@misc{10481/92908,
year = {2024},
month = {7},
url = {https://hdl.handle.net/10481/92908},
abstract = {We retrieve data from Dimensions, the World Bank Open Data (WBOA) and the UNESCO Institute for Statistics 
(UIS) to construct a country level longitudinal dataset including the yearly number of researchers by gender. Our aim is to predict when each country will reach gender parity and which factors may influence the increase of the proportion of women in science. Here we present some preliminary findings using the ARIMA and Exponential Smoothing forecasting models, and a first attempt to look into influencing factors using Bayesian Networks.},
organization = {Spanish Ministry of Science PID2020-117007RA-I00, PID2021-128429NB-I00, FPU2021/02320, RYC2019-027886-I},
keywords = {Scientometrics},
keywords = {Gender},
title = {What contributes to gender parity in science? A Bayesian Network analysis},
doi = {10.5281/zenodo.12609269},
author = {González-Salmón, Elvira and Chinchilla-Rodríguez, Zaida and Nane, Gabriela F. and Robinson García, Nicolás},
}