critical thinking: data management in research – why and how?

||Ana Sesartic / Matthias Töwe 1

Ana Sesartic & Matthias TöweDigital Curation Office

Critical Thinking:Data Management in Research – Why and How?

20.04.2016


from the ETH library’s Digital Curation Office sharing a scientific background ourselves

here to discuss what you need to think about in data management as part of your research

to learn more about your needs in the process

and to motivate you to think critically about the chances and limitations of data management and re-use.

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Nice to meet you, we’re…


Your scientific background

Previous experience with data management

Motivation to attend this course

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Shortly tell us something about…


What is data management and why should it concern you? Data exercise – what it takes to understand someone’s data Long term preservation of data

COFFEE BREAK

Data management plans ETH regulations, intellectual property, privacy and access rights Data management tools Feedback round

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Today’s Programme


Placemat Exercise

What does Research Data Management mean to you?

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Bild durch Klicken auf Symbol hinzufügen

||Ana Sesartic / Matthias Töwe 620.04.2016

Placemat Method

SummarisedGroup Inputs

Input participant 1

Input participant 3

Input participant

3

Input participant

4


What is data management and why should it concern you?

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Introduction

DigitalResearch Data

Hypothesis / Research question

Data capture or -collection

Analysis and interpretation

SynthesisPublication

Access and verification

Re-use


Share, publish, preserve your data for others – and for yourself !

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Two major issues

Manage your data while doing research

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What is data?

“A reinterpretable representation of information in a formalized manner suitable for communication, interpretation, or processing.”

Digital Curation Centre

20.04.2016Ana Sesartic / Matthias Töwe 9

Slide adapted from the PrePARe Project

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What is data management?

Data management is a general term covering how you organize, structure, store, and care for the information used or generated during a research project

It includes:

How you deal with information on a day-to-day basis over the lifetime of a project

What happens to data in the longer term – what you do with it after the project concludes



"A story told in file names":

Source:http://www.phdcomics.com/comics/archive.php?comicid=1323

Copyright: Jorge Cham

Does this remind you of something?

http://www.phdcomics.com/comics/archive.php?comicid=1323




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Why spend time and effort on this?

So you can work efficiently and effectively

Save yourself time and reduce frustration

Facilitate collaboration in your group

Highlight patterns or connections that might otherwise be missed

Because your data is precious – and costly

To enable data re-use and sharing – even for yourself

To meet funders’ and institutional requirements

You are the experts who can make informed decisions!


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Why spend time and effort on this? You as the authors are responsible for the work you deliver

during your PhD

As group leaders you will be held responsible for complying with your institution’s and funders’ regulations…

…and therefore you will have to set and enforce rules for your group to achieve this

You oversee students’ and staff members’ work and need to rely on them following good scientific practice

You may be able to influence the discussion in your community, in your institution and with funders



Idealised Data Life Cycle

DigitalResearch Data

Hypothesis / Research question

Data capture or -collection

Analysis and interpretation

SynthesisPublication

Access and verification

Re-use


Manage the transition from private to public

(Derived from Andrew Treloar’s Diagram: Private Research , Shared Research , Publication , and the Boundary Transitions, http://andrew.treloar.net/research/diagrams/data_curation_continuum.pdf, 2012)

http://andrew.treloar.net/research/diagrams/data_curation_continuum.pdf

http://andrew.treloar.net/research/diagrams/data_curation_continuum.pdf

Sesartic Ana

Not sure if we need this slide...

Töwe Matthias

At the moment, I still find it useful.


Benefits of data sharing and publication

SPEED: The research process becomes faster

EFFICIENCY: Data collection can be funded once, and used many times for a variety of purposes

ACCESSIBILITY: Interested third parties can (where appropriate) access and build upon publicly-funded research resources with minimal barriers to access

IMPACT and LONGEVITY: Open publications and data receive more citations, over longer period

TRANSPARENCY and QUALITY: The evidence that underpins research can be made open for anyone to scrutinise, and attempt to replicate findings. This leads to a more robust scholarly record

Benefits of data sharing and publication

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“In genomics research, a large-scale analysis of data sharing shows that studies that made data available in repositories received 9% more citations, when controlling for other variables; and that whilst self-reuse citation declines steeply after two years, reuse by third parties increases even after six years.”

(Piwowar and Vision, 2013)

Benefits of Open Data:Impact and Longevity

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Van den Eynden, V. and Bishop, L. (2014). Incentives and motivations for sharing research data, a researcher’s perspective. A Knowledge Exchange

Report, http://repository.jisc.ac.uk/5662/1/KE_report-incentives-for-sharing-researchdata.pdf

http://repository.jisc.ac.uk/5662/1/KE_report-incentives-for-sharing-researchdata.pdf




Bad News There is no Nobel Prize for data management… …but there is some data management behind any prize We are no experts in your field(s) of work – you are!

Good News Data management can spare you (real) trouble This is not Rocket Science You may already be doing a lot of this – as part of your research You are the experts on your data, anyway!

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Mixed News


Constraints for preservation and sharing

Data is usually created without its publication in mind

Research data requires comprehensive documentation for being properly interpreted or even being re-used

For text documents metadata can be improved at a later date

For research data, only technical metadata can be extracted later, but little to no documentation of content and context can be added:

„Garbage in, garbage out“


Exercise:What it takes to understand someone’s data

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1. What information is needed to understand this data?2. What does the metadata tell you and what not?3. Is this information sufficient? If not, what else do you

need to know?

Data, metadata and context are needed to properly understand a data set.

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Discuss


Critically re-thinking the re-use of third party data

Data management does not start with your own data, but also includes a critical view on other people’s data you use:

Do you understand how they were produced?

Do you have enough information on evaluating their reliability?

Are you comfortable with using data without talking to its producers?

Will you know in a few months time which data you re-used from other researchers?

Do you know how to cite the data you use?


Long term preservation of data

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And how to prepare for it

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What does long term mean?

Different time horizons and purposes

Keeping data for at least ten years (or for longer as is deemed appropriate in your field) to ensure accountability if results are challenged

Potentially unlimited retention of data with permanent value (e.g. long running series of observational data)

Permanent retention of published data which is considered as part of the scientific record and is expected to remain available just like articles and journals are

In general “long term” signifies any time period which spans technological changes in the way data is being used



Old files?

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Cartoon «Old Files»,taken from:http://xkcd.com/1360/

How old is your oldest file? Is it in use?

Age of files need not be a problem in itself…

…but obsolescence is

How much are you willing to invest to recover unreadable files?

Files in regular use will not become obsolete unnoticed

http://xkcd.com/1360/



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“Storage is becoming cheaper”

This is not (only) about storage

Cheaper storage helps – unless data grows faster than costs fall

Just keeping everything is neither archiving nor preservation

Preservation is about ensuring

Integrity and authenticity of stored data

Technical usability of data in the future: it can be rendered and used

Scientific usability of data in the future: it can be understood and interpreted

This is closely related to the issue of sharing and publishing data


||Ana Sesartic / Matthias Töwe

What is needed to ensure re-usability of data?

Data Curation

Content Preservation

Bitstream Preservation

What? Why? Who?

Ensure intellectualre-usability

Ensure technicalre-usability

Ensure technicalstability

Adapted after Jens Ludwig, Wissgrid

Data Producers

ETH-Bibliothek

IT-ServicesETH Zurich

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||Ana Sesartic / Matthias Töwe

What is needed to ensure re-usability of data?

Data Curation

Content Preservation

Bitstream Preservation

What? Why? Who?

Ensure intellectualre-usability

Ensure technicalre-usability

Ensure technicalstability

Adapted after Jens Ludwig, Wissgrid

Data Producers

ETH-Bibliothek

IT-ServicesETH Zurich

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You!

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How does this relate to data management?

Proper data management or its absence determine if preservation of data will be possible

For a period of ten years, data management alone might suffice, but thinking further ahead is useful

If data is to be kept and used for longer periods, further measures apply:

Data should be as self-contained as possible, including documentation of any tools used or better: the tools themselves;remember e.g. including reference outputs for model algorithms

More care is required in the choice and use of file formats



Open standards (non proprietary)

Well documented Widely used and supported by many tools

Uncompressed (or at least losslessly compressed)

Unencrypted When in doubt, keep original and create a copy in an open or

exchange format

Don’t rely on file extensions Consider that data might be used in different operating systems

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Preferences for file formats


Examples

Images: uncompressed TIFF; JPEG2000

Text: ASCII, including XML etc.Add encoding information and dependencies such as stylesheets or TeX-libraries

Text (page based): PDF/A1-b, (PDF)

Data from spreadsheets: CSV

Spreadsheets: (CSV), (ODF, OOXML)

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Examples


This does not mean you «must not» keep data in other formats

Just be aware that proprietary or undocumented formats (even your own!) might cause trouble in the future

Think about adding an alternative format (yes, redundantly) for a proprietary one…

…and add any context information you yourself would like to have on your own formats in a few years time in a readme-file, an accompanying document or as metadata

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Note


Data Management Planning

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Consequences for preservation and sharing

A structured process with the aim of data publication and/or long term preservation must apply early in the life cycle: data management

Quality depends on producers, any later curator can only perform certain limited checks

Preservation and re-use have limits as a matter of principle and strongly depend on today’s preparatory efforts


A brief plan written at the start of a project and updated during its course to define:

What data will be collected or created?

