U.S. Department of Energy Office of Biological and Environmental Research

PI-Submitted Research Highlights for
Terrestrial Ecosystem Science Program

Assessing the Challenges and Benefits of an Online ‘Open Experiment’

Ben Bond-Lamberty
Pacific Northwest National Laboratory


Powerpoint slide about study with image from the research. (JO)

PNNL scientists explore a new model for research and data sharing.

The Science                       
Scientists conducted an ‘open experiment’ in which every aspect of a laboratory experiment was documented online and in real time. This pushed them to write higher-quality analysis code, shortened the time required, allowed them to quickly identify problems, and resulted in a stronger manuscript.

The Impact
Researchers in every field of science are being pushed—by funders, journals, governments, and their peers—to increase transparency and reproducibility of their work. A key part of this effort is a move towards open data as a way to fight post-publication data loss, improve data and code quality, enable powerful meta- and cross-disciplinary analyses, and increase public trust in, and the efficiency of, publicly-funded research. The approach described here is way to help achieve these goals, and may serve as a model for interested researchers.

In early 2015, Department of Energy scientists at Pacific Northwest National Laboratory planned a laboratory incubation experiment to characterize the chemical and biological properties of sub-Arctic, active layer soils subjected to changes in temperature and moisture. This required (i) a multidisciplinary team that was not located in one time zone; (ii) integration of a variety of different data; (iii) performance of quality control and diagnostics rapidly, so if e.g. instrument problems arose the team would lose only the minimum amount of time and data; and (iv) tight integration of data, statistical analyses, and manuscript results. The team designed a data processing and analytical system written in an open-source and widely used language for statistical computing and graphics, and placed it in a publicly-available ‘repository’ that stored all code and data, making them available in real time. There were significant advantages for the project in using an automated analytical pipeline in an open repository, although the costs of such an approach and investments required should also be considered.

Contacts (BER PM)
Daniel Stover and Jared DeForest
Daniel.Stover@science.doe.gov (301-903-0289) and Jared.DeForest@science.doe.gov (301-903-1678)

(PI Contact)
Ben Bond-Lamberty
Pacific Northwest National Laboratory

This research was supported by the Office of Science of the US Department of Energy as part of the Terrestrial Ecosystem Sciences Program.

B Bond-Lamberty, P Smith, and V Bailey, "Running an open experiment: transparency and reproducibility in soil and ecosystem science." Environmental Research Letters 11:084004 (2016), [DOI: 10.1088/1748-9326/11/8/084004]

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