# Dataset Card for Dataset Name

## Dataset Description

- **Homepage:** 
- **Repository:** 
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary
This dataset contains datas being collected from Genbank. The dataset is organized in a way that it separate all the genes from an DNA , and was classified according to the region and coding type. In that way, people could get more detailed information regarding each DNA sequences.
The dataset also contain source, which is the whole DNA sequence, where the user can use it to compare to each segment to see the exact location. The dataset contains 937 files with about 200 million data and 300-400 GB storage space. Therefore user can specify the number
of files they are going to use by using the code below according to their own needs. If user want to download all of files, they can enter 937 as second arguement.
```python 
datasets.load_dataset('wyxu/Genome_database', num_urls = number of file you want to use)
```


### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

[More Information Needed]

## Dataset Structure

### Data Instances
```python
{DNA id: AP013063.1  
Organism: Serratia marcescens SM39  
year: 2017
region type:coding  
specific_class: Protein
Product:thr operon leader peptide
sequence: ATGCGCAACATCAGCCTGAAAACCACAATTATTACCACCACCGATACCACAGGTAACGGGGCGGGCTGA  
gc_content:0.52173913  
translation code: MRNISLKTTIITTTDTTGNGAG
start_position: 207
end_position: 276}  
```

### Data Fields
__DNA id__: id number for the whole DNA sequence, sequences with same DNA id are from same DNA  
__Organism__: Organism of the DNA  
__year__: the year of the DNA sequence       
__region type__: determine the general type of the sequence. For all the type that is typically classified as coding region, it was named with coding; while others including those that are case dependent were named according to their own type such as regulator, repeat_region,gap, intron,extron, etc.(__Note__: when classifying coding type, all the CDS, mRNA, tmRNA, tRNA,rRNA and others such as propetide, sig_propetide,mat_propetide was classified as coding. In order to minimize the missing coding part, all the other categories which has associated product was also classified as coding )   
__specific class__: if the sequence is coding sequence, it would be classified according to their production type such as RNA, Protein. The regulators would also be classified by their own class such as terminator, ribosome  
__Product__ : if the sequence produce protein, the product name would be listed  
__sequence__: the actual sequence   
__gc_content__: the gc_content of the sequence  
__translation code__: if the sequence produce protein, then the translation code would be provided as a reference  
__start_position__: the start position of the segment  
__end_position__: the end position of the segment

### Data Splits

first 80% of files was used as training dataset, while last 20% was used as testing dataset

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data
The data collected are all from the most recent release of genbank, genbank 255.
#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

[More Information Needed]

### Contributions

[More Information Needed]
task_categories:conversational task_categories:fill-mask size_categories:100M<n<1B size_categories:10M<n<100M language:en biology medical region:us
{"language": ["en"], "size_categories": ["100M<n<1B", "10M<n<100M"], "task_categories": ["conversational", "fill-mask"], "pretty_name": "genome database", "tags": ["biology", "medical"], "viewer": false}
TAGS
#task_categories-conversational #task_categories-fill-mask #size_categories-100M<n<1B #size_categories-10M<n<100M #language-English #biology #medical #region-us 

# Dataset Card for Dataset Name

## Dataset Description

- Homepage: 
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: 

### Dataset Summary
This dataset contains datas being collected from Genbank. The dataset is organized in a way that it separate all the genes from an DNA , and was classified according to the region and coding type. In that way, people could get more detailed information regarding each DNA sequences.
The dataset also contain source, which is the whole DNA sequence, where the user can use it to compare to each segment to see the exact location. The dataset contains 937 files with about 200 million data and 300-400 GB storage space. Therefore user can specify the number
of files they are going to use by using the code below according to their own needs. If user want to download all of files, they can enter 937 as second arguement.



### Supported Tasks and Leaderboards



### Languages



## Dataset Structure

### Data Instances


### Data Fields
__DNA id__: id number for the whole DNA sequence, sequences with same DNA id are from same DNA  
__Organism__: Organism of the DNA  
__year__: the year of the DNA sequence       
__region type__: determine the general type of the sequence. For all the type that is typically classified as coding region, it was named with coding; while others including those that are case dependent were named according to their own type such as regulator, repeat_region,gap, intron,extron, etc.(__Note__: when classifying coding type, all the CDS, mRNA, tmRNA, tRNA,rRNA and others such as propetide, sig_propetide,mat_propetide was classified as coding. In order to minimize the missing coding part, all the other categories which has associated product was also classified as coding )   
__specific class__: if the sequence is coding sequence, it would be classified according to their production type such as RNA, Protein. The regulators would also be classified by their own class such as terminator, ribosome  
__Product__ : if the sequence produce protein, the product name would be listed  
__sequence__: the actual sequence   
__gc_content__: the gc_content of the sequence  
__translation code__: if the sequence produce protein, then the translation code would be provided as a reference  
__start_position__: the start position of the segment  
__end_position__: the end position of the segment

### Data Splits

first 80% of files was used as training dataset, while last 20% was used as testing dataset

## Dataset Creation

### Curation Rationale



### Source Data
The data collected are all from the most recent release of genbank, genbank 255.
#### Initial Data Collection and Normalization



