Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,142 @@
+---
+pretty_name: Agent Eval Effector Hunt
+tags:
+  - biology
+  - genomics
+  - ai-agents
+  - agent-evaluation
+  - scientific-discovery
+task_categories:
+  - question-answering
+  - text-generation
+language:
+  - en
+---
+
+# Agent Eval: Effector Hunt
+
+While AI scientist agents like Claude Science and Google's AI co-scientist highlight the potential of autonomous research, compact and reproducible datasets for evaluating these agents on real scientific workflows remain scarce.
+
+**Agent Eval: Effector Hunt** is a genomics benchmark package designed around a real scientific discovery workflow from the *Science* paper [Chen et al. 2017](https://doi.org/10.1126/science.aao4810). It asks an AI agent, a computational biologist, or a hybrid human-agent workflow
+to analyze anonymized paired-end sequencing reads against an anonymized fungal
+reference genome and recover a biologically meaningful effector signal.
+
+## Task
+
+Given two anonymized paired-end readsets and an anonymized reference genome with
+annotations, identify the key effector-region difference between the samples.
+
+An agent should be able to:
+
+1. Inspect the provided reference genome and annotation files.
+2. Align or otherwise compare the anonymized reads to the reference.
+3. Detect the major sample-specific genomic signal.
+4. Prioritize candidate effector genes or regions.
+5. Produce a concise evidence-backed report explaining the finding.
+
+The dataset card includes an evaluator-facing rubric below so that users can
+score agent outputs without requesting a separate answer by email. For blind
+agent evaluation, do not include the dataset card or rubric in the agent prompt.
+
+## Dataset Contents
+
+```text
+data/raw/fastq_anonymized/
+  sample_1_R1.fastq.gz
+  sample_1_R2.fastq.gz
+  sample_2_R1.fastq.gz
+  sample_2_R2.fastq.gz
+
+reference/reference_anonymized/
+  reference_genome.fna.gz
+  reference_annotations.gff.gz
+  reference_annotations.gtf.gz
+  reference_cds.fna.gz
+  reference_protein.faa.gz
+  checksums.md5
+
+data_manifest.md
+```
+
+The sample names, read names, contig identifiers, gene identifiers, transcript
+identifiers, and protein identifiers have been replaced with neutral names.
+Read sequences, quality strings, genome sequences, protein sequences, annotation
+coordinates, strands, feature types, and phases are preserved.
+
+## Suggested Evaluation Setup
+
+For a clean agent evaluation, give the agent access to this dataset and a
+standard command-line bioinformatics environment, but do not provide the source
+paper, original sample names, original reference identifiers, or the private
+answer key.
+
+Recommended evaluation criteria:
+
+- Correctly identifies the major genomic difference between `sample_1` and
+  `sample_2`
+- Localizes the relevant region in the anonymized reference
+- Connects the region to plausible effector biology using the provided
+  annotations
+- Provides reproducible commands or analysis steps
+- Separates evidence from speculation
+- Reports uncertainty and checks alternative explanations
+
+If internet access is enabled during evaluation, benchmark integrity may be
+weaker because an agent could search for external provenance clues instead of
+solving the task from the anonymized data.
+
+## Rubric
+
+This rubric is intended for evaluators, not for the agent being tested. A strong
+submission should recover the anonymized target locus and explain the
+sample-specific evidence for it.
+
+### Expected Finding
+
+The key finding is a loss-of-heterozygosity signal in `sample_2` relative to
+`sample_1` that affects an effector-region candidate on:
+
+- Target contig: `ref_contig_000132`
+- Target interval: approximately `2553060-2553458`
+- Strand: `+`
+- Target gene: `gene_027287`
+- Target transcript: `transcript_028378`
+- Target protein: `protein_027771`
+- Public alias: `locus_X`
+
+The best answers should identify this region as the central candidate, describe
+the sample-specific haplotype/heterozygosity pattern, and avoid treating the
+signal as a simple loss of read depth unless their analysis supports that claim.
+
+## Intended Use
+
+This dataset is intended for research and evaluation of AI agents in scientific
+workflows. It is especially useful for testing whether an agent can:
+
+- plan a multi-step genomics analysis
+- choose appropriate command-line tools
+- recover from failed or uninformative analyses
+- synthesize sequence-level evidence into a scientific claim
+- write a report that a scientist can audit
+
+It is not intended as a clinical, diagnostic, agricultural decision-making, or
+production pathogen-surveillance dataset.
+
+## Public-Safe Anonymization
+
+The uploaded package is designed to be public-facing. Private mappings from
+neutral identifiers back to source identifiers are not included. Original raw
+downloads, source metadata, paper-derived notes, and answer-key files should be
+kept outside the Hugging Face dataset repository.
+
+
+## Citation And Provenance
+
+This benchmark package is derived from publicly available genomics data
+associated with a published plant-pathogen study in *Science*. The public upload
+intentionally uses anonymized names so that the dataset can function as an
+evaluation task rather than a paper-reading exercise.
+
+If you use this dataset in a paper, report, or benchmark suite, cite this
+dataset card and describe whether agents were allowed to use internet search,
+external biological databases, or only the files provided here.