Find research datasets worth reusing

opengpl-2.0Aug 2022View details →

Data and code for "A decade of monitoring micropollutants in urban wet-weather flows: what did we learn?"

Data and code for publication Lena Mutzner, Viviane Furrer, Hélène Castebrunet, Ulrich Dittmer, Stephan Fuchs, Wolfgang Gernjak, Marie-Christine Gromaire, Andreas Matzinger, Peter Steen Mikkelsen, William R. Selbig, Luca Vezzaro, A decade of monitoring micropollutants in urban wet-weather flows: what did we learn? Water Research, 2022. https://doi.org/10.1016/j.watres.2022.118968 Data Micropollutants concentrations in µg/l (incl. heavy metals) for 77 wet-weather discharge sites (36 combined sewer overflows, 41 stormwater outlets). The provided data set is a collection of raw data sets based on publications referenced below. The data description is provided in the folder Data\AA_DataDescription.txt Abstract Urban wet-weather discharges from combined sewer overflows (CSO) and stormwater outlets (SWO) are a potential pathway for micropollutants (trace contaminants) to surface waters, posing a threat to the environment and possible water reuse applications. Despite large efforts to monitor micropollutants in the last decade, the gained information is still limited and scattered. In a metastudy we performed a data-driven analysis of measurements collected at 77 sites (683 events, 297 detected micropollutants) over the last decade to investigate which micropollutants are most relevant in terms of 1) occurrence and 2) potential risk for the aquatic environment, 3) estimate the minimum number of data to be collected in monitoring studies to reliably obtain concentration estimates, and 4) provide recommendations for future monitoring campaigns. We highlight micropollutants to be prioritized due to their high occurrence and critical concentration levels compared to environmental quality standards. These top-listed micropollutants include contaminants from all chemical classes (pesticides, heavy metals, polycyclic aromatic hydrocarbons, personal care products, pharmaceuticals, and industrial and household chemicals). Analysis of over 30,000 event mean concentrations shows a large fraction of measurements (> 50%) were below the limit of quantification, stressing the need for reliable, standard monitoring procedures. High variability was observed among events and sites, with differences between micropollutant classes. The number of events required for a reliable estimate of site mean concentrations (error bandwidth of 1 around the “true value) depends on the individual micropollutant. The median minimum number of events is 7 for CSO (2 to 31, 80%-interquantile) and 6 for SWO (1 to 25 events, 80%-interquantile). Our analysis indicates the minimum number of sites needed to assess global pollution levels and our data collection and analysis can be used to estimate the required number of sites for an urban catchment. Our data-driven analysis demonstrates how future wet-weather monitoring programs will be more effective if the consequences of high variability inherent in urban wet-weather discharges are considered.

opencc-by-4.0Sep 2022View details →

Data samples for Flow-matching -- efficient coarse-graining molecular dynamics without forces

CG samples generated during the training and validation processes in the flow-matching project. Accompanying the preprint "Flow-matching -- efficient coarse-graining molecular dynamics without forces": https://arxiv.org/abs/2203.11167. Detailed descriptions can be found in the preprint as well as the included README.

opencc-by-4.0Oct 2022View details →

Data and movies for "Flow of the normal component of He II about bluff objects as recorded by 〖He〗_2^* excimers"

The directory is subdivided for the cylinder and flat plate bluff objects.  Each subdirectory is further subdivided into data and movie subdirectories. "Data" consists of raw data collected from the camera.  In the data directory is a listing of the filename with the power applied to the heater.  Filenames of background data are also listed.   "Movie" consists of frames integrated over 0.5 second intervals (28 frames).  The movies include only excimer peaks for which the intensity of the peak is >3 sigma (~45). Where sigma is the standard deviation of the per frame background. A basic Jupiter Notebook is included in the top-level directory.  This notebook (and the python code used by the notebook) will allow a user to upload a data file and a background file, subtract the background and place the net result into a NumPy array with dimensions of 256 x 256 x 1500, which represent the 2 directions in position and time in seconds.

opencc-zeroApr 2024View details →

Data from: Forestry impacts on stream flows and temperatures: A quantitative synthesis of paired catchment studies across the Pacific salmon range

