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Rich Search and Discovery for Research Datasets
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Rich Search and Discovery for Research Datasets
Building the Next Generation of Scholarly Infrastructure

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Research Methods

208 pages | SAGE Publications Ltd

Click here to find your FREE copy of the open access book Rich Search and Discovery for Research Datasets.

Rapid growth in science and technology offer opportunities to conduct social science research at a scale that was almost unimaginable a generation ago.

However, issues around reproducibility and replicability threaten the legitimacy and utility of such research. This ground-breaking open access book explores how automating the search for and discovery of datasets can help tackle irreproducibility in social science research.

Demonstrating how machine learning and natural language processing methods can be combined to create a new generation of automated tools for identifying datasets used in social research, the book also:

  • Shows how artificial intelligence can extract metadata from research data
  • Tackles issues like sharing confidential data and lacking social scientific infrastructure
  • Provides innovative ideas for using technology in research to implement positive change.

With contributions from across the globe, this insightful and topical book showcases how technology can advance social research and help us address key issues facing society today.

Ian Mulvany, Paco Nathan, Sophie Rand and Julia Lane
Chapter 1: Introduction
 
Part I: Motivation and approach
Stefan Bender, Hendrik Doll and Christian Hirsch
Chapter 2: Who’s Waldo? Conceptual issues when characterizing data in empirical research
Christian Herzog, Daniel W. Hook, Mark Hahnel, Stacy Konkiel and Duane E. Williams
Chapter 3: Digital Science Use Cases: Enriching context and enhancing engagement around datasets
 
Part II: New approaches to developing corpora and ontologies
Robert B. Allen
Chapter 4: Metadata for Social Science Datasets
Andrew Gordon, Ekaterina Levitskaya, Jonathan Morgan, Paco Nathan and Sophie Rand
Chapter 5: Competition design
 
Part III: Competition results
Daniel King, Waleed Ammar, Iz Beltagy, Christine Betts, Suchin Gururangan and Madeleine van Zuylen
Chapter 6: Finding datasets in publications: The Allen Institute for Artificial Intelligence approach
Haritz Puerto-San-Roman, Giwon Hong, Minh-Son Cao and Sung-Hyon Myaeng
Chapter 7: Finding datasets in publications: The KAIST approach
Wolfgang Otto, Andrea Zielinski, Behnam Ghavimi, Dimitar Dimitrov, Narges Tavakolpoursaleh, Karam Abdulahhad, Katarina Boland, Stefan Dietze
Chapter 8: Knowledge Extraction from scholarly publications: The GESIS contribution to the rich context competition
Rricha Jalota, Nikit Srivastava, Daniel Vollmers, René Speck, Michael Röder, Ricardo Usbeck, Axel-Cyrille Ngonga Ngomo
Chapter 9: Finding datasets in publications: The University of Paderborn approach
Philips Kokoh Prasetyo, Amila Silva, Ee-Peng Lim and Palakorn Achananuparp
Chapter 10: Finding datasets in publications: The Singapore Management University approach
 
Part IV: Looking forward
Tong Zeng and Daniel Acuna
Chapter 11: Finding datasets in publications: The University of Syracuse approach
Paco Nathan
Chapter 12: The Future of AI in Rich Context
Key features

•   It shows how artificial intelligence can extract metadata from research data

•   It tackles issues like sharing confidential data and lacking social scientific infrastructure

•   It provides innovative ideas for using technology in research to implement positive change

•   It showcases how technology can advance social research and help us address key issues facing society today, with contributions from across the globe


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