Recently, access to research data has become an important focus point among researchers. In 2018, The Norwegian Ministry of Education and Research created a national strategy on access to and sharing of research data. The main principles in the national strategy are that publicly funded research data should be shared and reused more widely, and that the data must be as open as possible, and as closed as necessary. Research data should be as transparent and reproducible as possible. Proper data management is a condition for reproducible data, and sharing data is important for the transparency.
Data should be as open as possible, and as closed as necessary (European Commission, 2016)
In this section, and in the Data Management section, you will learn about
You can find some quick information about searching for data, data management plans and data archiving by clicking the arrows in the figure below.
This models shows the different data handling stages in a project. Click the arrows for more information
There is no consensus on the definition on the term “data” or “research data” – it varies according to discipline and project funder. To start with, we can use a definition taken from the Research Council of Norway (NFR, 2017):
Even if data can be perceieved as self-sustained units existing in the world, the data we are dealing with in this section can be divided into the following:
The focus in this section is on the data you collect or generate during your research, not the data you have downloaded from a database. The most important issue is deciding if your data can in any way be regarded as personal or sensitive, and that you treat them accordingly.
You are responsible for your own results, how you store your data and, of course, how you share them later on. When starting a new project, find out if you need any kind of approval for dealing with sensitive data. Individual countries may have several officials or agencies responsible for approving the use and collection of data in new projects. Any project planning to deal with personal data of any kind will generally need approval from the national data protection authority (Datatilsynet in Norway), the data protection official at your institution or any board of health research ethics (REC in Norway)
If you use fully anonymized data you do not need to apply for approval by an external body. You still need to check if your institution has a research ethics board that must approve your project before you start.
Note that even though a single piece of data may not be enough to identify individuals, a combination of multiple variables could very well be enough to make the connection; typical cases could be sparsely populated areas or rare diseases.
Open research data is still data, defined as above. The term “open” refers to the fact that there are no restrictions on their access. Foster defines open data as “…online, free of cost, accessible data that can be used, reused and distributed provided that the data source is attributed and shared alike”(Fosteropenscience, 2014). According to the Norwegian national strategy on access to and sharing of research data (2018), publicly funded research data should be shared and reused more widely. The strategy is based on the principle that research data must be as open as possible, as closed as necessary.
The openness of data is one of the key elements in the FAIR guidelines on research data. Open research data may be e.g. government data or collected data sets published in a data repository or in a data journal. As we move towards more open data, you need to know if your data can be opened up for the research community, and you need to know how to do this correctly.
If you want to share your research data, it is a good idea to make a data management plan at an early stage. If you document and structure your data from the beginning, you save yourself a lot of work preparing the data for sharing later. A proper data management plan ensures that you plan ahead regarding future use of the data, and also make the potential re-use of your data as easy as possible. A possible starting point for a data management and sharing plan could be the Digital Curation Centre’s working level guide: “How to develop a data management and sharing plan“.
Remember that you need permission from all data collaborators before you share your research data, and you should sign a written agreement stating the conditions for ownership, reuse and sharing. It is common practice to follow the same guidelines for co-authorship as for publications when publishing data.
There are many reasons for sharing research data. Funders like the Research Council of Norway and the European Commission have policies on data sharing, and for some funding programmes, you are required to make your data openly available unless you are restricted by e.g. sensitivity issues. Some journals, like Nature, also require that published articles are accompanied by the underlying research data. If your journal of choice does not require the publishing of related datasets, you should still keep in mind that the data could be valuable for other researchers and should therefore be made available if possible. Also remember that your institution may have a policy on data sharing.
Although you may not be required to share your data, there are still good reasons for sharing data, both for you yourself and for other researchers (Piwowar & Vision, 2013; RECODE, 2013):
There are several possibilities for sharing data:
There is some evidence to indicate that you are better off by choosing a subject-specific, certified archive if this exists in your subject area.
Sending your data directly to another researcher or institution is of course also data sharing. Note that this kind of sharing is not considered as “open data”, even though the same rules on data safety and personal data apply.
