To help researchers in finding and understanding how to work with data from archival health sciences collections, we have compiled and published the Archives as Data research guide. “Archives as Data” refers to archival collection materials in digital form that can be shared, accessed, analyzed, and referenced as data. Using digital tools, researchers can work with archives as data to explore and evaluate characteristics of collection materials and analyze trends and connections within and across them.
AIDS History Project Collections document included in the No More Silence dataset with Python code used for analysis.
UCSF Archives and Special Collections makes data available from a number of our digital collections. Researchers will find guidance in the guide for accessing and using such data as well as descriptions of both the form and content this data takes. As well, you’ll find a growing set of links to learn more about various methods and tools used to work with archives as data, from training opportunities to learn programming foundations for data science to tutorials and other resources for self-study.
This new Archives as Data research guide provides researchers with a centralized resource hub with brief descriptions of collection materials as well as links to the datasets that have been prepared from them, including:
The No More Silence dataset, an aggregation of data from many, selected collections included in the AIDS History Project which range from the records of community activism groups to the papers of health researchers and journalists.
Data from the Industry Documents Library, comprising collections of documents from the tobacco, food, drug, fossil fuel, chemical, and opioid industries, all of which impact public health.
Selected datasets from the COVID Tracking Project, a volunteer organization launched from The Atlantic and dedicated to collecting and publishing the data required to understand the COVID-19 outbreak in the United States, with data collected from March 2020-March 2021.
Data from digitized UCSF University Publications, from course catalogs to annual reports, newsletters, and more.
We look forward to updating the guide as more data from UCSF Archives and Special Collections becomes available, and anticipate expanding to include links to “archives as data” of interest for digital health humanities work made available by other institutions and organizations.
To learn more about how we are making archives as data available at UCSF, check out recordings and resources from our recent sessions on Finding and Exploring Archives as Data for Digital Health Humanities!
The Archives as Data Research Guide has been published as part of the UCSF DIgital Health Humanities pilot program. Please reach out to the Digital Health Humanities Program Coordinator Kathryn Stine, at kathryn.stine@ucsf.edu with any questions about DHH at UCSF. The UCSF Digital Health Humanities Pilot is funded by the Academic Senate Chancellor’s Fund via the Committee on Library and Scholarly Communication.
Guest post by Heather Wagner, Digitization Coordinator at UC Merced Library
For the Pioneering Child Studies project the UC Merced Library’s Digital Curation and Scholarship unit was tasked with digitizing 68,000 pages of documents. So, how do we go about digitizing 68,000 pages of documents? With some help. That help comes from four undergraduate student assistants who play an important part in the digitization process.
The first part of the process is the actual digitization. Our undergraduate student assistants digitize materials on a variety of equipment. These include high speed document scanners and flatbed scanners for documents, book scanners for bound material, and cameras on stands for oversize or fragile materials.
Student Nicolas Fleming digitizing bound materials using a book scanner
Once the digitization is complete, the next step is quality checking. Students review each image in Adobe Bridge and zoom in to check for issues such as lines in scans or items out of focus. Some images may need minor editing such as straightening and cropping which is completed during the quality checking step in Photoshop. The quality checking step is time consuming but necessary, so we are sure we are receiving the best possible results from digitization.
PDFs with optical character recognition (OCR) are created from the digitized image files so they are accessible to users. OCR makes the PDF document searchable. The PDF documents are then quality checked by the students, and the documents are then optimized. Optimizing the PDF files reduces their file size, which makes them better suited for web viewing. The files are then ready for uploading.
We appreciate the hard work of our undergraduate student assistants. We would not be able to complete digitization projects of this size without them.
UCSF Archives and Special Collections is delighted to announce the publication of the Leona Mayer Bayer Correspondence digital collection on Calisphere. The digitization project is part of the NHPRC grant, Pioneering Child Studies: Digitizing and Providing Access to Collection of Women Physicians who Spearheaded Behavioral and Developmental Pediatrics. We worked in partnership with UC Merced Library’s Digital Assets Unit towards our goal of digitizing and publishing 68,000 pages from the collections of Drs. Hulda Evelyn Thelander, Helen Fahl Gofman, Selma Fraiberg, Leona Mayer Bayer, and Ms. Carol Hardgrove. To date we have digitized over 59,000 pages. Most digitized material is still undergoing quality assurance (QA) procedures. Here are some items we have digitized from Dr. Leona Mayer Bayer collection.
