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60 results for “behavioral measures”
A Case-Control Study to Measure Behavioral Risks of Malware Encounters in Organizations
<p>The behavior of enterprise users (e.g. browsing at night or visiting gambling sites) is a potential factor that might increase the chances of malware encounters (e.g. coinminers vs ransomware) on the field. This dataset report the aggregated results of a case-control study on telemetry data collected by Trend Micro, a global cybersecurity vendor, to identify users’ behavioral characteristics that can be used to differentiate cybersecurity risks profiles.</p>
Anabaena circadian clock behavior under nitrogen-poor conditions from single-cell measurements of fluorescence intensity
<p>Circadian clock arrays in multicellular filaments of the heterocyst-forming cyanobacterium Anabaena sp. strain PCC 7120 display remarkable spatio-temporal coherence under nitrogen-replete conditions. To shed light on the interplay between circadian clocks and the formation of developmental patterns, we followed the expression of a clock-controlled gene under nitrogen deprivation, at the level of individual cells. Our experiments showed that differentiation into heterocysts took place preferentially within a limited interval of the circadian clock cycle, that gene expression in different vegetative intervals along a developed filament was discoordinated, and that the circadian clock was active in individual heterocysts. Furthermore, Anabaena mutants lacking the kaiABC genes encoding the circadian clock core components produced heterocysts but failed in diazotrophy. Therefore, genes related to some aspect of nitrogen fixation, rather than early or mid-heterocyst differentiation genes, are likely affected by the absence of the clock. A bioinformatics analysis supports the notion that RpaA may play a role as master regulator of clock outputs in Anabaena, the temporal control of differentiation by the circadian clock and the involvement of the clock in proper diazotrophic growth. Together, these results suggest that under nitrogen-deficient conditions, the clock coherent unit in Anabaena is reduced from a full filament under nitrogen-rich conditions to the vegetative cell interval between heterocysts.</p>
Dataset for acceleration measurements at the research bridge openLAB in Bautzen, Germany - Change of dynamic behavior in the concrete hardening process
<p>This data set was collected during the construction phase of the openLAB in Bautzen, Germany during the period from 22.01.2024 - 30.04.2024. It includes acceleration measurements and temperature measurements that record the dynamic behavior of the bridge over a period of 49 days (from 22.01.2024 to 11.03.2024; the remaining data cannot be uploaded due to the Zenodo upload restriction, but can be released on request). The detailed documentation of the data set can be found in the file "Bartels, Dunkel, Marx_2024_Documentation.pdf". The documentation describes the structure, the applied monitoring system and the collected data in detail.</p>
Figure 1. CNN architecture (adopted from Krizhevsky et al. '12)-Measuring Customer Behavior with Deep Convolutional Neural Networks
<p>The architecture of a CNN can be described as following. A small pixel region goes to input neurons and then connects to a first convolution hidden layer (Figure1). There we can see a set of learnable filters, which are activated during the presentation some particular type of feature in pixel region in the input. On this phase, CNN does shift invariance, which is carried by feature map. Subsampling layer goes next. There we have two processes: local averaging and sampling. As a result, we get declining resolution of feature map. To correspond this task CNN needs supervised learning. Before starting the experiment, we gave a set of labeled videos with different emotional experience. The system analyses images and finds similar features. Then the system creates a map, where it arranges videos in accordance with similar features. Thereby, images with similar emotions form certain class. To test the system, we add other videos and correct the system when it refers them improperly. The proposed model consists of four convolutional layers, followed by max-pooling layers, and three fully-connected layers with a final classificatory presented with MLP (with six basic outputs, corresponding to basic emotions for emotion classification and two outputs for motion classification for typical and non-typical behavior). The input data was presented as infrared camera output.</p>
Auditory stream segregation and selective attention for cochlear implant listeners: Evidence from behavioral measures and event-related potentials
<p>Data set generated for the study "Auditory stream segregation and selective attention for cochlear implant listeners: Evidence from behavioral measures and event-related potentials" </p> <ol> <li><strong>behavioral.txt</strong>: d' scores obtained by the listeners on the deviant detection task. <ul> <li>subject: listener ID</li> <li>distractor: Electrode separation condition</li> <li>deviant: Deviant triplet</li> <li>d: d' scores</li> <li>exp: experimental session (BEH / ERP)</li> </ul> </li> <li><strong>ERP_by_condition.txt</strong>: <ul> <li>Subject: listener ID</li> <li>Type: Sound type (Target / Distractor)</li> <li>Dev: Deviant condition. Early = deviant triplets 1 or 2. Late = deviant triplet 3 or <em>none.</em></li> <li>rep: Triplet number</li> <li>sound: sound number within the triplet</li> <li>amplitude: amplitude difference between the active and the passive listening conditions.</li> </ul> </li> </ol> <p> </p>
