including data regarding the rotor without defects. Ten repetitions were performed for each experimental condition.

The IEEE Electronic Library (IEL) provides fulltext access to all IEEE-sponsored conference proceedings, journals, transactions as well as active IEEE standards.

signal data

Time-sensitive networking (TSN) is an emerging topic for the advancement of wireless networking for industrial applications. TSN, as defined under the umbrella of IEEE 802.1 working group standards, addresses issues related to providing deterministic communications over IEEE 802-based Local Area Networks (LANs). TSN was originally designed to support real-time audio/video applications over Ethernet providing better reliability and lower, more deterministic latency with traffic shaping capabilities. TSN has since expanded its scope and applicability to other applications such as those in industrial environments and automotive. Industrial examples include machine-machine communications for robot control, end-effector actuation, real-time sensing, and safety integrated systems. Applications utilizing an wireless local area network (WLAN) can also benefit from scheduling and traffic shaping as defined in the 802.1Qbv standard; however, factors such as clock stability, synchronization, resource requirements and protocol options come into play when selecting a schedule to support multiple application types on the same network. In this article, we present a scenario for a collaborative robot heavy lift operation, in which, two robots communicate over an IEEE 802.11 WLAN with TSN capabilities to lift a rigid body in three dimensions. Scheduling is performed using 802.1Qbv over WLAN with the robot operating system (ROS) used as the software middleware utilizing the transport control protocol (TCP). As a part of the research, we describe our process for schedule selection to accommodate the time-sensitive traffic of the robotic scenario while allowing an industrial internet of things (IIoT) high data rate traffic to coexist. We then provide an analysis of the impacts of TSN schedule selection on the operational performance of the collaborative robot application. The data provided within this data set was collected as a result of experiments conducted under this research effort.

Introduction

The 802.11 standard includes several management features and corresponding frame types. One of them are Probe Requests (PR), which are sent by mobile devices in an unassociated state to scan the nearby area for existing wireless networks. The frame part of PRs consists of variable-length fields, called Information Elements (IE), which represent the capabilities of a mobile device, such as supported data rates.

This dataset contains PRs collected over a seven-day period by four gateway devices in an uncontrolled urban environment in the city of Catania.

It can be used for various use cases, e.g., analyzing MAC randomization, determining the number of people in a given location at a given time or in different time periods, analyzing trends in population movement (streets, shopping malls, etc.) in different time periods, etc.

Related dataset

Same authors also produced the Labeled dataset of IEEE 802.11 probe requests with same data layout and recording equipment.

Measurement setup

The system for collecting PRs consists of a Raspberry Pi 4 (RPi) with an additional WiFi dongle to capture WiFi signal traffic in monitoring mode (gateway device). Passive PR monitoring is performed by listening to 802.11 traffic and filtering out PR packets on a single WiFi channel.

The following information about each received PR is collected: - MAC address - Supported data rates - extended supported rates - HT capabilities - extended capabilities - data under extended tag and vendor specific tag - interworking - VHT capabilities - RSSI - SSID - timestamp when PR was received.

The collected data was forwarded to a remote database via a secure VPN connection. A Python script was written using the Pyshark package to collect, preprocess, and transmit the data.

Data preprocessing

The gateway collects PRs for each successive predefined scan interval (10 seconds). During this interval, the data is preprocessed before being transmitted to the database. For each detected PR in the scan interval, the IEs fields are saved in the following JSON structure:

PR_IE_data = { 'DATA_RTS': {'SUPP': DATA_supp , 'EXT': DATA_ext}, 'HT_CAP': DATA_htcap, 'EXT_CAP': {'length': DATA_len, 'data': DATA_extcap}, 'VHT_CAP': DATA_vhtcap, 'INTERWORKING': DATA_inter, 'EXT_TAG': {'ID_1': DATA_1_ext, 'ID_2': DATA_2_ext ...}, 'VENDOR_SPEC': {VENDOR_1:{ 'ID_1': DATA_1_vendor1, 'ID_2': DATA_2_vendor1 ...}, VENDOR_2:{ 'ID_1': DATA_1_vendor2, 'ID_2': DATA_2_vendor2 ...} ...} }

Supported data rates and extended supported rates are represented as arrays of values that encode information about the rates supported by a mobile device. The rest of the IEs data is represented in hexadecimal format. Vendor Specific Tag is structured differently than the other IEs. This field can contain multiple vendor IDs with multiple data IDs with corresponding data. Similarly, the extended tag can contain multiple data IDs with corresponding data.
Missing IE fields in the captured PR are not included in PR_IE_DATA.

