Toward a definition of digital object reuse

Ayla Stein Kenfield (University Library, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA)
Liz Woolcott (University Libraries, Utah State University, Logan, Utah, USA)
Santi Thompson (University Libraries, University of Houston, Houston, Texas, USA)
Elizabeth Joan Kelly (LOUIS: The Louisiana Library Network, Louisiana Board of Regents, Baton Rouge, Louisiana, USA)
Ali Shiri (School of Library and Information Studies, University of Alberta, Edmonton, Canada)
Caroline Muglia (University of Southern California Libraries, University of Southern California, Los Angeles, California, USA)
Kinza Masood (Mountain West Digital Library, Salt Lake City, Utah, USA)
Joyce Chapman (Duke University Libraries, Duke University, Durham, North Carolina, USA)
Derrick Jefferson (University Library, American University, Washington, District of Columbia, USA)
Myrna E. Morales (Massachusetts Coalition of Domestic Workers, Boston, Massachusetts, USA)

Digital Library Perspectives

ISSN: 2059-5816

Article publication date: 17 January 2022

Issue publication date: 29 June 2022




The purpose of this paper is to present conceptual definitions for digital object use and reuse. Typically, assessment of digital repository content struggles to go beyond traditional usage metrics such as clicks, views or downloads. This is problematic for galleries, libraries, archives, museums and repositories (GLAMR) practitioners because use assessment does not tell a nuanced story of how users engage with digital content and objects.


This paper reviews prior research and literature aimed at defining use and reuse of digital content in GLAMR contexts and builds off of this group’s previous research to devise a new model for defining use and reuse called the use-reuse matrix.


This paper presents the use-reuse matrix, which visually represents eight categories and numerous examples of use and reuse. Additionally, the paper explores the concept of “permeability” and its bearing on the matrix. It concludes with the next steps for future research and application in the development of the Digital Content Reuse Assessment Framework Toolkit (D-CRAFT).

Practical implications

The authors developed this model and definitions to inform D-CRAFT, an Institute of Museum and Library Services National Leadership Grant project. This toolkit is being developed to help practitioners assess reuse at their own institutions.


To the best of the authors’ knowledge, this paper is one of the first to propose distinct definitions that describe and differentiate between digital object use and reuse in the context of assessing digital collections and data.



Kenfield, A.S., Woolcott, L., Thompson, S., Kelly, E.J., Shiri, A., Muglia, C., Masood, K., Chapman, J., Jefferson, D. and Morales, M.E. (2022), "Toward a definition of digital object reuse", Digital Library Perspectives, Vol. 38 No. 3, pp. 378-394.



Emerald Publishing Limited

Copyright © 2021, Ayla Stein Kenfield, Liz Woolcott, Santi Thompson, Elizabeth Joan Kelly, Ali Shiri, Caroline Muglia, Kinza Masood, Joyce Chapman, Derrick Jefferson and Myrna E. Morales.


Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at


As knowledge and memory organizations increase access to their collections by making them available online, digital library practitioners are increasingly tasked with assessing the value of their digitized and born-digital cultural heritage objects, institutional repository items, research data, learning objects, and more. An awareness of use cases for digital objects facilitates stronger digital collection building through a more nuanced understanding of how and why users interact with digital objects. Quantitative access statistics, like downloads and clicks, begin to show some indication of significance to users. But qualitative data showing how these materials are used and repurposed may be of greater value to digital library practitioners in building user-centric digital collections that are responsive to user needs and demonstrably valuable to stakeholders. The differences between access and repurposing of digital library objects can also be framed as a distinction between the consumption of a digital library object for its initially envisaged purpose and its recontextualization and repurposing. One method of differentiating between access and repurposing of digital objects is to focus on their use and reuse. Despite wishing to conduct more reuse assessment of their digital collections, digital library practitioners have expressed that the lack of an accepted methodology for gathering and/or interpreting reuse data is a major barrier in doing so (O’Gara et al., 2018). An integral factor of best practices for assessing use and reuse must make clear to practitioners how to discern use from reuse. But what differentiates use and reuse?

In 2019, the Content Reuse working group of the Digital Library Federation (DLF) Assessment Interest Group (AIG) was awarded an Institute of Museum and Library Services (IMLS) National Leadership Grant for Libraries to build the Digital Content Reuse Assessment Framework Toolkit (D-CRAFT). This toolkit will “…contain resources, recommended practices, and use cases for sustainably measuring and evaluating the reuse of digital assets held by cultural heritage knowledge organizations” (Thompson et al., 2019). The purpose of the project is to facilitate the assessment of digital repository content beyond traditional metrics such as clicks, views or downloads, as these metrics are insufficient for clearly demonstrating the impact of how digital content and objects are reused after viewing or downloading. Since practitioners cannot rely on traditional usage statistics for assessing reuse, this begs the question: what is in scope for assessing reuse? At this point in time, there are no community accepted definitions for use or reuse of digital content, of what differentiates them and of how their assessment methods and approaches differ. If assessing reuse is important to digital library practitioners, then digital object reuse must be clearly defined to facilitate the creation of said assessment toolkit (Kelly et al., 2018; O’Gara et al., 2018; Thompson et al., 2019).

In this article, the researchers propose answers to these three questions:


What defines use and reuse of digital content held by galleries, libraries, archives, museums and repositories (GLAMR)?


What differentiates use and reuse?


What constitutes instances, examples and indications of use and reuse?

The paper will present a review of prior research and literature aimed at defining use and reuse of digital content in GLAMR organizations, provides and builds on a review of findings of six focus groups conducted by Developing a Framework for Measuring Reuse of Digital Objects (Thompson et al., 2017), propose definitions for use and reuse of digital content held by GLAMR institutions, present the use-reuse matrix, unpack the concept of “permeability” and its bearing on the model and conclude with next steps for future research and application.

