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Journal articles on the topic 'Biomedical Library'

To see the other types of publications on this topic, follow the link: Biomedical Library.

Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Pedone, Elisa, Xiongwei Li, Neli Koseva, Oya Alpar, and Steve Brocchini. "An information rich biomedical polymer library." J. Mater. Chem. 13, no. 11 (2003): 2825–37. http://dx.doi.org/10.1039/b306857a.

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2

Ketchell, Debra S., Ryan Max Steinberg, Charles Yates, and Heidi A. Heilemann. "LaneConnex: An Integrated Biomedical Digital Library Interface." Information Technology and Libraries 28, no. 1 (March 1, 2009): 31. http://dx.doi.org/10.6017/ital.v28i1.3170.

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<span>This paper describes one approach to creating a search application that unlocks heterogeneous content stores and incorporates integrative functionality of Web search engines. LaneConnex is a search interface that identifies journals, books, databases, calculators, bioinformatics tools, help information, and search hits from more than three hundred full-text heterogeneous clinical and bioresearch sources. The user interface is a simple query box. Results are ranked by relevance with options for filtering by content type or expanding to the next most likely set. The system is built using component-oriented programming design. The underlying architecture is built on Apache Cocoon, Java Servlets, XML/XSLT, SQL, and JavaScript. The system has proven reliable in production, reduced user time spent finding information on the site, and maximized the institutional investment in licensed resources.</span>

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3

Testi, Debora, Paolo Quadrani, and Marco Viceconti. "PhysiomeSpace: digital library service for biomedical data." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, no. 1921 (June 28, 2010): 2853–61. http://dx.doi.org/10.1098/rsta.2010.0023.

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Every research laboratory has a wealth of biomedical data locked up, which, if shared with other experts, could dramatically improve biomedical and healthcare research. With the PhysiomeSpace service, it is now possible with a few clicks to share with selected users biomedical data in an easy, controlled and safe way. The digital library service is managed using a client–server approach. The client application is used to import, fuse and enrich the data information according to the PhysiomeSpace resource ontology and upload/download the data to the library. The server services are hosted on the Biomed Town community portal, where through a web interface, the user can complete the metadata curation and share and/or publish the data resources. A search service capitalizes on the domain ontology and on the enrichment of metadata for each resource, providing a powerful discovery environment. Once the users have found the data resources they are interested in, they can add them to their basket, following a metaphor popular in e-commerce web sites. When all the necessary resources have been selected, the user can download the basket contents into the client application. The digital library service is now in beta and open to the biomedical research community.

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4

Rickerby, Jenny, Roopa Prabhakar, Anita Patel, Jonathan Knowles, and Steve Brocchini. "A biomedical library of serinol-derived polyesters." Journal of Controlled Release 101, no. 1-3 (January 2005): 21–34. http://dx.doi.org/10.1016/j.jconrel.2004.07.021.

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5

Davidson, S. B., C. Overton, V. Tannen, and L. Wong. "BioKleisli: a digital library for biomedical researchers." International Journal on Digital Libraries 1, no. 1 (April 1997): 36–53. http://dx.doi.org/10.1007/s007990050003.

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6

Kaalaas-Sittig, J., and D. F. Sittig. "A Quantitative Ranking of the Biomedical Informatics Serials." Methods of Information in Medicine 34, no. 04 (July 1995): 397–410. http://dx.doi.org/10.1055/s-0038-1634609.

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Abstract:We have developed a quantitative serial ranking system based on multiple citation analysis techniques, library use statistics, expert opinion, and selected distinguishing publication characteristics. Evaluation criteria categories include: average Science Citation Index (Impact Factor, Immediacy Index, Total citations) rankings from 1987 to 1992; citation source counts of multiple “core” biomedical informatics publications; a questionnaire sent to American College of Medical Informatics Fellows; publication delay; distinguishing characteristics (e. g., subscription cost, total circulation, year established, places indexed, affiliation with a professional society, major biomedical resource library holdings); and the total number of interlibrary loan requests to the U. S. National Library of Medicine. The top serials were Computers and Biomedical Research, MD Computing, Methods of Information in Medicine, Medical Decision Making and Computers in Biology and Medicine.

