On the Use of Blockchain Technology to Improve the Reproducibility of

Preclinical Research Experiments

Eduardo C. Oliveira

1 a

, Rafael Z. Frantz

1 b

, Carlos Molina-Jim

enez

2 c

, Thiago Heck

1 d

Sandro Sawicki

1 e

and Fabricia Roos-Frantz

1 f

More Innovation Space, Uniju

ı University, Rua do Com

ercio, 3000, Universit

ario, Iju

ı/RS, 98700-000, Brazil

Department of Computer Science and Technology, University of Cambridge. CB2 1TN, Cambridge, U.K.

Keywords:

Blockchain, Distributed Ledger, Data Analysis, Decentralised Technology, Experiments Irreproducibility,

Experiments Reproducibility, DApps, DStorage, IPFS, Smart Contracts.

Abstract:

Preclinical research is crucial for the advancement of life sciences. The use of experimental animal models in

basic health sciences historically helped humanity to understand the pathological mechanisms of diseases and

to develop therapeutic strategies, medicines and vaccines. Progress in this direction depends, to a large de-

gree, on experimentation. Therefore, it is highly desirable that research experiments conducted on preclinical

research are reproducible. Regrettably, a large number of experiments are not reproducible. Factors leading

to irreproducible research on preclinical studies fall into four major categories: Biological reagents and ref-

erence materials, study design, data analysis and reporting and laboratory protocols. The data analysis and

reporting category concentrates 25.5% of the total factors. Is estimated that $7.19 billions of the total research

budget is funding irreproducible experiments. It is widely acknowledged that sharing experimental data be-

tween different institutions and cooperative researchers worldwide helps in experiment reproducibility which

results in science and technology acceleration and innovation. Data sharing involves several data operations:

The researcher needs to collect the data, protect it to prevent accidental and malicious deletion and corruption

and make it available to colleagues, possibly, to the general public. The execution of these operations is cum-

bersome and error prone unless appropriate technology is used. This paper suggests and explores the use of

blockchain to improve the reproducibility of experiments.A blockchain is a decentralised database that offers

several properties that can be used advantageously in the collection, storage and sharing of experimental data,

for instance, it prevents deletion.

1 INTRODUCTION

Preclinical research is a key aspect for the advance-

ment of life sciences. The use of experimental ani-

mal models in the basic health sciences historically

helped the humanity to ﬁnd pathological mechanisms

of different diseases as well has promoted the devel-

opment of many therapeutic strategies, medicines and

vaccines.

A crucial aspect of preclinical investigation is the

integrity of the research which strongly encourages

https://orcid.org/0000-0001-5283-7732

https://orcid.org/0000-0003-3740-7560

https://orcid.org/0000-0002-3617-8287

https://orcid.org/0000-0002-1242-5423

https://orcid.org/0000-0002-7960-0775

https://orcid.org/0000-0001-9514-6560

the achievement of the 3 R’s: Reﬁnement, Reduce

and Replace on the use of animal models as much as

possible.

In this way, ethically and scientiﬁcally, the qual-

ity and trustworthiness on the results is measured by

the methodological accuracy of the experiments, thus

research data must be reported with transparency and

submitted to the scrutiny of regulatory bodies, scien-

tiﬁc community, publishers, reviewers and readers.

It is paramount that experiments conducted on

preclinical research be reproducible. Sharing exper-

imental data between institutions and cooperative re-

searchers worldwide may turn science and technol-

ogy innovation in the health ﬁeld faster and efﬁcient

to solve different demands of the population.

However, a large number of experiments are not

reproducible. According to Freedman et al (2015)

a staggering rate ranging from 51% to 89% is esti-

Oliveira, E., Frantz, R., Molina-Jiménez, C., Heck, T., Sawicki, S. and Roos-Frantz, F.

On the Use of Blockchain Technology to Improve the Reproducibility of Preclinical Research Experiments.

DOI: 10.5220/0011851700003467

In Proceedings of the 25th International Conference on Enterprise Information Systems (ICEIS 2023) - Volume 1, pages 173-179

ISBN: 978-989-758-648-4; ISSN: 2184-4992

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

173

mated, a scenario that represents a big challenge for

the ﬁeld (Freedman et al., 2015).

Factors leading to irreproducible research on pre-

clinical studies fall into four major categories: The

ﬁrst category (C1) represents biological reagents and

reference materials, the second category (C2) is re-

lated to study design, the third category (C3) to data

analysis and reporting and the fourth category (C4) to

laboratory protocols.

Biological reagents and reference materials con-

centrates 36.1% of the failures in reproducing experi-

ments, usually related to issues with non validated or

contaminated reagents and inadequacies in biological

materials handling.

Study design presents 27.6% of the factors leading

to irreproducibility, it is largely associated to incon-

sistencies or lack of methods for sampling, randomi-

sation and blinding.

Data analysis and reporting concentrates 25.5% of

the total factors and is linked to poor statistical meth-

ods, unclear criteria for missing data, data inclusion

and exclusion and outliers handling. Topics such as

absence of disclosure of full results and primary data

also constitutes factors in this category.

Laboratory protocols accumulates 10.8% of the

overall causes and correlates to lab operations not ad-

hering to standards and best practices when conduct-

ing preclinical experiments.

This article focuses on the Data Analysis and Re-

porting category of factors leading to irreproducible

animal research.

The following statistics illustrate the economic

impact of the reproducibility problem: The annual

spend on preclinical research in the United States

alone is estimated to be $56.4 billions, applying a ir-

reproducibility rate of 50% means that $28.2 billions

of the total budget is funding researches that are not

reproducible, $7.19 billions only on the Data Analysis

and Reporting category.

Normally, critical data is generated, compiled,

analysed and reported at each stage of an experiment,

in many cases this data in manually written in paper

notebooks, or digitally recorded in spreadsheets, word

processors kept locally on the researcher computer or

shared by email or a cloud based storage.

To develop his or her activities a researcher needs

to preserve data collected and avoid data deletion and

tampering, get access to experiment data anytime,

anywhere, track changes in data, openly publish data

and manage data life cycle.

The blockchain technology is a digital ledger

where transactions are stored in a chronologi-

cally linked sequence of blocks (hence the name

blockchain), blocks are stored and processed at nodes

on a decentralised network, every node on the net-

work can receive transactions, all the participant

nodes should agree about the ﬁnal state of every trans-

action, accept or reject it, a process called consensus,

once accepted a transaction is permanently recorded

on the digital ledger.

Blocks are data structures that stores a speciﬁc

amount of transaction records, minting is the process

of creating and adding new blocks to the blockchain.

A public or open blockchain operates over the In-

ternet fabric with geographically dispersed nodes, a

blockchain protocol implements a code based opera-

tion with no central authority, nodes are free to enter

or leave the network. A incentive mechanism rewards

participant nodes and provides the proper balance of

activity on the network to protect it from malicious

attackers.

Bitcoin is the ﬁrst large scale implementation of

blockchain to gain mainstream adoption and along

with Ethereum represents the bedrock for several use

cases supporting the worldwide ﬂow of ﬁnancial as-

sets and fostering new applications and business mod-

els in property rights, healthcare, logistics, manufac-

turing, energy and transportation among others initia-

tives (Nakamoto, 2009).

The Blockchains feature universal principles that

can address the needs of a researcher regarding the

handling of data generated from experiments: Im-

mutability, resilience, traceability, decentralisation

and programmability. There is a set of technologies

with built-in functionalities that relates directly with

each of the universal principles, connecting them to

the needs of a preclinical researcher.

Blockchain networks are now widespread present

and adoption is attracting ﬁnancial and intellectual in-

vestments in large scale.

Applying this computational strategy in the shar-

ing of data between researchers might help science

and technology development and, at the same time,

contribute to the improvement of animal research con-

sidering the 3R’s international goals. In this paper we

explore the use of blockchain to improve the repro-

ducibility of experiments.

