Data Services http://dataservices.gmu.edu Fri, 23 Sep 2016 20:50:55 +0000 en-US hourly 1 Fall Hours and News http://dataservices.gmu.edu/2016/5314-fall-hours-and-news Tue, 20 Sep 2016 20:33:31 +0000 http://dataservices.gmu.edu/?p=5314 The Data Services’ Lab fall hours have been finalized. Note that the lab is closed during workshops.

We are continuing to add workshops to the workshop schedule, keep checking for updates!

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Fall Hours and Workshops Coming Soon! http://dataservices.gmu.edu/2016/5172-fall-hours-and-workshops-coming-soon Tue, 30 Aug 2016 22:00:06 +0000 http://dataservices.gmu.edu/?p=5172 Data Services’ finalized fall lab schedule will be available the week of September 12th.

We are in the process of adding workshops for the fall semester.

If you have any questions, please e-mail datahelp@gmu.edu

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Summer hours are by appointment http://dataservices.gmu.edu/2016/5106-summer-appointments Fri, 03 Jun 2016 14:14:43 +0000 http://dataservices.gmu.edu/?p=5106 If you need to access the Data Services Lab or set up a consultation, you will need to make an appointment.

Contact us for an appointment or for help by sending an e-mail.

Please note that our location for the summer is in 4607 Fenwick Library.

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Data Services on the move again http://dataservices.gmu.edu/2016/5091-data-services-on-the-move-again Tue, 10 May 2016 21:10:51 +0000 http://dataservices.gmu.edu/?p=5091 Data Services will be relocating for the duration of Summer 2016.

We will be closed on May 12 and May 13 and will re-open on May 16.

Our new location will be 4607 Fenwick Library.

We have limited staff during the summer. The Data Services lab will be open by appointment. The best way to get help is to send an e-mail to get started and to set up an appointment.

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Center for Open Science Workshop March 18 http://dataservices.gmu.edu/2016/4912-center-for-open-science-workshop-march-18 Wed, 17 Feb 2016 16:19:13 +0000 http://dataservices.gmu.edu/?p=4912 There are many actions researchers can take to increase the openness and reproducibility of their work. Please join us for a workshop, hosted by the Center for Open Science, to learn easy, practical steps researchers can take to increase the reproducibility of their work. The workshop will be hands-on. Attendees will need to bring their own laptops.

Using example studies, attendees will actively participate in creating a reproducible project from start to finish.

Topics covered:

* Project documentation
* Version control
* Pre-analysis plans
* Open source tools like the Center for Open Science’s Open Science Framework to easily implement these concepts in a scientific workflow.

Speaker: April Clyburne-Sherin, MSc

This workshop is aimed at faculty, staff, and students across disciplines, who are engaged in quantitative research. The workshop does not require any specialized knowledge of programming. Participants will gain a foundation for incorporating reproducible, transparent practices into their current workflows.

Registration Required: RSVP Here

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Virginia Data Management Bootcamp March 9 & 10 http://dataservices.gmu.edu/2016/4902-virginia-data-management-bootcamp-march-9-10 Wed, 17 Feb 2016 15:49:16 +0000 http://dataservices.gmu.edu/?p=4902 Graduate students, faculty, administrators, and staff interested in learning more about data management issues, best practices and tools are invited to participate in a Data Management Bootcamp sponsored by seven Virginia institutions, March 9 & 10, 2016. This free collaborative event will feature experts from across the state and will provide opportunities for local, hands-on practical experience. Presenters will be from remote and local locations.

Day one topics will include: Data Organization, Data Documentation, Rights and Access, Copyright, Finding and Reusing Data, and Sharing Data.  These are all basic, foundational concepts for working with research data. Day one is designed for full-day participation. On day two select one, two, or all three workshops to attend. Please bring your own laptop.

Registration is required.

March 9

March 10 workshop registrations.

See the detailed schedule.

The Bootcamp will take place on the Fairfax campus. Room details will be e-mailed.

If you are interested in more data management topics, check out the Center for Open Science workshop scheduled for March 18.

Data management is an important topic for researchers and scholars across domains, especially as funding agencies increasingly require submission of data management plans with grant proposals. These requirements encourage researchers to plan ahead for data management and sharing and help to promote practices that facilitate reproducible research, data use and reuse, and interoperability among distributed systems.

The Data Management Bootcamp is a collaborative initiative of the College of William & Mary, George Mason University, James Madison University, Old Dominion University, University of Virginia, Virginia Commonwealth University, and Virginia Tech.

Registration is limited to current George Mason University students, staff and faculty.

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#LYD16: Respect Your Data — Giving & Getting Credit http://dataservices.gmu.edu/2016/4878-lyd16-respect-your-data Thu, 11 Feb 2016 00:01:25 +0000 http://dataservices.gmu.edu/?p=4878 Data are becoming valued scholarly products instead of a byproduct of the research process. Federal funding agencies and publishers are encouraging, and sometimes requiring, researchers to share data that have been created with public funds. The benefit to researchers is that sharing your data can increase the impact of your work, lead to new collaborations or projects, enables verification of your published results, provides credit to you as the creator, and provides great resources for education and training. Data sharing also benefits the greater scientific community, funders, the public by encouraging scientific inquiry and debate, increases transparency, reduces the cost of duplicating data, and enables informed public policy.

There are many ways to comply with these requirements – talk to Data Services to figure out how, where, and when to share your data.

Remember to follow and participate in the discussion to learn more about today’s topic on Twitter #LYD16.

Want to talk about it? Join us for a 30-minute workshop/discussion Friday, 2/12 at noon. Sign up here.

Advice on sharing data from Data Services.

GOOD PRACTICE

  • Share your data upon publication.
  • Share your data in an open, accessible, and machine readable format (e.g., csv vs. xlsx, odf vs. docx, etc.)
  • Deposit your data in a subject or institutional repository so your colleagues can find and use it.
  • Deposit your data in your institution’s repository to enable long term preservation.
  • License your data so people know what they can do with it.
  • Tell people how to cite your data.
  • When choosing a repository, ask about the support for tracking its use. Do they provide a handle or DOI? Can you see how many views and downloads? Is it indexed by Google, Google Scholar, the Data Citation Index?

Things to Avoid

  • “Data available upon request” is NOT sharing the data.
  • Sharing data in PDF files.
  • Sharing raw data if the publication doesn’t provide sufficient detail to replicate your results.

TODAY’S ACTIVITY

Take the plunge and share some of your data today! Check out the list of resources below, or contact datahelp@gmu.edu to get started.

If your data are not quite ready to go public, go check out 1-2 of the repositories below and see what kinds of data are already being shared.

Talk to us, or check out these sites.

If you have used someone else’s data, make sure you are giving them credit. Take a minute to learn how to cite data:

Tell Us

How was the deposit process? Easier or harder than you expected?
What do you need to do before you can share your data?
What do you like or dislike about the repository?
Are people sharing data that is similar to yours?

Twitter: #LYD16

https://loveyourdata.wordpress.com/thursday/

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#LYD16: Write It Down http://dataservices.gmu.edu/2016/4874-lyd16-write-it-down Wed, 10 Feb 2016 12:32:59 +0000 http://dataservices.gmu.edu/?p=4874 Help You Future Self —Write It Down!

Remember to follow and participate in the discussion to learn more about today’s topic on Twitter #LYD16.

Want to talk about it? Join us for a 30-minute workshop/discussion today at noon. Sign up here.

Advice on documentation from Data Services.

Good practice

Document, document, document! You probably won’t remember that weird thing that happened yesterday unless you write it down. Your documentation provides crucial context for your data. So whatever your preferred method of record keeping is, today is the day to make it a little bit better!

Some general strategies that work for any format:

Be clear, concise, and consistent.
Write legibly.
Number pages.
Date everything, use a standard format (ex: YYYYMMDD).
Try to organize information in a logical and consistent way.
Define your assumptions, parameters, codes, abbreviations, etc.
If documentation is scattered across more than one place or file (e.g., protocols & lab notebook), remind yourself of the file names and where those files are located.
Review your notes regularly and keep them current.
Keep all of your notes for at least 7 years after the project is completed.

Things to avoid

Writing illegibly.
Using abbreviations or codes that aren’t defined.
Using abbreviations or codes inconsistently.
Forgetting to jot down what was unusual or what went wrong. This is usually the most important type of information when it comes to analysis and write up!

Today’s activity

Take a few minutes to think about how you document your data. What’s missing? Where are the gaps?
If your documentation could be better, try out some of these strategies and tools.

Readme files are a simple and low-tech way to start documenting your data better. Check out the sample readme.txt (filename =readme_template.txt) from IU.
Cornell University RDMSG also has a guide with tips for writing readme files
Check out Kristin Briney’s post on taking better notes
Cornell University RDMSG has some tips for writing metadata
Data dictionaries are an easy way to document spreadsheets

https://loveyourdata.wordpress.com/wednesday/

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#LYD16: Organizing Files http://dataservices.gmu.edu/2016/4866-lyd16-organizing-files Tue, 09 Feb 2016 12:27:15 +0000 http://dataservices.gmu.edu/?p=4866 Do You Know Where Your Data Is?

Remember to follow and participate in the discussion to learn more about today’s topic on Twitter #LYD16.

Want to talk about it? Join us for a 30-minute workshop/discussion today at noon. Sign up here.

Good Practice

Have a plan for organizing your data. This usually includes a folder structure and file naming scheme (plan). Easier said than done. Check out these tips and links to more in-depth guidance.

Activity

If you don’t already have a folder structure and/or file naming plan, come up with one and share it. Some good practices for naming files are described below.

Be Clear, Concise, Consistent, and Correct

Make it meaningful (to you and anyone else who is working on the project)

Provide context so it will still be a unique file and people will be able to recognize what it is if moved to another location.

For sequential numbering, use leading zeros. For example, a sequence of 1-10 should be numbered 01-10; a sequence of 1-100 should be numbered 001-010-100.

Do not use special characters: & , * % # ; * ( ) ! @$ ^ ~ ‘ { } [ ] ? < >Some people like to use a dash ( – ) to separate words

Others like to separate words by capitalizing the first letter of each (e.g., DST_FileNamingScheme_20151216)

Dates should be formatted like this: YYYYMMDD (e.g., 20150209). Put dates at the beginning or the end of your files, not in the middle, to make it easy to sort files by name:
OK: DST_FileNamingScheme_20151216
OK: 20151216_DST_FileNamingScheme
AVOID: DST_20151216_FileNamingScheme

Use only one period and before the file extension (e.g., name_paper.doc NOT name.paper.doc OR name_paper..doc)

There are generally two approaches to folder structures. Filing, or using a hierarchical folder structure. The other approach is piling, which relies on fewer folders and uses the search, sort, and tagging functions of your operating system or cloud storage tools like Box.

Check out the link below for more details and examples:
https://loveyourdata.wordpress.com/tuesday/

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Love Your Data Week: Keep Your Data Safe http://dataservices.gmu.edu/2016/4839-love-your-data-week-keep-your-data-safe Mon, 08 Feb 2016 12:50:23 +0000 http://dataservices.gmu.edu/?p=4839 GOOD PRACTICE

Follow the 3-2-1 Rule:

  • Keep 3 copies of any important file (1 primary, 2 backup copies)
  • Store files on at least 2 different media types (e.g., 1 copy on an internal hard drive and a second in a secure cloud account or an external hard drive)
  • Keep at least 1 copy offsite (i.e., not at your home or in the campus lab)

Things to Avoid: 

  • Storing the only copy of your data on your laptop or flash drive
  • Storing critical data on an unencrypted laptop or flash drive
  • Saving copies of your files haphazardly across 3 or 4 places
  • Sharing the password to your laptop or cloud storage account

TODAY’S ACTIVITY

Data snapshots or data locks are great for tracking your data from collection through analysis and write up. Librarians call this provenance, and it can be really important. Errors are inevitable. Data snapshots can save you lots of time when you make a mistake in cleaning or coding your data. Taking periodic snapshots of your data, especially before the next phase begins (collection or processing or analysis) can keep you from losing crucial data and time if you need to make corrections. These snapshots then get archived somewhere safe (not where you store active files) just in case you need them. If something should go wrong, copy the files you need back to your active storage location, keeping the original snapshot in your archival location. For a 5-year longitudinal study, you might take snapshots every quarter. If you will be collecting all the data for your study in a 2-week period, you will want to take snapshots more often, probably every day. How much data can you afford to lose? Oh, and (almost) always keep the raw data! The only time when you might not is it’s easier and less expensive to recreate the data than keep it around.

Instructions: Draw a quick workflow diagram of the data lifecycle for your project (check out our examples on Instagram and Pinterest). Think about when major data transformations happen in your workflow. Taking a snapshot of your data just before and after the transformation can save you from heartache and confusion if something goes wrong.

TELL US 

Where do you store your data? Why did you choose those platform(s), locations, or devices?

Twitter: #LYD16
Instagram: #LYD16
Facebook: #LYD16

Want to talk about it? Join us today for a 30-minute workshop.

RESOURCES

Research Data Management Basics: Storage & Backup

https://loveyourdata.wordpress.com/monday/

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