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4,276 results for “Estimation”
Standard Error Estimates from ARRI ensemble GAM model outputs
<p><strong>Summary (Purpose)</strong></p> <p>Basal area per acre (BAA) standard error estimate (SEE) for all trees, pine trees, and non-pine trees across three diameter at breast height (DBH) size classes, 2- to 10-inch, 10- to 14-inch, and 14+ inch. Models were informed by relative density rasters from 2018 Light Detection and Ranging (lidar) point clouds.</p> <p><strong>Description</strong></p> <p>The GAM_SEE_rasters are modelled basal area per acre standard error estimate single band rasters. The units for the rasters’ are square foot per acre. Rasters are divided into three tree species groups: 'All' trees, 'Pine' trees (defined as trees of the genus<em> Pinus)</em>, and 'No-Pine' trees, and three size classes: LT 10 for trees with DBH between 2- and 10-inches, 10-14 for trees with DBH between 10- and 14- inch DBH, and GT 14 for trees with DBH greater than 14-inches.</p> <p>Ensemble generalized additive models (GAM) of estimated tree basal area per DBH class were created from Restore field plots and relative density canopy cover rasters, or RDCC (St. Peter, et al., 2023). This ensemble GAM model was created using the R script detailed in Hogland, 2021. The parameters used were 0.75 for the percent of data used to train the model (selected using random sampling with replacement), 50 models, and using gaussian family. The estimated BAA for each of the 50 ensemble GAM models for each cell were averaged (mean) to produce the GAM estimate, additionally the variability between these estimates was used to create the standard error estimate (SEE) for each cell. </p> <p> </p> <p>The 246 Restore field plots used to train the GAM model of basal area include measurements of all trees within four non-overlapping 9m radius circular subplots within a 36m square plot. Tree diameter at breast height (DBH), species, count, and condition measurements were recorded. Measurements were summarized to the plot and DBH (square inches) was converted to basal area per acre (square feet per acre) using the formula 0.005454 * DBH^2. Restore field plots were measured in the Spring of 2018. The RDCC metrics are 5m resolution multiband rasters produced by applying a custom r software function that uses the r software’s ‘lidR’ package to produce forest metrics summarized from Light Imaging Detection and Ranging (LiDAR) point clouds. ARSA LiDAR is a combination of three collections, Block 2 and 3 were collected in early 2018 and has a NPS of 0.7-m using a Riegl VQ-1560i lidar system. Leon county LiDAR data has a nominal pulse spacing (NPS) of 0.35-m and was acquired between February 05, 2018 and April 25, 2018 using the Leica ALS80 HP SN8137 and SN8235 lidar systems. Choctawhatchee data was acquired in early 2017, using the Riegl LMS-Q1560 lidar system and has a NPS of 0.7-m. Rasters were generated in their vendor provided spatial projection before being reprojected to UTM Zone 16.</p> <p> </p> <p>The 5m resolution RDCC bands were summarized to 40x40m to correspond with the area of our plots and used as predictor variables in the models of all trees basal area. Each 5m pixel value represents the estimated standard error of the basal area per acre estimate as if it was the center of a 40x40m (8x8 cells) plot surrounding that pixel. </p> <p> </p> <p>Reference:</p> <p>Hogland, J. (2021). Ensemble Generalized Additive Models (EGAM). Retrieved from Jupyter Notebook: <a href="https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing">https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing</a></p> <p>St. Peter, Joseph, Drake, Jason, Medley, Paul, & Ibeanusi, Victor. (2023). Relative Density Canopy Cover Outputs for Leon Lidar data in the Florida Panhandle 2018 [Data set]. In Remote Sensing (Vol. 13, Number 23, p. 4763). Zenodo. https://doi.org/10.5281/zenodo.8222114</p> <p><strong>Credits</strong></p> <p>This dataset was built by Joseph St. Peter of FAMU’s Center for Spatial Ecology and Restoration using 2018 LiDAR data funded by Leon County, Northwest Florida Water Management District, US Geological Survey and the USDA Forest Service and processed using the r package lidR. Restore plots were funded by the Gulf Coast Ecosystem Restoration Council (RESTORE Council) through an interagency agreement with the USDA Forest Service (17-IA-11083150-001) for the Apalachicola Tate’s Hell Strategy 1 project.</p> <p><strong>Use Limitations</strong></p> <p>This spatial data is based on various data collection and processing techniques as well as on modeling or interpretation. While this data uses the most current and complete information available at the time of production, spatial data and derivative products may vary in accuracy. Spatial data are often developed from sources of differing accuracy which may be accurate only at certain scales. This data has been quality checked but may contain spurious errors or be incomplete or inappropriate for certain uses. Spatial data products used for purposes other than those for which they were created, may yield inaccurate or misleading results. The USDA Forest Service, Florida A&M University, and the Center for Spatial Ecology & Restoration (CSER) reserves the right to correct, update, modify, remove or replace GIS products at any time and without notification. This data may not be distributed without written permission from the Center for Spatial Ecology & Restoration (CSER) at Florida A&M University, and/or the USDA Forest Service.</p>
Data from: Biological clocks as age estimation markers in animals: a review and meta-analysis
<p>Complete dataset of Methylation and Telomere studies searched, retrieved, analysed, extracted and compared in the review and meta-analysis "<strong>Biological clocks as age estimation markers in animals: a review and meta-analysis".</strong></p>
Simulated reads for benchmarking SARS-CoV-2 lineage abundance estimation
<p>To evaluate the accuracy of lineage abundance estimates from amplicon-based and whole genome-based sequencing, we simulated paired-end reads from amplicons determined by AmpliDiff, and reads spanning full genomes. Abundances of lineages are based on the relative abundance of a lineage within the dataset. The data consists of the following 8 independent datasets:</p> <ul> <li>200 bp reads from the Netherlands based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage,</li> <li>400 bp reads from the Netherlands based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage,</li> <li>200 bp reads from the Netherlands based on whole genome sequencing at 100x coverage,</li> <li>400 bp reads from the Netherlands based on whole genome sequencing at 100x coverage,</li> <li>200 bp reads from Texas based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage,</li> <li>400 bp reads from Texas based on AmpliDiff amplicons (1, 2, 5 or 10 amplicons) at 1000x coverage,</li> <li>200 bp reads from Texas based on whole genome sequencing at 100x coverage,</li> <li>400 bp reads from Texas based on whole genome sequencing at 100x coverage.</li> </ul> <p>Every independent dataset contains 20 sets of reads (generated with different random seeds). The genomes used for the Netherlands-based simulations can be obtained via GISAID through accession id <a href="https://doi.org/10.55876/gis8.230825fe">EPI_SET_230825fe</a>, and the genomes used for the Texas-based simulations can be obtained via GISAID through accession id <a href="https://doi.org/10.55876/gis8.230825pe">EPI_SET_230825pe</a>.</p>
Figure Data for "Continuous Estimates of Glacier Mass Balance in High Mountain Asia Based on ICESat-1,2 and GRACE/GRACE Follow-On"
<p>Figure Data for "Continuous Estimates of Glacier Mass Balance in High Mountain Asia Based on ICESat-1,2 and GRACE/GRACE Follow-On" Data" </p>
Measurements obtained for USRP B210 used for Angle of Arrival estimation
<p>Results both for estimation of calibration phase (difference of phase between two RX antennas) and AoA error. attached python coeds for reading the samples.</p> <p>The whole setup described in <a href="https://doi.org/10.1007/s11276-022-03010-z">https://doi.org/10.1007/s11276-022-03010-z</a></p> <p>The code for USRP AoA estimation available at https://github.com/MarcinWachowiak/gr-aoa.git</p> <p> </p>
Data set: Statistically parameterizing and evaluating a positive degree-day model to estimate surface melt in Antarctica from 1979 to 2022
<p><strong>Version 2:</strong></p> <p><strong>Updates from version 1: Monthly, daily, and hourly dist-PDD and uni-PDD outputs have been added.</strong></p> <p><strong>https://doi.org/10.5194/tc-17-3667-2023</strong></p> <p> </p> <p>Version 1:</p> <p>This dataset accompanies Zheng et al. (2023): Statistically parameterizing and evaluating a positive degree-day<br> model to estimate surface melt in Antarctica from 1979 to 2022, The Cryosphere.</p> <p>This dataset contains annual PDD model output.</p>
