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400 results for “fingerprints”
Identification of foliar fingerprints in response to arbuscular mycorrhizal fungus in Nicotiana attenuata
GEO Series GSE85375. Nicotiana attenuata. 24 samples. Type: Expression profiling by array.
Fingerprinting of the hNGF P61R100E gene expression in rat Dorsal Root Ganglion detected by microarray analysis
GEO Series GSE80257. Rattus norvegicus. 8 samples. Type: Expression profiling by array.
Subtracted Diversity Array identifies novel molecular markers including retrotransposons for fingerprinting Echinacea species
GEO Series GSE44683. Echinacea purpurea; Echinacea paradoxa var. paradoxa; Echinacea; Echinacea angustifolia; Echinacea pallida; Echinacea paradoxa x Echinacea pallida; Echinacea angustifolia var. angustifolia; Echinacea angustifolia var. strigosa; Echinacea tennesseensis; Echinacea paradoxa var. neglecta. 27 samples. Type: Genome variation profiling by array.
Dataset for Fingerprints and early-warning signals of AMOC change
<p>This is the data used for the publication:</p> <p>Jackson, L.C. and R.A Wood (2020): Fingerprints and early-warning signals of AMOC change. Journal of Climate</p> <p>Most netcdf files contain timeseries of the different metrics examined. The name of the netcdf file is the metric name. Within these files are 1D variables of the metric for each experiment in the form varname_expname and separate variables for the times (in years).</p> <p>Exceptions to this are:</p> <p> SST_maps.nc with the 2D maps of SST shown in Fig 7</p> <p> eof_temp400m.nc with the first eof of the 400m temperature in the control also shown in Fig 7</p> <p> </p> <p> </p>
BAF: an audio fingerprinting dataset for broadcast monitoring
<p><strong>Overview</strong></p> <p>Broadcast Audio Fingerprinting dataset is an open, available upon request, annotated dataset for the task of music monitoring in broadcast. It contains 2,000 tracks from Epidemic Sound's private catalogue as reference tracks that represent 74 hours. As queries, it contains over 57 hours of TV broadcast audio from 23 countries and 203 channels distributed with 3,425 one-min audio excerpts.</p> <p>It has been annotated by six annotators in total and each query has been cross-annotated by three of them obtaining high inter-annotator agreement percentages, which validates the annotation methodology and ensures the reliability of the annotations.</p> <p><strong>Purpose of the dataset</strong></p> <p>This dataset aims to become the standard dataset to evaluate Audio Fingerprinting algorithms since it’s built on real data, without the use of any data-augmentation techniques. It is also the first dataset to address background music fingerprinting, which is a real problem in royalties distribution.</p> <p><strong>Dataset use</strong></p> <p>This dataset is available for conducting non-commercial research related to audio analysis. It shall not be used for music generation or music synthesis.</p> <p><strong>About the data</strong></p> <p>All audio files are monophonic, 8kHz, 128kb/s, pcm_s16le encoded in .wav. Annotations mark which tracks sound (either in foreground or background) in each query (if any) and also the specific times where it starts and ends sound in the query.</p> <p><em>Note that there are 88 queries that do not have any matches.</em></p> <p>For more information check the dedicated Github repository: <a href="https://github.com/guillemcortes/baf-dataset">https://github.com/guillemcortes/baf-dataset</a> and the dataset datasheet included in the files.</p> <p> </p> <p><strong>Dataset contents</strong></p> <p>The dataset is structured following this schema</p> <pre><code>baf-dataset/ ├── baf_datasheet.pdf ├── annotations.csv ├── changelog.md ├── cross_annotations.csv ├── queries_info.csv ├── queries │ ├── query_0001.wav │ ├── query_0002.wav │ ├── … │ └── query_3425.wav ├── queries_info.csv └── references ├── ref_0001.wav ├── ref_0002.wav ├── … └── ref_2000.wav</code></pre> <p>There are two folders named <strong>queries</strong> and <strong>references</strong> containing the <em>wav</em> files of TV broadcast recordings and the reference tracks, respectively.</p> <p><strong>annotations.csv</strong> file contains the annotations made by the 6 annotators, giving the following information:</p> <table align="center" summary="annotations.csv content summary"> <caption>annotations.csv content summary</caption> <thead> <tr> <th scope="col">query</th> <th scope="col">reference</th> <th scope="col">query_start</th> <th scope="col">query_end</th> <th scope="col">annotator</th> </tr> </thead> <tbody> <tr> <td>query_0692.wav</td> <td>ref_1235.wav</td> <td>0.0</td> <td>59.904</td> <td>annotator_6</td> </tr> </tbody> </table> <p><strong>cross_annotations.csv </strong>contains the resulting annotations after merging the overlapping annotations in <strong>annotations.csv</strong> file. x_tag has three different values:</p> <ul> <li> <p>single: the segment has only been annotated by one annotator.