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Restricted library services

During the period 27 September - 30 September, parts of the library's services will not be available due to the change to a new library data system. During this time you will not be able to get a library card, borrow books or request books.  


Research data


Research data may be numerical or textual, consist of images, video or sound recordings. It can be digital or analog (e.g. lab reports). Software code may also count as research data. In the context of 'Open Science', the term refers to digital data that has been collected or produced for scientific purposes.

Share your data

An important incitament for sharing data is to enable other researchers to validate the scientific results. Accessible and open data also encourage reuse of data in new projects. It may also inspire new collaborations between research groups, nationally as well as internationally.
Many funding agencies require that data is made openly accessible. Also, some scientific journals require datasets to be deposited along with the article (e.g. Nature) or ask for a statement on the authors’ willingness to share data.
To provide access by depositing data in a data center or certified repository is also a way of safeguarding the data, keeping it on secure servers, providing a backup for your own storage.

Write a Data management plan

A data management plan (DMP) is a formal, living document that defines what will happen to your research data during and after your research project. Many funding agencies require a data management plan (DMP) as part of the project application. It is important that all juridical implications of sharing data are made clear, preferably at the start of a project.
Writing a DMP is a good idea even when it is not required. Well organized, structured and documented data is easier to validate, reuse, share and preserve.

Here are some useful links to guides to and templates for DMPs:

Funder requirements

Carefully note what requirements your funder has regarding research data produced within your project, as many funders ask for DMPs and some mandate that data are published with open access.

Find your repository

For guidance and support in publishing and storing research data, contact the Department of Communication and Learning in Science (CLS): research.lib@chalmers.se

Chalmers recommends the following services:

  • Swedish National Data Service (SND) offer research data support to Swedish universities/researchers.
  • The online repository Zenodo (European Commission’s OpenAIREplus project), welcomes all researchers to preserve their research data regardless of size and format
  • re3data.org is a global registry of research data repositories that covers repositories from different academic disciplines.

Publish and cite data

The praxis of publishing and citing datasets creates a formalised system of recognition and reward to data producers. When you deposit data in a Core Certified Repository, it gets a persistent identifier (PID) that you can refer to in your publication. This makes the dataset both citable, and findable even if the data is moved to a new web address. There are many types of PIDs, but Digital Object Identifier (DOI) is the most widely used.

Data is cited in the same way as other information sources and a citation should include; author, title, year of publication, version, data archive and DOI, e.g.:

Barber, L.B., Weber, A.K., LeBlanc, D.R., Hull, R.B., Sunderland, E.M., and Vecitis, C.D., 2017, Poly-and perfluoroalkyl substances in contaminated groundwater, Cape Cod, Massachusetts, 2014-2015 (ver. 1.1, March 24, 2017): U.S. Geological Survey data release, https://doi.org/10.5066/F7Z899KT.

Be FAIR!

The FAIR principles were created to ensure that research data can be discovered, accessed, integrated and reused by humans as well as machines. They are widely adopted by publishers, data repositories and funding agencies, including the EU.

The FAIR acronym stands for Findable, Accessible, Interoperable och Reusable.

 fair_data_principles-768x261.jpg (1)

 

Bild: Sangya Pundir, Wikimedia Commons CC BY-SA 4.0

The ANDS-Nectar-RDS FAIR data self-assessment tool enables you to assess the 'FAIRness' of a dataset and gives advice on how to enhance it.

The tool poses questions related to the principles Findable, Accessible, Interoperable och Reusable and returns an overall rating of the FAIRness of the dataset for each principle.

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