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Forschungsdatenmanagement

Research data – a new aspect of research and publication

Research data – what‘s that exactly?

Research data are data that are collected or generated during the research process. They can include quantitative measuring data, survey data, statistical data, qualitative data like interview transliterations, notes from field research, audio and visual data or software and much more.

And what about OPEN research data?

As far as possible research data should be open. Open data are freely accessible and allocatable. Everybody can access these data in such a way that they can be distributed and reused.

And why is this relevant to you as a scientist?

More and more research funders like Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung or the European Union demand not only the publication of project results but also the publication and long-term preservation of research data that is generated in the funded projects. Besides, more and more publishers require additional research data with the delivered article.

And how do you as a scientist benefit from open research data?

Thanks to the open dissemination of research data research results become verifiable and reproducible. Research data that already is availabe may be used again in succeeding studies. New findings may be gathered from published data sets since they allow comparisons and new mixings of existing data. In this way data collecting gains a certain significance in the context of science. Data can be cited more easily. Therefore, corresponding text publications may be cited more often and data collection may be rewarded as part of doing research.

Our service – how we can support you

The research data lifecycle illustrates the management of research data from searching for available data and collecting data to archiving and reusing data. The chart (?) shows which services the university library offers to all members of Chemnitz University for a successful research data management:

Abbildung des Forschungsdatenzyklus

Research data management

The research process starts with the research question and the search for relevant literature - and the search for research data that may be used in the reseach project. Particular data research tools can help here:

  • DataCite Search - In DataCite you can search millions of records. Zenodo, figshare and PANGAEA are some of the repositories that are scanned.
  • BASE – Bielefeld Academic Search Engine is a global search engine for scientific web publications,among them research data.
  • GESIS - The data catalogue DBK contains descriptions of studies in social sciences.

In terms of the scientific and scolarly code of conduct research data that are taken from their original sources must be correctly cited - like quotations from texts . Several institutions have published recommendation and guidance that can help (in German only):

More information on searching for research data you can find here (in German only): https://www.forschungsdaten.info/themen/organisieren-und-arbeiten/forschungsdaten-finden/

Well planned research data management is absolutely essential to efficiently deal with research data.

Research data management plans support you when planning. If you apply for funding by the European Union handing in a research management plan is obligatory.

(In this document by HU Berlin you find an overview of several funder‘s requirements (in German only): https://www.cms.hu-berlin.de/de/dl/dataman/arbeiten/dmp_erstellen/foerderer)

A data management plan describes

  • which kind of data is collected or generated
  • how theses data is collected and documented
  • where the collected data is saved and who can access them
  • how the data may be reused and what will happen to them when the project is finished

These aspects are covered by the data management plan samples of several research funders:

In a DFG project the tool RDMO has been developped to support research data management. The tool allows structured recording of all project related planning details and the management of all data management tasks during the whole data lifecycle. This instrument is specific to applications for research projects in Germany (DFG, BMBF).

Regarding european applications for research funding you can consider the instructions on how to compile a data management plan (DMP) in horizon 2020.

More information on funding recommendations and guidance by single research funders you can find here (in German only): https://www.forschungsdaten.info/themen/planen-und-strukturieren/foerderrichtlinien/

From the start of a project collected data may be well organized. A well structured data collection helps you and others on your project anytime during the project to retrieve your data fast and easily.

The computing centre of Chemnitz University of Technology offers various services to save data during research: https://www.tu-chemnitz.de/urz/storage/index.html#info. Additionally, the computing centre makes Gitlab available, a version management tool in software engineering: https://www.tu-chemnitz.de/urz/storage/gitlab/.

It is not very exciting but the storage of data records in files that are named in a clear and consistent manner is absolutely necessary.

Another important aspect of data storage is version control which records the numerous different revisions of a document. You may allocate a unique version number and the date (YYYYMMDD) to each revision to allow sorting records.

As soon as you finished working with your dataset you may use a file format that can be read by free tools. Ensure that you store your data in one or better in more than one location.

More information on organising data you can find here (in German only): https://www.forschungsdaten.info/themen/organisieren-und-arbeiten/datenorganisation/

At the end of a research project you must consider which data needs to be kept and which data can be discarded:

  • What is needed to validate findings in your publication?
  • What might other researchers find useful?
  • How expensive will it be to collect this data again if it is destroyed?
  • How expensive will it be to archive and preserve the data?

Recommendation no. 17 in the DFG‘s recommendations on a Research Code of Conduct says that research data may be kept accessible for at least 10 years.

