Summary
Contents
Subject index
Although major funding agencies now require social scientists to share their documented raw data, scientists have been reluctant to comply. The reasons include unwillingness to divulge all of the conditions under which the data were generated, cost in time and money, and the desire by social scientists to carry the research further themselves. Data sharing, however, promises to foster more open, cost-effective and cumulative research, and to improve the quality of methodology, data and inference. Sharing Social Science Data presents the major accomplishments of social scientists who have pioneered in data sharing, highlighting the advantages for social science. It also includes an examination of the reasons for data sharing, the specific sharing practices in various disciplines, the factors affecting the usefulness of shared data (documentation, archiving, and marketing), and individual and institutional concerns about data sharing. A timely examination, this cohesive and well written volume will interest graduate students and researchers in all areas of the social sciences. “…the chapters are thoughtful and well written, and they address many of the crucial issues faced by the social sciences in the 1990s. …anyone who wants to help shape the future of the social and behavioral sciences can benefit from giving this book at least a quick read.” – Contemporary Psychology
The Science of Data Sharing: Documentation
The Science of Data Sharing: Documentation
1. Introduction
This chapter has two objectives: (a) to present a framework that organizes the description of data, and (b) to explain how data base management systems can implement large sections of that framework. The framework provides an explanation of why each bit of data description is needed. Relating the framework to data base management systems (DBMS) explains their role in providing a language for data. A language for data provides nouns and syntax for describing data, relationships among data elements, and procedures for generating derived values. The synthesis of the framework and DBMS clarifies debates about the merits of models of data structures and the role of statistical processors.
Perspectives on data and data sharing ...
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