Open Science is a movement to make scientific research, its data and dissemination accessible to all levels of society. This movement considers aspects such as Open Access, Open Data, Reproducible Research and Open Software. Each of these aspects presents discreteness that need to be evaluated and discussed by the scientific community so that guidelines are established that facilitate the dissemination of scientific information. The great challenge is to establish effective and efficient practices that allow journals to add these demands in their editorial processes, so as not only to allow data, software and methods to be accessible, but also to encourage the community to do so. Considering these questions, this panel has as a proposal to discuss important aspects about the advancement of research communication. Some of these aspects are placed in the SciELO indexing criteria, as is the case of referencing research materials in favor of transparency and reproducibility. Syllabus FAIR criteria, concepts and implementation; challenges for the publication of data and methods; institutional policies for open data; adoption of TOP guidelines (Transparency and Openness Promotion); software repositories; thematic areas data repositories.

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