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D-Lib Magazine

September/October 2016
Volume 22, Number 9/10


Robert M. Patton, Christopher G. Stahl and Jack C. Wells
Oak Ridge National Laboratory
{pattonrm, stahlcg, wellsjc}@ornl.gov

DOI: 10.1045/september2016-patton



The measurement of scientific progress remains a significant challenge exasperated by the use of multiple different types of metrics that are often incorrectly used, overused, or even explicitly abused. Several metrics such as h-index or journal impact factor (JIF) are often used as a means to assess whether an author, article, or journal creates an "impact" on science. Unfortunately, external forces can be used to manipulate these metrics thereby diluting the value of their intended, original purpose. This work highlights these issues and the need to more clearly define "impact" as well as emphasize the need for better metrics that leverage full content analysis of publications.


1 Introduction

Measuring scientific progress remains elusive. There is an intuitive understanding that, in general, science is progressing forward. New ideas and theories are formed, older ideas and theories are confirmed, rejected, or modified. Progress is made. But, questions such as how is it made, by whom, how broadly, or how quickly present significant challenges. Historically, scientific publications reference other publications if the former publication in some way shaped the work that was performed. In other words, one publication "impacted" a latter one. The implication of this impact revolves around the intellectual content of the idea, theory, or conclusion that was formed. Several metrics such as h-index or journal impact factor (JIF) are often used as a means to assess whether an author, article, or journal creates an "impact" on science. The implied statement behind high values for such metrics is that the work must somehow be valuable to the community, which in turn implies that the author, article, or journal somehow has influenced the direction, development, or progress of what others in that field do. Unfortunately, the drive for increased publication revenue, research funding, or global recognition has lead to a variety of external factors completely unrelated to the quality of the work that can be used to manipulate key metric values. In addition, advancements in computing and data sciences field have further altered the meaning of impact on science.

The remainder of this paper will highlight recent advancements in both cultural and technological factors that now influence scientific impact as well as suggest new factors to be leveraged through full content analysis of publications.


2 Impact Versus Visibility

Prior to the development of the computer, Internet, social media, blogs, etc., the scientific communities' ability to build upon the work of others relied solely on the specific and limited publication venues (e.g., conferences and journals) to publish and subscribe. With limited venues, the "noise" of excessive publishing was restricted and visibility of scientists across their respective fields was not diminished by this. Consequently, the clearer the visibility of work that is published by the respective scientific communities, the cleaner the interpretation of citation analysis and the stronger the association between citation and "impact".

Unfortunately, the "noise" of publications has increased significantly such that some fields are doubling their total publication count every fifteen years or less.[10] Over the course of the last 100 years, much of this growth rate could be attributed to a variety of factors such as the advent of computers, Internet, social media, blogs, to name a few. In fact, a new field, altmetrics, emerged in 2010 [14] for the sole purpose of attempting to measure "impact" of a publication via these additional factors. Further, another factor in influencing the growth rate of publications is an increasing level of focus on requiring justification for the investments made in scientific research.[2] In support of this increasing focus, a new set of metrics are being formed, snowball metrics [3], that seek to assess research performance using a range of data not just citation data.

While these two new areas, altmetrics and snowball metrics, are attempting to gain clarity on assessing impact as a result of the increasing "noise" of publishing, they are, unfortunately, not truly addressing the noise problem, nor do they reach into the heart of the matter, namely, does a researcher's content alter the progress of the scientific community? Ultimately, the vast majority of these metrics are simply indirect means of evaluating content. The underlying assumption remains: if a scientific publication references former publications, then a human researcher has determined that the former publication in some way shaped the work that was performed. Therein lies the problem. Indirect measures, along with increased pressures for assessing performance, means there are simply too many different ways of manipulating these metrics and their corresponding conclusions. In fact, there are publications that discuss such approaches. [4] identifies 33 different strategies that researchers can utilize to increase their citation frequency and thus their impact measure. These strategies involve increasing a researcher's visibility, and not necessarily the quality of their work. As stated by the authors, "The researchers cannot increase the quality of their published papers; therefore, they can apply some of these 33 key points to increase the visibility of their published papers."

There is some evidence to suggest that increasing visibility does, in fact, increase impact more broadly. In the work performed by [20], the authors performed a full content analysis of citing/cited article pairs. They found that there is "a general increase in explorative type citations, suggesting that researchers now reach further into our collective "knowledge space" in search of inspiration than they once did." In addition, they found that over time, the distance between the full content of these pairs grew, suggesting more interdisciplinary work was being performed. There are two key aspects about the work performed by [20] that are worth noting. First, it was only through a full content analysis of the publications that these conclusions could be drawn that citations are reaching more broad areas of research. Second, it does suggest a positive correlation between visibility and impact. Higher visibility leads to higher impact via increased multidisciplinary citations. However, despite these two key aspects, we are still left with several challenging questions: did a publication truly have an impact as opposed to simply being highly visible, and if so, how?

Unfortunately, efforts to effectively measure impact appear to only expand the entanglement between research funding, impact, and visibility.


3 Challenges of Defining & Measuring Impact

As evidenced by the formation of [2], increasing pressure to justify investments made in scientific research require a more quantitative approach to identifying return on investment. Unfortunately, this means determining an objective, quantifiable measure for something that is often subjective and qualitative. Before addressing that challenge, we must first identify what is meant by "impact".

There have been previous works that have studied the definition of impact and the various factors to consider. As an example, in the work of [5], the authors identify different types of impact beyond economic impact as well as different indicators for each type. Despite the attention to identifying different types of impact, many of the indicators are based on citation analysis. Unfortunately, the flaws of citation analysis have been identified [11][12][13], and with no clear solution or alternative approach. The primary issue with citation analysis is that there are simply too many assumptions that are made and most of those are assumptions about human behavior or human interpretation. In the work of [19], the authors explored the quantification of impact beyond citation analysis. However, their approach relied on a wide range of experts to review the content for a very specific field. Unfortunately, as the authors noted, their method "is time consuming to carry out as it requires a thorough literature review and gathering of scores for both research publications and interventions or solutions." Consequently, there exists a gap in attempts to measure impact. On one hand, there is citation analysis that can be easily tracked and automated, but relies on many assumptions that are easily manipulated. On the other hand, there is a more rigorous full content analysis by a panel of experts, but relies on the availability of those experts as well as an enormous amount of time to perform.

Our premise here, very simply, is that "impact" is defined as the level to which one resource is required by another resource in order to produce an outcome. Seminal publications epitomize this definition in that they are publications on which all others form their basis, and are usually considered to have made a significant contribution to the scientific body of knowledge. Most citation analysis and altmetrics focus on quantifying the "level" aspect of our definition. In works such as [5], others have worked to define the "outcome" aspect of our definition. What is missing is the need to more clearly define the "resource" aspect and the "required" aspect of our definition. How do we define these aspects?


3.1 Resources & Requirements for Scientific Progress

While publications of prior research is a primary resource on which new research is performed, today's scientific communities rely on a multitude of other resources beyond publications for example:

  • High performance computing facilities such as the Oak Ridge Leadership Computing Facility (OLCF) that enable advanced simulations of a wide range of natural phenomena
  • Visualization laboratories such as the one at the Texas Advanced Computing Center (TACC) that enable advanced visualization of scientific data sets
  • Specialized instrumentation facilities such as the Spallation Neutron Source (SNS) that enable the interrogation of advanced materials at the atomic level
  • Data centers such as the Distributed Active Archive Center (DAAC) that provide the capability to archive and management scientific data and models
  • Models of natural phenomena such as the Community Earth System Model that enable the simulation and study of these phenomena
  • Databases of measurements and experimental results such as MatNavi that enable scientists to search for various properties of different materials

Scientists often rely on such resources to accomplish their research, and ultimately publish their work. In many cases regarding these types of resources, the research performed simply would not have occurred if the corresponding resource did not exist or was not accessible. While these resources are not publications, they typically have corresponding representative publications that can be cited so as to provide "credit where credit is due". If they do not have a corresponding publication to cite, then they will have an acknowledgment statement that they request be used for any publication that leverages their resources. Unfortunately, in the case of acknowledgment statements, citation analysis simply does not provide appropriate attribution to assessing the level of impact. For many of these resources, such acknowledgments or citations are critical to continue receiving their operating funding levels and justify the financial investment made to create them.

When we investigate the use of these resources and the manner in which they are cited or acknowledged, a tremendous gap exists with respect to assessing impact. There are two issues:

  1. Not all cited works provide an equal contribution to a publication
  2. Not all required resources are provided appropriate credit or even credit at all

Both of these issues become evident only through full content analysis of the publications. To date, identifying these issues is a manual process that human experts can identify very easily, but requires significant time. In the first issue, some cited works are provided merely for reference or background purposes for the reader while other cited works are so critical to the citing work that the citing work would probably not have even existed if not for the existence of the cited work. However, citation analysis treats both citations equally, which is clearly not an accurate assessment of impact. In other words, some cited resources are required for the citing work to exist while others are not, but citation analysis alone does not make this distinction. Citation analysis only considers published resources and not other types of resources thus impact assessment is not complete. In the second issue, there does not exist any formal, standard means of citing such resources other than through a corresponding publication. This is the reason why some resources have resorted to publishing a corresponding paper so as to have some means of being cited for their contribution and they request their users to cite their paper. Citation analysis is then used on that corresponding paper in order to measure "impact". Some resources have attempted to create ARK (Archival Resource Key) Identifiers as a means of being cited [1]. However, this is not the intended purpose for ARK Identifiers and does not enable the benefits that may be achieved through citation analysis.


