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1,032 results for “decision making”
Collective Decision-Making and Change Detection with Bayesian Robots in Dynamic Environments
<p>The following folder structure holds all research data of my conducted experiments(h5-logfiles and plots). The Python-Script "show_h5.py" can be used to read out the logfile in h5-format (<em>$python3 show_h5.py expample_logfilename.h5</em>). However, this shouldn't be necessary because all plots are already generated.</p> <p>To find the results you want to see, this is a small guide through the structure:</p> <ol> <li> <p>First the trials are divided into the respective methods (PELT, DBB, DBBCPD). In the folders you find the experiments for the specific method.</p> </li> <li> <p>In the folder of PELT you find the results for the different feedback types and their combinations. The id for each feedback is noted in parentheses (e.g. XX_(id)_feedback_description). Feedback combinations have their ids added up (e.g. XX_(id1+...+idn)_feedback_description).</p> </li> <li> <p>In the folder to each feedback type the different test trials can be found. This means varying environment difficulties and parameter settings. In the name of the folders this information can be found (e.g. XX_method_environmentdifficulty_parametersetting).</p> </li> </ol> <p>All experiments follow the same procedure as long as it is stated otherwise. Each trial consists of 20 individual runs with a duration of 6000 seconds. At half time (3000 s) a change to the opposite fill ratio occurs (fill ratio of 1.0 defines a completely white and one of 0.0 a completely black environment).</p> <p><strong>Environment difficulty</strong></p> <ul> <li> <p>0901 --> easy environment, fill ratio changed from 0.9 to 0.1</p> </li> <li> <p>0703 --> easy environment, fill ratio changed from 0.7 to 0.3</p> </li> <li> <p>0604 --> easy environment, fill ratio changed from 0.6 to 0.4</p> </li> <li> <p>055045 --> easy environment, fill ratio changed from 0.55 to 0.45</p> </li> </ul> <p><strong>Parameter Setting</strong></p> <p>The setting is in the name of the folder composed of: feedbackID: intervalLength amountNeighbors</p> <ul> <li> <p>3c:50s3n --> feedback 3c with a 50s interval and 3 neighbors</p> </li> </ul> <p>In these folders all plots of the respective runs can be found showing a Boxplot of all 20 runs and for each run the swarm belief, the decision distribution and the reset histogram (before/after the change)</p>
Size-selective harvesting impacts learning and decision-making in zebrafish
<p><span>Size-selective harvesting common to fisheries is known to evolutionarily alter life-history and behavioural traits in exploited fish populations. Changes in these traits may in turn modify learning and decision-making abilities through energetic trade-offs with brain investment that can vary across development or via correlations with personality traits. We examined the hypothesis of size-selection-induced alteration of learning performance in three selection lines of zebrafish (<em>Danio</em> <em>rerio</em>) generated through intensive harvesting for large, small and random body-size for five generations followed by no further selection for ten generations that allowed examining evolutionarily fixed outcomes. We tested associative learning ability throughout ontogeny in fish groups using a colour-discrimination paradigm with a food reward, and the propensity to make group decisions in an associative task. All selection lines showed significant associative abilities that improved across ontogeny. The large-harvested line fish showed a significantly slower associative learning speed as subadults and adults than the controls. We found no evidence of memory decay as a function of size-selection. Decision-making speed did not vary across lines, but the large-harvested line made faster decisions during the probe trial. Collectively, our results show that size-selective harvesting evolutionarily alters associative and decision-making abilities in zebrafish, which could affect resource acquisition and survival in exploited fish populations. </span></p>
Meta-analysis on Stakeholder Engagement in the Co-Production of Knowledge for Environmental Decision-making
