NCN

The Data Management Plan is a tool for the effective management of research data. This document contains detailed information on the types of research data, the rules for using these data, as well as the procedures related to their sharing and archiving during and after the research project. From 2019, the Data Management Plan is a mandatory element of every research project carried out under NCN funding. It is worth noting that it is a “dynamic” document that can change as the project progresses. While submitting the final report, you have to update the Data Management Plan, which is subject to evaluation by NCN.

A well-prepared Data Management Plan offers the following benefits:

– DMP helps to effectively plan the research process and monitor the progress of the research;
– DMP facilitates cooperation between scientists working on a research project;
– DMP helps coordinate research implementation;
– DMP strengthens control of data access, security and quality;
– DMP minimizes project risks, such as the protection of personal data from unauthorized use;
– DMP supports the organization of long-term data storage.

According to NCN, the DMP should include six subject areas:

1. Description of the data, data collection, and reuse data.
2. Documentation and quality of the research.
3. Preservation and backup during the research.
4. Legal requirements, codes of conduct.
5. Sharing, long-term storage of data.
6. Sharing, long-term storage of data.

How to prepare Data Management Plan?

NCN prepared the guidebook, where you can find detailed information on how to complete the DMP: Guidelines for applicants to complete the Data Management Plan form in the proposal.

The NCN recognises that some projects will not generate, re-use or analyse research data and similar materials. In these cases, a short explanation is required.

If you can not make available your research data (in the case of industrial exploitation, confidentiality clauses, principles of safety and public interest, intellectual property rights including patent or trade secret protection, ethical issues and protection of personal data or due to third party licences), you should include such information in your DMP along with a short explanation of the reason for the restriction. Below you can find some hints for a Data Management Plan (DMP) based on NCN guidelines. Please remember to apply the suggested prompts to the aims of your project and planned actions. The DMP will be assessed by NCN experts in the context of your project.