#### Who are the source language producers?



### Annotations

#### Annotation process



#### Who are the annotators?



### 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







### Contributions


# Dataset Card for Dataset Name ## Dataset Description

- Homepage: 
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: ### Dataset Summary
This dataset contains datas being collected from Genbank. The dataset is organized in a way that it separate all the genes from an DNA , and was classified according to the region and coding type. In that way, people could get more detailed information regarding each DNA sequences.
The dataset also contain source, which is the whole DNA sequence, where the user can use it to compare to each segment to see the exact location. The dataset contains 937 files with about 200 million data and 300-400 GB storage space. Therefore user can specify the number
of files they are going to use by using the code below according to their own needs. If user want to download all of files, they can enter 937 as second arguement. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields
__DNA id__: id number for the whole DNA sequence, sequences with same DNA id are from same DNA  
__Organism__: Organism of the DNA  
__year__: the year of the DNA sequence       
__region type__: determine the general type of the sequence. For all the type that is typically classified as coding region, it was named with coding; while others including those that are case dependent were named according to their own type such as regulator, repeat_region,gap, intron,extron, etc.(__Note__: when classifying coding type, all the CDS, mRNA, tmRNA, tRNA,rRNA and others such as propetide, sig_propetide,mat_propetide was classified as coding. In order to minimize the missing coding part, all the other categories which has associated product was also classified as coding )   
__specific class__: if the sequence is coding sequence, it would be classified according to their production type such as RNA, Protein. The regulators would also be classified by their own class such as terminator, ribosome  
__Product__ : if the sequence produce protein, the product name would be listed  
__sequence__: the actual sequence   
__gc_content__: the gc_content of the sequence  
__translation code__: if the sequence produce protein, then the translation code would be provided as a reference  
__start_position__: the start position of the segment  
__end_position__: the end position of the segment ### Data Splits

first 80% of files was used as training dataset, while last 20% was used as testing dataset ## Dataset Creation ### Curation Rationale ### Source Data
The data collected are all from the most recent release of genbank, genbank 255. #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### 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 ### Contributions
TAGS
#task_categories-conversational #task_categories-fill-mask #size_categories-100M<n<1B #size_categories-10M<n<100M #language-English #biology #medical #region-us 
 # Dataset Card for Dataset Name ## Dataset Description

- Homepage: 
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact: ### Dataset Summary
This dataset contains datas being collected from Genbank. The dataset is organized in a way that it separate all the genes from an DNA , and was classified according to the region and coding type. In that way, people could get more detailed information regarding each DNA sequences.
The dataset also contain source, which is the whole DNA sequence, where the user can use it to compare to each segment to see the exact location. The dataset contains 937 files with about 200 million data and 300-400 GB storage space. Therefore user can specify the number
of files they are going to use by using the code below according to their own needs. If user want to download all of files, they can enter 937 as second arguement. ### Supported Tasks and Leaderboards ### Languages ## Dataset Structure ### Data Instances ### Data Fields
__DNA id__: id number for the whole DNA sequence, sequences with same DNA id are from same DNA  
__Organism__: Organism of the DNA  
__year__: the year of the DNA sequence       
__region type__: determine the general type of the sequence. For all the type that is typically classified as coding region, it was named with coding; while others including those that are case dependent were named according to their own type such as regulator, repeat_region,gap, intron,extron, etc.(__Note__: when classifying coding type, all the CDS, mRNA, tmRNA, tRNA,rRNA and others such as propetide, sig_propetide,mat_propetide was classified as coding. In order to minimize the missing coding part, all the other categories which has associated product was also classified as coding )   
__specific class__: if the sequence is coding sequence, it would be classified according to their production type such as RNA, Protein. The regulators would also be classified by their own class such as terminator, ribosome  
__Product__ : if the sequence produce protein, the product name would be listed  
__sequence__: the actual sequence   
__gc_content__: the gc_content of the sequence  
__translation code__: if the sequence produce protein, then the translation code would be provided as a reference  
__start_position__: the start position of the segment  
__end_position__: the end position of the segment ### Data Splits

first 80% of files was used as training dataset, while last 20% was used as testing dataset ## Dataset Creation ### Curation Rationale ### Source Data
The data collected are all from the most recent release of genbank, genbank 255. #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### 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 ### Contributions
passage: TAGS
#task_categories-conversational #task_categories-fill-mask #size_categories-100M<n<1B #size_categories-10M<n<100M #language-English #biology #medical #region-us 
# Dataset Card for Dataset Name## Dataset Description

- Homepage: 
- Repository: 
- Paper: 
- Leaderboard: 
- Point of Contact:### Dataset Summary
This dataset contains datas being collected from Genbank. The dataset is organized in a way that it separate all the genes from an DNA , and was classified according to the region and coding type. In that way, people could get more detailed information regarding each DNA sequences.
The dataset also contain source, which is the whole DNA sequence, where the user can use it to compare to each segment to see the exact location. The dataset contains 937 files with about 200 million data and 300-400 GB storage space. Therefore user can specify the number
of files they are going to use by using the code below according to their own needs. If user want to download all of files, they can enter 937 as second arguement.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances