I am team leader for this project at the Department of Applied IT. The project is coordinated by NILU, Norwegian Institute for Air Research. PI: Núria Castell. Funded by NordForsk under the Nordic Programme on Sustainable Urban Development and Smart Cities. 2020-2023. Project number 95326.
I am the principal investigator in this newly funded project Optimizing human computation using a game with a purpose (2019-2022). Funded by Marianne and Marcus Wallenberg Foundation, which allocated 3,740,000 SEK.
Human-machine integration in citizen science can harness the contributions of many human observers and use machine learning (ML) to process their contributed data. This project aims to support the development of human-machine integration in a citizen science classification project called Koster Seafloor Observatory, part of the Ocean Data Factory Sweden, which aims to study how climate change and human activities influence Sweden’s marine ecosystems. We aim to contribute to optimizing the overall experience of marine citizen scientists, not just the efficiency and speed of classifications.
We combine qualitative and quantitative analytical approaches, as we are using different types of data. Examples of data can include forum posts (qualitative) and accompanying meta-data (quantitative); information from documents (e.g., websites, reports, etc.) (qualitative), and interviews (qualitative). Analytical approach we will be using include trace ethnography and document analysis.
Recently completed research project:
From September 2018 until the end of August 2019, I worked as a researcher in Digitranscope, a EU Joint Research Centre’s project examining the governance of digitally transformed human societies. The programme aims to provide a deep understanding of digital transformation to help policy-makers address the challenges facing European society over the next decades.
Science increasingly turns to online volunteers through open calls for help in analysis of very large sets of data. This initiative goes under the banner of “citizen science”, crowdsourcing” or “crowd science” and is an important and innovative way for science to expand the workforce needed to manage large data sets. Contributions from a wider population into scientific knowledge production require arrangements to ensure quality. How are digital technologies used to enable volunteers with limited knowledge about theory and method to contribute to science? How is scientific rigor and data quality achieved? In this project, more seldom investigated aspects of citizen science will also be explored: the expanded role voluntary contributors might play as their relationship to science is mediated through digital technologies and the development of own epistemic practices among engaged amateurs. The project started late summer 2014 with funding from Marianne and Marcus Wallenberg Foundation (https://www.wallenberg.com/mmw/forskning/beviljade-anslag/projektanslag-2013).