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Template and guidelines

Horizon 2020

  • Guidelines and template as PDF.
  • Template as DOCX.
  • ERC-template as DOCX (special policies may apply).

SNSF

  • Guidance document as PDF.



DLCM template for the
SNSF Data Management Plan


prepared by

           


Please note

 Recommendations in this document are intended to illustrate the guidelines and other information provided by the SNSF for preparing Data Management Plans1. The SNSF’s guidelines are binding.

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Please contact data-archive@library.ethz.ch for feedback or questions concerning ETH Zurich.



Version ETH Zurich 2.0



License Creative Commons CC BY-SA

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1http://www.snf.ch/en/theSNSF/research-policies/open_research_data/Pages/default.aspx

2 http://www.dlcm.ch 




Table of Contents


1. Data collection and documentation

35028999

35028999

350289992. Ethics, legal and security issues

3. Data storage and preservation

4. Data sharing and reuse







SNSF Data Management Plan


Institution

...

ETH Zurich


Responsibilities

...

Principal Investigator:
(Specify name and email)

...

Anchor
1. Data collection and documentation
1. Data collection and documentation

...

1. Data collection and documentation

1.1 What data will you collect, observe, generate or re-use? 

Questions you might want to consider

  • What type, format and volume of data will you collect, observe, generate or reuse?

  • Which existing data (yours or third-party) will you reuse?

Briefly describe the data you will collect, observe or generate. Also mention any existing data that will be (re)used. The descriptions should include the type, format and content of each dataset. Furthermore, provide an estimation of the volume of the generated datasets.

This relates to the FAIR Data Principles F2, I3, R1 & R1.2
http://www.snf.ch/SiteCollectionDocuments/FAIR_principles_translation_SNSF_logo.pdf


Recommendations

...

For each dataset in your project (including data you might re-use) mention:

  • Data type: Briefly describe categories of datasets you plan to generate or use, and their role in the project

  • Data origin: to be mentioned if you are reusing existing data (yours or third-party one). Add the reference of the source if relevant.

  • Format of raw data (as created by the device used, by simulation or downloaded): open standard formats should be preferred, as they maximize reproducibility and reuse by others and in the future [see List of recommended file formats*]

  • Format of curated data (if applicable): open standard formats should be preferred [see List of recommended file formats*]

  • Estimation of volume of raw and curated data.


* List of recommended file formats, for ETH Zurich:
https://documentation.library.ethz.ch/display/DD/File+formats+for+archiving


Examples of answer to be adapted to your research application

...


Info
iconfalse
titleExample 1

The data produced from this research project will fall into two categories:

  1. The various reaction parameters required for optimization of the chemical transformation.

  2. The spectroscopic and general characterization data of all compounds produced during the work.

Data in category 1 will be documented in [file format].
Spectroscopic data in category 2 will be produced as [file format] and converted to [file format] for further use. Other characterisation data in this category will be collected in [file format].

We anticipate that the data produced in category 1 will amount to approximately 10 MB and the data produced in category 2 will be in the range of 4 - 5 GB.

...

Info
iconfalse
titleExample 4 (from a real Eawag DMP)

There will be two categories of data: NEW data from this project and EXISTING data from the FOEN Lake Monitoring
program.


The NEW data will consist of several file types, all CSV real number format, which are all organized along the same principle: matrixes of times series with various channels, each corresponding to a sensor (number of sensors varies from 1 to10) and very different length, as the sampling frequency varies by several orders-of-magnitudes.

  1. 6 files of CO2, DO, PAR and temperature (24 files at a time; Figure 2), each file only 1 sensor (Delta = 10 min; continuous),
  2. Thetis profiles corresponding to time series (equivalent to depth series) of 10 sensors (Delta = 1 s; 5-10 times per day).
  3. 5 files of CO2 time series for short-term surface flux measurements (several files, one per month),
  4. meteodata file (eight sensors; continuous),
  5. T-Microstructure profiles files (6 sensors at 512 Hz; several files, once per month) and
  6. excel files for individual chemical samples (such as alkalinity, sediment trap estimates, ect; sporadic).

The EXISTING data is already available (CIPAIS, CIPEL) in excel sheets with matrices for the individual samplings and a variable number of parameters (~10 to ~25). The EXISTING data will not be modified and remains with the organizations. We will keep a copy on our computers during the project. We anticipate the data produced in category 1 to amount to several hundred MB for the moored and profiled sensor files and ~100 GB for the T-microstructure profiles; the EXISTING data in category 2 is in the range of ~20 MB.


Contact for assistance – ETH Zurich
Digital Curation Office
data-archive@library.ethz.ch


...

1.2 How will the data be collected, observed or generated?

Questions you might want to consider

...

Discuss how the data management will be handled during the project, mentioning for example naming conventions, version control and folder structures.

This relates to the FAIR Data Principle R1
http://www.snf.ch/SiteCollectionDocuments/FAIR_principles_translation_SNSF_logo.pdf


Recommendations

...

What standards, methodologies or quality assurance processes will you use?

For each dataset in your project (including data you might re-use) mention:

...

  • a data management system, such as an Electronic Laboratory Notebook / Laboratory Information System (ELN/LIMS). Within ETH domain, examples of used ELN/LIMS: openBISSLims.


Examples of answer to be adapted to your research application

...

Info
iconfalse
titleExample 1

The reaction conditions will be recorded and collated using a spreadsheet application and named according to each generation of reaction as follows:
ProjectW-ReactionX-GenerationY-ScientistZ-YYYYMMDD-HHmm.csv

The various experimental procedures and associated compound characterization will be written up using the Royal Society of Chemistry standard formatting in a Word document, each Word document will also be exported to PDF-A. The associated NMR spectra will be collated in chronological order in a PDF-A document.

...

Info
iconfalse
titleExample 8 (template if you̕re using openBIS)

All files produced during this project will be stored in our Electronic Laboratory Notebook (ELN) and Laboratory Information Management System (LIMS) openBIS.

In this ELN, each scientist has a personal folder where to organize projects and experiments. Each experiment is described in the electronic notebook and all data related to the experiment is directly attached to it, in so called “datasets”. Each dataset is immutable, thus different file versions are stored in the lab notebook in different datasets with a manually generated version number. Very large datasets (100s of TBs) are not directly stored in openBIS datasets, but they are linked to the experimental description using an extension to openBIS called BigDataLink. This works similarly to the git version control software, so every time changes are made to the data, these need to be committed to openBIS, which automatically keeps track of the versioning.


Contact for assistance – ETH Zurich
Digital Curation Office
data-archive@library.ethz.ch
Scientific IT Services
https://sis.id.ethz.ch/


...

1.3 What documentation and metadata will you provide with the data?

Questions you might want to consider

  • What information is required for users (computer or human) to read and interpret the data in the future?

  • How will you generate this documentation?

  • What community standards (if any) will be used to annotate the (meta)data?

...

Wherever possible, the documentation should follow existing community standards and guidelines. Explain how you will prepare and share this information.

This relates to the FAIR Data Principles I1, I2, I3, R1, R1.2 & R1.3
http://www.snf.ch/SiteCollectionDocuments/FAIR_principles_translation_SNSF_logo.pdf.


Recommendations

...

Indicate all the information required in order to be able to read and interpret the data (context of data) in the future. General documentation of the data is often compiled into a plain text or markdown README file. These formats may be opened by any text editor and are future proofed.

In addition, for each data type

  • Provide the metadata standard used to describe the data (for concrete examples see: Research Data Alliance Metadata Standards Directory). If no appropriate (discipline oriented) existing standard is available, you may describe the ad hoc metadata format you will use in this section. Metadata* may also be embedded in the data (e.g. embedded comments for code). Or, when for example using Hierarchical Data Format HDF5, arbitrary machine readable metadata can be included directly at any level.

...

  • Describe the automatically generated metadata, if any.

  • Provide the data analysis or result together with the raw data, if possible.

Additional information that are helpful in a README file

  • description of the used software,

  • description of the used system environment,

  • description of relevant parameters such as:

    • geographic locations involved (if applicable)

    • all relevant information regarding production of data.

* Metadata refers to “data about data”, i.e., it is the information that describes the data that is being published with sufficient context or instructions to be intelligible for other users. Metadata must allow a proper organization, search and access to the generated information and can be used to identify and locate the data via a web browser or web based catalogue.


