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1,392 results for “Match”
Fig. 1 in The origins of the cochlea and impedance matching hearing in synapsids
Fig. 1. The skull of the non-mammalian synapsid Pristerodon mackayi Huxley, 1868 (MB.R.985) from the Late Permian of Biesjespoort, South Africa. Photograph (A) and virtual 3D model (B) of dorsolateral view.
Fig. 5 in The origins of the cochlea and impedance matching hearing in synapsids
Fig. 5. Virtual reconstruction of the left quadrate-quadratojugal complex of the non-mammalian synapsid Pristerodon mackayi Huxley, 1868 (MB.R.985) from the Late Permian of Biesjespoort, South Africa; in ventral (A), medial (B), and anterior (C) views.
Fig. 4 in The origins of the cochlea and impedance matching hearing in synapsids
Fig. 4. Virtual reconstruction of the left stapes of the non-mammalian synapsid Pristerodon mackayi Huxley, 1868 (MB.R.985) from the Late Permian of Biesjespoort, South Africa; in medial (A), posterior (B), ventral (C), and anterior (D) views.
T1234: A distortion-matched structural scan solution to misregistration of high resolution fMRI data, Part 1
<p>Raw and processed MRI data of the study entiled: T1234-Part 1</p> <p>Authors: Chung (Kenny) Kan1, Rüdiger Stirnberg2, Marcela Montequin1, Omer Faruk Gulban3,4, A Tyler Morgan1, Peter Bandettini1, Laurentius (Renzo) Huber1</p> <ol> <li>NIMH, NIH, Bethesda, United States,</li> <li>German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany,</li> <li>CN, FPN, University of Maastricht, The Netherlands,</li> <li>Brain Innovation, Maastricht, The Netherlands</li> </ol> <p><strong>Purpose</strong>: High-resolution fMRI at 7T is limited by limited registration quality between functional data with structural scans. We aim to provide a fast acquisition method that provides distortion-matched, artifact mitigated structural reference data.</p> <p><strong>Methods</strong>: We developed an efficient sequence approach with adjustable distortions, termed T1234: T1-weighted 2-inversion 3D-EPI with 4 directions for high-resolution fMRI. A forward Bloch model is implemented for T1 quantification and protocol optimization. 20 participants were scanned on 7T with structural and functional protocols to evaluate the utility of T1234.</p> <p><strong>Results</strong>: We find that a fast protocol provides reliable data for whole-brain segmentations in EPI-space in 3:00-3:40 min). It is robust across sessions, participants, and three 7T SIEMENS scanners. T1234 allows layer fMRI signal analysis with higher laminar precision.</p> <p><strong>Conclusion: </strong>This structural mapping approach allows precise registration with fMRI data. T1234 is implemented, validated, and tested to serve users of our sequence (locally and >50 centers worldwide). </p>
Coarse-Grained Sense Inventories Based on Semantic Matching between English Dictionaries
<p><strong>Abstract</strong> (our paper)</p> <p>WordNet is one of the largest handcrafted concept dictionaries visualizing word connections through semantic relationships. It is widely used as a word sense inventory in natural language processing tasks. However, WordNet's fine-grained senses have been criticized for limiting its usability. In this paper, we semantically match sense definitions from Cambridge dictionaries and WordNet and develop new coarse-grained sense inventories. We verify the effectiveness of our inventories by comparing their semantic coherences with that of Coarse Sense Inventory. The advantages of the proposed inventories include their low dependency on large-scale resources, better aggregation of closely related senses, CEFR-level assignments, and ease of expansion and improvement. Our inventories are publicly available for free use.</p> <p><strong>Publication</strong></p> <p>These datasets are part of our research results. If you make use of our datasets, please cite:</p> <ul> <li>Masato Kikuchi, Masatsugu Ono, Toshioki Soga, Tetsu Tanabe, Tadachika Ozono. Coarse-Grained Sense Inventories Based on Semantic Matching between English Dictionaries. In <em>Proceedings of the 11th International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA 2024)</em>. 6 pages, 2024.</li> </ul>
Data for: Tip of the Red Giant Branch Distances with JWST. II. I−band Measurements in a Sample of Hosts of 10 SN Ia Match HST Cepheids
<p>Data for: "Tip of the Red Giant Branch Distances with JWST. II. I−band Measurements in a Sample of Hosts of 10 SN Ia Match HST Cepheids". The photometry provided is after DOLPHOT quality cuts, foreground extinction corrections, and spatial cuts.</p>
