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5. Data and code for “Structured foraging of soil predators unveils functional responses to bacterial defenses”

Author(s):
Gregor, Thomas; Rossine, Fernando
Abstract:
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.
Type:
Dataset and Software
Issue Date:
11 November 2022

9. 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

12. 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

13. 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

14. 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

16. 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

29. Genetic Algorithm Crystal Optimization in a Chiral Tetramer Model System

Author(s):
Petsev, Nikolai D.; Nikoubashman, Arash; Latinwo, Folarin
Abstract:
Source code for our genetic algorithm optimization investigation of conglomerate and racemic chiral crystals. In this work, we address challenges in determining the stable structures formed by chiral molecules by applying the framework of genetic algorithms to predict the ground state crystal lattices formed by a chiral tetramer model. Using this code, we explore the relative stability and structures of the model’s conglomerate and racemic crystals, and extract a structural phase diagram for the stable Bravais crystal types in the zero-temperature limit.
Type:
Software
Issue Date:
8 August 2022

38. Molecular Dynamics Trajectories and Analysis Code Demonstrating Liquid-Liquid Criticality in the WAIL Water Model

Author(s):
Weis, Jack
Abstract:
These GROMACS trajectories show the existence of a critical point in deeply supercooled WAIL water. Also included is the code necessary to reproduce the figures in the corresponding paper from these trajectories. From this data the critical temperature, pressure, and density of the model can be found, and critical fluctuations in the deeply supercooled liquid can be directly observed (in a computer-simulation sense).
Type:
Dataset
Issue Date:
13 May 2022

41. Observation of a reduced-turbulence regime with boron powder injection in a stellarator

Author(s):
Nespoli, Federico; Masuzaki, Suguru; Tanaka, Kenji; Ashikawa, Naoko; Shoji, Mamoru; Gilson, Erik; Oishi, Tetsutarou; Ida, Katsumi; Yoshinuma, Mikirou; Takemura, Yuki; Kinoshita, Toshiki; Motojima, Gen; Kenmochi, Naoki; Kawamura, Gakushi; Suzuki, Chihiro; Nagy, Alex; Bortolon, Alessandro; Pablant, Novimir; Mollén, Albert; Tamura, Naoki; Gates, David; Morisaki, Tomohiro
Type:
Dataset
Issue Date:
2022

43. Princeton Dynamic Stall Database: Upward ramp-type pitching motions

Author(s):
Kiefer, Janik; Brunner, Claudia E.; Hansen, Martin O. L.; Hultmark, Marcus
Abstract:
This data set contains data of a NACA 0021 airfoil as it undergoes upward ramp-type pitching motions at high Reynolds numbers and low Mach numbers. The parametric study covers a wide range of chord Reynolds numbers, reduced frequencies and pitching geometries characterized by varying mean angle and angle amplitude. The data were acquired in the High Reynolds number Test Facility at Princeton University, which is a closed-loop wind tunnel that can be pressurized up to 23 MPa and allowed for variation of the chord Reynolds number over a range of 5.0 × 10^5 ≤ Re_c ≤ 5.5 × 10^6. Data were acquired using 32 pressure taps along the surface of the airfoil. The data are the phase-averaged results of 150 individual half-cycles for any given test case.
Type:
Dataset
Issue Date:
6 July 2022

44. Shakespeare and Company Project Dataset: Lending Library Books

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Aiyer, Priyanka; Alagappan, Serena; Allen, Paige; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chiossi, Robert; Chow, Jin Yun; Coles, Iliyah; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Gautreau, Violet; Gjaja, Alex; Green, Elspeth; Hart, Isaac; Hicks, Benjamin; Himmelfarb, Samuel; Joelson, Madeleine E.; Karmanov, Fedor; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Macksey, Elizabeth; Mahoney, Cate; Mancino, Francesca; McCarthy, Jesse D.; Naydan, Mary; Patch, Yvonne; Root, Sally; Ruehl, Isabel; Thode, Sylvie; Vandermel, Katherine; VanSant, Camey; Wulfman, Clifford E.
Abstract:
This dataset includes information about approximately 6,000 books and other items with bibliographic data as well as summary information about when the item circulated in the Shakespeare and Company lending library and the number of times an item was borrowed or purchased.
Type:
Dataset
Issue Date:
January 2022

