Evolutionary Biological Data Science Laboratory

EM-index

The EM-index (Bihaari and Tripathi 2017) combines elements of the multidimensional h-index, the two-sided h-index, the iteratively weighted h-index, and the e-index. The EM-index begins by creating a vector (E) which is equivalent to the upper/excess half of the two-sided h-index, namely a series of h values calculated from the citation curve of just the core publications, stopping when one reaches only a single remaining publication, no citations remain, or all remaining publications have only a single citation. From this vector, EM can be calculated as:

$$EM=\sqrt{\sum\limits_{i=1}^{n}{E_i}},$$

where E_i and n are the i^th element and length of E, respectively.

Example

Publications are ordered by number of citations, from highest to lowest. After each step, E_i is subtracted from the citations of the top E_i publications. All publications beyond the top E_i are ignored at subsequent steps.

Citations (Ci)	57	26	16	12	11	10	4	3	2	1	1	1	1	0	0	0	0	0
Rank (i)	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18
						E1 = 6
Adjusted Citations (Ci)	51	20	10	6	5	4
Rank (i)	1	2	3	4	5	6
					E2 = 5
Adjusted Citations (Ci)	46	15	5	1	0
Rank (i)	1	2	3	4	5
			E3 = 3
Adjusted Citations (Ci)	43	12	2
Rank (i)	1	2	3
		E4 = 2
Adjusted Citations (Ci)	41	10
Rank (i)	1	2
		E5 = 2
Adjusted Citations (Ci)	39	8
Rank (i)	1	2
		E6 = 2
Adjusted Citations (Ci)	37	6
Rank (i)	1	2
		E7 = 2
Adjusted Citations (Ci)	35	4
Rank (i)	1	2
		E8 = 2
Adjusted Citations (Ci)	33	2
Rank (i)	1	2
		E9 = 2

The sum of the 9 E values is 26. The EM-index is the square-root of this sum, thus EM = 5.0990.

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