TRY 6.0 - Species List from Taxonomic Harmonization – Matches with World Flora Online version 2023.12
<p>Standardized names for TRY 6.0 are available from the TRY File Archive (TFA; see here: <a href="https://www.try-db.org/TryWeb/Data.php#100">https://www.try-db.org/TryWeb/Data.php#100</a>); these were created for the following publication:</p> <p>Schellenberger Costa, D., Boehnisch, G., Freiberg, M., Govaerts, R., Grenié, M., Hassler, M., Kattge, J., Muellner-Riehl, A.N., Rojas Andrés, B.M., Winter, M., Watson, M., Zizka, A. and Wirth, C. (2023), The big four of plant taxonomy – a comparison of global checklists of vascular plant names. New Phytol, 240: 1687-1702. <a href="https://doi.org/10.1111/nph.18961">https://doi.org/10.1111/nph.18961</a></p> <p>Here matched records are provided with the taxonomic backbone of World Flora Online (WFO) version 2023.01, obtained from <a href="https://zenodo.org/records/10425161">https://zenodo.org/records/10425161</a>.</p> <p>Matches with WFO are given in the Zenodo archive in the fields for <em>SID</em> (=taxonID in WFO), <em>scientificName</em> (as in WFO) and <em>scientificNameAuthorship</em> (as in WFO). Fields of <em>TRY_SpeciesID</em>, <em>TRY_AccSpeciesNameScientific</em> and <em>RecommendedScientificName</em> were directly obtained from the TFA.</p> <p> </p> <p>Matching was done via following steps:</p> <p>1. Fungi matched in the TFA via the <a href="http://indexfungorum.org">http://indexfungorum.org</a> were excluded (4,099 records).</p> <p>2. The matching record in WFO was obtained by matching the TFA field of <em>TPL_ID</em> with the WFO field of <em>tplID</em>. Successful matches via this method are indicated in this Zenodo archive by the field of <em>MATCH</em> being set to <strong><em>TPL ID</em></strong>.</p> <p>3. Where matches in WFO included a non-empty <em>acceptedNameUsageID</em>, the currently accepted name of the taxon was obtained via this ID. Successful matches via this method are indicated in this Zenodo archive by the field of <em>MATCH</em> being set to <strong><em>TPL ID</em></strong> and fields of <em>synonymID, scientificName.synonym</em> and scientificNameAuthorship.synonym showing details for the match with the synonym.</p> <p>4. Taxa that were not matched in previous steps were matched for the TFA field of <em>MatchedName</em>. Matching was done via the <a href="https://cran.r-project.org/package=WorldFlora">WorldFlora package</a> version 1.14-5 (<a href="https://bsapubs.onlinelibrary.wiley.com/doi/full/10.1002/aps3.11388">Kindt 2020</a>). Matches expected to be acceptable after visual inspection are indicated in this Zenodo archive by the field of <em>MATCH</em> being set to <strong><em>manual</em></strong><em>.</em> Taxa at specific and infraspecific levels from the TFA where matches had only been achieved at generic levels for the <em>MatchedName</em> were excluded in this step.</p> <p>5. Taxa that were not matched in previous steps were matched for the TFA field of <em>TRY_AccSpeciesName</em>. Matching was done via the <a href="https://cran.r-project.org/package=WorldFlora">WorldFlora package</a> version 1.14-5 (<a href="https://bsapubs.onlinelibrary.wiley.com/doi/full/10.1002/aps3.11388">Kindt 2020</a>). Matches expected to be acceptable after visual inspection are indicated in this Zenodo archive by the field of <em>MATCH</em> being set to <strong><em>manual</em></strong><em>.</em></p> <p>6. Taxa that were not matched in previous steps were matched for the TFA field of <em>AlternativeName</em>. Matching was done via the <a href="https://cran.r-project.org/package=WorldFlora">WorldFlora package</a> version 1.14-5 (<a href="https://bsapubs.onlinelibrary.wiley.com/doi/full/10.1002/aps3.11388">Kindt 2020</a>). Matches expected to be acceptable after visual inspection are indicated in this Zenodo archive by the field of <em>MATCH</em> being set to <strong><em>manual</em></strong><em>.</em></p> <p>7. Taxa that were not matched in previous steps were matched for the TFA field of <em>MatchedName</em> against <a href="https://sftp.kew.org/pub/data-repositories/WCVP/Archive/">version 11 of the World Checklist of Vascular Plants</a>. Matching was also done via the <a href="https://cran.r-project.org/package=WorldFlora">WorldFlora package</a> version 1.14-5 (<a href="https://bsapubs.onlinelibrary.wiley.com/doi/full/10.1002/aps3.11388">Kindt 2020</a>), with similar scripts <a href="https://rpubs.com/Roeland-KINDT/1134151">as shown in this Rpub</a>. Matches expected to be acceptable after visual inspection are indicated in this Zenodo archive by the field of <em>MATCH</em> being set to <strong><em>manual</em></strong><em> </em>and by the field of <em>SID</em> (and possibly <em>synonymID</em>) containing records where the WCVP ID is preceded by WCVP-.</p> <p>8. 836 records where neither the TFA nor the previous steps managed to establish a match were removed.</p> <p>9. Taxa that could not be matched in previous steps are flagged by the field of <em>MATCH</em> being set to <strong><em>NONE - no species MatchedName</em></strong><em> </em>(where TFA also did not achieve a match at the required specific or infraspecific level) or <strong><em>NONE</em></strong> (other records where no match was achieved).</p> <p> </p> <p>The Zenodo archive contains 406,208 records with matches via the TPL, 96,040 records with manual matches, 1,822 records where the TFA also not achieve matches at required (infra-)specific levels and 361 records where no match was achieved (among this latter category were 202 records flagged in TFA as ‘not found in WFO’ and 140 records flagged in TFA as ‘found only here’).</p> <p> </p> <p>Version 2024.10b was created with added fields from TFA of <em>TRY_SpeciesName</em> and <em>BackboneDatabase</em>. This was done especially to explain different matches for records with the same <em>RecommendedScientificName </em>but with different matches to WFO, as for example for <em>Acacia adunca</em> A.Cunn. & G.Don and <em>Acacia aestivalis</em> E.Pritz.</p> <p> </p> <p><strong>References</strong></p> <ul> <li>Kattge J. 2023. TRY 6.0 - Species List from Taxonomic Harmonization. <a href="https://www.try-db.org/TryWeb/Data.php#100">https://www.try-db.org/TryWeb/Data.php#100</a></li> <li>Schellenberger Costa, D., Boehnisch, G., Freiberg, M., Govaerts, R., Grenié, M., Hassler, M., Kattge, J., Muellner-Riehl, A.N., Rojas Andrés, B.M., Winter, M., Watson, M., Zizka, A. and Wirth, C. (2023), The big four of plant taxonomy – a comparison of global checklists of vascular plant names. New Phytol, 240: 1687-1702. <a href="https://doi.org/10.1111/nph.18961">https://doi.org/10.1111/nph.18961</a></li> <li>The World Flora Online Consortium, Alan Elliott, Roger Hyam, William Ulate, Mark Watson, Gregory Anderson, Giovani carlos Andrella, et al. “World Flora Online Plant List December 2023”. Zenodo, December 22, 2023. <a href="https://doi.org/10.5281/zenodo.10425161" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10425161</a>.</li> <li>Kindt R (2020). “WorldFlora: An R package for exact and fuzzy matching of plant names against the World Flora Online taxonomic backbone data.” <em>Applications in Plant Sciences</em>, <strong>8</strong>(9), e11388. <a href="https://doi.org/10.1002/aps3.11388">https://doi.org/10.1002/aps3.11388</a></li> <li>Borsch, T., Berendsohn, W., Dalcin, E., Delmas, M., Demissew, S., Elliott, A., Fritsch, P., Fuchs, A., Geltman, D., Güner, A., Haevermans, T., Knapp, S., le Roux, M.M., Loizeau, P.-A., Miller, C., Miller, J., Miller, J.T., Palese, R., Paton, A., Parnell, J., Pendry, C., Qin, H.-N., Sosa, V., Sosef, M., von Raab-Straube, E., Ranwashe, F., Raz, L., Salimov, R., Smets, E., Thiers, B., Thomas, W., Tulig, M., Ulate, W., Ung, V., Watson, M., Jackson, P.W. and Zamora, N. (2020), World Flora Online: Placing taxonomists at the heart of a definitive and comprehensive global resource on the world's plants. TAXON, 69: 1311-1341. <a href="https://doi.org/10.1002/tax.12373">https://doi.org/10.1002/tax.12373</a></li> <li>Govaerts, R., Nic Lughadha, E., Black, N. <em>et al.</em> The World Checklist of Vascular Plants, a continuously updated resource for exploring global plant diversity. <em>Sci Data</em> <strong>8</strong>, 215 (2021). <a href="https://doi.org/10.1038/s41597-021-00997-6">https://doi.org/10.1038/s41597-021-00997-6</a></li> </ul> <p> </p> <p>The development of this archive supported by the <strong>Darwin Initiative</strong> to project DAREX001 of <em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em>, by <strong>Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia</strong> to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, by the <strong>Green Climate Fund</strong> through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> project and through the <em>Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso</em>, by the <strong>Bezos Earth Fund</strong> to the <em>Bezos Quality Tree Seed for Africa in Kenya and Rwanda</em> project and by the <strong>German International Climate Initiative (IKI)</strong> to the regional tree seed programme on <em>The Right Tree for the Right Place for the Right Purpose in Africa</em>.</p> <p> </p>
Open data source for "Optically reconfigurable quasi-phase-matching in silicon nitride microresonators"
<p>The folder includes includes the raw data as well as codes that were used for generation of all Figures in the paper "Optically reconfigurable quasi-phase-matching in silicon nitride microresonators".</p>
FIFA World Cups 2010, 2014 and 2018 matches' information datasets
<p>Data from FIFA World Cups 2010 (South Africa), 2014 (Brazil) and 2018 (Russia) collected from the FIFA website from the section of each match's facts. The original data were pdf files, which were cleaned and organized.</p> <p>The data was collected for the analysis of competition networks in soccer matches, where the high and low performance teams were studied.</p> <p>The zip file contains 3 folders of each world cup. Each folder contains the following files:</p> <p>- Players performance measures: input_worldcup.csv. This file contains information from 'fulltime statistics' and 'teams statistics' original files, complete for each match played in the world cup. More detailed player statistics is only complete for the teams that were champion, runner-up, and the teams that accumulated the less points and goals-against in their respective world cup.</p> <p>- The description of each column can be found in data_dictionary_worldcup.csv.</p> <p>- The list of countries that participated in the world cup with its abbreviation is found in countries_abbreviations_worldcup.csv.</p> <p>- The result of each match is found in tags_worldcup.csv</p> <p>- The players position information is the tactical line-up for World Cup 2010 and initial actual formation for World Cups 2014 and 2018. This information was manually set as a best approximation of position for the starting 11 players and a receiving shots nodes 'XS' - special for the visualization developed. The x-axis is between 0.5 and 6 and y-axis is flipped and between 0 and 6.</p> <p>- Folder of ball passing distributions for each match and team. The name of each file contains the match number and the team's ball passing distribution. The column names are the players numbers who received the ball. Rows represent 'from' and the columns 'to'. Each value are the number of passes between players.</p>
Using generative adversarial networks to match experimental and simulated inelastic neutron scattering data
<p>Files uploaded here are related to the paper titled "Using generative adversarial networks to match experimental and simulated inelastic neutron scattering data". Here we investigate how generative adversarial networks can be used to match simulated- and experimental INS data.</p>
[Dataset] Is it a match? Motivations on citizen science volunteers and recruitment arguments in project descriptions
<p>This dataset contains the necessary details to reproduce the experiments of the paper:</p> <p><em>Kai Nils, W., Gutiérrez Páez,N.F., Sabel, O. and Hämäläinen, R. (2022) “Is It a Match? Motivations on Citizen Science Volunteers and Recruitment Arguments in Project Descriptions.” In Proceedings of the ECSA2022 conference: Citizen Science for Planetary Health, 69–70. <a href="https://2022.ecsa-conference.eu/files/ecsa/Bilder/ECSA2022_Conference_Proceedings.pdf">https://2022.ecsa-conference.eu/files/ecsa/Bilder/ECSA2022_Conference_Proceedings.pdf</a></em></p> <p>Data has been collected by quantitative triangulation. 1076 participants in citizen science projects answered a survey about the 12 motivational factors for participating. They had access to the survey by social media posts or email invitations sent to people in charge of projects. Data regarding motivational arguments in recruitment come from quantitative content analysis of 367 project descriptions of the website Zooniverse. The content analysis of the project descriptions was done manually by two coders independently. Then, both coders analysed their codings and reached consensus.</p>
EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching
<p><strong>EyeFi Dataset</strong></p> <p>This dataset is collected as a part of the EyeFi project at Bosch Research and Technology Center, Pittsburgh, PA, USA. The dataset contains WiFi CSI values of human motion trajectories along with ground truth location information captured through a camera. This dataset is used in the following paper "<em>EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching</em>" that is published in the IEEE International Conference on Distributed Computing in Sensor Systems 2020 (DCOSS '20). We also published a dataset paper titled as "<em>Dataset: Person Tracking and Identification using Cameras and Wi-Fi Channel State Information (CSI) from Smartphones</em>" in Data: Acquisition to Analysis 2020 (DATA '20) workshop describing details of data collection. Please check it out for more information on the dataset.</p> <p><strong>Data Collection Setup</strong><br> <br> In our experiments, we used Intel 5300 WiFi Network Interface Card (NIC) installed in an Intel NUC and Linux CSI tools [1] to extract the WiFi CSI packets. The (x,y) coordinates of the subjects are collected from Bosch Flexidome IP Panoramic 7000 panoramic camera mounted on the ceiling and Angle of Arrivals (AoAs) are derived from the (x,y) coordinates. Both the WiFi card and camera are located at the same origin coordinates but at different height, the camera is location around 2.85m from the ground and WiFi antennas are around 1.12m above the ground.</p> <p>The data collection environment consists of two areas, first one is a rectangular space measured 11.8m x 8.74m, and the second space is an irregularly shaped kitchen area with maximum distances of 19.74m and 14.24m between two walls. The kitchen also has numerous obstacles and different materials that pose different RF reflection characteristics including strong reflectors such as metal refrigerators and dishwashers. </p> <p>To collect the WiFi data, we used a Google Pixel 2 XL smartphone as an access point and connect the Intel 5300 NIC to it for WiFi communication. The transmission rate is about 20-25 packets per second. The same WiFi card and phone are used in both lab and kitchen area.</p> <p><strong>List of Files</strong><br> Here is a list of files included in the dataset:</p> <pre><code>|- 1_person |- 1_person_1.h5 |- 1_person_2.h5 |- 2_people |- 2_people_1.h5 |- 2_people_2.h5 |- 2_people_3.h5 |- 3_people |- 3_people_1.h5 |- 3_people_2.h5 |- 3_people_3.h5 |- 5_people |- 5_people_1.h5 |- 5_people_2.h5 |- 5_people_3.h5 |- 5_people_4.h5 |- 10_people |- 10_people_1.h5 |- 10_people_2.h5 |- 10_people_3.h5 |- Kitchen |- 1_person |- kitchen_1_person_1.h5 |- kitchen_1_person_2.h5 |- kitchen_1_person_3.h5 |- 3_people |- kitchen_3_people_1.h5 |- training |- shuffuled_train.h5 |- shuffuled_valid.h5 |- shuffuled_test.h5 View-Dataset-Example.ipynb README.md </code></pre> <p>In this dataset, folder `1_person/` , `2_people/` , `3_people/` , `5_people/`, and `10_people/` contains data collected from the lab area whereas `Kitchen/` folder contains data collected from the kitchen area. To see how the each file is structured, please see below in section <em>Access the data</em><strong>.</strong> </p> <p>The training folder contains the training dataset we used to train the neural network discussed in our paper. They are generated by shuffling all the data from `1_person/` folder collected in the lab area (`1_person_1.h5` and `1_person_2.h5`). </p> <p><strong>Why multiple files in one folder?</strong></p> <p>Each folder contains multiple files. For example, `1_person` folder has two files: `1_person_1.h5` and `1_person_2.h5`. Files in the same folder always have the same number of human subjects present simultaneously in the scene. However, the person who is holding the phone can be different. Also, the data could be collected through different days and/or the data collection system needs to be rebooted due to stability issue. As result, we provided different files (like `1_person_1.h5`, `1_person_2.h5`) to distinguish different person who is holding the phone and possible system reboot that introduces different phase offsets (see below) in the system. </p> <p><strong>Special note:</strong></p> <p>For `1_person_1.h5`, this file is generated by the same person who is holding the phone, and `1_person_2.h5` contains different people holding the phone but only one person is present in the area at a time. Boths files are collected in different days as well.</p> <p><br> <strong>Access the data</strong><br> To access the data, hdf5 library is needed to open the dataset. There are free HDF5 viewer available on the official website: <a href="https://www.hdfgroup.org/downloads/hdfview/">https://www.hdfgroup.org/downloads/hdfview/</a>. We also provide an example Python code <em>View-Dataset-Example.ipynb</em> to demonstrate how to access the data.