The search box
Enter one or more words. You will be able to continue narrowing your search in the discovery tool EDS, where the majority of the library information resources can be found.
Filters in the search box
Articles: Articles from scholarly journals. Using the peer-review facet in the left menu you will limit to articles reviewed by experts in the field.
E-books and printed books: Online books in full text as well as print books. Please note that theses are not found using this filter.
Printed books: Make a request if the book is currently on loan or if you want to collect it at a different branch library.
E-journals: Enter words from the title of the journal to get to the list of e-journals.
Databases: Enter words from the database title or subject area to get to the list of List of databases. Use English terms. Databases often cover large areas and the search terms therefore need to be broad, for example Physics.
Additional search services
Chalmers Publications: In research.chalmers.se you will find information regarding research at Chalmers, projects, researchers and publications.
Student Theses: Theses by Chalmers students is found at studentarbeten.chalmers.se
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 incentive 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.
Useful links to guides, tools and templates for creating DMPs:
- Checklist for Data Management (Swedish National Data Service, SND)
- Checklist for a Data Management Plan (Digital Curation Centre, DCC)
- Guidelines on Data Management in Horizon 2020 (EC)
- Framework for Discipline-specific Research Data Management, (Science Europe)
- DMPonline (DCC)Tool for DMP creation, templates for EU and ERC are available.
- Template for the Data Management Plan [ODT format]
- ERC template for the Data Management Plan [ODT format]
Chalmers templates for data management plans
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.
- In Horizon 2020 the Open Research Data Pilot mandates open access to research data. Information on the requirements for data managemet, DMPs and open access is available in the Participant Portal H2020 Online Manual. The European Research Council (ERC) have simular requirements, please see specific guidelines.
- In Horizon Europe, the next framework programme (2021-2027), the principle of 'Open Science' is said to become modus operandi.
- The Swedish Research Council (Vetenskapsrådet, VR) will require DMPs for projects awarded grants in 2019, if they generate data.
- More on research funders’ data requirements is found on the website SHERPA/JULIET.
Find your repository
For guidance and support concerning publishing and storing research data, contact Chalmers Data Access Unit (DAU): email@example.com
Chalmers recommends the following services:
- Swedish National Data Service (SND) offers together with Chalmers DAU support for data publishing to Chalmers 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.
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.
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.