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342 results for “daily activity”
Figure 2 in Food habits and daily activity patterns of the North African ocellated lizard Timon pater from northeastern Algeria
Figure 2. Monthly variation in the number of faecal pellets collected (A) (total sample, n5164) and in the mean volume of each pellet (B) (total sample, n5139) of Timon pater at the study area.
Fig. 4 in Dependence of daily oviposition activity and total fecundity on body mass in the house cricket Acheta domesticus (L.) (Insecta: Orthoptera)
Fig. 4: Total fecundity (mean + sd) measured for female crickets belonging to mass category (MC) 1, 2, and 3. Asterisks indicate highly significant differences between adjacent mean values (p <0.001).
Fig. 3 in Dependence of daily oviposition activity and total fecundity on body mass in the house cricket Acheta domesticus (L.) (Insecta: Orthoptera)
Fig. 3: Temporal course of daily fecundity (mean + sd) in Acheta domesticus and its dependence on body mass. Three mass categories (MC) have been distinguished: MC1 ‒ 600-800 mg, MC2 ‒ 801- 100 mg, MC3 ‒ 1001-1200 mg; (a) 1st to 7th day of oviposition period; (b) 8th to 14th day; (c) 15th to 21st day, (d) 22nd to 28th day, (e) 29th to 35th day. Between most adjacent values significant differences (p <0.05) could be determined.
Fig. 2 in Dependence of daily oviposition activity and total fecundity on body mass in the house cricket Acheta domesticus (L.) (Insecta: Orthoptera)
Fig. 2: Equipment for the culture of the house cricket Acheta domesticus: (a) climate chamber with plastic boxes containing cricket nymphs, (b) glass vessels containing adults separated by gender, (c) small oviposition vessels for the investigation of daily fecundity.
Predicting daily activity time through ecological niche modeling and microclimatic data
<p><span>1. </span><span>Climate temporality is a phenomenon that affects species' activity and distribution patterns across spatial and temporal scales. Despite the global availability of microclimatic data, their use to predict activity patterns and distributions remains scarce, particularly at fine temporal scales (e.g., < month). Predicting activity patterns based on climatic data may allow us to foresee some of the consequences of climate change, particularly for ectothermic vertebrates. </span></p> <p><span>2. </span><span>The Gila monster exhibits marked daily and seasonal activity patterns linked to physiology and reproduction. Here we evaluate if ecological niche models fitted using microclimate data can predict temporal activity patterns using the Gila monster (<em>Heloderma suspectum</em>) as a study system. Further, we identified if the activity patterns are related to physiological constraints.</span></p> <p><span>3. </span><span>We used dated occurrences from museum specimens and human observations to generate and test ecological niche models using minimum-volume ellipsoids. We generated hourly microclimatic data for each occurrence site for ten years using the NicheMapR package. For ecological niche modeling, we compared the traditional seasonal approach versus a daily activity pattern strategy for model construction. We tested both using the omission rate of independent observations (citizen science data). Finally, we tested if unimodal and bimodal activity patterns for each season could be recreated through ecological niche modeling and if these patterns followed known physiological constraints.</span></p> <p><span>4. </span><span>The unimodal and bimodal activity patterns previously reported directly from tracking individuals across the year were recovered by using niche modeling and microclimate across the species' geographical range. We found that upper thermal tolerances can explain the daily activity patterns of this species. </span></p> <p><span>5. </span><span>We conclude that ecological niche models trained with microclimatic data can be used to predict activity patterns at fine temporal scales, particularly on ectotherm species of arid zones coping with rapid climate modifications. Further, the use of fine temporal scale variables can lead to a better niche delimitation, enhancing the results of any research objective that uses correlative models.</span></p>
Site occupancy of Dasypus novemcinctus (Mammalia, Cingulata) and daily activity of four armadillo species in the Uruguayan Savanna and southern Atlantic Forest
<p>Datasets and R code for the research article "Site occupancy of Dasypus novemcinctus (Mammalia, Cingulata) and daily activity of four armadillo species in the Uruguayan Savanna and southern Atlantic Forest", published in Mammalian Biology. <a href="https://doi.org/10.1007/s42991-023-00366-3">https://doi.org/10.1007/s42991-023-00366-3</a></p>
Fig.2 in On the daily activity of the Red Fox (Vulpes vulpes) in two village areas of Bulgaria: a case study
Fig.2. Number of all pictures of the red fox (Vulpes vulpes) made in 24 hours interval (number of images per hour) for NP "Sinite Kamani" (Georgiev et al., 2015).
