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1,474 results for “reliability”
Data from: Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates
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Speedy revelations: how alarm calls can convey rapid, reliable information about urgent danger
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Defensive colouration is not a reliable indicator of fungal infection in aposematic poison frogs
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Data from: Strength of female mate preferences in temperature manipulation study supports the signal reliability hypothesis
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Data from: Females can solve the problem of low signal reliability by assessing multiple male traits
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Raw data and outputs from: Sensory trap leads to reliable communication without a shift in nonsexual responses to the model cue
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Data from: Surrogate taxa and fossils as reliable proxies of spatial biodiversity patterns in marine benthic communities
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Data and statistical codes for: Reliability of urological telesurgery compared to local surgery: A multicenter, randomised controlled trial
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Raw data and R code for statistical analyses from: Sensory trap leads to reliable communication without a shift in nonsexual responses to the model cue
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Data from: Assessment of polytraumatized patients according to the Berlin Definition: Does the addition of physiological data really improve interobserver reliability?
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Reliable reconstruction of cricket song from biophysical models and preserved specimens
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Database for Study: Shear-wave elastography for assessment of trapezius muscle stiffness: reliability and association with low-level muscle activity
<p>This is the database for the study entitled <strong>Shear-wave elastography for assessment of trapezius muscle stiffness: reliability and association with low-level muscle activity. </strong>In this study, we explored the reliability of shear-wave elastography for assessment of the trapezius muscle stiffness and its relationship with low-level muscle activity.Twenty participants were included in a two-session experiment. Measurements of shear modulus and muscle activity were performed at rest and during low-level activity, induced by shoulder abduction without additional external resistance. Good to excellent intra-session repeatability (ICC > 0.80) and moderate inter-rater and inter-session reproducibility (ICC = 0.66-0.74) were observed. Typical errors were acceptable only for intra-session measurements in resting conditions. Inverse relationships between shear modulus and muscle activity at 40° and 60° of shoulder abduction (r = -0.53 and -0.57) were observed on a group level. We also found lower shear modulus in females, for perpendicular probe position and for the non-dominant side of the body. Previous studies have demonstrated positive within-individual relationships between muscle activity and shear modulus. Our results suggested that, at least when the activity levels are low, an inverse relationship exists between muscle activity and shear modulus on a group level, suggesting inherent passive stiffness could account for a larger portion of the variance (compared to muscle activity) in shear modulus when the muscle activity is low. Our results imply that shear-wave elastography can be used in research exploring muscle stiffness, however, caution is needed since only intra-session examination in resting conditions showed acceptable within-participant typical errors.</p>
Experts views on information evaluation and verification: source reliability, content credibility and audiovisual material checking
<p>Experts can be asked about nformation evaluation and verification: source reliability, content credibility and audiovisual material checking.</p> <p>OER available at: <a href="https://multimedia.ciberimaginario.es/genially/2020/CRESCEnt/4.2.1/">https://multimedia.ciberimaginario.es/genially/2020/CRESCEnt/4.2.1/</a> </p>
The reliability and validity of an Arabic version of the modified Japanese Orthopedic Association score for cervical myelopathy
<p><b>Purpose: </b>The modified Japanese Orthopedic Association score (mJOA) is considered as one of the most comprehensive scores in assessment of patients with cervical myelopathy. Hence, comes the need for providing a validated, translated and cross-culturally adapted versions in different languages in order to standardize patient's evaluation. This study aims to translate and validate an Arabic version of the mJOA.</p> <p><b>Methods: </b>65 patients of variable age and etiologies for compressive cervical myelopathy were recruited. Forward and backward translations were performed, followed by measurement of intra-observer and inter-observer reliability using intra-class correlation coefficient and Cronbach's alpha coefficient.</p> <p><b>Results: </b>The patients had a mean age of 58.08, predominantly males (69.2%). The intra-observer reliability and inter-observer reliability showed almost perfect agreement for the different sections and the total score, with 96.8 and 97.4, respectively.</p> <p><b>Conclusion: </b>This study provides a reliable, cross-culturally adapted Arabic version of mJOA for cervical myelopathy patients. Although the study was conducted on Egyptian patients, we believe it could be implemented with majority of Arabic speaking population.</p>
