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47 results for “Choice Modelling”

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zenodo32/100

Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model - Data

<p>This repository contains the data and additional information for the paper 'Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model'.&nbsp;</p> <p>The following files are included:</p> <ul> <li>accuracy.zip: raw performance files for all models;</li> <li>plots.zip: additional plots;</li> <li>tuning.zip: tuning log files.</li> </ul>

opencc-by-4.0Nov 2024View details →
zenodo32/100

Water migration through enzyme tunnels is sensitive to the choice of explicit water model (AldO + CYP2D6)

<p>This repository contains data for the alditol oxidase (AldO) and cytochrome P450 2D6 (CYP2D6). Data underpinning analyses of haloalkane dehalogenase DhaA are available from the related repository:&nbsp;<a href="https://doi.org/10.5281/zenodo.11489893">10.5281/zenodo.11489893</a>.</p> <p><strong>Content:</strong></p> <p><strong>AldO.tar.gz:</strong></p> <p><strong>tt_conda.yml -&gt; conda environment used for the calculations.&nbsp;</strong><br>&nbsp; &nbsp; &nbsp; &nbsp; Usage :<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;conda env create -f tt_conda.yml<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;conda activate tt_conda.yml&nbsp;</p> <p><strong>01_MD_simulations -&gt; the files to run simulation, out and restart files from simulation and simulation analysis results, organized by models and Tunnel Conformational Groups (TCGs).</strong></p> <p>├── 01_MD_simulations<br>│ &nbsp; ├── 01_inputs<br>│ &nbsp; │ &nbsp; ├── opc<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; │ &nbsp; ├── scripts<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 00_prepare_model.sh<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 01_minimization.sh<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 02_equilibration.sh<br>│ &nbsp; │ &nbsp; │ &nbsp; └── 03_production.sh<br>│ &nbsp; │ &nbsp; ├── tip3p<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; │ &nbsp; └── tip4pew<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; ├── 02_outputs<br>│ &nbsp; │ &nbsp; ├── opc<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; │ &nbsp; ├── tip3p<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; │ &nbsp; └── tip4pew<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; ├── 03_analysis<br>│ &nbsp; │ &nbsp; ├── rmsd_opc.csv<br>│ &nbsp; │ &nbsp; ├── rmsd_tip3p.csv<br>│ &nbsp; │ &nbsp; ├── rmsd_tip4pew.csv<br>│ &nbsp; │ &nbsp; ├── rmsf_opc.csv<br>│ &nbsp; │ &nbsp; ├── rmsf_tip3p.csv<br>│ &nbsp; │ &nbsp; └── rmsf_tip4pew.csv<br>│ &nbsp; └── readme.txt<br><br><strong>02_caver -&gt; results of CAVER calculations, organized by models and TCGs.</strong></p> <p>├── 02_caver<br>│ &nbsp; ├── config_files<br>│ &nbsp; │ &nbsp; ├── calculate_tunnels.txt<br>│ &nbsp; │ &nbsp; └── clustering.txt<br>│ &nbsp; ├── opc<br>│ &nbsp; │ &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; │ &nbsp; └── 5<br>│ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── 5<br>│ &nbsp; ├── readme.txt<br>│ &nbsp; ├── tip3p<br>│ &nbsp; │ &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; │ &nbsp; └── 5<br>│ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── 5<br>│ &nbsp; └── tip4pew<br>│ &nbsp; &nbsp; &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; └── 5<br>│ &nbsp; &nbsp; &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; └── 5</p> <p><strong>03_aquaduct -&gt; results of AQUA-DUCT calculations, organized by models and TCGs. &nbsp; </strong></p> <p>├── 03_aquaduct<br>│ &nbsp; ├── opc<br>│ &nbsp; │ &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; │ &nbsp; └── 5<br>│ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── 5<br>│ &nbsp; ├── readme.txt<br>│ &nbsp; ├── tip3p<br>│ &nbsp; │ &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; │ &nbsp; └── 5<br>│ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── 5<br>│ &nbsp; └── tip4pew<br>│ &nbsp; &nbsp; &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; └── 5<br>│ &nbsp; &nbsp; &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; └── 5&nbsp;</p> <p><strong>04_transport_tools -&gt; the results of TransportTools and analysis done from TransportTools results.</strong></p> <p>├── 04_transport_tools<br>│ &nbsp; ├── overall_results<br>│ &nbsp; │ &nbsp; ├── config.in<br>│ &nbsp; │ &nbsp; ├── data<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── exact_matching_analysis<br>│ &nbsp; │ &nbsp; │ &nbsp; └── super_clusters<br>│ &nbsp; │ &nbsp; ├── statistics<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1-initial_tunnels_statistics_bottleneck_residues.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1-initial_tunnels_statistics.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2-filtered_tunnels_statistics_bottleneck_residues.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2-filtered_tunnels_statistics.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3-initial_events_statistics_bottleneck_residues.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3-initial_events_statistics.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4-filtered_events_statistics_bottleneck_residues.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4-filtered_events_statistics.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; └── comparative_analysis<br>│ &nbsp; │ &nbsp; ├── transport_tools.log<br>│ &nbsp; │ &nbsp; └── visualization<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1-visualize_initial_tunnels.py<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2-visualize_filtered_tunnels.py<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3-visualize_initial_events.py<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4-visualize_filtered_events.py<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── comparative_analysis<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── sources<br>│ &nbsp; └── scripts<br>│ &nbsp; &nbsp; &nbsp; ├── bottleneck_residues<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 5_bottleneck_residues.py<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; └── T1.png<br>│ &nbsp; &nbsp; &nbsp; ├── presence_of_tunnels<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 6_tunnels_before_assignment.py<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── before.png<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; └── number_frames.pkl<br>│ &nbsp; &nbsp; &nbsp; └── water_transport_analysis<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 8_main_figure.py<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── figure2.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── output<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── percent_frames_events_AldO.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── transit_time_median.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── tt_events.