DeSci - Decentralized Science

1. Introduction
Blockchain ranks amongst the fastest expanding technologies mankind has ever seen.
Comparing user numbers, global blockchain adoption compares to the level of the internet
in the second half of the 1990s, but at a markedly stronger growth rate. Its ingenious
concept was conceived by an – arguably – still unknown persona or group publishing a
famous white paper [1] “Bitcoin: A peer-to-peer electronic cash system” under the pseudonym
“Satoshi Nakamoto” [2], likely in response to the global banking crises in the later
part of the 2000s.
Its underlying blockchain technology provided a practical solution to the doublespending
dilemma pertaining to digital assets, specifically by its unspent transaction output
(UTXO) accounting model [3]. Bitcoin [4, 5] and its technological precursors [6-13], or
objective-sharing initiatives [14-16], thus laid the foundation for digital scarcity in public
decentralized networks, which nowadays extends way beyond cryptocurrencies into a
whole slew of other, still emerging digital asset classes. Over now more than a decade,
Bitcoin has continued to remain the dominant player amongst a plethora of cryptocurrencies,
possessing a total market capitalization in the vicinity of 1 trillion US$ (status:December 2021), which is roughly on par with common fiat money [17], like the Russian
Ruble or the Swiss Franc.
Another seminal technological milestone was the launch of the Ethereum blockchain
[18] with its (quasi) Turing-complete Ethereum Virtual Machine (EVM) [19]. Its so-called
“smart contracts” [20, 21], occasionally also referred to as “programmable money”, set the
foundation of decentralized finance (“DeFi”), which, after only a few years, emerged into
a rapidly growing industry. Moreover, Ethereum set the groundwork of an entirely new,
burgeoning asset class called non-fungible token [22-28]; these “NFTs” are also traded on
a growing number of platforms [29-41]. While crypto-currencies themselves are, by their
very design, interchangeable, in a sense that swapping equal amounts of the same coin
leaves the economic impact for their owners unaltered, NFTs are unique, and their rarity
is secured by code.
NFTs are touted to play a paramount role in the ongoing uprise of the metaverse [42],
a notion coined in a 1992 book by Neal Stephenson [43], and later, for example, picked up
by the 2018 Spielberg film “Ready player One” [44]; in 2021, the metaverse garnered major
attention in the general public through the rebranding of a major social media giant [45,
46], and the accompanying, rather mind-blowing dynamics in their individual valuation
and supporting blockchains [47, 48].
Since the mid-2010s, these NFTs have been used to capture, trade, and immutably
track provenance and (digital) ownership of assets via a tamper-proof, decentralized
blockchain; these NFTs may represent physical items like property, e.g., real estate [49,
50] and artistic paintings [51], virtual collectables, e.g., digital graphics of kittens [52], apes
[53], pixelated “cypherpunks” [54], collectibles and fan tokens [55], in-game items [56],
and virtual land [57, 58], or “negative value” assets, e.g., loans, burdens and other responsibilities
[59, 60] with the potential to decisively disrupt present finance systems.
While initially rather modest, the market volume for NFTs has shot up to US$40 billion
just in 2021 [61]. However, this unprecedented type of asset is still in its adolescence,
potentially somewhat hyped phase, and it is thus naturally prone to significant volatility.
While a clear value proposition still needs to consolidate, it is commonly accepted that
NFTs well align with the massively increasing digitization and virtualization of our professional
and private lives, and the rapidly evolving metaverse (which, in the context of
blockchain technology, is occasionally also referred to as “cryptoverse”).
From a historic perspective, the advent of the internet of blockchains with its disruptive
concepts of decentralization and tokenization for an internet of value (“IoV”) [62, 63],
sometimes referred to as Web3 or Web3.0 [64, 65], might be viewed in the context of the
multi-stage industrial revolution; commencing in the years 1760 to 1820 / 1840, the first
industrial revolution saw the transition from hand production methods to machines, new
chemical manufacturing and iron production processes, the increasing use of steam and
water power, the development of machine tools and the rise of the mechanized factory
system [66]. The second industrial revolution saw rapid standardization and industrialization
from the late 19th century into the early 20th century [67].
The following “Digital Revolution” coined the second half of the 20th century, characterized
by a shift from mechanical and analogue electronic technology to computing,
digital record-keeping and communication [68]. Hallmarks of this third revolution are the
mass adoption of the internet / world wide web (“WWW”), online shopping, smartphones
and apps, social media, and the merger of artificial intelligence (AI), big data, the internet
of things (IoT), and various scientific disciplines. The ongoing automation of conventional
manufacturing and industrial practices, using modern smart technology is often termed
“Industry 4.0” [69].
It seems that these technological “revolutions” launch at increasing speed, narrowing
their succession from centuries at the start to, nowadays, only a few decades. In the eyes
of many futurists, a strong candidate for the next, or fifth, still somewhat silent industrial
revolution, might pivot around “decentralization” and “virtualization”.
Also, the century-old scientific culture has notably evolved over the recent decades;
there is an increasing institutional pressure on academics to optimize their publicationIn addition, while originating from good intentions, funder-driven open-access initiatives
have produced a plethora of new journals and conferences, which often display a “predator”
mindset towards maximizing the collection of fees from authors, while grossly
deprioritizing scientific integrity.
As an unquestionably detrimental fallout, researchers are implicitly pushed to artificially
spread out their findings over several publications, so that an abundance of manuscripts
hits the finite bandwidth of readers, leaving even possibly valuable efforts broadly
unnoticed. Moreover, a limited number of qualified referees tend to increasingly opt out
of an exuberant number of requests for review, so the delicate assessment process is exhaustively
delayed, or delegated down the ranks to less experienced experts.
The interplay of the rather manic hunt for metrics and unhealthy proliferation of subprime
publication outlets is accompanied by an insufficient methodological and experimental
verification by independent peers, and, occasionally, even by the very authors.
Common malpractices intervening with sound replication by other groups are rooted, for
instance, in improper experimental design, insufficient documentation of methods, incomplete
provision of (raw) data [70, 71], inappropriate statistical analysis [72-75] and deceptive
researcher bias [76-78]. Such highly undesirable trends are associated with the infamous
“reproducibility crisis” [79-84], which, depending on the field, leads to unfair academic
competition, significant economic losses, and even fatal damage to patients.
Furthermore, rising economic pressures on research institutions to raise external
funding entangle their researchers in very time-consuming grant preparation. It is an open
secret that these statistically overwhelmingly unsuccessful efforts are usually supported
by contracting costly ghost writers that tend to be largely unfamiliar with the core research
topic, but draw down a considerable share from the already tight research budgets in
many academic environments. Consequently, steadily increasing numbers of high-caliber
proposal submissions meet severely limited and rather static budgets through publicly
funded programs, causing counterproductive frustration by researchers, and a perceived
poor, or even objectively negative return on investment [85].
This paper outlines a new avenue to substantially improve research endeavors, encompassing
aspects of funding, dissemination, accessibility, management, governance
and exploitation, by the blockchain technology stack that has swiftly emerged since its
vastly unnoticed origins in the later 2000s. The proposed concept builds on its foundational,
decentralized setup, trust generation, tokenization, combined with the swiftly expanding
simulation capability in science and RTD through digital twins [86-88]; these are
virtual representations which serve as the real-time digital equivalents of physical objects
or processes. At the present state of the art, such digital twins may be mostly available in
engineering and physical sciences, with rapidly accelerating progress to be anticipated in
the life sciences. Note that a few promising and similar minded initiatives have already
formed, focusing on the publication aspects [89-92], data exchange [93], and broader topics
such as funding, replication, NFT-tokenomics, and DAOs also covered in this work
[94-98].
The next section covers relevant aspects of blockchain technology, specifically tokenization,
oracles, prediction markets, automated market makers (AMMs), curation and decentralized
finance (“DeFi”), which the traditional research might not be very familiar
with. Based on these ingredients, the novel concept of decentralized science (“DeSci”) [99,
100] is developed, including the toolbox comprising NFTs, reward and reputation systems,
crowdsourcing of research, community involvement by voting, arbitration and governance
schemes. (Note that the term “DeSci” is not uniquely defined, yet, and has been
used in somewhat different contexts [101-104]). Owing to the highly interdisciplinary
character of DeSci, terminology that may not be familiar for the general reader, but which
would be too complex to intersperse in the text and thus compromise readability, has been
amended with online links.

2. Blockchain

By virtue of its immutable, fully algorithmically controlled consensus mechanisms,
the distributed ledger technology (DLT) blockchain provides solid, battle-tested trust between
mutually unknown parties in a decentralized online environment without the need
for middlemen. The integrity of the blockchain is secured by demanding “skin in the
game”, i.e., the staking or personal investment from participants, and rewarding good
behavior, typically via its native digital assets and tokens; with a slight ironic touch, this
self-sustaining mechanism may arguably be viewed as the largest-scale behavioral incentivization
program in human history.
In conventional consensus architectures like proof-of-work (PoW) [105] in Bitcoin
and a few others [106-108] which are mainly focusing on payment function, the staked
asset are mined and awarded for investment in (specialized) computing infrastructure
and (electric) energy needed for solving cryptographic puzzles; many latest-generation
smart-contract blockchains, like Ethereum (after its next major upgrade expected in the
course of 2022) [109], Cardano [34], Polkadot [35], Solana [36], Terra [110], Avalanche
[111], Algorand [112], Stellar [113], Cosmos [114], Near [115] or Fantom [116] employ sophisticated
and extended (delegated / nominated) proof-of-stake (PoS) [117, 118] methods
that are secured by recruiting the collective of their users to amass a critically sized asset
pool. In either case, gaining a 51% majority is commonly required for being able to deliberately
manipulate decision making; gaining such ruling power on these blockchains
would involve massive financial means, rendering such nefarious tampering harshly lossmaking,
and thus pointless, at least for economically motivated actors (other than rogue
nations).
2.1. Tokenization
The possibility to immutably register and time stamp ownership of assets on a tamper-
proof distributed ledger has opened up the paradigm of tokenization [24, 65, 119].
Modalities of such tokens are transparently encoded in smart contracts and deterministically
executed on the blockchain’s virtual machine. A token economy directly enables access
to crowdfunding projects, and for the general public to take part in potentially highly
profitable investment tools, that were traditionally exclusive to the financial elites.
Fractional ownership through tokenized economies is slated to blur the lines between
owner and customer; for instance, social media giants like Facebook [120] (now “Meta”,
not to be confused with “Metamask” [121], the long-established cryptocurrency wallet
[122] for Ethereum) presently offer a value by connecting to the general or shared-interest
communities of account holders, while financially siphoning off profits arising from huge
network effects. In a tokenized, decentralized world, (social) network business models
could be run by code and community governance, thus letting the crowd, that epitomizes
the basis of value creation, reap the commercial fruit from its own inputs.
2.2. Oracles
The ability of blockchain technologies to interact with the world outside its own
ledger requires credible external data feeds, called oracles [123-129]. Such external information
might be provided from different types of sources and trust mechanisms. For instance,
exchange rates for currency trading pairs or stock values are intrinsically available
in digital form, while natively analog information, such as weather, traffic statistics or
time, needs to be converted for onboarding. Due to the finality of smart contracts that are
triggered by data inputs procured by these oracles, consensus needs to be fortified by
trust-endowing mechanisms, e.g., by sourcing data providers and validators from a sufficiently
large community of independent actors, and asking them for staking, and possibly
also time-locking, their crypto- or reputation tokens, to bestow credibility to their data
contributions to the oracles.

