CONCEPTUAL DATA MODEL FOR RESEARCH COLLABORATORS

Kedma B. Duarte (*) Master in Computer Engineering / Knowledge Engineering and Management / Federal University of Santa Catarina (EGC/UFSC) / [email protected] / Brazil

Rosina O. Weber PhD in Production Engineering / College of Computing and Informatics / Drexel University/ [email protected] / USA

Roberto C. S. Pacheco PhD in Production Engineering / Knowledge Engineering and Management / Federal University of Santa Catarina / [email protected] / Brazil

ABSTRACT We review the literature in search for attributes to characterize scholarly researchers, with a particular focus on collaborative work given its vast relevance and ubiquity. Our ultimate goal is to conduct studies to inform research related decisions, primarily focusing on researcher quality assessment. Recent efforts to design and maintain high-quality curriculum vitae (CV) databases, also called profiling systems, make them a valuable resource in data analyses to support advancements in science and technology. This paper is concerned with the use of CV data, particularly to enhance our understanding of the fields in CV databases, which tend to vary along different local contexts. Our contribution is a conceptual data model to assess researcher quality that aligns with the literature and accommodates collaboration-related concepts. Although we set out to investigate attributes for a data model with emphasis on research collaborators, our studies revealed that the model is independent of any particular emphasis. Any purpose or emphasis required to assess researcher quality should utilize the values of the attributes because they are the ones that reflect quality. Our studies also revealed that some dimensions of a researcher context entail meta-dimensions. For example, under the accomplishment dimension, every valued attribute (e.g., funded proposal) entails a process to deliver it that may derive more attributes (e.g., proposal written in collaboration with others) making processes a meta-dimension. This conceptual model of researcher’s metadata can be used as the basis to select fields to be used in data and reasoning studies that rely on CV databases. Keywords: collaboration; research collaborator; research assessment; science and technology; scientific collaboration. Type of paper: Full paper

1

INTRODUCTION In the quest to assess researcher performance to inform science and technology (S&T)

decisions, the use of data from curricula vitae (CVs) is a valuable resource that facilitates the study of researchers (Dietz et al. 2000). CV data retains information about the main actions of scientific processes. This valuable source has been recently target of improvement with the development of high quality research profiling systems such as Lattes (Cañibano and Bozeman, 2009; Lane, 2010; Pacheco et al., 2006) and VIVO (Börner et al., 2012). However, the form and contents in CVs vary vastly depending on various aspects such as culture, geography, and field of study. It is crucial therefore that we investigate what components in a researcher’s profile are valuable for such studies, and how to use them. Given how the delivery of scientific inquiry has evolved from an individual into a

collaborative task (e.g., Beaver and Rosen, 1978, 1979b), we are particularly interested in learning attributes that are relevant for collaborators. In this article, therefore, we search literature that discusses collaboration but focus on the collaborator as the unit to study processes and indicators for S&T. We refer to research collaborators as scientists, engineers, philosophers, and humanists, etc., all those whose profession is concerned with scientific inquiry and discovery, and who typically work in research collaborations. We rely on Katz and Martin's (1997, p.7) definition of research collaborations “as the working together of researchers to achieve the common goal of producing new scientific knowledge”. The vast literature in research collaboration (e.g., Yagi et al., 1996; Adams, 2013; Bozeman et al., 2013) has as one of its main purposes to understand what good science is, and what we can learn from it, in order to support a healthy and resilient society. With reliable sources of scientific data, we can measure academic production, know what the data means, how to interpret it, and reuse it (Lane, 2010). This paper bridges the gap to enable the use of high-quality profiling systems data to produce studies that can inform decisions to advance S&T by understanding the attributes that characterize researchers in CVs. This study is relevant because CVs vary substantially depending on aspects such as culture, geography, citation practices, and funding practices. The attributes listed in this article and the resulting model can be used to characterize research collaborators in data studies to capture, measure, and interpret data about individual research collaborators regardless of their contextual specificities. Using the methodology proposed by Tranfield et al. (2003), we review the literature to answer the question, “How to conceptualize a data model to assess researcher quality with

emphasis on research collaborators?” This review includes data originating from the research collaborator’s organizational context, as suggested by Bozeman et al. (2013) and Lane et al. (2015). In this review, we favor attributes that can be objectively valued and that are typically available in profiling systems. We focus on descriptors of individual research collaborators and not on those that describe relationships between different research collaborators because our goal is to support studies that can analyze one individual researcher at a time. We have no reason to believe that attributes of research collaborators would eliminate those to characterize researchers who do not collaborate. In the next sections, we describe the parameters of our literature review, the resulting conceptual model, and the analysis of the literature that substantiates the model.

