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1,392 results for “matching”
Gold-Caps_LMD-Matched_General
<p>This dataset contains captions for the <a href="https://colinraffel.com/projects/lmd/">Lakh MIDI Dataset-matched</a> music dataset (~30,000 tracks with accompanying MIDI files).</p><p>These captions were generated by the <strong>gpt-4-1106-preview</strong> chat endpoint prompted to describe each track based on the track title and artist. The captions have not been filtered or post-processed in any way.</p><p><strong>Prompt used:</strong><br>"Give a general description of the track <title> by <artist_name> in one sentence. Don't mention the title or artist."</p>
Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) and Gas Chromatography - Mass Spectrometry (GC-MS) Reference Libraries from Global Natural Products Social Molecular Networking (GNPS) and National Institute of Standards and Technology (NIST) WebBook Processed for Spectral Library Matching
<div>In order to obtain a high-quality LC-MS/MS reference database for spectral library matching, we selected 22 high-quality GNPS tandem mass spectrometry databases generated under the positive ion mode. Further preprocessing similar to Huber et al involving mass-to-charge (m/z) and intensity filtering yields the database found in the file LCMS_GNPS_reference_library.csv which contains 14,705 electrospray ionization (ESI) mass spectra, each of which corresponds to a unique compound. The NIST WebBook database was used to construct GC-MS database contained in the file GCMS_NIST_WebBook.csv. This database contains 23,721 electron ionization (EI) mass spectra, each of which corresponds to a unique non-hyphenated Chemical Abstract Service (CAS) Registry Number.</div> <div> </div> <div>Both LC-MS/MS and GC-MS databases are organized into three columns: one for the identifier, one for the m/z values, and one for the intensity values. For example, if spectrum A has 20 ion fragments, then there will be 20 rows corresponding to spectrum A in the corresponding database with the identifier A repeated 20 times with the corresponding m/z and intensity values.</div>
Food items matching
<p>It contains Slovenian food names from receipts linked to items in the Slovenian food composition database (FCDB). They are also annotated with the NAct ontology.<strong> </strong>Since food names on receipts are often abbreviated and vary from those in the FCDB, this dataset is crucial for future research in food, nutrition, data science, and AI. It can be used to develop food recommender systems commonly found in food applications.</p> <p> </p>
Matching results between landmark in different sources and landmark in a referenced dataset (BDTOPO)
<p>The four datasets represent the results of a two sequentials processus. The first processus consists on a automatic matching between landmark in different sources and landmark in a referenced dataset (french national topographic data: BDTOPO). Then the links 1:1 are manually validated by experts in the second processus.</p> <p>The four different datasets and the BDTOPO dataset are archived <a href="https://doi.org/10.5281/zenodo.6480986">here</a>.</p> <p>The data matching algorithm is described in this <a href="http://dx.doi.org/10.5311/JOSIS.2015.10.194">paper</a>.</p> <p>Each file represents the result matching for features belonging to a data source with:</p> <p>- the name of file depending on the data source</p> <p>- the column "id_source" corresponds to the identifier of the landmark in data source</p> <p>- the column "types_of_matching_results" describes the type of matching result:</p> <ul> <li>« 1:0 »: means that a landmark from a data source (e.g. Camptocamp) has no homologue landmark in BDTOPO</li> <li>« 1:1 validated »: means that a homologous feature exist in BDTOPO and the link was validated</li> <li>« 1:1 non validated »: means that the matching link was not validated</li> <li>« without candidates »: represents the non-matched landmarks because there are no candidates in BDTOPO or because the landmark in data source is far away from its homologous in BDTOPO</li> <li>« uncertain »: uncertainty cases are complex cases where any decision is taken by the data matching algorithm</li> </ul> <p>- the column "id_candidat" corresponds to the identifier of the landmark in BDTOPO if and only if there is a validated matching link</p> <p>- the column "samal" corresponds to the <a href="https://doi.org/10.1080/13658810410001658076">Samal distance</a></p> <p>The matching results are obtained using an ontology application named <a href="http://choucas.ign.fr/doc/ontologies/index-fr.html">OOR</a>. These specific results are obtained using the version of OOR V1.0.1 which is an improved version and contains new concepts compared to the first release 1.0.0. The new version of OOR (i.e. 1.0.1) will be released by the end of May 31 2022. The new link will be added here.</p> <p>This archive is released for transparency and reproducibility purposes.</p>
Vegetation and invertebrate communities in 500 plots in the Duplin and Dean Creek watersheds: ground truth data for matching hyperspectral imagery
We measured characteristics of vegetation (Aster tenuifolius, Batis maritima, Borrichia frutescens, Distichlis spicata, Iva frutescens, Juncus roemerianus, Limonium carolinianum, Salicornia biglovii, Salicornia virginica, Spartina alterniflora, Spartina patens, Sporobolus virginicus), soil (salinity, proportion organic and proportion water) and densities of common gastropods and bivalves in 500 plots in the Duplin and Dean Creek watersheds on Sapelo Island on June 20-26, 2006. Plot locations were determined using a high precision hand-held GPS. These data were used to help ground-truth hyperspectral aerial images collected at the same time by Dr. John Schalles.
