Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
126
datasets available to search
ShareScore release 0.9.0
Dataset results
126 results for “feelings”
Dataset of "Moving hands feel stimuli before stationary hands"
<p>In the flash lag effect (FLE), a moving object is seen to be ahead of a brief flash that is presented at the same spatial location; a haptic analogue of the FLE has also been observed. Some accounts of the FLE relate the effect to temporal delays in the processing of the stationary stimulus as compared to that of the moving stimulus [3–5]. We tested for movement-related processing effects in haptics. People judged the temporal order of two vibrotactile stimuli at the two hands: One hand was stationary, the other hand was executing a fast, medium, or slow hand movement. Stimuli at the moving hand had to be presented around 36 ms later, to be perceived to be simultaneous with stimuli at the stationary hand. In a control condition, where both hands were stationary, perceived simultaneity corresponded to physical simultaneity. We conclude that the processing of haptic stimuli at moving hands is accelerated as compared to stationary ones–in line with assumptions derived from the FLE.</p> <p>The dataset contains individual points of subjective simultaneity and just noticeable differences for each movement condition (stationary, fast , medium, slow) and individual response frequencies as a function of stimulus onset asynchrony (SOA) and movement condition.</p>
Maps of subjective feelings
<p>This is a snapshot of the code + data for the project "Map of subjective feelings".</p> <p>Up-to-date version of code and documentation at:</p> <p><a href="https://version.aalto.fi/gitlab/eglerean/sensations">https://version.aalto.fi/gitlab/eglerean/sensations</a></p> <p>Note: although the filesize is not big, the project contains ~43 thousand files; please consider that before unzipping.</p>
Data from: Sleep and subjective age: Protect your sleep if you want to feel young
<p>The current studies examined the impact of insufficient sleep and sleepiness on the subjective experience of age. Study 1, a cross-sectional study of 429 participants (282 females (66%), 144 males, 3 other; age range 18-70) showed that for each additional day of insufficient sleep in the last 30 days, subjective age increased by 0.23 years. Study 2, an experimental crossover sleep restriction study (N = 186; 102 females (55%); 84 males; age range 18-46) showed that two nights of sleep restriction (4h in bed/night) made people feel 4.44 years older compared to sleep saturation (9h in bed/night). Additionally, moving from feeling extremely alert (KSS score of 1) to feeling extremely sleepy (KSS score of 9) was associated with feeling 10 years older in both studies. These findings provide compelling support for insufficient sleep and sleepiness to exert a substantial influence on how old we feel, and that safeguarding sleep is likely a key factor in feeling young.<strong> </strong></p>
Mesh and configuration files to perform coupled heat+fluid simulations on a realistic human eyeball geometry with Feel++
<h1>Run the simulation</h1> <h2>With slurm</h2> <p>Set up position and desired mesh in the `run.slurm` file. Then, submit the job with the following command:</p> <p><code>sbatch run.slurm</code><br><br></p> <h2>Without slurm</h2> <p>Run by hand the command of the <code>run.slurm</code> file.<br><code>POSITION=prone # prone supine standing</code><br><code>SOLVER_TYPE=simple # simple lsc</code><br><code>MESH_INDEX=M4 # M1 M2 M3 M4 M5</code></p> <p><code>mpirun -np 128 feelpp_toolbox_heatfluid \</code><br><code> --config-files eye-${POSITION}.cfg pc_${SOLVER_TYPE}.cfg \</code><br><code> --heat-fluid.json.patch='{ "op": "replace", "path": "/Meshes/heatfluid/Import/filename", "value": "$cfgdir/mesh/Mr/'${MESH_INDEX}'/Eye_Mesh3D_p$np.json" }' \</code><br><code> --heat-fluid.scalability-save=1 --heat-fluid.heat.scalability-save=1 --heat-fluid.fluid.scalability-save=1</code></p> <h2>Available meshes</h2> <p>The meshes are available and are already partitioned for parallel computing:</p> <p><code>M0 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M1 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M2 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M3 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M4 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M5 : 1, 64, 128, 256, 384, 512, 640, 768</code><br><code>M6 : 128, 256, 384, 512, 640, 768</code></p>
Intrinsic motivation, perceived competence, negative feelings, and math academic performance.
<p>This dataset includes information about Primary School students' perceived competence, negative feelings, and intrinsic motivation with homework. The relationship between these variables and academic performance has been studied.</p>
CTU-50-FEEL
<p>This dataset contains HTTPS-based features of the CTU-50 dataset. </p> <p>These features dataset was created for the purposes of a thesis on Distributed Federated Learning for Network Security. The code of the thesis, as well as the code used for generating these features, can be found in <a href="https://github.com/stratosphereips/feel_project">this repository</a>.</p> <p>The dataset spans five days and is split into benign client data, which comes from the day-to-day traffic of real users, and malicious data, which was generated from captured traffic of malware. </p> <p>The dataset does not include any identifiable information. The features were created from the raw Zeek captures by splitting them into one-hour widows and grouping all connections with the same 4-tuple. This means every hour, a connection to one service will produce a single feature vector. </p> <p>The data is in the form of CSV, with the first 41 columns being numerical features. Each feature vector contains two columns with label information. Every vector is uniquely identifiable using these columns:</p> <ul> <li>id.orig_h - source IP address. The last octet of the addres is obfuscated with a value consistent across the dataset.</li> <li>id.resp_h - target IP address</li> <li>id.resp_p - target port</li> <li>proto - used protocol</li> <li>day - the day of the capture (value from 1 to 5)</li> <li>hour - an hour of the day from which the feature vector was computed</li> </ul>
Gut feeling: Host and habitat as drivers of the microbiome in blackbuck (Antilope cervicapra)
Open the record for dataset details and reuse information.
