Omkar2118/datasets huggingface dataset:
BirdClef 2020  (Pruned)

Due to the highly variable sample size of the original BirdClef2020 dataset and the issues that it presents with reproducibility, we propose a pruned version of the set, where samples longer than 180s are removed along with classes with fewer than 50 samples. This processing puts it further in line with other complex audio datasets and allows for experimentation on more consumer friendly hardware.

Introducing paper: MetaAudio: A Few-Shot Audio Classification Benchmark
Gasch1 Funcat

Hierarchical-multilabel classification dataset for functional genomics
Gasch2 Funcat

Hierarchical-multilabel classification dataset for functional genomics
IJB-S
Full name: IARPA Janus Benchmark-S

[Paper Abstract](http://biometrics.cse.msu.edu/Publications/Face/Kalkaetal_IJBSIARPPAJanusSurveillanceVideoBenchmark_BTAS2018.pdf)

We present IJB–S dataset, an open-source IARPA Janus Surveillance Video Benchmark and associated protocols. The dataset consists of images and surveillance video collected from 202 subjects at a Department of Defense (DoD) training facility. Surveillance video was captured across multiple vignettes representative of a variety of real-world surveillance use cases that are particularly of interest to law enforcement and national security communities. Each video was annotated by human subject matter experts in order to generate ground truth identity and bounding box face labels. In total, over 10 million annotations were collected for the dataset.
EVICAN

Deep learning use for quantitative image analysis is exponentially increasing. However, training accurate, widely deployable deep learning algorithms requires a plethora of annotated (ground truth) data. Image collections must contain not only thousands of images to provide sufficient example objects (i.e. cells), but also contain an adequate degree of image heterogeneity. We present a new dataset, EVICAN-Expert visual cell annotation, comprising partially annotated grayscale images of 30 different cell lines from multiple microscopes, contrast mechanisms and magnifications that is readily usable as training data for computer vision applications. With 4600 images and ∼26 000 segmented cells, our collection offers an unparalleled heterogeneous training dataset for cell biology deep learning application development. The dataset is freely available (https://edmond.mpdl.mpg.de/imeji/collection/l45s16atmi6Aa4sI?q=).
SR-RAW

Raw sensor dataset where each sequence captures 7 (few contain 6) images (RAW and JPG) taken by different focal lengths.

Introducing paper: Zoom To Learn, Learn To Zoom
bladderbatch
Full name: bladderbatch

Microarray gene expression data on 57 bladder samples from 5 batches.
RASFF

In the actual globalized world, the transportation of goods between any country is something normal. Considering that the protocols in quality and security vary from one country to another, there is a risk with the products that do not comply with the legislation of a country cross the border. In the case of edible products, the importance of avoiding this kind of situation is even higher. Since 1979, European Union members were obligated to register any risk to public health-related with the food and feed that is traded alongside the territory. This information has been registered in a portal called Rapid Alert System for Food and Feed (RASFF). The content of this paper provides a deep description of a set of records that goes from September 1979 to September 2019 both included. Each record represents an issue registered by RASFF workers containing a set of generic features that all issues have in common, and a set of features that are considered details of the issue. The nature of the data in these features is only categorical except in one case, which is a string of characters that correspond to a subject. All the data was downloaded by using an automatic scrapper and using techniques to clean and transform it, so they can be stored as a .csv file. The potential use of the dataset is related to feature engineering, predictions, a search of behavior patterns, etc. 

Description from [Mendeley](https://data.mendeley.com/datasets/yxkm4gs7zf/2)

Introducing paper: Food safety risk prediction with Deep Learning models using categorical embeddings on European Union data
DAVIS-585
Full name: DAVIS-585

A dataset for interactive segmentation with simulated initial masks.

Introducing paper: FocalClick: Towards Practical Interactive Image Segmentation
CNN Filter DB-Robust

Dataset for the Paper "Adversarial Robustness through the Lens of Convolutional Filters".

Introducing paper: Adversarial Robustness through the Lens of Convolutional Filters
CVL-DataBase
Full name: CVL-DataBase

The CVL Database is a public database for writer retrieval, writer identification and word spotting. The database consists of 7 different handwritten texts (1 German and 6 Englisch Texts). In total 310 writers participated in the dataset. 27 of which wrote 7 texts and 283 writers had to write 5 texts. For each text a rgb color image (300 dpi) comprising the handwritten text and the printed text sample is available as well as a cropped version (only handwritten). An unique id identifies the writer, whereas the Bounding Boxes for each single word are stored in an XML file.

The CVL-database consists of images with cursively handwritten german and english texts which has been choosen from literary works. All pages have a unique writer id and the text number (separated by a dash) at the upper right corner, followed by the printed sample text. The text is placed between two horizontal separatores. Beneath the printed text individuals have been asked to write the text using a ruled undersheet to prevent curled text lines. The layout follows the style of the IAM database. The database was updated on 12/09/2013 since one writer ID (265/266) was wrong. The version number was changed to 1.1.

Samples of the following texts have been used:

    Edwin A. Abbot – Flatland: A Romance of Many Dimension (92 words).
    William Shakespeare – Mac Beth (49 words).
    Wikipedia – Mailüfterl (73 words, under CC Attribution-ShareALike License).
    Charles Darwin – Origin of Species (52 words).
    Johann Wolfgang von Goethe – Faust. Eine Tragödie (50 words).
    Oscar Wilde – The Picture of Dorian Gray (66 words).
    Edgar Allan Poe – The Fall of the House of Usher (78 words).

Introducing paper: CVL-DataBase: An Off-Line Database for Writer Retrieval, Writer Identification and Word Spotting
Gait3D

Gait3D is a large-scale 3D representation-based gait recognition dataset. It contains 4,000 subjects and over 25,000 sequences extracted from 39 cameras in an unconstrained indoor scene.

Introducing paper: Gait Recognition in the Wild with Dense 3D Representations and A Benchmark
The Little Prince
Full name: The Little Prince Corpus

This corpus is an annotation of the novel The Little Prince by Antoine de Saint-Exupéry, published in 1943. We were inspired by the UNL project to include this novel, so that different groups could compare representations on the same text.
Bio
Full name: Bio AMR Corpus

This corpus includes annotations of cancer-related PubMed articles, covering 3 full papers (PMID:24651010, PMID:11777939, PMID:15630473) as well as the result sections of 46 additional PubMed papers. The corpus also includes about 1000 sentences each from the BEL BioCreative training corpus and the Chicago Corpus.

Introducing paper: Abstract Meaning Representation for Sembanking
RepCount
Full name: Repetitive Action Counting Dataset

Counting repetitive actions are widely seen in human activities such as physical exercise. Existing methods focus on performing repetitive action counting in short videos, which is tough for dealing with longer videos in more realistic scenarios. In the data-driven era, the degradation of such generalization capability is mainly attributed to the lack of long video datasets. To complement this margin, we introduce a new large-scale repetitive action counting dataset called RepCount covering a wide variety of video lengths, along with more realistic situations where action interruption or action inconsistencies occur in the video. Besides, we also provide a fine-grained annotation of the action cycles instead of just counting annotation along with a numerical value. Such a dataset contains **1451**  videos with about **20000** 
 annotations, which is more challenging.  Furthermore, the dataset consists of two subsets namely Part-A and Part-B. The videos in Part-A are fetched from YouTube, while the others in Part-B record simulated physical examinations by junior school students and teachers.

Introducing paper: TransRAC: Encoding Multi-scale Temporal Correlation with Transformers for Repetitive Action Counting
ZJU-RGB-P

Research on semantic segmentation of traffic scenes using color and polarization information (including training and testing sets).

Introducing paper: Polarization-driven Semantic Segmentation via Efficient Attention-bridged Fusion
Multispectral Image Database

We present a database of multispectral images that were used to emulate the GAP camera. The images are of a wide variety of real-world materials and objects. We are making this database available to the research community. Details of the database can be found in the following publication:

"Generalized Assorted Pixel Camera: Post-Capture Control of Resolution, Dynamic Range and Spectrum," 
F. Yasuma, T. Mitsunaga, D. Iso, and S.K. Nayar, 
Technical Report, Department of Computer Science, Columbia University CUCS-061-08, 
Nov. 2008.
RS-Haze

A large-scale non-homogeneous remote sensing image dehazing dataset

Introducing paper: Vision Transformers for Single Image Dehazing
CP2A dataset
Full name: CARLA Pedestrian Action Anticipation dataset

We present a new simulated dataset for pedestrian action anticipation collected using the CARLA simulator.
To generate this dataset, we place a camera sensor on the ego-vehicle in the Carla environment and set the parameters to those of the camera used to record the PIE dataset (i.e., 1920x1080, 110° FOV). Then, we compute bounding boxes for each pedestrian interacting with the ego vehicle as seen through the camera's field of view. We generated the data in two urban environments available in the CARLA simulator: Town02 and Town03.

The total number of simulated pedestrians is nearly 55k, equivalent to 14M bounding boxes samples. The critical point for each pedestrian is their first point of crossing the street (in case they will eventually cross) or the last bounding box coordinates of their path in the opposite case. The crossing behavior represents 25% of the total pedestrians. We balanced the training split of the dataset to obtain labeled sequences crossing/non-crossing in equal parts. We used sequence-flipping to augment the minority class (i.e., crossing behavior in our case) and then undersampled the rest of the dataset. The result is a total of nearly 50k pedestrian sequences.

Next, the pedestrian trajectory sequences were transformed into observation sequences of equal length (i.e., 0.5 seconds) with a 60% overlap for the training splits. The TTE length is between 30 and 60 frames. It resulted in a total of nearly 220k observation sequences.

Introducing paper: Is attention to bounding boxes all you need for pedestrian action prediction?
Amazon Review

Amazon Review is a dataset to tackle the task of identifying whether the sentiment of a product review is positive or negative. This dataset includes reviews from four different merchandise categories: Books (B) (2834 samples), DVDs (D) (1199 samples), Electronics (E) (1883 samples), and Kitchen and housewares (K) (1755 samples).
DIBCO 2019

DIBCO 2019 is the international Competition on Document Image Binarization organized in conjunction with the ICDAR 2019 conference. The general objective of the contest is to identify current advances in document image binarization of machine-printed and handwritten document images using performance evaluation measures that are motivated by document image analysis and recognition requirements.
OSS for Social Good Project List
Full name: Supplementary Material for Leaving My Fingerprints: Motivations and Challenges of Contributing to OSS for Social Good

# Leaving My Fingerprints: Motivations and Challenges of Contributing to OSS for Social Good

-> ICSE 2021 <- 

### Authors 

+ Yu Huang
+ Denae Ford
+ Thomas Zimmermann

### Abstract 
When inspiring software developers to contribute to open source software, the act is often referenced as an opportunity to build tools to support the developer community. However, that is not the only charge that propels contributions—growing interest in open source has also been attributed to software developers deciding to use their technical skills to benefit a common societal good. To understand how developers identify these projects, their motivations for contributing, and challenges they face, we conducted 21 semi-structured interviews with OSS for Social Good (OSS4SG) contributors. From our interview analysis, we identified themes of contribution styles that we wanted to understand at scale by deploying a survey to over 5765 OSS and Open Source Software for Social Good contributors. From our quantitative analysis of 517 responses, we find that the majority of contributors demonstrate a distinction between OSS4SG and OSS. Likewise, contributors described definitions based on what societal issue the project was to mitigate and who the outcomes of the project were going to benefit. In addition, we find that OSS4SG contributors focus less on benefiting themselves by padding their resume with new technology skills and are more interested in leaving their mark on society at statistically significant levels. We also find that OSS4SG contributors evaluate the owners of the project significantly more than OSS contributors. These findings inform implications to help contributors identify high societal impact projects, help project maintainers reduce barriers to entry, and help organizations understand why contributors are drawn to these projects to sustain active participation.

### Package

* The replication package containing the following:
    * interview protocol
    * codebook generated from our thematic analysis of interview data presented in this paper
    * survey distributed to both OSS and OSS4SG contributors
    * a list of OSS project repositories 
    * a list of OSS4SG project repositories

Introducing paper: Leaving My Fingerprints: Motivations and Challenges of Contributing to OSS for Social Good
Chart-to-text

**Chart-to-text** is a large-scale benchmark with two datasets and a total of 44,096 charts covering a wide range of topics and chart types.

Introducing paper: Chart-to-Text: A Large-Scale Benchmark for Chart Summarization
RELiC

**RELiC** is a large-scale dataset of 79k excerpts of literary scholarship, each containing a quotation from a primary source and the surrounding critical analysis. 79 public domain primary sources and over 8,836 secondary sources are represented in RELiC.

Introducing paper: RELIC: Retrieving Evidence for Literary Claims
SSD_PHONE
Full name: Sub-Slot Dialogue dataset phone domain

SSD (Sub-slot Dialog) dataset: This is the dataset for the ACL 2022 paper "A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots".

Introducing paper: A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots
SSD_ID
Full name: Sub-Slot Dialogue dataset id number domain

SSD (Sub-slot Dialog) dataset: This is the dataset for the ACL 2022 paper "A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots".

Introducing paper: A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots
SSD_NAME
Full name: Sub-Slot Dialogue dataset name domain

SSD (Sub-slot Dialog) dataset: This is the dataset for the ACL 2022 paper "A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots".

Introducing paper: A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots
SSD_PLATE
Full name: Sub-Slot Dialogue dataset license plate number domain

SSD (Sub-slot Dialog) dataset: This is the dataset for the ACL 2022 paper "A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots".

Introducing paper: A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots
SF-XL
Full name: San Francisco eXtra Large

Large scale dataset for visual geo-localization / visual place recognition.
It provides images from the city of San Francisco, labeled with GPS coordinates and heading.

Introducing paper: Rethinking Visual Geo-localization for Large-Scale Applications
SF-XL test v1
Full name: San Francisco eXtra Large test v1

Test set version 1 for the San Francisco eXtra Large dataset
SF-XL test v2
Full name: San Francisco eXtra Large test v2

Test set version 2 for the San Francisco eXtra Large dataset