Open Science Principles Research Articles

Robust Evidence in Integrative Medicine: Innovations, Challenges, and Future Directions

Integrative Medicine (IM), which includes therapies such as acupuncture, herbal medicine, yoga, and meditation, is gaining attention for managing chronic pain conditions. However, concerns about the quality of evidence supporting the use of these interventions persist. The 5th Annual Jaseng Academic conference 2024, in Seoul, South Korea, themed "Robust Evidence in Integrative Medicine: Innovations, Challenges, and Future Directions," addressed these concerns by focusing on advancements in study design, evidence synthesis, and open science practices. This conference proceeding summarizes key insights from the conference, emphasizing the role of pragmatic randomized controlled trials (pRCTs) in evaluating real-world effectiveness, and addressing the complexities involved in IM research such as sham controls. The integration of IM therapies into comprehensive pain management strategies (particularly in Korea), supported by government-backed research and policy initiatives was also discussed. Advancements in methodologies were addressed, such as bibliometric analysis, evidence mapping, and the development of clinical practice guidelines (CPGs) for integrative therapies. These methodologies offer valuable insights but face challenges due to the heterogeneity of IM interventions, and potential synergistic or antagonistic effects when combined with conventional medicine. Finally, the potential of open science to enhance transparency, reporting, and reproducibility in IM was explored, emphasizing the increased role of adherence to reporting guidelines (CONSORT and PRISMA). The future of IM research is built upon the continued efforts of refined study designs, rigorous evidence synthesis, and the integration of open science principles, for a robust and more credible evidence base.

Fragment Library of Colombian Natural Products: Generation and Comparative Chemoinformatic Analysis

Fragment libraries have a major significance in drug discovery due to their role in de novo design and enumerating large and ultra-large compound libraries. Although several fragment libraries are commercially available, most are derived from synthetic compounds. The number of fragment libraries derived from natural products is still being determined. Still, they represent a rich source of building blocks to generate pseudo-natural products and bioactive synthetic compounds inspired by natural products. In this work, we generated and analyzed a fragment library of natural products from Colombia, a highly diverse geographical region where fragment libraries are yet to be reported. We also generated and reported fragment libraries of three novel natural product libraries and, as a reference, the most updated version of FDA-approved drugs. In line with the principles of open science, the fragment libraries developed in this study are freely available.

Giving Away the Science of School Psychology

Within the field of school psychology, we often put a premium on evidence-based practices, but what is often missing from these conversations is a consideration of replication and open science principles in the evaluation of evidence. Amid growing concerns regarding the replicability of psychological science, calls for greater research transparency, and more careful consideration of the conditions under which a finding might replicate, research suggest that school psychologists’ uptake of open science practices may lag behind that of other social science disciplines. The present paper synthesizes the literature on the replication crisis and corresponding credibility revolution in psychology, describes various open science practices, and considers barriers and adaptations needed to implement these practices in school-based research. The paper ends with recommendations on how to better give away the science of school psychology via transparent research practices so that research consumers can better reap the benefits of evidence-based practice. Impact Statement This paper summarizes the literature on open science, including key concepts relevant to the replication crisis and corresponding credibility revolution. These concepts are then made applicable to the field of school psychology, including our roles as researchers, practitioners, research consumers, and publication editors. By engaging in more transparent research practices, school psychologists can promote a more replicable, reputable, and reliable science, which yields greater benefits and buy-in for the communities we serve.

Using Artificial Intelligence-Based Tools to Improve the Literature Review Process: Pilot Test with the Topic “Hybrid Meat Products”

Conducting a literature review is a mandatory initial stage in scientific research on a specific topic. However, this task is becoming much more complicated in certain areas (such as food science and technology) due to the huge increase in the number of scientific publications. Different tools based on artificial intelligence could be very useful for this purpose. This paper addresses this challenge by developing and checking different tools applicated to an emerging topic in food science and technology: “hybrid meat products”. The first tool to be applied was based on Natural Language Processing and was used to select and reduce the initial number of papers obtained from a traditional bibliographic search (using common scientific databases such as Web Science and Scopus) from 938 to 178 (a 87% reduction). The second tool was a project based on the interplay between Retrieval-Augmented Generation (RAG) and LLAMA 3, which was used to answer key questions relating to the topic under review (“hybrid meat products”) but limiting the context to the scientific review obtained after applying the first AI tool. This new strategy for reviewing scientific literature could be a major advance on from the traditional literature review procedure, making it faster, more open, more accessible to everyone, more effective, more objective, and more efficient—all of which help to fulfill the principles of open science.

