cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25934/00002124
title: Observer-blind Immunogenicity and Safety Study of GlaxoSmithKline Biologicals' Adjuvanted Quadrivalent Influenza Candidate Vaccines (GSK2584786A) in Children Aged 6 to 35 Months

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.5281/zenodo.1472121
title: Andydata-Lab-Onepersonwithshoes

description: This dataset contains motion and force measurements of humans performing various manual tasks. 2 participants performed 3 different tasks (5 trials per task): whole-body reaching with arm support, picking-up a heavy object of the floor, and applying a vertical downward force of 1/4th of one's weight. Participants' whole-body kinematics, ground reaction force and pressure force on the hand were recorded. Whole-body kinematics was recorded both with optical (gold standard) and with inertial motion capture systems. Ground reaction force was recorded both with force plates (gold standard) and sensorized shoes. Hand pressure force was recorded with a prototype glove embedding pressure sensors.

All data files are available in proprietary format (when existing), in standard notion analysis format, and in csv format. Videos of a human avatar replaying participants' motions are also provided.
DOI: 10.5281/zenodo.1472122
title: Andydata-Lab-Onepersonwithshoes

description: This dataset contains motion and force measurements of humans performing various manual tasks. 2 participants performed 3 different tasks (5 trials per task): whole-body reaching with arm support, picking-up a heavy object of the floor, and applying a vertical downward force of 1/4th of one's weight. Participants' whole-body kinematics, ground reaction force and pressure force on the hand were recorded. Whole-body kinematics was recorded both with optical (gold standard) and with inertial motion capture systems. Ground reaction force was recorded both with force plates (gold standard) and sensorized shoes. Hand pressure force was recorded with a prototype glove embedding pressure sensors.

All data files are available in proprietary format (when existing), in standard notion analysis format, and in csv format. Videos of a human avatar replaying participants' motions are also provided.
DOI: 10.13140/rg.2.2.20625.89446
title: Stock Calculator V0.0 GUI (Edited script )
DOI: 10.23721/100/1478779
title: Intrusion Detection Evaluation Dataset (ISCXIDS2012) (06/11/2010 to 06/17/2010)

description: To simulate user behaviour, the behaviours of our Center's users were abstracted into profiles. Agents were then programmed to execute them, effectively mimicking user activity. Attack scenarios were then designed and executed to express real-world cases of malicious behaviour. They were applied in real-time from physical devices via human assistance; therefore, avoiding any unintended characteristics of post-merging network attacks with real-time background traffic. The resulting arrangement has the obvious benefit of allowing the network traces to be labeled. This is believed to simplify the evaluation of intrusion detection systems and provide more realistic and comprehensive benchmarks.

The UNB ISCX 2012 intrusion detection evaluation dataset consists of the following 7 days of network activity (normal and malicious):

Day, Date, Description, Size (GB)

Friday, 11/6/2010, Normal Activity. No malicious activity, 16.1
Saturday, 12/6/2010, Normal Activity. No malicious activity, 4.22
Sunday, 13/6/2010, Infiltrating the network from inside + Normal Activity, 3.95
Monday, 14/6/2010, HTTP Denial of Service + Normal Activity, 6.85
Tuesday, 15/6/2010, Distributed Denial of Service using an IRC Botnet, 23.4
Wednesday, 16/6/2010, Normal Activity. No malicious activity, 17.6
Thursday, 17/6/2010, Brute Force SSH + Normal Activity, 12.3 ; cic@unb.ca
DOI: 10.23721/100/1478780
title: Intrusion Detection Evaluation Dataset (CICIDS2017) (07/03/2017 to 07/07/2017)

description: CICIDS2017 dataset contains benign and the most up-to-date common attacks, which resembles the true real-world data (PCAPs). It also includes the results of the network traffic analysis using CICFlowMeter with labeled flows based on the time stamp, source and destination IPs, source and destination ports, protocols and attack (CSV files). ; cic@unb.ca
DOI: 10.23721/100/1478781
title: Kyoto 2006+ Dataset (11/01/2006 to 12/31/2015)

description: With the rapid evolution and proliferation of botnets, largescale cyber attacks such as DDoS, spam emails are also becoming more and more dangerous and serious cyber threats. Because of this, network based security technologies such as Network based Intrusion Detection Systems (NIDSs), Intrusion Prevention Systems (IPSs), firewalls have received remarkable attention to defend our crucial computer systems, networks and sensitive information from attackers on the Internet. In particular, there has been much effort towards high-performance NIDSs based on data mining and machine learning techniques. However, there is a fatal problem in that the existing evaluation dataset, called KDD Cup 99’ dataset, cannot reflect current network situations and the latest attack trends. This is because it was generated by simulation over a virtual network many years ago. To the best of our knowledge, there is no alternative evaluation dataset. In this paper, we present a new evaluation dataset, called Kyoto 2006+, built on the 3 years of real traffic data (Nov. 2006 ? Aug. 2009) which are obtained from diverse types of honeypots.  ;
DOI: 10.23721/100/1478783
title: Unified Host and Network Dataset

description: The host event logs originated from most enterprise computers running the Microsoft Windows operating system on Los Alamos National Laboratory's (LANL) enterprise network. The network event data originated from many of the internal enterprise routers within the LANL enterprise network. The data values have been deidentified (anonymized) to protect the security of LANL’s operational IT environment. The identities match across both the host and network data allowing the two data elements to be used together for analysis and research. In some cases, including well-known network ports, system-level users names (not associated to people), and system-level hosts, the values were not deidentified. In addition, in some cases hosts were combined where they represented well-known redundant services including the Active Directory servers, LANL’s email servers, and LANL’s automated vulnerability scanning systems ; cyberdata@lanl.gov
DOI: 10.23721/100/1478784
title: Capture files from Mid-Atlantic CCDC (01/01/2010 to 01/01/2012)

description: The U.S. National CyberWatch Mid-Atlantic Collegiate Cyber Defense Competition (MACCDC) is a unique experience for college and university students to test their cybersecurity knowledge and skills in a competitive environment. The MACCDC takes great pride in being one of the premier events of this type in the United States.

While similar to other cyber defense competitions in many aspects, the MA CCDC, as part of the National CCDC, is unique in that it focuses on the operational aspects of managing and protecting an existing network infrastructure. The teams are physically co-located in the same building. Each team is given physically identical computer configurations at the start of the competition. Throughout the competition, the teams have to ensure the systems supply the specified services while under attack from a volunteer Red Team. In addition, the teams have to satisfy periodic “injects” that simulate business activities IT staff must deal with in the real world. ; info(at)netresec.com
DOI: 10.23721/100/1478785
title: Tor-nonTor dataset

description: To be sure about the quantity and diversity of this dataset in CIC, we defined a set of tasks to generate a representative dataset of real-world traffic. We created three users for the browser traffic collection and two users for the communication parts such as chat, mail, FTP, p2p, etc. For the non-Tor traffic we used previous benign traffic from VPN project and for the Tor traffic we used 7 traffic categories:

Browsing: Under this label we have HTTP and HTTPS traffic generated by users while browsing (Firefox and Chrome).

Email: Traffic samples generated using a Thunderbird client, and Alice and Bob Gmail accounts. The clients were configured to deliver mail through SMTP/S, and receive it using POP3/SSL in one client and IMAP/SSL in the other.

Chat: The chat label identifies instant-messaging applications. Under this label we have Facebook and Hangouts via web browser, Skype, and IAM and ICQ using an application called pidgin.

Audio-Streaming: The streaming label identifies audio applications that require a continuous and steady stream of data. We captured traffic from Spotify.

Video-Streaming: The streaming label identifies video applications that require a continuous and steady stream of data. We captured traffic from YouTube (HTML5 and flash versions) and Vimeo services using Chrome and Firefox.

FTP: This label identifies traffic applications whose main purpose is to send or receive files and documents. For our dataset we captured Skype file transfers, FTP over SSH (SFTP) and FTP over SSL (FTPS) traffic sessions.

VoIP: The Voice over IP label groups all traffic generated by voice applications. Within this label we captured voice-calls using Facebook, Hangouts and Skype.

P2P: This label is used to identify file-sharing protocols like Bittorrent. To generate this traffic we downloaded different .torrent files from the Kali linux distribution and captured traffic sessions using the Vuze application. We also used different combinations of upload and download speeds.

The traffic was captured using Wireshark and tcpdump, generating a total of 22GB of data. To facilitate the labeling process, as we explained in the related published paper, we captured the outgoing traffic at the workstation and the gateway simultaneously, collecting a set of pairs of .pcap files: one regular traffic pcap (workstation) and one Tor traffic pcap (gateway) file.

Later, we labelled the captured traffic in two steps. First, we processed the .pcap files captured at the workstation: we extracted the flows, and we confirmed that the majority of traffic flows were generated by application X (Skype, ftps, etc.), the object of the traffic capture. Then, we labelled all flows from the Tor .pcap file as X. ; cic@unb.ca ; a.habibi.l@unb.ca
DOI: 10.23721/100/1478786
title: Comprehensive, Multi-Source Cyber-Security Events (01/02/2015 to 01/02/2015)

description: This data set represents 58 consecutive days of de-identified event data collected from five sources within Los Alamos National Laboratory’s corporate, internal computer network.The data sources include Windows-based authentication events from both individual computers and centralized Active Directory domain controller servers; process start and stop events from individual Windows computers; Domain Name Service (DNS) lookups as collected on internal DNS servers; network flow data as collected on at several key router locations; and a set of well-defined red teaming events that present bad behavior within the 58 days. In total, the data set is approximately 12 gigabytes compressed across the five data elements and presents 1,648,275,307 events in total for 12,425 users, 17,684 computers, and 62,974 processes ; cyberdata@lanl.gov
DOI: 10.23721/100/1478787
title: User-Computer Authentication Associations in Time (01/01/2014 to 01/02/2014)

description: This anonymized data set encompasses 9 continuous months and represents 708,304,516 successful authentication events from users to computers collected from the Los Alamos National Laboratory (LANL) enterprise network.  The data sources include Windows-based authentication events from both individual computers and centralized Active Directory domain controller servers; process start and stop events from individual Windows computers; Domain Name Service (DNS) lookups as collected on internal DNS servers; network flow data as collected on at several key router locations; and a set of well-defined red teaming events that present bad behavior within the 58 days. In total, the data set is approximately 12 gigabytes compressed across the five data elements and presents 1,648,275,307 events in total for 12,425 users, 17,684 computers, and 62,974 processes.

Specific users that are well known system related (SYSTEM, Local Service) were not de-identified though any well-known administrators account were still de-identified. In the network flow data, well-known ports (e.g. 80, 443, etc) were not de-identified. All other users, computers, process, ports, times, and other details were de-identified as a unified set across all the data elements (e.g. U1 is the same U1 in all of the data). The specific timeframe used is not disclosed for security purposes. In addition, no data that allows association outside of LANL’s network is included. All data starts with a time epoch of 1 using a time resolution of 1 second. In the authentication data, failed authentication events are only included for users that had a successful authentication event somewhere within the data set. ; cyberdata@lanl.gov
DOI: 10.25934/00001172
title: RTG113388, a Long-term, Open-label Safety Extension Study of Retigabine/Ezogabine in Pediatric Subjects With Partial Onset Seizures (&gt;= 12 Years Old) and Subjects With Lennox-Gastaut Syndrome (&gt;=12 Years Old)

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.5281/zenodo.1472101
title: Федеральный Перечень Учебников, Рекомендованных Министерством Образования И Науки Российской Федерации К Использованию В Образовательном Процессе В Общеобразовательных Учреждениях, На 2012/2013 Учебный Год

description: <strong>Источник:</strong> "Приказ Министерства образования и науки Российской Федерации (Минобрнауки России) от 27 декабря 2011 г. N 2885 г. Москва"
description: Для раздела "Среднее (полное) общее образование" отсутствует указание стандарта
DOI: 10.5281/zenodo.1472102
title: Федеральный Перечень Учебников, Рекомендованных Министерством Образования И Науки Российской Федерации К Использованию В Образовательном Процессе В Общеобразовательных Учреждениях, На 2012/2013 Учебный Год

description: <strong>Источник:</strong> "Приказ Министерства образования и науки Российской Федерации (Минобрнауки России) от 27 декабря 2011 г. N 2885 г. Москва"
description: Для раздела "Среднее (полное) общее образование" отсутствует указание стандарта
DOI: 10.23721/100/1478788
title: Botnet Traffic Dataset (01/01/2014 to 01/01/2014)

