Published at MetaROR

April 4, 2025

Table of contents

Cite this article as:

Chodacki, J., Hanhel, M., Iacus, S., Scherle, R., Olson, E., Pfeiffer, N., ... & Hosseini, M. (2024). GREI Data Repository AI Taxonomy. arXiv preprint arXiv:2411.08054.

GREI data repository AI taxonomy

John Chodacki1, Mark Hanhel2, Stefano Iacus3, Ryan Scherle4, Eric Olson5, Nici Pfeiffer5, Kristi Holmes6, Mohammad Hosseini6

1. California Digital Library
2. figshare
3. Dataverse
4. Dryad
5. Center for Open Science
6. Zenodo

Originally published on November 7, 2024 at: 

https://arxiv.org/abs/2411.08054v1

Abstract

The Generalist Repository Ecosystem Initiative (GREI), funded by the NIH, developed an AI taxonomy tailored to data repository roles to guide AI integration across repository management. It categorizes the roles into stages, including acquisition, validation, organization, enhancement, analysis, sharing, and user support, providing a structured framework for implementing AI in repository workflows.

The Generalist Repository Ecosystem Initiative (GREI)

The Generalist Repository Ecosystem Initiative (GREI) is a NIH-funded program where repositories collaborate in a “coopetition” model to enhance the work of generalist data repositories, which are critical infrastructure across research domains. As part of our commitment to this work, we recognize the evolving importance of artificial intelligence (AI) in the future of science and infrastructure. To help our community navigate AI-driven changes, we have developed the following taxonomy to illustrate the roles AI can play in managing data repositories, improving data quality, and increasing accessibility.

Building on previously developed taxonomies and our coopetition efforts, the GREI repositories propose the following “GREI Data Repository AI Taxonomy,” specifically tailored for data repository roles.

Why do we need this?

A taxonomy like the one outlined below holds significant importance for GREI and other domain-specific or institutional data repositories, serving as a structured framework to develop strategies, facilitate discussions, and effectively address the opportunities and challenges presented by AI integration.

Firstly, this taxonomy can offer a systematic classification of the distinct roles AI can play in data repository operations. This classification enables a comprehensive understanding of AI’s potential contributions to repository operations, which, in turn, assists in strategic planning. By identifying specific areas where AI can bring the most value, interested parties can allocate resources efficiently, aligning AI initiatives with their organizational objectives.

Secondly, the taxonomy provides a common language and reference point, fostering productive discussions among participants. It ensures that organizations can communicate effectively about their AI workflow integrations, share best practices, and collaboratively address challenges. This shared understanding of AI’s role in data management promotes innovation and helps ensure that AI initiatives adhere to ethical and quality standards.

Additionally, the taxonomy can serve as a structured framework for addressing challenges related to AI integration in data repositories. By categorizing AI involvement into distinct areas, potential issues and ethical concerns can be systematically identified and explored . This structured approach enables the establishment of safeguards, transparent AI practices, and the incorporation of human oversight where necessary. It also helps in building trust with users, addressing concerns related to data accuracy, transparency, security and integrity.

The GREI Data Repository AI Taxonomy

Just as AI can revolutionize other forms of scholarly communications like peer-reviewed publications (Ref), it can bring significant improvements to data repositories (Ref). This taxonomy outlines seven areas for AI in data repository management:

  1. Acquire: Efficiently gather, collect, and ingest data and metadata from various sources, including researchers, sensors, and external datasets.

  2. Validate: Ensure the quality, accuracy, and integrity of the data and metadata to maintain its reliability and trustworthiness.

  3. Organize: Categorize, structure, and catalog data and metadata to facilitate easy retrieval, analysis, and sharing.

  4. Enhance: Enrich and augment data and metadata with metadata, annotations, or standardized formats to improve its utility and interoperability.

  5. Analyze: Employ AI-driven analytics to uncover insights, patterns, and trends within the data and metadata, aiding researchers and decision-makers.

  6. Share: Facilitate the discovery, access, and distribution of data and metadata within and beyond the repository, promoting collaboration and knowledge dissemination.

  7. Support: Provide suggestions and answer questions for users of the data and metadata, including the users who are submitting, the users who are consuming, and the repository staff.

 This taxonomy aims to provide a framework for understanding the specific roles that AI can play in data management and specifically data repository work including data discovery, documentation, and reuse. To further illustrate each category, we present these examples:

Acquire

The category of “Acquire” is important because efficiently gathering and ingesting data from researchers and other sources is the foundational step in data management. AI can streamline this process by automating data collection, saving time and reducing the risk of human errors. The ability to acquire data effectively ensures that repositories can stay up-to-date with the latest research and maintain a comprehensive database. Moreover, AI can be employed to adapt to the growing volume of data sources, making it crucial for categorizing AI usage in data repository work.

Examples of work in this area could include:

  • AI-assisted ingestion of research data from online sources.

  • Integrate data from various instruments and devices into the repository.

  • Aggregate data submissions from researchers and collaborators.

Validate

Data quality and reliability are non-negotiable in data repositories. “Validate” is a critical category because AI can play a pivotal role in verifying data accuracy and authenticity.

Inaccurate or unreliable data can lead to erroneous research findings, so AI tools can

cross-reference data against established standards, identify anomalies, and validate the source of incoming data. Proper validation ensures that researchers can trust the data they access, making this category indispensable in the taxonomy.

As AI evolves, examples of work in this area could include:

  • AI-assisted identification and flagging of potential data anomalies or errors.

  • Cross-reference data against established standards and guidelines.

  • Verify the authenticity and source of incoming data.

Organize

Effective data organization and description is key to making data repositories accessible and usable. AI can categorize, structure, and catalog data efficiently, ensuring that data is stored in a way that facilitates easy retrieval and analysis. Without proper organization, data repositories become unwieldy and less valuable to researchers. This category acknowledges the central role AI plays in ensuring that data is structured and categorized for maximum utility.

Examples of work in this area could include:

  • Categorize data by research discipline, project, or data type.

  • Create hierarchical structures to accommodate complex datasets.

  • Tag data with relevant keywords and descriptors for easy search.

Enhance

Enriching data with metadata, annotations, or standardized formats is crucial for ensuring data interoperability and usefulness. AI can significantly contribute by generating descriptive metadata or converting data to standardized schemas. This category underscores AI’s role in enhancing data quality and making it more accessible to a broader audience. Nevertheless, it also emphasizes the need for human oversight to ensure that enhancements align with the data’s intended purpose.

Examples of work in this area could include:

  • AI-assisted generation of metadata and descriptions for data entries.

  • Convert data formats to standardized schemas or ontologies.

  • Translate metadata and content to multiple languages for global accessibility.

  • Build knowledge graphs across ontologies that might assist in new discoveries

Analyze

The “Analyze” category highlights AI’s capability to extract insights, patterns, and trends from the vast amount of data within repositories. AI-driven data analytics tools can provide valuable insights that may not be immediately evident to human curators. These insights can guide research and decision-making processes. However, it’s equally important to categorize this area as it reminds us that human interpretation is essential to ensure that AI-generated findings are contextually accurate and meaningful.

Examples of work in this area could include:

  • Utilize AI-driven algorithms to identify correlations and trends in large datasets.

  • Perform sentiment analysis on textual data within the repository.

  • Assist in describing images for accessibility and other use cases.

  • Suggest datasets that are similar to a given dataset, aiding researchers in finding relevant information.

  • Create video to text transcriptions using AI algorithms

Share

The “Share” category is pivotal because data repositories exist to disseminate knowledge and facilitate reuse. AI can assist in recommending relevant datasets, securing data access, and generating citations. It is essential to categorize AI’s role in data sharing as it not only improves the efficiency of data distribution but also underlines the need for human-driven decisions regarding access controls and the suitability of sharing mechanisms. Trust and transparency in data sharing processes are paramount, and AI can support these aspects effectively.

Examples of work in this area could include:

  • Recommend relevant datasets to researchers based on their interests and previous usage.

  • Facilitate data sharing and collaboration through secure access controls and sharing options.

  • AI-assisted generation of citations and references for data used in research publications.

Support

The “Support” category is crucial for ensuring that users, whether they are data submitters, data consumers, or repository staff, can effectively interact with and benefit from the data repository. AI in this context acts as a bridge, providing intelligent assistance, automating routine queries, and facilitating a smoother user experience. This category recognizes that AI can enhance the overall user support ecosystem, making it more responsive, personalized, and efficient.

Examples of work in this area could include:

  • Provide immediate, intelligent support to users with AI-driven chatbots and virtual assistants.

  • Analyze user behavior to offer personalized recommendations, improving access to relevant datasets and resources.

  • Update documentation and tutorials, ensuring users have access to latest guidance.

  • Real-time feedback on data submissions, helping maintain high data quality standards.

  • Enhance search functionality by understanding natural language queries and providing accurate, contextual suggestions.

Balancing AI and Human Expertise in Data Repositories

The integration of AI in data repositories offers significant opportunities for enhancing efficiency, data quality, and user experience. However, the successful implementation of AI requires a careful balance between automated processes and human expertise. While AI can handle large-scale data processing tasks with speed and precision, human oversight remains crucial for ensuring that these processes align with ethical standards and meet the nuanced demands of research communities.

AI’s role in data repositories spans from data acquisition and validation to organization and sharing, as outlined in the above GREI Data Repository AI Taxonomy. However, at every stage, the balance between AI-driven automation and human intervention must be meticulously managed. For example, while AI could potentially ingest and validate vast amounts of data, human curators must also verify that AI-generated outputs adhere to the necessary quality standards and are contextually accurate. This human oversight ensures that the data remains reliable and trustworthy, particularly in fields where even minor errors can have significant consequences.

One effective approach to maintaining this balance is the implementation of tiered AI automation levels, where the degree of automation is matched to the task’s complexity and the potential impact of errors. For example:

  • No Automation: Human experts handle tasks entirely, appropriate for highly specialized or sensitive data.

  • Automation with 100% Certainty: AI handles tasks where outcomes are highly predictable, such as formatting metadata to standardized schemas.

  • AI with Human Review of Each Change: AI suggests actions that require human approval, ensuring that any AI-driven modifications meet the required standards.

  • AI with Human Review of a Sample of Changes: In less critical tasks, humans might review only a sample of AI-driven changes to ensure quality while benefiting from the efficiency of automation.

  • Fully Autonomous AI: For routine, low-risk tasks, AI operates independently, freeing human experts to focus on more complex issues.

This structured approach allows repositories to maximize the benefits of AI while safeguarding data integrity and trust. The transparency of AI processes is critical. Repositories must clearly communicate to users when and how AI is being used, especially in tasks that directly impact data quality or accessibility. Providing users with this transparency builds trust and allows for informed decision-making, as users can understand the role AI plays in the data they are accessing or contributing.

The provenance of any changes or enhancements created by non-human systems, such as AI, is critical for ensuring data integrity, trustworthiness, and transparency in data repositories.

Without clear provenance, it becomes difficult to verify the origins of data, understand the rationale behind AI-driven changes, or correct any potential errors introduced by automation. Repository systems should endeavor to expose this provenance in the GUI of the repository and metadata of any API retrieved records.

Trust and Transparency in Data Management

As AI becomes more integrated into data repository workflows, trust remains a crucial factor. To build and maintain trust, it’s essential to adhere to regulatory frameworks such as the Framework for Federal Scientific Integrity Policy and Practice developed by the Office of Science and Technology Policy (OSTP). For generalist repositories and their users to adhere to regulatory frameworks, ethical behavior that demonstrates honesty, transparency, and trust should be consistently practiced. Towards this end, three challenges are noticeable. First, generalist repositories strive to ensure that their infrastructure including the interface, instructions on how to use AI are up to date. However, given the range of international ethical and regulatory frameworks and their differences (e.g., AI Act in Europe versus AI Executive Order in the US), adhering to one regulatory framework without flouting/trespassing other frameworks might be challenging. Second, generalist repositories regularly prompt, and if necessary, apply pressure (e.g., screening and quality control) to ensure that uploaded content meets certain standards. Nevertheless, the sheer amount and diversity of newly uploaded content, and the range of roles conducted by AI combined with different levels of information/digital literacy, challenges adherence to regulatory frameworks. Third, since content is openly available and generalist repositories cannot control reuse and AI digestion of their data, they may inadvertently facilitate misuse or harm to individuals and groups, thereby eroding the public trust in science.

We propose three suggestions to promote trust and transparency when using AI in generalist repositories. 1) Devising balanced AI governance and stewardship policies, considerate of the diverse disciplinary and international ethical/legal norms that may affect AI use and potential data transfers (e.g., GDPR versus HIPAA); 2) Developing codes of practice related to AI use and signposting for users (e.g., tag AI-generated/-manipulated content, identify AI’s extent of involvement and limitations); 3) Initiating work to anticipate how content could be used (and misused) by various AI applications to provide adequate disclaimers (and if needed, restrictions).