Dr. Leona Mayer Bayer received her MD from Stanford University Medical School in 1928. She worked with the Institute of Human Development in Berkeley and focused on child development, human growth, and psychology of sick children. The collection consists of around 400 digitized pages and the collection features professional correspondence of Dr. Leona Mayer Bayer. Some items that may be of interest is her correspondence with Dr. Hilde Bruch and her acceptance remarks for the PSR Broadstreet Pump Award she received in March of 1987.
The UCSF Archives & Special Collections is delighted to welcome our new colleague, Alex Duryee who took over from Kevin Miller as the COVID Tracking Project Archive Lead. The project team continues the work of preserving, providing online access, and building educational resources for the organizational records and datasets of the COVID Tracking Project at The Atlantic (CTP).
Alex Duryee
Alex brings a background in metadata, digital archives, and archival access to the COVID Tracking Project Archive team. He holds a BA from The College of New Jersey and a MLIS from Rutgers University, and also serves as the Manager for Archival Metadata at the New York Public Library. In this position, he manages the Library’s archival metadata platforms and develops metadata policy for the Library’s archival collections. He also collaborates with staff across the organization to improve systems integrations and develop new methods for accessing and using archival materials. Alex also serves on the National Finding Aid Network (NAFAN) Technical Advisory Working Group, SAA’s Technical Subcommittee for Encoded Archival Standards, and as the chair of the SNAC (Social Networks and Archival Context) Technology & Infrastructure Working Group. He contributes to open-source projects such as ArchivesSpace, as well as developing open-source metadata tools. In 2019, his team was awarded the C. F. W. Coker Award for Archival Description by the Society of American Archivists.
Alex’s background also includes experience as a freelance ArchivesSpace developer, a consultant with AVP, and a digital archives fellow with Rhizome.
Alex enjoys puzzles of all sorts (including metadata), board games, baking, and dancing.
The 18th International Conference on Digital Preservation (iPRES) took place from September 12-16, 2022, in Glasgow, Scotland. First convened in 2004 in Beijing, iPRES has been held on four different continents and aims to embrace “a variety of topics in digital preservation – from strategy to implementation, and from international and regional initiatives to small organisations.” Key values are inclusive dialogue and cooperative goals, which were very much centered in Glasgow thanks to the goodwill of the attendees, the conference code of conduct, and the significant efforts of the remarkable Digital Preservation Coalition (DPC), the iPRES 2022 organizational host.
I attended the conference in my role as the UCSF Industry Documents Library’s managing archivist to gain a better understanding of how other institutions are managing and preserving their rapidly-growing digital collections. For me and for many of the delegates, iPRES 2022 was the first opportunity since the COVID pandemic began to join an in-person conference for professional conversation and exchange. It will come as no surprise to say that gathering together was incredibly valuable and enjoyable (in no small part thanks to the traditional Scottish ceilidh dance which took place at the conference dinner!) The Program Committee also did a fantastic job designing an inclusive online experience for virtual attendees, with livestreamed talks, online social events, and collaborative session notes.
Session themes focused on Community, Environment, Innovation, Resilience, and Exchange. Keynotes were delivered by Amina Shah, the National Librarian of Scotland; Tamar Evangelestia-Dougherty, the inaugural director of the Smithsonian Libraries and Archives; and Steven Gonzalez Monserrate, an ethnographer of data centers and PhD Candidate in the History, Anthropology, Science, Technology & Society (HASTS) program at the Massachusetts Institute of Technology.
Every session I attended was excellent, informative, and thought-provoking. To highlight just a few:
Amina Shah’s keynote “Video Killed the Radio Star: Preserving a Nation’s Memory” (featuring the official 1980 music video by the Buggles!) focused on keeping up with the pace of change at the National Library of Scotland by engaging with new formats, new audiences, and new uses for collections. She noted that “expressing value in a key part of resilience” and that the cultural heritage community needs to talk about “why we’re doing digital preservation, not just how.” This was underscored by her description of our world as a place where the truth is under attack, that capturing the truth and finding a way to present it is crucial, and that it is also crucial that this work be done by people who aren’t trying to make a profit from it.
“Green Goes with Anything: Decreasing Environmental Impact of Digital Libraries at Virginia Tech,” a long paper presented by Alex Kinnaman as part of the wholly excellent Environment 1 session, examined existing digital library practices at Virginia Tech University Libraries, and explored changes in documentation and practice that will foster a more environmentally sustainable collections platform. These changes include choosing the least-energy consumptive hash algorithms (MD4 and MD5) for file fixity checks; choosing cloud storage providers based on their environmental practices; including environmental impact of a digital collection as part of appraisal criteria; and several other practical and actionable recommendations.
The Innovation 2 session included two short papers (by Pierre-yves Burgi, and by Euan Cochrane) and a fascinatingly futuristic panel discussion posing the question “Will DNA Form the Fabric of our Digital Preservation Storage?” (Also special mention to the Resilience 1 session which presented proposed solutions for preserving records of nuclear decommissioning and nuclear waste storage for the very long term – 10,000 years!)
Tamar Evangelestia-Dougherty’s keynote Digital Ties That Bind: Effectively Engaging With Communities For Equitable Digital Preservation Ecosystemswas an electric presentation that called unequivocally for centering equity and inclusion within our digital ecosystems, and for recognizing, respecting, and making space for the knowledge and contributions of community archivists. She called out common missteps in digital preservation outreach to communities, and challenged all those listening to “get more people in the room” to include non-white, non-Western perspectives.
“’…provide a lasting legacy for Glasgow and the nation’: Two years of transferring Scottish Cabinet records to National Records of Scotland,” a short paper by Garth Stewart in the Innovation 4 session, touched on a number of challenges very familiar to the UCSF Industry Documents Library team! These included the transfer of a huge volume of recent and potentially sensitive digital documents, in redacted and unredacted form; a need to provide online access as quickly as possible; serving the needs of two major access audiences – the press, and the public; normalizing files to PDF in order to present them online; and dealing with incomplete or missing files.
After the conference I also had the opportunity to visit the Archives of the Royal College of Physicians and Surgeons of Glasgow, where our tour group was welcomed by the expert library staff and shown several fascinating items from their collections, including an 18th century Book of Herbal Remedies (which has been digitized for online access).
After five collaborative and collegial days in Glasgow, I’m looking forward to bringing these ideas back to our work with digital archival collections here at UCSF. Many thanks to iPRES, the DPC, the Program Committee, the speakers and presenters, and all the delegates for building this wonderful community for digital preservation!
An 18th-century Book of Herbal Remedies on display at the Archives of the Royal College of Physicians and Surgeons of Glasgow
This is a guest post from Lubov McKone, the Industry Documents Library’s 2022 Data Science Senior Fellow.
This summer, I served as the Industry Documents Library’s Senior Data Science Fellow. A bit about me – I’m currently pursuing my MLIS at Pratt Institute with a focus in research and data, and I’m hoping to work in library data services after I graduate. I was drawn to this opportunity because I wanted to learn how libraries are using data-related techniques and technologies in practice – and specifically, how they are contextualizing these for researchers.
Project Background
The UCSF Industry Documents Library is a vast collection of resources encompassing documents, images, videos, and recordings. These materials can be studied individually, but increasingly, researchers are interested in examining trends across whole collections, or subsets of it. In this way, the Industry Documents Library is also a trove of data that can be used to uncover trends and patterns in the history of industries impacting public health. In this project, the Industry Documents Library wanted to investigate what information is lost or changed when its collections are transformed into data.
