































































Open Science Recommendation Systems for Academic Libraries


Journal of eScience Librarianship 13 (1): e804
DOI: https://doi.org/10.7191/jeslib.804

ISSN 2161-3974 
Full-Length Paper

Open Science Recommendation Systems for 
Academic Libraries
Lencia Beltran, Carnegie Mellon University, Pittsburgh, PA, USA, lbeltran@andrew.cmu.edu

Chasz Griego, Carnegie Mellon University, Pittsburgh, PA, USA

Lauren Herckis, Carnegie Mellon University, Pittsburgh, PA, USA

Abstract

An interdisciplinary academic team offers a comprehensive case study describing the development 

of a predictive model as the cornerstone for an open science recommendation system tailored to the 

Carnegie Mellon University community. This initiative will empower users in choosing open science 

services that align with their academic requirements, introduce academics to resources they find 

valuable, and bridge gaps within academic library service offerings.

As an institution with a longstanding commitment to a science-informed approach and a focus on 

computer science, engineering, and artificial intelligence, Carnegie Mellon University has enthusiastically 

embraced open science practices. The Carnegie Mellon University’s Libraries has been instrumental in 

bringing these practices into our academic landscape.

Received: October 29, 2023 Accepted: February 5, 2024 Published: March 5, 2024

Keywords: open science, artificial intelligence, AI, recommendation system, higher education, academic library services, ethical 
considerations

Citation: Beltran, Lencia, Chasz Griego, and Lauren Herckis. “Open Science Recommendation Systems for Academic Libraries.” 
Journal of eScience Librarianship 13 (1): e804. https://doi.org/10.7191/jeslib.804.

Data Availability: Beltran, Lencia, Chasz Griego, and Lauren Herckis. 2024. “Open Science Recommendation Systems for 
Academic Libraries.” OSF. https://doi.org/10.17605/OSF.IO/PX6HJ.

The Journal of eScience Librarianship is a peer-reviewed open access journal. © 2024 The Author(s). This is an open-access 
article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits 
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  
See http://creativecommons.org/licenses/by/4.0.

 OPEN ACCESS

https://doi.org/10.7191/jeslib.804
mailto:lbeltran%40andrew.cmu.edu?subject=
https://doi.org/10.7191/jeslib.804
https://doi.org/10.17605/OSF.IO/PX6HJ
http://creativecommons.org/licenses/by/4.0
https://orcid.org/0009-0001-3333-8919
https://orcid.org/0000-0002-2051-7491
https://orcid.org/0000-0002-3177-2412


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Abstract continued

The authors strive to develop a predictive model which will evolve into a recommendation system. The 

pursuit of this endeavor has led the authors through several ethical considerations, such as data privacy, 

the involvement of student contributors, and the design of a persuasive recommendation system. We 

are committed to exploring ethical approaches for delivering user-centered recommendations and to 

preserving individual autonomy.

The authors have actively engaged with diverse academic departments, students, and faculty, embarking 

on data exploration, and applying open science principles throughout the process. The resulting system 

will raise awareness of library services and deliver tailored recommendations for the adoption of proven 

research tools and practices.

This case study serves as an exemplar of how universities can enact open science principles and develop 

systems that prioritize the user’s interests, navigate institutional complexities to forge interdisciplinary 

collaboration, and muster resources to support innovative, multi-disciplinary efforts. 

Introduction

A Carnegie Mellon University team aims to build a predictive model to act as a foundation for the 
development of an open science recommendation system for the campus community. This model will 
employ user characteristics to identify services that are a good fit for users’ academic needs from a universe 
of the Library’s Open Science resources. Developing this model will help us understand which users engage 
with each service and identify potential users who would benefit from additional Open Science resources. 
The recommendation system will introduce naive users to services they are likely to find valuable and, in 
parallel, introduce current users to alternative capabilities that are likely to be of value. At the same time, 
our team will consider and speak to the ethical implications of generating a novel system of this kind in 
an academic setting. This system will also serve as a proof of concept for other academic library service 
recommendation systems.

