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396 results for “Humidity”
FIG. 6. — A-D, Rossodes namorona n in Une sous-famille caractÉristique des forÊts humides primaires malgaches: les Rossodinae (Trichoptera, Philopotamidae)
FIG. 6. — A-D, Rossodes namorona n. sp.; A, vue latérale des genitalia; B, vue latérale de l'appareil phallique; C, vue dorsale du dixième tergite; D, vue dorsale partielle des appendices inférieurs; E-H, R. marojejyensis n. sp.; E, vue latérale des genitalia; F, vue latérale de l'appareil phallique; G, vue dorsale du dixième tergite et du neuvième segment; H, vue dorsale partielle des appendices inférieurs. Échelles: 0,1 mm.
FIG. 1 in Une sous-famille caractÉristique des forÊts humides primaires malgaches: les Rossodinae (Trichoptera, Philopotamidae)
FIG. 1. — Rossodes hertui, mâle, rivière des makis, 2.IV.1994. Photographie: H.P. Aberlenc (C.B.G.P.). Échelle: 1 mm.
FIG. 4. — A-D, Rossodes fabienneae n in Une sous-famille caractÉristique des forÊts humides primaires malgaches: les Rossodinae (Trichoptera, Philopotamidae)
FIG. 4. — A-D, Rossodes fabienneae n. sp.; A, vue latérale des genitalia; B, vue latérale de l'appareil phallique; C, vue dorsale du dixième tergite; D, vue dorsale partielle des appendices inférieurs; E-H, R. manantenina n. sp.; E, vue latérale des genitalia; F, vue latérale de l'appareil phallique; G, vue dorsale du dixième tergite et du neuvième segment; H, vue dorsale partielle des appendices inférieurs. Échelles: 0,1 mm.
FIG. 5. — A-D, Rossodes andohahela n in Une sous-famille caractÉristique des forÊts humides primaires malgaches: les Rossodinae (Trichoptera, Philopotamidae)
FIG. 5. — A-D, Rossodes andohahela n. sp.: A, vue latérale des genitalia; B, vue latérale de l'appareil phallique; C, vue dorsale du dixième tergite; D, vue dorsale partielle des appendices inférieurs; E-H, R. ambreensis n. sp.; E, vue latérale des genitalia; F, vue latérale de l'appareil phallique; G, vue dorsale du dixième tergite et du neuvième segment; H, vue dorsale partielle des appendices inférieurs. Échelles: 0,1 mm.
Datasets with weather forecasts (Temperature, Wind Direction, Humidity, Pressure, Wind Speed, GHI)
<p>Datasets with weather forecasts for HLU7 (Temperature, Wind Direction, Humidity, Pressure, Wind Speed, GHI) of the CROSSBOW project.</p>
Carbohydrate vitrification in aerosolized saliva is associated with the humidity-dependent infectious potential of airborne coronavirus - Datasets
<p>Data includes:</p> <p>- WIBS files for each individual chamber run</p> <p>- RT-qPCR results</p> <p>- TCID50 results</p> <p>- Spreadsheet calculations</p> <p>- MOUDI results</p> <p> </p>
Data and code from paper: The carbon sink of secondary and degraded humid tropical forests
<p>This repository contains the data and code produced for the following paper:</p> <p><strong>Title: </strong>The carbon sink of recovering secondary and degraded humid tropical forests</p> <p><strong>Contact:</strong> Viola Heinrich (viola.heinrich@bristol.ac.uk)</p> <p><strong>Please note:</strong></p> <ul> <li> throughout repository where files include reference to: <...<strong>congo_basin</strong>...> this refers to the <strong>Central Africa </strong>region as it is termed in the main paper.</li> <li>the <strong>code</strong> <strong>has not been amended</strong> for wider use and still contains set working directories for use with University of Bristol systems, you will need to change these for the scripts to run. </li> </ul> <p>The data produced in this project were produced using a combination of programming languages due to differences in the author's preferences and expertise. Overall, the initial data analysis was carried out in (i) Google Earth Engine, and (ii) Arcpy (Python3.6.10). Most of the post-processing of the initial data was then carried out in <strong>R (v3.6) for which the code and output datasets are available here.</strong></p> <p>To access the code used in <strong>Google Earth Engine</strong> that was used to produce and export data from the Tropical Moist Forest dataset (e.g. Years Since Last Disturbance of secondary/degraded forest), please follow the link: https://code.earthengine.google.com/d303fc21e7b57a8fc259e0ee2b58bfb4 </p> <p>This repository contains the following zipped folders:</p> <ul> <li><strong>data_folder</strong>: this folder contains further folders with all the data produced for this paper.</li> </ul> <ol> <li>Fig1_data_models: All data needed to produce Figure 1 of the main paper, including an .RDS version of the 6 main regrowth models produced for this paper (secondary and degraded forests in the three regions). These are the files beginning with "<strong>regrowthModel_..RDS</strong>. Additionally, the folder includes the dataframe files originally from GeoTiff files that were used to extract the Aboveground Biomass in old-growth (undisturbed forests) > e.g. the subfolder "amazon_basin_oldG_AGB" contains the .dbf files representing the AGB in old-growth forest pixels. There are 4 files as the Amazon was split up into 4 sections for computational reasons. Similarly, the Central Africa region (here referred to as congo_basin) was split up into 2 regions.