Introduction

Automated experimentation brings the promise of a much improved record of the research process. Where experiments are sufficiently well defined that they can be carried out by automated instrumentation or computational resources it is to be expected that an excellent record of process can and will be created. In "Big Science" projects from particle physics [##REF##] to genome sequencing (Batley and Edwards, 2009) the sharing of records about samples and objects, experimental conditions and outputs, and the processing of data is a central part of planning and infrastructure, and often a central part of justifying the investment of resources. As some segments biological science have become industrialized with greater emphasis on high throughput analysis and the generation of large quantities of data sophisticated systems have been developed to track the experimental process and to describe and codify the results of experiments through controlled vocabularies, minimal description standards (Taylor et al, 2008), and ontologies (Smith et al, 2007).

None of this has had a major impact on the recording process applied to the vast majority of research experiments, which are still carried out by single people or small teams in relative isolation from other research groups. The vast majority of academic research is still recorded in paper notebooks and even in industry the adoption of electronic recording systems is relatively recent and remains patchy. A paper notebook remains an excellent means of planning and recording experiments. In most modern laboratories, however, it is starting to fail as an effective means of recording, collating, and sharing data.

The majority of scientific data generated today is born digital. In some cases printouts make it into bound notebooks. In most cases the data remains distributed on a collection of laboratory and personal hard disks. The record of data analysis, the conversion of that digital data into new digital objects and finally into scientific conclusions is, in most cases, very poorly recorded. It is noteworthy in this context that a number of groups have felt it necessary to take an active advocacy position in trying to encourage the wider community that the reproducibility of data analysis is a requirement, and not an added bonus (http://reproducibleresearch.org, Pedersen, 2008). The promise of digital recording of the research process is that it can create a reliable record that would support automated reproduction and critical analysis of research results. The challenge is that the tools for generating these digital records must outperform a paper notebook while simultaneously providing enough advanced and novel functionality to convince users of the value of switching.

At the same time the current low level of adoption means that that field is wide open for a radical re-imagining of how the record of research can be created and used. It lets us think deeply about what value the different elements of that record have for use and re-use and to take inspiration from the wide variety of web-based data and object management tools that have been developed for the mass consumer market. This paper will describe a new way of thinking about the research record that is rooted in the way that the World Wide Web works and consider the design patterns that will most effectively utilize existing and future infrastructure to provide a useful and effective record.

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