A Truth Social data set containing a network of users, their associated posts, and additional information about each post. Collected from February 2022 through September 2022, this dataset contains 454,458 user entries and 845,060 Truth (Truth Social’s term for post) entries.

Comprised of 12 different files, the entry count for each file is shown below.

A readme file is provided that describes the structure of the files, necessary terms, and necessary information about the data collection.

We've put together a list of the latest Truth Social statistics so you can see who uses the platform and whether or not Truth Social is likely to become a dominant social media network in the future.

You might be surprised how much Truth Social is worth based on its small number of users.

A survey done in March 2022 found that 31% of Republican voters said they would use Truth Social often and 14% said they plan to use the platform a lot.

How does Truth Social compare to other social media platforms? There are around 2 million active Truth Social users.

In May 2022, an online survey in the United States found that only ***** percent of respondents were registered users of social media app Truth Social, launched in ************* by former U.S. President Donald Trump. Approximately **** percent of U.S. male respondents reported having a Truth Social account, while only *** percent of female respondents stated the same.

During the beginning of the launch, they had some pretty fast growth. Here are the key Truth Social statistics you need to know.

Youtube social network and ground-truth communities Dataset information Youtube is a video-sharing web site that includes a social network. In the Youtube social network, users form friendship each other and users can create groups which other users can join. We consider such user-defined groups as ground-truth communities. This data is provided by Alan Mislove et al.

We regard each connected component in a group as a separate ground-truth community. We remove the ground-truth communities which have less than 3 nodes. We also provide the top 5,000 communities with highest quality which are described in our paper. As for the network, we provide the largest connected component.

more info : https://snap.stanford.edu/data/com-Youtube.html

Population-level national networks on social media are precious and essential for network science and behavioural science. This study proposes a data collection strategy for scraping online social networks at the population level, and thereby serving as a “ground truth” for the validation of both ego-centric and socio-centric data collection approaches. We proposed a set of validation approaches to evaluate the validity of our approach. Finally, we re-examined classical network and communication propositions (e.g., 80/20 rule, six degrees of separation) on the national network. Our proposed strategy would largely flourish the data collection pool of population-level social networks and further develop the research of network analysis in digital media environment.

How high is the brand awareness of Truth Social in the United States?When it comes to social media users, brand awareness of Truth Social is at ** percent in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Truth Social in the United States?In total, * percent of U.S. social media users say they like Truth Social. However, in actuality, among the ** percent of U.S. respondents who know Truth Social, ** percent of people like the brand.What is the usage share of Truth Social in the United States?All in all, * percent of social media users in the United States use Truth Social. That means, of the ** percent who know the brand, ** percent use them.How loyal are the users of Truth Social?Around * percent of social media users in the United States say they are likely to use Truth Social again. Set in relation to the * percent usage share of the brand, this means that ** percent of their users show loyalty to the brand.What's the buzz around Truth Social in the United States?In February 2024, about * percent of U.S. social media users had heard about Truth Social in the media, on social media, or in advertising over the past four weeks. Of the ** percent who know the brand, that's ** percent, meaning at the time of the survey there's little buzz around Truth Social in the United States.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

com-LiveJournal: LiveJournal social network and ground-truth communities

LiveJournal is a free on-line blogging community where users declare friendship each other. LiveJournal also allows users form a group which other members can then join. We consider such user-defined groups as ground-truth communities. We provide the LiveJournal friendship social network and ground-truth communities.

We regard each connected component in a group as a separate ground-truth community. We remove the ground-truth communities which have less than 3 nodes. We also provide the top 5,000 communities with highest quality which are described in our paper. As for the network, we provide the largest connected component.

com-Friendster: Friendster social network and ground-truth communities

Friendster is an on-line gaming network. Before re-launching as a game website, Friendster was a social networking site where users can form friendship edge each other. Friendster social network also allows users form a group which other members can then join. We consider such user-defined groups as ground-truth communities. For the social network, we take the induced subgraph of the nodes that either belong to at least one community or are connected to other nodes that belong to at least one community. This data is provided by The Web Archive Project, where the full graph is available.

