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1,291 results for “gap”
Minding the gap: Learning and visual scanning behaviour in nocturnal bull ants
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Biogeography of the world’s worst invasive species has spatially-biased knowledge gaps but is predictable
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Bent Creek Experimental Forest site, station USGS Station 3447894, Bent Creek at Bent Creek Gap Rd. near Glen Bald, NC, study of mean daily streamflow in units of litersPerSecond on a monthly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Bent Creek Experimental Forest (BEN) contains mean daily streamflow measurements in litersPerSecond units and were aggregated to a monthly timescale.
Bent Creek Experimental Forest site, station USGS Station 3447894, Bent Creek at Bent Creek Gap Rd. near Glen Bald, NC, study of mean daily streamflow in units of litersPerSecond on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Bent Creek Experimental Forest (BEN) contains mean daily streamflow measurements in litersPerSecond units and were aggregated to a yearly timescale.
Canopy size, gap size, plant height, and scaled height in NEAT plots at Jornada Basin LTER, Summer 2017
This data package contains estimates of vegetation indicators derived from UAV overflights and comparable field observatons at the NEAT experiment in the Jornada Basin of southern New Mexico, USA. The purpose of this study is to develop a UAV-based remote sensing method that can estimate vegetation indicators in arid and semiarid rangelands. This method was used to characterize six rangeland indicators (canopy size, bare soil gap size, plant height, scaled height, vegetation cover, and bare soil cover) in a semiarid grass-shrub ecosystem at the NEAT site. The drone-based estimates were validated with field measurements by using the standard transect methods (gap intercept, drop disk, and line-point intercept methods) in the spring and summer of 2017. Further, we use these results to show possible applications of drone-based products on arid and semiarid rangelands: the spatially explicit input of an ecological model, to detect and characterize non-stationarity, and to detect landscape anisotropy.
Synthetic Datasets for Using synthetic datasets to bridge the gap between the promise and reality of basing health-related decisions on common single nucleotide polymorphisms
<p>Synthetic Dataset for Using synthetic datasets to bridge the gap between the promise and reality of basing health-related decisions on common single nucleotide polymorphisms, as published on F1000Research.</p>
FIGURE 2 in Filling biogeographic gaps about the shrimp Farfantepenaeus isabelae Tavares & Gusmão, 2016 (Decapoda: Penaeidae) in South America
FIGURE 2. Potential distribution map of Farfantepenaeus isabelae, the occurrence data from the literature and those obtained during the present work, with emphasis on the bioregionalization of coastal areas, respective marine ecoregions and their respective numbers according to Spalding et al. (2007): Tropical Atlantic (64—Eastern Caribbean, 66—Southern Caribbean, 67—Southwestern Caribbean, 71—Guianan, 72—Amazonia, 73—São Pedro and São Paulo Islands, and 74—Fernando de Noronha and Atoll das Rocas) and Temperate South America (176—Humboldtian, 177—Central Chile, 178—Araucanian, 180—Southeastern Brazil, 181—Rio Grande, and 182—Rio de la Plata).
FIGURE 1 in Filling biogeographic gaps about the shrimp Farfantepenaeus isabelae Tavares & Gusmão, 2016 (Decapoda: Penaeidae) in South America
FIGURE 1. Maximum Likelihood phylogram based on COI sequences of Farfantepenaeus isabelae. The numbers indicate bootstrap values (1000 replicates). Values below 50% are not shown. Sequences from new occurrences are in bold. Litopenaeus schmitti was used as outgroup.
MiRoR1 - P2 - Key stakeholders' perspectives and experiences with defining, identifying and displaying gaps in health research: a qualitative study protocol
<p>Supporting data items for protocol on key stakeholders’ perspectives and experiences with defining, identifying and displaying gaps in health research: a qualitative study protocol</p>
FIGURE5. Maximum likelihood tree based on the Kimura 2-parameter model of the COI sequences from the Siphamia species with P. kauderni as the outgroup. Tree shown here has the highest log likelihood following 10 000 replications. The percentage of trees in which the associated taxa clustered together is shown next to the branches, branch lengths are measured in the number of substitutions per site and all positions containing gaps and missing data have been eliminated. in Redescription and distributional range extension of the Speckled Siphonfish, Siphamia guttulata (Pisces: Apogonidae)
FIGURE5. Maximum likelihood tree based on the Kimura 2-parameter model of the COI sequences from the Siphamia species with P. kauderni as the outgroup. Tree shown here has the highest log likelihood following 10 000 replications. The percentage of trees in which the associated taxa clustered together is shown next to the branches, branch lengths are measured in the number of substitutions per site and all positions containing gaps and missing data have been eliminated.
