Code for Science and Society is happy to announce its first cohort of Event Fund grant recipients! We appreciate the response to our first request for proposals from members of the data science community and the time and effort that applicants spent on their proposals. The grantees were selected based on the proposed event’s alignment with the mission of the fund and the organizers’ plans to create inclusive and accessible spaces for their communities. We are supporting a diverse range of communities, including established and emerging organizers and events. This program is made possible through award number GBMF8449 from the Gordon and Betty Moore Foundation (https://doi.org/10.37807/GBMF8449). We thank the Event Fund Advisory Committee, Selection Committee, and the Gordon and Betty Moore Foundation for their guidance in developing this community-advised fund to support research-driven data science communities around the globe.
Please join us in congratulating the first cohort of event fund grantees - stay tuned for our next RFP opening later this week! To stay up to date, join our mailing list.
Grantee: Kelsey Breseman
Aim: Introduce participants to data science tools using data from the United States Environmental Protection Agency’s Enforcement and Compliance History Online (ECHO) database, help them explore and interpret data relevant to them, and cultivate a community in which they can further develop their skills.
Data Science By Design: Opening Best Practices Through Storytelling
Grantee: Ciera Martinez
Aim: Establish a community of data scientists who leverage creative mediums to convey research practices and results, demonstrating how design principles can advance the inclusivity and diversity of the data science field.
Core Team: Elio Campitelli; Laura Ación; Nicolás Palopoli; Paola Corrales; Yanina Bellini Saibene
Aim: Teach technical skills relevant to open and reproducible research to build capacity in Spanish-speaking Latin America.
Grantee: Benjamin Tully
Aim: Virtually empowering communities in bioinformatics by equipping them with open and reproducible data analysis skills, that can be developed further on a post-conference platform for sharing best practices in the field.
Culturally Relevant Data Science
Grantee: Natasha Gownaris
Aim: Develop inclusive open data science curricula using NEON (National Ecological Observatory Network) data.
Reproducible Silicon Landscapes
Grantee: Stuart Grieve & Fiona Clubb
Aim: Establish a community of practice around reproducible computational research in geomorphology.
Openscapes: Empowering Diverse Open Data Science Leaders
Grantee: Julia Lowndes
Aim: We empower resilient open data science habits, mindsets, and culture in science. Here we’ll be mentoring a cohort of research teams.
Data Umbrella: Contributing to Open Source / scikit-learn
Grantee: Reshama Shaikh
Aim: Increase participation of underrepresented persons in data science, open source, and Python from the regions of Africa, the Middle East and South Asia (Indian sub-continent).
CarpentryConnect South Africa 2021
Grantee: Angelique van Rensburg
Aim: Build capacity for workshops through instructor training and establish a network for the regional Carpentries community.
Grantee: Leonardo Collado Torres
Aim: Increase the diversity of the broader bioinformatics field by teaching computational skills in Spanish and creating a welcoming community for Latin Americans: CDSB + RMB + NNB-UNAM.
Grantee: Neema Iyer on behalf of Pollicy
Aim: Provide workshops and training sessions on data collection, analysis and communication for attendees from underserved groups and communities in African countries.
Open Life Science Training and Mentoring
Grantee: Yo Yehudi on behalf of the Open Life Science team
Aim: Provide structured training and mentoring in Open Data Science to early career scientists.
Expanding Data Science: Open Problem Workshops
Grantee: Beth Duckles
Aim: Host workshops to enable interdisciplinary conversations between data scientists and non-data science PhDs. During the workshops, attendees will discuss problems facing data science and explore potentials for collaboration.