Buch, Englisch, 146 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 410 g
ISBN: 978-3-032-08227-5
Verlag: Springer
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, the difference between experimental and observational data, reproducibility of data, and the implications of focusing on original data for peer review.
By considering these various subjects, the author has synthesized a philosophy to help students develop an effective and appropriate sensibility. The book 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.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Biomedizin, Medizinische Forschung, Klinische Studien
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken
- Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsvisualisierung
- Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
Weitere Infos & Material
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.




