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156 results for “explorative modeling”
Experimental Validation of Cryobot Thermal Models for the Exploration of Ocean Worlds
<p>The tables in this repository represent the data used in the figures and analyses of the paper "Experimental Validation of Cryobot Thermal Models for the Exploration of Ocean Worlds", published in the Planetary Science Journal. The provided data was collected between 2020 and 2022.</p> <ul> <li>AllResults.xlsx: compilation of tables 4, 5, 6, and 7 on the paper.</li> <li>WarmA1.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Total Power [W], H6 Power [W], H5 Power [W], H4 Power [W], H3 Power [W], H2 Power [W], H1 Power [W]".</li> <li>WarmA2.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Total Power [W], H6 Power [W], H5 Power [W], H4 Power [W], H3 Power [W], H2 Power [W], H1 Power [W]".</li> <li>CryoA1.xlsx: data presented in tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth Estimation [m], Power [W]".</li> <li>CryoB1.xlsx: data presented in figures 9 and 10, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoB2.xlsx: data presented in figures 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoB3.xlsx: data presented in figures 6, 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC1.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC2.xlsx: data presented in figures 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC3.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Truncated Coarse Depth [m], Power [W]".</li> </ul> <p> </p>
Dataset from Maith, O., Baladron, J., Einhäuser, W., & Hamker, F. H. (2023). Exploration behavior after reversals is predicted by STN-GPe synaptic plasticity in a basal ganglia model. Submitted to iScience.
<p>This dataset contains all analyzed data from the study "Maith, O., Baladron, J., Einhäuser, W., & Hamker, F. H. (2023). Exploration behavior after reversals is predicted by STN-GPe synaptic plasticity in a basal ganglia model. Submitted to iScience.". It includes the behavioral data of 20 human participants (folder "psychExp") and of simulations of a neuro-computational basal ganglia model (folder "simulations") of the study.</p> <p>To replicate the results of the study, the dataset can be analyzed using the code provided separately under the following identifier: https://doi.org/10.5281/zenodo.6555886. The dataset is organized in the directory structure required for this purpose.</p> <p>For the human participants, only preprocessed eye-tracking and general behavioral data (.mat files) and the final analyzed behavioral data (output files) generated with the script "get_vps_outputs.m" (folder psychExp/..../3_srcAna/) are available. For more information about preprocessing steps as well as raw data of the eye-tracking experiment, please contact us by email (click <a href="https://www.tu-chemnitz.de/urz/mail/adrx.html?1-d29sZmdhbmcuZWluaGFldXNlci10cmV5ZXJAcGh5c2lrLg==">here</a>).</p>
UAV-Based Height Measurement and Height-Diameter Model integrating Taxonomic Effects: Exploring Vertical Structure of Aboveground Biomass and Species Diversity in a Malaysian Tropical Forest
<p>These Excel files are the dataset used for the analysis in the submitted paper</p> <p>Dataset S1: Data for 6-ha pot in Pasoh Forest Researve</p> <p>Dataset S2: Data for height–diameter (HD) models</p>
Data and Supplementary Plots for A Shallow Water Model Exploration of Atmospheric Circulation on Sub-Neptunes
<p>This repository contains geopotential maps, zonal wind plots, gifs, and data for the ensemble of possible sub-Neptunes presented in the main manuscript. The data, individual plots, and plot grids are based on the averages of the last 100 simulated days. The data are in the pickle format (see https://docs.python.org/3/library/pickle.html)<br> The gifs are based on the last 1000 simulated hours. These figures and gifs support the analysis presented in the main manuscript.</p>
Exploring controls on coastal dune growth through a simplified model [Dataset]
<p>This dataset contains the Duna model output data, included in the article “Exploring controls on coastal dune growth through a simplified model” published in the <em>Journal of Geophysical Research - Earth Surface</em>.</p> <p>The data is provided as “Fig*.mat” files, processed in MatLab (R2023a), and organised following the structure of the figures presented in the manuscript (e.g., Fig1.mat corresponds to data shown in Fig.1). </p> <p>'Dataset.docx' provides information on the individual files contained in the dataset.</p>
Exploring the effect of microstructure and surface recombination on hydrogen effusion in Zn-Ni coated martensitic steels by advanced computational modelling
<p>Raw data pertaining to the publication "Exploring the effect of microstructure and surface recombination on hydrogen effusion in Zn-Ni coated martensitic steels by advanced computational modelling".</p>
Exploring the ENSO Modulation of the QBO Periods with GISS E2.2 Models
<p>These datasets are used in Zhou et al. (2023): Exploring the ENSO Modulation of the QBO Periods with GISS E2.2 Models.</p>
Exploration of Diagnosis and Treatment Strategies and Prognostic Prediction Models for Acute Respiratory Distress Syndrome Based on Radiographic Evaluations Assessed by Artificial Intelligence
ClinicalTrials.gov study NCT07328997. IPD Sharing: YES. Countries: 1. Publications: 30.
Explore the Effectors of The Transtheoretical Model on Nutritional Education in Patients on Hemodialysis
ClinicalTrials.gov study NCT05897502. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Worldwide exploration of the microbiome harbored by the cnidarian model, Exaiptasia pallida (Agassiz in Verrill, 1864) indicates a lack of bacterial association specificity at a lower taxonomic rank
Open the record for dataset details and reuse information.
