Reaping the benefits of open science in scholarly communication

Regardless of multiple efforts carried out across many countries to disseminate the ideas and the practice of open science, most scholars in the early 2020s do not self-archive their research articles and do not publish research papers in preprint form. Having received no education and training on open science, researchers are often puzzled on what to do, in practice, to start reaping the benefits of open science. This study offers a succinct vademecum on how to benefit from the open science approach to scholarly communication, no matter whether in natural or in humanistic and social sciences.

The Role of Open Science Practices in Scaling Evidence-Based Prevention Programs

The goal of creating evidence-based programs is to scale them at sufficient breadth to support population-level improvements in critical outcomes. However, this promise is challenging to fulfill. One of the biggest issues for the field is the reduction in effect sizes seen when a program is taken to scale. This paper discusses an economic perspective that identifies the underlying incentives in the research process that lead to scale up problems and to deliver potential solutions to strengthen outcomes at scale. The principles of open science are well aligned with this goal. One prevention program that has begun to scale across the USA is early childhood home visiting. While there is substantial impact research on home visiting, overall average effect size is .10 and a recent national randomized trial found attenuated effect sizes in programs implemented under real-world conditions. The paper concludes with a case study of the relevance of the economic model and open science in developing and scaling evidence-based home visiting. The case study considers how the traditional approach for testing interventions has influenced home visiting's evolution to date and how open science practices could have supported efforts to maintain impacts while scaling home visiting. It concludes by considering how open science can accelerate the refinement and scaling of home visiting interventions going forward, through accelerated translation of research into policy and practice.

Open science, data sharing and solidarity: who benefits?

Research, innovation, and progress in the life sciences are increasingly contingent on access to large quantities of data. This is one of the key premises behind the "open science" movement and the global calls for fostering the sharing of personal data, datasets, and research results. This paper reports on the outcomes of discussions by the panel "Open science, data sharing and solidarity: who benefits?" held at the 2021 Biennial conference of the International Society for the History, Philosophy, and Social Studies of Biology (ISHPSSB), and hosted by Cold Spring Harbor Laboratory (CSHL).

Assessing open science and citizen science in addictions and substance use research: A scoping review

BACKGROUND

The EU promotes 'Open Science' as a public good. Complementary to its implementation is Citizen Science, which redefines the relationship between the scientific community, civic society and the individual. Open Science and Citizen Science poses challenges for the substance use and addictions research community but may provide positive opportunities for future European addiction research. This paper explores both current barriers and potential facilitators for the implementation of Open Science and Citizen Science in substance use and addictions research.

METHODOLOGY

A scoping review was used to examine barriers and facilitators identified in the substance use and addiction research literature for the adoption of Open Science and Citizen Science.

RESULTS

'Technical' facilitators included the pre-registration of study protocols; publication of open-source datasets; open peer review and online tools. 'Motivational' facilitators included enhanced reputation; embracing co-creation; engaged citizenship and gamification. 'Economic' facilitators included the use of free tools and balanced remuneration of crowdworkers. 'Political' facilitators included better informed debates through the 'triple helix' approach and trust-generating transparency. 'Legal' facilitators included epidemiologically informed law enforcement; better policy surveillance and the validation of other datasets. 'Ethical' facilitators included the 'democratisation of science' and opportunities to explore new concepts of ethics in addiction research.

CONCLUSION

Open Science and Citizen Science in substance use and addictions research may provide a range of benefits in relation to the democratisation of science; transparency; efficiency and the reliability/validity of data. However, its implementation raises a range of research integrity and ethical issues that need be considered. These include issues related to participant recruitment; privacy; confidentiality; security; cost and industry involvement. Progressive journal policies to support Open Science practices; a shift in researcher norms; the use of free tools and the greater availability of methodological and ethical standards are likely to increase adoption in the field.

The importance of adherence to international standards for depositing open data in public repositories

There has been an important global interest in Open Science, which include open data and methods, in addition to open access publications. It has been proposed that public availability of raw data increases the value and the possibility of confirmation of scientific findings, in addition to the potential of reducing research waste. Availability of raw data in open repositories facilitates the adequate development of meta-analysis and the cumulative evaluation of evidence for specific topics. In this commentary, we discuss key elements about data sharing in open repositories and we invite researchers around the world to deposit their data in them.

