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Research Integrity

Tilburg University is committed to protecting and guaranteeing scientific integrity.

Data Management

Research data are of great value for Tilburg University and should be managed in a responsible manner. Responsible research data management is essential, on the one hand, from the principle of “verifiability” as is also stated in The Netherlands Code of Conduct for Research Integrity. On the other hand, it is in line with the conditions of national and European research funding organizations as regards the storage and management of research data for stimulating open access and reuse of research data.

The aim of research data management is to guarantee access to and protection of research data against theft, misuse, damage, and loss. The researcher should take care to adequately store and archive research data, and make them retrievable and accessible. Responsible data storage not only leads to transparency but also offers possibilities for knowledge dissemination, meta-analysis, and re-analysis.

In this section, you will learn more about the key elements of data management in three phases of your research: before your research starts, during your research, and after your research is finished.

  1. Data management before your research starts
    1. Research data policy
      The Research Data Management Regulationscontain Tilburg University’s policy on the safe handling and management of and sustained access to research data. It is important to read these regulations and, if applicable, the additional data management policy of your school before you start your research.
      By research data is meant: all (digital and non-digital) data collected and generated during academic research, as well as the instruments with which the research data were collected and other relevant information (for instance questionnaires, software, scripts, and lab journals). By metadata is meant: documentation and/or information on research data required to understand the content and context of the data.

      Data Management Plan
      Before you start your research, it is important to write a data management plan (DMP). A data management plan (DMP) describes how you handle your research data. It contains arrangements on such aspects as planning, choice of software and file formats, data storage locations, data documentation, legal aspects, costs, etc. The Tilburg University DMP template is part of the School’s "integrated form for Ethics, Data Management and the Data Processing Register", and is mandatory for all new research projects. For some Schools the G.E.D. Started tool is available to easily submit your data management plan. More information is available: https://www.tilburguniversity.edu/intranet/research-support-portal/prepare/tsb

      The data management plan is a dynamic plan, it is created at the start of the research and can be adapted and updated during the research. More information is available in the factsheet “Tips For Writing a Data Management Plan”
  1. Data management during your research
    1. Data storage during research
      An important element of data management during your research is data storage. Secure data storage is fundamental to prevent data loss and data breaches. For example, think about what would be the consequences if your computer or hard drive crashed. Or if you lost your USB-stick, or your laptop is stolen. There are several storage facilities available for researchers of Tilburg University to help to keep your research data safe.

      You could use one of the Microsoft365 facilities (i.e., Onedrive, Teams or Sharepoint Online), one of the SURF facilities (i.e., SURFdrive or SURF Research Drive) or the TiU Bulk storage (which is part of the university’s network drives). Which facility is best to use depends on, for example, whether you want to collaborate with others and with whom, and the storage capacity you need. In general, you could use the following guidelines: Onedrive and SURFdrive are meant for personal storage; Teams, Sharepoint and SURF Research Drive are suited to collaborate, and if you work with large amounts of data and / or need computing facilities, SURF Research Drive or TiU Bulk storage may best suit your needs. You can find an overview of these storage facilities and more information on the features of each of these facilities on the Intranet. More information is available here: https://www.tilburguniversity.edu/intranet/research-support-portal/rdm/data-storage

      Protecting (privacy)sensitive data
      If your data contain personal information, you have the responsibility to protect these personal data, and you should be able to demonstrate that these data are processed in compliance with the General Data Protection Regulation (GDPR) as described in the following chapter. If your data contains personal data, make sure you pseudonymize or, if possible, anonymize these data (see the next chapter for more information on pseudonymization and anonymization). If you have pseudonymized your data, keep the key file and the data file in different locations. Make sure as few people as possible have access to the key file. The key file should be deleted as soon as possible (within 6 months unless longer is necessary), as long as it does not conflict with interests of the research.

      If you work with special personal data (see next chapter), then these data must also be encrypted (if not anonymized). This also applies to data that cannot be anonymized or pseudonymized (such as video material), or other highly sensitive or confidential (business) information. You can use 7Zip for this. You can find 7Zip in the software center. Key management, however, is crucial in encryption. When you lose your password (key) your data are inaccessible! To create, manage, and share your passwords you can use the password manager Keeper.

      Safely sending data
      When you want to send sensitive research data to others, it is not safe to do so via e-mail. A safe option to share sensitive and/or large data files (up to 100 GB) is via SURF Filesender.  

      Discouraged storage facilities
      Tilburg University discourages the use of any public cloud storage facilities, such as Dropbox, Google Drive, and OneDrive (private account) for the storage of research data, especially for sensitive or confidential data. There is no processing agreement with these parties. There are a number of risks associated with the use of such facilities. For example, the data are not always stored within Europe, and often it is not clear who has access to the data. In addition, the terms of use of the storage facility may give the provider unwanted rights over the data.

