CSDE Affiliate Amelia Gavin, Associate Professor at the School of Social Work, was recently featured in a UW News story about a Journal of Health Disparities Research and Practice paper in which she connects racial discrimination to PTSD, and thus to preterm birth.
According to Gavin, “Pregnancy is a stress test for the body. If you’ve been stressed during your life through discrimination, poverty and residential segregation, then the likelihood of having a healthy birth outcome has been compromised.”
Gavin’s article finds that African-American women are nearly twice as likely to give birth prematurely as white women. Such births often coincide with low birth weight, and together are linked to other developmental delays and health effects believed responsible for almost one-fifth of infant deaths nationwide. The trend holds up regardless of socioeconomic factors.
Lee Fiorio, Geography doctoral student, and Connor Gilroy, Sociology doctoral student, are CSDE Trainees, former Funded Fellows, and leaders of the Computational Demography Working Group. The UW College of Arts & Sciences has recently published a story featuring their incredible research at the leading edge of data science and social science.
Both Fiorio, who focuses on migration across the US and the globe, and Gilroy, who looks at our willingness to share information about our sexual identity, use traditional sources like the US Census, but they find that the massive quantities of data generated through social media or cell phone use — digital trace data — provide a particularly rich snapshot of society. “The data are generated in real time, they are generated very quickly, and that’s very different from a traditional demographic data source like the US Census, which comes out once every ten years,” says Gilroy. According to Fiorio, “the research is about methodology — seeing what digital trace data can tell us that might be missing when we go about estimating migration in the standard, traditional way.”
The NIH Office of Behavioral and Social Sciences Research (OBSSR) invites Early Stage Investigators (ESI, within 10 years of their terminal degree) to participate in the NIH Matilda White Riley Honors Behavioral and Social Sciences Research Paper Competition. Initiated as an annual distinguished scholar lecture, OBSSR expanded the Matilda White Riley Honors event in 2016 to recognize emerging scientists with a competition for peer-reviewed articles by ESIs. OBSSR will pay the travel expenses for up to four ESI honorees to present the findings from their accepted paper and participate in a moderated discussion of future behavioral and social sciences research possibilities during the 12th annual meeting, to be held on Thursday, June 6.
ESIs can submit one research article meeting the following criteria:
– The first author of the paper is an Early Stage Investigator (as of the deadline for this paper competition submission), defined by the NIH as someone who has completed their terminal research degree or end of post-graduate clinical training, whichever date is later, within the past 10 years and who has not previously competed successfully as PD/PI for a substantial NIH independent research award.
– The article was published or accepted and in-press between 01/01/18 and 12/31/18.
– The article involves original research published in a peer-review journal. (Note: conceptual, review, or meta-analysis papers are not eligible for this competition).
The submission deadline is Sunday, March 31, 2019. Submission link: https://mwr.obssr.od.nih.gov/Contestant
The 12th NIH Matilda White Riley Behavioral and Social Sciences Honors will be held on Thursday, June 6, 2019, from 8:00 am to 12 noon on NIH’s Main Campus – Wilson Hall, Building 1. https://www.scgcorp.com/obssr12thmatilda/
If you have any questions, please contact NIHMWRHonors@nih.gov.
For more information about past NIH Matilda White Riley Behavioral and Social Sciences Honors, visit the OBSSR website: https://obssr.od.nih.gov
Purpose
The National Library of Medicine is issuing this Notice to highlight its interest in receiving grant applications through NLM Research Grants in Biomedical Informatics and Data Science (R01 Clinical Trial Optional) (PAR 18-896), focused on research to reduce or mitigate gaps and errors in health data sets.
