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264 results for “batch”
Signal recovery in single cell batch integration
GEO Series GSE280883. Mus musculus. 17 samples. Type: Expression profiling by high throughput sequencing.
ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints. Datasets, Benchmark Results, and Torch Files.
<div> <div># ConBatch-BAL: Batch Bayesian Active Learning under Budget Constraints. Datasets, Benchmark Results, and Torch Files.</div> <br> <div>## Table of Contents</div> <br> <div>- [Overview](#overview)</div> <div>- [Folder Structure](#folder-structure)</div> <div>- [Contents](#contents)</div> <div>- [Licenses](#licenses)</div> <br> <div>## Overview</div> <div>This project contains three datasets along with stored results from the conducted benchmark analysis and torch files for running or reproducing active learning experiments.</div> <br> <div>## Folder Structure</div> <br> <div>```plaintext</div> <div>conBatchBAL_datasets/</div> <div>├── benchmark_results/</div> <div>├── benchmark_torch_files/</div> <div>├── build6k/</div> <div>├── mnist6k/</div> <div>└── nieman17k/</div> <div>```</div> <br> <div>## Contents:</div> <div>- benchmark_results/: This directory contains the results and config files for reproducing the experiments presented in the paper.</div> <br> <div>- benchmark_torch_files/: This folder contains the required torch (and json) files to run/reproduce active learning experiments.</div> <br> <div>- build6k/: This folder contains approximately 6000 aerial images of buildings in Rotterdam with their corresponding energy efficiency class and geolocation.</div> <br> <div>- mnist6k/: This folder contains approximately 6000 images of digits *artificially* geolocated in Rotterdam. The geolocations correspond to the buildings contained on the *build6k* dataset.</div> <br> <div>- nieman17k/: This folder contains approximately 17000 aerial images of buildings in Rotterdam with their corresponding typology class and geolocation.</div> <br> <div>**Additional readme files are included in each directory.**</div> <br> <div>## Licenses</div> <br> <div>- build6k/</div> <div>The build6k dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) [LICENSE](https://creativecommons.org/licenses/by/4.0/), allowing for use, distribution, and modifications with proper attribution.</div> <br> <div>- mnist6k/</div> <div>The mnist6k dataset is released under the CC BY-SA 3.0 [LICENSE](https://creativecommons.org/licenses/by-sa/3.0/).</div> <br> <div>- nieman17k/</div> <div>The nieman17k dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) [LICENSE](https://creativecommons.org/licenses/by/4.0/), allowing for use, distribution, and modifications with proper attribution.</div> <br> <div>- Benchmark Results and Torch Files</div> <div>The benchmark results and Torch files generated as part of this project are released under the Creative Commons Attribution 4.0 International (CC BY 4.0) [LICENSE](https://creativecommons.org/licenses/by/4.0/), allowing for use, distribution, and modifications with proper attribution.</div> <br> <div>**License details are included separately in each directory**</div> </div>
Batch-processing Abstraction for Raw Data Integration
BARDI is a data engineering tool to make clinical data "AI-ready" by transforming raw, unstructured inputs (like pathology reports) into structured, model-friendly formats. It provides modular pipelines for preprocessing tasks such as tokenization, normalization, and batch data handling, simplifying the development and maintenance of large-scale data workflows for machine learning. Overall, prepares clinical text and related data so downstream model frameworks can efficiently perform tasks such as information extraction and prediction.
Histology Batch 3
<p>Histological slides scans sent by GIGA histo platform 18 july 2023</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.