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1,032 results for “Decision making”
The Decision-Making Process Behind Library Adoption
<p>This is the Factors/Process Book for the research paper "The Decision-Making Process Behind Library Adoption", submitted to: The 35th IEEE International Conference on Software Maintenance and Evolution (ICSME).</p>
The Decision-Making Process Behind Software Library Adoption
<p>Dataset supporting journal publication "The Decision-Making Process Behind Software Library Adoption".</p>
Decision-Making Tool for Road Preventive Maintenance Using Vehicle Vibration Data
<p>Corresponding data set for Tran-SET Project No. 18PLSU08. Abstract of the final report is stated below for reference:</p> <p>"Automated and timely road pavement damage inspection is critical to the preventive maintenance and the long-term sustainability and resilience of roads in Region 6. Current road inspection practices rely heavily on a manual process. Sensor-based methods (e.g., LiDAR scanning) are promising but can be too expensive for a wider adoption. This study employs a crowdsourcing approach of using the vibration patterns of regular vehicles in inferring specific types of road damages. A cloud-based smart phone app and system was developed to collect real-time vehicle vibrations, location data, and road damage images for training the detection model. However, there is a great challenge in using classic classification methods with crowdsourced vibration data containing high level of noises, as vehicle vibrations are greatly affected by the types and conditions of the vehicles, as well the varying driving behaviors of drivers. The study thus employed the recent developments in Deep Learning methods, including a Self-Taught Learning (STL) algorithm and Sparse Coding to tackle with the low-quality issues of collected data. A total of 310 miles of road-induced vehicle vibration data was collected in Texas and Louisiana, and the road damage detection model was trained on Texas A&M University (TAMU) supercomputing server. The results show that the features generated from Sparse Coding greatly contribute to enhancing detection performances, by addressing low-quality data issues."</p>
How to select predictive models for decision making or causal inference? Experiments data
<p>This is the full result data for the experiments of the paper : Doutreligne, M., & Varoquaux, G. (2023). How to select predictive models for decision making or causal inference?, https://hal.science/hal-03946902. <br><br>The code repository is : https://github.com/soda-inria/caussim/tree/main</p> <p>The files in this dataset are the one for the most computationnally costly experiments. There is one folder for each of the four datasets used in the paper. Then, one folder for each of the experimental setup. The files required for the main figure (Fig.3) of the paper are the one labelled #fig3 in the following descriptions.</p> <p>Details on the files : </p> <p>.<br>├── acic_2016_save<br>│ ├── acic_2016__nuisance_non_linear__candidates_hist_gradient_boosting__dgp_1-77__rs_1-5<br>│ │ └── run_logs.csv: results for the experiment with non linear models for both the nuisances and the candidates<br>│ ├── acic_2016__nuisance_non_linear__candidates_ridge__dgp_1-77__rs_1-10<br>│ │ └── run_logs.csv: results for the experiment with non linear models for the nuisances and linear models for the candidates<br>│ └── acic_2016__stacked_regressor__dgp_1-77__seed_1-10<br>│ └── run_logs.csv: results for the experiment with stacked models (linear and non linear) for the nuisances and non linear models for the candidates #fig3<br>├── acic_2018_save<br>│ └── acic_2018__nuisance_non_linear__candidates_hist_gradient_boosting__first_uid_432<br>│ └── run_logs.csv results for the experiment with stacked models (linear and non linear) for the nuisances models and non linear models for the candidates #fig3<br>├── caussim_save<br>│ ├── caussim__linear_regressor__test_size_5000__n_datasets_1000<br>│ │ ├── run_logs.csv: results for the experiment with stacked models for the nuisances models and linear models for the candidates <br>│ │ └── simu.yaml: configuration file of the experiment<br>│ ├── caussim__nuisance_non_linear__candidates_ridge__overlap_01-247_join_nuisance_train_set<br>│ │ └── run_logs.csv: results for the experiment with non linear models for the nuisances and linear models for the candidates, joined sets for the nuisances and the candidates<br>│ ├── caussim__nuisance_non_linear__candidates_ridge__overlap_01-247_separated_nuisance_train_set<br>│ │ └── run_logs.csv: results for the experiment with non linear models for the nuisances and linear models for the candidates, separated sets for the nuisances and the candidates<br>│ └── caussim__stacked_regressor__test_size_5000__n_datasets_1000<br>│ ├── run_logs.csv: results for the experiment with stacked models (linear and non linear) for the nuisances and linear models for the candidates #fig3<br>│ └── simu.yaml: configuration file of the experiment<br>└── twins_save<br> └── twins__stacked_regressor__rs_1-10__overlap_0.1-3<br> └── run_logs.csv: results for the experiment with stacked models (linear and non linear) for the nuisances and non linear models for the candidates #fig3</p>
