Database Reference
In-Depth Information
that the lifecycle of the workflows is coupled with the process of science — that
the human system of workflow use is coupled to the digital system of work-
flows. More workflows imply more users and more enactments, which in turn
provide scientists with more samples to assist in selecting workflows, to iden-
tify best practices, and to learn and build a reputation by sharing workflows
within the community.
From the scientist's perspective there are many factors guiding reuse of a
workflow, including descriptions of its function and purpose; documentation
about the services with which it has been used, with example input and output
data, and design explanations; provenance, including its version history and
origins; reputation and use within the community; ownership and permissions
constraints; quality, whether it is reviewed and still works; and dependencies
on other workflows, components, and data types. Workflows also enable us to
record the provenance of the data resulting from their enactment, and logs of
service invocations from workflow runs can inform later decisions about service
use. By binding workflows with this kind of information, a basis is provided for
workflows to be trusted, interpreted unambiguously, and reused accurately.
The community perspective brings “network effects.” By mining the sharing
behavior between users within a community we can provide recommendations
for use. By using the structure and interactions between users and workflow
tools we can identify what is considered to be of greater value to users. Prove-
nance information helps track down workflows through their use in content
syndication and aggregation. By sharing or publishing a workflow, with the
appropriate attribution, a scientist can allow their work to be reused with the
concomitant spread of their scientific reputation; but even if scientists do not
contribute workflows directly, their usage of workflows within the community
still adds value to the body of knowledge about those workflows.
13.6.3 Realizing myExperiment
The rise of harnessing the collective intelligence of the Web has dramatically
reminded us that it is people who generate and share knowledge and resources,
and people who create network effects in communities. Blogs and wikis, shared
tagging services, instant messaging, social networks, and semantic descriptions
of data relationships are flourishing. Within the scientific community we have
many examples, such as OpenWetWare, Connotea, PLoS on Facebook, and
so forth.
*
myExperiment is a virtual research environment to support scientists using
workflows by adopting a “Web 2.0 approach.” The myExperiment software
provides services and a user interface (a social Web site) to address the require-
ments of the social sharing of workflows. It aims to be a gossip shop to share
and discuss workflows and their related scientific objects, regardless of the
*
See corresponding .org Web sites and facebook.com. Accessed on July 20, 2009.
