This dataset reports on the operation state of traffic signals in Austin, TX. Traffic signals enter flash mode when something is preventing the signal from operating normally. This is typically the result of a power surge, power outage, or damage to signal equipment. A signal may also be intentionally placed into flash mode for maintenance purposes or be scheduled to flash overnight.

You can view an interactive map of flashing traffic signals here:

https://data.mobility.austin.gov/signal-monitor

Approximately 90% of the City’s signals communicate with our Advanced Transportation Management System. When these signals go on flash, they will be reported in this dataset. Although we are extending communications to all signals, approximately 10% are not currently captured in this dataset. It also occasionally happens that the event that disables a traffic signal also disables network communication to the signal, in which case the signal outage will not be reported here.

In this dataset the distinction between scheduled and unscheduled flash is identified by the 'operation state' column. A signal that is in unscheduled flash mode will have a status of 2 or 7. A signal that is in in scheduled flash mode will have a status of 1.

This product is for informational purposes and may not have been prepared for or be suitable for legal, engineering, or surveying purposes. It does not represent an on-the-ground survey and represents only the approximate relative location of traffic signals.

Check Signal Beam move data in Pokémon GO, including PvP, Gym & Raid power, gauge cost, move time, and more.

Daily-updated velocity score, classification, and multi-source adoption signal for amikos-tech/chroma-go: The Go client for Chroma vector database

This event has been computationally inferred from an event that has been demonstrated in another species.

The inference is based on the homology mapping from PANTHER. Briefly, reactions for which all involved PhysicalEntities (in input, output and catalyst) have a mapped orthologue/paralogue (for complexes at least 75% of components must have a mapping) are inferred to the other species. High level events are also inferred for these events to allow for easier navigation.

More details and caveats of the event inference in Reactome. For details on PANTHER see also: http://www.pantherdb.org/about.jsp

This dataset represents a snapshot of Traffic Signal data recorded in VDOT HMMS up through October 2023, beyond which the capture of signal data has transitioned out of HMMS. This feature service layer serves as a record of the latest version of signal inventory logged in HMMS.

Pedestrian Path Signal Detection

## Overview

Pedestrian Path Signal Detection is a dataset for object detection tasks - it contains Stop Go annotations for 2,762 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The aim of the present study was to investigate the speech preparation processes of adults who stutter (AWS). Fifteen AWS and fifteen adults with fluent speech (AFS) participated in the experiment. The event-related potentials (ERPs) were recorded in a foreperiod paradigm. The warning signal (S1) was a color square, and the following imperative stimulus (S2) was either a white square (the Go signal that required participants to name the color of S1) or a white dot (the NoGo signal that prevents participants from speaking). Three differences were found between AWS and AFS. First, the mean amplitude of the ERP component parietal positivity elicited by S1 (S1-P3) was smaller in AWS than in AFS, which implies that AWS may have deficits in investing working memory on phonological programming. Second, the topographic shift from the early phase to the late phase of contingent negative variation occurred earlier for AWS than for AFS, thus suggesting that the motor preparation process is promoted in AWS. Third, the NoGo effect in the ERP component parietal positivity elicited by S2 (S2-P3) was larger for AFS than for AWS, indicating that AWS have difficulties in inhibiting a planned speech response. These results provide a full picture of the speech preparation and response inhibition processes of AWS. The relationship among these three findings is discussed. However, as stuttering was not manipulated in this study, it is still unclear whether the effects are the causes or the results of stuttering. Further studies are suggested to explore the relationship between stuttering and the effects found in the present study.

Go-RTs and error rates means and standard deviations on go trials as a function of picture valence values.

A point feature representing the City of Alexandria's signalized traffic lights. Collected from Aerial imagery in 2007. Location of hung or mounted signals. Location of Traffic Signals within the City of Alexandria.

SPAT/MAP Signal Performance Analytics Market Outlook

According to our latest research, the global SPAT/MAP Signal Performance Analytics market size in 2024 stands at USD 2.1 billion, reflecting robust adoption across smart infrastructure initiatives. The market is expected to grow at a CAGR of 13.7% from 2025 to 2033, reaching a forecasted value of USD 6.1 billion by 2033. This growth trajectory is primarily driven by the increasing demand for intelligent traffic management solutions, rising urbanization, and the global emphasis on smart city development. The integration of advanced analytics and real-time data processing capabilities into traffic signal systems has further accelerated the adoption of SPAT/MAP Signal Performance Analytics across various regions.

