The Library Table

The Survey of Library Cafes (ISBN# 1-57440-089-4)presents data from more than forty academic and public libraries about their cafes and other food service operations. The report gives extensive data on library cafe sales volume, best selling products, impact on library maintenance costs, reasons for starting a cafe, effect on library traffic, and many other issues regarding the decision to start and manage a library cafe. The 60 page report presents data useful in managing a library cafe; it is not returnable.

For programmers who prefer content to frills, this guide has succinct and straightforward information for putting Access to its full, individually tailored use.

. Data in the report is based on a survey of 55 North American law libraries drawn from law firm, private company, university, courthouse and government agency law libraries. Data is broken out by size and type of library for ease in benchmarking. The 120+ page report covers developments in staffing, salaries, budgets, materials spending, use of blogs & wikis, use of legal directories, the library role in knowledge management, records management and content management systems. Patron and librarian training, reimbursement for library-related education and other issues are also covered in this latest edition.

college students in the United States evaluate their college library. The data in the report is based on a representative sample of more than 400 full time college students in the United States. Data is broken out by 16 criteria including gender, grade point average, major field of study, income level of students, type and size of college, and mean SAT acceptance score of colleges, among other variables. The report includes data on student satisfaction with electronic reserves, the range of databases and periodicals supplied, library supplied database use training, similar training on library workstations and software, reference services, photocopiers and printing services and other college library services. Just a few of the report¿s many conclusions are that: ¿Satisfaction with library electronic reserve was much greater among students from the higher income groups: more to 80% of students from homes with annual incomes of more than $150,000 per year said that they were either satisfied or greatly satisfied. ¿Students in the fine and performing arts seem the least satisfied with the provision of items on electronic reserve. Less than 40% of them expressed either satisfaction or great satisfaction. ¿In general, students seemed happy with the range of databases provided by their college library. Only 1.54% said that they would greatly dissatisfied and only 2.83% said that they were dissatisfied. Students who attend private college were someone more satisfied than students who attended public college. More than 73% of students attending private college said that they were either satisfied or greatly satisfied, while this was the case for a little more than 60% of public college students.

This 230+ page study looks at trends among corporate and other business libraries, such as those of MBA programs or trade associations. The report gives data on trends in spending, materials purchasing, employment, virtual library development, technology acquisitions, evolving role of the corporate librarian, use of internet resources and many other issues of interest to corporate and business librarians. Data is broken out for corporate and non-corporate business libraries, and also by library size and focus (sci-tech, business, etc.). For corporate libraries, the study presents per employee spending data on a range of library materials and services.

AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for ML methods, data contamination to test for the robustness of the classifiers. Key Features: Using ML methods by itself doesn’t ensure building classifiers that generalize well for new data Identifying optimal feature subsets and hyper-parameters of ML methods can be resolved using design and analysis of statistical experiments Using a bootstrapping approach to massive sampling of training and tests datasets with various data characteristics (e.g.: contaminated training sets) allows dealing with bias Developing of SAS-based table-driven environment allows managing all meta-data related to the proposed AI framework and creating interoperability with R libraries to accomplish variety of statistical and machine-learning tasks Computer programs in R and SAS that create AI framework are available on GitHub