Forestry is pervasive across temperate North America and may influence aquatic environmental conditions such as flows and temperatures, as well as important species such as Pacific salmon (Oncorhynchus spp.). While there have been many large-scale forestry experiments using paired catchment designs, these studies have yet to be quantitatively synthesized. Thus, it remains unclear whether forestry impacts are consistent, context-dependent, or unpredictable. This study aims to quantitatively synthesize forestry impacts on streamflow and temperature, through a systematic review and synthesis of paired catchment studies across the range of Pacific salmon. Specifically, we investigated whether generalizable relationships exist between forestry intensity (percent watershed harvested) and impacts to streamflow and temperature. We also examined whether watershed features (climate, hydrology, lithology) and harvest method mediated forestry impacts. We extracted information from 35 unique paired-catchments from California to Alaska. Forestry had strong impacts on peak and low flows and maximum summer water temperatures, but responses were quite variable. Across all catchments, forestry elevated peak flows ~20% (n = 31 catchments), reduced low flows ~25% (n = 13 catchments), and increased maximum summer temperatures ~15% (n = 35 catchments) on average. However, these impacts were variable and were not predictable based on forestry intensity, thus broader stressor-response relationships were not supported. Forestry impacts on peak flows and maximum summer temperatures varied spatially. Peak flow impacts increased with increased northward latitude and temperature impacts decreased with eastward longitude. However, the magnitude of impacts were unrelated to other watershed attributes, which included climate (precipitation and aridity), rain vs. snow hydrology, elevation, and bedrock lithology. Harvest method and riparian buffer presence also had no detected effects on forestry impacts across studies and statistical models explained a low proportion of variation overall. Collectively, our results indicate that forestry can have substantial impacts on key environmental conditions; however, the magnitude of impact was variable and could not be clearly linked to easily-measured watershed characteristics. This implies that forestry impacts are not broadly predictable. Probabilistic risk models based on distributions of potential impacts may therefore be more useful for watershed management in data-poor situations.

opencc-by-4.0May 2024View details →

Additional data for: Directing Min protein patterns with advective bulk flow

Data and analysis associated with 2024 addition to: "Direction Min protein patterns with advective bulk flow" (NatComms, 2023) Companion repository to <a href="../records/7339803">10.5281/zenodo.7339803</a> Please consider the readme for more information.

Stylidium armeria experimental gene flow data

Gene flow can have rapid effects on adaptation and is an important evolutionary tool available when undertaking biological conservation and restoration. This tool is underused partly because of the perceived risk of outbreeding depression and loss of mean fitness when different populations are crossed. In this article we briefly review some theory and empirical findings on how genetic variation is distributed across species ranges, describe known patterns of gene flow in nature with respect to environmental gradients, and highlight the effects of gene flow on adaptation in small or stressed populations in challenging environments (e.g., at species range limits). We then present a case study involving crosses at varying spatial scales among mountain populations of a trigger plant (Stylidium armeria: Stylidiaceae) in the Australian Alps to highlight how some issues around gene flow effects can be evaluated. We found evidence of outbreeding depression in seed production at greater geographic distances. Nevertheless, we found no evidence of maladaptive gene flow effects in likelihood of germination, plant performance (size), and performance variance, suggesting that gene flow at all spatial scales produces many offspring with high adaptive potential. This case study demonstrates a path to evaluating how increasing sources of gene flow in managed wild and restored populations could identify some offspring with high fitness that could bolster the ability of populations to adapt to future environmental changes. We suggest further ways in which managers and researchers can act to understand and consider adaptive gene flow in natural and conservation contexts under rapidly changing conditions.

Data from: Genetic divergence and one-way gene flow influence contemporary evolution and ecology of a partially migratory fish

Recent work has revealed the importance of contemporary evolution for shaping ecological outcomes. In particular, rapid evolutionary divergence between populations has been shown to impact the ecology of populations, communities, and ecosystems. While studies have focused largely on the role of adaptive divergence in generating ecologically-important variation among populations, much less is known about the role of gene flow in shaping ecological outcomes. After divergence, populations may continue to interact through gene flow, which may influence evolutionary and ecological processes. Here we investigate the role of gene flow in shaping the contemporary evolution and ecology of recently diverged populations of anadromous steelhead / resident rainbow trout (Oncorhynchus mykiss). Results show that resident rainbow trout introduced above waterfalls have diverged evolutionarily from downstream anadromous steelhead, which were the source of introductions. However, the movement of fish from above to below the waterfalls has facilitated gene flow, which has reshaped genetic and phenotypic variation in the anadromous source population. In particular, gene flow has led to an increased frequency of residency, which in turn has altered population density, size-structure, and sex ratio. This result establishes gene flow as a contemporary evolutionary process that can have important ecological outcomes. From a management perspective, anadromous steelhead are generally regarded as a higher conservation priority than resident rainbow trout, even when found within the same watershed. Our results show that anadromous and resident O. mykiss populations may be connected via gene flow, with important ecological consequences. Such eco-evolutionary processes should be considered when managing recently diverged populations connected by gene flow.

Data from: Salinity decline promotes growth and harmful blooms of a toxic alga by diverting carbon flow

Global climate change intensifies the water cycle and makes freshest waters become fresher and vice‑versa. But how this change impacts phytoplankton in coastal, particularly harmful algal blooms (HABs), remains poorly understood. Here, we monitored a coastal bay for a decade and found a significant correlation between salinity decline and the increase of Karenia mikimotoi blooms. To examine the physiological linkage between salinity decreases and K. mikimotoi blooms, we compare chemical, physiological and multi-omic profiles of this species in laboratory cultures under high (33) and low (25) salinities. Under low salinity, photosynthetic efficiency and capacity as well as growth rate and cellular protein content were significantly higher than that under high salinity. More strikingly, the omics data show that low salinity activated the glyoxylate shunt to bypass the decarboxylation reaction in the tricarboxylic acid cycle, hence redirecting carbon from CO2 release to biosynthesis. Furthermore, the enhanced glyoxylate cycle could promote hydrogen peroxide metabolism, consistent with the detected decrease in reactive oxygen species (ROS). These findings suggest that salinity declines can reprogram metabolism to enhance cell proliferation, thus promoting bloom formation in HAB species like K. mikimotoi, which has important ecological implications for future climate-driven salinity declines in the coastal ocean with respect to HAB outbreaks.

opencc-by-4.0May 2024View details →

Data products associated with "Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows"

These are the data products associated with "Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows" by J. F. Crenshaw, et. al. This includes the input catalog and the outputs of the workflow described here https://github.com/jfcrenshaw/pzflow-paper, as well as a gzip of the github repo.

Data from: Neuroprotection provided by hypothermia initiated with high transnasal flow with ambient air in a model of pediatric cardiac arrest

Clinical trials of hypothermia after pediatric cardiac arrest have not seen robust improvement in functional outcome, possibly because of the long delay in achieving target temperature. Previous work in infant piglets showed that high nasal airflow, which induces evaporative cooling in the nasal mucosa, reduced regional brain temperature uniformly in half the time needed to reduce body temperature. The mouth is kept open to allow the high nasal airflow to easily exit. Here, we evaluated whether initiation of hypothermia with high transnasal airflow (32 L/min) provides neuroprotection without adverse effects in the setting of asphyxic cardiac arrest. Anesthetized, mechanically ventilated piglets (approximately 2-weeks-old) underwent sham-operated procedures (Group 1) or asphyxic cardiac arrest (Groups 2-6). The asphyxic insult consisted of reducing the inspired oxygen from 30% to 9.5-10% for 45 minutes (hypoxia period), then briefly increasing the inspired oxygen to 21% for 5 min (to improve the later success of cardiac resuscitation), and then completely stopping ventilation for 7 minutes. Cardiopulmonary resuscitation (CPR) commenced by re-establishing ventilation, performing chest compression, and injecting epinephrine as needed. The five cardiac arrest groups were further divided into those with normothermic recovery (38.5°C; Group 2), with mild hypothermia (34°C) initiated by surface cooling at 10 minutes (Group 3) or 120 minutes (Group 5) after resuscitation, or with mild hypothermia (34°C) initiated by transnasal cooling initiated at 10 minutes (Group 4) or 120 minutes (Group 6) after resuscitation. In the two transnasal cooling groups, the high nasal airflow continued for 2 hours and was then stopped; thereafter, surface cooling was used to maintain hypothermia. In all four groups with induced hypothermia, rectal temperature was sustained at the targeted temperature of 34°C with surface cooling until 20 hours after resuscitation, followed by 6 hours of gradual rewarming and cessation of fentanyl/70% nitrous oxide anesthesia. At four days of recovery, the piglets were euthanized and their brains were analyzed for the density of morphologically intact neurons in putamen, sensorimotor cortex, ventrolateral thalamus, and prefrontal cortex. The data sheet shows the density of viable neurons in these 4 brain regions for the 45 piglets that completed the study. The data sheet also shows the serial measurements of rectal temperature, mean arterial blood pressure, heart rate, and arterial blood measurements of the partial pressure of oxygen (PO2) and carbon dioxide (PCO2), oxyhemoglobin saturation, and pH obtained at baseline, during the period of hypoxia, at 4 minutes of ventilation with 21% O2, during the period of asphyxia, and during the first 24 hours of recovery. The piglets are assigned the same unique identifier number, labelled 1-45, for each set of measurements. Transnasal cooling initiated at 10 minutes after resuscitation was able to significantly rescue neurons in the highly vulnerable putamen without adverse effects.

Data from: Amazonian rivers are leaky barriers to gene flow in forest understory birds

Ever since Alfred Russel Wallace's nineteenth-century observation that related terrestrial species are often separated on opposing riverbanks, major Amazonian rivers have been recognized as key drivers of speciation. However, rivers are dynamic entities whose widths and courses may vary through time. It thus remains unknown how effective rivers are at reducing gene flow and promoting speciation over long timescales. We fit demographic models to genomic sequence to reconstruct the history of gene flow in three pairs of avian taxa fully separated by different Amazonian rivers, and whose geographic ranges do not make contact in headwater regions. Models with gene flow were best fit, but still supported an initial period without any gene flow which ranged from 187,000 to over 959,000 years, suggesting that rivers are capable of initiating speciation through long stretches of allopatric divergence. Allopatry was followed by either bursts or prolonged episodes of gene flow that retarded genomic differentiation but did not homogenize populations. Our results support Amazonian rivers as key barriers that promoted speciation and the buildup of species richness, but they also suggest that river barriers are often leaky, with genomic divergence accumulating slowly due to episodes of substantial gene flow.

opencc-by-4.0Sep 2021View details →

Data from: Forest carbon sink in the U.S. (1870–2012) driven by substitution of forest ecosystem service flows

Open the record for dataset details and reuse information.

opencc-by-4.0Jul 2024View details →

RNA flow additional data sets

Additional raw data files and mathematical derivations to accompany the publication in Mol. Cell. (2024) by Ietswaart, Smalec, Xu, et al: Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the mammalian transcript life cycle.

opencc-by-4.0Mar 2024View details →

Collective flow of circadian clock information in honeybee colonies (Data)

