The Federal Big Data Research and Development Strategic Plan

This document was developed through the contributions of the NITRD Big Data SSG members and staff. A special thanks and appreciation to the core team of editors, writers, and reviewers: Lida Beninson (NSF), Quincy Brown (NSF), Elizabeth Burrows (NSF), Dana Hunter (NSF), Craig Jolley (USAID), Meredith Lee (DHS), Nishal Mohan (NSF), Chloe Poston (NSF), Renata Rawlings-Goss (NSF), Carly Robinson (DOE Science), Alejandro Suarez (NSF), Martin Wiener (NSF), and Fen Zhao (NSF). A national Big Data1 innovation ecosystem is essential to enabling knowledge discovery from and confident action informed by the vast resource of new and diverse datasets that are rapidly becoming available in nearly every aspect of life. Big Data has the potential to radically improve the lives of all Americans. It is now possible to combine disparate, dynamic, and distributed datasets and enable everything from predicting the future behavior of complex systems to precise medical treatments, smart energy usage, and focused educational curricula. Government agency research and public-private partnerships, together with the education and training of future data scientists, will enable applications that directly benefit society and the economy of the Nation. To derive the greatest benefits from the many, rich sources of Big Data, the Administration announced a “Big Data Research and Development Initiative” on March 29, 2012.2 Dr. John P. Holdren, Assistant to the President for Science and Technology and Director of the Office of Science and Technology Policy, stated that the initiative “promises to transform our ability to use Big Data for scientific discovery, environmental and biomedical research, education, and national security.” The Federal Big Data Research and Development Strategic Plan (Plan) builds upon the promise and excitement of the myriad applications enabled by Big Data with the objective of guiding Federal agencies as they develop and expand their individual mission-driven programs and investments related to Big Data. The Plan is based on inputs from a series of Federal agency and public activities, and a shared vision: We envision a Big Data innovation ecosystem in which the ability to analyze, extract information from, and make decisions and discoveries based upon large, diverse, and real-time datasets enables new capabilities for Federal agencies and the Nation at large; accelerates the process of scientific discovery and innovation; leads to new fields of research and new areas of inquiry that would otherwise be impossible; educates the next generation of 21st century scientists and engineers; and promotes new economic growth. The Plan is built around seven strategies that represent key areas of importance for Big Data research and development (R&D). Priorities listed within each strategy highlight the intended outcomes that can be addressed by the missions and research funding of NITRD agencies. These include advancing human understanding in all branches of science, medicine, and security; ensuring the Nation’s continued leadership in research and development; and enhancing the Nation’s ability to address pressing societal and environmental issues facing the Nation and the world through research and development

The Federal Big Data Research and Development Strategic Plan

This document was developed through the contributions of the NITRD Big Data SSG members and staff. A special thanks and appreciation to the core team of editors, writers, and reviewers: Lida Beninson (NSF), Quincy Brown (NSF), Elizabeth Burrows (NSF), Dana Hunter (NSF), Craig Jolley (USAID), Meredith Lee (DHS), Nishal Mohan (NSF), Chloe Poston (NSF), Renata Rawlings-Goss (NSF), Carly Robinson (DOE Science), Alejandro Suarez (NSF), Martin Wiener (NSF), and Fen Zhao (NSF). A national Big Data1 innovation ecosystem is essential to enabling knowledge discovery from and confident action informed by the vast resource of new and diverse datasets that are rapidly becoming available in nearly every aspect of life. Big Data has the potential to radically improve the lives of all Americans. It is now possible to combine disparate, dynamic, and distributed datasets and enable everything from predicting the future behavior of complex systems to precise medical treatments, smart energy usage, and focused educational curricula. Government agency research and public-private partnerships, together with the education and training of future data scientists, will enable applications that directly benefit society and the economy of the Nation. To derive the greatest benefits from the many, rich sources of Big Data, the Administration announced a “Big Data Research and Development Initiative” on March 29, 2012.2 Dr. John P. Holdren, Assistant to the President for Science and Technology and Director of the Office of Science and Technology Policy, stated that the initiative “promises to transform our ability to use Big Data for scientific discovery, environmental and biomedical research, education, and national security.” The Federal Big Data Research and Development Strategic Plan (Plan) builds upon the promise and excitement of the myriad applications enabled by Big Data with the objective of guiding Federal agencies as they develop and expand their individual mission-driven programs and investments related to Big Data. The Plan is based on inputs from a series of Federal agency and public activities, and a shared vision: We envision a Big Data innovation ecosystem in which the ability to analyze, extract information from, and make decisions and discoveries based upon large, diverse, and real-time datasets enables new capabilities for Federal agencies and the Nation at large; accelerates the process of scientific discovery and innovation; leads to new fields of research and new areas of inquiry that would otherwise be impossible; educates the next generation of 21st century scientists and engineers; and promotes new economic growth. The Plan is built around seven strategies that represent key areas of importance for Big Data research and development (R&D). Priorities listed within each strategy highlight the intended outcomes that can be addressed by the missions and research funding of NITRD agencies. These include advancing human understanding in all branches of science, medicine, and security; ensuring the Nation’s continued leadership in research and development; and enhancing the Nation’s ability to address pressing societal and environmental issues facing the Nation and the world through research and development

