Open science

The Evolving Landscape of Discrete Choice Experiments in Health Economics: A Systematic Review

INTRODUCTION

Stakeholder preference evaluations are increasingly emphasized in healthcare policy and health technology assessment. Discrete choice experiments (DCEs) are the most common method for quantifying preferences among patients, the public, and healthcare professionals. While prior reviews (1990-2017) have examined DCE trends, no comprehensive synthesis exists for studies published since 2018. This updated review (2018-2023) provides critical insights into evolving methodologies and global trends in health-related DCEs.

METHODS

A systematic search (2018-2023) of Medline, Embase, and Web of Science identified relevant studies. Studies were screened for inclusion and data were extracted, including details on DCE design and analysis. To enable trend comparisons, the search strategy and extraction items aligned with previous reviews.

RESULTS

Of 2663 identified papers, 1279 met the inclusion criteria, reflecting a significant rise in published DCEs over time. DCEs were conducted globally, with a remarkable increase in publications from Asia and Africa compared with previous reviews. Experimental designs and econometric models have advanced, continuing prior trends. Notably, most recent DCEs were administered online.

DISCUSSION

The rapid growth of DCE applications underscores their importance in health research. While the methodology is advancing rapidly, it is crucial that researchers provide full transparency in reporting their methods, particularly in detailing experimental designs and validity tests, which are too often overlooked. Key recommendations include improving reporting of experimental designs, applying validity tests, following good practices for presenting benefit-risk attributes, and adopting open science practices. Ensuring methodological rigor will maximize the impact and reproducibility of DCE research in health economics.

Structuring data analysis projects in the Open Science era with Kerblam!

Background

Structuring data analysis projects, that is, defining the layout of files and folders needed to analyze data using existing tools and novel code, largely follows personal preferences. Open Science calls for more accessible, transparent and understandable research. We believe that Open Science principles can be applied to the way data analysis projects are structured.

Methods

We examine the structure of several data analysis project templates by analyzing project template repositories present in GitHub. Through visualization of the resulting consensus structure, we draw observations regarding how the ecosystem of project structures is shaped, and what salient characteristics it has.

Results

Project templates show little overlap, but many distinct practices can be highlighted. We take them into account with the wider Open Science philosophy to draw a few fundamental Design Principles to guide researchers when designing a project space. We present Kerblam!, a project management tool that can work with such a project structure to expedite data handling, execute workflow managers, and share the resulting workflow and analysis outputs with others.

Conclusions

We hope that, by following these principles and using Kerblam!, the landscape of data analysis projects can become more transparent, understandable, and ultimately useful to the wider community.

EthoCRED: a framework to guide reporting and evaluation of the relevance and reliability of behavioural ecotoxicity studies

Behavioural analysis has been attracting significant attention as a broad indicator of sub-lethal toxicity and has secured a place as an important subdiscipline in ecotoxicology. Among the most notable characteristics of behavioural research, compared to other established approaches in sub-lethal ecotoxicology (e.g. reproductive and developmental bioassays), are the wide range of study designs being used and the diversity of endpoints considered. At the same time, environmental hazard and risk assessment, which underpins regulatory decisions to protect the environment from potentially harmful chemicals, often recommends that ecotoxicological data be produced following accepted and validated test guidelines. These guidelines typically do not address behavioural changes, meaning that these, often sensitive, effects are not represented in hazard and risk assessments. Here, we propose a new tool, the EthoCRED evaluation method, for assessing the relevance and reliability of behavioural ecotoxicity data, which considers the unique requirements and challenges encountered in this field. This method and accompanying reporting recommendations are designed to serve as an extension of the "Criteria for Reporting and Evaluating Ecotoxicity Data (CRED)" project. As such, EthoCRED can both accommodate the wide array of experimental design approaches seen in behavioural ecotoxicology, and could be readily implemented into regulatory frameworks as deemed appropriate by policy makers of different jurisdictions to allow better integration of knowledge gained from behavioural testing into environmental protection. Furthermore, through our reporting recommendations, we aim to improve the reporting of behavioural studies in the peer-reviewed literature, and thereby increase their usefulness to inform chemical regulation.

Reviewing manuscripts for scientific journals: recommendations for early career scientists

Publication of articles in international scientific journals has been one of the main strategies for the communication of scientific findings and ideas. Prepublication peer review is a fundamental aspect of the publishing process in indexed scientific journals and, associated with the large growth in journals and articles, there has been a recent challenge in having adequate peer reviewers for international journals. In this article, we provide a short overview of the publishing process, give recommendations to early career researchers about writing peer reviews of adequate quality, and discuss some possibilities for the future.

Daily life in the Open Biologist's second job, as a Data Curator

Background

Data reusability is the driving force of the research data life cycle. However, implementing strategies to generate reusable data from the data creation to the sharing stages is still a significant challenge. Even when datasets supporting a study are publicly shared, the outputs are often incomplete and/or not reusable. The FAIR (Findable, Accessible, Interoperable, Reusable) principles were published as a general guidance to promote data reusability in research, but the practical implementation of FAIR principles in research groups is still falling behind. In biology, the lack of standard practices for a large diversity of data types, data storage and preservation issues, and the lack of familiarity among researchers are some of the main impeding factors to achieve FAIR data. Past literature describes biological curation from the perspective of data resources that aggregate data, often from publications.

Methods

Our team works alongside data-generating, experimental researchers so our perspective aligns with publication authors rather than aggregators. We detail the processes for organizing datasets for publication, showcasing practical examples from data curation to data sharing. We also recommend strategies, tools and web resources to maximize data reusability, while maintaining research productivity.

