How I use the Zotero reference manager for collaborative grants or manuscripts

Why Zotero?

Zotero is an excellent and free reference manager that is my go-to for writing grants and manuscripts. It has some very handy features, like word processor plugins, web browser plugins that will grab PDF versions of documents if available, and ability to share “group libraries” with collaborators. It has all of the standard features of reference managers, like auto-formatting of references to meet submission requirements, automatic renumbering of in-line references, etc.

There are some silly things about Zotero’s initial setup that are important to get out of the way. For example, you don’t necessarily need to have a Zotero account to use Zotero (or at least you didn’t when I used it the first time). Make sure that you read about how to get set up with Zotero under the “Zotero” heading on this page. After you do that, come back here and read on!

How to use Zotero for collaborative projects

Zotero works well with MS Word and Google Docs. Examples here are taken from MS Word, but are also applicable to Google Docs. The main difference between Google Docs and MS Word is that the web browser plugin is also the Google Docs plugin. MS Word has a plugin separate from the web browser plugin. Regardless, whenever you use a Zotero plugin (eg the MS Word, Google Docs, or browser extensions), you also need to have the Zotero desktop app open. You’ll get an error if you try to insert a reference into a document or snag a reference from PubMed/a journal website if the Zotero desktop app isn’t also open.

Organizing your folders (“collections”) and subfolders (“subcollections”).

In your desktop app, navigate to the shared library that I’ll send you. Make folders/collections or subfolders/subcollections in there to help stay organized. To make a new folder/collection, right click on the shared group library and click “New Collection…”. To make subfolders/subcollections, right click on that new folder/subfolder that you made.

I suggest making collections/folders by section of your document, and numbering them so they stay in order, so: “01 Introduction”, “02 Methods”, “03 Results”, and “04 Discussion”. If you are writing something that doesn’t follow a usual flow (eg an opinion piece), number/name things by the major sections in your outline. You can always rename these folders/collections and renumber them so they show up in order.

Now, within each of these folders/collections, make specific groupings of subfolders/subcollections by topic. For example, in the introduction, you might have a sentence talking about the epidemiology/population prevalence of hypertension, then the costs (eg DALY lost) of hypertension, then an overview of the pathophysiology of hypertension, then how some biomarker relates to blood pressure. I recommend having a subfolder for each of these concepts separately in the “01 introduction” folder. You can also order these with numbers or letters, but it also might make sense to keep them unordered if you aren’t sure of how the introduction (or any other section) will flow.

Now repeat this for all of the other subfolders. The results folder might be pretty thin because usually (for me at least) there aren’t many references in that section. For the “discussion section”, I recommend including the suggested sections from my “your first epidemiology manuscript” example under “Download” here. It’ll look like this when you are done:

Next: Grabbing citations.

You need to install the Zotero browser extension, and make sure that you have the Zotero desktop app open when you are grabbing citations. I STRONGLY RECOMMEND GRABBING JOURNAL CITATIONS FROM PubMed AND ONLY PubMed. (For textbooks, Google Books works quite well.) Zotero uses metadata from websites to populate the reference. PubMed’s metadata is pristine. Journal websites have metadata, but it’s inconsistent and often incomplete. So, stick with PubMed when grabbing citations.

The first step is to find your article on PubMed. Let’s say you want to grab the AHA’s statistical update document, and using Google you find it on the Journal’s website. Welp, that’s not pubmed so don’t even think about grabbing the reference from here.

Instead, highlight the title and search Google again, appending the word “PubMed” to the search. This will get you to the Pubmed listing for that article.

Now this is very important! Go over to your Zotero desktop app and choose the subcollection/subfolder you want this to go in. This will be in the Introduction/Population Prevalence of HTN subfolder. You’ll notice it’s empty.

Now go back to the PubMed page for your document and click the Zotero plugin button.

Now when you go back to the Zotero Desktop app, you’ll see that the AHA Statistical Update is now saved in your “population prevalence of HTN” folder. If a PDF is available through Unpaywalled (a separate service that’s integrated into Zotero that grabs free/legal copies of journal PDFs), then a PDF will be saved as well.

When you grab references, make sure to sort them into a specific folder along the way. You’ll thank yourself later.

Next: Inserting citations in an MS Word document

Open up your manuscript file in MS Word. Make sure you click/set your cursor in the place in your document where you want the citation to land. (I’m a “citation goes after the period and not before the period” guy myself.) Then, click the Zotero tab. (If you don’t see the Zotero tab, you might need to try to reboot, or manually install the plugin. See the “Setting Things Up/Zotero” section above.) Now, click the “Add/Edit Citation” button. If this is the first citation/reference you are adding to this document, you’ll be prompted to select a formatting style, just pick anything since you can always change it later (I like the American Medical Association one to start with). You won’t see this pop up when adding other references.

Now you’ll see the hovering Zotero window. This isn’t a part of MS Word, it’s actually the Zotero desktop app. Sometimes this gets lost among your various programs/windows on your desktop and you need to go find it, it’ll be under the Zotero icon on your taskbar on windows. Or Alt+Tab until you find it. Anyway, this allows you to find a citation by text search (ie, by typing in the author name or title), but if you select the dropdown menu here, you can use the “classic view” and manually grab citations from your subcollections/subfolders. I recommend adding citations through the classic view.

In the classic view you will see all of your subcollections/subfolders. Navigate to your subcollection/subfolder of interest and click on the citation that you’d like to insert and hit “okay”. Notice in the “Classic View”, you can select multiple references at the same time by clicking the “Multiple Sources…” button at the bottom.

Now your citation is in-line! See the floating “1” after the first sentence. But where is the Reference list? Let’s plop one in. I added a new heading for references in MS Word and we’ll add it there. Click on the line after your “references” header, go to the zotero tab, and click “add/edit bibliography”.

End product is below. This reference list will update while you insert references in your manuscript. The reference numbers will also update automatically as you go.

Microsoft OneDrive

This is through LCOM. Not UVM, not your personal account.

  1. Open the OneDrive on your computer and sign in with your LCOM credentials if you aren’t already.
  2. I’ll share a research folder with you. You’ll need to sync it with your computer. To do that, go to onedrive.com, log in with your LCOM credentials (firstname dot lastname at med dot uvm dot edu). After you log in, you’ll be on the landing page for OneDrive. Click “Shared” on the left column. Find the research folder and click on it. On the top bar click “Sync” and allow the OneDrive desktop app to sync. Now all of the files should be available offline.

Microsoft Word

Unfortunately, writing papers in Google Drive is a bit too onerous.

Stata

You’ll be using Stata unless you are proficient in another statistical coding package. UVM has an institutional subscription. You can download and install it from the UVM Software page, here. For this you will log in with your UVM (not LCOM) credentials. To download it, hit the down arrow (1) then download. After it’s installed, you’ll need the serial number, code, and authorization to activate it. That’s under “more info” (2).

<– Two steps to install Stata from UVM

Part 4: Defining your population, exposure, and outcome

Getting the population, exposure, and outcome correct in your analytical dataset, and being able to come back and fix goofs later

Defining a study population, exposure variable, and outcome variable is a critical early step after determining your analysis plan. Most epidemiology projects come as a huge set of datasets, and you’ll probably need to join multiple files into one when defining your analytical population. Defining your analytical population is an easy place to make errors so you’ll want to have a specific script that you can come back and edit again if and when you find goofs.

For the love of Pete — Please generate your population, exposure, and outcome variables using a script so you can go back and reproduce these variables and fix any bugs you might find!

When you make these variables, you’ll likely need to combine several datasets. This will require mastery of importing datasets (if not in the native format for your statistical program) and combining datasets. For Stata, this means using –import– and –save– commands to bring everything over into Stata format, and then using –merge– commands to combine multiple datasets.

Make a variable for your population that is 0 (not included) or 1 (included)

One option in generating your dataset is to drop everyone who isn’t in your dataset. I recommend against dropping individuals who aren’t in your dataset. Instead, create a variable to define your population. Name it something simple like “included”, “primary population”, “group_a” or whatnot. If you will have multiple populations (say, one defined by prevalent hypertension using JNC7 vs. ACC/AHA 2017 hypertension thresholds), then you should have a variable for each addended with a simple way to tell them apart. Like “group_jnc7” and “group_aha2017”.

