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585 results for “Goal”
Eight archetypes of Sustainable Development Goal (SDG) synergies and trade-offs
<p>Data S1 - Details of system archetype application articles reviewed systematically.</p>
Goal Structuring Notation for Formal Methods in the Safety Case
<p>A Goal Structuring Notation goal structure for arguing safety of an Automated Driving System by the use of formal methods.</p>
Material Experimental - Comparison of Goal-oriented Analysis Techniques
<p>Experimental material and dataset used to compare GRL-Quant and VeGAn techniques.</p>
Quantitative Assessment of G7's Collaboration in Sustainable Development Goals
<div> </div> <p><strong>Authors</strong>: Kai Liu <sup>[1]</sup>, Ali Raisolsadat (<a href="mailto:arraisolsadat@uwaterloo.ca">arraisolsadat@uwaterloo.ca</a>) <sup>[2]</sup>, Xander Wang (<a href="mailto:xxwang@upei.ca">xxwang@upei.ca</a>) <sup>[3,4]</sup>, and Quan Van Dau (<a href="mailto:vdau@upei.ca">vdau@upei.ca</a>) <sup>[3,4]</sup></p> <p><strong>Institutions</strong>:</p> <ol> <li>School of Mathematical and Computational Sciences, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada C1A 4P3</li> <li>Faculty of Mathematics, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1</li> <li>Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, St. Peter's Bay, Prince Edward Island, Canada C0A 2A0</li> <li>School of Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada C1A 4P3</li> </ol> <p><strong>Corresponding Author</strong>: Dr. Xander Wang<br><strong>Contact Information</strong>: <a href="mailto:xxwang@upei.ca">xxwang@upei.ca</a></p> <div> <h2>Repository Contents</h2> </div> <p>This repository contains the code and data for the project titled "Quantitative Assessment of G7's Collaboration in Sustainable Development Goals".</p> <div> <h2>Information about the Folders</h2> </div> <ul> <li><strong><code>sdg_raw_data</code></strong>: Contains the raw Sustainable Development Goals indicator data from the "Our World in Data" database.</li> <li><strong><code>sdg_grouped_raw_data</code></strong>: Contains the raw SDG indicator data, but grouped for each goal (1-15).</li> <li><strong><code>results_datasets</code></strong>: Contains the main results for Domestic Changes, Foreign Changes, and Synergy data in <code>.CSV</code> and <code>.RData</code> formats.</li> <li><strong><code>main_manuscript_figures</code></strong>: Contains the 5 main figures used in the manuscript text.</li> <li><strong><code>s1_s12_supplementary_figures</code></strong>: Contains the 12 figures from the supplementary material of the manuscript.</li> <li><strong><code>partial_true_direction_un.csv</code></strong>: Contains the indicator directions from Table 1 of the manuscript.</li> <li><strong><code>SDG_Data.xlsx</code></strong>: An Excel file which contains all the data used in the manuscript results in multiple sheets, including SDG raw data and results datasets.</li> </ul> <div> <h2>Prerequisites</h2> </div> <ul> <li><strong>R</strong>: Please ensure that you have installed the latest version of the R software for your device. You can download it from <a href="https://cran.r-project.org/" rel="nofollow">CRAN</a>.</li> <li><strong>RStudio</strong>: It is recommended to use RStudio for running the R scripts. You can download it from <a href="https://rstudio.com/products/rstudio/download/" rel="nofollow">RStudio's official website</a>.</li> </ul> <div> <h2>How to Run</h2> </div> <ol> <li><strong>Download <a href="../api/records/11659806/draft/files/Synergy-2024-1.0.0.zip/content" target="_blank" rel="noopener noreferrer">Synergy-2024-1.0.0.zip</a> to your local computer and unzip it</strong></li> <li> <p><strong>Set the directory to the unzipped folder</strong></p> </li> <li><strong>Set the R working directory to the unzipped folder</strong></li> <li> <p><strong>Run Main Code</strong>:</p> <ul> <li>Open and run the <code>gross_synergy_markdown.Rmd</code> file. This is the main code for our manuscript.</li> <li>The resulting datasets will be saved in the <code>results_datasets</code> folder.</li> </ul> </li> <li> <p><strong>Generate Figure 1</strong>:</p> <ul> <li>Open and run <code>figure_1.R</code>.</li> <li>The resulting figure will be saved in the <code>main_manuscript_figures</code> folder.</li> </ul> </li> <li> <p><strong>Generate Figure 2</strong>:</p> <ul> <li>Open and run <code>figure_2.R</code>.</li> <li>The resulting figure will be saved in the <code>main_manuscript_figures</code> and <code>s1_s12_supplementary_figures</code> folders, respectively.</li> </ul> </li> <li> <p><strong>Generate Figure 3</strong>:</p> <ul> <li>Open and run <code>figure_3.R</code>.</li> <li>The resulting figure will be saved in the <code>main_manuscript_figures</code> folder.</li> </ul> </li> <li> <p><strong>Generate Figure 4</strong>:</p> <ul> <li>Open and run <code>figure_4.R</code>.</li> <li>The resulting figure will be saved in the <code>main_manuscript_figures</code> and <code>s1_s12_supplementary_figures</code> folders, respectively.</li> </ul> </li> <li> <p><strong>Generate Figure 5</strong>:</p> <ul> <li>Open and run <code>figure_5.R</code>.</li> <li>The resulting figure will be saved in the <code>main_manuscript_figures</code> folder.</li> </ul> </li> </ol>
Dataset for "Francisco Cirelli, Dalal Alrajeh, Sebastian Uchitel. Unavoidable Boundary Conditions: A Control Perspective on Goal Conflicts. In Proceedings of the 47th International Conference on Software Engineering 2025."
<p>This is the experimental data for the paper </p> <p>Francisco Cirelli, Dalal Alrajeh, Sebastian Uchitel<br>Unavoidable Boundary Conditions: A Control Perspective on Goal Conflicts. <br>In Proceedings of the 47th International Conference on Software Engineering 2025. </p> <p>We provide various specifications of reactive synthesis control problems taken from various sources. See paper for more details.</p>
Higher-order and distributed synergistic functional interactions encode information gain in goal-directed learning
<p>Dataset used in "Higher-order and distributed synergistic functional interactions encode information gain in goal-directed learning"</p> <p>https://www.biorxiv.org/content/10.1101/2024.09.23.614484v1</p>
How Do Clearly Defined Learning Goals Enhance Science Performance? An Investigation for Sustainable Education
Open the record for dataset details and reuse information.
Data from: Failure to coordinate management in transboundary populations hinders the achievement of national management goals: the case of wolverines in Scandinavia
1. Large carnivores are expanding in Europe, and their return is associated with conflicts that often result in policies to regulate their population size through culling. Being wide-ranging species, their populations are often distributed across several jurisdictions, which may vary in the extent to which they use lethal control. This creates the conditions for the establishment of source-sink dynamics across borders, which may frustrate the ability of countries to reach their respective management objectives. 2. To explore the consequences of this issue, we constructed a vec-permutation projection model, applied to the case of wolverines in south-central Scandinavia, shared between Norway (where they are culled) and Sweden (where they are protected). We evaluated the effect of compensatory immigration on wolverine population growth rates, and if the effect was influenced by the distance to the national border. We assessed to what extent compensatory immigration had an influence on the number of removals needed to keep the population at a given growth rate. 3. In Norway the model estimated a stable trend, whereas in Sweden it produced a 10% annual increase. The effect of compensatory immigration corresponded to a 0.02 reduction in population growth rate in Sweden and to a similar increase in Norway. This effect was stronger closer to the Norwegian-Swedish border, but weak when moving away from it. An average of 33 wolverines were shot per year in the Norwegian part of the study area. If no compensatory immigration from Sweden had occurred, 28 wolverines shot per year would have been sufficient to achieve the same goal. About 15.5% of all the individuals harvested in Norway between 2005-2012 were compensated for by immigrants, causing a decrease in population growth rate in Sweden. 4. Synthesis and applications. When a population is transboundary, the consequences of management decisions are also transboundary, even though the political bodies in charge of those decisions, the stakeholders who influence them, and the taxpayers who finance them are not. It is important that managers and citizens be informed that a difference in management goals can reduce the efficiency, and increase the costs, of wildlife management.
Understanding emergence of the UN Sustainable Development Goals Research
<p>Emerging new scientific knowledge associated with the UN Sustainable Development Goals (SDGs) can contribute to dealing with the complexities of social, economic, and environmental challenges. Identifying and understanding the underlying conditions that enable and constrain the emergence of SDGs research can help to increase the transformative potential of scientific knowledge. We investigate the role of universities in facilitating the emergence and consolidation of SDGs research using Utrecht University as a case. Our approach offers a systematic understanding of the development of research areas associated with the SDGs -including opportunities and constraints- to support universities in identifying the thematic orientation of their current research in the framework of the SDGs. This approach can thus increase reflexibility about the contribution of universities to implement the SDGs. Our results reveal that universities can enhance awareness about the SDGs, generate reflections on SDG research, offer institutional opportunities to interconnect diverse knowledge domains and diverse social actors, and nurture and develop emerging research associated with the SDGs.</p>
Dataset: Stimulus salience conflicts and colludes with endogenous goals during urgent choices
<p>This dataset (packaged as the zip file 3CS_datashare.zip) accompanies the article titled "Stimulus salience conflicts and colludes with endogenous goals during urgent choices" by EE Oor, TR Stanford, and E Salinas, iScience 26:106253 (2023).</p> <p>The experimental results in the paper are based on behavioral data collected from three monkey subjects during performance of visuomotor tasks, as described in the article. This dataset contains the trial-by-trial results collected for each subject and upon which all subsequent analyses were based.</p> <p>In addition to the trial-wise data arrays (stored in three *.csv files), the dataset includes Matlab functions and scripts (*.m files) used to analyze the data and generate figures in the article. Instructions and specifics are detailed in the README file.</p>
eu circular economy goal
<p>Circular economy data included predictions to 2035</p>
circular economy goals
<p>Data on circular economy targets in europe</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>
Decreasing Postoperative Complications by Goal-Directed Fluid Therapy During Esophageal Resection
ClinicalTrials.gov study NCT01416077. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Trauma-Informed Goal Management Training for Public Safety Personnel (PSP) With Post-traumatic Stress Disorder (PTSD)
ClinicalTrials.gov study NCT06354361. IPD Sharing: Not stated. Countries: 0. Publications: 3.
Obesity and Goal-directed Intraoperative Fluid Therapy
ClinicalTrials.gov study NCT01052519. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Assessing Habitual, Goal-Directed, and Pavlovian Influences in Alcohol Use Disorder
ClinicalTrials.gov study NCT06701500. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Goal Setting for Health Behavior and Psychosocial Issues in Primary Care
ClinicalTrials.gov study NCT01825746. IPD Sharing: Not stated. Countries: 1. Publications: 5.
The Effect of Goal-directed Hemodynamic Therapy in Radical Cystectomy
ClinicalTrials.gov study NCT03505112. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Early Goal Nutrition Therapy Guided by Indirect Calorimetry and Nitrogen Balance Among Critically Ill Patients With Acute Kidney Injury (ENGINE Study)
ClinicalTrials.gov study NCT06238674. IPD Sharing: YES. Countries: 1. Publications: 3.
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.