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733 results for “passive”
Data used in article 'Tuning Charge Carrier Dynamics and Surface Passivation in Organolead Halide Perovskites with Capping Ligands and Metal Oxide Interfaces'
<p>Data underlying the article 'Tuning Charge Carrier Dynamics and Surface Passivation in Organolead Halide Perovskites with Capping Ligands and Metal Oxide Interfaces' published in Advanced Optical Materials.</p>
Data for Non-Equilibrium Sensing of Volatile Compounds Using Active and Passive Analyte Delivery
<blockquote> <p>Version 2: Added missing files to <code>sniffing_data.zip</code></p> </blockquote> <p>See GitHub repository for data processing functions and examples: <a href="https://github.com/soerenbrandt/sniffing-sensor">https://github.com/soerenbrandt/sniffing-sensor</a></p> <p><strong>Abstract</strong>:<br>Sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch; however, the development of artificial noses is significantly behind their biological counterparts. This is largely due to the complexity of natural olfaction, as it incorporates complex fluid dynamics within the nasal anatomy together with the response patterns of hundreds to thousands of unique molecular-scale receptors for odor interpretation. We designed a sensing approach to identify volatiles that exploits time-dependent information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) by augmenting and accentuating differences in the non-equilibrium mass-transport dynamics of vapors stemming from their distinct physicochemical properties, thus obviating the need for a large sensor array. By training a machine learning algorithm on the sensor output, we clearly identify polar and nonpolar volatile organic compounds, determine the mixing ratios of binary mixtures, and accurately predict the boiling point, flash point, vapor pressure, and viscosity of several volatile liquids within those used for training as well as compounds unknown to the model. We further implement a bioinspired active sniffing approach, in which the fluid dynamics and patterns of analyte delivery are controlled, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. These results outline a strategy to build accurate and rapid artificial noses for volatile liquids that can provide useful information on chemicals such as their composition and properties, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.</p>
Unlabeled AnuraSet: A dataset for leveraging unlabeled data in machine learning models for passive acoustic monitoring
<p>The Unlabeled AnuraSet (U-AnuraSet) is an extension of the original AnuraSet dataset. It consists of soundscape recordings from passive acoustic monitoring conducted in Brazil. The recording sites are identical to those in the original AnuraSet. Each site comprises 2,666 one-minute raw audio files of unlabeled data. The U-AnuraSet is publicly available to encourage machine learning researchers to explore innovative methods for leveraging unlabeled data in the training of models aimed at solving problems such as anuran call identification.</p> <p>If you find the Unlabeled AnuraSet useful for your research, please consider citing it as follows:</p> <p>Cañas, J.S., Toro-Gómez, M.P., Sugai, L.S.M., et al. A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring. Sci Data 10, 771 (2023). https://doi.org/10.1038/s41597-023-02666-2</p>
Figure 5 in It's a Trap! An evaluation of different passive trap types to effectively catch and control the invasive red swamp crayfish (Procambarus clarkii) in streams of the Santa Monica Mountains
Figure 5. Mean crayfish counts by size (cm) class across twelve trap types with standard error bars.
Figure 2 in It's a Trap! An evaluation of different passive trap types to effectively catch and control the invasive red swamp crayfish (Procambarus clarkii) in streams of the Santa Monica Mountains
Figure 2. Six standard, base trap types used in this study; a = Steel silver Gee Minnow trap, b = Vinyl coated black Promar Minnow trap, c = Mountain Restoration Trust custom design pyramid trap, d = Collapsible red square mesh Promar 501 trap, e = Colapsible cylindrical black mesh Promar 503 trap, and f = Mountain Restoration Trust custom PVC tube/refuge traps. Specific modifications to these traps to create the 12 types tested are provided in Table 1.
Figure 1 in It's a Trap! An evaluation of different passive trap types to effectively catch and control the invasive red swamp crayfish (Procambarus clarkii) in streams of the Santa Monica Mountains
Figure 1. Placement of traps compared in study in Las Virgenes Creek within Malibu Creek State Park. Inset shows specific study location within a regional context. Section locations were selected based on their representativeness of habitat types occurring over the entire reach (i.e. amount of riffle, runs and pools being comparable) and presence of suitable habitat for trap placement and visual observance of crayfish, tadpoles and native chub.
Figure 7 in It's a Trap! An evaluation of different passive trap types to effectively catch and control the invasive red swamp crayfish (Procambarus clarkii) in streams of the Santa Monica Mountains
Figure 7. Mean catches by stream habitat type. Differences in mean catches between stream Pools vs. Runs was evaluated using Wilcoxon rank-sum tests. * P-value <0.05.
Figure 3 in It's a Trap! An evaluation of different passive trap types to effectively catch and control the invasive red swamp crayfish (Procambarus clarkii) in streams of the Santa Monica Mountains
Figure 3. Mean catch per trap type with standard errors. Letters indicate significantly different mean counts within groups.
Figure 6 in It's a Trap! An evaluation of different passive trap types to effectively catch and control the invasive red swamp crayfish (Procambarus clarkii) in streams of the Santa Monica Mountains
Figure 6. Mean chub counts by size class across twelve trap types with standard error bars. Size classes include small as ≤ 60mm, medium as 61–89 mm, and large as 89–150mm as described by O'Brien et al. (2011).
Figure 2 in The Distribution Of The Northern Bat Eptesicus Nilssonii (Keyserling & Blasius, 1839) In Latvia Assessed By Passive Acoustic Survey
Figure 2. Dot plot showing the activity of E. nilssonii in each observation site. The outer dot in each region represents the mean and the whiskers the standard error of the mean. Different letters indicate statistically significant differences (p<0.05) (A). Data visualization depicting the gradient of the activity of E. nilssonii in four parts of Latvia. The darker color represents the higher activity (B).
Figure 1 in The Distribution Of The Northern Bat Eptesicus Nilssonii (Keyserling & Blasius, 1839) In Latvia Assessed By Passive Acoustic Survey
Figure 1. The map of Latvia divided in four regions under LKS92 25x25 km square network. Bat activity was studied in randomly selected squares (visited squares marked grey). In total, 60 squares were surveyed with six survey sites chosen in each square (n=360).
Data from: Performance of unmarked abundance models with data from machine-learning classification of passive acoustic recordings
<p>The ability to conduct cost-effective wildlife monitoring at scale is rapidly increasing due to availability of inexpensive autonomous recording units (ARUs) and automated species recognition, presenting a variety of advantages over human-based surveys. However, estimating abundance with such data collection techniques remains challenging because most abundance models require data that are difficult for low-cost monoaural ARUs to gather (e.g., counts of individuals, distance to individuals), especially when using the output of automated species recognition. Statistical models that do not require counting or measuring distances to target individuals in combination with low-cost ARUs provide a promising way of obtaining abundance estimates for large-scale wildlife monitoring projects but remain untested. We present a case study using avian field data collected in forests of Pennsylvania during the Spring of 2020 and 2021 using both traditional point counts and passive acoustic monitoring at the same locations. We tested the ability of the Royle-Nichols and time-to-detection models to estimate abundance of two species from detection histories generated by applying a machine-learning classifier to ARU-gathered data. We compared abundance estimates from these models to estimates from the same models fit using point-count data and to two additional models appropriate for point counts, the N-mixture model and distance models. We found that the Royle-Nichols and time-to-detection models can be used with ARU data to produce abundance estimates similar to those generated by a point-count based study but with greater precision. ARU-based models produced confidence or credible intervals that were on average 31.9% ( 11.9 SE) smaller than their point-count counterpart. Our findings were consistent across two species with differing relative abundance and habitat use patterns. The higher precision of models fit using ARU data is likely due to higher cumulative detection probability, which itself may be the result of greater survey effort using ARUs and machine-learning classifiers to sample significantly more time for focal species at any given point. Our results provide preliminary support the use of ARUs in abundance-based study applications, and thus may afford researchers a better understanding of habitat quality and population trends, while allowing them to make more informed conservation actions and recommendations.</p>
Velocity and concentration measurements of passive tracer above gravelly seabeds under the wave influence
<p><strong>General description:</strong><br>This dataset was compiled and generated by Helena Stirnweiß as part of a series of Particle Image Velocimetry (PIV) and Laser-Induced Fluorescence (LIF) experiments conducted at the Institute of Fluid Mechanics at the University of Rostock. All data included herein is original and was acquired under controlled laboratory conditions. </p> <p>The experiments were conducted for 21 individual configurations of 3 different wave scenarios (description can be found in 'overview_wavescenarios.csv') and 7 seabed models (description can be found in 'overview_seabedmodels.csv'). The folders where the data is stored are named accordingly '[name of seabed model]_[name of wave scenario]'.