Omkar2118/datasets huggingface dataset:
AWS Documentation

We present the AWS documentation corpus, an open-book QA dataset, which contains 25,175 documents along with 100 matched questions and answers. These questions are inspired by the author's interactions with real AWS customers and the questions they asked about AWS services. The data was anonymized and aggregated. All questions in the dataset have a valid, factual and unambiguous answer within the accompanying documents, we deliberately avoided questions that are ambiguous, incomprehensible, opinion-seeking, or not clearly a request for factual information. All questions, answers and accompanying documents in the dataset are annotated by authors. There are two types of answers: text and yes-no-none(YNN) answers. Text answers range from a few words to a full paragraph sourced from a continuous block of words in a document or from different locations within the same document. Every question in the dataset has a matched text answer. Yes-no-none(YNN) answers can be yes, no, or none depending on the type of question. For example the question: “Can I stop a DB instance that has a read replica?” has a clear yes or no answer but the question “What is the maximum number of rows in a dataset in Amazon Forecast?” is not a yes or no question and therefore has a “None” as the YNN answer. 23 questions have ‘Yes’ YNN answers, 10 questions have ‘No’ YNN answers and 67 questions have ‘None’ YNN answers.

Introducing paper: Zero-Shot Open-Book Question Answering
TEM nanowire morphologies for classification and segmentation
Full name: Transmission electron microscopy (TEM) image datasets of peptide / protein nanowire morphologies

TEM image dataset containing four nanowire morphologies of bio-derived protein nanowires and synthetic peptide nanowires.

The peptide / protein nanowires used in this study were synthesized and imaged by Brian Montz in Prof. Todd Emrick's research group at the Department of Polymer Science and Engineering Department, University of Massachusetts Amherst. 

We acknowledge financial support from the U.S. National Science Foundation, Grant NSF DMREF #1921839 and DMREF #1921871.

Nanowires were classified into either of the four morphologies: bundle, singular, dispersed or network. Each morphology contains 100 images (jpg files).

For the dispersed and network morphologies, because these two morphologies are harder to visually distinguish, we have created manual segmentation labels of the nanowires (included in these two morphology folders as png files). Percolation analysis was done on these manually segmented nanowires to provide quantitative metric on whether the nanowires form a network in the image. 

encoders_trained_with_optimized_hyperparameter.zip contains 4 sets of encoders trained with either SimCLR or Barlow-Twins self-supervised methods on either generic TEM images, or generic everyday photographic images (each with 5 replicates with different random seed) with optimized hyperparameters.

seg_mask_5_resolutions.zip contains ground truth 2D binary encoding of segmented nanowires at 5 resolutions.

Introducing paper: Semi-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy images