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46 results for “statistical methods”
Statistical analysis and dataset for: A high-throughput and sensitive method for food preference assays in walking insects
<p>Linked to the journal article published in bioRxiv (https://doi.org/10.1101/2024.04.10.588882).</p> <p><em><strong>Abstract</strong></em></p> <p>Insects pose significant challenges in both pest management and ecological conservation. Often, the most effective strategy is employing toxicant-laced baits, which must also be designed to specifically attract and be preferred by the targeted species for optimal species-specific effectiveness. However, traditional methods for measuring bait preference are either non-comparative, meaning that most animals only ever taste one bait, or suffer from methodological or conceptual limitations. Here we demonstrate the value of direct comparison food preference assays using the invasive and pest ant <em>Linepithema humile </em>as a model. We compare the food preference sensitivity of non-comparative (one visit to a food source) and sequential comparative (visiting one type of food then another) assays at detecting low levels of aversive quinine in sucrose solution. We then introduce and test a novel dual-choice feeder method for simultaneous comparative evaluation of bait preferences, testing its effectiveness in discerning between foods with varying quinine or sucrose levels. While the non-sequential assay could not detect aversion to 1.25mM quinine in 1M sucrose, the sequential comparative approach detected aversion to quinine levels as low as 0.94mM. The novel dual feeder method approach could detect aversion to quinine levels as low as 0.31mM, and also preference for 1M sucrose over 0.75M sucrose. The dual-feeder method, combines the sensitivity of comparative evaluation with high throughput, ease of use, and avoidance of interpretational issues. This innovative approach offers a promising tool for rapid and effective testing of bait solutions, contributing to the development of targeted control strategies. Moreover, the method can be easily modified for application to a wide range of walking insects, such as cockroaches, crickets, and beetles.</p>
Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors: V4.NA.02.MAPLE
<p>We estimate ground-level fine particulate matter (PM<sub>2.5</sub>) total and compositional mass concentrations over North America by combining Aerosol Optical Depth (AOD) retrievals from the NASA MODIS, MISR, and SeaWIFS instruments with the GEOS-Chem chemical transport model, and subsequently calibrated to regional ground-based observations of both total and compositional mass using Geographically Weighted Regression (GWR) as detailed in the provided reference for V4.NA.02. V4.NA.02.MAPLE further modified the V4.NA.02 GWR method with additional developments as part of the MAPLE (Mortality–Air Pollution Associations in Low-Exposure Environments) project. This adjustment was of particular value over low concentrations. The GWR method of individual components remains unchanged from V4.NA.02, but are provided are percentages to ensure mass closure and recommended to be applied to the V4.NA.02.MAPLE total PM<sub>2.5</sub>.</p> <p>Annual datasets are provided in NetCDF [.nc]. Gridded files use the WGS84 projection. Compositional estimates are provided for sulfate (SO4), nitrate (NO3), ammonium (NH4), organic matter (OM), black carbon (BC), mineral dust (DUST), and sea-salt (SS). Percentages are denoted with a ‘p’ after component identifiers within filenames. A slight change in file name has been included for 2017, corresponding to minor internal changes compared to earlier years. Overall, however, the dataset is consistent throughout its entire time period and can be appropriately used for trend analysis.</p> <p><strong>Reference:</strong><br> van Donkelaar, A., R. V. Martin, et al. (2019). <strong>Regional Estimates of Chemical Composition of Fine Particulate Matter using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors.</strong> Environmental Science & Technology, 2019, doi:10.1021/acs.est.8b06392.</p>
Dataset for "Reflectance spectra of seven lunar swirls examined by statistical methods: A space weathering study"
<p>This archive corresponds to the source code, raw data, and results described in the article "Reflectance spectra of seven lunar swirls examined by statistical methods: A space weathering study" by Chrbolková et al. (2019) published in Icarus journal. See AA_README.txt for more information.</p>
Raw data used for COI delineation of the Eupolybothrus species: Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
<p>Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar</p>
Рис. 10. Блок-схема фиЗико-статистического прогноЗа уроЖайности спата приморского гребешка. Fig. 10. The block diagram of physical-statistical forecast of yield of spat of the Japanese scallop. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 10. Блок-схема фиЗико-статистического прогноЗа уроЖайности спата приморского гребешка. Fig. 10. The block diagram of physical-statistical forecast of yield of spat of the Japanese scallop.
