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4,404 results for “digits”
Digital twins for in vivo metabolic flux estimations in patients with brain cancer II
GEO Series GSE311464. Homo sapiens; Mus musculus. 7 samples. Type: Expression profiling by high throughput sequencing.
Digital proteomics by DNA sequencing
GEO Series GSE71486. synthetic construct; Homo sapiens. 41 samples. Type: Other.
Digital transformation of herbal medicine: Conversion to biological entity data using tonifying herbal medicine-induced transcriptome sequencing_HepG2_batchC
GEO Series GSE244704. Homo sapiens. 60 samples. Type: Expression profiling by high throughput sequencing.
Dataset of M. Marzano et al, Metrologia 58, 015002 (2020), "A fully digital bridge towards the realization of the farad from the quantum Hall effect"
<p>Compilation of the datasets used to generate the figures in the following journal publication;</p> <p>"A fully digital bridge towards the realization of the farad from the quantum Hall effect"</p> <p>by M. Marzano, M. Massimo, V. D'Elia, A. Müller, and L. Callegaro,</p> <p>Metrologia 58, 015002 (2020),</p> <p>DOI: 10.1088/1681-7575/abba86</p>
Digital competence profiles of first year, pre-service teachers. Analysis in the Catalan university system. RAWDATA
<p>Dataset for the ARMIF research project 2018-19</p>
Qialitative data WP2 and WP3: Examining acceptance, readability and comprehension of Digital communication as part of three population-based cancer screening programs
<p>Data for the the ZonMw project: <em>Examining acceptance, readability and comprehension of Digital communication as part of three population-based cancer screening programs</em></p> <p>Qualitative results of the co-creation sessions (WP2) in an excel file</p> <p>Qualitative results of the three posters for the workshop (WP3) in images</p> <p><em>Note. Part 1 (quantitative datasetwas for WP1) was stored in Zenodo, link (of persistent identifier) 10.5281/zenodo.6977196</em></p> <p><em> </em></p>
Dataset Peran Influencer Twitter_X dalam Membangun Preferensi Masyarakat terhadap Pembelian Produk di Era Digital
<p>Dataset Peran Influencer Twitter X dalam Membangun Preferensi Masyarakat terhadap Pembelian Produk di Era Digital Tahun 2024</p>
A workflow and digital filters for correcting speed and equalisation errors on digitised audio open-reel magnetic tapes - Audio Samples
<p>This repository makes available the audio samples related to the paper:</p> <p>Niccolò Pretto, Nadir Dalla Pozza, Alberto Padoan, Anthony Chmiel, Kurt James Werner, Alessandra Micalizzi, Emery Schubert, Antonio Rodà, Simone Milani and Sergio Canazza, <em>A workflow and digital filters for compensating speed and equalisation errors on digitised audio open-reel magnetic tapes</em>, Journal of the Audio Engineering Society, Special Issue on Audio Filter Design, 2022.</p> <p>The experiment and the three case studies are described in the publication above.</p> <p>This repository contains two main directories (<strong>bold</strong> indicates directory names):</p> <ul> <li> <p><strong>Experiment Samples</strong>: the 10 seconds long samples used in the experiment;</p> </li> <li> <p><strong>Long Samples</strong>: the original 6 minutes long samples, one for each of the identified cases.</p> </li> </ul> <p>Here is the notation of the file naming:</p> <ul> <li> <p>W: recording (writing);</p> </li> <li> <p>R: reproducing;</p> </li> <li> <p>3: 3.75 NAB;</p> </li> <li> <p>7N: 7.5 NAB;</p> </li> <li> <p>7C: 7.5 CCIR;</p> </li> <li> <p>15C: 15 CCIR.</p> </li> </ul> <p>For what concerns the <strong>Experiment Samples</strong> directory, here is the summary of the recording/reproducing standards of the adopted samples and their notation:</p> <ul> <li> <p>SET A: Recording 3.75 NAB (W3) - Reproducing 7.5 CCIR (R7C);</p> </li> <li> <p>SET B: Recording 3.75 NAB (W3) - Reproducing 15 CCIR (R15C);</p> </li> <li> <p>SET C: Recording 7.5 NAB (W7N) - Reproducing 15 CCIR (R15C).