Managing the data and metadata during the active development phase of an experimental project presents a significant challenge, particularly in collaborative research. This phase is frequently overlooked in Data Management Plans included in project proposals, despite its important role in ensuring reproducibility and preventing the need for retroactive reconstruction at the time of publication. Here we present Altar, a lightweight, domain-agnostic framework for structuring experimental data from the onset of a project without imposing rigid data models. Altar is built around the Sacred experiment-tracking model and captures experimental (meta)data and structures them. Parameters, metadata, curves and small files are stored in a flexible NoSQL database, while large raw data are maintained in dedicated storage and linked through unique identifiers, ensuring efficiency and traceability. This integration is composable with exiting workflows, allowing integration with minimial disruption of work habits. We document different pathways to use Altar based on users skillset (PhD students, Post-docs, Principal Investigators, Laboratory administrators, System administrators). While getting started with Altar does not require a specialized infrastructure, the framework can be easily deployed on a server and made publicly accessible when scaling up or preparing data for publication. By addressing the dynamic phase of research, Altar provides a practical bridge between exploratory experimentation and FAIR-aligned data sharing.

翻译：在实验项目的活跃开发阶段管理数据与元数据是一项重大挑战，尤其在协作研究中。尽管这一阶段对确保可复现性及避免发表时进行追溯性重构具有重要作用，却常被项目提案中的数据管理计划所忽视。本文提出Altar——一个轻量级、领域无关的框架，可在项目启动时即对实验数据进行结构化处理，且不强制采用僵化的数据模型。Altar围绕Sacred实验追踪模型构建，捕获实验（元）数据并对其进行结构化处理。参数、元数据、曲线及小型文件存储于灵活的NoSQL数据库中，大型原始数据则保存在专用存储中并通过唯一标识符进行关联，从而确保效率与可追溯性。该框架可与现有工作流组合使用，在最小化干扰工作习惯的前提下实现集成。我们根据用户技能背景（博士生、博士后、首席研究员、实验室管理员、系统管理员）记录了使用Altar的不同路径。虽然开始使用Altar无需专用基础设施，但在扩大规模或准备发表数据时，该框架可轻松部署于服务器并公开访问。通过应对研究的动态阶段，Altar为探索性实验与符合FAIR原则的数据共享提供了实用桥梁。