<commit_before>Fossil occurrence data from Pimiento et al. 2017 (doi:10.1038/s41559-017-0223-6)

The data are also used by Silvestro et al. in prep. to demonstrate the new 
RJMCMC algorithm implemented in PyRate.<commit_msg>Update README.txt<commit_after>Fossil occurrence data from [Pimiento et al. 2017](https://doi.org/10.1038/s41559-017-0223-6)

The data are also used by [Silvestro et al. 2019](https://doi.org/10.1017/pab.2019.23) and analyzed using the following command:

```python PyRate.py occs.py -qShift -filter_taxa mammals.txt -qShift epochs_q.txt -mG -pP 1.5 0```
      
The original dataset contained other marine megafauna organisms whereas here we decided to focus on mammals only, we used the command `-filter_taxa mammals.txt` to provide a list of mammalian taxa that we want to include in the analysis (whereas all other lineages are dropped).
Additional commands: `-qShift` specifies that preservation is modeled by a TPP process with independent rates for each epoch, `-mG` specifies that the TPP model should be coupled by a Gamma model of rate heterogeneity across lineages, and `-pP 1.5 0` specifies the shape and rate parameters of the gamma prior on the preservation rates. By setting the rate parameter to 0 we define the parameter as unknown, meaning that PyRate will estimate it after assigning it a hyper-prior.
{'fraction_non_alphanumeric': 0.06701414743112435, 'fraction_numerical': 0.05137751303052867, 'mean_word_length': 4.441295546558704, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '7051996', 'n_tokens_mistral': 444, 'n_tokens_neox': 365, 'n_words': 187}