Note: the submission deadline has been extended to 4 September 2026 to accommodate interest in this Call for Papers from participants in an 'AI for Sustainability' innovation summit at ESIIL. 

Jump to:

• Introduction
• Timetable
• How to Submit
• Open Access
• Open Materials
• Guest Editors

Introduction

This Call for Papers on Solution-Based Data Science for Environmental Biology Challenges will lead to a special collection (a virtual special issue) in the open-access journal Environmental Data Science (Cambridge University Press). 

We aim to capture studies that advance fundamental fields within Environmental Data Science (e.g., biology, evolutionary biology, ecology, earth science, geography, sociology, anthropology, geology, hydrology, economics) and utilize big data and methods from computer science, information science, and other fields. We also welcome papers that focus on training and education as well as collaboration and interdisciplinary team science in EDS. The goal of this special collection is to advance data-intensive science, improve management, or inform policy related to today’s environmental challenges. Some example themes may include:

1. Environmental Tipping Points and Transformations: Improving understanding of a) how we observe, measure, and detect critical thresholds, b) the drivers and their interactions, c) the post-transformation landscape including the collapse, transformation, and recovery trajectories, or d) management and intervention to enhance resilience.
2. Big Data and Advanced Analytics for Environmental Resilience and Adaptation: Using big data, advanced analytics, and data-intensive computing tools to understand resilience across genes, species, ecosystems and societies, advance ecological forecasting with solutions in mind, and inform adaptive management and nature-based solutions.
3. Emerging Topics within EDS: We aim to define the most pressing environmental challenges and opportunities for solutions-oriented science. Emerging topics may include extreme disturbances and climate, biodiversity and community dynamics, food webs and nutrient flows, evolutionary patterns, or complex socio-ecological systems, as some examples.

Timetable

New submission deadline: September 4 2026.

How to Submit

Please note the following key details, with more information available in the EDS Instructions for Authors:

Article Types: Authors should make sure that they select the most appropriate article type when they submit their work to EDS. The standard EDS article types are:

• 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.
• 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.
• Survey papers: providing a systematic overview of a method, tool or approach, or a field or subfield that is relevant to environmental data science.
• Position papers: a) providing an authoritative, personal view on the uptake or obstacles to AI and data science approaches for environmental problems, or b) exploring issues related to the use of environmental data, including ethical, legal and policy issues, as well as data standards, protocols and services. (Note that these are shorter articles: you should aim for 5,000 words in length)

Templates:  Authors have the option of using the following EDS templates to help structure their submission:

• LaTeX template files
• Word template
• Overleaf (a collaborative authoring tool based on LaTeX; authors are able to submit directly to through Overleaf; read more about benefits here)

Authors not using our templates are reminded to include:

• An impact Statement: 120 words beneath the abstract describing the significance of the findings in language that can be understood by a wide audience

And at the back of the article: 

• Author contributions (using the CRedIT taxonomy as a guide)
• Competing interest statement
• Data availability statement
• Funding statement 

See the EDS Instructions for Authors for more details about these statements.

Submission portal: Authors should submit via the EDS ScholarOne site and select the 'Solution-Based Data Science for Environmental Challenges' tag from the dropdown menu when prompted to identify whether the article is for a special collection.

Open Access

Any author can publish on an open access basis in EDS if accepted, irrespective of their funding situation or institutional affiliation. There are no financial barriers to publication. Many articles have publishing costs covered through the Transformative Agreements that Cambridge has set up with universities worldwide. If the corresponding author on an article is affiliated with a Transformative Agreement this effectively covers publishing costs. Authors not affiliated with these agreements who have received a grant that budgets for open access publication are encouraged to pay an article processing charge (APC). However, if an author has no funding and no institutional agreement, the charge will be waived. Please do not let concerns about your financial situation or affiliation put off your submission.

Open Materials

Authors are asked to make code and data that supports the findings openly available in a recognised repository and to link to them in the Data Availability Statement in the article. We recognise this may not be possible in all circumstances. See the EDS Research Transparency policy. Open Data and Open Materials badges will be displayed on published articles that link to replication materials, as a recognition of open practices.

Guest Editors

Chelsea Nagy, chelsea.nagy@colorado.edu
Cibele Amaral, cibele.amaral@colorado.edu
Ty Tuff, ty.tuff@colorado.edu

All of whom are affiliated with the Environmental Data Science Innovation and Impact Lab (ESIIL), University of Colorado, Boulder.