2.3. Prediction Markets

It has been shown in various contexts that the wisdom of the crowd can predict the
outcome future events with astonishing accuracy [130-140]. Community engagement can
thus generate tremendous value for economic decision making depending on, e.g., election
outcomes, sport results, or societal trends. This work investigates how similar staking
mechanisms, that are already applied for bestowing trust to PoS blockchains, can also be
harnessed to incentivize the sourcing of expertise and crowd intelligence.
2.4. DeFi
Leveraging collective wisdom in combination with liquidity from the crowd thus orchestrates
the price finding in spot and futures markets for trading physical or digital
commodities and assets through order books at the backbone of traditional finance
(“TradFi”); they inform decision making on investments or issuing loans in banking, for
putting a price tag on insurance policies, or for bookmaking in the betting industry.
Similarly, automated market makers (AMMs) are at the hub of decentralized finance
(“DeFi”) [141]. The widescale, sometimes rather turbulent success story of DeFi over the
recent years underpins the potential for value creation through blockchain technologies.
Nowadays many established financial institutions take a more positive stance on the
crypto space [142-144]. Decentralized exchanges (“DEXes”) [145, 146] and decentralized
applications (“Dapps”) [147] are blossoming, providing barrier-free global access to investment
vehicles, that historically have been a privilege of the wealthy few, while the
vast majority of the global population that either unbanked or exposed to high inflation
of their domestic currencies.
3. Decentralized Science (DeSci)
The objective of this paper is to scope viable avenues for fundamental science as well
as more commercially focused research and technology development (RTD) how to capitalize
on the continuing “blockchain revolution”. For the following considerations, it is
important to factor in the strong trends towards augmented (AR) and virtual reality (VR)
in the “metaverse” [42, 46], which represents a strong candidate for progressively influencing
science and RTD; this movement will be substantially fostered by the rapidly increasing
public availability of massive computing resources and data sets, e.g., through
cloud-based resources [148-152], of fab labs [153] for making “things”, of open accelerator
biochemical laboratories, and the proliferation of participatory research models [154-157].
Furthermore, decentralization through blockchain innovation dovetails with other exponential
technologies, such as artificial intelligence (AI), the internet of things (IoT) [158,
159], big data [160], digital manufacturing [161, 162], robotics, and the life sciences.
3.1. NFTs
Similar to the ownership of virtual assets, crowd engagement involves systematic
record keeping of the diverse contributions that are, in their entirety, crucial for the success
of research projects. Similar to creative art, such as paintings or music, scientific work
delivered by individuals or entities to the project should be captured by NFTs. These intellectual
artefacts may capture ideas, inventions, methods, materials, processes, modelling,
program code, and, last but not least, experimental and simulation results, and their
characterization, validation and optimization through new parameter sets. In addition,
accompanying activities, e.g., documentation, reporting, publication, communication, education,
promotion, as well as commercial and public engagement, should be recorded on
the ledger.
In the event where data is to be gathered for clinical research, patients may provide
their own samples; similar to current practices in the domain of genomics [163], patients
may then be entitled to a share of potential commercial revenues according to the value
of the data that can be attributed to them. Evidently, as for traditional science, e.g., involving
animal experiments or clinical trials, such studies must also be carried out under the
highest ethical, regulatory and privacy-preserving practices possible.

3.1.1. Content and Structure
The foundational idea behind DeSci is that, in a similar way to creative art or property,
different types of research outcomes are represented by NFTs; these contain a set of
basic elements outlined in Error! Reference source not found.: creator and owner, terms
and conditions for use, distribution algorithm of rewards and potential slashing. These
contributions are documented, e.g., in the form of a report that details methods and parameters
for proper reproduction of the posted experimental or simulation results, a
knowledge-graph [164] providing semantic context for facilitating machine reading, and
a link to an off-chain depository for relevant data; various decentralized storage platforms
[165-170] distinguish by their persistence mechanism, incentive structure, data retention
enforcement, level of decentrality, and consensus finding scheme [171].

Figure 1. Proposed structure of NFTs representing research outcomes. A standard should be defined
for a project or DeSci as a whole, which systematically captures information related to the research
outcomes, its embedding into the state-of-the art and tokenomics.

In the same way as scientific publications and patents, NFTs should point to relevant
state-of-the-art, including other NFTs; this structured NFT content will also support manual
or (semi-)automated linking to other preceding and upcoming NFTs. This way, NFTs
may be viewed as an advanced version of IP [172] rights where underlying, intangible
creations of the human intellect may not only be claimed, but also be executed, e.g., in
terms of automated payments triggered by oracles connected to smart contracts. Further
NFT fields also keep a dynamic record of the mandatory (minimum) stake and locking
period of the creator and / or owner, as well as tokens locked to the NFT by the crowd to
be utilized in reward or slashing algorithms, as stipulated in the project descriptor and
encoded in the blockchain executing transactions.
Scientific or commercial RTD projects are commonly organized in work packages
(WPs). With their features compiled in Error! Reference source not found., the outcomes
of WPs may be represented by NFTs, and the project itself as a collection of interconnected
NFTs. So-called “IP-NFT” constructs have been elaborated, with current emphasis on
managing data ownership and access in the biomedical space, specifically for development
of therapeutic drugs [173-175].
However, each project might have its own requirements and preferences on how to
handle its IP, such as methods, program code, data or designs. There are various opensource
license constructs available [176]. Blockchain may also preserve privacy, e.g., of
data sets, and also to robustly time-stamp IP to safely document prior art, essentially playing
the role of a tamper-proof electronic lab book for recording proof-of-knowledge or
freedom-to-operate for an invention. Eventually, the NFTs may set the foundation of a
patent application, which would mainly make sense in case competition ought to be mitigated
or royalties generated in more commercially focused projects. For initiatives pursuing
public Commons [177, 178] under open-source / open-access policies for outcomes,seeking patent protection might not necessarily be required. The underlying blockchain
technology would also facilitate time-locking or restricting access to IP-relevant project
information, for instance, by deploying privacy preserving techniques like multiparty
computation (“MPC”) [179] or homomorphic encryption [180]; these cryptographic techniques
would still allow its use for computation without disclosure of such private or proprietary
data.

3.1.2. Crowdsourcing Research Work on NFTs
Error! Reference source not found. sketches how NFTs are generated in a community-
based, participatory research approach. The project owner defines quantitative key
performance indicators (“qKPIs”) to accurately define the technical objectives of a WP /
NFT, a possibly multi-stage scheme for pre-selection and eventual ranking of multiple
submission, and delineates the formal framework, e.g., on the required stake, timelines
for delivery, and the reward and slashing procedure. Governance and arbitration panels
(see designated section further below) may be part of the decision-making structure.

Figure 2. Project as a collection of work packages (WPs) with their research outcomes represented
by NFTs. Crowdsourcing of quality outcomes is incentivized by bounties that are posted with reward
schedules and technical specification on KPIs and their validation. Reviewers are reworded
for ordering the submissions from the crowd in a competitive parallelization process.

expressing its outcomes via NFTs. The project owner initiates the process by posting technical
requirements in terms of quantitative key performance criteria (qKPIs), selection
process, staking and reward schedule. In response, the crowd submits proposals that they
need to stake for bestowing credibility, and to discourage spamming; the proposals received
are then ranked, either directly through the owner, or by a predetermined mechanism,
e.g., involving a committee or public vote. A single or a cohort of proposals are then
charged to carry out the WP and achieve the targets expressed as qKPIs, e.g., in a competitive
process.

3.1.3. Onboarding of NFTs to Projects
The selected initiatives then present their outcomes in form of a preliminary NFT that
is to be evaluated for its ranking amongst competing submissions, and its introduction to
the project (Error! Reference source not found.). The creator and / or owner of the pre-
NFTs are obliged to support the credibility of their research outcome by a time-locked
stake. The crowd, ideally comprising of independent experts, is then invited to thoroughly
validate the NFT, and strengthen the impact of their assessment by their own “skin-in-the
game”.

Figure 3. Introduction of new research NFT into science or RTD project. Any member of the crowd
can submit an NFT solving the challenge of a work package specified by the project. Other members
of the crowd independently validate the results. All these stakeholders put down a potentially timelocked
stake to bolster the credibility of their inputs. A consensus process then dismisses or accepts
the new NFT into the project results, represented by a network of NFTs.

on the quality and credibility of the new NFT, e.g., by a stake- and time-lock-weighted
majority vote; the process may be based on decentralized identification (DID) [181, 182]
for assigning crypto-wallets to individuals, and (optionally) on quadratic funding / voting
principles [183, 184] to somewhat lower the influence of economic heavyweights
(“whales”). The stakes are then either multiplied and issued, or (partially) lost to the project
treasury, depending on a positive or negative outcome of this stage.

3.1.4. Connecting and Appreciation of NFTs
Once onboarded to the project, a new, community-approved NFT is embedded into
the network of existing NFTs (Error! Reference source not found.). Starting with the root
information provided in each NFT (Error! Reference source not found.), links can be
added by their owners, or the crowd, through time-locked stakes at both ends of the connection.
The general idea is that the product of committed, time-locked stakes enter the
algorithm for issuing (one-off or periodic) rewards of an NFT (Error! Reference source not found.).

