Check out the resources our participants created at OSF: https://osf.io/r8tht/
Have you heard? With much generosity from our pals at facet publishing, we’re raffling off books to help you better Love Your Data! More details on the raffle page.
Keep up with all the events happening internationally during Love Your Data Week 2017 on Twitter!
When is it?
Monday, 2/13/17 – Friday, 2/17/17
Contact us on Twitter (#LYD17 #loveyourdata, or @IandPangurBan) or E-mail the planning committee (see list below) with any questions.
What is it?
Similar to Open Access Week, the purpose of the Love Your Data (LYD) campaign is to raise awareness and build a community to engage on topics related to research data management, sharing, preservation, reuse, and library-based research data services. We will share practical tips, resources, and stories to help researchers at any stage in their career use good data practices.
LYD week is a social media event coordinated by research data specialists, mostly working in academic and research libraries or data archives or centers. We believe research data are the foundation of the scholarly record and crucial for advancing our knowledge of the world around us. If you care about research data, please join us! This campaign is open to any institution – small, large, research intensive or not, so please feel free to share, adapt, and improve upon it. We encourage individuals, data librarians or otherwise, to participate in the campaign.
The theme for 2017 is emphasizing data quality for researchers at any stage in their career. Each day will have a message to drive the conversation and we will share resources, tips and tricks, stories (both success and horror!) and examples. All we ask in return is that you share your own experiences and results from the daily activities to keep the conversation lively.
Where is it?
The event will take place mainly online, although some institutions may choose to have in-person events (for more details contact the local site coordinator of a participating institution).
How can I get involved?
Join us by sharing the daily messages relating to the 2017 theme data quality and engaging researchers in the conversation. E-mail or Tweet (@IandPangurBan) with any questions. The more institutions that participate, the more successful LYD Week will be.
Librarians and data specialists: consider hosting local events/classes in conjunction with the campaign.
Specifics for During LYD Week:
Read, share, and adopt the information provided each day by the LYD planning committee.
Join the conversation on Twitter (#LYD17 #loveyourdata), share your insights on Instagram or Facebook (#LYD17 #loveyourdata), and check out the great resources on our Pinterest board.
There are several tools like IFTTT which can automate the connection between multiple social media accounts. It may be useful for larger groups to use these types of tools to set up connections between multiple accounts across the platforms.
The main conversation will take place on Twitter. Use the hashtag (#LYD17 #loveyourdata) in all your tweets so that you are part of the conversation.
Instagram will primarily be used for sharing student products resulting from the daily activities or anything else they want to share.
The LYD Pinterest board is created to share resources and stories, hopefully to spur conversation about tips and local resources.
Some campuses have a strong presence on Facebook, so it may be useful to post links to the Pinterest board and Twitter conversation to Facebook.
2017 Planning Committee
Thea Atwood – University of Massachusetts, Amherst
Michelle Bass – Stanford University
Heather Coates – Indiana University-Purdue University Indianapolis (IUPUI)
Patricia Condon – University of New Hampshire
Erin Foster – Indiana University School of Medicine
Carla Graebner – Simon Fraser University
Cinthya Ippoliti – Oklahoma State University
Renaine Julian – Florida State University
Sebastian Karcher – Syracuse University
Inna Kouper – Indiana University, Bloomington
Amy Neeser – University of Michigan
Melissa Ratajeski – University of Pittsburgh
|Monday: Defining Data Quality|
|Tuesday: Documenting, Describing, Defining|
|Wednesday: Good Data Examples|
|Thursday: Finding the Right Data|
|Friday: Rescuing Unloved Data|