Research data portal

Obtaining relevant data
To maximize the value of data gathered or generated in a lab, it has become a necessity and is becoming increasingly frequent, that data be made available in open archives so that it can be reused by others. When utilizing third-party data, the researcher must ensure that all applicable permissions and restrictions of use or sharing are followed.

If you are planning your next project, you should include a search for existing data in your plans. Perhaps there is data that you can use in combination with your data to broaden the analysis basis in your project. The most interesting comparison might be to compare your data with data collected by others. Spending time mapping out what data is available that you can use is therefore something you should make time for. Consider the following methods for finding useful data in open archives, and feel free to seek assistance if necessary: What sources are available for searching, and how should you search? Creating a so-called systematic search in the appropriate sources can be extremely beneficial.

Here are three international sources that we would recommend for searching for research data:

• DataCite Commons
• Google Dataset Search
• Bielefeld Academic Search Engine (BASE) 

The first two are sources that only provide hits on data material. BASE, on the other hand, contains all types of research material, including data.

Norwegian source: Microdata
UiT has an agreement with Statistics Norway (SSB) to use microdata.no, a service that allows researchers and students to process and analyze anonymized data from Statistics Norway on population, education, income, labor market, and social security. 

Various other sources
In UiT's archive for research data, DataverseNO, you can find data that other UiT researchers have archived. However, whatever you find is also available in the three international search engines indicated above.

The Tromsø survey (Tromsøundersøkelsen) is another interesting source. For more than 40 years, this has been Norway's most extensive and most visited demographic survey. All researchers affiliated with institutions with research competency are eligible to apply for access to the Tromsø survey data. You can find more information on how to apply for access to Tromsø survey data on their webpage.

Citation of datasets
If you use other people's data sets in your research, you should refer to them in the reference list in the same manner that you refer to the research literature on which you are based. This applies regardless of the license for reuse.

Normally, only digital versions of archived datasets are available. As a result, references to datasets must always include a link to the resource. The link should be based on persistent identifiers, such as DOI or handle, to ensure that the link does not corrupt. Such persistent identifiers are used by all quality-assured data archives.

There can be many versions of a dataset. It is possible that errors have been corrected, or that the data material has been modified or expanded for other reasons. All of these changes result in a new version of the dataset. As a result, the version number must be provided in the dataset reference. Others will know what version of the dataset your research is based on if they locate the reference in your reference list. A data archive should therefore have version tracking so that anyone who wants to test something can access all previous versions.

An example of a dataset reference is as follows:

Conzett, Philipp, 2022, "Dataverse Community Survey 2022 – Data", https://doi.org/10.18710/UOC8CP, DataverseNO, V1

(Author, Publication Year, "Title", persistent link, Name of Archive, V1)

If you wish to learn more about searching for and citing research data, a webinar on the subject is held every semester. A PowerPoint presentation with additional information is also available on the course page.

UiT and research funders such as The Research Council of Norway and the European Research Council require a research project to provide a good management plan for gathering, handling, archiving, and sharing data. 

A data management plan (DMP) must be prepared by the researcher early in the project, preferably within six months of its start. The data management plan is a document that details planned methods for data collection or generation, as well as how the data will be managed both during and after the research project is finished.
- Principles and guidelines for the management of research data at UiT. 

However, a DMP is more than just a requirement; it is also a beneficial tool for you as a researcher. A good plan will structure and streamline your workflow, saving you time and money in the long run. A DMP contributes to making data more FAIR and makes publishing and archiving easier.

The data management plan is a dynamic document that will be changed as needed throughout the project. It is essential that you evaluate the plan on a frequent basis to ensure that the intended routines are followed.

Data management plans at UiT must be implemented as specified here: Retningslinjer for oppfølging av datahåndteringsplaner ved UiTs enheter (in Norwegian only). More information regarding this can be obtained from your faculty's Ph.D./research administration.

How to get started?
What should be included in a DMP is illustrated by the research data cycle. Consider the cycle and define each step's areas of responsibility, work processes, safety measures, and data material. The researcher must produce a substantial amount of documentation that describes the processes and files at each level. The plan must include an explanation of how one expects to carry out this documentation. Science Europe has created a guide outlining the minimum needs for a data management plan.

A template is a very useful starting point when writing a DMP, to make sure that everything that should be included is included. There is a wide range of templates available, depending on the type of project, the type of data that is being collected and/or the funder of the project. It is advisable to check if your funder has recommendations or requirements for templates or tools to be used when creating a DMP.  

Below you will find an incomplete list of templates and tools. Text-based templates are generally more adaptable to the project's needs.  Web-based tools may offer multiple templates to choose from, provide guidance for completing the plan, can be machine-readable, facilitate tracking of version changes and often allow sharing with others.

Recommendations for text-based templates

• UiTs template – (Norwegian | English | note: file will automatically download) - generic template providing guiding texts on how to write a DMP in-line with UiTs principles and guidelines for research data management. The template complies to Science Europe’s core requirements for DMPs. Recommended to use when there are no specific requirements from your funder.
• Science Europe DMP template – generic template consisting of six core elements including guiding text and questions. See ‘RDM Guidance for Researchers’. Note that you can also use this template in the tool DMP online (see below). Recommended to use when there are no specific requirements from your funders.
• Horizon Europe template – recommended for Horizon Europe projects. Note that you can also use this template in the tool DMPonline (see below)

Recommendations for web-based tools

• DMPonline – generic tool, recommended for Horizon Europe projects. Note: You have to register your own user and choose “other” for Organization. Go to the “reference” tab > “Funder Requirements”, and create a new plan from the for example the ‘Horizon Europe’ or ‘Science Europe templates’.
• Sikt DMP – recommended for projects processing personal data.
• EasyDMP - recommend for projects using the Sigma2 infrastructure.
• DS Wizard ELIXIR Norway – recommended for projects within Life sciences.
• Data Stewardship Wizard – generic yet advanced tool that offers a guided and structured way to create a machine actionable DMP and provides FAIR metrics.
• ARGOS – generic yet technically advanced tool from OpenAIRE.

