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309 results for “Scale effects”
Climate and geology overwrite land use effects on soil organic nitrogen cycling on a continental scale
<p><strong>Abstract.</strong> Soil fertility and plant productivity are globally constrained by N availability. Proteins are the largest N reservoir in soils and the cleavage of proteins into small peptides and amino acids has been shown to be the rate limiting step in the terrestrial N cycle. However, we are still lacking a profound understanding of the environmental controls of this process. Here we show that integrated effects of climate and soil geochemistry drive protein cleavage across large scales. We measured gross protein depolymerization rates in mineral and organic soils sampled across a 4000-km-long European transect covering a wide range of climates, geologies and land uses. Based on structural equation models we identified that soil organic N cycling was strongly controlled by substrate availability, e.g. by soil protein content. Soil geochemistry was a secondary predictor, by controlling protein stabilization mechanisms and protein availability. Precipitation was identified as the main climatic control on protein depolymerization, by affecting soil weathering and soil organic matter accumulation. In contrast, land use was a poor predictor of protein depolymerization. Our results highlight the need to consider geology and precipitation effects on soil geochemistry when estimating and predicting soil N cycling at large scales.</p>
The Effects of Aerosol on Small-Scale Cloud-Environment Mixing: Implications for Observing Inhomogeneous Mixing
<p>This is the simulation output of the submitted manuscript "The Effects of Aerosol on Small-Scale Cloud-Environment Mixing: Implications for Observing Inhomogeneous Mixing" to the Journal of Geophysical Research: Atmospheres. </p> <p>Abstract: The mixing of clouds with their environment can change the size and number of cloud droplets, two important parameters to understand the role of clouds in the climate system. Because these changes are determined by the way a cloud mixes with its environment, they have been used to characterize the mixing process. Using a highly detailed numerical cloud model coupled with a statistical turbulence model, we show that the adequate consideration of aerosol particles from which cloud droplets grow, can alter the changes of cloud droplets during mixing. This has not been considered in the past. We identify biases that need to be considered when mixing processes are analyzed.</p> <p>The raw data presented here was produced by the aforementioned model only and is completely available.</p>
cellsig: a Bayesian sparse random-effect model and large-scale human bulk transcriptional catalogue support cell-type marker selection
<p><strong>Human Bulk Cell-type Catalogue (HBCC): This database harmonises 1,435 samples of 67 cell types from 58 datasets which can be utilised in the cellsig method for estimating cell-type transcriptional profiles.</strong></p>
Data for: Genomic variation across Chinook salmon populations reveals effects of a duplication on migration alleles and supports fine scale structure
<p>Distribution of ecotypic variation in natural populations is influenced by neutral and adaptive evolutionary forces that are challenging to disentangle without understanding of genomic architecture for phenotypic traits. This study provides a high-resolution portrait of genomic variation in Chinook salmon (<em>Oncorhynchus</em> <em>tshawytscha</em>) with emphasis on a region of major effect for ecotypic variation in migration timing. With a filtered dataset of ~13 million SNPs from low coverage whole genome resequencing of 53 populations (3,566 barcoded individuals), we contrasted patterns of genomic variation within and among major lineages and examined the extent of a selective sweep at a major effect region underlying migration timing (GREB1L/ROCK1). Allele frequency variation in GREB1L/ROCK1 was highly correlated with mean migration timing for early- and late-run populations within each of the lineages (r<sup>2</sup> between 0.58–0.95; P < 0.001). However, the extent of selection within the genomic region controlling migration timing was much narrower in one lineage (interior stream-type) compared to the other two major lineages which corresponded to the breadth of phenotypic variation in migration timing observed among lineages. Evidence of a duplicated block within GREB1L/ROCK1 may be responsible for reduced recombination in this portion of the genome and contributes to phenotypic variation within and across lineages. Lastly, SNP positions across GREB1L/ROCK1 were assessed for their utility in discriminating migration timing among lineages, and we recommend multiple markers nearest the duplication to provide highest accuracy in conservation applications such as those that aim to protect early migrating Chinook salmon. These results highlight the need to investigate variation throughout the genome and the effects of structural variants on ecologically relevant phenotypic variation in natural species.</p>