2

LITERATURE REVIEW AND CONCEPTUAL MODEL OF RESEARCH COLLABORATORS To conduct this systematic literature review, we adopted the SCOPUS digital library

because of its multiple scientific databases with 20,800 peer-reviewed journals (SCOPUS, 2014). We started the search in journal articles, reviews, and conference papers in English that included at least one of the following expressions: "scientific collaboration" or "science collaboration" or "research collaboration" or "collaborative science", or "collaborative research" or "research collaborator" or "researcher collaborator" or "collaborative researcher" or “scientific collaborator” or "science collaborator". This resulted 6988 items. Figure 1 - Growth of scientific collaboration according to search protocol (1976-2014)

We limited the selection of publications by recency because of our interest in collaborations that became more frequent with technological advances such as regular use of email and internet. The distribution of publications with the selected keywords is shown in

Figure 1, where we can see a substantial increase in the volume in the beginning of the new century. We then filtered the results by including works published post 2000. We filtered results based on relevance by including only works that have referenced at least one of the pioneers in the field of collaboration, which are the renowned authors Price, Beaver, and Garfield. We also included articles that have referenced Katz and Martin given their seminal contribution in defining collaboration. With this, the number of selected works reduced to 916 articles. Still on relevance, by reading abstracts only, we reduced the set to 101 papers, which we then have read in their entirety to obtain our final review set that contains 37 publications. This review set included nine authors from six countries that published in 16 different journals, the most common being Scientometrics, Research Policy, and Journal of Informetrics.

2.1

CONCEPTUAL DATA MODEL We propose the conceptual data model laid out in Figure 2 to organize and represent the

attributes of research collaborators. The model is motivated by the recommendation from Bozeman et al. (2013) and Lane et al. (2015) who suggest that studies should include the context in which the research collaborator operates. Figure 2 - The research collaborator conceptual data model includes the context where a research collaborator delivers scientific activities to achieve accomplishments RESEARCH COLLABORATOR IS A RESEARCHER

Relation

RESEARCHER Dimension

CAREER Meta-dimension

INSTITUTIONS

ACCOMPLISHMENTS

Dimension

Dimension

RESEARCH COLLABORATOR IS AFFILIATED TO INSTITUTIONS

RESEARCH COLLABORATOR ACHIEVES ACCOMPLISHMENTS Relation

Relation

RESEARCH COLLABORATOR We structure the universe of a research collaborator through four dimensions and rely on them to categorize the various attributes we found in our review. The dimensions are the researcher, the institutions, the accomplishments, and the career. As illustrated in Figure 2, the

first three are dimensions, while career is a meta-dimension that moves across the others to describe the research collaborator’s trajectory. The proposed model highlights the research collaborator as a researcher affiliated to institutions, whose accomplishments are achieved along their career. We found in our literature review approximately sixty attributes that we organized in these four categories of the research collaborator’s context, which we describe next. As we collected attributes and classified them under each dimension, we realized that some attributes do not originate from the dimensions, but from the relation between the research collaborator and some of the dimensions. Abramo et al. (2014a), for example, utilize as attribute the type of affiliation a research collaborator is with an institution. This attribute originates from the relation between the research collaborator and hers or his institution.

2.1.1 Researcher The first dimension that we use to categorize attributes of a research collaborator stems from the fact that a research collaborator is an instance of a general class of researchers. The attributes selected under this category researchers, are usually available in researchers’ CVs. They include attributes that Bozeman et al. (2013) describe as human capital. Table 1 lists attributes with their pertinent references. Table 1 - List of attributes characterizing research collaborators as researchers Label

Sources

Name

(Abramo et al., 2014b; Corley & Sabharwal, 2010; Gazni & Thelwall, 2014) (Bozeman et al., 2013; Dahlander & McFarland, 2013; Lee & Bozeman, 2005; Scellato et al., 2015)

Age Gender

(Abramo et al., 2014b, 2013a; Bozeman et al., 2013; Dahlander & McFarland, 2013; Hunter & Leahey, 2008; Jonkers & CruzCastro, 2013; Lee & Bozeman, 2005; Scellato et al., 2015)

Contact information, e.g., email address

(Youtie & Bozeman, 2014; Gazni & Thelwall, 2014)

Languages, Marital status, Citizenship, Nationality Ethnicity

(Lee & Bozeman, 2005)

Country of origin

(Scellato et al., 2015)

Country of residence

(Corley & Sabharwal, 2010)

Complementary skills

(Bozeman & Corley, 2004; Maglaughlin & Sonnenwald, 2005)

Race, Tacit knowledge, Fields of training, Network ties Tenure status

(Bozeman et al., 2013)

(Dahlander & McFarland, 2013)

(Boardman & Corley, 2008; Dahlander & McFarland, 2013)

Realizing that a research collaborator is primarily a researcher suggests that the model we are proposing can be used generally for all researchers. The next results in the following sections will better inform how we assess quality of researchers with respect to collaboration.

2.1.2 Institutions and affiliations Research collaborators perform their activities and participate in collaborations in physical and virtual interactive spaces (e.g., universities, research centers), which are part of their context (Hunter & Leahey, 2008). Many attributes of the dimension institution are available in a researcher’s CV; others may be easily available in online sources. Attributes like size and age contribute to the characterization of prestige (Carillo et al., 2013), which has been explicitly used as an attribute by Hunter and Leahey (2008). Abramo et al. (2009) substantiate the importance of the attribute size of institution because it may indicate the amount of opportunities available for researchers to collaborate internally. Table 2 - List of attributes characterizing research collaborators from the perspective of her or his institutions Label

Sources

Name of institutions Acronyms of institutions

(Boardman & Corley, 2008; Hunter & Leahey, 2008; Knobel et al., 2013; Kumar & Jan, 2013) (Kumar & Jan, 2013)

Size of institution

(Abramo et al., 2009; Bozeman et al., 2013)

Age of institution

(Carillo et al., 2013; Knobel et al., 2013)

Institutional prestige, Geographic location

(Hunter & Leahey, 2008)

Institutional Infrastructure and funds available, Institutional labor policies Number of PhDs and post-docs

(Abramo et al., 2009) (Carillo et al., 2013)

Table 3 lists attributes resulting from the relationship between a researcher and an institution he or she is affiliated with. Several authors recommended attributes to characterize researchers based on the relation with his or her affiliation. Some affiliation attributes may seem personal attributes at first, but they originate from the affiliation relation (e.g., institutional address, academic rank). As with institutions, a researcher can have multiple affiliations.

Table 3 - List of attributes characterizing research collaborators from the perspective of a research collaborator’s affiliations Label

Sources

Type of affiliation, Job position

(Scellato et al., 2015)

Institutional address

(Abramo et al., 2014b, 2013a, 2013b; De Stefano et al., 2013; Perez-Cervantes et al., 2012) (Abramo et al., 2014b, 2013a; De Stefano et al., 2013) (Abramo et al., 2014b, 2013a)

Academic rank Disciplines, fields and subfields

2.1.3 Accomplishments and Processes The third dimension of research collaborators stems from the accomplishments she or he achieves. We found in our literature review different types of research accomplishments, such as journal articles, books, books chapters, grants and patents, which are described by different attributes (Dietz et al., 2000). Some of the attributes can be used for various accomplishment types. For example, number of authors can be used to describe both publications and grants. Other attributes are specific to one type of accomplishment (e.g., journal impact factor and journal title).

Table 4 lists attributes characterizing research

collaborators from the perspective of researcher’s accomplishments. Table 4 - List of attributes characterizing research collaborators from the perspective of a researcher’s accomplishments Label

Sources

Digital object identifier (DOI)

(Perez-Cervantes et al., 2012)

Publication title

(De Stefano et al., 2013)

Journal title, Publication keywords

(Corley & Sabharwal, 2010; De Stefano et al., 2013)

Number of authors

(Gazni & Didegah, 2011; Newman, 2004)

Number of citations

(Abramo et al., 2014a; Corley & Sabharwal, 2010; Gazni & Didegah, 2011)

Journal impact factor

(Didegah & Thelwall, 2013)

Number of foreign countries

(Gazni & Didegah, 2011)

Epistemic authority

(Beaver, 2004; Bozeman et al., 2013)

Research grants

(Lee & Bozeman, 2005)

The relationship between a researcher and an accomplishment is the process or processes delivered to achieve it. Assigning values for attributes to characterize processes may

be difficult because researchers typically record accomplishments (e.g., an article accepted for publication) and not the process of preparing it (e.g., number if meetings to discuss article draft with collaborators). Nevertheless, it is in the processes executed to achieve accomplishments that research collaborators engage in collaborations. As attributes in Table 5 reveal, many authors consider these attributes important when studying collaborations and collaborators (e.g., Gazni and Didegah, 2011, Bozeman et al. 2013). A common attribute to describe a collaborative process is the type of collaboration, which has been pointed out since the work of Katz and Martin (1997). Authors use type to describe whether the collaborative process is institutional, domestic, or international (e.g., Abramo et al., 2014b; Didegah and Thelwall, 2013; Gazni and Didegah, 2011; Ibáñez et al., 2013). Gazni et al. (2012) utilize size of the collaboration as an attribute of the process, which differs from the attribute from the accomplishment called number of authors. As an attribute of the process, the number of participants (Corley et al., 2006) may include team members that may not have collaborated for the entire duration of the process or staff members who do not have their names added to scientific accomplishments due to their administrative role. Size of collaboration is more general than number of participants because it can be used to compute other aspects such as the number of institutions and other elements to compute size (Gazni et al., 2012). Several authors are concerned with the forms of communication in a process, particularly whether communication technologies are used (Abramo et al., 2013b). Aspects such as whether processes include face-to-face meetings are considered because of studies that investigate their relation to productivity (e.g., Cummings and Kiesler, 2007). Table 5 lists attributes characterizing research collaborators from the perspective of the processes they deliver to achieve accomplishments. These results lead us to conclude that the proposed data model is suitable to conceptualize researcher quality independent of the emphasis on collaboration or not. The emphasis on collaboration or on any other purpose of the research assessment is reflected in the values of the attributes of the model. Particularly to assess quality of research collaborators, it is necessary to consider the values assigned to accomplishment attributes that originate from collaborative processes.