Data: Algorithms for new types of fair stable matchings
<p>This data corresponds to the data and experiments described in Section 5 of<br> the following paper:</p> <p>Algorithms for new types of fair stable matchings<br> Authors: Frances Cooper and David Manlove</p> <ul> <li>The paper is located at: <a href="https://arxiv.org/abs/2001.10875">https://arxiv.org/abs/2001.10875</a></li> <li>The software is located at: <a href="https://zenodo.org/record/3630383">https://zenodo.org/record/3630383</a></li> <li>The data is located at: <a href="https://zenodo.org/record/3630349">https://zenodo.org/record/3630349</a></li> </ul> <p>See the README for more information.</p>
Mix-and-Match Dataset
<p>Benchmark results for "Mix-and-Match: A Model-driven Runtime Optimisation Strategy for BFS on GPUs" paper.</p> <p> </p> <p>Performance data for Breadth-First Search on NVidia TitanX. Including trained Binary Decision Tree model for predicting the best implementation on an input graph.</p>
High-resolution earthquake catalog obtained through template-matching in the Southern Apennine (Italy)
<p>This is an enhanced, high-resolution earthquake catalog obtained through template-matching (TM). It covers the area of the Southern Apennines (Italy), for the period 2009-2014</p> <p>Starting from about 4000 events used as templates, TM allowed to detect the hidden, small-magnitude seismicity in the 0-1 magnitude range, allowing a significant decrease of the magnitude of completeness in the resulting earthquake catalog.</p> <p>The catalog contains:</p> <ul> <li>templates (events catalogued by INGV and used as templates)</li> <li>template-matching detections (i.e. newly detected events by TM)</li> <li>events catalogued by INGV that are also found through template-matching</li> </ul> <p>All events are located with the same 1-D velocity model obtained by averaging several models that have been proposed in the literature, covering different portion of the Southern Apennines. </p> <p><strong>DATA STRUCTURE</strong></p> <p><strong>id</strong>: id of event. Events detected by template-matching start with 'TM', otherwise the id is the same as in the official INGV catalog.</p> <p><strong>lon</strong>: longitude (degrees)</p> <p><strong>lat</strong>: latitude (degrees)</p> <p><strong>depth</strong>: depth in km</p> <p><strong>time</strong>: origin time</p> <p><strong>M_l</strong>: local magnitude</p> <p><strong>lon_error</strong>: error on longitude (degrees)</p> <p><strong>lat_error</strong>: error on latitude (degrees)</p> <p><strong>depth_error</strong>: error on depth (km)</p> <p><strong>RMS</strong>: root-mean-square (sec)</p> <p><strong>az_gap</strong>: azimuthal gap</p> <p><strong>n_phases</strong>: total number of P and S arrivals </p> <p><strong>n_stations</strong>: total number of station recording the event</p> <p><strong>mag_diff</strong>: difference in magnitude between detection and its template</p> <p><strong>dt</strong>: difference in origin time between template and detected event (sec)</p> <p><strong>templ_id</strong>: id of the template event</p> <p><strong>as_template</strong>: =1 if the event was used as template, 0 otherwise</p> <p><strong>matched_TM</strong> (for events already catalogued by INGV): =1 if the events matched a detection made by template matching, =0 otherwise</p> <p><strong>matched_BSI</strong>: ==id of the corresponding event catalogued by INGV. For newly detected events (thus never catalogued before) this field is 'NA'</p>
OverWatch Match Data
<p>A dataset of 61867 matches from <a href="https://overtrack.gg/">OverTrack.gg</a> was loaded into memory. Most matches represent a team of 5 vs. 5, with no possibility for ties. Since tracking matches is volutary, a not insignificant percentage of matches (7.82%) are played against a team of unrated/unknown players. The average number of matchs per unique player is about 2.338, however a decent number of players were tracked for over a hundred matches.</p>
Trees of India Version 1: Standardization to Records in World Flora Online and the World Checklist of Vascular Plants, with matches in GlobalTreeSearch and GlobalUsefulNativeTrees
<p>The <strong>Trees of India (ToI, Version-I)</strong> includes data on 3708 tree species distributed across 35 states/union territories of India. The database is based on systematic review of 313 literature sources published from 1872-2022.This compendium is available via <a href="https://figshare.com/articles/dataset/ToI_Ver_-I_Trees_of_India_Version-I/23226281">Figshare</a> and was described by Mugal et al. <a href="https://link.springer.com/article/10.1007/s10531-023-02659-y">2023</a>:</p> <ul> <li>Khuroo, Anzar Ahmad; Mugal, Muzamil Ahmad; Wani, Sajad Ahmad (2023). ToI, Ver.-I : Trees of India, Version-I. figshare. Dataset. <a href="https://doi.org/10.6084/m9.figshare.23226281.v1">https://doi.org/10.6084/m9.figshare.23226281.v1</a></li> <li>Mugal, M.A., Wani, S.A., Dar, F.A. <em>et al.</em> Bridging global knowledge gaps in biodiversity databases: a comprehensive data synthesis on tree diversity of India. <em>Biodivers Conserv</em> <strong>32</strong>, 3089–3107 (2023). <a href="https://doi.org/10.1007/s10531-023-02659-y">https://doi.org/10.1007/s10531-023-02659-y</a></li> </ul> <p> </p> <p>Here I provide direct and fuzzy matches for taxa listed with accepted plant names in <strong>World Flora Online</strong> (<a href="https://www.worldfloraonline.org/downloadData">version 2023.03</a>; Borsch et al. <a href="https://doi.org/10.1002/tax.12373">2020</a>) and the <strong>World Checklist of Vascular Plants</strong> (WCVP <a href="https://doi.org/10.34885/nswv-8994">version 10</a>; Govaerts et al. <a href="https://www.nature.com/articles/s41597-021-00997-6">2021</a>). Matching was done in <em>R</em> through the <a href="https://cran.r-project.org/package=WorldFlora">WorldFlora</a> package (Kindt <a href="https://bsapubs.onlinelibrary.wiley.com/doi/full/10.1002/aps3.11388">2020</a>). The taxonomic standardization process was similar to the one completed <a href="https://www.worldagroforestry.org/output/agroforestry-species-switchboard-30">during the preparation of the third major release</a> of the <a href="https://apps.worldagroforestry.org/products/switchboard">Agroforestry Species Switchboard</a> and when preparing the <strong>GlobalUsefulNativeTrees database</strong> (GlobUNT; <a href="https://worldagroforestry.org/output/globalusefulnativetrees">https://worldagroforestry.org/output/globalusefulnativetrees</a>).</p> <p>After matching species with the WCVP, information was compiled on the <strong>native distribution</strong> documented in the WCVP for level-3 units of the <a href="https://github.com/tdwg/wgsrpd">World Geographical Scheme for Recording Plant Distributions</a> that correspond to India, including India (IND), Assam (ASS), West Himalaya (WHM), East Himalaya (EHM), Laccadive Is. (LDV), Andaman Is. (AND) and Nicobar Is. (NCB). Also included after matching with the WCVP is information on the geographic area, lifeform and main biome. Similar information is available when searching for species from <a href="https://powo.science.kew.org/">Plants of the World Online</a>.</p> <p>Where a matching species was found in <strong>GlobalTreeSearch</strong> (Beech et al. <a href="https://www.tandfonline.com/doi/full/10.1080/10549811.2017.1310049">2017</a>; <a href="https://tools.bgci.org/global_tree_search.php">https://tools.bgci.org/global_tree_search.php</a>; accessed on 28th June 2023) filtered for India, the species name in GlobalTreeSearch is shown. Note that GlobalTreeSearch documents the <strong>native country distribution</strong> of tree species.