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Node X research: Increasing women in engineering with Data Collaboration

Updated: Mar 24

The Data Collaboration Alliance has partnered with the University of Waterloo and the Ontario Women In Engineering (ONWiE) nonprofit to generate insights through Data Collaboration

Women are deeply under-represented in the engineering profession, and while things are definitely getting better, there is still much work to be done.


In Ontario, Canada's largest province (pop. 14.5M), approximately 13% of professional engineers (and 20% of those newly licensed) are women, who represent approximately 25% of undergraduate engineering students.


Engineers Canada, the professional body, has set a goal of raising the percentage of newly licensed engineers who are women to 30 per cent by the year 2030. This number represents a potential tipping point for representation that is predicted to set in motion other corrective forces.

Source: Engineers Canada website

In physics classes within Ontario High Schools, recent data indicates that women represent 30% of the classroom population - so this will also need to improve in order to establish gender balance within the profession.


Project stakeholders


The Women In Engineering project is a collaboration between students in the Faculty of Engineering at the University of Waterloo and the Ontario Network of Women In Engineering (OWiE), a nonprofit organization seeking to establish gender equality and balance in the engineering profession.


University of Waterloo:

Ontario Network of Women In Engineering:

Research goals


The goal of the Women In Engineering collaborative research project is to establish a prototype for a Collaborative Research Platform (CRP) capable of collecting data from stakeholders representing multiple organizations while enforcing variable data access and governance requirements.


The project team has established 4 guiding principles for their research:

  • Copies [e.g. elimination of spreadsheets]

  • Control

  • Consistency

  • Collaboration

The team has identified the following goals / phases:

Phase 1

Generate insights related to enrollment trends for women in engineering


Future phases

Build solutions to evaluate and accelerate women in engineering programs


What's next?


We'll be checking in with the Women In Engineering team in March 2022 to catch up with the progress they have made with establishing their Collaborative Research Platform.


About Node X


Node X is the research support program of the Data Collaboration Alliance that provides free and subsidized software in support of collaborative research projects.


Our goal is to empower teams with the ability to build unlimited digital solutions (data collection, analytics, real-time systems, automations) while simultaneously supporting the Zero-Copy Integration framework that provides data stakeholders with meaningful control and ownership of the information they contribute.


Data ownership controls include:

  • Setting data-level access grants (granular to the cell, universally-enforced)

  • Assigning temporary Data Custodians

  • Domain-level governance (aka 'Data Mesh' approach)

  • Fulsome deletion (aka Right to be Forgotten)

Visit our webpage to learn more about the Node X program.


Interview transcript


Chris: Welcome everyone. My name is Chris McLellan. I am the director of operations at the data collaborations Alliance. One of our key programs is the node X program, which is about providing. Data collaborations software for great causes. And I'm one of those we're speaking with today, it's called the on week data collaboration project. And it's based out of Waterloo in Canada. And we'd like to start things with a quick round of introductions. So, I'll hand it over to the.


Simran: Good afternoon, Chris, my name is Simran and this is my lovely team, Nancy and Merle, as you know, we're pursuing management engineering at the university of Waterloo, and we're very excited because we're entering our very last term in January and we'll be graduating.


And we're very passionate about women in engineering and enrolling in stem courses. And of course we have here Dr. Kim Jones, the chair of on we, and she's a strong advocate for inclusiveness equality and diversity in engineering, and is our first stakeholder. She served at McMaster university and currently she's the director.

And I like her to introduce herself.


Kim: Thank you. Yes. I'm a prof in chemical engineering at McMaster university, but importantly, to this project, I'm the chair of the Ontario network of women in engineering which is a group that works collaboratively to train, increase the diversity in our student population in engineering.


Nancy: Hi. My name is Nancy I as mentioned. I'm also an engineering student at university of Waterloo and I'm super passionate about getting more women to join engineering programs. And that's sort of where this project came to light and that's where it was birthed from is. More women to enroll and we are trying to use data to figure out what's the issue with women enrolling in engineering programs. And that's really what we're trying to build on.


Miral: Hi everyone. My name is Miral. I'm also in my last term, I'm at the university of Waterloo, studying management, engineering and women in engineering and this cause is so close to my heart as well. And I think all of us as a team is so passionate about the work.

On we does. And that's why we're putting our heart and our soul into this project, you know, making sure that that'll go as far as the can. And yeah, so it has been great working so far and I'm really looking forward to, to continuing on and see what we can do.


Chris: I think the best place to start is sort of the 10,000 foot level. And maybe I could ask upon Dr. Jones to give us a little context in terms of what are the challenges with women in engineering and, and what brought you to focus on this issue?


Kim: Definitely been an issue since I was an engineering student.

And well, before that, Women and engineer women are deeply underrepresented in engineering. Things are absolutely getting better, but we don't know how about how things are getting better. What's working unless we have data to support that. At the Ontario network of women in engineering was actually formed in 2003, because at that point, the data were showing that the number of percent of women in engineering programs was actually dropping and having those data, it gave us a huge incentive to work collaboratively, work together, to try and improve things.


As it stands now in Ontario, we have about 13% of professional engineers being women. We have a pro between around 25% of the undergraduate student population is women. We're striving. To get to 30% of newly licensed engineers being female by 2030. So that's engineers, Canada's 30 by 30 initiative.


And that's been a really good focus for collecting data and thinking about how to do things strategically. And the reason that we've set 30% as an initial goal is because. The psychological research that has suggested, but that's when representation hits a tipping point above 30%, then you don't have to feel isolated.


You don't have to make an extra effort to have your voice heard to be included. So it's really important to have representation above that amount in order to have your ideas shared. The true diversity happens. Easily in a room. So we've been collecting data around, you know, at each level in the first year, what percent of women do we have an engineering in each discipline?


What percentage of women do we have in engineering? What does our applicant pool look like? How many of those engineers are graduating? How long does it take them to graduate? How many of them then apply for professional engineering designation? Understanding those data are some of the things that help us understand what the barriers are.


So for example although it's beyond the scope of this project several years ago, we had done on, on, we identified that high school physics was actually one of the bottleneck points for women applying to engineering because in grade 12, In high school classes, they were only 30% women. And so it's difficult to get past 30% women because they you'd need physics to what, in most engineering programs, if your pool of potential applicants is stuck at 30% women.


So being really able to identify where are these bottlenecks, where are the real pinch points? You know, which disciplines need work where we struggling to really. Share those messages of, you know, engineers can do good with different tools and strategies. That you know, women belong in these spaces.


And I'm excited about the project, but I was invited to be a part of here because I think the, the real collaboration of which we've sort of structured our whole mission is embodied in this project. You know, we, we can think about how do we share those data most effectively to try and get to our solutions in the most quick and efficient way possible.


Chris: So thanks very much Dr. Jones. That's great context. And of course we're the data collaboration Alliance and really it's our mission to advance just that, which is a form of data sharing that doesn't require a data owners to give up control of their information as they engage with other groups, other stakeholders to develop outcomes.


The talk about some of the outcomes of this specific project with the the Henri data collaboration project Nancy, maybe you can tell us a little bit about, you know, more specifically what you're hoping to achieve through this through this.


Nancy: There is, there's really a couple of problems that exists right now. As Dr. Kim Jones can, can also talk a little bit more about, so right now, the data that we have is is being shared through spreadsheets, which makes it very stagnant. And so if someone's analyzing data in Ontario network of women in engineering and someone else's analyzing another spreadsheet in Waterloo and someone else in university of Toronto, these are all very stagnant analyses.

And people are redoing the work that someone else is doing at a different place. The second thing is that we're creating copies of data. If I want to share my, my data. With you, I will create a copy of it and email it to you, which right. As soon as I do that, I lose all control over that copy that I've made.


So I no longer maintain ownership over that copy of data. You could technically do whatever you wanted to do with that data and share it forward. So those are. Problems that we're currently having with sharing and collaborating on this data. So our goal in this project particularly is to create a collaborative research platform where researchers and universities can collaborate on this type of sensitive data by putting the principles of zero copy integration and data ownership at the center of it And really that's what we hope to achieve is a platform where universities can collaborate on data with other universities and research organizations, like Ontario network of women in engineering.


And the first, proof of concept that we're really building out for. This is with Ontario network of women in engineering. To collaborate on women in engineering data and our, our goal, our end vision, like our 10 X vision is really that this becomes one project. The initial pilot project that opens the gateway for universities to collaborate on all types of new data.


So not just women in engineering, but any type of research that universities do, they can collaborate with each other.


Chris: There was a time when, if we, all of us on this call wanted to collaborate on a document, I would have emailed a version of it to all of you or some of you, you, you would have made some changes and sent it back to me all at different times.

And of course, that sounds crazy, but that's how the world worked for a very long time as I can attest to. And so now fast forward, when you think about Google docs and things like that, where we're all working. The single document and, and simultaneously, but we're making changes, edits and writing new texts.


But under access controls, right? Not some people can view, some people can only comment, some people can edit. And so you have these different, this concept of rules. You have this concept of collaboration, but what you have is control there from the document owner. And now just take all of the good stuff that we love about Google docs and bring it into the world of dataset management data, operation, operationalization data analytics. And that's really what we're talking about with data collaboration versus versus other forms of sharing. So that's really a great, we're able to introduce that level of control and collaboration to what you're trying to achieve.


Simran and Miral, did you want to add anything to. To what the project's goals are, what you would excite you about what you're undertaking.


Simran: So, I wanted to add on that as we were going through the project and our goals, we had four CS in mind. The first one is control and of course, as Nancy had mentioned, we want data ownerships.

We want owners. To be able to govern their data and other people to not take over on that. We also had collaboration, which is having the data contracts as well as copies and consistency. And Nancy had almost touched on all of them there. So yeah, we're good to go.

Miral: And just adding onto that, we actually created a system diagram.

That would be a little bit of a visual representation of what our solution is aiming to achieve. And in that system diagram, we showed Waterloo and McMaster as two different notes. And each of them have kind of granted access to a certain part of their data and they have allowed for that data to be put into an aggregate.


Now this aggregate then gets put into a dashboard that's available to all user groups, including the public, while at the same time, they kind of have that governance over their data and are able to compare their own personal data at the same time. So it kind of allows them to to check off a lot of different boxes and it allows them to, to kind of, Check off all of their goals at the same time and they can revoke or grant that access whenever.

So that visual representation I think, is also very important to try to understand what we are trying to achieve.


Chris: I think this has been an excellent introduction. To the, to the project. And I can't wait to hear what's next. I mean, the idea here is that we'll check in with you and see how your network is developing your network of datasets within your node.

Over then in a few months time let's and I can't wait to do that. And so until we do then what, give us a hint of what, what we might expect in, in three or six months time, what would, what are you hoping you're going to see in three to six months?


Miral: I could speak on that. So up till now, we have been really focusing on the consistency C and we've kind of been going back and forth with our stakeholders and showing them our solution prototype and receiving that feedback moving forward.


We want to really integrate the other three CS. We want to focus on collaboration, copies, and control, and really kind of, integrate that now into our project. And I think moving forward, that's what we're going to be focusing on and using that technology aspect to also integrate those three CS into our project.


Nancy: So with the granularity in collaboration, One thing that really excites us as a next step is to understand how we can give access to sort of, sort of, as, as we talked about to certain domains within, within this initial node zero. So within like the, the Waterloo domain, they have their own set of access controls over their own data sets.


University of Toronto would have their own access sets, access controls. And we really want to configure this. So that I could create roles. So for example, university admin at Waterloo would be able to see all of the data entirely within Waterloo. Whereas, uh, someone from the Ontario network of women in engineering that they've given access.

I see their Waterloo data would only be able to see maybe the aggregate level data for example. So being able to create those dynamic roles and really adjust the access, to cater to those roles, I think is a really big business use case. That comes out, that we're really excited to implement.


Chris: Yeah, I'm, I'm just, it's a, it's, it's great to see people so excited about data governance, which I think in the history of data management, hasn't been like the hottest topic, but as we see breach after breach, as we see people realizing that their data has real value, then this control factor is coming into the conversation and at the center of the conversation.

And so I'm really excited to see where you guys go. is really fascinating.


And I'm really looking forward to touching base with you guys again, to seeing how you're going through that journey because of. You are the first project within the Node X program. And I think there couldn't be a better one. You're exemplifying everything that we're trying to support and your excitement, as I just mentioned around data, the data governance elements, and the access and the control elements.


For me, one of the most exciting parts as is collaborating to build outcomes like dashboards and systems, but it's figuring out how the world's data owners are gonna work together and what they control and where it's, it's just fascinating. And so I think you're going to get a lot out of that. That's it for this check-in I'd like to thank everyone from the University of Waterloo, Dr.

Jones for your time today, this has been really exciting learning about your project. And like I said, I can't wait to check back in and see how you're doing in a few months' time.


-ends


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