Рис. 1. Вероятность обнаружения меченых животных (среΑнее ± ошибка) при пяти- и Αесятиметровых интерваΛах межΑу прикормочными станциями в Αвух экспериментах. По второму эксперименту расчеты сΑеΛаны ΑΛя резуΛьтатов отΛова в течение первых трех и поΛных Αесяти Αней. Значение «p» отражает уровень статистической значимости разΛичий межΑу ΑоΛями животных с меткой при Αвух интерваΛах Fig. 1. Probability of finding marked animals (average±standard error) between feeding stations placed at intervals of five and ten meters in the two experiments. In the second experiment, calculations were made for the results of trapping during the first three days and during the whole period of ten days. The p value reflects the statistical significance of differences between the fractions of animals with a mark for two types of intervals in Verification of the bottle-based method for estimating abundance of small mammals using biomarkers
Рис. 1. Вероятность обнаружения меченых животных (среΑнее ± ошибка) при пяти- и Αесятиметровых интерваΛах межΑу прикормочными станциями в Αвух экспериментах. По второму эксперименту расчеты сΑеΛаны ΑΛя резуΛьтатов отΛова в течение первых трех и поΛных Αесяти Αней. Значение «p» отражает уровень статистической значимости разΛичий межΑу ΑоΛями животных с меткой при Αвух интерваΛах Fig. 1. Probability of finding marked animals (average±standard error) between feeding stations placed at intervals of five and ten meters in the two experiments. In the second experiment, calculations were made for the results of trapping during the first three days and during the whole period of ten days. The p value reflects the statistical significance of differences between the fractions of animals with a mark for two types of intervals
Statistical Methods for Identifying Sequence Motifs Affecting Point Mutations
<p>Scripts and derived data for the indicated paper. Links to the <a href="https://zenodo.org/record/1204695">original data</a> and <a href="https://zenodo.org/record/3497585">library source code</a> used to generate this material. Inflate the ENU_mutation_classification.tar.gz archive, remove the ENU_mutation_classification/classifier directory (this will be replaced). Move all others archives into the ENU_mutation_classification directory and inflate them.</p>
Figure 7 in Taxonomic notes on the Asian frogs of the tribe Paini (Ranidae, Dicroglossinae): 1. Morphology and synonymy of Chaparana aenea (Smith, 1922), with proposal of a new statistical method for testing homogeneity of small samples
Figure 7. Chaparana aenea (Smith, 1922), MNHN 1999.5821, topotype of Rana (Chaparana) fansipani Bourret, 1939, adult male from Fan Si Pan, Vietnam: ventral view of chest and throat showing shape of patch of nuptial spines.
Figure 6 in Taxonomic notes on the Asian frogs of the tribe Paini (Ranidae, Dicroglossinae): 1. Morphology and synonymy of Chaparana aenea (Smith, 1922), with proposal of a new statistical method for testing homogeneity of small samples
Figure 6. Plots of factors 1 and 2 of principal component analysis based on varimax rotated coefficients for logtransposed characters (25 measurements) for four adult specimens of Chaparana aenea (Smith, 1922) (see Table II) and six adult specimens of Chaparana unculuanus (Liu, Hu and Yang, 1960) (see Table IV).
Figure 3 in Taxonomic notes on the Asian frogs of the tribe Paini (Ranidae, Dicroglossinae): 1. Morphology and synonymy of Chaparana aenea (Smith, 1922), with proposal of a new statistical method for testing homogeneity of small samples
Figure 3. Chaparana aenea (Smith, 1922). (a–c) MNHN 1989.0712, non-breeding adult male from Doi Inthanon, Chiangmai Province, Thailand: (a) whole body in dorsal view; (b) whole body in ventral view; (c) head in right lateral view. (d–f) MNHN 1948.0139, holotype of Rana (Chaparana) fansipani Bourret, 1939, juvenile male from Fan Si Pan, Vietnam: (d) whole body in dorsal view; (e) whole body in ventral view; (f) head in right lateral view.
