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101. Data for Configurations and Characteristics of Simulated Single-Chain Nanoparticles

Author(s):
Webb, Michael; Patel, Roshan; Colmenares, Sophia
Abstract:
This item provides access to all configurations of single-chain nanoparticles analyzed in the manuscript "Sequence Patterning, Morphology, and Dispersity in Single-Chain Nanoparticles: Insights from Simulation and Machine Learning" by Roshan A. Patel, Sophia Colmenares, and Michael A. Webb (DOI: 10.1021/acspolymersau.3c00007). The single-chain nanoparticles derive from 320 unique precursor chains that are distinguished by the fraction of linker beads that decorate a fixed-length polymer backbone and the distribution or blockiness of those linker beads. The data is provided in the form of serialized object using the `pickle' python module. The data was compiled using Python version 3.8.8 and Clang 10.0.0. The Python object loaded from the .pkl file is a nested list, with the first dimension having 7,680 entries for the 7,680 unique single-chain nanoparticles produced in the aforementioned paper. Each of those 7,680 entries is itself a list with 20 entries, representing the 20 different simulation snapshots of the given single-chain nanoparticle. Each of the 20 entries is another list with two entries, with the first being a numpy.ndarray containing the x,y,z coordinates of all the beads comprising the single-chain nanoparticle and the second being a numpy.ndarray with a numerical encoding to indicate whether the beads are backbone (indicated as '0') or linker beads (indicated as '1'). Altogether, this provides 153,600 configurations of single-chain nanoparticles.
Type:
Dataset
Issue Date:
2 June 2023

102. Data for Nature Climate Change article 'Regional dry-season climate changes due to three decades of Amazonian deforestation'

Author(s):
Khanna, Jaya; Medvigy, David; Fueglistaler, Stephan; Walko, Robert
Abstract:
More than 20% Amazon rainforest has been cleared in the past three decades triggering important hydroclimatic changes. Small-scale (~few kilometers) deforestation in the 1980s has caused thermally-triggered atmospheric circulations that increase regional cloudiness and precipitation frequency. However, these circulations are predicted to diminish as deforestation increases. Here we use multi-decadal satellite records and numerical model simulations to show a regime shift in the regional hydroclimate accompanying increasing deforestation in Rondônia, Brazil. Compared to the 1980s, present-day deforested areas in downwind western Rondônia are found to be wetter than upwind eastern deforested areas during the local dry season. The resultant precipitation change in the two regions is approximately ±25% of the deforested area mean. Meso-resolution simulations robustly reproduce this transition when forced with increasing deforestation alone, showing a negligible role of large-scale climate variability. Furthermore, deforestation-induced surface roughness reduction is found to play an essential role in the present-day dry season hydroclimate. Our study illustrates the strong scale-sensitivity of the climatic response to Amazonian deforestation and suggests that deforestation is sufficiently advanced to have caused a shift from a thermally- to a dynamically-driven hydroclimatic regime.
Type:
Dataset and Software
Issue Date:
2017

104. Data for food consumption strategies in China

Author(s):
Guo, Yixin; Mauzerall, Denise; He, Pan
Abstract:
This dataset provides the data generated during the project analyzing ‘Food Consumption Strategies for Addressing Air Pollution, Climate Change, Water Use, and Public Health in China’. It includes the code for generating the alternative dietary scenarios, for analyzing the health impacts of alternative diets, and for visualization of results.
Type:
Dataset
Issue Date:
31 October 2020

106. Data for “Global adjoint tomography—model GLAD-M25”

Author(s):
Lei, Wenjie
Abstract:
The dataset contains the model file for the Global Adjoint Tomography Model 25 (GLAD-M25). The model file contains parameters defined on the spectral-element mesh and is recommend to be used in SPECFEM3D GLOBE for seismic wave simulation at the global scale.
Type:
Dataset
Issue Date:
5 April 2022

107. Data for: "Financing Carbon Lock-in in Developing Countries: Bilateral Financing for Power Generation Technologies from China, Japan, and the United States"

