You are here: Home / The IPN / Library / Research Data Management

Research Data Management

Research Data

Alliance initiative: “Research data is data produced by scientific projects, for example by means of digitization, the study of sources, experiments, measurements, surveys or questionnaires.” [1]

DFG (LIS): “Primary research data are data, which emerge in the course of source research, experiments, measurements, surveys or interviews. They represent the foundations of scientific publications.” [2, german only]

EU (Horizon 2020): “The Open Research Data Pilot applies to two types of data:

  1. The data, including associated metadata, needed to validate the results presented in scientific publications as soon as possible;

  2. Other data, including associated metadata, as specified and within the deadlines laid down in the data management plan.” [3]

Research data are, therefore, all kinds of information used in research such as statistics, interviews, simulations, experiential data, observed data from instruments, annotated text, 3D- scans, etc.

 

Why research data management?

Sometimes research promoters stipulate that research data management has to take place or at least be planned, so that certain projects can attain research funds.

The DFG stipulates the following in its 2014 guidelines [4] for the application of the project application under point 2.4 “Data handling”:

“If research data or information will be systematically produced using DFG project funds, describe if and how these will be made available  for future reuse by other researchers. Please regard existing standards and data repositories or archives in your discipline where appropriate.”

“Project costs associated with making research data available for future reuse can be requested with your project. In this case, please describe how the institutions participating in the project will contribute to data and information management.”

On the other hand, the BMBF states: “The applicant promises to make the data available which were collected in the context of the project. The applicant will do this in a transferable form after completion of the project. In order to guarantee the availability of the applicant’s data, the applicant is responsible for their personal research data management system […] (refer to checklist on data management) under http://www.empirische-bildungsforschung-bmbf.de (German only) The planned implementation should be explained in the project description.” [5, german only]

In the context of its promotion program, Horizon 2020, the EU is also interested in pressing forward with the publication of research data and for this reason initiated the Open Access Research Pilot, “In Horizon 2020 a limited pilot action on open access to research data will be implemented.Participating projects will be required to develop a Data Management Plan (DMP), in which they will specify what data will be open.” [6]

However, the most important consideration is: Research data management improves research and prevents data loss as well as unnecessary work.

 

How does research data management function?

The basis for research data management is the creation of a so-called

Data Management Plan. [7]

A data management plan helps you to describe systematically how you are going to deal with your research data, how you have conceptualized storing, recording, caring for and processing your data. Such a plan and documentation is important to make your data interpretable and usable afterwards for third parties.

Before you begin your project, it make sense to determine which responsibilities occur when dealing with research data and therefore the following aspects, for example, have to be clarified:

  • Which data will be produced and used?
  • What sort of data is it?
  • Which data should and has to be stored?
  • What additional information is necessary to understand the data?
  • When will the data selection take place?
  • How long should the data be stored?
  • When will the data be transferred?
  • Who is allowed to use the data?

This part of your project is about taking stock of the processes and technologies during the entire life cycle of research data.

The WissGrid project has published a guideline [8, german only] which you can use for your project.

You can also use the online tool DMPonline [9] which was developed by the British Digital Curation Centre (DCC) [10]. You can gain access by signing on with your email address and a password and then you have the possibility of developing a data management plan based on different issues. You can export this in different formats. For the case that you are going to make an application in the context of the EU program Horizon 2020, DMPonline explicitly offers you the possibility of being able to locate the respective specifications in this support program again.

Additionally, the DCC offers a checklist [11] for creating a data management plan and example data management plans [12].

In addition to the DMPonline tool, CLARIN-D [13] offers a research data infrastructure for the humanities and social sciences which is an online tool to create a data management plan [currently german only]. The appearance of a specific data management plan is normally dependent on your subject community.

 

Important aspects and tasks in research data management

 

1. Saving and storing data

Saving and storing data correctly prevents data loss; it also allows you to give authorized third parties access. Moreover, it creates high availability and access.

