Open Science

Question 1

Define Open data, open science, open-source research and open access

Answer 1

Open Data→ items of info
Open research/science→ set of practices
Open-source research→ use of publicity available datasets
Open access->unrestricted (free) online access to scholarly research

Question 2

What are the Ideals of open data?

Answer 2

CUDO
- Communism→ knowledge belongs to everyone
- Universalism→ knowledge evaluated objectively
- Disinterestedness→ not for personal gain
- Organized skepticism→ importance of community scrutiny

Question 3

What are the principles of open data?

Answer 3

FAIR
- Findable→ info easy to find for humans and computers (persistent Digital Object Identifier)
- Accessible→ no payment or gatekeeper to download data
- Interoperable→ readable by machines and humans
- Reusable→ enough details to be replicable

Question 4

What are the benefits of open-source research?

Answer 4

Transparency→ evaluate and verify claims
- minimize and correct errors
- avoid cognitive biases
Efficiency→ can build on each other’s work
Ethics→ verify the honesty of researchers
- avoid falsification and misrepresentation
- but can be a problem with confidentiality
Accessibility→ democratizes knowledge
Educational→ facilitates education
- open dissemination of tools
Alleviating constraints→ some phenomena would be too costly or unethical to conduct in lab

Question 5

What are the challenges of open-source research?

Answer 5

Large amounts of data
Limited control over data→ cannot perform new experiment
Unstructured data
Inconsistencies across datasets
Misinformation sources
Ethical dilemmas
- ownership and rights
- privacy

Question 6

Define Web-based research, crowd-sourced and citizen-science

Answer 6

Web based= online research
Crowd-sourced→ outsourcing of tasks to a large group of people usually paid
Citizen-science→ type of crowdsourcing that involves the public in scientific research

Question 7

How to design a web-based research?

Answer 7

• One-item-one-screen design→ only one item per screen
• Seriousness check→ ask participants if gonna be serious
• Subsampling check
• Multiple site entry→ recruit people from different location
• Warm-up→ warm participants up
• Attention checks→ ex: ask participant to click on one key to continue

Question 8

What are the strengths of both crowdsourced platforms and supervised laboratory setting?

Answer 8

Crowdsourced platforms (off-site)→ external validity
- Demographically representative
- Quick and accurate data collection
- Increases participant convenience and anonymity
- Promotes methodological standardization -> replicability
- Eco-friendly and economical

Supervised laboratory setting (on-site)→ internal validity
- Monitor participant impairment (e.g. fatigue)
- Monitor carelessness, independence, and distraction (e.g. multi-tasking)
- Increase conscientious participation
- Address questions and provide clarifications
—>influenced by location and sample population