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Amy Friedlander

Bio: Amy Friedlander is an academic researcher. The author has contributed to research in topics: Electronic publishing & Cataloging. The author has an hindex of 1, co-authored 1 publications receiving 245 citations.

Papers
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Journal ArticleDOI
TL;DR: The importance of persistent, location-independent identifiers (or names) is discussed primarily from the perspectives of information organization and the associated issues in cataloging resources in the MARC environment.
Abstract: Summary This paper describes the organization of material in D-Lib Magazine http://www.dlib.org, an online reference collection of pointers to sites containing resources in networked information and digital libraries, and a monthly, which addresses developments in advanced research and implementation projects in digital libraries and related topics. The importance of persistent, location-independent identifiers (or names) is discussed primarily from the perspectives of information organization and the associated issues in cataloging resources in the MARC environment.

245 citations


Cited by
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Journal ArticleDOI
TL;DR: The FAIR Data Principles as mentioned in this paper are a set of data reuse principles that focus on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.
Abstract: There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

7,602 citations

Journal ArticleDOI
06 Jan 2012-PLOS ONE
TL;DR: This paper describes the evolution and development of Darwin Core, a data standard for publishing and integrating biodiversity information, focusing on the categories of terms that define the standard, differences between simple and relational DarwinCore, how the standard has been implemented and the community processes that are essential for maintenance and growth of the standard.
Abstract: Biodiversity data derive from myriad sources stored in various formats on many distinct hardware and software platforms. An essential step towards understanding global patterns of biodiversity is to provide a standardized view of these heterogeneous data sources to improve interoperability. Fundamental to this advance are definitions of common terms. This paper describes the evolution and development of Darwin Core, a data standard for publishing and integrating biodiversity information. We focus on the categories of terms that define the standard, differences between simple and relational Darwin Core, how the standard has been implemented, and the community processes that are essential for maintenance and growth of the standard. We present case-study extensions of the Darwin Core into new research communities, including metagenomics and genetic resources. We close by showing how Darwin Core records are integrated to create new knowledge products documenting species distributions and changes due to environmental perturbations.

767 citations

Proceedings ArticleDOI
07 Jun 2004
TL;DR: Two supervised learning approaches to disambiguate authors in the citations are investigated, one uses the naive Bayes probability model, a generative model; the other uses support vector machines (SVMs) and the vector space representation of citations, a discriminative model.
Abstract: Due to name abbreviations, identical names, name misspellings, and pseudonyms in publications or bibliographies (citations), an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, Web search, database integration, and may cause improper attribution to authors. We investigate two supervised learning approaches to disambiguate authors in the citations. One approach uses the naive Bayes probability model, a generative model; the other uses support vector machines (SVMs) [V. Vapnik (1995)] and the vector space representation of citations, a discriminative model. Both approaches utilize three types of citation attributes: coauthor names, the title of the paper, and the title of the journal or proceeding. We illustrate these two approaches on two types of data, one collected from the Web, mainly publication lists from homepages, the other collected from the DBLP citation databases.

378 citations

Proceedings ArticleDOI
07 Jun 2005
TL;DR: An unsupervised learning approach using K-way spectral clustering that disambiguates authors in citations is proposed that utilizes three types of citation attributes: co-author names, paper titles, and publication venue titles.
Abstract: An author may have multiple names and multiple authors may share the same name simply due to name abbreviations, identical names, or name misspellings in publications or bibliographies (citations). This can produce name ambiguity which can affect the performance of document retrieval, web search, and database integration, and may cause improper attribution of credit. Proposed here is an unsupervised learning approach using K-way spectral clustering that disambiguates authors in citations. The approach utilizes three types of citation attributes: co-author names, paper titles, and publication venue titles. The approach is illustrated with 16 name datasets with citations collected from the DBLP database bibliography and author home pages and shows that name disambiguation can be achieved using these citation attributes

306 citations