In an effort to increase awareness of Open Access publishing at UNC Charlotte, Atkins Library is introducing a series of interviews with Open Access (OA) Champions around campus. These Champions are faculty and researchers at UNC Charlotte who have published their work in Open Access journals or books, deposited their work in the Niner Commons, or shared their scholarship via other Open Access avenues. Some Champions have received support from the Atkins Library Open Access Publishing Fund to publish in Open Access journals.
Today's OA Champion is Dr. Shi Chen from the Department of Public Health Sciences.
Q: Why did you decide to publish in an open access journal?
A: One of my research areas (health and medical informatics) is a niche field, and most journals that cover this field are open access (OA) journals, for example JMIR series, BMC series, Journal of American Medical Informatics Association, etc.
The other main reason that I choose to publish in OA journal is their relatively fast track from submission to decision, and from acceptance to online appearance. For example, I also work with the current COVID-19 pandemic. Timely dissemination of research findings is essential for the community to get up-to-date information. While there are pre-print archives such as MedRxiv, peer-reviewed OA journal articles carry more credibility.
Q: How did you obtain the funding? Was it written into your grant?
A: Support from Atkins Library OA funds; My own start-up funds in the first two years; Support from College of Health and Human Services; Support from School of Data Science; Publication funding of my co-authors.
I am also currently adding OA fees in all my grant proposals if the proposal allows publication fees or other expenditures (usually $4000-$6000 to cover 2-3 OA publications).
Q: How was your experience publishing open access different than publishing in a traditional pay journal? (licensing, process, etc.)
A: In general the process and licensing in OA journals are as smooth as in a traditional pay journal. So far my experience with OA journal is overwhelmingly positive. I understand that there are concerns about OA journals so I am very careful in selecting reputable ones to publish with.
Q: Do you believe that publishing in an open access journal or book increased the visibility of your material?
A: Yes, I have been publishing ~10 papers in open access journals (Scientific Reports, PLoS, JMIR, etc.) and received good visibility of my research. It benefits further research in developing regions of the world to understand infectious diseases dynamics and prevent them effectively.
Q: Are there any altmetrics or bibliometrics you wish to share in this Spotlight article to demonstrate the advantages of open access, as opposed to subscribed content?
A: Social media have become a good platform to promote OA publications. I have seen discussions on social media towards new studies on OA COVID-19 studies. This might be difficult to measure, but it definitely further disseminates the findings to both scientific communities and the general public. I believe that many OA studies serve well as educational materials for the general public, especially during this emerging COVID-19 pandemic. Increasing public awareness and knowledge is a powerful weapon against the pandemic and misinformation associated with it.
Q: If you are using open data tools to post or to obtain study data, how has that been helpful to your investigations?
A: I have been using a large volume of social media data on previous Zika pandemic (about 4 million tweets) and current COVID-19 pandemic (more than 20 million tweets) through GNIP API supported by School of Data Science. These “big data” enable me to understand public perceptions of these pandemics, attitude towards different interventions, identify potential health misinformation, and characterize how misinformation infiltrates on social media.
The other major open data tool that I use is the county-level Johns Hopkins COVID-19 case data. Based on this data, my collaborators and I are able to provide real-time visualization and modeling tools to track the pandemic in the U.S. at county level.