refactor(docs): move from reST to Markdown (#50)

Convert docs from reStructuredText to Markdown so that the changelog
file is compatible with Release Please.

AUTHORS.md

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+# Authors
+
+The list of contributors in alphabetical order:
+
+- [Audrius Mecionis](https://orcid.org/0000-0002-3759-1663)
+- [Daniel Prelipcean](https://orcid.org/0000-0002-4855-194X)
+- [Diego Rodriguez](https://orcid.org/0000-0003-0649-2002)
+- [Giuseppe Steduto](https://orcid.org/0009-0002-1258-8553)
+- [Jan Okraska](https://orcid.org/0000-0002-1416-3244)
+- [Lukas Heinrich](https://orcid.org/0000-0002-4048-7584)
+- [Marco Donadoni](https://orcid.org/0000-0003-2922-5505)
+- [Marco Vidal](https://orcid.org/0000-0002-9363-4971)
+- [Rokas Maciulaitis](https://orcid.org/0000-0003-1064-6967)
+- [Ronald Dobos](https://orcid.org/0000-0003-2914-000X)
+- [Tibor Simko](https://orcid.org/0000-0001-7202-5803)
+- [Vladyslav Moisieienkov](https://orcid.org/0000-0001-9717-0775)

README.md

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+# REANA example - ATLAS RECAST
+
+[![image](https://github.com/reanahub/reana-demo-atlas-recast/workflows/CI/badge.svg)](https://github.com/reanahub/reana-demo-atlas-recast/actions)
+[![image](https://img.shields.io/badge/discourse-forum-blue.svg)](https://forum.reana.io)
+[![image](https://img.shields.io/github/license/reanahub/reana-demo-atlas-recast.svg)](https://raw.githubusercontent.com/reanahub/reana-demo-atlas-recast/master/LICENSE)
+[![image](https://www.reana.io/static/img/badges/launch-on-reana-at-cern.svg)](https://reana.cern.ch/launch?url=https%3A%2F%2Fgithub.com%2Freanahub%2Freana-demo-atlas-recast&name=reana-demo-atlas-recast)
+
+## About
+
+This [REANA](http://www.reana.io/) reproducible analysis example demonstrates a
+[RECAST](https://arxiv.org/abs/1010.2506) analysis using [ATLAS](https://atlas.cern/)
+Analysis Software Group stack.
+
+## Analysis structure
+
+Making a research data analysis reproducible basically means to provide "runnable
+recipes" addressing (1) where is the input data, (2) what software was used to analyse
+the data, (3) which computing environments were used to run the software and (4) which
+computational workflow steps were taken to run the analysis. This will permit to
+instantiate the analysis on the computational cloud and run the analysis to obtain (5)
+output results.
+
+### 1. Input data
+
+The analysis takes the following inputs:
+
+- `dxaod` input ROOT file
+- `did` dataset ID e.g. 404958
+- `xsec_in_pb` cross section in picobarn e.g. 0.00122
+
+### 2. Analysis code
+
+The **event selection** code for this analysis example resides under the
+[eventselection](eventselection) subdirectory. It uses the official analysis releases
+prepared by the ATLAS Analysis Software Group (ASG).
+
+- `eventselection/CMakeLists.txt`
+- `eventselection/MyEventSelection/CMakeLists.txt`
+- `eventselection/MyEventSelection/MyEventSelection/MyEventSelectionAlg.h`
+- `eventselection/MyEventSelection/Root/LinkDef.h`
+- `eventselection/MyEventSelection/Root/MyEventSelectionAlg.cxx`
+- `eventselection/MyEventSelection/util/myEventSelection.cxx`
+
+The **statistical analysis** code for this analysis example resides in
+[statanalysis](statanalysis) subdirectory. It implements limit setting for outputs
+produced by the event selection package.
+
+- `statanalysis/data/background.root`
+- `statanalysis/data/data.root`
+- `statanalysis/make_ws.py`
+- `statanalysis/plot.py`
+- `statanalysis/set_limit.py`
+
+Notes that `make_ws.py` script generates a HistFactory configuration based on signal,
+data and background ROOT files. It performs a simple HistFactory-based fit based on a
+single channel (consisting of two bins).
+
+### 3. Compute environment
+
+In order to be able to rerun the analysis even several years in the future, we need to
+"encapsulate the current compute environment", for example to freeze the ATLAS software
+version our analysis is using. We shall achieve this by preparing a
+[Docker](https://www.docker.com/) container image for our analysis steps.
+
+The event selection stage uses official ATLAS `atlas/analysisbase` container on top of
+which we add and build our custom code:
+
+```console
+$ less eventselection/Dockerfile
+FROM docker.io/atlas/analysisbase:21.2.51
+ADD . /analysis/src
+WORKDIR /analysis/build
+RUN source ~/release_setup.sh &&  \
+    sudo chown -R atlas /analysis && \
+    sudo chmod -R 775 /analysis && \
+    sudo chmod -R 755 /home/atlas && \
+    cmake ../src && \
+    make -j4
+USER root
+RUN sudo usermod -G root atlas
+USER atlas
+```
+
+We can build our event selection analysis environment image and give it a name
