Why ResearchScorecard now links to LinkedIn Why ResearchScorecard now links to LinkedIn
examining a researcher's LinkedIn network
We’ve recently added functionality that links our Researcher Profiles to public LinkedIn profiles.
Why bother, you might think? The reasons are eloquently described in an interesting study by a group of researchers in academia, software companies and one of my favorite defense contractors, MITRE Corporation.
Having researched the requirements for expertise location systems for biomedical scientists, one of Schleyer et al.’s (2008) major findings is the need to exploit “… others’ social networks when searching for collaborators”. In plain language, this just means that when considering a collaboration, people find it helpful to understand who is associated with the prospective collaborator, perhaps to determine whether a common contact could perform introductions, but also to get a sense of the person (kind of like in high school, where one is often judged by their crowd). Yes, biomedical researchers are just like everyone else when it comes to socialization.
In short, after perusing the professional and scientific aspects of a potential collaborator, you’ll now be able to jump to LinkedIn to figure out whether there is a contact known to you both that can tell you more about him/her. Neat, huh?
Of course, such “social networking inter-connection” is one thing LinkedIn does admirably well in the professional realm, and so it didn’t take much to convince us to enable our Researcher Profiles to show a link to an individual’s profile when it’s available. Note that you will need your own LinkedIn account to be able examine someone else’s network.
Going back to the study, Schleyer et al. present ten major conclusions derived from interviews and a comprehensive literature review. The interviewees were from Carnegie Mellon University and the University of Pittsburgh. As with all expertise finding studies I know of, the results are retrospective only, since no scientist was actually observed in the process of seeking expertise. Though understandable, this limitation is unfortunate, given the relative inability of human subjects to recall and accurately describe their motivations and thought processes post facto.
| Requirements identified by study | Our plain language translation | What we’re doing about it |
|---|---|---|
| “The effort required to create and update an online profile should be commensurate with the perceived benefit of the system” | Scientists just don’t have the time to create and maintain their profile… | Our Researcher Profiles are not populated by the researcher. |
| “Online profiles should (…) reduce the effort involved in making collaboration decisions” | The study states that information about a scientist is “…very fragmented and inhomogeneous”. In short, creating a robust profile requires lots of manual Web searching and inability to construct a comprehensive data set by which to judge a given data point against a distribution (the only way to really understand data). | Resolving this problem is one of ResearchScorecard’s main value-added features: very different data sets are brought together and harmonized; statistical distributions are created and used to contextualized individual data points. |
| “Online profiles should be up-to-date” | Selecting a collaborator involves predicting aspects of the professional future of that person; leading indicators are preferred over trailing indicators. | ResearchScorecard is one of very few biomedical expertise systems that cover granting data, one of the “freshest” data sources to describe current researcher activity. And of course, we include funding amounts, not just title and grant number, and we do so for multiple funders, even private ones. |
| “Researchers should be able to exploit their own and others’ social networks when searching for collaborators” | Scientists want to assess their potential collaborator’s “clique”. | Now available! |
| “The system should model proximity, which influences the potential success in several respects” | “Proximity” = physical proximity, social proximity (clique), organizational proximity, and closeness of research area between the two parties. | RSC provides unit affiliation and research area proximity for this purpose through its Collaborator Network report, though we could do a better of showing physical proximity. Here’s an example report (takes a few minutes to compute). |
| “The system should facilitate the assessment of personal compatibility, similarity of work styles and other “soft” traits influencing collaborations” | Is the potential collaborator a nice person? Does he/she know how to collaborate? | We provide metrics of the number of collaborators over the years as a rough way to address this question. |
| “Social networks based on co-authorship may only partially describe a researcher’s collaborative network” | What about data from memberships in research consortia, clinical trials, etc, that are not always visible? | There is a lot here that we don’t address … yet. We do track co-PIships and are considsering mining the acknowledgment section of publications (see this 2004 paper for an example application). |
| “The system should account for researchers’ preferences regarding privacy and public availability of information about them” | This topic is replete with a plethora of aspects, but one elephant in the room is the desire from some researchers to not attract attention for any number of reasons… | We at ResearchScorecard believe that if a researcher works in a research institution that receives public funding, there are no strong reasons to exclude aspects of a professional persona from the profile if the underlying data are already publicly visible. |
| “The system should provide methods to search effectively across disciplines” | Biomedical research is vastly more cross-disciplinary than even ten years ago. Witness discoveries that rely on instruments that are heavily dependent upon physics, chemistry, computer science, engineering, etc. This dependency on other disciplines is likely to continue increasing. | This requirement is why we are investigating the merging of expertise data with data from compound analysis systems such as CDD (see our recent blog post). |
| “The system should help make “non-intuitive” connections between researchers” | Finding potential collaborators that look like you: easy. Finding potential collaborators that you should consider yet don’t look like you: hard. | This requirement is related to cross-disciplinary searching, though there are plenty of potential collaborators in proximal fields as well. For a software system to make non-intuitive yet useful recommendations would be very valuable, as long the recipients have confidence in the recommendations. Unfortunately, it’s our experience that the more non-intuitive the recommendation, the less likely the recipients’ confidence in the recommendation… |