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Dataset results
412 results for “sensor data”
Data for: Divergence of alternative sugar preferences through modulation of the expression and activity of the Gal3 sensor in yeast
<p>Optimized nutrient utilization is crucial for the progression of microorganisms in competing communities. Here we investigate how different budding yeast species and ecological isolates have established divergent preferences for two alternative sugar substrates: Glucose, which is fermented preferentially by yeast, and galactose, which is alternatively used upon induction of the relevant <em>GAL</em> metabolic genes. We quantified the dose-dependent induction of the <em>GAL1</em> gene encoding the central galactokinase enzyme, and found that a very large diversification exists between different yeast ecotypes and species. The sensitivity of <em>GAL1</em> induction correlates with the growth performance of the respective yeasts with the alternative sugar. We further define some of the mechanisms, which have established different glucose/galactose consumption strategies in representative yeast strains by modulating the activity of the Gal3 inducer. (1) Optimal galactose consumers, such as <em>Saccharomyces uvarum</em>, contain a hyperactive <em>GAL3</em> promoter, sustaining highly sensitive <em>GAL1</em> expression, which is not further improved upon repetitive galactose encounters. (2) Desensitized galactose consumers, such as <em>S. cerevisiae </em>Y12, contain a less sensitive Gal3 sensor, causing a shift of the galactose response towards higher sugar concentrations even in galactose experienced cells. (3) Galactose insensitive sugar consumers, such as <em>S. cerevisiae</em> DBVPG6044, contain an interrupted <em>GAL3</em> gene, causing extremely reluctant galactose consumption, which however still is improved upon repeated galactose availability. In summary, different yeast strains and natural isolates have evolved galactose utilization strategies, which cover the whole range of possible sensitivities by modulating the expression and/or activity of the inducible galactose sensor Gal3.</p>
Source data for the manuscript "CCR7 acts as both a sensor and a sink for CCL19 to coordinate collective leukocyte migration"
<p>The zip file includes source data used in the manuscript "CCR7 acts as both a sensor and a sink for CCL19 to coordinate collective leukocyte migration", as well as a representative Jupyter notebook to reproduce the main figures. Please see the <a href="https://www.biorxiv.org/content/10.1101/2022.02.22.481445v1">preprint on bioRxiv</a> and the DOI link there to access the final published version. Note the title change between the preprint and the published manuscript.</p> <p>A sample script for particle-based simulations of collective chemotaxis by self-generated gradients is also included (see Self-generated_chemotaxis_sample_script.ipynb) to generate exemplary cell trajectories. A detailed description of the simulation setup is provided in the supplementary information of the manuscipt.</p>
Data for Objective identification of pressure wave events from networks of 1-Hz, high-precision sensors
<p>Data from pressure sensors assembled by Matthew Miller which consist of either a Bosch BMP388 or Bosch BME280 Adafruit breakout board connected to a Raspberry Pi Zero W single-board computer used to log the data. Data are recorded at 1-second intervals. The data are stored in .csv files: one for each day for each sensor. Sensors were placed in networks in the Toronto, ON, Canada, New York, NY, USA, and Raleigh, NC, USA metro areas. Code for processing these data can be found at https://doi.org/10.5281/zenodo.8087843.</p>
Dataset for "Improving data quality of low-cost light-scattering PM sensors: Towards automatic air quality monitoring in urban environments"
<p>The dataset contains the data used in the article "Improving data quality of low-cost light-scattering PM sensors: Towards automatic air quality monitoring in urban environments".</p> <p>A low-cost monitoring system composed of 14 monitoring stations was positioned at the official monitoring station of Torino Rubino in the city of Turin (Italy). The official station is managed by the environmental agency ARPA Piemonte.</p> <p>Each low-cost station contains four low-cost light-scattering PM sensors (Honeywell HPMA115S0-XXX), one temperature and relative humidity sensor (DHT22), and one atmospheric pressure sensor (BME/BMP280).<br>The sampling time of the PM sensors was set to one second, while the other sensors generated measurements every 3-4 seconds.</p> <p>The official monitoring station uses both a gravimetric and a beta attenuation instrument for measuring PM.</p> <p>The data contained in this dataset was collected from October 2020 to November 2021. It contains the PM2.5, relative humidity, and temperature measurements of the low-cost monitoring system and the official measurements of the beta attenuation device.</p> <p>Measurements of low-cost sensors are expressed in UTC, while official measurements are expressed in UTC+1.</p> <p>Official PM measurements can be also found at https://aria.ambiente.piemonte.it/qualita-aria/dati.</p>
Feasibility of Monitoring Patients During Radiotherapy Using Biometric Sensor Data: the OncoWatch Study 1.0
ClinicalTrials.gov study NCT04613232. IPD Sharing: NO. Countries: 1. Publications: 1.
Recording of Physiological Data Via an Optical Sensor At the Fingertip Alongside with Double Auscultatory and Pulse Oximetry to Evaluate the Effectiveness of an Optical Blood Pressure Monitoring (OBPM
ClinicalTrials.gov study NCT06368206. IPD Sharing: NO. Countries: 1. Publications: 0.
Predicting Hospital Readmission for Surgical Patients Using Deep Learning Models With Smart Watch and Smart Ring Sensors Data
ClinicalTrials.gov study NCT07349901. IPD Sharing: NO. Countries: 1. Publications: 21.
