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278 results for “square”
Microplastic Abundance, Shape, and Color in Passerines Captured at Rushton Woods Preserve Bird Banding Station in Newtown Square, Pennsylvania, USA, April-September 2024
Fecal samples were collected from 5 species of passerine birds between April and September 2024 at the Rushton Woods Preserve Bird Banding Station. Samples were chemically digested and filtered for the purpose of extracting, quantifying, and describing microplastics.
Global monthly catch of tuna, tuna-like and shark species (1950-2023) by 1° or 5° squares (IRD level 2) - and efforts level 0 (1950-2023)
<div> </div> <p>This deposit contains various datasets describing tuna fisheries activities (currently catches and efforts) and different levels of processing on 1° or 5° spatial grids with a monthly temporal resolution.</p>
Plant Atlas 2020 — British and Irish vascular plant and charophyte 2 x 2 km grid square locations up to 2019
<p><span>This resource provides the data behind the 2 × 2 km grid square (tetrad) British and Irish distribution maps, for 3,431 taxa, presented in both the Plant Atlas 2020 book and website (</span><a href="http://www.plantatlas2020.org"><span><span>www.plantatlas2020.org</span></span></a><span><span>), up to 2019. These are presence-only data, indicating where a taxon was reported from a tetrad</span></span><span>. These 2 km square presences are based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s.</span></p>
Plant Atlas 2020 — British and Irish vascular plant and charophyte 10 x 10 km grid square locations, subdivided by survey period, up to 2019
<p><span>This resource provides the data behind the 10 × 10 km grid square (hectad) British and Irish distribution maps, for 3,497 taxa, presented in both the Plant Atlas 2020 book and website (</span><a href="http://www.plantatlas2020.org"><span><span>www.plantatlas2020.org</span></span></a><span><span>), subdivided by time period<a><span>.</span></a> These are presence-only data, indicating where a taxon was reported from a hectad, within a given</span><span><span></span></span></span><span> multi-year period, up to 2019. These time periods cover the 20<sup>th</sup> Century, but also extend back to the earliest botanical records known for Britain and Ireland in the first period (pre-1930). These 10 km square presences are based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s.</span></p>
Larval euphausiids collected using a 1 x 1 m square frame net with 333-μm mesh aboard Palmer LTER annual cruises off the coast of the Western Antarctic Peninsula, 1993-2013
Euphausiids (krill) are abundant along the Western Antarctic Peninsula where they have important impacts on the marine ecosystem and biogeochemical cycling. Euphausiids develop through a series of morphologically distinct larval stages within the first year of their life cycle. The calyptopis stages are followed by the furcilia stages before individuals recruit to the post-larval population. Larvae collected during Janury were most likely spawned during the preceding weeks or months of Antarctic spring/summer. Euphausia superba and Thysanoessa macrura are the most abundant euphausiid species along the Western Antarctic Peninsula. Euphausia crystallorophias, Euphausia frigida, and Euphausia triacantha are present but less abundant in the region. Samples were collected with a 1 x 1 m square frame net with 333-μm mesh towed obliquely to a depth of typically 300 m. Density of total calyptopis and furcilia larvae (all species combined) was determined for a subset of samples collected on the annual Palmer LTER cruises to cover the latitudinal and cross-shelf gradients of the study region. Larval euphausiid abundance varies spatially as spawning output is not homogeneous across the region. Larval euphausiid abundance also varies year-to-year due to changes in population demographics and environmental conditions.
