ifes-apt-tc-data-modeling 0.2.2

Foster exchange about data models and work towards clear specifications of file formats and data models in the research fields of atom probe tomography and related field-ion microscopy (atom probe microscopy).

ifes_apt_tc_data_modeling

Mission:

Foster exchange in the community of atom probe research to exchange about and document information content and formatting in their research field. Work towards ideally semantically specified file formats and data models.

Getting started:

Create an environment

To use this library create a conda or a virtual environment. We tested on Ubuntu with Python 3.8 and newer version. In what follows the version (tag) 3.8 is a placeholder whereby we show how to proceed when using e.g. Python version 3.8. Using newer versions of Python should work the same by replacing 3.8 with the respective version (tag). As of 2024, using Python in versions higher than 3.9 becomes more and more common. The support for users to install modern Python version has also improved. Therefore, the following commands typically enable you to create a specifically-versioned virtual environment:

mkdir <your-brand-new-folder>
cd <your-brand-new-folder>
pip install virtualenv
virtualenv --python=python3.8 .py38
source .py38/bin/activate

If you wish to use or still demand to use older versions of Python, like 3.8 or 3.9, you can conveniently install them via the deadsnakes repository (or via conda). For using deadsnakes proceed with the following commands:

sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt install python3.8 python3-dev libpython3.8-dev python3.8-venv

In some cases when using Python3.8, it was necessary to install python-numpy. Please consider this if you run into issues when continuing with this manual.

Install the ifes_apt_tc_data_modeling modules as a user

git clone https://www.github.com/atomprobe-tc/ifes_apt_tc_data_modeling.git
cd ifes_apt_tc_data_modeling
python -m pip install --upgrade pip
python -m pip install -e .
python -m pip install -e ".[dev]"
python -m pip list
jupyter-lab

Context, status quo, file formats used for atom probe research

A lack of detailed technical specifications of the file formats and a lack of usage of magic numbers as identifiers for specific file formats are a key blocker to parsing and semantic interpretation of information content stored in current file formats within the research field of atom probe microscopy.

A practical solution to raise at least awareness of this problem has been that scientists collect examples (instances) of files in respective formats. Pieces of information about the content and formatting of atom probe file formats were reported in the literature (e.g. in the books by D. Larson et al. https://doi.org/10.1007/978-1-4614-8721-0 or B. Gault et al. http://dx.doi.org/10.1007/978-1-4614-3436-8 ). Atom probers like D. Haley have contributed substantially through raising awareness of the issue within the community.

AMETEK/Cameca is the key technology partner in atom probe. AMETEK/Cameca has developed an open file format called APT which has improved the accessibility of specific numerical data and some metadata. Individuals like M. Kühbach have driven the implementation and communication of parsers for this APT file format. There are ongoing efforts by both AMETEK/Cameca and the scientific community to extent the APT file format with additional metadata. The main motivation behind these newer efforts is to improve the interoperability between research data collected within the IVAS/APSuite software and third-party software including research data management systems. Currently, most metadata have to be entered manually via e.g. electronic lab notebooks if one were to use or register atom probe data in solutions other than those developed by AMETEK/Cameca.

Nowadays, there is a global desire, a push by research funding agencies, and an increased interest of atom probers to make their research data and knowledge generation process better matching and more completely aligned to the aims and practices of the F.A.I.R. principles of research data stewardship and FAIR4RS research software development.

Therefore, it is useful to exchange more details about data models and file formats. Otherwise, it is not foreseeable how atom probe data can be made really interoperable with electronic lab notebooks, research data management systems (RDMS), and related software tools for data analyses, especially not if these tools should ever work with solutions from the stack of semantic web technologies. We are convinced there are substantial opportunities with making atom probe research communication more substantiated, the research itself better reproducible, and with enabling automated contextualization of atom probe research via computational agents.

