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  6. Call for Papers - Environmental Informatics
Call for Papers - Environmental Informatics

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Introduction

We invite researchers applying AI and data science for the environment to submit their work for a special collection of papers in Environmental Data Science (cambridge.org/eds): a new peer-reviewed open access journal published by Cambridge University Press. 

EDS was launched in order to serve a growing community of researchers at this interface who do not have a dedicated venue for the dissemination of their work. The EDS peer review process brings together expertise in data, methods and environmental domains, and in our publishing practices aim to engage with the burgeoning open science movement. 

For this special collection in the first volume of EDS, we call for papers addressing the topic of Environmental Informatics. Environmental Informatics refers to research that combines the environmental sciences with artificial intelligence/machine learning (AI/ML), data science, or statistics. Methods from these data-driven fields may be applied to address questions from the environmental sciences, and such questions may in turn stimulate methodical development that is tailored for environmental sciences and applicable for other applications.

Authors participating in the 3rd NOAA Workshop on Leveraging AI for the Environmental Sciences (September 2021) and Climate Informatics 2021-2022 (hackathon in Fall 2021 and conference in Spring 2022) will be invited to contribute papers by the Guest Editors (see below), but the call is also open for contributors not participating in these events.

This special collection also welcomes submissions from authors in the community who do not participate in these particular events. Authors unsure of the suitability of the work for this collection are encouraged to contact eds@cambridge.org or Guest Editors with an abstract.

Questions to explore

Topics of particular interest include but are not limited to:

  • Successful research and/or operational use cases of applying AI/ML and data science methods to address environmental science questions
  • Description of datasets that are a product of environmental informatics research or that can enable future environmental informatics research (e.g., datasets are preprocessed to enable AI/ML and data science applications)
  • Development of AI/ML and data science methods/softwares that are tailored for environmental science questions, such as knowledge-guided AI/ML models 
  • Comparison and evaluation of AI/ML and data science methods for application in the environmental sciences; What requirements should they fulfill?
  • Limitations of current general AI/ML and data science methods and future development needs in addressing environmental science questions
  • Development of hybrid methods that combine AI/ML and data science methods and conventional methods?
  • Recommended practices for robust applications of AI/ML and data science methods to address environmental science questions and potential pitfalls

Resulting publications will be presented on a dedicated special collections page on the EDS website, referenced in an introductory editorial and promoted through EDS and Cambridge University Press social media.

We strongly believe in the value of open science and reproducibility in promoting the development of environmental data science. Thus, we encourage authors sharing sample codes via Jupyter notebooks during the submission or after article acceptance, that allow readers to explore the research deeper and promote the value of published articles. Jupyter notebooks will be published alongside articles. Articles that link to open data and open materials will also be highlighted with Open Science badges. 

Submission process 

Authors interested in taking part should consult the EDS Instructions for Authors, which include (optional) LaTeX and Word templates to ease the submission process.

Key things to consider: 

1. Authors will need to select from the following EDS article types: 

  • Application papers: Research progress, or tackling a real-world problem, in an environmental field, enabled by data science. For example, AI or data science could be used for understanding of environmental processes, or improving forecasting tools.
  • Methods papers: Novel data science methodology inspired by an environmental problem or application. Typically the methodology should be demonstrated in one or more environmental applications.
  • Position papers that explore issues related to the use of environmental data, including security, legal and policy issues, as well as data standards, protocols and services. (Typically shorter articles: 3,000-4,000 words in length)
  • Data papers that describe in a structured way, with a short narrative and accompanying metadata, important and re-usable environmental data sets that reside in publicly accessible repositories. These papers promote data transparency and data re-use.

All submissions should contain (at the end of the manuscript):

  • An Impact Statement summarising in 120 words or less the significance of the finding for a broad audience.
  • A Data Availability Statement: EDS encourages but does not require authors to make replication materials (data, code) available in open repositories, but all articles should contain a statement that either describes where these materials can be accessed or explains why they cannot be made available. Articles linking to open data and open materials are awarded Open Science Badges, which display on publication. For more details see the EDS Transparency and Openness Promotion policy.

Authors should submit their work to the EDS ScholarOne site (https://mc.manuscriptcentral.com/eds) and select ‘Environmental Informatics’ from the list of special collections, as well as appropriate data and environmental keywords when prompted.

Note that, as an open access journal, EDS asks funded authors to contribute an article processing charge on acceptance but an increasing number of authors fall under Cambridge’s Read & Publish agreements with universities and research institutions that exempt them from this charge. There is a discretionary waiver process for unfunded authors, as described here. There are therefore no financial barriers to submission. Contact eds@cambridge.org with any questions.

Timeline

Full paper submission to EDS: encouraged by Fri 28 January (updated).

Publication: individual submissions will be handled in a timely manner, and published as soon as possible after acceptance. We anticipate the full collection of articles - which will be presented on a dedicated page, with an editorial overview - to be complete in summer 2022.

Guest Editors: