Take control of your PhD journey:

Manage your research data

according to best practice

(25. May, 2016)

Philipp Conzett & Lars Figenschou

University Library

Slides will be available at: http://www.slideshare.net/

Outline

• Course objectives and layout (Lars)

• General background and rationale for

research data management and sharing (Lars)

• Best practice during the life cycle of research data

– Searching/reusing (Philipp)

– Collecting (Philipp)

– Processing (Philipp)

– Archiving (Philipp)

– Planning (Lars)

• Support at the UiT Library

• Course evaluationwww.business.mcmaster.ca

Course objectives and layout

• Objectives

- Give you a glimpse into how research data should be managed

- Data management plan at the outset of your project

- Show how to structure, document, and preserve data

- …and how you can archive and share your data

Goals

- To help you crack some codes within “The Lifecycle of

Research Data Management”

- Be more prepared to fulfill present and future requests

from research funding agencies

- Make you share and re-use data

www.powerful-sample-resume-formats.com

Course objectives and layout

• Layout

– VERY dynamic…!!

– Large variation in your needs, so let us know!

– Disruptions are necessary (welcome…)

– Presentation

– Tasks and discussions

– Working with your own (or others) data

– 15 min. tea/coffee/fruit/chat break

layout.fm

Background

• This Module about Research Data …is the first ever… organized at UB

- dependent on the students

- we need inputs, suggestions, contributions from you (initiative)

• The University Library are building competence on the field to enable

OPEN SCIENCE

• I.e., UiT Open Research Data (from Sep. 2016)

Background

• Open Access to Research Data

• Share and Re-use

• Being transparent support your integrity

• Increased visibility – more citations

• Makes research more efficient

• Funders – EU & NFR (and others)

• Obligatory

• Add value for your self

• Add value for others

• https://www.youtube.com/watch?v=2JBQS0qKOBU

www.fosteropenscience.eu

Background

Invest some time now – save time later

«It is the planning itself that matters….»

So, be organized – from the start

Keep track of changes and pass on (your) knowledge

Checklist for (a) Data Management (Plan):

http://www.dcc.ac.uk/resources/data-management-plans

«Let your dataset`s live happily… ever after….»

Background: Discussion (or should we wait..?)

• Pros and cons of:

- having good routines/following best practice

for research data management

- research data sharing and/or Open Science

• From the video:

– What if someone in your lab quits?

– What if you need to use your old data?

– What if you are accused of fraud?

– What if you laptop is stolen?

– What if you could get more credit for your work?

– What if ...

depositphotos.com

The Lifecycle of Research Data Management

Planning

Phases:

Collecting

Processing

Archiving

Searching / reusing

Searching / reusing

• Good practice at the outset of your research project:

– Literature survey

– AND data survey

• Sources / places to search:

– Research group, supervisor, colleagues, ...

– (Electronic) literature

– Directories, e.g.

Registry of Research Data Repository

• Browse or search

• Reference to research data:

Persistent Identifier (PID)

– Research data archive(s)

– Databases, e.g. DataCite

Data Storage

Data Storage: Backups

Avoid data loss by establishing good backup routines:

• Regular backup

• Several backups:

– Here: e.g. your computer

– Near: e.g. your home directory at UiT (\\homer.uit.no)

– Far: e.g. a cloud service like myDoc/OneDrive at UiT

(https://mydoc.uit.no/)

• Shared storage areas, e.g. uDoc at UiT (https://udoc.uit.no/)

• Versioning = keep track of changes

Check http://orakel.uit.no/ for help.

File and Folder Naming and Organizing

Some fundamental file naming recommendations:

• Files should be named consistently

• File names should be descriptive, but short (< 25 characters)

• Use underscores ( _ ) instead of spaces

• Avoid characters like “ / \ : * . ? ‘ < > [ ] ( ) & $ æÆ øØ åÅ ...

• Use the international dating convention YYYY-MM-DD

Possible strategies:

• Order by date

• Order by subject

• Order by type

• Forced order with numbering

Folder naming and organization:

• Choose a consistent system, stick to it, and document it in a ReadMe-file

• Main structure should be visible in the file names

Documentation / metadata: ReadMe files

ReadMe file = description of your dataset / user guide to your data

Best practice recommendations for ReadMe files:

• Start early

• Describe

– contact information

– what the dataset is about

– file structure and naming conventions

– where to find which data = overview of your files

– methods and workflow

– column headings in tabular data

– abbreviations

– units of measure

– ...

• Save as Unicode .txt

• Check specific metadata requirements (discipline, archive, ...)

Archiving: Preparing

Preparing your data for archiving:

• Selection

• Do not exclude negative / null data

• Include raw version and analyzed version(s)

• Provide your data in original AND persistent file format

Persistent file formats are usually

• non-proprietary,

• open, with documented international standards,

• in common usage by the research community,

• using standard character encodings (i.e. ASCII, UTF-8), and

• uncompressed (space permitting)

Archiving: Persistent file formats

Persistent file formats for common document types:

Detailed data guide (for linguistics) available here.

Type of Document Non-persistent format (examples)

Persistent format

Text MS Word (.docx) PDF/A

Spreadsheet MS Excel (.xlsx) Tabulator separated Unicode text (.txt)

Image Windows Bitmap (.bmp) Uncompressed TIFF

Sound AAC (.m4a) WAV

Video Quicktime (.mov) MPEG-4

Databases MS Access (.accdb) XML or tabulator separated Unicode text (.txt)

Archiving: Choosing your archive

• Research group, supervisor, colleagues, ...

• Registry of Research Data Repository (http://www.re3data.org/)

• UiTs own archive for open research: UiT Open Research Data

(https://opendata.uit.no/)

Planning

• Context dependent- which phase are you planning for?(designing, collecting, processing or analyzing, saving, archiving, dissemination)

• General rules- procrastinate behavior (postpone….) does not work- use common sense- ask for help/guidance

• DATA MANAGEMENTHave in mind:How would you…- collect- store- describe- and share your data?

Exercises

1. Find a relevant research data archive (within your discipline).

2. Check whether your own data comply with best practice for research

data management. If you do not have own data available yet, you me

use the following data set: http://tinyurl.com/zgb34mp.

Support at the UiT Library

• Check out our research support pages at

https://uit.no/ub/forskningsstotte#linje2 (English version coming soon)

• Contact us at forskning@ub.uit.no or contact your subject librarian, see

https://uit.no/ub/fag#linje4 for more info.

Course evaluation

• Please use 5 min. to fill in our electronic evaluation form at

http://tinyurl.com/gnffdop