This data is in support of the paper "Impact of Cognitive Biases on Progressive Visualization", accepted to IEEE Transactions on Visualization and Computer Graphics


Run the provided .Rmd script in R. Data files must be in the same directory as the .Rmd script. Knitting this file will result in an HTML document with figures saved as .pdfs


In this paper, a PID controller is applied to control the temperature to a proposed model derived of the Newton’s law of cooling. The control problem considers a second-order model obtained that the rate at which the temperature of a body decreases with time is proportional to the difference in temperature between the body and its surroundings. This model represents a mathematical function the device's output for each possible input with initial conditions zero.


This dataset contains a list of 284 popular websites and URLs to their privacy statements. The websites belong to the three largest South Asian economies, namely, India, Pakistan, and Bangladesh. Each website is categorized into 10 sectors, namely, e-commerce, finance/banking, education, healthcare, news, government, telecom, buy and sell, job/freelance, and blogging/discussion. We hope that this dataset will help researchers in investigating website privacy compliance.


The dataset is split by country via sheets, i.e., one sheet per country. Each sheet contains five columns. The column description is as follows:
Column 1 specifies the sector that a specific website belongs to.
Column 2 specifies the sources that were leveraged to collect the websites belonging to a specific sector.
Column 3 specifies the name of a website.
Column 4 specifies the URL of a website.
Column 5 specifies the privacy policy/statement URL, if provided by the corresponding website. An empty cell in this column depicts that the respective website does not provide a privacy statement.
In case of any questions, please email at


Data validation of the article "A framework for assessing, comparing and predicting the performance of autonomous RFID-based inventory robots for retail"


This dataset contains 1.65 lakhs tweet ids related to death of Sushant Singh Rajput in English language. For whole dataset with all other fields drop a mail at


The file contains tweet ids. If anyone wants to hydrate the ids he/she may do so or you can mail at for drive link.


This document shows time-domain thermoreflectance (TDTR) measurements on blanket phase change multilayers [Fig. S1] and electro-thermal simulations of confined PCM cells [Fig. S2]. The extracted thermal resistance per unit area obtained from our measured TDTR signal ratio vs. time delay in Fig. S1 reveals that Bi2Te3 layer only introduces ~13% additional thermal resistance in the PCM stack. On the other hand, Fig. S2 shows the electro-thermal simulation of confined cells with 4 nm Bi2Te3 and 50 nm GST layer with a BE (TiN) diameter of 150 nm in Fig.


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In this data set, a power transistor’s uncertainty added S-parameters, its estimated S-parameters, uncertainty added S-parameters of a power amplifier circuit, uncertainty added S-parameters of a cascaded amplifier, and amplifier’s design files are presented. Sumitomo’s GaAs-FET FLL57MK is used for the measurements and design. Cadence AWR is used for the power amplifier design. The power amplifier is designed for 2.4 GHz, using S-parameters.


Uncertainty added S-parameters can be seen and analyzed with Maury Microwave’s Insight software or VNA Tools. Estimated S-parameters of the transistor and matching networks can be analyzed with S-parameter viewer or EDA tools. The circuit is designed with Cadence AWR. Matching networks are cascaded with uncertainty added S-parameters’ of the transistor using VNA Tool software to find the final uncertainty of the power amplifier and cascaded amplifier. The same amplifier is used to form the cascaded amplifier since it is a 50 ohm matched amplifier.


S-parameter data with uncertainty information are presented in the data set. High power RF transistors are measured with a regular measurement setup, consisting of a VNA, coaxial cables, bias tees, and fixture. Measured transistors are FLL57MK (GaAs-FET), LP601 (LDMOS-FET), CLF1G0060S-10 (GaN-HEMT), T2G6000528-Q3 (GaN-HEMT), CGH40010-F (GaN-HEMT). The data can be used to analyze for uncertainty studies and designing with uncertainty added S-parameters.


There are several software tools available to analyze uncertainty added S-parameters. For example, Maury Microwave’s Insight and VNA Tools are useful software tools. Uncertainty added S-parameters can not be used directly for design. Estimated s-parameters (s1p or s2p files) can be extracted and used to design circuit.


Dataset to accompany the manuscript "Adaptive Content Seeding for Information-Centric Networking under High Topology Dynamics".


Dataset to accompany the manuscript "Adaptive Content Seeding for Information-Centric Networking under High Topology Dynamics".
(Version 2)

The files contained in this dataset measure the following metrics:

Cache miss ratio: this metric, computed by every requester, is calculated as the ratio between the number of non-satisfied content requests (i.e., interests sent via \ac{D2D} links) and the total number of interest messages sent. The metric is recorded in the following files: CacheMissRatio_Low.csv (low density), CacheMissRatio_Medium.csv (medium density), CacheMissRatio_High.csv (high density).

Content hop count: this metric is computed by every requester and measures the number of hops to reach the content via \ac{D2D} communication links. The metric is recorded in the following files: CacheHitHops_Low.csv (low density), CacheHitHops_Medium.csv (medium density), CacheHitHops_High.csv (high density).

Sent control messages: this metric is computed by every vehicle by counting the total number of sent interest and acknowledgment messages. The metric is recorded in the following files: SentCtrlMsgs_Low.csv (low density), SentCtrlMsgs_Medium.csv (medium density), SentCtrlMsgs_High.csv (high density).

Content downloads: this metric is computed by the centralized controller and counts the number of content downloads from the backend service via RAN for all vehicles during one simulation run. The metric is recorded in the following files: ContentDownloads_Low.csv (low density), ContentDownloads_Medium.csv (medium density), ContentDownloads_High.csv (high density).

Channel busy ratio: this metric is computed by every vehicle and measures the ratio of time the channel is sensed busy over the total active simulation time. The metric is recorded in the following files: CBR_Low.csv (low density), CBR_Medium.csv (medium density), CBR_High.csv (high density).

Every file in the dataset has the following columns: <"Strategy","Clustering","reqProb","Value","sd","se","ci">

- "Strategy" contains the employed seeding algorithm
- "Clustering" contains the employed community detection algorithm
- "reqProb" contains the request probability p
- "Value" contains the mean value of the actual metric that is being measured, which is defined in the file name
- "sd","se","ci" are the standard deviation, standard error of the mean, and confidence interval (95%)

Changes with respect to the previous version:

- We repeated every simulation run using 100 different seeds, instead of 50 as in the previous version of the dataset
- We deleted the "Number of clusters" metric, as it is not being used in the manuscript