Herein, we present the study organised as follows:

Section 2, discusses the related works approaching

the preclinical research irreproducibility crisis; Sec-

tion 3 provides an overview on blockchain technolo-

gies that supports our proposal; Section 4, introduces

our proposal based on blockchain to improve the

reproducibility of preclinical research experiments;

and, ﬁnally, Section 5 concludes this paper.

ICEIS 2023 - 25th International Conference on Enterprise Information Systems

174

2 RELATED WORK

To address the reproducibility problem, several ap-

proaches have been proposed in the literature. To

achieve a higher level of reproducibility in preclinical

research Landis et al. (2012) recommend the adoption

of a core set of reporting standards for experimental

group randomisation, blinding processes for investi-

gators and animal care takers, sample size estimation,

statistical models and a priori criteria for stopping

data collection, inclusion, exclusion and removal of

data, handling of outliers and missing data (Landis

et al., 2012).

Vasilevsky et al. (2013) acknowledge the im-

portant contribution of guidelines for reporting of in

vivo experiments such as the ARRIVE guideline (Per-

cie du Sert et al., 2020) that was proposed to improve

research reproducibility. The authors point the lack

of proper unique identiﬁcation of material resources

such antibodies and model organisms as a critical ir-

reproducibility factor yet not properly addressed by

the current guidelines. They propose to increase the

identiﬁability through a better tracking of research re-

sources using electronic lab notebooks, management

software and resource sharing repositories that can

store and report the unique identities for each resource

through the entire experiment cycle (Vasilevsky et al.,

2013).

The study of Collins et al. (2014) explore ac-

tions the US National Institute of Health (NIH) is

planning to enhance reproducibility in preclinical re-

search, such as training on research reproducibility,

transparency and study design, implementation of

checklists to assert adequate experimental design on

the evaluation of grant applications, a big data ini-

tiative, the Data Discovery Index (DDI), for shar-

ing of unpublished primary research data sets and an

online open forum for comments on published arti-

cles, PubMed Commons, Manolagas et al. (2014)

subscribe on these recommendations for the bone re-

search ﬁeld (Collins and Tabak, 2014) (Manolagas

and Kronenberg, 2014).

To increase the value of research Ioannidis et al.

(2014) recommends the public availability of research

protocols, original data and statistical analysis scripts

of experiments, additionally public reviews and peri-

odic comparisons between study protocols and results

might provide valuable feedback for investigators and

journals (Ioannidis et al., 2014).

Elaborating on the importance of the reproducibil-

ity as a foundational component for advancements in

preclinical research Begley et al. (2015) points that

due to the biological variability of the subject sys-

tems is not possible to achieve precisely replication

Table 1: Categories approached by the literature.

Categories

Authors C1 C2 C3 C4

Landis et al., 2012 ✓ ✓

Vasilevsky et al., 2013 ✓ ✓

Collins and Tabak, 2014 ✓ ✓

Ioannidis et al., 2014 ✓ ✓

Manolagas and Kronenberg, 2014 ✓ ✓ ✓

Freedman et al., 2015 ✓ ✓ ✓ ✓

Begley and Ioannidis, 2015 ✓ ✓ ✓

Curtis et al., 2015 ✓ ✓

Our Proposal ✓

of experiments, but is still vital that the major ﬁnd-

ings of the original experiment could be confronted

with the results of further iterations while accepting

a reasonable uncertainty degree, states that address-

ing the challenge of the reproducibility crisis requires

a multidisciplinary approach by the stakeholders and

advocates for more rigour in study design, training,

statistical methods and primary data sharing (Begley

and Ioannidis, 2015).

Providing detailed guidance for experimental de-

sign and data handling Curtis et al. (2015) pro-

poses the eradication of undesirable and unneces-

sary sources of errors to promote experimental repro-

ducibility (Curtis et al., 2015).

3 BACKGROUND

In order to build a disruptive model is important to

put in perspective the core tenets of decentralisation

in the context of application architecture.

Decentralised applications, commonly referred as

DApps, are applications running on top of decen-

tralised peer to peer networks (Raval, 2016). As a

regular application, a DApp requires a front end in-

terface for user interaction, a middleware for business

logic processing and a persistence layer for read and

write transactions, the difference is that DApps relies

on a underlying decentralised infrastructure to get ac-

cess to such resources.

