RFP Example 2: Instructions

Program Description

To meet the workforce development goals and challenges described in the Synopsis of Program and Introduction sections, NSF invites proposals that identify the emerging and outstanding community needs in training and education outside the classroom that require significant innovations - including the challenge of broadening CI access and adoption by those communities and institutions with low CI adoption as well as underrepresented groups. These proposals shall engage the relevant set of partners required as investigators, collaborators, resource providers, and early adopters, and include plans for effective outreach to the stakeholder communities. Proposals shall articulate well-designed programs with potential for significant impacts, which can serve as templates and provide curricular material and supporting resources to be adopted by other institutions and potentially by sub-communities/subdisciplines. A key challenge is to design or update suitable training curriculum that will receive buy-in from the larger community of stakeholders as relevant, high quality and adoptable.

As investigators conceive of novel training models and activities, they are challenged to explore the following aspects for short-term impacts: (i) preparing a better scientific workforce for advanced CI; (ii) broadening adoption and accessibility both as users and contributors of institutional, regional, and national shared computing and data resources by various disciplines, institutions, and groups; (iii) complementing and leveraging the state of art in curricular offerings and material in academia, industry and elsewhere; (iv) creating alliances and backbones for collective impact; (v) providing on-demand, personalized accessibility; (vi) exploring innovative ways of drawing students into computational disciplines (X+Computing and Computing+X); (vii) identifying areas of workforce demand and career pathways; (viii) innovating in training/certification models, curriculum, educational material and activities, and their sustainability; and (ix) leveraging and contributing to NSF cyberinfrastructure and research projects (such as XSEDE, NanoHub, CyVerse, LIGO, and NHERI).

In the longer term, investigators should explore how their project contributes to one or more of the following program goals: (i) lead to an educational ecosystem enabling "Computational and Data Science for All" with understanding of computation as the third pillar and data-driven science as the fourth pillar of scientific discovery; (ii) establish deeper engagement with and impact on various disciplines, institutions, and groups; (iii) develop or update curriculum and instructional material that will feed into undergraduate courses and be formally adopted into the disciplinary or general education core curriculum, or guide best practices in teaching pedagogy and standards formulations for minimum skill sets in collaborations with key stakeholders; (iv) establish clear career pathways and employment opportunities for the scientific communities of concern; and (viii) result in an ubiquitous and scalable educational cloud infrastructure for online, dynamic, personalized lessons and certifications.

Investigators may explore various training modes and informal education models that may build upon and go beyond the following examples: (i) summer institutes hosting participants for a few weeks employing logistics similar to Research Experiences for Undergraduates (REU) sites (note that the CyberTraining solicitation will not accept submissions for REU sites); (ii) intensive, shortduration training workshops; (iii) workshop and conference training/tutorial tracks; (iv) massive open online courses, small private online courses, and online self-paced training; (v) collaboratively taught courses with remote and local instruction; and (vi) programming and other competitions and awards. PIs are encouraged to engage all stakeholders, including forging alliances and forming backbones for collective impact. Stakeholders may include academia (educators, researchers, and professional staff), supercomputing centers and related entities, public and private institutions, professional/disciplinary associations, government and industry research labs, industry, authors and publishers, and federal, state and local agencies, and may cross national boundaries (however, NSF funds may only be used to support US-based researchers).

The overall quality of the recruitment and selection processes for the trainees (and trainers) will be important. The recruitment plan should include the types of institutions from which trainees will be recruited, along with the plan to reach out to individuals from disciplines and institutions with lower levels of CI adoption as well as from underrepresented groups. Assessment of the project is another crucial element. Projects should include plans to evaluate the success of the training and the attainment of the planned short- and long-term goals. PIs should identify the expected competencies and outcomes along with performance measures and an evaluation timetable. There must be mechanisms for regular feedback from the evaluator and the trainees to the PI team and for feedback to inform practice. Proposers may consult The 2010 User-Friendly Handbook for Project Evaluation for guidance on the elements of a good evaluation plan.

A sample project may include a planning/coordination workshop of key stakeholder communities and partners to crystallize needs and create a robust roadmap, creation/gathering of curricular topics and training material, followed by a series of summer training workshops -- with feedback loops among the phases, and complementary activities for community engagement, dissemination, adoption, new partnerships, and backbone formation and strengthening. Some example projects, serving only to exemplify the nature of the three submission tracks, are as follows:

i. CI Professionals track: (a) Training and certification of CI Professionals in cybersecurity technology and management; and (b) Working with neuroscientists to effectively use advanced CI to share software and data;
ii. Domain science and engineering track: (a) Training geoscience graduate students to develop scalable, parallel, and distributed software for high-performance computing; and (b) Cross-training of computing and engineering students and faculty in advanced manufacturing; and
iii. Computational and data science literacy track: (a) Instructor training for computational science literacy across all science, technology, engineering, and mathematics (STEM) disciplines in minimum core topics; and (b) Software and data literacy for natural science undergraduates.