About Science Detective

Issue 084: Spreadsheets

Welcome to the 84th issue of De Programmatica Ipsum, about Spreadsheets. In this edition, we declare spreadsheets the most popular software programming environment of all time; in the Library section, we learn how to use Lotus 1-2-3 for science reading "Spreadsheet Physics" by Charles Misner and Patrick Cooney; and in our Vidéothèque section, we discover that Excel is a Turing-complete, functional programming language through the eyes of Dr. Felienne Hermans.

DataLicenses.org

Machine-readable hints for AI agents/crawlers; easy to adopt, rely on compliance.

Doctored data sets could trick AI agents

Researchers warn that ‘poisoned’ information can undermine the reliability of tools that analyse data autonomously.

Data dredging

Data dredging, also known as data snooping or p-hacking, is the misuse of data analysis to find patterns in data that can be presented as statistically significant, thus dramatically increasing and understating the risk of false positives. This is done by performing many statistical tests on the data and only reporting those that come back with significant results. Thus data dredging is also often a misused or misapplied form of data mining.

Scientific sleuths come in from the cold

Research integrity investigators are starting to organize, but the field, and the people, remain idiosyncratic

Distributed Denial of Science: How Indirect Data Poisoning of AI Systems Can Industrialize Scientific Fraud

Scientific fraud is the instrument of doubt that malicious entities can use to establish controversy in science. Historically, it required the resources of a company: deep pockets, ghostwritten articles, and corrupt academics. Today, Artificial Intelligence (AI) is increasingly automating scientific research, so we ask: Can a remote adversary weaponize the honest use of AI in science to compromise scientific integrity? We envision and empirically evaluate a new attack, indirect data poisoning, in which an adversary corrupts an open dataset and uploads the poisoned variant to a public repository. Autonomous research agents may independently retrieve and process this data, turning honest scientists into the unpaid and unwitting distributors of fraud at scale. Across five socially-salient topics, from hiring discrimination to the safety of autonomous vehicles, three widely used frontier AI systems (Claude Code with Claude Opus 4.7, Codex with GPT-5.5, Gemini CLI with Gemini 3.1 Pro), and 450 ethically contained experimental runs, we find that poisoning succeeds in 49.56% of runs, while the rate of poisoning detection is only 6.0%. The attack requires no topic-specific trigger-words, agent access, indirect prompt injection, or fabricated papers, only the open data ecosystem and misleading metadata. To mitigate the attacks, we propose and evaluate two measures: a scientist persona and a data provenance audit with five checks (referencing papers, social markers, statistical anomalies, related datasets, poisoning caution). We find that the persona still leaves 16.67% of runs with a poisoned conclusion, but provenance auditing reduces attack success rate to zero. Our results suggest that indirect data poisoning may enable scientific fraud at unprecedented scale, but these attacks can be mitigated with suitable auditing by agents during data retrieval.

Keeping your analysis correct: mycelium and scilintr

Coding agents have made data analysis far easier to produce. The hard part now is verifying what comes out. Here’s the scaffolding I built to help.

Home | Bellingcat's Online Investigation Toolkit

A toolkit for open source researchers

chrispsn/mesh-spreadsheet

A data and code editor that feels like a spreadsheet.

datasetpapers — a public research experiment

An experimental approach to versioned, forkable, machine-readable analyses. A prototype, not a product or service.

datasetpapers — a public research experiment

An experimental approach to versioned, forkable, machine-readable analyses. A prototype, not a product or service.

Introducing COSIG: The Collection of Open Science Integrity Guides

Investigating the integrity of published scientific papers is key to the scientific process, but the necessary knowledge is in short supply. We present COSIG, an open collection of meta-scientific guides enabling anyone to perform forensic peer review.

How to SNIFF out Good Scientific Software

New PLOS Computational Biology paper: "Ten Quick Tips to SNIFF Out Sustainable and Secure Scientific Software"

Data Is the New Copper

Data breaches fuel a complex cybercriminal ecosystem, similar to copper thefts after the financial crisis.