Regarding computing and storage services, de-

centralised systems consume resources provided by

nodes distributed through a network, the free ﬂow of

nodes entering and exiting the network is coordinated

by algorithms, at the operations level, this distribution

provides resources availability and fault tolerance.

A peculiar dynamics is at play on decentralised

networks, constituent parts are not obliged to trust

each other to collaborate, there is no central entity to

arbitrate the network behaviour, therefore to promote

and sustain decentralisation some strategies were de-

veloped, for instance, incentive mechanisms are used

to entice the addition of new nodes and discourage the

On the Use of Blockchain Technology to Improve the Reproducibility of Preclinical Research Experiments

175

participation of rogue actors. For data integrity, con-

sensus mechanisms are implemented to achieve a sin-

gle version of the truth for every transaction among all

participant nodes. To achieve a high degree of trans-

parency and trust, usually the code supporting this

type of governing model is open sourced and backed

by a active community of developers.

At the application level, a DApp implements the

business logic to execute processes through a smart

contract, a special program that runs on top of a

blockchain for the execution of well deﬁned instruc-

tions, like agreements between parts in a traditional

analogical contract (Szabo, 1994). Smart contracts

presents important features: Automation, predictabil-

ity, publicity and privacy.

Automation means that once a smart contract is

deployed and invoked, it will be executed if all logical

conditions are met, no human intervention is required.

Predictability relates to the precise outcomes gen-

erated by the execution of a smart contract, there is

no room for bias or misinterpretation of the contract,

unlike their traditional counterparts.

Publicity is grounded in the intrinsic nature of the

public blockchain, the smart contract and associated

transactions can be tracked and audited seamlessly,

furthermore the terms of a smart contract are openly

available for scrutiny.

Privacy is achieved through the pseudonymous

condition of users at a public blockchain, transac-

tions executed through a smart contract are linked to

a blockchain address and not to a person’s identity.

A blockchain network delivers processing power

and a minimal storage space for code and state of a

smart contract, usually a decentralised storage (dStor-

age) is used in conjunction with the blockchain to pro-

vide off chain storage capacity.

The Interplanetary File System (IPFS) is a proto-

col designed to provide decentralised storage services

for application deployments and secure distribution of

large data volumes, featuring version control and a

location independent name space, ﬁles and other con-

tent types are accessed through a content identiﬁer -

CID (Benet, 2022). Based on a peer to peer network

without central authority, it is not required that nodes

trust each other to connect and transfer content.

Blokchain is the central technology for immutable

transactions recording in the context of data han-

dling in our proposal of a infrastructure for handling

experiments data. It is a innovative approach that

emerged from the convergence of several well estab-

lished methods from the Mathematics and Computer

Science bodies of knowledge such as cryptography

hashes, asymmetrical encryption and peer to peer net-

works.

For a broader comprehension of the subject key

deﬁnitions are provided (Daniel Hellwig, 2020):

• Digital Ledger Technology (DLT): Is a data

repository that chronologically stores transactions

in sequence, it is a digital version of the ledger

book used to record property rights or ﬁnancial

records.

• Blockchain: A subset of the DLTs where transac-

tions are recorded. The blockchain is a database

containing all transactions. A copy of the

blockchain is stored in every node of the net-

work for availability and validation purposes. Bit-

coin and Ethereum are the most prevalent cases of

blockchains currently in use.

• Block: Chronologically linked data structures.

Each block consolidates transactions that will

be appended to the blockchain as a unit, the

blockchain protocol speciﬁes the number of trans-

actions or the block size. Only valid blocks are

appended to the blockchain, validation is obtained

through a consensus mechanism.

• Time Stamping: Every transaction and every

block are time stamped, allowing to trace back the

proper order of transaction events since the incep-

tion of the blockchain.

