In a world being driven largely by information, an enterprise defines itself by the way it manages its data.  Regrettably, biology is struggling to become a data-driven science because two of the ingredients essential to such a transformation are missing – open access to research data and a theory structure friendly to large amounts of quantitative information.  The point in playing a complexity game with biology is to bridge this gap by creating parallel complexities with databases.  Such a game is possible because complexity in biology is structured hierarchically as nested sets, which are connected quantitatively.  In short, a given game becomes a database exercise that includes identifying a specific goal, extracting a complexity, optimizing it, and analyzing the results.

Recall that literature databases can contain either original data or data derived therefrom.  Although both types of databases are included here, we will focus largely on the derived ones.  Such is the case because we will use databases to form quantitative links between specific questions and answers – much like setting up and solving a mathematical problem.

The databases included below provide open access to published research data, most of which are translated into configurations consistent with exploring biology as a complexity.  To this end, individual data points were translated into ratios and ratios into mathematical markers.  Although the MySQL databases allow us to preview of the data online, most of work, which involves manipulating large data sets, takes place locally on computers using Excel spreadsheets and Access databases (64 bit versions).  Such is the case because each problem requires a highly-tuned database to reach a solution.  By including examples of the databases as they become honed during the assembly process (type 1 → type 2 → type 3), the reader can experience – first hand – this approach to problem solving.  The advantage of this strategy is that it is entirely transparent, verifiable, and open to widespread testing and application.

Each database includes three components: a preview (MySQL database), a link to the report where it was used, and a complete copy of the database, saved as a text file (*.csv) that can be downloaded.  In turn, this text file can be imported into Excel, Access, et cetera.  Details related to these databases are given in a data sheet (click on the button).  If you would like to solve a complex problem from scratch, click on the instructions button and work through the step-by-step directions to produce a database for the reproducibility test.  Once successful, you will be ready to move all your published data onto a complexity platform where they will be keen to answer much harder questions.