Figure 4 in Taxonomic notes on the Asian frogs of the tribe Paini (Ranidae, Dicroglossinae): 1. Morphology and synonymy of Chaparana aenea (Smith, 1922), with proposal of a new statistical method for testing homogeneity of small samples
Figure 4. (a–c) Chaparana aenea (Smith, 1922), MNHN 1999.5818, topotype of Rana (Chaparana) fansipani Bourret, 1939, adult male from Fan Si Pan, Vietnam: (a) whole body in dorsal view; (b) whole body in ventral view; (c) head in right lateral view. (d–f) Chaparana unculuanus (Liu, Hu and Yang, 1960), MNHN 2001.0280, adult female from Jingdong Xian, Yunnan, China: (d) whole body in dorsal view; (e) whole body in ventral view; (f) head in right lateral view.
Figure 1 in Taxonomic notes on the Asian frogs of the tribe Paini (Ranidae, Dicroglossinae): 1. Morphology and synonymy of Chaparana aenea (Smith, 1922), with proposal of a new statistical method for testing homogeneity of small samples
Figure 1. Chaparana aenea (Smith, 1922). (a–c) BMNH 1947.2.2.31 (ex BMNH 1922.7.4.4, ex MAS 5821), holotype of Rana aenea Smith, 1922, juvenile female from Doi Chang, Thailand: (a) whole body in dorsal view; (b) whole body in ventral view; (c) head in right lateral view. (d–f) MNHN 1999.5821, topotype of Rana (Chaparana) fansipani Bourret, 1939, adult male from Fan Si Pan, Vietnam: (d) whole body in dorsal view; (e) whole body in ventral view; (f) head in right lateral view.
A new method for quantifying flake scar organisation on cores using orientation statistics
<p>Data and R code for reproducing the results presented in 'in “A new method for quantifying flake scar organisation on cores using orientation statistics” by Lin et al.</p>
Statistical methods and data for: Absence of heterosis for hypoxia tolerance in F1 hybrids of Tigriopus californicus
Open the record for dataset details and reuse information.
Efficient statistical method for single-cell QTL analysis
<p>This upload contains data objects associated with our paper "Efficient statistical method for single-cell QTL analysis" introducing the SAIGE-QTL method (preprint available soon!).</p>
Results of statistical analyses of the comparison of phosphorous measurement laboratory methods
<p>The study in the related article compares the phosphorus (P) analysis methods of ammonium lactate (AL), Mehlich 3 (M3); water extraction (P-WA(P)&P-WA(PO<sub>4</sub>)), cobalt hexamine (CoHex) and X-ray fluorescence (XRF, as an estimate of total soil P). The ratio of the P-content/XRF was calculated and compared with the whole dataset first. Based on the comparison of all the data there were significant differences between the results of P-WA(P) and P-WA(PO<sub>4</sub>) vs M3 and AL, CoHex vs M3 and CoHex vs AL methods (p<0.001). The influencing factors were also analysed for a more in-depth understanding of their role (CaCO<sub>3</sub>-content, pH, soil texture and clay content). The file contains the results of the statistical analyses.</p>
Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions
<p>This repo includes the GEOS-Chem simulations and R scripts that are needed to replicate and evaluate the conclusions from Qiu, Zigler, and Selin, ACP, 2022 "Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions".</p> <p><strong>The GEOS-Chem simulations</strong></p> <ul> <li>For the US (2011-2017): <ul> <li><em>observational_o3_pm_2011_2017_us.rds:</em> the simulated daily PM2.5 and O3 concentrations, and MERRA-2 meteorological features in the observational scenarios (<strong>changing</strong> meteorology, <strong>changing </strong>emissions).</li> <li><em>counterfactual_o3_pm_2011_2017_us.rds</em><em>:</em> the simulated daily PM2.5 and O3 concentrations, and MERRA-2 meteorological features in the counterfactual scenarios (<strong>constant</strong> meteorology, <strong>changing</strong> emissions).</li> <li><em>constant_emis_o3_pm_2012_2017_us.rds: </em>the simulated daily PM2.5 and O3 concentrations in the constant-emission scenarios (<strong>constant</strong> meteorology, <strong>constant</strong> emissions).