Research in the Biomedical Sciences
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ISBN/EAN:
9780128047262
Research in the Biomedical Sciences: Transparent and Reproducible documents the widespread concerns related to reproducibility in biomedical research and provides a best practices guide to effective and transparent hypothesis generation, experimental design, reagent standardization (including validation and authentication), statistical analysis, and data reporting. The book addresses issues in the perceived value of the existing peer review process and calls for the need for improved transparency in data reporting. It reflects new guidelines for publication that include manuscript checklists, replication/reproducibility initiatives, and the potential consequences for the biomedical research community and societal health and well-being if training, mentoring, and funding of new generations of researchers and incentives for publications are not improved. This book offers real world examples, insights, and solutions to provide a thought-provoking and timely resource for all those learning about, or engaged in, performing and supervising research across the biomedical sciences. - Provides a 'big picture perspective on the scope of reproducibility issues and covers initiatives that have potential as effective solutions - Offers real-world research context for transparent, reproducible experimental design, execution and reporting of biomedical research with the potential to address aspects of the translational gap in drug discovery - Highlights the importance of reproducibility and the necessary changes in biomedical and pharmaceutical research training and incentives to ensure sustainability
Autor: | Michael Williams, Michael Curtis, Kevin Mullane |
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EAN: | 9780128047262 |
eBook Format: | PDF/ePUB |
Sprache: | Englisch |
Produktart: | eBook |
Veröffentlichungsdatum: | 20.10.2017 |
Untertitel: | Transparent and Reproducible |
Kategorie: | |
Schlagworte: | animal models biomedical research culture data analysis drug discovery drug prices exploratory data analysis fraud guidelines. checklists healthcare hypothesis generation hypothesis testing inference investigator bias journal impact fact |
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