Researchers who’ve published their work on the open-access platform F1000Research come from many different fields, and their scholarship has advanced global knowledge in very diverse ways—from insights on the use of automated tools to extract data from social science papers to a study of the effectiveness of imaging technology in predicting long-term lung damage from COVID.

Despite their varied interests, these academics have a shared goal of making their work highly accessible for a worldwide audience—and they agree that F1000Research, which is part of Taylor & Francis and supports researchers in all subject areas, is an effective platform for achieving this goal.

Replicating social science studies

Amanda Legate, a Ph.D. candidate in the Soules College of Business at the University of Texas at Tyler, used F1000Research to publish her dissertation, in which she partnered with Dr. Kim Nimon to explore how AI could facilitate secondary data extraction across human resources development (HRD) literature.

“My research stems from a curiosity about metascience and a commitment to translating HRD research into actionable insights,” she said.

If researchers can use automated tools such as AI to extract survey information and other secondary data sources from published studies in HRD and other social science fields, they can replicate these studies across different contexts or use the findings to inform other work. However, “although automated tools showed promise, we found significant challenges due to the varied reporting standards and formats,” Legate said.

Her study found that automated data extraction in the social sciences lags far behind this practice within clinical research, often requiring domain-specific implementations for success.

“In social sciences, AI-driven tools face challenges with varied reporting formats, structures, and vocabularies,” she explained. “While benchmark datasets and evaluation standards do exist, they are not yet widely established or sufficiently adaptable across the diverse fields of social science research, leaving implementation inconsistent.”

Legate identified more than 50 open-source tools available to support data extraction tasks, but their usability varies widely. Her study highlights the need for standardized practices to enhance machine readability and better support AI-driven data extraction in the social sciences, which could “bridge knowledge gaps and drive innovations across disciplines,” she noted.

Sharing timely pandemic research

Tamas Dolinay, a pulmonologist and assistant professor in the UCLA Department of Medicine, led a research team that used F1000Research to publish its findings on the use of a specific chest imaging scan for predicting the long-term consequences of COVID-19 during the height of the global pandemic.

The team studied 16 patients who had survived ICU care and were transferred to Barlow Respiratory Hospital, a long-term care facility, to continue their treatment. “Most of these people were so sick that they were unable to leave a mechanical ventilator several weeks into their hospitalization,” Dolinay said.

Dolinay and his colleagues wanted to learn how effective the use of a certain radiology scan of these patients’ lungs would be in predicting their long-term prognosis. Although their study focused on a single facility with a limited number of patients, they found that the CT scan was effective in achieving this goal.

“We measured four different types of densities, and one correlated well with the potential for chronic scarring,” Dolinay said. “Those patients tend to have more severe, long-term debilitation. We were able to demonstrate that this test was easy to perform with radiology technology that’s readily available in hospitals. It could be done simply and safely this late in the condition, providing some degree of prognostication.”

When this study was published in 2020, it was very timely research, with important implications for treating COVID-19. “We know a lot more now,” Dolinay said, “but at the time it was very novel.”

Creating a more informed global community

Both Legate and Dolinay agreed that publishing their findings on F1000Research was a simple process that accomplished their goal of making their work more widely accessible worldwide.

“The F1000Research platform was ideal for disseminating our findings alongside similar studies,” Legate said. “The transparency of F1000Research’s open review process and their emphasis on data sharing and reproducibility aligned perfectly with our goals to make our research accessible and actionable.”

Publishing with F1000Research was a “seamless” experience, Legate added. She explained: “Their clear guidelines, supportive editorial team, and intuitive platform allowed for a quick, transparent process.”

Dolinay was also impressed with how smooth the process was, something he appreciated in a situation where speed was critical. “The turnaround was very quick,” he noted. “From submission to publication took only a month. With traditional journals, that’s unheard of.”

Open-access publishing helps create a more informed global community, Dolinay said. He concluded: “I think that was one reason we were able to tackle COVID successfully. Our goal was to publish to a broad community rather quickly, and certainly this journal provided that.”

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