Duplicate from Gugu8/Science

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bin filter=lfs diff=lfs merge=lfs -text
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+*.gz filter=lfs diff=lfs merge=lfs -text
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+*.pcm filter=lfs diff=lfs merge=lfs -text
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LICENSE

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+Open Data Attribution Training Disclosure License (ODATL‑1.0)
+Version 1.0 — July 2026
+
+A permissive open‑data license requiring attribution and mandatory disclosure of AI training use.
+
+--------------------------------------------------------------------
+SECTION 1 — DEFINITIONS
+--------------------------------------------------------------------
+1.1 “Dataset” refers to the collection of data, files, metadata, annotations, structures, or any other materials distributed under this License, including any updates, subsets, or modified versions.
+
+1.2 “Licensor” refers to the entity or individual who releases the Dataset under this License.
+
+1.3 “Licensee” refers to any person, organization, or system that accesses, uses, modifies, redistributes, or incorporates the Dataset.
+
+1.4 “Model” refers to any machine learning system, artificial intelligence system, algorithm, statistical model, or computational process trained, fine‑tuned, evaluated, or otherwise developed using the Dataset.
+
+1.5 “Public Notice” refers to a clear, visible, and publicly accessible statement acknowledging use of the Dataset.
+
+1.6 “Derivative Dataset” refers to any dataset created by modifying, transforming, filtering, augmenting, or otherwise altering the original Dataset.
+
+1.7 “Derivative Model” refers to any Model whose training data includes the Dataset or any Derivative Dataset.
+
+--------------------------------------------------------------------
+SECTION 2 — GRANT OF RIGHTS
+--------------------------------------------------------------------
+2.1 The Licensor grants the Licensee a worldwide, royalty‑free, non‑exclusive, irrevocable permission to:
+- Use, copy, and redistribute the Dataset for any purpose.
+- Modify, transform, or build upon the Dataset.
+- Create Derivative Datasets.
+- Train, fine‑tune, evaluate, or otherwise use the Dataset for machine learning or AI development.
+- Create Derivative Models based on the Dataset.
+
+2.2 These rights are intended to be compatible with open‑source and open‑data principles, including but not limited to CC‑BY, ODC‑BY, and other permissive data licenses.
+
+2.3 No patent rights are granted or implied by this License.
+
+--------------------------------------------------------------------
+SECTION 3 — MANDATORY ATTRIBUTION
+--------------------------------------------------------------------
+3.1 Any public use, redistribution, publication, or derivative work involving the Dataset must include the following attribution:
+
+“This work uses data provided under the Open Data Attribution Training Disclosure License (ODATL‑1.0).”
+
+3.2 Attribution must appear in:
+- Documentation
+- Research papers
+- Model cards
+- Public datasets
+- Product descriptions
+- Any public‑facing material referencing the Dataset
+
+3.3 Attribution must remain intact and may not be removed, obscured, or altered.
+
+--------------------------------------------------------------------
+SECTION 4 — MANDATORY TRAINING DISCLOSURE
+--------------------------------------------------------------------
+4.1 If the Dataset is used to train, fine‑tune, evaluate, or otherwise develop any Model, the Licensee must provide a Public Notice stating:
+
+“This model was trained using data provided under the Open Data Attribution Training Disclosure License (ODATL‑1.0).”
+
+4.2 This disclosure must appear in:
+- Model cards
+- Public releases of the Model
+- Research publications
+- Technical documentation
+- Product descriptions
+- Any public announcement or description of the Model
+
+4.3 Disclosure must be truthful, visible, and accessible to the general public.
+
+4.4 Failure to provide this disclosure immediately terminates all rights granted under this License.
+
+--------------------------------------------------------------------
+SECTION 5 — REDISTRIBUTION REQUIREMENTS
+--------------------------------------------------------------------
+5.1 If the Licensee redistributes the Dataset or any Derivative Dataset, the Licensee must:
+- Include this License in full.
+- Clearly indicate any modifications made.
+- Preserve all attribution and disclosure requirements.
+
+5.2 Derivative Datasets may be dual‑licensed under other open‑data licenses, provided this License remains included and enforceable.
+
+--------------------------------------------------------------------
+SECTION 6 — OPEN‑SOURCE COMPATIBILITY
+--------------------------------------------------------------------
+6.1 This License is designed to be compatible with:
+- Open Data Commons Attribution (ODC‑BY)
+- Creative Commons Attribution (CC‑BY)
+- Open‑source AI research workflows
+- Open‑data distribution platforms
+
+6.2 Redistribution through open‑source repositories (e.g., GitHub, HuggingFace, Kaggle) is permitted and encouraged.
+
+6.3 Licensees may combine the Dataset with other open datasets, provided attribution and training disclosure obligations remain intact.
+
+--------------------------------------------------------------------
+SECTION 7 — PROHIBITED USES
+--------------------------------------------------------------------
+7.1 The Licensee may not:
+- Claim exclusive ownership of the Dataset.
+- Remove or alter attribution or disclosure requirements.
+- Use the Dataset in violation of applicable laws or regulations.
+- Misrepresent the origin, nature, or licensing of the Dataset.
+
+7.2 The Licensee may not apply technical or legal measures that restrict others from exercising rights granted under this License.
+
+--------------------------------------------------------------------
+SECTION 8 — NO WARRANTY
+--------------------------------------------------------------------
+8.1 The Dataset is provided “as‑is,” without warranty of any kind, express or implied.
+
+8.2 The Licensor is not liable for any damages, losses, or claims arising from use of the Dataset or any Derivative Model.
+
+--------------------------------------------------------------------
+SECTION 9 — TERMINATION
+--------------------------------------------------------------------
+9.1 Rights under this License automatically terminate if the Licensee:
+- Fails to provide required attribution.
+- Fails to provide required training disclosure.
+- Violates any other term of this License.
+
+9.2 Rights may be reinstated upon correction of the violation, unless the Licensor explicitly revokes permission.
+
+--------------------------------------------------------------------
+SECTION 10 — ACCEPTANCE
+--------------------------------------------------------------------
+10.1 By accessing or using the Dataset, the Licensee agrees to be bound by the terms of this License.
+
+10.2 Continued use of the Dataset constitutes ongoing acceptance of all terms.
+
+--------------------------------------------------------------------
+SECTION 11 — CONTACT
+--------------------------------------------------------------------
+11.1 For permissions beyond this License, contact the Licensor.

