example Go-RTs of experiment 1 (subject 3)

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.

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.

This repository contains data accompanying: Kirchherr et al., 2023, "Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex".

Data collection methods:

Two adult female rhesus macaques (Macaca mulatta) trained on a reaching, and grasping, and placing task served as the subjects. The animal handling as well as surgical and experimental procedures complied with European guideline (2010/63/UE) and authorized by the French Ministry for Higher Education and Research (project # 2016112713202878) in force on the care and use of laboratory animals, and were approved by the ethics committee CELYNE (comité d’éthique Lyonnais pour les neurosciences expérimentale, C2EA 42). After initial training, we performed a sterile surgery to implant six floating multielectrode arrays (FMA, Microprobes for Life Science, Gaithersburg, MD, USA) in the right (monkey 1) or left (monkey 2) cortical hemisphere. Each array was comprised of 32 platinum/iridium electrodes (impedance 0.5 MΩ at 1 kHz) with lengths ranging from 1 to 6 mm, and with an inter-electrode spacing of 400 μm. One electrode array was implanted in the primary motor cortex (M1), two were implanted in the ventral premotor cortex (F5), one in the dorsal premotor cortex (F2), and two in the prefrontal cortex (45a and 46/12r), as estimated according to a previous magnetic resonance imaging scan. For the purposes of this study, we analyzed data from the M1 array of each monkey.

The wideband neural signal (bandpass filtered at 0.1 to 7500 kHz) was recorded at 30 kS/s, and amplified and digitized (16-bit; 0.192 μV resolution) with an Intan Tech-based (Intan Technologies, Los Angeles, CA, USA) open source acquisition system (Open Ephys; Siegle et al. 2017). This system uses a 256-channel Intan RHD2000 series acquisition board and 32-channel headstages (RHD2132). Spike detection was performed offline using Trisdesclous (Garcia & Pouzat,2015). The common reference was removed to reduce ambient noise. Spikes were then detected from each electrode using a threshold of 2 times the median absolute deviation (MAD), and analyzed as multi-unit activity (MUA) in 10 ms bins. All electrodes in which at least one well-isolated spike waveform was detected were selected for the following analyses. We thus used a sample of 21 electrodes out of 32 for monkey 1, and 25 out of 32 electrodes for monkey 2. Custom made detection panels were used to record the moments when the monkey’s hand released the handle, the hand contacted the target object, and when the object was placed in the groove. An Omniplex 16-channel recording system (Plexon, Dallas, TX, USA) was used to simultaneously record these behavioral events. Trials were discarded if the response time (time between the go signal and handle release) was less than 100 or greater than 1500 ms, the reach duration (time between handle release and object contact) was less than 100 or greater than 1000 ms, or the placing duration (time between object contact and placing the object in the groove) was less than 100 or greater than 1200 ms, leaving 19 - 68 trials per day for monkey 1 (M = 43.9, SD = 15.46, N = 439; left: M = 14.8, SD = 5.74; center: M = 14.4, SD = 5.15; right: M = 14.7, SD = 7.73), and 23 - 49 per day for monkey 2 (M = 38.3, SD = 9.87, N = 383; left: M = 14.2, SD = 3.91; center: M = 10.8, SD = 3.55; right: M = 13.3, SD = 3.37).

Abstract:

Neural populations, rather than single neurons, may be the fundamental unit of cortical computation. Analyzing chronically recorded neural population activity is challenging not only because of the high dimensionality of activity in many neurons, but also because of changes in the recorded signal that may or may not be due to neural plasticity. Hidden Markov models (HMMs) are a promising technique for analyzing such data in terms of discrete, latent states, but previous approaches have either not considered the statistical properties of neural spiking data, have not been adaptable to longitudinal data, or have not modeled condition specific differences. We present a multilevel Bayesian HMM which addresses these shortcomings by incorporating multivariate Poisson log-normal emission probability distributions, multilevel parameter estimation, and trial-specific condition covariates. We applied this framework to multi-unit neural spiking data recorded using chronically implanted multi-electrode arrays from macaque primary motor cortex during a cued reaching, grasping, and placing task. We show that the model identifies latent neural population states which are tightly linked to behavioral events, despite the model being trained without any information about event timing. We show that these events represent specific spatiotemporal patterns of neural population activity and that their relationship to behavior is consistent over days of recording. The utility and stability of this approach is demonstrated using a previously learned task, but this multilevel Bayesian HMM framework would be especially suited for future studies of long-term plasticity in neural populations.

experiment 2 - example subject (16) - Go-RTs

Locations of Rectangular Rapid Flashing Beacon (RRFB) signals in Arlington County. These are signs that exist at some crosswalks with pushbuttons and flashing lights. The signals and associated information are maintained in the Cartegraph asset management system. The associated data includes model information, installation and replacement dates, location descriptions, and other related data.Contact: Department of Environmental ServicesData Accessibility: Publicly AvailableUpdate Frequency: DailyDocumentation Last Revision Date: 1/24/2024Documentation Creation Date: 1/24/2024Feature Dataset Name: OMS_TrafficLayer Name: DES_Signal_RRFB_pnt

