@ man : in man_reference add workflow section; in man_tutoreal nextgen...

This howto walks you step by step through the process of installing

**development**  and **production** version of annot.

1. Install docker, docker-compose and the docker-machine as described in

1. On the host machine install docker, docker-compose and the docker-machine as described in

  *HowTo install the docker container platform?*

1. On the host machine install [Git](http://git-scm.com/).

    1. tar or zip the data to one big file.

    1. `docker exec -i annot_web_1 bash -c "cat > bigfile.tar.gz" < /host/path/bigfile.tar.gz` to upload a big junk of data into the docker container.

### HowTo get files from inside annot to your hostmachine?

for the **development version**:

the annot content every night. How to do this is described in the last

step of *HowTo install annot?*

### HowTo backup acpipeTemplateCode_*.py code?

**Warning:** it is your responsibility to back up the modified python3

Below is in short described, who such axis are specified.

Check out the Tutorial for an applied example.

#### About **axis sets**!

1. To define an axis set, one first has to gather the samples, the perturbation reagents, and the endpoint reagents used in the experiment.

   After a while You should see a message ` *runset_name* # successfully checked`

   or a warning when the acjson content differs.

#### About **date tracking**!

The tracking layer enables assay and superset related

date, protocol, and staff member metadata to be documented.

[Docker Machine](https://docs.docker.com/machine/) and

[Docker Compose](https://docs.docker.com/compose/)

as described here: [Install Docker](https://docs.docker.com/install/).

Additionally install [VirtialBox](https://www.virtualbox.org/),

which will be used as docker-machine disk driver.

### HowTo run the docker platform?

This howto will get you familiar with docker,

# Reference

## Annot **workflow**

---

This workflow representation gives an overview, how form a user point of view

experiments with annot are annotated.

![An! workflow](img/annot5_workflow_20190716_07.png "An! workflow")

Assay reagents and samples are first annotated via the annot web interface or via Excel spreadsheet that can be uploaded into annot. 

This annotation step enforces the use of controlled vocabulary and official gene, protein, compound and cell line identifiers. 

Annotated reagents and samples are next combined into endpoint, perturbation, and sample sets. 

In this step, additional experimental details can be specified, for example reagent concentrations, cell seeding density, or cell passage number (red arrows).

For assays that involve robot pipetting, array spotting, or cyclic staining, super sets can be generated (light green arrows). 

Finally, for each assay run, endpoint, perturbation, sample and super sets are merged to a run specific assay layout (dark green arrows).

Assays and supersets that are regularly processes by the lab can be directly tracked in annot. 

There the exact data, protocol and the person who did the lab work can be specified. 

Lastly, assays can be grouped into studies and studies into investigations (blue arrows).

## Docker and how the **annot** folder is structured

---

  d_record[s_gent].update({'timeBegin': ?})

  d_record[s_gent].update({'timeEnd': ?})

  d_record[s_gent].update({'timeUnit': 'hour_uo0000032'})

  d_record[s_gent].update({'recordSet': 'ds-lapatinib_750nM_v1'})

  d_record[s_gent].update({'recordSet': ['ds-lapatinib_750nM_v1']})

  d_record[s_gent].update({'externalId': 'LSM-1051'})

  d_acjson = ac.acfuseaxisrecord(

      d_acjson,

                    "cultType": "batch",

                    "externalId": "LSM-1051",

                    "manufacture": "Selleck_Own",

                    "recordSet": "ds-lapatinib_750nM_v1",

                    "recordSet": ["ds-lapatinib_750nM_v1"],

                    "timeBegin": 18,

                    "timeEnd": 90,

                    "timeUnit": "hour_uo0000032"