In light of these challenges, the idea of understanding formats just by examples, showed to be a slow and error-prone route as e.g. source code and workflows which have been used to write such files lack provenance information. As an example, the POS files only store a table of number quadruples which mostly are interpreted as reconstructed position and mass-to-charge- state ratio values but often are hijacked to report conceptually different quantities like identifier used to distinguish clusters of atoms. Nowhere in a POS file a magic number could identify the file as to be truely a POS file and no something else based on which software tools and human could make a substantiated assumption. Nowhere does the POS file document from which content and which tools it was generated. The situation is currently still similarly poor for ranging definitions files such as RRNG, RNG, or ENV: These merely store the resulting ranging definitions but no details based on which peak finding algorithm or even which mass-to-charge-state-ratio value array they were defined with. M. Kühbach et al. have summarized a more detailed discussion about these limitations https://doi.org/10.1017/S1431927621012241.

How can you support this work?

As a user with contacting us and providing examples of file formats. As a member of a company by documenting your file format and getting in contact to work together on improving the situation. Thank you very much for supporting this activity and your time.

Feedback, questions

Feel free to drop us a message via creating an issue or commenting on one.

Background information

File formats, data models, in (almost every) research field may not be fully documented. A checklist of the necessary pieces of information and documentation required to call a data model, data schema, and/or file format fully documented in accordance with the FAIR data and research software stewardship principles is given below:

1. Each piece of information (bit/byte) is documented.
2. This documentation fulfills the FAIR principles, i.e. Wilkinson et al., 2016 and Barker et al., 2022 For binary files, tools like kaitai struct offer a solution to describe the exact binary information content in a data item. This can be a file but also the storage of a database entry or the response of a call to an API. Let alone the binary structure is insufficient tough.
3. To each piece of information there has to exist also a parameterized description, what this piece of information conceptually means. One way to arrive at such description is to use a data schema or ontology. It is important to mention that the concepts in this schema/ontology have unique identifier so that each data item/piece of information is identifiable as an instance of an entry in a database or a knowledge graph. This holds independently of which research data management system or electronic lab notebook is used.
4. In addition, it is very useful if timestamps are associated with each data item (ISO8061 including time zone information) so that it is possible to create a timeline of the context in which and when the e.g. file was created.

The first and second point is known as a specification, while the third and fourth point emphasize that the contextualization and provenance is key to make a specification complete and useful.

Where to place your examples?

There is a examples_with_provenance and examples_without_provenance sub-directory for each file format.

When you do know with which software and measured dataset you have created a file, you should share the file and these pieces of information (software version). Do so by naming at least the respective raw files. Ideally, you share the examples via offering a link to an external data repository such as Zenodo or other providers. This not only avoids that this repository would get too much filled up with binary data. Also it enables you to share clearly under which license you would like make your example(s) accessible.

Provenance if possible, plain examples if in doubt

Use the examples_with_provenance sub-directory. With this it is at least possible to reproduce the file creation. A practical solution is to share (by uploading) the screenshot of the complete IVAS/APSuite version info screen, including the APSuite version, the CERN Root version, the CamecaRoot version, and the versions of libraries used by APSuite. This can help other atom probers and AMETEK/Cameca to improve their software as it will enable them to identify inconsistencies.

Atom probers should be aware that file formats like POS, ePOS, or APT are neither raw data nor follow a clear technical documentation. Therefore, all current file formats are not meeting the FAIR principles. Instead, share RRAW, STR, RHIT, and HITS files. Ideally, you add unique identifiers (such as SHA256 checksums) for each file. A documentation how you can do this was issued by your IFES APT TC colleagues (How to hash your data).

If you cannot provide such detailed pieces of information, you can still participate and support us a lot if you share your knowledge by adding at least a link to a repository or file share with content in the relevant atom-probe-specific file formats.

In this case, please use the examples_without_provenance directory. While these examples are stripped of the context in which they were created and used (provenance information), these examples can still be very useful to run the file formats parsers against to make the parsers more robust, i.e. that these can pick up formatting issues and act accordingly.