New Open-Access Journal Puts Food Models, Data and Software in the Spotlight

A new peer-reviewed, open-access journal has been launched with an unusual mission: to publish the often invisible building blocks of food science research. The Food Modelling Journal (FMJ), introduced by the AGINFRA+ community in an editorial published on 17 September 2019, is designed to give formal scientific credit to the models, datasets, software tools, workflows and visualisation methods that normally precede a conventional research article but rarely receive recognition of their own. Hosted on Pensoft’s ARPHA Publishing Platform and integrated with the AGINFRA+ Virtual Research Environment, the journal aims to make these digital research objects findable, citable and reusable across the food science community.

The rationale behind the launch lies in a long-standing imbalance in how science rewards its outputs. Mathematical models and experimental or observational data play a fundamental role in assuring and improving food supply, quality and safety in a globalised and rapidly changing world, prompting food scientists, agronomists and computer scientists to form strategic cross-disciplinary alliances. Yet the rapid development of new methods, software and hardware has produced an ever-growing flood of data, models and tools, often described as a data deluge. While scholarly tradition credits researchers primarily through conventional articles and citation counts, many invaluable products of the research cycle, so-called research objects such as data, software scripts, visualisation routines and models, remain undervalued, underused and uncredited despite their importance for scientific progress.

The consequences of this neglect are tangible. Scientists frequently repeat work already done by others instead of building on it as a stepping stone for further analysis, and reproducibility or reusability of published results can become impossible or restricted. Worse still, a vast share of precious data, software tools and other research outcomes has been lost outright, for example tools hosted on academic web pages that quietly go inactive over time, because there is no opportunity to publish them within a research article and no long-term preservation infrastructure exists to keep them alive. For industry and other stakeholders, these intermediate products are often of primary interest, which makes their disappearance from the scientific record all the more costly.

The Open Science movement has been steadily correcting this picture. What began as Open Access, a barrier-free route to research articles championed by initiatives such as the Budapest Open Access Initiative, evolved into Open Data, extending openness to the data and data-processing procedures underpinning research results. Following the natural logic of the research cycle, other valuable products, including software, methods, protocols and models, then emerged as discrete, openly publishable research objects linked to each other via repositories or within the research article itself, ensuring reproducibility and reusability. The idea has since influenced every stage of the research process, including scholarly publishing, and has been materialised in funder, governmental and European Union policy documents.

The Food Modelling Journal was created to help research scientists, modellers and software developers in the food domain share their valuable research objects more efficiently than conventional articles or information repositories allow. Its stated aim is to provide the much-needed scientific record and credit for dedicated models, data, data analytics workflows and software, thereby fostering the overall reproducibility and reusability of research. The journal emerged after intensive consultations with communities participating in the AGINFRA+ project and beyond, and enjoys particular support from the Risk Assessment Modelling and Knowledge Integration Platform (RAKIP) community, established by three European institutions specialising in food safety risk assessment: the French agency ANSES, the German Federal Institute for Risk Assessment (BfR) and Denmark’s National Food Institute (DTU Food).

RAKIP was created to provide infrastructure for hosting, saving and preserving a variety of scientific knowledge, including publications, experimental data and mathematical models, alongside resources such as databases and software tools for model generation and application, all based on harmonised data formats and consistent rules for knowledge annotation. The new journal is expected to support this and many other community activities in food modelling by adding a scholarly publishing infrastructure that facilitates the exchange of digital assets at community- and industry-accepted standards. Its scope covers information objects and digital resources related to food safety, food quality, food control, food defence and food design, documenting data, models, software and services, data analytics pipelines, visualisation methods and related digital resources.

Several key features distinguish the journal from conventional outlets. The first is a philosophy of quality rather than quantity: FMJ focuses on important outcomes of the research cycle other than research papers, which nonetheless meet rigorous scientific standards, with subject editors drawn from among world-leading international experts overseeing peer review. A cross-disciplinary approach from the outset aims to bridge basic and applied research through non-conventional article types such as model descriptions, data analytics papers, software descriptions and design concepts. The journal also enforces a strict open data and software code policy: authors must make all data, software or models described in their articles compliant with the FAIR principles, ensuring maximum transparency and reproducibility.

