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Figure 2 in Incubation parameters, offspring growth, and behavioral adaptations to heat stress of Black Skimmers (Rynchops niger) in a Neotropical inland colony (Aves, Charadriiformes, Laridae)
Figure 2. Egg mass development in three surveyed Black Skimmer (Rynchops niger) nests (n = 7 eggs, 84 measurements) until hatching at Praia do Totelão, Pantanal, Mato Grosso, Brazil, throughout the incubation period in July- September 2015. Note that the number of eggs decreased to three toward the end of incubation due to predation. The regression line indicates a negative trend (R² = 0.043, p <0.05, LME) of egg mass over incubation time.
Kero colonial Siglo XVIII
1. Vaso ceremonial de madera de la época colonial. Estos "keros" fueron usados para libar la chicha, bebida ceremonial de los andes elaborada de maíz fermentado. 2. Según documentos coloniales, los artesanos especialistas en tallar la madera eran conocidos como querocamayoc. 3. Su decoración polícroma se realizó con una resina conocida como mopa mopa y mezclada con pigmentos locales. 4. Se compone de tres escenas. La primera presenta el rostro de un personaje masculino con tocado y pendiente, flanqueado por flores. La segunda tiene motivos en forma de cruz y escaleras. Finalmente, en la parte inferior encontramos diseños representando la flora local, resaltando la flor de la cantuta. Source: Objaverse 1.0 / Sketchfab
Kero Incaico. Periodo Colonial. S.XVII
Vaso ceremonial Colonial Pieza de adscripción crono-cultural Inca - Colonial dado los elementos diagnósticos de su iconografía, confeccionado en madera, se puede definir como un vaso ceremonial o *Kero*. Su origen prehispánico se traza ya en el periodo Formativo (3.000 años AP) confeccionados en cerámica, posteriormente en madera y metal. De base menor a su boca, se proyecta una copa de bordes evertidos (hacia afuera), con un ensanchado progresivo desde la base hacia la boca. Esta pieza corresponde a una colección decomisada por Aduanas de Chile y Policía de Investigaciones, quienes identificaron el tráfico ilícito de este tipo de bienes patrimoniales arqueológicos. El tráfico ilícito de bienes patrimoniales desde Perú hacia Chile evidencia la existencia de un mercado negro de bienes arqueológicos que se expresa en la complejidad, antigüedad, conservación y particularidad de las piezas que son decomisadas. Juan del Encina (1468-1529) Todos los bienes del mundo Ars Musicae & Coro Alleluia LP 1990 Source: Objaverse 1.0 / Sketchfab
Quantitative modelling of nutrient-limited growth of bacterial colonies in microfluidic cultivation
<p>Data for "Quantitative modelling of nutrient-limited growth of bacterial colonies in microfluidic cultivation"</p> <p> </p> <p>GrowthChannelExperiments contains the data-folders of the following growth channel experiments:<br> ***********************************************************************************************</p> <p>Name Feeding Concentration [in units of 0.195mM PCA]<br> nd004_series1 0.5<br> nd004_series2 0.5<br> nd004_series3 0.5<br> nd004_series4 2.0<br> nd004_series5 2.0<br> nd004_series6 2.0<br> nd004_series7 3.0<br> nd004_series8 3.0<br> nd112_series2 0.25<br> nd112_series3 0.25<br> nd112_series7 3.0<br> nd112_series8 3.0</p> <p>Every folder contains:<br> - a tif-file with captured image series<br> - a PIV*-folder with four PIV-files for every frame pair. The four files belong to intermediate results of the multistep PIV. The final PIV-result is given in the file step2*.dat.nmt.<br> The PIV result will be stored in a plain text file. Each line in this file correspond to each PIV vector and comprised of 16 columns:<br> x y ux1 uy1 mag1 ang1 p1 ux2 uy2 mag2 ang2 p2 ux0 uy0 mag0 flag<br> -- (x,y) is the position of the vector (center of the interrogation window).<br> -- ux1, uy1 are the x and y component of the vector (displacement) obtained from the 1st correlation peak.