Despite the internet's dynamic and collaborative nature, scientists continue to produce grant proposals, lab notebooks, data les, conclusions etc. that stay in static formats or are not published online and therefore not always easily accessible to the interested public. Because of limited adoption of tools that seamlessly integrate all aspects of a research project (conception, data generation, data evaluation, peerreviewing and publishing of conclusions), much e ort is later spent on reproducing or reformatting individual entities before they can be repurposed independently or as parts of articles. We propose that work ows - performed both individually and collaboratively - could potentially become more e cient 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 accelerate the process of taking projects from conception to publication stages and allow for continuous updating of the data sets and their interpretation as well as their integration into other independent projects.
A major advantage of such work ows is the increased transparency, both with respect to the scienti c process as to the contribution of each participant. The latter point is important from a perspective of motivation, as it enables the allocation of reputation, which creates incentives for scientists to contribute to projects. Such work ow platforms o ering possibilities to ne-tune the accessibility of their content could gradually pave the path from the current static mode of research presentation into a more coherent practice of open science. 1
Like most areas of today's life, science has dramatically changed since the advent 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 work ow 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
selected stages of the scienti c process, a platform spanning every phase is missing
so far [
Independent of the nature of a research endeavor - hypotheses-driven or datadriven, 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 conception phase could help to avoid redundant research and to improve the design of the study. As the complexity and scope of scienti c projects are increasing, the application of project management tools can be useful for managing the processes and parties involved. 2.2
Today, data generation in academic research continues to rely strongly on
manual 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
could the e ciency of invested labor be improved by automation, but also
reproducibility could be signi cantly increased. To make this a ordable for the broader
research community, a shift from siloed proprietary devices to well-documented
pieces of standardized, open-source hardware developed by the scienti c
community itself in cooperation with potential vendors is needed. Open hardware
platforms like Arduino [
One promising example which visualizes the potential of such automation
of otherwise quite labor-intensive research is the robot scientist ADAM [
The online release of experimentally generated data should be done shortly after the generation and can potentially happen in real time. Downstream analysis 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 ful lls all these requirements, the conversion into alternative formats should be permitted. Access to the data could take place via a web interface or domain speci c clients. Especially for large or highly accessed data sets, the additional distribution via peer-to-peer networks is recommended. 2.4
Since every step in the data analysis should be transparent and easily
reproducible, it should take place preferably in the proposed platform, too. Systems
like the analysis work ow tool Taverna [
The results of analytical processing as well as the raw data can be used by
scientists - or machines [
As documentation of every step is an inherent feature of the work ow, the nal publications resulting from a study can be short reports linking to the major outcomes and putting them into the scienti c context. The platform should o er functionalities to perform open peer-review of this nal report. 3 3.1
As shown above, the many building blocks of a complete scienti c work ow already exist and only need to be connected seamlessly. The development of open standards de ning the required interfaces of these parts could enable di erent parties to assemble the pieces into a consistent work ow and to add further needed parts. This would o er the possibility to implement a platform either as one monolithic application or as separate interacting and exchangeable units. 3.2
Of similar importance as the technical realization is the adaptation of scienti c
culture and funding policies. While research institutions like the National
Institutes of Health (US) or the Welcome Trust (UK) already require open access for
nal peer-review manuscripts that results from research they funded [
The default copyright restrictions in most jurisdictions hamper the reuse of data.
It is therefore highly desirable that, with very few exceptions, each entity
generated in the research process is explicitly published under a less restrictive license,
e.g., the ones o ered by Creative Commons [
The gain of reputation is the most important incentive for scientists. It is
currently mostly determined on the basis of publications in scienti c 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
platform. The connection to research identi ers like ORCID [
As stated above, considering the allocation of reputation and funding in science is crucial when redesigning scienti c processes. To bridge a transient phase until the suggested political changes have taken place, ne granular access control in the research work ow 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 nal publication or at any other desired time.
It is very unlikely that there will be one single platform that can ful ll the requirements of all scienti c domains. Building and maintaining completely independent platforms for each domains, on the other hand, may not be sustainable. A modular and exible system, where possible re-using industry standard software is therefore called for.
Projects presently exploring this are, e.g.:
{ the eSciDoc platform which builds on the open-source repository software
Fedora Commons [
They span data collection, analysis and publishing (in collaboration with
Pensoft Publishers), are based on the established open-source platforms Drupal
[