Being a IT PM in health care, I usually have some of the best war stories among my fellow IT PMs from non-health care. Having to crowbar a door to an Labor & Delivery OR because of door control micro going bad while a women went into labor crisis makes for a great story at the bar... at least until the nuke engineer goes, 'Let me tell you about the time I set the radiation detectors off... dramatic pause... from ten feet away'. However, leaving through that stress sucks.

Talk with you doctor about antifungal medicines and testings. If you have an accute fungal infection, antibiotics can make it worse.

The worst place I had to code is when I had to reprogram a microcontroller in place using a laptop while waste deep in sewage. We had to fix an issue and didn't have enough time to pull it out of the controller box and put it into a test box. So I coded and tested the patch on an emulator then trudged across in waders and types as fast as I could into the terminal window. Thank goodness it took the first shot. Other places we had to code micros... 110 degree utility shaft and a 20 degree roof in high winds. I love my office job now.

I don't think that a full discourse on the basics of theory is required. In sociological work, which is funny to say because I am more of an economist and game theorist in approach, we have to state the strengths, weaknesses, and appropriateness of theory we apply to our research. For example, I use the powers approach in organization analysis, which is considered revolutionary in health care. (All you financial analysts, stop laughing) I have to state why the powers approach of financial analysis is appropriate, what the weakness of that approach, and what benefits it gives. Another example is when I use game theory approaches. I don't have to justify the math, just the method of analysis. Most of my other doctoral cohort is struggling with it at our stage, but I am overloading and progressing faster then the others. I am almost caught up with the next cohort.

What I find that is better about the social sciences in comparison to the hard sciences is that a good slice of us training in dealing with people. I think it improves presentation skills.

That's because they didn't handle the ice core at all. They simply applied a newer computational algorithm to the data collected from the ice core by other scientists years before they published. In fact, the second to last sentence in the paper says "We thank C. Genthon and J. Jouzel for performing the CO2 spectral analysis..." Their papers are, of course, listed at the end with all the other references.

Are you talking about: J R Petit, J Jouzel, D Raynaud, N I Barkov, et al. (1999). Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica. Nature, 399(6735), 429-436. Retrieved April 7, 2009, from ProQuest Medical Library database. (Document ID: 42351682)? Because the phrase is not in there. The paper reads like the researchers were involved in the drilling.

It might be a good idea to read at least the next few sentences before hurling accusations of plagiarism around. When you do, notice that the sentence you quoted is the "topic sentence" of the paragraph. Other sentences in that paragraph serve to expand on individual points in the topic sentence, and they're all referenced. In fact, there are no less than 14 references you can read (they're all listed at the end of the article) to catch up on the science contained in that sentence.

Actually, the statement is "leads it reading as opinion or possible plagerism" which is really close to the truth. The section is clumsily written, almost like they are trying to shove two thesis statements into the same flow. It goes warrant one, warrant two, evidence one, evidence two, synthesis two, synthesis one, closure. Since it is hard to track its flow, the section is a difficult read, which causes the warrant to come across badly, removing support from the general thesis.

Really? How about...

That is data analysis and synthesis. I am asking about the appropriateness and validity of the approach, not the data. If a research does not discuss the appropriateness of their approach, then it is impossible to validate the reasons for the approach.

Limiting the data set in what sense?

I would read p. 430-431 if I were you. They limited the ice core due to volcanic activity without discussing the impact. None of my editors would allow me to get away with that.

Really? how about...

If you want someone to research something, you state 'This study raises these questions for further research' or something similar. Another method is to state 'We suggest... but it would require further research'.

Excellent method in proving my point. Thanks. You are a hack, not a scientist. Every point I made is a valid point in academic discourse and is how grant committees should look at your work.