<br> -- mag1 is the magnitude (norm) of the vector.<br> -- ang1, is the angle between the current vector and the vector interpolated from previous PIV iteration.<br> -- p1 is the correlation value of the 1st peak.<br> -- ux2,uy2,mag2,ang2,p2 are the values for the vector obtained from the 2nd correlation peak.<br> -- ux0, uy0, mag0 are the vector value at (x,y) interpolated from previous PIV iteration.<br> -- flag is a column used for mark whether this vector value is interpolated (marked as 999) or switched between 1st and 2nd peak (marked as 21), or invalid (-1). <br> According to the PIV-Fiji-plugin as provided by Qingzong Tseng, used also in : <br> Tseng, Q. et al. Spatial organization of the extracellular matrix regulates cell-cell junction positioning. Proc. Natl. Acad. Sci. 109, 1506–1511 (2012)<br> - two traj*.dat files, belonging to particle positions of the corresponding simulation with monod/teissier uptake. <br> Columns correspond to <br> 1 : time | 2 : cellID | 3 : rx | 4 : ry | 5 : rz | 6: species | 7 : vx | 8 : vy | 9 : vz | 10 : fx | 11 : fy | 12 : fz | 13 : B(g) |<br> -- rx,ry,rz 3D coordinates of particle<br> -- species is either 0 (living cell) or 1 (wall-particle)<br> -- vx,vy,vz 3D velocity of particle<br> -- fx,fy,fz 3D force of particle<br> -- B(g) growth force constant dependent on local g-concentration<br> Note that due to the simulation being 2D, rx=constant and vx=0=fx.<br> - two g*.dat files, belonging to nutrient concentrations of the corresponding simulation with monod/teissier uptake. <br> Columns correspond to <br> 1 : time | 2 : gridx | 3 : gridy | 4 : gridz | 5 : g-conc | 6: kcons | 7 : kprod | 8: Dlocal |<br> -- gridx,gridy,gridz coordinates of lattice side<br> -- kcons local nutrient consumption rate<br> -- kprod local nutrient production rate (always zero)<br> -- Dlocal local diffusion constant</p> <p> </p> <p>GrowthChamberExperiments contains the the data-folders of the following growth chamber experiments:<br> ***************************************************************************************************</p> <p>Name Feeding Concentration [in units of 0.195mM PCA]<br> nd143_xy009 1.0<br> nd143_xy013 1.0<br> nd143_xy025 1.0<br> nd143_xy032 1.0<br> nd143_xy059 1.0<br> nd143_xy060 1.0<br> nd143_xy061 1.0<br> nd143_xy165 0.1<br> nd143_xy184 0.1<br> nd143_xy214 0.1</p> <p>Every folder contains:<br> - a tif-file with captured image series<br> - five traj*.dat files, belonging to particle positions of the corresponding simulation with monod-uptake and five different ratios of the diffusion constants in- and outside the colony.<br> - five g*.dat files, belonging to nutrient concentrations of the corresponding simulation with monod-uptake and five different ratios of the diffusion constants in- and outside the colony.</p>
Number of SHB in the colonies and colony defensiveness
<p>Small hive beetles (SHB), <em>Aethina tumida</em>, are free-flying parasites, which actively seek and invade honeybee host colonies. It seems as if SHB may prefer colonies in the shade. Further, it has been stated that SHB invade any colony with equal impunity. However, the impact of colony aggressiveness on SHB infestation levels has never been quantified. Here, we confirm significantly higher SHB infestation levels in shaded colonies and further suggest that host colony aggression is of minor importance only. In the state of Rio de Janeiro, Brazil, local Africanized honeybee colonies at a sunny (N=10) and at a shaded apiary (N=11) were tested for aggression and visually screened for SHB infestations using standard methods. Both colony aggression and infestation levels were variable, but not significantly correlated. The results confirm that infestation levels are significantly higher in the shaded apiary compared to the sun-exposed one. Further, host colony aggression is unlikely to interfere with SHB infestation levels of colonies. Instead, SHB seem to remain even in aggressive colonies. The underlying mechanisms for the significant differences in colony infestation levels due to sun exposure remain unknown. Beekeepers are advised to prefer sun-exposed apiary locations in regions, where SHB are a pest of concern.</p>
The scaling of metabolic traits differs among larvae and juvenile colonies of scleractinian corals
<p>Body size profoundly affects organism fitness and ecosystem dynamics through the scaling of physiological traits. This study tests for variation in metabolic scaling and its potential drivers among corals differing in life history strategies and taxonomic identity. Data were compiled from published sources and augmented with empirical measurements of corals in Moorea, French Polynesia. The data compilation revealed metabolic isometry in broadcasted larvae, but size-independent metabolism in brooded larvae; empirical measures of <em>Pocillopora acuta</em> larvae also supported size-independent metabolism in brooded coral larvae. In contrast, for juvenile colonies (i.e., 1–4 cm diameter), metabolic scaling was isometric for <em>Pocillopora</em> spp. and negatively allometric for <em>Porites</em> spp. The scaling of biomass with surface area was isometric for <em>Pocillopora</em> spp., but positively allometric for <em>Porites</em> spp., suggesting the surface area:biomass ratio mediates metabolic scaling in these corals. The scaling of tissue biomass and metabolism was not affected by light treatment (i.e., either natural photoperiods or constant darkness) in both juvenile taxa. However, biomass was reduced by 9–15% in the juvenile corals from the light treatments and this coincided with higher metabolic scaling exponents, thus supporting the causal role of biomass in driving variation in scaling. This study shows that metabolic scaling is plastic in the early life stages of corals, with intrinsic differences between life history strategy (i.e., brooded and broadcasted larvae) and taxa (i.e., <em>Pocillopora </em>spp<em>. </em>and <em>Porites </em>spp<em>.</em>), and acquired differences attributed to changes in area-normalized biomass.</p>
Twin colony isolate genome assemblies: Chlamydomonas 3112/3222 and WS3/WS7 and Scenedesmus ARA/ARA3
<p>Genomes and assemblies for twin isolate pairs from:</p> <p>David R. Nelson, Amphun Chaiboonchoe, Weiqi Fu, Khaled M. Hazzouri, Ziyuan Huang, Ashish Jaiswal, Sarah Daakour, Alexandra Mystikou, Marc Arnoux, Mehar Sultana, Kourosh Salehi-Ashtiani,<br>Potential for Heightened Sulfur-Metabolic Capacity in Coastal Subtropical Microalgae,<br>iScience,<br>Volume 11,<br>2019,<br>Pages 450-465,<br>ISSN 2589-0042,<br>https://doi.org/10.1016/j.isci.2018.12.035.<br>(https://www.sciencedirect.com/science/article/pii/S2589004218302657)</p> <p>Abstract: Summary<br>The activities of microalgae support nutrient cycling that helps to sustain aquatic and terrestrial ecosystems. Most microalgal species, especially those from the subtropics, are genomically uncharacterized. Here we report the isolation and genomic characterization of 22 microalgal species from subtropical coastal regions belonging to multiple clades and three from temperate areas. Halotolerant strains including Halamphora, Dunaliella, Nannochloris, and Chloroidium comprised the majority of these isolates. The subtropical-based microalgae contained arrays of methyltransferase, pyridine nucleotide-disulfide oxidoreductase, abhydrolase, cystathionine synthase, and small-molecule transporter domains present at high relative abundance. We found that genes for sulfate transport, sulfotransferase, and glutathione S-transferase activities were especially abundant in subtropical, coastal microalgal species and halophytic species in general. Our metabolomics analyses indicate lineage- and habitat-specific sets of biomolecules implicated in niche-specific biological processes. This work effectively expands the collection of available microalgal genomes by ∼50%, and the generated resources provide perspectives for studying halophyte adaptive traits.<br>Keywords: Global Nutrient Cycle; Phycology; Genomics; Metabolomics</p> <p>https://www.sciencedirect.com/science/article/pii/S2589004218302657</p> <p> </p> <p>Files are as follows:</p> <p> </p> <p>.fa = assembly</p> <p>.tr.fa = coding sequences</p> <p>.aa.fa = predicted proteins</p> <p>.gff = annotation files</p>
