BU Research Data
https://hdl.handle.net/2144/20283
2021-04-04T06:02:21ZComponents of clean delivery kits and newborn mortality in the Zambia Chlorhexidine Application Trial (ZamCAT): an observational study
https://hdl.handle.net/2144/42348
Components of clean delivery kits and newborn mortality in the Zambia Chlorhexidine Application Trial (ZamCAT): an observational study
Park, Jason; Hamer, Davidson; Mbewe, Reuben; Scott, Nancy; Herlihy, Julie; Yeboah-Antwi, Kojo; Semrau, Katherine
BACKGROUND: Infection, a leading cause of neonatal death in low- and middle-income countries, is often caused by pathogens acquired during childbirth. Clean delivery kits (CDKs) have shown efficacy in reducing infection-related perinatal and neonatal mortality. However, there remain gaps in our current knowledge, including the effect of individual components, timeline of protection, and benefit of CDKs in home and facility-based deliveries.
METHODS AND FINDINGS: A post-hoc, secondary analysis was performed using non-randomized data from the Zambia Chlorhexidine Application Trial (ZamCAT), a community-based, cluster-randomized controlled trial of chlorhexidine umbilical cord care in Southern Province of Zambia from February 2011 to January 2013. CDKs, containing soap, gloves, cord clamp, plastic sheet, razor blade, matches, and candles, were provided to all participants. Field monitors made home-based visit to each participant 4 days post-partum, during which CDK use and newborn outcomes were ascertained. Logistic regression was used to study the association between different CDK components and newborn mortality rate (NMR).Of 38,579 deliveries recorded during the study, 36,996 newborns were analyzed after excluding stillbirths and missing information. Gloves, cord clamp, and plastic sheets were the most frequently used CDK item combinations in both home and facility deliveries. Each of the 7 CDK components was associated with lower NMR in users versus non-users. Adjusted logistic regression showed that use of gloves (OR: 0.33, CI: 0.24-0.46), cord clamp (OR: 0.51, CI: 0.38-0.68), plastic sheets (OR: 0.46, CI: 0.34-0.63), and razor blades (OR: 0.69, CI: 0.53-0.89) were associated with lower risk of newborn mortality. Use of gloves and cord clamp was associated with reduced risk of immediate newborn death (<24 hours). Reduction in risk of early newborn death (1-7 days) was associated with use of gloves, cord clamp, plastic sheets, and razor blades. In examining perinatal mortality, similar patterns were observed. There was no significant reduction in risk of late newborn mortality (7-28 days) with CDK use. Study limitations included potential for potential recall bias of CDK use and inability to establish causality as a secondary observational study.
CONCLUSIONS: CDK use was associated with reductions in early newborn mortality at both home and facility deliveries, especially when certain kit components were used. While causality could not be established in this non-randomized secondary analysis, given these beneficial associations, scaling up the use of CDKs in rural areas of sub-Saharan Africa may improve neonatal outcomes.
2021-01-01T00:00:00ZDataset: Instagram use and its association with user outcomes
https://hdl.handle.net/2144/42224
Dataset: Instagram use and its association with user outcomes
Trifiro, Briana; Prena, Kelsey
An Excel-based template for estimating induction-phase treatment costs for cryptococcal meningitis in high HIV-burden African countries
https://hdl.handle.net/2144/41876
An Excel-based template for estimating induction-phase treatment costs for cryptococcal meningitis in high HIV-burden African countries
Larson, Bruce; Shroufi, Amir; Muthoga, Charles; Oladele, Rita; Rajasingham, Radha; Jordan, Alexander; Jarvis, Joe; Chiller, Tom; Govender, Nelesh
This repository contains a costing template for estimating induction-phase treatment costs for cryptococcal meningitis in high HIV-burden African countries. Four country-specific examples are included (and any of these examples can be used as a template for replication with new information, in other locations, etc. A brief User's Guide is included.
2021-01-01T00:00:00ZData and code for: Feedbacks among electric vehicle adoption, charging, and the cost and installation of rooftop solar
https://hdl.handle.net/2144/41462
Data and code for: Feedbacks among electric vehicle adoption, charging, and the cost and installation of rooftop solar
Kaufmann, Robert K.; Newberry, Derek; Xin, Chen; Gopal, Sucharita
Identifying feedback loops in consumer behaviors is important to develop policies to accentuate desired behavior. Here, we use Granger causality to provide empirical evidence for feedback loops among four important components of a low-carbon economy. One loop includes the cost of installing rooftop solar (Cost) and the installation of rooftop solar (PV); this loop is likely generated by learning by doing and reductions in the levelized cost of electricity. The second includes the purchase of electric vehicles (EV) and the installation of rooftop solar that is likely created by environmental complementarity. Finally, we address whether installing charging stations enhances the purchase of electric vehicles and vice versa; surprisingly, there is no evidence for a causal relation in either direction. Together, these results suggest ways to modify existing policy in ways that could trigger the Cost ↔PV ↔EV feedback loops and accelerate the transition to carbon free technologies.
These data and code can be used to reproduce the results described in the paper: "Feedbacks among electric vehicle adoption, charging, and the cost and installation of rooftop solar" that is published in Nature Energy.
The data file is EV_Table_0702.xls. The remaining files are RATS files that are used to generate the statistical output. The statistical software RATS is needed to run statistical analyses.
2020-10-01T00:00:00ZMechanical MNIST - Fashion
https://hdl.handle.net/2144/41450
Mechanical MNIST - Fashion
Lejeune, Emma
Each dataset in the Mechanical MNIST collection contains the results of 70,000 (60,000 training examples + 10,000 test examples) finite element simulation of a heterogeneous material subject to large deformation. Mechanical MNIST - Fashion is generated by first converting the fashion MNIST bitmap images (https://github.com/zalandoresearch/fashion-mnist) to 2D heterogeneous blocks of material. Consistent with the MNIST bitmap ($28 \times 28$ pixels), the material domain is a $28 \times 28$ unit square. In “Mechanical MNIST - Fashion,” the material is Neo-Hookean with a varying modulus. In the Uniaxial Extension (UE) case, the bottom of the domain is fixed (Dirichlet boundary condition), the left and right edges of the domain are free, and the top of the domain is fixed horizontally and moved vertically to a given fixed displacement (d). In the Equibiaxial Extension (EE) case, the top of the domain is free horizontally and moved vertically to a given fixed displacement (d), the right of the domain is free vertically and moved horizontally to a given fixed displacement (d), the bottom of the domain is free horizontally and moved vertically to a given fixed displacement (-d), and the left of the domain is free vertically and moved horizontally to a given fixed displacement (-d). The results of the simulations include: (1) change in strain energy at a perturbation level step (d=0.001), and at the final applied displacement (d=14 for UE, d=7 for EE) (2) total reaction force at a perturbation level step (d=0.001 for UE, d=.0005 for EE), and at the final applied displacement (d=14 for UE, d=7 for EE), and (3) full field displacement at a perturbation level step (d=0.001), and at the final applied displacement (d=14 for UE, d=7 for EE). The x-reaction (first column) and y-reaction (second column) forces are given. For the UE case, this corresponds to the top boundary. For the EE case, this correspond to the left and top boundaries. All simulations are conducted with the FEniCS computing platform (https://fenicsproject.org). The code to reproduce these simulations and import these text files is hosted on GitHub (https://github.com/elejeune11/Mechanical-MNIST-fashion).
The paper "Mechanical MNIST: A benchmark dataset for mechanical metamodels" can be found at https://doi.org/10.1016/j.eml.2020.100659. All code necessary to reproduce these finite element simulations is available on GitHub (https://github.com/elejeune11/Mechanical-MNIST-fashion). For questions, please contact Emma Lejeune (elejeune@bu.edu).
2020-01-01T00:00:00ZMechanical MNIST - Multi-Fidelity
https://hdl.handle.net/2144/41357
Mechanical MNIST - Multi-Fidelity
Lejeune, Emma
Each dataset in the Mechanical MNIST collection contains the results of 70,000 (60,000 training examples + 10,000 test examples) finite element simulation of a heterogeneous material subject to large deformation. Mechanical MNIST is generated by first converting the MNIST bitmap images (http://www.pymvpa.org/datadb/mnist.html) to 2D heterogeneous blocks of material. Consistent with the MNIST bitmap ($28 \times 28$ pixels), the material domain is a $28 \times 28$ unit square. The material is Neo-Hookean with a varying modulus dictated by the input bitmap. The simulation results included here are the change in strain energy at a fixed level of applied displacement. The cases considered are as follows:
*UE: uniaxial extension, full fidelity dataset (fully refined mesh, quadratic triangular elements, applied displacement is $1/2$ of a side length);
*EE: equibiaxial extension, full fidelity dataset;
*3D: uniaxial extension and out of plane twist, full fidelity three dimensional dataset (fully refined mesh, quadratic tetrahedral elements, applied displacement is $1/7$ of a side length, twist is $\pi/8$ radians, block thickness is $1/7$ of a side length);
*UE-CM-28: uniaxial extension, $28 \times 28 \times 2$ linear triangular elements;
*UE-CM-14: uniaxial extension, $14 \times 14 \times 2$ linear triangular elements;
*UE-CM-7: uniaxial extension, $7 \times 7 \times 2$ linear triangular elements;
*UE-CM-7-quad: uniaxial extension, $7 \times 7 \times 2$ quadratic triangular elements;
*UE-CM-4: uniaxial extension, $4 \times 4 \times 2$ linear triangular elements;
*UE-CM-4-quad: uniaxial extension, $4 \times 4 \times 2$ quadratic triangular elements;
*UE-perturb: uniaxial extension, applied displacement is a perturbation (.001 units);
*UE-CM-28-perturb: uniaxial extension, $28 \times 28 \times 2$ linear triangular elements, applied displacement is a perturbation (.001 units).
All simulations are conducted with the FEniCS computing platform (https://fenicsproject.org). The code to reproduce these simulations is hosted on GitHub (https://github.com/elejeune11/Mechanical-MNIST-Transfer-Learning).
The paper "Exploring the potential of transfer learning for metamodels of heterogeneous material deformation" is forthcoming. All code necessary to reproduce the metamodels demonstrated in the manuscript is available on GitHub (https://github.com/elejeune11/Mechanical-MNIST-Transfer-Learning). For questions, please contact Emma Lejeune (elejeune@bu.edu).
2020-07-01T00:00:00ZPioneer Venus Orbiter Radio Occultation Profiles
https://hdl.handle.net/2144/41269
Pioneer Venus Orbiter Radio Occultation Profiles
Withers, Paul
This archive contains derived data products from Pioneer Venus Orbiter radio occultations at Venus and their accompanying documentation. The derived data products include frequency data, ionospheric electron density profiles, and neutral atmospheric temperature profiles.
2020-07-01T00:00:00ZTraining in adolescent substance use and opioid misuse in US pediatric residency programs: a national survey
https://hdl.handle.net/2144/41197
Training in adolescent substance use and opioid misuse in US pediatric residency programs: a national survey
Allen, Emily; Michelson, Catherine D.; O’Donnell, Katherine A.; Bagley, Sarah M.; Earlywine, Joel J.; Hadland, Scott E.
Electronic survey distributed to associate program directors and chief residents of pediatric residency programs.
2020-06-15T00:00:00ZData for: Understanding glacial cycles: a multivariate disequilibrium approach
https://hdl.handle.net/2144/41127
Data for: Understanding glacial cycles: a multivariate disequilibrium approach
Kaufmann, Robert K.; Pretis, Felix
We find a consistent relation between orbital geometry and components of the climate system by returning to Milankovitch’s original hypothesis and focusing on the well-established physical concepts of an equilibrium state, disequilibrium from that state, and adjustment towards equilibrium. These mechanisms imply that the state of the climate system at any time depends on; (1) the state of the climate system in the previous period, (2) the degree to which this previous state is out-of-equilibrium with orbital geometry, and (3) the rate at which the climate system adjusts towards equilibrium. We evaluate this explanation by running experiments with a statistical model of climate that explicitly represents equilibria among variables and their movements towards equilibrium. Results indicate that; (1) skipped obliquity/precession beats are an artifact of ignoring adjustments towards an equilibrium state, (2) accounting for equilibrium and adjustments to equilibrium can account for all phases of the glacial cycle, and (3) glacial cycles are generated by adjustments to equilibrium relations between orbital geometry and climate and among components of the climate system. Together, these results suggest a new approach to understanding glacial cycles that is based on models which include a rich set of equilibria and adjustments to equilibria for a full suite of climate variables simulated over long periods.