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7

Bush, Renee. "Undersea Biomedical Research." Serials Review 13, no. 3 (September 1987): 55–56. http://dx.doi.org/10.1080/00987913.1987.10763765.

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8

Kail, Andrew, Kwai Wong, Henian Xia, and Xiaopeng Zhao. "Interoperable executive library for the simulation of biomedical processes." Journal of Computational and Applied Mathematics 270 (November 2014): 257–74. http://dx.doi.org/10.1016/j.cam.2014.01.011.

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9

Mark Hodges, T. "The Annette and Irwin Eskind biomedical library at Vanderbilt." Computer Methods and Programs in Biomedicine 44, no. 3-4 (September 1994): 209–12. http://dx.doi.org/10.1016/0169-2607(94)90115-5.

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10

Wong, S. T. C., and D. A. Tjandra. "A digital library for biomedical imaging on the Internet." IEEE Communications Magazine 37, no. 1 (January 1999): 84–91. http://dx.doi.org/10.1109/35.739310.

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11

&NA;. "Reference Library." Journal of Clinical Engineering 19, no. 5 (September 1994): 356–62. http://dx.doi.org/10.1097/00004669-199409000-00013.

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12

&NA;. "Reference Library." Journal of Clinical Engineering 20, no. 1 (January 1995): 27. http://dx.doi.org/10.1097/00004669-199501000-00009.

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13

&NA;. "Reference Library." Journal of Clinical Engineering 21, no. 6 (November 1996): 436–42. http://dx.doi.org/10.1097/00004669-199611000-00008.

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14

Ranjan, Prabhat, and Surya Nath Singh. "Library Use by Biomedical Scientists in India in Digital Era." International Journal of Information Dissemination and Technology 8, no. 4 (2018): 179. http://dx.doi.org/10.5958/2249-5576.2018.00039.0.

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15

Molina-Cantero, Alberto, Juan Castro-García, Clara Lebrato-Vázquez, Isabel Gómez-González, and Manuel Merino-Monge. "Real-Time Processing Library for Open-Source Hardware Biomedical Sensors." Sensors 18, no. 4 (March 29, 2018): 1033. http://dx.doi.org/10.3390/s18041033.

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16

Freiberger, Manuel, Florian Knoll, Kristian Bredies, Hermann Scharfetter, and Rudolf Stollberger. "The Agile Library for Biomedical Image Reconstruction Using GPU Acceleration." Computing in Science & Engineering 15, no. 1 (January 2013): 34–44. http://dx.doi.org/10.1109/mcse.2012.40.

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17

Zhao, Xia, Enjie Liu, Gordon J. Clapworthy, Marco Viceconti, and Debora Testi. "SOA-based digital library services and composition in biomedical applications." Computer Methods and Programs in Biomedicine 106, no. 3 (June 2012): 219–33. http://dx.doi.org/10.1016/j.cmpb.2010.08.009.

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18

Wong, Stephen T. C., Kent Soo Hoo, R. C. Knowlton, R. A. Hawkins, K. D. Laxer, Donny Tjandra, and Marco A. Abundo. "Issues and applications of digital library technology in biomedical imaging." International Journal on Digital Libraries 1, no. 3 (December 1997): 209–19. http://dx.doi.org/10.1007/s007990050017.

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19

Bloice, Marcus D., Peter M. Roth, and Andreas Holzinger. "Biomedical image augmentation using Augmentor." Bioinformatics 35, no. 21 (April 15, 2019): 4522–24. http://dx.doi.org/10.1093/bioinformatics/btz259.

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Abstract Motivation Image augmentation is a frequently used technique in computer vision and has been seeing increased interest since the popularity of deep learning. Its usefulness is becoming more and more recognized due to deep neural networks requiring larger amounts of data to train, and because in certain fields, such as biomedical imaging, large amounts of labelled data are difficult to come by or expensive to produce. In biomedical imaging, features specific to this domain need to be addressed. Results Here we present the Augmentor software package for image augmentation. It provides a stochastic, pipeline-based approach to image augmentation with a number of features that are relevant to biomedical imaging, such as z-stack augmentation and randomized elastic distortions. The software has been designed to be highly extensible meaning an operation that might be specific to a highly specialized task can easily be added to the library, even at runtime. Although it has been designed as a general software library, it has features that are particularly relevant to biomedical imaging and the techniques required for this domain. Availability and implementation Augmentor is a Python package made available under the terms of the MIT licence. Source code can be found on GitHub under https://github.com/mdbloice/Augmentor and installation is via the pip package manager (A Julia version of the package, developed in parallel by Christof Stocker, is also available under https://github.com/Evizero/Augmentor.jl).

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20

Ferraioli, Armando. "The Reference Library." Journal of Clinical Engineering 12, no. 6 (November 1987): 408. http://dx.doi.org/10.1097/00004669-198711000-00005.

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21

&NA;. "The Reference Library." Journal of Clinical Engineering 13, no. 1 (January 1988): 70. http://dx.doi.org/10.1097/00004669-198801000-00012.

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22

&NA;. "The Reference Library." Journal of Clinical Engineering 17, no. 2 (March 1992): 140. http://dx.doi.org/10.1097/00004669-199203000-00012.

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23

&NA;. "The Reference Library." Journal of Clinical Engineering 17, no. 2 (March 1992): 140. http://dx.doi.org/10.1097/00004669-199203000-00013.

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24

&NA;. "The Reference Library." Journal of Clinical Engineering 17, no. 2 (March 1992): 140. http://dx.doi.org/10.1097/00004669-199203000-00014.

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25

&NA;. "The Reference Library." Journal of Clinical Engineering 17, no. 2 (March 1992): 140. http://dx.doi.org/10.1097/00004669-199203000-00015.

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26

Brush, Linnea C. "The Reference Library." Journal of Clinical Engineering 19, no. 5 (September 1994): 355. http://dx.doi.org/10.1097/00004669-199409000-00011.

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27

Vidaurre, Carmen, Tilmann H. Sander, and Alois Schlögl. "BioSig: The Free and Open Source Software Library for Biomedical Signal Processing." Computational Intelligence and Neuroscience 2011 (2011): 1–12. http://dx.doi.org/10.1155/2011/935364.

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BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals.

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28

Bagirova, A. V., L. L. Sadovskaya, and P. A. Chesnyalis. "THE POTENTIAL OF INTERDICIPLINARY RESEARCHES: RUSSIAN BIOMEDICAL PUBLICATIONS IN COLLABORATION WITH LIBRARY SPECIALISTS." Proceedings of SPSTL SB RAS, no. 2 (July 5, 2020): 75–82. http://dx.doi.org/10.20913/2618-7515-2020-2-75-82.

