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18,960 results for “Healthy”
A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals (16S rRNA gene sequencing data)
<p>Microbiome data accompanying manuscript "A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals". Data is available for alpha- and beta- diversity, as well as for individual taxa both in binary and quantitative phenotypic representation. Data is available for 827 individuals that gave consent for their data to be shared outside of the Milieu intérieur consortium. </p>
A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals (Metadata)
<p>Associated demographic, lifestyle, environmental and biochemical metadata accompanying manuscript "A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals". Data is available for 827 individuals that gave consent for their data to be shared outside of the Milieu intérieur consortium. </p>
Mobile Device Voice Recordings at King's College London (MDVR-KCL) from both early and advanced Parkinson's disease patients and healthy controls
<p><strong>Dataset description</strong></p> <p>The dataset description will start with describing the local conditions and other metadata, then will continue with describing the recording procedure and annotation methodology. Finally, a brief description of the dataset deployment and publication will be given.</p> <p><strong>Meta Information</strong></p> <p>The dataset was recorded at King's College London (KCL) Hospital, Denmark Hill, Brixton, London SE5 9RS in the period from 26 to 29 September 2017. We used a typical examination room with about ten square meters area and a typical reverberation tome of approx. 500ms to perform the voice recordings. Due to the fact, that the voice recordings are performed in the realistic situation of doing a phone call (i.e. participant holds the phone to the preferred ear and microphone is in direct proximity to the mouth), one can assume that all recordings were performed within the reverberation radius and thus can be considered as “clean”.</p> <p><strong>Recording Procedure</strong></p> <p>We used a Motorola Moto G4 Smartphone as recording device. To perform the voice recordings on the device, we developed a “Toggle Recording App”, which uses the same functionalities as the voice recording module used within the i-PROGNOSIS Smartphone application, but deployed as a standalone android application. This means, that the voice capturing service runs as a standalone background service on the recording device and triggers voice recordings via on- and off-hook signals of the Smartphone. Due to the fact, that we directly record the microphone signal, and not the GSM (“Global System for Mobile Communications”) compressed stream, we end up with high quality recordings with a sample rate of 44.1 kHz and a bit depth of 16 Bit (audio CD quality). The raw, uncompressed data is directly written to the external storage of the Smartphone (SD-card) using the well-known WAVE file format (.wav). We used the following workflow to perform a voice recording:</p> <ul> <li>Ask the participant to relax a bit and then to make a phone call to the test executor (off-hook signal triggered).}</li> <li>Ask the participant to read out “The North Wind and the Sun”</li> <li>Depending on the constitution of the participant either ask to read out “Tech. Engin. Computer applications in geography snippet”</li> <li>Start a spontaneous dialog with the participant, the test executor starts asking random questions about places of interest, local traffic, or personal interests if acceptable.</li> <li>Test executor ends call by farewell (on-hook signal triggered).</li> </ul> <p><strong>Annotation Scheme</strong></p> <p>For each HC and PD participant, we labeled the data regarding scores on the Hoehn & Yahr (H&Y), as well as the UPDRS II part 5 and UPDRS III part 18 scale. The voice recordings are labeled in the following scheme:</p> <p>SI_ HS_ HYR_ UPDRS II-5_UPDRS III-18</p> <p>with</p> <ul> <li>SI as subject identification in the form ID<em>NN</em>, <em>N</em> in [0, 9]</li> <li>HS as the health status label (hc or pd accordingly)</li> <li>HYR as the expert assessed H&Y scale rating</li> <li>UPDRS II-5 as the according expert peer-reviewed score</li> <li>UPDRS III-18 as the according expert assessed score</li> </ul> <p>For example, an audio recording with the file name “ID02_pd_1_2_1.wav” represents a recording of the third participant (First participant was anonymized as ID00), which has PD and a H&Y rating of 1, a UPDRS II-5 score of 2 and a UPDRS III-18 score of 1. At this point, it should be noted, that also all healthy controls were evaluated with regard to the introduced scales, because Parkinson's disease and voice degradation correlate, but don't match exactly. This means, that the data set includes one HC participant (ID31) with UPDRS II-5 and III-18 rating of 1, and also includes PD patients with UPDRS II-5 and III-18 ratings of 0. It should be emphasized, that this does not mean the data set includes ambiguous information, but that an expert was not able to hear voice degradation that would end up in a UPDRS rating greater than zero. Machine learning approaches may be able to nevertheless classify correctly, or at least learn to correlate, but not match PD and voice degradation at any time.</p> <p><strong>Appendix</strong></p> <p>North Wind and the Sun (Orthographic Version):</p> <p>“The North Wind and the Sun were disputing which was the stronger, when a traveler came along wrapped in a warm cloak. They agreed that the one who first succeeded in making the traveler take his cloak off should be considered stronger than the other. Then the North Wind blew as hard as he could, but the more he blew the more closely did the traveler fold his cloak around him; and at last the North Wind gave up the attempt. Then the Sun shone out warmly, and immediately the traveler took off his cloak. And so the North Wind was obliged to confess that the Sun was the stronger of the two.”