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49 results for “Data Quality control”
Extended data for the paper: "SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters"
<p>Extended data 1 to 4 for the software article:<br>SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters. </p> <p>The extended data is tables and a Figure output and input from/to SentemQC runs relevant for the SentemQC paper.</p>
Data relating to Clyne et al. Quality, scope and reporting standards of randomised controlled trials in Irish Health Research: an observational study
<p>Data relating to the study reported in the paper "Quality, scope and reporting standards of randomised controlled trials in Irish Health Research: an observational study".</p>
Quality controlled observations of hourly incoming shortwave radiation data at the surface for solar resource mapping in Norway (2016-2020).
<p>Observed hourly incoming shortwave radiation data at the surface of Norway for the years 2016-2020 along with quality control flags, visualization plots and a descriptive report. The data has been collected, visually inspected and quality controlled within the SunPoint project (SUn in Norway - POtential and INTegration of the solar energy resource, Norwegian Research Council project 320750). The main data source is frost.met.no but some gaps were filled with data directly obtained by the station holders.</p> <p>There are three NetDCF files for 47 stations selected after quality control:</p> <ul> <li>rsds_1hr_selection_v5_2016-2020.nc: Raw data</li> <li>rsds_flagged_1hr_selection_v5_2016-2020.nc: Raw data with flags</li> <li>rsds_cleaned_1hr_selection_v5_2016-2020.nc: Filtered data (i.e. all flagged data has been removed)</li> </ul> <p>and one NetCDF file for all available stations (106 stations)</p> <ul> <li>rsds_1hr_frost_and_more_2016-2020.nc</li> </ul> <p>Version 3.5 of the McClear clear-sky model is used for flagging which reduces the bias to ground measurements compared to earlier versions. </p> <p>The visualization and automated quality control routines are available in the Scripts.zip file (python).</p>
Data used in the Final Draft Scientific Basis Report Supplement in Support of Proposed Voluntary Agreements for the Sacramento River, Delta, and Tributaries Update to the San Francisco Bay/Sacramento-San Joaquin Delta Water Quality Control Plan
This dataset includes modeled data describing the potential benefits of the Voluntary Agreements (VAs) from the Final Draft Scientific Basis Report Supplement in Support of Proposed Voluntary Agreements for the Sacramento River, Delta, and Tributaries Update to the San Francisco Bay/Sacramento-San Joaquin Delta Water Quality Control Plan.
mRNA expression data of genes related to mitochondrial quality control in hepatopancreas of the two marine bivalves, Mytilus edulis and Crassostrea gigas, during short-term hypoxia/reoxygenation stress
<p>Coastal environments commonly experience strong oxygen fluctuations. Resulting hypoxia/reoxygenation stress can negatively affect mitochondrial functions, since oxygen deficiency impairs ATP generation, whereas a surge of oxygen causes mitochondrial damage by oxidative stress mechanisms. Marine intertidal bivalves are adapted to fluctuating oxygen conditions, yet the underlying molecular mechanisms that sustain mitochondrial integrity and function during oxygen fluctuations are not yet well understood. We used targeted mRNA expression analysis to determine the potential involvement of the mitochondrial quality control mechanisms in responses to short-term hypoxia (24 h at <0.01% O<sub>2</sub>) and subsequent reoxygenation (1.5 h at 21% O<sub>2</sub>) in two hypoxia-tolerant marine bivalves, the Pacific oysters <em>Crassostrea gigas</em> and the blue mussels <em>Mytilus edulis</em>. To test these hypotheses, We focused on the transcript levels of the following marker genes: for mitochondrial fission and fusion - <em>mfn</em>2 (encoding mitofusin 2), <em>opa</em>1 (mitochondrial dynamin-like 120kDa protein), <em>dnm</em>1<em>l </em>(dynamin-1-like protein), <em>mff</em> (mitochondrial fission factor), <em>fis</em>1 (mitochondrial fission protein 1); for protein and DNA quality control - <em>tsfm</em> (encoding mitochondrial translation elongation factor Ts), <em>lonp</em>1 (mitochondrial Lon protease), <em>spg</em>7 (paraplegin), <em>oma</em>1 (mitochondrial metalloendopeptidase OMA1), <em>clpB</em> (mitochondrial caseinolytic matrix peptidase chaperone subunit B), <em>atp</em>23 (mitochondrial inner membrane protease ATP23), <em>twnk</em> (mitochondrial twinkle mtDNA helicase); and for mitophagy - <em>mieap</em> (encoding mitochondrial eating protein), <em>hyou</em>1 (hypoxia upregulated protein 1), <em>prkn</em> (parkin), <em>pink</em>1 (PTEN- induced kinase 1), and <em>pgam</em>5 (mitochondrial serine/threonine protein phosphatase PGAM5). The revealed species-specific differences in the expression of the mitochondrial quality control pathways shed light on the potentially important mechanisms of mitochondrial protection against H/R-induced damage that might contribute to hypoxia tolerance in marine bivalves. </p>
Data supplement to: Quality control of image sensors using gaseous tritium light sources
<p>In the article "Quality Control of Image Sensors using Gaseous Tritium Light Sources" (<a href="https://doi.org/10.1098/rsta.2021.0130)">https://doi.org/10.1098/rsta.2021.0130)</a> we propose a practical method for radiometrically calibrating cameras using widely available gaseous tritium light sources (<em>betalights</em>). This dataset includes all the recorded data along with the scripts necessary to reproduce the results and figures.</p>
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>
