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412 results for “sensor data”
Sensor data set of 3 electromechanical cylinder at ZeMA testbed (2kHz)
<p><strong>General information on the data set</strong></p> <p>The data set was generated at the ZeMA testbed. A working cycle lasts 2.8s and consists of a forward stroke, a waiting time and a return stroke. The data set does not consist of the entire working cycles. Only one second of the return stroke of each working cycle is used.</p> <p> </p> <p><strong>Structure of the data</strong></p> <ul> <li>data saved in three HDF5 file as a 3D-matrix, one file is for one axis</li> <li>one row represents one second of the return stroke of one working cycle<br> axis 3: 6292 cycles<br> axis 5: 6083 cycles<br> axis 7: 5732 cycles</li> <li>one column represents one datapoint of the cycle, that is resampled to 2 kHz (2000 columns)</li> <li>one page represent one sensor (11 pages: 11 sensors)</li> </ul> <p> </p> <p><strong>Allocation of the pages to the sensors</strong></p> <p>page 1: microphone<br> page 2: acceleration plain bearing<br> page 3: acceleration piston rod<br> page 4: acceleration ball bearing<br> page 5: axial force<br> page 6: pressure<br> page 7: velocity<br> page 8: active current<br> page 9: motor current phase 1<br> page 10: motor current phase 2<br> page 11: motor current phase 3</p> <p> </p> <p><strong>Remark</strong></p> <p>The datasets are not in SI units. For conversion, you can use the PDF documentation.</p> <p> </p> <p><strong>Further information</strong></p> <p>For an introduction and tutorial to this data, a set of Jupyter notebooks is available <a href="https://github.com/harislulic/ZeMA-machine-learning-tutorials">here</a>. These notebooks contain Python code and a documentation of example machine learning tasks and analysis of this data set. In the near future, these will be extended to also include uncertainties in the input data.</p>
Data-driven identification of reliable sensor species to predict regime shifts in ecological networks
<p>Signals of critical slowing down are useful for predicting impending transitions in ecosystems. However, in a system with complex interacting components not all components provide the same quality of information to detect system-wide transitions. Identifying the best indicator species in complex ecosystems is a challenging task when a model of the system is not available. In this paper, we propose a data-driven approach to rank the elements of a spatially-distributed ecosystem based on their reliability in providing early-warning signals of critical transitions. The proposed method is rooted in experimental modal analysis techniques traditionally used to identify structural dynamical systems. We show that one could use natural system fluctuations and the system responses to small perturbations to reveal the slowest direction of the system dynamics and identify indicator regions that are best suited for detecting abrupt transitions in a network of interacting components. The approach is applied to several ecosystems to demonstrate how it successfully ranks regions based on their reliability to provide early-warning signals of regime shifts. The significance of identifying the indicator species and the challenges associated with ranking nodes in networks of interacting components are also discussed.</p>
Data from: Give the machine a hand: a Boolean time-based decision-tree template for rapidly finding animal behaviours in multi-sensor data
1. The development of multi-sensor animal-attached tags, recording data at high frequencies, has enormous potential in allowing us to define animal behaviour. 2. The high volumes of data, are pushing us towards machine-learning as a powerful option for distilling out behaviours. However, with increasing parallel lines of data, systems become more likely to become processor limited and thereby take appreciable amounts of time to resolve behaviours. 3. We suggest a Boolean approach whereby critical changes in recorded parameters are used as sequential templates with defined flexibility (in both time and degree) to determine individual behavioural elements within a behavioural sequence that, together, makes up a single, defined behaviour. 4. We tested this approach, and compared it to a suite of other behavioural identification methods, on a number of behaviours from tag-equipped animals; sheep grazing, penguins walking, cheetah stalking prey and condors thermalling. 5. Overall behaviour recognition using our new approach was better than most other methods due to; (i) its ability to deal with behavioural variation and (ii) the speed with which the task was completed because extraneous data are avoided in the process. 6. We suggest that this approach is a promising way forward in an increasingly data-rich environment and that workers sharing algorithms can provide a powerful library for the benefit of all involved in such work.
