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47 results for “Weight estimation”
Soil Bacteria Community-Weighted rrn Operon Copy Number Estimation
<p>Datasets and R-Scripts for estimating community-weighted rrn operon copy number for soil bacteria communities collected from the Yukon-Kuskokwim River Delta, AK, USA, and from La Selva Biological Station, Costa Rica. File descriptions follow:</p> <p>"rrnDB_copy_number_database.csv": The Ribosomal RNA Database downloaded from <a href="rrndb.umms.med.umich.edu.">rrndb.umms.med.umich.edu.</a> Citation: </p> <ul> <li>Stoddard S.F, Smith B.J., Hein R., Roller B.R.K. and Schmidt T.M. (2015) <em>rrn</em>DB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. <em>Nucleic Acids Research</em> 2014; doi: 10.1093/nar/gku1201 [<a href="http://www.ncbi.nlm.nih.gov/pubmed/25414355">PMID:25414355</a></li> </ul> <p>"AK_16S_Genus_Abundance.csv": Count of ASVs by taxon (assigned to genus level) present in each soil sample collected in the Yukon_Kuskokwim River Delta, AK, USA.</p> <p>"Costa_Rica_16S_OTU_Abundance": Count of OTUs by taxon present in each soil sample collected in La Selva Biological Station, Costa Rica.</p> <p>"Alaska_rrn_copy_number_estimation_script.R": an R script for processing Alaska ASV count table and estimating community-weighted rrn operon copy numbers for each soil sample.</p> <p>"CostaRica_rrn_copy_number_estimation_script.R": an R script for processing Costa Rica OTU count table and estimating community-weighted rrn operon copy numbers for each soil sample.</p>
Human Capital-weighted population estimates for 185 countries from 1970 to 2100
<p>We provide a novel dataset of human capital-weighted population size (HCWP) for 185 countries from 1970 to 2100. HCWP summarizes a population's productive capacity and human capital heterogeneity in a single metric, enabling comparisons across countries and over time. The weights are derived from Mincerian earnings functions applied to multi-country census data on educational attainment. The model used to compute the returns to schooling accounts for the diminishing positive relative relationship between education and wages as the overall education of populations rises. The population weights are adjusted by a skills assessment factor representing differences in education quality across countries and years. HCWP is calculated by applying these adjusted human capital weights to population estimates and projections disaggregated by age, sex and education, spanning the period 1970-2020 and 2020-2100 for five Shared Socioeconomic Pathway scenarios. Validation analyses demonstrate the utility of the new HCWP data in explaining national income trends. As a more comprehensive population measure than basic size and age-sex indicators, HCWP enhances the power of statistical models aimed at the assessment of socioeconomic change impacts and forecasting.</p>
Data from: Improved robustness to gene tree incompleteness, estimation errors, and systematic homology errors with weighted TREE-QMC
Open the record for dataset details and reuse information.
Point-Quarter Harvested Plant Weight Measurements to Estimate Shrub ANPP in a Chihuahuan Desert Creosote Shrubland at the Sevilleta National Wildlife Refuge, New Mexico (2007- present)
In an effort to better quantify NPP of Creosotebush in the Five-Points region, it was decided to test the Point-Quarter method against the standard 1-m2 quadrat method that has been in use since 1998. Transects were laid out across the 5 mammal trapping webs as well as across burned and unburned plots of the Mixed Shrub site (MS). Repeat measures of the same bushes are performed seasonally. Whole shrubs of various size classes are collected and sorted and weighed to develop regressions for biomass.
