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958 results for “Data quality”
Data on physico-chemical quality and microbial community level physiological profiles of drinking water and freshwater resources in Mega Manila Philippines
<p><strong>Samples of drinking water and freshwater resources in Mega Manila, Philippines were analyzed for physical and chemical quality as well as physiological profiles of microbial communities. Temperature, pH, oxidation-reduction potential, absolute conductivity, resistivity, total dissolved solids, salinity, pressure, and dissolved oxygen were measured using a multi-parameter probe and reported as averages from three biological replicates per sample. Detection and quantification of arsenic were carried out via 3114 B: Hydride Generation AAS method, while analysis for lead and cadmium was carried out via 3111 B: Direct Air-Acetylene Flame method. Detection and quantification of mercury was performed via Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES). Detection and quantification of polyaromatic hydrocarbons (PAHs) and organochlorine pesticides (OCPs) were carried out via Gas Chromatography-Mass Spectrometry (GC-MS) and Gas Chromatography-Electron Capture Detection (GC-ECD). Assessment of the physiological activities of microbial communities in the water samples was done using the Biolog EcoPlateTM system. Eight samples returned less than the minimum detection limit for analysis of heavy metals and no samples contained polyaromatic hydrocarbons and organochlorine pesticides. In terms of microbial community physiological profiles, 66% of the samples (19 out 29) are found to demonstrate consistent increases in AMR and CMD over time. These data can be reused as comparators for retrospective and prospective quality assessments of drinking water and freshwater sources.</strong></p>
Script and data supporting Brand loyalty and price-quality perceptions of hybrid maize seed in La Frailesca Chiapas, Mexico
<p>Script and data material to:</p> <p><em>Brand loyalty and price-quality perceptions of hybrid maize seed in La Frailesca Chiapas, Mexico</em> by Ciro Domínguez, Jason Donovan, Romain Frelat, and C.S. Srinivasan currently in review.</p>
Data for: Assessment of quality of life (QoL) in cancer patients
<p><strong>Background</strong>: A cancer patient's quality of life (QoL) is the perception of their physical, functional, psychological, and social well-being as well as their mental and emotional state. QoL is one of the most important factors to consider when a person is being treated for cancer and during follow-up. The present study aimed to understand the status of QoL of cancer patients and determine the factors affecting it.</p> <p><strong>Methods</strong>: This cross-sectional study was conducted among 210 cancer patients attending the oncology unit of a medical college, within a 4-month consecutive time period in 2022. Data were collected by using the Bengali version of the European Organization for Research and Treatment of Cancer questionnaire.</p> <p><strong>Results</strong>: The present study reported a high number of female cancer patients (67.6%). Breast cancer was more common among females (31.43%) while lung and upper respiratory tract cancer was among males (19.05). Most of the patients in the present study were diagnosed with cancer in the past year (86.19%). The functional scales' overall mean scores varied from 54.92 for physical functioning to 38.89 for social functioning. The highest symptom scale score was for financial issues (63.02), while the lowest was for diarrhea (33.01). The overall QoL of cancer patients in the present study was 47.98 which was 45.71 for males and 49.10 for females respectively. </p> <p><strong>Conclusion</strong>: The overall QoL was poor in cancer patients in the present study compared to the developed countries. There was a low score for QoL for social and emotional function. Financial difficulty was the primary reason behind low QoL in the symptom scale. If the government supports cancer patients by providing subsidies for treatment and health insurance policies, cancer patients will benefit and QoL will improve.</p>
Data from: Environmental quality mediates the ecological dominance of cooperatively breeding birds
<p>Code and data associated with the publication: Lin, YH. et al. (2023) Environmental quality mediates the ecological dominance of cooperatively breeding birds. Ecology Letters, in press.</p>
Biogeosciences paper data for Hasanyar et al. "How much do bacterial growth properties and biodegradable dissolved organic matter control water quality at low flow?"
<p>The proposed dataset is related to the following publication:<br> Hasanyar, M., Romary, T., Wang, S., Flipo, N. (2023) How much do bacterial growth properties and biodegradable dissolved organic matter control water quality at low flow? Biogeosciences</p> <p>It consists in command files for the c-rive0.32 software available here<br> Shuaitao Wang, Lauriane Vilmin, Masihullah Hasanyar, & Nicolas Flipo. (2023). C-RIVE (0.32). Zenodo. https://doi.org/10.5281/zenodo.7849609</p> <p><br> To run the model :<br> 1. Compile crive0.32<br> 2. Copy the executable in the current directory<br> 3. In a terminal launch<br> > ./crive0.32 ./sensitivity.COMM test.log</p> <p>The “sensitivity.COMM” holds the settings for the C-RIVE simulation related to the paper mentioned in the front head of the current file<br> The information on different parameters of “sensitivity.COMM” are included in “layers”, “meteo” and “param_bio” folders.<br> The layer folder holds the information about the initial conditions of the model (Table 2 in the article).<br> The “meteo” folder holds the meteorological information and finally the “param_bio” folder holds the information on physiology of bacteria, phytoplankton and other model species.</p> <p><br> The output files are written in $HOME/OUTPUT_CRIVE. It is possible to change it directely in sensitivity.COMM, second entry 'OUTPUT_FOLDER'</p>
Application of deep neural networks to reconstruct coastal water quality data
<p>In this study ordinary and new integrated deep neural networks were developed for the reconstruction of measured specific conductance (<em>SC</em>) data. Five stations of USGS in the Gulf of Mexico were considered as case study.</p>
Data produced by the study "Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality in Africa".
<p>This is the processed data used in the manuscript entitled "Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality in Africa".</p> <p> </p>
Fig. 7 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe
Fig. 7. Multivariate data analysis (MVDA) of fresh and dried Peruvian ginger samples. A. Principal component analysis (PCA) showed clear separation of fresh and dried ginger samples. B. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) showed clear separation of fresh and dried ginger samples. C. Splot of OPLS-DA. D. VIP scores based on the metabolite data from fresh and dry ginger extracts.
Fig. 9 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe
Fig. 9. [6]-Gingerol (A) and [6]-shogaol (B) contents in μg/g fresh weight (μg/ g FW) among the tested fresh ginger samples using different extraction solvents. Error bars indicate mean ± SE for five replicates. Black asterisks indicate significance difference from the Peruvian samples (*P <0.05, Student's t-test).
Fig. 5 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe
Fig. 5. The influence of geographical distribution of ginger on the gingerols and gingerol-related metabolites. The figure also describes the biosynthetic pathway of [6]-gingerol and hexahydrocurcumin and subsequent transformations. The opposed abundances of the precursors (i.e., phenylalanine and cinnamic acid) and end products (i.e., [6]-gingerol, hexahydrocurcumin, and gingerenone A and B) hypothesize that they have the same biosynthetic pathway.
Fig. 3 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe
Fig. 3. The use of retention time, accurate mass, and co-elution pattern for compound validation. A. Total ion chromatogram (TIC) and extracted ion chromatograms (EIC) of [6]-gingerol measured by UPLC/MS in positive and negative ionization modes. B. Different gingerols within the same class show a retention time pattern according to their chain length. Intra-class variability is shown by XIC in positive ionization mode for the 6, 8 and 10-gingerol. C. Scatter plot representation of different gingerols annotated from the tested samples. The m/z of the loss of water from the protonated adducts is given on the x-axis and the observed RT (min) is given in the y-axis. The plot illustrates how the correlation between elution (RT) and chain length in the annotated gingerols can be used for the prediction of other compounds within the same class.
Fig. 4 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe
Fig. 4. Multivariate data analysis (MVDA) of fresh Ginger samples collected from different localities. A. Principal component analysis (PCA) showed clear separation of geographically different fresh ginger samples. B. VIP scores showing the top 25 metabolites discriminating ginger samples C. Dendrogram of the investigated fresh ginger samples based on the metabolites obtained after MS data analysis.
Fig. 2 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe
Fig. 2. Schematic diagram showing the steps for confident compound identification. The feature with the retention time of 12.94 min and 295.191 m/z, representing 6-gingerol was selected. Total ion chromatogram (TIC) and extracted ion chromatograms (EIC) were measured by UPLC/MS in the positive ionization mode.
Fig. 1 in The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe
Fig. 1. Experimental design for metabolic profiling of fresh ginger rhizomes collected from different geographical sources and the effect of drying.
A first high-quality vector data of buildings in East Asian countries based on a comprehensive large-scale mapping framework
<p>We generate a high-quality and complete vector data of buildings in East Asian regions, including China, Japan, North Korea, South Korea and Mongolia, filling the gaps in the existing public buildings data.</p>
Supplementary Materials and Raw Data for "Automated Test Suite Generation for Software Product Lines based on Quality-Diversity Optimisation"
<p>Supplementary Materials and Raw Data for "Automated Test Suite Generation for Software Product Lines based on Quality-Diversity Optimisation"</p> <p>1. OnlineSupplement.pdf------Online supplementary data for the paper</p> <p>2. RQ1-4.rar----Raw data for the paper</p> <p>3. Runtime to generate, optimise and execution test suites.xlsx -----Data used in Section 7 PRACTICAL IMPLICATIONS</p> <p>Source code of the algorithms used to produce these data can be found at GitHub https://github.com/gzhuxiangyi/SPLTestingMAP</p>
Supplementary data - Biochar and its potential to deliver negative emissions and better soil quality in Europe
<p>Additional data from </p> <p>- Matlab script of the logistic transport model for the transportation of forest and crop residues to biochar production plants</p> <p>- Gridded data from the maps in the publication and its supplementary information as geotiff, projection system "ETRS89-extended / LAEA Europe"</p>
The effects of two different fatigue protocols on movement quality during unanticipated change of direction in female soccer players (data file)
<p>This dataset contains the results of soccer players who participated in our study. In our study, we investigated the effects of two fatigue protocols on movement quality during change of direction in anticipated and unanticipated conditions.</p>
Dataset for "Improving data quality of low-cost light-scattering PM sensors: Towards automatic air quality monitoring in urban environments"
<p>The dataset contains the data used in the article "Improving data quality of low-cost light-scattering PM sensors: Towards automatic air quality monitoring in urban environments".</p> <p>A low-cost monitoring system composed of 14 monitoring stations was positioned at the official monitoring station of Torino Rubino in the city of Turin (Italy). The official station is managed by the environmental agency ARPA Piemonte.</p> <p>Each low-cost station contains four low-cost light-scattering PM sensors (Honeywell HPMA115S0-XXX), one temperature and relative humidity sensor (DHT22), and one atmospheric pressure sensor (BME/BMP280).<br>The sampling time of the PM sensors was set to one second, while the other sensors generated measurements every 3-4 seconds.</p> <p>The official monitoring station uses both a gravimetric and a beta attenuation instrument for measuring PM.</p> <p>The data contained in this dataset was collected from October 2020 to November 2021. It contains the PM2.5, relative humidity, and temperature measurements of the low-cost monitoring system and the official measurements of the beta attenuation device.</p> <p>Measurements of low-cost sensors are expressed in UTC, while official measurements are expressed in UTC+1.</p> <p>Official PM measurements can be also found at https://aria.ambiente.piemonte.it/qualita-aria/dati.</p>
Air quality data in Florence demo pilot (annex to D7.2)
<p>Research data acquired by ARPAT mobile lab. Procedures and information can be found in D7.2-Florence pilot project report of NEMO project</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.