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Hi, my name is Stephanie Labou and I am
the Data Science Librarian here at UC San Diego.
To me, data science is extracting meaning out
of the massive amount of data that is in everyday life.
It's also really automating
that extraction so it's not something
that we can necessarily do manually,
and it's also thinking critically about data.
We might have more data than ever before,
but we need to know what data
we're working with and where it came from
because that will inform
the results that we get out at the end.
Being a data science librarian is pretty new.
Data librarians have existed in libraries
for many decades and they
were the people that really had the old,
the microfiche films of census data back
before it was digital or CDs or floppy disks,
and now everything is pretty digital.
Data science librarian is
a little bit of classic data librarian.
It's a little bit of programming,
a little bit of help with statistics and visualization,
and computing and all of that wrapped together,
tied with the bow is the data science librarian.
As someone fortunate to be one
of the first official data science librarians,
I feel very honored to
help define the path of what that means
especially because we have
so many fantastic resources here in our major.
At HDSI, it means I can start to chart
my own path because there's not
a huge precedent because
data science is developing so quickly.
It's really fun to be able to
say data science librarianship
is 90 percent this and
10 percent whatever I want it to be. Just really fun.
For me personally,
I'm really excited in the Meta research of data science.
I'm working on a funded project right now,
looking at how data scientists take
their research outputs and
document them and preserve them.
We're specifically looking at machine-learning models,
which are usually really
computing expensive and time-intensive to
create and we would like them to be
shared and reused if possible.
However, there aren't really
formal guidelines yet for how
people should be documenting
all the different parts of their machine-learning model.
You've got your training data,
your test data,
all these other moving parts
that are often not documented.
Without that information, it's
really hard for another researcher
to reuse that model or run that model on their own data.
We're looking at how researchers share those,
what information they share,
and then how do we even
preserve this big research ecosystem that has
all these different sub-components and that is really
where data curation comes into play.
Our research data curation group
is really interested in helping
preserve the research output and share
it and make it reusable and reproducible.
Every time I meet with a student or a faculty member,
it's a chance to have a conversation
about what is good data,
because that is also really subjective.
As a librarian, I tend to focus on,
is it a authoritative source?
Government agency that has the directive to
study homelessness or mortality statistics,
they are the authoritative source.
That's a good way to find data that is
going to be correct.
Is the methodology available?
Can we look at it and
talk about how the data was collected?
These are the questions that I will talk
to students and faculty about when they come in and say,
"I just need this one thing,
" and so we'll have a deeper conversation
why you need that data.
Maybe what they're looking for doesn't exist,
but something very similar does.
Another good example is data that you can and cannot use.
Especially for students they want
to web scrape everything,
which is a great skill to learn.
However, web scraping often
violates a website's terms of service.
It's in the big block of texts that nobody ever reads,
but it actually is a violation.
You're not supposed to do that.
Having conversations about just because
you can doesn't mean you should.
You should still abide by
the terms of service and make sure that the data you're
using is legally accessed and
is then nobody's private data
that shouldn't be shared and so on and so forth.
One area that has come up a
lot recently with our grad students
and our faculty is advising on text-mining.
It's a really popular topic these days.
Everyone wants to look through
30 years of journals or newspapers or things like that.
We do have databases that have that information.
When we're talking about
information access and data literacy,
it's really sitting down with the researcher and
having that discussion about what they're looking for.
We want the newspaper or journal article
or whatever it may be to be machine-readable.
If it is a photograph of something of an old newspaper,
that's going to be really hard to work with.
We want to make sure that it's in
the right format for them.
We want to make sure that we don't have to
worry about any copyright issues.
It's already been rights cleared for text mining.
Then we want to make sure that they can
actually get their 50,000
journal articles or 30 years of
New York Times backlog essentially.
It's sometimes really hard to get
batch exports from databases and so
we've worked with those vendors
to really set up a way for,
instead of researchers bringing the data to them,
we can bring the researchers to the data.
We have special platforms that we can give them
access to and once they're there,
they have access to everything.
Instead of going through and
getting 10,000 articles from here for faculty member A,
50,000 from here for faculty member B,
we can set them both up with access and say,
"Do whatever you want to do,
we've made sure that you have permissions,
everything is machine readable,
have fun and let us know how it goes."
That's where we can set them up for success.
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For gold standard of data,
it really does vary.
Some of the things to consider though are is it current,
is the information recent?
If you are looking for recent information,
what is the timespan?
If you're looking for historical information,
is the agency or
organization that's putting out the data reputable?
Do they have a long history of good research or
did they come out of the
blue and they're fringe organization?
Also, we think about who
is interested in the information,
who might have agendas for the information,
so is it information that is coming out of
an organization that is trying
to prove a particular point?
This is often if organizations have political agendas.
Do we know the methodology well enough to be able to say,
I do trust that this survey was undertaken properly?
If the survey says,
how much do you love this thing?
A lot, a little,
or I love it so much,
there's no option to say,
I don't love it at all,
in which case your data's not really great,
it's pretty skewed and you're
not going to have great results,
and then is it accurate to the best of our ability?