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The article reviews interdisciplinary problems as a versatile phenomenon. Interdisciplinary is more often understood as interaction between different scientific disciplines, which can range from simple exchange of ideas to organization of joint research activities. Taking into account this understanding of the interdisciplinary phenomenon, the prerequisites for joint scientific activity between library specialists and the medical (biologists) community are considered. An attempt is made to analyze the possibilities of the library scientific community to participate in such studies. In the practical part, the Russian biomedical publications with the library employees’ participation, presented on the e Library platform, are analyzed: an array of interdisciplinary articles on this subject is revealed, the analysis of the sample by year, participation of authors, libraries, disciplines, scientometric indicators is carried out. The results made it possible to understand the features of the tasks and the specifics of research strategies. Suggestions on possible trends of interdisciplinary researches development in the libraries of Russia are formulated. Conditions, contributing to increasing the chances of library specialists to be included in interdisciplinary researches are stated based on the Reference and Bibliographic Department of SB RAS experience. Foreign interdisciplinary publications are considered by referring to reviews on this issue. The studied articles from authoritative journals on informatics and librarianship testifies to the number growth publications in biomedical disciplines prepared with participation of library specialists. A review publication in the Journal of the Medical Library Association covering interdisciplinary publications with participation of library professionals for 2008 – 2017 served as a model for the analysis. The authors concluded that the main topics of cooperation between librarians and tutors were related to the assessment of medical and social aspects of patients’ health, the search for medical information and clinical decisionmaking.

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29

Yasen, Wumaier, Ruijiao Dong, Aliya Aini, and Xinyuan Zhu. "Recent advances in supramolecular block copolymers for biomedical applications." Journal of Materials Chemistry B 8, no. 36 (2020): 8219–31. http://dx.doi.org/10.1039/d0tb01492c.

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Supramolecular block copolymers with a dynamically reversible nature and hierarchical microphase-separated structures can greatly enrich the library of pharmaceutical carriers and outline future research directions for biological applications.

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30

Bai, Ru Jiang, Xiao Yue Wang, and Xiao Fan Yu. "Comparative Analysis of the Major Ontology Library." Advanced Materials Research 267 (June 2011): 253–58. http://dx.doi.org/10.4028/www.scientific.net/amr.267.253.

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This paper introduces the major and general domestic and foreign ontology libraries: WordNet、DBpedia、Cyc and HowNet, and the more successful professional domain ontology libraries: Biomedical Ontology and Enterprise Ontology. Then separately compare and analyze them from the five aspects as the description language、storage mode、query language、platform build and application. We hope to provide assistance for the study of the domestic and foreign scholars in ontology library and its application.

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31

Federer, Lisa, and Douglas Joubert. "Providing Library Support for Interactive Scientific and Biomedical Visualizations with Tableau." Journal of eScience Librarianship 7, no. 1 (January 22, 2018): e1120. http://dx.doi.org/10.7191/jeslib.2018.1120.

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32

Hosburgh, Nathan. "Developing a Bioinformatics Program and Supporting Infrastructure in a Biomedical Library." Journal of eScience Librarianship 7, no. 2 (March 16, 2018): e1129. http://dx.doi.org/10.7191/jeslib.2018.1129.

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33

Hu, Xiaohua. "Mining novel connections from large online digital library using biomedical ontologies." Library Management 26, no. 4/5 (May 2005): 261–70. http://dx.doi.org/10.1108/01435120510596107.

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34

Zhang, Yuhao, Yuhui Zhang, Peng Qi, Christopher D. Manning, and Curtis P. Langlotz. "Biomedical and clinical English model packages for the Stanza Python NLP library." Journal of the American Medical Informatics Association 28, no. 9 (June 22, 2021): 1892–99. http://dx.doi.org/10.1093/jamia/ocab090.

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Abstract Objective The study sought to develop and evaluate neural natural language processing (NLP) packages for the syntactic analysis and named entity recognition of biomedical and clinical English text. Materials and Methods We implement and train biomedical and clinical English NLP pipelines by extending the widely used Stanza library originally designed for general NLP tasks. Our models are trained with a mix of public datasets such as the CRAFT treebank as well as with a private corpus of radiology reports annotated with 5 radiology-domain entities. The resulting pipelines are fully based on neural networks, and are able to perform tokenization, part-of-speech tagging, lemmatization, dependency parsing, and named entity recognition for both biomedical and clinical text. We compare our systems against popular open-source NLP libraries such as CoreNLP and scispaCy, state-of-the-art models such as the BioBERT models, and winning systems from the BioNLP CRAFT shared task. Results For syntactic analysis, our systems achieve much better performance compared with the released scispaCy models and CoreNLP models retrained on the same treebanks, and are on par with the winning system from the CRAFT shared task. For NER, our systems substantially outperform scispaCy, and are better or on par with the state-of-the-art performance from BioBERT, while being much more computationally efficient. Conclusions We introduce biomedical and clinical NLP packages built for the Stanza library. These packages offer performance that is similar to the state of the art, and are also optimized for ease of use. To facilitate research, we make all our models publicly available. We also provide an online demonstration (http://stanza.run/bio).