Figure 4. Anchoring of research NFT in existing project network and connection to future NFTs
through staked and time-locked links.

In the suggested model, the overall staking value at the ends of each link are pairwise
multiplied, and then aggregated over each NFT (Error! Reference source not found.),
possibly modulated by non-linear factors that saturate towards high values [184].

Figure 5. Rewarding of NFTs to stakeholders after their acceptance to the project. In its most basic
implementation, each link from a given “My NFT” to other members in the project’s NFT network
is rewarded in proportion to the total amount staked on the NFTs at its ends.

So, for instance, all 􀀁 NFTs of the project possess a total stake of 􀀂􀀃 may be indexed
by 􀀄, 􀀆 ∈ {1,2,3, … , 􀀁}. A (symmetrical) binary matrix Θ􀀃,􀀏 is filled displays a 1 at the position
(􀀄, 􀀆) for a connection between 􀀄 and 􀀆 ≠ 􀀄, otherwise zero. In the most basic implementation,
the value of each NFT 􀀄
􀀓􀀃 =
􀀂􀀃 ⋅ Σ 􀀗 ⋅ Θ􀀃,􀀏 ⋅ 􀀂􀀏
􀀘 􀀏
􀀙􀀚
Σ 􀀂􀀃 ⋅ Σ 􀀗 ⋅ Θ􀀃,􀀏 ⋅ 􀀂􀀏
􀀘 􀀏
􀀙􀀚
􀀘 􀀃
􀀙􀀚
⋅ 􀀛 (1)
is then derived from relative impact and the value of the entire project treasury 􀀛 while
setting 􀀗 to unity. In a more refined approach, this factor 􀀗 = 􀀗(􀀁, 􀀂􀀃, Θ􀀃,􀀏) may be a
function, which, for instance, similar to quadratic funding [183, 184], may be configured
for suppressing monopolization by a dominant player, in favor of decentralized decision
making in a diverse community. Furthermore, the parameters 􀀁, 􀀂􀀃, Θ􀀃,􀀏 and 􀀛, and thus
􀀓􀀃, might depend on the point in time, e.g., through the epochs introduced in the following.

3.2. Reward System
3.2.1. Treasury
The project maintains a reserve of tokens that are initially filled by the project owner,
which may be a commercial venture, private investor, funding agency, foundation, or by
a crowdfunding [185-187] campaign (Error! Reference source not found.). Tokens are
awarded according to a predefined rule set. Especially in research projects, it is typically
necessary to split the total pay-out for WPs and their NFTs into a guaranteed, upfront
payment after their selection, e.g., in order to pay salaries and bills, and a premium for
successful delivery after community assessment.+

Figure 6. Project treasury. To assure its economic sustainability, a project needs to strategically distribute
its initial (one-off start-up) funds and any follow-up income across the project execution and
exploitation phase, see also Error! Reference source not found.

Importantly, this treasury ought to stay open beyond the end with the delivery of the
final WP (Error! Reference source not found.), e.g., for funding upgrades, and for entertaining
promotional and commercialization activities; also, similar to royalties in intellectual
property (IP) [172] contributions, the owners and stakers may be rewarded according
to the sustainability of their NFT’s impact. Revenues may continue trickling into the treasury,
for instance, through trading profits, seigniorage [188] and arbitrage gains [189] on
project-associated cryptocurrencies.

3.2.2. Epochs
The time of project execution and its follow-up may then be partitioned into a sequence
of epochs, similar to the procedure for staking rewards in various PoS-type blockchains
(Error! Reference source not found.) [34, 35]. As already indicated in the context
of equation (1) on the valuation of NFTs, a fraction of the treasury, e.g., proportional to its
total stake, is allocated to each epoch, and distributed to the NFTs according to their relative,
token-weighted stake in the project.

Figure 7. Staking, trading, rewarding from project treasury through epochs representing a finite
time interval within the development and exploitation phase of a project (Error! Reference source
not found.). In each epoch, project tokens are spent, and income is collected to the project treasury.
A formula relating the balance on the treasury to rewards issued as a function of trusted parameters
accessible on the blockchain needs to be encoded in a smart contract.

3.3. Crowdsourcing of Research
The fundamental challenge in collaborating on research projects with possibly anonymous
or pseudonymous community members is trust; for this, the traditional scientific
community has established a culture where results are published with proper documentation
of data and sources to either support or dismiss the pitched scientific hypothesis.
Importantly, the research methods applied need to be adequately described for allowing
independent validation by peers.
3.3.1. On-Chaining of Research Outcomes
By its intrinsically digital nature, verification of submitted research outcomes in the
context of blockchain technology always needs to be carried out through computation,
ideally on decentralized networks. So, similar to multimedia recordings, trustful analogto-
digital (A/D) converters assuming the function of oracles are needed for on-chaining
results obtained from research carried out in the physical world.
The concept for on-chaining research outcomes to a blockchain elaborated in this
work predicates on the already very advanced, and swiftly emerging capability to virtualize
the actual world through simulation by means of the fast proliferation of unprecedently
powerful computing resources available to the public.

