This dataset encompasses three distinct sets of data analyzed in the study, namely the survey data on favorability to the US, the survey data on trust in Americans, and the social media data.
Physical and biogeochemical variables from the NOAA-GFDL Earth System Model 2M experiments (pre-processed), previously published observation-based datasets, and code to reproduce figures from these datasets, used for the study 'Hydrological cycle amplification reshapes warming-driven oxygen loss in Atlantic Ocean'.
Microscopy images are part of a paper entitled "Structured foraging of soil predators unveils functional responses to bacterial defenses" by Fernando Rossine, Gabriel Vercelli, Corina Tarnita, and Thomas Gregor. For detailed acquisition methods see the paper. Experiments were performed between 2019 and 2020 at Princeton University. Two types of images are provided, macroscopic and microscopic widefiled Images. Macroscopic images all show Petri dishes covered in fluorescent bacteria being consumed by amoebae. Images are shown for D. discoideum, P. violaceum, and A. castellanii. Images depicting drug treatments (Nystatin and Fluorouracil) were obtained using D. discoideum. Images used for the creation of a profile were all taken within 30 minutes of each other. Within each directory numbered images are independent replicates. The raw video directory contains time series for dishes under drug treatments. Each numbered folder is a sequence of photos (taken 30 minutes apart of each other) of a single dish. Microscopic images all show amoebae consuming bacteria on a petri dish. The 45 minute videos show either edge cells (located at the edge of amoebae colonies), or inner cells (located 2.5 millimeters towards the center of the colony, from the edge). Videos are confocal stacks, with bacteria showing in green and amoebae appearing as black holes within the bacterial lawn. As was for the macroscopic images, images are shown for D. discoideum, P. violaceum, and A. castellanii. Images depicting drug treatments (Nystatin and Fluorouracil) were obtained using D. discoideum.
Bhattacharjee, Tapomoy; Amchin, Daniel; Alert, Ricard; Ott, Jenna; Datta, Sujit
Abstract:
Collective migration -- the directed, coordinated motion of many self-propelled agents -- is a fascinating emergent behavior exhibited by active matter that has key functional implications for biological systems. Extensive studies have elucidated the different ways in which this phenomenon may arise. Nevertheless, how collective migration can persist when a population is confronted with perturbations, which inevitably arise in complex settings, is poorly understood. Here, by combining experiments and simulations, we describe a mechanism by which collectively migrating populations smooth out large-scale perturbations in their overall morphology, enabling their constituents to continue to migrate together. We focus on the canonical example of chemotactic migration of Escherichia coli, in which fronts of cells move via directed motion, or chemotaxis, in response to a self-generated nutrient gradient. We identify two distinct modes in which chemotaxis influences the morphology of the population: cells in different locations along a front migrate at different velocities due to spatial variations in (i) the local nutrient gradient and in (ii) the ability of cells to sense and respond to the local nutrient gradient. While the first mode is destabilizing, the second mode is stabilizing and dominates, ultimately driving smoothing of the overall population and enabling continued collective migration. This process is autonomous, arising without any external intervention; instead, it is a population-scale consequence of the manner in which individual cells transduce external signals. Our findings thus provide insights to predict, and potentially control, the collective migration and morphology of cell populations and diverse other forms of active matter.
This dataset encompasses two distinct sets of data analyzed in the study, namely Asian American Scholar Forum survey data and Microsoft Academic Graph bibleometrics data:
Yu Xie, Xihong Lin, Ju Li, Qian He, Junming Huang, Caught in the Crossfire: Fears of Chinese-American Scientists, Proceedings of the National Academy of Sciences, in press (2023).
This dataset encompasses three distinct sets of data analyzed in the study, namely the survey data on favorability to the US, the survey data on trust in Americans, and the social media data.
The first part of the dataset comprises the analysis in Study 1 and Study 3, which is collected from three surveys, including the Social Attitude Questionnaire of Urban and Rural Residents (SAQURR) in 2019 and 2020, the COVID-19 Multi-Wave Study (CMWS) between 2020 and 2022, and the Survey on Living Conditions (SLC) in 2023.
The second part of the datasets provides information used in Study 4, involving the 2018 and 2020 waves of the CFPS, Baidu Index data, and the COVID-19 cases and deaths data.
The third dataset is provided to depict trends in attitudes toward the US in Study 2.
This dataset contains example input files, training data sets and potential files related to the publication "First-principles-based Machine Learning Models for Phase Behavior and Transport Properties of CO2." by Mathur et al (2023). In this work, we developed machine learning models for CO2 based on different exchange-correlation DFT functionals. We assessed their performance on liquid densities, vapor-liquid equilibrium and transport properties.
Numerical data is tabulated for all plots (Figures 2, 3a-b, 4-89, S1, S4a-b,d, S5a-b,d, S6-S156) and included as separate spreadsheets categorized by figure in a .zip file in the Supplementary Material. Error bars in Figure 4 show the spread of data observed for 4 and 5 trials on independent samples for MIL-101 and MOF-235, respectively. Figure 6a shows the average of triplicate filtrate test conversions with error propagated based on this spread. Figures 6b and S165 error bars on rate constants are determined based on propagated conversion uncertainty for independent trials and extracted standard deviations of pseudo-first order rate constants from linearized plots. Error bars on other plots represent propagation of experimental uncertainty on single trials.
These files contain code used to segment D. virilis acoustic duets, quantification of courtship behaviors during acoustic duets, and measurements of duet song features.