This is the first draft for my upcoming talk in Sarasehan Geologi Populer, Badan Geologi, 13th March 2015.

1. Geological analysis with open-source software:
case Cikapundung River
Event: Sarasehan Geologi Populer, Badan Geologi Indonesia
Dasapta Erwin Irawan
13th March 2015

2. Part one: Introduction

3. Slide license
Creative commons:
need attribution (BY)
for non-commercial purposes only (NC)
can be copied, modified (SA, share-alike)

4. A bit about me
Personal data
Name: Dasapta Erwin Irawan
Job: Lecturer/researcher at Groundwater Engineering Program, ITB
Education (Geology, ITB):
Undergrad: 1994-1998
Master: 1999-2001
PhD: 2005-2009

5. A bit about me
Visiting program
Year: 2009 (1 month)
Institution: Center for Environmental Remote Sensing, Chiba
University (2009)
Supervisor: Prof. Josaphat T.S. Sumantyo
Research: remote sensing for hydrological purposes
Year: Feb 2014-Feb 2015
Institution: Faculty of Agriculture and Environment, University
of Sydney
Supervisor: Dr. Willem Vervoort
Research: hydrological modeling in R

6. A bit about me
Research
hydrogeology
hydrochemistry
multivariate [statistical] analysis

7. A bit about me
Media social
Website: - R and Linux - Writing - SlideShare
Twitter: @dasaptaerwin
Email: d_erwin_irawan[at]yahoo[dot]com

8. Skills in Geology

9. Essential skills for geologist

10. Software skills (not free version)
office: Microsoft Office (Word, Excel, ppt) -> annual
subscription (from USD 10 per month)
citation and referencing: EndNote -> from USD 250
statistical: Minitab, SPSS, Statistica, Stata -> basic version
from USD 700 (2012)
spatial / GIS: ArcGIS, Mapinfo, etc -> annual subscription
(basic version from USD 100 per year)
Sources:
Openwetware
ESRI

11. Software skills (free equivalent)
office: OpenOffice or LibreOffice
citation and referencing: Zotero, Mendeley, etc
statistical: R and R Studio, Orange Data Mining, PSPP, etc
GIS: QGIS, GRASSGIS, R **

12. Why open source?
free as breathing
mostly cross-platform (Linux, Mac, Win)
strong community, hence rapid development
supporting reproducibility

13. What is reproducibility in science?
Every step can be:
re-do
re-analysed and re-evaluate
re-developed

14. What is reproducibility in science?
Those principles are applied to:
data (items and locations)
software used in the analyses:
- each software has distinct feature and algorithm
- what would happen if not everyone could purchase the s

15. End of part one
This slides were made using open-source tools
Ubuntu Linux (14.04)
R
Dia flowcharter
Gimp image editor