Fig. 3.—Nonmetric multidimensional scaling ordination for the 4 in Effects of fire on small mammal communities in frequent-fire forests in California
Fig. 3.—Nonmetric multidimensional scaling ordination for the 4 habitat variables (overstory cover, oak tree cover, shrub cover, and fire severity index) and elevation and the 11 most commonly captured mammal species in 10 burned and 10 unburned sites from April to July 2004 and 2005 in Yosemite National Park, California. Only variables with a r ≥ 0.7 or the environmental variable with the highest r for that axis (i.e., fire severity r = −0.41 and oak tree cover r = 0.51) are listed on each axis. The R2 was 0.09 and 0.77 for axis 1 and 2, respectively, for a cumulative total of 0.86. The mammal species codes represent the following: CALA, Callospermophilus lateralis; GLSA, Glaucomys sabrinus (r = 0.73 on axis 1); MIXX, Microtus sp.; NEQU, Neotamias quadrimaculatus (r = −0.71 on axis 2); NESP, N. speciosus (r = −0.67 on axis 1); OTBE, Otospermophilus beecheyi; PEBO, Peromyscus boylii; PEMA; P. maniculatus (r = −0.88 on axis 2); SCGR, Sciurus griseus (r = 0.68 on axis 1); SOXX, Sorex sp.; TADO, Tamiasciurus douglasii.
Preclinical to Clinical Translation for Intervertebral Disc Repair: Effects of Species-Specific Scale, Metabolism and Matrix Synthesis Rates on Cell-Based Regeneration
<p>Supporting datasets for journal article funded by the ERC</p>
Data and R code for "Negative effects of wind on plant hydraulics at the global scale"
<p>To minimize ontogenetic and methodological variation, we only included trait data that met the following criteria: (a) plants were grown in natural ecosystems, excluding greenhouse and common garden experiments; (b) measurements were made on adult plants and not on seedlings; (c) hydraulic traits were measured on terminal stem or branch segments in the sapwood at the crown; and (d) trait data were calculated as the mean value for each species at the same site when data were from multiple sources.</p> <p>Climate data were obtained either from the original reports or from WorldClim version 2 (http://worldclim.org/version2) if the original data were not available. The following variables were extracted from WorldClim: mean annual wind speed, mean annual precipitation, mean annual temperature, precipitation seasonality, temperature seasonality, precipitation of driest month, and minimum temperature of coldest month. The VPD data was extracted from the TerraClimate dataset (http://www.climatologylab.org/terraclimate.html). Annual PET (potential evapotranspiration) data were extracted from the CGIAR-CSI consortium (http://www.cgiar-csi.org/data). The moisture index (MI) is the ratio of precipitation to PET.</p> <p>Simple linear regression was used to examine the relationships between two variables, utilizing the 'lm' function in R software. Partial regression analysis was conducted using the R package VISREG to investigate the relationships between wind speed and plant hydraulics while controlling for other variables. This analysis helped to illustrate the independent effect of wind on plant hydraulics. The Random Forest machine-learning algorithm (implemented using the R package randomForest) was utilized to assess the relative importance of environmental variables for each plant hydraulic trait. The Mean Decrease in Gini was calculated as the average of a variable's total decrease in node impurity, taking into account the proportion of samples that reach that node in each individual decision tree in the random forest. This provides a measure of a variable's importance in estimating the value of the target variable across all of the trees in the forest. A higher Mean Decrease in Gini value indicates greater importance of the variable. Multiple regression analyses were performed to develop predictive equations for plant hydraulic traits using environmental variables. To test for hydraulic traits-wind speed slope directions and differences among species groups in different climatic regions, we used standardized major axis (SMA) analyses. The R package SMATR was employed for these analyses. We considered <em>p </em>< 0.05 as the threshold for statistical significance in all models.</p>
