maps of research findings
"For an idea that does not at first seem insane, there is no hope." - Albert Einstein
The growth of the scientific literature in the last 30 years has been astronomic. The library of medicine now includes more than 20 million articles with an estimated 200 million experiments, and our own discipline (neuroscience) includes nearly two million research articles (approximately 20 million experiments.
Therefore, there is a great need to develop maps (simplified abstractions) of published information that could be used to characterize what is known and to guide research decisions. Essentially, we hope that our research maps will be to neuroscience what mathematics has been for physics: a formal language to represent research findings.
As a first step, our laboratory developed a taxonomy for experiments and a set of algorithms to generate these maps. This taxonomy allows us to classify experiments in our field into a small set of distinct categories, a critical first step in the development of simplified abstractions of research findings. The algorithms are essential for representing these experiments in causal networks (i.e., our maps).
Additionally, we are in the process of developing an App to facilitate the mapping process and a series of computer routines designed to use the taxonomy of experiments and algorithms we developed to automatically generate maps of research findings.
For a recent Ray Kurzweil's newsletter article on our work click here...
- Silva, AJ, Landreth, A, Bickle, J. Engineering the next revolution in neuroscience: the new science of experiment planning. Book from Oxford Press 2013
- Landreth, A, Silva, AJ, The Need for Research Maps to Navigate Published Work and Inform Experiment Planning Neuron (PDF)