So, I've actually got some tables and figures in here with some data and I'm
just not going to go through those, but I uploaded this presentation and made very
detailed notes about all of the figures, but since I don't have time to kind of
walk through them, I'm just going to just move through them.
But before we get started today, I just kind of wanted to provide a context of why we all came
together and the problem that we're trying to solve.
So, the United States is a very diverse country and when I'm talking about diversity, I'm not just
talking about ethnic and racial diversity, I'm also talking about all of
us as individuals and the perspectives that we can bring to these discussions
based on our life experiences.
But as you all know, and again I'm not going to go through all the all the numbers here, but STEM,
which stands for Science Technology Engineering and Mathematics, I'll be using that acronym a good bit,
but we're basically seeing that individuals in STEM occupations are not representative
of the U.S. population, and the demographics
of the U.S. population are changing pretty drastically
and so that's something that we need to really think about.
This is just a figure showing that women get paid a lot less than men in these occupations,
between twenty-thirty percent less.
If you control for that, that kind of goes down a little bit.
If you control for education, employment and some other demographics,
that goes down about five-ten percent less, but still something we all want to
think about when we have our conversations this week.
And the Pew Research Center asked the U.S. adults what they thought were the reasons why
we didn't have more minorities and women in STEM occupations.
And again, I don't have time to go through all of these but I did want to point out that one that was brought up
for women and was not brought up for minority groups, one that was brought up for minority groups
was not brought up for women, but you'll see "More difficult to balance work/family in STEM jobs",
that 33 percent said that and you hear that a lot with women having children
and being able to balance that as, you know, go through these types of careers,
especially if you're trying to receive tenure and things like that,
it could be very challenging.
But another one that I would really wanted to point out for minorities was the top one,
42% felt less likely to have access to quality education to prepare them for STEM fields.
And there's actual data that show this, that schools with high black and Latino enrollment have less
access to some math and high-level science courses then low black and native enrollment schools.
And so the NSF INCLUDES Conference which is who's funding this conference
and the reason that they were kind of developed was to try to start addressing
these issues realizing that it's going to require collective action across a
lot of different organizations with a lot of different expertise.
And the successful initiatives, things that we have shown to be working, how do we get those scaled?
So how do we get other groups adopting those practices and being able to utilize them in their programs?
And then we've also have not had very much success at determining
whether or not all of these efforts have really been able to move the needle.
So how do we start determining whether we are being able to do that through all of
this work that we're doing?
And then I wanted to touch just briefly on, because we're looking here in this conference
at the intersection of data science and environmental science.
I know Carolyn's going to be speaking a lot about the environmental sciences lately
and since I have a short amount of time I'm going to skip through a couple of these slides,
but I did want to say that the Ecological Society of America, which is the largest
ecological professional society in the world, they have been doing a lot of work
in diversity equity and inclusion, but they did a study in 2006 and found that
only 26 percent of their membership identified as female and that,
although they have been trying to do all these efforts, minority membership has only increased slightly.
So from 6.3 percent in 1999 to 8.8% in 2005.
And this table that's here is basically showing similar data to what I showed for across STEM fields,
that we have a lot less representation from minorities in that organization.
So now moving on into data science.
I know this may be something that comes up a lot of like what do we mean by data science?
And there are a lot of many definitions I know out there but for the purposes of this conference,
we're kind of trying to use a broad definition for it to just be "the extraction of knowledge from data."
And data science is a rapidly growing field.
It's one of the focuses right now of the National Science Foundation.
In their strategic plan they've talked about developing ways to implement mechanisms
to support the training and workforce development of data scientists,
and know we're going to be talking about that a lot this week,
but we're also seeing research coming out that we're going to need a lot more
data scientists and there just are not enough students entering the field.
There's also this question of this data divide that separates individuals communities and also institutions.
So thinking about individuals, who has the skills to make sense of all these data that are currently available?
Who has the ability to make decisions and drive policies with those data?
And then thinking about communities, who is collecting the data on these communities
and how are those data being used?
And who has access to those data after they're being collected?
And do those communities at the heart of data being collected,
do they have access to that data that's been collected?
And then specifically looking at institutions, research universities have been
developing comprehensive data science programs for many years,
but Tableau did a study in 2006 and found that most of the larger national universities offer
formal analytics programs but when they looked at two-year institutions they
found that only 2% of them had those programs and that that was due to
a lack of qualified instructors.
So we're also seeing that some of these institutions are lacking the necessary capacity
and resources to develop some of these programs.
And then there's been some discussion, too, about what is the role of open data and open science and all of this.
The Obama administration proposed an American graduation initiative and then they were
highlighting that open online education may be a way to improve recruitment and
retention of minority students in STEM, but a study that was done last year by GitHub,
which is an open source coding platform,
95% of its users were male and then only 16% of those users that were surveyed identified as minorities.
So we're really seeing that the open data,
open science is really not moving that needle to continue to kind of use that term or phrase.
Another thinking about the democratization of science, so open science is
becoming more popular, we have more open science publications like +1.
Open access data, like what's being provided by the National Ecological Observatory Network,
and there's also been a huge rise in citizen science.
Citizen science actually has a professional society now, the Citizen Science Association,
that's been developed to try to support researchers and practitioners because the field is growing so rapidly.
And there's another study that came out that basically talked about two challenges that they
thought need to be examined to start addressing in this issue.
And one was the lack of individuals from these group attaining skills for these careers and
then a lack of an organizational and professional culture that retains
and advances individuals from these groups.
And I know that some people may not feel that these are the major challenges,
you may want to add to these challenges,
and again that's going to be a lot of the part of the conversation that we have this week as well.
This is just an image of kind of showing the differences in what we're meaning by equality and equity.
So the three people on the left, they were all given a box so they could see the see the baseball game,
however there's someone that's a little shorter so even though they have the
same box as everyone else they can't see over the fence.
But when we're thinking about equity we need to make sure that it's not just about giving everyone a box,
it's all about making sure everyone can see over the fence.
There's been a report that came out, "Keeping Data Science Broad,"
that recently came out and I just wanted to let you know that there has been some work being done
in this field and we've been trying to upload and provide these reports on the
EDSIN website so that you guys can all look at these and have access to them
and we encourage everyone to continue uploading those resources so that we can
all share those with each other.
So the Environmental Data Science Inclusion Network was started to try to start addressing some of these issues.
Although we have everyone here in the room, this is an open network.
We're inviting anyone that would like to join to please join us.
You can go to our website EDSIN.QUBESHUB.org or EDSIN.QUBESHUB.org
to sign onto the network and we would love to have you, especially those that are participating remotely,
and I finally wanted to end, what we're hoping to achieve through the conference this week specifically,
we want to increase awareness of all the different initiatives and resources that are available to everyone.
We want all of you to identify collaboration opportunities that you can take home with you.
We want to think about developing shared goals for this network.
We don't want this to end after this conference, so what are we going to try to work towards after it's over.
We want to think about how can we can continue to develop and expand this.
And again thinking about how we move the needle,
what sort of data should we be collecting to demonstrate that what we're doing is effective?
And we're also wanting to prioritize activities and new initiatives,
again hoping that's all going to come out of these conversations this week.
So you guys have heard way too much from me already but again I was just wanting to
provide you with a little bit of context in these slides and detailed notes if
you want to look at some of those statistics they're all available.