@article{ioannidis2014published, abstract = {

In a 2005 paper that has been accessed more than a million times, John Ioannidis explained why most published research findings were false. Here he revisits the topic, this time to address how to improve matters.

Please see later in the article for the Editors' Summary

}, author = {Ioannidis, John P. A.}, doi = {10.1371/journal.pmed.1001747}, interhash = {8f87798566749594f170a42763ad239e}, intrahash = {2ced982df534cdc04b9feff0f4206b2a}, journal = {PLoS Med}, month = {10}, number = 10, pages = {e1001747}, publisher = {Public Library of Science}, title = {How to Make More Published Research True}, url = {http://dx.doi.org/10.1371%2Fjournal.pmed.1001747}, volume = 11, year = 2014 } @article{dunne2012rapid, abstract = {Keeping up with rapidly growing research fields, especially when there are multiple interdisciplinary sources, requires substantial effort for researchers, program managers, or venture capital investors. Current theories and tools are directed at finding a paper or website, not gaining an understanding of the key papers, authors, controversies, and hypotheses. This report presents an effort to integrate statistics, text analytics, and visualization in a multiple coordinated window environment that supports exploration. Our prototype system, Action Science Explorer (ASE), provides an environment for demonstrating principles of coordination and conducting iterative usability tests of them with interested and knowledgeable users. We developed an understanding of the value of reference management, statistics, citation text extraction, natural language summarization for single and multiple documents, filters to interactively select key papers, and network visualization to see citation patterns and identify clusters. A three-phase usability study guided our revisions to ASE and led us to improve the testing methods.}, author = {Dunne, Cody and Shneiderman, Ben and Gove, Robert and Klavans, Judith and Dorr, Bonnie}, doi = {10.1002/asi.22652}, interhash = {f031d712f64663242af6b6ec95b74f48}, intrahash = {045df67628ff0ae9b75bb1ecf915d025}, issn = {1532-2890}, journal = {Journal of the American Society for Information Science and Technology}, number = 12, pages = {2351--2369}, title = {Rapid understanding of scientific paper collections: Integrating statistics, text analytics, and visualization}, url = {http://dx.doi.org/10.1002/asi.22652}, volume = 63, year = 2012 } @article{ley2009lessons, abstract = {The DBLP Computer Science Bibliography evolved from an early small experimental Web server to a popular service for the computer science community. Many design decisions and details of the public XML-records behind DBLP never were documented. This paper is a review of the evolution of DBLP. The main perspective is data modeling. In DBLP persons play a central role, our discussion of person names may be applicable to many other data bases. All DBLP data are available for your own experiments. You may either download the complete set, or use a simple XML-based API described in an online appendix.}, acmid = {1687577}, author = {Ley, Michael}, interhash = {a75ae2987d55512b7d0731c7a11a1722}, intrahash = {bb968ff4ba9ae93bc80ba05d16a98ff4}, issn = {2150-8097}, issue_date = {August 2009}, journal = {Proceedings of the VLDB Endowment}, month = aug, number = 2, numpages = {8}, pages = {1493--1500}, publisher = {VLDB Endowment}, title = {DBLP: some lessons learned}, url = {http://dl.acm.org/citation.cfm?id=1687553.1687577}, volume = 2, year = 2009 } @article{bollen2009clickstream, abstract = {Background Intricate maps of science have been created from citation data to visualize the structure of scientific activity. However, most scientific publications are now accessed online. Scholarly web portals record detailed log data at a scale that exceeds the number of all existing citations combined. Such log data is recorded immediately upon publication and keeps track of the sequences of user requests (clickstreams) that are issued by a variety of users across many different domains. Given these advantages of log datasets over citation data, we investigate whether they can produce high-resolution, more current maps of science. Methodology Over the course of 2007 and 2008, we collected nearly 1 billion user interactions recorded by the scholarly web portals of some of