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273 results for “alert”
Gaia Photometric Science Alerts Crossmatch with Gaia DR3 - Feb. 2024
<p>The <a href="http://gsaweb.ast.cam.ac.uk/alerts/home">Gaia Photometric Science Alerts</a> were crossmatched to the Gaia DR3 catalog on <strong>February 23rd, 2024</strong>. We assumed a 1" separation for each crossmatch. The crossmatch was conducted using the <a href="https://lsdb.readthedocs.io/en/latest/">Large Survey Database</a> (LSDB). </p>
California Harmful Algal Bloom Monitoring and Alert Program Data (Darwin Core Archive format)
The Harmful Algal Bloom Monitoring and Alert Program (HABMAP) was formed in 2008 and provides updates on current algal blooms and facilitates information exchange among scientists, federal and state managers, and the general public in California. A major component of this program is regional HAB monitoring with support from the Southern California Coastal Ocean Observing System (SCCOOS) and the Central and Northern California Ocean Observing System (CeNCOOS). Water samples and net tows are collected once per week at piers to monitor for HAB species, the neurotoxin domoic acid, and water quality data including temperature, chlorophyll-a, and nutrients. The sampling stations represented in this dataset include Santa Cruz Wharf, Monterey Wharf, Cal Poly Pier, Stearns Wharf, Santa Monica Pier, Newport Beach Pier, and Scripps Pier. These data are consolidated and reformatted into Darwin Core Archive (DwC-A) format from the level 1 site-specific datasets hosted on the SCCOOS ERDDAP server: (https://erddap.sccoos.org/erddap/tabledap/index.html?page=1).
Dataset for Quieting the Static: A Study of Static Analysis Alert Suppressions
<h2><strong>Dataset for Quieting the Static: A Study of Static Analysis Alert Suppressions</strong></h2><p>This is the dataset for our empirical study on the practices of software bug suppression in open source projects.</p><h3><strong>Directory Structure</strong></h3><ul><li>./categorization: Contains the categorization spreadsheet data of sampled suppressions in csv format, as well as the raw JSON sample.</li><li>./categorization/html_files: Contains the annotated code fragments of the sampling process in HTML format.</li><li>./data: Contains the datasets of canonicalized configuration and annotation warning suppressions in JSON format.</li></ul><p><br> </p>
ERDS alerts based on WRF model output
<p>Heavy rainfall alerts based on WRF model output at 7.5 km resolution produced by ERDS (<a href="https://erds.ithacaweb.org/">https://erds.ithacaweb.org/</a>).</p>
AIT Alert Data Set
<p>This repository contains the AIT Alert Data Set (AIT-ADS), a collection of synthetic alerts suitable for evaluation of alert aggregation, alert correlation, alert filtering, and attack graph generation approaches. The alerts were forensically generated from the <a href="https://zenodo.org/record/5789064">AIT Log Data Set V2 (AIT-LDSv2)</a> and origin from three intrusion detection systems, namely Suricata, Wazuh, and AMiner. The data sets comprise eight scenarios, each of which has been targeted by a multi-step attack with attack steps such as scans, web application exploits, password cracking, remote command execution, privilege escalation, etc. Each scenario and attack chain has certain variations so that attack manifestations and resulting alert sequences vary in each scenario; this means that the data set allows to develop and evaluate approaches that compute similarities of attack chains or merge them into meta-alerts. Since only few benchmark alert data sets are publicly available, the AIT-ADS was developed to address common issues in the research domain of multi-step attack analysis; specifically, the alert data set contains many false positives caused by normal user behavior (e.g., user login attempts or software updates), heterogeneous alert formats (although all alerts are in JSON format, their fields are different for each IDS), repeated executions of attacks according to an attack plan, collection of alerts from diverse log sources (application logs and network traffic) and all components in the network (mail server, web server, DNS, firewall, file share, etc.), and labels for attack phases. For more information on how this alert data set was generated, check out our paper accompanying this data set [1] or our <a href="https://github.com/ait-aecid/alert-data-set">GitHub repository</a>. More information on the original log data set, including a detailed description of scenarios and attacks, can be found in [2].