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769 results for “Clock”
Two light sensors decode moonlight versus sunlight to adjust a plastic circadian/circalunidian clock to moon phase
<p>Many species synchronize their physiology and behavior to specific hours. It is commonly assumed that sunlight acts as the main entrainment signal for ~24h clocks. However, the moon provides similarly regular time information. Consistently, a growing number of studies have reported correlations between diel behavior and lunidian cycles. Yet, mechanistic insight into the possible influences of the moon on ~24hr timers remains scarce.</p> <div> <div> <div class="msocomtxt"> <p class="MsoNormal"><span>We have explored the marine bristleworm </span><em><span>Platynereis dumerilii</span></em><span> to investigate the role of moonlight in the timing of daily behavior. We uncover that moonlight, besides its role in monthly timing, also schedules the exact hour of nocturnal swarming onset to the nights' darkest times. Our work reveals that extended moonlight impacts on a plastic clock that exhibits <24h (moonlit) or >24h (no moon) periodicity. Abundance, light sensitivity, and genetic requirement indicate that the <em>Platynereis </em>light receptor molecule r-Opsin1 serves as a receptor that senses moonrise, whereas the cryptochrome protein L-Cry<em> </em>is required to discriminate the proper valence of nocturnal light as either moon- or sunlight. Comparative experiments in <em>Drosophila </em>suggest that cryptochrome's principal requirement for light valence interpretation is conserved. Its exact biochemical properties differ, however, between species with dissimilar timing ecology.</span></p> <p class="MsoNormal"><span>Our work advances the molecular understanding of lunar impact on fundamental rhythmic processes, including those of marine mass spawners endangered by anthropogenic change.</span></p> </div> </div> </div>
Data for: Pericytes' Circadian Clock Affects Endothelial Cells' Synchronization and Angiogenesis in a 3D Tissue Engineered Scaffold
<p>Raw data set and analysis files for Mastrullo et al., Frontiers in Pharmacology, 2022 <strong>DOI:</strong> 10.3389/fphar.2022.867070 </p>
dataset for "Long-lived Bell states in an array of optical clock qubits"
<p>Dataset for main figures in "Long-lived Bell states in an array of optical clock qubits".</p>
The intestinal circadian clock drives microbial rhythmicity to maintain gastrointestinal homeostasis
<p><strong>Diurnal (<em>i.e.</em>, 24-hour) oscillations of the gut microbiome have been described in various species including mice and humans. However, the driving force behind these rhythms remains less clear. In this study, we differentiate between endogenous and exogenous time cues driving microbial rhythms.</strong> <strong>Our results demonstrate that fecal microbial oscillations are maintained in mice kept in the absence of light, supporting a role of the host’s circadian system rather than representing a diurnal response to environmental changes. Intestinal epithelial cell-specific ablation of the core clock gene <em>Bmal1</em> disrupts rhythmicity of microbiota. Targeted metabolomics functionally link intestinal clock-controlled bacteria to microbial-derived products, in particular branched-chain fatty acids and secondary bile acids. Microbiota transfer from intestinal clock-deficient mice into germ-free mice altered intestinal gene expression, enhanced lymphoid organ weights and suppressed immune cell recruitment. These results highlight the importance of functional intestinal clocks for circadian microbiota composition and function, which is required to balance the host’s gastrointestinal homeostasis. </strong></p>
Two notorious nodes: A critical examination of relaxed molecular clock age estimates of the bilaterian animals and placental mammals