</p> <p>Where a matching species was found in the <strong>GlobalUsefulNativeTrees</strong> database (GlobUNT, version 2023.11) filtered for India, the species name in the GlobUNT database is shown. GlobUNT has been described in the following publication: Kindt et al. (<a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) <strong>GlobalUsefulNativeTrees, a database of 14,014 tree species, supports synergies between biodiversity recovery and local livelihoods in restoration</strong>. <em>Sci Rep</em> <strong>13</strong>, 12640. <a href="https://doi.org/10.1038/s41598-023-39552-1">https://doi.org/10.1038/s41598-023-39552-1</a>.</p> <p>See the metadata for information on versions.</p> <p> </p> <ul> <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> <li>E. Beech, M.Rivers, S. Oldfield & P. P. Smith (2017)GlobalTreeSearch: The first complete global database of tree species and country distributions, Journal of Sustainable Forestry, 36:5, 454-489, DOI: <a href="https://doi.org/10.1080/10549811.2017.1310049">10.1080/10549811.2017.1310049</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> 8(9): e11388. <a href="https://doi.org/10.1002/aps3.11388">https://doi.org/10.1002/aps3.11388</a></li> </ul> <p> </p> <p>The developments of this dataset and GlobUNT were supported by the Darwin Initiative to project DAREX001 of <a href="https://www.darwininitiative.org.uk/project/DAREX001/"><em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em></a>.</p>
Data supporting: "Trends in Europe storm surge extremes match the rate of sea-level rise"
<p><strong>Data supporting the paper:</strong></p> <p><strong>Calafat, F. M., T. Wahl, M. G. Tadesse, & S. Sparrow. Trends in Europe storm surge extremes match the rate of sea-level rise. <em>Nature</em> 603, 841-845.</strong></p> <p>Please cite the paper above when using this data set.</p> <p><em>Data description:</em></p> <ul> <li><strong>Bayesian_solutions_historical_total.nc</strong>: Bayesian estimates (posterior draws) of the GEV parameters, including trends in the GEV location parameter, at both tide gauge sites and prediction locations. This file also contains the observed surge annual maxima from tide gauge records on which these estimates are conditioned.</li> <li><strong>Bayesian_solutions_historical_contributions.nc</strong>: Bayesian estimates (posterior draws) of the contributions from external forcing and internal climate variability to the trends in the GEV location parameter.</li> <li><strong>Surge_annual_max_ensemble.nc</strong>: ensemble of surge annual maxima used to extract the pattern of response to external forcing.</li> </ul>
League of Legends KR High Elo 5v5 Match Data
<p>League of Legends KR High Elo 5v5 Match Data</p> <p>Related project link: <a href="https://github.com/JohnsonJDDJ/zilean">GitHub</a></p> <p>The dataset is retrieved using the <a href="https://developer.riotgames.com/apis">Riot API</a>. For documentation of the API please visit the website.</p> <p>The dataset contains information about all League of Legends KR server challengers (n=300) as of 2022-06-12. The account information is stored in<strong> accounts.json</strong>, whereas the information about the challenger league is in <strong>kr_challenger_league.json</strong>. </p> <p>Match data was retrieved from the 5 most recent 5v5 ranked solo matches for each challenger account. There are in total 2166 unique matches. The matches are further cleaned only to include games that last more than 16 minutes (n=2078), which are stored in <strong>matches.json.</strong></p>
Annex 1 – Actions and measures relevant to research integrity matched to the UK Concordat
