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149 results for “spatial variability”
Spatial and temporal variability of the freezing level in Patagonia's atmosphere
<h3>Short Summary:</h3> <p>This repository houses the Python preprocessing scripts utilized in generating the metadata for García-Lee et al., (2024) dataset. With these files and scripts, you gain access to the algorithm and examples for generating gridded products in netCDF format, specifically featuring the 0°C isotherm field.</p> <h3>Dependencies:</h3> <ul> <li>numpy (tested with 1.24.4 in py3)</li> <li>pandas (tested with 2.0.3 in py3)</li> <li>netCDF4 (tested with 1.6.0 in py3)</li> <li>re (tested with 2.2.1 in py3)</li> <li>glob</li> <li>OS: Tested in Windows.</li> </ul> <h3>Technical Info:</h3> <table> <tbody> <tr> <td> <p>File</p> </td> <td> <p>Type</p> </td> <td> <p>Description</p> </td> </tr> <tr> <td> <p><a href="../records/10523940/files/1_H0_Detect.py?download=1">1_H0_Detect.py</a></p> </td> <td> <p>Python script</p> </td> <td> <p>0°C Isotherm Detection Algorithm.</p> </td> </tr> <tr> <td> <p><a href="../records/10523940/files/2_Daily_Mean_H0.py?download=1">2_H0_Daily_Mean.py</a></p> </td> <td> <p>Python script</p> </td> <td> <p>Calculation of Daily Mean.</p> </td> </tr> <tr> <td> <p><a href="../records/10523940/files/ISO0_1959_2021_GRID.nc?download=1">ISO0_1959_2021_GRID.rar</a></p> </td> <td> <p>netCDF</p> </td> <td> <p>0°C Isotherm Data at 6-Hour Intervals (1959-2021) in meters above sea level (m a.s.l.).</p> </td> </tr> <tr> <td> <p><a href="../records/10523940/files/ERA5_PATAGONIA_6H_T_GPH_1959.rar?download=1">ERA5_PATAGONIA_6H_T_GPH_1959.rar</a></p> </td> <td> <p>netCDF</p> </td> <td> <p>Raw ERA5 data example for 1959: Temperature (°K) and Geopotential (m**2 s**-2).</p> </td> </tr> </tbody> </table> <h3>Extra:</h3> <p>The file 'Observations and Charts.pdf' shows averages, standard deviations, bias, and trends of the 0°C isotherm for Puerto Montt, Río Gallegos, Comodoro Rivadavia, and Punta Arenas. These values were estimated using both observations and reanalysis ERA5 data.</p> <h3>Reference:</h3> <p>García-Lee, N., Bravo, C., Gónzalez-Reyes, Á., and Mardones, P.: Spatial and temporal variability of the freezing level in Patagonia's atmosphere, Weather Clim. Dynam., 5, 1137–1151, https://doi.org/10.5194/wcd-5-1137-2024, 2024.</p>
Sensitivity of a Coarse-Resolution Global Ocean Model to a Spatially Variable Neutral Diffusivity - ACCESS-OM2 data and plotting routines
<p>This repository contains the processed data and plotting routines associated with the article</p> <p>Holmes, Groeskamp, Stewart and McDougall (2022), Sensitivity of a Coarse-Resolution Global Ocean Model to a Spatially Variable Neutral Diffusivity, Journal of Advances in Modeling Earth Systems (JAMES), doi: 10.1029/2021MS002914, http://dx.doi.org/10.1029/2021MS002914</p> <p>The contents includes post-processed data output from the 1-degree ACCESS-OM2 ocean-sea-ice model simulations and the python/jupyter plotting routines required to make the plots.</p> <p>The processing script is Holmes2022JAMES_Neutral_Diffusion_ACCESS-OM2_Plotting_Script.ipynb. The data files consist of time-averages or time series of certain metrics processed using NCO tools from the raw ACCESS-OM2 simulation output.</p>
Internal climate variability and spatial temperature correlations during the past 2000 years
<p>Climate model output of the iLOVECLIM model. The repository contains:</p> <p>Data on 9 ensemble members corresponding to ensemble members 1-9 in the publication:</p> <p>1: 0ka_trans_013000to015000.nc</p> <p>2: 0ka_trans_precipcorr_AC_x0.4_AS_x1.6_013000to015000.nc</p> <p>3: 0ka_trans_7params_6_013000to015000.nc</p> <p>4: 0ka_trans_7params_12_013000to015000.nc</p> <p>5: 0ka_trans_7params_30_013000to015000.nc</p> <p>6: 0ka_trans_8params_6_013000to015000.nc</p> <p>7: 0ka_trans_8params_26_013000to015000.nc</p> <p>8: 0ka_trans_8params_35_013000to015000.nc</p> <p>9: 0ka_trans_8params_40_013000to015000.nc</p> <p> </p> <p>Three different data files for every ensemble member</p> <p>atmym: yearly mean atmospheric output (t2m is used for temperature in the manuscript)</p> <p>CLIO2: yearly mean ocean surface output (temp is used for SST in the manuscript)</p> <p>graevolu: collection of monthly mean 1 dimensional ocean outputs (ADPro is used for AMOC strength in the manuscript)</p>