When a new MAC address is detected in the current scan time interval, the data from PR is stored in the following structure:

{'MAC': MAC_address, 'SSIDs': [ SSID ], 'PROBE_REQs': [PR_data] },

where PR_data is structured as follows:

{ 'TIME': [ DATA_time ], 'RSSI': [ DATA_rssi ], 'DATA': PR_IE_data }.

This data structure allows to store only 'TOA' and 'RSSI' for all PRs originating from the same MAC address and containing the same 'PR_IE_data'. All SSIDs from the same MAC address are also stored. The data of the newly detected PR is compared with the already stored data of the same MAC in the current scan time interval. If identical PR's IE data from the same MAC address is already stored, only data for the keys 'TIME' and 'RSSI' are appended. If identical PR's IE data from the same MAC address has not yet been received, then the PR_data structure of the new PR for that MAC address is appended to the 'PROBE_REQs' key. The preprocessing procedure is shown in Figure ./Figures/Preprocessing_procedure.png

At the end of each scan time interval, all processed data is sent to the database along with additional metadata about the collected data, such as the serial number of the wireless gateway and the timestamps for the start and end of the scan. For an example of a single PR capture, see the Single_PR_capture_example.json file.

Folder structure

For ease of processing of the data, the dataset is divided into 7 folders, each containing a 24-hour period. Each folder contains four files, each containing samples from that device.

The folders are named after the start and end time (in UTC). For example, the folder 2022-09-22T22-00-00_2022-09-23T22-00-00 contains samples collected between 23th of September 2022 00:00 local time, until 24th of September 2022 00:00 local time.

Files representing their location via mapping: - 1.json -> location 1 - 2.json -> location 2 - 3.json -> location 3 - 4.json -> location 4

Environments description

The measurements were carried out in the city of Catania, in Piazza Università and Piazza del Duomo The gateway devices (rPIs with WiFi dongle) were set up and gathering data before the start time of this dataset. As of September 23, 2022, the devices were placed in their final configuration and personally checked for correctness of installation and data status of the entire data collection system. Devices were connected either to a nearby Ethernet outlet or via WiFi to the access point provided.

Four Raspbery Pi-s were used: - location 1 -> Piazza del Duomo - Chierici building (balcony near Fontana dell’Amenano) - location 2 -> southernmost window in the building of Via Etnea near Piazza del Duomo - location 3 -> nothernmost window in the building of Via Etnea near Piazza Università - location 4 -> first window top the right of the entrance of the University of Catania

Locations were suggested by the authors and adjusted during deployment based on physical constraints (locations of electrical outlets or internet access) Under ideal circumstances, the locations of the devices and their coverage area would cover both squares and the part of Via Etna between them, with a partial overlap of signal detection. The locations of the gateways are shown in Figure ./Figures/catania.png.

Known dataset shortcomings

Due to technical and physical limitations, the dataset contains some identified deficiencies.

PRs are collected and transmitted in 10-second chunks. Due to the limited capabilites of the recording devices, some time (in the range of seconds) may not be accounted for between chunks if the transmission of the previous packet took too long or an unexpected error occurred.

Every 20 minutes the service is restarted on the recording device. This is a workaround for undefined behavior of the USB WiFi dongle, which can no longer respond. For this reason, up to 20 seconds of data will not be recorded in each 20-minute period.

The devices had a scheduled reboot at 4:00 each day which is shown as missing data of up to a few minutes.

 Location 1 - Piazza del Duomo - Chierici

The gateway device (rPi) is located on the second floor balcony and is hardwired to the Ethernet port. This device appears to function stably throughout the data collection period. Its location is constant and is not disturbed, dataset seems to have complete coverage.

 Location 2 - Via Etnea - Piazza del Duomo

The device is located inside the building. During working hours (approximately 9:00-17:00), the device was placed on the windowsill. However, the movement of the device cannot be confirmed. As the device was moved back and forth, power outages and internet connection issues occurred. The last three days in the record contain no PRs from this location.

 Location 3 - Via Etnea - Piazza Università

Similar to Location 2, the device is placed on the windowsill and moved around by people working in the building. Similar behavior is also observed, e.g., it is placed on the windowsill and moved inside a thick wall when no people are present. This device appears to have been collecting data throughout the whole dataset period.