Petit et al. (1999) takes no effort to describe the methodologies used in handling ice cores, which raises questions on the process used. The line "Ice cores give access to palaeoclimate series that includes local temperature and precipitation rate, moisture source conditions, wind strength and aerosol fluxes of marine, volcanic, terrestrial, cosmogenic and anthropogenic origin" is not attributed, which leads it reading as opinion or possible plagerism (Petit et al., 1999, p. 429). Since it is the bases of the work's analysis, it would make sense to give that sentence more concrete foothold in established theory. There is no discussion on this approach's appropriateness or flaws. There is a good discussion on the research team's reason for limiting the data set but not the impact of that limitation. There is no review of further research questions. It reads as a set of scientists too worried about analysis and not with synthesis. The work is biased to its approach and thusly flawed in its presentation.

In UT 03, we discovered that the nuke would reduce a player's score but not a team score in team death match. We would get the reclaimer and run into a group of the opposing side and shoot the floor.

Actually it is insightful into your work and position. Your blog and posting history on /. framed you differently.

Bias in the Vostok data is actually good exercise for you to work through, which helps enforce the fundamental flaws in your research so you can fix it. In my work, I have to depend on government data sources, since they are the major supplier of health care data. When working with interpreted data, I need to understand the bias of the department that produced the data, which is normally the Centers for Medicare and Medicaid Services (CMS). Their bias is centered around reducing the cost of provided services, which tends to deflate cost reports. That is why I am starting to work on longitudinal studies of the limited data set (lds), which means that some Patient Identification (PI) has been scrubbed out, is is about as raw as it gets. Comparing it to cost reports from CMS, surveys from hospitals, and the public financial documents of hospitals will allow for a more complete picture. However, I have to realize that lds is raw data and has not been fixed for errors in requests and the actions of the Recovery Audit Contractors. Comparison across all of this data will help reduce the amount of errors.

Brevity is not your goal. Conciseness is your goal. I have seen too many of my grad students cut their own throat by trying to be brief. Conversely, I have seen many of them try to beat me down with a wall of text. Usually structure, lack of citations, and the conclusion catch both parties.

What are the problems with the Vostok data? What are the bias in the sources you used for the Vostok data interpretation? You cover it in a haphazard method. What might be a better strategy is to cover it in clear sections. In addition, covering base assumptions in the data and the biases of the producers of the data is appropriate. For example, what are the biases of the IPCC?

When discussing your expertise, which you brought to the forefront by stating that you are a climate scientist, it might be better stating that you are a grad student working on theoretical and computational physics with work in using gravitational analysis on glaciers. When you use general terms with the assumption that people will not understand your work or question your appropriateness, then it makes it easy to question your statement of expertise. For example, I am a doctoral student with a masters with a specialization in health care management and advanced management studies, with significant course work in project management (almost done with that specialization) and organizational and strategic analysis, professional experience in IT project management, systems analysis, and non-profit management, and research work in evidence-based nursing and reimbursement systems. So, if I am talking about organizational factors with reimbursement, then I would be an appropriate expert. Discussing mechanical engineering from the position of an expert would not be appropriate for me. If your credentials were that you read some articles without a background in climatology or even critical analysis, then it would be harder to believe what you say.

Remember that most critical analysis techniques on your work, even when talking with people in public, will be to determine what the appropriateness of your background. When you continue to raise red flags in consideration of critical analysis, such as gross generalizations, you continue to present your side of a discussion as a politically motivated hack, not a scholar. This is exacerbated by the use of the Climate Action Network Canada as a source, a site that shows significant bias.

Actually, I am reading what academics should be reading into your writings. You make bold statements without considerations of what you say, which shows sloppy academic writing skills - which show whether your anonymous or not. I realize that you might not get this from your professors, but in my doctoral program these are important factors since it is stressed that policy makers will make legislation and regulation decisions off our work. We are taught to communicate to all levels and consider our audience.

It is not a sociological claim that all climate scientists are politically motivated. It is opinion. Just as your comments are opinion when not backed up by evidence, analysis, and synthesis. You can't refute someone by making poor statements. When you make easily defeatable statements based on gross generalizations, then you put yourself and any information you provide in question. Editors and peer reviewers should question your work.

