Thank you Swift Evo from DeSci Asia for reviewing this article and the ENS Public Goods Builders Grant for funding this work.

Decentralized science, or DeSci, uses open and decentralized technologies to experiment with new ways of funding, creating, reviewing, crediting, and sharing research. You do not need to launch a token or join a DAO to begin. A practical first step is to establish a recognizable onchain identity and use it to explore a DeSci platform.

In this guide, you will register an Ethereum Name Service name, set it as your primary name, create a FunDeSci account, and connect the wallet that owns the name. By the end, you will be ready to explore research projects and communities without being required to buy an NFT, fund a project, or publish anything.

Important distinction: An ENS name is attached to your wallet. It does not replace your FunDeSci email, Google login, or password. FunDeSci currently presents account creation and wallet connection as separate actions. The site may show your wallet address even after you set a primary ENS name, because each application chooses whether and how to resolve ENS names.

What DeSci, ENS, and FunDeSci each provide

DeSci is a broad movement rather than one website. Its projects explore public research infrastructure, open collaboration, alternative funding, verifiable attribution, and transparent records.

ENS, the Ethereum Name Service, lets you associate a readable name such as yourname.eth with an Ethereum address. A primary ENS name can help ENS-aware applications display that name instead of a long 0x address. ENS is your onchain identity layer in this tutorial.

FunDeSci is a DeSci platform for discovering research, participating in communities, publishing results, and supporting projects. Your FunDeSci account is your platform profile; your connected wallet is the account you use for blockchain interactions.

Before you begin

You will need:

• An Ethereum-compatible self-custody wallet. FunDeSci's current wallet picker includes MetaMask, WalletConnect, Coinbase Wallet, Trust Wallet, Rabby Wallet, Rainbow, and Ledger.

• Enough ETH on Ethereum Mainnet for the ENS registration fee and network gas. ETH on an L2 such as Base, Arbitrum, Optimism, or Polygon cannot pay for a Mainnet .eth registration.

• An email address and password, or a Google account, for FunDeSci account creation.

• A stable browser session and several uninterrupted minutes for ENS registration.

If you still need a wallet, begin with the official guidance from your chosen provider. MetaMask users can consult its official safety guidance.

Wallet and transaction safety

• Never enter your Secret Recovery Phrase or private key into ENS, FunDeSci, a pop-up, or a support chat. Legitimate wallet connection does not require it.

• Check the domain before connecting: use app.ens.domains for ENS and fundesci.com for FunDeSci.

• Read every wallet prompt. Confirm the selected account, network, destination, requested permission, and estimated cost.

• A signature proves control of a wallet and normally does not spend ETH. An onchain transaction changes blockchain state and can charge gas. A wallet prompt should tell you which one you are approving.

• Ethereum Mainnet gas is variable, and transactions are public and generally irreversible. If a prompt is unexpected or unclear, reject it and investigate.

Part 1: Register your .eth name

1. Open the official ENS app and search

Go directly to app.ens.domains. Select Connect, choose your wallet, and approve the connection request. Connecting lets the site view the selected public address; it is not the registration transaction.

Enter the name you want in Search for a name. The ENS app will add .eth when appropriate and report its current status. Available means it can be registered at the standard rate. A registered, grace-period, premium, invalid, or too-short name needs a different decision; the official status guide explains each label.

Start at the official ENS app, search for a name, and connect the wallet that should own it.

2. Choose the duration and review the live estimate

Open an available name and choose the registration duration. The interface may let you specify years or pick an expiry date. A longer registration reduces the need to submit a renewal transaction every year, but it also increases the registration fee paid now.

Review the live estimate shown by ENS. The total consists of the registration fee plus Ethereum network gas. Name length and registration duration affect the registration component; network demand affects gas. Recently expired names may also carry a temporary premium. Because these inputs change, rely on the quote in your own ENS session and the current official ENS pricing page—not a price copied from a tutorial.

The displayed values are an example captured at one moment, not a promised price. Your wallet shows the amount you are actually asked to approve.

3. Complete both registration transactions

ENS uses a commit-and-reveal process to reduce name sniping. According to the official registration guide, the sequence is:

1. Select Begin and approve the commit transaction. The transaction sends no registration payment, but it does require Mainnet gas.

2. Keep the same browser session open and wait for the approximately 60-second timer.

3. Select Complete Registration and inspect the second wallet prompt. This transaction includes the registration fee and gas.

4. Wait for confirmation before closing the flow.

The first transaction does not reserve the name. Do not clear the browser cache, switch browsers, or replace/speed up the commit while the flow is in progress. ENS stores a secret in the browser that must match the onchain commitment. Complete the second transaction promptly; the commitment eventually expires.

Part 2: Set and verify your primary ENS name

A primary name creates the reverse association applications use when they start with your wallet address and want to display an ENS name.

1. In the ENS app, open the name you just registered.

2. Open its Profile area and confirm that its ETH Address points to the wallet you intend to use.

3. Select Set as Primary Name.

4. Review and approve the wallet prompt. This is an onchain action and requires Mainnet gas.

5. After confirmation, look for the Your primary name badge and for your .eth name in the ENS app header.

Only the wallet set as the name's ETH Address can set that name as primary. If the ETH Address does not already point to your wallet, ENS may require an additional transaction. Use the official primary-name instructions if the button is missing or the connected wallet has a different role.

The primary-name setting helps ENS-aware apps display your name; it does not force every app to do so.

Part 3: Create your FunDeSci account

Open FunDeSci and select Sign Up / Log In in the header. On the sign-in panel, select Sign up if the Create Account form is not already visible.

Choose one of the current account methods:

• Continue with Google, then complete Google's authorization flow; or

• Enter an email, create a unique password, confirm it, and select Create Account.

Read the linked terms and privacy policy before submitting. Follow any email, Google, or profile-completion prompts that appear. FunDeSci can change the optional profile fields over time, so complete only the information you are comfortable making part of your platform profile.

Creating a FunDeSci account is an offchain platform action. It is separate from connecting your Ethereum wallet.

Part 4: Connect the wallet that owns your ENS name

Return to the FunDeSci header and select Connect Wallet. Choose the same wallet provider and account you used to register the ENS name. If you use WalletConnect, follow the QR-code or deep-link instructions shown by your wallet.

Select the ENS-owning account, then carefully review the request in your wallet.

Approve the connection request. If FunDeSci requests a sign-in signature, verify that it is a message signature—not a token transfer, approval, or paid transaction—and then sign only if the domain and selected address are correct. Connecting or signing in should not by itself require gas. A later action such as minting, joining an onchain membership, or funding a project may require a transaction and network-specific assets.

To verify the connection:

1. Open your wallet and copy or inspect the selected public address.

2. Compare its first and last characters with the address FunDeSci shows in its header, account menu, or wallet panel.

3. Confirm that this is the same address listed as the ETH Address for your ENS name.

4. Do not treat the absence of your .eth name in FunDeSci as a failed connection. Address display and ENS-name display are different features.

What you can do next without spending money

With your account and wallet connected, you can create a Profile, create a Project to receive, browse Projects, Discovery, and Communities, read public research information, fund projects, and inspect the network and payment requirements attached to individual opportunities. FunDeSci supports activity across multiple networks, so a community or project may use a network other than Ethereum Mainnet.

Stop before any mint, membership, donation, or funding confirmation unless you understand the asset, network, amount, gas estimate, and smart-contract permission. Creating the account and browsing do not obligate you to purchase anything.

Troubleshooting

• The ENS name is not available. Search for another name. Do not assume a grace-period name can be registered immediately, and inspect any premium before proceeding.

• The wallet says the balance is insufficient. Confirm that the ETH is on Ethereum Mainnet and covers both the registration fee and changing gas estimate.

• ENS is stuck after the first transaction. Stay in the original browser, do not clear its data, and wait for confirmation. If the commitment expires, restart through the official ENS app.

• “Set as Primary Name” is missing. Check that the connected wallet is the name's ETH Address, not merely a manager or owner with different records.

• FunDeSci connected the wrong account. Disconnect from FunDeSci, select the correct account in your wallet, and reconnect. Compare the complete address rather than relying on an account label.

• FunDeSci shows 0x… instead of the ENS name. That can be normal. Your ENS records may be correct even when an application chooses to display the raw address.

Frequently asked questions

Does an ENS name create my FunDeSci account?

No. Create the FunDeSci account with Google or email/password, then connect the ENS-owning wallet separately.

Does connecting a wallet cost gas?

A basic connection request and an offchain sign-in signature should not cost gas. Registration, primary-name changes, token approvals, mints, and other onchain actions can cost gas.

Can I use a different wallet on FunDeSci?

Technically, you can connect a supported wallet, but this tutorial uses the ENS-owning address so your onchain identity and platform activity stay aligned. Always verify which address is active before signing.

Is registration permanent?

No. A .eth registration has an expiry date and must be renewed. Record the expiry and renew early if you want to keep the name.

You are ready to explore

You now have three distinct pieces working together: a FunDeSci account for your platform profile, a wallet for blockchain interactions, and an ENS name for a readable onchain identity. Keep those roles clear, review every prompt, and begin with low-risk activities such as browsing projects and communities before deciding whether to participate onchain.

What else do you want to learn to participate in DeSci? Please share your feedback here.

What can you create?

It is a simple question, but it sits at the center of science, entrepreneurship, and technological progress.

Creation does not happen all at once. It usually moves through a sequence:

Discovery → Invention → Innovation → Diffusion

First, we create knowledge.
Then, we create tools, products, processes, systems, and materials.
Then, we create value.
Finally, we create impact at societal scale.

This framework is especially clear when we look at something as familiar as the apple.

1. Discovery: Creating knowledge

Let's use Malus sieversii, the wild apple species native to Central Asia and one of the key ancestors of the domestic apple, as an example.

At the discovery stage, the question is not yet: How do we build a product?

We ask:

What exists in nature?
What traits does it have?
What can we learn from it?
What biological potential is hidden in plain sight?

Discovery creates the scientific foundation. Without it, there is no invention.

2. Invention: Creating products, processes, systems, and materials

Once knowledge exists, humans begin to manipulate, combine, and apply it.

In apples, this includes methods like tree grafting and artificial pollination. These are not just agricultural techniques. They are biological engineering processes. They allow growers and breeders to preserve desirable traits, combine different genetic backgrounds, and create new possibilities from existing natural variation.

This is the invention stage: the knowledge becomes method.

3. Innovation: Creating value

Over time, these methods produce different varieties of apples with distinct flavors, colors, textures, storage qualities, disease resistance, and market uses.

At this stage, invention becomes innovation.

The output is not just a new biological form. It becomes something people value: a better fruit, a differentiated product, a commercial crop, a supply chain, a brand, an export market.

Innovation is where scientific and technical progress becomes economically meaningful.

4. Diffusion: Creating impact

The final stage is diffusion.

Apple varieties spread into orchards, food systems, markets, traditions, art, and culture.

They become part of industrial agriculture.
They become part of daily diets.
They become symbols in religion, mythology, education, and painting.

This is the moment when an idea, organism, or technology moves beyond its origin and becomes part of society.

Diffusion is how creation becomes impact.

Design Considerations for Helping People Create

If we want more people to create, we need to design systems that help them move across the full pathway: from discovery, to invention, to innovation, to diffusion. That means supporting curiosity at the earliest stage, giving people tools to test and combine ideas, helping them translate methods into useful outputs, and making it easier for those outputs to reach the people and markets that need them. Creation is not only about inspiration. It is also about reducing the friction between an idea and its execution.

This framework is useful because it reminds us that each stage requires a different kind of support. Discovery needs observation, access, and knowledge generation. Invention needs experimentation, technique, and iteration. Innovation needs value creation, usability, and market fit. Diffusion needs trust, communication, infrastructure, and adoption. When we design with the entire pathway in mind, we make it easier for knowledge to become useful, for useful things to become valuable, and for valuable things to create broader impact.

The Takeaway Lesson

The apple is more than a fruit. It is a story of how humans create with nature.

A wild species becomes scientific knowledge.
Knowledge becomes technique.
Technique becomes new varieties.
New varieties become markets, food systems, and culture.

That same pattern appears across agriculture, biotechnology, materials, medicine, environmental science, and many other fields.

The challenge is not only to discover what is possible.

The challenge is to build the systems, tools, and institutions that help possibilities move from knowledge to impact.

That is how science becomes infrastructure.

That is how invention becomes value.

And that is how creation scales.

For industries like reinsurance, the stakes are higher. Climate change affects both the risks being insured and the assets backing those risks. That makes climate strategy less about sustainability messaging and more about core business survival.

The Risk Landscape Has Shifted

Most companies now frame climate risk in two categories: transition risk and physical risk, consistent with frameworks such as the Task Force on Climate-related Financial Disclosures¹.

Transition Risk: Policy, Technology, Markets

Governments are tightening climate policy through carbon pricing, emissions limits, and mandatory disclosures. These measures are expected to increase operating costs in carbon-intensive sectors and reshape competitive dynamics².

Legal exposure is rising as well. Climate-related litigation has increased globally, adding another layer of uncertainty for companies navigating evolving regulatory environments³. However at the same time, capital is shifting. Investment in clean energy is accelerating, but many emerging technologies still lack long-term performance data, making risk assessment more difficult⁴. Companies are being pushed to allocate capital into systems that are not yet fully understood.

Markets are also repricing. Investor preferences are increasingly aligned with sustainability, influencing valuations and access to capital¹. For some firms, this creates opportunity. For others, it raises the risk of stranded assets.

Physical Risk: From Tail Event to Baseline

Physical risks are becoming more visible—and more frequent.

Extreme weather events such as floods, wildfires, and hurricanes are increasing in intensity and frequency⁵. These acute risks disrupt infrastructure, supply chains, and economic activity in real time. At the same time, chronic changes—rising temperatures, sea-level rise, and shifting rainfall patterns—are reshaping entire regions⁵. These long-term trends affect agriculture, water systems, and the viability of property and infrastructure.

For insurers, this creates a structural challenge. As risks increase, some assets may become too expensive to insure—or uninsurable altogether³.

Planning for an Uncertain Future

Because climate outcomes remain uncertain, companies are increasingly relying on scenario analysis. The Network for Greening the Financial System outlines three widely used scenarios⁶:

• Orderly transition: Coordinated policy action limits warming below 2°C, with manageable transition costs

• Disorderly transition: Delayed or fragmented policy increases economic disruption

• Hot house world: Limited climate action leads to severe warming and escalating physical risk

These scenarios are not predictions. They are stress tests—used to evaluate whether a company’s strategy holds under different futures.

What Companies Are Actually Doing

The gap between companies that understand climate risk and those that don’t is widening. The leaders are moving in three directions.

1. Embedding Climate into Governance

Climate risk is moving to the board level. Companies are aligning governance structures with disclosure frameworks such as TCFD, integrating climate considerations into investment decisions, risk management, and reporting¹.

They are also engaging more actively with regulators to shape disclosure standards and reduce fragmentation across jurisdictions³.

2. Rebuilding Risk Models

Historical data is becoming less reliable as climate patterns shift. Companies—especially insurers—are investing in updated modelling approaches to better capture extreme events and long-term trends⁵. This is not a technical detail, because mispricing climate risk can translate directly into financial losses.

3. Repricing Risk—and Opportunity

Climate change is widening the gap between insured and uninsured losses. In sectors like agriculture and infrastructure, this gap is growing quickly³. In response, companies are developing new tools—such as weather-indexed insurance and climate-linked risk transfer products—to manage volatility⁷. These solutions not only reduce exposure but also open new markets.

From Commitments to Metrics

Net-zero targets are now standard. What matters is execution, because investors and regulators are increasingly focused on measurable indicators:

• Exposure to climate-related risks

• Capital allocated to transition-aligned investments

• Performance of climate risk models

• Coverage of climate-related losses

Frameworks like TCFD emphasize linking these metrics directly to governance and strategy¹.

The Bottom Line

Climate change is forcing a repricing of risk across the global economy.

Assets are being reassessed. Insurance boundaries are shifting. Capital is moving. And companies are being judged not by what they say, but by how well they quantify and manage their exposure. Most organizations still treat climate change as a sustainability issue. It is not.

It is a structural shift in how risk is understood—and the companies that adapt fastest will define what comes next.

References

1. Task Force on Climate-related Financial Disclosures (TCFD). Recommendations of the TCFD (2017).

2. Intergovernmental Panel on Climate Change. Climate Change 2022: Mitigation of Climate Change (WGIII). Cambridge Univ. Press (2022).

3. Swiss Re. Sustainability Report 2023; Business Report 2023.

4. International Energy Agency. World Energy Investment 2023.

5. IPCC. Climate Change 2021: Impacts, Adaptation and Vulnerability (WGII) (2021).

6. Network for Greening the Financial System. NGFS Climate Scenarios Phase IV (2023).

7. Daron, J. & Stainforth, D. Climate Risk Management 1, 76–91 (2014).

8. Siebert, A. Climatic Change 134, 15–28 (2016).

On April 17 2026 , DeSci Asia shared community updates during an X podcast focused on strengthening an Asia-wide builder network during the bear market, with an emphasis on practical collaboration and resource sharing across projects. The group highlighted ongoing outreach efforts, including a recent university survey in Japan and plans to expand the DeSci awareness survey across additional countries such as China and India, alongside invitations to join upcoming survey workshops. Organizers also noted the scale and momentum of the community, including a large DeSci Asia Telegram group with over 550+ active builders, and reinforced the goal of shifting the blockchain narrative from speculation toward real scientific utility through coordinated, supportive participation.

Rohan (LabGPT) - 🧪 Prompt Your Way Into a Real Lab

Website | Github | Product Hunt

What it is: LabGPT is an open-source platform that turns plain AI prompts into real experiments running in physical labs. No million-dollar equipment. No pipetting expertise. Just describe what you want to test, and fleets of AI agents handle the protocol design, coordination, and bench work for you.

Why it matters: Rohan is tackling the two biggest gatekeepers of modern science — the cost of lab infrastructure and the years of hands-on technique required to use it. By letting AI agents run in parallel across real labs, LabGPT makes experimentation scale like software rather than creeping along at the pace of a single graduate student's hands.

🎁 Early-adopter drop: Engage with the GitHub repo and community channels to claim up to $3,000 in lab credits — real experiments, on the house, for builders who show up early.

Origo (Delblondo/Tapan)

X | Website | Telegram

Origo is a DeSci ecosystem featuring three core products designed to support scientific innovation. The flagship products include Origo Hub, a launchpad for DeSci IP tokens governed by a review board of expert scientists to evaluate projects, and Origo Pay, a B2B payment system tailored for international pharmaceutical companies. The team is currently building their infrastructure and preparing to launch new projects on the hub once market conditions become more favorable.

Walker (dLife)

X | Website | GitHub | Telegram

Walker shared updates on dLife and LifePass, addressing data security in genomics through Web3 technology. Drawing from his extensive 20-year background in informatics, Walker is developing LivePass, an NFT-based decentralized identity (DID) framework for personal genomic data, and Bloodline, a product that facilitates the transfer of digital assets based on biological relationships. His team is also incorporating zero-knowledge proofs and AI to verify biological ties, such as HLA verification for organ transplants, without compromising patient privacy.

Chris (AxonDAO)

X | Website | Telegram | Discord

As the CMO of AxonDAO, Chris detailed the project's focus on the intersection of AI, compute power, and decentralized science. AxonDAO is democratizing access to high-tier AI compute and offering them to researchers through the Axon GPU Vast.ai marketplace and Axon OS graphical interface. Furthermore, Chris highlighted Axon Academy, an educational initiative, and announced that researchers can currently apply for grants through the AxonOS Beta Grant Program to run scientific workloads on their testnet for free.

Rakhan (overlake.bio)

X | Website | LinkedIn

Overlake.bio is an independent DeSci research effort focused on longevity and aging, with an emphasis on extracellular matrix (ECM) changes over time. The project’s ECM Aging Atlas aggregates proteomics datasets into a comparable database and dashboard, and the team is exploring space biology collaborations to support drug discovery for astronaut health interventions.

Koko (DeSci Bridge)

X | Website | Telegram

Koko highlighted DeSci Bridge's mission to onboard and train African scientists, students, and medical professionals in Web3 and decentralized science. Koko announced an upcoming intensive bootcamp with over 400 interested students, aiming to provide them with meaningful contribution pathways and connect them with global DeSci infrastructures to solve real-world funding and research barriers. Koko extended an open invitation for other DeSci projects to participate as speakers to help them gain genuine adoption and users in Africa.

Hosseini (FunDeSci)

X | Website | Telegram

FunDeSci is a platform which enables DeSci projects to build loyalty membership programs. By creating VIP membership NFTs with access controls, communities can offer exclusive content, discussions, events, and perks to their holders. FunDeSci actively sought feedback from other builders on desired features to improve the platform's user experience and offered to support other projects through cross-promotion.

Rebecca (DeSci Asia)

X | Website | Telegram

Speaking as a moderator for DeSci Asia, Rebecca shared updates on community initiatives, including a recent survey conducted at Japanese universities to gauge awareness of decentralized science. She outlined plans to expand this survey across other Asian countries like China and India to better understand the regional landscape, inviting interested parties to collaborate on the upcoming survey workshops and continue building the network during the bear market.

Community Sentiment

Overall, the community was constructive and builder-focused, with repeated emphasis on collaborating through the bear market, sharing resources, and helping one another find practical paths to adoption. The community also highlighted a desire to shift blockchain’s narrative away from speculation and toward real scientific utility, especially by connecting Asia’s large research community into a reliable, supportive network.

Centralized knowledge institutions, primarily universities and research organizations, exert significant control over access to credentials, funding, and publication channels. This concentration profoundly shapes academic and research landscapes, determining who participates in knowledge production, what constitutes legitimate work, and the pace at which new fields emerge. As a result, critical barriers to equitable participation are created, particularly for those lacking the necessary affiliations. Developing neutral coordination platform that provides standardized, interoperable mechanisms for funding, contribution tracking, and credit attribution to enable collaboration across disciplines and jurisdictions without institutional lock-in is crucial for fostering interdisciplinary collaboration, facilitating equitable access to opportunities, and ensuring that all communities can benefit from technological advancements. Addressing these challenges is essential for enabling meaningful societal transformation in the face of evolving global issues.

1.1 Centralization and Its Implications for Knowledge Production

The centralization of educational and research institutions leads to a restrictive environment where pathways to visibility, resources, and academic trust are not only gated but also controlled by entrenched networks. Research indicates that institutions with higher funding success rates tend to favor narrower proposals, which diminishes support for interdisciplinary work that could yield innovative solutions to societal challenges (Bromham et al., 2016). The reliance on traditional metrics for evaluating research productivity leads to a chronic underproduction of specialized talent, specifically individuals who can effectively engage with and develop advanced technologies vital for modern society (Scholz, 2020).

The concentration within knowledge institutions often prioritizes established fields at the expense of emerging interdisciplinary domains that do not fit neatly into existing academic structures. Scholz argues that effective transdisciplinary approaches can enhance the relevance of academic research to societal needs, yet such approaches remain sporadically institutionalized (Scholz, 2020). Therefore, the pathways for interdisciplinary collaboration often remain obscure, leading to significant talent loss and underutilization of knowledge, which should ideally drive societal innovation.

1.2 Disciplinary Silos and the Mismatch with Societal Needs

The traditional structure of universities, based around distinct departments, often fails to adapt to the rapid pace of change in knowledge creation. This misalignment manifests as a lack of responsiveness to industry feedback and the needs of frontier communities, exacerbating gaps between educational outputs and actual societal demands (Caulfield & Ogbogu, 2015). For example, as new challenges arise from technological advancements, the existing academic incentive structures prioritize continuity over relevance, causing delays in the integration of new knowledge into practice (Caulfield & Ogbogu, 2015).

Bromham et al. highlight that interdisciplinary research continues to grapple with funding challenges, as proposals from these areas receive lower success rates (Bromham et al., 2016). This funding disparity leads to a significant lag in addressing pressing societal questions at the intersection of various disciplines. Consequently, research that combines fields such as computation, biology, and energy systems suffers from underfunding despite its potential to drive innovation and solve real-world problems.

1.3 The Need for Neutral Coordination Platform

To counteract the gatekeeping inherent in current educational and research structures, the development of neutral coordination platform is critical. Open platforms that enable funding, contribution, and collaboration without institutional lock-in are essential to fostering innovation across disciplines and jurisdictions (Caulfield & Ogbogu, 2015). Such systems would allow researchers to coordinate and gain credit for their work based on merit rather than institutional affiliation, effectively democratizing access to resources and opportunities (Scholz, 2020).

The need for a borderless platform for learning and research is imminent in a world where societal challenges are complex and multifaceted. However, there is currently limited evidence supporting the establishment of such platform across all areas, indicating that further efforts are needed to facilitate equitable global collaboration in research and educational spheres (Ferraz et al., 2024). By establishing inclusive networks, stakeholders can ensure that research efforts are more agile and responsive to the rapidly changing technological landscape, thus fostering a more equitable distribution of knowledge and resources.

2. Knowledge Centralization as a Driver of Inequality and Environmental Risk

Centralized knowledge institutions don’t just shape who gets educated—they shape who gets capacity. When credentials, networks, funding, and publishing access are concentrated, the ability to produce trusted knowledge and deploy it (skills, standards, tools, operational know-how) concentrates too. That concentration reinforces socioeconomic inequality by restricting mobility and slowing diffusion of expertise beyond elite pipelines. Inequality then feeds back into the environmental crisis: it allocates hazard exposure downward, concentrates high-impact consumption upward, and limits access to clean technologies and resilient infrastructure where they’re needed most. So an open, borderless coordination layer for learning and research isn’t merely “education reform”—it’s resilience solution: neutral rails that accelerate capability building, widen participation in innovation, and distribute the benefits of technological progress more fairly across communities and jurisdictions.

3. Socioeconomic Inequality is a Systemic Societal Problem

Socioeconomic inequality exacerbates the environmental crisis making vulnerable populations more exposed to hazards, limiting their ability to adapt, and driving unsustainable consumption patterns among the wealthy. Through technological advancement, humans have learned to extract and refined natural resources more efficiently through complex processes and machineries. The global socioeconomic disrepancy contributes to inefficient use of natural resources in low-income countries at the cost of ongoing environmental destruction.

3.1 The Link Between Socioeconomic Disparities and Environmental Exposure

Populations in low-income brackets are significantly more susceptible to hazardous environmental conditions, such as air pollution. Studies have shown that socioeconomic disparities lead to distinct differences in pollution exposure, with marginalized communities often residing in more polluted areas. This creates a broader pattern of health inequities linked to environmental factors, undermining the health and well-being of these populations (Hajat et al., 2015; Tomar et al., 2023). Hajat et al. point out that inequality metrics can highlight high-risk individuals within populations, demonstrating the need for targeted public health interventions (Hajat et al., 2015).

Moreover, the environmental exposure experienced by these communities is compounded by inadequate healthcare infrastructure and socioeconomic disadvantage. Dlamini et al. highlight that in low- and middle-income countries (LMICs), environmental pollutants and socioeconomic disparities converge to intensify health risks, including cancer, further exacerbating vulnerabilities (Dlamini et al., 2025). Factors such as limited access to clean water and sanitation facilities significantly affect the health outcomes of economically disadvantaged groups, emphasizing the necessity of addressing these social determinants (Chakraborty & Basu, 2021).

3.2 Unsustainable Consumption Patterns Driven by Wealth Inequality

The relationship between income inequality and ecological footprints reveals that wealthier individuals disproportionately drive environmental degradation through their consumption patterns. Wealth disparities manifest in consumption patterns where affluent classes consume a disproportionate amount of resources, escalating global ecological footprints while low-income communities often face the brunt of these environmental consequences (Andretti et al., 2024). For example, Ivanova et al. discuss how low-income households tend to use less carbon-intensive transportation options, such as public transport or cycling, resulting in a lower environmental impact (Ivanova et al., 2015). This discrepancy illustrates that while poorer populations might exhibit sustainability in consumption, systemic inequality fosters an environment where affluent consumers are primarily responsible for significant ecological degradation (Rainham et al., 2013).

3.3 Technological Advancements and Resource Inefficiency in Low-Income Regions

Technological advancements have undoubtedly improved the efficiency of resource extraction and refinement; however, the benefits of these technologies are often unevenly distributed globally. Low-income countries frequently lack access to advanced technologies, leading to inefficient resource utilization and further environmental damage (Li et al., 2025). Research indicates that these countries typically experience greater resource depletion due to a reliance on outdated extraction methods compared to wealthier nations (Hirschnitz-Garbers et al., 2016).

Furthermore, globalization and international trade may exacerbate these disparities by enabling wealthier nations to outsource environmentally damaging practices to poorer countries, intensifying resource extraction without adequate compensation for ecological and social damage (Figge et al., 2016). As a consequence, the interplay between global economic policies and local environmental practices often perpetuates a cycle of exploitation in low-income regions (Li et al., 2025).

4. Actionable Recommendations

• Treat knowledge governance as a systems lever. Explicitly map how credentialing, funding, and publishing gatekeeping affects capacity formation (skills, standards, tooling) and set measurable targets for reducing affiliation-dependence in your program/policy design.

• Reduce affiliation-based gatekeeping by building “neutral rails” for coordination. Implement standardized, interoperable, non-custodial mechanisms for (i) funding flows, (ii) contribution tracking, and (iii) portable credit attribution so contributors can earn recognition based on verifiable work rather than institutional signals.

• Operationalize interdisciplinarity instead of rewarding it rhetorically. Create dedicated interdisciplinary funding lanes with review criteria aligned to cross-domain integration (e.g., integration plan, deployment pathway, shared artifacts), and publish transparent rubrics to reduce discipline-specific penalty effects (Bromham et al., 2016).

• Align incentives with deployable capability, not only publication outputs. Add evaluation pathways that recognize concrete deliverables that increase real-world capacity (validated protocols, reference implementations, datasets, benchmarks, standards, open-source maintenance) alongside papers to close the specialized-talent gap (Scholz, 2020). Ideally the deliverable “works in the wild,” not “worked once in a lab.”

• Accelerate translation by shortening feedback loops with builders and end-users. Formalize recurring “external signal” inputs (industry, infrastructure builders, emerging research communities) into curriculum and research prioritization—e.g., quarterly needs assessments and rapid pilot cycles tied to adoption metrics (Caulfield & Ogbogu, 2015).

• Target inequality where it compounds environmental risk. Prioritize programs and funding that reduce hazard exposure and improve baseline public-health and infrastructure capacity in high-risk groups, using inequality metrics to target interventions (Hajat et al., 2015; Dlamini et al., 2025; Chakraborty & Basu, 2021).

• Address “luxury consumptions” and consumption asymmetry as a core policy dimension. Pair innovation policy with demand-side measures that reduce high-impact consumption among affluent groups while expanding low-emission access and options for lower-income households (Ivanova et al., 2015; Andretti et al., 2024; Rainham et al., 2013).

• Enable clean-technology diffusion by fixing access constraints, not blaming “inefficiency.” Design technology-transfer and financing mechanisms that allow lower-income regions to adopt best-available processes and infrastructure, reducing reliance on outdated methods (Hirschnitz-Garbers et al., 2016; Li et al., 2025).

• Make supply-chain externalities visible and accountable across borders. Incorporate consumption-based accounting, traceability, and environmental cost attribution into trade and procurement standards to reduce incentives to outsource damage to lower-income regions (Figge et al., 2016).

• Treat implementation evidence as a deliverable. Pilot the coordination platform in a bounded domain (one program, one funding stream, one cross-disciplinary challenge), publish outcomes and failure modes, and iterate—addressing the current evidence gap for scalable borderless coordination (Ferraz et al., 2024).

References

Andretti, B., Vieites, Y., Ramos, G., Elmor, L. & Andrade, E. B. Underestimations of the Income-Based ecological footprint inequality. Climatic Change 177 (2024). https://doi.org/10.1007/s10584-024-03719-0

Bromham, L., Dinnage, R. & Hua, X. Interdisciplinary Research Has Consistently Lower Funding Success. Nature 534, 684-687 (2016). https://doi.org/10.1038/nature18315

Caulfield, T. & Ogbogu, U. The Commercialization of University-Based Research: Balancing Risks and Benefits. BMC Medical Ethics 16 (2015). https://doi.org/10.1186/s12910-015-0064-2

Chakraborty, J. & Basu, P. Air Quality and Environmental Injustice in India: Connecting Particulate Pollution to Social Disadvantages. International Journal of Environmental Research and Public Health 18, 304 (2021). https://doi.org/10.3390/ijerph18010304

Dlamini, Z. et al. The Exposome Perspective: Environmental and Infectious Agents as Drivers of Cancer Disparities in Low- And Middle-Income Countries. Cancers 17, 2537 (2025). https://doi.org/10.3390/cancers17152537

Ferraz, P. F. P. et al. Assessment of Ammonia Emissions and Greenhouse Gases in Dairy Cattle Facilities: A Bibliometric Analysis. Animals 14, 1721 (2024). https://doi.org/10.3390/ani14121721

Figge, L., Oebels, K. & Offermans, A. The Effects of Globalization on Ecological Footprints: An Empirical Analysis. Environment Development and Sustainability 19, 863-876 (2016). https://doi.org/10.1007/s10668-016-9769-8

Hajat, A., Hsia, C. & O’Neill, M. S. Socioeconomic Disparities and Air Pollution Exposure: A Global Review. Current Environmental Health Reports 2, 440-450 (2015). https://doi.org/10.1007/s40572-015-0069-5

Hirschnitz-Garbers, M., Tan, A., Gradmann, A. & Srebotnjak, T. Key Drivers for Unsustainable Resource Use – Categories, Effects and Policy Pointers. Journal of Cleaner Production 132, 13-31 (2016). https://doi.org/10.1016/j.jclepro.2015.02.038

Ivanova, D. et al. Environmental Impact Assessment of Household Consumption. Journal of Industrial Ecology 20, 526-536 (2015). https://doi.org/10.1111/jiec.12371

Li, L., Liu, W., Khalid, S. & Mahmood, H. Investigating the Role of Natural Resource Depletion and Consumption Expenditures in Escalating Carbon Inequality. International Journal of Finance & Economics (2025). https://doi.org/10.1002/ijfe.3145

Millward-Hopkins, J. & Oswald, Y. ‘Fair’ Inequality, Consumption and Climate Mitigation. Environmental Research Letters 16, 034007 (2021). https://doi.org/10.1088/1748-9326/abe14f

Rainham, D., Cantwell, R. & Jason, T. Nature Appropriation and Associations With Population Health in Canada’s Largest Cities. International Journal of Environmental Research and Public Health 10, 1268-1283 (2013). https://doi.org/10.3390/ijerph10041268

Scholz, R. W. Transdisciplinarity: Science for and With Society in Light of the University’s Roles and Functions. Sustainability Science 15, 1033-1049 (2020). https://doi.org/10.1007/s11625-020-00794-x

Tomar, G., Nagpure, A. S., Jain, Y. & Kumar, V. High-Resolution PM 2.5 Emissions and Associated Health Impact Inequalities in an Indian District. Environmental Science & Technology 57, 2310-2321 (2023). https://doi.org/10.1021/acs.est.2c05636

DeSci Asia hosted its December community meeting on X recently. Speakers featured included Messi Boy (DeSci Asia contributor and host), Hosseini (co-founder of FunDeSci), and Swift Evo (DeSci Asia).

About FunDeSci

Hosseini introduced FunDeSci as a decentralized platform originally created to improve how scientific work gets funded by connecting researchers, investors, and organizations. He described FunDeSci as addressing gaps in traditional scientific funding—especially long review processes, limited grant availability for early-career researchers, and a lack of transparency in how some government funding is distributed.

FunDeSci’s approach, as described, includes enabling transparent peer-to-peer funding transfers and creating pathways for community-supported small to mid-range grants. Hosseini also shared several longer-term directions for the platform: a future science marketplace where researchers and developers can sell outputs/results, support for better access and ownership of scientific content, and future plans for intellectual property preservation via NFTs.

He also referenced a prior collaboration: FunDeSci previously ran a joint program with DeSci Asia and Hippocrat to provide grants for DeSci projects in Asia. He characterized it as a strong experience and noted that technical challenges during that period helped motivate the next phase of the platform’s development.

About DeSci Asia

Swift Evo described DeSci Asia as a community focused on sharing knowledge and building bridges between local Asian DeSci communities, while also serving as a contact point between international and Asian DeSci ecosystems. They shared that DeSci Asia hosts monthly scientific meetings featuring founders and builders across APAC, and provides retrospective funding to sponsor in-real-life DeSci meetings in different Asian cities.

Key Announcement: FunDeSci will become open source under DeSci Asia management 🧑‍💻

A central update from the session was the planned integration between FunDeSci and DeSci Asia. Hosseini described FunDeSci as a utility platform intended for community use (funding events, knowledge sharing, and future marketplace functions), while DeSci Asia is focused on building networks and growing the DeSci ecosystem across Asia.

Specifically, FunDeSci plans to transition into an open-source platform, with the intent to make the full codebase publicly available in the near future, and operate under DeSci Asia management. As described on the call, FunDeSci would remain community-driven, with technical and operational support from DeSci Asia, while development and platform growth would be driven by open-source community participation. Hosseini described this as a “win-win strategy”: DeSci Asia gains a dedicated platform for events and funding workflows, and FunDeSci benefits from access to a larger community and faster iteration through open development.

Key challenge discussed for DeSci in Asia

When the conversation zoomed out to Asia-wide challenges, both Hosseini and Swift Evo emphasized networking and coordination. Asia was described as having many promising communities and strong projects, but fragmentation prevents teams from combining strengths and scaling. Swift Evo added that Asian communities are still comparatively small relative to larger organizations, making it harder for their voices to be heard internationally. The discussion highlighted cross-city synergy and collaboration planning across regions including Hong Kong, Taiwan, Thailand, Malaysia, and the Middle East.

👉 Call to action

If you’re a researcher, PhD student, builder, or community organizer working in DeSci/DAO-adjacent domains in Asia:

• Join the next DeSci Asia monthly meeting to connect with founders, builders, and local communities across APAC.

• Prepare a short profile + project summary so you’re ready to participate in upcoming funding events when FunDeSci onboarding opens.

• Contribute to the open-source effort once the FunDeSci codebase is released—feature ideas, testing, documentation, and development are all valuable.

• If you run a local community, consider how you’d use a shared funding/events platform to host meetups and support early-stage researchers in your city.

TLDR

• Maximize social impact per unit time

• Optimize for readers + builders, not gatekeepers

• Ship knowledge in usable bites, not monoliths that die in review purgatory

Core takes + additional personal thoughts

1) Prestige is glorified marketing

The overvaluation of prestige and outdated metrics isn’t just “culture.” It’s a systemic barrier throttling knowledge flow from academia to society contributing to R&D stagnation and the currently actively collapsing global economy. I don't understand the metric-driven incentive structure that values citations and h-index instead of utility. Publishing in certain prestigious journals is like buying certain luxury bags 👜 for $10K+ when you can buy it 90% off in certain countries without the logo.

2) Conferences are overpriced ritual

I don’t understand why scientists pay so much to attend conferences in-person when we can host an e-conference in real-time on certain digital platforms. Borderless, permissionless streaming exists. Streamr exists. If you’re paying thousands for “networking,” what you’re really paying for is proximity to status and possibly a photo op that could have been a screenshot. 📷

3) Publishing is a distribution strategy

People should publish wherever they want depending on:

• who they want reading it,

• who’s funding it,

• and what kind of research impact they’re optimizing for.

Open-access vs closed-access should be a choice aligned with the funding agreement—not a morality badge, not a gatekeeping tool, not a “paywall = serious” cosplay. In a complex, multi-dimensional global society, I don't understand why certain centralized funding agencies mandate or coerce the researchers' distribution strategy through certain mandated incentive structures when they are not even the ones doing the work.

4) Peer review should be borderless + permissionless

All are welcome to peer review Bitcoin University's publications regardless of university affiliation (or not), country of origin, socioeconomic status, or language of choice. I care whether you can read carefully, think clearly, and provide constructive feedback. DMs, RT, comments all qualify as peer review feedback. Peer reviewers' privacy should be a choice. I recognize the wide variation of personalities, so blocking is also a choice.

5) The biggest treasure is what never gets published

The problem

Failed experiments. Negative data. “Which hills not to climb.”
The gold mine is in the failures when people learn through failures.

And yet it stays unpublished. The core issue isn’t that science fails—it’s that our systems fail to capture and reward the information content of failure.

Why it happens

Journals—especially the prestigious ones—cherry-pick “interesting” results, not failed experiments. When tenure and grant evaluations prioritize prestige over value contribution, it becomes unprofitable for career scientists to spend time reporting failures.

The consequence is professors shooting for a “big paper” or a fancy p<0.0001. Ambition isn’t bad, but the lesson is: publish in smaller bites. Ship useful units early even if it is a failed experiment. Let the world iterate with you in real-time.

Personal example

I’m sitting on three years of algal biofuels wet lab notes that could fundamentally transform and accelerate algal strain development—held hostage by the slow incentives of elite academia + COVID disruption + supervisor ambition. Lesson learned: I’m micro-publishing going forward.

Business design

The real business question in redesigning the incentive structure is: how do we reward failures that meaningfully update the shared map of reality without creating perverse incentives for spammy, low-effort “failure farming”? After all, failure only matters when it reduces uncertainty for others.

This is where values and incentives collide: when trust in judgment is replaced by rigid metrics, measurement stops reflecting reality and starts shaping behavior.

Values

But that raises a deeper question: do we even need rigid incentive structures if people participating in the system are educated with the correct values?

• Values education is societal engineering. The world we live in is the output of someone’s value system scaled to policy. Given the current state of the world, clearly some schools of thought suck—imperialism and patriarchy included.

• When money and artificial metrics are merely lagging indicators of a person’s value contribution, then in a natural, complex environment, incentives must be determined systemically and adaptively—not enforced dogmatically.

The problem is, when the system keeps changing the rules, people can’t optimize for truth—only for whatever the rules currently reward. You end up rewarding gaming instead of learning.

Metrics are lagging indicators

Nature is a lagging indicator of the physical environment (including infrastructure and policy). Data is a lagging indicator of nature. Metrics trail reality, not the other way around.

So if we mistake metrics for truth, we optimize dashboards while the system quietly degrades underneath.

6) The talent drain is engineered (and yes, Matilda Effect)

People “perish” in the academic pipeline. That’s not an accident. It’s a design flaw. When career interruptions due to family planning are not even included in tenure considerations, no wonder there aren't many female professors.

The Matilda Effect is real: women’s contributions are more likely to be discounted, delayed, or credited elsewhere. Male fragility in a patriarchal status system creates so much deadweight it’s honestly embarrassing for a species that claims it wants progress. Let’s be honest: some careers are materially propped up by invisible support labor. If the researcher's productivity depends on a full-time maid and babysitter (usually in the form of "wife" or "girlfriend"), that’s not pure merit—it’s subsidized output. Imagine failing to compete in a system designed for men.

Moreover, it’s not simply “talent drain” that we’re looking at. A lot of developed countries are staring at the same thing: demographic crisis + collapsing birth rates.

▼Reproductive Decision-Making Under Adverse Socioecological Conditions

Elephants don’t breed in hostile environments. Humans aren’t that different.

So why would women volunteer to birth children into a world with rising pediatric cancers, mental health crises, and accelerating ecological destruction—outcomes that have been normalized and rewarded within exploitative patriarchal structures?

Patriarchy fundamentally conflicts with the natural world because it optimizes for status, extraction, and control—not long-term sustainability. And here’s the part nobody wants to say out loud: as a top-of-food-chain species on a finite planet, not every man “gets” a wife. Entitlement isn’t a sustainable reproductive strategy.

Moreover, socioeconomic inequality exacerabates the global environmental crisis. However, considering the globally available tax structures and men's territorial nature, technically we have an inheritance + hoarding problem. If the billionaires actually wanted to reduce global poverty in one generation, they’d open-source their sperm and wealth, like Pavel Durov—progressive inheritance caps, real redistribution, universal basic services, and scholarship/endowment structures that outlive their territorial nature. If men can't be competitive on Earth, then they should explore whether they are competitive on Mars. There is a lot of unclaimed and untaxable land on Mars. Plus they already screwed up a planet through the establishment and propagation of patriarchy. It's kind of their responsibility to find a new planet that's more habitable.

7) Reproducibility is also an infrastructure problem (not just a “methods” lecture)

People love to say “just reproduce it” like everyone has the same equipment and budget.

Good luck reproducing results dependent on the latest HiSeq machine in a low-income country. Funding realities are not evenly distributed, and pretending otherwise is how you get performative “reproducibility discourse” that solves nothing.

Here is my experience trying to set up a research program in a certain low-income country, which is probably not unique to my circumstance. When government agencies in low-income countries are undereducated and unaware of the risks and benefits of scientific equipment, how can you import the latest models? When big scientific supply companies gatekeep operating software behind copyright licenses, how can underfunded independent labs reproduce anything? When there is no local supply chain for research consumables because instability keeps breaking logistics (and yes, a lot of this is geopolitical), where is the equipment supposed to come from — and how is it supposed to stay running? Without the local shipping routes and roads, where is the supply? How can a freezer stay running when power goes into a hiccup spree every time there is a storm?

▼Fancy illustration depicting the cause of the global socioeconomic inequality

We are currently looking at the rise of the Global South. The lesson from history should be: if you don’t want it done to you, then don’t do it to others.

8) Funding must be decentralized

I don’t understand why professors work so hard to write grants when the central banks have a magic money computer.

Central banks print. Funding agencies piggyback. Then they cherry-pick research directions like they’re omniscient. What do they know about what people living in chronic poverty need to participate in citizen science? How can remote communities that don't even have bank accounts pay publication fees?

Without access to international banking to pay journals in G7 currencies, how can people living in the middle of nowhere publish? The funding source needs to be decentralized. Tokenization isn’t “replacing science.” It’s adding another rail, the same way preprints complemented journals instead of killing them. People should use whatever currency that they recognise to fund and publish their work.

And I don’t recognise “prestige publications” from G7 countries that piggyback off Indigenous knowledge in low-income countries without equitable return. Here's the quiet part: when governments mandate public education curricula in ways that erase Indigenous language, worldview, and transmission pathways, that’s not “standardization.” That’s cultural cleansing by government mandate. Once the knowledge pipeline is broken, the same systems can repackage what survives as “novel discovery” later, post hoc—now conveniently detached from the people who carried it.

9) If the mission is cures, reward mission-driven collaboration

If producing cures is the mission, we need structures aligned with that objective.

Reward cross-discipline collaboration. Reward deployment. Reward people who move discoveries into real-world outcomes. And let’s be honest: nobody takes cancer research as seriously as someone with personal loss.

My publishing pipeline

Disclaimer: The specific platforms may change to adapt to the global market preference.

Stage 1 — Micro-publish first (fast feedback loop)

• Ideas, results, lessons, failures, negative data, “don’t climb this hill” notes

• Optimize for speed + clarity + conversation

• Where: X

Stage 2 — Long-form second (structured)

• Convert micro-posts into a coherent long writeup

• Living doc, versioned

• Borderless, permissionless peer reviews through DMs and article or tweet comments

• Where: Paragraph

I might add more stages depending on the market demand. This is scientific publishing in a global free market.

What I refuse to do

• Hold knowledge hostage for a “big paper”

• Treat prestige as the definition of quality. It is a waste of the taxpayers' money to pay $10K+ to publish my knowledge under a fancy logo or journal title.

  

• Hide behind paywalls to signal seriousness

• Filter reviewers by institution, country of origin, or language (there really is no excuse for language barriers with AI)

• Participate in labor systems that treat trainees as disposable inputs

Call to action

If you want science to move faster and reach more people:

• Review my work (borderless + permissionless)

• Publish your negative results and dead-end hills (wherever you want and however you want)

• Build your own curation + trust metrics that works for your research discipline

• Support decentralized funding rails that expand who gets to participate

Science should be a public good, not a status competition.

In Part I, I framed this series around three questions:

1. How are DeSci tokens distributed across blockchain networks, and to what extent are they multichain?

2. Which wallets, explorers, auditors, and social platforms constitute the core infrastructure supporting these tokens?

3. What structural features characterize the resulting token–infrastructure network, and which nodes act as hubs or bridges?

Part II showed that deployments are highly concentrated on a small set of major chains, with single-chain tokens dominating the sample dataset. Part III extended this picture to off-chain infrastructure, revealing similar concentration on a handful of wallets, explorers, and social platforms, along with sparse public audit coverage. Part IV then combined these layers into a single heterogeneous network, showing that tokens and infrastructure form one connected but sparse graph organized around a few structurally central hubs and a set of recurring “infrastructure bundles.”

In this final part, I will synthesize those findings through the lens of governance and security. Rather than just restating the distributions, I will focus on where power and systemic risk actually concentrate, what this implies for DeSci as a movement, and which design levers builders, infra providers, and auditors have if they want a more decentralized, resilient ecosystem.

2. Review of findings in Part II-IV

2.1 Networks: concentrated and mostly single-chain

Part II showed that DeSci tokens are clustered on a small set of chains, with Ethereum, Solana, Base, and BNB Smart Chain carrying the vast majority of deployments and only sporadic use of other networks. Most tokens are single-chain, with multichain deployments present but relatively rare. In practice, this concentrates liquidity, tooling, and risk on a few networks.

2.2 Off-chain infrastructure: a narrow core stack with long tails

Part III found a similar pattern off-chain. A handful of wallets (especially Trust Wallet and Binance Wallet), explorers and analytics tools (notably Nansen and Etherscan), and social platforms (X and Telegram) dominate access and visibility, while other tools are lightly used. Publicly reported audit coverage is limited and fragmented, with most tokens listing no auditor at all. Together, this points to a relatively narrow infra stack that many projects share.

2.3 Network topology: hubs, bridges, and infrastructure bundles

Part IV tied these layers together as a single heterogeneous graph. The resulting network is one connected component with a short average path length, but it is structured around a few central hubs (X, Trust Wallet, Nansen, Binance Wallet, Telegram) and a small set of broker tokens that link different parts of the system. Community detection reveals five multi-type “infrastructure bundles”—each mixing tokens, networks, wallets, explorers, social platforms, and sometimes auditors—which differ in how dense, audit-heavy, or socially connected they are. These bundles are not chain-specific; instead they are defined by recurring combinations of services.

3. Governance: Infrastructure Choice as Soft Power

DeSci narratives often focus on on-chain governance (DAOs, voting, funding rounds), but Parts II–IV suggest that a lot of practical governance power sits in the infrastructure layer.

• If most tokens rely on the same few wallets, those wallets effectively mediate who can easily hold or use DeSci assets. Moreover, with the emerging malwares (e.g. StilachiRAT, Trojan.Scavenger) that target browser wallets, keeping large holdings in any of these hot wallets would be a horrible idea in the first place.

  • What's the chance of biological seed phrase alternatives? Hardware can be seized, and software can be hacked.

• If most tokens rely on the same explorers and analytics platforms, those platforms mediate what activity is legible to the ecosystem. This concentration of interpretive power in a few infrastructure providers thus reintroduces a form of recentralization and creates a regulatory chokepoint, even in systems that are otherwise branded as decentralized. If one day EU decides to censor what these platforms can display, then they would effectively become gatekeepers of which tokens, addresses, or transaction types “exist” for most users. There are certain unnamed countries out there with their own "Great Fire Wall" for the internet, so hopefully by publishing on Paragraph where the data is stored on Arweave, they can read these articles too.

• If most tokens coordinate via X and Telegram, those platforms mediate which narratives spread, which communities grow, and which conflicts get attention (driven by social media algorithms).

None of this requires explicit on-chain control. This is soft governance: defaults, listings, UI choices, API policies, content rules, and business decisions on this layer can shape the DeSci ecosystem without touching the token contracts themselves.

4. Security and Systemic Risk

The community structure in Part IV adds another layer. By choosing a particular combination of wallets, explorers, and socials, projects effectively choose:

• Their neighbourhood (which other tokens they cluster with),

  • For example, if the community coordination of a token project is primarily on Discord, the token project will more likely cluster with other projects with a strong community presence on Discord. (Leave a message below if you want to see an analysis on this.)

