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9,677 results for “one”
MiRoR7-P1- Disagreements in risk of bias assessment for randomised controlled trials included in more than one Cochrane systematic reviews: a research on research study using cross-sectional design
<p>dataset referring to </p> <p><strong>Disagreements in risk of bias assessment for randomised controlled trials included in more than one Cochrane systematic reviews: a research on research study using cross-sectional design</strong></p> <p> </p> <p> </p> <p>Lorenzo Bertizzolo<sup>1</sup>, Patrick M Bossuyt<sup>2</sup>, Ignacio Atal<sup>1, 5</sup>, Philippe Ravaud<sup>1, 3-6</sup>, Agnès Dechartres<sup>7</sup></p> <p> </p> <p><sup>1</sup> INSERM, U1153 Epidemiology and Biostatistics Sorbonne Paris Cité Research Center (CRESS), Methods of therapeutic evaluation of chronic diseases Team (METHODS), Paris, F-75004 France; Paris Descartes University, Sorbonne Paris Cité, France.</p> <p><sup>2</sup> Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Netherlands.</p> <p><sup>3</sup> Centre d’Épidémiologie Clinique, Hôpital Hôtel Dieu, AP-HP (Assistance Publique des Hôpitaux de Paris), Paris, France.</p> <p><sup>4</sup> Faculté de Médecine, Université Paris Descartes, Sorbonne Paris Cité, Paris, France.</p> <p><sup>5</sup> Cochrane France, Paris, France</p> <p><sup>6</sup> Columbia University, Mailman School of Public Health, Department of Epidemiology, New York, USA</p> <p><sup>7</sup> Sorbonne Université, INSERM, Institut Pierre Louis de Santé Publique, Département Biostatistique, Santé Publique et Information Médicale, AP-HP, Hôpitaux Universitaires Pitié Salpêtrière – Charles Foix, Paris, France</p>
Data for 'Local food crop production can fulfil demand for less than one-third of the population'
<p><strong>This dataset is supplement to the following publication (<em>please cite that when using the data</em>):</strong></p> <p>Kinnunen et al. 2020. Local food crop production can fulfil demand for less than one-third of the population. Nature Food 1: 229–237. http://doi.org/10.1038/s43016-020-0060-7</p> <p> </p> <p><strong>Data description</strong></p> <p><strong><em>Distance to food:</em></strong> Globally optimized distance between crop production and consumption. The optimization creates a theoretical food allocation set-up that minimizes travel time cost from crop production to consumption. Data is in two formats: NetCDF (dist_food_netcdf.zip) and multi-band geotiff (dist_food_tif.zip).</p> <p>The data includes:</p> <ul> <li>baseline scenario (dist_food_baseline.nc / .tif)</li> </ul> <p>and three other scenarios where food availability is changed by</p> <ul> <li>decreasing food waste by half (dist_food_halfLoss.nc / .tif)</li> <li>halving the yield gap (dist_food_halfYieldGap.nc / .tif)</li> <li>both of these measures together (dist_food_halfLoss_halfYielGap.nc / .tif)</li> </ul> <p><em>The data covers six crop functional types</em>: maize, pulses, rice, temperate cereals, tropical cereals and tropical roots </p> <p><em>Dataset specifications:</em></p> <p>spatial extent: -180, 180, -90, 90 (xmin, xmax, ymin, ymax)</p> <p>spatial resolution: 0.5 degrees</p> <p>projection: long/lat WGS84</p> <p>layers: 1: maize, 2: pulses, 3: rice, 4: temp_cereals, 5: trop_cereals, 6: trop_roots </p> <p>no data value: -999</p> <p>unit: km</p> <p> </p> <p><em><strong>Foodsheds:</strong></em> The data contains global foodsheds which are areas that are connected by food flows between raster cells. The food flows are from a theoretical food allocation set-up that minimizes travel time cost from crop production to consumption. In addition to normal foodsheds (values>0), there are two special cases: ridge-cells (value: -99) and unconnected single cells (value: -50). Ridge-cells are raster cells connected to multiple foodsheds, while being able to satisfy their own demand locally. Unconnected single cells are not connected to any other foodshed. Each positivie value is a crop specific id, signifying a connected foodshed area. </p> <p>Data is in two formats: NetCDF (foodsheds_netcdf.zip) and multi-band geotiff (foodsheds_tif.zip).