README.md

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+---
+dataset_info:
+  features:
+  - name: id
+    dtype: int64
+  - name: domain
+    dtype: string
+  - name: topic
+    dtype: string
+  - name: subtopic
+    dtype: string
+  - name: difficulty
+    dtype: int64
+  - name: unit_type
+    dtype: string
+  - name: title
+    dtype: string
+  - name: content
+    dtype: string
+  - name: key_equations
+    dtype: string
+  - name: prerequisites
+    dtype: string
+  - name: learning_objective
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 11349918755
+    num_examples: 20133312
+  download_size: 1269042428
+  dataset_size: 11349918755
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+license: other
+language:
+- en
+tags:
+- synthetic
+- science
+---
+# Science Learning Curriculum
+
+A large collection of structured learning units covering the scientific method, physics, chemistry, biology, earth and space science, and supporting mathematics.  
+
+Content is organised by domain, topic, difficulty level (1–10), and unit type (explanation, worked example, practice problem, etc.). Each row contains a self-contained pedagogical text together with key equations, prerequisites, and an explicit learning objective.
+
+## Columns
+
+- `id` – unique identifier  
+- `domain` – broad scientific domain  
+- `topic` / `subtopic` – hierarchical organisation  
+- `difficulty` – integer difficulty rating  
+- `unit_type` – type of learning unit  
+- `title` – short descriptive title  
+- `content` – main pedagogical text  
+- `key_equations` – relevant equations  
+- `prerequisites` – required prior knowledge  
+- `learning_objective` – stated educational goal

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