Data repositories are an adequate place to archive and/or publish data. Appropriate repositories for long-term preservation of our data you can find in specific repository finders like:

  • re3data - re3data registrates data repositories from all over the world and from all subject disciplines.
  • HTW Dresden Repositorien Recommender - This tool allows access to more than hundred research data repositories.

When you archive your data in a repository you must add information on legal, commercial or contractual restrictions on publishing the data.

More information on legal aspects of research data management you can find here (in German only):
https://www.forschungsdaten.info/themen/rechte-und-pflichten/
https://www.cms.hu-berlin.de/de/dl/dataman/teilen/rechtliche-aspekte

A persistent identifier is absolutely necessary for long-term access and citation. This persistent identifier is allocated to a dataset and is unique. Datasets that are stored in a repository are allocated with a DOI (Digital Object Identifier).

The description of data by metadata enables data to be cited and to simplify their reuse. Description standards are either subject specific standards or general metadata standards (e.g. ISO-Standard, Dublin Core, DataCite Metadata Schema).

You as a scientist can use your personal ORCID iD that provides a persistent digital identifier to distinguish you from others as author of articles, research data or software. ORCID iD is non- proprietary and mostly numerical identifier for individual scientists. It is advicable to register with ORCID, particularly because some publishers demand an ORCID iD from their authors.

More information on ORCID iD you can find here (in German only): https://www.tu-chemnitz.de/ub/publizieren/bibliometrie/orcid_id.pdf

More information on archiving research data and metadata you can find here (in German only):
https://www.forschungsdaten.info/themen/bewahren-und-nachnutzen/langzeitarchivierung/
https://www.forschungsdaten.info/themen/bewahren-und-nachnutzen/persistente-identifikatoren/
https://www.forschungsdaten.info/themen/aufbereiten-und-veroeffentlichen/metadaten-und-metadatenstandards/

Repositories and data journals

Some examples of repositories which help you to save and archive research data:

  1. subject specific
    PANGAEA
    „The information system PANGAEA is operated as an Open Access library aimed at archiving, publishing and distributing georeferenced data from earth system research. The system guarantees long-term availability of its content through a commitment of the hosting institutions.“
    GESIS
    „GESIS preserves quantitative social research data to make it available to the scientific research community. All data are preserved for the long-term and documented to international standards. Access to data is free.“
    DRYAD
    „An international repository of data underlying peer-reviewed articles in the basic and applied biosciences.“
    Cambridge Structural Database
    „The Cambridge Structural Database is both a repository and a validated and curated resource for the three-dimensional structural data of molecules.“ (Wikipedia)
  2. institutional
    RADAR
    „RADAR offers institutions and researchers a comprehensive archiving and publishing service with reliable storage options for backing up and managing research data. With RADAR you can promote research data management at your institution and make an important contribution to improve availability, long-term preservation and independent publication of your research data.“
    OpARA
    „OpARA (Open Access Repository and Archive) is the repository for digital research data of the TU Dresden (TUD) and the TU Bergakademie Freiberg (TUBAF). It offers researchers the possibility of archiving their digital research data and optionally making it accessible to third parties under an Open Access license.“
  3. media specific
    TIB AV-Portal
    „The TIB AV-Portal provides access to high grade scientific films from the fields of engineering, architecture, chemistry, computer science, mathematics and physics.“
    GitHub
    „GitHub is a development platform.“
  4. Data Journals
    Journal of open archaeology data
    The Journal of Open Archaeology Data (JOAD) features peer reviewed data papers describing archaeology datasets with high reuse potential.
    Scientific Data
    „Scientific Data is a peer-reviewed, open-access journal for descriptions of scientifically valuable datasets, and research that advances the sharing and reuse of scientific data. Scientific Data primarily publishes Data Descriptors, a new type of publication that focuses on helping others reuse data, and crediting those who share. Scientific Data publishes content from all research disciplines, including descriptions of big or small datasets.“
  5. general / multidiscilinary
    Zenodo
    An open access data, software and publication repository for researchers who want to share multidisciplinary research results not available in other repositories. Developed and hosted by CERN.
    Figshare
    Figshare is an Open-Access-Repository in which researchers can post their data like datasets, pictures, videos and so on.

Documents (in German only)

Information about research data management (in German only)

Research data management in Saxony

Research data management in general

Contact

Questions on research data management? Please contact the open science team ( os@bibliothek.tu-chemnitz.de) or contact

Martina Jackenkroll M.A.
  • Phone:
    +49 371 531-33482
  • Fax:
    +49 371 531-833482
  • Email:
  • Room:
    Straße der Nationen 62, Room A13.137