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479 results for “human interaction”
STEHME files & HOLSEA spreadsheet for "Creel et al. 2022: Postglacial Relative Sea Level Change in Norway" and Balascio et al. 2023: "Refining Holocene sea-level dynamics for the Lofoten and Vesterålen archipelagos, northern Norway: Implications for prehistoric human-environment interactions"
<p><strong>Data Files for Creel et al. 2022: "Postglacial Relative Sea Level Change in Norway" and Balascio et al. 2023: "</strong><strong>Refining Holocene sea-level dynamics for the Lofoten and Vesterålen archipelagos, northern Norway: Implications for prehistoric human-environment interactions"</strong></p> <p>This repository contains the following files:</p> <p>1. Netcdf and csv files for the mean (stehme_mean.nc, stehme_mean_ts.csv) and standard deviation (stehme_std.nc, stehme_std_ts.csv) of the spatiotemporal empirical hierarchical model ensemble (STEHME) produced for Creel et al. 2022. The 'ts' suffix denotes time series for each unique lat/lon site. The netcdf files contain spatial maps at 100 yr resolution.</p> <p>2. mmc1.xlsx, the HOLSEA format Norway data compilation produced for Creel et al. 2022.</p> <p>3. Netcdf and csv files for the mean (stehme_mean_230721.nc, stehme_mean_ts_230721.csv) and standard deviation (stehme_std_230721.nc, stehme_std_ts_230721.csv) of the spatiotemporal empirical hierarchical model ensemble (STEHME) produced for Balascio et al. 2023. The 'ts' suffix denotes time series for each unique lat/lon site. The netcdf files contain spatial maps at 100 yr resolution. </p>
A Physical Human-Robot Interaction Dataset - TacAct
<p>This dataset is the supplementary material of the IROS 2021 article "Organization and Understanding of a Tactile Information Dataset TacAct During Physical Human-Robot Interactions".<br> The dataset was collected by a flexible supercapacitor tactile sensor installed in an imitated mechanical arm device. In a 32 × 32 grid, the sensor data can be sampled at 100 Hz (100 frames per second). A total of 12 touch actions, namely, pull, squeeze, push, hold, grasp, poke, static drag, strong hit, soft slide, scratch, soft tap, and sliding drag, were recorded. A single-action collected from a subject consists of a 32×32×N matrix (where N is the number of frames or frame length). For the same action, all subjects were asked to use as many postures as possible to apply different forces to different positions of the sensor, and repeat 20 times with each hand. Except for strongly hit and soft tap (complete in an instant, acquisition time 2 s), the duration of each action is 2 s, and the time is 4 s altogether. The experiment was conducted on 50 subjects (36 males and 14 females,ranged from 22 to 36 years and 44 were right-handed) in total, each subject consists of 480 actions (12 actions × 40 repetitions ) in total, and the dataset collected 24,000 actions from the subjects.</p>
Figure 2 in A not so natural history of the tarantula Brachypelma vagans: Interaction with human activity
Figure 2. Preferred orientations of the burrows for Brachypelma vagans. (A) For all sites (n5110); (B) for backyards (BY1 and BY2, n550) and for football field (FG and FC, n560).
Figure 3 in A not so natural history of the tarantula Brachypelma vagans: Interaction with human activity
Figure 3. Mean diameters of the burrow entrances of Brachypelma vagans. (A) Between backyards (BY: BY1 and BY2) and football field (FB: FC and FG); (B) among individuals. ANOVA test: **P,0.01; Fisher LSD, interclass group at 1% level.
Figure 1 in A not so natural history of the tarantula Brachypelma vagans: Interaction with human activity
Figure 1. Density of Brachypelma vagans in different vegetation/land use classes representing a gradient of human activity. F: mean of MF and SF. Site class codes are referred to in the Methods section.
Fig. 2 in The Effects Of Human-Dolphin Interaction Programmes On The Behaviour Of Three Captive Indo-Pacific Humpback Dolphins (Sousa Chinensis)
Fig. 2. Proportion of behaviours exhibited by the individual dolphins before and after the SWD programme. *P<0.05; **P<0.01; ***P<0.001.
Fig. 1 in The Effects Of Human-Dolphin Interaction Programmes On The Behaviour Of Three Captive Indo-Pacific Humpback Dolphins (Sousa Chinensis)
Fig. 1. Diagram of the dolphin lagoon consisting of the main pool (Length: 37 m, Width: 20 m, Depth: 3.5 m) alongside three holding pools. Locations that were utilised during scan sampling are marked out (1–3).
Fig. 3 in The Effects Of Human-Dolphin Interaction Programmes On The Behaviour Of Three Captive Indo-Pacific Humpback Dolphins (Sousa Chinensis)
Fig. 3. Proportion of behaviours exhibited by the individual dolphins before and after the MWD programme. *P<0.05; **P<0.01; ***P<0.001.
Microscale termophoresis fluorescence time traces testing the interaction between human survivin and a peptide derived from hSgol2
<p>Microscale termophoresis fluorescence time traces testing the interaction between human survivin and a peptide derived from hSgol2 ( sequence: ECQVKKVNKMTSKSKKRKTS). Survivin was chemically labelled and titrated with different concentrations of hSgol2 peptide.</p>
10 years of Human-NAO Interaction Research
<p>The evolving field of human-robot interaction (HRI) necessitates that we better understand how social robots operate and interact with humans. This scoping review provides an overview of about 300 research works focusing on the use of the NAO robot from 2010 to 2020. This study presents one of the most extensive and inclusive pieces of evidence on the deployment of the humanoid NAO robot and its global reach.</p>
Gene-environment interaction explains a part of missing heritability in human body mass index
<p>Gene-environment (G×E) interaction could partially explain missing heritability in traits; however, the magnitudes of G×E interaction effects remain unclear. Here, we estimate the heritability of G×E interaction for body mass index (BMI) by subjecting genome-wide interaction study data of 331,282 participants in the UK Biobank to linkage disequilibrium score regression (LDSC) and linkage disequilibrium adjusted kinships–software for estimating SNP heritability from summary statistics (LDAK-SumHer) analyses. Among 14 obesity-related lifestyle factors, MET score, pack years of smoking, and alcohol intake frequency significantly interact with genetic factors in both analyses, accounting for the partial variance of BMI. The G×E interaction heritability (%) and standard error of these factors by LDSC and LDAK-SumHer are as follows: MET score, 0.45% (0.12) and 0.65% (0.24); pack years of smoking, 0.52% (0.13) and 0.93% (0.26); and alcohol intake frequency, 0.32% (0.10) and 0.80% (0.17), respectively. Moreover, these three factors are partially validated for their interactions with genetic factors in other obesity-related traits, including waist circumference, hip circumference, waist-to-hip ratio adjusted with BMI, and body fat percentage. Our results suggest that G×E interaction may partly explain the missing heritability in BMI, and two G×E interaction loci identified could help in understanding the genetic architecture of obesity.</p>
Different Forms of Plasticity Interact in Adult Humans
<p>The data files are all in csv format, organized in columns for each variable and rows for each instance.<br> The column names denote the variables, explained below.</p> <p>---BINOCULAR RIVALRY DATA--- </p> <p>The experiment consisted of reporting the perceived orientation of a Gabor patch. Participants observed two <br> different Gabor patches (counter-clockwise -45° or clockwise 45°) projected separately on their two eyes <br> through a mirror stereoscope. They reported the perceived orientation by pressing the left (counter-clockwise), right (clockwise), or down (mixed) arrow keys.</p> <p>-FILES-<br> data_BR : main experimental cohort<br> data_BR_vc : visual control condition<br> data_BR_wm : working memory control condition</p> <p>-COLUMN NAMES-<br> grating : perceived grating orientation (1 counter-clockwise 45°, 2 clockwise 45°, 3 mixed)<br> trial : trial number (2 total in a block)<br> subtrial : sub-trial number (4 total in a block)<br> position : position of the counter-clockwise grating (1 left, 2 right)<br> perceiving_eye : the eye that is dominating perception at that instance (1 left, 2 right)<br> phase_duration : phase duration in seconds of the perception instance <br> ID : participant ID<br> block : block number (8 total in a session)<br> deprived_eye : code for the deprived eye (1 left, 2 right)<br> logPhaseDur : log10 transformed phase duration<br> session : session code (1 simple, 0 combined, 2 control)</p> <p>---MOTOR SEQUENCE LEARNING DATA---</p> <p>The experiment consisted of tapping four fingers (middle finger excluded) on a response pad, following a <br> sequence displayed on the monitor. The numbers on the sequence correspond to fingers (1 thumb, 2 index, <br> 3 ring, 4 little). Participants practiced two of the sequences for 14 blocks (test sequences) and two <br> others for only 1 block (control sequences) in the initial training. Each hand was trained separately. A <br> retest of two blocks for each of the sequences was done 45 minutes after the initial training per hand.</p> <p>-FILES-<br> data_MSL : main experimental cohort</p> <p>-COLUMN NAMES-<br> part : part number (4 in total: 2 test, 2 control)<br> block : block number (up to 14 for test sequences)<br> trial : trial number (5 in each block)<br> repetition : repetition number (i.e. completion of the sequence, 3 in each trial) <br> RT : reaction time in seconds<br> accuracy : whether the sequence was correct (i.e. no finger tap error) (1 correct, 0 incorrect)<br> ID : participant ID<br> sequence_num : sequence number (1: control, 2 test)<br> used_hand : is this the dominant hand (1 yes, 0 no)<br> first_hand : which is the first-trained hand (1 dominant, 0 non-dominant)<br> hand : which hand is currently used (1 left 2 right)<br> run : run number (1 initial training, 2 retest after 45 minutes)<br> logRT : log10 transformed reaction time<br> session : session code (1 simple, 0 combined)</p> <p>---WORKING MEMORY DATA---</p> <p>The experiment consisted of reporting whether a probe letter belonged to a previously seen set. The set of letters (3, 5, or 7 elements) was flashed (? sec) one by one (ISI ?) and after a 3 second pause a probe was flashed. Participants indicated if the probe belonged to the set by pressing arrow keys (left arrow no, right arrow yes). They received auditory feedback on their accuracy.