Literature review

Few attempts to define or differentiate between the concepts of digital object use and reuse exist in scholarly literature. More often than not, the terms use and reuse are applied interchangeably rather than as distinct concepts. Nevertheless, examples of digital use and reuse abound, and both theoretical papers and research studies reflect a range of impacts shown by different types of digital object practices, from reading or downloading to incorporating into teaching materials and creating new media works. This review focuses on literature and reports published during the past decade, with the inclusion of additional older, prominent articles when relevant. Some common themes in the concepts of use and reuse can be found in the areas of knowledge management, media studies and GLAMR. Differences in how these terms are applied in these fields as well as in relation to different digital content types (image, music, research data, learning objects, etc.) also surface. The following research provides some background as well as a basis for comparison with the definitions and examples of use and reuse proposed later in this paper.

From a library-focused perspective, use is not a singular concept but instead contains multiple facets manifested in different ways throughout library resource and service analysis (Fleming-May, 2011). Fleming–May’s typology breaks use into four major concepts including abstraction (vague notions such as “student library use”), implement (the library as entity, establishment or symbol and/or library information and resources as tools), instance (quantitatively measured transactions like circulation or door counts) and process (an individual’s use of the library to achieve a goal, such as writing a doctoral thesis) (Fleming-May, 2011, pp. 308–309). The concept of use becomes further muddled when evaluating usage of digital resources; while librarians may think it is obvious that online access to information resources provided by the library enables use, remote information seekers may not even realize they have “used” the library (Fleming-May, 2011, p. 316).

Image use is defined somewhat more definitively, with Beaudoin differentiating between “tangible use,” as in “lectures, presentations, articles, and artwork” and “intangible use,” as in “thematic illustration, intellectual reflection or inspiration” (Matusiak et al., 2019, p. 9). In attempting to differentiate between image use and reuse, Matusiak et al. (2019, pp. 495–497) further define image use as “an image is used in the context it was originally created or presented,” for example, a photograph or chart created by a student for the purposes of fulfilling an assignment. If a student then modifies their own photographs to create artistic content, this too is characterized as use. Fidel (1997) divides image needs into two types – illustration and information. Illustrative use involves including an image to depict what is in accompanying text, while informational use may instead lead to further analysis of the content of the image to better understand what is in it (McCay-Peet and Toms, 2009, p. 2420). Pasquetto et al. (2017) define data use as actions done by data creators, either when first compiling a data set to answer a specific question or returning to a previously created data set to replicate, reproduce or generate a new study. They further note that when that data set is contributed to a repository, retrieved by someone else and deployed for another project, it usually would be considered a reuse. Data use comprises a number of complex facets, including computation, aggregation, linkage, analysis and inferencing (Tempini, 2017).

Prior to the D-CRAFT project, the DLF AIG was awarded an IMLS grant for Developing a Framework for Measuring Reuse of Digital Objects (LG-73–17-0002–17) which, in addition to conducting a formal needs assessment of the Digital Library community to determine desired functionality for a future reuse assessment toolkit, explored the concepts of use and reuse (Kelly et al., 2018). The Measuring Reuse project conducted six focus groups with digital library practitioners from across the GLAMR landscape. Participants discussed the grant team’s definitions and examples for use and reuse alongside their own institution’s efforts to gather data in those categories. The project team derived the following definition for use: discovering and browsing objects in a digital library, often described as “clicks” or “downloads,” without knowing the specific context for this use (Kelly et al., 2018).

Use can perhaps best be encapsulated with the term “consumption,” especially as it relates to digital library collections. Tanner (2011) argues that consumption includes both the “entertainment” enjoyed by a user engaging with digital content as well as personal value accrued as a result of participating in a use community. From the perspective of media studies and, in particular, music remixes, Borschke (2017, p. 8) also defines use as “consumption practices.” Initial consumption of a digital object, such as downloading it, may count as use, while incorporating that object into future projects may count as reuse; for example, medical images downloaded from a repository (use) and then consulted to inform new product development (reuse) (Herman, 2014). Original creation and ownership of the digital object may also help differentiate between use and reuse, with creation of a scientific data set for novel analysis categorized as use and the analysis of an open data set created by someone else categorized as reuse (Pasquetto, 2019). Using digital objects as teaching aides in a face-to-face or online classroom may also qualify as use of digital objects (Dallmeier-Tiessen et al., 2014; Tanner, 2011; Terras, 2015).

Assessing use of digital objects tends to be quantitative and relatively straightforward; examples of digital object use assessment metrics include views, viewing time, clicks, downloads, sessions and other web analytics; printing; altmetrics (e.g. downloads, saves in citation managers, recommendations and shares); video coding and annotation; and clones of code or data (Baughman et al., 2018; Carpenter et al., 2016; Faniel and Yakel, 2011; Fouseki and Vacharopoulou, 2013; Frank et al., 2016; GitHub and Various Contributors, 2018; Ladd, 2015; He and Han, 2017; Perrin et al., 2017; White et al., 2018; O’Gara et al., 2018; Kelly et al., 2018).

Defining reuse proves to be as challenging as defining use, and attempts to do so in the literature vary in complexity. Situated among the more theoretical standpoints, Markus (2001) focuses on the reuse of knowledge. The author finds that knowledge reuse is completed with the “successful knowledge transfer” between two agents and outlines “a typology of different knowledge reuse situations” (Markus, 2001, pp. 59–60). Others, while not engaging in the theoretical composition of reuse to the same degree as Marcus, also emphasize the transactional nature that helps one identify the reuse of knowledge or information. Frank et al. (2016, pp. 3–4) claim that drawing upon information repeatedly signifies reuse, in addition to using information in a space other than where it was originally obtained. Pasquetto et al. (2017) state that data reuse occurs when a data set is retrieved by a researcher who was not the original creator and incorporated into a new, distinct study. In contrast, Donker (2016, p. 21) suggests that reuse, beyond accessing, downloading and “invoking” content must include feedback or input opportunities “for co-generated information.” An analysis of scholarly works that included both “reuse” and “data” in the title, triangulated with the study of the etymology of the word “reuse,” led van de Sandt et al. (2019) to deduce that there was no difference between applications of reuse and use. They determine that previous attempts to differentiate between use and reuse were based on the idea that an originating researcher uses their own data and any subsequent research involving that data is reuse. This interpretation of use and reuse is based on a paper-centered, linear research model, whereas contemporary, data-driven research is more complex and dynamic. Van de Sandt et al. (2019, p. 14) therefore define “(re)use” as “the use of any research resource regardless of when it is used, the purpose, the characteristics of the data and its user.”

