This is a condensed version of the raw data obtained through the Google Data Analytics Course, made available by Lyft and the City of Chicago under this license (https://ride.divvybikes.com/data-license-agreement).

I originally did my study in another platform, and the original files were too large to upload to Posit Cloud in full. Each of the 12 monthly files contained anywhere from 100k to 800k rows. Therefore, I decided to reduce the number of rows drastically by performing grouping, summaries, and thoughtful omissions in Excel for each csv file. What I have uploaded here is the result of that process.

Data is grouped by: month, day, rider_type, bike_type, and time_of_day. total_rides represent the sum of the data in each grouping as well as the total number of rows that were combined to make the new summarized row, avg_ride_length is the calculated average of all data in each grouping.

Be sure that you use weighted averages if you want to calculate the mean of avg_ride_length for different subgroups as the values in this file are already averages of the summarized groups. You can include the total_rides value in your weighted average calculation to weigh properly.

9 Columns:

date - year, month, and day in date format - includes all days in 2022 day_of_week - Actual day of week as character. Set up a new sort order if needed. rider_type - values are either 'casual', those who pay per ride, or 'member', for riders who have annual memberships. bike_type - Values are 'classic' (non-electric, traditional bikes), or 'electric' (e-bikes). time_of_day - this divides the day into 6 equal time frames, 4 hours each, starting at 12AM. Each individual ride was placed into one of these time frames using the time they STARTED their rides, even if the ride was long enough to end in a later time frame. This column was added to help summarize the original dataset. total_rides - Count of all individual rides in each grouping (row). This column was added to help summarize the original dataset. avg_ride_length - The calculated average of all rides in each grouping (row). Look to total_rides to know how many original rides length values were included in this average. This column was added to help summarize the original dataset. min_ride_length - Minimum ride length of all rides in each grouping (row). This column was added to help summarize the original dataset. max_ride_length - Maximum ride length of all rides in each grouping (row). This column was added to help summarize the original dataset.

Please note: the time_of_day column has inconsistent spacing. Use mutate(time_of_day = gsub(" ", "", time_of _day)) to remove all spaces.

Revisions

Below is the list of revisions I made in Excel before uploading the final csv files to the R environment:

• Deleted station location columns and lat/long as much of this data was already missing.

• Deleted ride id column since each observation was unique and I would not be joining with another table on this variable.

• Deleted rows pertaining to "docked bikes" since there were no member entries for this type and I could not compare member vs casual rider data. I also received no information in the project details about what constitutes a "docked" bike.

• Used ride start time and end time to calculate a new column called ride_length (by subtracting), and deleted all rows with 0 and 1 minute results, which were explained in the project outline as being related to staff tasks rather than users. An example would be taking a bike out of rotation for maintenance.

• Placed start time into a range of times (time_of_day) in order to group more observations while maintaining general time data. time_of_day now represents a time frame when the bike ride BEGAN. I created six 4-hour time frames, beginning at 12AM.

• Added a Day of Week column, with Sunday = 1 and Saturday = 7, then changed from numbers to the actual day names.

• Used pivot tables to group total_rides, avg_ride_length, min_ride_length, and max_ride_length by date, rider_type, bike_type, and time_of_day.

• Combined into one csv file with all months, containing less than 9,000 rows (instead of several million)

Condensation Nuclei (CN) data collected by the University of Hawaii group (Tony Clarke) in ACE1. All of the variables are average values for 15 second intervals. This dataset is a composite of all of the raw data files.

We developed an experimental method that can be used to study organization design and grouping decisions more specifically. We demonstrate the method in a study with 285 participants. The participants were asked to group a set of nine roles into units using card-sorting. The role descriptions indicated that there were interdependencies between some of the roles. Participants’ grouping decisions were quantified and compared against an algorithmic solution that minimized coordination costs. It was found that a relatively small difference in task complexity between groups greatly affected participants’ performance. The files that are uploaded here contain the raw data and "distance scores" for study of how people make organization design decisions. See the appendices in the article for tips on how to set up similar studies.