Data from: Exploring the multi-level impacts of a youth-led comprehensive sexuality education model in Madagascar using human-centered design methods
Open the record for dataset details and reuse information.
Data from: Exploring movement decisions: can Bayesian movement-state models explain crop consumption behaviour in elephants (Loxodonta africana)?
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Data from: How far can I extrapolate my species distribution model? Exploring Shape, a novel method
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Supplementary data and modeling code file for: Soft deployable airless wheel for lunar lava tube in-tact exploration
Open the record for dataset details and reuse information.
Data and Code to support COVID-19 - exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study
<p>Data and code to support paper published in Wellcome Open research on years of life lost among people who died with COVID-19.</p>
Extreme model exploration of a multi-scale simulation of tumor growth
<p>The dataset comprises the output of several simulations of a model of tumor growth with different parameter values. The model is a multi-scale agent-based model of a tumor spheroid that is treated with periodic pulses of the cytokine tumor necrosis factor (TNF). The multi-scale model simulates processes including i) the diffusion, uptake, and secretion of molecular entities such as oxygen, or TNF; ii) the mechanical interaction between cells; and iii) cellular processes including cell life cycle, cell death models, signal transduction.</p> <p>The multi-scale model was implemented and simulated using the PhysiBoSS framework (Letort et al. 2019). The dataset corresponds to different simulations trajectories obtained for alternative parameter values. The parameter explored are: i) the decay rate of the TNF after it binds the cell; ii) the TNF binding rate; and iii) the TNF secretion rate by NFkB activated cells.</p> <p>The dataset includes 48 different combinations of parameters. Each simulation is stored in a folder instance_[0-9]+ which includes the PhysiBoSS standard output files (<a href="https://github.com/gletort/PhysiBoSS/wiki">https://github.com/gletort/PhysiBoSS/wiki</a>). The root folder also includes other settings, logs, and outputs as well as the binary used to run the simulation.</p>
Data from: Exploring a Pool-seq only approach for gaining population genomic insights in non-model species
<p>Developing genomic insights is challenging in non-model species for which resources are often scarce and prohibitively costly. Here, we explore the potential of a recently established approach using Pool-seq data to generate a de novo genome assembly for mining exons, upon which Pool-seq data is used to estimate population divergence and diversity. We do this for two pairs of sympatric populations of brown trout (Salmo trutta); one naturally sympatric set of populations and another pair of populations introduced to a common environment. We validate our approach by comparing the results to those from markers previously used to describe the populations (allozymes and individual based SNPs) and from mapping the Pool-seq data to a reference genome of the closely related Atlantic salmon (Salmo salar). We find that genomic differentiation (FST) between the two introduced populations exceeds that of the naturally sympatric populations (FST = 0.13 and 0.03 between the introduced and the naturally sympatric populations, respectively), in concordance with estimates from the previously used SNPs. The same level of population divergence is found for the two genome assemblies but estimates of average genic diversity differ (π ≈0.002 and π ≈0.001 when mapping to S. trutta and S. salar, respectively), although the relationships between population values are largely consistent. This discrepancy might be attributed to biases when mapping to a haploid condensed assembly made of highly fragmented read data compared to using a high-quality reference assembly from a divergent species. We conclude that the Pool-seq only approach can be suitable for detecting and quantifying genome wide population differentiation, and for comparing genomic diversity in populations of non-model species where reference genomes are lacking.</p>
Data from: Exploring foraging decisions in a social primate using discrete choice models
There is a growing appreciation of the multiple social and nonsocial factors influencing the foraging behavior of social animals, but little understanding of how these factors depend on habitat characteristics or individual traits. This partly reflects the difficulties inherent in using conventional statistical techniques to analyze multi-factor, multi-context foraging decisions. Discrete choice models provide a way to do so, and we demonstrate this by using them to investigate patch preference in a wild population of social foragers (chacma baboons, Papio ursinus). Data were collected from 29 adults across two social groups encompassing 683 foraging decisions over a six-month period, and the results interpreted using an information theoretic approach. Baboon foraging decisions were influenced by multiple nonsocial and social factors, and were often contingent on the characteristics of the habitat or individual. Differences in decision-making between habitats were consistent with changes in interference competition costs but not changes in social foraging benefits. Individual differences in decision-making were suggestive of a trade-off between dominance rank and social capital. Our findings emphasize that taking a multi-factor, multi-context approach is important to fully understand animal decision-making. We also demonstrate how discrete choice models can be used to achieve this.
Supplementary Data for "Exploring the Integration of Large Language Models in Industrial Test Maintenance Processes"
<p>This package contains supplementary data not directly included in the paper, including per-commit results for each prototype and the prompts used in the proof-of-concept implementations.</p>
Exploring the Cognitive Effects of Ambiguity in Process Models
<p>This dataset contains the supplementary material for the paper "Exploring the Cognitive Effects of Ambiguity in Process Models", accepted for publication in the proceedings of BPM 2024.</p> <p>The dataset includes:</p> <ul> <li>All process models used in the experiments</li> <li>A report on the process models complexity metrics</li> <li>The list of all tasks and the respective modeling guidelines violations</li> <li>Participants demographics</li> <li>Higher resolution version of the figures in the paper</li> <li>The Python notebook used for the data analysis</li> <li>Process maps generated from the data analysis results</li> </ul>
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.