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7,195 results for “SET”
Divide and Remaster v3: Spanish Validation & Test Set
<p>Divide and Remaster v3 is a multilingual rework of the Divide and Remaster v2 dataset by Pétermann et al.</p> <p>This repository contains the <strong>metadata, validation set audio, and test set audio of the</strong> <strong>Spanish variant </strong>of DnR v3.</p> <p>The major changes from DnR v2 are as follows:</p> <ul> <li>the dialogue stem now contains content from more than 30 languages across various language families;</li> <li>speech, vocals, and/or vocalizations have been removed from the music and effects stems;</li> <li>loudness and timing parametrization have been adjusted to approximate the distributions of real cinematic content;</li> <li>the mastering process now preserves relative loudness between stems and approximates standard industry practices.</li> </ul> <p>See the linked GitHub repository for more details.</p>
Divide and Remaster v3: Bantu Test Set
<div> </div> <p>Divide and Remaster v3 is a multilingual rework of the Divide and Remaster v2 dataset by Pétermann et al.</p> <p>This repository contains the <strong>Bantu variant </strong>of DnR v3.</p> <p>The major changes from DnR v2 are as follows:</p> <ul> <li>the dialogue stem now contains content from more than 30 languages across various language families;</li> <li>speech, vocals, and/or vocalizations have been removed from the music and effects stems;</li> <li>loudness and timing parametrization have been adjusted to approximate the distributions of real cinematic content;</li> <li>the mastering process now preserves relative loudness between stems and approximates standard industry practices.</li> </ul> <p>See the linked GitHub repository for more details.</p>
Divide and Remaster v3: Basque Test Set
<p>Divide and Remaster v3 is a multilingual rework of the Divide and Remaster v2 dataset by Pétermann et al.</p> <p>This repository contains the <strong>Basque variant </strong>of DnR v3.</p> <p>The major changes from DnR v2 are as follows:</p> <ul> <li>the dialogue stem now contains content from more than 30 languages across various language families;</li> <li>speech, vocals, and/or vocalizations have been removed from the music and effects stems;</li> <li>loudness and timing parametrization have been adjusted to approximate the distributions of real cinematic content;</li> <li>the mastering process now preserves relative loudness between stems and approximates standard industry practices.</li> </ul> <p>See the linked GitHub repository for more details.</p>
Divide and Remaster v3: Indo-Aryan Test Set
<p>Divide and Remaster v3 is a multilingual rework of the Divide and Remaster v2 dataset by Pétermann et al.</p> <p>This repository contains the <strong>Indo-Aryan variant </strong>of DnR v3.</p> <p>The major changes from DnR v2 are as follows:</p> <ul> <li>the dialogue stem now contains content from more than 30 languages across various language families;</li> <li>speech, vocals, and/or vocalizations have been removed from the music and effects stems;</li> <li>loudness and timing parametrization have been adjusted to approximate the distributions of real cinematic content;</li> <li>the mastering process now preserves relative loudness between stems and approximates standard industry practices.</li> </ul> <p>See the linked GitHub repository for more details.</p>
Divide and Remaster v3: English Validation & Test Set
<p>Divide and Remaster v3 is a multilingual rework of the Divide and Remaster v2 dataset by Pétermann et al.</p> <p>This repository contains the <strong>metadata, validation set audio, and test set audio of the</strong> <strong>English variant </strong>of DnR v3.</p> <p>The major changes from DnR v2 are as follows:</p> <ul> <li>the dialogue stem now contains content from more than 30 languages across various language families;</li> <li>speech, vocals, and/or vocalizations have been removed from the music and effects stems;</li> <li>loudness and timing parametrization have been adjusted to approximate the distributions of real cinematic content;</li> <li>the mastering process now preserves relative loudness between stems and approximates standard industry practices.</li> </ul> <p>See the linked GitHub repository for more details.</p>
Divide and Remaster v3: Mandarin Chinese Validation & Test Set
<p>Divide and Remaster v3 is a multilingual rework of the Divide and Remaster v2 dataset by Pétermann et al.</p> <p>This repository contains the <strong>metadata, validation set audio, and test set audio of the</strong> <strong>Mandarin Chinese variant </strong>of DnR v3.</p> <p>The major changes from DnR v2 are as follows:</p> <ul> <li>the dialogue stem now contains content from more than 30 languages across various language families;</li> <li>speech, vocals, and/or vocalizations have been removed from the music and effects stems;</li> <li>loudness and timing parametrization have been adjusted to approximate the distributions of real cinematic content;</li> <li>the mastering process now preserves relative loudness between stems and approximates standard industry practices.</li> </ul> <p>See the linked GitHub repository for more details.</p>
Divide and Remaster v3: Standard German Validation & Test Set
<p>Divide and Remaster v3 is a multilingual rework of the Divide and Remaster v2 dataset by Pétermann et al.</p> <p>This repository contains the <strong>metadata, validation set audio, and test set audio of the</strong> <strong>Standard German variant </strong>of DnR v3.</p> <p>The major changes from DnR v2 are as follows:</p> <ul> <li>the dialogue stem now contains content from more than 30 languages across various language families;</li> <li>speech, vocals, and/or vocalizations have been removed from the music and effects stems;</li> <li>loudness and timing parametrization have been adjusted to approximate the distributions of real cinematic content;</li> <li>the mastering process now preserves relative loudness between stems and approximates standard industry practices.</li> </ul> <p>See the linked GitHub repository for more details.</p>
SGComp data sets
<p>Training data set for an expert finding system (SGComp_Train.csv), and Benchmarking data set for expert finding systems with adversarial data (SGComp_Adversarial.csv).</p> <p><br>Both data sets have the same field structure as relation triples (head: experts' full name, relation: relationship with key phrases, list of tail: []) in comma-separated values (.CSV) </p>
Supplementary File 8; The full data set used for the analysis presented herein
Open the record for dataset details and reuse information.
STILT footprints data set 2
<p>This repository contains the second batch of training data sets of measurement footprints.</p> <p>The footprints are used to train the deep learning model presented in our paper titled "FootNet v1.0: Development of a machine learning emulator of atmospheric transport".</p> <p>Preprint of the manuscript could be accessed at https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1526/.</p> <p>The footprints are provided in Numpy compressed array format, which could be decompressed with Python 3.10.6 and NumPy 1.23.4.</p>
Data set for "SARS-CoV-2 introductions to the island of Ireland: a phylogenetic and geospatiotemporal study of infection dynamics"
<p>Please see README.txt for detailed information about the contents of this data repository.</p>
PREPCLIM sample data set for publication in "Geoscientific Model Development" 2024
<p>Data set illustrating the functionality of software developed in the PREPCLIM project and used in the proposed paper:</p> <p>A Modeling System for Identification of Maize Ideotypes, optimal sowing dates and nitrogen<br>fertilization under climate change – PREPCLIM-v1</p> <p>https://doi.org/10.5194/gmd-2024-105<br>Preprint. Discussion started: 11 July 2024<br>c Author(s) 2024. CC BY 4.0 License.</p>
Data Set for "Analyzing Microbial Growth with R"
<p>Sample data set used in "<a href="http://bconnelly.net/2014/04/analyzing-microbial-growth-with-r/">Analyzing Microbial Growth with R</a>"</p>
Full Data Set of 16814 tweets from 17 different Twitter accounts of Catalonian Public Servants and Delegates collected between August 19 2013 and September 17 2017 in both original language and translated into Spanish later codified under para-diplomacy or nation branding macro-categories. Includes results for each year.
<p>Full Data Set of 16814 tweets from 17 different Twitter accounts of Catalonian Public Servants and Delegates collected between August 19 2013 and September 17 2017 in both original language and translated into Spanish later codified under para-diplomacy or nation branding macro-categories. Includes results for each year out of a significant sample of 1638 tweets.</p>
EVN measurement set of experiment N14C2
<p>EVN measurement set of experiment N14C2 (n14c2.ms) and calibration tables for Tsys (n14c2.tsys) and gain curve (n14c2.gcal).</p> <p>IDI files were downloaded from the EVN archive <a href="http://www.jive.nl/fitsfiles?experiment=N14C2_140610">here</a> and the associated EVN User Experiment Pipeline Feedback of N14C2 were downloaded from <a href="http://www.jive.nl/pipeline?experiment=N14C2_140610&pass=n14c2">here</a>. They were converted to a measurement set and the calibration tables were prepared following <a href="https://docs.google.com/document/d/1BbDsOTht7h7wgSCLweoycgB2Kei6qlwSp6ocOkgwG9Y/edit?usp=sharing">this tutorial</a>.</p> <p>The script fringe.n14c2_test_casa4.7_evn.py details how to run the <a href="https://github.com/as595/RadioNetRINGS/tree/master/PythonFringeFitter">fringe fitting python prototype</a> on these data.<br> </p>
A multimodal data-set of a unidirectional glass fibre reinforced polymer composite
<p>Please cite the following article when using the data-sets hereby shared:</p> <p>Emerson, M.J., Dahl, V.A., Conradsen, K., Mikkelsen, L.P. and Dahl, A.B., 2018. A multimodal data-set of a unidirectional glass fibre reinforced polymer composite. <em>Data in brief</em>, <em>18</em>, pp.1388-1393.</p> <p>These data-sets were used for validating the use of X-ray tomography and our dictionary-based probabilistic method for detection of individual fibres, for more information see the following article:</p> <p>Emerson, M.J., Dahl, V.A., Conradsen, K., Mikkelsen, L.P. and Dahl, A.B., 2018. Statistical validation of individual fibre segmentation from tomograms and microscopy. <em>Composites Science and Technology</em>, <em>160</em>, pp.208-215.</p>
ECODSE competition training set
<p>Data products</p> <p>Individual Tree Crown (ITC) data was collected by Sarah Graves and Justin Gearhart.<br> Data in the RSdata folder are derived from NEON-AOP data, collected in 2015 at Ordway Swisher Biological Station. NEON AOP data have been manipulated into the distributed product by the members of the ECODSE group, primarily Sarah Graves and Sergio Marconi. <br> Data in the Task2 folder were provided by NEON. Data have been cleaned, subset, and organized to better match the purposes of the competition. All members of the ECODSE group, primarily Sarah Graves and Sergio Marconi, were involved in the preparing the data products.<br> Data in Task3 folder are derived from combining the NEON-AOP and ITC data, collected at Ordway Swisher Biological Station. NEON AOP data have been manipulated into the provided product by the members of the ECODSE group, and primarily Sarah Graves and Sergio Marconi. </p> <p>Acknowledgments<br> We acknowledge the "Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative" through Grant GBMF4563, and the "National Institute of Standards and Technology (NIST). Data Science for Multimodal Plant Identification Task." who founded the production of the dataset and the ECODSE competition.</p>
