=Paper=
{{Paper
|id=None
|storemode=property
|title=Collaborative platforms for streamlining workflows in Open Science
|pdfUrl=https://ceur-ws.org/Vol-739/paper_8.pdf
|volume=Vol-739
|dblpUrl=https://dblp.org/rec/conf/okcon/ForstnerHKKM11
}}
==Collaborative platforms for streamlining workflows in Open Science ==
https://ceur-ws.org/Vol-739/paper_8.pdf
Collaborative platforms for streamlining
workflows in Open Science
Konrad U. Förstner12 , Gregor Hagedorn3 Claudia Koltzenburg4 , M. Fabiana
Kubke5 , and Daniel Mietchen6
1
Institute for Molecular Infection Biology
University of Würzburg
D-97080 Würzburg, Germany
2
Research Centre for Infectious Diseases
University of Würzburg
D-97080 Würzburg, Germany
3
Julius Kühn-Institute
Federal Research Center for Cultivated Plants
Berlin, Germany
4
Claudia Koltzenburg
Managing editor of Cellular Therapy and Transplantation (CTT)
5
Department of Anatomy with Radiology, University of Auckland
6
Daniel Mietchen
Science 3.0
Abstract. Despite the internet’s dynamic and collaborative nature, sci-
entists continue to produce grant proposals, lab notebooks, data files,
conclusions etc. that stay in static formats or are not published online
and therefore not always easily accessible to the interested public. Be-
cause of limited adoption of tools that seamlessly integrate all aspects of
a research project (conception, data generation, data evaluation, peer-
reviewing and publishing of conclusions), much effort is later spent on
reproducing or reformatting individual entities before they can be repur-
posed independently or as parts of articles.
We propose that workflows - performed both individually and collabora-
tively - could potentially become more efficient if all steps of the research
cycle were coherently represented online and the underlying data were
formatted, annotated and licensed for reuse. Such a system would ac-
celerate the process of taking projects from conception to publication
stages and allow for continuous updating of the data sets and their in-
terpretation as well as their integration into other independent projects.
A major advantage of such workflows is the increased transparency, both
with respect to the scientific process as to the contribution of each par-
ticipant. The latter point is important from a perspective of motiva-
tion, as it enables the allocation of reputation, which creates incentives
for scientists to contribute to projects. Such workflow platforms offering
possibilities to fine-tune the accessibility of their content could gradually
2
pave the path from the current static mode of research presentation into
a more coherent practice of open science.
Keywords: Open Science, Virtual Research Environments, collaborato-
ries, workflow platform, automation, transparency, reproducibitliy, rep-
utation, funding, Open Hardware
1 Introduction
Like most areas of today’s life, science has dramatically changed since the ad-
vent of the internet. However, the transformation that has taken place until now
is just the tip of the iceberg. In the following, we want to discuss the mostly
underutilized potential of representing all aspect of science in collaboratively
used online workflow platforms. Since such platforms could help to realize Open
Science, transparency of the funding cycles and access to all data in the research
process, we will shed light on this special aspect and make recommendations
regarding implementations.
While there are numerous projects developing and applying so called Virtual
Research Environments (VRE) - also known as Collaboratories - covering se-
lected stages of the scientific process, a platform spanning every phase is missing
so far [1]. Technically overcoming such gaps and creating a seamless transition
from bench to publication could speed up the research and, with it, the genera-
tion, distribution and reuse of knowledge.
2 The scientific workflow in open VREs
2.1 Conception and project planning
Independent of the nature of a research endeavor - hypotheses-driven or data-
driven, performed by a single person or a team - a solid conception phase is the
crucial basis for every project. Despite today’s common practice of limiting this
phase to a small group of people, utilizing collective intelligence during the con-
ception phase could help to avoid redundant research and to improve the design
of the study. As the complexity and scope of scientific projects are increasing,
the application of project management tools can be useful for managing the
processes and parties involved.
2.2 Experiments and data generation
Today, data generation in academic research continues to rely strongly on man-
ual labor. While this is mostly due to the relative low cost of labor force resulting
from the academic system and the limited interdisciplinary education of science
and engineering, the high potential of automation is mostly neglected. Not only
3
could the efficiency of invested labor be improved by automation, but also repro-
ducibility could be significantly increased. To make this affordable for the broader
research community, a shift from siloed proprietary devices to well-documented
pieces of standardized, open-source hardware developed by the scientific com-
munity itself in cooperation with potential vendors is needed. Open hardware
platforms like Arduino [2] could offer starting points for such a development and
first example of such tools are available (e.g. OpenPCR [3]). The devices could
and should enrich the primary data with further metadata, convert them into
semantified formats and directly upload the output into online repositories.
One promising example which visualizes the potential of such automation
of otherwise quite labor-intensive research is the robot scientist ADAM [4]. The
streamlining of mechanical steps and the evaluation of results would benefit from
formal languages that describe the necessary procedures and make the design
and exchange of experimental setups easy [5]. As a long term goal, scientists
would mostly engage in programming experiments and engineering the system
to automate those steps that have been performed manually so far. The motto
“work on the system, not in the system” should guide this development.