Results for paper "Energy dependent mesh adaptivity of discontinuous isogeometric discrete ordinate methods with dual weighted residual error estimators"
<p>This spreadsheet contains the results used to generate the plots in the paper "Energy dependent mesh adaptivity of discontinuous isogeometric discrete ordinate methods with dual weighted residual error estimators".</p>
code and data for A new method applied for the determination of relative weight ratios under the TensorFlow platform when estimating coseismic slip distribution
<p>The zip file contains three folders:"Data for Illapel earthquake", "HVCE and ABIC method implement on matlab" and "GDED method implement on tensorflow". Take the simulation experiments 1.2 and actual Illapel earthquakes as examples. the code for GDED method are placed on "GDED method implement on tensorflow" folders, and the code for the ABIC method and the HVCE method are place on "HVCE and ABIC method implement on matlab" folders.</p> <p>In the file"HVCE and ABIC method implement on matlab", the meaning of each code are represent as following</p> <p><br> ABIC_SIM.m:the slip distribution inversion results with the relative weight ratios determined by ABIC method Of simulation experiments and Illapel earthquakes</p> <p><br> HVCE.m:the slip distribution inversion results with the relative weight ratios determined by HVCE method Of simulation experiments and Illapel earthquakes</p> <p><br> GDED.m:the slip distribution inversion results with the relative weight ratios determined by GDED method Of simulation experiments and Illapel earthquakes(the relative weight ratios are from the "GDED method implement on tensorflow")</p> <p>savedata.m: that code are used for save matrix or data for the GDED method implement on tensorflow</p> <p>In the file"GDED method implement on tensorflow", the meaning of each code are represent as following<br> joint_inver_tensor_ex_1.0(1.1).py: the code for determining the relative weight ratios by the GDED method with(without) plot figures, which implement on Tensorflow platform<br> <br> the InSAR data and GPS data of Illapel earthquakes are palce on the folder" Data for Illapel earthquake/GPS_ori.txt and InSAR_ori.txt"</p>
Estimation of Nutritional Needs of Preterm Neonates Fed on Parenteral Nutrition and Effects on Body Weight Gain
ClinicalTrials.gov study NCT07236957. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: Individual weight estimates for Great Lakes benthic invertebrates
Open the record for dataset details and reuse information.
Rank likelihood-based estimation of low birth weight in Ethiopia
<p>Low birth weight is a significant risk factor associated with high rates of neonatal and infant mortality, particularly in developing countries. However, most studies conducted on this topic in Ethiopia have small sample sizes, often focusing on specific areas and using standard models employing maximum likelihood estimation, leading to potential bias and inaccurate coverage probability. This study used a novel approach, the Bayesian rank likelihood method, within a latent traits model, to estimate parameters and provide a nationwide estimate of low birth weight and its risk factors in Ethiopia. Data from the Ethiopian Demographic and Health Survey (EDHS) of 2016 were used as a data source for the study. Data stratified all regions into urban and rural areas. Among 15, 680 representative selected households, the analysis included complete cases from 10, 641 children. The evaluation of model performance considered metrics such as the root mean square error, the mean absolute error, and the probability coverage of the corresponding 95% confidence intervals of the estimates. Based on the values of root mean square error, mean absolute error, and probability coverage, the estimates obtained from the proposed model outperform the classical estimates. According to the result, 40.92% of the children were born with low birth weight. The study also found that low birth weight is unevenly distributed across different regions of the country. Furthermore, there were significant associations between birth weight and several factors, including the age of the mother, number of antenatal care visits, order of birth and the body mass index as indicated by the average posterior beta values of (β1= -0.269, CI = -0.320, -0.220), (β2= -0.235, CI = -0.268, -0.202), (β3= -0.120, CI = -0.162, -0.074) and (β5= -0.257, CI = -0.291, -0.225). The study showed that the low birth weight estimates obtained from the latent trait model outperform the classical estimates. The study also revealed that the prevalence of low birth weight varies between different regions of the country, indicating the need for targeted interventions in areas with a higher prevalence. To effectively reduce the prevalence of low birth weight and improve maternal and child health outcomes, it is important to concentrate efforts on regions with a higher burden of low birth weight. This will help implement interventions that are tailored to the unique challenges and needs of each area. Health institutions should take measures to reduce low birth weight, with a special focus on the factors identified in this study.</p>
Distribution. Now restricted to the Channel Country of SW Queensland and the Lake Eyre Basin in NE South Australia. Descriptive notes. Head-body 95-120 mm, tail 105-160 mm, ear 23-29 mm, hindfoot 32-37 mm; weight 30-50 g. The Fawn Hopping Mouse has body form typical of hopping mice, with very long hindfeet, long tail with distal brush of longer hairs, very long ears, and large protruberant eyes. Dorsal fur is of variable color, from pale pinkish fawn to gray; ventral fur white. Unlike most other hopping mice, it has no throat pouch, but males have a glandular area of naked skin on the chest. Habitat. Occurs in low shrublands and tussock grasslands on stony ("gibber") plains and claypans. Shows marked habitat segregation from the Dusky Hopping Mouse (N. fuscus), which is closely associated with sandy substrates. Food and Feeding. The Fawn Hopping Mouse is mostly granivorous, but also eats other plant material (stems, leaves) and occasionally invertebrates. It uses succulent, salt-adapted plants around edges of claypans as a source of water. Breeding. Reproduction is probably largely opportunistic and aseasonal, with high reproductive output from near-continuous breeding after periods of high rainfall; reported littersize is 1-5, most commonly three; gestation period 38-43 days for nonlactating females. Females may mature later than other hopping mice, with reproductive maturity reached at about six months. Activity patterns. Terrestrial and nocturnal. Fawn Hopping Mice shelter during day in burrow systems that are typically simpler and shallower than those of other hopping mice. Movements, Home range and Social organization. Fawn Hopping Mice generally live singly or in small groups; typically uncommon within range, but population density may increase by an order of magnitude following periods of high rainfall. Status and Conservation. Classified as Near Threatened on The IUCN Red List. The Fawn Hopping Mouse has shown marked decline in range (estimated at greater than 50%), and presumably population size, since European settlement of Australia. This is mostlikely due to predation by the introduced house cat and Red Fox (Vulpes vulpes), and to habitat degradation associated with pastoralism. Bibliography. Brazenor (1934), Burbidge et al. (2008), Finlayson (1939), Gould (1853), Jackson & Groves (2015), Murray et al. (1999), Ogilby (1892), Thomas (1921h), Van Dyck & Strahan (2008), Waite (1898), Watts & Aslin (1981), Woinarski et al. (2014), Wood Jones (1925). in Muridae
Distribution. Now restricted to the Channel Country of SW Queensland and the Lake Eyre Basin in NE South Australia. Descriptive notes. Head-body 95-120 mm, tail 105-160 mm, ear 23-29 mm, hindfoot 32-37 mm; weight 30-50 g. The Fawn Hopping Mouse has body form typical of hopping mice, with very long hindfeet, long tail with distal brush of longer hairs, very long ears, and large protruberant eyes. Dorsal fur is of variable color, from pale pinkish fawn to gray; ventral fur white. Unlike most other hopping mice, it has no throat pouch, but males have a glandular area of naked skin on the chest. Habitat. Occurs in low shrublands and tussock grasslands on stony ("gibber") plains and claypans. Shows marked habitat segregation from the Dusky Hopping Mouse (N. fuscus), which is closely associated with sandy substrates. Food and Feeding. The Fawn Hopping Mouse is mostly granivorous, but also eats other plant material (stems, leaves) and occasionally invertebrates. It uses succulent, salt-adapted plants around edges of claypans as a source of water. Breeding. Reproduction is probably largely opportunistic and aseasonal, with high reproductive output from near-continuous breeding after periods of high rainfall; reported littersize is 1-5, most commonly three; gestation period 38-43 days for nonlactating females. Females may mature later than other hopping mice, with reproductive maturity reached at about six months. Activity patterns. Terrestrial and nocturnal. Fawn Hopping Mice shelter during day in burrow systems that are typically simpler and shallower than those of other hopping mice. Movements, Home range and Social organization. Fawn Hopping Mice generally live singly or in small groups; typically uncommon within range, but population density may increase by an order of magnitude following periods of high rainfall. Status and Conservation. Classified as Near Threatened on The IUCN Red List. The Fawn Hopping Mouse has shown marked decline in range (estimated at greater than 50%), and presumably population size, since European settlement of Australia. This is mostlikely due to predation by the introduced house cat and Red Fox (Vulpes vulpes), and to habitat degradation associated with pastoralism. Bibliography. Brazenor (1934), Burbidge et al. (2008), Finlayson (1939), Gould (1853), Jackson & Groves (2015), Murray et al. (1999), Ogilby (1892), Thomas (1921h), Van Dyck & Strahan (2008), Waite (1898), Watts & Aslin (1981), Woinarski et al. (2014), Wood Jones (1925).
Supplementary material 3 from: García-Barros E (2015) Multivariate indices as estimates of dry body weight for comparative study of body size in Lepidoptera. Nota Lepidopterologica 38(1): 59-74. https://doi.org/10.3897/nl.38.8957
Documentation on phylogeny.: Explanation note: This is a list of references including the most relevant sources of information used to build the hypothesis on phylogenetic relationships which were not quoted in the main text.
Supplementary material 2 from: García-Barros E (2015) Multivariate indices as estimates of dry body weight for comparative study of body size in Lepidoptera. Nota Lepidopterologica 38(1): 59-74. https://doi.org/10.3897/nl.38.8957
Frequency distribution graph.: Explanation note: Frequency distribution of the dry body weight data (mg) across the species studied.
Supplementary material 5 from: García-Barros E (2015) Multivariate indices as estimates of dry body weight for comparative study of body size in Lepidoptera. Nota Lepidopterologica 38(1): 59-74. https://doi.org/10.3897/nl.38.8957
Mean by superfamily.: Explanation note: Mean dry body weight and wing length by superfamily, and sample sizes.
Supplementary material 4 from: García-Barros E (2015) Multivariate indices as estimates of dry body weight for comparative study of body size in Lepidoptera. Nota Lepidopterologica 38(1): 59-74. https://doi.org/10.3897/nl.38.8957
Tree topology.: Explanation note: Graphic display (dendrogram) to show the hypothesis on phylogenetic relations adopted in this work, after the sources quoted in the main texta and in the file: Supplementary material 3.
Supplementary material 6 from: García-Barros E (2015) Multivariate indices as estimates of dry body weight for comparative study of body size in Lepidoptera. Nota Lepidopterologica 38(1): 59-74. https://doi.org/10.3897/nl.38.8957
Alternative models.: Explanation note: Alternative or suboptimal regression models derived from the species means or from the independent contrasts.
Supplementary material 1 from: García-Barros E (2015) Multivariate indices as estimates of dry body weight for comparative study of body size in Lepidoptera. Nota Lepidopterologica 38(1): 59-74. https://doi.org/10.3897/nl.38.8957
Nexus format text.: Explanation note: Tree topology for the phylogenetic hypothesis adopted, to be used as input in applications reading nexus (requires some slight previous edition).
Data for "Bootstrapping outperforms community-weighted approaches for estimating the shapes of phenotypic distributions"
<p>This repository contains datasets used in the manuscript entitled "Bootstrapping outperforms community-weighted approaches for estimating the shapes of phenotypic distributions" by Maitner et al. For details of these datasets, see https://www.authorea.com/users/244803/articles/523535-on-estimating-the-shape-and-dynamics-of-phenotypic-distributions-in-ecology-and-evolution. All datasets contain individual (and in some cases, organ-level) trait measurements.</p> <p>The dataset "all_traits_unscaled_RMBL.rds" was compiled by Christine Lamanna, Lindsey L Sloat, Andrew J. Kerkhoff, and Brian J. Enquist, Full details in https://www.authorea.com/users/244803/articles/523535-on-estimating-the-shape-and-dynamics-of-phenotypic-distributions-in-ecology-and-evolution</p> <p>The dataset "Julies_panama_data.xlsx" was compiled by Julie Messier and collaborators, full details here: https://doi.org/10.1111/j.1461-0248.2010.01476.x</p> <p>The dataset "TreefrogTadpoles.xlsx" was compiled by Nick Rasmussen, full details here: https://www.jstor.org/stable/44082203 </p> <p>The dataset "zooplankton_2019.zip" was compiled by Ewa Merz and Francesco Pomati. For more details, see www.aquascope.ch , <a href="https://github.com/mbaityje/plankifier">https://github.com/mbaityje/plankifier</a>, <a href="https://github.com/tooploox/SPCConvert">https://github.com/tooploox/SPCConvert</a>, and https://www.authorea.com/users/244803/articles/523535-on-estimating-the-shape-and-dynamics-of-phenotypic-distributions-in-ecology-and-evolution .</p>
Using Mean Ages on Clothing Size Labels in Revised Advanced Pediatric Life Support Formulas for Weight Estimation
ClinicalTrials.gov study NCT02455674. IPD Sharing: Not stated. Countries: 1. Publications: 19.
Tall Stature Women Fetal Weight Estimation
ClinicalTrials.gov study NCT03206281. IPD Sharing: YES. Countries: 1. Publications: 1.
AI Support in Novice's Decision-making for Ultrasound Fetal Weight Estimation
ClinicalTrials.gov study NCT06232187. IPD Sharing: NO. Countries: 1. Publications: 16.
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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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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.
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