Collective credit allocation in science

Significance

The increasing dominance of multiauthor papers is straining the credit system of science: although for single-author papers, the credit is obvious and undivided, for multiauthor papers, credit assignment varies from discipline to discipline. Consequently, each research field runs its own informal credit allocation system, which is hard to decode for outsiders. Here we develop a discipline-independent algorithm to decipher the collective credit allocation process within science, capturing each coauthor’s perceived contribution to a publication. The proposed method provides scientists and policy-makers an effective tool to quantify and compare the scientific contribution of each researcher without requiring familiarity with the credit allocation system of the specific discipline.

Abstract

Collaboration among researchers is an essential component of the modern scientific enterprise, playing a particularly important role in multidisciplinary research. However, we continue to wrestle with allocating credit to the coauthors of publications with multiple authors, because the relative contribution of each author is difficult to determine. At the same time, the scientific community runs an informal field-dependent credit allocation process that assigns credit in a collective fashion to each work. Here we develop a credit allocation algorithm that captures the coauthors’ contribution to a publication as perceived by the scientific community, reproducing the informal collective credit allocation of science. We validate the method by identifying the authors of Nobel-winning papers that are credited for the discovery, independent of their positions in the author list. The method can also compare the relative impact of researchers working in the same field, even if they did not publish together. The ability to accurately measure the relative credit of researchers could affect many aspects of credit allocation in science, potentially impacting hiring, funding, and promotion decisions.

Reflecting the increasing complexity of modern research, in the last decades, collaboration among researchers became a standard path to discovery (1). Collaboration plays a particularly important role in multidisciplinary research that requires expertise from different scientific fields (2). As the number of coauthors of each publication increases, science’s credit system is under pressure to evolve (3–5). For single-author papers, which were the norm decades ago, credit allocation is simple: the sole author gets all of the credit. This rule, accepted since the birth of science, fails for multiauthor papers (6). The lack of a robust credit allocation system that can account for the discrepancy between researchers’ contribution to a particular body of work and the credit they obtain, has prompted some to state that “multiple authorship endangers the author credit system” (7). This situation is particularly acute in multidisciplinary research (8, 9), when communities with different credit allocation traditions collaborate (10). Furthermore, a detailed understanding of the rules underlying credit allocation is crucial for an accurate assessment of each researcher’s scientific impact, affecting hiring, funding, and promotion decisions.

Current approaches to allocating scientific credit fall in three main categories. The first views each author of a multiauthor publication as the sole author (11, 12), resulting in inflated scientific impact for publications with multiple authors. This system is biased toward researchers with multiple collaborations or large teams, customary in experimental particle physics or genomics. The second assumes that all coauthors contribute equally to a publication, allocating fractional credit evenly among them (13, 14). This approach ignores the fact that authors’ contributions are never equal and hence dilutes the credit of the intellectual leader. The third allocates scientific credit according to the order or the role of coauthors, interpreting a message agreed on within the respective discipline (15–17). For example, in biology, typically the first and the last author(s) get the lion’s share of the credit, and in some areas of physical sciences, the author list reflects a decreasing degree of contribution. An extreme case is offered by experimental particle physics, where the author list is alphabetic, making it impossible to interpret the author contributions without exogenous information. Finally, there is an increasing trend to allocate credit based on the specific contribution of each author (18, 19), specified in the contribution declaration required by some journals (20, 21). However, each of these approaches ignores the most important aspect of credit allocation: notwithstanding the agreed on order, credit allocation is a collective process (22–24), which is determined by the scientific community rather than the coauthors or the order of the authors in a paper. This phenomena is clearly illustrated by the 2012 Nobel prize in physics that was awarded based on discoveries reported in publications whose last authors were the laureates (25, 26), whereas the 2007 Nobel prize in physics was awarded to the third author of a nine-author paper (27) and the first author of a five-author publication (28). Clearly the scientific community operates an informal credit allocation system that may not be obvious to those outside of the particular discipline.

The leading hypothesis of this work is that the information about the informal credit allocation within science is encoded in the detailed citation pattern of the respective paper and other papers published by the same authors on the same subject. Indeed, each citing paper expresses its perception of the scientific impact of a paper’s coauthors by citing other contributions by them, conveying implicit information about the perceived contribution of each author. Our goal is to design an algorithm that can capture in a discipline-independent fashion the way this informal collective credit allocation mechanism develops.