Fig. 3 in On the daily activity of the Red Fox (Vulpes vulpes) in two village areas of Bulgaria: a case study
Fig. 3. Number of all pictures of the fox (Vulpes vulpes) made in 24 hours interval (number of images per hour) for the villages of Byaga and Isperihovo.
No relationship between chronotype and timing of breeding when variation in daily activity patterns across the breeding season is taken into account
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Predicting daily activity time through ecological niche modeling and microclimatic data
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Fine-Grained Activities of Daily Living Data with Structural Vibration and Electrical Load Sensing
<p>Fine-grained non-intrusive monitoring of activities of daily living (ADL) enables various smart building applications, including ADL pattern assessments for older adults at risk for loss of safety or independence. We utilize structural vibration sensing and electrical load sensing to acquire multiple fine-grained kitchen activities under a lab structure setting.</p> <p>Each file contains the following values:<br> -RawData: time series of data for each channel (vibration on the table, vibration on the floor, load)<br> -Label: manually fine-grained labels of events<br> -Table: detected events start/stop index for vibration sensor on the table<br> -Floor: detected events start/stop index for vibration sensor on the floor<br> -Load: detected events start/stop index for load sensor<br> <br> Label notation:<br> 1 -- operating the kettle<br> 2 -- kettle on<br> 3 -- operating the microwave<br> 4 -- microwave on<br> 5 -- put things on the stove<br> 6 -- operating with stove<br> 7 -- stove on<br> 8 -- operating vacuum<br> 9 -- sweep floor<br> 10 -- walking/step<br> 11 -- miscellaneous<br> 12 -- synchronization signal (knock on the floor)<br> 13 -- vacant<br> 14 -- microwave door open</p>
Daily activity time series of ants, Camponotus japonicus
<p>Social insects often share tasks among individuals. In this study, we analyzed the foraging activity of ants (<em>Camponotus japonicus</em>) and recorded the daily passage event counts of individual workers between a nest chamber and a foraging arena using five monodomous colonies. We proposed two hypotheses on the time series of foraging frequency by individual worker ants:</p> <p>(i) Regarding the time series of foraging frequency by individual worker ants, the foraging frequency on a certain day could be expressed by the product of the foraging frequency on the previous day and the exponential of a random number.<br>(ii) The random numbers are correlated between some pairs of worker ants.</p> <p>The results for the five tested ant colonies showed that the probability of total daily passage counts (the sum of an individual's passage count) were characterized by a log-normal distribution. The worker ants behaved differently in terms of active days and foraging frequency. However, for >54% of the worker ants, the probability of the daily passage count was characterized by a log-normal distribution, and these worker ants performed >72% of the tasks in each colony. Furthermore, for >73% of the worker ants, the time development of passage count was confirmed to be mathematically modelled; the logarithmic first difference between the passage counts on a certain day and those on the previous day was a random normal variable. These results support hypothesis (i). Additionally, the random numbers, that were equivalent to the logarithmic first difference, were correlated for some pairs of worker ants. These results support hypothesis (ii).</p>
Daily fraction of photosynthetically active radiation (fPAR), edaphic, and weather conditions from 20220410 to 20230409 in Kulen, Cambodia
<ol><li><strong>Sub-title 1:</strong> Daily mean and standard deviation of Soil water content, soil temperature, soil saturation extracted electrical conductivity of evergreen forests, regrowth forests, and cashew plantations in Kulen, Cambodia. (<strong>File name:</strong> <i>Edaphic_fPAR_Pub.txt</i>)</li><li><strong>Sub-title 2:</strong> Daily weather condition data from 20220410 to 20230409 in Kulen, Cambodia. (<strong>File name:</strong> <i>Weather_Pub.txt</i>)</li><li><strong>Sub-title 3:</strong> Photosynthetically active radiation (<i>PAR</i>) data from 20220410 to 20230409 in Kulen weather station, Cambodia. (<strong>File name:</strong> <i>PARa_Pub.txt</i>)</li></ol>
MOVMUS-UJI Dataset & ERGOMOVMUS: EMG and kinematics data of the hand in activities of daily living with special interest for ergonomics
<p>A dataset of <strong>human hand kinematics</strong> and <strong>forearm muscle activation</strong> collected during the performance of a wide variety of activities of daily living (ADLs) is presented, with tagged characteristics of products and tasks. A total of <strong>26 participants</strong> performed <strong>161 ADLs</strong>, selected to be representative of common elementary tasks, grasp types, product orientations and performance heights. 105 products were used, being varied regarding shape, dimensions, weight and type (common products and assistive devices).</p> <p>The data were recorded using CyberGlove instrumented gloves on both hands measuring 18 degrees of freedom on each and seven surface EMG sensors per arm recording muscle activity. The products and their arrangement were the same across subjects, and tasks were performed in a guided way. Data of <strong>more than 4100 ADLs</strong> is presented in this dataset as <strong>Matlab structures</strong> with full continuous recordings, which may be used in applications such as machine learning or to characterize healthy human hand behaviour.</p> <p>The dataset is accompanied with a <strong>custom data visualization application (ERGOMOVMUS)</strong> as a tool for ergonomics applications, allowing visualization and calculation of aggregated data from specific task, product and/or subjects’ characteristics.</p> <p> </p> <p><strong>v3.0 includes the following updates:</strong></p> <p>- Statistical summary of the recordings both in .xlsx and .ods file format (v1.1 only included it in .xlsx file format)</p> <p>- Updated experiment details in "MOVMUS-UJI DATASET GUIDE.pdf".</p>
Transhumeral Loading During Advanced Upper Extremity Activities of Daily Living
<p>Percutaneous osseointegrated (OI) implants for direct skeletal attachment of upper extremity prosthetics represent an alternative to traditional socket suspension that may yield improved patient function and satisfaction. This is especially true in high-level, transhumeral amputees where prosthetic fitting is challenging and abandonment rates remain high. However, maintaining mechanical integrity of the bone-implant interface is crucial for safe clinical introduction of this technology. The collection of population data on the transhumeral loading environment will aid in the design of compliance and overload protection devices that mitigate the risk of periprosthetic fracture. We collected marker-based upper extremity kinematic data from non-amputee volunteers during advanced activities of daily living (AADLs) that applied dynamic loading to the humerus. These kinematic data are available for download and will aid in the development of overload protection devices and appropriate post-operative rehabilitation protocols that balance return to an active lifestyle with patient safety.</p>
VHAKG: Multi-modal Knowledge Graphs with Multi-view Videos of Daily Activities
<h2>Outline</h2> <ul> <li>This dataset is a multimodal knowledge graph (MMKG) of daily activity videos.</li> <li>This dataset integrates a KG with embedded multi-view videos created by <a href="https://github.com/aistairc/virtualhome_aist">VirtualHome-AIST</a>, an extended version of the VirtualHome simulator, and an event-centric KG generated by <a href="https://github.com/aistairc/virtualhome2kg">VirtualHome2KG</a>.</li> <li>We named this dataset <strong>VHAKG</strong> (VirtualHome-AIST-KG).</li> </ul> <h2>Details</h2> <ul> <li>VHAKG describes 2D bounding boxes of objects every five frames, compositional activities, primitive actions, target objects, object states, 3D bounding boxes, and their time-series changes.</li> <li>The videos are encoded in base64 and embedded as a literal value.</li> <li>VHAKG consists of 706 daily activity scenarios (e.g., clean desk, cook fried bread, and relax on sofa) and 3,530 videos captured by five synchronized cameras per scenario.</li> <li>The file format is <a href="https://www.w3.org/RDF/">RDF</a> (<a href="https://en.wikipedia.org/wiki/Turtle_(syntax)">Turtle</a>), which can be loaded into various <a href="https://en.wikipedia.org/wiki/Triplestore">Triplestores</a>.</li> <li>VHAKG's vocabularies are defined as an ontology and can be found in vh2kg_schema_v2.0.0.ttl.</li> </ul> <h2>Contents</h2> <ul> <li>vh2kg_video_base64.tar.gz <ul> <li><strong>{activity name}{scene}_{camera}_2dbbox.ttl</strong>: KG with video embedded in base64 format, including 2D bounding box data every 5 frames. <ul> <li>To learn more about {scene}, check <a href="https://github.com/xavierpuigf/virtualhome/tree/v2.2.0/simulation#environment">here</a>.</li> <li>To learn more about {camera}, check <a href="https://github.com/aistairc/virtualhome_unity_aist?tab=readme-ov-file#addition-of-new-four-cameras">here</a>.</li> </ul> </li> </ul> </li> <li>vh2kg_event.tar.gz <ul> <li><strong>{activity name}_{scene}.ttl</strong>: Event-centric KGs representing video content as sequences of events.</li> <li><strong>vh2kg_schema_v2.0.0.ttl</strong>: The ontology file of this dataset.</li> <li><strong>affordance.ttl</strong>: The affordance data of objects that were created by crowdsourcing. <ul> <li>Please see Section III.B of <a href="https://doi.org/10.1109/ACCESS.2023.3253807" target="_blank" rel="noopener">this paper</a> for more information.</li> </ul> </li> <li><strong>add_places.ttl</strong>: Events in which agents moved from one room to another.</li> </ul> </li> </ul> <h2>Tools</h2> <ul> <li><a href="https://github.com/aistairc/vhakg-tools">A set of tools</a> for searching and extracting videos from VHAKG is available.</li> </ul>
