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.

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:

  • Create a project organization and add access for all members of the collaboration.
  • Enter a data set along with design variables such as subject ID, visit number, study center, sampling time and sample type.
  • Add additional datasets with the same design variables to allow smooth combination.
  • Team members can now download extracted data, to allow analyses and reporting using your usual tools for these purposes (e.g. R).

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

SUBJECT-ID

Both experimental and observational data usually have identifiers (a subject_ID), which represent unique IDs for the objects being studied. This is the primary design variable, and SQUIDR also generates one for internal use. You may have created your own identifiers for your study or you may use a globally unique identifier by applying a UUID generator, e.g. by registering samples in BioSamples (https://www.ebi.ac.uk/biosamples/) or similar registries.

STARTGROUP

The study objects may all belong to a single cohort or group, here termed a startgroup, or you may have an experimental design with 2 or more groups, thereby having several startgroups. In cross-over designs the objects may experience several treatments (a, b, c..) in the course of the experiment, and each sequence of treatments (abc, acb, cba,…) is then a unique startgroup.”

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. 

OTHER 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.

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.