COVID-19 and the research scholarship ecosystem: help!

OBJECTIVES

Data sharing practices remain elusive in biomedicine. The COVID-19 pandemic has highlighted the problems associated with the lack of data sharing. The objective of this article is to draw attention to the problem and possible ways to address it.

STUDY DESIGN AND SETTING

This article examines some of the current open access and data sharing practices at biomedical journals and funders. In the context of COVID-19 the consequences of these practices is also examined.

RESULTS

Despite the best of intentions on the part of funders and journals, COVID-19 biomedical research is not open. Academic institutions need to incentivize and reward data sharing practices as part of researcher assessment. Journals and funders need to implement strong polices to ensure that data sharing becomes a reality. Patients support sharing of their data.

CONCLUSION

Biomedical journals, funders and academic institutions should act to require stronger adherence to data sharing policies.

Prosociality in science

Science is unthinkable without collaboration between scientists. Yet, science is also unthinkable without competition (i.e., competing for the best and most solid arguments and limited, precious resources). In this review, we argue that scientific work routines represent social dilemmas and that two facets of prosociality help researchers solve these dilemmas: (i) sacrificing personal profit for the sake of collective profit (i.e., cooperation) and (ii) deciding to make oneself vulnerable to exploitation (i.e., trust). We use two contemporary developments in science to illustrate our reasoning: First, researchers' willingness to engage with the lay public (e.g., investing one's limited time to public engagement) and second, their commitment to 'open science' (e.g., sharing one's data and materials despite the risk of exploitation).

Study Registration for the Field of Prevention Science: Considering Options and Paths Forward

The practice of prospectively registering the details of intervention studies in a public database or registry is gaining momentum across disciplines as a strategy for increasing the transparency, credibility, and accessibility of study findings. In this article, we consider five registries that may be relevant for registration of intervention studies in the field of prevention science: ClinicalTrials.gov, the American Economic Association Registry of Randomized Controlled Trials (AEA RCT Registry), the Open Science Framework Preregistration (OSF Preregistration), the Registry for International Development Impact Evaluations (RIDIE), and the Registry of Efficacy and Effectiveness Studies (REES). We examine the five registries in terms of substantive focus, study designs, and contents of registry entries. We consider two paths forward for prospective registration of intervention studies in the field of prevention science: Path A: register all studies in ClinicalTrials.gov and Path B: allow individual researchers to select the registry with the "best fit." Lastly, we consider how the field might begin to establish norms around registration.

How to do Better N400 Studies: Reproducibility, Consistency and Adherence to Research Standards in the Existing Literature

Given the complexity of ERP recording and processing pipeline, the resulting variability of methodological options, and the potential for these decisions to influence study outcomes, it is important to understand how ERP studies are conducted in practice and to what extent researchers are transparent about their data collection and analysis procedures. The review gives an overview of methodology reporting in a sample of 132 ERP papers, published between January 1980 – June 2018 in journals included in two large databases: Web of Science and PubMed. Because ERP methodology partly depends on the study design, we focused on a well-established component (the N400) in the most commonly assessed population (healthy neurotypical adults), in one of its most common modalities (visual images). The review provides insights into 73 properties of study design, data pre-processing, measurement, statistics, visualization of results, and references to supplemental information across studies within the same subfield. For each of the examined methodological decisions, the degree of consistency, clarity of reporting and deviations from the guidelines for best practice were examined. Overall, the results show that each study had a unique approach to ERP data recording, processing and analysis, and that at least some details were missing from all papers. In the review, we highlight the most common reporting omissions and deviations from established recommendations, as well as areas in which there was the least consistency. Additionally, we provide guidance for a priori selection of the N400 measurement window and electrode locations based on the results of previous studies.

