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@INPROCEEDINGS{vanhulst_descriptive_2019,
     author = {Vanhulst, Pierre and Ev{\'{e}}quoz, Florian and Tuor, Rapha{\"{e}}l and Lalanne, Denis},
     editor = {Bechmann, Dominique and Chessa, Manuela and Cl{\'{a}}udio, Ana Paula and Imai, Francisco and Kerren, Andreas and Richard, Paul and Telea, Alexandru and Tremeau, Alain},
      title = {A Descriptive Attribute-{{Based Framework for Annotations in Data Visualization},
  booktitle = {Computer Vision, Imaging and Computer Graphics Theory and Applications},
       year = {2019},
      pages = {143-166},
  publisher = {{Springer International Publishing}},
       isbn = {978-3-030-26756-8},
        doi = {10.1007/978-3-030-26756-8_7},
   abstract = {Annotations are observations made during the exploration of a specific data visualization, which can be recorded as text or visual data selection. This article introduces a classification framework that allows a systematic description of annotations. To create the framework, a real dataset of 302 annotations authored by 16 analysts was collected. Then, three coders independently described the annotations by eliciting categories that emerged from the data. This process was repeated for several iterative phases, until a high inter-coder agreement was reached. The final descriptive attribute-based framework comprises the following dimensions: insight on data, multiple observations, data units, level of interpretation, co-references and detected patterns. This framework has the potential to provide a common ground to assess the expressiveness of different types of visualization over the same data. This potential is further illustrated in a concrete use case.}
}