Data from: Beyond the colony-forming-unit: Rapid bacterial evaluation in Osteomyelitis
<p>Examination of bacteria/host cell interactions is important for understanding the aetiology of many infectious diseases. The colony-forming-unit (CFU) has been the standard for quantifying bacterial burden for the past century, however, this suffers from low sensitivity and is dependent on bacterial culturability in vitro. Our data demonstrate the discrepancy between the CFU and bacterial genome copy number in an osteomyelitis-relevant co-culture system and we confirm diagnosis and quantify bacterial load in clinical bone specimens. This study provides insight into improving the quantification of bacterial burden in such cases.</p>
Fig. 14 in Mississippian colonial tabulate and rugose corals from the Flett Formation, Liard Basin, northwest Canada
Fig. 14. Variation in septal number and corallite diameters of rugose coral Cordilleria sp.
Fig. 4 in The Phenomenon Of A Mixed Colony: The Case Of Lasius Brunneus And Lasius Umbratus (Hymenoptera, Formicidae)
Fig. 4. Calculations of growth dynamics of a mixed colony according to hypotheses 1 (A) and 2 (B).
Fig. 1 in The Phenomenon Of A Mixed Colony: The Case Of Lasius Brunneus And Lasius Umbratus (Hymenoptera, Formicidae)
Fig. 1. Calculated growth dynamics of a mixed colony with only one L. brunneus queen.
Fig. 1 in Case of alarm vocalization in a colony of Microtus guentheri (Danford & Alston, 1880) (Mammalia, Rodentia, Arvicolidae) from Southern Bulgaria
Fig. 1. Spectrogram of the alarm whilst of Guenter's vole Microtus guentheri.
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>
Reliable Many-to-Many Routing in Wireless Sensor Networks Using Ant Colony Optimisation
<p>Results files for testing of ACO protocol for many to many routing in wireless sensor networks. </p>
Sea whip coral Leptogorgia virgulata in the Mid-Atlantic Bight: Colony complexity, age, and growth
<p>Datasets for the analyses described in the submitted publication: Sea whip coral <em>Leptogorgia virgulata </em>in the Mid-Atlantic Bight: Colony complexity, age, and growth. </p>
FIGURE 1 in Riparian and valley-margin hardwood species of pre-colonial Piedmont forests: A preliminary study of subfossil leaves from White Clay Creek, southeastern Pennsylvania, USA
FIGURE 1. Location of the White Clay Creek leaf mat site, Chester County, Pennsylvania.
Fig. 5 in Methods for collecting large numbers of exuviae from Coptotermes (Blattodea: Rhinotermitidae) termite colonies
Fig. 5. (A) Coptotermes gestroi exuviae (approximately 550); (B) close up of exuviae.
Data from 'Hot spots of bleaching in massive Porites coral colonies'
<p>Data from 'Hot spots of bleaching in massive Porites coral colonies' published in <em>Marine Environmental Research</em>.</p>
Beekeepers relocate a bee colony
<p>A new bee colony invaded a residential home in alpine Switzerland. Beekeepers were called to relocate the bee colony.</p> <p>Recorded 20 June 2021.</p>
Growth and productivity of monogynous and polygynous colonies in the ant Myrmecina graminicola
<p>This is the dataset used in the article:</p> <p><span>Taupenot A., Doums D., Molet M. (2024) No major difference in growth and productivity between monogynous and polygynous colonies in the ant <em>Myrmecina graminicola</em>. <em>Insectes Sociaux</em></span><br>https://doi.org/10.1007/s00040-024-01004-y</p>
ScienceDex guides
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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.