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19 results for “micro-scale”
Data and analysis code from: Micro-scale geography of synchrony in a serpentine plant community
This package includes data and code to reproduce analyses of micro-scale geography of synchrony in the plant community at Jasper Ridge Biological Preserve. Plant cover and soil depth data come from long-term experimental plots established by Richard Hobbs. Plant cover is aggregated into 36 1m2 plots across three treatments (control, gopher exclosure, rabbit exclosure) from 1983 to 2015; included herein are data on the 6 most abundant species (Plantago erecta, Bromus hordeaceous, Lasthenia californica, Microseris douglasii, Vulpia microstachys, and Calycadenia multiglandulosa), total plant cover across all species, and records of gopher disturbance in the plots. The data package also includes time series of monthly precipitation and growing season Palmer’s Drought Severity Index for the same time period. An R Markdown file is included that reproduces all analyses described in the manuscript and reproduces all data figures. Data to support: Walter, Hallett et al. in review “Micro-scale geography of synchrony in a serpentine plant community
TURDATA: a database of low-cost air quality and remote sensing measurements for the validation of micro-scale models in the real Prague urban environments
<p><strong>README</strong></p> <p>TURDATA is a supplementary data set for the TURBAN project Prague observation campaign described in the manuscript Bauerová et al. 2024 (submitted for publication). The measurement campaign was focused on air pollution and meteorological measurement, including vertical profiles in selected part of Prague city centre called here as Legerova domain. Within this area, one professional meteorological station (MS) Prague Karlov and one reference traffic air quality monitoring (AQM) station Prague 2-Legerova (classified as traffic hotspot) are located. To gain high spatial and temporal resolution data, the supplementary measurement network was established, which consisted of:</p> <p>- 20 combined low-cost sensor (LCS) stations for monitoring of PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations (using Plantower PMS7003 particle counters and Envea Cairsense electrochemical sensors) placed in different sites and different height levels AGL (higher = H, lower = L),</p> <p>- 1 mobile telescopic meteorological mast for measuring temperature, relative humidity, wind velocity and direction and air pressure (using 2D ultrasonic anemometer Gill WindSonic 60 and weather station Gill MetConnect THP),</p> <p>- 1 MTP-5-He microwave radiometer (MWR; Attex) for temperature vertical profile,</p> <p>- 1 StreamLine XR Doppler LIDAR (HALO Photonics) for wind vertical profile. </p> <p>The main Legerova campaign lasted from 30 May 2022 to 28 March 2023 with some exceptions (see <em>TURDATA_metadata.xlsx</em> with all details). Because LCSs are known for their highly variable measurement quality, before their deployment the Legerova campaign, a sufficiently long-term initial field comparative measurement of all LCSs at RM Prague 4-Libuš was carried out (lasting from 16/12/2021 to 30/5/2022). The results showed that most of the LCSs were in raw measurement differently zero-shifted against each other and against gaseous reference or aerosol optical equivalent monitors (RMs or EMs). Therefore, the Multivariate Adaptive Regression Splines (MARS) method was applied to calculate corrected LCS concentrations based on initial field comparative measurement complemented by meteorological data from MS Prague Libuš. To check the quality of raw and MARS corrected LCS concentrations at the end of the measurement campaign, the final comparative field measurement of all LCSs at Prague 4-Libuš RM station was performed.</p> <p>Therefore, in case of LCSs measurement (both raw and corrected) the important columns of location (measurement placement: RM_Prague_4-Libus and Legerova_domain) and measurement_program (Initial_comparative_measurement, Legerova_campaign and Final_comparative_measurement) were added.