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Figs 37–42 in Estimation of biomass of dung beetles (Coleoptera: Scarabaeoidea) from the Russian Far East

Figs 37–42. Models of length-weight relationship and functions for a large tunnelers

Figs 43–47 in Estimation of biomass of dung beetles (Coleoptera: Scarabaeoidea) from the Russian Far East

Figs 43–47. Models of length-weight relationship and functions for a medium tunnelers

Figs 19–24 in Estimation of biomass of dung beetles (Coleoptera: Scarabaeoidea) from the Russian Far East

Figs 19–24. Models of length-weight relationship and functions for a large dwellers

Figs 31–36 in Estimation of biomass of dung beetles (Coleoptera: Scarabaeoidea) from the Russian Far East

Figs 31–36. Models of length-weight relationship and functions for a large tunnelers

Figs 25–30 in Estimation of biomass of dung beetles (Coleoptera: Scarabaeoidea) from the Russian Far East

Figs 25–30. Models of length-weight relationship and functions for a large dwellers (figs

Figs 13–18 in Estimation of biomass of dung beetles (Coleoptera: Scarabaeoidea) from the Russian Far East

Figs 13–18. Models of length-weight relationship and functions for a medium dwellers

opencc-by-4.0Sep 2023View details →

The winter subset of the Saildrone 2021-2022 Mission to the Gulf Stream used for the publication "The importance of contemporaneous measurements for regional air-sea CO2 flux estimates"

Data from the Saildrone 2021-2022 observational mission to the Gulf Stream. These data are published to accompany the publication "The importance of contemporaneous measurements for regional air-sea CO2 flux estimates." Included in this dataset are the primary and processed variables used throughout the paper. The data associated with each saildrone is named by the drone number. Additionally, included in the structure for each drone are the gas transfer velocities for each scenario, MBL atmospheric CO2 interpolated to the time and location of the drone, and ERA-5 wind speed, sea level pressure, significant wave height, and drag coefficient interpolated to the time and location of the drone. These variables are used to calculate CO2 fluxes for each scenario and are named as follows: "F" + gas transfer velocity equation used (DM18 or W14) + drone ID + scenario. Scenario A-D correspond to those outlined in the paper. Scenarios E and F correspond to the calculation of air-sea fluxes using all saildrone observed variables except for atmospheric CO2 (from MBL product) and significant wave height (from ERA-5), respectively.

dryad36/100

Behavioral estimates of mating success corroborate genetic evidence for pre-copulatory selection

In promiscuous species, fitness estimates obtained from genetic parentage may often reflect both pre- and post-copulatory components of sexual selection. Directly observing copulations can help isolate the role of pre-copulatory selection, but such behavioral data are difficult to obtain in the wild and may also overlook post-copulatory factors that alter the relationship between mating success and reproductive success. To overcome these limitations, we combined genetic parentage analysis with behavioral estimates of size-specific mating in a wild population of brown anole lizards (Anolis sagrei). Males of this species are twice as large as females and multiple mating among females is common, suggesting the scope for both pre- and post-copulatory processes to shape sexual selection on male body size. Our genetic estimates of reproductive success revealed strong positive directional selection for male size, which was also strongly associated with the number of mates inferred from parentage. In contrast, a male's size was not associated with the fecundity of his mates or his competitive fertilization success. By simultaneously tracking copulations in the wild via the transfer of colored powder to females by males from different size quartiles, we independently confirmed that large males were more likely than small males to mate. We conclude that body size is primarily under pre-copulatory sexual selection in brown anoles, and that post-copulatory processes do not substantially alter this pre-copulatory selection. Our study also illustrates the utility of combining both behavioral and genetic methods to estimate mating success to disentangle pre- and post-copulatory processes in promiscuous species.

opencc-zeroNov 2023View details →

opencc-by-4.0Nov 2023View details →

Ocean biogeochemical reconstructions to estimate historical ocean CO2 uptake

The dataset presented here was employed for the visual representations and analysis in the submitted research article "Ocean Biogeochemical Reconstructions to Estimate Historical Ocean CO_2 Uptake" by Bernardello R. et al., which was a requirement established by the Earth System Journal. The presented data are post-processed results obtained through simulations of the EC-Earth-CC Earth System Model. In adherence to the journal's policy, we have provided access to this dataset.In the files where the name does not specify otherwise, the dataset comprises every case that was examined in the paper.In the case of Chlorophyll and Mixed Layer Depth, each file comprises a single case, which is denoted by its name.