 Location 4 - Piazza Università

This location is wirelessly connected to the access point. The device was placed statically on a windowsill overlooking the square. Due to physical limitations, the device had lost power several times during the deployment. The internet connection was also interrupted sporadically.

Recognitions

The data was collected within the scope of Resiloc project with the help of City of Catania and project partners.

.IEEE Whois Database, discover comprehensive ownership details, registration dates, and more for .IEEE TLD with Whois Data Center.

In collaboration with the IEEE Conference on Biomedical and Health Informatics (BHI) 2018 and the IEEE Conference on Body Sensor Networks (BSN), we are hosting a challenge to explore real clinical questions in critically ill patients using the MIMIC-III database. Participants in the challenge will be invited to present at the BHI & BSN Annual Conference in Las Vegas, USA (4-7 March 2018): https://bhi-bsn.embs.org/2018/

The dataset contains the resistance and reactance data of IEEE-118 distribution system, active and reactive power data of IEEE-118 distribution system.

The dataset contains 350 features engineered from the phasor measurements (PMU-type) signals from the IEEE New England 39-bus power system test case network, which are generated from the 9360 systematic MATLAB®/Simulink electro-mechanical transients simulations. It was prepared to serve as a convenient and open database for experimenting with different types of machine learning techniques for transient stability assessment (TSA) of electrical power systems.

Different load and generation levels of the New England 39-bus benchmark power system were systematically covered, as well as all three major types of short-circuit events (three-phase, two-phase and single-phase faults) in all parts of the network. The consumed power of the network was set to 80%, 90%, 100%, 110% and 120% of the basic system load levels. The short-circuits were located on the busbar or on the transmission line (TL). When they were located on a TL, it was assumed that they can occur at 20%, 40%, 60%, and 80% of the line length. Features were obtained directly from the time-domain signals at the pickup time (pre-fault value) and at the trip time (post-fault value) of the associated distance protection relays.

This is a stochastic dataset of 3120 cases, created from the population of 9360 systematic simulations, which features a statistical distribution of different fault types, as follows: single-phase (70%), double-phase (20%) and three-phase faults (10%). It also features a class imbalance, with less than 20% of cases belonging to the unstable class. Dataset is a compressed CSV file.

List of feature names in the dataset:

WmGx - rotor speed for each generator Gx, from G1 to G10,

DThetaGx - rotor angle deviation for each generator Gx, from G1 to G10,

ThetaGx - rotor mechanical angle for each generator Gx, from G1 to G10,

VtGx - stator voltage for each generator Gx, from G1 to G10,

IdGx - stator d-component current for each generator Gx, from G1 to G10,

IqGx - stator q-component current for each generator Gx, from G1 to G10,

LAfvGx - pre-fault power load angle for each generator Gx, from G1 to G10,

LAlvGx - post-fault power load angle for each generator Gx, from G1 to G10,

PfvGx - pre-falut value of the generator active power for each generator Gx, from G1 to G10,

PlvGx - post-falut value of the generator active power for each generator Gx, from G1 to G10,

QfvGx - pre-falut value of the generator reactive power for each generator Gx, from G1 to G10,

QlvGx - post-falut value of the generator reactive power for each generator Gx, from G1 to G10,

VAfvBx - pre-fault bus voltage magnitude in phase A for each bus Bx, from B1 to B39,

VBfvBx - pre-fault bus voltage magnitude in phase B for each bus Bx, from B1 to B39,

VCfvBx - pre-fault bus voltage magnitude in phase C for each bus Bx, from B1 to B39,

VAlvBx - post-fault bus voltage magnitude in phase A for each bus Bx, from B1 to B39,

VBlvBx - post-fault bus voltage magnitude in phase B for each bus Bx, from B1 to B39,

VClvBx - post-fault bus voltage magnitude in phase C for each bus Bx, from B1 to B39,

Stability - binary indicator (0/1) that determines if the power system was stable or unstable (0 - stable, 1 - unstable); this is the label variable.

License: Creative Commons CC-BY.

Disclaimer: This dataset is provided "as is", without any warranties of any kind.

This dataset contains CSV files for the figures in the paper titled "Multi-frequency Antenna Metrology with Sparse Measurements", to be submitted to IEEE Transactions on Antennas and Propagation or IEEE Transactions on Signal Processing. In this paper, we derive and experiment with approaches to use compressive sensing for multifrequency antenna radiation pattern measurements when samples are taken on a spherical domain. In particular, we develop sparsity and low-rank compressive sensing approaches and compare them for a simulated horn antenna. This work has applications in antenna metrology.

