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1,037 results for “large-scale”
Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration
<p>Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro/adipogenic progenitors defined by functional roles, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem cell quiescence to myofiber maturation and identified rare, transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. We also used the integrated dataset to explore ligand-receptor co-expression patterns and identify dynamic cell-cell interactions in muscle injury response. We provide a public web tool to enable interactive exploration and visualization of the data. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.</p>
A Large-scale Benchmark for Technical Debt Assessment
<p>This repository hosts the supplementary material of the paper entitled "A Large-scale Benchmark for Technical Debt Assessment". It comprises a very large-scale study on the Technical Debt of more than 55 million open-source Java files.</p>
An Exploratory Study on Faults in Web API Integration in a Large-Scale Payment Company: Appendix
<p>Appendix of our "An Exploratory Study on Faults in Web API Integration in a Large-Scale Payment Company: Appendix" paper.</p>
Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation (Unlabeled Data Part I)
<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>
Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation (Unlabeled Data Part II)
<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>
Incorporating the effect of large-scale vertical motion on convection through convective mass flux adjustment in E3SMv2
<p>Simulation data for the manuscript entitled of "Incorporating the effect of large-scale vertical motion on convection through convective mass flux adjustment in E3SMv2". </p>
Data from "Molecular Mapping of DR Tau's Protoplanetary Disk, Envelope, Outflow, and Large-Scale Spiral Arm"
<p>Data associated with Huang et al., 2023, "Molecular Mapping of DR Tau's Protoplanetary Disk, Envelope, Outflow, and Large-Scale Spiral Arm," ApJ, 943, 107</p> <p>The raw data can be obtained by requesting data from program W20BE (PI: J. Huang) from the NOEMA archive. </p> <p><strong>Images</strong></p> <p>images.tar: FITS files of image cubes and moment maps as shown in Figs. 2, 3, 5, 9, and 10 (see also finalimagingscript.py)</p> <p><strong>Spectra</strong></p> <p>spectra.tar: ASCII files of spectra shown in Figs. 1 and 9 (see also finalimagingscript.py) </p> <p><strong>Measurement sets (CASA format)</strong></p> <p>drtau_LINENAME_corrected.ms.tar: Measurement set for a given line, corresponding to drtau_LINENAME_selfcal-contsub.uvt in lines_contsub_uvt.tar </p> <p>drtau_ui_cont_corrected.ms.tar: Continuum measurement set corresponding to the UI baseband, generated from w20be-ui-cont-selfcal.uvt in continuum_uvt.tar </p> <p><strong>UV tables (GILDAS format)</strong></p> <p>LR_uvt.tar: Pipeline-calibrated UV tables of low-resolution data (see w20be-tables-LR.clic)</p> <p>HR_uvt.tar: Pipeline-calibrated UV tables of high-resolution data (see w20be-tables-HR.clic)</p> <p>continuum_uvt.tar: Self-calibrated, spectrally averaged continuum UV tables generated from LR_uvt.tar</p> <p>lines_contsub_uvt.tar: Self-calibrated, continuum-subtracted line UV tables generated from the UV tables in HR_uvt.tar</p> <p><strong>Scripts</strong></p> <p>w20be-tables-LR.clic: CLIC script used to output UV tables of low-resolution data from the pipeline-calibrated data (output contained in LR_uvt.tar)</p> <p>w20be-tables-HR.clic: CLIC script used to output UV tables of high-resolution data from the pipeline-calibrated data (output contained in HR_uvt.tar)</p> <p>uvfitstoms.py: Script to export uvfits files to measurement sets and to correct the metadata in the measurement sets </p> <p>finalimagingscript.py: CASA script used to generate imaging products in images.tar and spectra in spectra.tar </p> <p><strong>Other:</strong></p> <p>calib.tar: Files output by NOEMA pipeline </p> <p> </p>