</p> <p>The data includes:</p> <ul> <li>baseline scenario (foodsheds_baseline.nc / .tif)</li> </ul> <p>and three other scenarios where food availability is changed by</p> <ul> <li>decreasing food waste by half (foodsheds_halfLoss.nc / .tif)</li> <li>halving the yield gap (foodsheds_halfYieldGap.nc / .tif)</li> <li>both of these measures together (foodsheds_halfLoss_halfYielGap.nc / .tif)</li> </ul> <p><em>The data covers six crop functional types</em>: maize, pulses, rice, temperate cereals, tropical cereals and tropical roots </p> <p><em>Dataset specifications:</em></p> <p>spatial extent: -180, 180, -90, 90 (xmin, xmax, ymin, ymax)</p> <p>spatial resolution: 0.5 degrees</p> <p>projection: long/lat WGS84</p> <p>layers: 1: maize, 2: pulses, 3: rice, 4: temp_cereals, 5: trop_cereals, 6: trop_roots </p> <p>no data value: -999</p> <p>unit: -</p> <p> </p>
UM experiments for "Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection"
<p>NetCDF4 files containing UM vn 11.1 data used in Lambert et al., 2020, Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection, submitted to Journal of Advances in Modeling Earth Systems.</p> <p>Key:</p> <p>"summaryday.nc" contain eleven months of data in each year, excluding either February or March.</p> <p>"summarydat2.nc" contain one month of data in each year, either February or March.</p> <p>"last5" indicates that for this simulation only the last five years of data are available.</p> <p>"llcs" are simulations with Lambert-Lewis.</p> <p>"gr" are simulations with Gregory-Rowntree.</p> <p>"llcsemu" are simulations with the Lambert-Lewis emulator.</p> <p>"gremu" are simulations with the Gregory-Rowntree emulator.</p> <p>"llcsemu_llcs" is the test simulation wherein the LLCS emulator is run equatorward of 30 degrees and the original LLCS convection scheme is run poleward of 30 degrees.</p> <p>"4xco2" have 4 x pre-industrial atmospheric carbon dioxide concentration. (Others have 1 x pre-industrial atmospheric carbon dioxide concentration.)</p> <p>"rh0.7" and "rh0.9" have LLCS RHCRIT set to 70% and 90% respectively.</p> <p>"30day" are one month simulations for July for which daily output are available. Other data are monthly mean only.</p> <p> </p>
The One-Armed Spiral Instability in Neutron Star Mergers and its Detectability in Gravitational Waves
<p>We distribute complete gravitational-wave signals in the Advanced LIGO band (10 Hz - 8192 Hz) of the inspiral and merger of two neutron stars. These waveforms been constructed by hybridizing numerical-relativity data obtained with the WhiskyTHC code [1] with tidal effective-one-body waveforms [2,3]. More details on the procedure used to generate these waveforms are given in [4]. </p> <p>The waveforms are distributed as HDF5 files containing the amplitude and phase of the -2 spin-weighted spherical harmonics multipoles of the strain:</p> <p><span class="math-tex">\(( h_+ - \mathrm{i} h_\times )_{l,m} = \frac{A_{l,m}}{D_{\rm cm}} \exp(-\mathrm{i} \phi_{l,m} )\)</span></p> <p>where <span class="math-tex">\(D_{\rm cm}\)</span> is the distance in cm from the source.