JSTOR Digital Stewardship Services

Meet the integrated, cloud-hosted solution that helps you confidently scale digital stewardship efforts while ensuring your collections are preserved, and discoverable, for the future.

A woman working on a computer, with a visual workflow of digital preservation steps labeled as Describe, Preserve, Manage, and Share.

Community voices

“[The] AI tool for processing digital records has been a tremendous benefit for Brooklyn College. [We] have saved countless intern hours, allowing us to give them more complex and varied assignments, enhancing their internship experience. Overall, we highly recommend the tool.”

Professor Colleen Bradley-Sanders
Head of Archives and Special Collections, Brooklyn College

Solve common collections management challenges like never before

Developed by a mission-driven nonprofit in collaboration with librarians and archivists, these services integrate digital asset management, AI-powered collection processing support, long-term preservation, and paths for increased discovery.

Screenshot of a digital archiving platform showing metadata creation for a publication, with a highlighted cover of "Up from Under."

AI-assisted collections processing

JSTOR Seeklight accelerates the creation of descriptive metadata, and the generation of collection-level insights, while ensuring expert oversight and institutional control.

User interface of a digital archive system displaying options to create metadata, manage records, and preserve documents.

One unified, cloud-hosted solution

Combine tools for digital asset management, descriptive metadata creation, long-term preservation, and seamless sharing.

JSTOR digital archive interface featuring a scanned cover of "Up from Under" with zoom and navigation tools, alongside related archived materials.

Increase visibility and impact

Share on JSTOR or more broadly on Omeka, other platforms, or your library catalog via OAI-PMH.

Infographic illustrating three key steps of digital preservation: maintaining usability, addressing data corruption, and ensuring security through file duplication.

OAIS compliant digital preservation

Safeguard your valuable assets against data loss, obsolescence, and technological advancements. Powered by Portico.

Select the tier of functionality that best meets your needs

Designed for ease of use, with custom onboarding and migration support.

Tier 1

Share on JSTOR

Book a demo

Share collections on JSTOR to:

Provide access to users at your institution, or openly with researchers around the world
Increase visibility through indexing in major search engines such as Google
Enhance discoverability in common library discovery services, including EBSCO Discovery Service™ (EDS), OCLC WorldCat, and ProQuest/Ex Libris Primo/Alma
Increase usage and reach of your digital assets alongside primary and secondary sources on JSTOR
Strengthen your institution’s visibility with a dedicated, branded collections page
Increase discovery and access to audio and video content with auto-generated transcripts and streaming capabilities
Start with 5TB of content management allowance
Measure impact with a self-service usage analytics dashboard

Tier 2

Preserve, manage, and share

Book a demo

Everything from Tier 1, plus:

Optimize comprehensive digital asset management capabilities with long-term preservation and powerful sharing options
Organize records and collections with intuitive, user-friendly tools
Create structured metadata using standard or custom metadata schemas and linked authorities
Efficiently catalog using metadata templates, bulk editing, and customizable workflows
Share broadly beyond JSTOR: Exhibit on Omeka, and share on other platforms including DPLA and your library catalog via OAI-PMH
Preserve valuable assets with OAIS compliant digital preservation, safeguarding against obsolescence and loss, powered by Portico

Tier 3

AI-assisted stewardship with JSTOR Seeklight

Book a demo

Everything from Tier 1 and Tier 2, plus:

Use the AI-powered tools of JSTOR Seeklight to rapidly generate descriptive metadata and project-level intelligence at scale
Accelerate early-stage processing while maintaining expert oversight and control
Streamline review and editing with built-in tagging and confidence scores, and ensure responsible AI integration through transparent workflows that enhance, rather than replace, professional expertise
Process multiple formats, including images, handwritten documents, and text-based materials
Transform large volumes of uploaded materials into access- and preservation-ready resources
Organize files from box, folder, and file structures into cohesive groupings, generating structured summaries to support finding aids
Ensure standards-compliant metadata for seamless interoperability across archival and library systems
Option to join the charter program and help shape the future of AI-assisted digital stewardship

Looking for more flexibility? Add-on services are available for one-time content migration, ongoing collection harvesting, and additional content management or preservation allowances. Contact us to learn more.

Help shape the future of AI-assisted digital stewardship

Institutions have a unique opportunity to collaborate with our mission-driven nonprofit to shape AI-assisted digital stewardship. Join the charter program.

Developed in partnership with our community, including:

Perspectives from the field

Want to see who’s already involved? View participating institutions currently using JSTOR Digital Stewardship Services, including charter program members.

A cluttered metal shelf filled with stacks of old papers and folders. A blue sticky note taped to the shelf reads “SHELVES OF WOE, CONT’D” in black marker.

Bridging capacity and care: A field report on archives and special collections

Authored by archivist and librarian Emilie Hardman, this report draws on hundreds of interviews to surface the challenges and values guiding this work.

Read the full report

A yellowed Guilford College “Entrance Card” (No. 133) dated Sept. 16, 1918, with printed questions and black-ink handwritten answers. It records student Ada Lea McCracken of Guilford, North Carolina.

Small team, big reach

Guilford College uses JSTOR Digital Stewardship Services to scale digital collections, empower students, and share Quaker history.

See how Guilford scales stewardship

Drew University illuminates valuable scholarship and engages students in collection stewardship

Learn how Drew University’s manager of Special Collections, Candace Reilly, uses JSTOR Digital Stewardship Services to expand access to scholarship and increase student engagement.

Read Drew’s story of scholarship and stewardship

A glass award plaque on a sparkling blue background. The text on the plaque and image reads: “JSTOR Seeklight, 2025 Recipient of the C.F.W. Coker Award.” It also includes the Society of American Archivists Foundation logos.

Building together: Reflections on JSTOR Seeklight and the 2025 C.F.W. Coker Award

Reflections on receiving the Society of American Archivists’ Coker Award and designing AI tools rooted in archival values.

Celebrate JSTOR Seeklight’s Coker Award

A scanned newspaper clipping featuring an article with the title '1926 Bear Basketball Squad' and a black-and-white team photo at the top. The newspaper includes multiple columns of text with headlines about a train crash and a list of reported casualties. In the top right corner, a metadata input box with a red sparkle icon is overlaid, containing fields labeled 'Title,' 'Date,' and 'Author.'

Designed by the community, for the community

Discover how archivists and librarians shaped JSTOR’s AI-assisted tool that streamlines metadata creation, tackles backlogs, and enhances digital stewardship.

Explore the tool

Strengthening digital collections through collaboration

Explore how SCELC libraries share a growing array of academic, cultural, and historical collections through a values-aligned partnership with JSTOR.

Learn about our partnership

Frequently asked questions

General
AI-assisted stewardship with JSTOR Seeklight

What are the differences between the JSTOR Digital Stewardship Services offerings?
JSTOR Digital Stewardship Services is offered in three tiers to meet the diverse needs of institutions:
- Tier 1: Share collections on JSTOR, making them discoverable alongside scholarly research. Includes audio/video hosting, usage analytics, and indexing in search engines and library discovery services.
- Tier 2: Everything in Tier 1 plus digital asset management with flexible cataloging tools, broader sharing options, and OAIS-compliant preservation.
- Tier 3: Everything in Tiers 1 and 2 plus the AI-powered tools of JSTOR Seeklight for rapidly generating descriptive metadata at scale, and the ability to accelerate early-stage processing while maintaining expert oversight and control.
What are the fees to participate?
As a nonprofit, JSTOR operates on a cost-recovery model to sustain and expand our services while keeping them accessible to a broad range of institutions. Pricing varies based on institution type and selected services:
- Tier 1 and Tier 2: To find the most relevant pricing details for your organization, please visit our fees and classifications page.
- Tier 3: Institutions can participate in our charter program, which provides a unique opportunity to help shape AI-assisted digital stewardship while benefiting from:
  - Charter member savings.
  - Expanded processing capabilities.
  - Direct collaboration with a mission-driven nonprofit.
  Annual fees for charter program members begin at $6,000. Learn more about the charter program.
For additional questions or customized pricing guidance, reach out to participation@ithaka.org.
Is JSTOR offering a charter program for Digital Stewardship Services?
Yes. In keeping with our community-driven, continuous learning approach to developing solutions, JSTOR views charter programs as an essential part of our process, providing a structured onramp for institutions that are early adopters of new resources.

Institutions opting into Tier 3 of JSTOR Digital Stewardship Services can participate in our charter program, which offers additional support, peer collaboration, and other incentives for those integrating AI-powered collection processing support into their workflows.

Charter participants receive expanded processing capabilities, exclusive savings, and the opportunity to collaborate with JSTOR and peer institutions to shape the evolution of these services.

