As a scientist and a soon-to-be citizen journalist, each story you craft has to be more than a series of facts but also an engaging and accurate depiction of the truth. Your source of information should always include referenced facts and figures, but also including first-person accounts from scientists you meet at conferences, seminars, or at a local pub can add depth to your writing. Perspectives and insights gained from interviews are great for empowering you to tell your story and can help drive important research questions. And just like the journalist whose task it is to filter out someone’s opinion from a bona fide fact, so too must scientists learn how to talk to people in order to learn the facts and perspectives that are relevant for telling a science story.
I had the opportunity to interview four researchers from our institute this summer and was able to see the power of interviewing with and listening to researchers from fields other than my own. Talking to someone in an interview format is a terrifying prospect, but by approaching the conversation with an open and curious mind, I found that I learned more from the experience than the simple facts and figures I took home with me. In a post-truth world, the connections we make with people as we search for truth and understanding will continue to become as important as the data and the figures that we make to tell our story.
But let’s start out simple: What is an interview? Simply put, an interview is an opportunity to ask specific questions and receive answers, with the primary purpose being to get quotes, facts, insights, and to build a relationship with your interviewee. An interview is more formal than a casual conversation over coffee, just like how a job interview is more formalized than talking to someone at a conference about a job at their company. An interviewee is put on the spot to answer specific questions, and an interviewer is tasked with asking good questions, listening to responses, and collecting everything for analysis at a later date. It’s an intense process on both sides, and one that involves more than a simple series of questions and answers.
In the world of journalism, there are two types of interview styles. In a collaborative interview, your subject is willing to or very keen on telling a story. Your aim and theirs are the same: you both want to convey facts to the public and share their story for a specific purpose, such as making an audience more aware of a topic or sharing a new research finding. This is the most common type of interview you’ll be doing as a science communicator/citizen science journalist. Alternatively, an adversarial interview is when the interviewee is held to account on a topic while the subject is challenged to provide answers on something he or she might not want to answer. Perhaps if you stray into a controversial topic about someone’s research you might engage in this type of interviewing style, but for the most part working with other scientists there’s no need to put them in the hot seat.
There are also different types of questions you can ask at an interview. Open questions such as How does PCR work? or Why is your research important? are questions that put the power in the hands of the subject. These types of questions allow you to find out what the subject knows in a more open manner, especially related to things you don’t have any prior knowledge about. The disadvantage here is that it can allow your subject to ramble on about something beyond relevancy—leaving you to either intervene or to let them carry on while taking time from other questions.
Closed questions such as Did the new experiment work? or Were the findings statistically significant? can be answered very simply with a yes/no/short explanation, but the subject can also expand upon the answer if they feel like adding more. Closed questions give the interviewer the control and can enable you to focus on a topic and bring a discussion to a point, but it also limits what you hear—with these types of questions, you can’t find additional answers beyond what you’re asking or what you know about already.
No questions are an interesting approach I learned about in the citizen journalism Coursera course. It’s quite literally a question that’s actually a statement (I really don’t see the importance of that), and sometimes it’s not anything more than a Really?, Honestly?, or even just a period of silence from you. It can open up the subject for a reply, as people tend to want to fill the silence. It’s a way to get people to say things without a specific question preceding it. If you’re doing a collaborative interview you likely won’t need these types of approaches, but if you do run into someone that’s not providing a lot of feedback, this is one way to go about getting answers.
Interviewing as a journalist also means adhering to a code of ethics regarding consent and deception. Rules will vary internationally but in general they require you to identify yourself and your employer before an interview, to use fair and honest ways of obtaining materials for a story, and to never exploit a person’s vulnerability. Scientists working on science writing and communication activities should also strive to adhere to similar types of guidelines: be upfront about who you are and the purpose of your work, the intended output/audience, and be cautious when trying to sell a “breaking story” on research that hasn’t been published yet.
