Scientific information: tools for search and processing of scientific and didactics resources 7404-FIZ-IN

Topic:
L1 1. Progress in computer technology.
2. Current supercomputers and global village.
3. Fiber optics technology, www and new scientific tools.
4. Data acquisition problem in CERN: quest for Higgs boson.

L2 1. The general scheme of scientific method.
2. Computer modeling as “the third approach”
in the scientific methodology.
3. Artificial intelligence and extraction of scientific data.
4. How to interpret images and plots.

L3 1. Data bases – the idea, formal representations,
role in science.
2. Scientific information – journals, archives (arXiv),
Research Gate type services,
personal contacts, conferences.
3. Peer review system. Open access journals.
Digital depositories. Predatory journals.
4. “Web of knowledge” and how to use it.

L4 1. Data bases: How to use “Scopus”.
2. Data acquisition from “Google Scholar”.
3. An example of biological data base: “Protein Data Base”.
4. Examples of genetic, chemical, astronomical,
medical databases (PubMed).

L5 1. Critical approach to scientific data. Flaws in papers.
2. Data acquisition from experiments:
proper methodology and statistics.
3. Problems with data interpretation: examples
from bionanotechnology
4. Data acquisition in social sciences.
Sound and not sound results.
5. Data may lead to legal and ethical problems.
To patent or not to patent.

Total student workload

10h workshop + 5 h homework

Learning outcomes - knowledge

WG2 - Learn about the role of information in science and current trends WG3 - Learn about an important aspect of research methodology WG4 - Learn how to disseminate scientific results, including open access WK6 - Learn about the importance of copyright, the problem of predatory journals WK7 - Learn about patent databases and the need to inform society about scientific results

Learning outcomes - skills

UW1 - learn about publication requirements, novelty items, research hypotheses, quality of methods UW2 - critically search literature databases

Learning outcomes - social competencies

K1 - they acquire competence to critically evaluate the achievements of others KK2 - they learn about the requirements for manuscripts (critical evaluation of their own achievements) KO1 - they learn about the obligation of science to contribute to the development of society, information is important KR1 - they learn to respect intellectual property

Teaching methods

-interactive lecturing; -practicals with databases

Expository teaching methods

- participatory lecture

Exploratory teaching methods

- practical

Type of course

compulsory course

Prerequisites

General understanding of the need for information i modern science

Course coordinators

Wiesław Nowak

Assessment criteria

Assessment methods:

- written examination (test) - W1-W4
fail- <10 pts (<50%)
satisfactory 10-12 pts (50-59%)
satisfactory plus- 13-14 pts (60-69%)
good – 14-15 pts (70-79%)
good plus- 16-17 pts (80-89%)
very good- 18-20 pts (90-100%)

Practical placement

none

Bibliography

Sources:
1. https://www.slideshare.net/bibliotecasUA/scientific-information-sources
2. http://www.sciencemag.org/careers/2016/11/how-keep-scientific-literature
3. https://www.visionlearning.com/en/library/Process-of-Science/49/Utilizing-the-Scientific-Literature/173
4. https://www.visionlearning.com/en/library/Process-of-Science/49/Peer-Review-in-Scientific-Publishing/159
5. Evans, J. A., & Reimer, J. (2009). Open access and global participation in science. Science, 323(5917), 1025-.
6. https://www.nap.edu/read/9692/chapter/3 (Importance and Use of Scientific and Technical Databases).
7. http://ec.europa.eu/research/openscience/pdf/openaccess/npr_report.pdf (Access to and Preservation of Scientific Information in Europe)
8. https://en.wikipedia.org/wiki/List_of_academic_databases_and_search_engines
9. https://commons.lbl.gov/display/rst/Science+Databases+and+Other+Electronic+Resources+listed+Alphabetically
10. https://www.emergingedtech.com/2013/12/top-11-trusted-and-free-search-engines-for-scientific-and-academic-research/
11. https://www.plos.org/ ; https://doaj.org/
12. http://bigthink.com/age-of-engagement/how-to-search-the-scientific-literature-tips-from-an-expert-librarian
13. A.Aryani et al. , “Data Descriptor: A Research Graph dataset for connecting research data repositories using RD-Switchboard” , Scientific Data volume 5, Article number: 180099 (2018) doi:10.1038/sdata.2018.99
14. https://www.nature.com/subjects/databases
15. https://www.elsevier.com/connect/how-big-data-and-ai-can-generate-your-scientific-hypothesis
16. https://towardsdatascience.com/exploring-the-meaning-of-ai-data-science-and-machine-learning-with-the-latest-wikipedia-5fea5f0a2d46
17. http://www.sciencemag.org/news/2017/07/ai-revolution-science
18. www.nextscientist.com/phd-productivity-software/
19. http://www.worldofstatistics.org/wos/pdfs/Statistics&Science-TheLondonWorkshopReport.pdf
20. https://www.visionlearning.com/en/library/Process-of-Science/49/Data-Analysis-and-Interpretation/154

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 7404-FIZ-IN in USOSweb