UCSF Archives & Special Collections includes numerous digitized collections documenting health sciences topics ranging from institutional, community, and individual response to illness and disease to industry impacts on public health. We make many of these collections available as data that can be computationally analyzed for health sciences and humanities research.
Voyant Cirrus term frequency visualization generated from AIDS health crisis workshops file data, 1986 from the UCSF AIDS Health Project Records, UCSF Archives & Special Collections (data available in the No More Silence dataset).
If you are curious about working with data from the UCSF Archives and Special Collections, the Digital Health Humanities (DHH) pilot program will showcase our “archives as data” throughout the month. In two upcoming sessions, we’ll provide an orientation to available data as well as methods for finding, accessing, and exploring these data resources:
DHH programming also continues to partner with the Data Science Institute (DSI) to offer workshops on tools and methods well-suited to conducting research with “archives as data.” March workshops in the DSI Python for Data Analysis series will dig in to text analysis using natural language processing and building machine learning models:
Through these workshops and selected companion follow-up sessions with troubleshooting and guided process walkthroughs, researchers can learn and practice data analysis techniques and get familiar with data from our collections. Check out the library’s events calendar to find and register for the latest offerings!
OpenRefine workshops
If you have data you’d like to work with but it needs tidying and preparation attend a DSI OpenRefine workshop. This workshop will cover techniques for cleaning structured data, no programming required! There will be two OpenRefine sessions this month:
OpenRefine for Archives as Data, Wednesday, March 8, 12 – 1:30 p.m. PT (This is a DHH companion session to the Cleaning Spreadsheet Data with OpenRefine DSI workshop and all are welcome.)
Previously-held DHH session slides, linked resources, and recordings are available on the CLE. There you will find materials from a Digital Health Humanities Overview session and recorded walkthroughs for Unix, Python, and Jupyter notebooks basics. Related resources will be updated on the CLE following DHH sessions.
Questions?
Please contact DHH Program Coordinator, Kathryn Stine, at kathryn.stine@ucsf.edu. The UCSF Digital Health Humanities Pilot is funded by the Academic Senate Chancellor’s Fund via the Committee on Library and Scholarly Communication.
We are excited to launch digital health humanities pilot programming starting January 2023! Digital health humanities (DHH) is an emerging discipline that utilizes digital methods and resources to explore research questions investigating the human experience around health and illness. The Digital Health Humanities Pilot (DHHP) will facilitate new insights into historical health data. Participants will learn how to evaluate and integrate digital methods and “archives as data” into their research through a range of offerings and trainings.
Participants at the first workshop for the No More Silence project, a precursor to digital health humanities pilot programming
The programming from this pilot will bring a humanistic context to understanding institutional, personal and community responses to health issues, as well as social, cultural, political and economic impacts on individual and public health. The DHHP will offer researchers from all disciplines (including faculty, staff, and other learners) tailored workshops, classes, and skill-building sessions. Workshops will encourage the use of “archives as data” and utilize datasets from holdings within the UCSF Archives and Special Collections (including the AIDS History Project and Industry Documents Library, among others). Additionally, in spring 2023 we will be hosting the Digital Health Humanities Symposium. The symposium will provide space to consider theoretical issues central to this emerging field and highlight digital health humanities projects. More information on the symposium will be shared soon.
The UCSF Digital Health Humanities Pilot is funded by the Academic Senate Chancellor’s Fund via the Committee on Library and Scholarly Communication.
Register for an upcoming Digital Health Humanities overview session
This session will include an orientation led by Digital Health Humanities Program Coordinator, Kathryn Stine and Digital Archivist, Charlie Macquarie. We will discuss various approaches in DHH research, including getting familiar with data analysis and programming skills, and will share an overview of the UCSF Library’s archival collections data available for research.
For questions about digital health humanities at UCSF, please contact Digital Health Humanities Program Coordinator, Kathryn Stine at kathryn.stine@ucsf.edu.
The Data Science Initiative (DSI) is offering workshops in the coming months to support researchers interested in implementing DHH approaches. Follow-up sessions will be available for researchers to reinforce and contextualize programming foundations in practical application. Check out the upcoming sessions:
We invite you to check out the library’s events and classes calendar for upcoming DHHP (and related DSI) programming. If you are unable to attend any of the sessions listed above, we advise referring to the DSI Collaborative Learning Environment (CLE) (accessible with MyAccess credentials) for recordings and resources.
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!
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!
Please join us in giving a warm welcome to our two newest summer interns, May Yuan and Lianne de Leon!
May 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 May will be working (remotely) with the UCSF Industry Documents Library (IDL), and we are grateful to SFUSD and its partners for sponsoring these internships.
May and Lianne will be working on several collection description projects with IDL this summer, including correcting and enhancing document metadata, and creating descriptions for audio-visual materials. They have provided their introductions below.
My name is May Yuan and I’m a junior at Raoul Wallenberg Traditional High School. During my free time, I enjoy reading, learning and trying new things, and helping others academically. I’m super excited to work here at the UCSF IDL to help provide valuable information to the public as well as learn more about the various documents, lawsuits, etc. myself; I also hope to enhance my productivity and organization skills during my time working here as these skills are crucial to college and everyday life in general. The career paths I’m interested in are bioengineering (bioinformatics/biostatistics), law, and finance.
IDL Summer Intern May Yuan
Hi, my name is Lianne R. de Leon. I am a part of the Class of 2023 at Phillip and Sala Burton High School. In the past, I have worked on VEX EDR Robotics competition in 2018-2019. In my spare time I enjoy trying new foods and yoga. I aspire to become a computer hardware engineer and to travel across the entirety of Asia. I look forward to meeting and working with you all.
Please join us in giving a warm welcome to Khushi Bhat, who will be conducting a remote internship with the UCSF Industry Documents Library (IDL) this summer.
Khushi is currently a rising senior at Rutgers University where she is majoring in Biotechnology and minoring in Computer Science. This summer, she is working in the Industry Documents Library researching tools and methods to extract geographic locations from a collection of documents related to the tobacco industry’s influence in public policy.
Khushi will be conducting an independent course project to help the IDL team enhance descriptive metadata for our industry documents collections. We have long been aware of a research need to be able to filter documents by geographic location. Tobacco control researchers and other public health experts at UCSF and around the world use the documents in the Industry Documents Library to understand how corporations impact public health. This research is often used to inform policymakers who write laws and policies regulating the sale and use of products such as tobacco. Researchers and policymakers need information which relates to their local area such as their city, county, state, or country.
Geographic location is not currently included in IDL’s document-level metadata, and since IDL contains more than 15 million documents it is not feasible to manually catalog this information.
Khushi’s work will focus on researching Natural Language Processing (NLP) and Named Entity Recognition (NER) text analysis methods. She will investigate available tools which have the potential to automatically identify and label geographic information in text. Khushi’s research, recommendations, and pilot testing will help the IDL team outline workflows and strategies for enhancing our document metadata to include geographic information.
Khushi aspires to pursue a career in bioinformatics in the future and intends on pursuing higher education in this field upon graduation. In her spare time, Khushi enjoys dancing, baking, and hiking. Prior to joining Rutgers, she was an avid Taekwondo practitioner (and has a 2nd degree black belt to show for it!)
The conference was convened to celebrate a decade of digitizing and making available medical history resources. Keynote speaker Dr. Jaipreet Virdi, Assistant Professor for the Department of History at the University of Delaware, presented her work on Digitized Disability Histories. She discussed disability identity as represented through material objects of disability, and examined how disability history is separate from medical history.
The program also included fascinating talks from nine other speakers, ranging from the rhetoric used in early 20th century motherhood manuals to medicalize infant care and degrade traditional knowledge, to using convolutional neural networks (CNN) to identify and label objects in historical images in order to visualize thematic collections at scale, to studying the historical lessons from popular culture and medical discourse of face masks during the 1918-1919 Flu epidemic.
All talks were recorded and are being made available with captioning on the Medical Heritage Library YouTube channel (see Session 2 for the “Smoke on Screens” talk).
UCSF Archives & Special Collections is a contributing partner to the Medical Heritage Library. In 2015-2017 A&SC collaborated with four other medical libraries to digitize and make publicly accessible state medical journals, funded by a $275,000 National Endowment for the Humanities (NEH) grant. 97 journal titles were digitized (nearly every state medical journal in the U.S.) resulting in over 2.7 million full-text searchable pages.
The Industry Documents Library has contributed over 5,000 video recordings to the MHL, beginning in 2012. These videos are part of our Truth Tobacco Industry Documents collection and include recordings of cigarette commercials, marketing focus groups, internal corporate meetings and trainings, depositions of tobacco company employees, and congressional hearings. The recordings document the industry’s marketing and public relations strategies to cast doubt on the harms of smoking and to prevent or delay public health regulations.
Screenshot of 1960 Flintstones TV commercial for Winston cigarettes, available in the Industry Documents Library collection of the Medical Heritage Library on the Internet Archive: https://archive.org/details/tobacco_djq03d00
Image of a grocery store aisle with packaged foods.
The UCSF Archives and Special Collections and Industry Documents Library (IDL) are pleased to announce the launch of the Food Industry Documents Archive, a brand new collection of over 30,000 documents related to the food industry and its impact on public health. These documents, available online for the first time, highlight marketing, research, and policy strategies used by food companies and trade groups, and reveal the communications and connections between industry, academic, and regulatory organizations.
The Food Industry Documents Archive was created in collaboration with the UCSF Philip R. Lee Institute for Health Policy Studies and officially unveiled during the inaugural symposium on November 15, 2018. A full recording of the symposium can be viewed here.
The Food Industry Documents were digitized and made available online through partnerships with other libraries, archives, and related organizations, bringing together historical and contemporary materials to support inquiry into long-standing industry practices.
Topics include the Sugar Research Foundation, the International Sugar Research Foundation, the Sugar Institute, cane sugar and beet sugar production, sugar-sweetened beverages, sugared snack foods advertised to children, the U.S. Public Health Service, and the National Research Council and Food and Nutrition Board.
These documents have been used as the source for a number of publications including:
The Food Industry Documents Archive collection joins the existing Tobacco, Drug, and Chemical Industry Documents collections, allowing users to search across industries and identify common tactics used to sway scientific research, shape public opinion, and influence policies and regulations meant to protect public health.
Parnassus Library, 5th Floor, Lange Room: Join us to celebrate 20 years since the signing of the Master Settlement Agreement and the creation of the UCSF Center for Tobacco Control Research and Education and the UCSF Industry Documents Library!
In November 1998 the 5 largest cigarette manufacturers signed the Master Settlement Agreement (MSA) with 46 U.S. states and 6 U.S. jurisdictions. This was the largest civil litigation settlement in U.S. history.
The MSA imposed restrictions on the sale and marketing of cigarettes, especially to youth, and required hundreds of billions of dollar in payments to the states in perpetuity to partially compensate them for the Medicaid costs smoking causes. It also created the American Legacy Foundation (now known as the Truth Initiative) which funded the creation of the UCSF Center for Tobacco Control Research and Education and the Industry Documents Library.
This event is open to the public. Cake and beverages will be provided while supply lasts. RSVP using the link at the top of the post.
This event is co-organized by the UCSF Industry Documents Library and the Center for Tobacco Control Research and Education.
