Formatting and organising 
research data 
Research Data Management Support Services 
UK Data Service 
University of Essex 
April 2014 
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Overview 
File formats  
File conversions 
Organising files and folders 
File naming 
Version control and authenticity 
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Can you understand/use these data? 
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File formats 
Digital data can take countless different 
form(at)s…
A file format is a specific way of 
structuring information so that a machine, 
and therefore a person, can understand it 
should be readable by as many types of 
system as possible 
without compromising the purpose of the 
data 
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File formats 
Choice of software format for digital data: 
planned data analyses 
software availability/cost 
hardware used 
e.g. audio capture 
discipline-specific standards and customs 
Digital data is software dependent, so endangered by 
obsolescence of software/ hardware 
Best formats for long-term preservation: 
standard, interchangeable, open 
e.g. tab-delimited, comma-delimited (CSV), ASCII, RTF, PDF/A, 
OpenDocument format, SPSS portable, XML 
UK Data Archive optimal file formats for various data types 
Digital Preservation Coalition guidance on preservation formats 
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File format conversions 
Convert data for preservation or back-up:  
export  
save as 
scripts 
Beware of conversion errors or losses: 
loss of internal metadata 
e.g. convert mp3 audio to ogg 
loss of editing, formatting, formulae 
e.g. convert DOCX to RTF; XLSX to CSV 
truncation or loss of values 
e.g. string variables lost in SPSS 
Stata conversion; MS Access memo 
fields truncated in conversion to CSV 
Check for errors and changes after conversion 
Example: format conversion 
MS Excel (.xlsx) format 
Tab-delimited text format
Loss of 
annotation 
Formatting  
change 
Example: format conversion 
Different formats store date values in different ways, and format 
conversion can wreak havoc with these. 
e.g. 21:55 on the 21
st
April 2013 can be stored as: 
1366581312  
Unix time - seconds elapsed since midnight 1 January 1970 
or 
2013-04-21T21:55:12Z 
ISO 8601 time - and international standard for representing time 
and date stamps 
Quality assurance 
Quality assurance procedures should be undertaken throughout the 
research process, ensuring data are: 
clean 
verified 
validated 
Depending on the type of data, you may be able to automate aspects of 
this process using: 
statistical software to check e.g. frequencies on quantative data 
consistency checking with data manipulation tools like OpenRefine 
Qualitative data collectors in for a harder time 
manual proofreading 
Can you understand these data? 
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