Human Decision-Making Through the Lifecycle of Autonomous and Intelligent Systems in Defense Applications: Example Scenario with Comments
<p>This example scenario with comments has been created in conjunction with the report “A Framework for Human Decision-Making Through the Lifecycle of Autonomous and Intelligent Systems in Defense Applications” to be published by the IEEE Standards Association (IEEE SA) Research Group on Issues of AI and Autonomy in Defense Systems (the Research Group). It provides insights into the Research Group's working methods and process.</p>
Dataset for "Dissociable Roles of the mPFC-to-VTA pathway in the control od Impulsive action and Risk-Related Decision-Making in Roman High- and Low-Avoidance Rats"
<p>This dataset corresponds to the study "Dissociable Roles of the mPFC-to-VTA Pathway in the Control of Impulsive Action and Risk-Related Decision-Making in Roman High- and Low-Avoidance Rats". In this study, we used Positron Emission Tomography with [18F]-Fluorodeoxyglucose to evaluate brain metabolic activity in Roman High- (RHA) and Low-avoidance (RLA) rats, which exhibit innate differences in impulsivity. Notably, we used a viral-based intersectional chemogenetic strategy to isolate the role of the mPFC-to-VTA pathway in controlling impulsive behaviors. We selectively activated the mPFC-to-VTA pathway in RHA rats and inhibited it in RLA rats, assessing the effects on impulsive action and RDM in the rat gambling task. Our results showed that RHA rats displayed higher impulsive action, less optimal decision-making, and lower cortical activity than RLA rats at baseline. Chemogenetic activation of the mPFC-to-VTA pathway reduced impulsive action in RHA rats, whereas chemogenetic inhibition had the opposite effect in RLA rats. However, these manipulations did not affect RDM. Our findings suggest a dissociable role of the mPFC-to-VTA pathway in impulsive action and RDM, highlighting its potential as a target for investigating impulsivity-related disorders.</p> <p><strong>Contributions for usage of this data in publications:</strong></p> <p>If you publish any work using these data, please cite this repository and the associated publication.</p>
WaveMAP analysis of extracellular waveforms from monkey premotor cortex during decision-making
<p>Cortical circuits are thought to contain a large number of cell types that coordinate to produce behavior. Current <em>in vivo</em> methods rely on clustering of specified features of extracellular waveforms to identify putative cell types, but these capture only a small amount of variation. Here, we develop a new method (<em>WaveMAP</em>) that combines non-linear dimensionality reduction with graph clustering to identify putative cell types. We apply <em>WaveMAP</em> to extracellular waveforms recorded from dorsal premotor cortex of macaque monkeys performing a decision-making task. Using <em>WaveMAP</em>, we robustly establish eight waveform clusters and show that these clusters recapitulate previously identified narrow- and broad-spiking types while revealing previously unknown diversity within these subtypes. The eight clusters exhibited distinct laminar distributions, characteristic firing rate patterns, and decision-related dynamics. Such insights were weaker when using feature-based approaches. <em>WaveMAP</em> therefore provides a more nuanced understanding of the dynamics of cell types in cortical circuits.</p>
Visual processing and collective motion-related decision-making in desert locusts
<p>Collectively moving groups of animals rely on decision-making of locally interacting individuals in order to maintain swarm cohesion. However, the complex and noisy visual environment poses a major challenge to the extraction and processing of relevant information. We addressed this challenge by studying swarming-related decision-making in desert locust nymphs. Controlled visual stimuli, in the form of random dot kinematograms, were presented to tethered locust nymphs in a trackball setup, while monitoring movement trajectory and walking parameters. In a complementary set of experiments, the neurophysiological basis of the observed behavioral responses was explored. Our results suggest that locusts utilize filtering and discrimination upon encountering multiple stimuli simultaneously. Specifically, we show that locusts are sensitive to differences in speed at the individual conspecific level, and to movement coherence at the group level, and may use these to filter out non-relevant stimuli. The locusts also discriminate and assign different weights to different stimuli, with an observed interactive effect of stimulus size, relative abundance, and motion direction. Our findings provide insights into the cognitive abilities of locusts in the domain of decision-making and visual-based collective motion and support locusts as a model for investigating sensory-motor integration and motion-related decision-making in the intricate swarm environment.</p>