</li> <li>Fig2_data_models_plus_exFig3_to_5: The data needed to produce Figure 2 in the main paper as well as the Extended Data Figures 3 to 5. This includes .RDS versions of the regrowth models for secondary and degraded forests in the three regions for the different variables considered (files beginning with "<strong>regrowthModel_..RDS</strong>) e.g. "regrowtModel_borneo_deg_MaxTemo_low.rds", refers to the regrowth model shown in Figure 2c - the regrowth model for Bornean degraded forests for the variable "Maximum Temperature", where "low" refers to the lowest temperature range considered in the study. As before, files are provided giving information on the AGB in old-growth forests for each region within different conditions of each driving variable. </li> <li>Fig4: All the data needed to produce Figure 4 (and Supplementary Figure 18) of the main paper. This includes the file "regrowth_in_all_basins_by_country_input_data.csv", which contains data on the total number of cells for each forest type for each Years Since Last Disturbance (YSLD) in each region.</li> <li>Extended_dataFig1_input: The input for Extended Data Figure 1, including the values derived from other studies used in this comparison as well as additional notes/comments on how the data were assessed.</li> <li>Extended_dataFig2_input: the input data used to determine the standardised coefficients seen in the Extended Data Figure 2.</li> <li>Extended_data_table_inputs: The inputs for the Extended Data Tables 1 and 2. Inputs include the dataframe files (.dbf), of key variables that were extracted from the GeoTiff files. Only the .dbf files have been included here to limit excessively large data being uploaded. </li> </ol> <ul> <li><strong>code_folder.zip</strong>: The code in this folder was used to produce the main figures and results for the extended data tables shown in the paper. <ul> <li>this folder also contains a file "example_code_read_in_models.R" which provides an example of how best to read in the regrowth models for each region and forest type to extract important information such as the: (i) average growth rate in the first 20 years of analysis, (ii) all AGCs as a function of YSLD, and (iii) the estimated time it takes to reach the asymptote. </li> </ul> </li> </ul> <p><strong>Data and Code usage:</strong> When using any code or data in this repository or another related to this study please cite Heinrich et al. and the original paper as well as the DOI of this repository. </p> <p>Further source data in .xlsx format were also submitted with the main manuscript.</p> <p>If you need anything else, please contact the corresponding author: Viola Heinrich (viola.heinrich@bristol.ac.uk)</p>
Dataset for "Biodegradable materials as sensitive coatings for humidity sensing in S-band microwave frequencies"
<p>This dataset contains the data collected during the SNSF BRIDGE GREENsPACK project (Grant no. 40B2-0_187223) in association with the recent publication entitled “Biodegradable materials as sensitive coatings for humidity sensing in S-band microwave frequencies”.</p> <p>This work aims to study the humidity response of eco-friendly materials in the S-band (2-4GHz) using a microstrip line resonating at 3.3GHz. Several sensors coated with different biodegradable materials were tested and simulations have been performed. The S12 signal of the resonator was measured when varying the humidity. The data that was collected in the frame of this work is present in this repository.</p> <p>More information about the content of the dataset is present in the included README file.</p>
Modeling archive of How does humidity data impact land surface modeling of hydrothermal regimes at a permafrost site in Utqiaġvik, Alaska?
<p>Modeling archive contains the meteorological forcings, model input files, and Jupyter notebooks used to generate model meshes and figures for the paper entitled "How does humidity data impact land surface modeling of hydrothermal regimes at a permafrost site in Utqiaġvik, Alaska?"</p>
Hydration and evaporative water loss of lizards change in response to temperature and humidity acclimation
<p>Data and code associated with the 2023 publication in the Journal of Experimental Biology (doi:10.1242/jeb.246459).</p>
Data from: Evapotranspiration is resilient in the face of land cover and climate change in a humid temperate catchment
Open the record for dataset details and reuse information.
The influence of temperature, humidity and wind on the drinking behaviour of the Australian zebra finch
Open the record for dataset details and reuse information.
Data from: Habitat fragmentation drives pest termite risk in humid but not arid biomes
Open the record for dataset details and reuse information.
