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@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 reuse? 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
>
Best,
Emily
On Wed, Feb 8, 2023 at 2:27 PM Emily Lescak <elescak@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 reuse? 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
>