Author(s):
Chen, Xu; Li, Zhongshu; Gallagher, Kevin P.; Mauzerall, Denise L.
Abstract:
Power sector decarbonization requires a fundamental redirection of global finance from fossil fuel infrastructure towards low carbon technologies. Bilateral finance plays an important role in the global energy transition to non-fossil energy, but an understanding of its impact is limited. Here, for the first time, we compare the influence of overseas finance from the three largest economies – United States, China, and Japan – on power generation development beyond their borders and evaluate the associated long-term CO2 emissions. We construct a new dataset of Japanese and U.S. overseas power generation finance between 2000-2018 by analyzing their national development finance institutions’ press releases and annual reports and tracking their foreign direct investment at the power plant level. Synthesizing this new data with previously developed datasets for China, we find that the three countries’ overseas financing concentrated in fossil fuel power technologies over the studied period. Financing commitments from China, Japan, and the United States facilitated 101 GW, 95 GW, and 47 GW overseas power capacity additions, respectively. The majority of facilitated capacity additions are fossil fuel plants (64% for China, 87% for Japan, and 66% for the United States). Each of the countries’ contributions to non-hydro renewable generation was less than 15% of their facilitated capacity additions. Together, we estimate that overseas fossil fuel power financing through 2018 from these three countries will lock in 24 Gt CO2 emissions by 2060. If climate targets are to be met, replacing bilateral fossil fuel financing with financing of renewable technologies is crucial.
Type:
Dataset
Issue Date:
8 July 2021

108. Data for: "Structure and Density of Silicon Carbide to 1.5 TPa and Implications for Extrasolar Planets"

Author(s):
Kim, Donghoon
Abstract:
There has been considerable recent interest in the high-pressure behavior of silicon carbide, a potential major constituent of carbon-rich exoplanets. In this work, the atomic-level structure of SiC was determined through in situ X-ray diffraction under laser-driven ramp compression up to 1.5 TPa; stresses more than seven times greater than previous static and shock data. Here we show that the B1-type structure persists over this stress range and we have constrained its equation of state (EOS). Using this data we have determined the first experimentally based mass-radius curves for a hypothetical pure SiC planet. Interior structure models are constructed for planets consisting of a SiC-rich mantle and iron-rich core. Carbide planets are found to be ~10% less dense than corresponding terrestrial planets.
Type:
Dataset
Issue Date:
28 April 2022

109. Data for: "The origin of non-skeletal carbonate mud and implications for global climate"

Author(s):
Geyman, Emily C.; Wu, Ziman; Nadeau, Matthew D.; Edmonsond, Stacey; Turner, Andrew; Purkis, Sam J.; Howes, Bolton; Dyer, Blake; Ahm, Anne-Sofie C.; Yao, Nan; Deutsch, Curtis A.; Higgins, John A.; Stolper, Daniel A.; Maloof, Adam C.
Abstract:
Carbonate mud represents one of the most important geochemical archives for reconstructing ancient climatic, environmental, and evolutionary change from the rock record. Mud also represents a major sink in the global carbon cycle. Yet, there remains no consensus about how and where carbonate mud is formed. In this contribution, we present new geochemical data that bear on this problem, including stable isotope and minor and trace element data from carbonate sources in the modern Bahamas such as ooids, corals, foraminifera, and green algae.
Type:
Dataset
Issue Date:
16 June 2022

110. Data for: 'Facies control on carbonate δ13C on the Great Bahama Bank'

Author(s):
Geyman, Emily C.; Maloof, Adam C.
Abstract:
The carbon isotopic (δ13C) composition of shallow-water carbonates often is interpreted to reflect the δ13C of the global ocean and is used as a proxy for changes in the global carbon cycle. However, local platform processes, in addition to meteoric and marine diagenesis, may decouple carbonate δ13C from that of the global ocean. To shed light on the extent to which changing sediment grain composition may produce δ13C shifts in the stratigraphic record, we present new δ13C measurements of benthic foraminifera, solitary corals, calcifying green algae, ooids, coated grains, and lime mud from the modern Great Bahama Bank (GBB). This survey of a modern carbonate environment reveals δ13C variability comparable to the largest δ13C excursions in the last two billion years of Earth history.
Type:
Dataset
Issue Date:
6 May 2021