The formal definition of deception is to make people believe what we ourselves do not. This involves nefarious ways of developing empathy with a subject that are done under a false pretense or changing the story once new facts come in without your subject being on board. The rules on deception and entrapment are complicated for journalism, but as with the rule above: be clear about what you’re doing and be honest about what your goals are.
Prior consent means obtaining permission from a subject to interview them, including any media materials (like photos or videos) that you’ll collect for your story. Your University or institute might already have rules in place for using a picture or a video of someone on a blog or a news story that you’ll post on a Twitter account, so be sure to check with your publications office or a press officer before publishing any media online for your organization. This is especially true if you’re working with minors—get in contact with the appropriate press contacts before including any quotes or photographs of younger students, and do your homework before the event so you can collect any required permission from parents as needed.
Setting up an interview might seem too formal or unnecessary, but whether you’re a writer, a scientist, or just want to learn something from someone, an interview can be a great opportunity to gain information beyond the scope of a normal conversation. People do answer questions differently when in an interview setting, just as those of you who have applied for a job know that being put on the spot is different than talking about your life’s goals over a cup of coffee. Envision the interview with purpose, as a way to get information, insights, and also to build a relationship with another person. As we previously discussed in our networking post, building a professional relationship is crucial for progressing in your career. Interviews, and the information you’ll gain from them, can help you get there and can help you tell a story using more than just facts and figures.
The term “post-truth” was recently named Oxford Dictionary’s 2016 word of the year. This was in part thanks to the political movements fueled by strong emotions and sentiments, most notably in the UK and the US, but also possibly across Europe as many countries will face their own upcoming elections early in 2017. “Post-truth” isn’t a new concept, as authors and journalists in 2004 highlighted the actions of the Bush administration in a post-9/11 America. Just as last week we started our series with an overview of journalism, this week we’ll start by answering a simple question, given the fiery discussions surrounding the word truth: What is truth, anyways?
In journalism, truth is defined as the best obtainable version of the facts available at a given time, where facts must be consistent with the material available at that point in time. True statements should be based on facts and substantive claims, with verification and double-checking of facts a crucial step of telling any story. As the news-writing adage goes (and still stuck in my head from high school journalism class almost fifteen years ago now), “Believe half of what you see and none of what you hear.” But from this perspective, truth is also changeable. Truth is based on the knowledge you have at the time, and truth can change when new material comes to light.
Scientists have a similar means of coming to the truth. We use the scientific method to conduct experiments and generate data that tells us if our idea of how the world works could be possible or not. If it’s not possible, we move on to another hypothesis; if we’re right, we continue to blaze down that trail to learn more about the system we’re studying. And like the journalistic definition of truth, scientific truth is also changeable. We have to shift our idea of how things work if enough support comes in that refutes our original hypothesis or theory. In reality, good science and good journalism is all conducted in a “post-truth” manner, in the sense that the fields must embrace the best version of truth at the time while discarding any inconsistent theories they encounter as they progress through a story or through a series of experiments.
Unlike scientists who tell stories with data, journalists have to retrieve information leading to the truth in other ways. This can include attending events such as press conferences or sporting competitions or by reading official documents, papers, or books. Journalists also rely on other people to help provide stories and perspectives, which generally involves interviewing and cross-checking against other sources to provide support for statements (more on interviews in next week’s post). Truth-finding for journalists involves 1) gathering information and views/perspectives, 2) checking if statements can be supported by facts, 3) evaluating the relevance of new facts for telling a story, 4) helping the audience know what the truth means, and 5) telling the story accurately and clearly.
In order to tell the truth in an effective way, a journalist must be open-minded, especially when it comes to evaluating the relevance of facts for a story. Part of being involved in a post-truth world comes from cherry-picking results or statements that fulfill a central idea that we have already. Science is also guilty of cherry-picking facts in order to tell a story from a specific perspective, so making active considerations for any biases is crucial for telling any story, be it for news or for science. News also must be engaging; it can’t simply be presented as a list of facts. You have to explain the context, the meaning, and the significance. Scientists should also recognize that data and scientific evidence is more effective when provided within context, as tables and bar charts will only get you so far when trying to convince someone that your version of the truth is the best one out there.
While telling a story that’s a reflection of the truth, it’s crucial for both scientists and journalists to be impartial about the subject at hand. A writer (or scientist) is unbiased when he or she does not take sides when both researching and presenting new material and when the results of the work are a detached assessment of the facts uncovered. Achieving impartiality generally involves working towards the following goals: 1) accuracy, 2) fairness (presenting the subject in a way that deals with it proportionately), 3) balance (rather than presenting two sides equally, balance should be obtained by weighting things by the amount of evidence), 4) having no conflict of interest in the outcome of the story, 5) being open minded, and 6) telling the story with appropriate context.
We might envision journalists as being pressured to sell a story or to skew the facts that make a news piece more click-worthy, but can scientists say that they aren’t guilty of the same? Do we not also have our own favorite proteins or algorithms that we want to see succeed and become crucial pieces of some large scientific puzzle? Professional scientists should also recognize the importance of impartiality in doing good science and to avoid the pitfalls of becoming too enamored with a favorite technique, protein, or algorithm.
Our words and our papers have power to them, regardless of the impact factor of the journal or how many citations we get. Our work will inevitably be built upon by someone else, and our words that we use to tell our scientific stories should reflect our work in an accurate way. Every word we use contributes to the picture and supports our ideas—and being impartial also means we should choose our words accurately and fairly, words which are congruent with what we’re actually showing. In a “post-truth” world, it is our duty as scientists to strive for a truth that is not comprised but rather enhanced by our desire to share our science.
Next week in our series, we’ll discuss interviewing and working with other people to get facts—another step towards becoming a citizen science journalist. Until then, only 7 days left of #AcWriMo!!
I have a bad habit of overextending myself. It’s a habit that rears its head in many ways, from reading days where I end up printing more interesting papers than I actually read or opening tabs from Wikipedia that expand through the complete realm of time and space. To ensure that I had more than enough to do this autumn, I enrolled in an online journalism course available on Coursera. The six week course satisfied my goal of learning something new about a field that I’ve become more interested in lately, a chance to explore the underlying methods and philosophies behind something that people interact with everyday. Modern journalism has seen some controversy lately, especially in the wake of recent events leading up to Brexit and the US Presidential election.
This week has seen a lot of fall-out about the US election results. Everything from criticizing Facebook for not sifting out the false news from the real or creating a world of biased newsfeeds, as well as the endless spins on candidate statements or poll results that you could possibly imagine. But we don’t just see this in political news, and science is not immune to the shifting tides of news and the media. Take dietary guidelines, for example: Eggs were at one pointed considered unhealthy, but now they’re good for us. A beer a day can apparently prevent stroke and heart disease but low to moderate amounts of alcohol consumption causes several types of cancer. And who even knows what red wine is really doing.
As scientists we can easily evaluate and even criticize the bad science that goes viral or the poor reporting of a new research paper. But as a journalist, would you have the same level of discernment when readying a story for rapid publication? What can scientists learn from journalism in terms of making our stories clear accurate yet also gripping and impactful in a news-worthy way?
This week we’ll be introducing some basic concepts of journalism to give you a break from your paper writing during #AcWriMo. Next week we’ll talk about interviews and storytelling, and in the final week of November we’ll discuss how you can become an engaged citizen science journalist on your own. But first, the basics: what is journalism and who are journalists?*
*Note: This information is a summary of the excellent online course, “Journalism skills for engaged citizens”, by the University of Melborne. This course was really great, so be sure to check out Coursera and keep an eye out for the next session if you’re interested!
Journalism and journalists have a primary obligation to the truth. Good journalism is not marketing and it’s not personal opinion: it should be the most accurate depiction of a story based on the journalist’s understanding of the facts. In this sense, journalistic truth is the process of assembling and verifying facts, namely the facts which provide the most accurate depiction of truth at the time that the article is written. Sound familiar? In principal, the foundations of science and of journalism are more similar than not. The scientific method is also objective and one which uses experiments and hypotheses to come to an answer about how the world works, given the knowledge that we have at this stage in time. Ideas and theories change when we get new data, just as a story evolves when new angles or facts come in. Another important similarity to remember is that while the methods of both journalism and science are objective, journalists and scientists are not--we are all humans and make mistakes or can be biased to seeing things in a particular way. That being said, both fields also have guidelines and support for ensuring that objectivity and truth is the focus of the story or the research.
Journalism is storytelling with purpose. A news story must be interesting and relevant to an audience, which is also one reason why stories can become over-sensationalized or hyperbolized. While the audience is the one who decides if a story is relevant or exciting for them, it’s the role of the journalist to both find a story that will attract audience interest and to tell that story in a way that’s accurate. News is fundamentally something that people don’t know already and will also find interesting. News-worthy stories generally have a number of key ‘values’. The primary values include magnitude (the number affected/size of the event), negativity (bad news, conflict, or disruption tend to feel more news-worthy than good stories), and proximity (if the affected group is local or has some cultural/emotional empathy or connection). Secondary values include recency, prominence of the parties involved, stories that discuss emotion or the human condition (known as pathos), shock/surprise of the story, clarity (simple > complex), and the ability of the story to challenge what is already known.
Sound familiar? Probably not as much as the first point. In science, we tell our stories very objectively, much in how we also find out the story in the first place. When we write a manuscript we aren’t trying to over-sell our story or convince our audience of the newsworthy-ness of our article. We let the data speak for itself, in part because we are talking to other scientists and in part because that’s how science is typically done. Scientists tend to think that their own problems are interesting simply because they are interesting—we are engrossed with our projects and our data, with many of us believing that the publication in of itself is sufficient to gain further interest without the need for further reporting or promotion. Science communication efforts are focused on bridging this gap between science and the public in part by sharing science in forums beyond research journals and conferences. But scientists and science communicators also need to recognize that science communication is more than just telling the stories: if the work doesn’t feel close, relevant, big, or clear, it won’t resonate with an audience. People may never care about our work if it doesn’t connect to them in some convincing way.
Journalists put the biggest ideas first. Scientists and journalists present ideas very differently, which can explain in part why some stories seem to over-hype the results of research studies. In a research article, the long-term goals or broader impacts may make an appearance as a bit of text in an abstract or a discussion, and these may only have a secondary application in the overall findings of the paper. For example, a paper on the genetic regulations of prostate cancer might mention curing cancer as one of the aims of the research, but no cancer will be directly cured from the findings of the paper itself. An article popped up on my newsfeed several weeks about with an alarming headline connecting environmental pollutants in car exhaust to Alzheimer’s. While the paper does demonstrate a correlation between magnetite levels (evaluated in the brains of patients from urban areas in Mexico and Manchester, UK) with incidence of Alzheimer’s, the results were still only correlative, and with no non-urban control samples to compare these findings against.
The headline wasn’t a complete stretch, but also wasn’t exactly what the paper showed: you didn’t hear about the limitations of the article until you dug further into the text, after the important journalistic point of the connection between environmental nanoparticles and brain diseases. A scientist may put out a press release on findings from a research paper which from their perspective accurately separates the “big picture maybe” from the details and the facts presented in the paper itself. But a journalist might catch on to the big picture maybe as the most important part of the story—the one that will connect to readers more than the detailed methods and the relevance of the error bars. In this sense, understanding how stories are structured from a journalists’ perspective can help scientist understand that reporting casualties can arise not from fear-mongering or bad intentions but simply from looking at the parts of a paper or a press release and interpreting a big picture/long-term maybe as an immediate truth. In our last post of this series we’ll go into detail about news story structure and how to take this into account when working to become a better science communicator.
Journalism stands up to the principle that people have a right to information. In addition to the duty of truth telling, journalists also have their primary loyalty in informing citizens while “describing society to itself”. Journalists, editors, and news organizations undoubtedly have their own perspectives and bias, but they are also held accountable to their duty towards the public. Here we can envision a parallel between scientists and journalists: even in our own careers and interests, scientists have a duty to do good science and to ensure that work done with tax-payer dollars is of high-quality and open to scrutiny by others.