</p> <p>The data files include a machine readable "/metadata" group with:</p> <ul> <li>/metadata/EOS: name of the equation of state</li> <li>/metadata/M_{A|B}: mass in isolation of star A (or B) in grams</li> <li>/metadata/R_{A|B}: radius of star A (or B) in cm</li> <li>/metadata/k2T: tidal coupling constant of the binary (see [3])</li> <li>/metadata/kl_{A|B}: l=2,3,4 dimensionless Love numbers of star A (or B)</li> </ul> <p>We store amplitude and phase for multipoles modes up to l=4 as time series sampled at 16384 Hz.</p> <p>We make these waveforms freely available in the hope that they will be useful. We kindly ask you to cite [3] and [4] in any publication resulting from the use of these waveforms.</p> <p>---<br /> [1] http://www.tapir.caltech.edu/~david_e/whiskythc.html<br /> [2] https://eob.ihes.fr/<br /> [3] S. Bernuzzi, A. Nagar, T. Dietrich, T. Damour; Modeling the Dynamics of Tidally Interacting Binary Neutron Stars up to the Merger; Phys.Rev.Lett. 114 (2015) 16, 161103.<br /> [4] D. Radice, S. Bernuzzi, C. D. Ott; The One-Armed Spiral Instability in Neutron Star Mergers and its Detectability in Gravitational Waves; arXiv:1603.05726.</p>
Data for: Bound impurities in a one-dimensional Bose lattice gas: low-energy properties and quench-induced dynamics
<p>Dataset for <em>Bound impurities in a one-dimensional Bose lattice gas: </em><em>low-energy properties and quench-induced dynamics</em> [<a href="https://scipost.org/SciPostPhysCore.7.3.049">SciPost Phys. Core 7, 049 (2024)</a>].</p>
Sydney Morris Cockerell: an Appreciation by one of his Journeymen
<p>This is the recording and transcript of a lecture given by Anthony Cains in 1988 at the Fifth Anniversary Conference of the Parker Library Conservation Project. A publication based upon the conference paper, supplemented with new material and updated, was released in 1994:</p> <p>Cains, Anthony G. 1994. ‘Sydney Morris Cockerell. A Tribute by Anthony Cains, One of His Journeymen’. In <em>Conservation and Preservation in Small Libraries</em>, edited by Nicholas Hadgraft and Katherine Swift, 157–60. Cambridge: Parker Library Publications.</p>
Don't put all your eggs in one leaf-roll: a network analysis of candy-striped spider leaf preferences for egg deposition
<p>"Enoplognatha leaf choice.R" - R script for all analyses and figures for the manuscript.</p> <p>"Enop_LeafENNR_EnopChoice.csv" - Interaction matrix showing the leaves selected by each individual candy-striped spider.</p> <p>"Enop_LeafENNR_PlantComm.csv" - Proportional plant community composition in each quadrat within which candy-striped spiders were found.</p> <p>"Enop_LeafTSENNR_EnopChoice.csv" - Interaction matrix showing the tropho-species selected by each individual candy-striped spider.</p> <p>"Enop_LeafENNR_PlantComm.csv" - Proportional plant tropho-species community composition in each quadrat within which candy-striped spiders were found.</p> <p>"Enop_SNRENNR_EnopChoice.csv" - Interaction matrix showing the semi-natural habitats selected by each individual candy-striped spider.</p> <p>"Enop_SNRENNR_SNRs.csv" - Matrix equally representing the semi-natural habitats as the number of quadrats surveyed in each.</p> <p>"Leaf traits.csv" - The leaf trait data used to cluster leaves into tropho-species.</p> <p>"leaf.incidence.csv" - The number of candy-striped spiders that interacted with each plant species for assessment of interaction diversity and completeness.</p> <p>"Site Map.csv" - Latitude and longitude of the ten sampling sites used in this study.</p> <p>"TS plotting order.csv" - A file used to set the plotting order of tropho-species in a bipartite plot.</p>
Exploring the Pocillopora cryptic diversity: a new genetic lineage in the western Indian Ocean or remnants from an ancient one?