Speed and machine-readability are further hallmarks. Thanks to an innovative, entirely XML-based publishing workflow provided by Pensoft, accepted manuscripts can be published within two to three days of acceptance. All articles appear in semantically enhanced HTML, JATS XML and PDF formats, multiplying opportunities for content dissemination, data and text mining, discovery through automated and targeted searches, and barrier-free data sharing. Behind the scenes, the ARPHA-XML workflow supports every stage of publication, from manuscript writing through peer review, publication, distribution and archiving, within a single online collaborative working space.

The ARPHA Writing Tool at the heart of this workflow offers collaborative manuscript authoring with pre-defined but flexible templates covering most types of research outcomes. Co-authors can work together on a manuscript and invite external contributors, such as mentors, pre-submission reviewers, linguistic and copy editors or colleagues, who may correct and comment before submission without being listed as co-authors. Rich functionalities allow searching and importing literature and data references, cross-referencing in-text citations, importing tables, uploading images and multimedia, and building image plates. An automated technical validation step checks manuscripts for consistency, complemented by human pre-submission validation from the editorial office, while optional pre-submission external peer reviews can be submitted alongside the manuscript to speed up post-submission evaluation. The collaborative peer-review process supports change tracking, comments and replies, and automated, customisable email and social network notifications, with reviews automatically consolidated into a single online file for editors.

Published papers remain living objects. Readers can comment through an inbuilt commenting tool and an integrated hypothes.is plugin, and articles can undergo open post-publication peer review. Authors can convert published papers back into editing mode at the click of a button, revise them and republish them as new versions under different DOIs linked to previous versions via CrossMark, realising the concept of a living article. In a particularly practical touch, users of food model repositories can automatically convert FSK-ML, the Food Safety Knowledge Markup Language, metadata describing their models into manuscripts in the ARPHA Writing Tool, which can then be elaborated, submitted, peer reviewed and published in the journal.

The journal is also tightly woven into the AGINFRA+ Virtual Research Environment, which follows a system-of-systems approach, aggregating resources from domain-agnostic service providers such as D4Science, EGI and OpenAIRE as well as community-specific ones including AgroDataCube, AGROVOC and the RAKIP model repository. Through a web gateway offering a single access point, researchers gain a shared workspace for storing and sharing research artefacts, a social networking area for collaborative discussion, semantic data management solutions built on ontologies, thesauri and vocabularies, and a data analytics workbench integrated with RStudio, Jupyter notebook and Galaxy workflow environments. Data visualisation and publishing services range from a graphs management workbench producing interactive charts, from generic spline and scatter plots to highly specific plant-height-over-time graphs with values and images, to a catalogue-based publishing platform aligned with the FAIR principles. By relying on this platform, authors can mix the narrative of a traditional paper with links that give effective access to the digital versions of their research products, closing the loop between publication and the working tools of science.

The editorial team behind the Food Modelling Journal is convinced that the venture fills an obvious gap in the publishing landscape by bringing to light research outputs of exceptional value to their authors and to the food science community at large. By turning models, data and code into first-class, citable publications, the journal invites researchers to share their passion for Open Science and to spread the word throughout the community, so that the next breakthrough in food safety or quality can begin not from scratch, but from a well-documented, preserved and credited foundation left by those who came before.

Subject of Research: Open-access scholarly publishing of food science models, data and software as citable research objects

Article Title: Open Science meets Food Modelling: Introducing the Food Modelling Journal (FMJ)

Article References: Filter, M., Candela, L., Guillier, L., Nauta, M., Georgiev, T., Stoev, P., & Penev, L. (2019). Open Science meets Food Modelling: Introducing the Food Modelling Journal (FMJ). Food Modelling Journal, 1, Article e46561. https://doi.org/10.3897/fmj.1.46561

Image Credits: AI Generated

DOI: 10.3897/fmj.1.46561

Keywords: Food Modelling Journal, Open Science, food safety, food quality, mathematical modelling, FAIR principles, AGINFRA+, Virtual Research Environment, ARPHA Publishing Platform, peer review, research objects, FSK-ML