The Open Science movement is rapidly changing the scientific landscape. Because exact definitions are often lacking and reforms are constantly evolving, accessible guides to open science are needed. This paper provides an introduction to open science and related reforms in the form of an annotated reading list of seven peer-reviewed articles, following the format of Etz et al. (2018). Written for researchers and students - particularly in psychological science - it highlights and introduces seven topics: understanding open science; open access; open data, materials, and code; reproducible analyses; preregistration and registered reports; replication research; and teaching open science. For each topic, we provide a detailed summary of one particularly informative and actionable article and suggest several further resources. Supporting a broader understanding of open science issues, this overview should enable researchers to engage with, improve, and implement current open, transparent, reproducible, replicable, and cumulative scientific practices.

Poor research reporting is a major contributing factor to low study reproducibility, financial and animal waste. The ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines were developed to improve reporting quality and many journals support these guidelines. The influence of this support is unknown. We hypothesized that papers published in journals supporting the ARRIVE guidelines would show improved reporting compared with those in non-supporting journals. In a retrospective, observational cohort study, papers from 5 ARRIVE supporting (SUPP) and 2 non-supporting (nonSUPP) journals, published before (2009) and 5 years after (2015) the ARRIVE guidelines, were selected. Adherence to the ARRIVE checklist of 20 items was independently evaluated by two reviewers and items assessed as fully, partially or not reported. Mean percentages of items reported were compared between journal types and years with an unequal variance t-test. Individual items and sub-items were compared with a chi-square test. From an initial cohort of 956, 236 papers were included: 120 from 2009 (SUPP; n = 52, nonSUPP; n = 68), 116 from 2015 (SUPP; n = 61, nonSUPP; n = 55). The percentage of fully reported items was similar between journal types in 2009 (SUPP: 55.3 ± 11.5% [SD]; nonSUPP: 51.8 ± 9.0%; p = 0.07, 95% CI of mean difference -0.3–7.3%) and 2015 (SUPP: 60.5 ± 11.2%; nonSUPP; 60.2 ± 10.0%; p = 0.89, 95%CI -3.6–4.2%). The small increase in fully reported items between years was similar for both journal types (p = 0.09, 95% CI -0.5–4.3%). No paper fully reported 100% of items on the ARRIVE checklist and measures associated with bias were poorly reported. These results suggest that journal support for the ARRIVE guidelines has not resulted in a meaningful improvement in reporting quality, contributing to ongoing waste in animal research.

Open science practices have the potential to greatly accelerate progress in scientific research if widely adopted, but individual action may not be enough to...

The veracity of substantive research claims hinges on the way experimental data are collected and analyzed. In this article, we discuss an uncomfortable fact that threatens the core of psychology’s academic enterprise: almost without exception, psychologists do not commit themselves to a method of data analysis before they see the actual data. It then becomes tempting to fine tune the analysis to the data in order to obtain a desired result—a procedure that invalidates the interpretation of the common statistical tests. The extent of the fine tuning varies widely across experiments and experimenters but is almost impossible for reviewers and readers to gauge. To remedy the situation, we propose that researchers preregister their studies and indicate in advance the analyses they intend to conduct. Only these analyses deserve the label “confirmatory,” and only for these analyses are the common statistical tests valid. Other analyses can be carried out but these should be labeled “exploratory.” We illustrate our proposal with a confirmatory replication attempt of a study on extrasensory perception.

Ongoing technological developments have made it easier than ever before for scientists to share their data, materials, and analysis code. Sharing data and analysis code makes it easier for other researchers to re-use or check published research. These benefits will only emerge if researchers can reproduce the analysis reported in published articles, and if data is annotated well enough so that it is clear what all variables mean. Because most researchers have not been trained in computational reproducibility, it is important to evaluate current practices to identify practices that can be improved. We examined data and code sharing, as well as computational reproducibility of the main results, without contacting the original authors, for Registered Reports published in the psychological literature between 2014 and 2018. Of the 62 articles that met our inclusion criteria, data was available for 40 articles, and analysis scripts for 37 articles. For the 35 articles that shared both data and code and performed analyses in SPSS, R, Python, MATLAB, or JASP, we could run the scripts for 31 articles, and reproduce the main results for 20 articles. Although the articles that shared both data and code (35 out of 62, or 56%) and articles that could be computationally reproduced (20 out of 35, or 57%) was relatively high compared to other studies, there is clear room for improvement. We provide practical recommendations based on our observations, and link to examples of good research practices in the papers we reproduced.

The webinar features Dr. Joshua Rosenberg from the University of Tennessee, Knoxville and Dr. Cynthia D’Angelo from the University of Illinois at Urbana-Champaign discussing best practices examples for using R. They will present: a) general strategies for using R to analyze educational data and b) accessing and using data on the Open Science Framework (OSF) with R via the osfr package. This session is for those both new to R and those with R experience looking to learn more about strategies and workflows that can help to make it possible to analyze data in a more transparent, reliable, and trustworthy way.

This workshop demonstrates how using R can advance open science practices in education. We focus on R and RStudio because it is an increasingly widely-used programming language and software environment for data analysis with a large supportive community. We present: a) general strategies for using R to analyze educational data and b) accessing and using data on the Open Science Framework (OSF) with R via the osfr package. This session is for those both new to R and those with R experience looking to learn more about strategies and workflows that can help to make it possible to analyze data in a more transparent, reliable, and trustworthy way. Access the workshop slides and supplemental information at https://osf.io/vtcak/​.

Resources:

1) Download R: https://www.r-project.org/​
2) Download RStudio (a tool that makes R easier to use): https://rstudio.com/products/rstudio/...​
3) R for Data Science (a free, digital book about how to do data science with R): https://r4ds.had.co.nz/​
4) Tidyverse R packages for data science: https://www.tidyverse.org/​
5) RMarkdown from RStudio (including info about R Notebooks): https://rmarkdown.rstudio.com/​
6) Data Science in Education Using R: https://datascienceineducation.com/​

Python es un lenguaje de programación general que es útil para escribir scripts para trabajar con datos de manera efectiva y reproducible. Esta es una introducción a Python diseñada para participantes sin experiencia en programación. Estas lecciones pueden enseñarse en un día (~ 6 horas). Las lecciones empiezan con información básica sobre la sintaxis de Python, la interface de Jupyter Notebook, y continúan con cómo importar archivos CSV, usando el paquete Pandas para trabajar con DataFrames, cómo calcular la información resumen de un DataFrame, y una breve introducción en cómo crear visualizaciones. La última lección demuestra cómo trabajar con bases de datos directamente desde Python. Nota: los datos no han sido traducidos de la versión original en inglés, por lo que los nombres de variables se mantienen en inglés y los números de cada observación usan la sintaxis de habla inglesa (coma separador de miles y punto separador de decimales).

Recent research in psychology has highlighted a number of replication problems in the discipline, with publication bias – the preference for publishing original and positive results, and a resistance to publishing negative results and replications- identified as one reason for replication failure. However, little empirical research exists to demonstrate that journals explicitly refuse to publish replications. We reviewed the instructions to authors and the published aims of 1151 psychology journals and examined whether they indicated that replications were permitted and accepted. We also examined whether journal practices differed across branches of the discipline, and whether editorial practices differed between low and high impact journals. Thirty three journals (3%) stated in their aims or instructions to authors that they accepted replications. There was no difference between high and low impact journals. The implications of these findings for psychology are discussed.

