
# Dataset Card for ASRS Aviation Incident Reports
## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)
  
## Dataset Description
- **Homepage:** [https://huggingface.co/datasets/elihoole/asrs-aviation-reports]
- **Repository:** [ASRS Incident Reports Summarisation code repo](https://github.com/elihoole/asrs-incident-reports)
- **Point of Contact:** [Elijah Hoole](mailto:E.J.Hoole@sms.ed.ac.uk)
### Dataset Summary
This dataset collects 47,723 aviation incident reports published in the Aviation Safety Reporting System (ASRS) database maintained by NASA. 

### Supported Tasks and Leaderboards
- 'summarization': Dataset can be used to train a model for abstractive and extractive summarization. The model performance is measured by how high the output summary's [ROUGE](https://huggingface.co/metrics/rouge) score for a given narrative account of an aviation incident is when compared to the synopsis as written by a NASA expert. Models and scores to follow. 

### Languages
The BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.

## Dataset Structure


### Data Instances
For each instance, there is a string for the narrative account (Report 1_Narrative), a string for the synopsis (Report 1.2_Synopsis), and a string for the document id (acn_num_ACN). Some instances may have two narratives (Report 1_Narrative & Report 2_Narrative) and extended analyses produced by experts (Report 1.1_Callback & Report 2.1_Callback). Other fields contain metadata such as time, location, flight conditions, aircraft model name, etc. associated with the incident. See the [ASRS Incident Reports dataset viewer](https://huggingface.co/datasets/elihoole/asrs-aviation-reports/viewer/elihoole--asrs-aviation-reports/train) to explore more examples.

```
{'acn_num_ACN': '1206196',
 'Report 1_Narrative': 'While taxiing company B757 aircraft from gate to Hangar line; we were cleared by Ground Control to proceed via A-T-join runway XX. After receiving subsequent clearance to T1 [then associated taxiways] to the hangar; we caught up to a dark; apparently unpowered company livery RJ (ERJ-145) near the T1 intersection.  The RJ was being towed dark with absolutely no external lighting on; a completely dark aircraft.  This situation only presented itself as we drew close to the aircraft in tow.  The towbarless tractor (supertug) was lit externally; but minimally visible from our vantage point; with a completely dark aircraft between us and the tractor.  Once the towing operation completed a turn onto taxiway T; a single green light came in view which is somehow mounted on supertug; presented a similar appearance to a green wing navigation light common on all aircraft.  To say this presented a confusing situation is an understatement. [Aircraft] operation in Noncompliance with FARs; Policy and Procedures.  This is a situation never before observed in [my] 30 plus years as a taxi mechanic at our location.  There are long established standards in place regarding external light usage and requirements; both in gate areas; as well as movement in active controlled taxiways; most with an eye on safety regarding aircraft position (nav lights) and anti-collision lights signaling running engines and/or aircraft movement.',
 'Report 1.1_Callback': '',
 'Report 2_Narrative': '',
 'Report 2.1_Callback': '',
 'Report 1.2_Synopsis': 'A Line Aircraft Maintenance Technician (AMT) taxiing a company B757 aircraft reports coming up on a dark; unpowered ERJ-145 aircraft with no external lighting on. Light on the towbarless Supertug tractor only minimally visible; with completely dark aircraft between their B757 and Tow tractor. Technician notes long established standards requiring Anti-Collision and Nav lights not enforced during aircraft tow.'}
```

The average token count for the articles and the highlights are provided below. 

| Feature             | Number of Instances | Mean Token Count |
| ------------------- | ------------------  | ---------------- |
| Report 1_Narrative  | 47,723              | 281              |
| Report 1.1_Callback | 1,435               | 103              |
| Report 2_Narrative  | 11,228              | 169              |
| Report 2.1 Callback | 85                  | 110              |
|​ Report 1.2_Synopsis | 47,723              | 27               |

### Data fields

More data explanation. 

task_categories:summarization annotations_creators:expert-generated language_creators:other multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:en license:apache-2.0 region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["other"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["summarization"], "pretty_name": "ASRS Aviation Incident Reports "}
TAGS
#task_categories-summarization #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-apache-2.0 #region-us 

Dataset Card for ASRS Aviation Incident Reports
===============================================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


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


* Homepage: [URL
* Repository: ASRS Incident Reports Summarisation code repo
* Point of Contact: Elijah Hoole


### Dataset Summary


This dataset collects 47,723 aviation incident reports published in the Aviation Safety Reporting System (ASRS) database maintained by NASA.


### Supported Tasks and Leaderboards


* 'summarization': Dataset can be used to train a model for abstractive and extractive summarization. The model performance is measured by how high the output summary's ROUGE score for a given narrative account of an aviation incident is when compared to the synopsis as written by a NASA expert. Models and scores to follow.


### Languages


The BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.