<p><span>The popularity of relaxed clock Bayesian inference of clade origin timings has generated several recent publications with focal results considerably older than the fossils of the clades in question. Here we critically examine two such clades: the animals (with focus on the bilaterians); and the mammals (with focus on the placentals). Each example displays a set of characteristic pathologies which, although much commented on, are rarely corrected for. We conclude that in neither case does the molecular clock analysis provide any evidence for an origin of the clade deeper than what is suggested by the fossil record. In addition, both these clades have other features (including, in the case of the placental mammals, proximity to a large mass extinction) that allow us to generate precise expectations of the timings of their origins. Thus, in these instances the fossil record can provide a powerful test of molecular clock methodology, and why it goes astray; and we have every reason to think these problems are general. </span></p>
Carriage clock
Time and place of creation: mid–18th century, Kraków Creator: Piotr Jakub Ris Inventory number: 1683; 1403/V Museum: Jagiellonian University Museum Collegium Maius https://muzea.malopolska.pl/en/objects-list/2736 Digitalisation: Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
Grandfather Clock
Floor pendulum clock in a high wooden case * Clock case - 2048 x 2048 * Moon dial - 512 x 512 * Clock face - 512 x 512 Source: Objaverse 1.0 / Sketchfab
Low Poly, Old-Style Clock Hands
A simple, yet stylized free clock hands. This model is meant to go with the dynamic version of the wall clock Source: Objaverse 1.0 / Sketchfab
Clock bell
The bell was probably cast in the Kraków bell foundry of Kacper Franke operating in Kraków in the years 1601–1639. On the circumference there is a raised inscription: LAUDATE DOMINUM IN CYMBALIS BENE SONANTIBUS ANNO 1637 (Praise the Lord loudly in 1637). The order for the bell was probably issued by the parish priest of Miechów and at the same time the royal secretary, Piotr Gembicki of the Nałęcz coat of arms, who in 1638 became the great crown chancellor. The bell has no heart. The sound was obtained by hitting its outer part with the hammer of a mechanical clock. ID no.: MZM/D/R/6 Creator: Kacper Franke (?) Time and place of creation: 1637, Kraków, Poland The Museum of Ziemia Miechowska in Miechów https://muzea.malopolska.pl/en/objects-list/2993 Digitalisation: RDW MIC, Małopolska museum visits project. Source: Objaverse 1.0 / Sketchfab
Ardavin´s Medieval Clock. Model Completorium
Artisan work. Created with the same tools and technics used in the XV century. Ardavin´s Medieval Clocks never existed. The models are created based on designs, materials, styles and mechanical properties used by the real clockmakers during the XV century. Wood, iron, brass and a lot of handmade tools are used. You can find more info visiting [Ardavin´s Web](https://ardavin.net/historia-del-reloj/como-funciona-un-reloj-medieval/) (only spanish) or can see the real model in [this video]( https://www.youtube.com/watch?v=Gdbv2D1G_j4). Also, this [animation]( https://www.youtube.com/watch?v=BoeP0adbDKg) contains a very useful explanation of "how it works" Source: Objaverse 1.0 / Sketchfab
Tabletop clock (17-18th c.)
**Full name: Horizontal tile-shaped "kaflak" tabletop clock** Made by Georg Merckel, this horizontal tile-shaped tabletop clock is known as "kaflak" in Polish. Its mechanism is incomplete and the item was reconstructed in the 19th century. The mechanism has two spring drums (one non-original, dating back to the 19th century), a reduction screw with a chain, vintage movement gear, a crown wheel escapement protected by a decorative bow (the bow is missing). The ringing mechanism is incomplete (the bell is missing). The clock originally struck hours and half-hours. It has a ring dial and a single-hand mechanism (secondary hand, 21st century). **For more images and further information, visit:** https://muzea.malopolska.pl/en/objects-list/2737 Inventory number: 1681; 1401/V Localisation of the physical object: Jagiellonian University Museum, Collegium Maius **Digitalisation: Regional Digitalisation Lab, Małopolska Institute of Culture in Kraków, Poland; "Virtual Museums of Małopolska" project** Source: Objaverse 1.0 / Sketchfab
Vyborg Clock Tower city block in Old Town
