scholarly journals Thermostats: an Open Source Shiny App for Your Open Data Repository

2019 ◽  
Vol 3 (2-2) ◽  
pp. 233
Author(s):  
Dasapta Erwin Irawan ◽  
Muhammad Aswan Syahputra ◽  
Prana Ugi ◽  
Deny Juanda Puradimaja
Keyword(s):  
Water Quality ◽  
Open Source ◽  
Analytical Data ◽  
Open Data ◽  
Principal Component ◽  
Data Repository ◽  
Geothermal System ◽  
Online Application ◽  
Shiny App ◽  
And Cluster Analysis

Hydrochemical analysis has emerged as a powerful methodology in geothermal system profiling. Indonesia is the capital of geothermal energy with its more than 100 active volcanoes. Therefore we need to have an analytical, data-driven, and user-focused online application of geothermal water quality. Proudly we introduce Thermostats (https://aswansyahputra.shinyapps.io/thermostats/). We collected water quality from 416 geothermal sites across Indonesia. Three main objectives are to provide an online open-free to use data repository, to visualize the dataset to suit user’s needs, and to help users understand the geothermal system of each particular site. At the end, we hope they like this system and donate their own dataset to make it better for future users. We designed this online app using Shiny, because it’s open source, lightweight and portable. It’s very intuitive to load our descriptive, bivariate and multivariate statistics. We selected Principal Component Analysis and Cluster Analysis as two strong statistics for water sample classification. Users could add their own dataset by making a pull request on Github (https://github.com/dasaptaerwin/thermostats) or sending it to us by email to make it visible in the application and included in the visualization. We make this application portable, so it can be installed on a local computer or a server, to enable an easy and fluid way of data sharing between collaborators.

scholarly journals CNSA: a data repository for archiving omics data

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Xueqin Guo ◽  
Fengzhen Chen ◽  
Fei Gao ◽  
Ling Li ◽  
Ke Liu ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Analytical Data ◽  
Academic Research ◽  
Open Data ◽  
Data Repository ◽  
Free Access ◽  
Omics Data ◽  
Sequencing Data ◽  
Data Standards ◽  
Sample Information

Abstract With the application and development of high-throughput sequencing technology in life and health sciences, massive multi-omics data brings the problem of efficient management and utilization. Database development and biocuration are the prerequisites for the reuse of these big data. Here, relying on China National GeneBank (CNGB), we present CNGB Sequence Archive (CNSA) for archiving omics data, including raw sequencing data and its further analyzed results which are organized into six objects, namely Project, Sample, Experiment, Run, Assembly and Variation at present. Moreover, CNSA has created a correlation model of living samples, sample information and analytical data on some projects. Both living samples and analytical data are directly correlated with the sample information. From either one, information or data of the other two can be obtained, so that all data can be traced throughout the life cycle from the living sample to the sample information to the analytical data. Complying with the data standards commonly used in the life sciences, CNSA is committed to building a comprehensive and curated data repository for storing, managing and sharing of omics data. We will continue to improve the data standards and provide free access to open-data resources for worldwide scientific communities to support academic research and the bio-industry. Database URL: https://db.cngb.org/cnsa/.

Assessment of Water Quality Parameters on Uzuncayır Dam Lake Using Principal Component and Cluster Analysis

2016 ◽  
Vol 16 (3) ◽  
Author(s):  
Banu KUTLU ◽  
Azime KÜÇÜKGÜL ◽  
Osman SERDAR ◽  
Rahmi AYDIN ◽  
Durali DANABAŞ
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Principal Component ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
And Cluster Analysis

scholarly journals Statistical Assessment of Water Quality Parameters for Pollution Source Identification in Sukhnag Stream: An Inflow Stream of Lake Wular (Ramsar Site), Kashmir Himalaya

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
Author(s):  
Salim Aijaz Bhat ◽  
Gowhar Meraj ◽  
Sayar Yaseen ◽  
Ashok K. Pandit
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Principal Component ◽  
Pollution Source ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Accumulation Factor ◽  
Kashmir Himalaya ◽  
And Cluster Analysis ◽  
Maximum Similarity