While completing the Measuring Reuse project, the team also developed a definition of reuse based on focus group discussions. They articulated reuse as “how often and in what ways digital library materials are utilized and repurposed. In this definition, we do know the context of the use” (Kelly et al., 2018). By establishing working definitions for use and reuse, the Measuring Reuse project team essentially qualified the differences between the two concepts. Use is the initial access of an item. Nothing is known about how that item is used after it is initially accessed. Reuse is how the item is used after the initial access (Kelly et al., 2018).

Important themes emerge in the literature dedicated to digital object reuse. Thompson and Reilly (2017) and Matusiak et al. (2019) emphasize the need for a digital image to be re-contextualized, including changing the virtual location or intellectual meaning, for reuse to occur. Thompson and Reilly explain recontextualization as “using a digital image in a new setting that is not its original platform, purpose, or context” (2017, p. 2264). Matusiak et al. suggest reuse occurs when “an image is selected from a different source and is repurposed” and when “the visual content of an original image is used in a different context” (2019, p. 495). Eschenfelder and Caswell (2010, para. 11–16) align digital collections reuse with access levels and permissions. They cite three reuse categories: “virtual display case,” which makes content as accessible as is ethically possible; “regulated access/cultural property,” which monitors “access and use of culturally sensitive materials to protect the source group that generated the material”; and “cultural remix,” which encourages the use, reuse and distribution of knowledge without restrictions. Drawing from the field of media studies, Borschke (2017, pp. 33–39) articulates a remix as “a diverse range of activities of reuse and recombination” and “a new arrangement, an alternative mix of a composition.” She notes the pervasiveness in which the term is used to describe all “creative, unauthorized reuse that flourished with the mass uptake of digital technologies and is facilitated by broadband Internet access” (p. 48).

Scholars writing on the reuse of research data highlight the important role data plays in verifying existing knowledge and advancing new discoveries using combinations of existing research data. Curty and Qin (2014, p. 1) state, “not only data reuse helps address the issues of quality assessment and verification in science, but also allows for transforming outcomes from previous studies into new research.” Yoon (2017) broadens the practice of data reuse by suggesting that it is a “social process” that requires researchers to be engaged with one another and the public to communicate findings, processes and lessons learned.

Examples of reuse are diverse and expansive within the literature. Various studies focus on the reuse behavior of specific audience types. Thompson and Reilly (2017) document how general users download digital objects to repurpose as artwork or decoration in physical format and how social media users choose objects in GLAMR digital repositories to share across social media platforms. Johnston and Köntges (2016) and Terras (2015) articulate the need for GLAMR institutions to make bibliographic metadata, described by Terras as “open cultural data,” available to researchers to reconfigure and repurpose. Other investigations focus on the reuse patterns of educators and students interacting with digital objects in classroom settings (Tanner, 2011; Frank et al., 2016). Matusiak et al. (2019, pp. 497–499) identified types of reuse used by students when completing class assignments. Some examples include reuse as: “sources of information” when integrating digital images into a presentation “with the intent of communicating a concept related to the topic of their presentation” and “transformative reuse” when combining elements of multiple photographs to generate a different, unique image that “has new meaning and purpose.” Borschke (2017, pp. 48–49) includes: “cut/copy/paste technologies” (Navas, 2010, p. 157; Schütze, 2003), social networking (Davis et al., 2010; Markham, 2013), user-generated content (Lee, 2008), “commons-based peer production” (Benkler, 2006, p. 60), participatory media (Jenkins, 2009), distributed authorship (Logie, in Navas et al., 2015, p. 296–309) and a variety of concepts, descriptions, neologisms, and buzzwords associated with networked culture at the turn of the twenty-first century as examples of remix, itself an extension of reuse.

There is also an emerging body of literature focused exclusively on research data reuse examples. He and Nahar (2016) and Pasquetto (2019) identify various types of research data reuse, including: testing existing study results through replication, comparing the results of one version of a study to a previous version and conducting a “meta-analysis” of previous studies’ results. Peters et al. (2016, p. 724) suggest that citations of data sets or published research using data sets are markers of reuse because they show impact. Pasquetto et al. (2019, p. 4) argue that “data reuse is a process that occurs within knowledge infrastructures that evolve over time, encompassing expertise, trust, communities, technologies, policies, resources and institutions.” They propose a typology of data reuses ranging from comparative to integrative. Comparative data reuse requires enough expertise about the data to assess their quality and value for a specific comparison. The integrative involves the ability to perform the action, such as reusing data in a new experiment. An example of a more sophisticated method for tracking use and reuse to help evaluate the value of their collections is seen in the Inter-university Consortium for Political and Social Research (ICPSR) Bibliography of Data-related Literature, “a frequently-updated database of thousands of citations for publications that analyze data held at ICPSR [1].”

The literature also surfaces wide-ranging benefits for the practice of reuse among scientists. Faniel and Yakel (2017) note how the availability of research data and the opportunity to adapt it to fit the needs of various disciplines can make the work of a researcher more efficient. The availability and adaptability of data can also generate new research avenues and questions. However, others are quick to articulate the limits that some disciplines identify when observing reuse behavior, with Pasquetto (2019, p. 2) noting a preference in biomedicine for researchers to use personally collected data rather than to harness existing data for reuse.

Researchers and GLAMR practitioners use a variety of methods to assess reuse. He and Nahar (2016), for instance, evaluate publicly available research data reuse through citation analysis. A growing body of literature shows that practitioners use Web tools to identify instances of digital library content being placed on personal and commercial websites, social media platforms and online image galleries. Kousha et al. (2010), Kirton and Terras (2013), Kelly (2015) and Reilly and Thompson (2017) leverage reverse image lookup technology to locate images posted across the Web. Kelly (2018a) conducts a case study tracking reuse via Google Alerts. Ladd (2015) and Kelly (2018b) investigate the ways that placing content on popular third-party sites (Flickr and Wikipedia/Wikimedia, respectively) can increase the exposure of digital collections.

This paper complements and expands the scholarly conversation on the notions of use, reuse and their assessment. Following the lead of Matusiak et al. (2019) and the findings of the Measuring Reuse project (O’Gara et al., 2018; Kelly et al., 2018), this paper posits that there is a difference between digital object use and reuse. Echoing many of the thematic examples found in the use and reuse literature, including the acts of consuming, recontextualizing and transforming digital objects, this paper is the first to articulate a continuum of behavior indicative of use or reuse, and to situate this continuum in an assessment framework that digital repository practitioners can draw upon to demonstrate the impact of digital objects.

Defining use and reuse

The complexity of defining use and reuse is due largely to a sizable void in the field’s collective understanding of patron engagement with digital materials. For most GLAMR institutions, the focus in data collection has been primarily on use – the access of material provided by the institution. What users subsequently do with the materials they have accessed has not been a large part of how GLAMR institutions described their value until more recently. Outputs such as gate counts, checkouts or the number of visitors a year have traditionally been the measure of value for GLAMR institutions – primarily because these metrics are similar to metrics gathered for analog material in the past and provide a measure of consistency. However, access to an item does not equate value or impact so much as it indicates a measure of discoverability. As GLAMR institutions move toward efforts to measure outcomes instead of outputs, it becomes vital to understand what a user does with materials they have accessed and their impact.

As noted in the literature review above, in the previously completed Measuring Reuse grant, the research team conducted primary research of their own, discussing the concepts of use and reuse with digital repository practitioners across the GLAMR community (Kelly et al., 2018). The data uncovered through that process set the stage for the theory that underlines D-CRAFT. The Measuring Reuse grant work found that practitioners agreed that reuse should be an indicator of impact. Reuse metrics should provide contextual information that helps GLAMR institutions determine who their users are, where digital objects are being reused, what users do with digital objects, and most importantly, why those digital objects are meaningful to users. However, the consensus of reuse as an indicator of impact did not lead to a more robust understanding of how to define reuse. It became clear that the definition would need to be expansive with the metrics defined broadly enough to reflect how value is considered differently by institutions and communities.

While there was no clear consensus on the difference between use and reuse, two ways of viewing reuse emerged from discussions with practitioners: reuse as any action outside of the initial repository or exposure point; and reuse as a transformative act, either involving the object directly or its surroundings.

The clear break between use and reuse is the setting of the user’s ensuing actions. Any action that happens within the initial location of discovery is use. Such actions might include accessing, viewing, downloading, listening or reading the content of the digital object within the repository where the object is held. Any action that happens to the digital object outside of the repository is considered reuse. Examples provided by practitioners include social media sharing, printing a copy of the digital object or aggregation of genealogy data. Some discussion of the methods for measuring reuse with this definition noted that many impacts are not measurable, either because the reuse can happen offline or because it does not resolve in a tangible format. Practitioners reflected that reuse is not always visceral, such as a collection of protest material that inspires others to political action.

An alternative view of reuse that emerged from talking with practitioners was the concept of transformation or interpretation; the object is changed in some manner or given a meaning or nuance beyond its original intent. The difference between use of an object and reuse goes beyond the context or location of the action and addresses whether there is added value. Examples of this ranged from merely relocating the object to a new context, such as aggregating digital objects into a curated collection that could lend to reinterpretation of the object, to a complete transformation into a new object, such as a mashup of images. In these cases, practitioners noted that context might not be the most appropriate definition for reuse because the added value or transmutation of the object into a new object was the primary evidence that the original object was reused.

Digital repository practitioners’ nuanced views that reuse could be either the context of how/where interaction with a digital object occurs or the output of that interaction did not clarify a concrete expectation for the researchers to model in D-CRAFT. Therefore, the researchers aimed to incorporate these nuances into a Spectrum of Engagement that centered user engagement to better reflect the multi-dimensional way that GLAMR practitioners might understand reuse.

The matrix and the use-reuse spectrum of engagement

The subtlety in differentiating between use and reuse, as well as the distinctiveness of various use and reuse examples within different institutional contexts, only further emphasized how difficult it is to concretely define and distinguish between the two concepts. Members of the D-CRAFT and Measuring Reuse research teams have debated internally what constitutes reuse of digital materials and how that differs from use of digital materials for over two years now. While some areas of dispute have been resolved, others have been uncovered. This section will review the evolution of the team’s conceptualization of use and reuse.

The researchers needed to determine what would actually be evaluated when measuring reuse of a digital object, as well as the purpose of reuse assessment. Examples of reuse that GLAMR professionals had expressed interest in tracking in the Measuring Reuse focus groups have been discussed elsewhere (Kenfield et al., 2019, Slide 17).

As described above, the researchers posit that there is no clear-cut distinction between use and reuse. For this reason, the researchers determined that use and reuse exist on a spectrum and should not be considered discrete binary choices. Figure 1: Use-Reuse Engagement Spectrum (the Spectrum) depicts a series of activities arranged by “level of engagement.” On the use end of the Spectrum are activities that involve the least amount of interaction to perform, while at the other end of the Spectrum are activities that involve the highest degree of interaction with a digital object. The levels of engagement were defined as:

  • Access: To come into contact with a digital object.

  • Consumption: To view, read or listen to the intellectual content of a digital object.

  • Reformatting: To change the medium or delivery of a digital object.

  • Enhancement: To add functionality or accessibility to a digital object.

  • Sharing: To propagate the intellectual content of a digital object by distributing a means of access.

  • Recontextualization: To alter the surroundings or space that affect the meaning, purpose or intent of a digital object.

  • Transformation: To alter or change a digital object in such a way that results in the creation of a new, distinct entity.

