Data publishing repositories

Data repositories are services to publish and find data for reuse. Publishing the data in a repository helps to preserve it and increases the impact by data citations.
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Choosing the repository

There are a number of data repositories available, and the best one for you depends on your goals, needs and data. Some things to consider:

  • Persistent identifier: Does the repository provide a DOI or some other identifier?
  • Long-term availability of your data: Is the repository managed by a company, society, institution or government?
  • Impact and visibility: From which repository would the potential reusers best find your dataset?
  • Support for certain metadata, file format or data presentation features. Some discipline-specific repositories have useful features that add value and reuse potential to your data.

Recommended data repositories

We recommend using a reliable discipline-specific repository to maximize the impact of your dataset. You can look for a discipline-specific repository from the sources linked below. If a suitable discipline-specific repository is not available, we recommend Zenodo as a general purpose data repository.

Discipline-specific data repositories

Consider submitting your data to discipline-specific, community-recognised repositories when possible. To find a data repository, you can start with these services:

Examples of discipline-specific repositories

These repositories have been used in Aalto University to open datasets

General purpose data repositories

These repositories are not limited to any domain or discipline, but they accept any kind of research data.

Zenodo logo

Zenodo Data Repository

Zenodo is a catch-all repository for any kind of research data.

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B2SHARE Data repository

B2SHARE is the user-friendly, reliable and trustworthy EUDAT service to store and publish research data from diverse contexts.

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Fairdata services

The Fairdata services enable verifiable and reproducible science and secure preservation of digital research outputs.


Key features of general purpose repositories


Feature Zenodo EUDAT B2SHARE (B2DROP) Fairdata Dryad Notes
Data license Flexible Flexible ? CC0  
Metadata license CC0 CC0 CC0 CC0  
Metadata search ✔️ ✔️ ✔️ ✔️  
Data search ? ✔️ ? ?  
Cost to user None None for basic plan None $120 / upload  
Storage location in Europe CERN Data Centre, Geneva, Switzerland Finnish IT Center for Science, Kajaani, Finland IDA, Finland ✖ (Amazon Web Services, global)  
Project quota 50GB 20GB It depends 20GB, extendable with additional charge  
Max size for single file 50GB 10GB ? ?  
Handling data larger than quota (after contacting organisation) ✔️ ✔️ ✔️ ✔️  
Handling sensitive data ✔️* ✔️ * = does not provide encryption and does not guarantee security in case of data breach. However user can upload password protected files and limit access to specific users.
Access control (closed/restricted/open) ✔️ ✔️ ✔️ ✔️  
Ability to embargo files ✔️ ✔️ ✔️ ✔️  
Persistent identifier (DOI, URI, PID) ✔️ ✔️ ✔️ ✔️  
Long term storage ✔️** 2 years ✔️** ✔️** ** = as long as service exists
Metadata standard Compliant with DataCite’s Metadata Schema. Can be exported also as MARCXML, Dublin Core. Multiple formats available ✔️  
Web Interface ✔️ ✔️ ✔️ ✔️  
Comman line interface ✔️*** ✔️*** ✔️*** ✔️*** *** = limited features


Scientific data journals

The key issue in scientific data journals is that the articles do not include analysis of data, but the journals provide a forum to describe and document research data sets. For the researchers that have a specific interest to produce or create data, the data journals may be a relevant publishing channel.  

Examples on general or multidisciplinary data journals

Data in Brief by Elsevier:

  • Promotes external, public repositories and recommends interlinking the data and the article.
  • Elsevier recommends to publish a data article via the journal, even in cases where the article has been published in another Elsevier journal.
  • Enables authors “enrich online article by uploading relevant computer code and data to the RunMyCode repository … “.

Scientific Data by Nature:

  • Publish descriptions of data from new or published studies; in latter case dataset must provide new content.
  • Covers a broad range of natural science disciplines and will consider descriptions of quantitative datasets from the social sciences, too.


    Record description of your dataset to ACRIS, our local catalog

    ACRIS (Aalto Current Research Information System) is Aalto's research information management system - the home for Aalto's research information. It is important that you insert the description of your dataset in ACRIS, since universities are judged based on data produced and openness. The instructions for adding dataset description to ACRIS are attached below.

      Describing datasets in the data repositories

      Make a brilliant description to maximize re-use, citation and findability of your dataset.

      How to make a good data description
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      Links to research data management instructions

      Follow these links to navigate through research data management instructions.

      Aalto univerisity library

      Publishing and reusing open data

      Overview and instructions to services for sharing and publishing research data

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      Research Data Management (RDM) and Open Science

      Properly managed research data creates competitive edge and is an important part of a high-quality research process. Here you will find links to support, services and instructions for research data management.

      This service is provided by:

      Research and Innovation Services

      Did you find what you were looking for? If not, please contact us.
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