Skip to main content
ConductScienceScienceDex
Powered by ShareScore

Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

55

datasets available to search

ShareScore release 0.9.0

Reset

Dataset results

55 results for “CYP2D6”

Learn how ShareScore rates datasets ↗
ClinicalTrials.gov36/100

Implementing Genomics in Practice (IGNITE): CYP2D6 Genotype-Guided Pain Management in Patients Undergoing Arthroplasty Surgery

ClinicalTrials.gov study NCT03534063. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov36/100

CYP2D6 Polymorphism in Patients of General Practice in Austria

ClinicalTrials.gov study NCT03859622. IPD Sharing: YES. Countries: 1. Publications: 1.

controlledIPD-YESFeb 2026View details →
ClinicalTrials.gov36/100

Effects of Isotretinoin on CYP2D6 Activity

ClinicalTrials.gov study NCT03076021. IPD Sharing: YES. Countries: 1. Publications: 1.

controlledIPD-YESFeb 2026View details →
zenodo32/100

Transfer learning enables prediction of CYP2D6 haplotype function

<p>This data here were used to train the models described in the manuscript &quot;Transfer learning enables prediction of CYP2D6 haplotype function&quot;.&nbsp; The deep learning model described predicts metabolic function of <em>CYP2D6</em> star alleles.&nbsp; It uses two pretraining steps, first with simulated data, then with sequence data collected from liver microsomes, and finally using sequence data for <em>CYP2D6</em> star alleles.</p> <p>&nbsp;</p> <p>simulated_cyp2d6_diplotypes.tar.gz&nbsp; - This file contains sequence data and labels for simulated <em>CYP2D6 </em>data used in the first training step</p> <p>dalton_2019_cyp2d6_microsomes.txt&nbsp; - This file contains summary statistic data for liver microsome data used in the second pretraining step (originally from <a href="https://doi.org/10.1111/cts.12695">https://doi.org/10.1111/cts.12695)</a></p> <p>star_samples.vcf - This file contains sequence data for <em>CYP2D6 </em>star alleles derived from PharmVar (https://www.pharmvar.org/gene/CYP2D6) used in the final training step.</p>

opencc-by-4.0Jul 2020View 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

CYP2D6 Pharmacogenetics in Risperidone-Treated Children

ClinicalTrials.gov study NCT00783783. IPD Sharing: Not stated. Countries: 1. Publications: 3.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Genotyping and Phenotyping of CYP2D6 Breast Cancer Patients on Tamoxifen

ClinicalTrials.gov study NCT03504631. IPD Sharing: NO. Countries: 1. Publications: 11.

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

Impact of OCT1 and CYP2D6 Genotypes on Pharmacokinetics of Berberine in Healthy Volunteers

ClinicalTrials.gov study NCT05463003. IPD Sharing: NO. Countries: 1. Publications: 1.

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

CYP2D6 Genotypes and Breast Cancer Clinical Outcomes in the Indonesian Population

ClinicalTrials.gov study NCT05501158. IPD Sharing: NO. Countries: 1. Publications: 3.

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

Genetic Variability in CYP2D6 in U.S Active Duty Population

ClinicalTrials.gov study NCT02960568. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Cost-effectiveness of CYP2D6 and CYP2C19 Genotyping in Psychiatric Patients in Curacao

ClinicalTrials.gov study NCT02713672. IPD Sharing: YES. Countries: 1. Publications: 2.

controlledIPD-YESFeb 2026View details →
ClinicalTrials.gov32/100

Effects of Retinoids on CYP2D6 Activity During Pregnancy

ClinicalTrials.gov study NCT03117660. IPD Sharing: YES. Countries: 1. Publications: 3.

controlledIPD-YESFeb 2026View details →
ClinicalTrials.gov32/100

To Compare Blood and Urine Concentrations of Mirabegron (YM178) in Healthy Poor or Extensive Metabolizers for CYP2D6 and to Assess the Effect of Mirabegron on the Metabolism of Metoprolol

ClinicalTrials.gov study NCT01478490. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

Pharmacokinetics of Oral Morphine and Pharmacogenomics of CYP2D6 and UGT2B7, in an Urban Pediatric Population Presenting for Elective Surgery

ClinicalTrials.gov study NCT01071499. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

A Study to Evaluate Exemestane Tablets Combined With Ovarian Function Suppression/Ablation in Treatment of Premenopausal Breast Cancer Patients With CYP2D6*10 Mutations （STEP）

ClinicalTrials.gov study NCT03137368. IPD Sharing: NO. Countries: 1. Publications: 20.

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

Distribution of CYP2D6 Multiplication, CYP2D6*5, and Clinical Implications in Postoperative Patients Receiving Tramadol Analgesia in the Minangkabau Ethnic Group, Indonesia

ClinicalTrials.gov study NCT06642480. IPD Sharing: NO. Countries: 1. Publications: 1.

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

Tamoxifen Dose Adjustment on Indonesian Female ER+ Breast Cancer Patients Based on CYP2D6 Genotype and Endoxifen Levels

ClinicalTrials.gov study NCT04312347. IPD Sharing: Not stated. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov32/100

PharmacoKINEtics of TAMoxifen and Its Metabolites in Breast Cancer Patients: the Influence of a Dose Increase in Phenotypic Poor Metabolizers of CYP2D6 (KINETAM)

ClinicalTrials.gov study NCT01192308. IPD Sharing: Not stated. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →
zenodo28/100

Figure 2 from: Alvarado AT, Ybañez-Julca R, Muñoz AM, Tejada-Bechi C, Cerro R, Quiñones LA, Varela N, Alvarado CA, Alvarado E, Bendezú MR, García JA (2021) Frequency of CYP2D6*3 and *4 and metabolizer phenotypes in three mestizo Peruvian populations. Pharmacia 68(4): 891-898. https://doi.org/10.3897/pharmacia.68.e75165

Figure 2 Percentages (%) of poor metabolizers (gPM) extrapolated from the genotype in different populations of the tricontinent and Latin America previously studied and their clinical implication. ##: tricontinental population, **: Latin American population.

opencc-by-4.0Dec 2021View details →
ClinicalTrials.gov28/100

A Study to Evaluate Pharmacokinetic Parameters of Eliglustat in Healthy Volunteers Who Are CYP2D6 Extensive or Poor Metabolizers

ClinicalTrials.gov study NCT06188325. IPD Sharing: YES. Countries: 1. Publications: 0.

controlledIPD-YESFeb 2026View 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