Data Management and Digital Infrastructure in Social Sciences
1st Edition
Will be published approx. on 3. July 2026
Book
Paperback/Softback
144 pages
978-1-041-33948-9 (ISBN)
Description
Data Management and Digital Infrastructure in Social Sciences shows how to move from raw digital data to usable evidence, with practical guidance on data literacy, documentation, platforms, and workflows that support transparency and reproducibility.
Written for postgraduate students and early-career researchers in the social sciences and management, the volume also supports instructors and research support staff who need a grounded, course-ready guide to modern data practices, including links to evidence-based management and real-world research settings.
Written for postgraduate students and early-career researchers in the social sciences and management, the volume also supports instructors and research support staff who need a grounded, course-ready guide to modern data practices, including links to evidence-based management and real-world research settings.
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Postgraduate
Illustrations
2 s/w Tabellen
2 Tables, black and white
Dimensions
Height: 246 mm
Width: 174 mm
ISBN-13
978-1-041-33948-9 (9781041339489)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Lukasz Sulkowski | Andrzej Wozniak | Robert Seliga
Data Management and Digital Infrastructure in Social Sciences
Book
approx. 07/2026
1st Edition
Routledge
€191.50
Not yet published
Lukasz Sulkowski | Andrzej Wozniak | Robert Seliga
Data Management and Digital Infrastructure in Social Sciences
E-Book
approx. 07/2026
Routledge
€55.49
Not yet available
Lukasz Sulkowski | Andrzej Wozniak | Robert Seliga
Data Management and Digital Infrastructure in Social Sciences
E-Book
approx. 07/2026
Routledge
€55.49
Not yet available
Persons
Lukasz Sulkowski is a professor of economic sciences and humanities specializing in higher education management, social science methodology, HRM, and organizational culture, and serves as President of WSB University.
Andrzej Wozniak serves as Associate Dean for Development and lecturer at WSB University, specializing in organisational decision-making, management information systems, and business process improvement.
Robert Seliga holds a PhD in economics, specializing in management, higher education marketing, and the professionalization of management in universities.
Marcin Lis is Vice-Rector for Student Affairs and External Relations at WSB University and holds a PhD in engineering, his research interests include quality systems, innovation project management, and integrated management systems, with a particular focus on science - business collaboration and knowledge transfer.
Andrzej Wozniak serves as Associate Dean for Development and lecturer at WSB University, specializing in organisational decision-making, management information systems, and business process improvement.
Robert Seliga holds a PhD in economics, specializing in management, higher education marketing, and the professionalization of management in universities.
Marcin Lis is Vice-Rector for Student Affairs and External Relations at WSB University and holds a PhD in engineering, his research interests include quality systems, innovation project management, and integrated management systems, with a particular focus on science - business collaboration and knowledge transfer.
Content
Introduction, 1. The Data-Driven Transformation of Research, 2. Data Literacy and Researcher Competencies, 3. Data Governance, FAIR Principles, and Ethics, 4. Navigating Diverse Data Sources (Surveys, Sensors, Social Media, and Beyond), 5. Data Formats, Structures, and Metadata Standards, 6. Data Platforms and Digital Infrastructure (APIs, Repositories, and Data Lakes), 7. Building and Managing Data Pipelines, 8. Data Lifecycle Management - Provenance, Versioning, and Quality Assurance, 9. Data Sharing, Reuse, and Open Science, 10. Conclusion, 11. References.