As part of the Measurement of Agriculture Illuminating farm-Zone Emissions of N2O (MAIZE) project, in 2022 an aircraft platform sampled atmospheric concentrations of nitrous oxide (N2O) in the agriculture regions of Iowa. Vertical profiles were conducted on each flight to capture the vertical structure and mixing depths of the atmosphere. The data files contain the merged data for each individual flight day.
Airborne measurements reveal high spatiotemporal variation and the heavy-tail characteristic of nitrous oxide emissions in Iowa" by Natasha Dacic, Genevieve Plant, and Eric A Kort. Journal of Geophysical Research: Atmospheres. Submitted. and 2021 dataset: Kort, E. A., Plant, G., Dacic, N. (2022). Aircraft Data (2021) for Measurement of Agriculture Illuminating farm-Zone Emissions of N2O (MAIZE) [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/0jvh-0c91
As part of the Flaring & Fossil Fuels: Uncovering Emissions & Losses (F3UEL) project, in 2022 the aircraft measurement platform sampled offshore oil & gas facilities in the US Gulf of Mexico to quantify facility-level emissions using the approach detailed in Conley et al. (2017). Vertical profiles were conducted on each flight to capture the vertical structure and mixing depths of the atmosphere. The data file contains all merged flight data from each flight day.
Reference: Conley, S., Faloona, I., Mehrotra, S., Suard, M., Lenschow, D. H., Sweeney, C., Herndon, S., Schwietzke, S., Pétron, G., Pifer, J., Kort, E. A., and Schnell, R.: Application of Gauss’s theorem to quantify localized surface emissions from airborne measurements of wind and trace gases, Atmos. Meas. Tech., 10, 3345 – 3358, 2017.
As part of the Flaring & Fossil Fuels: Uncovering Emissions & Losses (F3UEL) project, in 2021 the aircraft measurement platform sampled offshore oil & gas facilities in Alaska and California to quantify facility-level emissions using the approach detailed in Conley et al. (2017). Onshore, the aircraft sampled downwind of flare combustion plumes in the Bakken region of North Dakota. Vertical profiles were conducted on each flight to capture the vertical structure and mixing depths of the atmosphere. The data file contains all merged flight data from each flight day. and Reference: Conley, S., Faloona, I., Mehrotra, S., Suard, M., Lenschow, D. H., Sweeney, C., Herndon, S., Schwietzke, S., Pétron, G., Pifer, J., Kort, E. A., and Schnell, R.: Application of Gauss’s theorem to quantify localized surface emissions from airborne measurements of wind and trace gases, Atmos. Meas. Tech., 10, 3345 – 3358, 2017.
As part of the Flaring & Fossil Fuels: Uncovering Emissions & Losses (F3UEL) project, in 2020 the aircraft measurement platform sampled offshore oil & gas facilities in the Gulf of Mexico to quantify facility-level emissions using the approach detailed in Conley et al. (2017). Onshore, the aircraft sampled downwind of flare combustion plumes in the Permian and Eagle Ford regions of Texas. Vertical profiles were conducted on each flight to capture the vertical structure and mixing depths of the atmosphere. The data file contains all merged flight data from each flight day. and Reference: Conley, S., Faloona, I., Mehrotra, S., Suard, M., Lenschow, D. H., Sweeney, C., Herndon, S., Schwietzke, S., Pétron, G., Pifer, J., Kort, E. A., and Schnell, R.: Application of Gauss’s theorem to quantify localized surface emissions from airborne measurements of wind and trace gases, Atmos. Meas. Tech., 10, 3345 – 3358, 2017.
Data is collected from research flights based in West Memphis, Arkansas, covering the Mississippi River Valley. The data file contains all merged flight data from each flight day.
Gvakharia, A., Kort, E.A., Smith, M.L., Conley, S., 2018. Testing and evaluation of a new airborne system for continuous N2O, CO2, CO, and H2O measurements: the Frequent Calibration High-performance Airborne Observation System (FCHAOS). Atmospheric Measurement Techniques; Katlenburg-Lindau 11, 6059. https://doi.org/10.5194/amt-11-6059-2018
The research adheres to PRISMA-HARM recommendations for systematic reviews. The reproducible search strategies for all databases, the citation export files from all databases, and the eligibility screening decisions are included in the dataset.
