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1,454 results for “large scale”
Data from: A cross-scale trophic cascade from large predatory fish to algae in coastal ecosystems
Trophic cascades occur in many ecosystems, but the factors regulating them are still elusive. We suggest that an overlooked factor is that trophic interactions (TIs) are often scale-dependent and possibly interact across spatial scales. To explore the role of spatial scale for trophic cascades, and particularly the occurrence of cross-scale interactions (CSIs), we collected and analysed food-web data from 139 stations across 32 bays in the Baltic Sea. We found evidence of a four-level trophic cascade linking TIs across two spatial scales: at bay scale, piscivores (perch and pike) controlled mesopredators (three-spined stickleback), which in turn negatively affected epifaunal grazers. At station scale (within bays), grazers on average suppressed epiphytic algae, and indirectly benefitted habitat-forming vegetation. Moreover, the direction and strength of the grazer–algae relationship at station scale depended on the piscivore biomass at bay scale, indicating a cross-scale interaction effect, potentially caused by a shift in grazer assemblage composition. In summary, the trophic cascade from piscivores to algae appears to involve TIs that occur at, but also interact across, different spatial scales. Considering scale-dependence in general, and CSIs in particular, could therefore enhance our understanding of trophic cascades.
Symbrain: A large-scale dataset of MRI images for neonatal brain symmetry analysis
Open the record for dataset details and reuse information.
Personalized Large-scale Functional Networks in the Adolescent Brain Cognitive Development (ABCD) Children
<ul><li>Data for our paper "<strong>Personalized Large-scale Functional Networks in ABCD Children: Linking Functional Network Topography with Socioeconomic Status</strong>".</li><li>This dataset contains personalized functional networks for <strong>3,921</strong> participants (age 9- and 10-year-olds) who had more than 20 min of high-quality (mean motion < 0.2mm) resting-state fMRI data from the Adolescent Brain Cognitive Development (<strong>ABCD</strong>) Study.</li><li>Using regularized non-negative matrix factorization (<strong>NMF</strong>) and preprocessed resting-state fMRI data from the ABCD-BIDS Community Collection (<strong>https://github.com/ABCD-STUDY/nda-abcd-collection-3165</strong>, NDA Collection 3165), we parcellated the cortex (<strong>fsLR_32k space</strong>) into 17 functional networks for each ABCD participant.</li><li>We provided two kinds of atlas data: discrete network parcellation (<strong>IndivAtlasLabel</strong>) and probabilistic network parcellation (<strong>IndivAtlasLoading</strong>). For discrete network parcellation, each value in the dscalar indicates which network this vertex belongs to; for probabilistic network parcellation, each value indicates the probability that this vertex belongs to each network (<strong>17 in total</strong>). We also provided group atlas label and atlas loading for comparison.</li><li>For dscalars contain individual atlas loading, we separated participants into 10 parts, each has nearly 400 individuals for easily uploading and downloading.</li><li>Each value corresponds with the following key for discrete network parcellation: 1=FP1, 2=AU, 3=VS1, 4=DM1, 5=DA, 6=DM2, 7=DA2, 8=DM3, 9=VS2, 10=SM1, 11=TMP, 12=LB 13=VA1, 14=FP2, 15=SM2, 16=SM3, 17=DA3 (VS: visual; AU: auditory; SM: somatomotor; VA: ventral attention; DA: dorsal attention; FP: fronto-parietal; DM: default mode; TM: temporo-parietal; LB: limbic).</li><li>For each participant, we provide his or her subject-key in the ABCD Study. Other related information can be found at <strong>https://wiki.abcdstudy.org</strong>.</li><li>Relevant analysis scripts can be found in <strong>https://github.com/CuiLabCIBR/SingleFuncParcel_ABCD</strong>.</li></ul>
Supplementary material to "SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme" : Pleiades snow depth maps analysis
<p>This Folder contains :</p><p> - necessary codes to reprduce Fig. 5, 12 and 13 of the third version of the submitted manuscript SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme"</p><p> - necessary data to run these codes, including extracts of simulation outputs</p><p> - A readme.txt which explains how to run everything</p><p> </p>
Data and code for Synoptic mechanisms of large-scale extreme precipitation events over southeastern China (Version 1.1.0)
<p>All data generated for the article <em>Synoptic mechanisms of large-scale extreme precipitation events over South China</em> and the code used to reproduce the article analysis and figures.</p>
Large-scale comparison of machine learning algorithms for target prediction of natural products
<p>Supplement Materials of the article named "Large-scale comparison of machine learning algorithms for target prediction of natural products".</p>
Data from: Despite short-lived changes, COVID-19 pandemic had minimal large-scale impact on citizen science participation in India
<p>This dataset contains the filtered and processed data files used in the analyses for the paper titled "Despite short-lived changes, COVID-19 pandemic had minimal large-scale impact on citizen science participation in India". These are <code>.Rdata</code> files that can be readily imported into the R environment (all analyses were performed in the R environment using RStudio).</p> <p>It is important to note that these data files were derived from publicly available datasets, and that the entire analytical workflow is reproducible (see <a href="https://github.com/rikudoukarthik/covid-ebirding/blob/main/README.md">the Github repository</a> for more details). Below is a summary of the datasets used:</p> <h2>Data</h2> <p>The analysis is centred around eBird data from India, which is publicly available but is a very large file.</p> <ul> <li><a href="https://ebird.org/data/download/ebd" rel="nofollow"><em>eBird Basic Dataset (EBD)</em></a><em>, relMay-2022</em>: The current version of this dataset is much more recent and contains data after May 2022, but filtering it should produce a fairly similar dataset to the ones used in this study, with only minor changes. (The processed version of this EBD ready for analyses is <code>data0_MY_d_slice.RData</code>).</li> <li><em>Shapefiles</em>: <code>maps_sf.RData</code> contains admin unit polygons, and <code>grids_st_sf.RData</code> contains country-wide grids of multiple resolutions.</li> <li><a href="https://lpdaac.usgs.gov/products/mcd12q1v006/" rel="nofollow"><em>MODIS Land Cover Type Product MCD12Q1</em></a>: The processed data is available as <code>rast_UNU.RData</code>, but deleting this before starting the analysis will force fresh LULC data files from MODIS to be produced from the script.</li> <li><em>Timeline of COVID classification</em>: Available <code>covid_classification.csv</code>. Classification of the timeline of interest into various COVID categories.</li> </ul> <h2>Abstract</h2> <p>Many parts of the world lack the large and coordinated volunteer networks required for systematic monitoring of bird populations. In these regions, citizen science (CS) programmes offer an alternative with their semi-structured data, but the utility of these data is contingent on how, where, and how comparably birdwatchers watch birds, year on year. Trends inferred from the data can be confounded during years when birdwatchers may behave differently, such as during the COVID-19 pandemic. We wanted to ascertain how the data uploaded from India to one such CS platform, eBird, were impacted by this deadly global pandemic. To understand whether eBird data from the pandemic years in India are comparable to data from adjacent years, we explored several characteristics of the data, such as how often people watched birds in groups or at public locations, at multiple spatial and temporal scales. We found that the volume of data generated increased during the pandemic years 2020--21 compared to 2019. Data characteristics changed largely only during the peak pandemic months (April--May 2020 and April--May 2021) associated with high fatality rates and strict lockdowns. These changes in data characteristics (e.g., greater site fidelity and less group birding) were possibly due to the decreased human mobility and social interaction in these periods. The data from the remainder of these restrictive years remained similar to those of the adjacent years, thereby reducing the impact of the aberrant peak months on any annual inference. Our findings show that birdwatchers in India as contributors to CS rapidly returned to their pre-pandemic behaviour, and that the effects of the pandemic on birdwatching effort and birdwatcher behaviour are scale- and context-dependent. In summary, eBird data from the pandemic years in India remain useful for abundance trend estimation and similar large-scale applications, but will benefit from preliminary data quality checks when utilised at a fine scale.</p>
Flow estimates example for k = 50 - Information-Theoretic Sensor Placement for Large-Scale Sewer Networks
<p>This dataset is used to generate Figure 7 without running the simulations in the paper Information-Theoretic Sensor Placement for Large-Scale Sewer Networks.</p>
FIG. 7 in Les mantes (Dictyoptera, Mantodea) du massif du Mitaraka (Guyane), in Touroult J. (ed.), "Our Planet Reviewed" 2015 large-scale biotic survey in Mitaraka, French Guiana.
FIG. 7. — Choeradodis strumaria (Linnaeus, 1758), femelle. Photos S. Hugel.
Data From: The Future of OA: A large-scale analysis projecting Open Access publication and readership
<p>This is the raw data behind the publication on bioRxiv at https://doi.org/10.1101/795310: </p> <p><strong>Piwowar, Priem, Orr (2019) The Future of OA: A large-scale analysis projecting Open Access publication and readership. bioRxiv: <a href="https://doi.org/10.1101/795310">https://doi.org/10.1101/795310</a></strong></p> <p>The jupyter notebook that produces the manuscript using the data here is available at: <a href="https://github.com/Impactstory/future-oa">https://github.com/Impactstory/future-oa</a></p> <p> </p> <p>Summary:</p> <p>Understanding the growth of open access (OA) is important for deciding funder policy, subscription allocation, and infrastructure planning.</p> <p>This study analyses the number of papers available as OA over time. The models includes both OA embargo data and the relative growth rates of different OA types over time, based on the OA status of 70 million journal articles published between 1950 and 2019.</p> <p>The study also looks at article usage data, analyzing the proportion of views to OA articles vs views to articles which are closed access. Signal processing techniques are used to model how these viewership patterns change over time. Viewership data is based on 2.8 million uses of the Unpaywall browser extension in July 2019.</p> <p>We found that Green, Gold, and Hybrid papers receive more views than their Closed or Bronze counterparts, particularly Green papers made available within a year of publication. We also found that the proportion of Green, Gold, and Hybrid articles is growing most quickly.</p> <p>In 2019:</p> <ul> <li> <p>31% of all journal articles are available as OA</p> </li> <li> <p>52% of article views are to OA articles</p> </li> </ul> <p>Given existing trends, we estimate that by 2025:</p> <ul> <li> <p>44% of all journal articles will be available as OA</p> </li> <li> <p>70% of article views will be to OA articles</p> </li> </ul> <p>The declining relevance of closed access articles is likely to change the landscape of scholarly communication in the years to come.</p>
Data from: Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance
Although activation/deactivation of specific brain regions have been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here we investigated time-varying functional connectivity patterns across the human brain in periods of 30-40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memo ry encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.
LMHLD: A Large-scale Multi-source High-resolution Landslide Dataset for Landslide Detection based on Deep Learning
<p>LMHLD collects remote sensing images of five satellite sensors in seven areas of the world: Wenchuan, China (2008); Rio de Janeiro, Brazil (2011); Gorkha, Nepal (2015); Jiuzhaigou, China (2015); Taiwan, China (2018); Hokkaido, Japan (2018); Emilia-Romagna, Italy (2023), a total of 25,365 patches by setting different patch sizes according to different landslide scale.</p> <p>Specifically, patch sizes vary across different areas: 32 for image segmentation in Emilia-Romagna, Italy and Gorkha, Nepal; 64 in Rio de Janeiro, Brazil; 128 in Jiuzhaigou, China and Hokkaido, Japan; and 224 in Wenchuan and Taiwan, China. All the above patches constitute LMHLD, a large-scale multi-source high-resolution heterogeneous landslide dataset.</p> <p><strong><em>LMHLD.rar</em></strong> includes all the Train, Validation, Test data used for the experiments.</p> <p>If you use our data, please cite our work published in <strong><em>IEEE Transactions on Geoscience and Remote Sensing</em></strong>. Or if you have any other questions about LMHLD, please contact us: <strong>guan.ting.liu2000@gmail.com</strong>.</p> <p><strong>Paper:</strong> Liu G, Wang Y, Chen X, et al. LMHLD: A Large-scale Multi-Source High-Resolution Landslide Dataset for Landslide Detection based on Deep Learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025. (<strong>DOI: </strong><a href="https://doi.org/10.1109/TGRS.2025.3619062" target="_blank" rel="noopener">10.1109/TGRS.2025.3619062</a>)</p>
Figure 2 from: Oever J, Gofferje M (2012) 'From Pilot to production': Large Scale Digitisation project at Naturalis Biodiversity Center. ZooKeys 209: 87-92. https://doi.org/10.3897/zookeys.209.3609
Figure 2 - Life cycle of Digistreets: stages of planning and executing of progect.