Clearinghouse Standards of Evidence on the Transparency, Openness, and Reproducibility of Intervention Evaluations

Clearinghouses are influential repositories of information on the effectiveness of social interventions. To identify which interventions are “evidence-based,” clearinghouses review intervention evaluations using published standards of evidence that focus primarily on internal validity and causal inferences. Open science practices can improve trust in evidence from evaluations on the effectiveness of social interventions. Including open science practices in clearinghouse standards of evidence is one of many efforts that could increase confidence in designations of interventions as “evidence-based.” In this study, we examined the policies, procedures, and practices of 10 federal evidence clearinghouses that review preventive interventions—an important and influential subset of all evidence clearinghouses. We found that seven consider at least one open science practice when evaluating interventions: replication (6 of 10 clearinghouses), public availability of results (6), investigator conflicts of interest (3), design and analysis transparency (3), study registration (2), and protocol sharing (1). We did not identify any policies, procedures, or practices related to analysis plan registration, data sharing, code sharing, material sharing, and citation standards. We provide a framework with specific recommendations to help federal and other evidence clearinghouses implement the Transparency and Openness Promotion (TOP) Guidelines. Our proposed “TOP Guidelines for Clearinghouses” includes reporting whether evaluations used open science practices, incorporating open science practices in their standards for receiving “evidence-based” designations, and verifying that evaluations used open science practices. Doing so could increase the trustworthiness of evidence used for policy making and support improvements throughout the evidence ecosystem.

The value-ladenness of transparency in science: Lessons from Lyme disease

Both philosophers and scientists have recently promoted transparency as an important element of responsible scientific practice. Philosophers have placed particular emphasis on the ways that transparency can assist with efforts to manage value judgments in science responsibly. This paper examines a potential challenge to this approach, namely, that efforts to promote transparency can themselves be value-laden. This is particularly problematic when transparency incorporates second-order value judgments that are underwritten by the same values at stake in the desire for transparency about the first-order value judgments involved in scientific research. The paper uses a case study involving research on Lyme disease to illustrate this worry, but it responds by elucidating a range of scenarios in which transparency can still play an effective role in managing value judgments responsibly.

Fusion neural networks for plant classification: learning to combine RGB, hyperspectral, and lidar data

Airborne remote sensing offers unprecedented opportunities to efficiently monitor vegetation, but methods to delineate and classify individual plant species using the collected data are still actively being developed and improved. The Integrating Data science with Trees and Remote Sensing (IDTReeS) plant identification competition openly invited scientists to create and compare individual tree mapping methods. Participants were tasked with training taxon identification algorithms based on two sites, to then transfer their methods to a third unseen site, using field-based plant observations in combination with airborne remote sensing image data products from the National Ecological Observatory Network (NEON). These data were captured by a high resolution digital camera sensitive to red, green, blue (RGB) light, hyperspectral imaging spectrometer spanning the visible to shortwave infrared wavelengths, and lidar systems to capture the spectral and structural properties of vegetation. As participants in the IDTReeS competition, we developed a two-stage deep learning approach to integrate NEON remote sensing data from all three sensors and classify individual plant species and genera. The first stage was a convolutional neural network that generates taxon probabilities from RGB images, and the second stage was a fusion neural network that “learns” how to combine these probabilities with hyperspectral and lidar data. Our two-stage approach leverages the ability of neural networks to flexibly and automatically extract descriptive features from complex image data with high dimensionality. Our method achieved an overall classification accuracy of 0.51 based on the training set, and 0.32 based on the test set which contained data from an unseen site with unknown taxa classes. Although transferability of classification algorithms to unseen sites with unknown species and genus classes proved to be a challenging task, developing methods with openly available NEON data that will be collected in a standardized format for 30 years allows for continual improvements and major gains for members of the computational ecology community. We outline promising directions related to data preparation and processing techniques for further investigation, and provide our code to contribute to open reproducible science efforts.

lab.js: A free, open, online study builder

Web-based data collection is increasingly popular in both experimental and survey-based research because it is flexible, efficient, and location-independent. While dedicated software for laboratory-based experimentation and online surveys is commonplace, researchers looking to implement experiments in the browser have, heretofore, often had to manually construct their studies’ content and logic using code. We introduce lab.js, a free, open-source experiment builder that makes it easy to build studies for both online and in-laboratory data collection. Through its visual interface, stimuli can be designed and combined into a study without programming, though studies’ appearance and behavior can be fully customized using html, css, and JavaScript code if required. Presentation and response times are kept and measured with high accuracy and precision heretofore unmatched in browser-based studies. Experiments constructed with lab.js can be run directly on a local computer and published online with ease, with direct deployment to cloud hosting, export to web servers, and integration with popular data collection platforms. Studies can also be shared in an editable format, archived, re-used and adapted, enabling effortless, transparent replications, and thus facilitating open, cumulative science. The software is provided free of charge under an open-source license; further information, code, and extensive documentation are available from https://lab.js.org/.