Macrozooplankton taxonomic density collected using a 1 x 1 m square net with 700-μm mesh at Palmer Station, Antarctica during Palmer LTER field seasons, 2017-2020
Zooplankton are a morphologically and taxonomically diverse group of animals. Many zooplankton feed on phytoplankton in surface waters and thus provide a link between primary producers and higher trophic levels. The numerical density of common macrozooplankton taxa was determined at Palmer LTER Stations B and E. Samples were collected with a 1 x 1 m square, 700-μm mesh Metro net towed obliquely from the surface to a target depth of 50 m and back. Duplicate tows typically were conducted at each sampling site. Tows were conducted during daytime, and sampling frequency was nominally twice weekly while personnel were at Palmer Station between the months of November and March. The catch was sorted and counted live. Data are provided for the following taxa, which dominated biomass: the euphausiids Euphausia superba and Thysanoessa macrura, the thecosome pteropod Limacina rangii, gymnosome pteropods, the salp Salpa thompsoni, amphipods, and larval fishes. Density varies across taxa, seasonally, among years, and between sampling stations. Units of density are individuals per cubic meter.
Approximate sum of squares decompositions for Adj₅ + k·Op₅ - λΔ₅ ∈ ISAut(F₅)
<p>This is the dataset accompanying <em>On property (T) for Aut(Fₙ) and SLₙ(</em>ℤ<em>) </em>paper (https://arxiv.org/abs/1812.03456). See the appendix thereof and Section 4 of (<a href="https://arxiv.org/abs/1712.07167">Aut(F₅) has property (T)</a>) for more details.</p> <p><strong>Content</strong></p> <ol> <li><code>1812.03456-cf6dee7.zip</code> contains a julia environment specification (<code>Project.toml</code> and <code>Manifest.toml</code>) as well as <code>1812.03456.jl</code> script used for automatic certification and jupyter noteboks in <code>./notebooks</code> directory.</li> <li><code>SAutF5_r2.tar.xz</code> contains the precomputed solutions for expressing <code>Adj₅+2·Op₅-0.28Δ₅</code> and <code>Adj₅+3·Op₅-1.4Δ₅</code> as sum of (hermitian) squares in the group ring of <code>SAut(F₅)</code>. The contents of this archive must be placed inside `<code>1812.03456`</code>directory from the <code>zip</code> file.</li> </ol> <p><strong>Preparation</strong></p> <p>The code needs to be run with <code>julia-1.4.0</code> or higher (tested versions include also versions <code>julia-1.5</code>). In principle any version in <code>[1.4-2.0)</code> should work due to the promise of forward compatibility.</p> <p>While located in the main directory (<code>1812.03456</code>) you should run the following code in <code>julia</code>s <code>REPL</code> console to instantiate the environment for computations:</p> <pre><code class="language-julia">using Pkg Pkg.activate(".") Pkg.instantiate()</code></pre> <p>(this needs to be done once per installation). Then the directory <code>SAutF5_r2</code> (from the <code>SAutF5_r2.tar.xz</code> archive) needs to be placed in <code>1812.03456</code>.</p> <p><strong>Replication: Jupyter notebook</strong></p> <p>A jupyter server may be launched then within the directory <code>1812.03456</code> by issuing from julia command-line (<code>REPL</code>) the following commands.</p> <pre><code>using Pkg Pkg.activate(".") using IJulia notebook(dir=".")</code></pre> <p>During the first run the user may be asked for installation of <code>Jupyter</code> program (a server for running this notebook) within <code>miniconda</code> environment, which will happen automatically after confirmation. To execute the commands in the notebook, one needs to navigate to <code>notebooks</code> subdirectory of <code>1812.03456</code> and click either of the notebooks.</p> <p>One can replicate the main computational results of the paper by executing all the cells in the <code>Positivity of Adj_n + kOp_n in ISAut(F_n)</code> notebook.