</p> </li> <li> <p>majority: the segment has been annotated by two annotators.</p> </li> <li> <p>unanimity: the segment has been annotated by the three annotators.</p> </li> </ul> <table align="center" summary="cross_annotations.csv content summary"> <caption>cross_annotations.csv content summary</caption> <thead> <tr> <th scope="col">query</th> <th scope="col">reference</th> <th scope="col">query_Start</th> <th scope="col">query_end</th> <th scope="col">annotators</th> <th scope="col">x_tag</th> </tr> </thead> <tbody> <tr> <td>query_0693.wav</td> <td>ref_1834.wav</td> <td>37.53</td> <td>38.07</td> <td>['annotator_3']</td> <td>single</td> </tr> <tr> <td>query_0693.wav</td> <td>ref_1834.wav</td> <td>18.18</td> <td>37.48</td> <td>['annotator_3', 'annotator_5', 'annotator_3']</td> <td>unanimity</td> </tr> <tr> <td>query_0693.wav</td> <td>ref_1834.wav</td> <td>37.48</td> <td>37.53</td> <td>['annotator_5', 'annotator_3']</td> <td>majority</td> </tr> </tbody> </table> <p><strong>queries_info.csv </strong>contains information about the queries as a citation reference. It contains the country, the channel and the date where the broadcast happened.</p> <table align="center" summary="queries_info.csv content summary"> <caption>queries_info.csv content summary</caption> <thead> <tr> <th scope="col">filename</th> <th scope="col">country</th> <th scope="col">channel</th> <th scope="col">datetime</th> </tr> </thead> <tbody> <tr> <td>query_0001.wav</td> <td>Norway</td> <td>Discovery Channel</td> <td>2021-02-26 14:45:26</td> </tr> </tbody> </table> <p><strong>changelog.md </strong>contains a curated, chronologically ordered list of notable changes for each version of the dataset.</p> <p><strong>baf_datasheet.pdf</strong> contains standardized documentation for datasets</p> <p> </p> <p><strong>Ownership of the data</strong></p> <p>Next, we specify the ownership of all the data included in BAF: Broadcast Audio Fingerprinting dataset. For licensing information, please refer to the “License” section.</p> <p><strong>Reference tracks</strong></p> <p>The reference tracks are owned by Epidemic Sound AB, which has given a worldwide, revocable, non-exclusive, royalty-free licence to use and reproduce this data collection consisting of 2,000 low-quality monophonic 8kHz downsampled audio recordings.</p> <p><strong>Query tracks</strong></p> <p>The query tracks come from publicly available TV broadcast emissions so the ownership of each recording belongs to the channel that emitted the content. We publish them under the right of quotation provided by the Berne Convention.</p> <p><strong>Annotations</strong></p> <p>Guillem Cortès together with Alex Ciurana and Emilio Molina from BMAT Music Licensing S.L. have managed the annotation therefore the annotations belong to BMAT.</p> <p> </p> <p><strong>Accessing the dataset</strong></p> <p>The dataset is available upon request. Please include, in the justification field, your academic affiliation (if you have one) and a brief description of your research topics and why you would like to use this dataset. Bear in mind that this information is important for the evaluation of every access request.</p> <p> </p> <p><strong>License</strong></p> <p>This dataset is available for conducting non-commercial research related to audio analysis. <strong>It shall not be used for music generation or music synthesis</strong>. Given the different ownership of the elements of the dataset, the dataset is licensed under the <strong>following conditions</strong>:</p> <ol> <li> <p>User’s access request</p> </li> <li> <p>Research only, non-commercial purposes</p> </li> <li> <p>No adaptations nor derivative works</p> </li> <li> <p>Attribution to Epidemic Sound and the authors as it is indicated in the ”citation” section.</p> </li> </ol> <p><strong>Please include, in the justification field, your academic affiliation (if you have one) and a brief description of your research topics and why you would like to use this dataset.</strong></p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>With the support of Ministerio de Ciencia Innovación y universidades through Retos-Colaboración call, reference: RTC2019-007248-7, and also with the support of the Industrial Doctorates Plan of the Secretariat of Universities and Research of the Department of Business and Knowledge of the Generalitat de Catalunya. Reference: DI46-2020.</p>
Single-molecule fingerprinting of protein-drug interaction using a funneled biological nanopore
<p>This dataset belongs to “Single-molecule fingerprinting of protein-drug interaction using a funneled biological nanopore” and describes the raw micrograph dataset from negative-stain electron microscopy for YaxAB-C8, -C9, -C10.</p>
Transcriptomic fingerprints in human peripheral blood mononuclear cells indicative of genotoxic and non-genotoxic carcinogenic exposure
GEO Series GSE24891. Homo sapiens. 89 samples. Type: Expression profiling by array.
Immunological fingerprint of 4CMenB recombinant antigens via protein microarray
GEO Series GSE152785. Homo sapiens; Neisseria meningitidis. 219 samples. Type: Protein profiling by protein array.
Gene expression fingerprinting of ameloblasts shows changes in the endocytosis/cytoskeleton interface across stages.
GEO Series GSE133330. Rattus norvegicus. 3 samples. Type: Expression profiling by high throughput sequencing.
Molecular fingerprint of bovine embryos produced by superovulation with high competence to establish pregnancy
GEO Series GSE131178. Bos taurus. 18 samples. Type: Expression profiling by high throughput sequencing.
DNA methylation fingerprint of a KDM5C loss-of-function mutation in patients with intellectual disability
GEO Series GSE108423. Homo sapiens. 21 samples. Type: Methylation profiling by genome tiling array.
Fingerprints of Hepatocellular Carcinoma-Infiltrated γδT Cells Revealed by Single-Cell Sequencing
GEO Series GSE175793. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Bovine pre-transfer endometrium and embryo transcriptome fingerprints as predictors of pregnancy success after embryo transfer (embryo study)
GEO Series GSE21047. Bos taurus. 6 samples. Type: Expression profiling by array.
Bovine pre-transfer endometrium and embryo transcriptome fingerprints as predictors of pregnancy success after embryo transfer (endometrial study)
GEO Series GSE20974. Bos taurus. 12 samples. Type: Expression profiling by array.
WiFi fingerprints
<p>Training and validation data for an indoor location test scenario.</p>
iCarB-Fingerprint
<h3><strong>Description</strong></h3> <p>Contains fingerprint images (.bmp files) from 200 data subjects, acquired using two different fingerprint scanners:</p> <ol> <li>OYSTER III from NEXT Biometrics (thermal sensor).</li> <li>CSD101i from Thales (optical sensor).</li> </ol> <p>During the recording, the data subjects were seated in the driver's seat of a vehicle, with the fingerprint scanners positioned to the right of the steering wheel. </p> <p>The fingerprint images were acquired in the following two scenarios:</p> <p>(1) <strong>Indoors</strong>: The car was parked inside a garage, with controlled (artificial) lighting.<br>(2) <strong>Outdoors</strong>: The car was parked outside, with uncontrolled (natural) lighting.</p> <p>In both scenarios, the following types of fingerprint images were captured to incorporate different variabilities into the recorded fingerprint data, using each of the two fingerprint scanners in turn:</p> <ul> <li>Normal: Fingerprint was captured as is (4 images).</li> <li>Dry: Finger was dried using hand disinfectant and a paper towel (2 images).</li> <li>Moist: Finger was moistened by rubbing it on the nose or forehead (2 images).</li> <li>Hot: Finger was heated by placing it on a hot-water bottle (2 images).</li> <li>Cold: Finger was cooled by placing it on an ice block (2 images).</li> </ul> <p><br>So in total, this dataset consists of 48 fingerprint images per data subject, for the majority of data subjects. For a few data subjects, it was not possible to acquire fingerprint images in some scenarios, so in those cases the number of fingerprint images is less than 48. Taking this point into consideration, the dataset consists of a total of 9,521 fingerprint images across all 200 data subjects.</p> <p> </p> <h3><strong>Reference</strong></h3> <p>If you use this dataset, please cite the following publication:</p> <p>V. Krivokuca Hahn, J. Maceiras, A. Komaty, P. Abbet and S. Marcel, 2024. "<em>in-Car Biometrics (iCarB) Datasets for Driver Recognition: Face, Fingerprint, and Voice</em><span>". arXiv:2411.17305, doi: <a href="https://doi.org/10.48550/arXiv.2411.17305" target="_blank" rel="noopener">https://doi.org/10.48550/arXiv.2411.17305</a>.</span></p>
DATASET RELATED TO ARTICLE "MILD MALFORMATION OF CORTICAL DEVELOPMENT WITH OLIGODENDROGLIAL HYPERPLASIA (MOGHE): NEUROPHYSIOLOGICAL FINGERPRINTS OF A NEW PATHOLOGICAL ENTITY"
<p>ppt: scalp eeg – brain mri – brain fdg-pet</p>
Bovine pre-transfer endometrium and embryo transcriptome fingerprints as predictors of pregnancy success after embryo transfer
GEO Series GSE21049. Bos taurus. 18 samples. Type: Expression profiling by array.
An Integrated Fingerprinting platform for the study of Chinese Medicineal Herb Cordyceps sinensis
GEO Series GSE3000. Homo sapiens. 16 samples. Type: Expression profiling by array.
Limitless per Customer: Understanding and Breaking User & Device Fingerprinting in Android
<p>Data and artifacts for the research work "Limitless per Customer: Understanding and Breaking User & Device Fingerprinting in Android"</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.