Putting Quantitatively Enhanced Qualitative Research to Work for the Life Sciences: Three Case Studies

Understanding the details of how scientific breakthroughs and discoveries emerge from prior work, knowledge, and collaboration is fundamental to science history. Traditional methods of collecting such relevant information and transforming it into rich narratives have existed since the inception of the scientific method. These typically involve qualitative research. Recently, modern computational tools have proved valuable in locating nuanced information regarding science history trends, allowing for fine-grained resolution, and adding a quantitative approach. This study features a quantitatively enhanced qualitative research approach using a combination of traditional and computational methodology to answer driving questions regarding influential ideas in the life sciences featuring three case studies. First, an investigation of how one author's work influenced the entire field of molecular cell biology in the 1980s and 1990s. Using linguistic software, a single publication is linked to influencing language changes within the overall field. Second, an examination of how development of a descriptive language allowed developmental biologists to port peripheral tools from molecular biology to diversify regeneration research areas from the 1980s through to the 2010s. Language analysis demonstrates that evolution of descriptive language combined with the rapid inclusion of molecular terms indicates periods of high organismal diversity from the 1980s to 2010s. Third, an examination of how nuanced but significant shifts in methodological aims of a specific research group bound two formerly distinct but related communities of synthetic biologists from the 2000s through the 2010s. Social networking tools allow location of an influential paper heralding shifts in methodological language that united these formerly distinct topics. All these approaches provide different tools for historical study, and we can imagine them making up a toolbox of options. Determining which tool to use in each case requires selection criteria to find an approach to answer the question. This dissertation draws on different tools for the different cases and explains the selection of those tools. The overall results demonstrate how computational and traditional methods of conducting historical studies can be implemented using my selection criteria tool, drawing from a need-based toolbox of useful methods from both the traditional and computational worlds. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]