We want to overcome the female data gap in order to integrate the female perspective and needs.

**What's a data gap?**
A data gap is missing, incomplete or biased datasets. For example if a specific medicine is only tested on men, the data on the effect of the medicine on women is lacking. You can find a general introduction to the topic in this video: https://www.srf.ch/play/tv/nouvo-srf/video/gender-data-gap-warum-lange-….

**Why is this relevant?**
Female data gaps can result in biased research or statistics, which hampers evidence-based decisions and policy. For example if a car is only security tested with male-shaped dummies, it can result in more severe car crash injuries for women.

**What needs to be done?**
The key problems and questions we perceive are:
1) Analysing the origins of the problem.
- Someone needs to evaluate whether some countries are doing better than others and if yes, why? Maybe the indicators of the SDG on gender equality can help with this. What do countries that are doing well have in common?
- We need to do a literature review in order to understand what studies have already been done on this. E.g. is representation the origin of the problem?
2) Discovering and overcoming data gaps.
- The state needs to implement national tests of structures and platforms that could help to overcome data gaps. E.g. through data crowd funding. Successful test should be expanded internationally by e.g. the OECD.
- International research associations need to establish research guidelines that ensure representative data collection.
- International companies could open up anonymised data in order to complete existing data gaps. But there are big privacy concerns.
3) Prioritising especially relevant fields.
- A map of the different fields that are affected by the female data gap needs to be developed, in order to overview and prioritise specific fields.
4) How to handle existing biased data?
- Countries with more complete datasets could help countries with bigger data gaps.
- An international organisation, e.g. UN Women, should set up a platform for best practices.
- General policy guidelines for the handling of incomplete or biased datasets should be established. Each research study has to go through the ethical commission and they need to include gender guidelines ...
5) Digitisation
Digitisation like algorithm will reinforce the data gaps. How can we handle this?