Results

We start by examining the simplest situation: given a paper p₀ with two authors, a₁ and a₂, who gets the credit? Consider the extreme case when author a₁ has published several other papers on the topic of paper p₀ that are often cited together with p₀; for author a₂, the target paper is his only publication. Given that a₁ has a track record in the particular discipline and a₂ is unknown to the community, the community views p₀ as a part of a₁’s body of work (Fig. 1A). The credit allocation system should recognize this and assign most or all credit to a₁. The other extreme case is when all papers pertaining to the topic of p₀ are joint publications between a₁ and a₂. Lacking any exogenous information, the two authors share equal credit for the target paper (Fig. 1B), a symmetry that should be captured by a credit allocation method. In practice, the situation is more complicated: authors a₁ and a₂ may publish some papers together and several with other coauthors on the topic of p₀. Hence, their credit share of the particular work diverges with time, based on the impact of the body of work they publish separately. Next we describe a method that can account for this collective credit allocation process.

Fig. 1.

Extreme cases of credit allocation. (A) Asymmetric credit: when author a₂ contributes to only one paper in a body of work, the community assigns credit to a₁, who publishes multiple papers on the topic. (B) Symmetric credit: when authors a₁ and a₂ publish all their papers on the topic of paper p₀ jointly, they equally share the credit. In both cases, p₀ is the target paper with two authors a₁ and a₂ colored in red and green, respectively; d_k (1 ≤ k ≤ 5) are citing papers of p₀; p_j (0 ≤ j ≤ 4) are papers that were cocited by the papers that cite p₀; A is the credit allocation matrix; s depicts the cocitation strength between cocited papers and target paper; and c is the final credit share for the authors of the target paper p₀.

Credit Allocation Algorithm.

Consider a paper p₀ with m coauthors {a_i}(1 ≤ i ≤ m). To determine the credit share of each author, we first identify all papers that cite p₀, forming a set $\mathcal{D}$ ≡ {d₁, d₂,…,d_l}. Next we identify all cocited papers $\mathcal{P}$ ≡ {p₀, p₁,…,p_n}, representing the complete set of papers cited by papers in the set $\mathcal{D}$. The relevance of each cocited paper p_j (0 ≤ j ≤ n) to the target paper p₀ is characterized by its cocitation strength s_j between p₀ and p_j, defined as the number of times p₀ and p_j are cited together by the papers in $\mathcal{D}$ (29). For example, for p₁ in Fig. 2A, we have s₁ = 1 because only one paper (d₁) cites p₀ and p₁ together, whereas s₂ = 4 as four papers (d₁, d₂, d₃, and d₅) cite p₀ and p₂ together. Cocitation strength captures the intuition that papers by an author that are perceived to be very relevant to paper p₀ should increase the author’s perceived contribution to p₀. Note that the target paper p₀ is also viewed as a cocited paper of itself with cocitation strength equal to the citation count of p₀. Consequently, for papers with high citation count, the credit share of coauthors is less likely to be affected by other cocited papers.

Fig. 2.

Illustrating the credit allocation process. (A) The target paper p₀ has two authors, a₁ and a₂, colored in red and green, respectively. We also show the citing papers d_k (1 ≤ k ≤ 5) and the cocited papers p_j (0 ≤ j ≤ 4) that were cited by these citing papers together with p₀. (B) The p₀-centric cocitation network constructed from A, where the weights of links denote the cocitation strength s between the cocited papers and the target paper p₀. (C) The author lists of the target paper p₀ and its cocited papers. (D) The credit allocation matrix A obtained from the author lists of the cocited papers in C. The matrix A provides for each cocited paper the authors’ share. For example, because p₂ has a₁ as one of its two authors but it lacks the author a₂, it votes 0.5 for author a₁ and 0.0 for author a₂. (E) With the matrix A and cocitation strength s, the credit share of the two authors of p₀ is computed according to Eq. 1 or Eq. 2 with a normalization.

Using the author list of the cocited papers, we next calculate a credit allocation matrix A, whose element A_ij denotes the amount of credit that author a_i gets from cocited paper p_j (SI Appendix, section S2.2). To develop a discipline-independent method for credit allocation, we use a fractional credit allocation matrix that does not depend on the order of authors in the author list. For example, paper p₁ assigns all credit to author a₁ who is the sole author of p₁, whereas p₀ assigns equal (half) credit to authors a₁ and a₂ (Fig. 1A). The total credit c_i of author a_i is the weighted sum of its local credit obtained from all cocited papers

$$c_i={\displaystyle \sum _j{A}_{ij}{s}_j},$$ [1]

or in the matrix form

$$\mathbf{c}=\mathbf{As}.$$ [2]

The vector c provides the credit of all authors of target paper p₀. By normalizing c, we obtain the fractional credit share among coauthors (Fig. 2E).