Despite strong interest in how noise affects marine mammals, little is known about the most abundant and commonly exposed taxa. Social delphinids occur in groups of hundreds of individuals that travel quickly, change behavior ephemerally, and are not amenable to conventional tagging methods, posing challenges in quantifying noise impacts. We integrated drone-based photogrammetry, strategically-placed acoustic recorders, and broad-scale visual observations to provide complimentary measurements of different aspects of behavior for short- and long-beaked common dolphins. We measured behavioral responses during controlled exposure experiments (CEEs) of military mid-frequency (3-4 kHz) active sonar (MFAS) using simulated and actual Navy sonar sources. We used latent-state Bayesian models to evaluate response probability and persistence in exposure and post-exposure phases. Changes in sub-group movement and aggregation parameters were commonly detected during different phases of MFAS CEEs but not control CEEs. Responses were more evident in short-beaked common dolphins (n=14 CEEs), and a direct relationship between response probability and received level was observed. Long-beaked common dolphins (n=20) showed less consistent responses, although contextual differences may have limited which movement responses could be detected. These are the first experimental behavioral response data for these abundant dolphins to directly inform impact assessments for military sonars. Methods We used complementary visual and acoustic sampling methods at variable spatial scales to measure different aspects of common dolphin behavior in known and controlled MFAS exposure and non-exposure contexts. Three fundamentally different data collection systems were used to sample group behavior. A broad-scale visual sampling of subgroup movement was conducted using theodolite tracking from shore-based stations. Assessments of whole-group and sub-group sizes, movement, and behavior were conducted at 2-minute intervals from shore-based and vessel platforms using high-powered binoculars and standardized sampling regimes. Aerial UAS-based photogrammetry quantified the movement of a single focal subgroup. The UAS consisted of a large (1.07 m diameter) custom-built octocopter drone launched and retrieved by hand from vessel platforms. The drone carried a vertically gimballed camera (at least 16MP) and sensors that allowed precise spatial positioning, allowing spatially explicit photogrammetry to infer movement speed and directionality. Remote-deployed (drifting) passive acoustic monitoring (PAM) sensors were strategically deployed around focal groups to examine both basic aspects of subspecies-specific common dolphin acoustic (whistling) behavior and potential group responses in whistling to MFAS on variable temporal scales (Casey et al., in press). This integration allowed us to evaluate potential changes in movement, social cohesion, and acoustic behavior and their covariance associated with the absence or occurrence of exposure to MFAS. The collective raw data set consists of several GB of continuous broadband acoustic data and hundreds of thousands of photogrammetry images. Three sets of quantitative response variables were analyzed from the different data streams: directional persistence and variation in speed of the focal subgroup from UAS photogrammetry; group vocal activity (whistle counts) from passive acoustic records; and number of sub-groups within a larger group being tracked by the shore station overlook. We fit separate Bayesian hidden Markov models (HMMs) to each set of response data, with the HMM assumed to have two states: a baseline state and an enhanced state that was estimated in sequential 5-s blocks throughout each CEE. The number of subgroups was recorded during periodic observations every 2 minutes and assumed constant across time blocks between observations. The number of subgroups was treated as missing data 30 seconds before each change was noted to introduce prior uncertainty about the precise timing of the change. For movement, two parameters relating to directional persistence and variation in speed were estimated by fitting a continuous time-correlated random walk model to spatially explicit photogrammetry data in the form of location tracks for focal individuals that were sequentially tracked throughout each CEE as a proxy for subgroup movement. Movement parameters were assumed to be normally distributed. Whistle counts were treated as normally distributed but truncated as positive because negative count data is not possible. Subgroup counts were assumed to be Poisson distributed as they were distinct, small values. In all cases, the response variable mean was modeled as a function of the HMM with a log link: log(Responset) = l0 + l1Z t where at each 5-s time block t, the hidden state took values of Zt = 0 to identify one state with a baseline response level l0, or Zt = 1 to identify an “enhanced” state, with l1 representing the enhancement of the quantitative value of the response variable. A flat uniform (-30,30) prior distribution was used for l0 in each response model, and a uniform (0,30) prior distribution was adopted for each l1 to constrain enhancements to be positive. For whistle and subgroup counts, the enhanced state indicated increased vocal activity and more subgroups. A common indicator variable was estimated for the latent state for both the movement parameters, such that switching to the enhanced state described less directional persistence and more variation in velocity. Speed was derived as a function of these two parameters and was used here as a proxy for their joint responses, representing directional displacement over time.
To assess differences in the behavior states between experimental phases, the block-specific latent states were modeled as a function of phase-specific probabilities, Z t ~ Bernoulli (pphaset), to learn about the probability pphase of being in an enhanced state during each phase. For each pre-exposure, exposure, and post-exposure phase, this probability was assigned a flat uniform (0,1) prior probability. The model was programmed in R (R version 3.6.1; The R Foundation for Statistical Computing) with the nimble package (de Valpine et al. 2020) to estimate posterior distributions of model parameters using Markov Chain Monte Carlo (MCMC) sampling. Inference was based on 100,000 MCMC samples following a burn-in of 100,000, with chain convergence determined by visual inspection of three MCMC chains and corroborated by convergence diagnostics (Brooks and Gelman, 1998). To compare behavior across phases, we compared the posterior distribution of the pphase parameters for each response variable, specifically by monitoring the MCMC output to assess the “probability of response” as the proportion of iterations for which pexposure was greater or less than ppre-exposure and the “probability of persistence” as the proportion of iterations for which ppost-exposre was greater or less than ppre-exposure. These probabilities of response and persistence thus estimated the extent of separation (non-overlap) between the distributions of pairs of pphase parameters: if the two distributions of interest were identical, then p=0.5, and if the two were non-overlapping, then p=1. Similarly, we estimated the average values of the response variables in each phase by predicting phase-specific functions of the parameters: Mean.responsephase = exp(l0 + l1pphase) and simply derived average speed as the mean of the speed estimates for 5-second blocks in each phase.

Supplementary Information and Raw Data for Hofstätter N., Hofer S., Duschl A., and Himly M. Children’s privilege in COVID-19: The protective role of the juvenile lung morphometry and ventilatory pattern on airborne SARS-CoV-2 transmission and severe pulmonary disease (2021). Biomedicines 9(10):1414. DOI: https://doi.org/10.3390/biomedicines9101414

1. pdf of deposition heatmaps (incl probability values) for 4 different VLP count medium diameters and 3 different age groups upon nose breathing

2. pdf of deposition heatmaps (incl probability values) for 4 different VLP count medium diameters and 3 different age groups upon mouth breathing

3. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 3 y upon nose breathing

4. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 3 y upon mouth breathing

5. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 8 y upon nose breathing

6. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 8 y upon mouth breathing

7. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 21 y upon nose breathing

8. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 21 y upon mouth breathing

Supplementary Information and Raw Data for Hofstaetter et al., 2021 1. pdf of deposition heatmaps (incl probability values) for 4 different VLP count medium diameters and 3 different age groups 2. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 3 y 3. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 8 y 4. xls-formatted file of MPPD-derived deposition raw data sets for 4 different VLP count medium diameters for age group 21 y

Data collected to compare the foraging/food consumption and impacts of Solenopsis invicta virus 3 on fire ant colonies, Solenopsis invicta. Ant colonies infected with Solenopsis invicta virus 3 were compared with uninfected (control) colonies. Four data sets include foraging/food consumption, brood changes, queen fecundity, and virus quantity. Resources in this dataset: Resource Title: Brood ratings of virus infected ant colonies File Name: Raw data brood rating.csv Resource Description: Change in brood quantity (rating) after treatment with Solenopsis invicta virus 3. Data include the number of days after exposure to virus, the brood ratings for four treatment (virus exposure) replicates and three control replicates, and the means for each group. Resource Title: Ant food consumption data File Name: Raw data consumption 1.csv Resource Description: Food consumption in ant colonies treated with Solenopsis invicta virus 3. Data include the number of days after treatment with SINV-3, the raw data for four treatment replicates and control replicates, means for treatment and control groups, the quantity (grams) of food consumed for each group, and the standard error for each group. Resource Title: Solenopsis invicta virus 3 genome quantification File Name: Raw data SINV3 quantity.csv Resource Description: Quantitative PCR data to detect Solenopsis invicta virus 3. Data include the day after treatment the measurements were taken, the raw data for four treatment replicates and 3 control replicates, means and standard deviation of each treatment group. Resource Title: Queen fecundity after treatment with Solenopsis invicta virus 3 File Name: Raw data eggs laid.csv Resource Description: Eggs laid (fecundity) in a 24 hour period by Solenopsis invicta queens after SINV-3 exposure. Data sheet includes colony group (four replicate treatment colonies and three replicate control colonies) and the number of eggs laid by queens from these colonies on 24, 31, and 39 days after virus exposure. Resources in this dataset:Resource Title: Brood ratings of virus infected ant colonies. File Name: Raw data brood rating.csvResource Description: Change in brood quantity (rating) after treatment with Solenopsis invicta virus 3. Data include the number of days after exposure to virus, the brood ratings for four treatment (virus exposure) replicates and three control replicates, and the means for each group.Resource Title: Solenopsis invicta virus 3 genome quantification. File Name: Raw data SINV3 quantity.csvResource Description: Quantitative PCR data to detect Solenopsis invicta virus 3. Data include the day after treatment the measurements were taken, the raw data for four treatment replicates and 3 control replicates, means and standard deviation of each treatment group.Resource Title: Queen fecundity after treatment with Solenopsis invicta virus 3. File Name: Raw data eggs laid.csvResource Description: Eggs laid (fecundity) in a 24 hour period by Solenopsis invicta queens after SINV-3 exposure. Data sheet includes colony group (four replicate treatment colonies and three replicate control colonies) and the number of eggs laid by queens from these colonies on 24, 31, and 39 days after virus exposure.Resource Title: Ant food consumption data. File Name: Raw data consumption 1.csvResource Description: Food consumption in ant colonies treated with Solenopsis invicta virus 3. Data include the number of days after treatment with SINV-3, the raw data for four treatment replicates and control replicates, means for treatment and control groups, the quantity (grams) of food consumed for each group, and the standard error for each group.

The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries, as well as an additional 2163 governance, trade, and competitiveness indicators from the World Bank Group GovData360 and TCdata360 platforms in a preprocessed form. The current version was compiled on July 27, 2020. Note that this version uses 20-40-60-80-day time windows and the first test data are based on the first country reports on tests.

Please cite as: • Kurbucz, M. T. (2020). A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms. Data in Brief, 105881.

Data generation: • Data generation (data_generation. Rmd): Datasets were generated with this R Notebook. It can be used to update datasets and customize the data generation process.

Datasets: • Country data (country_data.txt): Country data. • Metadata (metadata.txt): The metadata of selected GovData360 and TCdata360 indicators. • Joint dataset (joint_dataset.txt): The joint dataset of COVID-19 variables and preprocessed GovData360 and TCdata360 indicators. • Correlation matrix (correlation_matrix.txt): The Kendall rank correlation matrix of the joint dataset.

Raw data of figures and tables: • Raw data of Fig. 2 (raw_data_fig2.txt): The raw data of Fig. 2. • Raw data of Fig. 3 (raw_data_fig3.txt): The raw data of Fig. 3. • Raw data of Table 1 (raw_data_table1.txt): The raw data of Table 1. • Raw data of Table 2 (raw_data_table2.txt): The raw data of Table 2. • Raw data of Table 3 (raw_data_table3.txt): The raw data of Table 3.

