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74 results for “dynamic detection”

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zenodo44/100

Remote detection and recording of atomic-scale spin dynamics

<p>This folder contains all data and all processing files for the paper titled &quot;Remote detection and recording of atomic-scale spin dynamics&quot;. View full paper here: https://www.nature.com/articles/s42005-020-0361-z</p>

opencc-by-4.0Feb 2020View details →
zenodo40/100

An information theory-based machine learning approach to detecting functionally conserved and coordinated protein dynamics

<p>The application of machine learning classification to the molecular dynamics of the functional states of protein allows for application of an information theoretic framework familiar to traditional bioinformatics. The functional states of proteins involving binding interactions with partners comprised of protein, DNA or small molecules can first be defined in a binary fashion (i.e. bound vs unbound), subsequently simulated in molecular dynamics software, and then employed as a comparative training set for a binary machine learning classifier capable of discerning the complex dynamical consequences of binding interaction. This learner can subsequently be deployed on new simulations of the functionally bound state to validate its ability to recognize the molecular motions that are supporting binding function. Regions of proteins with functionally conserved dynamics will induce significant local correlations in learning performance across independent validation runs. Through case studies of Rbp subunit 4/7 interaction in RNA Pol II and DNA-protein interactions of TATA binding protein, we demonstrate this method of detecting functionally conserved protein dynamics. We also demonstrate how Shannon information, relative entropy and mutual information can be applied to these binary classification states of dynamic simulations in order to compare dynamics and identify concerted motions involved in dynamic interactions across sites.</p>

opencc-by-4.0May 2020View details →
zenodo40/100

Solvent sites detection from molecular dynamics in mixed solvents

<p>In this zip file, you will find 3 molecular dynamics (MDs) that were run using the AMBER molecular dynamics package (https://ambermd.org/), and the necessary scripts to obtain the solvent sites.</p> <p>The MDs correspond to the kinase and rubredoxin domains of protein kinase G from Mycobacterium tuberculosis.&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Nov 2021View details →
dryad40/100

Investigating cooccurrence patterns and dynamics for many imperfectly detected species, using a log-linear modelling parameterisation

<p>1. Patterns in, and the underlying dynamics of, species cooccurrence is of interest in many ecological applications. Unaccounted for, imperfect detection of the species can lead to misleading inferences about the nature and magnitude of any interaction. A range of different parameterisations have been published that could be used with the same fundamental modelling framework that accounts for imperfect detection, although each parameterisation has different advantages and disadvantages.</p> <p>2. We propose a parameterisation based on log-linear modelling that does not require a species hierarchy to be defined (in terms of dominance), and enables a numerically robust approach for estimating covariate effects.</p> <p>3. Conceptually the parameterisation is equivalent to using the presence of species in the current, or a previous, time period as predictor variables for the current occurrence of other species. This leads to natural, 'symmetric', interpretations of parameter estimates.</p> <p>4. The parameterisation can be applied to many species, in either a maximum-likelihood or Bayesian estimation framework. We illustrate the method using camera trapping data collected on three mesocarnivore species in South Texas.</p>

opencc-zeroApr 2022View details →
dryad40/100

Quantifying phenology and migratory behaviours of hummingbirds using single-site dynamics and mark-detection analyses

<p>Nuanced understanding of seasonal movements of partially migratory birds is paramount to species and habitat conservation. Using nascent statistical methods, we identified migratory strategies of birds outfitted with radio-frequency identification (RFID) tags detected at RFID feeders in two sites in California, USA. We quantified proportions of migrants and residents and the seasonal phenology for each movement strategy in Allen's and Anna's hummingbirds; we also validated our methodology by fitting our model to obligate migratory black-chinned hummingbirds. Allen's and Anna's hummingbirds exhibited characteristics of facultative migratory behaviour. We also quantified apparent annual survival for each migratory strategy and found that residents had significantly higher probabilities of apparent survival. Low survival estimates for migrants suggest that a high proportion of birds in the migrant group permanently emigrated from our study sites. Considered together, our analyses suggest that hummingbirds in both northern and southern California sites partake in diverse and highly plastic migratory behaviours. Our assessment elucidates the dynamics underlying idiosyncratic migratory behaviours of two species of hummingbirds, in addition to describing a framework for similar assessments of migratory behaviours using the multi-state open robust design with state uncertainty (MSORD-SU) model and single-site dynamics.</p>

opencc-zeroDec 2021View details →
zenodo40/100

Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings - The 4th China Physiological Signal Challenge 2021

