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1,474 results for “reliability”
FIGURE 2. Artemia persimilis female. A in Comparison of the amplexial morphology of two Argentine populations of Artemia (Crustacea, Anostraca, Artemiidae): a reliable taxonomical tool
FIGURE 2. Artemia persimilis female. A: Brood pouch, dorsal view (50x); B: Brood pouch, lateral view (50x). Abbreviations: FP: flattened projection; TP: tergal projection.
FIGURE 6. A in Comparison of the amplexial morphology of two Argentine populations of Artemia (Crustacea, Anostraca, Artemiidae): a reliable taxonomical tool
FIGURE 6. A. franciscana mating arrangement successive crosssections micrographs. Abbreviations: DA: distal articles of male second antennae; FK: frontal knobs; BP: brood pouch.
FIGURE 5. Artemia male heads anterior view. A in Comparison of the amplexial morphology of two Argentine populations of Artemia (Crustacea, Anostraca, Artemiidae): a reliable taxonomical tool
FIGURE 5. Artemia male heads anterior view. A: A. persimilis; B: A. franciscana. Abbreviations: DA: distal articles of second antennae.
FIGURE 7. A in Comparison of the amplexial morphology of two Argentine populations of Artemia (Crustacea, Anostraca, Artemiidae): a reliable taxonomical tool
FIGURE 7. A. persimilis mating arrangement successive crosssections micrographs. Abbreviations: DA: distal articles of male second antennae; FK: frontal knobs; FP: flattened projections; SM: Spiny mound.
FIGURE 4. Artemia male heads with second antennae extended forwards. A in Comparison of the amplexial morphology of two Argentine populations of Artemia (Crustacea, Anostraca, Artemiidae): a reliable taxonomical tool
FIGURE 4. Artemia male heads with second antennae extended forwards. A: A. persimilis; B: A. franciscana. Abbreviations: BA: basal articles of second antennae; DA: distal articles of second antennae; FK: frontal knobs.
Dataset for Reliability assessment of steel truss railway bridges at ultimate limit state under combined loads
<p>Dataset for Reliability assessment of steel truss railway bridges at ultimate limit state under combined loads (under review at Reliability Engineering & System Safety) by Mohammad Shihabuddin Khan, Colin Caprani, Roman Lenner, and Miroslav Sýkora.</p>
Improving the reliability of underwater gait analysis using wearable pressure and inertial sensors
<p><strong>Improving the reliability of underwater gait analysis using wearable pressure and inertial sensors</strong></p> <p> </p> <p>This work addresses the lack of reliable wearable methods to assess walking gaits in underwater environments by evaluating the lateral hydrodynamic pressure exerted on lower limbs. Sixteen healthy adults were outfitted with waterproof wearable inertial and pressure sensors. Gait analysis was conducted on land in a motion analysis laboratory using an optoelectronic system as reference, and subsequently underwater in a rehabilitation swimming pool. Differences between the normalized land and underwater gaits were evaluated using temporal gait parameters, knee joint angles and the total water pressure on the lower limbs. The proposed method was validated against the optoelectronic system on land; gait events were identified with low bias (0.01s) using Bland-Altman plots for the stride time, and an acceptable error was observed when estimating the knee angle (10.96° RMSE, Bland-Altman bias -2.94°). The kinematic differences between the land and underwater environments were quantified, where it was observed that the temporal parameters increased by more than a factor of two underwater (p<0.001). The subdivision of swing and stance phases remained consistent between land and water trials. A higher variability of the knee angle was observed in water (CV = 60.75%) as compared to land (CV = 31.02%). The intra-subject variability of the hydrodynamic pressure on the foot (CVz = 39.65%) was found to be substantially lower than that of the knee angle (CVz = 67.69%). The major finding of this work is that the hydrodynamic pressure on the lower limbs may offer a new and more reliable parameter for underwater motion analysis as it provided a reduced intra-subject variability as compared to conventional gait parameters applied in land-based studies.</p> <p> </p>
Quantitative analysis techniques for evaluating the reliability of Li-ion battery challenges and solutions
Open the record for dataset details and reuse information.
Dataset related to "Heterogeneous and higher-order cortical connectivity undergirds efficient, robust and reliable neural codes"
<p>This is an accompanying dataset to the article with the title "Heterogeneous and higher-order cortical connectivity undergirds efficient, robust and reliable neural codes" (DOI: <a href="https://doi.org/10.1101/2024.03.15.585196">10.1101/2024.03.15.585196</a>). It contains structural and activity data related to the morphologically detailed model of the rat somatosensory cortex (<a href="https://doi.org/10.1016/j.cell.2015.09.029" target="_blank" rel="noopener">Markram et al., 2015</a>), refered to as "BBP" data in the article. Specificaly, following data items are included:</p> <p><strong>Simulation data:</strong> <code>simulation.xz</code></p> <p><em>"Reliability" protocol:</em></p> <p>Separate folders <code>BlobStimReliability_O1v5-SONATA_<circuit-name></code> with simulation data using the baseline and all manipulated connectomes respectively (see Technical info below), each of which containing:</p> <ul> <li><code>working_dir/connectome.h5</code>: Connectivity matrix in <code>ConnectivityMatrix</code> format, which can be loaded using <a href="https://github.com/BlueBrain/ConnectomeUtilities" target="_blank" rel="noopener">ConnectomeUtilities</a>.</li> <li><code>working_dir/raw_spikes_exc_<sim>.npy</code>: Raw (excitatory) spikes in numpy .npy format, containing an array of spike times (first column) and corresponding neuron GIDs (second column). One file for each of the 10 simulations with different simulator seeds, i.e., <sim> is 0..9.</li> <li><code>working_dir/stim_stream.npy</code>: Stimulus train in numpy .npy format, containing the sequence of stimulus identities.</li> <li><code>working_dir/time_windows.npy</code>: Time windows in ms in numpy .npy format, corresponding to the stimulus train.</li> <li><code>working_dir/processed_data_store.h5</code>: Data store in HDF5 format with preprocessed spike signals (e.g., required for <em>Gaussian kernel reliability</em> computations), which contains... <ul> <li><code>spike_signals_exc</code>: Group of simulations datasets "sim_0" to "sim_9", each of which is an array of size <#gids x #t_bins> and contains binned spike signals filtered with a Gaussian kernel.</li> <li><code>sigma</code>: Sigma in ms of Gaussian kernel used for smoothing.</li> <li><code>gids</code>: List of excitatory neuron GIDs.</li> <li><code>t_bins</code>: List of time bins in ms.</li> <li><code>firing_rates</code>: Average firing rates per simulation (0..9; rows) and (excitatory) neuron GID (columns); average firing rates were computed as the inverse of the mean inter-spike interval per neuron.</li> </ul> </li> </ul> <p><em>"Classification" protocol:</em></p> <p>Single folder <code>Toposample_O1v5-SONATA</code> with simulation data using the baseline connectome, stored in a format compatible with the <a href="https://github.com/JasonPSmith/TriDy" target="_blank" rel="noopener">TriDy</a> (<a href="https://doi.org/10.1162/netn_a_00228">Conceição et al., 2022</a>) and <a href="https://github.com/BlueBrain/topological_sampling/tree/merge_samples" target="_blank" rel="noopener">TopoSampling</a> (<a href="https://doi.org/10.1371/journal.pone.0261702">Reimann et al., 2022</a>) pipelines, containing:</p> <ul> <li><code>toposample_input/connectivity.npz</code>: Sparse connectivity matrix in Compressed Sparse Column format , which can be loaded using <code>scipy.sparse.load_npz</code>.</li> <li><code>toposample_input/neuron_info.pickle</code>: Pandas dataframe in pickle format, which can be loaded using <code>pandas.read_pickle</code>, containing additional information about each neuron.</li> <li><code>toposample_input/raw_spikes_exc.npy</code>: Raw (excitatory) spikes in numpy .npy format, containing an array of spike times (first column) and corresponding neuron GIDs (second column).</li> <li><code>toposample_input/stim_stream.npy</code>: Stimulus train in numpy .npy format, containing the sequence of stimulus identities.</li> <li><code>toposample_input/time_windows.npy</code>: Time windows in ms in numpy .npy format, corresponding to the stimulus train.</li> </ul> <p> </p> <p><strong>Classification data:</strong> <code>classification.xz</code></p> <p>Selected neighborhoods and classification results for the "PCA" method (<a href="https://github.com/BlueBrain/topological_sampling/tree/merge_samples" target="_blank" rel="noopener">TopoSampling</a> pipeline) as well as the "network_based" method using active subnetworks (<a href="https://github.com/JasonPSmith/TriDy" target="_blank" rel="noopener">TriDy</a> pipeline, using <a href="https://github.com/jlazovskis/TriDy-tools" target="_blank" rel="noopener">TriDy-tools</a> wrapper), stored as:</p> <p><em>"PCA" method:</em></p> <ul> <li><code>PCA/community_database_PCA.pkl</code>: Pandas dataframe in pickle format, containing the binary selection of 50 neighborhood centers (neuron GIDs; rows) for the different selection parameters (columns).</li> <li><code>PCA/classification_results_PCA.pkl</code>: Pandas dataframe in pickle format, containing the classification accuracies for all selection parameters (rows) and 6 cross-validation folds plus mean (columns).</li> </ul> <p><em>"network_based" method:</em></p> <ul> <li><code>network_based/selections_reliability.pkl</code>: Pandas dataframe in pickle format, containing different combinations of first/second selection parameters for the <em>double selection procedure</em> (rows) together with neuron indices (w.r.t. the EXC subcircuit) of the corresponding 50 neighborhood centers (chief0..49; columns).</li> <li><code>network_based/partition_reliability.npy</code>: Partition indices in numpy .npy format required to launch the pipeline using <a href="https://github.com/jlazovskis/TriDy-tools" target="_blank" rel="noopener">TriDy-tools</a>, which is an array of 50 neuron indices (w.r.t. the full circuit!) of the neighborhood centers (columns) for each combination of selections as in <code>selections_reliability.pkl</code> (rows).</li> <li><code>network_based/results/...</code>: Subfolder containing a list of pickle files with the classification results using different featurization parameters, as indicated by the filename. Each file contains a Pandas dataframe with the classification accuracies and numbers of (non-zero) features (columns) for each combination of selections as in <code>selections_reliability.pkl</code> (rows).</li> </ul> <p> </p> <blockquote> <p><strong><em>Funding</em></strong></p> <p><em>Funding provided by the Swiss government’s ETH Board to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL).</em></p> </blockquote>
Experts fail to reliably detect AI-generated histological data
<p>This repository contains material related to the paper "<em>Experts fail to reliably detect AI-generated histological data</em>":</p> <ul> <li>Dreambooth parameters (<em>dreambooth_parameters.zip</em>)</li> <li>Images displayed during the study <em>(images.zip)</em></li> <li>Data collected during the survey (<em>results-survey.xlsx</em>)</li> <li>R Code to reproduce results and figures (<em>analysis_code.zip</em>)</li> </ul> <p>Please find our associated work here:</p> <p>Hartung, J., Reuter, S., Kulow, V.A., Fähling, M., Spreckelsen, C., and Mrowka, R. (2024). Experts fail to reliably detect AI-generated histological data. Sci Rep <em>14</em>, 28677. https://doi.org/10.1038/s41598-024-73913-8.</p>
Reliable and remote monitoring of absolute temperature during liver inflammation via luminescence lifetime-based nanothermometry
<p>Dataset of https://zenodo.org/record/5793930#.YcCqyGjMJPY</p>
Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment
<p>quantization benchmark</p>
Dataset of the study "Factor Structure, Validity, and Reliability of the STarT Back Screening Tool in Italian Obese and Non-obese Patients With Low Back Pain"
<p>Dataset of the study "Factor Structure, Validity, and Reliability of the STarT Back Screening Tool in Italian Obese and Non-obese Patients With Low Back Pain" published in Frontiers in Psychology 20 October 2021 12:740851, doi: 10.3389/fpsyg.2021.740851</p>
Source code and data used in "Reliable Editions from Unreliable Components"
<p>Source code and data used in Riddell, A. B. (2022). Reliable Editions from Unreliable Components: Estimating Ebooks from Print Editions Using Profile Hidden Markov Models.</p>
Testing the reliability and ecological implications of ramping rates in the measurement of Critical Thermal maximum