Haydar B, Baetzel A, Elliott A, MacEachern M, Kamal A, Christensen R. Adverse Events During Intrahospital Transport of Critically Ill Children: A Systematic Review. Anesth Analg. 2019. http://dx.doi.org/doi:10.1213/ANE.0000000000004585
This data is a subset of that originally produced as part of an effort to characterize GnRH neuron activity during prepubertal development in control and PNA mice and investigate the potential influences of sex and PNA treatment on this process (1). It was later used in (2) to further investigate the firing patterns of GnRH neurons in these categories of mice and determine how these patterns might differ based on age and treatment condition.
The data files can be opened and examined using Wavemetric's Igor Pro software. Code used to further examine and visualize the data can be found at https://gitlab.com/um-mip/mc-project-code.
This research was supported by National Institute of Health/Eunice Kennedy Shriver National Institute of Child Health and Human Development R01 HD34860 and P50 HD28934.
(1) Dulka EA, Moenter SM. Prepubertal development of gonadotropin-releasing hormone (GnRH) neuron activity is altered by sex, age and prenatal androgen exposure. Endocrinology 2017; 158:3941-3953
(2) Penix JJ, DeFazio RA, Dulka EA, Schnell S, Moenter SM. Firing patterns of gonadotropin-releasing hormone (GnRH) neurons are sculpted by their biology. Pending.
Dulka EA, Moenter SM. Prepubertal development of gonadotropin-releasing hormone neuron activity is altered by sex, age and prenatal androgen exposure. Endocrinology 2017; 158:3943-3953. https://dx.doi.org/10.1210%2Fen.2017-00768 and Penix JJ, DeFazio RA, Dulka EA, Schnell S, Moenter SM. Firing patterns of gonadotropin-releasing hormone (GnRH) neurons are sculpted by their biology. Pending.
Infant eating behavior is likely driven not only by hunger and satiety reflective of caloric need, but also by the reward value of food. The reward value of food can be understood in terms of wanting, liking, and salience. Little is understood about infant response to the reward value of food, or its predictors, particularly prenatally. This project sought to understand whether prenatal factors during pregnancy predict infant reward response to food, as measured by questionnaires in early infancy.
Healthy full-term infants were enrolled in a longitudinal study designed to examine the development of infant eating behavior. Infant weight and length was measured, mothers completed questionnaires regarding infant eating behaviors, and infants were weighed and length measured at ages 1, 2, 4, 6 and 10 months. Trajectories of eating behaviors were identified using latent class growth modeling and bivariate analyses examined associations of infant eating behavior trajectory membership with infant and maternal characteristics. Cross-lagged analyses examined associations between BEBQ subscales and infant weight-for-length z-score.
Harlan McCaffery, Julie Zaituna, Sophie Busch, Niko Kaciroti, Alison L. Miller, Julie C. Lumeng, Katherine L. Rosenblum, Ashley Gearhardt, Megan H. Pesch, Developmental trajectories of eating behaviors and cross-lagged associations with weight across infancy, Appetite, 2023, 106978
Healthy full-term infants were enrolled in a longitudinal study designed to examine the development of infant eating behavior. Infant weight and length was measured, mothers completed questionnaires regarding infant eating behaviors, and infant sucking behavior was quantified using the NFANT device during a typical feeding. The predictive value of the NFANT-generated sucking metrics for infant weight gain was evaluated.
Feldman, Keith, Katharine Asta, Ashley N. Gearhardt, Julie M. Sturza, Danielle Appugliese, Alison L. Miller, Katherine Rosenblum, Kai Ling Kong, Amanda K. Crandall, and Julie C. Lumeng. "Characterization of a Vigorous sucking style in early infancy and its predictive value for weight gain and eating behaviors at 12 months." Appetite (2023): 106525.