</p> <p>In case of PM<sub>10</sub> and PM<sub>2.5</sub> measurement the maximum raw and MARS-corrected concentrations were influenced by temporary pollution episode on 26 July 2022 around 4 a.m. and 9 p.m. (both UTC) caused by aerosol pollution transported from large forest fire in Hřensko (the northern part of the Czech Republic). </p> <p> </p> <p>TURDATA includes the following files:</p> <p>1. <strong>TURDATA_metadata_and_photos.zip</strong> containing:</p> <p>- "<em>TURDATA_metadata.xlsx</em>" with the important list of metadata about devices placement, locations parameters and measurement periods</p> <p>- Folder "<em>Photos_from_Legerova_campaign</em>" with photos from Legerova measurement campaign</p> <p>2. <strong>AQ_LCSs_raw_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> raw measured concentrations by all LCSs</p> <p>- "<em>O3_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM10_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM2_5_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> raw measured concentrations by all LCSs</p> <p>- "<em>AQ_LCSs_raw_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>3. <strong>AQ_data_RM_stations_Prague_TURDATA.zip</strong> containing:</p> <p>- "<em>AQ_data_Prague_RM_stations_TURDATA_12-2021_06-2023.xlsx</em>" with air quality data measured by reference AQM stations in Prague</p> <p>- "<em>AQ_data_RM_stations_Prague_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>4. <strong>Meteo_data_Prague_MS_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_12-2021_06-2023.xlsx</em>" with meteorological data measured by professional meteorological stations in Prague</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>5. <strong>AQ_LCSs_MARS-corrected_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_COR_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>O3_COR_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM10_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM2_5_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>AQ_LCSs_MARS-corrected_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use and brief description of MARS correction method</p> <p>6. <strong>Meteo-mast_PVK_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo-mast_PVK_TURDATA_06-2022_06_2023.xlsx</em>“ with non-referential meteorological data measured by mobile meteo-mast</p> <p>- "<em>Meteo-mast_data_PVK_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>7. <strong>MWR_temperature_profile_TURDATA.zip</strong> containing:</p> <p>- "<em>MWR_5min_temperature_TURDATA_02-2022_03-2023.xlsx</em>" with raw temperature vertical profile measurement from microwave radiometer</p> <p>- "<em>MWR_1hour_temperature_TURDATA.xlsx</em>" with 1-hour averaged temperature vertical profile from microwave radiometer</p> <p>- "<em>MWR_1hour_TMP_gradient_TURDATA.xlsx</em>" with 1hour temperature gradient calculated from raw temperature profiles measured by microwave radiometer</p> <p>- "<em>MWR_temperature_profile_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>8. <strong>LIDAR_wind_profile_TURDATA.zip</strong> contains:</p> <p>- Individual folders "yyyymm“ -> "yyyymmdd"</p> <p>- Each daily folder "yyyymmdd" contains files:</p> <p>a) "<em>Processed_Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with processed WV and WS data</p> <p>b) "<em>Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with non-processed Doppler wind profile data</p> <p>- "<em>LIDAR_wind_profile_TURADATA_readme.txt</em>" with all necessary information for correct data use</p>
Translaminar Fracture in a Mini-Protruded Compact Tension Specimen: A Dataset of Micro-Scale Tomograms of a Thin-Ply Carbon Fibre-Epoxy Composite acquired via Synchrotron Radiation Computed Tomography During In-Situ Loading
<p>In this study, we developed a scaled-down “mini-protruded compact tension specimen” to facilitate in-situ tensile testing coupled with synchrotron radiation computed tomography (SRCT). This innovative design provides valuable insights into in-situ translaminar damage mechanisms, significantly enhancing the accuracy of data used in finite element models.</p> <p>The specimen is made of HS40 carbon fibres and ThinPreg<sup>TM </sup>736LT epoxy resin, with the layup of [90<sub>2</sub>/0/90<sub>2</sub>/0/90<sub>2</sub>/0/90<sub>2</sub>]. The translaminar fracture experiments were conducted under continuous loading and scanning using ultra-fast SRCT at the Swiss Light Source (SLS) TOMCAT beamline (Paul Scherrer Institut in Villigen, Switzerland). A polychromatic beam with an energy of 24 keV was used. The achieved voxel size was 800 <em>nm</em>, and 1000 projections per scan and 2 <em>ms</em> exposure time were acquired per scan. The GigaFRoST camera served as the detector. The scans were reconstructed into 3D volumes using the SLS’s in-house absorption-based algorithm (Gridrec) for critical loading steps during a test—both before and after a load drop (detailed in the accompanying Excel file). The tensile loading was exerted on the specimen at a rate of 0.2 <em>mm/min</em> until failure during scanning with the Deben CT500.</p>
