Data Management and Digital Infrastructure in Social Sciences | 5.01 MB
Title: Data Management and Digital Infrastructure in Social Sciences
Author: Łukasz Sułkowski, Andrzej Woźniak, Robert Seliga, Marcin Lis
Category: Nonfiction, Social & Cultural Studies, Social Science, Methodology, Reference & Language, Reference
Language: English | 151 Pages | ISBN: 981168149X
Description:
Data Sources, Management, and Digital Infrastructure in Social Sciences and Management 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. Key features include: Strategies for working with diverse digital data sources, from social media and administrative records to geospatial and sensor data Data governance and research ethics, including the FAIR principles, privacy, consent, and responsible reuse Metadata and documentation practices that keep datasets interpretable over time, including codebooks and common standards Clear introductions to data platforms and infrastructure, including repositories, data warehouses and lakes, APIs, and cloud or high-performance computing Step-by-step approaches to data pipelines (ETL), quality assurance, provenance, version control, and open science data sharing 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.
Data Sources, Management, and Digital Infrastructure in Social Sciences and Management 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. Key features include: Strategies for working with diverse digital data sources, from social media and administrative records to geospatial and sensor data Data governance and research ethics, including the FAIR principles, privacy, consent, and responsible reuse Metadata and documentation practices that keep datasets interpretable over time, including codebooks and common standards Clear introductions to data platforms and infrastructure, including repositories, data warehouses and lakes, APIs, and cloud or high-performance computing Step-by-step approaches to data pipelines (ETL), quality assurance, provenance, version control, and open science data sharing 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.
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