Includes geodatabase with statewide address points, street centerlines, parcels, and county boundaries for Indiana, 2024. Also includes zipped shapefiles for individual counties, state geocoder, and real property geodatabase.
Includes geodatabase with statewide address points, street centerlines, parcels, and county boundaries for Indiana, 2020. Also includes zipped shapefiles for individual counties, state geocoder, and real property geodatabase. See inventory file for full description of geodatabase layers, and metadata file for more information.
Includes geodatabase with statewide address points, street centerlines, parcels, and county boundaries for Indiana, 2021. Also includes zipped geodatabases for individual counties, state geocoder, and real property geodatabase. See inventory and metadata files for more information.
Includes geodatabase with statewide address points, street centerlines, parcels, and county boundaries for Indiana, 2022. Also includes zipped shapefiles for individual counties, state geocoder.
Includes geodatabase with statewide address points, street centerlines, parcels, and county boundaries for Indiana, 2023. Also includes zipped shapefiles for individual counties, state geocoder, and real property geodatabase.
This collection contains yearly deposits of data from Indiana's Data Harvest - consisting of vector GIS layers for the state of Indiana as gathered and cleaned by the Indiana Geographic Information Office
We discuss event-related power differences (ERPDs) in low- and broadband-γ oscillations as the edge of embedded clauses is processed in wh-dependencies such as Which decision regarding/about him/her did Paul say that Lydie rejected without hesitation? in native and nonnative French speakers. The experimental conditions manipulated whether pronouns appeared in modifiers (Mods) or in noun complements (Comps) and whether they matched or mismatched a matrix-clause subject in gender. Across native and nonnative speakers, we found that anaphora-linked ERPDs for Mods vs. Comps in evoked power first arose in low γ and then in broadband γ. Therefore, referential elements first seem to be retrieved from working memory by narrowband processes in low-γ and then referential identification seems to be computed in broadband-γ output. Interactions between discourse- and syntax-based referential processes for the Mods vs. Comps in these ERPDs furthermore suggest that multidomain γ-range processing enables a range of elementary operations for discourse and semantic interpretation. We argue that a multidomain mechanism enabling operations conditioned by the syntactic and semantic nature of the elements processed interacts with local brain microcircuits representing features and feature sets that have been established in first- or second-language acquisition, accounting for a single language epistemology for native and nonnative language.
Spectroscopy data were collected on a BioTek Synergy H1 plate reader, then exported to and analyzed in Excel. 3D charts used in the manuscript were generated with OriginPro. Microscopy images are in proprietary .nd2 format of Nikon Elements software. .nd2 files were opened and processed with ImageJ. Gels were imaged on a BioRad Chemidoc.
Laughlin, P. M.; Young, K.; Gonzalez-Guiterrez, G.; Wang, J. C.-Y.,; Zlotnick, A. A narrow ratio of nucleic acid to SARS-CoV-2 N-protein enables phase separation. 2024.
Title:
Raw data for "A narrow ratio of nucleic acid to SARS-CoV-2 N-protein enables phase separation"
The data set consists of movement kinematic data from 92 participants that are neurotypical or have certain neurodevelopmental disorders (Autism, Attention Deficit/Hyperactivity Disorder, and their comorbidity). The data is of roughly 100 trials of a reaching paradigm per participant. The raw data consists of the linear acceleration (a), the roll (R), pitch (P), and yaw (Y), and the rate of change of the roll, pitch, and yaw.
Description of the data and file structure.
There are three primary folders: Data, Matlab Code, and the Deep Learning Code., Data:
Each folder corresponds to one participant. Inside is their motion data (sensor_data.csv) and a file describing their diagnosis and severity (diagnosis.txt). The columns of the csv are: a in x, a in y, a in z, dR/dt, dP/dt, dY/dt, R, P, Y
The diagnosis.txt file contains two lines: the first is their diagnosis (ASD, ADHD, A^2, NT), the second is their severity (HF for high functioning, MF for mid functioning, LF for low functioning, and NA for not applicable)., Matlab Code:
Information on how to analyze participants can be found in README.txt file within the folder.
The Matlab code was written and run on MATLAB_R2023b. The signal processing toolbox is required., and Deep Learning Code:
Information on how to perform the deep learning analysis can be found in README.txt file within the folder.
The deep learning code is written in Python3 and relies on PyTorch.
K. Doctor, C. McKeever, A. Phadnis, D. Wu, J. Nurnberger Jr., M. Plawecki, & J.V. Jose. Deep learning and statistical millisecond motor assessments of neurodevelopmental disorders. Science, In Submission.
Title:
Deep Learning and statistical millisecond motor assessments of neurodevelopmental disorders
