This dataset comprises the reconstructed DJF (December, January, February) averages for δ18O, precipitation, and wind-based data assimilation. It is important to highlight that this dataset has been validated for tropical South America and on centennial scales. Therefore, if it is intended to be used for studies concerning other regions or different time scales, the validity of the dataset should be verified beforehand.

This dataset comprises the reconstructed DJF (December, January, February) averages for δ18O, precipitation, and wind-based data assimilation. It is important to highlight that this dataset has been validated for tropical South America and on centennial scales. Therefore, if it is intended to be used for studies concerning other regions or different time scales, the validity of the dataset should be verified beforehand.

CT12K ceilometer data reports aerosol backscatter intensity and cloud base, up to 12000 feet, collected at the Gaviota Site by the Atmospheric Sciences Research Center (ASRC) at SUNY Albany during SWEX (Sundowner Winds Experiment) field campaign from 20 April through 16 May 2022.

Review of Economics and Statistics: Forthcoming.

Infrasound and Seismometer data collected from three infrasound station locations at the Gaviota Site by the Atmospheric Sciences Research Center (ASRC) at SUNY Albany during SWEX (Sundowner Winds Experiment) field campaign from 20 April through 16 May 2022.

The precipitation type (p-type) dataset (ptype.parquet) comprises observational weather reports sourced from the Meteorological Phenomena Identification Near the Ground (mPING) project, combined with corresponding numerical weather prediction data from the NOAA Rapid Refresh (RAP) model. These crowd-sourced mPING reports offer precipitation type labels (rain, snow, sleet, and freezing rain) across North America, while the RAP model provides atmospheric data, including temperature, humidity, and wind profiles, on pressure levels. The RAP data covers the contiguous United States (CONUS) from 2015 to 2022 on an hourly 13km grid. The mPING observations are matched to the nearest RAP grid cell and hour, allowing the two data sources to be merged into a labeled dataset suitable for classification tasks. The surface layer flux dataset (surface_layer.csv) contains high-frequency meteorological observations spanning from 2013 to 2015, collected at the Cabauw Experimental Site in the Netherlands. It includes measurements of various variables such as temperature, humidity, wind, radiation, and soil moisture, recorded every 10 minutes. The target output encompasses friction velocity, sensible heat, and latent heat.
The code used for processing the datasets and training neural network models is available in the Miles-Guess repository (https://github.com/ai2es/miles-guess).

The precipitation type (p-type) dataset (ptype.parquet) comprises observational weather reports sourced from the Meteorological Phenomena Identification Near the Ground (mPING) project, combined with corresponding numerical weather prediction data from the NOAA Rapid Refresh (RAP) model. These crowd-sourced mPING reports offer precipitation type labels (rain, snow, sleet, and freezing rain) across North America, while the RAP model provides atmospheric data, including temperature, humidity, and wind profiles, on pressure levels. The RAP data covers the contiguous United States (CONUS) from 2015 to 2022 on an hourly 13km grid. The mPING observations are matched to the nearest RAP grid cell and hour, allowing the two data sources to be merged into a labeled dataset suitable for classification tasks. The surface layer flux dataset (surface_layer.csv) contains high-frequency meteorological observations spanning from 2013 to 2015, collected at the Cabauw Experimental Site in the Netherlands. It includes measurements of various variables such as temperature, humidity, wind, radiation, and soil moisture, recorded every 10 minutes. The target output encompasses friction velocity, sensible heat, and latent heat.
The code used for processing the datasets and training neural network models is available in the Miles-Guess repository (https://github.com/ai2es/miles-guess).

Dataset associated with publication of the same name including qPCR results, measured variables, and other supplemental data.

Dataset associated with publication of the same name including qPCR results, measured variables, and other supplemental data.

Data were collected using online user surveys to assess participants' responses to textual information (words) in terms of participation, perception, and perseverance dimensions of information engagement (IE). The surveys were administered through the Qualtrics platform, ensuring efficient data collection and management.In this revised study, each participant was presented with 7 to 10 words randomly selected from the dataset, and the order of presentation was randomized to minimize potential biases. The use of multiple sets of words allowed for a more comprehensive investigation into the impact of phrasing on IE.A total of 80,500 observations were collected from 8,561 distinct participants, providing a substantial dataset for analysis. To ensure the validity of the findings, the survey design aimed to control for potential biases, such as selection bias and allocation bias. Chi-square analysis was conducted to assess the goodness of fit and ensure the representativeness of the samples. The analysis revealed that the composition of demographic groups who responded to each word sample was comparable to that of the overall population, indicating the reliability of the collected data.The measurement of perception, participation, and perseverance followed established scales and methodologies. Participants provided evaluations of the words' sensory appeal, attention-drawing capabilities, ease of understanding, and overall reward using a 5-point scale. The selection and retention rates were recorded to measure participants' active engagement and information retention, respectively.The comprehensive dataset and rigorous survey design provide a robust foundation for analyzing the impact of word choice on the dimensions of information engagement. The findings derived from this dataset will contribute to a deeper understanding of how word choice influences users' perceptions, participation, and perseverance, and inform strategies for effective communication and engagement in various domains.