</p> <p><strong>Replication: script</strong></p> <p>To verify that <em>(Adj₅ + 3.0·Op₅) - 1.4·Δ₅</em> admits an approximate sum of squares decomposition run in <code>1812.03456</code> directory</p> <blockquote> <p><code>julia --project=. --color=yes 1812.03456.jl -n 5 -k 3 -l 1.4</code></p> </blockquote> <p>On a modern laptop computer this should finish in less than 2h.</p> <p>At the end of computations you will see lines such as:</p> <blockquote> <p>┌ Info: λ is certified to be ><br> └ λ_cert.lo = 1.3701131733828074<br> [ Info: i.e Adj_5 + 3.0·Op_5 - (1.3701131733828074)·Δ_5 ∈ Σ²₂ ISAut(F_5)</p> </blockquote> <p>This means that <em>Adj₅ + 3.0·Op₅ - λΔ₅</em> is a sum of Hermitian squares of elements from <em>ISAut(F₅)</em> for every <code>λ < 1.370....</code></p> <p>A similar verification for <em>Adj₅ + 2.0·Op₅ - 0.28·Δ₅</em> can be run by executing</p> <blockquote> <p><code>julia --project=. --color=yes 1812.03456.jl -n 5 -k 2 -l 0.28</code></p> </blockquote> <p><strong>Generating the provided files</strong></p> <p>If you wish to produce the whole certificate on your own (including the generation of group ring and its multiplication table), delete all <code>*.jld</code> files from the <code>SAutF5_r2</code> folder and run one of the above commands with the same (or different) parameters again. Note: To do this you need at least 16GB of RAM and spare 24h of your CPU.</p> <p>This research was supported in part by National Science Center, Poland, grant 2017/26/D/ST1/00103.</p>
Sistan: part 1. Grid Squares
<h3>Deposit of Grid Squares from the Sistan region (Iran, Afghanistan) maintained within the EAMENA database.</h3> <p>These Grid Squares cover the central region of the Sistan region (Iran, Afghanistan) and represent a subset of the EAMENA grid system. Each square measures 0.25 degrees in both longitude and latitude, spanning the Middle East and North Africa (MENA) region from Afghanistan to Mauritania. These Grid Squares are the one surveyed to create the Sistan: part 1. Heritage Places. Their spatial coverage covers 16,875 km2 with these boundaries:</p> <ul> <li>Northern boundary: + 31.5</li> <li>Southern boundary: + 30</li> <li>Eastern boundary: + 62.5</li> <li>Western boundary: - 60.5</li> </ul> <p>Their geographic centre is the Iranian city of Zabol (زابل)</p> <p>---</p> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div>The two repositories (<a href="../records/10375902">Heritage Places</a> and Grid Squares) both pertain to Part 1 of the Sistan survey. This dataset primarily focuses on the central areas of Sistan, but it can be expanded through future surveys to include the eastern parts of Sistan in Afghanistan and additional undocumented areas in Iran.</div> </div> </div> </div> </div> </div> </div> </div> <div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div>
Geografisches hexagonales Gitter mit 1 Quadratkilometer Zellengröße für Deutschland - Geographical hexagonal grid with one square kilometer cell size for Germany
<p>Ein räumliches Gitter stellt eine abstrakte Definition von einheitlich großen Bezugszellen für statistische Auswertungen dar. Dieses macht Auswertungen und Vergleiche gegenüber räumlichen Abgrenzungen auf der Basis von administrativen Grenzen wie Kreisen oder Gemeinden unterschiedlicher Größen einfacher. Geografische Gitterdefinitionen werden eingesetzt, um eine vordefinierte Raumbezugsstruktur zu haben mit einer einheitlicheren Verteilung von Zellen in der Fläche.</p> <p><br> Ein Gitter mit sechseckigen, eben hexagonalen Zellen bietet eine Alternative zu rechteckigen statistischen Gittern wie das GeoGitter vom Bundesamt für Kartographie und Geodäsie (BKG, 2020). Ein Hexagon-Gitter bietet neben der Eigenschaft homogene Einheiten für statistische Analysen zu bilden (Schindler et al. 2008) mit sechs unmittelbaren Nachbarzellen vorteilhaftere Voraussetzungen für Nachbarschaftsanalysen im Vergleich zu quadratischen Zellen mit vier Kantennachbarn (White et al. 1992). Als Beispiel werden im Bild 1 mittels Hexagonen-Gitter Volumenunterschiede der aufragenden Vegetation pro Zelle dargestellt.</p>
Determinant Quantum Monte Carlo data for the Hubbard model on the square and honeycomb lattice.
<p>Data generated with QUEST 1.4.9. For documentation see these two homepages:<br> Original homepage: http://quest.ucdavis.edu/<br> Newest version available at: https://code.google.com/archive/p/quest-qmc/</p> <p>Available data from equal time measurements:</p> <ul> <li>up-up charge correlation function</li> <li>up-dn charge correlation function</li> <li>sz-sz spin correlation function</li> <li>pair correlation function</li> <li>kinetic energy</li> <li>total energy</li> <li>chi thermal</li> <li>squared magnetization</li> <li>ZZ AF structure factor</li> </ul> <p>Data for the square lattice calculated for</p> <ul> <li>lattice sizes 8x8, 10x10, 12x12</li> <li>trotter discretizations 0.05, 0.1, 0.2</li> <li>U 0.0 to 7.1 in steps of 0.1</li> </ul> <p>Data for the honeycomb lattice calculated for</p> <ul> <li>lattice sizes 6x6, 9x9, 12x12</li> <li>trotter discretizations 0.05, 0.1, 0.2</li> <li>U 0.0 to 7.1 in steps of 0.1</li> </ul> <p>All energies are in units of the hopping parameters which is set to t=1. All simulations are done for half filling.</p> <p>The data are used in the publication "First-order metal-insulator transitions in the extended Hubbard model due to self-consistent screening of the effective interaction" available on the arXiv (arXiv:1706.09644). There it is used to do an extrapolation of finite size and finite trotter errors and finally calculate derivatives of charge correlation functions w.r.t. the interaction U.</p> <p>The data are available in hdf5 archives and can easily be accessed, e.g., with python and h5py. An example python script is included. Relevant input parameters are included in the h5 files.</p> <p>All calculated quantities are averaged over multiple consecutive simulations, which is why the data is not presented in the usual QUEST output. This was necessary due to limited walltime on the used supercomputer.</p> <p>This version (v2) includes the number of bins used in each simulation and a slighlty changed python script to read the data.</p>
Continuous magnetic phase transition in artificial square ice
<p>Open access data set for manuscript "Continuous magnetic phase transition in artificial square ice" published in Physical Review B, <strong>99</strong>, 214430 (2019)</p>
Lattice subpolygons of a square with given sidelength
<p>This record contains all lattice subpolygons of the square of sidelength \(m\) for \(1 \leq m \leq 10\). There is one file for each sidelength. We use the <a href="https://www.hdfgroup.org/solutions/hdf5/" target="_blank" rel="noopener">HDF5</a> file format to store the polygons. The polygons have been obtained using <a href="https://github.com/justus-springer/RationalPolygons.jl" target="_blank" rel="noopener">RationalPolygons.jl</a>.</p> <h3>Structure of the HDF5 files</h3> <p>In each file, the polygons are split into different datasets according to their normalized area and number of vertices. We use "a" to denote the normalized area (twice the euclidian area) and "n" to denote the number of vertices. For example, the subpolygons of the square of sidelength \(4\) with normalized area \(20\) having \(6\) vertices are located in "m4.h5" under the dataset "/a20/n6". Each dataset of polygons is one-dimensional with one entry per polygon. A polygon is stored using a compound datatype with \(2 \cdot n\) fields of integers. These integers are the vertices of the polygon stored in column major layout. For example, a triangle with vertices \((x_1,y_1), (x_2,y_2)\) and \((x_3,y_3)\) is stored as the tuple \((x_1,y_1,x_2,y_2,x_3,y_3)\). Moreover, each file contains the special two-dimensional dataset "numbers_of_polygons" that stores the numbers of polygons for a given normalized volume and number of vertices.</p>
Raw data from Cao et al. (2023) "Electron exchange capacity of pyrogenic dissolved organic matter (DOM): Complementarity of square-wave voltammetry in DMSO and mediated chronoamperometry in water"
<p>Measured and fitted data from square-wave voltammetry (SWV) in DMSO for electron exchange capacities (EECs) of pyrogenic natural organic matter (pyDOM) and natural organic matter (NOM) standards. </p> <p>From Cao, H., A. S. Pavitt, J. M. Hudson, P. G. Tratnyek, and W. Xu. 2023. Electron exchange capacity of pyrogenic dissolved organic matter (DOM): Complementarity of square-wave voltammetry in DMSO and mediated chronoamperometry in water. Environ. Sci. Proc. Impacts: ASAP. [10.1039/d3em00009e]</p> <p>The manuscript reports electron accepting capacity (EAC), electron donating capacity (EDC), and electron exchange capacities (EECs) measured with a new method involving square-wave voltammetry in an aprotic solvent (dimethyl sulfoxide, DMSO). The measurement method, fitting of peak areas, and conversion of peak areas to EECs are described in the main text and supporting information of the manuscript.</p> <p>Here we provide the original measured data, baseline corrected data used in the peak fitting, and fitted peak area data that were used to obtain the final EEC values. The data are provided in one .xlsx file that contains multiple tabs: (i) a table of contents, (ii) a summary of the final fitting results, and (iii) tabs numbered R1-R40 containing raw measured data for each pyDOM/NOM sample.</p> <p>The data provided here should be sufficient to replicate and verify all of the analysis described in the manuscript. If you use these data, please cite this Zenodo record (DOI 10.5281/zenodo.7747020) and the original manuscript (DOI: 10.1039/d3em00009e).</p>
Snake Ancestor Illustration (Square)
<p>Illustration showing the lifestyle and the reconstructed brain model of the hypothetical ancestor of snakes (square)</p>
Thermal Phase Diagram of the Square Lattice Ferro-antiferromagnetic J1−J2 Heisenberg Model Data
<p>This repository contains raw data for the article "Thermal Phase Diagram of the Square Lattice Ferro-antiferromagnetic J1-J2 Heisenberg Model", Olivier Gauthé and Frédéric Mila, 2023.</p><p>Raw data is provided as json files into the archive data_PEPS_ferroJ1-J2/ subdirectory.zip. The file "data_mswt_ferroJ1-J2.json" contains modified spin wave theory data.</p><p><br>The jupyter notebook "plot_ferroJ1-J2.ipynb" provides scripts to load and visualize data, as well as reproducing figures from the paper.<br>It can be executed using<br>python version 3.9.17<br>numpy version 1.24.3<br>scipy version 1.10.1</p><p>All the data was generated using finite temperature PEPS. Refer to the paper for a complete methodological discussion. The source code to produce PEPS data is available upon reasonable request.</p><p>Olivier Gauthé<br>October 2023</p>
Total numbers per square meter and taxa of insects taken from the Kuparuk River during the summer of 2001, Arctic LTER 2001.
A Surber sampler (25 X 25 cm frame fitted with a 243 um mesh net) was used to sample invertebrates at several different stations. Two replicates were taken from each station. The same sampling procedure was used for all dates. The stations were measured relative to the site of the dripper ("-" = upstream of the dripper). Samples were preserved in 4% formaldehyde and transported to Orono, Maine, where invertebrates were removed by hand under 15X magnification and then identified and counted. All values are converted to individuals per square meter.
