pbtxt -> pb in README DOIs (open-reaction-database#54)

README.md

@@ -7,12 +7,12 @@
 ## DOIs covered by ORD
 
 * Mdluli, V. et al. High-throughput Synthesis and Screening of Iridium(III) Photocatalysts for the Fast and Chemoselective Dehalogenation of Aryl Bromides. ACS Catalysis 10, 6977–6987 (2020). [doi: 10.1021/acscatal.0c02247](https://doi.org/10.1021/acscatal.0c02247)
-  * [ord_dataset-b440f8c90b6343189093770060fc4098](https://github.com/Open-Reaction-Database/ord-data/blob/main/data/b4/ord_dataset-b440f8c90b6343189093770060fc4098.pbtxt)
+  * [ord_dataset-b440f8c90b6343189093770060fc4098](https://github.com/Open-Reaction-Database/ord-data/blob/main/data/b4/ord_dataset-b440f8c90b6343189093770060fc4098.pb)
 * Kutchukian, P. S. et al. Chemistry informer libraries: a chemoinformatics enabled approach to evaluate and advance synthetic methods. Chem. Sci. 7, 2604–2613 (2016). [doi: 10.1039/c5sc04751j](https://doi.org/10.1039/c5sc04751j)
-  * [ord_dataset-d319c2a22ecf4ce59db1a18ae71d529c](https://github.com/Open-Reaction-Database/ord-data/blob/main/data/d3/ord_dataset-d319c2a22ecf4ce59db1a18ae71d529c.pbtxt)
+  * [ord_dataset-d319c2a22ecf4ce59db1a18ae71d529c](https://github.com/Open-Reaction-Database/ord-data/blob/main/data/d3/ord_dataset-d319c2a22ecf4ce59db1a18ae71d529c.pb)
 * Buitrago Santanilla, A. et al. Nanomole-scale high-throughput chemistry for the synthesis of complex molecules. Science 347, 49–53 (2014). [doi: 10.1126/science.1259203](https://doi.org/10.1126/science.1259203)
-  * [ord_dataset-7d8f5fd922d4497d91cb81489b052746](https://github.com/Open-Reaction-Database/ord-data/blob/main/data/7d/ord_dataset-7d8f5fd922d4497d91cb81489b052746.pbtxt)
+  * [ord_dataset-7d8f5fd922d4497d91cb81489b052746](https://github.com/Open-Reaction-Database/ord-data/blob/main/data/7d/ord_dataset-7d8f5fd922d4497d91cb81489b052746.pb)
 * Perera, D. et al. A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow. Science 359, 429–434 (2018). [doi: 10.1126/science.aap9112](https://doi.org/10.1126/science.aap9112)
-  * [ord_dataset-33320f511ffb4f89905448c7a5153111](https://github.com/Open-Reaction-Database/ord-data/blob/main/data/33/ord_dataset-33320f511ffb4f89905448c7a5153111.pbtxt)
+  * [ord_dataset-33320f511ffb4f89905448c7a5153111](https://github.com/Open-Reaction-Database/ord-data/blob/main/data/33/ord_dataset-33320f511ffb4f89905448c7a5153111.pb)
 * Ahneman, D. T., Estrada, J. G., Lin, S., Dreher, S. D. & Doyle, A. G. Predicting reaction performance in C–N cross-coupling using machine learning. Science 360, 186–190 (2018). [doi: 10.1126/science.aar5169](https://doi.org/10.1126/science.aar5169)
-  * [ord_dataset-46ff9a32d9e04016b9380b1b1ef949c3](https://github.com/Open-Reaction-Database/ord-data/blob/main/data/46/ord_dataset-46ff9a32d9e04016b9380b1b1ef949c3.pbtxt)
+  * [ord_dataset-46ff9a32d9e04016b9380b1b1ef949c3](https://github.com/Open-Reaction-Database/ord-data/blob/main/data/46/ord_dataset-46ff9a32d9e04016b9380b1b1ef949c3.pb)