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832 results for “automatic”
Automatic speech recognition datasets for Gronings, Nasal, and Besemah
<p>Automatic speech recognition datasets for Gronings, Nasal, and Besemah for experiments reported in Bartelds, San, McDonnell, Jurafsky and Wieling (2023). <em>Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation</em>. ACL 2023.</p> <p>Model training code available at: https://github.com/Bartelds/asr-augmentation</p>
Supporting Material for "Lunar Surface Model Age Derivation: Comparisons Between Automatic and Human Crater Counting Using LRO-NAC And Kaguya TC Images"
<p>Supporting Material for "Lunar Surface Model Age Derivation: Comparisons Between Automatic and Human Crater Counting Using LRO-NAC And Kaguya TC Images"</p> <p>Contents of this material</p> <ul> <li>Supplemental Text S1 and Text S2.</li> <li>Figures S1, S2, S2, S4, S5.</li> <li>Tables S1, S2</li> </ul> <p>For any questions email JHF (john.h.fairweaher@gmail.com).</p>
Data and toolkit for: SoundScape learning: An automatic method for separating fish chorus in marine soundscapes
<p class="MsoNormal">Marine soundscapes provide the opportunity to non-invasively learn about, monitor, and conserve ecosystems. Some fishes produce sound in chorus, often in association with mating, and there is much to learn about fish choruses and the species producing them. Manually analyzing years of acoustic data is increasingly unfeasible, and is especially challenging with fish chorus, as multiple fish choruses can co-occur in time and frequency and can overlap with vessel noise and other transient sounds. SoundScape Learning (SSL) is a novel unsupervised automated method, to separate fish chorus from soundscape. SSL is an integrated technique that makes use of randomized robust principal component analysis (RRPCA), unsupervised clustering, and a neural network. SSL was applied to 14 recording locations off southern and central California and was able to detect a single fish chorus of interest in 5.3 yrs of acoustically diverse soundscapes. Through application of SSL, the chorus of interest was found to be nocturnal, increased in intensity at sunset and sunrise, and was seasonally present from late Spring to late Fall. Further application of SSL will improve understanding of fish behavior, essential habitat, species distribution, and potential human and climate change impacts, and thus allow for protection of vulnerable fish species. This repository provides example data and code for the JASA paper: <em><span>SoundScape Learning: an automatic method for separating fish chorus in marine soundscapes.</span></em></p>
Evaluating a Kinematic Data Glove with Pressure Sensors to Automatically Differentiate Free Motion from Product Manipulation (Experimental Data)
<p>Experimental data from <em>"Evaluating a kinematic data glove with pressure sensors to automatically differentiate free motion from product manipulation", </em>available at Applied Sciences.</p> <p>"DATA.zip" contains raw data collected using VMG30 and CyberGlove data gloves, in txt format. </p> <p>For further information please see the details in the manuscript or contact the corresponding author Alba Roda-Sales (rodaa@uji.es).</p>
Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms - Datasets
<p>Datasets for reproducibility of the results found in Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms. This study utilized data from the following 4 journals:</p> <p>Lake, B.B. et al. A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys. Nat Commun 10, 2832 (2019).</p> <p>Liao, J., Yu, Z., Chen, Y. et al. Single-cell RNA sequencing of human kidney. Sci Data 7, 4 (2020).</p> <p>Menon, R. et al. Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker. JCI Insight 5, e133267 (2020).</p> <p>Wu, H. et al. Single-cell transcriptomics of a human kidney allograft biopsy specimen defines a diverse inflammatory response. J Am Soc Nephrol 29: 2069–2080 (2018).</p> <p>Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).</p>
Search-based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functions - Dataset and Results
<p>This dataset contains the replication package for the paper: Federico Formica, Tony Fan, and Claudio Menghi. 2023. "Search-based Software Testing Driven by Automatically Generated and Manually Defined Fitness Functions".</p> <p>The dataset contains:</p> <ul> <li>The models used in the evaluation section.</li> <li>All the results obtained by running Athena-S on the models.</li> <li>The scripts that automatically analyze the results file and produce the tables and figures used in the paper.</li> </ul>
