With the launch of our new unified search interface, we restructured the Author Profile page on ScienceOpen, providing dynamic ways to explore an author’s output.
For a very prolific author like Ray Dolan, Director of the Wellcome Trust Centre for Neuroimaging at UCL and author of 674 articles, it can be hard work for a reader to even just scroll through the titles of his total output. The new ScienceOpen author profile, however, provides the researcher a variety of avenues to delve into this content on their own terms. They can sort publications by Altmetric score, citations, usage, date or reviews – to find the view that fits their needs.
New enhanced author profiles!
The left side-bar overview shows top collections, journals, publishers, keywords and disciplines. Users can also search within the publication list with a free-text search or add up to 14 filters to find exactly the content that is relevant to them.
The top metrics bar provides a view on total usage of the articles on the site and activity by the author. And if you want to know more about the background of the author just click on the profile button for biography and more.
How does it work? From the beginning ScienceOpen has worked closely with ORCID and required an ORCID ID for active participation in the network. We draw our information therefore from a user’s public profile. If we detect an author who is not identified in our network with an ORCID (we are tracking nearly 15 million authors), we mark the profile as “record” to indicate a lower level of reliability; for example, this profile from Jonathan A. Eisen:
Integrate your ORCID account to activate your full profile recordUseful author-level metrics and context
Below are several examples of interesting profiles on ScienceOpen to inspire you. We welcome you to search, explore, link your ORCID to your own profile and share your experience with us. At ScienceOpen we are striving to serve the academic community and always welcome your input.
As our thank you to all of our wonderful members and users, this year we have decided to give you a special gift. We’ve taken each of the individual interviews from our Open Science Stars series, which documents a range of experiences and perspectives into the world of Open Science, and assembled them here for you in one collection to download here: OPEN SCIENCE STARS. Only by listening to and understanding truly diverse voices can we gain a deeper appreciation of the issues surrounding Open Science. By taking on board what others have to say and learning from them, we strengthen ourselves and the community, and understand how to put things into practice more easily.
At ScienceOpen, we’ve just upgraded our search and discovery platform to be faster, smarter, and more efficient. A new user interface and filtering capabilities provide a better discovery experience for users. ScienceOpen searches more than 27 million full text open access or article metadata records and puts them in context. We include peer-reviewed academic articles from all fields, including pre-prints that we draw from the arXiv and which are explicitly tagged as such.
The current scale of academic publishing around the world is enormous. According to a recent STM report, we currently publish around 2.5 million new peer reviewed articles every single year, and that’s just in English language journals.
The problem with this for researchers and more broadly is how to stay up to date with newly published research. And not just in our own fields, but in related fields too. Researchers are permanently inundated, and we need to find a way to sift the wheat from the chaff.
The solution is smart and enhanced search and discovery. Platforms like ResearchGate and Google Scholar (GS) have just a single layer of discovery, with additional functions such as sorting by date to help narrow things down a bit. GS is the de facto mode of discovery of primary research for most academics, but it also contains a whole slew of ‘grey literature’ (i.e., non-peer reviewed outputs), which often interferes with finding the best research.
As well as this, if you do a simple search with GS, say just for dinosaurs, you get 161,000 returned results. How on Earth are you supposed to find the most useful and most relevant research based on this if you want to move beyond Google’s page rank, especially if you’re entering this from outside the area of specialisation? Simply narrowing down by dates does very little to prevent being overwhelmed with an absolute deluge of maybe maybe-not relevant literature. We need to do better at research discovery.