Learn more about the charter program.
Do JSTOR’s services integrate with other systems?
Yes, our services are designed for seamless interoperability with a range of library technologies, ensuring broad discovery, preservation, and data portability. Key integrations include:
- Discovery and access: Collections shared on JSTOR are indexed in Google and other major search engines, as well as library discovery services, including EBSCO Discovery Service™ (EDS), OCLC WorldCat, and ProQuest/Ex Libris Primo/Alma.
- Metadata sharing and harvesting: Support for OAI-PMH enables integration with Omeka, DPLA, library catalogs, and beyond, ensuring metadata can be harvested for broader dissemination.
- Long-term preservation: Portico integration provides trusted long-term digital preservation, safeguarding collections against obsolescence.
  Data portability: Flexible data export allows institutions to use their collections and metadata in other systems as needed.
How does JSTOR ensure long-term preservation of digital assets?
JSTOR ensures robust, standards-compliant digital preservation through its integration with Portico, a leading digital preservation service. JSTOR and Portico are both nonprofit services of ITHAKA. Key features include:
- Portico-powered preservation: JSTOR leverages Portico’s proven infrastructure to provide secure, long-term digital preservation.
- Active monitoring: Scheduled scans detect format obsolescence and perform necessary format migrations to maintain usability.
- Fixity checks: Regular integrity checks detect and address data corruption.
- Geographically distributed storage: Multiple redundant copies ensure long-term protection.
- Compliance with preservation standards: Adherence to established digital preservation standards, including the Open Archival Information System (OAIS) and Preservation Metadata (PREMIS), ensure a trusted preservation environment.
Learn more about our preservation standards and process or explore guidance on best practices for using our services to preserve your content.
How do JSTOR’s services improve discoverability?
Once your collections are shared to JSTOR, your institution benefits from the strengths of the JSTOR platform, including:
- Availability to millions of researchers worldwide.
- The ability to cross-search your collections alongside other scholarly materials on JSTOR.
- Indexing in Google and major search engines (JSTOR is one of the world’s top ranked websites on Google).
- Integration with library discovery services, ensuring seamless academic access.
Additionally, institutions can enable broad metadata sharing via OAI-PMH, allowing integration with Omeka, DPLA, and library catalogs, expanding discoverability beyond JSTOR’s platform.
Are collaborative workflows supported?
Yes, our cloud-based solution provides real-time access from anywhere, enabling unlimited collaboration and improved accessibility. With multi-user support, role-based access, and real-time updates, it seamlessly scales across departments and projects to streamline workflows.
What type of files are supported?
Our services support the management of a wide variety of digital assets, including images, documents, audio, and video. Learn more.

Please note: AI-assisted collection processing tools from JSTOR Seeklight (Tier 3) currently support text-based documents, handwritten materials (PDF), and images (JPG) only. We are exploring future expansions to support additional file types such as audio and video.
What training and support are available?
We offer a range of education and support resources to help ensure your success:
- Personalized onboarding
- Self-service resources
- Ongoing training
Is migration support available for transitioning from another platform?
Yes. For institutions moving from a legacy system into JSTOR Digital Stewardship Services, a one-time, fee-based content migration service is available. This add-on service is designed to support a full transition, making it easier to consolidate digital collections into JSTOR Digital Stewardship Services as the primary content management platform. Contact participation@ithaka.org to learn more.
Is there a service for regularly adding content to Digital Stewardship Services?
Yes. JSTOR Digital Stewardship Services offers harvesting as an add-on option for institutions that want to keep their collections up to date by regularly transferring content from another system into their JSTOR Stewardship environment.

Harvesting is ideal for institutions that manage collections across multiple platforms and need a reliable, hands-off way to bring new or updated content into JSTOR Stewardship on an ongoing basis. Learn more about what harvesting offers and how it works.
How can my institution participate?
You can book a demo or contact participation@ithaka.org to learn more. We look forward to hearing from you!

How does JSTOR Seeklight work?
JSTOR Seeklight is our AI-powered technology designed to enhance digital collections processing workflows—beginning with faster metadata generation and collection-level insights. Built on JSTOR’s trusted infrastructure and engineering expertise, and informed by deep community collaboration, JSTOR Seeklight features integrated workflows that ensure expert review and institutional control.

File analysis and grouping
Upon upload, JSTOR Seeklight analyzes filename syntax and folder structure to determine if any of the files are meant to be grouped together into compound objects. If so, it will group the requisite files together and describe them as one singular item.

Metadata processing workflow
- Text files:
- Images:
  - Images are classified by type. If applicable, JSTOR Seeklight identifies people in the image and identifies famous faces in a limited capacity.
  - The processed image data, along with specialized prompts, is sent to GPT-4o to generate metadata.
  - Users receive AI-generated descriptions, subjects, and keywords for verification and editing.
This approach ensures that AI serves as an assistive tool, not a replacement for expert judgment. Users retain full control, refining AI-generated suggestions from JSTOR Seeklight to meet institutional and scholarly standards. Learn more about how JSTOR Seeklight is being developed.
Which large language model (LLM) is JSTOR Seeklight using?
We are currently using GPT-4.1 from OpenAI, however we continuously evaluate the offerings of other service providers, as well as more bespoke alternatives, to ensure that we’re delivering the highest quality output.
Will my data be used to train the LLM?
No. Our large language model (LLM) provider, OpenAI, does not use data uploaded through its API (such as your uploaded materials) to train their LLM. This is stated in OpenAI’s API terms (see the OpenAI Trust Portal).
How is my content protected?
We employ strict security measures to protect uploaded materials and metadata:
- AI provider data handling: OpenAI stores uploaded data for a maximum of 30 days to check for abuse or misuse before deleting it entirely. Your content is never used to train AI models.
- Data rights and deletion: You retain full rights to your uploaded content and generated metadata. If you delete your project, all associated data is permanently removed from JSTOR’s systems.
- Security and compliance: JSTOR maintains physical, technical, and administrative safeguards to prevent unauthorized access or malicious use. As a SOC2 compliant organization, our data security practices undergo annual independent third-party audits.
- Metadata storage and use: JSTOR stores generated metadata and user interactions to improve system performance.
How does JSTOR Seeklight manage handwritten, historical, and/or non-English language materials?
- Handwritten and historical documents: JSTOR Seeklight is designed to effectively process handwritten documents and historical materials. While accuracy may vary depending on factors such as condition, language, legibility, and format, our technology is continuously improving to provide the best possible results.
- Non-English language materials: Our LLM service provider handles multiple languages by default, but optimization for specific non-English languages is still in progress.
Does JSTOR retain any rights to the files, file contents, and/or metadata itself?
Institutions will own the rights to the files, content, and metadata. We ask that JSTOR be provided a license in such content and metadata to provide, develop, and improve JSTOR Seeklight for accuracy, and to otherwise benefit our licensees.
Can users review and edit AI-generated metadata from JSTOR Seeklight?
Yes. Users have full control over metadata generated by JSTOR Seeklight and can edit AI-powered outputs before finalizing. During review, they can mark records as “Reviewed” to indicate completion. While unreviewed records can still be published, users will receive a prompt reminding them to review their items before finalizing publication. Learn more.
Can institutions customize the AI metadata generation rules in JSTOR Seeklight?
At this time, institutions will not be granted broad expansive controls to manipulate the rules governing the AI. However, we allow contributors to add additional context to augment the commands at the point of the prompt, to ensure that the LLMs are processing metadata with as much existing knowledge as possible. Additional customization options are currently being explored, and we’ll provide more information as plans are developed.
What formats are supported for JSTOR Seeklight?
Currently, JSTOR Seeklight supports text and image-based materials, with the following file types:
- Text: PDFs (.pdf)
- Images: JPEG/JPG (.jpeg, .jpg)
Within these formats, the tool can generate metadata for a diverse range of content, including:
- Typed documents, handwritten letters, newspapers, books, and periodicals.
- Historical photographs, maps, posters, postcards, and advertisements.
We are exploring future expansions to support metadata generation for additional file types, such as audio and video, in the future.
What is JSTOR doing to offset the environmental impacts of AI?
We recognize the environmental impact of AI and closely follow emerging research in this area. Our approach prioritizes making informed, responsible choices that minimize our footprint while supporting our commitment to advancing research and learning through new technologies. Wherever possible, we prioritize thoughtful implementation over scale for scale’s sake. Here are some of the ways we reduce our impact:
- We don’t train our own LLMs. Model training is resource-intensive, so we rely on existing models and focus on using them efficiently.
- We choose the right model for the task. Rather than defaulting to the largest model, we select the most appropriate option, using smaller models when possible to reduce energy use while maintaining quality.
- We keep prompts lean. We craft our system prompts to be as concise as possible while still providing the necessary context to ensure high-quality results. Smaller inputs mean less computational overhead.
What is JSTOR’s overall approach to AI and other emerging technologies?
Technology has always been an incredible accelerator for our mission to improve access to knowledge and education. As a trusted provider of scholarly materials, we have a responsibility to leverage content, technology, and deep subject matter expertise that enables a safe, effective, and affordable path forward for the use of AI.
- We honor our values first and foremost. JSTOR provides users with a credible, scholarly research and learning experience. Our use of technology must enhance that credibility, not undermine it.
- We listen closely and proceed with care. We recognize the concerns associated with AI and other emerging technologies and pursue this work in close collaboration with our community.
- We empower people, not replace them. These tools should not be used to “do the work.” They are designed to help people deepen and expand their work.
- We enable our systems to interact with users in ways that are intuitive. Traditionally, it has been the users’ responsibility to adapt to restricted language and structures to provide computers with inputs. Today, computers can interact effectively with users in natural language, and we should take advantage of that, while balancing the need to support user and institutional preferences.
This work is ongoing. As we learn and grow, we welcome your engagement to help shape AI tools that reflect shared values and advance meaningful access to scholarship. Learn more about how we are exploring emerging technologies on the JSTOR platform.

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