Figure 8. Trust in crowdsourcing of NFTs by staking. The blockchain implementation of a request
for a project contribution (eventually leading to a new NFT) requires a staked reward bounty to be
issued for successful delivery of the technical objectives, and a staked audit for assuring the integrity
of the smart contract. For a request that can be addressed by a purely virtual approach by a digital
twin, three basic levels of contributions can be crowdsourced, also by demanding stakes from contributors:
for refinement of the digital twin model, for generating and validating data submitted,
and for providing the usually comprehensive computing resources that is essential for performing
algorithmic design optimization in a highly multidimensional parameter space.

Error! Reference source not found. illustrates the concept of crowdsourcing research
through blockchain. Each step requires staking and time locking of tokens by the contributors
to underpin their credibility. A research goal is formulated in terms of objectively
verifiable parameters, e.g., qKPIs, and the crowd is incentivized by a clearly defined reward
schedule. An auditor certifies the integrity of the processes underlying evaluation
and remuneration from the project treasury. Also bug bounty [190, 191] programs can be
implemented, e.g., to fortify the soundness of the underlying smart contracts. Monetary
driven incentive schemes might be replaced or complemented by “meritocratic” reputation
systems [192, 193] that are already common in academic research, e.g., the (disputable)
Hirsch or h-factor [194], which is widely employed for quantifying research impact,
or global university rankings [195, 196].

