Maintenance and Support in Community-Driven Scientific Pipeline Ecosystems: A Cross-Platform Empirical Study of nf-core

Jul 16, 2026

Community-driven scientific pipeline ecosystems grow in importance for reproducible, data-intensive research, yet sustainability depends on more than engines, templates, and testing infrastructure. Long-term success requires communities to maintain pipelines, integrate contributions, and support users across heterogeneous execution environments. This paper performs a cross-platform empirical study of nf-core, examining 15,760 GitHub issues, 35,411 pull requests, and 895 Seqera Community Forum discussions to identify maintenance and support concerns, outcome drivers, and traceability between repository and community spaces.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages42

Reading Time1 h 46 min

Abstract

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Introduction

arXiv :2607 . 10839v 1 [ cs . SE] 12 Jul 2026 Maintenance and Support in Community-Driven Scientific Pipeline Ecosystems: A Cross-Platform Empirical Study of nf-core Khairul Alam 1*, Kowsik Roy2 , Md Shamimur Rahman3 , Banani Roy4 1,3,4* Department of Computer Science, University of Saskatchewan, Saskatoon, S7N 5A2, Canada. 2 Department of Computer Science and Engineering, BRAC University, Dhaka, 1212, Bangladesh. *Corresponding author(s). E-mail(s): [kha060@usask.ca](kha060@usask.ca) ; Contributing authors: [kowsik.roy@g.bracu.ac.bd](kowsik.roy@g.bracu.ac.bd) ; [shamimur.rahman@usask.ca](shamimur.rahman@usask.ca) ; [banani.roy@usask.ca](banani.roy@usask.ca) ; Abstract Community-driven scientific pipeline ecosystems are increasingly important for reproducible data-intensive research, but their sustainability depends on more than workflow engines, templates, and testing infrastructure. It also depends on how communities maintain pipelines, integrate contributions, and support users across heterogeneous execution environments. This paper presents a cross-platform empirical study of maintenance and support in nf-core, a large ecosystem of standardized Nextflow pipelines. We analyze 15,760 GitHub issues, 35,411 GitHub pull requests, and 895 Seqera Community Forum discussions to examine what maintenance and support concerns arise, how they differ across artifact types, which factors are associated with resolution outcomes, and how problems and solutions flow between repository-centered and community-centered spaces. Using topic modeling, statistical outcome analysis, direct-link mining, semantic similarity analysis, technical-signal overlap analysis, and qualitative inspection, we show that issues primarily capture repositorylevel problem reporting and maintenance coordination; pull requests capture implementation, review, testing, dependency, and template-update work; and forum discussions capture user-facing support around execution failures, containers, cloud and HPC environments, MultiQC reporting, and Nextflow usage. Resolution outcomes are associated with actionability, coordination, and diagnostic evidence. Issue closure is linked to assignees, comments, milestones, bug labels, error mentions, and version information. Pull request integration varies by author role, automation type, draft status, checklists, linked issues, and review routing. Forum accepted answers are more likely when discussions include code blocks, sustained interaction, and concrete technical evidence, while cloud, HPC, and workflow-semantics questions are harder to resolve. Cross-platform analysis reveals strong repository-internal traceability within GitHub, but limited explicit linkage between forum discussions and repository artifacts. These findings show that sustaining scientific pipeline ecosystems requires not only technical standardization, but also actionable support processes, review-ready contribution workflows, infrastructure-specific guidance, and stronger traceability between user support and repository maintenance. Keywords: nf-core, scientific workflow systems, pipeline maintenance, community support, cross-platform mining, empirical software engineering 1 Introduction Modern data-intensive science increasingly depends on computational pipelines to transform raw data into reliable scientific results [1, 2] . In domains such as bioinformatics, astronomy and cosmology, and earth and climate science, these pipelines coordinate many interdependent steps, including quality control, alignment, quantification, statistical analysis, reporting, visualization, simulation, and large-scale data processing. They also bind together software tools, parameters, reference datasets, containers, execution profiles, and infrastructure-specific configurations [1, 3 , 4] . As scientific analyses become more computationally intensive and distributed, reproducibility depends not only on sharing data and code, but also on making the full a

FAQs

What is the main goal of the study on nf-core maintenance and support?

To examine how maintenance and support concerns arise in nf-core and how they differ across GitHub issues, pull requests, and the Seqera Community Forum, including factors linked to resolution outcomes.

Which platforms and datasets are analyzed?

The study analyzes 15,760 GitHub issues, 35,411 GitHub pull requests, and 895 Seqera Community Forum discussions to understand problem reports, implementation work, and user support needs.

What characteristics are associated with better resolution outcomes?

Resolution outcomes correlate with actionability, coordination, and diagnostic evidence; issue closure and forum accepted answers are linked to factors such as assignees, comments, milestones, and the presence of concrete technical evidence.

Jul 16, 2026

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