Background
Recent successes with the use of data-centric artificial intelligence (AI) methods such as deep learning are stimulating interest in the promise of harnessing large and complex digital health data sets to advance the goals of precision medicine. Applying AI methods to large health data sets promises to provide new powers of discovery, diagnosis, prediction, and decision support aimed at improving health outcomes and reducing healthcare costs. Numerous public datasets of human and non-human data are available, and a rich array of specialized tools and platforms can be used in studies and applications. However, recent work in identifying and addressing systematic biases and blind spots in data, and in the AI systems derived from that data, have highlighted an array of potential problems with fairness, accuracy, safety, and reproducibility of inferences and conclusions. Work on bias and incompleteness in health data sets includes studies that find poor representation of minority groups, seniors, and women. (See, for example, https://www.eurekalert.org/pub_releases/2016-10/uoms-nsr100716.php, or https://datasociety.net/output/fairness-in-precision-medicine/?utm_source=STAT+Newsletters&utm_campaign=436e1528c5-Readout&utm_medium=email&utm_term=0_8cab1d7961-436e1528c5-150097429). A recent Wall Street Journal article (https://www.wsj.com/articles/a-crucial-step-for-avoiding-ai-disasters-11550069865) noted that computational tools developed by a diverse team can help avoid bias in algorithms. Beyond problems with biases and other gaps in data, research using health data from humans requires special care to protect the sources and the data (see https://www.ncbi.nlm.nih.gov/pubmed?term=Barocas%2C%20Solon%5BAuthor%5D ). The All of Us Research Program (https://allofus.nih.gov/) aims to develop an unbiased, representative health data resource, but there are many other health data sets already in use or being constructed. Tools developed using biased and incomplete data sets may contribute to erroneous analyses. Statistical fallacies and representational errors unrelated to the research question at hand could introduce systematic errors. The core questions for understanding and mitigating these and other problems in health data research are: “What can be done, computationally and/or statistically, to reduce or mitigate gaps and errors in data sets used for health research?” and, “How can we improve the tools used for discovery, understanding, and visualization in health data sets and their analyses?” Whether the problem is due to incomplete health data or inadequate tools, approaches are needed to strengthen the reproducibility and applicability of data-centered research on the etiology, epidemiology and treatment of health conditions.
Research Objectives
NLM invites research grant applications that propose state of the art methods and approaches to address problems with large health data sets or tools used to analyze them, whether the data are drawn from electronic health records or public health data sets, biomedical imaging, omics repositories or other biomedical or social/behavioral data sets. Areas of interest include but are not limited to (1) developing and testing computational or statistical approaches applied to large and/or merged health data sets holding human or non-human data, with a focus on understanding and characterizing the gaps, errors, biases, and other limitations in the data or inferences based on the data; (2) exploring approaches to correcting biases or compensating for missing data, including the introduction of debiasing techniques and policies or the use of synthetic data; (3) testing new statistical algorithms or other computational approaches to strengthen research designs for use with specific types of biomedical and social/behavioral data; (4) generating metadata that adequately characterizes the data, including its provenance, intended use, and processes by which it was collected and verified; (5) improving approaches for integrating, mining, and analyzing health data that preserve the confidentiality, accuracy, completeness and overall security of the data. Applicants should address ethical issues that might arise from their proposed approach.
The Director of the Institute for Portland Metropolitan Studies, reporting to the Dean of the College of Urban and Public Affairs, provides leadership for activities, projects and functions of the Institute. The Director also provides overall supervision of the Population Research Center, an interdisciplinary public service, research and training unit focused on urban demography which operates within the Institute. The Director oversees a staff that includes research faculty, administrative staff and graduate students.
The Director is expected to collaborate with the Institute’s Board of Directors, composed of civic and campus leaders, to create and implement effectively an annual work plan focused on creating knowledge and partnerships to advance the overall vitality of the greater Portland-Southwest Washington region. The Director is responsible for organizing and overseeing all projects of the Institute, raising funds through grants and contracts to support the Institute’s mission: Providing the Portland and metropolitan region with the tools and information policymakers and citizens need to advance the economic, environmental, and social goals of the region.
Chapin Hall at the University of Chicago and the University of Chicago School of Social Service Administration (SSA) invite applications for the Harold A. Richman Postdoctoral Fellowship Program. This year, the program will place one recent PhD into a two-year position under the joint mentorship of two nationally recognized researchers. The selected Fellow will participate in substantive research activity, benefit from professional mentoring, and have opportunity to pursue publication and other scholarly activities. This appointment offers both a competitive salary and benefits.
