 So good afternoon, everyone. I bring you greetings from sunny, Florida Again, my name is Kerry Jordan and I'm the senior director of equity and assessment for the carpentries And I'm so happy to be here to address you this afternoon The title of my keynote is building confidence achieving equity a call to action for open source communities I want to start by saying thank you to the CSV conference organizers And in particular to John Chidaki and Daniel Robinson for making sure that I got here on time and safely And I also want to recognize my carpentries colleagues in the room, Sarah Rono and Tracy Teal I'm truly blessed to be able to work with these women every day and just seeing them in the audience makes this a little less terrifying So thank you so much for being here So our journey today begins with thoughts on data analytics coming from someone who frequently feels like an imposter in this space And I'll talk a little bit about imposter syndrome as well I'll then share some thoughts on averting the data skills crisis and explain a little bit about the carpentries, who we are and what we do Then I'll share some personal stories about the role that data has played in my own life And what I've learned about building confidence to work with data We'll talk about mentoring and meaningful equity and finally we'll end with a call to action At some point I may burst into song so just be prepared for that and hopefully we'll have a lot of fun throughout the talk So here we go I remember the first time a colleague introduced me to someone who was looking to learn about assessment in data science And in their introduction they used the word expert I was totally thrown off because I had only been working in this space for about six months And I could barely import a CSE into our barely My research practices were horrible in my opinion, I had no idea what a workflow was I'd never heard of the term reproducibility and I had been storing my data in multiple formats all over the place If you don't believe me I'll prove it to you So here's a screenshot of an email that I received from a graduate student who wanted to replicate the study that I did for my dissertation My background is mechanical engineering as Sarah mentioned and my PhD is in engineering education So my dissertation research was an intervention to increase engineering self-efficacy for first year students of color in engineering So in this email the graduate student wanted to understand how many students were in my treatment group and how many students were in my control group And we had been emailing each other back and forth, back and forth to recover my notes, my spreadsheets It was very embarrassing, they were nowhere to be found This was more than two years after I defended and had since completed a postdoc and I was working for Data Carpentry A project that stresses the importance of reproducibility in research Alright, so needless to say, working in this space and being introduced as an expert while at the same time being ashamed that I couldn't help another student replicate my own work made me feel like a total imposter So in my own words, imposter syndrome is the belief that your success is illegitimate and at some point you'll be found out I was working for a project that develops and teaches lessons on the fundamental data skills needed to conduct research But my own research practices were horrible in my opinion And any moment now they were going to find me out The day I realized that I wasn't an imposter in this space was the day that I received this note from a colleague It reads, thank you for letting me help with the assessment process and driving our community forward with data You see what makes an expert? Isn't that the individual knows everything? Having comprehensive or authoritative knowledge is nothing if you aren't contributing or creating an environment where others feel comfortable contributing to the work So for this individual, I had done just that. I presented an environment where she could contribute as well I no longer feel like an imposter, plus I learned how to keep my raw data raw and all my stuff is on GitHub I'm proud to be in front of a room full of enthusiasts who are passionate about data and its application to society Because even as we sit in this room, decisions are being made on our behalf with all sorts of data Data analytics helps identify new opportunities that in turn solve problems and help us develop products, services to support the public What I've learned is that data analytics is not about one person's expertise, it's about collaboration and community From life sciences and banking to manufacturing and healthcare and government, data analytics is solving problems and saving lives But it's also causing distress and it's causing harm because not all of the experts or those with the comprehensive knowledge and skills in this space have a diversity of thoughts and experiences Let's take the United States Transportation Security Administration just for an example So I fly a lot, as I'm sure a lot of us do in this room, and I can't tell you how many times I've been pulled aside to have my hair pad down Thick locks, braids, wigs, and even turbines are often trigger alarms for airport security full body scanners Now don't get me wrong, I appreciate the work that our TSA does to keep us safe But being pulled out of line in the airport and having someone run their fingers through your hair is extremely frustrating It makes me think, it makes me wonder, are there experts in the room designing the technology that looks anything like me? Who would ever consider me an expert when so many see me as a threat? So I want to draw your attention to this text message thread from me and my father We're going to take a moment to do some jargon busting So he says, shout out to the acronym though, what are you doing? Okay, I'm learning Python, you need me to help? Let me know I said, hey, you know Python? Yep, it's a big snake No dad, no dad, it's a computer program So throughout this talk, I'll refer to several concepts and I want to take the opportunity to define them here The definitions for these terms were adapted from California State East Bay's Diversity Leadership and Employee Wellness website When I talk about accessibility, I'm referring to program and process design and implementation that offers multiple avenues for access and participation In other words, accessibility is the usability of a product, service, environment or facility by the widest range of capabilities For example, you may be familiar with accessibility accommodations like