The document discusses the author's experience with open source software development and its interaction with institutional contexts in research and higher education. The author has a long history developing open source software for applied statistics and geospatial applications using R. He notes a mismatch between institutional preferences for secrecy and open source practices of mutual trust and community-building. The author was an early contributor to the R project in 1997 and discusses the evolution of R from its beginnings as an academic initiative to the large open source community that exists today.

1. Experience
R project
R spatial
Managing research in collaborative networks
Open Source software, research and higher
education: a practitioner’s view
GoOpen 2010 (Fou thread), Aker Brygge, Oslo, 19–20 April.
Roger Bivand
Department of Economics
Norwegian School of Economics and Business Administration
Bergen, Norway
20 April 2010
Roger Bivand A practitioner’s view

2. Experience
R project
R spatial
Managing research in collaborative networks
Outline
This talk will examine how open source software development
and use may interact with their institutional contexts in
research and higher education
The talk will be based on experience of open source
development in applied statistics and geospatial applications
Reasons for mismatch between an institutional context
preferring secrecy when applying for funding, restricted
deliverables, and races to publication, and the ways in which
open source development occur will be discussed
In particular, the roles of mutual trust and community-building
in open source development will be stressed; these factors
appear to express externalities between developers and users
of software that are neglected in the exclusive management
models prevalent in research and higher education
Roger Bivand A practitioner’s view

3. Experience
R project
R spatial
Managing research in collaborative networks
Contextual background
In order to provide some justifications for presenting a
“practitioners view”, some background information beyond my
affiliation may be useful
Although employed in the Department of Economics at
Norges Handelshøyskole, I am an academic geographer,
educated in Cambridge, and the London School of Economics
My specialities within geography are in quantitative methods
and geographical information systems, and have used and
developed software since 1973, for research and teaching
During the EU 5th Framework, I was involved in the
evaluation of three open source Information Society
Technologies (IST) calls; I also founded the MBA programmes
at Warsaw University of Technology in 1991/92
Roger Bivand A practitioner’s view

4. Experience
R project
R spatial
Managing research in collaborative networks
Little languages
My first “open source” publication was an extra module for the
proprietary program Systat, with both source code and DOS binaries
available for FTP download, and an accompanying paper in
Computers & Geosciences in 1992
While much early software (Fortran, later C) was compiled (I only
had limited exposure to BASIC), by the 1980s little languages,
generally interpreted, began to appear as glue for compiled programs
The languages covered in two of my papers published in 1996 and
1997 were the Unix shell scripting language and AWK, used as glue
for the GRASS GIS, and for GMT for map production; I have been
using Unix/Linux since 1985
In these papers and other work in the mid 1990s, I pointed up the
benefits of scripting in permitting work to be reproduced and
audited, contrasted with non-journalling GUIs that were becoming
prevalent in academic practice
Roger Bivand A practitioner’s view

5. Experience
R project
R spatial
Managing research in collaborative networks
Glimpse from 1997
Here is a slide from a talk given in Italy about software for
handling geographical information (GI) in early 1997:
MAPPING GI USERS:
PRODUCTION: high training costs, PROFESSIONALS: as consultants customising
application specific macro languages, GI handling technologies for clients in long/
few linking requirements (cf. COTS) medium term relationships; as researchers
in GI handling technologies
MORE STANDARDISED TASKS LESS
LESS NEED OPEN SOFTWARE MORE
CASUAL: generic likeness to CURIOUS: as researchers analysing
familiar GUI, looks & behaves geographic information; as citizens
like Excel or Netscape (cf. challenging the use of GI by private
plug-ins) companies and public administration
Roger Bivand A practitioner’s view

6. Experience
R project
R spatial
Managing research in collaborative networks
Using the R project
My first message to the R project was in mid January 1997, as
I had begun using early alpha releases to re-implement a
number of spatial analysis functions
The initial motivation to systematise code for functions for
spatial data analysis was for a course given in the University of
Bergen Department of Geography; we were a joint department
until administrative changes split us
By 1998, Albrecht Gebhardt (Klagenfurt, Austria) and I had
provided code for most simple spatial data analysis for R,
either porting existing code, or writing fresh contributions
(presentation at a congress in Vienna)
But what is the R project?
Roger Bivand A practitioner’s view

7. Experience
R project
R spatial
Managing research in collaborative networks
www.r-project.org
While its website is non-candy, R is becoming a central resource for
statistical and computational data analysis across the sciences and
in business:
Roger Bivand A practitioner’s view

8. Experience
R project
R spatial
Managing research in collaborative networks
The R project
R is a language and environment for statistical computing and
graphics — it is a GNU project which is similar to the S language
and environment which was developed at Bell Laboratories (formerly
AT&T, now Alcatel–Lucent) by John Chambers and colleagues
R can be considered as a different implementation of S. There are
some important differences, but much code written for S runs
unaltered under R
The term “environment” is intended to characterize it as a fully
planned and coherent system, rather than an incremental accretion
of very specific and inflexible tools, as is frequently the case with
other data analysis software
Many users think of R as a statistics system. We prefer to think of
it of an environment within which statistical techniques are
implemented — R can be extended (easily) via packages
Roger Bivand A practitioner’s view

9. Experience
R project
R spatial
Managing research in collaborative networks
The R foundation
The R project began as an academic
initiative with no funding in Auckland,
New Zealand, and was licensed under
GPL as more collaborators joined. This
group was strengthed by academic
contributors to S, who began to work
with R in the late 1990s. By 2002, a
more formal structure was needed, and a
foundation was formed. I was invited to
join as an ordinary member in March
2003, so have seen things “from the
kitchen” since then.
Roger Bivand A practitioner’s view

10. Experience
R project
R spatial
Managing research in collaborative networks
The R community
While the software system was intended
to be “fully planned and coherent” the
,
community that has grown up around R
is neither planned nor coherent. Since
1997, there have been two main mailing
lists, one for users, the other for
developers. John Fox (another non-core
ordinary foundation member) has
described the social structure of the
project in a recent paper in the R
Journal, from which this graph is taken:
Roger Bivand A practitioner’s view

11. Experience
R project
R spatial
Managing research in collaborative networks
CRAN and contributed packages
The community has also grown thanks
to the ease with which packages may be
contributed. Both writing packages, and
their formal checking against R are not
hard — the check process executes all
the examples on the help pages and
other documentation. The
comprehensive R archive network
(CRAN) thus distributes R itself (source
and binaries for multiple platforms) and
packages (source and binaries), and
packages may also be installed and
updated from within R.
Roger Bivand A practitioner’s view

12. Experience
R project
R spatial
Managing research in collaborative networks
CRAN
Roger Bivand A practitioner’s view

13. Experience
R project
R spatial
Managing research in collaborative networks
CRAN task views
Since so many packages have been
contributed to R, and distributed
through CRAN, it became necessary to
provide a mechanism for guiding users
towards solutions to their problems. It is
helpful to see the complexity of CRAN
as an advantage, with “ecologically” more
fit packages establishing themselves in
“niches” possibly even in competition
with other packages providing similar
facilities. Task views have been added as
a light-weight non-authoritative way of
offering suggestions:
Roger Bivand A practitioner’s view

14. Experience
R project
R spatial
Managing research in collaborative networks
R Forge
In addition to CRAN running the
released, patched, and development
versions of R on the CRAN packages’
examples nightly, packages may also be
hosted on the R Forge repository. This
provides the usual *forge services, such
as SVN, but also builds Windows and
OSX binary packages, and checks
package source on multiple platforms
nightly. So even alpha or beta packages
may be made available, and may begin
to harvest user input, before being
released to CRAN:
Roger Bivand A practitioner’s view