ET-WB: water balance-based estimations of terrestrial evaporation over global land and major global basins
<p>This is the readme file for the ET-WB dataset described in the ESSD paper "ET-WB: water balance-based estimations of terrestrial evaporation over global land and major global basins" from Xiong et al. (2023)<br> ET-WB dataset-The monthly water balance data from May 2002-December 2021 for the 168 river basins and global land from 23 precipitation, 29 runoff, and 7 terrestrial water storage changes datasets.<br> The five dimensions (236*169*23*7*29) of the matrix represent the time, regions, precipitation, terrestrial water storage changes, and runoff datasets used respectively.<br> ET-WB is distributed in three kind of formats: Mat (ET-WB.mat), NetCDF (ET_WB.nc), and Shapefile (ET_WB.shp) (only for the ensemble median value). All the formats share the same definitions of dimensions (as below), except for the ArcGIS shapefile that is provided for individual regions (168 river basins and global land excluding Antarctic and Greenland).<br> File shapefile.rar is the geospatial database of the study area that can be opened in ArcGIS software. </p> <p>Please find more details in the Readme file.</p>
H2020 Platone Greek Demo State Estimation Results
<p>The dataset contains the results of the simulations of the State Estimation Tool for the Greek Demo of the Platone Project, for two separate instances during a day</p>
Estimation of biomass combustion carbon emissions data for 2016 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2016, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Estimation of biomass combustion carbon emissions data for 2015 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2015, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Data and R-scripts for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland
<p><strong> Introduction</strong></p> <p>A new method for estimating carbon dioxide emissions from rained peatland forest soils was developed for the Greenhouse Gas Inventory of Finland (GHG inventory). The method is based on a set of models (Ojanen et al. 2014, Tuomi et al., 2009) that dynamically compile all relevant carbon inputs and outputs into a time series of soil CO<sub>2</sub> emission. A complete description of the method is described in Alm et al. (2023). Here we present the input data and R-scripts (R Core Team, 2020) for computing the time series from year 1990 to 2022 of CO<sub>2</sub> emission from soil in forest land on drained organic soil, like it was reported by the Finnish GHG inventory (Statistics Finland, 2023).</p> <p><strong>Time series data </strong></p> <p>The source of forest and area data is the Finnish National Forest Inventory (NFI) as a part of Luke Statutory Services. The NFI standing forest data in the data files includes annual country-wide estimates of mean basal area and standing biomass of Scots pine (<em>Pinus sylvestris</em> L.), Norway spruce (Picea abies (L.) H. Karst) and all the broadleaved forest trees combined. The data concerns forest land on drained organic soil only (class FRA 1 according to the FAO forest land definition).</p> <p>The NFI data for each year has been averaged by different drained peatland forest site types (FTYPE) and by inventory regions of southern and northern Finland. The areas and proportions of FTYPEs of all drained peatland “forests remaining forests” (i.e., forests that have not undergone another change in land use in the past 20 years) in southern and northern Finland (Alm et al., 2023), derived from NFI12 (2014–2018).</p> <p>Annual litter input from harvest residues was estimated using statistics of harvested stem volumes by species, collected and published by Luke (Luke statistics). The stem volumes were converted to whole trees and further to litter fractions and further to The share of residues remaining in forest is estimated by subtracting the amount of the logging residues collected for energy use, the data obtained from Luke statistics/energy. The biomass of live trees, annual litterfall from live trees aboveground and root litter belowground are derived from the National Forest Inventory of Finland (inventory rounds NFI8 to NFI13). The R-code also includes calculation of annual litter production from the harvesting residues.