Research comm-part4

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  • The third and final theme we’ll explore in the Research Communication Workshop is the problem of scholarly representation.
  • This problem has several flavors. First, it is about who did what. It is often surprisingly difficult to accurately connect an author to her full body of work. Making it difficult for human and machine readers to access and understand an author’s scholarship.
  • Take, for example, the case of the ecologist JL Campbell. Searching Google Scholar for ecology and JL Campbell retrieves about 86 articles by or co-authored by JL Campbell. But, the results actually represent the research of two scholars who share a name and a field, but no other research ties.One John L Campbell is a Research Ecologist at Oregon State University. The other John L Campbell is a Research Ecologist at the Center for Research on Ecosystem Change in Durham, NCThis makes it difficult for the average human user to discover and use either author’s publications; and drives machine readers to inaccurate conclusions. Consider for instance, the important (if flawed) metrics derived from bibliographic data, like the h-index and how incorrect the scores would be in this case.
  • And, most tools and servcies don’t connect users to a scholars full body of research activities and products, specifically things that aren’t books or articles. This includes, but is not limited to, data sets, editorial activities, posters and presentations, funding, podcasts, and blogs. For example, here is Scopus’s view of my boss, Melissa Haendel’s scholarly footprint - showing mostly peer reviewed articles.
  • And here is her profile in SciVal Experts, which is primarily derived from the metadata about and abstracts from those publications.
  • This is her profile on GitHub.
  • And, here our her slide shares.
  • A related problem of representation is the incompleteness of the traditional metrics used to measure and understand the impact of a scholar’s work. These metrics, such and the h-index and impact factor are based on publication to publication citation data, and don’t address the attention and usage scholarly works that aren’t books or articles receive. Additionally, much like the traditional model of scholarly communication its self, these metrics are slow (not beginning to build until articles and books that reference a work are published), primarily address only a specific slice of communication (researcher to researcher) and don’t provide context (failing to reveal why a particular work was cited be it affirmation or refutation). A reliance and systematic use of such metrics, by hiring and tenure and review committees for instance, can lead to, among other things, a very narrow understanding of a scholars impact, and prevents some researchers from telling their most compelling impact stories. Additionally, the academy’s over emphasis of these metrics, can lead to an artificial preoccupation with publishing in “premier” journals (as measured by the impact factor) that may not be the best fit for a paper or prove the most useful for other researchers to build on or access. As Jackie and Nicole have done, I think it’s important to contextualize the problem of scholarly representation within the context of downstream effects and issues – it’s not just about discovering the work of one scholar or one tenure and promotion decision. Rather, it’s about the cumulative effect of hiring, funding, collaboration, policy, and research decisions based on incomplete and one-dimensional understandings of people’s scholarly footprints, specifically the research paths these decisions influence and the inefficiencies they promote.
  • The first solution to the problem of scholarly representation I’d like to highlight is author disambiguation and inclusive linkages. Author disambiguation refers to the process of associating people with unique, human and machine readable identifiers. Inclusive linkages refers to the ability to associate these identifiers with a broad and varied body of research activities and associations, including affiliation, funding awards, data sets, and poster presentations.
  • Both are exemplified in ORCID or ORCiD. ORCID provides a persistent digital identifier that distinguishes individual researchers. A researcher can link her ORCiD to the works and activities she wants to represent her scholarship, and when she or others cite her ORCiD in papers and grant applications, human and machine readers can easily connect to that larger body of work. I’ve included here my ORCID profile, which includes a personal statement, publications, presentations, posters, and links to my FigShare and Twitter accounts.
  • ORCiDs are free and are increasingly being integrated in publishers’ manuscript workflows and funders’ application materials. Institutions, including libraries, can use ORCiD API to create ID for researchers, and push, and pull information.
  • The second solution to the problem of scholarly representation I’d like to highlight is altmetrics.
  • Increasingly scientific research and information is communicated, accessed, and endorsed on the Web. This includes, of course, traditional journals and peer-reviewed papers, but also includes new channels such as blogs, social media, citation management software, video, and podcasts. As I’ve already discussed, traditional metrics—citation counts—do not adequately capture these new modes of scientific communication, offering an incomplete and often delayed indication of impact. Altmetrics or alternative metrics are intended to measure the diversity and speed of scholarly communication in the digital age. Furthermore, their utility extends beyond quantifying media mentions and usage; they help us understand the communities science and scholarship is reaching, and the tools scientists and the public are using to share scientific information. Used together citation counts and altmetrics can offer a fuller, more nuanced, and timely picture ofresearch productivity, impact, and engagement. OHSU is currently using PlumX to gather and analyze data altmetric data for OHSU’s research works and activities. We are profiling 50 OHSU scientists, including all members of two prominent research labs on campus. I’ve included an interesting snapshot of data in this slide, which illustrated the different kinds activity associated with roughly 1,100 unique articles. The goal of our pilot is to:Help the OHSU Library build and provide a more complete understanding of the impact of OHSU research.Provide us with a fuller understanding of the tools communities are using to share scientific information. Better understanding of the communities OHSU science is engaging.And, aid in our knowledge of the possible relationships between dissemination methods/modes and impact.   
  • Here are two very comprehensive resources for diving the altmetrics discussion.
  • We’d like to close the workshop by returning to the idea of strengthening the quality and efficiency of the research process and scholarly communication. We haven’t addressed all of the issues. Nor have we described all of the solutions related to the problem themes we covered. For instance, a longer workshop would cover data citation as related to research representation.But we hoped we’ve offered an interesting overview of some of the practices and resources libraries and librarians are using, building, and promoting to positively affect research communication. Thank you and please feel free to contact me, Nicole, and Jackie with questions and feedback.
  • Research comm-part4

    1. 1. Problem|Scholarly Representation
    2. 2. Image credit: http://www.salon.com/2010/03/28/contested_will/
    3. 3. I_PA _T
    4. 4. Solution|Disambiguation & Inclusive Linkages
    5. 5. RESOURCES ORCID Knowledge Base http://support.orcid.org/knowl edgebase
    6. 6. Solution|Altmetrics
    7. 7. RESOURCES Mendeley Altmetrics Group: http://www.mendeley.com/groups/58617 1/altmetrics/ NISO Altmetrics Project: http://www.niso.org/topics/tl/altmetrics_initiativ e/
    8. 8. Strengthening the Network Image credit: http://cygx1.deviantart.com/art/Translucent-Symbiosis-55747790

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