November 2018 – Geospatial Ecology of Marine Megafauna Laboratory

Scientific publishing: Impact factor, open access and citations

By Leila Lemos¹ and Rachel Ann Hauser-Davis²

¹PhD candidate, Fisheries and Wildlife Department, OSU

²PhD, CESTEH/ENSP/Fiocruz, Rio de Janeiro, Brazil

Scientific publishing not only communicates new knowledge, but also is a measure of each scientist’s success: the impact each scientist has on his/her field is often measured by his/her number of publications and the reputation of the journals he/she published in. Therefore, publishing in reputable journals, with a high impact factor, is often essential to get a job, promotion and tenure. So, what is an impact factor?

The impact factor (IF) was first created in the 1960’s and is a measure of a journal’s impact on science, as reflected by the yearly average number of citations to recent articles published in that journal. The IF is used to compare the impact of journals within disciplines. Journals with higher impact factors are deemed as more prestigious and of better quality than those with lower ones.

The IF of a journal for any given year is calculated as the number of citations, received in that year, of articles published in that journal during the two preceding years, divided by the total number of articles published in that journal during the two preceding years, as follows:

In recent years, open access (OA) journals have emerged, changing how we perceive publications. However, the role and significance of IF is still present, valuable and used worldwide.

Conventional (non-open access) journals cover publishing costs through access fees, such as subscriptions, site licenses or download charges, which can be paid by universities, research institutions and, sometimes, by individuals. Papers published in OA journals, on the other hand, are distributed online and free of cost. However, there are still publication costs, which are usually paid by the authors. And, open access article processing charges are not cheap, ranging from a few hundred to several thousand dollars, depending on the field (more thoughts on this theme here).

It seems imbalanced that researchers have to pay for their work to be published. They have carried out a study and have obtained results that should be shared with the community. These results should not be treated as a commercial item to be sold. Also, it ends strengthening those who have resources and weakening those who do not have, increasing the division between Northern and Southern hemispheres, and narrowing the knowledge-production system (Burgman 2018).

Thus, a free-of-charge research paper would be interesting for everyone. PeerJ is a good example of a recent OA, free of publishing costs, peer-reviewed, and scholarly journal, that was released in 2013. It’s a totally new model and pushes the boundaries. In addition, there are hybrid journals (i.e., Conservation Biology) that offer both conventional and OA modes, leaving it to the authors to decide what they prefer (Burgman 2018). In many cases, disadvantaged authors might also be able to appeal for waivers. Thus, authors who cannot pay publishing fees might still see their work getting published.

However, this is not how the publishing system typically works. Therefore, researchers need to determine where to publish based on the journal IF and focus/audience, on the different price structures and fees, and whether it is OA or not.

Researchers in general want their articles to be openly accessible for everyone, not just those who can afford to pay the journal for access, so they can increase visibility of their work. Open access can increase the impact/reach of a research paper by facilitating paper downloads, access, and use in other scientific research, which may, in turn, lead to higher citation rate (Eyesenbach 2006).

Higher citation rates would also improve researchers’ H-index: an author-level metric that measures both productivity and citation impact of a scientist or scholar, based on the scientist’s most cited papers and the number of citations that they have received in publications.

The graph below exemplifies the h-index that is based on the maximum value of h such that the given author/journal has published h papers that have each been cited at least h times. In other words, the index is designed to improve with number of publications or citations. The index can only be compared between researchers from same field, as citation conventions might differ widely among different fields.

H-index from a plot of decreasing citations for numbered papers
Source: Wikipedia

However, publishing in an OA journal might easily increase researchers’ H-index and journals’ IF. Many researchers have also considered OA as an “artificial citation enhancer”.

As with any new system, some are opposed to the establishment of the OA system, including researchers, academics, librarians, university administrators, funding agencies, government officials, publishers and editorial staff, among many others (Markin 2017). This opposition claims that OA publishing leads to financial damages to the large publishers worldwide, and, mainly, that this system may damage the peer review system in place today, leading to reduced scientific quality (such as “you pay, you publish” predatory journals that take advantage of the paid system by publishing as fast as possible, without any scientific rigor whatsoever).

However, many reputable journals, such as Elsevier, Springer, Wiley and Blackwell, now offer OA as an option for their established journals. This approach is simply another option for authors, where they may pay if they want for their paper to be available for everyone. Even if this option is available, manuscripts still go through a rigorous peer-review that occurs with both conventional and OA journals. Thus, publishing in OA should be just as rigorous.

Open access papers would be the most “scientifically ethical”, as science is aimed at society, for society, and this type of publishing furthers research reach. However, paying thousands of dollars is sometimes very complicated, as this means less money for fieldwork costs, gears, laboratory analyses, among others.

All in all, OA is a recent development that is changing scientist approach to publication. The future of scientific publication seems uncertain and likely to hold new developments in the near future.

