Related references
Note: Only part of the references are listed.Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R
Carsten Oliver Schmidt et al.
BMC MEDICAL RESEARCH METHODOLOGY (2021)
Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework
Katherine J. Lee et al.
JOURNAL OF CLINICAL EPIDEMIOLOGY (2021)
Hidden analyses: a review of reporting practice and recommendations for more transparent reporting of initial data analyses
Marianne Huebner et al.
BMC MEDICAL RESEARCH METHODOLOGY (2020)
A hypothesis is a liability
Itai Yanai et al.
GENOME BIOLOGY (2020)
Effective Visual Communication for the Quantitative Scientist
Marc Vandemeulebroecke et al.
CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY (2019)
Good enough practices in scientific computing
Greg Wilson et al.
PLOS COMPUTATIONAL BIOLOGY (2017)
Ten Simple Rules for Effective Statistical Practice
Robert E. Kass et al.
PLOS COMPUTATIONAL BIOLOGY (2016)
Comment: The FAIR Guiding Principles for scientific data management and stewardship
Mark D. Wilkinson et al.
SCIENTIFIC DATA (2016)
Degrees of Freedom in Planning, Running, Analyzing, and Reporting Psychological Studies: A Checklist to Avoid p-Hacking
Jelte M. Wicherts et al.
FRONTIERS IN PSYCHOLOGY (2016)
P values are just the tip of the iceberg
Jeffrey T. Leek et al.
NATURE (2015)
Ten Simple Rules for Reproducible Computational Research
Geir Kjetil Sandve et al.
PLOS COMPUTATIONAL BIOLOGY (2013)
Transparent and accurate reporting increases reliability, utility, and impact of your research: reporting guidelines and the EQUATOR Network
Iveta Simera et al.
BMC MEDICINE (2010)
Share Paper
