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Envisioning the next generation of scholarly communications

For years now, the journal and the publisher have held sway over many aspects of discovery and evaluation of research and researchers. The development of the Web was expected to disrupt this, but innovation has been slow. Collectively, the research community have been cautious in embracing the power that has been granted to us for integration, sharing, and using semantic technologies to enhance how we read, communicate, and re-use the scientific record.

At ScienceOpen, we believe that opening up article-level information will be part of the next wave of innovation in scholarly publishing and communications. Our CEO, Stephanie Dawson, spoke about this with Research Information recently, conveying the idea that we need to embrace the power of modern technologies to unlock the multi-dimensional intrinsic value of articles in their broader ‘context’.

What are the dimensions of context?

There are so many factors that contribute to the context of an article.

  1. Who has cited your work?
    • What is the academic context of a citation?
    • Was it cited in a neutral, positive or negative context?
    • What is the total number of citations for your work?
  1. What similar articles exist?
    • Based on keywords, subject area, or author
  2. What is the article metadata?
    • Does it have a PubMed Central ID?
    • Who published it, and in which journal?
    • When was it published, and by whom?
    • What institute(s) was the research conducted at?
    • Which funding body or bodies supported this research?

      Ioannidas stats 2
      Metadata galore! PubMed Central ID, DOI with link to full text, author, publication date, journal and publisher (Source).
  1. What are the most cited authors and references?
    • Discover who the more prominent researchers involved were
    • Discover the most relevant research an article is citing
  2. What are the article-level metrics beyond citations?
    • What do these tell you about the re-use of your articles, and the social context?
    • Who is sharing and writing about your work?
    • What has been the social impact of this?

      Ioannidas stats 1
      Number of readers, the number of citations in Open Access papers, the post-publication reviews, comments, recommendations, and shares (all on ScienceOpen). And a hefty Altmetric score! (Source)
  1. Has the article been peer reviewed?
    • Who peer reviewed it?
    • What did they say about it?
    • How did they rate it?
  1. Who is recommending your work?
    • Do your colleagues and broader society value your work?
  2. How can we use the above to smartly discover, sort, and filter relevant research to you?
    • Filter searches by citations, Altmetric score, relevance, date of publication and more

      Ioannidas stats 3
      Who is citing your work? What similar articles exist? (Source)

One of the major factors here is references as a carrier of context. References and citations track the genealogies of concepts, ideas, and trace the development of research through time. We have almost 24 million article records now on our platform, which form an interlinked network of how research is connected. We can use this to help create enriched article-level context.

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What are the most cited references? Who are the most referenced authors? (Source)

How do we create context at ScienceOpen?

Magic! Well actually, what we do is take the structured xml from each Open Access article record, and use it to create article metadata stubs for each reference included. We then cross-reference them across the entire database to remove duplicates and enhance the article-level metadata on our platform. Each article is tracked through a CrossRef DOI. We are also now integrating new content from other publishers, recently including Brill and SciELO.

Through this, we can generate a count of citations for each article, and create a huge semantic network that links the entire corpus through how they’re linked through citations!

You can then sort and filter by any type of metadata we can pull out or overlay from this: citation counts, Altmetric score, relevance based on keyword combinations, age, rating, and most read. All of these things tell you something about the article, are publisher and journal independent, and are designed to help you discover what you need in the most efficient way possible.

What is the future of context?

What all of this relies on is a well-developed scholarly infrastructure. Things like enriched xml tagging, ORCID, and Crossref DOIs all contribute to some degree to this. Importantly, all of these things operate at the article and the author level. Perhaps author-level metrics, such as the h-index, will be the next wave of innovation in this domain. Unlocking the context of individual researchers would be invaluable for aspects of evaluation for tenure, promotion, and grants.

As standards, unique identifiers, and semantic technologies improve, we too will be able to enhance article-level context. With this, we can ultimately help to accelerate the research process and make it more efficient, which I’m sure we can all agree on is a pretty good target to be aiming for!

We discussed what context meant in a previous post too, but we’d like to hear more from you all! What is conjured to your mind when you think of research context? What additional aspects can we integrate into our platform?

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