</p> <p>The alert data set contains two files for each of the eight scenarios, and a file for their labels:</p> <ul> <li><em><strong><scenario>_aminer.json</strong> </em>contains alerts from AMiner IDS</li> <li><em><strong><scenario>_wazuh.json</strong> </em>contains alerts from Wazuh IDS and Suricata IDS</li> <li><strong><em>labels.csv</em></strong> contains the start and end times of attack phases in each scenario</li> </ul> <p>Beside false positive alerts, the alerts in the AIT-ADS correspond to the following attacks:</p> <ul> <li>Scans (nmap, WPScan, dirb)</li> <li>Webshell upload (CVE-2020-24186)</li> <li>Password cracking (John the Ripper)</li> <li>Privilege escalation</li> <li>Remote command execution</li> <li>Data exfiltration (DNSteal) and stopped service</li> </ul> <p>The total number of alerts involved in the data set is 2,655,821, of which 2,293,628 origin from Wazuh, 306,635 origin from Suricata, and 55,558 origin from AMiner. The numbers of alerts in each scenario are as follows. fox: 473,104; harrison: 593,948; russellmitchell: 45,544; santos: 130,779; shaw: 70,782; wardbeck: 91,257; wheeler: 616,161; wilson: 634,246.</p> <p>Acknowledgements: Partially funded by the European Defence Fund (EDF) projects AInception (101103385) and NEWSROOM (101121403), and the FFG project PRESENT (FO999899544). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. The European Union cannot be held responsible for them.</p> <p><strong>If you use the AIT-ADS, please cite the following publications:</strong></p> <p>[1] Landauer, M., Skopik, F., Wurzenberger, M. (2024): <a href="https://doi.org/10.1145/3675741.3675748">Introducing a New Alert Data Set for Multi-Step Attack Analysis.</a> Proceedings of the 17th Cyber Security Experimentation and Test Workshop. [<a href="https://dl.acm.org/doi/pdf/10.1145/3675741.3675748">PDF</a>]</p> <p>[2] Landauer M., Skopik F., Frank M., Hotwagner W., Wurzenberger M., Rauber A. (2023): <a href="https://ieeexplore.ieee.org/abstract/document/9866880">Maintainable Log Datasets for Evaluation of Intrusion Detection Systems.</a> IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 4, pp. 3466-3482. [<a href="https://arxiv.org/pdf/2203.08580.pdf">PDF</a>]</p>
BP and RP spectra of the Gaia Photometric Alerts
<p>By means of Web Scraping techniques, the values of the BP (Blue Photometer) and RP (Red Photometer) spectra detected by <a href="https://gsaweb.ast.cam.ac.uk/alerts">Gaia</a> have been extracted. This data extraction was carried out with the aim of studying the feasibility of classifying the alerts automatically according to their origin. </p> <ul> <li><strong>Spectrums.csv</strong> <ul> <li><strong>id:</strong> Unique name of the alert.</li> <li><strong>order:</strong> Order of the spectrum on a given alert.</li> <li><strong>bp:</strong> Values as a list of the Blue Photometer.</li> <li><strong>rp:</strong> Values as a list of the Red Photometer.</li> <li><strong>a_d:</strong> If the spectrum corresponds to a detection it have the letter "D". If the spectrum corresponds to an alert it have the letter "A".</li> <li><strong>feature: </strong>It show us the class or comment of a spectrum.</li> </ul> </li> <li><strong>Spectrums_columns.csv</strong> <ul> <li>We have the same values as in the "Spectrums.csv" but, the bp and rp lists are shown as columns. This dataset is useful for machine learning models.</li> </ul> </li> </ul> <p> </p> <p> </p> <p>The databases are applied in the master's thesis: <a href="https://github.com/mariomartgarcia/Feasibility_study_of_a_spectra-based_classication_of_the_Gaia_Photometric_Alerts"><strong>Feasibility study of a spectra-based classication of the<br> Gaia Photometric Alerts.