The list of available DataSHIELD packages is listed below with links to their relevant repository. Please note packages are in various states of readiness, testing, disclosure audit and operation with real world data.
| Package Name | Package Description |
|---|---|
| dsBase |
R functions that enable the remote and non-disclosive analysis of sensitive research data. dsBase and dsBaseClient provide the non-domain specific data shaping, analysis and presentation methods. The dsBase being deployed to the server and dsBaseClient being deployed to the R environment used by the analyst/researcher and provide the method used by the initiate operations of the server. |
| dsBayesPathAnalysis | This package performs federated Bayesian inference through Markov Chain Monte Carlo (MCMC) on Structural Equation Regression (SEM) model. |
| dsBinVal | The package provides ROC-GLM and Calibration for DataSHIELD |
| dsBoltzmannMachines | Boltzmann machines are generative neural networks which are able to learn the distribution of data that are fed as input to the network. The dsBoltzmannMachines package allows to train and use these generative models in DataSHIELD for creating synthetic data that preserve patterns of input data. Synthetic data samples are not linked to individual samples in the original data but are generated via sampling from the distribution that is captured by a Boltzmann machine model. |
| dsCalibration | The package provides Calibration Functions for related to DataSHIELD |
| dsClusterAnalysis | This package perform non-disclosive cluster analysis in DataSHIELD. |
| dsCMS | The package provides functionality to conduct and visualize component-wise boosting on decentralized data. |
| dsCWB | The package provides component-wise boosting for DataSHIELD |
| dsDanger | This package contains utilities to aid the development and testing of new DataSHIELD methods and packages. |
| dsExposome | This package is mostly a wrapper of the rexposome package, which is an R package for exposome characterization and exposome-outcome test association. |
| dsGeo | dsGeo contains basic functions for defining geometries in space, create buffers around geometries and determine overlaps between geometries |
| dsHelper | This package contains a set of functions to automate processes in DataSHIELD to make data manipulation and analysis easier. Some functions now appear in dsTidyverse. |
| dsMediation | Methods to apply causal mediation analysis. |
| dsMicrobiome | The package is intended to be a collection of methods for microbiome analysis. Currently methods are wrappers around methods provided by the “vegan”. |
| dsML |
Non-disclosive Machine learning functions. Currently the methods being supported are:
|
| dsMTL | dsMTL (Federated Multi-Task Learning based on DataSHIELD) provided federated, privacy-preserving multi-task learning analysis. dsMTL aimed at simultaneously learning the outcome (e.g. diagnosis) associated patterns across datasets with dataset-specific, as well as shared, effects. Four sparse MTL methods were contained in dsMTL to disentangle specific structures of “shared-specific” components. In addition, Federated LASSO was also included due to its wide usage. dsMTL was suitable for biomedical applications, such as comorbidity analysis, multi-omics analysis, multi-modal data analysis, and high-dimensional molecular studies. |
| dsMIP | ... |
| dsMice | encorporated into dsBase |
| dsOMOP | The package is designed to facilitate the interaction with remote databases formatted in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) from within a DataSHIELD environment. |
| dsOMOPHelper | The dsOMOPHelper package is an extension of dsOMOPClient, designed to streamline the interaction with databases in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) format within the DataSHIELD environment. This package simplifies the process of fetching tables and integrating them into the DataSHIELD workflow, adhering to the privacy standards and disclosure control mechanisms of DataSHIELD. |
| dsOMOP.oracle | dsOMOP.oracle is an extension of the dsOMOP package. It expands its functionality to support the interaction with Oracle databases |
| dsOmics |
Omics analysis. Currently the methods being supported are:
|
| dsPrivacy | The package for running differentially private statistical analysis for DataSHIELD |
| dsPredictBase | The package provides base functionality to push R objects to servers using the DataSHIELD |
| dsQueryLibrary | Implements a no-frills version of https://github.com/OHDSI/QueryLibrary on remote datashield/opal servers |
| dsResource | ... |
| dsROCGLM | This packages is now encorporated into dsBinVal. |
| dsSandBox | ... |
| dsSurvival | This is a standalone package for survival analysis in DataSHIELD. These are client side functions for building survival models and Cox models. |
| dsSwissKnife | dsSwissKnife is a software package which builds upon (and extends) DataSHIELD to allow non-disclosive remote federated analysis on sensitive data. Functionality include implementations for a kmeans clustering algorithm, PCA, as well as a number of functions from specific packages (imputation with VIM, random forests with randomForest, synthetic data generation with Synthpop, etc) |
| dsSynthetic | This package can be used to generate a synthetic data set on the client side by running the generation on the server side. Users can then perform harmonisation while working with full access to synthetic data on the client to confirm algorithms are working as expected. When the user is happy that the algorithms are working correctly, they can then be applied to the real data on the server side. The user therefore has the benefit of being able to see the data they are working with, but without the need to go through labourious data transfer processes. The same benefits are realised for an analysis user. |
| dsTidyverse | Implementation of selected Tidyverse functions for data manipulation within DataSHIELD. |
| dsUpload | A collections of tools used to upload data into DataSHIELD backends. It aids data mangers in the initial stages of uploading data to DataSHIELD backends. |