Examples of answer to be adapted to your research application

...

Info
iconfalse
titleExample 1

The data will be accompanied by the following contextual documentation, according to standard practice for synthetic methodology projects:

  1. Spreadsheet documents which detail the reaction conditions.

  2. Text files which detail the experimental procedures and compound characterization.

Files and folders will be named according to a pre-agreed convention YXZ, which includes for each dataset, identifications to the researcher, the date, the study and the type of data (see section 1.2).

The final dataset as deposited in the chosen data repository will also be accompanied by a README file listing the contents of the other files and outlining the file-naming convention used.

...

Info
iconfalse
titleExample 6 (template* if you̕re using openBIS)

In the data management system (openBIS ELN-LIMS), metadata are provided as attributes of the respective datasets. Based on the defined metadata schema, openBIS ELN-LIMS will be configured so that the required metadata is automatically assigned to datasets and / or manually provided by the researcher.

*Information required to read and interpret data (incl. metadata standards) to be filled by researchers.


Contact for assistance – ETH Zurich
Digital Curation Office
data-archive@library.ethz.ch
Scientific IT Services
https://sis.id.ethz.ch/

 

Anchor
2. Ethics, legal and security issues
2. Ethics, legal and security issues

...

2. Ethics, legal and security issues

2.1 How will ethical issues be addressed and handled?

Questions you might want to consider

  • What is the relevant protection standard for your data? Are you bound by a confidentiality agreement?
  • Do you have the necessary permission to obtain, process, preserve and share the data? Have the people whose data you are using been informed or did they give their consent?
  • What methods will you use to ensure the protection of personal or other sensitive data?

Ethical issues in research projects demand for an adaptation of research data management practices, e.g. how data is stored, who can access/reuse the data and how long the data is stored. Methods to manage ethical concerns may include: anonymization of data; gain approval by ethics committees; formal consent agreements. You should outline that all ethical issues in your project have been identified, including the corresponding measures in data management.

This relates to the FAIR Data Principle A1
http://www.snf.ch/SiteCollectionDocuments/FAIR_principles_translation_SNSF_logo.pdf


Recommendations

...

Description and management of ethical issues

Describe which ethical issues are involved in the research project (for example, human participants, collection/use of biological material, privacy issues (confidential/sensitive data), animal experiments, dual use technology, etc.).  

For more information, see
Guidelines for Research Integrity
https://doi.org/10.3929/ethz-b-000179298 
The ETH Zurich Compliance Guide
https://rechtssammlung.sp.ethz.ch/Dokumente/133en.pdf


Explain how these ethical issues will be managed, for example:

...

  • For research involving work with human cells/tissues, a description of the types of cells/tissues used in their project needs to be provided, together with copies of the accreditation for using, processing or collecting the human cells or tissues.

  • Research which involves the collection or use of personal data needs to be reviewed by the cantonal ethics commission or the ETH Zurich Ethics Commission’s (depending on what kind of data is involved). ETH Zurich: For more information, see the ETH Zurich Ethics Commission’s web-site: https://www.ethz.ch/de/die-eth-zuerich/organisation/gremien-gruppen-kommissionen/ethikkommission.html (German)

  • If animal experiments are conducted in the context of the research project, an authorization of the cantonal veterinarian office is needed.
    (See also: ETH Zurich Animal Welfare Officer (
    https://www.ethz.ch/en/research/ethics-and-animal-welfare/animal-welfare.html).

  • Dual-purpose technologies (civil and military): Transfer of knowledge, software, demonstrators or prototypes could fall under the scope of the Federal Law on the control of dual-purpose goods (LCB) and its Ordinance (OCB) in the context of technology transfer or research proposals, but also in informal personal contacts. Before transmission of information, research results, prototypes etc. to a company, person or institution (even academic) outside of Switzerland, it must be checked whether the data/information to be transmitted are apt to authorization.
    For more information, see the ETH Zurich Ethics Commission’s web-site: https://www.ethz.ch/de/die-eth-zuerich/organisation/gremien-gruppen-kommissionen/ethikkommission.html (German)
    and the ETH Zurich Animal Welfare Officer website https://www.ethz.ch/en/research/ethics-and-animal-welfare/

  • Research that may have a negative impact on the environment, for example research with Genetically Modified Organisms (GMO), requires an authorization from the Federal Office for the Environment (FOEN). If the research project has a negative impact on the health and safety of the researchers involved (for example if the research proposal involves the use of elements that may cause harm to humans), authorizations for the processing or possession of harmful materials must be requested.
    More information can be obtained from the ETH Zurich Safety, Security, Health, Environment department (SSHE / SGU): https://www.ethz.ch/en/the-eth-zurich/organisation/departments/safety-security-health-environment.html.


Examples of answer to be adapted to your research application

...

Info
iconfalse
titleExample 1

Please check if your project involves one of the following ethical issues:

  • Human participants (This includes all kinds of human participation, incl. non-medical research, e.g. surveys, observations, tracking the location of people)

  • Human cells/tissues

  • Human embryonic stem cells

  • A clinical trial

  • The collection of personal/private data

  • Animal experimentation

  • Third countries (access and benefit sharing) / export law issues.

  • Environmental and/or health and safety issues (for example, a negative impact on the environment and/or on the health and safety of the researchers.)

  • The potential for military applications (dual-use technology).

If you consider that there are no ethical issues in your project, you can use the following statement:

There are no ethical issues in the generation of results from this project.

...

Info
iconfalse
titleExample 3

Research in this proposal involves the use of animals of the species mouse (Mus Musculus). Animal studies will be preceded by multiple biochemical experiments in vitro and in cultured cells. Mouse experiments will only be used at advanced stages of investigations when few, specific and highly relevant questions can be addressed by a limited number of experiments.

The PI and the research team will work in conformity with all applicable rules, guidelines and principles such as the EU directive 2010/63/EU on the protection of animals used for scientific purposes, the Swiss federal law on animal protection (RS 455), the federal ordinance on animal protection (RS 455.1), and the federal ordinance on animal experimentation, production, and housing (RS 455.163). All animal experiments will only be initiated after having received the approval of the Cantonal and Federal authorities.

Details on animal usage

In performing the experiments, we strive to strictly adhere to the 3Rs principle of Replacement, Refinement, and Reduction.

  • Reduction: Each experiment will be designed to use the minimum number of mice required to obtain statistical significance. For the proposed pharmacokinetic experiments, a total number of 24 mice will be required.

  • Refinement: The animals will be housed in the animal facilities of EPFL, which meet international housing norms, and the animal health status is monitored by a certified veterinarian. To reduce stress and discomfort of the animals, all procedures will be performed only after animals are anaesthetized. After experiments animals will be euthanized. Also, as soon as animals show signs of severe discomfort and/or tumor burden during experiments, they will be euthanized by cervical dyslocation after being anaesthetized.

  • Replacement: Alternatives for mouse experiments will be considered at all stages during the project. Whenever possible, these alternatives will replace the mouse experiments.

Training

All researchers and technicians working with the animals receive proper animal welfare training in conformity with DFE Ordinance 455.109.1 on ‘Training in animal husbandry and in the handling of animals’.

...

Info
iconfalse
titleExample 4

Environmental protection and safety

The PI assures that appropriate health and safety procedures conforming to relevant local / national guidelines / legislation are followed for staff involved in this project. The health and safety of all participants in the research (investigators, subjects involved or third parties) must be a priority in all research projects (see also ETH regulations on health and safety). The project will be conducted in collaboration with ETH’s Safety, Security, Health and Environment department (SSHE).

...

Info
iconfalse
titleExample 8

Dataset X was obtained from the BAFU and is subject to a confidentiality agreement to keep information about the sampling locations secret. We are allowed to share this information among researchers involved in the project. The dataset is being stored in a location to which only project member have access. Please refer to Section 2.2 for technical details about access restrictions. All project members will be informed about sensitivity of this data and agree not to copy it to other places. This dataset and intermediate datasets containing the sampling locations will be excluded from the data package published along with the final report and replaced with instructions about how to obtain them from the BAFU.


