Conservation experts and researchers perform field observations that they want to document and store simply, reliably, and in a way that allows easy retrieval. They seek a stable, long-term service that enables them to work efficiently without unnecessary effort. For this, they need a flexible data management solution that can adapt to continuously changing and evolving requirements.

For most researchers and conservationists, conducting field observations is a true pleasure. However, documenting these observations and processing the recorded data often pose challenges — and can be stressful — as it is easy to make mistakes that lead to data loss or missing information. In a poorly organized data management process, recording and handling data consumes disproportionate time — both for fieldworkers and for database curators. It can even result in several people redundantly performing the same administrative tasks, wasting valuable time and effort.

We offer solutions developed in collaboration with hundreds of researchers and conservation professionals to ensure that recording, storing, and processing field observations can be carried out as efficiently as possible — in a fully configurable system suitable for various biodiversity research and conservation projects.

With OpenBioMaps, you can create your own customized database and data management system on your own server. Field data collection, data handling, visualization, and access become straightforward and reliable.

All you need to do is set up your own OpenBioMaps server or join a trusted partner organization. Create an OpenBioMaps project for your data collection and management, define your database structure, design data upload forms, and invite your colleagues to participate in collaborative data collection and management.

If you choose this path, you will have a PostgreSQL-based database where your data is stored transparently and can be easily integrated with other systems — such as QGIS or R. Field data is uploaded quickly and with full documentation, allowing fieldworkers to focus on their actual work instead of post-processing. Data reliability can be continuously and automatically verified, and data transfers or transformations no longer require manual intervention.

If you do not choose this solution, you will need to use multiple different systems and take responsibility for maintaining their integrity. Updating and maintaining the individual software components will also fall on you. As a result, you become the sole expert and operator of your workflow — creating strong dependencies and a significant personal workload, which in turn reduces the flexibility of your system.

Reliable and flexible data management directly improves the quality of data collection. It enables professional growth, the adoption of new methods, and the refinement of existing ones. The ability to view and understand data easily helps users follow and correct their own work. As a result, data becomes more widely usable, leading to further — and even more exciting — data collection opportunities.

If you do not take this approach, you will have to rely on several separate systems whose integrity, updates, and maintenance are entirely your responsibility. Your workflow becomes highly dependent on your own expertise, creating an inflexible and burdensome system over time.