There are many ways to generate data from digital collections. In this project we focused on a combination of collections metadata and computer-generated transcripts of video files. Like all information, data is not objective but constructed. Metadata is usually entered manually and is subject to human error. Video transcripts generated by computer programs are never 100% accurate. If accuracy varies based on factors such as the age of the video or the type of event being recorded, how might this impact conclusions drawn by researchers who are treating all video transcriptions as equally accurate? What guidance can the library provide to prevent researchers from drawing inaccurate conclusions from computer-generated text?
Project Team
Kate Tasker, Industry Documents Library Managing Archivist
Rebecca Tang, Industry Documents Library Applications Programmer
Geoffrey Boushey, Data Science Initiative Application Developer and Instructor
Lubov McKone, Senior Data Science Fellow
Lianne De Leon, Junior Data Science Fellow
Rogelio Murillo, Junior Data Science Fellow
Project Summary
Research Questions
Based on the background and the goals of the Industry Documents Library, the project team identified the following research questions to guide the project:
Taking into account factors such as year and runtime, how does computer transcription accuracy differ between television commercials and court proceedings?
How might transcription accuracy impact the conclusions drawn from the data?
What guidance can we give to researchers to prevent uninformed conclusions?
Uses
This project is a case study that evaluates the accuracy of computer-generated transcripts for videos within the Industry Documents Library’s Tobacco Collection. These findings provide a foundation for UCSF’s Industry Documents Library to create guidelines for researchers using video transcripts for text analysis. This case study also acts as a roadmap and a collection of instructional materials for similar studies to be conducted on other collections. These materials have been gathered in a public github repo, viewable here.
Sourcing the Right Data
At the beginning of the project, we worked with the Junior Fellows to determine the scope of the project. The tobacco video collection contains 5,249 videos that encompass interviews, commercials, court proceedings, press conferences, news broadcasts, and more. We wanted to narrow our scope to two categories that would illustrate potential disparities in transcript accuracy and meaning. After transcribing several videos by hand, the fellows proposed commercials and court proceedings as two categories that would suit our analysis. We felt 40 would be a reasonable sample size of videos to study, so each fellow selected 10 videos from each category, selecting videos with a range of years, quality, and runtimes. The fellows were selecting videos from a list that was generated by the InternetArchive python API, containing video links and metadata such as year and runtime.
Computer & Human Transcripts
Once the 40 videos were selected, we extracted transcripts from each URL using the Google AutoML API for transcription. We saved a copy of each computer transcription to use for the analysis, and provided another copy to the Junior Fellows, who edited them to accurately reflect the audio in the videos. We saved these copies as well for comparison to the computer-generated transcription.
Comparing Transcripts
To compare the computer and human transcripts, we conducted research on common metrics for transcript comparison. We came up with two broad categories to compare – accuracy and meaning.
To compare accuracy, we used the following metrics:
Word Error Rate – a measure of how many insertions, deletions, and substitutions are needed to convert the computer-generated transcript into the reference transcript. We subtracted this number from 1 to get the Word Accuracy Rate (WAR).
BLEU score – a more advanced algorithm measuring n-gram matches between the transcripts, normalized for n-gram frequency.
Human-evaluated accuracy – a score from Poor, Fair, Good, and Excellent assigned by the fellows as they were editing the computer-generated transcripts.
Google AutoML confidence score – a score generated by Google AutoML during transcript generation indicating how accurate Google believes its transcription to be.
To compare meaning, we used the following metrics:
Sentiment – We generated sentiment scores and magnitude for both sets of transcripts. We wanted to see whether the computer transcripts were under- or over- estimating sentiment, and whether this differed across categories.
Topic modeling – We ran a k-means topic model for two categories to see how closely the computer transcripts matched the pre-determined categories vs. how closely they were matched by the human transcripts
Findings & Recommendations
Relationships in the data
From an initial review of the significant correlations in the data, we gained some interesting insights. As shown in the correlation matrix, AutoML confidence score, fellow accuracy rating, and Word Accuracy Rate (WAR) are all significantly positively correlated. This means that the AutoML confidence score is a relatively good proxy for transcript accuracy. We recommend that researchers who are seeking to use computer-generated transcripts look to the AutoML confidence score to get a sense of the reliability of the computer-generated text they are working with.
Correlation matrix showing that AutoML confidence score, fellow accuracy rating, and Word Accuracy Rate (WAR) are all significantly positively correlated
We also found a significant positive correlation between year and fellow accuracy rating, Word Accuracy Rate, and AutoML confidence score – suggesting that the more recent the video, the better the quality. We suggest informing researchers that newer videos may generate more accurate computer transcriptions.
Transcript accuracy over time
One of the Junior Fellows suggested that we look into whether there is a specific cutoff year where transcripts become more accurate. As shown in the visual below, there’s a general improvement in transcription quality after the 1960s, but not a dramatic one. Interestingly, this trend disappears when looking at each video type separately.
Line graph showing transcript accuracy over time for all video typesLine graph showing transcript accuracy over time, separated into two categories: commercials and court proceedings
Transcript accuracy by video type
Bar graphs showing transcript accuracy by video type (commercials and court proceedings) according to four ratings: AutoML Confidence Average; Bleu Score; Fellow Accuracy Rating; and Word Accuracy Rate (WAR)
When comparing transcript accuracy between the two categories, we found that our expectations were challenged. We expected the accuracy of the advertising video transcripts to be higher, because advertisements generally have a higher production quality, and are less likely to have features like multiple people speaking over each other that could hinder transcription accuracy. However, we found that across most metrics, the court proceeding transcripts were more accurate. One potential reason for this is that commercials typically include some form of singing or more stylized speaking, which Google AutoML had trouble transcribing. We recommend informing researchers that video transcripts from media that contain singing or stylized speaking may be less accurate.
The one metric that the commercials were more accurate in was BLEU score, but this should be interpreted with caution. BLEU score is supposed to range from 0-1, but in our dataset its range was 0.0001 – 0.007. BLEU score is meant to be used on a corpus that is broken into sentences, because it works by aggregating n-gram accuracy on a sentence level, and then averaging the sentence-level accuracies across the corpus. However, the transcripts generated by Google AutoML did not contain any punctuation, so we were essentially calculating BLEU score on a corpus-length sentence for each transcript. This resulted in extremely small BLEU scores that may not be accurate or interpretable. For this reason, we don’t recommend the use of the BLEU score metric on transcripts generated by Google AutoML, or on other computer-generated transcripts that lack punctuation.
Transcript sentiment
We looked to sentiment scores to evaluate differences in meaning between the test and reference transcripts. As we expected, commercials, which are sponsored by the companies profiting off of the tobacco industry, tend to have a positive sentiment, while court proceedings, which tend to be brought against these companies, tend to have a negative sentiment. As shown in the plot to the left, the sentiment of the computer transcripts was a slight underestimation in both video types, though this was not too dramatic of an underestimation.
Graph comparing average sentiment scores from computer and human transcriptions of commercials and court proceedings
Opportunities for Further Research
Throughout this project, it was important to me to document my work and generate a research dataset that could be used by others interested in extended this work beyond my fellowship. There were many questions that we didn’t get a chance to investigate over the course of this summer, but my hope is that the work can be built upon – maybe even by a future fellow! This dataset lives in the project’s github repository under data/final_dataset.csv.
One aspect of the data that we did not investigate as much as we had hoped was topic modeling. This will likely be an important next step in assessing whether transcript meaning varies between the test and reference transcripts.
Professional Learnings & Insights
My main area of interest in the field of library data services is critical data literacy – how we as librarians can use conversations around data to build relationships and educate researchers about how data-related tools and technologies are not objective, but subject to the same pitfalls and biases as other research methods. Through my work as the Industry Documents Library Senior Data Science Fellow, I had the opportunity to work with a thoughtful team who is thinking ahead about how to responsibly guide researchers in the use of data.
