| Over the past decade, there has been an explosion of quantitative, high-throughput data generated by a host of new measurement technologies. Public access to these data provides information on a host of phenomena from behavioral to genomic, which allows for biological processes at multiple scales to be more fully characterized. Concurrently, new forms of collaboration and education have emerged which allow these data to be utilized by a broader community of scientists from many different fields and career stages. This can not only lead to new opportunities for scientific discovery, but also result in new forms of scientific practice: open-source computational biology, theory hackathons, the creation of open data repositories, and a wide range of published output. It is this combination of data recycling and collaborative flexibility which defines this new type of "open" model for scientific discovery. To demonstrate the I will draw from work being conducted in the DevoWorm group (http://devoworm.weebly.com) to demonstrate how this new kind of developmental neuroscience can proceed. By integrating multiple sources of data into a framework of computational modeling and analysis, we can demonstrate global processes in the developing nervous system as well as undiscovered phenomena related to developmental/newly-differentiated, emerging neural systems, and the nature of spatiotemporal complexity at multiple scales. Understanding the nervous system and developmental processes by using approaches such as networks/connectomics, the analysis of biological dynamics, and comparisons among a diversity of model organisms contributes to a richer view of developmental. Bringing together diverse sources of biological data also leads us to new insights into biological emergence and the complexity of cellular systems. We will conclude with considering how this work provides innovations in model-building, visualization, and perhaps even new biological laws. |
