Annotation has been identified as one of the "scholarly primitives", and plays a pivotal role in facilitating access to audio-visual (AV) media in a scholarly context. However, there is a lack of understanding of scholars' annotation needs and behavior. This paper is part of a group of studies aiming to understand how to improve annotation support of AV media, in order to facilitate research activities of media scholars and other scholars who make intensive use of AV media. The main findings confirm previous research discerning stages in media scholars' research processes, and indicate a great variety of research activities which occur in a non-linear order. Our studies also show that different annotation activities occur along those stages. The main contribution of this paper is a generic process model capturing AV media annotation, potentially applicable to a variety of research use cases in a scholarly context.
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To help scholars to extract meaning, knowledge and value from large volumes of archival content, such as the Dutch Common Lab Research Infrastructure for the Arts and Humanities (CLARIAH), we need to provide more 'generous' access to the data than can be provided with generalised search and visualisation tools alone. Our approach is to use Jupyter Notebooks in combination with the existing archive APIs (Application Programming Interface). This gives access to both the archive metadata and a wide range of analysis and visualisation techniques. We have created notebooks and modules of supporting functions that enable the overview, investigation and analysis of the archive. We demonstrate the value of our approach in preliminary tests of its use in scholarly research, and give our observations of the potential value for archivists. Finally, we show that good archive knowledge is essential to create correct and meaningful visualisations and statistics.
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