BACKGROUND: Research gaps refer to unanswered questions in the existing body of knowledge, either due to a lack of studies or inconclusive results. Research gaps are essential starting points and motivation in scientific research. Traditional methods for identifying research gaps, such as literature reviews and expert opinions, can be time consuming, labor intensive, and prone to bias. They may also fall short when dealing with rapidly evolving or time-sensitive subjects. Thus, innovative scalable approaches are needed to identify research gaps, systematically assess the literature, and prioritize areas for further study in the topic of interest.
OBJECTIVE: In this paper, we propose a machine learning-based approach for identifying research gaps through the analysis of scientific literature. We used the COVID-19 pandemic as a case study.
METHODS: We conducted an analysis to identify research gaps in COVID-19 literature using the COVID-19 Open Research (CORD-19) data set, which comprises 1,121,433 papers related to the COVID-19 pandemic. Our approach is based on the BERTopic topic modeling technique, which leverages transformers and class-based term frequency-inverse document frequency to create dense clusters allowing for easily interpretable topics. Our BERTopic-based approach involves 3 stages: embedding documents, clustering documents (dimension reduction and clustering), and representing topics (generating candidates and maximizing candidate relevance).
RESULTS: After applying the study selection criteria, we included 33,206 abstracts in the analysis of this study. The final list of research gaps identified 21 different areas, which were grouped into 6 principal topics. These topics were: \"virus of COVID-19,\" \"risk factors of COVID-19,\" \"prevention of COVID-19,\" \"treatment of COVID-19,\" \"health care delivery during COVID-19,\" \"and impact of COVID-19.\" The most prominent topic, observed in over half of the analyzed studies, was \"the impact of COVID-19.\"
CONCLUSIONS: The proposed machine learning-based approach has the potential to identify research gaps in scientific literature. This study is not intended to replace individual literature research within a selected topic. Instead, it can serve as a guide to formulate precise literature search queries in specific areas associated with research questions that previous publications have earmarked for future exploration. Future research should leverage an up-to-date list of studies that are retrieved from the most common databases in the target area. When feasible, full texts or, at minimum, discussion sections should be analyzed rather than limiting their analysis to abstracts. Furthermore, future studies could evaluate more efficient modeling algorithms, especially those combining topic modeling with statistical uncertainty quantification, such as conformal prediction.

Papers on COVID-19 are being published at a high rate and concern many different topics. Innovative tools are needed to aid researchers to find patterns in this vast amount of literature to identify subsets of interest in an automated fashion.
We present a new online software resource with a friendly user interface that allows users to query and interact with visual representations of relationships between publications.
We publicly released an application called PLATIPUS (Publication Literature Analysis and Text Interaction Platform for User Studies) that allows researchers to interact with literature supplied by COVIDScholar via a visual analytics platform. This tool contains standard filtering capabilities based on authors, journals, high-level categories, and various research-specific details via natural language processing and dozens of customizable visualizations that dynamically update from a researcher\'s query.
PLATIPUS is available online and currently links to over 100,000 publications and is still growing. This application has the potential to transform how COVID-19 researchers use public literature to enable their research.
The PLATIPUS application provides the end user with a variety of ways to search, filter, and visualize over 100,00 COVID-19 publications.

This paper examines how scientific literature and policy documents frame the ecosystem concept and how these frames have shaped scientific dialogue and policy making over time. This was achieved by developing a frame typology, as a basis for organizing relevant value expressions, to assess how different frames have altered perspectives of the ecosystem concept. The frame typology and analysis is based on a semi-grounded and longitudinal document analysis of scientific literature and policy documents using the ecosystem concept. Despite changing discourses and public priorities (e.g., cultural constructs of biodiversity) both science and policy documents are characterized by stable value systems that have not changed substantially since the 1930s. These value systems were defined based on ethical principles that delineate 6 core frames: humans first, dual systems, eco-science, eco-holism, animals first, and multicentrism. Specific crises (e.g., climate change) and cross-disciplinary uptake and re-uptake of, for example, the ecosystem services concept, have brought new perspectives to the forefront of public discourse. These developments triggered changes in the core frames that, rather than being value based, are based on how the ecosystem is conceptualized under fixed value systems and over time. Fourteen subframes were developed to reflect these longitudinal changes. There are as such clear framing effects in both scientific literature and in policy. Ecosystem research is for instance often characterized by unstated value judgments even though the scientific community does not make these explicit. In contrast, policy documents are characterized by clear value expressions but are principally management driven and human centered.