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28 results for “automated recorders”
Sample dataset to accompany Hamilton, Chang, Lee, & Chang. Semi-automated anatomical labeling and inter-subject warping of high-density intracranial recording electrodes in electrocorticography
<p>This dataset accompanies the following paper: <br> Hamilton, Chang, Lee, and Chang. Semi-automated anatomical labeling and inter-subject warping of <br> high-density intracranial recording electrodes in electrocorticography</p> <p>This data includes an anonymized and de-identified CT and T1 MRI scan, plus all intermediate and final files<br> produced by the img_pipe software for testing and instructional purposes. This subject had a right hemisphere implantation including high density grids, strip electrodes, and depth electrodes.</p> <p>img_pipe software and installation instructions can be found at http://github.com/changlabucsf/img_pipe</p> <p>If you wish to follow along yourself, we recommend creating a new subject in your Freesurfer $SUBJECTS_DIR, <br> then copy the acpc and CT directories from this dataset into that new subject directory. </p> <p>The electrode montage is provided in test_subj_montage.txt and describes the type of electrodes implanted<br> (grid, strip, or depth) and their general location. </p>
An automated workflow for parallel processing of large multiview SPIM recordings
<p>Selective Plane Illumination Microscopy (SPIM) allows to image developing organisms in 3D at unprecedented temporal resolution over long periods of time. The resulting massive amounts of raw image data requires extensive processing interactively via dedicated graphical user interface (GUI) applications. The consecutive processing steps can be easily automated and the individual time points can be processed independently, which lends itself to trivial parallelization on a high performance computing (HPC) cluster. Here, we introduce an automated workflow for processing large multiview, multichannel, multiillumination time-lapse SPIM data on a single workstation or in parallel on a HPC cluster. The pipeline relies on <em>snakemake</em> to resolve dependencies among consecutive processing steps and can be easily adapted to any cluster environment for processing SPIM data in a fraction of the time required to collect it.</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>
Remote automated delivery of mechanical stimuli coupled to brain recordings in behaving mice
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Wavelet filters for automated recognition of birdsong in long-time field recordings
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Could dementia be detected from UK primary care patients' records by simple automated methods earlier than by the treating physician? A retrospective case-control study - Extended Data
<p>Extended data for Article published in Wellcome Open Research (Appendices 1,2 & 3). </p> <p>Abstract of Article: </p> <p><strong>Background:</strong> Timely diagnosis of dementia is a policy priority in the United Kingdom (UK). Primary care physicians receive incentives to diagnose dementia; however, 33% of patients are still not receiving a diagnosis. We explored automating early detection of dementia using data from patients’ electronic health records (EHRs). We investigated: a) how early a machine-learning model could accurately identify dementia before the physician; b) if models could be tuned for dementia subtype; and c) what the best clinical features were for achieving detection.</p> <p><strong>Methods:</strong> Using EHRs from Clinical Practice Research Datalink in a case-control design, we selected patients aged >65y with a diagnosis of dementia recorded 2000-2012 (cases) and matched them 1:1 to controls; we also identified subsets of Alzheimer’s and vascular dementia patients. Using 77 coded concepts recorded in the 5 years before diagnosis, we trained random forest classifiers, and evaluated models using Area Under the Receiver Operating Characteristic Curve (AUC). We examined models by year prior to diagnosis, subtype, and the most important features contributing to classification.</p> <p><strong>Results:</strong> 95,202 patients (median age 83y; 64.8% female) were included (50% dementia cases). Classification of dementia cases and controls was poor 2-5 years prior to physician-recorded diagnosis (AUC range 0.55-0.65) but good in the year before (AUC: 0.84). Features indicating increasing cognitive and physical frailty dominated models 2-5 years before diagnosis; in the final year, initiation of the dementia diagnostic pathway (symptoms, screening and referral) explained the sudden increase in accuracy. No substantial differences were seen between all-cause dementia and subtypes.</p> <p><strong>Conclusions:</strong> Automated detection of dementia earlier than the treating physician may be problematic, if using only primary care data. Future work should investigate more complex modelling, benefits of linking multiple sources of healthcare data and monitoring devices, or contextualising the algorithm to those cases that the GP would need to investigate.</p>
Data from: Using automated digital recording systems as effective tools for the monitoring of birds and amphibians
There is a need to improve the quantity and quality of data in biodiversity monitoring projects. We compared an automated digital recording system (ADRS) with traditional methods (point-counts and transects) for the assessment of birds and amphibians. The ADRS proved to produce better quantity and quality of data. This new method has 3 additional advantages: permanent record of a census, 24 h/d data collection and the possibility of automated species identification.
FIGURE 5. Automate evermanni Rathbun, 1901 s in Decapod crustaceans from the state of Ceará, northeastern Brazil: an updated checklist of marine and estuarine species, with 23 new records
FIGURE 5. Automate evermanni Rathbun, 1901 s. lat.: ovigerous female (OUMNH.ZC.2015-01-0003) from Icapuí, Ceará, Brazil, in dorsolateral view. Scale bar: 2 mm. Photograph by A. Anker.
A Framework for Automated Construction of Heterogeneous Large-Scale Biomedical Knowledge Graphs (Recorded Talk)
<p>This entry contains the recording of the presentation that was presented at the 2020 Intelligent Systems for Molecular Biology as part of the Bio-Ontologies COSI (https://www.iscb.org/ismb2020).</p>
Automated bird sound classifications of long-duration recordings produce occupancy model outputs similar to manually annotated data
<p>Occupancy modeling is used to evaluate avian distributions and habitat associations, yet it typically requires extensive survey effort because a minimum of three repeat samples are required for accurate parameter estimation. Autonomous recording units (ARUs) can reduce the need for surveyors on site, yet ARUs utility were limited by hardware costs and the time required to manually annotate recordings. Software that identifies bird vocalizations may reduce expert time needed, if classification is sufficiently accurate. We assessed the performance of BirdNET – an automated classifier capable of identifying vocalizations from >900 North American and European bird species – by comparing automated to manual annotations of recordings of 13 breeding bird species collected in northwestern California. We compared the parameter estimates of occupancy models evaluating habitat associations supplied with manually annotated data (9 min recording segments) to output from models supplied with BirdNET detections. We used three sets of BirdNET output to evaluate the duration of automatic annotation needed to approach manually annotated model parameter estimates: 9-min, 87-min, and 87-min of high-confidence detections. We incorporated 100 3-sec manually validated BirdNET detections per species to estimate true and false positive rates within an occupancy model. BirdNET correctly identified 90% and 65% of the bird species a human detected when data were restricted to detections exceeding a low or high confidence score threshold, respectively. Occupancy estimates, including habitat associations, were similar regardless of method. Precision (proportion of true positives to all detections) was >0.70 for 9 of 13 species, and a low of 0.29. However, processing of longer recordings was needed to rival manually annotated data. We conclude that BirdNET is suitable for annotating multispecies recordings for occupancy modeling when extended recording durations are used. Together, ARUs and BirdNET may benefit monitoring and, ultimately, conservation of bird populations by greatly increasing monitoring opportunities. </p>
Enhancing georeferenced biodiversity inventories: automated information extraction from literature records reveal the gaps
<p>Data and code supplement to our article revised submission to PeerJ.</p> <p> </p> <p>The file is compressed using standard zip. The uncompressed size is about 50 GB. There is a readme.md in the archive, which explains the structure of the contents.</p> <p> </p> <p>Abstract:</p> <p>We use natural language processing (NLP) to retrieve location data for cheilostome bryozoan species (text-mined occurrences [TMO]) in an automated procedure. We compare these results with data combined from two major public databases (DB): the Ocean Biogeographic Information System (OBIS), and the Global Biodiversity Information Facility (GBIF). Using DB and TMO data separately and in combination, we present latitudinal species richness curves using standard estimators (Chao2 and the Jackknife) and range-through approaches. Our combined DB and TMO species richness curves quantitatively document a bimodal global latitudinal diversity gradient for extant cheilostomes for the first time, with peaks in the temperate zones. 79% of the georeferenced species we retrieved from TMO (N = 1408) and DB (N = 4549) are non-overlapping. Despite clear indications that global location data compiled for cheilostomes should be improved with concerted effort, our study supports the view that many marine latitudinal species richness patterns deviate from the canonical latitudinal diversity gradient (LDG). Moreover, combining online biodiversity databases with automated information retrieval from the published literature is a promising avenue for expanding taxon-location datasets.</p>
buzzfindr: Automating the detection of feeding buzzes in bat echolocation recordings
<p>Quantification of bat communities and habitat heavily rely on non-invasive acoustic bat surveys the scope of which has greatly amplified with advances in remote monitoring technologies. Despite the unprecedented amount of acoustic data being collected, analysis of these data is often limited to simple species classification which provides little information on habitat function. Feeding buzzes, the rapid sequences of echolocation pulses emitted by bats during the terminal phase of prey capture, have historically been used to evaluate foraging habitat quality. Automated identification of feeding buzzes in recordings could benefit conservation by helping identify critical foraging habitat. I tested if detection of feeding buzzes in recordings could be automated with bat recordings from Ontario, Canada. Data were obtained using three different recording devices. The signal detection method involved sequentially scanning narrow frequency bands with the "Bioacoustics" R package signal detection algorithm, and extracting temporal and signal strength parameters from detections. Buzzes were best characterized by the standard deviation of the time between consecutive pulses, the average pulse duration, and the average pulse signal-to-noise ratio. Classification accuracy was highest with artificial neural networks and random forest algorithms. I compared each model's receiver operating characteristic curves and random forest provided better control over the false-positive rate so it was retained as the final model. When tested on a new dataset, buzzfindr's overall accuracy was 93.4% (95% CI: 91.5% - 94.9%). Overall accuracy was not affected by recording device type or species frequency group. Automated detection of feeding buzzes will facilitate their integration in the analytical workflow of acoustic bat studies to improve inferences on habitat use and quality.</p>
FIGURE 3 in The first record of the alpheid shrimp Automate isabelae Ramos-Tafur, 2018 from Brazil (Decapoda: Caridea)
FIGURE 3. Automate isabelae Ramos-Tafur, 2018, ovigerous female (cl indet., specimen not examined) from Araçá Bay, São Sebastião Channel (Stn 10H, MC, 23°49'03"S 45°23'52"W, 20 m), São Paulo, Brazil; A, shrimp photographed alive, dorsal view; B, same, left lateral view; C, same, right lateral view. Photographs courtesy of Alvaro E. Migotto.
