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1,454 results for “large scale”
Increasing sustainability in palaeoproteomics by optimizing digestion times for large-scale archaeological bone analyses
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Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex
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Data for: Large-scale long-term passive-acoustic monitoring reveals spatiotemporal activity patterns of boreal bats
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Data from: Using large-scale tropical dry forest restoration to test successional theory
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Rare and declining bee species are key to consistent pollination of wildflowers and crops across large spatial scales
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Linking the microarchitecture of neurotransmitter systems to large-scale MEG resting state networks
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Data for: Zebra finch song ecology: monitoring of breeding, observational transects, focal and year-round acoustic recordings, and a large-scale simultaneous playback experiment
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Data and code from: Learning a deep language model for microbiomes: The power of large scale unlabeled microbiome data
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Data from: Using environmental DNA metabarcoding to monitor fish communities in small rivers and large brooks: Insights on the spatial scale of information
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Large differences in herbivore performance emerge from simple herbivore behaviors and fine-scale spatial heterogeneity in phytochemistry
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Validating eDNA Measurements of the Richness and Abundance of Anurans at a Large Scale
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Large-scale interventions may delay decline of the Great Barrier Reef
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ASTRAL-MP: scaling ASTRAL to very large datasets using randomization and parallelization
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Data from: A reusable pipeline for large-scale fiber segmentation on unidirectional fiber beds using fully convolutional neural networks
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Replication data for impact evaluation of two large-scale forestry incentive programs in Guatemala
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Chronicles of Nature Calendar, a Long-term and Large-scale Multitaxon Database on Phenology
<p>We present an extensive, large-scale, long-term and multitaxon database on phenological and climatic variation, involving 506,186 observation dates acquired in 471 localities in Russian Federation, Ukraine, Uzbekistan, Belarus and Kyrgyzstan. The data cover the period 1890-2018, with 96% of the data being from 1960 onwards. The database is rich in plants, birds and climatic events, but also includes insects, amphibians, reptiles and fungi. The database includes multiple events per species, such as the onset days of leaf unfolding and leaf fall for plants, and the days for first spring and last autumn occurrences for birds. The data were acquired using standardized methods by permanent staff of national parks and nature reserves (87% of the data) and members of a phenological observation network (13% of the data). The database is valuable for exploring how species respond in their phenology to climate change. Large-scale analyses of spatial variation in phenological response can help to better predict the consequences of species and community responses to climate change.</p> <p>The recording scheme implemented at nature reserves offers unique opportunities for addressing community-level change across replicate local communities. These data have been systematically collected not as independent monitoring efforts, but using a shared and carefully standardized protocol adapted for each local community. Thus, variability in observation effort is of much less concern than in most other distributed cross-taxon phenological monitoring schemes. To enable analyses of higher-level taxonomical groups, we have included taxonomic classifications for the species in the database.</p> <p>The compilation of the data in a common database was initiated in the context of the project “Linking environmental change to biodiversity change: long-term and large-scale data on European boreal forest biodiversity” (EBFB), funded for 2011-2015 by the Academy of Finland, and continued with the help of other funding to OO since 2016. We organized a series of project meetings that were essential for data acquisition, digitalization and unification. These meetings were organized in Ekaterinburg (Russia) by the Institute of Plant and Animal Ecology, Ural Branch of RAS (Russian Academy of Sciences) in 2011; in Petrozavodsk (Russia) by the Forest Research Institute, at the Karelian Research Center, RAS in 2013; in Miass (Russia) by the Ilmen Nature Reserve in 2014; in Krasnoyarsk (Russia) by the Stolby Nature Reserve in 2014; in Artybash (Russia) by the Altaisky Nature Reserve in 2015; in Listvyanka, Lake Baikal (Russia) by the Zapovednoe Pribajkalje Nature Reserve in 2016; in Roztochja (Ukraine) by the Ministry of Natural Resources of Ukraine in 2016; in Puschino (Russia) by the Prioksko-Terrasnyj Nature Reserve in 2017, in Vyshinino (Russia) by the Kenozero National Park in 2018, and in St Petersburg (Russia) by the Komarov Botanical Institute of the Russian Academy of Sciences in 2019.