This data set models the IEEE 14-bus system for studies on P2P electricity markets, including real data of consumption, solar and wind power from Australia. This data set is characterized by 30 minutes time-step over one year, i.e. from July 2012 to June 2013.

The transmission system comprises 14 buses and 20 lines, and its characteristics are based on [1]. The original number of generators was increased to 8 generators, i.e. 1 coal-based generator, 2 gas-based generators, 3 wind turbines and 2 PV plants. The data set uses the original number of 11 loads.

The bus 1 represents the upstream connection to the main grid, where the generator assumes an infinite power. The market price from the Australian Energy Market Operator is used in this generator. It is assumed the same period from July 2012 to June 2013 [4]. This data set supposes a tariff of 10$/MWh for using the main grid. The energy imported and exported in bus 1 has to account this extra cost. Thus, the exportation price is equal to the market price minus this grid tariff. On the other hand, the importation price is equal to the market price plus this grid tariff.

The wind production has been based on the data set from [2]. The time resolution has been converted from 5 minutes to 30 minutes. The authors would like to acknowledge that the data set in [2] was processed by Stefanos Delikaraoglou and Jethro Dowell. The solar production and load consumption are taken from [3]. The load consumption is split into fixed and flexible consumption per time-step. Since there is no access to the total capacity of the flexible consumption, we split the daily flexible consumption over each time-step. In this way, the maximum consumption is equal to the fixed consumption plus twice this flexible consumption per time-step. The minimum consumption is equal to the fixed consumption in each time-step.

The wind, solar and load data sets have been normalized, i.e. values relative to rated power. Then, these normalized sequences were multiplied by the capacity of each element. The data is intended for use in studies related to consumer-centric electricity markets, e.g.:

• Validate new market designs or business models;
• Assess the impact of new grid operation strategies;
• Test the effect of strategic behavior by producers or consumers.

This repository contains the final release of the development and evaluation datasets for the LOCATA Challenge.The challenge of sound source localization in realistic environments has attracted widespread attention in the Audio and Acoustic Signal Processing (AASP) community in recent years. Source localization approaches in the literature address the estimation of positional information about acoustic sources using a pair of microphones, microphone arrays, or networks with distributed acoustic sensors. The IEEE AASP Challenge on acoustic source LOCalization And TrAcking (LOCATA) aimed at providing researchers in source localization and tracking with a framework to objectively benchmark results against competing algorithms using a common, publicly released data corpus that encompasses a range of realistic scenarios in an enclosed acoustic environment.Four different microphone arrays were used for the recordings, namely:

Planar array with 15 channels (DICIT array) containing uniform linear sub-arraysSpherical array with 32 channels (Eigenmike)Pseudo-spherical array with 12-channels (robot head)Hearing aid dummies on a dummy head (2-channel per hearing aid).

An optical tracking system (OptiTrack) was used to record the positions and orientations of talker, loudspeakers and microphone arrays. Moreover, the emitted source signals were recorded to determine voice activity periods in the recorded signals for each source separately. The ground truth values are compared to the estimated values submitted by the participants using several criteria to evaluate the accuracy of the estimated directions of arrival and track-to-source association. The datasets encompass the following six, increasingly challenging, scenarios:

Task 1: Localization of a single, static loudspeaker using static microphones arraysTask 2: Multi-source localization of static loudspeakers using static microphone arraysTask 3: Localization of a single, moving talker using static microphone arraysTask 4: Localization of multiple, moving talkers using static microphone arraysTask 5: Localization of a single, moving talker using moving microphone arraysTask 6: Multi-source localization of moving talkers using moving microphone arrays.

The development and evaluation datasets in this repository contain the following data:

Close-talking speech signals for human talkers, recorded use DPA microphonesDistant-talking recordings using four microphone arrays:Spherical Eigenmike (32 channels)Pseudo-spherical prototype NAO robot (12 channels)Planar DICIT array (15 channels)Hearing aids installed in a head-torso simulator (4 channels)

Ground-truth annotations of all source and microphone positions, obtained using an OptiTrack system of infrared cameras. The ground-truth positions are provided at the frame rate of the optical tracking system

The following software is provided with the data:

Matlab code to read the datasets: github.com/cevers/sap_locata_ioMatlab code for performance evaluation of localization and tracking algorithms: github.com/cevers/sap_locata_eval

For further information, see:

C. Evers, H. W. Löllmann, H. Mellmann, A. Schmidt, H. Barfuss, P. A. Naylor, W. Kellermann""The LOCATA Challenge: Acoustic Source Localization and Tracking,"" in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 1620-1643, 2020, doi: 10.1109/TASLP.2020.2990485Documentation: https://www.locata.lms.tf.fau.de/files/2020/01/Documentation_LOCATA_final_release_V1.pdf

SciVis

IEEE 1394 Connector Market Outlook

The global IEEE 1394 connector market size was valued at approximately $400 million in 2023 and is projected to reach around $550 million by 2032, registering a compound annual growth rate (CAGR) of 3.5% during the forecast period. The growth in this market is driven by increasing demand for high-speed data transfer and connectivity solutions across various industries. The adoption of IEEE 1394 connectors, also known as FireWire, is expected to be bolstered by advancements in consumer electronics, automotive, and industrial applications where fast and reliable data transmission is crucial.

One of the primary growth factors in the IEEE 1394 connector market is the surge in consumer electronics that require high-speed data transfer capabilities. Devices such as digital cameras, camcorders, and external hard drives rely heavily on the IEEE 1394 standard for quick data exchange, ensuring that users experience minimal delays and high efficiency. As consumer preference shifts towards devices with superior performance, the demand for IEEE 1394 connectors is expected to rise correspondingly. Moreover, the rapid technological advancements in electronic gadgets continue to set the stage for consistent market growth.

The automotive industry's increasing reliance on sophisticated electronic systems is another significant driver for the IEEE 1394 connector market. Modern vehicles are equipped with complex infotainment systems, advanced driver-assistance systems (ADAS), and other electronic control units that necessitate robust and high-speed data transfer protocols. IEEE 1394 connectors offer the reliability and speed required for these automotive applications, thus driving their adoption. The trend towards electric and autonomous vehicles further propels the need for advanced data transmission solutions, contributing to market growth.

Industrial applications are also playing a crucial role in the expansion of the IEEE 1394 connector market. Industries such as manufacturing, aerospace, and medical equipment manufacturing are increasingly adopting automation and robotics, which demand seamless and efficient data communication systems. IEEE 1394 connectors provide the necessary bandwidth and reliability for these industrial applications, enhancing their operational efficiency and productivity. The growing emphasis on Industry 4.0 and smart manufacturing further underscores the importance of reliable data transmission protocols, thereby supporting market growth.

Regionally, North America is expected to dominate the IEEE 1394 connector market due to the high concentration of consumer electronics manufacturers and automotive companies in the region. The presence of leading technology firms and high adoption rates of advanced electronic devices contribute significantly to market growth. Europe follows closely, driven by robust automotive and industrial sectors. The Asia Pacific region is also poised for substantial growth during the forecast period, thanks to the booming consumer electronics market and increasing industrial activities in countries like China, Japan, and South Korea.

Type Analysis

The IEEE 1394 connector market is segmented by type into FireWire 400, FireWire 800, FireWire S1600, and FireWire S3200. FireWire 400, the original version of the IEEE 1394 standard, has been widely adopted since its inception due to its capability to transfer data at speeds up to 400 Mbps. Despite being the oldest, FireWire 400 continues to be used extensively in various applications, especially where basic, reliable data transfer is required. Its widespread compatibility with older devices ensures that it remains relevant even as newer standards emerge.

FireWire 800 improves upon the FireWire 400 standard by offering data transfer speeds of up to 800 Mbps. This enhanced speed makes it suitable for applications requiring faster data exchange, such as video editing and large file transfers. The adoption of FireWire 800 is particularly prevalent in consumer electronics and professional audio/video equipment, where high-speed connectivity is crucial. As the demand for faster data transfer rates continues to grow, FireWire 800 is expected to maintain a significant share of the market.

FireWire S1600 and S3200 represent the next generations of IEEE 1394 connectors, offering data transfer speeds of 1.6 Gbps and 3.2 Gbps, respectively. These types are designed to meet the needs of high-bandwidth applications, such as HD video streaming and advanced industrial automation. While their adoption is currently limited com

IEEE 68 bus system data set

High quality data set for data-driven power system assessment.

