SQUIDR

A platform for structured and reusable research data

Squidr is a flexible platform for organizing, sharing, and exploring scientific study data. It combines a structured database with a central data hub, enabling data management and sharing in line with FAIR principles.

The platform supports diverse data types, including measurements, questionnaires, omics datasets, and study designs, organized in a consistent, machine-readable structure.

Users can query across both study metadata and variables, making it possible to identify specific biomarkers, features, or study populations without manual data extraction.

Squidr includes fine-grained access control and supports linking to external repositories for large raw datasets, while maintaining a unified layer for documentation, legal agreements, and publication outputs.

Search studies

A GUIDE

Using the platform

Collaborative projects

When collaborating on 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:

  • Combine several data sets into a study to allow queries and data extraction across all datasets.
  • Team members can now download extracted data, to allow analyses and reporting using your usual tools for these purposes (e.g. R).

Study design

Studies can be defined by a set of 7 design variables​

  • EVENT: 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.
  • SUBEVENT: 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.
  • SAMPLING EVENT: During an event or subevent you may take samples from your study objects, thereby creating a sampling event. Herefter fortsættes med
  • OTHER DESIGN VARIABLES: Study objects may be recruited at several geographical places, each using a similar numbering system and grouping, and the Center is therefore an additional but optional design variable. Other optional design variables may be introduced if needed.

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.
  • 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.