• Hash: Hash is a mathematical function that re-

ceives an arbitrary size input and generates a ﬁxed

size unique output. Hashes are heavily deployed

in blockchain operations for data integrity veri-

ﬁcation and block linkage. The hash algorithm

SHA256 generates a 64 bytes output and its used

for hash operations in Bitcoin (NIST, 2015).

• Merkle Tree: Is a tree like hierarchical data struc-

ture with a unique root, each level of the tree

stores hashes computed from hashes of the pre-

vious level. In the context of blockchains, Merkle

Trees holds the hashes of every transaction in the

current block plus the root hash of the previous

block providing a linkage of every block in the

chain for integrity veriﬁcation, preventing a mali-

cious attacker to tamper data.

• Consensus: Every node on the network can

accept transactions, but each transaction on a

blockchain is only committed if the block is ap-

proved through a validation process, the network

nodes must agree about the the block to be com-

mitted, this is settled trough a consensus mech-

anism, Proof of Work (PoS) and Proof of Stake

(PoW) are the most prevalent protocols. On Proof

of Work (PoW), all the participant nodes com-

petes to win a compute intensive mathematical

challenge, the winer node mints the block and ap-

pends it to the blockchain. On Proof of Stake

ICEIS 2023 - 25th International Conference on Enterprise Information Systems

176

(PoS) blockchains randomly selected participants

are obliged to hold a certain amount of coins or

tokens to participate in the validation process, the

stake of each participant deﬁnes the likelihood of

wining the validation.

Blockchains can also be deﬁned by the gover-

nance model:

• Public Blockchains: Participant nodes can freely

join or leave the network, there is no control or

veto to add or remove a node and no central au-

thority for data veriﬁcation, transactions are trans-

parent to users and outsiders. Trust and privacy

are not enforced by code or processes. Examples:

Bitcoin and Ethereum.

• Private Blockchains: A central authority regu-

lates the enrolment process and life cycle of par-

ticipant nodes, all participants are acknowledged

and the governance process provides a high de-

gree of trust among the parts. Applies mostly

to enterprise use cases and consortiums like the

CDBC (Central Bank Digital Currency). Hyper-

ledger and Ethereum Enterprise are the main play-

ers.

The ﬁrst successful use case for the applica-

tion of the blockchain technology, Bitcoin, devel-

oped by an individual or collective known as Satoshi

Nakamoto released in 2015 a network protocol and

digital coin currently in use by individuals and in-

stitutions, storing economic value and moving ﬁnan-

cial assets across borders worldwide. Bitcoin sparked

a wave of initiatives on decentralisation and new

plaforms such as Ethereum are ﬂourishing creating an

ecosystem for the digital economy and the Web 3.0

(Nakamoto, 2009).

4 OUR PROPOSAL

In this section we provide a detailed discussion about

the main needs of a researcher related to data gener-

ated by experiments, we also contextualise the uni-

versal principles of the blockchain that are relevant to

the improvement of experiments reproducibility and

present a decentralised architecture for an application

supporting the recording of experiments

4.1 Approaching Data

On conducting experimental preclinical studies, a re-

searcher needs to preserve the integrity of generated

data avoiding deletion or tampering or even fraudu-

lent manipulation, preferably the researcher should

safely access data anytime, anywhere and not only

on lab premises. For accuracy of reporting is also

fundamental that a researcher could track changes in

data reported and most important could openly pub-

lish data with transparency.

4.2 Blockchain Properties

Universal principles featured in blockchain imple-

mentations:

• Immutability: An intrinsic property of

blockchain based systems, records can only

be appended to the database. Once a transaction

record is committed to a valid block it cannot be

modiﬁed or deleted. This provides the proper

safeguard and integrity for experiment’s data,

avoiding further changes or even frauds that could

harm research results.

• Resiliency: Blockchain architectures are based

on peer to peer networks where each node plays

a egalitarian role, holding the full blockchain

database, providing resilience and fault tolerance,

this features provides a layer of resilience and

availability against failures and malicious attacks,

protecting research records from deliberate or ac-

cidental deletions or corruption.

• Traceability: Every transaction committed to the

blockchain is timestamped and all the records

are openly available for tracing, providing trans-

parency for research data along with the history

of every new version.

