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Citizen Science, Notable Women, and the Wikimedia Ecosystem
October 4, 2021
About Siobhan
Siobhan is an Independent Wikimedian from New Zealand and a seasoned citizen scientist who has worked since 2014 on many projects including the Smithsonian Transcription Center, iNaturalist, BHL’s Flickr Tagging efforts, and contributes and speaks regularly about her data work in Wikimedia’s core projects like Wikipedia, Wikimedia Commons, and Wikidata. She is an open-knowledge advocate whose work elevates and exposes new knowledge around women in science, biodiversity, and natural history. She currently serves as an expert advisor on the BHL Metadata and Cataloging Committee. (Wikidata Profile: Q54823671)
1. What motivates you to do this work and what is a current passion project?
Currently, my main goal is to make women in science visible and surface when someone does a Google search. A knowledge graph panel is nice but even a Wikidata Q entry, at the very least is helpful. However, generally for all my contributions my goal is to assist in improving access to knowledge and to help link everything.
A timeline of the evolution of my interests over time:
Smithsonian Transcription Center - gravitated towards the Natural History subject matter.
Flickr Tagging on BHL content - tagging the illustrator tags, focusing on women and illustrators, collecting research about the women I’d tagged.
The research, I was collecting I wanted to save and share so I started adding references to Wikipedia.
Discovered Wikidata through Mix’n’Match work (MixNMatch BHL creator ID to Wikidata) and now spend a lot of my time there; particularly adding women to Wikidata. Also check out the Author Disambiguator Tool. It’s useful to match the articles to the author Q entry. (Both Magnus created tools but see Arthur Smith’s extended work)
2. If you could name 1-2 crowdsourcing projects that BHL partner staff should/could be actively participating in to maximize impact what would those be?
Mix’n’match is THE entry point. It’s so easy, you are editing one thing at a time and it's either a yes or no. Look at Diane Shaw’s Wikidata entries for a good example of BHL authors.
3. Can you walk me through the steps in a typical workflow to add a new name/author to the Wikimedia ecosystem?
Get the name: JSTOR/BHL/scholarly articles
Google the name: look and see, what do I get?
Full-text search in BHL: find your references for the name
Plus, sometimes genealogy work on ancestry.com [for women the husband’s name can help]
Plus, sometimes references can be found in the Internet Archive
Add the entity to Wikidata
Connect women to appropriate identifiers eg BHL creator id, VIAF, FindAGrave, Harvard Index of botanists etc.
The addition of entries in Wikidata generates lists for Wikipedia editors to work on. This feeds into Wikipedia projects e.g Women in Red.
From there, someone can see the name and will create a Wikipedia article if the woman is notable.
There is a greater need to be the creator of Q items to feed this data flow.
4. You are quite active in Wikidata, Wikipedia, and Wikimedia Commons – any thoughts on Wikisource and Wikispecies?
Wikispecies - the data in Wikispecies is great but it isn't linked, nor is it complete. I much prefer to add data (that might otherwise be added to Wikispecies) to Wikidata as there is more interlinking in the Wikidata ecosystem. Also, you can query that data.
Wikisource - small community. I personally am more involved in Digivol for transcription. However, Wikisource is a valid platform for BHL transcription and should be explored further.
5. Do you think BHL should invest in its own crowdsourcing platform?
No. Farm it out. Don’t build it, they won’t come. Use the various established projects I mentioned above.
Smithsonian Transcription Center
DigiVol - there are now more volunteers at DigiVol than previously and it is growing all the time. Also, BHL related organizations such as Kew and Edinburgh Botanic Garden use DigiVol.
Zooniverse - Notes for Nature (biggest volunteer community)
Wikisource - A small community but integrated into the Wikimedia system
6. Capacity and data literacy are two barriers BHL has faced internally since its inception – in your experience with leading volunteer projects and edit-a-thons, what are your go-to strategies to build capacity and expertise across global networks?
Get your people over the learning hump
Communicate the benefits to them and communicate the power of it
I’ve seen that Librarians are afraid of the “anyone as curator” aspect and learning to let go of control for the greater good is a part of this information evolution
Give the feeling of empowerment
One example: Wikidata drives Google and Alexa and the data points populate on the web really quickly. Wikidata is really important to surface information in Google search
7. Can you tell me about your recent participation in the BiCIKL-Hackathon 2021 and how various platforms and initiatives are working together to improve the visibility of notable women botanists?
This was a fabulous collaboration helping to surface women in science. But again the notability criteria is often a challenge to get these women into Wikipedia. Adding them to Wikidata is a step in the journey of collecting enough sources to get them over the notability criteria.
One thing to emphasize: I’m of the opinion that manual curation is underestimated in the Wikidata world. It’s really important to remember that humans can do more complex interlinkages between entities and knowledge bases. Not everything can be solved by bulk uploads of data.
8. Any interest in collaborating and organizing a BHL + Wikidata workshop in the future?
Yes!
9. Is this notion of “notability” a bit amorphous to an outsider? What makes something notable in Wikipedia?
Notability is hard to define, firstly several references or sources are needed to get a person over the notability criteria – after working in Wikipedia “you’ll know it when you see it.”
Also note: writing blogs about women can help push them over the notability criteria. See the BHL Her Natural History campaign as a great example of encouraging further research into women. The results of this research helped push some women over the notability criteria for a Wikipedia page.
10. Final thoughts for the BHL Community on Wikidata, Transcriptions, and Data?
People can’t really understand Wikidata or its power of it unless they understand linked open data and what it is doing, how it’s transforming the information landscape.
Siloed data equals siloed thinking
Let’s follow-up on getting more transcriptions into BHL (from DigiVol and Smithsonian Transcription Center)