<p>Cryptic species and lineages have been widely reported during the last decades, particularly in the marine realm. Misidentifications and ignoring species complexes imply many consequences, notably biasing biodiversity and connectivity assessments, which in turn mislead our understanding of ecosystems and impact the effective design and management of conservation plans. Focusing on the Indo-Pacific coral genus <em>Pocillopora</em>, playing key roles in reef ecosystems as one of the main bio-constructors, we report the first <em>Pocillopora</em> PSH16 (ORF53; <em>sensu</em> Gélin et al. 2017, Mol Phylogenet Evol 109:430–446) colonies (<em>N</em> = 19) in the western Indian Ocean (Nosy Tanikely, Madagascar), 6,000 km further from its current distribution. Colonies were identified according to their mitochondrial Open Reading Frame (ORF) haplotype and Bayesian assignment tests based on 13-microsatellite genotypes. Additionally, we performed genetic structure and diversity analyses with sympatric colonies from other <em>Pocillopora</em> species and <em>Pocillopora</em> PSH16 colonies from the tropical southwestern Pacific, revealing (1) a weak clonal richness, (2) a weak genetic diversity and (3) a relative isolation for the newly reported PSH16 colonies. These colonies thus represent either a new, distinct and uncommon, genetic lineage, or isolated remnants of a wider one. In any case, unless specific management measures are implemented, their long-term maintenance seems compromised due to restricted gene flow within a restricted pool of genes.</p> <p> </p> <p>This dataset contains the microsatellite genotypes analysed (98 <em>Pocillopora</em> colonies × 13 loci + ORF). Missing data are encoded as "?". The sampling marine province and the population are indicated for each individual.</p>
Data set of article entitled: "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study"
<p>Data set of the immagies showed in figure 3 of the article entiteled "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study". All files contain the matrix of the dispersion relations of the two analysed Magnonic Crystals: the SAMPLE A and the SAMPLE B for different value of the interfacial Dzyaloshinskii-Moriya interaction (constant D). The first row is the set of values of k-vector, while the first column is the set of value of the frequencies. The other elements of the matrix are the values of the pixel related to the first row and first column. These elements are been obtanied by the Fast Fourier Transform in time and space of the micromagnetic simulations .</p>
Data: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management
<p>2018 Boreal forest fires in Sweden: Measurements of soil CO2 and CH4 fluxes, soil microclimate and nutrient content during the first growing season after a wildfire, from forest sites impacted by different fire severity (tree mortality) and post-fire management.</p> <p> </p> <p>Data used in: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management; Julia Kelly, Theresa S. Ibáñez, Cristina Santín, Stefan H. Doerr, Marie-Charlotte Nilsson, Thomas Holst, Anders Lindroth, Natascha Kljun; Global Change Biology, 27, 4181-4195, https://doi.org/10.1111/gcb.15721</p> <p> </p> <p> </p> <p> </p>
One Classifier Ignores a Feature
<p>The data sets are used in a controlled experiment, where two classifiers should be compared. train_a.csv and explain.csv are slices from the original data set. train_b.csv contains the same instances as in train_a.csv, but with feature x1 set to 0 to make it unusable to classifier B.</p> <p>The original data set was created and split using this Python code:</p> <pre><code class="language-python">from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression X, y = make_classification(n_samples=300, n_features=2, n_redundant=0, n_informative=2, n_clusters_per_class=1, class_sep=0.75, random_state=0) X *= 100 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0) lm = LogisticRegression() lm.fit(X_train, y_train) clf_a = lm clf_b = LogisticRegression() X2 = X.copy() X2[:, 0] = 0 X2_train, X2_test, y2_train, y2_test = train_test_split(X2, y, test_size=0.5, random_state=0) clf_b.fit(X2_train, y2_train) X_explain = X_test y_explain = y_test</code></pre>
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>
Data for: Mobile impurities interacting with a few one-dimensional lattice bosons
<p>Dataset for <em>Mobile impurities interacting with a few one-dimensional lattice bosons</em> (<a href="https://iopscience.iop.org/article/10.1088/1361-6455/acb51b">10.1088/1361-6455/acb51b</a>). It corresponds to exact diagonalization results for energies and bipolaron sizes.</p> <p>The files correspond to the following figures in the preprint:</p> <p>Fig. 1a: Ep_UBB2.dat <br> Fig. 1b: Ep_UBB4.dat <br> Fig. 1c: Ep_UBB6.dat <br> Fig. 1d: Ep_UBB8.dat <br> Fig. 2: Ep_UBI50.dat<br> Fig. 5a: Ebp_UBB2.dat <br> Fig. 5b: Ebp_UBB4.dat <br> Fig. 5c: Ebp_UBB6.dat <br> Fig. 5d: Ebp_UBB8.dat <br> Fig. 6: Ebp_UBI50.dat <br> Fig. 7a: rbp_UBB2.dat <br> Fig. 7b: rbp_UBB4.dat <br> Fig. 7c: rbp_UBB6.dat <br> Fig. 7d: rbp_UBB8.dat <br> Fig. 8: rbp_UBI50.dat</p>
