Scholarly communication and the web  (Page 3/4)

• Replayable – go back and see what happened. Experiments are automated and may occur in milliseconds or in months. Either way, the ability to replay the experiment, and to study parts of it, is essential for human understanding of what happened.
• Repeatable – run the experiment again. There's enough in a Research Object for the original researcher or others to be able to repeat the experiment, perhaps years later, in order to verify the results or validate the experimental environment. This also helps scale to the repetition of processing needed for the scale of data intensive science.
• Reproducible – run a new experiment to reproduce the results. To reproduce (or replicate) a result is for a third party to start with the same materials and methods and see if a prior result can be confirmed.
• Reusable – use as part of new experiments or Research Objects. One experiment may call upon another, and by assembling methods in this way we can conduct research, and ask research questions, at a higher level.
• Repurposeable – reuse the pieces in a new experiment. An experiment which is a black box is only reuseable as a black box. By opening the lid we find parts, and combinations of parts, available for reuse, and the way they are assembled is a clue to how they can be re-used.
• Reliable – robust under automation, which brings systematic and unbiased processing, and also “unattended experiments” without a human in the loop. In data-intensive science, Research Objects promote reliable experiments, but also they must be reliable for automated running.

To achieve these behaviours it is crucial to store provenance records and full contextual metadata in the Research Object, so that results can be properly interpreted and replicated. This complete digital chain from laboratory bench to scholarly output is exemplified by the work on repositories and blogs in laboratories (Coles and Carr 2008), and also in the use of electronic laboratory notebooks.

We believe that in the fullness of time, objects such as these will replace academic papers as the entities that researchers share, because they plug straight into the tooling of e-Research. This means it is Research Objects rather than papers that will be collected in our repositories, and as well as a workflow repository, myExperiment has become a prototypical Research Object repository.

Linked data

To achieve these properties, a Research Object must be self-contained and self-describing – containing enough metadata to have all the above characteristics and have maximal potential for re-use, whether anticipated or unanticipated. To support this, myExperiment provides a SPARQL endpoint (rdf.myexperiment.org) that makes myExperiment content available according to the myExperiment data model – a modularised ontology drawing on a set of emerging ontologies and standards in open repositories, scientific discourse, provenance and social networking.

myExperiment also aims to be a source of Linked Data so that myExperiment content can be readily integrated with other scientific data. The Linked Data initiative (linkeddata.org) enables people to share structured data on the Web as easily as they can share documents – as with documents, the value and usefulness of data increases the more it is interlinked with other data. To be part of the Linked Data web, data has to be accessible as RDF over the HTTP protocol in line with guidelines. At the time of writing there are 8 billion triples in Linked Data datasets.