fill placeholders w/ links

src/ehrdata/dt/datasets.py

@@ -188,11 +188,11 @@ def physionet2012(
 ) -> EHRData:
     """Loads the dataset of the `PhysioNet challenge 2012 (v1.0.0) <https://physionet.org/content/challenge-2012/1.0.0/>`_.
 
-    If interval_length_number is 1, interval_length_unit is "h" (hour), and num_intervals is 48, this is equivalent to the SAITS preprocessing (insert paper/link/citation).
+    If interval_length_number is 1, interval_length_unit is "h" (hour), and num_intervals is 48, this is equivalent to the `SAITS <https://arxiv.org/pdf/2202.08516>`_ preprocessing.
     Truncated if a sample has more num_intervals steps; Padded if a sample has less than num_intervals steps.
     Further, by default the following 12 samples are dropped since they have no time series information at all: 147514, 142731, 145611, 140501, 155655, 143656, 156254, 150309,
     140936, 141264, 150649, 142998.
-    Taken the defaults of interval_length_number, interval_length_unit, num_intervals, and drop_samples, the tensor stored in .r of edata is the same as when doing the PyPOTS <insert citation/link/reference> preprocessing.
+    Taken the defaults of interval_length_number, interval_length_unit, num_intervals, and drop_samples, the tensor stored in .r of edata is the same as when doing the `PyPOTS <https://github.com/WenjieDu/PyPOTS>`_ preprocessing.
     A simple deviation is that the tensor in ehrdata is of shape n_obs x n_vars x n_intervals (with defaults, 3000x37x48) while the tensor in PyPOTS is of shape n_obs x n_intervals x n_vars (3000x48x37).
     The tensor stored in .r is hence also fully compatible with the PyPOTS package, as the .r tensor of EHRData objects generally is.