Project Details 

Carnegie Mellon University (CMU) is a private, global research university that has championed a science-
informed approach for more than five decades and is consistently ranked among international leaders in 
computer science, engineering, and artificial intelligence. Nobel laureate, Turing award winner and father 
of artificial intelligence Herb Simon created interdisciplinary pathways for research and innovation that 
are still characteristic of Carnegie Mellon University’s unique academic ecosystem today. Today, Carnegie 
Mellon University offers degrees with a focus on artificial intelligence at the bachelors, masters, and doctoral 
levels. A transdisciplinary effort focused on artificial intelligence, CMU-AI unites students, faculty, and 

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staff from all areas of the university to engage with complex challenges and to partner with corporations, 
non-profits, and research institutions around the world. In addition to extensive research and development 
efforts, CMU actively fosters entrepreneurship. This has resulted in a number of spin-off corporations which 
embed CMU-developed, AI-enabled innovations ranging from self-driving cars to smart textbooks.

This case study is authored by the project team, Lencia Beltran, Chasz Griego, and Lauren Herckis. Lencia 
Beltran is the Open Science Program Coordinator for Carnegie Mellon University Libraries Open Science 
Program. Her educational background is in Linguistics, Speech-Language Pathology, and Librarianship 
and Archival studies. Beltran received training in data science from Drexel University’s LEADING and AI 
applications from the IDEA Institute on AI. Her research falls within the spheres of AI, geospatial mapping, 
social networks, and implications of technology on language, including diversity, identifying, and belonging 
in higher education and academia. For this project, Beltran supported project establishment, initiation and 
management, research design, documentation, and the building of institutional collaborations. Dr. Chasz 
Griego is a Science and Engineering Liaison Librarian and formerly an Open Science Postdoctoral Associate 
at the Carnegie Mellon University Libraries. His educational background is in Chemical Engineering, with 
a focus in computational chemistry and catalysis. His doctoral work focused on physical models coupled 
with machine learning to expedite catalyst screening projects. His research focuses on the influence of 
open science tools on reproducibility in computational research related to AI, simulations, and modeling. 
For this project, Dr. Griego supported research design, data curation, and technical recommendations for 
model development.  Dr. Lauren Herckis is an anthropologist by training and has a faculty appointment in 
the University Library and the School of Computer Science’s Human-Computer Interaction Institute. Her 
research explores the adoption and use of AI-augmented and collaborative learning tools, the digitalization 
of higher education, and the design of tools to help faculty employ effective technology-enhanced 
learning tools with fidelity. For this project, Dr. Herckis supported research design and data analysis, and 
co-developed strategies for tool deployment, data curation, service delivery, and evaluation, as well as 
facilitating partnerships with institutional collaborators.

Background

Carnegie Mellon has championed a science-informed approach for more than five decades and is committed 
to designing and facilitating transformative educational experiences, accelerating research and creative 
inquiry, developing innovative library infrastructure, and evolving to enable students, staff, and faculty to 
discover, access, and use scholarly information. Core project team members are affiliated with the University 
Library and have a professional interest in enhancing Library services. Carnegie Mellon has invested in an 
Open Science Program in recent years, and project personnel are both personal and professional champions 
of open science practices. 

The proposal of a recommendation system derived from the idea to create a predictive model that would 
shed light on usage patterns of open science services. In 2021, members of the Open Science team ran an 

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analysis to evaluate the program’s impact, using data collected over the span of two years, which included 
service offerings and, in most cases, high-level user information like departmental affiliation. The analysis 
of the preliminary data showed the effectiveness of the program in the campus community, yet it did not 
provide further details on why users opted for specific services and their motivations behind those decisions. 

In an effort to understand our users and their motivations, we pursued the next step to build a predictive 
model. The predictive model will not only give us insight into these essential areas but has the potential 
to identify probable user groups who would benefit from open science resources. The findings from the 
predictive analysis can lead to discussions on where gaps in our services lie in terms of which departments 
are not using our services so that we can begin to develop resources to meet the needs of those departments. 