References
Guidelines for Research Integrity
 https://doi.org/10.3929/ethz-b-000179298
The ETH Zurich Compliance Guide
https://rechtssammlung.sp.ethz.ch/Dokumente/133en.pdf
Federal Data Protection and Information Commissioner
https://www.edoeb.admin.ch/edoeb/en/home.html

Contact for assistance – ETH Zurich
Ethics Commission
https://www.ethz.ch/services/en/organisation/boards-university-groups-commissions/ethics-commission.html
Contact
raffael.iturrizaga@sl.ethz.ch)
Legal Office (e.g. for Data Protection issues)
https://www.ethz.ch/en/the-eth-zurich/organisation/staff-units/rechtsdienst.html
Animal Welfare Officer
https://www.ethz.ch/en/research/ethics-and-animal-welfare/animal-welfare.html
ETH transfer
https://www.ethz.ch/en/industry-and-society/tto.html
Safety, Security, Health, Environment department (SSHE / SGU)
https://www.ethz.ch/en/the-eth-zurich/organisation/departments/safety-security-health-environment.html



...

2.2 How will data access and security be managed?

Questions you might want to consider

  • What are the main concerns regarding data security, what are the levels of risk and what measures are in place to handle security risks?

  • How will you regulate data access rights/permissions to ensure the security of the data?

  • How will personal or other sensitive data be handled to ensure safe data storage and transfer?

If you work with personal or other sensitive data you should outline the security measures in order to protect the data. Please list formal standards which will be adopted in your study. An example is ISO 27001-Information security management. Furthermore, describe the main processes or facilities for storage and processing of personal or other sensitive data. (This relates to the FAIR Data Principle A1; see http://www.snf.ch/SiteCollectionDocuments/FAIR_principles_translation_SNSF_logo.pdf.)

Recommendations

...

The main concerns regarding data security are data availability, integrity and confidentiality, in particular the levels of risks involved and technical and organizational measures as named in the Swiss Federal Act on Data Protection.

...

  • All data will be backed-up on a regular basis and access to backup media will be managed according to data access rules. Backups will be stored in another location.
  • All damaged media containing sensitive data will be physically destroyed.
  • All servers will be located in a datacentre with restricted access. The datacentre is based in [country] (preferably data are stored at EPFL/ETH Zurich).
  • No data will be stored on a public cloud / cloud hosted outside Switzerland.
  • No sensitive/personal data will be stored in cloud service external to EPFL/ETH Zurich. “Sensitive data can be for example data related to individuals, data under a non-disclosure agreement, data injuring third-parties rights or legal expertise).
  • All computers storing or computing sensitive data will not be connected to the Internet.
  • All computers storing or computing sensitive data will have a hardened configuration (disk encryption, restricted access to privileged accounts to a small, controlled group of users, restricted or disabled remote access using privileged accounts, disabled guest or default accounts, local firewall, automatic screen lock with password protection, disabled remote out-of-band management (IPMI, Active Management Technology (AMT), etc.), disabled USB ports, removable privacy filter on screens, automatic updates via “Windows Update”, Apple’s “Software Update” or Linux “yum auto-update”, anti-virus software, Adobe’s “Flashplayer” and “Java” runtime).


Please note

In May 2018, the EU General Data Protection Regulation (GDPR, Regulation (EU) 2016/679) will come into force. This already now influences future cooperation with any EU-based partners and will be implemented in Swiss law, as well.
GDPR introduces an approach of “Privacy by Design” for parties working with personal or other sensitive data, requiring projects to define their data protection measures from the beginning.

Where the GDPR applies you must outline in a Data Protection Impact Analysis (DPIA, text or table, see example: https://www.icrc.org/en/download/file/18149/dpia-template.pdf) the risks involved to the rights of your studies’ subjects and the security measures foreseen in order to protect the data. This is crucial for your project. The less risks you have, the better. The more data safeguards you can imply, the better. The earlier stage you imply them at, the better.

(Cf. Art 35 of the EU General Data Protection Regulation entering into force May 2018:http://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32016R0679&from=EN#d1e3265-1-1)
DPIA-Template of the ICRC
https://www.icrc.org/en/download/file/18149/dpia-template.pdf

Examples of answer to be adapted to your research application

...

Info
iconfalse
titleExample 1

The data will be processed and managed in a secure non-networked environment using virtual desktop technology.

...

Info
iconfalse
titleExample 6 (template if you̕re using openBIS)

All data generated in the project will be stored in our open- BIS ELN-LIMS. This operates in a client-server model, which is installed and maintained by the ETH Zurich IT services on ETH Zurich infrastructure. Researchers can access openBIS via any of the most common web browsers. openBIS requires user authentication with ETH Zurich credentials and it provides user right management, so that different users can have different access to all or different parts of the system, as required. Below is a description of the default openBIS roles, which can be modified upon request:

  1. Instance admin. Has full admin powers. Can customize settings, create, modify and delete entities, assign user roles, create data spaces.
  2. Instance observers. Has read-only access to everything in openBIS.
  3. Space admin. Can create, modify, delete entities and assign roles only within a given data space.
  4. Space power user. Can create, modify and delete entities only within a given data space.
  5. Space user. Can create and modify entities only within a given data space.
  6. Space observer. Has read-only access limited to a given data space.

openBIS does not offer any specific option for sensitive data, but the data will be encrypted prior to upload to openBIS. Furthermore, all operations on the system (incl. which users log in and when) are logged, so that it is fully transparent who did what to the data and when.

The data stored in openBIS is physically located on a NAS (network attached storage) provided by the ETH Zurich IT Services. The access to the share’s data is governed by the latest security best practices and only a limited number of employees of the ETH Zurich IT services have access to that share.


References
Guidelines for Research Integrity
https://doi.org/10.3929/ethz-b-000179298 
The ETH Zurich Compliance Guide
https://rechtssammlung.sp.ethz.ch/Dokumente/133en.pdf


Contact for assistance – ETH Zurich

Digital Curation Office
data-archive@library.ethz.ch
Legal Office (e.g. for Data Protection issues)
https://www.ethz.ch/en/the-eth-zurich/organisation/staff-units/rechtsdienst.html
IT Support Groups in the Departments
https://www.ethz.ch/services/en/it-services/service-desk/contacts-departments.html
Scientific IT Services
https://sis.id.ethz.ch/



...

2.3 How will you handle copyright and Intellectual Property Rights issues?

Questions you might want to consider

  • Who will be the owner of the data?

  • Which licenses will be applied to the data?

  • What restrictions apply to the reuse of third-party data?

Outline the owners of the copyright and Intellectual Property Right (IPR) of all data that will be collected and generated including the licence(s). For consortia, an IPR ownership agreement might be necessary. You should comply with relevant funder, institutional, departmental or group policies on copyright or IPR. Furthermore, clarify what permissions are required should third-party data be re-used.

This relates to the FAIR Data Principles I3 & R1.1
http://www.snf.ch/SiteCollectionDocuments/FAIR_principles_translation_SNSF_logo.pdf


Recommendations

...

Attaching a clear license to a publicly accessible data set allows other to know what can legally be done with its content. When copyright is applicable, Creative Commons licenses are recommended. However, Creative Commons licenses are not recommended for software.

...

Reuse of third-party data may be restricted. If authorised, the data must be shared according to the third party’s original requirement or license.


Examples of answer to be adapted to your research application

...

Info
iconfalse
titleExample 1

The research is not expected to lead to patents. Other Intellectual Property Rights (IPR)issues will be dealt in line with the institutional recommendation. As the data is not subjected to a contract and will not be patented, it will be released as open data under Creative Commons CC0 license.

...

Info
iconfalse
titleExample 4 (EAWAG example)

The source code for analysis will most likely utilize the GNU Scientific Library (GSL), which is licensed under the GNU General Public License (GPL). Therefore we will make our analysis software available under the GPL as well.



References
Guidelines for Research Integrity
https://doi.org/10.3929/ethz-b-000179298 
The ETH Zurich Compliance Guide
https://rechtssammlung.sp.ethz.ch/Dokumente/133en.pdf


Contact for assistance – ETH Zurich
Digital Curation Office
data-archive@library.ethz.ch
 ETH transfer (e.g. for research contracts) 
https://www.ethz.ch/en/industry-and-society/tto.html


Anchor
3. Data storage and preservation
3. Data storage and preservation

...

