5 datasets found
  1. o

    Demographic Analysis Workflow using Census API in Jupyter Notebook:...

    • openicpsr.org
    delimited
    Updated Jul 23, 2020
    + more versions
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    Donghwan Gu; Nathanael Rosenheim (2020). Demographic Analysis Workflow using Census API in Jupyter Notebook: 1990-2000 Population Size and Change [Dataset]. http://doi.org/10.3886/E120381V1
    Explore at:
    delimitedAvailable download formats
    Dataset updated
    Jul 23, 2020
    Dataset provided by
    Texas A&M University
    Authors
    Donghwan Gu; Nathanael Rosenheim
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Boone County, Kentucky, US Counties
    Description

    This archive reproduces a table titled "Table 3.1 Boone county population size, 1990 and 2000" from Wang and vom Hofe (2007, p.58). The archive provides a Jupyter Notebook that uses Python and can be run in Google Colaboratory. The workflow uses Census API to retrieve data, reproduce the table, and ensure reproducibility for anyone accessing this archive.The Python code was developed in Google Colaboratory, or Google Colab for short, which is an Integrated Development Environment (IDE) of JupyterLab and streamlines package installation, code collaboration and management. The Census API is used to obtain population counts from the 1990 and 2000 Decennial Census (Summary File 1, 100% data). All downloaded data are maintained in the notebook's temporary working directory while in use. The data are also stored separately with this archive.The notebook features extensive explanations, comments, code snippets, and code output. The notebook can be viewed in a PDF format or downloaded and opened in Google Colab. References to external resources are also provided for the various functional components. The notebook features code to perform the following functions:install/import necessary Python packagesintroduce a Census API Querydownload Census data via CensusAPI manipulate Census tabular data calculate absolute change and percent changeformatting numbersexport the table to csvThe notebook can be modified to perform the same operations for any county in the United States by changing the State and County FIPS code parameters for the Census API downloads. The notebook could be adapted for use in other environments (i.e., Jupyter Notebook) as well as reading and writing files to a local or shared drive, or cloud drive (i.e., Google Drive).

  2. Off the Books, Off the Clock: Alcohol, Ethics, and the Informal Institutions...

    • zenodo.org
    bin
    Updated Jul 20, 2025
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    Anon Anon; Anon Anon (2025). Off the Books, Off the Clock: Alcohol, Ethics, and the Informal Institutions of Power [Dataset]. http://doi.org/10.5281/zenodo.16181604
    Explore at:
    binAvailable download formats
    Dataset updated
    Jul 20, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Anon Anon; Anon Anon
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Off the Books, Off the Clock: Alcohol, Ethics, and the Informal Institutions of Power

    DOI:
    10.5281/zenodo.16181604

    Publication Date:
    2025-07-20

    Creators:
    Anon, Anon Annotator

    Description:
    This dataset supports the article “Off the Books, Off the Clock: Alcohol, Ethics, and the Informal Institutions of Power,” which examines how workplace drinking rituals function as informal governance structures, shaping ethical norms and power dynamics. Drawing on data from the 2021 General Social Survey (GSS), this research integrates quantitative regression models with institutional theory to explore how demographic and cultural factors predict alcohol use—and how such use mediates access to informal authority, camaraderie, and moral disengagement in professional contexts.

    Included Files:

    • Drink.ipynb: Python notebook used for data cleaning, transformation, and OLS regression analysis.

    • GSS.xlsx: Extracted and cleaned subset of the 2021 General Social Survey (GSS) relevant to alcohol use, religion, gender, income, and education.

    • STEP 1: Open Google Colab

      1. Go to https://colab.research.google.com.

      2. Log in with your Google account if you haven’t already.

      STEP 2: Upload the Notebook File

      1. Click on the “File” menu at the top left.

      2. Select “Upload notebook…”

      3. In the upload dialog:

        • Click “Choose File”.

        • Select the file Drink.ipynb from your computer (download from here first if needed).

      Colab will automatically open the notebook once uploaded.

      STEP 3: Upload the Dataset (GSS.xlsx)

      In your notebook:

      1. Add a new code cell at the top (if not already present).

      2. Paste the following code to upload GSS.xlsx:

      python
      CopiarEditar
      from google.colab import files uploaded = files.upload()
      1. Run the cell (Shift + Enter).

      2. A file upload box will appear—select GSS.xlsx.

      Once uploaded, the file is available in your Colab session.

      STEP 4: Run the Notebook

      • Simply run each cell in the notebook using Shift + Enter.

      • Make sure the code references the file exactly as GSS.xlsx.

        • If your code has a different filename, update it accordingly:

      python
      CopiarEditar
      import pandas as pd df = pd.read_excel('GSS.xlsx')

      ✅ Optional: Mount Google Drive (to save or load from Drive)

      python
      CopiarEditar
      from google.colab import drive drive.mount('/content/drive')

      This allows you to load or save files directly from your Google Drive.

    Licenses:
    Creative Commons Attribution 4.0 International (CC BY 4.0)

    Copyright:
    © 2025 The Authors.

