1. Getting Started with the Texas Data Repository and Data Competencies
Laying the Foundation for Research Data Services: Session 1

2. Today’s speakers Kristi Park Santi Thompson Peace Williamson
Executive Director, Texas Digital Library
Santi Thompson
Head of Digital Research Services, University of Houston Libraries
Peace Williamson
Director for Research Data Services, University of Texas at Arlington Libraries
I’m joined today by two other speakers: Santi Thompson, the head of digital research services at the University of Houston Libraries. Santi was also the chair of the dataverse implementation working group which worked with TDL to launch the Texas Data Repository.
Secondly, we’re pleased to welcome Peace Ossom Williamson, the Director for Research Data Services at another TDL member institution, the UT Arlington Libraries.

3. The Texas Digital Library is a consortium of Texas higher education institutions that builds capacity for preserving, managing, and providing access to unique digital collections of enduring value.
In the fall of 2016, after several years of evaluation and implementation work, the TDL launched a new service called the Texas Data Repository, intended for sharing, archiving, and managing research data produced by higher education institutions in Texas.
This Texas Data repository (or TDR) service fits well within the TDL’s stated mission of building capacity for preserving managing, and providing access to unique digital collections of enduring value.
And currently, we are working actively with 7 members to offer the TDR service on their campuses.

4. Member Priorities for Research Data Management
Outcome of structured discussion at TDL Data Symposium, November 2016:
Offer more tools and marketing materials for faculty outreach
Assess the impact of data reuse and repository functionality
More training for librarians
Support for large datasets
Refine system functionality and documentation over time
Present on/promote the repository at disciplinary conferences (not just library conferences)
In November of 2016, as part of the soft launch of the TDR service, we held the first TDL Data Symposium at Baylor Libraries in Waco. And one fo the things we did there was hold a facilitated, structured discussion with those who were attending, focused on identifying and prioritizing needs related to research data management.
The outcome of that discussion was this list of priorities, and we’re using these to guide our efforts over the next year as we onboard members using the service and promote it to new users.
You can see here that two of the identified priorities revolve around more training and tools for promotion of the TDR and research data services more generally. This idea for this webinar series, grew out internal discussions at TDL and with Peace, about how we could begin to meet these needs with our members -- needs for more education, more tools, and training that will equip our members to confidently promote these services on their campus, and to confidently discuss research data management issues with faculty, offices of sponsored research, and others on their campuses.

5. About this series
In “Laying the Foundation for Research Data Services,” we will:
Discuss the Texas Data Repository’s role within the context of research data infrastructure at Texas universities.
Introduce the Data Information Literacy (DIL) model and illustrate its adaptation and implementation at one TDL member institution as a framework for building a data services program.
Equip librarians with more information on how to communicate data services to their campus communities.
This is the first in a series of 3 webinars that we’re holding over the next few weeks on “Laying the foundation for Research Data Services.”
The series is part of our efforts to educate our membership about the TDR and to situate that service within the larger context of research data infrastructure and services at Texas universities.
But we’re also going to talk about the Data Information Literacy (DIL) model and illustrate how one TDL member -- UT Arlington -- adapted and implemented this model as a framework for building data education programs that are a foundation for their library’s research data services.
And in the concluding webinar, we’ll be taking those competencies and demonstrate how you might teach the competencies using the Texas Data Repository, as a way of bringing it all together, and as an example that other members might replicate in their local outreach efforts.
So, overall, through this series of discussions, we hope to continue TDL’s work of equipping librarians with more information and training in how to communicate data services to their campus communities, and how TDR can help them do that.

