Publishing Agronomic Data

Abstract

A typical agricultural experiment produces vastly more data than can be reproduced in a research paper. Th us, data provided in a paper oft en are not detailed enough to allow the analysis to be checked, nor can the data be reanalyzed, for example, by combining them with data from other experiments or by use of techniques developed long aft er the original paper was published. Limitations to reanalysis are particularly unfortunate because agricultural research increasingly deals with issues that require examining large or detailed sets of data. Journals increasingly favor publishing data in digital supplements because this improves the likelihood that papers will be cited while reducing the length of journal articles. In this paper we argue that formal methods for publishing datasets from agricultural research should be established, analogous to publishing research fi ndings. Criteria for determining whether a dataset merits publication include originality, utility, signifi cance, completeness, quality, and usability. Mechanisms for distributing datasets also merit attention. Ensuring long-term accessibility, respecting intellectual property rights, and managing possible corrections or modifi cations to datasets are among issues that require consideration.

Meta‐Analysis Constrained by Data: Recommendations to Improve Relevance of Nutrient Management Research

“…Whereas standard criteria on data completeness and quality can determine the suitability of data for publication (White and van Evert, 2008), meta-analysis can extend the value of these data beyond the original experiment, and beyond replication, to application in policy and other decision-making. Experimental data are appropriately complete when the data and supporting information sufficiently allow the experiment to be reproduced, either in the field or with a simulation model (Hunt et al, 2001(Hunt et al, , 2006.…”

Big Data for weed control and crop protection

“…Big Data applications typically involve several data owners. For research data, the issue of ownership, archiving and sharing has received ample attention (King, ; White & Van Evert, ). The consensus is that the scientific method calls for sharing data liberally, although care should be taken to respect concerns such as privacy of people, the need to protect rare species and habitats by withholding details about location, and the need to publish before sharing (Duke & Porter, ).…”

Data Practices in the Crop Sciences: A Review of Selected Faculty Publications

“…One prominent example is the International Consortium for Agricultural Systems Applications (ICASA), which develops standards "to provide a reliable, portable, flexible structure both for documenting field experiments (or their equivalents in greenhouses or growth chambers) and for specifying conditions for running dynamic simulation models" (Hunt, Hoogenboom, Jones, & White, 2006, p. 4). Researchers using simulation models have been the primary adopters of these standards, but White and van Evert (2008) noted a developing trend toward using the standards to document other types of agricultural research. ICASA provides a data exchange system.…”