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Dataset results
19 results for “automated recognition”
Not So Weak-PICO: Leveraging weak supervision for Participants, Interventions, and Outcomes recognition for systematic review automation
<p>EBM-PICO is a widely used dataset with PICO annotations at two levels: span-level or coarse-grained and entity-level or fine-grained. Span-level annotations encompass the full information about each class. Entity-level annotations cover the more fine-grained information at the entity level, with PICO classes further divided into fine-grained subclasses. For example, the coarse-grained Participant span is further divided into participant age, gender, condition and sample size in the randomised controlled trial. This dataset comes pre-divided into a training set (n=4,933) annotated through crowd-sourcing and an expert annotated gold test set (n=191) for evaluation.</p> <p>The <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6174533/bin/NIHMS988059-supplement-Appendix.pdf">EBM-PICO annotation guidelines</a> caution about variable annotation quality. <a href="http://ceur-ws.org/Vol-2429/paper1.pdf">Abaho et al.</a> developed a framework to post-hoc correct EBM-PICO outcomes annotation inconsistencies. <a href="https://arxiv.org/pdf/1904.09557.pdf">Lee et al.</a> studied annotation span disagreements suggesting variability across the annotators. Low annotation quality in the training dataset is excusable, but the errors in the test set can lead to faulty evaluation of the downstream ML methods. We evaluate 1% of the EBM-PICO training set tokens to gauge the possible reasons for the fine-grained labelling errors and use this exercise to conduct an error-focused PICO re-annotation for the EBM-PICO gold test set. The file 'test_ebm_correctedlabels.tsv' has error corrected EBM-PICO gold test set.</p> <p> </p> <p>The upload also contains two zip files containing labelling sources mentioned in the Distant-PICO paper. </p> <ol> <li>ds_cto_dict.zip: contains the four distant supervision dictionaries (P: participant.txt, I = intervention.txt, intervetion_syn.txt, O: outcome.txt) generated from clinicaltrials.gov using the methodology described in Distant-CTO. </li> <li>handcrafted_dictionaries.zip: contains three files <ul> <li>gender_sexuality.txt: contains a list of possible genders and sexual orientations found across the web. The list is not comprehensive.</li> <li>endpoints_dict.txt: contains outcome names and the names of questionnaires used to measure outcomes assembled from PROM questionnaires and PROMs.</li> <li>comparator_dict: contains a list of idiosyncratic comparator terms like a sham, saline, placebo, etc., compiled from the literature search. The list is not comprehensive.</li> </ul> </li> </ol>
Dataset for ICFHR2018 Competition on Automated Text Recognition on a READ Dataset
<p>The main idea of this dataset is to analyse the impact of training data. How many training data specific to the document, you are transcribing, is necessary? </p> <p><strong>general data: </strong>This is a collection of heterogeneous documents to train an initial system. For each text line there is an image file of that line, a file with the ground truth text and an information file containing an automatically generated surrounding polygon.</p> <p><strong>specific data: </strong>The specific data contains documents related to the test data. For the specific systems only the images of the train list may be used. The file are of the same type as the general data.</p> <p><strong>test data: </strong>The test data contains only the images and the information files.</p> <p>More Information, some published results and an evaluation procedure at https://scriptnet.iit.demokritos.gr/competitions/10/</p>
Data from: Domain-specific neural networks improve automated bird sound recognition already with small amount of local data
<p><span><span>An automatic bird sound recognition system is a useful tool for collecting data of different bird species for ecological analysis. Together with autonomous recording units (ARUs), such a system provides a possibility to collect bird observations on a scale that no human observer could ever match. During the last decades progress has been made in the field of automatic bird sound recognition, but recognizing bird species from untargeted soundscape recordings remains a challenge. <br></span></span></p> <p><span><span>In this article we demonstrate the workflow for building a global identification model and adjusting it to perform well on the data of autonomous recorders from a specific region. We show how data augmentation and a combination of global and local data can be used to train a convolutional neural network to classify vocalizations of 101 bird species. We construct a model and train it with a global data set to obtain a base model. The base model is then fine-tuned with local data from Southern Finland in order to adapt it to the sound environment of a specific location and tested with two data sets: one originating from the same Southern Finnish region and another originating from a different region in German Alps.<br></span></span></p> <p><span><span>Our results suggest that fine-tuning with local data significantly improves the network performance. Classification accuracy was improved for test recordings from the same area as the local training data (Southern Finland) but not for recordings from a different region (German Alps). Data augmentation enables training with a limited number of training data and even with few local data samples significant improvement over the base model can be achieved. Our model outperforms the current state-of-the-art tool for automatic bird sound classification.<br></span></span></p> <p><span><span>Using local data to adjust the recognition model for the target domain leads to improvement over general non-tailored solutions. The process introduced in this article can be applied to build a fine-tuned bird sound classification model for a specific environment.</span></span></p>
