cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.2558687
title: Domain shares on Twitter containing news and misinformation

description: This dataset contains a set of domain sharing actions that occurred on Twitter during the month of June 2017. Each domain sharing action can be thought of as a triple <em>(twitter user id, tweet id, domain).</em> The tweets in the dataset were collected through Twitter's decahose API. Each user in the dataset was responsible for sharing at least one news article, and at least one article that can be labeled as misinformation. 

The data is distributed in <em>domain-shares.data</em> in the following JSON format:

<pre><code class="language-json">{
    "&lt;twitter-user-id&gt;": {
        "&lt;tweet-id&gt;": ["&lt;domain&gt;", "&lt;domain&gt;", ...],
        ...
    },
    ...
}</code></pre>

For example:

<pre><code class="language-json">{
    "2274775459": {
        "880214796779573249": ["palmerreport.com"], 
        "870801925054373888": ["reuters.com", "abcn.ws"], 
        "879899831808012292": ["mobile.nytimes.com"]
    },
    "2909434459": {
        "879856813755256832": ["dallasnews.com"]
    }
}</code></pre>

In addition, a TAB-separated version with <em>(twitter user id, tweet id, domain)</em> triples is also available.

For further information on how the dataset was constructed and on analyses that have been conducted on it, please refer to the accompanying Github repository at https://github.com/dimitargnikolov/twitter-bias.
DOI: 10.5281/zenodo.2559447
title: LexiRumah 2.1.1: Eastwards Expansion

description: A lexical database of languages of the Lesser Sunda Islands and their suggested relations. Available through an online-interface on https://lexirumah.model-ling.eu

Changes to this version:


	Fixing to several minor issues
	Adding languages further to the east
	Resetting IPA segmentation
	Small improvements to the online UI (see the UI repository)
description: Funding:
Reconstructing the past through languages of the present: the Lesser Sunda Islands
Netherlands Organisation for Scientific Research (The Hague)
GRANT_NUMBER: 277-70-012
URL: https://www.nwo.nl/en/research-and-results/research-projects/i/91/22391.html
DOI: 10.21227/apc2-s596
title: RGB Images from Wearable Cameras: Applications for Autonomous Lower-Limb Biomechatronic Device Control

description: This RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.
DOI: 10.21227/g2w9-rw55
title: RGB Images from Wearable Cameras: Applications for Autonomous Lower-Limb Biomechatronic Device Control

description: This RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.
DOI: 10.21227/cdsa-wh31
title: RGB Images from Wearable Cameras: Applications for Autonomous Lower-Limb Biomechatronic Device Control

description: This RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.
DOI: 10.14469/hpc/5023
title: WORKY

description: This should work
DOI: 10.21227/1xq2-8n57
title: Environment Recognition and Wearable RGB Camera Sensors: Implications for Autonomous Lower-Limb Biomechatronic Devices

description: This RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.
DOI: 10.21227/8meq-mq65
title: Environment Recognition and Wearable RGB Cameras: Applications for Autonomous Lower-Limb Biomechatronic Devices

description: This RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.
DOI: 10.21227/gpbn-e884
title: RGB Images from Wearable Cameras: Applications for Autonomous Lower-Limb Biomechatronic Devices

description: This RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.
DOI: 10.21227/01vw-1z14
title: Environment Recognition and Wearable RGB Camera Sensors: Implications for Autonomous Lower-Limb Biomechatronic Devices

description: The following RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.
DOI: 10.21227/06bp-8n69
title: Environment Recognition and Wearable RGB Camera Sensors: Implications for Autonomous Lower-Limb Biomechatronic Devices

description: The following RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.
DOI: 10.21227/3nyx-hz76
title: Environment Recognition and Wearable RGB Camera Sensors: Implications for Autonomous Lower-Limb Biomechatronic Devices

description: The following RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.
DOI: 10.21227/yypw-1t96
title: Environment Recognition and Wearable RGB Camera Sensors: Implications for Autonomous Lower-Limb Biomechatronic Devices

description: The following RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.
DOI: 10.21227/542m-bp70
title: Wearable Camera Images of Human Locomotion: Environment Recognition for Autonomous Lower-Limb Biomechatronic Devices

description: This RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.
DOI: 10.21227/61tt-vn85
title: Environment Recognition and Wearable RGB Camera Sensors: Implications for Autonomous Lower-Limb Biomechatronic Devices

description: The following RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.
DOI: 10.21227/72yf-b760
title: Environment Recognition and Wearable RGB Camera Sensors: Implications for Autonomous Lower-Limb Biomechatronic Devices

description: The following RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.
DOI: 10.21227/c09x-k633
title: Environment Recognition and Wearable RGB Cameras: Implications for Autonomous Lower-Limb Biomechatronic Devices

description: The following RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.
DOI: 10.5281/zenodo.2559236
title: Image Synthesis with a Convolutional Capsule Generative Adversarial Network- Dataset

description: <strong>Dataset of 152 two-photon images (512x512) of axons with segmentation labels. </strong>

We combined data from two published sources (Bass et al., 2017; Canty et al., 2018) to get 152 (512×512) 2D images (produced from 3D image stacks), and manually produced the corresponding labels. These images were collected using in-vivo two-photon microscopy from the mouse somatosensory cortex.  To generate the 2D images, we used a max projection over the 3D stack. The labels are binary segmentation maps of the axons.

This dataset is split into a train (132 images) and test (20 images) sets. The raw 2D images of axons are in /original folder, and the segmentation labels are in /mask folder.

<strong>Please cite the following paper when using this dataset:</strong>

Bass, C., Dai, T., Billot, B.,  Arulkumaran, K., Creswell, A., Clopath, C., De Paola, V., and Bharath, A. A., 2019. “Image synthesis with a convolutional capsule generative adversarial network,” <em>Medial Imaging with Deep Learning.</em>

<strong>This dataset was complied from the following publications:</strong><br>
Bass, C., Helkkula, P., De Paola, V., Clopath, C. and Bharath, A.A., 2017. Detection of axonal synapses in 3D two-photon images. PloS one, 12(9), p.e0183309.<br>
Canty, A.J., Jackson, J.S., Huang, L., Trabalza, A., Bass, C., Little, G. and De Paola, V., 2018. Single-axon-resolution intravital imaging reveals a rapid onset form of Wallerian degeneration in the adult neocortex. <em>bioRxiv</em>, p.391425.
description: {"references": ["Bass, C.,\u00a0Dai, T., Billot,\u00a0B.,\u00a0\u00a0Arulkumaran,\u00a0K.,\u00a0Creswell, A.,\u00a0Clopath,\u00a0C.,\u00a0De Paola,\u00a0V., and\u00a0Bharath,\u00a0A. A., 2018. \"Image synthesis with a convolutional capsule generative adversarial network,\" Open Review.", "Bass, C., Helkkula, P., De Paola, V., Clopath, C. and Bharath, A.A., 2017. Detection of axonal synapses in 3D two-photon images. PloS one, 12(9), p.e0183309.", "Canty, A.J., Jackson, J.S., Huang, L., Trabalza, A., Bass, C., Little, G. and De Paola, V., 2018. Single-axon-resolution intravital imaging reveals a rapid onset form of Wallerian degeneration in the adult neocortex. bioRxiv, p.391425."]}
DOI: 10.5281/zenodo.2559237
title: Image Synthesis with a Convolutional Capsule Generative Adversarial Network- Dataset

description: <strong>Dataset of 152 two-photon images (512x512) of axons with segmentation labels. </strong>

We combined data from two published sources (Bass et al., 2017; Canty et al., 2018) to get 152 (512×512) 2D images (produced from 3D image stacks), and manually produced the corresponding labels. These images were collected using in-vivo two-photon microscopy from the mouse somatosensory cortex.  To generate the 2D images, we used a max projection over the 3D stack. The labels are binary segmentation maps of the axons.

This dataset is split into a train (132 images) and test (20 images) sets. The raw 2D images of axons are in /original folder, and the segmentation labels are in /mask folder.

<strong>Please cite the following paper when using this dataset:</strong>

Bass, C., Dai, T., Billot, B.,  Arulkumaran, K., Creswell, A., Clopath, C., De Paola, V., and Bharath, A. A., 2019. “Image synthesis with a convolutional capsule generative adversarial network,” <em>Medial Imaging with Deep Learning.</em>

<strong>This dataset was complied from the following publications:</strong><br>
Bass, C., Helkkula, P., De Paola, V., Clopath, C. and Bharath, A.A., 2017. Detection of axonal synapses in 3D two-photon images. PloS one, 12(9), p.e0183309.<br>
Canty, A.J., Jackson, J.S., Huang, L., Trabalza, A., Bass, C., Little, G. and De Paola, V., 2018. Single-axon-resolution intravital imaging reveals a rapid onset form of Wallerian degeneration in the adult neocortex. <em>bioRxiv</em>, p.391425.
description: {"references": ["Bass, C.,\u00a0Dai, T., Billot,\u00a0B.,\u00a0\u00a0Arulkumaran,\u00a0K.,\u00a0Creswell, A.,\u00a0Clopath,\u00a0C.,\u00a0De Paola,\u00a0V., and\u00a0Bharath,\u00a0A. A., 2018. \"Image synthesis with a convolutional capsule generative adversarial network,\" Open Review.", "Bass, C., Helkkula, P., De Paola, V., Clopath, C. and Bharath, A.A., 2017. Detection of axonal synapses in 3D two-photon images. PloS one, 12(9), p.e0183309.", "Canty, A.J., Jackson, J.S., Huang, L., Trabalza, A., Bass, C., Little, G. and De Paola, V., 2018. Single-axon-resolution intravital imaging reveals a rapid onset form of Wallerian degeneration in the adult neocortex. bioRxiv, p.391425."]}
DOI: 10.21227/21nm-ym90
title: Environment Recognition and Wearable RGB Cameras: Applications for Autonomous Lower-Limb Biomechatronic Devices

description: This RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.
DOI: 10.21227/3hy1-7x24
title: Environment Recognition and Wearable RGB Cameras: Applications for Autonomous Lower-Limb Biomechatronic Devices

description: The following RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.
DOI: 10.21227/5p46-e251
title: Environment Recognition and Wearable RGB Cameras: Applications for Autonomous Lower-Limb Biomechatronic Devices

description: This RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.
DOI: 10.21227/pt18-2850
title: Environment Recognition and Wearable RGB Cameras: Applications for Autonomous Lower-Limb Biomechatronic Devices

description: This RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.
DOI: 10.3886/e107288v2
title: Occupation Scores in Canada 1911-31

description: Replication files (syntax) and disclosed summary data from: <br>Occupational income scores and immigrant assimilation.  Evidence from the Canadian Census<br>November 2018
description: Non-farm Males age 16-65 reporting earnings and occupation
DOI: 10.21227/2dhq-2853
title: Environment Recognition and Wearable RGB Cameras: Applications for Autonomous Lower-Limb Biomechatronic Devices

description: This RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.
DOI: 10.21227/9jrp-7h38
title: Environment Recognition and Wearable RGB Cameras: Applications for Autonomous Lower-Limb Biomechatronic Devices

description: This RGB image dataset was experimentally collected using a wearable camera system and postprocessed for image classification of different walking environments conducive to controlling autonomous lower-limb biomechatronic devices (e.g., robotic lower-limb prostheses and exoskeletons). Overall, 34,254 sampled images were collected and individually labelled. The authors request that prospective users reference the following publication: Laschowski B, McNally W, Wong A, and McPhee J. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics. Toronto, Canada.