How the data will be documented and described?

Where the data will be stored?

Who will be responsible for data security and backup?

Which data will be shared and/or preserved?

How the data will be shared and with whom?

What is a Data Management Plan (DMP)?

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DMPs are increasingly required with grant applications, but are useful whenever researchers are creating data.

They can help researchers to:

Make informed decisions to anticipate & avoid problems

Develop procedures early on for consistency

Ensure data are accurate, complete, reliable and secure

Avoid duplication, data loss and security breaches

Save time and effort to make their lives easier!

Why develop a DMP?

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What to do?Data Management Planning

Data Sharing and Publishing should be part of Data Management Planning from the beginning of a project

Check mandates with funders, in your community and with your institution

What can and may be made publicly available at a later stage?

Which means of publication should be used?

Will there be additional or consequential costs?

What are the prerequisites for re-using your data? Are there dependencies from soft- or hardware?

Will any restrictions for access and re-use be required?


What to do?Data Management Checklist ETH / EPFL

Supports you in the creation of a DMP or in discussing data management in general

Covers general planning and the phases of the data life-cycle, from data collection and creation to data sharing and long-term management

Special sections cover documentation and metadata, file formats, storage, ethical and intellectual property issues

http://bit.ly/rdmchecklist



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DMPOnline

20.04.2016 39Ana Sesartic / Matthias Töwe

https://dmponline.dcc.ac.uk/

The DMPOnline tool by the UK Digital Curation Centre helps you create Horizon 2020 compliant data management plans, by answering a questionnaire, ensuring that your scientific data are:

Discoverable Accessible Assessable and intelligible Usable beyond the original purpose for which it was collected Interoperable to specific quality standards




What to do?Data production and filing

From the start of data production, data publishing should be considered and prepared – if appropriate

Complying with requirements from third parties (funders, universities, community standards, law)

Use of comprehensible, documented structures

Use of transparent, speaking or documented naming conventions (e.g. with key to abbreviations); names should not exceed reasonable limits

Versioning, supporting an easy selection of data e.g. for publication or further processing

Try to make your data as self-contained as possible

Avoid unclear redundancies


What to do?Group Policy

Self-critical question:

What must data look like to enable us to re-use it with scientific conviction and trust into its quality and correctness?

Is this true for our own data? What is missing?

Tasks for group leaders within the group

Agree on binding rules and fix them in writing

Instruct new members

«Archive in an ordered way or delete» - do not create «data graveyards»

Control implementation while staff members are still there


An altruistic exercise?

There are benefits for yourself!

Your data is transparent and well managed – no matter if it’s public or not

Data can better be shared within group, e.g. handed over to new students

Sharing and publishing with little additional effort and at short notice

Visibility of your work increases through Open Access to transparently organised and qualitatively relevant data

Should a publication be challenged, all material is within reach

Requirements by funders and institutions can be fulfilled with little additional effort


Benefits of data sharing

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Limits to planning – our know-how

Learning processes within communities

What will really work over the next years, what is missing, what is not necessary?

It is not clear, how much re-use will actually happen Are expectations realistic?

In which disciplines and for which data types is re-use an attractive option?

Where lies a balance between effort and opportunities for re-use?

Researchers and service units can and must learn together


ETH regulations, intellectual property, privacy and access rights

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Recent Overview

https://itsecurity.ethz.ch/en/#/manage_your_data 20.04.2016

https://itsecurity.ethz.ch/en/#/manage_your_data




Guidelines for Research Integrity

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At the ETH Zurich research is founded on intellectual honesty. Researchers […] are committed to scientific integrity and truthfulness in research and peer review.

https://www.ethz.ch/content/dam/ethz/main/research/pdf/forschungsethik/Broschure.pdf






Article 11. Collection, documentation and storage of primary data

All steps in the treatment of primary data (statistical analyses, reorganizations, etc.) must be documented in a form appropriate to the discipline in question (e.g. laboratory logs, other data carriers) in such a way as to ensure that the results obtained from the primary data can be reproduced completely.

The project management is responsible for data management (data collection, storage, data access, compliance with data protection requirements, etc.). In particular, it should ensure that, following completion of the project, the data and materials are retained for the period prescribed in the discipline, and are duly destroyed within the period prescribed by law, if appropriate.

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Compliance Guide […] all ETH members […] are

required to integrate the general conditions and internal directives into the work process.

