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Register a new userA New Ranking Scheme for the Institutional Scientific Performance
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We propose a new performance indicator to evaluate the productivity of research institutions by their disseminated scientific papers. The new quality measure includes two principle components: the normalized impact factor of the journal in which paper was published, and the number of citations received per year since it was published. In both components, the scientific impacts are weighted by the contribution of authors from the evaluated institution. As a whole, our new metric, namely, the institutional performance score takes into account both journal based impact and articles specific impacts. We apply this new scheme to evaluate research output performance of Turkish institutions specialized in astronomy and astrophysics in the period of 1998-2012. We discuss the implications of the new metric, and emphasize the benefits of it along with comparison to other proposed institutional performance indicators.
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The goal of our research is to understand how ideas propagate, combine and are created in large social networks. In this work, we look at a sample of relevant scientific publications in the area of high-frequency analog circuit design and their citation distribution. A novel aspect of our work is the way in which we categorize citations based on the reason and place of it in a publication. We created seven citation categories from general domain references, references to specific methods used in the same domain problem, references to an analysis method, references for experimental comparison and so on. This added information allows us to define two new measures to characterize the creativity (novelty and usefulness) of a publication based on its pattern of citations clustered by reason, place and citing scientific group. We analyzed 30 publications in relevant journals since 2000 and their about 300 citations, all in the area of high-frequency analog circuit design. We observed that the number of citations a publication receives from different scientific groups matches a Levy type distribution: with a large number of groups citing a publication relatively few times, and a very small number of groups citing a publication a large number of times. We looked at the motifs a publication is cited differently by different scientific groups.
The rapid development of modern science and technology has spawned rich scientific topics to research and endless production of literature in them. Just like X-ray imaging in medicine, can we intuitively identify the development limit and internal evolution pattern of scientific topic from the relationship of massive knowledge? To answer this question, we collect 71431 seminal articles of topics that cover 16 disciplines and their citation data, and extracts the idea tree of each topic to restore the structure of the development of 71431 topic networks from scratch. We define the Knowledge Entropy (KE) metric, and the contribution of high knowledge entropy nodes to increase the depth of the idea tree is regarded as the basis for topic development. By observing X-ray images of topics, We find two interesting phenomena: (1) Even though the scale of topics may increase unlimitedly, there is an insurmountable cap of topic development: the depth of the idea tree does not exceed 6 jumps, which coincides with the classical Six Degrees of Separation! (2) It is difficult for a single article to contribute more than 3 jumps to the depth of its topic, to this end, the continuing increase in the depth of the idea tree needs to be motivated by the influence relay of multiple high knowledge entropy nodes. Through substantial statistical fits, we derive a unified quantitative relationship between the change in topic depth ${Delta D}^t(v)$ and the change in knowledge entropy over time ${KE}^tleft(vright)$ of the article $v$ driving the increase in depth in the topic: ${Delta D}^t(v) approx log frac{KE^{t}(v)}{left(t-t_{0}right)^{1.8803}}$ , which can effectively portray evolution patterns of topics and predict their development potential. The various phenomena found by scientific x-ray may provide a new paradigm for explaining and understanding the evolution of science and technology.
Research grants have played an important role in seeding and promoting fundamental research projects worldwide. There is a growing demand for developing and delivering scientific influence analysis as a service on research grant repositories. Such analysis can provide insight on how research grants help foster new research collaborations, encourage cross-organizational collaborations, influence new research trends, and identify technical leadership. This paper presents the design and development of a grants-based scientific influence analysis service, coined as GImpact. It takes a graph-theoretic approach to design and develop large scale scientific influence analysis over a large research-grant repository with three original contributions. First, we mine the grant database to identify and extract important features for grants influence analysis and represent such features using graph theoretic models. For example, we extract an institution graph and multiple associated aspect-based collaboration graphs, including a discipline graph and a keyword graph. Second, we introduce self-influence and co-influence algorithms to compute two types of collaboration relationship scores based on the number of grants and the types of grants for institutions. We compute the self-influence scores to reflect the grant based research collaborations among institutions and compute multiple co-influence scores to model the various types of cross-institution collaboration relationships in terms of disciplines and subject areas. Third, we compute the overall scientific influence score for every pair of institutions by introducing a weighted sum of the self-influence score and the multiple co-influence scores and conduct an influence-based clustering analysis. We evaluate GImpact using a real grant database, consisting of 2512 institutions and their grants received over a period of 14 years...
The number of publications and the number of citations received have become the most common indicators of scholarly success. In this context, scientific writing increasingly plays an important role in scholars scientific careers. To understand the relationship between scientific writing and scientific impact, this paper selected 12 variables of linguistic complexity as a proxy for depicting scientific writing. We then analyzed these features from 36,400 full-text Biology articles and 1,797 full-text Psychology articles. These features were compared to the scientific impact of articles, grouped into high, medium, and low categories. The results suggested no practical significant relationship between linguistic complexity and citation strata in either discipline. This suggests that textual complexity plays little role in scientific impact in our data sets.
We present a bibliographic analysis of Chandra, Hubble, and Spitzer publications. We find (a) archival data are used in >60% of the publication output and (b) archives for these missions enable a much broader set of institutions and countries to scientifically use data from these missions. Specifically, we find that authors from institutions that have published few papers from a given mission publish 2/3 archival publications, while those with many publications typically have 1/3 archival publications. We also show that countries with lower GDP per capita overwhelmingly produce archival publications, while countries with higher GDP per capital produce guest observer and archival publications in equal amounts. We argue that robust archives are thus not only critical for the scientific productivity of mission data, but also the scientific accessibility of mission data. We argue that the astronomical community should support archives to maximize the overall scientific societal impact of astronomy, and represent an excellent investment in astronomys future.
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