run with less than 16 GB of RAM (recommended: 32 GB).</p> <p>Visualisations (e.g. cortical depth profiles and signal timecourses) and group-level statistical tests are implemented in <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">py_depthsampling</a>.</p> <p><strong>Further resources</strong></p> <p>Please refer to the research paper for more details: <a href="https://doi.org/10.1101/653626">https://doi.org/10.1101/653626</a></p> <p>The analysis pipeline can be found on <a href="https://github.com/ingo-m/PacMan">https://github.com/ingo-m/PacMan</a></p> <p>A separate repository contains the code used for visualisation of depth-sampling results: <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">https://github.com/ingo-m/py_depthsampling/tree/PacMan</a></p> <p>Free & open source software package for population receptive field mapping: <a href="https://github.com/ingo-m/pyprf">https://github.com/ingo-m/pyprf</a></p> <p> </p>
Feedback from experts on participatory governance
<p>Feedback from of nine experts, conducted in the context of task 2.5 of the PANTHEON project, to assess the current practices on participatory governance and the techniques and experiences in how to involving civilian in risk management. The respondents are with public organizations, associations, first responders and researchers involved in risk management and research projects dealing with emergency response. (Contact: Simon.Bittner@johanniter.at)</p> <div></div>
Maintenance of Convectively Coupled Kelvin waves: Relative Importance of Internal Thermodynamic Feedback and External Momentum Forcing (Code and Data)
<p>This is the dataset and code for generating all figures for the journal article named "Maintenance of Convectively Coupled Kelvin Waves: Relative Importance of Internal Thermodynamic Feedback and External Momentum Forcing," The article was written by Mu-Ting Chien and Daehyun Kim and submitted to Geophysical Research Letters in 2024.</p>
Multimodal poster presentation feedback dataset
<p>This is a dataset used for a multimodal poster presentation experiment and data analysis performed at the Division of Speech, Music and Hearing (TMH) at KTH Royal Institute of Technology in Stockholm Sweden in 2019 and 2020. The compressed elan_files.zip file contains raw ELAN annotated data, and the various .csv files contain formatted data for training for statistical learning models. Used in an upcoming publication.</p>
Supporting data for the manuscript: "The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback"
<p>This repository contains supporting data for the manuscript "The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback" in <em>Geophysical Research Letters</em>. Detailed descriptions of these datasets can be found in the manuscript text as well as in the file descriptions.</p>
Raw data: Local-scale feedbacks influencing cold-water coral growth and subsequent reef formation
<p>Spreadsheets with the raw data of ADV-measured current velocity, coral growth derived from buoyant weight measurements and stress-related protein activities and concentrations.</p>
Reproducibility package for Using root economics traits to predict biotic plant soil-feedbacks
<p>Using root economics space to predict biotic plant soil-feedbacks presents a novel framework linking below ground ecological theory to plant soil feedback effects. We show how to calculate root functional distance and location of two plant species in root economics space and how these measures can help to predict the strength and direction of the plant soil feedback between them. </p> <p>Contains data and scripts to reproduce analysis and figures for the manuscript (https://github.com/ggpmrutten/linkingRES-PSF)</p>
Replication Package: Exploring the Relationship Between Personality Traits and User Feedback
<p>This is the replication package for the paper titled 'Automated User Feedback Analysis: Processing the Feedback Quantity and Quality' accepted for the AffectRE23 workshop track at RE 2023.</p> <p>Full abstract:</p> <p>Previous research has studied the impact of developer personality in different software engineering scenarios, such as team dynamics and programming education. However, little is known about how user personality affect software engineering, particularly user-developer collaboration. Along this line, we present a preliminary study about the effect of personality traits on user feedback. 56 university students provided feedback on different software features of an e-learning tool used in the course. They also filled out a questionnaire for the Five Factor Model (FFM) personality test. We observed some isolated effects of neuroticism on user feedback: most notably a significant correlation between neuroticism and feedback elaborateness; and between neuroticism and the rating of certain features. The results suggest that sensitivity to frustration and lower stress tolerance may negatively impact the feedback of users. This and possibly other personality characteristics should be considered when leveraging feedback analytics for software requirements engineering.</p>
