Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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
Log in with your Google account if you haven’t already.
Click on the “File” menu at the top left.
Select “Upload notebook…”
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.
GSS.xlsx
)In your notebook:
Add a new code cell at the top (if not already present).
Paste the following code to upload GSS.xlsx
:
from google.colab import files
uploaded = files.upload()
Run the cell (Shift + Enter
).
A file upload box will appear—select GSS.xlsx
.
Once uploaded, the file is available in your Colab session.
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:
import pandas as pd
df = pd.read_excel('GSS.xlsx')
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
https://user-images.githubusercontent.com/78155393/233774342-287492bb-26c1-4acf-bc2c-9462e97a03ca.png" alt="Survey">
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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/
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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).