Arizona State University Research Data Repository
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TOMNET/D-STOP Transformative Technologies in Transportation (T4) Survey
No full textIn 2019, four universities comprising the TOMNET (Transformative Transportation Technologies) and D-STOP Tier 1 University Transportation Centers, namely, Arizona State University, Georgia Tech, The University of Texas at Austin, and University of South Florida, conducted a survey to understand traveler attitudes, behaviors, and mobility and lifestyle choices in the context of new mobility services and rapidly evolving transportation technologies. An identical survey was administered to a random sample of individuals in the four metro regions of Phoenix, Atlanta, Tampa, and Austin
Replication Data for: Adolescent cerebellar nuclei manipulation alters reversal learning and perineuronal net intensity independently in male and female mice
No full textReplication data for: Adolescent cerebellar nuclei manipulation alters reversal learning and perineuronal net intensity independently in male and female mice
This dataset supports Adolescent cerebellar nuclei manipulation alters reversal learning and perineuronal net intensity independently in male and female mice and contains a spreadsheet of raw values and code plus link to the GitHub code library.
Contains R code and Excel files. The README documentation file provides a variable list and additional information</p
APPLE (Assessing Positive Peer Learning Environments) Partnership
No full textGiven that there has been no focused examination of gender integration in classrooms in almost 40 years, this project focused on reexamining this issue to better understand how boys and girls in contemporary US classrooms are (or are not) engaged with each other and the impact this has on academic outcomes (school engagement, academic perceptions and achievement). These files include SPSS datasets and a Codebook of data gathered from 3 cohorts of 3rd, 4th, and 5th grade students and their teachers on the current state of gender integration in upper elementary school classrooms
Low-Cost Approaches in Neuroscience to Teach Machine Learning Using a Cockroach Model
No full textDataset of manuscript, SLEAPing with cockroaches: low-cost approaches to teaching machine learning in neuroscience
Phoenix Metro Parking Space Infrastructure Inventory
No full textA parking inventory for metropolitan Phoenix, Arizona, USA is developed by cross-referencing geospatial cadastral and roadway data with minimum parking requirements. Historical growth of parking is also estimated by linking year of property development to required off-street and nearby on-street parking spaces. As of 2017, we estimate that there were 12.2 million parking spaces in the metropolitan region with 4.04 million inhabitants, 2.86 million registered personal vehicles, and 1.84 million jobs. Growth of parking in metro Phoenix has also been significant; since 1960, 10.9 million spaces have been added to the region compared to a population growth of 3.41 million, vehicle fleet growth of 2.63 million, and employment growth of 1.56 million jobs. Since the 2008 recession, parking growth in metro Phoenix has significantly slowed, but continued urban growth combined with substantial minimum parking requirements may promote more parking infrastructure than is needed
Replication Data for: Raman thermometry for temperature assessment of inorganic transformations under microwave heating
No full textReplication data for the manuscript titled "Raman thermometry for temperature assessment of inorganic transformations under microwave heating" for submission to the Journal of Raman Spectroscopy. The raw experimental data and MATLAB (R2024) scripts for analysis are included for verification of the studies conclusions. TOPAS 6 (Bruker) has been used to process the data.
Dataset contains 31 delimited data files (.csv), 1 text file (.txt), 2 MATLAB live scripts, and 2 MATLAB functions.
See README for additional information.</p
Replication Data for: Breeding bird response to adaptive multi-paddock and continuous grazing practices in Southeastern United States
No full textThe dataset consists of survey data results for multiple unlimited distance, single observer point count breeding bird surveys at ten selected ranches over two consecutive years. The methods selected and the time intervals used to collect the data are arranged in a manner that is consistent with many state, federal, and international protocols for bird population data collection with the expressed intention of allowing flexibility/usability in comparison with other studies. The data rows are unique observations, and the columns are the varying metrics (ex. species, taxonomic order, location, date, time, distance, direction, abundance, behavior, etc.). Additionally, a custom R package for analyzing effective detection radii per species was developed and the script is provided. Review of the data will require Microsoft Excel and analysis/review of the statistical analyses will require R (Version 4.3.1). It is anticipated that other researchers studying breeding bird population dynamics within working lands and/or grasslands in the southeastern USA might be able to compare or include these data into larger studies
San Francisco Bay Area Parking Space Inventory
No full textThe San Francisco Bay Area is one of the most progressive transportation regions in the deployment of high-capacity transit and the use of policies to encourage active transportation. Yet, there remains a dearth of knowledge on the abundance and location of parking infrastructure. The extent and location of parking supply, including on-street and off-street spaces, are estimated for the nine-county Bay Area by creating a federated database that joins land use, transportation, parcel, building, and parking code layers to estimate the number and characteristics of parking spaces at the census block scale. This bottom-up parking space inventory results in an estimated 15 million parking spaces in the region: 8.6 million on-street and 6.4 million off-street. Residential parking dominates the share of supply at 70%, followed by commercial at 9.4%. Space density is greatest in downtown San Francisco, Oakland, and San Jose—largely attributed to high-rise structures. On-street parking is dominant in the North Bay, commanding 78% of total parking in Napa, 75% in Solano, 68% in Sonoma, and 67% in Marin County. Parking area constitutes 7.9% of the total incorporated area. Notably, when compared to other southwest cities (Phoenix Metropolitan Area and Los Angeles County), the Bay Area parking supply appears better utilized considering spaces per person, per car, and per job. The density and quantity of parking spaces in the Bay Area are critical insights toward developing targeted policies that encourage active mobility and support affordable housing
Los Angeles County Parking Space Infrastructure Inventory
No full textReplication data for the 2015 article "Parking Infrastructure: A Constraint on or Opportunity for Urban Redevelopment? A Study of Los Angeles County Parking Supply and Growth." We estimate how parking has grown in Los Angeles County from 1950 to 2010. We find that since 1975 the ratio of residential offstreet parking spaces to automobiles in Los Angeles County is close to 1.0 and the greatest density of parking spaces is in the urban core while most new growth in parking occurs outside of the core. 14% of incorporated land in Los Angeles County is committed to parking. Uncertainty in our space inventory is attributed to our building growth model, onstreet space length, and the assumption that parking spaces were created as per the requirements
Artificial Social Intelligence for Successful Teams (ASIST) Study 4 Dragon Testbed Dataset
No full textArtificial Social Intelligence for Successful Teams (ASIST) Study 4 Dragon Testbed Dataset was developed in a human subjects research study designed to assess the capability of artificial intelligence to instantiate a Machine Theory of Teams and apply it to generate and issue (or withhold) advice to team members that improve team state (e.g., motivation), process (e.g., synchronization), and mission effects (e.g., game score). These agents -- called Artificial Social Intelligence Advisors (ASI Advisors) -- draw measurements of team states and processes from agents called Analytic Components (AC). They take inputs from survey responses and behaviors of a three-person team executing a bomb disposal task in Minecraft.
This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001119C0130. Any opinions, findings conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the DARPA.
Data Overview
The dataset was collected between 2023-08-03 and 2023-11-20. This dataset consists of 1160 games that had valid end states, and 1112 of them included post-trial surveys. Each game has one Zip file (e.g., 230804001812+P000003_P000004_P000005_1+NO_ADVISOR+9c5f329e-0c52-4254-b165-60f3a57b4fd3.zip). The Zip file name starts with a UTC in the form of YYMMDDHHMMSS, followed by the team name in the format of the three participants' ID in ascending order and the number of games the team has played together, followed by one of three advisor types (i.e., NO_ADVISOR, ASI_DOLL_TA1_RITA, or ASI_CMU_TA1_ATLAS), and followed by the unique 36 digit trial ID. Inside the Zip file, it has seven files: the testbed data in .metadata format, an overview of the testbed data in JSON format, and five CSV files (i.e., agent_tests, chat_measures, individual_measures, intervention_measures, trial_measures). The metadata files include all the raw data, such as surveys, chat messages, state of the task environment, etc. The CSV files are data extracted from the metadata files to show certain aspects of the data variables for the convenience of those not used to reading metadata files.
The Study 4 dataset has a size of 4GB.
A readme file (README.txt) describes the dataset contents in detail.
For full details on methods available for downloading files, please see the ASU Research Data Repository Depositor Guide page on downloading files.
For a quick reference on using the download methods mentioned in the ASU Research Data Repository guide, please download and view the ASIST Dataset File Downloads Instructions PDF file.</p