Datasets:

FartLabs
/

FartDB

@@ -49,7 +49,7 @@ tags:
 <!-- Provide a quick summary of the dataset. -->
-Composite dataset of 15,032 molecules and their taste (sweet, bitter, umami, sour, undefined).
 ## Dataset Details
@@ -106,14 +106,6 @@ This dataset is intended for the training of machine learning models, in particu
 This dataset has been used to finetune ChemBERTa to be able to predict the flavor from an arbitrary SMILES input.
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-[More Information Needed]
 ## 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. -->
@@ -132,7 +124,6 @@ PubChem descriptors: Where available, the dataset was enriched with descriptors
 This dataset contains the majority of all known SMILES to flavor mappings publicly available. In order to use this data for supervised machine learning, both the SMILES and the flavor categories had to be made consistent.
-[More Information Needed]
 ### Source Data
@@ -155,72 +146,11 @@ Tas2R Agonists DB: This dataset contains molecules which bind the human bitter r
 Scifinder: A random subset of small molecules (< 500 Da) that are listed with a pKa between 2 and 7 which is a safe range for human tasting.
 Umami DB: A small dataset of known umami compounds was manually curated from the literature.
-#### 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. -->
-[More Information Needed]
-### Annotations [optional]
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-#### 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. -->
-[More Information Needed]
-#### Who are the annotators?
-<!-- This section describes the people or systems who created the annotations. -->
-[More Information Needed]
-#### 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. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-## Citation [optional]
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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-## More Information [optional]
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-## Dataset Card Authors [optional]
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-## Dataset Card Contact
-[More Information Needed]

 <!-- Provide a quick summary of the dataset. -->
+Composite dataset of 15,031 molecules and their taste (sweet, bitter, umami, sour, undefined).
 ## Dataset Details
 This dataset has been used to finetune ChemBERTa to be able to predict the flavor from an arbitrary SMILES input.
 ## 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. -->
 This dataset contains the majority of all known SMILES to flavor mappings publicly available. In order to use this data for supervised machine learning, both the SMILES and the flavor categories had to be made consistent.
 ### Source Data
 Scifinder: A random subset of small molecules (< 500 Da) that are listed with a pKa between 2 and 7 which is a safe range for human tasting.
 Umami DB: A small dataset of known umami compounds was manually curated from the literature.
+## Citation
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+```
+Zimmermann Y, Sieben L, Seng H, Pestlin P, Görlich F. A Chemical Language Model for Multi-Class Molecular Taste Prediction. ChemRxiv. 2024; doi:10.26434/chemrxiv-2024-d6n15  This content is a preprint and has not been peer-reviewed.
+```