About this Research Topic
This Research Topic is part of the Women in Research Metrics and Analytics series. Other titles in the series are:
Women in Research Policy and Strategic Management 2022
Women in Scholarly Communication 2022
We are delighted to present the inaugural Frontiers in Research Metrics and Analytics 'Women in Text-mining and Literature-based Discovery” 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 Research Metrics and Analytics is proud to offer this platform to promote the work of women scientists, across the fields of Text-mining and Literature-based Discovery and Network analysis methods applied to knowledge graphs derived from texts. This editorial initiative of particular relevance is led by Editors Melissa Haendel, Bridget McInnes, and Susan McRoy.
The work presented here highlights the diversity of research performed across the entire breadth of Text-mining and Literature-based Discovery 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 identifies as a woman.
Women in Research Policy and Strategic Management 2022
Women in Scholarly Communication 2022
We are delighted to present the inaugural Frontiers in Research Metrics and Analytics 'Women in Text-mining and Literature-based Discovery” 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 Research Metrics and Analytics is proud to offer this platform to promote the work of women scientists, across the fields of Text-mining and Literature-based Discovery and Network analysis methods applied to knowledge graphs derived from texts. This editorial initiative of particular relevance is led by Editors Melissa Haendel, Bridget McInnes, and Susan McRoy.
The work presented here highlights the diversity of research performed across the entire breadth of Text-mining and Literature-based Discovery 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 identifies as a woman.
Keywords: Natural language processing, Entity extraction, relation extraction, Intelligent search, semantic search, Full-text mining, Entity linking, Text-based hypothesis generation, Mining clinical text, Evaluation and validation
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