We regard each connected component in a group as a separate ground-truth community. We remove the ground-truth communities which have less than 3 nodes. We also provide the top 5,000 communities with highest quality which are described in our paper. As for the network, we provide the largest connected component.

com-Orkut: Orkut social network and ground-truth communities

Orkut is a free on-line social network where users form friendship each other. Orkut also allows users form a group which other members can then join. We consider such user-defined groups as ground-truth communities. We provide the Orkut friendship social network and ground-truth communities. This data is provided by Alan Mislove et al.

We regard each connected component in a group as a separate ground-truth community. We remove the ground-truth communities which have less than 3 nodes. We also provide the top 5,000 communities with highest quality which are described in our paper. As for the network, we provide the largest connected component.

com-Youtube: Youtube social network and ground-truth communities

Youtube is a video-sharing web site that includes a social network. In the Youtube social network, users form friendship each other and users can create groups which other users can join. We consider such user-defined groups as ground-truth communities. This data is provided by Alan Mislove et al.

We regard each connected component in a group as a separate ground-truth community. We remove the ground-truth communities which have less than 3 nodes. We also provide the top 5,000 communities with highest quality which are described in our paper. As for the network, we provide the largest connected component.

com-DBLP: DBLP collaboration network and ground-truth communities

The DBLP computer science bibliography provides a comprehensive list of research papers in computer science. We construct a co-authorship network where two authors are connected if they publish at least one paper together. Publication venue, e.g, journal or conference, defines an individual ground-truth community; authors who published to a certain journal or conference form a community.

We regard each connected component in a group as a separate ground-truth community. We remove the ground-truth communities which have less than 3 nodes. We also provide the top 5,000 communities with highest quality which are described in our paper. As for the network, we provide the largest connected component.

com-Amazon: Amazon product co-purchasing network and ground-truth communities

Network was collected by crawling Amazon website. It is based on Customers Who Bought This Item Also Bought feature of the Amazon website. If a product i is frequently co-purchased with product j, the graph contains an undirected edge from i to j. Each product category provided by Amazon defines each ground-truth community.

We regard each connected component in a product category as a separate ground-truth community. We remove the ground-truth communities which have less than 3 nodes. We also provide the top 5,000 communities with highest quality which are described in our paper. As for the network, we provide the largest connected component.

email-Eu-core: email-Eu-core network

The network was generated using email data from a large European research institution. We have anonymized information about all incoming and outgoing email between members of the research institution. Th...

Social networks are a battlefield for political propaganda. Protected by the anonymity of the internet, political actors use computational propaganda to influence the masses. Their methods include the use of synchronized or individual bots, multiple accounts operated by one social media management tool, or different manipulations of search engines and social network algorithms, all aiming to promote their ideology. While computational propaganda influences modern society, it is hard to measure or detect it. Furthermore, with the recent exponential growth in large language models (L.L.M), and the growing concerns about information overload, which makes the alternative truth spheres more noisy than ever before, the complexity and magnitude of computational propaganda is also expected to increase, making their detection even harder. Propaganda in social networks is disguised as legitimate news sent from authentic users. It smartly blended real users with fake accounts. We seek here to detect efforts to manipulate the spread of information in social networks, by one of the fundamental macro-scale properties of rhetoric—repetitiveness. We use 16 data sets of a total size of 13 GB, 10 related to political topics and 6 related to non-political ones (large-scale disasters), each ranging from tens of thousands to a few million of tweets. We compare them and identify statistical and network properties that distinguish between these two types of information cascades. These features are based on both the repetition distribution of hashtags and the mentions of users, as well as the network structure. Together, they enable us to distinguish (p − value = 0.0001) between the two different classes of information cascades. In addition to constructing a bipartite graph connecting words and tweets to each cascade, we develop a quantitative measure and show how it can be used to distinguish between political and non-political discussions. Our method is indifferent to the cascade’s country of origin, language, or cultural background since it is only based on the statistical properties of repetitiveness and the word appearance in tweets bipartite network structures.

Pollution of online social spaces caused by rampaging d/misinformation is a growing societal concern. However, recent decisions to reduce access to social media APIs are causing a shortage of publicly available, recent, social media data, thus hindering the advancement of computational social science as a whole. We present a large, high-coverage dataset of social interactions and user-generated content from Bluesky Social to address this pressing issue. The dataset contains the complete post history of over 4M users (81% of all registered accounts), totalling 235M posts. We also make available social data covering follow, comment, repost, and quote interactions. Since Bluesky allows users to create and like feed generators (i.e., content recommendation algorithms), we also release the full output of several popular algorithms available on the platform, along with their timestamped “like” interactions. This dataset allows novel analysis of online behavior and human-machine engagement patterns. Notably, it provides ground-truth data for studying the effects of content exposure and self-selection and performing content virality and diffusion analysis.