We apply the proposed procedure to the two extreme cases of Fig. 1. When author a₂ has only one paper on the topic of p₀, the fact that the community cites p₀ together with other papers of author a₁ indicates that they perceive p₀ a part of a larger body of work by author a₁ (Fig. 1A). Our method in this case obtains c = (0.75, 0.25)^T, hence allocating most credit to author a₁. When all subsequent work are joint, it gives c = (0.5, 0.5)^T, i.e., credit is equally shared between a₁ and a₂ (Fig. 1B).

Validation.

To validate our method, we apply it to Nobel prize-winning publications, representing a case where the community (and the Nobel committee) has decided where the main credit goes. We therefore collected all Nobel prize-winning papers in Physics (1995–2013), Chemistry (1998–2013), Medicine (2006–2013), and Economics (1995–2013), since the Nobel committee started offering a detailed explanation with references for the prize. Table 1 shows the obtained credit share for five Nobel prize-winning physics papers in the year before the Nobel prizes were awarded, hence discounting the influence of the prize (see SI Appendix, Table S5–S8 for the complete set of results). We find that in four of the five cases, the laureates have the largest credit share, no matter whether they are the first (2010) or the last authors (2012) or occupy some intermediate position in the author list (2007). For example, as the third author of the prize-winning paper with nine coauthors (27), the 2007 Nobel laureate A. Fert gets nearly one fourth of all credit, and the remaining credit is almost evenly distributed among the other coauthors. A particularly interesting case is the 2010 prize-winning paper (30), where two of eight coauthors were awarded the Nobel prize, consistent with the predicted credit share. Indeed, the credit share of the laureates is almost equal, and it is 2.5 times higher than the credit share of the third-ranked coauthor. Another interesting example, out of the validation sample, is offered by the 1974 Nobel prize in Physics awarded to A. Hewish for the discovery of pulsars (31), with S. J. Bell as the second of five authors. Researchers in the community occasionally refer to the 1974 Nobel prize as the “No-Bell” prize because many feel that Bell should have shared it (http://en.wikipedia.org/wiki/Antony_Hewish). Applying our method to the prize-winning paper, we obtain c = [0.250, 0.189, 0.196, 0.185, 0.180]^T, assigning the largest credit to the laureate, indicating that the committee’s choice was consistent with the perceived credit within the scientific community. Fig. 3 shows the accuracy of our method at identifying the laureates from the author list of all of the 63 multiauthor prize-winning papers across three disciplines. We find that the authors with top credit share correspond to laureates in 51 papers (81%), despite the diversity of positions the laureates had in the author list. Note that we did not count single-author papers, for which credit is obvious. Counting those as well, accuracy increases to 86%.

Table 1.

Credit share for five Nobel prize winning papers

Awarding year/paper	Authors	Credit share
		WOS	APS
2012/Phys Rev Lett 77, 4887 (1996)	M. Brune	0.204	0.209
	E. Hagley	0.074	0.080
	J. Dreyer	0.065	0.070
	X. Maître	0.068	0.074
	A. Maali	0.073	0.077
	C. Wunderlich	0.069	0.074
	J. M. Raimond	0.212	0.206
	S. Haroche*	0.236	0.211
2012/Phys Rev Lett 76, 1796 (1996)	D. M. Meekhof	0.160	0.149
	C. Monroe	0.198	0.182
	B. E. King	0.173	0.158
	W. M. Itano	0.200	0.239
	D. J. Wineland*	0.270	0.272
2010/Science 306, 666 (2004)	K. S. Novoselov*	0.244	NA
	A. K. Geim*	0.253	NA
	S. V. Morozov	0.111	NA
	D. Jiang	0.102	NA
	Y. Zhang	0.064	NA
	S. V. Dubonos	0.075	NA
	I. V. Grigorieva	0.075	NA
	A. A. Firsov	0.075	NA
2007/Phys Rev Lett 61, 2472 (1988)	M. N. Baibich	0.094	0.093
	J. M. Broto	0.090	0.090
	A. Fert*	0.242	0.252
	F. Nguyen Van Dau	0.093	0.093
	F. Petroff	0.114	0.100
	P. Etienne	0.089	0.093
	G. Creuzet	0.097	0.093
	A. Friederich	0.091	0.093
	J. Chazelas	0.090	0.093
1997/Phys Rev Lett 55, 48 (1985)	S. Chu*	0.244	0.196
	L. Hollberg	0.087	0.096
	J. E. Bjorkholm	0.134	0.162
	A. Cable	0.138	0.160
	A. Ashkin	0.397	0.386