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; └── water_per_frame_AldO.png</p> <p>&nbsp;</p> <p><strong>CYP2D6.tar.gz:</strong></p> <p><strong>tt_conda.yml -&gt; conda environment used for the calculations.&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp; </strong>Usage :<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;conda env create -f tt_conda.yml<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;conda activate tt_conda.yml&nbsp;</p> <p><strong>01_MD_simulations -&gt; the files to run simulation, out and restart files from simulation and simulation analysis results, organized by models and Tunnel Conformational Groups (TCGs).</strong></p> <p>├── 01_MD_simulations<br>│ &nbsp; ├── 01_inputs<br>│ &nbsp; │ &nbsp; ├── opc<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; │ &nbsp; ├── scripts<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 01_minimization_heating_CPU_ARES_prep.sh<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 02_equilibration_GPU_ARES_prep.sh<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 03_production_ARES.sh<br>│ &nbsp; │ &nbsp; │ &nbsp; └── prepare_model_3tbg.sh<br>│ &nbsp; │ &nbsp; ├── tip3p<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; │ &nbsp; └── tip4pew<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; ├── 02_outputs<br>│ &nbsp; │ &nbsp; ├── opc<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; │ &nbsp; ├── tip3p<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; │ &nbsp; └── tip4pew<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; ├── 03_analysis<br>│ &nbsp; │ &nbsp; ├── rmsd_opc.csv<br>│ &nbsp; │ &nbsp; ├── rmsd_tip3p.csv<br>│ &nbsp; │ &nbsp; ├── rmsd_tip4pew.csv<br>│ &nbsp; │ &nbsp; ├── rmsf_opc.csv<br>│ &nbsp; │ &nbsp; ├── rmsf_tip3p.csv<br>│ &nbsp; │ &nbsp; └── rmsf_tip4pew.csv<br>│ &nbsp; └── readme.txt</p> <p><strong>02_caver -&gt; results of CAVER calculations, organized by models and TCGs.</strong></p> <p>├── 02_caver<br>│ &nbsp; ├── config_files<br>│ &nbsp; ├── opc<br>│ &nbsp; │ &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; │ &nbsp; └── 5<br>│ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── 5<br>│ &nbsp; ├── readme.txt<br>│ &nbsp; ├── tip3p<br>│ &nbsp; │ &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; │ &nbsp; └── 5<br>│ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── 5<br>│ &nbsp; └── tip4pew<br>│ &nbsp; &nbsp; &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; └── 5<br>│ &nbsp; &nbsp; &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; └── 5</p> <p><strong>03_aquaduct -&gt; results of AQUA-DUCT calculations, organized by models and TCGs. &nbsp; </strong></p> <p>├── 03_aquaduct<br>│ &nbsp; ├── opc<br>│ &nbsp; │ &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; │ &nbsp; └── 5<br>│ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── 5<br>│ &nbsp; ├── readme.txt<br>│ &nbsp; ├── tip3p<br>│ &nbsp; │ &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; │ &nbsp; └── 5<br>│ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── 5<br>│ &nbsp; └── tip4pew<br>│ &nbsp; &nbsp; &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; └── 5<br>│ &nbsp; &nbsp; &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; └── 5</p> <p><strong>04_transport_tools -&gt; the results of TransportTools and analysis done from TransportTools results.</strong></p> <p>├── 04_transport_tools<br>│ &nbsp; ├── overall_results<br>│ &nbsp; │ &nbsp; ├── config.in<br>│ &nbsp; │ &nbsp; ├── data<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── exact_matching_analysis<br>│ &nbsp; │ &nbsp; │ &nbsp; └── super_clusters<br>│ &nbsp; │ &nbsp; ├── statistics<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1-initial_tunnels_statistics_bottleneck_residues.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1-initial_tunnels_statistics.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2-filtered_tunnels_statistics_bottleneck_residues.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2-filtered_tunnels_statistics.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3-initial_events_statistics_bottleneck_residues.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3-initial_events_statistics.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4-filtered_events_statistics_bottleneck_residues.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4-filtered_events_statistics.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; └── comparative_analysis<br>│ &nbsp; │ &nbsp; ├── transport_tools.log<br>│ &nbsp; │ &nbsp; └── visualization<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1-visualize_initial_tunnels.py<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2-visualize_filtered_tunnels.py<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3-visualize_initial_events.py<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4.pse<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4-visualize_filtered_events.py<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── comparative_analysis<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── sources<br>│ &nbsp; └── scripts<br>│ &nbsp; &nbsp; &nbsp; ├── bottleneck_analyses<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 5_bottleneck_residues.py<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── Ch2B-F.png<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── Ch2C.png<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; └── ChS.png<br>│ &nbsp; &nbsp; &nbsp; ├── presence_of_tunnels<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 6_tunnels_before_assignment.py<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── before.png<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; └── number_frames.pkl<br>│ &nbsp; &nbsp; &nbsp; └── water_transport_analysis<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 8_main_figure.py<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── figure2_Ch2B-Ch2F.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── figure2_Ch2C.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── figure2_Ch2S.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── output<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── percent_frames_events_Ch2B-Ch2F.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── percent_frames_events_Ch2C.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── percent_frames_events_ChS.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── transit_time_median_Ch2B-Ch2F.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── transit_time_median_Ch2C.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── transit_time_median_ChS.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── tt_events_Ch2B-Ch2F.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── tt_events_Ch2C.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── tt_events_Ch2S.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── water_per_frame_Ch2B-Ch2F.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── water_per_frame_Ch2C.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; └── water_per_frame_ChS.png</p>

