Data Pool
Advancing open science and secondary research with real-world health data
The data pool is a dedicated initiative to make high-quality, multimodal research datasets available to the wider scientific community.
This effort is founded on the successful partnerships utilizing our D4L Collect solution to conduct digital studies, including the Digital Health Cluster at the Hasso Plattner Institute.
Available data sets
Open science
Unlock data, advance research: our solution promotes open science.
At Data4Life, open science is central to our mission. With the D4L Data Pool, we provide access to high-quality, multimodal datasets collected in collaboration with our research partners through the D4L Collect platform.
As a nonprofit healthtech organization, we are committed to enabling transparent, inclusive, and reproducible health research. By making anonymized, research-ready real-world data available, we support evidence generation and global collaboration - and maximize the societal value of donated data.
Equity & accessibility
Access to diverse real-world multimodal data for researchers worldwide - regardless of geography or institutional funding.
Reproducibility
Transparent access to underlying datasets to allow independent researchers to validate findings and strengthen scientific integrity.
Global collaboration
Promotion of open science to enable multidisciplinary and cross-border research efforts.
Secondary research
Data you can build on: ready for analysis, rich in insights
The D4L Data Pool makes selected datasets from digital health studies available for reuse in secondary research. These datasets combine self-reported data with continuous, objective data from sensor and wearables - collected using our D4L Collect platform.
Why use the D4L Data Pool for secondary research?
Save time & resources
Skip data collection and focus on analysis and hypothesis testing.
Explore new questions
Use existing data to identify correlations, trends, or outliers for future research.
Leverage diversity
Work with data from a growing, heterogeneous study population across various domains.
Available datasets
SLICE (Simulated Location-based Identification of Compulsive Events)
The SLICE (Simulated Location-based Identification of Compulsive Events) study was a semi-controlled feasibility study investigating the potential of wearable devices and indoor localization to detect routine and repetitive activity patterns. The study consisted of a one-hour session in which participants engaged in both naturalistic and protocol-driven activities within a semi-controlled multi room residential lab environment. It was approved by the ethics commission of the University of Potsdam (Approval No.: 38/2022).
Abstract:
The feasibility study was conducted at the Hasso Plattner Institute in Potsdam, Germany, and was designed to replicate everyday living conditions. The environment consisted of a kitchen, bathroom, main room, and hallways, and was designed to support the collection of high-resolution, multimodal sensor data.
The main session lasted approximately one hour, with the total time commitment per participant (including setup and debriefing) being around two hours. During the one-hour recording, participants were left alone in the residential lab to minimize the Hawthorne effect and to create a realistic everyday setting. They were instructed to engage in natural activities such as reading, preparing food, or working on a laptop, thereby generating baseline or NULL data. At the same time, they were asked to perform a specified number of simulated compulsive-like activities, namely handwashing, table cleaning, and door checking, at self-chosen times during the session. Each behavior was performed in two distinct ways to represent routine and compulsive-like patterns.