<p>The present dataset is an Annex to the Discussion Document entitled “<a href="https://doi.org/10.5281/zenodo.6827947">Indicators of Research Integrity: An initial exploration of the landscape, opportunities and challenges</a>”. </p> <p>It consists in a longlist of actions and measures that organisations may put in place to support research integrity, building on a set of documents that we considered to represent the perspectives of the stakeholder groups mentioned in the UK Concordat to Support Research Integrity, including: </p> <ul> <li> <p>researchers; </p> </li> <li> <p>employers of researchers (i.e. bodies that conduct or host research; employ, support or host researchers; teach research students; or allow research to be carried out under their auspices); </p> </li> <li> <p>research funders; and </p> </li> <li> <p>other organisations (e.g. professional, statutory and regulatory bodies; academies and learned societies; professional and subject-specific representative bodies; journals and publishers; and organisations offering advice, guidance and support). </p> </li> </ul> <p>The table below provides an overview of the documents covered in the dataset. It should be noted that our selection of documents is not meant to imply that other efforts are of lesser importance: it is only a starting point for discussion and seeks to represent a breadth of stakeholder views. </p> <table> <tbody> <tr> <td> <p>Document </p> </td> <td> <p>Lead </p> </td> <td> <p>Main perspective(s) </p> </td> </tr> <tr> <td> <p><a href="https://ukrio.org/wp-content/uploads/UKRIO-Self-Assessment-Tool-for-The-Concordat-to-Support-Research-Integrity-V2.pdf">UKRIO Self-Assessment Tool for The Concordat to Support Research Integrity</a> </p> </td> <td> <p>UK Research Integrity Office (UKRIO) </p> </td> <td> <p>Employers of researchers </p> </td> </tr> <tr> <td> <p><a href="https://doi.org/10.1371/journal.pbio.3000737">The Hong Kong Principles for assessing researchers: Fostering research integrity</a> </p> </td> <td> <p>Moher et al. (academic article) </p> </td> <td> <p>Researchers, Employers of researchers, Research funders </p> </td> </tr> <tr> <td> <p><a href="https://www.vitae.ac.uk/vitae-publications/reports/research-integrity-a-landscape-study">Research integrity: a landscape study</a> </p> </td> <td> <p>UK Research and Innovation (UKRI), Vitae, UK Research Integrity Office (UKRIO), UK Reproducibility Network (UKRN) </p> </td> <td> <p>All stakeholders </p> </td> </tr> <tr> <td> <p><a href="https://wellcome.org/reports/what-researchers-think-about-research-culture">What Researchers Think About the Culture They Work In</a> </p> </td> <td> <p>Wellcome </p> </td> <td> <p>Researchers, Employers of researchers, Research funders </p> </td> </tr> <tr> <td> <p><a href="https://www.allea.org/wp-content/uploads/2017/05/ALLEA-European-Code-of-Conduct-for-Research-Integrity-2017.pdf">The European Code of Conduct for Research Integrity</a> </p> </td> <td> <p>All European Academies (ALLEA) </p> </td> <td> <p>All stakeholders </p> </td> </tr> <tr> <td> <p><a href="http://www.enrio.eu/wp-content/uploads/2019/03/INV-Handbook_ENRIO_web_final.pdf">Handbook on Research Integrity</a> </p> </td> <td> <p>European Network for Research Ethics and Integrity (ENERI) </p> </td> <td> <p>Researchers, Employers of researchers, Research funders </p> </td> </tr> <tr> <td> <p><a href="https://sops4ri.eu/wp-content/uploads/Guideline-for-Promoting-RI-in-RFOs_final.pdf">Guideline for Promoting Research Integrity in Research Funding Organisations</a> </p> </td> <td> <p>Standard Operating Procedures for Research Integrity (SOPs4RI) </p> </td> <td> <p>Research funders </p> </td> </tr> <tr> <td> <p><a href="https://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/horizon/guidance/guideline-for-promoting-research-integrity-in-research-performing-organisations_horizon_en.pdf">Guideline for Promoting Research Integrity in Research Performing Organisations</a> </p> </td> <td> <p>Standard Operating Procedures for Research Integrity (SOPs4RI) </p> </td> <td> <p>Employers of researchers </p> </td> </tr> <tr> <td> <p><a href="https://doi.org/10.24318/cope.2018.1.3">Cooperation between research institutions and journals on research integrity cases: guidance from the Committee on Publication Ethics</a> </p> </td> <td> <p>Committee on Publication Ethics (COPE) </p> </td> <td> <p>Publishers and Employers of researchers </p> </td> </tr> <tr> <td> <p><a href="https://doi.org/10.24318/cope.2019.1.4">COPE Retraction Guidelines</a> </p> </td> <td> <p>Committee on Publication Ethics (COPE) </p> </td> <td> <p>Publishers </p> </td> </tr> </tbody> </table> <p>Find more outputs of this project in the <a href="https://zenodo.org/communities/research-integrity-indicators/">dedicated Zenodo community</a>. </p>
The data for SKYSURF-5: Probing the Integrated Galaxy Light with a SDSS-SKYSURF Cross-Matched Catalog
<p>The SKYSURF Project (Windhorst et al. 2022) analyzes the extragalactic background light (both directly using sky background measurements and indirectly using galaxy counts) using the HST Archive. While HST images probe faint galaxies unseen by ground-based imaging, its small field of view prevents it from probing the large-scale structure around its observations.</p> <p>To supplement SKYSURF analysis, we cross-match SKYSURF pointings with SDSS observations able to probe the surrounding large-scale environment (Bhatia et al. 2024). The tables in this database include galaxies brighter than r=22.5 AB mag photometrically identified in SDSS, within +/-5 arcmin around a SKYSURF pointing.</p> <p>The tables in the Object_AB directory include information on all SDSS objects, organized by the HST camera and filter of the central pointing.</p> <p>The tables in the IGL directory include the total galaxy counts and integrated galaxy light (down to AB mag=22.5) for all SDSS objects surrounding a given SKYSURF image.</p>
Dual color DMD-SIM by temperature-controlled laser wavelength matching [raw datasets]
<p>Raw data set accompanying the publication "Dual color DMD-SIM by temperature-controlled laser wavelength matching".</p>
Transcriptomic profiles of resected pancreatic adenocarcinoma, whole-slide match
<p>RNA was extracted from the whole-slide tumor regions of 100 pancreatic adenocarcinomas, consecutively resected at the Beaujon hospital (Clichy, FRANCE). Tumors were sequenced in two batches, using 3' RNA-sequencing for FFPE compatibility.</p>
Dataset with the results and matches of La Liga (2013/14 to 2022/23)
<p>The dataset contains information about soccer matches played in the Spanish professional league, La Liga, from the 2013/2014 season to the current season (2022/2023). The dataset includes 3800 rows, where each row represents a match and contains information such as season, matchday, date, time, home team, away team, result, and referee (referees are only available from the 2014/2015 season onwards). The dataset can be used to analyze and compare the performance of teams over the last 10 years, as well as to investigate the influence of referees and match schedules on match results.</p>
Numerical convergence of model Cauchy-Characteristic Extraction and Matching (data)
<p>This dataset was used to produce the convergence plots in the paper "Numerical convergence of model Cauchy-Characteristic Extraction and Matching", as well as additional convergence tests that can be found in the repository https://github.com/ThanasisGiannakopoulos/model_CCE_CCM_public. The data can be used to reproduce the aforementioned convergence plots or for comparison against data obtained if one performs the same simulations independently.</p>
Velociraptor mongoliensis and Troodon mongoliensis squabble over a Protoceratops andrewsi carcass. The firsfls greater firepower matched the second 's larger size. in Predatory Dinosaurs of the World
Velociraptor mongoliensis and Troodon mongoliensis squabble over a Protoceratops andrewsi carcass. The firsfls greater firepower matched the second 's larger size.
