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Library Advanced Research Competencies: Data & Stats

LARC, the Library Advanced Research Competencies tutorial, provides an overview of research concepts and practices from the perspective of the student as an active participant in the production of information.

Finding Data and Statistics

Here are some useful search strategies for finding the data and statistics you need for your research:

 

Consider using one of these statistical resources provided by the UWM libraries.

Think about who may be creating data and search their websites/publications

Scan publications for data

  • Published articles (newspaper and journal articles) may contain data tables and references to data sources.

Add the word 'statistics' or 'data' to your searches

  • Using Google, the library catalog, or another search tool.

Ask for help

Visualizing Data

Citing Data and Statistics

A general format for citing datasets:

Format:

  • Creator (PublicationYear): Title. Publisher. Identifier
  • Creator (PublicationYear): Title. Version. Publisher. ResourceType. Identifier

Example:

Lowe T, Garwood RJ, Simonsen TJ, Bradley RS, Withers PJ (2013) Data from: Metamorphosis revealed: time-lapse three-dimensional imaging inside a living chrysalis. Dryad Digital Repository. doi:10.5061/dryad.b451g

Citing Data

Format:

  • Author. (Year). Title of data set (Version number) [Description of form]. Location: Name of producer.
  • Author. (Year). Title of data set (Version number) [Description of form]. Retrieved from http://
  • Author. (Year). Title of data set (Version number) [Description of form]. doi:xxx.

Example:

Lowe, Tristan, Garwood, Russell J., Simonsen, Thomas J., Bradley, Robert S., and Withers, Philip J. (2013). Data from: Metamorphosis revealed: time-lapse three-dimensional imaging inside a living chrysalis. [Dataset]. doi:10.5061/dryad.b451g

Citing Statistics

In text format:

  • (Author, Year, Table #)

Bibliography format:

  • Author. (Year). Title of entry. In Editor (Eds.), Title of reference book (pp. xxx-xxx). Retrieved from http://.
  • Author. (Year). Title of entry. In Editor (Eds.), Title of reference book (pp. xxx-xxx). Location: Publisher.
  • Author. (Year). Title of entry. In Editor (Eds.), Title of reference book (pp. xxx-xxx). doi:xxxx.

Examples:

(U.S. Census Bureau, 2012, Table 270)

U.S. Census Bureau. (2012) "Table 270. Public High School Graduates by State: 1980 to 2009." In U.S. Census Bureau (131st ed.), Statistical Abstract of the United States. Retreived from http://www.census.gov/compendia/statab/2012/tables/12s0270.pdf.

Citing Data

Format:

  • Author. Title. Version. Source, Year. Medium of publication.
  • Author. Title. Version. Source, Year. Web. Access Date.
  • Author. Title. Version. Source, Year. Web. Access Date. <URL>.

Example:

Lowe, Tristan et al. “Data from: Metamorphosis Revealed: Time-lapse Three-dimensional Imaging Inside a Living Chrysalis.” Dryad Digital Repository, 2013. Web. 24 October 2013.

Citing Statistics

Format:

  • Author. "Title of entry." Title of book. Edition. Ed. Editor's name(s). Place of publication: Publisher, Year. Page range. Medium of publication.
  • Author. "Title of entry." Title of book. Edition. Ed. Editor's name(s). Place of publication: Publisher, Year. Page range. Medium of publication. Date of access.
  • Author. "Title of entry." Title of book. Edition. Ed. Editor's name(s). Place of publication: Publisher, Year. Page range. Medium of publication. Date of access. <URL>.

Example:

U.S. Census Bureau. "Table 270. Public High School Graduates by State: 1980 to 2009." Statistical Abstract of the United States. 131st ed. U.S. Census Bureau. Washington D.C.: U.S. Census Bureau, 2012. Web. 25 October 2013. <http://www.census.gov/compendia/statab/2012/tables/12s0270.pdf>.

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Why Do Data Management?

Improve Your Documentation

Improving your documentation often means improving your note taking. Good notes are:

  • Clear and concise
  • Legible if handwritten
  • Understandable to someone with similar training
  • Include all relevant information (eg. methods, thoughts about current project, ideas for new research, etc.) – opt to record more information, not less

README.txt's document digital files and add context whenever clarity is required. They are useful for:

  • Providing a general project overview in the main project folder
  • Describing the organization or naming conventions for files in a folder
  • Providing clarity for a group of files
  • Detailing one particular file's contents

See this post for more information on README.txt files.

Methods are important documentation that you need to remember to keep with your data. Methods include:

  • Protocols
  • Code
  • Survey
  • Codebook
  • Data dictionary
  • Anything that lets someone reproduce your results

Data dictionaries are particularly useful for spreadsheet data. Data dictionaries describe:

  • Variable names
  • Variable meanings
  • Null values
  • Variable codings
  • Relationships between variables in a dataset
  • Anything unusual about the data

Use a data dictionary when you share data, when you expect to reuse the data, or have a particularly large and complex dataset.

Read more about data dictionaries.

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Data Management Best Practices

Consistent file naming means you can tell at a glance what a file contains. This is useful for searching through content, for organizing data, and for collaborations/your own reuse.

Use consistent naming for groups of related files. Pick 2-3 things that will help you distinguish a file's contents, such as:

  • Date
  • Site
  • Analysis
  • Sample
  • Short description

Combine into a patten for naming your files. Follow these other rules:

  • Files should be named consistently
  • Files names should be descriptive but short (<25 characters)
  • Use underscores instead of spaces
  • Avoid these characters: “ / \ : * ? ‘ < > [ ] & $
  • Use the dating convention: YYYY-MM-DD

Examples:

YYYYMMDD_site_sampleNum
    "20140422_PikeLake_03", "20140424_EastLake_12", etc.

AuthorLastName-Year-Title
    "Smith-2010-ImpactOfStressOnSeaMonkeys", "Hailey-1999-VeryImportantDNAStudy", etc.

Files using proprietary file formats are more difficult to use over time. Convert your data into an open, non-proprietary format that is in wide use. This will improve the odds on reusing your data in the future.

Keep a copy of your data in both formats. The original file lets you use all of the file features and the open copy is a backup in case something happens to the software.

Recommended File Formats
Text files .txt
Images .tiff
Tabular data .csv, .tsv
Video .mp4
Audio .mp3
Other .pdf
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Licensing

The contents of the Library Advanced Research Competencies tutorial may be reused with attribution. Please copy the following into new works based on the Library Advanced Research Competencies:
Creative Commons LicenseLibrary Advanced Research Competencies by Board of Regents of the University of Wisconsin System is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Based on a work at guides.library.uwm.edu.