CROWD-BASED DATA-DRIVEN HYPOTHESIS GENERATION FROM DATA AND THE ORGANISATION OF PARTICIPATIVE SCIENTIFIC PROCESS
| Autor: | Akin Kazakçi, Yohann Sitruk |
|---|---|
| Přispěvatelé: | Centre de Gestion Scientifique i3 (CGS i3), Centre National de la Recherche Scientifique (CNRS)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Process (engineering)
business.industry Computer science media_common.quotation_subject Design science Hypothesis 16. Peace & justice Creativity Crowdsourcing Data science Data-driven [SHS]Humanities and Social Sciences [SCCO]Cognitive science 13. Climate action Design process [SHS.GESTION]Humanities and Social Sciences/Business administration business media_common |
| Zdroj: | Design 2018 Conference Design 2018 Conference, May 2018, Dubrovnik, Croatia |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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