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Chapter 2: Team Oceanography

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Our understanding of how the world ocean works as a system emerges from the scientific method, the systematic and self-correcting set of practices that scientists use to discover knowledge about the natural world. But while textbooks often present the scientific method as a set of rigid rules that scientists must follow, I’m here to tell you that science is messy—occasionally very messy—with twists and turns as dramatic and spine-tingling as an Agatha Christie novel. As you’ll see, the scientific “method” is more of a scientific “process,” a back-and-forth, collaborative, social—and did I say messy?—web of activities carried out by a community of scientists on a quest to understand how nature works. 

The Hollywood model of the scientific method goes something like this: A scientist—usually a man in a white lab coat working in a dark basement filled with bubbling test tubes—poses a question, creates a hypothesis, tests the hypothesis, gets a result, forms a conclusion, communicates the results, and wins a Nobel Prize. The crowd roars. But science rarely, if ever, works this way. 

Scientific research proceeds in fits and starts, maybe even two steps forward, one backward, and a couple sideways. The standard sequence may be taken out of order. An initial approach may prove to be a dead end. Early results may be shared to get feedback. Methods may be refined. Hypotheses may be revised, and revised again, as new observations and measurements lead to a deeper understanding of a problem. All kinds of things, theoretical, methodological, statistical, and conceptual, may interrupt the smooth sequence of textbook science. Instead, scientists multitask and work with other scientists and skillfully jump between different activities to ensure that by the end of a particular study, the data they collect, the methods they use, and the interpretations they offer meet rigorous standards and pass muster with their fellow scientists, a key step called peer review. 

This modern approach has been called the activity model of the scientific method (Harwood 2004). The activity model emphasizes the social character of science, which proceeds in small, careful steps with numerous refinements and do-overs. Preliminary results are scrutinized to refine methods and make adjustments prior to full-scale implementation. Scientists may share data in the cloud or an online database to gain feedback from other scientists for improving their approach or to support scientists working on similar problems. An exchange of ideas and new findings happens via telephone, email, social media, and at any of the regular gatherings of professional organizations, scientific conferences, or virtual meetings. In this way, science in the 21st century takes on many of the aspects of a thriving, dynamic, and exciting entrepreneurial enterprise. 

Oceanography, perhaps more than any other discipline, embodies the activity model of the scientific method. If you’re looking for a scientific career that brings you into contact with people from all walks of life, you’ll likely enjoy working in ocean science. We call it team oceanography. Come join us!

2.1 Channel Your Inner Scientist

To understand the scientific process, it’s helpful to learn how to think like a scientist—a real scientist, not the fact-quoting one you learned about in secondary school or the nerdy one on TV (although I do love Sheldon). In fact, your natural “inner child” instincts are those of a scientist. 

Anyone who spends time with young children appreciates their innate curiosity, their desire to know. Not long after children learn to speak, they learn to ask “what” and “why.” “What’s that?” they may say, pointing to a squirrel in a tree. “Why did the cat bite me?” they might cry, learning to never again pull the cat by its tail. “Why do stars fall down from the sky?” as they point to the trail of a shooting star. 

2.2 Investigate the Natural World

Science starts with curiosity. To see the world as scientists do requires a childlike wonder to see and hear and smell and touch and taste the world in high definition. In the lab or in the field, a scientist’s senses are on alert. Every detail matters. Like the Bene Gesserit of the 1965 novel Dune—written by Frank Herbert (1920–1986) and considered the best-selling science fiction novel of all time—scientists are “trained in the minutiae of observation.” Only through repeated observations of the same organism, behavior, event, or occurrence can scientists establish a set of facts upon which experiments or further observations can be based. 

Phenomena that can be observed and measured consistently and repeatedly lend themselves to scientific inquiry, the multifaceted process of investigating the natural world. Things that cannot be observed or measured, or that occur once or rarely, do not lend themselves to scientific inquiry. Such phenomena fall outside the domain of science. Scientific inquiry encompasses a diverse set of tools and approaches. To the extent that the researchers carrying out the inquiry report their methods and results truthfully, and as long as they remain willing to modify their conclusions based on their results, then these efforts may be considered legitimate science. 

2.3 Ask a Good Question

Science aims to ask questions about the universe and everything in it, including us. In my classroom and online teaching experience, it’s a rare student who asks a question about a topic or concept being covered in the course. Perhaps students fear that they will appear unknowledgeable and their instructor or their classmates will judge them. Maybe it’s because nobody likes a smarty-pants (e.g., Rentzsch and Schröder-Abé 2014). Or maybe students just aren’t curious about the topics on the syllabus—the saddest of all possibilities.

Whatever the reason, I like to think that many students don’t realize that asking questions is a good thing. Journalists and scientists learn very early in their careers that asking great questions reaps great benefits. Experienced journalists and scientists can ask questions that other folks never think of. Just like surfing Bonzai Pipeline takes practice (and a bit of nerve), asking good questions takes practice (and a bit of confidence). Like the big wave that got away, an unasked question is an opportunity missed. 

In science, however, we differentiate between scientific and nonscientific questions. A scientific question concerns the natural and observable world. As outlined by the National Research Council (NRC 2002), a scientific question is one that “can be investigated empirically,” that is, by using our senses to gather evidence. Questions like why is the ocean blue, why do waves break at the shore, and how do sperm whales hold their breath so long can be answered using science. Questions about if God exists or whether humans have untapped mental powers aren’t scientific. They don’t lend themselves to observation (at least not at this time).

A scientific question typically builds upon previous research and scientific theory; it “links research to relevant theory” (NRC 2002). A newly posed scientific question contributes to a chain of reasoning, a logical extension of previous work. It’s not enough to simply work on a question in a field of study in which others are working. A scientist’s work requires an intimate knowledge of what has been published in that field and what questions remain unanswered. A scientist’s questions pick up where others left off or take the next step in a series designed to figure something out. A 12th-century quote popularized by Sir Isaac Newton (1643–1727) sums it up nicely: “If I have seen further, it is by standing on the shoulders of giants” (Newton 1675). The discoveries of modern science owe their existence to the discoveries of scientists in previous times. 

Scientific questions must be general enough to apply in different settings. A study that focuses on one event in one place at one time will be difficult to replicate and generalize. Research on human behavior in social sciences and education often faces problems of replicability and generalization. Replicability stands at the heart of scientific progress. If a scientist’s results cannot be replicated, then those results will be questioned and potentially dismissed. 

2.4 Propose an Idea or Model

Collecting observations and forming questions naturally lead to consideration of possible answers. Humans relish in coming up with ideas about why something happened. Because of the way our brains work, we try to fit observations into mental frameworks of what we already know. These frameworks become conceptual models (introduced in Chapter 1), idealized representations of the world around us. 

Conceptual models often take the form of illustrations, diagrams, and even animations. Descriptions and drawings of imaginary things like dragons, mermaids, and extraterrestrials represent someone’s conceptual model of these creatures. Ideas also take the form of illustrations or graphics, primarily as a means of refining those ideas and explaining them to others. In fact, most illustrations that you see in textbooks represent conceptual models. An illustration of the major ocean currents and a diagram of the interactions of species in an oceanic food web serve as examples of conceptual models of how a part of the ocean works. 

Scientists, like everyone else, employ other kinds of models to help them understand and test hypotheses about the natural world. As a child, you likely played with toys. Toys are a kind of physical model, a scaled-down, three-dimensional, semirealistic representation of an object or part of nature. A Matchbox car is a physical model of a real car. A Cabbage Patch doll is a physical model of a human baby. Lincoln Logs represent real logs; Legos represent construction materials. These physical models allow kids to experience through pretend play what it’s like to drive a real car, care for a real baby, or build a house. They provide a sense of the excitement and, hopefully, the responsibilities involved with an actual car, baby, or house. 

A third kind of model uses mathematics to formulate and test ideas. Mathematical models represent simple or complex expressions used to describe or simulate how something works. We usually think of them as quantitative—giving numerical results—but they may also be qualitative, helping scientists describe properties or relationships between variables (e.g., Dambacher et al. 2015). Mathematical models underpin the stock market, retirement planning, business development, economics, and a whole host of other human activities.

2.5 Test Your Hypotheses

Models form the cornerstone of human thinking, but a model, any model—conceptual, physical, or mathematical—is only as good as the facts that support it. To determine whether a model accurately describes or predicts a natural event, scientists carry out what may be the most important part of their work: creating testable hypotheses.

