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26 results for “Composite Cross”
Crossing Treeline: Bacterioplankton community composition in alpine and subalpine lakes of the Rocky Mountain southern ecoregion and associated physical and chemical characteristics
This dataset includes lake water samples collected in the summer of 2016 from 16 different mountain lakes in the Rocky mountains in both Rocky Mountain National Park and the Snowy Range of southern Wyoming. Each lake was sampled twice: once in the early summer when hydrologic connections with the surrounding terrestrial environment were high and again in the late summer when hydrologic connections were low. The main goal of the study was to compare communities of bacterioplankton in alpine and subalpine lakes to determine if communities differed across treeline as soil microbes in the surrounding terrestrial environment were. To do so, we collected water samples from the deepest point of each lake, mixed it with a surface water sample and characterized bacterioplankton communities with 16S sequencing technology. Additionally, we wanted to identify abiotic factors that may correlate with community dissimilarity and characterized a suite of chemical attributes for each lake. Lake characteristics reported included surface temperature, soluble reactive phosphorous (SRP), ammonia (NH3+), pH, total dissolved nitrogen (TDN), total dissolved phosphorus (TDP), and total dissolved organic carbon (DOC), and chlorophyll a (chl-a).
Unstable Ion Composition Boundary Layer Crossings
<p>A list of unstable ion composition boundary layer crossings by the MAVEN spacecraft, separated into three categories. "Snowplow" events have discrete parcels of low energy escaping heavy ion plasma interspersed with more energetic magnetosheath plasma. "Plume" events have higher energy escaping heavy ions with a beam-like distribution, interspersed with magnetosheath plasma. "Unclassified" events do not neatly fit in either of these categories. </p>
Ultrasonic guided-waves in quasi-isotropic and cross-ply composite beams using a finite element model
<p>Ultrasonic guided-wave data taken from numerical evaluations of a finite element model (FEM) considering two composite beam structures and applying three noise levels in signal-to-noise ratio (SNR):</p> <ul> <li>Quasi-isotropic carbon fiber composite beam (dimensions: 1m x 4mm x 2mm) with a [+45/-45/90/0]_s layup and a transverse crack (1mm deep at 0.40m from excitation) in it and the following noise levels (the file directories are specified in parentheses): <ul> <li>Noisefree (/Quasi-Isotropic_Crack/Noisefree/MeasurementCrack_NF.mat)</li> <li>SNR 10 dB (/Quasi-Isotropic_Crack/SNR_10dB/MeasurementCrack_10dB.mat)</li> <li>SNR 5 dB (/Quasi-Isotropic_Crack/SNR_05dB/MeasurementCrack_5dB.mat)</li> </ul> </li> <li>Cross-ply carbon fiber composite beam (dimensions: 1m x 3mm x 2mm) with [0_{2}/90]_s stacking sequence and a delamination (5mm long at 0.45m from excitation) in it and the following noise levels: <ul> <li>Noisefree (/Cross-Ply_Delam/Noisefree/MeasurementDelam_NF.mat)</li> <li>SNR 10 dB (/Cross-Ply_Delam/SNR_10dB/MeasurementDelam_10dB.mat)</li> <li>SNR 5 dB (/Cross-Ply_Delam/SNR_05dB/MeasurementDelam_5dB.mat)</li> </ul> </li> </ul> <p>FEM notes: solid elements (i.e. C3D8R in Abaqus) with the following dimensions: 0.5mm thick, 0.5mm width, and 0.2mm long. Damage modes are modeled by node duplication.</p> <p>Ultrasonic tests data: 6-cycle Hanning windowed sinusoid centered at 100 kHz of frequency. Excitation signal at one end of the beam in the vertical direction. Sensor situated at 0.2m from the point of excitation and measuring for 720 \mu s. First antisymmetric mode (A0) is excited.</p>
Data for "Associations between 3D surface scanner derived anthropometric measurements and body composition in a cross-sectional study"
<p>Datasets underlying the analysis of the paper: "Associations between 3D surface scanner derived anthropometric measurements and body composition in a cross-sectional study"</p> <p>This upload includes the following:</p> <ul> <li><strong>data_study.csv </strong>: contains socio-demographic, health- and lifestyle factors, and body scan variables of each participant</li> <li><strong>healthscore.csv</strong> : contains the "healthy score" from the food frequency questions calculated from from five food categories: fruits, vegetables, wholegrain products, meat, and sweet/salty snacks. For each category the officially recommended minimum or maximum amount of weekly intake was used as the cut-off value and a point was assigned if the recommendation was met. A score from 0 to 5 was built to reflect the overall healthiness of the diet.</li> </ul>
Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"
<h1><strong>Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"</strong></h1> <p>This repository contains pre-processed data sets and code scripts to reproduce the data processing and analyses that are presented in the corresponding manuscript. Some minor pre-processing was completed before presenting this -- namely, the land cover raster was clipped to the study area of Knox County, Tennessee, USA, prior to placing in the repository to reduce the file size. </p> <h2><strong>How to use this repository to reproduce results </strong></h2> <p>This repository is designed to support the reproduction of analyses in the associated manuscript. The entire project can be downloaded and stored anywhere on your computer, as long as the file structure is not altered. The project contains folders with all data sets and code scripts necessary for analysis.</p> <p><strong>What you will need: </strong><br> - Installed R and RStudio for purely spatial cluster and global model analyses<br> - Basic understanding of how to open R and run code </p> <p><strong> You do NOT need:</strong><br> - To download or install R packages on your own; that is taken care of within this environment<br> - To write any code <br> - To set up any working directories in R </p> <h3><strong>Important: Using `renv`</strong></h3> <p>Short Version: When you open the R project, run `renv::restore()` and follow the prompts to install the necessary R packages. </p> <p>The R package `renv` was used to create a <strong>project library</strong>, which contains all R packages that are used by the project. The packages in the project library are <strong>the versions used during the original analysis</strong>. This means that if any packages are updated by developers in ways that would change the results of the analysis, this project can still produce the original results because of `renv`. When you open this project for the first time, `renv` will automatically download and install itself and ask you to run `renv::restore()`. <strong>You should run `renv::restore()` to automatically download and install all of the packages within this reproducible environment</strong>. </p> <h2><strong>## Basic step-by-step guide:</strong></h2> <p>- 1. Download the entire repository by clicking "Code -> Download ZIP" on GitHub or by downloading the ZIP file in Zenodo<br>- 2. Extract the ZIP file anywhere on your computer (do not change the structure of the files once extracted)<br>- 3. In RStudio, click *File -> Open Project* and browse to the location where you extracted the repository; in the repository file, open the knoxaedeslandcover R Project file <br>- 4. Open any of the R scripts in the `analysis/` folder<br>- 5. Run the code `renv::restore()` in the script or in the console and follow the prompt to install the packages <br> - Now you can run the R Scripts; start from the top with loading the packages and data, then work your way down line-by-line</p> <h3><strong># `analysis/` Folder</strong></h3> <p>The `analysis/` folder contains scripts for processing data and conducting analyses. Each file is an R script that should be opened in R studio. The first shows how to process and aggregate the various raw data files; if you are only interested in reproducing analyses from the manuscript, you can skip to the second file and work from there. </p> <p><strong><em>## Files within the `analysis/` folder</em></strong></p> <p>The files are numbered in the order that they were run for the original analysis. In this case, none of the analyses are dependent on the others, so they can technically be used in any order. The numbers associated with each file describe the order that the analyses would normally be run. </p> <p> - `(1)dataprep.R` contains the code for cleaning and combining the land cover, climate, and mosquito data -- this includes calculating the land cover percentages at different scales and calculating weekly and timelagged climate values<br> - `(2)summary_analysis.R` contains code for reproducing summary data and creating graphs from the manuscript<br> - `(3)variable_selection.R` contains code for asssessing collinearity and fitting models to identify the best fitting variables for each speceis<br> - `(4)finalmodels.R` contains code for fitting the final models using the selected variables for each species </p> <h3><strong># `data/` Folder</strong></h3> <p>This folder contains several datasets, including one that compiles them all for analyses (`knox_joined`). The raw data are included to show how the data was processed and aggregated, but the individual raw data files are not needed for analyses. See `data dictionary.txt` for a description of all attributes contained within each file. </p> <p><strong><em>## Files within the `data/` folder</em></strong></p> <p> - `knox22_joined.RDS` contains a cleaned