Snow depth, air temperature, humidity, soil moisture and temperature, and solar radiation data from the basin-scale wireless-sensor network in American River Hydrologic Observatory (ARHO)
Open the record for dataset details and reuse information.
Temperature and Relative Humidity Time Series across 60 forest plots at Sagehen Creek Field Station, 2016-2019
Database contains data downloaded from HOBO loggers placed at 60 sites within the Sagehen Experimental Forest. These plots are a subset of 500+ forest monitoring plots established in 2004 and 2005 for the purpose of testing strategically-placed land area treatments (SPLATS) that impede forest fire progression (Vaillant 2008, UC Berkeley Doctoral Dissertation). HOBO loggers sampled dates between fall 2016 and spring 2019. Some sites have intermittent data due to deactivation for logging activities and some interference from being buried in snow or from wild animals. Please see comments in the plot information file (Logger_Plot_Data.csv) detailing all plot metadata. This monitoring is ongoing through the Sagehen Forest Monitoring Project. Contact the Tahoe National Forest for forest treatment dates, additional information and GIS data.
Murphy Dome: Hourly temperature of air and soil, PAR, relative humidity and soil moisture in three pairs of adjacent black spruce and birch stands 2012-2018
This dataset contains weather station data (air and soil temperature, relative humidity, moisture, and PAR) from 2012 to 2019. The data was collected in three blocks (A, B, and C) of adjacent black spruce and paper birch stands at Murphy Dome (access via Cache Creek road).
Data from: The extension of internal humidity levels beyond the soil surface facilitates mound expansion in Macrotermes
Termites in the genus Macrotermes construct large-scale soil mounds above their nests. The classic explanation for how termites coordinate their labour to build the mound, based on a putative cement pheromone, has recently been called into question. Here we present evidence for an alternate interpretation based on sensing humidity. The high humidity characteristic of the mound internal environment extends a short distance into the low-humidity external world, in a "bubble" that can be disrupted by external factors like wind. Termites transport more soil mass into on-mound reservoirs when shielded from water loss through evaporation, and into experimental arenas when relative humidity is held at a high value. These results suggest that the interface between internal and external conditions may serve as a template for building, with workers moving freely within a zone of high humidity and depositing soil at its edge. Such deposition of additional moist soil will increase local humidity, in a feedback loop allowing the "interior" zone to progress further outward and lead to mound expansion.
Data for "Aerosol invigoration of atmospheric convection through increases in humidity"
<p>Codes, simulation input files, and simulation output data supporting “Aerosol invigoration of atmospheric convection through increases in humidity”. Enclosed README files provide detailed descriptions of the archive contents.</p>
The diurnal data of the aerosol extinction coefficient of the Mount Qomolangma lidar, as well as precipitation, low cloud cover, relative humidity of three adjacent stations (Tingri, Lazi, Nyalam) of the Mount Qomolangma
<p><strong>The diurnal data of the the vertical average aerosol extinction coefficient of 0.15-2.5 km of the Mount Qomolangma lidar, as well as precipitation, low cloud cover, relative humidity of three adjacent stations (Tingri, Lazi, Nyalam) of the Mount Qomolangma in July 2018 and July 2019.</strong></p>
Hibernating female big brown bats (Eptesicus fuscus) adjust huddling and drinking behaviour, but not arousal frequency, in response to low humidity
<p>Many mammals hibernate during winter, reducing energy expenditure via bouts of torpor. The majority of a hibernator's energy reserves are used to fuel brief, but costly, arousals from torpor. Although arousals likely serve multiple functions, an important one is to restore water stores depleted during torpor. Many hibernating bat species require high humidity, presumably to reduce torpid water loss, but big brown bats (<em>Eptesicus fuscus</em>) appear tolerant of a wide humidity range. We tested the hypothesis that hibernating female <em>E. fuscus </em>use behavioural flexibility during torpor and arousals to maintain water balance and reduce energy expenditure. We predicted: (1) <em>E. fuscus </em>hibernating in dry conditions would exhibit more compact huddles during torpor and drink more frequently than bats in high humidity conditions; and (2) frequency and duration of torpor bouts and arousals, and thus, total loss of body mass would not differ between bats in both environments. We housed hibernating <em>E. fuscus</em> in temperature- and humidity-controlled incubators at 50% or 98% relative humidity (8°C, 110 days). Bats in the dry environment maintained a more compact huddle during torpor and drank more frequently during arousals. Bats in both environments<em> </em>had a similar number of arousals, but arousal duration was shorter in the dry environment. However, total loss of body mass over hibernation did not differ between treatments indicating that both groups used similar amounts of energy. Our results suggest that behavioural flexibility allows hibernating <em>E. fuscus</em> to maintain water balance and reduce energy costs across a wide range of hibernation humidities.</p>
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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.