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9,302 results for “DETECT”
Detecting axisymmetric magnetic fields using gravity modes in intermediate-mass stars
<p>Typical MESA and GYRE inlists associated with <a href="https://ui.adsabs.harvard.edu/abs/2020arXiv200502411V/abstract">Van Beeck et al. (2020)</a>. MESA version 10398 and GYRE version 5.2.</p> <p>Context: Angular momentum (AM) transport models of stellar interiors require improvements to explain the strong extraction of AM from stellar cores that is observed with asteroseismology. One of the often invoked mediators of AM transport are internal magnetic fields, even though their properties, observational signatures and influence on stellar evolution are largely unknown.</p> <p>Aims: We study how a fossil, axisymmetric internal magnetic field affects period spacing patterns of dipolar gravity mode oscillations in main-sequence stars with masses of 1.3, 2.0 and 3.0 <span class="math-tex">\(\mathrm{M}_{\odot}\)</span> . We assess the influence of fundamental stellar parameters on the magnitude of pulsation mode frequency shifts.</p> <p>Methods: We compute dipolar gravity mode frequency shifts due to a fossil, axisymmetric poloidal-toroidal internal magnetic field for a grid of stellar evolution models, varying stellar fundamental parameters. Rigid rotation is taken into account using the traditional approximation of rotation and the influence of the magnetic field is computed using a perturbative approach.</p> <p>Results: We find magnetic signatures for dipolar gravity mode oscillations in terminal-age main-sequence stars that are measurable for a near-core field strength larger than 10<sup>5</sup> G. The predicted signatures differ appreciably from those due to rotation.</p> <p>Conclusions: Our formalism demonstrates the potential for the future detection and characterization of strong fossil, axisymmetric internal magnetic fields in gravity-mode pulsators near the end of core-hydrogen burning from Kepler photometry, if such fields exist.</p> <blockquote> <p>The publication date is the date of acceptance.</p> </blockquote> <p>J. Van Beeck would like to thank researchers M. Michielsen, C. Johnston, and dr. M. G. Pedersen for their valuable input in the MESA and GYRE computations.</p>
An information theory-based machine learning approach to detecting functionally conserved and coordinated protein dynamics
<p>The application of machine learning classification to the molecular dynamics of the functional states of protein allows for application of an information theoretic framework familiar to traditional bioinformatics. The functional states of proteins involving binding interactions with partners comprised of protein, DNA or small molecules can first be defined in a binary fashion (i.e. bound vs unbound), subsequently simulated in molecular dynamics software, and then employed as a comparative training set for a binary machine learning classifier capable of discerning the complex dynamical consequences of binding interaction. This learner can subsequently be deployed on new simulations of the functionally bound state to validate its ability to recognize the molecular motions that are supporting binding function. Regions of proteins with functionally conserved dynamics will induce significant local correlations in learning performance across independent validation runs. Through case studies of Rbp subunit 4/7 interaction in RNA Pol II and DNA-protein interactions of TATA binding protein, we demonstrate this method of detecting functionally conserved protein dynamics. We also demonstrate how Shannon information, relative entropy and mutual information can be applied to these binary classification states of dynamic simulations in order to compare dynamics and identify concerted motions involved in dynamic interactions across sites.</p>
CyclomicsSeq: Accurate detection of circulating tumor DNA using nanopore consensus sequencing
<p>CyclomicsSeq is a protocol designed to produce and sequence long DNA concatemers with a linear repetition to acquire high accuracy consensus reads. In this dataset, we used CyclomicsSeq for sequencing TP53 in cell-free DNA of healthy individuals and of head and neck cancer patients and for sequencing synthetic TP53 DNA sequences that mimic the length of cell-free DNA. This dataset contains data (mainly base calls of the backbone and the insert) of 32 nanopore sequencing runs. <br> </p>
RealVAD: A Real-world Dataset for Voice Activity Detection
<p><strong>RealVAD: A Real-world Dataset for Voice Activity Detection</strong></p> <p>The task of automatically detecting “Who is Speaking and When” is broadly named as Voice Activity Detection (VAD). Automatic VAD is a very important task and also the foundation of several domains, e.g., human-human, human-computer/ robot/ virtual-agent interaction analyses, and industrial applications.</p> <p>RealVAD dataset is constructed from a YouTube video composed of a panel discussion lasting approx. 83 minutes. The audio is available from a single channel. There is one static camera capturing all panelists, the moderator and audiences.</p> <p>Particular aspects of RealVAD dataset are:</p> <ul> <li> It is composed of panelists with different nationalities (British, Dutch, French, German, Italian, American, Mexican, Columbian, Thai). This aspect allows studying the effect of ethnic origin variety to the automatic VAD.</li> <li> There is a gender balance such that there are four female and five male panelists.</li> <li> The panelists are sitting in two rows and they can be gazing audience, other panelists, their laptop, the moderator or anywhere in the room while speaking or not-speaking. Therefore, they were captured not only from frontal-view but also from side-view varying based on their instant posture and head orientation.</li> <li> The panelists are moving freely and are doing various spontaneous actions (e.g., drinking water, checking their cell phone, using their laptop, etc.), resulting in different postures.</li> <li> The panelists’ body parts are sometimes partially occluded by their/other's body part or belongings (e.g., laptop).</li> <li> There are also natural changes of illumination and shadow rising on the wall behind the panelists in the back row.</li> <li> Especially, for the panelists sitting in the front row, there is sometimes background motion occurring when the person(s) behind them moves.</li> </ul> <p>The annotations includes:</p> <ul> <li> The upper body detection of nine panelists in bounding box form.</li> <li> Associated VAD ground-truth (speaking, not-speaking) for nine panelists.</li> <li> Acoustic features extracted from the video: MFCC and raw filterbank energies.</li> </ul> <p><em>All info regarding the annotations are given in the ReadMe.txt and Acoustic Features README.txt files.</em></p> <p><strong>When using this dataset for your research, please cite the following paper in your publication:</strong></p> <ol> <li>C. Beyan, M. Shahid and V. Murino, "RealVAD: A Real-world Dataset and A Method for Voice Activity Detection by Body Motion Analysis", in IEEE Transactions on Multimedia, 2020.</li> </ol>
SMART2: Age-specific social mixing of school-aged children in a US setting using proximity detecting sensors and contact surveys
<p>To increase the evidence base supporting specific methods to measure social interaction, we compared data from self-reported contact surveys and wearable proximity sensors from a cohort of schoolchildren in the Pittsburgh metropolitan area.</p> <p> </p> <p>Enrollment in the Social Mixing and Respiratory Transmission (SMART) study operated on an opt-out basis, and all students registered in a participating school before the start of the study were eligible to participate. Students in kindergarten (typically aged 5 years) to 12<sup>th</sup> grade (typically aged 18 years) from two elementary (K to 4<sup>th</sup> grade, K to 5<sup>th</sup> grade), two middle (5<sup>th</sup> to 6<sup>th</sup> grade, 7<sup>th</sup> to 8<sup>th</sup> grade), two elementary-middle (K to 8<sup>th</sup> grade), and two high (both 9<sup>th</sup> to 12<sup>th</sup> grade) schools were eligible to participate in SMART. Participation rates were high in all schools (82 to 99%). Each school provided aggregate demographic information about the school population, and individual grade and sex of participating students.</p> <p><em>Proximity sensor deployments</em></p> <p>The details of proximity sensor deployments have been described in detail elsewhere (60). In brief, participating students were given proximity sensors in plastic pouches and instructed to wear the pouch around their neck for the duration of the school day without removing or otherwise tampering with the sensor. In six of the eight schools, all participating students were given a sensor; in two schools, the large student population limited the deployment to randomly selected classrooms in each grade. Deployments typically lasted from the first class period (08:00 – 09:00) to the last class period (14:00 – 15:00). Deployment days in each school were chosen to be representative of a typical school day, without any special schoolwide or grade-specific activities that could modify normal contact patterns.</p> <p> </p> <p>We used TelosB wireless sensors (61) programmed in the NesC language to send beacons every 20 seconds (beacon frequency 3 per minute). The receiving sensor recorded the contacting sensor’s identity, an internal time stamp, and a radio strength signal indicator (RSSI). Signal strength provided an estimate of physical proximity, but was highly dependent on the orientation of the two sensors and any obstructions between them and therefore could not be used to define an exact distance between contacts. Based on pilot studies and previous work on effective distances of respiratory virus transmission (29, 62), we chose a signal threshold (-80 dBm) that should correspond to contacts of relevance to respiratory disease transmission.</p> <p> </p> <p>The number of unique proximity sensor contacts recorded for a participant was defined as the total number of other participants with whom their proximity sensor recorded at least one interaction during each deployment. To explore patterns of contacts of varying length, we considered several values of the contact threshold, or the minimum number of recorded interactions between two proximity sensors required to be considered a unique contact. The number of interactions between any given pair of sensors was taken to be the maximum number of interactions recorded by either sensor, to account for battery failure, measurement error, or other malfunctions.</p> <p> </p> <p><em>Contact survey design</em></p> <p>Contact surveys were completed by participants in school under the supervision of project staff and teachers. Each sheet of the paper version allowed for information on up to 30 contacts to be recorded; additional sheets could be requested. Two versions were designed: one for middle- and high-school students, and a simplified version for elementary school students (although some elementary school children completed the middle- and high-school version, upon consultation with school administrators and teachers). Classrooms were randomly selected to participate from each grade, and students of several classrooms completed more than one contact survey over the course of the study period.</p> <p> </p> <p>Participants were asked to report information about any individual they talked with, played with, or touched the previous day, including the contact’s age and sex, whether they attended the same school as the participant, the context in which the contact was made, whether the contact involved direct or indirect (through a shared object) touch, and approximate duration of the contact. Students reported the total number of contacts made in the previous day, without detailed information, and additional demographic information about themselves and their household. The surveys were completed either on paper or by computer, depending on resources available in each school.</p> <p> </p> <p>We defined total survey contacts as the total number of individuals a student reported having interacted with on the day before the survey was completed. Detailed contacts were the subset of total contacts for which the student reported contact age, sex, duration, and context. We considered further subsets of detailed survey contacts, including those occurring within school, those reported to have lasted more than 10 minutes over the course of the day, and those occurring on the same day as a sensor deployment.</p> <p> </p>
Detection of very low frequency earthquakes based on matched-filter technique
<p>List of origin times of detected Very low frequency earthquakes based on Baba et al. (2020, Journal of Geophysical Research).</p> <p>Analysis period: January 2003-June 2019</p> <p>The content of this analysis is published in Geophysical Research Letters: <a href="https://doi.org/10.1029/2020GL088089">https://doi.org/10.1029/2020GL088089</a></p> <p>First column: year, second column: month, third column: day, forth column: hour, fifth column: minute, sixth column: second, seventh column: longitude, eighth column: latitude, ninth column: depth, and tenth column: region name. Times are described in JST (UT+9).</p> <p>Data Set S1 of supporting information of GRL corresponds to version 2. We updated the catalog because the information on Line 19035 in version 2 was incomplete.</p>
Recombination detection program (RDP4) results for ICMV and SLCMV DNA-B
<p>A DNA-B multiple alignment (Zenodo record 3964977) was scanned with RDP4.100 (D. Martin et al. 2015). Recombination breakpoints for events with statistical support from at least 5 of 7 methods were adjusted per the manual (high-confidence accepted events). A CSV file with the results for the accepted events was exported and then saved a second time with Excel to normalize format (a comma in between every column).</p> <p>Note added 2020-09-07: AJ575821 is listed in the files as ICMV based on its assignment in the NCBI Taxonomy database (taxa 341701 and 31600) but it is better classified as SLCMV.</p> <p>These results will be described in a manuscript by Crespo-Bellido et al.</p>
Robust step detection from different waist-worn sensor positions – implications for clinical studies
<p>The dataset contains tri-axial acceleration and gyroscope data (100 Hz sampling) from walks from 19 healthy volunteers, each walking up to three times a parcours of 20 meters with self-selected speed, slow speed or with five soft turns at self-selected speed. Each participant wore 11 time-synchronized sensors during these tests: left/right foot, 5 around waist, non-dominant wrist and upper arm and collar and pocket. In addition to the sensor recordings each 20 meter walk was timed with a stop-watch. </p> <p>Also see: <a href="https://doi.org/10.1159/000511611">https://doi.org/10.1159/000511611</a></p>
Fall Detection Dataset Modified - Unicomfacauca
<p>Modification of the original TST Fall detection dataset (available at <a href="https://meet.google.com/linkredirect?authuser=0&dest=https%3A%2F%2Fwww.ieee-dataport.org%2Fdocuments%2Ftst-fall-detection-dataset-v2">https://www.ieee-dataport.org/documents/tst-fall-detection-dataset-v2</a>) in order to more accurately represent the event of a fall, and also adds a class related to people standing.</p> <p>Source Code Repository: <a href="https://github.com/CristianChilito/Fall-Detection-System-Unicomfacauca">https://github.com/CristianChilito/Fall-Detection-System-Unicomfacauca</a></p>
Evaluation of MALDI‐ToF Mass Spectrometry for Rapid Detection of Cereulide from Bacillus cereus Cultures - MALDI-ToF Mass Spectra
<p>Datasets in support of the <em>bioRxiv </em>submitted paper Doellinger et al. (<strong>2019</strong>) "<em>Evaluation of MALDI‐ToF Mass Spectrometry for Rapid Detection of Cereulide from Bacillus cereus Cultures"</em> - MALDI-ToF Mass Spectra.</p> <p>The experiment and sample description and spectra numbering is consistent with the publication. Mass spectral data files are provided as unprocessed raw data in the manufacturer's original data format (Bruker Daltonics). Data is compressed using the freely available 7zip software.</p> <p><strong>Content:</strong></p> <p><em><strong>Figure 1.zip</strong></em>: Cereulide detection in <em>B. cereus</em> samples cultivated using different cultivation media and different sample preparation, or cereulide extraction methods.</p> <p><em><strong>Figure 2.zip</strong></em>: Effectivity of cereulide extraction by different solvents from <em>B. cereus</em> F4810/72 colony material.</p> <p><em><strong>Figure 3.zip</strong></em>: MALDI LIFT-ToF /ToF MS spectrum of cereulide.</p> <p><em><strong>Figure 4.zip</strong></em>: Determination of the limit of detection (LOD) of cereulide by MALDI- and LDI-ToF</p> <p><em><strong>Table 1.zip</strong></em>: Analysis of cereulide in <em>B. cereus</em> strains by MALDI-ToF MS.</p> <p><em><strong>Fig.SI.01.zip: </strong></em> Ultraperformance Liquid Chromatography – Mass Spectrometry (UPLC-MS/MS) analysis of ethanolic washing solutions of <em>B. cereus</em> F4810/72.</p> <p><em><strong>Fig.SI.02.zip:</strong></em> A selection of MALDI-ToF and LDI-ToF technical replicate mass spectra obtained from a commercial cereulide standard.</p> <p><em><strong>Fig.SI.03.zip:</strong></em> Limit of detection (LOD) of cereulide determined by MALDI- and LDI-ToF MS of ethanol wash solutions from <em>B. cereus</em> ATCC 10987 spiked by a cereulide standard. </p> <p><em><strong>Fig.SI.04.zip:</strong></em> Direct cereulide detection by means of MALDI- (panels <strong>A</strong>-<strong>F</strong>) and LDI-ToF MS (panels <strong>G</strong>-<strong>M</strong>) in linear and reflectron measurement mode.</p>
Understanding and Detecting Harmful Code
<p>Code smells typically indicate poor design implementation and choices that may degrade software quality. Hence, they need to be carefully detected to avoid such poor design. In this context, some studies try to understand the impact of code smells on the software quality, while others propose rules or machine learning based techniques to detect code smells. However, none of those studies or techniques focus on analyzing code snippets that are really harmful to software quality. This paper presents a study to understand and detect code harmfulness. We analyze harmfulness in terms of Clean, Smelly, Buggy, and Harmful code. By Harmful Code, we define a Smelly code element having one or more bugs reported.These bugs may have been fixed or not. Thus, the incidence of Harmful Code may represent a increased risk of introducing new defects and/or design problems during its fixing. We perform our study with 22 smell types, 803 versions of 13 open-source projects,40,340 bugs and 132,219 code smells. The results show that even though we have a high number of code smells, only 0.07% of those smells are harmful. The Abstract Function Call From Constructor is the smell type more related to Harmful Code. To cross-validate our results, we also perform a survey with 60 developers. Most of them (98%) consider code smells harmful to the software, and 85% of those developers believe that code smells detection tools are important. But, those developers are not concerned about selecting tools that are able to detect Harmful Code. We also evaluate machine learning techniques to classify code harmfulness: they reach the effectiveness of at least 97% to classify Harmful Code.While the Random Forest is effective in classifying both Smelly and Harmful Code, the Gaussian Naive Bayes is the less effective technique. Our results also suggest that both software and developers’ metrics are important to classify Harmful Code.</p>
Data for the MLCS 2020 paper "A Year of Automated Anomaly Detection in a Datacenter"
<p>This contains the data used for the paper by Ahmed et. al in the MLCS 2020 paper "A Year of Automated Anomaly Detection in a Datacenter". Each of the four CSV files corresponds to one of the quarters discussed in the paper, and each has a metadata file containing information about the query that produced them. The CSV files contain the 'raw' log messages, and an eventID that identifies which pattern the log entry matched; the eventID is used to group together log messages of the same type. These logfiles were collected on the CloudLab facility (https://cloudlab.us/) from Jan 1 - Dec 30, 2019.</p> <p>The violated_unviolated_sessions_*.txt files each contain 20 randomly-selected sessions: half of the sessions were labeled by the invariant miner as being 'normal', and the other half 'anomalous'. CloudLab developers and system administrators were asked to label these sessions manually (and were not given the invariant miner's labels). The corresponding *_manual_labels.txt contain the labels that the administrators assigned, and in some cases additional correspondence with the administrators and information about which manual labels matched the invariant miner and which did not.</p>
GECCO Industrial Challenge 2019 Dataset: A water quality dataset for the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition at the Genetic and Evolutionary Computation Conference 2019, Prague, Czech Republic.
