Delineating the citation impact of scientific discoveries
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Delineating the Citation Impact of Scientific Discoveries . Chaomei Chen 1 , Jian Zhang 1 , Weizhong Zhu 1 , Michael Vogeley 2 1 College of Information Science and Technology, Drexel University 2 Department of Physics, Drexel University .

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Delineating the Citation Impact of Scientific Discoveries

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Delineating the Citation Impact of Scientific Discoveries

Chaomei Chen1, Jian Zhang1, Weizhong Zhu1, Michael Vogeley2

1College of Information Science and Technology, Drexel University

2Department of Physics, Drexel University

This work is supported by the National Science Foundation under Grant No. 0612129.

Thomson ISI provides the bibliographic data for the analysis.


As We May Thinkby Vannevar Bush

There is a growing mountain of research. But there is increased evidence that we are being bogged down today as specialization extends. The investigator is staggered by the findings and conclusions of thousands of other workers—conclusions which he cannot find time to grasp, much less to remember, as they appear. Yet specialization becomes increasingly necessary for progress, and the effort to bridge between disciplines is correspondingly superficial.


massive scientific data are being collected by one group of scientists

and

being analyzed by another group of scientists.

Two notable examples:

1. The SDSS project in astrophysics

2. The human genome project in biomedicine

An Increasingly Strong Trend in ScienceGray & Szalay 2004


Sloan Digital Sky SurveyThe most ambitious astronomical survey ever undertaken

There is an increasingly strong trend in science that massive scientific data are being collected by one group of scientists and being analyzed by another group of scientists (Gray & Szalay 2004). Two notable examples: the SDSS project in astrophysics and the human genome project in biomedicine.

Sloan Survey Data

  • June, 2006: Data Release Five:8000 square degrees, 1,048,960 spectra.

  • June, 2005: Data Release Four:6670 square degrees, 806,400 spectra.

  • September, 2004: Data Release Three:5282 square degrees, 528,640 spectra.

  • March, 2004: Data Release Two:3324 square degrees, 367,360 spectra.

  • April, 2003: Data Release One:2099 square degrees, 186,240 spectra.

  • June, 2001: Early Data Release:462 square degrees, 52,896 spectra.

SDSS Literature

  • Total number of articles: 1,478

  • Total citations: 47,282

  • June 18, 2007: H = 95

  • January 30, 2007: H = 89


Integrating Microscopic and Macroscopic perspectives

  • Connecting text-level patterns (microscopic) and paper-level citation impacts (macroscopic)

    • improve our understanding of science in the making

    • develop data mining and visual analytics algorithms


Figure 3. Prominent keywords assigned by authors and burst terms extracted from titles and abstracts (2002-2006).


Class I

Hc, Ht Split

Class II


Fast-Growing SDSS Literature

  • 1,400 papers

  • 40,000 citations

  • The total citation number doubled in the past 1.5 years.

  • H-index of SDSS literature = 89 95


As of June 18, 2007, 95 SDSS papers

have 95 or more citations.

It was 89 in January 2007.


Measuring the Citation Impact

Sc discounts citations accumulated over a long period of time.

  • Sc is adjusted for publication age.

    St measures the recent impact:

  • St gives heavier weights to relatively recent citations than earlier citations.


Hg Indices and Splits

  • The 1,293 records

    • H-index = 65, including 3 papers have 65 citations

    • Hc index =52

    • Ht index = 53

  • The H split

    • 67 papers in the highly cited group

    • 1,226 remaining papers in the second group


Class I

Class II

Class I


Significant Noun Phrases

  • 22,665 noun phrases identified by a part-of-speech tagging and pattern matching process.

  • 290 of them are selected based on their log-likelihood ratios.


Figure 4. An overview of a decision tree generated based on 216 terms selected by log-likelihood ratio values (p<0.01) and a geometric mean split (74.44% of classification accuracy). The tree should be read from the root downwards .


Figure 5. A part of the tree shown in Figure 4. The presence (>0) or absence (<=0) of a term is associated with a citation status group, i.e. highly and timely cited group.


Figure 6. An ADTree derived from the data selected with the same selection criteria with 70.55% of accuracy.


n-

Figure 7. A decision tree of 95.82% classification accuracy derived from 721 terms and 1,267 records.


Figure 10. The citation history of timeliness papers shows recently published papers are moved up in the rankings.


Future Work

  • Unsupervised ontology construction to smooth the feature space

  • Incremental classification of incoming new data and scholarly publications

  • Self-directed optimization of existing decision trees based on new evidence

  • Full-text analysis that can model associative relations between hypotheses and evidence and between facts and opinions


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