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\textbf{Academic impact in the era of forking.} A number of \href{http://altmetrics.org/manifesto/}{alternative metrics for evaluating academic impact} are emerging. These include metrics to give scholars credit for sharing of raw science (like datasets and code), semantic publishing, and social media contribution, based not solely on citation but also on usage, social bookmarking, conversations. We, at Authorea, strongly believe that these alternative metrics will be a fundamental ingredient of how scholars will be evaluated in the future. In fact, \href{https://www.authorea.com}{Authorea} already welcomes data, code, and raw science materials alongside its articles and is built on an infrastructure (Git) that naturally poses as a framework for distributing, versioning, and tracking those materials. \href{http://en.wikipedia.org/wiki/Git_(software)}{Git} is a versioning control platform currently employed by developers for collaborating on source code, but its features perfectly fit the needs of most scientists too. A versioning system as GitHub empowers \textbf{forking} of projects, allowing a colleague of yours to make a copy of your published research data in their repository and further develop it in a new direction, inheriting the history of the work and preserving the value chain of science (who did what). In other words, forking in science means \textit{standing on the shoulder of giants} (or dwarves) and do so in a transparent manner, sharing with the world where your idea and work originated.  \textbf{More sharing, more forking, more impact.} Obviously, the more of your research projects data  are shared, the higher are you chances that they will be forked and used as a basis for groundbreaking work, and in turn, the higher the interest in your work and your academic impact. Whether your projects are data-driven articles on Authorea discussing a new finding, raw datasets detailing some novel findings on Zenodo or Figshare, source code repositories hosted on Github presenting a new stastical statistical  package, every bit of your work that can be reused, will be forked and will give you credit. \textbf{And now onto the nerdy part: The Fork Factor}. So, we'd we would  like to imagine what academia would be like if forking actually mattered in determining a scholar's reputation. How would you calculate it? Here, we give it a shot. We define the \textbf{Fork Factor} (FF) as: \begin{equation}  FF = N*(L^{\frac{1}{\sqrt{N}}}-1)  \end{equation}  Where N is the number of forks on your work and L their median length. In order to take into account the reproducibility of research projects, data,  the length of forks has a higher weigh in the FF formula. Indeed, forks with length equal to one likely represent a failure to reproduce the forked research datum.  Anyone out there care to improve the formula above or help us collect some data and test this out? Let us know at \verb|[email protected]| or by commenting here.