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Fig. 9 in Long distance dispersal and pseudo-cryptic species in Gastrotricha: first description of a new species (Chaetonotida, Chaetonotidae, Polymerurus) from an oceanic island with volcanic rocks
Fig. 9. Maximum Likelihood tree based on multigene approach with 18S and 28S sequences. Highlighted branches correspond to the Polymerurus Remane, 1927 species sequences. Values on the branches correspond, respectively, to: SH-aLRT support (%) / aBayes support / ultrafast bootstrap support (%).
Fig. 2 in Long distance dispersal and pseudo-cryptic species in Gastrotricha: first description of a new species (Chaetonotida, Chaetonotidae, Polymerurus) from an oceanic island with volcanic rocks
Fig. 2. Light microscopy – DIC. Polymerurus insularis sp. nov., holotype (ZUEC GCH 55). Full body view. A. Dorsal view. B. Internal view. C. Ventral view. Scale bars = 30 µm.
Data supplement to "Aromatics and agriculture: A spatial approach to long-distance trade and the local economy of the Nabataeans"
<p>Data used to analyze the role of long-distance trade in the local agricultural economy of the Nabataeans, as described in Weaverdyck, E. J. S. forthcoming. "Aromatics and agriculture: A spatial approach to long-distance trade and the local economy of the Nabataeans." In S. von Reden (ed.). <em>Handbook of Ancient Afro-Eurasian Economies</em>, vol. 3. Oldenbourg: De Gruyter.</p> <p>Data sources are provided in the chapter.</p> <p>With the exception of runoff, environmental variables are rasters created using the focal statistics tool to sum the number of cells within a radius around each cell. They are named according to the following convention: f{radius}_{factor abbreviation}_{variable}.tif</p> <p>Factor abbreviations:</p> <table> <tbody> <tr> <td>A</td> <td>Aspect</td> </tr> <tr> <td>D</td> <td>DEV geomorphological land form</td> </tr> <tr> <td>H</td> <td>Hydrology</td> </tr> <tr> <td>S</td> <td>Slope</td> </tr> <tr> <td>T</td> <td>TPI geomorphological land form</td> </tr> </tbody> </table> <p>Of these environmental variables, the following were used as background variables in the MaxEnt models:</p> <table> <thead> <tr> <th scope="col">ASKP-LA</th> <th scope="col">WHS</th> </tr> </thead> <tbody> <tr> <td>f500_H_prec_soak</td> <td>f500_H_Runoff</td> </tr> <tr> <td>f500_T_Valley</td> <td>f5k_D_Ridge</td> </tr> <tr> <td>f5k_T_Ridge</td> <td>f5k_H_Runoff</td> </tr> <tr> <td>f5k_H_Runoff</td> <td>f500_D_UpperSlope_or_low_rise</td> </tr> <tr> <td>f5k_A_Northeast</td> <td>f5k_A_Southwest</td> </tr> <tr> <td>f500_D_UpperSlope_or_low_rise</td> <td>f500_D_Valley</td> </tr> <tr> <td>f5k_H_springs</td> <td> </td> </tr> <tr> <td>f5k_D_Ridge</td> <td> </td> </tr> <tr> <td>f5k_A_South</td> <td> </td> </tr> <tr> <td>f5k_A_Southwest</td> <td> </td> </tr> <tr> <td>f5k_A_Northwest</td> <td> </td> </tr> <tr> <td>f500_T_UpperSlope_or_low_rise</td> <td> </td> </tr> <tr> <td>f500_D_Lower_slope_or_shallow_valley</td> <td> </td> </tr> <tr> <td>f5k_A_North</td> <td> </td> </tr> </tbody> </table> <p> </p>
IEEE 802.15.4 TSCH dataset for phase-based distance estimation
<p><strong>Introduction</strong></p> <p>This data set contains two collections of phase angle measurements created in two indoor and one outdoor environment that can be used for phase-based distance estimates. The measurements include phase samples created on two different frequency sets:</p> <ul> <li> <strong>TSCH standard frequencies</strong>: measurements are performed on default 16 channel frequencies {2405.0, 2410.0, 2415.0, 2420.0, 2425.0, 2430.0, 2435.0, 2440.0, 2445.0, 2450.0, 2455.0, 2460.0, 2465.0, 2470.0, 2474.0, 2480.0}MHz.</li> <li> <strong>Golomb ruler frequencies</strong>: the measurements are performed on 15 custom selected frequencies according to the Golomb ruler technique {2400.5, 2406.0, 2407.5, 2408.0, 2412.5, 2423.0, 2431.0, 2442.5, 2452.0, 2460.5, 2463.0, 2466.5, 2476.5, 2479.5, 2480.5}MHz.</li> </ul> <p><br> <strong>Measurement setup</strong></p> <p>Measurements were performed using AT86RF233 transceivers connected to the in-house <a href="https://log-a-tec.eu/hw-vesna.html">VESNA</a> platform. Two nodes were placed on a stand 1.6 m above the ground in three separate environments:</p> <ul> <li>in a 5x5m square office with no furniture</li> <li>in an indoor hallway with dimensions of 4x40m</li> <li>in a park without any nearby obstacles</li> </ul> <p>The actual distance between nodes was measured with a laser ranger with an accuracy of ±1.5 mm. There was no obstacle between the devices. Indoors, 17 WiFi access points were in operation during the measurement campaign.