</p> <p>Each file is structured as (except the files under *"training/"* folder):<br> </p> <pre><code>|- csi_imag |- csi_real |- nPaths_1 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- nPaths_2 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- nPaths_3 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- nPaths_4 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- num_obj |- obj_0 |- cam_aoa |- coordinates |- obj_1 |- cam_aoa |- coordinates ... |- timestamp </code></pre> <p>The `csi_real` and `csi_imag` are the real and imagenary part of the CSI measurements. <strong>The order of antennas and subcarriers are as follows for the 90 `csi_real` and `csi_imag` values : [subcarrier1-antenna1, subcarrier1-antenna2, subcarrier1-antenna3, subcarrier2-antenna1, subcarrier2-antenna2, subcarrier2-antenna3,… subcarrier30-antenna1, subcarrier30-antenna2, subcarrier30-antenna3]</strong>. `nPaths_x` group are SpotFi [2] calculated WiFi Angle of Arrival (AoA) with `x` number of multiple paths specified during calculation. Under the `nPath_x` group are `offset_xx` subgroup where `xx` stands for the offset combination used to correct the phase offset during the SpotFi calculation. We measured the offsets as:</p> <pre><code>|Antennas | Offset 1 (rad) | Offset 2 (rad) | |:-------:|:---------------:|:-------------:| | 1 & 2 | 1.1899 | -2.0071 | 1 & 3 | 1.3883 | -1.8129 </code></pre> <p>The measurement is based on the work [3], where the authors state there are two possible offsets between two antennas which we measured by booting the device multiple times. The combination of the offset are used for the `offset_xx` naming. For example, `offset_12` is offset 1 between antenna 1 & 2 and offset 2 between antenna 1 & 3 are used in the SpotFi calculation.</p> <p>The `num_obj` field is used to store the number of human subjects present in the scene. The `obj_0` is always the subject who is holding the phone. In each file, there are `num_obj` of `obj_x`. For each `obj_x1`, we have the `coordinates` reported from the camera and `cam_aoa`, which is estimated AoA from the camera reported coordinates. The (x,y) coordinates and AoA listed here are chronologically ordered (except the files in the `training` folder) . It reflects the way the person carried the phone moved in the space (for `obj_0`) and everyone else walked (for other `obj_y`, where `y` > 0). </p> <p>The `timestamp` is provided here for time reference for each WiFi packets.</p> <p>To access the data (Python):</p> <pre><code class="language-python">import h5py data = h5py.File('3_people_3.h5','r') csi_real = data['csi_real'][()] csi_imag = data['csi_imag'][()] cam_aoa = data['obj_0/cam_aoa'][()] cam_loc = data['obj_0/coordinates'][()] </code></pre> <p><strong>For file inside `training/` folder:</strong></p> <p>Files inside training folder has a different data structure:</p> <pre><code> |- nPath-1 |- aoa |- csi_imag |- csi_real |- spotfi |- nPath-2 |- aoa |- csi_imag |- csi_real |- spotfi |- nPath-3 |- aoa |- csi_imag |- csi_real |- spotfi |- nPath-4 |- aoa |- csi_imag |- csi_real |- spotfi </code></pre> <p><br> The group `nPath-x` is the number of multiple path specified during the SpotFi calculation. `aoa` is the camera generated angle of arrival (AoA) (can be considered as ground truth), `csi_image` and `csi_real` is the imaginary and real component of the CSI value. `spotfi` is the SpotFi calculated AoA values. The SpotFi values are chosen based on the lowest median and mean error from across `1_person_1.h5` and `1_person_2.h5`. All the rows under the same `nPath-x` group are aligned (i.e., first row of `aoa` corresponds to the first row of `csi_imag`, `csi_real`, and `spotfi`. There is no timestamp recorded and the sequence of the data is not chronological as they are randomly shuffled from the `1_person_1.h5` and `1_person_2.h5` files. </p> <p><strong>Citation</strong><br> If you use the dataset, please cite our paper:</p> <pre><code>@inproceedings{eyefi2020, title={EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching}, author={Fang, Shiwei and Islam, Tamzeed and Munir, Sirajum and Nirjon, Shahriar}, booktitle={2020 IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS)}, year={2020}, organization={IEEE} } </code></pre> <p>Thanks!</p> <p><strong>References</strong></p> <p>1. Halperin, Daniel, et al. "Tool release: Gathering 802.11 n traces with channel state information." ACM SIGCOMM Computer Communication Review 41.1 (2011): 53-53.</p> <p>2. Kotaru, Manikanta, et al. "Spotfi: Decimeter level localization using wifi." Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication. 2015.</p> <p>3. Zhang, Dongheng, et al. "Calibrating Phase Offsets for Commodity WiFi." IEEE Systems Journal (2019).<br> </p>
Cleaning stations in coral reefs – matching services to reef-dwelling and pelagic clients?
<p>Cleaning, the removal of parasites and dead tissue from clients, is common in the Sea. Reef-based cleaning stations are visited by many fish clients, some by both resident and visitor pelagic species, while others are visited solely by resident species. Nonetheless, no distinction has ever been made between the potentially different cleaning stations. Here we describe two distinct categories of cleaning stations: Pelagic Cleaning Stations (PCS) and Residential Cleaning Stations (RCS). We suggest that the two station types differ not only in their clientele but also in the characteristics of their cleaning services. We examined the behaviour of the cleaner wrasse, <em>Labroides</em> <em>dimidiatus</em>, at six cleaning stations on isolated knolls in Palawan, Philippines – three stations that are routinely visited by pelagic manta rays (i.e., PCS), and three stations that service only resident clients (i.e., RCS). Our results indicate PCS have a higher number of cleaners per station, that also forage at greater distances from the focal point of the station. These distinct patterns may be due to the effectiveness of the cleaning process for both the clients and the cleaners. Our findings may aid in the identification and conservation of shark and manta cleaning stations.</p>
Data from: Testing background matching and disruptive colouration in a sexually dichromatic grasshopper: a computer detection experiment.
<p>Cryptic colouration is an adaptative mechanism against predators. Colour patterns can become cryptic through background matching and disruptive colouration, which breaks up the outlines of an animal because the pattern does not coincide with the shape and outline of the animal’s body. Background matching could be advantageous in chromatically homogeneous microhabitats, whereas disruptive colouration can be favoured in visually heterogeneous microhabitats. Grasshoppers of the genus <em>Sphenarium</em> (Orthoptera: Pyrogomorphidae) inhabit very heterogeneous environments and exhibit both strategies. Adults show substantial continuous variation in colouration and longitudinal and transverse bands on the thorax and abdomen. However, males often exhibit considerably more variation in the number of longitudinal and transverse bands than females, which tend to have more uniform colouring (flatter patterns). In this study, we analysed the cryptic properties of the colour patterns of males and females of <em>Sphenarium </em><em>zapotecum</em><em> </em>Sanabria-Urbán, H. Song & Cueva del Castillo and tested the effectiveness of background matching and disruptive colouration using humans as ‘predators’ in a computer detection experiment. We found that the females and males are dichromatic and seem to follow different cryptic strategies in their colouration: males are more disruptive to the background than females, whereas females have a higher level of background matching. In addition, in visually heterogeneous areas, predators spent most time searching for striped male morphs with lower background matching and higher disruptive properties, as well as for female morphs with high background matching, potentially increasing prey survival. As background matching is associated with females and disruptive colouration with males, our results could help explain the evolution of sexual dichromatism in this and other species of grasshoppers of the genus <em>Sphenarium.</em></p> <p> </p>
Gollum: A Gold Standard for Large Scale\\Multi Source Knowledge Graph Matching
<p>The set of Knowledge Graphs (KGs) generated with automatic and manual approaches is constantly growing.<br> For an integrated view and usage, an alignment between these KGs is necessary on the schema as well as instance level.<br> There are already approaches which try to tackle this multi source knowledge graph matching problem,<br> but large gold standards are missing to evaluate their effectiveness and scalability.<br> In particular, most existing gold standards are fairly small and can be solved by matchers which match exactly two KGs (1:1), which are the majority of existing matching systems.<br> <br> We close this gap by presenting Gollum -- a gold standard for large-scale multi source knowledge graph matching with over 275,000 correspondences between 4,149 different KGs.<br> They originate from knowledge graphs derived by applying the DBpedia extraction framework to a large wiki farm.<br> <br> Three variations of the gold standard are made available:<br> (1) a version with all correspondences for evaluating unsupervised matching approaches, and two versions for evaluating supervised matching: (2) one where each KG is contained both in the train and test set, and (3) one where each KG is exclusively contained in the train or the test set.</p> <p><br> We plan to extend our KG track at the Ontology Alignment Evaluation Initiative (OAEI) to allow for matching systems <br> which are specifically designed to solve the multi KG matching problem.<br> As a first step towards this direction, we evaluate multi source matching approaches which reuse two-KG (1:1) matchers from the past OAEI.</p> <p> </p> <p>Due to the size of the KG files, they are hosted at the institute:</p> <p><a href="http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/40K.tar">http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/40K.tar</a> (50,3 GB)<br> <a href="http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/all.tar">http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/all.tar</a> (74,7 GB)<br> <a href="http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/gold.tar">http://data.dws.informatik.uni-mannheim.de/dbkwik/gollum/gold.tar</a> (25,3 GB)</p>
