Frequently asked questions

Answer: Research data is any information collected, observed or created during a research project and used as a basis for obtaining research results and drawing conclusions. Research data may include:

• numerical measurements, such as temperature measurements in laboratory experiments;
• text, such as literature analysis notes;
• images such as microscopy images;
• videos, such as experiment records;
• audio recordings, such as interview recordings;
• program codes, such as data analyzing software code;
• simulation data, such as computational models and outputs;
• other data formats.

Answer: Data not directly related to scientific research should not be considered as research data. Research data are not:

• administrative records of the study, such as financial statements or staff documents;
• commercial or private communications, such as e-mails or correspondence documents;
• legal documents, such as employment contracts or cooperation agreements;
• marketing materials, including advertising leaflets;
• preliminary drafts or personal notes not intended for publication.

These data do not contribute to the scientific analysis or evidence base of the research project and therefore do not constitute research data.

Answer: Research data Management (RDM) is a systematic approach that involves planning, collecting, storing, sharing and archiving data. It is important to ensure the quality of the data, its long-term availability and its reuse in other studies. Effective RDM also facilitates compliance with legal and ethical requirements, enhances research efficiency, and supports data integrity throughout the research lifecycle.

Answer: FAIR principles are guidelines that require research data to be made:

• Findable: data and their metadata are readily available to other researchers and systems;
• Accessible: data is available, and access conditions are clearly specified;
• Interoperable: data is compatible with other systems and datasets;
• Reusable: the data is prepared so that it can be reused in the future.

Answer: Open data is publicly available research data that can be freely used, shared and analyzed. However, you are not always required to make your research data public. Data can be protected if it contains sensitive or personal information, or if its publication could harm study participants, authors or the public. In such cases, data should be managed according to privacy laws and ethical guidelines, and access may be restricted or controlled through data sharing agreements.

Answer: Open data is publicly available research data that can be freely used, shared and analyzed. However, you are not always required to make your research data public. Data can be protected if it contains sensitive or personal information, or if its publication could harm study participants, authors or the public. In such cases, data should be managed according to privacy laws and ethical guidelines, and access may be restricted or controlled through data sharing agreements.

Answer: Open data and FAIR data are not synonymous as they have different approaches and objectives. Open data is freely available to everyone, while FAIR data means data can be found, available, interoperable and reusable. FAIR principles do not provide for mandatory data opening but ensure that the data is easily accessible and usable as widely as possible, maintaining confidentiality where necessary. Respectively, FAIR principles focus on enhancing the usability of data, regardless of its openness. This means that the data may comply with the FAIR principles but not be publicly available if protected by privacy or proprietary restrictions.

Answer: The Research Data Management Plan (DMP) is a document describing how information and data generated or obtained will be organized, processed, stored and shared as part of a research project. DMP is an essential part of modern research as it helps to ensure the quality, availability and sustainability of data. It outlines strategies for data handling throughout the project's lifecycle and helps anticipate potential challenges.

Answer: A Data Management Plan (DMP) is usually started by determining what data will be collected, how it will be stored and shared, and what security and privacy requirements must be met. In general, you should:

• identify the types of data you will collect or generate;
• determine how the data will be stored and backed up securely;
• assess any security and privacy requirements that must be met;
• consult DMP templates offered by your university or study funders.

We recommend using DMP templates offered by university or study funders, as well as systems such as Argos or DMPonline that help structure the plan step by step.

Answer: We recommend storing research data in encrypted and secure locations, such as cloud servers with access control, university servers, or specialized data repositories. It is essential to make regular back-up copies and to comply with data protection requirements such as General Data Protection Regulation (GDPR). We recommend to:

• implement strong access controls and authentication methods;
• make regular backup copies stored in separate locations;
• use encryption for sensitive data, both at rest and in transit;
• comply with data protection regulations such as the General Data Protection Regulation (GDPR).

Answer: A dataset is a structured set of data, usually arranged in tables or other structured forms, that consists of several data elements or values that have been collected and prepared for analysis. Here are examples for different research fields:

• in an epidemiological study, for example, the dataset could include patients age, gender, symptoms and treatment outcomes;
• in sociological research, the dataset could include responses from survey respondents to different questions;
• in environmental science, a dataset might consist of temperature readings collected over time from various locations.

Answer: Data repositories are digital platforms designed to safely store, organize, share and restore research data. They provide long-term data storage and access to the wider research community, while promoting good data management practices. Repositories can be institutional, discipline-specific, or general-purpose. Zenodo, for example, is a well-known repository used by scientists from different fields, or GenBank, a specialized repository for biological data.

Answer: When choosing a repository to deposit (publish) your dataset, several factors should be considered. In general, it is essential to select a trusted repository that offers metadata standards, long-term data storage and access, meets ethical and confidentiality requirements, and meets funders and institutional requirements. It's also beneficial to choose a repository that assigns persistent identifiers (like DOIs) to datasets, enhancing discoverability and citation.

It may be useful for the specific scientific sector to choose an industry-specific repository. For example, GenBank or Dryad repositories may be suitable for biological data, while data related to social sciences may be suitable for ICPSR, CESSDA or Figshare.

Answer: Research data can be placed in data repositories that are appropriate for your research industry or institution. Some of the most popular repositories are Zenodo, Figshare, or specialized repositories like Dryad or Pangaea.

