UpSkills: Research Data Management for the Sciences

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Research Data Planning ...for the Sciences

MSGR UpSkills ProgramJeff Christiansen & Steve Bennett

13 September 2012

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Why data management What data Where you store it Who owns it How you manage it

Bonus: start work on a data management plan!

Intro – who we are

Dr Jeff Christiansen [email protected]

Australian National Data ServicePreviously researcher in molecular genetics

Steve Bennett: [email protected]

Victorian e-Research Strategic InitiativeHelps researchers with systems for digital data

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Why data management What data Where you store it Who owns it How you manage it

Becoming aware of data management in research BSc (Hons)

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Experiment 1

Experiment 2

?

Becoming aware of data management in research PhD

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CCACGCGTCCGGTGTGAGCTCTCCTTCAGCTGCTGCAGGCATTACACTCAGCTCTGCTGT CCAAGCTGCTCATGTGATTGCCCTCTAATCCATTCAGGCAAAGTGAGCTAGACTTGTTTA AGCTGCAGGTCTTATTTTGATTGTAGCAGGCTAGTGAACAGTCACAGAAGTGGTTCAAGT ATTGTGCCCCTTGGAGCTGTTATCTTTGAAAATGTGGCCGTGGCTGGAAAAGGATGCATC TGCACCAATGGCACAGTGACCAGCCAGTTGCTTAGGGGCTTAGCTGGTGGATTTGGACCT GTCTTCTGCAACCTGGGGAAAGCATAATCTACTGTGTTATTTGATAATGGAAGCGCCGTG ATCAGATCCATCCCTCTGCTTTGAATTTTCAAACAAATAATCAAGAATTTGGCTCGTGTT AAAAAAAAAAAAAAAA


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Becoming aware of data management in research Postdoc

Becoming aware of data management in research EMAGE Database Project Manager




Becoming aware of data management in research EMAGE Database Project Manager Cross DB queries need to use appropriate descriptors, not just free text E.g. Gene name identifiers

Becoming aware of data management in research Being organised, having systems in place and adopting

community standards are all helpful in data management.

Think about what you will be required to do when publishing.

There are obligations for having data available for others post publication.

It’s useful to have your data organised so you can collaborate with others easily.

What will happen to your data when you leave the lab? Your supervisor would like to know what’s what/where.

Data Planning & ManagingMotivators

#1 Meet your obligations legal, ethical, funding requirements; uni, department, group policies Find out now – avoid hassle later (ask [email protected])

#2 Make your life easier a data management system to make your research work a data management plan to save time keeping data, finding stuff again, labelling, security sharing & collaborating

#3 Helping your career being a professional researcher data – your assets and records – finding, understanding data in years to come contributing to global research community manage your data now, help your future self.

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mailto:[email protected]

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Why data management

What data Where you store it Who owns it How you manage it

Ask: [email protected]

What is data?

Observational data Sensor readings, telemetry (non-reproducible)

Experimental data Gene sequences, chromatograms (reproducible,

but expensive)

Simulation data Climate models (model the most important thing)

Derived/compiled data Compiled database (reproducible but expensive)

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What else is data?

Social sciencesSurveys, statistical data

HumanitiesCultural artefacts (video, photos, sound…)

Physical samplesSoil, biological, water, archeological…

Does anyone here not have data?

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The University’s definitions

Research Data laboratory notebooks; field notebooks; primary research data (hardcopy or

in computer); questionnaires; audiotapes; videotapes; models; photographs;films; test responses; slides; artefacts; specimens; samples

Research Records Includes correspondence (electronic mail and paper-based correspondence);

project files; grant applications; ethics applications; technical reports; researchreports; master lists; signed consent forms; and information sheets for researchparticipants

Administrative Records (Research Office, Central Records) Includes contracts and agreements, patents, licences, grants, intellectual property

and trademarks, policies, ethics, research project files, reports, publications

What is often included as “Research Data”:= data + records + copies (physical & digital)

= stuff you used and/or created

Group activity (15 mins)

Form groups of similar discipline Earth sciences/forestry/botany/agriculture Health/medical biology/physio/social work Engineering/computer science/linguistics

Discuss: What kind of data do you collect? How do you get it?

Your data management checklist: Section 1.1

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Discu

ss

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Why data management What data

Where you store it Who owns it How you manage it

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Research trends

Research Data is increasing in size Protein crystallography100 GB/experiment Gene sequencing 1,000 GB/day High-energy physics 10,000,000s GB/year Astronomy (SKA) 1,000,000,000 GB/day

Research Collaborations are increasing Human Genome project (1990-2003)

113 people, 20 orgs

Belle collaboration (1994-..) ~370 people, 60 inst., 14 countries

ATLAS collaboration @ LHC CERN (1994-2020+) ~2500 people, 169 inst., 37 countries

Research Data is increasingly digital Wonderful opportunities for reuse,

sharing, collaboration, analysis Data science (4th paradigm) “eResearch”!

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Research trends

Large scale data intensive science “A totally new way of doing research” New research methods, new skills,

therefore new training needed

New skills... Specialists – in both technology and

research Informatics – dealing with data from

collection through analysis Data Management and Planning –

collecting, maintaining, sharing dataEveryone!

How big?

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1mb(spreadsheets)

10 Gb(numerical, video)

1Tb(simulations, synchrotron) 1Pb

Limit of Google Drive, DropBox…

Easy?(Probably already solved)

AwkwardEasy!

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Where to keep it?

Possibilities:Research group storage

Ask!

Local computer Backups crucial. Sharing hard. Disaster looms.

Cloud (Dropbox, Google Drive) Check security, legals. How to archive?

Ask [email protected]

Sharing

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Group activity #2 (15 mins)

DiscussHow much data will you have?Where will you store it?What data formats?

Data management checklistComplete section 2.3 & 2.4 If non-digital: 2.1, 2.2

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Discu

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Why data management What data Where you store it

Who owns it How you manage it

In collaborations, get IP right early. Find out:

Does the University own your data?Can you still share it?Restrictions?Licences?

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IP – who claims to own it Copyright – who has legal backing

(not all data can be copyright)

Ethics – more rules you agreed toMust you keep the data private?Must you share it?

Privacy – can you de-identify the data?

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DiscussWho owns your data?What data can you share? With whom?How will you protect confidential information?

Data management checklistComplete section 1.3

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Discu

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Why data management What data Where you store it Who owns it

How you manage it

University Code of Conduct for Research

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University Policy on Management of Research Data and Records

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Starting your system

Consider your goals – what do you want to get out of managing your data?

Figure out your criteria for keeping data Picture your data three years from now Consider the metadata you want to collect

to document your datasets

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Benefits

Find your data 3 years from now Get more papers out of your data Save time and stress – get organised Share with collaborators Some journals require data submission

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Not rocket science! Stop and think about what data you have, what you’re doing, what you should be doing

Some scary facts: Microfilm, non-acidic paper last 100+ years magnetic media lasts 10+ years optical media lasts 20+ years 2-10% of hard drives fail every year software & hardware can outdate quickly

Scary stories: US study 100’s charges “research misconduct”

40% avoided by better data management! UniMelb ~20 cases research misconduct 2008.

Most involved students. All needed good records! Climategate scandal, UK – FOI

Proper Planning & Management is needed!!!

Being more professional...

Burroughs 1977 – B 9495Magnetic Tape Subsystem

High level view

Your data management system needs to cover:

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Create,Capture,Describe

(Use, Transform, Update)

Store, Secure, Preserve

Keep,Transfer,Destroy

(National Archives)

A simple Data Man. System Identify key data in your context, important stuff to keep (your Data Assets) Find secure places to keep physical & digital Records + Data (filing cabinet, department shared

drive) – backups are essential Where and when should there be checks on your data (sanity checks, quality control, standards) File your data and records into logical divisions, say activities, projects, or pieces of work

eg. folders /DeptShare/johnsmith/Records/ProteinABC Investigation Don’t break things down too much, makes things harder to find!