Associations between Perceived Cancer Impact and Measures of Health Behavior in Survivors of Childhood Cancer
<p>This dataset corresponds to a manuscript titled "Associations between Perceived Cancer Impact and Measures of Health Behavior in Survivors of Childhood Cancer" which can be found in the Journal of Cancer Survivorship. https://doi.org/10.1007/s11764-024-01667-3</p>
Dataset for the collected responses for the items measuring constructs affecting eHS non-acceptance behavior in Nigeria
<p>This dataset is a collection of responses from the questionnaire distributed to study the e-health service non-acceptance behavior prominent in Nigeria. A total of 543 valid responses were collected. This research used an integration model based on TPB and SOR theory The dataset were analysed using PLS-SEM. Refer to the article for the results of this study.<br><br></p> <p><strong>Note:</strong> CO = Communication overload; CHO = Choice overload; PR = Perceived oisk; HL = Health literacy; NA = Negative attitude; SN = Subjective norms; PBC = Perceived behavioral control; INTU = Intention not to use eHS; NAB = Non-acceptance behavior</p>
Anabaena circadian clock behavior under nitrogen-poor conditions from single-cell measurements of fluorescence intensity
Open the record for dataset details and reuse information.
Data S1. Laboratory behavioral data from: Measuring the fitness advantage conferred by autotomy in the wild
<p>Data S1. Laboratory behavioral data. The composition is summarized in figure S1.</p> <p>Autotomy, the self-amputation of body parts, serves as an anti-predator defense in many taxonomic groups of animals. However, its adaptive value has seldom been quantified. Here, we propose a novel modeling approach for measuring the fitness advantage conferred by the capability for autotomy in the wild. Using a predator-prey system where a land snail autotomizes and regenerates its foot specifically in response to snake bites, we conducted a laboratory behavioral experiment and a 3-year multi-event capture–mark–recapture (CMR) study. Combining these empirical data, we developed a hierarchical model and estimated the basic life history parameters of the snail. Using samples from the posterior distribution, we constructed the snail's life table as well as that of a snail variant incapable of foot autotomy. As a result of our analyses, we estimated the monthly encounter rate with snake predators at 3.3% (95% CI: 1.6–4.9%), the contribution of snake predation to total mortality until maturity at 43.3% (15.0–95.3%), and the fitness advantage conferred by foot autotomy at 6.5% (2.7–11.5%). This study demonstrated the utility of the multi-method hierarchical modeling approach for the quantitative understanding of the ecological and evolutionary processes of anti-predator defenses in the wild.</p>
A Repeated Measures Dataset on Public Responses to the COVID-19 Pandemic: Social Norms, Attitudes, Behaviors, Conspiracy Thinking, and (Mis)Information
<p>We present a quantitative repeated measures dataset from intensive longitudinal research (diary survey) conducted with participants drawn from a nationally representative sample of the German population. This diary survey addresses a wide range of variables relevant to the COVID-19 pandemic, including information seeking, attitudes towards science, public health, and related behaviors. Respondents were asked about their thoughts, challenges, concerns, and experiences related to the pandemic. Survey responses were requested every two weeks from November 2020 and September 2021 for a total of 18 stages of data collection. The diary survey responses were all matched to the same individual. Using the unique identifier for each individual, the diary survey dataset can be analyzed in concert with a larger representative survey sample that was conducted three times within the same research project over this time period. The diary dataset we present here includes socio-demographic information and other potentially relevant variables, ready for secondary analysis.<br> <br> Initial sample frame: <em>N </em>= 1,480</p> <p>Initial diary survey sub-sample: <em>N </em>= 133</p>
Trial of Methyl B12 on Behavioral and Metabolic Measures in Children With Autism
ClinicalTrials.gov study NCT01039792. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Measuring Smoking Behaviors While Using Varenicline
ClinicalTrials.gov study NCT00948155. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Measuring Ambulation, Motor, and Behavioral Outcomes With Post-Stroke Fluoxetine in Tanzania
ClinicalTrials.gov study NCT03728153. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Measuring behavior patterns and evaluating time sampling methodology to characterize brush use in weaned beef cattle
Open the record for dataset details and reuse information.
Data S1. Laboratory behavioral data from: Measuring the fitness advantage conferred by autotomy in the wild
Open the record for dataset details and reuse information.
Predicting disease in transition dairy cattle based on behaviors measured before calving
<p>Data set and code for the article "Predicting disease in transition dairy cattle based on behaviors measured before calving"</p>
Targeted micro-fiber arrays for measuring and manipulating localized multi-scale neural dynamics over large, deep brain volumes during behavior: resources and main figure data
<p><strong>Summary:</strong></p> <p>Neural population dynamics relevant for behavior vary over multiple spatial and temporal scales across 3-dimensional volumes. Current optical approaches lack the spatial coverage and resolution necessary to measure and manipulate naturally occurring patterns of large-scale, distributed dynamics within and across deep brain regions such as the striatum. We designed a new micro-fiber array approach capable of chronically measuring and optogenetically manipulating local dynamics across over 100 targeted locations simultaneously in head-fixed and freely moving mice. We developed a semi-automated micro-CT based strategy to precisely localize positions of each optical fiber. This highly-customizable approach enables investigation of multi-scale spatial and temporal patterns of cell-type and neurotransmitter specific signals over arbitrary 3-D volumes at a spatial resolution and coverage previously inaccessible. We applied this method to resolve rapid dopamine release dynamics across the striatum volume which revealed distinct, modality specific spatiotemporal patterns in response to salient sensory stimuli extending over millimeters of tissue. Targeted optogenetics through our fiber arrays enabled flexible control of neural signaling on multiple spatial scales, better matching endogenous signaling patterns, and spatial localization of behavioral function across large circuits. </p> <p><strong>Files included: </strong></p> <ul> <li>Vu, M-A. et al. (2023) - Key Resources Table.xlsx -- key resources used in this study</li> <li>README_figure_data_and_analysis.txt --a description of the organization of the files within this repository</li> <li>README_preprocessing_code.txt -- details about the preprocessing pipeline (including registration and localization pipelines) and annotation of the preprocessed data structure</li> <li>preprocessing_code.zip -- contains all the preprocessing code</li> <li>Fig01.zip -- preprocessed data and analysis code for main Figure 1</li> <li>Fig03.zip -- preprocessed data and analysis code for main Figure 3</li> <li>Fig04.zip -- preprocessed data and analysis code for main Figure 4</li> <li>Fig05.zip -- preprocessed data and analysis code for main Figure 5</li> <li>Fig06.zip -- preprocessed data and analysis code for main Figure 6</li> <li>Fig07.zip -- preprocessed data and analysis code for main Figure 7</li> <li>SuppFig01.zip -- preprocessed data and analysis code for supplemental Figure 1</li> <li>SuppFig02.zip -- preprocessed data and analysis code for supplemental Figure 2</li> <li>SuppFig03.zip -- preprocessed data and analysis code for supplemental Figure 3</li> <li>SuppFig04.zip -- preprocessed data and analysis code for supplemental Figure 4</li> <li>SuppFig05.zip -- preprocessed data and analysis code for supplemental Figure 5</li> <li>SuppFig06.zip -- preprocessed data and analysis code for supplemental Figure 6</li> <li>SuppFig07.zip -- preprocessed data and analysis code for supplemental Figure 7</li> </ul> <p>Each .zip file contains a folder corresponding to a figure. Within each figure folder will be a folder corresponding to the letter (e.g., A, B, C) of the panel within the figure. In addition to the panel folders, when applicable, there may also be a folder containing general-use functions or scripts, or interim results .mat files, that are called by the scripts within the subfolders. In each subfolder there will be 3 things:</p> <ol> <li>a folder of preprocessed data (see README_preprocessing_code.txt)</li> <li>a folder of the analysis scripts that analyzed the preprocessed data and generated the figure</li> <li>a README text file detailing the contents of both folders and the analysis workflow.</li> </ol> <p> </p>
Data from: Behavioral and neural measures of confidence using a novel auditory pitch identification task
<p>Observers can discriminate between correct versus incorrect perceptual decisions with feelings of confidence. The centro-parietal positivity build-up rate (CPP slope) has been suggested as a likely neural signature of accumulated evidence, which may guide both perceptual performance and confidence. However, CPP slope also covaries with reaction time, which also covaries with confidence in previous studies, and performance and confidence typically covary; thus, CPP slope may index signatures of perceptual performance rather than confidence per se. Moreover, perceptual metacognition – including neural correlates – has largely been studied in vision, with few exceptions. Thus, we lack understanding of domain-general neural signatures of perceptual metacognition outside vision. Here we designed a novel auditory pitch identification task and collected behavior with simultaneous 32-channel EEG in healthy adults. Participants saw two tone labels which varied in tonal distance on each trial (e.g., C vs D, C vs F), then heard a single auditory tone; they identified which label was correct and rated confidence. We found that pitch identification confidence varied with tonal distance, but performance, metacognitive sensitivity (trial-by-trial covariation of confidence with accuracy), and reaction time did not. Interestingly, however, while CPP slope covaried with performance and reaction time, it did not significantly covary with confidence. We interpret these results to mean that CPP slope is likely a signature of first-order perceptual processing and not confidence-specific signals or computations in auditory tasks. Our novel pitch identification task offers a valuable method to examine the neural correlates of auditory and domain-general perceptual confidence.</p>
The OpenFeeder: a flexible automated RFID feeder to measure inter and intraspecies differences in cognitive and behavioral performance in wild birds
<p>Understanding the ecology and evolution of personality and cognition requires the development of new tools to measure individual and species differences in behavioral and cognitive performances in wild populations. Furthermore, such tools should facilitate collection of large sample sizes, evaluate the repeatability of measured traits and allow direct comparison of species performances across a variety of behavioural tasks. Here we present a RFID-based feeder (OpenFeeder) designed to run visual cognitive tasks in wild animals. We illustrate the flexibility of the tool showing performances of three wild passerine species (<em>Parus major</em>, <em>Cyanistes caeruleus</em> and <em>Poecile palustris</em>) in an associative learning task. We recorded performances of a large number of individuals (>300) in the wild and showed both inter and intraspecific differences in associative learning. We also found moderate to high repeatability in individual differences in associative learning in each species. We show that the OpenFeeder is a flexible tool to record performance in multiple cognitive and behavioural tasks in free ranging animals across a variety of passerine species. The design, firmware, and software are open source to facilitate use in a wide variety of species and thus allow continuous improvement of the system and development of new behavioural and cognitive tasks. In doing so, we hope that this tool will be used by a large community of cognitive ecologists and comparative psychologists for both within and across species studies. Furthermore, our system should facilitate replication of results across populations along large-scale environmental gradients to improve our understanding of the role ecology plays in the evolution of cognitive traits.</p>
Raw Data from - Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures
<p><em><strong>Folder and Data Description for dataset of:</strong></em></p> <p><strong>Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures</strong></p> <p>Ghada BinKhamis, Antonio Elia Forte, Tobias Reichenbach, Martin O’Driscoll, and Karolina Kluk</p> <p><strong>Please site the paper when using this dataset</strong> (DOI: 10.1177/2331216519848297)</p> <p> </p> <p><strong>Shared dataset is as follows:</strong></p> <ul> <li><strong>Behavioral data is in the excel spread sheet entitled:</strong> “BinKhamis_et_al_behavioral_data .xlsx”<br> </li> <li><strong>Speech-ABRs (raw EEG (speech-ABR) data) are contained within the five 'zip' folders.</strong></li> </ul> <p><strong>Description of the “Speech-ABRs” folders, subfolders, and raw EEG files:</strong></p> <p><strong>“Speech-ABRs” Folder Information:</strong></p> <ul> <li><strong>Each Speech_ABR folder</strong> contains subfolders from a subset of participants (e.g. Speech_ABR_1_20.zip contains data from participant number 1 to participant number 20)</li> <li><strong>Subfolders:</strong> <ul> <li>Each subfolder starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, …, HA98</li> <li>Next is the background condition: noise or quiet</li> <li>Next is whether recordings were: aided or unaided</li> </ul> </li> <li><strong>Example subfolder names:</strong> <ul> <li><strong><em>HA1 noise aided:</em></strong> participant number 1, aided speech-ABRs in background noise</li> <li><strong><em>HA4 noise unaided:</em></strong> participant number 4, unaided speech-ABRs in background noise</li> <li><strong><em>HA55 quiet aided:</em></strong> participant number 55, aided speech-ABRs in quiet</li> <li><strong><em>HA97 quiet unaided</em></strong>: participant number 97, unaided speech-ABRs in quiet</li> </ul> </li> <li>Each participant has 4 subfolders for the four recording conditions (aided quiet, aided noise, unaided quiet, unaided noise) <ul> <li><strong>Each subfolder contains four ‘.mat’ files, ‘.mat’ file names:</strong> <ul> <li>Each ‘.mat’ file starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, …, HA98</li> <li>Next is the stimulus: 40 da</li> <li>Next is ‘unaided’ only if recordings were without HA</li> <li>Next is ‘noise’ only if the background condition was noise</li> <li>Next is the stimulus polarity: <ul> <li>‘Pos’ for positive/standard</li> <li>‘Neg’ for negative (reversed polarity stimulus)</li> </ul> </li> <li>And finally the test ear and recording number for that polarity <ul> <li>R1 is the first recording from the right ear, R2 is the second recording from the right ear</li> <li>L1 is the first recording from the left ear, L2 is the second recording from the left ear</li> </ul> </li> <li><strong>Example ‘.mat’ file name:</strong> <ul> <li><strong><em>HA1 40 da Neg Noise R1.mat: </em></strong>participant number 1, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, in background noise, right ear recording number 1.</li> <li><strong><em>HA4 40 da unaided Pos Noise L2.mat:</em></strong> participant number 1, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, left ear recording number 2.</li> <li><strong><em>HA7 40 da Neg R2.mat:</em></strong> participant number 7, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, right ear recording number 2.</li> <li><strong><em>HA10 40 da unaided Pos Noise L1.mat:</em></strong> participant number 10, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, in background noise, left ear recording number 1.</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p><strong>File Information:</strong></p> <p><strong>Description of ‘.mat’ files that can be accessed and processed using MATLAB (MathWorks):</strong></p> <p>Each ‘.mat’ file is a structure that contains the following fields:</p> <ul> <li>The first nine fields are informational, for example:</li> <li><strong><em>xunits</em></strong>: ‘s’ indicates that the recording time window is in seconds, conversion to milliseconds would be required to plot the data in milliseconds</li> <li><strong><em>start: </em></strong>‘0’ indicates that both stimulus and recording start at 0 seconds</li> <li><strong><em>points:</em></strong> <strong>2200</strong> is the number of sample points</li> <li><strong><em>chans:</em></strong> 2 is the number of channels <ul> <li><em>Right ear:</em> channel 2, <em>Left ear:</em> channel 1</li> </ul> </li> <li><strong><em>frames:</em></strong> 2500 is the number of epochs</li> <li>The last filed <strong>‘values’</strong> is what contains the raw EEG data (2200x2x2500) <ul> <li><strong>2200 </strong>is the number of samples</li> <li><strong>2 </strong>is the number of channels (channel one is recorded from the left ear lobe (A1) and channel two is from the right ear lobe (A2))</li> <li><strong>2500 </strong>is the number of epochs <ul> <li>Stimulus starts at 0 seconds per epoch, pre-stimulus baseline may be extracted from the end of each epoch (i.e. before the next stimulus).</li> <li>Data are in Volts; conversion to <strong>μVolts </strong>(multiply by 1000) is required.</li> </ul> </li> </ul> </li> </ul> <p><strong>Date of data collection: </strong>October 2017 to July 2018</p>
ScienceDex guides
Understand access before you commit
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.