2020-01-01T00:00:00ZData and code for: Testing Hypotheses About Glacial Dynamics and the Stage 11 Paradox Using a Statistical Model of Paleo-Climate
https://hdl.handle.net/2144/40340
Data and code for: Testing Hypotheses About Glacial Dynamics and the Stage 11 Paradox Using a Statistical Model of Paleo-Climate
Kaufmann, Robert K.; Pretis, Felix
To test hypotheses about glacial dynamics, the Mid-Brunhes event, and the stage 11 paradox, we evaluate the ability of a statistical model to simulate climate during the previous ~800,000 years. Throughout this period, the model simulates the timing and magnitude of glacial cycles, including the saw-tooth pattern in which ice accumulates gradually and ablates rapidly, without nonlinearities or threshold effects. This suggests that nonlinearities and/or threshold effects do not play a critical role in glacial cycles. Furthermore, model accuracy throughout the previous ~800,000 years suggest that changes in glacial cycles associated with the Mid-Brunhes event, which occurs near the division between the out-of-sample period and the in-sample period, are not caused by changes in the dynamics of the climate system. Conversely, poor model performance during MIS stage 11 and Termination V is consistent with arguments that the ‘stage 11 paradox’ represents a mismatch between orbital geometry and climate. Statistical orderings of simulation errors indicate that periods of reduced accuracy start with significant reductions in the model’s ability to simulate carbon dioxide, non-sea-salt sodium, and non-sea-salt calcium. Their importance suggests that the stage 11 paradox is generated by changes in atmospheric and/or oceanic circulation that affect ocean ventilation of carbon dioxide.
These data and code can be used to reproduce the results described in the paper Testing Hypotheses About Glacial Dynamics and the Stage 11 Paradox Using a Statistical Model of Paleo-Climate that is submitted for peer review in Climate of the Past.
2020-04-01T00:00:00ZBuckling Instability Classification (BIC)
https://hdl.handle.net/2144/40085
Buckling Instability Classification (BIC)
Lejeune, Emma
The Buckling Instability Classification (BIC) datasets contain the results of finite element simulations where a heterogeneous column is subject to a fixed level of applied displacement and is classified as either "Stable" or "Unstable." Each model input is a 16x1 vector where the entries of the vector dictate the Young's Modulus (E) of the corresponding portion of the physical column domain. Each input file has 16 columns one for each vector entry. For each 16x1 vector input, there is a single output that indicates if the column was stable or unstable at the fixed level of applied displacement. An output value of "0" indicates stable, and an output value of "1" indicates unstable. In BIC-1, we only allow two possible discrete values for E: E=1 or E=4. In BIC-2, we allow three possible discrete values for E: E=1, E=4, or E=7. In BIC-3, we allow continuous values (to three digits of precision) of E in the range E=1–8. BIC-1 consists of 65,536 simulation results. This exhausts the entire possible input domain. BIC-2 consists of 100,000 simulation results. This is less than 1% of the entire possible input domain. BIC-3 also consists of 100,000 simulation results. This is a tiny fraction of the entire possible input domain.
Link to the manuscript “Geometric stability classification: datasets, metamodels, and adversarial attacks” is forthcoming. All code necessary to generate the BIC datasets and reproduce the metamodels demonstrated in the manuscript is available on GitHub (https://github.com/elejeune11/BIC). For questions, please contact Emma Lejeune (elejeune@bu.edu).
2020-01-01T00:00:00ZDiagnosis Codes for Addiction and Mental Health Research
https://hdl.handle.net/2144/39358
Diagnosis Codes for Addiction and Mental Health Research
Bagley, Sarah M.; Gai, Mam Jarra; Earlywine, Joel J.; Schoenberger, Samantha F.; Morgan, Jake R.; Hadland, Scott E.; Barocas, Joshua A.
These diagnosis codes can be used in the study of opioid use disorder and related conditions. The spreadsheets include International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10) diagnosis codes for opioid-related complications (i.e., opioid use disorder and opioid-related overdose) and other substance use disorders, as well as comorbid mental health conditions. Sources: International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10).
These diagnosis codes are part of two studies:
1. Scott E. Hadland, MD, MPH, MS, Sarah M. Bagley, MD, MSc, Mam Jarra Gai, MPH, Joel J. Earlywine, BA, Samantha F. Schoenberger, BA, Jake R. Morgan, PhD, & Joshua A. Barocas, MD, MPH. 2020. "Diagnosis Codes for Addiction and Mental Health Research".; 2. Bagley, S.M., Gai, M.J., Earlywine, J.J., Schoenberger, S.F., Hadland, S.E., and Barocas, J.A. 2020-07-21. "Sex-based differences in nonfatal opioid overdose among male and female youth". This study was funded by: National Institute on Drug Abuse and Charles A. King Trust.
2020-01-01T00:00:00ZGeorinex
https://hdl.handle.net/2144/39121
Georinex
Hirsch, Michael
RINEX 3 and RINEX 2 reader and batch conversion to NetCDF4 / HDF5 in Python or Matlab. Batch converts NAV and OBS GPS RINEX (including Hatanaka compressed OBS) data into xarray.Dataset for easy use in analysis and plotting. This gives remarkable speed vs. legacy iterative methods, and allows for HPC / out-of-core operations on massive amounts of GNSS data. GeoRinex works in Python ≥ 3.6 and has over 150unit tests driven by Pytest.
2019-03-28T00:00:00ZMechanical MNIST - Uniaxial Extension
https://hdl.handle.net/2144/38693
Mechanical MNIST - Uniaxial Extension
Lejeune, Emma
Each dataset in the Mechanical MNIST collection contains the results of 70,000 (60,000 training examples + 10,000 test examples) finite element simulation of a heterogeneous material subject to large deformation. Mechanical MNIST is generated by first converting the MNIST bitmap images (http://www.pymvpa.org/datadb/mnist.html) to 2D heterogeneous blocks of material. Consistent with the MNIST bitmap ($28 \times 28$ pixels), the material domain is a $28 \times 28$ unit square. In “Mechanical MNIST - Uniaxial Extension,” the material is Neo-Hookean with a varying modulus. The bottom of the domain is fixed (Dirichlet boundary condition), the left and right edges of the domain are free, and the top of the domain is fixed horizontally and moved vertically to a set of given fixed displacements (d = [0.0, 0.001, 0.01, 0.1, 0.5, 1.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0 ]). The results of the simulations include: (1) change in strain energy at each step, (2) total reaction force at the top boundary at each step, and (3) full field displacement at each step. All simulations are conducted with the FEniCS computing platform (https://fenicsproject.org). The code to reproduce these simulations is hosted on GitHub (https://github.com/elejeune11/Mechanical-MNIST/tree/master/generate_dataset).
The paper "Mechanical MNIST: A benchmark dataset for mechanical metamodels" can be found at https://doi.org/10.1016/j.eml.2020.100659. All code necessary to reproduce the metamodels demonstrated in the manuscript is available on GitHub (https://github.com/elejeune11/Mechanical-MNIST). For questions, please contact Emma Lejeune (elejeune@bu.edu).
2019-12-01T00:00:00ZInteractive single cell RNA-Seq analysis with Single Cell Toolkit (SCTK)
https://hdl.handle.net/2144/38691
Interactive single cell RNA-Seq analysis with Single Cell Toolkit (SCTK)
Johnson, W. Evan; Jenkins, David; Khan, Mohammed Muzamil; Faits, Tyler; Zhang, Yuqing; McFarlane, Ada; Zhao, Yue; Campbell, Joshua D.; Yajima, Masanao
I will present the Single Cell Toolkit (SCTK), an R package and interactive single cell RNA-sequencing (scRNA-Seq) analysis package that provides the first complete workflow for scRNA-Seq data analysis and visualization using a set of R functions and an interactive web interface. Users can perform analysis with modules for filtering raw results, clustering, batch correction, differential expression, pathway enrichment, and scRNA-Seq study design. The toolkit supports command line or pipeline data processing, and results can be loaded into the GUI for additional exploration and downstream analysis. We demonstrate the effectiveness of the SCTK on multiple scRNA-seq examples, including data from mucosal-associated invariant T cells, induced pluripotent stem cells, and breast cancer tumor cells. While other scRNA-Seq analysis tools exist, the SCTK is the first fully interactive analysis toolkit for scRNA-Seq data available within the R language.
