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sami2py sample output files
<p>Example output datasets generated by the sami2py model. These files are in support of the manuscript "The sami2py model -- overview and applications", submitted to Frontiers in Astronomy and Space Science in October 2022. The sami2py model can be accessed via github.</p> <p>sami2py: </p> <p>https://github.com/sami2py/sami2py</p> <p>https://doi.org/10.5281/zenodo.2875799</p>
Multi-LEX: a database of multi-word frequencies (French files)
<p>Written word frequency is a key variable used in many psycholinguistic studies and is central in explaining visual word recognition. Indeed, methodological advances on single word frequency estimates have helped to uncover novel language-related cognitive processes, fostering new ideas and studies. In an attempt to support and promote research on a related emerging topic, visual multi-word recognition, we extracted from the exhaustive Google Ngram datasets a selection of millions of multi-word sequences and computed their associated frequency estimate. Such sequences are presented with Part-of-Speech information for each individual word. An online behavioral investigation making use of the French 4-gram lexicon in a grammatical decision task was carried out. The results show an item-level frequency effect of word sequences. Moreover, the proposed datasets were found useful during the stimulus selection phase, allowing more precise control of the multi-word characteristics.</p>
Multi-LEX: a database of multi-word frequencies (English files)
<p>Written word frequency is a key variable used in many psycholinguistic studies and is central in explaining visual word recognition. Indeed, methodological advances on single word frequency estimates have helped to uncover novel language-related cognitive processes, fostering new ideas and studies. In an attempt to support and promote research on a related emerging topic, visual multi-word recognition, we extracted from the exhaustive Google Ngram datasets a selection of millions of multi-word sequences and computed their associated frequency estimate. Such sequences are presented with Part-of-Speech information for each individual word. An online behavioral investigation making use of the French 4-gram lexicon in a grammatical decision task was carried out. The results show an item-level frequency effect of word sequences. Moreover, the proposed datasets were found useful during the stimulus selection phase, allowing more precise control of the multi-word characteristics.</p>
Output files corresponding to "Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States"
<p>This dataset corresponds to the output files that were produced for the study reported in:</p> <p>Knights, Deon, Kevin C. Parks, Audrey H. Sawyer, Cédric H. David, Trevor N. Browning, Kelsey M. Danner, and Corey D. Wallace, (2017), Direct groundwater discharge and vulnerability to hidden nutrient loads along the Great Lakes coast of the United States, <em>Journal of Hydrology,</em> 554, 331-341</p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>The National Hydrography Dataset Plus (NHDPlus) Version 2, obtained from http://www.horizon-systems.com/nhdplus/NHDplusV2_data.php. Region used is: Great Lakes (04)</li> <li>The second phase of the North American Land Data Assimilation System (NLDAS2), obtained from ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS. Model outputs used are: NLDAS_MOS0125_MC.002, NLDAS_NOAH0125_MC.002, and NLDAS_VIC0125_MC.002.</li> <li>The United States 2011 National Land Cover Database (NLCD 2011), obtained from: http://www.mrlc.gov/nlcd2011.php.