fix creedo and jupyter links

atomic-charges/learning_atomic_charges.demoinfo.yaml

 {
 "title": "Learning atomic charges",
 "authors": ["Csányi, Gábor", "Kermode, James R."],
-"editLink": "/jupyter/notebooks/shared/afekete/tutorial/learning_atomic_charges.ipynb",
+"editLink": "/jupyter/cM/start/data/shared/afekete/tutorial/learning_atomic_charges.ipynb",
 "isPublic": true,
 "username": "tutorialsNew",
 "description": "In this tutorial, we will use Gaussian process regression, GPR (or equivalently, Kernel Ridge Regression, KRR) to train and predict charges of atoms in small organic molecules.", 

demos/creedo.demoinfo.yaml

@@ -3,7 +3,7 @@
 "type": "demos",
 "attributes": {
 "title": "Discovering simple descriptors for crystal-structure classification",
-"logicalPath": "/Creedo/index.htm",
+"logicalPath": "/Creedo/cM/start/",
 "authors": ["Boley, Mario", "Goldsmith, Bryan", "Kariryaa, Ankit", "Ghiringhelli, Luca"],
 "editLink": "/Creedo/index.htm",
 "isPublic": true,

grain-boundary/GB_tutorial_Berlin_2017.demoinfo.yaml

 {
 "title": "Grain boundaries of alpha-Fe tutorial",
 "authors": ["Fekete, Ádám", "Stella, Martina", "Lambert, Henry", "De Vita, Alessandro", "Csányi, Gábor"],
-"editLink": "/jupyter/notebooks/shared/afekete/tutorial/GB_tutorial_Berlin_2017.ipynb",
+"editLink": "/jupyter/cM/start/data/shared/afekete/tutorial/GB_tutorial_Berlin_2017.ipynb",
 "isPublic": true,
 "username": "tutorialsNew",
 "description": "In this tutorial we will be using a machine learning method (clustering) to analyse results of Grain Boundary (GB) calculations of alpha-iron. Along the way we will learn about different methods to describe local atomic environment in order to calculate properties of GBs. We will use these properties to separate the different regions of the GB using clustering methods. Finally we will determine how the energy of the GB is changing according to the angle difference of the regions.",