Introducing new ``Just Data'' Section in Mathematical and Computational Forestry & Natural-Resource Sciences

John A. Kershaw

Abstract


Data archiving has become a requirement for many journals and an increasingly important part of the everyday life of a scientist. While data from published studies often have more safeguards, unpublished data often is lost. In this new section we are looking for previously unpublished data and pseudo-data that may be of interest to biometricians or other forest researchers as test cases for new analysis approaches, classroom exercises, or challenging analytical tests. These may be unique, small, one-of-a-kind datasets, or larger datasets one discovered or inherited.

The journal of Mathematical and Computational & Natural-Resource Sciences (MCFNS) is soliciting submissions of data sets that can be published in the MCFNS’ “Just Data” section, and be archived on the journal site and made available to other researchers.

The submitted to the MCFNS’ “Just Data” section dataset needs to be accompanied by a brief write up describing the rationale for the data, what variables were collected, the basic experimental design, how treatments were applied, and, if applicable, what is unique or wrong with the data and what are the data strengths. Maps showing treatment layouts or plot designs are encouraged. Variable descriptions should include how the measurements were made or calculated, what units were used, and their precision. Number data should be submitted in a csv file format with a header line identifying the variables. All submissions will be peer reviewed to assess usefulness and meaningfulness of the data, and subsequent comments accepted showing novel analyses of the submitted data.


Keywords


databases; public data; shared data; online data

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