The following Compliance Guide is designed to serve as an orientation tool. […]To facilitate implementation, each point is supplemented with further information and contact persons available for consultation.

https://rechtssammlung.sp.ethz.ch/Dokumente/133en.pdf

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Privacy

People-related data need to be preserved according to Swiss data protection law

Appropriate anonymization might be required The deletion of individual datasets must be possible at all

times The study subjects need to sign a declaration of consent

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Intellectual Property Rights

Intellectual Property Rights (IPR) can be defined as rights acquired over any work created or invented with the intellectual effort of an individual. These rights are time-limited and do not require explicit assertion.

Owners of works are granted exclusive rights

to publish the work

to license the work’s distribution to others

to sue in case of unlawful or deceptive copying

It may be possible to waive these rights through a public declaration similar to a licence

20.04.2016Ana Sesartic / Matthias Töwe


What you need to consider

Respect the rights of others Third parties Individuals you work with

In case of doubt: seek permission even when a CC-licence is assigned

Note that according to ETH law, ETH reserves most immaterial rights in works by its employees. When in doubt, contact ETH transfer (www.transfer.ethz.ch)

Make sure you keep sufficient rights Eg. for Open Access Publishing (green path) Eg. with respect to patent applications: ETH transfer (www.transfer.ethz.ch

)20.04.2016

http://www.transfer.ethz.ch/

http://www.transfer.ethz.ch/


share-alikeby non-derivative Some rights

reserved

share

non-commercial public domainremix

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www.dcc.ac.uk/resources/how-guides/license-research-data

Licensing research data

Outlines pros and cons of each approach and gives practical advice on how to implement your licence

CREATIVE COMMONS LIMITATIONS

NC Non-CommercialWhat counts as

commercial?

SA Share AlikeReduces interoperability

ND No DerivativesSeverely restricts use

Horizon 2020 guidelines point to

OR

Ana Sesartic / Matthias Töwe 20.04.2016 54

http://www.dcc.ac.uk/resources/how-guides/license-research-data

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Open Access & Article Processing Charges


For a number of OA publishers, ETH-Bibliothek either covers the Article Processing Charges (APC) or has negotiated reduced rates

For more information:http://www.library.ethz.ch/en/ms/Open-Access-at-ETH-Zurich/Publishing-in-open-access-journals/Publishing-in-open-access-journals-Funding

http://www.library.ethz.ch/en/ms/Open-Access-at-ETH-Zurich/Publishing-in-open-access-journals/Publishing-in-open-access-journals-Funding







Further Information on Open Access

Critical Thinking Workshop:Think, check, submit: Making informed decisions in open access publishingThu, 26 May 2016http://www.library.ethz.ch/en/Dienstleistungen/Schulungen-Tutorials-Fuehrungen/Workshops/Think-check-submit-Informierte-Entscheidungen-ueber-das-Open-Access-Publizieren-treffen

Open Access at ETH Zurichhttp://www.library.ethz.ch/en/Open-Access

Open Research Datahttp://www.library.ethz.ch/en/ms/Open-Access-at-ETH-Zurich/What-is-Open-Access/Open-Research-Data 20.04.2016

http://www.library.ethz.ch/en/Dienstleistungen/Schulungen-Tutorials-Fuehrungen/Workshops/Think-check-submit-Informierte-Entscheidungen-ueber-das-Open-Access-Publizieren-treffen




http://www.library.ethz.ch/en/Open-Access


http://www.library.ethz.ch/en/ms/Open-Access-at-ETH-Zurich/What-is-Open-Access/Open-Research-Data




Tools

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Versioning:How do you currently handle it? What works well? What went wrong?

Naming conventions:Do you have any? Which rules apply?

Sharing:Which tools or services do you use? What are your experiences?

Literature Management:Which tools do you use? What are their pros and cons?

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Group discussion: current practice


Repositories and Registries

http://www.re3data.orghttp://datadryad.org

https://zenodo.org

http://figshare.com

https://www.openaire.eu/search/data-providers

http://www.re3data.org/

http://www.re3data.org/

http://datadryad.org/

http://datadryad.org/

https://zenodo.org/

https://zenodo.org/

http://figshare.com/

http://figshare.com/



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Deposit in a repository – but in which one?

http://databib.org

http://service.re3data.org/search Ana Sesartic / Matthias Töwe 20.04.2016 60

http://service.re3data.org/search

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OpenAIRE

Open Access Infrastructure for Research in Europe

Aggregates data on OA publications Mines and enriches its content by linking things together Provides services & APIs e.g. to generate publication lists www.openaire.eu


http://www.openaire.eu/


Where will your data reside? Which legislation applies, e.g. in terms of data protection? Is the service sustainable? Do you trust the provider? Who else can access and use which of your data?