the most significant publishers, aggregators and institutional consortia. The resulting reference data set covers a significant part of world-wide use of scholarly web portals in 2006, and provides a balanced coverage of the humanities, social sciences, and natural sciences. A journal clickstream model, i.e. a first-order Markov chain, was extracted from the sequences of user interactions in the logs. The clickstream model was validated by comparing it to the Getty Research Institute's Architecture and Art Thesaurus. The resulting model was visualized as a journal network that outlines the relationships between various scientific domains and clarifies the connection of the social sciences and humanities to the natural sciences. Conclusions Maps of science resulting from large-scale clickstream data provide a detailed, contemporary view of scientific activity and correct the underrepresentation of the social sciences and humanities that is commonly found in citation data.}, author = {Bollen, Johan and van de Sompel, Herbert and Hagberg, Aric and Bettencourt, Luis and Chute, Ryan and Rodriguez, Marko A. and Balakireva, Lyudmila}, doi = {10.1371/journal.pone.0004803}, interhash = {3a371a1ed31d14204770315b52023b96}, intrahash = {e61bd0c26cc1c08cff22a8301d03044f}, journal = {PLoS ONE}, month = mar, number = 3, pages = {e4803}, publisher = {Public Library of Science}, title = {Clickstream Data Yields High-Resolution Maps of Science}, url = {http://dx.doi.org/10.1371/journal.pone.0004803}, volume = 4, year = 2009 } @article{newman2001structure, abstract = {The structure of scientific collaboration networks is investigated. Two scientists are considered connected if they have authored a paper together and explicit networks of such connections are constructed by using data drawn from a number of databases, including MEDLINE biomedical research, the Los Alamos e-Print Archive physics, and NCSTRL computer science. I show that these collaboration networks form ” small worlds,” in which randomly chosen pairs of scientists are typically separated by only a short path of intermediate acquaintances. I further give results for mean and distribution of numbers of collaborators of authors, demonstrate the presence of clustering in the networks, and highlight a number of apparent differences in the patterns of collaboration between the fields studied.}, author = {Newman, M. E. J.}, doi = {10.1073/pnas.98.2.404}, eprint = {http://www.pnas.org/content/98/2/404.full.pdf+html}, interhash = {8c5edd915b304ae09fc08e0a51dfd5e9}, intrahash = {a4d3149c7198762a99102935da4d1bdb}, journal = {Proceedings of the National Academy of Sciences}, number = 2, pages = {404--409}, title = {The structure of scientific collaboration networks}, url = {http://www.pnas.org/content/98/2/404.abstract}, volume = 98, year = 2001 } @inproceedings{1271658, abstract = {Bibliometric analysis is used as a measuring activity technique for basic research. There are many country level analyses of trends in scientific publications. These analyses give us an understanding of the macro-scale character of scientific activities. However, it is difficult to capture the qualitative evolution of scientific activities through them. In this regard, a meso-scale analysis of science activities, i.e., analysis of "research areas", is suitable for grasping qualitative changes in scientific activities. In this study, we develop a new method for mapping science at the research area level. Our method consists of two parts: constructing research areas from scientific publications and content analysis by experts. Research areas are explored through a co-citation analysis, and a map of science was generated to analyze how research areas relate to each other. This method contributes to endeavours to understand and track the changing nature of science.}, address = {Washington, DC, USA}, author = {SAKA, Ayaka and IGAMI, Masatsura}, booktitle = {IV '07: Proceedings of the 11th International Conference Information Visualization}, doi = {http://dx.doi.org/10.1109/IV.2007.77}, interhash = {1586085e24335ab7d0f8f5530d32552d}, intrahash = {a9168950512836c2155af1ed6dc99453}, isbn = {0-7695-2900-3}, pages = {453--458}, publisher = {IEEE Computer Society}, title = {Mapping Modern Science Using Co-citation Analysis}, url = {http://portal.acm.org/citation.cfm?id=1270398.1271658}, year = 2007 }