The Sustainable Development Oxymoron: Quantifying and Modelling the Incompatibility of Sustainable Development Goals

In 2015, the UN adopted a new set of Sustainable Development Goals (SDGs) to eradicate poverty, establish socioeconomic inclusion and protect the environment. Critical voices such as the International Council for Science, however, have expressed concerns about the potential incompatibility of the SDGs, specifically the incompatibility of socio-economic development and environmental sustainability. In this paper we test, quantify and model the alleged inconsistency of SDGs. Our analyses show which SDGs are consistent and which are conflicting. We measure the extent of inconsistency and conclude that the SDG agenda will fail as a whole if we continue with business as usual. We further explore the nature of the inconsistencies using dynamical systems models, which reveal that the focus on economic growth and consumption as a means for development underlies the inconsistency. Our models also show that there are factors which can contribute to development (health programs, government investment in education) on the one hand and ecological sustainability (renewable energy) on the other, without triggering the conflict between incompatible SDGs

Dinâmicas comunitárias em deslocados e não deslocados residentes em áreas de exclusão social em Barranquilla (Colômbia)

El sentido de comunidad, la participación y el empoderamiento permiten comprender el proceso de desplazamiento y reasentamiento en el contexto de recepción, así como las consecuencias derivadas de ambos fenómenos. Los objetivos de la investigación son a) evaluar los tres constructos mencionados, b) analizar la sinergia que existe entre estos y c) proponer estrategias para aumentar su capacidad de influencia en los procesos de toma de decisiones. Llevamos a cabo una investigación exploratoria y transversal con población desplazada (n=30) y no desplazada (n=32) en dos localidades de bajos ingresos en Barranquilla (Colombia). Existe retroalimentación positiva entre los procesos evaluados, aunque no se observan diferencias significativas entre el grupo de desplazados y el de no desplazados. La dimensión Pertenencia (sentido de comunidad) es la que mejor explica la varianza del empoderamiento y de la participación en ambos grupos. Presentamos iniciativas para reforzar el sentido de comunidad y facilitar el acceso a los recursos socio-comunitarios en población desplazada.The sense of community, participation and empowerment enable us to understand the process of displacement and resettlement in the context of reception, as well as the consequences of both phenomena. Our objectives are a) to assess the three constructs mentioned above, b) to analyze the synergy existing among them and c) to propose strategies for increasing their capacity to influence the decision-making processes. We carried out a cross-sectional exploratory study with displaced (n=30) and non-displaced (n=32) people in two low-income districts of Barranquilla (Colombia). There is positive feedback between the processes evaluated, although no significant differences are observed between the displaced and the non-displaced groups. The dimension of belonging (sense of community) is the one that best explains the variance of empowerment and participation in both groups. Finally, we present a set of initiatives to reinforce the sense of community and to facilitate access to the community’s social resources for the displaced population.O sentido de comunidade, a participação e o empoderamento permitem compreender o processo de deslocamento e reassentamento no contexto de recepção bem como as consequências derivadas de ambos os fenômenos. Os objetivos desta pesquisa são: a) avaliar os três construtos mencionados; b) analisar a sinergia que existe entre estes e c) propor estratégias para aumentar sua capacidade de influência nos processos de tomada de decisões. Realizamos uma pesquisa exploratória e transversal com população deslocada (n=30) e não deslocada (n=32) em duas localidades de baixa renda em Barranquilla (Colômbia). Existe retroalimentação positiva entre os processos avaliados, embora não se observem diferenças significativas entre o grupo de deslocados e o de não deslocados. A dimensão Pertencimento (sentido de comunidade) é a que melhor explica a variância do empoderamento e da participação em ambos os grupos. Apresentamos iniciativas para reforçar o sentido de comunidade e facilitar o acesso aos recursos sociocomunitários em população deslocada

Patterns of geohelminth infection, impact of albendazole treatment and re-infection after treatment in schoolchildren from rural KwaZulu-Natal/South-Africa

BACKGROUND: Geohelminth infection is a major health problem of children from rural areas of developing countries. In an attempt to reduce this burden, the Department of Health of the province of KwaZulu-Natal (KZN) established in 1998 a programme for helminth control that aimed at regularly treating primary school children for schistosomiasis and intestinal helminths. This article describes the baseline situation and the effect of treatment on geohelminth infection in a rural part of the province. METHODS: Grade 3 schoolchildren from Maputaland in northern KZN were examined for infections with hookworm, Ascaris lumbricoides, and Trichuris trichiura, treated twice with 400 mg albendazole and re-examined several times over one year after the first treatment in order to assess the impact of treatment and patterns of infection and re-infection. RESULTS: The hookworm prevalence in the study population (83.2%) was considerably higher than in other parts of the province whereas T. trichiura and especially A. lumbricoides prevalences (57.2 and 19.4%, respectively) were much lower than elsewhere on the KZN coastal plain. Single dose treatment with albendazole was very effective against hookworm and A. lumbricoides with cure rates (CR) of 78.8 and 96.4% and egg reduction rates (ERR) of 93.2 and 97.7%, respectively. It was exceptionally ineffective against T. trichiura (CR = 12.7%, ERR = 24.8%). Re-infection with hookworm and A. lumbricoides over 29 weeks after treatment was considerable but still well below pre-treatment levels. CONCLUSION: High geohelminth prevalences and re-infection rates in the study population confirm the need for regular treatment of primary school children in the area. The low effectiveness of single course albendazole treatment against T. trichiura infection however demands consideration of alternative treatment approaches

Hypertension in pregnancy and risk of coronary heart disease and stroke: A prospective study in a large UK cohort.

BACKGROUND: Many studies investigating long-term vascular disease risk associated with hypertensive pregnancies examined risks in relatively young women among whom vascular disease is uncommon. We examined the prospective relation between a history of hypertension during pregnancy and coronary heart disease (CHD) and stroke in middle-aged UK women. METHODS: In 1996-2001, 1.1 million parous women (mean age=56years) without vascular disease at baseline reported their history of hypertension during pregnancy and other factors. They were followed for incident CHD and stroke (hospitalisation or death). Adjusted relative risks (RRs) were calculated using Cox regression. RESULTS: Twenty-six percent (290,008/1.1 million) reported having had a hypertensive pregnancy; 27% (79,163/290,008) of women with hypertensive pregnancy, but only 10% (82,145/815,560) of those without hypertensive pregnancy, reported being treated for hypertension at baseline. Mean follow-up was 11.6years (mean ages at diagnosis/N of events: CHD=65years/N=68,161, ischaemic stroke=67years/N=8365, haemorrhagic stroke=64years/N=5702). Overall, the RRs (95% confidence interval [CI]) of incident disease in women with hypertensive pregnancy versus those without such history were: CHD=1.29 (1.27-1.31), ischaemic stroke=1.29 (1.23-1.35), and haemorrhagic stroke=1.14 (1.07-1.21). However, among women with hypertensive pregnancy who were not taking hypertension treatment at baseline, their RRs (95% CI) were only modestly increased: CHD=1.17 (1.14-1.19), ischaemic stroke=1.18 (1.11-1.25), and haemorrhagic stroke=1.09 (1.02-1.18). CONCLUSION: Hypertension during pregnancy was associated with increased CHD and stroke incidence in middle age, largely because such women also had hypertension in their 50s and 60s, which has a substantially greater effect on vascular disease risk than hypertension during pregnancy without hypertension later in life

Awareness in Practice: Tensions in Access to Sensitive Attribute Data for Antidiscrimination

Organizations cannot address demographic disparities that they cannot see. Recent research on machine learning and fairness has emphasized that awareness of sensitive attributes, such as race and sex, is critical to the development of interventions. However, on the ground, the existence of these data cannot be taken for granted. This paper uses the domains of employment, credit, and healthcare in the United States to surface conditions that have shaped the availability of sensitive attribute data. For each domain, we describe how and when private companies collect or infer sensitive attribute data for antidiscrimination purposes. An inconsistent story emerges: Some companies are required by law to collect sensitive attribute data, while others are prohibited from doing so. Still others, in the absence of legal mandates, have determined that collection and imputation of these data are appropriate to address disparities. This story has important implications for fairness research and its future applications. If companies that mediate access to life opportunities are unable or hesitant to collect or infer sensitive attribute data, then proposed techniques to detect and mitigate bias in machine learning models might never be implemented outside the lab. We conclude that today's legal requirements and corporate practices, while highly inconsistent across domains, offer lessons for how to approach the collection and inference of sensitive data in appropriate circumstances. We urge stakeholders, including machine learning practitioners, to actively help chart a path forward that takes both policy goals and technical needs into account

Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China

Air pollution exposure remains a leading public health problem in China. The use of chemical transport models to quantify the impacts of various emission changes on air quality is limited by their large computational demands. Machine learning models can emulate chemical transport models to provide computationally efficient predictions of outputs based on statistical associations with inputs. We developed novel emulators relating emission changes in five key anthropogenic sectors (residential, industry, land transport, agriculture, and power generation) to winter ambient fine particulate matter (PM2.5) concentrations across China. The emulators were optimized based on Gaussian process regressors with Matern kernels. The emulators predicted 99.9% of the variance in PM2.5 concentrations for a given input configuration of emission changes. PM2.5 concentrations are primarily sensitive to residential (51%–94% of first‐order sensitivity index), industrial (7%–31%), and agricultural emissions (0%–24%). Sensitivities of PM2.5 concentrations to land transport and power generation emissions are all under 5%, except in South West China where land transport emissions contributed 13%. The largest reduction in winter PM2.5 exposure for changes in the five emission sectors is by 68%–81%, down to 15.3–25.9 μg m−3, remaining above the World Health Organization annual guideline of 10 μg m−3. The greatest reductions in PM2.5 exposure are driven by reducing residential and industrial emissions, emphasizing the importance of emission reductions in these key sectors. We show that the annual National Air Quality Target of 35 μg m−3 is unlikely to be achieved during winter without strong emission reductions from the residential and industrial sectors

Unhealthy Snack Food and Beverage Consumption Is Associated with Lower Dietary Adequacy and Length-for-Age z-Scores among 12-23-Month-Olds in Kathmandu Valley, Nepal.

BACKGROUND: Consumption of unhealthy snack foods and beverages (USFBs) in low- and middle-income countries (LMICs) is rising, with global awareness increasing about risks of overnutrition. However, little is known about the relation between USFB consumption and young children's diet/nutritional outcomes in contexts where nutrient density of complementary foods is often low. OBJECTIVES: This study assessed the association of high USFB consumption, compared with low consumption, with nutrient intakes, dietary adequacy, iron status, and growth in young children in Kathmandu Valley, Nepal. METHODS: A cross-sectional survey was conducted in a representative sample of 745 primary caregivers of children aged 12-23 mo. Food consumption was measured through quantitative 24-h recalls, and child anthropometric measurements and capillary blood samples were collected. Using adjusted linear/logistic regression models, nutrient intakes, dietary adequacy, length-for-age and weight-for-length z-scores (LAZ and WLZ, respectively), and iron status were compared between lowest and highest tertiles of consumption based on the contribution of USFBs to total energy intakes (TEIs). Mediation of the relation between USFB consumption and LAZ via lowered dietary adequacy was explored using structural equations modeling. RESULTS: On average, USFBs contributed 46.9% of TEI among the highest tertile of consumers, compared with 5.2% of TEI among the lowest. Compared with low-USFB consumers, high-USFB consumers had lower nutrient intakes and a greater proportion were at risk of inadequate intakes for 8 nutrients. Mean LAZ was nearly 0.3 SD lower among high-USFB consumers than low consumers (P = 0.003), with this relationship partially mediated through dietary adequacy. No associations were found with stunting prevalence or iron status. Prevalence of overweight/obesity was low. CONCLUSIONS: In this LMIC context, high USFB consumption among young children was associated with inadequate micronutrient intakes, which can contribute to poor growth outcomes. Addressing increased availability of USFBs in LMIC food systems should be a priority for policies and programs aiming to safeguard child nutrition