If you give the programmer a long list of inputs and actions, and they fire up their own copy of the program and nothing goes wrong, then you haven't given them enough information.

En este estudio, se destaca la importancia de la investigación reproducible y la narrativa de datos para garantizar la transparencia, verificabilidad y validez de los hallazgos. La reproducibilidad permite a otros investigadores verificar y validar los resultados, fortaleciendo la credibilidad de los estudios. La narrativa de datos proporciona un marco coherente para interpretar los resultados y comunicarlos efectivamente.

Auto-update aside, you might also have found it hard to find a Chrome binary with a specific version. Google intentionally doesn’t make versioned Chrome downloads available, since users shouldn’t have to care about version numbers—they should always get updated to the latest version as soon as possible. This is great for users, but painful for developers needing to reproduce a bug report in an older Chrome version.

When we are collecting new data to address a research question addressed in another context, it can be near impossible to re-create ex-act contexts with participants; researchers simply do not have that sort of control over any research context. This suggests that reproduction, rather than replication, may be a more useful goal.

In general, I've found Selenium to be unreliable and not deterministic as laid out here although I still don't entirely know why.

Quarto Basics

The analysis that I present is applicable to all branches of science whose models are based on continuous mathematics, such as algebraic, differential, or integral equations.

In order to move forwards with this issue I'll need an example app that reproduces the behavior (https://www.codetriage.com/example_app).

But I can describe a simple reproduction case to demonstrate the developer inconvenience (can provide a sample app if it's helpful).

An example app is an application that is designed to reproduce a bug or demonstrate an issue.

Before a bug can be fixed, it has to be understood and reproduced. For every issue, a maintainer gets, they have to decipher what was supposed to happen and then spend minutes or hours piecing together their reproduction. Usually, they can’t get it right, so they have to ask for clarification. This back-and-forth process takes lots of energy and wastes everyone’s time. Instead, it’s better to provide an example app from the beginning. At the end of the day, would you rather maintainers spend their time making example apps or fixing issues?

lack of data provenance i

Also as an aside, your REPL is far, far too complicated. You've got a much better chance of us being able to see your issue and fix it if you boil it down to just the minimum required code in order to reproduce the issue.

Now that Rails has a dependency on Webpack, it then has a dependency on Node.js and Yarn, surprise! Which means we also need to ensure the versions used for those binaries are specific.

Omitted details change everything here. There won't be circular dependency because unused import is skipped. Consider providing stackoverflow.com/help/mcve .

Made the repro above more manageable: https://svelte.dev/repl/a55ba18ceca44c898f2b011a0978de85?version=3.6.6

If you can't understand where it's coming from in the stack traces, please post screenshots or create reproducing sandboxes and we'll try to help. Most of these are probably coming from a few libraries, so the most productive thing to do is to reduce these cases and then file issues with those libraries.

CoyIM supports reproducible builds for Linux on AMD64

problem in my app is fixed. It was not related to devise . I removed 'rails-api' gem and it started working.

We used the program STRUCTURE v2.3.4 [16]

NA extractions were attempted for all taxa for which recent and sufficient tissues were available. Of the samples that were successfully extracted, at least two individuals for each taxon were included when available. This yielded 44 Malagasy Canarium accessions, to which we added an additional four Southeast Asian Canarium species to serve as outgroups. Genomic libraries were prepared for genotyping-by-sequencing (GBS) following the protocol described by Escudero et al. [14], but with the addition of a size selection step. After restriction digestion, fragment sizes were visualized on an Agilent 2100 Bioanalyzer and selected in the range of 300–800 bp using a Pippin Prep system. The final library containing 48 barcoded individuals was sequenced on two lanes of an Illumina HiSeq 2500 at Yale University’s Center for Genome Analysis to generate 75 bp single-end reads.

Fully reproducible code is available in the form of jupyter notebooks (https://jupyter.org) with instructions to install all necessary software, download sequence data, assemble it, and run genomic analyses (https://github.com/dereneaton/Canarium-GBS) (DOI 10.5281/zenodo.1273357). For all genomic analyses in this study we used the ipyrad Python API and the ipyrad-analysis toolkit (https://github.com/dereneaton/ipyrad) which provides a set of wrappers around common genomic analysis tools to run highly parallelized analyses using simple Python scripts.

En total, se analizaron los datos de 18.662 estudiantes de pregrado en el período 1530 y de 18.989 en el período 1610.

IsPreviousVersionOf doi:10.5524/100148

Similarly, in science there exists substantial expertise making brilliant connectionsbetween concepts, but it is being conveyed in silos of English prose known as journalarticles. Every scientific journal article has a methods section, but it is almost impossibleto read a methods section and subsequently repeat the experiment—the English languageis inadequate to precisely and concisely convey what is being done.