h-index

The h-index is an author-level metric that attempts to measure both the productivity and citation impact of the publications of a scientist or scholar. The index is based on the set of the scientist's most cited papers and the number of citations that they have received in other publications. The index can also be applied to the productivity and impact of a scholarly journal[1] as well as a group of scientists, such as a department or university or country.[2] The index was suggested in 2005 by Jorge E. Hirsch, a physicist at UC San Diego, as a tool for determining theoretical physicists' relative quality[3] and is sometimes called the Hirsch index or Hirsch number.

Definition and purpose

H-index-en
h-index from a plot of decreasing citations for numbered papers

The h-index is defined as the maximum value of h such that the given author/journal has published h papers that have each been cited at least h times.[4] The index is designed to improve upon simpler measures such as the total number of citations or publications. The index works properly only for comparing scientists working in the same field; citation conventions differ widely among different fields.

Calculation

Formally, if f is the function that corresponds to the number of citations for each publication, we compute the h index as follows. First we order the values of f from the largest to the lowest value. Then, we look for the last position in which f is greater than or equal to the position (we call h this position). For example, if we have a researcher with 5 publications A, B, C, D, and E with 10, 8, 5, 4, and 3 citations, respectively, the h index is equal to 4 because the 4th publication has 4 citations and the 5th has only 3. In contrast, if the same publications have 25, 8, 5, 3, and 3 citations, then the index is 3 because the fourth paper has only 3 citations.

f(A)=10, f(B)=8, f(C)=5, f(D)=4, f(E)=3 → h-index=4
f(A)=25, f(B)=8, f(C)=5, f(D)=3, f(E)=3 → h-index=3

If we have the function f ordered in decreasing order from the largest value to the lowest one, we can compute the h index as follows:

h-index (f) =

The Hirsch index is analogous to the Eddington number, an earlier metric used for evaluating cyclists. The h-index serves as an alternative to more traditional journal impact factor metrics in the evaluation of the impact of the work of a particular researcher. Because only the most highly cited articles contribute to the h-index, its determination is a simpler process. Hirsch has demonstrated that h has high predictive value for whether a scientist has won honors like National Academy membership or the Nobel Prize. The h-index grows as citations accumulate and thus it depends on the "academic age" of a researcher.

Input data

The h-index can be manually determined using citation databases or using automatic tools. Subscription-based databases such as Scopus and the Web of Science provide automated calculators. Harzing's Publish or Perish program calculates the h-index based on Google Scholar entries. From July 2011 Google have provided an automatically-calculated h-index and i10-index within their own Google Scholar profile.[5] In addition, specific databases, such as the INSPIRE-HEP database can automatically calculate the h-index for researchers working in high energy physics.

Each database is likely to produce a different h for the same scholar, because of different coverage.[6] A detailed study showed that the Web of Science has strong coverage of journal publications, but poor coverage of high impact conferences. Scopus has better coverage of conferences, but poor coverage of publications prior to 1996; Google Scholar has the best coverage of conferences and most journals (though not all), but like Scopus has limited coverage of pre-1990 publications.[7][8] The exclusion of conference proceedings papers is a particular problem for scholars in computer science, where conference proceedings are considered an important part of the literature.[9] Google Scholar has been criticized for producing "phantom citations," including gray literature in its citation counts, and failing to follow the rules of Boolean logic when combining search terms.[10] For example, the Meho and Yang study found that Google Scholar identified 53% more citations than Web of Science and Scopus combined, but noted that because most of the additional citations reported by Google Scholar were from low-impact journals or conference proceedings, they did not significantly alter the relative ranking of the individuals. It has been suggested that in order to deal with the sometimes wide variation in h for a single academic measured across the possible citation databases, one should assume false negatives in the databases are more problematic than false positives and take the maximum h measured for an academic.[11]

Comparing results across fields and career levels

Little systematic investigation has been done on how the h-index behaves over different institutions, nations, times and academic fields. Hirsch suggested that, for physicists, a value for h of about 12 might be typical for advancement to tenure (associate professor) at major [US] research universities. A value of about 18 could mean a full professorship, 15–20 could mean a fellowship in the American Physical Society, and 45 or higher could mean membership in the United States National Academy of Sciences.[12] Hirsch estimated that after 20 years a "successful scientist" would have an h-index of 20, an "outstanding scientist" would have an h-index of 40, and a "truly unique" individual would have an h-index of 60.[3]

For the most highly cited scientists in the period 1983–2002, Hirsch identified the top 10 in the life sciences (in order of decreasing h): Solomon H. Snyder, h = 191; David Baltimore, h = 160; Robert C. Gallo, h = 154; Pierre Chambon, h = 153; Bert Vogelstein, h = 151; Salvador Moncada, h = 143; Charles A. Dinarello, h = 138; Tadamitsu Kishimoto, h = 134; Ronald M. Evans, h = 127; and Axel Ullrich, h = 120. Among 36 new inductees in the National Academy of Sciences in biological and biomedical sciences in 2005, the median h-index was 57.[3] However, Hirsch noted that values of h will vary between different fields.[3]

Among the 22 scientific disciplines listed in the Thomson Reuters Essential Science Indicators Citation Thresholds [thus excluding non-science academics], physics has the second most citations after space science.[13] During the period January 1, 2000 – February 28, 2010, a physicist had to receive 2073 citations to be among the most cited 1% of physicists in the world.[13] The threshold for space science is the highest (2236 citations), and physics is followed by clinical medicine (1390) and molecular biology & genetics (1229). Most disciplines, such as environment/ecology (390), have fewer scientists, fewer papers, and fewer citations.[13] Therefore, these disciplines have lower citation thresholds in the Essential Science Indicators, with the lowest citation thresholds observed in social sciences (154), computer science (149), and multidisciplinary sciences (147).[13]

Numbers are very different in social science disciplines: The Impact of the Social Sciences team at London School of Economics found that social scientists in the United Kingdom had lower average h-indices. The h-indices for ("full") professors, based on Google Scholar data ranged from 2.8 (in law), through 3.4 (in political science), 3.7 (in sociology), 6.5 (in geography) and 7.6 (in economics). On average across the disciplines, a professor in the social sciences had an h-index about twice that of a lecturer or a senior lecturer, though the difference was the smallest in geography.[14]

Advantages

Hirsch intended the h-index to address the main disadvantages of other bibliometric indicators, such as total number of papers or total number of citations. Total number of papers does not account for the quality of scientific publications, while total number of citations can be disproportionately affected by participation in a single publication of major influence (for instance, methodological papers proposing successful new techniques, methods or approximations, which can generate a large number of citations), or having many publications with few citations each. The h-index is intended to measure simultaneously the quality and quantity of scientific output.

Criticism

There are a number of situations in which h may provide misleading information about a scientist's output:[15] Most of these however are not exclusive to the h-index.

  • The h-index does not account for the typical number of citations in different fields. It has been stated that citation behavior in general is affected by field-dependent factors,[16] which may invalidate comparisons not only across disciplines but even within different fields of research of one discipline.[17]
  • The h-index discards the information contained in author placement in the authors' list, which in some scientific fields is significant.[18][19]
  • The h-index has been found in one study to have slightly less predictive accuracy and precision than the simpler measure of mean citations per paper.[20] However, this finding was contradicted by another study by Hirsch.[21]
  • The h-index is a natural number that reduces its discriminatory power. Ruane and Tol therefore propose a rational h-index that interpolates between h and h + 1.[22]
  • The h-index can be manipulated through self-citations,[23][24][25] and if based on Google Scholar output, then even computer-generated documents can be used for that purpose, e.g. using SCIgen.[26]
  • The h-index does not provide a significantly more accurate measure of impact than the total number of citations for a given scholar. In particular, by modeling the distribution of citations among papers as a random integer partition and the h-index as the Durfee square of the partition, Yong[27] arrived at the formula , where N is the total number of citations, which, for mathematics members of the National Academy of Sciences, turns out to provide an accurate (with errors typically within 10–20 percent) approximation of h-index in most cases.

Alternatives and modifications

Various proposals to modify the h-index in order to emphasize different features have been made.[28][29][30][31][32][33] As the variants have proliferated, comparative studies have become possible showing that most proposals are highly correlated with the original h-index and therefore largely redundant,[34] although alternative indexes may be important to decide between comparable CVs, as often the case in evaluation processes.

  • An individual h-index normalized by the number of authors has been proposed: , with being the number of authors considered in the papers.[28] It was found that the distribution of the h-index, although it depends on the field, can be normalized by a simple rescaling factor. For example, assuming as standard the hs for biology, the distribution of h for mathematics collapse with it if this h is multiplied by three, that is, a mathematician with h = 3 is equivalent to a biologist with h = 9. This method has not been readily adopted, perhaps because of its complexity. It might be simpler to divide citation counts by the number of authors before ordering the papers and obtaining the h-index, as originally suggested by Hirsch.
  • The m-index is defined as h/n, where n is the number of years since the first published paper of the scientist;[3] also called m-quotient.[35][36]
  • There are a number of models proposed to incorporate the relative contribution of each author to a paper, for instance by accounting for the rank in the sequence of authors.[37]
  • A generalization of the h-index and some other indices that gives additional information about the shape of the author's citation function (heavy-tailed, flat/peaked, etc.) has been proposed.[38]
  • A successive Hirsch-type-index for institutions has also been devised.[39][40] A scientific institution has a successive Hirsch-type-index of i when at least i researchers from that institution have an h-index of at least i.
  • Three additional metrics have been proposed: h2 lower, h2 center, and h2 upper, to give a more accurate representation of the distribution shape. The three h2 metrics measure the relative area within a scientist's citation distribution in the low impact area, h2 lower, the area captured by the h-index, h2 center, and the area from publications with the highest visibility, h2 upper. Scientists with high h2 upper percentages are perfectionists, whereas scientists with high h2 lower percentages are mass producers. As these metrics are percentages, they are intended to give a qualitative description to supplement the quantitative h-index.[41]
  • The g-index can be seen as the h-index for an averaged citations count.[42]
  • It has been argued that "For an individual researcher, a measure such as Erdős number captures the structural properties of network whereas the h-index captures the citation impact of the publications. One can be easily convinced that ranking in coauthorship networks should take into account both measures to generate a realistic and acceptable ranking." Several author ranking systems such as eigenfactor (based on eigenvector centrality) have been proposed already, for instance the Phys Author Rank Algorithm.[43]
  • The c-index accounts not only for the citations but for the quality of the citations in terms of the collaboration distance between citing and cited authors. A scientist has c-index n if n of [his/her] N citations are from authors which are at collaboration distance at least n, and the other (Nn) citations are from authors which are at collaboration distance at most n.[44]
  • An s-index, accounting for the non-entropic distribution of citations, has been proposed and it has been shown to be in a very good correlation with h.[45]
  • The e-index, the square root of surplus citations for the h-set beyond h2, complements the h-index for ignored citations, and therefore is especially useful for highly cited scientists and for comparing those with the same h-index (iso-h-index group).[46][47]
  • Because the h-index was never meant to measure future publication success, recently, a group of researchers has investigated the features that are most predictive of future h-index. It is possible to try the predictions using an online tool.[48] However, later work has shown that since h-index is a cumulative measure, it contains intrinsic auto-correlation that led to significant overestimation of its predictability. Thus, the true predictability of future h-index is much lower compared to what has been claimed before.[49]
  • The h-index has been applied to Internet Media, such as YouTube channels. The h-index is defined as the number of videos with ≥ h × 105 views. When compared with a video creator's total view count, the h-index and g-index better capture both productivity and impact in a single metric.[50]
  • The i10-index indicates the number of academic publications an author has written that have been cited by at least ten sources. It was introduced in July 2011 by Google as part of their work on Google Scholar.[51]
  • The h-index has been shown to have a strong discipline bias. However, a simple normalization by the average h of scholars in a discipline d is an effective way to mitigate this bias, obtaining a universal impact metric that allows comparison of scholars across different disciplines.[52] Of course this method does not deal with academic age bias.
  • The h-index can be timed to analyze its evolution during one's career, employing different time windows.[53]
  • The o-index corresponds to the geometric mean of the h-index and the most cited paper of a researcher.[54]
  • The RA-index accommodates improving the sensitivity of the H-index on the number of highly cited papers and has many cited paper and uncited paper under the H-core. This improvement can enhance the measurement sensitivity of the H-index. [55]

See also

References

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Further reading

External links

Clifford Stein

Clifford Seth Stein (born December 14, 1965), a computer scientist, is a professor of industrial engineering and operations research at Columbia University in New York, NY, where he also holds an appointment in the Department of Computer Science. Stein is chair of the Industrial Engineering and Operations Research Department at Columbia University. Prior to joining Columbia, Stein was a professor at Dartmouth College in New Hampshire.

Stein's research interests include the design and analysis of algorithms, combinatorial optimization, operations research, network algorithms, scheduling, algorithm engineering and computational biology.

Stein has published many influential papers in the leading conferences and journals in his fields of research, and has occupied a variety of editorial positions including in the journals ACM Transactions on Algorithms, Mathematical Programming, Journal of Algorithms, SIAM Journal on Discrete Mathematics and Operations Research Letters. His work has been funded by the National Science Foundation and the Sloan Foundation. As of November 1, 2015, his publications have been cited over 46,000 times, and he has an h-index of 42.Stein is the winner of several prestigious awards including an NSF Career Award, an Alfred Sloan Research Fellowship and the Karen Wetterhahn Award for Distinguished Creative or Scholarly Achievement. He is also the co-author of two textbooks:

Introduction to Algorithms, with T. Cormen, C. Leiserson and R. Rivest, which is currently the best-selling textbook in algorithms and has been translated into 8 languages. About 39,500 of Stein's 46,000 citations are made to this book.

Discrete Math for Computer Science, with Ken Bogart and Scot Drysdale, which is a new textbook that covers discrete math at an undergraduate level.Stein earned his B.S.E. from Princeton University in 1987, a Master of Science from The Massachusetts Institute of Technology in 1989, and a PhD also from the Massachusetts Institute of Technology in 1992.In recent years, Stein has built up close ties with the Norwegian research community which earned him an honorary doctorate from the University of Oslo (May 2010).

Craig E. Manning

Craig E. Manning is a professor of geology and geochemistry in the Department of Earth, Planetary, and Space Sciences at the University of California, Los Angeles, where he served as department chair between 2009 and 2012. Manning's research interests include water chemistry, thermodynamics, gas chemistry, geochemistry, igneous petrology, and metamorphic petrology.

David S. Johnson

David Stifler Johnson (December 9, 1945 – March 8, 2016) was an American computer scientist specializing in algorithms and optimization. He was the head of the Algorithms and Optimization Department of AT&T Labs Research from 1988 to 2013, and was a visiting professor at Columbia University from 2014 to 2016. He was awarded the 2010 Knuth Prize.Johnson was born in 1945 in Washington, D.C.. He graduated summa cum laude from Amherst College in 1967, then earned his S.M. from MIT in 1968 and his Ph.D. from MIT in 1973. All three of his degrees are in mathematics. He was inducted as a Fellow of the Association for Computing Machinery in 1995, and as a member of the National Academy of Engineering in 2016.

He was the coauthor of Computers and Intractability: A Guide to the Theory of NP-Completeness (ISBN 0-7167-1045-5) along with Michael Garey. As of March 9, 2016, his publications have been cited over 96,000 times, and he has an h-index of 78. Johnson died on March 8, 2016 at the age of 70.

Eigenfactor

The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. As a measure of importance, the Eigenfactor score scales with the total impact of a journal. All else equal, journals generating higher impact to the field have larger Eigenfactor scores.

Eigenfactor scores and Article Influence scores are calculated by eigenfactor.org, where they can be freely viewed. The Eigenfactor score is intended to measure the importance of a journal to the scientific community, by considering the origin of the incoming citations, and is thought to reflect how frequently an average researcher would access content from that journal. However, the Eigenfactor score is influenced by the size of the journal, so that the score doubles when the journal doubles in size (measured as number of published articles per year). The Article Influence score measures the average influence of articles in the journal, and is therefore comparable to the traditional impact factor.

