# Good Enough Practices in Scientific Computing

arXiv: https://arxiv.org/abs/1609.00037 · HTML: https://mlsanity.com/paper/1609.00037/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2016.

## Built on by (most cited first, 9 of 9)

- ["Garbage In, Garbage Out" Revisited: What Do Machine Learning Application Papers Report About Human-Labeled Training Data?](https://mlsanity.com/paper/2107.02278/) (2021)
- [SurvBench: A Standardised Preprocessing Pipeline for Multi-Modal Electronic Health Record Survival Analysis](https://mlsanity.com/paper/2511.11935/) (2025)
- [AutoMOOSE: An Agentic AI for Autonomous Phase-Field Simulation](https://mlsanity.com/paper/2603.20986/) (2026)
- [Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents](https://mlsanity.com/paper/2609.00065/) (2026)
- [AI Data Development: A Scorecard for the System Card Framework](https://mlsanity.com/paper/2506.02071/) (2025)
- [How Usable is Automated Feature Engineering for Tabular Data?](https://mlsanity.com/paper/2508.13932/) (2025)
- [Coding-agents can replicate scientific machine learning papers](https://mlsanity.com/paper/2607.02134/) (2026)
- [A Configuration-First Framework for Reproducible, Low-Code Machine Learning: a Localization Use Case](https://mlsanity.com/paper/2510.25692/) (2025)
- [Accelerated Genetic Programming Hyper-Heuristics for Simulation-Based Scheduling via Agentic AI](https://mlsanity.com/paper/2608.19487/) (2026)

## Abstract (arXiv)

We present a set of computing tools and techniques that every researcher can and should adopt. These recommendations synthesize inspiration from our own work, from the experiences of the thousands of people who have taken part in Software Carpentry and Data Carpentry workshops over the past six years, and from a variety of other guides. Unlike some other guides, our recommendations are aimed specifically at people who are new to research computing.