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ShareScore release 0.9.0
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105 results for “IMPACT DETECTION”
Heterogeneity in the rate of molecular sequence evolution substantially impacts the accuracy of detecting shifts in diversification rates
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Miniaturization eliminates detectable impacts of drones on bat activity
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Data from: Early detection of human impacts using acoustic monitoring: an example with forest elephants
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Data from: High detectability with low impact: Optimising large PIT tracking systems for cave-dwelling bats
Passive integrated transponder (PIT) tag technology permits the 'resighting' of animals tagged for ecological research without the need for physical re-trapping. While this is effective if animals pass within centimetres of tag readers, short-distance detection capabilities have prevented the use of this technology with many species. To address this problem, we optimised a large (15 m-long) flexible antenna system to provide a c. 8 m2 vertical detection plane for detecting animals in flight. We installed antennas at two roosting caves, including the primary maternity cave, of the critically endangered southern bent-winged bat (Miniopterus orianae bassanii) in south-eastern Australia. Testing of these systems indicated PIT-tags could be detected up to 105 cm either side of the antenna plane. Over the course of a three-year study, we subcutaneously PIT-tagged 2966 bats and logged over 1.4 million unique detections, with 97% of tagged bats detected at least once. The probability of encountering a tagged bat decreased with increasing environmental 'noise' (unwanted signal) perceived by the system. During the study we mitigated initial high noise levels by earthing both systems, which contributed to an increase in daily detection probability (based on the proportion of individuals known to be alive that were detected each day) from <0.2 (noise level ≥30%) to 0.7-0.8 (noise level 5-15%). Conditional on a low (5%) noise level, model-based estimates of daily encounter probability were highest (>0.8) during peak breeding season when both female and male southern bent-winged bats congregate at the maternity cave. In this paper we detail the methods employed and make methodological recommendations for future wildlife research using large antennas, including earthing systems as standard protocol and quantifying noise metrics as a covariate influencing the probability of detection in subsequent analyses. Our results demonstrate that large PIT antennas can be used successfully to detect small volant species, extending the scope of PIT technology and enabling a much broader range of wildlife species to be studied using this approach.
Data from: Natural disturbances can produce misleading bioassessment results: identifying metrics to detect anthropogenic impacts in intermittent rivers
<p>Ecosystems experience natural disturbances and anthropogenic impacts that affect biological communities and ecological processes. When natural disturbance modifies anthropogenic impacts, current widely used bioassessment metrics can prevent accurate assessment of biological quality.</p> <p>Our aim was to assess the ability of biomonitoring metrics to detect anthropogenic impacts at both perennial and intermittent sites, and in the latter including both flowing and disconnected pool aquatic phases. Specifically, aquatic macroinvertebrates from 20 rivers were sampled along gradients of natural flow intermittence (natural disturbance) and anthropogenic impacts to investigate their combined effects on widely used river biomonitoring metrics (i.e. taxonomic richness and standard biological indices) and novel functional metrics, including functional redundancy (i.e. the number of taxa contributing similarly to an ecosystem function, here a trophic function) and response diversity (i.e. how functionally similar taxa respond to natural disturbance and anthropogenic impacts).</p> <p>Our results showed that natural flow intermittence can confound river bioassessment, and that a set of new functional metrics could be used as effective alternatives to standard metrics in naturally disturbed intermittent rivers.</p>
Resources for the paper: "Social Context in Political Stance Detection: Impact and Extrapolation"
<p>This repository contains the resources in our paper <strong>[Social Context in Political Stance Detection: Impact and Extrapolation]</strong><br><em>Ramon Villa-Cox, Evan Williams, Kathleen M. Carley</em></p> <p>In this work, we explore the performance and extrapolation power of political stance-detection models using an existing large-scale weakly-labeled Twitter dataset collected around the 2019 South American Protests [1]. We construct transformer-based user and tweet encoders to embed users in a low-dimensional space using their text and ego-networks. We then train heterogeneous graph attention networks to predict user stances and contrast their ability to extrapolate stance predictions to different country contexts.</p> <p>The protest dataset, which was collected between September 25 and December 24 of 2019, contains 550k labeled users split unevenly across the four countries and contains over 36 million labeled tweets. It contains an additional 1.1 million unlabeled neighbors and 40 million unlabeled tweets. This repository includes the anonymized datasets necessary to reproduce the results and tables of the paper. In addition, we include the corresponding anonymized resources for the new weakly-labeled dataset around the 2020 Chilean Referendum presented in our paper.</p> <p>Following Twitter's January 2023 User Protection Policy update, tweet or user IDs related to sensitive political events cannot be publicly shared. We respect this policy, and only share:</p> <ul> <li>The anonymized user ID, their weak-stance label, the label predicted by each model and the data split (train, validation or test) the user was assigned to.</li> <li>Anonymized user network edges used by the different network classifiers</li> <li>The type of tweet the edge represents (Original, Reply, or Quote)</li> <li>The User Embeddings produced by the User Transformer and which serve as input for the different network models.</li> </ul> <p>This repository is comprised of the following files:</p> <ol> <li>Main_Predictions.7z: Compressed folder containing anonymized user IDs their stance label and each model’s prediction for the country it was trained on. The performance metrics for each model can be obtained based on the test split for each country. This folder includes the results for the Chilean Referendum.