3. Data storage and preservation

3.1 How will your data be stored and backed-up during the research?

Questions you might want to consider

  • What is your storage capacity and where will the data be stored?

  • What are the back-up procedures?

...

Please specify your back-up procedure (frequency of updates, responsibilities, automatic/manual process, security measures, etc.)


Recommendations

...

Institutional storage solutions:

For ETH Zurich, see storage options here
https://www.ethz.ch/services/en/it-services/catalogue/storage.html
and consult the IT Support Group of your Department
https://www.ethz.ch/services/en/it-services/service-desk/contacts-departments.html.


Examples of answer to be adapted to your research application

...

Info
iconfalse
titleExample 1

Storage and back up will be in three places:

  • On Laptop of [Name of Researcher]

  • On a portable storage device (hard drive)

  • On institutional collaborative storage

[Name of Researcher] will be responsible for the storage and back up of data. This will be done weekly. Backups on the institutional infrastructure are automated using the RSYNC tool.

...

Info
iconfalse
titleExample 6 (template* if you̕re using openBIS)

by the ETH Zurich IT Services. openBIS uses a postgres database that stores all metadata. This database is backed up (“pg_dump”) every night with a 7 days retention of the dumps and fully backed-up twice a week with a backup retention of 20 days. The full backup procedure includes a point-in-time recovery that allows a finer granularity (up to minutes) of data recovery in case of a disaster. The database backup is stored on the NAS (network attached storage) provided by the ETH Zurich IT services. The same NAS is used to store the data uploaded to openBIS. This network attached storage is snapshot every night with a 7 days retention, and data is backed up on a proprietary tape library with a retention of 90 days.

Data which is no longer actively needed is moved to the long term storage (i.e. tapes). The tape library where openBIS moves the data has a read-only replica in a different geographical location in order to minimize any data loss.

*For data linked to openBIS with the BigDataLink tool, please provide details of the data location and back-up.


Contact for assistance
Digital Curation Office
data-archive@library.ethz.ch
Scientific IT Services
https://sis.id.ethz.ch/


...

3.2 What is your data preservation plan?

Questions you might want to consider

  • What procedures would be used to select data to be preserved?

  • What file formats will be used for preservation?

...

In particular, comment on the choice of file formats and the use of community standards.


Recommendations

...

Describe the procedure, (appraisal methods, selection criteria …) used to select data to be preserved.  Note that preservation does not necessarily mean publication (e.g. personal sensitive data may be preserved but never published), but publication means generally preservation. 

...

Other criteria from the Digital Curation Center (UK). In addition, select appropriated preservation formats (see section 1.1) and data description or metadata (see section 1.3).


Examples of answer to be adapted to your research application

...

Info
iconfalse
titleExample 1

Data will be stored for a minimum of three years beyond award period, per funder’s guidelines.  If inventions or new technologies are made in connection data, access to data will be restricted until invention disclosures and/or provisional patent filings are made with the institutional Technology Transfer Office (TTO).

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titleExample 4

Data will be stored on ETH servers and will be preserved for the long term at the ETH Data Archive.


Contact for assistance – ETH Zurich
Digital Curation Office
data-archive@library.ethz.ch
 IT Support Groups in the Departments
https://www.ethz.ch/services/en/it-services/service-desk/contacts-departments.html

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4. Data sharing and reuse
4. Data sharing and reuse

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4. Data sharing and reuse

4.1 How and where will the data be shared?

Questions you might want to consider

  • On which repository do you plan to share your data?

  • How will potential users find out about your data?

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Please also consider how the reuse of your data will be valued and acknowledged by other researchers.

This relates to the FAIR Data Principles F1, F3, F4, A1, A1.1, A1.2 & A2
http://www.snf.ch/SiteCollectionDocuments/FAIR_principles_translation_SNSF_logo.pdf


Recommendations

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It is recommended to publish data in well established (or even certified) domain specific repositories, if available:

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  • each data packet and publication has a DOI (or similar persistent identifier) assigned,

  • they are deposited Open Access in a repository harvested by the main data services (e.g.: OpenAire, EUDAT,…).