1. Data description and collection or re-use of existing data
1.1 How will new data be collected or produced and/or how will existing data be re-used? LIMIT: 1000 characters Questions you might want to consider: − what standards, methodologies or software will be used if new data are collected or produced? − what quality assurance processes will you use? − which existing data (yours or third party) will you re-use? − how data provenance will be documented? − how will you organize your files and handle versioning? Primary data is data generated during scientific research or projects (e.g., surveys, questionnaires and their analyses, photos, notes, software, results of computer simulations, algorithms, samples, laboratory protocols, methodological Secondary data is data derived from previous research, analysis or source documents (e.g. previously published research datasets, published publications, library collections, archives, legal acts, official documents (e.g. from statistic offices), compilations, statistics, etc.).	In this section, you should: ► describe the primary research data that will be produced or acquired during the project or/and describe the origin and methods of obtaining secondary research data                » for primary data, you can write that the data will be produced during, e.g. queries, field research, laboratory studies, experiments, in-depth interviews, observations, etc. (depending on the project).             » for secondary data, please indicate the source(s) from which the data will be obtained and how the data will be processed (Do you plan to use a specific program to process such data? What type of data will be received after processing it, e.g. summaries, graphs, tables, etc.) Please identify any restrictions connected with the re-use of this data, such as licences, agreements with third parties, and property rights. ► in case when similar or the same data already exists, you need to explain why new data should be collected instead of using existing data.   The NCN recognises that some projects will not generate, re-use or analyse research data and similar materials. However, these are rare cases that must be very well explained.   Please note! Availability issue To ensure that secondary data can be safely used and shared, please check that it is available as open data or under which licence it is made available. You may need to obtain consent or sign an appropriate agreement setting out the terms of use (if it is third-party data). In the case of processing archive data, you will need to obtain permission to use and possibly share the acquired data.     Please note! External agreements issue  If you plan to use an external company to carry out planned research (e.g. conducting a survey), the chosen company must be informed about the guidelines imposed by NCN and the assumptions of DMP. The signed contract has to adequately regulate the ownership of the data and terms of sharing in the open repository according to agreements with NCN. If you outsource a report or other analysis, please be sure that the agreement with the external company includes a paragraph that obliges that company to submit the research data along with that report or analysis.   Please note! Digitisation issue If it will be needed to digitise analogue or paper-based research data (such as maps, photographs, and texts), please contain such information in DMP. If this will generate additional costs, please estimate and describe them in section 6.2 (under indirect costs). Please note! Open data issue Research data should be ‘as open as possible and as closed as necessary’. According to this assumption, NCN accepts cases where it may not be possible to make data openly available. However, in such a situation, it is mandatory to include a brief explanation.   Possible restrictions on the sharing of research data: in the case of industrial exploitation, confidentiality clauses, principles of safety and public interest, intellectual property rights including patent or trade secret protection, ethical issues and protection of personal data or due to third party licences. If this applies to your project, you should include such information in your DMP along with a short explanation of the reason for the restriction.   Please consider! If you cannot make your research data available in an open repository due to the reasons mentioned above, you may still deposit them in a repository and set the embargo or close it indefinitely. The benefit of such action is to increase the visibility of your research and scientific activity through metadata describing the deposited dataset.
1.2 What data (for example the kinds, formats, and volumes) will be collected or produced? LIMIT: 1000 characters   Questions you might want to consider: − what type, format and volume of data will you collect, generate or reuse?	In this section, you should describe: ► what type of data will be produced or acquired during the project? This can be, e.g. textual data, numerical data, audio recordings, video recordings, photographs, contents of a database, software, results of computer simulations, source codes, etc. ► in what formats research data will be stored? Please remember to give preference to open and standard formats (e.g. .odt, .csv, .wav, .avi, .pdf and others). Microsoft Office formats are not open formats because you have to pay for software to use its applications.   If you intend to collect or produce data in closed formats, please consider converting to open formats (preferred by NCN) at least that part of the research data that will be selected for sharing in an open repository. If conversion is not possible, a short explanation is required. In addition, when depositing and sharing research data in an open repository, appropriate documentation, e.g. a README-type file, should be included to inform potential users how to use the data and what software is required to read it (in the case of data stored in closed formats).   ► what will be the estimated size of the data, including the size of the backups (expressed in KB, MB, GB or TB)? In exceptional situations, it can be written that it is not possible to estimate the size of the data at the time of application.
