Research Data Science Services

Our mission is to assist researchers at the NYUAD in carrying out their research projects and faculty members in their research-related courses and projects. Research Data Science services are broadly classified below

• Data Analytics

• Data Visualization

• Data Management

• Big Data

• Artificial Intelligence (AI) Support

We provide extended support over the lifecycle of a research project by embedding a data scientist in your research team. We can design and implement a data analysis pipeline for many stages of your research project, and/or develop a prototype of your research focused software tool. Specifically, we can help with the following:

• Writing reproducible, version-controlled code

• Data organization and cleaning

• Model estimation and post-estimation

• Visualization of raw data and model output

• Interpretation of results

• Writing methods and results sections of papers

• Responding to peer-reviews of our analyses

• Developing tool prototypes in R / Python

Acknowledgement

In return for this extended support, we ask for:

Authorship: The data science specialist who collaborates significantly will receive author / developer credit on all resulting research / software output (e.g., publications, talks, posters, documents, software packages).

• Data Management & Big Data

  • Developing customized data management plans

  • Organizing data (data collection and analysis)

  • Big Data handling and Processing

  • High-memory, multi-processing computational support.

• Data Visualization

  • GIS and Web Maps

  • ggplot2 and Matplotlib

  • Visualization using available software or customized tools (Power BI, Tableau & QlikView)

• Data Analytics

  • Assistance with data analysis using available software or customized tools (Power BI, Tableau & QlikView).

  • Developing analysis software and customized pipelines

  • Statistical analysis of results.

• Artificial Intelligence (AI) Support

  • Advanced Algorithms Design and implementation.

  • Parallelization and optimizing of code.

  • State of Art advanced Model implementation like Image Recognition, Social Network Analysis, Recommendation engine, Speech and Text mining using Deep learning frameworks.

Research Data Scientist Support Guidelines

The research data scientist can support the faculty and researchers in 2 ways; 1. the first, could be through research collaboration on projects and, 2. the second, could be through support services.

• Research collaboration will entail the data scientist contributes significantly to the research project.

• Depending on the nature of the collaboration and the project, the number of project collaborations will be limited to a few projects per year.

• The expectation is that the data scientist would be a coauthor on any publications resulting from the project.

• For support services, we kindly ask that any publication acknowledges the use of the resources.

Ways to Contact/Requests:

The Best way for researchers to contact or raise a request is through the ticketing system and also other informal ways are either by email, phone call or face to face. All the informal ways will again have logged in to the ticketing system for better tracking and record keeping purposes.

Prioritization of the requests:

All the received requests will be dealt with on the priority of first come first serve basis. However, we will consider conference/publication/grant funding as exception to the priority list based on the urgency.