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103
datasets available to search
ShareScore release 0.9.0
Dataset results
103 results for “drinking water”
Is Mg do Improve the Glycemic Control in Patients Drink a Desalinate Water
ClinicalTrials.gov study NCT04632277. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Survey Data of the socio-demographic, economic and water source types that influences HHs drinking water supply
Open the record for dataset details and reuse information.
Time-series drinking water metagenomes: Assemblies & MAGs
Open the record for dataset details and reuse information.
GECCO Industrial Challenge 2018 Dataset: A water quality dataset for the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition at the Genetic and Evolutionary Computation Conference 2018, Kyoto, Japan.
<p>Dataset of the 'Internet of Things: Online Anomaly Detection for Drinking Water Quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2018, Kyoto, Japan</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of water quality data</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, M. Rebolledo, S. Moritz, S. Chandrasekaran, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p>GECCO Industrial Challenge: 'Internet of Things: Online Anomaly Detection for Drinking Water Quality'</p> <p>Description:</p> <p>For the 7th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2017 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.<br> Additionally to the competition, for the first time in GECCO history we are now able to provide the opportunity for all participants to submit 2-page algorithm descriptions for the GECCO Companion. Thus, it is now possible to create publications in a similar procedure to the Late Breaking Abstracts (LBAs) directly through competition participation!</p> <p> </p> <p>Accepted Competition Entry Abstracts<br> - Online Anomaly Detection for Drinking Water Quality Using a Multi-objective Machine Learning Approach (Victor Henrique Alves Ribeiro and Gilberto Reynoso Meza from the Pontifical Catholic University of Parana)<br> - Anomaly Detection for Drinking Water Quality via Deep BiLSTM Ensemble (Xingguo Chen, Fan Feng, Jikai Wu, and Wenyu Liu from the Nanjing University of Posts and Telecommunications and Nanjing University)<br> - Automatic vs. Manual Feature Engineering for Anomaly Detection of Drinking-Water Quality (Valerie Aenne Nicola Fehst from idatase GmbH)</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/">http://www.spotseven.de/gecco/gecco-challenge/gecco-challenge-2018/</a></p>
Data from: Bacterial characterization of Beijing drinking water by flow cytometry and MiSeq sequencing of the 16S rRNA gene
Flow cytometry (FCM) and 16S rRNA gene sequencing data are commonly used to monitor and characterize microbial differences in drinking water distribution systems. In this study, to assess microbial differences in drinking water distribution systems, 12 water samples from different sources water (groundwater, GW; surface water, SW) were analyzed by FCM, heterotrophic plate count (HPC), and 16S rRNA gene sequencing. FCM intact cell concentrations varied from 2.2 × 103 cells/mL to 1.6 × 104 cells/mL in the network. Characteristics of each water sample were also observed by FCM fluorescence fingerprint analysis. 16S rRNA gene sequencing showed that Proteobacteria (76.9–42.3%) or Cyanobacteria (42.0–3.1%) was most abundant among samples. Proteobacteria were abundant in samples containing chlorine, indicating resistance to disinfection. Interestingly, Mycobacterium, Corynebacterium, and Pseudomonas, were detected in drinking water distribution systems. There was no evidence that these microorganisms represented a health concern through water consumption by the general population. However, they provided a health risk for special crowd, such as the elderly or infants, patients with burns and immune-compromised people exposed by drinking. The combined use of FCM to detect total bacteria concentrations and sequencing to determine the relative abundance of pathogenic bacteria resulted in the quantitative evaluation of drinking water distribution systems. Knowledge regarding the concentration of opportunistic pathogenic bacteria will be particularly useful for epidemiological studies.
Dataset of " Removal of the waterborne parasite Cryptosporidium parvum from drinking water using granular activated carbon"
<p>Dataset of Figure 2 of the manuscript "Removal of the waterborne parasite Cryptosporidium parvum from drinking water using granular activated carbon"</p>
Supplementary material 2 from: Horváthová E (2022) Analysis of Drinking Water treatment costs – with an Application to Groundwater Purification Valuation. One Ecosystem 7: e82125. https://doi.org/10.3897/oneeco.7.e82125
Ecosystem types
Supplementary material 1 from: Horváthová E (2022) Analysis of Drinking Water treatment costs – with an Application to Groundwater Purification Valuation. One Ecosystem 7: e82125. https://doi.org/10.3897/oneeco.7.e82125
Regression results
Data for paper Linear and Non-linear Modelling of Bromate Formation During Ozonation of Surface Water in Drinking Water Production
<p>Data for journal paper</p>
Water Drinking Test and Its Reproducibility in Goldmann Applanation Tonometry and Pneumatic Tonometry
ClinicalTrials.gov study NCT03014349. IPD Sharing: NO. Countries: 0. Publications: 9.
The Effects of the Water Drinking Test on Intraocular Pressure
ClinicalTrials.gov study NCT01507584. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effectiveness of Safe Drinking Water in Treatment of Severe Acute Malnutrition (Pakistan)
ClinicalTrials.gov study NCT02751476. IPD Sharing: UNDECIDED. Countries: 0. Publications: 1.
Effects of Water and Glucose Drinks on Cardiovascular Function in Subjects With and Without Postprandial Hypotension
ClinicalTrials.gov study NCT02713412. IPD Sharing: YES. Countries: 0. Publications: 9.
Water Drinking Test Study and Disc Hemorrhages in Normal Tension Glaucoma
ClinicalTrials.gov study NCT05075369. IPD Sharing: YES. Countries: 0. Publications: 3.
The Effect of Drinking-Water pH on the Human Gut Microbiota
ClinicalTrials.gov study NCT02917616. IPD Sharing: YES. Countries: 0. Publications: 1.
Drinking Water to Reach or Maintain a Healthier Weight
ClinicalTrials.gov study NCT02331316. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Bacterial characterization of Beijing drinking water by flow cytometry and MiSeq sequencing of the 16S rRNA gene
Open the record for dataset details and reuse information.
Transcriptomic analyses of the liver in female mice exposed to 1,4-dioxane in drinking water
GEO Series GSE154899. Mus musculus. 85 samples. Type: Expression profiling by high throughput sequencing.
Human cell toxicogenomic analysis links reactive oxygen species to the toxicity of monohaloacetic acid drinking water disinfection byproducts
GEO Series GSE49698. Homo sapiens. 12 samples. Type: Expression profiling by RT-PCR.
Transcriptional profiling of bulk liver tissue from male and female PPARa knockout mice and mice expressing a human PPARa transgene following treatment with PFOA or vehicle drinking water in conjuncti
GEO Series GSE290187. Mus musculus. 20 samples. Type: Expression profiling by high throughput sequencing.
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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.