Performance and lifetime testing of batteries requires considerable effort and expensive specialist equipment. A wide range of potentiostats and battery testers are available on the market, but there is no standardization of data exchange and data storage between them. To address this, we present Galv, a battery test database developed to manage the growing challenges of collating, managing and accessing data produced by multiple different battery testers. During an experiment, the cells used, the equipment employed, and the schedules run are effectively tracked. Data and metadata are packaged together in an organized manner, allowing for comprehensive tracking of how each component integrates within the system. Galv is composed of three components: backend, frontend, and harvester. The Galv backend is a REST API that provides programmatic access to the data and metadata stored in the platform. The Galv frontend is a web interface that allows users to interact with the data and metadata stored in the platform. The Galv harvester is a tool that can be used to automatically collect data and metadata from experiments and store it in the platform. Galv provides comprehensive access control functionalities, allowing team and lab administrators to effectively manage user permissions and data accessibility, thus enhancing collaborative efficiency. With integrated JSON-LD and schema validation support, Galv ensures data compatibility and integrity across platforms, facilitating seamless interoperability with standardized data models. Galv promotes a highly structured, opinionated data file framework that supports the effortless sharing of data across diverse cycler formats, enhancing usability and adaptability for varied experimental setups. The software includes versatile export capabilities, allowing users to conveniently export datasets in JSON format, thus ensuring compatibility with a wide range of data-processing environments. Galv’s batch export feature enables efficient datafile generation for large datasets, streamlining the process of bulk data analysis and documentation. By leveraging PyBaMM-based experiment definitions, Galv simplifies the setup of battery modeling experiments, providing researchers with an accessible framework for defining and running simulations.