Organisation

The organisation module is used to describe the processes and workflows utilised for capturing structured data. This defines the procedures used to reliably capture high quality information. It is distinct from the execution module providing separation between the plan and reality. This allows scientists to use the system across the full range of scientific investigations from routine activities such as ADME to more exploratory research such as in vivo behavioural studies.

The organisation module builds on the data concepts and parameter types defined in the foundation module, to form a set of parameters, defining the conceptual space of data capture. The data entry forms and relationships between parameters are defined using 'templates'. The combination of the parameters and the template define the type and structure of data captured and is referred to as an 'Assay'. The template concept is realised in two forms

Processes -every process has a default layout defining the structure or arrangement of parameters on the data entry sheet of corresponding tasks

Recipes - processes can be sequenced into a default workflow for an experiment

An example HTS Dose response process. Complex protocols can be distilled into simple re-usable process definitions used across many different assays.

The recipe defines the workflow for experiments allowing a whole experiment to be defined from a single method definition in one step.

Flexibility

The separation of the assay definition from its execution in an experiment means that the experiment workflow can deviate from the intended 'blue-print'. In addition tasks can be modified from the process template to include more rows of data or new parameters. This unique flexibility allows the system to be applied to a wide range of research activities.

This flexibility extends to the deployment of BioRails within an organisation

"Breadth first - simple process definitions can be defined using only a few high level parameters; the experiment folders are used to store files and documents containing information relevant to the activity
"Depth first - parameters can be defined to capture all the information generated during a study, from the conditions used to the results generated, in a series of processes combined into an assay definition; raw data can be archived in the experiment and task folders together with analysis and reports; this can be supported by analysis templates used to automate activities and increase efficiency

A breadth first approach facilitates rapid rollout with the advantage of bringing all groups into an electronic environment, providing centralised access to data and reports. The use of simple process definitions reduces the number of tasks required to capture data and simplifies training.

In contrast the depth first approach requires more planning and time to design the experiment workflow and all processes used to capture and analyse data. The advantage of this approach is that it facilitates capture of more structured data, and enables a higher degree of automation to be applied to the experiment, reducing the number of manual tasks and increasing efficiency. Data captured using process definitions is immediately available for analysis and open to analytical queries. This facilitates data mining and makes it easier and more direct to ask and answer questions from the data, such as, what was the historical performance of my control? Data quality is improved since the system enforces the validity and dimensionality of data entered.

Freedom

Fortunately BioRails does not mandate either approach but allows scientists the freedom to choose the best approach for their needs. This is always a pragmatic decision based on effort and reward. In summary the system can be used as a generic ELN using simple generic process definitions and the content module to capture unstructured data related to the experiment. Alternatively it can be used as a structured data capture system by specialising the assay definition to include more detail and making use of processing and workflow templates to automate tasks. This flexibility is unique to BioRails.

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