We investigated the scientific attention garnered by iGEM and the Registry of Standard Parts. A data-driven approach was chosen: We extracted data from search results to various queries (such as ("iGEM" OR "International Genetically Engineered Machine") AND ("synthetic biology" OR "genetic engineering")) from various publication search engines. We searched Web of Knowledge, Scopus, PubMed and Google Scholar. Google Scholar was chosen due to the alternatives' various shortcomings.

We conclude with hypotheses to explain the results and discuss their implications for the iGEM competition.

TODO Put findings summary here too.

Data collection Data and where to find it

Why we used Google Scholar

We must admit iGEM is somewhat of a niche topic. WoK, Scopus and PubMed are strictly curated and limited in scope, so they missed many obviously relevant publications. We also found their search options unsuitable: Many of them did not support full text search (they looked at titles, keywords and abstracts only) or boolean operators. These were requirements, for the following reasons: iGEM cannot be expected to always be the main subject of a paper, hence full text search. There are many relevant terms floating about iGEM, hence boolean operators like "OR" to allow treating papers that contain "International Genetically Engineered Machine" or the acronym "iGEM" equally. Google Scholar, however, fulfilled these requirements.

What sort of scope range are we talking about here? Here is an example: PubMed gave so few results (16 for iGEM genetic*) that we quickly discarded it. Manually merging the Web of Knowledge and Scopus results for the query iGEM AND genetic* (discarding obviously irrelevant results) gave 43 results. Then we queried Google Scholar. It gave us 770 for (“synthetic biology” OR "genetic engineering") AND (“iGEM” OR “International Genetically Engineered Machine"). Imagine our expressions.

Of course, Google Scholar too is but a bronze bullet: It brings its own drawbacks. It is engineered to pick up things that only seem like scholarly articles. Like Google's search results in general, the results are not curated by a human. This has been criticised in the literature. Also we found the occasional hilarious total miss. Google Scholar is also known to somewhat overestimate citation counts. However, we concluded from empirical manual examination of a random sample that the majority of the results are plausible and (most importantly) far greater in scope than searches in curated databases. We only want statistics to identify trends. For this, large and coarce pieces suffice. We discourage using our method for obtaining precise values!

Browse the data

Our tables

...are online in a nifty and very usable Google Docs folder.

An introduction is included in case you get lost or want more information.

On extraction tools

We made extensive use of Harzing Publish or Perish (TODO Cite as "Harzing, A.W. (2007) Publish or Perish, available from http://www.harzing.com/pop.htm"!) to scrape Google Scholar results. The tool has many limitations. However, it is in our experience the best out there for managing the mess that scientific publication data scraping tends to become!

We did try other things: We quickly found manual methods too slow. Various Firefox browser plugins simply failed outright, were extremely awkward to use or produced clearly erroneous results. The Mac OS program Papers was easy to use and found huge numbers of papers (as it could access many sources), but had unacceptably high rates of error, problems with duplicates and could not export the results into a form we could easily process. Hence Publish or Perish.

Query summary

Here's a quick breakdown of what we queried for on Google Scholar and what sort of data was returned.

Note Searches were capped at a maximum of 1000 results. Hence getting 1000 results for a query implies that more exist! Those first 1000 are only the ones the search engine judged most relevant.

There are many ways you could quantify success of a paper. Here are a few we investigated:

Is iGEM getting attention?

What's "attention"? We will operate on the axiom that getting attention correlates very strongly with being mentioned. With this assumption, the attention a term is getting is quantifiable simply by searching for that term and summing up result counts.

Here is a chart summarising how many papers mention various terms floating about iGEM over time:

Looking good!

TODO Commentate more.

Old data (in case it still becomes useful)

Graphs (currently nonsense test data)

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