Data and analysis for "Association of self-reported musculoskeletal pain with school furniture suitability and daily activities among primary school and university students"
<p>Datasets and R analysis code and report for the article "Association of self-reported musculoskeletal pain with school furniture suitability and daily activities among primary school and university students".</p>
SINS database - Node 1 - Daily activities in a home environment recorded using a Acoustic Sensor Network
<p>The SINS database contains continuous recordings of a single person living in a vacation home over a period of one week. It was collected using a network of 13 microphone arrays distributed over the entire home. The microphone array consists of 4 linearly arranged microphones.Recordings were manually annotated on the level of daily activities performed in the environment.<br> <br> To obtain more information on the database please visit <a href="https://kuleuvenadvise.github.io/SINS_database/">this website</a> and read the published paper.<br> <strong>Please read to license file (available in this repository) before using the database.</strong><br> <br> When using this database you should <strong>cite the following paper</strong>:<br> Gert Dekkers, Steven Lauwereins, Bart Thoen, Mulu Weldegebreal Adhana, Henk Brouckxon, Toon van Waterschoot, Bart Vanrumste, Marian Verhelst, and Peter Karsmakers, The SINS database for detection of daily activities in a home environment using an acoustic sensor network, Proceedings of the Detection and Classification of -Acoustic Scenes and Events 2017 Workshop (DCASE2017), pp 32–36, November 2017.</p>
Fig. 1 in Dependence of daily oviposition activity and total fecundity on body mass in the house cricket Acheta domesticus (L.) (Insecta: Orthoptera)
Fig. 1: Biological traits demonstrably influenced by body mass in orthopterans (WITHMAN 2008).
Data from: Long-term capture data uncover shifts in the daily activity patterns of Amazonian birds
<p>Although the duration of biological rhythms varies from milliseconds to decades, daily cycles are especially widespread for individual organisms. These circadian rhythms are synchronized by light-dark cycles, which govern predictable changes in other microclimate variables. Despite the fixed, regular nature of these abiotic cycles at low latitudes, little is known about how different animals structure activity along these gradients. We used 25 years of capture-recapture data and ~25,000 unique captures to characterize 'activity' (time of capture) for 65 species of Amazonian birds in the lowest forest stratum (0–2.5 m). To quantify vertical niche breadths, we also measured foraging heights for these same species in the field. Activity peaked within the first two hours after sunrise for most species (71%), while others peaked 1–3 hours later (29%). This difference was associated with an apparent preference for specific vertical strata, as terrestrial and near-ground birds peaked ~1 hour after sunrise, while birds from higher strata delayed their first bouts of activity in the lower understory (by an average of 40 to 100 minutes). Peak capture times also correlated with long-term abundance trends, as only species with the earliest capture times showed strong declines, suggesting that associations with mid- or late-morning microclimates may contribute to resiliency in birds typical of higher strata. Because arboreal species largely avoid the lower understory in the early morning, when terrestrial birds are active, we infer that they are poorly adapted to low-light environments. However, with their broad vertical niches and reliance on brighter microhabitats, we suggest that an arboreal lifestyle diminishes specialization and may increase the capacity to cope with a wider range of daily microclimate conditions. We predict that subtle, but important, shifts in daily activity patterns would materialize if other biodiversity-monitoring programs could analyze similar temporal datasets.</p>
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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.