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1,899 results for “Running”
Fig. 22 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Fig. 22. Elaphropeza lowioides, new species, male habitus lateral. Genitalia removed and illustrated in Figs. 23–25.
Figs. 3–5 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Figs. 3–5. Elaphropeza albicornis, new species: 3, right epandrial lamella, lateral view; 4, epandrium with cerci, dorsal view; 5, left surstylus, lateral view. Scale bar = 0.1 mm. lc: left cercus; ls: left surstylus; rel: right epanderial lamella, lel: left epandrial lamella.
Figs. 41–43 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Figs. 41–43. Elaphropeza singulata Shamshev & Grootaert, 2007. 41, right epandrial lamella, lateral view; 42, epandrium with cerci, dorsal view; 43, left surstylus, lateral view. Scale bar = 0.1 mm.
Fig. 40 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Fig. 40. Elaphropeza singulata Shamshev & Grootaert, 2007 male habitus lateral. Genitalia removed and illustrated in Figs. 41–43.
Text-fig. 10. Phylogenetic relationships of Miocene hyaenodonts (for definitions of character states see Table 2). The data matrix was compiled in MacClade 4.05 and run in PAUP 4.0b10 (Macintosh version). We chose Cimolestes magnus CLEMENS et RUSSELL, 1965, (additional data from Lillegraven 1969), as the outgroup. The unordered and unweighted analysis produced 16 trees. a: Majority-rule consensus. b: Strict consensus. Consistency index (CI): 0.5882; Homoplasy index (HI): 0.4118; Retention index (RI): 0.7742. in New Hyaenodonts (Ferae, Mammalia) From The Early Miocene Of Napak (Uganda), Koru (Kenya) And Grillental (Namibia)
Text-fig. 10. Phylogenetic relationships of Miocene hyaenodonts (for definitions of character states see Table 2). The data matrix was compiled in MacClade 4.05 and run in PAUP 4.0b10 (Macintosh version). We chose Cimolestes magnus CLEMENS et RUSSELL, 1965, (additional data from Lillegraven 1969), as the outgroup. The unordered and unweighted analysis produced 16 trees. a: Majority-rule consensus. b: Strict consensus. Consistency index (CI): 0.5882; Homoplasy index (HI): 0.4118; Retention index (RI): 0.7742.
MAgPIE v4.3.x model run outputs including dynamic forestry sector
<p>Archive of runs produced for the forestry paper using MAgPIE 4.3.1+</p> <p><a href="https://github.com/magpiemodel/magpie">Model Code</a></p> <p><a href="https://rse.pik-potsdam.de/doc/magpie/4.3/index.htm">Model documentation</a></p> <p><a href="https://github.com/magpiemodel/tutorials">Model tutorials</a></p>
Holocene Reconstruction: Model and proxy data for running code
<p>This is the model output and proxy data for running the Holocene data assimilation.</p> <p>The data assimilation code can be found by searching for "Holocene reconstruction code". Newer versions of the code may be found at https://github.com/Holocene-Reconstruction/Holocene-code. To read more about the Holocene data assimilation, see the paper Erb et al., 2022: "Reconstructing Holocene temperatures in time and space using paleoclimate data assimilation".</p> <p>Once downloaded, unzip this holocene_da_data.zip file. It contains a variety of directories. Some are empty but are present to accommodate output generated by the data assimilation code mentioned above. Directories with data are:</p> <ul> <li>models/original_model_data/TraCE_21ka/: TraCE-21ka temperature output from: https://www.earthsystemgrid.org/project/trace.html</li> <li>models/original_model_data/HadCM3_transient21k/: HadCM3 temperature output (the spatially smoothed version, signified by "_s" at the end of the filename)</li> <li>proxies/temp12k/ - Temperature 12k proxy records from: https://lipdverse.org/</li> </ul>
B->Dh, D->KKpipi bin yields from LHCb Run 1 and 2
<p>The attached files contain the bin yields from an analysis of B->Dh, D->KKpipi, using the full Run 1 and 2 dataset at LHCb. The binning scheme can be found <a href="https://zenodo.org/record/6940031#.Y9fRGhzP1as">here</a>.</p>
Dataset to run the NeuralFRG software for the t-t' Hubbard model on the square lattice "Phys. Rev. Lett. 129, 136402 (2022)"