A hypothesis is a carefully worded, conditional, and testable explanation of how nature works. You’ll often hear people call them educated guesses, but there’s little guesswork involved. Scientists typically frame hypotheses within the context of known explanations. Because of previous observations and experiments, they often have a pretty good idea of what’s happening—or at least they think they do—and so hypotheses represent the next step in a progression of observations and experiments designed to figure something out. They aren’t random and they aren’t long lists that include every possible explanation. They are focused, narrowly interpreted, tentative statements that can be tested using carefully planned observations and carefully designed experiments. For example, you might state the hypothesis, “Whales jump out of the water to remove barnacles from their skin.” You could test this by observing whales. 

One often overlooked and misunderstood characteristic of hypotheses is how they are worded. Scientists pose hypotheses in a manner that allows them to be rejected by observations and experiments. It may seem a curious way to go about something, but science generally attempts to disprove possibilities—eliminate hypotheses—rather than prove them. Scientists call this method falsification, the process of disproving a hypothesis. While some argue that falsification can be limiting in certain fields, especially branches of modern particle and theoretical physics (e.g., Carroll 2014), most scientists view it as a cornerstone of the scientific method. Our test of the whale-jumping hypothesis would be rejected (i.e., falsified) if we observed whales without barnacles jumping or if the act of jumping had no effect on whale-attached barnacles. See how that works?

The process of eliminating competing hypotheses was developed by Austrian-British science philosopher Karl Popper (1902–1994; Popper 2005). It belongs to a broader approach called deductive reasoning. Deduction operates as a kind of top-down logic: If A is true, and B belongs to A, then B is also true. For example, if all mammals breathe air, and a dolphin is a mammal, then dolphins breathe air.

While most scientists use deductive reasoning, the opposite form, inductive reasoning, also plays a part in science. Inductive reasoning operates as a bottom-up approach to figuring something out. Specific observations lead to general principles. For example, if dolphins use tools, and sea lions use tools, and both dolphins and sea lions are mammals, then, inductively thinking, all mammals use tools. Induction helps in the process of generating appropriate scientific questions and hypotheses from observations or preliminary experiments. Many scientists consider this the most interesting part of their research because it draws upon the creative mind. Science requires a person to imagine all the possibilities. For that reason, scientists tend to be highly creative people, despite their characterization as boring nerds. 

A third form of reasoning, abductive reasoning, plays a larger role outside of science, where explanations or conclusions are needed to fix something, make a diagnosis, or render a judgment. Abductive reasoning is best explained as the quickest way to the most logical and usually simplest explanation, even though facts and observations may be incomplete. Car mechanics, doctors, and jurors often use abductive reasoning to complete a task because they are presented with limited data. Scientists often use abductive reasoning to generate hypotheses. It’s considered the more creative approach to scientific inquiry. 

Hypothesis making through inductive and abductive reasoning and hypothesis falsification through deductive reasoning make up what’s known as the hypothetico-deductive approach, also known as the hypothetico-deductive model or simply the H-D method. It relies on the process of generating a hypothesis that can be falsified by a set of observations or experiments and then carrying out those observations or experiments to reject or support the hypothesis. 

The H-D method remains a mainstay of science because it aspires to objectivity. Scientists go to great lengths to be impartial in their work from beginning to end. They don’t always get it right, but the self-correcting nature of science and the willingness of scientists to admit their errors make the H-D method the best thing going. To quote American philosopher and education reformer John Dewey (1859–1952):

Science represents the safeguard of the race against . . . natural propensities and the evils that flow from them. It consists of the special appliances and methods which the race has slowly worked out in order to conduct reflection under conditions whereby its procedures and results are tested. . . . Without initiation into the scientific spirit one is not in possession of the best tools which humanity has so far devised. (Dewey 1916)

2.6 Conduct an Investigation

All scientific investigations take time, patience, and funding. But ocean science presents special challenges. At least eight broad and overlapping approaches characterize the discipline of oceanography:

  • laboratory investigation, usually carried out in a laboratory or special facility outfitted with instrumentation, artificial habitats, and other tools for conducting controlled experiments and observations
  • oceangoing (or field) investigation, typically carried out in the ocean, including along the shore, using snorkel or scuba, or on a small vessel, large ship, submersible, or other manned vehicle
  • robotic investigation, using remotely operated vehicles, autonomous vehicles, drifters, gliders, and similar craft
  • time-series investigation, using instruments deployed on moorings, autonomous seafloor instruments, and cabled ocean observatories
  • animal-tagging investigation, in which oceanographers attach instrument packages to marine mammals (e.g., elephant seals) or large fishes (e.g., great white sharks)
  • aerial investigation (confined to Earth’s atmosphere), including instruments flown on drones, tethered balloons, and manned aircraft
  • spaceborne investigation, mostly carried out by instruments aboard satellites but occasionally conducted by instruments and personnel aboard spacecraft and space stations
  • computer modeling investigation, ranging from studies done on personal computers to those requiring supercomputers

An oceanographic research program may include any or all of the above approaches. Investigations may be carried out by a single investigator or multiple investigators. They may be supported by undergraduate and graduate research assistants, postdoctoral researchers, lab and field technicians, and personnel from private industry. An investigator may work with colleagues at their own institution or collaborate with colleagues, students, technicians from multiple institutions. The largest oceanographic programs involve scientists from across the globe cooperating on different aspects of a scientific problem and sharing the costs of personnel, equipment, travel, and ships. It’s not uncommon to find people from different countries spending several weeks at sea together, working through language barriers, getting used to different customs, and attempting to enjoy what are often unfamiliar foods (like fiskeboller). It works (most of the time) because everyone depends on each other for assistance and safety and is focused on a common goal.

As a real-life example, an international expedition involving hundreds of researchers from 19 countries began a yearlong mission aboard the icebreaker Polarstern in the Arctic Ocean in September 2019. Dubbed the Multidisciplinary drifting Observatory for the Study of Arctic Climate, or MOSAiC, the expedition froze their ship into the ice to conduct investigations designed to shed new light on climate change. MOSAiC, the largest shipboard polar expedition ever mounted, recalls the historic journey of Norwegian oceanographer Fridtjof Nansen (1861–1930). Nansen froze his round-bottom wooden sailing ship, Fram, into the Arctic ice from 1893 to 1896. (He’s also the only oceanographer to win a Nobel Prize, albeit for his humanitarian work.) Oceanographers participating in the MOSAiC mission—which ended in October 2020—were able to take measurements and perform experiments at polar latitudes over all four seasons, including Arctic winter. Though larger than most, MOSAiC serves as just one example of dozens of similar oceanographic investigations carried out in coastal waters and across the world ocean annually.

2.7 Analyze Your Results

Data analysis represents the worst of times and the best of times for scientists. Entering, organizing, reducing, summarizing, tabulating, graphing, and analyzing scientific observations and data—in some cases, millions upon millions of data points—can be time consuming, frustrating, tedious, and a downright pain in the caboose. On the other hand, as the averages emerge, as the trends become clear, as the relationships make themselves known, and as something that no one has previously witnessed or discovered sees the light of day, data analysis can be one of the most exhilarating experiences in a scientist’s life. After all, the data tell the story. They refute or confirm the hypotheses and ideas. They tell the scientists whether they’re on the right track or whether a particular set of experiments is an utter failure. Data analysis is often the make-or-break moment in a scientific study. Occasionally, a scientist’s work results in something so extraordinary that it changes the world. Our modern, industrialized civilization—including the smartphone you’re checking as you read this—represents an accumulation of these moments in science.

Without question, the tools, methods, and approaches of data analysis have been transformed by the development of technology, notably computing. Scientists have been using computers since the 1950s, but computers’ speed and ease of use have changed dramatically in the past 20 years. As one oceanographer put it, computers did for the 20th century what adoption of Arabic numerals did for the 17th century: they transformed how we think (Warren 2006). Three examples illustrate Warren’s point: numerical models, big data, and artificial intelligence. 

In recent decades numerical models, also known as mathematical or computer models, have evolved alongside theory and observation into what oceanographers call the third element of oceanographic research. Models generate data by solving sets of equations that represent—in the scientist’s mind—how a system works. When the model’s predictions (or forecasts) are compared to observations from nature, scientists can determine how well they do or don’t understand a system. When the model outputs fail to match observations, scientists know that some part of their model needs revision. This stepwise process—generating model predictions, comparing them to real-world observations, and revising the model—helps scientists improve their understanding of the natural world.