Dataset for "Separate measurement- and feedback-driven entanglement transitions in the stochastic control of chaos"
<p>Raw datasets used in the paper "Separate measurement- and feedback-driven entanglement transitions in the stochastic control of chaos". Use with the GitHub repository to reproduce results and figures from the referenced paper (https://github.com/clema12/CliffordBernoulli)</p>
Acoustic Data for Endotracheal Intubation Simulation with Machine Learning Feedback
<p>This dataset contains raw acoustic data collected during endotracheal intubation simulations, utilized for developing a machine learning-based performance feedback system. The data includes .wav audio recordings sampled at 192 kHz, organized by buzzer and microphone location and intubation states.</p><p>The data is associated with the following paper:</p><p>Steffensen, T. L., Bartnes, B., Fuglstad, M. L., Auflem, M., & Steinert, M. (2023). Playing the pipes: Acoustic sensing and machine learning for performance feedback during endotracheal intubation simulation. <i>Frontiers in Robotics and AI</i>, <i>10–2023</i>. https://doi.org/10.3389/frobt.2023.1218174</p>
Supplementary material for "Inverse Modeling of the Initial Stage of the 1991 Pinatubo Volcanic Cloud Accounting for Radiative Feedback of Volcanic ash" paper
<p>Supplementary material for "Inverse Modeling of the Initial Stage of the 1991 Pinatubo<br>Volcanic Cloud Accounting for Radiative Feedback of Volcanic ash" by A. Ukhov, <br>G. Stenchikov, S.Osipov, N. Krotkov, N. Gorkavyi, C. Li, O. Dubovik, and A. Lopatin.</p> <p>Corresponding author: Alexander Ukhov, alexander.ukhov@kaust.edu.sa</p> <p>Contents<br>0. This 'README' file</p> <p>1. Emission profiles for ash and SO2<br> 1.1 In pickle and txt format, when radiative feedback is accounted for:<br> 1.1.1 Files 'ash_2d_emission_profiles_rad_on' [Mt/sec] and 'ash_2d_emission_profiles.txt' [Mt/(m sec)]<br> 1.1.2 Files 'so2_2d_emission_profiles_rad_on' [Mt/sec] and 'so2_2d_emission_profiles.txt' [Mt/(m sec)]</p> <p> 1.2 In pickle format, when radiative feedback is not accounted for:<br> 1.2.1 Files 'ash_2d_emission_profiles_rad_off' [Mt/sec]<br> 1.2.2 Files 'so2_2d_emission_profiles_rad_off' [Mt/sec]</p> <p>2. python script 'draw_supplementary_profiles.py' plots inverted emission profiles <br> (in pickle format) and their time integrated variants.</p> <p>3. WRF-Chem output file 'wrfout_d01_1991-06-16_00:00:00' in netcdf format contains <br> 3-D fields of ash, sulfate, and SO2 concentrations at 0000 UTC on 16 of June. <br> Instructions on how to process WRF-Chem output are available at the Appendix of [1].</p> <p>4. WRF-Chem domain grid description in the file 'wrf_small_grid.txt'. This file can be<br> used for conservative interpolation of 3-D fields to another grid, for example <br> using 'cdo remapcon'.</p> <p>There are two options: <br>1. Use inverted ash and SO2 emission profiles (see p.1 and p.2)<br>2. Use ash, sulfate, and SO2 concentrations from WRF-Chem output file (see p.3 and p.4)<br> as initial conditions for another run.</p> <p><br>References:<br>1. Ukhov, A., Ahmadov, R., Grell, G., and Stenchikov, G.: Improving dust simulations<br> in WRF-Chem v4.1.3 coupled with the GOCART aerosol module, <br> Geosci. Model Dev., 14, 473–493, https://doi.org/10.5194/gmd-14-473-2021, 2021.</p> <p>2. Ukhov et. al, Enhancing Volcanic Eruption Simulations with the WRF-Chem v4.7.x</p>
Alpine plant seed microbiomes, germination, and plant-soil feedbacks, Niwot Ridge and Green Lakes Valley, 2018.
Seed and soil microbiomes strongly affect plant performance, and these effects can scale-up to influence plant community structure. However, seed and soil microbial community composition are variable across landscapes, and different microbial communities can differentially influence multiple plant metrics (biomass, germination rate), and community stabilizing mechanisms. We measured how microbiomes inside seeds and in soils varied among alpine plant species and communities that differed in plant species richness and density. Across 10 common alpine plant species, we found a total of 318 bacterial and 128 fungal operational taxonomic units (OTUs) associated with seeds, with fungal richness affected by plant species identity more than sampling location. However, seed microbes had only marginally significant effects on plant germination success and timing. In contrast, soil microbes associated with two different plant species had significant effects on plant biomass, and their effect depended both on the plant species and the location the soils were sampled from.
