The dataset includes 51 children (age range = 6-12 years) who listened to the first chapter of Alice’s Adventures in Wonderland during fNIRS neuroimaging. We also provide the text of the story with several word-by-word predictors motivated by research in Theory of Mind development and language. These annotated, naturalistic datasets can be used to replicate prior work and test new hypotheses about everyday social cognition and natural language comprehension in the developing brain.
As part of the Measurement of Agriculture Illuminating farm-Zone Emissions of N2O (MAIZE) project, in 2021 the aircraft platform sampled the agriculture regions of Nebraska and Iowa. Vertical profiles were conducted on each flight to capture the vertical structure and mixing depths of the atmosphere. The data files contains the merged data for each individual file day.
Gvakharia A, Kort EA, Smith M, Conley S, Testing and evaluation of a new airborne system for continuous N2O, CO2, CO, and H2O measurements: the Frequent Calibration High-performance Airborne Observation System (FCHAOS), Atmos. Meas. Tech. 11, 6059-6074, https://doi.org/10.5194/amt-11-6059-2018, 2018, Conley S, Faloona I.C, Lenschow D.H, Karion A, Sweeney S, (2014) A low-cost system for measuring horizontal winds from single-engine aircraft, Journal of Atmospheric and Oceanic Technology, 31(6), 1312-1320, https://doi.org/10.1175/JTECH-D-13-00143.1, Airborne measurements reveal high spatiotemporal variation and the heavy-tail characteristic of nitrous oxide emissions in Iowa" by Natasha Dacic, Genevieve Plant, and Eric A Kort. Journal of Geophysical Research: Atmospheres. Submitted., and 2022 dataset: Kort, E. A., Plant, G., Dacic, N. (2024). Aircraft Data (2022) for Measurement of Agriculture Illuminating farm-Zone Emissions of N2O (MAIZE) [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/tmfd-nw87
In a broad sense, this dataset explores morphological and phonological processing in English monolinguals and two bilingual populations, Chinese-English and Spanish-English, using a battery of standardized and self-developed behavioral measures.
Language: English - Spanish - Chinese
The accurate and rapid prediction of generic nanoscale interactions is a challenging problem with broad applications. Much of biology functions at the nanoscale, and our ability to manipulate materials and purposefully engage biological machinery requires knowledge of nano-bio interfaces. While several protein-protein interaction models are available, they leverage protein-specific information, limiting their abstraction to other structures. Here, we present NeCLAS, a general, and rapid machine learning pipeline that predicts the location of nanoscale interactions, providing human-intelligible predictions. Two key aspects distinguish NeCLAS: coarse-grained representations, and the use of environmental features to encode the chemical neighborhood. We showcase NeCLAS with challenges for protein-protein, protein-nanoparticle and nanoparticle-nanoparticle systems, demonstrating that NeCLAS replicates computationally- and experimentally-observed interactions. NeCLAS outperforms current nanoscale prediction models, and it shows cross-domain validity, qualifying as a tool for basic research, rapid prototyping, and design of nanostructures., Software:
- To reproduce all-atom molecular dynamics (MD) NAMD is required (version 2.14 or later is suggested). NAMD software and documentation can be found at https://www.ks.uiuc.edu/Research/namd/, - To reproduce coarse-grained MD simulations, LAMMPS (version 29 Sep 2021 - Update 2 or later is suggested). LAMMPS software and documentation can be found at https://www.lammps.org, - To rebuild free energy profiles, the PLUMED plugin (version 2.6) was used. PLUMED software and documentation can be found at https://www.plumed.org/ , and - To generate force matching potentials, the was used the OpenMSCG software was used. OpenMSCG software and documentation can be found at https://software.rcc.uchicago.edu/mscg/
The data sources and methods used to process the raw data are described in the paper
www.doi.org/10.1073/pnas.2118046119 and the associated Supplementary Information.
These data are anonymized (see Methodology for details). Consequently, running the same code on these data vs. the data in the paper does not yield *identical* results but qualitatively similar ones.
This research was completed to statistically validate that a data-model refinement technique could integrate real measurements to remove bias from physics-based models via changing the forcing parameters such as the thermal conductivity coefficients.
Ponder, B. M., Ridley, A. J., Goel, A., & Bernstein, D. S. (2023). Improving forecasting ability of GITM using data-driven model refinement. Space Weather, 21, e2022SW003290. https://doi.org/10.1029/2022SW003290
Each row in the file contains the museum ID (museum where the specimen is located and the accession number), the species name, and the values for centroid size, followed by the x,y coordinates for each landmark. Any program that can read in a csv file can read this file.
The data and scripts are meant to show how burster dynamics determine response to a single biphasic stimulus. The files include data which show trends in the propensity of termination for different burster types and the MATLAB scripts used to generate this data. The MATLAB scripts also allow the user to generate their own data sets for alternative bursting paths and stimulus parameter combinations. Furthermore, they allow the user to visually examine the effects of single stimuli in the voltage timeseries and in state space. How the user can access these features of the script is described in the file "ReadMe.pdf."
The characterization of HFO networks through functional connectivity analysis and network centrality. Details of the code repository can be found in the README.txt file.
These are datasets released from our manuscript "A Comparison of Lossless Compression Methods in Microscopy Data Storage Applications".
Included in this data release are: `noise16.tif`: a file containing background noise collected from a 1000-frame acquisition of a ORCA-Fusion camera; `noise8.tif`: a file containing the 16-bit data collective above converted into a 8-bit form; `brainbow.tif`: This is a mouse Brainbow image originally published and described in Roossien, et al. Bioinformatics 2019; `bead.tif`: This is a 3D image of 100nm Invitrogen TetraSpeck fluorescent microspheres imaged in a blue channel using a custom microscope; `fly.tif`: This is a 3D image of a fly Bitbow brain collected as described in Li, et al. Front. Neural Circuits 2021; and `neurite.tif`: This is a 3D image of DiD-labeled mouse V1 tissue, collected using a custom microscope.