A primary growth factor for the SPAT/MAP Signal Performance Analytics market is the rapid urbanization and the resultant surge in vehicular traffic, which has created a pressing need for efficient traffic management systems. Urban areas worldwide are experiencing unprecedented growth, leading to congested roadways and increased travel times. Governments and municipal authorities are turning to intelligent transportation systems (ITS), including SPAT/MAP analytics, to optimize traffic flow, reduce congestion, and enhance road safety. The ability of SPAT/MAP solutions to deliver real-time signal phase and timing data, combined with map-based analytics, allows for dynamic adjustment of traffic signals, thereby improving overall network performance and minimizing delays. This has made these solutions indispensable for cities aiming to modernize their transportation infrastructure and provide seamless mobility to residents.

Another significant driver is the advancement of connected vehicle technologies and Vehicle-to-Everything (V2X) communications, which rely heavily on accurate and timely SPAT/MAP data. As automotive manufacturers and technology providers push towards autonomous and semi-autonomous vehicles, the importance of reliable signal performance analytics becomes paramount. SPAT/MAP analytics facilitate communication between traffic signals and vehicles, enabling predictive routing, efficient intersection management, and proactive incident response. This not only enhances driver safety but also supports regulatory compliance and environmental sustainability by reducing idling times and emissions. The growing investments in smart mobility initiatives and the proliferation of IoT sensors at intersections further amplify the demand for sophisticated analytics platforms in this space.

The increasing focus on sustainability and environmental impact reduction is also propelling the SPAT/MAP Signal Performance Analytics market forward. Cities are under pressure to meet stringent emission targets and improve air quality, making efficient traffic signal management a top priority. By leveraging SPAT/MAP analytics, municipalities can optimize signal timings to decrease stop-and-go traffic, thereby reducing fuel consumption and greenhouse gas emissions. Furthermore, the integration of these analytics with broader smart city platforms enables holistic urban planning, resource allocation, and emergency response coordination. This has encouraged public and private sector stakeholders to invest in scalable, interoperable analytics solutions that align with long-term sustainability goals.

From a regional perspective, North America currently dominates the SPAT/MAP Signal Performance Analytics market, owing to its advanced transportation infrastructure, high adoption of smart city technologies, and proactive government initiatives. The United States, in particular, has seen significant deployments of SPAT/MAP analytics in metropolitan areas, driven by federal funding and public-private partnerships. Europe follows closely, with a strong emphasis on sustainable urban mobility and regulatory compliance. Meanwhile, the Asia Pacific region is poised for the fastest growth, fueled by rapid urbanization, expanding smart city projects, and increasing investments in intelligent transportation systems. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by infrastructural upgrades and strategic collaborations with technology providers.

Title: Directionally modulated MEG activity

Short description: The data set contains directionally modulated MEG activity that was recorded while subjects performed wrist movements in four different directions.

Detailed Description: Brain activity during wrist movements was recorded with MEG at 625 Hz from two healthy, right-handed subjects. The subject sat relaxed in an MEG chair, the elbow rested on a pillow to prevent upper arm and shoulder movements, and the head was stabilized by small pillows. The task was to move a joystick from a center position toward one of four targets located radially at 90° intervals (four-class center-out paradigm) using exclusively the right hand and wrist. Movement amplitude was 4.5 cm. In each trial, the target was self-chosen by the subject. Targets were arranged in the form of a rhombus in the horizontal plane with corners pointing left, right, away from and toward the subject's body. Visual trigger signals were presented on a screen to start a trial or to indicate possible time violations. A trial started with the joystick in the center position and the appearance of a gray circle. After a variable delay (1-2 s), the disappearance of the circle indicated the “go” signal (cued movements). Then, within 0.75 s the subject had to start the movement and reach the target. For a trial to be valid, the subject also had to rest at the target for at least 1 s. These time constraints allowed for temporal consistency across trails and the hold period at the target prevented interference of in- and outward movements. A red cross was presented continuously for fixation. The trials were cut to contain data from 0.4 s before to 0.6 s after movement onset and the signals were band pass filtered (0.5 to 100 Hz) and resampled at 400 Hz, whereas in Waldert et al. (JNeurosci 28(4), 2008) we showed that especially the low-frequency activity (<8 Hz) contains information about movement direction. The data are composed of signals from ten MEG channels which were located above the motor areas. We will evaluate the competitor's submissions with respect to the percentage of correct classifications (decoding accuracy). Please note that classification using cross-validation on the provided training data does not necessarily yield the average decoding accuracy reported in our original study because of the reduced number of channels (approx. one quarter), inter-subject variability and the reduced number of trials in the training set due to separation of data into training and test set. The data are provided as mat-files (Matlab), detailed description is provided along with the files. Predicted class labels for the test sets must be submitted as a single mat-file containing two variables (vectors): PredictedLabelsS1 and PredictedLabelsS2, e.g. PredictedLabelsS1=[3 2 4 1 ...]. The name of this mat-file must identify the competitor.

A signal station is a place on shore from which signals are made to ships at sea. Traffic signal stations regulate the movement of traffic.

S-57 Object Class: Signal station, traffic

S-57 Acronym: SISTAT

This data was compiled for the use in the scale range 1:4,000 to 1:22,000.

THIS DATA DOES NOT REPLACE NAUTICAL CHARTS AND MUST NOT BE USED FOR NAVIGATION.

This data is based on the S-57 data format used in Electronic Navigational Charts (ENCs) published and maintained by the New Zealand Hydrographic Authority at Land Information New Zealand (LINZ). Refer to the following link for information about S-57 data: http://www.linz.govt.nz/hydro/regulation/

RadChar is a synthetic radar signal dataset designed to facilitate the development of multi-task learning models. Unlike existing datasets that only provide labels for classification tasks, RadChar provides labels that support both classification and regression tasks in radar signal recognition. This makes it the first multi-task labelled dataset of its kind released to help the research community to advance machine learning for radar signal characterisation.

To use the dataset, please following the instructions at: - ⚙️ https://github.com/abcxyzi/RadChar

Note, RadChar-Tiny is a subset of RadChar-Small, while RadChar-Small is a subset of RadChar-Baseline, etc. It is recommended a train-val-test split should be created from a single RadChar dataset (e.g., RadChar-Baseline) to support model development.

Further information about the dataset is available in our paper: - 📑 IEEE Xplore: https://ieeexplore.ieee.org/document/10193318 - 📑 arXiv: https://arxiv.org/abs/2306.13105

Please cite our work if you find this dataset useful for your project:

Z. Huang, A. Pemasiri, S. Denman, C. Fookes and T. Martin, "Multi-Task Learning For Radar Signal Characterisation," 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSPW59220.2023.10193318.

Traffic Signal Optimization Software Market Outlook

As per our latest research, the global traffic signal optimization software market size reached USD 1.37 billion in 2024, registering a robust growth trajectory that is projected to continue with a CAGR of 14.2% from 2025 to 2033. By the end of the forecast period, the market is anticipated to attain a valuation of USD 4.63 billion. This notable expansion is driven by increasing urbanization, rising congestion in metropolitan areas, and the growing adoption of smart city initiatives worldwide. The demand for real-time traffic management and the integration of artificial intelligence in transportation systems are pivotal factors fueling this market's growth.

The accelerating pace of urbanization is a primary growth driver for the traffic signal optimization software market. Rapid expansion of urban areas has led to a surge in vehicular traffic, resulting in frequent congestion and longer commute times. Governments and municipal authorities are increasingly investing in advanced traffic management solutions to mitigate these challenges and enhance the efficiency of urban transportation networks. The integration of traffic signal optimization software enables real-time monitoring and adaptive control of traffic signals, which translates into improved traffic flow, reduced congestion, and lower emissions. Furthermore, the proliferation of connected vehicles and the Internet of Things (IoT) has amplified the need for intelligent traffic management, thereby propelling the adoption of such software solutions.

Another significant growth factor is the worldwide push towards smart city development. With urban populations swelling, city planners are leveraging technology to create sustainable, efficient, and livable urban environments. Traffic signal optimization software plays a crucial role in this transformation by facilitating data-driven decision-making and automating traffic signal operations based on real-time traffic conditions. The integration of artificial intelligence and machine learning algorithms allows these systems to predict traffic patterns, adjust signal timings dynamically, and prioritize emergency or public transport vehicles. As cities continue to invest in digital infrastructure, the deployment of advanced traffic management systems is expected to witness exponential growth, further accelerating the expansion of the traffic signal optimization software market.

The growing emphasis on environmental sustainability also drives the adoption of traffic signal optimization software. Traffic congestion not only wastes time and fuel but also contributes significantly to urban air pollution. By optimizing signal timings and reducing stop-and-go traffic, these solutions help minimize vehicle idling and emissions, aligning with global efforts to combat climate change. Additionally, government regulations and incentives aimed at reducing urban pollution and promoting green mobility are encouraging municipalities and transportation authorities to upgrade their traffic management infrastructure. This trend is expected to create lucrative opportunities for vendors offering innovative and scalable traffic signal optimization software.

From a regional perspective, North America currently leads the traffic signal optimization software market, accounting for the largest market share in 2024. This dominance is attributed to the early adoption of smart city technologies, substantial government investments in intelligent transportation systems, and a high concentration of technology providers. Europe follows closely, driven by stringent environmental regulations and strong public sector initiatives for urban mobility enhancement. Meanwhile, the Asia Pacific region is poised for the fastest growth during the forecast period, fueled by rapid urbanization, expanding transportation networks, and increasing government focus on modernizing urban infrastructure. Latin America and the Middle East & Africa are also witnessing steady growth, supported by ongoing urban development projects and the gradual adoption of smart traffic management solutions.

Product Review and Consumer Trust Signals — Social Intel

Review authenticity debates, consumer protection discussions, and trust signal analysis across Trustpilot, Reddit, G2, Yelp, and 90+ platforms. Useful for review fraud detection, consumer trust research, and product quality signal extraction.

Includes 54 fields per row — sentiment (VADER, deterministic, English-validated), emotion primary/secondary, sarcasm flag, toxicity score, topic tags, claim type, verified/debunked flags, source credibility, echo chamber signal, bot signal, coordinated inauthentic flag, and narrative type.

69,871 records. Updated Jul 11, 2026.

License: Social Intel Commercial v1 — See socialintel.io for terms.

A warning signal transmitted by a vessel, or aid to navigation, during periods of low visibility. Also, the device producing such a signal.

S-57 Object Class: Fog signal

S-57 Acronym: FOGSIG

This data was compiled for the use in the scale range 1:4,000 to 1:22,000.

THIS DATA DOES NOT REPLACE NAUTICAL CHARTS AND MUST NOT BE USED FOR NAVIGATION.

This data is based on the S-57 data format used in Electronic Navigational Charts (ENCs) published and maintained by the New Zealand Hydrographic Authority at Land Information New Zealand (LINZ). Refer to the following link for information about S-57 data: http://www.linz.govt.nz/hydro/regulation/

This is the location information of unmanned surveillance cameras in Gimje-si, Jeollabuk-do Special Self-Governing Province. The data provides information on unmanned surveillance equipment installed to prevent violations of traffic laws and enhance traffic safety. The main items provided are types of surveillance cameras (speeding, signals, etc.), installation locations (addresses based on road names), and can be used for establishing traffic policies, analyzing road safety, and providing citizen guidance services. It can also be used as data for guiding compliance with the Road Traffic Act and managing accident-prone areas, and is useful for linking with GIS-based location visualization data. In addition, the data can be used as basic data for establishing traffic volume analysis and safe driving inducement policies.

Inside the human body, there are tiny invisible workers called proteins. They do almost everything in our body: they help digest food, transport oxygen in the blood, and even protect us from diseases. But for each protein to know where it needs to go and what it needs to do, it receives special instructions—like an address on a letter!

These "addresses" are called localization signals, and they tell the protein where it should go inside the cell. Scientists have created different ways to identify these signals and predict where each protein will go. Let's explore some of them!

🔬 McGeoch and von Heijne: These methods help find a special part in the protein called the signal sequence. This sequence works like a GPS that guides the protein to the right place inside the cell.

🧪 ALOM: This method tries to discover if a protein can pass through the cell membrane, like crossing an invisible wall.

⚡ Mitochondrial Score: Some proteins need to go to the mitochondria, which are like tiny power plants inside cells. This method helps identify proteins that should work there.

🏠 ER Signal (HDEL): Some proteins need to stay inside a place called the endoplasmic reticulum, which works like a factory inside the cell. The HDEL code helps keep these proteins in that place.