The members of the crowd, possibly pre-selected via competitive tendering (Error!
Reference source not found.) or randomized selection, then measure and confirm the validity
of submitted research data. To construct an A/D interface for trustful on-chaining,
the project also sources realistic modelling and simulation to create a digital twin [86-88]
that can accurately virtualize experimental scenarios, including the statistical spread of
results due to tolerances of input parameters. For data validation, the recruitment of computational
resources is incentivized for decentralized validation of findings; the same
computational capabilities may then also be employed for numerical parameter optimization.
3.3.2. Digital Twins
Trustful onboarding of research outcomes to a blockchain primarily hinges on producing
a digital twin capable of realistic simulation of experiments. As, for instance, observed
in the massively expanding computer gaming arena, the interplay of computational
power and simulation methods already manages to produce an increasingly immersive,
3-dimensional user experience (UX); the notion “realistic” technically means that
the laws governing nature, combined with initial and boundary conditions, are accurately
reflected. While numerous projects, particularly in the domain of physical sciences and
classical engineering, already possess a solid track record on this itinerary towards virtualization,
other disciplines slated to follow along an exponential path. For the specific example
of microfluidic “Lab-on-a-Disc” systems for bioassay automation at the point-ofcare,
we published several implementations of digital twin technology, including methods
for their algorithmic performance optimization [197-202].
3.3.3. Compute-Enabled Oracles
While at its very foundation, blockchain, especially those implementing PoW like
Bitcoin, may be deemed a sophisticated incentivization scheme for computing resources.
However, in the interest of bandwidth and security, it is wise to devote this infrastructure
to transactions. Pricing structures, e.g., on the EVM, thus deter users from running comprehensive,
time- and memory consuming calculations on-chain.
In case a state change of the blockchain is to be induced by complex simulation of a
digital twin model, complex computation can be offloaded to decentralized oracle networks
(“DONs”) composed of nodes. Confidence in the validity of outcomes from these
compute-enabled oracles may, for instance, be established by open market solutions including
reputation scores derived from performance history, or network trusted nodes
[203].
3.3.4. Experimental Results
Many scientific fields, such as clinical research, the life and social sciences still mainly
rely on often extensive experimental campaigns on biosamples, animals or humans. In the
event where data is to be gathered for life science projects, patients may provide their own
data and the value of that data may be attributable to those patients if the data results in
products that make it to a marketplace. This is already happening within the genomics
domain [204].
In the context of sourcing data, it is worthwhile pointing out the composability of
financial instruments and processes within the smart contract domain in DeFi. In DeSci,
this compelling opportunity may be fostered through streamlined formats for data files
and their exchange, e.g., on the analogy of Ethereum’s ERC-20 [205], ERC-721 [206, 207]
or EIP [208] standards.
In these cases, a hybrid approach needs to be implemented: as previously, submission
of empirical, real-world data sets needs to be supported by a time-locked stake from
a number of independently acquired data sets to endow optimum credibility and protection,
e.g., against fabrication of data or collusion of nefarious actors; data submissions are
then filtered through a digital twin in the form of data analytical algorithms which takeinto account factors like stake and reputation of its producers and voters from the crowd,
as well as plausibility between data sets as a prerequisite for on-boarding them as NFTs.
3.3.5. International Data Spaces
The International Data Spaces (IDS) reference architecture [209, 210] deals with data
sovereignty, secure data exchange and sharing using the IDS Connector concept. Some of
the features, such as decentralization and distribution of trust, are compatible in both,
blockchain and IDS systems. Blockchain technology can therefore act as a key enabler for
maintaining shared data assets in an IDS environment. Here, large datasets are made
available through its IDS connector, where the shared data asset might encompass a hash
code or NFT to verify a larger file (e.g., a complex federated model).
When a business community decides to store shared data assets on a blockchain and
make this data accessible to the IDS ecosystem. 1) The blockchain acting as a data consumer
registers certain data from the IDS ecosystem on the cryptographically secured,
distributed ledger. For instance, a measurement, which has taken place, or certain sensor
data. 2) The blockchain acting as a data provider makes data accessible to other parties
in the IDS ecosystem, for instance, by recording certain transaction data.
3.4. Community Involvement
3.4.1. Participatory Models
On top of the benefits in quality, efficiency and costs through eliminating the need
for middlemen, crowdsourcing in DeSci bears the opportunity of running science and
RTD projects in a more inclusive and democratic approach. Following this game-changing
paradigm shift takes research out of its many silos into a global community, thus triggering
crucial network effects to enhance its value in a non-linear manner (Metcalfe's law,)
[211]; sourcing the wisdom of the crowd is broadly recognized for improving the quality
and credibility of research outcomes. The active involvement of a community also tends
to substantially increase their commitment to deliver on the technical objectives, as well
as bolstering the long-term impact of a research project. Participatory models also facilitate
the adoption of (quasi) standards and platform strategies [212] that are crucial to tap
into important economy-of-scale effects [213, 214].
3.4.2. Governance
While a “code is law” mantra is feverishly nurtured among crypto purists, it is also
widely acknowledged that governance structures need to be established for decision-making
and conflict resolution in community-led projects. Particularly in the process of onchaining
research outcomes, decisions, e.g., on quality, selections and rankings for issuing
rewards by smart contracts, may be challenged; there is always a chance that an originally
scoped algorithm might not fully echo the actual situation, conceivably leading to unfair
or even random outcomes, and thus undermining the vital community spirit and discouraging
contributors.
Moreover, community-agreed improvements of the ledger-encoded rule set should
be permitted. In this regard, DeSci can be well guided by existing mechanisms, like community-
appointed judges, arbitration panels and technical councils wielding distinct veto
privileges; such, at times, token-weighted governance structures have already been successfully
introduced in various blockchain ecosystems [215-222], in these cases mainly to
be able to upgrade their own ecosystem; they have, after some painful lessons were learnt
[223, 224], led to the formation of a rapidly growing number of chiefless, entirely community
governed decentralized autonomous organizations (DAOs) [225-230]. Several topnotch
universities, including Harvard, MIT, UC Berkeley and Oxford, have launched Education
DAO (“EduDAO”) [231, 232] with the objective of tackling the prevalent “funding
crisis and skills gap”. Rather convenient development kits and exemplary application
cases for composing and customizing the (Science / RTD) DAOs conceived in this workfrom its constituent elements, e.g., for minting designated crypto-tokens and voting on a
treasury, are publicly available [233-235].
4. Summary and Outlook
4.1. Summary
The swiftly growing Web3 technology blockchain ecosystem provides a highly potent
toolset for seminally upgrading the legacy organization of academic research. For the
proposed concept of decentralized science (DeSci), research projects are interpreted as
networks of non-fungible tokens (NFTs) representing the outcome of its work packages
and a set of attributes, such as its creator(s), ownership, stakes, scientific roots and semantics;
each crowdsourced contribution to these NFTs, such as the original idea, conceptualization,
improvements, scientific methodology, experimentation, simulation, analysis,
validation, verification, documentation, forecasting and promotion, is recorded and timestamped
in conjunction with metadata, on a distributed, tamper-free, and immutable public
ledger. Adapting mechanisms already established in other blockchains, the underlying
tokenomics, governance and arbitration schemes can be geared to incentivize broad participation
of competent experts and their good behavior in the very spirit of the project
objectives.
With the presented, crypto-enabled mechanisms, the quality, credibility, efficiency,
transparency, inclusiveness, impact, and sustainability of scientific projects can be distinctly
improved, hence offering a much-needed resolution to the progressively endemic
funding, credibility and sustainability crisis of science. Due to the intrinsically digital nature
of the distributed ledger technology, disciplines that lend themselves to virtualization
enabled by digital twins, such as engineering or physical sciences, are deemed easier to
onchain than fields like the life or social sciences, which may need to pursue an empirical
or hybrid approach, such as the life sciences.
Proper cross-checking, especially of experimental data or real-world sensors, by independent,
even pseudonymous peers can be decidedly improved through tokenization
mechanisms known from decentralized finance (DeFi). By incorporating commercially
critical mechanisms for confidentiality and intellectual property (IP), e.g., through introduction
of privacy elements, digital identity, curation services and access-restricted blockchain
setups, the above-described instruments enabling DeSci may readily be extended to
application-focused research and technology development (RTD).
The combined action of NFTs, competitive validation and virtualization through digital
twins on trusted nodes forms the ideal link to connect science and RTD to blockchain,
unleashing it from its academic and institutional siloes, and laying the groundwork for
effective self-administration of the entire research stack in decentralized autonomous organization
(RTD or Science DAOs); similar to the coding scene, a new class of freelance
scientist may emerge. Other mechanisms, that are already used on DeFi, may well be incorporated
into DeSci, e.g., crowdfunding.
4.2. Opportunity, Risks and Barriers
Blockchain constitutes a still comparatively young technology, the maturity of its
technological backbone, application space and application space may somewhat compare
with the internet in the mid-1990s: a space marked by rather poor user experience mainly
populated by tech-savvy experts, still lacking smartphones and killer apps like online
shopping or social media. On the one hand, this early stage of development offers great
opportunity. New applications that are not even on the radar, yet may disrupt entire businesses.
For instance, if only a small fraction of present global gold reserves were invested
in cryptoassets, such as Bitcoin, the valuation of its tokens would inevitably have to increase
by orders of magnitude. On the other hand, the still budding ecosystem has suffered
from serious vulnerabilities and major exploits; occasionally, downward-incompatible
upgrades were infuriating developers, even leading to community-splitting hardforks.

