Data management (newest version)

Attribution-NonCommercial-ShareAlike
1. Plan ahead
 Managing needs
 Ethics
 Plagiarism
 Note-taking
2. Organizing your data
 Files
 Metadata
 RSS feeds
 Manage your email
 References
 Remote access
 Safekeeping
3. Preserving your data
 What to keep/delete
 Long-term storage
4. Market your data
 Reasons to share
 Reasons not to share
 How ?
G. Gabriel
LSC Library
Pocock House
235 Southwark Bridge Road
London SE1 6NP
library@lsclondon.co.uk
© jannoon028, FreeDigitalPhotos.net
Manage
your data

What is data?
©EpicGraphic.com
PresentationInformationData Knowledge

The Royal Society. (2012). Science as an open
enterprise. Available at www.oecd.org/sti/sci-
tech/38500813.pdf (retrieved 18 October 2014).
What is data?

“’research data’ are defined as factual records
(numerical scores, textual records, images and
sounds) used as primary sources for scientific
research, and that are commonly accepted in the
scientific community as necessary to validate
research findings. A research data set
constitutes a systematic, partial representation of
the subject being investigated.”
What is research data?
OECD. (2007). OECD Principles and guidelines for access to
research from public funding. Available at www.oecd.org/sti/sci-
tech/38500813.pdf (retrieved 1 October 2014).

EMC. (2012). The digital
universe: 50-fold growth
from the beginning of
2010 to the end of 2020
[picture]. Available at
http://www.emc.com/lead
ership/digital-
universe/iview/executive-
summary-a-universe-
of.htm (retrieved 14
August 2014).
Digital universe

• Video;
• Audio;
• Databases;
• Still images;
• Spreadsheets;
• Text documents;
• Instrument measurements;
• Experimental observations;
• Quantitative/qualitative data;
• Slides, artefacts, specimens, samples;
• Survey results & interview transcripts;
• Simulation data, models & software;
• Sketches, diaries, lab notebooks;
…
©Supertrooper, FreeDigitalPhotos.net
Types/formats of research data
©thmvmnt on Flickr
©David Castillo Dominici, FreeDigitalPhotos.net

Consider your data needs:
• Type of data created
• Consider what data will be created (e.g.
interviews/transcripts, experimental
measurements);
• Consider how data will be created/captured (e.g.
recorded, written, printed);
• Consider the equipment/software required (find
out if there is funding in case new software is
needed).
Plan ahead  data management needs

• Choose format(s)
• What software/formats have you (or your
colleagues) used in past projects;
• What software/formats can be easily
modified/shared (e.g. Microsoft Excel, SPSS);
• What formats are at risk of obsolescence;
• What software is compatible with hardware you
already have.

• Volume of data created
• Consider where data is going to be stored;
• Consider if the scale of data poses challenges
when sharing/ transferring data.
• Plan how to sort and analyse data;
• Investigate about Intellectual property rights (IPR)
concerning your research and its dissemination, future
related research projects, and associated profit/credit.

• Investigate about data protection and ethics -
according to the Data Protection Act 1998 (governs the
processing of personal data), information must follow
eight data protection principles:
 processed fairly and lawfully
 obtained for specified and lawful purposes
 adequate, relevant and not excessive
 accurate and, where necessary, kept up-to-date
 not kept for longer than necessary
 processed in accordance with the subject's rights
 kept secure
 not transferred abroad without adequate protection
Available at
http://www.legisl
ation.gov.uk/ukp
ga/1998/29/cont
ents (retrieved 17
August 2014).
Plan ahead  ethics

“Plagiarism is defined as submitting as one's own
work, irrespective of intent to deceive, that
which derives in part or in its entirety from the
work of others without due acknowledgement. It
is both poor scholarship and a breach of
academic integrity.”.
© Thomas Hawk via Flickr
University of Cambridge. (2011). University-wide statement on plagiarism. Available at
http://www.admin.cam.ac.uk/univ/plagiarism/students/statement.html (Retrieved 10 July
2014).
Plan ahead  plagiarism

While you are reading/writing, make sure you identify:
• Which part is your own thought and which is taken from
other authors;
• Which parts of your own writing are a response to the
argument or directly inspired by ideas in the text;
• Which parts are paraphrases of the author’s points;
• Which parts were done in collaboration with others.
Plan ahead  avoiding plagiarism

Design a reading grid to take notes of the main ideas/data/
research (including specific citations you may use later on).
• Quivy and Campenhoudt
Main ideas/content Evaluation of
ideas/content
1. e.g. Theory A considers… (pages x-x) e.g. Different
theories;
Take further
research on those
supporting theory x
and theory y;
2. e.g. Theory B considers…
3. e.g. Theory C…
Plan ahead  note-taking
Translated from: Quivy, R.; Campenhoudt, L. (2008). Manual
de investigação em ciências sociais (5 ed.). Lisboa: Gradiva.

