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Small scale bibliographic databases

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Published by Academic Press in Sydney, Orlando .
Written in English

Subjects:

  • Machine-readable bibliographic data -- Congresses.,
  • Bibliography -- Data processing -- Congresses.,
  • Small libraries -- Automation -- Congresses.,
  • Bibliography -- Databases -- Congresses.,
  • Information technology -- Congresses.

Book details:

Edition Notes

Includes bibliographical references and index.

Statementedited by Peter Judge, Brenda Gerrie.
SeriesLibrary and information science, Library and information science (New York, N.Y.)
ContributionsGerrie, Brenda., Judge, Peter J.
Classifications
LC ClassificationsZ699.35.M28 S63 1986
The Physical Object
Paginationviii, 198 p. :
Number of Pages198
ID Numbers
Open LibraryOL2558760M
ISBN 100123919703
LC Control Number85073034

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MARC RTP will read files of bibliographic records in MARC format, and convert them to a text-delimited format for importing into a database or spreadsheet. Records can also be converted into other formats are possible. The program can also produce a listing or summary of the structure of a file of records. View all The Free Library of Philadelphia's subscription databases to perform research, find citations and full text articles. The Free Library of Philadelphia is one of the largest public library systems in the world, FLP has established its Internet presence to enhance and extend its services to the citizens of Philadelphia, the Delaware Valley, and beyond.   The key bibliographic database relevant to Psychology is PsycInfo. This is the best database to start with when you are doing your literature search. Tip: Before you search PsycInfo, have a look at the separate page on Searching fo is set up slightly differently from the other databases and can cause confusion until you understand how it works. Summary. Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides .