A partnership of various libraries and archives, led by Greenhouse Studios at the University of Connecticut and including the Northeastern University Library, has recently been awarded a $805,000 grant from the Andrew W. Mellon Foundation.
The two-year grant will support the continued development and outreach of Sourcery, “a mobile application that streamlines the scanning of remote of archival materials, provides better connections between researchers and archivists, and offers new and more equitable pathways for archival research.”
According to Greenhouse Studios: “Sourcery is an open-source web application that expands access to non-digitized archival sources. The app, developed by Greenhouse Studios and supported by the non-profit Corporation for Digital Scholarship (CDS), is accessible on any device connected to the Internet. Sourcery provides archivists with a streamlined reference scanning workflow, payment processing services, and analytics on document requests. It provides researchers with a single interface for placing document requests across multiple remote repositories–a practice that has taken on new urgency during this time of limited in-person access to collections.”
Northeastern University Library is one of three partner repositories from which researchers can request documents. The others are Hartford Public Library and the University of Connecticut Archives and Special Collections. A fourth repository—Folger Shakespeare Library—will join the partnership upon completion of a renovation in 2023.
The grant is the second awarded to the group for the Sourcery project, after an initial Andrew W. Mellon Foundation grant in 2020.
Students of history become familiar with the vast array of human accomplishments. With that knowledge also comes an understanding of human cruelty and racial violence: a perspective humanity shies away from. Perhaps one of the greatest examples of social depravity was in the Jim Crow-era South, a topic I only knew from textbooks and lectures. Working for the Civil Rights and Restorative Justice Project completely changed my awareness of the subject and opened my eyes to the importance of restorative justice.
The CRRJ (part of the Northeastern University School of Law) spearheads a variety of projects meant to bring justice to the victims of racial crimes. Examples of restorative justice include public apologies, memorials, and reconciliation through education. The efforts of the CRRJ not only provide closure and honorable memory to the families of victims, but also valuable opportunities for law students to advance in their field.
George Stinney
The Civil Rights and Restorative Justice Project, “George Stinney,” Year End Report 2014, 12.
An essential part of the CRRJ’s efforts is the Burnham-Nobles Archive. With an abundance of records (such as police records and death certificates, among many others), the archive serves as the CRRJ’s central hub of information. The latest archive project (set to unveil in 2022) is to transform this data into an interactive and accessible platform that is open to students, researchers, and families. Blending academia, restorative justice, and technology isn’t an easy feat, but it is a relevant and necessary undertaking in today’s society.
I was hired in April of 2021 to work part time assisting the CRRJ’s Burnham-Nobles Archive. I was interested in the position as I recently entered an MA program in Public History and want to work in the archival field. Before working with the project, I was simply passionate about doing archive-related tasks: I didn’t quite realize the breadth of the CRRJ’s project.
It was not until I started doing actual work that I realized the depths of the horror that was the Jim Crow South. It’s one thing to learn about racial violence, but it’s entirely different to work “face to face” with it. One of my first assigned projects was to code cases from Alabama according to the CRRJ’s v1 data dictionary. This seemed straightforward until I began learning about each victim’s story, their age, and their manner of death. Suddenly, the task had taken on a new level of importance: these weren’t faceless victims of race crimes. They were children, parents, siblings, soldiers, students, and workers—human beings senselessly cut down and unprotected by the law. A tragic example is 14-year-old George Stinney (above), a young boy sent to the electric chair on an unfounded accusation of the murder of two white girls.
Today, my outlook on the project is entirely different, and I have learned so much about the history of racial violence in the South, as well as the important connection between archives, history, and social justice. I have worked on a variety of assignments for the CRRJ, including coding work, GeoNames verifications, case abstract extraction/organization, and work on AirTable.
Working for the CRRJ has been essential for my Public History studies because it has given me the “human” element so often missing from the academic world. While I have learned about racial injustice and violence in the past, working for the CRRJ has allowed me to see each incident on an individual level. Additionally, I feel as if I am actually doing something with my work. Rather than just learning about what happened in the Jim Crow era, I feel that my work is helping the CRRJ accomplish its restorative goals to bring justice to the victims.
The CRRJ and the Burnham-Nobles Archives are leaders in the restorative justice movement, and they have given me valuable experience on both a technical level and a deeply human level.
This grant will help fund a series of digital projects currently underway through the DSG and NULab, but that were delayed or postponed due to the COVID-19 pandemic. It will support efforts to conduct collaborative research, digitize and process archival materials, create metadata, increase web accessibility, and more, while creating many graduate and undergraduate student research positions to conduct this work.
