Sift - Knowledge Discovery

Sift is designed to be a tool that helps you, the user, discover useful knowledge from your data. This process of knowledge discovery is iterative, requiring users to collect, clean, and shape their data before performing analysis and then communicating their results. Each of these steps requires experience to be done well, the aim of this article is to outline the goal of each step, how Sift lets you accomplish these goals, and to point you on to additional resources.

Collecting Data

The first step in learning from you data is to collect that data and bring it all together fro analysis. Challenges can arise here if the data you are interested in has been collected by different researchers in different locations over many years. No matter how complex you study is, questions about how data is collected, how meta-data and data are linked, how data is shared or centralized, and how data is protected should always be answered before you start collecting.

So you have establidef a data collection plan, worked hard to collect you data, and now you have an intitial or complete data set to analyse. At this point you want to bring your data set under one roof and explore what you've collected to see what patterns and connections you can find. Sift lets you do this by loading CMZ files which contain all of the .c3d files as a Library. Since the .c3d format is standard across biomechanics research, this lets you easily group your data according to collection session and trials and to import this data with only a few clicks. You can also include arbitrary metadata alongside you .c3d files by using the Build CMZs feature.