</p> <p>BNC – Tech. Engin. Computer applications in geography snippet:</p> <p>“[...] This is because there is less scattering of blue light as the atmospheric path length and consequently the degree of scattering of the incoming radiation is reduced. For the same reason, the sun appears to be whiter and less orange-coloured as the observer's altitude increases; this is because a greater proportion of the sunlight comes directly to the observer's eye. Figure 5.7 is a schematic representation of the path of electromagnetic energy in the visible spectrum as it travels from the sun to the Earth and back again towards a sensor mounted on an orbiting satellite. The paths of waves representing energy prone to scattering (that is, the shorter wavelengths) as it travels from sun to Earth are shown. To the sensor it appears that all the energy has been reflected from point P on the ground whereas, in fact, it has not, because some has been scattered within the atmosphere and has never reached the ground at all. [...]”</p>
Figure 2 in Orientation of Hippodamia variegata (Coleoptera: Coccinellidae) to healthy and Beauveria bassiana-infected Aphis fabae (Hemiptera: Aphididae) in an olfactometer system
Figure 2. Response of Hippodamia variegata to broad bean plants infested by A. fabae infected by Beauveria bassiana at the interval of 0 h after infection versus broad bean plants infested by A. fabae infected by B. bassiana at intervals of 24 (a), 48 (b), and 72 (c) h after infection. The white bars indicate the numbers of insects choosing broad bean plants infested by A. fabae infected by B. bassiana at intervals of 24, 48, and 72 h after infection, whereas the black bars indicate the numbers of insects that chose broad bean plants infested by A. fabae infected by B. bassiana at the interval of 0 h after infection.
Figure 4 in Orientation of Hippodamia variegata (Coleoptera: Coccinellidae) to healthy and Beauveria bassiana-infected Aphis fabae (Hemiptera: Aphididae) in an olfactometer system
Figure 4. Response of Hippodamia variegata to broad bean plants infested by A. fabae infected by Beauveria bassiana at the interval of 48 h after infection versus broad bean plants infested by A. fabae infected by B. bassiana at the interval of 72 h after infection. The white bars indicate the numbers of insects choosing broad bean plants infested by A. fabae infected by B. bassiana at the interval of 72 h after infection, whereas the black bars indicate the numbers of insects that chose broad bean plants infested by A. fabae infected by B. bassiana at the interval of 48 h after infection.
Fig. 4 in Giardia duodenalis in a clinically healthy population of captive zoo chimpanzees: Rapid antigen testing, diagnostic real-time PCR and faecal microbiota profiling
Fig. 4. | Faecal bacterial community profile of captive chimpanzees infected with Giardia duodenalis detected by rapid antigen test. (A) Relative abundance of colour coded bacterial phyla separated based on presence (+) or absence (‒) of Giardia using rapid antigen test (RAT). The sample identity is located at the bottom of the graph with two labels (C20, C3) shaded indicating samples that were found as Giardia positive by real-time PCR. (B) Alpha diversity based on observed OTU and Shannon's index plotted as box plot and evaluated using t-tests. (C) Principal coordinates analysis (PCoA) 2D plot using first two principal components from Bray-Curtis dissimilarity matrix at the genus taxonomic levels. The clustering between Giardia positive (RAT+) and negative (RAT-) samples was tested using ANOSIM. (D) Linear discriminant analysis effect size (LEfSe) used plot of significant factors discriminating G. duodenalis positive from negative sample. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3 in Giardia duodenalis in a clinically healthy population of captive zoo chimpanzees: Rapid antigen testing, diagnostic real-time PCR and faecal microbiota profiling
Fig. 3. | Faecal bacterial community profile of captive chimpanzees infected with Giardia duodenalis as detected by rapid antigen test and real-time PCR combined. (A) Relative abundance of colour coded bacterial phyla separated based on presence (+) or absence (‒) of Giardia. The sample identity is located at the bottom of the graph. (B) Alpha diversity based on observed OTU and Shannon's index plotted as box plot and evaluated using t-tests. (C) Principal coordinates analysis (PCoA) 2D plot using first two principal components from Bray-Curtis dissimilarity matrix at the genus taxonomic levels. The clustering between Giardia positive (+) and negative (‒) samples was tested using ANOSIM. (D) Linear discriminant analysis effect size (LEfSe) used plot of significant factors discriminating G. duodenalis positive from negative sample. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Giardia duodenalis in a clinically healthy population of captive zoo chimpanzees: Rapid antigen testing, diagnostic real-time PCR and faecal microbiota profiling
Fig. 2. | Results of Giardia duodenalis rapid antigen test applied on faecal samples from chimpanzees. A positive result for the Giardia duodenalis rapid antigen test (RAT, Anigen Rapid Giardia AG Test Kit) is represented by the line in the 'T' position in the window along with the positive control line in the 'C' position.
