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575 results for “Quality assessment”
Genetic diversity, population structure, and linkage disequilibrium among tropical quality protein maize (QPM) lines assessed with high-density SNP markers
<p>The study of genetic diversity (GD), population structure, and linkage disequilibrium (LD) provides a better understanding of the genetic relationships between individuals in a population which can be utilized in crop research and improvement. Genotyping-by-sequencing (GBS) was used to detect and genotype single nucleotide polymorphisms (SNPs) in a collection of 74 quality protein maize (QPM) lines and further to characterize their genetic diversity, population structure, and linkage disequilibrium. A total of 235,214 high-quality SNPs were used for different genetic analyses except for structure analysis where 11,950 SNPs were used. Analysis of molecular variance (AMOVA) based on these SNPs revealed high genetic heterozygosity among the five populations with 1% of the total genetic variation present among the subpopulations and 99% of the variation among individuals within the populations. Population structure analysis using Bayesian-based clustering revealed that the 74 lines could be clustered into four groups. However, neighbor-joining trees indicate the lines are grouped into three major clusters. Further analysis using principal component analyses (PCA) clustered the genotypes into five groups which are concordant with the groups based on pedigree information. Higher genetic diversity was detected in population 1 with a GD value of 0.484 and the lowest in population 5 (0.396) and overall, with a mean of 0.434. The LD pattern in the quality protein maize was investigated and we observed a relatively rapid LD decay of 3.53kb and 10.66kb at r<sup>2</sup> =0.2 and r<sup>2</sup>= 0.1, respectively. Our findings provide important information for future Linkage mapping studies, genome-wide association analyses, and marker-assisted selective breeding of maize as well as genomic prediction-based selection in tropical germplasm.</p>
FISHPASS ASSESSMENT PLAN LONG-TERM MONITORING OF HYDROLOGIC AND WATER QUALITY DATA
The Great Lakes Fishery Commissions’ (GLFC) FishPass project seeks to reconnect the waterscape for only desired species (i.e., selective passage) by integrating a multitude of existing and novel passage techniques and technologies. The probability of a fish passing through a sorting system is dependent on environmental conditions and a fish’s motivation ─ its internal state in relation to environmental stimuli. While fish decision making abilities introduce complexity to the sorting operations, they also provide an opportunity to exploit behavioral tendencies and abilities to achieve selective sorting. The FishPass Assessment Plan details a monitoring program aimed at quantifying fish movement and sorting capabilities associated with both individual mechanisms and integrated sorting systems. The results of the monitoring program will be used to inform future adjustments to the selection of techniques and technologies and their configuration to optimize passage of desirable species while blocking and/or removing undesirable species. A key component to the Assessment Plan is the long-term monitoring of abiotic variables in and around FishPass. This data set contains the hydrologic (e.g., river discharge, water level) and water quality data (e.g., temperature, specific conductivity, conductivity, and turbidity) collected at mostly static stations throughout the Boardman/Ottaway River. The dataset is updated annually. These data are collected until the initiation and/or substantial completion of the FishPass structure. Collection of this type of data are expected to continue after FishPass construction completion but modifications to the extent and location of monitoring stations are anticipated. As a result, a new dataset will be updated in the future containing all long term hydrologic and water quality monitoring post construction. R. Swanson, GLFC Assessment Biologist, is primarily responsible for maintaining the monitoring equipment, data retrieval, quality assuran
A Tool for Uncertainty Quantification in Reconstructing Sparse Water Quality Time Series Data to Assess Risk Metrics for Watershed Health and TMDL Analysis
<p>The uploaded file contains the input and output data which can be used to reproduce the results in the research article 'Uncertainty Quantification in Reconstruction of Sparse Water Quality Time Series: Implications for Watershed Health and Risk-Based TMDL Assessment'. Please refer to the file '<a href="https://zenodo.org/api/files/31b59cce-8eb2-4ee7-93aa-61474c6f6359/dst_2019_SJRW_TP_TDS.zip?versionId=2af2b54d-d5fb-4720-919d-de2e827595e2">dst_2019_SJRW_TP_TDS.zip'</a> for updated files..</p>
Metadata Quality Assessment Results of data.europa.eu in November 2023
