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ShareScore release 0.9.0
Dataset results
165 results for “prompting”
【outputs_encode_to_representations】only two 70B LLMs, only Ir task, augmented responses representations w/o their language materials, w/ original prompt and response representations
<ul> <li>only two 70B LLMs</li> <li>only Ir task augmented responses representations w/o their language materials <ul> <li>12 (valid prompts) ⨉ 2 (LLMs) ⨉ 2 (scores) = 48 files </li> </ul> </li> <li>original prompt and response representations <ul> <li>[1 (prompt) + 1 (response)] ⨉ 2 (LLMs) = 4 files</li> </ul> </li> <li>in total, 48 + 4 = 52 files</li> </ul>
【outputs_encode_to_representations】only two 70B LLMs, only SS 012 task, augmented responses representations w/o their language materials, w/ original prompt and response representations
<ul> <li>only two 70B LLMs, </li> <li>only SS 012 task,</li> <li>only augmented responses representations w/o their language materials, <ul> <li>7 (valid prompts) ⨉ 2 (LLMs) ⨉ 1 (Scores) = 14 files </li> </ul> </li> <li>original prompt and response representations <ul> <li>1 (response) ⨉ 2 (LLMs) = 2 files </li> </ul> </li> <li>in total, 14 + 2 = 16 files</li> </ul>
【outputs_encode_to_representations】only two 70B LLMs, only SS task, augmented responses representations w/o their language materials, w/ original prompt and response representations
<ul> <li>only two 70B LLMs, </li> <li>only SS task, augmented responses representations w/o their language materials, <ul> <li>12 (valid prompts), 2 (LLMs), 2 (Scores)</li> <li>16 ⨉ 2 (LLMs) = 32 files </li> </ul> </li> <li>original prompt and response representations <ul> <li>[1 (prompt) + 1 (response)] ⨉ 2 (LLMs) = 4 files</li> </ul> </li> <li>in total, 32 + 4 = 36 files</li> </ul>
PharmaVQA: A Retrieval-Augmented Visual Question Answering Framework for Molecular Representation via Pharmacophores Guided Prompts
<p>The dataset includes the preprocessed Li's dataset, MoleculeACE dataset, and 3 ligands dataset.</p>
Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning
<p>File A: Datasets (.txt)</p> <p> A1: Webex</p> <p> A2: Zoom</p> <p> A3: Teams</p> <p> A4: Word</p> <p> A5: PowerPoint</p> <p> A6: Excel</p> <p> </p> <p>File B: Python Code (Both ours and baseline)</p> <p> B1: pert_class.ipynb</p> <p> B2: Baseline.ipynb</p> <p> </p> <p>FIle C: Result Tables (.xlsx)</p> <p> C1: Table of perturbed outputs in Webex </p> <p> C2: Table of perturbed outputs in Zoom</p> <p> C3: Table of perturbed outputs in Teams</p> <p> C4: Table of perturbed outputs in Word</p> <p> C5: Table of perturbed outputs in PowerPoint</p> <p> C6: Table of perturbed outputs in Excel </p> <p> </p> <p>File D: Trend of Adversarial Shifts (Graphs)</p> <p> D1: Webex Adversarial Shifts</p> <p> D2: Zoom Adversarial Shifts</p> <p> D3: Teams Adversarial Shifts</p> <p> D4: Word Adversarial Shifts</p> <p> D5: Powerpoint Adversarial Shifts</p> <p> D6: Excel Adversarial Shifts</p> <p>File E: Trend of Non-Adversarial Shifts (Graphs)</p> <p> E1: Webex Non-Adversarial Shifts</p> <p> E2: Zoom Non-Adversarial Shifts</p> <p> E3: Teams Non-Adversarial Shifts</p> <p> E4: Word Non-Adversarial Shifts</p> <p> E5: Powerpoint Non-Adversarial Shifts</p> <p> E6: Excel Non-Adversarial Shifts</p> <p> </p> <p>File F: Adversarial Examples (.pdf)</p> <p> F1: Adversarial vs original in Webex</p> <p> F2: Adversarial vs original in Zoom</p> <p> F3: Adversarial vs original in Teams</p> <p> F4: Adversarial vs original in Word</p> <p> F5: Adversarial vs original in PowerPoint</p> <p> F6: Adversarial vs original in Excel</p> <p> </p> <p>File G: Questionnaire<br> <br> <br> <br> <br> </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.