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Dataset results
650 results for “Workflow”
Patient-Specific Guides vs. Classical Workflow in Class IV Mandibular Fracture Fixation
ClinicalTrials.gov study NCT07153354. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Subcutaneous Recombinant Human Hyaluronidase: Workflow Analysis and Emergency Department Design
ClinicalTrials.gov study NCT01020513. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Optimizing Surgical and Prosthetic Workflow for Implant-supported Ear and Nose Prostheses With Early Loading and Distant Prosthesis Fabrication: A Prospective Cohort Study
ClinicalTrials.gov study NCT06506695. IPD Sharing: YES. Countries: 0. Publications: 0.
Benefit of the Digital Workflow for Screw-retained Single Implant Restorations
ClinicalTrials.gov study NCT03234868. IPD Sharing: Not stated. Countries: 0. Publications: 0.
A unified workflow to define multipotent progenitor hierarchies (TEA-Seq)
GEO Series GSE266524. Mus musculus. 12 samples. Type: Other.
A scalable, low cost, multi-omic interrogation and sample hashing workflow for single-cell analysis using the Seq-Well S3 platform
GEO Series GSE266385. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing; Other.
A fully automated high throughput-workflow for human neural organoids
GEO Series GSE119060. Homo sapiens. 80 samples. Type: Expression profiling by high throughput sequencing.
TIRE-seq: an Integrated Sample Extraction and Transcriptomics Workflow for High Throughput Perturbation Studies
GEO Series GSE284395. Homo sapiens; Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
An end-to-end workflow to study newly synthesized mRNA following rapid protein depletion in Saccharomyces cerevisiae
GEO Series GSE261145. Saccharomyces cerevisiae. 18 samples. Type: Expression profiling by high throughput sequencing.
A unified workflow to define multipotent progenitor hierarchies
GEO Series GSE266609. Mus musculus. 418 samples. Type: Other; Expression profiling by high throughput sequencing.
A unified workflow to define multipotent progenitor hierarchies (HIVE)
GEO Series GSE266523. Mus musculus. 6 samples. Type: Other.
A unified workflow to define multipotent progenitor hierarchies (CITE-Seq and CellTag)
GEO Series GSE266608. Mus musculus. 63 samples. Type: Expression profiling by high throughput sequencing; Other.
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>
Replication package for the paper on contract generation for workflows
<p>The replication package for the paper on contract generation for workflows.</p> <p>Contains the prototype and the experimental results from the paper.</p>
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>
Synthetic dataset for the testing of an MT-Mag geophysical integration workflow
<p>This datasets is related to the manuscript "<strong>Utilisation of probabilistic MT inversions to constrain magnetic data inversion: proof-of-concept and field application</strong>", by Jérémie Giraud, Hoël Seillé, Gerhard Visser, Mark D. Lindsay, Vitaliy Ogarko, and Mark W. Jessell, intended for publication in Solid Earth. </p> <p>It is organised as follows. <br> <br> MT FOLDER<br> model subfolder: contains the synthetic resistivity model, 2 formats available:<br> - ModEM format (.mod)<br> - WinGLink format (.out)<br> <br> responses subfolder: contains the synthetic model responses, 2 formats available:<br> - ModEM format (.dat): This data has not been perturbed by synthetic noise. <br> - EDI format (.edi): This data has been perturbed by 5% Gaussian noise, this is the data used in the synthetic part of the study.<br> <br> coordinates of the synthetic MT sites with respect to the model: coordinates.txt <br> - it assumes the origin (0,0) in the center of the synthetic 3D model.<br> <br> Mag FOLDER<br> model subfolder: contains the magnetic susceptibility model at the Tomofast format<br> - mag_voxet_true_model.txt <br> <br> responses subfolder: contains the synthetic model responses, for both the noisy and clean data. <br> - fwd_mag_data_clean.txt<br> - fwd_mag_data_with_noise.txt<br> <br> Rock units FOLDER: contains the file with indices of the rock unit model, in 3D, of the modified Mansfield model. <br> It is of dimensions 128 * 128 * 36. The indices are stored as a column vector. </p>
EVA Workflow 1 Package
Open the record for dataset details and reuse information.
Alternative splicing detection workflow needs a careful combination of sample prep and bioinformatics analysis
GEO Series GSE58001. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Development of a user-friendly next-generation epigenomic chip adaptable to automation workflows
GEO Series GSE298383. Homo sapiens. 1 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
A unified workflow to define multipotent progenitor hierarchies [Fluidigm]
GEO Series GSE266522. Mus musculus. 337 samples. Type: Other.
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.