A research data management (RDM) community for ELIXIR.

Research data management (RDM) is central to the implementation of the FAIR (Findable Accessible, Interoperable, Reusable) and Open Science principles. Recognising the importance of RDM, ELIXIR Platforms and Nodes have invested in RDM and launched various projects and initiatives to ensure good data management practices for scientific excellence. These projects have resulted in a rich set of tools and resources highly valuable for FAIR data management. However, these resources remain scattered across projects and ELIXIR structures, making their dissemination and application challenging. Therefore, it becomes imminent to coordinate these efforts for sustainable and harmonised RDM practices with dedicated forums for RDM professionals to exchange knowledge and share resources. The proposed ELIXIR RDM Community will bring together RDM experts to develop ELIXIR's vision and coordinate its activities, taking advantage of the available assets. It aims to coordinate RDM best practices and illustrate how to use the existing ELIXIR RDM services. The Community will be built around three integral pillars, namely, a network of RDM professionals, RDM knowledge management and RDM training expertise and resources. It will also engage with external stakeholders to leverage benefits and provide a forum to RDM professionals for regular knowledge exchange, capacity building and development of harmonised RDM practices, keeping in line with the overall scope of the RDM Community. In the short term, the Community aims to build upon the existing resources and ensure that the content of these remain up to date and fit for purpose. In the long run, the Community will aim to strengthen the skills and knowledge of its RDM professionals to support the emerging needs of the scientific community. The Community will also devise an effective strategy to engage with other ELIXIR structures and international stakeholders to influence and align with developments and solutions in the RDM field.

Forest Fire Hazard Forecasting Based on Google Earth Engine Open Satellite Data

Introduction. Forest fires cause significant damage to both the natural fund and the national economy. In recent years, their harmful influence has increased due to global warming, and in Ukraine also due to the armed conflict. Thus, traditional methods of patrolling (ground and air) are not only costly, but also dangerous due to mines and possible shelling of border areas. Therefore, the role of surveillance using satellite systems is increasing. Space monitoring is more efficient and covers a larger area of ??the Earth's surface. Another important advantage is open access to information. The purpose of the paper is to build a mathematical model for determining fire danger based on climatic and biophysical satellite data for the forests of Ukraine, as well as a similar climatic zone with the possibility of further scaling to other climatic regions and types of vegetation cover. To adhere to the principles of open science, Google Earth Engine (GEE), a cloud-based platform that provides open access to dynamic collections of pre-processed Earth remote sensing results, was chosen. Results. Climatic and biophysical data for the forests of Ukraine for the years 2017-2020 were collected using the tools of the Python library for working with GEE data. Further, the obtained data were processed by two methods: linear (PCA) and non-linear (UMAP) in order to obtain statistically independent attributes. Both obtained datasets were subjected to statistical processing using the Bayesian method. Finally, for each point on the map for which information was collected, an indicator was calculated that predicted fire danger if the obtained value was greater than 1 and its absence in the opposite case. The resulting model showed its efficiency on training data. On the test dataset (data on Polish forests for the same period), the results turned out to be worse, in particular, the model using PCA did not predict absence of fire danger, and the model using UMAP generally showed lower performance. This can be due to both the imperfection of the model and the small size of the test dataset or factors unrelated to natural processes (in particular the human factor). Conclusions. An approach to forest fires forecasting based on satellite data is proposed. The obtained results indicate that the model is already effective at this stage, although machine learning methods have not yet been applied. However, it needs further improvement, so work on the model will be continued. Along with improving the quality of forecasts, attention will be paid to the geographic expansion and the creation of a web application. Keywords: fire danger in forests, satellite data, correlation, PCA, UMAP, hard-to-reach areas.