Before this fellowship, I wasn’t sure exactly how opportunities to educate researchers around data would come up in a real library setting. Because I previously worked for the government, I tended to imagine researchers sourcing data from government open data portals such as NYCOpenData, or other public data sources. This fellowship opened my eyes to how often researchers might be using library collections themselves as data, and to the unique challenges and opportunities that can arise when contextualizing this “internal” data for researchers. As the collecting institution, you might have more information about why data is structured the way it is – for instance, the Industry Documents Library created the taxonomy for the archive’s “Topic” field. However, you are also often relying on hosting systems that you don’t have full control over. In the case of this project, there were several quirks of the Internet Archive API that made data analysis more complicated – for example, the video names and identifiers don’t always match. I can see how researchers might be confused about what the library does and does not have control over.
Another great aspect of this fellowship was the opportunity to work with our high school Junior Fellows, who were both exceptional to work with. Not only did they contribute the foundational work of editing our computer-generated transcripts – tedious and detail-oriented work – they also had really fresh insights about what we should analyze and what we should consider about the data. It was a highlight to support them and learn from them.
I also appreciated the opportunity to work with this very unique and important collection. Seeing the breadth of what is contained in the Industry Documents Library opened my eyes to not only the wealth of government information that exists outside of government entities, but also to the range of private sector information that ought to be accessible to the public. It’s amazing that an archive like the Industry Documents Library is also so invested in thinking critically about the technical tools that it’s reliant upon, but I guess it’s not such a surprise! Thanks to the whole team and to UCSF for a great summer fellowship experience!
We are at the one-year point of the project Pioneering Child Studies: Digitizing and Providing Access to Collection of Women Physicians who Spearheaded Behavioral and Developmental Pediatrics. UCSF Archives & Special Collections and UC Merced have made significant headway towards our goal of digitizing and publishing 68,000 pages from the collections of Drs. Hulda Evelyn Thelander, Helen Fahl Gofman, Selma Fraiberg, Leona Mayer Bayer, and Ms. Carol Hardgrove.
To date we have digitized over 33,000 pages. The digitized material are still undergoing quality assurance (QA) procedures. Here are some items we have digitized so far.
Dr. Leona Mayer Bayer
This collection features professional correspondence of Dr. Leona Mayer Bayer. Her work focused on child development and human growth and psychology of sick children.
MSS 86-54, Leona Mayer Bayer Correspondence, Carton 1, Folder 9, PSR Award acceptance speechMSS 86-54, Leona Mayer Bayer Correspondence, Carton 1, Folder 9, Memo thanking Nancy W. Zin for collecting her archive
Dr. Selma Horwitz Fraiberg
This collection includes several drafts of her research papers on important aspects of developmental-behavioral pediatrics.
MSS 83-9, Selma Fraiberg Papers, Carton 14, Folder 8, draft of “Transference and Resistance in the Analysis of a Child with a Severe Behavior Disorder” MSS 83-9, Selma Fraiberg Papers, Carton 14, Folder 6, draft of “Observations On The Homosexual Conflicts of Adolescence”
In the next year we will continue digitizing and will soon publish our collections on Calisphere. Stay tuned for our next update.
The Industry Documents Library (IDL) is excited to welcome three Data Science Fellows to our team this summer. The Data Science Fellows will be working with the IDL and with the UCSF Library Data Science Initiative (DSI) to to assess the impact of transcription accuracy on text analysis of digital archives, using the IDL collections.
Through tagging, human transcription, and computer-generated transcription, the team will assess how accuracy may differ between media or document types, and how and whether this difference is more or less pronounced in certain categories of media (for example, video recordings of focus groups, community meetings, court proceedings, or TV commercials, all of which are present in the IDL’s video collections). After identifying transcript accuracy in different media types, we aim to provide guidelines to researchers and technical staff for proper analysis, measurement, and reporting of transcript accuracy when working with digital media.
Our Junior Data Science Fellows are Rogelio Murillo and Lianne De Leon. Rogelio and Lianne are both participating in the San Francisco Unified School District (SFUSD) Career Pathway Summer Fellowship Program. This six-week program provides opportunities for high school students to gain work experience in a variety of industries and to expand their learning and skills outside of the classroom. Lianne and Rogelio will be learning about programming and creating transcription for selected audiovisual materials. The IDL thanks SFUSD and its partners for running this program and providing sponsorship support for our fellows.