45. Shakespeare and Company Project Dataset: Lending Library Events

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Aiyer, Priyanka; Alagappan, Serena; Allen, Paige; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chiossi, Robert; Chow, Jin Yun; Coles, Iliyah; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Gautreau, Violet; Gjaja, Alex; Green, Elspeth; Hart, Isaac; Hicks, Benjamin; Himmelfarb, Samuel; Joelson, Madeleine E.; Karmanov, Fedor; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Macksey, Elizabeth; Mahoney, Cate; Mancino, Francesca; McCarthy, Jesse D.; Naydan, Mary; Patch, Yvonne; Root, Sally; Ruehl, Isabel; Thode, Sylvie; Vandermel, Katherine; VanSant, Camey; Wulfman, Clifford E.
Abstract:
The Shakespeare and Company Project: Lending Library Events dataset includes information about approximately 35,000 lending library events including membership activities such as subscriptions, renewals and reimbursements and book-related activities such as borrowing and purchasing. For events related to lending library cards that are available as digital surrogates, IIIF links are provided.
Type:
Dataset
Issue Date:
January 2022

46. Shakespeare and Company Project Dataset: Lending Library Members

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Aiyer, Priyanka; Alagappan, Serena; Allen, Paige; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chiossi, Robert; Chow, Jin Yun; Coles, Iliyah; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Gautreau, Violet; Gjaja, Alex; Green, Elspeth; Hart, Isaac; Hicks, Benjamin; Himmelfarb, Samuel; Joelson, Madeleine E.; Karmanov, Fedor; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Macksey, Elizabeth; Mahoney, Cate; Mancino, Francesca; McCarthy, Jesse D.; Naydan, Mary; Patch, Yvonne; Root, Sally; Ruehl, Isabel; Thode, Sylvie; Vandermel, Katherine; VanSant, Camey; Wulfman, Clifford E.
Abstract:
The Shakespeare and Company Project: Lending Library Members dataset includes information about approximately 5,200 members of Sylvia Beach's Shakespeare and Company lending library.
Type:
Dataset
Issue Date:
January 2022

47. Shakespeare and Company Project Dataset: Lending Library Members, Books, Events

Author(s):
Kotin, Joshua; Koeser, Rebecca Sutton; Adair, Carl; Aiyer, Priyanka; Alagappan, Serena; Allen, Paige; Bauer, Jean; Browne, Oliver J.; Budak, Nick; Calver, Harriet; Chiossi, Robert; Chow, Jin Yun; Coles, Iliyah; Davis, Ian; Doroudian, Gissoo; Engel, Currie; Gautreau, Violet; Gjaja, Alex; Green, Elspeth; Hart, Isaac; Hicks, Benjamin; Himmelfarb, Samuel; Joelson, Madeleine E.; Karmanov, Fedor; Kelly, Carolyn; Krolewski, Sara; Li, Xinyi; Maag, Ellie; Macksey, Elizabeth; Mahoney, Cate; Mancino, Francesca; McCarthy, Jesse D.; Naydan, Mary; Patch, Yvonne; Root, Sally; Ruehl, Isabel; Thode, Sylvie; Vandermel, Katherine; VanSant, Camey; Wulfman, Clifford E.
Abstract:
The Shakespeare and Company Project makes three datasets available to download in CSV and JSON formats. The datasets provide information about lending library members; the books that circulated in the lending library; and lending library events, including borrows, purchases, memberships, and renewals. The datasets may be used individually or in combination site URLs are consistent identifiers across all three.
Type:
Dataset
Issue Date:
January 2022