The Group Nurturance Inventory - initial psychometric evaluation using Rasch and factor analysis

BACKGROUND

This paper describes the development and psychometric evaluation of a behavioral assessment instrument primarily intended for use with workgroups in any type of organization. The instrument was developed based on the Nurturing Environments framework which describes four domains important for health, well-being, and productivity; minimizing toxic social interactions, teaching and reinforcing prosocial behaviors, limiting opportunities for problem behaviors, and promoting psychological flexibility. The instrument is freely available to use and adapt under a CC-BY license and intended as a tool that is easy for any group to use and interpret to identify key behaviors to improve their psychosocial work environment.

METHODS

Questionnaire data of perceived frequency of behaviors relevant to nurturance were collected from nine different organizations in Sweden. Data were analyzed using confirmatory factor analysis, Rasch analysis, and correlations to investigate relationships with relevant workplace measures.

RESULTS

The results indicate that the 23-item instrument is usefully divided in two factors, which can be described as risk and protective factors. Toxic social behaviors make up the risk factor, while the protective factor includes prosocial behavior, behaviors that limit problems, and psychological flexibility. Rasch analysis showed that the response categories work as intended for all items, item fit is satisfactory, and there was no significant differential item functioning across age or gender. Targeting indicates that measurement precision is skewed towards lower levels of both factors, while item thresholds are distributed over the range of participant abilities, particularly for the protective factor. A Rasch score table is available for ordinal to interval data transformation.

CONCLUSIONS

This initial analysis shows promising results, while more data is needed to investigate group-level measurement properties and validation against concrete longitudinal outcomes. We provide recommendations for how to work in practice with a group based on their assessment data, and how to optimize the measurement precision further. By using a two-dimensional assessment with ratings of both frequency and perceived importance of behaviors the instrument can help facilitate a participatory group development process. The Group Nurturance Inventory is freely available to use and adapt for both commercial and non-commercial use and could help promote transparent assessment practices in organizational and group development.

Lots of movement, little progress: a review of reptile home range literature

Reptiles are the most species-rich terrestrial vertebrate group with a broad diversity of life history traits. Biotelemetry is an essential methodology for studying reptiles as it compensates for several limitations when studying their natural history. We evaluated trends in terrestrial reptile spatial ecology studies focusing upon quantifying home ranges for the past twenty years. We assessed 290 English-language reptile home range studies published from 2000-2019 via a structured literature review investigating publications' study location, taxonomic group, methodology, reporting, and analytical techniques. Substantial biases remain in both location and taxonomic groups in the literature, with nearly half of all studies (45%) originating from the USA. Snakes were most often studied, and crocodiles were least often studied, while testudines tended to have the greatest within study sample sizes. More than half of all studies lacked critical methodological details, limiting the number of studies for inclusion in future meta-analyses (55% of studies lacked information on individual tracking durations, and 51% lacked sufficient information on the number of times researchers recorded positions). Studies continue to rely on outdated methods to quantify space-use (including Minimum Convex Polygons and Kernel Density Estimators), often failing to report subtleties regarding decisions that have substantial impact on home range area estimates. Moving forward researchers can select a suite of appropriate analytical techniques tailored to their research question (dynamic Brownian Bridge Movement Models for within sample interpolation, and autocorrelated Kernel Density Estimators for beyond sample extrapolation). Only 1.4% of all evaluated studies linked to available and usable telemetry data, further hindering scientific consensus. We ultimately implore herpetologists to adopt transparent reporting practices and make liberal use of open data platforms to maximize progress in the field of reptile spatial ecology.

Opinion: Independent third-party entities as a model for validation of commercial antibodies

A vast array of commercial antibodies covers a large percentage of human gene products, but determining which among them is most appropriate for any given application is challenging. This leads to use of non-specific antibodies that contributes to issues with reproducibility. It is our opinion that the community of scientists who use commercial antibodies in their biomedical research would benefit from third-party antibody characterization entities that use standardized operating procedures to assess and compare antibody performance. Ideally, such entities would follow the principles of open science, such that all antibodies against any given protein target would be tested in parallel, and all data generated released to the public domain without bias. Furthermore, there should be no financial incentive for the entity beyond cost-recovery. Such non-profit organizations, combined with other scientific efforts, could catalyse new discoveries by providing scientists with better validated antibody tools.