</li> </ul> <p> </p> <p><strong>Description of files</strong></p> <p>The files in this dataset contain are described below:</p> <ul> <li><em>Flowlines</em>: This folder contains a shapefile (<em>GL_coastcatchment_NHDflowline</em>) with the coastline of the Contiguous United States as described by NHDPlus V2, and was merged from a subsample of all river reaches available in the region used. </li> <li><em>Catchment</em>: This folder contains a shapefile (GL_coastcatchment_polygon) with the contributing catchments of NHDPlus V2 corresponding to the above coastline, and was merged from a subsample of all catchments available in the region used. </li> <li><em>Centroid</em>: This folder contains a shapefile (GL_coastcatchment_centroid) with the centroids of the above catchments. </li> <li><em>DischargeVulnerabilities.csv</em>. This .csv file contains the following data (units are in parentheses): <ul> <li>COMID: Unique feature identifier in NHDPlusV2 ().</li> <li>Length_km: Length of coastline feature (km).</li> <li>Area_sqkm: Area of coastal catchment feature (km<sup>2</sup>).</li> <li>Infiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>REACHCODE: Reach identifier in NHDPlusV2; reaches can include multiple features; Submarine Groundwater Discharge (SGD) is computed by reach, not feature ().</li> <li>RLength_km: Total length of coastline accumulated by REACHCODE (km).</li> <li>RArea_sqkm: Total area of coastal catchment accumulated by REACHCODE (km<sup>2</sup>).</li> <li>RInfiltration_kgsqm: Average annual infiltrating runoff for REACHCODE (kg/m<sup>2</sup>)</li> <li>DGWD: Average annual direct groundwater discharge for REACHCODE (m<sup>2</sup>/y).</li> <li>Vulnerable_percent: Percentage of reach area with developed or agricultural land use in 2011 (%).</li> <li>Vulnerable: Vulnerability to coastal contamination (0- not vulnerable; 1-vulnerable)</li> </ul> </li> </ul> <p> </p>
LOFAR Observation (MS file) from the Boötes Field and the Toothbrush cluster used in the paper: "Looking beyond pixels with continuous-space EstimAtion of Point sources"
<p>The dataset contains the measurement sets (MS file) of the LOFAR observations from the Boötes field and the Toothbrush cluster. The dataset was used in the experiments of the paper: </p> <blockquote> <p>LEAP: Looking beyond pixels with continuous-spaceEstimAtion of Point sources</p> <p>Pan, H., Simeoni, M., Hurley, P., Blu, T. & Vetterli, M. In: Astronomy & Astrophysics, in press, 2017</p> </blockquote> <p>The data was provided as a collaboration between ASTRON and IBM within the DOME project. The data was acquired for a LOFAR sky survey of the Boötes field:</p> <blockquote> <p>LOFAR 150-MHz observations of the Boötes field: Catalogue and Source Counts</p> <p>Williams, W. L. , Hardcastle, M. J. & 33 others In: Monthly Notices of the Royal Astronomical Society. 460, 3, p. 2385–2412</p> </blockquote> <p>and the Toothbrush cluster (RX J0603.3+4214):</p> <blockquote> <p>Simulating the toothbrush: evidence for a triple merger of galaxy clusters</p> <p>Brüggen, M., van Weeren, R. J., Röttgering, H. J. A. In: Monthly Notices of the Royal Astronomical Society: Letters. 425, 1, p. L76--L80</p> </blockquote> <p>In case of questions concerning the measurement set, please contact the original authors for details.</p> <p> </p> <p>We have also included the three catalogs used in the experiments, which are converted from their original FITS table to Numpy arrays:</p> <ul> <li>skycatalog.npz is the catalog of the Boötes field: https://academic.oup.com/mnras/article-lookup/doi/10.1093/mnras/stw1056</li> <li>TGSSADR1_7sigma_catalog.npz is the TGSS ADR1 source catalog: http://tgssadr.strw.leidenuniv.nl/catalogs/TGSSADR1_7sigma_catalog.fits</li> <li>NVSS_CATALOG.npz is the NRAO/VLA Sky Survey: ftp://nvss.cv.nrao.edu/pub/nvss/CATALOG/</li> </ul>
Supplementary files for Vertical Displacements and Sea-Level Changes in Eastern North America Driven by Glacial Isostatic Adjustment: an Ensemble Modeling Approach
<p>Model input and output files associated with the manuscript entitled "Vertical Displacements and Sea-Level Changes in Eastern North America Driven by Glacial Isostatic Adjustment: an Ensemble Modeling Approach" that will be submitted to Journal of Geophysical Research.</p>
Network Measurements while Uploading 5.6 KB Files from Moving Buses to Cellular Networks in Varmland, Sweden.
