How can you join the Global Data Sharing Initiative?
Two webinars are available for data custodians:
- Introduction demo: “How registries can join the Global Data Sharing Initiative?”
- Technical demo: “How import into QMENTA platform?“
Step 1: Data collection
We recommend all data custodians wanting to work on COVID-19 and MS should implement the COVID-19 core dataset within their protocols. This has been developed by a global data taskforce and is based on the International MuSC-19 Case Reporting platform and UK MS Register sub-study protocols.
If you are planning to implement the COVID-19 core dataset, please let us know by filling in this google document. This will allow us to put your initiative on the website (patient-reported initiatives, initiatives open to healthcare professionals). This way, we can redirect people with MS and healthcare professionals to your initiative.
Step 2: Data sharing
We invite all MS registries and cohorts to join this global data sharing initiative. We can work with both clinician reported and patient reported data on COVID-19 in MS. You can either share:
(1) de-identified patient-level data or
(2) aggregated data.
1) Sharing de-identified patient-level data into the central platform
To take part you will be invited to:
- Sign an agreement to make sure that the import and the use data is agreed upon between all parties involved.
- Create an anonymous subset of your COVID-19 and MS data at regular intervals to share.
- Import the data subset into the QMENTA central platform.
Find more detailed information about sharing patient-level data, and the ethical, legal and technical aspects on the MS Data Alliance website.
2) Aggregated data sharing (federated pipeline)
If you are not able to share patient-level data, but would be willing to share aggregated data, you could run the federated Python script. The script assumes the data is harmonized locally to the COVID-19 core dataset as described in the dictionary. After running the scripts locally, the counts are shared and combined with the counts of the data inside the central platform. The advantage of this federated pipeline is that regulatory and privacy concerns are reduced. However, the main disadvantages of this approach are:
- We lose some of the ability to explore and fish in a fully combined dataset, so looking for patterns and forming hypotheses is more difficult.
- Also the running of localized data queries can become time consuming as the data analysis evolves.
Please contact Lotte Geys if you’re interested in becoming a “federated registry” sharing insights with the GDSI. Then, we will provide you with a docker, a manual for this option and a video. Additionally, we are happy to schedule a “live session” to run the script together and we can troubleshoot you all the way through.
Questions? Please contact Lotte Geys.