# IFLA panel preparation notes

Topic: the impact, value, and challenges for women and open data sharing

- Laura:
- nuances, e.g. divisions within each gender "bracket", intersectionality, what data is collected and why?
- data is a useful tool to advocate for changes (but needs to be accompanied with other pressures), e.g. UK's mandatory reporting for pay gap data since 2017
- small scale studies
- Irene:
- working w/ local communities, separating genders in focus groups in biodiversity studies; see a difference between genders e.g. in the plants that they recall from their experiences
- Women have different interests in how they choose what seeds to grow, for example they might like the color of a particular maize landrace.
- sociocultural dimension of biodiversity - thinking about whether studies should have gender-disaggregated data
- Most of the people in her organisation/discipline/environment are women; is this about gender data specifically or our experiences working with data?
- Esther:
- her faculty has 5-20% women as professors; discipline has 40-60% women
- librarianship is a female heavy profession - there are studies that shows women are more likely to be in service professions
- bropenscience
- Laura:
- making data accessible and FAIR is work, but more likely unacknolwedged and often devalued/feminized - it needs to be valued to make it more visible
- Esther: maintainance of data & data repository
- Irene:
- building a data platform, funders want to be able to play with it and see the ML algorithms in application, but to reach that stage there's a lot of data wrangling and cleaning - often invisible
- data science and analytics are more male dominated disciplines - also big salary discrepancies
- Points of entry for data work: humanities vs technical fields
- Paz:
- difficulties with working with data outside of science and academy
- in running community projects, sees that there is an engrained preconception that STEM work is very much male-orientated
- Laura: who gets free time to take part in poking around
- Emmy:
- Men/women division where data work is often invisible and often done by women. How do we get out of this? Is more recognition needed?
- At the journal I worked at we noticed that there is a gatekeeping effect at what gets published. What is the impact of women in this role or the people that were involved in this process?
- Gender data: Having disaggregated data
- Worked with mice during PhD which was only based on male mice (for the only reason to keep the variables low)
- Laura:
- **What data do people choose to collect?** If you have women in the team you're more likely to collect data that is relevant to women.
- Irene:
- The focus groups are seperated in gender, reflects the type of data that is being collected. For example, women share recipes among eachother.
- Differences in context: here in Mexico there are not a lot of people in Humanities. Librarians here are not working with open data - most people come from natural science/technical perspectives.
- Paz:
- open data is perceived as scientific data, but there's also a surveillance angle to it, open data being collected and used by state actors and corporations;
- often this data collected is gendered
- there should be a bigger push towards more transparency and accountability in how this data is used
- state collects a lot more data on women - e.g. how subsidies are spent;
- even if the state says it's open data
- Laura:
- panama had a gendered curfew; this affects trans people whose gender that didn't match their ID's (see http://repository.essex.ac.uk/28041/)
- Emmy:
- Its about the accountability of the state and the data that they gather: This might negatively impact people (for example, period data when abortion is re-criminalised).

gender and how that shapes the data and the data culture

data about women
women in data professions

why is this topic important to discuss