Models serve a practical function too. A branch of applied ocean science known as operational oceanography serves as a kind of ocean weather forecasting service. Its models produce near-real-time output called nowcasts. To create nowcasts, oceanographers use observational data from various near-real-time electronic streams, such as buoys, floats, satellites, and even ships. Operational oceanography serves commercial marine interests (fishing and shipping, for example), the military (which depends on up-to-date knowledge of the ocean to conduct its operations), and recreational interests (like boaters and surfers). 

Increasingly, oceanographers are turning to computer scientists for help managing large oceanographic data sets. Big data refers to an emerging subdiscipline of computer science that tackles the challenges of analyzing data sets whose size or complexity exceeds the capabilities of traditional software and computers. Hey et al. (2009) refer to this “data-intensive computing” as the fourth paradigm, the use of computers to explore and mine big data for information. In a sense, the fourth paradigm emerges from the ability of computers to think. They detect patterns that we cannot. Big data scientists emphasize five key properties of big data: volume (lots of it), variety (many different sources), velocity (coming at you fast), veracity (ensuring the data are real and free of errors), and value (what you can learn; e.g., De Mauro et al. 2016). The terminology has largely developed in response to the proliferation of electronic devices such as smartphones, watches, tablets, virtual assistants, and similar devices. Capable of tracking, storing, and archiving (via the cloud) practically everything you do on your device, the resultant data stream (popularly known as digital exhaust) generates mind-blowing quantities of information.

Oceanographers have long faced the challenges of big data, marine big data, as they refer to it (e.g., Huang et al. 2015, 2019). They note that American oceanographer Matthew Maury’s (1806–1873) compilation of wind and current data from ships’ logs contained 1.2 million data points. Results from the world’s first global oceanographic voyage, the Challenger expedition (1872–1876), spanned 50 volumes and more than 29,500 pages (Murray 1895). More recently, large, multi-institutional oceanographic expeditions churn out data by the petabyte. That’s a thousand times a terabyte, the typical storage capacity of the current generation of desktop computers. A single Earth-observing satellite, the Aquarius, logs more data in two months than the first 125 years of ship and buoy data gathering (Huang et al. 2015). Developing the architecture and algorithms to collect, verify, store, process, integrate, and analyze such massive volumes will require new ways of thinking and a new generation of marine data science techies. Perhaps you will be among them.

One possible solution for extracting meaningful information from big data lies in artificial intelligence, or AI, a broad category of data science aimed at building computer and software systems that mimic human intention, intelligence, and adaptability (e.g., West and Allen 2018). While application of AI-based data analysis in the marine sciences has been slow to develop, the field holds great promise for extracting greater value from big data. Working side by side, humans and machines engaged in ocean science research may accelerate our knowledge and understanding of the world ocean as never before.

2.8 Form Your Conclusions 

The grand finale of the scientific method—forming your conclusions—proves to be the most challenging for many scientists. This is where the rubber meets the road, so to speak. A scientist must now make sense of their data. They must extract meaning and significance. They must use the evidence of their observations, measurements, and experiments to tell a story. And they must tell that story in a way that other scientists and even the public can understand. Most often, this story forms the basis of a scientific discussion, the presentation in oral or written form of the conclusions of a particular scientific study. Though forming conclusions differs depending on the nature of the research, in general, four steps are crucial (e.g., Heard 2016):

  1. How do the results shed light on (i.e., answer) your original research questions?
  2. What are the strengths and weaknesses of your results in answering your research questions?
  3. How do your conclusions build upon, extend, or contradict previous work on this topic?
  4. What questions and considerations arise for future research on this topic?

Here’s where science most often tests the modesty of a scientist. After all, if you designed and carried out the research, you’re naturally (and forgivably) inclined to think it’s brilliant! Yet a scientist’s interpretation of their results may be subject to bias, a less-than-objective, partial, and sometimes distorted view of scientific evidence. Scientific bias almost certainly stems from a worldview formed as a result of years or decades of experience in the field. There’s nothing intentional or malevolent about it. Scientists are human, after all, but they must always remain aware of the inherent biases in their work. As Milroy (2016) puts it:

Regardless of the strength of your hypothesis or the veracity of your data, as human beings we are always prone to making errors in our analyses. In the context of science, this almost always occurs because we have some preconceived notion of how our experiments should turn out, and if our data seem to support our original hypothesis, it is difficult to step back from all of your hard work and look at the results with a truly cynical eye. Results can be easily misinterpreted unless you are very careful to devise an experiment where the results can only be construed in one way. 