111. Data for: 'How is sea level change encoded in carbonate stratigraphy?'

Author(s):
Geyman, Emily C.; Maloof, Adam C.; Dyer, Blake
Abstract:
The history of organismal evolution, seawater chemistry, and paleoclimate is recorded in layers of carbonate sedimentary rock. Meter-scale cyclic stacking patterns in these carbonates often are interpreted as representing sea level change. A reliable sedimentary proxy for eustasy would be profoundly useful for reconstructing paleoclimate, since sea level responds to changes in temperature and ice volume. However, the translation from water depth to carbonate layering has proven difficult, with recent surveys of modern shallow water platforms revealing little correlation between carbonate facies (i.e., grain size, sedimentary bed forms, ecology) and water depth. We train a convolutional neural network with satellite imagery and new field observations from a 3,000 km2 region northwest of Andros Island (Bahamas) to generate a facies map with 5 m resolution. Leveraging a newly-published bathymetry for the same region, we test the hypothesis that one can extract a signal of water depth change, not simply from individual facies, but from sequences of facies transitions analogous to vertically stacked carbonate strata. Our Hidden Markov Model (HMM) can distinguish relative sea level fall from random variability with ∼90% accuracy. Finally, since shallowing-upward patterns can result from local (autogenic) processes in addition to forced mechanisms such as eustasy, we search for statistical tools to diagnose the presence or absence of external forcings on relative sea level. With a new data-driven forward model that simulates how modern facies mosaics evolve to stack strata, we show how different sea level forcings generate characteristic patterns of cycle thicknesses in shallow carbonates, providing a new tool for quantitative reconstruction of ancient sea level conditions from the geologic record.
Type:
Dataset
Issue Date:
1 February 2021

112. Data for: Persistent hepatitis B virus and HIV coinfections in dually humanized mice engrafted with human liver and immune system

Author(s):
Ploss, Alexander; Hogan, Glenn; Winer, Benjamin; Ahodantin, James; Sellau, Julie; Huang, Tiffany; Douam, Florian; Funaki, Masaya; Chiriboga, Luis; Su, Lishan
Abstract:
Chronic hepatitis B (CHB), caused by hepatitis B virus (HBV), remains a major medical problem. HBV has a high propensity for progressing to chronicity and can result in severe liver disease, including fibrosis, cirrhosis and hepatocellular carcinoma. CHB patients frequently present with viral coinfection, including HIV and hepatitis delta virus. About 10% of chronic HIV carriers are also persistently infected with HBV which can result in more exacerbated liver disease. Mechanistic studies of HBV-induced immune responses and pathogenesis, which could be significantly influenced by HIV infection, have been hampered by the scarcity of immunocompetent animal models. Here, we demonstrate that humanized mice dually engrafted with components of a human immune system and a human liver supported HBV infection, which was partially controlled by human immune cells, as evidenced by lower levels of serum viremia and HBV replication intermediates in the liver. HBV infection resulted in priming and expansion of human HLA-restricted CD8+ T cells, which acquired an activated phenotype. Notably, our dually humanized mice support persistent coinfections with HBV and HIV which opens opportunities for analyzing immune dysregulation during HBV and HIV coinfection and preclinical testing of novel immunotherapeutics.
Type:
Dataset
Issue Date:
12 June 2023

114. Data from "A Neural Network Water Model Based on the MB-pol Many-Body Potential"

Author(s):
Muniz, Maria Carolina
Abstract:
This dataset contains input files, training data and other files related to the machine learning models developed during the work by Muniz et al. In this work, we construct machine learning models based on the MB-pol many-body model. We find that the training set should include cluster configurations as well as liquid phase configurations in order to accurately represent both liquid and VLE properties. The results attest for the ability of machine learning models to accurately represent many-body potentials and provide an efficient avenue for water simulations.
Type:
Dataset
Issue Date:
27 September 2023

116. Data from "Homogeneous ice nucleation in an ab initio machine learning model"

Author(s):
Piaggi, Pablo
Abstract:
This dataset contains input and output files to reproduce the results of the manuscript "Homogeneous ice nucleation in an ab initio machine learning model" by Pablo M. Piaggi, Jack Weis, Athanassios Z. Panagiotopoulos, Pablo G. Debenedetti, and Roberto Car (arXiv preprint https://arxiv.org/abs/2203.01376). In this work, we studied the homogeneous nucleation of ice from supercooled liquid water using a machine learning model trained on ab initio energies and forces. Since nucleation takes place over times much longer than the simulation times that can be afforded using molecular dynamics simulations, we make use of the seeding technique that is based on simulating an ice cluster embedded in liquid water. The key quantity provided by the seeding technique is the size of the critical cluster (i.e., a size such that the cluster has equal probabilities of growing or shrinking at the given supersaturation). Using data from the seeding simulations and the equations of classical nucleation theory we compute nucleation rates that can be compared with experiments.
Type:
Dataset
Issue Date:
4 April 2022