The inspiration for developing a recommendation system arose as we considered strategies for simplifying 
the discovery process of our services to users. The recommendation system built from this predictive model 
will extend how the Open Science Program delivers services and how users discover these services. This 
recommendation system will facilitate how information is accessed/retrieved and alleviate information 
overload that students, as well as faculty and staff, may feel as a product of having too many options and not 
knowing which services will be the most helpful. 

The mechanics of the recommendation system will work similarly to other well-known models, like Pinterest, 
Amazon, and Netflix, by providing users with a curated list of results. How the services are delivered to users 
is an aspect we are thinking through. As we move forward and look to other projects for guidance, we intend 
to keep our users at the center of each approach. 

Members of the Open Science Program in the University Libraries have spearheaded the effort to implement 
this recommendation system. The Libraries serve the efforts of the University to continually innovate 
education and research by supporting the curriculum as well as faculty and student research. One area in 
which the Libraries are leading is in innovation around open science, a fairly new concept in the United 
States. Our Open Science Program has helped propel the integration of many open science elements into the 
education landscape of our community. This team is composed of several faculty and staff in the Libraries, 
and the members of this program who are actively involved in this project include Lencia Beltran, the Open 
Science Program Coordinator and Chasz Griego, a Science and Engineering Librarian who was formerly 
an Open Science Postdoctoral Associate. Along with these associates, Lauren Herckis, an anthropologist 
and affiliate of the Libraries, Simon Initiative, and the Human Computer Interaction Institute at CMU, has 
contributed in our efforts to identify collaborators and develop strategies to assess how users will engage 
with open science service and tool delivery in educational and research settings. We also recruited an 
undergraduate student, Zhijin Wu, majoring in information systems, human-computer interaction, and 
business administration, who is volunteering their efforts as a project manager and coordinator to gain 
academic and professional experience. 

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The Open Science Program offers many resources, which include data and research consultations and 
Libraries workshops in data and software Carpentries. Along with these resources, our patrons also have 
access to several tools and platforms that facilitate open science practices. These include KiltHub, our 
institutional repository, LabArchives, a platform for electronic lab notebooks, protocols.io, a repository for 
sharing records of research methods, and Open Science Framework, a platform for collaborative research 
management. With such resources and tools in place, Open Science Program personnel have initiated an 
ambitious plan to amplify engagement at Carnegie Mellon. In 2021, the CMU Open Science team, including 
past and present members, explored and gathered user data dating back to 2019 of over 900 users at CMU 
that counted usage and numbers of items uploaded on digital platforms as well as interactions with our 
other tools and services (Wang et al. 2019). This dataset has driven initial insight into our efforts to establish 
an open science recommendation system in the Libraries.

In the spring 2023 semester, we partnered with a faculty-led team of four Master of Statistical Practice 
(MSP) capstone students who agreed to undertake data exploration and develop a proof-of-concept 
AI-enabled predictive model that would identify likely use cases for open science tools and resources at 
CMU as a Master’s degree capstone project. Currently, this team has described the distribution of past open 
science tool users among schools and departments at CMU, with faculty and Ph.D. students being the most 
common academic positions held by users. As these efforts continue, this team is helping us identify the 
features of our current dataset that will provide statistically significant predictions. Before establishing this 
partnership, we requested, as one of the deliverables, a written report describing the development, decision-
making process, outputs, and findings so that we, including others, could reproduce their work. In keeping 
with our posture of openness, we also asked the team to apply open science practices such as reproducible 
tools and code and version control. The students have since shared their research materials, including 
documentation of data, code, and analysis using open platforms like Open Science Framework. In addition 
to the existing usage data for open science tools and services, a subset of the metrics data for students along 
with de-identified demographic information from the CMU Registrar, and TartanDataSource (TDS) from 
the University Institutional Research and Analysis Office were used in the analysis, all of which can be 
shared. As library and information professionals, among other titles, we understand the significance of an 
individual’s right to privacy and, as we carry on, preserving this right will be at the forefront of our mind. 
As long as our research materials do not contain personally identifiable information that is unable to be 
anonymized, our team plans to share any code and scripts, documentation, and other information openly 
since one of our objectives is for this system to serve as a blueprint for other academic libraries.

