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Strategies for Managing Excessive Publications in the Field of Environmental Science


Composite image showing stacks of paper with a forest in the background

Every year, numerous peer-reviewed articles are published in environmental science, hydrology, geology, ecology, climatology, and related fields. These papers are of high quality and contribute to our knowledge of the world. However, the existing vast bodies of work make it challenging for experienced scientists to stay updated and for new scientists to familiarize themselves with the foundations and advancements in their respective fields.

Given that the rate of scientific progress and publication is not expected to decrease, it is necessary to develop a more efficient method for combining the vast amount of information currently available.

The excessive amount of published material is hindering our progress in pushing the boundaries of science, tackling important societal issues, and assisting emerging scientists. It can also result in duplication of research efforts, rediscovery of previously known concepts, or even perpetuation of errors – all of which are inefficient uses of limited resources. Additionally, smaller, location-specific studies that offer vital context and comparisons for larger or worldwide studies in environmental science are frequently overlooked.

Given the continuous growth of science and its publications, it is necessary to develop a more effective method for synthesizing the vast amount of knowledge available. Our suggestion is to utilize a human-operated, technology-assisted online synthesis tool that adapts and improves over time. This tool would seamlessly integrate a large volume of interconnected research and information, while maintaining the intricate details found in individual peer-reviewed articles.

A Publications Avalanche

Presently, over 3 million scientifically reviewed documents are released annually in various fields. In the United States alone, there are about 500,000 papers published each year [Jinha, 2010; Johnson et al., 2018; White, 2019]. The number of papers addressing critical environmental concerns such as drought, fire, and climate change is particularly high. For instance, a simple search for academic peer-reviewed literature on “fire or wildfire” in the western United States yields over 20,000 results. Since 2016, over a thousand papers have been published each year. These high rates of publication make it challenging for experts in the field to keep up with all the latest research, which can hinder the development of new hypotheses and the adoption of new observation and modeling techniques.

The burden of being overwhelmed with publications is particularly challenging for scientists who are just starting their careers. They are trying to establish their expertise and make progress in their careers [Atkins et al., 2020; Thakore et al., 2014]. To become an expert, one must read, comprehend, and incorporate the work of others. However, determining what to read and in what order can significantly enhance one’s understanding of essential concepts. Without proper guidance from mentors, early-career scientists are left to navigate through the vast digital libraries on their own, often relying solely on Google searches, hoping to find and comprehend the fundamental and cutting-edge knowledge they need. The lack of effective mentorship is a significant obstacle to academic growth and achievement, particularly for those from marginalized backgrounds [Deanna et al., 2022].

The excessive amount of publications can hinder scientists from receiving acknowledgement for their contributions. This issue affects the entire scientific community, but it may have a greater impact on early-career researchers. Despite publishing their work, it often gets lost among the overwhelming volume of new literature being produced. The exact percentage of papers that go uncited is hard to determine and differs based on the field, but it is often significant.

In the beginning of their professional journeys, scientists frequently conduct smaller-scale studies. These may involve testing established theories in different settings or circumstances, or utilizing new techniques in unique manners. While this type of research is crucial to the scientific community, it is typically published in specialized journals rather than more widely recognized, diverse publications. Consequently, this research is often disregarded, particularly by individuals outside of the field, leading to its undervaluation.

The current efforts are just the starting point.

Researchers have attempted to address the issue of too many publications by developing different methods and products for synthesis.

The issue of too many publications is not new. Scientists have attempted to address this issue by creating various synthesis methods and materials. For instance, review articles like the Tamm Reviews, which concentrate on studies in forest ecology and management, typically summarize findings from multiple studies related to a specific topic. Additionally, there are journals like Wiley’s WIREs series that primarily publish review papers. There are also established criteria for high-quality systematic reviews, such as those provided by Collaboration for Environmental Evidence. Furthermore, we have established synthesis institutes, such as the National Center for Ecological Analysis and Synthesis, the U.S. Geological Survey’s John Wesley Powell Center for Analysis and Synthesis, and the National Socio-Environmental Synthesis Center, which are increasingly focused on producing synthesis materials such as review papers and databases.