Examples of answer to be adapted to your research application

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titleExample 1

Some of the ongoing data will be shared on [Researcher1]’s Github repository (results and code from the project, data from twitter searches). Major revisions of this page will be baked up using the Github-Zenodo connection (see: https://guides.github.com/activities/citable-code/). All other data we will be published on Zenodo under CC0 license.

We chose Zenodo because it supports the FAIR principles (http://about.zenodo.org/principles/). The immediate publication at the end of the project aims to minimize the data loss risk, while the 2 years embargo guarantees us to be first to exploit our data. Zenodo implements long-term preservation features, notably bitstream preservation.

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titleExample 3

For this project, the National Geoscience Data Centre (NGDC) (see http://www.bgs.ac.uk/services/ngdc/home.html) is the most suited repository. As it is adapted to geodata, it facilitates storage and allows interactive geographical search. In addition, many other researchers in our field are familiar with it.

This repository requires the deposition under Open Governement Licence (see : http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/), which demands attribution when the data is reused (our dataset must by cited, similarly to the CC BY license).


References
SNSF’s criteria for non-commercial respositories
http://www.snf.ch/en/theSNSF/research-policies/open_
research_data/Pages/data-management-plan-dmp-guidelinesfor-
researchers.aspx
Contact for assistance – ETH Zurich
Digital Curation Office
data-archive@library.ethz.ch
Research Collection
https://www.research-collection.ethz.ch/



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4.2 Are there any necessary limitations to protect sensitive data?

Questions you might want to consider

  • Under which conditions will the data be made available (timing of data release, reason for delay if applicable)?

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Consider whether a non-disclosure agreement would give sufficient protection for confidential data.

This relates to the FAIR Data Principles A1 & R1.1
http://www.snf.ch/SiteCollectionDocuments/FAIR_principles_translation_SNSF_logo.pdf.


Recommendations

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You may mention specifically the conditions under which the data will be made available:

  • there are no sensitive data

  • the data are not available at the time of publication

  • the data are not available before publication

  • the data are available after the embargo of …

  • the data are not available because of the patent of … for a period of…


Examples of answer to be adapted to your research application

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titleExample 1

Data which underpins any publication will be made available at the time of publication.

All unpublished data will be deposited in a data repository 12 months after the end of the award.

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titleExample 4 (Eawag example)

The extensive household survey about water-born diseases poses severe challenges with regard to anonymization, since simple pseudonymization might not be sufficient to guard against the identification of individual households by an inference attack that uses other available information.

Therefore we will be only able to publish summary statistics together with the associated article. If a sufficiently anonymized dataset turns out to still hold scientific value, we will publish it no later than one year after completion of the project.


References
Guidelines for Research Integrity
https://doi.org/10.3929/ethz-b-000179298 
 The ETH Zurich Compliance Guide
https://rechtssammlung.sp.ethz.ch/Dokumente/133en.pdf


Contact for assistance – ETH Zurich
Digital Curation office
data-archive@library.ethz.ch
Ethics Commission
https://www.ethz.ch/services/en/organisation/boards-university-groups-commissions/ethics-commission.html
Contact: raffael.iturrizaga@sl.ethz.ch



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4.3 All digital repositories I will choose are conform to the FAIR Data Principles 


[CHECK BOX]


Recommendations

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The SNSF requires that repositories used for data sharing are conformed to the FAIR Data Principles. For more information, please refer to the SNSF’s explanation of the FAIR Data Principles.

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ETH Zurich’s Research Collection (https://www.research-collection.ethz.ch/) also complies with the FAIR Principles.



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4.4 I will choose digital repositories maintained by a non-profit organisation

[RADIO BUTTON yes/no]



Recommendations

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If you do not choose a repository maintained by a non-profit organization, you have to provide reasons for that.

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Please note that the SNSF supports the use of non-commercial repositories for data sharing. Costs related to data upload are only covered for non-commercial repositories. Check the SNSF’s criteria for non-commercial repositories (http://www.snf.ch/en/theSNSF/research-policies/open_research_data/Pages/data-management-plan-dmp-guidelines-for-researchers.aspx, section 5.2).



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External useful resources

Digital Curation Centre glossary: http://www.dcc.ac.uk/digital-curation/glossary

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