But there are also some striking differences in this regard. While science is becoming more open, there is still a tendency to keep data and information within a research community and to focus on the peers who judge our work and its quality instead of members the public. Good journalism is meant to provide a map that enables people to navigate society on their own, when provided with the truth and the facts in a clear and accurate way. Does good science do the same? Do scientists actively help the world reflect on where it came from, what it is, and where it’s going next?
As scientists working in one of the most well-connected eras in terms of communication opportunities, we have a chance to make an even bigger impact than simply publishing research papers. But we’re up against a flurry of news, stories, and sensationalism, and it’s a time where folks in different fields are better off working together than pointing fingers at one another. Scientists can learn a lot from the approaches used by journalists in order to better connect and resonate with a broader audience. Next week we’ll talk about interviewing/fact-finding and will follow up the last week with some tips that will enable you to start telling impactful and accurate stories about science and the world around us.
I attended the #scidata16 meeting last week as an amateur reporter and as part of an award for being selected as a finalist for the SciData writing contest. After the conference I returned to my own office and my own project, searching for the code and datasets I needed to re-make some figures from some data analysis months prior, with the discussions of the previous day all of a sudden feeling even more relevant. I thought it would be worthwhile for our Science with Style readers to provide some highlights from the conference and some tips and tricks for data management and sharing. You’ll be able to read my upcoming report on one of the keynote presentations in a future post on the Nature Jobs blog.
Early career researchers, especially PhD students, tend to focus on their own work and their own project. But as you progress through a career in research, the projects you’ll be involved in will become much larger efforts, with not as much of the project that’s yours and yours alone. Anyone who’s dug through a freezer full of boxes to find some crucial samples that a student who graduated 3 years ago left in a box labelled “E. coli samples” will know the struggles facing those of us in lab management.
But for researchers who are working on large datasets or large collaborative projects, the concepts and importance of data management might not be as evident. As science students we learn how to keep lab notebooks organized and in graduate school we learn how to organize our samples and important reagents, but when your entire project is stored digitally, how should it be organized? When do we learn as Phd students or early career researchers how to manage digital information?
While the conference was focused on quite a few topics related to data science, management, and open data, I’ll focus on just a few of the highlights from the keynotes. You can read more in-depth about the meeting in upcoming posts by myself and other #scidata16 contest winners in the coming weeks.
Reproducibility: When comparing data science with wet lab science, there are more overlaps than you think in how both are conducted and managed. One overlapping concept is that both types of data need to be reproducible. The first keynote speaker, Dr Florian Markowetz of the University of Cambridge, gave an example of a paper which was later retracted after two bioinformaticians noticed that the incredible findings they discovered were only due to Excel copy-paste errors. And those incredible figures you made once but now can’t find the original code? You need to have the data and the plan in order to make them again, or else it’s not a trustworthy result. My favorite quote from this talk was “A project is more than a beautiful result.”
Dr. Markowetz also gave the audience 5 things that data reproducibility can do for you. It can 1) help you avoid disaster, like having a retracted paper, 2) help you write a paper since it’s easier to look up numbers and be confident in your figures, 3) help you during peer-review since you can share your data and let the reviewer take a look for themselves, 4) help you achieve continuity in your work so you can come back to a problem later and you don’t have to start all over again, and 5) it will help you build a better reputation, which will allow you to submit your work to better journals and can establish yourself as a solid scientist.
Dr. Markowetz gave a great talk and emphasized that reproducibility is not a waste of time but is a part of science—think if your lab mate or a future student in your lab could repeat the ground-breaking results you generate in your thesis. The big take-home message here is to make reproducibility a part of your work flow early on in your career.