Conclusion

We propose a simple approach to address research data management challenges for experimentalists, designed to promote FAIR data sharing. This strategy not only simplifies data management, but also enhances data visibility, recognition and impact, ultimately benefiting the entire scientific community.

Comment on Uzun Ozsahin et al. COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach. Diagnostics 2023, 13, 1264

The declaration of the COVID-19 pandemic by the World Health Organization (WHO) in March 2020 has triggered the publication of thousands of papers covering a plethora of aspects of the pandemic, from epidemiology models [...].

lucie: An Improved Python Package for Loading Datasets from the UCI Machine Learning Repository

The University of California-Irvine (UCI) Machine Learning (ML) Repository (UCIMLR) is consistently cited as one of the most popular dataset repositories, hosting hundreds of high-impact datasets. However, a significant portion, including 28.4% of the top 250, cannot be imported via the ucimlrepo package that is provided and recommended by the UCIMLR website. Instead, they are hosted as .zip files, containing nonstandard formats that are difficult to import without additional ad hoc processing. To address this issue, here we present lucie-load University California Irvine examples-a utility that automatically determines the data format and imports many of these previously non-importable datasets, while preserving as much of a tabular data structure as possible. lucie was designed using the top 100 most popular datasets and benchmarked on the next 130, where it resulted in a success rate of 95.4% vs. 73.1% for ucimlrepo. lucie is available as a Python package on PyPI with 98% code coverage.

Reporting guidelines for terrestrial respirometry: Building openness, transparency of metabolic rate and evaporative water loss data

Respirometry is an important tool for understanding whole-animal energy and water balance in relation to the environment. Consequently, the growing number of studies using respirometry over the last decade warrants reliable reporting and data sharing for effective dissemination and research synthesis. We provide a checklist guideline on five key sections to facilitate the transparency, reproducibility, and replicability of respirometry studies: 1) materials, set up, plumbing, 2) subject conditions/maintenance, 3) measurement conditions, 4) data processing, and 5) data reporting and statistics, each with explanations and example studies. Transparency in reporting and data availability has benefits on multiple fronts. Authors can use this checklist to design and report on their study, and reviewers and editors can use the checklist to assess the reporting quality of the manuscripts they review. Improved standards for reporting will enhance the value of primary studies and will greatly facilitate the ability to carry out higher quality research syntheses to address ecological and evolutionary theories.

"Best Paper" awards lack transparency, inclusivity, and support for Open Science

Awards can propel academic careers. They also reflect the culture and values of the scientific community. But do awards incentivize greater transparency, inclusivity, and openness in science? Our cross-disciplinary survey of 222 awards for the "best" journal articles across all 27 SCImago subject areas revealed that journals and learned societies administering such awards generally publish little detail on their procedures and criteria. Award descriptions were brief, rarely including contact details or information on the nominations pool. Nominations of underrepresented groups were not explicitly encouraged, and concepts that align with Open Science were almost absent from the assessment criteria. At the same time, 10% of awards, especially the recently established ones, tended to use article-level impact metrics. USA-affiliated researchers dominated the winner's pool (48%), while researchers from the Global South were uncommon (11%). Sixty-one percent of individual winners were men. Overall, Best Paper awards miss the global calls for greater transparency and equitable access to academic recognition. We provide concrete and implementable recommendations for scientific awards to improve the scientific recognition system and incentives for better scientific practice.

Are Open Science instructions targeted to ecologists and evolutionary biologists sufficient? A literature review of guidelines and journal data policies

Open science (OS) awareness and skills are increasingly becoming an essential part of everyday scientific work as e.g., many journals require authors to share data. However, following an OS workflow can seem challenging at first. Thus, instructions by journals and other guidelines are important. But how comprehensive are they in the field of ecology and evolutionary biology (Ecol Evol)? To find this out, we reviewed 20 published OS guideline articles aimed for ecologists or evolutionary biologists, together with the data policies of 17 Ecol Evol journals to chart the current landscape of OS guidelines in the field, find potential gaps, identify field-specific barriers for OS and discuss solutions to overcome these challenges. We found that many of the guideline articles covered similar topics, despite being written for a narrow field or specific target audience. Likewise, many of the guideline articles mentioned similar obstacles that could hinder or postpone a transition to open data sharing. Thus, there could be a need for a more widely known, general OS guideline for Ecol Evol. Following the same guideline could also enhance the uniformity of the OS practices carried on in the field. However, some topics, like long-term experiments and physical samples, were mentioned surprisingly seldom, although they are typical issues in Ecol Evol. Of the journals, 15 out of 17 expected or at least encouraged data sharing either for all articles or under specific conditions, e.g. for registered reports and 10 of those required data sharing at the submission phase. The coverage of journal data policies varied greatly between journals, from practically non-existing to very extensive. As journals can contribute greatly by leading the way and making open data useful, we recommend that the publishers and journals would invest in clear and comprehensive data policies and instructions for authors.

Through the looking glass: attempting to predict future opportunities and challenges in experimental biology

To celebrate its centenary year, Journal of Experimental Biology (JEB) commissioned a collection of articles examining the past, present and future of experimental biology. This Commentary closes the collection by considering the important research opportunities and challenges that await us in the future. We expect that researchers will harness the power of technological advances, such as '-omics' and gene editing, to probe resistance and resilience to environmental change as well as other organismal responses. The capacity to handle large data sets will allow high-resolution data to be collected for individual animals and to understand population, species and community responses. The availability of large data sets will also place greater emphasis on approaches such as modeling and simulations. Finally, the increasing sophistication of biologgers will allow more comprehensive data to be collected for individual animals in the wild. Collectively, these approaches will provide an unprecedented understanding of 'how animals work' as well as keys to safeguarding animals at a time when anthropogenic activities are degrading the natural environment.