description: The CTU-13 is a dataset of botnet traffic that was captured in the CTU University, Czech Republic, in 2011. The goal of the dataset was to have a large capture of real botnet traffic mixed with normal traffic and background traffic. The CTU-13 dataset consists in thirteen captures (called scenarios) of different botnet samples. On each scenario we executed a specific malware, which used several protocols and performed different actions.  ; stratosphereips@agents.fel.cvut.cz
DOI: 10.23721/100/1478789
title: Android Adware and General Malware Dataset (01/01/2017 to 01/01/2017)

description: The AAGM dataset is captured by installing the Android apps on the real smartphones semi-automated. The dataset is generated from 1900 applications from 1) Adware , 2) General malaware and 3) Benign apps ; cic@unb.ca
DOI: 10.23721/100/1478790
title: CIC DoS dataset (01/01/2017 to 01/01/2017)

description: In this study the focus was on the universal type of application DoS slow-rate attacks that are often seen in two variations: slow send and slow read.

The lack of data with application layer DoS attacks prompted us to create an evaluation dataset. We have set up a testbed environment with a victim webserver running Apache Linux v.2.2.22, PHP5 and Drupal v.7 as a content management system. The attacks were selected to represent the most common types of application layer DoS. We assume that an attacker is non-oblivious, i.e., he understands the attack, knows exactly when and how much traffic to send to maximize the attack damage.

Since the main premise of low-volume DoS attacks is their ability to impact a service without significant resources on an attacker side, the attacks were generated with just enough traffic to impact the targeted service, i.e, the attacks were stopped once a server became unresponsive. As a result we noticed that to be successful it was sufficient for these attacks to produce small amounts of traffic during short periods of time. ; cic@unb.ca
DOI: 10.23721/100/1478791
title: UGR’16

description: The dataset is built with real traffic and up-to-date attacks. These data come from several netflow v9 collectors strategically located in the network of a spanish ISP. It is composed of two differentiated sets of data that are previously split in weeks:
The main advantage of this dataset over previous ones is its usefulness for evaluating IDSs that consider long-term evolution and traffic periodicity. Models that consider differences in daytime/night or labour weekdays/weekends can also be trained and evaluated with it.

 ; gmacia@ucr.es ; rmagan@ugr.es
DOI: 10.23721/100/1478792
title: NSL-KDD dataset (01/01/2009 to 01/01/2009)

description: NSL-KDD is a data set suggested to solve some of the inherent problems of the KDD'99 data set . Although, this new version of the KDD data set still suffers from some of the problems discussed by McHugh and may not be a perfect representative of existing real networks, because of the lack of public data sets for network-based IDSs, we believe it still can be applied as an effective benchmark data set to help researchers compare different intrusion detection methods.

Furthermore, the number of records in the NSL-KDD train and test sets are reasonable. This advantage makes it affordable to run the experiments on the complete set without the need to randomly select a small portion. Consequently, evaluation results of different research work will be consistent and comparable.

Data files

KDDTrain+.ARFF: The full NSL-KDD train set with binary labels in ARFF format
KDDTrain+.TXT: The full NSL-KDD train set including attack-type labels and difficulty level in CSV format
KDDTrain+_20Percent.ARFF: A 20% subset of the KDDTrain+.arff file
KDDTrain+_20Percent.TXT: A 20% subset of the KDDTrain+.txt file
KDDTest+.ARFF: The full NSL-KDD test set with binary labels in ARFF format
KDDTest+.TXT: The full NSL-KDD test set including attack-type labels and difficulty level in CSV format
KDDTest-21.ARFF: A subset of the KDDTest+.arff file which does not include records with difficulty level of 21 out of 21
KDDTest-21.TXT: A subset of the KDDTest+.txt file which does not include records with difficulty level of 21 out of 21
 ; cic@unb.ca.
DOI: 10.23721/100/1478794
title: Botnet dataset (01/01/2010 to 01/01/2014)

description: Botnet traces can be merged with benign data by mapping malicious data to either machines existing in the home network or machines outside of the current network. Considering the wide range of IP addresses in the traces, we mapped botnet IPs to the hosts outside of the current network using BitTwist packet generator. Malicious and benign traffic were then replayed using TCPReplay and captured by TCPdump as a single dataset. ; cic@unb.ca.
DOI: 10.5281/zenodo.1404097
title: Entwicklung Stofflicher Konzentrationen In Moosen (Stickstoff, Schwermetalle) Von 1990 Bis 2015 In Deutschland, Link Zu Forschungsdaten

