Related papers: Reproducible Workflow
Over the past decade alongside increased focus on computational reproducibility significant efforts have been made to define reproducibility. However, these definitions provide a textual description rather than a framework. The community…
In recent years, a series of high-profile retractions and fraud cases have arisen in physics, sparking a conversation about research integrity and replicability. Here, we discuss how the practice of science is shaped by the social and…
As research increasingly relies on computational methods, the reliability of scientific results depends on the quality, reproducibility, and transparency of research software. Ensuring these qualities is critical for scientific integrity…
Achieving complete reproducibility in science, particularly in research fields such as biodiversity, is challenging due to analytical choices, bias and interpretation. Here, we examine examples of reproducibility in biological systematics,…
Scientific peer review increasingly struggles to assess reproducibility at the scale and complexity of modern research output. Evaluating reproducibility requires reconstructing experimental dependencies, methodological choices, data flows,…
Reproducibility remains a central challenge in computational social science, where complex workflows, evolving software ecosystems, and inconsistent documentation hinder researchers ability to re-execute published methods. This study…
Those seeking to reproduce a computational experiment often need to manually look at the code to see how to build necessary libraries, configure parameters, find data, and invoke the experiment; it is not automatic. Automatic…
Building Performance Simulation (BPS) uses advanced computational and data science methods. Reproducibility, the ability to obtain the same results by using the same data and methods, is essential in BPS research to ensure the reliability…
Recently, much attention has been focused on the replicability of scientific results, causing scientists, statisticians, and journal editors to examine closely their methodologies and publishing criteria. Experimental particle physicists…
Ascertaining the feasibility of independent falsification or repetition of published results is vital to the scientific process, and replication or reproduction experiments are routinely performed in many disciplines. Unfortunately, such…
Objective: Reproducibility is a core tenet of scientific research. A reproducible study is one where the results can be recreated by different investigators in different circumstances using the same methodology and materials. Unfortunately,…
The FAIR principles for scientific data (Findable, Accessible, Interoperable, Reusable) are also relevant to other digital objects such as research software and scientific workflows that operate on scientific data. The FAIR principles can…
The sharing and citation of research data is becoming increasingly recognized as an essential building block in scientific research across various fields and disciplines. Sharing research data allows other researchers to reproduce results,…
Scientific workflows have become essential for orchestrating complex computational processes across distributed resources, managing large datasets, and ensuring reproducibility in modern research. The Workflows Community Summit 2025, held…
Evaluating and comparing the academic performance of a journal, a researcher or a single paper has long remained a critical, necessary but also controversial issue. Most of existing metrics invalidate comparison across different fields of…
Research is facing a reproducibility crisis, in which the results and findings of many studies are difficult or even impossible to reproduce. This is also the case in machine learning (ML) and artificial intelligence (AI) research. Often,…
Replicability and reproducibility of experimental results are primary concerns in all the areas of science and IR is not an exception. Besides the problem of moving the field towards more reproducible experimental practices and protocols,…
The advancement of scientific knowledge increasingly depends on ensuring that data-driven research is reproducible: that two people with the same data obtain the same results. However, while the necessity of reproducibility is clear, there…
As machine learning systems become ubiquitous, there has been a surge of interest in interpretable machine learning: systems that provide explanation for their outputs. These explanations are often used to qualitatively assess other…
Consistent confirmations obtained independently of each other lend credibility to a scientific result. We refer to results satisfying this consistency as reproducible and assume that reproducibility is a desirable property of scientific…