and joined version of land cover, climate, and mosquito data in R Data Serialization format, which maintains predefined factor and numeric designations for columns. <br> - `knox22_joined.csv` contains a cleaned and joined version of land cover, climate, and mosquito data in CSV format -- identical to 'knox22_joined.RDS'<br> - `sites22.csv` contains the names, site codes, and coordinates of the study sites<br> - `aedes22_clean.csv` contains the raw mosquito collection data for the study without any climate or land cover information <br> - `NLCD_2019_landcover_clippedtoKnox.tif` contains the NLCD land cover data, already clipped to Knox County, TN, USA<br> - `knox22_temperature.csv` contains raw daily temperatures for the city of Knoxville in 2022<br> - `knox22_rainfall.csv` contains raw daily precipitation for the city of Knoxville watersheds in 2022<br> - `rainfall_stations.csv` contains the descriptions, approximate street addresses, and geographic coordinates for rainfall monitoring sites <br> - `data dictionary.txt` file that defines column names and other data attributes for every dataset </p> <h3><strong># `renv/` Folder</strong></h3> <p>The `renv/` folder contains bits and pieces needed for the `renv` package. Nothing should be altered in this folder. </p> <p> </p> <h2><strong>References for source data </strong></h2> <p> - Some of the data in this repository were originally obtained from open access sources. </p> <p> - Land cover data was obtained from the National Land Cover Database (NLCD) 2019 data product, specifically the "NLCD 2019 Land Cover (CONUS)" product. The original, unclipped raster can be freely downloaded here: https://www.mrlc.gov/data/nlcd-2019-land-cover-conus</p> <p> - Temperature data was downloaded from the United States National Oceanic and Atmospheric Administration (NOAA) weather station for Knoxville, Tennessee. The source data can be downloaded from this site: https://www.weather.gov/mrx/tysclimate</p> <p> - Rainfall data was obtained from the City of Knoxville rainfall data website, located here: https://www.knoxvilletn.gov/government/city_departments_offices/engineering/stormwater_engineering_division/rainfall_data</p> <p> - All mosquito collection data was collected directly by the manuscript authors</p>
Hydrolase and Peroxidase Activity of Gold Nanocluster Composites with Lysozyme and Cross-Linked Lysozyme Crystals
<div> <div> <p>Lysozyme (Lyz) and cross-linked Lyz crystals (CLLC) are among the proteins and templates used in the bio-inspired synthesis of subnanometer-sized gold nanoclusters (AuNCs) utilized in various biological applications as biosensors or bioimaging agents and recyclable catalyst, respectively. While previous studies have explored the formation and stabilization of AuNCs in the presence of Lyz, little is known about the enzymatic activity of Lyz after stabilization of AuNCs with the protein (AuNC@Lyz) and in CLLC (AuNC@CLLC). In this study, we conducted a comprehensive analysis of AuNC@Lyz, including hydrodynamic characteristics using multiwavelength fluorescence analytical ultracentrifugation and enzymatic activity assays. AuNC@Lyz exhibited bactericidal performance as efficiently as Lyz at pH 4.0, with enzymatic activity assays indicating more efficient hydrolytic activity of AuNC@Lyz compared to Lyz under the same conditions. Additionally, AuNC@Lyz showed peroxidase-like performance due to the catalytically active surface of AuNCs in the presence of H<sub>2</sub>O<sub>2</sub> and a chromogenic substrate. The multifunctional catalytic capabilities of AuNC@Lyz, including lytic activity and mimic peroxidase function, were highlighted. Additionally, we conducted fluorescent-based hydrolytic activity assays of CLLC, revealing that morphological variations of CLLC influence the catalytic performance of the enzyme crystals depending on the size of solvent channels and Lyz conformation within the CLLC. Furthermore, the bio-inspired synthesis of AuNCs in CLLC resulted in size distributions of AuNCs correlated with pore sizes. As a consequence of the occluded solvent channels and limited breathing of CLLC, AuNC@CLLC displayed a diminishing hydrolytic performance.</p> </div> </div>
Data to the manuscript 'Imperfect cross-linking of Xanthan for pH-responsive bio-based composite moist wound dressings by stencil printing'
<p>Evaluation data for the manuscript stated above. For follow-up evaluation, files have to be arranged according to file designation in the scripts uploaded. There is no additional description of how to arrange. Please feel free to contact the first author for help and information.</p>
Plant Species Composition percent cover:Nutrient Network: A cross-site investigation of bottom-up control over herbaceous plant community dynamics and ecosystem function.