While modeling use and reuse as a spectrum was a step in the right direction, it was not nuanced enough because it did not adequately highlight how the middle tier levels such as enhancement, sharing and recontextualization could encompass actions that are sometimes use and other times reuse. The Spectrum, shown in Figure 1, was then re-envisioned as a matrix, shown in Table 1. There were several iterations of the use-reuse matrix model, only the final version is presented, in Table 1, and discussed here. For the curious, the other revolutions of the matrix model are available for viewing elsewhere [2].

The use-reuse matrix model builds on the Spectrum of Engagement by mapping it to a grid structure. The Spectrum is rotated so that it runs vertically along the y-axis with a similar continuum of “Simple Engagement” to “Complex Engagement.” Use and reuse become distinct variables that intersect with each level of the Spectrum.

Table 1 represents the final version of the use-reuse matrix model. The definitions for use and reuse were revised to emphasize that significance to users is the primary differentiation between use and reuse. The definition of reuse was simplified to “active use that indicates an interest or value to the user.” As in Figure #1, Transformation is the final activity on the Complex end of the Spectrum.

The researchers also added “Reproducibility” as a level of engagement. In this model, Reproducibility is defined as “to draw upon a digital object or data set to validate or verify a previous study’s methods and/or results.” Positioning Reproducibility as its own level emphasizes the uniqueness of validating previous studies, which is dissimilar to reformatting or enhancing data, and also acknowledges the likelihood that this type of engagement will only become more prominent as various academic fields grapple with the “reproducibility crisis” impacting scholarly research. Influenced by the work of Pasquetto et al. (2017, p. 4), who state that reproducibility is “an example of independent reuse of a dataset,” the researchers position reproducibility toward the reuse side of the Spectrum.

As can be seen in Table 1, the row for Sharing on the Spectrum is unique. On all of the other rows, the columns for use and reuse are separate and distinct. Only in the row for Sharing has the delineation between use and reuse been removed. This indicates the permeability of use and reuse in Sharing; that is to say, Sharing cannot be confined solely to use or reuse.

Throughout the revisions of the matrices, Sharing has moved along the Spectrum of Engagement, sometimes appearing as high level and other times as low level. Examples of Sharing contain a mixture of active and passive interactions and do not line up neatly with “transformation.” In the early matrices where the definition was determined by whether the action “added substantive, interpretive or transformative meaning,” Sharing actions such as posting a digital object on social media fell distinctly under use because they did not necessarily add meaning or interpretation. In the later matrices where the definition of use and reuse was determined by a demonstration of interest to a user, the same action could be considered an example of reuse because the simple act of reposting a digital object showed interest in a clear, measurable way that practitioners could potentially track. Nevertheless, as noted at the beginning of this article, not everything that demonstrates value can be measured, and the method of measuring for value often falls short. Similarly, the evidence of interest also does not equate with value to a user. The presence of thought and involvement with a digital object, such as citing a digital object in a scholarly article or Wikipedia entry that examines and interprets the item, may be more in line with what an institution wants to consider to be reuse because the value to the user can be determined more reliably. In choosing to show Sharing as spanning both use and reuse, the authors of this paper argue that the context for making that designation may be best left to the institution.

In the following section, the researchers will expand on the complexities of Sharing, the concept of permeability and why this is significant for the Matrix.

Embracing permeability

First articulated by philosopher and legal scholar Cornell (1991), permeability invokes alternative voices, ideas and frameworks to dissolve the current limits of a system. Information studies scholars like Hope Olson (2001, p. 659) contend that permeability is a fitting paradigm for GLAMR standards because it offers practitioners the ability to “develop an ethical relationship with the other” – to engage with diverse audiences and allow those groups to help (re)shape the construct. Olson (2001, p. 661) offered several ways of promoting permeability in cataloging, including by “using systems designed for multilingual catalogs to allow more than authoritative headings for a topic” such as “women, wimmin, womyn, femmes, Frauen, and so on, would all retrieve the same collection of documents without an intervening instruction to ‘USE Women’.”

Formulating and building consensus around the final iteration of the matrix surfaced several internal debates among the researchers. These debates forced them to acknowledge the limits of their ability to outline all aspects of the Use-Reuse Spectrum. In part, this reckoning emerged from literature grounded in permeability. The researchers draw upon one element in the matrix, the process of sharing a digital object over social media, as a lens to illustrate these internal discussions and the influence that permeability has on their evolving construction of the matrix and the spectrum.

Examples of sharing digital objects over social media in the literature reinforce the complexity of sharing and how challenging it is for a practitioner to classify where Sharing falls. The researchers cited documented instances of Sharing that suggested the act fell into the reuse category because of the recontextualization of an object’s meaning or the transformation of an object itself. Examples include instances of users:

  • Placing content from an online lesson plan dedicated to immigration into a Pinterest board focused on denim fashion (Thompson and Reilly, 2019).

  • Repurposing an image of railway workers on the Union Pacific Railroad to illustrate minimum wage advocacy (Kelly, 2019).

  • Remixing digital objects from the United Kingdom’s National Gallery as part of an “update art” initiative (Kirton and Terras, 2013).

As the conversation progressed, it became clear that the specific circumstances surrounding a sharing event would determine passive/active and use/reuse status. It was also evident that they could not, and should not, neither try to anticipate every type of example that might encompass sharing nor should they attempt to categorize hypothetical sharing events as passive/use or active/reuse. Instead, the researchers elected to embrace the idea of permeability to allow for other perspectives to inform the boundaries of sharing over time.

Professional, educational, cultural, ethical and legal variables will all challenge the current limits of the matrix. As such, practitioners who use the spectrum to assess use and reuse will continue to interrogate the definitions, examples and positioning of engagement types along the Spectrum. The researchers’ commitment to a permeable matrix allows for the adaptation of the framework beyond the goals of their current project and the collective imagination of the team members. This admission presents long-term project sustainability questions that the researchers have not fully processed: as the matrix filters into the GLAMR professional discussion, and as practitioners identify ways to adapt, revise and reshape the framework, how will the researchers keep the matrix updated? How are permeable contributions represented in a matrix and in a future assessment toolkit? As diverse perspectives interrogate the matrix, who will be responsible for making decisions on what is and is not included in the model? While the concept of permeability has freed the researchers to move forward with a level of ambiguity built into the matrix, it still presents challenges for long-term maintenance. Despite the challenges, the team believes a permeable framework offers the most potential for an assessment tool that is flexible and adaptable enough to serve the diversity of needs and interests of the GLAMR profession.