Chapin Hall and SSA seek applications from recent PhDs who aspire to receive advanced training, learning, and independent research opportunities. The program guidelines below include important details regarding topical areas, mentors, and eligibility. The application period is open until April 19, 2019.
This is an excellent opportunity; please share with interested talent within and across your networks. Please send questions regarding the fellowship program to Richmanpostdoc@uchicago.edu.
The Population Research Institute at Pennsylvania State University seeks a Research Associate in Applied Demography. With support from Penn State, PRI is helping to lead a new initiative to develop the Pennsylvania Population Network (PPN) – a coalition of related research centers and resources from across the Pennsylvania State University. The PPN provides analyses focused on the role of population structure and change on health outcomes that can inform decision making by stakeholders in Pennsylvania and beyond. We are seeking a highly qualified applied demographer who can take a lead role in research and coordinate graduate internships across participating centers and programs. We seek candidates with strong motivation, ability to conduct research with a proven track record, excellent project management, written and oral communication skills and the ability to effectively interact with and mentor graduate students. Candidates must have a Ph.D. in Demography, Geography, Sociology or related field. Strong data analysis skills are required. Experience in spatial modeling, GIS, complex and multi-level data is preferred. The initial appointment will be for one-year, but renewable for additional years pending funding availability. Applications must be submitted electronically and include a cover letter, CV and the names, addresses and email contact information for three professional references. The review of applications begins on 4/15/2019 and will continue until the position is filled. Contact Dora Hunter, PRI Project Manager (email: dmh63@psu.edu).
We seek a highly motivated Postdoctoral Research Fellow with a background in epidemiology, psychology, statistics, or econometrics with a strong interest in ageing and gerontology. The Postdoctoral Research Fellow will conduct research for a project examining lifecourse determinants and social variation in Healthy and Working Life Expectancies in Australia. The project also includes cross-national comparisons with UK and (potentially) other European data.
Any experience working with longitudinal household panel data, and knowledge of multi-state modelling, mortality modelling, or other methods and software used to calculate healthy life expectancies and disability free life expectancies is highly desirable.
The position will be based at the University of New South Wales (Australia) and at Neuroscience Research Australia (NeuRA).
To apply and for more information please see: http://external-careers.jobs.unsw.edu.au/cw/en/job/496455/postdoctoral-fellow-psychology
Informal inquiries please contact Dr Kim Kiely (k.kiely@unsw.edu.au)
Take the Next Four Weeks to Prepare Your Submission for the WSDS 2019 Program
Wherever you are in your career, participating in WSDS is a valuable opportunity to present your work or research or to share your perspective on the role of women in today’s statistics and data science fields. Take some time to think about what you might contribute, then prepare a concurrent, panel, or speed session abstract to submit for the WSDS 2019 program!
Text as Data – eScience Special Interest Group
Summary
Text is a ubiquitous and valued data source in the computer and information sciences, many areas of the natural and social sciences, engineering, business and more. This eScience Special Interest Group is for students, faculty and researchers interested in sharing and learning about UW research and teaching that uses text as data. The group’s objectives include:
- Promote connections, conversations and collaborations among domains with text-as-data interests on the UW campus
- Provide a forum for sharing research and receiving feedback
- Learn about new research advances, data, and funding opportunities
- Provide a forum discussing teaching resources and pedagogy
- Discuss methods and software tools for processing and understanding text as data
Activities
Spring Seminar and Discussion Group
The text-as-data discussion group is a forum for learning and and sharing information. The bi-weekly session is for discussing research and receiving feedback on text methods, data, discoveries, and tools.
Time: TBD, Spring Quarter 2019
Location: WRF Data Science Studio, Physics/Astronomy Tower, 6th Floor
Please contact Spencer Wood (spwood@uw.edu) if you are interested in presenting on research or teaching during the discussion-group this Spring. The date and time will be determined based on
availability of interested participants.
Moderated list serve
Anyone interested in text-as-data at UW can sign-up to receive and share information about research and activities on campus from textasdata@u.washington.edu.
Contacts
Spencer Wood (eScience Institute) spwood@uw.edu
John Wilkerson (Department of Political Science) jwilker@uw.edu
UW Text-as-data Listserve textasdata@u.washington.edu