</strong></a></p> <p><strong>Abstract</strong></p> <p>The photometric alerts obtained by the Gaia satellite are collected when a significant change from a constant magnitude is detected. This alert is recorded to be later studied and classified, in other words, to know what has caused it (variable star, microlensing effects, transits...). The project focuses on the alerts that have been published and classified in order to study the feasibility of automating the process of classifying these alerts.</p> <p>After selecting the alerts with the greatest representation, a web scraping process is carried out where the photometric spectra of each of the alerts participating in the study are obtained. Once we obtain a dataset formed by the photometric spectra and the classification of the alert, various supervised machine learning techniques are implemented. Given the large volume of data we work with, a balanced random subset of 4000 elements is selected to obtain the best hyperparameters and evaluate the performance of the following classifiers: Decision Trees, Support Vector Machines, Random Forests and Gradient Boosting Classifier. This process is repeated on the complete dataset using the hyperparameters obtained in the subset. Finally, the performance of different models for an Artificial Neural Network is built and evaluated.</p> <p>The best model obtained is the Gradient Boosting Classifier which, with a maximum depth of 7 nodes, 200 estimators and a learning rate of 0.1, gives an accuracy of 66.8%. Although the results are not excessively good, we can affirm that the classification of Gaia photometric alerts according to their spectra is feasible.</p> <p><br> - Keywords: "Gaia", "Photometry", "Classification", "Machine Learning", "Web Scraping".</p> <p> </p> <p> </p>
Universal safety distance alert device for road vehicles - Testing videos
<p>Testing the Universal safety distance alert device for road traffic in simulated and in real traffic.</p>
Data supplementing Einhäuser, W., Neubert, C. R., Grimm, S., & Bendixen, A. (2024). High visual salience of alert signals can lead to a counterintuitive increase of reaction times. Scientific Reports, 14, 8858.
<p>These files supplement the publication <br>Einhäuser, W., Neubert, C. R., Grimm, S., & Bendixen, A. (2024). High visual salience of alert signals can lead to a counterintuitive increase of reaction times. <em>Scientific Reports, </em>14, 8858. https://doi.org/10.1038/s41598-024-58953-4</p> <p>The files data_expX.mat, where X is the experiment number (1-4), contain the data as described below. </p> <p>The files dataTraining_expX.mat contain the data of the first (training) block of each experiment. They are needed only for the supplemental material. </p> <p>To exemplify the usage, the functions figure2and3.m, figure4.m, figure5.m, figure6.m and Table1.m output the paper's figures and the data of Table 1, respectively; figureS2.m, figureS3.m, figureS4.m and figureS5.m output the figures of the supplemental material (figure S1 needs substantial amounts of external source code to compute the salience maps and is therefore not included).</p> <p><br>data_exp1.mat contains the following variables<br>For alert trials, variable of dimensions subjects x blocks x alert trials (20x10x64); note that only used participants and blocks with alert trials (2 through 11) are included in the data set:<br>alert_aud - the salience level of the alert tone (1-8, corresponding to 54dB(A) through 89 dB(A))<br>alert_vis - the salience level of the alert frame (1-8, corresponding to 0.10 to 8.50 Weber contrasts in logarithmic steps)<br>alert_side - the side on which the alert frame and the tone were presented (1-left, 2-right)<br>alert_fixOk - derived from eye movement data, was the first fixation closer to the alert square than to the center?<br>alert_primaryRT - primary-task reaction time (for alert trials)<br>alert_alertRT - alert-task reaction time <br>alert_correctAlert - was the response (up/down) to the alert correct?<br>alert_intrusionAlert - was there an intrusion (left/right pressed before up or down)?<br>alert_correctPrimary - was the primary task conducted correctly?<br>alert_intrusionPrimary - was there an intrusion for the primary task?