Some of you may be wondering if this is the right course for you.
This course is designed to be a
practical, hands-on, high level introduction to the field.
And it's geared towards people with experience in related fields.
Like medicine, computers, science research in general.
Or data research outside of medicine.
That might include clinicians that are interested in
learning about the data management aspects of research.
Researchers who want to learn best practices for data
management and practical skills to apply in their studies.
And lab or research study staff who want hands on practice.
It might also include medical and informatics students, data managers
in other fields who are interested in how to collect and
manage data in clinical research.
Or biostatisticians who are interested in data capture and management.
This course is right for anyone interested in an overview of the field.
The key take away is that this course is not
designed for people who are already experts in the field.
If that's you, you'll probably recognize all the material.
But don't let that discourage you from participating if you want to.
We're very interested in your
incites and participation on the discussion boards.
You should be comfortable using the internet and web applications in general.
You can fill out web forms easily.
You know the general lingo about computers and user interfaces.
You should understand basic medical concepts too.
We'll be mentioning concepts like systolic and diastolic blood
pressure, specific symptoms, diseases.
Or diagnoses without explaining exactly what they are, nothing obscure though.
You should also have a general idea about basic research concepts like.
What we mean by demographic data, a research hypothesis, a steady protocol, a
steady outcome or the difference between quantitative and qualitative research.
do you not need to know in advance, you don't need to know how to use REDCap.
We're going to be using a program called
REDCap for the applied parts of this course.
Red cap is a web application for capturing clinical research data.
It has lots of features that will help us provide
you with hands on experience with the concepts we're teaching.
Even though we're doing our work in REDCap, you
should realize there's a world of software solutions out there.
Many great tools are designed to help you capture data for your research studies.
Redcap is a great general purpose program for research data capture.
But it's not the only one.
I originally had a slide here filled with the logos of
many popular programs, but that didn't fly by our copyright reviewers.
So we can talk about the other systems on the discussion boards instead.
Some tools are designed for surveys.
Some for electronic data capture, others for managing every aspect of a trial.
your biospecimens in huge freezers, complex billing
operations and printing shipping labels for your packages.
Other software solutions are designed for very specific
environments, like sending out surveys to mobile phones.
Data collection using interactive voice response systems, or public health
data collection in places where you have no Internet access.
Every week in this class you can expect to have
a couple hours of video lectures from Paul Fierus or me.
As well as some segments from guest speakers.
Once we get to the section involving REDCap, we'll also
have some tutorial videos on how to get started with REDCap.
Design your own data collection forms and test them.
And then use some of the advacned features of the applcitation.
Your score in this course will be based primarily on the results of
quizzes which will be un timed an the assignments you will complete in REDCap.
The key to the assignments will be understanding
the concepts and best practices that we present.
And then being able to apply them by critiquing a poor
data collection instrument or designing a practical survey based on specifications.
Specifications we provide you.
Check the Coursera web page for data management for clinical
research for the latest specifics on the assignment grading policies.
We'll also be on twittter.
You can tweet
us at DataCourse. Which we hope will be easy to remember.
Or use the hashtag, DMCRMOOC.
Which is a bit harder to remember, sorry.
It's the first letters of our course title.
Data management for clinical research, plus mooc.
But at least we hope the hash tag won't overlap with other content.
That's it for what you need to know about how the course works.
In the next video we'll cover the basic
concepts for this course plus what topics are included
and excluded in the material of the next few weeks.
Before using the apps described above, you should also be aware that these apps do not fall under the agreement between Columbia University and Google that offers enhanced security that applies to LionMail's core apps. The apps above are subject to Google's Consumer Terms, which do not protect confidential information to the extent required by FERPA. Therefore, any sensitive or confidential data, as defined in the Columbia University Data Classification Policy, must not be disclosed or transmitted through any apps other than LionMail, Calendar, Contacts or Drive. CUIT highly recommends that you review Google's Consumer Terms, as they are an agreement between you and Google.
Furthermore, please note that when you first access any of the enabled apps, you will see the message shown in the screenshot below, reminding you of our security and privacy policies. Although this standard wording from Google may make it sound as if there have been changes to our security and privacy policies, be assured that CUIT's standard policies are still in effect and that no additional data or information will be visible to, or shared with, CUIT than is currently the case.