National Data Science Alliance (NDSA)
Interest Survey

The National Data Science Alliance (NDSA), based in Atlanta, Georgia, invites you to share your interest in collaborating to enhance data science research and education at Historically Black Colleges and Universities (HBCUs). The NDSA is an Alliance of the National Science Foundation’s Eddie Bernice Johnson INCLUDES program that was funded $10 million to increase the number of Black people earning data science credentials by at least 20,000 by 2027 and expand data science research that advocates for social justice and strive to eliminate bias.

We aim to increase the number of Blacks with credentials in data science and analytics by leveraging the strength of HBCUs that significantly contribute to a diverse STEM workforce.

Please provide any feedback so that we can best support you and your organization as you move forward in data science and analytics. 

Team:
  • Research Lead H. Justin Ballenger, Morehouse College
  • Co-PI LaTanya Brown-Robertson, Howard University
  • Co-PI Moses Garuba, Howard University
  • Co-PI Sajid Hussain, Fisk University
  • Co-PI Eric Mintz, Clark Atlanta University
  • PI Talitha Washington, Atlanta University Center Data Science Initiative and Clark Atlanta University 
If you have questions, please contact:
PI Talitha Washington
twashington@ndsalliance.org
404-992-2570

You are invited to participate in a research study and if you agree to be part of this research study, we ask that you fill out the survey. This research will inform the development and strengths of data science academic and research programs at HBCUs.

Participating in this study is completely voluntary. Even if you decide to participate now, you may change your mind and stop at any time. You may choose not to answer any survey question or complete the survey for any reason. Information collected in this project may be shared with other researchers, but we will not share any information that could identify you.

As part of their review, the Clark Atlanta University Institutional Review Board has determined that this study is no more than minimal risk and exempt from on-going IRB oversight.

IRB Protocol Approval Code: HR2023-4-1212-1/A

If you have questions about this research study, please contact:
PI Talitha Washington
twashington@ndsalliance.org or twashington@cau.edu
404-992-2570


1.By checking the box below, I consent to participating:(Required.)
2.Contact Information
3.What is your role/position?
4.What is your HBCU institution?
5.Choose the area that best matches your primary expertise or area of study.
6.Would you be interested in participating in workshops with other HBCU faculty and researchers? Topics may vary and will be data science-related.
7.Would you be interested in participating in a research group with other HBCU faculty and researchers? Topics may vary and will be data science-related.
8.Would you be interested in participating in a virtual curriculum development working group with other HBCU faculty and researchers? The area of focus may vary and will be data science-related.
9.Would you be interested in serving on a committee?
10.Is your organization interested in learning more about how to become a member of the NDSA?
11.What data science academic or training programs does your organization host? This may include majors, minors, postbaccalaureate, and certificate programs. If available, include corresponding website links.
12.What are your organization's key strengths in supporting the development of data science (a) curriculum and (b) research?
13.What are your barriers to developing or applying data science (a) curriculum or (b) research?
14.Which faculty/researchers/staff at your organization teach data science courses or are engaged in data science research? Provide any emails and website links, if available.
15.Please provide any additional information so we understand your needs, interests, and opportunities for advancing data science that advocates for social justice, strives to eliminate bias, and broadens participation.
This material is based upon work supported by the National Science Foundation under Grant No. 2217346. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.