Improve hashing implementation, add convenience methods and add tutorial on Tracker usage

examples/dataset2/info_day1.json

@@ -0,0 +1,6 @@
+{
+    "name": "Day 1 experiment",
+    "description": "This is the first day of the experiment.",
+    "experimenter": "John Doe",
+    "date": "2023-10-01"
+}

examples/dataset2/info_day2.json

@@ -0,0 +1,6 @@
+{
+    "name": "Day 2 experiment",
+    "description": "This is the second day of the experiment.",
+    "experimenter": "John Doe",
+    "date": "2023-10-02"
+}

examples/dataset2/info_day3.json

@@ -0,0 +1,6 @@
+{
+    "name": "Day 3 experiment",
+    "description": "This is the third day of the experiment.",
+    "experimenter": "Mary Smith",
+    "date": "2023-10-05"
+}

examples/dataset2/info_day4.json

@@ -0,0 +1,6 @@
+{
+    "name": "Day 3 experiment",
+    "description": "This is the fourth day of the experiment.",
+    "experimenter": "Mary Smith",
+    "date": "2023-10-06"
+}

examples/dataset2/info_day5.json

@@ -0,0 +1,6 @@
+{
+    "name": "Day 5 experiment",
+    "description": "This is the fifth day of the experiment.",
+    "experimenter": "Paul Brown",
+    "date": "2023-10-09"
+}

notebooks/01_orcabridge_core_concepts copy.ipynb

@@ -59,11 +59,6 @@
                         "* Different pipeline DAG defintion -- In `orcapod` the directed acyclic graph (DAG) for the `pipeline` should be defined using YAML file (or less frequently using API on `pipeline` struct in the Rust library). In `orcabridge` you will find that a `pipeline` DAG is defined dynamically through a series of application of `operation`. This is very much akin to how some DAG-based neural network library like TensorFlow defines a computation graph. While this works well for simple examples, it is rather difficult to track changes to the pipeline defined dynamically/programmatically using version control system. Since *how* you define the pipeline DAG is strictly speaking an orthogonal problem to the everything else that concerns the operation of the `pipeline`, no effort will be given to align the DAG definition in `orcabridge` and `orcapod`.\n",
                         "* Limited usage of a `stream` -- Currently `orcabrdige` only support single producer single consumer (SCSP) `stream`x, whereas in `orcapod`, `stream` should support single producer multiple consumer (SPMC) paradigm. While the same stream can be used in multiple downstream operations, each iteration of the stream actually results in recomputations of the entire chain of pipeline leading up to that stream. This inefficiency can be ameliorated by `CacheStream` operation after particularly computationally expensive segment of the pipeline. Using storage-backed `FunctionPod` will also help ameliorate the cost of recomputation by retrieving memoized computation result instead of recomputing."
                   ]
-            },
-            {
-                  "cell_type": "markdown",
-                  "metadata": {},
-                  "source": []
             }
       ],
       "metadata": {