closed captioning or displaying the audio portion of a program as text on the screen Others include identifying unusual words or jargon busting like we're doing right now Certain conditions make it difficult to understand non-literal word usage or figurative language I'll tell you a story about how I learned this the hard way A group of carpentries instructors were putting together a series of community calls with various themes ranging from teaching your first workshop to navigating unpredictable learning environments Well, I had the bright idea of a call about running workshops with little to no money and I propose that we call it balling on a budget Now, this title would have gone over fantastically in the black community because balling means to have wealth or influence However, in other areas across the globe, balling is extremely offensive So in our work, what can we do to make our code, our content knowledge, our documentation accessible and the language understandable to all? That's what accessibility is about So now let's take a look at diversity Diversity refers to individual differences and group social differences that can be engaged in the service of learning Our individual differences can be personality, language, language preferences and life experiences Our group social differences can be race, ethnicity, class, gender, gender identity, sexual orientation, country, origin, ability status, cultural, political, religious and any other affiliations Rather than labeling one another and seeing our differences as a threat, we can use our differences to engage in the service of learning and we can see each other as human When I talk about equity, it's really creating opportunities for equal access and participation in programs that are capable of closing participation gaps in our community This is me teaching my engineer like a girl after school program So for example, Angus McGuire adapted this image to illustrate the difference between equality and equity Equality is about sameness, it promotes justice by giving everyone the same thing But if it can only work if everyone starts from the same place, right? So in this example, equality only works if everyone is the same height Equity by contrast is about fairness, it's about making sure people have the access to the same opportunities Sometimes our differences or our history can create barriers to participation, so we must first achieve equity before we can enjoy equality Lastly, inclusion is the active, intentional and ongoing engagement of diverse people and communities This increases awareness, content knowledge and empathetic understanding of the ways in which we interact within and change our community I want to draw your attention to one of my favorite quotes by Verna Myers Diversity is being invited to the party, inclusion is being asked to dance Alright, come on people, how would you feel? You put your best foot forward, you work so hard so that there's no way you don't get invited to the party And then when you get there, you're standing in the corner the whole night because nobody asks you to dance You know the song? It's more than inviting people to the conversation or the workshop or the conference who don't look like you It's ensuring that they're able to interact and contribute in ways that are meaningful to them So now that we're all hopefully familiar with the terminology, let's talk about approaches to building confidence to work with data with these concepts in mind I'm going to take a drink of water, it's a lot of words Training for data skills is more critical now than ever In the past decade, we've seen the creation of certification programs and graduate programs for data science as well as interactive, self-paced online learning opportunities Today's learners are often learning on the job and need the flexibility of short, self-paced learning experiences Though much of training and learning is happening online, the importance of guided instruction and learner-instructor interaction is still apparent Data-heavy research fields require the use of sophisticated tools and computational resources Unfortunately, many researchers or individuals who have data but don't know what to do with it are prone to using tools that introduce errors such as automatic data formatting and unexpected sorting results There's a better way and there are better tools available today for researchers including statistical programs such as R&Python and cloud resources The problem with these solutions though is in the training, training the attended user base and how to leverage them There's just not enough time in your day and my day for formal training programs and even if there were, there are many institutions that don't access the technology or have the bandwidth to participate in formal training programs Low hanging fruit, in my opinion, is to reach diverse populations through training Exposing diverse communities to coding and data will expand the number, type and caliber of these projects So, how do we bring people with diverse perspectives to data? How do we empower more people to work with data when the keepers of the conversation don't look like or identify with them? We know that empowering more people to work with data allows us to answer more questions in science and society, but what does it take to attract and retain them? Something the Carpentries is doing to address this issue is aiming for inclusive pedagogy in our training and resources So I'll tell you a little bit about what we do The Carpentries were formed to teach computation and data science skills through short impactful workshops The model that we follow is to train the trainer where we certify volunteer instructors within the communities that they're trying to reach All course material are open source and in the public domain and free to use and contribute to Hint, hint In this way, we are able to scale beyond our organization and build regional self-sustaining learning and support communities for advancing research The Carpentries is a fiscally sponsored project of community initiatives of registered 501c3 nonprofit based in California in the United States The Carpentries project includes software carpentry, data carpentry and library carpentry And all of our community of instructors, maintainers, helpers, supporters who share a mission to teach foundational computation and data science skills for researchers, technologists and librarians The Carpentries builds global capacity and essential data and computation skills for conducting efficient, open and reproducible research We train and foster an active, inclusive, diverse community of learners and instructors that promotes and models the importance of software and data in research We collaboratively develop openly available lessons and deliver these lessons using evidence-based teaching approaches All Carpentries workshops are community driven and taught globally So before I tell you about each of the lesson programs, are there any Carpentries instructors in the room? Talk to them, talk to them! So software carpentry was founded in 1998 to develop materials and train instructors to teach computing skills to researchers in science, medicine, engineering and other disciplines Since 2012, volunteer instructors have run workshops globally and impacted more than 34,000 researchers Software carpentry lessons include three core topics, which are the Unix Shell, version control with Git and then a programming language, either R or Python We have lessons in English and a few of our lessons are also in Spanish Yes! Data carpentry was founded in 2015 Its curriculum centers around the fundamental data skills needed to conduct reproducible research Data carpentry lessons cover several domains including social sciences, ecology, geospatial, and genomics And lessons are developed collaboratively Lesson content includes data organization and spreadsheets, data cleaning with open refine, SQL, and then of course R and Python programming languages And then lastly onboarded as a lesson program in 2018, library carpentry develops lessons and teaches workshops for people who work in library or information related roles Library carpentry's goal is to create an on-ramp to empower this community to use software and data Additionally, library carpentry seeks to train individuals on efficient and effective reproducible data and software practices Library carpentry's core curriculum includes an intro to data, the Unix Shell, and then data cleaning with open refine So this is the exciting part, this is the meat of my keynote For the next few minutes, I'd like to focus on the pedagogy that has made our approach sustainable For the communities that we reach and what we, the people in this room and those who are watching online, thanks mom and dad Can do to democratize data, build confidence in ourselves and others, and support our mission and vision to solve problems in a collaborative way So I'll do this by discussing how the carpentry's teaching practices align with what I've learned in my personal and my professional journey And then we'll wrap up with a call to action and I'm going to collect some data on you because I am a researcher This is what we do So I took some time to reflect on my own life and where data played a significant role I was born in Detroit, Michigan in the United States in the 1980s During a time when my city was at or near the top of unemployment, poverty per capita and infant mortality My parents were wed and brought me into this unpredictable world, my parental During this period, my city had gone through horrific disappointment with the reversal of Milliken v. Bradley A federal court decision that would have actively desegregated Detroit and suburban communities It would have fixed many of my city's problems In the 80s, my city became notorious for crime and was repeatedly dubbed the arson capital of America The murder capital of America and the most dangerous city in America It wasn't all doom and gloom for my city though in the 80s The Detroit Tigers won the World Series during that time and both Nelson Mandela and Pope John Paul II visited Detroit But you know what? I don't remember any of that I remember growing up in this house I remember backyard barbecues I remember slumber parties with my cousin Shawnee who looked exactly like me I remember all of my uncles living in our basement at one point or another I remember making snow angels in the winter I remember jumping into the fire hydrant in the summer I remember when I found out it was illegal to open fire hydrant I remember Christmas lights and Thanksgiving dinners The first time my mom added baby carrots to our spaghetti that was really interesting today I remember loving my house and loving my family and loving my city You may have noticed that I repeatedly referred to Detroit as my city It was then, and even though I don't still live there, it still is my city Because though the plight of Detroit was gruesome, living there taught me persistence and resilience And I need that to work in data I remember some other things about growing up in Detroit I remember dropping to the floor as gunshots rang in the new year or on a random Tuesday I remember having our home broken in two and all our Christmas gifts being stolen I remember my brother being arrested Because he fit the description of an armed robber I remember how difficult it was to plan celebrations with my parents Since they've been divorced since I was three All of these anecdotes are data points And if we were to trust this data A Detroiter like me born in the 80s would presently live below the poverty line And work in either accommodation or food services If we trusted the data A Detroiter like me born in the 80s would presently rely on federally funded programs to support herself But there was another plan from me, and that plan begins with these two Miley and Albert My parents taught me to work my tail off so I can get good grades I played sports, I sang in the choir, obviously I did everything that I needed to do to get into engineering school About a 10 hour drive north of Detroit There's a small town in the upper peninsula of Michigan named Houghton Anybody heard of it? Thank you Nobody, nobody, no sorry That's where I went to school for my undergrad and masters Michigan Technological University was a place that I never thought I would end up I was one of the only women and one of the only people of color in most of my classes But I was determined to do well because I got accepted into engineering school I was invited to the party After earning a bachelor's and master's in mechanical engineering, I decided to pursue my PhD But I wanted to address the fact that there were not enough people who looked like me studying in the field And so I switched my focus from mechanical engineering to engineering education That's how I learned many of the techniques that the carpentries uses to teach workshops and build community So let's explore them together now Building confidence to work with data requires multiple approaches Combining the power of guided instruction with the flexibility of short focused learning experiences Has the potential to achieve equity among practitioners