The code and data to analyze the HKS Misinformation Review paper, "Cognitive reflection is associated with greater truth discernment for COVID-19 headlines, less trust but greater use of formal information sources, and greater willingness to pay for masks among social media users in Pakistan"

The real dataset consists of movie evaluations from IMDB, which provides a platform where individuals can evaluate movies on a scale of 1 to 10. If a user rates a movie and clicks the share button, a Twitter message is generated. We then extract the rating from the Twitter message. We treat the ratings on the IMDB website as the event truths, which are based on the aggregated evaluations from all users, whereas our observations come from only a subset of users who share their ratings on Twitter. Using the Twitter API, we collect information about the follower and following relationships between individuals that generate movie evaluation Twitter messages. To better show the influence of social network information on event truth discovery, we delete small subnetworks that consist of less than 5 agents. The final dataset we use consists of 2266 evaluations from 209 individuals on 245 movies (events) and also the social network between these 209 individuals. We regard the social network to be undirected as both follower or following relationships indicate that the two users have similar taste.

Youtube social network and ground-truth communities

https://snap.stanford.edu/data/com-Youtube.html

Dataset information

Youtube (http://www.youtube.com/) is a video-sharing web site that includes a social network. In the Youtube social network, users form friendship each other and users can create groups which other users can join. We consider
such user-defined groups as ground-truth communities. This data is provided by Alan Mislove et al.
(http://socialnetworks.mpi-sws.org/data-imc2007.html)

We regard each connected component in a group as a separate ground-truth
community. We remove the ground-truth communities which have less than 3
nodes. We also provide the top 5,000 communities with highest quality
which are described in our paper (http://arxiv.org/abs/1205.6233). As for
the network, we provide the largest connected component.

Network statistics
Nodes 1,134,890
Edges 2,987,624
Nodes in largest WCC 1134890 (1.000)
Edges in largest WCC 2987624 (1.000)
Nodes in largest SCC 1134890 (1.000)
Edges in largest SCC 2987624 (1.000)
Average clustering coefficient 0.0808
Number of triangles 3056386
Fraction of closed triangles 0.002081
Diameter (longest shortest path) 20
90-percentile effective diameter 6.5
Community statistics
Number of communities 8,385
Average community size 13.50
Average membership size 0.10

Source (citation)
J. Yang and J. Leskovec. Defining and Evaluating Network Communities based on Ground-truth. ICDM, 2012. http://arxiv.org/abs/1205.6233

Files
File Description
com-youtube.ungraph.txt.gz Undirected Youtube network
com-youtube.all.cmty.txt.gz Youtube communities
com-youtube.top5000.cmty.txt.gz Youtube communities (Top 5,000)

Notes on inclusion into the SuiteSparse Matrix Collection, July 2018:

The graph in the SNAP data set is 1-based, with nodes numbered 1 to
1,157,827.

In the SuiteSparse Matrix Collection, Problem.A is the undirected Youtube
network, a matrix of size n-by-n with n=1,134,890, which is the number of
unique user id's appearing in any edge.

Problem.aux.nodeid is a list of the node id's that appear in the SNAP data set. A(i,j)=1 if person nodeid(i) is friends with person nodeid(j). The
node id's are the same as the SNAP data set (1-based).

C = Problem.aux.Communities_all is a sparse matrix of size n by 16,386
which represents the communities in the com-youtube.all.cmty.txt file.
The kth line in that file defines the kth community, and is the column
C(:,k), where C(i,k)=1 if person nodeid(i) is in the kth community. Row
C(i,:) and row/column i of the A matrix thus refer to the same person,
nodeid(i).

Ctop = Problem.aux.Communities_top5000 is n-by-5000, with the same
structure as the C array above, with the content of the
com-youtube.top5000.cmty.txt.gz file.

THÖR is a dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for the position, head orientation, gaze direction, social grouping and goals. THÖR contains sensor data collected by a 3D lidar sensor and involves a mobile robot navigating the space. In comparison to other, our dataset has a larger variety in human motion behaviour, is less noisy, and contains annotations at higher frequencies.

The dataset includes 9 separate recordings in 3 variations:

• ``One obstacle" - features one obstacle in the environment and no robot
• ``Moving robot" - features one obstacle in the environment and the moving robot
• ``Three obstacles" - features three obstacles in the environment and no robot

THOR - point clouds is the part of THÖR data set containing bag files with 3D scans collcted during the experiments.

Reference:

For more details check project website thor.oru.se or check our publications:

@article{thorDataset2019,
title={TH\"OR: Human-Robot Indoor Navigation Experiment
and Accurate Motion Trajectories Dataset},
author={Andrey Rudenko and Tomasz P. Kucner and
Chittaranjan S. Swaminathan and Ravi T. Chadalavada
and Kai O. Arras and Achim J. Lilienthal},
journal={arXiv preprint arXiv:1909.04403},
year={2019}
}

Task

Fake news has become one of the main threats of our society. Although fake news is not a new phenomenon, the exponential growth of social media has offered an easy platform for their fast propagation. A great amount of fake news, and rumors are propagated in online social networks with the aim, usually, to deceive users and formulate specific opinions. Users play a critical role in the creation and propagation of fake news online by consuming and sharing articles with inaccurate information either intentionally or unintentionally. To this end, in this task, we aim at identifying possible fake news spreaders on social media as a first step towards preventing fake news from being propagated among online users.

After having addressed several aspects of author profiling in social media from 2013 to 2019 (bot detection, age and gender, also together with personality, gender and language variety, and gender from a multimodality perspective), this year we aim at investigating if it is possbile to discriminate authors that have shared some fake news in the past from those that, to the best of our knowledge, have never done it.

As in previous years, we propose the task from a multilingual perspective:

• English
• Spanish

NOTE: Although we recommend to participate in both languages (English and Spanish), it is possible to address the problem just for one language.

Data

Input

The uncompressed dataset consists in a folder per language (en, es). Each folder contains:

• A XML file per author (Twitter user) with 100 tweets. The name of the XML file correspond to the unique author id.
• A truth.txt file with the list of authors and the ground truth.

The format of the XML files is:

The format of the truth.txt file is as follows. The first column corresponds to the author id. The second column contains the truth label.

  b2d5748083d6fdffec6c2d68d4d4442d:::0
  2bed15d46872169dc7deaf8d2b43a56:::0
  8234ac5cca1aed3f9029277b2cb851b:::1
  5ccd228e21485568016b4ee82deb0d28:::0
  60d068f9cafb656431e62a6542de2dc0:::1
  ...
  

Output

Your software must take as input the absolute path to an unpacked dataset, and has to output for each document of the dataset a corresponding XML file that looks like this:

The naming of the output files is up to you. However, we recommend to use the author-id as filename and "xml" as extension.

IMPORTANT! Languages should not be mixed. A folder should be created for each language and place inside only the files with the prediction for this language.

Evaluation

The performance of your system will be ranked by accuracy. For each language, we will calculate individual accuracies in discriminating between the two classes. Finally, we will average the accuracy values per language to obtain the final ranking.

Submission

Once you finished tuning your approach on the validation set, your software will be tested on the test set. During the competition, the test set will not be released publicly. Instead, we ask you to submit your software for evaluation at our site as described below.

We ask you to prepare your software so that it can be executed via command line calls. The command shall take as input (i) an absolute path to the directory of the test corpus and (ii) an absolute path to an empty output directory:

mySoftware -i INPUT-DIRECTORY -o OUTPUT-DIRECTORY

Within OUTPUT-DIRECTORY, we require two subfolders: en and es, one folder per language, respectively. As the provided output directory is guaranteed to be empty, your software needs to create those subfolders. Within each of these subfolders, you need to create one xml file per author. The xml file looks like this:

The naming of the output files is up to you. However, we recommend to use the author-id as filename and "xml" as extension.

Note: By submitting your software you retain full copyrights. You agree to grant us usage rights only for the purpose of the PAN competition. We agree not to share your software with a third party or use it for other purposes than the PAN competition.

Related Work

• Bilal Ghanem, Paolo Rosso, Francisco Rangel. An Emotional Analysis of False Information in Social Media and News Articles. arXiv preprint arXiv:1908.09951 (2019). ACM Transactions on Internet Technology (TOIT). In Press.
• Anastasia Giachanou, Paolo Rosso, Fabio Crestani. Leveraging Emotional Signals for Credibility Detection. Proceedings of the 42nd International ACM Conference on Research and Development in Information Retrieval (SIGIR). pp 877–880. (2019)
• Andre Guess, Jonathan Nagler, and Joshua Tucker. Less than you think: Prevalence and predictors of fake news dissemination on Facebook. Science Advances vol. 5 (2019)
• Andrew Hall, Loren Terveen, Aaron Halfaker. Bot Detection in Wikidata Using Behavioral and Other Informal Cues. Proceedings of the ACM on Human-Computer Interaction. 2018 Nov 1;2(CSCW):64.
• Kashyap Popat, Subhabrata Mukherjee, Andrew Yates, Gerhard Weikum. DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP). pp 22-32. (2018)
• Francisco Rangel and Paolo Rosso. Overview of the 7th Author Profiling Task at PAN 2019: Bots and Gender Profiling in Twitter. In: L. Cappellato, N. Ferro, D. E. Losada and H. Müller (eds.) CLEF 2019 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings.CEUR-WS.org, vol. 2380
• Francisco Rangel, Paolo Rosso, Martin Potthast, Benno Stein. Overview of the 6th author profiling task at pan 2018: multimodal gender identification in Twitter. In: CLEF 2018 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 2125.
• Francisco Rangel, Paolo Rosso, Martin Potthast, Benno Stein. Overview of the 5th Author Profiling Task at PAN 2017: Gender and Language Variety Identification in Twitter. In: Cappellato L., Ferro N., Goeuriot L, Mandl T. (Eds.) CLEF 2017 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 1866.
• Francisco Rangel, Paolo Rosso, Ben Verhoeven, Walter Daelemans, Martin Pottast, Benno Stein. Overview of the 4th Author Profiling Task at PAN 2016: Cross-Genre Evaluations. In: Balog K., Capellato L., Ferro N., Macdonald C. (Eds.) CLEF 2016 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 1609, pp. 750-784
• Francisco Rangel, Fabio Celli, Paolo Rosso, Martin Pottast, Benno Stein, Walter Daelemans. Overview of the 3rd Author Profiling Task at PAN 2015.In: Linda Cappelato and Nicola Ferro and Gareth Jones and Eric San Juan (Eds.): CLEF 2015 Labs and Workshops, Notebook Papers, 8-11 September, Toulouse, France. CEUR Workshop Proceedings. ISSN 1613-0073, http://ceur-ws.org/Vol-1391/,2015.
• Francisco Rangel, Paolo Rosso, Irina Chugur, Martin Potthast, Martin Trenkmann, Benno Stein, Ben Verhoeven, Walter Daelemans. Overview of the 2nd Author Profiling Task at PAN 2014. In: Cappellato L., Ferro N., Halvey M., Kraaij W. (Eds.) CLEF 2014 Labs and Workshops, Notebook Papers. CEUR-WS.org, vol. 1180, pp. 898-827.
• Francisco Rangel, Paolo Rosso, Moshe Koppel, Efstatios Stamatatos, Giacomo Inches. Overview of the Author Profiling Task at PAN 2013. In: Forner P., Navigli R., Tufis D. (Eds.)Notebook Papers of CLEF 2013 LABs and Workshops. CEUR-WS.org, vol. 1179
• Francisco Rangel and Paolo Rosso On the Implications of the General Data Protection Regulation on the Organisation of Evaluation Tasks. In: Language and Law / Linguagem e Direito, Vol. 5(2), pp. 80-102
• Kai Shu, Suhang Wang, and Huan Liu. Understanding user profiles on social media for fake news detection. Proceedings of the IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 430--435 (2018)
• Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. Fake News Detection on Social Media: A Data Mining Perspective. ACM SIGKDD Explorations Newsletter. (2017)

