I need you to analyze the following content and extract dataset metadata following the "Datasheets for Datasets" (D4D) schema.

**Source Information:**
- File: dataverse_10.18130_V3_B35XWX_row13.txt
- Project Column: CM4AI
- File Type: .txt
- Validation: ✅ Relevant

**Instructions:**
1. Analyze the content below and extract all available dataset metadata
2. Format the output as valid YAML following the D4D schema structure
3. Include these key sections if information is available:
   - id, name, title, description
   - creators (with names, affiliations, roles)
   - purposes and intended uses
   - instances (data types, counts, representations)
   - collection_mechanisms and timeframes
   - preprocessing_strategies and cleaning
   - distribution_formats and access
   - ethical_reviews and consent
   - license_and_use_terms
   - maintainers and funding

**D4D Schema Reference:**
```yaml
# Key D4D fields (use as template):
id: dataset-identifier
name: Dataset Name
title: "Full Dataset Title"
description: "Detailed description..."

creators:
  - name: "Author Name"
    affiliation: "Institution"
    role: "Principal Investigator"

purposes:
  response: "Why was this dataset created..."

instances:
  representation: "What the data represents"
  data_type: "Type of data (text, images, etc.)"
  counts: 1000

collection_mechanisms:
  description: "How data was collected..."

# Add other relevant sections...
```

