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281 results for “community model”
Agent‐based modeling of the effects of forest dynamics, selective logging, and fragment size on epiphyte communities
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Data from: An evolutionary modelling approach to understanding the factors behind plant invasiveness and community susceptibility to invasion
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Data from: Integrated culturing, modeling and transcriptomics uncovers complex interactions and emergent behavior in a three-species synthetic gut community
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Data from: A hierarchical distance sampling model to estimate abundance and covariate associations of species and communities
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Data from: Recasting the dynamic equilibrium model through a functional lens: the interplay of trait-based community assembly and climate
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Data from: Using a forest dynamics model to link community assembly processes and traits structure
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Data from: Achieving a step change in the tuberculosis epidemic through comprehensive community-wide intervention: A model-based analysis
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Mobilisation of data to stakeholder communities: Bridging the research-practice gap using a commercial shellfish species model
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Multi-species occupancy models as robust estimators of community richness
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Evaluation of a novel community-based COVID-19 ‘Test-to-Care’ model for low-income populations
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NACP: Climate Data Inputs (3-hourly) for Community Land Model, Western USA, 1979-2015
This dataset provides sub-daily, high-resolution, climate data inputs including temperature, precipitation, near surface specific humidity, incoming short-wave radiation, and near-surface wind speed over 11 states of the western USA. States included are Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming. These data were derived for use in the Community Land Model (CLM v4.5) and are at 3-hourly temporal and 4 x 4 km spatial resolutions for the 1979 through 2015 time period. The source for observational data was METDATA (now called GRIDMET), at a daily resolution. Modeling efforts using these data estimated annual carbon stocks, fluxes, and productivity across the western United States.
Output of CAM simulations performed for study "Impact of cloud physics on the Greenland Ice Sheet near-surface climate: a study with the Community Atmosphere Model"
<p>Output of CAM simulations performed for study "Impact of cloud physics on the Greenland Ice Sheet near-surface climate: a study with the Community Atmosphere Model" in JGR-Atmospheres (2020). </p> <p>Output are NetCDF files containing annual means (named 'yearmean', 2007-2013), or multi-annual monthly means ('ymonmean', 2007-2012) of various variables that are of interest and/or used for analysis in this study. The file name starts with the variable name. Fields are global, at a resolution of 0.9 x 1.25 degrees latitude/longitude.</p> <p>The test simulations are named (as discussed in the paper):</p> <p>cam4_clm5<br> cam5_clm5<br> cam6_noicenucl_clm5<br> cam6_noclubb_clm5<br> cam6_mg1_clm5<br> cam6</p>
Data from: Assessing the impacts of imperfect detection on estimates of diversity and community structure through multispecies occupancy modeling
Detecting all species in a given survey is challenging, regardless of sampling effort. This issue, more commonly known as imperfect detection, can have negative impacts on data quality and interpretation, most notably leading to false absences for rare or difficult‐to‐detect species. It is important that this issue be addressed, as estimates of species richness are critical to many areas of ecological research and management. In this study, we set out to determine the impacts of imperfect detection, and decisions about thresholds for inclusion in occupancy, on estimates of species richness and community structure. We collected data from a stream fish assemblage in Algonquin Provincial Park to be used as a representation of ecological communities. We then used multispecies occupancy modeling to estimate species‐specific occurrence probabilities while accounting for imperfect detection, thus creating a more informed dataset. This dataset was then compared to the original to see where differences occurred. In our analyses, we demonstrated that imperfect detection can lead to large changes in estimates of species richness at the site level and summarized differences in the community structure and sampling locations, represented through correspondence analyses.