This is the multimodal SWELL knowledge work (SWELL-KW) dataset for research on stress and user modeling. The dataset was collected in an experiment, in which 25 people performed typical knowledge work (writing reports, making presentations, reading e-mail, searching for information). We manipulated their working conditions with the stressors: email interruptions and time pressure. A varied set of data was recorded: computer logging, facial expression from camera recordings, body postures from a Kinect 3D sensor and heart rate (variability) and skin conductance from body sensors. Our dataset not only contains raw data, but also preprocessed data and extracted features. The participants' subjective experience on task load, mental effort, emotion and perceived stress was assessed with validated questionnaires as a ground truth. The resulting dataset on working behavior and affect is suitable for several research fields, such as work psychology, user modeling and context aware systems.The collection of this dataset was supported by the Dutch national program COMMIT (project P7 SWELL). SWELL is an acronym of Smart Reasoning Systems for Well-being at Work and at Home. Notes on the content of the dataset:- The uLog XML files refer to documents in the dataset. Most extensions of these files have changed due to file conversions. The original extension is now included in the file names at the end.- Due to copyrights not all original documents and images are included in the dataset.- Variable C in 'D - Physiology features (HR_HRV_SCL - final).csv' refers to the type of block, 1, 2 or 3.

THÖR is a dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for the position, head orientation, gaze direction, social grouping and goals. THÖR contains sensor data collected by a 3D lidar sensor and involves a mobile robot navigating the space. In comparison to other, our dataset has a larger variety in human motion behaviour, is less noisy, and contains annotations at higher frequencies. The dataset includes 13 separate recordings in 3 variations: One obstacle" - features one obstacle in the environment and no robotMoving robot" - features one obstacle in the environment and the moving robot ``Three obstacles" - features three obstacles in the environment and no robot THOR - people tracks is the part of THÖR data set containing ground truth position of people in the environment, including information about head orientation. The data are available in three formats: mat - Matlab binary file TSV - text file bag - ROS bag file MAT files File - [char] Path to original QTM file Timestamp - [string] Date and time of the startof the data collection Start Fram - [char] 1 Frames - [double] Number of frames in the file FrameRate - [double] Number of frames per second Events - [struct] 0 Trajectories - [struct] 3D postion of observed reflective markers Labeled - [struct] Markers belonging to the tracked agents: Count - [double] Number of tracked markers Labels - [cell] List of marker labels Data - [double] Array of dimension {Count}x4x{Frames}, contains the 3D position of each marker and residue RigidBodies - [struct] 6D pose of the helmet, corresponds to head poistion and orientation: Bodies - [double] Number of tracked bodies Name - [cell] Bodies Names Positions - [double] Array of dimension {Bodies}x3x{Frames} contains the position of the centre of the mass of the markers defining the rigid body Rotations - [double] Array of dimension {Bodies}x9x{Frames} contains rotation matrix describing the orientation of the rigid body RPYs - [double] Array of dimension {Bodies}x3x{Frames} contains orientation of the rigid body described as RPY angles Residual - [double] Array of dimension {Bodies}x1x{Frames} contains residual for each rigid body TSV files 3D data File Header NO_OF_FRAMES - number of frames in the file NO_OF_CAMERAS - number of cameras tracking makers NO_OF_MARKERS - number of tracked markers FREQUENCY - tracking frequency [Hz] NO_OF_ANALOG - number of analog inputs ANALOG_FREQUENCY - frequency of analog input DESCRIPTION - -- TIME_STAMP - the beginning of the data recording DATA_INCLUDED - the type of data included MARKER_NAMES - names of tracked makers Column names Frame - frame ID Time - frame timestamp [marker name] [C] - coordinate of a [marker name] along [C] axis 6D data File Header NO_OF_FRAMES - number of frames in the file NO_OF_CAMERAS - number of cameras tracking makers NO_OF_MARKERS - number of tracked markers FREQUENCY - tracking frequency [Hz] NO_OF_ANALOG - number of analog inputs ANALOG_FREQUENCY - frequency of analog input DESCRIPTION - -- TIME_STAMP - the beginning of the data recording DATA_INCLUDED - the type of data included BODY_NAMES - names of tracked rigid bodies Colum Names Frame - frame ID Time - frame timestamp The columns are grouped according to the rigid body. Each group starts with the name of the rigid body and then is followed by the position of the centre of the mas and the orientation expressed as RPY angles and rotation matrix Reference: For more details check project website thor.oru.se or check our publications: @article{thorDataset2019, title={TH"OR: Human-Robot Indoor Navigation Experiment and Accurate Motion Trajectories Dataset}, author={Andrey Rudenko and Tomasz P. Kucner and Chittaranjan S. Swaminathan and Ravi T. Chadalavada and Kai O. Arras and Achim J. Lilienthal}, journal={arXiv preprint arXiv:1909.04403}, year={2019} }