cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.23721/100/1478798
title: Autonomous systems AS-733 (11/18/1997 to 01/02/2000)

description: The data was collected from University of Oregon Route Views Project - Online data and reports. The dataset contains 733 daily instances which span an interval of 785 days from November 8 1997 to January 2 2000. In contrast to citation networks, where nodes and edges only get added (not deleted) over time, the AS dataset also exhibits both the addition and deletion of the nodes and edges over time.

Dataset statistics are calculated for the graph with the highest number of nodes and edges (dataset from January 02 2000):

Dataset statistics
Nodes	6474
Edges	13895
Nodes in largest WCC	6474 (1.000)
Edges in largest WCC	13895 (1.000)
Nodes in largest SCC	6474 (1.000)
Edges in largest SCC	13895 (1.000)
Average clustering coefficient	0.2522
Number of triangles	6584
Fraction of closed triangles	0.003218
Diameter (longest shortest path)	9
90-percentile effective diameter	4.6 ; jure@cs.stanford.edu.
DOI: 10.23721/100/1478799
title: Gnutella peer-to-peer network, August 4 2002

description: There are total of 9 snapshots of Gnutella network collected in August 2002. Nodes represent hosts in the Gnutella network topology and edges represent connections between the Gnutella hosts.
(example of one dataset):
Dataset statistics
Nodes        10876
Edges        39994
Nodes in largest WCC        10876 (1.000)
Edges in largest WCC        39994 (1.000)
Nodes in largest SCC        4317 (0.397)
Edges in largest SCC        18742 (0.469)
Average clustering coefficient        0.0062
Number of triangles        934
Fraction of closed triangles        0.001807
Diameter (longest shortest path)        9
90-percentile effective diameter        5.4 ; jure@cs.stanford.edu.
DOI: 10.23721/100/1478800
title: Bitcoin OTC trust weighted signed network

description: Members of Bitcoin OTC rate other members in a scale of -10 (total distrust) to +10 (total trust) in steps of 1. 
Dataset statistics
Nodes        5,881
Edges        35,592
Range of edge weight        -10 to +10
Percentage of positive edges        89% ; jure@cs.stanford.edu.
DOI: 10.23721/100/1478801
title: KDD Cup 1999 Data

description: This is the data set used for intrusion detector learning task in the Third International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-99, The Fifth International Conference on Knowledge Discovery and Data Mining.   The intrusion detector learning task is to build a predictive model (i.e. a classifier) capable of distinguishing between ``bad'' connections, called intrusions or attacks, and ``good'' normal connections.

The 1998 DARPA Intrusion Detection Evaluation Program was prepared and managed by MIT Lincoln Labs. The objective was to survey and evaluate research in intrusion detection.  A standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment, was provided.  The 1999 KDD intrusion detection contest uses a version of this dataset.

Lincoln Labs set up an environment to acquire nine weeks of raw TCP dump data for a local-area network (LAN) simulating a typical U.S. Air Force LAN.  They operated the LAN as if it were a true Air Force environment, but peppered it with multiple attacks.

The raw training data was about four gigabytes of compressed binary TCP dump data from seven weeks of network traffic.  This was processed into about five million connection records.  Similarly, the two weeks of test data yielded around two million connection records. ; gcounsel@ics.uci.edu
DOI: 10.23721/100/1478802
title: Detecting Malicious URLs

description: The long-term goal of this research is to construct a real-time system that uses machine learning techniques to detect malicious URLs (spam, phishing, exploits, and so on). This dataset shows the recorded attempts to use machine learning to detect malicious URLs. UCSD explored techniques that involve classifying URLs based on their lexical and host-based features, as well as online learning to process large numbers of examples and adapt quickly to evolving URLs over time. The data set consists of about 2.4 million URLs (examples) and 3.2 million features. ; csestudent@eng.ucsd.edu
DOI: 10.5281/zenodo.1471573
title: Dataset: Dopamine Neurons Projecting To Medial Shell Of The Nucleus Accumbens Drive Heroin Reinforcement.

description: This dataset contains the data presented in the paper Dopamine neurons projecting to medial shell of the nucleus accumbens drive heroin reinforcement. (doi: 10.7554/eLife.39945 )
DOI: 10.5281/zenodo.1471574
title: Dataset: Dopamine Neurons Projecting To Medial Shell Of The Nucleus Accumbens Drive Heroin Reinforcement.

description: This dataset contains the data presented in the paper Dopamine neurons projecting to medial shell of the nucleus accumbens drive heroin reinforcement. (doi: 10.7554/eLife.39945 )
DOI: 10.5281/zenodo.1472066
title: Stemforyouth: Trials And Outreach Data

description: Data collected during the trials implementation of the STEM4youth project from September 2017 to May 2018. 

Students developed the following STEM4youth sub-courses: Physics, Chemistry, Mathematics, Engineering, Astronomy and Medicine in six European countries: Poland, Greece, Czech Republic, Slovenia, Italy and Spain.

Students freely accepted to fill questionnaires before and after carrying out the activities, being conscious that they were part of a research study. The data was collected in paper-based format or computer-based format. In the last case, questionnaires were generated on Google Forms. In addition, a control group was designated in Greece, Czech Republic, Slovenia and Spain to ensure the validity of the results.

All participants in the trials signed an informed consent to participate, in agreement with the Spanish Law for Personal Data Protection or similar laws in participant countries. This procedure was approved by the Ethics Committee in each country, and all methods were performed in accordance with the relevant guidelines and regulations.

The questionnaire employed for gathering this data is an amended version of Diego-Mantecón’s (2012) student mathematics-related beliefs instrument. If you like to use this questionnaire and get further information about how to use it, please contact the original author at this email: diegojm@unican.es
DOI: 10.5281/zenodo.1472067
title: Stemforyouth: Trials And Outreach Data

description: Data collected during the trials implementation of the STEM4youth project from September 2017 to May 2018. 

Students developed the following STEM4youth sub-courses: Physics, Chemistry, Mathematics, Engineering, Astronomy and Medicine in six European countries: Poland, Greece, Czech Republic, Slovenia, Italy and Spain.

Students freely accepted to fill questionnaires before and after carrying out the activities, being conscious that they were part of a research study. The data was collected in paper-based format or computer-based format. In the last case, questionnaires were generated on Google Forms. In addition, a control group was designated in Greece, Czech Republic, Slovenia and Spain to ensure the validity of the results.

All participants in the trials signed an informed consent to participate, in agreement with the Spanish Law for Personal Data Protection or similar laws in participant countries. This procedure was approved by the Ethics Committee in each country, and all methods were performed in accordance with the relevant guidelines and regulations.

The questionnaire employed for gathering this data is an amended version of Diego-Mantecón’s (2012) student mathematics-related beliefs instrument. If you like to use this questionnaire and get further information about how to use it, please contact the original author at this email: diegojm@unican.es
DOI: 10.23721/100/1478803
title: FCCX-15 Cyber Exercise (06/01/2015 to 11/04/2007)

description: The FCCX data is separated into packet data and SiLK data.

FCCX-pcap.tar.gz(5.4GiB)
21 Packet capture data files and supporting documentation
(SHA256=18a0bf030866b2712953593edbcd46de975c1c3bddce14bf4cb855550b00a704)

FCCX-silk.tar.gz(327MiB)
2442 hourly SiLK flow files, supporting documentation, and configuration files
(SHA256=ffa086cd3ea707cb38d91a57befa9dd3123cc2ea89441568b9112b90d12f6c42) ; netsa-help@cert.org
DOI: 10.23721/100/1478805
title: IoT devices captures (01/01/2016 to 11/07/2007)

description: Each directory contains several pcap files, each representing a setup of the given device directory. Files are named Setup-X-Y-STA.pcap where X is the person realizing the setup and Y is the sequence number of the given capture. The file _iotdevice-mac.txt contains the MAC address of the considered IoT device.