117. Data from "Liquid-liquid transition in water from first principles"

Author(s):
Gartner, Thomas
Abstract:
This dataset contains all data relevant to a forthcoming publication in which we used molecular simulation methods to study the phase behavior of supercooled water. The dataset contains simulation input and output files, processed data files, and image files used to create all plots in the manuscript. Python analysis scripts are also included, including instructions for how to re-generate all plots in the manuscript.
Type:
Dataset
Issue Date:
26 July 2022

119. Data from "Melting curves of ice polymorphs in the vicinity of the liquid-liquid critical point"

Author(s):
Piaggi, Pablo M; Gartner, Thomas E; Car, Roberto; Debenedetti, Pablo G
Abstract:
The possible existence of a liquid-liquid critical point in deeply supercooled water has been a subject of debate in part due to the challenges associated with providing definitive experimental evidence. Pioneering work by Mishima and Stanley [Nature 392, 164 (1998) and Phys.~Rev.~Lett. 85, 334 (2000)] sought to shed light on this problem by studying the melting curves of different ice polymorphs and their metastable continuation in the vicinity of the expected location of the liquid-liquid transition and its associated critical point. Based on the continuous or discontinuous changes in slope of the melting curves, Mishima suggested that the liquid-liquid critical point lies between the melting curves of ice III and ice V. Here, we explore this conjecture using molecular dynamics simulations with a purely-predictive machine learning model based on ab initio quantum-mechanical calculations. We study the melting curves of ices III, IV, V, VI, and XIII using this model and find that the melting lines of all the studied ice polymorphs are supercritical and do not intersect the liquid-liquid transition locus. We also find a pronounced, yet continuous, change in slope of the melting lines upon crossing of the locus of maximum compressibility of the liquid. Finally, we analyze critically the literature in light of our findings, and conclude that the scenario in which melting curves are supercritical is favored by the most recent computational and experimental evidence. Thus, although the preponderance of experimental and computational evidence is consistent with the existence of a second critical point in water, the behavior of the melting lines of ice polymorphs does not provide strong evidence in support of this viewpoint, according to our calculations.
Type:
Dataset
Issue Date:
February 2023

121. Data from "Signatures of a liquid-liquid transition in an ab initio deep neural network model for water"

Author(s):
Gartner, Thomas III; Zhang, Linfeng; Piaggi, Pablo; Car, Roberto; Panagiotopoulos, Athanassios; Debenedetti, Pablo
Abstract:
This dataset contains all data related to the publication "Signatures of a liquid-liquid transition in an ab initio deep neural network model for water", by Gartner et al., 2020. In this work, we used neural networks to generate a computational model for water using high-accuracy quantum chemistry calculations. Then, we used advanced molecular simulations to demonstrate evidence that suggests this model exhibits a liquid-liquid transition, a phenomenon that can explain many of water's anomalous properties. This dataset contains links to all software used, all data generated as part of this work, as well as scripts to generate and analyze all data and generate the plots reported in the publication.
Type:
Dataset
Issue Date:
July 2020

123. Data from "Transferability of data-driven, many-body models for CO2 simulations in the vapor and liquid phases"

Author(s):
Yue, Shuwen; Riera, Marc
Abstract:
This dataset comprises of data associated with the publication "Transferability of data-driven, many-body models for CO2 simulations in the vapor and liquid phases", which can be found at https://doi.org/10.1063/5.0080061. The data includes calculations for a Many-Body decomposition, virial coefficient calculations, orientational molecular scan energies, potential energy fields, correlation plots of training and testing data, vapor-liquid equilibrium simulations, liquid density simulations, and solid cell simulations.
Type:
Dataset
Issue Date:
30 March 2022

124. Data from "Vapor-liquid equilibrium of water with the MB-pol many-body potential"

Author(s):
Muniz, Maria Carolina; Gartner III, Thomas E.; Riera, Marc; Knight, Christopher; Yue, Shuwen; Paesani, Francesco; Panagiotopoulos, Athanassios Z.
Abstract:
This dataset contains all data (including input files, simulation trajectories as well as other data files and analysis scripts) related to the publication "Vapor-liquid equilibrium of water with the MB-pol many-body potential" by Muniz et al. in preparation (2021). In this work, we assessed the performance of the MB-pol many-body potential with respect to water's vapor-liquid equilibrium properties. Through the use of direct coexistence molecular dynamics, we calculated properties such as coexistence densities, surface tension, vapor pressures and enthalpy of vaporization. We found that MB-pol is able to predict these properties in good agreement with experimental data. The results attest to the chemical accuracy of MB-pol and its large range of application across water's phase diagram.
Type:
Dataset
Issue Date:
2021