Answer: To ensure data is reused, add detailed and standardized metadata that explains the structure, format, and context in which the data is collected. It is recommended to provide documentation and data dictionaries explaining variables, codes, and methodologies. Make sure that the data are published in an acceptable and widely used format and include clear rules on data use licenses such as Creative Commons.

Answer: Metadata is data that provides information about the content, structure, origin, and format of a dataset. For example, photo metadata may include information about the date, location, and camera settings of the capture, while research data metadata may specify the method and sources of data collection. Metadata is essential for other researchers to find, understand and use your data. Without metadata, data can be difficult to understand and use.

Answer: Yes, in most cases, research funders and institutions are demanding a Data Management Plan (DMP), especially for larger studies. Even if a plan is optional, when you develop a DMP, you can systematically plan how to manage, protect, and share data, thereby improving project quality and transparency.

Answer: If the study data contains sensitive information or personal data, it requires anonymization or pseudonymization to protect individuals' privacy. Restricting access to data and using secure data storage and transfer solutions is also essential. In addition, data protection regulations such as GDPR need to be complied with to ensure compliance with legal requirements. In some cases, you may require to obtain informed consent from participants specifying how their data will be used and shared.

Answer: ARGOS is an online platform dedicated to the development and management of research data management plans (DMP). It helps researchers’ structure, develop and comply with funders' requirements for data management throughout the study. ARGOS offers templates and automated processes to make it easier to create and maintain DMP.

Answer: Yes, ARGOS is a free tool that researchers can use to create, manage, and submit research data management plans. Many universities and research funders recommend or require its use as part of research projects.

Answer: To create a DMP on the ARGOS platform, you must log on or create an account. You can then choose a template that meets the requirements of your fundraiser or institution, and fill in the relevant fields about data collection, storage, sharing, and security. ARGOS offers guides and directions to help you fill out the plan.

Answer: This section should specify where data will be stored, such as on a university server, cloud service, and what security measures will be taken, such as data encryption, access control. It should also be mentioned how backup data will be backed up and how sensitive information will be protected to meet safety requirements and comply with regulations like GDPR.

Answer: Yes, ARGOS offers templates designed to meet the requirements of various national and international funders, such as Horizon Europe or the Latvian Council of Science. Templates contain specific issues to help ensure compliance with these requirements.

Answer: The FAIR principles state that research data can be Findable, Accessible, Interoperable and Reusable. When designing a DMP, make sure you describe how the data will be annotated with metadata, how access to the data will be provided, and what formats will be used to ensure compatibility and re-use of the data. ARGOS provides guidance to help integrate these principles into the data management plan.

Answer: If you have not already identified all solutions during DMP development, you may indicate that these aspects will be clarified later in the study. ARGOS provides an opportunity to update the DMP at any stage of the project, so the plan can be modified, complemented, complemented or refined when additional information is available. It's important to document any anticipated challenges and outline provisional plans where possible.

Answer: Yes, ARGOS supports collaboration functions, allowing multiple people to work on one data management plan at a time. You can invite colleagues or collaborators to join the plan by giving them the access rights they need.

Answer: In the DMP section on data sharing, you must specify where the data will be stored after the end of the study, such as the institutional repository or open data repositories. It is important to mention whether the data will be made public, as well as to define access conditions such as licensing.

Answer: The most serious errors are providing an insufficient detailed description of data storage and security requirements, misstating the availability of data after the end of the project, and failing to comply with the FAIR principles. ARGOS provides automated guidance to help you avoid these errors, but it's important to carefully review and populate all fields accurately.

Answer: According to GDPR, personal data is any information relating to an identified or identifiable natural person. Personal data is considered sensitive under the protection of EU law when it relates to religion, politics, health, racial or ethnic origin, genetic data, biometric data, sexual orientation, etc.

Answer: The solution may vary depending on the individual case. You can contact the appropriate department at your faculty or contact the LBTU ethics committee for an opinion. The departments responsible for faculty shall be:

• Ethics Committee for Research at LBTU LPTF Institute of Food;
• LBTU VMF Animal Welfare and Defence Ethics Council.

Alternatively, you may need to submit your study for an ethics review and obtain the necessary approval before resubmitting your manuscript.

Answer: Before you start working on personal data, you must develop and submit a Data Management Plan (DMP) describing data protection measures and a data storage plan. It is also necessary to provide reconciliation documentation for the processing of personal data in accordance with the requirements of GDPR, e.g. to obtain informed consent from participants. It is recommended to prepare a confidentiality agreement on data security for stakeholders and to set access restrictions. Finally, document data anonymization or pseudonymization methodology to protect the privacy of participants.

Answer: Yes, you need it. With the Privacy Statement, explain how members' personal data will be processed. Even if polling data is collected anonymously, they can form an identifiable personal profile using information related to, for example, occupation, age or education. Additionally, online data collection may capture IP addresses or metadata that are considered personal data. Even email addresses are considered personal data.

Answer: You must enter into a contract with the data provider (i.e., the company) and describe the issues related to data ownership, reuse, confidentiality, and any restrictions in this contract. This agreement should address data licensing, intellectual property rights, and any obligations for data security and privacy, etc. in this contract. Ask your university's legal department or lawyer for help with preparing such a contract.