

## Dataset Description

- **Paper:** Examining the Potential of Generative Language Models for Aviation Safety Analysis: Insights from ASRS Case Study
- **Point of Contact:** archanatikayatray@gmail.com

### Dataset Summary

The dataset contains a total of 9984 incident records and 9 columns. Some of the columns contain ground truth values whereas others contain information generated by ChatGPT based on the incident _**Narratives**_.
The creation of this dataset is aimed at providing researchers with columns generated by using ChatGPT API which is not freely available. 

## Dataset Structure

The column names present in the dataset and their descriptions are provided below: 


|Column Name|Description|Generated by|
| :----:        |    :----:              |                                :----:                              |
ACN | Unique identifier for incident reports | - |
Narrative | Incident narrative  | Reporter |
synopsis_groundtruth | Synopsis of the incident | Safety Analyst |
(GPT-3.5-turbo) Synopsis | Synopsis generated by ChatGPT based on narrative | ChatGPT |
human_factors_groundtruth | Human factor issues that contributed to the incident | Safety Analyst |
(GPT-3.5-turbo) Human Factor issue| Human factor issue that contributed to the incident identified by ChatGPT based on incident narrative | ChatGPT |
(GPT-3.5-turbo) Rationale - Human Factor issue | Rationale behind human factor issue identified by ChatGPT | ChatGPT |
(GPT-3.5-turbo) Incident attribution | Incident attribution identified by ChatGPT based on incident narrative | ChatGPT |
(GPT-3.5-turbo) Rationale - Incident attribution | Rationale behind incident attribution by ChatGPT | ChatGPT |

## Dataset Creation

### Source Data

The initial dataset was obtained from the Aviation Safety Reporting System (ASRS) database and comprises incident reports that encompass the time period from January 2009 to July 2022.
This was followed by retaining only the records where the _**Primary Problem**_ that led to the incident was _**Human Factors**_. 

### Importing dataset into Python environment 

Use the following code chunk to import the dataset into Python environment as a DataFrame. 

```
from datasets import load_dataset
import pandas as pd

dataset = load_dataset("archanatikayatray/ASRS-ChatGPT")

#Converting the dataset into a pandas DataFrame
dataset = pd.DataFrame(dataset["train"])
dataset = dataset.astype({'ACN':'string'})

#Viewing the last 10 rows of the annotated dataset
dataset.tail(10)

```

### Limitations

Certain columns within this dataset include information generated by ChatGPT and therefore may not be entirely accurate. Consequently, it is advised to exercise caution when utilizing the generated data for decision making purposes.
### Citation Information

```
@Article{TikayatRay-ASRS,
AUTHOR = {Tikayat Ray, Archana and Bhat, Anirudh Prabhakara and White, Ryan T. and Nguyen, Van Minh and Pinon Fischer, Olivia J. and Mavris, Dimitri N.},
TITLE = {Examining the Potential of Generative Language Models for Aviation Safety Analysis: Case Study and Insights Using the Aviation Safety Reporting System (ASRS)},
JOURNAL = {Aerospace},
VOLUME = {10},
YEAR = {2023},
NUMBER = {9},
ARTICLE-NUMBER = {770},
URL = {https://www.mdpi.com/2226-4310/10/9/770},
ISSN = {2226-4310},
DOI = {10.3390/aerospace10090770}
}

```
task_categories:zero-shot-classification task_categories:text-generation task_categories:summarization task_categories:question-answering size_categories:1K<n<10K language:en license:apache-2.0 ChatGPT doi:10.57967/hf/0830 region:us
{"language": ["en"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["zero-shot-classification", "text-generation", "summarization", "question-answering"], "tags": ["ChatGPT"]}
TAGS
#task_categories-zero-shot-classification #task_categories-text-generation #task_categories-summarization #task_categories-question-answering #size_categories-1K<n<10K #language-English #license-apache-2.0 #ChatGPT #doi-10.57967/hf/0830 #region-us 

Dataset Description
-------------------


* Paper: Examining the Potential of Generative Language Models for Aviation Safety Analysis: Insights from ASRS Case Study
* Point of Contact: archanatikayatray@URL


### Dataset Summary


The dataset contains a total of 9984 incident records and 9 columns. Some of the columns contain ground truth values whereas others contain information generated by ChatGPT based on the incident *Narratives*.
The creation of this dataset is aimed at providing researchers with columns generated by using ChatGPT API which is not freely available.


