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ShareScore release 0.9.0
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1,454 results for “large scale”
Large-Scale Data of Traditional Chinese Medicine bioactivity
<p>These files work with KNIME diagram to handle all training data and external validation data. </p> <p>1. <strong>TCM_inchikey.xlsx</strong> includes all chemical structure data from 16 TCM databases.</p> <p>2. After data augmentation, the original TCM structure data was expanded and stored as the <strong>2000w_inchikey.txt</strong>.</p> <p>3. Then TCM_inchikey.xlsx and 2000w_inchikey.txt were separatly mapped on the ChEMBL32 to acquire the bioassay data. Due to ChEMBL32 was acquired by SQL, please check KNIME diagram stored in github site for ChEMBL32 data and the mapped bioassay data. </p> <p>4. After the screening process strored in KNIME, the <strong>training_data.zip</strong> was acquired.</p> <p>5. <strong>BindingDB_bioassay.csv</strong> and <strong>pubchem_bioassay.csv</strong> were used to form the external validation data, that is <strong>external data.zip</strong>.</p>
Large-scale analysis of grooming in modern social networks
<p>We provide a large-scale dataset of the messages exchanged publicly by the streamers and viewers during the live broadcasts of users identified as adult content producers from the <a href="https://www.liveme.com/">LiveMe</a> platform, a major Social Live Streaming Service (SLSS). The dataset comprises 39,382,838 chat messages exchanged by 1,428,284 users, in the context of 293,271 live broadcasts during a period of approximately two years, from July 2016 to June 2018. The analysis of this dataset can be found in our paper <strong><em>"Large-scale analysis of grooming in modern social networks" </em></strong>(<a href="https://arxiv.org/abs/2004.08205">arXiv:2004.08205</a> [cs.SI]).</p>
【outputs_search_best_number_tokens_large_scales_SS】only two 8B LLMs, only SS task, [1 (best # of tokens) + 1 (corresponding language materials)] ⨉ 6 (parts) = 12 files
<ul> <li>only two 8B LLMs, </li> <li>only SS task,</li> <li>[1 (best # of tokens) + 1 (corresponding language materials)] ⨉ 6 (parts) = 12 files</li> </ul>
【outputs_search_best_number_tokens_large_scales_Ir】only two 8B LLMs, only Ir task, [1 (best # of tokens) + 1 (corresponding language materials)] ⨉ 6 (parts) = 12 files
<ul> <li>only two 8B LLMs, </li> <li>only Ir task,</li> <li>[1 (best # of tokens) + 1 (corresponding language materials)] ⨉ 6 (parts) = 12 files</li> </ul>
【outputs_search_best_number_tokens_large_scales_HT】only two 8B LLMs, only HT task, [1 (best # of tokens) + 1 (corresponding language materials)] ⨉ 6 (parts) = 12 files
<ul> <li>only two 8B LLMs, </li> <li>only HT task,</li> <li>[1 (best # of tokens) + 1 (corresponding language materials)] ⨉ 6 (parts) = 12 files</li> </ul>
【outputs_search_best_number_tokens_large_scales_FB】only two 8B LLMs, only FB task, [1 (best # of tokens) + 1 (corresponding language materials)] ⨉ 6 (parts) = 12 files
<ul> <li>only two 8B LLMs, </li> <li>only FB task,</li> <li>[1 (best # of tokens) + 1 (corresponding language materials)] ⨉ 6 (parts) = 12 files</li> </ul>
【outputs_search_best_number_tokens_large_scales_SS 012】only two 8B LLMs, only SS 012 task, [1 (best # of tokens) + 1 (corresponding language materials)] ⨉ 6 (parts) = 12 files
<ul> <li>only two 8B LLMs, </li> <li>only SS 012 task,</li> <li>[1 (best # of tokens) + 1 (corresponding language materials)] ⨉ 6 (parts) = 12 files</li> </ul>
【outputs_best_number_tokens_to_tpm_large_scales】/best_number_tokens_log_part_12 (2), only two 8B LLMs, 30 (tpms) = 30 files
<ul> <li>Part 12 (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 (tpms) = 30 files</li> </ul>
【outputs_best_number_tokens_to_tpm_large_scales】/best_number_tokens_log_part_12 (1), only two 8B LLMs, 30 (tpms) = 30 files
<ul> <li>Part 12 (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 (tpms) = 30 files</li> </ul>
【outputs_best_number_tokens_to_tpm_large_scales】/best_number_tokens_log_part_18 (2), only two 8B LLMs, 5 (tpms) = 5 files
<ul> <li>Part 18 (2) 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>5 (tpms) = 5 files</li> </ul>
【outputs_best_number_tokens_to_tpm_large_scales】/best_number_tokens_log_part_18 (1), only two 8B LLMs, 25 (tpms) = 25 files
<ul> <li>Part 18 (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>25 (tpms) = 25 files</li> </ul>
【outputs_best_number_tokens_to_tpm_large_scales】/best_number_tokens_log_part_18 (4), only two 8B LLMs, 8 (tpms) = 8 files
<ul> <li>Part 18 (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>8 (tpms) = 8 files</li> </ul>
【outputs_best_number_tokens_to_tpm_large_scales】/best_number_tokens_log_part_18 (3), only two 8B LLMs, 22 (tpms) = 22 files
<ul> <li>Part 18 (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>22 (tpms) = 22 files</li> </ul>
【outputs_best_number_tokens_to_tpm_large_scales】/best_number_tokens_log_part_1 (1), only two 8B LLMs, 30 (tpms) = 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 (tpms) = 30 files</li> </ul>
【outputs_best_number_tokens_to_tpm_large_scales】/best_number_tokens_log_part_1 (2), only two 8B LLMs, 30 (tpms) = 30 files
<ul> <li>Part 1 (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 (tpms) = 30 files</li> </ul>
【outputs_best_number_tokens_to_tpm_large_scales】/best_number_tokens_log_part_1 (4), only two 8B LLMs, 30 (tpms) = 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 (tpms) = 30 files</li> </ul>
【outputs_best_number_tokens_to_tpm_large_scales】/best_number_tokens_log_part_1 (3), only two 8B LLMs, 30 (tpms) = 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 (tpms) = 30 files</li> </ul>
【outputs_best_number_tokens_to_tpm_large_scales】/best_number_tokens_log_part_6 (5), only two 8B LLMs, 20 (tpms) = 20 files
<ul> <li>Part 6 (5) </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>20 (tpms) = 20 files</li> </ul>
【outputs_best_number_tokens_to_tpm_large_scales】/best_number_tokens_log_part_6 (6), only two 8B LLMs, 10 (tpms) = 10 files
<ul> <li>Part 6 (6) </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>10 (tpms) = 10 files</li> </ul>
【outputs_best_number_tokens_to_tpm_large_scales】/best_number_tokens_log_part_6 (7), only two 8B LLMs, 23 (tpms) = 23 files
<ul> <li>Part 6 (7) </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>23 (tpms) = 23 files</li> </ul>
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.