Upload dataset card

README.md

@@ -0,0 +1,146 @@
+---
+pretty_name: MOSAIC
+license: cc-by-nc-sa-4.0
+language:
+  - zh
+  - en
+task_categories:
+  - summarization
+  - text-generation
+  - feature-extraction
+size_categories:
+  - 10K<n<100K
+tags:
+  - education
+  - multimodal
+  - subtitles
+  - knowledge-graph
+  - slides
+---
+
+# MOSAIC
+
+## Dataset Summary
+
+MOSAIC is a course-centric multimodal dataset released with an ACL 2026 paper. The dataset centers on `mosaic.jsonl`, a JSONL file that stores course-level metadata together with nested video-level summaries, subtitles, captions, and auxiliary references.
+
+The dataset also includes:
+
+- `data/graph_p_results/`: course-level knowledge graph JSON files keyed by `kg`
+- `data/all.csv`: URL-to-filename mapping for slide references
+- `data/pdfs/shard_xx/`: sharded reference slide PDFs
+
+## Supported Tasks
+
+- multimodal educational data understanding
+- subtitle and caption analysis
+- document-aware summarization
+- course knowledge graph grounding
+- retrieval over linked videos, graphs, and slides
+
+## Languages
+
+The dataset is primarily in Chinese, with a smaller amount of English content in slide titles, references, and course materials.
+
+## Dataset Structure
+
+```text
+.
+├── README.md
+└── data/
+    ├── mosaic.jsonl
+    ├── all.csv
+    ├── graph_p_results/
+    │   ├── BIT-1001604004.json
+    │   └── ...
+    └── pdfs/
+        ├── shard_00/
+        ├── shard_01/
+        └── ...
+```
+
+## Data Instances
+
+### Main file: `data/mosaic.jsonl`
+
+Each line is one course record with the following top-level fields:
+
+- `url`
+- `course_title`
+- `contents`
+- `kg`
+- `caption_anno`
+- `overview`
+- `objectives`
+- `prerequisites`
+- `references`
+
+Each video entry inside `contents[*].courses[*]` contains:
+
+- `video_url`
+- `srt_url`
+- `summary`
+- `subtitle`
+- `caption`
+- `video_title`
+- `ref`
+
+The `ref` object includes:
+
+- `cate`: reference category
+- `doc`: list of reference document URLs
+
+### Knowledge graphs: `data/graph_p_results/*.json`
+
+Each knowledge graph file contains a top-level object with:
+
+- `code`
+- `message`
+- `sampled`
+- `traceId`
+- `result`
+
+The main graph payload is stored in:
+
+- `result.mocKgNodeDtoList`
+
+### Slide mapping: `data/all.csv`
+
+Columns:
+
+- `doc_url`: document URL referenced in `mosaic.jsonl`
+- `filename`: corresponding PDF filename
+
+### PDFs: `data/pdfs/shard_xx/`
+
+Reference slide PDFs are sharded into directories of up to 500 files each for more reliable upload and browsing.
+
+## Dataset Creation
+
+MOSAIC is constructed from public courses on iCourse163, a major Chinese MOOC platform. The source data follows a four-level hierarchy of course, chapter, video, and topic. Each course provides course-level metadata such as objectives and prerequisite information; chapters group related videos and associated slide decks; videos include timestamped ASR transcripts, instructor-provided knowledge-point outlines, and summaries; and topics correspond to the predefined knowledge points used for alignment. Because the platform does not provide high-quality alignment between transcripts, topic inventories, and slides, the dataset constructs these links from scratch. MOSAIC is released in two subsets: MOSAIC-G, a fully human-annotated gold benchmark built from 6 diverse courses with utterance-level topic labels and utterance-to-slide alignment, and MOSAIC-S, a large silver subset for the remaining courses produced with DORA, a two-stage pipeline that first refines noisy topic inventories and then performs joint segmentation and topic assignment. For slide linkage in MOSAIC-S, the paper describes an automatic pipeline combining title matching, rule-based filtering, and LLM verification.
+
+## Statistics
+
+| Metric | Value |
+| --- | ---: |
+| Courses | 179 |
+| Videos | 14,942 |
+| Knowledge graph JSON files | 167 |
+| PDF files | 10,566 |
+| Slide mapping rows | 10,566 |
+| Raw size | ~12.17 GB (11.34 GiB) |
+
+## Licensing Information
+
+This dataset is released under **CC BY-NC-SA 4.0**.
+
+## Citation Information
+
+```bibtex
+@inproceedings{ai-etal-2026-mosaic,
+  title = {MOSAIC: A Large-Scale Multimodal Open-Course Segmentation and Alignment Corpus in Chinese},
+  author = {Ai, Yuming and Fan, Shuai and Xu, Hua and Kong, Fang},
+  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics},
+  year = {2026}
+}
+```