The precursors of deterioration of immaculate Kashmir Himalaya water bodies are apparent. This study statistically analyzes the deteriorating water quality of the Sukhnag stream, one of the major inflow stream of Lake Wular. Statistical techniques, such as principal component analysis (PCA), regression analysis, and cluster analysis, were applied to 26 water quality parameters. PCA identified a reduced number of mean 2 varifactors, indicating that 96% of temporal and spatial changes affect the water quality in this stream. First factor from factor analysis explained 66% of the total variance between velocity, total-P, NO3–N, Ca2+, Na+, TS, TSS, and TDS. Bray-Curtis cluster analysis showed a similarity of 96% between sites IV and V and 94% between sites II and III. The dendrogram of seasonal similarity showed a maximum similarity of 97% between spring and autumn and 82% between winter and summer clusters. For nitrate, nitrite, and chloride, the trend in accumulation factor (AF) showed that the downstream concentrations were about 2.0, 2.0, and 2.9, times respectively, greater than upstream concentrations.

The influence of supply and sewerage area characteristics on water and sewerage companies responses to the Water Framework Directive

2009 ◽  
Vol 60 (7) ◽  
pp. 1811-1819 ◽  
Author(s):  
M. Spiller ◽  
B. S. McIntosh ◽  
R. A. F. Seaton
Keyword(s):  
Water Quality ◽  
Water Framework Directive ◽  
Thematic Analysis ◽  
Principal Component ◽  
Raw Water ◽  
Business Function ◽  
Raw Water Quality ◽  
And Cluster Analysis ◽  
Agricultural Areas ◽  
The Relationship

Using the example of raw water quality this paper examines the relationship between different spatial characteristics (geographical and physical properties) of Water and Sewerage Companies (WaSCs) supply and sewage areas and response to the Water Framework Directive. Results were obtained from thematic analysis and content analysis of 14 interviews with WaSCs representatives. Principal component analysis and cluster analysis of 51 WaSCs business function characteristics was employed to derive groups of similar WaSCs. Results indicate that there is difference in how WaSCs approach raw water quality issues. It appears that small WaSCs with relatively large agricultural areas in their supply catchments are more likely to seek managerial solutions to raw water quality problems.

scholarly journals Assessment of the water quality in a large reservoir in semiarid region of Brazil

2014 ◽  
Vol 18 (4) ◽  
pp. 437-445 ◽  
Author(s):  
Fernando B. Lopes ◽  
Eunice M. de Andrade ◽  
Ana C. M. Meireles ◽  
Helena Becker ◽  
Adriana A. Batista
Keyword(s):  
Water Quality ◽  
Cost Savings ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Temporal Variations ◽  
Semiarid Region ◽  
Large Reservoir ◽  
Total Ammonia ◽  
And Cluster Analysis ◽  
Major Factors

The aim of this study was to identify spatial and temporal variations in water quality of Orós reservoir, Ceará, Brazil, as well as the sources of contamination. To get this information the Principal Component Analysis (PCA) and Cluster Analysis (CA) was used. Water samples were collected at seven (geo-referenced) points, from April 2008 to March 2011, totalling 4,032 samples. The following attributes of the waters were analysed: temperature, pH, CE, Ca2+, Mg2+, Na+, K+, Cl-, HCO3-, SO4--, turbidity, colour, Sechi transparency, TS, TVS, TFS, TSS, VSS, FSS, TDS, DO, BO5D, total phosphorus, soluble orthophosphate, EC, TTC, total ammonia, TKN, nitrate, SAR and chlorophyll-a. The PCA promoted the reduction from the 32 initial variables to 14, accounting for 84.39% of the total variance. The major factors responsible for water quality composition are: the natural weathering of geological soil components; the entrainment of suspended solids through surface runoff from agricultural areas; and anthropogenic action in the Upper Jaguaribe basin in Ceará. The similarity of the water of the Orós reservoir allows a reduction in the number of sampling points, which may result in significant cost savings without sacrificing the water quality monitoring. The similarity of the waters was influenced by anthropic activities being carried out near the reservoir and all along the watershed.