Credit share is computed according to Eq. 1 or Eq. 2 in the awarding year of each paper, using the WOS and the APS datasets. Coauthors are shown according to their positions in the author list. The maximum credit share is highlighted in bold and the laureates are marked with asterisks. For papers not contained in the dataset, we put NA for credit share.

Fig. 3.

Identifying Nobel laureates from the prize-winning papers in Physics, Chemistry, and Medicine. We apply our method to all multiauthor Nobel prize-winning papers in Physics (1995–2013), Chemistry (1998–2013), and Medicine (2006–2013), covering the periods since the Nobel committee started offering a detailed explanation with references for the prize. For each Nobel prize-winning paper, the laureates are shown in red-filled circles. The author with top credit share (for a paper with k laureates we consider all of the top-k credit share) is shown as a black-filled circle when he/she is not a laureate. Other coauthors are shown as empty circles. Hence the presence of black-filled circles indicates that the credit allocation offered by our algorithm is inconsistent with the decision made by the Nobel committee. The individuals to whom we assign the top credit correspond to laureates in 51 of the 63 prize-winning papers. To accommodate papers with more than 23 authors, we put in the adjacent column the circles corresponding to the authors after the 23rd one, forming irregular blocks. Results are based on the Web of Science dataset. Papers on Economics are not shown because they are either single-author papers or are not contained in our dataset.

Finally, it is useful to understand potential reasons for the method’s occasional failure. For example, for the two prize-winning papers of the 2011 Physics Nobel, our method fails to correctly identify the laureates, caused by the fact that one researcher (Filippenko) coauthors both prize-winning papers but is not the intellectual leader for either of them. Consequently, he gets the top credit on both papers. The laureates get the highest credit among the remaining coauthors; hence, if the anomaly is removed, our method correctly identifies them. This case could be corrected by incorporating contextual or exogenous information into the credit allocation matrix, like the order of the authors, as we discuss below. Another fascinating anomaly is the 1997 Nobel prize in Physics (32): S. Chu was awarded the prize, although A. Ashkin has the highest credit share according to our method. Considered by many scientists the father of the field of optical tweezers (33), Ashkin published several high-impact papers (34, 35) preceding the collaboration with Chu, developing the technology that made the Nobel prize-winning discovery possible. The prize-winning paper is repeatedly cocited with the preceding papers, explaining Ashkin’s higher score. As we show below, credit to Chu is restored if we restrict the cocited pool to papers published after the joint 1985 (Nobel-winning) paper, removing the influence of the preceding work.

Credit Share Evolution.

The proposed methodology also allows us to determine the temporal evolution of credit share between coauthors. To illustrate this, we explore whether the Nobel prize affects the credit share of Nobel laureates relative to their coauthors. Fig. 4A shows the evolution of credit share for the 1997 Nobel prize-winning paper in Physics (32). We find that right after the publication, Ashkin gets virtually all of the credit for the discovery, and Chu’s credit share is tiny, given his lack of previous track record in this area (Fig. 4A). However, with time his credit share increases, whereas Ashkin’s credit share decreases, partly because Ashkin stopped publishing papers after 1986 and retired in 1992. The method also helps us explore how the papers preceding the publication (i.e., previous reputation) of a prize-winning paper influence the credit allocation. Indeed, when we consider all cocited papers, Ashkin’s credit share is higher than the credit share of the laureate Chu, given his work preceding the 1985 paper. However, Chu gets higher credit share than Ashkin if we only consider the cocited papers published after 1985 (Fig. 4A, Inset). This example indicates that, although established scientists receive more credit than their junior colleagues from their coauthored publications, this situation can change if the junior colleague makes important independent contribution to the field.

Fig. 4.