Research Data Personal Data

How can you get your data back? Is a certain license required? Are there immediate or longer term costs?

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Criteria for chosing services and tools


Collaboration - Organisation

https://www.openproject.org

http://www.redmine.org

https://trello.com

https://slack.com

https://tagpacker.com

https://www.openproject.org/

https://www.openproject.org/

http://www.redmine.org/

http://www.redmine.org/

https://trello.com/

https://trello.com/

https://slack.com/

https://slack.com/

https://tagpacker.com/

https://tagpacker.com/


Collaboration - Sharing

https://owncloud.org

https://www.dropbox.com

https://www.switch.ch/drive/

https://cifex.ethz.ch/

https://polybox.ethz.ch

https://www.wetransfer.com

https://owncloud.org/

https://www.dropbox.com/

https://www.dropbox.com/



https://cifex.ethz.ch/

https://polybox.ethz.ch/

https://polybox.ethz.ch/

https://www.wetransfer.com/

https://www.wetransfer.com/


Collaboration – Versioning

https://subversion.apache.org

https://github.com

https://bitbucket.org

https://subversion.apache.org/

https://subversion.apache.org/

https://github.com/

https://github.com/

https://bitbucket.org/

https://bitbucket.org/


Collaboration - Writing

https://www.overleaf.com

https://www.authorea.com

https://atlas.oreilly.com

https://hypothes.is

https://evernote.com

http://simplenote.com

https://www.overleaf.com/

https://www.overleaf.com/

https://www.authorea.com/

https://www.authorea.com/

https://atlas.oreilly.com/

https://atlas.oreilly.com/

https://hypothes.is/

https://hypothes.is/

https://evernote.com/

https://evernote.com/

http://simplenote.com/

http://simplenote.com/


www.zotero.org

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Collaboration – Reference Management

www.mendeley.com

endnote.com

www.citavi.com

www.citeulike.org www.bibsonomy.org

http://www.zotero.org/

http://www.mendeley.com/

http://endnote.com/

http://www.citavi.com/

http://www.citeulike.org/

http://www.bibsonomy.org/


ETH-Bibliothek Digital Curation (http://www.library.ethz.ch/Digital-Curation)

ETH Data Archive Docuteam packer DMP-Checklist Archival formats Consulting and training

DOI registration (http://www.library.ethz.ch/DOI-Desk-EN) Open Access (http://www.library.ethz.ch/en/Open-Access) ETH E-Collection (http://e-collection.library.ethz.ch/index.php?lang=en) ETH E-Citations (http://e-citations.ethbib.ethz.ch/index.php?lang=en)

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Services at ETH Zurich I

http://www.library.ethz.ch/Digital-Curation



http://www.library.ethz.ch/DOI-Desk-EN

http://www.library.ethz.ch/DOI-Desk-EN



http://e-collection.library.ethz.ch/index.php?lang=en

http://e-collection.library.ethz.ch/index.php?lang=en

http://e-citations.ethbib.ethz.ch/index.php?lang=en

http://e-citations.ethbib.ethz.ch/index.php?lang=en


Scientific IT Services Research Data Analysis and Management

(http://www.sis.id.ethz.ch/researchdatamanagement/)

openBIS / openBIS ELN-LIMS (https://sis.id.ethz.ch/software/openbis.html)

IT Services Storage provision, usually via your IT Support Group, e.g.

HSM (Hierarchical Storage Management) (http://www1.ethz.ch/id/services/list/storage/nas)

LTS (Long-Term Storage)(http://www1.ethz.ch/id/services/list/storage/LTS)

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Services at ETH Zurich II

http://www.sis.id.ethz.ch/researchdatamanagement/

https://sis.id.ethz.ch/software/openbis.html

http://www1.ethz.ch/id/services/list/storage/nas

http://www1.ethz.ch/id/services/list/storage/LTS


Think about what you do! Start early Agree on clean concepts and simple tools You do not need the latest sophisticated apps Talk to colleagues Check what your local service providers can offer «Keep it as simple as possible – but distrust it!»

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Take home message


Thank you!Are there any questions?

Dr. Matthias TöweHead Digital CurationETH-BibliothekRämistrasse 1018092 Zurich044 632 60 [email protected]

Dr. Ana SesarticDigital CurationETH-BibliothekRämistrasse 1018092 Zurich044 632 73 [email protected]

www.library.ethz.ch/Digital-Curation

[email protected]

mailto:[email protected]