<br>alert_timeToFixation - time to first fixation on alert square <br>alert_fixationToResp - time from beginning of fixation to response to the alert <br>alert_fixDur - duration of first fixation after trial onset</p> <p>For no-alert trials, variable of dimensions subjects x blocks x no-alert trials (20x10x448):<br>noalert_correctPrimary - was the primary task conducted correctly?<br>noalert_intrusionPrimary - was there an intrusion for the primary task? (i.e., up/down pressed before left/right)?</p> <p>For all trials, variable of dimensions subjects x blocks x no-alert trials (20x10x512):<br>all_correctPrimary - was the primary task conducted correctly?<br>all_intrusionPrimary - was there an intrusion for the primary task? (i.e., up/down pressed before left/right)?<br>all_RT - reaction time in the primary task<br>all_isAlertTrial - was the trial an alert trial? (useful to map no-alert trials and alert trials on all trials)</p> <p>In addition, there are some raw eye movement data for the alert blocks:<br>alert_eyeX, alert_eyeY - dimension 20 x 10 x 64 x 6000; x and y position in pixel coordinates relative to trial (and alert) onset, 1ms/sample, ends at conclusion of trials, filled up with NaN if duration was less than 6000ms <br>alert_eyeFixX, alert_eyeFixY, alert_eyeFixTon, alert_eyeFixDur - 20 x 10 x 64 x 15; x and y position, onset (in ms relative to trial onset) and duration of fixations during the trial (from onset to primary-task response), filled with NaN when less than 15 fixations were made. Note that the first entry of alert_eyeFixDur along the forth dimension will usually equal the alert_fixDur</p> <p><br>data_exp2.mat contains the same variables as data_exp1.mat with the following exceptions:<br>alert_vis - contains only two levels (1 and 2) corresponding to Weber contrasts of 0.10 and 2.39, respectively<br>alert_dur - the level of duration of the alert frame (1 through 8, corresponding to 25ms, 50ms, 100ms, 200ms, 300ms, 400ms, 600ms, 800ms)<br>alert_aud is not included (all tones were at 54 dB(A))<br>there are only 19 participants; hence the variables are of size 19 x ...<br>note: block 8 for subject 6 contains only 450 trials (57 alert trials), the remainder is filled with NaN.</p> <p><br>data_exp3.mat contains the same variables as data_exp1.mat with the following exceptions:<br>alert_aud - contains only two levels (1 and 2) corresponding to sound levels of 54 dB(A) and 79 dB(A) respectively<br>alert_dur - the level of duration of the alert tone (1 through 8, corresponding to 25ms, 50ms, 100ms, 200ms, 300ms, 400ms, 600ms, 800ms)<br>alert_vis is not included (all alert frames were at 0.10 contrast)</p> <p> </p> <p>data_exp4.mat contains the same variables as data_exp1.mat with the following exceptions:<br>alert_aud - contains only two levels (1 and 2) corresponding to sound levels of 54 dB(A) and 79 dB(A) respectively<br>alert_vis is not included and replaced by<br>alert_condBefore - alert frame contrast level before the saccade (1 - 0.10 contrast, 2 - 2.39 contrast)<br>alert_condAfter - alert frame contrast level after the saccade (1 - 0.10 contrast, 2 - 2.39 contrast)</p> <p><br>dataTraining_expX.mat contains for the first (training) block of experiment X (X being 1, 2, 3 or 4) the following variables of size 20x512 (participant x trial) [19x512 in case of Experiment 2]:<br>all_correctPrimary - was the primary task conducted correctly?<br>all_RT - reaction time in the primary task<br>[Note that there are no alert trials in this block and these data are only used in the supplementary material (part 4)]</p>
FIGURE 1 in Genetic diversity in two threatened species of guitarfish (Elasmobranchii: Rhinobatidae) from the Brazilian and Argentinian coasts: an alert for conservation
FIGURE 1 | Median-joining network of mtCR haplotypes for A. Pseudobatos horkelii and B. Pseudobatos percellens. Haplotypes are represented by circles with size proportional to frequency in the total sample. All hatch marks correspond to one mutation. Samples from northern Argentina (AR), Torrinha/RS (RS), Florianópolis/SC (SC), Pontal do Paraná/PR (PR), Cananéia/ SP (SP1), Mongaguá/SP (SP2), Santos/SP (SP3), Rio de Janeiro/RJ(RJ).
