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564
datasets available to search
ShareScore release 0.9.0
Dataset results
564 results for “ABS”
γδ T-PD-1 Ab Cells in the Treatment of Malignant Meningioma
ClinicalTrials.gov study NCT07172178. IPD Sharing: NO. Countries: 0. Publications: 0.
A Multicenter, Randomized, Double-Blind Parallel-Group, Placebo-Controlled Efficacy Study Comparing 4 Weeks of Treatment With Esomeprazole 20 mg Once Daily to Placebo qd for the Resolution of Upper Ab
ClinicalTrials.gov study NCT00626535. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Phase 1 Clinical Trial to Evaluate the Safety and Anti-Tumor Activity of AB-201
ClinicalTrials.gov study NCT06341647. IPD Sharing: NO. Countries: 0. Publications: 0.
Combination of AB-LIFE Probiotic Plus Monacolin K to Reduce Blood Cholesterol
ClinicalTrials.gov study NCT04677335. IPD Sharing: NO. Countries: 0. Publications: 0.
Scl-Ab: Exploratory 26-Week Subcutaneous Toxicology Study in the Aged Ovariectomized Female Sprague Dawley Rat with an 18-week Recovery [vertebrae]
GEO Series GSE71306. Rattus norvegicus. 294 samples. Type: Expression profiling by array.
Affymetrix microarray for gene expression patterns influenced by syndecan-1 overexpression in malignant mesothelioma STAV-AB cells
GEO Series GSE21401. Homo sapiens. 6 samples. Type: Expression profiling by array.
Induction of Muscle Regenerative Multipotent Stem Cells from Human Adipocytes by PDGF-AB and 5-Azacytidine
GEO Series GSE151527. Homo sapiens. 17 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Methylation profiling by high throughput sequencing.
MESSENGER E/V/H MASCS 2 UVVS UNCALIBRATED DATA V1.0
Abstract ======== This data set consists of the MESSENGER MASCS UVVS uncalibrated observations, also known as EDRs. The MASCS UVVS experiment is a scanning grating monochromator equipped with three photomultiplier tubes. There are three UUVS EDR data products, one for each detector, which cover the wavelength ranges of the far ultraviolet (FUV), middle ultraviolet (MUV), and visible (VIS).
MAGELLAN V RSS 5 OCCULTATION PROFILE ABS H2SO4 VOLMIX V1.0
This data set includes vertical profiles of 13-cm and 3.6-cm absorptivity and abundance of sulfuric acid vapor (H2SO4). The data set is composed of the following parameter fields (listed as the field name followed by a description).
Gene expression differences between LPS, anti-CD36 Abs, or LPS plus anti-CD36 Abs treated RAW264.7 cells.
GEO Series GSE290268. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Gene expression profiling of tumor-infiltrating CD45+CD8+ cells and CD45- tumor cells of tumor bearing mice treated with metformin or anti-PD-1 Ab or both
GEO Series GSE134191. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing.
Comparative transcriptome of the fertile and sterile buds of genic multiple-allele inherited male sterile AB line in Chinese cabbage
GEO Series GSE77427. Brassica rapa subsp. pekinensis. 2 samples. Type: Expression profiling by high throughput sequencing.
Gene expression differences between LPS, anti-CD36 Abs, or LPS plus anti-CD36 Abs treated RAW264.7 cells.
GEO Series GSE318498. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
3 Week IV Study of RGMC Ab in Female Sprague Dawley Rats
GEO Series GSE63200. Rattus norvegicus. 12 samples. Type: Expression profiling by array.
Ab Initio Design of Amphipathic-Symmetric Peptides against SARS-CoV-2
GEO Series GSE182562. Homo sapiens. 9 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Zebrafish fbln1 mutant (ulg075) larvae at 10dpf versus AB wt
GEO Series GSE238059. Danio rerio. 6 samples. Type: Expression profiling by high throughput sequencing.
VoiceWukong: Benchmarking Deepfake Voice Detection (part_ab)
<h1>VoiceWukong</h1> <p><em><strong>VoiceWukong </strong>is a comprehensive benchmark for deepfake voice detection, designed to evaluate the performance of various detectors in real-world application scenarios.</em></p> <p><strong>Dataset Features</strong></p> <ul> <li>Large Scale: Contains 265,200 English and 148,200 Chinese deepfake voice samples</li> <li>Diverse Sources: Covers voice samples generated by 19 commercial tools and 15 open-source tools</li> <li>Real-world Scenarios: Constructed 38 data variants covering 6 types of audio manipulations common in practical applications</li> <li>Bilingual Support: Supports evaluation in both Chinese and English languages</li> </ul> <p><strong>Evaluation Results</strong></p> <ul> <li>Conducted comprehensive evaluations on 12 state-of-the-art deepfake voice detectors</li> <li><a href="https://github.com/TakHemlata/SSL_Anti-spoofing">AASIST2</a> achieved the best performance with an Equal Error Rate (EER) of 13.50%</li> <li>Other detectors showed EERs exceeding 20%</li> <li>Results indicate significant challenges for current detectors in practical applications</li> </ul> <p><strong>Human-Machine Comparison Study</strong></p> <ul> <li>Conducted user studies with over 300 participants</li> <li>Comparative analysis of detection capabilities among humans, detectors, and multimodal large language models (<a href="https://github.com/QwenLM/Qwen2-Audio">Qwen2-Audio</a>)</li> <li>Different detectors and humans showed varying identification capabilities for deepfake voices at different deception levels</li> <li>Multimodal large language models demonstrated no effective detection ability</li> </ul> <h2>Dataset</h2> <p>This is the second part of the dataset, and it requires the complete download of both <a href="https://zenodo.org/records/13731918"><strong><em>part_aa</em></strong></a> and <a href="https://zenodo.org/records/13732412"><strong><em>part_ab</em></strong></a> for proper extraction and use. Please ensure that both files are in the same folder. For a detailed introduction to the data, please refer to our paper (to be made available).</p> <p>The first part (part_aa) is at <a href="../records/13731918">part_aa</a><br>extract command : <code>cat VoiceWukong.part_* | tar -xz</code></p> <h2>Leaderboard</h2> <p>Our leaderboard presents comprehensive evaluation results in three main sections:</p> <ol> <li><strong>Overall Performance</strong> - General evaluation metrics for each detector across the entire dataset, providing a broad view of detection capabilities.</li> <li><strong>Manipulation-specific Performance</strong> - Detailed results showing how each detector performs under different types of audio manipulations, offering insights into specific strengths and weaknesses.</li> <li><strong>User Study-based Evaluation</strong> - Performance analysis of detectors on deepfake voices categorized by difficulty levels based on our user study results, demonstrating detector effectiveness across varying deception capabilities.</li> </ol> <p>Visit our <a href="https://voicewukong.github.io/">leaderboard(github.io)</a> for detailed performance metrics and rankings. Additionally, we provide a copy of the leaderboard code <a href="https://zenodo.org/uploads/14650793">here</a> for permanent storage.</p> <div> <h2>Evaluated Detectors' Weighted Models</h2> <ul> <li>All evaluated detectors’ weighted models can be obtained from <a title="https://huggingface.co/VoiceWukong/VoiceWukong/" href="https://huggingface.co/VoiceWukong/VoiceWukong/">huggingface.co</a>. Additionally, we provide a copy of the weights files <a href="https://zenodo.org/uploads/14650793">here</a> for premanent storage.</li> </ul> <h2>User Study Results & Original Outputs</h2> <ul> <li>This <a href="https://github.com/VoiceWukong/VoiceWukong">code repository(github)</a> stores our <a href="https://github.com/VoiceWukong/VoiceWukong/tree/main/Userstudy/result">user study results</a> and the <a href="https://github.com/VoiceWukong/VoiceWukong/tree/main/OutputScore">original outputs</a> of the evaluation detectors. Additionally, we provide a copy of the code repository <a href="https://zenodo.org/uploads/14650793">here</a> for permanent storage.</li> </ul> </div> <p>Note: <em><strong>VoiceWukong</strong></em> <strong>prohibits use for <em>commercial purposes.</em></strong></p>
ABS-rich model waste characterization for different sampling strategies – ATR FTIR data
<p>The purpose of this analysis is the development of an efficient sampling protocol for plastic waste streams. </p> <p>A model waste from different polymers was formulated, rich in ABS and containing PS, PP and PE in smaller proportions. Additionally, one bromine containing flame retardant is added to a final concentration of either 500ppm or 50ppm. Different sampling approaches were followed including extrusion and/or cryogenic grinding as a homogenization step. Each approach was assessed via various analytical techniques as to homogenization efficiency. </p> <p>This dataset contains raw ATR-FTIR data of the model waste from the different sampling approaches. The content is:</p> <ul> <li>One Excel file containing ATR-FTIR data of the model waste, wherein the approach was based on extrusion and measurement protocol</li> <li>One Excel file containing ATR-FTIR data of the model waste, wherein the approach was based on cryogenic grinding and measurement protocol</li> <li>One Readme file containing further information about the methodology and nomenclature </li> </ul> <p>This dataset was generated in the framework of PRecycling Horizon Europe project (101058670)</p> <p> </p> <p> </p>
ABS-rich model waste characterisation for different sampling strategies – MFR data
<p>The purpose of this analysis is the development of an efficient sampling protocol for plastic waste streams. </p> <p>A model waste from different polymers was formulated, rich in ABS and containing PS, PP and PE in smaller proportions. Additionally, one bromine containing flame retardant is added to a final concentration of either 500ppm or 50ppm. Different sampling approaches were followed including extrusion and/or cryogenic grinding as a homogenization step. Each approach was assessed via various analytical techniques as to homogenization efficiency. </p> <p>This dataset contains raw MFR data of the model waste from the different sampling approaches. The content is:</p> <p>·One Excel file containing MFR data of the model waste, wherein the approach was based on extrusion and measurement protocol</p> <p>·One Excel file containing MFR data of the model waste, wherein the approach was based on cryogenic grinding and measurement protocol</p> <p>·One Word file containing further information about the methodology and nomenclature </p> <p>This dataset was generated in the framework of PRecycling Horizon Europe project (101058670)</p>
ABS-rich model waste characterisation for different sampling strategies – TGA data
<p>The purpose of this analysis is the development of an efficient sampling protocol for plastic waste streams. </p> <p>A model waste from different polymers was formulated, rich in ABS and containing PS, PP and PE in smaller proportions. Additionally, one bromine containing flame retardant is added to a final concentration of either 500ppm or 50ppm. Different sampling approaches were followed including extrusion and/or cryogenic grinding as a homogenization step. Each approach was assessed via various analytical techniques as to homogenization efficiency. </p> <p>This dataset contains raw TGA data of the model waste from the different sampling approaches. The content is:</p> <p>·One Excel file containing TGA data of the model waste, wherein the approach was based on extrusion and measurement protocol</p> <p>·One Excel file containing TGA data of the model waste, wherein the approach was based on cryogenic grinding and measurement protocol</p> <p>·One Word file containing further information about the methodology and nomenclature </p> <p>This dataset was generated in the framework of PRecycling Horizon Europe project (101058670)</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.