DDSS prioritizes projects that:

1. Encourage collaboration across and within social science disciplines and fields.
2. Advance the use of innovative research methods, computational analysis, or challenging data sets.
3. Impact the larger social science community and beyond through production of sharable data sets, computer code, training for students, or findings/methods that can be communicated to a diverse campus audience.
4. Demonstrate the need for additional computing infrastructure.

DDSS offers two grants, one small ($5,000 or less) and one large (over $5,000). Applications for small grants are accepted on a semi-rolling (reviewed monthly) basis. Applications for large grants are invited twice a year in an official call for proposals. DDSS also provides funds for workshops, trainings, software, and other technical needs, which can be requested through our co-sponsorship application.

For the most part, our grantmaking is reactive. However, there may be circumstances when the DDSS identifies unique research opportunities and reaches out to faculty members, post-docs, or graduate students to assemble and fund a research team.

The DDSS accepts grant applications from Princeton University faculty members, research scholars, administrators, post-doctoral fellows, and enrolled students.

DDSS adopts the definition of Principal Investigator (PI) eligibility set by the Institutional Review Board (IRB). For those who are not PI eligible, we only consider funding with supervision in the case of Princeton graduate students, advanced full-time research positions such as postdoctoral research associates, or research staff working on a larger, collaborative project with other members of the Princeton research community.

All of our grant applications are submitted online. Applicants will be required to authenticate with their Princeton login credentials in order to access the online application form.

Following the priorities of our mission, there are 3 key pillars on which applications are evaluated:

1. Focus on research infrastructure and innovation in quantitative and computational social science.
2. Positive externalities and significant contributions both within and outside the field. This may include bridging social science to the fields of computer science and statistics.
3. Projects must demonstrate contemporary relevance or apply truly pioneering techniques to longstanding questions.

In addition, applicants should demonstrate that they have exhausted other funding resources.

Although applications will not be available to the general public, the Director and Executive Committee reserve the right to discuss details of any application with other University faculty members, outside experts, or the applicant’s faculty advisors.

When an application for funding is denied, the notification to the applicant will include an explanation as to why the grant was denied. Applicants may request a more detailed explanation for the decision. Please remember that it is discouraging to reject applications from researchers doing important work. Applications may be turned down because they fall outside the scope of the DDSS mission. Applicants should read the guidelines carefully before investing the time and energy in making an application. Funding requests may also be denied when applicant pools are competitive and DDSS receives more in requests than our grant budget allows us to fund.

E-mail [email protected] with any technical questions or problems. If you have questions about the application itself, please contact us at [email protected] or by calling 609-258-3149.

Yes, provided that any additional grant proposals are for separate projects with additional collaborators. In cases where a researcher has multiple grant proposals with no collaborators, they should first consult with DDSS to identify the strongest proposal.

Yes, but DDSS views additional requests for funding as individual requests. We apply the same considerations as we do with an original application. This requires detailing the other funding sources to which you have applied.

Unless otherwise specified, DDSS expects funded research projects to be completed in one calendar year. Grantees may request additional time to complete their work. Decisions to grant no-cost extensions will be made by the Director.

DDSS accepts applications for research work done at Princeton University as part of a multi-institutional study. In reviewing the funding request, the DDSS will consider how the work done specifically at Princeton contributes to the overall study, and what contributions the overall study can make to the DDSS knowledge base and infrastructure for supporting future projects.

While there is no hard cap on grants, in past years, the highest awards for large grants ranged from $35-55k.

All applications, small and large, must provide an itemized budget, with strong justification for each expense.

The DDSS prefers to fund projects where applicants can demonstrate that efforts for alternative funding have been exhausted.

Funds have been used for data acquisition and management, research support, specialized programming and computational resources. Barring exceptional circumstances, DDSS does not award funds for travel, conference attendance, summer schools, faculty summer pay, student tuition or summer funding, hardware, or large equipment investments.

Spending must align with the itemized budget in the original proposal. If there are any substantive changes to the budget, researchers must first submit an amended budget and receive DDSS approval.

Yes, DDSS allows requests for general operating expenses, which can include salary coverage for research support (but not faculty summer pay or student stipends unrelated to research assistance) and other direct costs. 

Yes, DDSS has research software engineers and specialists in data science whose time can be apportioned to projects. Priority is given to projects that (a) would not be possible without the addition of labor hours from specialized DDSS staff and (b) lead to the accumulation of skills or creation of code that can be used in future research projects.

Due to increased volume in funding requests in recent years, DDSS has become much more selective in participant compensation requests.

In addition to prioritizing innovation, we consider demonstrated rigor, significant advancement in the field, potential public good creation, impact and scope for meaningful interdisciplinary collaborations.

Due to the notable increase in requests to fund LLM API calls, we only offer funding in exceptional circumstances. Faculty and students should inquire with the Center for Statistics and Machine Learning about the availability of Microsoft Azure credits that can be used for this purpose.

DDSS does, on occasion, fund the purchase of datasets. However:

a. applicants should first check with subject librarians to ensure that Princeton does not already hold a subscription to the desired data.

b. the data must be of interest to a broader audience and available through a multi-user or departmental/institutional license. We do not provide funding for data that are too niche or licensed to just one individual.

We do not fund individual licenses or subscriptions as a rule.

Exceptions may be in the case of a truly innovative project where the license or subscription is a necessity, or in the construction of a notable public good.

DDSS will consider technical goods (e.g., software, subscriptions) on a departmental/institutional level if wider demand is demonstrated, the product aligns with our mission, the cost is reasonable, and institutional support cannot other be found.

Applications for large grant requests (over $5,000) are reviewed twice per year by the DDSS Executive Committee. The Technical Advisory Group may be consulted for guidance during the grant review process, but final decisions on which grants to fund are made by the Executive Committee. Applications for small grants ($5,000 or less) are reviewed on an ongoing basis by the Director in consultation with DDSS senior staff and, when necessary, members of the Executive Committee.

Once approved, grantees must submit all interim and final reports online through our online reporting system as required in their grant agreement, unless otherwise specified.  

The Graduate Fellowship in Social Data Science offers a one-time award and Graduate Fellows will play a key role in community-building across the quantitative social sciences and related departments. Graduate Fellows also share their expertise through consultations and office hours.

DDSS Consultants fit the more traditional RA role. Though, they can also be hired to lead workshops or provide replication services. They are paid an hourly wage.

Yes, but a student's advisor should be made aware of all of the student's commitments and hours worked cannot exceed those set by the university.

Yes. Graduate Fellows can serve multiple terms, whether consecutively or not.

Likewise, graduate students who were not selected in previous terms can reapply, especially as skill levels advance.

Fellows should expect to dedicate approximately 1-3 hours a week to fellowship activities. They must:

• Respond to consultation requests relevant to their specific technical skillset during the semester.
• Attend DDSS Coffee Hours and present at one meeting. 
• Provide updates on research projects, relay common consultation requests, identify areas where advanced training workshops would be most effective, and propose other novel ideas for advancing the mission of the DDSS.
• Organize and promote DDSS informal social events, discussion groups, or seminars.
• Contribute to public good creation, such as reference guides and contributions to the DDSS GitHub repository.

Yes, as long as requests can suitably be addressed online, office hours an be remote.

We will have some dedicated space for Graduate Fellows at 169 Nassau. Because the space is limited, it will be offered as hot desking space.