<p>The dataset and the collection methodology are described and used in the following papers:</p> <ul> <li>Ben Abdesslem, Fehmi, Henrik Abrahamsson, and Bengt Ahlgren.<br> "<em><strong>Measuring Mobile Network Multi-Access for Time-Critical C-ITS Applications" </strong></em><br> Network Traffic Measurement and Analysis Conference (TMA'18), Vienna, Austria (2018).</li> <li>Ben Abdesslem, Fehmi, Henrik Abrahamsson, and Bengt Ahlgren.<br> "<em><strong>Cellular Network Multi-Access Measurements on the Roads of Värmland, Sweden.</strong></em>" <br> <em>arXiv preprint arXiv:1805.06814</em> (2018).</li> <li>Henrik Abrahamsson, Ben Abdesslem, Fehmi, Bengt Ahlgren, Anna Brunstrom, Ian Marsh and Mats Björkman.<br> "<em><strong>Connected Vehicles in Cellular Networks: Multi-access versus Single-access Performance</strong></em>" <br> 2nd Workshop on Mobile Network Measurement (MNM’18), Vienna, Austria (2018).</li> </ul> <p>The CSV file has the following columns:</p> <ul> <li>Index: Unique number for the transaction</li> <li>Timestamp: Time and date of the transaction</li> <li>Interface: Interface used by the transaction [op0, op1 or op2]</li> <li>TransactionTime: Duration of the transaction (in sec)</li> <li>Status: Result of the transaction [failed, senderror, timeout, or number of received bytes acknowledged]</li> <li>GpsTimestamp: Time and date of the GPS coordinates</li> <li>GpsLatitude: Last GPS latitude known</li> <li>GpsLongitude: Last GPS longitude known</li> <li>ModemTimestamp: Time and date of the modem properties</li> <li>ModemOperator: Name of the operator [op0, op1, op2]. The original names (Telia, Telenor, 3) have been replaced in a different order.</li> <li>ModemRSSI: RSSI (in dBm)</li> <li>ModemCID: Cell ID</li> <li>ModemDeviceMode: <ul> <li>UNKNOWN (0).</li> <li>DISCONNECTED (1).</li> <li>NO_SERVICE (2).</li> <li>2G (3).</li> <li>3G (4).</li> <li>LTE (5).</li> </ul> </li> <li>ModemDeviceSubmode: <ul> <li>UNKNOWN (0).</li> <li>UMTS (1).</li> <li>WCDMA (2).</li> <li>EVDO (3).</li> <li>HSPA (4).</li> <li>HSPA+ (5).</li> <li>DC HSPA (6).</li> <li>DC HSPA+ (7).</li> <li>HSDPA (8).</li> <li>HSUPA (9).</li> <li>HSDPA+HSUPA (10).</li> <li>HSDPA+ (11).</li> <li>HSDPA+HSUPA (12).</li> <li>DC HSDPA+ (13).</li> <li>DC HSDPA + HSUPA (14).</li> </ul> </li> <li>ModemLAC: Location Area Code</li> <li>ModemRSRP: RSRP (in dBm)</li> <li>ModemFrequency: Frequency in Mhz</li> <li>ModemRSRQ: RSRQ (in dBm)</li> <li>ModemBand: LTE band</li> <li>ModemPCI: LTE Physical Cell ID</li> <li>ModemECIO: Ec/Io</li> <li>ModemENODEBID: eNodeB ID</li> <li>ModemRSCP: RSCP (in dBm)</li> <li>bus: Bus number (head node number in Monroe)</li> <li>country: Country of operation [Sweden]</li> <li>protocol: protocol used [UDP, TCP or HTTPS]</li> <li>experiment: Experiment ID (one hour experiments)</li> <li>diff: Max time difference between the three simultaneous uploads (in ms)</li> <li>TransactionTime200: Transaction duration if timeout=200ms</li> <li>TransactionTime1000:Transaction duration if timeout=1000ms</li> <li>TransactionTime6000: Transaction duration if timeout=6000ms</li> <li>bestAvailability: Best availability over the whole experiment ID (%)</li> <li>bestAvailability200: Best availability over the whole experiment ID (%) if timeout=200ms</li> <li>bestAvailability1000: Best availability over the whole experiment ID (%) if timeout=1000ms</li> <li>best: Best duration (in sec)</li> <li>best1000: Best duration (in sec) if timeout=1000ms</li> <li>availability: Availability over the whole experiment ID (%)</li> <li>availability200: Availability over the whole experiment ID (%) if timeout=200ms</li> <li>availability1000: Availability over the whole experiment ID (%) if timeout=1000ms</li> <li>DayOfWeek: Day of the Week [Monday, ..., Sunday]</li> </ul>
Example files to A FAIR archive based on the CERIF model