This dataset provides processed and normalized/standardized indices for the management tool group focused on 'Mission and Vision Statements', including related concepts like Purpose Statements. Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding Mission/Vision dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "mission statement" + "vision statement" + "mission and vision corporate". Processing: None. Utilizes the original base-100 normalized Google Trends index. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Mission Statements + Vision Statements + Purpose Statements + Mission and Vision. Processing: Annual relative frequency series normalized (peak year = 100). Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching Mission/Vision-related keywords [("mission statement" OR ...) AND (...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly Crossref publications. Deduplicated via DOIs. Processing: Monthly relative share calculated (Mission/Vision Count / Total Count). Monthly relative share series normalized (peak month's share = 100). Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years: Mission/Vision (1993); Mission Statements (1996); Mission and Vision Statements (1999-2017); Purpose, Mission, and Vision Statements (2022). Processing: Semantic Grouping: Data points across the different naming conventions were treated as a single conceptual series. Normalization: Combined series normalized relative to its historical peak (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years (same names/years as Usability). Processing: Semantic Grouping: Data points treated as a single conceptual series. Standardization (Z-scores): Using Z = (X - 3.0) / 0.891609. Index Scale Transformation: Index = 50 + (Z * 22). Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding Mission/Vision dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

Despite current clinical knowledge of the risks associated with tobacco consumption, the bioarchaeological investigation of tobacco’s effect on health in past populations remains woefully underexamined. This study explores the potential respiratory health implications of the rapid incorporation of tobacco-use into the everyday lives of English citizens during the post-medieval period. Adult skeletons from urban post-medieval St James’s Gardens Burial Ground, Euston, London (N = 281; CE1789–1853) and rural post-medieval (N = 151; CE1500–1855) and medieval (N = 62; CE1150–1500) Barton-upon-Humber were examined. Individuals were assessed for tobacco consumption status using osteoarchaeological and biomolecular methods. Individuals were observed for bone changes related to inflammation within the maxillary sinuses and within the pleural/pulmonary regions. Statistical tests revealed a significant association between tobacco consumption and the presence of pulmonary/pleural inflammation in the Barton-upon-Humber post-medieval group. Tobacco consumers at Barton-upon-Humber were also more than twice as likely to present with maxillary sinusitis or pleural/pulmonary inflammation, although the results were not statistically significant. Differences between tobacco consumers and non-consumers in the London group were not apparent, but the odds of having maxillary sinusitis increased by two-fold in middle adults (compared to young adults) and lower socio-economic groups (compared to higher socio-economic groups). Significant differences in respiratory disease frequencies were apparent between rural and urban groups. The results highlight the complexity of factors affecting upper and lower respiratory disease, indicating the potential impacts of not only tobacco consumption, but household, environmental, and occupational air pollution, as well as poor water sanitation, on frequencies of respiratory disease in different population groups.

Raw dataset and analysis for Figure 3E:Figure 3: AAV9-GFP transduction in kidneys of wild type and Alport mice injected at low (6.6x1012 vg/kg), medium (3.3x1013 vg/kg) and high (1.65x1014 vg/kg) dose. In total, 6 (3 WT and 3 Col4a5 KO) 8-week-old mice per group were injected with either AAV9-GFP or saline. E) Colocalization for WT1 and GFP signals was quantified using QuPath and plotted as the percentage of GFP-positive podocytes. The entirety of glomeruli from 2 whole kidney cross-sections (per genotype/condition) were quantified. Data are presented as mean ± SEM of the values from 3 mice per group and per genotype. P value

This dataset contains raw data for all of the variables measured for the iUTAH GAMUT Network Provo River at Riverwoods Aquatic (PR_RW_A). Each file contains a calendar year of data. The file for the current year is updated on a daily basis. The data values were collected by a variety of sensors at 15 minute intervals. The file header contains detailed metadata for site and the variable and method of each column.

