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106 results for “vertical distribution”
Data and R code used in Alonso-Crespo et al (2024) Exploring priority and year effects on plant diversity, productivity and vertical root distribution: first insights from a grassland field experiment
<p>This release contains the raw data, R code, and RootPainter model supporting the results described in Alonso-Crespo et al (2024) Exploring priority and year effects on plant diversity, productivity and vertical root distribution: first insights from a grassland field experiment.</p>
Data and Codes used in Alonso-Crespo et al (2021) Assembly history modulates vertical root distribution in a grassland experiment.
<p>This repository contains the raw data, the RootPainter model and the annotated R and Python codes supporting the results reported in the following paper: Alonso-Crespo et al (2021) Assembly history modulates vertical root distribution in a grassland experiment.</p>
Measurement report: Vertical profiling of particle size distributions over Lhasa, Tibet: Tethered balloon-based in-situ measurements and source apportionment
<p>Particle size distribution data in summer 2020 in Lhasa, Tibet for https://doi.org/10.5194/acp-2021-810</p>
data sets from "Updated trends of the stratospheric ozone vertical distribution in the 60S–60N latitude range based on the LOTUS regression model"
<p>Monthly means data sets from satellite, ground-based and model records used in the article entitled: "Updated trends of the stratospheric ozone vertical distribution in the 60 S–60 N latitude range based on the LOTUS regression model"</p> <p> </p>
data sets from "Updated trends of the stratospheric ozone vertical distribution in the 60S–60N latitude range based on the LOTUS regression model"
<p>Monthly means data sets from satellite, ground-based and model records used in the article entitled: "Updated trends of the stratospheric ozone vertical distribution in the 60 S–60 N latitude range based on the LOTUS regression model".</p> <p>Information about and the most recent versions of each dataset can be found at their individual source locations:</p> <p>Merged satellite datasets</p> <ol> <li>SBUV MOD – https://acd-ext.gsfc.nasa.gov/Data_services/merged/index.html (NASA GSFC, USA)</li> <li>SBUV COH: https://ftp.cpc.ncep.noaa.gov/SBUV_CDR/ (NOAA, USA).</li> <li>GOZCARDS: https://www.earthdata.nasa.gov/esds/competitive-programs/measures/gozcards (JPL, NASA, USA)</li> <li>SWOOSH: https://csl.noaa.gov/groups/csl8/swoosh/ (NOAA, USA).</li> <li>SAGE-CCI-OMPS and MEGRIDOP datasets are available through https://climate.esa.int/en/projects/ozone/data/ and ftp://cci_web@ftp-ae.oma.be/esacci (ESA Climate Office). They are provided by FMI, Finland</li> <li>SAGE-SCIAMACHY-OMPS: data record is available upon registration via the following link: http://www.iup.uni-bremen.de/DataRequest/ (U. Bremen, Germany).</li> <li>SAGE-OSIRIS-OMPS: downloading instructions can be found at https://research-groups.usask.ca/osiris/data-products.php#OSIRISLevel3andMergedDataProducts (U. Saskatchewan, Canada).</li> </ol> <p>Ground-based records:</p> <ol> <li>Umkehr – https://gml.noaa.gov/aftp/data/ozwv/Dobson/AC4/Umkehr/Monthly/ (NOAA, USA)</li> <li>ozonesondes – https://hegiftom.meteo.be/datasets/ozonesondes (HEGIFTOM). Measurements at the various stations are provided by the following institutions: <ul> <li>Hohenpeissenberg: DWD, Germany</li> <li>Payerne:MeteoSwiss, Switzerland</li> <li>OHP, CNRS, France</li> <li>Hilo, NOAA, USA</li> <li>Lauder, NIWA, New Zealand</li> </ul> </li> <li>lidar: <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> . Measurement at the various stations are provided by the following institutions: <ul> <li>Hohenpeissenberg: DWD, Germany</li> <li>OHP: CNRS, France</li> <li>MLO: JPL, NASA, USA</li> <li>Lauder: NIWA, New Zealand</li> </ul> </li> <li>FTIR spectrometers – <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> Three sites only provided quality checked measurements relevant for the article. For other ozone FTIR measurements, data in <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> must be used. Measurement used in the article are provided by the following institutions: <ul> <li>Zugspitze: KIT, Germany</li> <li>Jungfraujoch: ULiège, GIRPAS team, Belgium</li> <li>Lauder: NIWA, New Zealand</li> </ul> </li> <li>Microwave spectrometers: <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> Measurement at the various stations are provided by the following institutions: <ul> <li>Payerne: MeteoSwiss, Switzerland</li> <li>Mauna Loa: NRL, USA</li> <li>Lauder: NRL, USA</li> </ul> </li> </ol> <p>Chemistry Climate Model (CCM) CCMI simulations are avilable at https://blogs.reading.ac.uk/ccmi</p>
Water vapor vertical distribution on Mars during perihelion season of MY 34 and MY 35 with ExoMars-TGO/NOMAD observations [Dataset]