Please refer to the following publication when citing this dataset:
Markus Miettinen, Samuel Marchal, Ibbad Hafeez, N. Asokan, Ahmad-Reza Sadeghi, Sasu Tarkoma, “IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT,” in Proc. 37th IEEE International Conference on Distributed Computing Systems (ICDCS 2017), Jun. 2017. ; samuel.marchal@aalto.fi
DOI: 10.23721/100/1478806
title: Phishing Website Data Set

description: Although many articles about predicting phishing websites have been disseminated, no reliable training dataset has been previously published publically, maybe because there is no agreement in literature on the definitive features that characterize phishing webpages, hence it is difficult to shape a dataset that covers all possible features.  This dataset collected mainly from: PhishTank archive, MillerSmiles archive, Googles searching operators.
Data Set Characteristics:  N/A
Number of Instances:2456
Area:Computer Security
Attribute Characteristics:Integer
Number of Attributes:30
Date Donated 2015-03-26
Associated Tasks: Classification
Missing Values? N/A
 ; ml-repository@ics.uci.edu
DOI: 10.23721/100/1478807
title: Special Eurobarometer 423: Cyber security

description: The aim of this Eurobarometer is to understand EU citizens’ experiences and perceptions of cyber security issues. The survey examines the nature and frequency of Internet usage; their awareness and experience of cybercrime; and the level of concern that they feel about this type of crime. This survey, covering over 27 000 people in all EU member states, follows a previous survey from 2013 (Special Eurobarometer 404).

The results by volumes are distributed as follows:
Volume A: Countries
Volume AA: Groups of countries
Volume A' (AP): Trends
Volume AA' (AAP): Trends of groups of countries
Volume B: EU/socio-demographics
Volume C: Country/socio-demographics ; Eurobarometer@ec.europa.eu
DOI: 10.23721/100/1478808
title: The CTU-13 Dataset. A Labeled Dataset with Botnet, Normal and Background Traffic (01/01/2011 to 11/11/2007)

description: Each scenario was captured in a pcap file that contains all the packets of the three types of traffic. These pcap files were processed to obtain other type of information, such as NetFlows, WebLogs, etc. The first analysis of the CTU-13 dataset, that was described and published in the paper "An empirical comparison of botnet detection methods" used unidirectional NetFlows to represent the traffic and to assign the labels. These unidirectional NetFlows should not be used because they were outperformed by our second analysis of the dataset, which used bidirectional NetFlows. The bidirectional NetFlows have several advantages over the directional ones. First, they solve the issue of differentiating between the client and the server, second they include more information and third they include much more detailed labels. The second analysis of the dataset with the bidirectional NetFlows is the one published here. 

The relationship between the duration of the scenario, the number of packets, the number of NetFlows and the size of the pcap file is shown in Table 3. This Table also shows the malware used to create the capture, and the number of infected computers on each scenario. ; STRATOSPHEREIPS@AGENTS.FEL.CVUT.CZ
DOI: 10.23721/100/1478809
title: Malware Capture Facility Project

description: Machine learning algorithms need to be verified to find out their precise performance in real data. Specially in network computer security it is really important to have good datasets, because the data in the networks is infinite, changing, varied and with a high concept drift. These issues force us to obtain good datasets to train, verify and test the algorithms.

To make a good verification we need three types of traffic: Malware, Normal and Background. The Malware traffic will include all the things we want to detect, specially C&C (Command and Control) connections. The Normal traffic is very important to find out the real performance of our algorithms by computing the False Positives and True Negatives. The Background traffic is necessary to saturate the algorithms, verify its memory/speed performance and to test if the algorithm gets confused with the data. ; STRATOSPHEREIPS@AGENTS.FEL.CVUT.CZ
DOI: 10.23721/100/1478810
title: DDS Dataset Collection

description: From Data-Driven security blog. "I recently got some data from my friend Daniel Blander. He and I were talking about learning from honeypots some time ago, and he spun up several instances across the world and just let iptables capture packets. He let these run from March to September last year and shared the data so we can play around with it." ;
DOI: 10.25934/00001199
title: Study PTG116878, a Dose-Optimization Study of Ezogabine/Retigabine Immediate Release Tablets Versus Placebo in the Adjunctive Treatment of Subjects With Partial-Onset Seizures

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: 10.23721/100/1478812
title: Anonymized enterprise packet header traces: LBNL-05 (01/01/2014 to 11/15/2007)

description: Information of packet traces that span more than 100 hours of activity has been collected from a total of several thousand internal hosts. This wealth of data, which we are publicly releasing in anonymized form, spans a wide range of dimensions.

