DIMA: Data Integration and Metadata Annotation

DIMA (Data Integration and Metadata Annotation) is a Python package designed for the Laboratory of Atmospheric Chemistry to support the integration of multi-instrument data in HDF5 format. It is tailored for data from diverse experimental campaigns, including:

• beamtimes,
• kinetic flowtube studies,
• smog chamber experiments, and
• field campaigns.

Key Features

DIMA provides reusable operations for data integration, manipulation, and extraction using HDF5 files. These serve as the foundation for the following higher-level operations:

1. Data Integration Pipeline: Harmonizes and integrates multi-instrument data sources by converting a human-readable campaign descriptor YAML file into a unified HDF5 format.

2. Metadata Revision Workflow: Updates and refines metadata through a human-in-the-loop process, optimizing HDF5 file metadata serialization in YAML format to align with conventions and develop campaign-centric vocabularies.

3. Visualization pipeline: Generates a treemap visualization of an HDF5 file, highlighting its structure and key metadata elements.

4. Jupyter notebooks Demonstrates DIMA’s core functionalities, such as data integration, HDF5 file creation, visualization, and metadata annotation. Key notebooks include examples for data sharing, OpenBis ETL, and workflow demos.

Adaptability to Experimental Campaign Needs

The instruments/ module is designed to be highly adaptable, accommodating new instrument types or file reading capabilities with minimal code refactoring. The module is complemented by instrument-specific dictionaries of terms in YAML format, which facilitate automated annotation of observed variables with:

• standard_name
• units
• description

as suggested by CF metadata conventions.

Versioning and Community Collaboration

The instrument-specific dictionaries in YAML format provide a human readable interface for community-based development of instrument vocabularies. These descriptions can potentially be enhanced with semantic annotations for interoperability across research domains.

Repository Structure

Software arquitecture

Installation

Follow these steps to install and set up the project:

1. Download our GitLab repository in your GitLab folder, or alternatively open a Git Bash terminal and run the following commands:

cd Path/to/GitLab
git clone https://gitlab.psi.ch/5505/data-integration-and-metadata-annotation.git

2. Open an Anaconda Prompt (Anaconda3) as administrator, and set the current directory to the path of the project's folder.

3. Create the project's environment multiphase_chemistry_env by running the following command:

conda env create -f environment.yml

Working with Jupyter Notebook on the multiphase_chemistry_env

1. Open an Anaconda Prompt as a regular user, ensure that multiphase_chemistry_env is in the list of available enviroments and activate it by running the following commands:

conda env list
conda activate multiphase_chemistry_env

2. Register the associated kernel in Jupyter by running:

python -m ipykernel install --user --name multiphase_chemistry_env --display-name "Python (multiphase_chemistry_env)"  

3. Start a Jupyter Notebook by running the command:

jupyter notebook

and select the multiphase_chemistry_env environment from the kernel options.

Working with Visual Studio Code (VS Code) on the multiphase_chemistry_env

1. Open the project in VS Code, click on the Python interpreter in the status bar and choose the multiphase_chemistry_env environment.

Data integration workflow

Metadata review workflow

- review through branches
- updating files with metadata in Openbis

Metadata

Specifying a compound attribute in yaml language.

Consider the compound attribute relative_humidity, which has subattributes value, units, range, and definition. The yaml description of such an attribute is as follows:

relative_humidity:
  value: 65
  units: percentage
  range: '[0,100]'
  definition: 'Relative humidity represents the amount of water vapor present in the air relative to the maximum amount of water vapor the air can hold at a given temperature.'  

Deleting or renaming a compound attribute in yaml language.

• Assume the attribute relative_humidity already exists. Then it should be displayed as follows with the subattribute rename_as. This can be set differently to suggest a renaming of the attribute.
• To suggest deletion of an attribute, we are required to add a subattribute delete with value as true. Below for example, the attribute relative_ humidity is suggested to be deleted. Otherwise if delete is set as false, it will have no effect.

relative_humidity:
  delete: true # we added this line in the review process
  rename_as: relative_humidity
  value: 65
  units: percentage
  range: '[0,100]'
  definition: 'Relative humidity represents the amount of water vapor present in the air relative to the maximum amount of water vapor the air can hold at a given temperature.'

How to Extend DIMA’s File Reading Capabilities for New Instruments

We now explain how to extend DIMA's file-reading capabilities by adding support for a new instrument. The process involves adding instrument-specific files and registering the new instrument's file reader.

1. Create Instrument Files

You need to add two files for the new instrument:

• A YAML file that contains the instrument-specific description terms.

  • Location: instruments/dictionaries/

• A Python file that reads the instrument's data files (e.g., JSON files).

  • Location: instruments/readers/

Example:

• YAML file: ACSM_TOFWARE_flags.yaml
• Python file: flag_reader.py (reads flag.json files from the new instrument).

2. Register the New Instrument Reader

To enable DIMA to recognize the new instrument's file reader, update the filereader registry:

1. Open the file: instruments/readers/filereader_registry.py.
2. Add an entry to register the new instrument's reader.

Example:

# Import the new reader
from instruments.readers.flag_reader import read_jsonflag_as_dict
# Register the new instrument in the registry
file_extensions.append('.json') 
file_readers.update({'ACSM_TOFWARE_flags_json' : lambda x: read_jsonflag_as_dict(x)})

-------------------

Getting started

To make it easy for you to get started with GitLab, here's a list of recommended next steps.

Already a pro? Just edit this README.md and make it your own. Want to make it easy? Use the template at the bottom!

Add your files

• Create or upload files
• Add files using the command line or push an existing Git repository with the following command:

cd existing_repo
git remote add origin https://gitlab.psi.ch/5505/functionspython.git
git branch -M main
git push -uf origin main

cd existing_repo
git remote add origin https://gitlab.psi.ch/5505/functionspython.git
git branch -M main
git push -uf origin main

Integrate with your tools

Integrate with your tools

• Set up project integrations

Collaborate with your team

• Invite team members and collaborators
• Create a new merge request
• Automatically close issues from merge requests
• Enable merge request approvals
• Automatically merge when pipeline succeeds

Test and Deploy

Use the built-in continuous integration in GitLab.

• Get started with GitLab CI/CD
• Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)
• Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy
• Use pull-based deployments for improved Kubernetes management
• Set up protected environments

Editing this README

When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to makeareadme.com for this template.

Suggestions for a good README

Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.

Name

Choose a self-explaining name for your project.

Description

Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.

Badges

On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.

Visuals

Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.

Installation

Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.

Usage

Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.

Support

Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.

Roadmap

If you have ideas for releases in the future, it is a good idea to list them in the README.

Contributing

State if you are open to contributions and what your requirements are for accepting them.

For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.

You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.

Authors and acknowledgment

Show your appreciation to those who have contributed to the project.

License

For open source projects, say how it is licensed.

Project status

If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.