2.Documentation and data quality
2.1 What metadata and documentation (for example methodology or data collection and way of organising data) will accompany data? LIMIT: 2000 characters   Questions you might want to consider: − what information is required for users (computer or human) to read and interpret the data in the future? − is the data machine-readable? − how will you generate this documentation? − what community standards (if any) will be used to annotate the (meta)data? − what international standards or schemes (i.e. Dublin Core, DDI) will be used to structure metadata?	In this section, you should: ► describe the adopted arrangement for organizing the acquired or produced data: folders and subfolders, file names, versions,  e.g.:             » ” Data will be stored in separate folders…”             » ” File titles will clearly describe the contents…”             » ” Versions will be created…” ► write what standard will be used to structure metadata. This issue is connected to the repository. If you decide to deposit and share your data in the RODBUK repository (Cracow Open Research Data Repository), you can write that the RODBUK repository is using the Dublin Core standard for metadata. If you are going to deposit your data in another repository, you should check what metadata standard is used there and include this information in your DMP (e.g. DDI, DataCite). ► indicate what additional documentation will be attached to the dataset to inform potential users how to reuse published research data and how it will be presented (a database with links to sources, a README file, file titles, code books, laboratory notebooks, etc.)             » “Information about the research as well as the organization of the data deposited in the repository will be included in the README file attached to the dataset.”                Please note! Even if the project assumes to use only secondary data (i.e. obtained from databases), it is also necessary to describe how the downloaded data will be managed (how the folder/files will be organized, for example, day of download, source, basic information defining the content of each file, etc.). It is important to maintain appropriate documentation to prove the origin of secondary data.
2.2 What data quality control measures will be used? LIMIT: 1000 characters   Questions you might want to consider: − how the data collection, analysis and processing methods used may affect the quality of data? − how measurement error and bias will be eliminated? − how you will minimise the risks related to data accuracy?	In this section, you should include the following information:   ► How data quality control will be carried out? e.g.:             » Who will perform data quality control?             » What procedures will be implemented to obtain the highest quality results and avoid measurement errors during laboratory testing? If the laboratory operates under specific procedures for safe and proper testing, it is worth mentioning them in the DMP.             » Will the laboratory equipment be properly calibrated according to the manufacturer’s instructions during testing?             » Will data be checked by qualified personnel during laboratory testing?             » Does the project assume to use specialized software to check and analyze the data?   ► If quantitative data will require cleaning, describe how cleaning of the data will be carried out so that it can be shared and used by other researchers.   ► How the data will be protected from unauthorized modification? Please indicate the procedures implemented in your institution to secure the data from unauthorized modification, e.g., authorization procedures, data recovery, and regulations for working in the cloud.   Please note! If there are several institutions involved in the project, please describe how the data quality control process will be carried out in each of these institutions.
3. Storage and backup during the research process
3.1 How will data and metadata be stored and backed up during the research process? LIMIT: 1000 characters   Questions you might want to consider: − what is your storage capacity and where will the data be stored? − what are the back-up procedures? − are special measures needed to transfer data from mobile devices, from fieldwork sites or from home equipment to a central work server? − do analogue or paper-based research data (maps, photographs, text) need to be digitised to increase their potential for sharing?	The NCN recommends backups according to the 3-2-1 rule, which means that you store at least 3 backups, 2 backups on different devices, and 1 of the devices is kept in another location than your office. According to these recommendations, you should describe where data will be stored and how backups will be made. You may consider the following options, which are available at KUE: ► business laptop (password-protected, with antivirus installed), backup will be created …(How often?) ► business desktop computer (password-protected, with antivirus installed), backup will be made…(How often?) ► KUE cloud (drive.uek.krakow.pl) has 5 GB of space for each employee, research data will be protected by login and password, and a backup will be made once every 2 days (backups are set automatically) ► OneDrive in the Microsoft cloud – has 1 TB of space for each employee with the possibility of increasing to 5 TB, datafiles can be shared directly with other members of the research team. The backup will be made by the project manager …(How often?) ► in addition, there may be an external drive purchased as part of the project, (password protected). Backup will be made …(How often?). Information about the cost of purchasing the drive should be included in section 6.2.   Storing data on flash drives, CDs or DVDs is not preferred.   In the case of fieldwork, please describe the procedure of transferring research data from mobile devices and/or field stations to the server at the office.   In the case of a research trip, please describe how data will be backed up. Please note! If there are several institutions involved in the project, please describe how data storage will be managed in each of these institutions and how data will be exchanged between them.