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35

Graaf, Maurits v. d. "Retrieval of biomedical reviews." Journal of Information Science 16, no. 6 (December 1990): 403–4. http://dx.doi.org/10.1177/016555159001600612.

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36

&NA;. "New Online Assay Library." Journal of Clinical Engineering 30, no. 2 (April 2005): 77. http://dx.doi.org/10.1097/00004669-200504000-00032.

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37

Kumar, Srividya, Taru Verma, Ria Mukherjee, Freek Ariese, Kumaravel Somasundaram, and Siva Umapathy. "Raman and infra-red microspectroscopy: towards quantitative evaluation for clinical research by ratiometric analysis." Chemical Society Reviews 45, no. 7 (2016): 1879–900. http://dx.doi.org/10.1039/c5cs00540j.

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38

Masic, Izet, and Asima Ferhatovica. "Review of Most Important Biomedical Databases for Searching of Biomedical Scientific Literature." Donald School Journal of Ultrasound in Obstetrics and Gynecology 6, no. 4 (2012): 343–61. http://dx.doi.org/10.5005/jp-journals-10009-1258.

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ABSTRACT We are living in the time of displossion of medical scientific information. Only in PubMed/MedLine, one of the largest host of scientific biomedical literature is indexed in almost 5,000 scientific biomedical journals. Each scientific paper is recorded in data written by rules recommended by several scientific associations and institutions. Databases can contain information about the author(s) and his/their published scientific works or results of research/investigation, including bibliographic data, abstract or full text of the paper. The databases are collecting and processing the best scientific and professional papers, or reviews and case reports published in scientific and professional journals or other publications. The reliability and quality of information guarantees producers of databases. Most important databases are located in famous university/academic centers like Bethesda [National Library Medicine (NLM)], Philadelphia (ISI), Amsterdam (Elsevier), Ipswitch (EBSCO), Geneva (WHO), Moscow (RAS), Shiraz [Islamic World Science Citation Center (ISC)], Warsaw [Index Copernicus (IC)]. Author of this review article shortly described most important online databases of biomedical literature today which will be usefull for scientists or other medical professionals. How to cite this article Masic I. Review of Most Important Biomedical Databases for Searching of Biomedical Scientific Literature. Donald School J Ultrasound Obstet Gynecol 2012;6(4):343-361.

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39

Crawford, Susan Y. "Evolution of biomedical communication as reflected by the National Library of Medicine." Journal of the Medical Library Association : JMLA 104, no. 1 (January 2016): 67–71. http://dx.doi.org/10.3163/1536-5050.104.1.011.

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40

Korhonen, I., J. Ojaniemi, K. Niieminen, M. Van Gils, A. Heikela, and A. Kari. "Building the IMPROVE data library." IEEE Engineering in Medicine and Biology Magazine 16, no. 6 (November 1997): 25–32. http://dx.doi.org/10.1109/51.637114.

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41

Rambo, Neil. "E-science and biomedical libraries." Journal of the Medical Library Association : JMLA 97, no. 3 (July 2009): 159–61. http://dx.doi.org/10.3163/1536-5050.97.3.001.

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42

Moore, Mary. "Battling the Biomedical Information Explosion." Medical Reference Services Quarterly 8, no. 1 (April 7, 1989): 13–19. http://dx.doi.org/10.1300/j115v08n01_02.