</p> <p>Horizontal (u [m/s]) and vertical (w [m/s]) velocities were measured simultaneously to the concentration (c [l/l]) of a tracer fluid released from the seabed. The collected data was analyzed and phase averages and phase-resolved covariances were derived and are given for the field of view of each configuration as .npy-files in the respective folder. <br>Time-averaged and horizontally averaged profiles were determined for the concentration and all covariances in dependence on the bottom distance. The profiles are stored as .npy-files in the respective Folders. </p> <p>Mass mixing length (l_c) and Eddy diffusivity (D_t) profiles were derived for each configuration from the measured data as described in the corresponding article. The variables are given in dependence on the bottom distance as .npy-files in the respective folder. Slopes of the vertical mass mixing length profiles and Eddy diffusivity profiles from linear regression are given in 'slopes_turbmodels.csv'. </p> <p>TIME-RESOLVED DATA IS NOT PROVIDED IN THIS DATASET DUE TO EXTENSIVE DATA SIZE but will be shared upon request. Please contact the creators.</p> <p> </p> <p><strong>Description of .npy files in .zip-folders:</strong></p> <p>The time-averaged, horizontally averaged profiles (named '[c/RS/TF/TKE]_[optional: names of covariates]_time_averaged_[name of seabed model]_[name of wave scenario].npy') are given in each folder.<br>All time-averaged data is stored in the following format.</p> <p>import numpy as np</p> <p>data = np.load(filename.npy, allow_pickle=True)<br>#data[0] -> z-dimensions in mm<br>##data[0][z]</p> <p>#data[1] -> respective quantity (c, Reynolds stresses (RS), turbulent fluxes (TF), turbulent kinetic energy (TKE))<br>##data[1][z]</p> <p> </p> <p>The phase-averages (named '[u/w/c]_phase_averaged_[name of seabed model]_[name of wave scenario].npy'), phase-resolved covariances of the fluctuations (named '[RS/TF]_[names of the covariates]_[name of seabed model]_[name of wave scenario].npy', note: RS stands for Reynolds stresses, TF stands for turbulent fluxes), and the turbulent kinetic energy (named 'TKE_[name of seabed model]_[name of wave scenario].npy') are given in each folder.<br>All phase-resolved data is stored in the following format.</p> <p>import numpy as np</p> <p>data = np.load(filename.npy, allow_pickle=True)<br>#data[0] -> x-dimensions in mm<br>##data[0][z, x]</p> <p>#data[1] -> z-dimensions in mm<br>##data[1][z, x]</p> <p>#data[2] -> respective quantity (c, u, w, Reynolds stresses (RS), turbulent fluxes (TF), turbulent kinetic energy (TKE))<br>##data[2][phi_idx, z, x]</p> <p>#(phase-averaging is performed with 100 phase bins -> phi_idx ranges from 0 to 99)</p> <p> </p> <p>Mass mixing length (lc) and eddy diffusivity (Dt) profiles were derived as described in the corresponding article and are given in each folder under '[lc/Dt]_prof_[name of seabed model]_[name of wave scenario].npy' in the following format:<br>data = np.load(filename.npy, allow_pickle=True)<br>#data[0] -> z-dimensions in mm<br>##data[0][z]</p> <p>#data[1] -> respective quantity (l_c in mm, D_t in m^2/s)<br>##data[1][z]</p>
Wood Buffalo Environmental Association (WBEA) Historical Monitoring Data used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes"
<p>Wood Buffalo Environmental Association (WBEA) Historical Monitoring Data from two monitoring stations Bertha Ganter – Fort McKay and Barge Landing for 20 August 2013 to 2 September 2013. This data was used in "Passive Tracer Modelling at Super-Resolution with WRF-ARW to Assess Mass-Balance Schemes" (Fathi et al., 2022 - egusphere-2022-1125) for model output and observational data comparisons. The same data can be accessed and downloaded from "<a href="https://wbea.org/historical-monitoring-data/">https://wbea.org/historical-monitoring-data/</a>".</p>
Backtracked trajectories of passive particles in the Virgin Islands Basin (2007-2017) using Ocean Parcels
<p>Backtracked trajectories of 100 passive particles released in the Virign Islands Basin daily from 2007-2017. Backtracking is done for 100 days. Surface velocity fields are from GLORYS reanalysis. <br> <br> Corresponding author: Giovanni Seijo-Ellis (giovanni.seijo@colorado.edu)</p>
Data for: Large-scale long-term passive-acoustic monitoring reveals spatiotemporal activity patterns of boreal bats
<p class="MsoNormal"><span>The distribution ranges and spatio-temporal patterns in the occurrence and activity of boreal bats are yet largely unknown due to their cryptic lifestyle and lack of suitable and efficient study methods. We approached the issue by establishing a permanent passive-acoustic sampling setup spanning the area of Finland to gain an understanding on how latitude affects bat species composition and activity patterns in northern Europe. The recorded bat calls were semi-automatically identified for three target taxa; <em>Myotis</em> spp., <em>Eptesicus nilssonii</em> or <em>Pipistrellus nathusii</em> and the seasonal activity patterns were modeled for each taxa across the seven sampling years (2015–2021). We found an increase in activity since 2015 for <em>E. nilssonii</em> and <em>Myotis </em>spp. For <em>E. nilssonii</em> and <em>Myotis</em> spp. we found significant latitude -dependent seasonal activity patterns, where seasonal variation in patterns appeared stronger in the north. Over the years, activity of <em>P. nathusii</em> increased during activity peak in June and late season but decreased in mid season. We found the passive-acoustic monitoring </span><span>network to be an effective and cost-efficient method for gathering b</span><span>at activity data to analyze spatio-temporal patterns. Long-term data on the composition and dynamics of bat communities facilitates better estimates of abundances and population trend directions for conservation purposes and predicting the effects of cli</span><span>mate change.</span></p>
