Can your mountains of unpublished data be turned into papers? (Very often, YES!)
All academics, all over the world, want to publish more papers, as fast as possible, in the best journals possible. Who doesn’t?
We are all under extreme pressure to publish our work. But sometimes the write-up just isn’t happening. The well is dry. You’re experiencing ‘writers block’.
This article examines a situation we often find ourselves in as researchers: We’ve collected (or have access to) lots of data but are unsure what sorts of questions we might be able to assess, write-up and publish about. How can we turn data into articles?
This is topsy-turvy: Of course, research is not supposed to work this way. You’re supposed to first formulate a question – a hypothesis – and then go out and collect data to either refute or validate it. Busy researchers often end up surrounded by data, but unsure what can happen next.
Can your mountains of unpublished data be turned into papers? The answer, very often, is YES. This article outlines some steps you can take.
What kind of data do you have? (How to know?)
Busy academic researchers tend to collect and have access to lots of data but then very often simply don’t have the time to write up papers, select journals, make submissions, and steer the work through the whole arduous process. Data often accumulates at a much faster rate than we are able to process, analyze, and write up papers.
It’s often a good idea to take a step back and think: What kind of data am I collecting? What would be the best way to analyze my measurements, numbers, measurements, or counts?
At the most basic level, what kind of data do you have? It could be either quantitative or qualitative:
Quantitative data tends to come in the form of numbers or counts of things. You’ll have generated sets of data with unique numerical values, often using measurements.
Qualitative data tends to describe qualities or characteristics of things. These data are usually collected based on surveys, interviews, or observations.
Within the first category, quantitative, are the data you’ve already collected discrete or continuous? Presentation methods and the kinds of analyses required will be different.
Numbers and counts of things (e.g., number of spots, or animals within a given area) are discrete data and can often be managed and analyzed more easily that other forms of quantitative measurement data (such as speeds, heights, or areas). Compare these kinds to qualitative data where information describes qualities of characteristics such as gender or color, position in a list, letter grade or status. These data are often much harder to visualize and analyze.
Medical researchers are often in the unique position of collecting, or at least having daily access to, both data types: quantitative and qualitative. Having determined what kind of data you have, the next thing to ask is: ‘What’s the question?’ What could I test using these data?
What can your data be used to test?
We find that researchers across all disciplines often rush into the data collection phase without thinking carefully about what they wish to test. You’re not in that position, however, if you already have lots of data and are looking for ways to write up additional papers.
The first thing to do is ask yourself: What is the question that underlies my research? What is it I am seeking to test with my research as a whole? It might be to alleviate a certain kind of cancer, or develop a new therapy, or to address the issue of childhood obesity.
To refine your question within a thematic research field, you’ll need to carry out what’s called a ‘Gap Analysis’ in order to determine an area where further, meaningful progress can be made using your existing data. This usually involves a review of the current literature to look for gaps in knowledge – clear unmet needs – where the further analysis of existing data can lead to useful insights.
Taking this step slowly and carefully means you will be better prepared to both collect more (if needed) and then subsequently analyze the data you already have access to.
In other words, is the question you seek to test descriptive or analytic?
In the first case, descriptive, you’ll be collecting (or will have collected) specific parameters to assess something specific such as average income or minimum height within a population. These are quantitative parameters but your question is simpler: A clear one-on-one trend line drawn between two parameters will usually be sufficient to show a relationship which you can then assess for statistical significance.
In the second case, analytic, you might be assessing more abstract parameters such as the relationship between two variables, perhaps average income and height. Quantitative data types, but which will require analysis in a different, usually more complex way.
It’s well worth consulting with a statistician if in doubt about how to perform further analysis at this stage. Which tests are appropriate to demonstrate correlations, for example, or which data filtering approaches should be applied.
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Formulating a question
Once you’ve determined the type of data you’ve collected and what sorts of issues it could be used to assess (descriptive or analytic), it’s time to formulate a clear question.
There are two key steps here: Ensuring your question is both INTERESTING (i.e., non-trivial) and TESTABLE. This will come out of your gap analysis. Research questions should not be incremental: A small step forward that only slightly builds on previous work is not going to prove particularly interesting and will therefore have a much smaller chance of publication.
You might have seen, for example, some recent work assessing the efficacy of a particular drug on one condition. It won’t be all that interesting just to tweak this question slightly and repeat analyses using, for example, a slightly different sample. Take time to formulate questions that are going to be worthwhile to address: Questions that will represent considerable steps forward within your research area. Examples of research questions can either be quantitative, qualitative, or mixed.