Co-creation Decision Making Tool
<p>Should I use co-creation?</p> <p>Decision-making tool</p>
dhaw/fluCodeImperial: Using real-time data to guide decision-making during an influenza pandemic: a modelling analysis
<p><strong>All codes and data used for "Using real-time data to guide decision-making during an influenza pandemic: a modelling analysis" are included in this folder. The file "runExamples.m" contains a step-by-step method for reproducing figures and running model fits. Ensure that all data and code files are in the same directory, then a single execution of “runExamples” on the command line will generate all main and supplementary figures in the manuscript. There is one line of code per figure, clearly marked, that can be commented out as desired. In order to run the MCMC adaptive algorithm, a single line of code, also clearly marked, must be commented back in. Instructions to change the single-state example are given at the top of the file “runExamples.m”. The saved state selection of California (“state=1”) is consistent with all results presented in the manuscript. </strong></p> <p><strong> </strong></p> <p><strong>Plots make use of files from the following sources, with some modifications:</strong></p> <p><strong>Holger Hoffmann (2022). Violin Plot (https://www.mathworks.com/matlabcentral/fileexchange/45134-violin-plot);</strong></p> <p><strong>Evan (2022). Plot Groups of Stacked Bars (https://www.mathworks.com/matlabcentral/fileexchange/32884-plot-groups-of-stacked-bars);</strong></p> <p><strong>John Onofrey (2022). Shaded Plots and Statistical Distribution Visualizations (https://www.mathworks.com/matlabcentral/fileexchange/69203-shaded-plots-and-statistical-distribution-visualizations)</strong></p>
Real-time Alignments for Predicting Decision-Making Time for Diagnostics over NGS Cycles
<p>Input datasets for an interpretable learning approach to predict decision-making time for diagnostics over NGS cycles. The dataset contains alignment files of two clinical sputum samples sequenced with an Illumina MiSeq sequencing device, and following a real-time sequencing protocol.</p> <p>The datasets consist of real-time alignment files for specific sequencing cycle intervals. The alignment has been performed to a database of respiratory microbes. Data preprocessing, including the removal of human host DNA, is integrated into the real-time alignment approach that has been applied to generate the alignments.</p>
Data and code for "Representing storylines with causal networks to support decision making: framework and example"
<p>Data and code for the paper "Representing storylines with causal networks to support decision making: framework and example", along with the Shiny webapp code accompanying the paper. The paper has been submitted to the journal of Climate Risk Management and is currently under review.</p>
Optimal policy for uncertainty estimation concurrent with decision making
<p>Dataset for "Optimal policy for uncertainty estimation concurrent with decision making".</p> <p>The dataset should be merged into the code folder, thus the program can work.</p>
Information integration for decision-making in desert locusts
<p>Locust swarms can extend over hundred kilometers, and starvation compels this ancient pest to devour everything in its path. Theory suggests that gregarious behavior benefits foraging efficiency, yet the role of social cohesion remains elusive. To this end, we collected high-resolution trajectories of individual and grouped gregarious desert locusts in a 2-choice assay with patches of either similar or different quality. Carefully maintaining animals' identities allowed us to monitor each individual's experience and estimate the leaky accumulation process of personally-acquired and socially-derived evidence. We fitted data to a Bayesian model to gain insight into the decision-making system for patch selection. By disentangling the relative contribution of each information class, our study suggests that locusts balance incongruent evidence but reinforce congruent ones. We provide insight into the collective foraging decisions of social (but non-eusocial) insects and present locusts as a powerful empirical system to study individual choices and consequent collective dynamics.</p>
[DATA LINK] Clearing the way to the external world: do ants make optimal decisions when removing obstacles from their subterranean galleries?