Passive acoustic monitoring applied to black-and-white ruffed lemurs (Varecia variegata) in Ranomafana National Park, Madagascar
<p>Data accompanying the paper: <strong>"An integrated passive acoustic monitoring and deep learning pipeline applied to black-and-white ruffed lemurs (\textit{Varecia variegata}) in Ranomafana National Park, Madagascar"</strong></p> <p>Fieldwork was conducted at Mangevo (21.3833S, 47.4667E), an isolated and undisturbed forest location within Ranomafana National Park (RNP), located in southeastern Madagascar, during the period of May to July 2019. To facilitate passive acoustic monitoring, we deployed a total of two SongMeter SM4 devices (manufactured by Wildlife Acoustics) and two Swift units (provided by the Cornell Yang Center for Conservation Bioacoustics). The placement of these recorders was strategically chosen within the central regions of known subgroups, ensuring a minimum distance of 300 meters between each device. The SongMeter devices operated at a sampling rate of 48 kHz, while the Swift units operated at 32 kHz, respectively, enabling comprehensive audio data collection throughout the study period.</p> <p>We provide the audio data (.wav) used to train and test our neural network classifier along with the corresponding labelled text files (.data).</p> <p><strong>Files provided</strong></p> <ul> <li><strong>Test_Audio.zip </strong>-- contains (.wav) testing audio files</li> <li><strong>Test_Annotations.zip </strong>-- contains (.svl) manually annotated testing files which can be read in using Sonic Visualiser or by parsing the XML file in Python or another programming language. Load in the audio file into Sonic Visualiser and then drag-and-drop the corresponding .svl file.</li> <li><strong>Training_Audio_batch_x.zip -</strong>- several .zip files were created to simplify downloading. There are 10 batches, each is roughly 4GB. Each batch contains (.wav) training audio files</li> <li><strong>Training_Annotations.zip</strong> -- contains (.svl) manually annotated training files which can be read in using Sonic Visualiser or by parsing the XML file in Python or another programming language. Load in the audio file into Sonic Visualiser and then drag-and-drop the corresponding .svl file.</li> <li><strong>model_weights_tensorflow.hdf5 </strong>-- the Tensorflow model. Load the model using: model = tf.keras.models.load_model(model_filepath) note that the model expects a three channel input as explained in the research article.</li> </ul>
Raw data and analysis code for "Higher-order Process Matrix Tomography of a passively-stable Quantum SWITCH"
<p>This folder contains the raw data and analysis coded need to reproduce all of the major results in the manuscript "Higher-order Process Matrix Tomography of a passively-stable Quantum SWITCH". </p>
Data from: Narwhal (Monodon monoceros) echolocation click rates to support cue counting passive acoustic density estimation
<p class="MsoNormal"><span>The datasets correspond to the data used to obtain the results shown in the manuscript "Narwhal (<em>Monodon monoceros</em>) echolocation click rates to support cue counting passive acoustic density estimation".</span></p> <p class="MsoNormal"><span>When the manuscript is accepted we will also edit and add here the full reference including the DOI of the publication.</span></p>
Combining active and passive temperature signals for estimating sediment depths
<p>This dataset contains the results of the experimental campaign and how data were collected on the the <a href="https://co-udlabs.eu/">Co-UDlabs</a> Transnational Access project: <em>Characterization of thermal properties in sediment samples from urban drainage systems with temperature probes</em>, by Langeveld et al. The Transnational Access project was funded under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101008626.</p> <p>The experiments were designed to further develop an innovative methodology for measuring sediment bed deposits in UDS based on temperature data analysis (<a href="https://doi.org/10.5281/zenodo.7258998">Anta et al., 2022</a>; <a href="https://doi.org/10.1039/D2EW00820C">Regueiro-Picallo et al., 2023</a>). Particularly, the aim of these experiments was to prove the application of this methodology for measuring sewer sediment accumulation. Therefore, the main tasks of this project were the development of a system based on active temperature sensors and its application in the measurement of sediment thermal properties of UDS. Furthermore, this system included additional temperature sensors installed at different height locations to allow the estimation of the sediment depth. The purpose of these measurements is to deepen the understanding of sedimentation and sediment transport processes.</p> <p>The data are described so that others can use and reproduce.</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.