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Data Policy / Implementation

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The UT has since many years a data policy and ITC has developed guidelines based on this UT Data Policy. 

Good scientific practice aims to carefully manage research data during and archive the data after every research project. It is important to keep the raw, processed and/or analyzed data available, as well as any documentation that is necessary for understanding the data and the way it was collected, processed and analyzed. Archiving plays an important role in accountability issues and allows the researcher to reuse his/her own data or to return to earlier stages of the research process when needed. As a result, research is becoming increasingly reproducible and verifiable.

"Proper RDM makes science more transparent and improves scientific integrity and societal trust”.

Research data means data in the form of facts, observations, images, computer program results, recordings, measurements or experiences on which an argument, theory, test or hypothesis, or another research output is based. Data may be numerical, descriptive, visual or tactile. It may be raw, cleaned or processed, and may be held in any format or media.”  (University of Southhampton)

The ITC/UT policy on data policy is based on leading principles in the area of research data management, such as the FAIR principles, and on national frameworks such as the Netherlands Code of Conduct for Research Integrity

Data archiving should not conflict with agreements and conditions set by data suppliers and should be stored according to the GDPR.

Herewith a nice introduction video on the importance of Data Sharing and Management

data management plan is a project document which tells the story of research data. It outlines what research data were collected, how they were collected and what will be done with the data during and after your research. Thinking in advance about how data are to be collected, stored, described, and archived and how access, sharing, reuse and linking to publications will be realised (keep track of the research progress more efficiently; easily find and understand the data created earlier; prepare the research data for future use (e.g., data publication, verification purposes or reuse of your data by others); comply with ethical guidelines, and institutional, funder and journal requirements is part of good research design

For any question about Research Data Management please contact Alice Nikuze: a.nikuze@utwente.nl or Masoome Shariat : m.shariat@utwente.nl

  • ITC Implementation Research Data Management (RDM) policy

    Important for ITC is that the ITC guidelines should not constrain any research or researchers, and the implementation of ITC/UT data policy is part of the research workflow. At ITC we have chosen the DANS Easy trusted repository for storing our research data. All datasets deposited in DANS Easy repository will receive a DOI so can be easily cited.

    ITC data policy for research will focus first of all on the reproduction, verification and validation of our research results. Secondly it will deal with the safety and accessibility of research data, both during and after research. Good research data management allows for the publishing of datasets and consequently its citation. It improves the visibility of the research and therefore its citation rate, reputation, and its scientific impact.

    On 28 March 2018, the EASY online archiving system of DANS received CoreTrustSeal (CTS) certification. CTS is an international quality mark for data repositories focussed on sustainability, reliability and accessibility. CoreTrustSeal is a worldwide data quality mark which offers interested repositories a core level of certification based on the catalogue and procedures of the CoreTrustSeal Data Repositories Requirements. CoreTrustSeal replaces the Data Seal of Approval (DSA) and World Data Systems (WDS) Regular Members certifications. With its recently achieved CoreTrustSeal certification, DANS demonstrates that the sustainability and reliability of the data stored in EASY are guaranteed. The international research community will also benefit from placing their data in sustainable and reliable archiving systems. Unambiguous, coherent data repository standards enhance researchers’ and research funders’ trust of the future-proofness of these crucial repositories.

    At this moment we have over 200 datasets (12 May 2021) deposited to DANS Easy: most of them are published. Some are submitted but awaiting approval from DANS and some are still a draft. Please take a look here and look for Faculty of Geo-Information.

    ITC wants all ITC researchers to deposit their research data as soon as an article is published. This is important for validating our research. The data can be stored publicly or restricted, depending on the type of the data or the requirements of data providers. Despite strong reasons for making data as open as possible, there are legitimate reasons to restrict access to and reuse of data including interests of national security, law enforcement, privacy, confidentiality, intellectual property, and indigenous data governance, among others. If the data need to be closed, an effort should be made to provide responsible and proportionately controlled access.

    Therefore, as soon as you have published an article you will receive an e-mail requesting you to deposit your data in DANS Easy. Even better is to store your data before you publish. As soon as the data are deposited in DANS Easy, the dataset receives a DOI and this can be used when you publish your article. One advantage of this workflow (deposit your data at DANS before publishing) is that you do not have to store your data with the commercial publisher.

  • Deposit your research data in the trusted repository DANS easy

    Please use our institutional account for uploading your data to DANS Easy. You browse to: https://easy.dans.knaw.nl/ui/home, ask data management support for the login: Alice Nikuze: a.nikuze@utwente.nl or Ana Duarte Bustamante: a.m.bustamanteduarte@utwente.nl, in order to publish a dataset, DANS needs a number of 'minimum requirements'. They examine de dataset in the following sections before “publishing” (public or restricted).

    1. Data: the files that are send. 

    2. Personal data: only tick the box when the data itself contains personal data. In general you should always anonymize (making the identification of persons impossible, i.e. no personal data any more!)  the data before uploading it to DANS. So in theory you always tick the "no" box. 
    DANS needs to know whether your dataset contains personal data in the sense of the General Data Protection Regulation (GDPR). In the GDPR, personal data are defined as follows: "'Personal data' means any information relating to an identified or identifiable natural person ('data subject'); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person."
    This includes pseudonymised data but not anonymized data.
    Please choose "yes" if your dataset contains personal data other than the personal data to account for the dataset, such as the creator, the rights holder or citations. For instance, this applies when your dataset contains personal data from research subjects. Please choose "no" if your dataset only contains personal data to account for the dataset, such as the creator, the rights holder or citations. This would typically be information that you provide as metadata below.
    When you choose "yes", please note:

    • The access category for your dataset will be Restricted Access, for which the DANS licence automatically applies.
    • Personal data other than the personal data to account for the dataset must not be included in the metadata or in file names of your dataset.
    • You and DANS must conclude a processing agreement. DANS will offer you a standard agreement.

    3. Metadata: this is the metadata of the project; this part that is filled in as a form in DANS EASY: straightforward. The metadata should be filled in in such a way that the user can get a good general impression of the content of the dataset in a fast way. Not all of the fields are obligatory, but use of as many fields as possible and ‘keywords’ will increase the findability and the clarity your dataset. 

    4. Supporting data: supporting data is information that is too specific or too detailed for the metadata but, that is needed to make the data understandable to users without previous knowledge of the data set. For example, background information, a description of the research method, the codebook, a notebook and READ ME files. The supporting data must be delivered as files to be added to the data (pdf’s, txt, etc). This will make the data set understandable. They are not able to publish data sets where they encounter problems as variables with no further explanation: e.g. codes of single numbers in a column with no statement of what they represent. Also, the structure of the dataset can be of importance, to clarify which files contain which information. 

    5. When your files are too big to upload through the web form you can use SURF filesender: https://www.surf.nl/en/surffilesender-send-large-files-securely-and-encrypted. The e-mail address to be used is: dans-itc@utwente.nl.

    6. After sending the files with SURF Filesender, you can complete your deposit in DANS EASY without making use of the ‘upload files’ part of the deposit module. You can add a notification in the Remarks field of the metadata that the files will be sent to DANS outside of EASY. 

    7. When you have finished the draft upload please inform the research data steward. So, do not submit it to DANS after you finished the deposit. ITC will check if the affiliation name and the names of the ITC researchers are correctly spelled and make changes if necessary.

    ITC will submit it to DANS. After submission, you receive immediately the DOI of your dataset, DANS will do a check on your data and if everything is clear they will officially publish the data with the restrictions you have set for the data.

    Under My datasets you will see a list of your datasets including the download history under the Activity log tab. The download history is not available for datasets in the Open access category. If a user has chosen not to be anonymous when downloading datasets, you can also see who downloaded your data. 

    Instruction video's on how to deposit your data:
    In this video Valentijn Gilissen M.A. (DANS Data manager / project leader) explains how to deposit a dataset in EASY, the online archiving system of DANS. 

    In this video Emilie Kraaikamp (DANS, Advisor for legal affairs) talks about the practical aspects of sharing personal research data.

  • Center of Expertise in Big Geodata Science CRIB

    Center  of Expertise in Big Geodata Science (CRIB) is a horizontal facility that supports all ITC departments for teh bette ruse of big geodata tecxhnology in education, research and institutional strenghthening activities. 

