Friday, October 23, 2009
NextBio is the provider of an innovative platform that enables life science researchers to search, discover, and share knowledge locked within public and proprietary data. NextBio's platform seamlessly combines powerful tools with unique correlated content to transform information into knowledge, providing the foundation for new scientific discoveries. (Source: Nextbio website)
Novoseek
Novoseek is a novel search engine for the biomedical sciences. It indexes biomedical literature & US grants using text mining technology. It extracts and retrieves key biomedical concepts and bibliographic information for a keyword search. One can highlight concepts and filter results quickly. It has many more features....
Video courtesy: Novoseek website
Tuesday, September 8, 2009
Statistical analysis is for PhDs, right? Well ...
Some researcher believe that statistical analysis is something which is so complex that people should only try to touch the subject after a 5 year PhD program. Well, like any other subject, statistics has multiple levels of knowledge and by absolutely no means is it a one or nothing type of deal like many statisticians would like us to think.
For starters, let's state the obvious: A little knowledge can be dangerous if you work alone, but in modern clinical and translational research, researchers nearly always work in interdisciplinary groups with data analysis specialists. In an environment like that a little knowledge is not only a good thing, but an essential skill to establish a successful communication with your peers. Having established that knowing some statistics is better than knowing none, where should you get started?
Two areas are essential. First, you should know in which situations a statistical test should be used. Although this might initially sound like a daunting task, the basic knowledge can be simple, allowing you to open a bilateral discussion with your data analyst. The decision algorithms on how to choose a test are largely determined by the type of variables involved (continuous, categorical, representing time, etc), their distribution (normal, count, etc), and the role each variable is representing in the research question (outcome, predictor, confounder, etc). Does it mean you should blindly follow what these algorithms tell you to do. No, you are working in an interdisciplinary team and so everything can be discussed with your data analysis specialists, but knowing some general concepts is essential for you to start a discussion and not simply accept what they say as a true and "sent from heaven".
Second, it is important to understand how results from individual statistical tests should be interpreted. For example, when a survival regression model gives you a point estimate, called hazard ratio, with 95% confidence intervals, you should be able to understand what exactly that means in the context of the tables of your paper and of your specific research question. Luckily, this interpretation is easy if you have access to a few examples from previous tables and graphics from other articles using the same statistical test. Of course, your initial interpretation should always be confirmed by other expert data analysts in your interdisciplinary group.
Knowing the indication of statistical tests and interpretation of their results is just the start. That said, these skills constitute the very basic knowledge that you can use to start conducting research while working with your peers. These basic tools are all explained in detail in the context of your individual research project during our Research Coaching Program, which you can learn more about at [Research on Research].
For starters, let's state the obvious: A little knowledge can be dangerous if you work alone, but in modern clinical and translational research, researchers nearly always work in interdisciplinary groups with data analysis specialists. In an environment like that a little knowledge is not only a good thing, but an essential skill to establish a successful communication with your peers. Having established that knowing some statistics is better than knowing none, where should you get started?
Two areas are essential. First, you should know in which situations a statistical test should be used. Although this might initially sound like a daunting task, the basic knowledge can be simple, allowing you to open a bilateral discussion with your data analyst. The decision algorithms on how to choose a test are largely determined by the type of variables involved (continuous, categorical, representing time, etc), their distribution (normal, count, etc), and the role each variable is representing in the research question (outcome, predictor, confounder, etc). Does it mean you should blindly follow what these algorithms tell you to do. No, you are working in an interdisciplinary team and so everything can be discussed with your data analysis specialists, but knowing some general concepts is essential for you to start a discussion and not simply accept what they say as a true and "sent from heaven".
Second, it is important to understand how results from individual statistical tests should be interpreted. For example, when a survival regression model gives you a point estimate, called hazard ratio, with 95% confidence intervals, you should be able to understand what exactly that means in the context of the tables of your paper and of your specific research question. Luckily, this interpretation is easy if you have access to a few examples from previous tables and graphics from other articles using the same statistical test. Of course, your initial interpretation should always be confirmed by other expert data analysts in your interdisciplinary group.
Knowing the indication of statistical tests and interpretation of their results is just the start. That said, these skills constitute the very basic knowledge that you can use to start conducting research while working with your peers. These basic tools are all explained in detail in the context of your individual research project during our Research Coaching Program, which you can learn more about at [Research on Research].