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>
Data supplement to: Quality control of image sensors using gaseous tritium light sources
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Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation
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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>
Vesuvio cGNSS Network - Rinex data quality control (2020)
<p>For each cGNSS station, the file contains a summary report with information about Rinex observation data. For each day, the summary line shows the following information:</p><ol><li>cGNSS station name (Name),</li><li>the start time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date) and GNSS week (Week),</li><li>the end time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date), and GNSS week (Week),</li><li>the start and end times of the window (time format is year month day hour min),</li><li>window time laps (Hrs),</li><li>observation interval (OI),</li><li>the number of possible observations (#expt) above the elevation mask,</li><li>the number of complete observations (#obs),</li><li>the ratio of complete to possible observations as a percent (DCP),</li><li>the RMS "multipath combinations" values MP1 and MP2, in meters, limited by the elevation mask (MP1, MP2) rounded to two decimal points,</li><li>cycle slips (CS),</li><li>the ratio of complete observations to cycle slips (obs/CS).</li></ol><p>A full description of cGNSS network is reported in:<br>- De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.</p><p>Please cite this when using the dataset</p>
Campi Flegrei cGNSS Network - Rinex data quality control (2020)
<p>For each cGNSS station, the file contains a summary report with information about Rinex observation data. For each day, the summary line shows the following information:</p><ol><li>cGNSS station name (Name),</li><li>the start time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date) and GNSS week (Week),</li><li>the end time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date), and GNSS week (Week),</li><li>the start and end times of the window (time format is year month day hour min),</li><li>window time laps (Hrs),</li><li>observation interval (OI),</li><li>the number of possible observations (#expt) above the elevation mask,</li><li>the number of complete observations (#obs),</li><li>the ratio of complete to possible observations as a percent (DCP),</li><li>the RMS "multipath combinations" values MP1 and MP2, in meters, limited by the elevation mask (MP1, MP2) rounded to two decimal points,</li><li>cycle slips (CS),</li><li>the ratio of complete observations to cycle slips (obs/CS).</li></ol><p>A full description of cGNSS network is reported in:<br>- De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.</p><p>Please cite this when using the dataset</p>
Vesuvio cGPS network – RINEX data quality control (2001 – 2019)
<p>For each cGPS station, the file contains a summary report with information about Rinex observation data. For each day, the summary line shows the following information:</p> <ol> <li>cGPS station name (Name),</li> <li>the start time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date) and GPS week (Week),</li> <li>the end time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date), and GPS week (Week),</li> <li>the start and end times of the window (time format is year month day hour min),</li> <li>window time laps (Hrs),</li> <li>observation interval (OI),</li> <li>the number of possible observations (#expt) above the elevation mask,</li> <li>the number of complete observations (#obs),</li> <li>the ratio of complete to possible observations as a percent (DCP),</li> <li>the RMS “multipath combinations” values MP1 and MP2, in meters, limited by the elevation mask (MP1, MP22) rounded to two decimal points,</li> <li>cycle slips (CS),</li> <li>the ratio of complete observations to cycle slips (obs/CS).</li> </ol> <p>A full description of cGPS network is reported in:<br> - De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.</p> <p>Please cite this when using the dataset</p>
Campi Flegrei cGPS network – RINEX data quality control (2000 – 2019)
<p>For each cGPS station, the file contains a summary report with information about Rinex observation data. For each day, the summary line shows the following information:</p> <ol> <li>cGPS station name (Name),</li> <li>the start time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date) and GPS week (Week),</li> <li>the end time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date), and GPS week (Week),</li> <li>the start and end times of the window (time format is year month day hour min),</li> <li>window time laps (Hrs),</li> <li>observation interval (OI),</li> <li>the number of possible observations (#expt) above the elevation mask,</li> <li>the number of complete observations (#obs),</li> <li>the ratio of complete to possible observations as a percent (DCP),</li> <li>the RMS “multipath combinations” values MP1 and MP2, in meters, limited by the elevation mask (MP1, MP22) rounded to two decimal points,</li> <li>cycle slips (CS),</li> <li>the ratio of complete observations to cycle slips (obs/CS).</li> </ol> <p>A full description of cGPS network is reported in:<br> - De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.</p> <p>Please cite this when using the dataset</p>
Ischia cGPS network – RINEX data quality control (2001 – 2019)
