STEM Data Fellow Spotlight: Diego Bustamante

For Love Data Week 2022, we are highlighting our FSU STEM Libraries Data Fellows! These posts, written by the fellows themselves, tell their stories of how they became interested in data-related work and their experience as a data fellow to this point. Today’s post is contributed by Diego Bustamante.

Prior to my role as a Data Fellow, my idea of what data is was defined by my previous work with quantitative data collected from laboratory experiments. For example, when I worked as a Research Assistant I recorded quantitative data for chemistry experiments, like mass, temperature, volume, etc. I then conducted statistical analysis on the data in order to draw conclusions from each experiment. I personally enjoy collecting and analyzing data, especially because it can lead to many scientific and technological advancements!

While searching for jobs in FSU’s NoleNetwork in summer 2021, one job title that immediately caught my attention was “FSU STEM Libraries Data Fellow.” The job description was unique amongst other jobs offered on campus. As a data fellow, I was offered the opportunity to develop several professional skills in data reference, co-hosting programming language workshops, writing and publishing blog posts, and many more. I felt like it was a great opportunity and a good fit with my previous experience and skills, and so I decided to apply. Thankfully, I was selected as one of the inaugural data fellows, leading to a journey of professional and personal development that has thus far surpassed my initial expectations. 

One of my first tasks in the program was meeting with different librarians at FSU Libraries. In these meetings I was able to learn about different methods and applications for data analysis in a variety of disciplines. For example, I learned that the Digital Humanities Librarian uses a text-mining software to find specific words from books published in the 1800s. She used the data drawn from the software to analyze certain traits of the story by counting the amount of times a character participates in an interaction of this type. This experience helped me realize that qualitative data sets can be used to draw similar conclusions about a study as quantitative data. 

Another concept that I have become familiar with while working as a Data Fellow is open data. We discussed this concept during a workshop where we talked about the potential benefits of making research data openly accessible to the wider research community. Initially, I was hesitant regarding the concept of open data, because I saw academic research as a “race” to find a solution to a given problem. However, further discussion of how researchers are compensated for sharing their data made me realize that it is possible to benefit from open data on a personal and global level. 

Currently, I am still learning about the many different types of data, its definitions, applications, and its importance. I am also working on developing an open source Canvas module on MATLAB where I explain the basics of the math based programming language in a student friendly manner. I look forward to sharing more about this work in the future!

STEM Data Fellow Spotlight: William-Elijah Clark

For Love Data Week 2022, we are highlighting our FSU STEM Libraries Data Fellows! These posts, written by the fellows themselves, tell their stories of how they became interested in data-related work and their experience as a data fellow to this point. Today’s post is contributed by William-Elijah Clark.

It’s hard to say exactly when I first got interested in data. After all, my mother was a statistician, so I’ve always been surrounded by data since I was in elementary school — from Arkansas Department of Health public health and mortality statistics to Disney World focus groups and market research. Personally, I started liking statistics when I took UCF’s equivalent to QMB 3200 and Econometrics. This experience extended into being a research assistant at UCF, and even into conducting and monitoring surveys at Universal Orlando Resort! Through my Econometrics course and from additional professional development opportunities at Universal, I was also able gain experience with R (although I didn’t learn it to the extent that I would call myself a professional data analyst or a data scientist.)

Due to the COVID-19 pandemic and subsequent lockdowns in Orlando back in 2020, I decided to go back to school here at Florida State University for Statistics, especially considering that FSU has a SAS coding certificate! Overall, I came to Florida State University with over two years of professional survey experience between academia and hospitality industry work.           

I spent time in 2020 taking calculus courses and statistics electives here at FSU to hone my data analysis skills further. I then saw an opportunity to apply for a FSU Libraries data fellowship beginning in Fall 2021. I decided to apply, as this position would give me the opportunity to utilize some of the skills I obtained from my previous positions and coursework at UCF and FSU, and hopefully develop some new skills to further myself in my goals of becoming a data analyst (and hopefully even an econometrician).

So far in my fellowship here at FSU Libraries, I have had the opportunity to gain some experience with MATLAB and SQL through the Data @ Your Desk workshops at Dirac, as well as some experience writing surveys in Qualtrics (as opposed to just conducting and monitoring surveys). I’ve also had the opportunity to learn more about citation management, library research, and data management. I’ve even been able to explain concepts for MS Excel to a patron via the online “Ask a Data Librarian” feature on the FSU Libraries website. This all said, I’m looking forward to applying some of my previous R coding and statistical analysis skills to some survey data for FSU Libraries this semester.

How do the Pros do Data Analysis?

By: Diego Bustamante and William-Elijah Clark

INTRODUCTION

As technology continues to evolve, the infrastructure needed to run this technology gets more and more sophisticated. Processes and tasks carried out by personal computers, smartphones, and appliances are increasingly automated and run with minimal input from the user. This is made possible through code that is developed with one or more computer programming languages.  However, with the increase in the quantity of software and programming applications, the demand for programmers and the number of languages they are required to learn has increased.  Furthermore, many employers now require skills in data analysis and computer programming as prerequisites for job applications.  In this blog post, we will discuss the most in demand languages in the market and give a brief explanation of each.  (Grand Canyon University 2020; Jiidee 2020; Meinke 2020; University of California – Berkeley, n.d.) 

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What is ‘Big Data’ Anyway?

By: Diego Bustamante and William-Elijah Clark

Maybe you’re on Twitter one day and search ‘#Statistics’ to look up some information for your Introductory Statistics course. Before you know it, you scroll through and see several tweets that are also marked with ‘#BigData’, and you’re left with more questions than you had when you started your search. Maybe you try to search for “big data” on Google, see the definition from Oxford, and are then left with even more questions: 

  • How large is “extremely large?”
  • What kind of patterns, trends, and interactions are we talking about?
  • What isn’t big data?

Big data as a term has become synonymous with the growth of digital data and the glut of information available to researchers and the public. Furthermore, there is a growing interest by both the public and private sector in utilizing large datasets to provide insight into market trends and to improve decision making. However, the exact definition of big data is sometimes unclear and can vary widely depending on who you ask. Businesses, nonprofit organizations, government agencies, and academic researchers each view big data in a different context and with different goals for its use. (University of Wisconsin Data Science, n.d.)

Above: a Google Trends graph that shows the number of searches for the term “Big Data” from 2007 to 2017

In this blog post, we aim to provide clarity and insight into the origins and definitions of big data.  We will also discuss the potential benefits and challenges surrounding big data. In doing so, we will provide some examples linking big data to applications or data that you may interact with on a daily basis.

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My Experience as a STEM Research Data Services Assistant

By: Paxton Welton

Welcome to the third post in the Get Data Lit! blog series. This post will focus on my experience working as a STEM Research Data Services Associate with FSU Libraries during the 2020-2021 school year. In this role, I assisted with outreach and education to FSU students, groups, and organizations at Florida State University around STEM research data services. 

My name is Paxton Welton and I will be graduating with a bachelor’s degree in Finance this semester. One question that you might have right from the start-why is a finance major working in a STEM-focused role? 

When applying for jobs prior to this academic year, I knew I wanted a role that would challenge me and allow me to develop new skills. I believed that being the Research Data Services Assistant would provide me the appropriate level of challenge and opportunity that I was looking for. By and large, I believe that my experience provided me with just that. There was a major learning curve that I faced when I first started this role. While I had a grasp of the basics of data literacy and research data services, I quickly realized I did not know nearly enough to be able to properly speak to student groups about these topics. During the first few weeks of the fall semester, I spent a significant portion of my time getting a stronger understanding of data and everything FSU STEM Libraries had to offer to its students in regards to research data. By reading countless articles about data literacy and engaging in weekly discussions with my supervisor Dr. Nick Ruhs, the STEM Data & Research Librarian, I became confident in my working knowledge on these topics. 