This repository contains the data used in the paper "Collective flow of circadian clock information in honeybee colonies".   Paper: Collective flow of circadian clock information in honeybee colonies   Code and more details are provided in the README file of<a href="https://github.com/BioroboticsLab/speedtransfer.git"> speedtransfer</a> repository.   <h2>Description of included files</h2> All data sets exist for the period 01.08.-25.08.2016 and 20.08-14.09.2019.   <h3>mean_velocity_2016.csv and mean_velocity_2019.csv</h3> The mean velocity for each bee and age is averaged over 10-minute time windows. Keys: <ul> <li> velocity: Mean euclidean distance of two consecutive points of a bee's hive position. </li> <li> datetime: Date in year-month-day hour:minute:seconds+ms:ns format. </li> <li> age: Age in days. Can be NaN if the bee has no associated death_date. </li> </ul>   <h3>velocity_2088_2019.csv and velocity_5101_2019.csv</h3> The movement speed [mm/s] of two individual bees with the bee id 2088 and the bee id 5101 for the period 2019. Keys: <ul> <li> velocity: Euclidean distance of two consecutive points of a bee's hive position. </li> <li>time_passed: Time [s] in between the datetime of that current and the last previous detection.</li> <li> datetime: Date in year-month-day hour:minute:seconds+ms:ns format. </li> </ul>   <h3>cosinor_2016.csv and cosinor_2019.csv</h3> 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>. Keys: <ul> <li> 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. </li> <li> 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. </li> <li> 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. </li> <li> 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. </li> <li> 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. </li> <li> 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. </li> <li> 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. </li> <li> p_reject: P-value of a F-test for model validity. </li> <li> 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. </li> <li> 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. </li> <li> p_ks: P-value of a Kalgomorov-Smirnoff test of residual normality. </li> <li> p_hom: P-Value of F-test of variance homogeneity of cosinor fit. </li> <li> dw: Durbin-Watson statistic for the independence of the residuals. </li> <li> p_runs: P-value of runs test of independence of residuals. </li> <li> RSS: Residual sum of squared - the sum of squared differences between the data and the estimated values from the fitted model </li> <li> SSPE: Pure error sum of squares of cosinor fit. </li> <li> bee_id: Numeric unique identifier per individual bee. </li> <li> age: Age in days. Can be NaN if the bee has no associated death_date. </li> <li> date: Date and time in year-month-day hour:minute:seconds+ms:ns format. The hour is always 12. </li> <li> n_data_points: Number of data points per cosinor fit. </li> <li> data_point_dist_max: Maximum temporal distance between two consecutive timepoints of velocity data. </li> <li> data_point_dist_min: Minimum temporal distance between two consecutive timepoints of velocity data. </li> <li> data_point_dist_mean: Mean temporal distance between two consecutive timepoints of velocity data. </li> <li> data_point_dist_median: Median temporal distance between two consecutive timepoints of velocity data. </li> <li> day_mean: Mean velocity during the daytime defined as the time between 9 and 18 o'clock. </li> <li> day_std: Standard deviation of velocity during the daytime defined as the time between 9 and 18 o'clock. </li> <li> night_mean: Mean velocity during the nighttime defined as the time between 21 and 6 o'clock. </li> <li> night_std: Standard deviation of velocity during the nighttime defined as time between 21 and 6 o'clock. </li> <li> ad_fuller: P-value of augmented Dickey-Fuller test for testing whether the velocity data is stationary. </li> <li> 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. </li> <li> ci_acrophase_lower: Confidence interval lower bound for the acrophase fit value. </li> <li> ci_acrophase_upper: Confidence interval upper bound for the acrophase fit value. </li> <li> ci_mesor_lower: Confidence interval lower bound for the mesor fit value. </li> <li> ci_mesor_upper: Confidence interval upper bound for the mesor fit value. </li> <li> ci_amplitude_lower: Confidence interval lower bound for the amplitude fit value. </li> <li> ci_amplitude_upper: Confidence interval upper bound for the amplitude fit value. </li> </ul>   <h3>interactions_side0_2016.csv, interactions_side0_2019.csv and interactions_side1_2016.csv, interactions_side1_2019.csv</h3> 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. Keys: <ul> <li> bee_id0: Numeric unique identifier per individual bee. </li> <li> bee_id1: Numeric unique identifier per individual bee. </li> <li> interaction_start: Timestamp indicating interaction start time point. </li> <li> interaction_end: Timestamp indicating interaction end time point. </li> <li> x_pos_start_bee0: Numeric x-position of bee relative to hive at interaction start. </li> <li> y_pos_start_bee0: Numeric y-position of bee relative to hive at interaction start. </li> <li> theta_start_bee0: Numeric angle of bee relative to hive at interaction start. </li> <li> x_pos_start_bee1: Numeric x-position of bee relative to hive at interaction start. </li> <li> y_pos_start_bee1: Numeric y-position of bee relative to hive at interaction