<h2>Description</h2> <p>Metadata Quality Assessment Results of data.europa.eu in November 2023 to evaluate the quality of the datasets harvested by the European portal in terms of FAIR principles</p> <h2>Paper</h2> <ul> <li>Paper: <a href="https://arxiv.org/abs/2402.06693">Fostering the integration of European Open Data into Data Spaces through High-Quality Metadata</a></li> <li>Cite:</li> </ul> <p><code>@misc{conde2024fostering,</code><br><code> title={Fostering the integration of European Open Data into Data Spaces through High-Quality Metadata}, </code><br><code> author={Javier Conde and Alejandro Pozo and Andrés Munoz-Arcentales and Johnny Choque and Álvaro Alonso},</code><br><code> year={2024},</code><br><code> eprint={2402.06693},</code><br><code> archivePrefix={arXiv},</code><br><code> primaryClass={cs.DB}</code><br><code>}</code></p> <p> </p>
High quality figures of "Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China"
<p>This repository provides the figures for the publication "Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China" in their original resolution, ensuring clarity and high-quality visual representations for readers.</p>
Data for the submitted paper by Yasunari et al., "Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments"
<p>The dataset contains some of the outputs from the global climate model experiments by MIROC5 on changing Siberian wildfire severities, the other data used in the paper (see READ_ME files on the data sources), the analyzed data, and the scripts for analyses, which were used in the following submitted paper. Note that this dataset also includes unused data for the paper:</p> <p><br>Yasunari, T. J., D. Narita, T. Takemura, S. Wakabayashi, and A. Takeshima, Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments, submitted.</p> <p>Please read the READ_ME files for detailed information in each directory (especially see the "about_figures_and_tables/" directory first). Because of their large sizes, the data were separated into three zipped files.</p>
Data for: D3.6 - Assessment of organoleptic and nutritional quality of fish products from the demonstration tests
<p>Data for: D3.6 - Assessment of organoleptic and nutritional quality of fish products from the demonstration tests </p> <p>https://ifishienci.eu/wp-content/uploads/2024/01/iFishIENCi_D3.6.pdf</p> <p>Corresponding Author</p> <p>Name: Anneli Rost<br>ttz Bremerhaven, Germany<br>Address: Knurrhahnstraß 22-24 /Packhalle X 27572 Bremerhaven<br>Email: arost@ttz-bremerhaven.de</p>
Supplementary materials for the paper "Hyperstyle : A Tool for Assessing the Code Quality of Solutions to Programming Assignments"
<p>The set of the artefacts for a SIGCSE-2022 paper. The SIGCSE_atrifacts.pdf document contains detailed information about each folder, also this document has supplementary materials.</p> <p>Folders:<br> - comparison. This folder contains a public dataset in Java for comparison of our code assessment tool with the Tutor tool.<br> - dynamics. This folder contains two datasets of Python and Java public submissions from Stepik and Hyperskill platforms to check the influence of our tool on students code style.<br> - plots. This folder contains examples of charts that were plotted to analyze the tool thresholds.</p> <p>Supplementary materials:<br> - Examples of code quality issues categories. This section in the document contains code snippets with examples of issues for each code quality issue category for Python and Java.<br> - List of available subcategories in the tool.<br> - Tables with penalty coefficients for detection of recurring errors algorithm<br> - Examples of charts that were plotted to analyze the tool thresholds. This section is the same with the plots folder.</p>
Experimental assessment of the mixing quality in a circular cross-sectional t-shaped mixer for the precipitation of sparingly soluble compounds
<p>Precipitation processes have been successfully proved to be a feasible route to extract high-value products from wastes. However, reactive crystallization is characterized by fast kinetics, especially when highly concentrated solutions are used or sparingly soluble compounds precipitate, thus requiring a fast mixing of the reactants. In this context, T-shaped mixers have been extensively investigated both experimentally and numerically as a mean to achieve a rapid reactants homogenization. Many research efforts have been focused on the analysis of rectangular cross-sectional T-shaped mixers, while less attention has been devoted to the study of circular cross-sectional ones. In the present work, mixing times in a 3 mm circular cross-sectional T-shaped mixer have been optically estimated by exploiting the extremely fast neutralization reaction between sodium hydroxide and hydrochloric acid solutions. Reynolds numbers ranging between 1,000 and 6,000 were investigated. Mixing phenomena were (i) captured by detecting the colour change of a pH indicator by means of a high frame rate camera and (ii) quantified by a suitable image analysis technique. The assessed mixing times were found in accordance with available data in the literature.</p>
Fig. 2 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 2. Linear relationship (solid line) and 95 % confidence interval (gray area) between habitat quality predicted by the BART model (x-axis) and shell height (H in millimeters, y-axis), derived from the linear mixed model.
