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533 results for “Quality control”
Open-source quality control routine and multi-year power generation data of 175 PV systems
<p><strong>Description</strong></p> <p>The repository contains an extensive dataset of PV power measurements and a python package (qcpv) for quality controlling PV power measurements. The dataset features four years (2014-2017) of power measurements of 175 rooftop mounted residential PV systems located in Utrecht, the Netherlands. The power measurements have a 1-min resolution.</p> <p><strong>PV power measurements</strong></p> <p>Three different versions of the power measurements are included in three data-subsets in the repository. Unfiltered power measurements are enclosed in <em>unfiltered_pv_power_measurements.csv</em>. Filtered power measurements are included as <em>filtered_pv_power_measurements_sc.csv </em>and<em> filtered_pv_power_measurements_ac.csv</em>. The former dataset contains the quality controlled power measurements after running single system filters only, the latter dataset considers the output after running both single and across system filters. The metadata of the PV systems is added in<em> metadata.csv</em>. This file holds for each PV system a unique ID, start and end time of registered power measurements, estimated DC and AC capacity, tilt and azimuth angle, annual yield and mapped grids of the system location (north, south, west and east boundary).</p> <p><strong>Quality control routine</strong></p> <p>An open-source quality control routine that can be applied to filter erroneous PV power measurements is added to the repository in the form of the Python package qcpv (<em>qcpv.py</em>). Sample code to call and run the functions in the qcpv package is available as <em>example.py.</em></p> <p><strong>Objective</strong></p> <p>By publishing the dataset we provide access to high quality PV power measurements that can be used for research experiments on several topics related to PV power and the integration of PV in the electricity grid.</p> <p>By publishing the qcpv package we strive to set a next step into developing a standardized routine for quality control of PV power measurements. We hope to stimulate others to adopt and improve the routine of quality control and work towards a widely adopted standardized routine. </p> <p><strong>Data usage</strong></p> <p>If you use the data and/or python package in a published work please cite: <em>Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2022. Open-source quality control routine and multi-year power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy.</em></p> <p><strong>Units</strong></p> <p>Timestamps are in UTC (YYYY-MM-DD HH:MM:SS+00:00).</p> <p>Power measurements are in Watt.</p> <p>Installed capacities (DC and AC) are in Watt-peak.</p> <p><em><strong>Additional information</strong></em></p> <p>A detailed discussion of the data and qcpv package is presented in: <em>Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2022. Open-source quality control routine and multi-year power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy. Corrections are discussed in: Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2024. </em><em>Erratum: Open-source quality control routine and multiyear power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy.</em></p> <p><strong>Acknowledgements </strong></p> <p>This work is part of the Energy Intranets (NEAT: ESI-BiDa 647.003.002) project, which is funded by the Dutch Research Council NWO in the framework of the Energy Systems Integration & Big Data programme. The authors would especially like to thank the PV owners who volunteered to take part in the measurement campaign. </p>
Dataset for "Quality and Contamination control" workflow
<p>This dataset is associated with the workflow "Quality and Contamination control for paired end data".</p>
Figure 2 in Quality control of the predatory mite Euseius scutalis (Acari: Phytoseiidae) againstTetranychus turkestani (Acari: Tetranychidae) over 30 generations of rearing on cattail pollen
Figure 2 The age-specific survivorship (lx), age-stage specific fecundity of femalesf(xj) (eggs) and age-specific fecundity (mx) of Euseius scutalis fedTetranychus turkestani before (G0) and after (G10 –G30) long-term rearing on cattail pollen.
Fig. 5. A in Quality Control Aspects in Relation to Rearing of Moths New rearing method and larval diet for the mahogany shoot borer Hypsipyla grandella (Lepidoptera: Pyralidae)
Fig. 5. A) Average (± SE) weight of 3rd instar larvae, and B) percent survival of larvae reared on fresh cedar leaves or on artificial diet either in well plates or in Petri dishes.
Fig. 4. A in Quality Control Aspects in Relation to Rearing of Moths New rearing method and larval diet for the mahogany shoot borer Hypsipyla grandella (Lepidoptera: Pyralidae)
Fig. 4. A) Average (± SE) 3rd instar larval weight, and B) percent survival of larvae reared on fresh cedar leaves or on artificial diet in well plates.
Fig. 2. A in Quality Control Aspects in Relation to Rearing of Moths New rearing method and larval diet for the mahogany shoot borer Hypsipyla grandella (Lepidoptera: Pyralidae)
Fig. 2. A) Average (± SE) pupal weight (g), and B) average (± SE) number of d to pupation and emergence of insects reared on original or modified diets.
