This dataset contains images of dissected and fixed retinae in which cones of specific subtypes are labeled either by transgenic expression of a fluorescent reporter or by antibody staining (Figures 1 and 2 and 6A and Supplementary Figure 7A).
This dataset also contains images of dissected and fixed retinae in ZO1 is immunostained (Figure 6C-E and Supplementary Figure 7B).
Please see the readme file for which files correspond to which figures.
Nunley, H., Nagashima, M., Martin, K., Gonzalez, A. L., Suzuki, S. C., Norton, D. A., Wong, R. O. L., Raymond, P. A., & Lubensky, D. K. (2020). Defect patterns on the curved surface of fish retinae suggest a mechanism of cone mosaic formation. PLOS Computational Biology, 16(12), e1008437. https://doi.org/10.1371/journal.pcbi.1008437 and Hayden Nunley, Mikiko Nagashima, Kamirah Martin, Alcides Lorenzo Gonzalez, Sachihiro C. Suzuki, Declan Norton, Rachel O. L. Wong, Pamela A. Raymond, David K. Lubensky. Defect patterns on the curved surface of fish retinae suggest mechanism of cone mosaic formation. bioRxiv 806679; doi: https://doi.org/10.1101/806679
For Drifters, HYCOM, MITgcm: Spectra and kinetic energy files. Please see readme.txt for a description of all data and code contained here. and - Compare kinetic energies (KE) of high-resolution global ocean models estimated from rotary spectra to KE in surface drifter observations.
- Near-inertial KE is closer to drifter observations in models with frequently updated wind forcing
- Internal tide KE is closer to drifter observations in models with topographic wave drag
Elipot, S., Lumpkin, R., Perez, R. C., Lilly, J. M., Early, J. J., & Sykulski, A. M. (2016). A global surface drifter data set at hourly resolution. Journal of Geophysical Research: Oceans, 121(5), 2937–2966. https://doi.org/10.1002/2016JC011716
Boles, A., & Pluijm, B. van der. (2020). Locally Derived, Meteoric Fluid Infiltration Was Responsible for Widespread Late Paleozoic Illite Authigenesis in the Appalachian Basin. Tectonics, 39(7), e2020TC006137. https://doi.org/10.1029/2020TC006137
These datasets support the findings of Townsend et al. (2020). In this article, we quantify rock strength using two novel applications of hillslope stability models, resulting in estimates of cohesive and frictional strength at the spatial scale of small watersheds. We compare these results against the direct-shear test dataset here for validation of our approach. We find that cohesive strength is dependent on the original burial depth of the sedimentary rocks studied here. The low-temperature thermochronometry data was used to assess the magnitude of burial.
The object of this project is to provide researchers and students with a tool to allow them to develop an intuitive understanding of singular vectors and singular values. 2x2 matrices A with real entries map circles to ellipses; in particular, unit circles centered at the origin to ellipses centered at the origin. It is known that the points on the ellipse farthest from the origin correspond to the singular vectors of A. Users can use the GUI to enter matrices of their choice and explore to visually self-determine the singular vectors/values.
We sampled the near-Earth plasma sheet using data from the NASA Time History of Events and Macroscale Interactions During Substorms mission. For the observations of the plasma sheet, we used corresponding interplanetary observations using the OMNI database. We used these data to develop a data-driven model that predicts plasma sheet electron flux from upstream solar wind variations. The model output data are included in this work, along with code for analyzing the model performance and producing figures used in the related publication. and Data files are included in hdf5 and Python pickle binary formats; scripts included are set up for use of Python 3 to access and process the pickle binary format data.
Swiger, B. M., Liemohn, M. W., & Ganushkina, N. Y. (2020). Improvement of Plasma Sheet Neural Network Accuracy With Inclusion of Physical Information. Frontiers in Astronomy and Space Sciences, 7. https://doi.org/10.3389/fspas.2020.00042
We use the MHD with embedded particle-in-cell model (MHD-EPIC) to study the Geospace Environment Modeling (GEM) dayside kinetic processes challenge event at 01:50-03:00 UT on 2015-11-18, when the magnetosphere was driven by a steady southward IMF. In the MHD-EPIC simulation, the dayside magnetopause is covered by a PIC code so that the dayside reconnection is properly handled. We compare the magnetic fields and the plasma profiles of the magnetopause crossing with the MMS3 spacecraft observations. Most variables match the observations well in the magnetosphere, in the magnetosheath, and also during the current sheet crossing. The MHD-EPIC simulation produces flux ropes, and we demonstrate that some magnetic field and plasma features observed by the MMS3 spacecraft can be reproduced by a flux rope crossing event. We use an algorithm to automatically identify the reconnection sites from the simulation results. It turns out that there are usually multiple X-lines at the magnetopause. By tracing the locations of the X-lines, we find the typical moving speed of the X-line endpoints is about 70~km/s, which is higher than but still comparable with the ground-based observations.
