Limiting the debilitating consequences of ageing is a major medical challenge of our time. Robust pharmacological interventions that promote healthy ageing across diverse genetic backgrounds may engage conserved longevity pathways. Here we report results from the Caenorhabditis Intervention Testing Program in assessing longevity variation across 22 Caenorhabditis strains spanning 3 species, using multiple replicates collected across three independent laboratories. Reproducibility between test sites is high, whereas individual trial reproducibility is relatively low. Of ten pro-longevity chemicals tested, six significantly extend lifespan in at least one strain. Three reported dietary restriction mimetics are mainly effective across C. elegans strains, indicating species and strain-specific responses. In contrast, the amyloid dye ThioflavinT is both potent and robust across the strains. Our results highlight promising pharmacological leads and demonstrate the importance of assessing lifespans of discrete cohorts across repeat studies to capture biological variation in the search for reproducible ageing interventions.

The last ten years have witnessed increasing awareness of questionable research practices (QRPs) in the life sciences, including p-hacking, HARKing, lack of replication, publication bias, low statistical power and lack of data sharing (see Figure 1). Concerns about such behaviours have been raised repeatedly for over half a century but the incentive structure of academia has not changed to address them. Despite the complex motivations that drive academia, many QRPs stem from the simple fact that the incentives which offer success to individual scientists conflict with what is best for science. On the one hand are a set of gold standards that centuries of the scientific method have proven to be crucial for discovery: rigour, reproducibility, and transparency. On the other hand are a set of opposing principles born out of the academic career model: the drive to produce novel and striking results, the importance of confirming prior expectations, and the need to protect research interests from competitors. Within a culture that pressures scientists to produce rather than discover, the outcome is a biased and impoverished science in which most published results are either unconfirmed genuine discoveries or unchallenged fallacies. This observation implies no moral judgement of scientists, who are as much victims of this system as they are perpetrators.

Recently, many psychological effects have been surprisingly difficult to reproduce. This article asks why, and investigates whether conceptually replicating an effect in the original publication is related to the success of independent, direct replications. Two prominent accounts of low reproducibility make different predictions in this respect. One account suggests that psychological phenomena are dependent on unknown contexts that are not reproduced in independent replication attempts. By this account, internal replications indicate that a finding is more robust and, thus, that it is easier to independently replicate it. An alternative account suggests that researchers employ questionable research practices (QRPs), which increase false positive rates. By this account, the success of internal replications may just be the result of QRPs and, thus, internal replications are not predictive of independent replication success. The data of a large reproducibility project support the QRP account: replicating an effect in the original publication is not related to independent replication success. Additional analyses reveal that internally replicated and internally unreplicated effects are not very different in terms of variables associated with replication success. Moreover, social psychological effects in particular appear to lack any benefit from internal replications. Overall, these results indicate that, in this dataset at least, the influence of QRPs is at the heart of failures to replicate psychological findings, especially in social psychology. Variable, unknown contexts appear to play only a relatively minor role. I recommend practical solutions for how QRPs can be avoided.

Workshop goals
- Why are we teaching this
- Why is this important
- For future and current you
- For research as a whole
- Lack of reproducibility in research is a real problem

Materials and how we'll use them
- Workshop landing page, with

- links to the Materials
- schedule

Structure oriented along the Four Facets of Reproducibility:

- Documentation
- Organization
- Automation
- Dissemination

Will be available after the Workshop

How this workshop is run
- This is a Carpentries Workshop
- that means friendly learning environment
- Code of Conduct
- active learning
- work with the people next to you
- ask for help

Data Carpentry lesson to learn how to work with Amazon AWS cloud computing and how to transfer data between your local computer and cloud resources. The cloud is a fancy name for the huge network of computers that host your favorite websites, stream movies, and shop online, but you can also harness all of that computing power for running analyses that would take days, weeks or even years on your local computer. In this lesson, you’ll learn about renting cloud services that fit your analytic needs, and how to interact with one of those services (AWS) via the command line.

Data Carpentry lesson to understand data structures and common storage and transfer formats for spatial data. The goal of this lesson is to provide an introduction to core geospatial data concepts. It is intended for learners who have no prior experience working with geospatial data, and as a pre-requisite for the R for Raster and Vector Data lesson . This lesson can be taught in approximately 75 minutes and covers the following topics: Introduction to raster and vector data format and attributes Examples of data types commonly stored in raster vs vector format Introduction to categorical vs continuous raster data and multi-layer rasters Introduction to the file types and R packages used in the remainder of this workshop Introduction to coordinate reference systems and the PROJ4 format Overview of commonly used programs and applications for working with geospatial data The Introduction to R for Geospatial Data lesson provides an introduction to the R programming language while the R for Raster and Vector Data lesson provides a more in-depth introduction to visualization (focusing on geospatial data), and working with data structures unique to geospatial data. The R for Raster and Vector Data lesson assumes that learners are already familiar with both geospatial data concepts and the core concepts of the R language.

Data Carpentry lesson to open, work with, and plot vector and raster-format spatial data in R. The episodes in this lesson cover how to open, work with, and plot vector and raster-format spatial data in R. Additional topics include working with spatial metadata (extent and coordinate reference systems), reprojecting spatial data, and working with raster time series data.

This video will introduce how to calculate statistical power in R using the pwr package.

All materials shown in the video, as well as content from our other videos, can be found here: https://osf.io/7gqsi/.

This is a recording of a 45 minute introductory webinar on preprints. With our guest speaker Philip Cohen, we’ll cover what preprints/postprints are, the benefits of preprints, and address some common concerns researcher may have. We’ll show how to determine whether you can post preprints/postprints, and also demonstrate how to use OSF preprints (https://osf.io/preprints/) to share preprints. The OSF is the flagship product of the Center for Open Science, a non-profit technology start-up dedicated to improving the alignment between scientific values and scientific practices. Learn more at cos.io and osf.io, or email contact@cos.io.

In this webinar, Doctors David Mellor (Center for Open Science) and Stavroula Kousta (Nature Human Behavior) discuss the Registered Reports publishing workflow and the benefits it may bring to funders of research. Dr. Mellor details the workflow and what it is intended to do, and Dr. Kousta discusses the lessons learned at Nature Human Behavior from their efforts to implement Registered Reports as a journal.

The goal of this lesson is to provide an introduction to R for learners working with geospatial data. It is intended as a pre-requisite for the R for Raster and Vector Data lesson for learners who have no prior experience using R. This lesson can be taught in approximately 4 hours and covers the following topics: Working with R in the RStudio GUI Project management and file organization Importing data into R Introduction to R’s core data types and data structures Manipulation of data frames (tabular data) in R Introduction to visualization Writing data to a file The the R for Raster and Vector Data lesson provides a more in-depth introduction to visualization (focusing on geospatial data), and working with data structures unique to geospatial data.

This video is an introduction to power analyses to improve the reproducibility of your research.

Command line interface (OS shell) and graphic user interface (GUI) are different ways of interacting with a computer’s operating system. The shell is a program that presents a command line interface which allows you to control your computer using commands entered with a keyboard instead of controlling graphical user interfaces (GUIs) with a mouse/keyboard combination. There are quite a few reasons to start learning about the shell: The shell gives you power. The command line gives you the power to do your work more efficiently and more quickly. When you need to do things tens to hundreds of times, knowing how to use the shell is transformative. To use remote computers or cloud computing, you need to use the shell.

Data Carpentry lesson to learn to navigate your file system, create, copy, move, and remove files and directories, and automate repetitive tasks using scripts and wildcards with genomics data. Command line interface (OS shell) and graphic user interface (GUI) are different ways of interacting with a computer’s operating system. The shell is a program that presents a command line interface which allows you to control your computer using commands entered with a keyboard instead of controlling graphical user interfaces (GUIs) with a mouse/keyboard combination. There are quite a few reasons to start learning about the shell: For most bioinformatics tools, you have to use the shell. There is no graphical interface. If you want to work in metagenomics or genomics you’re going to need to use the shell. The shell gives you power. The command line gives you the power to do your work more efficiently and more quickly. When you need to do things tens to hundreds of times, knowing how to use the shell is transformative. To use remote computers or cloud computing, you need to use the shell.

This video will introduce how to calculate confidence intervals around effect sizes using the MBESS package in R. All materials shown in the video, as well as content from our other videos, can be found here: https://osf.io/7gqsi/

Welcome to R! Working with a programming language (especially if it’s your first time) often feels intimidating, but the rewards outweigh any frustrations. An important secret of coding is that even experienced programmers find it difficult and frustrating at times – so if even the best feel that way, why let intimidation stop you? Given time and practice* you will soon find it easier and easier to accomplish what you want. Why learn to code? Bioinformatics – like biology – is messy. Different organisms, different systems, different conditions, all behave differently. Experiments at the bench require a variety of approaches – from tested protocols to trial-and-error. Bioinformatics is also an experimental science, otherwise we could use the same software and same parameters for every genome assembly. Learning to code opens up the full possibilities of computing, especially given that most bioinformatics tools exist only at the command line. Think of it this way: if you could only do molecular biology using a kit, you could probably accomplish a fair amount. However, if you don’t understand the biochemistry of the kit, how would you troubleshoot? How would you do experiments for which there are no kits? R is one of the most widely-used and powerful programming languages in bioinformatics. R especially shines where a variety of statistical tools are required (e.g. RNA-Seq, population genomics, etc.) and in the generation of publication-quality graphs and figures. Rather than get into an R vs. Python debate (both are useful), keep in mind that many of the concepts you will learn apply to Python and other programming languages. Finally, we won’t lie; R is not the easiest-to-learn programming language ever created. So, don’t get discouraged! The truth is that even with the modest amount of R we will cover today, you can start using some sophisticated R software packages, and have a general sense of how to interpret an R script. Get through these lessons, and you are on your way to being an accomplished R user! * We very intentionally used the word practice. One of the other “secrets” of programming is that you can only learn so much by reading about it. Do the exercises in class, re-do them on your own, and then work on your own problems.

Today we are going to learn the basics of literate programming using Jupyter Notebooks, a popular tool in data science, with the R kernel, so we can run R code in our notebooks. We’ll then take a look at how we use Git and GitHub to keep track of all the versions of our work, collaborate with others, and be open!

Software Carpentry lección para la terminal de Unix La terminal de Unix ha existido por más tiempo que la mayoría de sus usuarios. Ha sobrevivido tanto tiempo porque es una herramienta poderosa que permite a las personas hacer cosas complejas con sólo unas pocas teclas. Lo más importante es que ayuda a combinar programas existentes de nuevas maneras y automatizar tareas repetitivas, en vez de estar escribiendo las mismas cosas una y otra vez. El uso del terminal o shell es fundamental para usar muchas otras herramientas poderosas y recursos informáticos (incluidos los supercomputadores o “computación de alto rendimiento”). Esta lección te guiará en el camino hacia el uso eficaz de estos recursos.

Purpose: To introduce methods and tools in organization, documentation, automation, and dissemination of research that nudge it further along the reproducibility spectrum.OutcomeParticipants feel more confident applying reproducibility methods and tools to their own research projects.ProcessParticipants practice new methods and tools with code and data during the workshop to explore what they do and how they might work in a research workflow. Participants can compare benefits of new practices and ask questions to help clarify which would provide them the most value to adopt.

Library Carpentry lesson: An introduction to Git. What We Will Try to Do Begin to understand and use Git/GitHub. You will not be an expert by the end of the class. You will probably not even feel very comfortable using Git. This is okay. We want to make a start but, as with any skill, using Git takes practice. Be Excellent to Each Other If you spot someone in the class who is struggling with something and you think you know how to help, please give them a hand. Try not to do the task for them: instead explain the steps they need to take and what these steps will achieve. Be Patient With The Instructor and Yourself This is a big group, with different levels of knowledge, different computer systems. This isn’t your instructor’s full-time job (though if someone wants to pay them to play with computers all day they’d probably accept). They will do their best to make this session useful. This is your session. If you feel we are going too fast, then please put up a pink sticky. We can decide as a group what to cover.