</p> </li> </ul> <p>Here are the variants of the samples:</p> <ul> <li> <p>REFERENCE: produced by using the correct equalization standard;</p> </li> <li> <p>ANCHOR: the "Reference" altered with a low-pass filter, with pass band set at 7 kHz for music and 3.5 kHz for speech;</p> </li> <li> <p>INCORRECT: produced by using an intentionally incorrect equalization, created by mismatching the recording and reading curves and resampled to the correct speed;</p> </li> <li> <p>MATLAB: the “Incorrect” variant corrected by means of a Matlab script;</p> </li> <li> <p>API: the “Incorrect” variant corrected by means of an <em>ad hoc</em> web interface adopting Web Audio API, for simulating real-time correction in web applications.</p> </li> </ul> <p>Here is the samples list:</p> <ul> <li> <p>SET A:</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample4: Richard Wagner - <em>Ride of the Valkyries</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample1: Taylor Swift - <em>Shake It Off</em>;</p> </li> <li> <p>sample5: Queen - <em>We Will Rock You</em>;</p> </li> <li> <p>sample8: Bruno Maderna - <em>Continuo</em>;</p> </li> <li> <p>sample9: Luciano Berio - <em>Différences</em>.</p> </li> </ul> </li> </ul> </li> <li> <p>SET B (the track title reflects the name of the file from which the track itself was extracted, from the CLIPS project of the University of Napoli: <a href="http://www.clips.unina.it/en/index.jsp">http://www.clips.unina.it/en/index.jsp</a>):</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample22: CLIPS project - <em>LP4m18bZ</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample15: CLIPS project - <em>LP1f20bZ</em>;</p> </li> <li> <p>sample16: CLIPS project - <em>LP4m20bZ</em>;</p> </li> <li> <p>sample17: CLIPS project - <em>LP1f19bZ</em>;</p> </li> <li> <p>sample18: CLIPS project - <em>LP4m19bZ</em>.</p> </li> </ul> </li> </ul> </li> <li> <p>SET C:</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample3: Carl Orff - <em>Carmina Burana - O Fortuna</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample2: The Weeknd - <em>Save Your Tears</em>;</p> </li> <li> <p>sample6: Eagles - <em>Hotel California</em>;</p> </li> <li> <p>sample10: Bruno Maderna - <em>Musica su Due Dimensioni</em>;</p> </li> <li> <p>sample12: Bruno Maderna - <em>Syntaxis</em>.</p> </li> </ul> </li> </ul> </li> </ul>
Telegram and digital methods: mapping networked conspiracy theories through platform affordances
<p>This data accompanies the paper 'Telegram and digital methods: mapping networked conspiracy theories through platform affordances'. It comprises the the network files describing the found community's topology.</p> <p>We are currently <span><em><strong>not</strong></em></span> granting requests for access to this data.</p>
DDGP - Logotipo do Dicionário Digital Grego-Português- Digital Dictionary Greek-Portuguese symbol.
<p>Davi Rosa é o designer que produziu o logotipo.<br> Anise Ferreira propôs o conceito. </p>
Datasets of Marzano et al, Metrologia 59, 065001 (2022), "PTB–INRIM comparison of novel digital impedance bridges with graphene impedance quantum standards"
<p>Compilation of the datasets used to generate the figures in the following journal publication:</p> <p>"PTB–INRIM comparison of novel digital impedance bridges with graphene impedance quantum standards"</p> <p>by Martina Marzano, Yaowaret Pimsut, Mattias Kruskopf, Yefei Yin, Marco Kraus, Vincenzo D’Elia, Luca Callegaro, Massimo Ortolano, Stephan Bauer and Ralf Behr</p> <p>Metrologia 59, 065001 (2022),</p> <p>doi.org/10.1088/1681-7575/ac9187</p> <p> </p>
[SUMMER - Digital_Twin_beta_release_geospatial_platform_2024-04-29]
<p><span>Source code of the actual Digital Twin (beta version)</span></p>
Innovative Methodologies Based on eXtended Reality and Immersive Digital Environments in Natural Risk education: A Scoping Review
<p><span>Faced with the rise in natural disasters, studies on Disaster Risk Reduction Education (DRRE) have emerged since the ‘90s, predominantly employing a transmissive teaching approach, while literature advocates for interactive models, including eXtended Reality (XR) simulations, which offer cost-effective solutions. This scoping review explores XR in DRRE for teachers, students, and citizens aiming to discern its pedagogical affordances. The databases search identified 34 papers published between 2013 and 2023. The majority centered on seismic events and floods, with Asia, notably Japan, as a primary source. Methodologically, 26 were empirical, using various research designs, and 8 were non-empirical. While XR-based tools demonstrated pedagogical affordances in teaching risk management, the lack of specific educational frameworks and a predominant focus on the acquisition of procedural knowledge and skills indicate that a broader approach is needed, by the incorporation of uncertainty education and complex competences, including attitudes like risk perception.</span></p>
Ethical digital leadership
<p>This dataset was collected by researchers at the Isfahan University of Technology's business laboratory to investigate the concept of Ethical Digital Leadership (EDL) in the context of digital transformation. This data offers valuable insights for researchers and practitioners studying leadership, digital ethics, and organizational legitimacy.</p>
Corporate Digital Responsibility
<p>This dataset has been collected by a research team in the business laboratory of Isfahan University of Technology for future research to realize a better society. These researches are about the responsibility of companies in the digital age and in facing the challenges of digitalization. It is hoped that this data can fulfill the goals of this team and provide a better future for the inhabitants of the planet.</p>
DAMP-VP1k: Digital Archive of Mobile Performances - Smule Vocal Performances 100x10
<p>Digital Archive of Mobile Performance DAMP-VP1k archive of Smule vocal performances, 100 singers, 10 Performance each.</p> <p>The dataset contains sung musical performances from the Smule app. Data files include audio.zip with all the compressed audio files, and metadata.csv describing some metadata about each performance, including unique identifiers for each recording, song, and singer, as well as binary gender labels, region labels, and social "love" counts from the Smule app.</p> <p>This archive is a subset of the DAMP-Multiple Songs archive hosted at https://ccrma.stanford.edu/damp/, which contains multiple performances from each of multiple singers, singing different songs, without much verification of the data. This subset has been reduced to a subset of 10 performances per singer, with cleaner recordings for each singer preselected.</p> <p>Users of this dataset must read and accept Smule's Research Data License Agreement (LICENSE.txt).</p>
DAMP-VPB: Digital Archive of Mobile Performances - Smule Vocal Performances Balanced
<p>The DAMP-balanced dataset contains 24874 solo singing performances from 5429 singers singing a collection of 14 songs.<br> The structure of the DAMP-balanced is that the last 4 songs are designed to be the test set, and the first 10 songs<br> could be partitioned into any 6/4 train/validation split (permutation) that the singers in train and validation set sang<br> the same 6/4 songs collections according to the 6/4 split (the number of total recordings for train and validation set<br> are different from split to split, since there are different number of singers that all sang the same 6/4 split for<br> different split).</p> <p>For example, a subset from the dataset splitting the 14 songs into 6/4/4 train/validation/test sets having 276/88/224<br> performances sang by 46/22/56 singers could be extracted from the dataset and be used for machine learning tasks.<br> Each singer in the train, validation and test set, sang each of the 6/4/4 songs once respectively, thus making the<br> collections of performances "balanced" respect to the song collections.</p> <p>List of songs:</p> <ol> <li>one call away - Charlie Puth(3912)</li> <li>say you won`t let go - James Arthur(3255)</li> <li>allof me - John Legend(2856)</li> <li>closer -The Chainsmokers(2873)</li> <li>seven years - Lukas Graham(2942)</li> <li>despacito- Luis Fonsi(1287)</li> <li>more than words - Extreme(586)</li> <li>lostboy - Ruth B.(1183)</li> <li>loveyourself - Justin Bieber(3019)</li> <li>rockabye- Clean Bandit(2737)</li> <li>part of your world - Jodi Benson(56)</li> <li>when I was your man - Bruno Mars(56)</li> <li>chandelier- Sia(56)</li> <li>cups - Anna Kendrick(56)</li> </ol> <p>Users of this dataset must read and accept Smule's Research Data License Agreement (LICENSE.txt).</p>
DAMP-VSEP: Smule Digital Archive of Mobile Performances - Vocal Separation