IG. 6. — A, Trunk vertebra of Alsophis sp. 2 from Pointe du Helleux archaeological site (Square 2 – crab layer) located on Grande-Terre Island; B, trunk vertebra of Erythrolamprus juliae cf. copeae (Parker, 1936) from Sainte-Rose La Ramée archaeological site (US 2058) located on Basse-Terre Island. Abbreviations: cd., condyle; ct., cotyle; di., diapophysis; h. k., hemal keel; m. c., medial constriction; n. a., neural arch; n. s., neural spine; p. c., precondylar constriction; p. d., paracotylar depression; p. n., postero-medial notch of the zygantrum; pa., parapophysis; pz. f., prezygapophyseal facet; pz. p., prezygapophyseal process; s. d., subcentral depression; s. r., subcentral ridge; s. t., sub-cotylar tubercle; zs., zygosphene. Scale bars: 4 mm in Fossil dipsadid snakes from the Guadeloupe Islands (French West-Indies) and their interactions with past human populations
IG. 6. — A, Trunk vertebra of Alsophis sp. 2 from Pointe du Helleux archaeological site (Square 2 – crab layer) located on Grande-Terre Island; B, trunk vertebra of Erythrolamprus juliae cf. copeae (Parker, 1936) from Sainte-Rose La Ramée archaeological site (US 2058) located on Basse-Terre Island. Abbreviations: cd., condyle; ct., cotyle; di., diapophysis; h. k., hemal keel; m. c., medial constriction; n. a., neural arch; n. s., neural spine; p. c., precondylar constriction; p. d., paracotylar depression; p. n., postero-medial notch of the zygantrum; pa., parapophysis; pz. f., prezygapophyseal facet; pz. p., prezygapophyseal process; s. d., subcentral depression; s. r., subcentral ridge; s. t., sub-cotylar tubercle; zs., zygosphene. Scale bars: 4 mm
Fig.ç3.R elationships of (A) barbel length and (B) pectoral- n length to standard length in Upeneus guttatus (closed symbols: stars from Japan, squares from Indo–West Paci c) and U. japonicus (open circles). in First Records of the Two-tone Goatfish, Upeneus guttatus, from Japan, and Comparisons with U. japonicus (Perciformes: Mullidae)
Fig.ç3.R elationships of (A) barbel length and (B) pectoral- n length to standard length in Upeneus guttatus (closed symbols: stars from Japan, squares from Indo–West Paci c) and U. japonicus (open circles).
Рис. 11. Распространение Otiorhynchus digitalis Yunakov et Davidian, 2002 (круг) и O. paradigitalis sp. n. (кваΑрат). Fig 11. Distribution of Otiorhynchus digitalis Yunakov et Davidian, 2002 (circle) and O. paradigitalis sp. n. (square). in A new weevil of the subgenus Otismotilus Reitter, 1912, genus Otiorhynchus Germar, 1824 (Coleoptera: Curculionidae: Entiminae) from the Caucasus
Рис. 11. Распространение Otiorhynchus digitalis Yunakov et Davidian, 2002 (круг) и O. paradigitalis sp. n. (кваΑрат). Fig 11. Distribution of Otiorhynchus digitalis Yunakov et Davidian, 2002 (circle) and O. paradigitalis sp. n. (square).
Transition between distinct hybrid skyrmion textures through their hexagonal-to-square crystal transformation in a polar magnet
<p>The file Manuscript data files.7z contains the experimental data used for creating the figures in the manuscript entitled "Transition between distinct hybrid skyrmion textures through their hexagonal-to-square crystal transformation in a polar magnet" that appear in Nature Communications 14, 8050 (2023).</p><p>Paper abstract: Magnetic skyrmions, topological vortex-like spin textures, garner significant interest due to their unique properties and potential applications in nanotechnology. While they typically form a hexagonal crystal with distinct internal magnetisation textures known as Bloch- or Néel-type, recent theories suggest the possibility for direct transitions between skyrmion crystals of different lattice structures and internal textures. To date however, experimental evidence for these potentially useful phenomena have remained scarce. Here, we discover the polar tetragonal magnet EuNiGe3 to host two hybrid skyrmion phases, each with distinct internal textures characterised by anisotropic combinations of Bloch- and Néel-type windings. Variation of the magnetic field drives a direct transition between the two phases, with the modification of the hybrid texture concomitant with a hexagonal-to-square skyrmion crystal transformation. We explain these observations with a theory that includes the key ingredients of momentum-resolved Ruderman–Kittel–Kasuya–Yosida and Dzyaloshinskii-Moriya interactions that compete at the observed low symmetry magnetic skyrmion crystal wavevectors. Our findings underscore the potential of polar magnets with rich interaction schemes as promising for discovering new topological magnetic phases.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.