Love Data Week, Feb 8-12, 2021.

Love Data Week 2021

Love Data Week 2021

Love Data Week takes place the week of Valentine’s Day. Created to raise awareness of research data management, sharing, reuse, and preservation of data, it has been promoted by library and data professionals since 2016 in both online and in-person events.  You can see some of the events taking place around the world at the International Love Data Week 2021 schedule.

One of the ways we at RDS/UCL celebrate is to participate in the Adopt a Dataset program that ICPSR hosts every year.  ICPSR choses to highlight interesting data sets from their collection for individuals to explore to learn more about data. Once a dataset is chosen, you may fill out the Dataset adoption form and have your name added to the wall of adopters.  Adopters are encouraged to delve into the data by reading about the research, look at the variables, try out the analysis tools online, and read related publications.

ICPSR is a member consortium that UC belongs to that provides access to datasets from over 15,000 studies, over 5.6 million variables, which have had over 95,000 publications that cited those studies. It also provides curated data repository services for researchers, including secure data enclaves, which fulfill finding requirements for data management plans. Although most studies are quantitative in the social and behavioral sciences, there are also themed collections in the arts, humanities, and some health sciences. Teaching resources, online analysis tools, and pre-made exercise modules also are available to members.

There are a wide variety of datasets to choose from this year, ranging from education, to social media, social justice, to health. I chose to adopt one on music, Study of Jazz Artists, 2001 (ICPSR 35593). I started out by looking at the description of the study and how it was conducted. I then jumped into browsing the variables, one of the tabs found on the data set page. One of the great features of ICPSR is that you can search by variable if you are looking for specific studies to replicate or want to see if your own survey questions fit what others have asked in the past.

One variable jumped out at me – Q32- Age Began Playing First Instrument. My own children play instruments and I always wonder if we’ve started them at the right age or not. Looking at the result for this variable, I can see the unweighted results, including summary statistics and a variables chart. The median age was 9, the mode was 10, the maximum was 35, and the minimum was 1! I guess there’s still hope if you’re in your 30s to pick up an instrument to become a professional jazz musician!   The sweet spot to start your child appears to be the 8-10 year old range.

If you have questions about Love Data Week,  ICPSR, Data, Data Management Plans, Cleaning, Storing, Finding, or Using Data, contact us at Research and Data Services here at UC Libraries. We would love to help you with your projects, offer a workshop to your department or class, or discuss your data needs.

Love Your Data Week Day 5 Rescuing Unloved Data

Today’s LYD post is by Amy Koshoffer, Science Informationist based at the Geology Math and Physics Library with editorial support from Dr. Eric J. Tepe, Assistant Professor of Biology and Curator of the Margaret H. Fulford Herbarium.

It has been sometime since I stepped over the threshold of my old lab in the Care/Crawley Building. Many changes occurred in the interim including a move to another floor of the building. There are times I miss the bench research and the data I created in my time as a senior research assistant. One of my favorite techniques was microscopy and particularly Electron Microscopy (EM). I remember the multitude of samples processed, the long wait for samples to be ready to image and then finally all the amazing images we captured. Processing samples for EM imagining is a long and sometimes challenging technique. The samples need to be dehydrated and then infiltrated with a resin to stabilize the structures and prevent destruction from the electron beam during viewing. You might not know if a sample has been ideally preserved until you get to the imaging lab and begin to examine the sample. But what joy when the images look amazing with crisp detail and no water holes. So much work and resources went into the sample preservation and acquiring images.

I wonder what will happen to that effort in the years and decades to come. Are there others who might want to use the physical samples and digital images in their own work? Did I do what was needed to make sure that someone could reuse all the data created? Continue reading

Love Your Data Week Day 4 – Finding the Right Data

Today’s LYD post is by Don P. Jason III, MLIS, MS, Clinical Informationist based at the Donald C. Harrison Health Sciences Library.

Welcome to Day 4 of “Love Your Data Week!” Whether you’re a student analyzing a data set for a school project or a researcher combining data sets to create new insights, finding the right data is essential! This blog post will list a few places you can look to find free, authoritative and unique data sets. The data sets have be broken down into three categories:  US Government Data Sets, International Data Sets and Google Data Sets.

US Government Data Sets

Data.gov http://data.gov – This web site has an eclectic mix of datasets from criminal justice to climate data.  This government site encourages people to use the data to create web and mobile applications and design data visualizations.

US Census Bureau http://www.census.gov/data.html – This web site provides data on the US population and economy.  Utilizing this site’s data has never been easier thanks to new: API’s, data visualizations, mobile apps and interactive web apps.

Healthdata.gov https://www.healthdata.gov/ – This web site includes US healthcare data.  The site is dedicated to making high value health data more accessible to entrepreneurs, researchers and policy makers.

National Climatic Data Center http://www.ncdc.noaa.gov/data-access/quick-links#loc-clim – This is the world’s largest archive of weather data. It has a robust collection of environmental, meteorological and climate data sets from the US National Climatic Data Center.

Continue reading

Love Your Data Week Day 3 – Good data examples

Today’s Love Your Data Week’s post is by Tiffany Grant PhD, Interim Assistant Director for Research and Informatics at the Health Sciences Library (HSL) and Research Informationist.

Data, FAIR Data

If asked to define good data, the definitions would run the gamut, as the interpretation of the term will be specific to the types and formats of data typically collected by the individual. However, simply put, good data meets the standard of being of good quality, and data quality generally refers to the ability of data to serve the use it was intended. In short, data quality hinges on the reliability and application efficiency of data. The combination of good data quality and data documentation ensures accurate interpretation and reproducibility. Beyond documentation, a number of federal mandates dictate that data be shared beyond one’s own lab notebook, and in order to ensure proper interpretation and reproducibility of your data, it must be FAIR.

 

 

 

 

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Love Your Data Week Day 2 Documenting, Describing and Defining

Today’s Love Your Data Week’s post is by Tiffany Grant PhD, Interim Assistant Director for Research and Informatics at the Health Sciences Library (HSL) and Research Informationist.

The Big 3 of Data

Documenting, describing and defining your data are the 3 most critical components of good data management and your data legacy. If done properly, documentation ensures accurate interpretation and reproducibility of your data. Additionally, it improves the integrity of the scholarly record by providing a more complete picture of how your research was conducted.

Data Things to Do

  1. Document all file names and formats associated with your project
  2. Describe how your data was derived including a description of any equipment and/or software used in the process
    1. Describe your file naming conventions and folder structures
  3. Define any abbreviations, variables or codes used in your data or your file names/folders

Big 3 Data Basics

Who: Who are the contributors?

What: What kind of data was collected and what analyses were done to generate the data?

Why: Why was the project started, i.e. what questions did you hope to answer?

Where: Where did you get your data (if you aren’t the creator)? What is the physical location of the data?

How: How was your data generated?  

Message of the day

Good documentation tells people they can trust your data by enabling validation, replication, and reuse.

Love Your Data Week Day 1 Defining Data Quality

Today’s LYD post features the thoughts of Dylan Shields, the Graduate Assistant for the Chemistry-Biology Library and Chemistry Graduate Student in Anna Gudmundsdottir’s Lab.

Welcome back to another edition of Love Your Data Week!!

The first topic for this week is going to focus on DEFINING DATA QUALITY!

So what IS data quality? Well, first off it is important to note that data quality definitions and practices can differ quite vastly depending on the field of study. However, there are a few markers of data quality that can be broadly applicable to most research. These markers include: accuracy, consistency, completeness, and accessibility.

So what are these markers and why are they important?

Continue reading

Love Your Data Week 2017

Drop the roses and the box of chocolate because Love Your Data week is almost here.  All next week, the UC Libraries informationist team will be blogging loving tips about how to best care for your research data.  The theme for 2017 is emphasizing data quality for researchers at any stage in their career and the daily topics are:

Feb 13th  – Defining Data Quality

Feb 14th Documenting, Describing, Defining

Feb 15th  Good Data Examples

Feb 16th –  Finding the Right Data

Feb 17thRescuing Unloved Data

Follow the action or join in on Twitter using hashtags (#LYD17 #loveyourdata)

 

Other social media outlets will be Facebook, Instagram and Pinterest.

So much better for you and your data than a box of chocolate!

Questions and comments to AskData@uc.edu