This dataset includes 20 identified challenges employers face with Gen Z workers, categorized by behavioral trends, workplace expectations, and communication gaps.

Dataset contains summary data (mean, standard error, sample size (n)) for all measured endpoints reported and depicted in the corresponding manuscript. This dataset is associated with the following publication: Conley, J., C. Lambright, N. Evans, M. Strynar, J. McCord, B. McIntyre, G. Travlos, M. Cardon, E. MedlockKakaley, P. Hartig, V. Wilson, and E. Gray. Adverse maternal, fetal, and postnatal effects of Hexafluoropropylene oxide dimer acid (GenX) from oral gestational exposure in Sprague Dawley rats. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 1-13, (2019).

Complete summary data (mean, error, sample size) for all figures and tables associated with the present manuscript, which characterizes maternal, fetal, and neonatal effects of oral exposure to HFPO-DA (GenX) during gestation in the Sprague-Dawley rat . This dataset is associated with the following publication: Conley, J., C. Lambright, N. Evans, J. McCord, M. Strynar, D. Hill, E. MedlockKakaley, V. Wilson, and E. Gray. Hexafluoropropylene oxide-dimer acid (HFPO-DA or GenX) alters maternal and fetal glucose and lipid metabolism and produces neonatal mortality, low birthweight and hepatomegaly in the Sprague-Dawley rat. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, NETHERLANDS, 146: 106204, (2021).

The Excel file includes data transcribed from Thermo QualBrowser software into the spreadsheet. The first tab of the spreadsheet is the data dictionary. This dataset is associated with the following publication: Liberatore, H., S.R. Jackson, M. Strynar, and J. McCord. Solvent Suitability for HFPO-DA (“GenX” Parent Acid) in Toxicological Studies. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 7(7): 477-481, (2020).

This dataset is a rich collection of handwritten Sindhi alphabet images, carefully curated to capture a diverse range of writing styles. The dataset includes samples from multiple generations, including Gen X, Millennials, Gen Z, and Gen Alpha, ensuring a broad representation of handwriting variations. Additionally, it encompasses contributions from individuals of different genders and varying levels of handwriting proficiency, making it highly valuable for research in handwriting recognition and computer vision.

This dataset is ideal for training machine learning models on tasks such as:
- Optical Character Recognition (OCR) for Sindhi script
- Handwriting style analysis across generations
- Character classification and dataset augmentation experiments
- Computer vision research involving regional scripts

Dataset Details:

• Total Sindhi Letters: 52
• Images per Letter: 58
• Total Images: 3016
• Format: JPG
• Diversity Factors:
  • Generations Covered: Gen X, Millennials, Gen Z, Gen Alpha
  • Gender Representation: Male and Female contributors
  • Writing Styles: Includes cursive, bold, thin, and uneven strokes

Dataset Structure:

The dataset is structured into 52 folders, each representing a unique Sindhi letter. Each folder contains 31 handwritten samples of that letter, captured from various contributors.

Usage & Applications:

This dataset can be used by researchers, educators, and developers working on:
- Handwriting Recognition Models
- AI-powered OCR for Sindhi Language
- Multi-generational Handwriting Studies
- Sindhi Language Digitization & Preservation

License & Attribution

This dataset is publicly available under the CC BY 4.0 License, meaning you can use it freely with proper attribution.

This dataset was created through the combined efforts of multiple contributors who provided handwritten samples.

• Mudasir Murtaza
• Farhad Ali

This dataset is now open-source and we encourage researchers, developers, and students to use this dataset for AI projects and Sindhi handwriting recognition models!

This dataset is the most comprehensive look at volunteering and civic life in the 50 states and 51 cities across the country. Data include volunteer rates and rankings, civic engagement trends, and analysis.

Exposure to PFAS such as GenX (HFPO dimer acid) has become increasingly common due to the replacement of older generation PFAS in manufacturing processes. While neurodegenerative and developmental effects of legacy PFAS exposure have been studied in depth, there is a limited understanding specific to the effects of GenX exposure. To investigate the effects of GenX exposure, we exposed Drosophila melanogaster to GenX and assessed the motor behavior and performed quantitative proteomics of fly brains to identify molecular changes in the brain. Additionally, metabolic network-based analysis using the iDrosophila1 model unveiled a potential link between GenX exposure and neurodegeneration. Since legacy PFAS exposure has been linked to Parkinson’s disease (PD), we compared the proteome data sets between GenX-exposed flies and a fly model of PD expressing human α-synuclein. Considering the proteomic data- and network-based analyses that revealed GenX may be regulating GABA-associated pathways and the immune system, we next explored the effects of GenX on astrocytes, as astrocytes in the brain can regulate GABA. An array of assays demonstrated GenX exposure may lead to mitochondrial dysfunction and neuroinflammatory response in astrocytes, possibly linking non-cell autonomous neurodegeneration to the motor deficits associated with GenX exposure.

Due to its wide usage and recent detection in environmental matrices, hexafluoropropylene oxide dimer acid (HFPO–DA, commercial name GenX) has attracted considerable attention. Here, we explored and compared the toxicity of GenX and its novel analogs with that of perfluorooctanoic acid (PFOA) to provide guidance on the structural design and optimization of novel alternatives to poly- and perfluoroalkyl substances (PFASs). Adult male BALB/c mice were continuously exposed to PFOA, GenX, perfluoro-2-methyl-3,6-dioxo-heptanoic acid (PFMO2HpA), and perfluoro-2-methyl-3,6,8-trioxo-nonanoic acid (PFMO3NA; 0, 0.4, 2, or 10 mg/kg/d) via oral gavage for 28 days. The PFOA, GenX, and PFMO3NA treatment groups showed an increase in relative liver weight, and bile acid metabolism was the most significantly affected pathway in all treatment groups, as shown via weighted gene coexpression network analysis. The highest total bile acid levels were observed in the 2 and 10 mg/kg/d PFMO3NA groups. The ratios of primary bile acids to all bile acids increased in the high-dose groups, while the ratios of secondary bile acids showed a downward trend. Thus, bile acid metabolism disorder may be a prominent adverse effect induced by exposure to GenX, its analogs, and PFOA. Results also showed that the hepatotoxicity of PFMO2HpA was lower than that of GenX, whereas the hepatotoxicity of PFMO3NA was stronger, suggesting that PFMO2HpA may be a potential alternative to GenX.

Raw data file outputs of serum and urine measurements of GenX in dosed rodents.

This dataset is associated with the following publication: Rushing, B., Q. Hu, J. Franklin, R. McMahen, S. Dagnino, C. Higgins, M. Strynar, and J. DeWitt. Evaluation of the Immunomodulatory Effects of 2,3,3,3-tetrafluoro-2-(heptafluoropropoxy)-propanoate (“GenX”) in C57BL/6 Mice. ENVIRONMENTAL TOXICOLOGY. John Wiley & Sons, Ltd., Indianapolis, IN, USA, 156(1): 179-189, (2017).

This dataset is about book subjects. It has 1 row and is filtered where the books is Generation impact : how next gen donors are revolutionizing giving. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

X-ray measurements are critical for the understanding of cycles of matter and energy in the Universe, for understanding the nature of dark matter and dark energy, and for probing gravity in the extreme limit of matter accretion onto a black hole. We propose a program to mature the current x-ray microcalorimeter technology, while developing transformational technology that will enable megapixel arrays. X-ray calorimeters based on superconducting transition-edge sensors achieve the highest energy resolution of any non-dispersive detector technology. The performance of single x-ray calorimeter pixels has reached that required for many possible future missions such as IXO, RAM, and Generation-X, but further optimization is still useful. In the last years, we have made progress in developing techniques to control and engineer the properties of the superconducting transition. We propose to continue this single-pixel optimization, and to improve both the practical and theoretical understanding of the correlation between alpha, beta, and noise to identify favorable regions of parameter space for different instruments. A greater challenge is the development of mature TES x-ray calorimeter arrays with a very large number of pixels. Advances in the last several years have been significant. We have developed modestly large (256 pixel) x-ray calorimeter arrays with time-division SQUID multiplexing, and demonstrated Walsh code-division SQUID multiplexing, which has the potential to allow scaling to much larger arrays. Here we propose to extend this work, and also to introduce a new code-division SQUID multiplexing circuit with extremely compact, low-power elements. Using this approach, it is possible for the first time to fit all of the detector biasing and multiplexing elements underneath an x-ray absorber, allowing in-focal-plane multiplexing. This approach eliminates the requirement to bring leads from each pixel out of the focal plane, while reducing the power dissipati

"We propose to continue our detector development program in X-ray astronomy. Under our current grant we are developing a new type of active pixel detector. The current funding allows us to carry this design through CDR, but will not cover fabrication of the detectors. Here we propose to build and test these innovative detectors, which could potentially be employed in future missions such as IXO, Xenia, Gen-X, and SMEX/MIDEX-class missions. This proposal supports NASA's goals of technical advancement of technologies suitable for future missions and training of graduate students."

Multimodal models trained on large natural image-text pair datasets have exhibited astounding abilities in gener-ating high-quality images. Medical imaging data is fundamentally different to natural images, and the language used to succinctly capture relevant details in medical data uses a different, narrow but semantically rich, domain-specific vocabulary.