Fig. 1 in Giardia duodenalis in a clinically healthy population of captive zoo chimpanzees: Rapid antigen testing, diagnostic real-time PCR and faecal microbiota profiling
Fig. 1. Captive chimpanzees and their enclosure in Sydney, Australia. (A) Main chimpanzee open air exhibit with multiple climbing structures. (B) View from the other direction showing entry to the indoor area at the end of the exhibit. (C) smaller exhibit with mesh covering and more climbing and sleeping structures. (D) Members of the chimpanzee troop at the Taronga Zoo.
Resources for "BMF CP 96: The roles of healthy eating information seeking ability and e-health literacy in healthy eating"
<p><span>The current study is conducted to examine the following research questions:</span></p> <ul> <li><span>How are healthy eating information-seeking ability and e-health literacy associated with healthy eating intention?</span></li> </ul>
Data from: Age estimation using methylation-sensitive high-resolution melting (MS-HRM) in both healthy felines and those with chronic kidney disease
<p>Age is an important ecological tool in wildlife conservation. However, it is difficult to estimate in most animals, including felines — most of whom are endangered. Here, we developed the first DNA methylation-based age-estimation technique — as an alternative to current age-estimation methods — for two feline species that share a relatively long genetic distance with each other: domestic cat (<i>Felis catus</i>; 79 blood samples) and an endangered <i>Panthera</i>, the snow leopard (<i>Panthera uncia</i>; 11 blood samples). We measured the methylation rates of two gene regions <span>using </span>methylation-sensitive high-resolution melting (MS-HRM). Domestic cat age was estimated with a mean absolute deviation (MAD) of 3.83 years. Health conditions influenced accuracy of the model. Specifically, the models built on cats with chronic kidney disease (CKD) had lower accuracy than those built on healthy cats. The snow leopard-specific model (i.e. the model that resets the model settings for snow leopards) had a better accuracy (MAD = 2.10 years) than that obtained on using the domestic cat model directly. This implies that our markers could be utilised across species, although changing the model settings when targeting different species could lead to better estimation accuracy. The snow leopard-specific model also successfully distinguished between sexually immature and mature individuals.</p>
Simultaneous dynamic glucose-enhanced (DGE) MRI and fiber photometry measurements of glucose in the healthy mouse brain
<p>This dataset was acquired for the DGE and fiber photometry study published in NeuroImage ( <a href="https://doi.org/10.1016/j.neuroimage.2022.119762">https://doi.org/10.1016/j.neuroimage.2022.119762</a>).<br> Comprises of three datasets: DGE MRI, fiber photometry and two-photon microscopy.</p>
MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs obtained through electrophysiological simulations
<p>Mechanistic cardiac electrophysiology models allow for personalized simulations of the electrical activity in the heart and the ensuing electrocardiogram (ECG) on the body surface. As such, synthetic signals possess precisely known ground truth labels of the underlying disease (model parameterization) and can be employed for validation of machine learning ECG analysis tools in addition to clinical signals. Recently, synthetic ECG signals were used to enrich sparse clinical data for machine learning or even replace them completely during training leading to good performance on real-world clinical test data.<br> <br> We thus generated a large synthetic database comprising a total of 16,900 12 lead ECGs based on multi-scale electrophysiological simulations equally distributed into 1 normal healthy control and 7 pathology classes. The pathological case of myocardial infraction had 6 sub-classes. A comparison of extracted timing and amplitude features between the virtual cohort and a large publicly available clinical ECG database demonstrated that the synthetic signals represent clinical ECGs for healthy and pathological subpopulations with high fidelity. The novel dataset of simulated ECG signals is split into training, validation and test data folds for development of novel machine learning