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This dataset contains only the COCO 2017 train images (118K images) and a caption annotation JSON file, designed to fit within Google Colab's available disk space of approximately 50GB when connected to a GPU runtime.

If you're using PyTorch on Google Colab, you can easily utilize this dataset as follows:

Manually downloading and uploading the file to Colab can be time-consuming. Therefore, it's more efficient to download this data directly into Google Colab. Please ensure you have first added your Kaggle key to Google Colab. You can find more details on this process here

from google.colab import drive
import os
import torch
import torchvision.datasets as dset
import torchvision.transforms as transforms

os.environ["KAGGLE_KEY"] = userdata.get('KAGGLE_KEY')
os.environ["KAGGLE_USERNAME"] = userdata.get('KAGGLE_USERNAME')

# Download the Dataset and unzip it
!kaggle datasets download -d seungjunleeofficial/coco2017-image-caption-train
!mkdir "/content/Dataset"
!unzip "coco2017-image-caption-train" -d "/content/Dataset"


# load the dataset
cap = dset.CocoCaptions(root = '/content/Dataset/COCO2017 Image Captioning Train/train2017',
            annFile = '/content/Dataset/COCO2017 Image Captioning Train/captions_train2017.json',
            transform=transforms.PILToTensor())

You can then use the dataset in the following way:

print(f"Number of samples: {len(cap)}")
img, target = cap[3]
print(img.shape)
print(target)
# Output example: torch.Size([3, 425, 640])
# ['A zebra grazing on lush green grass in a field.', 'Zebra reaching its head down to ground where grass is.', 
# 'The zebra is eating grass in the sun.', 'A lone zebra grazing in some green grass.', 
# 'A Zebra grazing on grass in a green open field.']

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This dataset provides information about top-rated TV shows, collected from The Movie Database (TMDb) API. It can be used for data analysis, recommendation systems, and insights on popular television content.

Key Stats:

Total Pages: 109 Total Results: 2098 TV shows Data Source: TMDb API Sorting Criteria: Highest-rated by vote_average (average rating) with a minimum vote count of 200 Data Fields (Columns):

id: Unique identifier for the TV show name: Title of the TV show vote_average: Average rating given by users vote_count: Total number of votes received first_air_date: The date when the show was first aired original_language: Language in which the show was originally produced genre_ids: Genre IDs linked to the show's genres overview: A brief summary of the show popularity: Popularity score based on audience engagement poster_path: URL path for the show's poster image Accessing the Dataset via API (Python Example):

python Copy code import requests

api_key = 'YOUR_API_KEY_HERE' url = "https://api.themoviedb.org/3/discover/tv" params = { 'api_key': api_key, 'include_adult': 'false', 'language': 'en-US', 'page': 1, 'sort_by': 'vote_average.desc', 'vote_count.gte': 200 }

response = requests.get(url, params=params) data = response.json()

Display the first show

print(data['results'][0]) Dataset Use Cases:

Data Analysis: Explore trends in highly-rated TV shows. Recommendation Systems: Build personalized TV show suggestions. Visualization: Create charts to showcase ratings or genre distribution. Machine Learning: Predict show popularity using historical data. Exporting and Sharing the Dataset (Google Colab Example):

python Copy code import pandas as pd

Convert the API data to a DataFrame

df = pd.DataFrame(data['results'])

Save to CSV and upload to Google Drive

from google.colab import drive drive.mount('/content/drive') df.to_csv('/content/drive/MyDrive/top_rated_tv_shows.csv', index=False) Ways to Share the Dataset:

Google Drive: Upload and share a public link. Kaggle: Create a public dataset for collaboration. GitHub: Host the CSV file in a repository for easy sharing.

This dataset accompanies the study The Cultural Resource Curse: How Trade Dependence Undermines Creative Industries. It contains country-year panel data for 2000–2023 covering both OECD economies and the ten largest Latin American countries by land area. Variables include GDP per capita (constant PPP, USD), trade openness, internet penetration, education indicators, cultural exports per capita, and executive constraints from the Polity V dataset.

The dataset supports a comparative analysis of how economic structure, institutional quality, and infrastructure shape cultural export performance across development contexts. Within-country fixed effects models show that trade openness constrains cultural exports in OECD economies but has no measurable effect in resource-dependent Latin America. In contrast, strong executive constraints benefit cultural industries in advanced economies while constraining them in extraction-oriented systems. The results provide empirical evidence for a two-stage development framework in which colonial extraction legacies create distinct constraints on creative industry growth.

All variables are harmonized to ISO3 country codes and aligned on a common panel structure. The dataset is fully reproducible using the included Jupyter notebooks (OECD.ipynb, LATAM+OECD.ipynb, cervantes.ipynb).