<p>Data repository of the submitted paper:</p> <p>“Clearing the way to the external world: do ants make optimal decisions when removing obstacles from their subterranean galleries?”</p> <p>Authors : Louis Devers (0009-0001-3726-1891), Clémentine Cléradin, Zoé Bescond-Michel, Gérard Latil and Vincent Fourcassié (0000-0002-3605-6351).</p> <p>Contains :</p> <ul> <li>Extraction.csv [database] : the observed orientations of the obstacle extraction</li> <li>Traction.csv [database] : censing the orientation of mandibular grasps and effective pulls observed</li> <li>Model_traction.csv [database] : censing the orientation of virtual pulls</li> <li>Model_extraction.csv [database] : censing the orientation of virtual extraction, resulting from sequential tractions presented in model_traction.csv</li> <li>TreatmentAndGraphs [R script] : script including statistical analysis, and survival analysis graphs</li> </ul>
Dataset for Preferred and actual involvement of caregivers in oncologic treatment decision-making: A systematic review
<p>This dataset shows the search that was done for the publication Preferred and actual involvement of caregivers in oncologic treatment decision-making: A systematic review, which can be found at <a href="https://doi.org/10.1016/j.jgo.2023.101525">https://doi.org/10.1016/j.jgo.2023.101525</a>. </p>
Code and Datasets for "A simple mechanism for collective decision-making in the absence of payoff information"
<p>This repository contains R code and datasets used for analyses in the paper:</p> <p>"A simple mechanism for collective decision-making in the absence of payoff information"</p> <p>Published in <em>Proceedings of the National Academy of Sciences of the United States of America</em> (PNAS) in 2023</p> <p>by Daniele Carlesso, Justin M. McNab, Christopher J. Lustri, Simon Garnier, and Chris. R. Reid.</p> <pre><code>✓ Ensure that all files are in the same folder for the code to run properly. ✓ the repository also includes a summary HTML file for easier visualisation. </code></pre> <p>Datasets:</p> <pre><code>• chain_growth.csv • cumsum_df.csv • growth_data.csv • GrowthMP.csv • JL_chaingrowth.csv • joining_distance.csv • leave_10.csv • leaving_positions.csv • timespent.csv • traffic_mean.csv </code></pre> <p>Code:</p> <pre><code>• Code.Rmd </code></pre> <p>Markdown summary:</p> <pre><code>• Code.html </code></pre>
Dataset: Recruitment responses of shade-tolerant and heliophilous trees in human-degraded areas: the necessity of knowing the recruitment autecology of species for making reforestation decisions
<p>This repository contains the files associated with the following article:</p> <p>Johanna Croce, Ernesto I. Badano, Andrés Tálamo. Recruitment responses of shade-tolerant and heliophilous trees in human-degraded areas: the necessity of knowing the recruitment autecology of species for making reforestation decisions Submitted to <em>Land Degradation & Development</em>.</p> <p>The first Microsoft Excel file (Dataset 01 - Microclimate.xlsx) contains two sheets with the microclimatic data (average, maximum and minimum soil temperatures, and volumetric soil water content) measured in Cerro Chachapoyas and Cerro Fachacano. These measurements were performed on three plots of each trearment, including shrub-protected treatment with high shade, shrub-protected treatment with medium shade, sunny treatment with high herbaceous cover, sunny treatment with medium herbaceous cover and controls. The second Microsoft Excel file (Dataset 02 -Plant responses.xlsx Dataset 02 - Plant responses.xlsx) contains three sheets with the data used to estimate the seedling emergence rates, plant survival rates and net aboveground growth rates of the tree species, including <em>Anadenanthera colubrina</em> and <em>Ceiba chodatii</em> in Cerro Chachapoyas, and <em>Jacaranda mimosifolia</em> in Cerro Fachacano.</p>
The essential-use concept: a valuable tool to guide decision-making on applications for authorisation under REACH?
<p>Supporting Information from The essential-use concept: a valuable tool to guide decision-making on applications for authorisation under REACH?</p>
Shared decision making on mode of delivery following a prior cesarean delivery in Dar es Salaam, Tanzania
<p><strong>Background</strong>: Shared decision-making between clinicians and pregnant women with prior cesarean on the subsequent mode of delivery improves trial of labor rates, and reduces the number of repeat cesarean sections and their related complications. However, this practice is insufficient worldwide and the factors influencing it are still unknown. The study aimed at determining the proportion of pregnant women involved in shared decision-making and its associated factors in Dar es Salaam.</p> <p><strong>Methods</strong>: A cross-sectional analytical study among 350 pregnant women with one prior cesarean section. Data was collected using a structured questionnaire and SPSS 23 was used for analysis. A score of 80 or higher on the nine-item Shared Decision-Making Questionnaire (SDM-Q9) was used to calculate the proportion of women, and the associated factors were obtained using a logistic regression model. A p-value of < 0.05 was considered significant.</p> <p><strong>Results</strong>: The proportion of pregnant women involved in shared decision-making was 38%. Factors that were significantly associated with sharing decision-making were; having low level of education (AOR 0.55 95% CI 0.33–0.91), being married/having partner (AOR 2.58 95% CI 1.43–4.63), having a companion who had active participation (AOR 3.31 95% CI 1.03–10.6) and being familiar with the clinician (AOR 5.01 95% CI 1.30–19.2).</p> <p><strong>Conclusion</strong>: To promote the practice of shared decision-making in our setting, encouragement of socially vulnerable pregnant women's participation in decision-making by health care professionals, encouragement of companion participation during antenatal care and promotion of personal continuity of care to improve familiarity to clinicians are needed.</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.