algorithms and their objective assessment. </p> <p>This folder WP2_largeDataset_Noise contains the 12 lead ECGs of 10 seconds length. Each ECG is stored in a separate CSV file with one row per lead (lead order: I, II, III, aVR, aVL, aVF, V1-V6) and one sample per column (sampling rate: 500Hz). Data are split by pathologies (avblock = AV block, lbbb = left bundle branch block, rbbb = right bundle branch block, sinus = normal sinus rhythm, lae = left atrial enlargement, fam = fibrotic atrial cardiomyopathy, iab = interatrial conduction block, mi = myocardial infarction). MI data are further split into subclasses depending on the occlusion site (LAD, LCX, RCA) and transmurality (0.3 or 1.0). Each pathology subclass contains training, validation and testing data (~ 70/15/15 split). Training, validation and testing datasets were defined according to the model with which QRST complexes were simulated, i.e., ECGs calculated with the same anatomical model but different electrophysiological parameters are only present in one of the test, validation and training datasets but never in multiple. Each subfolder also contains a "siginfo.csv" file specifying the respective simulation run for the P wave and the QRST segment that was used to synthesize the 10 second ECG segment. Each signal is available in three variations:</p> <ul> <li>*_raw.csv contains the synthesized ECG without added noise and without filtering</li> <li>*_noise.csv contains the synthesized ECG (unfiltered) with superimposed noise</li> <li>*_filtered.csv contains the filtered synthesized ECG (fiter settings: highpass cutoff frequency 0.5Hz, lowpass cutoff frequency 150Hz, butterworth filters of order 3).</li> </ul> <p>The folder WP2_largeDataset_ParameterFiles contains the parameter files used to simulate the 12 lead ECGs. Parameters are split for atrial and ventricular simulations, which were run independently from one another. <br> See <a href="https://doi.org/10.48550/arXiv.2211.15997">Gillette*, Gsell*, Nagel* et al. "MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs obtained through electrophysiological simulations"</a> for a description of the model parameters.</p>
Raw Data for Unpublished Study "Healthy Women Show More Experimentally-Induced Central Sensitization Compared to Men"
<p>Version sex_v01_20230409</p> <p>Main authors: Alexandros Guekos, Janis Saxer, Diego Salinas Gallegos, and Petra Schweinhardt</p> <p>The present data was collected from April 2021 to December 2022 for a study on sex-specific differences in central sensitization (CS) proxies in healthy young adults.</p> <p>The results are to be published soon. A link to the manuscript will be provided.</p> <p>Full details of the paradigm, the experimental setup, and the analysis will be given there. Therefore, the following description only summarizes the most important steps.</p> <p>In brief, 66 subjects (33 men and 33 women) participated in a single experimental session during which a CS induction protocol consisting of repetitive noxious heat stimuli over about 12 minutes of 10 blocks at 48 degrees centigrade was applied to the lateral dorsum of the foot proximal to the malleolus. A sham protocol (where temperature remained at baseline for the whole time) was performed on the contralateral foot. Order of protocol and choice of starting side was pseudorandomly varied across participants. Participants had to rate mean temperature intensity per block on a scale from 0 (no sensation) to 200 (maximum tolerable pain) with 100 corresponding to the individual heat pain threshold (HPT).</p> <p>The CS induction protocol has previously been published (Jürgens, Tim P., et al. "An improved model of heat-induced hyperalgesia?repetitive phasic heat pain causing primary hyperalgesia to heat and secondary hyperalgesia to pinprick and light touch." PLoS One 9.6 (2014): e99507, https://doi.org/10.1371/journal.pone.0099507) and validated (Scheuren, Paulina S., et al. "Pain?autonomic interaction: A surrogate marker of central sensitization." European journal of pain 24.10 (2020): 2015-2026, https://doi.org/10.1002/ejp.1645). Details on this CS induction protocol can be found in the two aforementioned publications.</p> <p>In the present paradigm, two measures of CS proxies were collected before and about 20 minutes after CS induction.