Visual Analytics for Sustainability and Climate Change/Lane Rasberry

Interview with Lane Rasberry by Iolanda Pensa and Chiara Somajni, 4 September 2025, CC BY 4.0.

Documentation on OSF - Open Science Framework: https://osf.io/2buvq/
ORCID of Lane Rasberry (bluerasberry): https://orcid.org/0000-0002-9485-6146

Lane Rasberry is a scientist and an activist for the open movement. A chemist by education, he works full-time as a wikimedian – since 2018 as the Wikimedian-in-Residence at the School of Data Science, University of Virginia (US). His areas of interest include open movement, public health communication, source metadata, clinical trials, climate change and LGBT+. He organises research and content development collaborations between Wikipedia and universities, Wikipedia community projects, and wiki-training sessions.

Notes from the interview

Profile and engagement with Wikimedia projects

Based in the US
Started editing Wikipedia in 2004
Professional Wikipedia work since 2012
Wikimedian-in-Residence at the School of Data Science, University of Virginia (US)
Works in academic contexts and with academic material
Produced and edited lots of Wikipedia articles
Focus on health and medicine, science information; climate change-related work is ca. 20%
Tends to work on projects initiated by others that trigger his interest.

Work with students and training sessions:

Currently spending more time training and overseeing students
Often pays students to develop particular content areas in Wikipedia
Organised around 500 training sessions
Usually works with undergraduates, who are new to Wikipedia editing. He teaches them to edit Wikipedia, how to use the university library, to work with citations, and to find sources, considering what makes them reliable. Goal: getting these articles to a level adequate for an undergraduate college student who knows nothing about the topic.

Wikidata:

For the last 10 years, everything he does on Wikipedia is also related to WikiData.

WikiCite and Scholia:

Major focus on the WikiCite project, developing the collection of open citation metadata.
"WikiCite has lots of applications. It is useful for scholarly profiles. What are researchers there studying? You should be able to query this with a database and get quick answers by examining all the papers that are published by people at a university. I also sort this out by topic: You should be able to search a database for all the papers ever published on a given topic. And if you have that list of papers, sort them out in other ways, like which papers are cited most or by the most prominent people and make sure that those are cited in Wikipedia articles. This is a process for identifying content gaps".

Examples of current projects:

WikiData: Wiki Project Gov Directory (https://www.govdirectory.org/, https://www.wikidata.org/wiki/Wikidata:WikiProject_Govdirectory)
Help the Bahamas open up governmental data sets. Raspberry focuses on climate change (climate, disaster, tourism data), making the high-level datasets accessible on Wikipedia. Data is often owned by third parties, e.g. the University of East Anglia (weather sensors). The work consists of accessing these datasets and wikidata, interlinking them, and producing wiki sites to curate papers about them. Students edit Wikipedia articles about the Bahamas and try to make these datasets available.

Languages

Fluent in English only
Commissioning and monitoring a large number of translations in Hindi, Bengali, and Spanish, and monitoring the content.

Criteria to assess articles' quality

Pageviews always examined: low-quality articles that people aren't reading are not concerning
Adequacy more important than excellence
Quality of citations: presence of most relevant sources; old, fringe or minor sources should be removed.
"A typical popular article, equivalent to 10 pages of text in English Wikipedia, has 100 citations and is within the top 1% of popularity by traffic. It's not uncommon for such articles to have never had a complete copy edit, never had anyone check all the citations, never had anyone cross-reference it with topics that should be in the manual of style, and never made an attempt to get the very most popular, easiest to find sources cited in that article.

Climate change

Climate change is a significant area of engagement for Rasberry (approximately 20%).
IPCC reports are still under copyright. The visualisations they produce, even those commented on extensively by journalists like this, need to be redesigned to be included in Wikimedia projects.
Public benefit communication messages (the IPCC reports, but also medical messages) are supposed to be shared, but are not made available openly. It's "discouraging"
"I am aware of a lot of misinformation in Wikipedia, but I'm less concerned about the misinformation than I'm concerned with the lack of cooperation from our closest allies. They are one of the biggest barriers to sharing climate change information.

Identifying gaps

Today, the preferred strategy to identify content gaps is using informatics and AI, trying to come up with a database of citations, examining papers at the scale of tens of millions at a time, and then trying to determine information gaps.
In the past, to identify what is missing in Wikipedia articles, Raspberry tried attending academic conferences, recruiting experts to review articles, and asking for expert opinions.

Feedback on the visualisation prototype

First visualisation focusing on the lead.
"I've wanted this for many years and I think this is one of one of the most important things that's ever happened to Wikipedia".
"Top importance, it's hard for me to imagine any project anywhere in the Wikimedia movement that's more important than this. This is worth $100 million" by getting 100 universities involved.
"Metrics is what keeps me funded: I haven't been able to show this kind of information and didn't get funded for this reason".
Make it look more like a social media dashboard.
There should be a basic view for this dashboard.
The basic view should be the default and have just a few options to prevent a wikipedian from going to a funder and putting their metrics on the table. And the funders, who are used to seeing Twitter, YouTube and website metrics, don't know how to interpret Wikipedia metrics.
It has to be understandable immediately.
The most important metric is pageviews.
Use the total pageviews

AI

Rasberry's university is a school of data science: data science is another term for artificial intelligence. So, all the projects he undertakes with Wikipedia involve artificial intelligence.

Collaboration

Rasberry is available to write the documentation for this project.
He is also available and happy to support clustering and categorisation work: this would need 6 months' time and students can work on it ("they need challenges, not money")

Suggested requirements

Make it look like a social media dashboard (so it can be included in communication strategies)
Select basic metrics to show everyone that are immediately understandable
Provide a basic visualisation and an advanced one
Pageviews is the lead metric (not daily views but total views), it can orient efforts to check on the adequacy of content

Knowledge gaps about climate change on Wikipedia

Suggestion to use informatics, AI and a database of citations, to examine papers at the scale of tens of millions at a time.