Data from: Travelling Wave Pulse Coupled Oscillator (TWPCO) Using a Self-Organizing Scheme for Energy-efficient Wireless Sensor Networks
Recently, Pulse Coupled Oscillator (PCO)-based travelling waves have attracted substantial attention by researchers in wireless sensor network (WSN) synchronization. Because WSNs are generally artificial occurrences that mimic natural phenomena, the PCO utilizes firefly synchronization of attracting mating partners for modelling the WSN. However, given that sensor nodes are unable to receive messages while transmitting data packets (due to deafness), the PCO model may not be efficient for sensor network modelling. To overcome this limitation, the current study proposed a new scheme called the Travelling Wave Pulse Coupled Oscillator (TWPCO). For this, the study used a self-organizing scheme for energy-efficient WSNs that adopted travelling wave biologically inspired network systems based on phase locking of the PCO model to counteract deafness. From the simulation, it was found that the proposed TWPCO scheme attained a steady state after a number of cycles. It also showed superior performance compared to other mechanisms, with a reduction in the total energy consumption of 25 %. The results showed that the performance improved by 13 % in terms of data gathering. Based on the results, the proposed scheme avoids the deafness that occurs in the transmit state in WSNs and increases the data collection throughout the transmission states in WSNs.
Data from: Preferred gait and walk–run transition speeds in ostriches measured using GPS-IMU sensors
The ostrich (Struthio camelus) is widely appreciated as a fast and agile bipedal athlete, and is a useful comparative bipedal model for human locomotion. Here, we used GPS-IMU sensors to measure naturally selected gait dynamics of ostriches roaming freely over a wide range of speeds in an open field and developed a quantitative method for distinguishing walking and running using accelerometry. We compared freely selected gait–speed distributions with previous laboratory measures of gait dynamics and energetics. We also measured the walk–run and run–walk transition speeds and compared them with those reported for humans. We found that ostriches prefer to walk remarkably slowly, with a narrow walking speed distribution consistent with minimizing cost of transport (CoT) according to a rigid-legged walking model. The dimensionless speeds of the walk–run and run–walk transitions are slower than those observed in humans. Unlike humans, ostriches transition to a run well below the mechanical limit necessitating an aerial phase, as predicted by a compass-gait walking model. When running, ostriches use a broad speed distribution, consistent with previous observations that ostriches are relatively economical runners and have a flat curve for CoT against speed. In contrast, horses exhibit U-shaped curves for CoT against speed, with a narrow speed range within each gait for minimizing CoT. Overall, the gait dynamics of ostriches moving freely over natural terrain are consistent with previous lab-based measures of locomotion. Nonetheless, ostriches, like humans, exhibit a gait-transition hysteresis that is not explained by steady-state locomotor dynamics and energetics. Further study is required to understand the dynamics of gait transitions.
Data from: Effect of sensor location on continuous intraperitoneal glucose sensing in an animal model
In diabetes research, the development of the artificial pancreas has been a major topic since continuous glucose monitoring became available in the early 2000's. A prerequisite for an artificial pancreas is fast and reliable glucose sensing. However, subcutaneous continuous glucose monitoring carries the disadvantage of slow dynamics. As an alternative, we explored continuous glucose sensing in the peritoneal space, and investigated potential spatial differences in glucose dynamics within the peritoneal cavity. As a secondary outcome, we compared the glucose dynamics in the peritoneal space to the subcutaneous tissue. Eight-hour experiments were conducted on 12 anesthetised non-diabetic pigs. Four commercially available amperometric glucose sensors (FreeStyle Libre, Abbott Diabetes Care Ltd., Witney, UK) were inserted in four different locations of the peritoneal cavity and two sensors were inserted in the subcutaneous tissue. Meals were simulated by intravenous infusions of glucose, and frequent arterial blood and intraperitoneal fluid samples were collected for glucose reference. No significant differences were discovered in glucose dynamics between the four quadrants of the peritoneal cavity. The intraperitoneal sensors responded faster to the glucose excursions than the subcutaneous sensors, and the time delay was significantly smaller for the intraperitoneal sensors, but we did not find significant results when comparing the other dynamic parameters.
Data from: Synthesis and characterization of azo-guanidine based alcoholic media naked eye DNA sensor
DNA sensing always has an open meadow of curiosity for biotechnologists and other researchers. Recently, in this field, we have introduced an emerging class of molecules containing azo and guanidine functionalities. In this study, we have synthesized three new compounds (UA1, UA6 and UA7) for potential application in DNA sensing in alcoholic medium. The synthesized materials were characterized by elemental analysis, FTIR, UV-visible, 1H NMR and 13C NMR spectroscopies. Their DNA sensing potential were investigated by UV-visible spectroscopy. The insight of interaction with DNA was further investigated by electrochemical (cyclic voltammetry) and hydrodynamic (viscosity) studies. The results showed that compounds have moderate DNA binding properties, with the binding constants range being 7.2 × 103, 2.4 × 103 and 0.2 × 103 M−1, for UA1, UA6 and UA7, respectively. Upon binding with DNA, there was a change in colour (a blue shift in the λmax value) which was observable with a naked eye. These results indicated the potential of synthesized compounds as DNA sensors with detection limit 1.8, 5.8 and 4.0 ng µl−1 for UA1, UA6 and UA7, respectively.