<p>This dataset is part of the available dataset for <em>Paroxysmal Atrial Fibrillation Events Detection from Dynamic ECG Recordings: The 4th China Physiological Signal Challenge 2021</em>, available at&nbsp;https://physionet.org/static/published-projects/cpsc2021/paroxysmal-atrial-fibrillation-events-detection-from-dynamic-ecg-recordings-the-4th-china-physiological-signal-challenge-2021-1.0.0.zip (last accessed today 2022-07-22).</p> <p>The dataset is licensed under Creative Commons Attribution 4.0 International Public License:</p> <p>Permissions:</p> <ul> <li>Share &mdash; copy and redistribute the material in any medium or format</li> <li>Adapt &mdash; remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <p>Conditions:</p> <ul> <li> <p>Attribution &mdash; You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.</p> </li> </ul> <p>Limitations:</p> <ul> <li>No additional restrictions &mdash; You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.</li> </ul> <p>For more information about the license, check:&nbsp;https://creativecommons.org/licenses/by/4.0/</p> <p>The following modifications were made:</p> <ul> <li>Only the training and test set folders are used</li> <li>Only the files *.hea,&nbsp;*.dat&nbsp;and *.atr are used, being the *.dat and *.atr converted to CSV and compressed in .bz2 format</li> <li>Added the LICENSE.txt file as required.</li> </ul>

opencc-by-4.0May 2021View details →
zenodo40/100

Dataset from "Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes"

<p>These are&nbsp;the output land cover and land cover change raster maps from the paper, &quot;<a href="https://doi.org/10.3390/rs10020306">Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes</a>,&quot; published in Remote Sensing journal.</p> <p>ABSTRACT.&nbsp;Robust quantitative estimates of land use and land cover change are necessary to develop policy solutions and interventions aimed towards sustainable land management. Here, we evaluated the combination of Landsat and L-band Synthetic Aperture Radar (SAR) data to estimate land use/cover change in the dynamic tropical landscape of Tanintharyi, southern Myanmar. We classified Landsat and L-band SAR data, specifically Japan Earth Resources Satellite (JERS-1) and Advanced Land Observing Satellite-2 Phased Array L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2), using Random Forests classifier to map and quantify land use/cover change transitions between 1995 and 2015 in the Tanintharyi Region. We compared the classification accuracies of single versus combined sensor data, and assessed contributions of optical and radar layers to classification accuracy. Combined Landsat and L-band SAR data produced the best overall classification accuracies (92.96% to 93.83%), outperforming individual sensor data (91.20% to 91.93% for Landsat-only; 56.01% to 71.43% for SAR-only). Radar layers, particularly SAR-derived textures, were influential predictors for land cover classification, together with optical layers. Landscape change was extensive (16,490 km<sup>2</sup>; 39% of total area), as well as total forest conversion into agricultural plantations (3,214 km<sup>2</sup>). Gross forest loss (5,133 km<sup>2</sup>) in 1995 was largely from conversion to shrubs/orchards and tree (oil palm, rubber) plantations, and gross gains in oil palm (5,471 km<sup>2</sup>) and rubber (4,025 km<sup>2</sup>) plantations by 2015 were mainly from conversion of shrubs/orchards and forests. Analysis of combined Landsat and L-band SAR data provides an improved understanding of the associated drivers of agricultural plantation expansion and the dynamics of land use/cover change in tropical forest landscapes.</p>

opencc-by-4.0Feb 2018View details →
zenodo40/100

Collective Decision-Making and Change Detection with Bayesian Robots in Dynamic Environments

<p>The following folder structure holds all research data of my conducted experiments(h5-logfiles and plots). The Python-Script &quot;show_h5.py&quot; can be used to read out the logfile in h5-format (<em>$python3 show_h5.py expample_logfilename.h5</em>). However, this shouldn&#39;t be necessary because all plots are already generated.</p> <p>To find the results you want to see, this is a small guide through the structure:</p> <ol> <li> <p>First the trials are divided into the respective methods (PELT, DBB, DBBCPD). In the folders you find the experiments for the specific method.</p> </li> <li> <p>In the folder of PELT you find the results for the different feedback types and their combinations. The id for each feedback is noted in parentheses (e.g. XX_(id)_feedback_description). Feedback combinations have their ids added up (e.g. XX_(id1+...+idn)_feedback_description).</p> </li> <li> <p>In the folder to each feedback type the different test trials can be found. This means varying environment difficulties and parameter settings. In the name of the folders this information can be found (e.g. XX_method_environmentdifficulty_parametersetting).</p> </li> </ol> <p>All experiments follow the same procedure as long as it is stated otherwise. Each trial consists of 20 individual runs with a duration of 6000 seconds. At half time (3000 s) a change to the opposite fill ratio occurs (fill ratio of 1.0 defines a completely white and one of 0.0 a completely black environment).</p> <p><strong>Environment difficulty</strong></p> <ul> <li> <p>0901 --&gt; easy environment, fill ratio changed from 0.9 to 0.1</p> </li> <li> <p>0703 --&gt; easy environment, fill ratio changed from 0.7 to 0.3</p> </li> <li> <p>0604 --&gt; easy environment, fill ratio changed from 0.6 to 0.4</p> </li> <li> <p>055045 --&gt; easy environment, fill ratio changed from 0.55 to 0.45</p> </li> </ul> <p><strong>Parameter Setting</strong></p> <p>The setting is in the name of the folder composed of: feedbackID: intervalLength amountNeighbors</p> <ul> <li> <p>3c:50s3n --&gt; feedback 3c with a 50s interval and 3 neighbors</p> </li> </ul> <p>In these folders all plots of the respective runs can be found showing a Boxplot of all 20 runs and for each run the swarm belief, the decision distribution and the reset histogram (before/after the change)</p>

