This is an update for COASP (http://oaspa.org/conference/) on the representation of attribution beyond authorship of a publication. Publications are proxies for the projects and people that area actually engaged in the work, and represent the dissemination aspect. How can we better understand the individual contributions and their impact? The openRIF, openVIVO and FORCE11 Attribution WG efforts aim to represent scholarship in a computationally tractable manner so as to enable credit and evaluation of all types of scholarly contributions.
7. Many contributions don’t lead to
authorship
NIH BD2K co-authorship
D.Eichmann
N.Vasilevsky
> 20% key personnel are not profiled using publications
8. Some contributions are anonymous
Data deposition
Image credit: http://disruptiveviews.com/is-your-data-anonymous-or-just-encrypted/
Anonymous review
11. Credit extends beyond the
publication
Johannes creates stim1 mouse
Melissa curates patient data for UDP_2542
Will performs analysis of UDP_2542 data that includes
stim1 mouse to generate a dataset of
prioritized variants
Tom writes publication pmid:25577287 about the
STIM1 diagnosis
Tom explicitly credits Will as an author but not Melissa.
13. Who contributed?
Melissa Haendel
Peter Robinson
Chris Mungall
Sebastian Kohler
Cindy Smith
Nicole Vasilevsky
Sandra Dolken
Johannes Grosse
Attila Braun
David Varga-Szabo
Niklas Beyersdorf
Boris Schneider
Lutz Zeitlmann
Petra Hanke
Patricia Schropp
Silke Mühlstedt
Carolin Zorn
Michael Huber
Carolin Schmittwolf
Wolfgang Jagla
Philipp Yu
Thomas Kerkau
Harald Schulze
Michael Nehls
Bernhard Nieswandt
Thomas Markello
Dong Chen
Justin Y. Kwan
Iren Horkayne-Szakaly
Alan Morrison
Olga Simakova
Irina Maric
Jay Lozier
Andrew R. Cullinane
Tatjana Kilo
Lynn Meister
Kourosh Pakzad
Sanjay Chainani
Roxanne Fischer
Camilo Toro
James G. White
David Adams
Cornelius Boerkoel
William A. Gahl
Cynthia J. Tifft
Meral Gunay-Aygun
Melissa Haendel
David Adams
David Draper
Bailey Gallinger
Joie Davis
Nicole Vasilevsky
Heather Trang
Rena Godfrey
Gretchen Golas
Catherine Groden
Michele Nehrebecky
Ariane Soldatos
Elise Valkanas,
Colleen Wahl
Lynne Wolfe
Elizabeth Lee
Amanda Links
Will Bone
Murat Sincan
Damian Smedley
Jules Jacobson
Nicole Washington
Elise Flynn
Sebastian Kohler
Orion Buske
Marta Girdea
Michael Brudno
Jeremy Band
Hans Goeble
Karen Balbach
Nadine Pfeifer
Sandra Werner
Christian Linden
Clinical/care Pathology Ontologist CS/informatics Curator Basic research
14. Contribution and Attribution in the Context of the Scholar
workshop – Force 2015, Oxford, January 2015
Measuring success through improved attribution VIVO
2015, Austin, August 2015
OpenRIF: semantic infrastructure for the scholarly
research landscape, Portland, April 2016
Project CRediT workshop,
Washington DC, December 2014
The evolution of credit
NISO Alternative Metrics Initiative: Outputs in Scholarly
Communications, May 2016
15. EXAMPLE OUTPUTS related to software:
Outputs: binary redistribution package (installer), algorithm, data analytic software tool,
analysis scripts, data cleaning, APIs, codebook (for content analysis), source code, software to
make metadata for libraries archives and museums, data analytic software tool, source code,
program codes (for modeling), commentary in code(thinking of open source-need to attribute
code authors and commentator/enhancers/hackers, who can document what they did and
why), computer language (a syntax to describe a set of operations or activities), software patch
(set of changes to code to fix bugs, add features, etc.), digital workflow (automated sequence of
programs, steps to an outcome), software library (non-stand alone code that can be
incorporated into something larger), software application (computer code that accomplishes
something)
Roles: catalog, design, develop, test, hacker, bug finder, software developer, software engineer,
developer, programmer, system administrator, execute, document, software package maintainer,
project manager, database administrator
Workshop results:
>500 scholarly products
16. Introducing open Research Information
Framework (openRIF)
and the Contribution Ontology
Interoperable standard for representing people and
organizations within the research ecosystem
Ontology A
http://bit.ly/
ConnectedResearchersDavid Eichmann
Contribution Ontology
17. Acknowledgements
MARIJANE WHITE, KRISTI HOLMES, DAVID EICHMANN, KAREN E.
GUTZMAN, STACY KONKIEL, MATTHEW BRUSH, VIOLETA ILIK, MIKE
CONLON, AMY BRAND, DAN KATZ, LIZ ALLEN,
FIGSHARE, FORCE11 ATTRIBUTION WORKING GROUP, CASRAI,
OPENVIVO, SCIENCV, DIGITAL SCIENCE,
OPENRIF DEVELOPMENT TEAM
Join the Force Attribution Working Group at:
https://www.force11.org/group/attributionwg
Join the openRIF listserv at: http://group.openrif.org
Notas do Editor
Even more critical in research today – more interdisciplinary, more moving pieces, more team-based
(translational workforce is a good example of this: eg for a clinical trial may have PI, study coordinators, ethicist, lab tech, biobanking facility, analyst, etc.)
(another good example is an open source software project like VIVO where contributors have different roles, produce software and data models, different workflow and dissemination patterns)
The contributions of all of these people are required for research to move forward, but there are not mechanisms in place to properly recognize these contributions and represent them in a meaningful manner
http://g3journal.org/content/5/5/719
co-authorship is cross-award, but expertise is within award
There are key persons that connect communities
20% awardees are not adequately profiled using publications
Social network visualization of 282 BD2K awardees (key personnel) on 38 grants of 6 award types. Coauthorship between personnel from those same publications. Edge length is inversely proportional to publication count (200 edges). Note that K01 key personnel include senior mentors and there are still a few non-responders missing.
Connectivity and cluster composition changes when comparing domain expertise to co-authorship. For example, there is substantive co-authorship across awards, but expertise tends to stay in the same award. A final key finding from this SNA was that approximately 20% of the awardees did not have publications as a primary outcome (often in roles like software engineers, developers, programmers, analysts, etc.) from these and prior efforts, implying that traditional means for profiling learners, experts, and collaborations does not provide a complete picture of the data science landscape