About this page
A DMP is expected for any research project with clear temporal boundaries that is conducted at ETH Zurich (see Guidelines for Research Data Management at ETH Zurich). The purpose of this Data Management Plan (DMP) template and instructions is to assist ETH researchers with writing a DMP in line with good practice for data management and complies with the Guidelines for Research Data Management at ETH Zurich (RDM Guidelines) and the ETH Zurich Guidelines on scientific integrity (Integrity Guidelines). The actual blank ETH DMP template can be found here.
If your project is supported by a research funding agency, please adhere to the requirements of the respective funder when writing and implementing the associated DMP. In case you have to prepare a DMP for the Swiss National Science Foundation (SNSF), this website can provide you with information about ETH-specific solutions. However, in such a case, please follow mainly the guidance for an SNSF DMP to address all relevant questions. The guide for the SNSF DMP can be found here.
The structure of the DMP template provided by the Swiss National Science Foundation (SNSF) served as the main basis for the elaboration of this ETH specific DMP template and was further complemented with content of the Checklist for a Data Management Plan from the Digital Curation Center (DCC) and the DLCM template for the SNSF Data Management Plan.
The next paragraph provides key questions and links to further information that should help to comprehend and answer the questions of each section in the template. Example answers for the questions in each subsection included in the DMP are provided below. Please consider your DMP as a declaration of intent. It should reflect your decisions and practices in data management, but necessary changes can and should be made within the time frame of your project. To refer to this page please use the following: “ETH Library (2021), DMP Instructions for ETH Zurich Researchers”.
DMP Instructions for ETH Zurich Researchers © 2021 by ETH Library is licensed under CC BY 4.0 . For reusing example answers from each of the subsections, no citation is necessary.
Guidance and instructions for the ETH Zurich DMP template
This section incorporates the structure and content of the SNSF’s DMP template. Every subsection comprises the same questions as found in the SNSF’s template (as expandable section) with some slight modifications. This guide is supplemented by key questions (bold and underlined) and corresponding suggestions (in italic) as well as useful information to provide the minimum content required for each specific sub-section.
Note that you are not expected to describe technical services in detail if you rely on an established service at ETH. However, please at least name the service and its provider.
- Section 1: Data collection and documentation
- Section 2: Ethics, legal and security issues
- Section 3: Data storage and preservation
- Section 4: Data sharing and reuse
Section 1: Data collection and documentation
1.1 What data will you collect, generate or reuse?
- What are the types and formats of your research data?
Please list all the data types that will arise during your project and add the corresponding file formats.
Are the file formats and software you use open source? Do they enable sharing and long-term access to the data?
You can of course use proprietary file formats and software, but keep in mind that they are not open to everybody and therefore they limit the reusability of your data. To ensure reusability, data in proprietary formats should be converted into open and properly documented standard formats.
List of recommended standard file formats (most suitable for long-term reuse and archiving) from the ETH Library, Research Data Management and Digital curation
- What will be the estimated volume of data generated during the project?
Indicate the approximate volume, ideally per data type.
Are you reusing existing data (yours or third-party)?
If yes, please detail the nature of these data and ideally list the persistent identifiers (e.g. DOIs) if available.
1.2 How will the data be collected or generated?
- What methodologies and standard procedure will you use for data collection?
Briefly describe every collecting and processing step related to your research data during the entire project. The use of community standard procedures is to be preferred in case there are some already established in your research field or discipline.
- What measures are you taking to assure quality and consistency of generated data?
Measures may include but are not limited to usage of a controlled vocabulary, calibration processes, repetition of measurements or whole experiments, standardised data capture, data entry validation or data peer review.
What file naming conventions and file folder structure will you use? How will you handle versioning of files?
File names should represent content and should not exceed 30 characters. Use ASCII characters, no spaces, no upper cases and points only before file extension. Example: YYYYMMDD_experiment1_scientist2.pdf
Worksheet on naming and organizing your files and folders from MIT Libraries
Data management system openBIS at ETH Zurich provided and supported by the ETH Scientific IT Services (SIS)
Git server hosted at ETH Zurich for code revision management (gitlab.ethz.ch)
1.3 What documentation and metadata will you provide with the data?
- What information about your data (i.e., metadata) is required to make reuse of your data in the future?
Just imagine what you would need to know so that you are able to reuse other researchers’ data within your scientific field. At least it should include basic metadata such as the name of the data(set), persistent identifier, names of creators and contributors, date of creation and access conditions. However, it might further contain more specific details as on methodology, analytical and procedural information, definition of variables, defined vocabularies, units of measurements. Do not forget to mention system environments as well as software and the software version, which you have used, for example, for data analysis.
Are you using certain community standards for the annotation of metadata?
The usage of metadata standards enables interoperability meaning that computers and other machines can read this information. If community standards exist in your research field or discipline, they should be preferred.
Discipline-specific metadata standards:
- Disciplinary Metadata on the website of the Digital Curation Centre (DCC)
- RDA Metadata Standards Catalog
- Metadata standards from Fairsharing.org
How will data documentation be carried out?
A README file should be attached, which contains all the metadata associated with the dataset that are relevant for verification, validation, and exploitation of research results. The information in the README file usually exceeds the amount and type of metadata gathered at the data repository that you have selected for uploading your research data.
Section 2: Ethics, legal and security issues
2.1 How will ethical issues be addressed and handled?
Are parts of your data sensitive data? What makes them sensitive?
Sensitive personal data is information that makes a person identifiable. Sensitive data can also be confidential data such as contractual agreements with external partners. If the project deals with human subjects, ethical approval from the Ethics Committee of the Canton of Zurich (KEK) or the ETH Ethics Commission is needed.
- Ethics self-check to determine if your research project needs ethics clearance
- ETH webpage for further information on human subject research including links to the document to apply for ethical approval and an online form.
- Have you applied for ethical approval and was it granted?
If your study requires ethical approval, your DMP must be in line with the conditions under which ethical approval was granted.
Did you inform study participants, and did they give their consent to collection, sharing and preservation of their data?The consent form is part of the application for the ETH Ethics Commission. Normally, you can briefly outline what is written in the consent form of the approved application. Please keep in mind that sensitive data can only be published in a completely anonymized form. 1
- What methods will you use to ensure the protection of personal or other sensitive data?
Please describe any necessary protection measure such as the storage location, data encryption, pseudonymization, anonymization 2 as well as the time of application. Be aware that the identification key of pseudonymized data should be kept in a separate physical storage location. Please also describe what happens with the data after the end of the project: Is part of these data supposed to be archived or does it need to be deleted?
2.2 How will data access and security be managed?
How will you regulate data access rights to ensure data security?
Please define who will have authorized access and if it is traceable. Determine responsibilities for managing access rights.
- General Information on IT security at ETH Zurich: House Rules Information Security
- Further information on the IT identity and user management system IAM at ETH Zurich
- Contact information to IT support groups in the departments
- How will sensitive data be handled to ensure safe data storage and transfer?
For any research project that involves secure handling and processing of confidential research data and/or research data related to identifiable persons 4, ETH Scientific IT Services operate the secure data and IT infrastructure Leonhard Med which is part of the national BioMedIT network. Such confidential and/or person-related research data (as defined in footnote 1) must mostly be classified as strictly confidential and may thus not be stored in cloud services. 5 Conditions under which such confidential and/or person-related research data are published and/or shared are strictly regulated and must be further specified in the DMP Section 4 ‘Data sharing and reuse’ (see below).
2.3 How will you handle copyright and Intellectual Property Rights issues?
Who will be the owner of the data?
In principle, the data creator is the owner (i.e., copyright holder) of the data and therefore has the right to allocate a licence to these data. For multi-partner projects a consortium agreement might be worthwhile. Make sure that you keep enough rights when a transfer of rights occurs as for instance during data upload into a commercial data repository. In case you would lose all rights when using such a repository service, look for alternatives. In addition, all the data that have been generated during an employment at ETH Zurich shall in principle remain at ETH Zurich (see Integrity Guidelines).
- Which licenses will be applied to the data?
For research data we recommend the use of Creative Commons (CC) licenses such as CC0 and CC-BY, because they allow easy reuse. However, CC-licences should neither be applied for sensitive data, nor for software and code. Furthermore, all code and software developed at ETH Zurich must be registered at ETH transfer prior to sharing them. Appropriate software licenses apply.
- Are there any constraints for data sharing and/or reuse of third-party data?
If you have specific contract agreements on parts of your data with for instance an industrial partner you just need to state that you cannot disclose that data due to this fact. Do not name the partner in case this is already confidential. Please contact ETH transfer, if you are in doubt about contract agreements with research or industrial partners as well as if you are aiming for a patent application.