FIGURE 2 in Genetic diversity in two threatened species of guitarfish (Elasmobranchii: Rhinobatidae) from the Brazilian and Argentinian coasts: an alert for conservation
FIGURE 2 | Graph of the Bayesian analysis of population structure of mtCR for A. Pseudobatos horkelii and B. Pseudobatos percellens. Samples from northern Argentina (AR), Torrinha/RS (RS), Florianópolis/SC (SC), Pontal do Paraná/PR (PR), Cananéia/ SP (SP1), Mongaguá/SP (SP2), Santos/SP (SP3), Rio de Janeiro/RJ(RJ).
Alert Type Frequency Assessment of Open-Source Static Analysis Tools and Codebases
<p>This includes all data needed to replicate and validate our frequency analysis of static analysis (SA) alerts produced using open-source SA tools on several OSS codebases. It includes instructions how to get and run the SA tools, a Dockerfile to conveniently get and use the SA tools, raw SA tool output, some python scripts to parse that output, parsed SA data and aggregate analyses, and SA data augmented with CERT coding rule and CWE data. </p> <p>The SA tools used:</p> <ul> <li>clang-tidy version 15.07 </li> <li>cppcheck version 2.9 </li> <li>CERT Rosecheckers </li> </ul> <p>The codebases analyzed:</p> <ul> <li>zeek version 5.1.1</li> <li>git version 2.39.0</li> <li>dos2unix version 7.4.3</li> </ul>
Food fraud data based on the European Rapid Alert System for Food and Feed (RASFF)
<p>The data contains information on food fraud and was used to predict food fraud type using a Bayesian Network model. Food fraud notifications for the period 2000-2014 were downloaded from the Rapid Alert System for Food and Feed (RASFF) database. Each record contains detailed information on the kind of notification and the products and countries involved. Based on the description in each notification we added a variable "food fraud type" (i.e. six different types of food fraud). A set of 749 notifications for the years 2000-2013 was used to train a Bayesian Network model to predict food fraud type. This model was validated using the 88 notifications for the year 2014.</p> <p>Interpretation of the data and details on the performance of the BN model can be found in the research article titled “Prediction of food fraud type using data from Rapid Alert System for Food and Feed (RASFF) and Bayesian network modelling” <a href="https://doi.org/10.1016/j.foodcont.2015.09.026">https://doi.org/10.1016/j.foodcont.2015.09.026</a></p> <p> </p> <p> </p> <p><strong>Column names</strong></p> <p>year - year notification was made</p> <p>product - categorization of the different products</p> <p>notification - categorization of the notifications</p> <p>notified - country that made the notification</p> <p>origin - country where the product originated from</p> <p>fraud - classification of fraud type</p>
Randomized trial of AKI alerts in hospitalized patients
<p><b>Objective: </b>To determine whether electronic health record (EHR) alerts for Acute Kidney Injury (AKI) would improve patient outcomes of mortality, dialysis and progression of AKI. </p> <p><b>Design: </b>Double-blinded, multicenter, parallel, randomized, controlled trial of an electronic AKI alert versus usual care (no alert). Participants were electronically identified and randomized via a best practice alert build using simple randomization with allocation concealment.</p> <p><b>Setting:</b> Six diverse hospitals (four teaching and two non-teaching) ranging from small community hospitals to large tertiary care centers.</p> <p><b>Participants:</b> 6,030 adult inpatients with AKI, as defined by the Kidney Disease: Improving Global Outcomes (KDIGO) creatinine criteria.</p> <p><b>Interventions:</b> An EHR-based "pop-up" alert for AKI with an associated AKI order set upon provider opening of the patient's medical record.