A few data repositories are nationally available in Norway. Follow the links in the non-exhaustive list below for a description of some of the Norwegian repositories, their focus, services and security level.
UiT The Arctic University of Norway provides an open data repository, UiT Open Research Data. The service is geared towards employees and students of the university, but is available for any researcher through the Dataverse network. Since UiT Open Research data is an open repository, it is unsuitable for any personal or sensitive data. UiT Open Research Data complies with the DataCite schema, and stored data are therefore reproducible and transparent.
Uninett runs a data storage and high performance computing service called Sigma 2. Sigma2 services are available for Norwegian researchers and projects funded by the Norwegian government. The Sigma2-services include high-security data storage and a tool for data management plans. Uninett’s storage facility is called NorStore, and offers storage, sharing and management of active datasets. Note that storing data in NorStore is not a permanent solution, and it should not be used as a data repository. Uninett’s repository service is called Nird, which is also compliant with the DataCite schema. Nird will store your data for a period up to 10 years. Nird is free and you log in via FEIDE.
The Norwegian Centre for Research Data (NSD) is developing a framework for storing, searching and managing research data called NORDi. The current services include a tool for creating data management plans. NORDi is still in progress but is planned as a complete platform for finding, sharing and using research data. NORDi also focuses on training courses in subjects related to research data.
The Norwegian Ministry of Education and Research is launching a data repository interface (BIRD), through its service group Unit. Norwegian institutions can create repositories within this interface for their own needs. Currently, BIRD contains only one archive belonging to BI Norwegian Business School. BIRD is mainly intended as a data storage facility and should not be treated as a proper repository for sharing data openly. Sharing is possible through requests.
There are a few things to consider to make sure you choose the right archive. The Digital Curation Centre has a checklist and a more detailed guide for evaluating data repositories, aimed at research support staff in UK higher education institutions, but the information is general in character and will be useful for you as well.
These points involve long time preservation, possibilities for adding sufficient metadata and assigning a persistent identifier like a DOI. It is a a good idea to make sure that the archive you choose complies with the FAIR-guidelines on research data.
Under some circumstances data cannot be shared:
These issues can sometimes be resolved if addressed at an early stage. You cannot share personal information about others, but if properly de-identified and with suitable consent forms, you may share a processed version of such data. Read more about how to treat personal data and data ethics.
Sending your data to another person, institution or project for further analysis or normal re-use is another kind of data sharing. When you send your data directly to an identifiable unit, you are responsible for making sure that the receiver of the data will treat them according to legislation and regulations. The best way of doing this is to sign a data processor agreement with the receiver of the data. Ask your institution’s data protection official if there is a standard template for a data processor agreement, or create one using the information on the Norwegian Centre for Research Data’s topic page on data treatment.
As the data owner, you decide what is proper handling of your data, and you are also responsible for obtaining any approval needed for processing personal data. However, any receiver of research data must make sure that all approvals are in place before they start processing the data.
The information below is important if you are transferring personal data. Read more on personal data on the Ethics-page.
If you transfer your data to another institution, you must make sure that the receiving institution has a proper information handling system. Note that the Personal Data Act also applies to data transfer, meaning that any data that needs approval for research or collection also needs approval for transfer. If the receiving organization is subject to current industry or conduct norms, or is a certified information handler, you could probably go ahead with the transfer. Try contacting the receiver’s data protection officer if you are unsure; the Norwegian Centre for Research Data (NSD) acts as data protection agency for many Norwegian research institutions.
Your institution is the data controller in this setting. It is your task alone to determine the purpose of any data transfer as well as the way the data are to be transferred.
First of all, the EU General Data Protection Regulation (GDPR), also in force in Norway, is quite strict and detailed. Writing a GDPR-compliant data processing agreement is probably the one area where you need of help from your local data protection officer. If you need to issue a data processing agreement to a third party, even within Norway, you can use the following template as guidance.
The template is a freely available example published by DLA Piper in the UK.
The Norwegian Data Protection Authority (Datatilsynet) has published a guide to the data processing agreement explaining what must be included in a general agreement if you should choose to write one on your own.
Note that the Personal Data Act was replaced by the EU GDPR in May 2018.
Some kinds of data handling are regarded as transferring or sending the data abroad. If you are part of a larger project where a collaborating university needs your data transferred for further processing, you are in fact transferring your data out of the country. The regulations in the Personal Data Act apply regardless of how long the data is stored abroad; international data transfer is regulated by the same requirements for approval as your own collection and handling of the same data. If your project needs approval before collecting the data, you also need approval if you need to send the data abroad.
Searching research data may help you to get to know your field. Even though your field is not data-intensive, you may be surprised what you can find, as many types of research content may be considered to be research data.
If there are research data in your field, there are several ways you can benefit from a good data search strategy.
There are numerous data repositories, and they cannot all be listed here. However, there are sites where you can search data sets or repositories.
DataCite provides services to help the researchers locate, identify and cite research data.
At https://search.datacite.org/, you can search data sets that are assigned a DOI. Each data set has the citation shown in different reference styles and with export to BibTeX and RIS.
Bielefeld Academic Search Engine is a search engine especially for academic web resources. The BASE advanced search allows you to limit the search to data sets.
Zenodo and Figshare are multidisciplinary data archives. Their user interface is similar to that of literature databases, and you can use common operators like AND and OR. Figshare also provides a guide on how to use it.
Here is a quick video showing how re3data works (youtube)
The Norwegian Centre for Research Data (NSD) has available data sets for personal data, regional data, institutional data, and more.
The Norwegian Institute of Public Health provides data for research and analysis. Data on health records, health surveys and biobanks are available. You will also find information on how to access the data.
Statistics Norway (SSB) can supply you with research data at a personal level. The procedure for obtaining data is clearly outlined.
When using data collected or generated by others, you need to cite the data set, similar to citing all articles, books and other sources you use in a publication. This facilitates description and information retrieval, access and persistence, verification and reproducibility, and integration with other data (Altman & Crosas, 2013). Force 11 has developed a set of data citation principles on how and why data need to be cited properly (Martone, 2014). Many repositories have citation export or clear guidelines on how the data sets should be cited. If not, the citation should include
Most data repositories will explain which elements should be part of a proper reference to their data, and there is no need to create an exhaustive and hard-to-read reference if you have a persistent identifier leading the reader to all the relevant metadata information.
In the text:
The hypothesis is supported by observations of the Atlantic puffin (Barret, 2016).
In the reference list:
Barrett, R. T. (2016). Atlantic puffin Fratercula arctica field data, Hornoya [Data set]. UiT Open Research Data Dataverse, V2. https://doi.org/10.18710/4LABGF.
Altman, M, & Crosas, M. (2013). The evolution of data citation: from principles to implementation. IASSIST Quarterly, 37. Retrieved from: http://www.iassistdata.org/sites/default/files/iqvol371_4_altman.pdf
Foster.(2014). Open Data Definition. Foster’s Open Data Taxonomy. Retrieved from: https://www.fosteropenscience.eu/taxonomy/term/110
Martone, M. (2014). Data Citation Synthesis Group: Joint Declaration of Data Citation Principles. San Diego CA: FORCE11. https://doi.org/10.25490/a97f-egyk.
Ministry of Education and Research, Norway: National Strategy of access to and sharing of research data. Retrieved from https://www.regjeringen.no/en/dokumenter/national-strategy-on-access-to-and-sharing-of-research-data/id2582412/sec1
The Research Council of Norway. (2017). Open access to research data : Policy for the Research Council of Norway. Retrieved from https://www.nfr.no/PolicyOpenDataWEBrev2017.pdf
Piwowar, H. A., & Vision, T. J. (2013). Data reuse and the open data citation advantage. PeerJ 1:e175, https://doi.org/10.7717/peerj.175.
RECODE (2013). Policy recommendations for open access to research data in europe – Stakeholder values and ecosystems. Retrieved from http://recodeproject.eu/wp-content/uploads/2013/10/RECODE_D1-Stakeholder-values-and-ecosystems_Sept2013.pdf