Open science practices are designed to enhance the utility, integrity, and credibility of scientific research. This article highlights how preregistration in open science practice can be leveraged to enhance the rigor and transparency of single-case experimental designs within an applied behavior analysis framework. We provide an overview of the benefits of preregistration including increased transparency, reduced risk of researcher bias, and improved replicability, and we review the specific contexts under which these practices most benefit the proposed framework. We discuss potential concerns with and unique considerations for preregistering experiments that use single-case designs, with practical guidance for researchers who are seeking to preregister their studies. We present a checklist as a tool for researchers in applied behavior analysis to use for preregistration and provide recommendations for our field to strengthen the contingencies for open science practices that include preregistration.

Open Science practices are integral to increasing transparency, reproducibility, and accessibility of research in health psychology and behavioral medicine. Drives to facilitate Open Science practices are becoming increasingly evident in journal editorial policies, including the establishment of new paper formats such as Registered Reports and Data Notes. This paper provides: (i) an overview of the current state of Open Science policies within health psychology and behavioral medicine, (ii) a call for submissions to an Article Collection of Registered Reports and Data Notes as new paper formats within the journal of Health Psychology & Behavioral Medicine, (iii) an overview of Registered Reports and Data Notes, and (iv) practical considerations for authors and reviewers of Registered Reports and Data Notes.

UNASSIGNED: A discussion on social media led to the formation of a multidisciplinary group working on this project to highlight women\'s contributions to science. The role of marginalised groups in science has been a topic of much discussion, but data on these contributions are largely lacking. Our motivation for the development of this dataset was not only to highlight names of plant genera that honour women, but to enrich this information with data that would allow the names, roles and lives of these women to be shared more widely with others, both researchers and data sources like Wikidata. Amplification of the contributions of women to botany through multiple means will enable the community to better recognise and celebrate the role of this particular marginalised group in the history and development of science.
UNASSIGNED: The innovative approach of our study resulted in a dataset that is dynamic, expansive and widely shared. We have published a static dataset with this paper and have also created a dynamic dataset by linking flowering plant genera and the women in whose honour those genera were named in Wikidata. This concurrent addition of the data to Wikidata, a linked open data repository, enabled it to be enriched, queried and proactively shared during the whole process of dataset creation and into the future. This innovative workflow allowed wide, open participation throughout the research process. The methodology and workflows applied can be used to create future datasets celebrating and amplifying the contributions of marginalised groups in science.

Registered reports are a publication format that involves peer reviewing studies both before and after carrying out research procedures. Although registered reports were originally developed to combat challenges in quantitative and confirmatory study designs, today registered reports are also available for qualitative and exploratory work. This article provides a brief primer that aims to help researchers in choosing, designing, and evaluating registered reports, which are driven by qualitative methods.

The GLES Open Science Challenge 2021 was a pioneering initiative in quantitative political science. Aimed at increasing the adoption of replicable and transparent research practices, it led to this special issue. The project combined the rigor of registered reports-a new publication format in which studies are evaluated prior to data collection/access and analysis-with quantitative political science research in the context of the 2021 German federal election. This special issue, which features the registered reports that resulted from the project, shows that transparent research following open science principles benefits our discipline and substantially contributes to quantitative political science. In this introduction to the special issue, we first elaborate on why more transparent research practices are necessary to guarantee the cumulative progress of scientific knowledge. We then show how registered reports can contribute to increasing the transparency of scientific practices. Next, we discuss the application of open science practices in quantitative political science to date. And finally, we present the process and schedule of the GLES Open Science Challenge and give an overview of the contributions included in this special issue.
Die GLES Open Science Challenge 2021 war ein Pilotprojekt in der quantitativen Politikwissenschaft, die darauf abzielte, die Akzeptanz von replizierbaren und transparenten Forschungspraktiken zu erhöhen. Dieses Sonderheft enthält die im Rahmen dieses Projektes entstandenen Artikel. Die GLES Open Science Challenge verband die strengen Regeln von Registered Reports – einem neuen Publikationsformat, bei dem Studien vor der Datenanalyse begutachtet werden – mit quantitativer Politikwissenschaft im Kontext der Bundestagswahl 2021. Die Beiträge dieses Sonderheftes zeigen, dass transparente Forschung, die den Prinzipien von Open Science folgt, unserer Disziplin zugutekommt und einen wesentlichen Beitrag zur quantitativen Politikwissenschaft leistet. In dieser Einleitung zum Sonderheft erläutern wir zunächst, warum transparentere Forschungspraktiken notwendig sind, um den kumulativen Fortschritt der wissenschaftlichen Erkenntnisse zu gewährleisten. Dann zeigen wir auf, wie Registered Reports dazu beitragen können, die Transparenz wissenschaftlicher Praktiken zu erhöhen. Anschließend diskutieren wir die bisherige Anwendung von Open-Science-Praktiken in der quantitativen Politikwissenschaft. Nachfolgend stellen wir den Prozess und den Zeitplan der GLES Open Science Challenge vor und geben abschließend einen Überblick über die in diesem Sonderheft enthaltenen Beiträge.

Being on the bleeding edge of research requires the use of new and regularly updated software. The result is the occasional and inevitable occurrence of bugs. In the following work we present a case study where a feature request introduced a bug in a neuroimaging software package, which had consequences for the quality of results in a published article. We discuss the process of diagnosis, rectification and analysis replication.

The search for novel therapeutic compounds remains an overwhelming task owing to the time-consuming and expensive nature of the drug development process and low success rates. Traditional methodologies that rely on the one drug-one target paradigm have proven insufficient for the treatment of multifactorial diseases, leading to a shift to multitarget approaches. In this emerging paradigm, molecules with off-target and promiscuous interactions may result in preferred therapies. In this study, we developed a general pipeline combining machine learning algorithms and a deep generator network to train a dual inhibitor classifier capable of identifying putative pharmacophoric traits. As a case study, we focused on dual inhibitors targeting DNA methyltransferase 1 (DNMT) and histone deacetylase 2 (HDAC2), two enzymes that play a central role in epigenetic regulation. We used this approach to identify dual inhibitors from a novel large natural product database in the public domain. We used docking and atomistic simulations as complementary approaches to establish the ligand-interaction profiles between the best hits and DNMT1/HDAC2. By using the combined ligand- and structure-based approaches, we discovered two promising novel scaffolds that can be used to simultaneously target both DNMT1 and HDAC2. We conclude that the flexibility and adaptability of the proposed pipeline has predictive capabilities of similar or derivative methods and is readily applicable to the discovery of small molecules targeting many other therapeutically relevant proteins.

A novel perception of botanic gardens as complex \"factories of molecules\" (Lombardy Region Project-Lr. 25/2016, year 2021), that mediate plant-environment interactions, and are the basis of their utility for humans, is presented. The core-topic is the medicinal plant heritage of the Ghirardi Botanic Garden (Toscolano Maderno, Brescia, Italy) of the University of Milan. In this work, we studied Myrtus communis L. subsp. communis (Myrtaceae) at multiple scale levels: macro- and micromorphological, with special emphasis on the secretory structures responsible for the production of secondary metabolites; phytochemical, with the analysis of the essential oil (EO) composition from leaves (fresh, dried, stored at -20 °C and at -80 °C) and fruits over two consecutive years (2018 and 2019); bio-ecological, with a focus, based on literature data, on the ecology and biological activity of the main EO components. The occurrence of secretory cavities producing terpenes, along with flavonoids, was proven. A high level of chemical variability across the obtained EO profiles emerged, especially that concerning quantitative data. However, regardless of the different conservation procedures, the examined plant part, or the phenological stage, we detected the presence of three ubiquitous compounds: α-pinene, 1,8-cineole, and linalool. The overall results will serve to enrich the Ghirardi Botanic Garden with novel labeling showing accurate and updated scientific information in an Open science perspective.

As part of the Reproducibility Project: Cancer Biology, we published Registered Reports that described how we intended to replicate selected experiments from 29 high-impact preclinical cancer biology papers published between 2010 and 2012. Replication experiments were completed and Replication Studies reporting the results were submitted for 18 papers, of which 17 were accepted and published by eLife with the rejected paper posted as a preprint. Here, we report the status and outcomes obtained for the remaining 11 papers. Four papers initiated experimental work but were stopped without any experimental outcomes. Two papers resulted in incomplete outcomes due to unanticipated challenges when conducting the experiments. For the remaining five papers only some of the experiments were completed with the other experiments incomplete due to mundane technical or unanticipated methodological challenges. The experiments from these papers, along with the other experiments attempted as part of the Reproducibility Project: Cancer Biology, provides evidence about the challenges of repeating preclinical cancer biology experiments and the replicability of the completed experiments.

ICEES (Integrated Clinical and Environmental Exposures Service) provides a disease-agnostic, regulatory-compliant approach for openly exposing and analyzing clinical data that have been integrated at the patient level with environmental exposures data. ICEES is equipped with basic features to support exploratory analysis using statistical approaches, such as bivariate chi-square tests. We recently developed a method for using ICEES to generate multivariate tables for subsequent application of machine learning and statistical models. The objective of the present study was to use this approach to identify predictors of asthma exacerbations through the application of three multivariate methods: conditional random forest, conditional tree, and generalized linear model. Among seven potential predictor variables, we found five to be of significant importance using both conditional random forest and conditional tree: prednisone, race, airborne particulate exposure, obesity, and sex. The conditional tree method additionally identified several significant two-way and three-way interactions among the same variables. When we applied a generalized linear model, we identified four significant predictor variables, namely prednisone, race, airborne particulate exposure, and obesity. When ranked in order by effect size, the results were in agreement with the results from the conditional random forest and conditional tree methods as well as the published literature. Our results suggest that the open multivariate analytic capabilities provided by ICEES are valid in the context of an asthma use case and likely will have broad value in advancing open research in environmental and public health.