15. Experience
R project
R spatial
Managing research in collaborative networks
R spatial
In 1999 I had interfaced R and the open source GIS GRASS, and
presented a paper on this at a Scandinavian GIS meeting — the
paper was rejected by Norsk Geografisk Tidsskrift, but published in
extended form in Computers & Geosciences in 2000
This, and the publication of a paper based on my 1998 presentation
with Albrecht Gebhardt in Journal of Geographical Systems, and a
presentation with Markus Neteler, the lead GRASS developer at the
2000 GeoComputation conference, led to closer personal contacts
with R core
Kurt Hornik, who runs CRAN, encouraged me to talk about R and
GIS at the March 2001 Distributed Statistical Computing meeting in
Vienna, at which I got to know active developers personally
By the next DSC meeting in March 2003, I was organising a
thematic session on spatial statistics, and a crucial fringe developers’
workshop to discuss how to advance spatial data analysis in R
Roger Bivand A practitioner’s view

16. Experience
R project
R spatial
Managing research in collaborative networks
CRAN Spatial task view
Since 2003, a number of
community-building steps have been
made over and above developing
contributed packages. From the CRAN
side, the Spatial task view is the hub, to
which traffic is channelled to package
pages and to ancilliary websites, as well
as the special interest group mailing list.
Some package authors contact me to
ask to be included, others are asked
whether they want to be added to the
web of information
Roger Bivand A practitioner’s view

17. Experience
R project
R spatial
Managing research in collaborative networks
R-sig-geo mailing list
Following the 2003 workshop, we monthly number of emails on r−sig−geo
started a project on Sourceforge to q
permit joint development, and a mailing
300
list served within the family of R lists
q
250
from Zurich. Traffic on the list has q
q
q
q
grown steadily, with a subscribed q
q
qq q
q
200
q
q
# of emails
membership in April 2010 of over 1600. q
q q q
q
q q
Naturally, many of these “lurk” without q
150
q q q
q
q q
q q
posting, while others post without q
qq
q q
100
q
q q
helping, and many fewer help by q
q qq
q
q
q q
answering posted questions. This final 50
q
q
q q q
q
q
q
q
q qq
q qq
group is however growing, and since the qqq q
qqqq
qq
q
q
q
q q
q q
list archives are also kept on Nabble, qq
q
0
they are easy to search for information. 2004 2005 2006 2007 2008 2009 2010
Roger Bivand A practitioner’s view

18. Experience
R project
R spatial
Managing research in collaborative networks
The sp package
In 2003, we agreed that a shared system of
new-style classes to contain spatial data would
permit many-to-one and on-to-many conversion
of representations, avoiding the then prevalent
many-to-many conversion problem. The idea
was to make it easier for GIS people and stats
people to work together by creating objects that
“looked” familiar to both groups, although the
groups differ a lot in how they “see” data
objects. Package dependencies have grown, here
the upper diagram shows packages depending
on sp in April 2008, the lower diagram in April
2010:
Roger Bivand A practitioner’s view

19. Experience
R project
R spatial
Managing research in collaborative networks
R Wiki
In addition to the “coordinated”
information sources, a community Wiki
does exist. While it seems to suit some
users, the general impression (among
older people?) is that there is little
feeling of responsibility for following up
tips given there. On the mailing list and
its archive, usually experienced
developers or users will clarify
misunderstandings, while on the Wiki,
posters do not feel obliged to update
their contributions, as when examples
stop working (they are not run ever,
unlike CRAN package examples):
Roger Bivand A practitioner’s view

20. Experience
R project
R spatial
Managing research in collaborative networks
Spatial on R Forge
R Forge is used actively by individuals
and groups in developing packages for
spatial data analysis, with 52 projects
registered in April 2010. Some projects
are registered in more than one topical
area, some may never mature, but some
are already in active use; the raster
package is already frequently discussed
on R-sig-geo — it was released to CRAN
in late March 2010 after a gestation of
16 months.
Roger Bivand A practitioner’s view

21. Experience
R project
R spatial
Managing research in collaborative networks
Book website
Finally, I’ll mention a book that I wrote
with Edzer Pebesma and Virgilio
G´mez-Rubio, and published in the
o
Springer useR series in 2008. Not only
does the book seem to be doing OK, but
the website with dataset and code
download is visited frequently (450–600
unique visitors per month). The code is
run nightly against current R and the
various required contributed packages. It
may be of interest to note that the text
was written using the literate
programming tool Sweave in R, which is
designed to support reproducible
research (as indeed is this talk).
Roger Bivand A practitioner’s view