</p> <p><br> <strong>Phase measurement process</strong></p> <p>The devices involved first establish an IEEE 802.15.4 TSCH network. In it, they measure the phase difference on pre-selected frequencies. The phase measurement has been seamlessly integrated into a communication so that the devices obtain phase measurement with every packet sent.</p> <p><em>Why two collections?</em></p> <p>The set labelled "TSCH standard channels" contains phase measurements created at frequencies defined in the IEEE.802.15.4 standard for the 2.4 GHz band. The frequency step (<span class="math-tex">\(\Delta freq = freq_{i+1} - freq_{i}\)</span>) between two phase samples is equal to 5MHz, which results in a maximum distinguishable range of 30m for the distance estimation.</p> <p>To increase the range up to 300m, the frequency step must be reduced to 0.5 MHz. This requires 160 phase samples in the 2.4 GHz band used with a bandwidth of 80 MHz. However, measuring 160 phase samples on 160 frequencies would take a lot of time and therefore interfere with TSCH communications. One way to shorten the procedure is to use the Golomb ruler technique. This allows a large set of phase differences to be created from a small number of measured phases. This method was used in the creation of the set named "Golomb ruler frequencies". The data set also contains a Python example script that expands the set of 15 measured frequencies to a set of 160 samples.</p> <p><br> <strong>Folder structure</strong></p> <p>Each record collection is stored in a corresponding folder. Each folder contains .json files representing different environments. In addition to the data sets, the folders also contain figures and a sample Python script. The measurements are stored in JSON format. Each measured distance contains the number of measurements and the actual data. With each packet sent (identified by its Absolute Slot Number (ASN)), the phase difference between the devices is measured. The phase value is stored as an 8-bit value representing the range from 0 to 2 pi.</p>
Supplementary Data to *Informative and adaptive distances and summary statistics in approximate Bayesian computation*
<p>Supplementary code and data to <strong>Informative and adaptive distances and summary statistics in approximate Bayesian computation</strong> by <strong>Y. Schaelte et al., 2021</strong>.</p> <p>The archive contains a <strong>README.rst </strong>for information on what is where and how to execute the study and generate the figures. The underlying code without the data can be found at the repository https://github.com/yannikschaelte/study_abc_slad, of which this archive is a snapshot.</p>
A near-field Head-Related Transfer Function (HRTF) data set of KEMAR with high distance resolution
<p>A near-field Head-Related Transfer Function (HRTF) data set measured on a KEMAR head and torso simulator with high distance resolution and multiple elevations is presented ('KEMAR_NFHRIRmea_1cm.sofa'). HRTFs are measured at 83448 spatial points at distances ranging from 20 to 110 cm, elevations from -25° to 35°, and azimuths from 0° to 355°. The distance resolution of the HRTF data is 1 cm, higher than that of any existing public near-field HRTF databases. Therefore, the dataset enables further exploration of the distance dependence of near-field HRTFs, and is beneficial for applications of realistic and dynamic binaural rendering of nearby sound sources. An additional data set of simulated HRTFs with 1.5 cm distance resolution is also provided ('KEMAR_NFHRIRsim_1.5cm.sofa') for a direct comparison with the measured HRTFs or other purposes.</p>
Data and code from: Three decades of wildlife-vehicle collisions in a protected area: main roads and long-distance commuting trips to migratory prey increase spotted hyena roadkills in the Serengeti
<p>This is the first release. Potential updates will be available on GitHub: <a href="https://github.com/MarwanNaciri/Three_decades_of_spotted_hyena_roadkill_in_a_protected_area">https://github.com/MarwanNaciri/Three_decades_of_spotted_hyena_roadkill_in_a_protected_area</a></p>
Divorce rate in birds increases with male promiscuity and migration distance
