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zenodo32/100

Fort Canning - Sally Port Tunnel

"There were originally three Sally Ports on Fort Canning Hill. Today, only one remains. A Sally Port is a small door leading in and out of the fort." <br> ~ Description taken from 6 Historical Spots To Explore At Fort Canning Park <br> [https://www.southeast-asia.com/singapore/6-historical-spots-to-explore-at-fort-canning-park/](https://www.southeast-asia.com/singapore/6-historical-spots-to-explore-at-fort-canning-park/) <br> Google Maps Location: [https://goo.gl/maps/PMXGhBCHCktxsRNY9](https://goo.gl/maps/PMXGhBCHCktxsRNY9) Source: Objaverse 1.0 / Sketchfab

opencc-by-nc-sa-2.0Jan 2022View details →
zenodo32/100

Ancien tunnel

Ancien tunnel Source: Objaverse 1.0 / Sketchfab

opencc-byJun 2022View details →
zenodo32/100

FIGURE 15. Endoclita spp. larval tunnel structure. E in Notes on Endoclita C. & R. Felder (Lepidoptera: Hepialidae) feeding on Eucalyptus in Vietnam, with new records and a new species

FIGURE 15. Endoclita spp. larval tunnel structure. E. phuthoensis sp. n. (15a–b), E. coomani (15c–d) (15c – tunnel of early instar larva reared to adult). Arrows mark position of pupa or larva. Distal tunnel extends upwards, proximal tunnel extends downwards. Photos 15a, b, d by Nguyen Minh Chi, 15c by Duy Long Pham.

opennotspecifiedMay 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 →
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A digital twin model of urban utility tunnels and its application:One-dimensional comparison verification

<div> <div> <div> <div> <div>&nbsp;</div> 重点词汇</div> <div> <div>93<em>/</em>5000</div> </div> </div> </div> </div> <div>&nbsp;</div> <div> <div> <div> <div> <div> <div>通用场景</div> <div>&nbsp;</div> </div> </div> </div> </div> <div> <div> <p><span>论文《城市综合管廊数字孪生模型及其应用》中一维综合管廊中天然气浓度分布快速预测模型结果对比</span></p> </div> </div> </div>

openmit-licenseJul 2024View details →
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Scanning tunneling microscopy study of epitaxial Fe3GeTe2 monolayers on Bi2Te3

<p>Scanning tunneling microscopy study of epitaxial Fe3GeTe2 monolayers on Bi2Te3. Data associated with the publication which can be found at: https://iopscience.iop.org/article/10.1088/2053-1583/ad1c6d</p>

opencc-by-4.0Jul 2024View details →
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Scanning tunneling microscopy of pentacene on Cu(111)

<p>STM images and spectroscopy of pentacene molecules on Cu(111). Data were taken at 5K. Data format is CreaTec ca 2007. (unpublished)</p>

opencc-by-4.0Jul 2024View details →
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Scanning tunneling microscopy imaging of vanadyl phthalocyanine on Ag(100)

<p>Compilation of raw STM images of VOPc on Ag(100). Related publication can be found at: DOI: 10.1021/acs.jpcc.4c02017</p> <p>&nbsp;</p>

opencc-by-4.0Jun 2024View details →
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Supplementary material 2 from: Cahoon AB, Huffman AG, Krager MM, Crowell RM (2018) A meta-barcoding census of freshwater planktonic protists in Appalachia – Natural Tunnel State Park, Virginia, USA. Metabarcoding and Metagenomics 2: e26939. https://doi.org/10.3897/mbmg.2.26939

Figure 2. Rarefaction analysis estimates demonstrate that family and genus collections were approaching saturation :

opencc-zeroOct 2018View details →
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Data for "Highly symmetric and tunable tunnel couplings in InAs/InP nanowire heterostructure quantum dots"

<p>Data for the publication &quot;Highly symmetric and tunable tunnel couplings in InAs/InP nanowire heterostructure quantum dots&quot;.</p>

opencc-by-4.0Sep 2019View details →
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Synethic dataset for differential OGI images and wind tunnel dataset