22. Experience
R project
R spatial
Managing research in collaborative networks
Managing research in higher education
While the links between the knowledge economy and Open
Source software are evident, there are very real challenges to
the management of research and higher education in policy
terms that need to be addressed
Most research and higher education organisations have been
rationalised and subjected to the styles of management
practices introduced in commercial corporations years and
even decades ago
In particular, budget discipline is a favoured tool in attempting
to point organisational units in directions seen as being
appropriate
Given that these organisations clearly face a “missing market” ,
in that neither potential students nor grant-giving bodies are
analogues of customers in a fast-food restaurant, those
responsible for management have a measurement problem
Roger Bivand A practitioner’s view

23. Experience
R project
R spatial
Managing research in collaborative networks
Grant processes
Universities and research institutions appear to “compete” in
grant processes, and thereby seem to have an interest in
locking potential competitors out, by securing privileged
access to knowledge
While such advantage may be quite real in the case of
laboratory skills and quality — the institution does deliver
services of higher quality, or when the institution has secured
the services of high-flying academics — this model is not
directly transferable to software
Given the steadily increasing importance of software in
teaching and research, it seems clear that care is needed in
constructing management tools for activities which may
produce or modify software (see the UEA “climategate”
scandal)
Roger Bivand A practitioner’s view

24. Experience
R project
R spatial
Managing research in collaborative networks
Software deliverables
It does make sense for institutions to develop expertise in
customising software, in training, and in publishing materials
of benefit to software users on a for-profit basis
It does not in general, however, make sense to mandate source
closure in research programs or projects, in the same way that
mandating openness might be mistaken
The question as to whether software deliverables, or software
developed in the process of creating deliverables should be
opened is one that is relevant in all grant processes
It is also highly relevant in evaluation routines associated with
program and project execution
Roger Bivand A practitioner’s view

25. Experience
R project
R spatial
Managing research in collaborative networks
Handling software in research projects
In grant awarding and evaluation processes, the grant-making
body should consider at least two factors: the importance of
Open Source for enhanced efficiency in providing the software
needed in a project, and the importance of reproducibility and
peer-review in the scientific process generally
It can thus be argued that the management of the boundary
between what the institution “owns”, what can sensibly be
commercialised on a for-profit basis, and research productivity
and efficiency deserves attention
Otherwise, naive and rather outdated management practises
can endanger research quality and productivity with regard to
software innovation and incremental improvement by seeing
products where one should see services
Roger Bivand A practitioner’s view

26. Experience
R project
R spatial
Managing research in collaborative networks
Software, research and higher education
There are clearly cases in which source code should not be
opened, although when the public purse has funded the
research involves, the number of real cases will in practice be
very few, even for projects with very small communities of
interest
It is of importance for the enabling, for the empowering of
actors in the knowledge economy, that unnecessary barriers to
the diffusion of knowledge be removed, and that new ones not
be permitted to emerge
As a corollary, researchers should perhaps be given incentives
in career terms to contribute to the pool of knowledge by
opening source code, and by contributing to the improvement
of software in their domain of science, in the same way that
publications are rewarded
Roger Bivand A practitioner’s view

27. Experience
R project
R spatial
Managing research in collaborative networks
Round-up
As far as I am aware, no research council has played any
relevant role in the progress of the R project directly
Indirectly, research council funded projects have included
software deliverables defined as contributed packages,
including spatial packages (but none that I have handled)
Even more indirectly, people in research council funded
doctoral and post-doctoral positions have not only used R and
R spatial, but have contributed to software development, even
though this was not required or mentioned in their projects
Finally, the diffuseness and unpredictability of collaborative
networks of “amator” developers makes it very hard to reply to
calls; if a research council wanted to be pro-active, it might
fund travel for active developers to enable them to meet, or
similar enabling measures
Roger Bivand A practitioner’s view