Do Agile Scaling Approaches Make A Difference? An Empirical Comparison of Team Effectiveness Across Popular Scaling Approaches
<p>This bundle contains supplementary materials for an upcoming academic publication <em>Do Agile Scaling Approaches Make A Difference? An Empirical Comparison of Team Effectiveness Across Popular Scaling Approaches?</em>, by Christiaan Verwijs and Daniel Russo. Included in the bundle are the dataset and SPSS syntaxes. This replication package is made available by C. Verwijs under a "Creative Commons Attribution Non-Commercial Share-Alike 4.0 International"-license (CC-BY-NC-SA 4.0).</p> <p><strong>About the dataset</strong></p> <p>The dataset (SPSS) contains anonymized response data from 15,078 team members aggregated into 4,013 Agile teams that participated from <a href="https://scrumteamsurvey.org">scrumteamsurvey.org</a>. Stakeholder evaluations of 1,841 stakeholders were also collected for 529 of those teams. Data was gathered between September 2021, and September 2023. We cleaned the individual response data from careless responses and removed all data that could potentially identify teams, individuals, or their parent organizations. Because we wanted to analyze our measures at the team level, we calculated a team-level mean for each item in the survey. Such aggregation is only justified when at least 10% of the variance exists at the team level (Hair, 2019), which was the case (ICC = 35-50%). No data was missing at the team level.</p> <p>Question labels and option labels are provided separately in Questions.csv. To conform to the privacy statement of <a href="https://scrumteamsurvey.org">scrumteamsurvey.org</a>, the bundle does not include response data from before the team-level aggregation.</p> <p><strong>About the SPSS syntaxes</strong></p> <p>The bundle includes the syntaxes we used to prepare the dataset from the raw import, as well as the syntax we used to generate descriptives. This is mostly there for other researchers to verify our procedure.</p>
Choose to Move - Next Steps: Can 'Booster Sessions' Sustain Health Benefits of an Effective, Scaled-up, Health Promotion Program?
ClinicalTrials.gov study NCT04592614. IPD Sharing: NO. Countries: 1. Publications: 13.
Effects of Azithromycin as an Adjunct to Scaling and Root Planning in the Treatment of Periodontitis
ClinicalTrials.gov study NCT05506371. IPD Sharing: NO. Countries: 1. Publications: 15.
Inter- and Intra-Observer Reliability of the Iowa Level of Assistance Scale and the Effect of Staff Retraining
ClinicalTrials.gov study NCT07330011. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Postoperative Basal Bolus or Sliding Scale Insulin Regimen in DM2 and Its Effect on Surgical Site Infections.
ClinicalTrials.gov study NCT06638567. IPD Sharing: YES. Countries: 1. Publications: 17.
the Effectiveness of Different Risk Scales In Predicting VTE in Respiratory Inpatients
ClinicalTrials.gov study NCT04963374. IPD Sharing: NO. Countries: 1. Publications: 1.
The Effect of Scaling and Root Planing on Oral Inflammatory Biomarkers Among E-cig Users and Non-smokers
ClinicalTrials.gov study NCT05745324. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effect of Aspirin Vs Omega 3 Fatty Acid After Scaling and Root Planing in Type II Diabetic Patients With Chronic Periodontitis
ClinicalTrials.gov study NCT03599401. IPD Sharing: NO. Countries: 1. Publications: 2.
SCALE: Small Changes and Lasting Effects
ClinicalTrials.gov study NCT01198990. IPD Sharing: NO. Countries: 1. Publications: 2.
Differing, multi-scale landscape effects on genetic diversity and differentiation in eastern chipmunks
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Data from: Priority effects within coinfected hosts can drive unexpected population-scale patterns of parasite prevalence
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Data from: How far is enough? Prediction of the scale of effect for wild bees
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Data from: Predicting the effects of increasing temporal scale on species composition, diversity, and rank-abundance distributions
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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.