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data_summary_card.md

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+# Data Summary for microsoft_Dayhoff-170m-UR90, Dayhoff-3b-UR90, Dayhoff-170m-GR, Dayhoffm-UR-50-BRn, Dayhoff-3b-GR-HM-c, Dayhoff-3b-GR-HM, Dayhoff-170m-UR50, Dayhoff-170m-UR50-BRq, Dayhoff-170m-UR50-BRu 
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+## 1. General information 
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+**1.0.1 Version of the Summary:** 1.0 
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+**1.0.2 Last update:** 4-Dec-2025 
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+## 1.1 Model Developer Identification 
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+**1.1.1 Model Developer name and contact details:** Microsoft Corporation at One Microsoft Way, Redmond, WA 98052. Tel: 425-882-8080 
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+## 1.2 Model Identification 
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+**1.2.1 Versioned model name(s):** Dayhoff 
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+**1.2.2 Model release date:** 25-Jul-2025 
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+## 1.3 Overall training data size and characteristics 
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+### 1.3.1 Size of dataset and characteristics 
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+**1.3.1.A Text training data size:** Not applicable.  
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+**1.3.1.B Text training data content:** Not applicable. Text data is not part of the training data. 
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+**1.3.1.C Image training data size:** Not applicable. 
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+**1.3.1.D Image training data content:** Not applicable. Images are not part of the training data. 
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+**1.3.1.E Audio training data size:** Not applicable. 
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+**1.3.1.F Audio training data content:** Not applicable. Audio data is not part of the training data. 
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+**1.3.1.G Video training data size:** Not applicable. 
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+**1.3.1.H Video training data content:** Not applicable. Video data is not part of the training data. 
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+**1.3.1.I Other training data size:** Training data consists of protein sequences and multiple sequence alignments; sizes include 3.34 billion sequences across 1.7 billion clusters (Gigaref), 46 million structure-derived synthetic sequences (BackboneRef), and 16 million MSAs (OpenProteinSet) 
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+**1.3.1.J Other training data content:**  
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+**1.3.2 Latest date of data acquisition/collection for model training:** Uniref (January 2024), Gigaref (July 2024), BackboneRef (July 2024), OpenProteinSet (August 2023) 
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+**1.3.3 Is data collection ongoing to update the model with new data collection after deployment?** No 
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+**1.3.4 Date the training dataset was first used to train the model:** April 2024 
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+**1.3.5 Rationale or purpose of data selection:** Datasets combine large-scale metagenomic and structure-based synthetic protein sequences to maximize coverage, diversity, and novelty of protein sequence space, supporting tasks like zero-shot mutation effect prediction, motif scaffolding, and guided generation of novel proteins with improved cellular expression rates 
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+## 2. List of data sources 
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+### 2.1 Publicly available datasets 
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+**2.1.1 Have you used publicly available datasets to train the model?** Yes 
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+ 
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+## 2.2 Private non-publicly available datasets obtained from third parties 
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+### 2.2.1 Datasets commercially licensed by rights holders or their representatives 
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+**2.2.1.A Have you concluded transactional commercial licensing agreement(s) with rights holder(s) or with their representatives?** No 
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+ 
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+### 2.2.2 Private datasets obtained from other third-parties 
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+**2.2.2.A Have you obtained private datasets from third parties that are not licensed as described in Section 2.2.1, such as data obtained from providers of private databases, or data intermediaries?** No 
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+## 2.3 Personal Information 
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+**2.3.1 Was personal data used to train the model?** Microsoft follows all relevant laws and regulations pertaining to personal information. 
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+## 2.4 Synthetic data 
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+**2.4.1 Was any synthetic AI-generated data used to train the model?** Yes  
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+## 3. Data processing aspects 
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+### 3.1 Respect of reservation of rights from text and data mining exception or limitation 
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+**3.1.1 Does this dataset include any data protected by copyright, trademark, or patent?** Microsoft follows all required regulations and laws for processing data protected by copyright, trademark, or patent. 
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+## 3.2 Other information 
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+**3.2.1 Does the dataset include information about consumer groups without revealing individual consumer identities?** Microsoft follows all required regulations and laws for protecting consumer identities. 
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+**3.2.2 Was the dataset cleaned or modified before model training?** Yes 
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