Category Archives: How To
The following will allow a visual project-level overview of what is on your plate. It is comprised of 3 files: a python script, a CSS file and a PDF README file (a copy of this post). It can be executed from the terminal or as a system service using the macOS Automator app (thus never needing to open a terminal and can be associated to a keyboard short cut).
*** read below for the download link ***
For sometimes now, I went fully digital when attending meetings (one on one, research, scientific congress or even committee meetings). I adopted the iPad for that task just a few month after it came out on the market. There are multiple choices of apps out there for note taking. Apple Notes actually is probably the most simple, and quite efficient, one. Since I bring all of my meeting documents with me in DevonThink To Go or DTTG (see my e-office series to see how I make this work), I now take almost all of my meeting notes directly in DTTG. DTTG sync with DevonThink Pro Office (DTPO) edition on my Mac. I am looking forward for the new sync features of DTTG 2.0 but for now this works really fine.
The e-Office series now has a new home. All of the posts related to getting an efficient digital office workflow have been gather in single page. If you look at the menu-bar above you will find the dedicated button or you can simply click here 😉
In the previous post I was describing the free Zotero scientific manuscript management software. Through a comment via this blog and others, I was pointed out that there are some solutions for access to your PDFs on the go.
If, as starting graduate students, you are following my first key advice of reading on a regular basis scientific manuscripts related to your field of research in general and your project in particular, you’ve probably reach an obvious observation: you are collecting a large number of PDF files very quickly.
There are, of course, a few more observations to be made:
In the previous posts, we went over the hardware requirements and selection, software and finally mobile software. It is now time to address the sources of digital documents, the true inputs of the digital workflow.
Not so long ago, there was a single inbox for all incoming “stuff” that requires your attention. Stuff is here define as anything that needs for you to decide what to do with it, including throwing in the garbage. In the analog world, that single inbox was the good old paper tray: correspondence, various documents, business cards, memos, telephone notes… everything ended-up there for further processing.
This semester I thought an undergraduate laboratory to last year engineering students. These will be entering the job market (or pursue graduate studies) in a few days. I was really surprised going over the lab reports how very basic things are omitted. Things like error analysis or displaying error bars on figures. How can one make any valid statement on the results or general behavior of data without them?
While the final format is not unique a few elements are quite common
- Your report will likely contain an introduction, review of the theory, method and materials with measurement set-up, results presented and discussed and a conclusion.
- If a calibration of an instrument is involved, shows the graph, give the mathematical relationship with the fit parameters and the quality of the fit (R2 at the very least). Do not forget the error bars.
- Make your figures readable! The experimental measurements should be represented by symbols while any fits should be represented by lines. Please avoid Excel. It can make acceptable figures if you work hard on them but specialized software (even GNUplot) are way better.
- Do not underestimate the power of well design tables to present a resume of multiple measurements. Going over 10 or more pages of lab report to be able to sum your results is not a good way to get extra points. Furthermore, it is likely that this exercise will lead you to make a better discussion of (or find obvious mistakes in) your data and analyses.
- To present (or unearth) trend in data, nothing beat graphical representations: do figures as often as you deem necessary and decide afterward to include them or not in the report. Again you might uncover interesting things in your data, which you have not thought of.
- If data are expected to be compared to a theoretical model, display the results of that model along with the data (see previous comment!). If the model can be fitted to the data, again give the fit parameters and the quality of the fit. If the fit parameters are related to physical values, they also need to be discussed.
- If data are to be compared to expected values or a theoretical model, your discussion should be quantitative ( not qualitative). Is the match perfect (within the error of x%)? If deviation are important, gives the deviation either in the form of absolute or relative differences or even replotting the data relative to the expected reference values or theoretical model. In any case, you are expected to make your discussion quantitative be default.
- Any deviation or unexpected behavior in the data must be discussed (even if you do not know the reason for a deviation)
While the above might seems a lot of work, it will in fact save you work when it is time to write your discussion, not mentioning that you should see an immediate improvement of your grades.