🌱 Peroxisome Signal (POX): This signal shows that a protein needs to go to the peroxisomes, which are like the cell’s garbage collectors—they help clean up toxic substances.

📦 Vacuole Score: Some proteins must go to the vacuoles, which are like large storage bags where the cell keeps nutrients or gets rid of useless things.

🧬 Nuclear Signal: Some proteins need to go to the nucleus of the cell, where all the genetic material is stored. This signal acts like a password that allows them to enter this special space.

All this information helps scientists understand how proteins work and where they need to go. This way, they can study diseases, create medicines, and even develop new ways to treat health problems!

City of Boston traffic signals.

Aim

I hope that by uploading some of the data previously shared by [SETI][1] onto Kaggle, more people will become aware of SETI’s work and become engaged in the application of machine learning to the data (amongst other things). Note, I am in no way affiliated with SETI, I just think this is interesting data and amazing science.

Finding ET

If you’re reading this, then I’m guessing you have an interest in data science. And if you have an interest in data science, you’ve probably got an interest in science in general.

Out of every scientific endeavour undertaken by humanity, from mapping the human genome to landing a man on the moon, it seems to me that the Search for Extra-terrestrial Intelligence (SETI) has the greatest chance to fundamentally change how we think about our place in the Universe.

Just imagine if a signal was detected. Not natural. Not human. On the one hand it would be a Copernican-like demotion of mankind’s central place in the Cosmos, and on the other an awe-inspiring revelation that somewhere out there, at least once, extra-terrestrial intelligence emerged.

SETI

Over the past few years, SETI have launched a few initiatives to engage the public and ‘citizen scientists’ to help with their search. Below is a summary of their work to date (from what I can tell).

In January 2016, the Berkeley SETI Research Center at the University of Berkley started a program called Breakthrough Listen, described as “*the most comprehensive search for alien communications to date*”. Radio data is being currently been collected by the Green Bank Observatory in West Virginia and the Parkes Observatory in New South Wales, with optical data being collected by the Automated Planet finder in California. Note that (for now at least), the rest of this description focusses on the radio data.

The basic technique for finding a signal is this; point the telescope at a candidate object and listen for 5 minutes. If any sort of signal is detected, point slightly away and listen again. If the signal drops away, then it’s probably not terrestrial. Go back to the candidate and listen again. Is the signal still there? Now point to a second, slightly different position. How about now? The most interesting finding is, as you might expect, SIGNAL - NO SIGNAL – SIGNAL - NO SIGNAL – SIGNAL.

The Breakthrough Listen project has just about everything covered. The hardware and software to collect signals, the time, the money, and the experts to run the project. The only sticking point is the data. Even after compromising on the raw data’s time or frequency resolution, Breakthrough Listen is archiving 500GB and data every hour (!).

The resulting data are stored in something called a filterbank file, which are created at three different frequency resolutions. These are,

• High frequency resolution (~3 Hz frequency resolution, ~18 second sample time)
• High time resolution (~366 kHz frequency resolution, ~349 microsecond sample time)
• Medium resolution (~3 kHz frequency resolution, ~1 second sample time)

To engage the public, Breakthrough listen’s primary method is something called SETI@Home, where a program can be downloaded and installed, and your PC used when idle to download packets of data and run various analysis routines on them.

Beyond this, they have shared a number of starter scripts and some data. To find out more, a general landing page can be found [here][2]. The scripts can be found on GitHub [here]3, and a data archive can be found [here]4. Note that the optical data from the Automated Planet Finder is also in a different format called a FITS file.

Entering the Cloud

The second initiative by SETI to engage the public was the SETI@IBMCloud project launched in September 2016. This provided the public with access to an enormous amount of data via the IBM Cloud platform. This initiative, too, came with an excellent collection of starter scripts which can still be found on GitHub [here][5]. Unfortunately, at the time of writing, this project is on hold and the data cannot be accessed.

SETI & Machine Learning

There are a few other sources of data online from SETI, one of which is the basis for this dataset.

In the summer of 2017, SETI hosted a machine learning challenge where simulated datasets of various sizes were provided to participants along with a blinded test set. The winning team achieved a classification accuracy of 94.67% using a convolution neural network. The aim of this challenge was to attempt a novel approach to signal detection, namely to go beyond traditional signal analysis approaches and to turn the problem into an image classification task, after converting the signals into spectrograms.

The primary traini...