The projects that will benefit from this grant all involve collaborative engagement with communities outside of Northeastern, with many of them focused on resources related to underrepresented groups and social justice efforts. These include:
The grant also includes funding for additional projects organized through the NULab.
Julia Flanders, the director of the Digital Scholarship Group, is excited to get started: “We are honored and energized by this award. It creates wonderful research opportunities for students and will help the entire digital humanities ecology at Northeastern.”
Northeastern University Library’s procedure for digitizing physical materials utilizes a few different workflows for processing print documents, photographs, and analog audio and video recordings. Each step in the digitization workflow, from collection review to scanning to metadata description, is performed with thorough attention to detail, and it can take years to completely process a collection. For example, the approximately 1.6 million photographs in The Boston Globe Library collection held by the Northeastern University Archives and Special Collections may take several decades to complete!
What if some of these steps could be improved by using artificial intelligence technologies to complete portions of the work, freeing staff to focus more effort on the workflow elements that require human attention? Read on for a very brief overview of artificial intelligence and three potential options for processing The Boston Globe Library collection and other digital collections held by the Library.
What is artificial intelligence and machine learning? Artificial intelligence (AI) is a broad term used for many different technologies that attempt to emulate human reasoning in some way. Machine learning (ML) is a subset of AI where a program is taught how to learn and reason on its own. The program learns by using an algorithm to process existing data and find patterns. Every pattern prediction is evaluated and scored according to how accurate the prediction may or may not be until the predictions reach an acceptable level of accuracy.
ML may be supervised or unsupervised, depending on the type of result needed. Supervised learning is when instructions are provided to assist the algorithm to learn how to identify patterns expected to the researcher. Unsupervised learning is when the algorithm is fed data and discovers its own patterns that may be unknown to the researcher.
Ethics As we undertake this work, it is important to be aware that AI technologies are human-made and therefore human biases are embedded directly within the technology itself. Because AI technologies can be employed at such a large scale, the potential for negative impact caused by these biases is greater than with tools that require standard human effort. Although it is tempting to adopt and employ a useful technology as quickly as possible, this is an area of research where it is imperative that we make sure the work aligns with our institutional ethics and privacy practices before it is implemented.
What AI or ML techniques could be used to help process digital collections? OCR: The most widely known and used form of AI in digital collections practices may be recognition of printed text using Optical Character Recognition, or OCR. OCR is the process of analyzing printed text and extracting the text objects, like letters, words, sentences. The results may be embedded directly in the file, like a PDF with OCR’d text, or stored separately, like in a METS-ALTO file, or both.
Image source: Screenshot of an OCR page of The Winchester News with METS-ALTO encoding opened in AltoViewer.
OCR works rather well for modern text documents, especially those in English, but a particular challenge for OCR is historical documents. For more about this challenge, I recommend A Research Agenda for Historical and Multilingual OCR, a fairly recent report published by NULab.
We can already see the benefit of using OCR in the library’s Digital Repository Service, as files with OCR text embedded in the file have the full text extracted and stored alongside the text file. That text is indexed and improves discoverability of text files by retrieving files that match search terms in the file’s metadata or the full text.
Digitized back of a photograph from The Boston Globe Library collection.
HTR: Handwritten Text Recognition, or HTR, is like OCR, but for handwritten, not typewritten, text. Handwriting is very unique to an individual and poses a difficult challenge for teaching machines to interpret it. HTR relies heavily on having lots of data to train a model (in this case, lots of digitized images of handwriting), so even once a model is accurately trained on one set of handwriting, it may not be useful for accurately interpreting another set. Transkribus is a project attempting to navigate this challenge by creating training sets for batches of handwriting data. Researchers submit at least 100 transcribed images for a particular handwriting set to Transkribus and Transkribus uses that set as training data to create an HTR model to process the remaining corpus of handwritten text. HTR is appealing for the Boston Globe collection, as the backs of the photographs contain handwritten text describing the image, including the photographer name, date the photograph was taken, classification information, and perhaps a description or an address.
Computer Vision: Computer vision refers to AI technologies that allow machines to work with images and video, essentially training a machine to “see”. This type of AI is particularly challenging because it requires the machine to learn how to observe and analyze a picture and understand the content. Algorithms for computer vision are trained to identify patterns of different objects or people and attempt to accurately sort and identify the patterns. In a picture of the Northeastern campus, for example, a computer vision algorithm may be able to identify building objects or people objects or tree objects.