<p>This dataset corresponds to a meta-analysis to examine the peer-reviewed scholarship on stakeholder engagement in co-production processes in environmental decision-making.</p> <p>The dataset was developed by searching keywords, abstracts, and titles in Scopus on three key themes: co-production, transdisciplinary, and community-based participatory research. A total of 709 publications were reviewed, from which 144 met the selection criterion. The 144 articles constitute this dataset. They pertain to articles published between January 2005 and June 2020.</p> <p>The data includes information on the following topics:</p> <ol> <li>Number of unique case studies reported</li> <li>The projects’ time span</li> <li>When stakeholder engagement activities occur during the project cycle</li> <li>Are stakeholders (non-university researchers) an author</li> <li>Geographic region of project</li> <li>Environmental issues addressed</li> <li>Geographic scale of project </li> <li>Types of stakeholders engaged </li> <li>How stakeholders were engaged</li> <li>What output resulted from stakeholder engagement</li> <li>Approach to stakeholder engagement</li> <li>Factors that enabled stakeholder engagement</li> <li>Barriers or challenges to engagement</li> <li>Societal impacts associated with stakeholder engagement</li> <li>Direct evidence of societal impacts of stakeholder engagement</li> </ol> <p>The methods and analysis are presented in "Stakeholder Engagement in the Co-Production of Knowledge for Environmental Decision-making," which was submitted for publication in 2022 to the Journal <em>World Development</em>. A draft of the manuscript can be provided upon request to authors.</p>
Dataset of TMLR 2024 Paper "Perceptual Similarity for Measuring Decision-Making Style and Policy Diversity in Games"
<p>This is a part of dataset of the paper published in TMLR 2024 (Transactions on Machine Learning Research, <a href="https://jmlr.org/tmlr/" target="_blank" rel="noopener">https://jmlr.org/tmlr/</a>).</p> <p>The example program for using this file will be put on the author's github repo branch: <a href="https://github.com/DSobscure/cgi_drl_platform/tree/game_balance_measures_tmlr" target="_blank" rel="noopener">https://github.com/DSobscure/cgi_drl_platform/tree/playstyle_similarity_tmlr</a></p> <p> </p>
MEND 2: Making Treatment Decisions Using Genomic Testing
ClinicalTrials.gov study NCT03183050. IPD Sharing: YES. Countries: 1. Publications: 2.
Will Veterans Engage in Prevention After HRA-guided Shared Decision Making?
ClinicalTrials.gov study NCT01828567. IPD Sharing: YES. Countries: 1. Publications: 5.
Improving Providers' Decision-Making and Reducing Information Overload Using Information Visualization in EHRs
ClinicalTrials.gov study NCT05937646. IPD Sharing: YES. Countries: 1. Publications: 1.
Shared Decision Making in the Emergency Department: Chest Pain Choice Trial
ClinicalTrials.gov study NCT01969240. IPD Sharing: YES. Countries: 1. Publications: 2.
Helping Patients and Providers Make Better Decisions About Colorectal Cancer Screening
ClinicalTrials.gov study NCT04683731. IPD Sharing: YES. Countries: 1. Publications: 1.
Communication to Improve Shared Decision-Making in ADHD
ClinicalTrials.gov study NCT02716324. IPD Sharing: YES. Countries: 1. Publications: 6.
Supporting Oral Pre-exposure Prophylaxis Decision Making Among Pregnant Women in Lilongwe, Malawi
ClinicalTrials.gov study NCT06394323. IPD Sharing: YES. Countries: 1. Publications: 1.
Decision-making in dynamic, continuously evolving environments: Quantifying the flexibility of human choice
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Data from: Quantity discrimination, decision-making, and the role of early-life conditions in a lizard
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Energetic trade-offs in migration decision-making, reproductive effort, and subsequent parental care in a long-distance migratory bird
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Size-selective harvesting impacts learning and decision-making in zebrafish
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Data from: Frontal noradrenergic and cholinergic transients exhibit distinct spatiotemporal dynamics during competitive decision-making
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Resource allocation underlies parental decision-making during incubation in the Manx shearwater
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Data from: Distinct developmental trajectories for risky and impulsive decision-making in chimpanzees
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Intraparietal stimulation disrupts negative distractor effects in human multi-alternative decision-making
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Data from: Anthropogenic noise exposure over development increases baseline auditory activity and decision-making time in adult crickets
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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.