Search

The scope of this project covers the storing of result data produced by generic laboratory devices during processing of analytical experiments, the data describing the examination methods, and the audit trail using the Analytical Information Markup Language (AnIML) standard. This project also considers the integration of generic devices in automated laboratory environments. AnIML is an upcoming ASTM standard format for recording analytical data and workflows with accompanying experimental metadata. Adapting this standard to existing instruments currently requires manual intervention. The goal of this project is to automate as many steps as possible in generating an AnIML document with all its essential information supplied directly by the analytical instrument. Software with such functionality could be integrated into analytical instruments or reside in firmware boxes hooked to the instruments. This would allow a smooth transition to the new standard even in complex existing environments. A set of prerequisites have to be fulfilled before the feasibility of this approach can be shown. The prototype application we describe here integrates the generic description of an instrument using the Laboratory Equipment Control Interface Specification, Object Management Group (LECIS OMG) Device Capability Dataset. Information about the device's commands and the device's data stream with its semantics can be found there. The experiment's metadata are provided by the test order. In both cases, XML schemas contain the information syntax. Using this information, we developed a generic interface that maps the result stream semantically and then transforms it into an AnIML document without manual intervention. At this time, we have completed and tested a prototype implementation and are working to support the full functionality of both the LECIS OMG and the ASTM AnIML standards. (JALA 2006;11:247–53)

SpectroML, a markup language for ultraviolet-visible spectroscopy data, has been developed as a “Web-aware” mechanism for instrument-to-instrument, instrument-to-application, and application-to-application data interchange and archiving. This article documents the application of SpectroML to the interchange and archiving of measurement data from three spectrophotometers that are used in the NIST optical filter standards program. It describes how result data from the NIST national reference spectrophotometer and two commercial spectrophotometers are converted into SpectroML format and how SpectroML-formatted data and metadata are imported into the optical filter standards database.

Laboratory environments are controlled more and more by automated systems. Written procedures and lab journals are replaced by workflow description languages and electronic notebooks, which not only describe the processes but are used also for the control of the entire workbench, data acquisition, and documentation. Dynamic scheduling is needed in such an environment. Multiple samples with different procedures are processed in parallel, competing for the instruments. The whole environment may be also underlaid by optimization strategies like throughput or minimum sample processing time. The modeling of all components interacting on the workbench—samples, devices, sensors, results, database systems, and so on—needs to rely on concepts building a consistent framework. This article gives a set of terms and definitions used in a dynamic scheduling environment. It describes most of these entities in detail, including their functionality and attributes as well as their logical and physical interactions. It also describes concepts such as workflows with activities and constraints, functional libraries, hidden transport, and dynamic execution. Maintenance, calibration, and error management also are included. Finally, it discusses how the entities interact with the different components in the scheduling system.