Founded in 1743, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) is one of the largest research universities in Germany. 

Julius Kirschbaum is a research associate at the university, conducting research into artificial intelligence – especially in the field of Natural Language Processing (NLP) – and innovation and business ecosystems. His work therefore spans both technology and management, and he is also a doctoral candidate.

Analyzing literature more effectively

The amount of academic literature in the world is vast. And every year, millions of new papers add to it.

This poses a tremendous challenge for researchers like Julius, who seek to research a particular topic in order to advance the body of knowledge around it. From establishing the key ideas and trends, to evaluating existing research and identifying the gaps in the field, they need to analyze and synthesize a huge mountain of literature – methodically, comprehensively, and accurately.

In Julius’s case, he and his team use quantitative and qualitative methods to analyze a large amount of academic literature on business and innovation ecosystems. They employ several bibliographic and bibliometric analyses (using topic modelling techniques) to investigate two areas: the evolution of ecosystem concepts over time and the core characteristics that have developed. They use this to categorize and potentially synthesize types of ecosystems.

“There’s such a lot happening in the field of business and innovation ecosystems and a lot of subtypes of ecosystems are discussed,” explains Julius. “It’s a complex and fast-moving area, and reviewing all the literature manually would be completely impossible for us. So we have to limit ourselves to a sample data set that’s manageable – say 100 papers, or 200 at most. Of course, this barely scratches the surface and we know we could be missing a lot of very important information.”

Because of this, Julius and his fellow-researchers wanted to take a different approach to analyzing literature. They sought a way to be able to analyze a much larger sample that would give them a far more complete picture – a data set with over 34,000 references, ranging from journal articles to conference contributions and book chapters.

”We had to use a computational approach to doing this,” says Julius, “and for that we needed to have a data set that would sufficiently reflect all the literature on ecosystems and contain most of the references published on the topic.”

Uncovering valuable new information

One of the difficulties Julius encounters is that not every data entry is complete; for example, the abstract or citation count might be missing, or the authors’ names are not given in the correct format.

After hearing about Dimensions, Julius and his team created a combined data set from multiple sources and tried it out. The Dimensions API provides full-text search and data retrieval for use in complex analyses and visualizations.

“Dimensions was really helpful. Not only did it find the missing pieces and complete these entries, it also delivered new entries that we hadn’t captured before. So we can now conduct a far broader literature analysis and get a far more complete picture.”

Julius Kirschbaum, Research Associate at the University of Erlangen-Nuremberg, Germany

Saving time and increasing efficiency

Providing the data to users through a good API is “very valuable”, says Julius, as is the ability to download a huge data set from the provider. “Not all providers give you that, but it’s very useful. Our access meant that almost half our final data set came from Dimensions. And not having to download everything manually saved us a huge amount of time.”

Another feature that optimized the project’s efficiency was the research category selection. “I found it very interesting that Dimensions provides not only initial categories but also subcategories,” says Julius. “The word ‘ecosystem’ appears in multiple contexts, so it’s useful for me to be able to really pinpoint which subject area I want to get data from. It means I get only the results that are focused purely on innovation and business ecosystems, filtering out all the irrelevant mentions such as natural ecosystems.”

Small details streamline his workload, too – such as the email notification he gets when an export is ready for download, and the ability to look up his exports in the Export Centre.

To find out how Dimensions can support research in your organization, get in touch and one of our experts will be happy to speak with you.