Notice Number: NOT-DA-16-027

Key Dates
Release Date: April 12, 2016
Response Date: July 3, 2016

Related Announcements
None

Issued by
National Institute on Drug Abuse (NIDA)

Purpose

Training the next generation of addiction researchers is a critical priority for the National Institute on Drug Abuse (NIDA). The need for expertise in biomedical informatics, statistics and Big Data science is increasing across all of biomedical research, including addiction. NIDA seeks to create a massive open online course (MOOC) providing teaching materials to begin the training of the next generation of quantitative addiction researchers. The purpose of this Request for Information (RFI) is to seek broad public input on the design of the course, and the best methods of implementation.

Background

The mission of NIDA is to lead the nation in bringing the power of science to bear on drug abuse and addiction. Part of fulfilling this mission includes creating a pipeline of training opportunities to ensure the research workforce is adequately skilled. The need for increased education in statistics, informatics and data science has grown with the development of new technologies for neuroscience and biomedical research.

MOOCs are able to reach audiences in the thousands. They are available online on websites such as Coursera and EDx, offering a huge advantage over local courses requiring travel. There are already MOOCs available focusing on informatics and neuroscience data analysis. However, there are no MOOCs for biomedical informatics directly related to addiction research. More specifically, NIDA is interested in strengthening the training pipeline to produce an influx of researchers trained in these techniques by providing MOOC-distributed training materials to students and/or their teachers.

Information Requested

This RFI is intended to gather broad public input on the biomedical informatics, statistics or Big Data lecture topics deemed valuable for training the next generation of addiction researchers and the most appropriate methods to distribute those topics. Advice sought includes but is not limited to the following items ranked in priority for NIDA, please provide feedback to as many as possible:

• Ideas on the target audience: NIDA has considered advanced high school students may have the sophistication to integrate materials related to addiction research and biomedical informatics. For example, this includes ideas on constraining the audience to those students considering experiences in a research lab or ideas on the value of broadly targeting students in hopes of exciting those not considering experience in a research lab.
• Ideas on whether it is more appropriate to constrain the topic domain to addiction, or whether it is more valuable for students early in their training arc to obtain a broader biomedical informatics training that spans disease areas. Ideas on whether placing statistics and biomedical informatics in the context of addiction research provides greater intuition.
• Ideas on how the MOOC materials will fit into existing curriculum: For example, if a high school audience is targeted, should the MOOC be integrated into existing curriculum, be used in class as a supplement to such curriculum or serve as an outside activity such as an after-school club.
• Ideas on whether such a course can serve as a virtual lab for students who do not have the opportunity to view and analyze research data, but are interested in research and related majors.
• Ideas on whether such a course can serve as a strength in the resume of students applying to work in labs at the undergraduate level and beyond, including the community college/associate level.
• Ideas on whether teachers should serve as the primary viewers of these online courses by going through the materials and using them to create customized curriculum for their students.
• Ideas on topic areas within biomedical informatics that should be included in the MOOC. Ideas on whether regression and z-scores could be a suitable degree of challenge if high school students are sought. Ideas on whether multi-dimensional analysis or machine are learning suitable challenges if pre-associates or pre-baccalaureate levels are targeted.
• Ideas on how many lectures should the MOOC be comprised of, and how long should each lecture be: This response may or may not be provided in consideration with the above bullet on lecture topics.
• Ideas on whether funded research investigators who are experts in these areas should be the presenters in the MOOC lectures: If a high school audience is sought, comment on funded research investigators working with high school teachers and if so the best way to foster such collaborations.
• Ideas on what media (e.g. websites, technologies) should be used to disseminate the materials.
• Insights on format and materials of each individual lecture (e.g. 1 video directed at teachers, 1 video for use with students, data, handouts or homework assignments).
• Insights of the formatting of the entire compiled course.

NIDA has collected the following listing of topics with relevance to advance biomedical informatics within addiction research. Please comment on these as potential MOOC lecture topics and what level of education/aptitude they should be targeted towards. Please also provide additional topics that would prepare students early in the training pipeline for experiences in the lab:

• Analysis of voltammetry signaling;
• Longitudinal analysis of calcium imaging or microelectrode data over the temporal course of self-administration
• Analysis of temporal geospatial data from mHealth studies
• Construction of correlation matrices during resting state fMRI tasks
• Epidemiological analysis of national or local drug use
• Construction of Manhattan plots in genetics analysis
• Dimensionality reduction allowing visualization of high-dimensional data;
• Single trial analyses or other high-resolution investigations of research data;
• Investigating individual variability on self-administration behavioral data to explore resilience and vulnerability factors;
• Automated analysis and machine learning classification of "big behavioral data," such as multiple camera and long-term video monitoring of naturalistic behaviors (e.g. in the home cage setting), recording of ultrasonic vocalizations or other behavioral measures;
• Analysis of electronic health record (EHR) data to identify patterns in health care data that could identify those at risk for developing substance misuse or substance use disorders or those at risk of relapsing (e.g. integration of EHRs with administrative data to examine the impact of the design or performance of the service delivery system on patient outcomes);
• Methods to integrate and analyze multiple sources of health data (i.e., EHR, mobile device, etc.)

How to Submit a Response

To ensure consideration, responses must be received by July 3, 2016, and should be emailed to vani.pariyadath@nih.gov. Respondents will not receive individualized feedback. All respondents are encouraged to sign up for NIDA E-News updates (http://www.drugabuse.gov/international/sign-up-e-news) to receive information related to Institute activities, including updates on the development and release of the final Strategic Plan.

Responses to this RFI are voluntary. Please do not include any personally identifiable or other information that you do not wish to make public. Proprietary, classified, confidential, or sensitive information should not be included in responses. Comments submitted will be compiled for discussion and incorporated into the NIDA Strategic Plan as appropriate. Any personal identifiers (personal names, email addresses, etc.) will be removed when responses are compiled.

This RFI is for informational and planning purposes only and should not be construed as a solicitation or as an obligation on the part of the Federal Government in general, the NIH, or NIDA specifically. NIDA does not intend to make any awards based on responses to this RFI or pay for the preparation of any information submitted or for the Government’s use of such information.

Inquiries

Please direct all inquiries to:

Vani Pariyadath
National Institute on Drug Abuse (NIDA)
Telephone: 301-443-3209
Email: vani.pariyadath@nih.gov