Reproducible research and GIScience: an evaluation using AGILE conference papers

EBRAINS Live Papers - Interactive Resource Sheets for Computational Studies in Neuroscience

We present here an online platform for sharing resources underlying publications in neuroscience. It enables authors to easily upload and distribute digital resources, such as data, code, and notebooks, in a structured and systematic way. Interactivity is a prominent feature of the Live Papers, with features to download, visualise or simulate data, models and results presented in the corresponding publications. The resources are hosted on reliable data storage servers to ensure long term availability and easy accessibility. All data are managed via the EBRAINS Knowledge Graph, thereby helping maintain data provenance, and enabling tight integration with tools and services offered under the EBRAINS ecosystem.

The role of metadata in reproducible computational research

Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results. In addition to its role in research integrity, improving the reproducibility of scientific studies can accelerate evaluation and reuse. This potential and wide support for the FAIR principles have motivated interest in metadata standards supporting reproducibility. Metadata provide context and provenance to raw data and methods and are essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described how metadata enable reproducible computational research. This review employs a functional content analysis to identify metadata standards that support reproducibility across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our review provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work.

Understanding experiments and research practices for reproducibility: an exploratory study

Scientific experiments and research practices vary across disciplines. The research practices followed by scientists in each domain play an essential role in the understandability and reproducibility of results. The "Reproducibility Crisis", where researchers find difficulty in reproducing published results, is currently faced by several disciplines. To understand the underlying problem in the context of the reproducibility crisis, it is important to first know the different research practices followed in their domain and the factors that hinder reproducibility. We performed an exploratory study by conducting a survey addressed to researchers representing a range of disciplines to understand scientific experiments and research practices for reproducibility. The survey findings identify a reproducibility crisis and a strong need for sharing data, code, methods, steps, and negative and positive results. Insufficient metadata, lack of publicly available data, and incomplete information in study methods are considered to be the main reasons for poor reproducibility. The survey results also address a wide number of research questions on the reproducibility of scientific results. Based on the results of our explorative study and supported by the existing published literature, we offer general recommendations that could help the scientific community to understand, reproduce, and reuse experimental data and results in the research data lifecycle.

Publishing computational research - a review of infrastructures for reproducible and transparent scholarly communication

BACKGROUND

The trend toward open science increases the pressure on authors to provide access to the source code and data they used to compute the results reported in their scientific papers. Since sharing materials reproducibly is challenging, several projects have developed solutions to support the release of executable analyses alongside articles.

METHODS

We reviewed 11 applications that can assist researchers in adhering to reproducibility principles. The applications were found through a literature search and interactions with the reproducible research community. An application was included in our analysis if it (i) was actively maintained at the time the data for this paper was collected, (ii) supports the publication of executable code and data, (iii) is connected to the scholarly publication process. By investigating the software documentation and published articles, we compared the applications across 19 criteria, such as deployment options and features that support authors in creating and readers in studying executable papers.

RESULTS

From the 11 applications, eight allow publishers to self-host the system for free, whereas three provide paid services. Authors can submit an executable analysis using Jupyter Notebooks or R Markdown documents (10 applications support these formats). All approaches provide features to assist readers in studying the materials, e.g., one-click reproducible results or tools for manipulating the analysis parameters. Six applications allow for modifying materials after publication.

CONCLUSIONS

The applications support authors to publish reproducible research predominantly with literate programming. Concerning readers, most applications provide user interfaces to inspect and manipulate the computational analysis. The next step is to investigate the gaps identified in this review, such as the costs publishers have to expect when hosting an application, the consideration of sensitive data, and impacts on the review process.

Leveraging Container Technologies in a GIScience Project: A Perspective from Open Reproducible Research

Scientific reproducibility is essential for the advancement of science. It allows the results of previous studies to be reproduced, validates their conclusions and develops new contributions based on previous research. Nowadays, more and more authors consider that the ultimate product of academic research is the scientific manuscript, together with all the necessary elements (i.e., code and data) so that others can reproduce the results. However, there are numerous difficulties for some studies to be reproduced easily (i.e., biased results, the pressure to publish, and proprietary data). In this context, we explain our experience in an attempt to improve the reproducibility of a GIScience project. According to our project needs, we evaluated a list of practices, standards and tools that may facilitate open and reproducible research in the geospatial domain, contextualising them on Peng’s reproducibility spectrum. Among these resources, we focused on containerisation technologies and performed a shallow review to reflect on the level of adoption of these technologies in combination with OSGeo software. Finally, containerisation technologies proved to enhance the reproducibility and we used UML diagrams to describe representative work-flows deployed in our GIScience project.

O ecossistema da Ciência Aberta

Resumo O presente estudo descritivo tem como objetivo apresentar as dimensões da Ciência Aberta e o impacto desse contexto na vida dos pesquisadores. O surgimento de revistas científi cas no século XVII ajudou a promover a Revolução Científi ca,permitindo que os pesquisadores se comunicassem através do tempo e do espaço usando as tecnologias da época para gerar conhecimento confi ável de maneira mais rápida e efi ciente. Aproveitando os contínuos avanços nas tecnologias da informação, os pesquisadores estão caminhando para um novo ecossistema científi co, o qual é mais aberto para acompanhar o ciclo de vida investigativo. É nesse contexto que a Ciência Aberta surge para garantir a disponibilidade e a usabilidade sem barreiras de acesso às publicações acadêmicas e aos dados resultantes de pesquisas e metodologias acadêmicas, incluindo códigos oualgoritmos que foram usados para gerar esses dados.