FAIR chemical structures in the Journal of Cheminformatics

The ability to access chemical information openly is an essential part of many scientific disciplines. The Journal of Cheminformatics is leading the way for rigorous, open cheminformatics in many ways, but there remains room for improvement in primary areas. This letter discusses how both authors and the journal alike can help increase the FAIRness (Findability, Accessibility, Interoperability, Reusability) of the chemical structural information in the journal. A proposed chemical structure template can serve as an interoperable Additional File format (already accessible), made more findable by linking the DOI of this data file to the article DOI metadata, supporting further reuse.

Twitter integration of chemistry software tools

Social media activity on a research article is considered to be an altmetric, a new measure to estimate research impact. Demonstrating software on Twitter is a powerful way to attract attention from a larger audience. Twitter integration of software can also lower the barriers to trying the tools and make it easier to save and share the output. We present three case studies of Twitter bots for cheminformatics: retrosynthetic analysis, 3D molecule viewer, and 2D chemical structure editor. These bots make software research more accessible to a broader range of people and facilitate the sharing of chemical knowledge, concepts, and ideas.

Design-Based Approaches to Causal Replication Studies

Recent interest in promoting replication efforts assumes that there is well-established methodological guidance for designing and implementing these studies. However, no such consensus exists in the methodology literature. This article addresses these challenges by describing design-based approaches for planning systematic replication studies. Our general approach is derived from the Causal Replication Framework (CRF), which formalizes the assumptions under which replication success can be expected. The assumptions may be understood broadly as replication design requirements and individual study design requirements. Replication failure occurs when one or more CRF assumptions are violated. In design-based approaches to replication, CRF assumptions are systematically tested to evaluate the replicability of effects, as well as to identify sources of effect variation when replication failure is observed. The paper describes research designs for replication and demonstrates how multiple designs may be combined in systematic replication efforts, as well as how diagnostic measures may be used to assess the extent to which CRF assumptions are met in field settings.

Physcraper: a Python package for continually updated phylogenetic trees using the Open Tree of Life

BACKGROUND

Phylogenies are a key part of research in many areas of biology. Tools that automate some parts of the process of phylogenetic reconstruction, mainly molecular character matrix assembly, have been developed for the advantage of both specialists in the field of phylogenetics and non-specialists. However, interpretation of results, comparison with previously available phylogenetic hypotheses, and selection of one phylogeny for downstream analyses and discussion still impose difficulties to one that is not a specialist either on phylogenetic methods or on a particular group of study.

RESULTS

Physcraper is a command-line Python program that automates the update of published phylogenies by adding public DNA sequences to underlying alignments of previously published phylogenies. It also provides a framework for straightforward comparison of published phylogenies with their updated versions, by leveraging upon tools from the Open Tree of Life project to link taxonomic information across databases. The program can be used by the nonspecialist, as a tool to generate phylogenetic hypotheses based on publicly available expert phylogenetic knowledge. Phylogeneticists and taxonomic group specialists will find it useful as a tool to facilitate molecular dataset gathering and comparison of alternative phylogenetic hypotheses (topologies).

CONCLUSION

The Physcraper workflow showcases the benefits of doing open science for phylogenetics, encouraging researchers to strive for better scientific sharing practices. Physcraper can be used with any OS and is released under an open-source license. Detailed instructions for installation and usage are available at https://physcraper.readthedocs.io.

Open science saves lives: lessons from the COVID-19 pandemic

In the last decade Open Science principles have been successfully advocated for and are being slowly adopted in different research communities. In response to the COVID-19 pandemic many publishers and researchers have sped up their adoption of Open Science practices, sometimes embracing them fully and sometimes partially or in a sub-optimal manner. In this article, we express concerns about the violation of some of the Open Science principles and its potential impact on the quality of research output. We provide evidence of the misuses of these principles at different stages of the scientific process. We call for a wider adoption of Open Science practices in the hope that this work will encourage a broader endorsement of Open Science principles and serve as a reminder that science should always be a rigorous process, reliable and transparent, especially in the context of a pandemic where research findings are being translated into practice even more rapidly. We provide all data and scripts at https://osf.io/renxy/ .