Estimating Lexical Complexity in Multi-Domain Settings for the Russian language

Open the record for dataset details and reuse information.

On the estimation of the shear modulus of a honeycomb sandwich panel from X-ray mapping of its core: supplementary files

This dataset is a Supplementary Material accompanying a paper [1] entitled  On the estimation of the shear modulus of a honeycomb sandwich panel from X-ray mapping of its core by Q. Leclère et al., published in the <a href="https://doi.org/10.25518/2684-6500.161">Journal of Structural Dynamics</a>. (Diamond Open Access) This dataset contains two data types from two experimental setups implemented on the same sandwich panel sample. .TIF images are X-ray radiographies of the sample, showing the honeycomb structure of the core. (1pixel=0.19mm) .UNV file is the result of a measurement of the vibration field of the sample excited by a piezoactuator realized using a Laser Doppler Vibrometer (regular grid, 2mm resolution).  More information in the paper, that has to be cited in any publication exploiting this dataset. [1] Q. Leclère, K. Ege, F. Marchetti, N.B. Roozen, Ph. Duvauchelle, M. Tahraoui & V. Kaftandjian, «On the estimation of the shear modulus of a honeycomb sandwich panel from X-ray mapping of its core.», Journal of Structural Dynamics [En ligne], Issue 2, URL : https://popups.uliege.be/2684-6500/index.php?id=161.

opencc-zeroNov 2023View details →

dryad36/100

Quantitative precipitation estimate (QPE) and forecast (QPF) exceedance comparison with flash flood reports

Flash flooding remains a challenging prediction problem, which is exacerbated by the lack of a universally accepted definition of the phenomenon. In this article, we extend prior analysis to examine the correspondence of various combinations of quantitative precipitation estimates (QPE) and precipitation thresholds to observed occurrences of flash floods, additionally considering short-term quantitative precipitation forecasts from a convection-allowing model. Consistent with previous studies, there is large variability between QPE datasets in the frequency of "heavy" precipitation events. There is also large regional variability in the best thresholds for correspondence with reported flash floods. In general, Flash Flood Guidance (FFG) exceedances provide the best correspondence with observed flash floods, except in the interior western US where recurrence interval thresholds (for the southwestern US) and static thresholds (for the northern and central Rockies) provide better correspondence. Six-hour QPE provides better correspondence with observed flash floods than 1-h QPE in all regions except the west coast and southwestern US. Exceedances of precipitation thresholds in forecasts from the operational High-Resolution Rapid Refresh (HRRR) generally do not correspond with observed flash flood events as well as QPE datasets, but they outperform QPE datasets in some regions of complex terrain and sparse observational coverage such as the southwestern US. These results can provide context for forecasters seeking to identify potential flash flood events based on QPE or forecast-based exceedances of precipitation thresholds.

Telemetry data from: Realized thermal niche approach eliminates temperature bias in 3 bioenergetic model estimates

<h4>Raw data for Ivanova et al paper in Ecology and Evolution</h4>Datafile is an .rds file.