Additional data for "In-situ full field out of plane displacement and strain measurements at the micro-scale in single reinforcement composites under transverse load"
<p>The following document is an extension of the <em>In-situ full field out of plane displacement and strain measurements at the micro-scale in single reinforcement composites under transverse load</em> publication. It contains guidelines for the experimental results for the single fiber experiments and bundle of carbon fiber ones. The detailed data from the experiments is provided with this document as <em>CSV </em>files.</p>
A narrow ear canal reduces sound velocity to 1 create additional acoustic inputs in a micro-scale insect ear
<p><span><span><span><span><span><span><span><span><span><span><span>Located in the forelegs, katydid ears are unique among arthropods in having outer, middle and inner component, analogous to the mammalian ear. Unlike mammals, sound is received externally, and internally via a narrow ear canal (EC) derived from the respiratory tracheal system. Inside the EC sound travels slower than in free air, causing temporal and pressure differences between external and internal inputs. The delay is suspected to arise as sound propagation changes from adiabatic to isothermal, imposed by EC geometry. If true, a reduction in sound velocity should persist independently of the gas composition in the EC. Integrating experimental (laser Doppler vibrometry, micro-CT) and numerical methods, we demonstrate that the narrow radius of the EC is the major cause of the signal time delay. Results imply that the EC is asymmetrically bifurcated, creating four notable auditory paths for each ear. Implication of methods and findings in avian hearing are discussed.</span></span></span></span></span></span></span></span></span></span></span></p>
Chang'E-4 rover spectra revealing micro-scale surface thermophysical properties of the Moon
<p>Dataset of the article 'Chang’E-4 rover spectra revealing micro-scale surface thermophysical properties of the Moon'</p>
Data from: Micro-scale environmental variation amplifies physiological variation among individual mussels
The contributions of temporal and spatial environmental variation to physiological variation remain poorly resolved. Rocky intertidal zone populations are subjected to thermal variation over the tidal cycle, superimposed with micro-scale variation in individuals' body temperatures. Using the sea mussel (<i>Mytilus californianus</i>), we assessed the consequences of this micro-scale environmental variation for physiological variation among individuals, first by examining the latter in field-acclimatized animals, second by abolishing micro-scale environmental variation via common garden acclimation, and third by restoring this variation using a reciprocal outplant approach. Common garden acclimation reduced the magnitude of variation in tissue-level antioxidant capacities by ~30% among mussels from a wave-protected (warm) site, but it had no effect on antioxidant variation among mussels from a wave-exposed (cool) site. The field-acclimatized level of antioxidant variation was restored only when protected-site mussels were outplanted to a high, thermally stressful site. Variation in organismal oxygen consumption rates reflected antioxidant patterns, decreasing dramatically among protected-site mussels after common gardening. These results suggest a highly plastic relationship between individuals' genotypes and their physiological phenotypes that depends on recent environmental experience. Corresponding context-dependent changes in the physiological mean-variance relationships within populations complicate prediction of responses to shifts in environmental variability that are anticipated with global change.
