1.
The People Behind
Research Software
crediting from the informatics,
technical point of view
Professor Carole Goble,
University of Manchester, UK
Software Sustainability Institute UK
ELIXIR, ISBE, FAIRDOM
Views are my own
Science Europe LEGS Committee: Career Pathways in Multidisciplinary Research:
How to Assess the Contributions of Single Authors in Large Teams, 1-2 Dec 2015, Brussels.

2.
Team Science: Ego-System
• Experimental scientists
• Theoretical scientists
• Modellers
• Social scientists
• Computer scientists
• Computational Scientists
• Scientific informaticians
• Specialist Tool developers
• Research Software Engineers
• Data engineers and curators
• Service & resource providers
• Infrastructure developers
• System Administrators
Many software, services
and public data resources
are team based
collaborations

3.
Service vs Science in Projects
teams within teams
Biologists
Software frameworks
Tools, Infrastructure
Data platforms
Public data archives
Bioinformaticians
Comp Biologists
Local data curators

4.
Informatics contribution to team
Reputation, Recognition, Productivity, Respect
Contribution to the informatics
– Technical publications in their own right
– Software publications: citation proxies
• Fosselise snapshot of authors as
contributors
– Specific code and curation tracking
– Usage metrics (downloads, reuse)
– Comp Sci - Conferences matter
– IMPACT

5.
Compound, collaborative, living nature of
data and software

6.
Acknowledgement by research teams
– “We are not the janitors” It’s not “free”.
– The Craftsmen of Science
– Careers, credibility and sustainability
– Recognised career role of Research Software
Engineer and BioCurator
– Recognition of professionalism, software and
data quality.
– Reward for LABOUR.
Informatics contribution to team
Reputation, Recognition, Productivity, Respect

7.
*Survey of researchers from 15 UK Russell Group universities conducted by SSI between August - October
2014. 406 respondents covering representative range of funders, discipline and seniority.

8.
Credit
Biologists
Bioinformaticians
Cite
Local tool providers
Public data set providers

9.
Service vs Science
Background vs Foreground
Data [and software] in
foreground most likely cited.
Same data [and software] viewed
as background not / explicitly
cited though equally essential
Wynholds, et al (2012) Data, data use, and scientific inquiry: two case studies of data practices
10.1145/2232817.2232822
25% Publications that used
the public Arrayexpress
Archive cited it*
The invisibility of software
esp software that is widely
used, infrastructural,
components or cross-discipline
*Rung, Brazma Reuse of public wide gene expression data Nature Review Genetics 2012

10.
What is a Team? Credit drift
Immediate
team
Background
team
“Foreground”
informatics
Authorship Authorship?
Cited?
Acknowledged
Cited?
Mentioned
Ignored
“Background”
informatics
Cited

11.
The Currency of Recognition
Person Career
Peers
Funders
Institutions
Public
Resource Sustainability

12.
Software mentions in the
biology literature (90 articles)
Howison and Bullard 2015 The visibility of software in the scientific literature: how
do scientists mention software and how effective are those mentions? J Assoc for
Info Science and Technology DOI: 10.1002/asi.23538
37% citations formal
87% software could be found
informal mentions very common
-> poor at providing crediting information
18% software author offered preferred citation
-> 32% who cited it ignored it
24% journals had a citation policy Legal License
attribution
obligations
ignored

13.
Team reciprocity rules
Download and Go. No.
Jam for Everyone.

14.
sciencecodemanifesto.org

15.
1. Software and Data Research Objects
into the Publishing Workflow
informal
mentions
replaced by
formal

16.
http://ivory.idyll.org/blog/2015-authorship-on-software-papers.html

17.
*http://arxiv.org/pdf/1407.5117v3.pdf
• Research Object-specific credit models
– Software, data, models….
– Credit based on use: downloads, reusability, reuse, FAIR
• Contribution: Credit distribution, propagation, dividends
– Transitive credit maps (Katz and Smith)* , CReDIT**
• Use: Credit trajectories: tracing, tracking, mining
– Recovery from literature, identifier and provenance infrastructure,
standards, data/software level metrics services (Datacite),
repositories, machine readable and processable metadata.
3. Credit
networks &
credit currency
**http://casrai.org/CRediT
http://depsy.org/

18.
2. Stop conflating credit with
Authorship
Contribution
Roles
Usage
Liz Allen: CreDiT

19.
4. Research units and credit models
that reflect software
Not Publish. Release paradigm. Portfolio paradigm.
Jennifer Schopf,Treating Data Like Software: A Case for Production Quality Data,JCDL 2012
Evolving Multi-stewarded
Multi-authored
Multi-platform
Reproducible
Executable papers
Connected
Body of work
Compound, Aggregated

20.
https://dx.doi.org/10.1111/febs.13237
https://doi.org/10.15490/seek.1.investigation.56
http://www.fair-dom.org

21.
28/01/2016 22
An “evolving manuscript” would begin with a pre-
publication, pre-peer review “beta 0.9” version of an
article, followed by the approved published article itself, [
… ] “version 1.0”.
Subsequently, scientists would update this paper with
details of further work as the area of research develops.
Versions 2.0 and 3.0 might allow for the “accretion of
confirmation [and] reputation”.
Ottoline Leyser […] assessment criteria in science revolve
around the individual. “People have stopped thinking
about the scientific enterprise”.
http://www.timeshighereducation.co.uk/news/evolving-manuscripts-the-future-of-scientific-communication/2020200.article

22.
Ramps vs Revolutions
Technical ramps
• Machinery, tools, platforms,
repositories
Process ramps
• Research processes and
Publisher workflows
Social ramps
• Rules and policies
• Adoption by stakeholders
– interventions & automations
• Recognition by stakeholders
Credit is like love not money
Citations and across discipline boundaries.
Within discipline more like dividends.
All research products and all scholarly
labour are equally valued
(except by institutional promotion,
funding review and REF committees)
Public software and data resources
are not free.
Stewardship costs and needs crediting
Publishers adapt to “Publications”
that are dynamic Research Objects
(still need to snapshot)

23.
http://www.software.ac.uk/software-credi

24.
https://www.force11.org/group/software-citation-working-group

25.
Links
• FAIRDOM
– http://www.fair-dom.org
• SEEK Platform
– http://www.seek4science.org
• Research Objects
– http://www.researchobject.org
• Software Sustainability Institute
– http://www.software.ac.uk
• Software Carpentry
– http://www.software-carpentry.org
• Force11
– http://www.force11.org