</p> <p><b>Main Outcome Measures: </b>A composite of AKI progression, receipt of dialysis, or death within 14 days of randomization. Pre-specified secondary outcomes included per-hospital outcome rates and rates of various AKI care practices. </p> <p><b>Results: </b>6,030 patients were randomized over 22 months. The primary outcome occurred in 653 (21.4%) patients in the alert group and 622 (20.9%) in the usual care group (relative risk 1.02, 95% confidence interval [CI] 0.93 to 1.13, p=0.67). Per-hospital analysis revealed worse outcomes in the two non-teaching hospitals (N=765, 13%), where alerts were associated with a relative risk of the primary outcome of 1.49 (95% CI, 1.12 to 1.98, p=0.006). More deaths (15.6% in the alert group vs. 8.6% in the usual care group) occurred at these centers (p=0.003). Certain AKI care practices were increased in the alert group but did not appear to mediate these outcomes.</p> <p><b>Conclusions: </b>Alerts did not reduce rates of our primary outcome among hospitalized patients with AKI. The overall lack of clinical benefit and signals of harm in non-teaching hospitals should lead to a re-evaluation of existing AKI alerting systems.</p> <p><b>Trial Registration: </b>ClinicalTrials.gov NCT02753751.</p>
Realistic LIGO/Virgo/KAGRA observing scenarios based on O3 public alerts
<p>Efforts to search for electromagnetic counterparts of gravitational-wave sources have intensified dramatically since the 2017 discovery a binary neutron star merger with an associated gamma-ray burst, optical/infrared kilonova, and panchromatic afterglow. Now, one LIGO/Virgo observing run later, there has not yet been a second secure identification of electromagnetic counterpart. This is not unexpected, and can be mostly explained by the localization uncertainty of events from LIGO and Virgo’s most recent, third observing run (“O3”). The official LIGO/Virgo observing scenarios fail to account for improvements in data analysis that allow LIGO/Virgo to detect fainter and hence worse-localized gravitational- wave sources, which increases the number of detections while decreasing the proportion of well-localized “gold-plated” events. Realistic forecasting of gravitational-wave localization performance is paramount because electromagnetic counterpart searches require large commitments of telescope time. We present simulations of the next several LIGO/Virgo/KAGRA observing runs that are based on the statistics of O3 public alerts.</p>
Food fraud data based on the European Rapid Alert System for Food and Feed (RASFF)
<p>The data contains information on food fraud. A total of 1634 food fraud notifications for the period 2000-2020 were downloaded from the Rapid Alert System for Food and Feed (RASFF) database. Each record contains detailed information on the kind of notification and the products and countries involved. Based on the description in each notification we added a variable "food fraud type" (i.e. six different types of food fraud). This dataset can be used to analyze food fraud, and a subset was used to train a Bayesian network model to predict food fraud type. This research article titled “Prediction of food fraud type using data from Rapid Alert System for Food and Feed (RASFF) and Bayesian network modelling” can be found here: <a href="https://doi.org/10.1016/j.foodcont.2015.09.026">https://doi.org/10.1016/j.foodcont.2015.09.026</a></p> <p> </p> <p><strong>Dataset column names</strong></p> <p>year - year notification was made</p> <p>product - categorization of the different products</p> <p>notification - categorization of the notifications</p> <p>notified - country that made the notification</p> <p>origin - country where the product originated from</p> <p>fraud - classification of fraud type</p>
OCDHAL : alertes sur les doublons titres et DOI, revues et congrès en texte intégral
<p>Suite des tutoriels OCDHAL rapports HCERES</p>
Earthquakes worldwide alert
<p>Dataset with data from Earthquake Hazard Program with a subset of the latest earthquakes registered since 1984 with a Magnitude of 2.5+</p>
Measurement of bacterial concentration with FLUIDION ALERT System - Milan site
<p>The dataset includes data collected during the monitoring campaign in Milan in 2019 using ALERT and in 2021 campaign, using ALERT V2, and laboratory determinations, performed in the laboratories of CAP. </p> <p><br> From September 2019 to January 2020 the ALERT LAB was tested, and its outcomes were compared with laboratory determinations. Bacteriological analyses of wastewater samples were performed by the Fluidion ALERT Lab device and in the microbiological laboratory of the CAP Group. The monitoring campaign was implemented in Line 2 of the WWTP, where three different sampling points were selected:<br> • IN-BIO: Before biological treatment, performed with BIOFOR system;<br> • IN-UV: Before the UV disinfection treatment;<br> • OUT-UV: After the UV disinfection treatment (i.e., the final wastewater effluent).</p> <p>A lower number of samples was analysed also from the Line 1 of the WWTP. Sampling points were:<br> • IN-OXI: Before biological oxidation;<br> • IN-PAA: Before the disinfection treatment with peracetic acid;<br> • OUT-PAA: After the disinfection treatment with peracetic acid, corresponding to WWTP effluent.</p> <p> </p> <p>The ALERT V2 SYSTEM has been installed in Peschiera Borromeo WWTP from July 2021 to September 2021, and its outcomes were compared with laboratory determinations.</p> <p> </p>
ALERT Doctoral School 2022: Data for photoelasticity lesson
<p>Data used for the two-hour photoelasticity lesson on September 29, 2022 at the 2022 ALERT Doctoral School in Aussois, France. </p> <p>In the Data directory, you will find the PEGS-master, PhotoelasticDisks, and Results subdirectories. You will also find the Jupyter notebook ALERTPhotoelasticity_220929_v1.ipynb.</p> <p>Photoelasticity data is in the PhotoelasticDisks subdirectory. N_Image and P_Image contain a sequence of images of 511 bidisperse birefringent disks in simple shear as viewed with unpolarized light and polarized light, respectively. The Positions subdirectory contains the position and radii of the disks in disks in each image. The G2images and radii_highlighted subdirectories contain, respectively: (1) images of each particle colored by G^2 as computed from the photoelasticity images via methods described in (Daniels, et al., Review of Scientific Instruments, 88, 051808 (2017)); (2) images of deformation of the particle with the outlines of each particle highlighted. Computations are performed in the accompanying ALERTPhotoelasticity_220929_v1.ipynb Jupyter notebook, which may be opened on any computer supporting jupyter notebooks or through Google Colab.</p> <p>Within PEGS-master, you can open PeGSDiskSolve.m to solve for inter-particle forces using methods described in Sec. V of (Daniels, et al., Review of Scientific Instruments, 88, 051808 (2017)) and in the thesis of James Puckett (thesis titled "State Variables in Granular Materials: an Investigation of Volume and Stress Fluctuations" and completed at North Carolina State University in 2012). You can also find a script titled "PlotExpVsSynth.m" that compares results from G^2 calculations; results are put into the Results subdirectory.</p> <p>Paths may need to be changed in all scripts.</p> <p>Related content from the doctoral school can be found here: https://github.com/alert-geomaterials/2022-doctoral-school. </p>
Data from "Relaxed alertness in novice and advanced meditators – A neurophysiological and psychological study of Isha Yoga practices"
<p>This dataset contains EEG power spectral data, statistical values and codes used for the findings of the paper titled:</p> <p>"Relaxed alertness in novice and advanced meditators – A neurophysiological and psychological study of Isha Yoga practices", accepted for publication in Mindfulness Journal.</p> <p>Authored by Saketh Malipeddi, Arun Sasidharan, Ravindra P.N., Seema Mehrotra, John P John and Bindu M. Kutty</p> <p>from Centre for Consciousness Studies, Department of Neurophysiology, NIMHANS, Bengaluru, India</p> <p> </p> <p> </p> <p><br> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.