Skip to end of metadata
Go to start of metadata

Research Objects are semantically rich aggregations of resources [1] that bring together data, methods and people in scientific investigations. As described in [2] their goal is to create a class of artifacts that can encapsulate our digital knowledge and provide a mechanism for sharing and discovering assets of reusable research and scientific knowledge. In the context of Wf4Ever we focus on those Research Objets whose methods are implemented as scientific workflows. Hence a Workflow-Centric Research Object can be viewed as an aggregation of resources that bundles a workflow specification and additional auxiliary resources, including documents, input and output data, annotations, provenance traces of past executions of the workflow, etc. 

  • [1] Bechhofer, S., De Roure, D., Gamble, M., Goble, C. and Buchan, I. (2010) Research Objects: Towards Exchange and Reuse of Digital Knowledge. In: The Future of the Web for Collaborative Science (FWCS 2010), April 2010, Raleigh, NC, USA.
  • [2] Bechhofer, S., Ainsworth, J., Bhagat, J., Buchan, I., Couch, P., Cruickshank, D., Delderfield, M., Dunlop, I., Gamble, M., Goble, C., Michaelides, D., Missier, P., Owen, S., Newman, D., De Roure, D. and Sufi, S. (2010) Why Linked Data is Not Enough for Scientists. In: Sixth IEEE e--Science conference (e-Science 2010), December 2010, Brisbane, Australia.

Another longer version (to go into the Website)

Scientific workflows are used to describe series of structured activities and computations that arise in scientific problem-solving, providing scientists from virtually any discipline with a means to specify and enact their experiments. From a computational perspective, such experiments (workflows) can be defined as directed acyclic graphs where the nodes correspond to analysis operations, which can be supplied locally or by third party web services, and where the edges specify the flow of data between those operations.
Besides being useful to describe and execute computations, workflows also allow encoding of scientific methods and know-how. Hence they are valuable objects from a scholarly point of view, for several reasons: (i) to allow assessment of the reproducability of results; (ii) to be reused by the same or by a different scientist; (iii) to be repurposed for other goals than those for which it was originally built; (iv) to validate the method that led to a new scientific insight; (v) to serve as live-tutorials, exposing how to take advantage of existing data infrastructure, etc. This follows a trend that can be observed in disciplines such as Biology and Astronomy, with other types of objects, such as databases, increasingly becoming part of the research outcomes of an individual or a group, and hence also being shared, cited, reused, versioned, etc. 
However, the use of workflow specifications on their own does not guarantee to support reusability, shareability, reproducibility, or better understanding of scientific methods. Workflow environment tools evolve across the years, or they may even disappear. The services and tools used by the workflow may change or evolve too. Finally, the data used by the workflow may be updated or no longer available. To overcome these issues, additional information may be needed. This includes annotations to describe the operations performed by the workflow; annotations to provide details like authors, versions, citations, etc.; links to other resources, such as the provenance of the results obtained by ex- ecuting the workflow, datasets used as input, etc.. Such additional annotations enable a comprehensive view of the experiment, and encourage inspection of the different elements of that experiment, providing the scientist with a picture of the strengths and weaknesses of the digital experiment in relation to decay, adaptability, stability, etc.
These richly annotation objects are what we call workflow-centric Research Objects. The notion of Research Object (as discussed in [1]) is a general idea that aims to extend traditional publication mechanisms and take us "beyond the pdf" [2]. An RO is an aggregation of resources along with annotations on those resources. The aggregation itself may also be annotated, where by annotation, we mean the association of arbitrary additional information with a resource. The Research Object thus collects together relevant resources along with annotations that enable the understanding, reuse etc. of its constituent parts. In a workflow-centric Reearch Object describing an investigation for example, annotations could describe how data sources have been used or how intermediate results were derived.
Research Objects within Wf4Ever are being realised or grounded through the use of Semantic Web technologies. Standard vocabularies for aggregation and annotation provide container structures, while domain vocabularies (both new and existing) are used to describe particular resources. The project is also developing a collection of Research Object services that support the construction, manipulation and storage of ROs along with an architecture that describes the interaction of such services.
[1] S.Bechhofer et.al. Why linked data is not enough for scientists. Future Generation Computer Systems, 2011.
[2] Improving Future Research Communication and e-Scholarship. FORCE11 Manifesto. http://force11.org/white_paper

  • No labels