Update README.md

README.md

@@ -18,6 +18,7 @@ configs:
   data_files:
   - split: test
     path: data/test-*
+license: cc
 ---
 
 # Detectability - Wang
@@ -31,7 +32,7 @@ This dataset represents 30 healthy tissues from the Human Protein Atlas Project.
 
 ### Dataset Sources
 
-The data is based on the datasets introduced in [1] and available at: https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD010154
+The data is based on the datasets introduced in [[1]](#ref1) and available at: https://www.ebi.ac.uk/pride/archive/projects/PXD010154
 
 ## Uses
 
@@ -40,19 +41,33 @@ The dataset is used for testing detectability prediction models. However, users
 
 ## References
 
-[1] Wang, D., Eraslan, B., Wieland, T., Hallström, B., Hopf, T., Zolg, D. P., ... & Kuster, B. (2019). A deep proteome and transcriptome abundance atlas of 29 healthy human tissues. Molecular systems biology, 15(2), e8503.
+<a id="ref1">[1]</a> Wang, D., Eraslan, B., Wieland, T., Hallström, B., Hopf, T., Zolg, D. P., ... & Kuster, B. (2019). A deep proteome and transcriptome abundance atlas of 29 healthy human tissues. Molecular systems biology, 15(2), e8503.
 
 
 ## Citation
 
 **BibTeX:**
 
-[More Information Needed]
+```bibtex
+@article {Abdul-Khalek2024.10.28.620610,
+	author = {Abdul-Khalek, Naim and Picciani, Mario and Wimmer, Reinhard and Overgaard, Michael Toft and Wilhelm, Mathias and Echers, Simon Gregersen},
+	title = {To fly, or not to fly, that is the question: A deep learning model for peptide detectability prediction in mass spectrometry},
+	elocation-id = {2024.10.28.620610},
+	year = {2024},
+	doi = {10.1101/2024.10.28.620610},
+	publisher = {Cold Spring Harbor Laboratory},
+	URL = {https://www.biorxiv.org/content/early/2024/10/31/2024.10.28.620610},
+	eprint = {https://www.biorxiv.org/content/early/2024/10/31/2024.10.28.620610.full.pdf},
+	journal = {bioRxiv}
+}
+```
 
 **APA:**
 
+Abdul-Khalek, N., Picciani, M., Wimmer, R., Overgaard, M. T., Wilhelm, M., & Gregersen Echers, S. (2024). To fly, or not to fly, that is the question: A deep learning model for peptide detectability prediction in mass spectrometry. bioRxiv, 2024-10.‏
+
 ## Dataset Card Contact
 
 Simon Gregersen, sgr@bio.aau.dk, Department of Chemistry and Biosciences, Aalborg University.
 
-Mathias Wilhelm, mathias.wilhelm@tum.de, Wilhelmlab, TU Munich, School of Life Sciences, Germany.
+Mathias Wilhelm, mathias.wilhelm@tum.de, Wilhelmlab, TU Munich, School of Life Sciences, Germany.