The Eigenfactor approach is thought to be more robust than the impact factor metric, which purely counts incoming citations without considering the significance of those citations. While the Eigenfactor score is correlated with total citation count for medical journals, these metrics provide significantly different information. For a given number of citations, citations from more significant journals will result in a higher Eigenfactor score.Originally Eigenfactor scores were measures of a journal's importance; it has been extended to author-level. It can also be used in combination with the h-index to evaluate the work of individual scientists.

G-index

The g-index is an index for quantifying productivity in science, based on publication record (an author-level metric). It was suggested in 2006 by Leo Egghe.

The index is calculated based on the distribution of citations received by a given researcher's publications, such that given a set of articles ranked in decreasing order of the number of citations that they received, the g-index is the unique largest number such that the top g articles received together at least g2 citations.

It can be equivalently defined as the largest number n of highly cited articles for which the average number of citations is at least n. This is in fact a rewriting of the definition

as

The g-index is an alternative for the older h-index, which does not average the numbers of citations. The h-index only requires a minimum of n citations for the least-cited article in the set and thus ignores the citation count of very highly cited papers. Roughly, the effect is that h is the number of papers of a quality threshold that rises as h rises; g allows citations from higher-cited papers to be used to bolster lower-cited papers in meeting this threshold. Therefore, in all cases g is at least h, and is in most cases higher. However, unlike the h-index, the g-index saturates whenever the average number of citations for all published papers exceeds the total number of published papers; the way it is defined, the g-index is not adapted to this situation.

The g-index has been characterized in terms of three natural axioms by Woeginger (2008). The simplest of these three axioms states that by moving citations from weaker articles to stronger articles, one's research index should not decrease. Like the h-index, the g-index is a natural number and thus lacks in discriminatory power. Therefore, Tol (2008) proposed a rational generalisation.[clarification needed]

Tol also proposed a collective g-index.

Given a set of researchers ranked in decreasing order of their g-index, the g1-index is the (unique) largest number such that the top g1 researchers have on average at least a g-index of g1.
Google Scholar

Google Scholar is a freely accessible web search engine that indexes the full text or metadata of scholarly literature across an array of publishing formats and disciplines. Released in beta in November 2004, the Google Scholar index includes most peer-reviewed online academic journals and books, conference papers, theses and dissertations, preprints, abstracts, technical reports, and other scholarly literature, including court opinions and patents. While Google does not publish the size of Google Scholar's database, scientometric researchers estimated it to contain roughly 389 million documents including articles, citations and patents making it the world's largest academic search engine in January 2018. Previously, the size was estimated at 160 million documents as of May 2014. Earlier statistical estimate published in PLOS ONE using a Mark and recapture method estimated approximately 80–90% coverage of all articles published in English with an estimate of 100 million. This estimate also determined how many documents were freely available on the web.

Google Scholar has been criticized for not vetting journals and including predatory journals in its index.

H index

H/h index may refer to:

Herfindahl index, a measure of the quantity and competition of firms in an industry

h-index, a measure of scientific research impact

Henning Schulzrinne

Henning Schulzrinne was the Chief Technology Officer (CTO) for the United States Federal Communications Commission, having been appointed to that role on December 19, 2011 to 2014 Previously he was chair and Julian Clarence Levi Professor of the Computer Science department at Columbia University. He is a co-chair of the Internet Technical Committee of the IEEE Communications Society.

Schulzrinne studied at the German TU Darmstadt in Darmstadt, where he earned his Vordiplom (cf. Diplom), then went on to earn his M.Sc. at the University of Cincinnati and his Ph.D. at the University of Massachusetts Amherst. From 1994 to 1996 he worked in Berlin at the Forschungs-Institut für Offene Kommunikationssysteme (GMD FOKUS), an institute of the now-defunct Gesellschaft für Mathematik und Datenverarbeitung (GMD) and now part of the Fraunhofer Society as Fraunhofer Institute for Open Communication Systems. Schulzrinne is an editor of the Journal of Communications and Networks.