<h2>Fluid Flow Dataset</h2> <p>&nbsp;</p> <h2>1. Introduction</h2> <p>A total of three datasets are included here, namely, (1) the synthetic dataset used for model training as mentioned in the paper, (2) the wind tunnel dataset used to validate the segmentation performance of the model, including the corresponding manually labeled labels, and (3) the original wind tunnel experiment dataset.</p> <h2>2. Synthetic differential image dataset for segmentation</h2> <p>To address the lack of semantic segmentation datasets for infrared fluid flow imagery, we created a synthetic dataset from the ScalarFlow dataset, focusing on pixel-level labels suitable for neural network training. Our process, illustrated as shown in the paper, includes generating realistic noise by capturing images under controlled conditions with an Optical Gas Imaging (OGI) camera, followed by image subtraction, normalization, and merging with ScalarFlow data. This method ensures the inclusion of real-world disturbances such as camera jitter effects, enhancing the dataset's robustness and applicability. The dataset, enriched with various data augmentation techniques, comprises over 30,000 images split into training, validation, and testing sets, catering to the rigorous demands of practical applications in fluid dynamics analysis.</p> <h2>3. Wind tunnel dataset</h2> <p>To enable the determination of velocities of fluid flow&nbsp;by using optical flow algorithms, a wind tunnel data set that&nbsp;includes fluid images captured by different cameras was&nbsp;recorded.&nbsp;</p> <p>The fluid flow is created in the wind tunnel and generated by different substances, i.e., dry ice, smoke matches, or paraffin oil. In addition, velocity data collected from the 3D ultrasonic anemometer were used as a reference to evaluate the performance and accuracy of the optical flow algorithms. The fluid flow rate was set at three different velocities in Euclidean space, i.e., 0.7 m/s,&nbsp; 1.4 m/s, and 2.0 m/s.</p> <div> <div>This dataset is captured by using a wind tunnel, the OGI camera FLIR GF320 and a 3D anemometer for obtaining reference flow velocities. Below is the information of the used camera.</div> </div> <h3>FLIR GF320 Camera Info</h3> <table> <tbody> <tr> <td> <div> <div><strong>Parameter</strong></div> </div> </td> <td> <div> <div><strong>Value</strong></div> </div> </td> </tr> <tr> <td> <div> <div>Spectral Range</div> </div> </td> <td> <div> <div>3.2 &ndash; 3.4 &mu;m</div> </div> </td> </tr> <tr> <td> <div> <div>Standard Temperature Range</div> </div> </td> <td> <div> <div>&ndash;20&deg;C to +350&deg;C</div> </div> </td> </tr> <tr> <td> <div> <div>Accuracy</div> </div> </td> <td> <div> <div>&nbsp;&plusmn;1 &deg;C for 0 &deg;C to 100 &deg;C; &plusmn;2% &gt; 100 &deg;C</div> </div> </td> </tr> <tr> <td> <div> <div>Lenses</div> </div> </td> <td> <div> <div>24&deg; &times; 18&deg;</div> </div> </td> </tr> <tr> <td> <div> <div>Resolution</div> </div> </td> <td> <div> <div>320 &times; 240 Pixel</div> </div> </td> </tr> </tbody> </table> <p>&nbsp;</p> <p>The dataset consists of three parts, i.e., OGI images of fluids/smoke generated from three different substances.</p> <ul> <li>1. Smoke matches dataset<br>&nbsp;<br> <table> <tbody> <tr> <td> <div> <div><strong>Id</strong></div> </div> </td> <td><strong>Substances</strong></td> <td> <div> <div><strong>Velocity in m/s</strong></div> </div> </td> <td> <div> <div><strong>Group</strong></div> </div> </td> <td> <div> <div><strong>Number of frames</strong></div> </div> </td> </tr> <tr> <td>1</td> <td>Smoke matches</td> <td>0.7</td> <td>1</td> <td>143</td> </tr> <tr> <td>2</td> <td>Smoke matches</td> <td>0.7</td> <td>2</td> <td>597</td> </tr> <tr> <td>3</td> <td>Smoke matches</td> <td>1.4</td> <td>1</td> <td>145</td> </tr> <tr> <td>4</td> <td>Smoke matches</td> <td>1.4</td> <td>2</td> <td>598</td> </tr> <tr> <td>5</td> <td>Smoke matches</td> <td>2.0</td> <td>1</td> <td>145</td> </tr> <tr> <td>6</td> <td>Smoke matches</td> <td>2.0</td> <td>2</td> <td>596</td> </tr> </tbody> </table> </li> <li>2. Paraffin oil dataset<br><br> <table> <tbody> <tr> <td> <div> <div><strong>Id</strong></div> </div> </td> <td><strong>Substances</strong></td> <td> <div> <div><strong>Velocity in m/s</strong></div> </div> </td> <td> <div> <div><strong>Group</strong></div> </div> </td> <td> <div> <div><strong>Number of frames</strong></div> </div> </td> </tr> <tr> <td>7</td> <td> <div> <div>Paraffin oil</div> </div> </td> <td>0.7</td> <td>1</td> <td>597</td> </tr> <tr> <td>8</td> <td>Paraffin oil</td> <td>0.7</td> <td>2</td> <td>597</td> </tr> <tr> <td>9</td> <td>Paraffin oil</td> <td>1.4</td> <td>1</td> <td>597</td> </tr> <tr> <td>10</td> <td>Paraffin oil</td> <td>1.4</td> <td>2</td> <td>597</td> </tr> <tr> <td>11</td> <td>Paraffin oil</td> <td>2.0</td> <td>1</td> <td>597</td> </tr> <tr> <td>12</td> <td>Paraffin oil</td> <td>2.0</td> <td>2</td> <td>597</td> </tr> </tbody> </table> </li> <li>3. Dry ice dataset<br><br> <table> <tbody> <tr> <td> <div> <div><strong>Id</strong></div> </div> </td> <td><strong>Substances</strong></td> <td> <div> <div><strong>Velocity in m/s</strong></div> </div> </td> <td> <div> <div><strong>Group</strong></div> </div> </td> <td> <div> <div><strong>Number of frames</strong></div> </div> </td> </tr> <tr> <td>13</td> <td>Dry ice</td> <td>0.7</td> <td>1</td> <td>598</td> </tr> <tr> <td>14</td> <td>Dry ice</td> <td>0.7</td> <td>2</td> <td>597</td> </tr> <tr> <td>15</td> <td>Dry ice</td> <td>1.4</td> <td>1</td> <td>595</td> </tr> <tr> <td>16</td> <td>Dry ice</td> <td>1.4</td> <td>2</td> <td>596</td> </tr> <tr> <td>17</td> <td>Dry ice</td> <td>2.0</td> <td>1</td> <td>185</td> </tr> <tr> <td>18</td> <td>Dry ice</td> <td>2.0</td> <td>2</td> <td>628</td> </tr> </tbody> </table> </li> </ul> <p>&nbsp;</p> <p>We also provide the corresponding 3D anemometer data, which allows the user to convert the pixel displacement from the image to the actual flow rate, as shown in below.</p> <div> <h3>Velocities in m/s and pixel</h3> <table> <tbody> <tr> <td> <div> <div><strong>Velocity in m/s</strong></div> </div> </td> <td> <div> <div><strong>Settings of WindChannel</strong></div> </div> </td> <td> <div> <div><strong>&nbsp;Velocity in pixel</strong></div> </div> </td> </tr> <tr> <td> <div> <div>0.7</div> </div> </td> <td>1.88</td> <td>4.57</td> </tr> <tr> <td>1.4</td> <td>2.50</td> <td>9.14</td> </tr> <tr> <td>2.0</td> <td>3.06</td> <td>13.06</td> </tr> </tbody> </table> <p>&nbsp;</p> <h2>4. Wind tunnel segmentation dataset</h2> <p>This part of the dataset is from the dry ice dataset portion of the wind tunnel test dataset described above. And labels are generated by manual labeling for evaluating the performance of the image segmentation model in real-world scenarios, a total of 100 differential images and 100 labels.</p> <p>&nbsp;</p> </div>

