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This document discusses the conventional and enhanced research data lifecycles, highlighting the importance of managing research datasets effectively. It outlines the critical phases of hypothesis formulation, experimentation, interpretation, and publication. The enhanced model emphasizes organizing and conditioning raw data in local databases before sharing and archiving, enabling routine submissions to institutional repositories. This systematic approach helps prevent loss of valuable research data, facilitating better dissemination and reuse through open access platforms.
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Scholarly publications: conference papers and journal articles Institutional repositories Publication activities Hypothesis formulation and project design Research results and conclusions Research plan Data selection and interpretation Experimentation and data creation Research datasets abandoned on local hard drives or CD-ROMs Raw data in research note-books and live PC files Figure 1: The conventional research data lifecycle Four phases mark the activities undertaken in traversing the lifecycle: formulation, experimentation, interpretation and publication. The publication outputs from one cycle provide the input to the next. However, only selected research data are conventionally published. The original research datasets are abandoned on local hard drives or CD-ROMs, and neither datasets nor papers are submitted to the institutional repository.
Dissemination Open data on Web Scholarly publications: conference papers and journal articles Institutional repositories Papers and datasets Publication activities Hypothesis formulation and project design Preservation Research results and conclusions Research plan Data selection and interpretation Experimentation and data creation Local filestore Private and sharable Raw data in research note-books and live PC files Management Figure 2: The enhanced research data lifecycle Raw research data are first organized and ‘conditioned’ in local personal research databases. From there they can be shared and used to support publications, and can be automatically archived to the institutional repository, from which they can optionally be published as linked data on the Web for public dissemination and reuse. This figure differs from the DCC Data Cycle model by emphasising the importance of the local research data filestore.
Figure 3: The effort involved in submitting data to an institutional repository As investment is made in the local organization and annotation of research datasets, the effort involved in submission to an institutional repository reduces to the point where it becomes feasible on a routine basis.