Figure 1 from: Oever J, Gofferje M (2012) 'From Pilot to production': Large Scale Digitisation project at Naturalis Biodiversity Center. ZooKeys 209: 87-92. https://doi.org/10.3897/zookeys.209.3609
Figure 1 - Structure of the digitization programme at Naturalis Biodiversity Center.
Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation (Unlabeled Data Part III)
<p>Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. The paper can be found at https://arxiv.org/pdf/2206.08023.pdf</p> <p>In addition to providing the labeled 600 CT and MRI scans, we expect to provide 2000 CT and 1200 MRI scans without labels to support more learning tasks (semi-supervised, un-supervised, domain adaption, ...). The link can be found in:</p> <ul> <li><a href="https://zenodo.org/deposit/7262581">labeled data (500CT+100MRI)</a></li> <li><a href="https://zenodo.org/record/7262757#.Y2iSQ9JBwYs">unlabeled data Part I (900CT)</a></li> <li><a href="https://zenodo.org/record/7295661#.Y2iR_9JBwYs">unlabeled data Part II (1100CT)</a> (Now there are 1000CT, we will replenish to 1100CT)</li> <li><a href="https://zenodo.org/record/7295816">unlabeled data Part III (1200MRI)</a></li> </ul> <p>if you found this dataset useful for your research, please cite:</p> <blockquote> <pre>@article{ji2022amos, title={AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation}, author={Ji, Yuanfeng and Bai, Haotian and Yang, Jie and Ge, Chongjian and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhang, Lingyan and Ma, Wanling and Wan, Xiang and others}, journal={arXiv preprint arXiv:2206.08023}, year={2022} }</pre> </blockquote>
MIntRec2. 0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversations
<p><span>MIntRec2.0, a large-scale benchmark dataset for multimodal intent recognition in multi-party conversations. It contains 1,245 high-quality dialogues with 15,040 samples, each annotated within a new intent taxonomy of 30 fine-grained classes, across text, video, and audio modalities. In addition to more than 9,300 in-scope samples, it also includes over 5,700 out-of-scope samples appearing in multi-turn contexts, which naturally occur in real-world open scenarios, enhancing its practical applicability.</span> <span>This dataset will be released under the CC BY-NC-SA 4.0 license.</span></p>
A large scale photonic matrix processor enabled by charge accumulation
<p>Integrated neuromorphic photonic circuits aim to power complex artificial neural networks (ANNs) in an energy and time efficient way by exploiting the large bandwidth and the low loss of photonic structures. However, scaling photonic circuits to match the requirements of modern ANNs still remains challenging. In this perspective, we give an overview over the usual sizes of matrices processed in ANNs and compare them with the capability of existing photonic matrix processors. To address shortcomings of existing architectures, we propose a time multiplexed matrix processing scheme which virtually increases the size of a physical photonic crossbar array without requiring any additional electrical post-processing. We investigate the underlying process of time multiplexed incoherent optical accumulation and achieve accumulation accuracy of 98.9% with 1 ns pulses. Assuming state of the art active components and a reasonable crossbar array size, this processor architecture would enable matrix vector multiplications with 16,000 × 64 matrices all optically on an estimated area of 51.2 mm<sup>2</sup>, while performing more than 110 trillion multiply and accumulate operations per second.</p>
Dataset for journal article "Understanding the timeliness of automated data feeds is important for determining their usability for large-scale surveillance of healthcare-associated infections"
<p>This dataset was used for the journal article "Understanding the timeliness of automated data feeds is important for determining their usability for large-scale surveillance of healthcare-associated infections".</p> <p>For a selection of "activity dates", it contains the number of records available centrally at UKHSA on each day after the specified activity date, from a selection of data feeds that could potentially be used for national surveillance of healthcare-associated infections in England.</p> <p>The associated R code is available from https://github.com/oxfordmmm/ukhsa-datafeeds-timeliness-anon</p>
Male Reproductive Health of Floating Population: a Large-Scale Survey in Dongguan
ClinicalTrials.gov study NCT01901029. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Randomized Controlled Trial of Mindfulness-Based Stress Reduction for Reducing Perceived Stress in Women Impacted by Large-Scale Disasters
ClinicalTrials.gov study NCT07283601. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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.