<p>Socially monogamous birds may break up their partnership by a so-called 'divorce' behaviour. Divorce rate immensely varies across avian taxa that have a predominantly monogamous social mating system. Although a range of factors associated with divorce have been tested, broad-scale drivers of divorce rate remain contentious. Moreover, the impact of sexual roles in divorce still needs further investigation due to the conflicting interest of males and females. Here we applied phylogenetic comparative methods to analyse one of the largest datasets ever compiled that included divorce rates from published studies of 186 avian species from 25 orders and 61 families. We tested correlations between divorce rate and a group of factors: 'promiscuity' of both sexes (propensity of polygamy), migration distance, and adult mortality. Our results showed that only male promiscuity, but not female promiscuity, had a positive relationship with divorce rate. Furthermore, migration distance was positively correlated with divorce rate, while adult mortality rate showed no direct relationship with divorce rate. These findings indicated that divorce might not be a simple adaptive (by sexual selection) or non-adaptive strategy (by accidental loss of a partner), but could be a mixed response to sexual conflict and stress from the ambient environment.</p>
Estimated stand-off distance between ADS-B equipped aircraft and obstacles
<p><strong>Summary</strong>:</p> <p>Estimated stand-off distance between ADS-B equipped aircraft and obstacles. Obstacle information was sourced from the FAA Digital Obstacle File and the FHWA National Bridge Inventory. Aircraft tracks were sourced from processed data curated from the OpenSky Network. Results are presented as histograms organized by aircraft type and distance away from runways.</p> <p> </p> <p><strong>Description:</strong></p> <p>For many aviation safety studies, aircraft behavior is represented using encounter models, which are statistical models of how aircraft behave during close encounters. They are used to provide a realistic representation of the range of encounter flight dynamics where an aircraft collision avoidance system would be likely to alert. These models currently and have historically have been limited to interactions between aircraft; they have not represented the specific interactions between obstacles and aircraft equipped transponders. In response, we calculated the standoff distance between obstacles and ADS-B equipped manned aircraft.</p> <p>For robustness, this assessment considered two different datasets of manned aircraft tracks and two datasets of obstacles. For robustness, MIT LL calculated the standoff distance using two different datasets of aircraft tracks and two datasets of obstacles. This approach aligned with the <a href="https://doi.org/10.2514/1.D0091">foundational research</a> used to support the <a href="https://doi.org/10.1520/F3442_F3442M-20">ASTM F3442/F3442M-20</a> well clear criteria of 2000 feet laterally and 250 feet AGL vertically.</p> <p>The two datasets of processed tracks of ADS-B equipped aircraft curated from the OpenSky Network. It is likely that rotorcraft were underrepresented in these datasets. There were also no considerations for aircraft equipped only with Mode C or not equipped with any transponders. The first dataset was used to train the <a href="https://github.com/Airspace-Encounter-Models/em-model-manned-bayes/releases/tag/v1.3">v1.3 uncorrelated encounter models</a> and referred to as the “Monday” dataset. The second dataset is referred to as the “aerodrome” dataset and was used to train the v2.0 and v3.x terminal encounter model. The Monday dataset consisted of 104 Mondays across North America. The other dataset was based on observations at least 8 nautical miles within Class B, C, D aerodromes in the United States for the first 14 days of each month from January 2019 through February 2020. Prior to any processing, the datasets required 714 and 847 Gigabytes of storage. For more details on these datasets, please refer to "Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing" and “Benchmarking the Processing of Aircraft Tracks with Triples Mode and Self-Scheduling.”