Links and Resources

In my research we take advantage of a number of openly available tools to help us do everything from taking notes, to making graphs, to building complex statistical models. I have a preference for open tools so, in addition to referencing these tools in published papers, I would like to acknowledge these tools and their creators here. I am deeply indebted to an awful lot of people who make these tools possible. 

• Mozilla

• R and the R Foundation for Statistical Computing

• R Studio

• Python

• The Anaconda Distribution

• Inkscape

• The Gnu Image Manipulation Program

• GitHub
  
  
  

• My GitHub Repository

• My Published Papers

• My Lectures and Videos

(My institution asks for an executive summary of teaching activities as part of our annual review. This is the statement I provide.)

Philosophy: Teaching is a big part of what attracted me to being a professor back in graduate school. Currently, I have the joy of teaching students how to be rigorous scientists in the movement science PhD program, and how to be critical consumers of research in the physical therapy doctoral program (DPT). These are very different skillsets and I love the challenge of needing sufficient depth of knowledge to mentor student PhD students through esoteric study designs and statistical analyses, while at the same time having sufficient mastery over the material to de-mystify research and present sound foundational principles to clinicians. Most importantly, I want to impress upon our students that medical knowledge is not a static body of facts. Although science is a relatively stable method, medical knowledge is constantly changing, full of controversy, and the “facts” are updated as we learn more.

Outputs: For the DPT program: I have designed content and provide lectures for the "evidence" thread, designed content and provided lectures for research ethics, and created a series of asynchronous modules on social constructs in medicine. For the movement science PhD program: I have revised the syllabus for and co-taught "Instrumentation and Measurement in Movement Science" with Dr. Jacob McPherson, and I regularly facilitate directed readings for data analysis and methodology (e.g., machine learning and statistical parametric mapping). With respect to mentorship: I have served as a mentor for one KL2 awardee, as a mentor for three post-doctoral trainees, as the primary supervisor for three pre-doctoral trainees, and have served on thirty dissertation committees. I also maintain an un-monetized YouTube channel where various lectures I have created and workshops I have led are freely available (https://tinyurl.com/5n88msx4). I use YouTube as a platform to quickly and easily share this information with students and collaborators (e.g., rather than re-explain Type I versus Type III sums of squared errors every year), an added benefit is that this content has proven to be beneficial for people all over the world.

(My institution asks for an executive summary of research activities as part of our annual review. This is the statement I provide.)

Research Focus: I study measurement, design, and analysis as they pertain to neurological recovery and disease. Recovery is a complex, dynamic process with many interacting factors at physiological, psychological, and sociological levels. To that end, I have specialized in longitudinal and multivariate modeling techniques to help disentangle these problems and mechanistically explore the recovery process.

Outputs: I have a unique expertise in neurological rehabilitation and applied statistics that make me a valuable team scientist for many projects in physical therapy and neurology. For my part, I have helped bring in over 5 million dollars of extramural funding as a co-investigator/collaborator on grants from the National Institutes of Health, the Department of Defense, and private foundations. As of 2024-10-30, my work has been cited over 6,800 times, with an h-index of 40 and an i10-index of 91 (estimated from Google Scholar). I also maintain a GitHub repository where code that I have written and tutorials I have created are freely available (https://github.com/keithlohse).

(My institution asks faculty to report on diversity, equity, and inclusion activities as part of our annual review. This is the statement I provide.)

Philosophy: I have a deep and abiding commitment to giving everyone equal opportunities in life. We are unfairly born into different circumstances and as much as we might strive for meritocracy, no one’s successes are completely their own and neither are their failures. I think it is imperative that we make high quality education available to everyone, giving all students the greatest chance of realizing their full potential and the ability to use their skills for the greater good. To that end, I value making science accessible to everyone and I recognize that we sometimes need to do the difficult work of changing current practices to attract and retain people who have been under-represented in higher education and science. None of us individually are better than all of us together.

Outputs: I teach content that is relevant to students’ understanding of diversity, equity, and inclusion. For instance, for our DPT students I teach a series of modules called “Measurement in Society” that explains what social constructs are, how social constructs can be useful, and when they can be misleading. The specific social constructs we deal with are sex, gender, race, ethnicity, and disability. In my research I have studied the representation of minority groups in clinical trials and the effects that biases from missing data an unrepresentative sampling can create when trying to generalize the efficacy and effectiveness of treatments across groups (e.g., https://www.ahajournals.org/doi/full/10.1161/STROKEAHA.122.042168). More generally, I use open source software and share data (making analyses and processes more accessible for all), I try to share my published work through pre-prints and open-access publications (so that results and implications of data are freely available), and I share many of the lectures I give/workshops I have led on an unmonetized YouTube channel (making my pedagogical content more accessible). 

(My institution asks faculty to report on entrepreneurial activities as part of our annual review. This is the statement I provide.)

At present, I have no entrepreneurial responsibilities. However, I would be remiss if I did not comment on how I do not like “entrepreneurship” and its growing presence in research.

Entrepreneurship prioritizes profit and proprietary control, which clashes with the values of collaboration and accessibility in open science. For an entrepreneur, protecting intellectual property is crucial for maintaining a competitive advantage and generating profit. In contrast, open science advocates for transparency and sharing of research tools, outputs, and findings. There is also a well-documented history of commercial interests influencing the selective publication of favorable results, undermining the validity of scientific inquiry. Open science, in contrast, aims for unbiased dissemination of all findings. It is my personal view that the pursuit of patents and exclusive rights hinders the free flow of information, which in turn slows (or even stops) the collective advancement of scientific knowledge. I understand that as scientists we need to participate in the economic systems of society, but I think it is important that we balance the drive for profit with the principles of openness, collaboration, accessibility, and reproducibility. I think that adopting these scientific virtues will accelerate innovation and discovery in a way that privatization and entrepreneurship cannot.