In an effort to solve the so-called “trilemma” [236], certain blockchains opted to sacrifice
one of its cornerstones of decentralization, speed and security for another to optimize
their performance towards their main applications. Various second layer solutions
have been suggested [236-245]. Bridges between blockchains, and side or parachains have
been constructed to promote interoperability [246-248]. (Decentralized) digital identity
(DID) and privacy solutions, e.g., via conventional or even decentralized mechanisms,
such as, zero-knowledge proofs [249], have been elaborated to combat monopolization, as
well as to conform with legally compulsory know-your-customer (KYC) and anti-moneylaundering
(AML) practices, or nationally often starkly diverging legislation on securities.
The futuristic Internet Computer [250, 251] initiative describes a decentralized alternative
to currently dominating corporate cloud services that, in practice, also still play a role in
many blockchains [148, 149].
There are further risks, such as the still rather elevated short-term volatility of cryptocurrencies,
which is often closely tied to poorly understood behavioral economics of
market participants, and the competition of central bank digital currencies (CBDCs) that
are expected to be launched in the near future. Imminent regulatory pressures may force
cryptocurrencies to truly decentralize, and to revisit the risk strategy of system-critical
stablecoins. Blockchains may also lose the entire value of their tokens after token rugpulls,
along a rapidly emerging innovation, and only projects having a profound loyalty
and size of their user base, high utility and speed may survive.
Other threats are cyberattacks, e.g., of (distributed) denial of service (DDoS) [252], on
blockchains, especially by non-economical players, e.g., nation states, or the power games
and collective, nefarious usage of coins by economically dominant “whales”. While the
frequently practiced open-source character of code gives transparency, it might induce
vulnerabilities to blockchains and smart contracts. As cryptographically secured systems,
blockchains may also be exposed to future quantum computing capabilities; note that various
strategies for post-quantum cryptography have been elaborated [253]. So far, history
has taught that the blockchain economy may take substantial blows on the chin from such
setbacks, but emerges even stronger in the aftermath.
For on-chaining real world inputs, significant opportunity for collusion and or fabrication
of data inputs exists. Careful design of crypto-economic incentives will be required
but it seems possible to, at the very least, increase the reliability and auditability of sensor
outputs that may be inserted into the scientific literature and records.
Extra ethical and privacy requirements are at play when patient related data is involved
in scientific and medical processes; as such, it will be critical to ensure that decentralized
science and medicine abides by the highest regulatory standards possible. Deviation
from such rigorous regulatory processes for acquiring such data may prompt authorities
to block approval of resultant therapeutic and diagnostic products.
To disperse these quite valid concerns, which often revolve in the context of compliance
with ESG (environment, social, governance), major blockchains, with the most notable
exception of Bitcoin, already run, or are planned to transition to Proof-of-Stake (PoS)
or similar consensus mechanisms that are radically lowering their often-quoted carbon
footprint [254]. Some blockchain initiatives even, immediately or indirectly, incentivize
the protection of global commons, such as poverty, education, charity, climate, and biodiversity
[255-258]. Overall, the tremendous potential of blockchain is recognized across an
increasing number of traditional industries. Even in recent years, several key stakeholders
have shifted from blanket dismissal of blockchain’s utility to adamant supporters.
From a scientific perspective, it is difficult to keep track of the far-reaching spectrum
of the technology, and distinguish facts from interest-driven announcements, as much of
the communication is channeled by frequently pseudonymous sources through social media,
websites, and non-peer reviewed whitepapers. Certainly, there will be massive inertia
in the scientific community to migrate to an unprecedented alternative. The disruptive
model for DeSci (version 0.1) proposed here will, quite expectedly, be encountered by
some frequently valid skepticism; such critical reception will necessarily raise the awareness
of present issues, and trigger fruitful discussions leading to widely backedadvancements and refinements. Nevertheless, the giant benefits of integrating the 21st century
Web3 technology blockchain to seminally improve the legacy culture of science and
RTD in terms of the quality, credibility, transparency, efficiency, sustainability, community
engagement and adoption will hopefully be increasingly recognized over time.
Author Contributions: All authors edited and approved the manuscript. JD developed the concept,
authored the first full draft and performed the final edit. MC engaged in much discussion with the
authors regarding DeSci and provided insight & additions with respect to biomedical translation
and addressed issues related to data integrity and reproducibility. RW contributed to international
data spaces and blockchain. SB revised the manuscript and contributed to its scientific context.
Conflicts of Interest: All authors declare that they do not have a conflict of interest. MC is a founder
of Fleming Protocol (https://flemingprotocol.io), a company working to develop products in the decentralized
science ecosystem. SB is the sole proprietor and owner of Blockchain for Science GmbH
(http://www.blockchainforscience.com).