I worry about the field of climatology and related research, since they present themselves as walking lockstep and any disagreements being arisen from wankers. This is also my problem with discrimination research, were it is very difficult to get results of no discrimination published. In the health care management, public health, and project management fields, where I specialize, we can sometimes have holy wars over theory. Take someone with more of a sociological background and mix them with someone with more of an economic background and start bets on who wins the fist fight. We don't even try to give a perception that we agree with each other. It would be fruitless. Informed disagreement is what drives scientific advancements.

I think you are just an impassioned student that has been involved in some research and think of yourself as an expert. You just have poor presentation skills, which is quite common among the hard sciences, especially the faddish ones. Work on learning to be eloquent in your communication and you will not get other scholars, such as I, questioning your work due to the lack of presentation skills.

Get and read the book I suggest, study epistemology, and learn how to apply critical thought to what you write. Otherwise, your just an elitist dick who posts on /. and worries too much about the people who disagree with him, since his god complex means that he isn't ever wrong. Good science is not just playing out in the field or lab, but communicating with both the profession and the public in constructive ways. You have to admit that several climatologists have managed to present themselves as being raving lunatics.

First day in epistemology: Who is the source? What are the biases inherit to such sources? Does the author recognize those biases? What epistemological, axiological, ontological, and methodological approaches and beliefs create the theoretical lens that the author uses? These questions show that who the author is and what the author's theoretical lens does matter. I highly suggest you read the The Craft of Research by Booth, Colomb, and Williams.

I already see a set theory flaw within your statement, where you assume that all climatologists are computational physicists. Thurston is a Bioinformatics Specialist, associated with Climate Dynamics group at Oxford. Kininmonth is a meteorologist. Ruddiman is a marine geologist. If I saw an academic paper with such an inclusive statement from a student of mine, then they would told to rewrite.

By a literature review, it seems mostly statistics and modeling, forming a physical science discipline. This would mean that you are casting a very wide net in your definition, making someone studying computational physics an economist, game theorist (which may or may not be an economist), or even a positivist sociologist. Just the measurement and analysis of data does not make one a climatologist. The specialization education makes someone a climatologist, where they study in depth the literature and body of knowledge to understand the field.

For example, Dr. Kielpinski, a significant quantum physicist, would likely have a difficulty understanding game theory, even though the math between the two are similar. It is the detail of the body of knowledge, with field-based assumptions, that brings true understanding of a subject. It is that what makes a scholar of a subject.

Ok, I have to adapt an internet mime for this: Peer reviewed or it didn't happen. Even then it would have to pass the critical analysis test. You present no metrics, sample pool data, or descriptive statistics and yet expect your personnal experience to be taken at face value. You make to many assumptions. Bad scientist. No grant money for you.

It is also hard to take a 'climate scientist' seriously when you qoute yourself as studing computational and theoretical physics, which is outside climatology. A good scientist knows when not to present themselves as an expert in a field they are not.

Had a discussion with one of my security guys and he had a very insightful point. Security is the best when there is a disparate security apparatus, where I might use X, Y, and Z vendors for my security solution and my competitor uses A, B, and C. This creates complexity for malicious hackers due to complexity created by this disparity. By mandating standards, the Government creates a target that security vendors have to reach and have no incentive to go beyond that standard. This might create an unintended consequence that net security value goes down due to similar approaches.

My current research is centered on the reimbursement systems of the Centers for Medicare and Medicaid Services (CMS). I have noticed that CMS and Congress are good at making decisions that focus on some hot button issue, without considering the fallout of those decisions. For example, the Prospective Payment System (PPS) of Medicare and Medicaid has lowered health quality due to changes in incentive. Conversely, the proposed fixes to PPS, Pay for Performance (P4P) and non-payments of Hospital Acquired Conditions (HACs) are regressive in nature, targeting the urban and rural poor disproportionally.

The fact of these unintended consequences that the government creates gives me a nice, warm feeling on the future of cyber security.