OpenML is a platform for machine learning researchers to share and organize data, experiments, and algorithms in detail, enabling more effective collaboration and faster discovery. It builds on the principles of networked science, where scientists work together to share knowledge, reuse data, and build on each other's work. OpenML allows researchers to automatically share, organize, and discuss machine learning experiments, data, and algorithms, making it easier to collaborate globally. Networked science tools have evolved from the traditional journal system, which focused on long-term memory and individual contributions, to the internet, which serves as a short-term working memory for collaborative research. These tools enable scientists to share data, code, and ideas in real time, leading to faster discoveries and more efficient research. OpenML follows this model, allowing researchers to share data in fine detail, organize it, and collaborate on a global scale. OpenML is designed to facilitate designed serendipity and a dynamic division of labor. Designed serendipity refers to the chance that someone with the right expertise will contribute to a shared idea or problem. A dynamic division of labor allows scientists to focus on tasks they are best suited for, leading to more efficient progress. OpenML supports these principles by allowing scientists to contribute in small increments, split complex tasks into smaller subtasks, and build on prior knowledge. OpenML also enables massive collaborative science, where scientists from around the world work together to solve complex problems. This is exemplified by projects like the Polymath projects in mathematics, where scientists collaborate online to solve unsolved problems. Similarly, OpenML allows scientists to collaborate on machine learning research, sharing data, code, and results to build on each other's work. OpenML also supports open data, where data is shared publicly to maximize its impact. This is important for scientific research, as it allows others to build on the data, leading to new discoveries. OpenML links to data available online and integrates with popular data mining platforms, making it easy for researchers to access and use the data. OpenML also supports citizen science, where the public contributes to scientific research. This is exemplified by projects like Galaxy Zoo, where volunteers help classify galaxies. OpenML allows scientists to share their data and results with the public, enabling more people to contribute to scientific research. In machine learning, OpenML is particularly valuable because it allows researchers to share and reuse data, code, and results, leading to more efficient and effective research. OpenML helps with reusability and reproducibility, which are important for scientific research. By sharing data and results, researchers can build on each other's work, leading to faster discoveries. OpenML also benefits individual scientists, students, and the broader scientific community. It allows scientists to share their work with a wider audience, increasing their visibility and reputation. Students can use OpenML to learn more about machine learning and contribute to ongoing research. OpenML also helps with funding, as open data sharing is increasinglyOpenML is a platform for machine learning researchers to share and organize data, experiments, and algorithms in detail, enabling more effective collaboration and faster discovery. It builds on the principles of networked science, where scientists work together to share knowledge, reuse data, and build on each other's work. OpenML allows researchers to automatically share, organize, and discuss machine learning experiments, data, and algorithms, making it easier to collaborate globally. Networked science tools have evolved from the traditional journal system, which focused on long-term memory and individual contributions, to the internet, which serves as a short-term working memory for collaborative research. These tools enable scientists to share data, code, and ideas in real time, leading to faster discoveries and more efficient research. OpenML follows this model, allowing researchers to share data in fine detail, organize it, and collaborate on a global scale. OpenML is designed to facilitate designed serendipity and a dynamic division of labor. Designed serendipity refers to the chance that someone with the right expertise will contribute to a shared idea or problem. A dynamic division of labor allows scientists to focus on tasks they are best suited for, leading to more efficient progress. OpenML supports these principles by allowing scientists to contribute in small increments, split complex tasks into smaller subtasks, and build on prior knowledge. OpenML also enables massive collaborative science, where scientists from around the world work together to solve complex problems. This is exemplified by projects like the Polymath projects in mathematics, where scientists collaborate online to solve unsolved problems. Similarly, OpenML allows scientists to collaborate on machine learning research, sharing data, code, and results to build on each other's work. OpenML also supports open data, where data is shared publicly to maximize its impact. This is important for scientific research, as it allows others to build on the data, leading to new discoveries. OpenML links to data available online and integrates with popular data mining platforms, making it easy for researchers to access and use the data. OpenML also supports citizen science, where the public contributes to scientific research. This is exemplified by projects like Galaxy Zoo, where volunteers help classify galaxies. OpenML allows scientists to share their data and results with the public, enabling more people to contribute to scientific research. In machine learning, OpenML is particularly valuable because it allows researchers to share and reuse data, code, and results, leading to more efficient and effective research. OpenML helps with reusability and reproducibility, which are important for scientific research. By sharing data and results, researchers can build on each other's work, leading to faster discoveries. OpenML also benefits individual scientists, students, and the broader scientific community. It allows scientists to share their work with a wider audience, increasing their visibility and reputation. Students can use OpenML to learn more about machine learning and contribute to ongoing research. OpenML also helps with funding, as open data sharing is increasingly