</p> <p>The regression-based transfer models, implemented in the R-code, also need meteorological time series inputs: The soil organic matter decomposition model (Ojanen et al. 2014) uses May-October mean temperature. Decomposition model yasso07 (Tuomi et al., 2009), applied for estimating the CO<sub>2</sub> release by decomposition of harvesting residues and above ground litter from natural mortality, is constrained by annual temperature, annual temperature amplitude and annual precipitation. Starting from the original country-wide grid produced by the Finnish Meteorological Institute (FMI) the weather time series were spatially averaged so that the FMI weather grid values were collected from those locations where peatlands representing each FTYPE in southern and northern Finland were observed by the NFI, respectively.</p> <p>The pre-prepared input data are given in files, see Table 1 for descriptions.</p> <p> </p> <p> </p> <p>Table 1. Description of input data files.</p> <table> <tbody> <tr> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description of data</strong></p> </td> </tr> <tr> <td> <p>basal.areas.csv</p> </td> <td> <p>Time series of years 1990-2022 for annual average basal area (m<sup>2</sup> ha<sup>-1</sup>) by year, by peatland forest site type (peat_type) and by tree species or group (tree_type).</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> <p>Values of tree species or group correspond to:</p> <p>1 Scots pine</p> <p>2 Norway spruce</p> <p>3 Broadleaved species</p> </td> </tr> <tr> <td> <p>biomass.csv</p> </td> <td> <p>Time series of years 1990-2022 for annual biomass (biomass, t ha<sup>-1</sup> of dry mass) by year, by biomass component, by tree species and by peatland forest site type (tkg).</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> </td> </tr> <tr> <td> <p>dead_litter.csv</p> </td> <td> <p>Time series of years 1990-2022 of annual aboveground litter from dead wood: Harvesting residues and natural mortality combined (C, t ha<sup>-1</sup> of dry mass; lognat_litter).</p> <p> </p> <p>Values of region correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> </td> </tr> <tr> <td> <p>ghgi_litter.csv</p> </td> <td> <p>Time series of years 1990-2022 for litter AWEN-fractions (A=acid soluble, W=water soluble, E=ethanol soluble, N=non-soluble; C, t ha<sup>-1</sup>) by different litter types: Above-ground coarse woody litter (coarse_woody_litter), fine woody litter (fine_woody_litter), non-woody litter (non_woody_litter) by litter source and deposition type by region. “org” denotes organic soil.</p> <p> </p> <p>Values of region correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> <p>Values of ground correspond to litter deposition environment:</p> <p>above Above-ground litter</p> <p>below Below-ground litter</p> </td> </tr> <tr> <td> <p>lognat_decomp.csv</p> </td> <td> <p>Time series of years 1990-2022 for C, t ha<sup>-1</sup> of dry mass, decomposed from logging residues and natural mortality by region.</p> <p> </p> <p>Values of variable “region” correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> </td> </tr> <tr> <td> <p>logyasso_weather_data.csv</p> </td> <td> <p>Time series of years 1990-2022 for regional (region) precipitation sum (mm, sum_P), average annual temperature (°C, mean_T) and amplitude of the annual temperature (°C , ampli_T).</p> <p> </p> <p>Values of region correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> </td> </tr> <tr> <td> <p>total_area.csv</p> </td> <td> <p>Areas (ha) of drained peatland forests remaining forest land by region and peat_type.</p> <p> </p> <p>Values of variable “region” correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> </td> </tr> <tr> <td> <p>weather_data.csv</p> </td> <td> <p>Time series of years 1990-2022 for 30-year rolling mean temperature for the May-October period (roll_T) used by the soil decomposition models. The values are calculated for each FTYPE (peat_type) using their spatial distributions (see details in Alm et al., 2023).</p> <p> </p> <p>Values of variable “region” correspond to GHG inventory region:</p> <p>south South Finland</p> <p>north North Finland</p> <p> </p> <p>Values of peat_type correspond to FTYPE:</p> <p>1 Herb-rich type</p> <p>2 <em>Vaccinium myrtillus</em> type</p> <p>4 <em>Vaccinium vitis-idaea</em> type</p> <p>6 Dwarf shrub type</p> <p>7 <em>Cladina</em> type</p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <p><strong>The R-scripts</strong></p> <p>The scripts are an excerpt from the Finnish greenhouse gas inventory code set, applying the necessary pre-processed input data and producing the soil CO<sub>2</sub> emissions for each FTYPE separately. The necessary R-packages (R Core Team, 2020) are managed in the script LIBRARIES.R.