4 Toward Full Content Analysis to Assess Impact

As identified by [19] and the issues stated previously regarding scientific resources, additional information and value from publications, with respect to assessing impact, can be gained through full content analysis. The key challenge, however, is that this analysis is currently a manual effort such that it can not be performed for all publications, but rather only for a select few for a specific purpose.

In the work of [9][7], a new approach called semantometrics was established as a first step toward leveraging automation of full content analysis for impact assessment. In [9], semantometrics is used to automatically quantify how well a cited/citing pair "creates a "bridge" between what we already know and something new". If the similarity of the content between the pair is high, then the "bridge" is likely to be very short. If the similarity is low, then the "bridge" is likely to be very long. Longer bridges then are considered to have high impact values. While not citing the semantometric approach first developed in [9], the work of [20] confirmed the effectiveness of this approach. In the work of [7], semantometrics is used to automatically quantify the diversity of collaboration between co-authors referred to as research endogamy. Higher values of endogamy imply low cross-disciplinary efforts, and vice versa. Further, using semantometrics to investigate co-authorship and endogamy provided evidence to support that "collaboration across disciplines happens more often on a short-term basis".

We posit that this prior work should be further extended in order to more fully assess scientific impact at a higher resolution of detail than what is currently capable with citation analysis. In our assessment of publications that have acknowledged using the Oak Ridge Leadership Computing Facility (OLCF), we have identified at least two areas of opportunity to leverage full content analysis: 1) context-aware citation analysis and 2) leading edge impact assessment.


4.1 Context-Aware Citation Analysis

Depending on the context of the work performed, not all citations convey equal impact to a citing work.

As a specific example, the work of [21] cites the work of [8], which in turn, acknowledges having used OLCF resources. [21] cites [8] in the following way, "We used the Community Earth System Model28 to simulate UHI." From this context and the scope of the work performed in [21], it can be concluded that the work of [21] most likely could not have been performed if not for the work performed by [8]. Further, a possible conclusion could also be drawn that the work of [8] could not have been performed if not for the availability and existence of a resource such as OLCF, although the evidence for this is not as strong as it's not clear whether that work could have been performed with another resource of the same type.

In contrast, the work of [21] also cites the work of [6] in the following way, "A measure of heatwave intensity is the degree of deviation, in multiples of standard deviation (North American mean value, s < 0.6 K) of summertime temperature from the climatological mean21." From this context and the scope of work performed in [21], it can be concluded that this citation is simply used as reference material for the reader. If the work of [6] had not been cited, the value of the work performed in [21] would likely not be diminished.

Unfortunately, there does not appear to be a current, automated method to address this discrepancy in the way publications are cited. Only through the context of the full content does the discrepancy appear, and traditional means of citation analysis could possibly hide the value of a higher impact of one cited work over another.


4.2 Leading Edge Impact Assessment

Scientific research areas often "ebb and flow" as progress is made. Unfortunately, as a result of the noise from an increasing number of scientific publications annually, the probability of appropriately citing relevant works diminishes each year. This leads to a severely restricted ability to observe the "ebb and flow"of research areas through citation analysis alone. Consequently, assessment of impact is also restricted. We postulate that the ebb and flow of research areas likely resembles the Everett Rogers' Innovation Adoption Lifecycle [18] as shown in Figure 1. Seminal papers would fall within the leftmost leading edge of innovators, and based on interest in the research area, the number of papers resulting from the seminal paper would grow until the research area no longer remains of interest or morphs into another topic.


Figure 1: Everett Rogers' Innovation Adoption Lifecycle model [18]
(Licensing information for this figure is available from Wikipedia here.)

