cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4121/5f3a6a70-377b-42eb-9f46-5fd1141bed78.v1
title: Data underlying the publication: Simulation-optimization configurations for real-time decision-making in fugitive interception

description: This repository is part of the Ph.D. thesis of Irene S. van Droffelaar, Delft University of Technology.<br>This repository accompanies the paper "Simulation-optimization configurations for real-time decision-making in fugitive interception" (https://doi.org/10.1016/j.simpat.2024.102923).<br>The optimization algorithm can be found under<em> platypus-fork.zip</em>.<br>In<em> simopt-configs.zip</em>, the folders _cleaned data_ and _models_ contain all results and software for:- Sequential Simulation Optimization (abbreviated as _seq_ in file names), solved with MIP solver CBC (https://github.com/coin-or/Cbc)- Sequential Simulation Optimization (_seq_), solved with metaheuristic Borg- Simulation Model Optimization (abbreviated as _smo_ in file names), solved with metaheuristic Borg
DOI: 10.15468/dl.8sz2py
title: Occurrence Download

description: A dataset containing 560497 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Spain, Portugal)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Falco tinnunculus Linnaeus, 1758"
 ]
}

 The dataset includes 560497 records from 83 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032500-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.27604449
title: Underlying data: Does Happiness Impact Behavioral Intention in Adverse Services?

description: This file includes the raw data used for data analysis. The data was analyzed using SmartPLS.
DOI: 10.6084/m9.figshare.21679469
title: Learning from Deep Learning: lessons from using computer vision to identify (urban) form and function in open data satellite imagery

description: Data and code for the "Learning from Deep Learning: lessons from using computer vision to identify (urban) form and function in open data satellite imager" academic article.
DOI: 10.15468/dl.x9bst6
title: Occurrence Download

description: A dataset containing 69 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Genlisea lobata Fromm"
 ]
}

 The dataset includes 69 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032538-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/6d0f025b-967e-4007-9b6d-b920366d8f74
title: Data underlying the publication: The effect of models of fugitive behavior on police interception strategies

description: This repository is part of the Ph.D. thesis of Irene S. van Droffelaar, Delft University of Technology.<br><em>fug_behavior </em>contains the experiments and data files:- prep_graph_*.ipynb (e.g., [prep_graph_Manhattan.ipynb](prep_graph_Manhattan.ipynb) import the graph from OpenStreetMap and the camera data from the data folder. The output of these files is the plotted graph of the respective area and the saved graphs.- [enrich_graph_cool.ipynb](enrich_graph_cool.ipynb) and [enrich_graph_cool.ipynb](enrich_graph_cool.ipynb) import the relevant factors from OpenStreetMap and other data sources. These factors are used to create the graph attribute 'travel_time_adj' which reflects the attractiveness of a particular road segment. After, the best routes from the incident to each of the defined escape nodes are generated and saved.- [run_model_cool.py](run_model_cool.py) and [run_model_hot.py](run_model_hot.py) import the escape routes and pass them to a pyDSOL model, which adds a jitter factor to the routes. The output of these files is the resulting, final simulated escape routes.- [optimize/optimize_positions.py](optimize/optimize_positions.py) import the resulting routes and optimizes the positions of the police units. The optimized positions and the resulting interception dictionary (reflecting which routes are intercepted by the chosen strategy) are saved.- In [optimize/cross_evaluation_optimization.ipynb](optimize/cross_evaluation_optimization.ipynb), these positions and the routes are imported to evaluate their relative robustness. I.e., how well does a strategy perform on a set of routes generated using a different rationale or profile - also see the figure below. The resulting heatmaps are saved in the [optimize/](optimize/) folder.<br><em>platypus-fork</em> contains the optimization algorithm.
DOI: 10.4121/6d0f025b-967e-4007-9b6d-b920366d8f74.v1
title: Data underlying the publication: The effect of models of fugitive behavior on police interception strategies