When used in digital collections workflows, the output produced by computer vision tools will need to be evaluated for its usefulness and accuracy. In the above example, the terms returned to describe the image are technically present in the photo (the subjects are wearing shoes and hats and overcoats), but the terms do not adequately capture the spirit of the image (a person being detained at a demonstration).
There are a lot of ethical concerns about using computer vision, especially for recognizing faces and assigning emotions. If we were to employ this particular technology, it may be able to generate keywords or other descriptive metadata for the Boston Globe collection that may not be present on the back of an image, but we would need to be careful to make sure that the process does not embed problematic assessments into the description, like describing an image of a protest as a riot.
Computer vision is already being employed in some digital collection workflows. Carnegie Mellon University Libraries has developed an internal tool called CAMPI to help archivists enhance metadata. An archivist uses the software to tag selected images, then the program returns other images it identifies as visually similar, regardless of its box and folder, allowing the archivist to easily apply the same tags to those visually similar images without having to manually seek them out.
Many other aspects of AI and ML technologies will need to be researched and evaluated before they can be integrated into our digital collections workflows. We will need to evaluate tools and identify the skills that are needed to train staff to perform the work. We will also continue to watch leaders in this space as they dive deep into the world of artificial intelligence for library work.
When Jackson Davidow was looking for information on Boston’s gay community in the 1970s, he knew where to go.
“I’ve long been interested in the relationship between queer politics and queer art, particularly in Boston in the 1970s, a point at which the city was a crucial hub of gay discourse, activism, nightlife, and sex,” said Davidow, a postdoctoral fellow in the “Translating Race” Lab at the Center for the Humanities at Tufts University. Gay Community News “was grounded in the political, cultural, and social environments of Boston. For that reason, it is an invaluable resource for researchers who study gay and lesbian life and liberation in Boston and beyond.”
The January 12, 1974, issue of the Gay Community News, one of its first published.
Gay Community News (GCN) was started in 1973 by eight Bostonians seeking to create a community voice for gays and lesbians in the Boston area. Originally published as a 2-page mimeographed sheet, the newspaper grew to have a national and international audience by the late 1970s and became one of the longest-running and most progressive national newspapers in the gay community. It was a natural place to start to gather the information Davidow needed. Issues of the GCN and records from its parent organization, the Bromfield Street Educational Foundation were subsequently donated to the Northeastern University Archives and Special Collections (NUASC).
While today’s researchers can contact many archives by email and receive scans of collections remotely, there was a time when physically visiting an Archives was only possible for those who lived in or could travel to the area. To provide more access to collections in the 1980s and 1990s, some Archives made arrangements to microfilm high use portions of their collections. In recent years those microfilms have been digitized and are offered via subscription to libraries — usually at a high cost — and then made available to the students and faculty affiliated with that university, a practice commonly described as “paywalling.”
The August 2-8, 1987, of the Gay Community News.
Unfortunately, this means that the many of the volunteers who wrote and edited articles, turned the crank on the mimeograph machine, or paid to advertise a queer night at a local club no longer have access to the content they created. It’s a trend that Giordana Mecagni, Head of the NUASC, knows all too well. Troubled, she recently published “Tear Down This (Pay)wall!: Equality, Equity, and Liberation for Archivists” in the Journal of Critical Library and Information Studies. The piece describes the negative effect paywalled archives have on institutions, archives, and researchers, and focuses on the GCN.
“Having the Gay Community News behind a paywall results in uneven access, where affiliates of universities can access the resource but members of marginalized groups within the queer community may not,” Mecagni wrote.
“Paywalls restrict who has access to archival materials. Many scholars are independent and unattached to academic institutions, or attached to academic institutions that do not have the money to subscribe to special historical resources,” Davidow added.
The NUASC recently completed an effort to made the Gay Community News freely available to anyone by re-scanning the GCN with help from the Boston Public Library’s “Library for the Commonwealth” program. This program provides free scanning services to Massachusetts libraries who have unique materials they want to share widely and freely. Now researchers, students, members of the LGBTQIA+ community, writers, and anyone else can browse through 26 years of the GCN to get a glimpse of the gay community in Boston and around the world.
Researchers like Davidow are thrilled.
“The digitization of GCN helps scholars and community members learn about and revisit these important histories,” he said. “During my research for my recent essay in The Baffler, ‘Against Our Vanishing,’ I talked with many people involved in GCN, and everyone was thrilled to learn that the full run is available online.”
