CROWD-BASED DATA-DRIVEN HYPOTHESIS GENERATION FROM DATA AND THE ORGANISATION OF PARTICIPATIVE SCIENTIFIC PROCESS

Yohann Sitruk and Akin Kazakçi ()
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Yohann Sitruk: CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique
Akin Kazakçi: CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique

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Abstract: In scientific process, hypothesis generation is one the most important steps where creativity is needed most. As the science becomes more open and data-driven, it becomes interesting to analyse whether a crowdsourcing approach might be beneficial in this step. First, we characterize the process as a design process. Then, based on a real-life case study, we analyse and highlight difficulties and challenges for crowd-based hypothesis generation. Last, we give a generic process model for organizing in similar challenges in other data-based scientific hypothesis generation contexts.

Date: 2018-05-20
Note: View the original document on HAL open archive server: https://hal.science/hal-01787696v1
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Published in Design 2018 Conference, May 2018, Dubrovnik, Croatia

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01787696

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