start. </li> <li> theta_start_bee1: Numeric angle of bee relative to hive at interaction start. </li> <li> x_pos_end_bee0: Numeric x-position of bee relative to hive at interaction end. </li> <li> y_pos_end_bee0: Numeric y-position of bee relative to hive at interaction end. </li> <li> theta_end_bee0: Numeric angle of bee relative to hive at interaction end. </li> <li> x_pos_end_bee1: Numeric x-position of bee relative to hive at interaction end. </li> <li> y_pos_end_bee1: Numeric y-position of bee relative to hive at interaction end. </li> <li> theta_end_bee1: Numeric angle of bee relative to hive at interaction end. </li> <li> 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. </li> <li> 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. </li> <li> 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. </li> <li> 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. </li> </ul> 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>: <ul> <li> x_trans_focal_bee0: Translated and rotated x-position relative to hive. </li> <li> y_trans_focal_bee0: Translated and rotated y-position relative to hive. </li> <li> theta_trans_focal_bee0: Translated and rotated theta relative to hive. </li> <li> x_trans_focal_bee1: Translated and rotated x-position relative to hive. </li> <li> y_trans_focal_bee1: Translated and rotated y-position relative to hive. </li> <li> theta_trans_focal_bee1: Translated and rotated theta relative to hive. </li> <li> overlapping: Bool indicating whether rectangular masks modeling the body of bees overlap. </li> </ul> 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: <ul> <li> amplitude_bee0: Amplitude of the cosinor fit per bee of the day of the interaction. </li> <li> phase_bee0: Phase of the cosinor fit per bee of the day of the interaction. </li> <li> p_value_bee0: P-value of the cosinor fit per bee of the day of the interaction. </li> <li> r_squared_bee0: R² value of the cosinor fit per bee of the day of the interaction. </li> <li> amplitude_bee1: Amplitude of the cosinor fit per bee of the day of the interaction. </li> <li> phase_bee1: Phase of the cosinor fit per bee of the day of the interaction. </li> <li> p_value_bee1: P-value of the cosinor fit per bee of the day of the interaction. </li> <li> r_squared_bee1: R² of the cosinor fit per bee of the day of the interaction. </li> </ul>   <h3>interactions_side0_null_model_2016.csv and interactions_side0_null_model_2019.csv</h3> 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. 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. Keys: <ul> <li> bee_id0 </li> <li> bee_id1 </li> <li> interaction_start </li> <li> interaction_end </li> <li> x_pos_start_bee0 </li> <li> y_pos_start_bee0 </li> <li> theta_start_bee0 </li> <li> x_pos_start_bee1 </li> <li> y_pos_start_bee1 </li> <li> theta_start_bee1 </li> <li> x_pos_end_bee0 </li> <li> y_pos_end_bee0 </li> <li> theta_end_bee0 </li> <li> x_pos_end_bee1 </li> <li> y_pos_end_bee1 </li> <li> theta_end_bee1 </li> <li> vel_change_bee0 </li> <li> rel_change_bee0 </li> <li> vel_change_bee1 </li> <li> rel_change_bee1 </li> <li> age_bee0 </li> <li> phase_bee0 </li> <li> amplitude_bee0 </li> <li> r_squared_bee0 </li> <li> p_value_bee0 </li> <li> age_bee1 </li> <li> amplitude_bee1 </li> <li> r_squared_bee1 </li> <li> p_value_bee1 </li> <li> phase_bee1 </li> </ul>   <h3>interaction_tree_paths_2016.csv and interactions_tree_paths_2019.csv</h3> 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. Keys: <ul> <li> bee_id: Numeric unique identifier per individual bee which is a node in the tree. </li> <li> datetime: Date in year-month-day hour:minute:seconds+ms:ns format when the interaction takes place. </li> <li> x_pos: Numeric x-position of bee relative to hive at interaction start. </li> <li> y_pos: Numeric y-position of bee relative to hive at interaction start. </li> <li> 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. </li> <li> age: Age in days of node bee. Can be NaN if the bee has no associated death date. </li> <li> is_root: Bool indicating if node is root of tree. </li> <li> depth: Depth of node in tree. E.g. depth of root is 0. </li> <li> is_leaf: Bool indicating if node is leaf of tree. </li> <li> n_children: Number of children of the subtree of the node. </li> <li> parent: Numeric bee_id of parent node. </li> <li> tree_id: Numeric unique identifier of tree. </li> <li> time_gap: Python datetime.timedelta object of time delta in between the parent and child node interaction. </li> <li> path_id: Numeric unique identifier of path. </li> </ul> 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: <ul> <li>  r_squared: R² value of the cosinor fit per bee of the day of the interaction. </li> <li>  phase: Phase of the cosinor fit per bee of the day of the interaction. </li> </ul>   <h2>Software used to acquire and analyze the data:</h2> <a href="https://github.com/BioroboticsLab/speedtransfer.git">speedtransfer: Cosinor fit and interaction calculation and further analyses.</a> <a href="https://github.com/BioroboticsLab/bb_rhythm">bb_rhythm: Cosinor fit and interaction calculation and further analyses.</a> <a href="https://github.com/BioroboticsLab/bb_behavior">bb_behavior: Database interaction and data (pre)processing, velocity calculation.</a> <a href="https://github.com/BioroboticsLab/bb_utils">bb_utils: Database settings and interaction.</a> <a href="https://github.com/walachey/slurmhelper">slurmhelper: A package for slurm script handling.</a>