<p>1. Description of methods used for collection/generation of data:<br> NOMAD SO channel acquires transmittance spectra at different diffraction orders sounding the limb of the Martian atmosphere in solar occultation. It uses an echelle grating with a density of ∼4 lines/mm in a litrow configuration. An Acousto-Optical Tunable Filter (AOTF) is used to select different spectral windows (with a width that varies from 20 to 35 cm−1). Each window corresponds to the desired diffraction order to be used during the atmospheric scan. The spectral resolution of the SO channel is λ/∆λ=20,000.<br> After spectral calibration, the inversion problem is solved by fitting the data with a forward model and the vertical profiles are obtained.</p> <p>2. Methods for processing the data:<br> For the H2O inversion we use the Retrieval Control Program (RCP) developed at Institut für Meteoriologie und Klimaforschung (IMK), which incorporates the Karlsruhe Optimized and Precise Radiative transfer Algorithm (KOPRA) forward model. After providing an a priori, a first-guess and the measured spectra, RCP solves the inversion problem iteratively until the convergence of the solution. The IMK-IAA level-2 processor relies on multi-parameter non-linear least squares fitting of measured and modeled spectra (von Clarmann et al., 2003). Further information about RCP and the inversion problem can be found in (Jurado Navarro et al., 2016).<br> Retrievals of NOMAD diffraction orders 134 (3011-3035 cm−1) and 168 (3775-3805 cm−1) have been obtained and merged when collocated.</p>
Figure 1 in Vertical distribution and migration of planktonic polychaete larvae in Onagawa Bay, north-eastern Japan
Figure 1. Location of the sampling station in Onagawa Bay.
Figure 1 in Diurnal vertical distribution of zooplankton in a newly formed reservoir (Tahtalı Reservoir, Kocaeli): the role of abiotic factors and chlorophyll a
Figure 1. Study area and sampling station.
Data sources and code for: "Species-specific acclimation capacity of key traits explains global vertical distributions of seagrass species"
<p>Minguito-Frutos_etal_2023_Data1.xlsx contains the data for analyzing the relationship between plant size and seagrass growth reproductive strategy and the species-specific vertical distribution of seagrasses. </p> <p>Minguito-Frutos_etal_2023_Data2.xlsx contains the data for the meta-analityc approach studying the relationship between the vertical distribution of seagrass species and the plasticity of their traits (physiological, morphological, structural and growth). </p> <p>Scripts_Minguito_Frutos_etal_2023_GEB_Ref.GEB-2022-0592.R contains the R reproducible code to run all the analyses carried out in this study. </p>
Thermal stratification and fish thermal preference explain vertical eDNA distributions in lakes
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Substrate quality and not dominant plant community determines the vertical distribution and C assimilation of enchytraeids in peatlands
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Processed EK60 acoustics data used to examine changes in vertical distribution of mesopelagic fish
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Vertical distribution of epiphytic lichens on Quercus laurina Humb. & Bonpl. in a remnant of cloud forest in the state of Veracruz, México
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Data from: Vertical distribution of the soil microbiota along a successional gradient in a glacier forefield
Spatial patterns of microbial communities have been extensively surveyed in well-developed soils, but few studies investigated the vertical distribution of microorganisms in newly developed soils after glacier retreat. We used 454-pyrosequencing to assess whether bacterial and fungal community structures differed between stages of soil development (SSD) characterized by an increasing vegetation cover from barren (vegetation cover: 0%/ age: 10 years), sparsely-vegetated (13%/ 60 years), transient (60%/ 80 years) to vegetated (95%/ 110 years) and depths (surface, 5 and 20 cm) along the Damma glacier forefield (Switzerland). Stage of soil development significantly influenced the bacterial and fungal communities. Based on indicator species analyses, metabolically versatile bacteria (e.g. Geobacter) and psychrophilic yeasts (e.g. Mrakia) characterized the barren soils. Vegetated soils with higher C, N and root biomass consisted of bacteria able to degrade complex organic compounds (e.g. Candidatus Solibacter), ligno-cellulolytic Ascomycota (e.g. Geoglossum) and ectomycorrhizal Basidiomycota (e.g. Laccaria). Soil depth only influenced bacterial and fungal communities in barren and sparsely-vegetated soils. These changes were partly due to more silt and higher soil moisture in the surface. In both soil ages, the surface was characterized by OTUs affiliated to Phormidium and Sphingobacteriales. In lower depths, however, bacterial and fungal communities differed between both SSDs. Lower depths of sparsely-vegetated soils consisted of OTUs affiliated to Acidobacteria and Geoglossum whereas depths of barren soils were characterized by OTUs related to Gemmatimonadetes. Overall, plant establishment drives the soil microbiota along the successional gradient but does not influence the vertical distribution of microbiota in recently deglaciated soils.