World Padel Tour historical match data
<p>This dataset contains all World Padel Tour match information available on its webpage. It was obtained via web scraping.</p> <p>This dataset is obtained for educational purposes, it is part of a university course in UOC (Universitat Oberta de Catalunya).</p> <p>The data is in Spanish.</p>
Data from: Pre‐adaptation and adaptation shape trait‐environment matching in the Neotropics
<p>The resources provided here comprise raw and final data sets used in our study 'Preadaptation and adaptation shape trait-environment matching in the Neotropics'<span> (DOI: 10.1111/geb</span>.<span>13730) comprising curated g</span>eographic occurrences for 156 Swartzia taxa across its distribution in the Neotropics, phylogenetic information for 69 Swartzia taxa, and corresponding climatic (i.e. 19 climatic variables taken from WorldClim), soil (<span>i.e. </span>sand, nitrogen, and clay content taken from the SoilGrids 2.0 portal), and trait data <span>for each taxa</span> (i.e. <span>trait measurements for </span>lefleats, petals, and fruits)<span>. We also provide t</span>he script used to extract trait information from botanical monographs<span>.</span></p>
Tallo database with World Flora Online taxonomic matches, also including information on presence in the TreeGOER and GlobalUsefulNativeTrees databases
<p>The <strong>Tallo database</strong> (v 1.0.0) provides tree measurements for 5,163 tree species and 61,856 globally distributed sites. The database can be accessed via <a href="https://zenodo.org/record/6637599">https://zenodo.org/record/6637599</a> and has been fully described in Jucker <em>et al.</em> 2022. <strong>Tallo: A global tree allometry and crown architecture database</strong>. Global Change Biology, 28, 5254–5268. <a href="https://doi.org/10.1111/gcb.16302">https://doi.org/10.1111/gcb.16302</a>. Data provided in this archive show the taxonomic matches with <a href="https://www.worldfloraonline.org/">World Flora Online</a> (Borsch et al. <a href="https://onlinelibrary.wiley.com/doi/10.1002/tax.12373">2020</a>) for two versions of its taxonomic backbone data set. The first version (<a href="https://www.worldfloraonline.org/downloadData">v. 2021.12</a>) was used also to standardize species names for the latest version of the <a href="https://www.worldagroforestry.org/output/agroforestry-species-switchboard-30">Agroforestry Species Switchboard</a> and during the compilation of the <strong>TreeGOER</strong> (<a href="https://zenodo.org/record/8052331">Tree Globally Observed Environmental Ranges</a>; Kindt <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">2023</a>) and <strong>GlobalUsefulNativeTrees</strong> (<a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobUNT</a>; Kindt <em>et al.</em> <a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) databases. The second version (<a href="https://www.worldfloraonline.org/downloadData">v. 2023.03</a>) was the most recent version available online. Taxonomic matching was done via the <a href="https://cran.r-project.org/package=WorldFlora">WorldFlora</a> R package (Kindt <a href="https://bsapubs.onlinelibrary.wiley.com/doi/10.1002/aps3.11388">2020</a>, see example scripts for a different taxonomic matching exercise <a href="https://rpubs.com/Roeland-KINDT/996500">here</a>).</p> <p>Taxonomic matching was achieved for all taxa listed in the Tallo database, except for <em>Lithocarpus orocola</em>.</p> <p>Presence of a taxon in the TreeGOER and GlobalUsefulNativeTrees databases has been flagged by showing the number of records used in TreeGOER to calculate ranges for environmental variable bio01 and by the number of native countries in GlobUNT, respectively.</p> <p>The development of this archive was supported by the Darwin Initiative to project DAREX001 of <em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em>, by Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, and by the Green Climate Fund through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> project.</p>
League of Legends Match Data at Various Time Intervals
<p>This dataset comprises comprehensive information from ranked matches played in the game League of Legends, spanning the time frame between January 12, 2023, and May 18, 2023. The matches cover a wide range of skill levels, specifically from the Iron tier to the Diamond tier.</p> <p>The dataset is structured based on time intervals, presenting game data at various percentages of elapsed game time, including 20%, 40%, 60%, 80%, and 100%. For each interval, detailed match statistics, player performance metrics, objective control, gold distribution, and other vital in-game information are provided.</p> <p>This collection of data not only offers insights into how matches evolve and strategies change over different phases of the game but also enables the exploration of player behavior and decision-making as matches progress. Researchers and analysts in the field of esports and game analytics will find this dataset valuable for studying trends, developing predictive models, and gaining a deeper understanding of the dynamics within ranked League of Legends matches across different skill tiers.</p>
Persian emoji matching dataset
<p>This is a data set used for Creating NFT-Backed Emoji Art from User Conversations on the Blockchain paper. </p>
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.