In order to design service models that accommodate recommendations from our system and accurately 
meet the needs of the campus community, our team is also investigating perspectives from researchers 
and implementing open science tools into educational settings. Through 2023, the Libraries Open Science 
Program is conducting a needs assessment and environmental scan that includes focus group interviews 
of research with diverse areas of study and identifying open science services and practices among peer 

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https://library.cmu.edu/services/lab-archives
https://www.library.cmu.edu/services/protocols
https://www.library.cmu.edu/services/open-science-framework
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institutions and other units at CMU. In summer 2023, Chasz Griego will lead an eight-week undergraduate 
course, hosted through the Office of the Vice Provost for Education, delivering opportunities for students 
to use open science tools and assess how these tools influence collaboration and reproducibility in research. 
This course will serve as a testing space to investigate how open sciences tools, and practices, can be 
implemented in practice within educational settings at CMU. 

Our findings from these assessments and collaborations will aid in guiding us to develop a proof-of-concept 
recommendation system that will introduce naive Carnegie Mellon University students, faculty, and staff to 
existing open science tools and resources. Developing a predictive model and associated recommendation 
system is a novel approach to scaling adoption of, and engagement with, academic tools at a university like 
Carnegie Mellon. Eventually, we will need to instantiate a functional version of the predictive model-driven 
recommendation system so that it can begin effectively delivering resource recommendations to educators. 
The work proposed here will meet this substantial challenge and make successful implementation possible.

Ethical considerations 

Already within our preliminary exploration, our team has identified ethical challenges that relate to 
developing both a predictive model and a recommendation system in three areas. First, many of our 
concerns connect to users’ rights, such as using personally identifiable information and privacy. Second, 
this project leverages student labor in exchange for academic credit and learning opportunities. Finally, this 
project is designed to promote specific tools and practices through persuasive design. 

Data collection for our system will include personally identifiable information that can be used to 
describe patron behavior with respect to Library tools and services. Generally speaking, predictive and 
recommendation systems take information about a user’s preferences as input and predict an output of an 
item that is likely to meet the user’s needs. As a result of the underlying nature, the collection and curation 
of vast amounts of personal information are inevitable for generating personalized recommendations. On 
the surface, these systems appear to be user-centered, because they generate curated content, but many 
of them are driven by business objectives and applications. Consequently, this leads to less consideration 
of the user and their privacy. More often than not, user data is being collected and analyzed without the 
consent or knowledge of the user. If users are aware data is being collected, then it is likely they do not 
understand its actual or intended uses. In our pursuit, we are seeking approaches that will allow us to 
design a recommendation system that curates open science resources, takes into account the users’ rights, 
and carefully balances the risks of user privacy and accuracy, as well as fairness and explainability without 
merely shifting the responsibility to the users. For example, a solution might be to embrace a macro-ethical 
approach which considers ethical problems related to data, algorithms, and practices and how the problems 
relate, depend on, and impact each other (Milano, Taddeo, and Floridi 2020).

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This project currently entails collaboration with students individually and in teams and will likely expand 
in the coming year to include other graduate and undergraduate students working for credit, hourly, and 
for free. Students regularly engage in generative activity as part of their educational experiences, and there 
is substantial literature about the effective design of capstone courses (Tenhunen et al. 2023) and recent 
literature also addresses the ownership and intellectual property associated with work that students have 
completed in the course of their education (Allen 2021). As this case study is being written, an undergraduate 
triple-major in information systems, human-computer interaction, and business administration is 
volunteering approximately five hours per week to serve as a project manager on this project. This student 
has a background in AI research and development and an interest in developing project management skills. 
In order to ensure that this student is gaining useful professional and/or academic experience, we worked 
with her to identify learning outcomes and desired skills and to agree on mutually agreeable communication 
and collaboration strategies. We have asked her to create project plans and visualizations, such as Gantt 
charts, maintain records and manage communications. In order to ensure that data and products of work 
are handled ethically, we used a collaboration agreement and discussed the need for explicit communication 
about future use of project assets. We expect that she will use visualizations and other assets as part of her 
portfolio. This student will gain substantial educational benefit through the hands-on learning experiences 
that our collaboration requires. The project will gain several durable assets which will outlast the student’s 
collaboration on the project.

