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1,940 results for “data sample”
Data from: Estimating abundance of the federally endangered Mitchell’s satyr butterfly using hierarchical distance sampling
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Data from: An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou
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Data from: Phylogenomic incongruence, hypothesis testing, and taxonomic sampling: the monophyly of characiform fishes
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Data from: Genotyping-in-Thousands by sequencing (GT-seq) panel development and application to minimally-invasive DNA samples to support studies in molecular ecology
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Data from: 10 years of fish species sampling in Rouge River, Michigan
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Kenya heel prick and cord blood sample data
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Boardman River 2019 eDNA metabarcoding water sample data
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Data from: A new lineage of Galapagos giant tortoises identified from museum samples
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Data from: Bayesian analyses in phylogenetic palaeontology: interpreting the posterior sample
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Data from: The utility of environmental DNA from sediment and water samples for recovery of observed plant and animal species from four Mojave Desert springs
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Synoptic water chemistry and discharge data for the Glyndon and Baismans Run sampling sites - 2001 - 2002
This is stream water quality and discharge data collected by Steven Kenworthy using flow velocity measurement method. Samples and discharge measurments were taken approximately once per month from June 2001 to June 2002. Locations within Pond Branch, Baisman Run, and Glyndon are denoted in the MetadataCodes Tab of the Excel Workssheet. Flow and Streamwater Sampling Station Codes........ Pond Branch........ PB4 (PBC2)........Upper PB spring (PBC2 was an error in labeling) PB3a........PB @ upper riparian wells PB3........PB below gas line PB2a........PB @ lower riparian wells POBR........Pond Branch at gage Baisman Run........ BR5a........Baisman Run @ upper gage (Red's flume) BR5........Baisman Run Northwest tributary basin BR6........Baisman Run Southwest tributary basin BR4........Northern tributary (west of Pond Branch) BR3........Southern tributary BR7........Baisman Run upstream of Pond Branch Confluence BR2........Pond Branch between pond and yellow trail BARN........Baisman at gage (Ivy Hill Rd.) Gwynns Falls @ Glyndon........ GL9........GF southern tributary (Glyndon Gate) in woods upstream of pond and ditch GL6........GF main channel in forest downstream of log cabin GL4........GF downstream of Sacred Heart Rd. GL3........GF upstream of Chatsworth ave (@ lower riparian wells) GL2 (GFGL side) ........Schoolbus lot / Dyer Rd. tributary GL2a (GFGL main) ........GF above schoolbus trib junction (reg. stream crew did this > sometimes) GFGL/GL1........Gwynns Falls @ Glyndon Gage NO3 and TN are mg of N/L........ Cl and SO4 are mg/L........ PO4 and TP are mg of P/L Concentrations that were below the lowest standard run are reported as follows: NO3 and TN 0.01 CL and SO4 0.05 PO4 and TP 1.5
Synoptic water chemistry and discharge data for the Baismans Run sampling sites - 2007 - 2008
This is stream water quality and discharge data collected by Monica Smith using flow velocity measurment method. Samples and discharge measurments were taken approximately once per month from August 2008 to October 2007. Locations within Baisman Run are denoted below: Department of Geography University of North Carolina at Chapel Hill. UTM 18N BA3-SF1B 353952.4775 4370933.534 BA3-SF2A 353824.1488 4370802.221 BA3-SF2B 353871.899 4370793.268 BA3-SF3A 353746.5547 4370644.048 BA3-SF3B 353794.3049 4370638.08 BA3-SF2R 353991.2746 4370888.768 BA3-SF1L 353979.337 4370918.612 BA3 354500.9953 4371320.992 BA5a-JC1 353278.0057 4371970.732 BA5a-JC2 353474.9753 4372018.482 BA3-SF1A 353913.6804 4370964.855
Macrosystems Gigante Soil Sample Data on N, P, K and Micronutrient Treated Plots 16S rRNA Resampled
Patterns of biodiversity, such as the increase toward the tropics and the peaked curve during ecological succession, are fundamental phenomena for ecology. Such patterns have multiple, interacting causes, but temperature emerges as a dominant factor across organisms from microbes to trees and mammals, and across terrestrial, marine, and freshwater environments. However, there is little consensus on the underlying mechanisms, even as global temperatures increase and the need to predict their effects becomes more pressing. The purpose of this project is to generate and test theory for how temperature impacts biodiversity through its effect on biochemical processes and metabolic rate. A combination of standardized surveys in the field and controlled experiments in the field and laboratory measure diversity of three taxa -- trees, invertebrates, and microbes -- and key biogeochemical processes of decomposition in seven forests distributed along a geographic gradient of increasing temperature from cold temperate to warm tropical. This data set captures abundance of OTUs (Operational Taxonomic Units) sampled for in forest soils at the Gigante Peninsula plots in Panama. Prior to macrosystems collection, these plots had been fertilized with N, P, K, and micronutrients for 14 years. This data represents abundance of 16S rRNA genes in soil samples at Gigante processed by the University of Oklahoma Institute for Environmental Genomics as part of a macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836.