Data from: Empirical phylogenies and species abundance distributions are consistent with pre-equilibrium dynamics of neutral community models with gene flow
Community characteristics reflect past ecological and evolutionary dynamics. Here, we investigate whether it is possible to obtain realistically shaped modelled communities – i.e., with phylogenetic trees and species abundance distributions shaped similarly to typical empirical bird and mammal communities – from neutral community models. To test the effect of gene flow, we contrasted two spatially explicit individual-based neutral models: one with protracted speciation, delayed by gene flow, and one with point mutation speciation, unaffected by gene flow. The former produced more realistic communities (shape of phylogenetic tree and species-abundance distribution), consistent with gene flow being a key process in macro-evolutionary dynamics. Earlier models struggled to capture the empirically observed branching tempo in phylogenetic trees, as measured by the gamma statistic. We show that the low gamma values typical of empirical trees can be obtained in models with protracted speciation, in pre-equilibrium communities developing from an initially abundant and widespread species. This was even more so in communities sampled incompletely, particularly if the unknown species are the youngest. Overall, our results demonstrate that the characteristics of empirical communities that we have studied can, to a large extent, be explained through a purely neutral model under pre-equilibrium conditions.
Finite Volume Community Ocean Model-based Arctic Ocean Forecast System: A Comprehensive Assessment of Sea Ice Forecast Results without Data Assimilation
<p>The dataset contains sea ice forecast results from the Finite Volume Community Ocean Model-based Arctic Ocean Forecast System (FVCOM-AOFS) for the period 2019-2020. The output result of the system is presented as daily mean values.</p><p>Each netCDF file within the dataset includes the following variables: lon, lat, lonc, latc, aice, vice, uuice, and vvice. Specifically, the variables of lon and lat represent the longitude and latitude of the unstructured triangular grid. The variables of lonc and latc represent the longitude and latitude of the unstructured triangular cell. The variables of aice and vice indicate sea ice concentration and sea ice thickness. The variables of uuice and vvice indicate eastward and northward sea ice drift velocity. The scalars including aice and vice use lon and lat coordinates, and the vectors including uuice and vvice use lonc and latc coordinates. </p>
A Mediation Model of the Usability and Intergroup Relation for Online Project Management Community Effectiveness with Microsoft Teams
<p>A Mediation Model of the Usability and Intergroup Relation for Online Project Management Community Effectiveness with Microsoft Teams</p>
Implementation of an Ensemble Kalman Filter in the Community Multiscale Air Quality Model (CMAQ Model v5.1) for Data Assimilation of Ground-level PM2.5: Model Simulation Outputs
<p>This data sets are model outputs from CMAQ simulations. The output contains only PM2.5 variable after combining related aerosol species. File format is netCDF binary. File naming convention for Domain 1 (D1) is D1_EXP_DATE_TIME_e000.nc where EXP is the control experiment (CTR) or the assimilation experiments (DA_icbc), DATE means YYYYMMDD format date, TIME indicates 2 digits UTC time, and e000 represents ensemble mean result. Also, file naming convention for Domain 2 (D2) is D2_EXP_CASE_DATE_TIME_e000 where EXP is the control experiment (CTR) or the assimilation experiments (DA_ic and DA_icbc), CASE is the simulation cases for ANL or PRD, DATE means YYYYMMDD format date, TIME indicates 2 digits UTC time, and e000 represents ensemble mean result. For the processed and assimilated observation data in this study for D1 and D2, the file names are D1_OBS_DATA_YYYYMMDDhh.txt and D2_OBS_DATA_YYYYMMDDhh.txt, respectively, where YYYYMMDD is date format and hh is UTC.</p>
SPARCS_WP4_Leipzig_Virtual_Modelled Residential Load for Community Demand Response
<p>modelled Residential Load for the 1000 households participating in the Community Demand Response. 900 are typical private 2-person households (RL1),100 are typical private 2-person households with heat pumps (RL2)</p>
Peer to Community (P2C) Model: A Pilot Randomized Controlled Trial
ClinicalTrials.gov study NCT05884749. IPD Sharing: NO. Countries: 1. Publications: 0.
Minimizing Risk and Maximizing Outcomes in Geriatric Patients Through Integrated Clinical Pharmacy Services in an Innovative Model of Community Practice
ClinicalTrials.gov study NCT01351441. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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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.