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1,454 results for “large scale”
【outputs_best_number_tokens_to_span_vectors_large_scales】/best_number_tokens_log_part_1 (1), only two 8B LLMs, 30 (span_vectors) = 30 files
<ul> <li>Part 1 (1) </li> <li>only two 8B LLMs, </li> <li>only valid results (both MP p value and CI),</li> <li>all permutation controls,</li> <li>all seeds,</li> <li>all linguistic spans,</li> <li>all 5 tasks, </li> <li>30 (span_vectors) = 30 files</li> </ul>
【outputs_best_number_tokens_to_span_vectors_large_scales】/best_number_tokens_log_part_6 (2), only two 8B LLMs, 30 (span_vectors) = 30 files
<ul> <li>Part 6 (2) </li> <li>only two 8B LLMs, </li> <li>only valid results (both MP p value and CI),</li> <li>all permutation controls,</li> <li>all seeds,</li> <li>all linguistic spans,</li> <li>all 5 tasks, </li> <li>30 (span_vectors) = 30 files</li> </ul>
【outputs_best_number_tokens_to_span_vectors_large_scales】/best_number_tokens_log_part_6 (1), only two 8B LLMs, 30 (span_vectors) = 30 files
<ul> <li>Part 6 (1) </li> <li>only two 8B LLMs, </li> <li>only valid results (both MP p value and CI),</li> <li>all permutation controls,</li> <li>all seeds,</li> <li>all linguistic spans,</li> <li>all 5 tasks, </li> <li>30 (span_vectors) = 30 files</li> </ul>
【outputs_best_number_tokens_to_span_vectors_large_scales】/best_number_tokens_log_part_1 (4), only two 8B LLMs, 30 (span_vectors) = 30 files
<ul> <li>Part 1 (4) </li> <li>only two 8B LLMs, </li> <li>only valid results (both MP p value and CI),</li> <li>all permutation controls,</li> <li>all seeds,</li> <li>all linguistic spans,</li> <li>all 5 tasks, </li> <li>30 (span_vectors) = 30 files</li> </ul>
【outputs_best_number_tokens_to_span_vectors_large_scales】/best_number_tokens_log_part_1 (3), only two 8B LLMs, 30 (span_vectors) = 30 files
<ul> <li>Part 1 (3) </li> <li>only two 8B LLMs, </li> <li>only valid results (both MP p value and CI),</li> <li>all permutation controls,</li> <li>all seeds,</li> <li>all linguistic spans,</li> <li>all 5 tasks, </li> <li>30 (span_vectors) = 30 files</li> </ul>
【outputs_best_number_tokens_to_span_vectors_large_scales】/best_number_tokens_log_part_6 (4), only two 8B LLMs, 30 (span_vectors) = 30 files
<ul> <li>Part 6 (4) </li> <li>only two 8B LLMs, </li> <li>only valid results (both MP p value and CI),</li> <li>all permutation controls,</li> <li>all seeds,</li> <li>all linguistic spans,</li> <li>all 5 tasks, </li> <li>30 (span_vectors) = 30 files</li> </ul>
【outputs_best_number_tokens_to_span_vectors_large_scales】/best_number_tokens_log_part_6 (3), only two 8B LLMs, 30 (span_vectors) = 30 files
<ul> <li>Part 6 (3) </li> <li>only two 8B LLMs, </li> <li>only valid results (both MP p value and CI),</li> <li>all permutation controls,</li> <li>all seeds,</li> <li>all linguistic spans,</li> <li>all 5 tasks, </li> <li>30 (span_vectors) = 30 files</li> </ul>
【outputs_best_number_tokens_to_span_vectors_large_scales】/best_number_tokens_log_part_11 (3), only two 8B LLMs, 30 (span_vectors) = 30 files
<ul> <li>Part 11 (3) </li> <li>only two 8B LLMs, </li> <li>only valid results (both MP p value and CI),</li> <li>all permutation controls,</li> <li>all seeds,</li> <li>all linguistic spans,</li> <li>all 5 tasks, </li> <li>30 (span_vectors) = 30 files</li> </ul>
【outputs_best_number_tokens_to_span_vectors_large_scales】/best_number_tokens_log_part_11 (4), only two 8B LLMs, 30 (span_vectors) = 30 files
<ul> <li>Part 11 (4) </li> <li>only two 8B LLMs, </li> <li>only valid results (both MP p value and CI),</li> <li>all permutation controls,</li> <li>all seeds,</li> <li>all linguistic spans,</li> <li>all 5 tasks, </li> <li>30 (span_vectors) = 30 files</li> </ul>
【outputs_best_number_tokens_to_span_vectors_large_scales】/best_number_tokens_log_part_11 (2), only two 8B LLMs, 30 (span_vectors) = 30 files
<ul> <li>Part 11 (2) </li> <li>only two 8B LLMs, </li> <li>only valid results (both MP p value and CI),</li> <li>all permutation controls,</li> <li>all seeds,</li> <li>all linguistic spans,</li> <li>all 5 tasks, </li> <li>30 (span_vectors) = 30 files</li> </ul>
【outputs_best_number_tokens_to_span_vectors_large_scales】/best_number_tokens_log_part_11 (1), only two 8B LLMs, 30 (span_vectors) = 30 files
<ul> <li>Part 11 (1) </li> <li>only two 8B LLMs, </li> <li>only valid results (both MP p value and CI),</li> <li>all permutation controls,</li> <li>all seeds,</li> <li>all linguistic spans,</li> <li>all 5 tasks, </li> <li>30 (span_vectors) = 30 files</li> </ul>
Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation
<p>Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. The paper can be found at https://arxiv.org/pdf/2206.08023.pdf</p> <p>In addition to providing the labeled 600 CT and MRI scans, we expect to provide 2000 CT and 1200 MRI scans without labels to support more learning tasks (semi-supervised, un-supervised, domain adaption, ...). The link can be found in:</p> <ul> <li><a href="https://zenodo.org/deposit/7262581">labeled data (500CT+100MRI)</a></li> <li><a href="https://zenodo.org/record/7262757#.Y2iSQ9JBwYs">unlabeled data Part I (900CT)</a></li> <li><a href="https://zenodo.org/record/7295661#.Y2iR_9JBwYs">unlabeled data Part II (1100CT)</a> (Now there are 1000CT, we will replenish to 1100CT)</li> <li><a href="https://zenodo.org/record/7295816">unlabeled data Part III (1200MRI)</a></li> </ul> <p>if you found this dataset useful for your research, please cite:</p> <blockquote> <pre>@inproceedings{NEURIPS2022_ee604e1b, author = {Ji, Yuanfeng and Bai, Haotian and GE, Chongjian and Yang, Jie and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhanng, Lingyan and Ma, Wanling and Wan, Xiang and Luo, Ping}, booktitle = {Advances in Neural Information Processing Systems}, editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh}, pages = {36722--36732}, publisher = {Curran Associates, Inc.}, title = {AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation}, url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/ee604e1bedbd069d9fc9328b7b9584be-Paper-Datasets_and_Benchmarks.pdf}, volume = {35}, year = {2022} } </pre> </blockquote> <p> </p>
STAVER: A Standardized Dataset-Based Algorithm for Efficient Variation Reduction in Large-Scale DIA MS Data
<p>This project focuses on the development and application of STAVER, a standardized dataset-based algorithm designed to reduce variation in large-scale data-independent acquisition (DIA) mass spectrometry data. By using a reference dataset to standardize mass spectrometry signals, STAVER effectively reduces noise and enhances protein quantification accuracy, especially in the context of multi-library search. The effectiveness of STAVER is demonstrated in several large-scale DIA datasets, showing improved identification and quantification of thousands of proteins. The project aims to promote the adoption of multi-library search and improve the quality of DIA proteomics data through the open-source STAVER software package.</p>
Codes for A hidden connection of meso-scale convective rainfall and large-scale circulations over the eastern Pacific
<h4><strong>README</strong></h4><h4><strong>System requirments</strong></h4><ul><li><strong>Figure 1, ED_Figure_1, and ED_FIGURE_3</strong>: IDL (v8.5) on Linux OS</li><li><strong>Figure 3, Figure 4, ED_Figure_4, and ED_FIGURE_5</strong>: Python (v3.7.3) on Linux OS</li><li><strong>Figure 2 and ED_Figure_2</strong>: Sigmaplot (v10.0)</li></ul><h4><strong>Installation guide</strong></h4><p>The modules needed to run are in each directory. Each code requires execution time of less than 1 minute.</p><h4><strong>Demo</strong></h4><p>Sample data, code, and expected output are located in their repective directories.</p><h4><strong>Instruction for use</strong></h4><p>IDL code) type <i>idl</i> to enter IDL mode</p><p>idl> .r (figure name).pro </p><p>Python code) </p><p>> python (figurename).py</p>
Large-scale Chemical Screen Identifies a Geroprotective Role of Gallic Acid in Premature Aging
GEO Series GSE181445. Homo sapiens. 20 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Large-scale tethering of RNA binding proteins discovers alternative splicing regulators
GEO Series GSE100638. Homo sapiens. 220 samples. Type: Expression profiling by high throughput sequencing; Expression profiling by array.
Large scale transcriptomic characterization of mice blood inflammation during Acute Lung Injury
GEO Series GSE52685. Mus musculus. 92 samples. Type: Expression profiling by array.
Genome-scale CRISPR screen identifies nucleotide metabolism as an actionable therapeutic vulnerability in diffuse large B-cell lymphoma [RNA-seq]
GEO Series GSE215222. Mus musculus. 13 samples. Type: Expression profiling by high throughput sequencing.
Large-scale characterisation of cell line xenografts identifies SMAD4 as key regulator of breast cancer morphology and aggressiveness
GEO Series GSE263474. Homo sapiens. 39 samples. Type: Expression profiling by high throughput sequencing.
Engineering Large-Scale and Innervated Functional Human Gut for Transplantation
GEO Series GSE233439. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
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.