Archive for the ‘News From NN/LM PNR’ Category
Tuesday, March 21st, 2017
Data is a hot topic these days and it’s a challenge to keep up with all the new titles being released. Here are eight books that are definitely worth a look if you’d like to learn more about the world of data and its influence on librarianship…
Big Data: A revolution that will transform how we live, work, and think, by Viktor Mayer-Schönberger and Kenneth Cukier (2013) A great overview of “big data” and it’s impact on the way we do science.
Data Management for Libraries: A LITA guide, by Laura Krier and Carly A. Strasser (2013) A quick introduction to data management.
Data Management for Researchers: Organize, maintain and share your data for research success, by Kristin Briney (2015) A detailed guide for researchers.
The Accidental Data Scientist: Big data applications and opportunities for librarians and information professionals, by Amy Affelt (2015) A playful primer for the curious librarian.
Big Data, Little Data, No Data: Scholarship in the networked world (MIT Press), by Christine L. Borgman (2015) An interesting exploration of data’s impact on the future of scholarship.
Databrarianship: The academic data librarian in theory and practice, edited by Lynda Kellam and Kristi Thompson (2016) A scholarly collection of articles on data librarianship specifically for the academic librarian.
The Medical Library Association Guide to Data Management for Librarians (Medical Library Association Books Series), by Lisa Federer (2016) An indispensable resource for the medical librarian interested in data management.
The Data Librarian’s Handbook, by John Southall and Robin Rice (2016) A manual for the library student, teacher or working professional on data librarianship.
Next month, a list of some of the best data blogs…
Friday, March 17th, 2017
Please join us for the first NNLM PNR Twitter chat, on Tuesday, April 11, from 12:00 – 1:00 pm, PST. The subject will be data, and the hashtag is #nnlmpnrchat. A twitter chat is a prearranged time to meet on Twitter and discuss a particular topic. To find the tweets in our conversation, search #nnlmpnrchat in the search box.
To learn more about Twitter chats, see Twitter Chat 101.
From the the Symplur Project: “A Twitter chat affords Twitter users the opportunity to engage in conversation with each other. A chat can either emerge from a new community that coalesces around a particular subject or keyword, or serve to focus the conversation of an existing community … Twitter hashtags, including those relevant to the healthcare industry, help to organize conversations on specific topics.”
Our two newest staff members, Research & Data Coordinators Ann Glusker and Ann Madhavan, will be featured! Topics include:
- Data literacy
- Access to NIH data
- Research Data Management Plans
- Training and Technology
- What is Big Data?
- Advocacy for Open Access
- Data respositories
- Reporting requirements for clinicaltrials.gov
If you have any specific questions, please email them to firstname.lastname@example.org, or just attend the chat and ask questions then. A transcript of the chat will be available from the Symplur Healthcare Hashtag Project. Questions? Contact Patricia Devine at 206-543-8275 or email@example.com. See you then!
Monday, March 13th, 2017
The NN/LM PNR is pleased to request applications for a new round of funding opportunities to support projects from May 1, 2017 through April 30, 2018. Applications submitted by April 14, 2017 will receive fullest consideration and will be reviewed on a first come first serve basis.
New funding opportunities include:
Community Health Outreach Award, two awards up to $9,500 each.
This award is to support outreach projects with aims to improve access and use of quality online health information for informed decisions about health in underserved communities. Possible activities include: 1) Promotional activities, including health fairs, exhibits and events to increase awareness and use of electronic resources; 2) Hands-on training sessions at conferences of health care providers about skills to identify, access, retrieve, evaluate, and use relevant electronic health information resources for patient and consumer health education; 3) Collaboration by one or more of the following: libraries (all types), public health agencies, academic or K-12 programs, healthcare workforce, or community organizations. (more…)
Friday, February 17th, 2017
How do data become unloved? We data users don’t love data that are messy, poorly documented, incomplete, or unwieldy, to name just a few frustrations. However, one important way that data become unloved is that they are just plain old. Older data tend not to be machine-readable, which can pretty much be the kiss of death. Digitization, while it’s improving, is still somewhat labor-intensive and costly, and so unless a data set is obviously worth the trouble, it may languish.
However, researchers are starting to explore whether there may be some hidden gems worth rescuing. One area in which this is happening is climate data, and a great example is the Glacier Photograph Collection from the National Snow and Ice Data Center (NSIDC). Before this collection was digitized, users had to travel to the NSIDC in Colorado, ask staff to find physical images or microfilm for them in the collection, and then deal with those physical artefacts. Not surprisingly, the collection had few users. However, digitizing these photographs (which can be considered data sources, as they contain information that can be analyzed) has made them not only accessible, but an important resource for documenting changes in glacier size and coverage. Digitizing some of the old photographs also suggests locations for repeat photographs from the same vantage point, which can indicate changes across time periods.
PHOTO: Left: William O. Field, 1941; Right: Bruce F. Molnia, 2004. Muir Glacier: From the Glacier Photograph Collection. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media.
But, using the above example is cheating a little bit; these photographs were unloved because they were undigitized, but it was clear that they were worth digitizing. In fact, it was so clear that NSIDC was able to get funding and enter into partnerships to get that work done. So, what if a researcher has a great idea, but needs sheer person-power to bring it to fruition? These days, crowd-sourcing may do the trick! Check out the Swiss project Data Rescue @ Home, in which citizen-volunteers are entering German climate data collected during WWII, and also have completed entering data from a weather station in the Solomon Islands collected in the early to mid-1900s. By January 2014, they reported having digitized 1.3 million values! They note: “The old data are expected to be very useful for different international research and reanalysis projects…[for example,] historical weather data from the Azores Islands are particularly valuable since the islands are located at the southern node of the most important climatic variability mode in the North Atlantic-European region, the so-called North Atlantic Oscillation (NAO), and there are not much other historical data available from the larger region.”
PHOTO: Example of data collected in the Solomon Islands, entered electronically by citizen-volunteers of the Data Rescue @ Home project (Accessed 2-13-17).
Interested in getting involved in a citizen-science project yourself? Here’s a list of possibilities! And, if you really get hooked, you may want to dive into some collections of older non-digitized data and consider starting your own project, to rescue the unloved data and give them new life.
OK, I’m off now to figure out how to get on the project where I can hang out on the beach in New Jersey and count horseshoe crabs!
Thursday, February 16th, 2017
The theme for Day 4 of “Love Your Data Week” is “Finding the Right Data”. There’s a lot of open national health data out there– Data.gov’s health portal, and the “Data and Tools” tab on the main page of the National Center for Health Statistics are good sources (also this list of open access data repositories has a good section on medicine).
But, any open data on the internet can be vulnerable if there isn’t a commitment to preserve it or if organizational priorities change, and government data are no exception. Enter DataLumos! This service, launched (not coincidentally) during “Love Your Data Week”, aims to preserve government data by archiving it into the future. The data will be gathered and maintained by ICPSR, the respected data center at the University of Michigan. Want to hear more? There’s a webinar about it tomorrow! You can register here.
Also, check out the wider work of DataRefuge and Data Rescue projects springing up across the United States (in fact, the University of Washington is hosting a Data Rescue event next weekend). We may not know yet why a data set could be important to preserve for the future, but careful and committed archiving at least will give future data scientists and seekers the option to use it.
And, it’s also no coincidence that the creators of the archive are using the term Lumos; it is the spell, in the Harry Potter series by J.K. Rowling, that turns a wand into a flashlight. The idea is that they are working to keep data sets well-lit by keeping them open. In future, there will be these and many other open data sets to choose from, to advance research and data science!
Wednesday, February 15th, 2017
Welcome to day three of Love Your Data Week 2017! Today’s topic is Good Data Examples. What makes data “good” or “well managed?” The Fair Data Principles: Findability, Accessibility, Interoperability, and Reusability are a good place to start. Published by Mark Wilkinson and his colleagues in 2016, these principles “put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.” 1A brief description of the principles, excerpted from Wilkinson’s article, explains:
To be Findable:
- F1. (meta)data are assigned a globally unique and persistent identifier
- F2. data are described with rich metadata (defined by R1 below)
- F3. metadata clearly and explicitly include the identifier of the data it describes
- F4. (meta)data are registered or indexed in a searchable resource