who want to work with data By acknowledging and building upon the four pillars of self-efficacy The carpentries has been able to develop and train communities of practice by sharing good data practices So here's what I mean by self-efficacy, a little more jargon busting One of my favorite quotes is from Mahatma Gandhi And it reads, if I have the belief that I can do it I shall surely acquire the capacity to do it Even if I may not have it at the beginning Gandhi's quote describes how I think about self-efficacy Self-efficacy refers to an individual's belief that she is capable of solving a problem or taking action in a certain goal Such as writing a computer program to solve a problem that she's interested in Self-efficacy is formed by performance outcomes, vicarious experiences, verbal persuasions, and physiological feedback Increasing self-efficacy can ultimately improve access and equity for individuals who are historically underrepresented and underserved in the data science space So I didn't come up with any of that, that was all Albert Bandura, just in case you were wondering So I get this question a lot, what's the difference between self-confidence and self-efficacy? Self-confidence is trusting in your abilities, your qualities, and your judgment However, self-confidence refers to a more generalized personality characteristic Self-efficacy points to your ability to succeed in a very specific task or accomplish a specific situation So we'll go over the four pillars of self-efficacy. I want to start with performance outcomes with a short video So when we talk about performance outcomes, it means previous success that leads you to believe you are capable of completing a similar task in the future Comparing the Carpentries pre- and post-workshop scores, we see full-point confidence gains in learners' ability to write a program, to solve a problem in their work, and even in their overall confidence in programming So this is what we talk about performance outcomes, and this is after a two-day workshop When you see someone else completing a task, you believe you can do the same This is one of the reasons why I love the Carpentries teaching practices Rather than displaying a slide deck of code, instructors deliver content by live coding These are vicarious experiences This method provides learners the opportunity to practice and receive continuous feedback about their code It is important to keep in mind, however, that feedback is not helpful if you cannot understand it Live coding facilitates tacit knowledge or learning by watching how others do things And the best way I can describe verbal persuasions is by showing you another short video It took out a moment of senior hello recording for me I was about to send a still shot and I thought, well, not just video, take me saying hello to my darling Carrie I'm about to read. Anyway, love you. Bye Supportive people in your life, such as teachers, family, or mentors, help you build self-efficacy The Carpentries engages in verbal persuasion by way of our mentoring groups Mentoring groups are small groups of instructors led by a mentor, usually four to five participants And they focus on contributing to lessons, development confidence and skill in teaching The logistics of running a workshop and tried and true events that foster community building Lastly, we'll talk about physiological feedback Experiencing sensations and emotions can affect your ability to see yourself completing a task Let's check out this video to see what I mean We tend to think of emotional arousal in terms of excitement and joy, like you saw from my father dancing on his birthday But at times, learning environments can create a sense of anxiety If they aren't designed and facilitated with equity, inclusion, and accessibility in mind Again One more time back The Carpentries are committed to making participation in our workshops a harassment-free experience for everyone Regardless of level of experience, gender, gender identity, and expression Sexual orientation, your personal parents, body size, race, ethnicity, age, religion We establish norms for interaction by having, discussing, and enforcing a code of conduct Such that our workshops provide open and inclusive learning environments So to recap, I said a lot just now Considering any or all of the factors determining self-efficacy Can help us not only build our own self-efficacy and general confidence to work with data But it can ultimately achieve equity and help each of us feel as if our contributions are welcome and acknowledged I've asked us to consider many things this afternoon And now it's time to bring it home with this thought Science, scholarship, and society is better by having diverse people with the skills And the abilities to address questions that are important to them I encourage us to work together to provide easily accessible resources For people who are unfamiliar with the tools and the technology that we've grown to love and hate Sometimes I hate it, sometimes I love it What if there were a greater diversity in the spoken languages that we teach and interact How can we recognize and appreciate the different cultural norms that exist around data Programming, teaching, and volunteering in different regions How can we recognize and value the various types of contributions that we see in this space How can we work with existing organizations to reach broader communities Rather than building or reinventing our own networks How can we authentically work with broader communities Rather than approach our work with the we're doing it for them mentality We won't be able to answer all these questions or solve these issues over the next two days But what I do want us to realize is that our story and our contributions matter And if we're going to build confidence in ourselves and the way we interact with one another We can only go further when we go together Now I wouldn't be a researcher if I didn't collect data So if you're able to, please follow the URL that I put on my call to action slide It leads to the link from my slide deck and also a prompt that I'd like us to take about five Yes, we have five minutes The prompt I would like us to take five minutes to share Please share an experience that may have created a barrier to you participating in data Or open source communities and how that barrier could have been removed So we'll take five minutes to, and if you have any issues with the prompt I was going to say put up a red sticky but I didn't pass out any sticky I'm so used to saying that We'll take five minutes to answer the prompt and then questions