<p>An archival structure based on the CERIF model is proposed. The archive tree is represented by cfProjects and the archived objects by cfResult* entities with their descriptive metadata given in attached CERIF entities. Archival preservation metadata is stored in the Premis format inside attached cfMeasurment entities. An example in which EPrints repository items are transferred to the archive is presented. When CERIF is employed in relevant archive processes, a FAIR compliant archive is easier to achieve. </p>
R script and data files for Oakley et al (2017) Journal of Proteome Research. DOI: 10.1021/acs.jproteome.6b00797
<p>This R script and data replicates the analysis of Oakley et al (2017) Thermal shock induces host proteostasis disruption and endoplasmic reticulum stress in the model symbiotic Cnidarian <em>Aiptasia</em>. <em>Journal of Proteome Research</em>. 16:2121-2134. DOI: 10.1021/acs.jproteome.6b00797. </p>
ncrncornell/ced2ar-nqwi-codebook: Codebook for the National QWI [Codebook file]
<p>Codebook for the early research version of National QWI.</p> <p>Live version of the DDI codebook at <a href="https://www2.ncrn.cornell.edu/ced2ar-web/codebooks/nqwi/">https://www2.ncrn.cornell.edu/ced2ar-web/codebooks/nqwi/</a></p>
Genomes plasmids MDR B. fragilis ONT sequence read files in fastq format
<p>Supporting data for the manuscript <em>Complete genome assembly of clinical multidrug resistant Bacteroides fragilis isolates enables comprehensive identification of antimicrobial resistance genes and plasmids.</em></p> <p>Oxford Nanopore reads demultiplexed with <a href="https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=2ahUKEwjNjqL5tY7iAhUawMQBHZHfDasQFjAAegQIAhAB&url=https%3A%2F%2Fgithub.com%2Frrwick%2FDeepbinner&usg=AOvVaw0wikvIUagLuFV38CwKZtia">Deepbinner</a> v0.2.0 and base-called (with demultiplexing) using Albacore v2.3.3. Barcodes and adapters were removed with <a href="https://github.com/rrwick/Porechop">Porechop</a> v0.2.4 with the --discard_middle option.</p> <p>Data from each isolate was produced from two runs per isolate. Data for the individual runs are included here. They can easily be concatenated eg with cat. Runs are named TVS_01,. TVS_02, TVS_03 and TVS_04.</p> <p>Fast5 (only demultiplexed with deepbinner and basecalled with albacore) as well as illumina reads and genome assemblies can be found via the NCBI bioproject accessions:</p> <p>Isolates, NCBI bioproject accession no:</p> <p>CCUG4856T, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA525024">PRJNA525024</a></p> <p>BFO17, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA244943">PRJNA244943</a></p> <p>BFO18, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA244944">PRJNA244944</a></p> <p>S01, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA244942">PRJNA244942</a></p> <p>BFO42, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA253771">PRJNA253771</a></p> <p>BFO67, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA254401">PRJNA254401</a></p> <p>BFO85, <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA254455">PRJNA254455</a></p> <p> </p> <p><strong>md5sum's (also found in the file md5.md5):</strong></p> <p>135d0570a1e49e25c8fde59f321cca68 BFO17_TVS_03_99377.barcode02_trimmed.fastq.gz<br> 3c6ca800a1f735937c0cffccd263fb82 BFO18_TVS_01_97673.barcode03_trimmed.fastq.gz<br> dc37823950f529d3a859b7a7c514af8c BFO18_TVS_03_99377.barcode03_trimmed.fastq.gz<br> abd707404f9ebbc38652e45ed378ca21 BFO42_TVS_02.barcode10_trimmed.fastq.gz<br> 9761e9ab082e276624c5778dcbeaffd2 BFO42_TVS_04.barcode10_trimmed.fastq.gz<br> ffc27009c0f7fead1af84ce045dbe5f3 BFO67_TVS_02.barcode09_trimmed.fastq.gz<br> 06c363a7feeeb88f9d195b3769b37f2b BFO67_TVS_04.barcode09_trimmed.fastq.gz<br> 5553c95cc98f4b9d4cfb38c4f8f8f037 BFO85_TVS_02.barcode08_trimmed.fastq.gz<br> b1a8013cba7a079cee6d3bdd6cd97ff2 BFO85_TVS_04.barcode08_trimmed.fastq.gz<br> 8169225219a5fb20935d5f0304aa80c5 CCUG4856T_TVS_01_97673.barcode01_trimmed.fastq.gz<br> a509b0ae912a798a91e677795198c1c6 CCUG5846T_TVS_03_99377.barcode01_trimmed.fastq.gz<br> 8a9d2eb8b626a6e87ed267d31aa220e3 S01_TVS_01_97673.barcode04_trimmed.fastq.gz<br> 69e239a17becc25a4f103dfc3bf5886a S01_TVS_03_99377.barcode04_trimmed.fastq.gz</p>