    Keywords:
    alcohol, ethics, informal institutions, workplace rituals, religion, gender, regression, General Social Survey, moral disengagement

    Languages:
    English

    Version:
    1.0

    Publisher:
    Zenodo

    Funding:
    Not externally funded.

  3. R

    Accident Detection Model Dataset

    • universe.roboflow.com
    zip
    Updated Apr 8, 2024
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    Accident detection model (2024). Accident Detection Model Dataset [Dataset]. https://universe.roboflow.com/accident-detection-model/accident-detection-model/model/1
    Explore at:
    zipAvailable download formats
    Dataset updated
    Apr 8, 2024
    Dataset authored and provided by
    Accident detection model
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Accident Bounding Boxes
    Description

    Accident-Detection-Model

    Accident Detection Model is made using YOLOv8, Google Collab, Python, Roboflow, Deep Learning, OpenCV, Machine Learning, Artificial Intelligence. It can detect an accident on any accident by live camera, image or video provided. This model is trained on a dataset of 3200+ images, These images were annotated on roboflow.

    Problem Statement

    • Road accidents are a major problem in India, with thousands of people losing their lives and many more suffering serious injuries every year.
    • According to the Ministry of Road Transport and Highways, India witnessed around 4.5 lakh road accidents in 2019, which resulted in the deaths of more than 1.5 lakh people.
    • The age range that is most severely hit by road accidents is 18 to 45 years old, which accounts for almost 67 percent of all accidental deaths.

    Accidents survey

    https://user-images.githubusercontent.com/78155393/233774342-287492bb-26c1-4acf-bc2c-9462e97a03ca.png" alt="Survey">

    Literature Survey

    • Sreyan Ghosh in Mar-2019, The goal is to develop a system using deep learning convolutional neural network that has been trained to identify video frames as accident or non-accident.
    • Deeksha Gour Sep-2019, uses computer vision technology, neural networks, deep learning, and various approaches and algorithms to detect objects.

    Research Gap

    • Lack of real-world data - We trained model for more then 3200 images.
    • Large interpretability time and space needed - Using google collab to reduce interpretability time and space required.
    • Outdated Versions of previous works - We aer using Latest version of Yolo v8.

    Proposed methodology

    • We are using Yolov8 to train our custom dataset which has been 3200+ images, collected from different platforms.
    • This model after training with 25 iterations and is ready to detect an accident with a significant probability.

    Model Set-up

    Preparing Custom dataset

    • We have collected 1200+ images from different sources like YouTube, Google images, Kaggle.com etc.
    • Then we annotated all of them individually on a tool called roboflow.
    • During Annotation we marked the images with no accident as NULL and we drew a box on the site of accident on the images having an accident
    • Then we divided the data set into train, val, test in the ratio of 8:1:1
    • At the final step we downloaded the dataset in yolov8 format.
      #### Using Google Collab
    • We are using google colaboratory to code this model because google collab uses gpu which is faster than local environments.
    • You can use Jupyter notebooks, which let you blend code, text, and visualisations in a single document, to write and run Python code using Google Colab.
    • Users can run individual code cells in Jupyter Notebooks and quickly view the results, which is helpful for experimenting and debugging. Additionally, they enable the development of visualisations that make use of well-known frameworks like Matplotlib, Seaborn, and Plotly.
    • In Google collab, First of all we Changed runtime from TPU to GPU.
    • We cross checked it by running command ‘!nvidia-smi’
      #### Coding
    • First of all, We installed Yolov8 by the command ‘!pip install ultralytics==8.0.20’
    • Further we checked about Yolov8 by the command ‘from ultralytics import YOLO from IPython.display import display, Image’
    • Then we connected and mounted our google drive account by the code ‘from google.colab import drive drive.mount('/content/drive')’
    • Then we ran our main command to run the training process ‘%cd /content/drive/MyDrive/Accident Detection model !yolo task=detect mode=train model=yolov8s.pt data= data.yaml epochs=1 imgsz=640 plots=True’
    • After the training we ran command to test and validate our model ‘!yolo task=detect mode=val model=runs/detect/train/weights/best.pt data=data.yaml’ ‘!yolo task=detect mode=predict model=runs/detect/train/weights/best.pt conf=0.25 source=data/test/images’
    • Further to get result from any video or image we ran this command ‘!yolo task=detect mode=predict model=runs/detect/train/weights/best.pt source="/content/drive/MyDrive/Accident-Detection-model/data/testing1.jpg/mp4"’
    • The results are stored in the runs/detect/predict folder.
      Hence our model is trained, validated and tested to be able to detect accidents on any video or image.