References:

Burgman M. 2018. Open access and academic imperialism. Conservation Biology 0 (0): 1–2. DOI: 10.1111/cobi.13248.

Eysenbach G. 2006. Citation Advantage of Open Access Articles. PLoS Biology 4 (5): e157. doi:10.1371/journal.pbio.0040157. PMC 1459247. PMID 16683865.

Markin P. 2017. The Sustainability of Open Access Publishing Models Past a Tipping Point. Open Science. Retrieved 26 April 2017.

The Intersection of Science and Politics

By Alexa Kownacki, Ph.D. Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

As much as I try to keep politics out of my science vocabulary, there are some ties between the two that cannot be severed. Often, science in the United States is very linked to the government because funding can be dependent on federal, state, and/or local government decisions. Therefore, it is part of our responsibility as scientists to be, at least, informed on governmental proceedings.

The United States has one agency that is particularly important to those of us conducting marine science: the National Oceanic and Atmospheric Administration (NOAA). NOAA’s mission is science, service, and stewardship with three major components:

To understand and predict changes in climate, weather, oceans and coasts
To share that knowledge and information with others
To conserve and manage coastal and marine ecosystems and resources

noaa org chart — Organizational Chart of NOAA. (Image source: OrgCharting)

Last year, the U.S. Senate confirmed Retired Rear Admiral Timothy Gallaudet, Ph.D., as the Assistant Secretary of Commerce for Oceans and Atmosphere for the Department of Commerce in NOAA. This position is an appointment by the current President of the United States, and is tasked with overseeing the daily functions and the strategic and operational future of NOAA. NOAA oversees the National Marine Fisheries Service (NMFS), which is an agency responsible for the stewardship and management of the nation’s living marine resources. NMFS is a major player when it comes to marine science, particularly through the determination of priorities for research and management of marine species and habitats within the United States’ exclusive economic zone (EEZ).

In dark blue, the United States’ Exclusive Economic zones, surrounding land masses in green. (Figure by K. Laws)

Recently, I had the opportunity to hear Dr. Gallaudet speak to scientists who work for, or in conjunction with, a NMFS office. After the 16% budget cut from the fiscal year 2017 to 2018, many marine scientists are concerned about how budget changes will impact research. Therefore, I knew Dr. Gallaudet’s visit would provide insight about the future of marine science in the United States.

Dr. Gallaudet holds master’s and doctoral degrees in oceanography from Scripps Institution of Oceanography, as well as a bachelor’s degree from the United States Naval Academy. He spent 32 years in the Navy before stepping into his current role as Assistant Secretary. Throughout the meeting, Dr. Gallaudet emphasized his leadership motto: All in, All Good, and All for One.

Dr. Gallaudet also spoke about where he sees NOAA moving towards: the private sector.

A prominent conservation geneticist asked Dr. Gallaudet how NOAA can better foster advanced degree-seeking students. The geneticist commented that a decade ago there were 10-12 PhD students in this one science center alone. Today, there is “maybe one”. Dr. Gallaudet responded that the science centers should start reaching out to private industry. In response to other questions, he continued to redirect scientists toward United States-based corporations that could join forces with government agencies. He believes that if NMFS scientists share data and projects with local biotechnology, medical, and environmental companies, the country can foster positive relationships with industry. Dr. Gallaudet commented that the President wants to create these win-win situations: where the US government pairs with for-profit companies. It is up to us, as the scientists, how we make those connections.

As scientists, we frequently avoid heated political banter in the hopes of maintaining an objective and impartial approach to our research. However, these lines can be blurred. Much of our science depends on political decisions that mold our future, including how funding is allocated and what goals are prioritized. In 2010, Science Magazine published an online article, “Feeding your Research into the Policy Debate” where Elisabeth Pain highlighted the interdisciplinary nature of science and policy. In Pain’s interview with Troy Benn, a PhD student in Urban Ecology at the time, Benn comments that he learned just how much scientists play a role in policy and how research contributes to policy deliberations. Sometimes our research becomes of interest to politicians and sometimes it is the other way around.

From my experiences collaborating with government entities, private corporations, and nonprofit organizations, I realize that science-related policy is imperative. California established a non-profit, the California Ocean Science Trust (OST), for the specific objective supporting management decisions with the best science and bridging science and policy. A critical analysis of the OST by Pietri et al., “Using Science to Inform Controversial Issues: A Case Study from the California Ocean Science Trust”, matches legislation with science. For example, the Senate Bill (SB) 1319, better known as the California Ocean Protection Act (COPA), calls for “decisions informed by good science” and to “advance scientific understanding”. Science is explicitly written into legislation and I think that is a call to action. If an entire state can mobilize resources to create a team of interdisciplinary experts, I can inform myself on the politics that have potential to shape my future and the future of my science.

