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412
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ShareScore release 0.9.0
Dataset results
412 results for “Verification”
Transcriptomic analysis and experimental verification reveal the involvement of PI3K/AKT signaling pathway in high-altitude cognitive dysfunction [6_OHG]
GEO Series GSE307180. Mus musculus. 13 samples. Type: Expression profiling by high throughput sequencing.
Bacillus velezensis LG37: Transcriptome profiling and functional verification of GlnK and NmtA in ammonia (NH4+) assimilation
GEO Series GSE136178. Bacillus velezensis. 6 samples. Type: Expression profiling by high throughput sequencing.
Cardiac Fibrosis in Dilated Cardiomyopathy: Transcriptomics Insights, Histological Correlations, and Organoid Model Verifications
GEO Series GSE246299. Homo sapiens. 67 samples. Type: Expression profiling by high throughput sequencing.
Prediction and verification of mouse tRNAs identifies intron-containing families
GEO Series GSE8224. Mus musculus. 12 samples. Type: Expression profiling by array.
ASH1L-MRG15 methyltransferase deposits H3K4me3 and FACT for damage verification in nucleotide excision repair
GEO Series GSE227009. Homo sapiens. 97 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
A versatile platform for locus-scale genome rewriting and verification
GEO Series GSE159488. Homo sapiens; Mus musculus; synthetic construct. 44 samples. Type: Other.
Halobacterium NRC-1 in response to oxic/anoxic transitions (initial and verification experiments)
GEO Series GSE5929. Halobacterium salinarum NRC-1. 86 samples. Type: Expression profiling by array.
Sequential verification of transcription by Integrator and Restrictor [ZC3H4_INTS4_chrRNA-seq]
GEO Series GSE297595. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Sequential verification of transcription by Integrator and Restrictor [ZC3H4_mNET-seq]
GEO Series GSE297599. Homo sapiens. 6 samples. Type: Non-coding RNA profiling by high throughput sequencing.
An antibody independent verification method quantifies m6A in the chicken β-actin zipcode
GEO Series GSE185078. Gallus gallus. 6 samples. Type: Other.
ASH1L methyltransferase deposits H3K4me3 and FACT for damage verification in nucleotide excision repair [ChIP-Seq]
GEO Series GSE214182. Homo sapiens. 61 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Sequential verification of transcription by Integrator and Restrictor [ZC3H4_EXOSC3_nucRNA-seq]
GEO Series GSE297602. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Development and Verification of an RNA-Seq Assay for the Detection of Gene Fusions in Tumors
GEO Series GSE111320. Homo sapiens. 54 samples. Type: Expression profiling by high throughput sequencing.
Tamoxifen-induced apoptosis of MCF-7 cells via GPR30/PI3K/MAPKs interactions: Verification by ODE modeling and RNA sequencing
GEO Series GSE115737. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Sequential verification of transcription by Integrator and Restrictor [INTS4_U1AMO_chrRNA-seq]
GEO Series GSE297603. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
ASH1L methyltransferase deposits H3K4me3 and FACT for damage verification in nucleotide excision repair [ATAC-Seq]
GEO Series GSE227007. Homo sapiens. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
RNA expression data from Halobacterium NRC-1 in response to oxic/anoxic transitions, verification experiment
GEO Series GSE5925. Halobacterium salinarum NRC-1. 30 samples. Type: Expression profiling by array.