This study investigated the relationship between the stiffness of the upper trapezius muscle and the range of rotational movement of the cervical spine. A total of 60 right-handed asymptomatic students participated in the study. Participants (N = 22) characterised by asymmetry in rotational movements were selected for the experimental group. A difference of ≥10° between right and left rotation of the cervical spine was considered asymmetrical. The control group (N = 38) included participants whose rotation difference was < 10°.

This is raw data of my MRI data.The file contains three subfolders.The three folders are the mania group, the remission group and the control group.

A total of 99 HIV-infected PCP patients and 61 HIV-infected patients diagnosed with non-PCP pneumonia between March 2019 and December 2022 were enrolled. The raw data of clinical symptoms and laboratory test results was collected in the excel table.

Abstract From samples of the Paleozoic Tamatán Group (Huizachal–Peregrina Anticlinorium, Tamaulipas, Mexico), petrographic (qualitative and modal) and geochemical analyses (major, trace, and rare earth elements) were conducted. The first U–Pb geochronological data on detrital zircons of the Tamatán Group were generated using four samples. The data presented here contains a broad overview of photomicrography, recalculated modal point-count data, raw geochemical data, and simple statistics of selected geochemical parameters. The data presented in this article are interpreted and discussed in the research article titled “Provenance and tectonic setting of the Tamatán Paleozoic sequence, NE Mexico: Implications for the closure of the Rheic Ocean at the northwestern part of Gondwana” [1]. Value of the data • Important data available for researchers conducting research on the Northwestern margin of Gondwana and adjacent areas. • Data collection available for sedimentologists, working with geochemical data. • Data made availabla to construct integrated geological models for the Northwestern Margin of Gondwana and adjacent areas. • A complete geochemical dataset for the Tamatán Group. • Tectonic activity, weathering, and provenance data of the Tamatán Group are provided. • First U-Pb geochronological data of the Tamatán Group Data This article provides data from 105 samples. From 70 samples, photomicrographs were taken and point-counted and modal analyses on recalculated petrographic parameters were provided were provided. Geochemical analyses (major, trace, and rare earth elements [REE]) of 73 samples were conducted. Four samples for U–Pb geochronological zircon analyses were made. The sample location is given with the geographical and UTM coordinates of each sample. Each sample is located on a geological map. The petrographic and geochemical data are presented as raw data and displayed as a simple statistic of the selected petrography and geochemical parameters, respectively. Additionally, outcrop photographs are provided. Acknowledgements Financial support for this work was provided by a Ph.D. fellowship from the National Council of Science and Technology (CONACYT). The first author, a Ph.D. student at the postgraduate program of the Facultad de Ciencias de la Tierra, Universidad Autónoma de Nuevo León (FCT/UANL), wants to thank Sergio Padilla-Ramírez, Centro de Investigación Científica y de Educación Superior de Ensenada B.C, México and Susana Rosas-Montoya and Daniela Tazzo (CICESE) for their help in the preparation and analysis of the geochronological data. Special thanks to L.A. Elizondo-Pacheco, N.Z. Morales-Alemán, and D.C. Rodríguez-Campero y M. Rodríguez-Escamilla (FCT/UANL) for their assistance in the field. The geochemical and geochronological analyses were supported by the PAICyT projects CT-129-09 and CN-940-19, which was granted by the Universidad Autónoma de Nuevo León.

Raw sequencing data as generated by the five different methods used are provided for each of the three samples used in the comparison. The files are in FASTQ format as exported from the Oxford Nanopore’s MK1C using MinION flowcells. Files are labeled according to the method (as described in the paper) and the Sample ID). The MK1C exports data in blocks of 6000 reads per FASTQ file and all the FASTQ files from each method and sample are grouped in a common folder.