An image of the NOAA ship Bell M. Shimada transiting between stations. Multiple members of the GEMM Lab conducted surveys from this NOAA vessel in 2018. (Image source: Alexa Kownacki)

Hundreds and hundreds and hundreds of models: An ecologist’s love for programming

By Dawn Barlow, PhD student, Department of Fisheries & Wildlife, Geospatial Ecology of Marine Megafauna Lab

When people hear that I study blue whales, they often ask me questions about what it’s like to be close to the largest animal on the planet, where we do fieldwork, and what data we are interested in collecting. While I love time at sea, my view on a daily basis is rarely like this:

Our small research vessel at sunset in New Zealand’s South Taranaki Bight at the end of a day of blue whale survey. Photo by D. Barlow.

More often than not, it looks something like this:

In my application letter to Dr. Leigh Torres, I wrote something along the lines of “while I relish remote fieldwork, I also find great satisfaction in the analysis process.” This statement is increasingly true for me as I grow more proficient in statistical modeling and computer programming. When excitedly telling my family about how I am trying to model relationships between oceanography, krill, whales, and satellite imagery, I was asked what I meant by “model”. Put simply, a model is a formula or equation that we can use to describe a pattern. I have been told, “all models are wrong, but some models work.” What does this mean? While we may never know exactly every pattern of whale feeding behavior, we can use the data we have to describe some of the important relationships. If our model performance is very good, then we have likely described most of what drives the patterns we see. If model performance is poor, then there is more to the pattern that we have not yet captured in either our data collection or in our analytical methods. Another common saying about models is, “A model is only ever as good as the data you put into it.” While we worked hard during field seasons to collect a myriad of data about what could be influencing blue whale distribution patterns, we inevitably could not capture everything, nor do we know everything that should be measured.

So, how do you go about finding the ‘best’ model? This question is what I’ve been grappling with over the last several weeks. My goal is to describe the patterns in the krill that drive patterns in whale distribution, the patterns in oceanography that drive patterns in the krill, and the patterns in the oceanography that drive patterns in whale distribution. The thing is, we have many metrics to describe oceanographic patterns (surface temperature, mixed layer depth, strength of the thermocline, integral of fluorescence, to name just a few), as well as several metrics to describe the krill (number of aggregations, aggregation density, depth, and thickness). When I multiplied out how many possible combinations of predictor variables and parameters we’re interested in modeling, I realized this meant running nearly 300 models in order to settle on the best ten. This is where programming comes in, I told myself, and caught my breath.

I’ve always loved languages. When I was much younger, I thought I might want to study linguistics. As a graduate student in wildlife science, the language I’ve spent the most time learning, and come to love, is the statistical programming language R. Just like any other language, R has syntax and structure. Like any other language, there are many ways in which to articulate something, to make a particular point or reach a particular end goal. Well-written code is sometimes described as “elegant”, much like a well-articulated piece of writing. While I certainly do not consider myself “fluent” in R, it is a language I love learning. I like to think that the R scripts I write are an attempt to eloquently uncover and describe ecological patterns.

Rather than running 300 models one by one, I wrote an R script to run many models at a time, and then sort the outputs by model performance. I may look at the five best models of 32 options in order to select one. But this is where Leigh reminds me to step back from the programming for a minute and put my ecologist hat back on. Insight on the part of the modeler is needed in order to discern between what are real ecological relationships and what are spurious correlations in the data. It may not be quite as simple as choosing the model with the highest explanatory power when my goal is to make ecological inferences.

So, where does this leave me? Hundreds of models later, I am still not entirely sure which ones are best, although I’ve narrowed it down considerably. My programming proficiency and confidence continue to grow, but that only goes so far in ecology. Knowledge of my study system is equally important. So my workflow lately goes something like this: write code, try to interpret model outputs, consider what I know about the oceanography of my study region, re-write code, re-interpret the revised results, and so on. Hopefully this iterative process is bringing us gradually closer to an understanding of the ecology of blue whales on a foraging ground… stay tuned.

A blue whale lunges on an aggregation of krill in New Zealand’s South Taranaki Bight. Drone piloted by Todd Chandler.

Why Feeling Stupid is Great: How stupidity fuels scientific progress and discovery

By Alexa Kownacki, Ph.D. Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

It all started with a paper. On Halloween, I sat at my desk, searching for papers that could answer my questions about bottlenose dolphin metabolism and realized I had forgotten to check my email earlier. In my inbox, there was a new message with an attachment from Dr. Leigh Torres to the GEMM Lab members, saying this was a “must-read” article. The suggested paper was Martin A. Schwartz’s 2008 essay, “The importance of stupidity in scientific research”, published in the Journal of Cell Science, highlighted universal themes across science. In a single, powerful page, Schwartz captured my feelings—and those of many scientists: the feeling of being stupid.