Conclusion

AI has the potential to revolutionize data repository management, improving efficiency, data quality, and accessibility. By adapting this GREI Data Repository AI Taxonomy and embracing a balanced approach that combines AI and human expertise, the wider data repository community can harness the full potential of AI while maintaining data integrity and ethical standards. Each of these taxonomic areas is uniquely important in categorizing the usage of AI in data repository work because they represent distinct stages in the data management process, and AI’s contribution to each stage is pivotal in achieving efficient, reliable, and accessible data repositories. Categorizing these roles ensures that the specific strengths and responsibilities of AI within data management are acknowledged and properly managed.

As we move forward, we must continue to explore and iterate on the application of AI in data management, building a strong foundation for the future of research and knowledge sharing across GREI repositories as well as the wider repository community.

Many thanks to the authoring team of “An Initial Scholarly AI Taxonomy” which offered inspiration for this article. In addition, thank you to NIH and the Generalist Repository Ecosystem Initiative (GREI) for supporting us writing this. This work was supported by the National Institutes of Health (NIH) Office of Data Science Strategy/Office of the NIH Director pursuant to OTA-21-009, “Generalist Repository Ecosystem Initiative (GREI)” through Other Transactions Agreement (OTA) Numbers OT2DB000001, OT2DB000002, OT2DB000003, OT2DB000004, OT2DB000005, OT2DB000006, and OT2DB000013.

Editors

Kathryn Zeiler
Editor-in-Chief

Wolfgang Kaltenbrunner
Handling Editor

Editorial assessment

by Wolfgang Kaltenbrunner

DOI: 10.70744/MetaROR.33.1.ea

The authors present a seven-part taxonomy aimed at identifying how AI can be integrated into workflows in data repositories. The taxonomy focuses on functions such as ‘acquire’, ‘validate’, ‘enhance’, and ‘organize’. The authors suggest that the piece is meant for the community of librarians and Open Science practitioners working with data repositories. The piece was reviewed by two metaresearchers. Both emphasized the potential value of the proposed taxonomy, but also noted a number of areas in which the paper needs substantial improvement. The reviewers admitted to be struggling with what they saw as a lack of clarity and practical applicability of the taxonomy. They emphasized the need for more detailed explanations of the various functions, such as case studies or examples of how AI is currently used in repositories. A second main point mentioned by both reviewers was the lack of a section explaining how  exactly the taxonomy was developed. This makes it hard to see how and why the authors arrived at the seven functions and what knowledge/insights they are based on – is it previous literature or based on practical work experience of the authors? This also ties in with a third point raised by the reviewers, namely the lack of references. Finally, especially reviewer 2 felt that the concluding section on balancing human and AI expertise is interesting, but also noted that it currently feels a bit disconnected from the main part of the text.

The reviewers and I agree that the paper can potentially become a valuable contribution to the metaresearch literature related to data repositories, provided that the authors address the various criticisms raised by the reviewers.

Competing interests: None.

Peer review 1

Anonymous reviewer

DOI: 10.70744/MetaROR.33.1.rv1

General review

The article describes a taxonomy which can be used to help facilitate the use of AI in data repositories. The taxonomy is similar to other descriptions of the data or research life cycle and is not particularly AI specific although brief descriptions of how AI could be used in each stage are included. The article is not currently written as a journal article, and does not engage with the current literature or research within the field, which would help to explain the importance or value of this specific taxonomy.

A literature review should be included and references should also be included throughout using an inline referencing style rather than hyperlinks. A references section should be included at the end of the article.

It is not clear how the 7 areas for AI in data repositories were decided. Is this opinion, based on currently literature, based on user experience…? Without knowing this it is hard for the reader to understand how justified the categories are. Towards the end of the paper 5 categories of AI involvement are discussed, but these don’t appear to be a part of the framework although they seem to be the most AI specific part. I’d suggest that the taxonomy could benefit from including more AI specific terms.

The article could be improved by more detail about how this framework could be used in practice (or is currently being used). It is unclear whether this is just a suggestion of shared language or something more formal like CReDIT or MeSH?

The Balancing AI and Human Expertise in Data Repositories section gives a nice overview of some of the concerns, however this section needs to be referenced as there have been lots of discussions in the literature about the important of ‘humans in the loop’.