Coastal SEES Collaborative Research: Coastal Sustainability: A cross-site comparison of salt marsh persistence in response to sea-level rise and feedbacks from social adaptations
Coastal ecosystems are often valued for decision-making purposes based on monetized market and non-market values of goods and services, and associated economic impacts. Examples include values of fishery landings, price changes for waterfront homes, and tourism revenues. Monetized quantities such as these do not provide a comprehensive characterization of the values provided by these ecosystems. Human reliance on the goods and services provided by ecosystems and the global decline in the health of many of these ecosystems suggests the need for ecosystem valuation to help inform decision-making and conservation policy. However, traditionally employed economic valuation methods are rarely able to capture the full scope of the benefits ecosystems provide, including benefits provided by "cultural" ecosystem services. Qualitative methods such as focus groups can provide insight on these values not available through quantitative methods alone. This research explores public perceptions of salt marsh value through the use of semi-structured focus groups in marsh-adjacent communities in Massachusetts, Virginia, and Georgia. The data include de-identified focus group transcripts from three 90-minute focus groups held in each state. Initial questions were drawn from the same semi-structured question list in each focus group, with exploratory follow-up questions based on participant responses. Results of text analysis suggest that in case study communities, outdoor experiences in salt marshes inspire serenity in Massachusetts, influence shore identities in Virginia, and promote stewardship cultivation in Georgia. Perceived threats to these benefits, such as the threat of residential development, industrial pollution, and increasing flood risk, together constitute the context for various community responses related to marsh protection. Results supplement information from extant economic valuations and show the importance of utilizing diverse methods to elicit information on soci
Modular organization of the murine locomotor pattern in the presence and absence of sensory feedback from muscle spindles
<p>In this study, we made use of non-negative matrix factorization (NMF) to extract muscle synergies from electromyographic (EMG) data. We implemented the NMF algorithm in R version 3.5.1 (R Foundation for Statistical Computing, R Core Team, Vienna, Austria), a programming language available in a free software environment. However, even if the software does not require a paid license, often researchers are either not confident with or prefer not to spend time writing the code required to perform NMF. We make available, as we recently did with human data (Santuz <em>et al.</em>, 2018), an example open access data set of EMG and muscle synergy data for murine walking and swimming. The data presented in this supplementary information part is available in three formats: 1) the raw EMG of two example trials (one recorded during walking and the other during swimming in a wild type animal, six muscles), unprocessed together with the touchdown and lift-off timings of the recorded limb for walking and the cycle timings for swimming; 2) the filtered and time-normalized EMG and 3) the muscle synergies extracted via NMF. Moreover, we provide the R code for obtaining the results described in the previous three points. We do not report any metadata, since trials are relative to a single representative animal. The R code is profusely commented.</p>
Data of LAI-L20C in Vegetation masking effect on future warming and snow albedo feedback in a boreal forest region of northern Eurasia according to MIROC-ESM
<p>Data of LAI-L20C experiment in the research paper: Vegetation masking effect on future warming and snow albedo feedback in a boreal forest region of northern Eurasia according to MIROC-ESM.</p> <p>The paper was submitted to JGR-Atmosphere.</p> <p>Variables are limited to those used in the paper.</p> <ul> <li>snow water equivalent (swe)</li> <li>snow cover fraction (snc)</li> <li>clear-sky downward shortwave radiation at surface (rsdscs)</li> <li>clear-sky upward shortwave radiation at surface (rsuscs)</li> <li>surface air temperature (tas)</li> </ul> <p>See the paper for the detail.</p>
Data for: Collective signalling is shaped by feedbacks between signaller variation, receiver perception, and acoustic environment in a simulated communication network
<p>Communication takes place within a network of multiple signallers and receivers. Social network analysis provides tools to quantify how an individual's social positioning affects group dynamics, and the subsequent biological consequences. However, network analysis is rarely applied to animal communication, likely due to the logistical difficulties of monitoring natural communication networks. We generated a simulated communication network to investigate how variation in individual communication behaviours generates network effects, and how this communication network's structure feeds back to affect future signalling interactions. We simulated competitive acoustic signalling interactions among chorusing individuals and varied several parameters related to communication and chorus size to examine their effects on calling output and social connections. Larger choruses had higher noise levels, and this reduced network density and altered the relationships between individual traits and communication network position. Hearing sensitivity interacted with chorus size to affect both individuals' positions in the network and the acoustic output of the chorus. Physical proximity to competitors influenced signalling, but a distinctive communication network structure emerged when signal active space was limited. Our model raises novel predictions about communication networks that could be tested experimentally, and identifies aspects of information processing in complex environments that remain to be investigated. </p>