      It is also not recommended to store your data (exclusively) on your laptop, PC, or external storage devices such as a hard drive or USB stick. Because these devices can break down, the chances of data loss are high. If you need to use such devices, make sure they are encrypted with Bitlocker when it comes to storing sensitive data. The use of private devices such as PCs and laptops is discouraged as much as possible. This is because Tilburg University's PCs and laptops are equipped with various security measures, such as an encrypted hard drive, an up-to-date antivirus program, and a set firewall.

    2. Data organization and documentation
      Next to securely storing your research data, it is important to properly organize and document your research. Proper data organization and documentation makes it easier to find and re(use) your data (for yourself and others), it contributes to the understanding of the information, and it helps to prevent errors and misinterpretations.
      1. Data organization
        1. File naming
          Before your start storing your data, think about a file naming system. A file name should uniquely identify a file, and give information about the content, status and version of a file. Useful elements in a file name are for example the date of creation (YYYY-MM-DD), a description of the content, the name of the creator, or version number (_v01, _v02)
        2. Versioning
          The most important aspect of versioning is that you always keep the original ‘raw’ data file. To keep the different versions of your files apart, you should not use confusing file names, such as ‘final’, ‘final 2’, ‘new’, ‘revised’, etc. Instead, best practices are to use ordinal numbers (v.1, v.2) for major version changes and decimals for minor changes (v.1, v.1.1, v.1.2), to include the date in the new versions, and to document the changes you make to (data) files.
        3. Folder structure
          It is also helpful to think about your folder structure before you start. A clear folder structure makes it easier to locate files and versions, it is essential when you collaborate with others and it is time consuming to make changes to an existing folder structure.
      2. Data Documentation
        Data documentation is essential to make the data understandable, discoverable and reusable, for yourself and others. It is important to document all information that is needed for others to understand your data. In general, there are three levels of documentation:
        1. Project level documentation includes for example a description of the research questions and hypotheses, used methods, date of collection, etc.;
        2. File or dataset level documentation could be captured in a ‘readme’ file that could describe how the files that make up the dataset relate to each other, what format they are in, and what the order of the files is;
        3. Variable or item level documentation includes for example names, labels and values of variables, codes or missing values, or syntaxes/command files.

 

  1. Data management after your research
    1. Storage period
      If no other period is required by an applicable legal, contractual, or subsidy rule, all raw and processed research data must be stored for a period of at least 10 years or 15 years (for medical data) after the moment that the research is formally completed (article 5 Research Data Management Regulations).

    2. Creating a data package
      During research it is important to systematically document your research data. Clear and detailed documentation improve the overall data quality.
      It helps you understand your data years from now and ensures easy replication of your research if required for example for verification purposes. Journals and funders sometimes require a data package as well.

      The data package contains metadata (data documentation), raw and processed data sets and the materials used for the research (e.g. codes, questionnaires, stimuli, instructional texts). The data documentation is all the information needed to enable reuse and/or replication: administrative, descriptive, structural. For example methodology, analytical and procedural information, definitions of variables, units of measurement, reasons for missing values, etc. The documentation is available through for example README files, codebooks, text files, information included in data files or syntaxes. Some schools have specific guidelines for creating data packages. Please check if this applies to your school before you create your data package.

  2. Archiving in a repository
    Tilburg University applies the basic principle that research data are made publicly available for academic research, as far as reasonably possible, if and insofar as this does not conflict with agreements with research funders, confidentiality, the General Data Protection Regulation (GDPR) and/or intellectual property rights of third parties (article 7 Research Data Management Regulations). Tilburg University (TiU) Dataverseis the designated facility for researchers of Tilburg University to archive and share research data. It is a certified central online data repository and is managed by the Research Data Office (RDO). Before you deposit your data, you should also check whether your funder or the journal you are submitting your paper to have specific requirements. A listing of available data repositories worldwide is available at https://www.re3data.org/

    The FAIR principles (Findable, Accessible, Interoperable, Reusable) are a useful framework for thinking about sharing data in a way that will enable maximum use and reuse. Some examples are:
    Findable: (Meta)data are assigned a globally unique and persistent identifier, for example a DOI (digital object identifier)
    Accessible: for example Dataverse can be used to publish and grant access to your data, either publicly downloadable as open data or available upon request or with restricted access
    Interoperable: use well-known and preferably open formats and software
    Reusable: data are well-documented to support proper data interpretation and have a clear and accessible data usage license so others know what kinds of reuse are permitted.

    Publishing your research data creates additional exposure for your research. When you publish your data in TiU Dataverse, your data are harvested by NARCIS and search engines. In the near future your datasets in TiU Dataverse will also be automatically visible as research output in the Tilburg University Research Portal.