The Status of Surface Water in West Tripura District, India: An Approach by Using Water Quality Index and Multivariate Statistical Technique

2021 ◽  
Vol 18 (2) ◽  
pp. 27-36
Author(s):  
Biplab Roy ◽  
Ajay Kumar Manna
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Principal Component ◽  
Statistical Technique ◽  
Total Hardness ◽  
Multivariate Statistical ◽  
And Cluster Analysis ◽  
Entropy Weighted

The present investigation provides a better interpretation of surface water (rivers, ponds, bills, lakes, etc.) quality utilising entropy weighted water quality index (EWWQI) and different multivariate statistical techniques. Eleven physicochemical parameters including alkalinity, dissolved oxygen (DO), pH, total dissolved solids (TDS), electrical conductivity (EC), calcium (Ca), turbidity, magnesium (Mg), total hardness (TH), chloride (Cl-), and iron (Fe) were analysed and monitored at 23 sampling sites (in December 2018) of West Tripura district. Experimental outcomes of turbidity followed by Fe contamination exceeded recommended WHO standard limit. The maximum values of Fe and turbidity were estimated as 8.745 mg/L and 797.7 NTU, respectively. WQI values confirmed that most of the monitoring locations had poor water quality except three reported areas (S7, S14, and S15) but without Fe and turbidity, estimated WQI confirmed drinkable water condition for entire samples. Multivariate statistical approaches like correlation analysis, principal component analysis (PCA) and cluster analysis (CA) were applied to explore water quality. PCA outcomes recognised three principal factors explaining almost 85% of the total variance. CA investigated three major clusters of 23 sampling sites namely less polluted, highly polluted and moderately polluted zone. Confirming all above, the surface water at the monitoring locations is a major concern which may lead to serious health issues in local people.

scholarly journals Street Network Models and Indicators for Every Urban Area in the World

2020 ◽  
Author(s):  
Geoff Boeing
Keyword(s):  
Open Source ◽  
Urban Area ◽  
Open Data ◽  
Network Models ◽  
Data Repository ◽  
Street Network ◽  
Data Repositories ◽  
Urban Street ◽  
The World ◽  
Network Form

Cities worldwide exhibit a variety of street network patterns and configurations that shape human mobility, equity, health, and livelihoods. This study models and analyzes the street networks of each urban area in the world, using boundaries derived from the Global Human Settlement Layer. Street network data are acquired and modeled from OpenStreetMap with the open-source OSMnx software. In total, this study models over 160 million OpenStreetMap street network nodes and over 320 million edges across 8,914 urban areas in 178 countries, and attaches elevation and grade data. This article presents the study's reproducible computational workflow, introduces two new open data repositories of ready-to-use global street network models and calculated indicators, and discusses summary findings on street network form worldwide. It makes four contributions. First, it reports the methodological advances of this open-source workflow. Second, it produces an open data repository containing street network models for each urban area. Third, it analyzes these models to produce an open data repository containing street network form indicators for each urban area. No such global urban street network indicator dataset has previously existed. Fourth, it presents a summary analysis of urban street network form, reporting the first such worldwide results in the literature.

scholarly journals SPATIAL AND TEMPORAL ASSESSMENT OF WATER QUALITY DATA USING MULTIVARIATE STATISTICAL TECHNIQUES

2016 ◽  
Vol 38 (2) ◽  
pp. 577
Author(s):  
Nícolas Reinaldo Finkler ◽  
Taison Anderson Bortolin ◽  
Jardel Cocconi ◽  
Ludmilson Abritta Mendes ◽  
Vania Elisabete Schneider
Keyword(s):  
Water Quality ◽  
Anthropogenic Activities ◽  
Principal Component ◽  
Quality Data ◽  
Spatial And Temporal Variation ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
And Cluster Analysis ◽  
Quality Variation ◽  
Human Density

The natural factors and anthropogenic activities that contribute to spatial and temporal variation in superficial waters in Caxias do Sul’s urban hydrographic basins were determined applying multivariate analysis of data. The techniques used in this study were Principal Component Analysis and Cluster Analysis. The monitoring was executed in 12 sampling stations, during January, 2009 to January, 2010 with monthly periodicity in total of 13 campaigns. Between chemical, biological and physical, 20 parameters were analyzed. The results state that with the use of ACP, a data variance of 70.94% was observed. Therefore, it testifies that major pollutants that contribute to a water quality variation in the county are classified as domestic and industrial pollutants, mainly from galvanic industry. Moreover, two clusters were found which differentiated regarding their location and distance from areas with a high human density, corroborating on identifying of impact due to human activities in urban rivers.

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