In this study, we examined participants' choice behavior in a sequential risk-taking task. We were especially interested in the extent to which participants focus on the immediate next choice or consider the entire choice sequence. To do so, we inspected whether decisions were either based on conditional probabilities (e.g., being successful on the immediate next trial) or on conjunctive probabilities (of being successful several times in a row). The results of five experiments with a simplified nine-card Columbia Card Task and a CPT-model analysis show that participants' choice behavior can be described best by a mixture of the two probability types. Specifically, for their first choice, the participants relied on conditional probabilities, whereas subsequent choices were based on conjunctive probabilities. This strategy occurred across different start conditions in which more or less cards were already presented face up. Consequently, the proportion of risky choices was substantially higher when participants started from a state with some cards facing up, compared with when they arrived at that state starting from the very beginning. The results, alternative accounts, and implications are discussed.

There is broad interest to improve the reproducibility of published research. We developed a survey tool to assess the availability of digital research artifacts published alongside peer-reviewed journal articles (e.g. data, models, code, directions for use) and reproducibility of article results. We used the tool to assess 360 of the 1,989 articles published by six hydrology and water resources journals in 2017. Like studies from other fields, we reproduced results for only a small fraction of articles (1.6% of tested articles) using their available artifacts. We estimated, with 95% confidence, that results might be reproduced for only 0.6% to 6.8% of all 1,989 articles. Unlike prior studies, the survey tool identified key bottlenecks to making work more reproducible. Bottlenecks include: only some digital artifacts available (44% of articles), no directions (89%), or all artifacts available but results not reproducible (5%). The tool (or extensions) can help authors, journals, funders, and institutions to self-assess manuscripts, provide feedback to improve reproducibility, and recognize and reward reproducible articles as examples for others.

To increase transparency in research, the International Committee of Medical Journal Editors required, in 2005, prospective registration of clinical trials as a condition to publication. However, many trials remain unregistered or retrospectively registered. We aimed to assess the association between trial prospective registration and treatment effect estimates. Methods This is a meta-epidemiological study based on all Cochrane reviews published between March 2011 and September 2014 with meta-analyses of a binary outcome including three or more randomised controlled trials published after 2006. We extracted trial general characteristics and results from the Cochrane reviews. For each trial, we searched for registration in the report’s full text, contacted the corresponding author if not reported and searched ClinicalTrials.gov and the International Clinical Trials Registry Platform in case of no response. We classified each trial as prospectively registered (i.e. registered before the start date); retrospectively registered, distinguishing trials registered before and after the primary completion date; and not registered. Treatment effect estimates of prospectively registered and other trials were compared by the ratio of odds ratio (ROR) (ROR <1 indicates larger effects in trials not prospectively registered). Results We identified 67 meta-analyses (322 trials). Overall, 225/322 trials (70 %) were registered, 74 (33 %) prospectively and 142 (63 %) retrospectively; 88 were registered before the primary completion date and 54 after. Unregistered or retrospectively registered trials tended to show larger treatment effect estimates than prospectively registered trials (combined ROR = 0.81, 95 % CI 0.65–1.02, based on 32 contributing meta-analyses). Trials unregistered or registered after the primary completion date tended to show larger treatment effect estimates than those registered before this date (combined ROR = 0.84, 95 % CI 0.71–1.01, based on 43 contributing meta-analyses). Conclusions Lack of trial prospective registration may be associated with larger treatment effect estimates.

Objectives Prospective registration of animal studies has been suggested as a new measure to increase value and reduce waste in biomedical research. We sought to further explore and quantify animal researchers’ attitudes and preferences regarding animal study registries (ASRs). Design Cross-sectional online survey. Setting and participants We conducted a survey with three different samples representing animal researchers: i) corresponding authors from journals with high Eigenfactor, ii) a random Pubmed sample and iii) members of the CAMARADES network. Main outcome measures Perceived level of importance of different aspects of publication bias, the effect of ASRs on different aspects of research as well as the importance of different research types for being registered. Results The survey yielded responses from 413 animal researchers (response rate 7%). The respondents indicated, that some aspects of ASRs can increase administrative burden but could be outweighed by other aspects decreasing this burden. Animal researchers found it more important to register studies that involved animal species with higher levels of cognitive capabilities. The time frame for making registry entries publicly available revealed a strong heterogeneity among respondents, with the largest proportion voting for “access only after consent by the principal investigator” and the second largest proportion voting for “access immediately after registration”. Conclusions The fact that the more senior and experienced animal researchers participating in this survey clearly indicated the practical importance of publication bias and the importance of ASRs underscores the problem awareness across animal researchers and the willingness to actively engage in study registration if effective safeguards for the potential weaknesses of ASRs are put into place. To overcome the first-mover dilemma international consensus statements on how to deal with prospective registration of animal studies might be necessary for all relevant stakeholder groups including animal researchers, academic institutions, private companies, funders, regulatory agencies, and journals.

