Harvard Dataverse
Not a member yet
92223 research outputs found
Sort by
Galaxies Going Bananas: Inferring the 3D Geometry of High-Redshift Galaxies with JWST-CEERS
No full textThis repository provides all figures for the Astrophysical Journal article "Galaxies Going Bananas: Inferring the 3D Geometry of High-Redshift Galaxies with JWST-CEERS" by Viraj Pandya et al. We also include a machine readable version of Table 2.
Below we describe the four figure sets corresponding to Figures 7, 8, 13 and 23 in the paper as well as Table 2. This repository also includes all individual figures not comprising sets -- for a description of these, we refer the user to their corresponding captions in the paper.
Figure 7 shows corner plots from our constrained Bayesian model for 3D galaxy shapes in a single mass-redshift bin. The figure set here includes analogous corner plots for the other mass-redshift bins.
Figure 8 shows the fractional contribution of ellipsoids of different types (prolate, oblate, spheroidal) to the observed joint distribution of projected axis ratios and sizes. This figure also shows that we can use these fractional model contributions to assign 3D shape probabilities to individual observed galaxies. The figure set here includes analogous figures for other mass-redshift bins and for our model applied independently to the SE++ and Galfit data.
Figure 13 shows a histogram of 3D axis ratios (C/A vs B/A) computed as the average of 500 draws from our model posterior for every mass-redshift bin. The version in the paper is for our model applied to the SE++ data. The additional figure here is for our model applied to the Galfit data.
Figure 23 shows mock parameter recovery tests for Hamiltonian Monte Carlo applied to our Bayesian 3D galaxy shape model with different sample sizes. The version in the paper is for a mock population of ellipsoids dominated by prolate objects. The additional figures here are for additional mock populations dominated by either spheroids, oblate (axisymmetric) disks, or triaxial (oval) disks.
Table 2 summarizes the means and standard deviations of our Bayesian model as well as ellipsoid class fractions for every mass-redshift bin. The results from both of our models based on Galfit and SE++ have been combined into this single table. This is a machine readable table that can easily be read in with, e.g., the Python astropy.table module
Offgridders energy system modelling results - Covid-case
No full textEnergy system modelling results based on Offgridders optimisation and simulation for different energy system setups (diesel-only and PV, battery diesel), sensitivities (0, 5, and 10 % shortage allowance). Parameters such as installed capacities, first investment, net present value and levelised cost of electricity are provided. Two different result files are available for: 1) health care facilities only considering covid equipment, 2) health care facilities and their surrounding households considering covid equipment
Psychological Capital: A Comprehensive Introduction
No full textIn Aotearoa New Zealand’s dynamic and ever-evolving organizational landscape, success is often contingent upon more than just financial capital, tangible assets, or market positioning. The intangible aspects of human capital, particularly psychological capital, have garnered increasing attention among scholars, researchers, and practitioners in Aotearoa New Zealand. Psychological capital, often abbreviated as PsyCap, represents a multifaceted construct encompassing positive psychological resources that individuals possess, and which have been demonstrated to play a pivotal role in personal well-being and organizational performance. This introduction aims to provide a comprehensive overview of psychological capital, elucidating its conceptual foundations, measurement approaches, antecedents, consequences, and implications for individuals and organizations across our country
Replication Data for: "Managing Mental Accounts Payment Cards and Consumption Expenditures"
No full textStata do-files to produce the tables
Replication Data for: Culpability, Redistribution, Inequality and Public Support for Government Assistance for Struggling Firms and Workers
No full textReplication data for Culpability, Redistribution, Inequality and Public Support for Government Assistance for Struggling Firms and Workers, published in Political Behavio
Factors associated with clinical nurses’ preconception health behavior in Korea: a cross-sectional survey
No full textPurpose: Nurses have been reported to be at an increased risk for miscarriage and preterm labor. However, there is limited knowledge regarding nurses’ preconception health behaviors. Therefore, this study aimed to identify factors influencing these behaviors.
Methods: One hundred sixty nurses, who were planning their first pregnancy within the upcoming year, participated in an online survey from August 11 to October 31, 2021. Data on preconception health behavior, perceived health status, pregnancy anxiety, nursing practice environment, and social support were analyzed using the t-test, Pearson correlation coefficients, and multiple regression analysis.
Results: Age (р=.024), educational level (р=.010), marital status (р=.003), work experience (р=.003), satisfaction with the work department (р<.001), smoking status (р=. 039), and previous health problems related to pregnancy outcomes (р=.004) were significantly associated with nurses’ preconception health behaviors. Furthermore, perceived health status (р<.001), pregnancy anxiety (р=.011), nursing practice environment (р=.003), and social support (р<.001) showed significant correlations with preconception health behaviors. Social support (β=. 28, р=.001), satisfaction with the work department (β=.23, р=.032), marital status (β=.22, р=.002), and perceived health status (β=.23, р=.002) were confirmed as factors associated with preconception health behaviors. These factors explained 40.9% of the variance in preconception health behaviors (F=6.64, р<.001).
Conclusion: Clinical nurses’ preconception health behaviors were influenced by social support, perceived health status, satisfaction with the work department, and marital status. Interventions to improve clinical nurses’ preconception health behaviors should target social support and perceived health status. A preconception health behavior education program considering clinical nurses’ marital status and satisfaction with the workplace can also be implemented
Replication Data for: Flexible sensitivity analysis for causal inference in observational studies subject to unmeasured confounding
No full textR code and datasets for “Flexible sensitivity analysis for causal inference in observational studies subject to unmeasured confounding” by Lu and Ding
Replication data for "Evaluating Bias and Noise Induced by the U.S. Census Bureau's Privacy Protection Methods"
No full textThe United States Census Bureau faces a difficult trade-off between the accuracy of Census statistics and the protection of individual information. We conduct the first independent evaluation of bias and noise induced by the Bureau's two main disclosure avoidance systems: the TopDown algorithm employed for the 2020 Census and the swapping algorithm implemented for the three previous Censuses. Our evaluation leverages the Noisy Measure File (NMF) as well as two independent runs of the TopDown algorithm applied to the 2010 decennial Census. We find that the NMF contains too much noise to be directly useful, especially for Hispanic and multiracial populations. TopDown's post-processing dramatically reduces the NMF noise and produces data whose accuracy is similar to that of swapping. While the estimated errors for both TopDown and swapping algorithms are generally no greater than other sources of Census error, they can be relatively substantial for geographies with small total populations
Replication Data for Fear and Loathing, Chat GPT in the Political Science Classroom
No full textChatGPT has captured the attention of the academic world with its remarkable ability to write, summarize, and even pass rigorous exams. This article provides a brief summary of the primary concerns of political science faculty with ChatGPT and similar AI software with regard to academia. Additionally, we discuss results of a national survey of political scientists conducted in March of 2023 to assess faculty attitudes towards ChatGPT and their strategies for effectively engaging with it in the classroom. Next, we present several assignment ideas that limit the potential for cheating with ChatGPT, a primary concern of faculty, and provide opportunities for incorporating ChatGPT into faculty teaching. Finally, several suggestions for syllabi addressing political science students' use of ChatGPT are provided