Data sharing: We started off the second keynote by Dr. Jenny Molloy (also from the University of Cambridge) with an answer to the seemingly apparently question of ‘What is Data?’, which she defined as collected observations and tabular calculations. Explaining what data you have is the first step for data sharing. It’s also important to understand that you can retain ownership and restrict how other uses and reuse data you share, similar to copyright on images and written works.
In another series of 5 items, we also learned the 5 steps for data sharing: 1) get motivated and start early, 2) stay on top of your data, 3) share the way you want to, 4) make the most of your sharing experience, and 5) set an example to your colleagues. If you ask why sharing is important, Dr. Molloy emphasized how open data can lead to better career recognition, connections to new collaborators and employers, and even gave some examples of how open science is creating new jobs for researchers with experience in data management. Other presentations on open data also highlighted tools available to researchers—if you’re interested in learning more, be sure to check out the Open Knowledge Framework website for examples and data management training.
Data management: Dr. Kevin Ashley from the University of Edinburgh discussed tools and infrastructure already in place for data management. He first emphasized that data management is not something that happens at the end of a project but something that begins when you conceptualize an idea and think about what data might look like in the end. The importance of good data collection and management was also highlighted in discussions on astronomy data and their use in research today. Measurements from 8th century astronomers are still being used by researchers today, although for purposes not connected to what the observers originally intended. Dr. Ashley also mentioned the volume of data collection efforts from the Hubble telescope, where numerous publications and observations were made not on data collected by the researcher who wrote the paper. This keynote highlighted the importance of clear and open data management policies that allow researchers to tap into their own ideas without even having collected the data themselves.
Dr. Ashley also mentioned that we’ll be running out of storage space in the long term, based on how quickly storage capacities and the number of datasets are both increasing. Because of that, it’s important for ECRs to consider what needs to be kept and for how long. And for curious ECRs wondering about the details of data management, recommendations for project budgets (5%) as well as the role of institutional infrastructure for data storage were also discussed.
The future of data science: Dr. Andrew Hufton, the editor of Scientific Data, talked about the role of data journals as well as the importance of meeting journal requirements for open data sharing. Data journals are one way to get credit for reproducibility of your results and to have your data cited even when you’re not involved with the new paper itself. Data should also be seen before it can be believed, and it needs to be able to be shared or it’s not science. Dr. Hufton also emphasized how data sharing drives the impact of your work, especially for researchers working in emerging or timely fields (such as zika virus research).
Dr. Hufton also presented an acronym for good data sharing, the type of sharing that allows other authors to replicate and build off of the author’s claims. This includes making data FAIR: findable, accessible, interoperable (i.e. in the right format), and reusable (i.e. having really good descriptors for each header). Dr. Hufton also emphasized that while supplementary materials are great, they are not curate and machine-readable and should not be the only place you put your results.
What’s next? One of the last points discussed really hit home for me: when it comes to being a scientist, we need to take time to remember the reason that we do research: we are tackling the problems facing our world and need to remember that people’s lives can be directly impacted by our work. Any work we do that’s not open, repeatable, or manage properly can negatively impact others, not just our own career, and work poorly done work can be harmful to people who rely on our work for bettering their lives. A recent article about incentives in science highlighted this concept, which again brings up the need for incentivizing well-done, repeated studies instead of just more publications.
While it will take some time for the research culture to change, you can already find Open Science peers through the OSF network as well as reaching out to your institution for support in terms of data management and open data platforms available. With just a few of the potential benefits to your career laid out in this post, there are certainly a number of reasons for having open, repetitive, and well-managed datasets—and if I didn’t manage to convince you in this post, you can catch up on the #scidata16 tweets or see the presentations posted later on the Nature Jobs website.
I greatly enjoyed #scidata16 not only for the experiences as a reporter-in-training but also as a bioinformatician and as a person who is interested in finding ways to improve the research experiences of PhD students and ECRs. The conference had a great set of speakers as well as tips and tricks for researchers at all stages in their careers and across a range of fields. Whether it’s a big or small dataset, making it readable, available, and interpretable by others in the long run is a more powerful tool than I would have thought before attending this conference. Who knows—it could even get you a publication in a Nature journal!