Universities on the way to open science implementation: theoretical and practical context

Problem setting. Despite certain progressive movements of universities towards open science, currently one of the problems that hinders progress is the achievement of balance, systematicity, and consolidation in taking measures that contribute to the openness of scientific researches, to ensuring wide access to scientific inventions, discoveries and developments, contribute to expanding the possibility of their use for the advancement of knowledge and real changes in the world, and also to stimulation of innovations. Purpose of the research. The experience and practices of implementing open science in universities become an illustration to note as soon as possible the extent of their impact on achieving a greater level of transparency, integrity, reliability, reproducibility and accessibility of research discoveries in order to make certain social impacts for the benefit of social progress. Analysis of recent researches and publications. The scientific and theoretical understanding of the trends in the development of open science, the evolution of information systems, and electronic archives that support the availability of research data and discoveries is reflected in the works of T. Yaroshenko, S. Nazarovets, A. Lyubchych, L. Luparenko, P. Gaitanou, I. Andreou, J. Leitao, D. Pereira and others. Article`s main body. The article examines the new roles and impacts of open science, open data, and open access on the modernization of the scientific research field in the context of global challenges and the digital transformation of universities. It has been proven that an essential characteristic of the changes taking place under the influence of the modification of means and systems for the implementation of the open science principles is the mandatory online presentation of research data, facts recorded in research materials, which helps not only to acquire, but also to overcome the gap in the knowledge of interested persons, ensures the transparency of scientific researches. in the difficult conditions of martial law, remote work, the importance of implementing a system approach when calculating the main indicators of scientific and scientific and technical activity in universities is increasing, in connection with this, a new “Regulatory framework of open science” and metrics for evaluating scientific researchers are being developed, taking into account the principles of open science. Such important aspects of open science as storage, management and sharing of industry research data must comply with the FAIR guidelines (Findability, Accessibility, Interoperability, Reusability) and be based on the development and application of modern technologies. This, in our opinion, will allow: better integration of data on scientific activity in electronic information systems (IS); to consolidate research datasets and make them accessible and compatible; to support their storage, legal multiple use for scientific purposes; to make easier the search for scientific information in systems and on the Internet. Thanks to technology and the intensification of the universities movement towards open science, scientific cooperation and information exchange is expanding, and that benefits science and society; scientific knowledge becomes more open, more accessible for use; the processes of creation, assessment and circulation of scientific knowledge are accelerated; everyone’s right to participate in scientific progress and its benefits is more fully realized. Conclusions and prospects for the development. It was made the conclusion about the importance of the formation and use of the entire complex of network resources and platforms, international and national practices of the open science implementation in universities as an action plan agenda in the construction of modern ecosystem of scientific research activities of higher education institutions and in the support of the national scientific infrastructure in ensuring the availability of Ukrainian research with their further integration into world science.

A how-to guide for code sharing in biology.

In 2024, all biology is computational biology. Computer-aided analysis continues to spread into new fields, becoming more accessible to researchers trained in the wet lab who are eager to take advantage of growing datasets, falling costs, and novel assays that present new opportunities for discovery. It is currently much easier to find guidance for implementing these techniques than for reporting their use, leaving biologists to guess which details and files are relevant. In this essay, we review existing literature on the topic, summarize common tips, and link to additional resources for training. Following this overview, we then provide a set of recommendations for sharing code, with an eye toward guiding those who are comparatively new to applying open science principles to their computational work. Taken together, we provide a guide for biologists who seek to follow code sharing best practices but are unsure where to start.

ReproduceMe: Lessons from a pilot project on computational reproducibility

If a scientific paper is computationally reproducible, the analyses it reports can be repeated independently by others. At the present time most papers are not reproducible. However, the tools to enable computational reproducibility are now widely available, using free and open source software. We conducted a pilot study in which we offered ‘reproducibility as a service’ within a UK psychology department for a period of 6 months. Our rationale was that most researchers lack either the time or expertise to make their own work reproducible, but might be willing to allow this to be done by an independent team. Ten papers were converted into reproducible format using R markdown, such that all analyses were conducted by a single script that could download raw data from online platforms as required, generate figures, and produce a pdf of the final manuscript. For some studies this involved reproducing analyses originally conducted using commercial software. The project was an overall success, with strong support from the contributing authors who saw clear benefit from this work, including greater transparency and openness, and ease of use for the reader. Here we describe our framework for reproducibility, summarise the specific lessons learned during the project, and discuss the future of computational reproducibility. Our view is that computationally reproducible manuscripts embody many of the core principles of open science, and should become the default format for scientific communication.

