The document discusses the intersection of open science and data science, highlighting the importance of open access to research data and the principles of FAIR data (Findable, Accessible, Interoperable, Reusable). It addresses the challenges faced by both fields, including issues related to privacy, reproducibility, and data quality. The document emphasizes the need for proper curation, visualization, and the role of interdisciplinary approaches in extracting knowledge from data.

1. Open Science
meets
Data Science:
Some challenges to be faced
Claudia Bauzer Medeiros
Institute of Computing – Unicamp

2. Open Science – G7 Priority
1. Human Capital Formation – research and
innovation
2. Financing – inclusive science, research and
innovation
3. Global Research Infrastructures
4. Open Science

3. RDA - https://www.rd-alliance.org

4. Open Science
• Open to all the access to scientific research
• How ??????
• Why ????
• What ???????

5. COLLABORATION THROUGH DATA –
OPEN SCIENCE = OPEN DATA

6. 6/10000
Open Science – slide
adapted from Gray
Respost
Perguntas
Data driven-science
Models
Simulations
Papers
Files
Experiments
Instruments
XXXXX

7. National Academies of Sciences,
Engineering, Medicine
July 2018
Open science =
Open access = papers
Open data
Open methods = open source

8. What is Open Data?
• “What is OPEN DIGITAL DATA”
– Share “everything”? Not necessarily
• Everyone can
– Discover if data exist
– Discover how to obtain them
Under constraints – security, confidentiality,
ethics, intellectual property
8

9. OPEN SCIENCE – OPEN METADATA

10. HOW??? Datacite.org
(Find, share, cite, connect)

11. Open science requires FAIR Data
• Findable
• Accessible
• Interoperable
• Reusable
• ??? Have you fairicized your data???
11

12. Open Science – Basic concepts
Curation
Preservation
Data (which?)
Processes: Workflows Reproducibility
Cyberinfrastructure
Reusability
Provenance

13. WHAT ABOUT DATA SCIENCE?

14. The “sexiest job of the 21st century”
14
@Altigran Silva, Brasnam’18 keynote

15. Data Science (CACM)
• Processes and systems to extract knowledge or
insight from data in various forms and translate it
into action.
• Interdisciplinary field that integrates approaches
from statistics, data mining, predictive analytics
• Incorporates advances in scalable computing and
data management.
Berman et al. CACM 61(4), April 2018
@Altigran Silva, Brasnam’18 keynote
15

16. Data Science: Reality (FORBES 2016)
• 80% of time of data scientists spent on data
pre-processing, cleansing, etc.
16
https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says

17. Open Science meets data science
Open science
• Fairicized data
• Concerns with
– Privacy
– Accessibility
– Quality
– Provenance
– Reproducibility
WHERE, HOW, will be used?
Data Science
• Mine and correlate data
• Concerns with
– Pattern extraction
– Algorithmic efficiency
– Production of knowledge
– Ask interesting questions
from data
Big data and VVVVVVVVVV…

18. Open Science meets Data Science
Open science
• Fairicized data
• Concerns with
– Privacy
– Accessibility
– Quality
– Provenance
– Reproducibility
WHERE, HOW, will be used?
Data Science
• Mine and correlate data
• Concerns with
– Pattern extraction
– Algorithmic efficiency
– Production of knowledge
– Ask interesting questions
from data
Big data and VVVVVVVVVV…

19. Open Science meets Data Science
Open science
• Fairicized data
• Concerns with
– Privacy
– Accessibility
– Quality
– Provenance
– Reproducibility
WHERE, HOW, will be used?
Data Science
• Mine and correlate data
• Concerns with
– Pattern extraction
– Algorithmic efficiency
– Production of knowledge
– Ask interesting questions
from data
Big data and VVVVVVVVVV…

20. WHAT ABOUT
VISUALIZATION????

21. Challenges
• Fairicization
• Curation
• Visualization!!!!!!!!!!!!!!
• For xxx science to work, interpretation is
needed (who are the “appropriate” experts?)

22. www.fapesp.br/gestaodedados

23. Obrigada!!!!
cmbm@ic.unicamp.br