Use Case Mendeley Research Networks for linking researchers and publications
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Mendeley Research Networks for linking researchers and publications.
Background and Current Practice
The existing system by which scientific knowledge flows from discovery to publication to new discovery involves a publishing and peer review process. For many fields, particularly life sciences, this is time consuming and inefficient as the knowledge discovered must be distilled into a form suitable for a printed publication. In the process, much of the context and links are lost, as when a data set is rendered as an image and published as a PDF. This leads to the body of knowledge existing as different silos of unstructured information and creates a large delay as the work goes through the editorial and printing processes of the publisher to which the work was submitted. In response to this, Mendeley proposes to add context back to publications through crowdsourced social and attention-based metadata as well as algorithmic approaches to linking documents.
1. Collect the the world's academic literature. By the end of 2011, if current rates continue, we expect to have 80-90% coverage.
2. Collect annotations, tags, and document usage data on the document corpus.
3. Use this information as well as established algorithmic approaches to enrich the publication metadata and make this information available via API so that further applications can be built, such as a more advanced way of understanding paper-paper, paper-researcher, and researcher-researcher relationships besides "A cites B".
The target audience is researchers, developers, and publishers of scholarly communications.
Use Case Scenario
Researcher A wants to know if a method described in Paper A has been criticized and what the commentary was. Researcher A searches for the paper in the Mendeley research catalog, papers listed on the catalog page for the paper as related to Paper A and reads those, discovering related commentary both in the paper itself and added to the document by the readers of the paper within Mendeley. Researcher A then searches for the method from Paper A in the Mendeley research catalog and retrieves results based on the relative level of readership of the paper, thereby accessing the most relevant published commentary about the method.
Application of linked data for the given use case
The related papers would be identified by means of readership, but co-citation is also possible. The strength of the relationships can be derived by citation and readership graph analysis. Ranking of search results can be derived in a similar fashion. Custom search operators could also enable searches for only criticizing or supporting publications, for example.
Existing Work (optional)
The existing work at http://mendeley.com implements some of this and plans to implement the rest.
Related Vocabularies (optional)
We may use existing vocabularies such as the Citation Typing Ontology.
Problems and Limitations (optional)
The key to overcoming this challenge is collecting enough crowdsourced information on enough documents that the dataset becomes dense enough that algorithms yield useful results for the majority of the content.
Related Use Cases and Unanticipated Uses (optional)
It is also possible that publishers may use the information to optimize the readership of their own publications or that researchers may begin to demand publication forms that preserve more of the structured data.