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Leigh 1994 - This paper explores the relationship between captive and wild body mass data. Correlations are generally high but there are consistent differences, suggesting possible underestimates in wild values.

 

 

 

 

Nunn and Barton 2000 - This paper explores the relationship between home range and metabolic rate, and used multiple ways of measuring the key variables (overall averages, paired values, etc).

 

 

 

 

Patterson et al. 2014 - An investigation of data quality in various datasets of group size in primates reveals inconsistencies. Comparative studies should be more careful about what data they include and include detailed information on inclusion criteria.

 

 

 

 

Smith and Jungers 1997 - This paper discusses many of the factors that impact adult body mass in primates (including the definition of "adult") as well as biological and statistical concerns with regard to data quality, and it also provides what has been the standard database of wild primate body mass for the past 17 years.

 

 

 

 

Borries et al. 2013 - Values from historic datasets may differ significantly from more recent data, highlighting the importance of adequate labeling of data and suggesting new repositories.

 

 

 

 

Wheeler et al. 2013 - The method section describes in detail how data was selected (agonism, diet, substrate use, group size), what problems were encountered (e.g., types of agonistic behaviors, different observational methods) and what decisions were made.

 

 

 

 

Goff et al. 2011 - This paper describes the iPlant Collaborative, which is a cyberinfrastructure project to understand plant biology at multiple levels of organization. It deals with open source data integration, data dissemination, and data analysis.

 

 

 

Jetz et al. 2012 - It focuses on spatial biodiversity data and databases (their Map of Life project) and discusses data integration, data storage, data visualization, data dissemination and feedback tools.

 

 

 

 

van den Broeck et al. 2005 - A short paper focusing on data cleaning. They discuss biomedical data in particular but the issues they discuss should be applicable to most types of data.

 

 



 

 

Additional Papers: