Squidr

Store your research.

Share it instantly.

Connecting nutrition researchers through open, accessible study data.

Build by community for community.

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key benefits

Powerful tools for data management and collaboration

Advanced features built to streamline data management, enable seamless sharing, and accelerate discovery.

Central & local repositories connected

Store all your datasets in a local repository with full institutional control or add them in a central, secure, accessible location.

Community Driven

Collaborate with researchers worldwide. Share insights, review studies, and build knowledge together.

Open & Transparent

Full transparency, open-source code. Your data, your control, your way.

Fast Search

Powerful search capabilities to find relevant studies instantly with advanced filters.

Version Control

Track changes, manage versions, and maintain a complete history of your research.

Multiple Formats

Support for various file formats including questionnaires, ’omics datasets, and more.

How it works​

In Squidr the steps to sharing and discovering research, is easy.

01

Upload your study​

Create your own protected organization or login to Common. Upload your dataset as a csv-file, add additional datasets and metadata to create a study. Add publication, protocol or other materials in any format.

02

Discover and explore​

Browse through the available collection of studies, filter by topic, author, or methodology, and find exactly what you need.​

03

Collaborate and share

Add collaborators to share your data set, study or data hub. Connect with others to work on existing datasets in Squidr. Science is better as a community.

core features

Squidr

A flexible repository for sharing, structuring, and collaborating on research data.

Squidr is a structured database designed to work at the same time as a data hub, for storage of study data, and as a FAIR data sharing system. Squidr is highly versatile allowing any type of data, i.e. measurements, questionnaires, ‘omics datasets, and study designs to be stored in a logical way. 

It allows searching for specific kinds of variables, e.g. for mass spectrometric features, microbials, or resilience-related biomarkers or for specific study objects, e.g. those with a certain characteristic at baseline. It allows for detailed control of permissions, e.g. for data upload or download.

Squidr can also hold data-related documents, e.g. legal documents related to a study or publication output. You can link to repositories with the corresponding raw data files but not store them directly within Squidr. 

Joint projects

For collaborative projects, you need a data hub to share curated output data openly across the project group.

Squidr can easily help you in sharing structured data within your project group:

  • Study to allow queries and data extraction across the whole study.
  • Download extracted data, then make data analyses and reports using your usual tools for these purposes (e.g. R).  

Study definition

Studies can defined by a set of 7 variables

  • In the course of a study, you may  interact your object only on one occasion, e.g. at baseline, or you may do it repeatedly, so the occasion number (event) is a unique identifier.
  • On any such occasion you may interact with the object for instance by challenging it, thereby creating one or more subevents and/or you may take a sample from your study object, creating a sampling event.
  • If the sampling is repeated within a specific sampling event it is necessary to add a specific sampling ordinal time (e.g. 1,2,4, 8, 16… min).
  • The Subject_ID, Startgroup, Center, Event, Subevent, Sampling event, and Sampling ordinal time, comprise the design variables that must accompany any measurements during upload. 
  • Study objects may be recruited at several geographical places, each using a similar numbering system and grouping, and the Center is therefore also a design variable.

Upload & search

Metabolomics data

  • To upload a metabolomics data set first make sure it is formatted in a matrix of samples by features, which is the typical output after raw data prepressing and curation. The file must be saved in a .csv file or similar flat text file.
  • To upload a metabolomics data set first make sure it is formatted in a matrix of samples by features, which is the typical output after raw data prepressing and curation. The file must be saved in a .csv file or similar flat text file.
  • After uploading the file you must add the variable info, which is the information identifying the features in the dataset; all features have a retention time (rt) and a mass (m/z), and the simplest variable info is to add this information as e.g. “rt3.54mz234.0543”, which is a format that can be parsed by squidr. The variable info allows several layers, such as feature id (chemical name), molecular formula, feature group, etc. 
Growth of Open Science

A decade of progress

Open science has experienced remarkable growth over the past decade, transforming the way research is shared and accessed worldwide.

100000 K
Indexed Repositories

The use of indexed data repositories went from 10K in 2015 to more than 20K in 2025.

100000 K
Gold Open Access

Publishers in the STEM field experienced a growth of 27% in the last decade.

100000 M
Registred Users​

An increased growth in registered users by 2M from 2020 to 2025.

Team Squidr

The dedicated team that made it happen.

Jan Stanstrup

Tester

Lars Ove Dragsted​

System Outline & Vision​

Finn Sandø​

Programmer

Susanne Riber

UI/UX Designer

Thank you

For your

generous support

NuGO, FAIR, Phenotype Database,

University of Copenhagen, Carlsberg Fondet,

JPI - A Healthy Diet for a Healthy Life.

suppoert-squidr-open-science

FAQ

Things you might like to know

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