A city block in the center of [Vyborg](https://en.wikipedia.org/wiki/Vyborg) Old Town. The quarter is home to the Clock Tower and the Old Cathedral. Please see more info in the annotations. Location: https://goo.gl/maps/Ln4rA8EuGyXb9CiW6 Panorama: https://yandex.ru/maps/-/CCUFe8c-SB Captured with DJI mini 2 in June 2021 **Квартал Часовой башни в Выборге**. Башня была построена в 1678-1793гг. Нынешний часовой механизм работает с 1848 г. Рядом с башней расположены руины Старого Кафедрального собора, построенного в XV веке и разрушенного в 1939 г. во время советско-финской войны. Больше информации можно найти в аннотациях. Страница в Википедии: https://ru.wikipedia.org/wiki/Часовая_башня_(Выборг) Местоположение: https://yandex.ru/maps/-/CCUFe8cDOA Панорама: https://yandex.ru/maps/-/CCUFe8c-SB Снято на DJI mini 2 в июне 2021 Source: Objaverse 1.0 / Sketchfab
Clock Tower (Big Ben)
Big Ben is the nickname for the Great Bell of the clock at the north end of the Palace of Westminster in London and is usually extended to refer to both the clock and the clock tower. The official name of the tower in which Big Ben is located was originally the Clock Tower, but it was renamed Elizabeth Tower in 2012 to mark the Diamond Jubilee of Elizabeth II. Source: Objaverse 1.0 / Sketchfab
Collective flow of circadian clock information in honeybee colonies (Data)
<p>This repository contains the data used in the paper "Collective flow of circadian clock information in honeybee colonies".</p> <p><strong> </strong></p> <p>Paper: Collective flow of circadian clock information in honeybee colonies</p> <p><strong> </strong></p> <p>Code and more details are provided in the README file of<a href="https://github.com/BioroboticsLab/speedtransfer.git"> speedtransfer</a> repository.</p> <p><strong> </strong></p> <h2>Description of included files</h2> <p>All data sets exist for the period 01.08.-25.08.2016 and 20.08-14.09.2019.</p> <p><strong> </strong></p> <h3><strong>mean_velocity_2016.csv and mean_velocity_2019.csv</strong></h3> <p>The mean velocity for each bee and age is averaged over 10-minute time windows.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>velocity: Mean euclidean distance of two consecutive points of a bee's hive position.</p> </li> <li> <p>datetime: Date in year-month-day hour:minute:seconds+ms:ns format.</p> </li> <li> <p>age: Age in days. Can be NaN if the bee has no associated death_date.</p> </li> </ul> <p> </p> <h3><strong>velocity_2088_2019.csv and velocity_5101_2019.csv</strong></h3> <p>The movement speed [mm/s] of two individual bees with the bee id 2088 and the bee id 5101 for the period 2019.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>velocity: Euclidean distance of two consecutive points of a bee's hive position.</p> </li> <li>time_passed: Time [s] in between the datetime of that current and the last previous detection.</li> <li> <p>datetime: Date in year-month-day hour:minute:seconds+ms:ns format.</p> </li> </ul> <p> </p> <h3><strong>cosinor_2016.csv and cosinor_2019.csv</strong></h3> <p>A cosinor fit of the velocity per bee for a time window of 3 consecutive days according to the method proposed by <a href="https://doi.org/10.1186/1742-4682-11-16">Cornelissen</a>.</p> <p><strong>Keys</strong>:</p> <ul> <li> <p>mesor: Rhythm-adjusted mean of a cosine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>amplitude: Amplitude of a cosine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>phase: Acrophase of a cosine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_value: P-value of an F-test for overall significance of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_mesor: P-value of the mesor coefficient of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_amplitude: P-value of the amplitude coefficient of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_acrophase: P-value of the phase coefficient of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_reject: P-value of a F-test for model validity.</p> </li> <li> <p>r_squared: R² value of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>r_squared_adj: Adjusted R² value of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_ks: P-value of a Kalgomorov-Smirnoff test of residual normality.</p> </li> <li> <p>p_hom: P-Value of F-test of variance homogeneity of cosinor fit.