Statistics in biosciences
Abbreviation:
Stat. Biosci.
Copyright: International Chinese Statistical Association
Published by: Springer Science+Business Media
Publisher Location: New York, NY, USA
Journal Website:
http://link.springer.com/journal/12561
Range of citations in the SafetyLit database:
2017; 9(2) --
2023; ePub(ePub)
Publication Date Range:
2009 --
Number of articles from this journal included in the SafetyLit database:
4
(Download all articles from this journal in CSV format.)
pISSN = 1867-1764 | eISSN = 1867-1772
LCCN = 2009243675 | USNLM = 101498115 | OCLC = 444803619
Find a library that holds this journal: http://worldcat.org/issn/18671764
Journal Language(s):
English
Aims and Scope (from publisher):
Statistics in Biosciences (SIB) is published twice a year in print and electronic form. It aims at development and application of statistical methods and their interface with other quantitative methods, such as computational and mathematical methods, in biological and life science, health science, and biopharmaceutical and biotechnological science.
SIB publishes scientific papers and review articles in four sections, with the first two sections as the primary sections. Original Articles publish novel statistical and quantitative methods in biosciences. The Bioscience Case Studies and Practice Articles publish papers that advance statistical practice in biosciences, such as case studies, innovative applications of existing methods that further understanding of subject-matter science, evaluation of existing methods and data sources. Review Articles publish papers that review an area of statistical and quantitative methodology, software, and data sources in biosciences. Commentaries provide perspectives of research topics or policy issues that are of current quantitative interest in biosciences, reactions to an article published in the journal, and scholarly essays. Substantive science is essential in motivating and demonstrating the methodological development and use for an article to be acceptable. Articles published in SIB share the goal of promoting evidence-based real world practice and policy making through effective and timely interaction and communication of statisticians and quantitative researchers with subject-matter scientists in biosciences.
