By Margaret Hogarth
Facts use within the library has particular features and customary difficulties. information Clean-up and administration addresses those, and gives ways to freshen up frequently-occurring facts difficulties utilizing readily-available purposes. The authors spotlight the significance and strategies of knowledge research and presentation, and supply guidance and proposals for a knowledge caliber coverage. The publication offers step by step how-to instructions for universal soiled information issues.
- Focused in the direction of libraries and training librarians
- Deals with functional, real-life matters and addresses universal difficulties that every one libraries face
- Offers cradle-to-grave remedy for getting ready and utilizing info, together with obtain, clean-up, administration, research and presentation
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Additional resources for Data Clean-Up and Management. A Practical Guide for Librarians
A librarian will need to explain patiently the source and meaning of data. COUNTER vs non-COUNTER usage data Non-standardized data is problematic and complicated from acquisition to storage. It may require an email inquiry in order to receive updates, which adds to the labor load. If the data comes in a format such as PDF, it requires manipulation in order to get it into a usable form such as Excel. A librarian must also make decisions about how to interpret the data. For example, non-standardized usage data, to be used to answer ARL survey questions (see “ARL statistics”, below), must be interpreted to “fit” in a standards-based category.
Simply specify the input and output files and their locations, choose which fields to display, and then view and format the file in Excel. See Chapter 8, “Additional tools”, for details. gov/marc/ umb/. Microsoft Access Access, also by Microsoft, is the Office Suite database function. For libraries without programming staff or funds to buy proprietary products, Access can satisfy an institution’s data needs. It readily imports data from Excel, XML and other applications. Data is stored in tables and manipulated through queries, forms and reports.
See Chapter 9 for more information on normalizing data. Why talk about databases, defining and normalizing data? In our datarich environment, librarians will benefit from thinking about their information in these terms. Think like a database. It will clarify many tasks and workflows. The need to have a “master list” and avoid capturing redundant data will make one’s work more efficient and accurate. Additionally, automation and machine matching are a necessity as we deal with more and more data.