This is really where if the methodology is available,
we can look at the survey instruments,
look at the methodology.
Then one that I always like to think about
too and have students and faculty consider is,
did we get the data by legal and best means?
Oftentimes this doesn't mean that
someone is going and doing something shady.
It means that they found some data
online and ran with it.
However, when I've gone and looked at it, I've said,
this definitely was not accessed
or collected in a way that was appropriate,
so it's got people's personal information.
Nobody gave consent to
have their information collected in this way.
A lot of information that is public doesn't
necessarily mean that you
can go and do whatever you want with it,
and Twitter data is a really good example of this.
So yes, all tweets are public,
unless your account is private. We all agree.
However, there is a difference between
tweeting and if a researcher grabbed
all your tweets and then was publishing
them and calling you out in these published papers,
you would feel violated.
We often try to think that people are
behind every data point
or something real is behind every data point.
For the Twitter example,
if you want to then share out
the status that you have of tweets,
you need to make sure to,
again abide by the terms of service.
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For every single data point or gene sequence,
knowing where it came from,
what it is supposed to be.
If it is a combination of multiple other things,
if it's a derivative of different variables,
and so really having
that documentation is something that we encourage.
You never know when someone's going to
want to reuse your own data,
and when you reuse other data,
you want to have the best possible information.
It's playing nice.
If you want to get
really well-documented data that is clean and
ready to use and there are no errors
and everything has been clarified in the documentation.
You want to do the same thing with
your own data because it's really frustrating to
find mistakes in publish and share data.
But it's even more frustrating
if you don't know there are
mistakes and find out way after the fact,
and so really exploring any data that you find
before you use it from
the basics of are there any missing values.
Does this look suspicious?
Let's say I have a temperature dataset and I have a value
of 19,000 degrees and
this is supposed to be temperatures in San Diego.
I would be suspicious that something has gone wrong.
It's been really hot this week,
but it's not that hot,
and so really taking a look
at any outliers in the dataset,
anything that doesn't look
quite right and diving into that.
It might be a real data point or it might be a mistake,
and so making sure that you explore your data to get
a really good feel for it before
you throw it in the model.
I think is crucial,
and that sometimes is a step that
people skip over because they're really
excited but it is really important to really
understand the data that you're putting into a model.
Because otherwise what comes out of it
is not going to be accurate.
I think it is important to remember that
for a lot of data,
there are real people under it,
and even for data that's not necessarily people driven.
Climate data or oceanographic data,
there is always some real thing
that is underlying these data points.
It's especially important for
demographic data or user data or social media data,
and I think that's something that can be hard to
remember and people lose sight of when
we're talking about really big datasets.
I think it's important to remember that
each data point is actually a person.
It doesn't mean that you can't do the research,
but I think it means approaching data science with
the level of empathy and understanding.
A lot of what I will
tell students when they're thinking about
projects is imagine if
someone took your data this way and
if that would make you
flinch and make you feel uncomfortable,
that is a hint to you.
It's also really important to think of and not be
blind to data is just the way society is.
A common example is predictive policing.
Every year I have students that come,
it's a really common first project to
make a model that will predict crime.
However, this carries a lot of baggage,
and so it's really easy to go out and find
arrest statistics and demographic data
and information about crimes,
and if you're not thinking
critically about the data you're working with,
it is all too easy to write
a few lines of code and make a model have
a result and assume that result is somehow
an inviolable truths of
the universe that you have managed to find.
Really, your data is a product
of hundreds of years
of societal pressures and influences.
Who gets arrested,
for what crimes, what sentences they have,
levels of recidivism is really impacted by social issues.
That's an example that gets used a lot
predictive policing as something where the model
is pretty inherently biased
just because of who gets arrested.
But that's a pretty straightforward example.
I think what's important is to take
that thought process and apply it to other things,
so really thinking through,
if you were putting demographic data into a model,
what does that data really represents?
Where do you have confounding factors?
I'm really excited
about the focus on data in data science.
I know that sounds a little
strange since it's in the name.
But there's been a lot of focus and
excitement about methodologies for data science.
Machine learning and neural networks and AI,
and those are all very exciting things.
But I think recently we've
seen a trend that I hope continues in
the future of really critically
analyzing the data itself.
The data that goes into your machine learning
model or your neural network and saying,
where was it collected,
how was it collected,
is the data biased in any way,
is there anything in this that
would then travel through my model and
influence my outputs and my results in a way that is not
necessarily accurate to the underlying scenario?
So I think that increasing level
of data literacy and really
interrogating the underlying data
is an exciting avenue for data science.
I don't think it takes anything away.
I think it only makes the results richer.
I think being in the mindset of we
are stewards of the research
that comes out of our university,
and we want to make it accessible,
and ideally for it to have
an impact and it can't have an impact if it is
poorly documented or not
shared or not shared with the appropriate context,
or if we are not communicating
the nuances in what is
coming out of our data science research.
I think that mindset of helping it
along is a real opportunity
and a strength of the university.
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