This article posited the following questions:


What defines use and reuse of digital content held by galleries, libraries, archives, museums, and repositories (GLAMR)?


What differentiates use and reuse?


What constitutes instances, examples and indications of use and reuse?

To address these queries, the researchers introduced the use-reuse matrix. The use-reuse matrix includes new, refined definitions for use and reuse, as well as the levels of engagement, which aims to provide further nuance when defining use and reuse. Examples of activities in either the use or reuse columns for each of the levels of engagement are presented as well. By articulating levels of engagement for use and reuse, the researchers make one of the first attempts to devise categorical types of use and reuse as well as to construct boundaries that can help practitioners classify use or reuse on a case-by-case basis. The researchers’ adoption of permeability also invites other perspectives to inform and shape the future composition of the use-reuse matrix and its implementation.

Collectively, these efforts are important building blocks to establishing a foundation for D-CRAFT: an open access, collaboratively-developed toolkit that offers guidelines on assessing reuse. D-CRAFT will centralize documentation, templates and existing tools to assist GLAMR practitioners in the entire lifecycle of reuse assessment. The integration of the use-reuse matrix into D-CRAFT’s design and intellectual framework aims to help GLAMR practitioners identify reuse and use vetted assessment methods to evaluate the value and impact of their collections.


Use-reuse engagement spectrum

Figure 1.

Use-reuse engagement spectrum

Use-reuse matrix

Use, Reuse Use
Passive interaction
with a digital object that indicates potential interest and/or value to an external user
Active interaction
with a digital object(s) that demonstrates an interest or value to an external user
Simple Engagement

Spectrum of Engagement

Complex Engagement
To come into contact with a digital object
• Browsing digital repositories for content
• Clicking a link for a digital object
• Downloading digital objects
• Accessing a web archive

To view, read, listen or expose oneself to the intellectual content of a digital object

• Watching a video online
• Reading an article
• Viewing a photograph
• Listening to a song

To change the medium or delivery of a digital object without changing the content itself

• Printing digital objects
• Scanning a document

To expose others to the intellectual content of a digital object by distributing a means of display or access, such as a link or doi
• Displaying digital collection materials on social media or e-mail
• Citing a digital object in a scholarly article without adding interpretation
• Citing a digital object in a Wikipedia article without adding interpretation
• Publishing/reposting content in online or print publication without adding interpretation
• Incorporating digital images into documentaries or movies without adding interpretation

To draw upon a digital object or data set to validate or verify a previous study’s methods and/or results
• Confirming a journal article’s results by using an existing data set to reproduce its methods and conclusions
• Verifying a research study’s methodology by replicating its process using a different data set

To add functionality or accessibility to a digital object
• Annotating an image or document
• Translating the text of a digital object from one language to another
• Transcribing a digital object
• Creating closed captioning for a video
• Adjusting lighting or coloring of digital items to faithfully represent the original object
• Charting a dataset in a graph or infographic to communicate with others
• Recording a book to make an audio book
To alter the surroundings or space that affect the meaning, purpose or intent of a digital object
• Aggregating metadata in a discovery tool • Curating sets of digital material, such as People of Color in Medieval European Art History, doi:
• Curriculum planning K-12 education, e.g. DocTeachs, LOC Teaching with Primary Sources, etc.
• Creating a Pinterest board of digital objects
• Citing a digital object in a scholarly article and adding interpretation
• Citing a digital object in a Wikipedia article and adding interpretation
• Publishing/reposting content in an online or print publication that adds interpretation
• Incorporating digital images into documentaries or movies while adding interpretation
To change or alter a digital object substantially, resulting in a new, distinct entity, including, but not limited to recreations, versions and mashups
• Creating “then and now” photographs for an exhibit, doi:
• Painting, drawing or otherwise artistically representing a digital object
• Combining two or more datasets for analysis
• Creating a GIF or meme from digital objects
• Revising an existing OER object with new content
• Overlaying a map with data points
• Adding color to a black and white photo or video to add artistic value to the original object
• Combining datasets from multiple sources and disciplines to produce a new result, intellectual framework or model



Baughman, S., Roebuck, G. and Arlitsch, K. (2018), “Reporting practices of institutional repositories: analysis of responses from two surveys”, Journal of Library Administration, Vol. 58 No. 1, pp. 65-80, doi: 10.1080/01930826.2017.1399705.

Benkler, Y. (2006), The Wealth of Networks: How Social Production Transforms Markets and Freedom, Yale University Press, New Haven, CT.

Borschke, M. (2017), “This is not a remix: piracy, authenticity and popular music”, Bloomsbury Academic, available at:

Carpenter, T.A., Lagace, N. and Bahnmaier, S. (2016), “Developing standards for emerging forms of assessment: the NISO altmetrics initiative”, The Serials Librarian, Vol. 70 Nos 1/4, pp. 85-88, doi: 10.1080/0361526X.2016.1157737.

Cornell, D.L. (1991), “The philosophy of the limit, systems theory and feminist legal reform symposium on feminist critical legal studies and postmodernism: part one: a diversity of influence”, New England Law Review, Vol. 26 No. 3, pp. 783-804.

Curty, R.G. and Qin, J. (2014), “Towards a model for research data reuse behavior”, Proceedings of the American Society for Information Science and Technology, Vol. 51 No. 1, pp. 1-4, doi: 10.1002/meet.2014.14505101072.

Dallmeier-Tiessen, S., Darby, R., Gitmans, K., Lambert, S., Matthews, B., Mele, S., Suhonen, J. and Wilson, M. (2014), “Enabling sharing and reuse of scientific data”, New Review of Information Networking, Vol. 19 No. 1, pp. 16-43.

Davis, A., Webb, S., Lackey, D. and DeVoss, D.N. (2010), “Remix, play, and remediation: undertheorized composing practices”, in Urbanski, H. (Ed.), Writing and the Digital Generation: Essays On New Media Rhetoric, McFarland & Company, Jefferson, NC, pp. 186-197.

Donker, F.W. (2016), “From access to re-use: a user’s perspective on public sector information availability”, A+BE | Architecture and the Built Environment, No. 21, available at:

Eschenfelder, K.R. and Caswell, M. (2010), “Digital cultural collections in an age of reuse and remixes”, First Monday, Vol. 15 No. 11, doi: 10.5210/fm.v15i11.3060.

Faniel, I.M. and Yakel, E. (2011), “Significant properties as contextual metadata”, Journal of Library Metadata, Vol. 11 Nos 3/4, pp. 155-165, doi: 10.1080/19386389.2011.629959.

Faniel, I.M. and Yakel, E. (2017), “Practices do not make perfect: disciplinary data sharing and reuse practices and their implications for repository data curation”, Johnson, L.R. (Ed.), Curating Research Data: practical Strategies for Your Digital Repository, Association of College and Research Libraries Press, Chicago, Vol. 1, pp. 103-125, available at:

Fidel, R. (1997), “The image retrieval task: implications for the design and evaluation of image databases”, New Review of Hypermedia and Multimedia, Vol. 3 No. 1, pp. 181-199, doi: 10.1080/13614569708914689.

Fleming-May, R.A. (2011), “What is library ‘use’? Facets of concept and a typology of its application in the literature of library and information science”, The Library Quarterly, Vol. 81 No. 3, pp. 297-320, doi: 10.1086/660133.

Fouseki, K. and Vacharopoulou, K. (2013), “Digital museum collections and social media: ethical considerations of ownership and use”, Journal of Conservation and Museum Studies, Vol. 11 No. 5, doi: 10.5334/jcms.1021209.

Frank, R.D., Suzuka, K. and Yakel, E. (2016), “Examining the reuse of qualitative research data: digital video in education”, 13th Annual Archiving Conference, April 19, 2016 – April 22, 2016, pp. 146-151.

GitHub and Various Contributors (2018), “Open source metrics”, [Tutorials], Open Source Guides, available at:

He, L. and Han, Z. (2017), “Do usage counts of scientific data make sense? An investigation of the dryad repository”, Library Hi Tech, Vol. 35 No. 2, pp. 332-342.

He, L. and Nahar, V. (2016), “Reuse of scientific data in academic publications: an investigation of dryad digital repository”, Aslib Journal of Information Management, Vol. 68 No. 4, pp. 478-494, doi: 10.1108/AJIM-01-2016-0008.

Herman, D. (2014), “Asset reuse of images from a repository”, Doctoral, Walden University, available at:

Johnston, M. and Köntges, T. (2016), “Increasing metadata usability for the New Zealand cartoon archive at the Alexander Turnbull library”, Alexandria: The Journal of National and International Library and Information Issues, Vol. 26 No. 2, pp. 91-106.

Kelly, E.J. (2015), “Reverse image lookup of a small academic library digital collection”, Codex: Journal of the Louisiana Chapter of the ACRL, Vol. 3 No. 2, available at:

Kelly, E.J. (2018a), “Content analysis of Google alerts for cultural heritage institutions”, Journal of Web Librarianship, Vol. 12 No. 1, pp. 28-45, doi: 10.1080/19322909.2017.1369374.

Kelly, E.J. (2018b), “Use of Louisiana’s digital cultural heritage by Wikipedians”, Journal of Web Librarianship, Vol. 12 No. 2, pp. 85-106, doi: 10.1080/19322909.2017.1391733.

Kelly, E.J. (2019), “Reuse of Wikimedia commons cultural heritage images on the wider web”, Evidence Based Library and Information Practice, Vol. 14 No. 3, pp. 28-51, doi: 10.18438/eblip29575.

Kelly, E.J., Kenfield, A.S., O’Gara, G., Muglia, C., Thompson, S. and Woolcott, L. (2018), “Setting a foundation for assessing content reuse: a white paper from the developing a framework for measuring reuse of digital objects project”, available at: 10.17605/OSF.IO/BQJVR

Kenfield, A.S., Thompson, S., Kelly, E.J., Muglia, C., O’Gara, G. and Woolcott, L. (2019), “Debates in lexicography: defining use and reuse”, presented at Open Repositories 2019, Hamburg, Germany, doi: 10.17605/OSF.IO/W4TY9

Kirton, I. and Terras, M. (2013), “Where do images of art go once they go online? A reverse image lookup study to assess the dissemination of digitized cultural heritage”, presented at Museums and the Web, 2013, Silver Springs, MD, USA, available at:

Kousha, K., Thelwall, M. and Rezaie, S. (2010), “Can the impact of scholarly images be assessed online? An exploratory study using image identification technology”, Journal of the American Society for Information Science and Technology, Vol. 61 No. 9, pp. 1734-1744, doi: 10.1002/asi.21370.

Ladd, M. (2015), “Access and use in the digital age: a case study of a digital postcard collection”, New Review of Academic Librarianship, Vol. 21 No. 2, pp. 225-231, doi: 10.1080/13614533.2015.1031258.

McCay-Peet, L. and Toms, E. (2009), “Image use within the work task model: images as information and illustration”, Journal of the American Society for Information Science and Technology, Vol. 60 No. 12, pp. 2416-2429.

Markham, A. (2013), “Remix cultures, remix methods: reframing qualitative inquiry for social media contexts”, in Denzin, N. and Giardina, M. (Eds), Global Dimensions of Qualitative Inquiry, Routledge, New York, NY, pp. 63-82.