• The Cornell Method
Major themes Detailed points
1st main point
e.g. There are several types of theories
More detailed information.
e.g. Theory A explains…
More detailed information.
e.g. Theory B explains…
e.g. Theory C explains…
2nd main point
e.g. Why do some believe in theory A
e.g. Reason 1…
e.g. Reason 2…
critical evaluation
e.g. Both theories A and B do not explain the occurrence of xxx.
Plan ahead  note-taking
Pauk, W. (1993). How to study in college
(5th ed.). Boston: Houghton Mifflin Co.

Plan ahead  further information
JISC Legal: copyright and intellectual property law
http://www.jisclegal.ac.uk/LegalAreas/CopyrightIPR.aspx
JISC Legal: data protection overview
www.jisclegal.ac.uk/LegalAreas/DataProtection/DataProtectionOvervie
w.aspx
UK Data Archive: duty of confidentially
http://www.data-archive.ac.uk/create-manage/consent-
ethics/legal?index=1
The Information Commissioners Office guide to data protection
http://www.ico.org.uk/for_organisations/data_protection/the_guide

LEKO via Jalopnik, ThePimp.Blog

When naming files:
• Adhere to existing procedures (within your research
group, or preferred by your supervisor);
• Use folders and subfolders
– Name folders appropriately (e.g. after the areas of
work) and consistently;
– Structure folders hierarchically (limited number of
folders for the broader topics, and more specific
folders within these);
– Separate on-going and completed work;
Organize your data  files

When naming files:
• Be consistent with filenames
– Choose a standard vocabulary like a numbering
system (e.g. xxxx_v01.doc; 1930film0001.tif), and
specify the amount of digits to use (standard: eight-
character limit);
– Decide on the use of dates so that documents are
displayed chronologically;
– Include a version control table for important
documents;

When naming files:
• Be consistent with filenames
– Avoid characters such as / : * ? < > | (because they
are reserved for the operating system) and spaces;
use hyphens or underscores, particularly with files
destined for the Web;
– When drafts are circulating, decide how to identify
individuals (e.g. xxxx_v01.doc);
– Mark the final document as “Final” and prevent
further changes.

• Review records (assess materials regularly or at the
end of a project to ensure files aren’t kept needlessly);
• Backup everything: your files, data, and even your
favourites.

• Use metadata (data about data -
usually embedded in the data
files/documents themselves) to
add information to your
documents (e.g. use Microsoft
Office’s “Document properties”).
– Provide searchable information
to help you/others find
information.
Organize your data  metadata

• Standard metadata fields:
– Title (name of the dataset or research project);
– Creator (who created the data);
– Identifier (number used to identify the data);
– Subject(s) (keywords);
– Intellectual property rights held for the data;
– Access information (where/how data can be
accessed by others);
– Methodology (how the data was generated);
– Versions (date/time stamp for each file).
Organize your data  metadata

• Structure information from the web
(news websites, blogs, etc.) into a
feeds reader (e.g. feedly, digg reader,
NewsBlur, NetVibes); ©Vector, www.youtoart.com
• Set up RSS
feeds from
databases.
Organize your data  RSS feeds

• Structure your folders by subject, activity or
project;
• Set up a separate folder for personal emails
(create filters);
• Archive old emails;
• Delete useless emails and block junk
email;
• Limit the use of attachments (use
alternative ‘data sharing’ options);
• Try applications to help you manage your
email (see “7 great services for taking back
control of your inbox”)
Organize your data  manage your email

• Keep track of every
bibliographic reference
used/seen;
• Use a reference
management software;
• Backup your
bibliographic data.
Organize your data  references

©winnond,
FreeDigitalPhotos.net
• Use a single technology/method of
remote access
or
• Decide on clear rules for managing
your remote access technologies
• Designate one device as your “master”
storage location;
• Transfer the latest versions of your
files to your master device ASAP,
every time that you do work away from
your master storage location;
• Back up your important files regularly.
Organize your data  remote access

• Key printed data should be kept in a secure location
(e.g. locked cupboards);
• Keep sensitive electronic data password protected,
encrypted or sett privileged levels of access
(including backups);
• Do not use printouts with sensitive data as scrap
paper. Decide on efficient methods of disposing
(e.g. shredding);
Organize your data  safekeeping

• Computer terminals should not be left unattended
and should be logged off at the end of each
session;
• Protect your computer with anti-virus, firewall and
anti-keylogging;
• Choose strong passwords and change them
frequently (if you store passwords on a computer
system, encrypt the file);

• Store crucial data in more than one secure location:
• Networked drives;
• Personal computers/laptops;
• External storage devices (CDs, DVDs, USB flash
drives);
• Remote or online systems for storing (Dropbox, Mozy,
A-Drive, etc.).

Organize your data  further information
Data Documentation Initiative
www.ddialliance.org
UK Data Archive: documenting your data
www.data-archive.ac.uk/create-manage/document/overview
MIT Libraries documentation and metadata
http://libraries.mit.edu/guides/subjects/data-
management/metadata.html
Online services that provide storage (e.g. DropBox)
Online/desktop programs to storage and keep track of the changes
made to documents (e.g. Git)

See: http://datalib.edina.ac.uk/mantra/

Jones, S. (2011). How to Develop a Data Management and Sharing
Plan. Edinburgh: Digital Curation Centre. Available at:
http://www.dcc.ac.uk/resources/how-guides/develop-data-
plan#sthash.hwE7pntn.dpuf (retrieved 17 February 2014).

