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115 results for “biological processes”
Isotopic and GDGTs analysis of methane-related biological processes (methane production and oxidation) in a Miocene sedimentary pool
<p>Methane is one of the most important greenhouse gases. A substantial proportion of this gas originates from marine environments where both its biological formation and oxidation occur. Variations in seawater properties due to climatic perturbations are likely to be reflected in the living conditions of both methanotrophic and methanogenic microbiota. The debate about the fate of the marine methane cycle facing global change may benefit from the inclusion of data concerning marine palaeoenvironments, especially salt-bearing sediments, which are a relic of the evaporitic sea. The study presents a GDGT-derived palaeorecord of methane-related biological processes in Miocene Wieliczka Formation sediments (a remnant of the Paratethys sea) as well as a description of present-day microbial communities subsisting in evaporates using activity measurements and high throughput sequencing. The results revealed that GDGT-based indices corresponded with the reconstructed sedimentary conditions and microbial ecophysiology which confirms their applicability of reconstruction for biological methane formation and oxidation in saline environments.</p>
Collision between biological process and statistical analysis revealed by mean-centering
<p>Animal ecologists often collect hierarchically-structured data and analyze these with linear mixed-effects models. Specific complications arise when the effect sizes of covariates vary on multiple levels (e.g., within vs among subjects). Mean-centering of covariates within subjects offers a useful approach in such situations, but is not without problems.</p> <p>A statistical model represents a hypothesis about the underlying biological process. Mean-centering within clusters assumes that the lower level responses (e.g. within subjects) depend on the deviation from the subject mean (relative) rather than on absolute values of the covariate. This may or may not be biologically realistic. We show that mismatch between the nature of the generating (i.e., biological) process and the form of the statistical analysis produce major conceptual and operational challenges for empiricists.</p> <p>We explored the consequences of mismatches by simulating data with three response-generating processes differing in the source of correlation between a covariate and the response. These data were then analyzed by three different analysis equations. We asked how robustly different analysis equations estimate key parameters of interest and under which circumstances biases arise. </p> <p>Mismatches between generating and analytical equations created several intractable problems for estimating key parameters. The most widely misestimated parameter was the among-subject variance in response. We found that no single analysis equation was robust in estimating all parameters generated by all equations. Importantly, even when response-generating and analysis equations matched mathematically, bias in some parameters arose when sampling across the range of the covariate was limited.</p> <p>Our results have general implications for how we collect and analyze data. They also remind us more generally that conclusions from statistical analysis of data are conditional on a hypothesis, sometimes implicit, for the process(es) that generated the attributes we measure. We discuss strategies for real data analysis in face of uncertainty about the underlying biological process.</p>
Integrative processing in artificial and biological vision predicts the perceived beauty of natural images
<p><em>Data, code, and materials for Nara & Kaiser (2023). </em></p> <p><em>Preprint: </em><a href="https://www.biorxiv.org/content/10.1101/2023.05.05.539579v1">https://www.biorxiv.org/content/10.1101/2023.05.05.539579v1</a></p> <p>Paper: <a href="https://doi.org/10.1126/sciadv.adi9294">https://doi.org/10.1126/sciadv.adi9294</a></p> <p>In this version (v2) of the repository, we:</p> <ul> <li>fixed an error in the fMRI data, where only the data from one participant, instead of all participants was uploaded previously - now all data are available,</li> <li>added brain masks (extracted by SPM) for each participant, and</li> <li>added realignment parameter text files (created by SPM) to the functional data for each participant.</li> </ul>
Extended data for "The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing"
<p>Extended data for "The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing"</p>
Processed Data for Short Gianotti et al., "Two Sub-Annual Time-Scales and Coupling Modes for Terrestrial Water and Carbon Cycles" (2024), Global Change Biology.
