# Why to Use the Scientific Research Ontology ## Overview The advent of modern information technologies such as the internet, wikis, open source repos, artificial intelligence, and blockchain presents unprecedented opportunities to update and enhance the ecosystem of scientific research. This document outlines the importance of mapping the institutional ecology of scientific research using the provided ontology, focusing on integrating new organizational forms that leverage these technologies. ## Background ### People, Purpose, and Environment The essence of any organizational structure can be distilled into three core tenets: people, purpose, and environment. - **People**: The individuals and groups who are part of an organization or affected by its operations. In the context of scientific research, this includes researchers, engineers, administrators, and the broader community benefiting from scientific advancements. - **Purpose**: The goals, objectives, and missions that drive an organization. For scientific institutions, purposes can range from generating new knowledge to solving specific societal problems. - **Environment**: The broader context within which an organization operates, encompassing economic, social, legal, and technical dimensions. This includes the regulatory landscape, funding mechanisms, technological infrastructure, and societal needs and values. Mapping the environment of scientific research institutions using the proposed ontology allows for a comprehensive understanding of how these entities interact within the larger ecosystem, facilitating the development and integration of innovative organizational forms. Our approach is based on the [Method of Functional Decomposition of Organizations and Their Environments](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4606672). ## Examples The landscape of scientific research is already experiencing shifts toward more open, collaborative, and decentralized models. Wikipedia's success in _canonizing_ general knowledge through a peer-to-peer internet-based protocol illustrates the potential for institutional change in the digital age. Building on this foundation, we explore three forward-looking examples of new organizational forms within the scientific research ecosystem. ### Autonomous Research Organization An **Autonomous Research Organization (AROs)** operates as a research institution where the primary actors are the researchers themselves. These organizations, which can be for-profit corporations, cooperatives, or non-profits, specialize in providing the infrastructure and administrative support necessary for researchers to focus on their work. Examples include [BlockScience](https://blog.block.science/), a small private engineering R&D firm, and [Metagov](https://metagov.org) a community of practice; both exemplify this model by prioritizing the needs of its contributors and specializing in an area of expertise. AROs are a more general version of [Focused Research Organizations](https://fas.org/publication/focused-research-organizations-a-new-model-for-scientific-research/). ### Autonomous Funding Organization An **Autonomous Funding Organization (AFOs)** functions as a funding institution with its own treasury, expected to operate democratically. These organizations articulate goals, appropriate funds, and delegate grant administration to grant programs, ensuring transparency and accountability. The [Ethereum Foundation](https://ethereum.foundation/) (non-profit) is an example, managing its resources to support innovation and development within ecosystem around the Ethereum Network. As more peer-to-peer public infrastructures emerge, we see the corresponding emergence of Decentralized Autonomous Organizations (DAOs) which serve as public forums populated by the diverse stakeholders of those infrastructures. ### Decentralized Science Protocol **Decentralized Science Protocols (DSPs)** facilitate one or more subprocesses of scientific research through standardized protocols, reducing bureaucratic overhead and increasing accessibility. These protocols, developed by a collaborative community of researchers and software engineers, provide alternative approaches to accreditation, credentialing, and publication. By leveraging open-source contribution models and where appropriate blockchain technology to maintain a distributed shared history of events. These protocols offer fine-grained, collaborative scientific practices and tools for technical development and funding. Examples range from the [Open Source Science Initiative (OSSci) at NumFOCUS](https://numfocus.org/open-source-science-initiative-ossci), to the [HyperCerts frameowkr being developed by a research team at Protocol Labs](https://protocol.ai/blog/hypercert-new-primitive/). While our interest is largely in increasing access to more scientific contributors, large companies like IBM and Microsoft have demonstrated interests in these protocols, eg [Decentralized Collaborative AI at Microsoft](https://www.microsoft.com/en-us/research/project/decentralized-collaborative-ai-on-blockchain/). ## Conclusion The integration of modern information technologies into the fabric of scientific research institutions represents a critical evolution in how knowledge is generated, shared, and applied. By utilizing the Scientific Research Ontology to map this evolving landscape, we can better understand and engage with the complex interplay of people, purpose, and environment, driving forward a more open, collaborative, and effective scientific research ecosystem.