2019-05-01T00:00:00ZAssociations between Maternal Thyroid Function in Pregnancy and Obstetric and Perinatal Outcomes - Supplemental Table 1
https://hdl.handle.net/2144/38476
Associations between Maternal Thyroid Function in Pregnancy and Obstetric and Perinatal Outcomes - Supplemental Table 1
Lee, Sun Young; Cabral, Howard J.; Aschengrau, Ann; Pearce, Elizabeth N.
Supplemental table 1 for "Associations between Maternal Thyroid Function in Pregnancy and Obstetric and Perinatal Outcomes." Covariates included in the multivariable regression analyses.
MATLAB code and data processing guide for phase resolved Doppler Optical Coherence Tomography
https://hdl.handle.net/2144/37076
MATLAB code and data processing guide for phase resolved Doppler Optical Coherence Tomography
Tang, Jianbo; Erdener, Sefik Evren; Fu, Buyin; Boas, David A.
This guide and the MATLAB code are for post data processing of prDOCT, which outputs 3D vascular blood flow velocity.
Example data is available through:
https://drive.google.com/open?id=168HD4lKt0K97g09zus6H9h7lAyO0jOBZ
https://drive.google.com/open?id=1QvTO_41cPN3_wM9wxCh9NECv_hypVZPC
MATLAB code and data processing guide for Optical Coherence Tomography Angiography
https://hdl.handle.net/2144/37075
MATLAB code and data processing guide for Optical Coherence Tomography Angiography
Tang, Jianbo; Erdener, Sefik Evren; Sunul, Smrithi; Boas, David A.
This guide is for post data processing of OCTA which outputs the vascular structure.
Example data is available through:
https://drive.google.com/a/bu.edu/file/d/1q9_F93_5p_pgmXIwzKZOCQ36m7sE95d2/view?usp=sharing
https://drive.google.com/a/bu.edu/file/d/1nNzjBI2JZFf4epRr4XSKhRYiT7FJcZT7/view?usp=sharing
https://drive.google.com/a/bu.edu/file/d/1dAwY56dSBoRLX246ALTeV3ZiV3dehrEH/view?usp=sharing
https://drive.google.com/a/bu.edu/file/d/1TUA9L170blYQdrI6L9oiSkxae7XpV--E/view?usp=sharing
https://drive.google.com/a/bu.edu/file/d/1J3gG6HFBK2uVjDgCRHbRV6fvA2Ei9YHp/view?usp=sharing
MATLAB code and data processing guide for g1OCTA
https://hdl.handle.net/2144/37074
MATLAB code and data processing guide for g1OCTA
Tang, Jianbo; Erdener, Sefik Evren; Sunil, Smrithi; Boas, David A.
This guide is for post data processing of g1OCTA which outputs 3D vascular structure with flow
direction and minimized tail artifacts.
Example data is available through:
https://drive.google.com/open?id=168HD4lKt0K97g09zus6H9h7lAyO0jOBZ
https://drive.google.com/open?id=1QvTO_41cPN3_wM9wxCh9NECv_hypVZPC
MATLAB code and data processing guide for Dynamic Light Scattering-Optical Coherence Tomography
https://hdl.handle.net/2144/37072
MATLAB code and data processing guide for Dynamic Light Scattering-Optical Coherence Tomography
Tang, Jianbo; Erdener, Sefik Evren; Li, Baoqiang; Fu, Buyin; Sakadzic, Sava; Carp, Stefan A.; Lee, Jonghwan; Boas, David A.
This guide is for post data processing of DLSOCT, which outputs axial velocity (Vz), transverse
velocity (Vx), total velocity(V), the ratio of static component (Ms), the ratio of dynamic component (Mf), and fitting accuracy (R). The speed upper limit is determined by OCT system Aline rate and 3Dvoxel size.
An example data is available through:
https://drive.google.com/open?id=168HD4lKt0K97g09zus6H9h7lAyO0jOBZ
https://drive.google.com/open?id=1QvTO_41cPN3_wM9wxCh9NECv_hypVZPC