<p>C<span>ritical Thermal maximum (CTmax) is often used to characterize the upper thermal limits of organisms and represents a key trait for evaluating the fitness of ectotherms. The lack of standardization in CTmax assays has, however, introduced methodological problems in its measurement, which can lead to questionable estimates of species' upper thermal limits. Focusing on ants, which are model organisms for research on thermal ecology, we aim to obtain a </span><span>reliable ramping rate that will yield the most rigorous measures of CTmax for the most species. </span><span>After </span><span>identifying three commonly used ramping rates (i.e., 0.2, 0.5 and 1.0 °C min<sup>-1</sup>) in the literature, we experimentally determine their effects on the CTmax values of 27 species measured using dynamic assays. Next, we use static assays to evaluate the accuracy of these values in function of the time of exposure.</span></p> <p><span> Finally, we use field observations of species' foraging activities across a wide range of ground temperatures to identify the most biologically relevant CTmax values and to develop a standardized method. Our results demonstrate that the use of a 1 °C min<sup>-1</sup> ramping rate in dynamic assays yields the most reliable CTmax values for comparing ant species' upper thermal limits, which are further validated in static assays and field observations. We further illustrate how methodological biases in physiological trait measurements can affect subsequent analyses and conclusions on community comparisons between strata and habitats, and the detection of phylogenetic signal </span><span>(</span><span>Pagel's λ and Bloomberg's K</span><span>)</span><span>.</span></p> <p><span>Overall, our study presents a methodological framework for identifying a reliable and standardized ramping rate to measure CTmax in ants, which can be applied to other ectotherms. Particular attention should be given to CTmax values obtained with less suitable ramping rates, and the potential biases they may introduce to </span><span>trait-based research on global warming and habitat conversion, as well as</span> <span>inferences about phylogenetic conservatism</span><span>.</span></p>
Osteochondral explants as a reliable experimental model to test a promising cell-free approach against osteoarthritis
<p><a href="https://zenodo.org/record/4062030">Datasets of the paper 'Osteochondral explants as a reliable experimental model to test a promising cell-free approach against osteoarthritis</a>'</p>
Dataset: On Cost-effective, Reliable Coverage for LoS Communications in Urban Areas
<p>Dataset containing the geodata needed to replicate the analysis and the results related the research article: <a href="https://doi.org/10.1109/TNSM.2022.3190634">"On Cost-effective, Reliable Coverage for LoS Communications in Urban Areas"</a> published on Transactions of Network and Service Management.</p> <p> </p> <p>This dataset contains all the data used in the research article: "On Cost-effective, Reliable Coverage for LoS Communications in Urban Areas" published on Transactions of Network and Service Management.</p> <p>It is divided into three main archives:</p> <ul> <li>The first archive, called <strong>data.zip</strong>, contains the Data Surface Model (DSM) used to generate the intervisibility graphs. These maps have been aggregated from different sources and they are all released under a CC-BY-SA 4.0 license. The files follow the naming format {area}_{type}.tif, where type can be one of the followings: <ul> <li>'<strong>buildings_mask</strong>': contains the buildings' shapes in raster format. These have been obtained by rasterizing the OpenStreetMap vectorial buildings data.</li> <li>'<strong>roads_mask</strong>': contains the roads' shapes in raster format. These have been obtained by expading the OpenStreetMap road graph by a given a mount of meters and rasterizing the result.</li> <li>'<strong>dtm</strong>': the Data Terrain Model in raster format</li> <li>'': the polished version of the Data Surface Model, where the values of the DSM are used only for the buildings (to map the roofs) and outside of the buildings the values from the dtm are used. In this way trees and unmapped buildings are not considered.</li> </ul> </li> <li>The second archive, called <strong>results.zip</strong>, contains the outcome of our algorithm for the optimal BS locations. The folder format is the following: 'results/{area}/threestep/{sa_id}/{ranking_function}/{k}/{ratio}/{lambda} : <ul> <li>'area': corresponds to the macro area used for that specific run</li> <li>'sa_id': corresponds to the subarea id (from 0 to 4) of a specific block of that area</li> <li>'ranking_function': corresponds to the ranking function used by the algorithm for that specific result (see the research article for more information)</li> <li>'ratio': corresponds to the percentage of buildings used (see the research article for more information)</li> <li>'lambda': corresponds to the density of Base Stations deployed (see the research article for more information).