</p> <p>Two different datasets of obstacles were also considered. First was point obstacles defined by the FAA digital obstacle file (DOF) and consisted of point obstacle structures of antenna, lighthouse, meteorological tower (met), monument, sign, silo, spire (steeple), stack (chimney; industrial smokestack), transmission line tower (t-l tower), tank (water; fuel), tramway, utility pole (telephone pole, or pole of similar height, supporting wires), windmill (wind turbine), and windsock. Each obstacle was represented by a cylinder with the height reported by the DOF and a radius based on the report horizontal accuracy. We did not consider the actual width and height of the structure itself. Additionally, we only considered obstacles at least 50 feet tall and marked as verified in the DOF.</p> <p>The other obstacle dataset, termed as “bridges,” was based on the identified bridges in the FAA DOF and additional information provided by the National Bridge Inventory. Due to the potential size and extent of bridges, it would not be appropriate to model them as point obstacles; however, the FAA DOF only provides a point location and no information about the size of the bridge. In response, we correlated the FAA DOF with the National Bridge Inventory, which provides information about the length of many bridges. Instead of sizing the simulated bridge based on horizontal accuracy, like with the point obstacles, the bridges were represented as circles with a radius of the longest, nearest bridge from the NBI. A circle representation was required because neither the FAA DOF or NBI provided sufficient information about orientation to represent bridges as rectangular cuboid. Similar to the point obstacles, the height of the obstacle was based on the height reported by the FAA DOF. Accordingly, the analysis using the bridge dataset should be viewed as risk averse and conservative. It is possible that a manned aircraft was hundreds of feet away from an obstacle in actuality but the estimated standoff distance could be significantly less. Additionally, all obstacles are represented with a fixed height, the potentially flat and low level entrances of the bridge are assumed to have the same height as the tall bridge towers. The attached figure illustrates an example simulated bridge.</p> <p>It would had been extremely computational inefficient to calculate the standoff distance for all possible track points. Instead, we define an encounter between an aircraft and obstacle as when an aircraft flying 3069 feet AGL or less comes within 3000 feet laterally of any obstacle in a 60 second time interval. If the criteria were satisfied, then for that 60 second track segment we calculate the standoff distance to all nearby obstacles. Vertical separation was based on the MSL altitude of the track and the maximum MSL height of an obstacle.</p> <p>For each combination of aircraft track and obstacle datasets, the results were organized seven different ways. Filtering criteria were based on aircraft type and distance away from runways. Runway data was sourced from the FAA runways of the United States, Puerto Rico, and Virgin Islands <a href="https://adds-faa.opendata.arcgis.com/datasets/4d8fa46181aa470d809776c57a8ab1f6_0/about">open dataset</a>. Aircraft type was identified as part of the <a href="https://github.com/Airspace-Encounter-Models/em-processing-opensky">em-processing-opensky</a> workflow.</p> <ul> <li><em>All</em>: No filter, all observations that satisfied encounter conditions</li> <li><em>nearRunway</em>: Aircraft within or at 2 nautical miles of a runway</li> <li><em>awayRunway</em>: Observations more than 2 nautical miles from a runway</li> <li><em>glider</em>: Observations when aircraft type is a glider</li> <li><em>fwme</em>: Observations when aircraft type is a fixed-wing multi-engine</li> <li><em>fwse</em>: Observations when aircraft type is a fixed-wing single engine</li> <li><em>rotorcraft</em>: Observations when aircraft type is a rotorcraft</li> </ul> <p><strong>License</strong></p> <p>This dataset is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International(CC BY-NC-ND 4.0).</p> <p>This license requires that reusers give credit to the creator. It allows reusers to copy and distribute the material in any medium or format in unadapted form and for noncommercial purposes only. Only noncommercial use of your work is permitted. Noncommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. Exceptions are given for the not for profit standards organizations of ASTM International and RTCA.