Here are some pointers that can help with the development of an effective research question in cases where data has already been collected. It’s important to keep in mind that:
- Question development is an ongoing process that will involve you continually updated your understanding of an area and refining your ideas. Don’t get stuck on one question, especially if you already have data. Be flexible.
- It’s very important to stay updated on current research in your field so you can see and understand how other researchers formulate questions.
- Your question should be formulated to be as specific and concise as possible to ensure clarity. It’s a good idea to talk to others: Seek out experts, mentors, and colleagues working across your field and talk to them about your questions. Is this question interesting?
Data comes in many shapes and sizes. In this article, we’ve attempted to summarize the main types you might have collected. We’ve also considered the broad kinds of questions different data types might be used to usefully test as well as how to formulate an effective question with the information you have.
As a busy researcher, you often won’t be able to work in the ‘standard’ way with your research: Formulate a question and then work to collect data to address that issue, or hypothesis. As we’ve discussed, very often we end up sitting atop piles of data wondering how we might do something with the information already collected.
Still stuck trying to figure out what to do with your data? Need help with a gap analysis? Need a consultation on managing that pile of unpublished data?
Edanz can help!
Our expert team of project managers, writers, editors, and analytical specialists can guide you through the publication process. As one of our services, we can take your existing data, perform a gap analysis of your research field, identify an ‘unmet need’ and then assist with the writing and publishing process. We’ll also target a journal, help with presubmission enquiries (informal letters to editors to gauge their level of interest) and then advise on the actual submission itself.
Our support really works: Researchers we’ve worked with have significantly enhanced their publication success from one year to the next. Imagine: In one year you publish say, two papers, but then start working with Edanz and then go on to publish eight or ten the next year. Getting in touch with our team is a worthwhile step to take, career-wise.
One of our key services at Edanz is helping researchers to make the most of their existing data. We can assess your current data sets, evaluate them against what has already been published and determine an unmet need in the literature, before proposing both a review article topic and a range of potential journals you might go to. We’ll even help with the presubmission enquiry and the actual paper writing itself. All within ethical guidelines, of course. Our writers and publication team members are always acknowledged at the end of the articles we help with: We are the only company in our industry that does this. Complete transparency.
How does the process work?
Our team will assess your data as a first step and advise as to whether further collection needs to be carried out. We’ll then perform an overview of your field to identify publication opportunities – a gap analysis – which then informs suggested article type as well as journal selection.
One example is a recent paper we worked on that was published in The Lancet in 2021 entitled ‘Adjuvant S-1 plus endocrine therapy for oestrogen receptor-positive HER2-negative, primary breast cancer: A multi-center, open-label, randomized, controlled, phase 3 trial’. Edanz team members analysed data and helped with the article writing as well as journal selection, steering the work into one of the best journals globally. Obviously, this can be a huge help for busy doctors and medical researchers, especially. In these fields, data tends to accumulate as a far faster rate than it’s possible to write up and publish articles.
Is this ethical? Of course!
At Edanz, we pride ourselves on our ethics. We are one of the most transparent operations out there in the market and we do not “ghostwrite” papers.
We follow Certified Medical Publication (CMP), International Committee of Medical Journal Editors (ICMJE), and Committee on Publication Ethics (COPE) guidelines and are one of the only outfits to routinely have our editors and proofreaders added to the acknowledgements sections of papers. Openness and transparency are our positioning and ethos: You can be sure to get the best service from Edanz if you choose us to support your next research project.
What about a review paper?
One of our most popular services is review article development. Here, we scrutinize your data and look across your field for other recent work relevant to a predetermined research question or area. We then work to propose areas where review articles might be appropriate, something we’ve covered in detail in other posts. One of the best ways to enhance your reputation in your field as well as your publication list is to work with us to identify and write a review article. We work with authors to publish hundreds of such articles each year across a wide range of fields.
If you have unpublished data that needs to be assessed, or even just a desire to publish a few more papers to add to your CV, then our review article development service is ideal for you.
What are my chances of publication success?
Good question. Edanz is a unique research solutions provider and so our success rate with publications is also very high. Indeed, more than 99 percent of projects we work on end up getting published, and almost always in an initially selected target journal.
We understand that article writing can be extremely hard if English is not your first language. We also understand that finding the time to work on data as a busy researcher can also be a massive struggle!