</p> <p>The compilation of the data in a common database was initiated in the context of the project “Linking environmental change to biodiversity change: long-term and large-scale data on European boreal forest biodiversity” (EBFB), funded for 2011-2015 by the Academy of Finland, and continued with the help of other funding to OO since 2016. We organized a series of project meetings that were essential for data acquisition, digitalization and unification. These meetings were organized in Ekaterinburg (Russia) by the Institute of Plant and Animal Ecology, Ural Branch of RAS (Russian Academy of Sciences) in 2011; in Petrozavodsk (Russia) by the Forest Research Institute, at the Karelian Research Center, RAS in 2013; in Miass (Russia) by the Ilmen Nature Reserve in 2014; in Krasnoyarsk (Russia) by the Stolby Nature Reserve in 2014; in Artybash (Russia) by the Altaisky Nature Reserve in 2015; in Listvyanka, Lake Baikal (Russia) by the Zapovednoe Pribajkalje Nature Reserve in 2016; in Roztochja (Ukraine) by the Ministry of Natural Resources of Ukraine in 2016; in Puschino (Russia) by the Prioksko-Terrasniy Nature Reserve in 2017, in Vyshinino (Russia) by the Kenozero National Park in 2018, and in St Petersburg (Russia) by the Komarov Botanical Institute of the Russian Academy of Sciences in 2019.</p> <p>The compilation of the data into a common database was conducted by the database coordinators (EM and CL) in Helsinki (Finland). Those participants that already held the data in digital format submitted it in the original format, and those that had the data only in paper format digitized it using Excel-based templates developed in the project meetings. Submitted data were processed by the database coordinators according to the following steps:</p> <p> </p> <ol> <li>The data were formatted so that each observation (the phenological date of a particular event in a particular locality and year) formed one row in the data table (e.g. un-pivoting tables that involved several years as the columns). The phenological event names were split into event type (e.g. “first occurrence“) and species name.</li> <li>The event type names (provided originally typically in Russian) were translated into English and the species names (usually provided in Russian) were identified to scientific names, using dictionaries that were partly developed and verified in the project meetings. All scientific names were periodically verified by mapping them to the Global Biodiversity Information Facility (GBIF) backbone taxonomy.</li> <li>We associated each data record with the following set of information fields: (1) project name, i.e. the source organization, (2) dataset name, (3) locality name, (4) unique taxon identifier, (5) scientific taxon name, and (6) event type.</li> <li>We imported the data records in the main database (maintained as an EarthCape database at <a href="https://ecn.ecdb.io">https://ecn.ecdb.io</a>). During the import, the taxonomic names, locality names, and dataset names were matched against already existing records.</li> </ol> <p>There are at least 200 National Parks and Nature Reserves that collect Chronicles of Nature Book data (in Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russian Federation, Tajikistan, Turkmenistan, Ukraine and Uzbekistan). Out of these, the current database covers data from 62 organizations, with the highest coverage in European Russia. The collection of new data continues in most parks. Thus, the database is not complete, and we aim to support the database with updates, depending on the interest of new partners to join, as well as resources and funding. The technical validation procedures described below will also be applied to any new information included in the database.</p> <p>Data is available as a data package (<a href="http://frictionlessdata.io/docs/data-package/#tabular-data">http://frictionlessdata.io/docs/data-package/#tabular-data</a>) and will be updated with new versions as project goes on.</p>
ProGene - A Large-scale, High-Quality Protein-Gene Annotated Benchmark Corpus