For the next few minutes, I stood at the printer and absorbed the article, while commenting out loud, “YES!”, “So true!”, and “This person can see into my soul”. Meanwhile, colleagues entered my office to see me, dressed in my Halloween costume—as “Amazon’s Alexa”, talking aloud to myself. Coincidently, I was feeling pretty stupid at that moment after just returning from a weekly meeting, where everyone asked me questions that I clearly did not have the answers to (all because of my costume). This paper seemed too relevant; the timing was uncanny. In the past few weeks, I have been writing my PhD research proposal —a requirement for our department— and my goodness, have I felt stupid. The proposal outlines my dissertation objectives, puts my work into context, and provides background research on common bottlenose dolphin health. There is so much to know that I don’t know!

Alexa dressed as “Amazon Alexa” on Halloween at her office in San Diego, CA.

When I read Schwartz’s 2008 paper, there were a few takeaway messages that stood out:

People take different paths. One path is not necessarily right nor wrong. Simply, different. I compared that to how I split my time between OSU and San Diego, CA. Spending half of the year away from my lab and my department is incredibly challenging; I constantly feel behind and I miss the support that physically being with other students provides. However, I recognize the opportunities I have in San Diego where I work directly with collaborators who teach and challenge me in new ways that bring new skills and perspective.
(Image source: St. Albert’s Place)
Feeling stupid is not bad. It can be a good feeling—or at least we should treat it as being a positive thing. It shows we have more to learn. It means that we have not reached our maximum potential for learning (who ever does?). While writing my proposal I realized just how little I know about ecotoxicology, chemistry, and statistics. I re-read papers that are critical to understanding my own research, like “Nontargeted biomonitoring of halogenated organic compounds in two ecotypes of bottlenose dolphins (Tursiops truncatus) from the Southern California bight” (2014) by Shaul et al. and “Bottlenose dolphins as indicators of persistent organic pollutants in the western north Atlantic ocean and northern gulf of Mexico” (2011) by Kucklick et al. These articles took me down what I thought were wormholes that ended up being important rivers of information. Because I recognized my knowledge gap, I can now articulate the purpose and methods of analysis for specific compounds that I will conduct using blubber samples of common bottlenose dolphins
Image source: memegenerator.net
Drawing upon experts—albeit intimidating—is beneficial for scientific consulting as well as for our mental health; no one person knows everything. That statement can bring us together because when people work together, everyone benefits. I am also reminded that we are our own harshest critics; sometimes our colleagues are the best champions of our own successes. It is also why historical articles are foundational. In the hunt for the newest technology and the latest and greatest in research, it is important to acknowledge the basis for discoveries. My data begins in 1981, when the first of many researchers began surveying the California coastline for common bottlenose dolphins. Geographic information systems (GIS) were different back then. The data requires conversions and investigative work. I had to learn how the data were collected and how to interpret that information. Therefore, it should be no surprise that I cite literature from the 1970s, such as “Results of attempts to tag Atlantic Bottlenose dolphins, (Tursiops truncatus)” by Irvine and Wells. Although published in 1972, the questions the authors tried to answer are very similar to what I am looking at now: how are site fidelity and home ranges impacted by natural and anthropogenic processes. While Irvine and Wells used large bolt tags to identify individuals, my project utilizes much less invasive techniques (photo-identification and blubber biopsies) to track animals, their health, and their exposures to contaminants.
(Image source: imgflip.com)
Struggling is part of the solution. Science is about discovery and without the feeling of stupidity, discovery would not be possible. Feeling stupid is the first step in the discovery process: the spark that fuels wanting to explore the unknown. Feeling stupid can lead to the feeling of accomplishment when we find answers to those very questions that made us feel stupid. Part of being a student and a scientist is identifying those weaknesses and not letting them stop me. Pausing, reflecting, course correcting, and researching are all productive in the end, but stopping is not. Coursework is the easy part of a PhD. The hard part is constantly diving deeper into the great unknown that is research. The great unknown is simultaneously alluring and frightening. Still, it must be faced head on. Schwartz describes “productive stupidity [as] being ignorant by choice.” I picture this as essentially blindly walking into the future with confidence. Although a bit of an oxymoron, it resonates the importance of perseverance and conviction in the midst of uncertainty.
(Image source: Redbubble)

Now I think back to my childhood when stupid was one of the forbidden “s-words” and I question whether society had it all wrong. Maybe we should teach children to acknowledge ignorance and pursue the unknown. Stupid is a feeling, not a character flaw. Stupidity is important in science and in life. Fascination and emotional desires to discover new things are healthy. Next time you feel stupid, try running with it, because more often than not, you will learn something.

Image may contain: 1 person, sitting, table, child and outdoor — Alexa teaching about marine mammals to students ages 2-6 and learning from educators about new ways to engage young students. San Diego, CA in 2016. (Photo source: Lori Lowder)

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