Section 3: Data storage and preservation
3.1 How will your data be stored and backed up during the research?
How will the data be backed up and who is responsible?
State how often the data will be backed up: daily back-up is recommended. You can also indicate the retention time of backed up files (e.g., for recovering of older versions). As long as data is stored on standard ETH Zurich storage infrastructure, your departmental IT services take care of the automatic backup (see below). If you are not certain, which storage locations are included, contact your departmental IT services.
- Find the IT support group in your department
- Information on the automatic backup and restore service at ETH Zurich
- Where will the data be stored during the research project and are there limitations regarding storage capacity?
Please consider the various types of storage that exist at ETH Zurich. For more information on departmental procedures, consult the IT support group in your department. Keep in mind that sensitive data require special solutions for data storage (see above).
3.2 What is the long-term preservation plan for your research data?
Which parts of your research data could be of use in the long term and should therefore be preserved?
You should describe here what happens to the research data far beyond the lifetime of a project. Data with long-term value could be, but are not limited to, all the data underlying a publication that are necessary to verify, validate and reproduce published research results. Please also consider ethical and legal issues that might prohibit long-term storage of certain data types. Data with long-term value might be placed into a digital archive for long-term preservation. The standard way how research data can be preserved at ETH Zurich is by submitting data to the ETH Research Collection. A copy of the data is then automatically transferred to the ETH Data Archive, ETH Zurich’s long-term digital archive (except for data deposited via Libdrive).
- Which file formats will be used for preservation?
Please consider our recommendation table for file formats that are suitable for digital long-term preservation. Comment also on the choice of your file formats and on the use of community standards.
Section 4: Data sharing and reuse
4.1 How and where will the data be shared?
On which data repository do you plan to share your data?
ETH Zurich requires sharing of at least research data and programming code that forms the basis of published research output at the time of publication (see RDM Guidelines). Data should be made available in compliance with the FAIR data principles including assignment of a persistent identifier (e.g. DOI) and open access to the data with the exception of sensitive personal and confidential data that cannot be publicly shared. If possible, deposit your data in a well-established, trustworthy, non-commercial repository that implements the FAIR principles. The institutional FAIR data repository of ETH Zurich is the ETH Research Collection. Alternatively, a FAIR data repository can be selected that operates within your field of research. If such a repository exists, it might be more suitable regarding metadata community standards.
- A wide range of data repositories are listed on www.re3data.org (most should be listed there) or fairsharing.org/databases.
- The SNSF has its own recommendation list of repositories.
We provide a Step-by-Step Guide on Data Publication that supports you in preparing your datasets for publication in a FAIR data repository.
You might also want to check our recommendations for a Data Availability Statement, which is necessary to indicate the location of your research data underlying your publication.
4.2 Are there any necessary limitations to protect sensitive data?
- Under which conditions will you make the data publicly available?
Describe your restrictions for data sharing due to ethical or legal constraints, preparation for patent application, security constraints, contractual obligations, intended commercial purposes and copyright issues as outlined in the Guidelines for Research Data Management at ETH Zurich. Be aware that confidential and/or person-related research data (as defined in footnote 4) can only be published in completely anonymised 9 form and in line with consent obtained from study participants. This purpose should already be considered when preparing consent forms for study participants.
1 “all items which, when combined, would enable the data subject to be identified without disproportionate effort, must be irreversibly masked or deleted” (Human Research Ordinance, Art 25)
2 see footnote 1 [definition of anonymization]
3 see footnote 1 [definition of anonymization]
4 “A person is identifiable if a third party having access to the data of the person is able to identify such person with reasonable effort. [This can refer to health or biomedical traits,] religious, ideological or trade union related views or activities; […] the intimate sphere or racial origin; social security measures; administrative or criminal proceedings and sanctions” (From the document Data Protection in Research Projects by ETH Zurich, accessed 15.10.2021).
5 in accordance with the Directive on “Information Security at ETH Zurich” (accessed 15.10.2021)
6 If you or the principal investigator of your project are not sure about the conditions at your ETH Zurich department, please check with your departmental IT services at ETH whether daily backups are ensured for your standard department servers. A list of contacts can be found here.
7 see footnote 1 [definition of anonymization]
8 see footnote 1 [definition of anonymization]
9 see footnote 1 [definition of anonymization]
10 see footnote 1 [definition of anonymization]
11 see footnote 1 [definition of anonymization]