</li> <li><em>regional_features_2011_2017_4x5_us.rds: </em>the MERRA-2 meteorological features in the observational scenarios (aggregated to 4x5 degrees), inputs for the "RF-regional" model.</li> </ul> </li> <li>For China (2013-2017): <ul> <li><em>observational_o3_pm_2013_2017_china.rds:</em> the simulated daily PM2.5 and O3 concentrations, and MERRA-2 meteorological features in the observational scenarios (<strong>changing</strong> meteorology, <strong>changing </strong>emissions).</li> <li><em>counterfactual_o3_pm_2013_2017_china.rds:</em> the simulated daily PM2.5 and O3 concentrations, and MERRA-2 meteorological features in the counterfactual scenarios (<strong>constant</strong> meteorology, <strong>changing</strong> emissions).</li> <li><em>constant_emis_o3_pm_2014_2017_china.rds</em><em>: </em>the simulated daily PM2.5 and O3 concentrations in the constant-emission scenarios (<strong>constant</strong> meteorology, <strong>constant</strong> emissions).</li> <li><em>regional_features_2013_2017_4x5_china.rds: </em>the MERRA-2 meteorological features in the observational scenarios (aggregated to 4x5 degrees), inputs for the "RF-regional" model.</li> </ul> </li> </ul> <p><strong>R scripts:</strong></p> <ul> <li><a href="https://zenodo.org/api/files/065be469-ef8d-4c9b-9bd8-a6a808275237/main.r">main.r</a>: the main script to perform statistical correction of meteorological variability.</li> <li>main.r uses functions from the other R script files (see below) which perform different statistical correction methods, respectively. </li> <li><a href="https://zenodo.org/api/files/065be469-ef8d-4c9b-9bd8-a6a808275237/parametric_regression_methods.r">parametric_regression_methods.r</a>: performs meteorological correction with parametric regression methods (MLR, polynomial, spline, GAM)</li> <li><a href="https://zenodo.org/api/files/065be469-ef8d-4c9b-9bd8-a6a808275237/tune_RF_regional.r">tune_RF_regional.r</a> and <a href="https://zenodo.org/api/files/065be469-ef8d-4c9b-9bd8-a6a808275237/RF_regional.r">RF_regional.r</a>: perform the meteorological correction with the "RF-regional" model</li> <li><a href="https://zenodo.org/api/files/065be469-ef8d-4c9b-9bd8-a6a808275237/GEOS_Chem_constant_emis.r">GEOS_Chem_constant_emis.r</a>: performs the meteorological correction using the simulations from the constant emission scenarios from the GEOS-Chem model</li> </ul> <p> </p>
Experimental and synthetic datasets supporting FITSA: Statistical analysis of fluorescence intensity transients with Bayesian methods
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Statistical model training data for "Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection"
<p>Gzipped CSV files containing convection scheme inputs and outputs used for training.</p> <p>Column format of each file:</p> <p>THETA_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,Q_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DTHETA_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DQ_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28</p> <p>where THETA_IN are input values of potential temperature [K], Q_IN are input values of specific humidity [kg/kg], DTHETA are changes in potential temperature due to convection [K], DQ are changes in specific humidity due to convection [kg/kg].</p> <p>Key:</p> <p>"llcs" are simulations with Lambert-Lewis.</p> <p>"gr" are simulations with Gregory-Rowntree.</p> <p>"4xco2" have 4 x pre-industrial atmospheric carbon dioxide concentration. (Others have 1 x pre-industrial atmospheric carbon dioxide concentration.)</p> <p>"rh0.7" and "rh0.9" have LLCS RHCRIT set to 70% and 90% respectively.</p> <p>All are 30 day simulations either for January "jan" or July "jul".</p> <p> </p>
Comparative analysis of statistical methods used for detecting differential expression in label-free mass spectrometry proteomics - Data Supplement
<p>This the is Data Supplement for the article "Comparative analysis of statistical methods used for detecting differential expression in label-free mass spectrometry proteomics" submitted to the Journal of Proteomics 2015.</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.