<p>Dataset of the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 13th-17th 2019, Prague, Czech Republic</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>1. Original train dataset of water quality data provided to participants (identical to gecco2019_train_water_quality.csv)</p> <p>2. Call for Participation</p> <p>3. Rules and Description of the Challenge</p> <p>4. Resource Package provided to participants</p> <p>5. The complete dataset, consisting of train, test and validation merged together (gecco2019_all_water_quality.csv)</p> <p>6. The test dataset, which was used for creating the leaderboard on the server (gecco2019_test_water_quality.csv)</p> <p>7. The train dataset, which participants had available for training their models (gecco2019_train_water_quality.csv)</p> <p>8. The validation dataset, which was used for the end results for the challenge (gecco2019_valid_water_quality.csv)</p> <p> </p> <p>The challenge required the participants to submit a program for event detection. A training dataset was available to the participants (gecco2019_train_water_quality.csv). During the challenge the participants were able to upload a version of their program to out online platform, where this version was scored against the testing dataset (gecco2019_test_water_quality.csv), thus an intermediate leaderboard was available. To avoid overfitting against this dataset, at the end of the challenge, the end result was created from scoring with the validation dataset (gecco2019_valid_water_quality.csv). </p> <p>Train, Test, Validation dataset are from the same measuring station and are in chronological order. So the timestamps from the test dataset begin directly after the train timestamps, while the validation timestamps begin directly after the test timestamps. </p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, S. Moritz, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p> </p> <p>Internet of Things: Online Event Detection for Drinking Water Quality Control</p> <p> </p> <p>Description:</p> <p>For the 8th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2018 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.</p> <p><br> Competition Opens: End of January/Start of February 2019<br> Final Submission: 30 June 2019</p> <p>Official webpage:</p> <p><a href="https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php">https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php</a></p> <p> </p>
Data From: Powerful detection of polygenic selection and environmental adaptation in US beef cattle
<p>GEMMA output containing summary statistics for generation proxy selection mapping (GPSM) and environmental GWAS (envGWAS) selection analyses from <br> Rowan et al. "Powerful detection of polygenic selection and environmental adaptation in US beef cattle" 2021<br> https://doi.org/10.1101/2020.03.11.988121 </p> <p>File names identify the analysis run, for example<br> "Gelbvieh_envgwas_desert_summary_stats.txt.gz"<br> Is the Gelbvieh dataset analyzed using the Desert ecoregion as the dependent variable <br> in a univariate envGWAS model. </p> <p>Files are formated according to GEMMA output.</p>
Detection of ultra-weak laser pulses by free-running single-photon detectors: modeling dead time and dark counts effects
<p>In quantum communication systems, the precise estimation of the detector´s response to the incoming light is necessary to avoid security breaches. The typical working regime uses a free-running single-photon avalanche diode in combination with attenuated laser pulses at telecom wavelength for encoding information. We demonstrate the validity of an analytical model for this regime which considers the effects of dark counts and dead time on the measured count rate. For the purpose of gaining a better understanding of these effects, the photon detections were separated from the dark counts via a software-induced gating mechanism. The model was verified by experimental data for mean photon numbers covering three orders of magnitude as well as for laser repetition frequencies below and above the inverse dead time. Consequently, our model would be of interest for predicting the detector response not only in the field of quantum communications, but also in any other quantum physics experiment where high detection rates are needed.</p>
A Static-Based Approach to Detect SQL Semantic Bugs Dataset