Table 5 - List of attributes characterizing research collaborators from the perspective of the processes delivered to achieve accomplishments Label

Sources

Types of collaboration

Administrative roles in the collaborative group

(Abramo et al., 2014b; Didegah and Thelwall, 2013; Gazni and Didegah, 2011; Ibáñez et al., 2013; Thelwall and Sud, 2014) (Beaver, 2001; Bozeman et al., 2013)

Management style within research team

(Bozeman et al., 2013; Chompalov et al., 2002)

Collaboration strategies

(Lee and Bozeman, 2005)

Size of collaboration Number of institutions

(Carillo et al., 2013; Cummings and Kiesler, 2007; Gazni et al., 2012) (Gazni and Didegah, 2011)

Budget

(Corley et al., 2006)

Duration of collaboration Participants of collaboration

(Brocke and Lippe, 2013; Cummings and Kiesler, 2007; Jeong et al., 2014) (Corley et al., 2006)

Participation incentives

(Corley et al., 2006)

Communications, Physical interaction, Physical meetings, Informal communications, Communication technologies

(Abramo et al., 2013b; Bozeman et al., 2013; Jeong et al., 2014)

2.1.4 Career The career of a researcher is a longitudinal account of an individual’s productivity (Dietz et al., 2000, Woolley et al. 2016), and this concept entails the remaining attributes we found in our review (see Table 6). Given the nature of a career trajectory that describes the historical progress of a research collaborator’s path, the values for its attributes are mostly derived from the other dimensions. This justifies our formulation of career as a meta-dimension (see Figure 2). Table 6 - Attributes that characterize research collaborators from the perspective of their careers Label Career age

Effects of seniority International mobility data Trajectories

Sources (Bozeman et al., 2013; Lee & Bozeman, 2005; PerezCervantes et al., 2012) (Bozeman et al., 2013; Dahlander & McFarland, 2013) (Bozeman et al., 2013; Dahlander & McFarland, 2013; Jonkers & Tijssen, 2008) (Jonkers & Tijssen, 2008) (Scellato et al., 2015) (Bozeman et al., 2013)

Years since PhD

(Jonkers & Tijssen, 2008; Lee & Bozeman, 2005)

Career productivity, Publications since PhD

(Lee & Bozeman, 2005)

Career stages Previous collaboration / Experiences

The career dimension associates researchers with temporal trajectories. This suggests that attributes under trajectories such as years since PhD may be used as basis for normalizing various researchers whose quality is under assessment.

3

CONCLUDING REMARKS The conceptual model of the research collaborator’s context has three categories,

namely, researcher, institutions, accomplishments, and one meta-dimension, the research collaborator’s career. They form the basis of the proposed conceptual model of research collaborator’s data showed in Figure 2. We set our study to conduct a systematic literature review to answer the question, How to conceptualize a data model to assess researcher quality with emphasis on research collaborators? We learned that research collaborators are, primarily, researchers and thus the model should conceptualize researcher quality while collaborative quality should be assessed from the values of other attributes. We found that accomplishments are one of the main categories that could be used to predict quality. We also learned that accomplishments entail a process that leads to its completion. Our conclusion is that the quality of a researcher with respect to its collaborative potential should be determined through the accomplishments that originate from processes that were delivered as a result of collaborations. The proposed conceptual data model to assess researcher quality enables future studies using data from CVs from different contextual realities (e.g., geography, culture of citation). The definition of the categories of attributes in which a researcher can be characterized helps guide selection of relevant attributes for analyses based on their alignment with the model. The model presented here is conceptual, thus it aims to capture the essence of the context of a research collaborator making it easier to identify which data is relevant for S&T investigation.

Acknowledgements Authors thank the STELA Institute. First author is supported by Brazilian’s Goiás Research Foundation (FAPEG) and University of the State of Goiás (UEG) under agreement number 201310267000099.

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