FIGURE 2 in The first record of the alpheid shrimp Automate isabelae Ramos-Tafur, 2018 from Brazil (Decapoda: Caridea)
FIGURE 2. Automate isabelae Ramos-Tafur, 2018, non-ovigerous specimen (cl 4.3 mm) from Araçá Bay, São Sebastião Channel, São Paulo, Brazil (MZUSP 33932) [A–D]; non-ovigerous specimen (cl 3.3 mm) from the same area (MZUSP 33933) [E, F]; A, major (right) cheliped, lateral; B, same, mesial; C, minor (left) cheliped, lateral; D, same, mesial; E, major (right) cheliped, lateral; F, same, carpus and chela, mesial.
FIGURE 1 in The first record of the alpheid shrimp Automate isabelae Ramos-Tafur, 2018 from Brazil (Decapoda: Caridea)
FIGURE 1. Automate isabelae Ramos-Tafur, 2018, non-ovigerous specimen (cl 4.3 mm) from Araçá Bay, São Sebastião Channel, São Paulo, Brazil (MZUSP 33932): A, frontal region, dorsal; A', dorsomesial margin of first article of left and right antennular peduncles, showing two spiniform setae, dorsal; B, telson, dorsal; C, third maxilliped, lateral; D, same, ultimate article, dorsomesial; E, second pereiopod, lateral; F, third pereiopod, lateral; G, same, distal portion of propodus and dactylus, mesial; H, fifth pereiopod, lateral; I, uropod, dorsal (marginal and dorsal setae omitted).
Artificial Intelligence for Automated Clinical Data Exploration From Electronic Medical Records (CardioMining-AI)
ClinicalTrials.gov study NCT05176769. IPD Sharing: YES. Countries: 1. Publications: 7.
Data from: Using automated digital recording systems as effective tools for the monitoring of birds and amphibians
Open the record for dataset details and reuse information.
buzzfindr: Automating the detection of feeding buzzes in bat echolocation recordings
Open the record for dataset details and reuse information.
Automated bird sound classifications of long-duration recordings produce occupancy model outputs similar to manually annotated data
Open the record for dataset details and reuse information.
Data from: Towards large scale automated cage monitoring – Diurnal rhythm and impact of interventions on in-cage activity of C57BL/6J mice recorded 24/7 with a non-disrupting capacitive-based technique
Abstract Background and aims Automated recording of laboratory animal's home cage behavior is receiving increasing attention since such non-intruding surveillance will aid in the unbiased understanding of the normal animal cage behavior potentially improving animal experimental reproducibility. Material and methods Here we investigate activity of group held (5 mice/cage) female C57BL/6J mice (mus musculus) housed in standard Individually Ventilated Cages (IVC cages) across three test-sites: Consiglio Nazionale delle Ricerche (Rome, Italy), The Jackson Laboratory (Bar Harbor, USA) and Karolinska Insititutet (Stockholm, Sweden). Additionally, comparison of female and male C57BL/6J mice was done at KI. Activity was recorded using a capacitive-based sensor placed non-intrusively on the cage rack under the home cage collecting activity data every 250 msec, 24/7. The data collection was analyzed using non-parametric rank-based analysis of variance (nparLD) for longitudinal data comparing sites, weekdays and sex. Results The system detected an increase in activity preceding and peaking around lights-on followed by a decrease to a rest pattern. At lights off, activity increased substantially displaying a distinct temporal variation across this period. We also documented impact on mouse activity that standard animal handling procedures have, e.g. cage-changes, and show that even simple procedures are stressors impacting in-cage activity. These key observations replicated across the three test-sites, however, it is also clear that, apparently minor local environmental differences generate significant behavioral variances between the sites and within sites across weeks. Comparison of gender revealed differences in activity in the response to cage-change lasting for days in male but not female mice; and apparently also impacting the response to other events such as lights-on in males. Females but not males showed a larger tendency for week-to-week variance in activity possibly reflecting estrous cycling. Conclusions These data demonstrate that real-time home cage monitoring is scalable and run in real time, providing complementary information for animal welfare measures, experimental design and phenotype characterization.
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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.