opencc-by-4.0Jun 2024View details →
ClinicalTrials.gov32/100

Model Predictive Control (MPC) Artificial Pancreas vs. Sensor Augmented Pump (SAP)/Predictive Low Glucose Suspend (PLGS) With Different Food Choices in the Outpatient Setting

ClinicalTrials.gov study NCT03767790. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov32/100

The Choice of a Violent Suicidal Means: a MRI Study With Computational Modeling of Decision-making

ClinicalTrials.gov study NCT05230043. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
dryad32/100

Data from: Testing the validity of functional response models using molecular gut content analysis for prey choice in soil predators

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publicDec 2017View details →
dryad32/100

Data from: Influence of device accuracy and choice of algorithm for species distribution modelling of seabirds: a case study using black-browed albatrosses

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publicFeb 2017View details →
dryad32/100

Data from: Objective choice of phylogeographic models

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publicSep 2017View details →
dryad32/100

Data from: Re-evaluating neonatal-age models for ungulates: does model choice affect survival estimates?

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publicSep 2015View details →
dryad32/100

Data from: Trial marriage model – female mate choice under male interference

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publicApr 2020View details →
dryad32/100

Data from: Exploring foraging decisions in a social primate using discrete choice models

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publicMay 2012View details →
dryad32/100

Data from: Discrete choice modelling of natal dispersal: "choosing" where to breed from a finite set of available areas

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publicApr 2016View details →
dryad32/100

Data from: Examining temporal sample scale and model choice with spatial capture-recapture models in the common leopard Panthera pardus

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publicOct 2016View details →
dryad28/100

Data from: The effects of model choice and mitigating bias on the ribosomal tree of life

Deep-level relationships within Bacteria, Archaea, and Eukarya as well as the relationships of these three domains to each other require resolution. The ribosomal machinery, universal to all cellular life, represents a protein repertoire resistant to horizontal gene transfer, which provides a largely congruent signal necessary for reconstructing a tree suitable as a backbone for life's reticulate history. Here, we generate a ribosomal tree of life from a robust taxonomic sampling of Bacteria, Archaea, and Eukarya to elucidate deep-level intra-domain and inter-domain relationships. Lack of phylogenetic information and systematic errors caused by inadequate models (that cannot account for substitution rate or compositional heterogeneities) or improper model selection compound conflicting phylogenetic signals from HGT and/or paralogy. Thus, we tested several models of varying sophistication on three different datasets, performed removal of fast-evolving or long-branched Archaea and Eukarya, and employed three different strategies to remove compositional heterogeneity to examine their effects on the topological outcome. Our results support a two-domain topology for the tree of life, where Eukarya emerges from within Archaea as sister to a Korarchaeota/Thaumarchaeota (KT) or Crenarchaeota/KT clade for all models under all or at least one of the strategies employed. Taxonomic manipulation allows single-matrix and certain mixture models to vacillate between two-domain and three-domain phylogenies. We find that models vary in their ability to resolve different areas of the tree of life, which does not necessarily correlate with model complexity. For example, both single-matrix and some mixture models recover monophyletic Crenarchaeota and Euryarchaeota archaeal phyla. In contrast, the most sophisticated model recovers a paraphyletic Euryarchaeota but detects two large clades that comprise the Bacteria, which were recovered separately but never together in the other models. Overall, models recovered consistent topologies despite dataset modifications due to the removal of compositional bias, which reflects either ineffective bias reduction or robust datasets that allow models to overcome reconstruction artifacts. We recommend a comparative approach for evolutionary models to identify model weaknesses as well as consensus relationships.