</p> <p>MIT is releasing this dataset in good faith to promote open and transparent research of the low altitude airspace. Given the limitations of the dataset and a need for more research, a more restrictive license was warranted. Namely it is based only on only observations of ADS-B equipped aircraft, which not all aircraft in the airspace are required to employ; and observations were source from a crowdsourced network whose surveillance coverage has not been robustly characterized.</p> <p>As more research is conducted and the low altitude airspace is further characterized or regulated, it is expected that a future version of this dataset may have a more permissive license.</p> <p><strong>Distribution Statement</strong></p> <p>DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.</p> <p>© 2021 Massachusetts Institute of Technology.</p> <p>Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.</p> <p>This material is based upon work supported by the Federal Aviation Administration under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Federal Aviation Administration.</p> <p>This document is derived from work done for the FAA (and possibly others); it is not the direct product of work done for the FAA. The information provided herein may include content supplied by third parties. Although the data and information contained herein has been produced or processed from sources believed to be reliable, the Federal Aviation Administration makes no warranty, expressed or implied, regarding the accuracy, adequacy, completeness, legality, reliability or usefulness of any information, conclusions or recommendations provided herein. Distribution of the information contained herein does not constitute an endorsement or warranty of the data or information provided herein by the Federal Aviation Administration or the U.S. Department of Transportation. Neither the Federal Aviation Administration nor the U.S. Department of Transportation shall be held liable for any improper or incorrect use of the information contained herein and assumes no responsibility for anyone’s use of the information. The Federal Aviation Administration and U.S. Department of Transportation shall not be liable for any claim for any loss, harm, or other damages arising from access to or use of data or information, including without limitation any direct, indirect, incidental, exemplary, special or consequential damages, even if advised of the possibility of such damages. The Federal Aviation Administration shall not be liable to anyone for any decision made or action taken, or not taken, in reliance on the information contained herein.</p> <p><strong>Download and Format:</strong></p> <p>The dataset is provided as a single .zip archive with multiple directories. The directories indicate the aircraft track dataset and types of obstacles. Within each directory are csv files corresponding to different the filtering criteria. These files are the total counts of observations (histogram) where columns corresponding to lateral (range) distance between the simulated obstacle and aircraft and rows correspond to the relative vertical separation. The files centers_x_ft.csv and centers_y_ft.csv are the center of each bin.</p> <p>Simply download the .zip file and extract. The MD5 checksum of the .zip file prior to uploading to Zenodo was 03e5aed725a4cf393683730e05521ba5.</p> <p><strong>MIT Lincoln Laboratory LL Group and Division this dataset is associated with</strong>:</p> <ul> <li>Group 42 / Division 4</li> <li>Topic: collision avoidance</li> <li>R&D Area: Air Traffic Control</li> <li>R&D Group: Surveillance Systems</li> </ul>
Data accompanying the paper "The electron flow in marine sediments experiencing microbial long-distance electron transport"
<p>Data accompanying the paper "The electron flow in marine sediments experiencing microbial long-distance electron transport"</p>
Suppl. figures of otoliths of pelagic shorefish larvae captured over the Galapagos Rift for Victor, B.C. (2023) Rapid long-distance multispecies transport of shorefish larvae to the oceanic tropical eastern Pacific, revealed by DNA-barcodes and otolith aging of larvae captured over the Galapagos Rift
<p>Supplementary figures of otoliths of pelagic shorefish larvae captured over the Galapagos Rift</p> <p>Victor, B.C. (2023)</p> <p><strong>Rapid long-distance multispecies transport of shorefish larvae to the oceanic tropical eastern Pacific, revealed by DNA-barcodes and otolith aging of larvae captured over the Galapagos Rift</strong></p> <p>in volume: Early Life History and Biology of Marine Fishes: Research inspired by the work of H Geoffrey Moser</p> <p>Figure Sup A1 Sagittal otolith of 5.9 mm SL Stegastes sp. fish larva (Pomacentridae) captured over the Galapagos Rift, age since hatching is 23 days.</p>
