Moreover, this study explores data mining, statistical analysis, and semantic analysis methods to obtain valuable information on public opinion and requirements based on Chinese microblog data.
Though communication has a central role in interdisciplinary collaboration and since miscommunication can have a negative impact on research processes, to the best of our knowledge, no study has yet explored how data analysis specialists and clinical researchers communicate over time.
Specifically, an existing single-user desktop analytical tool for exploring data using principal component analysis (called iPCA [6]) is adapted into a collaborative touch-table application (called iPCA-CE [7]).
We have developed POMO (http://pomo.cs.tut.fi), an interactive web-based application to visually explore omics data analysis results and associations in circular, network and grid views.
To systematically explore data growth over time, our analysis focused on data for which release dates were available, which included 913,972 compounds, 10,142 targets, 872,577 assays, and 5,258,052 activity records (Table 1).
We set out to (i) review the different methods used and (ii) use simulations to explore how the data analysis method influences the quantification of wetland hydraulics and pollutant removal.
This tension is explored further in the data analysis that follows.
This may be caused by not only experimental discrepancies, but also not fully explored possibilities of the data analysis.
The falls rate reduction remains statistically significant under all scenarios explored in the missing data analysis (Additional file 6: Table S6).
This paper presents a novel approach to social data analysis, exploring the use of microblogging to manage interaction between humans and robots, and presenting and evaluating an architecture that extends the use of social networks to connect humans and devices.