opencc-zeroDec 2012View details →
dryad28/100

Data from: Predictions of response to temperature are contingent on model choice and data quality

The equations used to account for the temperature dependence of biological processes, including growth and metabolic rates are the foundations of our predictions of how global biogeochemistry and biogeography change in response to global climate change. We review and test the use of 12 equations used to model the temperature dependence of biological processes across the full range of their temperature response, including supra- and sub-optimal temperatures. We focus on fitting these equations to thermal response curves for phytoplankton growth, but also tested the equations on a variety of traits across a wide diversity of organisms. We found that many of the surveyed equations have comparable abilities to fit data and equally high requirements for data quality (number of test temperatures and range of response captured), but lead to different estimates of cardinal temperatures and of the biological rates at these temperatures. When these rate estimates are used for biogeographic predictions, differences between the estimates of even the best fitting models can exceed the global biological change predicted for a decade of global warming. As a result, studies of the biological response to global changes in temperature must make careful consideration of model selection and of the quality of the data used for parametrizing these models.

opencc-zeroDec 2016View details →
dryad28/100

Data from: Model choice for phylogeographic inference using a large set of models

Model-based analyses are common in phylogeographic inference because they parameterize processes such as population division, gene flow and expansion that are of interest to biologists. Approximate Bayesian Computation is a model-based approach that can be customized to any empirical system and used to calculate the relative posterior probability of several models, provided that suitable models can be identified for comparison. The question of how to identify suitable models is explored using data from Plethodon idahoensis, a salamander that inhabits the North American inland northwest temperate rainforest. First, we conduct an ABC analysis using five models suggested by previous research, calculate the relative posterior probabilities, and find that a simple model of population isolation has the best fit to the data (PP = 0.70). In contrast to this subjective choice of models to include in the analysis, we also specify models in a more objective manner by simulating prior distributions for 143 models that included panmixia, population isolation, change in effective population size, migration, and range expansion. We then identify a smaller subset of models for comparison by generating an expectation of the highest posterior probability that a false model is likely to achieve due to chance and calculate the relative posterior probabilities of only those models that exceed this expected level. A model that parameterized divergence with population expansion and gene flow in one direction, offered the best fit to the P. idahoensis data (in contrast to an isolation only model from the first analysis). Our investigation demonstrates that the determination of which models to include in ABC model choice experiments is a vital component of model-based phylogeographic analysis.

opencc-zeroDec 2013View details →
zenodo28/100

Self-progressive choice models

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opencc-by-4.0Jun 2024View details →
zenodo28/100

Understanding The Impact of Solver Choice in Model-Based Test Generation

<p><strong># Solver Experiment - Data Package</strong></p> <p>This repository is intended to allow replication and extension of the study<br> conducted in the following paper:</p> <p>Ying Meng and Gregory Gay. Understanding The Impact&nbsp;of Solver Choice in Model-Based Test Generation. To appear, 2020&nbsp;ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). Available from&nbsp;<a href="https://greg4cr.github.io/pdf/20solvers.pdf">https://greg4cr.github.io/pdf/20solvers.pdf</a>.</p> <p>This package includes all data used or generated in this experiment.<br> &nbsp; * Models<br> &nbsp; * Mutants<br> &nbsp; * Test Suites<br> &nbsp; * Execution Traces<br> &nbsp; * Fault-detection Results</p> <p>If you have any questions on this data, please contact us at greg@greggay.com.&nbsp;<br> &nbsp;</p>

opencc-by-4.0Oct 2019View details →
dryad28/100

Data from: Machine learning biogeographic processes from biotic patterns: a new trait-dependent dispersal and diversification model with model choice by simulation-trained discriminant analysis

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publicDec 2015View details →
dryad28/100

Data from: Predictions of response to temperature are contingent on model choice and data quality

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publicOct 2018View details →
dryad28/100

Data from: The effects of model choice and mitigating bias on the ribosomal tree of life

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publicJun 2013View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record