<p>The Pro(tein)/Gene corpus was developed at the JULIE Lab Jena under supervision of Prof. Udo Hahn.</p> <p>The goals of the annotation project were</p> <ul> <li>to construct a consistent and (as far as possible) subdomain-independent/-comprehensive protein-annotated corpus</li> <li>to differentiate between protein families and groups, protein complexes, protein molecules, protein variants (e.g. alleles) and elliptic enumerations of proteins.</li> </ul> <p>The corpus has the following annotation levels / entity types:</p> <ul> <li>protein</li> <li>protein_familiy_or_group</li> <li>protein_complex</li> <li>protein_variant</li> <li>protein_enum</li> </ul> <p>For definitions of the annotation levels, please refer to the Proteins-guidelines-final.doc file that is found in the download package.</p> <p>To achieve a large coverage of biological subdomains, document from multiple other protein / gene corpora were reannotated. For further coverage, new document sets were created. All documents are abstracts from PubMed/MEDLINE. The corpus is made up of the union of all the documents in the different subcorpora.<br> All document are delivered as MMAX2 (http://mmax2.net/) annotation projects.</p>
Lyman-alpha tomographic map of the large-scale matter distribution using the eBOSS - Stripe 82 data
<p><strong>Lyman-alpha tomography map using DR16 eBOSS data</strong></p> <p>This data release contains products associated to the Lyman-alpha large-scale tomographic map realized with the 16<sup>th</sup> data release of SDSS-eBOSS. The densest Lyman-alpha forest set of eBOSS in the Stripe 82 field is used : 220 deg<sup>2</sup> wide field with a 37 deg<sup>-2</sup> density. A Lyman-alpha flux contrast map over a volume of 0.94 h<sup>-3</sup>Gpc<sup>3</sup> is obtained. Voids and protoclusters are detected in this portion of the sky.</p> <p>For more details : see https://arxiv.org/abs/2004.01448</p> <p>All the data detailed below can be opened by using the short python script read_data_release.py.</p> <p><strong>Pixels and map data</strong></p> <p>The pixel file pixels_lya_tomography_stripe82.bin was used to create the Lya tomographic map map_lya_tomography_stripe82.bin by using the dachshund algorithm (https://github.com/caseywstark/dachshund).</p> <p>Pixel and map file are in the binary numpy format. Pixels are created from 8999 Lya forests positioned from -43° to +45° in the RA(J2000) direction, from -1.25° to +1.25° in the DEC(J2000) direction and from z=2.1 to z=3.2. The (RA,DEC,z) coordinates are converted to (X,Y,Z) coordinates in Mpc.h<sup>-1</sup>. The pixel file shares the same origin than the map. Each pixel is defined by a set of five floats corresponding to (X, Y, Z, σ, 𝛿) where 𝛿 is the Lyman-alpha flux contrast and σ its associated error based on pipeline noise.</p> <p>The map is also a binary numpy file. It contains a (6354,181,834) Mpc.h<sup>-1</sup> cube of Lyman-alpha flux contrast with a pixel shape (2928,90,417). The origin of the map (0,0,0) corresponds to the coordinates RA=-43°, DEC=-1.25° and z=2.1 and in the cube coordinate to (X,Y,Z)=(0,0,0) Mpc.h<sup>-1</sup>.</p> <p>Note that to obtain this map, the pixel file was first separated to parallelize the tomographic procedure. In comparison to the map detailed in the linked article, this map is rebinned to be less voluminous. Furthermore, a mask is applied to the map where the distance to the nearest line-of-sight of the pixel file is below 20 Mpc.h<sup>-1</sup>. At these locations, map flux contrast is put to 0.</p> <p> </p> <p><strong>Derived catalog data</strong></p> <p>Voids and proto-cluster searches are applied to the tomographic map. The results of this procedure, along with additional catalog cut is given in this data release. Catalogs of voids (catalog_voids_lya_stripe82_*.fits) and proto-clusters (catalog_protoclusters_lya_stripe82_*.fits) are given in the (X,Y,Z) and (RA,DEC,z) coordinates of the map, sharing the same origin. The catalogs are in the FITS format which can be opened with the fitsio python library.</p>
Replication code and data for: "Machine Learning Predicts Large Scale Declines in Native Plant Phylogenetic Diversity."
<p>Replication code and data for the paper: "Machine Learning Predicts Large Scale Declines in Native Plant Phylogenetic Diversity." The following files are included in this repository:</p> <p>1) R scripts (numbered 0 through 9) include replication code for data analysis</p> <p>2) Datasets (6 zip folders) contain the data analyzed in the R scripts</p> <p> </p> <p> </p>
Large scale simulation of pressure induced phase-field fracture propagation using Utopia
<p>Utopia is an open-source C++ library for parallel non-linear multilevel solution strategies. Utopia provides the advantages of high-level programming interfaces while at same time a framework to access low level data-structures without breaking code encapsulation. Complex numerical procedures can be expressed with few lines of code, and evaluated by different implementations, libraries, or computing hardware. In this paper we investigate the parallel performance of our implementation of the recursive multilevel trust-region (RMTR) method based on the Utopia library. RMTR is a globally convergent multilevel solution strategy designed to solve non-convex constrained minimization problems. In particular, we solve pressure induced phase-field fracture propagation in large and complex fracture networks. Solving such problems is deemed challenging even for a few fractures, however, here we are considering realistic and idealized networks with up to 1000 fractures.</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.