**Content to Analyze:**
```





Cell Maps for Artificial Intelligence - March 2025 Data Release (Beta) - Cell Maps for Artificial Intelligence

































































































































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Cell Maps for Artificial Intelligence - March 2025 Data Release (Beta)
Version 1.4









Clark T; Parker J; Al Manir S; Axelsson U; Ballllosero Navarro F; Chinn B; Churas CP; Dailamy A; Doctor Y; Fall J; Forget A; Gao J; Hansen JN; Hu M; Johannesson A; Khaliq H; Lee YH; Lenkiewicz J; Levinson MA; Marquez C; Metallo C; Muralidharan M; Nourreddine S; Niestroy J; Obernier K; Pan E; Polacco B; Pratt D; Qian G; Schaffer L; Sigaeva A; Thaker S; Zhang Y; Bélisle-Pipon JC; Brandt C; Chen JY; Ding Y; Fodeh S; Krogan N; Lundberg E; Mali P; Payne-Foster P; Ratcliffe S; Ravitsky V; Sali A; Schulz W; Ideker T, 2025, "Cell Maps for Artificial Intelligence - March 2025 Data Release (Beta)", https://doi.org/10.18130/V3/B35XWX, University of Virginia Dataverse, V1
                


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                                                            Dataset Description
                                                            

This dataset is the March 2025 Data Release of Cell Maps for Artificial Intelligence (CM4AI; CM4AI.org), the Functional Genomics Grand Challenge in the NIH Bridge2AI program. This Beta release includes perturb-seq data in undifferentiated KOLF2.1J iPSCs; SEC-MS data in undifferentiated KOLF2.1J iPSCs and iPSC-derived NPCs, neurons, and cardiomyocytes; and IF images in MDA-MB-468 breast cancer cells in the presence and absence of chemotherapy (vorinostat and paclitaxel). CM4AI output data are packaged with provenance graphs and rich metadata as AI-ready datasets in RO-Crate format using the FAIRSCAPE framework. Data presented here will be augmented regularly through the end of the project. CM4AI is a collaboration of UCSD, UCSF, Stanford, UVA, Yale, UA Birmingham, Simon Fraser University, and the Hastings Center. This data is Copyright (c) 2025 The Regents of the University of California except where otherwise noted. Spatial proteomics raw image data is copyright (c) 2025 The Board of Trustees of the Leland Stanford Junior University. Dataset licensed for reuse under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license (https://creativecommons.org/licenses/by-nc-sa/4.0/). Attribution is required to the copyright holders and the authors. Any publications referencing this data or derived products should cite the Related Publication below, as well as directly citing this data collection (2025-03-04). (2025-03-07)
                                                                    

                                                            Subject
                                                            
Medicine, Health and Life Sciences

                                                            Keyword
                                                            

AI, affinity purification, AP-MS, artificial intelligence, breast cancer, Bridge2AI, cardiomyocyte, CM4AI, CRISPR/Cas9, induced pluripotent stem cell, iPSC, KOLF2.1J, machine learning, mass spectroscopy, MDA-MB-468, neural progenitor cell, NPC, neuron, paclitaxel, perturb-seq, perturbation sequencing, protein-protein interaction, protein localization, single-cell RNA sequencing, scRNAseq, SEC-MS, size exclusion chromatography, subcellular imaging, vorinostat

                                                            Related Publication
                                                            
References: Clark T, Parker J, Schaffer L, Obernier K, Al Manir S, Churas CP, Dailamy A, Doctor Y, Forget A, Hansen JN, Hu M, Lenkiewicz J, Levinson MA, Marquez C, Nourreddine S, Niestroy J, Pratt D, Qian G, Thaker S, Bélisle-Pipon JC, Brandt C, Chen J, Ding Y, Fodeh S, Krogan N, Lundberg E, Mali P, Payne-Foster P, Ratcliffe S, Ravitsky V, Sali A, Schulz W, Ideker T. Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines. 2024.doi: http://doi.org/10.1101/2024.05.21.589311


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                        ro-crate-metadata.json
                    

CRISPR Perturbation Cell Atlas/JSON - 31.1 KB
Published Mar 3, 2025
31 Downloads
MD5: cbdb263b1c099396d75e16f00a79a818

This dataset represents an expressed genome-scale CRISPRi Perturbation Cell Atlas in undifferentiated KOLF2.1J human induced pluripotent stem cells (hiPSCs) mapping transcriptional and fitness phenotypes associated with 11,739 targeted genes, as part of the Cell Maps for Artificial Intelligence (CM4AI; CM4AI.org) Functional Genomics Grand Challenge, a component of the U.S. National Institute of Health’s (NIH) Bridge2AI program. We validated these findings via phenotypic, protein-interaction, and metabolic tracing assays. 


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                        ro-crate-metadata.json
                    

CRISPR Perturbation RNA Sequences - Raw Sequences/JSON - 1000.1 KB
Published Mar 3, 2025
17 Downloads
MD5: 1cafefa32a897998e3e2ba0a29a3ef5c

This dataset represents raw sequence data from an expressed genome-scale CRISPRi Perturbation Cell Atlas in KOLF2.1J human induced pluripotent stem cells (hiPSCs) mapping transcriptional and fitness phenotypes associated with 11,739 targeted genes, as part of the Cell Maps for Artificial Intelligence (CM4AI; CM4AI.org) Functional Genomics Grand Challenge, a component of the U.S. National Institute of Health’s (NIH) Bridge2AI program. 


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                        cm4ai-v0.6-beta-if-images-paclitaxel.zip
                    

Protein Localization Subcellular Images/ZIP Archive - 2.6 GB
Published Mar 3, 2025
87 Downloads
MD5: 9422486c80bc9e1d35b2fbbc72a5f043