description: Forschungsdaten zu einer Studie über die Entwicklung von deutschlandweit zwischen 1990 und 2015 gesammelten Moosen. Die räumliche Verteilung und die zeitliche Entwicklung der Schwermetalle Cadmium (Cd), Blei (Pb) und Quecksilber (Hg) sowie von Stickstoff (N) wurde geostatistisch ausgewertet und kartografisch unter Verwendung von Perzentilklassen dargestellt.
description: {"references": ["Nickel S, Schr\u00f6der W, Drehwald U, Dreyer A, Preu\u00dfing M, Stapper NJ, Struve S, Teuber D, V\u00f6lksen B 2018. Entwicklung der Schwermetall- und Stickstoffkonzentrationen in Moosen in Deutschland. Schweizerische Zeitschrift f\u00fcr Forstwesen 169(6):340-346"]}
DOI: 10.5281/zenodo.1404098
title: Entwicklung Stofflicher Konzentrationen In Moosen (Stickstoff, Schwermetalle) Von 1990 Bis 2015 In Deutschland, Link Zu Forschungsdaten

description: Forschungsdaten zu einer Studie über die Entwicklung von deutschlandweit zwischen 1990 und 2015 gesammelten Moosen. Die räumliche Verteilung und die zeitliche Entwicklung der Schwermetalle Cadmium (Cd), Blei (Pb) und Quecksilber (Hg) sowie von Stickstoff (N) wurde geostatistisch ausgewertet und kartografisch unter Verwendung von Perzentilklassen dargestellt.
description: {"references": ["Nickel S, Schr\u00f6der W, Drehwald U, Dreyer A, Preu\u00dfing M, Stapper NJ, Struve S, Teuber D, V\u00f6lksen B 2018. Entwicklung der Schwermetall- und Stickstoffkonzentrationen in Moosen in Deutschland. Schweizerische Zeitschrift f\u00fcr Forstwesen 169(6):340-346"]}
DOI: 10.17632/drgh6dv6mb.1
title: Data for: Telomere Length and its association with hippocampal gray matter volume in Antipsychotic-Naïve/Free Schizophrenia Patients

description: Volume Estimates of Hippocampus along with demographic data of study subjects
DOI: 10.23721/100/1478795
title: Android validation dataset (01/01/2014 to 01/01/2014)

description: This data set wich consist in 72 original apps from different origins, and the following 10 transformations on each app, resulting in 792 apps with different relationships between them.

The transformations performed featured the following operations: insert junk code, insert junk files, replace icons, replace files, different aligns, replace strings and more.

 ; cic@unb.ca.
DOI: 10.23721/100/1478796
title: Android Botnet dataset (01/01/2014 to 01/01/2014)

description: The accumulated dataset combines some botnet samples from the Android Genome Malware project, malware security blog, VirusTotal and samples provided by well-known anti-malware vendor. Overall, the dataset includes 1929 samples spawning a period of 2010 (the first appearance of Android botnet) to 2014.
The Android Botnet dataset consists of 14 families:
Family, Year of discovery, No. of samples

AnserverBot, 2011, 244
Bmaster, 2012, 6
DroidDream, 2011, 363
Geinimi, 2010, 264
MisoSMS, 2013, 100
NickySpy, 2011, 199
Not Compatible, 2014, 76
PJapps, 2011, 244
Pletor, 2014, 85
RootSmart, 2012, 28
Sandroid, 2014, 44
TigerBot, 2012, 96
Wroba, 2014, 100
Zitmo, 2010, 80
 ;  cic@unb.ca.
DOI: 10.23721/100/1478797
title: Honeynet Project Challenges (04/15/2015 to 04/16/2015)

description: The purpose of Honeynet Challenges is to take this learning one step farther regarding cyber security. Instead of having the Honeynet Project analyze attacks and share their findings, Challenges give the security community the opportunity to analyze these attacks and share their findings. The end results is not only do individuals and organizations learn about threats, but how to learn and analyze them. Even better, individuals can see the write-ups from other individuals, learning new tools and technique for analyzing attacks. Best of all, these attacks are from the wild, real hacks.
 ; project@honeynet.org