The ‘three suggestions to promote trust and transparency’ at the end are interesting but come rather out of nowhere. These should be tied into the body of the paper. How are they related to the taxonomy?

Specific suggestions

Engagement with the literature would help to show why this taxonomy is important or relevant. Some areas which the literature review could cover are:

  • How is AI being used with repositories at the moment? (or not)

  • What is the importance of standard taxonomies?

  • Are there other examples of AI taxonomies?

  • Are there other ways of describing AI roles which are currently being used and why are they not sufficient?

Each of the taxonomy descriptions include examples of how AI could benefit this role, but they would be greatly enhance by giving more detail of projects where this is already happening or explaining where (and why) it isn’t happening. A specific example of this is within the Organize section where a useful project to discuss might be the Library of Congress metadata labelling project.

A more comprehensive introduction to the project and what you are trying to do would be helpful. Readers may not be specialists in either AI or digital repositories.

The introduction should also explain the current situation and problem that this taxonomy is trying to solve. Future possible benefits are described, but it does not discuss what else is happening in this space or the current situation.

“Just as AI can revolutionize other forms of scholarly communications like peer-reviewed publications” – the reference justifying this is an editorial, a research paper would be more authoritative. There have been a lot of articles arguing the positives and negatives of AI within the field of scholarly communication and this should be discussed more thoroughly.

“it can bring significant improvements to data repositories” What are these improvements? Has anyone done this yet or is it only theoretical?

“As AI becomes more integrated into data repository workflows” Is it becoming more integrated into these workflows? Or do you mean “if”?

Competing interests: None.

Peer review 2

Anonymous reviewer

DOI: 10.70744/MetaROR.33.1.rv2

This commentary (or perhaps it is more similar to a blogpost) proposes a taxonomy for tasks for how AIs could be used within workflows in data repositories. Although it is not the stated aim, it also reflects on the relationship between AIs and humans in these workflows and how to develop trust and transparency in a repository while using AIs.

The idea of having a taxonomy which can be used to spur discussions or to classify the roles of AI in repositories is useful, and the taxonomy roles themselves are sensible, although I am not sure how feasible they actually are at the moment. This is perhaps related to a lack of clarity about if these capabilities already exist for AIs to be used in these ways, or if this is the imagined future. It is also not clear which AIs the authors are referring to; is this targeted to LLMs specifically? To other AIs? I imagine this is important in identifying the potential tasks which AI could perform.

Although the authors provide examples of possible tasks for each part of the taxonomy, I still find it a bit abstract and vague. Maybe it would help to include a case study or example of a repository to carry through the steps and demonstrate how this looks/could look in practice? Some of the statements introducing each section of the taxonomy could be a bit more clear also, especially in terms of the target audience. Overall it is unclear to me who the exact target audience is – is this aimed for all repositories or just generalist repositories, given that it was developed within GREI? The latter part of the article refers to generalist repositories, but the earlier part (the taxonomy) does not. This raises the question of whether these steps would apply to all types and sizes of data repositories, or just to larger “generalist” ones. Some of the names for the taxonomy tasks may also be confusing, e.g. “share” – which usually tends to refer to the act of researchers sharing/depositing data in repositories rather than repositories making data available and facilitating reuse,.

One of the biggest points for improvement is that it is not very clear how the taxonomy was developed. The authors mention that it is based on other taxonomies (but they do not provide links or references) and their “coopetition efforts” within the GREI consortium. What did these activities entail exactly? There is a link in the acknowledgements section to authors of a very similar taxonomy (actually a very similar piece overall) that was developed for publishing workflows. Why is this not referenced in the article earlier, or the relationship between the two pieces described? Overall, the referencing should be improved and standardized (although again how this is done depends a bit on how the authors envision the future of this work) – there is currently no reference list at all.

It also seems as if the taxonomy part of the article is disconnected from the latter sections (aside from the conclusion). I actually find the section on balancing human and AI expertise to be quite valuable as its own contribution, as it makes it clear that using AIs is not an all-or-nothing proposition in repositories’ workflows. I think it would help if the authors could let readers know (at least) that this section, and the one on trust, are a part of the article in the introduction. Or perhaps there is room for restructuring the argument here, and somehow foregrounding the human/AI section.

Other reflections

It is a bit difficult to review this piece, not knowing whether it is intended to be a commentary, blogpost, or other type of article.

Competing interests: None.

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