Dataset to "Persistent Identifiers for File Formats: enabling preservation and re-use of research data"
<p>This fileset includes a "preprint" and the main dataset <em>fileformatRecognizer</em> (as .xlsx and .csv) to the paper "Persistent identifiers for file formats: enabling preservation and re-use of research data" submitted to iPRES 2019, but subsequently rejected after peer review. For the sake of transparency, permission to make available here the anonymous reviews motivating the rejection (<em>ReviewsPIDs4fileFormats.odt</em>) was asked, but was left without response. Some images (screendumps) and text result files from file identification tools tested are included. Further, a simple xquery command file (BaseX) for <em>fetch:content-type</em>()<em>, </em>used for getting MIME-types for files, is also provided.</p>
Lookup table data files for MOABS software
<p>These three lookup table data files are required for MOABS software to speed up computation. See more at <a href="https://github.com/sunnyisgalaxy/moabs">https://github.com/sunnyisgalaxy/moabs</a></p>
MetFrag Local CSV: CompTox (7 March 2019 release) Smoking MetaData File
<p>This is the CSV file that can be used as a local database in MetFrag (https://msbi.ipb-halle.de/MetFrag/), for those who wish to integrate this into the command line version.</p> <p>Note that this file is TOO LARGE to be uploaded via the web interface, this is already integrated in the web interface.</p> <p>This file is based off the "SelectMetaData" CompTox MetFrag file from the 7 March 2019 release, available from:</p> <p><a>ftp://newftp.epa.gov/COMPTOX/Sustainable_Chemistry_Data/Chemistry_Dashboard/MetFrag_metadata_files</a></p> <p>The Smoking MetaData file contains the following fields, in addition to the regular (basic) CompTox data fields:</p> <p>- PubMedNeuro: the <a href="https://comptox.epa.gov/dashboard/chemical_lists/LITMINEDNEURO">LITMINEDNEURO</a> list with total PubMed reference counts in the column</p> <p>- <a href="https://comptox.epa.gov/dashboard/chemical_lists/CIGARETTES">CIGARETTES</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/INDOORCT16">INDOORCT16</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/SRM2585DUST">SRM2585DUST</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/SLTCHEMDB">SLTCHEMDB</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/THSMOKE">THSMOKE</a> as suspect lists.</p> <p>First release (July 2019, not archived) contained a smaller subset of the SRM2585DUST list.</p>
MetFrag Local CSV: CompTox (7 March 2019 release) Wastewater MetaData File
<p>This is the CSV file that can be used as a local database in MetFrag (<a href="https://msbi.ipb-halle.de/MetFrag/">https://msbi.ipb-halle.de/MetFrag/</a>), for those who wish to integrate this into the command line version.</p> <p>Note that this file is TOO LARGE to be uploaded via the web interface, this is already integrated in the web interface.</p> <p>This file is based off the "SelectMetaData" CompTox MetFrag file from the 7 March 2019 release, available from:</p> <p>ftp://newftp.epa.gov/COMPTOX/Sustainable_Chemistry_Data/Chemistry_Dashboard/MetFrag_metadata_files</p> <p>The Wastewater MetaData file contains the following fields, in addition to the regular (basic) CompTox data fields:</p> <p>Suspect Lists (1=presence, 0=absence):</p> <p>- ITNANTIBIOTIC, STOFFIDENT, REACH2017, ZINC15PHARMA and PFASMASTER</p> <p>Suspect Lists with scores from KEMI (see details on <a href="https://www.norman-network.com/nds/SLE/">NORMAN-SLE</a> and hyperlinks below):</p> <p>- <a href="https://zenodo.org/record/2628787">KEMIMARKET_EXPO</a>, <a href="https://zenodo.org/record/2628787">KEMIMARKET_HAZ</a>, <a href="https://zenodo.org/record/2653567">KEMIWW_WDUIndex</a>, <a href="https://zenodo.org/record/2653567">KEMIWW_StpSE</a>, <a href="https://zenodo.org/record/2653567">KEMIWW_SEHitsOverDL</a></p>