The main dataset is a 130 MB file of trajectory data (I90_94_moving_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) and non-automated vehicles on a highway in an urban environment. Supporting files include aerial reference images for four distinct data collection “Runs” (I90_94_moving_RunX_with_lanes.png, where X equals 1, 2, 3, and 4). Associated centerline files are also provided for each “Run” (I-90-moving-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I90_94moving.csv” for more details). The dataset defines six northbound lanes using these centerline files. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. The northbound lanes are shown visually from left to right in I90_94_moving_lane1.png through I90_94_moving_lane6.png. This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using one high-resolution 8K camera mounted on a helicopter that followed three SAE Level 2 ADAS-equipped vehicles (one at a time) northbound through the 4 km long segment at an altitude of 200 meters. Once a vehicle finished the segment, the helicopter would return to the beginning of the segment to follow the next SAE Level 2 ADAS-equipped vehicle to ensure continuous data collection. The segment was selected to study mandatory and discretionary lane changing and last-minute, forced lane-changing maneuvers. The segment has five off-ramps and three on-ramps to the right and one off-ramp and one on-ramp to the left. All roads have 88 kph (55 mph) speed limits. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a cloudy day. As part of this dataset, the following files were provided: I90_94_moving_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the automated test vehicles ("yes" or "no") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion. I90_94_moving_RunX_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound lanes) for each run X. I-90-moving-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. The "x" and "y" columns represent the horizontal and vertical locations in the reference image, respectively. The "ramp" columns define the type of roadway segment (0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments). In total, the centerline files define six northbound lanes. Annotation on Regions.zip, which includes images that visually map lanes (I90_9

This dataset includes three zip files and several other files to replicate the runs. It also includes the load time-series of commercial and industrial electricity usage of each zone of the manuscript. It also includes the full 8760hour time-series of the gross load.

1. The file "2030.zip" includes the inputs and outputs from each case run by GenX.

2. The file "CompiledResult_upload.zip" includes the compiled results of all cases; in the unzipped folder, there are nine (9) ".csv" files. There are four common columns in these nine files:

• case: this is the long case name, can be represented by Scenario, and TechSensitivity
• year: this is 2030 because of the target year is 2030
• Scenario: this is the name of each scenario modeled in the study. There are nine scenarios.
  • 10% CI Part, Curt. Tech. = 10% C&I Participation Rate, Current Technology Scenario
  • 10% CI Part, Curt. Tech., No Ex. Limit = 10% C&I Participation Rate, Current Technology Scenario, No Excess Limit
  • 25% CI Part, Curt. Tech. = 25% C&I Participation Rate, Current Technology Scenario
  • 10% CI Part, Adv. Tech. no Comb. = 10% C&I Participation Rate, Advanced Technology no Combustion Scenario
  • 10% CI Part, Adv. Tech. no Comb., no Ex. Limit = 10% C&I Participation Rate, Advanced Technology no Combustion Scenario, No Excess Limit
  • 25% CI Part, Adv. Tech. no Comb. = 25% C&I Participation Rate, Advanced Technology no Combustion Scenario
  • 10% CI Part, Adv. Tech. Full = 10% C&I Participation Rate, Advanced Technology, Full Portfolio
  • 10% CI Part, Adv. Tech. Full, no Ex. Limit = 10% C&I Participation Rate, Advanced Technology, Full Portfolio, No Excess Limit
  • 25% CI Part, Adv. Tech. Full = 25% C&I Participation Rate, Advanced Technology, Full Portfolio
• TechSensitivity: There are eleven cases
  • Hourly 80%-100%, these are the 24/7 CFE procurement cases with different CFE Score Target
  • Annual 100%: this is the 100% annual matching case
  • No 24x7 Purchase: this is the reference case