Supplementary Material for "Intrusion Tolerance for Networked Systems Through Two-Level Feedback Control"
<h2>Supplementary material for the paper "Intrusion Tolerance for Networked Systems Through Two-Level Feedback Control" </h2><p>The paper is submitted to "International Conference on Dependable Systems and Networks, 2024". Author names withheld for double-blind reviewing.</p><ul><li>The file <strong>proofs_and_hyperparameters.pdf </strong>contains proofs of Theorem 1--2 and Corollary 1 in the paper. It also includes formulas for computing the belief state (Eq. 4) and for computing the curves in Fig. 6. It also includes a complete list of hyperparameters used for all experiments detailed in the paper.</li><li>The file <strong>ids_alerts_statistics.json</strong> contains the statistics used to produce Fig. 10 in the paper and to define the parameter Z for the experiments in section VIII.<ul><li>The JSON file contains a single object with the following keys: 'conditionals_counts', 'conditionals_kl_divergences', 'conditionals_probs', 'conditions', 'descr', 'emulation_name', 'id', 'initial_distributions_counts', 'initial_distributions_probs', 'initial_maxs', 'initial_means', 'initial_mins', 'initial_stds', 'maxs', 'means', 'metrics', 'mins', 'num_conditions', 'num_measurements', 'num_metrics', 'stds'. </li><li>The key "conditionals_counts" leads to another object with the following keys: 'A:CVE-2010-0426 exploit_D:Continue_M:[]', 'A:CVE-2015-3306 exploit_D:Continue_M:[]', 'A:CVE-2015-5602 exploit_D:Continue_M:[]', 'A:CVE-2016-10033 exploit_D:Continue_M:[]', 'A:Continue_D:Continue_M:[]', 'A:DVWA SQL Injection Exploit_D:Continue_M:[]', 'A:FTP dictionary attack for username=pw_D:Continue_M:[]', 'A:Ping Scan_D:Continue_M:[]', 'A:SSH dictionary attack for username=pw_D:Continue_M:[]', 'A:Sambacry Explolit_D:Continue_M:[]', 'A:ShellShock Explolit_D:Continue_M:[]', 'A:TCP SYN (Stealth) Scan_D:Continue_M:[]', 'A:Telnet dictionary attack for username=pw_D:Continue_M:[]', 'intrusion', 'no_intrusion'</li><li>The above keys correspond to different types of intrusions, see Table 6 in the paper.</li><li>Each of the keys listed above leads to a new object with 1551 keys which correspond to different types of metrics collected from the infrastructure. The metric used for produce Fig. 10 in the paper is called "alerts_weighted_by_priority". This key leads to another object where the keys correspond to the number of alerts weighted by priority and the values correspond to the measurements from the system.</li></ul></li><li>The file <strong>intrusion_traces.zip</strong> contains 6400 intrusion traces. Each trace contains a list of attacker actions and the corresponding measurements from the system. When unzipped, it is a directory with 64 files which take up 1500GB. Each file contains 100 traces in JSON format.</li><li>The file <strong>source_code_and_docker_files.zip </strong>contains the source code and the docker containers used for the experiments. It is a system we have developed for 3 years. It includes 225,000 lines of Python, 40,000 lines of JavaScript, 3000 lines of Dockerfiles, 2500 lines of Makefile, and 1800 lines of Bash. When unzipped one can find documentation about the source code in a file called "documentation.pdf" and in the README file.</li></ul>
Supplementary Material—Evaluating tactile feedback in addition to kinesthetic feedback for haptic shape rendering: a pilot study
<p>Supplementary Material of the journal article: "<a href="https://www.frontiersin.org/articles/10.3389/frobt.2024.1298537/abstract">Evaluating tactile feedback in addition to kinesthetic feedback for Haptic Shape rendering: a pilot study</a>": DOI: <a href="https://doi.org/10.3389/frobt.2024.1298537">10.3389/frobt.2024.1298537</a></p> <p> </p>
Time dependent interaction modification generated from plant-soil feedback
<p>Pairwise interactions between species can be modified by other community members, leading to emergent dynamics contingent on community composition. Despite the prevalence of such higher-order interactions, little is known about how they are linked to the timing and order of species' arrival. We generate population dynamics from a mechanistic plant-soil feedback model, then apply a general theoretical framework to show that the modification of a pairwise interaction by a third plant depends on its germination phenology. These time-dependent interaction modifications emerge from concurrent changes in plant and microbe populations and are strengthened by higher overlap between plants' associated microbiomes. The interaction between this overlap and the specificity of microbiomes further determines plant coexistence. Our framework is widely applicable to mechanisms in other systems from which similar time-dependent interaction modifications can emerge, highlighting the need to integrate temporal shifts of species interactions to predict the emergent dynamics of natural communities.</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.