919 resultados para Library catalogues.


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The paper discusses the use of online information resources for organising knowledge in library and information centres in Cochin University of Science and Technology (CUSAT). The paper discusses the status and extent of automation in CUSAT library. The use of different online resources and the purposes for which these resources are being used, is explained in detail. Structured interview method was applied for collecting data. It was observed that 67 per cent users consult online resources for assisting knowledge organisation. Library of Congress catalogue is the widely used (100 per cent) online resource followed by OPAC of CUSAT and catalogue of British Library. The main purposes for using these resources are class number building and subject indexing

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A database of books published by Italian Academies between 1525 and 1700. The Italian Academies Themed Collection provides a detailed searchable database for locating printed material relating to the Italian learned Academies active in Avellino, Bari, Benevento, Bologna, Brindisi, Caltanissetta, Catania, Catanzaro, Enna, L’Aquila, Lecce, Mantua, Naples, Padua, Palermo, Rome, Salerno, Siena, Syracuse, Trapani, and Venice in the period 1525-1700 and now held in the collections of the British Library.

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The use of indexing language in university libraries collective catalogs and the socio-cognitive context of indexing and users were evaluated. The methodology consisted of a diagnostic study elaboration of the functioning and treatment procedures of the indexing information from nine libraries of the UNESP Network, representing the Civil Engineering, Pedagogy and Dentistry areas from a data collection using the Verbal Protocol introspective technique in the Individual and Group forms. The study conducted a reflection upon the statements issued by the seventy-two participating individuals whose the results revealed unsatisfactory results about the use of the Subject Headings List of the BIBLIODATA Network, indexing language utilizing by the UNESP Libraries Network, Brazil, in the representation and in the information retrieval process in the ATHENA catalog, about the sequent aspects of the language: lack of specialized vocabulary as well as updated; lack of remissives and of specific headings, and others. We have concluded that the adequate use of indexing languages of specialized scientific areas becomes by means of evaluation as to updating, specificity and compatibility in order to meet the needs of indexing and information retrieval.

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Leather and marbled hardcover binding. Substantially annotated. The volume consists of pages from the published catalogues pasted into a blank volume. The bulk of the volume is comprised of the printed list of graduate names found in the Triennial Catalogue accompanied by handwritten biographical information, usually a sentence in length. It begins with a handwritten section titled "Settled Ministers (in the first Parish in Cambridge)." The entries generally contain a residence, date of death (abbreviated ob), age of death (abbreviated ae), and professional information. While the 1794 Catalogue comprises the majority of the volume, names were added from Triennial Catalogues through the 1812 edition. An example of an entry, for John Hancock (Harvard AB 1754), reads “Rep. for Boston, Maj. Gen. Militia. Ob. Octo. 8. 1793 AE 57 Son of Rev. John of Brantree [sic]." A March 27, 1798 letter to Judge Richard Cranch (1726-1818) from Jeremy Belknap (1744-1798, Harvard AB 1762) pasted into the back of the volume. Written only two months before his death, Belknap describes his plan to "go thro’ the whole Catalogue of the graduates of Harvard College, & relate all that’s proper to be related." Four leaves of biographical notes for the classes of 1642-1686 towards the beginning of the volume are in a different hand with the note "Rev Dr. Holmes's handwriting."

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Prepared by a British expert for the use of Dr. Reese and for the guidance of the Virginia Committee: William J. Van Schreeven, Virginia State Library and Francis L. Berkeley, Jr., University of Virginia Library.

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Accompanied by "Second supplementary catalogue of Bengali books in the library of the British museum acquired during the years 1911-1934. Comp. by the late J. F. Blumhardt, M.A. and J. V. S. Wilkinson. Printed by order of the Trustees." (2 p. L., 678 col., [1] p. 29 cm.) Published: London, British museum; [etc., etc.] 1939.

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Accompanied by "A supplementary catalogue of Hindustani books in the library of the British museum acquired during the years 1889-1908. By J. F. Blumhardt. Printed by order of the Trustees." (vi p., 1 l., 678 col. 29 x 22 cm.) Published: London, British museum [etc.] 1909.

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Mode of access: Internet.

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We consider the statistical problem of catalogue matching from a machine learning perspective with the goal of producing probabilistic outputs, and using all available information. A framework is provided that unifies two existing approaches to producing probabilistic outputs in the literature, one based on combining distribution estimates and the other based on combining probabilistic classifiers. We apply both of these to the problem of matching the HI Parkes All Sky Survey radio catalogue with large positional uncertainties to the much denser SuperCOSMOS catalogue with much smaller positional uncertainties. We demonstrate the utility of probabilistic outputs by a controllable completeness and efficiency trade-off and by identifying objects that have high probability of being rare. Finally, possible biasing effects in the output of these classifiers are also highlighted and discussed.

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An emerging issue in the field of astronomy is the integration, management and utilization of databases from around the world to facilitate scientific discovery. In this paper, we investigate application of the machine learning techniques of support vector machines and neural networks to the problem of amalgamating catalogues of galaxies as objects from two disparate data sources: radio and optical. Formulating this as a classification problem presents several challenges, including dealing with a highly unbalanced data set. Unlike the conventional approach to the problem (which is based on a likelihood ratio) machine learning does not require density estimation and is shown here to provide a significant improvement in performance. We also report some experiments that explore the importance of the radio and optical data features for the matching problem.

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ACM Computing Classification System (1998): J.2.