Types of Partnerships

At the core of the Digital Integration Teaching Initiative model is an ethos of collaboration: we aim to help faculty integrate digital proficiencies into the curriculum of their field-specific courses. The DITI team will work carefully with you on this integration: we will not parachute into a course to teach a discrete unit on a particular digital skill. The integrative model we support takes time, careful planning, and collaborative work on the part of faculty members and the DITI team.

The DITI team offers several types of partnerships between faculty and DITI-trained graduate students to incorporate computational and digital skills into existing course content:

One-day or several-day workshop
DITI team members will visit the course during one or more days to teach a workshop on digital or computational skills. Workshops can either present new content – created in collaboration with the faculty partner- or be adapted from existing materials. Developing new workshop materials will require additional time. Visit our example materials page to explore some previous workshops.

Multi-class assignment
One or more DITI team members will work one-on-one with the faculty partner to develop an assignment that incorporates digital or computational skills. Usually, this involves adding a digital component to an existing assignment (e.g. mapping, Excel, visualizations, websites), but developing a new assignment is also a possibility. Visit our example materials page to explore some multi-class assignments.

Semester-long assignment
This partnership involves a more in-depth assignment that spans the course semester. This sustained assignment will help students become more proficient with a particular digital or computational skill and apply it to a long-term project. DITI team members will work with the faculty partner throughout the semester. This may also involve guest lectures and workshops run by DITI members and will result in final computationally or digitally enhanced projects by students.

We are also able to accommodate other types of partnerships, with sufficient advance notice. Please note that some partnerships will take longer than others to develop, especially if the content is new. For all partnerships, however, both the class date and type of partnership must be specified before the semester begins.

Partnership Timeline

To ensure that we are able to accommodate all requests, we have established a timeline for DITI partnerships. Partnerships begin early in the semester before the class is offered in order to provide sufficient time to create class materials, reserve times for class visits, and modify materials based on faculty partner feedback. After the initial consultation, DITI team members will remain in touch throughout the process to ensure the success of digital teaching modules.

Timeline for Fall semesters:

  • Request by April 1st: let us know by the end of the Spring semester if you are interested in a DITI partnership for the Fall by filling out this brief survey. You do not need to know explicit details about your course.
  • Consultation by May 15th: an in-person or Skype meeting will be held by May 1st to collect course details and plan for the partnership. A specific date or course syllabus is not needed at this time, but the initial meeting will serve as the foundation for determining which types of partnerships best suit the class goals, creating a timeline, and clarifying expectations for what can be taught during the given timeframe.

Timeline for Spring semesters:

  • Request by November 1st: let us know by the end of the Fall semester if you are interested in a DITI partnership for the Fall by filling out this brief survey. You do not need to know explicit details about your course
  • Consultation by December 15th: an in-person or Skype meeting will be held by May 1st to collect course details and plan for the partnership. A specific date or course syllabus is not needed at this time, but the initial meeting will serve as the foundation for determining which types of partnerships best suit the class goals, creating a timeline, and clarifying expectations for what can be taught during the given timeframe.

Faculty Partnership Roles

You do not need any specialized technical knowledge or skills to include a digital module in your course. That’s what the team members are for! Our goal is for instructors to work closely with the DITI team so that in the future they will be able to teach the modules themselves, but development team members will be available in class for assistance. To ensure the module is successful and to help you understand what to expect, we have set out a few guidelines. Faculty members should plan to:

  • Attend the class during workshop days and guest lectures. This helps faculty partners learn the material themselves, and address any course-specific questions from students as they arise.
  • Distribute the appropriate materials, arrange downloads of data/software, and send advance instructions to students so they are well-prepared for the class as needed.
  • Reserve time that works best for both the course and the DITI team members’ schedule; without sufficient lead time, DITI team members may not be able to accommodate a specific date.

Data Considerations

DITI team members span multiple disciplines and their familiarity with data in disciplines other than their own may be limited. We recommend that faculty partners select useful data for workshops, projects, and presentations during advance planning for the module. Faculty partners should plan to provide or point to datasets that are relevant to the course and to consult with DITI team members on what kinds of data are needed for different assignments or workshops. In some cases, the DITI team may be able to assist with data gathering. If faculty are unable to provide data resources, then DITI team members may be able to supply datasets; however, this data may not always be the most relevant for the course.

The DITI is guided by three principles: open data, analyzable data, and archivalable data. If data are not arranged in an open, standard, analyzable format, then users of that data might have difficulty accessing it in the short- or long-term. We strive to remove all unnecessary restrictions over the data that we create, use, and share in the classroom and on our GitHub Digital Showcase.* If at all possible, we would like faculty to follow these principles when it comes to sharing data with us, though we will do our best to work with files in any form. We prefer faculty send us data that are analyzable and non-proprietary, so we can avoid problems like: major software updates causing datasets to become inaccessible, difficulty with reading files on different operating systems and with different software, or proprietary data types becoming obsolete over the course of a few years.

The DITI follows data and file formatting guidelines that allow for long-term storage and wide-range accessibility. Below are lists outlining the data file formatting and data values we observe at the DITI. More resources explaining the reasoning behind these guidelines can be found through the links below. If you have any questions about these data considerations, please contact a DITI member.

*Before posting modules on GitHub, we confirm with faculty that they wish to have the data and resources made public.

Data File Formatting:

  • Containers: TAR, GZIP, ZIP
  • Databases: CSV, XML
  • Tabular data: CSV
  • Geospatial vector data: SHP, GeoJSON, KML, DBF, NetCDF
  • Geospatial raster data: GeoTIFF/TIFF, NetCDF, HDF-EOS
  • Moving images: MOV, MPEG, AVI, MXF, MP4
  • Sounds: WAVE, AIFF, MP3, MXF
  • Still images: TIFF, JPEG 2000, PDF, PNG, GIF, BMP
  • Text: XML, HTML, TXT, UTF-8
  • Web archive: WARC

Data Values:

  • Standardize all coded and null values within a dataset.
  • Use an explicit value for missing or no data, rather than an empty field.
    • For numeric fields, represent missing data with a specified extreme value (e.g., -9999), the IEEE floating point NaN value (Not a Number), or the database NULL. Be advised that NULL and NaN can cause problems, particularly with some older programs. For character fields, use NULL, “not applicable”, “n/a” or “none”.
  • If there are multiple reasons that cells might not contain values, include a separate code for each reason.
  • The null value(s) should be consistently applied within and among data files.
  • If data values are encoded, be sure to provide a definition in the metadata. We recommend using UTF-8 when possible.
  • Don’t include rows with summary statistics. It is best to put summary statistics, figures, analyses, and other summary content into a separate companion data file.

Resources

“Data and File Formatting,” Axiom Data Science. 2017.
https://www.axiomdatascience.com/best-practices/DataandFileFormatting.html

Tauberer, Joshua. “Analyzable Data in Open Formats (Principles 5 and 7).” Open Government Data: The Book. Second Edition, 2014.
https://opengovdata.io/2014/analyzable-data-in-open-formats/