Be data-driven. Continuously collect data to monitor progress & test solutions.
We are all biased. What can we do? The ALBA Declaration on Equity & Inclusion advises to commit to recognizing & counteracting bias, by being data-driven: continuously collect data to monitor progress & test solutions. This article provides useful tips & resources on best practices about data collection.
Issues
The underrepresentation by women and minorities remains an ongoing issue. Many people think sexism, racism, and other disparities were fixed in the past; they see small samplings of the underrepresented groups and think enough progress has already been made. But the numbers and the lived experienced of discrimination and unfair treatment by many in neuroscience and other STEM fields show us there is still a problem today.
Suggested actions
- Establish data collection and reporting methods to track student, hiring, faculty, committee, conference and other EDI statistics.
- Report and publicize your results on a regular basis.
- Celebrate achievements and give credit where due at milestones.
- Assess stumbling blocks and obstacles and seek solutions.
Resources
Examples of data collection tools:
- Using EDI data and evidence; Advance Higher Education (UK)
- Diversity monitoring data collection: tips and guidance; Equality Diversity and Inclusion in Science and Health EDIS group (UK)
- Institutional Report Card for Gender Equality; Initiative on Women in Science (IWISE) (US)
Examples of data reporting:
- Diversity, equity and inclusion in European higher education; European University Association (EU)
- Student Diversity at More Than 3,900 U.S. Institutions; The Chronicle of Higher Education (US)
- Evaluation of gender inequities in Latin American neuroscience community, FALAN IBRO-LARC
- Report of the Commission on the Status of Women Faculty, EPFL (CH)