Raw Data - Photo-Responsive Doped 3D-Printed Copper Electrodes for Water Splitting: Refractory One-Pot Doping Dramatically Enhances the Performance
<p>The dataset contains raw data that complements the article:</p> <p>Photo-Responsive Doped 3D-Printed Copper Electrodes for Water Splitting: Refractory One-Pot Doping Dramatically Enhances the Performance</p> <p>Christian Iffelsberger, Daniel Rojas, and Martin Pumera<strong>*</strong></p> <p>https://doi.org/10.1021/acs.jpcc.1c10686</p> <p>Related to the MSCA Project: 888797 LoCatSpot</p>
Regional model results (combined) for the six transition potentials (one for Africa, Australia, Asia, Europe, North America, and South America)
<p>Results for the six regional models showing areas of high potential to transition from tree cover to tree cover loss to areas of low potential to transition.</p>
Data from: Andriollo T., Gillet F., Michaux J.R., Ruedi M. (2019). The menu varies with metabarcoding practices: A case study with the bat Plecotus auritus. PLoS ONE 14(7)
<p><strong>Supporting data for: </strong>Andriollo T., Gillet F., Michaux J.R., Ruedi M. (2019). The menu varies with metabarcoding practices: a case study with the bat <em>Plecotus auritus</em>. PLoS ONE 14(7): e0219135. https://doi.org/10.1371/journal.pone.0219135</p> <p>Raw DNA sequences of prey of <em>Plecotus auritus</em>. Sampling information separated by semicolums as folows:</p> <p>>Sequence number; Colony; Date; Sample name; Dataset; Is the sequence attributable to the diet or not (Diet); Read numbers (Size); DNA sequence</p>
Circular seal in a copper alloy engraved with a standing female figure, probably Lakṣmī; inscription at one side.
<p>Circular seal in a copper alloy engraved with a standing female figure, probably Lakṣmī; inscription at one side. British Museum 1897,0528.4.</p>
Attribution of intentional agency towards robots reduces one's own sense of agency.
<p>### Attribution of intentional agency towards robots reduces one’s own sense of agency ###</p> <p>The data presented here are reported in Ciardo, Beyer, De Tommaso & Wykowska (accepted). Attribution of intentional agency towards robots reduces one’s own sense of agency. Cognition.</p> <p>Please refer to that paper for context and method. </p> <p>Files descriptions:<br> Raw Data.csv: Raw data of the three experiments. Please read the .txt file for variables definition.<br> Exp3_Data_Goodspeed.csv: Goodspeed questionnaire data of Experimet 3.<br> Listof Variables:..txt file with definition of variables and labels.</p>
Ubuntu One multi-cloud object storage trace sublement to "SkyPIE: A Fast & Accurate Oracle for Object Placement"
<p>These are the workload traces of Ubuntu one used in the evaluation of "SkyPIE: A Fast & Accurate Oracle for Object Placement".</p> <p>These traces derive diverse multi-cloud access pattern on object stores from the trace published in "Dissecting UbuntuOne: Autopsy of a Global-Scale Personal Cloud Back-End." The derivation is described in the SkyPIE paper.</p> <p>The traces are stored in Parquet file format, hence can be read with a Parquet reader such as the one included in Pandas. The file names specify the number of regions issuing accesses and the percentage of accesses to object that originate from regions other than the home region, see the publication.</p>
SU(2) gauge theory with one and two adjoint fermions towards the continuum limit—data release
<p>This package contains all data generated in preparing the publication <a href="https://arxiv.org/abs/2408.00171">SU(2) gauge theory with one and two adjoint fermions towards the continuum limit</a>. It includes four classes of data:</p> <ol> <li>Raw data, as generated from the measurement code running on HPC, in their native formats (raw_data.zip).</li> <li>Metadata around the analysis of the ensembles, in YAML format (ensembles.yaml).</li> <li>Data obtained by analysing the above data and presented in <a href="https://arxiv.org/abs/2408.00171">arXiv:2408.00171</a>, for specific ensembles, in sqlite3 format (su2.sqlite).</li> <li>The above data in (3), and additional data obtained by further analysing them, in CSV format (ensemble_results.csv and gammastar_results.csv).</li> <li>For convenience, the data in (1) above, repackaged in HDF5 format (package.h5).</li> </ol> <p>Each of these is documented in more detail in the file README.md.</p> <p>Due to their size, raw gauge configurations are not included in this package.</p> <p> </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.