</li> </ul> </li> </ul> <p>The data are licensed as follows:</p> <p>The DSM and DTM are licensed depending on the area:</p> <ul> <li><strong>Trento</strong>: The data are released by <a href="https://www.provincia.tn.it/">Provincia Autonoma di Trento</a> under a <a href="https://creativecommons.org/licenses/by/2.5/">CC-BY 2.5 License</a></li> <li><strong>Firenze</strong>: The data are released by <a href="https://www.regione.toscana.it/">Regione Toscana</a> under a <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0 License</a></li> <li><strong>Napoli</strong>: The data are released by <a href="https://cittametropolitana.na.it/">Città Metropolitana di Napoli</a> under a <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA 4.0 License</a></li> </ul> <p>The OpenStreetMap data have been obtained by <a href="https://download.geofabrik.de/">geofabrik.de</a> and are released under an <a href="https://opendatacommons.org/licenses/odbl/">Open Data Commons Open Database License</a></p> <p> </p> <p>All the results can be replicated using our code, available on <a href="https://github.com/UniVe-NeDS-Lab/TrueBS">Github</a>.</p> <p> </p> <p>In order to cite this dataset please cite the original research article:</p> <p> </p> <pre><code>@article{9828530, author={Gemmi, Gabriele and Cigno, Renato Lo and Maccari, Leonardo}, journal={IEEE Transactions on Network and Service Management}, title={On Cost-effective, Reliable Coverage for LoS Communications in Urban Areas}, year={2022}, volume={}, number={}, pages={1-1}, doi={10.1109/TNSM.2022.3190634} } </code></pre> <p> </p>
Distribution. Reliably in N & C Vietnam (Lao Cai, Son La, Nhge An, and Kon Tum provinces) and N Laos (Xiangkhouang and [Luang Prabang provinces), but range limits still unknown. in Muridae
Distribution. Reliably in N & C Vietnam (Lao Cai, Son La, Nhge An, and Kon Tum provinces) and N Laos (Xiangkhouang and [Luang Prabang provinces), but range limits still unknown.
The intra- and inter-rater reliability of a variety of testing methods to measure shoulder range of motion, hand-behind-back and external rotation strength in healthy participants
<p>Dataset for a intra- and inter-rater reliability study of a variety of testing methods to measure shoulder range of motion, hand-behind-back and external rotation strength in healthy participants</p>
Data from: Backfire effects after correcting misinformation are strongly associated with reliability
<p>The backfire effect is when a correction increases belief in the very misconception it is attempting to correct, and it is often used as a reason not to correct misinformation. The current study aimed to test whether correcting misinformation increases belief more than a no-correction control. Furthermore, we aimed to examine whether item-level differences in backfire rates were associated with test-retest reliability or theoretically meaningful factors. These factors included worldview-related attributes, namely perceived importance and strength of pre-correction belief, and familiarity-related attributes, namely perceived novelty and the illusory truth effect. In two nearly identical experiments, we conducted a longitudinal pre/post design with N = 388 and 532 participants. Participants rated 21 misinformation items and were assigned to a correction condition or test-retest control. We found that no items backfired more in the correction condition compared to test-retest control or initial belief ratings. Item backfire rates were strongly negatively correlated with item reliability (⍴ = -.61 / -.73) and did not correlate with worldview-related attributes. Familiarity-related attributes were significantly correlated with backfire rate, though they did not consistently account for unique variance beyond reliability. While there have been previous papers highlighting the non-replicable nature of backfire effects, the current findings provide a potential mechanism for this poor replicability. It is crucial for future research into backfire effects to use reliable measures, report the reliability of their measures, and take reliability into account in analyses. Furthermore, fact-checkers and communicators should not avoid giving corrective information due to backfire concerns.</p>
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.