opencc-by-4.0Mar 2018View details →

A Power-Aware, Self-Adaptive Macro Data Flow Framework

Abstract: The dataflow programming model has been extensively used as an effective solution to implement efficient parallel programming frameworks. However, the amount of resources allocated to the runtime support is usually fixed once by the programmer or the runtime, and kept static during the entire execution. While there are cases where such a static choice may be appropriate, other scenarios may require to dynamically change the parallelism degree during the application execution. In this paper we propose an algorithm for multicore shared memory platforms, that dynamically selects the optimal number of cores to be used as well as their clock frequency according to either the workload pressure or to explicit user requirements. We implement the algorithm for both structured and unstructured parallel applications and we validate our proposal over three real applications, showing that it is able to save a significant amount of power, while not impairing the performance and not requiring additional effort from the application programmer. This dataset contains the raw data of the experiments and the scripts used to plot them.

opencc-by-4.0Apr 2018View details →

Pressure-based algorithm for compressible interfacial flows with acoustically-conservative interface discretisation (Supporting data)

The dataset contains sample numerical results associated with the manuscript under the same title, "Pressure-based algorithm for compressible interfacial flows with acoustically-conservative interface discretisation", published in Journal of Computational Physics (2018), https://doi.org/10.1016/j.jcp.2018.04.028.

opencc-by-nc-nd-4.0Oct 2018View details →

AllScale: Reference data of a flow over a plane. DPW-4 performed with Fine/Open

The present computation has been performed on a 66 millions cells mesh. The turbulence model used is the Spalart Allmaras using CPU-Booster.   Flow configuration :  <ul> <li>M = 0.85</li> <li>Re = 5 000 000</li> <li>AoA = 0.040472087 radians</li> </ul> Integral results:  <ul> <li>CL = 0.49328</li> <li>CD = 2.701862e-2</li> <li>CM = -4.89841e-2</li> </ul> Geometry and refernce data can be found on the DPW-4 website  : https://aiaa-dpw.larc.nasa.gov/Workshop4/DPW4-geom.html The data set provide one cgns file of the 3D mesh with the primitives variables in order to compare the reference application and the AllScale prototype.

opencc-by-4.0Jan 2019View details →

Test and validation data for Robbie: A Batch Processing Work-flow for the Detection of Radio Transients and Variables

Robbie: a general work-flow for the detection and characterization of radio variability and transient events in the image domain. Robbie is designed to work in a batch processing paradigm with a modular design so that components can be swapped out or upgraded to adapt to different input data, whilst retaining a consistent and coherent methodological approach. Robbie is based on commonly used and open software, and is encapsulated in a Makefile to aid portability and reproducibility. In the description paper we describe the methodology behind Robbie, and demonstrate its use on real and simulated data. This repository contains the observed and simulated data that was used in the description paper.

opencc-by-4.0Nov 2018View details →

One-dimensional shallow flow solution data with uncertain topography

Raw tabulated data generated by <a href="https://www.seamlesswave.com/">SEAMLESS-WAVE</a> stochastic models.