Useful code in R and Stata to do this:

  • Count
  • Generate and replace (Stata), mutate (R)
  • Combine these with assigning single equals sign “=” (Stata & R, I say out loud “assign” when using this) and “<-" (R)
  • use –if–, –and–, & –or– statements
  • Tests of equality: >, =, <=, != (not), == ("equals exactly"), not single equal sign

Example Stata code to count # of people with diabetes, generate a variable for group_a and assign someone to group_a if they have diabetes.

count if diabetes==1
gen group_a=0
replace group_a=1 if diabetes==1

Here’s example R code to do the same (df=data frame).

nrow( df %>% filter(diabetes == 1) )
df = df %>% mutate(group_a = ifelse(diabetes == 1, 1, 0) )

Make an inclusion flowchart

These are essential charts in observational epidemiology. As you define your population, generate this sort of figure. Offshoots of the nodes define why folks are dropped from the subsequent node. Here’s how I approach this, folks might have different approaches:

  • Node 1 is the overall population.
  • Node 2 is individuals who you would not drop for baseline eligibility reasons (had prior event that discounts them or missing data to prevent assessment of their eligibility)
  • Node 3 is individuals who you would not drop because you can assess them for necessary follow-up (incomplete follow-up, died before required follow-up time, missing data)
  • Node 4 is individuals who you would not drop because they had all required exposure covariates (if looking at stroke by cholesterol level, people who all have cholesterol). This is your analytical population.

If you have two separate populations (eg, different hypertension populations by JNC7 or ACC/AHA 2017), you might opt to make two entirely separate figures. If you have slightly different populations because of multiple exposures (e.g., 3 different inflammatory biomarkers, but you have different missingness between the 3), you might have the last node fork off into different nodes, like this:

I generate these via text output in Stata then manually generate them in PowerPoint.

Defining exposure and outcome

This seems simple, but define clearly what your exposure is and your outcome is. Each should have a simple 0 or 1 variable (if dichotomous) with an intuitive name. You might need 2 separate outcomes if you are using different definitions, like “incident_htn_jnc7” and “incident_htn_aha2017”.

Table 1

“Table 1” shows core features of the population by the exposure. Don’t include the outcome as a row, but include demographics and key risk factors/covariates for outcome (eg if CVD, then diabetes, blood pressure, cholesterol, etc.). Some folks include a 2nd column that presents the N for that row. Some folks also include a P-value comparison as a final row. I tend to generate the P value every time but only present it if the reviewers ask for it.

In Stata, I use the excellent table1_mc program to generate these, which you can read about here. For R, I am told that gtsummary works well.

Part 2: Effective collaborations in epidemiology projects

Stay organized

Have one folder where you save everything. Use an intuitive versioning system. If you use dates to keep track of versions, append your documents with the ISO convention of YYYY-MM-DD. Trust me. Lots of details on this post.

Have realistic goals and stick to deadlines

Come up with some firm deadlines. For a 2 month epidemiology manuscript-writing project, here’s a possible schedule:

  • Week 1 – Combine all existing written documents (eg, proposal) into one manuscript
  • Week 2 – Draft tables. Write methods.
  • Week 3 – Baseline characteristics. Describe in results.
  • Week 4 – Describe exposure and outcome. Describe in results.
  • Week 5 – Estimate primary outcome. Describe in results.
  • Week 6 – Secondary analyses. Describe in results.
  • Week 7 – Finish first draft.
  • Week 8 – Finish second draft

Managing your mentor: Send reminder emails more frequently than you probably realize

I block off time to work on your stuff, but clinical priorities or other professional/personal life crises might bump that time. I try to find other time to work on your stuff, but a big crisis might mean that I don’t have a chance to reschedule.

Please, please, please, please email me early and persistently about your projects. This will never annoy me — these emails are very helpful. Quick focused emails are helpful here, especially if you re-forward your prior email threads. Eg, “Hi Tim, wondering if you had a chance to take a look at that draft from last week, reforwarded here. Thanks, [name].”

Working on revisions

Use tracked changes

And remember to turn them on when you send around a draft!

Append your initials to the end of the document that you are editing for someone else

For me, I’ll change a name to “My cool document v1 tbp.docx”.

Part 3: Introduction to Stata

Stata is a popular commercial statistical software package that was first released 30+ years ago. It has some really nice features, loads of top-rate documentation, a very active community, and approachable syntax. For beginners, I think it’s the simplest to learn.

Learning how to use Stata

Stata has really, really, really good documentation.

The documentation is outstanding. Let’s say that you want to learn how to use the –destring– command. In the command line (1a under “Stata’s Interface” below), type:

help destring

…and up will pop a focused help file. There’s the “View complete PDF manual entry” option that has EXTENSIVE documentation of the command. (Note: This file seems to only work well with Adobe PDF reader, not alternative PDF readers like Sumatra). If the focused help file isn’t sufficient to answer your questions, try the complete PDF manual.

The focused help file has multiple parts, but the syntax example is gold. Further down you’ll see example uses of the command.