An extensible and unifying approach to retrospective clinical data modeling: the BrainTeaser Ontology

Automatic disease progression prediction models require large amounts of training data, which are seldom available, especially when it comes to rare diseases. A possible solution is to integrate data from different medical centres. Nevertheless, various centres often follow diverse data collection procedures and assign different semantics to collected data. Ontologies, used as schemas for interoperable knowledge bases, represent a state-of-the-art solution to homologate the semantics and foster data integration from various sources. This work presents the BrainTeaser Ontology (BTO), an ontology that models the clinical data associated with two brain-related rare diseases (ALS and MS) in a comprehensive and modular manner. BTO assists in organizing and standardizing the data collected during patient follow-up. It was created by harmonizing schemas currently used by multiple medical centers into a common ontology, following a bottom-up approach. As a result, BTO effectively addresses the practical data collection needs of various real-world situations and promotes data portability and interoperability. BTO captures various clinical occurrences, such as disease onset, symptoms, diagnostic and therapeutic procedures, and relapses, using an event-based approach. Developed in collaboration with medical partners and domain experts, BTO offers a holistic view of ALS and MS for supporting the representation of retrospective and prospective data. Furthermore, BTO adheres to Open Science and FAIR (Findable, Accessible, Interoperable, and Reusable) principles, making it a reliable framework for developing predictive tools to aid in medical decision-making and patient care. Although BTO is designed for ALS and MS, its modular structure makes it easily extendable to other brain-related diseases, showcasing its potential for broader applicability.Database URL https://zenodo.org/records/7886998.

Enhancing precision and safety in metallographic sample preparation: Reduce the stochasticity and workload with robotization

Abstract Despite advancements in metallography automation, sample preparation remains largely semi-automated with isolated subprocesses like sectioning, grinding, and polishing. Leveraging modern technologies such as collaborative robotics, AI-driven computer vision, and advanced sensors could enable fully integrated automation. However, the diversity of processes requires skilled human oversight. Integrating user-friendly cobot interfaces may promote a synergistic workspace that enhances safety, reduces monotony, and supports complex studies and documentation aligned with open science principles. Our study explores cost-effective mini robots in critical preparation stages, highlighting steps toward complex automation in metallography.

Sharing, collaborating, and building trust: How Open Science advances language testing

The Open Science movement is taking hold around the world, and language testers are taking part. In this viewpoint, I discuss how sharing, collaborating, and building trust, guided by Open Science principles, benefit the language testing field. To help more language testers join in, I present a standard definition of Open Science and describe four ways language testing researchers can immediately partake. Overall, I share my views on how Open Science is an accelerating process that improves language testing as a scientific and humanistic field.

A software-agnostic benchmark for DEM simulation of cohesive and non-cohesive materials

Although DEM (Discrete Element Method) was introduced >40 years ago, there are still various challenges in applying it with a certain level of confidence. To develop a realistic material model, calibration, validation, particle shape representation and scaling are well-known challenges that have been studied by various researchers over the past two decades. In addition, once a realistic DEM material model is developed and published in line with open science principles, it is unknown to what extent the calibrated values can be used across software packages. Although a benchmark between 8 open source codes was recently published, it did not cover cohesive materials, rolling friction models, realistic calibrated material behaviour, and commercial software that is widely used in industries.The aim of this study is to investigate the portability of input parameters between different codes and to identify if software independent calibration is possible. We propose a framework to assist DEM users in industry and academia to reach a software independent DEM simulation when required.To compare the results between software packages, the framework considers the following aspects: 1) identical implementation of contact models, 2) single contact modelling, 3) bulk level simulation, and 4) identical post-processing. This framework is demonstrated for two contact models (Hertz-Mindlin and Edinburgh Elasto Plastic Adhesive) with rolling friction model and two commercial software packages (EDEM and PFC) but is applicable for any other (open-source) software. Moreover, two realistic calibrated material models are included to cover a range of material characteristics, from free-flowing incompressible materials to cohesive compressible materials. This study shows that individual particle level simulations give identical results, bulk level simulations show differences for both contact models. In general, users should use parameter values from other software packages with caution, especially where critical or sensitive applications are modelled. This paper also highlights the use of novel computation techniques, such as the GPU engine, to achieve practical computation times when modelling industrial applications.

Maximizing the Utility of Alzheimer’s Disease Trial Data: Sharing of Baseline A4 and LEARN Data

BackgroundThe Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) and Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) studies were conducted between 2014 and 2023, with enrollment completed in 2017 and final study results reported in 2023. The study screening process involved the collection of initial clinical, cognitive, neuroimaging, and genetic measures to determine eligibility. Once randomized, enrolled participants were assessed every four weeks over a 4.5-year follow-up period during which longitudinal clinical, cognitive, and neuroimaging measures were collected. A large number of longitudinal fluid biospecimens were also collected and banked. Consistent with the NIH data sharing policy and the principles of Open Science, the A4/LEARN investigators aimed to share data as broadly and early as possible while still protecting participant privacy and confidentiality and the scientific integrity of the studies.ObjectivesWe describe the approach, methods, and platforms used to share the A4 and LEARN pre-randomization study data for secondary research use. Preliminary results measuring the impact of these efforts are also summarized. We conclude with a discussion of lessons learned and next steps.DesignThe materials shared included de-identified quantitative and image data, analysis software, instruments, and documentation.SettingThe A4 and LEARN Studies were conducted at 67 clinical trial sites in the United States, Canada, Japan, and Australia.ParticipantsThe A4 study screened (n=6763), enrolled, and randomized (n=1169) participants between the ages of 65 and 85 with a blinded follow-up period of 240 weeks followed by an open-label period of variable length. The LEARN study screened and enrolled individuals (n=538) who were ineligible for the A4 study based on nonelevated measures of amyloid accumulation using positron emission tomography imaging (amyloid PET).MeasurementsWe provide descriptive measures of the data shared and summarize the frequency, characteristics, and status of all data access requests submitted to date. We evaluate the scientific impact of the data-sharing effort by conducting a literature search to identify related publications.ResultsThe A4 and LEARN pre-randomization study data were released in December 2018. As of May 8, 2024, 1506 requests have been submitted by investigators and citizen scientists from more than 50 countries. We identified 49 peer-reviewed publications that acknowledge the A4/LEARN study.ConclusionsOur initial results provide evidence supporting the feasibility and scientific utility of broad and timely sharing of Alzheimer’s disease trial data.

A desalination research agenda fit for the 21st century

Desalination is expanding exponentially across the world. Yet, there is little research on the economic, institutional, and social dimensions of desalination, as well as on policy-relevant lessons for managing the risks and opportunities from this technology, especially in low- and middle-income countries. This perspective article proposes a broad research agenda anchored in five key themes – energy and materials, institutions and regulations, social dynamics and perception, strategic planning, and financial sustainability – and grounded in the principles of open science to help steer the sustainable and equitable roll-out of desalination globally.

UVLHub: A feature model data repository using UVL and open science principles

Feature models are the de facto standard for modelling variabilities and commonalities in features and relationships in software product lines. They are the base artefacts in many engineering activities, such as product configuration, derivation, or testing. Concrete models in different domains exist; however, many are in private or sparse repositories or belong to discontinued projects. The dispersion of knowledge of feature models hinders the study and reuse of these artefacts in different studies. The Universal Variability Language (UVL) is a community effort textual feature model language that promotes a common way of serializing feature models independently of concrete tools. Open science principles promote transparency, accessibility, and collaboration in scientific research. Although some attempts exist to promote feature model sharing, the existing solutions lack open science principles by design. In addition, existing and public feature models are described using formats not always supported by current tools. This paper presents ▪ , a repository of feature models in UVL format. ▪ provides a front end that facilitates the search, upload, storage, and management of feature model datasets, improving the capabilities of discontinued proposals. Furthermore, the tool communicates with Zenodo – one of the most well-known open science repositories – providing a permanent save of datasets and following open science principles. ▪ includes existing datasets and is readily available to include new data and functionalities in the future. It is maintained by three active universities in variability modelling.