  • Guide to open data

    There are many misconceptions around the sharing of research data. We’ve put together a helpful guide that covers data types and the forms data exists in; shows you that support is available and where you can seek further information; and how data sharing actually establishes and confirms ownership of your data via authorship:
    Types of research data + Data sharing in different subject areas + Planning and suppor with data sharing  +  Rights to share data  +  Sensitive data  +  Misinterpretation of data  +  Inappropriate reuse of data  +  Claiming prioroity to results through data sharing  +   Impact of data sharing on your career  +  Data sharing for commercial innovation and industry applied research
    Open Research Europe
    Read further

  • FAIR data in 10 easy steps

    A video instruction on the FAIR-Aware tool is now available online on the DANS YouTube account.

    Make your data FAIR in 10 easy steps!

    The FAIR-Aware tool is an online self-assessment tool which helps researchers and data stewards to assess and increase their knowledge on how to make a dataset Findable, Accessible, Interoperable, and Reusable (FAIR). This video takes you through the process of using the tool and providing feedback for further development.

  • Digital Competence Center DCC

    The Hub for expertise on open science, especially fair data and open access, - digitalization of science and research ICT facilities 

  • E-learning courses and other background material

    Research Data Management Course UT: 
    At the end of this course you will:
    be able to write a data management plan (DMP)
    have adequate knowledge about management of data for verification and reuse
    have adequate knowledge about the value of research data as scientific output of your research
    have basic knowledge about legal issues when handling research data

    GODAN; Global Open Data for Agriculture & Nutrition
    The Global Open Data for Agriculture and Nutrition (GODAN) initiative seeks to support global efforts to make agricultural and nutritionally relevant data available, accessible, and usable for unrestricted use worldwide. The initiative focuses on building high-level policy, and public and private institutional support for open data.

    This webinar describes a flexible, multifaceted approach to evaluating and improving metadata collections (in multiple dialects).https://www.youtube.com/watch?v=QvrXpLJsjDY
    (duration: 1:13)

    Geospatial data is data specific to objects or phenomena that are directly or indirectly associated with a location relative to the Earth.

    Dealing with pseudonymization and key files in small-scale research. A few basic steps
    Report LCRDM

    Essentials 4 Data Support is an introductory course for those who provide support to researchers in storing, managing, archiving and sharing their research data (data support staff). With this course, Research Data Netherlands aims to contribute to the professional development of and coordination between data support staff. The course covers the basic building blocks of the discipline. After completing the course, participants will have a good overview of the different phases in the life-cycle of scientific research data.

    Eleven tips for working with large data sets; Big data are difficult to handle. These tips and tricks can smooth the way.

    RDMLS Research Data Management Librarian Academy (RDMLA)
    RDMLA is the result of a unique and successful partnership between a LIS academic program, academic health sciences and research libraries, and Elsevier. 
    Questions about this course 

    Supporting Output and Useful Resources by OpenAIRE; FAQ Open Science in Europee

    Engaging Researchers with Data Management: The Cookbook
    Connie Clare, Maria Cruz, Elli Papadopoulou, James Savage, Marta Teperek, Yan Wang, Iza Witkowska, and Joanne Yeomans

    OpenAIRE blogpost about electronic notebooks: https://www.openaire.eu/write-a-blogpost/electronic-lab-notebooks-should-you-go-e-1

    The Australian FAIR metrics tool: https://www.ands-nectar-rds.org.au/fair-tool

    The Beijing Declaration on Research Data
    Grand challenges related to the environment, human health, and sustainability confront science and society. Understanding and mitigating these challenges in a rapidly changing environment require datai to be FAIR (Findable, Accessible, Interoperable, and Reusable) and as open as possible on a global basis. Scientific discovery must not be impeded unnecessarily by fragmented and closed systems, and the stewardship of research data should avoid defaulting to the traditional, proprietary approach of scholarly publishing. Therefore, the adoption of new policies and principles, coordinated and implemented globally, is necessary for research data and the associated infrastructures, tools, services, and practices. The time to act on the basis of solid policies for research data is now.
    Read full text

    The RDA Working Group on FAIR Data Maturity Model made a (not too detailed) overview of several FAIR data metrics tools: webpage

    There is a collection of public DMPs which have been to some extent reviewed by LIBER, the organisation of research libraries. These reviews are not formal and LIBER doesn’t endorse the DMPs or the projects in any way. See https://libereurope.eu/dmpcatalogue 

    NDSA Announces the Levels of Digital Preservation 2.0