• Their gatekeepers (which platforms can amplify or suffocate them), and

  • Refer to my hypothetical example of EU censoring explorers and analytics platforms earlier.

• Their failure modes (for example, whether they live in an audit-heavy but small bundle, or a socially loud but audit-thin one). Different bundles come with different correlated risks:

  • An audit-heavy but small infrastructure bundle might be robust to basic contract bugs but fragile to the disappearance of a single auditor or analytics provider.

  • A socially loud but audit-thin bundle might mobilize quickly on X/Telegram, but be more exposed to contracts that fail in subtle ways.

  • A wallet/explorer-heavy but socially thin bundle might have great technical access and observability, but almost no community capacity to coordinate a response.

The key takeaway here is that picking an infrastructure bundle is already a governance decision, even when projects treat it as a purely technical integration checklist. By picking a bundle, you are implicitly deciding:

• Which kinds of failures you’re more okay with (e.g., “we’re fine being dependent on X, but we care a lot about audits”),

• Which system-level shocks will hit you hardest (social platform policy change vs explorer API change vs wallet support removal).

Security here is broader than smart contract bugs. It includes technical risk, operational risk, and information risk.

• Technical risk: buggy contracts, unsafe tokenomics, lack of audits.

• Operational risk: outages, deprecations, or policy shifts at central infra providers.

• Information risk: parts of the network that are poorly observed or poorly communicated.

The analyses suggest at least three systemic concerns:

1. Hubs as single points of failure. High-betweenness nodes like X, Trust Wallet, Nansen, and Telegram connect many otherwise distant parts of the graph. If they fail, change user policies, or face censorship pressure, large portions of the ecosystem become more isolated at once. This is a classic systemic risk where shocks can propagate through shared dependencies, not just through project-specific flaws.

2. Uneven audit coverage. Audit coverage is sparse overall and concentrated in one of the five communities. Some bundles are effectively audit deserts, where users have little formal information about contract reviews, even if security work exists off-chain. Others have strong social presence but limited public security signalling, which can amplify hype faster than risk awareness.

3. Uneven observability and coordination capacity. In some communities of the DeSci ecosystem, there are few monitoring tools and almost no active social channels. This means, no one is really watching, and no one is really talking, so problems are harder to spot and harder to fix. Some regions of the DeSci network are well-instrumented and can more easily detect abnormal activity or coordinate responses.

From a governance and security standpoint, community outreach and funding support to integrate isolated communities into the broader DeSci ecosystem is neccessary not only for accountability but also to potentially track censorship pressure to elucidate the political reasons behind why certain DeSci projects are targeted.

5. Design Lessons and Practical Levers

5.1 For DeSci builders and token projects

• Treat infra stack selection as a policy choice.
  Don’t just ask, “Which wallet/explorer is easiest to integrate?” Also ask, “Which infrastructure bundle does this put us into, and what does that imply for our risks and dependencies?”

• Avoid absolute single points of failure where feasible.
  Full redundancy is unrealistic, but:

  • Support more than one major wallet where your users actually are. Know any non-mainstream alternatives to the usual wallets, explorers, or socials? Drop them in the comments below.

  • Integrate with at least one alternative explorer or analytics tool.

  • Diversify live social channels (e.g., X + Telegram + a “home base” like Discord or a forum).

• Make security work legible.
  If you have audits, report them consistently across platforms. If you don’t, be explicit about your roadmap and threat model. This doesn’t magically make you safe, but it does reduce information asymmetry.

• Think in bundles, not one-off tools.
  Design a target infra bundle—networks, wallets, explorers, socials, auditors—and move towards it deliberately, instead of accreting integrations organically.

5.2 For infra providers (wallets, explorers, analytics, social)

• Acknowledge de facto governance roles.
  Infra providers are chokepoints and coordination layers. Listing policies, moderation rules, and API changes are governance events that can directly effect the DeSci ecosystem research output, even if they are not on-chain.

• Invest in transparent, predictable policies.
  Publish criteria for listings and removals, communicate major changes early, and provide clear channels for DeSci projects to understand and adapt.

• Support portability and exit options.
  Make it easier for tokens to mirror or migrate their presence, export data, and integrate with alternatives. A resilient DeSci ecosystem is one where no single infra provider becomes a trap.

5.3 For auditors, risk analysts, and stewards

• Use community structure as a risk map.
  Token-dense but audit-poor communities are natural targets for security outreach. Socially heavy but audit-light communities may benefit from extra scrutiny and user education.

• Measure systemic risk, not just individual risk.
  Don’t only assess tokens in isolation; also analyze how reliance on central hubs changes the risk profile of entire bundles.

6. Directions for Future Analyses

This series is based on a static snapshot of 35 DeSci tokens from CoinMarketCap. It is only a starting point. Future research directions could:

• Track the network over time to see how communities evolve, which infra players rise or fall, and how shocks propagate.

• Run experiments to simulate the removal or degradation of central nodes (e.g., X, major wallets, key explorers) to gain better understanding of the systemic risks.

• Compare DeSci’s dependency structure with other crypto subsectors (DeFi, NFTs, gaming) to see whether DeSci is unusually concentrated or just differently organized.

• Layer in on-chain governance data and off-chain institutional ties (foundations, labs, DAOs, universities) to build a fuller picture of who actually governs DeSci in practice.

If you are interested in any of these research directions, please leave a message below.

7. Closing

The starting point of this series is straightforward: map where DeSci tokens live, what infrastructure they use, and how the resulting network is structured. The picture that emerges is more political than the raw data might suggest. DeSci tokens rely heavily on a small set of chains, wallets, explorers, and social platforms, and they cluster into a handful of infrastructure bundles with uneven security signalling and observability.

This pattern doesn't necessarily invalidate the DeSci projects. Instead, it shifts our question from “Is DeSci decentralized?” to “Decentralized relative to what, and who currently holds the meaningful levers?” If we can answer that honestly and design around it, then we have a much better chance of building a DeSci ecosystem that is not only novel and exciting, but also robust, transparent, and actually useful to science.

Table 3 lists the top nodes that, on average, lie “closest” to all others in the DeSci token–infrastructure network. Overall, we can see that DeSci relies heavily on a small set of wallets, analytics tools, and social platforms that are both highly connected and structurally central. Besides the key findings mentioned in Part IV.a reinforced by the results in this table, I highlight the fact that GitHub (0.468927) showing up in the table reflects the central role of code hosting and open-source collaboration across projects.

When centralities are averaged by node type (Figure 3), social platforms and tokens have the highest average degree and between-ness, with tokens and social nodes also exhibiting the highest average closeness, indicating that they tend to sit near the structural centre of the network. So, does the network actually split into communities?

2.4 Overall community structure

Community detection identifies five structural communities comprising 24, 21, 18, 13, and 8 nodes, respectively (Figure 4). Each cluster contains a mixture of node types—tokens, networks, wallets, auditors, social platforms, and explorers—rather than a single homogeneous category. Visually, there is no “Ethereum-only” or “wallet-only” community; instead, tokens tend to cluster with the particular combinations of infrastructure services they use.

The layout also highlights that communities are not isolated components. Edges connect nodes across community boundaries, and several tokens and infrastructure nodes sit near the interfaces between clusters. This is consistent with the global statistics reported earlier in Part IV.a (single connected component, short average path length): communities represent regions of relatively denser connection, not disconnected subgraphs. Next, we will find out what’s inside each community.

2.5 Community composition by node type

Figure 5 summarizes the size and composition of each community. In absolute terms, C0 and C1 are the largest groups, with 24 and 21 nodes respectively, whereas C2, C3, and C4 contain 8, 18, and 13 nodes. Tokens are concentrated in C0 and C1 (11 and 10 tokens), with the remaining communities each containing four tokens. This means that most projects in this snapshot fall into two large token-dense communities, while a smaller number of tokens populate three more specialized clusters.

When node types are normalized to percentages, C0 and C1 remain strongly token-oriented—roughly half of their nodes are tokens—but they also include multiple wallets, explorers, and at least one network apiece. In contrast, the smaller communities have more distinctive profiles. C2, despite containing four tokens, has relatively few supporting nodes overall (one network, one wallet, one explorer, and a single social platform) (Figure 5). C3 and C4 show higher shares of infrastructure relative to tokens: C3 combines tokens with a notable presence of auditors and social platforms, whereas C4 is characterized by many wallets and explorers but no social nodes (Figure 5).

These patterns support the interpretation of communities as “infrastructure bundles” rather than purely chain-specific groupings. The tokens in C0 and C1 appear to use a broad, relatively standard set of services; whereas the tokens in C2–C4 tend to rely on narrower or more specialized combinations of infrastructure.

2.6 Distribution of each node type across communities

The per–node-type community breakdown (Figure 6) clarifies how the five detected communities differ in composition.

• Tokens. Communities C0 and C1 contain the largest numbers of tokens (11 and 10, respectively), while the remaining communities each contain four tokens.

• Networks. Every community contains one to two blockchain network(s). Networks are broadly distributed and do not by themselves determine community boundaries.

• Wallets. Wallet nodes are more unevenly distributed. C1 and C4 each contain five wallets, whereas C0 has three, and C2 and C3 only one wallet each. This suggests that communities C1 and C4 represent infrastructure bundles where user-access options are relatively diversified, while C2 and C3 are anchored around a smaller set of wallets.

• Auditors. Auditors show a pronounced skew. C3 contains three auditor nodes, C0 and C4 contain one each, and C1 and C2 contain none. Thus, any publicly signalled audit activity in this snapshot is concentrated in a subset of communities, with C3 standing out as relatively “audit-heavy.”

• Social platforms. Social nodes are present only in C0 (4), C2 (1), and C3 (4). C1 and C4 contain no social nodes. Combined with the earlier centrality results, this implies that active, linked social infrastructure (e.g., project accounts on X or Telegram) is unevenly distributed: some communities appear much more socially connected than others.

• Explorers. Explorers are relatively frequent in C0, C1, and C3 (each with 4 explorers), but less common in C2 and C4 (one explorer each). This hints that observability and analytics tooling are richer in the larger communities and in C3, while tokens in C2 and C4 rely on a narrower set of explorer integrations.

Taken together, these distributions show that each community has a characteristic profile:

• C0 and C1 are token-dense and broadly provisioned with wallets and explorers.

• C2 is small and relatively sparse, with minimal supporting infrastructure.

• C3 is audit- and social-intensive.

• C4 is wallet- and explorer-heavy but socially thin.

No single node type dominates community membership; instead, communities correspond to different combinations of services that tokens tend to adopt together.
These community assignments should be treated as structural groupings driven by connection patterns, not as definitive categorizations of project type, quality, or function.

