Omkar2118/datasets huggingface dataset:
Twitter-COMMs
Full name: Twitter-COMMs

Detecting out-of-context media, such as "mis-captioned" images on Twitter, is a relevant problem, especially in domains of high public significance. Twitter-COMMs is a large-scale multimodal dataset with 884k tweets relevant to the topics of Climate Change, COVID-19, and Military Vehicles. This dataset can be used to develop methods to detect misinformation on social media platforms related to these three topics.

Introducing paper: Twitter-COMMs: Detecting Climate, COVID, and Military Multimodal Misinformation
https://osf.io/73c4q/
Full name: Empathy dataset

Briganti et al. 2018
https://osf.io/mj5wa/
Full name: PTSD dataset

Armour et al. 2017
Streetscore
Full name: STREETSCORE--PREDICTING THE PERCEIVED SAFETY OF ONE MILLION STREETSCAPES

Paper abstract: 

Social science literature has shown a strong connection between the visual appearance of a city’s neighborhoods and the behavior and health of its citizens. Yet, this re- search is limited by the lack of methods that can be used to quantify the appearance of streetscapes across cities or at high enough spatial resolutions. In this paper, we de- scribe ‘Streetscore’, a scene understanding algorithm that predicts the perceived safety of a streetscape, using training data from an online survey with contributions from more than 7000 participants. We first study the predictive power of commonly used image features using support vector re- gression, finding that Geometric Texton and Color His- tograms along with GIST are the best performers when it comes to predict the perceived safety of a streetscape. Us- ing Streetscore, we create high resolution maps of perceived safety for 21 cities in the Northeast and Midwest of the United States at a resolution of 200 images/square mile, scoring ∼1 million images from Google Streetview. These datasets should be useful for urban planners, economists and social scientists looking to explain the social and eco- nomic consequences of urban perception.
HowMany-QA

HowMany-Qa is a object counting dataset. It is taken from the counting-specific union of VQA 2.0 (Goyal et al., 2017) and Visual Genome QA (Krishna et al., 2016).

Introducing paper: Interpretable Counting for Visual Question Answering
Identity Access Management dataset

We release 280 synthetic IAM graphs generated using IAM graphs of commercial companies.
Specifically, we vary the number of nodes, but keep graph density as is, i.e. in the range of 0.259 ± 0.198 (avg ± std). 
To generate a synthetic graph, we first
sample the number of users and datastores from uniform distributions over the following intervals [10, 150] and [50, 300]
respectively that cover variations of those parameters across
real graphs. After fixing node counts we sample with replacement
the actual nodes from a real world graph, which is chosen
at random. Then we add Gaussian N(0, 0.01) noise to node
embeddings and renormalize them. To match the graph density with the density of the underlying baseline we sample
edges from a multinomial distribution, where each component is proportional to the cosine distance between a user and
a datastore embeddings. Also we enforce the invariant that
dynamic edges are always a subset of all permission edges.
A synthetic graph generated in such a way is an ”upsampled”
version of an underlying real world graph.

Introducing paper: Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security Policies
GMD-12

A dataset for medical consultation dialogues. 
See our related paper for more details: https://arxiv.org/pdf/2204.13953.pdf

Introducing paper: "My nose is running.""Are you also coughing?": Building A Medical Diagnosis Agent with Interpretable Inquiry Logics