<p>This hdf5 repository contains the fRG vertices for the t-t' Hubbard model on the square lattice required to reproduce the results shown in the publication</p> <p>Di Sante et al., Phys. Rev. Lett. 129, 136402 (2022)</p> <p>by means of the NeuralFRG software (https://github.com/BITMAPdds/NeuralFRG).</p> <p>A train-test split can be performed with:</p> <p>python3 train_validation_split.py NeuralFRG_train_and_validation_data.h5 --verbose</p> <p>and training can be started with:</p> <p>python3 train.py path/to/file_train.h5 (...) #Additional flags here</p>
Sample data for JRC_seeker test run
<p>This is sample data as part of the test run for JRC_seeker, a Snakemake pipeline for the genome-wide discovery of jointly regulated CpGs (JRCs) in pooled whole genome bisulfite sequencing (WGBS) data (https://github.com/BenjaminPlanterose/JRC_seeker).</p>
Sample data for JRC_sorter test run
<p>This is sample data as part of the test run for JRC_sorter, a classifier for Jointly Regulated CpGs (JRCs) (https://github.com/BenjaminPlanterose/JRC_sorter).</p>
Example run script for ligand dissociation in wepy
<p>This provides an example script for initializing a wepy script with files from CHARMM-GUI. The wepy software and installation instructions can be found here: https://github.com/ADicksonLab/wepy .</p>
Input files to run free subduction model by Keum and So (2023), submitted to Tectonophysics.
<p>This release contains a series of Lagrangian free subduction models, and MATLAB script used to create the initial temperature condition.</p>
NASA GISS-E2-1G model runs with and without irrigation
<p>This dataset includes NASA GISS ModelE2.1G model runs with irrigation at the Year 2000 (Irrig_2000), halved irrigation at the Year 2000 (Irrig_2000half), and irrigation at the Year 1850 (Irrig_1850). The climate variables included in this dataset are: daily maximum temperature, equivalent temperature, and minimum relative humidity, and monthly latent heat flux, sensible heat flux, specific humidity, precipitation, incident solar radiation, and total cloud cover.</p>
Figure 11 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 11 An example of how water mites appear under a stereoscope. a – water mites of mixed taxa in a single sample ready to be sorted through and identified; b – water mites belonging to the genusTestudacarus and their easily observable characteristic dorsal plates; c – water mites belonging to the genusKongsbergia and their easily distinguishable posterior body shape.
Figure 6 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 6 The sieving process proposed in this manuscript. a – collector fills the composited sample container with water and shakes the container for approximately 10 seconds; b – immediately after shaking the collector removes the lid and pours the water through the 3mm and 250 μm sieves; c and d – the 3mm will be placed on top of the 250 μm sieve so it can filter out larger sized substrate and debris. This process is repeated 10x.
Figure 5 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 5 An example of the collector emptying the net contents from the first of four site collections into sample container; the next three site
Figure 2 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 2 Collection nets discussed in this manuscript. a – a standard, commercially available, truncated D-frame net with 500 μm mesh; b – our custom made, non-truncated D-frame net with 250 μm mesh.
Figure 1 in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 1 Examples of impaired and attaining riffle-run stream habitats in central Pennsylvania. a – Warriors Mark Run, impaired stream; b – Muddy Run, impaired stream; c – Spruce Run, attaining stream; d – Laurel Run, attaining stream.
Figure 3 What a in A quantitative method for collecting water mites in lotic, riffle-run habitats for water quality biomonitoring
Figure 3 What a single collection site should look like after the proposed three-minute collection effort. The blue arrows indicate stacked rocks that were stacked upstream of the net to better direct flow into the net. Large rocks were moved before digging into the substrate, while medium-sized and smaller rocks were placed as the substrate was dug up.
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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.