125. Data from a flume experiment of passive scalar diffusion within vegetation canopies using laser-induced fluorescence

Author(s):
Ghannam, Khaled; Poggi, Davide; Katul, Gabriel; Bou-Zeid, Elie
Abstract:
This dataset is a sequence of laser-induced fluorescence images of a dye injected in a channel flow with canopy-like stainless steel rods simulating a vegetation canopy stand. The data is acquired close to the channel bottom at z/h=0.2, where z is the height referenced to the channel bed and h is the canopy height. The dataset provides spatial distribution of scalar concentration in a plane parallel to the channel bed. The data has been used (but the data itself has not been published or available to the public) in previous work. The references are: Ghannam, K., Poggi, D., Porporato, A., & Katul, G. (2015). The spatio-temporal statistical structure and ergodic behaviour of scalar turbulence within a rod canopy. Boundary-Layer Meteorology,157(3), 447–460. Ghannam, K, Poggi, D., Bou-Zeid, E., Katul, G. (2020). Inverse cascade evidenced by information entropy of passive scalars in submerged canopy flows. Geophysical Research Letters (accepted).
Type:
Dataset
Issue Date:
22 April 2020

127. Data on Enzyme Activity Retention in glucose oxidase, lipase, and horseradish peroxidase

Author(s):
Webb, Michael; Patel, Roshan; Gormley, Adam; Tamasi, Matt; Borca, Carlos; Kosuri, Shashank
Abstract:
This distribution contains experimentally measured data for the extent of retained enzyme activity post thermal stressing for three distinct enzymes: glucose oxidase, lipase, and horseradish peroxidase. The data is used to form conclusions and develop machine learning models as reported in the publication "Machine Learning on a Robotic Platform for the Design of Polymer-Protein Hybrids" by Matthew Tamasi, Roshan Patel, Carlos Borca, Shashank Kosuri, Heloise Mugnier, Rahul Upadhya, N. Sanjeeva Murthy, Michael Webb*, and Adam Gormley. Details regarding the experimental protocols are reported in the aforementioned paper but are briefly discussed in the README.
Type:
Dataset
Issue Date:
17 May 2022

128. Data set for "Film drop production over a wide range of liquid conditions"

Author(s):
Shaw, Daniel
Abstract:
Data set for "Film drop production over a wide range of liquid conditions." One .csv file is provided that contains data about the number of film drops produced by bursting bubbles of multiple sizes in various liquid conditions.
Type:
Dataset
Issue Date:
5 January 2024

129. Data set for "NAPS: Integrating pose estimation and tag-based tracking"

Author(s):
Wolf, Scott W.
Abstract:
Data set corresponding to "NAPS: Integrating pose estimation and tag-based tracking." This dataset contains the corresponding videos, tracking scripts, and SLEAP models along with SLEAP, NAPS, and ArUco tracking results.
Type:
Dataset
Issue Date:
7 December 2022

130. Data set for "Ocean emission of microplastic by bursting bubble jet drops"

Author(s):
Shaw, Daniel B.; Deike, Luc
Abstract:
Data set for "Ocean emission of microplastic by bursting bubble jet drops." Two .csv files are provided: one for the size of a jet drop carrying microplastic, and another for the amount of microplastic captured by a jet drop.
Type:
Dataset
Issue Date:
21 December 2022

135. Dataset for 'Auditory Activity is Diverse and Widespread Throughout the Central Brain of Drosophila'

Author(s):
Pacheco, Diego A; Thiberge, Stephan; Pnevmatikakis, Eftychios; Murthy, Mala
Abstract:
Sensory pathways are typically studied starting at receptor neurons and following postsynaptic neurons into the brain. However, this leads to a bias in analysis of activity towards the earliest layers of processing. Here, we present new methods for volumetric neural imaging with precise across-brain registration, to characterize auditory activity throughout the entire central brain of Drosophila and make comparisons across trials, individuals, and sexes. We discover that auditory activity is present in most central brain regions and in neurons responsive to other modalities. Auditory responses are temporally diverse, but the majority of activity is tuned to courtship song features. Auditory responses are stereotyped across trials and animals in early mechanosensory regions, becoming more variable at higher layers of the putative pathway, and this variability is largely independent of spontaneous movements. This study highlights the power of using an unbiased, brain-wide approach for mapping the functional organization of sensory activity.
Type:
Dataset
Issue Date:
October 2020

