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2,353 results for “channel”
The longer arc of channel recovery post-dam removal
<p>This repository includes all data and analyses referenced in the paper "The Longer Arc of Channel Recovery Post-Dam Removal" by Fields et al. (submitted 2024). The R files used to plot figures and complete additional data analysis are also included. </p> <p>This project is a follow-up to earlier work at the same site by Fields et al. (2021) "A Mechanistic Understanding of Channel Evolution following Dam Removal" (10.1016/j.geomorph.2021.107971). Data for that paper is available on the CUAHSI database (http://www.hydroshare.org/resource/ ae0589f6a2e54effb5514d126ecb6908). </p> <p>If any neccessary data is missing or if you require more of our data for your analsyses please contact Jordan Fields (jordan.f.fields@gmail.com). </p>
IODP Expedition 360 RGB channels (calculated from core photos)
Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.
IODP Expedition 397 RGB channels (calculated from core photos)
Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.
Dataset of molecular docking data of neuropeptides to acid-sensing ion channels
<p>The *.dock4 files are result files of molecular docking with the software Autodock Vina to the human ASIC1a closed state model, of the peptides FRRFa and KNFLRFa (FRRF.dock4, KNFLRF.dock4) that can be visualized with structure viewing programs such as UCSF Chimera on the closed ASIC1a model file (closed_ASIC_pH7.4.pdb). The file “FRRF_KNFLRF_complexes.pdb” provides the structures of selected poses of FRRFa and KNFLRFa peptides docked to the closed conformation of the human ASIC1a model.</p>
EXPLORATORY SPATIAL ANALYSIS OF "ACCESS" TO PHYSICAL AND DIGITAL RETAIL BANKING CHANNELS IN THE UK
<p>File built in order to explore access to banking channels in the UK (February 2019)</p> <p>The report "Exploratory Spatial Analysis of Access to Physical and Digital Retail Banking Channels in the UK" has been published by Think Forward Initiative in October 2019. You can download the full report from here: <a href="https://www.thinkforwardinitiative.com/research/exploratory-spatial-analysis-of-access-to-physical-and-digital-retail-banking-channels-in-the-uk">https://www.thinkforwardinitiative.com/research/exploratory-spatial-analysis-of-access-to-physical-and-digital-retail-banking-channels-in-the-uk</a></p> <p>Related code: <a href="https://github.com/andrasonea">andrasonea</a>/<strong><a href="https://github.com/andrasonea/TFI_AccessToBanking">TFI_AccessToBanking</a></strong></p> <p> </p>
1X4 radio channel data at 3.42 GHz for device-free human sensing
<p>Datasets for <em>CAMPAIGN-I and CAMPAIGN-II </em>from paper 'Beamsteering for Ad-Hoc Recognition of Multi-Human Targets Performing Distinct Activities'. The datasets belong to the <a href="http://ambientintelligence.aalto.fi/radiosense/">Radiosense</a> project.</p> <p>'.</p>
Water Body Checklists 2019: Mozambique Channel Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Mozambique Channel using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists 2019: English Channel Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the English Channel using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists 2019: Bristol Channel Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Bristol Channel using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists: Mozambique Channel Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Mozambique Channel using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists: Bristol Channel Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Bristol Channel using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists: English Channel Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the English Channel using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
YouTube RAI channel dataset
<p>id, title and youtube segmentation of videos from the official youtube RAI channel (<a href="https://www.youtube.com/@rai" rel="nofollow">https://www.youtube.com/@rai</a>) longer than 5 minutes. For each video the segmentation is a list composed by the start time (in milliseconds) and the title of each chapter. The dataset is already divided in two non-overlapping sets: 614 in "test_yt_over5min.json" and 2460 in "train_yt_over5min.json".</p>
IODP Expedition 398 RGB channels (calculated from core photos)
Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.
IODP Expedition 355 RGB channels (calculated from core photos)
Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.
WiFiCam Dataset | Through-Wall Imaging based on WiFi Channel State Information
<p><strong>Through-Wall Imaging based on WiFi Channel State Information</strong></p> <p>This repository contains the <strong>WiFiCam dataset</strong> for through-wall imaging based on WiFi channel state information proposed in [1]. The corresponding source code repository is located at: <a href="https://github.com/StrohmayerJ/wificam">https://github.com/StrohmayerJ/wificam</a></p> <p>The demo video (demo.mp4) showcases the through-wall imaging capabilities of our approach. </p> <p> </p> <p><strong>Dataset Structure</strong></p> <p>/wificam</p> <p>├── j3</p> <p> └── 320 <-- 320x240 resolution subset</p> <p> └── csi.csv <-- raw WiFi packet sequence recorded with the ESP32-S3</p> <p> └── csiComplex.npy <-- complex CSI sequence (cache)</p> <p> └── 92108.png <-- 320x240 RGB image</p> <p> └── 92112.png</p> <p> └── ...</p> <p> └── 640 <-- 640x480 resolution subset</p> <p> └── csi.csv <-- raw WiFi packet sequence recorded with the ESP32-S3</p> <p> └── csiComplex.npy <-- complex CSI sequence (cache)</p> <p> └── 154.png <-- 640x480 RGB image</p> <p> └── 155.png</p> <p> └── ...</p> <p>├── statistics320.csv <-- per-channel means and standard deviations for 320x240 images</p> <p>├── statistics640.csv <-- per-channel means and standard deviations for 640x480 images</p> <p> </p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].</p> <p>[1] J. Strohmayer, R. Sterzinger, C. Stippel and M. Kampel, "Through-Wall Imaging Based On WiFi Channel State Information," <em>2024 IEEE International Conference on Image Processing (ICIP)</em>, Abu Dhabi, United Arab Emirates, 2024, pp. 4000-4006, doi: 10.1109/ICIP51287.2024.10647775.</p> <p>BibTeX:</p> <pre>@INPROCEEDINGS{10647775, author={Strohmayer, Julian and Sterzinger, Rafael and Stippel, Christian and Kampel, Martin}, booktitle={2024 IEEE International Conference on Image Processing (ICIP)}, title={Through-Wall Imaging Based On WiFi Channel State Information}, year={2024}, volume={}, number={}, pages={4000-4006}, doi={10.1109/ICIP51287.2024.10647775}}</pre>
IODP Expedition 356 RGB channels (calculated from core photos)
Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.
IODP Expedition 353 RGB channels (calculated from core photos)
Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.
IODP Expedition 359 RGB channels (calculated from core photos)
Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.
Connexin 46 and connexin 50 gap junction channel properties are shaped by structural and dynamic features of their N-terminal domains
<p>Provided are reduced trajectories (.dcd) of the MD simulations -- each trajectory has 100 ps/frame with only protein and ion atoms remaining. Each set of trajectories are accompanied by a protein structure file (.psf) which is required to visualize the trajectories in VMD. Additionally, the z-trajectories of each intracellular ion (2 ps/frame) are provided in zipped files.<br> <br> To re-create the potentials of mean force (PMF) in Yue & Haddad et al., use the scripts provided with the paper (https://github.com/reichow-lab/Yue-Haddad_et-al.JPhysiol2021):<br> <br> </p> <pre><code class="language-bash">python3 GapJ_Analysis.py "Cx46_Ace_Produc-1_POT_*"</code></pre> <ul> <li>Choose a bin size in Å (3)</li> <li>Choose an output name (Cx46_Ace)</li> <li>Choose option (M)</li> <li>Choose time (ps) / frame (2)</li> <li>Choose column from file (1)</li> <li>Choose bin<sub>min</sub>/bin<sub>max </sub>(auto)</li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.