Accumulating evidence indicates high risk of bias in preclinical animal research, questioning the scientific validity and reproducibility of published research findings. Systematic reviews found low rates of reporting of measures against risks of bias in the published literature (e.g., randomization, blinding, sample size calculation) and a correlation between low reporting rates and inflated treatment effects. That most animal research undergoes peer review or ethical review would offer the possibility to detect risks of bias at an earlier stage, before the research has been conducted. For example, in Switzerland, animal experiments are licensed based on a detailed description of the study protocol and a harm–benefit analysis. We therefore screened applications for animal experiments submitted to Swiss authorities (n = 1,277) for the rates at which the use of seven basic measures against bias (allocation concealment, blinding, randomization, sample size calculation, inclusion/exclusion criteria, primary outcome variable, and statistical analysis plan) were described and compared them with the reporting rates of the same measures in a representative sub-sample of publications (n = 50) resulting from studies described in these applications. Measures against bias were described at very low rates, ranging on average from 2.4% for statistical analysis plan to 19% for primary outcome variable in applications for animal experiments, and from 0.0% for sample size calculation to 34% for statistical analysis plan in publications from these experiments. Calculating an internal validity score (IVS) based on the proportion of the seven measures against bias, we found a weak positive correlation between the IVS of applications and that of publications (Spearman’s rho = 0.34, p = 0.014), indicating that the rates of description of these measures in applications partly predict their rates of reporting in publications. These results indicate that the authorities licensing animal experiments are lacking important information about experimental conduct that determines the scientific validity of the findings, which may be critical for the weight attributed to the benefit of the research in the harm–benefit analysis. Similar to manuscripts getting accepted for publication despite poor reporting of measures against bias, applications for animal experiments may often be approved based on implicit confidence rather than explicit evidence of scientific rigor. Our findings shed serious doubt on the current authorization procedure for animal experiments, as well as the peer-review process for scientific publications, which in the long run may undermine the credibility of research. Developing existing authorization procedures that are already in place in many countries towards a preregistration system for animal research is one promising way to reform the system. This would not only benefit the scientific validity of findings from animal experiments but also help to avoid unnecessary harm to animals for inconclusive research.

A Software Carpentry lesson to learn how to use Make Make is a tool which can run commands to read files, process these files in some way, and write out the processed files. For example, in software development, Make is used to compile source code into executable programs or libraries, but Make can also be used to: run analysis scripts on raw data files to get data files that summarize the raw data; run visualization scripts on data files to produce plots; and to parse and combine text files and plots to create papers. Make is called a build tool - it builds data files, plots, papers, programs or libraries. It can also update existing files if desired. Make tracks the dependencies between the files it creates and the files used to create these. If one of the original files (e.g. a data file) is changed, then Make knows to recreate, or update, the files that depend upon this file (e.g. a plot). There are now many build tools available, all of which are based on the same concepts as Make.