Have a consistent file naming convention: perhaps: ActivityOrContents-LocationOrPerson-CreateDate-Id-Description.ext eg. “ProteinABC-LJW-20100409-0001 Raw data from instrument.dat”

Keep good metadata (notes, records) on how you captured your data, particularly for physical records

Descriptions of collections or files – Structured text files good enough eg. FileOrCollectionName-metadata.txt

On other things, entities that are not files – Structured text files or spreadsheets Have a good labeling/ID/coding system Perhaps keep a registry (spreadsheet will do; IDs, names, location, basic metadata)

Find the right balance in digitising physical stuff (easy and quick) Digital is easy to keep/transfer/search if stored properly. However, digitising/scanning everything can be

time consuming and without good descriptions may not be useful. Link digital notes/metadata to physical stuff (IDs, names, labels, codes, location) Have some basic digital representations or notes of important physical stuff 45

Free Tools jEdit – text file editor (private notes, metadata and records) local disk + file share + Cobian Backup (private project records, data) Google Desktop (file and email search) Zotero (reference material) (EndNote is Uni default) EVO & Skype & Google chat (video/tele/chat communication)

http://evo.arcs.org.au/

Sakai@Melbourne (project workspace) https://sakai.unimelb.edu.au/

Google docs + Sites (collaborative editing) Google groups (email list)

research data storage, a tricky one… use local storage in preference, ask around DropBox, Google Drive, Microsoft SkyDrive, box.com…

too many others to list, heaps on the web… See Digital Research Tools (DiRT) wiki for a huge list

http://digitalresearchtools.pbworks.com/ Check with your supervisor,

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see Info Skills classeson EndNote,

UpSkills 29 June on VC

http://evo.arcs.org.au/

https://sakai.unimelb.edu.au/

http://digitalresearchtools.pbworks.com/

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Data Security 2 aspects to security

Safety from damage or loss How important is the data to you?

Safety from incorrect use What are the possible consequences?

Safety from damage or loss (unintended and intentional)… What’s acceptable loss (safety can cost, use up time) Backups (data, software, system)

How often (hourly, daily, weekly, monthly, manually, automated)? How many and where (onsite, offsite, both, multiple)? Departmental storage? Probably backed up already!

Disaster Recovery Quality hardware, multiple/spare servers, spare disk drives, Operating System and Applications image backups

(talk with someone technical, your local IT guys)

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Data Security

Safety from damage or loss (continued)… Make sure Backup is occurring

Essential data and records... “Your Archive” Frequency should depend on how often your data changes Incremental backups are essential. Replication IS NOT SAFE!!! Keep some copies (one?) offsite. Database backups should use database tools (mysqldump, pg_dump etc.)

Departmental storage is best... probably backed up already! Worst case... DIY, use external hard drives or remote storage Seek advice on software

for Windows I use... Cobian Backup, DriveImage XML for Linux I use... rsync (see http://rsync.samba.org/examples.html ) for Mac there is... Time Machine

(talk with someone technical, your local IT guys)

http://rsync.samba.org/examples.html

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Data Security Safety from incorrect use (unintended and malicious)…

PCI DSS - a recommendation (Payment Card Industry Data Security Standard) eg. google for: “nacubo.org payment card data security” 12 requirements that are good practice (first 10 are the basics)

10 IT basics… Firewall servers Do not use default usernames/password Physically protected stored data (lock up servers, disk, tape, source material) Use encrypted transmission over internet (VPN, SSL, SSH, GridFTP, S/MIME email) Update antivirus/antimalware software regularly Use secure and trusted applications Restrict access to sensitive data (tighter control, or put it somewhere else) Assign unique IDs for each user Record and monitor all access to data

Plus some good practice… Don’t retain sensitive data Or encrypt sensitive information

Read up!

Google: research data toolkit http://researchdata.unimelb.edu.au ANDS guides To consider: identifiers, DOIs, archival,

security, licensing, metadata formats, ontologies, controlled vocabularies, definition of “collection”, data reuse, metadata stores…!

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http://researchdata.unimelb.edu.au/


Data management checklistComplete section 3.1

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Discu

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Questions?

[email protected]

researchdata.unimelb.edu.au

Copyright (c) 2012, VeRSI Consortium, Lyle Winton , Steve Bennett, Jeff Christiansen

UpSkills: Research Data Management for the Sciences

Technology

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