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


### Data Instances


For each instance, there is a string for the narrative account (Report 1\_Narrative), a string for the synopsis (Report 1.2\_Synopsis), and a string for the document id (acn\_num\_ACN). Some instances may have two narratives (Report 1\_Narrative & Report 2\_Narrative) and extended analyses produced by experts (Report 1.1\_Callback & Report 2.1\_Callback). Other fields contain metadata such as time, location, flight conditions, aircraft model name, etc. associated with the incident. See the ASRS Incident Reports dataset viewer to explore more examples.


The average token count for the articles and the highlights are provided below.


Feature: Report 1\_Narrative, Number of Instances: 47,723, Mean Token Count: 281
Feature: Report 1.1\_Callback, Number of Instances: 1,435, Mean Token Count: 103
Feature: Report 2\_Narrative, Number of Instances: 11,228, Mean Token Count: 169
Feature: Report 2.1 Callback, Number of Instances: 85, Mean Token Count: 110
Feature: ​ Report 1.2\_Synopsis, Number of Instances: 47,723, Mean Token Count: 27


### Data fields


More data explanation.



### Dataset Summary


This dataset collects 47,723 aviation incident reports published in the Aviation Safety Reporting System (ASRS) database maintained by NASA. ### Supported Tasks and Leaderboards


* 'summarization': Dataset can be used to train a model for abstractive and extractive summarization. The model performance is measured by how high the output summary's ROUGE score for a given narrative account of an aviation incident is when compared to the synopsis as written by a NASA expert. Models and scores to follow. ### Languages


The BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.


Dataset Structure
----------------- ### Data Instances


For each instance, there is a string for the narrative account (Report 1\_Narrative), a string for the synopsis (Report 1.2\_Synopsis), and a string for the document id (acn\_num\_ACN). Some instances may have two narratives (Report 1\_Narrative & Report 2\_Narrative) and extended analyses produced by experts (Report 1.1\_Callback & Report 2.1\_Callback). Other fields contain metadata such as time, location, flight conditions, aircraft model name, etc. associated with the incident. See the ASRS Incident Reports dataset viewer to explore more examples.


The average token count for the articles and the highlights are provided below.


Feature: Report 1\_Narrative, Number of Instances: 47,723, Mean Token Count: 281
Feature: Report 1.1\_Callback, Number of Instances: 1,435, Mean Token Count: 103
Feature: Report 2\_Narrative, Number of Instances: 11,228, Mean Token Count: 169
Feature: Report 2.1 Callback, Number of Instances: 85, Mean Token Count: 110
Feature: ​ Report 1.2\_Synopsis, Number of Instances: 47,723, Mean Token Count: 27 ### Data fields


More data explanation.
TAGS
#task_categories-summarization #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-apache-2.0 #region-us 
 ### Dataset Summary


This dataset collects 47,723 aviation incident reports published in the Aviation Safety Reporting System (ASRS) database maintained by NASA. ### Supported Tasks and Leaderboards


* 'summarization': Dataset can be used to train a model for abstractive and extractive summarization. The model performance is measured by how high the output summary's ROUGE score for a given narrative account of an aviation incident is when compared to the synopsis as written by a NASA expert. Models and scores to follow. ### Languages


The BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.


Dataset Structure
----------------- ### Data Instances


For each instance, there is a string for the narrative account (Report 1\_Narrative), a string for the synopsis (Report 1.2\_Synopsis), and a string for the document id (acn\_num\_ACN). Some instances may have two narratives (Report 1\_Narrative & Report 2\_Narrative) and extended analyses produced by experts (Report 1.1\_Callback & Report 2.1\_Callback). Other fields contain metadata such as time, location, flight conditions, aircraft model name, etc. associated with the incident. See the ASRS Incident Reports dataset viewer to explore more examples.


The average token count for the articles and the highlights are provided below.


Feature: Report 1\_Narrative, Number of Instances: 47,723, Mean Token Count: 281
Feature: Report 1.1\_Callback, Number of Instances: 1,435, Mean Token Count: 103
Feature: Report 2\_Narrative, Number of Instances: 11,228, Mean Token Count: 169
Feature: Report 2.1 Callback, Number of Instances: 85, Mean Token Count: 110
Feature: ​ Report 1.2\_Synopsis, Number of Instances: 47,723, Mean Token Count: 27 ### Data fields


More data explanation.
passage: TAGS
#task_categories-summarization #annotations_creators-expert-generated #language_creators-other #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-apache-2.0 #region-us 
### Dataset Summary


This dataset collects 47,723 aviation incident reports published in the Aviation Safety Reporting System (ASRS) database maintained by NASA.### Supported Tasks and Leaderboards


* 'summarization': Dataset can be used to train a model for abstractive and extractive summarization. The model performance is measured by how high the output summary's ROUGE score for a given narrative account of an aviation incident is when compared to the synopsis as written by a NASA expert. Models and scores to follow.### Languages


The BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.


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