</p> <p>-FILES-<br> data_WM : working memory control condition</p> <p>-COLUMN NAMES-<br> response : participant's response (1 yes, 0 no)<br> accuracy : accuracy (1 correct, 0 incorrect)<br> block : block number (3 in each run)<br> trial : trial number (30 total in each block)<br> length : set length (3,5 or 7)<br> set : the set of letters shown<br> RT : reaction time in seconds<br> probe : probe letter<br> ID : participant ID<br> run : run number (1-4)<br> logRT : log10 transformed reaction time</p>
"It was recorded on Sunday, morning of the 28th of September as some of the slower runners of the Berlin Marathon made it past Torstrasse near my flat. Iwas out to buy some bread for breakfast, but Iusually bring a camera and my Edirol R-1 recorder whenever Igo out. Since Iwas freshly returned to Berlin Iguess Iwas sensitive to the more antiquated sounds which still survive there, like that of the organ grinder. Iam generally interested in how human beings are replacing the presence of Nature with an artificial environment made entirely by human hands (and thus far more understandable, it is hoped). In this new Human Nature, the sounds of Nature are also Human made. Iwrite about these things, but Ialso use the sounds in my videos and my interactive and generative media work, so generally Iam wandering around building up my archive of media documents for use as material in future works." [Baruch/ gottlieb]17 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"It was recorded on Sunday, morning of the 28th of September as some of the slower runners of the Berlin Marathon made it past Torstrasse near my flat. Iwas out to buy some bread for breakfast, but Iusually bring a camera and my Edirol R-1 recorder whenever Igo out. Since Iwas freshly returned to Berlin Iguess Iwas sensitive to the more antiquated sounds which still survive there, like that of the organ grinder. Iam generally interested in how human beings are replacing the presence of Nature with an artificial environment made entirely by human hands (and thus far more understandable, it is hoped). In this new Human Nature, the sounds of Nature are also Human made. Iwrite about these things, but Ialso use the sounds in my videos and my interactive and generative media work, so generally Iam wandering around building up my archive of media documents for use as material in future works." [Baruch/ gottlieb]17
Real-world human-robot interaction data with robotic pets in user homes in the United States and South Korea
<p>Socially-assistive robots (SARs) hold significant potential to transform the management of chronic healthcare conditions (e.g. diabetes, Alzheimer's, dementia) outside the clinic walls. However doing so entails embedding such autonomous robots into people's daily lives and home living environments, which are deeply shaped by the cultural and geographic locations within which they are situated. That begs the question of whether we can design autonomous interactive behaviors between SARs and humans based on universal machine learning (ML) and deep learning (DL) models of robotic sensor data that would work across such diverse environments. To investigate this, we conducted a long-term user study with 26 participants across two diverse locations (the United States and South Korea) with SARs deployed in each user's home for several weeks. We collected robotic sensor data every second of every day, combined with sophisticated ecological momentary assessment (EMA) sampling techniques, to generate a large-scale dataset of over 270 million data points representing 173 hours of randomly-sampled naturalistic interaction data between the human and robot pet. Interaction behaviors included activities like playing, petting, talking, cooking, etc.</p>
Implementing social network analysis to understand the socio-ecology of wildlife co-occurrence and joint interactions with humans in anthropogenic environments
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A dataset for predicting protein-protein interactions in humans
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Synergistic label-free fluorescence imaging and miRNA studies reveal dynamic human neuron-glial metabolic interactions following injury
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Data from: Social robots as conversational catalysts: Enhancing long-term human-human dyadic interaction at home
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Data from: Neural interactions in the human frontal cortex dissociate reward and punishment learning
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Real-world human-robot interaction data with robotic pets in user homes in the United States and South Korea
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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.