The open source Electronic Laboratory Notebook (ELN) is a collaborative, distributed, web-based notebook system, designed to provide researchers with a means to record and share their primary research notes and data. As with most electronic notebook (EN) systems, the ELN was originally designed as a closed system with its own data repository and implicit semantics. The Scientific Annotation Middleware (SAM) project, a Department of Energy (DOE)-funded effort at Pacific Northwest and Oak Ridge National Laboratories, envisions a new model in which ENs are simply one application contributing to a much richer and semantically explicit record. Such a record would include, for example, data provenance, descriptive metadata, and annotations from a wide range of applications, problem solving environments, and agents. This paper reports the adaptation of the ELN client to use SAM and the development ofan initial set of SAM-based notebook services and semantic model, and then discusses the advantages of such an architecture in creating federated, human- and machine-interpretable, electronic research records.
Electronic Laboratory Notebooks, Scientific Annotation Middleware, Collaboration, Semantic Data Management 1.
Traditionally, scientists have used paper-based laboratory notebooks to record their ideas, observations, and data. Regulatory and scientific standards require notebooks for purposes of intellectual property protection, but they also have value as a knowledge base of previously performed work. In recent years, many organizations in research-intensive fields have begun to look closely at Electronic Notebooks (ENs) as a new way to record, store, and manipulate experimental data. ENs have some compelling advantages over paper-based notebooks. They allow scientists to work in distributed teams, sharing data with peers in real time, and receiving comments on their research. ENs eliminate the need for manual transcription of data that already exists in electronic form and they can directly display the large, multidimensional, and time-dependent data sets produced in modem experiments. Further, they can have automated searching, indexing, and metadata generation capabilities to aid in enterprise knowledge discovery. ENs also have advantages in terms of legal defensibility because of the strength of digital signatures and as records because of the low cost of digital media storage. Thus, ENs become much more than just a record-book-they provide a collaborative, intelligent working environment.[ 1]
Although an EN has many advantages over paper notebooks, the model of an EN as a stand-alone system has become limiting. While stand-alone ENs can associate manually entered notes with output and analysis from scientific instruments on a single page view, they are limited in their ability to interact with other producers, curators, and consumers of data and annotations. Feedback from users of the Electronic Laboratory Notebook (ELN), as well as developers of other types of scientific software, indicates growing interest in ENs capable of acting as a component in more comprehensive semantic systems incorporating components such as instrument control software, problem solving environments (PSEs), autonomous feature-detection agents, digital libraries, and data pedigree mechanisms. While a number of notebooks, ranging from the Spectro-Microscopy EN developed in the mid-1990’s  to those based on the CENSML language  and to commercial notebooks such as the Rescentris CERF/Notebook , incorporate a notebook schema and can be extended with science-domainspecific schema, the concept of ENs interacting with other components as equals annotating shared resources has not been fully explored.
The Scientific Annotation Middleware (SAM) is middleware designed to manage semantic information directly and provide a shared ‘schema-less’ metadata store usable by multiple annotation and records-related applications to create rich scientific documentation. We report here on work to modify the ELN Client to use SAM as a notebook server and discuss the initial benefits of this model of using an EN with a general semantic store. In updating the ELN, we had to consider how to represent the ELN data model with explicit semantics and define a mapping between the ELN client-service functionality and SAM’s existing API, aiming to simplify and standardize communication between client and server. The result is a new generation of the ELN capable of storing data in a variety of underlying repositories, exposing its metadata in the resource description framework (RDF) syntax, and integrating much more deeply with other systems producing and consuming data and metadata.