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9,302 results for “DETECT”
MIntRec2. 0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations
<p><span>MIntRec2.0, a large-scale benchmark dataset for multimodal intent recognition in multi-party conversations. It contains 1,245 high-quality dialogues with 15,040 samples, each annotated within a new intent taxonomy of 30 fine-grained classes, across text, video, and audio modalities. In addition to more than 9,300 in-scope samples, it also includes over 5,700 out-of-scope samples appearing in multi-turn contexts, which naturally occur in real-world open scenarios, enhancing its practical applicability.</span> <span>This dataset will be released under the CC BY-NC-SA 4.0 license.</span></p>
BITS-2 Dataset for Fall detection
<p>Data was collected using a custom built-wrist worn end worn on the left-wrist. Qualcomm Snapdragon 820c. We used MAX30102 Heart rate and SP02 sensor, MPU6500, which gives 3-axis acceleration, 3-axis linear acceleration and 3-axis gyroscope data and GY273 Magnetometer chip for data collection. All the sensors are interfaced to the SoC via the I2C interface using a Mezzanine board.</p> <p>Table, each table having six columns, time-stamp, x axis data, y-axis data and z axis data, number of axis and type of sensor(label) except in case of heart-rate where there will be only three columns, time-stamp, beats per minute and label (hrt for heart-rate)</p> <p>In this data set, the data was collected from a total of 41 volunteers performing 16 ADLs and 8 Falls . Every activity was repeated for 5 trials.</p>
Anomalies Detection in Student Activity in Solving Unique Programming Exercises: Motivated Students against Suspicious Ones
<p>The proposed dataset contains messages from the Digital Teaching Assistant (DTA) system, which captures the results of automatic verification of students’ solutions of unique programming exercises for 11 tasks of various types, which are automatically generated by the same system that automates a massive Python programming course at MIREA - Russian Technological University (RTU MIREA). The dataset contains anonymous information about students, their groups, variants, the time of successful and unsuccessful attempts to submit solutions to exercises for 11 tasks to the DTA system, as well as the ways in which a particular student performed a particular unique exercise. The dataset can be the subject of exploratory analysis in terms of detecting various anomalies and outbursts both in the structure of a multidimensional time series formed from messages sent to the DTA system as a whole, and in the behavior models of student groups and individual students when they interact with the DTA system.</p>
The Detectability of Organic Biosignatures in Artificially Matured Iron-rich Mars Analogues [Dataset]
<p>Dataset comprising py-GC-MS spectra from artifically matured circumneutral iron-rich deposits. This data were presented in Tan et al. in "The Detectability of Organic Biosignatures in Artificially Matured Iron-rich Mars Analogues".</p>
Datasets used in Detecting Plumes in Mobile Air Quality Monitoring Time Series with Density-based Spatial Clustering of Applications with Noise v01
<p>This repository contains the following data sets related to Detecting Plumes in Mobile Air Quality Monitoring Time Series with DBSCAN published in . Please cite the following: .</p> <p>Validated_Data.csv: A .csv file containing the validation set used in the study. Column headings are the following:</p> <p>"Lat1": GPS latitude of car location in degrees.<br> "Long1": GPS longitude of car location in degrees.<br> "LST": Measurement time stamp. Time zone US/Central.<br> "BC": Black carbon measurements in ng/m^3<br> "CO2": Carbon dioxide measurements in ppm.<br> "UFP": Ultrafine particle count in particles/cc.<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2, in ppb.<br> "Anomaly": What has been manually flagged as "Anomaly" (2) or "Normal" (1).<br> "Uniq_Fac": Factor from 1-30 mapping to different days of the campaign. For example, all measurements with Uniq_Fac = 1 belong to the same day.</p> <p>Labeled_DBSCAN_Anomalies.csv: A .csv file containing points labeled as anomalies by the DBSCAN algorithm described in the manuscript. Columns are the following.</p> <p>"BC": Black carbon measurement (ng/m^3)<br> "CO2": Carbon dioxide measurement (ppm)<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2 (ppb)<br> "UFP": Ultrafine particle count (p/cc)<br> "Anomaly": Whether the DBSCAN algorithm has labeled this point as "Anomaly" (2) or "Normal" (1)<br> "Uniq_Fac": Factor spanning from 1-277 grouping measurements taken on separate days by car. E.g. all measurements with Uniq_Fac=1 were grouped and analyzed together.<br> "LST": Timestamp (US/Central)<br> "Road_Class": TigerLINE census road class designation for the given point. Possible road classes are S1100 - Primary Road, S1200 - Secondary Road, S1400 - Local Road, S1630 - Ramps, S1640 - Service Drives, S1730 - Private Roads<br> "X": Universal Transverse Mercator Easting for Zone 15N (m).<br> "Y": Universal Transverse Mercator Northing for Zone 15N (m).</p> <p>*_To_Be_Validated.csv: A series of files where * denotes the following.</p> <p>"DB": DBSCAN Algorithm<br> "QOR": QOR Algorithm<br> "QAND": QAND Algorithm<br> "Drew": Drewnick Algorithm</p> <p>Each file contains the following columns:</p> <p>"Lat1": GPS latitude of car location in degrees.<br> "Long1": GPS longitude of car location in degrees.<br> "LST": Measurement time stamp. Time zone US/Central.<br> "BC": Black carbon measurements in ng/m^3<br> "CO2": Carbon dioxide measurements in ppm.<br> "UFP": Ultrafine particle count in particles/cc.<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2, in ppb.<br> "Anomaly": What has been flagged as "Anomaly" (2) or "Normal" (1).<br> "Uniq_Fac": Factor from 1-30 mapping to different days of the campaign. For example, all measurements with Uniq_Fac = 1 belong to the same day.</p>
PAN23 Trigger Detection
<p>This is the dataset for the shared task on <a href="https://pan.webis.de/clef23/pan23-web/trigger-detection.html">Trigger Detection at PAN@CLEF2023</a>. Please consult the task's page for further details on the format, the dataset's creation, and links to baselines and utility code.</p> <p>You can find a more refined version of this work here: <a href="https://github.com/webis-de/ACL-23">github.com/webis-de/ACL-23</a>.</p> <p><strong>Task:</strong> In trigger detection, we want to assign trigger warning labels to documents that contain potentially discomforting or distressing (triggering) content. We model trigger detection as a multi-label document classification challenge: assign each document all appropriate trigger warnings, but not more. All warnings are chosen from the author's perspective, i.e. the work's author decided which kind of trigger the document contains.</p> <p><strong>Dataset: </strong>This dataset contains annotated works of fanfiction, extracted from <a href="https://archiveofourown.org/">archiveofourown.org</a> (AO3). Each work is between 50 and 6,000 words long and has between 1 and many trigger warnings assigned. Our training dataset contains 307,102 examples, with 17,104 in validation and 17,040 in the test split. The label set contains 32 different trigger warnings. All labels are based on the <a href="https://archiveofourown.org/wrangling_guidelines/11#Warnings%20and%20triggers:~:text=Warnings%20and-,triggers,-General%20warnings%20(e">freeform content warnings</a> added to a fanwork by its author.</p> <p><strong>Versioning:</strong> </p> <ul> <li>1.0: initial upload</li> <li>1.1 fixed a minor bug where some works in the labels.jsonl contained labels that are not used in the competition (heteronormativity and religious-discrimination). Those labels have been removed. </li> <li>1.2 added labels.jsonl for the test dataset.</li> </ul>
Synthetic Training Dataset for Real-World Terminal Strip Object Detection
<p>This dataset provides synthetic training data for the real-world industrial application of terminal strip object detection to investigate the sim-to-real generalization performance of modern object detectors based on state-of-the-art image synthesis methods. It consists of 30.000 randomly generated synthetic images of terminal strips covering 36 different terminal blocks in five colors and additional accessories such as plug-in bridges, test adapters, end covers and markings. Except from the markings and the DIN rail all objects of the terminal strips are labeled with a bounding box and the respective object class for supervised learning. Additionally, 300 real images of terminal strips were taken and manually labeled for the real-world test.</p> <p>If you use this datset for your research, please consider citing this: <a href="https://arxiv.org/abs/2403.04809">Investigation of the Impact of Synthetic Training Data in the Industrial Application of Terminal Strip Object Detection</a></p>
Study to Evaluate NA-NOSE for Monitoring and Detecting Recurrence in Early Stage Lung Cancer
ClinicalTrials.gov study NCT01840150. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Epidemiological Tool To Detect Angina Pectoris in Blacks
ClinicalTrials.gov study NCT00005218. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Exploration on the Value of MRD Based on ctDNA Detection in Predicting Recurrence of Resected Non-small Cell Lung Cancer
ClinicalTrials.gov study NCT05965024. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
An Imaging Agent (I-124 M5A) in Detecting CEA-Positive Liver Metastases in Patients With Colorectal Cancer
ClinicalTrials.gov study NCT03993327. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Screening for Early Detection and Prevention of Pompe Disease in Israel Using Tandem Mass Spectrometry
ClinicalTrials.gov study NCT01409486. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Biomarker to Detect Salt Sensitivity
ClinicalTrials.gov study NCT03147170. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Pilot of AbStats /AEGIS System for Detecting Motility Abnormalities
ClinicalTrials.gov study NCT02233270. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Methylated DNA Detect in Paraneoplastic Syndrome of Nervous System
ClinicalTrials.gov study NCT04454853. IPD Sharing: NO. Countries: 1. Publications: 0.
68Ga-NYM096 PET/ CT for the Detection of Clear Cell Renal Cell Carcinoma in Presurgical Patients With Renal Masses
ClinicalTrials.gov study NCT07101900. IPD Sharing: YES. Countries: 1. Publications: 0.
Non Invasive dP/dt to Detect an Acute Ischemic Myocardial Dysfunction in Emergency Department
ClinicalTrials.gov study NCT02138604. IPD Sharing: NO. Countries: 1. Publications: 0.
Validation of Digital Heart Rhythm Devices in the Detection of Atrial Fibrillation
ClinicalTrials.gov study NCT06023290. IPD Sharing: Not stated. Countries: 1. Publications: 0.
PSMA PET Scan and mpMRI for Prostate Cancer Detection
ClinicalTrials.gov study NCT05820724. IPD Sharing: NO. Countries: 1. Publications: 0.
FH Detection in Children by Salivary Test
ClinicalTrials.gov study NCT04261803. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.