Crowdsourced-Data Normalization with Python and Pandas

cDNA Microarray Data Normalization

Stimfit: quantifying electrophysiological data with Python

Intracellular electrophysiological recordings provide crucial insights into elementary neuronal signals such as action potentials and synaptic currents. Analyzing and interpreting these signals is essential for a quantitative understanding of neuronal information processing, and requires both fast data visualization and ready access to complex analysis routines. To achieve this goal, we have de...

Metabolomics Data Normalization with EigenMS

Liquid chromatography mass spectrometry has become one of the analytical platforms of choice for metabolomics studies. However, LC-MS metabolomics data can suffer from the effects of various systematic biases. These include batch effects, day-to-day variations in instrument performance, signal intensity loss due to time-dependent effects of the LC column performance, accumulation of contaminant...

Query Processing over Crowdsourced Data

We are building Deco, a comprehensive system for answering declarative queries posed over stored relational data together with data gathered from the crowd. In this paper we present Deco’s query processor, building on Deco’s data model and query language presented earlier. In general, it has been observed that query processing over crowdsourced data must contend with issues and tradeoffs involv...

Maximizing benefits from crowdsourced data

Crowds of people can solve some problems faster than individuals or small groups. A crowd can also rapidly generate data about circumstances affecting the crowd itself. This crowdsourced data can be leveraged to benefit the crowd by providing information or solutions faster than traditional means. However, the crowdsourced data can hardly be used directly to yield usable information. Intelligen...