3.2 How will data security and protection of sensitive data be taken care of during the research? LIMIT: 1000 characters   Questions you might want to consider: − how the data will be recovered in the event of an incident? − who will have an access to the data during the research and how access to data will be controlled, especially in collaborative partnerships? Sensitive data: personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, genetic or biometric data for the purpose of uniquely identifying a particular individual, data on a person’s health, sexuality or sexual orientation, or data on criminal convictions and offenses. Anonymization of data is the processing of data in such a way that it is not possible to attribute the information to a specific or identifiable natural person. This process is irreversible. Secure document anonymizer to remove sensitive data. Pseudonymization means the processing of personal data in such a manner that the personal data can no longer be attributed to a specific data subject (individual person who can be identified) without the use of additional information, provided that such additional information is kept separately and is subject to technical and organizational measures to ensure that the personal data are not attributed to an identified or identifiable natural person.	If the project does not assume the processing of sensitive data, please include such information in the DMP, e.g. “Sensitive data will not be processed in this project” or “The research does not assume collecting, processing and storing sensitive data.”   On the other hand, if the project assumes the use of sensitive data, it is recommended to consult the planned research linked with processing and sharing sensitive data with the university’s Data Protection Officer. It will help to avoid potential mistakes at further stages of the project. Information about such consultation (carried out or planned) should be included in the DMP.   In addition, please describe the following issues: ► Where will sensitive data be stored? ► How will data be recovered in the case of an incident? ► How will sensitive data be protected from unauthorized access (e.g., password protection)? ► Will the identifiable data be anonymized or pseudonymized? And if data is pseudonymized, where will the encryption key for this data be stored? (The encryption key must be stored separately from the data.) ► Who will have access to the sensitive data (project manager, research team members)? It is also recommended to refer in the DMP to selected KUE internal legislation applicable to the project, e.g. ► the policy of personal data security; ► IT system management instruction; ► information security incident management procedure; ► clean desk policy; ► cloud working regulations.   Please note! If there are several institutions involved in the project, please describe the procedure for managing sensitive data in each institution, including the procedure for sharing data between them.
4. Legal requirements, codes of conduct
4.1 If personal data are processed, how will compliance with legislation on personal data and on data security be ensured? LIMIT: 1000 characters   Questions you might want to consider: − do you need to use anonymisation throughout a data collection? − do you need to remove identifying information or conceal the identity of participants (e.g.) using pseudonymisation) before data can be shared?	If the project does not assume the processing of personal data, please include such information, e.g.: “Personal data will not be processed in this project” or “The research does not assume collecting, processing and storing personal data.” On the other hand, if the project assumes the processing of personal data, it is recommended to consult the planned research linked with collecting and processing personal data with the university’s Data Protection Officer (iod@uek.krakow.pl). It will help to avoid potential mistakes at further stages of the project. Information about such consultation (carried out or planned) should be included in the DMP. It is also worth including the following information: ► If you plan to pseudonymize or anonymize the data (e.g., in the case of interviews or surveys), you should explain how this process will be conducted; ► It is worth declaring compliance with the specific legislation applicable to personal data protection. In the case of the KUE, you can invoke the Policy of Personal Data Security – Annex No. 1 to the Rector’s Ordinance (R.0211.34.2022, dated July 4, 2022).   Please note! When conducting a survey connected with collecting personal data, you have to gain informed consent from respondents for the preservation and sharing of personal data.   In the case of full anonymization, consent to share data is not required, but it is part of the research ethics code, research culture and good practice to take into account: > information on archiving research data and personal data: anonymized research data will be public, available to anyone and retained for the time specified in the guidelines of the funding organization; personal data will be securely stored, classified and eventually destroyed > information on long-term storage and use of anonymized data; > a statement from the study participant confirming informed consent to participate.