Advances in metabarcoding techniques bring us closer to reliable monitoring of the marine benthos
<p>1. Reliable and accurate biodiversity census methods are essential for monitoring ecosystem health and assessing potential ecological impacts of future development projects. Although metabarcoding is increasingly used to study biodiversity across ecological research, morphology-based identification remains the preferred approach for marine ecological impact assessments. Comparing metabarcoding to morphology-based protocols currently used by ecological surveyors is essential to determine whether this DNA-based approach is suitable for the long-term monitoring of marine ecosystems. 2. We compared metabarcoding and morphology-based approaches for the analysis of invertebrates in low diversity intertidal marine sediment samples. We used a recently developed bioinformatics pipeline and two taxonomic assignment methods to resolve and assign amplicon sequence variants (ASVs) from Illumina amplicon data. We analysed the community composition recovered by both methods and tested the effects, on the levels of diversity detected by the metabarcoding method, of sieving samples prior to DNA extraction. 3. Metabarcoding of the mitochondrial marker cytochrome c oxidase I (COI) gene recovers the presence of more taxonomic groups than the morphological approach. We found that sieving samples results in lower alpha diversity detected and suggests a community composition that differs significantly from that suggested by un-sieved samples in our metabarcoding analysis. We found that whilst metabarcoding and morphological approaches detected similar numbers of species, they are unable to identify the same set of species across samples. 4. Synthesis and Applications We show that metabarcoding using the COI marker provides a more holistic, community-based, analysis of benthic invertebrate diversity than a traditional morphological approach. We also highlight current gaps in reference databases and bioinformatic pipelines for the identification of intertidal benthic invertebrates that need to be addressed before metabarcoding can replace traditional methods. Ultimately, with these limitations taken into consideration, resolving community-wide diversity patterns with metabarcoding could improve the management of non-protected marine habitats in the U.K.14-Jul-2020</p>
Data from: On the objectivity, reliability, and validity of deep learning enabled bioimage analyses
<p>Bioimage analysis of fluorescent labels is widely used in the life sciences. Recent advances in deep learning (DL) allow automating time-consuming manual image analysis processes based on annotated training data. However, manual annotation of fluorescent features with a low signal-to-noise ratio is somewhat subjective. Training DL models on subjective annotations may be instable or yield biased models. In turn, these models may be unable to reliably detect biological effects. An analysis pipeline integrating data annotation, ground truth estimation, and model training can mitigate this risk. To evaluate this integrated process, we compared different DL-based analysis approaches. With data from two model organisms (mice, zebrafish) and five laboratories, we show that ground truth estimation from multiple human annotators helps to establish objectivity in fluorescent feature annotations. Furthermore, ensembles of multiple models trained on the estimated ground truth establish reliability and validity. Our research provides guidelines for reproducible DL-based bioimage analyses.</p>
Transfer Learning Models and Datasets for a Reliable Emergency Landing Field Identification
<p>The file <em>data.tar.gz</em> compromises three HDF5 datasets. This file has been split into 100 files. The files can be merged, decompressed and unpacked with the following commands:</p> <pre><code class="language-bash">cat data* > data.tar.gz tar -xzf data.tar.gz</code></pre> <p>Afterwards, the three files: <em>train_test_data_ss8_supervised_new.hdf5</em>, <em>train_test_data_ss16_supervised_new.hdf5</em>, <em>train_test_data_ss32_supervised_new.hdf5</em> are ready to get processed. </p> <p>Internal structure of the datasets:<br> <strong>Search Window (SW) 8 m^2:</strong><br> HDF5 "train_test_data_ss8_supervised_new.hdf5" {<br> GROUP "/" {<br> GROUP "test" {<br> DATASET "fus_data" {<br> DATATYPE H5T_IEEE_F32LE<br> DATASPACE SIMPLE { ( 76288, 40, 40, 8 ) / ( 76382, 40, 40, 8 ) }<br> }<br> DATASET "labels" {<br> DATATYPE H5T_STD_I64LE<br> DATASPACE SIMPLE { ( 76382, 1 ) / ( 76382, 1 ) }<br> }<br> }<br> GROUP "train" {<br> DATASET "fus_data" {<br> DATATYPE H5T_IEEE_F32LE<br> DATASPACE SIMPLE { ( 380928, 40, 40, 8 ) / ( 380998, 40, 40, 8 ) }<br> }<br> DATASET "labels" {<br> DATATYPE H5T_STD_I64LE<br> DATASPACE SIMPLE { ( 380998, 1 ) / ( 380998, 1 ) }<br> }<br> }<br> }}</p> <p><br> <strong>SW 16 m^2:</strong><br> HDF5 "train_test_data_ss16_supervised_new.hdf5" {<br> GROUP "/" {<br> GROUP "test" {<br> DATASET "fus_data" {<br> DATATYPE H5T_IEEE_F32LE<br> DATASPACE SIMPLE { ( 17024, 80, 80, 8 ) / ( 17054, 80, 80, 8 ) }<br> }<br> DATASET "labels" {<br> DATATYPE H5T_STD_I64LE<br> DATASPACE SIMPLE { ( 17054, 1 ) / ( 17054, 1 ) }<br> }<br> }<br> GROUP "train" {<br> DATASET "fus_data" {<br> DATATYPE H5T_IEEE_F32LE<br> DATASPACE SIMPLE { ( 84992, 80, 80, 8 ) / ( 85068, 80, 80, 8 ) }<br> }<br> DATASET "labels" {<br> DATATYPE H5T_STD_I64LE<br> DATASPACE SIMPLE { ( 85068, 1 ) / ( 85068, 1 ) }<br> }<br> }<br> }}</p> <p><br> <strong>SW 32 m^2:</strong><br> HDF5 "train_test_data_ss32_supervised_new.hdf5" {<br> GROUP "/" {<br> GROUP "test" {<br> DATASET "fus_data" {<br> DATATYPE H5T_IEEE_F32LE<br> DATASPACE SIMPLE { ( 3328, 160, 160, 8 ) / ( 3359, 160, 160, 8 ) }<br> }<br> DATASET "labels" {<br> DATATYPE H5T_STD_I64LE<br> DATASPACE SIMPLE { ( 3359, 1 ) / ( 3359, 1 ) }<br> }<br> }<br> GROUP "train" {<br> DATASET "fus_data" {<br> DATATYPE H5T_IEEE_F32LE<br> DATASPACE SIMPLE { ( 16768, 160, 160, 8 ) / ( 16793, 160, 160, 8 ) }<br> }<br> DATASET "labels" {<br> DATATYPE H5T_STD_I64LE<br> DATASPACE SIMPLE { ( 16793, 1 ) / ( 16793, 1 ) }<br> }<br> }<br> }}</p> <p>The sample count of the various generated dataset is as follows: <br> <strong>SW 8 m^2:</strong> {train: 380,928 with {0: 190,464, 1: 190,464}, test: 76,288 with {0: 38,152, 1: 38,136}}<br> <strong>SW 16 m^2: </strong>{train: 84,992 with {0: 42,498, 1: 42,494}, test: 17,024 with {0: 8,516, 1: 8,508}}<br> <strong>SW 32 m^2: </strong>{train: 16,768 with {0: 8,424, 1: 8,344}, test: 3,328 with {0: 1,672, 1: 1,656}}</p> <p>Each sample is composed as follows:<br> RGB = sample[:,:,<strong>:3</strong>]; Theoretically: [0, 1] per color channel<br> NIR = sample[:,:,<strong>3</strong>]; Theoretically: [0, 1]<br> Slope = sample[:,:,<strong>4</strong>]; Theoretically: [0, 90]<br> Roughness = sample[:,:,<strong>5</strong>]; Theoretically: [0, 78.78]<br> NDVI = sample[:,:,<strong>6</strong>]; Theoretically: [-1, 1]<br> DOM = sample[:,:,<strong>7</strong>]; Theoretically: [0, 429.90]</p> <p>====================================================================================================</p> <p>The following three files compromise the model and optimizer state variable of our PyTorch models trained on the aforementioned datasets: <em>best_alexnet_final.pth</em>, <em>best_resnet18_final.pth</em>, <em>best_wide_resnet50_2_final.pth</em></p> <p>Below find a more precise description of each model:<br> <strong>best_resnet18_final.pth</strong></p> <ul> <li>Model: ResNet-18</li> <li>Dataset: SW 8</li> <li>Input: RGB-NIR-Slope -> R: [0,224,224], G: [1,224,224], B: [2,224,224], NIR: [3,224,224], Slope: [4,224,224]</li> </ul> <p><strong>best_wide_resnet50_2_final.pth</strong></p> <ul> <li>Model: Wide-ResNet-50-2</li> <li>Dataset: SW 16</li> <li>Input: NDVI-Slope -> NDVI: [0,224,224], Slope: [1,224,224]</li> </ul> <p><strong>best_alexnet_final.pth</strong></p> <ul> <li>Model: AlexNet</li> <li>Dataset: SW 32</li> <li>Input: RGB-Slope -> R: [0,224,224], G: [1,224,224], B: [2,224,224], Slope: [3,224,224]</li> </ul> <p>Each model is capable of performing a binary classification, distinguishing between landable and unlandable samples</p>
Code for: Threshold assessment, categorical perception, and the evolution of reliable signaling
<p>Animals often use assessment signals to communicate information about their quality to a variety of receivers, including potential mates, competitors, and predators. But what maintains reliable signaling and prevents signalers from signaling a better quality than they actually have? Previous work has shown that reliable signaling can be maintained if signalers pay fitness costs for signaling at different intensities and these costs are greater for lower quality individuals than higher quality ones. Models supporting this idea typically assume that continuous variation in signal intensity is perceived as such by receivers. In many organisms, however, receivers have threshold responses to signals, in which they respond to a signal if it is above a threshold value and do not respond if the signal is below the threshold value. Here, we use both analytical and individual-based models to investigate how such threshold responses affect the reliability of assessment signals. We show that reliable signaling systems can break down when receivers have an invariant threshold response, but reliable signaling can be rescued if there is variation among receivers in the location of their threshold boundary. Our models provide an important step towards understanding signal evolution when receivers have threshold responses to continuous signal variation.</p>
Dynamic Byzantine Reliable Broadcast
Full video presentation of the paper: Dynamic Byzantine Reliable Broadcast.<br><br>Appears in Session 1 of the 24th International Conference on Principles of Distributed Systems OPODIS 2020<br><a href="https://opodis2020.unistra.fr">https://opodis2020.unistra.fr</a>
Multi-Threshold Asynchronous Reliable Broadcast and Consensus
Full video presentation of the paper: Multi-Threshold Asynchronous Reliable Broadcast and Consensus.<br><br>Appears in Session 2 of the 24th International Conference on Principles of Distributed Systems OPODIS 2020<br><a href="https://opodis2020.unistra.fr">https://opodis2020.unistra.fr</a>
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.