This experiment is one implementation of a globally distributed experiment, known as the Nutrient Network. At Cedar Creek, as in over 70 other sites in grasslands around the world, the experiment aims to describe impacts of increased nutrients (nitrogen, phosphorus, potassium, sulfur and other metals) and decreased herbivory (removal of mammals by fencing). Two overarching questions are being explored with these manipulations: 1. To what extent are plant production and diversity co-limited by multiple nutrients in herbaceous-dominated communities? 2. Under what conditions do grazers or fertilization control plant biomass, diversity, and composition? By utilizing identical protocols at diverse grassland sites around the world, NutNet aims to uncover both the generalities in ecosystem functioning, and the contingencies or differences which can obscure those common mechanisms. In addition to the standard NutNet protocol, e247 includes an additional low Nitrogen gradient (1 gram Nitrogen per meter squared per year and 5 grams Nitrogen per meter squared per year in addition to the standard 10 grams Nitrogen per meter squared per year).
Composite dolmen Spunetam-2 (cross-section)
The dolmen is located on Mezetsu range (Spunetam group of monuments), Tuapse region, Caucasus, Russia, Middle Bronze Age (4000-2000 BC), Dolmen Culture of Caucasus. The stone tomb is placed on the stone cairn with remains of retaining wall. Sepulcher chamber is built with almost wild stone blocks. That is one of the most rare constructions for this culture. Facade wall is absent now. Дольмен расположен на хребете Мезецу, Туапсинский район. Относится к типу составных сооружений. Особенности: дольмен выполнен преимущественно из необработанных блоков, что является редкостью, курган, вероятно, был укреплен крепидой, фасад сейчас отсутсвует. Source: Objaverse 1.0 / Sketchfab
Dataset for "Definition and rationale for placebo composition: Cross-sectional analysis of randomized trials and protocols published in high-impact medical journals"
<p>Contains our data extraction sheets</p>
High buffering potential of winter wheat composite cross populations to rapidly changing environmental conditions
<p><span>A winter wheat composite cross population (CCP), created in the UK in 2001, has been grown in Germany, Hungary and the UK since 2005 (F<sub>5</sub> generation). In 2008/9 (F<sub>8</sub>), a cycling pattern for the populations was developed between partners to test the effects of rapidly changing environments on agronomic performance. One CCP was grown by eight partners for one year and subsequently sent to the next partner, creating "cycling CCPs" with different histories. In 2013, all eight cycling CCPs and the three non-cycling CCPs (from Germany, Hungary and UK) were included in a two-year experiment in Germany with three line varieties to compare agronomic performance and morphological characteristics. Differing seed weight of the F<sub>13</sub> at sowing affected some agronomic parameters under drought conditions in 2014/15, but not under less stressful conditions in 2013/14. In both experimental years, the CCPs were comparable to the line varieties in terms of agronomic performance, with some CCPs outyielding the varieties under drought conditions of 2015. The results highlight the potential of CCPs to compete with line varieties while the</span> <span>overall similarity of the CCPs based on their origin and cycling history for agronomic traits indicate a high buffering potential under highly variable environmental conditions. </span></p>
High buffering potential of winter wheat composite cross populations to rapidly changing environmental conditions
Open the record for dataset details and reuse information.
Impact of smoking cessation, coffee and bread consumption on the intestinal microbial composition among Saudis: A cross-sectional study
The gut microbiota is often affected by the dietary and lifestyle habits of the host, resulting in a better efficacy that favors energy harvesting from the consumed food. Our objective was to characterize the composition of gut microbiota in adult Saudis and investigate possible association with lifestyle and dietary practices. Feces from 104 Saudi volunteers (48% males) were tested for microbiota by sequencing the V3-V4 region of bacterial 16S ribosomal RNA (rRNA). For all participants, data were collected related to their lifestyle habits and dietary practices. The relative abundance (RA) of Fusobacteria was significantly higher in normal weight Saudis ( p =0.005, FDR=0.014). Individuals who consumed more coffee presented marginally significant more RA of Fusobacteria ( p =0.02, FDR=0.20) in their gut microbiota compared to those reporting low or no coffee intake, but the RA of Fusobacteria was significantly higher in smokers compared to non-smokers p =0.009, FDR=0.027) . The RA of Fusobacteria was also significantly higher in those reporting daily consumption of bread ( p =0.005, FDR=0.015). At the species level, the gut microbiota of people who consumed coffee was dominated by Bacteroidetes thetaiotaomicron followed by Phascolarctobacterium faecium and Eubacteriumrectale . Similarly, the gut microbiota of smokers was also enriched by B. thetaiotaomicron and Lactobacillus amylovorus. Smoking cessation, bread and coffee consumption induce changes in the intestinal microbial composition of Saudis. This indicates the significance of diet and lifestyle practices in the determination of the composition of the gut microbiota, which could possibly lead later to changes in metabolic profile and weight.
Supplemental Table S2. Least square means of BW, DMI, milk yield (MY) and fat- and protein-corrected milk yield (FPCMY), milk protein yield (MPY), milk composition (milk fat, milk protein, milk lactose and MUN), milk N efficiency (MNE) and feed efficiency for the CTRL, MetLys and MetLysHis treatment groups in the pre-experimental, depletion and cross-back period.
<p><strong>Supplemental Table S2.</strong> Least square means of BW, DMI, milk yield (MY) and fat- and protein-corrected milk yield (FPCMY), milk protein yield (MPY), milk composition (milk fat, milk protein, milk lactose and MUN), milk N efficiency (MNE) and feed efficiency for the CTRL, MetLys and MetLysHis treatment groups in the pre-experimental, depletion and cross-back period.</p>
Cross Validation of Body Composition Measurement
ClinicalTrials.gov study NCT06453564. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Body Composition and Lipid Metabolism at Rest and During Exercise: A Cross-Sectional Analysis.
ClinicalTrials.gov study NCT03029364. IPD Sharing: UNDECIDED. Countries: 1. Publications: 21.
Impact of smoking cessation, coffee and bread consumption on the intestinal microbial composition among Saudis: A cross-sectional study
Open the record for dataset details and reuse information.
CCAN Assembly Configures Composite Binding Interfaces to Promote Cross-Linking of Ndc80 Complexes at the Kinetochore
GEO Series GSE86587. Saccharomyces cerevisiae. 28 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Cross-species single-cell transcriptomic analysis reveals divergence of cell composition and functions in mammalian ileum epithelium
GEO Series GSE196663. Rattus norvegicus; Sus scrofa; Mus musculus; Macaca mulatta. 4 samples. Type: Expression profiling by high throughput sequencing.
Single-nucleus transcriptomic sequencing and cross integration comparison of multiple species revealed new cell composition and specific gene marker in hippocampal aging of tree shrew
GEO Series GSE229035. Tupaia belangeri. 15 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
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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.