Research Data - Open Research Data
Research data is any information that has been collected or generated during the research process. Their sharing allows you to verify the presented test results and allows you to re-use them in subsequent tests. Research data is typically digital, but also includes non-digital formats such as lab notebooks and journals that can be digitized. Research data includes both raw data (not analysed, collected in the research process) and processed data. When collecting non-digital data, its long-term usefulness should be assessed and plan how to guarantee their durability. For the sake of universal data availability, you should use formats that do not require commercial software to read them.
Forms of research data:
- diaries, diaries,
- laboratory and field notebooks, notes from experiments,
- laboratory protocols, methodological descriptions,
- text documents and spreadsheets,
- questionnaires and interviews,
- test answers,
- photos and slides,
- audio and video recordings,
- artifacts, specimens, samples,
- data files,
- standard operating procedures and protocols,
- mathematical models, algorithms,
- computer simulation results.
Open Research Data is research data made available in accordance with the idea of Open Access - each user can analyse, reuse, modify and redistribute it. In order for research data to be open, it must be deposited in open national or international repositories and made public under open licenses, such as Creative Commons.
- Open Research Data (ORD) - the uptake in Horizon 2020
- Facts and Figures for open research data
- The Open Data Research network
The FAIR Principles are a set of guidelines that define the most important principles of good research data management.
In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data highlighted the need to improve data re-use infrastructure and sets out four principles for data reuse. Since then, research funding organisations, universities and research centres have supported the implementation of good practices in data sharing and management to support education and innovation.
FAIR is an acronym formed from the first letters of words: Findable, Accessible, Interoperable and Reusable.
Findable - easy to find
Research data, according to the FAIR principles, should be described with rich metadata and have a unique and permanent identifier (e.g. DOI). They should also be stored and indexed in a searchable resource (e.g. a repository).
Accessible - widely available
It is recommended that access and reading of data should be possible through open, free and universal communication channels. Data should be "as open as possible - as closed as necessary". If access to data is limited (e.g. sensitive data, patent proceedings, time embargo), you should justify the reason or specify the conditions under which it can be made available. If the data cannot be made available, a description of the created resource should be provided in the form of metadata.
Interoperable - interoperable, easy to read and process by both humans and computers
The FAIR principles assume that the data presented uses a formal, accessible and widely used language to represent knowledge and allows it to be linked with other data sets.
Reusable - reusable
In order to enable the re-use of data and their proper interpretation, the data should be properly documented, providing information on the objectives of the project, entities involved in data collection and explaining how the data was created.
In addition, research data should have a clearly defined license governing the conditions for its re-use, e.g. commonly used Creative Commons licenses.
The FAIR principles are constantly being developed as more and more organizations and institutions are interested in introducing good practices in the field of research data management.
More about the FAIR principles you can find on:
- GO FAIR
- The FAIR Guiding Principles for scientific data management and stewardship
- Turning FAIR into reality
- Guidelines on FAIR Data Management in Horizon 2020
Data Management Plan (DMP)
Data Management Plan (DMP) is a document that defines how research data will be managed during the research project as well as after its completion. Such a plan is required, inter alia, by the Polish National Science Centre, the European Commission (in the Horizon 2020 and Horizon Europe programs), the European Research Council, the National Science Foundation from the United States or the British Research Councils.
There is no universal template for a Data Management Plan, its content depends on the guidelines of the institution financing a given research project. The data necessary to verify the test results should be made available. The plan may change as the project progresses.
The National Science Centre has identified six thematic areas that must be included in the data management plan:
- Data description and collection or re-use of existing data
- Documentation and data quality
- Storage and backup during the research process
- Legal and ethical requirements, codes of conduct
- Data sharing and long-term preservation
- Data management responsibilities and resources
- Horizon 2020 Online Manual - Data Management
- Data Management Expert Guide (Consortium of European Social Science Data Archives CESSDA)
- DMP Tool - a tool for preparing DMP templates tailored to the requirements of American funders
- DMP online - a tool very similar to DMPtool containing a database of UK science funding institutions
- The Data Curation Centre – British service specializing in research data management
Science Europe papers:
- Practical Guide to the International Alignment of Research Data Management Extended Edition with DMP Evaluation Rubric
- Guidance Document Presenting a Framework for Discipline-specific Research Data Management
- Implementing Research Data Management Policies Across Europe. Experiences form Science Europe Member Organisations
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