Credit share evolution. (A) The credit share of the authors of the 1997 Nobel prize-winning paper (32). (Inset) Credit share obtained when we only consider the cocited papers published after the publication of the prize-winning paper. (B) The credit share of the authors of the other 1997 Nobel prize-winning paper (36). (C) Credit share of six physicists who contributed to the theory of Higgs boson in 1964 (47–49), obtained by our method using the Web of Science dataset as input. (Inset) The same as in C but based on the APS dataset. (D) The influence of the Nobel prize on laureates’ credit share. For each laureate, we use the increase ratio (c_a/c_b) to quantify the change of her credit share after she was awarded the Nobel prize. In the box plot, whiskers are higher than 90th percentile or lower than 10th percentile. Results are based on the Web of Science dataset.

The effect of Nobel prize on credit share is also remarkable for the other 1997 Nobel prize-winning paper (36): the Nobel laureate W. D. Phillips’s credit share jumps after the prize year (Fig. 4B). Indeed, the prize, by canonizing credit, alters the subsequent citation patterns (37–39), reflecting a “rich get richer” phenomenon in science (40–46). To quantify how widespread this effect is, we systematically studied how the credit share of laureates relative to their coauthors changes when they are awarded the Nobel prize. Therefore, for each laureate, we calculate her average credit share c_b over 3 y before the award year and the average credit share c_a over 3 y after the award year. We quantify the increase of the credit share using the ratio c_a/c_b. For most Nobel laureates, the Nobel prize does improve their credit share relative to their coauthors (Fig. 4D), an effect that is strongest in Physics and weakest in Medicine. The two cases with the strongest effect in Physics (the outlier points in Fig. 4D) are shown in Fig. 4 A and B.

Comparing Independent Authors.

The developed method allows us to compare authors that are in the same research field but may not have published papers together. In this case, the cocitation strength is based on the citing papers that simultaneously cite at least one paper of each compared author, automatically identifying their common research topic (SI Appendix, Fig. S1). Therefore, the credit share of the compared authors reflects their relative contribution to their common research topic, just as the credit share quantifies the coauthors’ relative contribution to a joint paper. An excellent example is provided by the 2013 Nobel prize in Physics (see SI Appendix, Fig. S2 for more examples). The prize posed a widely publicized dilemma: six physicists and three key papers are credited for the 1964 discovery pertaining to the theory of Higgs boson, but the prize could be shared by a maximum of three individuals. F. Englert and R. Brout published the theory first (47) but failed to spell out the Higgs boson, whose existence was predicted in a subsequent paper by P. W. Higgs (48). G. S. Guralnik, C. R. Hagen, and T. W. B. Kibble, 1 mo later proposed the same theory (49), explaining how the building blocks of the universe get their mass. In 2010, the six physicists were given equal recognition by the American Physical Society (APS), sharing the Sakurai prize for theoretical particle physics. This symmetry was broken by the Nobel committee, awarding the prize to Higgs and Englert in 2013. To explore their credit share, we apply our method to compare these researchers (Fig. 4C) and find that Higgs gets the most credit, followed by Kibble, whereas Englert is third, getting only slightly higher credit than his coauthor Brout (deceased). Finally, Guralnik and Hagen equally share the remaining credit. Therefore, the scientific community assigns credit for the discovery recognized by the 2013 Nobel Physics prize to Higgs, Kibble, and Englert (and the deceased Brout), in this order. The committee, by bypassing Kibble, has clearly deviated from the community’s perception of where the credit lies (50).

Robustness of the Method.

We validate the robustness of our method by applying it to two disparate datasets, the publicly available APS dataset and the Web of Science (WOS) dataset (SI Appendix, section S1). The APS dataset consists of papers published by journals of APS between 1893 and 2009; hence, its coverage is biased toward the US-based physics community (51). The dataset does not contain papers published in interdisciplinary journals, like Science, where the 2010 Nobel winning paper was published. The WOS dataset, in contrast, contains all papers indexed by Thomson Reuters between 1955 and 2012 (52). By comparing the results obtained using these two datasets, we can evaluate the robustness of our method. In Table 1, we show the credit share of the papers obtained using each dataset individually. Overall, we find that the data incompleteness of the APS dataset never alters the relative ranking of the researchers. The same robustness is documented in Fig. 4C, where we calculated using both the APS dataset and the WOS dataset the dynamic credit share of the contenders for the 2013 Nobel prize, particularly important given that the European experimental particle physics community shuns the APS journals. Consequently, there are huge difference in coverage between the APS and WOS datasets. For example, for Higgs’ prize winning paper, we have $N_{\mathcal{D}}$ = 187, $N_{\mathcal{P}}$ = 1,847 in the APS dataset and $N_{\mathcal{D}}$ = 879, $N_{\mathcal{P}}$ = 24,596 in the WOS dataset. Despite the bias of the APS dataset and the huge difference in coverage, the relative credit of the six authors remains unchanged (Fig. 4C).