Dataset for paper "Global method for gender profile estimation from distribution of first names"

Full dataset for paper "Global method for gender profile estimation from distribution of first names". See https://arxiv.org/abs/2305.07587

CityPark enviroment for Depth Estimation

The CityPark simulation environment is a park scenario that contains diverse vegetation, wooden, metal and lighting poles, fences and walking trails. Three flights were conducted at various times of the day to have images with varied lighting, collecting 9K RGB images and their corresponding depth image.

opencc-by-4.0Jan 2024View details →

Passive Non-Cooperative Intruder State Estimation and Optimal-Feedback Avoidance System for UAVs

In recent years, numerous applications for unmanned aircraft systems (UAS) have emerged, such as manufacturing inspections and reconnaissance. Ensuring safety is crucial for integrating UAS into the National Airspace System (NAS); this integration is being conducted on the basis of a century of experience that has made manned aircraft operations incredibly safe.A key challenge for unmanned flight is the inability to "detect-and-avoid" (DAA) obstacles. Various DAA systems have been proposed in recent years, each employing different sensor modalities. Cooperative systems enable air vehicles to exchange state information, while devices like the Automatic Dependent Surveillance-Broadcast (ADS-B) and Traffic Collision Avoidance System (TCAS) use satellite navigation sensors and transponders, respectively, to broadcast position data. Additionally, the Airborne Collision Avoidance System (ACAS) led to the creation of the ACAS-XU standard for unmanned aircraft.The DAA capability for UAS must be extended to address non-cooperative intruders. This paper introduces an integrated vision-based passive collision alert system (PCAS) and guidance system that is designed to detect and optimally avoid collision with non-cooperative intruders. The system can adhere to recently-introduced regulations for safety zones and can be customized pre-flight. Hardware-in-the-loop (HITL) simulation demonstrates the feasibility for deployment on UAS in a plug-and-play fashion.

Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease

Connectome, traits and behavior data for APOE234 mice. <ul> <li>1. connectome.zip: mouse brain structural connectivity matrices from diffusion MRI.</li> <li>2. FAGNN_Phenotype.csv: a sheet of trait information of mice used in the study.</li> </ul> columns: winding numbers, total distance, normalized NE time, normalized NE distance, normalized NW time, normalized NW distance, normalized SE time, normalized SE distance, normlaized SW time, normalized SW distance, island latency to first entry, island entries, normalized thigmataxis time, and normalized thigmotaxis distance <div>rows: 4 trials for each day from day 1 to day 5 with 1 probing test each at day 5 and day 8</div> <ul> <li>3. mouse_anatomy.csv: brain region information regarding the connectivity matrix.</li> <li>4. behavior.zip: behavioral data for each mouse from Morris Water Maze experiments.</li> </ul>

Data and codes for Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be

Data and codes for Landslide hazard spatiotemporal prediction based on data-driven models: Estimating where, when and how large landslide may be