Structural and functional connectomes from 27 schizophrenic patients and 27 matched healthy adults
<p><strong><em>Data Acquisition</em></strong></p> <p>The cohort consists of a total of 27 healthy participants (age 35 ± 6.8 years) and 27 schizophrenic patients (age 41 ± 9.6), scanned in a 3-Tesla MRI scanner (Trio, Siemens Medical, Germany) using a 32-channel head-coil. The schizophrenic patients are from the Service of General Psychiatry at the Lausanne University Hospital (CHUV). All of them were diagnosed with schizophrenic and schizoaffective disorders after meeting the DSM-IV criteria (American Psychiatric Association (2000): Diagnostic and Statistical Manual of Mental Disorders, 4th ed. DSM-IV-TR. American Psychiatric Pub, Arlington, VA22209, USA). The Diagnostic Interview for Genetic Studies assessment was used to recruits the healthy controls (Preisig et al. 1999). 24 out of the 27 schizophrenics were under medication with mean chlorpromazine equivalent dose (CPZ) of 431 ± 288 mg. The written consent was obtained for all subjects - in accordance with institutional guidelines of the Ethics Committee of Clinical Research of the Faculty of Biology and Medicine, University of Lausanne, Switzerland, #82/14, #382/11, #26.4.2005). All subjects were fully anonymised.</p> <p>The session protocol consisted of (1) a magnetization-prepared rapid acquisition gradient echo (MPRAGE) sequence sensitive to white/gray matter contrast (1-mm in-plane resolution, 1.2-mm slice thickness), (2) a Diffusion Spectrum Imaging (DSI) sequence (128 diffusion-weighted volumes and a single b0 volume, maximum b-value 8,000 s/mm<sup>2</sup>, 2.2x2.2x3.0 mm voxel size), and (3) a gradient echo EPI sequence sensitive to BOLD contrast (3.3-mm in-plane resolution and slice thickness with a 0.3-mm gap, TE 30 ms, TR 1,920 ms, resulting in 280 images per participant). During the fMRI scan, participants were not engaged in any overt task, and the scan was treated as eyes-open resting-state fMRI (rs-fMRI).</p> <p><strong><em>Data Pre-processing </em></strong></p> <p>Initial signal processing of all MPRAGE, DSI, and rs-fMRI data was performed using the Connectome Mapper pipeline (Daducci et al. 2012). Grey and white matter were segmented from the MPRAGE volume using freesurfer (Desikan<em> </em>et al. 2006) and parcellated into 83 cortical and subcortical areas. The parcels were then further subdivided into 129, 234, 463 and 1015 approximately equally sized parcels according to the Lausanne anatomical atlas following the method proposed by (Cammoun et al. 2012). DSI data were reconstructed following the protocol described by (Wedeen et al. 2005), allowing us to estimate multiple diffusion directions per voxel. The diffusion probability density function was reconstructed as the discrete 3D Fourier transform of the signal modulus. The orientation distribution function (ODF) was calculated as the radial summation of the normalized 3D probability distribution function. Thus, the ODF is defined on a discrete sphere and captures the diffusion intensity in every direction.</p> <p><strong><em>Structural Connectivity</em></strong></p> <p>Structural connectivity matrices were estimated for individual participants using deterministic streamline tractography on reconstructed DSI data, initiating 32 streamline propagations per diffusion direction, per white matter voxel (Wedeen et al. 2008). Structural connectivity between pairs of regions was measured in terms of fiber density, defined as the number of streamlines between the two regions, normalized by the average length of the streamlines and average surface area of the two regions (Hagmann et al. 2008). The goal of this normalization was to compensate for the bias toward longer fibers inherent in the tractography procedure, as well as differences in region size. The number of fibers and fiber length were also included in the dataset. For the quantitative measure of structural connectivity, the generalised fractional anisotropy (gFA, Tuch et al. 2004) and average apparent diffusion coefficient (ADC, Sener et al. 2001) were also computed for each tract.</p> <p> </p> <p><strong><em>Functional Connectivity</em></strong></p> <p>Functional data were pre-processed using routines designed to facilitate subsequent network exploration (Murphy et al. 2009, Power et al. 2012). The first four time points were excluded from subsequent analysis to allow the time series to stabilize. The signal was linearly detrended and further physiological (white-matter and cerebrospinal fluid regressors) and motion artefacts (three translational and three rotational regressors) confounds were regressed. Then, the signal was spatially smoothed and bandpass-filtered between 0.01-0.1 Hz with Hamming windowed sinc FIR filter. To obtain the brain regions for different atlas scales the signal was linearly registered to the MPRAGE image and averaged within a given region (Jenkinson et al. 2012). Functional matrices were obtained by computing Pearson’s correlation between the individual pairs of regions. All of the above was carried out in subject’s native space (Daducci et al. 2012, Griffa et al. 2017).</p> <p>Brain cortical bert freesurfer rendering for the 5 scales of the Lausanne2008 atlas is available on <a href="https://github.com/jvohryzek/bert4lausanne2008">https://github.com/jvohryzek/bert4lausanne2008</a>.</p>
ScienceDex guides
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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.