3. Outlook

Part IV has treated the DeSci token–infrastructure ecosystem as a single heterogeneous network and shown that it is neither a flat marketplace of interchangeable tools nor a set of neatly separated silos. Instead, a small group of wallets, explorers, analytics platforms, and social channels sit at the structural centre, while tokens cluster into distinct infrastructure bundles with uneven levels of social visibility, observability, and security signalling. Read through a governance lens, this means that “infrastructure choice” is already a form of soft coordination: by plugging into a particular bundle, projects implicitly co-locate with specific intermediaries, norms, and failure modes, and shocks to a few central nodes—whether from outages, policy changes, censorship, or business model shifts—are likely to propagate through many tokens at once.

In Part V, I reinterpret infrastructure choice as a system design problem, treating these structural patterns as inputs to questions of governance (“who effectively has power over whom?”), security (“which correlated failures are we implicitly accepting?”), and architecture (“how might we rewire the system to reduce single points of failure and audit deserts?”). The aim is not to prescribe a single “correct” stack, but to extract practical design lessons—checklists, stress tests, and alternative patterns—that DeSci projects, ecosystem funders, and even traditional funding agencies (which may never touch a token directly but do underwrite digital platform development) can use to understand how their choices shape the infrastructure available to participants in this emerging market.

4. Analysis Limitations

This analysis is subject to several important limitations:

• Snapshot design. It is based on a single snapshot dataset and does not consider network evolution over time. The reported network structure reflects the ecosystem state at the time of data collection and may not predict future trends.

• Binary relationships. All network relationships are treated as binary (present/absent) without weighting by importance or frequency.

• Undirected graph assumption. The network graph assumes symmetric relationships; directionality (e.g., flows of value or information) is not modelled.

• Equal edge weight. All edges have equal weight, regardless of metrics outside the working dataset such as token market capitalization or transaction volume.

• Independence between entity types. The analysis implicitly assumes that relationships between different entity types (e.g., network–wallet) are independent of each other beyond what is encoded in the observed connections.

• Artificial “no auditor” node. Tokens without audits are connected to an auditor_none node, which is an artificial construct that may affect network metrics (e.g., centrality and community structure).

• Multipartite structure. The network is a multi-partite graph (tokens connect to other entity types, but entities of the same type do not directly connect to each other). This means that if two tokens interact with each other, this information is not included. This limitation arises from data availability on CoinMarketCap, because token listings do not report token–token interactions.

• Small sample size. With only a few dozen tokens (≈35) used to map the network, the sample is relatively small, which may limit the generalization of findings.

• Approximate community detection. Community detection is based on the Louvain algorithm, which is approximate and may not find the globally optimal partition.

These limitations should be kept in mind when interpreting the structural patterns described above and when considering any governance or design implications drawn from them.

1. Introduction

Parts II and III examined DeSci token infrastructure in terms of the marginal distributions: how many tokens are deployed on each network, and how many tokens list each wallet, auditor, explorer, or social platform. Those results showed clear token concentration on a small set of blockchain networks (Ethereum, Solana, Base, BNB Smart Chain), wallets (Trust Wallet, Binance Wallet), explorers (Nansen, Etherscan), and social platforms (X, Telegram), as well as limited reported audit coverage.

Since marginal distributions treat each attribute domain in isolation, they do not describe how networks, wallets, explorers, auditors, and socials co-occur across the same tokens, nor do they reveal which elements sit at the structural “center” of the resulting ecosystem. In Part IV, I will represent the tokens and infrastructure elements as nodes in a single heterogeneous graph to describe:

• Global network properties (size, connectivity, density, path length, clustering),

• Node-level centralities (degree, betweenness, closeness) for both tokens and infrastructure, and

• Community structure, interpreted as recurring infrastructure bundles.

2. Results & Discussion

2.1 Network Topology and Centrality

The DeSci token-infrastructure network graph (Figure 1) forms a single connected component, meaning that every node (token, network, wallet, explorer, auditor, social platform) is reachable from every other via some sequence of relationships. Despite this connectivity, the global structure is sparse, and most node pairs do not have a direct link (network density = 0.1087). The lack of triadic closure (average clustering coefficient = 0) aligns with the properties of the node types. Tokens connect to wallets / explorers / socials, but wallets don’t connect to each other, and explorers don’t connect to each other, etc. The average shortest path length is 2.6583, suggesting that any node is, on average, fewer than three steps away from any other.

2.2 High-degree and high-centrality nodes

Table 1 shows that a small set of services sit at the centre of the DeSci token–infrastructure network. The most connected node is X, with direct links to 33 tokens. It is followed closely by Trust Wallet and Nansen, each connected to 31 tokens, and by Binance Wallet and Telegram, each with 26 connections. In other words, many different projects rely on the same social platform, the same two wallets, and the same analytics provider. Rather than a flat landscape of many small tools, the snapshot looks more like a hub-and-spoke system built around a few core platforms.

The “none” auditor node among the top-degree nodes (25 connections) represents the group of tokens that did not list any auditor on their CoinMarketCap profile, yet it is almost as connected as the largest wallets and social platforms. However just because the auditor information of a token is not on CoinMarketCap does not mean that the token was not audited. The more accurate interpretation is that publicly signalled audits in the projects considered here are uncommon.

Tokens themselves start to appear only in the lower half of Table 1. These projects are plugged into a relatively large number of networks, wallets, explorers and social platforms compared to other tokens in the sample. Tokens with a high degree here should be interpreted as “broadly integrated into the surrounding infrastructure,” NOT automatically as “more successful” or “more important,” but it does suggest that changes at central infrastructure providers would affect these tokens through many different channels.

Table 2 highlights which nodes act as bridges between otherwise weakly connected parts of the network. The token Rejuve.AI has the highest betweenness value (0.157592), meaning that a large share of the shortest paths between other nodes pass through it. In practical terms, Rejuve.AI sits “in the middle” of many token–infrastructure relationships: it connects to multiple wallets, explorers, and social platforms in ways that link different parts of the graph together. I wouldn’t recommend any market speculation of Rejuve.AI’s token price based on this metric as it simply means that Rejuve.AI is well integrated with other infrastructures in the ecosystem.

Several infrastructure nodes also play strong bridging roles. X (0.136406), Trust Wallet (0.100413), Nansen (0.100413), and Telegram (0.090511) all appear among the top positions. This pattern is consistent with their high degrees (Table 1) but adds an important nuance: not only are they connected to many tokens, but they also lie on critical routes that connect one portion of the ecosystem to another. This means if any of these services were removed from the graph many tokens and infrastructure elements would become more distant from each other in terms of path length resulting in habitat fragmentation. The Institute for Creativity, Arts, and Technology has a good introduction on the concept of habitat fragmentation from the ecological perspective in their YouTube video Wildlife, Habitat Fragmentation, and You [1] .

It is notable that several other tokens also have comparatively high betweenness scores: SUI Desci Agents (0.085082), AxonDAO Governance Token (0.078421), ValleyDAO (0.066565), and OriginTrail (0.066462). These projects are not the highest-degree nodes overall, but they occupy strategic positions that link multiple infrastructure bundles. They function as “connectors” between different combinations of wallets, explorers, and social platforms. Finally, Binance Wallet (0.063062) appears as a bridge alongside Trust Wallet, reinforcing the idea that a small number of wallets route connectivity between large parts of the DeSci ecosystem.

Together, these betweenness results suggest that both infrastructure providers (especially X, Trust Wallet, Nansen, Telegram, and Binance Wallet) and a small set of tokens (Rejuve.AI, SUI Desci Agents, AxonDAO Governance Token, ValleyDAO, OriginTrail) act as structural brokers. Their positions do not necessarily reflect economic size or scientific impact, but they do indicate that changes to these nodes—such as policy shifts, outages, or deprecations—would disproportionately alter how different parts of the network can reach one another. (Thank you, Elon Musk, for protecting X from EU’s censorship policy.)

To be continued in the next article.

Here's an open-ended discussion question for my readers:

Alice

@wAliceChen

Are “central” tokens real structural brokers… or just well-funded/organized enough to integrate everywhere?

Alt explanations:
- default stack effect (everyone uses the same wallets/explorers)
- reporting bias (better docs = more edges)
- lifecycle age (older tokens expand

paragraph.com

The DeSci Token Landscape (Part IV.a): One Big but Sparse Graph

Network Topology and Structural Hubs in the DeSci Infrastructure

0

8:24 AM • Dec 17, 2025

In Part II, I covered where DeSci tokens are deployed across blockchain networks. Most tokens in the snapshot are single-chain, and the deployments are concentrated on a small set of major networks (Ethereum, Solana, Base, BNB Smart Chain). The choice of network deployment, however, is only one dimension of a token’s operational environment. To participate in a DeSci ecosystem in practice, users and developers require:

• Wallets to hold and transact tokens,

• Block explorers and analytics platforms to inspect contracts and monitor transaction activities,

• Security auditors (when present) to review smart contracts and tokenomic design risks, and

• Social media platforms to learn about, coordinate around, and contribute to projects.

In Part III, I will characterize which wallets, explorers, auditors, and social platforms are most frequently associated with DeSci tokens, and to assess the extent to which these dependencies are concentrated using the same CoinMarketCap dataset (see Part I).

3. Results & Discussion

3.1 Wallet Support

Wallet support is similarly concentrated (Figure 1). Trust Wallet is associated with 33 tokens, making it the most frequently listed access point. Binance’s wallet follows with 28 tokens. A second tier of wallets—including Solflare, Phantom, Backpack, and Jupiter—each appears in connection with 11 tokens. MetaMask is associated with 7 tokens. All other wallets (Coinbase Wallet, Ledger, Rabby, Daedalus, Lace, Eternl, Vespr, Gnosis Safe) are connected to ≤ 2 tokens each.

Block explorer usage mirrors this distribution pattern (Figure 2). Nansen is linked to 33 tokens, indicating extensive reliance on a single commercial analytics and exploration platform. Etherscan supports 19 tokens, Solscan 11, and BaseScan 7. Other explorers—including Ethplorer and BscScan—are each used by fewer than six tokens, with an “other” category aggregating explorers that appear for only one token each (8 total). Taken together, these distributions suggest a core infrastructure stack in which most DeSci tokens rely on a small cluster of wallets (Trust, Binance) and explorers (Nansen, Etherscan), with long tails of alternative providers.

3.2 Security Audits

Security audit coverage is limited and fragmented (Figure 3). A majority of tokens—27 in the sample—listed no auditors at all. Among those that do, coverage is fragmented: Cyberscope appears for 4 tokens, Certik for 3, and Quantstamp and Hacken for one token each. This pattern indicates that formal third-party auditing is far from universal among DeSci tokens, at least as reported on CoinMarketCap. It also shows that security expertise is not concentrated in a single auditor but distributed across a few firms, each serving only a small subset of tokens.