Macrosystems Gigante Soil Sample Data on N, P, K and Micronutrient Treated Plots - ITS Resampled
Patterns of biodiversity, such as the increase toward the tropics and the peaked curve during ecological succession, are fundamental phenomena for ecology. Such patterns have multiple, interacting causes, but temperature emerges as a dominant factor across organisms from microbes to trees and mammals, and across terrestrial, marine, and freshwater environments. However, there is little consensus on the underlying mechanisms, even as global temperatures increase and the need to predict their effects becomes more pressing. The purpose of this project is to generate and test theory for how temperature impacts biodiversity through its effect on biochemical processes and metabolic rate. A combination of standardized surveys in the field and controlled experiments in the field and laboratory measure diversity of three taxa -- trees, invertebrates, and microbes -- and key biogeochemical processes of decomposition in seven forests distributed along a geographic gradient of increasing temperature from cold temperate to warm tropical. This data set captures abundance of OTUs (Operational Taxonomic Units) sampled for in forest soils at the Gigante Peninsula plots in Panama. Prior to macrosystems collection, these plots had been fertilized with N, P, K, and micronutrients for 14 years. This data represents abundance of ITS (fungi) genes in soil samples at Gigante processed by the University of Oklahoma Institute for Environmental Genomics as part of a macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836.
RYDL: the sample data of the RY product for deep learning applications
<p>RYDL is the sample data of the RY product provided by the German Weather Service (DWD). This data has been utilized for training the RainNet model -- a deep convolutional neural network for radar-based precipitation nowcasting.</p> <p>You can find the latest RY product data on DWD open data repository: https://opendata.dwd.de/weather/radar/.</p> <p>The RainNet model is available on GitHub: https://github.com/hydrogo/rainnet.</p>
Supporting publication for 'Prevalence sample-based guidance for reporting 2019 data'
<p>These two Excel documents help you to map terms from the matrix catalogue ZOO_CAT_MATRIX used in the aggregated prevalence data model to FoodEx2 codes and offer you examples on how prevalence data can be reported using SSD2</p>
Raw data related to: Sample Size for Oxidative Stress and Inflammation When Treating Multiple Sclerosis with Interferon-b1a and Coenzyme Q10
<p><strong>Abstract </strong></p> <p>Studying multiple sclerosis (MS) and its treatments requires the use of biomarkers for underlying pathological mechanisms. We aim to estimate the required sample size for detecting variations of biomarkers of inflammation and oxidative stress. This is a post-hoc analysis on 60 relapsing-remitting MS patients treated with Interferon-1a and Coenzyme Q10 for 3 months in an open-label crossover design over 6 months. At baseline and at the 3 and 6-month visits, we measured markers of scavenging activity, oxidative damage, and inflammation in the peripheral blood (180 measurements). Variations of laboratory measures (treatment eect) were estimated using mixed-eect linear regression models (including age, gender, disease duration, baseline expanded disability status scale (EDSS), and the duration of Interferon-1a treatment as covariates; creatinine was also included for uric acid analyses), and were used for sample size calculations. Hypothesizing a clinical trial aiming to detect a 70% eect in 3 months (power <strong>= </strong>80% alpha-error <strong>= </strong>5%), the sample size per treatment arm would be 1 for interleukin (IL)-3 and IL-5, 4 for IL-7 and IL-2R, 6 for IL-13, 14 for IL-6, 22 for IL-8, 23 for IL-4, 25 for activation-normal T cell expressed and secreted (RANTES), 26 for tumor necrosis factor (TNF)-, 27 for IL-1, and 29 for uric acid. Peripheral biomarkers of oxidative stress and inflammation could be used in proof-of-concept studies to quickly screen the mechanisms of action of MS treatments.</p> <p><strong>Progetto giovani ricercatori </strong>[GR-2016-02363725] dal titolo: "Immune Tolerance, Metabolism and Multiple Sclerosis: Novel Molecular Tools to Monitor Disease Pathogenesis and Progression"</p>
Data Scimago 2017 sample
<p>Esto es una prueba</p>
Raw data on Elements of eating pattern and intensity of dysmenorrhea – cross sectional study in sample of Polish women
<p>Raw data used in the analysis on the paper "Elements of eating pattern and intensity of dysmenorrhea - a cross-sectional study in sample of Polish women".</p>
Field and Genetic Profile Data for Grizzly Bear Hair-Snag Sampling and Scat Sampling along Roads in North America
<p>These datasets contain field or genetic profile data for grizzly bear hair-snag sampling and scat sampling along roads in North America. See method details in the published article: Phoebus et al. 2020. A Comparison of Grizzly Bear Hair-Snag Sampling and Systematic Scat Sampling along Roads to Inform Wildlife Population Monitoring. Wildlife Biology, WLB-00697. </p> <p>An inadequate sample in the field data means that the particular lab test failed (i.e. species, sex or individual identification test) likely due to poor sample quality (i.e. too little DNA in the sample). </p>
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.