6. TDL Services Manager: Courtney Mumma
Started February 1st
Managing and promoting digital preservation and research data services including the Texas Data Repository
I also want to mention that another step we have taken is to fill a new position at TDL -- which we’re calling “services manager” -- that will be dedicated to managing and promoting both our preservation services and research data services within our membership, including the Texas Data Repository.
Courtney Mumma, who has worked previously with Internet Archive and before that with Artefactual, started in that role on February 1, and will be taking on a substantial role in continuing the training, consulting, and community-building around the Texas Data Repository going forward. She is on this call and has spent the last two weeks absorbing a lot of information about TDL and its services and thinking about her priorities over the next year or so. You all will be hearing more from her over the next few weeks, including at the next TDL Forum conference call on March 15, and we’re very excited to have her join the team.

7. Today’s webinar: Review of the Texas Data Repository (Santi)
Role and value
Service model
Scope and functionality
How to participate
Overview of the Data Competencies (Peace)
Their background and purpose
How they were developed
How they can be used
Preview of what’s next (Kristi)

8. Problem: Increasing demands for researchers to make data accessible for a variety of reasons
Variety of storage options -- with specific rules, requirements, and restrictions
TDL members have expressed an interest and several needs around storing research data and making it available for reuse.
Those expressed needs have been driven by funders, governments, advocacy groups, and others pushing for improved accessibility and usability of research data (as well as other research outputs).
This increased focus on data sharing and re-use was famously accelerated by the 2013 OSTP Directive that required plans from federal agencies to support increased public access to the results of the research they fund.
All of these factors were driving researchers to explore potential repository options.
This grew to be a complicated process as there are various repository options available to researchers, with many choices being restricted by the researcher’s institutional affiliation, particular research topic/discipline, and their level of expertise with any given repository software.

9. comply with funding mandates
Goal: Provide infrastructure (and other) support that will help researchers:
comply with funding mandates
receive greater recognition for research data and researchers (DOIs, discoverability)
produce better research (more reproducible, more efficient)
So, from the beginning, TDL and its members knew that any data service or repository had to, first and foremost, help researchers comply with funding mandates -- providing them with infrastructure for compliance at the end of the research process and also for data management planning at the beginning of the process.
Additionally, we wanted the service to facilitate greater recognition for research data and researchers through the assignment of DOIs and enhanced discoverability.
And finally, we hope that improved infrastructure for data sharing will ultimately produce better research by enabling sharing of reproducible results and making the research process more efficient.

10. Goal: Provide infrastructure (and other) support that will help libraries:
Become an integral part of the Research Data Lifecycle.
Provide services at whatever level they are able
Benefit from collaborative infrastructure/management and mutual support.
Showcase institutional collections of research data
Those are the goals for end users of the service.
For our direct stakeholders -- academic libraries, we wanted to offer a service that would enable their participation in the research data life cycle in a way that is flexible and mutually beneficial -- so that any institution regardless of its own staff resources could use the service.
We also wanted to enable libraries to help their institutions showcase the research outputs produced by faculty, staff, and students on their campuses.

11. Texas Data Repository Centrally hosted, collectively managed
Built in open-source Dataverse
With these goals in mind and with the contributions of several working groups, TDL and its membership has created the Texas Data Repository, a platform for publishing and archiving datasets (and other data products) created by faculty, staff, and students at Texas higher education institutions.
The TDR is a single repository hosted by TDL staff, but managed collectively by our member institutions.
It is built in open-source Dataverse, originally developed and in use by Harvard University

12. Add Data Share Data Version Data Organize, publish, and archive
Share data with trusted group
Version Data
Maintain multiple versions of data
The primary function of the Texas Data Repository is to provide a mechanism for users to upload data and share their datasets as widely as they desire, from limited access within a trusted group that they designate to completely open access.
Part of the storing and dissemination of data includes the user’s ability to generate versions of the uploaded data, ensuring that that evolution of their work is documented and, if so desired, made available.
These benefits all align nicely with the data competency model that Peace will discuss. We will explore these benefits in more depth during the third webinar session taking place on March 9th.

13. Scope & Collecting policies
Research data and products
Any discipline; any file type
Midsized-data set size
Free of confidential or sensitive information
Policies that identify the scope of the repository and the kinds of content and collections accepted into it are critical for long-term sustainability and maintenance
The Texas Data Repository will focus on research data and products, such as codebooks. The repository is not intended for published papers, which are frequently found in institutional or discipline-based repositories.
The repository will accept data from any discipline and with any file type.
It is designed for regular to mid-sized datasets. As such, individual file sizes up to 2 GB and research projects with up to 10GB total per project will be accepted.
And while the repository is incredibly flexible, accepting any kind of file formats, it is important to note that the repository ONLY accepts de-identified data. Because datasets are intended to be accessed by any number of people, the repository is not intended for data with sensitive or confidential information like social security numbers, health information, etc.

14. Texas Data Repository Texas Digital Library (technology)
Steering Committee
(TDL & Data Repository Librarians)
Member Libraries
(service & outreach)
Researchers
(deposit, search, publish)
Administering the Texas Data Repository will be done in a hybrid model.
There is a single Dataverse repository hosted by the Texas Digital Library.
TDL provides organizational support in the form of training, tech support, and limited coordination.
Member institutions will provide services based on their local needs and resources. Their roles include:
Each member institution will supply a data repository librarian to manage the data uploaded from their respective institutions, act as a local expert for the repository, and serve on the TDL data librarian repository steering committee.
The steering committee will help TDL recognize trends in research data management, address repository issues, and recommend future groups needed for sustaining data management support.
Any data curation support will also be provided by the member institution
The user is responsible for depositing data
This hybrid model is flexible enough to accommodate different processes at different institutions (some requiring library intervention during ingest, some may not).

15. Learning More about TDR
Explore documentation (including policies and user guides) at
Watch the previously recorded webinar “Launching the Texas Data Repository: How to Implement TDR at Your Institution”
If you are interested in learning more about the Texas Data Repository, there are several sources of information that you would find valuable:
The TDR homepage includes key documentation on the repository, including TDR policies, metadata dictionary, and a step-by-step user guide
Last Fall, Kristi and I also hosted a webinar on launching the TDR at your institution. This webinar offers a more in-depth look at the repository and outlines the key steps each TDL member institution needs to complete prior to making it available to your respective campus.

16. Getting Started with TDR
Submit MOU to TDL
Integrate the system with Shibboleth
Select representative for TDR Steering Committee
Questions? Contact TDL
Implementation requirements include:
Signing a Memorandum of Understanding that outlines the various roles and responsibilities of TDL and member libraries
Integrating the repository with Shibboleth locally at your institution
Identifying a member of your library to serve on the Steering Committee
For those institutions who have not initiated implementation, please contact TDL via the helpdesk to begin this process. And, it goes without saying, that any questions you have can also be directed to TDL staff, using the TDL helpdesk or by calling TDL.
Finally, it is worth noting that there will be no additional cost to members for using the repository in FY

17. University of Santa Cruz Library, “Research Data Management,”
Through the contributions of multiple TDL working groups, the TDR is now another option for researchers in need of data storage and sharing solutions. It becomes a valuable tool libraries can provide as part of suite of services focused on research support.
Many of us see the establishment of the Texas Data Repository as an important first step towards providing more comprehensive services for researchers.
While the conversations we have with faculty, staff, and students may start with questions related to where one places research data, many of us also realize that there are other questions and hurdles that need to be addressed, like how to describe data, how to manage files, etc. As this graphic suggests, data storage and archiving is just one part of a larger research data management lifecycle.
But never fear. Our colleagues have also been tackling questions related to other aspects of research support. Some of these issues are framed and addressed by identifying data competencies.
Next I will hand it over to Peace, who will introduce and provide an overview of the data competencies

18. Background About UTA data services’ development
Started as research data librarian in 2016
Began planning what RDS would look like

19. Data Information Literacy
datainfolit.org
Twelve competencies were proposed, and skills that demonstrate the mastery of these competencies were listed.
It was agreed that these twelve competencies were reasonable starting points for the exploration of the data management processes of a range of STEM disciplines.
IMLS-funded research project (Purdue, Minnesota, Oregon, Cornell)
Aim: develop and implement curriculum around data information literacy

20. Use at UTA
To meet the needs of students, faculty, and staff of UTA campus in regards to working with data for teaching, learning, and research.