Schulzrinne has contributed to standards. He co-designed the Session Initiation Protocol along with Mark Handley, the Real Time Streaming Protocol, the Real-time Transport Protocol, the General Internet Signaling Transport Protocol,

part of the Next Steps in Signaling protocol suite. Overall, as of November 5, 2015, his publications have been cited over 45,000 times, and he has an h-index of 80.He was elected to ACM Fellow (2014) for contributions to the design of protocols, applications, and algorithms for Internet multimedia.

International Conference on Acoustics, Speech, and Signal Processing

ICASSP, the International Conference on Acoustics, Speech, and Signal Processing, is an annual flagship conference organized of IEEE Signal Processing Society. All papers included in its proceedings have been indexed by Ei Compendex.

The first ICASSP was held in 1976 in Philadelphia, Pennsylvania based on the success of a conference in Massachusetts four years earlier that had focused specifically on speech signals.As ranked by Google Scholar's h-index metric in 2016, ICASSP has the highest h-index of any conference in Signal Processing field.

Also, It is considered a high level conference in signal processing and, for example, obtained an 'A1' rating from the Brazilian ministry of education based on its H-index.

Marvin L. Cohen

Marvin L. Cohen (born Montreal on March 3, 1935) is a Canadian-born University Professor of Physics at the University of California, Berkeley. Nobel laureate Robert B. Laughlin studied under John D. Joannopoulos, a student of Cohen's.

Cohen received his PhD from the University of Chicago in 1964, under Professor Jim Phillips. He has received the Oliver E. Buckley Prize in 1979, the Julius Edgar Lilienfeld Prize in 1994, the National Medal of Science in 2001, and the Dickson Prize in Science in 2011. He is a member of the National Academy of Sciences, and in 2005, he served as President of the American Physical Society. He is noted for studies of materials, especially semiconductors, which are the basis for computers and Internet lasers From the top down

Top physical scientists by h-index:

Physics

1. Ed Witten 124

(Institute for Advanced Study, Princeton)

2. Marvin Cohen 102

(University of California, Berkeley)

3. Philip Warren Anderson 102

(Princeton University)

4. Manuel Cardona 100

(Max Planck Institute for Solid

State Research, Stuttgart, Germany)

5. Pierre-Gilles de Gennes 88

(ESPCI, Paris)

Nature Reviews Molecular Cell Biology

Nature Reviews Molecular Cell Biology is a peer-reviewed monthly review journal that was established in October 2000 and is published by Nature Publishing Group. It covers all aspects of molecular and cell biology.

According to the Journal Citation Reports, the journal had a 2016 impact factor of 46.602, ranking it first in the category "Cell Biology". In 2016, it has an h-index of 324.

Physical Review Letters

Physical Review Letters (PRL), established in 1958, is a peer-reviewed, scientific journal that is published 52 times per year by the American Physical Society. As also confirmed by various measurement standards, which include the Journal Citation Reports impact factor and the journal h-index proposed by Google Scholar, many physicists and other scientists consider Physical Review Letters to be one of the most prestigious journals in the field of physics.PRL is published as a print journal, and is in electronic format, online and CD-ROM. Its focus is rapid dissemination of significant, or notable, results of fundamental research on all topics related to all fields of physics. This is accomplished by rapid publication of short reports, called "Letters". Papers are published and available electronically one article at a time. When published in such a manner, the paper is available to be cited by other work. The Lead Editor is Hugues Chaté. The Managing Editor is Reinhardt B. Schuhmann.

Robert S. Langer

Robert Samuel Langer, Jr. FREng (born August 29, 1948 in Albany, New York) is an American chemical engineer, scientist, entrepreneur, inventor and one of the 10 Institute Professors at the Massachusetts Institute of Technology.He was formerly the Germeshausen Professor of Chemical and Biomedical Engineering and maintains activity in the Department of Chemical Engineering and the Department of Biological Engineering at MIT. He is also a faculty member of the Harvard-MIT Division of Health Sciences and Technology and the David H. Koch Institute for Integrative Cancer Research.

