Moving beyond a journal-based filtering system

The amount of published scientific research is simply enormous. Current estimates are over 70 million individual research articles, with around 2 million more being published every year. We are in the midst of an information revolution, with the World Wide Web offering rapid, structured and practical distribution of knowledge. But for researchers, this creates the monolith task of manually finding relevant content to fuel their work, and begs the question, are we doing the best we can to leverage this knowledge?

There are already several well-established searchable archives, scientific databases representing warehouses for all of our knowledge and data. The most well-known include the Web of Science, Scopus, PubMed, and Google Scholar, which together are the de facto mode for current methods of information retrieval. The first two of these are paid services, and attempts to replicate searches between all platforms produce inconsistent results (e.g., Bakkalbasi et al., Kulkarni et al.), raising questions about each of their methods of procurement. The search algorithms for each are also fairly opaque, and the relative reliability of each is quite uncertain. Each of them, though, have their own benefits and pitfalls, which are far better discussed elsewhere (e.g. Falagas et al.).

So where does this leave discoverability for researchers in a world that is becoming more and more ‘open’?

Well, you can break this down into two related questions about what it is researchers want:

How do you find research relevant to you?

How do you find research relevant to your community?

At ScienceOpen, our primary target is to resolve these issues, as research cannot progress without a solid foundation of discovery. In order to recognise this, we provide context-driven filtering of our entire article archive, which currently comprises over 11 million individual records. This is based on two layers for how we facilitate searching: firstly, through focussed keyword or categorical searching, you can find research that is directly relevant to you; and secondly, through filtering and sorting this by citation counts, altmetrics, and other usage statistics, so that you can discover research that is more broadly relevant to your research community.

This flexible first layer is nothing new, and similar to the search engine employed by Google Scholar. The second layer of enhanced searching is where ScienceOpen really stands out from the others! Typically, the only level of discovery you have will be by keyword search, with secondary filtering being by date of publication. By adding this extra layer of attention, both social and academic, you can identify which papers are being most re-used by your research community and more broadly, helping you to identify the most important work. More importantly, ScienceOpen is currently the only platform which offers this service for free, besides Europe PMC.

Beyond this, ScienceOpen also offers a dual functionality by combining this enhanced filtering of article records with a suite of overlayed and interactive post-publication evaluation tools:

What this means is that, unlike the other platforms, everything subsequent to article discovery at ScienceOpen is completely open! The combination of these last two ensures we maintain a high level of quality in peer review, while also retaining a social and public aspect for it. Collections are thematic groups of articles drawn from our archive that can serve as the basis for post-publication re-use such as for journal clubs or research collaboration. For example, just recently we have created two Collections for the Zika Virus and for the discovery of gravitational waves, both of which can be used as the basis for further discovery, discussion, and research. Collection editors at ScienceOpen can be either individual experts in that specific field, scholarly institutions or academic organizations. In the future Collections may substitute classical journals when filtering relevant research, being independent of publication date or source.

Consequently, and importantly for researchers, this provides a time saving tool that performs across publishers and journals at the article level. Searching is context-driven and informed by usage statistics and metadata, creating a smart and efficient way to discover relevant content. Unlike Scopus and Web of Science, both the platform and context data is also completely free. And as the majority of our content is sourced from PubMed or the arXiv, we are transparent about what results are showing up when you search.

This enhanced discoverability is just one part of how we are aiming to build an infrastructure designed for making science open through sharing and democratising access to knowledge.