Dataset Structure
-----------------


The column names present in the dataset and their descriptions are provided below:



Dataset Creation
----------------


### Source Data


The initial dataset was obtained from the Aviation Safety Reporting System (ASRS) database and comprises incident reports that encompass the time period from January 2009 to July 2022.
This was followed by retaining only the records where the *Primary Problem* that led to the incident was *Human Factors*.


### Importing dataset into Python environment


Use the following code chunk to import the dataset into Python environment as a DataFrame.


### Limitations


Certain columns within this dataset include information generated by ChatGPT and therefore may not be entirely accurate. Consequently, it is advised to exercise caution when utilizing the generated data for decision making purposes.



### Dataset Summary


The dataset contains a total of 9984 incident records and 9 columns. Some of the columns contain ground truth values whereas others contain information generated by ChatGPT based on the incident *Narratives*.
The creation of this dataset is aimed at providing researchers with columns generated by using ChatGPT API which is not freely available.


Dataset Structure
-----------------


The column names present in the dataset and their descriptions are provided below:



Dataset Creation
---------------- ### Source Data


The initial dataset was obtained from the Aviation Safety Reporting System (ASRS) database and comprises incident reports that encompass the time period from January 2009 to July 2022.
This was followed by retaining only the records where the *Primary Problem* that led to the incident was *Human Factors*. ### Importing dataset into Python environment


Use the following code chunk to import the dataset into Python environment as a DataFrame. ### Limitations


Certain columns within this dataset include information generated by ChatGPT and therefore may not be entirely accurate. Consequently, it is advised to exercise caution when utilizing the generated data for decision making purposes.
TAGS
#task_categories-zero-shot-classification #task_categories-text-generation #task_categories-summarization #task_categories-question-answering #size_categories-1K<n<10K #language-English #license-apache-2.0 #ChatGPT #doi-10.57967/hf/0830 #region-us 
 ### Dataset Summary


The dataset contains a total of 9984 incident records and 9 columns. Some of the columns contain ground truth values whereas others contain information generated by ChatGPT based on the incident *Narratives*.
The creation of this dataset is aimed at providing researchers with columns generated by using ChatGPT API which is not freely available.


Dataset Structure
-----------------


The column names present in the dataset and their descriptions are provided below:



Dataset Creation
---------------- ### Source Data


The initial dataset was obtained from the Aviation Safety Reporting System (ASRS) database and comprises incident reports that encompass the time period from January 2009 to July 2022.
This was followed by retaining only the records where the *Primary Problem* that led to the incident was *Human Factors*. ### Importing dataset into Python environment


Use the following code chunk to import the dataset into Python environment as a DataFrame. ### Limitations


Certain columns within this dataset include information generated by ChatGPT and therefore may not be entirely accurate. Consequently, it is advised to exercise caution when utilizing the generated data for decision making purposes.
passage: TAGS
#task_categories-zero-shot-classification #task_categories-text-generation #task_categories-summarization #task_categories-question-answering #size_categories-1K<n<10K #language-English #license-apache-2.0 #ChatGPT #doi-10.57967/hf/0830 #region-us 
### Dataset Summary


The dataset contains a total of 9984 incident records and 9 columns. Some of the columns contain ground truth values whereas others contain information generated by ChatGPT based on the incident *Narratives*.
The creation of this dataset is aimed at providing researchers with columns generated by using ChatGPT API which is not freely available.


Dataset Structure
-----------------


The column names present in the dataset and their descriptions are provided below:



Dataset Creation
----------------### Source Data


The initial dataset was obtained from the Aviation Safety Reporting System (ASRS) database and comprises incident reports that encompass the time period from January 2009 to July 2022.
This was followed by retaining only the records where the *Primary Problem* that led to the incident was *Human Factors*.### Importing dataset into Python environment


Use the following code chunk to import the dataset into Python environment as a DataFrame.### Limitations


Certain columns within this dataset include information generated by ChatGPT and therefore may not be entirely accurate. Consequently, it is advised to exercise caution when utilizing the generated data for decision making purposes.