Not so weak-PICO: Leveraging weak supervision for Participants, Interventions, and Outcomes recognition for systematic review automation
<p><strong>Objective: </strong>PICO (Participants, Interventions, Comparators, Outcomes) analysis is vital but time-consuming for conducting systematic reviews (SRs). Supervised machine learning can help fully automate it, but a lack of large annotated corpora limits the quality of automated PICO recognition systems. The largest currently available PICO corpus is manually annotated, which is an approach that is often too expensive for the scientific community to apply. Depending on the specific SR question, PICO criteria are extended to PICOC (C-Context), PICOT (T-timeframe), and PIBOSO (B-Background, S-Study design, O-Other) meaning the static hand-labelled corpora need to undergo costly re-annotation as per the downstream requirements. We aim to test the feasibility of designing a weak supervision system to extract these entities without hand-labelled data.</p> <p><strong>Methodology:</strong> We decompose PICO spans into its constituent entities and re-purpose multiple medical and non-medical ontologies and expert-generated rules to obtain multiple noisy labels for these entities. These labels obtained using several sources are then aggregated using simple majority voting and generative modelling approaches. The resulting programmatic labels are used as weak signals to train a weakly-supervised discriminative model and observe performance changes. We explore mistakes in the currently available PICO corpus that could have led to inaccurate evaluation of several automation methods.</p> <p><strong>Results: </strong>We present Weak-PICO, a weakly-supervised PICO entity recognition approach using medical and non-medical ontologies, dictionaries and expert-generated rules. Our approach does not use hand-labelled data.</p> <p><strong>Conclusion: </strong>Weak supervision using weak-PICO for PICO entity recognition has encouraging results, and the approach can potentially extend to more clinical entities readily. </p>
Datasets and codes for De Lorm et al. 2023: Optimising the automated recognition of individual animals to support population monitoring
<p>Reliable estimates of population size and demographic rates are central to assessing the status of threatened species. However, obtaining individual-based demographic rates requires long-term data, which is often costly and difficult to collect. Photographic data offer an inexpensive, non-invasive method for individual-based monitoring of species with unique markings, and could therefore increase available demographic data for many species. However, selecting suitable images and identifying individuals from photographic catalogues is prohibitively time-consuming. Automated identification software can significantly speed up this process. Nevertheless, automated methods for selecting suitable images are lacking, as are studies comparing the performance of the most prominent identification software packages.</p> <p> </p> <p>In this study, we develop a framework that automatically selects images suitable for individual identification, and compare the performance of three commonly used identification software packages; Hotspotter, I<sup>3</sup>S-Pattern, and WildID. As a case study, we consider the African wild dog <em>Lycaon pictus</em>, a species whose conservation is limited by a lack of cost-effective large-scale monitoring. To evaluate intra-specific variation in the performance of software packages, we compare identification accuracy between two populations (in Kenya and Zimbabwe) that have markedly different coat colouration patterns. </p> <p> </p> <p>The process of selecting suitable images was automated using Convolutional Neural Nets that crop individuals from images, filter out unsuitable images, separate left and right flanks, and remove image backgrounds. Hotspotter had the highest image-matching accuracy for both populations. However, the accuracy was significantly lower for the Kenyan population (62%), compared to the Zimbabwean population (88%). </p> <p> </p> <p>Our automated image pre-processing has immediate application for expanding monitoring based on image-matching. However, the difference in accuracy between populations highlights that population-specific detection rates are likely and may influence certainty in derived statistics. For species such as the African wild dog, where monitoring is both challenging and expensive, automated individual recognition could greatly expand and expedite conservation efforts. </p>
Data from: Domain-specific neural networks improve automated bird sound recognition already with small amount of local data
Open the record for dataset details and reuse information.