opencc-by-4.0Feb 2023View details →
dryad40/100

Data and software for: Temporal novelty detection and multiple timescale integration drive Drosophila orientation dynamics in temporally diverse olfactory environments

<p>To survive, insects must effectively navigate odors plumes to their source. In natural plumes, turbulent winds break up smooth odor regions into disconnected patches, so navigators encounter brief bursts of odor interrupted by bouts of clean air. The timing of these encounters plays a critical role in navigation, determining the direction, rate, and magnitude of insects' orientation and speed dynamics. Disambiguating the specific role of odor timing from other cues, such as spatial structure, is challenging due to natural correlations between plumes' temporal and spatial features. Here, we use optogenetics to isolate temporal features of odor signals, examining how the frequency and duration of odor encounters shape the navigational decisions of freely-walking <em>Drosophila</em>. We find that fly angular velocity depends on signal frequency and intermittency – fraction of time signal can be detected – but not directly on durations. Rather than switching strategies when signal statistics change, flies smoothly transition between signal regimes, by combining an odor offset response with a frequency-dependent novelty-like response. In the latter, flies are more likely to turn in response to each odor hit only when the hits are sparse. Finally, the upwind bias of individual turns relies on a filtering scheme with two distinct timescales, allowing rapid and sustained responses in a variety of signal statistics. A quantitative model incorporating these ingredients recapitulates fly orientation dynamics across a wide range of environments and shows that temporal novelty detection, when combined with odor motion detection, enhances odor plume navigation.</p>

opencc-zeroApr 2023View details →
zenodo40/100

Research data supporting: "Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling"

<p>This repository contains the set of data shown in the paper &quot;Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling&quot; published on PNAS (DOI: 10.1073/pnas.2300565120).</p>

opencc-by-4.0Jun 2023View details →
dryad40/100

Investigating cooccurrence patterns and dynamics for many imperfectly detected species, using a log-linear modelling parameterisation

Open the record for dataset details and reuse information.

publicNov 2021View details →
dryad40/100

Quantifying phenology and migratory behaviours of hummingbirds using single-site dynamics and mark-detection analyses

Open the record for dataset details and reuse information.

publicJun 2022View details →
dryad40/100

Data and software for: Temporal novelty detection and multiple timescale integration drive Drosophila orientation dynamics in temporally diverse olfactory environments

Open the record for dataset details and reuse information.

publicApr 2023View details →
dryad36/100

Data from: Detecting the population dynamics of an autosomal sex-ratio distorter transgene in malaria vector mosquitoes

<p>1. The development of genetically modified mosquitoes and their subsequent field release offers innovative and cost-effective approaches to reduce mosquito-borne diseases, such as malaria. A sex-distorting autosomal transgene has been developed recently in G3 mosquitoes, a lab strain of the malaria vector Anopheles gambiae s.l. The transgene expresses an endonuclease called I-PpoI during spermatogenesis, which selectively cleaves the X chromosome to result in ~95% male progeny. Following the World Health Organization Guidance Framework for the testing of genetically modified mosquitoes, we assessed the dynamics of this transgene in large cages using a joint experimental-modelling approach.</p> <p>2. We performed a four-month experiment in indoor large cages to study the population genetics of the transgene. The cages were set up to mimic a simple tropical environment with a diurnal light-cycle, constant temperature, and constant humidity. We allowed the generations to overlap to engender a stable age structure in the populations. We constructed a model to mimic the experiments, and used the experimental data to infer the key model parameters.</p> <p>3. We identified two fitness costs associated to the transgene. First, transgenic adult males have reduced fertility and, second, their female progeny have reduced pupal survival rates. Our results demonstrate that the transgene is likely to disappear in less than three years under our confined conditions. Model predictions suggest this will be true over a wide range of background population sizes and transgene introduction rates.</p> <p>4. Synthesis and applications: Our semi-field indoor cage experiments are in line with WHO guidance recommendations in regards to the development and testing of self-limiting technologies. Since the transgenic strain (Ag(PMB)1) has been considered for genetic vector control of malaria, our results are fundamentally important for determining expectations on the persistence of the transgene post-release. Our results provide a demonstration of the self-limiting nature of the transgene, and indicate that longevity will be further reduced by fitness costs that were not previously identified. Finally, our study has showcased an alternative and effective method for characterising the phenotypic expression of a transgene in an insect pest population.</p>

opencc-zeroJul 2020View details →
dryad36/100

Data from: The stochastic dynamics of early epidemics: probability of establishment, initial growth rate, and infection cluster size at first detection

<p>Emerging epidemics and local infection clusters are initially prone to stochastic effects that can substantially impact the epidemic trajectory. While numerous studies are devoted to the deterministic regime of an established epidemic, mathematical descriptions of the initial phase of epidemic growth are comparatively rarer. Here, we review existing mathematical results on the epidemic size over time, and derive new results to elucidate the early dynamics of an infection cluster started by a single infected individual. We show that the initial growth of epidemics that eventually take off is accelerated by stochasticity. These results are critical to improve early cluster detection and control. As an application, we compute the distribution of the first detection time of an infected individual in an infection cluster depending on the testing effort, and estimate that the SARS-CoV-2 variant of concern Alpha detected in September 2020 first appeared in the United Kingdom early August 2020. We also compute a minimal testing frequency to detect clusters before they exceed a given threshold size. These results improve our theoretical understanding of early epidemics and will be useful for the study and control of local infectious disease clusters.</p>

opencc-zeroOct 2021View details →
zenodo36/100

Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to detect breast malignant Lesions

<p>We uploade the dataset of the manuscript &quot;Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to detect breast malignant Lesions&quot; by Current Oncology.</p> <p>&nbsp;</p>

opencc-by-4.0Mar 2022View details →
zenodo36/100

An exploratory study (with and without time pressure) using mouse dynamics to detect faking-good behavior in the MMPI-2 and PPI-R validity scales

<p>Please find here&nbsp;the dataset&nbsp;generated and analyzed during the study entitled &quot;Can mouse dynamics detect faking-good behavior in personality questionnaires? An exploratory study (with and without time pressure) using the MMPI-2 and PPI-R validity scales&quot;.&nbsp;&nbsp;Moreover, here you can find the source code of the experiment to execute the task using MouseTracker software, the code of the statistical analysis and a file containing the instructions to replicate ML model results reported in the original paper.</p>

opencc-by-4.0Oct 2019View details →
dryad36/100

Data from: Remotely sensed environmental measurements detect decoupled processes driving population dynamics at contrasting scales

<p class="MsoNormal">The increasing availability of satellite imagery has supported a rapid expansion in forward-looking studies seeking to track and predict how climate change will influence wild population dynamics. However, these data can also be used in retrospect to provide additional context for historical data in the absence of contemporaneous environmental measurements. We used 167 Landsat-5 Thematic Mapper (TM) images spanning 13 years to identify environmental drivers of fitness and population size in a well-characterized population of banner-tailed kangaroo rats (<em>Dipodomys spectabilis</em>) in the southwestern United States. We found evidence of two decoupled processes that may be driving population dynamics in opposing directions over distinct time frames. Specifically, increasing mean surface temperature corresponded to increased individual fitness, where fitness is defined as the number of offspring produced by a single individual. This result contrasts with our findings for population size, where increasing surface temperature led to decreased numbers of active mounds. These relationships between surface temperature and (i) individual fitness and (ii) population size would not have been identified in the absence of remotely sensed data, indicating that such information can be used to test existing hypotheses and generate new ecological predictions regarding fitness at multiple spatial scales and degrees of sampling effort. To our knowledge, this study is the first to directly link remotely sensed environmental data to individual fitness in a nearly exhaustively sampled population, opening a new avenue for incorporating remote sensing data into eco-evolutionary studies.</p>

opencc-zeroJul 2023View details →
dryad36/100

Data from: The stochastic dynamics of early epidemics: probability of establishment, initial growth rate, and infection cluster size at first detection

Open the record for dataset details and reuse information.

publicJul 2022View details →
dryad36/100

Data from: Remotely sensed environmental measurements detect decoupled processes driving population dynamics at contrasting scales

Open the record for dataset details and reuse information.

publicJul 2023View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record

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