Hello everyone,
The next Research Showcase will be livestreamed next Wednesday, February 15
at 9:30AM PT / 17:30 UTC. The theme is The Free Knowledge Ecosystem.
YouTube stream:
https://www.youtube.com/watch?v=8VJmR-3lTac
We welcome you to join the conversation on IRC at #wikimedia-research. You
can also watch our past research showcases:
https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase
This month's presentations:
The evolution of humanitarian mapping in OpenStreetMap (OSM) and how it
affects map completeness and inequalities in OSMBy *Benjamin Herfort,
Heidelberg Institute for Geoinformation Technology*Mapping efforts of
communities in OpenStreetMap (OSM) over the previous decade have created a
unique global geographic database, which is accessible to all with no
licensing costs. The collaborative maps of OSM have been used to support
humanitarian efforts around the world as well as to fill important data
gaps for implementing major development frameworks such as the Sustainable
Development Goals (SDGs). Besides the well-examined Global North - Global
South bias in OSM, the OSM data as of 2023 shows a much more spatially
diverse spread pattern than previously considered, which was shaped by
regional, socio-economic and demographic factors across several scales.
Humanitarian mapping efforts of the previous decade have already made OSM
more inclusive, contributing to diversify and expand the spatial footprint
of the areas mapped. However, methods to quantify and account for the
remaining biases in OSM’s coverage are needed so that researchers and
practitioners will be able to draw the right conclusions, e .g. about
progress towards the SDGs in cities.
Dataset reuseː Toward translating principles to practiceBy *Laura Koesten,
University of Vienna*The web provides access to millions of datasets. These
data can have additional impact when used beyond the context for which they
were originally created. But using a dataset beyond the context in which it
originated remains challenging. Simply making data available does not mean
it will be or can be easily used by others. At the same time, we have
little empirical insight into what makes a dataset reusable and which of
the existing guidelines and frameworks have an impact.In this talk, I will
discuss our research on what makes data reusable in practice. This is
informed by a synthesis of literature on the topic, our studies on how
people evaluate and make sense of data, and a case study on datasets on
GitHub. In the case study, we describe a corpus of more than 1.4 million
data files from over 65,000 repositories. Building on reuse features from
the literature, we use GitHub’s engagement metrics as proxies for dataset
reuse and devise an initial model, using deep neural networks, to predict a
dataset’s reusability. This demonstrates the practical gap between
principles and actionable insights that might allow data publishers and
tool designers to implement functionalities that facilitate reuse.
We hope you can join us!
Warm regards,
Emily
--
Emily Lescak (she / her)
Senior Research Community Officer
The Wikimedia Foundation