</li> <li>Cross_Predictions.7z: Compressed folder containing the results of the cross-country experiments for each anonymized user. The performance metrics for each model, when applied on a different country can be obtained based on each complete file. Users seen during the training of each model are excluded as described in the paper.</li> <li>Tweet_Level_Edgelists.7z: Compressed folder containing anonymized tweet edge lists indicating its interaction type (Original, Reply, or Quote).</li> <li>User_Networks.7z: Compressed folder containing different anonymized user edge lists for each interaction type.</li> <li>Embeddings.zip: Compressed Pytorch tensor files containing the User Embeddings produced by the User Transformer and which serve as input for the different network models. This are provided for the main results and the cross-country and referendum experiments.</li> </ol> <p>The code developed for this study is available at: https://github.com/rvillaco/Protest_Stance_Detection</p>
Ancient geological dynamics impact neutral biodiversity accumulation and are detectable in phylogenetic reconstructions
<p><strong>Aim</strong> Landmasses have been continuously modified by tectonic activity, the breakup and collision of landmasses is thought to have generated or suppressed ecological opportunities, altering the rates of speciation, dispersal and extinction. However, the extent to which the signatures of past geologic events are retained in modern biodiversity patterns - or obliterated by recent ecological dynamics - remains unresolved. We aim to identify the fingerprint of different scenarios of geological activity on phylogenetic trees and geographic range size distributions.<br> <strong>Location</strong> Global.<br> <strong>Time period</strong> Geological time.<br> <strong>Major taxa studied</strong> Theoretical predictions for any taxa.<br> <strong>Methods </strong>We conducted spatially explicit simulations under a neutral model of range evolution, speciation and extinction for three different geological scenarios that differed in their geological histories. We set a limit in the number of populations that locally can coexist which along with the geographic boundaries of landmasses, influences the rate of range expansion.<br> <strong>Results</strong> Our results demonstrate regions of similar size, age and ecological limits will differ in richness and macroevolutionary patterns based solely on the geological history of landmass breakup-collision even in the absence of species' ecological differences i.e., neutrality. When landmasses collide, regional richness is higher, lineages exhibit more similar rates of speciation and phylogenetic trees are more balanced than in the geologically static scenario. Stringent local limits to coexistence yield lower regional diversity but in general do not affect our ability to distinguish geological scenarios.<br> <strong>Main conclusions</strong> These findings provide an alternative explanation for existence of some hotspots of diversity in areas of high geological activity. Although a limit in the number of coexisting species largely influences regional diversity, its contribution to phylogenetic patterns is lower than variation in per-capita rates of speciation and extirpation. Importantly, these findings demonstrate the potential for inferring past geological history from distributions of phylogenies and range sizes.</p>
Pupillary dynamics reflect the impact of temporal expectation on detection strategy - data
<p>This repository contains data from work that investigated how humans can extract hidden temporal cues from the occurrences of probabilistic targets and utilize them to inform target detection in a complex acoustic stream, using a combination of behavioral measures and pupillometry. It accompanies the article entitled: " <strong>Pupillary dynamics reflect the impact of temporal expectation on detection strategy</strong>" from the same authors. It contains:<br> - preprocessed pupil data for all subjects<br> - similarly matched behavior data for all subjects<br> - custom code for both behavioral and pupil analysis</p>
Developing Advanced MRI Methods for Detecting the Impact of Nutrients on Infant Brain Development
ClinicalTrials.gov study NCT02058225. IPD Sharing: NO. Countries: 1. Publications: 3.
Detection and Neurological Impact of Cerebrovascular Events in Cardiac Surgery Patients
ClinicalTrials.gov study NCT04241289. IPD Sharing: NO. Countries: 1. Publications: 8.
Detection and Neurological Impact of CerebroVascular Events In Noncardiac Surgery PatIents: A COhort EvaluatioN
ClinicalTrials.gov study NCT01980511. IPD Sharing: Not stated. Countries: 9. Publications: 6.
The Impact of Artificial Intelligence on Dentists' Decision-Making Process During Caries Detection
ClinicalTrials.gov study NCT07027189. IPD Sharing: UNDECIDED. Countries: 1. Publications: 6.
Impact of Systematic Early Tuberculosis Detection Using Xpert MTB/RIF Ultra in Children With Severe Pneumonia in High Tuberculosis Burden Countries (TB-Speed Pneumonia)
ClinicalTrials.gov study NCT03831906. IPD Sharing: YES. Countries: 6. Publications: 3.
Impact of Standardized MONitoring for Detection of Atrial Fibrillation in Ischemic Stroke
ClinicalTrials.gov study NCT02204267. IPD Sharing: Not stated. Countries: 1. Publications: 7.
The Impact of Experienced Endoscopy Nurse Participation on Polyp and Adenoma Detection During Colonoscopy
ClinicalTrials.gov study NCT02292563. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Impact of an Innovative Childhood TB Diagnostic Approach Decentralized to District Hospital and Primary Health Care Levels on Childhood Tuberculosis Case Detection and Management in High Tuberculosis
ClinicalTrials.gov study NCT04038632. IPD Sharing: NO. Countries: 6. Publications: 3.
IMPACt of an Enhanced Screening Program on the Detection of Non-AIDS NEOplasms in HIV Patients
ClinicalTrials.gov study NCT04735445. IPD Sharing: Not stated. Countries: 1. Publications: 19.
The Impact of Active Nurse Participation on Adenoma Detection During Routine Colonoscopy
ClinicalTrials.gov study NCT00859625. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Impact of Split Dose of Low-volume Polyethylene Glycol on Adenoma Detection Rate
ClinicalTrials.gov study NCT02178033. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Impact of Distraction on Adenoma Detection Rate
ClinicalTrials.gov study NCT02978664. IPD Sharing: Not stated. Countries: 1. Publications: 3.
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.