Contents:

• GDPPC.csv — GDP per capita series from the World Bank.

• explanatory.csv — Trade openness, internet penetration, and education indicators.

• culture_exports.csv — UNESCO cultural export data.

• p5v2018.csv — Polity V institutional indicators.

• Jupyter notebooks for data processing and replication.

Potential uses: Comparative political economy, cultural economics, institutional development, and resource curse research.

How to Run This Dataset and Code in Google Colab

These steps reproduce the OECD vs. Latin America analyses from the paper using the provided CSVs and notebooks.

1) Open Colab and set up

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

2. Click File → New notebook.

3. (Optional) If your files are in Google Drive, mount it:

2) Get the data files into Colab

You have two easy options:

A. Upload the 4 CSVs + notebooks directly

• In the left sidebar, click the folder icon → Upload.

• Upload: GDPPC.csv, explanatory.csv, culture_exports.csv, p5v2018.csv, and any .ipynb you want to run.

B. Use Google Drive

• Put those files in a Drive folder.

• After mounting Drive, refer to them with paths like /content/drive/MyDrive/your_folder/GDPPC.csv.

README – Sample Park Analysis## OverviewThis repository contains a Google Colab / Jupyter notebook and accompanying dataset used for analyzing park features and associated metrics. The notebook demonstrates data loading, cleaning, and exploratory analysis of the Hope_Park_original.csv file.## Contents- sample park analysis.ipynb — The main analysis notebook (Colab/Jupyter format)- Hope_Park_original.csv — Source dataset containing park information- README.md — Documentation for the contents and usage## Usage1. Open the notebook in Google Colab or Jupyter.2. Upload the Hope_Park_original.csv file to the working directory (or adjust the file path in the notebook).3. Run each cell sequentially to reproduce the analysis.## RequirementsThe notebook uses standard Python data science libraries:```pythonpandasnumpymatplotlibseaborn

S2 File. Colab notebook for AquaWave-BiLSTM model analysis, and results. S3 File. Colab notebook containing SHAP visualizations and interpretability analysis related to PM2.5 prediction. (ZIP)

This archive reproduces a figure titled "Figure 3.2 Boone County population distribution" from Wang and vom Hofe (2007, p.60). The archive provides a Jupyter Notebook that uses Python and can be run in Google Colaboratory. The workflow uses the Census API to retrieve data, reproduce the figure, 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 2000 Decennial Census (Summary File 1, 100% data). Shapefiles are downloaded from the TIGER/Line FTP Server. All downloaded data are maintained in the notebook's temporary working directory while in use. The data and shapefiles are stored separately with this archive. The final map is also stored as an HTML file.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 that performs the following functions:install/import necessary Python packagesdownload the Census Tract shapefile from the TIGER/Line FTP Serverdownload Census data via CensusAPI manipulate Census tabular data merge Census data with TIGER/Line shapefileapply a coordinate reference systemcalculate land area and population densitymap and export the map to HTMLexport the map to ESRI shapefileexport 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 TIGER/Line shapefile and Census API downloads. The notebook can 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).

Here's a clear Zenodo description for your dataset:

Dataset Description

This dataset supports the research paper "Nou Pa Bèt: Civic Substitution and Expressive Freedoms in Post-State Governance" which examines how civic participation functions as institutional substitution in fragile states, with Haiti as the primary case study. The dataset combines governance indicators from the World Bank's Worldwide Governance Indicators (WGI) with civic engagement measures from the Varieties of Democracy (V-Dem) project.

Files Included:

1. wgidataset.xlsx (2.57 MB) - Complete World Bank Worldwide Governance Indicators dataset covering multiple governance dimensions across countries and years
2. CivicEngagement_SelectedCountries_Last10Years.xlsx (25.03 KB) - Processed V-Dem civic engagement indicators for fragile states sample (2015-2024) including variables for participatory governance, civil society participation, freedom of expression, freedom of assembly, anti-system movements, and direct democracy
3. civic.ipynb (10.35 KB) - Complete Python analysis notebook containing all data processing, regression analysis, and visualization code used in the study

How to Use in Google Colab:

Step 1: Upload Files

from google.colab import files
import pandas as pd
import numpy as np

# Upload the files to your Colab environment
uploaded = files.upload()
# Select and upload: CivicEngagement_SelectedCountries_Last10Years.xlsx and wgidataset.xlsx

Step 2: Load the Datasets

# Load the civic engagement data (main analysis dataset)
civic_data = pd.read_excel('CivicEngagement_SelectedCountries_Last10Years.xlsx')