Automatic message sequence chart creation from simulation run of the Chandy-Lamport algorithm modeled by colored Petri net
<p>Videos of two message sequence chart creation from simulation runs of the Chandy-Lamport algorithm modeled by colored Petri net using the CPN tool.</p> <p><strong>Message Sequence Chart Of Model With Automatic Simulation Run_SuppInfo.mp4</strong>: This video shows the automatic generation of a message sequence chart of a proposed colored Petri net model of the Chandy-Lamport distributed global snapshot algorithm using the CPN tool version 4.0.1. The video has been generated by the authors' updated extension server of the CPN tool. The automatic simulation run of the model has been used to create this video. The CPN tool randomly selects the enabled transition in an automatic simulation run.</p> <p><strong>Message Sequence Chart Of Model With Step-By-Step Simulation Run_SuppInfo.mp4:</strong> This video shows the automatic generation of a message sequence chart of the proposed colored Petri net model of the Chandy-Lamport algorithm in a step-by-step simulation run with our updated extension server of the CPN tools version 4.0.1. We fired our selected enabled transition of the model to create this video.</p>
Data for Automatic Mechanism Generation Involving Kinetics of Surface Reactions with Bidentate Adsorbates
<p>Data and scripts for the preprint "Automatic Mechanism Generation Involving Kinetics of Surface Reactions with Bidentate Adsorbates".</p>
PiJAMA: Piano Jazz with Automatic MIDI Annotations
<p>Release of the automatic MIDI transcriptions that constitute the PiJAMA dataset. See below for the abstract of the publication.<br> <br> Abstract:</p> <p>Recent advances in automatic piano transcription have enabled large scale analysis of piano music in the symbolic domain. However, the research has largely focused on classical piano music. We present <strong>PiJAMA</strong> (<strong>Pi</strong>ano <strong>J</strong>azz with <strong>A</strong>utomatic <strong>M</strong>IDI <strong>A</strong>nnotations): a dataset of over 200 hours of solo jazz piano performances with automatically transcribed MIDI. In total there are 2,777 unique performances by 120 different pianists across 244 recorded albums. The dataset contains a mixture of studio recordings and live performances. We use automatic audio tagging to identify applause, spoken introductions, and other non-piano audio to facilitate downstream music information retrieval tasks. We explore descriptive statistics of the MIDI data, including pitch histograms and chromaticism. We then demonstrate two experimental benchmarks on the data: performer identification and generative modeling. The dataset, including a link to the associated source code is available at <a href="https://almostimplemented.github.io/PiJAMA/">https://almostimplemented.github.io/PiJAMA/</a>.</p>
NLAS-multi: A Multilingual Corpus of Automatically Generated Natural Language Argumentation Schemes
<p>The multilingual corpus of natural language argumentation schemes (NLAS-multi) consists of 3,810 natural language argumentation schemes of which 1,893 are in English and 1,917 in Spanish. It has a total of 253,516 words distributed in 118,493 words in English and 135,023 words in Spanish. In terms of inferences, our corpus has a total of 7,964 (3,949 in English and 4,015 in Spanish). Furthermore, the NLAS-multi corpus contains a total of 23,781 conflict relations between arguments in the same topic.</p>
Data from: Automatic patient-level recognition of four Plasmodium species on thin blood smear by a Real Time Detector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation
<p>Automatic patient-level recognition of four <em>Plasmodium</em> species on thin blood smear by a Real Time Dectector Transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation</p> <p>Emilie Guemas, Baptiste Routier, Théo Ghelfenstein-Ferreira, Camille Cordier, Sophie Hartuis, Bénédicte Marion, Sébastien Bertout, Emmanuelle Varlet-Marie, Damien Costa, Grégoire Pasquier</p> <p><strong>Abstract:</strong></p> <p>Malaria remains a global health problem with 247 million cases and 619,000 deaths in 2021. Diagnostic of <em>Plasmodium</em> species is important for administering the appropriate treatment. The gold-standard diagnosis from accurate species identification remains the thin blood smear. Nevertheless, this method is time-consuming and requires highly skilled and trained microscopists. To overcome these issues, new diagnostic tools based on deep learning are emerging. This study aimed to evaluate the performances of a RT-DETR (Real-Time