The scientific method embodies a set of checks and balances that guard against this very thing, a system called peer review, which may be defined as the feedback on a scientist’s work by other scientists or experts. A scientist may meet with or Zoom with a trusted colleague and ask them what they think, or present preliminary conclusions to gain feedback. Scientific papers undergo scrutiny by anonymous reviewers, a formal peer review process that further ensures the validity of the data and the veracity of a scientist’s conclusions. Of course, errors, misstatements, and, on rare occasions, deliberate misrepresentation of results may occur. But science rests on the accumulation of evidence from multiple studies and multiple scientists. The self-correcting nature of science is what makes it one of the most valuable tools ever invented by humans. Scientists don’t always get it right the first time, but eventually, through the scrutiny of the ages, after decades to centuries, the truth emerges. Truths become well-established scientific theories, and the extent of our knowledge and understanding progresses.

2.9 Communicate Your Science

In the 21st century, communication of scientific results takes many forms, including:

  • peer-reviewed scientific journals
  • talks and poster presentations at scientific conferences
  • workshop or symposium compilations
  • popular magazines (e.g., National Geographic, Scientific American)
  • science books (of all varieties, including textbooks)
  • public social media (especially Twitter, but also Facebook and Instagram)
  • professional and scientific social media (such as LinkedIn, ResearchGate, Academia)
  • videos (e.g., YouTube, Vimeo, TikTok)
  • live appearances (e.g., Zoom)
  • blogs, podcasts, websites, email, and more!

Unfortunately, most scientists are trained only to present papers and talks. That’s fine if you’re communicating solely with scientists in your own discipline. But nearly all forms of science these days—and the agencies that fund the work—require communication with people outside the discipline, including scientists in other fields.

In its 2017 report, Communicating Science Effectively: A Research Agenda, the National Academy of Sciences, Engineering, and Medicine (NASEM) defines science communication, popularly known as SciComm, as “the exchange of information and viewpoints about science to achieve a goal.” A science communicator may strive to develop “a greater understanding of science and the scientific method” or to “help people understand the science relevant to a decision.” NASEM acknowledges that: 

The methods scientists use to understand the world are unlike the ways people typically think on a day-to-day basis. The results of science also can be insufficient, ambiguous, or uncertain, and scientific conclusions can change over time as new findings emerge. These inherent characteristics of science can create barriers to communication and understanding.

You can’t really blame the scientists. American biologist and book author Paul Ehrlich (b. 1932) famously opined that “If what does is comprehensible to the general public, it means he’s not a good scientist. That’s what I thought. I was wrong” (from Goodell 1977). Indeed, the importance of communicating science to the public is greater than ever before. Public interest in climate change, vaccines, environmental pollution, genetically modified foods, fracking, and a host of similar socioscientific issues underscores the need to bridge the gap between scientists and the public. As Fischhoff (2013) puts it, “Effective SciComm informs people about the benefits, risks, and other costs of their decisions, thereby allowing them to make sound choices.” Increasingly, educators view SciComm training as a priority in the education of future scientists (e.g., Brownell et al. 2013; Montgomery et al. 2022).

Even if you’re pursuing a career outside science, you play a role in SciComm. When you tell family or friends about your science courses, you represent something about science. Your experience in a science class (and reading a science textbook!) shapes your opinions, good, bad, or otherwise. And those opinions are expressed every time you post something (hopefully, good) about science.

A common refrain I hear from students is “I’m not good at science.” Of course not! You’re probably not good at fixing spaceships, either. These things take years of training and experience. But having a sense of where scientists are coming from, learning how to decipher their statements, and understanding that behind every statement is a body of evidence may be more valuable to the world than just knowing a bunch of science facts. If the end result of a science course is that you understand and appreciate how science works—and have a positive feeling about the work that scientists do—then you deserve an A in my book!