Dataset for WRR journal "Pore space deformation and its implications for two-phase flow through porous media: A micro-scale experimental investigation"
<p>Pressure drop, pore network model</p> <p>Calculation of REVs, strains, absolute and relative permeabilities, porosities profiles, grain size distribution, pore and throat size distribution. coordination number, aspect ratio, etc.</p>
Micro-scale Surface Thermophysical Properties Affected by Sample Scooping of the Moon as Revealed by Chang'E-5 Lander Spectra
<p>This archive includes data supporting the manuscript entitled "<span>Micro-scale Surface Thermophysical Properties</span><span><span> Affected by Sample Scooping</span><span> of the Moon as Revealed by Chang'E-5 Lander Spectra</span></span>" by Hengyue Jiao et al.</p>
Raman data for "Flow-driven micro-scale pH variability affects the physiology of corals and coralline algae under ocean acidification"
<p>This file contains the Raman data and code for "Flow-driven micro-scale pH variability affects the physiology of corals and coralline algae under ocean acidification" by Comeau et al. in Scientific Reports. Run the file, "run.R" in R to reproduce the analysis.</p> <p>Please see the published paper for methods and details: https://doi.org/10.1038/s41598-019-49044-w</p>
Micro-scale spatial preference and temporal cyclicity linked to foraging in harbour porpoises
<p><span>Habitat heterogeneity is a crucial driver for species distribution across scales</span><span>. Harbour porpoise <em>Phocoena phocoena</em> basin-wide distribution is linked to prey availability, and small-scale (kilometres to tens of kilometres) differences in distribution are prevalent. However, information on porpoise distribution and foraging-behaviour variations on a micro-scale (hundred meters to kilometres) is limited. To monitor harbour porpoise distribution and foraging activity on a micro-scale we deployed passive acoustic dataloggers, logging porpoise acoustic activity at six sites in a small, high porpoise-density area in southern Sweden. Data were collected for almost a year, giving detailed time series on porpoise activity. The time series were analysed using dynamic time warping to compare activity patterns between sites.</span><span> La</span><span>rge </span><span>differences were found between sites separated by only a few hundred meters, indicating micro-scale spatial preference. Spectral analysis for temporal cyclicity in activity revealed a dominant peak for 24-hour cycles with higher activity during the night for all sites. All sites also had a second peak for 29.5 days, linked to the lunar cycle with higher activity during the full moon. Activity was overall highest during autumn and winter (September–December). Spatial and temporal patterns were linked to foraging, showing a positive correlation between porpoise presence and the percent of time present with detected foraging. The study demonstrates that harbour porpoise spatial distribution on a micro-scale should be considered in e.g. behavioural, management, and conservation studies and actions. In addition, we show that </span><span>time series statistical methodology, such as dynamic time warping and spectral analysis, are informative and appropriate for analysis of acoustic temporal data. </span></p>
New insights on micro-scale variations of geochemical and oxygen isotope compositions in conodont and shark tooth bioapatite: Datasets
<p>The files contain the Research Data for the article entitled <strong>'New insights on micro-scale variations of geochemical and oxygen isotope compositions in conodont and shark tooth bioapatite'</strong>. A table of contents is provided. In summary, the files are: the raw geochemical data (scanning electron microscopy, electron microprobe analysis, stable isotope analysis), and profilometer images showing samples’ topography (please consult the README file) used in the referred article. The data normalisation was given as Supplementary Data in the publication. Any doubt or question about these are welcomed by me (Zoneibe Luz) for discussion or further considerations.</p>
data shown in manuscript "WRF-Comfort: Simulating micro-scale variability of outdoor heat stress at the city scale with a mesoscale model"
<blockquote> <p>data shown in manuscript "WRF-Comfort: Simulating micro-scale variability of outdoor heat stress at the city scale with a mesoscale model"</p> </blockquote>
A narrow ear canal reduces sound velocity to 1 create additional acoustic inputs in a micro-scale insect ear
Open the record for dataset details and reuse information.
Micro-scale spatial preference and temporal cyclicity linked to foraging in harbour porpoises
Open the record for dataset details and reuse information.
Data from: Micro-scale environmental variation amplifies physiological variation among individual mussels
Open the record for dataset details and reuse information.
Optimization of a Micro-scale Air-Liquid-Interface Model of Human Proximal Airway Epithelium for Moderate Throughput Drug Screening for SARS-CoV-2
GEO Series GSE266907. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Micro-scale genomic copy number aberrations as another means of mutagenesis in breast cancer
GEO Series GSE36889. Homo sapiens. 192 samples. Type: Genome variation profiling by array; Genome variation profiling by genome tiling array.
Exploring micro-scale heterogeneity as a driver of biogeochemical transformations and gas transport in peat - raw data and code
<p>Raw data and data processing code for the manuscript of the same name.</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.