This post summarizes findings and recommendations from the Library’s Ithaka S+R Local Report, “Supporting Big Data Research at the University of California, Berkeley” released on October 1, 2021. The research was conducted and the report written by Erin D. Foster, Research Data Management Program Service Lead, Research IT & University of California, Berkeley (UCB) Library, Ann Glusker, Sociology, Demography, Public Policy, & Quantitative Research Librarian, UCB Library, and Brian Quigley, Head of the Engineering & Physical Sciences Division, UCB Library.
In 2020, the Ithaka S+R project “Supporting Big Data Research” brought together twenty-one U.S. institutions to conduct a suite of parallel studies aimed at understanding researcher practices and needs related to data science methodologies and big data research. A team from the UCB Library conducted and analyzed interviews with a group of researchers at UC Berkeley. The timeline appears below. The UC Berkeley team’s report outlines the findings from the interviews with UC Berkeley researchers and makes recommendations for potential campus and library opportunities to support big data research. In addition to the UCB local report, Ithaka S+R will be releasing a capstone report later this year that will synthesize findings from all of the parallel studies to provide an overall perspective on evolving big data research practices and challenges to inform emerging services and support across the country.
After successfully completing human subjects review, and using an interview protocol and training provided by Ithaka S+R, the team members recruited and interviewed 16 researchers from across ranks and disciplines whose research involved big data, defined as data having at least two of the following: volume, variety, and velocity.
After transcribing the interviews and coding them using an open coding process, six themes emerged. These themes and sub-themes are listed below and treated fully in the final report. The report includes a number of quotes so that readers can “hear” the voices of Berkeley’s big data researchers most directly. In addition, the report outlines the challenges reported by researchers within each theme.
The most important part of the entire research process was developing a list of recommendations for the UC Berkeley Library and its campus partners. Based on the needs and challenges expressed by researchers, and influenced by our own sense of the campus data landscape including the newly formed Library Data Services Program, these recommendations are discussed in more detail in the full report. They reflect the two main challenges that interviewees reported Berkeley faces as big data research becomes increasingly common. One challenge is that the range of discrete data operations happening all over campus, not always broadly promoted, means that it is easy to have duplications of services and resources — and silos. The other (related) challenge is that Berkeley has a distinctive data landscape and a long history of smaller units on campus being at the cutting edge of data activities. How can these be better integrated while maintaining their individuality and freedom of movement? Additionally, funding is a perennial issue, given the fact that Berkeley is a public institution in an area with a high cost of living and a very competitive salary structure for tech workers who provide data supports.
Here are the report’s recommendations in brief:
Create a research-welcoming “third place” to encourage and support data cultures and communities.
The creation of a “data culture” on campus, which can infuse everything from communications to curricula, can address challenges related to navigating the big data landscape at Berkeley, including collaboration/interdisciplinarity, and the gap between data science and domain knowledge. One way to operationalize this idea is to utilize the concept of the “third place,” first outlined by Ray Oldenburg. This can happen in, but should not be limited to, the library, and it can occur in both physical and virtual spaces. Encouraging open exploration and conversation across silos, disciplines, and hierarchies is the goal, and centering Justice, Diversity, Equity and Inclusion (JEDI) as a core principle is essential.
- The University Library, in partnership with Research IT, conducts continuous inquiry and assessment of researchers and data professionals, to be sure our efforts address the in-the-moment needs of researchers and research teams.
- The University Library, in line with being a “third place” for conversation and knowledge sharing, and in partnership with a range of campus entities, sponsors programs to encourage cross-disciplinary engagement.
- Research IT and other campus units institute a process to explore resource sharing possibilities across teams of researchers in order to address duplication and improve efficiency.
- The University Library partners with the Division of Computing, Data Science, and Society (CDSS) to explore possibilities for data-dedicated physical and virtual spaces to support interdisciplinary data science collaboration and consultation.
- A consortium of campus entities develops a data policy/mission statement, which has as its central value an explicit justice, equity, diversity and inclusion (JEDI) focus/requirement.
Enhance the campus computing and data storage infrastructure to support the work of big data researchers across all disciplines and funding levels.
Researchers expressed gratitude for campus computing resources but also noted challenges with bandwidth, computing power, access, and cost. Others seemed unaware of the full extent of resources that were available to them. It is important to ensure that our computing and storage options meet researcher needs and then encourage them to leverage those resources.
- Research, Teaching & Learning and the University Library partner with Information Services & Technology (IST) to conduct further research and benchmarking in order to develop baseline levels of free data storage and computing access for all campus researchers.
- Research IT and the University Library work with campus to develop further incentives for funded researchers to participate in the Condo Cluster Program for Savio and/or the Secure Research Data & Computing (SRDC) platform.
- The University Library and Research IT partner to develop and promote streamlined, clear, and cost-effective workflows for storing, sharing, and moving big data.
Strengthen communication of research data and computing services to the campus community.
In the interviews, researchers directly or indirectly expressed a lack of knowledge about campus services, particularly as they related to research data and computing. In light of that, it is important for campus service providers to continuously assess how researchers are made aware of the services available to them.
- The University Library partners with Research IT to establish a process to reach new faculty across disciplines about campus data and compute resources.
- The University Library partners with Research IT and CDSS (including D-Lab and BIDS) to develop a promotional campaign and outreach model to increase awareness of the campus computing infrastructure and consulting services.
- The University Library develops a unified and targeted communication method for providing campus researchers with information about campus data resources – big data and otherwise.
Coordinate and develop training programs to support researchers in “keeping up with keeping up”
One of the most-cited challenges researchers stated in terms of training is that of keeping up with the dizzying pace of advances in the field of big data, which necessitate learning new methods and tools. Even with postdoc/grad student contributions, it can seem impossible to stay up to date with needed skills and techniques. Accordingly, the focus in this area should be to help researchers to keep up with staying current in their fields.
- The University Library addresses librarians’/library staff needs for professional development to increase comfort with the concepts of and program implementation around the research life cycle and big data.
- The University Library’s newly formed Library Data Services Program (LDSP) is well-positioned to offer campus-wide training sessions within the Program’s defined scope, and to serve as a hub for coordination of a holistic and scaffolded campus-wide training program
- The University Library’s LDSP, departmental liaisons, and other campus entities offering data-related training should specifically target graduate students and postdocs for research support.
- CDSS and other campus entities investigate the possibility of a certificate training program — targeted at faculty, postdocs, graduate students — leading to knowledge of the foundations of data science and machine learning, and competencies in working with those methodologies.
The full report concludes with a quote from one of the researchers interviewed, which we team members feel encapsulates much of the current situation relating to big data research at Berkeley, as well as the challenges and opportunities ahead:
[Physical sciences & engineering researcher] “The tsunami is coming. I sound like a crazy person heaping warning, but that’s the future. I’m sure we’ll adapt as this technology becomes more refined, cheaper… Big data is the way of the future. The question is, where in that spectrum do we as folks at Berkeley want to be? Do we want to be where the consumers are or do we want to be where the researchers should be? Which is basically several steps ahead of where what is more or less the gold standard. That’s a good question to contemplate in all of these discussions.
Do we want to be able to meet the bare minimum complying with big data capabilities? Or do we want to make sure that big data is not an issue? Because the thing is that it’s thrown around in the context that big data is a problem, a buzzword. But how do we at Berkeley make that a non-buzzword?
Big data should be just a way of life. How do we get to that point?”
The University Library, Research IT, and Berkeley Institute for Data Science will host a series of events on February 12th-16th during the Love Data Week 2018. Love Data Week a nationwide campaign designed to raise awareness about data visualization, management, sharing, and preservation.
Please join us to learn about multiple data services that the campus provides and discover options for managing and publishing your data. Graduate students, researchers, librarians and data specialists are invited to attend these events to gain hands-on experience, learn about resources, and engage in discussion around researchers’ data needs at different stages in their research process.
To register for these events and find out more, please visit: http://guides.lib.berkeley.edu/ldw2018guide
Intro to Scopus APIs – Learn about working with APIs and how to use the Scopus APIs for text mining.
01:00 – 03:00 p.m., Tuesday, February 13, Doe Library, Room 190 (BIDS)
Refreshments will be provided.
Data stories and Visualization Panel – Learn how data is being used in creative and compelling ways to tell stories. Researchers across disciplines will talk about their successes and failures in dealing with data.
1:00 – 02:45 p.m., Wednesday, February 14, Doe Library, Room 190 (BIDS)
Refreshments will be provided.
Planning for & Publishing your Research Data – Learn why and how to manage and publish your research data as well as how to prepare a data management plan for your research project.
02:00 – 03:00 p.m., Thursday, February 15, Doe Library, Room 190 (BIDS)
Hope to see you there!
Love Your Data (LYD) Week is a nationwide campaign designed to raise awareness about research data management, sharing, and preservation. In UC Berkeley, the University Library and the Research Data Management program will host a set of events that will be held from February 13th-17th to encourage and teach researchers how to manage, secure, publish, and license their data. Presenters will describe groundbreaking research on sensitive or restricted data and explore services needed to unlock the research potential of restricted data.
Graduate students, researchers, librarians and data specialists are invited to attend these events and learn multiple data services that the campus provides.
To register for these events and find out more, please visit our LYD Week 2017 guide:
- Securing Research Data – Explore services needed to unlock the research potential of restricted data.
11:00 am-12:00 pm, Tuesday, February 14, Doe Library, Room 190 (BIDS)
For more background on the Securing Research Data project, please see this RIT News article.
- RDM Tools & Tips: Box and Drive – Learn the best practices for using Box and bDrive to manage documents, files, and other digital assets.
10:30 am-11:45 am, Wednesday, February 15, Doe Library, Room 190 (BIDS)
Refreshments are provided by the UC Berkeley Library.
- Research Data Publishing and Licensing – This workshop covers why and how to publish and license your research data.
11:00 am-12:00 pm, Thursday, February 16, Doe Library, Room 190 (BIDS)
The presenters will share practical tips, resources, and stories to help researchers at different stages in their research process.
Software is as important as data when it comes to building upon existing scholarship. However, while there has been a small amount of research into how researchers find, adopt, and credit software, there is currently a lack of empirical data on how researchers use, share, and value software and computer code.
The UC Berkeley Library and the California Digital Library are investigating researchers perceptions, values, and behaviors around the software generated as part of the research process. If you are a researcher, we would appreciate if you could help us understand your current practices related to software and code by spending 10-15 minutes to complete our survey. We are aiming to collect responses from researchers across different disciplines. The answers of the survey will be collected anonymously.
Results from this survey will be used in the development of services to encourage and support the sharing of research software and to ensure the integrity and reproducibility of scholarly activity.
Take the survey now:
Last week, one of my teammates, at Old Dominion University, contacted me and asked if she could apply some of the techniques I adopted in the first paper I published during my Ph.D. She asked about the data and any scripts I had used to pre-process the data and implement the analysis. I directed her to where the data was saved along with a detailed explanation of the structure of the directories. It took me awhile to remember where I had saved the data and the scripts I had written for the analysis. At the time, I did not know about data management and the best practices to document my research.
I shared the scripts I generated for pre-processing the data with my colleague, but the information I gave her did not cover all the details regarding my workflow. There were many steps I had done manually for producing the input and the output to and from the pre-processing scripts. Luckily I had generated a separate document that had the steps of the experiments I conducted to generate the graphs and tables in the paper. The document contained details of the research process in the paper along with a clear explanation for the input and the output of each step. When we submit a scientific paper, we get reviews back after a couple of months. That was why I documented everything I had done, so that I could easily regenerate any aspect of my paper if I needed to make any future updates.
Documenting the workflow and the data of my research paper during the active phase of the research saved me the trouble of trying to remember all the steps I had taken if I needed to make future updates to my research paper. Now my colleague has all the entities of my first research paper: the dataset, the output paper of my research, the scripts that generated this output, and the workflow of the research process (i.e., the steps that were required to produce this output). She can now repeat the pre-processing for the data using my code in a few minutes.
Funding agencies have data management planning and data sharing mandates. Although this is important to scientific endeavors and research transparency, following good practices in managing research data and documenting the workflow of the research process is just as important. Reproducing the research is not only about storing data. It is also about the best practices to organize this data and document the experimental steps so that the data can be easily re-used and the research can be reproduced. Documenting the directory structure of the data in a file and attaching this file to the experiment directory would have saved me a lot of time. Furthermore, having a clear guidance for the workflow and documentation on how the code was built and run is an important step to making the research reproducible.
While I was working on my paper, I adopted multiple well known techniques and algorithms for pre-processing the data. Unfortunately, I could not find any source codes that implemented them so I had to write new scripts for old techniques and algorithms. To advance the scientific research, researchers should be able to efficiently build upon past research and it should not be difficult for them to apply the basic tenets of scientific methods. My teammate is not supposed to re-implement the algorithms and the techniques I adopted in my research paper. It is time to change the culture of scientific computing to sustain and ensure the integrity of reproducibility.
On October 26-28, I had the honor of attending the Library Leaders Forum 2016, which was held at the Internet Archive (IA). This year’s meeting was geared towards envisioning the library of 2020. October 26th was also IA’s 20th anniversary. I joined my Web Science and Digital Libraries (WS-DL) Research Group in celebrating IA’s 20 years of preservation by contributing a blog post with my own personal story, which highlights a side of the importance of Web preservation for the Egyptian Revolution. More personal stories about Web archiving exist on WS-DL blog.
In the Great room at the Internet Archive Brewster Kahle, the Internet Archive’s Founder, kicked off the first day by welcoming the attendees. He began by highlighting the importance of openness, sharing, and collaboration for the next generation. During his speech he raised an important question, “How do we support datasets, the software that come with it, and open access materials?” According to Kahle, the advancement of digital libraries requires collaboration.
After Brewster Kahle’s brief introduction, Wendy Hanamura, the Internet Archive’s Director of Partnership, highlighted parts of the schedule and presented the rules of engagement and communication:
- The rule of 1 – Ask one question answer one question.
- The rule of n – If you are in a group of n people, speak 1/n of the time.
Before giving the microphone to the attendees for their introductions, Hanamura gave a piece of advice, “be honest and bold and take risks“. She then informed the audience that “The Golden Floppy” award shall be given to the attendees who would share bold or honest statements.
Next was our chance to get to know each other through self-introductions. We were supposed to talk about who we are, where we are from and finally, what we want from this meeting or from life itself. The challenge was to do this in four words.
After the introductions, Sylvain Belanger, the Director of Preservation of Library and Archives in Canada, talked about where his organization will be heading in 2020. He mentioned the physical side of the work they do in Canada to show the challenges they experience. They store, preserve, and circulate over 20 million books, 3 million maps, 90,000 films, and 500 sheets of music.
“We cannot do this alone!” Belanger exclaimed. He emphasized how important a partnership is to advance the library field. He mentioned that the Library and Archives in Canada is looking to enhance preservation and access as well as looking for partnerships. They would also like to introduce the idea of innovation into the mindset of their employees. According to Belanger, the Archives’ vision for the year 2020 includes consolidating their expertise as much as they can and also getting to know how do people do their work for digitization and Web archiving.
After the Belanger’s talk, we split up into groups of three to meet other people we didn’t know so that we could exchange knowledge about what we do and where we came from. Then the groups of two will join to form a group of six that will exchange their visions, challenges, and opportunities. Most of the attendees agreed on the need for growth and accessibility of digitized materials. Some of the challenges were funding, ego, power, culture, etc.
— Yasmina Anwar (@yasmina_anwar) October 27, 2016
Chris Edward, the Head of Digital Services at the Getty Research Institute, talked about what they are doing, where they are going, and the impact of their partnership with the IA. Edward mentioned that the uploads by the IA are harvested by HathiTrust and the Defense Logistics Agency (DLA). This allows them to distribute their materials. Their vision for 2020 is to continue working with the IA and expanding the Getty research portal, and digitize everything they have and make it available for everyone, anywhere, all the time. They also intend on automating metadata generation (OCR, image recognition, object recognition, etc.), making archival collections accessible, and doing 3D digitization of architectural models. They will then join forces with the International Image Interoperability Framework (IIIF) community to develop the capability to represent these objects. He also added that they want to help the people who do not have the ability to do it on their own.
