pokemon_cards / README.md
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---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: original
path: data/original-*
- split: augmented
path: data/augmented-*
dataset_info:
features:
- name: Card
dtype: string
- name: Year
dtype: int64
- name: Card Set
dtype: string
- name: Artwork Style
dtype: string
- name: Condition
dtype: string
- name: Market Value
dtype: float64
- name: Collector's Item
dtype: string
- name: Set Number Eq
dtype: float64
splits:
- name: original
num_bytes: 2891
num_examples: 34
- name: augmented
num_bytes: 27713
num_examples: 300
download_size: 16713
dataset_size: 30604
language:
- en
size_categories:
- n<1K
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset contains information about real-world Pokémon cards, including both an original set of 34 cards and an augmented set of 300 synthetic cards. It includes features such as card name, year, card set, artwork style, condition, market value, and whether the card is considered a collector's item.
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
This dataset is a collection of real-world Pokémon card data. The original portion consists of 34 real cards with various attributes. The augmented portion was generated synthetically to expand the dataset size to 300 entries by varying existing features and adding a unique identifier to each card. The goal is to provide a larger dataset for potential analysis or machine learning tasks which work to predict a given card's collectors item status.
- **Curated by:** Emily Copus
- **Shared by:** ecopus (Hugging Face Hub)
- **Language(s) (NLP):** English
- **License:** apache-2.0
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://huggingface.co/datasets/ecopus/pokemon_cards
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
This dataset can be used for:
- Exploratory data analysis of Pokémon card attributes.
- Training machine learning models to correlate card attributes to identify a card's collector's item status.
- Practicing data manipulation and augmentation techniques.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
This dataset is not suitable for:
- Making financial decisions based on the market values, as these are based on a small sample and synthetic augmentation.
- Representing a comprehensive and accurate historical record of all Pokémon cards.
- Uses requiring real-time market data (market values are current as of 9/14/2025).
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
The dataset is split into two configurations: 'original' and 'augmented'. Both configurations have the following features:
- Card (string): The name of the Pokémon card. Augmented cards are a random choice of card, and have a random number appended.
- Year (int): The year the card was released or assigned. Augmented years are within a 4 year range of the original year.
- Card Set (string): The set the card belongs to. Augmented cards use existing sets from the original data.
- Artwork Style (string): The style of the card's artwork (e.g., Standard, Full Art, Holo).
- Condition (string): The condition of the card (e.g., Mint, Near Mint).
- Set Number Eq (float): A calculated value related to the set number (which number card it is wrt the full set). For example, if a card is 118 out of 220 total cards for that set, its set number eq is 0.536. Set number eq values greater than one correspond to "Secret Rare cards", thus indicating desirability.
- Market Value (float): The estimated market value of the card, based upon real time values from tcgplayer.com. Augmented values are based on the original with a +/- 0.2 scale factor.
- Collector's Item (string): Indicates if the card is considered a collector's item (Yes/No), based on artwork style, market value, and condition for augmented data.
The 'original' split contains 34 examples, and the 'augmented' split contains 385 examples.
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
This dataset was curated as a basic learning tool for implementing ML tools with tabular datasets. The simplicity of this dataset allows for easy implementation into basic ML classification algorithms, pefect for a first time user.
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
The source data for this dataset is 34 real-world pokemon cards and their attrubutes.
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
The Pokemon cards were chosen randomly from the curator's personal collection, with roughly 50% being collectors items and 50% not. The cards chosen span from 1995-2025 across a range of 22 sets.
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
The curator was the producer of this dataset.
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
Annotations were created based off the physical features printed on the cards, along with the target market value from tcgplayer.com. It is important to note that the collector's item status was based off intimate knowledge of the curator's personal collection - these cards have not been professionally graded. For the augmented dataset, a conditional was used to assign collector's item status to the augmented data. Ultimately, if a card is not heavily played, not standard art, and have a current market value greater than $15, is is assigned a collector's item.
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
The curator is the annotator for this dataset.
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
This dataset contains no personal nor sensitive information.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
The original dataset is very small (only 34 cards) and only contains cards from 22 sets, which limits the diversity and representativeness of the data. The augmented dataset, while larger (300 cards), is synthetically generated based on the small original set, meaning it may not fully capture the complexities and nuances of real-world Pokémon card data (many of these likely do not respresent real world cards). The augmentation process involves some randomness and simple rules (like the one for determining 'Collector's Item'), which may not reflect actual market dynamics or collector behavior accurately.
Sociotechnically, the market values are estimations and are subject to the limitations of the small original sample and the augmentation process, so relying on them for actual financial decisions is not advisable. The dataset also doesn't account for regional variations in card releases, market values, or collector preferences, which are significant factors in the real world of Pokémon cards.
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be aware of the risks, biases and limitations of the dataset before use (see above).
Relying on models making predictions from this dataset for actual financial decisions is not advisable.
Simply, this tool should be utilized to better make connections between a card's features and its status as a collector's item.
## Dataset Card Authors [optional]
Emily Copus
## Dataset Card Contact
ecopus@andrew.cmu.edu