<p>This dataset contains audio content for performing vocal separation research. It contains performances with (1) backing tracks, (2) one or more isolated vocals files, and (3) a mixture of the two. This is a real-world dataset from performances on the Smule app, which includes non-linear effects processes on the mixture of vocals and backing track. The dataset contains data from 155 countries, 36 languages, 6456 artists, and 11494 compositions.</p> <p>Users of this dataset must read and accept Smule's Research Data License Agreement (LICENSE.txt).</p>
Fig. 10 The Layers menu. a Create a New Layer. b in Adding depth to line artwork by digital stippling-a step-by-step guide to the method
Fig. 10 The Layers menu. a Create a New Layer. b Selected paths can be moved to different layers
A workflow and novel digital filters for compensating speed and equalization errors on digitized audio open-reel tapes: audio samples
<p>This repository makes available the audio samples related to the experiments described in the paper: </p> <p><em>Niccolò Pretto, Nadir Dalla Pozza, Alberto Padoan, Anthony Chmiel, Kurt James Werner, Alessandra Micalizzi, Emery Schubert, Antonio Rodà, Simone Milani and Sergio Canazza. 2021. A workflow and novel digital filters for compensating speed and equalization errors on digitized audio open-reel tapes. In Proceedings of the 16th International Conference on Audio Mostly (AM '21). Association for Computing Machinery, New York, NY, USA</em></p> <p>The experiment and the three case studies are described in the publication below. Here is the summary of their recording/reproducing standards and their notation.</p> <ul> <li>SET A: Recording 3.75 NAB (W3) - Reproducing 7.5 CCIR (R7C);</li> <li>SET B: Recording 3.75 NAB (W3) - Reproducing 15 CCIR (R15C);</li> <li>SET C: Recording 7.5 NAB (W7N) - Reproducing 15 CCIR (R15C).</li> </ul> <p>Here is the notation of the file naming: </p> <ul> <li>W: recording (writing);</li> <li>R: reproducing;</li> <li>3: 3.75 NAB;</li> <li>7N: 7.5 NAB;</li> <li>7C: 7.5 CCIR;</li> <li>15C: 15 CCIR.</li> </ul> <p>Here are the variants of the samples:</p> <ul> <li>REFERENCE: produced by using the correct equalization standard;</li> <li>ANCHOR: the "Reference" altered with a low-pass filter, with pass band set at 7 kHz for music and 3.5 kHz for speech;</li> <li>INCORRECT: produced by using an intentionally incorrect equalization, created by mismatching the recording and reading curves and resampled to the correct speed;</li> <li>MATLAB: the “Incorrect” variant corrected by means of a Matlab script;</li> <li>API: the “Incorrect” variant corrected by means of an <em>ad hoc</em> web interface adopting Web Audio API, for simulating real-time correction in web applications.</li> </ul> <p>Here is the samples list:</p> <p>SET A:</p> <ul> <li>Training: <ul> <li>sample4: Richard Wagner - <em>Ride of the Valkyries</em>;</li> </ul> </li> <li>Test: <ul> <li>sample1: Taylor Swift - <em>Shake It Off</em>;</li> <li>sample5: Queen - <em>We Will Rock You</em>;</li> <li>sample8: Bruno Maderna - <em>Continuo</em>;</li> <li>sample9: Luciano Berio - <em>Différences</em>.</li> </ul> </li> </ul> <p>SET B (the track title reflects the name of the file from which the track itself was extracted, from the CLIPS project of the University of Napoli: <a href="http://www.clips.unina.it/en/index.jsp">http://www.clips.unina.it/en/index.jsp</a>):</p> <ul> <li>Training: <ul> <li>sample22: CLIPS project - <em>LP4m18bZ</em>;</li> </ul> </li> <li>Test: <ul> <li>sample15: CLIPS project - <em>LP1f20bZ</em>;</li> <li>sample16: CLIPS project - <em>LP4m20bZ</em>;</li> <li>sample17: CLIPS project - <em>LP1f19bZ</em>;</li> <li>sample18: CLIPS project - <em>LP4m19bZ</em>.</li> </ul> </li> </ul> <p>SET C:</p> <ul> <li>Training: <ul> <li>sample3: Carl Orff - <em>Carmina Burana - O Fortuna</em>;</li> </ul> </li> <li>Test: <ul> <li>sample2: The Weeknd - <em>Save Your Tears</em>;</li> <li>sample6: Eagles - <em>Hotel California</em>;</li> <li>sample10: Bruno Maderna - <em>Musica su Due Dimensioni</em>;</li> <li>sample12: Bruno Maderna - <em>Syntaxis</em>.</li> </ul> </li> </ul> <p>Supplementary material can be found at the following DOI: 10.5281/zenodo.5118708</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.