Observation files of CG-5 gravity meters for the Zhetygen calibration line for five years
<p>Observation files of three Scintrex CG-5 gravimeters obtained during six field campaigns for calibration of these meters. Measurements were carried out at seven points of the Zhetygen calibration line, located 20 km north of Almaty in Kazakhstan. Also attached is a file with the coordinates of the stations.</p>
Expedited Modeling of Burn Events Results (EMBER) Data Files
<p>This dataset includes photochemical air quality modeling files for simulations of fire impacts on ground-level ozone cocnentrations in the U.S. during the summer of 2023. A data dictionary describes what is included in the each of the files. Detailed information on the model simulations and the file contents is included in a journal article documenting the dataset: Simon, H., Beidler, J., Baker, K.R., Henderson, B.H., Fox, L., Misenis, C., Campbell, P., Vukovich, J. Possiel, N., Eyth, E. Expediated Modeling of Burn Events Results (EMBER): A Screening-Level Dataset of 2023 Ozone Fire Impacts in the US, <em>Data in Brief</em>, https://doi.org/10.1016/j.dib.2024.111208</p> <p>A web-based tool for browsing this dataset is also available at: https://www.epa.gov/air-quality-analysis/expedited-modeling-burn-events-results-ember</p>
Data files: Electric vehicle charging dataset with 35,000 charging sessions from 12 residential locations in Norway
<p>Please refer to the data article where the data is described (Data-in-brief, <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2024.110883" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.dib.2024.110883</span></span></a>).</p> <p>The data article refers to the paper "A method for generating complete EV charging datasets and analysis of residential charging behaviour in a large Norwegian case study". The Electric Vehicle (EV) charging dataset includes detailed information on plug-in times, plug-out times, and energy charged for over 35,000 residential charging sessions, covering 267 user IDs across 12 locations within a mature EV market in Norway. Utilising methodologies outlined in the paper, realistic predictions have been integrated into the datasets, encompassing EV battery capacities, charging power, and plug-in State-of-Charge (SoC) for each EV-user and charging session. In addition, hourly data is provided, such as energy charged and connected energy capacity for each charging session.</p> <p>The comprehensive dataset provides the basis for assessing current and future EV charging behaviour, analysing and modelling EV charging loads and energy flexibility, and studying the integration of EVs into power grids.</p>
Supplementary File: Entertainment interspersed with propaganda: How non-legacy-news accounts deliver explicitly political content to mass audiences on Russia's most popular social network VK
<p>Supplementary file and dataset for the paper "Entertainment interspersed with propaganda: How non-legacy-news accounts deliver explicitly political content to mass audiences on Russia’s most popular social network VK"</p>
Correction by focus: Sound files and transcriptions
<p>This data was collected to examine the prosodic reflexes of corrective focus in canonical and cleft clauses of Chinese, English, French, German. Experimental factors: FOCUS: subject|object, CONSTRUCTION: canonical|cleft; ITEMS: 4, SPEAKERS: 16 (per language). See text file ALI.txt for further details.</p> <p> </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.