Leslie A. Hook

Archiving numerical models of biogeochemical dynamics

No adequate community-wide mechanism or set of standards currently exists to ensure the long-term reproducibility of results from numerical modeling in biogeochemical research. Various investigators maintain copies of code and supporting materials through ad hoc methods, and some models and modeling studies are not archived by any mechanism other than the published manuscripts describing specific research results. This state of affairs invites trouble. To illustrate this situation, one of the authors of this article was recently involved in producing a state-of-the-art review on the topic of the effects of climate change on forests. The literature on this topic contains a rich array of results from dynamic models of global or regional vegetation produced by forcing these models with climate change scenarios.

A Conceptual Framework for Management of Carbon Sequestration Data and Models

The purpose of this chapter is to provide an overview of data management options and recommendations for different forms of carbon sequestration, based on results of previous major data system efforts. Data management systems are essential for preserving the primary information from carbon sequestration observations, experiments, and management activities, and making them available for others to build upon. This chapter outlines a conceptual framework for carbon sequestration data management, a first step in capturing the information that can lead to archives organized in a way that is useful for synthesis and integration activities. This framework will also inform carbon resource managers about sequestration processes and efficacy from local to global scales.

Preserve: Protecting Data for Long-Term Use

This chapter provides guidance on fundamental data management practices that investigators should perform during the course of data collection to improve both the preservation and usability of their data sets over the long term. Topics covered include fundamental best practices on how to choose the best format for your data, how to better structure data within files, how to define parameters and units, and how to develop data documentation so that others can find, understand, and use your data easily. We also showcase advanced best practices on how to properly specify spatial and temporal characteristics of your data in standard ways so your data are ready and easy to visualize in both 2-D and 3-D viewers. By following this guidance, data will be less prone to error, more efficiently structured for analysis, and more readily understandable for any future questions that the data products might help address.