There are a variety of terms that you’ll come across throughout the dHealth Knowledge Portal and Synapse. Refer back to this page if you’re ever unsure about what something means, or the difference between terms.

Annotations

Annotations are extra pieces of information that get associated to data and other resources to make these data query-able on the portal. This additional information, in the form of controlled vocabulary, helps to surface the data in a structured way.

As a user, annotations are what allow you to systematically search for and find specific data of interest. If you haven’t already, learn how to filter and find data on the dHealth Knowledge Portal here.

Collection

In our context, this refers to the studies found on the portal leveraging digital health technologies, as well as analytical efforts and benchmarking challenges.

Explore all collections on the portal here.

Data

In our context, this refers to the raw and processed data from the collections (including studies, analyses, and benchmarking challenges) catalogued in this portal.

Explore all data on the portal here.

Data Subtype

Data Subtype (also referred to as dataSubtype) is a file annotation that indicates if data in the file are raw, processed, or normalized, or if the file contains metadata.

Governance

Due to the open-access nature of the platform, Synapse operates under comprehensive governance policies that define the rights and responsibilities of Synapse users. This includes our standard operating procedures (SOPs), privacy policy, code of conduct, community standards, and more.

Metadata

Metadata is additional, standardized information included alongside the data to give it context—data about the data, if you will. Metadata is what allows data in the portal to be searchable, discoverable, accessible, re-usable, and understandable to others, including those who were not involved in the data generation process.

Metadata can be descriptive (i.e., the name of the file), administrative (i.e., provenance information), or research-based (i.e., information about the sampling and handling of data).

Open Data/Open Science

Open data represents transparent and accessible knowledge that is shared and developed through collaborative networks, based on the principles of open science. The goal of open science is to make scientific research—including publications, data, physical samples, and software—and its dissemination accessible to all levels of an inquiring society, whether amateur or professional.

The general driving idea behind open science and data is that scientific research can and should be accessible to anyone—because, well, why not? This system benefits all parties involved—the researchers gain wider-reaching recognition and appreciation for their work, the study subjects get to witness the palpable value of providing their personal data, scientists and other professionals are able to use properly funded research to aid in their own research/work, and the general public gains helpful information and knowledge from trusted sources. This is truly a win-win—collective consciousness is a global good!

Publications

Publications highlight the lessons learned and models built from digital health data, documented in peer-reviewed journal articles.

Explore all publications on the portal here.

Schema

An overlapping concept to data model, a metadata schema provides further rules and standardization of a data model. It outlines additional rules governing the management of metadata through constraints such as the optionality or valid values of attributes.

Tools

The portal features the computational resources for the processing and analysis of digital health data.

Explore all tools used by studies on the portal here.