Web searches will find even more answers

Odds are that someone has already hit the same problem you have in using Stata. Queries in your favorite search engine are likely to find answers on the Statalist archive or UCLA’s excellent website.

You can install Stata programs that other users have written

There are MANY MANY MANY user-written programs out there that can be installed and used in your code. You only need to install them once. Most are on BU’s repository called SSC. I use the table1_mc program extensively (it makes pretty table 1s, you can read about it here). To install table1_mc from SSC, you type:

ssc install table1_mc

…and Stata will download it and install it for you. It’s ready to use when it finishes installing. And, there’s no need to re-install it, it will load each time you start Stata.

Quirks of Stata

Stata only works with rectangular datasets

Think of a rectangular dataset as a single spreadsheet in Excel. It has vertical columns (like a y axis) and horizontal rows (like an x axis). There’s no data on a Z axis coming out of the computer at your face.

A rectangular dataset is the only type that Stata works with. Other statistical software like R or Python can handle many more complex data structures. For learners, forcing data to fit within a rectangular dataset is a huge advantage in my mind since that structure is intuitive, and you can always browse your data with the built-in data browser (see 3c under Stata’s Interface, below).

Stata only works with one dataset at a time*

One dataset in Stata is akin to one spreadsheet in a workbook in Excel. In Excel, you can have multiple spreadsheets in one .xlsx file, with each spreadsheet appearing on a different tab at the bottom. All spreadsheets are in the memory at the same time. You can do math across spreadsheets in a workbook in excel, summarize costs in one column in spreadsheet A and have the result appear in one cell on spreadsheet B. In Stata, you can only have one spreadsheet (here, dataset) open at a time.* Because of this, Stata users spend a good deal of time merging and appending multiple datasets to make a single dataset that has all of the necessary variables in the best format from the get-go.

A big problem historically with Stata was that datasets are loaded in the RAM, and big datasets would be too big for conventional computers. That’s not an issue anymore since even cheap computers have several gigabytes of RAM.

*This isn’t true anymore. Starting in version 16, Stata can actually now have multiple datasets in memory, each stored in its own frame. These frames can be very useful in certain scenarios, but for our purposes, we are going to pretend that you can have just one dataset open at a time.

Data are either string or numeric. Their color changes in the data browser

Strings are basically text that are thought to be words and not numbers. But sometimes a dataset will be imported wrong and things that are actually numbers (“1.5”, “2.5” in different rows of the same column) will be imported and considered to be strings and not numbers. This might be because they were imported incorrectly. This might be that later down in the list there is a word in a different cell (“1.5”, “2.5”, “Specimen error”). If any row of a variable contains something that isn’t a number, Stata makes the entire column, and with it the variable, a string.

IMPORTANT: When viewing strings in the data browser (3c under “Stata’s Interface” below), they appear in RED text. When specifying strings in commands, you need to enclose them in quotations (eg count if name==”Old”). Missing strings are two quotes with nothing in between them (eg count if name==””).

In order to do math, you need to have things be numbers. There are several different numerical formats that you can read about here. If something is an integer (nothing after the decimal), it can be byte, int, and long. If something has a decimal point, it’s float or double. Stata does a nice job selecting which numerical format your data should be in, so you probably don’t need to think much about the difference between byte, int, long, float, or double again.

IMPORTANT: When viewing numeric variables in the data browser, they appear in BLACK text (or BLUE if they have a label applied). When specifying strings in commands, no quotations are needed (eg count if quartile==1). Missing strings are periods (eg count if quartile==.), and a period is positive infinity (a missing value is bigger than a value of one billion).

To convert from a string to a numerical value (change the “1” to a 1), you use the –destring– command. You might need to include the force and replace options, but read up on those by typing –help destring–.

To convert from a numerical value to a string (change the “1” to a 1), use the –tostring– command. Note that missing numerical values will go from a dot to a dot in quotations (. becomes “.”), which is not the same as a missing value for a string, which is just empty quotations (“”). It’s a good idea to follow up a –tostring– command with a command that replaces “.” values with “” values.

Stata’s output is only 255 characters wide, max

The output window of Stata will print (“display”) the inputted command and results from that command. It will clip the output at up to 255 characters, and insert a line break to the next row. You can specify:

set linesize 255

…so that the output is always 255 characters wide. Otherwise, it’ll adjust the output to match how wide your output window is.

The working directory is your “documents folder” unless you manually set the working directory with the cd command or open up Stata by double clicking on a .do file in Windows explorer

The working directory is where Stata is working from. If you save a dataset with the –save– command, it’ll save it in the working directory unless you specify all of the files from the C: drive on. If you double click on the Stata icon to open it up in Windows and type the present working directory command to see where it’s working from (that’s –pwd–), it’ll print out:

. pwd 
C:\Users\USERNAME\Documents

So, if you type:

save "dataset.dta", replace

…it’ll save dataset.dta in C:\Users\USERNAME\Documents

Let’s say that you really want to be working in your OneDrive folder because that’s secure and backed up and your Documents folder isn’t. The directory for your desired folder is:

C:\Users\USERNAME\OneDrive\Research project\Analysis

In order to save your file there, you’d type:

save "C:\Users\USERNAME\OneDrive\Research project\Analysis\dataset.dta", replace

Note that there’s a space in the Research project folder name so the directory needs to be in quotations. If there was no space anywhere in the directory, you could omit the quotations. I’m including quotations everywhere here because it’s good practice.

One option is to change your working directory to the OneDrive folder. You use the –cd– command to do that then any save command will automatically save in that folder:

cd "C:\Users\USERNAME\OneDrive\Research project\Analysis\"
save "dataset.dta", replace

Alternatively, you can save your project’s Do file in the “C:\Users\USERNAME\OneDrive\Research project\Analysis\” folder. Rather than opening Stata by clicking on the icon, find the Do file in your OneDrive folder in Windows Explorer and double click on it. It’ll open Stata AND set that folder as the working directory!! For a new project, this means opening Stata by clicking on its icon, opening a blank do file, saving that do file in your OneDrive folder, closing the Do File Editor and Stata, then reopening stata by double clicking on your blank do file in Windows Explorer.

Stata is most effectively used with with command-line input, specifically through the Do File Editor. There is a graphical user interface that can be handy.

I think that everything in Stata should be completed through Do files. These are text files with sequential lines of codes that make Stata perform commands in order.

There is a graphical user interface (GUI) with clickable menus. You can click through commands and it’ll generate the code and run the command of interest, and these can be handy for stealing syntax to run an annoying command. The command from the GUI will appear in the Command History (1c below) and you can right click and copy/paste it into your do file.

I find –import excel– to be frustrating and use the GUI probably 90% of the time to generate that command then copy/paste the syntax into my do file.

Stata won’t let you close a dataset in the memory or overwrite an existing dataset without some effort

The –use– command will open up a dataset in the memory. If you don’t have a dataset opened yet, this will open one:

use dataset.dta

Remember that Stata can only have one dataset opened at a time, so any time you open one when you already have a different dataset opened in memory, Stata will need to drop the open dataset. If you spent a lot of time on the open dataset creating new variables or merging with other datasets, closing it will make you lose all of your work unless you have also saved it. Stata doesn’t want you to make this mistake so if you already have a dataset opened and you type in the above command, Stata will say “No” and you won’t be opening the new dataset.

Instead, you need to put “–, clear–” at the end of the command, like this:

use dataset.dta, clear

And now Stata will drop whatever you have open. It’s really just a nice check to keep you from discarding your work accidentally.

Similarly, if you are trying to save a dataset with the –save– command into an empty folder, you just need to type:

save newdataset.dta

…and Stata will save it no problem. HOWEVER if you are trying to overwrite an existing dataset with that same name, Stata will say “No” and you won’t be saving your dataset today. This is another check. instead, you just need to use “–, replace–” to overwrite. Example:

save newdataset.dta, replace

Stata’s interface

Here’s a quick overview of the Stata interface in Windows. Note: the Mac interface looks a bit different. There’s some way to make the Mac interface look like the Windows interface, but I don’t know how to do that. I’ll try to remember to update this page when I help a Mac user in the future.

  1. Ways to input and interact with commands:
    1a. Command line – This is where you type command by command. Unless you are just poking around in your data, you should avoid using this. Anything that you want to reproduce in your analysis should be done in the Do file editor.
    1b. Open Do file editor button – The Do File Editor is the most important part of Stata in my opinion. A do file is a long text file saving command after command. This is where you should do all of your analytical work.
    1c. Command history – If you use the command line or GUI to make a command, it’ll be saved here. You can right click on old commands and copy/paste them into your do file.
  2. Output window – Your command will appear here with a preceding dot (“. sysuse auto” means that I had previously typed in “sysuse auto”). The output from your do file or command will appear immediately below.
  3. Ways to interact with data
    3a. Variable list – This is a list of variables in the open dataset. You can double click on them and the variable name will be copied to the command line. You can ctrl+click and select multiple and then copy them to the clipboard. This is quite handy.
    3b. Variable and dataset properties – This will let you see details about a selected variable in the variable list and the current dataset in memory.
    3c. Data browser – You can also pop this open with the –bro– command. this views all data in a spreadsheet format that looks like Excel.

Summer medical student research project series Part 1: Getting set up

Summer goals and expectations

Hi there! Thanks for expressing interest in working on an epidemiology project with me this summer. This project will entail:

  • Using cohort study or clinical trial data trying to extend knowledge of cardiovascular disease (CVD).
  • You getting your hands dirty with statistical coding via Stata. If you have high proficiency with another coding package (e.g., R, SAS), then you can do that. If we have an R expert available, they will help you use R. Otherwise, get ready for Stata, since that’s what I use.

My expectations for all LCOM summer students are:

  • You have a computer that works and internet that is dependable enough to allow Zoom conferences. You don’t have to be in VT.
  • In advance of the summer, you will submit a manuscript proposal to the cohort that will be used, and apply for funding through the CVRI or LCOM (typically due in February). If we need an IRB proposal (which we likely don’t), then you’ll lead the completion of that.
  • We’ll meet weekly via Zoom for an hour or so during the summer to review the project’s progress. I’ll be available in between meetings via email, Zoom, or whatnot.
  • You’ll complete the analysis, with help from me in learning the ins and outs of coding.
  • If doing a REGARDS project, you’ll attend lab meetings.
  • At the end of the summer you will have: 1) A complete first draft of a manuscript, 2) a completed abstract that can be submitted to a conference, and 3) a completed first draft of a conference poster.

Things to set up now.

Zotero

I use Zotero as a reference manager. It’s the bomb diggity. We will share references in a private group library that we can both edit. Only the people who have this library shared with them can see its contents.

To install Zotero, do the following:

  1. A free Zotero account. Sign up here. Please tell me your username so I can start a group library with you.
  2. Zotero’s desktop app. Make sure to log into your account in the desktop app. It’s under edit –> preferences.
  3. The Zotero web browser plugin for your web browser of choice. You need to have the Zotero desktop app open for this to work.
  4. The Zotero MS Word plugin (in Zotero desktop app, click Edit –> Preferences –> Cite –> Word Processors). This has been finicky with the specific MS Office install on the LCOM laptops so it might take some working to get it to work. But! It’ll work.

I’ll send you a shared library invitation. To accept the group library invite, do the following:

  • Go to zotero.org, log in.
  • In the top right, click on your username. A menu should drop down, click “inbox”.
  • Accept the group library invitation.
  • Open up the Zotero desktop app and let it sync (again, you need to be signed in on the desktop app, seen #2 above). The group library folder should appear in the left column all the way on the bottom.

Group libraries are awesome because we can compile references that either of us can insert into a document. Please keep the group library organized. If you add a new reference, please make a subfolder to stick it in so you don’t have to search for references one by one.

How to use Zotero

This is covered in a separate post, here.

Microsoft OneDrive

This is through LCOM. Not UVM, not your personal account.

  1. Open the OneDrive on your computer and sign in with your LCOM credentials if you aren’t already.
  2. I’ll share a research folder with you. You’ll need to sync it with your computer. To do that, go to onedrive.com, log in with your LCOM credentials (firstname dot lastname at med dot uvm dot edu). After you log in, you’ll be on the landing page for OneDrive. Click “Shared” on the left column. Find the research folder and click on it. On the top bar click “Sync” and allow the OneDrive desktop app to sync. Now all of the files should be available offline.

Microsoft Word

Unfortunately, writing papers in Google Drive is a bit too onerous. Please download this manuscript file, which you will be using to draft your manuscript.

Your statistical software, option 1: Stata

At this point you should know if you are using Stata or R. I use Stata. UVM has an institutional subscription. You can download and install it from the UVM Software page, here. For this you will log in with your UVM (not LCOM) credentials. To download it, hit the down arrow (1) then download. After it’s installed, you’ll need the serial number, code, and authorization to activate it. That’s under “more info” (2).

<– Two steps to install Stata from UVM

Your statistical software, option 2: R

R is a free and open source computer language intended for use in statistics. RStudio is a commercial software that makes using R much more user-friendly. I have only used R a few times and don’t have expertise in the language.