From an impact assessment perspective, we conjecture that, in light of inadequate citations of appropriate publications, a publication could be assessed based on its relative position within this lifecycle model. The closer to the leftmost leading edge of the model, the higher the impact in that the publication has the opportunity to shape the field. To evaluate this conjecture, we randomly selected a publication from 2009 [15] that acknowledged having used resources of the Oak Ridge Leadership Computing Facility (OLCF). This publication, entitled "Systematic assessment of terrestrial biogeochemistry in coupled climate—carbon models", is focused on research involving the carbon cycle, and was published in the journal Global Change Biology. Abstracts from every article published in this journal from 2009 to 2015 were collected. These abstracts were then clustered using techniques described in [17][16]. For 2009, we cluster all of the abstracts and observed that a cluster formed of abstracts that are topically oriented around the "carbon cycle". This cluster contains the [15] paper. For each year after 2009, we cluster all the abstracts for the respective year along with the [15] paper. The [15] paper continues to cluster within the "carbon cycle" group for each year, and we count the number of publications within that cluster for each year. The plot for this is shown in Figure 2.


Figure 2: Cluster Size vs Publication Year for publications relating to the "Carbon Cycle"

From 2009, there is a significant growth of this cluster, followed by a severe decline and then resurgence of the cluster size. While not an entirely conclusive result, the growth and decline does seem to emulate the lifecycle model of Figure 1. Future work will examine additional journals beyond Global Change Biology to determine if this growth and decline is evident in other journals, thereby representative of the research field, or if not evident, simply a reflection of submissions and acceptance rate of Global Change Biology. Based on this initial result, an impact assessment could incorporate the location in time of when paper was published with respect to the growth and decline of the topic cluster. In this example, the [15] paper occurred at the leftmost leading edge of the curve, thus increasing its ability to impact the field even though it may not receive appropriate citing and defining it as a more innovating paper relative to the rest of the field.


5 Summary

This work highlights some of the issues involving impact assessment of publications when using citation analysis and the need to more clearly define "impact". In addition, we emphasize the need for better metrics that leverage full content analysis of publications. Analysis of the full content reveals additional detail of how citations differ where some papers are cited simply as references while others are absolutely critical to the citing work, and that the topics with respect to when the paper is published provide additional insight into possible impact. We posit that further work should be performed to leverage full content analysis in two ways. First, a context-aware citation analysis that takes into account that the citations are have different value to a citing work and should be a factor in assessing impact. Second, a leading edge impact assessment from the full content can reveal how quickly a research area begins, grows, and fades, and that the timing of the publication should be a factor in assessing impact. Future work will expand on these areas in support of more clearly evaluating publication impact on science and technology.



This manuscript has been authored by UT-Battelle, LLC and used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan.



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About the Authors

Robert M. Patton received his PhD in Computer Engineering with emphasis on Software Engineering from the University of Central Florida in 2002. He joined the Computational Data Analytics group at Oak Ridge National Laboratory (ORNL) in 2003. His research at ORNL has focused on nature-inspired analytic techniques to enable knowledge discovery from large and complex data sets, and has resulted in approximately 30 publications pertaining to nature-inspired analytics and 3 patent applications. He has developed several software tools for the purposes of data mining, text analyses, temporal analyses, and data fusion, and has developed a genetic algorithm to implement maximum variation sampling approach that identifies unique characteristics within large data sets.


Christopher G. Stahl recently graduated with a Bachelor of Science from Florida Southern College. For the past year he has been participating in the Higher Education Research Experiences (HERE) program at Oak Ridge National Laboratory. His major research focuses on data mining, and data analytics. In the future he plans on pursing a PhD in Computer Science with a focus on Software Engineering.


Jack C. Wells is the director of science for the National Center for Computational Sciences (NCCS) at Oak Ridge National Laboratory (ORNL). He is responsible for devising a strategy to ensure cost-effective, state-of-the-art scientific computing at the NCCS, which houses the Department of Energy's Oak Ridge Leadership Computing Facility (OLCF). In ORNL's Computing and Computational Sciences Directorate, Wells has worked as group leader of both the Computational Materials Sciences group in the Computer Science and Mathematics Division and the Nanomaterials Theory Institute in the Center for Nanophase Materials Sciences. During a sabbatical, he served as a legislative fellow for Senator Lamar Alexander, providing information about high-performance computing, energy technology, and science, technology, engineering, and mathematics education issues.Wells began his ORNL career in 1990 for resident research on his Ph.D. in Physics from Vanderbilt University. Following a three-year postdoctoral fellowship at Harvard University, he returned to ORNL as a staff scientist in 1997 as a Wigner postdoctoral fellow. Jack is an accomplished practitioner of computational physics and has been supported by the Department of Energy's Office of Basic Energy Sciences. Jack has authored or co-authored over 70 scientific papers and edited 1 book, spanning nanoscience, materials science and engineering, nuclear and atomic physics computational science, and applied mathematics.

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