Data from: Sleep and subjective age: Protect your sleep if you want to feel young
Open the record for dataset details and reuse information.
Data from: Sensing, feeling, and regulating: Investigating the association of focal brain damage with voluntary respiratory and motor control
Open the record for dataset details and reuse information.
Feeling the future: A meta-analysis of 90 experiments on the anomalous anticipation of random future events
<p>The research that created this data is described in:</p> <ul> <li>Bem D, Tressoldi P, Rabeyron T and Duggan M. Feeling the future: A meta-analysis of 90 experiments on the anomalous anticipation of random future events. F1000Research 2015, 4:1188 (<a href="https://f1000research.com/articles/4-1188">doi: 10.12688/f1000research.7177.1</a>)</li> </ul>
Dataset manuscript "Does it Help to Feel your Body? Evidence is Inconclusive that Interoceptive Accuracy and Sensibility Help Cope with Negative Experiences
<p>In four studies (total <em>N</em> = 534), we examined the moderating impact of Interoceptive Accuracy (i.e., IAcc, as measured with the heartbeat counting task) and Interoceptive Sensibility (IS, assessed via questionnaire) on negative affect, following social exclusion or after receiving negative feedback. Results from an integrative data analysis combining the four studies confirmed that the manipulations were successful at inducing negative affect. However, no significant interaction between mood induction (control versus negative affect induction) and interoception on mood measures was observed, and this was true both for objective (i.e., IAcc) and subjective (i.e., IS) measures of interoception. Hence, previous conclusions on the moderating impact of interoception in the relationship between mood induction and self-reported mood were neither replicated nor generalized to this larger sample. We discuss these findings in light of theories of emotion regulation as well as recent concerns raised about the validity of the heartbeat counting task. </p>
Seeing the piles of the velvet bending under our finger sliding over a tactile stimulator improves the feeling of the fabric
<p>Using friction modulation to simulate fabrics with a tactile stimulator (i.e. virtual surface) is not<br>sufficient to render fabric touch and even more so for hairy fabrics. We hypothesized that seeing<br>the pile of the velvet darken or lighten depending on changes in the finger movement direction<br>on the virtual surface should improve the velvet fabric rendering.</p> <p><strong>Experiment 1</strong> : Participants actively rubbed a tactile device or a velvet fabric looking at a screen that showed a synthesized image of a velvet which either remained static (V-static) or darkening/lightening with the direction of touch (V-moving). We showed that in V-moving condition, the touched surface was always perceived rougher, which is a descriptor of a real velvet<strong>.</strong></p> <p><em>Files : data_brutes.csv</em> and <em> ordre_xx_c.csv</em> for data,<em> Calculus_data_expe1.Rmd</em> for code</p> <p><strong>Experiment 2</strong> : Using electroencephalography and sources localization analyses, we found increased activity in the occipital and inferiorparietal lobes when seeing dark and shinning traces during back and forth finger<br>movements over the virtual surface. This suggests that these two posterior cortical regions work<br>together to evaluate visuo-tactile congruence between the seen and the felt (tactile). The visuotactile binding, evidenced by neural synchronization (specifically, theta band [5-7 Hz] oscillation) in the left inferior posterior parietal lobule, is consistent with enhanced integration of information and likely contributed to the emergence of a more realistic velvet representation. </p> <p><em>Files </em>: <em>EEG time frequency individual data.xlsx</em> and all <em>.eeg</em></p>
Narratives on the present and the future in the time of Covid-19 pandemic: Uncertainty, subjective feeling and the role of positive anticipatory states.
<p><strong>Narratives on the present and the future in the time of Covid-19 pandemic: Uncertainty, subjective feeling and the role of positive anticipatory states.</strong></p>
Study on the Feelings of Euphoria Caused by Remimazolam During Gastroscopy and Colonoscopy and Their Related Factors
ClinicalTrials.gov study NCT06721195. IPD Sharing: YES. Countries: 1. Publications: 6.
Comparing Augmented Reality and Virtual Reality Glasses to Help Children Feel Less Pain and Anxiety During Local Anesthesia
ClinicalTrials.gov study NCT07060170. IPD Sharing: NO. Countries: 1. Publications: 5.
Feeling and Body Investigators for Pediatric Abdominal Pain
ClinicalTrials.gov study NCT02075437. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Move and Feel Good : Effects of Intensive Physical Training on Brain Plasticity, Cognition and Psychological Well-being.
ClinicalTrials.gov study NCT02970825. IPD Sharing: NO. Countries: 1. Publications: 1.
Touch and Feel Evaluation of Two Foam Hand Sanitizers
ClinicalTrials.gov study NCT05755282. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: Feeling disconnected: River fragmentation alters parenting, aggression, and risk-taking in threespine stickleback
Open the record for dataset details and reuse information.
I see how you feel: facial expressions' recognition and distancing in the time of COVID-19
<p>Data sets and R script for anlysis.</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.