2. Metadata and documentation

You should organize your research and metadata in such a way that they can be easily located and used when required – this should be done in such a way that both you and third parties can easily access the data. With the help of metadata and sensible documentation, it is possible to preserve research data for 10, 20 or even 50 years. Furthermore, it is also possible to verify research findings, to make them traceable for other scientists and to make your research data usable for other research issues.

3. Data sharing, access, legal and ethical issues

Publishing research data and sharing them (data sharing) with others increases your reputation. You receive acknowledgement for your qualitatively valuable research as a result of data sharing. Moreover, your colleagues can gain a better understanding of your applied methods and a verification of the work carried out by third parties. Your contribution to the community will be recognized and it is possible for you and others to expand your research to other fields.

Making data available or publishing data is frequently one of the promoter’s (e.g. DFG) requirements. Moreover, you can upgrade your own project application by declaring your willingness to publish your findings or by formulating a clear strategy on your willingness to share data, even though this is not explicitly required from you for a successful application.

An important aspect of data sharing is the access administration. From the outset, you should consider the following aspects: “Sharing, yes, but what, with whom and when?” Furthermore, you should clarify and make arrangements about the authorship and intellectual property rights.

Additional consideration should be given to licensing models. In Germany, you are always the author. By using free licenses (e.g. CC0) you could relinquish your rights as a result of data sharing. On the other hand, as a semi-standard system, the Creative Commons (CC) licenses could come into question, because they allow different gradations of rights management, e.g. All rights reserved/ Some rights reserved/ No rights reserved.

 

What do you do with the research data?

The final point in research data management is clarifying the issue where should you store your research data. On the one hand, your data can be published as data publication in a data journal. Another alternative would be to store your data in a research data repository (e.g. europena, figshare, Dryad or Wikidata).

You will find a suitable repository under the following offers:

  • Directory of Open Access Journals (DOAJ) [14]
  • Directory of Open Access Repositories (OpenDOAR) [15]
  • SHERPA/RoMEO [16]
  • Registry of Research Data Repositories (Re3data.org) [17]

 

As a researcher at IPN you can use the portal forschungsdaten-bildung.de for a centralized reporting, description and delivering of your resreach data.

 

And What Else?

Our library can offer you further information and support. You can also attend one of the numerous further education events dealing with the subject research data management. Or participate in the Helmholtz open science webinars about research data [german only]:

http://oa.helmholtz.de/bewusstsein-schaerfen/workshops/webinare-zu-forschungsdaten.html#c3939

 


[1] www.allianzinitiative.de/en/core-activities/research-data.html

[2] http://www.dfg.de/download/pdf/foerderung/programme/lis/ua_inf_empfehlungen_200901.pdf

[3] http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-pilot-guide_en.pdf

[4] www.dfg.de/formulare/54_01/54_01_en.pdf

[5] http://www.bmbf.de/foerderungen/25771.php (Abschnitt 4 – Zuwendungsvoraussetzung)

[6] http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf

[7] data.uni-bielefeld.de/en/data-management-plan

[8] http://www.wissgrid.de/publikationen/Leitfaden_Data-Management-WissGrid.pdf

[9] http://www.dcc.ac.uk/

[10] https://dmponline.dcc.ac.uk/

[11] http://www.dcc.ac.uk/resources/data-management-plans/checklist

[12] http://www.dcc.ac.uk/resources/data-management-plans/guidance-examples

[13] www.clarin-d.de/en/preparation/data-management-plan

[14] https://doaj.org/

[15] http://www.opendoar.org/countrylist.php

[16] http://www.sherpa.ac.uk/romeo/

[17] http://www.re3data.org/

[18] www.neps-data.de/en-us/home.aspx

[19] www.bibb.de/en/53.php

[20] http://www.forschungsdaten-bildung.de/ueber-fdz

[21] www.iqb.hu-berlin.de/fdz/studies