+`docker.io/reanahub/reana-demo-atlas-recast-eventselection`:
+
+```console
+$ cd eventselection
+$ docker build -t docker.io/reanahub/reana-demo-atlas-recast-eventselection .
+```
+
+The statistical analysis stage also extends `atlas/analysisbase` by the custom code:
+
+```console
+$ less statanalysis/Dockerfile
+FROM docker.io/atlas/analysisbase:21.2.51
+ADD . /code
+RUN sudo sh -c "source /home/atlas/release_setup.sh && pip install hftools==0.0.6"
+USER root
+RUN sudo usermod -G root atlas && sudo chmod -R 755 /home/atlas && chmod -R 775 /code
+USER atlas
+```
+
+We can build our statistical analysis environment image and give it a name
+`docker.io/reanahub/reana-demo-atlas-recast-statanalysis`:
+
+```console
+$ cd statanalysis
+$ docker build -t docker.io/reanahub/reana-demo-atlas-recast-statanalysis .
+```
+
+We can upload both images to the DockerHub image registry:
+
+```console
+$ docker push docker.io/reanahub/reana-demo-atlas-recast-eventselection
+$ docker push docker.io/reanahub/reana-demo-atlas-recast-statanalysis
+```
+
+(Note that typically you would use your own username such as `johndoe` in place of
+`reanahub`.)
+
+### 4. Analysis workflow
+
+This analysis example consists of a simple workflow where event selection is run first
+and its output serve as an input for the statistical analysis.
+
+We shall use the [Yadage](https://github.com/yadage) workflow engine to express the
+computational steps in a declarative manner:
+
+![](https://raw.githubusercontent.com/reanahub/reana-demo-atlas-recast/master/docs/workflow.png){.align-center}
+
+The full analysis pipeline is defined in [workflow.yml](workflow/workflow.yml) and the
+individual steps are defined in [steps.yml](workflow/steps.yml).
+
+### 5. Output results
+
+The analysis produces several pre-fit and post-fit plots:
+
+![](https://raw.githubusercontent.com/reanahub/reana-demo-atlas-recast/master/statanalysis/example_results/pre.png){.align-center}
+
+![](https://raw.githubusercontent.com/reanahub/reana-demo-atlas-recast/master/statanalysis/example_results/post.png){.align-center}
+
+The limit plot:
+
+![](https://raw.githubusercontent.com/reanahub/reana-demo-atlas-recast/master/statanalysis/example_results/limit.png){.align-center}
+
+The limit data is also stored in JSON format for both an entire µ-scan as well as for
+µ=1.
+
+## Running the example on REANA cloud
+
+There are two ways to execute this analysis example on REANA.
+
+If you would like to simply launch this analysis example on the REANA instance at CERN
+and inspect its results using the web interface, please click on the following badge:
+
+<a href="https://reana.cern.ch/launch?url=https%3A%2F%2Fgithub.com%2Freanahub%2Freana-demo-atlas-recast&name=reana-demo-atlas-recast">
+ <img src="https://www.reana.io/static/img/badges/launch-on-reana-at-cern.svg" />
+</a>
+<p></p>
+
+If you would like a step-by-step guide on how to use the REANA command-line client to
+launch this analysis example, please read on.
+
+We start by creating a [reana.yaml](reana.yaml) file describing the above analysis
+structure with its inputs, code, runtime environment, computational workflow steps and
+expected outputs:
+
+```yaml
+version: 0.3.0
+inputs:
+  parameters:
+    did: 404958
+    xsec_in_pb: 0.00122
+    dxaod_file: https://recastwww.web.cern.ch/recastwww/data/reana-recast-demo/mc15_13TeV.123456.cap_recast_demo_signal_one.root
+workflow:
+  type: yadage
+  file: workflow/workflow.yml
+outputs:
+  files:
+    - outputs/statanalysis/fitresults/pre.png
+    - outputs/statanalysis/fitresults/post.png
+    - outputs/statanalysis/fitresults/limit.png
+    - outputs/statanalysis/fitresults/limit_data.json
+```
+
+We can now install the REANA command-line client, run the analysis and download the
+resulting plots:
+
+```console
+$ # create new virtual environment
+$ virtualenv ~/.virtualenvs/reana
+$ source ~/.virtualenvs/reana/bin/activate
+$ # install REANA client
+$ pip install reana-client
+$ # connect to some REANA cloud instance
+$ export REANA_SERVER_URL=https://reana.cern.ch/
+$ export REANA_ACCESS_TOKEN=XXXXXXX
+$ # create new workflow
+$ reana-client create -n myanalysis
+$ export REANA_WORKON=myanalysis
+$ # upload input code, data and workflow to the workspace
+$ reana-client upload
+$ # start computational workflow
+$ reana-client start
+$ # ... should be finished in about a minute
+$ reana-client status
+$ # list workspace files
+$ reana-client ls
+$ # download output results
+$ reana-client download
+```
+
+Please see the [REANA-Client](https://reana-client.readthedocs.io/) documentation for
+more detailed explanation of typical `reana-client` usage scenarios.