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1,037 results for “large-scale”
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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Large-scale interventions may delay decline of the Great Barrier Reef
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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>
Large-scale comparison of bibliographic data sources: Scopus, Web of Science, Dimensions, Crossref, and Microsoft Academic
<p>This data set contains supplementary material for the paper 'Large-scale comparison of bibliographic data sources: Scopus, Web of Science, Dimensions, Crossref, and Microsoft Academic' by Martijn Visser, Nees Jan van Eck, and Ludo Waltman. The data set provides the statistics presented in the figures in the paper.</p>
Data from: Quantifying natural disturbances using a large-scale dendrochronological reconstruction to guide forest management
Estimates of historical disturbance patterns are essential to guide forest management aimed at ensuring the sustainability of ecosystem functions and biodiversity. However, quantitative estimates of various disturbance characteristics required in management applications are rare in longer-term historical studies. Thus, our objectives were to: (1) quantify past disturbance severity, patch size, and stand proportion disturbed, and (2) test for temporal and sub-regional differences in these characteristics. We developed a comprehensive dendrochronological method to evaluate an approximately two-century-long disturbance record in the remaining Central and Eastern European primary mountain spruce forests, where wind and bark beetles are the predominant disturbance agents. We used an unprecedented large-scale nested design dataset of 541 plots located within 44 stands and 6 sub-regions. To quantify individual disturbance events, we used tree-ring proxies, which were aggregated at plot and stand levels by smoothing and detecting peaks in their distributions. The spatial aggregation of disturbance events was used to estimate patch sizes. Data exhibited continuous gradients from low- to high-severity and small- to large-size disturbance events. In addition to the importance of small disturbance events, moderate-scale (25-75% of the stand disturbed, >10 ha patch size) and moderate-severity (25-75% of canopy disturbed) events were also common. Moderate disturbances represented more than 50% of the total disturbed area and their rotation periods ranged from one to several hundred years, which is within the lifespan of local tree species. Disturbance severities differed among sub-regions, whereas the stand proportion disturbed varied significantly over time. This indicates partially independent variations among disturbance characteristics. Our quantitative estimates of disturbance severity, patch size, stand proportion disturbed, and associated rotation periods provide rigorous baseline data for future ecological research, decisions within biodiversity conservation, and silviculture intended to maintain native biodiversity and ecosystem functions. These results highlight a need for sufficiently large and adequately connected networks of strict reserves, more complex silvicultural treatments that emulate the natural disturbance spectrum in harvest rotation times, sizes, and intensities, and higher levels of tree and structural legacy retention.
Data from: Large-scale mutation in the evolution of a gene complex for cryptic coloration
<p class="western">The types of mutations affecting adaptation in the wild are only beginning to be understood. In particular, whether structural changes shape adaptation by suppressing recombination or by creating new mutations is unresolved. Here we show that multiple, linked but recombining loci underlie cryptic color morphs of <i>Timema chumash </i>stick insects. In a related species, these loci are found in a region of suppressed recombination, forming a supergene. However, in seven species of <i>Timema </i>we find that a mega-base size 'supermutation' has deleted color loci in green morphs. Moreover, we find that balancing selection likely contributes more to maintaining this mutation than does introgression. Our results show how suppressed recombination and large-scale mutation can help package gene complexes into discrete units of diversity, such as morphs, ecotypes, or species.</p>
Data from: The earliest baleen whale of the mediterranean: Large-scale implications of an early miocene thalassotherian mysticete from Piedmont, Italy
The discovery of an Early Miocene chaeomysticete from the Pietra da Cantoni Group in Piedmont (north-western Italy) allowed for the creation of Atlanticetus n. gen. and Atlanticetus lavei n. sp. The new species is represented by a partial skeleton including the earbones and shows anatomical resemblance with Atlanticetus patulus (new combination) from the western North Atlantic. The Early Miocene age of the new specimen supports the view that it represents the oldest record of Chaeomysticeti from the Mediterranean. A new phylogenetic analysis showed that both A. patulus and A. lavei belong to a radiation of basal thalassotherian taxa. The basal thalassotherians are found being monophyletic to the exclusion of Cetotheriidae and Balaenopteroidea. The reconstruction of ancestral characters at selected nodes informs that the group including Atlanticetus and living balaenopterid taxa independently evolved rostra wide at base, anterolateral expansion in the tympanic bulla and a peculiar arrangement of the endocranial foramina of the periotic exhibiting a noteworthy phenomenon of convergent evolution in feeding and hearing functions with Balaenopteridae. The paleobiogeographic analysis showed that the North Pacific was the center of origin of Balaenomorpha (crown mysticetes), Thalassotherii and Balaenoidea. The recurrent invasion of the Mediterranean by balaenomorph mysticetes occurred from both the North Atlantic and North Pacific.
A large-scale systematic framework of Chinese snakes based on a unified multilocus marker system
Snakes are one of the most diverse groups of terrestrial vertebrates, with approximately 3500 extant species, some of which are closely related to human life. A robust phylogeny and taxonomy of snakes is crucial for us to know, study, protect and make use of them. For a large group such as snakes, cross-study data utilization is important which can be facilitated by standardization of the loci used for systematic analyses. In this study, we combined 5 mitochondrial markers, 19 vertebrate-universal nuclear protein coding (NPC) markers and 72 snake-specific noncoding intron markers, and generated a unified multilocus marker system for studies of snake systematics. This marker system is an addition to the squamate conserved locus set compiled for large-scale sequence capture experiments. We applied this marker system to over 440 snake samples and constructed the currently most comprehensive systematics framework of the snakes in China. Robust snake phylogenetic relationships were recovered at both deep and shallow evolutionary depths, demonstrating the usefulness of this multilocus marker system. Discordance was revealed by a parallel comparison between the snake tree based on our multilocus marker system and that based on only the mitochondrial loci, highlighting the necessity of using multiple marker types to better understand the snake evolutionary histories. The divergence times of different snake groups were estimated with the multilocus data set. Our comprehensive snake tree not only confirms many important nodes inferred in previous studies but also contributes new insights into the phylogenetic relationships among snakes. Suggestions are made for the current snake taxonomy.
Supplementary datasets for: Large-scale genome sequencing reveals the driving forces of viruses in microalgal evolution
<p>Microalgae are integral primary producers for global ecosystems whose genomes can be mined for ecological insights, but representative genome sequences are lacking for many phyla. We cultured and sequenced 107 microalgae species from 11 different phyla indigenous to varied geographies and climates. This genome collection was used to resolve genomic differences between saltwater and freshwater microalgae. Freshwater species showed domain-centric ontology enrichment for nuclear and nuclear membrane functions, while saltwater species were enriched in organellar and cellular membrane functions. Marine species contained significantly more viral families in their genomes (<span>p-value = 8 x 10(-4))</span>. Viral sequences were identified from Chlorovirus, Coccolithovirus, Pandoravirus, Marseillevirus, Tupanvirus, and others integrated into algal genomes. Algal, viral-origin sequences were found to be expressed and to code for a wide variety of functions. Our results clarify the poorly characterized occurrences of viral elements in algal genomes and define a unified adaptive strategy for algal halotolerance.</p>
Setpoints obtained for generator G in large-scale study
<p>The attached JSON files contain the setpoints obtained for the generator G in the large-scale study.</p> <ul> <li>results_enwl_exp_1.json: results with the exponents set to 1.0</li> <li> results_enwl_exp_3.json: results with the exponents set to 3.0</li> </ul> <p>Each json file is a tree, where the first two levels indicate the network and feeder from the ENWL dataset. Per (network, feeder) dictionary, the following data is available</p> <ul> <li><strong>pg_swl</strong>: per-phase active power set-point obtained with the SWL formulation, in kW</li> <li><strong>qg_swl</strong>: per-phase reactive power set-point obtained with the SWL formulation, in kvar</li> <li><strong>pg_su</strong>: per-phase active power set-point obtained with the SU formulation, in kW</li> <li><strong>qg_su</strong>: per-phase reactive power set-point obtained with the SU formulation, in kvar</li> <li><strong>gen_bus</strong>: bus to which the generator is connected</li> </ul>
The dataset of figures for "The GPU version of LICOM3 under HIP framework and its large-scale application"
<p>A high-resolution (1/20°) global ocean general circulation model with Graphics processing units (GPUs) code implementations is developed based on the LASG/IAP Climate system Ocean Model version 3 (LICOM3) under Heterogeneous-compute Interface for Portability (HIP) framework. The dynamic core and physics package of LICOM3 are both ported to the GPU, and 3-dimensional parallelization is applied. The HIP version of the LICOM3 (LICOM3-HIP) is 42 times faster than what the same number of CPU cores dose, when 384 AMD GPUs and CPU cores are used. The LICOM3-HIP has excellent scalability; it can still obtain speedup of more than four on 9216 GPUs comparing to 384 GPUs. In this phase, we successfully performed a test of 1/20° LICOM3-HIP using 6550 nodes and 26200 GPUs, and at the grand scale, the model’s time to solution can still obtain an increasing, about 2.72 simulated years per day (SYPD). The high performance was due to putting almost all of computation processes inside GPUs, and thus greatly reduces the time cost of data transfer between CPUs and GPUs. At the same time, a 14-year spin-up integration following the phase 2 of Ocean Model Intercomparison Project (OMIP-2) protocol of surface forcing has been conducted, and the preliminary results have been evaluated. We found that the model results have little differences from the CPU version. Further comparison with observations and lower-resolution LICOM3 results suggests that the 1/20° LICOM3-HIP can not only reproduce the observations, but also produce much smaller scale activities, such as submesoscale eddies and frontal scales structures.</p>
Data from: Large-scale meta-analysis on rheumatoid arthritis across East Asian and European populations