Chen, Y., Tóth, G., Hietala, H., Vines, S. K., Zou, Y., Nishimura, Y., Silveira, M. V. D., Guo, Z., Lin, Y., & Markidis, S. (2020). Magnetohydrodynamic With Embedded Particle-In-Cell Simulation of the Geospace Environment Modeling Dayside Kinetic Processes Challenge Event. Earth and Space Science, 7(11), e2020EA001331. https://doi.org/10.1029/2020EA001331 and Chen, Yuxi, et al. "Magnetohydrodynamic with embedded particle-in-cell simulation of the Geospace Environment Modeling dayside kinetic processes challenge event." arXiv preprint arXiv:2001.04563 (2020). https://arxiv.org/abs/2001.04563
This data was collected and processed as part of ongoing research to characterize waterway infrastructure performance in the Great Lakes. These dataset enable researchers to evaluate both travel time and vessel carrying capacity in the waterway., I assembled AIS data from the MarineCadastre website for UTM Zones 15-18 for the years 2015-2017 available in csv format. I combined files for Navigation Seasons, defined as March to January and clipped data for a set of predefined features using a python code (AIS Data Processor.ipynb). The code writes the appended and clipped files to csv for a single Navigation Year. The written files are submitted here:
Trimmed_NY2015_new.csv (n=13,228,824);
Trimmed_NY2016_new.csv (n=18,782,779);
Trimmed_NY2017_new.csv (n=16,816,603), Data fusion of AIS and LPMS used the following algorithm for a subset of 30 vessels on the waterway. Let A be the original AIS data and let B be the subset of records for vessel i within geographic feature j. The script for this analysis is attached (Maritime Data Fusion.ipynb), For Connecting Channels and select segments of the Great Lakes: 1. Subset A for vessel i. Let B_i⊆A | 2. Subset B_i in geographic feature, Gj. Let B_ij⊆B_i | 3. Select tmin for each unique date or any consecutive dates, record as vessel i arrival to feature j, b_ijt | 4. IF feature j is a harbor or lock, select tmax for each unique date or any consecutive dates, record as departure from feature j, b_ijt | 5. Calculate time elapsed between features for each vessel, For vessel passage through the Soo Locks:
1. Subset A for vessel i. Let B_i⊆A | 2. Subset B_i in geographic boundaries (46.5<Lat<46.6, -84.4<Lon<-84.3). Let C_(i,lock)⊆B_i | 3. Select tmin for each unique date or any consecutive dates, record as arrival to Soo Locks | 4. Select tmax for each unique date or any consecutive dates, record as departure to Soo Locks | 5. Calculate time delta between arrival and departure times, and The merged dataset is included here along with the raw LPMS data:
Merged_Data_new.csv (n=42,021),
LPMS obscured.csv (n=55,342).
VesselNames have been obscured in these datasets to protect proprietary information for shipping companies.
Sugrue, D., Adriaens, P. (in review) Multi-dimensional Data Fusion to Evaluate Waterway Performance: Maritime Transport Efficiency of Iron Ore on the Great Lakes. Water Resources Research.
Micron-scale robots require systems that can morph into arbitrary target configurations controlled by external agents such as heat, light, electricity, and chemical environment. Achieving this behavior using conventional approaches is challenging because the available materials at these scales are not programmable like their macroscopic counterparts. To overcome this challenge, we propose a design strategy to make a robotic machine that is both programmable and compatible with colloidal-scale physics. Our strategy uses motors in the form of active colloidal particles that constantly propel forward. We sequence these motors end-to-end in a closed chain forming a two-dimensional loop that folds under its mechanical constraints. We encode the target loop shape and its motion by regulating six design parameters, each scale-invariant and achievable at the colloidal scale. The research dataset includes simulation, visualization, and analysis scripts and results generated for the 2D chain loops of self-propelling particles.
File Description:, -- arrows_folding - Contains the data for the folded chain loop shapes resembling an arrowhead., -- bending_vs_variation - Contains the data to study the stability of a particular shape in simulations as one of the segments of the shape bends and/or the distribution of propulsion on it varies., -- curved_triangle - Contains the data to study motion and bending of a triangle shape made using chain loop., -- example_shapes - Contains data for various examples of shapes that can be generated by designing the chain loops., -- nskT_vs_fakT - Contains the data for a specific shape to study the effect of scaling up the number of particles (governed by ns) and the propulsion (governed by fa) in its chain., -- stability - Contains the data and theoretical model (stability.py) to study the stability of the six different shapes., -- tuning_design_forM - Contains the data for sequential tuning the design parameters to fold the shape "M" as described in the corresponding publication., and -- two_neighboring_cds_segments_ - Contains the data to study a system of two neighboring chain segments with respect to different parameters discussed in the publication.
The locations ("locs") files in this directory contain indices pointing to the locations in the CMA superset datafiles that were used in the Luecke et al. 2020 comparison of HYCOM and MITgcm model output to CMA observations.
Luecke, C. A., Arbic, B. K., Richman, J. G., Shriver, J. F., Alford, M. H., Ansong, J. K., Bassette, S. L., Buijsman, M. C., Menemenlis, D., Scott, R. B., Timko, P. G., Voet, G., Wallcraft, A. J., & Zamudio, L. (2020). Statistical Comparisons of Temperature Variance and Kinetic Energy in Global Ocean Models and Observations: Results From Mesoscale to Internal Wave Frequencies. Journal of Geophysical Research: Oceans, 125(5), e2019JC015306. https://doi.org/10.1029/2019JC015306