# Load the WGI data (if needed for extended analysis)
wgi_data = pd.read_excel('wgidataset.xlsx')

# Display basic information
print("Civic Engagement Dataset Shape:", civic_data.shape)
print("
Columns:", civic_data.columns.tolist())
print("
First few rows:")
civic_data.head()

Step 3: Run the Analysis Notebook

# Download and run the complete analysis notebook
!wget https://zenodo.org/record/[RECORD_ID]/files/civic.ipynb
# Then open civic.ipynb in Colab or copy/paste the code cells

Key Variables:

Dependent Variables (WGI):

• Control_of_Corruption - Extent to which public power is exercised for private gain
• Government_Effectiveness - Quality of public services and policy implementation

Independent Variables (V-Dem):

• v2x_partip - Participatory Component Index
• v2x_cspart - Civil Society Participation Index
• v2cademmob - Freedom of Peaceful Assembly
• v2cafres - Freedom of Expression
• v2csantimv - Anti-System Movements
• v2xdd_dd - Direct Popular Vote Index

Sample Countries: 21 fragile states including Haiti, Sierra Leone, Liberia, DRC, CAR, Guinea-Bissau, Chad, Niger, Burundi, Yemen, South Sudan, Mozambique, Sudan, Eritrea, Somalia, Mali, Afghanistan, Papua New Guinea, Togo, Cambodia, and Timor-Leste.

Quick Start Analysis:

# Install required packages
!pip install statsmodels scipy

# Basic regression replication
import statsmodels.api as sm
from statsmodels.stats.outliers_influence import variance_inflation_factor

# Prepare variables for regression
X = civic_data[['v2x_partip', 'v2x_cspart', 'v2cademmob', 'v2cafres', 'v2csantimv', 'v2xdd_dd']].dropna()
y_corruption = civic_data['Control_of_Corruption'].dropna()
y_effectiveness = civic_data['Government_Effectiveness'].dropna()

# Run regression (example for Control of Corruption)
X_const = sm.add_constant(X)
model = sm.OLS(y_corruption, X_const).fit(cov_type='HC3')
print(model.summary())

Citation: Brown, Scott M., Fils-Aime, Jempsy, & LaTortue, Paul. (2025). Nou Pa Bèt: Civic Substitution and Expressive Freedoms in Post-State Governance [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.15058161

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

Contact: For questions about data usage or methodology, please contact the corresponding author through the institutional affiliations provided in the paper.

This description provides clear, step-by-step instructions for researchers to immediately begin working with your data in Google Colab while explaining the theoretical and methodological context.

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Food/Not Food Image Caption Dataset

Small dataset of synthetic food and not food image captions. Text generated using Mistral Chat/Mixtral. Can be used to train a text classifier on food/not_food image captions as a demo before scaling up to a larger dataset. See Colab notebook on how dataset was created.

  Example usage

import random from datasets import load_dataset

Load dataset

loaded_dataset = load_dataset("mrdbourke/learn_hf_food_not_food_image_captions")

Get… See the full description on the dataset page: https://huggingface.co/datasets/mrdbourke/learn_hf_food_not_food_image_captions.

Please follow the steps below to download and use Kaggle data within Google Colab:

1) from google.colab import files files.upload()

Choose the kaggle.json file that you downloaded 2) ! mkdir ~/.kaggle

! cp kaggle.json ~/.kaggle/

Make directory named kaggle and copy kaggle.json file there. 4) ! chmod 600 ~/.kaggle/kaggle.json

Change the permissions of the file. 5) ! kaggle datasets list - That's all ! You can check if everything's okay by running this command.

Use unzip command to unzip the data:

unzip train data there,

! unzip train.zip -d train

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

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📚 Overview: This dataset provides a compact and efficient way to explore the massive "Wikipedia Structured Contents" dataset by Wikimedia Foundation, which consists of 38 large JSONL files (each ~2.5GB). Loading these directly in Kaggle or Colab is impractical due to resource constraints. This file index solves that problem.

🔍 What’s Inside: This dataset includes a single JSONL file named wiki_structured_dataset_navigator.jsonl that contains metadata for every file in the English portion of the Wikimedia dataset.

Each line in the JSONL file is a JSON object with the following fields: - file_name: the actual filename in the source dataset (e.g., enwiki_namespace_0_0.jsonl) - file_index: the numeric row index of the file - name: the Wikipedia article title or identifier - url: a link to the full article on Wikipedia - description: a short description or abstract of the article (when available)

🛠 Use Case: Use this dataset to search by keyword, article name, or description to find which specific files from the full Wikimedia dataset contain the topics you're interested in. You can then download only the relevant file(s) instead of the entire dataset.

⚡️ Benefits: - Lightweight (~MBs vs. GBs) - Easy to load and search - Great for indexing, previewing, and subsetting the Wikimedia dataset - Saves time, bandwidth, and compute resources

📎 Example Usage (Python): ```python import kagglehub import json import pandas as pd import numpy as np import os from tqdm import tqdm from datetime import datetime import re

def read_jsonl(file_path, max_records=None): data = [] with open(file_path, 'r', encoding='utf-8') as f: for i, line in enumerate(tqdm(f)): if max_records and i >= max_records: break data.append(json.loads(line)) return data

file_path = kagglehub.dataset_download("mehranism/wikimedia-structured-dataset-navigator-jsonl",path="wiki_structured_dataset_navigator.jsonl") data = read_jsonl(file_path) print(f"Successfully loaded {len(data)} records")

df = pd.DataFrame(data) print(f"Dataset shape: {df.shape}") print(" Columns in the dataset:") for col in df.columns: print(f"- {col}")

This dataset is perfect for developers working on:
- Retrieval-Augmented Generation (RAG)
- Large Language Model (LLM) fine-tuning
- Search and filtering pipelines
- Academic research on structured Wikipedia content

💡 Tip:
Pair this index with the original [Wikipedia Structured Contents dataset](https://www.kaggle.com/datasets/wikimedia-foundation/wikipedia-structured-contents) for full article access.

📃 Format:
- File: `wiki_structured_dataset_navigator.jsonl`
- Format: JSON Lines (1 object per line)
- Encoding: UTF-8

---

### **Tags**

wikipedia, wikimedia, jsonl, structured-data, search-index, metadata, file-catalog, dataset-index, large-language-models, machine-learning ```

Licensing

CC0: Public Domain Dedication

(Recommended for open indexing tools with no sensitive data.)

Open-Orca Augmented FLAN Dataset

Unlocking Advanced Language Understanding and ML Model Performance

By Huggingface Hub [source]

About this dataset

The Open-Orca Augmented FLAN Collection is a revolutionary dataset that unlocks new levels of language understanding and machine learning model performance. This dataset was created to support research on natural language processing, machine learning models, and language understanding through leveraging the power of reasoning trace-enhancement techniques. By enabling models to understand complex relationships between words, phrases, and even entire sentences in a more robust way than ever before, this dataset provides researchers expanded opportunities for furthering the progress of linguistics research. With its unique combination of features including system prompts, questions from users and responses from systems, this dataset opens up exciting possibilities for deeper exploration into the cutting edge concepts underlying advanced linguistics applications. Experience a new level of accuracy and performance - explore Open-Orca Augmented FLAN Collection today!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This guide provides an introduction to the Open-Orca Augmented FLAN Collection dataset and outlines how researchers can utilize it for their language understanding and natural language processing (NLP) work. The Open-Orca dataset includes system prompts, questions posed by users, and responses from the system.

Getting Started The first step is to download the data set from Kaggle at https://www.kaggle.com/openai/open-orca-augmented-flan and save it in a project directory of your choice on your computer or cloud storage space. Once you have downloaded the data set, launch your ‘Jupyter Notebook’ or ‘Google Colab’ program with which you want to work with this data set.

Exploring & Preprocessing Data: To get a better understanding of the features in this dataset, import them into Pandas DataFrame as shown below. You can use other libraries as per your need:

import pandas as pd   # Library used for importing datasets into Python 

df = pd.read_csv('train.csv') #Imports train csv file into Pandas};#DataFrame 

df[['system_prompt','question','response']].head() #Views top 5 rows with columns 'system_prompt','question','response'

After importing check each feature using basic descriptive statistics such Pandas groupby statement: We can use groupby statements to have greater clarity over the variables present in each feature(elements). The below command will show counts of each element in System Prompt column present under train CVS file :

 df['system prompt'].value_counts().head()#shows count of each element present under 'System Prompt'column
 Output: User says hello guys 587 <br>System asks How are you?: 555 times<br>User says I am doing good: 487 times <br>..and so on   

Data Transformation: After inspecting & exploring different features one may want/need certain changes that best suits their needs from this dataset before training modeling algorithms on it.
Common transformation steps include : Removing punctuation marks : Since punctuation marks may not add any value to computation operations , we can remove them using regex functions write .replace('[^A-Za -z]+','' ) as

Research Ideas

• Automated Question Answering: Leverage the dataset to train and develop question answering models that can provide tailored answers to specific user queries while retaining language understanding abilities.
• Natural Language Understanding: Use the dataset as an exploratory tool for fine-tuning natural language processing applications, such as sentiment analysis, document categorization, parts-of-speech tagging and more.
• Machine Learning Optimizations: The dataset can be used to build highly customized machine learning pipelines that allow users to harness the power of conditioning data with pre-existing rules or models for improved accuracy and performance in automated tasks

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. [See Other Information](ht...

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