When creating your DMP using one of these DMP tools, we recommend reading Cessda Data Management expert guide (social sciences), DS Wizard fra ELIXIR (life sciences), Science Europe guidelines (generic), DCC's checklist (generic).

Examples of DMPs

It can also be helpful to begin by creating a tabular overview of the project's processes and materials. This table will outline the project's data flow and can be attached to the DMP. See for an example the table Digital Data Handling within ADLab.

It might also be helpful to have a look at good examples of DMPs.  You can for example have a look at the following resources:

• Example DMPs and guidance from the Digital Curation Centre
• The curated collection of Horizon 2020 DMPs from the University of Vienna
• The Zenodo DMP catalogue by LIBER Europe.
• The public plans from DMPTool

Support when writing or evaluating a DMP 

The University library organizes an introductory webinar on how to write a data management plan for all staff and students. You will find handout material in our RDM Training @ UiT Zenodo collection.

You can contact the University Library at researchdata@hjelp.uit.no for advice on picking a template or tool suitable for your project and content of your DMP. We can also provide you with feedback on your plan. Please make sure to give us some time to provide you with adequate feedback. If you are using the Sikt Data Management tool (person sensitive data) or the DS Wizard fra ELIXIR (Life Sciences) we recommend you to use their support services for guidance. 

There is a starterpack for UiT employees who supervise and/or evaluate DMPs. The document provides an introduction to DMPs, information on relevant training resources, examples of DMP templates, tips on how to evaluate DMPs, links to relevant documents where you can learn more, as well as information on where to get help and guidance at UiT. For now, the file is only in Norwegian.

Agreements and ownership

UiT's Principles and guidelines for the management of research data state the following about ownership of research data (§2):

According to the Regulations on securing and managing work results at UiT, the University has the right to take over ownership and use of all research data generated by personnel in positions at UiT. This also applies to Ph.D. students and other temporary workers. 

 

UiT typically does not own research data generated by students, exchange students, guest researchers, externally funded Ph.D. students, or other projects with external funding. Students who work at UiT are exempt from this rule.

 

Ownership and rights should be clarified in a formal agreement when external partners are involved or the research is externally funded. The same is true for student projects in which the research group should retain data rights.

When research data is generated as part of a research project that is entirely or partially funded by a third party, ownership, and rights to use the data and research results must be agreed upon prior to the beginning of the project. The same is applicable for student projects, where it is important for the research group to retain data rights.

Consider the following agreements for various collaborative projects:

• Project agreements (for grant and commissioned projects)
• Participant declaration
• Data Management Agreement
• Confidentiality Agreements/NDA (Non-Disclosure Agreement)
• MTA (Material Transfer Agreement)
• Agreement on disclosure of data to countries outside the EU/EEA.
• Memorandum of Understanding (MoU)
• Agreements with students
• Agreement on business Ph.D.
• Agreements with guest researchers

More information about agreements can be found on the Prosjektstøtteportalen. The intranet page «Maler for samarbeidsavtaler ved UiT» contains templates for several sorts of agreements. (Click here or go to: www.intranett.uit.no > Organisasjon > Fellesadministrasjon > Avdeling for forskning og utviklingsarbeid > Skjemaer).

If you wish to learn more about searching for and citing research data, a webinar on the subject is held every semester. A PowerPoint presentation with additional information is also available on the course page.

Lastly, keep in mind that the right to use and/or publish research data should never be handed to commercial actors unless UiT retains the right to make the data publically available for reuse. (§3 - Principles and guidelines for the management of research data at UiT).

Licenses

Reuse rights on published research data are limited unless the material is accompanied by a user license. As a result, it is critical to provide your datasets with a clear and machine-readable license that allows for re-use. This will contribute to the dataset becoming more "reusable," and hence more FAIR.

In UiTs Principles and guidelines for the management of research data at UiT state the following about licenses for research data:

• As long as there are no legal, ethical, security, or financial grounds to do otherwise, the researcher must make research data readily available for further use to all relevant users.
• Research data shall be equipped with licenses for access, reuse, and redistribution.
• The licenses should be universally recognized, with as minimal restrictions on data access, re-use, and redistribution as possible.

In practice, this means that research data uploaded to an open research data archive should be licensed with the fewest restrictions feasible. This might be a standard Creative Commons or Open Data Commons license. Such a license allows data to be reused without first obtaining permission from the licensee, but there may be some restrictions. In addition to the attribution constraint, Creative Commons licenses allow you to specify terms such as non-commercial re-use (NonCommercial), sharing in the same way (ShareAlike), and no modifications (NoDerivatives). It is important to note that different terms impose distinct (and often unexpected) constraints on data reuse.

For practical recommendations on licensing research data, have a look at OpenAIRes guide "How do I license my research data". 
If your dataset contains code or software, we recommend that you have a look at the following "Research Software Licensing guide" 

The Creative Commons CC0 license is used by default at UiT's institutional collection on DataverseNO. It is important to remember that even if CC0 does not restrict re-use, the re-user is required by research ethical standards to credit the sources. If you prefer a more limited license, we recommend selecting one with the fewest restrictions on reuse.

Reliable research data archives will allow you to choose a standard license for data archiving, which will be presented under "Terms of Use." At the same time, it is advised that the appropriate license be specified in the attached documentation (ReadMe file or separate license file).

If you want to learn more about licenses and rights to research data, a webinar on the topic is arranged every semester. On the course page, you will also find a PowerPoint presentation with more information.