4.2 How will other legal issues, such as intellectual property rights and ownership, be managed? What legislation is applicable? LIMIT: 1000 characters   Questions you might want to consider: − who will be the owner of the data? − which licenses will be applied to the data? − what restrictions apply to the reuse of third-party data? − do you need to seek copyright clearance before sharing data?	In this section, please indicate what intellectual property laws will be applied during project implementation:   In the field of intellectual property rights, the following legal acts will be applied:   > Act on Copyright and Related Rights > Act of 30 June 2000 Industrial Property Law  > Regulations for the Management of Intellectual and Industrial Property and the Principles of Commercialization at Krakow University of Economics (Annex to Senate Resolution No. 11/2015 of March 9, 2015).  > The agreement signed with the research funder; > if RODBUK has been selected to deposit research data, then the Terms and Conditions for the use of the RODBUK Cracow Open Research Data Repository The following information should also be included: ►  Who will own the research data collected and produced during the project? Please note! If there are several institutions involved in the project, it may be necessary to sign an ownership agreement.   ► In the case of the use of third-party data, please write whether there are any legal restrictions on the reuse of these data, especially if they are made available under a specific license or additional agreement. ►  Which licenses will be applied to the shared data? Currently, according to NCN guidelines, the research data accompanying the publication and validating its results should be made available under a Creative Commons license: CC0 or CC BY.   As for licenses for databases, you can choose PDDL, ODC or ODbl, and for computer programs GNU GPL or GNU LGPL. You can find more information about the licenses.   Please note! If data will be obtained on request by external entities, it is necessary to sign an appropriate agreement that will regulate intellectual property issues and the possibility of sharing data in an open repository. The chosen company must be informed about the guidelines imposed by NCN and the aims of DMP, especially on the issue of sharing research data under the CC0 or CC BY license.
5. Data sharing and long-term preservation
5.1 How and when will data be shared? Are there possible restrictions to data sharing or embargo reasons? LIMIT: 1000 characters   Questions you might want to consider: − how will potential users find out about your data? − for how long will the data be stored? − are there any barriers and constraints to making the research data fully or partially accessible? − will journal publishers require deposit of data supporting article findings? − do you need to ask participants for their consent for data to be shared?	In this section, you should include the following information: ► What is the planned date for sharing data in an open repository? NCN obliges beneficiaries of grants to make research data available no later than the date of publication accompanying the project.   ► For how long will the data be stored? According to the potential usefulness and value of research data, the entire collected data (including agreements and consents accompanying the research) should be stored, for example, in the principal investigator archive for a minimum of 10 years. The research data made available in an open repository should be stored for a minimum of 10 years, too. When choosing a repository, please check the period for which the repository commits to store the data. The RODBUK repository stores data indefinitely. ► What are the possible restrictions and prohibitions on sharing the data? If there are restrictions on sharing a part or all of the data, describe which data and for what reasons will not be shared. Please note! Research data may not be available only if: ► the project processes personal, including sensitive data and it is impossible to anonymize or pseudonymize them; ► commercialization of research results is planned; ► agreements have been signed (e.g., with external entities) prohibiting the sharing of data; ► the project processes data of state importance, e.g., concerning state security.   If you have planned to conduct a survey, please consult the type of data processing consent (which should be obtained from respondents) with the KUE Personal Data Inspector first, so that the acquired data may be made available in an open repository.
5.2 How will data for preservation be selected, and where will data be preserved long- term (for example a data repository or archive)? LIMIT: 1000 characters   Questions you might want to consider: − what data must be retained or destroyed for contractual, legal, or regulatory purposes? − how it will be decided what data to keep? − what procedure would be used to select data to be preserved? − what repository will you be using? Is this repository conform to the FAIR Data Principles? − does the institution provide regular data backup or not?	In this section, it is necessary to describe how the selection of data for long-term preservation in an open repository will be carried out. Please remember that: ► It is up to the principal investigator (or research team) to decide what data will be made available in an open repository. This can be either raw or processed data, depending on the project and the planned research, licenses or agreements; ►  NCN obliges beneficiaries of grants to select firstly these research data that verifies and confirms the conclusions from the accompanying publication. The research data selected for sharing must:             » meet FAIR principles;             » present the highest quality and scientific value. This section should also include information on which data should be destroyed according to external agreements or other legal reasons.   Please specify in which open repository the research data will be stored. We recommend RODBUK (Cracow Open Research Data Repository), which is a specially dedicated repository for KUE academics as well as PhD students. It complies with FAIR principles and is free of charge. You may choose another open repository, but firstly please check if it meets the conditions of openness according to the FAIR principles.   If you have to pay for depositing data, please estimate these costs and consider how you will provide funds for long-term storage (for a minimum of 10 years). Information about these costs should be included in section 6.2.
