Transferability of new affordances in collaborative analyses of complex data?

Kristine Lund (1, 2) , Elizaveta Chernyshova (2) , Laurène Smykowski , Lydia Heiden (2) , Thomas Franco Pinto , Heike Baldauf-Quilliatre , Isabel Colón de Carvajal (2) , Carole Etienne (2)

1 LabEx ASLAN - Laboratoire d'excellence ASLAN
2 ICAR - Interactions, Corpus, Apprentissages, Représentations

ASLAN – Advanced Studies on Language Complexity, gathers over 200 researchers from one French computer science and two French language sciences laboratories. ASLAN views language as a complex dynamic system and studies the acquisition, use, diversity, and history of languages, including their biological, cognitive, and social aspects. Interdisciplinary projects are favored, for example collaborations on 1) embodiment, social behavior and collaborative games, 2) language change, geography, and machine learning, and 3) a cross-linguistic typology of the expression of space and a machine-processable ontology of spatial relations. All such projects share the necessity to visualize and analyze complex data. One example includes audio-visual recordings, synchronized transcriptions of talk and gesture and digital traces of human interaction. Analyses are done on indicators within temporal data and it’s desirable to synchronize separate visualizations of these indicators with their locations in recordings, transcriptions, and traces. Researchers study many audio-visual recordings with the goal of categorizing and describing salient phenomena. The problem becomes one of facilitating collaborative visualization, analysis and management of these complex data sets. The DatAgora project has explored the use of a wall of screens in conjunction with SAGE2 collaborative software that enabled researchers in interactional linguistics to capitalize on such a combination’s affordances. In this splash talk, we show how two of the authors created vocabulary and modified their typical analytical approach, based on the different modes of visualization and manipulation offered. First, content was initially organized and different types of content were associated. Pursuant to categorizing extracts of audio-visual content in terms of their illustrating specific phenomena, pairs of associated content were miniaturized thus leaving room on the wall of screens for new content to be added, analyzed, and categorized. Simultaneous uploading of content from different laptops to the wall of screens and manipulations in parallel allowed for spontaneous assignment of roles, depending on analytical needs. In sum, it was easier to visualize and analyze large quantities of complex data — compared to the use of one laptop and a video projector — even if it required much preparation. We plan to explore complex data from other projects involving language and computer sciences. Even if each project has its own data set, a large shared space with the affordances we present are hypothesized to enhance analysis capacity in other contexts. In terms of the field of Science of Team Science, we aim to generate discussion about the extent to which these techniques of shared visualization and analysis of complex synchronized data can be transferred to other interdisciplinary contexts where stakeholders from outside of academic could also participate in collaborative analysis and decision making.

Mots clés: •Data Visualization; •Inter- & Trans-disciplinary Teams and Approaches; •Sociotechnical Systems

Domaines: Sciences cognitives; Sciences de l'Homme et Société

Kristine Lund, Elizaveta Chernyshova, Laurène Smykowski, Lydia Heiden, Thomas Franco Pinto, et al.. Transferability of new affordances in collaborative analyses of complex data?. Science of Team Science 2021, International Network for the Science of Team Science and Virginia Tech, Jun 2021, Blacksburg, Virginia (on-line), United States. ⟨hal-04035758⟩