This book offers an alternative to faith-dependent analytical approaches, explaining how original data can be transformed into cogent and compelling interpretations with analytical techniques that are straightforward and accessible to biomedical scientists. Some data-related topics covered in the book are the aesthetics of data or how beauty in data inspires, erroneous data by fraud or honest mistake, and what role it plays in biomedical science, the difference between experimental and observational data, reproducibility of data, and the implications of focusing on original data for peer review, leading to both publication and scientific funding.

By considering these various subjects, this book has synthesized a philosophy to help students develop an effective and appropriate sensibility. It can serve as a guide for biomedical research investigators in their studies, assist practitioners in making sense of complex mechanisms for patient benefit, and for business professionals who may learn from a thoughtful consideration of biomedical science. This book is intentionally accessible for those without an extensive biomedical science background, and hopes to motivate readers to expand their data literacy and comprehension, in the age of AI.

Autorentext

Dr. David Kaplan graduated with A.B, Ph.D., and M.D. degrees from the University of Chicago. He was a pathology resident at Barnes and Jewish Hospitals and a postdoctoral fellow at Washington University. Since 1984 he served as a faculty member at Case Western Reserve University and an attending physician at University Hospitals Cleveland. In 2016 he founded a biotechnology company based on his invention of a powerful technology that enhanced the sensitivity of flow cytometric analysis. Dr. Kaplan has made many advancements throughout his scientific career including the first cloning of human influenza-specific cytotoxic T cells, the first gene transfer into human T cells, the first use of antisense transfection to inhibit expression of a protein to probe its function in the context of the human T cell, and the first demonstration of intercellular immunoregulation via cell surface receptors.

Inhalt

Preface.- Chapter One Original Data.- Chapter Two Data Quality.- Chapter Three Data Collection.- Chapter Four Data Analysis.- Chapter Five Aesthetics of Data.- Chapter Six Erroneous Data.- Chapter Seven Data Reproducibility.-Chapter Eight Converting Data into Interpretation.- Chapter Nine Oral Presentation of Data.- Chapter Ten Evaluation of Data by Peer Review.- Chapter Eleven The Value of Data.