Editorial: The publication of geoscientific model developments v1.0

Abstract

In 2008, the first volume of the European Geosciences Union (EGU) journal Geoscientific Model Development (GMD) was published. GMD was founded because we perceived there to be a need for a space to publish comprehensive descriptions of numerical models in the geosciences. The journal is now well established, with the submission rate increasing over time. However, there are several aspects of model publication that we believe could be further improved. In this editorial we assess the lessons learned over the first few years of the journal's life, and describe some changes to GMD's editorial policy, which will ensure that the models and model developments are published in such a way that they are of maximum value to the community. <br><br> These changes to editorial policy mostly focus on improving the rigour of the review process through a stricter requirement for access to the materials necessary to test the behaviour of the models. <br><br> Throughout this editorial, "must" means that the stated actions are required, and the paper cannot be published without them; "strongly encouraged" means that we encourage the action, but papers can still be published if the criteria are not met; "may" means that the action may be carried out by the authors or referees, if they so wish. <br><br> We have reviewed and rationalised the manuscript types into five new categories. For all papers which are primarily based on a specific numerical model, the changes are as follows: <br><br> &ndash; The paper must be accompanied by the code, or means of accessing the code, for the purpose of peer-review. If the code is normally distributed in a way which could compromise the anonymity of the referees, then the code must be made available to the editor. The referee/editor is not required to review the code in any way, but they may do so if they so wish. <br><br> &ndash; All papers must include a section at the end of the paper entitled "Code availability". In this section, instructions for obtaining the code (e.g. from a supplement, or from a website) should be included; alternatively, contact information should be given where the code can be obtained on request, or the reasons why the code is not available should be clearly stated. <br><br> &ndash; We strongly encourage authors to upload any user manuals associated with the code. <br><br> &ndash; For models where this is practicable, we strongly encourage referees to compile the code, and run test cases supplied by the authors where appropriate. <br><br> &ndash; For models which have been previously described in the "grey" literature (e.g. as internal institutional documents), we strongly encourage authors to include this grey literature as a supplement, when this is allowed by the original authors. <br><br> &ndash; All papers must include a model name and version number (or other unique identifier) in the title. <br><br> It is our perception that, since Geoscientific Model Development (GMD) was founded, it has become increasingly common to see model descriptions p...

Case Studies and Challenges in Reproducibility in the Computational Sciences

“…We also noted a lack of domain-specific workflows for recomputation in non-CS journals. A counterexample is the Geoscientific Model Development (GMD) journal, which encourages reviewers to get acquainted with the code behind papers under review [18]. Yet, the same journal, as of now, imposes a 50 MB limit on the size of electronic supplements which effectively rules out shipping a VM together with the paper.…”

Editorial: The publication of geoscientific model developments v1.1

“…This topic was not previously mentioned in the peer-review criteria. For such papers, authors are encouraged to also consider open-access data journals (Candela et al, 2015) which are more suitable than GMD for the publication of new observational data. There is, however, an area of development directly relevant to numerical modelling which may suit publication in GMD.…”

SEDA: A software package for the Statistical Earthquake Data Analysis

“…This allows replication of results and ensures that the scientific community can apply the methodology to their own data, without the need of re-implementing the algorithms. Moreover, making the software available allows the evaluation of its performance on any dataset, leading to better quality of codes themselves, and the comparison of different methodologies5678.…”