</p> <p>Guidance for running the R-scripts is given in the README.txt.</p> <p><strong>References</strong></p> <p>Alm, J., Wall, A., Myllykangas, J-P., Ojanen, P., Heikkinen, J., Henttonen, H. M., Laiho, R., Minkkinen, K., Tuomainen, T. and Mikola, J. A new method for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland. Biogeosciences https://doi.org/10.5194/bg-20-1-2023, 2023.</p> <p>LUKE Statistics</p> <ul> <li>https://www.luke.fi/en/statistics/total-roundwood-removals-and-drain, last access 8.12.2022.</li> </ul> <ul> <li>https://www.luke.fi/en/statistics/commercial-fellings/commercial-fellings-72023. last access 8.12.2022.</li> </ul> <p>Statistics Finland 2023. URL: https://unfccc.int/documents/627718 (last access 13.9.2023).</p> <p>Ojanen, P., Lehtonen, A., Heikkinen, J., Penttilä, T., and Minkkinen, K.: Soil CO2 balance and its uncertainty in forestry drained peatlands in Finland, Forest Ecol. Manage., 325, 60–73, 2014.</p> <p>R Core Team: R: A language and environment for statistical computing. R Foundation forStatistical Computing, Vienna, Austria, URL https://www.R-project.org, 2020.</p> <p>Tuomi, M., Thum, T., Järvinen, H., Fronzek, S., Berg, B., Harmon, M., Trofymow, J.A., Sevanto, S. and Liski, J.: Leaf litter decomposition - Estimates of global variability based on Yasso07 model, Ecol. Modell. 220 (23):3362-3371, 2009.</p>
Fig. 3. Maximum likelihood tree estimated from the 215 in Morphological and Molecular Identification of Isospora sepetibensis (Chromista: Miozoa: Eimeriidae) from a New Host, Trichothraupis melanops (Passeriformes: Thraupidae: Tachyphoninae) in South America
Fig. 3. Maximum likelihood tree estimated from the 215 bp long cox1 sequences. Numbers at nodes represent bootstrap support (1,000 replicates; only values> 50% shown) for Neighbor-Joining and Maximum Likelihood, respectively. The scale-bar represents the number of nucleotide substitutions per site.
Fig. 2. Maximum likelihood tree estimated from the cox1 in Morphological and Molecular Identification of Isospora sepetibensis (Chromista: Miozoa: Eimeriidae) from a New Host, Trichothraupis melanops (Passeriformes: Thraupidae: Tachyphoninae) in South America
Fig. 2. Maximum likelihood tree estimated from the cox1 sequences. Numbers at nodes represent bootstrap support (1,000 replicates; only values> 50% shown) for Neighbor-Joining and Maximum Likelihood, respectively. The scale-bar represents the number of nucleotide substitutions per site.
FIGURE 3 in Inferring global species richness from megatransect data and undetected species estimates
FIGURE 3 Latitudinal distribution of currently valid species of Pholcidae. Numbers of Pholci- dae species (x-axis) known from different latitudes (y-axis; N, north; S, south), with the land mass distribution shown in grey (from www.ecoclimax.com; excluding Antarctica). Pholcidae species richness is slightly shifted towards the north, possibly as a result of the unbalanced land masses and/or taxonomists' biases, but most diversity is in tropical regions.
FIGURE 2 in Inferring global species richness from megatransect data and undetected species estimates
FIGURE 2 Cumulative percentages of new species (y-axis) as a function of cumulative field days (x-axis) (left) and cumulative number of total (upper/blue line) and new (lower/red line) species (y-axis) as a function of culumative field days (x-axis) (right), for the three major tropical megatransects shown on the map. Each green dot represents a sampling locality. For raw data of all geographic regions, see supplementary tables S1–S5.
FIGURE 4 Temporal estimates derived from a in Split distribution, biogeography and morphological and genetic diversity of the Iberobathynellini Tribe in the family Parabathynellidae (Crustacea, Malacostraca, Bathynellacea)
FIGURE 4 Temporal estimates derived from a relaxed molecular clock analysis. Divergence times are depicted in the coalescence-based MCC tree. Units of X-axis address Mya and blue bars at nodes indicate the 95% highest posterior density intervals (HPD).
FIGURE 1 in Inferring global species richness from megatransect data and undetected species estimates
FIGURE 1 Cumulative curve of currently valid species of Pholcidae (y-axis) as a function of time (x-axis). The curve suggests that we are far from approaching a complete taxonomic knowledge of the family.
Acclimation capacity of critical thermal maximum varies among populations: Consequences for estimates of vulnerability
<p>Adaptive plasticity in thermal tolerance traits may buffer organisms against changing temperatures, making such responses of particular interest in the face of global climate change. Although population variation is integral to the evolvability of this trait, many studies inferring proxies of physiological vulnerability from thermal tolerance traits extrapolate data from one or few populations to represent the species. Estimates of physiological vulnerability can be further complicated by methodological effects associated with experimental design. We evaluated how populations varied in their acclimation capacity (i.e., the magnitude of plasticity) for critical thermal maximum (CT<sub>max</sub>) in two species of tailed frogs (Ascaphidae), cold-stream specialists. We used the estimates of acclimation capacity to infer physiological vulnerability to future warming. We performed CT<sub>max</sub> experiments on tadpoles from 14 populations using a fully factorial experimental design of two holding temperatures (8℃, 15℃) and two experimental starting temperatures (8℃, 15℃). This design allowed us to investigate the acute effects of transferring organisms from one holding temperature to a different experimental starting temperature, as well as fully acclimated responses by using the same holding and starting temperature. We found that most populations exhibited beneficial acclimation, where CT<sub>max</sub> was higher in tadpoles held at a warmer temperature, but populations varied markedly in magnitude of the response and the inferred physiological vulnerability to future warming. We also found that the response of transferring organisms to different starting temperatures varied substantially among populations, although accounting for acute effects did not greatly alter estimates of physiological vulnerability at the species-level or for most populations. These results underscore the importance of sampling widely among populations when inferring physiological vulnerability, as population variation in acclimation capacity and thermal sensitivity may be critical when assessing vulnerability to future warming. </p>
Data for: A modified Michaelis-Menten equation estimates growth from birth to 3 years in healthy babies in the US
<p class="MsoNormal"><strong><span>Background</span></strong><span>: Standard pediatric growth curves cannot be used to impute missing height or weight measurements in individual children</span><span>. The Michaelis-Menten equation, <span>used for characterizing substrate-enzyme saturation curves, has been shown to model growth in many organisms including nonhuman vertebrates. </span>We investigated whether this equation <span>could be used</span> to interpolate missing growth data in children in the first three years of life.</span></p> <p class="MsoNormal"><strong><span>Methods</span></strong><span>: We developed a modified Michaelis-Menten equation and compared expected to actual growth, first in a local birth cohort (N=97) then in a large, outpatient, pediatric sample (N=14,695). </span></p> <p class="MsoNormal"><strong><span>Results</span></strong><span>: The modified Michaelis-Menten equation showed excellent fit for both infant weight (median RMSE: boys: 0.22kg [IQR:0.19; 90%<0.43]; girls: 0.20kg [IQR:0.17; 90%<0.39]) and height (median RMSE: boys: 0.93cm [IQR:0.53; 90%<1.0]; girls: 0.91cm [IQR:0.50;90%<1.0]). Growth data were modeled accurately with as few as four values from routine well-baby visits in year 1 and seven values in years 1-3; birth weight or length was essential for best fit. </span></p> <p class="MsoNormal"><strong><span>Conclusions</span></strong><span>: A modified Michaelis-Menten equation accurately describes growth in healthy babies aged 0-36 months, allowing interpolation of missing weight and height values in individual longitudinal measurement series. The growth pattern in healthy babies in resource-rich environments mirrors an enzymatic saturation curve.</span></p>
Verzehrsstudie zur Ermittlung der Lebensmittelaufnahme von Säuglingen und Kleinkindern für die Abschätzung eines akuten Toxizitätsrisikos durch Rückstände von Pflanzenschutzmitteln (VELS) [Consumption study to determine the food intake of infants and young children for the estimation of an acute toxicity risk through residues of plant protection products]
<p>The VELS study (<strong>V</strong>erzehrsstudie zur <strong>E</strong>rmittlung der Lebensmittelaufnahme von <strong>S</strong>äuglingen und Kleinkindern für die Abschätzung eines akuten Toxizitätsrisikos durch Rückstände von Pflanzenschutzmitteln; VELS) was conducted in 2001/2002. Data were collected from 804 infants, toddlers and children (age group 0.5 to <5 years) in Germany. The food consumed was recorded by the parents as "food as eaten" over a total of 6 days (2x3-day food record with an interval of 3-6 months for toddlers and 4-8 weeks for infants). Anthropometric data are also included.</p> <p>Foods are coded according to the German food coding system (BLS; Bundeslebensmittelschlüssel) and food names and additional information is available in German only.</p> <p>Dataset owner: German Federal Institute for Risk Assessment (BfR)</p> <p>Data collection was carried out by the University of Paderborn on behalf of the Federal Ministry of Food and Agriculture [Bundesministerium für Ernährung und Landwirtschaft; BMEL] – formerly Federal Ministry for Food, Agriculture and Consumer Protection [Bundesministerium für Verbraucherschutz, Ernährung und Landwirtschaft; BMVEL]</p>
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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.