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33 results for “Transition rates”
Supplementary material 2 from: Azzaro M, Packard TT, Monticelli LS, Maimone G, Rappazzo AC, Azzaro F, Grilli F, Crisafi E, La Ferla R (2019) Microbial metabolic rates in the Ross Sea: the ABIOCLEAR Project. In: Mazzocchi MG, Capotondi L, Freppaz M, Lugliè A, Campanaro A (Eds) Italian Long-Term Ecological Research for understanding ecosystem diversity and functioning. Case studies from aquatic, terrestrial and transitional domains. Nature Conservation 34: 441-475. https://doi.org/10.3897/natureconservation.34.30631
: Data type: parameters data
Supplementary material 1 from: Azzaro M, Packard TT, Monticelli LS, Maimone G, Rappazzo AC, Azzaro F, Grilli F, Crisafi E, La Ferla R (2019) Microbial metabolic rates in the Ross Sea: the ABIOCLEAR Project. In: Mazzocchi MG, Capotondi L, Freppaz M, Lugliè A, Campanaro A (Eds) Italian Long-Term Ecological Research for understanding ecosystem diversity and functioning. Case studies from aquatic, terrestrial and transitional domains. Nature Conservation 34: 441-475. https://doi.org/10.3897/natureconservation.34.30631
: Data type: measurement
Biome classification influences the projected rate of future biome transitions
<p><strong>Aim. </strong>Biome classification schemes are widely used to map biogeographic patterns of vegetation formations on large spatial scales. Future climate change will influence biome patterns, and vegetation models can be used to assess the susceptibility of biomes to experience transitions. However, biome classification is not unique, and various classification schemes and biome maps exist. Here, we aimed to assess how the choice of biome classification schemes influences current and projected future biome patterns.</p> <p><strong>Location.</strong> Africa, Australia, Tropical Asia</p> <p><strong>Time period.</strong> 2000-2099</p> <p><strong>Major taxa studied.</strong> Tropical vegetation</p> <p><strong>Methods.</strong> We used adaptive dynamic global vegetation model version 2 (aDGVM2) to simulate vegetation in the study region. We classified vegetation into biomes using (1) a classification scheme based on the cover of functional types, (2) a cluster analysis based on the cover of functional types, and (3) a cluster analysis based on trait patterns simulated by the aDGVM2. We compared the resulting biome maps to multiple observation-based biome maps and quantified differences in projected biome changes under the RCP8.5 scenario for the different classification schemes.</p> <p><strong>Results.</strong> As expected, biome patterns were strongly related to the scheme used for biome classification. The highest data-model agreement was derived for a cluster analysis using 21 simulated traits. Traits related to size were most important for classification. Considering all classification schemes, the area projected to undergo biome transitions under climate change varied between 16.5% and 32.1%. Despite this variability, different schemes consistently showed that grassland and savanna areas are most susceptible to climate change, whereas tropical forests and deserts are stable. Our results demonstrate that traits simulated by aDGVM2 are appropriate to delimit biomes.</p> <p><strong>Main conclusions. </strong> Studies projecting biome patterns and transitions under current and future climate should consider applying different biome classification schemes to avoid biases in such projections caused by biome classification schemes.</p>
Data from: Experiments and modelling of rate-dependent transition delay in a stochastic subcritical bifurcation
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Macroevolution of flower color patterning: biased transition rates and correlated evolution with flower size
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Data from: Is specialization an evolutionary dead-end? Testing for differences in speciation, extinction and trait transition rates across diverse phylogenies of specialists and generalists.
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Data from: Gradual assembly of avian body plan culminated in rapid rates of evolution across dinosaur-bird transition
[No abstract entered]
Respiration Rate Monitoring During Transitions
ClinicalTrials.gov study NCT01881269. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Gradual assembly of avian body plan culminated in rapid rates of evolution across dinosaur-bird transition
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Meta-Analyses of Dehalococcoides mccartyi Strain 195 Transcriptomic Profiles Identify a Respiration Rate-Related Gene Expression Transition Point and Interoperon Recruitment of a Key Oxidoreductase Su
GEO Series GSE26287. Dehalococcoides mccartyi 195. 53 samples. Type: Expression profiling by array.
Reducing Readmission Rates by Providing a Comprehensive Transition Plan From Hospital to Home for Cardiac Surgery Patients.
ClinicalTrials.gov study NCT04373850. IPD Sharing: Not stated. Countries: 0. Publications: 0.
The transition between transcriptional initiation and elongation in E. coli is highly variable and often rate-limiting
GEO Series GSE6069. Escherichia coli. 12 samples. Type: Expression profiling by genome tiling array; Genome binding/occupancy profiling by genome tiling array.
Nitric oxide required for transition to slower hepatic protein synthesis rates during long-term caloric restriction
GEO Series GSE274644. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
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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.