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.
UCSF Archives and Special Collections makes data available from a number of our digital collections. Researchers will find information in the guide about 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 to learning resources about various data analysis methods used to work with archives as data.
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 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.
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 email@example.com 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.
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.
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!
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.
Please contact DHH Program Coordinator, Kathryn Stine, at firstname.lastname@example.org. 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.
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.