Background: The systematic capture of appropriately annotated experimental data is a prerequisite for most bioinformatics analyses. Data capture is required not only for submission of data to public repositories, but also to underpin integrated analysis, archiving, and sharing - both within laboratories and in collaborative projects. The widespread requirement to capture data means that data capture and annotation are taking place at many sites, but the small scale of the literature on tools, techniques and experiences suggests that there is work to be done to identify good practice and reduce duplication of effort. Results: This paper reports on experience gained in the deployment of the Pedro data capture tool in a range of representative bioinformatics applications. The paper makes explicit the requirements that have recurred when capturing data in different contexts, indicates how these requirements are addressed in Pedro, and describes case studies that illustrate where the requirements have arisen in practice. Conclusion: Data capture is a fundamental activity for bioinformatics; all biological data resources build on some form of data capture activity, and many require a blend of import, analysis and annotation. Recurring requirements in data capture suggest that model-driven architectures can be used to construct data capture infrastructures that can be rapidly configured to meet the needs of individual use cases. We have described how one such model-driven infrastructure, namely Pedro, has been deployed in representative case studies, and discussed the extent to which the model-driven approach has been effective in practice. © 2008 Jameson et al; licensee BioMed Central Ltd.