Data from: Neonatal activation of the xenobiotic-sensors PXR and CAR results in acute and persistent down-regulation of PPARα-signaling in mouse liver
Safety concerns have emerged regarding the potential long-lasting effects due to developmental exposure to xenobiotics. The pregnane X receptor (PXR) and constitutive androstane receptor (CAR) are critical xenobiotic-sensing nuclear receptors that are highly expressed in liver. The goal of this study was to test our hypothesis that neonatal exposure to PXR- or CAR-activators not only acutely but also persistently regulates the expression of drug-processing genes (DPGs). A single dose of the PXR-ligand PCN (75 mg/kg), CAR-ligand TCPOBOP (3 mg/kg), or vehicle (corn oil) was administered intraperitoneally to 3-day-old neonatal wild-type mice. Livers were collected 24 h post-dose or from adult mice at 60 days of age, and global gene expression of these mice was determined using Affymetrix Mouse Transcriptome Assay 1.0. In neonatal liver, PCN up-regulated 464 and down-regulated 449 genes, whereas TCPOBOP up-regulated 308 and down-regulated 112 genes. In adult liver, there were 15 persistently up-regulated and 22 persistently down-regulated genes following neonatal exposure to PCN, as well as 130 persistently up-regulated and 18 persistently down-regulated genes following neonatal exposure to TCPOBOP. Neonatal exposure to both PCN and TCPOBOP persistently down-regulated multiple Cyp4a members, which are prototypical-target genes of the lipid-sensor PPARα, and this correlated with decreased PPARα-binding to the Cyp4a gene loci. RT-qPCR, western blotting, and enzyme activity assays in livers of wild-type, PXR-null, and CAR-null mice confirmed that the persistent down-regulation of Cyp4a was PXR and CAR dependent. In conclusion, neonatal exposure to PXR- and CAR-activators both acutely and persistently regulates critical genes involved in xenobiotic and lipid metabolism in liver.
Data from: Elucidating the functional evolution of heat sensors among Xenopus species adapted to different thermal niches by ancestral sequence reconstruction
Ambient temperature fluctuations are detected via the thermosensory system which allows animals to seek preferable thermal conditions or escape from harmful temperatures. Evolutionary changes in thermal perception have thus potentially played crucial roles in niche selection. The genus Xenopus (clawed frog) is suitable for investigating the relationship between thermal perception and niche selection due to their diverse latitudinal and altitudinal distributions. Here we performed comparative analyses of the neuronal heat sensors TRPV1 and TRPA1 among closely related Xenopus species (X. borealis, X. muelleri, X. laevis, and X. tropicalis) to elucidate their functional evolution and to assess whether their functional differences correlate with thermal niche selection among the species. Comparison of TRPV1 among four extant Xenopus species and reconstruction of the ancestral TRPV1 revealed that TRPV1 responses to repeated heat stimulation were specifically altered in the lineage leading to X. tropicalis which inhabits warmer niches. Moreover, the thermal sensitivity of TRPA1 was lower in X. tropicalis than the other species, although the thermal sensitivity of TRPV1 and TRPA1 was not always lower in species that inhabit warmer niches than the species inhabit cooler niches. However, a clear correlation was found in species differences in TRPA1 activity. Heat-evoked activity of TRPA1 in X. borealis and X. laevis, which are adapted to cooler niches, was significantly higher than in X. tropicalis and X. muelleri which are adapted to warmer niches. These findings suggest that the functional properties of heat sensors changed during Xenopus evolution, potentially altering the preferred temperature ranges among species.
Data from: Systematic study of the surface plasmon resonance signals generated by cells for sensors with different characteristic lengths
The objectives of this study were to establish an in-depth understanding of the signals induced by mammalian cells in surface plasmon resonance (SPR) sensing. To this end, two plasmonic structures with different propagation and penetration distances were used: conventional surface plasmon resonance and long-range surface plasmon resonance. Long-range SPR showed a lesser sensitivity to the absolute number of round cells but a greater resolution due to its very narrow spectral dip. The effect of cell spreading was also investigated and the resonance angle of long-range SPR was mostly insensitive unlike in the conventional SPR counterpart. Experimental data was compared with suitable models used in the SPR literature. Although these simple averaging models could be used to describe some of the experimental data, important deviations were observed which could be related to the fact that they do not take into consideration critical parameters such as plasmon scattering losses, which is particularly crucial in the case of long-range SPR structures. The comparison between conventional and long-range SPR for cellular schemes revealed important fundamental differences in their responses to the presence of cells, opening new horizons for SPR-based cell assays. From this study, long-range SPR is expected to be more sensitive towards both the detection of intracellular events resulting from biological stimulation and the detection of microorganisms captured from complex biological samples.
Hydrostatic pressure sensor logger data for floodX urban flooding experiments
<p>Film of pressure sensor loggers for floodX dataset</p>
Raw data for 'Long-baseline Quantum Sensor Network as Dark Matter Haloscope'
Open the record for dataset details and reuse information.
Data Set For: Raw Data Collected From NO2, O3 And NO Air Pollution Electrochemical Low-Cost Sensors
<p>This data set contains data from two Captor nodes. These are prototypes nodes that were developed at the Universitat Politècnica de Catalunya (UPC) in order to study the effects of the sensor data gathering process on the use of low-cost sensors for air quality monitoring. Specifically, the captors nodes have tropospheric ozone, nitrogen dioxide, and nitrogen monoxide electrochemical sensors. They also have a temperature and relative humidity sensor inside the box. <br> Two Captor nodes were placed in a reference station in Barcelona (Spain) for four months (January 2021 to May 2021), at a sampling frequency of 0.5 Hz. The data set contains a "readme" file with a brief description of the five files that make up the data set.</p>
Supporting data to "Open-source, low-cost, in-situ turbidity sensor for river network monitoring"
<p>This folder contains the Supporting Dataset that is part of the Manuscript "Open-source, low-cost, in-situ turbidity sensor for river network monitoring."</p>
Relaxation sensors: raw data
<p>Raw data for the publication "Relaxation Sensors" (<a href="https://doi.org/10.5281/zenodo.5810930">doi.org/10.5281/zenodo.5810930</a>)</p>
Time variation of accelerometer sensor data from a smartphone placed on the same surface next to the mobilefuge
<p>Time variation of accelerometer sensor data from a smartphone placed on the same surface next to the mobilefuge is recorded to show the vibrations caused by the mobilefuge.</p> <p>This data set has three csv files that are used to create the Figure 9 in the mobilefuge article. </p> <p>Figure.9a_mobilefuge_off.csv</p> <p>Fugure.9b_mobilefuge_on.csv</p> <p>Figure.9c_mobilefuge_with_pad.csv</p> <p>The labels in the first row of each of the file are Time (s), acceleration along the x-direction (m/s^2), acceleration along the y-direction (m/s^2) and acceleration along the z-direction (m/s^2).</p>
Demo and Source Data for the Paper "Development of a genetically-encoded sensor for probing endogenous nociceptin opioid peptide release"
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Replication Package for: Scalable and Reliable Multi-Dimensional Aggregation of Sensor Data Streams
<p>This repository contains a replication package and experimental results for our study on <em>Scalable and Reliable Multi-Dimensional Aggregation of Sensor Data Streams</em>.</p> <p>It features the presented implementation with Kafka Streams, tools for load generation and data collection, scripts for executing the presented evaluations as well as our raw results and script for analysis. A detailed description is given in the top-level README.md file.</p>
TAMU - ARPA-E SMARTFARM Grain Sorghum 2022 Texas site comprehensive sensor modalities data set.
<p>Comprehensive Year 2 data of ARPA-E SMARTFARM Grain Sorghum project titled "Establishing Validation Sites for Field-Level Emissions Quantification from Grain Sorghum in Southern Great Plains". Data sets includes Eddy Caovariance measurements of GHGs (CO2, CH4 and N2O) along with sub acre level soil moisture, soil temperarature, soil N and carbon, plant biomass and yield. This data is from the Texas site of the project. </p>
UF/OSU - ARPA-E SMARTFARM Grain Sorghum 2022 Oklahoma site comprehensive sensor modalities data set.
<p>Comprehensive Year 2 data of ARPA-E SMARTFARM Grain Sorghum project titled "Establishing Validation Sites for Field-Level Emissions Quantification from Grain Sorghum in Southern Great Plains". Data sets includes Eddy Caovariance measurements of GHGs (CO2, CH4 and N2O) along with sub acre level soil moisture, soil temperarature, soil N and carbon, plant biomass and yield. This data is from the Oklahoma site of the project. </p>
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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.
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.