These simulated live cell microscopy sequences were generated by the CytoPacq web service [R1]. The dataset is composed of 51 2D sequences and 41 3D sequences. The 2D sequences are divided into distinct 44 training and 7 test sets. The 3D sequences are divided into distinct 34 training and 7 test sets. Each sequence contains up to 200 frames.


The dataset represents the negative interaction dataset of the Drugbank that has been generated from our proposed machine learning method based on drug similarity, which achieved an average accuracy of 95% compared to the randomly generated negative datasets in the literature. Drugbank was used as the drug target interaction dataset from


Biomechanics has predominantly relied upon the trajectory optimization method for the analysis and prediction of the movement of the limbs. Such approaches have paved the way for the motion planning of biped and quadruped robots as well. Most of these methods are deterministic, utilizing first-order iterative gradient-based algorithms incorporating the constrained differentiable objective functions.




This dataset contains about 140,000 Tweets related to exoskeletons. that were mined for a period of 5-years from May 21, 2017, to May 21, 2022. The tweets contain diverse forms of communications and conversations which communicate user interests, user perspectives, public opinion, reviews, feedback, suggestions, etc., related to exoskeletons.


The dataset contains only tweet identifiers (Tweet IDs) due to the terms and conditions of Twitter to re-distribute Twitter data ONLY for research purposes. They need to be hydrated to be used. The process of retrieving a tweet's complete information (such as the text of the tweet, username, user ID, date and time, etc.) using its ID is known as the hydration of a tweet ID. For hydrating this dataset the Hydrator application (link to download and a step-by-step tutorial on how to use Hydrator) may be used.


Data Description

This dataset consists of 7 .txt files. The following shows the number of Tweet IDs and the date range (of the associated tweets) in each of these files. 

Filename: Exoskeleton_TweetIDs_Set1.txt

Number of Tweet IDs – 22945, Date Range of Tweets - July 20, 2021 – May 21, 2022

Filename: Exoskeleton_TweetIDs_Set2.txt

Number of Tweet IDs – 19416, Date Range of Tweets - Dec 1, 2020 – July 19, 2021

Filename: Exoskeleton_TweetIDs_Set3.txt

Number of Tweet IDs – 16673, Date Range of Tweets - April 29, 2020 - Nov 30, 2020

Filename: Exoskeleton_TweetIDs_Set4.txt

Number of Tweet IDs – 16208, Date Range of Tweets - Oct 5, 2019 - Apr 28, 2020

Filename: Exoskeleton_TweetIDs_Set5.txt

Number of Tweet IDs – 17983, Date Range of Tweets - Feb 13, 2019 - Oct 4, 2019

Filename: Exoskeleton_TweetIDs_Set6.txt

Number of Tweet IDs – 34009, Date Range of Tweets - Nov 9, 2017 - Feb 12, 2019

Filename: Exoskeleton_TweetIDs_Set7.txt

Number of Tweet IDs – 11351, Date Range of Tweets - May 21, 2017 - Nov 8, 2017


For any questions related to the dataset, please contact Nirmalya Thakur at


Skeleton datasets for Normal, Antalgic, Stiff legged, Lurching, Steppage, and Trendelenburg gaits.


Sequential skeleton and average foot pressure data for normal and five pathological gaits (i.e., antalgic, lurching, steppage, stiff-legged, and Trendelenburg) were simultaneously collected. The skeleton data were collected by using Azure Kinect (Microsoft Corp. Redmond, WA, USA). The average foot pressure data were collected by GW1100 (GHIWell, Korea). 12 healthy subjects participated in data collection. They simulated the pathological gaits under strict supervision. A total of 1,440 data instances (12 people x 6 gait types x 20 walkings) were collected.


This dataset was created for an Eli Lilly and Company employee information management training program. It was part of a project that explored the potential use of ( as a tool to evaluate the severity of changes to inclusion and exclusion criteria on clinical trial operations. is a public clinical study registry that records summary data about clinical trials. The registry includes a historical record of changes (change history) to inclusion and exclusion criteria and other data that is accessible by users.


The research were incorporated an extended cohort monitoring campaign, validation of an existing exposure model and development of a predictive model for COPD exacerbations evaluated against historical electronic health records.A miniature personal sensor unit were manufactured for the study from a prototype developed at the University of Cambridge. The units monitored GPS position, temperature, humidity, CO, NO, NO2, O3, PM10 and PM2.5.Three 6-month cohort monitoring campaigns were carried out, each including of 65 COPD patients.


Columns are genes, miRNAs, drugs, or cnv. Rows are patient identifiers or cell lines.


Supplementary materials for paper 

'In silico drug repositioning using integrated target information'