</p> </li> <li> <p>dw: Durbin-Watson statistic for the independence of the residuals.</p> </li> <li> <p>p_runs: P-value of runs test of independence of residuals.</p> </li> <li> <p>RSS: Residual sum of squared - the sum of squared differences between the data and the estimated values from the fitted model</p> </li> <li> <p>SSPE: Pure error sum of squares of cosinor fit.</p> </li> <li> <p>bee_id: Numeric unique identifier per individual bee.</p> </li> <li> <p>age: Age in days. Can be NaN if the bee has no associated death_date.</p> </li> <li> <p>date: Date and time in year-month-day hour:minute:seconds+ms:ns format. The hour is always 12.</p> </li> <li> <p>n_data_points: Number of data points per cosinor fit.</p> </li> <li> <p>data_point_dist_max: Maximum temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>data_point_dist_min: Minimum temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>data_point_dist_mean: Mean temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>data_point_dist_median: Median temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>day_mean: Mean velocity during the daytime defined as the time between 9 and 18 o'clock.</p> </li> <li> <p>day_std: Standard deviation of velocity during the daytime defined as the time between 9 and 18 o'clock.</p> </li> <li> <p>night_mean: Mean velocity during the nighttime defined as the time between 21 and 6 o'clock.</p> </li> <li> <p>night_std: Standard deviation of velocity during the nighttime defined as time between 21 and 6 o'clock.</p> </li> <li> <p>ad_fuller: P-value of augmented Dickey-Fuller test for testing whether the velocity data is stationary.</p> </li> <li> <p>fit_type: Median time bin in seconds used for fit, e.g. 3600 means that the median over a time window of 3600s is used for the fit.</p> </li> <li> <p>ci_acrophase_lower: Confidence interval lower bound for the acrophase fit value.</p> </li> <li> <p>ci_acrophase_upper: Confidence interval upper bound for the acrophase fit value.</p> </li> <li> <p>ci_mesor_lower: Confidence interval lower bound for the mesor fit value.</p> </li> <li> <p>ci_mesor_upper: Confidence interval upper bound for the mesor fit value.</p> </li> <li> <p>ci_amplitude_lower: Confidence interval lower bound for the amplitude fit value.</p> </li> <li> <p>ci_amplitude_upper: Confidence interval upper bound for the amplitude fit value.</p> </li> </ul> <p><strong> </strong></p> <h3><strong>interactions_side0_2016.csv, interactions_side0_2019.csv and interactions_side1_2016.csv, interactions_side1_2019.csv</strong></h3> <p>The bee interactions and their post-interaction velocity change. An interaction between two bees (bee0 and bee1) is defined when two bees are detected simultaneously in the hive with a confidence threshold of 0.25, the distance between the markings on their thorax bodies is no more than 14 mm. These interactions are combined into one interaction if the same detections occur within a time interval of 1 second or less between them. The resulting interaction data frames per bee are concatenated with the estimates of the cosinor dataframe.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>bee_id0: Numeric unique identifier per individual bee.</p> </li> <li> <p>bee_id1: Numeric unique identifier per individual bee.</p> </li> <li> <p>interaction_start: Timestamp indicating interaction start time point.</p> </li> <li> <p>interaction_end: Timestamp indicating interaction end time point.</p> </li> <li> <p>x_pos_start_bee0: Numeric x-position of bee relative to hive at interaction start.</p> </li> <li> <p>y_pos_start_bee0: Numeric y-position of bee relative to hive at interaction start.</p> </li> <li> <p>theta_start_bee0: Numeric angle of bee relative to hive at interaction start.</p> </li> <li> <p>x_pos_start_bee1: Numeric x-position of bee relative to hive at interaction start.</p> </li> <li> <p>y_pos_start_bee1: Numeric y-position of bee relative to hive at interaction start.</p> </li> <li> <p>theta_start_bee1: Numeric angle of bee relative to hive at interaction start.</p> </li> <li> <p>x_pos_end_bee0: Numeric x-position of bee relative to hive at interaction end.