Fundamentals of digital scholarship

Definitions

• Define digital scholarship in terms of activities and objects
  • Understand how DS is different from traditional scholarship
  • Know that DS is not just ‘adding computers to scholarship’
• Explain the relationships between Digital Scholarship and Open Science, RDM and Data Science

Project planning and management

• Explain how digital scholarship activities fit the definition of a project
  • Identify example digital scholarship projects
  • Understand the difference between project, product and service in the context of DS
• Find (technical) resources to get the project going
• Plan for the end of the project
  • Understand that projects end at some point and what that means for project outcomes

Random

Persistent identification

• understand link rot and content drift in web pages
• determine whether a persistent identifier system supports persistent identification
• explain what makes an identifier ‘persistent’ or ‘persistable’

Research Data basics

• translate research questions into data needs and potential data sources
• recognise/know stages data life cycle (and research life cycle?)
• understand how a character encoding relates bytes in files to characters from a certain set
  • identify that Unicode is a character set and that e.g. UTF-8 is a character encoding
  • identify that the Unicode Consortium determines what characters (and emoji) are accepted into Unicode
• understand the difference between text and fonts
• file formats and their characteristics
  • plain text
  • HTML
  • JSON
  • Word documents
  • JPEG, PNG, SVG
  • PDF
• Understand the difference between markup and layout
• Understand the differences between static and dynamic web pages
• data in files versus data mediated through interfaces (e.g. DBMS)

Obtaining data

• Identify data sources
• Understand license terms
• Understand effects of copying data or linking to data
• get data
  • download or request from archive
  • use provided API
  • scrape from web pages
• understand data provenance
  • identify common ways to record provenance
    • file metadata
    • commonly provided/used files in a folder
  • understand the influence of file templates on correctness/completeness of provenance
    • people do not always care to provide metadata
• determine data completeness
  • identify measures for data completeness

Data management

• understand the goals and content of a data management plan
• storage
• access control
  • cloud storage basics
  • encryption basics

Data criticism

• Identify data as anything that serves as evidence in research
• perform data modelling
  • understand how data relates to ‘real-world’ entities
• perform data quality assurance
  • understand how data quality can be assured
  • apply data quality checking
• understand data ethics
  • identify potential privacy issues
• understand legal rights: copyright, database rights
  • identify basic rules: what can and cannot be copyrighted?
  • acknowledge that experts are needed to determine copyright applicability
• determine whether data is fit for (your) purpose

Data Science

Methods

• list common data science methods
  • Text and data mining
  • Network analysis
  • Visualisation
  • Annotation
  • GIS
  • software development
  • Machine learning

Data transformation

• understand that you likely need to prepare (preprocess) data before use

Tool criticism

• distinguishing between hosted tools and locally running tools
  • understand that hosted tools need data in their control
• identify tools that are fit for purpose
  • find a description of the tool in its manual or in reviews
• identifying combinations of tools that solve problems
• know the four rights users have with FLOSS
• understand difference between open-source and propriety software
• influence of algorithms on research

Attitudes

• understand the trade-off between efforts needed for automation and repeating a task manually
• understand that not every new shiny tool is necessarily better
• learn from code forums like StackOverflow
• support folks who ask good questions
  • when asking a question, explain your thought process and show your efforts
  • help people ask good questions

Machine learning

• know that supervised machine learning is based on models, training using data and predicting results for unseen data
• understand that (especially in deep learning) how ML models predict often not easily explainable

Collaborating

• collaborative data maintenance
• preventing data conflicts
• resolving data conflicts
• agree on using a system for exchanging information in a project
• keep confidential data safe

Sharing results and credits

• Understand that (technical) support is part of the research and deserves credit too
• Identify the legal owner(s) of an artifact

Publishing

• know that publishing is making something public
  • know that some disciplines have narrower/other definitions of publishing
  • understand the paradox that it is hard to remove things from the internet and that it is hard to keep things available online for the long term
• identify various ways of publishing DS
  • web
  • data and publication repositories
  • code repositories
  • (binary) package repositories
  • preprint
  • traditional publisher
  • data paper
• identify publishing activities that cost money
• identify who is paying for DS activities and why

FAIR

• know that FAIR does not imply Open (or vice versa)
• explain how produced research objects relate
• describe the provenance of produced results
• list terms commonly used in research to describe research objects
• understand the meaning of generic data terms
  • dataset, file, metadata

Accessibility

• acknowledge that research should be accessible and understandable to anyone regardless of (dis)abilities (FIXME)
• apply basic guidelines for making text content accessible
  • use headings
  • provide relevant textual descriptions of images, videos and audio elements