• Decentralisation: There is no central authority to

manage the network operations, nodes can freely

join or leave the network, the rule set that co-

ordinates the network is based on code. Incen-

tive mechanisms regulates the economics of the

network, providing rewards and inhibiting mali-

cious attackers. Therefore, research data could be

freely and openly shared with no need for a cen-

tral repository controlled by an authority or insti-

tution.

• Programmability: The interactions with the

blockchain can be controlled by special programs

called Smart Contracts, these programs resides on

the blockchain and their execution is enforced by

a set of embedded rules, as in traditional analog-

ical contracts. In the context of pre-clinical re-

search Smart Contracts can establish the proper

input and workﬂow of the data, checking for va-

lidity, authorship and expiration dates.

On the Use of Blockchain Technology to Improve the Reproducibility of Preclinical Research Experiments

177

4.3 Blockchain-Based Architecture

To provide a fully decentralised architecture to sup-

port the handling of experiment’s data, we have de-

signed a DApp with four main components, as fol-

lows (see Fig. 1): Front-end, smart contract, dis-

tributed digital ledger (DLT) and decentralised stor-

age (DStorage).

The interactions between the DApp front-end and

the other components is executed through API calls.

The front-end presents a web based user interface for

contract deployed on the blockchain is invoked for

business logic processing. (2) Every transaction is

permanently recorded on the DLT. (3) Uploaded ﬁles

are stored on the DStorage which is implemented on

a IPFS ﬁle system. (4) Every stored ﬁle receives an

unique CID (content identiﬁer) that is sent back to the

DApp and written on the DLT for ﬁle hash check and

retrievals.

The DApp web engine and user interface ﬁles are

fully deployed on the DStorage as well, eliminating

any point of dependency on central services or author-

ity.

The user, typically a experimental researcher, ac-

cess the application through the Internet and authen-

ticates using his or her private key. Upon authentica-

tion the researcher inputs experiment’s data following

a proper protocol or guideline.

Inputs (e.g., authorship, study design, materials,

statistical models, lab and environmental conditions,

images and reports) are validated through a smart con-

tract and the transactions are committed permanently,

this step is backed by the immutability property of the

blockchain and assures experiment’s data integrity,

avoiding data tampering, violation or fraud.

Files generated by the experiment are uploaded

through the application to the DStorage and are ver-

sioned and protected, every ﬁle generates a CID that

is written on the blockchain, future ﬁle retrievals are

based on the CID.

Resiliency and availability of transactions and

ﬁles is achieved through the decentralised nature of

both the blockchain and the IPFS ﬁle system, the

complete record of an experiment’s data and ﬁles can

be traced back to any point in time since the beginning

of the experiment, data is openly exposed and shared

with stakeholders through the public blockchain.

5 CONCLUSIONS

Our work aims to present the important role that de-

centralisation can play in the improvement of repro-

Figure 1: Proposed Architecture.

ducibility of preclinical research through the applica-

tion of its core principles on data generated by exper-

iments.

By providing an DApp architecture based on

blockchain and a distributed ﬁle system, it is possible

to achieve consistency and validation for data input

through smart contracts, safeguarding data records

permanently in the blockchain and ﬁles in the DStor-

age, also allowing traceability and version control of

the data and ﬁles in any point in time. Both the

blockchain and the IPFS network provide availability

and fault tolerance for records and ﬁles.

Since it relies totally on a decentralised model,

no central authority can inﬂuence, dictate or veto

the publishing of experiment’s data, promoting trans-

parency that leads to better practices of the whole ex-

periment life cycle.

Future works can explore incentive mechanisms

to attract institutions, publishers, researchers and re-

viewers to use the proposed architecture and also dis-

cuss interoperability with the current centralised ap-

plications.

ACKNOWLEDGEMENTS

This work was supported by the Research and Devel-

opment Programme at UNIJUI; the Coordination for

the Brazilian Improvement of Higher Education Per-

sonnel (CAPES); and, the Brazilian National Council

for Scientiﬁc and Technological (CNPq) under grant

309315/2020-4.

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