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Research Data

The Jagiellonian University has introduced the possibility of depositing open research data on the Jagiellonian University Repository platform.

Research data are materials of factual nature (in a numerical, text, graphic, or sound form) collected during application of various research techniques and considered by the scientific community to be indispensable for the purpose of assessing and replicating research results.
A distinction is usually made between the raw (non-analysed) research data, i.e. data obtained directly from measuring devices, in various scientific ventures, or collected in the frames of specific projects, and the data that have been processed.

One can deposit data from all areas of knowledge that have been produced, collected or described for further research use. The scope of data constituting one batch to be deposited shall be determined by their creator.

Collecting, processing, storage, protection and sharing of research data involves a number of activities that need to be planned for the sake of proper management. Increasingly, researchers have to submit a Data Management Plan (DMP) to the research funding body (RDF) already during the submission and evaluation phase of their grant application, which sets out how the data are managed both during and after conclusion of the research project. The main areas of activities that should be analysed in the preparation of the data management plan are described below.

Data Management Plan – examples

Jagiellonian University Repository

The types of data collected are very diverse, depending on the field of science and the research methodology adopted; for instance:

  • textual documents and notes
  • numerical data
  • questionnaire and survey results
  • audio and video recordings and photos
  • database content (video, audio, text, images)
  • mathematical models, algorithms
  • software (scripts, input files, etc)
  • output of computer simulations
  • laboratory protocols and methodological descriptions
  • samples, artefacts, objects*

The types of data, the way in which they are going to be collected and/or processed, their quantity, and frequency of occurrence are issues that have to be carefully considered in advance.

The file formats can be arbitrary, while in order to ensure universal access and openness, it is advisable to use formats that do not require proprietary reading software. Multiple files can be deposited under one description. If there are a great number of files, a good solution is to group and pack them, e.g. into a .zip form. One should also be attentive about file naming. An adequately named file/set of files can significantly facilitate accessing the relevant data by the user. These elements all contribute to the consequent efficient use of the data in the proper context.

The documentation should describe the methodology of the research conducted as well as its context and source, informing about the way the data were organized during the project, e.g. the adopted convention, version, and structure of the folders. It often includes additional files needed to use the data (e.g. scripts) or standard dictionaries. One can create a separate ReadMe.txt file. If the research has been already documented in a published paper, its link should be provided in the URL field.

The metadata serve to characterize research data for a potential user, providing description of the entire data set (author, title, date of creation, license, scientific discipline, etc.). The research data must be always made available but with their metadata.
The research data in the JUR can be described using dedicated fields:
Description: the person submitting the research data should briefly characterize their content, origin, test methods used, research context etc.
Time range: The start date and the end date should be given, indicating the duration of the study, which is often the same as the period of performance of the grant.
Data provider: for any data deposited in the JUR, the Jagiellonian University Repository should be indicated as a provider.
Area of research: one should indicate a relevant field of research/art, narrowing it down to a particular discipline, basing on the classification of fields of research and scholarly and artistic disciplines included in the Regulation of the Minister of Science and Higher Education of 20 September 2018.
When updates or extensions of data are expected, the JUR makes it possible to version research data, using the fields: version, connections and DOI. In the version field, the person entering the description should indicate which version of research data it is (e.g. 1.0, 1.0.0).
When depositing the first version of research data, two DOI identifiers will be attributed to it at the verification stage in JL: one for the version and one for the concept. After submitting subsequent versions of the test data, one should specify a new version number (in version field), while the DOI for the concept (in connection field) remains the same, which is very important as it indicates that they are actually related and will allow the JUR administrators to properly link the records. It can also be noted in the description that this is a subsequent version of previously published research data.

For example, a correctly approved description for the second version of the research data could look like this:

 

The issue of security and data storage needs to be carefully addressed in the entire process of collecting and/or processing research data. The issue of access to data (especially of sensitive kind) should be thoroughly analysed and precautionary steps taken in order to avert any undesirable leakage of confidential information. A backup plan has also to be implemented to prevent accidental data loss due to e.g. hardware failure.

Sensitive data are data related to racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership and genetic profile. Biometric data that unambiguously identify a particular person or data concerning his or her health, sexuality or sexual orientation also fall into this category.
If any research work involves collecting and/or processing of sensitive data, it is necessary to ensure their adequate protection. The Jagiellonian University has implemented a policy for the protection of sensitive data and appointed a Data Protection Officer to see to its compliance and develop a data management plan that will solve any potential privacy or legal problems. See: https://iod.uj.edu.pl.

Copyright and licences

The owners of copyrights and intellectual property rights to all the data acquired and produced should be clearly indicated. It should be also specified if there are any legal restrictions on the reuse of data obtained from third parties.

Furthermore, one should indicate the licences for the research data made available. It is recommended to use open Creative Commons licenses, while it is also possible to share the data as public domain. The creator depositing research data in the repository is responsible for obtaining any relevant permission to share it, as well as for proper anonymization/pseudonymization of personal and sensitive data. One should keep in mind that under the effective data protection law (RODO), it is obligatory to obtain an informed consent of all the study’s participants to record and share their personal data.

*Source: https://www.fosteropenscience.eu/content/open-research-data-definitions.

 

Providing access to research data consists in their proper description and making it available. It should be specified when one will be able to access (whether during or after the study, along with appropriate deadlines) and if the access will be open or restricted (any limitations should be clearly formulated). The Jagiellonian University Repository can provide both open and restricted access.
The reuse of research data in a different context will be enabled by assigning a unique permanent identifier in the form of DOI by the JUR administrator when the data is deposited by the author (actually there will be two DOI numbers: one for the version and the other for the concept, serving to connect the consecutive versions of research data). The DOI facilitates locating the data, tracking citations, and multiple reusing.

Long-term archiving consists in storing research data over a large period of time. The data management plan should determine where the data are to be stored. In choosing an external institution to provide access to a research data repository, it is important to consider, among others, whether it has a long-term data storage plan, whether the files in which the data are stored can be described with metadata, who will be responsible for providing access to the data in, say, 10 or 15 years, who finances the repository and what the storage conditions are.
The Jagiellonian University Repository ensures long-term archiving of the deposited data on the university servers managed by the Jagiellonian University Integrated Systems Development Centre. Data security is further enhanced by performing regular backups.

The Jagiellonian University Repository complies with the FAIR Data principles, which can be expanded as follows:

  • Findable – easy to be searched for and found.
  • Accessible - available for everyone.
  • Interoperable - it can be combined with other data.
  • Reusable – suitable for multiple use.

The FAIR Data principles serve as guidelines to enable the reuse of research data under precisely described conditions both by humans and by machines. See more about FAIR Data here.

  1. Research Data. Jagiellonian University Repository https://ruj.uj.edu.pl/xmlui/help/0067 (access: 25.10.2019).
  2. FAIR Principles. https://www.go-fair.org/fair-principles (access: 25.10.2019).
  3. Open research data. Definitions. https://www.fosteropenscience.eu/content/openresearch-data-definitions (access: 25.10.2019).
  4.  National Science Center. Guidelines for applicants to supplement the Data Management Plan in the research project. https://www.ncn.gov.pl/sites/default/files/pliki/regulaminy/wytyczne_zarzadzanie_dany mi.pdf (access: 25.10.2019), (obecnie https://www.ncn.gov.pl/sites/default/files/pliki/regulaminy/wytyczne_zarzadzanie_danymi_16_03_2020.pdf)
  5. Science Europe (2018). Science Europe Practical Guide to the International Alignment of Research Data Management. https://www.scienceeurope.org/media/jezkhnoo/se_rdm_practical_guide_final.pdf (access: 25.10.2019).