GAP model parameter files for "Combining phonon accuracy with high transferability in Gaussian approximation potential models"
<p>GAP model parameter files to accompany the publication</p> <p>"Combining phonon accuracy with high transferability in Gaussian approximation potential models"</p> <p>by Janine George, Geoffroy Hautier, Albert P. Bartók, Gábor Csányi, and Volker L. Deringer</p> <p>All models are defined by the main parameter file "gp_iter6C.xml" and the associated file "gp_iter6C.xml.sparseX.GAP_00001", where "GAP_0000" is a placeholder for the unique identifier of the potential (also given in the XML header), and the trailing "1" indicates that only one set of descriptor (here, SOAP) parameters is given.</p> <p>The directories in this dataset follow the figures in the publication for which the respective potentials have been first used.</p> <p>Fig_2/only_random/M_1000<br> Fig_2/only_random/M_3000<br> Fig_2/only_random/M_5000<br> Fig_2/only_random/M_7000<br> Fig_2/only_random/M_9000</p> <p>Fig_2/only_individual/M_1000<br> Fig_2/only_individual/M_3000<br> Fig_2/only_individual/M_5000<br> Fig_2/only_individual/M_7000<br> Fig_2/only_individual/M_9000</p> <p>Fig_2/combined/M_1000<br> Fig_2/combined/M_3000<br> Fig_2/combined/M_5000<br> Fig_2/combined/M_7000<br> Fig_2/combined/M_9000</p> <p>Fig_4/only_individual/f_0.1000<br> Fig_4/only_individual/f_0.0100<br> Fig_4/only_individual/f_0.0010<br> Fig_4/only_individual/f_0.0001</p> <p>Fig_4/combined/f_0.1000<br> Fig_4/combined/f_0.0100<br> Fig_4/combined/f_0.0010<br> Fig_4/combined/f_0.0001</p> <p>Fig_5/GAP-18_plus_SCs_f0.01 <br> Fig_5/GAP-18_plus_SCs_f0.001</p> <p> </p>
The impact of stellar rotation on the black hole mass-gap from pair-instability supernovae
<p>Necessary files to reproduce the simulations of the paper "The impact of stellar rotation on the black hole mass-gap from pair-instability supernovae", as well as simulation output.</p> <p>These simulations were performed using MESA version 13311, with a small bugfix that can be applied with the provided <a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/hydro_riemann.f90?versionId=36bcfc7a-4535-4494-ba2b-c7ea69b9b362">hydro_riemann.f90</a> file.</p> <p>A description of the included files is as follows:</p> <ul> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/final_profiles.tar.gz?versionId=b85755ce-cd64-4e6b-ac14-d9e5364ee86b">final_profiles.tar.gz</a>: Individual profiles for all models at iron-core collapse (or last profile for PISN models). Files are separated into three folders, 'non_rot', 'rot_ST', 'rot_no_ST' corresponding to non-rotating models, rotating models with ST and rotating models without ST. Names of individual files contain the initial mass and the initial ratio between surface omega and its critical value. So for example, a file named "50.00_0.90_profile.data" represents an initial mass of 50 Msun and an initial rotation rate of 0.9 times critical. This work only contains non-rotating models and models at 0.9 critical though.</li> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/histories.tar.gz?versionId=d3876d5e-c95e-4945-8715-9da428837afa">histories.tar.gz</a>: MESA history files containing various stellar properties describable by a single number at each timestep of the simulation (for instance, mass, effective temperature, etc...). Files follow the same naming scheme as those in final_profiles.tar.gz. For each simulation there is an additional file with a name ending in "pulse_indexes" that contains various lines with two integers per line. These specify for PPISN models the simulation step (or model_number in MESA-speak) at which a pulsation begins, and the step at which it ends.</li> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/tables.tar.gz?versionId=69974ece-20e5-4509-a1a4-7dad7a92f4b6">tables.tar.gz</a>: Machine readable tables summarizing our simulations, these correspond to the data shown in the tables in the paper. The names of the tables "non_rot.txt", "rot_ST.txt" and "rot_no_ST.txt" correspond to non-rotating models, rotating models with ST and rotation models without ST respectively. The meaning of each column is: <ul> <li>M_i[Msun]: Initial mass of the helium star</li> <li>(om/omc)_i: Ratio between omega and its critical value at the star of core helium burning.</li> <li>M_Hedep[Msun]: Mass of the star at core helium depletion</li> <li>M_CO[Msun]: Carbon oxygen core mass at core helium depletion. Defined as the innermost mass coordinate at which Y<0.01.</li> <li>M_preSN[Msun]: Mass at the onset of PPISN/PISN. For models that do not undergo pair-instability, it corresponds to the mass at iron-core collapse.</li> <li>M_He,preSN[Msun]: Total mass in helium of the star at the onset of PPISN/PISN, or at iron-core collapse for non pair-unstable models. This does not represent the mass of the helium-rich envelope, but the total baryonic mass in helium of the star.</li> <li>M_ejecta[Msun]: Mass ejected by pulsations.</li> <li>M_final[Msun]: Final mass of the star at iron-core collapse.</li> <li>number_of_pulses: Number of pulsations.</li> <li>time_to_coll[yrs]: Time between the first pulse and iron-core collapse.</li> <li>max_KE[foe]: Maximum kinetic energy achieved by any pulse, measured in 10^51 erg/s=1 foe.</li> <li>a_i: Dimensionless spin of the star (a=jc/Gm) at the beginning of core helium burning.</li> <li>a_he_dep: Dimensionless spin at core helium depletion</li> <li>a_preSN: Dimensionless spin at the onset of PPISN/PISN.</li> <li>a_f: Final dimensionless spin from our simulations at iron-core collapse.</li> <li>BH_mass[Msun]: Final BH mass predicted from the properties of our model at iron-core collapse following Batta & Ramirez-Ruiz (2019).</li> <li>BH_spin: Final BH spin predicted from the properties of our model at iron-core collapse following Batta & Ramirez-Ruiz (2019).</li> </ul> </li> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/pulses.tar.gz?versionId=5afe548e-3f3f-429a-98f6-b3cb14c77cc6">pulses.tar.gz</a>: Information on individual pulses for each PPISN simulation in our grid. Files are contained in three separate folders "non_rot", "rot_ST" and "rot_no_ST" for models that are non-rotating, rotating with ST and rotating without ST respectively. Naming of individual files is the same as those in final_profiles.tar.gz. Information contained in these tables is: <ul> <li>pulse_num: Integer identifying the pulse.</li> <li>ejecta[Msun]: Total mass ejected by the pulse.</li> <li>time_to_coll[yrs]: Time between the onset of this pulse and iron-core collapse.</li> <li>KE[foe]: Maximum kinetic energy reached during the pulse.</li> </ul> </li> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/template_ST.tar.gz?versionId=ac1563c9-5b0c-422a-8739-2fbc82ff5788">template_ST.tar.gz</a>: Template used for simulations. This template is setup for the simulations that include the ST dynamo and have an initial rotation rate of 90% critical. To modify the rotation rate and the initial mass, modify new_omega_div_omega_crit and initial_mass in the inlist_extra file. To turn of ST, set am_nu_ST_factor=0 in inlist_ppisn. Only one model required additional tuning to comple, the 60 Msun rotating model with ST. For this one, the model was restarted at step 98000 with the value of the option x_ctrl(18) switched from 0.025d0 to 0.25d0. This particular simulation did not reach the automated terminating condition and was stopped manually, but at the end it does have a collapsing iron-core with an infall velocity ~5000 km/s.</li> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/notebook.tar.gz?versionId=d59d2404-1517-4c0a-910e-4adbc14511f8">notebook.tar.gz</a>: jupyter notebook used to process the data from our simulations and produce tables and figures (except for the information on the spin of the primary BH of GW170729).</li> <li><a href="https://zenodo.org/api/files/b21500e4-6cb5-4e3e-b8c5-83d769b24161/GW170729_spin.tar.gz?versionId=47f46740-3c0d-4355-b2a2-9d7a454f388b">GW170729_spin.tar.gz</a>: jupyter notebook used to compute the spin posterior of GW170729 using the data from the first catalogue of gravitational wave transients.</li> </ul>
Data from: Filling knowledge gaps in a threatened shorebird flyway through satellite tracking
1. Satellite-based technologies that track individual animal movements enable the mapping of their spatial and temporal patterns of occurrence. This is particularly useful in poorly studied or remote regions where there is a need for the rapid gathering of relevant ecological knowledge to inform management actions. One such region is East Asia, where many intertidal habitats are being degraded at unprecedented rates and shorebird populations relying on these habitats show rapid declines. 2. We examine the utility of satellite tracking to accelerate the identification of coastal sites of conservation importance in the East Asian-Australasian Flyway. In 2015–2017 we used solar-powered satellite transmitters to track the migration of 32 great knots (Calidris tenuirostris), an 'Endangered' shorebird species widely distributed in the Flyway and fully dependent on intertidal habitats for foraging during the non-breeding season. 3. From the great knot tracks, a total of 92 stopping sites along the Flyway were identified. Surprisingly, 63% of these sites were not known as important shorebird sites before our study; in fact, every one of the tracked individuals used sites that were previously unrecognized. 4. Site knowledge from on-ground studies in the Flyway is most complete for the Yellow Sea and generally lacking for Southeast Asia, Southern China, and Eastern Russia. 5. Policy implications: Satellite tracking highlighted coastal habitats that are potentially important for shorebirds but lack ecological information and conservation recognition, such as those in Southern China and Southeast Asia. At the same time, the distributional data of tracked individuals can direct on-ground surveys at the lesser-known sites to collect information on bird numbers and habitat characteristics. To recognize and subsequently protect valuable coastal habitats, filling knowledge gaps by integrating bird tracking with ground-based methods should be prioritized.19-Jun-2019
FIGURE S1. Automatic Barcode Gap Discovery genetic distances calculated for 18 Asteronotus Ehrenberg, 1831 in A tale of two genera: the revival of Hoplodoris (Nudibranchia: Discodorididae) with the description of new species of Hoplodoris and Asteronotus
FIGURE S1. Automatic Barcode Gap Discovery genetic distances calculated for 18 Asteronotus Ehrenberg, 1831 and Hoplodoris Bergh, 1880 COI sequences using three genetic distance calculations: (A) Jukes-Cantor (JC69); (B) Kimura (K80); (C) Simple Distance. Presumed intraspecific variation is shown in white, interspecific variation in grey.
