Datasets:

hugging-science
/

dataset-quest-index

@@ -4,3 +4,4 @@
 {"id": "71fcbca937a0443a920606be6dcc4060", "created_at": "2025-09-19T19:05:48Z", "dataset_name": "International Brain Lab Data", "dataset_url": "http://internationalbrainlab.com/data; https://www.biorxiv.org/content/10.1101/2023.07.04.547681v4", "description": "Multiple Brain-Related Datasets. A key challenge is that this dataset contains data from multiple experiments and different modalities. The dataset comprises 459 Neuropixel experimental sessions encompassing 699 distinct recordings (probe insertions). These recordings were obtained from 139 subjects performing the IBL task across 12 different laboratories. Spike-sorting analysis yielded 621,733 units, of which 75,708 were classified as good quality. In total, 241 brain regions were recorded(from their website).", "approx_size": 50.0, "size_unit": "TB", "field": "Multisite Neural Recording Local Field Potential(LFP)", "user": "Ken Fahmidur Rahman"}
 {"id": "a77b360636984b4ea3391c1757cdca50", "created_at": "2025-09-19T19:34:53Z", "dataset_name": "Insect-1M", "dataset_url": "https://uark-cviu.github.io/projects/insect-foundation/", "description": "Large-scale Insect Dataset comprising more than a million images with hierarchical labels from the high to the low taxonomy level, including class, order, family, genus, and species. Paper: https://arxiv.org/abs/2502.09906", "approx_size": 100.0, "size_unit": "GB", "field": "Entomology", "user": "Mohammed Hamdy"}
 {"id": "92b0add1ee9b446fb234f13438ea93e0", "created_at": "2025-09-20T08:04:57Z", "dataset_name": "ToT-Biology", "dataset_url": "https://huggingface.co/datasets/moremilk/ToT-Biology", "description": "The ToT-Biology dataset emphasizes mechanistic understanding and explanatory biological reasoning, rather than just providing correct answers. It aims to train AI models in interpretability and logical deduction within the biological realm. Spanning a wide range of biological complexities, it starts with foundational concepts in cell biology, genetics, and ecology, and progresses to advanced areas like systems biology, synthetic biology, and computational biophysics. The dataset incorporates real-world applications across diverse fields such as medicine, agriculture, environmental science, and bioengineering, ensuring practical relevance alongside rigorous theoretical exploration.", "approx_size": 80.0, "size_unit": "MB", "field": "Biology", "user": "SHIVAM DUBEY"}

 {"id": "71fcbca937a0443a920606be6dcc4060", "created_at": "2025-09-19T19:05:48Z", "dataset_name": "International Brain Lab Data", "dataset_url": "http://internationalbrainlab.com/data; https://www.biorxiv.org/content/10.1101/2023.07.04.547681v4", "description": "Multiple Brain-Related Datasets. A key challenge is that this dataset contains data from multiple experiments and different modalities. The dataset comprises 459 Neuropixel experimental sessions encompassing 699 distinct recordings (probe insertions). These recordings were obtained from 139 subjects performing the IBL task across 12 different laboratories. Spike-sorting analysis yielded 621,733 units, of which 75,708 were classified as good quality. In total, 241 brain regions were recorded(from their website).", "approx_size": 50.0, "size_unit": "TB", "field": "Multisite Neural Recording Local Field Potential(LFP)", "user": "Ken Fahmidur Rahman"}
 {"id": "a77b360636984b4ea3391c1757cdca50", "created_at": "2025-09-19T19:34:53Z", "dataset_name": "Insect-1M", "dataset_url": "https://uark-cviu.github.io/projects/insect-foundation/", "description": "Large-scale Insect Dataset comprising more than a million images with hierarchical labels from the high to the low taxonomy level, including class, order, family, genus, and species. Paper: https://arxiv.org/abs/2502.09906", "approx_size": 100.0, "size_unit": "GB", "field": "Entomology", "user": "Mohammed Hamdy"}
 {"id": "92b0add1ee9b446fb234f13438ea93e0", "created_at": "2025-09-20T08:04:57Z", "dataset_name": "ToT-Biology", "dataset_url": "https://huggingface.co/datasets/moremilk/ToT-Biology", "description": "The ToT-Biology dataset emphasizes mechanistic understanding and explanatory biological reasoning, rather than just providing correct answers. It aims to train AI models in interpretability and logical deduction within the biological realm. Spanning a wide range of biological complexities, it starts with foundational concepts in cell biology, genetics, and ecology, and progresses to advanced areas like systems biology, synthetic biology, and computational biophysics. The dataset incorporates real-world applications across diverse fields such as medicine, agriculture, environmental science, and bioengineering, ensuring practical relevance alongside rigorous theoretical exploration.", "approx_size": 80.0, "size_unit": "MB", "field": "Biology", "user": "SHIVAM DUBEY"}
+{"id": "67a803cadc7b4fa1966ffdac2780ccb2", "created_at": "2025-09-20T12:41:49Z", "dataset_name": "Enhancer", "dataset_url": "https://sid.erda.dk/cgi-sid/ls.py?share_id=aNQa0Oz2lY", "description": "Dataset for the problem of findings enhancer regions for a given gene", "approx_size": 2.0, "size_unit": "MB", "field": "genomics", "user": "anonymous"}