A survey of sensor network use and data management among academic ecologists
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Data from: Performance of social network sensors during Hurricane Sandy
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Data from: High-performance resistive humidity sensor based on Ag nanoparticles decorated with graphene quantum dots
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Data from: Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors
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Microclimate sensor data from 3 locations
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Data for: Divergence of alternative sugar preferences through modulation of the expression and activity of the Gal3 sensor in yeast
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Data from: CDCA7 is an evolutionarily conserved hemimethylated DNA sensor in eukaryotes
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Conductivity Temperature Depth (CTD) sensor profile data binned by depth from stations within the CCE region from CCE LTER P0904 student cruise, April 2009.
Data from deployed Seabird 911 CTD mounted on a 24-bottle rosette during P0904 student cruise in the CCE region. The CTD-rosette is lowered into the ocean (to depths up to 1000m) at selected hydrographic stations and multiple times across fronts, using the ship's conductive-wire winch. Data from many sensors are transmitted up the conductive wire and displayed real-time on a data aquistion computer. Discrete seawater samples are collected in 10L bottles at specific depths determined by the chlorophyll maximum and mixed layer depth. These samples are analyzed at sea and used to assess the CTD sensor data quality, plus measure additional properties. Processed CTD profile data are binned by depth and include: depth, temperature, salinity, density (sigma theta), oxygen, O2 saturation, PAR (radiation, surface radiation, and % irradiance), fluorescence, transmission, and nitrate. These data are then compared to the seawater sample data, and corrected if necessary.
Conductivity Temperature Depth (CTD) sensor profile data binned by depth from stations within the CCE region from CCE LTER process cruises, 2006 - 2017 (ongoing).
Since 2006 (ongoing), the CCE LTER program has deployed a Seabird 911 CTD mounted on a 24-bottle rosette during Process cruises in the CCE region. The CTD-rosette is lowered into the ocean (to depths up to 1000m) at selected hydrographic stations and multiple times across fronts, using the ship's conductive-wire winch. Data from many sensors are transmitted up the conductive wire and displayed real-time on a data aquistion computer. Discrete seawater samples are collected in 10L bottles at specific depths determined by the chlorophyll maximum and mixed layer depth. These samples are analyzed at sea and used to assess the CTD sensor data quality, plus measure additional properties. Processed CTD profile data are binned by depth and include: depth, temperature, salinity, density (sigma theta), oxygen, O2 saturation, PAR (radiation, surface radiation, and % irradiance), fluorescence, transmission, and nitrate. These data are then compared to the seawater sample data, and corrected if necessary.
EMIT L1B At-Sensor Calibrated Radiance and Geolocation Data 60 m V001
The Earth Surface Mineral Dust Source Investigation (EMIT) instrument measures surface mineralogy, targeting the Earth’s arid dust source regions. EMIT is installed on the International Space Station (ISS) and uses imaging spectroscopy to take mineralogical measurements of sunlit regions of interest between 52° N latitude and 52° S latitude. An interactive map showing the regions being investigated, current and forecasted data coverage, and additional data resources can be found on the VSWIR Imaging Spectroscopy Interface for Open Science (VISIONS) [EMIT Open Data Portal](https://earth.jpl.nasa.gov/emit/data/data-portal/coverage-and-forecasts/).The EMIT Level 1B At-Sensor Calibrated Radiance and Geolocation (EMITL1BRAD) Version 1 data product provides at-sensor calibrated radiance values along with observation data in a spatially raw, non-orthocorrected format. Each EMITL1BRAD granule consists of two Network Common Data Format 4 (NetCDF4) files at a spatial resolution of 60 meters (m): Radiance (EMIT_L1B_RAD) and Observation (EMIT_L1B_OBS). The Radiance file contains the at-sensor radiance measurements of 285 bands with a spectral range of 381-2493 nanometers (nm) and with a spectral resolution of ~7.5 nm, which are held within a single science dataset layer (SDS). The Observation file contains viewing and solar geometries, timing, topographic, and other information related to the observation. Each NetCDF4 file holds a location group containing geometric lookup tables (GLT), which are orthorectified images that provide relative x and y reference locations from the raw scene to allow for projection of the data. Along with the GLT layers, the files also contain latitude, longitude, and elevation layers. The latitude and longitude coordinates are presented using the World Geodetic System (WGS84) ellipsoid. The elevation data was obtained from Shuttle Radar Topography Mission v3 (SRTM v3) data and resampled to EMIT’s spatial resolution.Each granule is approximately 75 kilometers (km) by 75 km, nominal at the equator, with some granules at the end of an orbit segment reaching 150 km in length.Known Issues* Data acquisition gap: From September 13, 2022, through January 6, 2023, a power issue outside of EMIT caused a pause in operations. Due to this shutdown, no data were acquired during that timeframe.
Elbow and wrist range of motion assessment comparing inertial sensors against goniometry_Raw data clinical validation
<p>These data were taken during the validity and reliability analysis of inertial sensors (S) against goniometry (G) for the assessment of the elbow and wrist range of motion. To study the intra-inter-rater reliability, two physiotherapists (A and B) and a technician were in charge of taking the measurements. 29 subjects were evaluated in two different sessions (1 and 2). In the elbow assessment, the flexo-extension, pronation and supination movements were performed. For the wrist, flexo-extension and radial-ulnar deviation movements were evaluated.</p>
Replication Package for: Scalable and Reliable Multi-Dimensional Sensor Data Aggregation in Data-Streaming Architectures
<p>This repository contains a replication package and experimental results for our study on <em>Scalable and Reliable Multi-Dimensional Sensor Data Aggregation in Data-Streaming Architectures</em>.</p> <p>It features the presented implementations 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>
Sensor data set, 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 HDF5 file as a 3D-matrix</li> <li>one row represents one second of the return stroke of one working cycle (6292 rows: 6292 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>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.