Scientific literature review: Doing the necessary, no more and definitely no less
Most people think of a literature review for a scientific paper as something equivalent to a gigantic effort that has to encompass everything under the sky. Well, a literature review definitely has to be complete and it cannot miss important points, but overdoing it is also a mistake since it will waste time that could be used in other areas of your manuscript. So, how do you determine what is the right amount?
First and foremost, your search should use efficient tools that will allow you to quickly get to the core of the matter. In our Research Coaching Program we have gathered information on a large number of tools that can streamline the process of getting to the central articles in a topic. Slides with information on these tools can be found at [add link]
Second, your literature review should be directed at the areas of the manuscript that need to be supported by citations. Although this is fully explained in the Manuscript Template section of our Research Coaching program [link], these areas are usually the significance of the topic, a literature review emphasizing why your paper is needed in face of a gap in the literature, and a review of articles agreeing or disagreeing with your main study findings. These topics are absolutely essential, and missing any of them would be considered a serious flaw. Although additional areas could be included, it is important that researchers don't overdo it, since the article might lose its focus and start digressing into areas that just don't belong in the manuscript.
For more details on our Research Coaching program, you might want to visit our site at [link]
First and foremost, your search should use efficient tools that will allow you to quickly get to the core of the matter. In our Research Coaching Program we have gathered information on a large number of tools that can streamline the process of getting to the central articles in a topic. Slides with information on these tools can be found at [add link]
Second, your literature review should be directed at the areas of the manuscript that need to be supported by citations. Although this is fully explained in the Manuscript Template section of our Research Coaching program [link], these areas are usually the significance of the topic, a literature review emphasizing why your paper is needed in face of a gap in the literature, and a review of articles agreeing or disagreeing with your main study findings. These topics are absolutely essential, and missing any of them would be considered a serious flaw. Although additional areas could be included, it is important that researchers don't overdo it, since the article might lose its focus and start digressing into areas that just don't belong in the manuscript.
For more details on our Research Coaching program, you might want to visit our site at [link]
Why is scientific writing so hard?
Writing scientific manuscripts is frequently associated with a painful experience. It is easy to find a thousand reasons why we should not be doing it, and this just drags the manuscript to being completed tomorrow. But why is it so?
Apparently the main reason for the writing experience being so difficult is that there is a lot going on when researchers try to write their manuscripts. To name a few factors, they have to be concerned with focusing on their main topic, grammar, vocabulary, getting the facts right, convincing their audience, citing others where appropriate, following guidelines of a specific journal, and the list keeps going. With so much going on, it's no surprise that writing carries a huge burden, more specifically the cognitive burden of having to manage multiple tasks at once. But can anything be done?
In the research coaching program we have developed a platform called Manuscript Templates that is specifically designed to alleviate this burden. Put simply, researchers are given simple and straightforward steps that allow them to start writing the manuscript from day one in the project. At each step of the manuscript, they have very clear guidelines on how each section should include or not include, examples of well-written manuscripts similar in structure to theirs, and a constant feedback from more experienced researchers that keeps guiding them into a manuscript that will be considered well-written by reviewers and peers.
If this sounds of interest, you might want to check further details on the Research Coaching Program at [http://www.researchonresearch.org/?q=node/107]
Apparently the main reason for the writing experience being so difficult is that there is a lot going on when researchers try to write their manuscripts. To name a few factors, they have to be concerned with focusing on their main topic, grammar, vocabulary, getting the facts right, convincing their audience, citing others where appropriate, following guidelines of a specific journal, and the list keeps going. With so much going on, it's no surprise that writing carries a huge burden, more specifically the cognitive burden of having to manage multiple tasks at once. But can anything be done?
In the research coaching program we have developed a platform called Manuscript Templates that is specifically designed to alleviate this burden. Put simply, researchers are given simple and straightforward steps that allow them to start writing the manuscript from day one in the project. At each step of the manuscript, they have very clear guidelines on how each section should include or not include, examples of well-written manuscripts similar in structure to theirs, and a constant feedback from more experienced researchers that keeps guiding them into a manuscript that will be considered well-written by reviewers and peers.
If this sounds of interest, you might want to check further details on the Research Coaching Program at [http://www.researchonresearch.org/?q=node/107]
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