This Research Topic is part of the Women in Big Data series. Other titles in the series are:
Women in Data Mining and Management 2022
Women in Cybersecurity and Privacy
Women in Machine Learning and Artificial Intelligence 2022
Women in Data-driven Climate Sciences 2022
Women in Recommender Systems
Women in AI: Medicine and Public Health 2022
Women in Data Science 2022We are delighted to present the inaugural
Frontiers in Big Data "Women in Big Data Networks” series of article collections.
At present, less than 30% of researchers worldwide are women. Long-standing biases and gender stereotypes are discouraging girls and women away from science-related fields, and STEM research in particular. Science and gender equality are, however, essential to ensure sustainable development as highlighted by UNESCO. In order to change traditional mindsets, gender equality must be promoted, stereotypes defeated, and girls and women should be encouraged to pursue STEM careers.
Therefore,
Frontiers in Big Data is proud to offer this platform to promote the work of women scientists, across the fields of Big Data Networks. This editorial initiative of particular relevance is led by Associate Editors Osnat Mokryn and Xiangliang Zhang. The work presented here highlights the diversity of research performed across the entire breadth of Big Data Networks research and presents advances in theory, experiment, and methodology with applications to compelling problems.
Please note: To be considered for this collection, the first or last author should be a researcher who is a woman. This Research Topic is part of the Women in Big Data series. Other titles in the series are:
Women in Data Mining and Management 2022
Women in Cybersecurity and Privacy
Women in Machine Learning and Artificial Intelligence 2022
Women in Data-driven Climate Sciences 2022
Women in Recommender Systems
Women in AI: Medicine and Public Health 2022
Women in Data Science 2022We are delighted to present the inaugural
Frontiers in Big Data "Women in Big Data Networks” series of article collections.
At present, less than 30% of researchers worldwide are women. Long-standing biases and gender stereotypes are discouraging girls and women away from science-related fields, and STEM research in particular. Science and gender equality are, however, essential to ensure sustainable development as highlighted by UNESCO. In order to change traditional mindsets, gender equality must be promoted, stereotypes defeated, and girls and women should be encouraged to pursue STEM careers.
Therefore,
Frontiers in Big Data is proud to offer this platform to promote the work of women scientists, across the fields of Big Data Networks. This editorial initiative of particular relevance is led by Associate Editors Osnat Mokryn and Xiangliang Zhang. The work presented here highlights the diversity of research performed across the entire breadth of Big Data Networks research and presents advances in theory, experiment, and methodology with applications to compelling problems.
Please note: To be considered for this collection, the first or last author should be a researcher who is a woman.