This dataset contains raw data for all of the variables measured for the iUTAH GAMUT Network Provo River at Charleston Advanced Aquatic (PR_CH_AA). Each file contains a calendar year of data. The file for the current year is updated on a daily basis. The data values were collected by a variety of sensors at 15 minute intervals. The file header contains detailed metadata for site and the variable and method of each column.

The following submission includes raw and processed data from the in water deployment of NREL's Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC), in the form of parquet files, TDMS files, CSV files, bag files and MATLAB workspaces. This dataset was collected in March 2024 at the Jennette's pier test site in North Carolina. This submission includes the following: - Data description document (HERO WEC FY24 Hydraulic Deployment Data Descriptions.doc) - This document includes detailed descriptions of the type of data and how it was processed and/or calculated. - Processed MATLAB workspace - The processed data is provided in the form of a single MATLAB workspace containing data from the full deployment. This workspace contains data from all sensors down sampled to 10 Hz along with all array Value Added Products (VAPs). - MATLAB visualization scripts - The MATLAB workspaces can be visualized using the file "HERO_WEC_2024_Hydraulic_Config_Data_Viewer.m/mlx". The user simply needs to download the processed MATLAB workspaces, specify the desired start and end times and run this file. Both the .m and .mlx file format has been provided depending on the user's preference. - Summary Data - The fully processed data was used to create a summary data set with averages and important calculations performed on 30-minute intervals to align with the intervals of wave resource data reported from nearby CDIP ocean observing buoys located 20km East of Jennette's pier and 40km Northeast of Jennette's pier. The wave resource data provided in this data set is to be used for reference only due the difference in water depth and proximity to shore between the Jennette's pier test site and the locations of the ocean observing buoys. This data is provided in the Summary Data zip folder, which includes this data set in the form of a MATLAB workspace, parquet file, and excel spreadsheet. - Processed Parquet File - The processed data is provided in the form of a single parquet file containing data from all HERO WEC sensors collected during the full deployment. Data in these files has been down sampled to 10 Hz and all array VAPs are included. - Interim Filtered Data - Raw data from each sensor group partitioned into 30-minute parquet files. These files are outputs from an intermediate stage of data processing and contain the raw data with no Quality Control (QC) or calculations performed in a format that is easier to use than the raw data. - Raw Data - Raw, unprocessed data from this deployment can be found in the Raw Data zip folder. This data is provided in the form of TDMS, CSV, and bag files in the original format output by the MODAQ system. - Python Data Processing Script - This links to an NREL public github repository containing the python script used to go from raw data to fully processed parquet files. Additional documentation on how to use this script is included in the github repository. This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

The Environmental Justice Index (EJI) is a tool that may aid in identifying environmental justice populations using demographic data at the Census block group and Tract levels. This dataset includes raw data associated with the EJI at the Block Group level. Consult the user guide for more information.Executive Order 12898 defines environmental justice populations as low-income and minority groups. This rule states that federally-funded agencies must identify and address disproportionately high and adverse impacts of their programs, policies, and activities on environmental justice populations. In addition, Executive Order 13985, signed on January 20, 2021, requires the federal government to pursue a comprehensive approach to advancing equity. Equity is defined as “the consistent and systematic fair, just, and impartial treatment of all individuals,” including people of color, religious minorities, LGBTQ+ persons, people with disabilities, people who live in rural areas, and people “otherwise adversely affected by persistent poverty or inequality.” This order may affect how equity is addressed in transportation planning in the future.The Environmental Justice Index can support implementation of US Department of Transportation environmental justice principles during transportation planning and project delivery. Because federal transportation agencies recommend against using bright-line thresholds while identifying environmental justice populations, the regional percentages in the Environmental Justice Index should be a starting point for planning, analysis, and outreach. Communities in block groups below the regional percentage threshold should not be excluded. The applications described below are synthesized from educational materials developed and published by federal transportation agencies and from analyses conducted by NCTCOG. The applications may not be appropriate for all analyses and do not represent all potential uses of environmental justice data.