<p><span><span><span><b>Objective:</b> Nearly 110 susceptibility loci for rheumatoid arthritis (RA) with modest effect sizes have been identified by population-based genetic association studies, suggesting a large number of undiscovered variants behind a highly polygenic genetic architecture of RA. Here, we performed the largest-ever trans-ancestral meta-analysis with the aim to identify new RA loci and to better understand RA biology underlying genetic associations.</span></span></span></p> <p><span><span><span><b>Methods:</b> Genome-wide RA association summary statistics in three large case-control collections consisting of 311,292 individuals of Korean, Japanese, and European populations were used in an inverse-variance-weighted fixed-effects meta-analysis. Several computational analyses using public omics resources were conducted to prioritize causal variants and genes, RA variant-implicating features (tissues, pathways, and transcription factors), and potentially repurposable drugs for RA treatment. </span></span></span></p> <p><span><span><span><b>Results:</b> We identified 11 new RA susceptibility loci that explained 6.9% and 1.8% of the SNP-based heritability in East Asians and Europeans, respectively, and confirmed 71 known non-HLA susceptibility loci, identifying 90 independent association signals. The RA variants were preferentially located in binding sites of various transcription factors and in cell type-specific transcription-activation histone marks that simultaneously highlighted the importance of CD4<sup>+</sup> T-cell activation and the potential role of non-immune organs in RA pathogenesis. A total of 615 plausible effector genes, based on gene-based associations, expression-associated variants, and chromatin interaction, included targets of drugs approved for RA treatments and potentially repurposable drugs approved for other indications.</span></span></span></p> <p><span><span><span><b>Conclusion:</b> Our findings provide useful insights regarding RA genetic etiology and variant-driven RA pathogenesis.</span></span></span></p>
easyMF: A Web Platform for Matrix Factorization-based Biological Discovery from Large-scale Transcriptome Data
<p>With the development of high-throughput experimental technologies, large-scale RNA sequencing (RNA-Seq) data have been and continue to be produced, but have led to challenges in extracting relevant biological knowledge hidden in the produced high-dimensional gene expression matrices. Here, we present easyMF, a user-friendly web platform that aims to facilitate biological discovery from large-scale transcriptome data through matrix factorization (MF). The easyMF platform enables users with little bioinformatics experience to streamline transcriptome analysis from raw reads to gene expression and to decompose expression matrix from thousands of genes to a handful of metagenes. easyMF also offers a series of functional modules for metagene-based exploratory analysis with an emphasis on functional gene discovery. As a modular, containerized and open-source platform, easyMF can be customized to satisfy users’ specific demands and deployed as a web server for broad applications. easyMF is freely available at https://github.com/cma2015/easyMF. We demonstrated the application of easyMF with four case studies using 940 RNA sequencing datasets from maize (<em>Zea mays </em>L<em>.</em>).</p>
Data for "Potential of mid-tropospheric water vapor isotopes to improve large-scale circulation and weather predictability"
<p>This contains simulation results of "Potential of mid-tropospheric water vapor isotopes to improve large-scale circulation and weather predictability".</p>
Large-scale patterns of green turtle trophic ecology in the eastern Pacific Ocean
<p><span><span><span><span><span><span><span><span><span><span><span>Trophic position and niche width are fundamental components of a species' ecology, reflecting resource use, and influencing key demographic parameters such as somatic growth, maturation, and survival. The present data file contains results of stable isotope analysis (stable-carbon, δ<sup>13</sup>C; stable-nitrogen, δ<sup>15</sup>N values) that was conducted on bulk skin tissue of 718 green sea turtles (<i>Chelonia mydas</i>) distributed among 16 foraging areas in the eastern Pacific from the US to Chile, a range spanning ~10,000 km. These study sites </span></span></span></span></span></span></span></span></span></span></span>were distributed across a latitudinal range from 33.736 °N to 23.098°S in the Eastern Pacific (Site Code): Long Beach, USA (LB); San Diego Bay, USA (SDB); north Gulf of Ulloa, Mexico (NGU); Magdalena Bay, Mexico (BMA); Los Angeles Bay, Mexico (BLA); Infiernillo Channel, Mexico (CIN); Navachiste Bay, Mexico (NAV); Dulce Gulf, Costa Rica (DUL); Cocos Island, Costa Rica (COC); Gorgona Island, Colombia (GOR); Punta Espinosa, Galapagos Islands, Ecuador (IGP); Bahia Elizabeth, Galapagos Islands, Ecuador (IGE); Caleta Derek, Galapagos Islands, Ecuador (IGD); oceanic waters, Peru (PPE); Pisco Paracas Bay, Peru (PAR); and Mejillones Bay, Chile (MEJ). <span><span><span><span><span><span><span><span><span><span><span>Substantial variability in bulk tissue δ<sup>13</sup>C and δ<sup>15</sup>N values was found within and among sites. These data were also used to calculate the isotope niche space (used as a proxy for ecological niche space) using the Bayesian ellipse approach, and we found that isotope niche space varied among sites, likely influenced by the diversity of prey types and relative input of terrestrial- vs. marine-derived nutrients. In addition to providing additional spatial resolution for δ<sup>13</sup>C and δ<sup>15</sup>N isoscapes in the eastern Pacific, especially in coastal habitats, this study and resultant dataset further establish stable isotope analysis as an effective tool to study the trophic ecology of sea turtles across a variety of food webs and habitats. </span></span></span></span></span></span></span></span></span></span></span></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.