description: This repository is part of the Ph.D. thesis of Irene S. van Droffelaar, Delft University of Technology.<br><em>fug_behavior </em>contains the experiments and data files:- prep_graph_*.ipynb (e.g., [prep_graph_Manhattan.ipynb](prep_graph_Manhattan.ipynb) import the graph from OpenStreetMap and the camera data from the data folder. The output of these files is the plotted graph of the respective area and the saved graphs.- [enrich_graph_cool.ipynb](enrich_graph_cool.ipynb) and [enrich_graph_cool.ipynb](enrich_graph_cool.ipynb) import the relevant factors from OpenStreetMap and other data sources. These factors are used to create the graph attribute 'travel_time_adj' which reflects the attractiveness of a particular road segment. After, the best routes from the incident to each of the defined escape nodes are generated and saved.- [run_model_cool.py](run_model_cool.py) and [run_model_hot.py](run_model_hot.py) import the escape routes and pass them to a pyDSOL model, which adds a jitter factor to the routes. The output of these files is the resulting, final simulated escape routes.- [optimize/optimize_positions.py](optimize/optimize_positions.py) import the resulting routes and optimizes the positions of the police units. The optimized positions and the resulting interception dictionary (reflecting which routes are intercepted by the chosen strategy) are saved.- In [optimize/cross_evaluation_optimization.ipynb](optimize/cross_evaluation_optimization.ipynb), these positions and the routes are imported to evaluate their relative robustness. I.e., how well does a strategy perform on a set of routes generated using a different rationale or profile - also see the figure below. The resulting heatmaps are saved in the [optimize/](optimize/) folder.<br><em>platypus-fork</em> contains the optimization algorithm.
DOI: 10.15468/dl.dkew9n
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is one of (Primulina bogneriana (B.L.Burtt) Mich.Möller & A.Weber)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032539-241107131044228/datasets/export for details.
DOI: 10.18419/darus-4518/1
title: command_line_arguments.yaml
DOI: 10.18419/darus-4518/17
title: events.out.tfevents.1726044593.pcsgs11.2045409.0
DOI: 10.18419/darus-4518/29
title: command_line_arguments.yaml
DOI: 10.18419/darus-4518/31
title: avg_error.png
DOI: 10.18419/darus-4518/32
title: avg_error.png
DOI: 10.18419/darus-4518/5
title: avg_error.png
DOI: 10.18419/darus-4518/8
title: command_line_arguments.yaml
DOI: 10.18419/darus-4518/9
title: events.out.tfevents.1726044378.simcl1n2.1741344.0
DOI: 10.18419/darus-4518/12
title: info.yaml
DOI: 10.18419/darus-4518/15
title: info.yaml
DOI: 10.18419/darus-4518/16
title: events.out.tfevents.1727768317.pcsgs11.2022953.0
DOI: 10.18419/darus-4518/18
title: info.yaml
DOI: 10.18419/darus-4518/19
title: learning_rate_history.tab
DOI: 10.18419/darus-4518/2
title: log_best_loss_per_epoch.tab
DOI: 10.18419/darus-4518/20
title: learning_rate_history.tab
DOI: 10.18419/darus-4518/21
title: log_best_loss_per_epoch.tab
DOI: 10.18419/darus-4518/30
title: events.out.tfevents.1727686070.simcl1n1.136646.0
DOI: 10.18419/darus-4518/33
title: log_best_loss_per_epoch.tab
DOI: 10.18419/darus-4518/4
title: learning_rate_history.tab
DOI: 10.18419/darus-4518/10
title: model_structure.txt
DOI: 10.18419/darus-4518/11
title: model.pt
DOI: 10.18419/darus-4518/13
title: model.pt
DOI: 10.18419/darus-4518/14
title: model_structure.txt
DOI: 10.18419/darus-4518/22
title: measurements_train.yaml
DOI: 10.18419/darus-4518/23
title: measurements_train.yaml
DOI: 10.18419/darus-4518/26
title: model.pt
DOI: 10.18419/darus-4518/27
title: log_loss_per_epoch.tab
DOI: 10.18419/darus-4518/3
title: log_loss_per_epoch.tab
DOI: 10.18419/darus-4518/6
title: measurements_train.yaml
DOI: 10.18419/darus-4518/7
title: log_loss_per_epoch.tab
DOI: 10.18419/darus-4518/28
title: model_structure.txt
DOI: 10.15468/dl.aubv46
title: Occurrence Download