</p> </li> <li> <p>y_pos_end_bee0: Numeric y-position of bee relative to hive at interaction end.</p> </li> <li> <p>theta_end_bee0: Numeric angle of bee relative to hive at interaction end.</p> </li> <li> <p>x_pos_end_bee1: Numeric x-position of bee relative to hive at interaction end.</p> </li> <li> <p>y_pos_end_bee1: Numeric y-position of bee relative to hive at interaction end.</p> </li> <li> <p>theta_end_bee1: Numeric angle of bee relative to hive at interaction end.</p> </li> <li> <p>vel_change_bee0: Numeric post-interaction absolute change of velocity: abs = vafter-vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> <li> <p>rel_change_bee0: Numeric post-interaction relative change of velocity: rel = (vafter-vbefore)/vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> <li> <p>vel_change_bee1: Numeric post-interaction absolute change of velocity: abs = vafter-vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> <li> <p>rel_change_bee1: Numeric post-interaction relative change of velocity:rel = (vafter-vbefore)/vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> </ul> <p>Modeling bees as rectangular mask to determine if bees body overlap when interacting - see more details in <a href="https://github.com/BioroboticsLab/speedtransfer.git">speedtransfer repository</a>:</p> <ul> <li> <p>x_trans_focal_bee0: Translated and rotated x-position relative to hive.</p> </li> <li> <p>y_trans_focal_bee0: Translated and rotated y-position relative to hive.</p> </li> <li> <p>theta_trans_focal_bee0: Translated and rotated theta relative to hive.</p> </li> <li> <p>x_trans_focal_bee1: Translated and rotated x-position relative to hive.</p> </li> <li> <p>y_trans_focal_bee1: Translated and rotated y-position relative to hive.</p> </li> <li> <p>theta_trans_focal_bee1: Translated and rotated theta relative to hive.</p> </li> <li> <p>overlapping: Bool indicating whether rectangular masks modeling the body of bees overlap.</p> </li> </ul> <p>Cosinor fit parameters - see more detailed in <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.wjr1wzfvmipm">Cosinor</a> data frame:</p> <ul> <li> <p>amplitude_bee0: Amplitude of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>phase_bee0: Phase of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>p_value_bee0: P-value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>r_squared_bee0: R² value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>amplitude_bee1: Amplitude of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>phase_bee1: Phase of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>p_value_bee1: P-value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>r_squared_bee1: R² of the cosinor fit per bee of the day of the interaction.</p> </li> </ul> <p> </p> <h3><strong>interactions_side0_null_model_2016.csv and interactions_side0_null_model_2019.csv</strong></h3> <p>A null model for bee interactions and their post-interaction velocity change. The interaction null model is created by taking the distribution of the start and end times of a given interaction dataframe and selecting two random bees at those times that the bees were detected in the hive at that time. These pairs of bees are considered as "interacting" and their post-interaction speed change is calculated. The resulting interaction data frames per bee are concatenated with the estimates of the cosinor dataframe. The position data is relative to pixels not to the hive coordinates.</p> <p>As this is a null model the following keys are the same as described in the <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.728ge538e768">Interaction</a> data frame.