Search

Abstract Background All aspects of our society, including the life sciences, need a mechanism for people working within them to represent the concepts they employ to carry out their research. For the information systems being designed and developed to support researchers and scientists in conducting their work, conceptual models of the relevant domains are usually designed as both blueprints for a system being developed and as a means of communication between the designer and developer. Most conceptual modelling concepts are generic in the sense that they are applied with the same understanding across many applications. Problems in the life sciences, however, are especially complex and important, because they deal with humans, their well-being, and their interactions with the environment as well as other organisms. Results This work proposes a “systemist” perspective for creating a conceptual model of a life scientist’s problem. We introduce the notion of a system and then show how it can be applied to the development of an information system for handling genomic-related information. We extend our discussion to show how the proposed systemist perspective can support the modelling of precision medicine. Conclusion This research recognizes challenges in life sciences research of how to model problems to better represent the connections between physical and digital worlds. We propose a new notation that explicitly incorporates systemist thinking, as well as the components of systems based on recent ontological foundations. The new notation captures important semantics in the domain of life sciences. It may be used to facilitate understanding, communication and problem-solving more broadly. We also provide a precise, sound, ontologically supported characterization of the term “system,” as a basic construct for conceptual modelling in life sciences.

Citizen science projects that collect natural history observations often do not have an underlying research question in mind. Thus, data generated from such projects can be considered “use-agnostic.” Nevertheless, such projects can yield important insights about species distributions. Many of these projects use a class-based data schema, whereby contributors must supply a species identification. This can limit participation if contributors are not confident in their identifications, and can introduce data quality issues if species identification is incorrect. Some projects, such as iNaturalist, circumvent this with crowdsourced species identifications based on contributed photographs, or by grading confidence in the data based on attributes of the sighting and/or contributor. An alternative to a class-based data schema is an open-ended (instance-based) one, where contributors are free to identify their sighting at whatever taxonomic resolution they are most confident, and/or describe the sighting based on attributes. This can increase participation (data completeness) and have the benefit of adding additional (and sometimes unexpected) information. The regionally-focused citizen science website NLNature.com was designed to experimentally examine how class-based versus instance-based schema affected contributions and data quality. Here, we show that the instance-based schema yielded not only more contributions, but also several of ecological importance. Thus, allowing contributors to supply natural history information at a level familiar to them increases data completeness and facilitates unanticipated contributions.