Experiments for 'Verification of indefinite-horizon POMDPs'
<p>Supplementary material for the ATVA 2020 paper on Verification of indefinite-horizon POMDPs</p>
ASVspoof 2019: The 3rd Automatic Speaker Verification Spoofing and Countermeasures Challenge database
<p>This is a database used for the Third Automatic Speaker Verification Spoofing and Countermeasures Challenge, for short, ASVspoof 2019 (http://www.asvspoof.org) organized by Junichi Yamagishi, Massimiliano Todisco, Md Sahidullah, Héctor Delgado, Xin Wang, Nicholas Evans, Tomi Kinnunen, Kong Aik Lee, Ville Vestman, and Andreas Nautsch in 2019. The ASVspoof challenge aims to encourage further progress through (i) the collection and distribution of a standard dataset with varying spoofing attacks implemented with multiple, diverse algorithms and (ii) a series of competitive evaluations for automatic speaker verification. The ASVspoof 2019 challenge follows on from three special sessions on spoofing and countermeasures for automatic speaker verification held during INTERSPEECH 2013, 2015, and 2017. While the first edition in 2013 was targeted mainly at increasing awareness of the spoofing problem, the 2015 edition included the first challenge on the topic, accompanied by commonly defined evaluation data, metrics and protocols. The task in ASVspoof 2015 was to design countermeasure solutions capable of discriminating between bona fide (genuine) speech and spoofed speech produced using either text-to-speech (TTS) or voice conversion (VC) systems. The ASVspoof 2017 challenge focused on the design of countermeasures aimed at detecting replay spoofing attacks that could, in principle, be implemented by anyone using common consumer-grade devices. The ASVspoof 2019 challenge extends the previous challenge in several directions. The 2019 edition is the first to focus on countermeasures for all three major attack types, namely those stemming from TTS, VC and replay spoofing attacks. Advances with regards to the 2015 edition include the addition of up-to-date TTS and VC systems that draw upon substantial progress made in both fields during the last four years. ASVspoof 2019 thus aims to determine whether the advances in TTS and VC technology post a greater threat to automatic speaker verification and the reliability of spoofing countermeasures. Advances with regards to the 2017 edition concern the use of a far more controlled evaluation setup for the assessment of replay spoofing countermeasures. Whereas the 2017 challenge was created from the recordings of real replayed spoofing attacks, the use of an uncontrolled setup made results somewhat difficult to analyse. A controlled setup, in the form of replay attacks simulated using a range of real replay devices and carefully controlled acoustic conditions is adopted in ASVspoof 2019 with the aim of bringing new insights into the replay spoofing problem. Last but not least, the 2019 edition aligns ASVspoof more closely with the field of automatic speaker verification. Whereas the 2015 and 2017 editions focused on the development and assessment of stand-alone countermeasures, ASVspoof 2019 adopts for the first time a new ASV-centric metric in the form of the tandem decision cost function (t-DCF). The ASVspoof 2019 database encompasses two partitions for the assessment of logical access (LA) and physical access (PA) scenarios. Both are derived from the VCTK base corpus which includes speech data captured from 107 speakers (46 males, 61 females). Both LA and PA databases are themselves partitioned into three datasets, namely training, development and evaluation which comprise the speech from 20 (8 male, 12 female), 10 (4 male, 6 female) and 48 (21 male, 27 female) speakers respectively. The three partitions are disjoint in terms of speakers, and the recording conditions for all source data are identical. While the training and development sets contain spoofing attacks generated with the same algorithms/conditions (designated as known attacks), the evaluation set also contains attacks generated with different algorithms/conditions (designated as unknown attacks). Reliable spoofing detection performance therefore calls for systems that generalise well to previously-unseen spoofing attacks. Full descriptions are available in the ASVspoof 2019 evaluation plan. ## Codec ## The original waveform format is PCM and compressed using Flac. No telephone or mobile codec was used.</p>
UTKPAD: Replay Attack Database for Face Age Verification
<p><strong>Description</strong></p> <p>UTKPAD is a replay attack database prepared for face age verification as part of the paper "Vulnerability of Face Age Verification to Replay Attacks" published in ICASSP 2024 conference. This database is originated from the well-known large dataset, UTKFace of face images with age labels ranging from 1 to 100 plus years old. Replay attack are recorded with three different mobiles phones: Apple iPhone 12, Samsung Galaxy S9 and Huawei Mate 30. With UTKPAD database, we also provide file lists that can be used for training and testing of replay attacks detection and for vulnerability assessment of age verification systems.</p> <p>The face images in the <a href="https://susanqq.github.io/UTKFace/">UTKFace</a> are first enhanced with a face restoration CodeFormer method. Each enhanced image is converted into a video clip with a subsequent concatenation process to have one video clip including all image-to-video converted files. The final video clip is then replayed on Apple iPad Pro in order to record it with three different mobiles phones: Apple iPhone 12, Samsung Galaxy S9 and Huawei Mate 30. And, finally, the recordings are de-concatenated/segmented into sub video clips and each video clip is sampled by taking the middle frame to construct the database of replay attack images.</p> <p>To avoid breaching the copyright, we release the dataset in the form of deltas that are not actual images. The attack images can be recovered only if the user also obtains and downloads the original UTKFace dataset. When UTKFace is downloaded, however, the attack images of UTKPAD can be easily computed using the script we provide. </p> <p> </p> <p><strong>Reference</strong></p> <p>If you're using this dataset, please cite the following publication</p> <p>@INPROCEEDINGS{Korshunov_ICASSP_2024,<br> author = {Korshunov, Pavel and George, Anjith and {\"O}zbulak, G{\"o}khan and Marcel, S{\'{e}}bastien},<br> projects = {Idiap, Biometrics Center},<br> title = {Vulnerablity of Face Age Verification to Replay Attacks},<br> booktitle = {ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},<br> year = {2024},<br>}</p>
ScienceDex guides
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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.