A central experimental task in executive control research is the Stop-signal task, which allows measuring the ability to inhibit dominant responses. A crucial aspect of this task consists of varying the delay between the Go- and Stop-signal. Since the time necessary to administer the task can be long, a method of optimal delay choice was recently proposed: the PSI method. In a behavioral experiment, we show a variant of this method, the PSI marginal method, to be unable to deal with the Go-response slowing often observed in the Stop-signal task. We propose the PSI adjusted method, which is able to deal with this response slowing by correcting the estimation process for the current reaction time. In several sets of behavioral simulations, as well as another behavioral experiment, we document and compare the statistical properties of the PSI marginal method, our PSI adjusted method, and the traditional staircase method, both when reaction times are constant and when they are linearly increasing. The results show the PSI adjusted method’s performance to be comparable to the PSI marginal method in the case of constant Go-response times, and to outperform the PSI marginal method as well as the staircase methods when there is response slowing. The PSI adjusted method thus offers the possibility of efficient estimation of Stop-signal reaction times in the face of response slowing.

Arlington County VA, school beacon signal data. Includes retired signals

Inter-animal communication allows signals released by an animal to be perceived by others. Scent-marking is the primary mode of such communication in giant pandas (Ailuropoda melanoleuca). Signal detection theory propounds that animals choose the substrate and location of their scent marks so that the signals released are transmitted more widely and last longer. We believe that pandas trade off scent-marking because they are an energetically marginal species and it is costly to generate and mark chemical signals. Existing studies only indicate where pandas mark more frequently, but their selection preferences remain unknown. This study investigates whether the marking behavior of pandas is consistent with signal detection theory. Feces count, reflecting habitat use intensity, was combined with mark count to determine the selection preference for marking. The results showed that pandas preferred to mark ridges with animal trails and that most marked tree species were locally dominant. In addition, marked plots and species were selected for lower energy consumption and a higher chance of being detected. Over 90% of the marks used were the longest-surviving anogenital gland secretion marks, and over 80% of the marks were oriented toward animal trails. Our research demonstrates that pandas go out of their way to make sure their marks are found. This study not only sheds light on the mechanisms of scent-marking by pandas but also guides us toward more precise conservation of the panda habitat.

Description
The dataset contains raw surface EMG signals recorded while performing pushups. The Participant first performed 3 sets of 5 pushups. with ~ 45 seconds rest in between. After the initial 3 sets the participant performs a last set till failure. (The first set starts with the participant supporting his weight awaiting a go signal.

Signal Description
The signal consists of sEMG channels from left and right biceps 1000Hz. The recording was performed using BTS FREEEMG1000.

Data Format
Data is in the from of a list of JSON payloads each consisting of 200 data points each.

Minimum Recomended data processing procedures
Pre-Preperation --> Convert from JSON to DataFrame
Band pass filter --> 20 - 450 Hz
Notch filter --> 50Hz (Power line frequency)

This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No's 101093069 (P2CODE) and 101120657(ENFIELD). Disclaimer: Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commission. Neither the European Union nor the European Commission can be held responsible for them.

Citation
The users of this dataset are kindly asked to cite the following paper:
Syed, D.A., Quadrini, W., Rahmani Choubeh, N., Pinzone, M., Gusmeroli, S. (2025). Approaching Interoperability and Data-Related Processing Issues in a Human-Centric Industrial Scenario. In: Presser, M., Skarmeta, A., Krco, S., González Vidal, A. (eds) Global Internet of Things and Edge Computing Summit. GIECS 2024. Communications in Computer and Information Science, vol 2328. Springer, Cham. https://doi.org/10.1007/978-3-031-78572-6_2

What happens when the brain awaits a signal of uncertain arrival time, as when a sprinter waits for the starting pistol? And what happens just after the starting pistol fires? Using functional magnetic resonance imaging (fMRI), we have discovered a novel correlate of temporal expectations in several brain regions, most prominently in the supplementary motor area (SMA). Contrary to expectations, we found little fMRI activity during the waiting period; however, a large signal appears after the “go” signal, the amplitude of which reflects learned expectations about the distribution of possible waiting times. Specifically, the amplitude of the fMRI signal appears to encode a cumulative conditional probability, also known as the cumulative hazard function. The fMRI signal loses its dependence on waiting time in a “countdown” condition in which the arrival time of the go cue is known in advance, suggesting that the signal encodes temporal probabilities rather than simply elapsed time. The dependence of the signal on temporal expectation is present in “no-go” conditions, demonstrating that the effect is not a consequence of motor output. Finally, the encoding is not dependent on modality, operating in the same manner with auditory or visual signals. This finding extends our understanding of the relationship between temporal expectancy and measurable neural signals.

Non-traditional data signals from social media and employment platforms for GOAC stock analysis

Non-traditional data signals from social media and employment platforms for GO stock analysis

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 1:1,500,000 and smaller.

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/

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!

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

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.

This open document spreadsheet (.ods) file contains summary data from all subjects in all conditions from "Subthreshold transcranial magnetic stimulation applied after the go-signal facilitates reaction time under control but not startle conditions" Published in the European Journal of Neuroscience.

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/

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.