Supplementary Material - Bridging the Python Training Gap for Bioscientists in Brazil: Improvements and Challenges
Creators: name: Schiavone Crestana, Gustavo
name: da Silva Batista, Ubiratan
name: Inácio Gonçalves Funncelli, Michelli
name: Braga, Larissa
name: Zuvanov, Luíza
name: Bizarria Júnior, Rodolfo
name: Melo de Sousa, Raissa
name: Ferreira, Pedro
name: Vischi Winck, Flavia
name: Rodrigues Alves Margarido, Gabriel
name: de Mello Varani, Alessandro
name: Riaño Pachón, Diego Mauricio
name: Corrêa dos Santos, Renato Augusto
Publisher: name: Zenodo
Year: 2025

description: This file contains supplementary figures and tables related to the study "Bridging the Python Training Gap for Bioscientists in Brazil: Improvements and Challenges". The study presents the advances made during the 2021 and 2022 editions of the Brazilian Python Workshop for Biological Data, an event first held in 2017 (published in Zuvanov et al., 2021). It details the improvements implemented over the years, incorporating suggestions and recommendations from previous editions, while also discussing new strategies for the continuous enhancement of the workshop, which is designed for bioscientists with little or no programming experience.The supplementary files include the course schedule, the topics covered in the event’s Code Clubs, a partial characterization of course participants and workshop followers on social media, as well as an analysis of students’ responses from the final course evaluation forms. These materials provide additional context to the study, offering a deeper understanding of the impact and reception of the initiatives introduced in recent editions.

These files are an integral part of the study: Bridging the Python Training Gap for Bioscientists in Brazil: Improvements and Challengesdoi: https://doi.org/10.1101/2024.11.25.624749Gustavo Schiavone Crestana*1, Ubiratan da Silva Batista*2, Michelli Inácio Gonçalves Funnicelli*3, Larissa Graciano Braga4,5, Luíza Zuvanov6, Rodolfo Bizarria Jr.7, Raissa Melo de Sousa8, Pedro Henrique Narciso Ferreira9, Flavia Vischi Winck10, Gabriel Rodrigues Alves Margarido11, Alessandro de Mello Varani12, Diego Mauricio Riaño-Pachón13, Renato Augusto Corrêa dos Santos13, 14* The authors contributed equally to this work

Institutions1University of São Paulo (USP), Campus Luiz de Queiroz,  Department of Genetics, Genomics Group, Piracicaba, São Paulo, Brazil.2Federal University of Ouro Preto (UFOP), Department of Biological Science, Ouro Preto, Minas Gerais, Brazil.3São Paulo State University (UNESP), Vector-Borne Bioagents Laboratory, Department of Pathology, Reproduction and One Health, School of Agricultural and Veterinary Sciences, Jaboticabal, SP, Brazil.4São Paulo State University/ School of Agricultural and Veterinary Sciences, Department of Exact Sciences, Jaboticabal, São Paulo, Brazil.5University Of Guelph, Department of Animal Biosciences, N1G 2W1, Guelph, ON, Canada6Free University of Berlin, Institute of Chemistry and Biochemistry, Berlin, Berlin, Germany.7São Paulo State University (UNESP), Institute of Biosciences, Rio Claro, São Paulo, Brazil.8Federal University of Pará (UFPA)/Francisco Mauro Salzano Molecular Biology Laboratory, Institute of Biological Sciences, Belém, Pará, Brazil.9State University of Campinas/Department of Genetics, Evolution, Microbiology, and Immunology/Laboratory of Genomics and BioEnergy (LGE), Campinas, SP, Brazil.10Laboratório de Biologia de Sistemas Regulatórios LABIS, Centro de Energia Nuclear na Agricultura, Universidade de São Paulo, Piracicaba, São Paulo, Brazil.11University of São Paulo (USP) - Campus Luiz de Queiroz, Department of Genetics, Piracicaba, São Paulo, Brazil.12São Paulo State University (UNESP), Department of Agricultural and Environmental Biotechnology, Varani's LAB, Jaboticabal, São Paulo, Brazil.13Laboratory of Computational, Evolutionary, and Systems Biology, Centro de Energia Nuclear na Agricultura, Universidade de São Paulo, Piracicaba, São Paulo, Brazil.14The Wallace Lab, Center for Applied Genetic Technologies (CAGT), University of Georgia, Athens, Georgia, United States of America.

Included Materials1 - S1 Table. Brazilian introductory training initiatives focused on manipulating biological data using Python.This document provides an overview of courses related to biological data manipulation using Python in the Brazilian context.

2 - S2 Table. Schedule of the Brazilian Python Workshop for Biological Data in 2021.This document details the workshop schedule for 2021, including lecture topics, keynote sessions, and flash talks. It also includes a designated break day for individual and group activities.

3 - S3 Table. Schedule of the Brazilian Python Workshop for Biological Data in 2022.This document details the workshop schedule for 2022, including lecture topics, keynote sessions, and flash talks. It also includes a designated break day for individual and group activities.

4 - S1 Text. Code-club topics explored by the 2022 organizing team.This document outlines all topics explored by the 2022 organizing team during the Code Club sessions, serving as a means for team members to update their knowledge on fundamental concepts to be taught in the course.

5 - S1 Fig. Age characterization of Instagram followers of the Brazilian Python Workshop for Biological Data. Age distribution of male and female followers of the workshop’s Instagram page (@brazilpythonws) as of September 14, 2023.

6 - S2 Fig. The Brazilian Python Workshop for Biological Data Instagram profile statistics since third edition (2020). This figure illustrates the growth in the number of followers on the workshop’s Instagram profile, alongside the increase in the number of posts. The donut plot represents the distribution between male and female of the workshop's Instagram page followers as of September 14, 2023.

7 - S3 Fig. Course structure evaluation about (A) Evaluation of instructors and presentations, where Q1 = The instructors were effective, Q2 = The presentations were clear and organized, Q3 = The instructors fostered student engagement, Q4 = The instructors managed their time effectively during the classes, and Q5 = The instructors were accessible and helpful.; (B) The objectives and materials presented, where Q1 =  The objectives were clear, Q2 = The course content was organized and well-planned, and Q3 = The instructional material was well-developed.; (C) The organization and the tools used, where Q4 = The course workload was appropriate, Q5 = The tools were suitable for the course, and Q6 = The course was structured to enable the participation of all students.

8 - S4 Fig. Gender ratio among selected participants. Distribution of gender among participants in the 2021 edition (A) and the 2022 edition (B).

9 - S5 Fig. Participants' Knowledge of the Python Programming Language. Changes in participants' knowledge of the Python programming language, where Q1 = Programming knowledge level at the beginning of the course and Q2 = Programming knowledge level at the end of the course.

10 - S4 Table. Representative answers obtained from participants at the end of the IV Python for Biological Data Workshop, held in 2021.This document provides an overview of representative responses from participants to the final course evaluation survey distributed at the end of the 2021 edition. The survey included questions assessing various aspects of the course organization and execution, as well as suggestions for improvements.

11 - S5 Table. Representative answers obtained from participants at the end of the 5th Python Workshop for Biological Data, held in 2022.This document provides an overview of representative responses from participants to the final course evaluation survey distributed at the end of the 2022 edition. The survey included questions assessing various aspects of the course organization and execution, as well as suggestions for improvements.