    When to deposit
    The policy at Tilburg University is that the empirical data of all academic research studies which are reported in academic publications should be documented and archived in such a way that they can be replicated. Also when the data are unique in their kind and when the exact same data collection cannot be replicated (for example, because the data are related to a unique time and place), it is important to share these data with other researchers. Please note that data can only be deposited in TiU Dataverse (or any other repository) when they are free of privacy sensitive information and intellectual property rights or other legal rights of third parties
    It may be an option to publish only the metadata of the dataset and save the data in another location that fits all legal requirements. Data sets should be deposited at the latest when you publish a paper or other publication based on the data. Data sets can also be deposited before the publication of research results. TiU Dataverse is, however, not meant for storage of working versions, but your data package should be finished and complete.

    How to deposit
    Tilburg University researchers who want to deposit their data set(s) are requested to follow the instructions from the website. Compliance with these instructions is necessary in order for TiU Dataverse to meet the requirements of the CoreTrustSeal Certification. There’s also a list preferred file formats available. 

    Data ownership and sharing
    Research data, generated in the scope of employment, internship, or secondment to or on behalf of Tilburg University, are, in principle the property of Tilburg University, unless otherwise agreed in a separate contract by Tilburg University (article 6 Research Data Management Regulations).

    When publishing your data in a repository or share it with others in another way then it is important to determine under what license you make the data available. You deal with intellectual property rights, contract rights and privacy rights both in sharing and reuse of research data. A license agreement is a legal arrangement between the creator/depositor of the data set and the data repository, signifying what a user is allowed to do with the data. When you want to publish and share your data without any restrictions it is common to assign “CC0” or “CC Zero Waiver” to the dataset. For more information go to the following link.

    Data Management Support
    The Research Data Office (RDO) is the virtual one-stop shop for researchers and Schools for all questions about research data management and offers support and advice on:
    1. Managing, storing and preserving research data.
    2. Data management plans for funding proposals.
    3. Issues related to the university’s Research Data Management Regulations.

    On a regular basis the Research Data Office also organizes trainings and meetings on research data management for researchers. The Research Data Office can be reached via rdo@tilburguniversity.edu or http://www.tilburguniversity.edu/rdo. In addition, the Research Support Team at your School will also offer support.

Dilemma

I am a junior researcher who has painstakingly hand-collected a large amount of data. My first paper using these data has just been accepted for publication. A senior colleague in my department contacts me to ask for the data. He has an important say in my career development. What do I do?
A: I send the senior colleague the data.: 27 votes (10.07%)
B: I tell the senior colleague that the data are available as soon as the last paper I want to write on the subject has been published. This can easily be one or two years.: 94 votes (35.07%)
C: I tell the senior colleague that I do not want to give him a preferential treatment.: 33 votes (12.31%)
D: I tell the senior colleague that I am willing to send the data on the condition that I will be mentioned as co-author on all publications that use the data.: 114 votes (42.54%)
Total Votes: 268
 

Schools

Tilburg School of Social and Behavioral Sciences Science Committee

Tilburg School of Social and Behavioral Sciences (TSB) has established the Science Committee which publishes a guideline on ‘Data Handling and Methods Reporting’ (DHMR) for TSB researchers. The Science Committee periodically investigates the quality of data handling and the reporting of research methods by researchers of the School. The following things are considered:

 

The Management Team of TSB appoints the members of the TSB Science Committee. The committee consists of a chairman/professor and at least three members of the scientific staff (PhD and at least 0,8 fte tenured).

Guideline on Data Handling and Methods Reporting (DHMR) 2020
TSB Science Committee Regulations

  • The storage of research data (in a data archive in the own institute; in a national data archive such as DANS; on a server that is accessible via the internet);
  • The completeness of the stored research data (completed questionnaires; raw research data of experiments; data obtained from transformation of raw data, including missed observations and outliers; data files as used for statistical analysis; etc
  • The reporting of used research methods;
  • The compliance with rules regarding the privacy of participants in experiments and other forms of research;
  • The accessibility of data to other researchers, reviewers of journals, and everybody who wants to check the data and the results and conclusions based on these data.

 

The Management Team of TSB appoints the members of the TSB Science Committee. The committee consists of a chairman/professor and at least three members of the scientific staff (PhD and at least 0,8 fte tenured).


Guideline on Data Handling and Methods Reporting (DHMR) 2020

TSB Science Committee Regulations

Tilburg School of Economics and Management

As part of responsible data management, TiSEM (Tilburg School of Economics and Management) requires replication packages of published research. Getting a paper accepted is a good moment to ‘clean up’ the files and archive the data. The replication package consists for example of metadata and data documentation, raw and processed data sets, information on statistical processing and analysis. Creating a replication package is much easier while working on the research than afterwards and researchers are encouraged to get familiar with the requirements. More information can be found on the TiSEM replication package webpage.