By releasing these traces we hope to provide a resource for others to use in studying patterns and dynamics within enterprises. Further, we hope that providing a corpus of "background traffic" for security researchers will allow for the sound evaluation of defense mechanisms in the context of the "crud" that appears on real networks.

 ; netsa-help@cert.com
DOI: 10.23721/100/1478813
title: BioTracker Project- Mouse Dynamics Dataset (01/01/2003 to 11/17/2007)

description: The datasets consist of several files which can be opened using matlab. 
Each file consists of a number of matrices, each representing the data collected from one user.
Matrix columns represent the following:
Column 1: Type of Action (1: Mouse Move 2: Silence 3: Point Click 4: Drag drop)
Column 2: Traveled Distance in pixels
Column 3: Elapsed Time (in seconds)
Column 4: Direction of Movement (1 to 8) (actions performed within 45-degree intervals clockwise).  ; itraore at ece.uvic.ca
DOI: 10.23721/100/1478814
title: ISOT Mouse Gestures Dataset

description: The raw data in this dataset consists of the horizontal coordinate (X-axis), vertical coordinate (Yaxis), and the elapsed time (T) in milliseconds starting from the origin of the drawn gesture.  There are two folders. The data in the first folder consists of genuine samples from 41 users, where users were asked to draw five different gestures outlined from various figures. The data was collected over a period that ranges from 10 days to a month. The data in the second folder corresponds to the second experiment where some users attempted to forge the gestures of other users by observing how they draw the gestures. Forgery attacks
were conducted against 26 different (genuine) users, each attacked by 5 different users.  ; itraore at ece.uvic.ca
DOI: 10.23721/100/1478817
title: Wireless Datasets

description: Research on wireless networks and mobile computing benefits from access to data from real networks and real mobile users. Data captured from production wireless networks help us understand how real users, applications, and devices use real networks under real conditions.  CRAWDAD archives data sets relevant to a variety of measurement purposes, including:
Computer malware
Human behavior modeling
Localization
Nework diagnosis
Network performance analysis
Network security
Network simulation
Routing protocols
Routing protocols for Disruption Tolerant Neworks (DTNS)
Social Network Analysis ; crawdad@crawdad.org
DOI: 10.23721/100/1478818
title: Cambridge/Haggle

description: Smartphones perform Wifi scans to adapt to the changing wireless environments causes by mobility. From network monitoring perspective, such scans provide a natural stream of network measurements from client's point of view. In order to see whether such measurements can provide new insights in monitoring large scale wireless networks, we collected the Wifi scan results data, together with other Wifi related logs, from the PhoneLab smartphone testbed over 5 months. All data are collected passively from the smartphones.

 ; jamesscott@acm.org
DOI: 10.23721/100/1478819
title: Cambridge/Inmotion Dataset

description: Dataset of UDP and TCP transfers between a car traveling at speeds from 5 mph to 75 mph, and an 802.11b access point. ; jamesscott@acm.org
DOI: 10.23721/100/1478820
title: The Cister/Rssi Dataset

description: We use a sensor network composed of TelosB motes deployed in the library building to collect RF energy level samples (RSSI) on all 802.15.4 channels in the 2.4 GHz ISM Band. The building has several collocated Wi-Fi networks in normal operation. These networks produce interference for the 802.15.4 radios. Sensor nodes record RSSI values every 20 us, simultaneously on all channels, for 130 ms and then write the result to the respective files. This process is repeated every 8 seconds for around 4h. ; cand@isep.ipp.pt
DOI: 10.23721/100/1478821
title: The CMU/Hotspot Dataset

description: The performance was measured and the application support of all visible APs at 13 hotspot locations around University Avenue, Seattle, WA, near the University of Washington over the course of 1 week. ; jeffpang@cs.cmu.edu
DOI: 10.25934/00002167
title: A Randomized, Open-label, Active-Controlled, Parallel-Group, Exploratory Study on the Effects of Repeated Doses of Albiglutide Compared to Exenatide on Gastric Myoelectrical Activity and Gastric Emptying in Subjects With Type 2 Diabetes Mellitus