    Challenges I ran into

    I majorly ran into 3 problems while making this model

    • I got difficulty while saving the results in a folder, as yolov8 is latest version so it is still underdevelopment. so i then read some blogs, referred to stackoverflow then i got to know that we need to writ an extra command in new v8 that ''save=true'' This made me save my results in a folder.
    • I was facing problem on cvat website because i was not sure what
  4. h

    Dataset-EfficientDrivingTimeDeterminationSystem

    • huggingface.co
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    ACHMAD AKBAR, Dataset-EfficientDrivingTimeDeterminationSystem [Dataset]. https://huggingface.co/datasets/jellysquish/Dataset-EfficientDrivingTimeDeterminationSystem
    Explore at:
    Authors
    ACHMAD AKBAR
    Description

    import re import pandas as pd from sklearn.tree import DecisionTreeClassifier from sklearn.preprocessing import LabelEncoder from google.colab import drive from sklearn.tree import export_text from sklearn.metrics import accuracy_score

      1. Mount Google Drive
    

    drive.mount('/content/drive')

      2. Baca file Excel
    

    file_path = '/content/drive/MyDrive/Colab Notebooks/AI_GACOR_Cleaned.xlsx' data = pd.read_excel(file_path)

      3. Encode kolom 'Hari'
    

    label_encoder_hari =… See the full description on the dataset page: https://huggingface.co/datasets/jellysquish/Dataset-EfficientDrivingTimeDeterminationSystem.

  5. Data from: Including Data Management in Research Culture Increases the...

    • zenodo.org
    bin, csv, pdf, txt
    Updated Jul 16, 2024
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    Christian Riedel; Christian Riedel; Hendrik Geßner; Hendrik Geßner; Anja Seegebrecht; Safial Islam Ayon; Safial Islam Ayon; Shafayet Hossen Chowdhury; Shafayet Hossen Chowdhury; Ralf Engbert; Ralf Engbert; Ulrike Lucke; Ulrike Lucke; Anja Seegebrecht (2024). Including Data Management in Research Culture Increases the Reproducibility of Scientific Results [Dataset]. http://doi.org/10.5281/zenodo.6543497
    Explore at:
    pdf, csv, txt, binAvailable download formats
    Dataset updated
    Jul 16, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Christian Riedel; Christian Riedel; Hendrik Geßner; Hendrik Geßner; Anja Seegebrecht; Safial Islam Ayon; Safial Islam Ayon; Shafayet Hossen Chowdhury; Shafayet Hossen Chowdhury; Ralf Engbert; Ralf Engbert; Ulrike Lucke; Ulrike Lucke; Anja Seegebrecht
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    General Information:

    This dataset contains artifacts related to Riedel et al. (2022) (https://dx.doi.org/10.18420/inf2022_114). Here, we investigate the reproducibility of 108 research papers published between 2017 and 2021 by members of the Collaborative Research Center 1294 – Data Assimilation. To that end, we relate to a previous study by Stagge et al. (2019) that relies on a questionnaire that we extended.

    The publication by Stagge et al. (2019) is available here: https://doi.org/10.5281/zenodo.2562268
    The dataset by Stagge et al. (2019) is available here: https://doi.org/10.1038/sdata.2019.30

    This dataset contains the questionnaire that we used to evaluate the reproducibility of scientific publications, a csv file containing the questionnaire’s answers, and a Jupyter notebook script to evaluate the given data.

    Run the code:

    To run the code, you must install Anaconda [1] and then open the jupyter notebook. All necessary libraries are listed in "requirement.txt".

    Alternatively, you can import the .ipyab file in the colab [2] and run it.


    [1]. https://www.anaconda.com/
    [2]. https://research.google.com/colaboratory/

  6. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
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TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Donghwan Gu; Nathanael Rosenheim (2020). Demographic Analysis Workflow using Census API in Jupyter Notebook: 1990-2000 Population Size and Change [Dataset]. http://doi.org/10.3886/E120381V1

Demographic Analysis Workflow using Census API in Jupyter Notebook: 1990-2000 Population Size and Change

Explore at:
delimitedAvailable download formats
Dataset updated
Jul 23, 2020
Dataset provided by
Texas A&M University
Authors
Donghwan Gu; Nathanael Rosenheim
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
Boone County, Kentucky, US Counties
Description

This archive reproduces a table titled "Table 3.1 Boone county population size, 1990 and 2000" from Wang and vom Hofe (2007, p.58). The archive provides a Jupyter Notebook that uses Python and can be run in Google Colaboratory. The workflow uses Census API to retrieve data, reproduce the table, and ensure reproducibility for anyone accessing this archive.The Python code was developed in Google Colaboratory, or Google Colab for short, which is an Integrated Development Environment (IDE) of JupyterLab and streamlines package installation, code collaboration and management. The Census API is used to obtain population counts from the 1990 and 2000 Decennial Census (Summary File 1, 100% data). All downloaded data are maintained in the notebook's temporary working directory while in use. The data are also stored separately with this archive.The notebook features extensive explanations, comments, code snippets, and code output. The notebook can be viewed in a PDF format or downloaded and opened in Google Colab. References to external resources are also provided for the various functional components. The notebook features code to perform the following functions:install/import necessary Python packagesintroduce a Census API Querydownload Census data via CensusAPI manipulate Census tabular data calculate absolute change and percent changeformatting numbersexport the table to csvThe notebook can be modified to perform the same operations for any county in the United States by changing the State and County FIPS code parameters for the Census API downloads. The notebook could be adapted for use in other environments (i.e., Jupyter Notebook) as well as reading and writing files to a local or shared drive, or cloud drive (i.e., Google Drive).

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