Exogenous Information.

The proposed algorithm can incorporate exogenous information to improve its accuracy. To show this, we explored five priors for constructing the credit allocation matrix A, each reflecting a different hypothesis about the role of the authors. They are as follows: (i) count prior (12), each author is viewed as the sole author of the particular publication; (ii) fractional prior (14), authors equally share one credit independent of their position in the author list; (iii) harmonic prior (15), authors share one credit with their credit share proportional to the reciprocal of authors’ rank in the author list; (iv) axiomatic prior (17), authors share one credit but the credit share of each author is determined by the number of coauthors with lower rank in the author list; and (v) Zhang’s prior (16), the first and the corresponding authors get one credit, whereas other authors share one credit dependent on their rank in the author list (see SI Appendix, section S2.2 for details). The first two priors do not depend on the order of authors, whereas the last three do. We summarize the results of each prior separately for three Nobel-awarding disciplines (SI Appendix, Table S3). We find that when we incorporate corresponding author information (if not available, we take the last author as the corresponding author) for Medicine and Chemistry, the accuracy increases, but it decreases for Physics. Therefore, if contextual or exogenous information is available, our method can absorb that, improving its predictive power. However, these other priors should be only used in a disciplinary fashion.

Discussion

In this paper, we proposed a method to quantify the credit share of coauthors by reproducing the collective credit allocation process informally used by the scientific community. The method captures several key aspects of credit allocation in science. (i) Credit is allocated among scientists based on their perceived contribution rather than their actual contribution. (ii) Established scientists receive more credit than their junior collaborators from coauthored publications (40). This balance can change, however, if the junior colleague makes important independent contribution to the field. (iii) Credit share changes with the evolution of the field.

Our method has several distinguishing characteristics, differentiating it from current credit allocation procedures that are based on the author list (13–17). (i) The method offers topic-dependent credit share, as each paper’s research topic is automatically defined by the body of papers that cite it. (ii) It performs consistently better than existing methods across disciplines. Indeed, previous methods that assign credit to the first or the corresponding authors work only for disciplines that have clear agreed-on rule on authorship and credit allocation. (iii) The method is flexible, being able to incorporate the order of coauthors in the author list, allowing us to construct a credit allocation matrix that captures exogenous information (SI Appendix, section S2.2). (iv) The method provides a natural way to directly compare the relative scientific impact of researchers that did not collaborate with each other but work in the same research field. (v) The method could be used to refine some established measures for scientific impact by considering the credit share of coauthors. (vi) As a further improvement, we could consider a page-rank style algorithm, where the citing set $\mathcal{P}$ is weighted based on their citation count. Hence, citations from more influential papers would gain more weight.

The proposed credit allocation method is based on citations, the most elementary form of visibility and credit in the scientific community. Consequently, it does not explicitly account for other tokens of impact, such as invited talks, keynotes, mentoring, and books, each of which can alter the reputation of a scientist relative to its coauthors. However, our method may implicitly incorporate these effects: if these activities enhance an author’s visibility compared with her/his coauthors, it could result in long-term changes in citations and credit share that are captured by the proposed method.

Finally, credit allocation has potential long-term impact on the career of individuals, affecting hiring, funding, and promotion decisions. We wish to clarify that our algorithm does not capture the precise role of an individual in a paper or a discovery—it only captures the community’s perception of each individual’s contribution, as reflected by their body of work. Hence, we would caution turning this algorithm into the sole tool for credit allocation—letters from coauthors could offer a more nuanced or altogether different picture. Hence, it should be used in conjunction with the other available evaluation tools. The method may also offer feedback to an individual of the need to seek ways to strengthen the credit for a work. It may also have adverse effects: uncovering the mechanism of credit allocation may increase the likelihood that some authors can “jockey” for position, seeking to change the outcome. However, such credit manipulation may be realistic only for lower impact work, where collective effects do not dominate the citation count. Finally, we must keep in mind that the algorithm relies on citation patterns that take time to accumulate. Hence, young scientists, with fewer citations, no matter how important their contribution is, will be at disadvantage. We therefore must learn to account for age- and time-dependent factors in credit allocation, opening up avenues for further research.