He is a widely recognized and cited researcher in biotechnology, especially in the fields of drug delivery systems and tissue engineering. His publications have been cited over 273,000 times and his h-index is 261. According to Google Scholar, Langer is one of 7 most cited individuals in history. He is the most cited engineer in history. Langer's research laboratory at MIT is the largest biomedical engineering lab in the world; maintaining over $10 million in annual grants and over 100 researchers.In 2015, Langer was awarded the Queen Elizabeth Prize for Engineering.

SCIgen

SCIgen is a computer program that uses context-free grammar to randomly generate nonsense in the form of computer science research papers. All elements of the papers are formed, including graphs, diagrams, and citations. Created by scientists at the Massachusetts Institute of Technology, its stated aim is "to maximize amusement, rather than coherence."

Scopus

Scopus is Elsevier’s abstract and citation database launched in 2004. Scopus covers nearly 36,377 titles (22,794 active titles and 13,583 inactive titles) from approximately 11,678 publishers, of which 34,346 are peer-reviewed journals in top-level subject fields: life sciences, social sciences, physical sciences and health sciences. It covers three types of sources: book series, journals, and trade journals. All journals covered in the Scopus database, regardless of who they are published under, are reviewed each year to ensure high quality standards are maintained. The complete list is on the SCImago Journal Rank website. Searches in Scopus also incorporate searches of patent databases. Scopus gives four types of quality measure for each title; those are h-Index, CiteScore, SJR (SCImago Journal Rank) and SNIP (Source Normalized Impact per Paper).

Solomon H. Snyder

Solomon Halbert Snyder (born December 26, 1938) is an American neuroscientist who is known for wide-ranging contributions to neuropharmacology and neurochemistry. He studied at Georgetown University, and has conducted the majority of his research at the Johns Hopkins School of Medicine. Many advances in molecular neuroscience have stemmed from Dr. Snyder's identification of receptors for neurotransmitters and drugs, and elucidation of the actions of psychotropic agents, making him one of the most highly cited biologists in the world. He is most famous for his research on the opioid receptor, for which he received the Albert Lasker Award for Basic Medical Research in 1978. He is one of the most highly cited researchers in the biological and biomedical sciences, with the highest h-index in those fields for the years 1983–2002.

Tobin J. Marks

Tobin Jay Marks (born November 25, 1944) is the Vladimir N. Ipatieff Professor of Catalytic Chemistry and Professor of Material Science and Engineering, Department of Chemistry, Northwestern University. Among the themes of his research are synthetic organo-f-element and early-transition metal organometallic chemistry, polymer chemistry, materials chemistry, homogeneous and heterogeneous catalysis, molecule-based photonic materials, superconductivity, metal-organic chemical vapor deposition, and biological aspects of transition metal chemistry.

Marks received his B.S. from the University of Maryland in 1966 in chemistry, which is part of the University of Maryland College of Computer, Mathematical, and Natural Sciences. Then he received his Ph.D. from the Massachusetts Institute of Technology in 1971. He came to Northwestern University in the fall of 1970.

As of April 2009, Marks has mentored over 100 PhD students and nearly 100 postdoctoral fellows. More than 90 of these alumni hold academic positions worldwide. He has published over 1245 research articles and holds 260 patents. His h-index is 141.

Varun Grover

Varun Grover (born 1959) is an American Information systems researcher, who is the David D. Glass Endowed Chair and Distinguished Professor at the Walton School of Business, University of Arkansas. From 2002-17, he was the William S. Lee (Duke Energy) Distinguished Professor of Information Systems at Clemson University, where he taught doctoral seminars on methods and information systems. He is consistently in the top 3 IS researchers in the world (ranked by volume in top journals). He has an h-index of 79, among the top 5 in his field (see https://ai.arizona.edu/sites/ai/files/MIS510/h-index-2015-04.pdf).

Ward Whitt

Ward Whitt (born 1942) is an American professor of operations research and management sciences. He is the Wai T. Chang Professor of Industrial Engineering and Operations Research at Columbia University. His research focuses on queueing theory, performance analysis, stochastic models of telecommunication systems, and numerical transform inversion. He is recognized for his contributions to the understanding and analyses of complex queues and queuing networks, which led to advances in the telecommunications system. As of November 2, 2015, his publications have been cited over 25,000 times, and he has an h-index of 82.

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