Data journals publish articles with descriptions of research data. This distinguishes them from conventional scientific journals, which focus on scientific interpretation. Data journal articles (also known as data papers) usually describe large and complex datasets, and the articles usually go through a peer-review process, as is common in conventional scientific journals. The data itself, which is described in data journal articles, is mostly published separately in data repositories.

Data journals promote the reuse of research data and its recognition as a scientific achievement, and seek to improve the transparency of scientific methods and results and support good data management practices.

An overview of quality-assured data journals is provided by forschungsdaten.org.

A data management plan systematically describes how research data are managed within research projects. It documents the storage, indexing, maintenance and processing of data. A data management plan is essential in order to make data interpretable and re-usable for third parties. It is therefore recommended to assign data management responsibilities before the start of a project.

Many research funding institutions now ask that research grant proposals address research data management and include at least some elements of a data management plan. See, for example, the German Research Foundation’s website on handling research data. The University Library’s Research Data Management Team can provide you with helpful advice when putting together your data management plan.

A digital artefact is the end result of the proces of digitalisation during which an analog object (a book, manuscript, picture, sculpture etc.) is transformed into digital values in order to store it electronically. As opposed to an analog object, a digital artefact can be distributed in the form of digital research data and machine-processed. Another advantage of working with digital artefacts is that further alteration or damage to sensitive analog objects can be avoided

The University Library provides a variety of services to help with digitizing media from the Freie Universität’s libraries’ collections.

Documenting research data makes it easier to retrieve, search, understand, contextualize, and apply research data. The use of structured metadata helps to improve the findability of research data, while extra information describing the context of the data collection, research methods, and the data itself promote a better understanding and reuse of the data. For this reason, data stored in the Refubium, Freie Universität Berlin’s institutional repository, is accompanied by a description of the research data (as a text file).

The term FAIR (Findable, Accessible, Interoperable und Reusable) Datawas coined by the FORCE11-Community for sustainable research data management in 2016. It is the main goal of the FAIR principles to promote professional management of research data in order to make them more findable, accessible, interoperable and reusable. The FAIR principles were adopted by the European Commission and integrated into the Horizon 2020 funding guidelines.

The FORCE11 website provides a list of these far-reaching principles. Initiatives like GO-FAIR have developed implementation guidelines for the FAIR principles for research data and the criteria derived from them.

The German Research Foundation’s “Guidelines for Safeguarding Good Research Practice” serve as a reference work on research integrity. The 19 guidelines listed therein establish a framework and standards for conducting research.

Research data management comes up as an important topic in several of the guidelines, for example, in the context of quality assurance or when it comes to documenting, publishing, or archiving research data. The guidelines also recommend that all of the materials and data used to establish research findings be stored for a certain period of time, usually ten years. They also note that good scientific practice involves documenting and publishing research software.

See:

• Deutsche Forschungsgemeinschaft. 2022. ‘Guidelines for Safeguarding Good Research Practice. Code of Conduct’. https://doi.org/10.5281/zenodo.6472827.
• Freie Universität Berlin. 2020. ‘Satzung zur Sicherung guter wissenschaftlicher Praxis (GWP-Satzung)’. Mitteilungen. Amtsblatt der Freien Universität Berlin 2020 (42). https://www.fu-berlin.de/service/zuvdocs/amtsblatt/2020/ab422020.pdf.

In order to ensure maximum reusability of scientific reserach data, which might be subject to copyright law, the additional allocation of usage rights by using a suitable license should be considered. One possibility of determining reuse conditions of published research data is the use of liberal licensing models such as the widely accepted Creative Commons (CC) model.

Structured research data is considered machine-readable when it can be read and interpreted by machines. Data collected and stored in standardized formats enable an efficient exchange of information between systems, for example, by adhering to metadata properties (like the ones provided by DataCite Metadata Schema) when processing data.

Meta data are independent data which contain structured information about other data and/or ressources and their characteristics. Meta data are stored either independently of or together with the data they describe. An exact defintion of meta data is difficult since the term is being used in different contexts and distinctions can vary according to perspective.

Usually there is a distinction between discipline-specific and technical/administrative meta data.

In order to raise the effectiveness of meta data, a standardisation of descriptions is necessary. By using meta data standards, meta data from different sources can be linked and processed together.

As part of good scientific practice, research institutions and research funders require a certain minimum storage period for research data generated over the course of a project, dissertation, or other research endeavor. Typically, the minimum storage period is ten years.

See:

• Deutsche Forschungsgemeinschaft. 2022. ‘Guidelines for Safeguarding Good Research Practice. Code of Conduct’. https://doi.org/10.5281/zenodo.6472827.
• Freie Universität Berlin. 2020. ‘Satzung zur Sicherung guter wissenschaftlicher Praxis (GWP-Satzung)’. Mitteilungen. Amtsblatt der Freien Universität Berlin 2020 (42). https://www.fu-berlin.de/service/zuvdocs/amtsblatt/2020/ab422020.pdf.