The NSF offers various programs, including Research Coordination Networks, which assist teams of researchers in organizing and combining research, training, and educational efforts. Additionally, government and non-government entities create synthesis materials and reports, such as California’s Climate Change Assessments and the Intergovernmental Panel on Climate Change’s reports. While serving a similar purpose, data provision websites like Google Earth offer data and model outputs that address key environmental science inquiries.

These existing tools and initiatives clearly contribute to information synthesis, although the array of syntheses and their various products can themselves be overwhelming. Critically, these synthesis products are often static or are limited in scope, meaning users can easily mistake outdated syntheses for up-to-date understanding or misapply generalized findings to specific locations or circumstances where they aren’t relevant.

Furthermore, subsequent discoveries may deviate from initial theories in previous synthesis papers or provide more detailed information to expand upon fundamental principles (such as measuring the correlation between rising temperatures and earlier snowmelt in a specific area). However, due to the infrequent revisiting of synthesis papers, this progression in our current knowledge can easily go unnoticed or be forgotten.

Synthesis papers and reports also typically focus on specific topics, but links to reviews of related topics are not consistently included, particularly if topics cross disciplines. For example, a review paper discussing the effectiveness of wildfire fuel treatment methods may not mention or link to reviews of relevant science, such as how climate affects fire risk.

Artificial intelligence guided by humans.

Can artificial intelligence (AI) be of assistance in relieving publication overload, given that current human-made synthesis products are insufficient? According to Matthews (2021), this is indeed possible. There are already machine learning-based products such as Iris.ai, Semantic Scholar, Connected Papers, Open Knowledge Maps, and Local Citation Network that can automate searches and condense information.

However, it is difficult to extract meaningful information from publications related to environmental science on specific topics [Romanelli et al., 2021]. This is because simply finding literature does not guarantee understanding, especially when the search yields a large number of papers. Relying solely on highly cited papers may also pose a challenge, as their popularity may not necessarily align with the goal of understanding [Romanelli et al., 2021]. For instance, high citation counts may be driven by current trends rather than advancements in expert knowledge. In some cases, high citation counts may also be a result of scientific disagreements or papers being repeatedly cited as examples of outdated assumptions.

AI algorithms can use semantic terms to automatically map domain knowledge and group papers together, revealing trends in publication topics over time. However, this method of clustering does not necessarily combine ideas about a topic. This limitation is seen in the work of Borner and Polley (2014), Franconeri et al. (2021), and Lafia et al. (2021). On the other hand, more generalized syntheses from tools like ChatGPT do not provide the level of nuanced and detailed understanding that researchers require to advance environmental science.

A revolutionary solution would be a dynamic online tool for metasynthesis that promotes fairness and efficiency in discovering, comprehending, and updating scientific information.

A revolutionary solution to address these limitations would be a dynamic online tool for metasynthesis that streamlines the process of discovering, comprehending, and updating scientific information. This tool could merge the advantages of human-driven syntheses with advancements in visualization and AI technology. It could also adapt as our understanding grows, utilizing customizable searches to synthesize research while retaining the detail and context found in individual peer-reviewed papers upon request.

Traditional review articles structure various concepts into theoretical frameworks. They emphasize similarities and differences in fundamental theories. They assess the methods employed in obtaining observational data, analyzing data, and constructing models. Additionally, they incorporate specific research papers into conceptual frameworks to facilitate comprehension.

Using recent advancements in AI, such as natural language processing and visualization tools, we can potentially create user-friendly interfaces that utilize “on the fly” rendering to better connect review papers and reports from various fields and subjects. These interfaces could assist both novice and expert users in navigating knowledge landscapes, with options for beginners to explore general topics like snow accumulation and melt, and for experts to specify location- and scale-specific research hypotheses. However, it is crucial for scientists to take an active leadership role in designing and updating the knowledge synthesis, including conceptual models, hypotheses, and the application of current techniques.

Working on this project will involve teamwork and alliances between scientists, visualization specialists, database experts, ontologists (language engineers), and machine learning professionals. We believe that having scientists lead and participate in every stage of this collaborative effort will result in a product that best serves our community’s needs. While there may be advancements in private sector tools, such as an enhanced version of Google Scholar or a more comprehensive ChatGPT, they may not necessarily maintain the advantages of science syntheses driven by humans.