Fig. 3 in Quality Control Aspects in Relation to Rearing of Moths New rearing method and larval diet for the mahogany shoot borer Hypsipyla grandella (Lepidoptera: Pyralidae)
Fig. 3. Average (± SE) numbers of fertile eggs, infertile eggs and hatched larvae of insects reared either on the original diet or on the new modified diet.
The loss of SMG1 causes defects in quality control pathways in Physcomitrella patens
<p>Nonsense-mediated mRNA decay (NMD) is important for both RNA quality control and gene regulation. NMD targets aberrant mRNA transcripts for decay and also directly influences the abundance of specific, non-aberrant transcripts in a wide range of eukaryotes. In animals, the PIK kinase, SMG1, plays an essential role in NMD, by phosphorylating the core NMD effector, UPF1. Despite being absent from the genome of the model plant, <em>Arabidopsis thaliana</em>, SMG1 is ubiquitous throughout the plant kingdom. Here we utilize RNA-seq to reveal the full range of processes involving <em>SMG1</em> in plants. In NMD-compromised, <em>smg1</em> mutant moss, 30% of multi-isoform genes produce NMD targeted transcript isoforms. Taking a machine learning approach, we show that an exon-exon junction downstream of a stop codon acts as the major feature to target transcripts to NMD and that retained intron isoforms are underrepresented among NMD targets. Furthermore, we show that <em>SMG1</em> is involved in other quality control pathways, affecting DNA repair and the unfolded protein response, in addition to its role in mRNA quality control. <em>smg1</em> plants have increased susceptibility to DNA damage, but increased tolerance to unfolded protein inducing agents. The involvement of SMG1 in RNA, DNA and protein quality control has major implications for the study of these processes in plants.</p>
A Galaxy-based training resource for single-cell RNA-seq quality control and analyses
<p>This is the tutorial data for the 'Single-cell quality control with scater' tutorial on the Galaxy Training Network. The data is the same dataset that is used as the inbuilt example dataset within scater, but has been implemented as individual files.</p>
Fig. 2 in Applications of molecular diagnostics for quality control in rearing of Spodoptera frugiperda (Lepidoptera: Noctuidae) larvae for experimental use
Fig. 2. Relative abundance of diversity of viruses found in the metagenomic analysis of dead and healthy larvae of Spodoptera frugiperda.
Fig. 1 in Applications of molecular diagnostics for quality control in rearing of Spodoptera frugiperda (Lepidoptera: Noctuidae) larvae for experimental use
Fig. 1. Relative abundance of bacterial diversity found in samples of healthy (LH1 and LH2) and dead larvae (LD1 and LD2) of Spodoptera frugiperda.
Supporting data for "Quality control for the target decoy approach for peptide identification"
<p>Supporting data for the manuscript "Quality control for the target decoy approach for peptide identification". Input files are raw mass spectrometry runs, to be downloaded from the PRIDE Archive (https://www.ebi.ac.uk/pride/), which can be processed with the parameter files, Nextflow workflow, and Python scripts in "workflow-scripts-parameters.zip". The resulting output files that were used for the manuscript are provided in search-results.zip.</p>
Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation
<p>1. Animal tracking data are indispensable for understanding the ecology, behaviour and physiology of mobile or cryptic species. Meaningful signals in these data can be obscured by noise due to imperfect measurement technologies, requiring rigorous quality control as part of any comprehensive analysis. </p> <p>2. State-space models are powerful tools that separate signal from noise. These tools are ideal for quality control of error-prone location data and for inferring where animals are and what they are doing when they record or transmit other information. However, these statistical models can be challenging and time-consuming to fit to diverse animal tracking data sets. </p> <p>3. The R package <em><span>aniMotum</span></em> eases the tasks of conducting quality control on and inference of changes in movement from animal tracking data. This is achieved via: 1) a simple but extensible workflow that accommodates both novice and experienced users; 2) automated processes that alleviate complexity from data processing and model specification/fitting steps; 3) simple movement models coupled with a powerful numerical optimization approach for rapid and reliable model fitting. </p> <p>4. We highlight <em>aniMotum</em>'s<em> </em>capabilities through three applications to real animal tracking data. Full R code for these and additional applications are included as Supporting Information so users can gain a deeper understanding of how to use <em>aniMotum</em> for their own analyses. </p>
Resource quantity and quality differentially control stream invertebrate biodiversity across spatial scales
<p class="MsoNormal"><span>Resource quantity controls biodiversity across spatial scales, however the importance of resource quality to cross-scale patterns in species richness has seldom been explored. We evaluated the relationship between stream basal resource quantity (periphyton chlorophyll-<em>a</em>) and invertebrate richness and compared this to the relationship of resource quality (periphyton stoichiometry) and richness at local and regional scales across 27 North American streams. At the local scale, invertebrate richness peaked at intermediate levels of chlorophyll-<em>a</em>, but had a shallow negative relationship with periphyton C:P and N:P. However, at the regional scale richness had a strong negative relationship with both chlorophyll-<em>a</em> and periphyton C:P and N:P. The divergent effects of periphyton chl-<em>a</em> and stoichiometry on invertebrate richness suggest that basal resource quantity limits diversity more than resource quality, consistent with patterns of eutrophication. Collectively, we demonstrate that resource quantity and quality play important, yet differing roles in shaping freshwater biodiversity across spatial scale.</span></p>
Data supplement to: Quality control of image sensors using gaseous tritium light sources
Open the record for dataset details and reuse information.
Resource quantity and quality differentially control stream invertebrate biodiversity across spatial scales
Open the record for dataset details and reuse information.
Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation
Open the record for dataset details and reuse information.
Quality control for modern bone collagen stable carbon and nitrogen isotope measurements
<p><strong>(1)</strong> Isotopic analyses of collagen, the main protein preserved in sub fossil bone and tooth, has long provided a powerful tool for the reconstruction of ancient diets and environments. Although isotopic studies of contemporary ecosystems have typically focused on more accessible tissues (e.g., muscle, hair), there is growing interest in the potential for analyses of collagen because it is often available in hard tissue archives (e.g., scales, skin, bone, tooth), allowing for enhanced long-term retrospective studies. The quality of measurements of the stable carbon and nitrogen isotopic compositions of ancient samples are subject to robust and well-established criteria for detection of contaminants and digenesis. Among these quality control (QC) criteria, the most widely utilized is the atomic C:N ratio (C:N<sub>Atomic</sub>), which for ancient samples has an acceptable range between 2.9 and 3.6. While this QC criterion was developed for ancient materials, it has increasingly being applied to collagen from modern tissues.</p> <p><strong>(2)</strong> Here we use a large survey of published collagen amino acid compositions (n<em> </em>= 436) from 193 vertebrate species as well as recent experimental isotopic evidence from 413 modern collagen extracts to demonstrate that the C:N<sub>Atomic</sub> range used for ancient samples is not suitable for assessing collagen quality of modern and archived historical samples.</p> <p><strong>(3) </strong>For modern tissues, collagen C:N<sub>Atomic</sub> falling outside 3.00–3.30 for fish and 3.00–3.28 for mammals and birds can produce systematically skewed isotopic compositions and may lead to significant interpretative errors. These findings are followed by a review of protocols for improving C:N<sub>Atomic</sub> criteria for modern collagen extracts.</p> <p><strong>(4)</strong> Given the tremendous conservation and environmental policy-informing potential that retrospective isotopic analyses of collagen from contemporary and archived vertebrate tissues have for addressing pressing questions about long-term environmental conditions and species behaviours, it is critical that QC criteria tailored to modern tissues are established.</p>
Software Evolution and Quality Data from Controlled, Multiple, Industrial Case Studies
<p>This data was obtained from a controlled, multiple case study involving six professional developers and four real-life, industrial systems. The study was designed to control for the moderator factors: programmer skill, maintenance task and learning effect. The primary data set contains multiple sets of defects, in the form of reports (excel files) extracted from six issue tracking systems. The secondary data consists of a series of attributes extracted from the software systems (i.e., code smells) and their evolution (i.e., code churn), and a log specifying the dates on which developers worked on each of the systems/tasks, in the form of excel files. Details on the controlled, multiple case study can be found in the doctoral dissertation by Yamashita titled: "Assessing the Capability of Code Smells to Support Software Maintainability Assessments: Empirical Inquiry and Methodological Approach" (online) Available at: https://www.duo.uio.no/handle/10852/34525</p>
CMIP5 Quality Control Level 2 Exception Codes with Categories
<p>This document contains a list of exception codes for the interpretation of the quality control level 2 (QC L2) results of CMIP5 including exception categories. It was originally available on a web page under the url: http://cera-www.dkrz.de/CMIP5/QC/2/qc2list.html. The QC L2 results are available at: http://cera-www.dkrz.de/WDCC/CMIP5/QCResult.jsp .</p>
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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.