Evaluating implementation of the Transparency and Openness Promotion (TOP) guidelines: the TRUST process for rating journal policies, procedures, and practices

BACKGROUND

The Transparency and Openness Promotion (TOP) Guidelines describe modular standards that journals can adopt to promote open science. The TOP Factor is a metric to describe the extent to which journals have adopted the TOP Guidelines in their policies. Systematic methods and rating instruments are needed to calculate the TOP Factor. Moreover, implementation of these open science policies depends on journal procedures and practices, for which TOP provides no standards or rating instruments.

METHODS

We describe a process for assessing journal policies, procedures, and practices according to the TOP Guidelines. We developed this process as part of the Transparency of Research Underpinning Social Intervention Tiers (TRUST) Initiative to advance open science in the social intervention research ecosystem. We also provide new instruments for rating journal instructions to authors (policies), manuscript submission systems (procedures), and published articles (practices) according to standards in the TOP Guidelines. In addition, we describe how to determine the TOP Factor score for a journal, calculate reliability of journal ratings, and assess coherence among a journal's policies, procedures, and practices. As a demonstration of this process, we describe a protocol for studying approximately 345 influential journals that have published research used to inform evidence-based policy.

DISCUSSION

The TRUST Process includes systematic methods and rating instruments for assessing and facilitating implementation of the TOP Guidelines by journals across disciplines. Our study of journals publishing influential social intervention research will provide a comprehensive account of whether these journals have policies, procedures, and practices that are consistent with standards for open science and thereby facilitate the publication of trustworthy findings to inform evidence-based policy. Through this demonstration, we expect to identify ways to refine the TOP Guidelines and the TOP Factor. Refinements could include: improving templates for adoption in journal instructions to authors, manuscript submission systems, and published articles; revising explanatory guidance intended to enhance the use, understanding, and dissemination of the TOP Guidelines; and clarifying the distinctions among different levels of implementation. Research materials are available on the Open Science Framework: https://osf.io/txyr3/ .

The fall of the innovation empire and its possible rise through open science

There is growing concern that the innovation system's ability to create wealth and attain social benefit is declining in effectiveness. This article explores the reasons for this decline and suggests a structure, the open science partnership, as one mechanism through which to slow down or reverse this decline. The article examines the empirical literature of the last century to document the decline. This literature suggests that the cost of research and innovation is increasing exponentially, that researcher productivity is declining, and, third, that these two phenomena have led to an overall flat or declining level of innovation productivity. The article then turns to three explanations for the decline - the growing complexity of science, a mismatch of incentives, and a balkanization of knowledge. Finally, the article explores the role that open science partnerships - public-private partnerships based on open access publications, open data and materials, and the avoidance of restrictive forms of intellectual property - can play in increasing the efficiency of the innovation system.

Fostering global data sharing: highlighting the recommendations of the Research Data Alliance COVID-19 working group

The systemic challenges of the COVID-19 pandemic require cross-disciplinary collaboration in a global and timely fashion. Such collaboration needs open research practices and the sharing of research outputs, such as data and code, thereby facilitating research and research reproducibility and timely collaboration beyond borders. The Research Data Alliance COVID-19 Working Group recently published a set of recommendations and guidelines on data sharing and related best practices for COVID-19 research. These guidelines include recommendations for clinicians, researchers, policy- and decision-makers, funders, publishers, public health experts, disaster preparedness and response experts, infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations), and other potential users. These guidelines include recommendations for researchers, policymakers, funders, publishers and infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations). Several overarching themes have emerged from this document such as the need to balance the creation of data adherent to FAIR principles (findable, accessible, interoperable and reusable), with the need for quick data release; the use of trustworthy research data repositories; the use of well-annotated data with meaningful metadata; and practices of documenting methods and software. The resulting document marks an unprecedented cross-disciplinary, cross-sectoral, and cross-jurisdictional effort authored by over 160 experts from around the globe. This letter summarises key points of the Recommendations and Guidelines, highlights the relevant findings, shines a spotlight on the process, and suggests how these developments can be leveraged by the wider scientific community.