This data set displays the spatial localization of 563 proteins of interest in cells of the breast cancer cell line MDA-MB-468 treated with paclitaxel as imaged by immunofluorescence-based staining (ICC-IF) and confocal microscopy in the Lundberg Lab at Stanford University, as part of the Cell Maps for Artificial Intelligence (CM4AI; CM4AI.org) project. Nuclei were stained with DAPI (blue channel); endoplasmic reticulum with a calreticulin antibody (yellow channel); microtubules with tubulin antibody (red channel); and antibody against protein of interest (green channel). 


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                        cm4ai-v0.6-beta-if-images-untreated.zip
                    

Protein Localization Subcellular Images/ZIP Archive - 3.2 GB
Published Mar 3, 2025
67 Downloads
MD5: 0b4d129f5fbc3bb7f7ea564cd032cef7

This data set displays the spatial localization of 563 proteins of interest in untreated cells of the breast cancer cell line MDA-MB-468 as imaged by immunofluorescence-based staining (ICC-IF) and confocal microscopy in the Lundberg Lab at Stanford University, as part of the Cell Maps for Artificial Intelligence (CM4AI; CM4AI.org) project. Nuclei were stained with DAPI (blue channel); endoplasmic reticulum with a calreticulin antibody (yellow channel); microtubules with tubulin antibody (red channel); and antibody against protein of interest (green channel). 


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                        cm4ai-v0.6-beta-if-images-vorinostat.zip
                    

Protein Localization Subcellular Images/ZIP Archive - 2.8 GB
Published Mar 3, 2025
57 Downloads
MD5: ac577109a41a9806978461157b777d52

This data set displays the spatial localization of 563 proteins of interest in cells of the breast cancer cell line MDA-MB-468 treated with vorinostat as imaged by immunofluorescence-based staining (ICC-IF) and confocal microscopy in the Lundberg Lab at Stanford University, as part of the Cell Maps for Artificial Intelligence (CM4AI; CM4AI.org) project. Nuclei were stained with DAPI (blue channel); endoplasmic reticulum with a calreticulin antibody (yellow channel); microtubules with tubulin antibody (red channel); and antibody against protein of interest (green channel). 


Preview "Protein Localization Subcellular Images/cm4ai-v0.6-beta-if-images-vorinostat.zip"


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                        ro-crate-metadata.json
                    

Protein-protein Interaction SEC-MS/JSON - 2.9 KB
Published Mar 3, 2025
20 Downloads
MD5: cb67e7749b15ce87b9042a9feba9d032

This dataset was generated by size exclusion chromatography-mass spectroscopy (SEC-MS) on undifferentiated KOLF2.1J human induced pluripotent stem cells (hiPSCs), in the Nevan Krogan laboratory at the University of California San Francisco, as part of the Cell Maps for Artificial Intelligence (CM4AI; CM4AI.org) Functional Genomics Grand Challenge, a component of the U.S. National Institute of Health’s (NIH) Bridge2AI program. The data will be uploaded to Pride when available.


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                    Citation Metadata  







                                            Persistent Identifier
                                            

doi:10.18130/V3/B35XWX


                                            Publication Date
                                            

2025-03-03


                                            Title
                                            
Cell Maps for Artificial Intelligence - March 2025 Data Release (Beta)



                                            Author
                                            
Clark T (University of Virginia) - ORCID: https://orcid.org/0000-0003-4060-7360
Parker J (University of California, San Diego) - ORCID: https://orcid.org/0000-0003-4535-3486
Al Manir S (University of Virginia) - ORCID: https://orcid.org/0000-0003-4647-3877
Axelsson U (KTH Royal Institute of Technology,)
                                                        Ballllosero Navarro F (Stanford University) - ORCID: https://orcid.org/0000-0002-4180-422X
Chinn B (University of California San Diego)
                                                        Churas CP (University of California San Diego) https://orcid.org/0000-0001-9998-705X
                                                        Dailamy A (University of California, San Diego) - ORCID: https://orcid.org/0000-0002-6711-8260
Doctor Y (University of California, San Diego) - ORCID: https://orcid.org/0009-0009-0483-7506
Fall J (KTH - Royal Institute of Technology)
                                                        Forget A (University of California San Francisco) - ORCID: https://orcid.org/0000-0003-0223-0312
Gao J (University of California San Diego) - ORCID: https://orcid.org/0000-0002-6311-3526
Hansen JN (Stanford University) - ORCID: https://orcid.org/0000-0002-4650-9094
Hu M (University of California San Diego) https://orcid.org/0000-0002-1571-8029
                                                        Johannesson A (KTH - Royal Institute of Technology)
                                                        Khaliq H (University of California San Diego)
                                                        Lee YH (University of California San Diego) - ORCID: https://orcid.org/0000-0003-0917-355X
Lenkiewicz J (University of California San Diego) https://orcid.org/0000-0001-7252-8638
                                                        Levinson MA \n\n... [Content truncated for length] ...
```

**Output Instructions:**
- Provide ONLY valid YAML output (no explanations or markdown formatting)
- Start directly with the YAML content
- If specific information isn't available, omit those fields rather than guessing
- Focus on extracting concrete, factual information from the source material