On top of our search and discovery platform ScienceOpen has built a ‘social networking’ layer to allow researchers to interact with each other and with the content on our site.
We don’t see ourselves so much as a social platform like Facebook or ResearchGate, but more as a professional community space for researchers to exchange knowledge and progress their research field in the open, and receive credit for doing so.
But what are the key features needed for any modern research platform like this?
Unlike other platforms, we don’t expect you to manually upload your papers. We automate this via ORCID integration instead. I mean, it’s 2020, this just makes sense.
Manual uploading of research often requires a lot of effort and time. Furthermore, there is a total loss of legal certainty, as often it is copyrighted publisher versions which are uploaded onto the platforms, and integrated into their data systems.
The impact factor is academia’s worst nightmare. So much has been written about its flaws, both in calculation and application, that there is little point in reiterating the same tired points here (see here by Stephen Curry for a good starting point).
Recently, I was engaged in a conversation on Twitter (story of my life..), with the nice folks over at the Scholarly Kitchen and a few researchers. There was a lot of finger pointing, with the blame for impact factor abuse being aimed at researchers, at publishers, funders, Thomson Reuters, and basically any player in the whole scholarly communication environment.
As with most Twitter conversations, very little was achieved in the moderately heated back and forth about all this. What became clear though, or at least more so, is that despite what has been written about the detrimental effects of the impact factor in academia, they are still widely used: by publishers for advertising, by funders for assessment, by researchers for choosing where to submit their work. The list is endless. As such, there are no innocents in the impact factor game: all are culpable, and all need to take responsibility for its frustrating immortality.
The problem is cyclical if you think about it: publishers use the impact factor to appeal to researchers, researchers use the impact factor to justify their publishing decisions, and funders sit at the top of the triangle facilitating the whole thing. One ‘chef’ of the Kitchen piped in by saying that publishers recognise the problems, but still have to use it because it’s what researchers want. This sort of passive facilitation of a broken system helps no one, and is a simple way of failing to take partial responsibility for fundamental mis-use with a problematic metric, while acknowledging that it is a problem. The same is similar for academics.
(Note: these are just smaller snippets from a larger conversation)
What some of us did seem to agree on, in the end, or at least a point remains important, is that everyone in the scholarly communication ecosystem needs to take responsibility for, and action against, mis-use of the impact factor. Pointing fingers and dealing out blame solves nothing, and just alleviates accountability without changing anything, and worse, facilitating what is known to be a broken system.
So here are eight ways to kick that nasty habit! The impact factor is often referred to as an addiction for researchers, or a drug, so let’s play with that metaphor.
Eugene Garfield, one of the founders of biliometrics and scientometrics, once claimed that “Citation indexes resolve semantic problems associated with traditional subject indexes by using citation symbology rather than words to describe the content of a document.” This statement led to the advent and a new dawn of Web-based measurements of citations, implemented as a way to describe the academic re-use of research.
However, Garfield had only reached a partial solution to a problem about measuring re-use, as one of the major problems with citation counts is that they are primarily contextless: they don’t tell us anything about why research is being re-used. Nonetheless, citation counts are now at the very heart of academic systems for two main reasons:
They are fundamental for grant, hiring and tenure decisions.
They form the core of how we currently assess academic impact and prestige.
Working out article-level citation counts is actually pretty complicated though, and depends on where you’re sourcing your information from. If you read the last blog post here, you’ll have seen that search results between Google Scholar, Web of Science, PubMed, and Scopus all vary to quite some degree. Well, it is the same for citations too, and it comes down to what’s being indexed by each. Scopus indexes 12,850 journals, which is the largest documented number at the moment. PubMed on the other hand has 6000 journals comprising mostly clinical content, and Web of Science offers broader coverage with 8700 journals. However, unless you pay for both Web of Science and Scopus, you won’t be allowed to know who’s re-using work or how much, and even if you are granted access, both services offer inconsistent results. Not too useful when these numbers matter for impact assessment criteria and your career.
Google Scholar, however, offers a free citation indexing service, based, in theory, on all published journals, and possibly with a whole load of ‘grey literature’. For the majority of researchers now, Google Scholar is the go-to powerhouse search tool. Accompanying this power though is a whole web of secrecy: it is unknown who Google Scholar actually crawls, but you can bet they reach pretty far given by the amount of self-archived, and often illegally archived, content they return from searches. So the basis of their citation index is a bit of mystery and lacking any form of quality control, and confounded by the fact that it can include citations from non-peer-reviewed works, which will be an issue for some.
Academic citations represent the structured genealogy or network of an idea, and the association between themes or topics. I like to think that citation counts tell us how imperfect our knowledge is in a certain area, and how much researchers are working to change that. Researchers quite like citations; we like to know how many citations we’ve got, and who it is who’s citing and re-using our work. These two concepts are quite different: re-use can be reflected by a simple number, which is fine in a closed system. But to get a deeper context of how research is being re-used and to trace the genealogy of knowledge, you need openness.
At ScienceOpen, we have our own way to measure citations. We’ve recently implemented it, and are only just beginning to realise the importance of this metric. We’re calling it the Open Citation Index, and it represents a new way to measure the retrieval of scientific information.
But what is the Open Citation Index, and how is it calculated? The core of ScienceOpen is based on a huge corpus of open access articles drawn primarily from PubMed Central and arXiv. This forms about 2 million open access records, and each one comes with its own reference list. What we’ve done using a clever metadata extraction engine is to take each of these citations and create an article stub for them. These stubs, or metadata records, form the core of our citation network. The number of citations derived from this network are displayed on each article, and each item that cites another can be openly accessed from within our archive.
So the citation counts are based exclusively on open access publications, and therefore provide a pan-publisher, article-level measure of how ‘open’ your idea is. Based on the way these data are gathered, it also means that every article record has had at least one citation, and therefore we explicitly provide a level of cross-publisher content filtering. It is pertinent that we find ways to measure the effect of open access, and the Open Citation Index provides one way to do this. For researchers, the Open Citation Index is about gaining prestige in a system that is gradually, but inevitably and inexorably, moving towards ‘open’ as the default way of conducting research.
In the future, we will work with publishers to combine their content with our archives and enhance the Open Citation Index, developing a richer, increasingly transparent and more precise metric of how research is being re-used.