| --- |
| license: apache-2.0 |
| language: |
| - en |
| task_categories: |
| - text-generation |
| tags: |
| - nsf |
| - scientific-claims |
| - science-communication |
| - generated-data |
| pretty_name: NSF-SciFy Full Awards |
| size_categories: |
| - 100K<n<1M |
| configs: |
| - config_name: default |
| data_files: |
| - split: full |
| path: data/*.parquet |
| --- |
| |
| # NSF-SciFy Full Awards |
|
|
| NSF-SciFy contains 412,155 NSF award records enriched with generated nontechnical abstracts, verifiable claims, and investigation proposals. |
|
|
| The source technical abstracts come from the NSF awards database snapshot `nsf_awards-0325.json`. The generated fields were produced sequentially with the following task-specific LoRA adapters: |
|
|
| - [`darpa-scify/nsf-scify-matsci-20k-nta`](https://huggingface.co/darpa-scify/nsf-scify-matsci-20k-nta) |
| - [`darpa-scify/nsf-scify-matsci-20k-claims`](https://huggingface.co/darpa-scify/nsf-scify-matsci-20k-claims) |
| - [`darpa-scify/nsf-scify-matsci-20k-ip`](https://huggingface.co/darpa-scify/nsf-scify-matsci-20k-ip) |
|
|
| These adapters were trained on the materials-science plus 20K NSF-SciFy data. This repository contains their generated annotations for the full awards snapshot; it is a dataset, not a new model trained on all 412,155 records. |
|
|
| ## Load |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("darpa-scify/nsf-scify") |
| full = dataset["full"] |
| print(full[0]) |
| ``` |
|
|
| ## Fields |
|
|
| - `award_id`, `award_year`, `title`, `directorate`, `division`: NSF award metadata. |
| - `abstract`: original technical abstract. |
| - `non_technical_abstract`: parsed generated nontechnical abstract. |
| - `verifiable_claims`: parsed list of generated verifiable claims. |
| - `investigation_proposals`: parsed list of generated investigation proposals. |
| - `*_raw`: raw model response retained for auditing and possible repair. |
| - `generation_error`, `*_parse_error`: error details when generation or parsing failed. |
| - `generation_success`: `true` only when all three parsed outputs are present and nonempty and no error marker is present. |
|
|
| ## Generation quality |
|
|
| Using the strict `generation_success` definition: |
|
|
| | Status | Records | Percentage | |
| |---|---:|---:| |
| | Successful | 410,313 | 99.553% | |
| | Unsuccessful | 1,842 | 0.447% | |
| | Total | 412,155 | 100% | |
|
|
| The unsuccessful set includes overlapping categories: 1,085 records with missing, empty, or unparsed claims; 790 with missing, empty, or unparsed investigation proposals; 42 with an empty or missing nontechnical abstract; and 12 with an explicit generation error. Raw model responses are retained where available. |
|
|
| ## Important limitations |
|
|
| All three enriched fields are model-generated and have not been exhaustively verified by humans. A successfully parsed record can still contain factual, extraction, relevance, repetition, or writing-quality errors. Users should validate generated content for their application and use `generation_success` and the error fields when filtering records. |
|
|
| ## Related resources |
|
|
| - [`darpa-scify/nsf-scify-20k`](https://huggingface.co/datasets/darpa-scify/nsf-scify-20k) |
| - [NSF-SciFy collection](https://huggingface.co/collections/darpa-scify/nsf-scify) |
| - [NSF-SciFy paper](https://arxiv.org/abs/2503.08600) |
|
|