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43

Fortney, Lynn M., Judith Rieke, and Barbara A. Carlson. "Collection Development Assessment for Biomedical Serials Collections." Serials Librarian 23, no. 3-4 (March 29, 1993): 289–92. http://dx.doi.org/10.1300/j123v23n03_40.

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44

Gschneidtner, Tina A., Sarah Lerch, Erik Olsén, Xin Wen, Amelia C. Y. Liu, Alicja Stolaś, Joanne Etheridge, Eva Olsson, and Kasper Moth-Poulsen. "Constructing a library of metal and metal–oxide nanoparticle heterodimers through colloidal assembly." Nanoscale 12, no. 20 (2020): 11297–305. http://dx.doi.org/10.1039/d0nr02787a.

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Nanoparticle dimers composed of different metals or metal oxides, as well as different shapes and sizes, are of wide interest for applications ranging from nanoplasmonic sensing to nanooptics to biomedical engineering.

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45

Shereff, Denise. "Electronic Books for Biomedical Information." Journal of Electronic Resources in Medical Libraries 7, no. 2 (June 7, 2010): 115–25. http://dx.doi.org/10.1080/15424065.2010.482903.

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46

Skalak-S, R., and Armando Ferraioli. "The Reference Library Handbook of Bioengineering." Journal of Clinical Engineering 13, no. 1 (January 1988): 18. http://dx.doi.org/10.1097/00004669-198801000-00003.

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47

Deardoff, Ariel, and Dylan Romero. "From Python to Raspberry Pi: Celebrating Pi Day with data science." College & Research Libraries News 79, no. 11 (December 5, 2018): 613. http://dx.doi.org/10.5860/crln.79.11.613.

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The University of California-San Francisco (UCSF) Library is a graduate-only health science university with four professional schools (medicine, pharmacy, nursing, and dentistry), a graduate division, and an academic medical center. For several years UCSF has been the number one public recipient of NIH funding, reflecting the school’s dedication to biomedical research. Around 2015, the UCSF Library began investigating new ways to serve the university’s research population. Seeing a need for more computational and entrepreneurship training the library piloted two new programs: the Data Science Initiative (DSI) and the Makers Lab.

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48

Shelstad, Kirsten. "Landmark United States Biomedical Ethics Cases." Medical Reference Services Quarterly 18, no. 2 (June 1999): 27–53. http://dx.doi.org/10.1300/j115v18n02_03.

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49

Humphreys, B. L., and D. A. B. Lindberg. "Rising Expectations: Access to Biomedical Information." Yearbook of Medical Informatics 17, no. 01 (August 2008): 165–72. http://dx.doi.org/10.1055/s-0038-1638596.

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Summary Objective To provide an overview of the expansion in public access to electronic biomedical information over the past two decades, with an emphasis on developments to which the U.S. National Library of Medicine contributed. Methods Review of the increasingly broad spectrum of webaccessible genomic data, biomedical literature, consumer health information, clinical trials data, and images. Results The amount of publicly available electronic biomedical information has increased dramatically over the past twenty years. Rising expectations regarding access to biomedical information were stimulated by the spread of the Internet, the World Wide Web, advanced searching and linking techniques. These informatics advances simplified and improved access to electronic information and reduced costs, which enabled inter-organizational collaborations to build and maintain large international information resources and also aided outreach and education efforts The demonstrated benefits of free access to electronic biomedical information encouraged the development of public policies that further increase the amount of information available. Conclusions Continuing rapid growth of publicly accessible electronic biomedical information presents tremendous opportunities and challenges, including the need to ensure uninterrupted access during disasters or emergencies and to manage digital resources so they remain available for future generations.

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Lewison, Grant, Anne-Marie Rafferty, and Michael Traynor. "Is nursing research typical of biomedical research?" Research Evaluation 10, no. 2 (August 1, 2001): 97–103. http://dx.doi.org/10.3152/147154401781777114.

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