<p>For each cGPS station, the file contains a summary report with information about Rinex observation data. For each day, the summary line shows the following information:</p> <ol> <li>cGPS station name (Name),</li> <li>the start time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date) and GPS week (Week),</li> <li>the end time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date), and GPS week (Week),</li> <li>the start and end times of the window (time format is year month day hour min),</li> <li>window time laps (Hrs),</li> <li>observation interval (OI),</li> <li>the number of possible observations (#expt) above the elevation mask,</li> <li>the number of complete observations (#obs),</li> <li>the ratio of complete to possible observations as a percent (DCP),</li> <li>the RMS “multipath combinations” values MP1 and MP2, in meters, limited by the elevation mask (MP1, MP22) rounded to two decimal points,</li> <li>cycle slips (CS),</li> <li>the ratio of complete observations to cycle slips (obs/CS).</li> </ol> <p>A full description of cGPS network is reported in:<br> - De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.</p> <p>Please cite this when using the dataset</p>
Data for the manuscript 'Cover crop root morphology rather than quality controls the fate of root and rhizodeposition C into distinct soil C pools'
<p><strong>Data for manuscript</strong></p> <p>The data provided in the present document corresponds to the manuscript:</p> <p>Engedal, T., Magid, J., Hansen, V., Rasmussen, J., Sørensen, H., Jensen, L. S. (2023): Cover crop root morphology rather than quality controls the fate of root and rhizodeposition C into distinct soil C pools. <em>Global Change Biology, in press</em>.</p> <p> </p> <p><strong>Short abstract</strong></p> <p>In order to investigate the fate of cover crop-derived belowground C as rhizodeposition and, over time, into the distinct soil organic carbon pools of particulate- and mineral-associated organic carbon (POC and MAOC), a column trial was esblished with 0.25 m top soil and 0.25 m sub soil. Four cover crops were grown for 3 months and 14CO2-labelled twice a week. Four out of eight replicate columns were destructively harvested to quantify root C and the carbon lost via rhizodeposition in absolute (qClvR) and relative terms (%ClvR) in bulk soil and rhizosphere soil from top- and subsoil (t1). The other four replicate columns were harvested for undisturbed incubation for one year, before final sampling (t2). Bulk soil from both sampling times were subject to a simple fractionation protocol by size, where particles larger from 50 microns were assigned to POC and smaller than 50 microns assigned to MAOC after dispersion in NaHMP. All fractions were dried, weighed and analyzed for 14C activity as disintegrations per minute (DPM).</p> <p> </p> <p><strong>Further details</strong></p> <p>Column ID 1-16 refer to columns sampled at t1, while column ID 17-32 refer to columns sampled at t2. Underlying assumptions and detailed descriptions of the different fractions are to be found in the manuscript.</p>
Data from: Litter quality controls tradeoffs in soil carbon decomposition and replenishment in a subtropical forest
<p><span>Species-rich forests can produce litter of varying carbon (C) and nitrogen (N) composition (<em>i.e</em>., quality), which can affect decomposition and play a central role in long-term soil organic carbon (SOC) accumulation. However, how differences in litter quality affect SOC decomposition and formation remains unclear over the full litter decomposition trajectory. </span></p> <p><span>We followed the <em>in-situ</em> complete decomposition of added <sup>13</sup>C-labelled high- (low C:N) and low-quality (high C:N) leaf-litter and its effect on particulate (POM) and mineral-associated (MAOM) organic matter fractions over two years in a natural subtropical forest.</span></p> <p><span>We found that during early stages of decomposition</span><span>, low-quality litter inputs decreased SOC via a positive priming effect (i.e., new C inputs favored decomposition of native SOC), but these SOC losses were offset by SOC gains observed via a negative priming effect during decomposition of high-quality litter. In contrast, this pattern reversed during </span><span>late</span><span> stages of decomposition</span><span>—SOC losses via a positive priming effect induced by </span><span>high-quality litter were offset by SOC gains via a negative priming effect induced by low-quality litter. </span><span>Over the full decomposition of litter, b</span><span>oth high- and low-quality litter stimulated</span><span> microbial breakdown of SOC tied to POM,</span><span> but </span><span>also replenished more persistent SOC that associated with soil minerals (MAOM).</span><span> Altogether, we observed</span><span> that low-quality litter formed twice as much new SOC as high-quality litter (24% vs. 12% of added litter-C). We extend the notion of the priming effect </span><span>from primarily a negative role promoting losses of native SOC, to a functional role that can replenish persistent SOC.</span> </p> <p><strong><em><span>Synthesis</span></em></strong><span><strong><em>.</em></strong> Our measurements</span><span> raise the possibility that, in species-rich forests, high- and low-quality litter decomposition play opposite but dynamically complementary roles in renewing POM—both by inducing its decomposition and formation—while exclusively favoring MAOM formation, which can help explain how differences in litter quality favor SOC accumulation and persistence. Global change factors that shift plant community composition may ultimately affect the fate of soil C, as changes in litter quality may force soil transitions </span><span>from sinks to sources or sources to sinks of atmospheric CO<sub>2</sub>.</span></p>
Data from: Litter quality controls tradeoffs in soil carbon decomposition and replenishment in a subtropical forest
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