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The term open access refers to free and unimpeded access to digital scientific content. Users are usually given a wide range of usage rights and provided with easy modes of access. The copyright, however, generally remains in the hands of the author. Through open access scientific information can be widely disseminated, used and re-processed.

Open access is a fundamental aspect of the research practices associated with open science / open research / open scholarship.

The Open Access Policy of Freie Universität Berlin (last updated: 2021) provides recommendations and guidelines on how to implement open access. Freie Universität’s Open Access Strategy 2018–2020, which was developed in 2017, announced the goal of significantly increasing the number of open access publications by 2020. 

More information: Open Access at Freie Universität

Open science / open research / open scholarship refers to a set of research practices characterized by accessibility, transparency, and participation. In this context, researchers are encouraged to make research data (as well as research methods, software, educational resources, and publications) accessible early on, share them, and work together to develop them collaboratively. The field of open science / open research / open scholarship also includes areas such as open access, open data, open source, open educational resources, and open methods.

More information about open science at Freie Universität, see Open Science Working Group.

Persistent identification is the process of assigning a permanent, digital identifier consisting of numbers and/or alphanumerical characters to a data set (or any other digital object).

Frequently used identification systems include DOI (Digital Object Identifier) and URN (Uniform Resource Name). As opposed to other serial identifiers (such as URL addresses), a persistent identifier refers to the object itself rather than to its location on the internet. Even if the location of a persistently identified object changes, the identifier remains the same. All that needs to be changed is the URL location in the identification database. This process ensures that data sets are permanently findable, retrievable, and citable.

Freie Universität’s DOI Service can provide you with persistent identifiers.

A repository serves an administered storage space for digital objects, where research data can be published and archived. Alongside institutional repositories, there are a large number of subject-specific repositories (see re3data to search for repositories by keyword or subject).

The Refubium at the University Library is an institutional repository that allows members of Freie Universität to publish dissertations, research papers, and research data free of charge.

More information: Refubium A-Z

Research data are all research-related analog data, documents, and objects that are to be digitized in the course of a given research process as well as to “born digital” (i.e., originally created in a digital medium) data, documents, and objects produced in the course of a research process and/or that are the object or result of such a process. Research data are also defined as any data that facilitate the documentation, transparency, and – depending on the research area – replication of research outcomes (metadata).

Common examples of such research data are digitizations, audiovisual data, digital representations of analog data, measurement data, observation data, survey data, texts and text editions, databases, object collections, protocols, methodological test procedures, questionnaires, software, and simulations. The German Research Foundation (DFG) also defines source code and software as research data where they represent central outcomes of scientific research. The broad spectrum of data types reflects the diversity of scientific disciplines and their different research methods and processes.

In the course of the research process, research data can take on several forms. They may vary in quality depending on how they are prepared or if additional data are added. The form of the data may also depend on the stage of processing, or they may be provided in different formats for presentation purposes. Furthermore, they may be subject to varying access regulations (open data, restricted data, closed data).

See: Deutsche Forschungsgemeinschaft. 2022. ‘Guidelines for Safeguarding Good Research Practice. Code of Conduct’. https://doi.org/10.5281/zenodo.6472827.

The management of research data is an ongoing part of the entire research process. It includes the organization, documentation, storage, back-up, archiving, sharing, and publication of data. Research data management not only increases the visibility of the generated and/or processed data, but also the related research. It also helps to improve data quality and data processing, while making subsequent use of the data easier both for the original researchers and for others. Moreover, it facilitates the use of data in new contexts created when different data sets are linked. Sustainable research data management ensures the fulfillment of subject-specific requirements, as well as potential obligations related to funding agreements. It also helps meet publishers’ standards and requirements, as well as ensuring compliance with the principles of good scientific practice.

Standard formats refer to file or data formats usually designed for unrestricted use. For different types of files, the relevant standardized, open format (e.g., CSV for spreadsheets) allows the file or data to be read and processed by a variety of applications. Certain standard formats have discipline-specific specifications.

Common standard formats: OpenAire's guide on Data formats for preservation.

When it comes to preserving digital research data, the following three aspects should be kept in mind:

Storage refers to the processes and methods of (physically) storing data on a computer or other IT system (for example, during a project that is still being conducted or at the planning stage).

Protection refers to the methods of data security used to prevent unauthorized access to research results (for example, access control, access restrictions, encryption).

Archiving refers to the methods used to preserve and store research data for the long term (usually in relation to the outcomes of completed projects). Standard formats are often used. Research results must be stored for a specific minimum period of time.

Please contact the University Library or ZEDAT if you have questions about these topics.

See: Freie Universität Berlin. 2019. 'IT-Sicherheitsrichtlinie der Freien Universität Berlin (Version 4.0)'. https://www.fu-berlin.de/sites/it-sicherheit/downloads/IT-Sicherheitsrichtlinie.pdf.