5.3 What methods or software tools will be needed to access and use the data? LIMIT: 1000 characters     Questions you might want to consider: − do data need to be converted to a standard or open format with long-term validity for long-term preservation? − is additional equipment or software needed for scanning or conversion? − what mechanism will be used for data sharing; e.g. request handled directly, repository?	In this section, you should include the following information: ► In what format the data will be made available in an open repository? ► What hardware or software will be needed to read this data?   Please note! NCN prefers standard open formats so that access to data deposited in a repository does not require specialized software. Therefore, you should consider converting data stored in closed formats. If such conversion is not possible due to data quality preservation – appropriate documentation (e.g. a README file), should be included to inform potential users how to use the data and what software is required to read it.
5.4 How will the application of a unique and persistent identifier (such us a Digital Object Identifier (DOI)) to each data set be ensured? LIMIT: 1000 characters   Questions you might want to consider: − will persistent identifier for the data be obtained? − which existing persistent identifier will be used (e.g. Digital Object Identifiers, Accession Numbers)?	If your chosen repository provides a digital object identifier (DOI), you should include this information in this section. If not, consider changing the repository to one that meets NCN requirements.   The RODBUK repository assigns a DOI (digital object identifier) to research data shared as a dataset.
6. Data management responsibilities and resources
6.1 Who will be responsible for data management (i.e. data steward)? LIMIT: 1000 characters   Questions you might want to consider: − what is the role of data steward in your institution? − what is his/her position in the institution?	In this section, you should include the following information: ► Who will be responsible for managing the research data, i.e., who will be the data steward? ► Who will be responsible for data collection, and who will be responsible for data quality, data selection, data storage and backup, data archiving and sharing? ► Who will be responsible for implementing and updating the Data Management Plan? ► Does the project assume the participation of other professionals who will assist in the data management and development process: – Data Protection Officer (in the case of personal and sensitive data); – lawyer (in the case of settling legal issues, e.g. property rights, contracts with external parties); – IT team (in the case of data collection on university servers); – KUE librarians (in the case of choosing the RODBUK Repository as the place for depositing research data. Librarians can provide help in terms of verification of metadata and research data deposited by researchers in the RODBUK.); – KUE librarians in cooperation with IT specialists of the Academic Computer Centre Cyfronet AGH (in terms of archiving and long-term management of data deposited in the RODBUK Repository). Please note! NCN considers it as an error if one person is responsible for all research data management activities.
6.2 What resources will be dedicated to data management and ensuring that data will be FAIR? LIMIT: 1000 characters   Questions you might want to consider: − what are the costs for making data FAIR in your project? − how will these be covered? Research data management costs can be covered by indirect costs: – Open Acess indirect costs of up to 2% of allocated direct costs, which can only be used for expenses related to making publications or research data available in open access; – other indirect costs of up to 20% on (used) direct costs, which may be allocated to costs indirectly related to the project, including the cost of making publications or research data available in open access.	If the project may generate additional costs, they should be listed in this section. These may include: ►  purchase of equipment or service for digitizing data to be made available in an open repository; ► purchase of external drives for backups; ► fee for the storage of data in the repository for the entire required period (min. 10 years).   If the project will not generate additional costs for data sharing, please include such information. This may include: ► depositing research data in a repository that does not require fees,  e.g. RODBUK; ► using the existing IT infrastructure of the KUE;

The information on this site was prepared mainly based on the guidelines and presentations of the National Science Center:

– Wytyczne dla wnioskodawców do uzupełnienia PLANU ZARZĄDZANIA DANYMI w projekcie badawczym, https://ncn.gov.pl/sites/default/files/pliki/regulaminy/wytyczne_zarzadzanie_danymi.pdf
– Czarny G., Wskazówki dot. weryfikacji planów zarzadzania danymi i otwartych (meta)danych badawczych (prezentacja PDF)
– Galica N., Otwarte dane badawcze w polityce Narodowego Centrum Nauki (08.05.2024) https://www.ncn.gov.pl/sites/default/files/pliki/prezentacje/2024_05_08_otwarte_dane_badawcze_webinarium.pdf

Pages for creating Data Management Plan: 

• DMP Tool – a tool that prepares DMP templates tailored to the requirements of US grantors
• DMP online – a tool very similar to DMPtool containing the UK science funding body database
• The Digital Curation Centre (DDC) – a UK-based service specializing in research data management