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568 results for “SIT”
EEG data offline and online during motor imagery for standing and sitting
<p>The experiments were conducted in an acoustically isolated room where only the participant and the experimenter were present. Participants voluntarily signed an informed consent form in accordance with the experimental protocol approved by the ethics committee of the Universidad Antonio Nariño. The participant was seated in a chair in a posture that was comfortable for him/her but did not affect data collection. In front of the participant, a 40-inch TV screen was placed at about 3 m. On this screen, a graphical user interface (GUI) displayed images that guided the participant through the experiment. Each experimental session was divided into two phases: an offline phase and an online phase. </p> <p>The offline experiments consisted of recording participants' EEG signals during motor imagery trials for standing and sitting that were guided by the GUI presented on the TV screen. Six offline runs were conducted in which the participants were standing in three runs and sitting in the other three runs. In each run, the participant had to repeat a block of 30 trials of mental tasks indicated by visual cues continuously presented on the screen in a pseudo-random sequence.</p> <p>The first phase of the experimental session was conducted to construct the offline parts of the dataset: (A) Sit-to-stand and (B) Stand-to-sit. The participant's EEG data were collected from 90 sequences for part A (45 trials of MotorImageryA tasks and 45 trials of IdleStateA tasks) and 90 sequences for part B (45 trials of MotorImageryB tasks and 45 trials of IdleStateB tasks).</p> <p>For each participant, the two machine learning models obtained in the offline phase were used to carry out the online experiment parts of the dataset: (C) Sit-to-stand and (D) Stand-to-sit. Each participant was instructed to select, in no particular order, 30 sequences for part C (15 trials of MotorImageryA tasks and 15 trials of IdleStateA tasks) and 30 other sequences for part D (15 trials of MotorImageryB tasks and 15 trials of IdleStateB tasks). Each trial was unique and was generated pseudo-randomly before the experiment.</p> <p>The database consisted of 32 electroencephalographic files corresponding to the 32 participants. All recordings were collected on channels F3, Fz, F4, FC5, FC1, FC2, FC6, C3, Cz, C4, CP5, CP1, CP2, CP6, P3, Pz, and P4 according to the 10-20 EEG electrode placement standard, grounded to AFz channel and referenced to right mastoid (M2). Each data file contained the data stream in a 2D matrix where rows corresponded to channels and columns corresponded to time samples with a sampling frequency of 250Hz.</p> <p>The following marker numbers encoded information about the execution of the experiment. Marker numbers 200, 201, 202, and 203, indicated the beginning and end of the four steps of the sequence in a trial (resting, fixation, action observation, and imagining). Marker numbers 1, 2, 3, and 4, indicated the figure activated on the screen to the participant perform the task corresponding to 1. actively imagining the sit-to-stand movement (labeled as MotorImageryA), 2. sitting motionless without imagining the sit-to-stand movement (labeled as IdleStateA), 3. standing motionless while actively imagining the stand-to-sit movement (labeled as MotorImageryB), or 4. standing motionless without imagining the stand-to-sit movement (labeled as IdleStateB). Finally, marker numbers 101, 102, 103, and 104, indicated the task detected by the BCI in real time during the online experiment: 101. MotorImageryA, 102. IdleStateA, 103. MotorImageryB, or 104. IdleStateB.</p>
MultiPosture: A Dataset of body joints keypoints extracted using MediaPipe for multi-task sitting posture recognition with upper and lower body labels
<p>This dataset contains skeletal pose data extracted from video recordings of 13 participants performing various sitting postures in home environments. The data was processed using MediaPipe Pose Heavy model and includes 4,800 frames of 3D skeletal coordinates (x, y, z) for 11 key body joints, with each frame manually labeled for both upper and lower body posture classifications.</p> <p>The data is stored in CSV format with normalized coordinates relative to hip center, containing 33 input dimensions (11 joints × 3 coordinates) representing key skeletal points. To protect participant privacy, only the processed skeletal coordinates are included, with no raw video or image data due to privacy constraints.<br><br></p> <p>Upper Body Labels:</p> <ul> <li>TUP: Upright trunk position</li> <li>TLB: Trunk leaning backward</li> <li>TLF: Trunk leaning forward</li> <li>TLR: Trunk leaning right</li> <li>TLL: Trunk leaning left</li> </ul> <p>Lower Body Labels:</p> <ul> <li>LAP: Legs apart</li> <li>LWA: Legs wide apart</li> <li>LCS: Legs closed</li> <li>LCR: Legs crossed right over left</li> <li>LCL: Legs crossed left over right</li> <li>LLR: Legs lateral right</li> <li>LLL: Legs lateral left</li> </ul> <p>Each frame in the dataset has been manually labeled and validated by experts, making it particularly suitable for developing and evaluating machine learning models for ergonomic monitoring systems, ambient assisted living applications, and general posture recognition research.</p> <p>This dataset was collected as part of the study:</p> <p><strong>D. Carneros-Prado, L. Cabañero-Gómez, E. Johnson, I. González, J. Fontecha and R. Hervás, "A Comparison Between Multilayer Perceptrons and Kolmogorov-Arnold Networks for Multi-Task Classification in Sitting Posture Recognition," in <em>IEEE Access</em>, vol. 12, pp. 180198-180209, 2024, doi: 10.1109/ACCESS.2024.3510034. </strong><em><a href="https://ieeexplore.ieee.org/abstract/document/10772228" target="_blank" rel="noopener">link</a></em></p> <p> </p>