2.10 Chapter References

Alfred Wegener Institute. “MOSAiC.” Accessed May 27, 2022.  https://mosaic-expedition.org/

Brownell, Sara E., Jordan V. Price, and Lawrence Steinman. 2013. “Science Communication to the General Public: Why We Need to Teach Undergraduate and Graduate Students This Skill as Part of Their Formal Scientific Training.” Journal of Undergraduate Neuroscience Education 12(1): E6–E10. https://pubmed.ncbi.nlm.nih.gov/24319399

Carroll, Sean. 2014. Falsifiability. Contribution to What Scientific Idea Is Ready for Retirement? Edge.org. https://www.edge.org/response-detail/25322 

Dambacher, Jeffrey M., Peter C. Rothlisberg, and Neil R. Loneragan. 2015. “Qualitative Mathematical Models to Support Ecosystem-Based Management of Australia’s Northern Prawn Fishery.” Ecological Applications 25(1): 278–298. https://doi.org/10.1890/13-2030.1 

De Mauro, Andrea, Marco Greco, and Michele Grimaldi. 2016. “A Formal Definition of Big Data Based on Its Essential Features.” Library Review 65(3): 122–135. https://doi.org/10.1108/LR-06-2015-0061

Dewey, J. 1916. Democracy and Education: An Introduction to the Philosophy of Education. New York: Macmillan. https://www.google.com/books/edition/Democracy_and_Education/8P0AAAAAYAAJ?hl=en

Fischhoff, Baruch. 2013. “The Sciences of Science Communication.” Proceedings of the National Academy of Sciences 110: 14033–14039. https://doi.org/10.1073/pnas.1213273110

Goodell, Rae. 1977. “The Visible Scientists.” The Sciences 17(1): 6–9. https://doi.org/10.1002/j.2326-1951.1977.tb01494.x

Harwood, William. 2004. “A New Model for Inquiry: Is the Scientific Method Dead?” Journal of College Science Teaching 33(7): 29–33. https://www.jstor.org/stable/10.2307/26491315

Heard, Stephen B. 2016. The Scientist’s Guide to Writing. Princeton, NJ: Princeton University Press. https://doi.org/10.2307/j.ctvcmxs67

Herbert, Frank. 1965. Dune. New York: Ace Books. https://archive.org/details/dune0000herb_a7n1/mode/2up

Hey, Tony, Stewart Tansley, and Kristin Tolle, eds. 2009. The Fourth Paradigm: Data-Intensive Scientific Discovery. Seattle: Microsoft Research. https://www.microsoft.com/en-us/research/publication/fourth-paradigm-data-intensive-scientific-discovery/

Huang, Dongmei, Wei Song, and Guoliang Zou. 2019. Marine Big Data. Hackensack: World Scientific. https://doi.org/10.1142/11337

Huang, Dongmei, Danfeng Zhao, Lifei Wei, Zhenhua Wang, and Yanling Du. 2015. “Modeling and Analysis in Marine Big Data: Advances and Challenges.” Mathematical Problems in Engineering. https://doi.org/10.1155/2015/384742

Milroy, Scott. 2016. Field Methods in Marine Science: From Measurements to Models. New York: Garland Science. https://www.routledge.com/Field-Methods-in-Marine-Science-From-Measurements-to-Models/Milroy/p/book/9780815344766#

Montgomery, Thomas D., Joanne Rae Buchbinder, Ellen S. Gawalt, Robbie J. Iuliucci, Andrew S. Kock, Evangelia Kotsikorou, Patrick E. Lackey, Min Soo Lim, Jeffrey Joseph Rohde, Alexander J. Rupprecht, Matthew N. Srnec, Brandon Vernier, and Jeffrey D. Evanseck. 2022. “The Scientific Method as a Scaffold to Enhance Communication Skills in Chemistry.” Journal of Chemical Education. https://doi.org/10.1021/acs.jchemed.2c00113 

Murray, John. 1895. Report on the Scientific Results of the Voyage of the H.M.S. Challenger During the Years 1872–76: A Summary of the Scientific Results, First Part. Published by Order of Her Majesty’s Government. https://doi.org/10.5962/bhl.title.6513

National Academies of Sciences, Engineering, and Medicine. 2017. Communicating Science Effectively: A Research Agenda. Washington, DC: National Academies Press. https://doi.org/10.17226/23674

National Research Council. 2002. Scientific Research in Education. Washington, DC: National Academies Press. https://doi.org/10.17226/10236

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