Code: A model of wild bee populations accounting for spatial heterogeneity and climate induced temporal variability of food resources at the landscape level
<p><span>The viability of wild bee populations and the pollination services that they provide are driven by the availability of food resources during their activity period and within the surroundings of their nesting sites. Changes in climate and land use influence the availability of these resources and are major threats to declining bee populations. Because wild bees may be vulnerable to interactions between these threats, spatially explicit models of population dynamics that capture how bee populations jointly respond to land use at a landscape scale and weather are needed. Here, we developed a spatially and temporally explicit theoretical model of wild bee populations aiming for a middle ground between the existing mapping of visitation rates using foraging equations and more refined agent-based modelling. The model is developed for <em>Bombus</em> sp. and captures within-season colony dynamics. The model describes mechanistically foraging at the colony level and temporal population dynamics for an average colony at the landscape level. Stages in population dynamics are temperature-dependent triggered with a theoretical generalized seasonal progression, which can be informed by growing degree days (GDD). The purpose of the LandscapePhenoBee model is to evaluate the impact of systematic changes and within-season variability in resources on bee population sizes and crop visitation rates. In a simulation study, we used the model to evaluate the impact of the shortage of food resources in the landscape arising from extreme drought events in different types of landscapes (ranging from different proportions of semi-natural habitats and early and late flowering crops) on bumblebee populations.</span></p>
Spatial and temporal variability of phytoplankton photophysiology in the Atlantic Southern Ocean
<p>The datasets in this repository are part of the manuscript entitled "<strong>Spatial and temporal variability of phytoplankton photophysiology in the Atlantic Southern Ocean</strong>".</p>
Spatial variability of biogenic CO2 flux in Helsinki in 2020
<p>Data used in the CO-CARBON project to simulate the biogenic CO<sub>2</sub> flux for Helsinki 2020. This data set includes the Surface Urban Energy and Water balance Scheme (SUEWS) model run to simulate the biogenic CO<sub>2</sub> flux in the city of Helsinki, Finland. The model is run over two year period (2019-2020) with hourly resolution with the whole city divided into 250x250 m<sup>2</sup> grids. (Note: anthropogenic CO<sub>2</sub> emissions are not included in this data set. )</p> <p>The data files are:</p> <p><strong>SUEWS_ModelRun</strong></p> <ul> <li>SUEWS source code version V2020b</li> <li>RunControl.nml</li> <li>Input folder for the model run</li> </ul>
Fig. 1 in Spatial, Temporal And Individual Variability In The Autumn Diet Of European Hare (Lepus Europaeus) In Hungary
Fig. 1. Localities of the study areas. Study areas are shown as gray patches, the capital (Budapest) by striped gray area, Lake Balaton and Lake Tisza by black ones. Black lines are Hungarian rivers and
Data for: Spatial variability in the contribution of termites to the decay of plant detritus