Scientific data and tools should, as much as possible, be free as in beer and free as in freedom. The vast majority of science today is paid for by taxpayer-funded grants; at the same time, the incredible successes of science are strong evidence for the benefit of collaboration in knowledgable pursuits. Within the scientific academy, sharing of expertise, data, tools, etc. is prolific, but only recently with the rise of the Open Access movement has this sharing come to embrace the public. Even though most research data is never shared, both the public and even scientists in their own fields are often unaware of just much data, tools, and other resources are made freely available for analysis! This list is a small attempt at bringing light to data repositories and computational science tools that are often siloed according to each scientific discipline, in the hopes of spurring along both public and professional contributions to science.

Background: The reproducibility policy at the journal Biostatistics rewards articles with badges for data and code sharing. This study investigates the effect of badges at increasing reproducible research. Methods: The setting of this observational study is the Biostatistics and Statistics in Medicine (control journal) online research archives. The data consisted of 240 randomly sampled articles from 2006 to 2013 (30 articles per year) per journal. Data analyses included: plotting probability of data and code sharing by article submission date, and Bayesian logistic regression modelling. Results: The probability of data sharing was higher at Biostatistics than the control journal but the probability of code sharing was comparable for both journals. The probability of data sharing increased by 3.9 times (95% credible interval: 1.5 to 8.44 times, p-value probability that sharing increased: 0.998) after badges were introduced at Biostatistics. On an absolute scale, this difference was only a 7.6% increase in data sharing (95% CI: 2 to 15%, p-value: 0.998). Badges did not have an impact on code sharing at the journal (mean increase: 1 time, 95% credible interval: 0.03 to 3.58 times, p-value probability that sharing increased: 0.378). 64% of articles at Biostatistics that provide data/code had broken links, and at Statistics in Medicine, 40%; assuming these links worked only slightly changed the effect of badges on data (mean increase: 6.7%, 95% CI: 0.0% to 17.0%, p-value: 0.974) and on code (mean increase: -2%, 95% CI: -10.0 to 7.0%, p-value: 0.286). Conclusions: The effect of badges at Biostatistics was a 7.6% increase in the data sharing rate, 5 times less than the effect of badges at Psychological Science. Though badges at Biostatistics did not impact code sharing, and had a moderate effect on data sharing, badges are an interesting step that journals are taking to incentivise and promote reproducible research.

Beginning January 2014, Psychological Science gave authors the opportunity to signal open data and materials if they qualified for badges that accompanied published articles. Before badges, less than 3% of Psychological Science articles reported open data. After badges, 23% reported open data, with an accelerating trend; 39% reported open data in the first half of 2015, an increase of more than an order of magnitude from baseline. There was no change over time in the low rates of data sharing among comparison journals. Moreover, reporting openness does not guarantee openness. When badges were earned, reportedly available data were more likely to be actually available, correct, usable, and complete than when badges were not earned. Open materials also increased to a weaker degree, and there was more variability among comparison journals. Badges are simple, effective signals to promote open practices and improve preservation of data and materials by using independent repositories.

We revisit the results of the recent Reproducibility Project: Psychology by the Open Science Collaboration. We compute Bayes factors—a quantity that can be used to express comparative evidence for an hypothesis but also for the null hypothesis—for a large subset (N = 72) of the original papers and their corresponding replication attempts. In our computation, we take into account the likely scenario that publication bias had distorted the originally published results. Overall, 75% of studies gave qualitatively similar results in terms of the amount of evidence provided. However, the evidence was often weak (i.e., Bayes factor < 10). The majority of the studies (64%) did not provide strong evidence for either the null or the alternative hypothesis in either the original or the replication, and no replication attempts provided strong evidence in favor of the null. In all cases where the original paper provided strong evidence but the replication did not (15%), the sample size in the replication was smaller than the original. Where the replication provided strong evidence but the original did not (10%), the replication sample size was larger. We conclude that the apparent failure of the Reproducibility Project to replicate many target effects can be adequately explained by overestimation of effect sizes (or overestimation of evidence against the null hypothesis) due to small sample sizes and publication bias in the psychological literature. We further conclude that traditional sample sizes are insufficient and that a more widespread adoption of Bayesian methods is desirable.