description: A dataset containing 2360 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Leucocarbo chalconotus (G.R.Gray, 1845)",
 "Year 1000-2016"
 ]
}

 The dataset includes 2360 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032540-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/fa299948-661f-4003-a4c1-a4f3a6bb2809
title: Data underlying the publication: Timely Adaptive Strategies for Fugitive Interception

description: This repository is part of the Ph.D. thesis of Irene S. van Droffelaar, Delft University of Technology.<br>This repository accompanies the paper "Timely Adaptive Strategies for Fugitive Interception".<br>Folders <em>DirectPolicySearch, OneShot, PeriodicReOpt, and PolicyTreeOpt</em> contain the models and results for each of the solution approaches used in the paper. Each folder contains the folders <em>testgraph, grid</em>, and <em>city</em>. Each of those contains the runfiles, model files, and HPC results.<br>DirectPolicySearchtestgraphresults_HPC/fugitive_interception_model_city.pyrun_city.pyrun_experiment_city.pygridcityOneShotPeriodicReOptPolicyTreeOpt<br>The optimization algorithm can be found under<em> platypus-fork.zip</em>.
DOI: 10.4121/fa299948-661f-4003-a4c1-a4f3a6bb2809.v1
title: Data underlying the publication: Timely Adaptive Strategies for Fugitive Interception

description: This repository is part of the Ph.D. thesis of Irene S. van Droffelaar, Delft University of Technology.<br>This repository accompanies the paper "Timely Adaptive Strategies for Fugitive Interception".<br>Folders <em>DirectPolicySearch, OneShot, PeriodicReOpt, and PolicyTreeOpt</em> contain the models and results for each of the solution approaches used in the paper. Each folder contains the folders <em>testgraph, grid</em>, and <em>city</em>. Each of those contains the runfiles, model files, and HPC results.<br>DirectPolicySearchtestgraphresults_HPC/fugitive_interception_model_city.pyrun_city.pyrun_experiment_city.pygridcityOneShotPeriodicReOptPolicyTreeOpt<br>The optimization algorithm can be found under<em> platypus-fork.zip</em>.
DOI: 10.23668/psycharchives.14072
title: EAS data from two raters for a sample of preschool aged children

description: Two certified raters rated N=116 dyade of preschool aged children and a parent for a control and clinical sample. The related code (including variable descriptions as well as data processing and analyses steps) was published under https://doi.org/10.23668/psycharchives.14044.
DOI: 10.15148/b4b4a2e1-3d00-45b7-b3b2-2ca05d054346
title: ECOPATH: brown skua Pasteurella multocida epidemiological data and GPS tracking data (Amsterdam Island, 2015-2017)

description: Pateurella multocida epidemiological data and GPS tracking data collected on brown skuas from Amsterdam Island in naturally exposed birds between 2015 and 2017. ams_species_altitude (.csv and correcponding .meta): altitude of the breeding grounds of the main vertebrate species of Amsterdam Island. sku_epidemio (.csv and correcponding .meta): capture data and Pasteurella multocida serological and PCR data from brown skuas. sku_gps (.csv and correcponding .meta): gps tracking data from brown skuas. yna_nestlings (.csv and correcponding .meta): survival and Pasteurella multocida prevalence data from yellow-nosed albatross nestlings during the brown skua study period. immuno_functions.r: homemade functions for basic epidemiological data exploration (del_dup(), get_prev(), get_stat()). ecopath_yna_pcr_notebook.html: R notebook presenting the codes for the analyses and figures presented in the manuscript. BRB_MKDE (.txt and corresponding .meta): example of a BRB_MKDE file for the analysis of GPS tracking data
DOI: 10.23668/psycharchives.14075
title: Dataset for: Academic risk taking and teaching quality in higher education