For the rest of the files, see description below

• Gen_Output_California.csv: this file includes the annual generation output of California generators. The unit is "MWh"
• NetImport_California.csv: this file include the annual net imports of California (negative number stands for net exports. The unit is "MWh"
• tfs_cfe_table.csv: this file includes a table with the following columns:
  • Policy: this column is all 1 because there is only one 24/7 CFE buyers
  • Shortfall: this column includes the annual grid supply that is not clean. Unit is MWh
  • Excess: this column includes the annual excess. Unit is MWh
  • Shortfall price: this column includes the shadow prices of the CFE target constraint. Unit is $/MWh
  • Excess Price: this column includes the shadow prices of the CFE excess limit constraint. Unit is $/MWh
  • Load: this column includes the annual load of 24/7 participants. Unit is MWh.
  • Storage loss: this column includes the annual storage loss of the 24/7 participating storage. Unit is MWh.
  • Post-Grid CFE Score Local_n_Import: this column includes the final CFE score of the model. Unitless
• tfs_gen_capacity_California.csv: this documents the capacity procured by the 24/7 or 100% annual matching participants. Unit is MW
• tfs_gen_output_California.csv: this documents the energy procured by the 24/7 or 100% annual matching participants. Unit is MWh
• tfs_gents_table.csv: this documents the time-series of the 24/7 CFE procurement. Unit is MW.
• tfs_lse_cost.csv: this documents the cost of 24/7 CFE participants in each case. Unit is 2020USD
• tfs_modified_load.csv: this documents the modified load time-series of the 24/7 CFE participants. Unit is MW
• tfs_system_emissions.csv: this documents the carbon footprint of the 24/7 CFE participants and the rest of the California load.
  • participating_load_emission: this is the carbon footprint of the 24/7 CFE participants. Unit is metric tons
  • emission_rest_of_local_w_import: this is the carbon footprint of the rest of the California load. Unit is metric tons
  • emission_local_n_import: this is the carbon footprint of the California load, i.e., the sum of the "participating_load_emission" and "emission_rest_of_local_w_import". Unit is metric tons

3. GenX_upload.zip: this includes a workable version of GenX that, if installed correctly, can be run with provided inputs to replicate the runs. Please check GenX's github website https://github.com/GenXProject/GenX for the installation guide. However, because GenX (the uploaded one) has been updated since the results were generated, there are a few changes to be made so that GenX can run those slightly outdated files. Use Run_247.jl (uploaded) to run the cases; this 'jl' file will also calculate several other files that are needed for 24/7 analysis; furthermore, this jl file includes the iteration algorithm used for finding grid supply clean share.

The necessary changes to the case files include

• in Generators_data.csv, add a column of "CapRes_duration_requirement" with entries being 1 for all generators; further, change the Min_Duration and Max_Duration of pumped hydro generators to 1 and 20;
• Add Capacity_reserve_margin_slack.csv (provided in this zenodo repo) to the case to be run.
• Add RPSH_MER (uploaded) to the case to be run
• in RPSH.csv, add three columns
  • MWhExtraBudget = 0
  • Carbon_Offset_Target = 0
  • Carbon_Offset_Penalty = 0
• Add Energy_share_requirement_slack.csv to the cases to be run; if this case is an annual matching case, i.e., the name ends with "cipannual100", copy and paste Energy_share_requirement_slack_annualmatching.csv but rename it to Energy_share_requirement_slack.csv before running.
• Add a new column in any Maximum_capacity_limit.csv called "PriceCap" with all entries being 1000000 (1 million).
• in Settings/genx_settings.yml; add three rows

  • CO2Capture: 1

  • ref_case_id: if a case name begins with p[x]_, put p[x]_ncp in this row; for example, if a case name begins with p1, then put p1_ncp.

  • ref_case_name:

    • if a case name begins with p2, use currentpolicy_10ci_nocip

    • if a case name begins with p3, use currentpolicy_25ci_nocip

    • if a case name begins with p7, use currentpolicy_10ci_adv_nocip

    • if a case name begins with p8, use currentpolicy_25ci_adv_nocip

    • if a case name begins with p12, use currentpolicy_10ci_adv_nocip_noccszcf

    • if a case name begins with p13, use currentpolicy_25ci_adv_nocip_noccszcf

    • if a case name begins with p19, use currentpolicy_10ci_adv_nocip_noccszcf_noexcess

    • if a case name begins with p30, use currentpolicy_10ci_nocip_noexcess

    • if a case name begins with p31, use currentpolicy_10ci_adv_nocip_noexcess

4. CI_Load_Formatted_google.csv included the Commercial and Industrial load for each zone; This study uses columns with "current_policy," year = 2030

5. Total_Load_Formatted_google.csv included the gross load of each zone; This study uses columns with "current_policy," year = 2030

Total number of young adults aged 15 to 34 years and total number of young adults aged 20 to 34 years in the UK living with their parents.

Input and output data for 40 scenarios for the 2040 Indian electricity sector.

Compatible with old versions of GenX: https://github.com/GenXProject/GenX, available upon request.

"We propose to continue our detector development program in X-ray astronomy. Under our current grant we are developing a new type of active pixel detector. The current funding allows us to carry this design through CDR, but will not cover fabrication of the detectors. Here we propose to build and test these innovative detectors, which could potentially be employed in future missions such as IXO, Xenia, Gen-X, and SMEX/MIDEX-class missions. This proposal supports NASA's goals of technical advancement of technologies suitable for future missions and training of graduate students."

Comprehensive dataset of 209 X-ray labs in Illinois, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 87 X-ray labs in Colorado, United States as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.