Linear breaking strength of porcine cystic ducts and distance to the gallbladder are not associated to allometric parameters
<p>Raw data for the analyses in the manuscript with the same title.</p>
A 5 m vertical distance to channel network index (VDCNI) across France
<p>The vertical distance to channel network index (VDCNI) expresses the vertical height (in meter) between the elevation of a pixel and the nearest channel. It was derived from the national airborne DTM (RGE ALTI ®) at 5 m spatial resolution, which is available from the website of the French National Geographic Institute (IGN) (<a href="https://geoservices.ign.fr/">https://geoservices.ign.fr/</a>), and the GIS layer of the channel network in the national hydrological database <a href="https://www.sandre.eaufrance.fr/atlas/srv/fre/catalog.search#/metadata/3b3d3c56-d9b6-4625-a57e-ba054e798274">https://www.sandre.eaufrance.fr/atlas/srv/fre/catalog.search#/metadata/3b3d3c56-d9b6-4625-a57e-ba054e798274</a>.</p> <p>Dataset includes:</p> <ul> <li>280 GeoTIFF raster files (VDCNI_000.tif) projected in the French Lambert-93 system (EPSG code 2154), each file corresponding to a 50 x 50 km tile. The number indicates the tile of interest ;</li> <li>1 vector tile index at Google Earth format (tile_index.kmz) showing the location of each tile. This file has been created to facilitate download layer only on area of interest.</li> </ul> <p>To reduce storage space and download time, each raster file has been packed at 7-Zip freeware format.</p> <p>A complete description of the dataset can be found in Panhelleux, L., Rapinel, S., Lemercier, B., Gayet, G., Hubert-Moy, L., 2023. A 5 m dataset of digital terrain model derivatives across mainland France. Data in Brief 109369. https://doi.org/10.1016/j.dib.2023.109369</p>
Suppl. figures of pelagic shorefish larvae captured over the Galapagos Rift for Victor, B.C. (2023) Rapid long-distance multispecies transport of shorefish larvae to the oceanic tropical eastern Pacific, revealed by DNA-barcodes and otolith aging of larvae captured over the Galapagos Rift
<p>Supplementary figures of pelagic shorefish larvae captured over the Galapagos Rift</p> <p>Victor, B.C. (2023)</p> <p><strong>Rapid long-distance multispecies transport of shorefish larvae to the oceanic tropical eastern Pacific, revealed by DNA-barcodes and otolith aging of larvae captured over the Galapagos Rift</strong></p> <p>in volume: Early Life History and Biology of Marine Fishes: Research inspired by the work of H Geoffrey Moser</p> <p>Figure S1 Gobioid fish larvae captured over the Galapagos Rift.</p> <p>Figure S2 Labrid fish larvae captured over the Galapagos Rift.</p> <p>Figure S2. Pomacentrid fish larvae captured over the Galapagos Rift.</p> <p>Figure S4 Lythrypnus sp 5.4 mm SL fish larva captured over the Galapagos Rift.</p> <p>Figure S4a Lythrypnus sp 5.4 mm SL fish larva (head) captured over the Galapagos Rift.</p> <p>Figure S5 Abudefduf troschelii 7.3 mm SL fish larva captured over the Galapagos Rift.</p> <p>Figure S6 Chaetodon humeralis 8.9 mm SL fish larva captured over the Galapagos Rift.</p> <p>Figure S7 Gerreidae 10.6 mm SL fish larva captured over the Galapagos Rift.</p> <p>Figure S8 Neoniphon suborbitalis 6.8 mm SL fish larva captured over the Galapagos Rift.</p> <p>Figure S9 Ophioblennius steindachneri 10.6 mm SL fish larva captured over the Galapagos Rift.</p> <p>Figure S9a Ophioblennius steindachneri 10.6 mm SL fish larva (head) captured over the Galapagos Rift.</p> <p>Figure S9b Ophioblennius steindachneri 10.6 mm SL fish larva (ventral) captured over the Galapagos Rift.</p> <p>Figure Sup10 Sphoeroides lobatus 12.0 mm SL fish larva captured over the Galapagos Rift.</p>
Data for Numerical Study on the Impact of Large Air Purifiers, Physical Distancing, and Mask Wearing in Classrooms
<p>This is a reference case setup to re-generate all the data for the paper titled "Numerical Study on the Impact of Large Air Purifiers, Physical Distancing, and Mask Wearing in Classrooms"<br> </p>
To design, or not to design? Comparison of beetle ultraconserved element probe set utility based on phylogenetic distance, breadth, and method of probe design
