Emory Dataverse
Not a member yet
6274 research outputs found
Sort by
Data Management and Sharing Plan for: Understanding alpha-gal red meat allergy
No full textThe Data Management and Sharing Plan describes the scientific data to be generated and/or used in the research and outlines a strategy for managing and sharing project data
Data Management and Sharing Plan for: Microfluidics Consortium: In Situ Molecular Weight Determination
No full textThe Data Management and Sharing Plan describes the scientific data to be generated and/or used in the research and outlines a strategy for managing and sharing project data
EQUATES Evaluation Data -- 01/01/2002 - 12/31/2019 12km CONUS
No full textData SummaryCMAQv5.3.2 air quality and deposition estimates from the EPA’s Air Quality Time Series (EQUATES; www.epa.gov/cmaq) project paired in space and time to measurement data for 01/01/2002 - 12/31/2019.
Measurements of ozone (O3), particulate matter with an effective aerodynamic diameter less than 2.5 µm (PM2.5), PM2.5 constituents (SO42-, NO3-, NH4+, OC, EC), and nitrogen oxides (NOX) and other gases are acquired from the USEPA's Air Quality System (AQS; https://www.epa.gov/aqs), which hosts data from networks maintained by various federal, state, and tribal agencies, including the Clean Air Status and Trends Network (CASTNET), the Interagency Monitoring of Protected Visual Environments (IMPROVE), the Chemical Speciation Network (CSN) and the National Air Pollution Surveillance Program (NAPS) in Canada. Weekly total deposition data are acquired from the National Atmospheric Deposition Program (NADP) National Trends Network (NTN).
Observational data from the different networks are reformatted and then paired in space and time with model values using the Atmospheric Model Evaluation Tool (AMET; Appel et al., 2011) version 1.4 (https://github.com/USEPA/AMET/tree/1.4). Hourly O3 values are aggregated to Maximum Daily 8-hr averages (MDA8 O3), the metric used for determining attainment of EPA’s National Ambient Air Quality Standard for ozone.
There are 7 .csv files provided for every month/year combination in the EQUATES timeseries. These are referred to as ‘sitecompare’ files.
Files names (and approximate size per file); brief description:
AQS_Daily_CMAQv532_12US1_{YEAR}.csv, (~30-50 MB); daily average PM2.5 and constituents from EPA’s AQS (including data from CSN and IMPROVE networks)
AQS_Daily_O3_CMAQv532_12US1_{YEAR}.csv, (~3-5 MB); MDA8 O3 and other daily ozone metrics from EPA’s AQS (including data from CASTNET)
AQS_Hourly_CMAQv532_12US1_{YEAR}.csv, (~500-900 MB); hourly PM2.5, ozone, NOX, other gaseous pollutants, and meteorological variables from EPA’s AQS (including data from CASTNET)
CASTNET_Daily_CMAQv532_12US1_{YEAR}.csv, (~400-500 KB); MDA8 O3 and other daily ozone metrics from CASTNET
NADP_CMAQv532_12US1_{YEAR}.csv, (~450-550 KB); weekly total precipitation, wet deposition, and wet concentration from NADP/NTN
NAPS_CMAQv532_12US1_{YEAR}.csv, (~40-50 MB); hourly PM2.5, ozone, NOX, other gaseous pollutant from NAPS (Canadian sites)
NAPS_Daily_CMAQv532_12US1_{YEAR}.csv, (~ 1MB or less); MDA8 O3 and other daily ozone metrics from NAPS (Canadian sites)
See Section 8.2 of the CMAQv5.3.2 User’s Guide for information on how model output is matched to observational data using the combine, sitecmp, and sitecmp_dailyo3 Fortran utilities in the CMAQ GitHub code repository.File Location and Download Instructions
Link to EQUATES Data Dictionary and Data Use Statement
Link to sitecompare files for 2002 – 2019 for the EQUATES 12US1 domain
Link to download instructions
Publication on EQUATES US emissions: Foley et al. (2023)File Format
Each of the .csv files includes 17 or 21 columns with information on spatial location and timestamp (in local time) of the model and observed data: The remaining columns are the paired observational data (columns ending with ‘_ob’) and model estimates (columns ending with ‘_mod’). Note that the AQS_Hourly files have several ‘_mod’ columns and no corresponding ‘_ob’ columns. These are to provide modeled meteorological variables for the grid cells with measurement data to inform model evaluation. Descriptions of the observed and modeled variables for each file are provided in the EQUATES Data Dictionary.Recommended citations
For traceability, please cite use of these evaluation files with the dataset DOI of https://doi.org/10.15139/S3/YJTJ1F
Data Management and Sharing Plan for: 5/6 HBCD Prenatal Experiences and Longitudinal Development (PRELUDE) Consortium
No full textThe Data Management and Sharing Plan describes the scientific data to be generated and/or used in the research and outlines a strategy for managing and sharing project data
Gift Unexpectedness and Other-Praising, 2016
No full text249 participants from the United States were recruited from Amazon Mechanical Turk (MTurk) to participate in a study about a Christmas present they had just received (135 female, 114 male; Mage = 34.06, SDage = 11.07). Participants received approximately $0.35 in monetary compensation.
Participants named a Christmas present they had received that year for which they felt grateful and indicated who had given it to them. Then, they answered questions regarding their general feelings of surprise upon receiving this present, unexpectedness about receiving this specific present, and unexpectedness about receiving this present from the gift-giver. Subsequently, participants wrote a thank-you note to the gift-giver, which was content-coded for other-praising and self-benefiting expressions based on a coding-scheme developed by Algoe and colleagues (2016), adapted to the gift-giving context. Next, participants were asked to indicate what they would like to express to the gift-giver by rating additional items assessing other-praising (e.g. “I would like to praise him or her for making me this present”) and self-benefiting expression intentions (e.g. “I would like to express how perfectly the present suits me”). The remainder of the survey assessed additional relevant variables and demographics, such as closeness to the gift-giver, how many presents they had received from that person, happiness with the present itself, and estimated monetary value of the present.
There was no formal institutional ethics approval available at the time these data were collected. The research project on the relationship between unexpectedness and gratitude expressions was approved and funded by the funding institution (Center of Social and Economic Behavior at the University of Cologne)
Data Management and Sharing Plan for: A Cluster Randomized Trial Implementing the Diabetes Prevention Program in WIC Clinics in Louisiana
No full textThe Data Management and Sharing Plan describes the scientific data to be generated and/or used in the research and outlines a strategy for managing and sharing project data
City of Des Moines Vision Zero Transportation Safety Action Plan (2023). Des Moines, Iowa
No full textVision Zero is a strategy to reduce all traffic fatalities and severe injuries to zero, while increasing safe, equitable, and healthy mobility for all. Vision Zero Plans help guide municipalities, counties, and other areas to address these strategies considering the local context
Data Management and Sharing Plan for: CC Integration-Small: Top Down Approach to Implementing Scientific Workflows in Transit using In-Network And Near-Network Resources
No full textThe Data Management and Sharing Plan describes the scientific data to be generated and/or used in the research and outlines a strategy for managing and sharing project data
Data Management and Sharing Plan for: Intrinsic and extrinsic spindle orientation mechanisms in mammalian epidermis
No full textThe Data Management and Sharing Plan describes the scientific data to be generated and/or used in the research and outlines a strategy for managing and sharing project data
Data Management and Sharing Plan for: Probing Ethanol Dysregulation of Hypothalamic CRH neurons
No full textThe Data Management and Sharing Plan describes the scientific data to be generated and/or used in the research and outlines a strategy for managing and sharing project data