</p> <p>On the one hand, mechanical sensitivity testing (by von 256 mN Frey filament and by soft brush of about 300 mN) was performed on the foot dorsum along five radial lines of 5 cm length (separated at 45 degrees) in the skin area adjacent to the primary area of painful stimulation. For the von Frey filament, testing was done every 5 mm along each line, always starting from the outermost point moving inwards (10 points in total). For the soft brush, strokes of 10mm were applied (5 strokes in total). Participants had to report a definite change in sensation w.r.t. to the outermost point.</p> <p>On the other hand, the nociceptive withdrawal (NWR) reflex was elicited at the retromalleolar pathway of the sural nerve and surface electromyography (sEMG) was recorded from the biceps femoris (BF), rectus femoris (RF), and tibialis anterior (TA) muscles.</p> <p>For NWR elicitation, the reflex threshold was first determined at the BF. For threshold determination, a single ascending staircase with either single electrical stimulations or triplets (at 2Hz) were used. From the triplets, only the muscle response to the third stimulation was analysed. The higher of the two obtained currents was then used as the NWR threshold.</p> <p>Then 15 stimulations at 120% threshold current and 15 at 140% were applied. The order of stimulations was random with the restriction that not more than five identical stimulations in a row were allowed. The interstimulus interval was randomly varied between 5 and 15 s.</p> <p>Every electrical stimulation consisted of a train of five rectangular stimuli of 1 ms duration delivered at 200 Hz. Muscle responses were recorded from 120 pre- to 380 ms post-stimulation. The recorded sEMG signals were sampled at 48 kHz and downsampled to 6 kHz, rectified, band-pass filtered from 10 Hz to 500 Hz and amplified up to 125 times. Between 120 ms pre- and 380 ms post-stimulation, traces for all applied stimulations were automatically saved into separate txt files.</p> <p>Please consult the README.txt file for details on the structure of the uploaded data and for information w.r.t. potential instances of incompleteness or unusability.</p> <p>The study was funded by the Swiss National Science Foundation as part of a grant to PS (grant number 320030_179191/1).</p> <p>The study was registered at clinicaltrials.gov (NCT05031286).</p>
Data for: A modified Michaelis-Menten equation estimates growth from birth to 3 years in healthy babies in the US
<p class="MsoNormal"><strong><span>Background</span></strong><span>: Standard pediatric growth curves cannot be used to impute missing height or weight measurements in individual children</span><span>. The Michaelis-Menten equation, <span>used for characterizing substrate-enzyme saturation curves, has been shown to model growth in many organisms including nonhuman vertebrates. </span>We investigated whether this equation <span>could be used</span> to interpolate missing growth data in children in the first three years of life.</span></p> <p class="MsoNormal"><strong><span>Methods</span></strong><span>: We developed a modified Michaelis-Menten equation and compared expected to actual growth, first in a local birth cohort (N=97) then in a large, outpatient, pediatric sample (N=14,695). </span></p> <p class="MsoNormal"><strong><span>Results</span></strong><span>: The modified Michaelis-Menten equation showed excellent fit for both infant weight (median RMSE: boys: 0.22kg [IQR:0.19; 90%<0.43]; girls: 0.20kg [IQR:0.17; 90%<0.39]) and height (median RMSE: boys: 0.93cm [IQR:0.53; 90%<1.0]; girls: 0.91cm [IQR:0.50;90%<1.0]). Growth data were modeled accurately with as few as four values from routine well-baby visits in year 1 and seven values in years 1-3; birth weight or length was essential for best fit. </span></p> <p class="MsoNormal"><strong><span>Conclusions</span></strong><span>: A modified Michaelis-Menten equation accurately describes growth in healthy babies aged 0-36 months, allowing interpolation of missing weight and height values in individual longitudinal measurement series. The growth pattern in healthy babies in resource-rich environments mirrors an enzymatic saturation curve.</span></p>
Reaching and grasping objects in depth for people with stereovision deficits and healthy controls.
<p>Decades of research into hand-object interaction and manipulation skills has yielded fundamental insights with applications in robotics and motor learning. Nevertheless, integrating visual function (especially binocular function, important to perceive depth) into this equation is crucial, forming a triangle between vision, reaching, and object manipulation.</p> <p> </p> <p>The ReGraD dataset provides kinematic data during hand-object interaction in monocular and binocular conditions at different depths and monocular/binocular conditions. It comprises two sub-datasets: ReGraD A (two measurements) can determine its test-retest reliability, whilst ReGraD B (one measurement) can characterize individuals with and without visual disorders. ReGraD includes 35 controls and 3 patients with amblyopia aged 6 to 35.</p> <p> </p> <p>The ReGraD dataset may aid to (1) gain insights into hand-object interaction under various eye conditions and depths, (2) assess reliability and reproducibility and (3) examine the effects of groups (control vs. patients) and age, among others. The ReGraD dataset contains raw data that can also be used to develop algorithms for data segmentation and data interpolation in the kinematic field.</p>
Raw Data for Unpublished Study "Physiologically Induced State Anxiety Reduces Perceived Heat Pain in Healthy Subjects"
<p>Version hyper_v01_20231019</p><p>Authors: Alexandros Guekos, Madeleine Hau, Sabina Grob, Gil Sharvit, Petra Schweinhardt</p><p>The present data was collected from February 2022 to May 2023 for a study on the influence of hypercapnia on pain perception.</p><p>The results are to be published soon. A link to the manuscript will be provided.</p><p>Full details of the paradigm, the experimental setup, and the analysis will be given there. Therefore, the following description only summarizes the most important steps.</p><p>In total, 47 subjects (22 men and 25 women) participated in a single experimental session during which they inhaled two different air mixtures, one coresponding to regular room air (normocapnic air, 21% O2 and 0.04 CO2) and one with identical O2 concentration (21%) but 7.5% CO2 concentration (hypercapnic air) for about 13 minutes each. Starting condition, i.e. first mixture, was pseudorandomized across participants.</p><p>Each condition began with a task-free phase of 90s. Then, self-assessment manikin (SAM) ratings for valence and arousal had to be entered (10s) before the first heat stimulus. SAM ratings were prompted again in the middle of the condition, i.e. during the ninth interstimulus interval, and after rating the last, i.e. 18th stimulus. After the last stimulus, the body sensations questionnaire (BSQ) had to be filled in (60s). After a further 60s of task-free time the air mixture was switched and the entire procedure was repeated for the second condition.</p><p>Instructions for SAM were given before the start of the experiment (see SAM_instructions_final.pdf).</p><p>BSQ was obtained before the experiment as well (see paper version BSQ_new.pdf).</p><p>During inhalation participants received 18 painful heat stimuli of 6s each on three locations on the calf muscle. The stimuli were divided into low (6x), medium (6x), and high (6x). Stimulus temperature was determined based on the individual heat pain tolerance threshold (ToT), corresponding to roughly 95, 96.5, 98% ToT. ToT was determined as a mean of three method of limits beforehand.</p><p>Participants had to rate every stimulus on two scales, one for intensity and one for unpleasantness. The intensity scale ranged from 0 (no sensation) to 200 (maximum tolerable pain) with 100 being the individual pain threshold (HPT). The unpleasantness scale ranged from -100 (extremely unpleasant) to 100 (extremely pleasant) with 0 corresponding to neutral.</p><p>Both scales were presented as horizontal bars with anchors for the lowest, highest, quarter-, and mid-point values. Ratings had to be filled in using a mouse-controlled slider on the two scales displayed on the screen (see Ratings.png). The whole protocol was programmed and displayed with PsychoPy v3.2021.2.3 (Open Science Tools Ltd, Nottingham, UK). The total duration of each condition was approximately 13 minutes (778s).</p><p>During the whole experiment, physiological data was recorded. This included galvanic skin response from the toes (electrodes on biggest and middle toes, in muS), heart rate (three point measurement, in bpm), as well as exhaled O2 and C02 concentration (in %). Heart beat and end-tidal were directly calculated from the registered data during recording and saved with the data.</p><p>The study was funded by the Swiss National Science Foundation as part of a grant to PS (grant number 320030_179191/1).</p>
Testing Digital Technologies to Help Families Build Healthy Habits
ClinicalTrials.gov study NCT04845568. IPD Sharing: YES. Countries: 1. Publications: 1.
Immunogenicity and Safety Study of a Quadrivalent Meningococcal Conjugate Vaccine Administered Concomitantly With Routine Pediatric Vaccines in Healthy Infants and Toddlers
ClinicalTrials.gov study NCT03691610. IPD Sharing: YES. Countries: 2. Publications: 0.
Immunogenicity and Safety of an Investigational Quadrivalent Meningococcal Conjugate Vaccine in Healthy Toddlers
ClinicalTrials.gov study NCT03205358. IPD Sharing: YES. Countries: 1. Publications: 1.
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.