Research Data Management Training for IAC

Research Collection

Educational Material

Research Data Management Training for IAC

Author(s):

Petrus, Ana; Töwe, Matthias Publication Date:

2017-02-03 Permanent Link:

https://doi.org/10.3929/ethz-b-000296488

Rights / License:

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

Research Data Management Training for IAC

Why manage your research data?

©“The Carrot and the Stick Set”(4.9.2018) by Frits Ahlefeldt

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!

 You as the authors are responsible for the work you deliver

 To meet funders’ and institutional requirements

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

regulations

 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

ETH regulations, intellectual property, privacy and access rights

Recent Overview

Guidelines for Research Integrity

 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/eth z/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.

Roles and Responsibilities

 Project Members :

 adhere to the principles of good scientific practice and the guidelines for Research Integrity at ETH.

 All steps of treatment of primary data must be documented and results must be reproducible.

 Project Manager:

 responsible for execution of a scientific project and data management (data collection, storage, data access, compliance with data protection requirements...).

 Ensures that all research project participants are aware of the guidelines.

 Determines together with the professor, which departed project members should retain access to the primary data or materials.

Licences / Open Access / Creative Commons

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/133_en.pdf

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.

share-alike by non-derivative some rights reserved

share

“Creative Commons” (4.9.2018) by Michael Porter CC BY-NC-ND 2.0

What you need to consider

 Respect the rights of others

 Third parties

 Individuals you work with

 Licence shouldn’t clash with any previous licences (e.g. when collaborating on models)

 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

 E.g. for Open Access Publishing (green path)

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-Commercial

What counts as commercial?

SA Share Alike

Reduces interoperability ND No Derivatives

Severely restricts use

Horizon 2020 guidelines point to

OR

Best practices for personal data management

“MGB Grau Blau WP” (4.9.2018) by Bidgee/ CC BY-SA 3.0

GARBAGE IN,

GARBAGE OUT!

 Open standards (non proprietary)

 If proprietary, convert or if not possible include data viewer

 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

Preferences for file formats

 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

Note

"A story told in file names“ from

Does this remind you of something?

Keep in mind, this is just a suggestion.

Generally, keep stuff together that belongs together.

For further file and folder organisation tips, see:

 http://www.data.cam.ac.uk/data-management-guide/organising- your-data

 http://www.wur.nl/en/Expertise-Services/Data-Management-

Support-Hub/Browse-by-Subject/Organising-files-and-folders.htm

 http://datalib.edina.ac.uk/mantra/organisingdata/

Try this instead…

 Context information needed

 How to run the model

 Which software is needed (incl. compilers)

 Which hardware is needed

 Always preserve a reference output together with the code, to ensure reproducibility

 When writing a publication , preserve the model code, input and output needed to reproduce the results/figures presented in the article

 Save aggregated data if possible (eg. daily or monthly means, depending on time resolution, saving raw output with each timestep not always necessary.

Preservation of (climate) models and data

«Gulf Stream Sea Surface Currents and Temperatures» (12.9.2018) by NASA Goddard Space Flight Center /CC BY

Long-term preservation

What does long term mean?

 Different time horizons and purposes

 Keeping data for at least ten years to ensure accountability if results are challenged (as defined in the ETH “Guidelines for Research Integrity”)

 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

if results are challenged (as defined in the

Old files?

 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

ETH Services

 ETH Data-Archive (http://www.library.ethz.ch/Digital-Curation)

 Not for mass storage and active data

 DOI registration (http://www.library.ethz.ch/DOI-Desk-EN)

 Open Access (http://www.library.ethz.ch/en/Open-Access) including payment of Article Processing Charges with a range of publishers

 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)

Will be merged into

«research collection»

and allow publication of documents and data

Services at ETH Library

IT Services

 Storage provisioning, usually via your IT Support Group

 HSM (Hierarchical Storage Management) https://www.ethz.ch/services/en/it- services/catalogue/storage/nas.html

 LTS (Long-Term Storage) https://www.ethz.ch/services/en/it- services/catalogue/storage/lts.html

 openBIS ELN-LIMS https://openbis-eln-lims.ethz.ch/

ETH-Transfer https://www.ethz.ch/en/the-eth-zurich/organisation/staff-units/eth-transfer.html

IT services and ETH transfer

 Training courses and workshops on information research, reference

management, data management, scientific writing and open access by the ETH-Library:

http://www.library.ethz.ch/en/Services/Training-courses-guided-tours

 Courses offered by the ETH Information Center for Chemistry/Biology/Pharmacy:

http://www.infozentrum.ethz.ch/en/whats-up/events/

 Further topics on demand

Trainings

Thank you!

Digital Curation ETH-Bibliothek Rämistrasse 101 8092 Zurich

www.library.ethz.ch/Digital-Curation data-archive@library.ethz.ch

Dr. Ana Sesartic 044 632 73 76

ana.sesartic@library.ethz.ch Dr. Matthias Töwe

044 632 60 32

matthias.toewe@library.ethz.ch