<p>These files include all data used to create Figures in Short Gianotti et al., "Two Sub-Annual Time-Scales and Coupling Modes for Terrestrial Water and Carbon Cycles" (2024), Global Change Biology. Raw data provenances and methodological processing are cited in the published manuscript.</p> <p>See README file for metadata information.</p>
Biological Process Summary (shared genes)
<p><strong>Compiled data for Gene Ontology analysis done using TOPPFUN for significant genes from pairwise Prakriti comparisons (PK,VK,VP)</strong></p>
Data from: Processing citizen science- and machine-annotated time-lapse imagery for biologically meaningful metrics
Time-lapse cameras facilitate remote and high-resolution monitoring of wild animal and plant communities, but the image data produced require further processing to be useful. Here we publish pipelines to process raw time-lapse imagery, resulting in count data (number of penguins per image) and 'nearest neighbour distance' measurements. The latter provide useful summaries of colony spatial structure (which can indicate phenological stage) and can be used to detect movement – metrics which could be valuable for a number of different monitoring scenarios, including image capture during aerial surveys. We present two alternative pathways for producing counts: 1) via the Zooniverse citizen science project Penguin Watch and 2) via a computer vision algorithm (Pengbot), and share a comparison of citizen science-, machine learning-, and expert- derived counts. We provide example files for 14 Penguin Watch cameras, generated from 63,070 raw images annotated by 50,445 volunteers. We encourage the use of this large open-source dataset, and the associated processing methodologies, for both ecological studies and continued machine learning and computer vision development.
Effects of thermal fluctuations on biological processes: A meta-analysis of experiments manipulating thermal variability
<p>Thermal variability is a key driver of ecological processes, affecting organisms and populations across multiple temporal scales. Despite the ubiquity of variation, biologists lack a quantitative synthesis of the observed ecological consequences of thermal variability across a wide range of taxa, phenotypic traits, and experimental designs. Here, we conduct a meta-analysis to investigate how properties of organisms, their experienced thermal regime, and whether thermal variability is experienced in either the past (prior to an assay) or present (during the assay) affect performance, relative to the performance of organisms experiencing constant thermal environments. Our results – which draw upon 1,712 effect sizes from 75 studies – indicate that the effects of thermal variability are not unidirectional and become more negative as mean temperature and fluctuation range increase. Exposure to variation in the past decreases performance to a greater extent than variation experienced in the present and increases the costs to performance more than diminishing benefits across a broad set of empirical studies. Further, we identify life history attributes that predictably modify the ecological response to variation. Our findings demonstrate that effects of thermal variability on performance are context-dependent, yet negative outcomes may be heightened in warmer, more variable climates.</p>
Process controlling iron-manganese regulation of the Southern Ocean biological carbon pump
<p>Netcdf output files at the end of 100-year run Fe-Mn co-limitation model experiments on the ORCA2 grid, as described and discussed in Anugerahanti, et al., in<em> Process controlling iron-manganese regulation of the Southern Ocean biological carbon pump,</em> Philosophical Transactions of the Royal Society A, 2022</p>
Hyperspectral Placenta Dataset: Hyperspectral Image Acquisition, Annotations, and Processing of Biological Tissues in Microsurgical Training
<p>The dataset consists of 101 hyperspectral images of four fresh human placentas and six hyperspectral images of contrast dyes (i.e., indocyanine green and red and blue food colorant) that were captured in the range 515-900 nm, step = 5 nm. The hyperspectral images were manually annotated, delineating the key anatomical structures: arteries, veins, stroma, and the umbilical cord. Standard reference materials were used for flat-field correction. The dataset can be used to develop machine learning algorithms for the automated classification of biological structures, particularly the classification of superficial and deep vessels and transparent tissue layers.</p>
A Study to Evaluate the Safety and Immunogenicity of GlaxoSmithKline (GSK) Biologicals' Quadrivalent Influenza Candidate Vaccine (GSK2321138A) Manufactured Using a New Process in Adults and Children
ClinicalTrials.gov study NCT02207413. IPD Sharing: Not stated. Countries: 7. Publications: 1.
Evaluation of the Immune Responses of GSK Biologicals' HPV Vaccine Following Manufacturing Process Adaptation.
ClinicalTrials.gov study NCT00250276. IPD Sharing: YES. Countries: 3. Publications: 1.
Data from: Biological processes underpin the persistence of dryland productivity following extreme wet years
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Data from: Processing citizen science- and machine-annotated time-lapse imagery for biologically meaningful metrics
Open the record for dataset details and reuse information.
Data from: Disentangling the impact of forest management intensity components on soil biological processes
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Effects of thermal fluctuations on biological processes: A meta-analysis of experiments manipulating thermal variability
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Data from: Biological and statistical processes jointly drive population aggregation: using host–parasite interactions to understand Taylor's power law
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Collision between biological process and statistical analysis revealed by mean-centering
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Local plant diversity and soybean biological control 2011 Aphid and Enemy Surveys:Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Local plant diversity and soybean biological control 2012 Enemy Surveys:Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
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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.