Not so weak-PICO: Leveraging weak supervision for Participants, Interventions, and Outcomes recognition for systematic review automation
Open the record for dataset details and reuse information.
Supporting Data: A large-scale dataset of solar event reports from automated feature recognition modules.
<p>This is the supporting dataset for the paper:</p> <p>A large-scale dataset of solar event reports from automated feature recognition modules. Michael A. Schuh, Rafal A. Angryk, Petrus C. Martens. Journal of Space Weather and Space Climate, 2016.</p>
Vanellus chilensis dataset accompanying PLOS ONE paper "Automated Sound Recognition Provides Insights into the Behavioral Ecology of a Tropical Bird"
<p>Southern Lapwing <em>Vanellus chilensis</em> dataset accompanying the PLOS ONE article</p> <p>O. Jahn, T. Ganchev, M.I. Marinez and K.L. Schuchmann: Automated Sound Recognition Provides Insights into the Behavioral Ecology of a Tropical Bird. DOI:10.1371/journal.pone.0169041</p> <p>---<br> BL01:<br> Background Library 01 consists of</p> <p>BL01 >> BL_CHVACH_free_final:<br> 54 hand-cleaned (<em>Vanellus chilensis</em>-free) background files and</p> <p>BL01 >> PSC008_forest:<br> 36 original soundscapes recorded inside forest, which may contain a few target signals from overflying lapwings.</p> <p>---<br> PONE_VACH_AnnualCycle_Statistics:<br> Contains the Excel files<br> - 2013CHVACH_BreedingCycle_Statistics_PONE: statistics on <em>V. chilensis</em> activity patterns, Apr. 2013 to Sep. 2013.<br> - 2013CHVACH_FalseNegatives_PONE: determination of the false negative rate based on an expert-annotated sample of 26 soundcsape recordings<br> - 2013CHVACH_FalsePositives_PONE: determination of the false positive rate based on an expert-annotated random sample of 1250 automated <em>V. chilensis</em> detections.</p> <p>---<br> TL01_BIAVCHCHVACH_20130813v2_HandCleaned:<br> Training library for the development of the <em>V. chilensis</em> recognizer, consisting of 90 hand-filtered recordings of the target species.</p> <p>---<br> VACH_Detector_results >> VACHdetectorOutput_PONE.zip:<br> TXT detector output files, listing timestamps of potential <em>V. chilensis</em> sound events.</p> <p>---<br> VACH_FNrate_20160706:<br> Validation library used to determine the false negative rate. The library consists of 26 expert-annotated sound files. Annotations were made in Adobe Audion v3.0.</p> <p>---<br> VL01_VACH_MonoB<br> and<br> VL01_VACH_MonoB:<br> Validation library used for the fine-tuning of the recognizer settings.</p> <p>---<br> Important notes on the annotation of the VACH_FNrate_20160706 library:</p> <p>1) In general, we used the procedure described in Ganchev et al. 2015 for the annotation of VACH validation libraries (see next section).</p> <p>2) However, the detector-generated timestamps were not changed! For the following reasons, it is not possible to use the VACH_FN_rate library as a validation library for the development of improved recognizer versions:</p> <p>(a) The VACH detector overlooked many of the weaker signals within a VACH call series. Therfore the detector-generated annotations are incomplete.<br> (b) For the same reason some automatically-generated detections may refer to a single VACH call event (double hits).</p> <p>Details on the method used for the annotation of the VACH validation libraries are described in Ganchev et al. 2015, pp.6100f: 2.1.3.3.Vanellus chilensis validation dataset.</p>
Automated recognition of RNA structure motifs by their SHAPE data signatures
<p>Datasets, code and results supporting the manuscript:</p> <p>Radecki P., Ledda M. & Aviran S., Automated recognition of RNA structure motifs by their SHAPE data signatures</p>
Wavelet filters for automated recognition of birdsong in long-time field recordings
<p>1. Ecoacoustics has the potential to provide a large amount of information about the abundance of many animal species at a relatively low cost. Acoustic recording units are widely used in field data collection, but the facilities to reliably process the data recorded -- recognising calls that are relatively infrequent, and often significantly degraded by noise and distance to the microphone -- are not well developed yet. 2. We propose a call detection method for continuous field recordings that can be trained quickly and easily on new species, and degrades gracefully with increased noise or distance from the microphone. The method is based on the reconstruction of the sound from a subset of the wavelet nodes (elements in the wavelet packet decomposition tree). It is intended as a preprocessing filter, therefore we aim to minimise false negatives: false positives can be removed in subsequent processing, but missed calls will not be looked at again. 3. We compare our method to standard call detection methods, and also to machine learning methods (using as input features either wavelet energies or Mel-Frequency Cepstral Coefficients (MFCC)) on real-world noisy field recordings of six bird species. The results show that our method has higher recall (proportion detected) than the alternative methods: 87% with 85% specificity on >53 hrs of test data, resulting in an 80% reduction in the amount of data that needed further verification. It detected >60% of calls that were extremely faint (far away), even with high background noise. 4. This preprocessing method is available in our AviaNZ bioacoustic analysis program and enables the user to significantly reduce the amount of subsequent processing required (whether manual or automatic) to analyse continuous field recordings collected by spatially and temporally large-scale monitoring of animal species. It can be trained to recognise new species without difficulty, and if several species are sought simultaneously, filters can be run in parallel.</p>
Wavelet filters for automated recognition of birdsong in long-time field recordings
Open the record for dataset details and reuse information.
Automated Telephone Outreach With Speech Recognition to Improve Diabetes Care: A Randomized Controlled Study
ClinicalTrials.gov study NCT00790530. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Automated Medication Platform With Video Observation and Facial Recognition to Improve Adherence to Antiretroviral Therapy in Patients With HIV/AIDS
ClinicalTrials.gov study NCT05454514. IPD Sharing: NO. Countries: 1. Publications: 0.
Automated Assessment of Difficult Airway With Facial Recognition Techniques
ClinicalTrials.gov study NCT02022397. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Computer-based Tutorial and Automated Speech Recognition for Intravitreal Drug Injections
ClinicalTrials.gov study NCT04142164. IPD Sharing: NO. Countries: 1. Publications: 0.
Preventing Medication Mismanagement in People Living with Dementia Through Automated Medication Dispensing with Facial Recognition and Video Observation
ClinicalTrials.gov study NCT06691256. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Clinical Correlation Study Between Anomalous Pancreaticobiliary Ductal Junction and Malignant Tumors of the Biliary System and the Establishment of an AI-based Automated Recognition System
ClinicalTrials.gov study NCT06903520. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Computerized Facial Recognition for Automated Diagnosis of the Facio-Scapulo-Humeral Muscular Dystrophy (FSMHD)
ClinicalTrials.gov study NCT04377217. IPD Sharing: NO. Countries: 1. Publications: 0.
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.