We Test 3 Tools for Visually ‘Mapping’ your Research Area

If you’re a visual person, the text-heavy terrain of academic research can be a foreign landscape. Piles of papers promise deep insights into your research area, but sometimes it’s hard to see the bigger picture.

For visual learners, a research ‘map’ can be a great way to get a mental picture of the overall research area, and of the connections between the individual scholars, studies, and papers that comprise it. But how can you create a map of your research area?

I tested three online tools that can do the job. In order to see what they could do, I performed the same search query across all three tools and compared the powers of each. My search query — “”genetic engineering” AND posthumanism” — relates to my own area of PhD research, so I can recognise reasonably well whether the results of these tools are a good reflection of the research landscape. Let’s go!

1. Connected Papers

Connected Papers (CP) builds a picture of the research landscape based on one key paper. When you enter a search term, CP returns a list of what it deems to be the most closely-related publications from the Semantic Scholar database. You then select the paper that appears most relevant to your research, and CP generates an interactive map showing a network of closely related papers. You can click around that map, exploring different readings. Papers with more citations are represented with larger nodes, so it’s easy to visually identify the more impactful papers in the field. Lines show connections between the papers, which represents not only mutual citations, but also overlapping bibliographies – great for seeing at a glance how papers relate to each other.

In my test, CP returned only a very limited number of results – 59, compared to 43,000 that I got by using the same search term in the Semantic Scholar database that CP draws from. CP’s sort order didn’t appear to take any measure of impact or citations into account, meaning I got lots of results that were closely-related to my search term, but not very influential in my field. I could easily create a map based on these papers, but without prior knowledge of which were most relevant, it would have been easy to fall into the trap of building maps based on fringe papers. However, on the plus side, the tool was easy to use and generated an interactive map based on my chosen paper in just 12 seconds.

Overall, I think a CP map serves well as a kind of smart ‘reading list,’ if the paper you based your map on is, in fact, a core paper for your research area. (I’d suggest having a paper in mind before you search.) One thing to note is that CP doesn’t tend to represent publications over a long historical period. The papers in the map in my test were all clustered within one particularly active decade-or-so. There are ‘Prior Works’ and ‘Derivative Works’ buttons that allow you to view tables of papers that came before or followed after; but these are not built in to the map itself. This may best suit those in fast-moving disciplines; but if you were to rely entirely on one of these maps without using those additional tables, you may miss the most seminal or most recent papers.

Publication map from Connected Papers, generated 3 March 2023, using search term “”genetic engineering” AND posthumanism”

Pros: Quick & easy to use. Finds connections between papers even where they don’t cite each other. Good for those with prior knowledge of the research area; especially if you are working with one key paper.

Cons: Map is built based on just one paper as a focal point. Not easy to identify the most relevant paper to choose without prior knowledge. Doesn’t represent publications over a long time period.

Best for: Researchers wanting to find connections to one seminal paper.

2. Dimensions Database

Huh? Why is a database in here? Dimensions is no ordinary database. It is a tool for searching literature, yes; but also for mapping factors related to that literature. Upon performing a search, you’ll get just a list of results. But by playing with the ‘Analytical Views’ tab on the right-hand-side, you can generate a huge variety of maps showing all manner of different factors relating to those general search results. By focusing on the papers, their authors, the research funders, institutions, publishers, locations, and more, you can generate deep insights into how and where this type of research takes place; who funds it; when the research area has been most active, and across which disciplines; and who are the major players. It’s a powerful, powerful tool. And it’s right there in the AUT Library‘s Databases tab – just login with your AUT credentials.

In my test, Dimensions returned a whopping 2155 results for my search term – by far the most of the three tools tested here. Because of that breadth, the maps and graphs it produced were more detailed than with the other tools. They were also much more varied. The ‘Source Titles’ tool allowed me to see at a glance which journals were publishing most often in my area; and the ‘Funders’ section allowed me to see which groups were funding a lot of this research. The researcher network map was also useful for identifying which scholars are most active in the field, and how they are connected. However, Dimensions determines connectedness based on co-authorship, which doesn’t reflect those working separately on highly similar research.

Overall, Dimensions was the most powerful and most complex of the tools tested here. It takes a little time to get to grips with it, but for those committed to a career in their field, it’s well-worth the time invested.

Researchers network map from Dimensions database, generated 3 March 2023, using search term “”genetic engineering” AND posthumanism”

Pros: Incredibly powerful, with many types of maps and charts available. Returns lots of search results and delivers deep insights.

Cons: Higher learning curve due to the increased complexity of combining many different insights. Definitions of ‘connectedness’ are limited.

Best for: Intermediate to advanced researchers building a more complex picture of their research area, particularly if planning to publish or establish a career in the research area.

3. Open Knowledge Maps

Open Knowledge Maps calls itself “a visual interface to the world’s scientific knowledge.” Sure enough, it can create a map of knowledge on a given topic in about 20 seconds, based on your keywords. It builds its maps based on the textual similarity between records and works on ‘clusters’ (rather than line connections). Each ‘cluster’ is expandable so you can explore the papers within, and there are quick links so you can click through to papers of interest.

In my test, Open Knowledge Maps returned just 21 results for my (admittedly quite specific) search term; the least of any tool on this list. Because of that, the number of clusters was very limited. However, I could see the logic behind the cluster groupings, and many of the papers within were highly relevant to my research area. It was also incredibly easy to navigate the map, and to access the papers I wanted to read.

While this tool didn’t prove useful to me with my specific research niche, I would recommend it for those just starting out in a research area and wanting a broad overview.

Research map from Open Knowledge Maps, generated 3 March 2023, using search term “”genetic engineering” AND posthumanism”

Pros: Easy to use and navigate; includes an icon for open access research; adds concept labels to help you learn the language of a new field.

Cons: Limited results when using specific search terms.

Best for: Beginner researchers (or those tackling a field that’s new to them) who want a broad overview of a research area.

Bonus tool: Old-School Mind-Mapping

The smart digital solutions above are great for instantly generating an overview of your research area. But when it comes time to deep-dive into the literature, sometimes a good old-fashioned mind-map is the best tool. A map that you generate will have the most meaning for your research, and the process of creating that map will enable you to mentally process, digest, and organise the information. An instantly-generated map will reflect what an AI engine thinks is the shape of the research area; but your map will reflect what is most relevant to you and your project.

In terms of creating your own map, there are loads of tools that can help. There’s nothing wrong with pen-on-paper; but if you want to create a digital map, then Microsoft OneNote and Visio are good options (both included with your student Office365 package). Mindomo and Scapple are paid purpose-build mind-mapping tools that are worth a look if you plan to do lots of mind-mapping (the latter integrates with Scrivener, for those using that as a writing tool).

If mind-mapping works for you, you may like to explore variations on the idea. Many researchers swear by ‘concept mapping,’ which adds layers of complexity by focusing on the specific nature of the connections between ideas (as opposed to just laying out the ideas in a structure). Cmap Tools (a software suite developed by researchers) can be helpful to produce concept maps. There are many more variations on mind-mapping to explore as well.

Whatever your method of choice, treat your map as a living document. Your project plans will evolve, and your research area will grow every day as new findings are published around the world. Let your map reflect your growing and deepening knowledge of the research landscape!

About Anaise Irvine

Dr Anaise Irvine is the Editor of Thesislink and leads the Researcher Education and Development team at Auckland University of Technology. Her PhD research analysed how contemporary films and novels represent genetic engineering as a social justice issue. These days she works with researchers at all levels to improve their research skills, and the most obscure of her own research skills is being able to turn novels into phylogenetic trees!

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