description: Background: Academic risk taking (ART) is a type of learning-beneficial student engagement with a risk of making errors. Students for example respond to questions during seminars, even though they are not sure about the correctness of their answer. Next to a trait-component, ART is assumed to have a state component which can change depending on the context. Aims: In this study, we explore whether students’ ART differs by seminar belonging, and whether teaching quality explains these differences. Additionally, we consider the association between students’ goal orientation and ART. Sample: For this purpose, a sample of 381 university students nested in 38 seminars and instructors was acquired. Methods: Data was analyzed following a two-level structural equation modeling approach. Results: We find significant differences in students’ ART by seminar group, explaining between 11.0% and 16.3% of variance. Reproduction orientation in teaching seems to discourage ART. Conclusions: Our results contribute to an understanding of the multidimensional dynamics of teaching and learning, and offer implications for teaching quality development.
description: Dataset for: Hübner, V., & Pfost, M. (2024). Academic risk taking and teaching quality in higher education. In Learning and Instruction (Vol. 90, p. 101877). Elsevier BV. https://doi.org/10.1016/j.learninstruc.2024.101877
DOI: 10.4121/07643762-6038-4ccc-bf94-4bf56b5abeae
title: Data underlying the publication: Graph Coarsening for Fugitive Interception

description: This repository is part of the Ph.D. thesis of Irene S. van Droffelaar, Delft University of Technology.<br><em>coarseningFIP.zip </em>contains all algorithms and data accompanying the paper "Graph Coarsening for Fugitive Interception":Directories <em>pruning</em>, <em>consolidate_nodes</em>, <em>heuristic</em>, and <em>onthefly</em> contain the algorithms for graph coarsening. The <em>heuristic</em> coarsening algorithm is the Python implementation of the algorithm proposed by Krishnakumari et al. (2020) and can also be found in <em>heuristic_coarsening.zip</em> and https://github.com/irene-sophia/HeuristicCoarsening.The directory <em>HPC_results</em> contains the results of the experiments for all five road networks for all algorithms.The directory <em>analysis</em> contains the notebooks that analyze the experiments, also including the cross-evaluation, counting the numbers of nodes in each network, and the timing experiments. Most plots in the paper are generated in the <em>compare_methods.ipynb</em> notebook.The directory <em>data</em> contains the coarsened networks resulting from each algorithm, and the simulated routes.The directory <em>route_simulation</em> contains the route generation code.<br><em>platypus-fork.zip</em> contains the optimization algorithm.<br>
DOI: 10.4121/07643762-6038-4ccc-bf94-4bf56b5abeae.v1
title: Data underlying the publication: Graph Coarsening for Fugitive Interception

description: This repository is part of the Ph.D. thesis of Irene S. van Droffelaar, Delft University of Technology.<br><em>coarseningFIP.zip </em>contains all algorithms and data accompanying the paper "Graph Coarsening for Fugitive Interception":Directories <em>pruning</em>, <em>consolidate_nodes</em>, <em>heuristic</em>, and <em>onthefly</em> contain the algorithms for graph coarsening. The <em>heuristic</em> coarsening algorithm is the Python implementation of the algorithm proposed by Krishnakumari et al. (2020) and can also be found in <em>heuristic_coarsening.zip</em> and https://github.com/irene-sophia/HeuristicCoarsening.The directory <em>HPC_results</em> contains the results of the experiments for all five road networks for all algorithms.The directory <em>analysis</em> contains the notebooks that analyze the experiments, also including the cross-evaluation, counting the numbers of nodes in each network, and the timing experiments. Most plots in the paper are generated in the <em>compare_methods.ipynb</em> notebook.The directory <em>data</em> contains the coarsened networks resulting from each algorithm, and the simulated routes.The directory <em>route_simulation</em> contains the route generation code.<br><em>platypus-fork.zip</em> contains the optimization algorithm.<br>
DOI: 10.23668/psycharchives.14078
title: Dataset for: Children's statistical learning capacity and arithmetic principle use

description: Statistical learning—an unconscious cognitive process used to extract regularities—is well-established as a fundamental mechanism underlying learning. Yet, despite the prominence of patterns in the number system and operations, little is known about the relation between statistical learning and mathematics knowledge. This study examined the associations among statistical learning, executive control, and arithmetic knowledge among first graders (N = 54). Individual differences in visual statistical learning negatively predicted response time on addition problems and positively predicted accuracy on subtraction problems. These relations remained even after accounting for executive control. These findings provide an impetus for testing new models of mathematics learning that include statistical learning as a potentially important mechanism.
DOI: 10.15468/dl.8hzx2e
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is one of (Primulina brachystigma (W.T.Wang) Mich.Möller & A.Weber)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032541-241107131044228/datasets/export for details.
DOI: 10.15468/dl.6yb4tj
title: Occurrence Download

description: A dataset containing 204 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Glis glis (Linnaeus, 1766)",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 204 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032542-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dm5dam
title: Occurrence Download

description: A dataset containing 815 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Leucocarbo colensoi (Buller, 1888)",
 "Year 2000-2024"
 ]
}

 The dataset includes 815 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032543-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.23668/psycharchives.14087
title: Dataset for: CFA, MI, Alpha, Criterion Validity, and Test-retest for AGS

description: The academic grit scale (AGS) is a new measure developed to assess the level of adolescents’ grit in an academic-specific context. The main purpose of this study was to examine the psychometric properties, factor structure, and measurement invariance of the AGS among Chinese adolescents. A cross-sectional design and convenient sampling were conducted in a sample of Chinese adolescents (N = 619, 50.6% female, Mage = 14.56 years, SD = 1.47 years) using the AGS. Confirmatory factor analysis (CFA) supported the original unidimensional model of the AGS, and multiple-group CFA further verified the AGS scores had strong measurement invariance across gender (i.e., boys and girls) and grade level (i.e., middle school students and high school students). Moreover, the AGS also showed satisfactory internal consistency (using Cronbach’s α, McDonald’s ω, and mean inter-item correlation) and criterion validity, supported by expected relationships with scores on external variables (i.e., general grit, anxiety, and depression). In conclusion, these findings suggest that the AGS has satisfactory psychometric properties and can be a reliable tool to measure the academic grit level of Chinese adolescents.
description: Dataset for: Dong-Li Bei; Yang Liu; Chuan-Zhang Zheng; Cheng-Kui Yao; Jie Luo; Ying Ge (2024). The Psychometric Properties and Measurement Invariance of the Academic Grit Scale (AGS) in Chinese Adolescents. European Journal of Psychological Assessment. https://doi.org/10.1027/1015-5759/a000832
DOI: 10.15468/dl.r7mb93
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Neolebias lozii Winemiller & Kelso-Winemiller, 1993"
 ]
}

 The dataset includes 23 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032544-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.na958t
title: Occurrence Download

description: A dataset containing 106 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Genlisea pygmaea A.St.-Hil."
 ]
}

 The dataset includes 106 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032545-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j4z9u7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is one of (Primulina brachytricha (W.T.Wang & D.Y.Chen) R.B.Mao & Yin Z.Wang)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032546-241107131044228/datasets/export for details.
DOI: 10.23668/psycharchives.14096
title: Dataset for: Production benefits on encoding are modulated by language experience: Less experience may help

description: Dataset for: Production benefits on encoding are modulated by language experience: Less experience may help