<p>Drylands are characterized by high spatial variability in resource availability due to sporadic rainfall, topography of the landscape and important effects of animals. Resource availability gradients may trigger patterns in decomposer population abundances and activity which could affect ecosystem functions such as decomposition. Here, we examined the influence of resource availability gradients on the importance of termites in the decomposition of wood and grass litter. We placed wood blocks and grass litter baits in bags accessible and inaccessible to termites across wood and grass resource gradients as determined by the presence or absence of a top mammalian predator and across topographic gradients during a 9-month period in arid Australia. We hypothesized that grass-eating termite activity would track grass abundance and wood-eating termite activity would track wood abundance. Termites were the predominant decomposition agent at these sites. Termites contributed to 99.5% of wood decomposition and 83.9% of grass decomposition during our study period. For wood, the termite effect was spatially variable and increased with habitat wood availability which was greatest on dunes and where top predators were absent. However, the contribution of termites to grass litter decomposition did not track grass availability or termite abundance. The highest effects of termites on grass decomposition rates were found in habitats where the absence of top predators led to low grass availability. Our findings highlight how spatial variability in resources in addition to other factors that we do not document but are known to be influenced by the presence of top predators, such as insectivore predation rates, across the landscape could affect ecosystem functions such as decomposition. </p>
Figure 3 in Tintinnina (Ciliophora) and Foraminifera in plankton of hypersaline Lagoon Bardawil (Egypt): spatial and temporal variability
Figure 3. Dependence of number of found tintinnid species on number of analyzed samples in Lagoon Bardawil (a) and the Mediterranean Sea (b).
Figure 2 in Tintinnina (Ciliophora) and Foraminifera in plankton of hypersaline Lagoon Bardawil (Egypt): spatial and temporal variability
Figure 2. Dependence of total tintinnid abundance on number of tintinnid species in Lagoon Bardawil during 2009 and 2010 (a- winter, b- all seasons).
Fig. 2 in The importance of considering small-scale variability in macrobenthic distribution: spatial segregation between two fiddler crab species (genus Leptuca) (Decapoda, Ocypodidae)
Fig. 2. NMDS ordination (stress = 0.16) of sites based on similarity of group composition. Leptuca leptodactyla (Rathbun in Rankin, 1898): JLM (juvenile males), JLF (juvenile females), ALM (adult males) and ALF (adult females). Leptuca uruguayensis (Nobili, 1901): JUM (juvenile males), JUF (juvenile females), AUM (adult males) and AUF (adult females).
Fig. 1 in The importance of considering small-scale variability in macrobenthic distribution: spatial segregation between two fiddler crab species (genus Leptuca) (Decapoda, Ocypodidae)
Fig. 1. Schematic representation of the sampling design, with subarea separation and the six random replicates. (Area=10 m²).
Fig. 3 in Influence of environmental variables on stream fish fauna at multiple spatial scales
Fig. 3. Venn diagrams representing the results of the variance partitioning with partial CCA (canonical correspondence analysis): percentage of variation in fish abundance (a) and incidence (b) explained by land use and land cover, site, and spatial variables, as well as that shared between the three sets of variables in the Upper Araguari River basin, Minas Gerais. See Table 4 for a list of all explanatory variables.
Fig. 1 in Influence of environmental variables on stream fish fauna at multiple spatial scales
Fig. 1. Locations of the 38 randomly selected sites sampled in the Upper Araguari River basin, State of Minas Gerais, Brazil.
Fig. 2 in Influence of environmental variables on stream fish fauna at multiple spatial scales
Fig. 2. Detrended correspondence analysis (DCA) of fish abundance (a) and incidence (b) along the sampling sites. The species are shown in triangle and sampling sites in X-mark.