opencc-by-4.0Jul 2020View details →

Virtually estimated endocranial volumes of the Krapina Neandertals

To use any of these data, please cite: Cofran, Z., Boone, M., and Petticord, M. 2021. Virtually estimated endocranial volumes of the Krapina Neandertals. American Journal of Physical Anthropology 174:117–128. <a href="https://doi.org/10.1002/ajpa.24165">https://doi.org/10.1002/ajpa.24165</a> S2. Endocranial volumes of Middle Pleistocene hominins from this and other studies A comma separated values (.csv) format file containing 10 ECVs for each of the Krapina hominins as determined by warping human reference endocasts, and single ECV estimates for the Sima de los Huesos hominins, pre-Würm Neandertals, and Würm Neandertals. The Sima de los Huesos data are from Poza-Rey and colleagues (2018). Würm and pre-Würm Neandertal values come mainly from DeSilva (2018), but in some cases more recent and/or virtually-estimated values were used instead (i.e., Braüer et al., 2020; Bruner & Manzi, 2008; Neubauer et al., 2018; Rougier, 2003). References Braüer, G., Pitsios, T., Säring, D., von Harling, M., Jessen, F., Kroll, A., & Groden, C. (2020). Virtual reconstruction and comparative analyses of the Middle Pleistocene Apidima 2 cranium (Greece). Anatomical Record 303: 1374–1392. doi: 10.1002/ar.24225 Bruner, E., & Manzi, G. Paleoneurology of an “Early” Neandertal: Endocranial size, shape, and features of Saccopastore 1. Journal of Human Evolution 54: 729–742. Neubauer, S., Hublin, J.-J., & Gunz, P. (2018). The evolution of modern human brain shape. Science Advances, 4: eaao5961. doi: 10.1126/sciadv.aao5961 Poza-Rey, E. M., Gómez-Robles, A., & Arsuaga, J. L. (2019). Brain size and organization in the Middle Pleistocene hominins from Sima de los Huesos. Inferences from endocranial variation. Journal of Human Evolution, 129: 67–90. doi:10.1016/j.jhevol.2019.01.006. Rougier, H. (2003). Étude descriptive et comparative de Biache-Saint-Vaast 1 (Biache-Saint-Vaast, Pas-de-Calais, France) (doctoral dissertation). Sciences de l’Homme et Société. Université de Bordeaux, Bordeaux. S3. Terry Collection endocast landmark data A comma separated values (.csv) format file containing all 441 landmarks for all 40 adult human endocasts from the Terry Collection (Copes, 2012), used in this study. Note that the original CT scan data can be downloaded from Dr. Lynn Copes’ website (http://www.lynncopes.com/human-ct-scans.html). The .csv file contains a column for individual ID, landmark name, and the x-, y-, and z-coordinates of the landmark. The ten individuals used as references to reconstruct the fossil endocasts are identified as TC 712, TC 942, TC 956, TC 1015, TC 1155, TC 1233R, TC 1293, TC 1306, TC 1323, and TC 1356. Reference Copes, L. E. (2012). Comparative and Experimental Investigations of Cranial Robusticity in Mid-Pleistocene Hominins (doctoral dissertation). Arizona State University, Tempe. S4. Fossil endocast landmark data A comma separated values (.csv) format file containing all 441 landmarks for Spy II and each of the five Krapina hominins. For each fossil, the dataset contains only the landmarks located on preserved endocranial surface, as originally digitized in Viewbox4 (dHAL Software) prior to missing data estimation as described in the Methods section of the manuscript. .csv file contains a column for individual ID, landmark name, and the x-, y-, and z-coordinates of the landmark.

Comparative Analysis of Methods to Estimate Geodetic Strain Rates from GNSS Data in Italy

This dataset comprises GNSS velocity field and strain rate maps for Italy. List of Files: <ol> <li> velocity_dataset.dat <ul> <li>GNSS velocity field of stations with time series longer than 4.5 years.</li> <li>Columns: Longitude (degrees), Latitude (degrees), East component of velocity (mm/yr), North component of velocity (mm/yr), Uncertainty on the East component (mm/yr), Uncertainty on the North component (mm/yr), Station Name.</li> </ul> </li> <li> velocity_dataset_filtr.dat <ul> <li>Filtered velocity field.</li> <li>Columns: Longitude (degrees), Latitude (degrees), East component of velocity (mm/yr), North component of velocity (mm/yr), Uncertainty on the East component (mm/yr), Uncertainty on the North component (mm/yr), Station Name.</li> <li>Stations ending with 'GPM' represent velocity values obtained by merging neighboring stations.</li> </ul> </li> <li> strain_rate_nn.dat (strain_rate_visr.dat, strain_rate_wav.dat) <ul> <li>Strain rate computed on cells spaced by 0.025°.</li> <li>Suffixes in the file names: 'nn' refers to the Nearest Neighbor method, 'visr' refers to the VISR method, and 'wav' refers to the Wavelet-based method</li> <li>Columns: Longitude (degrees), Latitude (degrees), exx (east) component of the strain rate tensor (nstr/yr), exy (east, north) component of the strain rate tensor (nstr/yr), eyy (north) component of the strain rate tensor, second invariant of the strain rate (nstr/yr), most extensive eigenvalue (nstr/yr), most compressive eigenvalue (nstr/yr), angle between north and the direction of the eigenvector corresponding to the most compressive eigenvalue (degrees, positive clockwise).</li> </ul> </li> </ol> Reference: For further details, please refer to the article "Comparative Analysis of Methods to Estimate Geodetic Strain Rates from GNSS Data in Italy", published in Annals of Geophysics.