Transforming an Idea into a Tangible Outcome

How would the tool we are proposing appear when put into action? In general, we imagine interconnected web pages that display conceptual diagrams and current working theories (and opposing theories) regarding specific research inquiries. These pages would also include examples of evidence that support or challenge these theories in specific locations and time frames. We acknowledge that identifying exceptions to general principles or measuring the impact in specific contexts is often how progress is made in environmental science. This data would all be connected to peer-reviewed publications.

Figures 1 and 2 illustrate potential front-end pages focusing on the question of how changing snowpacks relate to changing vegetation in semiarid, mountainous regions of the U.S. West. A dashboard would allow users to move quickly among pages covering different aspects of this broad research question, and a navigation pane would show connections between the selected question and other related questions, such as how snowpacks, which store water for vegetation, are changing in the region.

Hypothetical page in the proposed online synthesis tool addresses how changing snowpacks are affecting vegetation in semiarid mountainous regions of the western United States.

In Figure 1, a mock page of an online synthesis tool is shown, discussing the impact of changing snowpacks on vegetation in semiarid mountainous regions of the western United States. The navigation pane on the left allows users to move between different research questions (represented by yellow dots) and related conceptual models (represented by orange dots). The right pane displays a visual representation of the central processes, variables, and connections involved in addressing the research question. This pane also provides links to related conceptual models and questions. In the center, the vertical bars in the dashboard pane link to other pages (refer to Figure 2) that highlight information on hypotheses, models, observations, space, and time, as well as peer-reviewed papers relevant to the selected research question. The space and time pages contain examples of papers that either support or contradict a hypothesis, organized by location/period and time/space scale, respectively. Clicking on the image will display a larger version.

As the most valuable form of scientific knowledge is current information, the system would need to have a strong method for staying updated with modern understanding.

Because scientific knowledge is most valuable when it is current, the system would require a robust process by which it could keep up with changes in contemporary understanding. This process, more so than its design, would be the system’s key innovation. Developing the details of the updating process—including how often, by whom, and by what criteria it would be updated—would require careful thought and rigorous debate by the scientific community. And the system’s success ultimately would depend on scientists’ willingness to contribute. The more users, and the larger the updating community, the better the end product.

We propose using established peer review processes to ensure credibility and establish a system that is adaptable, dynamic, and accessible. The development of conceptual diagrams and hypotheses would involve direct participation from the scientific community, with working group engagement being crucial. We plan to use iterative working group methods, similar to those used by the Intergovernmental Panel on Climate Change, and utilize existing community organizations like AGU to achieve this goal.

Individuals who assist with curation, conceptual model development, hypothesis creation, or software development, as well as those who actively contribute to and manage the tool, should be acknowledged formally for their contributions.

When creating the suggested tool, it will be crucial to provide incentives that encourage involvement. This may include recognizing researchers who contribute to tasks such as curation, conceptual model advancement, hypothesis development, or software development, as well as those who create, contribute to, and maintain the tool. This emphasis on recognition aligns with current movements in universities and funding agencies, like NSF, that acknowledge the value of software and database development and other types of contributions.

The peer-reviewed article, created in the 17th century, has been beneficial for science. However, with the advancement of scientific knowledge in various subjects and inquiries, a new method is needed to access information that offers a comprehensive overview of multiple studies while also maintaining the crucial details of each individual study.

This hypothetical page would be accessed via the “hypotheses” bar in the dashboard shown in Figure 1.

Figure 2 illustrates a theoretical webpage that can be accessed through the “hypotheses” tab on the dashboard shown in Figure 1. This page displays a collection of current working hypotheses related to the chosen research question and includes papers that either support, clarify, or contradict each hypothesis (highlighted in green). It also includes tags for cited sources to provide more context. Users can navigate to other categories of information from this page or click “return to navigation” to go back to the main page where they can search for other interconnected hypotheses (a more detailed version of the left box in Figure 1). Click on the image for a larger version.

As an initial step, we recommend that groups of environmental scientists come together to form working groups dedicated to creating a tool similar to the one we have proposed. It is crucial that these groups also establish guidelines for how scientists can contribute to the ongoing development of the tool. At the same time, it is important for these groups to collaborate with experts in artificial intelligence and other fields to take advantage of advancements in data visualization and information retrieval. Government agencies and organizations such as NSF and AGU should provide support for these endeavors by organizing and financing these working groups, as well as supporting the development of prototypes and other community engagement initiatives.

Given the increasing amount of peer-reviewed articles and the availability of tools such as ChatGPT, it is crucial for these communities to acknowledge the strengths and limitations of current and upcoming methods for sharing and combining scientific information. As leaders in their fields, they should take an active role in creating innovative tools. A drastic solution must be implemented to address the issue of publication overload and support researchers of all levels in advancing scientific boundaries.

References

Atkins, K., et al. (2020), “Looking at myself in the future”: How mentoring shapes scientific identity for STEM students from underrepresented groups, Int. J. STEM Educ., 7(1), 42, https://doi.org/10.1186/s40594-020-00242-3.

Borner, K., and D. E. Polley (2014), Visual Insights: A Practical Guide to Making Sense of Data, 297 pp., MIT Press, Cambridge, Mass.

The authors of this article (Carter, R.G., et al.) discuss the relationship between innovation, entrepreneurship, and promotion and tenure in academia. Their research was published in the journal Science, with the title “Innovation, Entrepreneurship, Promotion, and Tenure.” The article can be found in Volume 373, issue 6561, with pages 1,312-1,314. The article can also be accessed online at https://doi.org/10.1126/science.abj2098.

Deanna, R., et al. (2022), Community voices: The importance of diverse networks in academic mentoring, Nat. Commun., 13, 1681, https://doi.org/10.1038/s41467-022-28667-0.

The article “The Science of Visual Data Communication: What Works” by Franconeri et al. (2021) discusses effective methods for presenting data visually. Published in Psychol. Sci. Public Interest, the article delves into various techniques and their effectiveness. The DOI for this article is 10.1177/15291006211051956.

According to Jinha (2010), there are approximately 50 million scholarly articles in existence. This estimate was published in Learned Publishing and can be found in volume 23, issue 3, pages 258-263, with a DOI of https://doi.org/10.1087/20100308.

Johnson, R., A. Watkinson, and M. Mabe (2018), The STM report: An overview of scientific and scholarly publishing, 5th ed., 212 pp., Int. Assoc. of Sci., Tech., and Med. Publ., The Hague, Netherlands, https://www.stm-assoc.org/2018_10_04_STM_Report_2018.pdf.

Lafia, S., et al. (2021), Mapping research topics at multiple levels of detail, Patterns, 2(3), 100210, https://doi.org/10.1016/j.patter.2021.100210.

Matthews, D. (2021), Drowning in the literature? These smart software tools can help, Nature, 597(7874), 141–142, https://doi.org/10.1038/d41586-021-02346-4.

The authors of this study (Romanelli, J. P., et al., 2021) address four difficulties that arise when conducting bibliometric reviews and provide solutions for managing them. The research was published in Environmental Science and Pollution Research, volume 28, issue 43, pages 60,448-60,458, and can be accessed through the following link: https://doi.org/10.1007/s11356-021-16420-x.

Reworded: In 2014, Thakore and colleagues conducted a randomized controlled trial on the effectiveness of theory-driven coaching in promoting the growth and diversity of young scientists in the Academy for Future Science Faculty. The results were published in BMC Medical Education, Vol. 14, No. 1, under the title “Randomized controlled trial of theory-driven coaching to shape development and diversity of early-career scientists”. The article can be accessed at https://doi.org/10.1186/1472-6920-14-160.

White, K. (2019), Publications output: U.S. trends and international comparisons, Sci. Eng. Indicators, NSB-2020-6, 35 pp., Natl. Sci. Found., Alexandria, Va., https://ncses.nsf.gov/pubs/nsb20206/.

Author Information

William Brandt from Scripps Institution of Oceanography at the University of California, San Diego; and Christina Tague at the University of California, Santa Barbara (email: [email protected]).

Reference: Brandt, W., and C. Tague (2023), Managing the problem of excessive publications in environmental science, Eos, 104, https://doi.org/10.1029/2023EO230361. Published on September 22, 2023.

The views expressed in this article are not endorsed by AGU, Eos, or any associated organizations. They are solely the views of the author(s).

The text was published in 2023 by the authors under a CC BY-NC-ND 3.0 license.
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