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>bee_id0</p> </li> <li> <p>bee_id1</p> </li> <li> <p>interaction_start</p> </li> <li> <p>interaction_end</p> </li> <li> <p>x_pos_start_bee0</p> </li> <li> <p>y_pos_start_bee0</p> </li> <li> <p>theta_start_bee0</p> </li> <li> <p>x_pos_start_bee1</p> </li> <li> <p>y_pos_start_bee1</p> </li> <li> <p>theta_start_bee1</p> </li> <li> <p>x_pos_end_bee0</p> </li> <li> <p>y_pos_end_bee0</p> </li> <li> <p>theta_end_bee0</p> </li> <li> <p>x_pos_end_bee1</p> </li> <li> <p>y_pos_end_bee1</p> </li> <li> <p>theta_end_bee1</p> </li> <li> <p>vel_change_bee0</p> </li> <li> <p>rel_change_bee0</p> </li> <li> <p>vel_change_bee1</p> </li> <li> <p>rel_change_bee1</p> </li> <li> <p>age_bee0</p> </li> <li> <p>phase_bee0</p> </li> <li> <p>amplitude_bee0</p> </li> <li> <p>r_squared_bee0</p> </li> <li> <p>p_value_bee0</p> </li> <li> <p>age_bee1</p> </li> <li> <p>amplitude_bee1</p> </li> <li> <p>r_squared_bee1</p> </li> <li> <p>p_value_bee1</p> </li> <li> <p>phase_bee1</p> </li> </ul> <p> </p> <h3><strong>interaction_tree_paths_2016.csv and interactions_tree_paths_2019.csv</strong></h3> <p>Graph-theoretic interaction tree paths. By tracing back interactions that positively influenced the speed of a focal bee, we constructed a graph-theoretic tree structure starting from a young rhythmic bee and recursively adding activating (velocity change parent > 0) individuals. We examined the impact of sequential interactions among bees occurring between 10 am and 3 pm, focusing on a subgroup of n = 1000 bees that are significantly rhythmic, younger than 5 days old, and peak in activity after 12 pm. We limited the time window between interactions to 30 minutes and capped the cascade duration at 2 hours to ensure causal relevance. The resulting interaction trees are collected and each node of all paths in the interaction trees are concatenated to this dataframe.</p> <p><strong>Keys</strong>:</p> <ul> <li> <p>bee_id: Numeric unique identifier per individual bee which is a node in the tree.</p> </li> <li> <p>datetime: Date in year-month-day hour:minute:seconds+ms:ns format when the interaction takes place.</p> </li> <li> <p>x_pos: Numeric x-position of bee relative to hive at interaction start.</p> </li> <li> <p>y_pos: Numeric y-position of bee relative to hive at interaction start.</p> </li> <li> <p>vel_change_parent: <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.728ge538e768">Absolute velocity change</a> of bee of parent node.</p> </li> <li> <p>age: Age in days of node bee. Can be NaN if the bee has no associated death date.</p> </li> <li> <p>is_root: Bool indicating if node is root of tree.</p> </li> <li> <p>depth: Depth of node in tree. E.g. depth of root is 0.</p> </li> <li> <p>is_leaf: Bool indicating if node is leaf of tree.</p> </li> <li> <p>n_children: Number of children of the subtree of the node.</p> </li> <li> <p>parent: Numeric bee_id of parent node.</p> </li> <li> <p>tree_id: Numeric unique identifier of tree.</p> </li> <li> <p>time_gap: Python datetime.timedelta object of time delta in between the parent and child node interaction.</p> </li> <li> <p>path_id: Numeric unique identifier of path.</p> </li> </ul> <p>Cosinor fit parameters - see more detailed in <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.wjr1wzfvmipm">Cosinor</a> data frame:</p> <ul> <li> <p> r_squared: R² value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p> phase: Phase of the cosinor fit per bee of the day of the interaction.</p> </li> </ul> <p> </p> <h2>Software used to acquire and analyze the data:</h2> <p><a href="https://github.com/BioroboticsLab/speedtransfer.git">speedtransfer: Cosinor fit and interaction calculation and further analyses.</a></p> <p><a href="https://github.com/BioroboticsLab/bb_rhythm">bb_rhythm: Cosinor fit and interaction calculation and further analyses.</a></p> <p><a href="https://github.com/BioroboticsLab/bb_behavior">bb_behavior: Database interaction and data (pre)processing, velocity calculation.</a></p> <p><a href="https://github.com/BioroboticsLab/bb_utils">bb_utils: Database settings and interaction.</a></p> <p><a href="https://github.com/walachey/slurmhelper">slurmhelper: A package for slurm script handling.</a></p> <p> </p>
The Cortical Synaptic Transcriptome is Organized by Clocks, but its Proteome is Driven by Sleep
<p>Images, ROIs and databases with the results of Particle analysis from RNA in situ hybridization performed with the RNA scope technology onto CA1 and cortex from mice collected at 6 different times of day .</p>
IMF clock angle proxy: orbital averaged magnetic field data from MAVEN in Martian magnetosheath
