Docs: Enhancements to Installation, Introduction, and Tutorial Sections (aiidateam#6806)

Fixes several typos, and formatting problem. 
Some of the links were not even shown properly.

Author: Shibu778

docs/source/installation/docker.rst

@@ -77,7 +77,7 @@ Any data that was created in a previous session is still available.
 
 .. caution::
 
-    When the container is not just stopped but *deleted*, any data stored in the container, including the data stored in the profile's storage, is permanently localhost
+    When the container is not just stopped but *deleted*, any data stored in the container, including the data stored in the profile's storage, is permanently deleted.
     To ensure the data is not lost, it should be persisted on a volume that is mounted to the container.
     Refer to the section on :ref:`persisting data <installation:docker:persisting-data>` for more details.
 

docs/source/installation/guide_complete.rst

@@ -157,7 +157,7 @@ Although it is possible to run AiiDA without a daemon it does provide significan
 
     .. tab-item:: MacOS X
 
-        #. Install `Homebrew <https://docs.brew.sh/Installation>`.
+        #. Install `Homebrew <https://docs.brew.sh/Installation>`__.
 
         #. Install RabbitMQ:
 
@@ -315,7 +315,7 @@ The options specific to the ``core.sqlite_dos`` storage plugin are:
 This storage plugin uses `PostgreSQL <https://www.postgresql.org/>`_ and the `disk-objectstore <https://disk-objectstore.readthedocs.io/en/latest/>`_ to store data.
 The ``disk-objectstore`` is a Python package that is automatically installed as a dependency when installing ``aiida-core``, which was covered in the :ref:`Python package installation section <installation:guide-complete:python-package>`.
 The storage plugin can connect to a PostgreSQL instance running on the localhost or on a server that can be reached over the internet.
-Instructions for installing PostgreSQL is beyond the scope of this guide.
+Instructions for installing PostgreSQL is beyond the scope of this guide. You can find the installation instructions for your system on the `PostgreSQL website <https://www.postgresql.org/docs/current/tutorial-install.html>`__.
 
 Before creating a profile, a database (and optionally a custom database user) has to be created.
 First, connect to PostgreSQL using ``psql``, the `native command line client for PostgreSQL <https://www.postgresql.org/docs/current/app-psql.html>`_:

docs/source/intro/index.rst

@@ -10,14 +10,14 @@ Introduction
 
 AiiDA is an open-source Python infrastructure to help researchers with automating, managing, persisting, sharing and reproducing the complex workflows associated with modern computational science and all associated data.
 
-AiiDA is built to support and streamline the four core pillars of the ADES model: Automation, Data, Environment, and Sharing (described `here <https://arxiv.org/abs/1504.01163>`__). Some of the key features of AiiDA include:
+AiiDA is built to support and streamline the four core pillars of the ADES model: Automation, Data, Environment, and Sharing (described `here <https://arxiv.org/abs/1504.01163>`__, `doi <https://doi.org/10.1016/j.commatsci.2015.09.013>`__). Some of the key features of AiiDA include:
 
 *  **Workflows:** AiiDA allows to build and execute complex, auto-documenting workflows linked to multiple codes on local and remote computers.
 *  **High-throughput:** AiiDA's event-based workflow engine supports tens of thousands of processes per hour with full check-pointing.
 *  **Data provenance:** AiiDA automatically tracks and records inputs, outputs and metadata of all calculations and workflows in extensive provenance graphs that preserve the full lineage of all data.
 *  **Advanced queries:** AiiDA's query language enables fast graph queries on millions of nodes.
 *  **Plugin interface:** AiiDA can support via plugins any computational code and data analytics tool, data type, scheduler, connection mode, etc. (see `public plugin repository <https://aiidateam.github.io/aiida-registry/>`__).
-*  **HPC interface:** AiiDA can seamlessly deal with heterogeneous and remote computing resources; it works with many schedulers out of the box (`SLURM <https://slurm.schedmd.com>`__, `PBS Pro <https://www.pbspro.org/>`__, `torque <http://www.adaptivecomputing.com/products/torque/>`__, `SGE <http://gridscheduler.sourceforge.net/>`__ or `LSF <https://www.ibm.com/docs/en/spectrum-lsf>`__).
+*  **HPC interface:** AiiDA can seamlessly deal with heterogeneous and remote computing resources; it works with many schedulers out of the box (`SLURM <https://slurm.schedmd.com>`__, `PBS Pro <https://www.pbspro.org/>`__, `torque <https://docs.adaptivecomputing.com/torque/5-0-0/help.htm>`__, `SGE <http://gridscheduler.sourceforge.net/>`__ or `LSF <https://www.ibm.com/docs/en/spectrum-lsf>`__).
 *  **Open science:** AiiDA allows to export both full databases and selected subsets, to be shared with collaborators or made available and browsable online on the `Archive <https://archive.materialscloud.org/>`__ and `Explore <https://www.materialscloud.org/explore>`__ sections of `Materials Cloud <https://www.materialscloud.org>`__.
 *  **Open source:** AiiDA is released under the `MIT open-source license <https://github.com/aiidateam/aiida-core/blob/main/LICENSE.txt>`__.
 

docs/source/tutorials/basic.md

@@ -172,7 +172,7 @@ def multiply(x, y):
     return x * y
 ```
 
-This converts the `multiply` function into an AiIDA *calculation function*, the most basic execution unit in AiiDA.
+This converts the `multiply` function into an AiiDA *calculation function*, the most basic execution unit in AiiDA.
 Next, load the `Int` node you have created in the previous section using the `load_node` function and the PK of the data node:
 
 ```{code-cell} ipython3
@@ -251,7 +251,7 @@ graph.graphviz
 ```
 
 :::{note}
-Remember that the PK of the `CalcJob` can be different for your database.
+Remember that the PK of the `calcfunction` can be different for your database.
 :::
 
 (tutorial:basic:calcjob)=
@@ -500,7 +500,7 @@ class MultiplyAddWorkChain(WorkChain):
 ```
 
 :::{note}
-Besides WorkChain's, workflows can also be implemented as {ref}`work functions <topics:workflows:concepts:workfunctions>`.
+Besides WorkChains, workflows can also be implemented as {ref}`work functions <topics:workflows:concepts:workfunctions>`.
 These are ideal for workflows that are not very computationally intensive and can be easily implemented in a Python function.
 :::