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: 10.25934/00002168
title: An Exploratory Randomized, 2-Part, Single-blind, 2-Period Crossover Study Comparing the Effect of Albiglutide With Exenatide on Regional Brain Activity Related to Nausea in Healthy Volunteers

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: 10.5255/ukda-sn-853055
title: Good germs, bad germs: A participatory model for mapping the domestic microbiome 2016-2017
DOI: 10.23721/100/1478822
title: The CMU/Supermarket Dataset

description: The dataset is meant to aid development and evaluation of indoor location in complex indoor environments using round-trip time-of-flight (RToF) and magnetometer measurements. The dataset provides Round-trip time-of-flight (RToF) and magnetometer measurements at 1589 mobile node locations from 30 deployed stationary anchors in the 26m x 24m New Wing Yuan supermarket in Sunnyvale, CA. The data was collected during working hours over a period of 15 days. The nodes are equipped with chirp spread spectrum Nanotron nanoLoc radios (802.15.4a) and a Honeywell's HMC5843, a 3-axis digital magnetometer. At every location, 20 distinct readings were obtained for RToF signatures as well magnetometer measurements, to capture the variance, giving a total of 31780 readings. ; apurohit@ece.cmu.edu
DOI: 10.23721/100/1478823
title: The CNU/cdma dataset

description: Tcpdump data was collected from a CDMA 1x EV-DO network in South Korea that provides high-speed "always on" Internet connectivity in a wide-area mobile environment. ; lee@cnu.ac.kr
DOI: 10.23721/100/1478824
title: The Columbia/ecsma Dataset

description: This dataset contains packet transmission traces collected from an experimental wireless sensor network testbed, where E(Enhanced)-CSMA MAC protocol is implemented using TinyOS on Mica2 motes.

 ; shane@ee.columbia.edu
DOI: 10.23721/100/1478826
title: The Columbia/Kinetic Dataset

description: To help us better understand the properties of various energy sources and their impact on energy harvesting adaptive algorithms, we collected acceleration traces from different participants. The volunteers were asked to perform normal daily routines while comfortably carrying SparkFun Electronics ADXL345 accelerometer boards. For our long-term studies, we collected over 200 hours of acceleration information in 25 days from 5 participants. The data simulates the natural motions that participants' belongings (keys, phone, or wallet) experience on a daily basis. ; mc3415@columbia.edu
DOI: 10.23721/100/1478827
title: The Copelabs/Usense Dataset

description: This dataset comprises experiments carried out with the open-source middleware NSense (fomerly named as USense), available at https://github.com/COPELABS-SITI/NSense. The data has been collected based on four sensors: bluetooth; Wi-Fi; microphone; accelerometer. NSense then relies on four different pipelines to compute aspects such as relative distance (Wi-Fi); social strength (based on bluetooth contact duration); sound activity level; motion. We set up experiments making use of Samsung Galaxy S3 devices. For each experiment, there is the following set of data files: - SocialProximity.dat has three columns: Timestamp, DeviceName, Encounter Duration, Average Encounter Duration, Social Strength (Per hour) and Social Strength(Per minute) towards DeviceName - DistanceOutput.dat has three columns: Timestamp, DeviceName, and Distance towards DeviceName - Microphone.dat has two columns: Timestamp, and Soundlevel(QUIET, NORMAL, ALERT and NOISY) - PhysicalActivity.dat has two columns: Timestamp, and Activity as STATIONARY, WALKING and RUNNING There are two tracesets. A first traceset has been collected relying on a first NSense version in 2015. Then, a second traceset has been collected in 2016, with a refined version of NSense. In all tracesets, devices have been carried around by people that share the same affiliation during their individual daily routines (24 hour periods).

 ; saeik.firdose@ulusofona.pt
DOI: 10.23721/100/1478828
title: The RioBuses Dataset

description: Real-time position data reported by buses, updated every minute, from the city of Rio de Janeiro, Brazil. The file is CSV, containing the date, time(24h format), bus ID, bus line, latitude, longitude and speed of more than 12,000 buses ; dias@gta.ufrj.br