<p>The By and Bz components can be used as IMF clock angle proxy (IMF direction in MSO y-z plane). See the publication by Dong et al. 2019 for details: Dong, Y., Fang, X., Brain, D. A., Hurley, D. M., Halekas, J. S., Espley, J. R., et al. (2019). Magnetic field in the Martian magnetosheath and the application as an IMF clock angle proxy. <em>Journal of Geophysical Research: Space Physics</em>, 124, 4295–4313. <a href="https://doi.org/10.1029/2019JA026522">https://doi.org/10.1029/2019JA026522</a></p> <p>Formats: IDL save file and ASCII file</p> <p>Data descirption:</p> <p>Time span: Nov 11 2014 - May 21 2024</p> <p>Time: averaged unix time of MAVEN in magnetosheath of each s/c orbit</p> <p># of data points: # of data points (time resolution: 4s) taken in magnetosheath of each s/c orbit. Suggest using only # of data points >150 (i.e. duration > 10 min)</p> <p>Bx, By, Bz in MSO coordinate system</p> <p> </p>
Data associated with the manuscript "Complex epistatic interactions between ELF3, PRR9, and PRR7 regulates the circadian clock and plant physiology"
<p>Datasets associated with the figures in the paper entilted: "<strong>Complex epistatic interactions between ELF3, PRR9, and PRR7 regulates the circadian clock and plant physiology"</strong></p>
Quantum coherent spin-electric control in a molecular nanomagnet at clock transitions. Open data set
<p>Data supporting the related publication.</p>
Mercury isotope clocks predict coastal residency and migration timing of hammerhead sharks
<p>1. The management of migratory taxa relies on the knowledge of their movements. Among them, ontogenetic habitat shift, from nurseries to adult habitats, is a behavioral trait shared across marine taxa allowing resource partitioning between life stages and reducing predation risk. As this movement is consistent over time, characterizing its timing is critical to implement efficient management plans, notably in coastal areas to mitigate the impact of fisheries on juvenile stocks.</p> <p>2. In the Mexican Pacific, habitat use of the smooth hammerhead shark (<em>Sphyrna zygaena</em>) is poorly described, while the species is heavily harvested. Given the large uncertainties associated with the timing of out-migration from coastal nursery grounds to offshore waters prior to reproductive maturity, a more precise assessment of smooth hammerhead shark movements is needed. </p> <p>3. Photochemical degradation of mercury imparts mass-independent isotope fractionation (Δ<sup>199</sup>Hg) which can be used to discriminate between neonate coastal shallow habitats and the offshore deep foraging patterns of late juveniles. Here, we present the application of muscle Δ<sup>199</sup>Hg as molecular clocks to predict the timing of ontogenetic habitat shifts by smooth hammerhead sharks, based on their isotopic compositions at the initial and arrival habitats and on muscle isotopic turnover rate.</p> <p>4. We observed decreases in Δ<sup>199</sup>Hg values with shark body length, reflecting increasing reliance on offshore mesopelagic prey with age. Coastal residency estimates indicated that smooth hammerhead sharks utilize coastal resources for two years prior to offshore migration, suggesting a prolonged residency in these ecosystems.</p> <p>Policy implications: This study demonstrates how mercury stable isotopes and isotopic clocks can be implemented as a complementary tool for stock management by predicting the timing of animal migration—a key aspect in the conservation of marine taxa. In the Mexican Pacific, fishing pressure on shark species occurs in coastal habitats depleting juvenile stocks. Consequently, management decision support tools are imperative for effectively maintaining early life stage population levels over time. The finding that smooth hammerhead sharks extensively rely on highly fished habitats for two years after parturition supports the relevance of establishing a size limit in coastal fisheries and demonstrates how the current temporal shark fishing closure could lack efficiency for the species.</p>
Datasets for 176Lu+ clock comparison at the 10E-18 level via correlation spectroscopy
<p>Datasets and figure data for "176Lu+ clock comparison at the 10E-18 level via correlation spectroscopy"</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.