This project was integrated into the Master of Statistical Practice (MSP) graduate curriculum in the Spring 
of 2023. A student team working under faculty supervision undertook data exploration, developed project 
constraints and documentation, and built a proof of concept predictive model that met our specifications. 
The project team is positioned as a client, and related student efforts will be evaluated and graded as a 
capstone project to meet Masters degree requirements. Faculty associated with the capstone course and 
project will guide student work and frame the experience to best serve students’ educational goals. While 
these efforts represent curricular and educational benefits to the students, they can also be understood 
as an appropriation of student labor to produce university assets. Development of a predictive model is 
a non-trivial task which requires substantial investment of time and resources. These resources are being 
extracted from students as a component of requirements for degree completion and can be understood as 
appropriation of student labor.

Following the pattern of other recommendation designs, our system seeks to help users discover new 
services and minimize the cognitive information overload that exists in academic settings. Yet we are 
grappling with the inherent persuasive design of recommendation models. How can we build a model 
that does not invite the undue or unwanted influence of library services or introduce bias but ultimately is 
helpful and protects the users’ autonomy? Fortunately, there are a number of different approaches we can 
explore for building a recommendation system, yet they all generally involve constructing a user model 
or profile. A user profile is a set of characteristics and/or preferences for a given user and is used by the 

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system to make personalized recommendations. Although we do want users to receive curated services, 
these constructions can limit the range of options recommended to users as it places them into categories 
(e.g., department, academic level, etc.). As a result of this algorithmic classification, an individual’s ability to 
make self-driven and reflected decisions on which services are extended to them is hindered, and ultimately 
users are nudged toward a particular outcome. Being that the area of Artificial Intelligence is still in its 
infancy, there is extensive research that explores the ethicality of recommendation systems and their effect 
on citizens as a by-product. Some research suggests strategies to assuage bias and quell autonomy issues, 
such as deploying a conversational recommender system that provides users with explanations for why a 
particular recommendation was made (Musto et al. 2019). Musto et al. (2019) found the selections were 
better received when explanations were provided to users. 

This general description illustrates how these systems can potentially shape an individual’s experience of the 
digital world. As echoed throughout, our intent is to aid in facilitating the discovery of these tools within 
our community, which may be beneficial for their academic and personal goals, rather than participate 
in influencing the choices or altering the perception of what services are readily available to them. At this 
current stage, we have more questions than answers. The ethical challenges presented here, including 
others we encounter as we move forward, shape how we approach conversations with individuals who have 
experience with designing and deploying predictive and recommendation systems.

Who is affected by this project? 

Over the course of the project, we expect many individuals, services, and programs to be involved to some 
degree. The Libraries Dean has been the essence of support for open science practices in our community, 
including this project. In 2018, he endorsed the development of the Open Science and Data Collaborations 
Program spearheaded by three library liaisons. As the highest level of support within the Libraries, he has 
helped advocate for the many open science resources we offer and paved the way for our Open Science 
team to hold discussions and establish relationships with Deans from the Schools/Colleges. Through these 
many conversations, we have already seen an uptick in interest in open science practices and services from 
disciplines (e.g., Language Technologies Institute and Statistics) across campus. Since the Open Science 
Program was initiated by three library liaisons it has helped increase internal support from other library 
service providers (e.g., functional specialists, liaisons, and staff ). The support from library service providers 
has been valuable for raising awareness about our services and building internal and external partnerships 
as each person engages with distinct departments and individuals on campus. 

As implied to some degree, the Open Science team embodies a range of specialists whose work is diverse. 
Our team includes a staff who manages and supports the Institutional Repository, a functional specialist 
who provides training and support on data curation and literacy, and three library liaisons who support 
tools and provide training on topics related to open access, data management, scholarly communications, 
and more. Their involvement in our project is indirect but essential for the ongoing success of the program 

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and the resources we can offer our community. Our project has many moving parts, including partnering 
with faculty and educators to integrate our services into their curriculum, which will discursively involve the 
students who engage with the open science resources through their professors’ class transformation. 

Altogether our project can increase awareness of library services, begin to glean user motivations, and open 
up new approaches for gathering information on how users practice open science at an institution and 
measure satisfaction/success with each tool. As our project unfolds, there are potential opportunities to 
partner with University service areas, including the Student Academic Success Center and the Eberly Center 
for Teaching and Excellence. The Student Academic Success Center facilitates student learning by providing 
academic coaching, subject-specific tutoring, effective communication strategies, accommodations for 
students with disabilities, and language support for multilingual learners. The Eberly Center supports 
faculty, graduate students, and other educators that aim to design courses and curricula that put students at 
the center of the teaching process. Our effort will enhance our understanding of Carnegie Mellon Library 
service use and provide more effective open science support to the Carnegie Mellon community. More 
broadly, this project can serve as a proof of concept for other academic libraries, which we hope will build 
on our work.

Lessons learned and future work 

While this project is still in preliminary stages and much of the work is ongoing, we have already learned 
several lessons to improve our approach to create a robust recommendation system while considering the 
ethics of data usage and implementation of AI. Many of these lessons were learned through our work with 
the Master of Statistical Practice (MSP) capstone team. Collaborations with Statistics faculty and students 
first revealed challenges for us to communicate our goals and intentions in a way that aligns with the 
knowledge base of these subject specialists. We chose terminology that helped translate our goals into action 
items that could reasonably be executed by statistic students. However, we did observe some disconnects 
with information exchange. For instance, the students treated the variables in our dataset as arbitrary. When 
analyzing trends such as academic departments that are more likely to use our institutional repository, 
Kilthub, the students would tend to focus on how these trends contribute to model parameters and not 
question the reasoning behind why a certain department would be more drawn to Kilthub. These points 
were usually addressed during team meetings where the faculty adviser led the efforts to ask the more 
subject-specific questions. Overall, the statistics students successfully applied their education to real-world 
problems and data, but the team encountered challenges connecting the data analysis to the context of the 
problems that were specific to the university libraries. 

The Master of Statistical Practice (MSP) capstone team delivered preliminary results that signaled a need for a 
larger dataset so that the predictive model can deliver results with higher confidence. Challenges arose when 
considering ways to obtain expansive data that represents Library users at CMU. A major consideration is 
the privacy of individuals currently or previously affiliated with the university. While our aim is to develop 

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user profiles that describe prior or potential attraction to open science practices, we plan to only rely on 
user information that is publicly or internally available. However, there are challenges specific to library user 
data. Within the rights of the Fourth Amendment of the United States Constitution, the privacy of patrons 
that access information from libraries is protected. For instance, these laws protected patrons from seizures 
of library records from the Federal Bureau of Investigation under the USA PATRIOT act. Appropriately, 
the CMU libraries does not record user-specific circulation information, which teaches us that a predictive 
model built for library services cannot be established with such data, but other, carefully considered records 
that describe academic behavior and motivations. 

Future work will address approaches to evaluate the effectiveness of recommendations to users. We will develop 
strategies to assess user responses to the recommended tools or services and how they influence research 
and/or educational outcomes. This will include developing metrics to measure how the recommendation 
system supports decision making. To evaluate the performance of decisions, we can refer to the strategies 
outlined by Jameson (2015) that identify choice patterns based upon attributes, consequences, experience, 
social conditions, policies, or trial-and-error (Jameson et al. 2015). We will survey responses from users 
in a variety of settings including electronic surveys, focus groups, and case studies. In case studies we will 
analyze changes in educational outcomes in academic courses that incorporate recommended open science 
tools and services.

Data Availability
Many of the materials mentioned within the case study can be found on our Open Science Framework 
project, Open Science Recommendation Systems for Academic Libraries (Beltran, Griego, and Herckis 
2024). Please reach out to our team if you have any questions.

Acknowledgements
The research case study was developed as part of an IMLS-funded Responsible AI project, through grant 
number LG-252307-OLS-22.

Competing Interests
The authors declare that they have no competing interests.

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