<p>This repository contains Materials and designed UCE probe sets for the manuscript entitled "To design or not to design? Comparison of beetle ultraconserved element probe set utility based on phylogenetic distance, breadth, and method of probe design".</p>
Beyond trait distances: Functional distinctiveness captures the outcome of plant competition
<p>Functional trait distances between coexisting organisms reflect not only complementarity in the way they use resources, but also differences in their competitive abilities. Accordingly, absolute and relative trait distances have been widely used to capture the effects of niche dissimilarity and competitive hierarchies, respectively, on the performance of plants in competition. However, multiple dimensions of the plant phenotype are involved in these plant-plant interactions (PPI), challenging the use of relative trait distances to predict their outcomes. Furthermore, estimating the effects of competitive hierarchy on the performance of a group of coexisting plants remains particularly difficult since relative trait distances relate to the effects of a focal plant on another. We argue that trait distinctiveness, an emerging facet of functional diversity that characterizes the eccentric position of a species (or genotype) in a phenotypic space, can reveal the unique role played by a given individual plant in a group of competing plants . We used the model crop species <em>Oryza sativa</em> spp. japonica to evaluate the ability of trait distances and trait distinctiveness to predict the outcome of intraspecific PPI on the performance of single genotype and genotype mixtures. We performed a screening experiment to characterize the phenotypic space of 49 rice genotypes based on 11 aboveground and root traits. We selected nine genotypes with contrasting positions in the phenotypic space and grew them in pots following a complete pairwise interaction design. Relative distances and distinctiveness based on traits associated with light competition were by far the best predictors of the performance of single genotypes - taller genotypes that acquired resource faster being the best competitors - while absolute trait distances had no effect. These results indicate that competitive hierarchy for light dominates PPI in this experiment. Consistently, trait distinctiveness in plant height and age at flowering had the strongest, positive effects on mixture performance, confirming that functional distinctiveness captures the effects of trait hierarchies and asymmetric PPI at this scale. Our findings shed new light on the role of trait diversity in regulating PPI and ecosystem processes and call for a greater consideration of functional distinctiveness in studies of coexistence mechanisms.</p>
Distance to the edge and other landscape features influence nest predation in grey partridges
<p><span>Predation and habitat deterioration are the main reasons for the strong decline of ground-nesting farmland birds such as the grey partridge <em>Perdix</em> <em>perdix</em> in Europe. Grey partridge nests and incubating females are especially vulnerable to predation. We have previously demonstrated that predator activity is much lower inside flower blocks (agri-environment schemes sown with a flower seed mix) than at their edges and that predator activity in flower blocks depends on the surrounding landscape. Here, we investigate whether these differences in predator activity translate into differences in grey partridge nest predation and assess predation patterns relative to landscape and nest site characteristics. We recorded the success of 56 nests of radio-tagged grey partridges between 2009–2017 in an agricultural landscape in Central Germany. We used Bayesian logistic regression to analyse the effects of nest site and landscape characteristics on nest predation on a subset of 46 nests (21 nests successful, 25 predated). Distance to the edge of the nesting habitat was the most important predictor, reducing predation probability from 66.8% at the edge to 18.5% at 85.5 m. Predation probability decreased with increasing length of habitat borders, habitat diversity and the area of permanent grasslands and fallows. Predation probability was higher further from settlements and increased with increasing woodland area in the agricultural matrix. When considering linear landscape structures, nest predation patterns matched the patterns of predator activity from our previous studies. Results suggest that the distance to the edge of the nesting habitat is most important, and that nest predation may be reduced by providing sufficiently broad nesting habitats. Nest predation may further be minimized by increasing habitat diversity and coverage of extensive vegetation types and by establishing conservation measures for grey partridges further away from woodlands. These measures may also benefit other ground-nesting farmland birds. </span></p>