Data from: Carbon flux and forest dynamics: increased deadwood decomposition in tropical rainforest tree-fall gaps
<p>This study was carried out within an area of lowland, old growth dipterocarp rainforest in the Maliau Basin Conservation Area, Sabah, Malaysia (4° 44' 35" to 55" N and 116° 58' 10" to 30" E; mean annual rainfall 2838 mm ± 93 mm). On the 20<sup>th</sup> of July 2017, there was a storm at the study site, which generated winds speeds of 8.4 m/s (Fig. S1). These were among the strongest winds normally experienced in inland forests of the region, which placed extreme sheer stress on trees. Consequently, a large number of trees fell within the same 24-hour period in the study location. Ten tree-fall gaps (mean length: 32 m ± 2.8, mean width: 24.5 m ± 3; see table S1 for gap characteristics) created during this event were selected for use in this investigation, along with ten adjacent closed canopy sites, located 20 m from the edge of each gap. We took 10 hemispherical photos in each gap and closed canopy sites to quantity canopy openness at each location.</p>
FIGURE 10 in New early Pleistocene Alligator (Eusuchia: Crocodylia) from Florida bridges a gap in Alligator evolution
FIGURE 10. Appendicular elements of the Alligator hailensis holotype (UF 224688). (A.) Proximal portion of right humerus in posterior view. (B.) Anterior portion of left ilium in lateral view. (C.) Right and left femora in posterodorsal view. (D.) Left tibia in posterior view. (E.) Left metatarsal I and III, and right metatarsal I, all in dorsal view. (F.) Left calcaneum and astragalus, lateral view. (G.) Unguals of the manus, lateral view. Scale in each = 2 cm.
FIGURE 8 in New early Pleistocene Alligator (Eusuchia: Crocodylia) from Florida bridges a gap in Alligator evolution
FIGURE 8. Representative thoracic vertebra of the Alligator hailensis holotype (UF 224688) in (clockwise from top left) anterior, posterior, right lateral, and left lateral views. Scale = 2 cm.
FIGURE 12 in New early Pleistocene Alligator (Eusuchia: Crocodylia) from Florida bridges a gap in Alligator evolution
FIGURE 12. Strict consensus of 25,106 most parsimonious cladograms of Alligatoridae with Bernissartia fagesii, Crocodylus niloticus, and Leidyosuchus canadensis, as outgroups (A. and B.). (C.) Plurality consensus (majority rule allowing less than 50 percent recovery) offers no clade support but does reflect the biostratigraphic record, illustrating the intermediate nature of Alligator hailensis in time and morphology (numbers represent percent clade recovery of most parsimonious cladograms).
FIGURE 2 in New early Pleistocene Alligator (Eusuchia: Crocodylia) from Florida bridges a gap in Alligator evolution
FIGURE 2. Holotype of Alligator hailensis (UF 224688) skull shown in dorsal view with interpretive line drawing below. Abbreviations are angular (a), dentary (d), frontal (f), jugal (j), lacrimal (l), maxilla (m), nasal (n), parietal (p), postorbital (po), prefrontal (pf), premaxilla (pm), quadrate (q), quadratojugal (qj), squamosal (sq), and surangular (sa). Scale = 4 cm.
FIGURE 1 in New early Pleistocene Alligator (Eusuchia: Crocodylia) from Florida bridges a gap in Alligator evolution
FIGURE 1. View of the Haile 7G quarry (with fossil excavation seen at left) and approximate location of Haile 7C and Haile 7G in Alachua County, North-central Florida (at right). Left image courtesy of S. C. Wallace.
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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.