A reminder that this is starting in about an hour! We hope you can join us!
Best,
Emily
On Wed, Feb 8, 2023 at 2:27 PM Emily Lescak <elescak(a)wikimedia.org> wrote:
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