Getting your data onto Squidr is simpler than you might think, and the guides below walk you through everything you need to get started.
You’ll learn how to set up a collaborative project space and share structured datasets with your team. You’ll get to grips with Squidr’s study design framework, a consistent set of variables that makes your data comparable and combinable with other studies. And you’ll learn how to format and upload your datasets, including specific guidance for metabolomics data.
Whether you’re publishing for the first time or moving an existing project onto the platform, you’ll find everything you need to get your data working harder.
Getting your data onto Squidr is simpler than you might think, and the guides below walk you through everything you need to get started.
You’ll learn how to set up a collaborative project space and share structured datasets with your team. You’ll get to grips with Squidr’s study design framework, a consistent set of variables that makes your data comparable and combinable with other studies. And you’ll learn how to format and upload your datasets, including specific guidance for metabolomics data.
Whether you’re publishing for the first time or moving an existing project onto the platform, you’ll find everything you need to get your data working harder.
When collaborating on projects, you need a data hub to share curated output data openly across the project group.
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 (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.
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.