Detection Transformer)object detection algorithm to discriminate <em>Plasmodium</em> species on thin blood smears images. The algorithm was trained and validated on a dataset consisting in 24,720 images from 475 thin blood smears corresponding to 2,002,597 labels. Performances were calculated with a test dataset of 4,508 images from 170 smears corresponding to 358,825labels coming from six French university hospital. At the patient level, the RT-DETR algorithm exhibited an overall accuracy of 79.4% (135/170) with a recall of 74% (40/54) and 81.9% (95/116) for negative and positive smears, respectively. Among <em>Plasmodium </em>positive smears, the global sensitivity was 82.7% (91/110) with a sensitivity of 90% (38/42), 81.8% (18/22) and 76.1% (35/46) for <em>P. falciparum</em>, <em>P. malariae </em>and <em>P. ovale/vivax,</em> respectively. The YOLOv5 model achieved a World Health Organization (WHO) competence level 2 for species identification. Besides, the RT-DETR algorithm may be run in real-time on low-cost devices such as a smartphone and could be suitable for deployment in low-resource setting areas where microscopy experts are lacking.</p> <p><strong>Data collection:</strong></p> <p>The training and validation dataset included 24,720 pictures taken from 475 manually May Grunwald-Giemsa (MGG)-stained thin blood smears from the Montpellier University Hospital collection and for a smaller part from the Toulouse University Hospital collection. In Montpellier, the pictures were taken with a Flexcam C1 microscope camera (Leica) attached to a Leica DM 2000 microscope and Leica DF450C microscope camera adapted with a Leica DM2500 microscope at X1000 magnification. Labelling of pictures was performed manually, and then automatically with manual correction with a Computer Visual Annotation Tools (CVAT) free software. Nine categories of labels were used: white blood cells (n=3,338), red blood cells (n=1,887,781), platelets (n=48,520), <em>Trypanosoma brucei </em>(n=2,773), and red blood cells infected by <em>P. falciparum </em>(n=43,545), <em>P. ovale </em>(n=4,651), <em>P. vivax </em>(n=4,115), <em>P. malariae </em>(n=2,849) and <em>Babesia divergens</em> (n=5,142).</p> <p>The test dataset included 4,508 pictures taken from 170 thin blood smears from the same number of patients from the Parasitology laboratories of University Hospitals of Montpellier, Toulouse, Rouen, Lille, Nantes and Saint-Louis in Paris (Table 1). Among these 170 patients, 54 were not infected, including two patients with Howell-Jolly bodies, and 116 were infected with hematozoa. For each patient, between 20 and 30 photos were taken from one thin blood smear with at least one hematozoan parasite per picture for infected patients.</p> <p>Accurate species diagnostic was made by a senior parasitologist, and for recent smears, it was confirmed by specific PCR, either performed locally (Toulouse) or at the Malaria French National Reference Center (Montpellier, Saint Louis, Rouen, Lille, Nantes).</p>
Dataset from : "Automatic extraction of former WWI battlefields from ancient maps"
<p>The folder contains 3 shapefiles usable in GIS (geographic information system). These data result from the processing of the french map of devastated regions ("carte des régions dévastées"). The map was edited in 1920 by the geographic service of French army. The objective was to classify lands depending on the intensity of destruction, and to locate areas where substantial restoration work was necessary. The 47 map sheets of the collection at scale 1:50,000 have been scanned and can be obtained from the National Geographic Institute (IGN) in .jpg format. The map shows large red-colored zones representing heavily damaged front-line area by trenches and bombing according to the map legend. There are also red-hatched features locating destroyed cities, roads and destroyed or cut forests. The blue-colored symbols show new constructions, such as memorials and cemeteries. For the methodology of georeferencing, classification and vectorization, see Nelly Paradelle, Marianne Laslier, Guillaume DeCocq, "Automatic extraction of former WWI battlefields from ancient maps," Proc. SPIE 12727, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV, 127270H (17 October 2023);<a href="https://doi.org/10.1117/12.2684009"> https://doi.org/10.1117/12.2684009</a></p><p>-MANUAL_ENVELOPE.shp : This dataset contains the envelope bordering the local destructions from the dataset "RED POLYGONS", and drawn manually within QGIS.</p><p>-RED_POLYGONS.shp : This dataset contains only polygons of local destructions (cities, roads, buildings, destroyed or cut forests etc.) extracted from the map of devastated regions</p><p>-RED_ZONE.shp : This dataset contains only polygons of the large red-colored areas representing heavily damaged front-line area by trenches and bombing extracted from the map of devastated regions</p><p>Files with extension .qmd provide metadata.</p>