Data from: Vertical distribution of marine invertebrate larvae in response to thermal stratification in the laboratory
We investigated the effect of the presence of an experimentally generated thermocline on the vertical distribution of larval Strongylocentrotus droebachiensis, Asterias rubens and Argopecten irradians. Vertical distributions were recorded over 90 min in rectangular plexiglass thermocline chambers designed to regulate the temperature of a central observation compartment to the desired values. The temperature in the bottom water layer (B) and the temperature difference between layers (ΔT) were manipulated in an orthogonal design. We used, for S. droebachiensis: 4 levels of ΔT (0, 3, 6 and 12 °C) and 3 levels of B (3, 6 and 9 °C); for A. rubens: 3 levels ΔT (0, 6 and 12 °C) and 2 levels of B (6 and 12 °C); and for A. irradians: 3 levels of ΔT (0, 5 and 11 °C) and 2 levels of B (5 and 11 °C). The difference in temperature between water layers did not affect the vertical distribution of echinoderms consistently, while the distribution of A. irradians was limited to the bottom layer when any thermal stratification was present regardless of strength. Our results suggest that the vertical position of larvae of S. droebachiensis and A. rubens is related to the temperatures of the surface layer and that the presence alone or the steepness of the thermocline has less influence on their distribution. Consequently, in the field, echinoderm larvae would aggregate at the surface unless temperature extremes were encountered. In contrast, the position of A. irradians was limited to the bottom layer in the presence of a thermocline of at least 5 °C (the shallowest used in our study). Such thermoclines are common in a natural setting and could affect the vertical distribution and horizontal dispersal of larvae by acting as a barrier to vertical migration.
Figure 5 in Vertical distribution of liverwort communities and their relationship with environmental factors in a karst sinkhole in south-western China
Figure 5. The canonical correspondence analysis between liverwort community distribution and environmental factors: humidity, temperature and light levels.
Figure 3 in Vertical distribution of liverwort communities and their relationship with environmental factors in a karst sinkhole in south-western China
Figure 3. Variation in abundance of species, genera and families recorded in liverwort communities at different depths.
Figure 2 in Vertical distribution of liverwort communities and their relationship with environmental factors in a karst sinkhole in south-western China
Figure 2. The variation in diversity and the number of species, genera and families recorded in liverwort communities at different depths.
Distribution of ant assemblage, microclimate and microhabitat along vertical gradients
<p><span>Abiotic and biotic factors structure species assembly in ecosystems both horizontally and vertically. However, the way community composition changes along comparable horizontal and vertical distances in complex three-dimensional habitats, and the factors driving these patterns, remains poorly understood. By sampling ant assemblages at comparable vertical and horizontal spatial scales in a tropical rain forest, we tested hypotheses that predicted differences in vertical and horizontal turnover explained by different drivers in vertical and horizontal space. These drivers included environmental filtering, such as microclimate (temperature, humidity, and photosynthetic photon flux density) and microhabitat connectivity (leaf area) which are structured differently across vertical and horizontal space. We found that both ant abundance and richness decreased significantly with increasing vertical height. Although dissimilarity between ant assemblages increased with vertical distance, indicating a clear distance-decay pattern, the dissimilarity was higher horizontally where it appeared independent of distance. The pronounced horizontal and vertical structuring of ant assemblages across short distances is likely explained by a combination of microclimate and microhabitat connectivity. Our results demonstrate the importance of considering three-dimensional spatial variation in local assemblages and reveal how highly diverse communities can be supported by complex habitats.</span></p>
Figure 5 in Vertical distribution of brown and red macroalgae along the central Western Antarctic Peninsula
Figure 5: Vertical distribution of seven additional red macroalgal species by the number of transects in which they were present in the diver by-hand collections. Other details as in Figure 4.
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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.