Visualization of Composite Plots in R Using a Programmatic Approach and smplot2

In psychology and human neuroscience, the practice of creating multiple subplots and combining them into a composite plot has become common because the nature of research has become more multifaceted and sophisticated. In the last decade, the number of methods and tools for data visualization has surged. For example, R, a programming language, has become widely used in part because of ggplot2, a free, open-source, and intuitive plotting library. However, despite its strength and ubiquity, it has some built-in restrictions that are most noticeable when one creates a composite plot, which currently involves a complex and repetitive process with steps that go against the principles of open science out of necessity. To address this issue, I introduce smplot2, an open-source R package that integrates ggplot2’s declarative syntax and a programmatic approach to plotting. The package aims to enable users to create customizable composite plots by linearizing the process of complex visualization. The documentation and code examples of the smplot2 package are available online ( https://smin95.github.io/dataviz ).

Nine quick tips for open meta-analyses.

Open science principles are revolutionizing the transparency, reproducibility, and accessibility of research. Meta-analysis has become a key technique for synthesizing data across studies in a principled way; however, its impact is contingent on adherence to open science practices. Here, we outline 9 quick tips for open meta-analyses, aimed at guiding researchers to maximize the reach and utility of their findings. We advocate for outlining preregistering clear protocols, opting for open tools and software, and the use of version control systems to ensure transparency and facilitate collaboration. We further emphasize the importance of reproducibility, for example, by sharing search syntax and analysis scripts, and discuss the benefits of planning for dynamic updating to enable living meta-analyses. We also recommend publication in open-access formats, as well as open data, open code, and open access publication. We close by encouraging active promotion of research findings to bridge the gap between complex syntheses and public discourse, and provide a detailed submission checklist to equip researchers, reviewers and journal editors with a structured approach to conducting and reporting open meta-analyses.

PHIRI: lessons for an extensive reuse of sensitive data in federated health research.

The extensive and continuous reuse of sensitive health data could enhance the role of population health research on public decisions. This paper describes the design principles and the different building blocks that have supported the implementation and deployment of Population Health Information Research Infrastructure (PHIRI), the strengths and challenges of the approach and some future developments. The design and implementation of PHIRI have been developed upon: (i) the data visiting principle-data does not move but code moves; (ii) the orchestration of the research question throughout a workflow that ensured legal, organizational, semantic and technological interoperability and (iii) a 'master-worker' federated computational architecture that supported the development of four uses cases. Nine participants nodes and 28 Euro-Peristat members completed the deployment of the infrastructure according to the expected outputs. As a consequence, each use case produced and published their own common data model, the analytical pipeline and the corresponding research outputs. All the digital objects were developed and published according to Open Science and FAIR principles. PHIRI has successfully supported the development of four use cases in a federated manner, overcoming limitations for the reuse of sensitive health data and providing a methodology to achieve interoperability in multiple research nodes.

The Gab Project: The Methodological, Epistemological, and Legal Challenges of Studying the Platformized Far-Right

In this article we describe our five-year research project on the notorious radical free speech service and fringe platform Gab. During these years we scraped an entire platform, prepared it into a dataset for analysis, and opened it up to a broader community of students and researchers. Each of these projects provides us not just with a small slice of platformized far-right culture but also with a larger sphere of a fringe platform. However, the overarching goal of the Gab project was to contribute to a methodology for the study of the contemporary platformized far right. The atypical nature of the project posed many methodological, epistemological, and legal challenges. It therefore kicked off an institutional learning process about the possibilities, legal boundaries, and best practices for research compliant with General Data Protection Regulation (GDPR). In this article we argue that the study of the platformized far right should have a thorough understanding of the medium on which the object is present, as well as the methods with which the object is captured. What is more, scholars that use digital tools and data methods for capture and analysis of web platforms must become literate in operating them. Consequently, data-driven research on the far right is naturally interdisciplinary and therefore cooperative and adherent to the principles of open science.