136. Dataset for China's foreign direct investment in the global power sector

Author(s):
Li, Zhongshu; Gallagher, Kevin P.; Mauzerall, Denise L.
Abstract:
The dataset include a list of power projects outside of China that receive Chinese foreign direct investment from 2000 to 2018. Detailed information including project capacity, location, share of Chinese ownership, type of power generating technologies are collected for each power project.
Type:
Dataset
Issue Date:
January 2020

137. Dataset for JGR Article (DOI: 10.1002/2014JD022278)

Abstract:
This dataset contains all the data, model and MATLAB codes used to generate the figures and data reported in the article (DOI: 10.1002/2014JD022278). The data was generated during September 2013 and February 2014 using the Ocean-Land-Atmosphere Model also provided with this package. The data was generated using the computational resources supported by the PICSciE OIT High Performance Computing Center and Visualization Laboratory at Princeton University. The dataset contains a pdf Readme file which explains in detail how the data can be used. Users are recommended to go through this file before using the data.
Type:
Dataset, Image, Software, and text
Issue Date:
19 November 2014

142. Deep Potential training data for crystalline and disordered TiO2 phases

Author(s):
Calegari Andrade, Marcos; Selloni, Annabella
Abstract:
Data set used to train a Deep Potential (DP) model for crystalline and disordered TiO2 phases. Training data contain atomic forces, potential energy, atomic coordinates and cell tensor. Energy and forces were evaluated with the density functional SCAN, as implemented in Quantum-ESPRESSO. Atomic configurations of crystalline systems were generated by random perturbation of atomic positions (0-0.3 A) and cell tensor (1-10%). Amorphous TiO2 was explored by DP molecular dynamics (DPMD) at temperatures in the range 300−2500 K and pressure in the range 0−81 GPa.
Type:
Dataset
Issue Date:
9 October 2020

143. Deep Potential training data for subcritical and supercritical water

Author(s):
Calegari Andrade, Marcos; Ko, Hsin-Yu; Car, Roberto
Abstract:
Data set used to train a Deep Potential (DP) model for subcritical and supercritical water. Training data contain atomic forces, potential energy, atomic coordinates and cell tensor. Energy and forces were evaluated with the density functional SCAN. Atomic configurations were extracted from DP molecular dynamics at P = 250 bar and T = 553, 623, 663, 733 and 823 K. Input files used to train the DP model are also provided.
Type:
Dataset
Issue Date:
19 August 2020

145. Delivery Gig Worker Interviews on Automation at Work

Author(s):
Enriquez, Diana
Abstract:
These data include 39 structured interview transcripts. Each case is someone who worked at the time for Uber, UberEats, Lyft, and/or Amazon Flex (Amazon’s contractor delivery service). These data were collected between July and September 2019. All but one of the interviews occurred over the phone. My questions are focused on the structure of their gig work jobs and the technology they used at work or expected to use at work in the future. I included a description of the data, the recruitment methods, and the discussion guide in this ReadMe file. 
Type:
Dataset
Issue Date:
21 September 2021

149. Derrida's Margins datasets

Author(s):
Chenoweth, Katie; Koeser, Rebecca Sutton; Altergott, Renée; Baron-Raiffe, Alexander; Bauer, Jean; Budak, Nick; Córdova, Chad; Hancock, Austin; Hicks, Benjamin; McElwee, Kevin; Vettier, Chloé
Abstract:
Derrida’s Margins <derridas-margins.princeton.edu> is a website and online research tool for annotations from the Library of Jacques Derrida, housed at Princeton University Library (PUL) <library.princeton.edu>. Jacques Derrida is one of the major figures of twentieth-century thought, and his library--which bears the traces of decades of close reading--represents a major intellectual archive. This project focused on annotations related to Derrida’s landmark 1967 work De la grammatologie (Of Grammatology).
Type:
Dataset
Issue Date:
29 October 2021

150. Derrida's Margins datasets

Author(s):
Chenoweth, Katie; Baron-Raiffe, Alexander; Sutton Koeser, Rebecca
Abstract:
Derrida’s Margins <derridas-margins.princeton.edu> is a website and online research tool for annotations from the Library of Jacques Derrida, housed at Princeton University Library (PUL) <library.princeton.edu>. Jacques Derrida is one of the major figures of twentieth-century thought, and his library--which bears the traces of decades of close reading--represents a major intellectual archive. This project focused on annotations related to Derrida’s landmark 1967 work De la grammatologie (Of Grammatology).
Type:
Dataset
Issue Date:
15 October 2021

161. Distinct cytoskeletal proteins define zones of enhanced cell wall synthesis in Helicobacter pylori

Author(s):
Taylor, Jenny A.; Bratton, Benjamin P.; Sichel, Sophie R.; Blair, Kris M.; Jacobs, Holly M.; DeMeester, Kristen E.; Kuru, Erkin; Gray, Joe; Biboy, Jacob; VanNieuwenhze, Michael S.; Vollmer, Waldemar; Grimes, Catherine L.; Shaevitz, Joshua W.; Salama, Nina R.
Abstract:
Helical cell shape is necessary for efficient stomach colonization by Helicobacter pylori, but the molecular mechanisms for generating helical shape remain unclear. We show that the helical centerline pitch and radius of wild-type H. pylori cells dictate surface curvatures of considerably higher positive and negative Gaussian curvatures than those present in straight- or curved-rod bacteria. Quantitative 3D microscopy analysis of short pulses with either N-acetylmuramic acid or D-alanine metabolic probes showed that cell wall growth is enhanced at both sidewall curvature extremes. Immunofluorescence revealed MreB is most abundant at negative Gaussian curvature, while the bactofilin CcmA is most abundant at positive Gaussian curvature. Strains expressing CcmA variants with altered polymerization properties lose helical shape and associated positive Gaussian curvatures. We thus propose a model where CcmA and MreB promote PG synthesis at positive and negative Gaussian curvatures, respectively, and that this patterning is one mechanism necessary for maintaining helical shape.
Type:
Dataset and Image
Issue Date:
April 2019

163. Drosophila melanogaster Long Timescale Data 2022

Author(s):
McKenze-Smith, Grace C.; Wolf, Scott W.
Abstract:
This dataset contains lifetime recordings and associated postural data and ethograms for 47 male Drosophila melanogaster.
Type:
Dataset and MovingImage
Issue Date:
September 2023

164. Dust and Starlight Maps for Galaxies in the KINGFISH Sample

Author(s):
Aniano, G.; Draine, B.T.; Hunt, L.K.; Sandstrom, K.; Calzetti, D.; Kennicutt, R.C.; Dale, D.A.; Galametz, M.; Gordon, K.D.; Leroy, A.K.; Smith, J.-D.T.; Roussel, H.; Sauvage, M.; Walter, F.; Armus, L.; Bolatto, A.D.; Boquien, M.; Crocker, A.; De Looze, I.; Donovan Meyer, J.; Helou, G.; Hinz, J.; Johnson, B.D.; Koda, J.; Miller, A.; Montiel, E.; Murphy, E.J.; Relano, M.; Rix, H.-W.; Schinnerer, E.; Skibba, R.; Wolfire, M.G.; Engelbracht, C.W.
Abstract:
Dust and starlight have been modeled for the KINGFISH project galaxies. For each pixel in each galaxy, we estimate: (1) dust surface density; (2) q_PAH, the dust mass fraction in PAHs; (3) distribution of starlight intensities heating the dust; (4) luminosity emitted by the dust; and (5) dust luminosity from regions with high starlight intensity. The modeling is as described in the paper "Modeling Dust and Starlight in Galaxies Observed by Spitzer and Herschel: The KINGFISH Sample", by G. Aniano, B.T. Draine, L.K. Hunt, K. Sandstrom, D. Calzetti, R.C. Kennicutt, D.A, Dale, and 26 other authors, accepted for publication in The Astrophysical Journal.
Type:
Dataset and Image

165. Dynamic reconfiguration of the default mode network during narrative comprehension

Author(s):
Simony, Erez; Honey, Christopher; Chen, Janice; Lositsky, Olga; Yeshurun, Yaara; Wiesel, Ami; Hasson, Uri
Abstract:
Does the default mode network (DMN) reconfigure to encode information about the changing environment? This question has proven difficult, because patterns of functional connectivity reflect a mixture of stimulus-induced neural processes, intrinsic neural processes and non-neuronal noise. Here we introduce inter-subject functional correlation (ISFC), which isolates stimulus-dependent inter-regional correlations between brains exposed to the same stimulus. During fMRI, we had subjects listen to a real-life auditory narrative and to temporally scrambled versions of the narrative. We used ISFC to isolate correlation patterns within the DMN that were locked to the processing of each narrative segment and specific to its meaning within the narrative context. The momentary configurations of DMN ISFC were highly replicable across groups. Moreover, DMN coupling strength predicted memory of narrative segments. Thus, ISFC opens new avenues for linking brain network dynamics to stimulus features and behaviour.
Type:
Dataset
Issue Date:
18 July 2016

166. Dynamical Magnetic Fields of Antialigned Nonspinning Dipoles

Abstract:
This movie shows the dynamical behavior of field lines seeded on one of the stars. We find a clear cyclical process operating in the magnetosphere. First, field lines from one star can attach to the second star. Second, as the orbit progresses these field lines develop twist and are expelled outward past the second star as closed loops. Third, these loops open up to infinity and then reconnect on the far side of the first star opposite to the second. Fourth, the orbital motion will bring the second star back into contact with the closed loops, and they reattach to the second star.
Type:
moving image
Issue Date:
21 November 2013

189. Enhancement of edge turbulence concomitant with ELM suppression during boron powder injection in EAST

Author(s):
Sun, Zhen; Maingi, Rajesh; Diallo, Ahmed; Xu, Wei; Qian, Yuzhong; Tritz, Kevin; Ye, Yang; Li, Chenglong; Xu, Zong; Wang, Yifeng; Ye, Kaixuan; Bortolon, A.; Nagy, Alex; Zhang, Ling; Duan, Yanmin; Lu, Zhiyuan; Wang, Huiqian; Shi, Tonghui; Zhao, Hailin; Wei, Gao; Xu, Jichan; Chen, Ran; Huang, Ming; Zuo, Guizhong; Xu, Guosheng; Gong, Xianzu; Hu, Jiansheng
Type:
Dataset
Issue Date:
2021

190. Estimated The New York Times paragraph- and article-level sentiment on China topics, and aggregated survey attitude toward China

Author(s):
Huang, Junming; Cook, Gavin; Xie, Yu
Abstract:
Here we publish the data used in paper "Junming Huang, Gavin Cook, and Yu Xie, Large-scale Quantitative Evidence of Media Impact on Public Opinion toward China". This dataset include estimated sentiments on The New York Times on China in eight topics from 1970 to 2019, and a time series of public attitude aggregated from surveys on China.
Type:
Dataset
Issue Date:
July 2021

194. Experimental data for paper "hydraulic transmissivity inferred from ice-sheet relaxation following Greenland supraglacial lake drainages"

Author(s):
Lai, Ching-Yao
Abstract:
This setup mimics ice lying above the drainage system. In the experiment, a fluid-filled blister is generated via liquid injection into the interface between a transparent elastic layer and a porous substrate. After injection of liquid, the fluid permeates from the blister through the porous substrate, the blister volume V(t) relaxes exponentially with time. Our lab experiments show that varying the permeability of the porous substrate k significantly impacts the relaxation timescale in the experiments.
Type:
Dataset
Issue Date:
18 May 2021

198. Fast animal pose estimation using deep neural networks

Author(s):
Pereira, Talmo D.; Aldarondo, Diego E.; Willmore, Lindsay; Kislin, Mikhail; Wang, Samuel S.-H.; Murthy, Mala; Shaevitz, Joshua W.
Abstract:
Recent work quantifying postural dynamics has attempted to define the repertoire of behaviors performed by an animal. However, a major drawback to these techniques has been their reliance on dimensionality reduction of images which destroys information about which parts of the body are used in each behavior. To address this issue, we introduce a deep learning-based method for pose estimation, LEAP (LEAP Estimates Animal Pose). LEAP automatically predicts the positions of animal body parts using a deep convolutional neural network with as little as 10 frames of labeled data for training. This framework consists of a graphical interface for interactive labeling of body parts and software for training the network and fast prediction on new data (1 hr to train, 185 Hz predictions). We validate LEAP using videos of freely behaving fruit flies (Drosophila melanogaster) and track 32 distinct points on the body to fully describe the pose of the head, body, wings, and legs with an error rate of <3% of the animal's body length. We recapitulate a number of reported findings on insect gait dynamics and show LEAP's applicability as the first step in unsupervised behavioral classification. Finally, we extend the method to more challenging imaging situations (pairs of flies moving on a mesh-like background) and movies from freely moving mice (Mus musculus) where we track the full conformation of the head, body, and limbs.
Type:
Dataset
Issue Date:
30 May 2018