Data for: Temporal consistency and spatial variability in detection: implications for monitoring of macroinvertebrates from shallow groundwater aquifers (Subterranean Biology, 2024)
<p>Original research article: Knüsel M., Alther R., Couton M. & Altermatt F. (2024) Temporal consistency and spatial variability in detection: implications for monitoring of macroinvertebrates from shallow groundwater aquifers. Subterranean Biology 49: 139-161. <a href="https://doi.org/10.3897/subtbiol.49.132515" target="_blank" rel="noopener">https://doi.org/10.3897/subtbiol.49.132515</a></p>
Spatial interpolation of air pollutant and meteorological variables in Central Amazonia
<p>This dataset presents data of aerosol, trace-gases and meteorological variables from the Amazon Rainforest region, resulting from an interpolation process. The original data were collected from the GOAmazon 2014/15 project, from the Atmospheric Radiation Measurement (ARM) repository. </p>
Dataset for: 'Patterns in the Plankton – Spatial distribution and long-term variability of copepods on the Agulhas Bank'
<p>This dataset contains environmental data (in situ temperature and chlorophyll <em>a</em>) and integrated biomass (mg C m<sup>-2</sup>) data for a number of copepod taxa, as well as total copepod biomass and abundance, on the Agulhas Bank, South Africa, as predicted by a Generalized Additive Model (GAM), during late austral spring (October-December) from 1988 to 2011. Mean environmental and copepod biomass parameters for each area and year are also provided. Relevant information on sampling and statistical analysis of spatial distributions has been extracted from the paper. Please see paper for full details and figures, including supplementary data; <a href="https://doi.org/10.1016/j.dsr2.2023.105265">https://doi.org/10.1016/j.dsr2.2023.105265</a>. Please see the Word document Huggett_et_al_2023_README.docx for a list of the data files and descriptions of the contents.</p>
Recalibration of the lunar chronology due to spatial cratering-rate variability - Supporting Information
<p>CR_moon.csv: Relative cratering rate shown in Fig.3 and 5.a. The<strong> </strong>data are provided over the full range of latitudes and longitudes, with a 1-degree bin.</p> <p>SI_convert_age.m: Matlab code computing model ages of Plutarch and Kirkwood craters using the chronology function presented in this study.</p>
Code and data: Understanding temporal variability across trophic levels and spatial scales in freshwater ecosystems
<p>Code and data to reproduce the results in Siqueira et al. (submitted) published as a Preprint (https://doi.org/10.32942/osf.io/mpf5x)</p> <p>The full set of results, including those made available as supplementary material, can be reproduced by running five scripts in the <strong>R_codes</strong> folder following this sequence:</p> <ul> <li>01_Dataprep_stability_metrics.R</li> <li>02_SEM_analyses.R</li> <li>03_Stab_figs.R</li> <li>04_Stab_supp_m.R</li> <li>05_Sensit_analysis.R</li> </ul> <p>and using the data available in the <strong>Input_data</strong> folder.</p> <p>The original raw data made available include the abundance (individual counts, biomass, coverage area) of a given taxon, at a given site, in a given year. See details here https://doi.org/10.32942/osf.io/mpf5x</p> <p>However, this is a collaborative effort and not all authors are allowed to share their raw data. One data set (LEPAS), out of 30, was not made available due to data sharing policies of The Ohio Division of Wildlife (ODOW). So, in code "01_Dataprep_stability_metrics.R" all data made available are imported, except the LEPAS data set. For this specific data set, code "01_Dataprep_stability_metrics.R" imports variability and synchrony components estimated using the methods described in Wang et al. (2019 Ecography; doi/10.1111/ecog.04290), diversity metrics (alpha and gamma diversity), and some variables describing the data set.</p> <p>A protocol for requesting access to the LEPAS data sets can be found here:<br> https://ael.osu.edu/researchprojects/lake-erie-plankton-abundance-study-lepas</p> <p>Dataset owner: Ohio Department of Natural Resources – Division of Wildlife, managed by Jim Hood, Dept. of Evolution, Ecology, and Organismal Biology, The Ohio State University. Email: hood.211@osu.edu</p> <p>Anyone who wants to reproduce the results described in the preprint can just download the whole R project (that includes code and data) and run codes from 01 to 05.</p> <p>I am making the whole R project folder (with everything needed to reproduce the results) available as a compressed file.</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)
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DANDI Archive for NWB datasets
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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.