As the STEM Research Data Services Assistant, one of my main responsibilities was conducting targeted outreach to different student organizations across campus. When I first started this process I reached out specifically to STEM-focused groups. This process involved me initiating conversations via email with registered student organizations (RSOs) to introduce them  to the research data services FSU Libraries offers them.  In several cases, we were invited to meet and/or present synchronously to these groups. This gave us a chance to share more in-depth information about our services and just how valuable they are to students. It also gave students a chance to ask us any questions they may have. Getting the chance to directly interact with students and help them find the right resources to feel more prepared for their future was by far my favorite part of this role.

I also had the opportunity to contribute to data-related events hosted by FSU STEM Libraries. Two examples include Love Data Week in February and the Virtual FSU Libraries Data Services Quest in March. My involvement in these events allowed me to see the entire process of creating programming for students. I was able to sit in on brainstorming meetings, give my input on the marketing materials, and create content for the events.

One of my main focuses throughout this year has been to develop and create this blog series you are reading right now–Get Data Lit! The focus of this blog series was data literacy and its applicability to student’s educational experiences. As such, I had the chance to put into practice the new data literacy skills I learned in this role. I also had the opportunity to connect data literacy to real-world practice and explain the importance of critically evaluating data. Doing so made me realize just how important learning data skills are for my future career and education.

One thing that proved to be a common theme throughout all the work I was doing is that data is powerful and knowing how to work with it is even more powerful. From a career in law to a career in fashion, you are going to be working with data in some form. Learning how to critically evaluate data is going to give you the skills you need to stand out in the future. 

By taking on a job in a discipline that I knew very little about, I was able to challenge myself and make the most out of this past year. From getting to work on student programming events to developing a blog series, I was constantly challenged and learning something new. 

Research Data Services: Vital data skills for future career and academic growth

By: Paxton Welton and Nick Ruhs

Introduction 

Welcome to our second post in the Get Data Lit! blog series. This post is inspired by the theme of Love Data Week (Feb. 8-12), “Data: Building a Better Future.” Here we will focus on the research data services provided by FSU Libraries and how utilizing these valuable services can have positive effects on your future research, learning, and career prospects.  While the focus of this post is primary undergraduate students, much of the information we provide below is useful for students at any stage of their academic careers.

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The 2014 Election and the power of open data!

I spend a considerable portion of my time convincing researchers of the benefits associated with publishing their data online in open repositories. Bringing up things like reproducibility of research and the idea of others using their original data sets to advance scholarship in their field or another are my usual selling points. Academics produce vast amounts of data that has value well beyond the scope of their original project. That being said, government agencies produce endless amounts of data and information as they conduct their day to day business. There are obvious products that have mounds of useful information in them, like the U.S. Census or the American Community Survey. Governments rely on information in all sorts of formats to perform countless tasks on a day to day basis. For example, many local governments rely on spatial data of their infrastructure (roads, sewers, power lines) to set maintenance schedules or to select an ideal space for new residential development.

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What is a Census Research Data Center and Why Should You Care?

This semester, FSU became the newest consortial member of Atlanta’s Census Research Data Center. Funded primarily by the College of Social Sciences and the Office of Research, the Florida State community can now use Census micro-data without paying lab fees, which can range upwards of $15,000 per project.  There are currently 18 Census Research Data Centers in the United States, and outside of North Carolina’s Research Triangle the only one located in the southeastern United States is The Federal Reserve Bank of Atlanta.

So, what is a Census Research Data Center? The Center for Economic Studies defines Census Research Data Centers (RDCs) as U.S. Census Bureau facilities, staffed by a Census Bureau employee, which meet all physical and computer security requirements for access to restricted–use data. At RDCs, qualified researchers with approved projects receive restricted access to selected non–public Census Bureau data files.

Where do college graduates work? Visualization based on 2012 Census data.

To understand the true value of doing research with non-public data from the RDC, it’s important to note the difference between micro data and macro data, which is often referred to as aggregate data. When most of us use datasets for research or analysis, we’re looking at summary figures. For example, if you extract Census data for analysis, you’re typically looking at some sort of summary or aggregation for a specific geographic unit. These geographic units range from state, county, city as well as much smaller units such as census tracts and block groups. Regardless of unit of analysis, the data itself is a summarization of individual survey responses for participants in that specific area.