Journal of Cheminformatics will only publish research or software that is entirely reproducible by third parties. This means that any datasets, software and algorithms that are required to reach the conclusions stated in the paper must be provided as supplemental materials, or be otherwise accessible without the need for registration, login or agreement with license terms other than Creative Commons licenses for data and text and OSI-approved Open Source Licenses for software. For any software, the source code must be provided.
A Data Note should describe a curated, Public Domain, CCZero or CC-BY-licensed dataset (explicit statement is required), and likely to be of broad utility. The dataset must be curated as detailed in this publication and archived for long-term reuse. A Data Note must describe the content of the data set, the method used to curate the data, and a description of how the dataset deposition complies with the FAIR principles. Unlike with a Database paper, Data Notes focus on the data itself and not how the data can be graphically or otherwise explored.
The structure of a Data Notes paper should include the following components:
- an abstract that does not exceed 350 words
- an Objective section
- a Data description, with two subsections called Curation and FAIR-ification
- a Limitations section
- followed by the common sections Abbreviations, Declarations, and References
The main section of the article starts with a Objective section explaining where the data came from and why it was collected citing a selection of relevant literature, particularly articles describing data (re)used in this article, and how this data is or can be used. The Limitations section should discuss the limitations of the data to help the reader to assess the reusability. When your data is collected from other data, adhere to the DataCite principles (and please use [cito:usesDataFrom], see the Citation Typing Ontology section below).
The Data description should contain a table describing the data files presented in this publication in a general paragrah, following by at least two subsections describing the methodologies used curate the data (in the "Curation" subsection) and to make the data FAIR (in the "FAIR-ification" subsection). The Curation section must describe how the data was curated and include a qualitative or quantitative characterization how the data was improved this way.
The FAIR-ification section must describe for all FAIR Guiding Principles what choices were made for the presented data, citing appropriate literature. For example, describe where (meta)data is made findable, what globally unique persistent identifiers are used, such as the the InChI for chemical structures, and what open standards or ontologies are used in the dataset. Of particular interest is the domain-specific community standards employed in the dataset. This can include applied minimal reporting standards. A summary of this can be provided as a table.
If you are invited to review a Data Note article, you can find guidelines here.