Dataset of the study "Thirty seconds sit-to-stand test as an alternative for estimating peak oxygen uptake and six-minutes walking distance in women with breast cancer: a cross-sectional study"
<p>Data was collected to study the usefulness of the thirty seconds sit-to-stand test as an alternative for estimating peak oxygen uptake and six-minutes walking distance in women with breast cancer, which is a cross-sectional study derived from the ONCORE project (Randomized controlled trial on comprehensive exercise-based cardiac rehabilitation program for the prevention of anthracyclines and/or anti-HER2 antibodies-induced cardiotoxicity in breast cancer), ClinicalTrials.gov Identifier: NCT03964142</p> <p> </p> <p>DATASET FILE (xlsx) includes 4 sheets:<br> - Dataset_variables: all variables collected pre-post intervention<br> - Descriptive data (baseline): variables used for the descriptive analysis before the intervention (baseline)<br> - Data_CPET-30STS(pre-post): pooled data from CPET-30STS pre-post intervention<br> - Data_6MWD-30STS(pre-post): pooled data from 6MWD-30STS pre-post intervention</p>
Ultrafst whole cell imaging data of SiT-Golgi vesicles over 10,000 time points
<p>This dataset is the raw lattice light-sheet microscopy data of SiT-Golgi vesicles over 10,000 consecutive time points, which was used to demonstrate TiS-rDL denoising algorithm in our Nature Biotechnology paper (DOI: 10.1038/s41587-022-01471-3). This dataset can be used for non-commercial purposes with proper citations of our NBT paper.</p>
Fig. 11 in Area freedom in Mexico from Mediterranean fruit fly (Diptera: Tephritidae): a review of over 30 years of a successful containment program using an integrated area-wide SIT approach
Fig. 11. Average monthly temperatures (0C) and monthly rainfall totals (mm) in the region of Marques de Comillas, Chiapas, Mexico, for the years 2011, 2012 and 2013.
Fig. 4 in Area freedom in Mexico from Mediterranean fruit fly (Diptera: Tephritidae): a review of over 30 years of a successful containment program using an integrated area-wide SIT approach
Fig. 4. Number of Mediterranean fruit fly transient entries (detections and outbreaks) in the Mexican States of Chiapas and Tabasco during 1982 - 2013.
Fig. 8 in Area freedom in Mexico from Mediterranean fruit fly (Diptera: Tephritidae): a review of over 30 years of a successful containment program using an integrated area-wide SIT approach
Fig. 8. Relationship between the distance of the leading edge of the medfly infestation in Guatemala and the number of medfly transient entries (detections and outbreaks) into the PFA of Soconusco, Chiapas, for the Southwest–Soconusco (SW) Front for years the 2004-2013.
Fig. 12 in Area freedom in Mexico from Mediterranean fruit fly (Diptera: Tephritidae): a review of over 30 years of a successful containment program using an integrated area-wide SIT approach
Fig. 12. Number of potential generations of the Mediterranean fruit fly in the Marques de Comillas pest free area (PFA) estimated for the periods Jun to Oct and Nov to May for the years 2011-2013 based on the Tassan degree-day model (Tassan et al. 1982).
Fig. 10 in Area freedom in Mexico from Mediterranean fruit fly (Diptera: Tephritidae): a review of over 30 years of a successful containment program using an integrated area-wide SIT approach
Fig. 10. Number of fruit samples collected during 2012 and 2013 in the Marques de Comillas PFA in Chiapas, Mexico, reflecting availability of Mediterranean fruit fly hosts and fruit phenology in this region.
Fig. 6 in Area freedom in Mexico from Mediterranean fruit fly (Diptera: Tephritidae): a review of over 30 years of a successful containment program using an integrated area-wide SIT approach
Fig. 6. Mediterranean fruit fly average FTD (flies per trap per day) index in the Mexican States of Chiapas and Tabasco from 2004 to 2013.
Fig. 9 in Area freedom in Mexico from Mediterranean fruit fly (Diptera: Tephritidae): a review of over 30 years of a successful containment program using an integrated area-wide SIT approach
Fig. 9. Numbers of Mediterranean fruit fly transient entries (detections and outbreaks) per month during the years 2011-2013 into the PFA of Marques de Comillas, Chiapas, for the North Transversal Strip–Marques de Comillas (NTS) front.
Fig. 2 in Area freedom in Mexico from Mediterranean fruit fly (Diptera: Tephritidae): a review of over 30 years of a successful containment program using an integrated area-wide SIT approach
Fig. 2. Mediterranean fruit fly pest free areas (PFAs) in Chiapas, Tabasco, Guatemala and Belize, as well as the location of the current containment barrier, infestation fronts and leading edge of the infestation within Guatemala. The white areas to the southwest and southeast represent the Pacific Ocean and the Caribbean Sea, respectively.
Fig. 5 in Area freedom in Mexico from Mediterranean fruit fly (Diptera: Tephritidae): a review of over 30 years of a successful containment program using an integrated area-wide SIT approach
Fig. 5. Location and percentage of Mediterranean fruit fly transient entries into Mexico during 1982-2013.
Fig. 7 in Area freedom in Mexico from Mediterranean fruit fly (Diptera: Tephritidae): a review of over 30 years of a successful containment program using an integrated area-wide SIT approach
Fig. 7. Mediterranean fruit fly fluctuation (based on routine trapping) within suppression areas in Guatemala associated with El Niño/Southern Oscillation weather events and the population suppression effects of Hurricane Stan and Hurricane Agatha.
Fig. 3 in Factors and Variables that Affect Quality of Lepidopterans Used in SIT Programs General biology of Eldana saccharina (Lepidoptera: Pyralidae): A target for the sterile insect technique
Fig. 3. Frequency distribution of multiple matings of Eldana saccharina males during 1 to 7 days afer emergence (n = 30).
Fig. 1 in Factors and Variables that Affect Quality of Lepidopterans Used in SIT Programs General biology of Eldana saccharina (Lepidoptera: Pyralidae): A target for the sterile insect technique
Fig. 1. Mean percentage of eggs (± SE; n = 20) oviposited by Eldana saccharina adult females per night afer emergence.
Fig. 3 in Limited gene flow among Cydia pomonella (Lepidoptera: Tortricidae) populations in two isolated regions in China: Implications for utilization of the SIT
Fig. 3. Dendrogram generated by NJ analysis representing the genetic distance among populations of Cydia pomonella. The topology was tested by bootstrap analysis with 1,000 replicates. The scale bar represents 2.0% genetic distance. The sampling locations include JinTa (JJT), YinDa (JYD), XiDong (JXD) and ZongZai (JZZ) of Jiuquan Region, and Luo Tuocheng (ZLT), NiJiaying (ZNJ), MinYong (ZMY) and XiaoMan (ZXM) of Zhangye Region.
Fig. 1 in Limited gene flow among Cydia pomonella (Lepidoptera: Tortricidae) populations in two isolated regions in China: Implications for utilization of the SIT
Fig. 1. Sampling regions and locations of Cydia pomonella in Hexi Corridor of China. (I) map of China, the Hexi Corridor is indicated in the dash box; (II) enlarged map of Hexi Corridor, the 2 sampling regions are indicated as (A) Jiuquan region and (B) Zhangye region; (III) enlarged map of the sampling regions and sampling locations. The sampling locations includes JinTa (JJT), YinDa (JYD), XiDong (JXD), and ZongZai (JZZ) of the Jiuquan Region, and Luo Tuocheng (ZLT), NiJiaying (ZNJ), MinYong (ZMY) and XiaoMan (ZXM) of the Zhangye Region.
Fig. 2 in Limited gene flow among Cydia pomonella (Lepidoptera: Tortricidae) populations in two isolated regions in China: Implications for utilization of the SIT
Fig. 2. Bayesian clustering analysis by use of STRUCTURE, which indicates the presence of 2 clusters. Proportion of membership coefficient for 8 Cydia pomonella populations falling into the 2 clusters is depicted by 2 different shades of gray, respectively. The sampling locations include JinTa (JJT), YinDa (JYD), XiDong (JXD) and ZongZai (JZZ) of Jiuquan Region, and Luo Tuocheng (ZLT), NiJiaying (ZNJ), MinYong (ZMY) and XiaoMan (ZXM) of Zhangye Region.
Dataset and Data Dictionary for "Enhancing Consumer Satisfaction in Live Commerce: A Study of Middle-Aged Women's Cosmetics Purchases Using TAM, PVT, and SIT Models
<p>This dataset is part of a study investigating the underexplored factors driving middle-aged Chinese women’s purchasing behavior in live commerce, particularly in the context of their decision-making amidst the rapid expansion of e-commerce. The study employs a comprehensive theoretical framework based on the Technology Acceptance Model (TAM), Perceived Value Theory (PVT), and Social Influence Theory (SIT).</p> <p>Data were collected through a structured survey administered to 653 women aged 40 to 59. The dataset captures key variables including ease of use, pricing, consumer trust, platform interactivity, and purchase satisfaction. These variables are essential for understanding the complex relationships that influence purchasing decisions in live-stream shopping environments.</p> <p>The dataset has been analyzed using Structural Equation Modeling (SEM), revealing that factors such as ease of use, perceived value from competitive pricing, consumer trust, and real-time platform interactivity significantly enhance purchase satisfaction. Moreover, the results demonstrate that perceived value moderates these relationships, amplifying their effects under conditions of high perceived value.</p> <p>This dataset provides valuable insights into the psychological and social factors that shape e-commerce behavior, offering implications for optimizing platform design to promote consumer trust and long-term engagement.</p> <p>Keywords: Live commerce, Middle-aged women, Purchasing behavior, Technology Acceptance Model (TAM), Perceived Value Theory (PVT), Social Influence Theory (SIT), Structural Equation Modeling (SEM), Consumer trust, E-commerce.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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