3.3 Social Media Footprint

The social media footprint of DeSci projects is dominated by a single platform (Figure 4). X is associated with 35 tokens, effectively universal within this sample. Telegram is also widely used, with 28 tokens listing active channels. Discord and GitHub appear for a smaller but substantial subset (15 and 14 tokens, respectively). In contrast, Facebook and Reddit are each used by only 5 tokens, and YouTube and “other” platforms by 2 tokens each. This distribution demonstrate that DeSci coordination and communication are overwhelmingly organized via X and Telegram, with Discord and GitHub forming secondary hubs for community discussion and open-source development.

4. Conclusion

Key Takeaways:

• Off-chain infrastructure dependencies are concentrated in a small set of providers and platforms.

• Wallet access. Trust Wallet and Binance Wallet are listed by the majority of tokens, while other wallets are used more selectively. I recommend using the Trust Wallet as the go-to hot wallet option to HODL your DeSci tokens if you don’t want to keep switching the wallets.

• Block explorer. Nansen and Etherscan dominate the explorer and analytics landscape for this sample. This means either option would be a safe pick to setup comprehensive dashboard to monitor trading metrics.

• Social media. X and Telegram function as nearly universal and widely used social hubs, respectively, with Discord and GitHub forming a secondary layer. If you are community manager or looking to become one, then learn how to use these platforms.

• Security audits. Most tokens do not have any listed auditors. There is a small number of audited tokens distributed across several different firms. Token security auditors should DM me for a list of potential clients in this emerging market.

5. Outlook

In Part III, we treated each off-chain attribute—wallets, auditors, social media, and explorers—largely in isolation, with focus on the marginal distributions (tokens per wallet, tokens per explorer, and so on). In Part IV, we will move forward to a joint, network-based perspective. By interpreting the token attributes as a heterogeneous network graph in which tokens and infrastructure elements are the nodes and observed relationships are the edges, we can answer the following questions:

• How is the entire DeSci token–infrastructure system connected?

• Which nodes act as structural hubs or bridges?

• How do tokens and infrastructure cluster together into communities or “infrastructure bundles”?

In Part IV, I will address these questions by analyzing the topology, centrality patterns, and community structure of the full token–infrastructure network constructed from the same dataset.

One of the most basic questions one can ask about a token is: on which blockchain(s) does it exist? For DeSci projects, the choice of network shapes not only technical properties (e.g., transaction throughput, fees, programming model) but also ecosystem alignment, liquidity access, and tooling. This post focuses on the network layer of the DeSci token ecosystem. Using a snapshot of the top 35 DeSci tokens on CoinMarketCap (refer to Part I), I will show you:

• How many tokens are present on each major network, and

• How many networks, in turn, each token uses (single-chain versus multichain deployment).

2. Method Recap

As described in Part I, tokens were identified by manually scanning CoinMarketCap for projects clearly aligned with DeSci. For each included token, I recorded the blockchain networks on which the project lists a deployed contract (e.g., Ethereum, Solana, Base, BNB Smart Chain, Gnosis, Cardano, Polygon, Sui).

3. Results & Discussion

DeSci token deployment is highly skewed toward a small number of chains (Figure 1). Ethereum accounts for the largest share, with 19 tokens deploying smart contracts on the Ethereum main net. Solana hosts 11 tokens, followed by Base with 9, and BNB Smart Chain (BSC) with 4. The remaining networks—Gnosis, Cardano, Polygon, and Sui—each hosts only one token.

This distribution indicates a clear concentration of DeSci tokens on a small set of major chains. Ethereum has the largest footprint, with Solana, Base, and BSC forming a second tier. Other networks appear only sporadically, with single-token representation.

It is important to emphasize that these counts are per network, not per unique token. Because some tokens are deployed on multiple networks, the sum across networks exceeds the number of distinct tokens. The token–network matrix in Figure 2 (below) illustrates this multichain structure.

The token–network matrix (Figure 2) illustrates that tokens can maintain contracts on multiple networks. However, multichain deployment remains uncommon. As shown in Figure 3 (below), 24 tokens are deployed on one network, 4 on two networks, and 5 on three networks. Several multichain tokens are deployed on multiple EVM-compatible networks (e.g., combinations of Ethereum, Base, BSC, and Gnosis), and a subset also bridges between EVM chains and Solana (Figure 2).

Overall, these results indicate that DeSci tokens in this sample are heavily concentrated on a small set of major chains—Ethereum, Solana, Base, and BSC—while Gnosis, Cardano, Polygon, and Sui each host only a single token (Figure 1). In theory, this means token liquidity can be available on multiple networks. (Do verify for permissionless exit liquidity before investing.)

4. Conclusion

Key Takeaways:

• Concentration on a small set of major networks. Ethereum, Solana, Base, and BSC together account for all but a handful of token deployments in the snapshot, with Gnosis, Cardano, Polygon, and Sui each associated with only a single token.

• Predominance of single-chain deployment. Among 33 tokens with explicit network-count information, 24 are single-chain, 4 are deployed on two networks, and 5 on three networks. Multichain deployment is thus present but remains relatively uncommon in this sample.

5. Outlook

This post has focused exclusively on where DeSci tokens are deployed across blockchain networks. In Part III, I will walk through the off-chain infrastructure that surrounds these deployments:

• Which wallets do these tokens list as supported?

• Which block explorers and analytics platforms do they rely on for visibility?

• How often do they list formal security auditors?

• On which social media platforms do they coordinate their communities?

By examining these additional layers—wallets, explorers, auditors, and social platforms—we can begin to understand not only where DeSci tokens reside on-chain 🏘, but also how users access, observe 🐦, and coordinate around them in practice.

Decentralized Science (DeSci) has emerged as a movement to apply Web3 tools—such as programmable tokens, smart contracts, and decentralized governance—to fund, conduct, and coordinate of scientific research. Tokens associated with DeSci projects are not only speculative assets; they also function as access keys, governance instruments, and reward mechanisms when used with the intentions to contribute to the society. Their utility depends on a broader infrastructure stack that includes blockchain networks, user wallets, block explorers, security auditors, and social media platforms. Despite rapidly growing interest in DeSci, systematic analysis of this token–infrastructure ecosystem remains scarce. I have only come across largely narrative or project-specific, with little quantitative characterization of how DeSci tokens are distributed across networks, what wallets and explorers they depend on, or how centrally structured the ecosystem is.

Understanding these patterns is important for assessing systemic risks (e.g., dependence on a few infrastructure providers), barriers to user access, and opportunities for protocol and tooling development. Here, I present a descriptive, exploratory analysis of a manually curated sample of DeSci tokens from CoinMarketCap, focusing on three research questions:

1. How are DeSci tokens distributed across blockchain networks and to what extent are they multichain?

2. Which wallets, explorers, auditors, and social platforms constitute the core infrastructure supporting these tokens?

3. What structural features characterize the resulting token–infrastructure network, and which nodes act as hubs or bridges?

2. Methods

2.1 Data Collection and Scope

Tokens were identified by manually searching CoinMarketCap for projects explicitly aligned with DeSci. Only the top 35 tokens listed on CoinMarketCap at the time of collection—November 19, 2025—were included. For each token, I collected following publicly reported attributes from the token project websites: blockchain networks and contract addresses, wallet integrations, block explorers, security auditors (if any), and linked social media platforms (X, Telegram, Discord, GitHub, and others).

2.2 Overview of the Dataset

Simple counts and frequency distributions were computed for:

• Tokens per network,

• Networks per token,

• Tokens per wallet, auditor, social platform, and block explorer.

These descriptive statistics provide the backbone for the subsequent posts: first, a view of where DeSci tokens live on-chain; second, how they plug into off-chain infra; and finally, how all of these elements combine into a heterogeneous network.

2.3 Mapping the Heterogeneous Network

While marginal distributions are informative, they treat each variable in isolation (networks separate from wallets, wallets separate from social platforms, and so on). To study the joint structure of the DeSci token ecosystem, I constructed a network graph where:

• Nodes represent both tokens and infrastructure elements (networks, wallets, explorers, auditors, social platforms).

• Edges represent the presence of an observed relationship (e.g., “token A is deployed on Ethereum,” “token B lists Trust Wallet,” “token C links to X and Telegram”).

Formally, the graph is constructed as an undirected, unweighted network: edges indicate the existence of a relationship, but no directionality or weight (such as transaction volume or user counts) is encoded at this stage.

In subsequent posts, this graph is used to compute:

• Network-level statistics, such as the number of nodes and edges, density, average degree, average shortest path length, and clustering coefficient;

• Node-level centrality measures, including degree, betweenness centrality, and closeness centrality for tokens and infrastructure nodes; and

• Community structure, via a standard community-detection algorithm that partitions the graph into clusters of tokens and infrastructure elements that are more densely connected to each other than to the rest of the network

All such analyses are based strictly on the dataset and coding scheme described in this post. The original code used to perform the analysis and generate the figures are stored on GitHub.

2.4 Caveats

1. Sampling and coverage. The analysis includes only the top 35 DeSci tokens listed on CoinMarketCap as of November 19, 2025 that meet a DeSci-relevance criterion. Tokens not listed on CoinMarketCap, or listed after this date, are not captured.

2. Metadata dependence. Network deployments are inferred from publicly reported contract information. Undocumented deployments, or deployments not reflected in the sources used are not included in the analysis.

3. No assessment of economic or technical quality. The analysis does not incorporate market capitalization, liquidity, transaction volume, or protocol-level features. A single-chain token may be heavily used, and a multichain token may be largely inactive; these possibilities are not distinguished here. The same applies with the adequacy, security, or desirability of wallet, social media, and security auditor options.

4. Static snapshot. The results reflect one point in time. Tokens can and do migrate, add new deployments, or deprecate old ones, none of which is captured in a cross-sectional design.

3. Upcoming in this Series

This first post has focused on research design: defining the sampling frame, specifying the scope of the dataset, and outlining how qualitative project metadata were transformed into a structured matrix and a heterogeneous graph. In the next few posts, I will walk you through the empirical analysis:

• Part II examines how DeSci tokens are distributed across blockchain networks and characterizes the prevalence of multichain deployment.

• Part III analyzes the distribution of wallets, auditors, block explorers, and social platforms, highlighting patterns of concentration in access, observability, and coordination.

• Part IV uses the heterogeneous graph to study network topology, identify structurally central hubs and bridges, and describe the community structure of the token–infrastructure ecosystem.

• Part V synthesizes these findings, discusses their implications for DeSci governance and security, and outlines directions for future analyses.