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README.md

DIMA: Data Integration and Metadata Annotation

Description

DIMA (Data Integration and Metadata Annotation) is a Python package developed to support the findable, accessible, interoperable, and reusable (FAIR) data transformation of multi-instrument data at the Laboratory of Atmospheric Chemistry as part of the project IVDAV: Instant and Versatile Data Visualization During the Current Dark Period of the Life Cycle of FAIR Research, funded by the ETH-Domain ORD Program Measure 1.

The FAIR data transformation involves cycles of data harmonization and metadata review. DIMA facilitates these processes by enabling the integration and annotation of multi-instrument data in HDF5 format. This data may originate 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: Searches for, retrieves, and integrates multi-instrument data sources in HDF5 format using a human-readable campaign descriptor YAML file that points to the data sources on a network drive.

  2. Metadata revision pipeline: Enables updates, deletions, and additions of metadata in an HDF5 file. It operates on the target HDF5 file and a YAML file specifying the required changes. A suitable YAML file specification can be generated by serializing the current metadata of the target HDF5 file. This supports alignment with conventions and the development of 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.

Requirements

For Windows users, the following are required:

  1. Git Bash: Install Git Bash to run shell scripts (.sh files).

  2. Conda: Install Anaconda or Miniconda.

  3. PSI Network Access: Ensure access to PSI’s network and access rights to source drives for retrieving campaign data from YAML files in the input_files/ folder.

💡 Tip: Editing your system’s PATH variable ensures both Conda and Git are available in the terminal environment used by Git Bash.

Getting Started

Download DIMA

Open a Git Bash terminal.

Navigate to your GitLab folder, clone the repository, and navigate to the dima folder as follows:

cd path/to/GitLab
git clone --recurse-submodules https://gitlab.psi.ch/5505/dima.git
cd dima

Install Python Interpreter

Open Git Bash terminal.

Option 1: Install a suitable conda environment multiphase_chemistry_env inside the repository dima as follows:

cd path/to/GitLab/dima
Bash setup_env.sh

Open Anaconda Prompt or a terminal with access to conda.

Option 2: Install conda enviroment from YAML file as follows:

cd path/to/GitLab/dima
conda env create --file environment.yml
Working with Jupyter Notebooks

We now make the previously installed Python environment multiphase_chemistry_env selectable as a kernel in Jupyter's interface.

  1. Open an Anaconda Prompt, check if the environment exists, and activate it: 
    conda env list
conda activate multiphase_chemistry_env
  2. Register the environment in Jupyter: 
    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.

Repository Structure and Software arquitecture

Directories

  • input_files/ stores some example raw input data or campaign descriptor YAML files.

  • output_files/ stores generated outputs for local processing.

  • instruments/ contains instrument-specific dictionaries and file readers.

  • src/ contains the main source code, HDF5 Writer and Data Operations Manager.

  • utils/ contains generic data conversion operations, supporting the source code.

  • notebooks/ contains a collection of Jupyter notebooks, demonstrating DIMA's main functionalities.

  • pipelines/ contains source code for the data integration pipeline and metadata revision workflow.

  • visualization/ contains primarily functions for visualization of HDF5 files as treemaps.

Software arquitecture

Alt Text

File standardization module (instruments/)

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).

  1. Register the New Instrument Reader To enable DIMA to recognize the new instrument's file reader, update the filereader registry:

  2. Open the file: instruments/readers/filereader_registry.py.

  3. 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)})

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.

How-to tutorials

Data integration workflow

This section is in progress!

Metadata review workflow
  • review through branches
  • updating files with metadata in Openbis

Metadata

Attribute CF Equivalent Definition
campaign_name - Denotes a range of possible campaigns, including laboratory and field experiments, beamtime, smog chamber studies, etc., related to atmospheric chemistry research.
project - Denotes a valid name of the project under which the data was collected or produced.
contact contact (specifically E-mail address) Denotes the name of data producer who conducted the experiment or carried out the project that produced the raw dataset (or an aggragated dataset with multiple owners)
description title (only info about content), comment (too broad in scope), source Provides a short description of methods and processing steps used to arrive at the current version of the dataset.
experiment - Denotes a valid name of the specific experiment or study that generated the data.
actris_level - Indicates the processing level of the data within the ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure) framework.
dataset_startdate - Denotes the start datetime of the dataset collection.
dataset_enddate - Denotes the end datetime of the dataset collection.
processing_file - Denotes the name of the file used to process an initial version (e.g, original version) of the dataset into a processed dataset.
processing_date - The date when the data processing was completed.

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.

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.'

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.

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

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Usage

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Support

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Roadmap

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Contributing

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