EMC (2012). The
digital universe in
2020: big data, bigger
digital shadows, and
biggest growth in the
Far East. Available at
http://www.emc.com
/leadership/digital-
universe/iview/execut
ive-summary-a-
universe-of.htm
(retrieved 14 January
2014).
Preserving your data  the cloud

• Does your funder needs to keep data and /or make
it available for a certain amount of time?
• Is the data a vital record of a project/organisation/
and therefore needs to be retained indefinitely?
• Do you have the legal and intellectual property
rights to keep and re-use the data? If not, can
these be negotiated?
• Does sufficient metadata exist to allow data to be
found wherever it is stored?
Preserving your data  what to
keep/delete?

• If you need to pay to keep the data, can you afford
it?
• Only store what you need to keep! Storage costs
money and/or effort and storing massive amounts of data
require a well thought plan to organize it so that
information is easily found;
Preserving your data  what to
keep/delete?

• Digital repository
Provides online archival storage – usually open access –
and cares for digital materials, ensuring that they remain
readable for as long as the repository survives.
• Archive/data center
Ensure data safe-keeping in the long term: datasets are
fully documented with all bibliographical details and
users of the data are aware of the need to acknowledge
the data sources in publications.
e.g. Archaeology Data Service
Preserving your data  long term
storage

Preserving your data  further reading
https://dmponline.dcc.ac.uk
Digital Curation Centre: the value of digital curation
www.dcc.ac.uk/digital-curation
UK Data Archive FAQ
www.data-archive.ac.uk/help/user-faq#2
National Preservation Office: caring for CDs and DVDs
www.bl.uk/blpac/pdf/cd.pdf
Wikipedia: list of backup software
http://en.wikipedia.org/wiki/List_of_backup_software
Wikipedia: comparison of online back-up services
http://en.wikipedia.org/wiki/List_of_online_backup_services

Digital Curation Centre.
(cop. 2004-2014). DCC
curation lifecycle model
[image]. Available at
http://www.dcc.ac.uk/res
ources/curation-lifecycle-
model (retrieved 17
February 2014).

• Scientific integrity - publishing your data and citing
its location in published research papers can allow
others to replicate, validate, or correct your results,
thereby improving the scientific record.
• Funding mandates - UK research councils are
increasingly mandating data sharing so as to avoid
duplication of effort and save costs.
• Raise/Increase the impact of your research - those
who make use of your data and cite it in their own
research will help to increase your impact within your
field and beyond it.
Market your data  reasons to share

• Preserve your data for future use – anyone can
benefit by being able to identify, retrieve, and
understand the data by themselves after you have lost
familiarity with it (perhaps several years hence).
• Making publicly funded research available publicly
- there is a growing movement for making publicly
funded research available to the public, as indicated
for example, in the Organisation for Economic Co-
operation and Development (OECD) Principles and
Guidelines for Access to Research Data from Public
Funding.

• Increase transparency through creating,
disseminating and curating knowledge.
• Increase collaboration - the use of archived data by
other researchers may lead to with the data owner and
to co-authorship of publications based on re-use of the
data.

• If your data has financial value or is the basis for
potentially valuable patents, it may be unwise to share
it, even with a data licence or terms and conditions
attached.
• If the data contains sensitive, personal information
about human subjects, it may violate the Data
Protection Act, ethics codes, or written consent forms.
Do not even share data with other researchers. Note:
often there are ways to anonymise the data to remove
the personally identifying information from it, thus
making it sharable as a public use dataset.
Market your data  reasons not to share

• If parts of the data are owned by others (such as
commercial entities or authors) you may not have the
rights to share the data, even if you have derived
wholly new data from the original sources.
Market your data  reasons not to share

• Publish in Open Access journals;
• Enhance your online presence through social
media (Facebook, Twitter, start and maintain a blog);
• Use author identification (researcherID from Web of
Science; Scopus ID, ORCID);
• Share research in ”academic” platforms (LinkedIn,
Academia.edu, ResearchGate, Microsoft Academic
Search, Mendeley);
• Keep track of different metric statistics (number of
citations);
Market your data  how?

Digital Curation Centre Overview of major funders’ data policies
SHERPA JULIET searchable international database of funders' open access
and archiving requirements.
Times Higher Education supplement "Research intelligence - Request hits
a raw spot" (15 July 2010).
DOAJ – Directory of Open Access Journals (with information on OA
journal preservation program and OA quality standards.
OAD – Open Access Directory.
Market your data  Further information

Guidance Leaflet by DICE, SHARD and PrePARe projects.
Summary

LSC Library
Pocock House
235 Southwark Bridge Road
London
SE1 6NP
library@lsclondon.co.uk
www.slideshare.net/lsclondon
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Data management (newest version)

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