The dramatic explosion of research data, the developing information and computing technologies, the changes in European values have had a profound impact on the scientific community. We experience a policy and strategic paradigm shift, meanwhile the new European data strategy, Open Science and Open Access movements require and promote a change in perspective on research data management. Adopting core values such as sharing and reusing scientific research data also sets researcher with a myriad of new challenges, which the following information can help to address.
Qualitative and quantitative scientific research generates a significant amount of research data1 which researchers want to keep safe and for which they are responsible in terms of security, storage and preservation. Therefore, it is necessary to set up a data management plan which is a 1–2-page document, tailored to the different stages of the research and describing in detail how the data generated will be managed during the whole research process.
When preparing research plans, it is becoming increasingly common for research funders or funding organisations (e.g., OTKA, Élvonal proposals, Horizon 2020) to request that a data management plan be included within the research concept, thus promoting thoughtful data management, accessible, sustainable, and reusable data, knowledge sharing and more active participation in open science. The principle of “as open as possible, as closed as necessary” should be considered and taken into account when providing access to data and datasets
In fact, a data management plan should be outlined at the very beginning of the research, as early as the planning stage. This plan should include the most important information on data management:
Although there are some important common points in the data management plan (some proposals use separate forms, such as OTKA proposals mentioned above) which the researcher must answer, these RDMPs may differ significantly from each other, reflecting the specific characteristics of the particular research project.
Managing your research data should follow the FAIR Principles2. Therefore, your archived data should be:
F: findable by others and therefore properly supplied by detailed metadata (globally unique persistent identifier),
A: accessible through an easy access service (standardised communication protocol),
I: Interoperable (data should be readable by machines and humans without specialised algorithms, Metadata formats should use shared vocabularies)
R: reusable by others, with appropriate data use permissions (clear licenses and other conditions)
1 Research data are data collected and recorded in a non-digital (paper-based) or digital format during the research.
2 The FAIR guiding principles proposed by the EU and scientific communities promote Open Science and support open access and reusability of research data.
Open data sharing (in particular through the use of data repositories) allows research results to be exploited much more quickly, efficiently and widely, as they can be accessed by other members of the research community (in a pre-defined way, controlled by the researcher or funder), which facilitates knowledge sharing.
The safest way to store research data is to upload it to a data repository, which helps to ensure that your data is secure in the long term by providing a number of data security requirements.
This requires you to think about your data management strategy, how to manage your data throughout the research, how to store it physically and virtually, how to protect it and ensure that only authorised people have access to it, and how to share it with the wider scientific community after the research is completed.
The three most important data security requirements to keep in mind when choosing where to store data are:
Data safety storage options
It is important to store your data under secure conditions on at least 2-3 physically separated locations to avoid data loss. The safest option may be uploading your data to an on-line data repository.
Data and information generated during the research are usually managed and stored on one of the following devices or channels, therefore data and information security should also apply to the following areas:
Cloud-based data repositories
Hungarian data repositories (still in test function):
International interdisciplinary repositories:
Of the above, the most popular free repository with appropriate data security requirements is Zenodo1 , a general-purpose, open-access data repository developed in the framework of the European OpenAIRE programme, where you can upload up to 50GB/database. Researchers and institutions can also upload their databases related to their publications. There are also other data repository search engines which are specifically designed to help you choose the most appropriate repository for your purposes and data:
1 The Zenodo data repository is operated by the CERN Data Centre.
The protection of personal data 1 is of paramount importance. Any data or information that can be used to identify a person, family or household member is considered personal data, and you have to protect it as it is subject to the Hungarian Data Protection Regulation2 and, since 2018, you have to comply with the GDPR3 (General Data Protection Regulation) as well.
In the research data management plan (RDMP):
1 Examples of personal data: surname and first name, address, ID card number, location data (including virtual data such as IP address), mobile phone number etc.
2 Act LXIII of 1992 On the Protection of Personal Data and the Publicity of Data of Public Interest.
3 The GDPR (General Data Protection Regulation) is regulation No. 2016/679 passed by the European Parliament and the Council, which entered into force in Hungary in May 2018 and protects the data of persons and provides regulation for the free flow of information between member states.
There are open source tools available on the Internet to help you prepare a data management plan online, which will guide you through all the main points and provide you with a personalised template to help you prepare your data management plan – unless your research funder requires the use of a specific form. Examples of such effective tools include:
The data management plan should include:
1 DMPTool is produced by the University of California Curation Centre of the California Digital Library.
2 DMPOnline is a product of the UK Digital Curation Centre.
3 Metadata is “data about the data”, for a document or database it contains the most important descriptive properties, author, year of publication, etc. This data can greatly help in finding documents and data.
4 The Dublin Core Metadata Initiative is an internationally accepted method for standardising the metadata of documents available online, and which makes it easier for search engines to access documents by creating a digital “library card catalogue”.
5 DataCite is a non-profit organisation that aims to facilitate the on-line availability and discoverability of research data and results for its members through the use of DOIs or other identifiers.
6 DOI (Digital Object Identifier), registered with the CrossRef agency, is a unique identifier that helps to make scientific publications available in on-line format.
7 The ORCID (Open Research and Contributor Identifier) is an international author identification code that collects researchers’ publications based on DOI identifiers and helps to identify the researcher.
8 Including a time-table in your RDMP may help us revise your document from time to time.