AutoMate: a dataset and learning approach for automatic mating of CAD assemblies
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Automatic monitoring of neural activity with single-cell resolution in behaving Hydra
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Robust semi-automatic vessel tracing in the human retinal image by an instance segmentation neural network
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A web-based tool for automatically linking clinical trials to their publications - example calculation
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TreeFlow: Probabilistic programming and automatic differentiation for phylogenetics
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Data and toolkit for: SoundScape learning: An automatic method for separating fish chorus in marine soundscapes
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Automatic image-based identication and biomass estimation of invertebrates
<p>Dataset containing images of macroinvertebrates from updated BIODISCOVER (2019). The task of the image collection was to determine optimal camera settings, from 2 parameters: Exposure time and Aperture.</p> <p>A combination of 3 exposure time and aperture settings was tested (total of 9 combinations of settings):</p> <p>Exposure time: 1000, 1500, 2000</p> <p>Aperture: 3.8, 8, 16</p> <p> </p> <p>All images contained within the dataset are separated by camera settings, species and sample number. Samples containing several specimen within one sample number are indexed (example: 10023_1, 10023_2, 10023_3). Further labeling and information can be found within "setup data.csv".</p> <p> </p>
Supplementary Material for the paper: Automatic Document Screening of Medical Literature Using Word and Text Embeddings in an Active Learning Setting
<p>This is the dataset used in the paper: Automatic Document Screening of Medical Literature Using Word and Text Embeddings in an Active Learning Setting. </p> <p>It is composed of: </p> <p>- Pre-trained models using active learning for document screening on HealthCLEF and Epistemonikos datasets. </p> <p>- Epistemonikos and HealthCLEF datasets containing medical questions and relevant/non relevant articles. </p> <p>- Embeddings and Document Representations used for experiments on both datasets. </p> <p>Scripts to run experiments can be found at: <a href="https://github.com/afcarvallo/active_learning_document_screening">https://github.com/afcarvallo/active_learning_document_screening</a></p> <p> </p> <p><strong>Paper abstract:</strong></p> <p>Document screening is a fundamental task within Evidence-based Medicine (EBM), a practice that provides scientific evidence to support medical decisions. Several approaches have tried to reduce physicians' workload of screening and labeling vast amounts of documents to answer clinical questions. Previous works tried to semi-automate document screening, reporting promising results, but their evaluation was conducted on small datasets, which hinders generalization. Moreover, recent works in natural language processing have introduced neural language models, but none have compared their performance in EBM. In this paper, we evaluate the impact of several document representations such as TF-IDF along with neural language models (BioBERT, BERT, Word2vec, and GloVe) on an active learning-based setting for document screening in EBM. Our goal is to reduce the number of documents that physicians need to label to answer clinical questions. We evaluate these methods using both a small challenging dataset (HealthCLEF 2017) as well as a larger one but easier to rank (Epistemonikos). Our results indicate that word as well as textual neural embeddings always outperform the traditional TF-IDF representation. When comparing among neural and textual embeddings, in the HealthCLEF dataset the models BERT and BioBERT yielded the best results. On the larger dataset, Epistemonikos, Word2Vec and BERT were the most competitive, showing that BERT was the most consistent model across different corpuses. In term of active learning, an uncertainty sampling strategy combined with logistic regression achieved the best performance overall, above other methods under evaluation, and in fewer iterations.</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.