Fig. 4. Partial dependence plot for topographic Fig. 5 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 4. Partial dependence plot for topographic Fig. 5. Partial dependence plot for terrain roughness wetness index (TWI). index (tri).
Fig. 6 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 6. Partial dependence plot for pH water (phh2o). Fig. 7. Partial dependence plot for silt content (SLT).
Fig. 3. Partial dependence plot for BIO17 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 3. Partial dependence plot for BIO17 = Precipitation of Driest Quarter; gray area = 95 % confidence interval.
Rapid assessment of lipidomics sample quality and quantity using attenuated total reflectance Fourier-transform infrared spectroscopy
<p>In this work, we aimed to develop a simple lipid quality and quantification method for biological lipid extracts, as a step in lipidomics workflows, with minimal sample requirement. We chose FTIR spectroscopy with an Attenuate Total Reflectance (ATR) sampling method as it requires just 1 microliter of MS-ready sample without additional sample preparation. We validated the proposed lipidomics sample quality control workflow using a set of plasma samples (n=107, with 3-4 technical replicates) with comparison to LC-MS-based lipidomics. The following file contains the resulting spectra acquired by ATR-FTIR spectrometry for these plasma samples, standard curves and contaminated samples used for method development. Spectrometer was ambient blanked and detector cleaned between each measurement. Lipid samples were extracted by butanol-methanol (3:1) precipitation, and dried directly onto the ATR-FTIR detector. Absorbance was measured between 4,000 and 650 cm-1 wavenumbers, at a resolution of 8cm-1. Each spectra has been baseline corrected (whole spectra).</p>
Dataset related to article: QUALITY ASSESSMENT OF THE MRI-RADIOMICS STUDIES FOR MGMT PROMOTER METHYLATION PREDICTION IN GLIOMA: A SYSTEMATIC REVIEW AND META-ANALYSIS
<p><strong><span>This table contains the raw data used to generate the heatmap illustrated in Fig 2. Each row of the table shows the distribution of the scores achieved by the studies for a domain. Colors from red to green denote progressive increase from minimum to maximum score obtainable for each domain.</span></strong></p>
Fig. 8 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 8: Percentage of assigned quality flags for a) temperature and salinity, b) nutrients and c) chlorophyll a in the LTER-MC dataset grouped by period, following the simplified QF scheme.
Fig. 4 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 4: A) Schematic representation of a possible spike (V2) evaluated based on its previous (V1) and subsequent (V3) values of the profile. B) Examples of identified spikes in nitrate profiles at low (pMC 535, -5 m), medium (●M276, -5m) and high (○MC48, -2 m) concentrations. Spikes are in orange.
Fig. 1 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 1: Geographical position of sampling stations for LTER-MC (●), Si.Di.Mar (●) and TYR (●) datasets.
Fig. 2 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 2: Frequency distribution of individual parameters in the LTER-MC dataset before applying QC (TEMP: temperature, PSAL: salinity, NTRI: nitrites, PHOS: phosphates, AMON: ammonia, NTRA: nitrates, SLCA: silicates, CHLT: chlorophyll a, S: skewness and K: kurtosis).
Fig. 9 in Assessing the quality of biogeochemical coastal data: a step-wise procedure Abstract
Fig. 9: Flags (¡ QF0, pQF2, and ÚQF6) for surface nitrates (NO3) concentrations in the Si.Di.Mar. dataset.
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.