<p>The dataset used for our study: A Static-Based Approach to Detect SQL Semantic Bugs.</p> <p>This dataset contains more than 172,000 queries extracted from StackOverflow posts. It was built for analysing the prevalence of semantic bugs in SQL queries.</p> <p>For more information about our study and tools see our GitHub repository: <a href="https://github.com/SERG-Delft/sql-bug-finder">https://github.com/SERG-Delft/sql-bug-finder</a></p> <p>Description of included files:</p> <ul> <li>sql_db.png: database ER diagram</li> <li>homedb_queries.sql: contains queries extracted from StackOverflow posts</li> <li>homedb_questions.sql: contains SQL related question posts extracted from StackOverflow</li> <li>homedb_answers.sql: contains the answers to SQL related question posts extracted from StackOverflow</li> <li>homedb_bugs.sql: contains queries with semantic bugs extracted from StackOverflow posts</li> <li>homedb_owners.sql: contains data related to the owners (users) of SQL StackOverflow posts</li> <li>homedb_pages.sql: artifact from book-keeping script, tracking the StackOverflow pages from which SQL queries were extracted (SQL tagged pages, ordered by votes in descending order)</li> </ul>
Truck Axle Detection
<p>This dataset was created to train a neural network to recognize truck axles, applied to videos recorded in a highway in the State of São Paulo, Brazil. This is still a work in progress and will be updated regularly. More info can be found in our <a href="https://www.researchgate.net/lab/Andre-Luiz-Cunha-Lab">Researchgate Lab Page</a> or on our OrcID Profiles.</p> <p>The dataset includes 1737 cropped images of truck axles, divided in two folders:</p> <ul> <li>Axle_Dark - Images of shadowed or dark truck axles, with few features and details visible. <ul> <li>1034 images</li> <li>Format: JPEG</li> <li>Resolution: Various, 96dpi</li> </ul> </li> <li>Axle_Clear - Images of clear truck axles, with visible details and features. <ul> <li>703 images</li> <li>Format: JPEG</li> <li>Resolution: Various, 96dpi</li> </ul> </li> <li>Naming pattern: <video_name>_<color|gray>-<Region_of_Interest_ID>-<truck_ID>_<axle_number>.jpg</li> </ul> <p>If this dataset helps in any way your research, please feel free to contact the authors. We really enjoy knowing about other researcher's projects and how everybody is making use of the images on this dataset. We are also open for collaborations and to answer any questions.</p>
Unbiased metagenomic sequencing complements specific routine diagnostic methods and increases chances to detect rare viral strains
<p>Raw Illumina MiSeq data in zipped FASTQ format.</p> <p>Files are named by sample type and time point (weeks after transplantation).</p>
Sensor Defect Detection Datasets
<p><strong>Deprecated</strong> - Use https://zenodo.org/record/48728 for a more comprehensive version.</p> <p> </p> <p>Two datasets of sensor values, with each dataset including one defect sensor that delivers incorrect values. The datasets where gathered during tests in a hazardous material storage demonstrator.</p> <p>The datasets are given as comma-separated values in text files. The first line in each file holds time stamps, while the following lines hold the sensor values. The first entry in every line gives the name of the sensor.</p> <p>The first dataset (data_scenario_1.txt) was recorded under normal operating conditions, with the sensor Temperature_Inside_8 delivering incorrect values. In the second scenario there is a leakage of fluid inside the hazardous material storage. At the same time the sensor Smoke_Inside_0 delivers incorrect values.</p>
Sensor Defect Detection Datasets with Configuration
<p>Two datasets of sensor values, with each dataset including one defect sensor that delivers incorrect values. The datasets where gathered during tests in a hazardous material storage demonstrator.</p> <p>The datasets are given as comma-separated values in text files. The first line in each file holds time stamps, while the following lines hold the sensor values. The first entry in every line gives the name of the sensor.</p> <p>The first dataset (data_scenario_1.csv) was recorded under normal operating conditions, with the sensor Temperature_Inside_8 delivering incorrect values. In the second scenario (data_scenario_2.csv) there is a leakage of fluid inside the hazardous material storage. At the same time the sensor Smoke_Inside_0 delivers incorrect values.</p> <p>Additionally attached is configuration data (Configurations.pdf) for the sensor fusion approach that was used to classify the datasets.</p> <p>For more information please contact the uploader.</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.