The data used in my PhD thesis comes from internet repositories (see within files). It is based on detailed information from both IEEE n-bus test power systems and synthetic power grid test cases developed by the scientific community. The dataset, stored in spreadsheet format (.xlsx), includes comprehensive technical details such as line impedances, equivalent internal impedances, locations of generators, load demands and their locations, and shunt element specifications. All values are normalized to per-unit (pu), assuming a base power of 100 MVA. Additionally, the reference to the internet repository is available in the same file for potential further consultation.

monitoring

The data for IEEE 14 bus system is given

This dataset is a list of literature used in a review chapter named From Urban Surveillance to Urban Care: Care-full Justice in the Age of AI. Literature collection source: IEEE Xplore Database (https://ieeexplore.ieee.org/Xplore/home.jsp) Literature collection date: 10 June 2024 Timeframe: open timeframe (database returned results from 2016) Search string: ("All Metadata":surveillance OR "All Metadata":cctv) AND ("All Metadata":"artificial intelligence" OR "All Metadata":AI) AND ("All Metadata":urban OR "All Metadata":cit*) AND ("All Metadata":india OR "All Metadata":indian) Metadata includes abstract, title and indexing terms Initial results: 104 records (articles and conference proceedings) Records removed after abstract screening: 47 Records removed after full-text assessment: 3 Records reviewed: 54

Includes all articles indexed by Scopus® for the seven journals listed on the Design Society website and all papers indexed for DESIGN and ICED (accessed on 05/Nov/2016).

The full search query used to extract the articles is:

"( SRCTITLE ( "Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM" OR "Journal of Engineering Design" OR "Design Studies" OR "Research in Engineering Design" OR "CoDesign" OR "Journal of Design Research" OR "Design Science Journal" OR "The International Journal of Design Creativity and Innovation" OR "International Conference on Engineering Design" OR "INTERNATIONAL DESIGN CONFERENCE" ) ) AND ( "data collection" OR "data acquisition" OR "data source" OR database OR "empirical data" OR "empirical grounding" OR interview OR documents OR "data logs" OR "case study" OR observation OR experiment* OR "empirical finding*" OR "empirical result*" ) AND ( EXCLUDE ( EXACTSRCTITLE , "Hardware Software Codesign Proceedings Of The International Workshop" ) OR EXCLUDE ( EXACTSRCTITLE , "Journal Of Engineering Design And Technology" ) OR EXCLUDE ( EXACTSRCTITLE , "Research In Engineering Design Theory Applications And Concurrent Engineering" ) OR EXCLUDE ( EXACTSRCTITLE , "Chinese Journal Of Engineering Design" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 2005 International Conference On Hardware Software Codesign And System Synthesis" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 12 Proceedings Of The 10th ACM International Conference On Hardware Software Codesign And System Synthesis Co Located With Esweek" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 2006 Proceedings Of The 4th International Conference On Hardware Software Codesign And System Synthesis" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 2007 International Conference On Hardware Software Codesign And System Synthesis" ) OR EXCLUDE ( EXACTSRCTITLE , "Embedded Systems Week 2008 Proceedings Of The 6th IEEE ACM IFIP International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2008" ) OR EXCLUDE ( EXACTSRCTITLE , "Second IEEE ACM IFIP International Conference On Hardware Software Codesign And Systems Synthesis Codes Isss 2004" ) OR EXCLUDE ( EXACTSRCTITLE , "Embedded Systems Week 2011 Esweek 2011 Proceedings Of The 9th IEEE ACM IFIP International Conference On Hardware Software Codesign And System Synthesis Codes Isss 11" ) OR EXCLUDE ( EXACTSRCTITLE , "2010 IEEE ACM IFIP International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2010" ) OR EXCLUDE ( EXACTSRCTITLE , "2013 International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2013" ) OR EXCLUDE ( EXACTSRCTITLE , "2014 International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2014" ) OR EXCLUDE ( EXACTSRCTITLE , "2015 ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2015" ) OR EXCLUDE ( EXACTSRCTITLE , "8th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2010" ) ) AND ( EXCLUDE ( EXACTSRCTITLE , "2015 International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2015" ) OR EXCLUDE ( EXACTSRCTITLE , "9th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2011" ) OR EXCLUDE ( EXACTSRCTITLE , "11th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2013" ) OR EXCLUDE ( EXACTSRCTITLE , "10th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2012" ) OR EXCLUDE ( EXACTSRCTITLE , "A Practical Introduction To Hardware Software Codesign" ) )"

The keywords used in the article "DATA-DRIVEN ENGINEERING DESIGN RESEARCH: OPPORTUNITIES USING OPEN DATA" are:

"data collection" OR "data acquisition" OR "data source" OR database OR "empirical data" OR "empirical grounding" OR interview OR documents OR "data logs" OR "case study" OR observation OR experiment* OR "empirical finding*" OR "empirical result*"