ranked science-ranked institutions...ranked people? What is really important making science? To be competitive is important, but what about knowledge? When you had choice an excellent institution, with the goal of to be prepared in the best postgraduate program, but be careful, because YOU will be tested and selected also.
Gustavo Hernandez show me a interesting place for computational biologist, http://gmod.org/wiki/Main_Page, I will reproduce some paragraph from this site, which I consider closer to own ambitions in science.
"GMOD has been created for biologists and in the real world it's used by biologists. However, the creators of GMOD are mostly not practicing biologists and the look and the feel of most GMOD documentation reflects this. What we will attempt to do is discuss GMOD from the researchers' perspective. This does not simply mean describe what the software does. If you look, for example, at a typical GBrowse page like this GBrowse view of human chromosome 7 you'll understand immediately what GBrowse is built to do, and a few more minutes of clicking and scrolling will reveal all sorts of useful ways to display and query the data. A modern biologist knows a great deal about bioinformatics functionality already. What we're more concerned with here are the practical details. Like given the data I have what database should I use? or do I even need a database? Or how hard is this going to be?
In our experience we find that most biologists want to focus on the science. They may have little knowledge of programming languages or databases, and only passing interest in the IT minutiae. They have deep knowledge of their own data, needless to say, and know how data like their own can be viewed and analyzed. What they want to know is how to create their own useful set of tools for their own data in as efficient a way as possible. And when this tool set is created they want to rest assured that their platform can be easily maintained in an environment where resources may be limited. We will attempt to address these sorts of questions"
I recommend test these plataforms and enjoy doing science and data analysis.
Also Juan Caballero explain simples rules for being efficients using software and analyzing data, please check it. http://linxe-eye.blogspot.com/2008/10/rules-for-biocomputing-happiness.html Nowadays there are a hug explosion of data, we need more efficient using the right tool and the best database Cesar.
When we look at the domesticated plants, we also review and must to be grateful with the people that worked into the process. Now we enjoy the Banana, maize, wheat, potato, barley, etc. We must consider what we have to do for the next generation, our work could improve the life style for the human being in the next 400 years, please think and act. think.
It is time to learn other language for bioinformatics, some people said we shall learn phyton, other said we could learn R, I will start with R, some linux commands to made scripts, and then Phyton, I do not suggest that we must invent the wheal, but for bioinformatic introduction, could be so useful to know differents ways for solving and analyzing biological problems
7 comments:
How much it cost make science? Every day we spent time, energy, and reagents to get a better understanding of biological problems, but, it is?
ranked science-ranked institutions...ranked people? What is really important making science? To be competitive is important, but what about knowledge? When you had choice an excellent institution, with the goal of to be prepared in the best postgraduate program, but be careful, because YOU will be tested and selected also.
what about quatum data!
the time of big information, zero background, the fastest information, is coming!
Gustavo Hernandez show me a interesting place for computational biologist, http://gmod.org/wiki/Main_Page, I will reproduce some paragraph from this site, which I consider closer to own ambitions in science.
"GMOD has been created for biologists and in the real world it's used by biologists. However, the creators of GMOD are mostly not practicing biologists and the look and the feel of most GMOD documentation reflects this. What we will attempt to do is discuss GMOD from the researchers' perspective. This does not simply mean describe what the software does. If you look, for example, at a typical GBrowse page like this GBrowse view of human chromosome 7 you'll understand immediately what GBrowse is built to do, and a few more minutes of clicking and scrolling will reveal all sorts of useful ways to display and query the data. A modern biologist knows a great deal about bioinformatics functionality already. What we're more concerned with here are the practical details. Like given the data I have what database should I use? or do I even need a database? Or how hard is this going to be?
In our experience we find that most biologists want to focus on the science. They may have little knowledge of programming languages or databases, and only passing interest in the IT minutiae. They have deep knowledge of their own data, needless to say, and know how data like their own can be viewed and analyzed. What they want to know is how to create their own useful set of tools for their own data in as efficient a way as possible. And when this tool set is created they want to rest assured that their platform can be easily maintained in an environment where resources may be limited. We will attempt to address these sorts of questions"
I recommend test these plataforms and enjoy doing science and data analysis.
Also Juan Caballero explain simples rules for being efficients using software and analyzing data, please check it. http://linxe-eye.blogspot.com/2008/10/rules-for-biocomputing-happiness.html
Nowadays there are a hug explosion of data, we need more efficient using the right tool and the best database
Cesar.
It seems that Firefox is comming seriously, and improve version 3 faster and more secure, google chrome is in troubles
When we look at the domesticated plants, we also review and must to be grateful with the people that worked into the process. Now we enjoy the Banana, maize, wheat, potato, barley, etc.
We must consider what we have to do for the next generation, our work could improve the life style for the human being in the next 400 years, please think and act.
think.
It is time to learn other language for bioinformatics, some people said we shall learn phyton, other said we could learn R, I will start with R, some linux commands to made scripts, and then Phyton, I do not suggest that we must invent the wheal, but for bioinformatic introduction, could be so useful to know differents ways for solving and analyzing biological problems
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