Lubov McKone is our Senior Data Science Fellow and will be using automated transcription tools to extract text from audiovisual files, run sentiment and topic analyses, and compare automated results to human transcription. Lubov will also provide guidance and mentoring to the Junior Fellows.
Our Fellows have introduced themselves below. Please join us in welcoming Rogelio, Lianne, and Lubov to the UCSF Library this summer!
Hi my name is Lianne R. de Leon and I go to Phillip and Sala Burton High School as a rising senior. I love playing volleyball in my free time and you may see me at numerous open gyms around the city. In the future I hope to major in computer science or computer engineering. I’m looking forward to meeting many wonderful people here at UCSF and learning more about the data science industry from the inside.
IDL Junior Data Science Fellow Lianne de Leon
Hi, my name is Rogelio Murillo and I’m a rising junior at Ruth Asawa School of the Arts. I enjoy playing a variety of music and percussion. I’ve played Japanese Taiko, Afro Brazilian drumming, and Latin Jazz. I’m also learning guitar over the summer. I’m a responsible and respectful person.
IDL Junior Data Science Fellow Rogelio Murillo
My name is Lubov McKone and I’m currently pursuing my Masters in Library and Information Science from Pratt Institute in Brooklyn, NY. I also hold a Bachelor’s degree in Statistics, and prior to entering graduate school I worked as a data analyst in local government. My professional interests include supporting researchers in the accurate and responsible use of data, and I aspire to work as a data librarian in an academic library after graduation. Outside of work, I spend my time cooking, doing yoga, and writing music. I’m very excited to be joining the UCSF Industry Documents Library this summer, and I’m looking forward to learning more about how researchers use digital collections!
The UCSF Archives and Special Collections is pleased to announce the completion of the Subaward: “The San Francisco Bay Area’s Response to the AIDS Epidemic: Digitizing and Providing Universal Access to Historical AIDS Records Network of the National Library of Medicine, Pacific Southwest Region Subaward: “The San Francisco Bay Area’s Response to the AIDS Epidemic: Digitizing and Providing Universal Access to Historical AIDS Records.” This project chronicles the stories of marginalized communities and communities of color during the AIDS epidemic.
African- Americans, AIDS history project — ephemera collection, MSS 2000-31, box 1, folder 3
AIDS Events Photos, San Francisco General Hospital Historical Documents, SFGH 2015-002, box 11, folder 32Lesbians, San Francisco AIDS Foundation (SFAF) records, MSS 94-60, carton 22, folder 27
In collaboration with UC Merced Library’s Digital Assets Unit, we digitized over 45,000 pages from 14 archival collections related to the early days of the AIDS epidemic in the San Francisco Bay Area. The digitized material is now accessible to the public via the California Digital Library platform, Calisphere. This new corpus includes correspondence, brochures, reports, notebooks, negatives, newspaper clips, and photographic prints. Several new digital collections have been added to our digital holdings related to AIDS history including:
Latinos, AIDS history project — ephemera collection, MSS 2000-31, box 1, folder 4
Native Americans, AIDS history project — ephemera collection, MSS 2000-31, box 1, folder 1Asian-Americans, AIDS history project — ephemera collection, MSS 2000-31, box 1, folder 2
Another accomplishment of the project was the development of an AIDS history primary source set in collaboration with Aimee Medeiros, Associate Professor of History of Health Sciences at UCSF. The primary source set titled “BIPOC Activism” highlights BIPOC activism and AIDS outreach campaigns to communities of color during the early days of the AIDS epidemic. This new educational resource and tool can be used by students, teachers, and researchers and is accessible on the archives’ website.
Once again we contributed to the New York Academy of Medicine’s #ColorOurCollections. We’ve created a coloring book featuring images from our collection of Japanese woodblock prints. Please download the book, color, and tweet your creations @ucsf_archives using #ColorOurCollections.