After lunch, Wendy Hanamura walked us quickly through the Archive’s strategic plan for 2015-2020 and IA’s tools and projects. Some of these plans are:
- Next generation Wayback Machine
- Test pilot with Mozilla so they suggest archived pages for the 404
- Wikimedia link rots
- Building libraries together
- The 20 million books
- Table top scribe
- Open library and discovery tool
- Digitization supercenter
- Collaborative circulation system
- Television Archive — Political ads
- Software and emulation
- Proprietary code
- Scientific data and Journals – Sharing data
- Music — 78’s
“No book should be digitized twice!”, this is how Wendy Hanamura ended her talk.
Then we had a chance to put our hands on the new tools by the IA and by their partners through having multiple makers’ space stations. There were plenty of interesting projects, but I focused on the International Research Data Commons– by Karissa McKelvey and Max Ogden from the Dat Project. Dat is a grant-funded project, which introduces open source tools to manage, share, publish, browse, and download research datasets. Dat supports peer-to-peer distribution system, (e.g., BitTorrent). Ogden mentioned that their goal is to generate a tool for data management that is as easy as Dropbox and also has a versioning control system like GIT.
After a break Jeffrey Mackie-Mason, the University Librarian of UC Berkeley, interviewed Brewster Kahle about the future of libraries and online knowledge. The discussion focused on many interesting issues, such as copyrights, digitization, prioritization of archiving materials, cost of preservation, avoiding duplication, accessibility and scale, IA’s plans to improve the Wayback Machine and many other important issues related to digitization and preservation. At the end of the interview, Kahle announced his white paper, which wrote entitled “Transforming Our Libraries into Digital Libraries”, and solicited feedback and suggestions from the audience.
— Yasmina Anwar (@yasmina_anwar) October 27, 2016
— Merrilee Proffitt (@MerrileeIAm) October 27, 2016
— Yasmina Anwar (@yasmina_anwar) October 27, 2016
— Merrilee Proffitt (@MerrileeIAm) October 27, 2016
— Mouse Reeve (@tripofmice) October 27, 2016
— Mouse Reeve (@tripofmice) October 27, 2016
— Dr.EB (@LNBel) October 27, 2016
At the end of the day, we had an unusual and creative group photo by the great photographer Brad Shirakawa who climbed out on a narrow plank high above the crowd to take our picture.
On day two the first session I attended was a keynote address by Brewster Kahle about his vision for the Internet Archive’s Library of 2020, and what that might mean for all libraries.
— Yasmina Anwar (@yasmina_anwar) October 28, 2016
Heather Christenson, the Program Officer for HathiTrust, talked about where HeathiTrust is heading in 2020. Christenson started by briefly explaining what is HathiTrust and why HathiTrust is important for libraries. Christenson said that HathiTrust’s primary mission is preserving for print and digital collections, improving discovery and access through offering text search and bibliographic data APIs, and generating a comprehensive collection of the US federal documents. Christensen mentioned that they did a survey about their membership and found that people want them to focus on books, videos, and text materials.
Our next session was a panel discussion about the Legal Strategies Practices for libraries by Michelle Wu, the Associate Dean for Library Services and Professor of Law at the Georgetown University Law Center, and Lila Bailey, the Internet Archive’s Outside Legal Counsel. Both speakers shared real-world examples and practices. They mentioned that the law has never been clearer and it has not been safer about digitizing, but the question is about access. They advised the libraries to know the practical steps before going to the institutional council. “Do your homework before you go. Show the usefulness of your work, and have a plan for why you will digitize, how you will distribute, and what you will do with the takedown request.”
After the panel Tom Rieger, the Manager of Digitization Services Section at the Library of Congress (LOC), discussed the 2020 vision for the Library of Congress. Reiger spoke of the LOC’s 2020 strategic plan. He mentioned that their primary mission is to serve the members of Congress, the people in the USA, and the researchers all over the world by providing access to collections and information that can assist them in decision making. To achieve their mission the LOC plans to collect and preserve the born digital materials and provide access to these materials, as well as providing services to people for accessing these materials. They will also migrate all the formats to an easily manageable system and will actively engage in collaboration with many different institutions to empowering the library system, and adapt new methods for fulfilling their mission.
In the evening, there were different workshops about tools and APIs that IA and their partners provided. I was interested in the RDM workshop by Max Ogden and Roger Macdonald. I wanted to explore the ways we can support and integrate this project into the UC Berkeley system. I gained more information about how the DAT project worked through live demo by Ogden. We also learned about the partnership between the Dat Project and the Internet Archive to start storing scientific data and journals at scale.
We then formed into small groups around different topics on our field to discuss what challenges we face and generate a roadmap for the future. I joined the “Long-Term Storage for Research Data Management” group to discuss what the challenges and visions of storing research data and what should libraries and archives do to make research data more useful. We started by introducing ourselves. We had Jefferson Bailey from the Internet Archive, Max Ogden, Karissa from the DAT project, Drew Winget from Stanford libraries, Polina Ilieva from the University of California San Francisco (UCSF), and myself, Yasmin AlNoamany.
Some of the issues and big-picture questions that were addressed during our meeting:
- The long-term storage for the data and what preservation means to researchers.
- What is the threshold for reproducibility?
- What do researchers think about preservation? Does it mean 5 years, 15 years, etc.?
- What is considered as a dataset? Harvard considers anything/any file that can be interpreted as a dataset.
- Do librarians have to understand the data to be able to preserve it?
- What is the difference between storage and preservation? Data can be stored, but long-term preservation needs metadata.
- Do we have to preserve everything? If we open it to the public to deposit their huge datasets, this may result in noise. For the huge datasets what should be preserved and what should not?
- Privacy and legal issues about the data.
Principles of solutions
- We need to teach researchers how to generate metadata and the metadata should be simple and standardized.
- Everything that is related to research reproducibility is important to be preserved.
- Assigning DOIs to datasets is important.
- Secondary research – taking two datasets and combine them to produce something new. In digital humanities, many researchers use old datasets.
- There is a need to fix the 404 links for datasets.
- There is should be an easy way to share data between different institutions.
- Archives should have rules for the metadata that describe the dataset the researchers share.
- The network should be neutral.
- Everyone should be able to host a data.
- Versioning is important.
Notes from the other Listening posts:
- LIBRARY 2020: Refining the vision, mapping the collection, and identifying the contributors
- WEB ARCHIVING: What are the opportunities for collaborative technology building?
- DIGITIZATION: Scanning services–develop a list of key improvements and innovations you desire
- DISCOVERY: Open Library– ideas for moving forward
At the end of the day, Polina Ilieva, the Head of Archives and Special Collections at UCSF, wrapped up the meeting by giving her insight and advice. She mentioned that for accomplishing their 2020 goals and vision, there is a need to collaborate and work together. Ilieva said that the collections should be available and accessible for researchers and everyone, but there is a challenge of assessing who is using these collections and how to quantify the benefits of making these collections available. She announced that they would donate all their microfilms to the Internet Archive! “Let us all work together to build a digital library, serve users, and attract consumers. Library is not only the engine for search, but also an engine for change, let us move forward!” This is how Ilieva ended her speech.
It was an amazing experience to hear about the 2020 vision of the libraries and be among all of the esteemed library leaders I have met. I returned with inspiration and enthusiasm for being a part of this mission and also ideas for collaboration to advance the library mission and serve more people.
Every time you download a spreadsheet, use a piece of someone else’s code, share a video, or take photos for a project, you’re working with data. When you are producing, accessing, or sharing data in order to answer a research question, you’re working with research data, and Berkeley has a service that can help you.
Research Data Management at Berkeley is a service that supports researchers in every discipline as they find, generate, store, share, and archive their data. The program addresses current and emerging data management issues, compliance with policy requirements imposed by funders and by the University, and reduction of risk associated with the challenges of data stewardship.
In September 2015, the program launched the RDM Consulting Service, staffed by dedicated consultants with expertise in key aspects of managing research data. The RDM Consulting Service coordinates closely with consulting services in Research IT, the Library, and other researcher-facing support organizations on the campus. Contact a consultant at firstname.lastname@example.org.
The RDM program also developed an online resource guide. The Guide documents existing services, providing context and use cases from a research perspective. In the rapidly changing landscape of federal funding requirements, archiving tools, electronic lab notebooks, and data repositories, the Guide offers information that directly addresses the needs of researchers at Berkeley. The RDM Guide is available at researchdata.berkeley.edu.
Research Data Management Service Design Analyst
Contact me at wittenberg[@]berkeley.edu