Markus, L.M. (2001), “Toward a theory of knowledge reuse: types of knowledge reuse situations and factors in reuse success”, Journal of Management Information Systems, Vol. 18 No. 1, pp. 57-93, doi: 10.1080/07421222.2001.11045671.

Matusiak, K.K., Harper, A. and Heinbach, C. (2019), “Use and reuse of visual resources in student papers and presentations”, The Electronic Library, Vol. 37 No. 3, doi: 10.1108/EL-09-2018-0185.

Navas, E. (2010), “Regressive and reflexive mashups in sampling culture”, in Sonvilla-Weiss, S. (Ed.), Mashup Cultures, Springer Wien, New York, NY, pp. 157-177.

Navas, E., Gallagher, O. and burrough, x. (2015), The Routledge Companion to Remix Studies, Routledge, New York, NY.

O’Gara, G.M., Woolcott, L., Joan Kelly, E., Muglia, C., Stein, A. and Thompson, S. (2018), “Barriers and solutions to assessing digital library reuse: preliminary findings”, Performance Measurement and Metrics, Vol. 19 No. 3, pp. 130-141, doi: 10.1108/PMM-03-2018-0012.

Olson, H.A. (2001), “The power to name: representation in library catalogs”, Signs: Journal of Women in Culture and Society, Vol. 26 No. 3, pp. 639-668, available at:

Pasquetto, I. (2019), “Do scientists reuse open data?”, [Publisher], Sage Bionetworks, available at:

Pasquetto, I., Randles, B. and Borgman, C. (2017), “On the reuse of scientific data”, Data Science Journal, Vol. 16, p. 8, doi: 10.5334/dsj-2017-008.

Pasquetto, I.V., Borgman, C.L. and Wofford, M.F. (2019), “Uses and reuses of scientific data: the data creators’ advantage”, Harvard Data Science Review, Vol. 1 No. 2, doi: 10.1162/99608f92.fc14bf2d.

Perrin, J.M., Yang, L., Barba, S. and Winkler, H. (2017), “All that glitters isn’t gold”, The Electronic Library, Vol. 35 No. 1, pp. 185-197, doi: 10.1108/EL-09-2015-0179.

Peters, I., Kraker, P., Lex, E., Gumpenberger, C. and Gorraiz, J. (2016), “Research data explored: an extended analysis of citations and altmetrics”, Scientometrics, Vol. 107 No. 2, pp. 723-744, doi: 10.1007/s11192-016-1887-4.

Reilly, M. and Thompson, S. (2017), “Reverse image lookup: assessing digital library users and reuses”, Journal of Web Librarianship, Vol. 11 No. 1, pp. 56-68, doi: 10.1080/19322909.2016.1223573.

Schütze, B. (2003), “Samples from the heap: notes on recycling the detritus of a remixed culture”, Horizon Zero, No 8, available at:

Tanner, S. (2011), “The value and impact of digitized resources for learning, teaching, research and enjoyment”, in Hughes, L. (Ed.), Evaluating and Measuring the Value, Use and Impact of Digital Collections, 1st ed., Facet, London, pp. 103-120, doi: 10.29085/9781856049085.009.

Tempini, N. (2017), “Till data do us part: understanding data-based value creation in data-intensive infrastructures”, Information and Organization, Vol. 27 No. 4, pp. 191-210, doi: 10.1016/j.infoandorg.2017.08.001.

Terras, M. (2015), “Opening access to collections: the making and using of open digitised cultural content”, Online Information Review, Vol. 39 No. 5, pp. 733-752, doi: 10.1108/OIR-06-2015-0193.

Thompson, S. and Reilly, M. (2017), “‘A picture is worth a thousand words’: reverse image lookup and digital library assessment”, Journal of the Association for Information Science and Technology, Vol. 68 No. 9, pp. 2264-2266, doi: 10.1002/asi.23847.

Thompson, S. and Reilly, M. (2019), “‘Everyone’s a curator’: identifying the everyday curator”, The International Journal of the Image, Vol. 10 No. 2, pp. 25-38, doi: 10.18848/2154-8560/CGP/v10i02/25-38.

Thompson, S., Kelly, E.J., Kenfield, A.S., Muglia, C., O’Gara, M. and Woolcott, L. (2017), “LG-73-17-0002-17: ‘developing a framework for measuring reuse of digital objects’”, Institute of Museum and Library Services, available at:

Thompson, S., Kelly, E.J., Kenfield, A.S., Masood, K., Muglia, C., Shiri, A. and Woolcott, L. (2019), “LG-36-19-0036-19: the digital content reuse assessment framework toolkit”, Institute of Museum and Library Services, available at:

van de Sandt, S., Dallmeier-Tiessen, S., Lavasa, A. and Petras, V. (2019), “The definition of reuse”, Data Science Journal, Vol. 18 No. 1, p. 22, doi: 10.5334/dsj-2019-022.

White, H.C., Chen, S. and Liu, G. (2018), “Relationships between metadata application and downloads in an institutional repository of an American law school”, LIBRES E-Journal, Vol. 28 No. 1, pp. 13-24.

Yoon, A. (2017), “Role of communication in data reuse”, Proceedings of the Association for Information Science and Technology, Vol. 54 No. 1, pp. 463-471, doi: 10.1002/pra2.2017.14505401050.

Further reading

Thompson, S. and Reilly, M. (2018), “Embedded metadata patterns across web sharing environments”, available at:


The researchers would like to acknowledge that this project was made possible in part by the Institute of Museum and Library Services National Forum Grant LG-36–19-0036–19. The views, findings, conclusions or recommendations expressed in this paper do not necessarily represent those of the Institute of Museum and Library Services.

The researchers would like to extend their gratitude to members of the D-CRAFT Advisory Group, who provided helpful feedback on the Matrix and Spectrum. The members are: Paige Dansinger, Latoya Devezin, Genya O’Gara, Anna Naruta-Moya, Kelly Riddle, Betty Rozum, Kayla Siddell and Holly Smith.

Funding: Institute of Museum and Library Services (LG-36-19-0036-19).

Corresponding author

Santi Thompson can be contacted at:

Related articles