opencc-by-4.0Sep 2024View details →
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InSb Nanowires with Built-In GaxIn1−xSb Tunnel Barriers for Majorana Devices

<p>This file contains electron transport data and code of the paper &quot;InSb Nanowires with Built-In GaxIn1&minus;xSb Tunnel Barriers for Majorana Devices&quot; Nano Lett. 2017, 17, 2, 721-727&nbsp;</p>

opencc-by-4.0Jul 2021View details →
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Fig. 1 in Observations on the Tunneling Behavior and Seed Dispersal Efficacy of Copris nubilosus Kohlmann, Cano, and Delgado (Coleoptera: Scarabaeinae: Coprini)

Fig. 1. The dung-lined interior of a tunnel (A) and the soil stained with FlashGel® Loading Dye indicating the tunnel trajectory (B).

opennotspecifiedDec 2017View details →
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Fig. 2 in Observations on the Tunneling Behavior and Seed Dispersal Efficacy of Copris nubilosus Kohlmann, Cano, and Delgado (Coleoptera: Scarabaeinae: Coprini)

Fig. 2. The effect of seed size on burial depth for small (asterisk) and large (triangle) seeds. The trendline (solid grey line) is the non-linear trend obtained using loess. The hatched box denotes the optimum depth range for successful seed germination (Estrada and Coates-Estrada 1991).

opennotspecifiedDec 2017View details →
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Fig. 1 in A Novel Method for Measuring Dung Removal by Tunneler Dung Beetles (Coleoptera: Scarabaeidae: Scarabaeinae) in Pastures

Fig. 1. Methodology for measuring dung removal by tunneller dung beetles. A) Bucket design, B) Bucket cut in half lengthwise. Galleries, beetles, and quantity of dung removed by the beetles are shown.

opennotspecifiedMar 2016View details →
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Dataset - Radially resolved dynamic inflow pitch step experiment in wind tunnel

<p>The relevant turbine data of MoWiTO 1.8 and the preprocessed data presented in the following accepted paper are uploaded:</p> <p><br> Berger, F., Onnen, D., Schepers, J. G., and K&uuml;hn, M.: Experimental analysis of radially resolved dynamic inflow effects due to pitch steps, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2021-70, accepted, 2021.</p> <p>&nbsp;</p> <p>The dataset describes a pitch step experiment with a model wind turbine in a wind tunnel between a high rotor load and a low rotor load. Measurements of turbine loads, near wake flow&nbsp;and radius resolved induction measurements are available.</p>

opencc-by-4.0Oct 2021View details →
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Data of Thermal Insulation and Shock Absorption Effect for Cross-fault Tunnel in High Geothermal Area

<p>Data of manuscript &quot;Thermal Insulation and Shock Absorption Effect for Cross-fault Tunnel in High Geothermal Area&quot;</p>

opencc-by-4.0Oct 2021View details →
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Figure 4. a in One tree, many colonies: colony structure, breeding system and colonization events of host trees in tunnelling Melissotarsus ants

Figure 4. a, number of matings per queen for each monogyne colony in each population. b, relatedness values among nestmate workers for each colony. Arrows indicate relatedness values between alate queens (rA-A) and the triangle indicates relatedness value between queens in the SL11 polygyne colony.

opennotspecifiedFeb 2021View details →
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Figure 3 in One tree, many colonies: colony structure, breeding system and colonization events of host trees in tunnelling Melissotarsus ants

Figure 3. Clustering of nests in the overall sampling using principal component analysis of the microsatellite markers. Clustering analyses were subsequently run for each of the four populations of nests.

opennotspecifiedFeb 2021View details →
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Figure 1 in One tree, many colonies: colony structure, breeding system and colonization events of host trees in tunnelling Melissotarsus ants

Figure 1. Geographic positions of the 34 nests of Melissotarsus sampled in four localities in South Africa, and one pooled sample from Mozambique. Insets indicate sampling positions of nests within the localities of uMkhuze (left) and St Lucia (right). Nests located on the same branch or tree are indicated with the same label.

opennotspecifiedFeb 2021View 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