Introducing a Framework for Open and Reproducible Research Training (FORRT)

Current norms for the teaching and mentoring of higher education are rooted in obsolete practices of bygone eras. Improving the transparency and rigor of science is the responsibility of all who engage in it. Ongoing attempts to improve research credibility have, however, neglected an essential aspect of the academic cycle: the training of researchers and consumers of research. Principled teaching and mentoring involve imparting students with an understanding of research findings in light of epistemic uncertainty, and moreover, an appreciation of best practices in the production of knowledge. We introduce a Framework for Open and Reproducible Research Training (FORRT). Its main goal is to provide educators with a pathway towards the incremental adoption of principled teaching and mentoring practices, including open and reproducible research. FORRT will act as an initiative to support instructors, collating existing teaching pedagogies and materials to be reused and adapted for use within new and existing courses. Moreover, FORRT can be used as a tool to benchmark the current level of training students receive across six clusters of open and reproducible research practices: 'reproducibility and replicability knowledge', 'conceptual and statistical knowledge', 'reproducible analyses', 'preregistration', 'open data and materials', and 'replication research'. FORRT will strive to be an advocate for the establishment of principled teaching and mentorship as a fourth pillar of a true scientific utopia.[working document here: https://tinyurl.com/FORRTworkingDOC]

Reproducibility, Preservation, and Access to Research with ReproZip and ReproServer

The adoption of reproducibility remains low, despite incentives becoming increasingly common in different domains, conferences, and journals. The truth is, reproducibility is technically difficult to achieve due to the complexities of computational environments. To address these technical challenges, we created ReproZip, an open-source tool that automatically packs research along with all the necessary information to reproduce it, including data files, software, OS version, and environment variables. Everything is then bundled into an rpz file, which users can use to reproduce the work with ReproZip and a suitable unpacker (e.g.: using Vagrant or Docker). The rpz file is general and contains rich metadata: more unpackers can be added as needed, better guaranteeing long-term preservation. However, installing the unpackers can still be burdensome for secondary users of ReproZip bundles. In this paper, we will discuss how ReproZip and our new tool, ReproServer, can be used together to facilitate access to well-preserved, reproducible work. ReproServer is a web application that allows users to upload or provide a link to a ReproZip bundle, and then interact with/reproduce the contents from the comfort of their browser. Users are then provided a persistent link to the unpacked work on ReproServer which they can share with reviewers or colleagues.

A checklist for maximizing reproducibility of ecological niche models

Reporting specific modelling methods and metadata is essential to the reproducibility of ecological studies, yet guidelines rarely exist regarding what information should be noted. Here, we address this issue for ecological niche modelling or species distribution modelling, a rapidly developing toolset in ecology used across many aspects of biodiversity science. Our quantitative review of the recent literature reveals a general lack of sufficient information to fully reproduce the work. Over two-thirds of the examined studies neglected to report the version or access date of the underlying data, and only half reported model parameters. To address this problem, we propose adopting a checklist to guide studies in reporting at least the minimum information necessary for ecological niche modelling reproducibility, offering a straightforward way to balance efficiency and accuracy. We encourage the ecological niche modelling community, as well as journal reviewers and editors, to utilize and further develop this framework to facilitate and improve the reproducibility of future work. The proposed checklist framework is generalizable to other areas of ecology, especially those utilizing biodiversity data, environmental data and statistical modelling, and could also be adopted by a broader array of disciplines.

Semantic Web Technologies for Data Curation and Provenance

The Reproducibility issue even if not a crisis, is still a major problem in the world of science and engineering. Within metrology, making measurements at the limits that science allows for, inevitably, factors not originally considered relevant can be very relevant. Who did the measurement? How exactly did they do it? Was a mistake made? Was the equipment working correctly? All these factors can influence the outputs from a measurement process. In this work we investigate the use of Semantic Web technologies as a strategic basis on which to capture provenance meta-data and the data curation processes that will lead to a better understanding of issues affecting reproducibility.

Exploring Reproducibility and FAIR Principles in Data Science Using Ecological Niche Modeling as a Case Study

Reproducibility is a fundamental requirement of the scientific process since it enables outcomes to be replicated and verified. Computational scientific experiments can benefit from improved reproducibility for many reasons, including validation of results and reuse by other scientists. However, designing reproducible experiments remains a challenge and hence the need for developing methodologies and tools that can support this process. Here, we propose a conceptual model for reproducibility to specify its main attributes and properties, along with a framework that allows for computational experiments to be findable, accessible, interoperable, and reusable. We present a case study in ecological niche modeling to demonstrate and evaluate the implementation of this framework.