Data set for: Proposal for a micromagnetic standard problem for materials with Dzyaloshinskii-Moriya interaction
<p>Scripts and Jupyter notebooks for the data reproduction of the paper: "Proposal for a micromagnetic standard problem for materials with Dzyaloshinskii-Moriya interaction"</p> <p>Files include notebooks and scripts with simulations using the OOMMF, MuMax3 and Fidimag codes. Data analysis is performed using Python tools such as Numpy and Scipy.</p> <p>The latest version of this data set can be found in the Github repository:</p> <p><a href="https://github.com/fangohr/paper-supplement-standard-problem-dmi">https://github.com/fangohr/paper-supplement-standard-problem-dmi</a></p>
Clinical usefulness of library and information services in Japan: The detailed use and value of information in clinical settings
<p>Abstract</p> <p><strong>Objectives</strong></p> <p>Considering that there is a lack of evidence regarding the contribution of library and information services to evidence-based medicine in actual clinical practice in Japan, the purpose of the study is to explore the current status of use and value of library and information services in clinical settings to examine the usefulness of information in implementing evidence-based medicine (EBM) into practice.</p> <p><strong>Methods</strong></p> <p>A Web-based survey was conducted at seven sites (hospitals with 300–1,200 beds) and interviews conducted at five sites to investigate information behavior among health professionals (physicians, residents, and nurses) in 2016, replicating the Value Study carried out in the United States in 2010 and 2011. Using a critical incident technique, respondents answered questions about their information topics, information resources used, search location, access points, and evaluation of the information.</p> <p><strong>Results</strong></p> <p>Analysis from 598 valid responses (275 physicians, 55 residents, and 268 nurses) revealed the characteristics of information use and recognition of the value of information. Physicians and residents showed their information needs regarding clinical care using PubMed (80.4%, 65.5%), Ichushi-Web (61.8%, 63.6%), and UpToDate (40.4%, 65.5%). While physicians rely more on electronic journals (37.8%), residents use more hybrid resources including Japanese print books (38.2%) and online books (30.9% for Japanese, 32.7% for English) to confirm their knowledge. Nurses need more information close to patients and explore a wider variety of information resources such as Japanese print books (60.4%), Ichushi -Web (40.3%), Japanese online books (20.5%), and websites of academic organizations (19.0%). Although the overall recognition of the value of information was relatively modest, concrete changes in clinical practice were found in some areas. Environments with insufficient information and availability of electronic resources should be improved to increase the use of library and information services for implementing EBM.</p>
Data set for "Chiral Magnonic Crystals: Unconventional Spin-Wave Phenomena Induced by a Periodic Dzyaloshinskii-Moriya Interaction"
<p>Scripts for the micromagnetic simulations of the publication "Chiral Magnonic Crystals: Unconventional Spin-Wave Phenomena Induced by a Periodic Dzyaloshinskii-Moriya Interaction", using the OOMMF software. These codes reproduce the result of magnonic waveguides with periodic Dzyaloshinskii-Moriya interactions. A Dockerfile and a Makefile are included for the reproducibility of the results.</p> <p>The repository containing these results, together with a explanatory README document can be found in:</p> <p>https://github.com/davidcortesortuno/paper-2018-chiral_magnonic_crystals</p> <p> </p> <p>The files included in this Zenodo release refer to the v1.0 version of the data set. For an updated version of the scripts refer to the Github repository.</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.