21. Campus data needs & competency-based learning
Reactions
Campus data needs & competency-based learning
Purdue’s DIL competencies were not created for direct translation to beginner-level data literacy instruction
Data ethics
Integration of data as support for argumentative assignments
Data & digital literacy
Data literacy - the ability to read, create, utilize, communicate, and criticize data.

22. Original DIL competencies
Intro to Datasets & Data Formats
Data Discovery & Acquisition
Data Management & Organization
Data Conversion & Interoperability
Quality Assurance
Metadata
Data Curation & Re-Use
Cultures of Practice
Data Preservation
Data Analysis
Data Visualization
Ethics, including citation of data
The learning outcomes can be pinpointed by faculty and create the structure for customized instruction for their classes.
What are the two additional and two re-defined competencies?
New:
Data awareness and knowledge - understanding the role of data in research
Data responsibility - recognizing and minimizing bias and/or ambiguous or misleading representation in reporting
Data structuring and cleaning - preparing data for analysis
Modified:
Cultures of practice - changed from a competency to a component of each competency
Quality assurance → Data quality and documentation
Metadata → Data description

23. Changes Levels: emerging, intermediate & expert
Three additional, one removed & two re-defined competencies
Learning outcomes for each
The learning outcomes can be pinpointed by faculty and create the structure for customized instruction for their classes.
What are the two additional and two re-defined competencies?
New:
Data awareness and knowledge - understanding the role of data in research
Data responsibility - recognizing and minimizing bias and/or ambiguous or misleading representation in reporting
Data structuring and cleaning - preparing data for analysis
Modified:
Cultures of practice - changed from a competency to a component of each competency
Quality assurance → Data quality and documentation
Metadata → Data description

24. Ethics Plagiarism Intellectual Property
Privacy and Confidentiality issues
“In the case of the Oregon team, it was determined that existing trainings provided by the Institutional Review Board at Oregon was sufficient to meet the needs of the graduate students.”

25. Cultures of Practice
Recognizes practices, values, and norms of chosen field, discipline, or subdiscipline as they relate to managing, sharing, curating, and preserving data.
Recognizes relevant data standards of field (metadata, quality, etc.)

26. Implementation
Piloting training and instruction content in various disciplines to further develop categories and learning outcomes.

27. Implementation a) instruction scaffolding in classes
b) workshop series
b) targeted training, including partnerships with graduate & faculty training groups

28. Coming up . . .
Session 2. Teaching Data: Developing Data Instruction Using a Multi-Level Competency Model
Tuesday, February 28, 2017 | 11:00 AM - 12:00 PM (CST)
In this webinar, Peace Ossom Williamson will discuss how the Data Information Literacy (DIL) model has been expanded at the University of Texas at Arlington as an adaptable frame on which to build an entire data services program. The presenter will describe the data competencies, their levels, and expectations for teaching emerging, intermediate, and expert level audiences.
Session 3. Learning By Example: Connecting Data Competencies with the Texas Data Repository
Thursday, March 9, 2017 | 11:00 AM - 12:00 PM (CST)
This webinar session will connect teaching data competencies explored in Session 2 with core functionality of the Texas Data Repository. It will address data competency learning outcomes, including: using the TDR to add, describe, share, and publish data; managing data, including versioning and de-accessioning data; and downloading and using data.
Talk about how you’ll be expanding on what we have introduced using the data competencies. Then when introducing the 3rd webinar will be where all of it comes together as Kristi and Santi will be teaching the data competencies using the Texas Data Repository and how the TDR can provide the infrastructure underlying the competencies.

29. Questions and Discussion