2.3 Data release
The online release of experimentally generated data should be done shortly after
the generation and can potentially happen in real time. Downstream analy-
sis within the research project but also the reuse by other parties should be
kept in mind when selecting data formats. These should, as far as possible, be
non-proprietary, machine readable (semantically enriched) and common for the
respective domain of research. If no format fulfills all these requirements, the con-
version into alternative formats should be permitted. Access to the data could
take place via a web interface or domain specific clients. Especially for large or
highly accessed data sets, the additional distribution via peer-to-peer networks
is recommended.
2.4 Data analysis
Since every step in the data analysis should be transparent and easily repro-
ducible, it should take place preferably in the proposed platform, too. Systems
like the analysis workflow tool Taverna [6] could be used for such processing.
Already today, many research institution offer grid computing infrastructure for
such purposes. Analyses using external tools, especially GUI-tools that do not
offer any possibility to log the performed actions, should be avoided if possible,
as otherwise documentation has to be created manually. For some computation-
ally intensive analyses the use of shared systems is a more economical usage of
the needed infrastructure, provided the management overhead does not exceed
the computational efficiency gain. As done for the raw experimental data, the
protocols and the result of the data processing should be documented and stored
in repositories to be accessible.
4
2.5 Knowledge generation
The results of analytical processing as well as the raw data can be used by
scientists - or machines [7] - to draw conclusions and to generate knowledge
out of the available information in a well documented way. The platform should
assist to make this happen collaboratively by offering commenting and rating of
statements. Discussions - text, audio- and/or video-based - should be recorded
to make the path to finding reconstructible.
2.6 Final publication
As documentation of every step is an inherent feature of the workflow, the final
publications resulting from a study can be short reports linking to the major
outcomes and putting them into the scientific context. The platform should offer
functionalities to perform open peer-review of this final report.
3 Implementation
3.1 Technology
As shown above, the many building blocks of a complete scientific workflow
already exist and only need to be connected seamlessly. The development of open
standards defining the required interfaces of these parts could enable different
parties to assemble the pieces into a consistent workflow and to add further
needed parts. This would offer the possibility to implement a platform either as
one monolithic application or as separate interacting and exchangeable units.
3.2 Funding
Of similar importance as the technical realization is the adaptation of scientific
culture and funding policies. While research institutions like the National Insti-
tutes of Health (US) or the Welcome Trust (UK) already require open access for
final peer-review manuscripts that results from research they funded [8, 9], the
regulations are much weaker for the underlying data, and almost nonexistent for
proper annotation. However, the first attempts to establish such requirements
are on the horizon [10].
3.3 Licensing
The default copyright restrictions in most jurisdictions hamper the reuse of data.
It is therefore highly desirable that, with very few exceptions, each entity gener-
ated in the research process is explicitly published under a less restrictive license,
e.g., the ones offered by Creative Commons [11] or is released into the public
domain. As the latter concept may differ or be missing in some countries, release
through the CC0 license [12] is recommended.
5
3.4 Reputation
The gain of reputation is the most important incentive for scientists. It is cur-
rently mostly determined on the basis of publications in scientific journals and
the related measure of success in funding applications. As every contribution to
a research project can be attributed to a distinct person and could be rated by
others, the allocation of reputation is an inherent element of the proposed plat-
form. The connection to research identifiers like ORCID [13] and the analysis of
such microcontributions could assemble a precise image of a scientist’s skills and
achievements.
4 Challenges
As stated above, considering the allocation of reputation and funding in science
is crucial when redesigning scientific processes. To bridge a transient phase until
the suggested political changes have taken place, fine granular access control in
the research workflow platform could permit that the technology is adapted by
scientists despite objection regarding the loss of reputation. With such a control
in place, the full process could be opened up after the final publication or at any
other desired time.
It is very unlikely that there will be one single platform that can fulfill the
requirements of all scientific domains. Building and maintaining completely inde-
pendent platforms for each domains, on the other hand, may not be sustainable.
A modular and flexible system, where possible re-using industry standard soft-
ware is therefore called for.
Projects presently exploring this are, e.g.:
– the eSciDoc platform which builds on the open-source repository software
Fedora Commons [14] and is mainly developed for the for the Max Planck
Society has a similar aim and strategy [15].
– the FP7 funded Virtual Biodiversity Research and Access Network for Tax-
onomy” (ViBRANT) [16–18]
They span data collection, analysis and publishing (in collaboration with
Pensoft Publishers), are based on the established open-source platforms Drupal
[19] and Mediawiki [20] and equipped with specific extensions.
References
1. Annamaria Carusi, Torsten Reimer. Virtual Research Environment - Collaborative
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2. Arduino, http://www.arduino.cc/
3. OpenPRC, http://openpcr.org/
6
4. Ross D. King, Jem Rowland, Stephen G. Oliver, Michael Young, Wayne Aubrey,
Emma Byrne, Maria Liakata, Magdalena Markham, Pinar Pir, Larisa N. Soldatova,
Andrew Sparkes, Kenneth E. Whelan, Amanda Clare. The automation of science.
Science. 3 April 2009: Vol. 324 no. 5923 pp. 85-89
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us/Policy/Policy-and-position-statements/WTD002766.htm
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