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8 results for “Goal Models”
Resource-Centric Goal Model Slicing for Detecting Feature Interactions
<p>Supplementary materials of the paper entitled:</p> <p>“Resource-Centric Goal Model Slicing for Detecting Feature Interactions”</p> <p>file01 - The features related to the concept of Decline-Mutual-Exclusion.<br> file02 - The features related to the concept of Decline-Produce-and-Use<br> file03 - The features related to the concept of Decline-State-Changing<br> file04 - The features related to the concept of Enhanced-State-Changing<br> file05 - Zoom-features-2022</p>
Identifying Implicit Vulnerabilities through Personas as Goal Model: Case study model
<p>CAIRIS model to accompany 'Identifying Implicit Vulnerabilities through Personas as Goal Model: Case study model' SECPRE 2020 paper.</p> <p>Instructions for importing the model package: https://cairis.readthedocs.io/en/latest/io.html#importing-models</p> <p>Instructions for working with user goal models: https://cairis.readthedocs.io/en/latest/usergoals.html#user-goals-and-user-goal-models</p>
Replication package for the paper "Modeling Europe's role in the global LNG market 2040: balancing decarbonization goals, energy security, and geopolitical tensions"
<p>This package contains folders and files with code and data used in the study described in the paper. Further information can be found on <a href="https://github.com/sebastianzwickl/lng-trade-europe" target="_blank" rel="noopener">GitHub</a>.</p>
Data source and projections of maintenance energy gaps for "Caloric reductions needed to achieve obesity goals by 2030 and 2040: A modeling study"
<p><strong>Variables in "data_ENSANUT_waves.xlsx"</strong></p> <table> <thead> <tr> <th scope="col">Name</th> <th scope="col">Variable</th> </tr> </thead> <tbody> <tr> <td><em>id</em></td> <td>Identifier for each individual in the data.</td> </tr> <tr> <td><em>est_var</em></td> <td>Strata for the estimation of variances, accounting for survey design.</td> </tr> <tr> <td><em>svy_weights</em></td> <td>Complex survey weight.</td> </tr> <tr> <td>code_upm</td> <td>Identifier of the primary sampling unit.</td> </tr> <tr> <td>sex</td> <td>Sex of the individual (``male'' or ``female'').</td> </tr> <tr> <td>age</td> <td>Age (yrs).</td> </tr> <tr> <td>body_weight</td> <td>Measured body weight (kg).</td> </tr> <tr> <td>height</td> <td>Measured height (cm).</td> </tr> <tr> <td>bmi</td> <td>Body mass index, estimated before the simulation process (kg/m<sup>2</sup>).</td> </tr> <tr> <td>SES</td> <td>Socioeconomic level, divided in tertiles. This variable was constructed using Principal Components Analysis.</td> </tr> <tr> <td>year</td> <td>Indicator for each ENSANUT wave (2000, 2006, 2012, 2016, 2018).</td> </tr> <tr> <td>svy_weights_raking_2030</td> <td>Complex survey weight, constructed for the baseline sample (ENSANUT 2018) to replicate the expected age and sex distribution in 10-year age groups for 2030.</td> </tr> <tr> <td>svy_weights_raking_2040</td> <td>Complex survey weight, constructed for the baseline sample (ENSANUT 2018) to replicate the expected age and sex distribution in 10-year age groups for 2040.</td> </tr> <tr> <td>body_weight_final_2030_Nordpred</td> <td>Simulated body weight by 2030 based on MEGs projections of the Nordpred-based fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_Nordpred</td> <td>Simulated body weight by 2040 based on MEGs projections of the Nordpred-based fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_Nordpred</td> <td>Simulated body mass index by 2030 based on MEGs projections of the Nordpred-based fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_Nordpred</td> <td>Simulated body mass index by 2040 based on MEGs projections of the Nordpred-based fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_Nordpred</td> <td>Indicator of obesity by 2030, based on MEGs projections of the Nordpred-based fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_Nordpred</td> <td>Indicator of obesity by 2040, based on MEGs projections of the Nordpred-based fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_Gompertz</td> <td>Simulated body weight by 2030 based on MEGs projections of the Gompertz model (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_Gompertz</td> <td>Simulated body weight by 2040 based on MEGs projections of the Gompertz model (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_Gompertz</td> <td>Simulated body mass index by 2030 based on MEGs projections of the Gompertz model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_Gompertz</td> <td>Simulated body mass index by 2040 based on MEGs projections of the Gompertz model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_Gompertz</td> <td>Indicator of obesity by 2030, based on MEGs projections of the Gompertz model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_Gompertz</td> <td>Indicator of obesity by 2040, based on MEGs projections of the Gompertz model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_linear</td> <td>Simulated body weight by 2030 based on MEGs projections of the linear fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_linear</td> <td>Simulated body weight by 2040 based on MEGs projections of the linear fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_linear</td> <td>Simulated body mass index by 2030 based on MEGs projections of the linear model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_linear</td> <td>Simulated body mass index by 2040 based on MEGs projections of the linear model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_linear</td> <td>Indicator of obesity by 2030, based on MEGs projections of the linear model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_linear</td> <td>Indicator of obesity by 2040, based on MEGs projections of the linear model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_rootSquare</td> <td>Simulated body weight by 2030 based on MEGs projections of the root square fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_rootSquare</td> <td>Simulated body weight by 2040 based on MEGs projections of the root square fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_rootSquare</td> <td>Simulated body mass index by 2030 based on MEGs projections of the root square fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_rootSquare</td> <td>Simulated body mass index by 2040 based on MEGs projections of the root square fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_rootSquare</td> <td>Indicator of obesity by 2030, based on MEGs projections of the root square fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_rootSquare</td> <td>Indicator of obesity by 2040, based on MEGs projections of the root square fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> </tbody> </table>
Glucose to Goal: A Model to Support Diabetes Management in Primary Care
ClinicalTrials.gov study NCT02715934. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Neurobehavioral Signatures of Sign- and Goal-Tracking in Emerging Adults: Translation of a Preclinical Model
ClinicalTrials.gov study NCT07094061. IPD Sharing: YES. Countries: 1. Publications: 0.
Models and data in support of "EMFEM: a parallel 3D modeling code for frequency-domain electromagnetic method using goal-oriented adaptive finite element method"
<p>These directories contain the model and data files for "EMFEM: a parallel 3D modeling code for frequency-domain electromagnetic method using goal-oriented adaptive finite element method".<br> </p>
Data Set Used for Biblometric Analaysis in "Optimizing Rainfall-Runoff Models Over Three Decades: Progress, Innovations, Challenges, and Insights for Sustainable Development Goals (SDGs) Based on Bibliometric Analysis"
<p>Data Set Used for Biblometric Analaysis in "Optimizing Rainfall-Runoff Models Over Three Decades: Progress, Innovations, Challenges, and Insights for Sustainable Development Goals (SDGs) Based on Bibliometric Analysis"</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.