A proposed equation for calculating the total horizontal distance in projectiles of varying launch and landing levels
<p><strong>Abstract</strong></p> <p>The projectile draws a path for the flight of the tool, a horizontal distance can be calculated according to the general law, but only if the two points of the launch and fall of the tool are equal, otherwise we need an equation to be added to the general law to calculate the real distance that was generated due to the difference between the launch and fall points. There are several equations to calculate this, but they are complex and can be simplified, the proposed equation was tested on large samples for three different throwing events from the track and field games, (javelin, shotput, disc). The proposed equation was based on the basics of mathematics and geometry. The equation was tested in terms of the advantage of deleting the height different, if the height is zero, the proposed equation is suitable even for projectiles with equal levels, and the credibility of the proposed equation with the previous equation was tested statistically, and there were no differences between the two equations (p>0.05). and due to the relative ease of access of the proposed equation to very similar results, researcher suggests applying the proposed equation.</p>
Data from: A user-friendly guide to using distance measures to compare time series in ecology
<p>Time series are a critical component of ecological analysis, used to track changes in biotic and abiotic variables. Information can be extracted from the properties of time series for tasks such as classification (e.g. assigning species to individual bird calls); clustering (e.g. clustering similar responses in population dynamics to abrupt changes in the environment or management interventions); prediction (e.g. accuracy of model predictions to original time series data); and anomaly detection (e.g. detecting possible catastrophic events from population time series). These common tasks in ecological research rely on the notion of (dis-) similarity, which can be determined using distance measures. A plethora of distance measures have been described, predominantly in the computer and information sciences, but many have not been introduced to ecologists. Furthermore, little is known about how to select appropriate distance measures for time-series-related tasks. Therefore, many potential applications remain unexplored.</p> <p>Here we describe 16 properties of distance measures that are likely to be of importance to a variety of ecological questions involving time series. We then test 42 distance measures for each property and use the results to develop an objective method to select appropriate distance measures for any task and ecological dataset. We demonstrate our selection method by applying it to a set of real-world data on breeding bird populations in the UK and discuss other potential applications for distance measures, along with associated technical issues common in ecology.</p> <p>Our real-world population trends exhibit a common challenge for time series comparisons: a high level of stochasticity. We demonstrate two different ways of overcoming this challenge, first by selecting distance measures with properties that make them well-suited to comparing noisy time series, and second by applying a smoothing algorithm before selecting appropriate distance measures. In both cases, the distance measures chosen through our selection method are not only fit-for-purpose but are consistent in their rankings of the population trends.</p> <p>The results of our study should lead to an improved understanding of, and greater scope for, the use of distance measures for comparing ecological time series, and help us answer new ecological questions.</p>
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.