module-idea.md

Ideas for Modules (M) and Lessons (L) for Infusing FAIR Principles into Materials Science

M+: Software Carpentry and Data Management Related Tools

programming, version control, unix shell for automating tasks, database tools for programmatic data access interaction and labbook for documenting science

Potential resources:

• https://swcarpentry.github.io/shell-novice
• https://swcarpentry.github.io/python-novice-gapminder
• https://swcarpentry.github.io/sql-novice-survey/
• https://swcarpentry.github.io/git-novice
• https://www.writethedocs.org/

M0: World beyond spreadsheets and metadata in filenames

L1: data formats for machine-readable data (CSV, JSON, XML for materials data)

Motivating examples:

• https://www.youtube.com/watch?v=N2zK3sAtr-4
• “The Semantic Web” from Scientific American 284(5): 28–37, 2001
• http://tinyurl.com/vxk2s9kb

L2: Introduction to knowledge representation: data + metadata

introducing structure and syntax into the data or how to make the data machine-actionable through self-describing key/value data structure (e.g., XML, JSON with semantically relevant key names)

Potential resources:

• https://carpentries-incubator.github.io/scientific-metadata/index.html
• https://github.com/marda-alliance/FAIR_2023_Workshop/ (tabular data, data documents)

L3: Prepare your first dataset and code repository

(git or zenodo, jupyterlab vs notebook) and pay attention to documentation (https://www.writethedocs.org/)

L4: Verify if data is well structured for correctness - schema and schema validators

M1: Data reuse (use prior data from last year's course/literature/database)

L1: Work with data from prior work or last year course (CSV, JSON, XML)

(load data, and visualize it using the script (python/matplotlib, gnuplot) – write notebook to capture the workflow)

Potential resources:

• https://swcarpentry.github.io/python-novice-gapminder/

L2: Use REST API (Materials Project, Citirination, AWFLOW)

L3: Data scraping and data restructuring

(e.g., Beautiful Soup for XML and HTML documents in Python, rvest and xml2 in R)

M2: Data integration (taking data from other groups/work, integrating experimental and computational work)

L1: Data cleaning

Potential resources:

• tool OpenRefine https://librarycarpentry.org/lc-open-refine/01-introduction.html

L2: Data integration: metadata standardization challenges

(dealing with synonymous and homonymous terms, singular/plural word forms, lexical/dialectical variants, etc.)

Potential resources:

• https://www.yamz.net/
• YAMZ in materials science: https://doi.org/10.1162/dint_a_00211

L3: Knowledge representation

data + metadata + rules - adding semantics to structure and syntax: Resource Description Framework (RDF) and schema (RDFS), database and ontology

Potential resources:

• https://github.com/marda-alliance/FAIR_2023_Workshop/
• (tabular data, documents, knowledge graphs)

Manufacturing ontologies:

• https://matportal.org/,
• https://terminology.nfdi4ing.de/ts/

Mechanical testing ontologies:

• https://doi.org/10.1016/j.compind.2023.104016
• https://github.com/emmo-repo/domain-mechanical-testing/blob/master/emmo-mechanical-testing.owl

M3: FAIR data generation

L1: Generate data with metadata

L2: Generate data with metadata and annotate with existing ontology/vocabulary

L3: Prepare FAIR publication

(publication space: zenodo, MDF), or plan for effortless and reproducible work: describe a computational/experimental environment, provide notebook as an essay/journal article, plan for incremental work.

Other resources

• https://github.com/Materials-Data-Science-and-Informatics/awesome-fair
• https://github.com/tilde-lab/awesome-materials-informatics (see standarization initiatives)
• https://howtofair.dk/
• https://codemeta.github.io/user-guide/
• https://github.com/marda-alliance/FAIR_2023_Workshop/
• https://open.hpi.de/courses/semanticweb2016/overview
• https://librarycarpentry.org/Top-10-FAIR/
• Shanahan, H., Hoebelheinrich, N. and Whyte, A., 2021. Progress toward a comprehensive teaching approach to the FAIR data principles. Patterns, 2(10).
• https://openscience101.org/
• https://nasa.github.io/Transform-to-Open-Science/