Large-scale survey of excitatory synapses reveals sublamina-specific and asymmetric synapse disassembly patterns in a neurodegenerative circuit
<p>Dataset associated with the manuscript titled "<strong>Large-scale survey of excitatory synapses reveals sublamina-specific and asymmetric synapse disassembly patterns in a neurodegenerative circuit"</strong></p>
Appendix of Large-Scale Evaluation of Method-Level Bug Localization with FinerBench4BL
<p>Appendix of "Large-Scale Evaluation of Method-Level Bug Localization with FinerBench4BL"</p> <p> </p> <p>FinerBench4BL is available at the following URL</p> <p><a href="https://github.com/salab/FinerBench4BL">https://github.com/salab/FinerBench4BL</a></p>
Long-term responses to large-scale disturbances: Spatiotemporal variation in gastropod populations and communities
<p>The Anthropocene is characterized by complex, primarily human-generated, disturbance regimes that include combinations of long-term press (e.g. climate change, pollution) and episodic pulse (e.g. cyclonic storms, floods, wildfires, land use change) disturbances. Within any regime, disturbances occur at multiple spatial and temporal scales, creating complex and varied interactions that influence spatiotemporal dynamics in the abundance, distribution, and biodiversity of organisms. Moreover, responses to disturbance are context-dependent, with the legacies of previous disturbances affecting responses to ensuing perturbations. We use three decades of annual data to evaluate the effects of repeated pulse disturbances and global warming on gastropod populations and communities in Puerto Rico at multiple spatial scales. More specifically, we quantify (1) the relative importance of large-scale and small-scale aspects of disturbance on variation in abundance, biodiversity, and species composition; and (2) the spatial scales at which populations and communities integrate information in the spatially heterogenous environments created by disturbances. Gastropods do not exhibit consistent decreases in abundance or biodiversity in association with global warming: abundance for many species has increased over time and species richness does not evince a temporal trend. Nonetheless, gastropods are sensitive to hurricane severity, spatial environmental variation, and successional trajectories of the flora. In addition, they exhibit context-dependent (i.e. legacy effects) responses that are scale-dependent. The Puerto Rican biota has evolved in a disturbance-mediated system. This historical exposure to repeated, severe hurricane-induced disturbances has imbued the biota with high resistance and resilience to the current disturbance regime, resulting in an ability to persist or thrive under current environmental conditions. Nonetheless, these ecosystems may yet be threatened by worsening direct and indirect effects of climate change. In particular, more frequent and severe hurricanes may prevent the establishment of closed-canopy forests, negatively impacting populations and communities that rely on these habitats.</p>
supplementary to "SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme" , 2D wind forcing
<p>This is a supplementary material to article "SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme" ( unpublished at the publication date of this dataset)</p> <p>It contains the 2D wind forcing for Crocus-SnowPappus simulations on the Grandes Rousses test zone. It was generated using DEVINE wind downscaling method ( Le Toumelin et al., 2022 )</p>
Data from "Enhanced radiative forcing from aerosol-cloud interactions due to large-scale circulation adjustments" by Guy Dagan, Netta Yeheskel and Andrew I. L. Williams.
<p>This is the data presented in "Enhanced radiative forcing from aerosol-cloud interactions due to large-scale circulation adjustments" by Guy Dagan, Netta Yeheskel and Andrew I. L. Williams.</p> <p> </p> <p>Please read the README file for explanations about the data.</p> <p> </p>
Large-scale saliva pooling as a screening strategy to control transmission_raw data
<p>This dataset include the raw data of our experience after building a bespoke laboratory to control critical areas by pooling samples of saliva. From August 2020 to February 2022; 928,528 individual self-samples of saliva were processed in 52,580 pools, 4,935 of which were positive and helped us detect 5,806 nonsymptomatic individuals. </p> <p>The dataset was collected directly from the laboratory information system and was processed to delete the columns with sensitive information.</p> <p>The Dataset was charged as XLSX file and only need a compatible software.</p> <p>To facilitate the understanding of the data set, which uses the Galician language and an internal nomenclature of our laboratory, we include a sheet named "description" and a README file in which it defines the terms used and their equivalence with those used in the article.</p>
Data from: Chilean bee diversity: Contrasting patterns of species and phylogenetic turnover along a large-scale ecological gradient
<p>Title of dataset</p> <p>Data from: Chilean bee diversity: Contrasting patterns of species and phylogenetic turnover along a large-scale ecological gradient</p> <p>Authors of dataset</p> <p>Leon Marshall<sup>1,2</sup>, John S. Ascher<sup>3</sup>, Cristian Villagra<sup>4</sup>, Amaury Beaugendre<sup>1</sup>, Valentina Herrera<sup>4</sup>, Patricia Henríquez-Piskulich<sup>4</sup>, Alejandro Vera<sup>5</sup>, Nicolas J. Vereecken<sup>1</sup></p> <ol> <li>Agroecology Lab, Université libre de Bruxelles (ULB), Boulevard du Triomphe CP 264/2, B 1050 Brussels, Belgium</li> <li>Naturalis Biodiversity Center, Darwinweg 2, 2333 CR Leiden, The Netherlands</li> <li>Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543, Singapore</li> <li>Instituto de Entomología, Universidad Metropolitana de Ciencias de la Educación, Santiago, Región Metropolitana, Chile</li> <li>Departamento de Biología, Universidad Metropolitana de Ciencias de la Educación, Santiago, Región Metropolitana, Chile</li> </ol> <p>Abstract</p> <p>Chile's isolation and varied climates have driven the evolution of a unique biodiversity with a high degree of endemism. As a result, Chile encompasses diverse environments, including the Mediterranean-type ecosystem, a global biodiversity hotspot. These environments are currently threatened by anthropogenic land use change impacting the integrity of local biomes and associated species. This area is the most intensively sampled of the country with high endemicity of native bee species. Characterising habitat requirements of bees is a pressing priority to safeguard these insects and the ecosystem services they provide. We investigated broad-scale patterns of bee (Hymenoptera: Apoidea: Anthophila) diversity using newly accessible expert-validated datasets comprising digitized specimen records from Chilean and US collections, and novel expert-validated type specimen data for the bees of Chile. We used a generalised dissimilarity modelling (GDM) approach to explore both compositional and phylogenetic β-diversity patterns across latitudinal, altitudinal, climate and habitat gradients in well-sampled bee assemblages in Central Chile. Using the GDM measures of increasing compositional and environmental dissimilarity we categorised and compared the most important drivers of these patterns and used them to classify 'wild bee ecoregions' (WBE) representing unique assemblages. Turnover of bee assemblages was explained primarily by latitudinal variation (proxy for climate) from south to north in Chile. However, temperature variations, precipitation and the presence of bare soil also significantly explained turnover in bee assemblages. In comparison, we observed less turnover in phylogenetic biodiversity corresponding to spatial gradients. We identified six de novo ecoregions (WBE), all with distinct taxa, endemic lineages, and representative species. The WBE represent distinct spatial classifications but have similarities to existing biogeographical classifications, ecosystems and bioclimatic zones. This approach establishes the baseline needed to prioritise bee species conservation efforts across this global biodiversity hotspot. We discuss the novelty of this classification considering previous biogeographical characterisations and their relevance in assessing conservation priorities for bee conservation. We argue that Chile's WBE highlight areas in need of funding for bee species surveys and description, distribution mapping and strengthening of conservation policies.</p> <p>Usage notes</p> <p>The dataset contains species occurrence data of chilean bees aggregated to a 5 x 5 km grid shapefile. The shapefile of the grid and the raster mask of the Central Chilean study area are also included. Finally, a database of type specimen data used to supplement the dataset is included here. The code for the analysis can be found at: <a href="https://github.com/lmar116/ChileanBeeDiversity">https://github.com/lmar116/ChileanBeeDiversity</a>.</p> <p>Shapefiles, rasters, CSV files and code were all loaded and analyzed using R statistics software. </p> <p>Four files are included:</p> <ol> <li>Marshall-et-al-2023_Ecosphere_DataTable_bee_grid: contains all species occurrence data used in GDM analysis, Grid column refers to cl.5km.shp.</li> <li>cl.5km.shp (and associated files): 5 x 5 km grid shapefile of the Central Chilean study area</li> <li>chile.mask.tif: raster outline of the Central Chilean study area</li> <li>Marshall-et-al-2023_Ecosphere_CentralChileTypeSpecimens.xlsx: contains type specimen data used to supplement species occurrence dataset.</li> </ol>
MMpedia: A Large-scale Multi-modal Knowledge Graph
<p><strong>List of files:</strong></p> <ul> <li>entity2image.json: The entity to map images file</li> <li>MMpedia_triples.ttl: The triples file</li> <li>EntlistXX.tar: The image file</li> </ul> <p>MMpedia dataset is split into 132 subsets and each subset is compressed into a <code>.tar</code> file. After unziping files under the folder "MMpedia", the data structure are as following:</p> <pre><code>|-MMpedia |-Entitylist1 |-Entity1 |-1.jpg |-2.jpg |-3.jpg ... |-Entity2 |-Entity3 ... |-Entitylist2 |-Entitylist3</code></pre> <p>For example, the path <code>MMpedia/Entlist141/Bart Tanski/Bart Tanski+1.jpg</code> means the image corresponding to the entity "Bart Tanski"</p> <p><strong>Other image files can be found in following URLs:</strong></p> <p><a href="https://zenodo.org/record/7854781#.ZEU7Uc7iu38">MMpedia2 | Zenodo</a></p> <p><a href="https://zenodo.org/record/7855010">MMpedia3 | Zenodo</a></p> <p><a href="https://zenodo.org/record/7855226">MMpedia4 | Zenodo</a></p> <p> <strong>For more details, please refer to </strong><a href="https://github.com/Delicate2000/MMpedia">Delicate2000/MMpedia (github.com)</a></p>
READ: Large-Scale Neural Scene Rendering for \\ Autonomous Driving
<p>With the development of advanced driver assistance systems~(ADAS) and autonomous vehicles, conducting experiments in various scenarios becomes an urgent need. Although having been capable of synthesizing photo-realistic street scenes, conventional image-to-image translation methods cannot produce coherent scenes due to the lack of 3D information. In this paper, a large-scale neural rendering method is proposed to synthesize the autonomous driving scene~(READ), which makes it possible to generate large-scale driving scenes in real time on a PC through a variety of sampling schemes. In order to effectively represent driving scenarios, we propose an ω rendering network to learn neural descriptors from sparse point clouds. Our model can not only synthesize photo-realistic driving scenes but also stitch and edit them. The promising experimental results show that our model performs well in large-scale driving scenarios.</p> <p> </p> <p>Source Code: https://github.com/JOP-Lee/READ</p>
Experimental dataset on a large-scale urban drainage physical facility
<p>The present dataset contains the results from the test carried out at a large-scale urban drainage facility located in the Hydraulics Laboratory of the Centre of Technological Innovation in Construction and Civil Engineering (CITEEC) at the University of A Coruña (Spain) within the scope of the project SATURNO - Early warning against pluvial flooding in urban areas [PID2020-118368RB-I00]. The main goal of the project SATURNO is to establish a robust methodology for the development and implementation of operational Early Warning System (EWS) for pluvial flood risk in urban areas.</p> <p>The experimental facility represents a T-intersection street of 100 m<sup>2</sup> linked to a sewer system, and it is equipped with a rainfall simulator able to generate spatially homogeneous rainfall intensities. The dataset includes the results of experiments on roofs, street surfaces, inlets, manholes and the outfall of the drainage system as well as all products obtained during the tests.</p> <p>The dataset is structured in the following items:</p> <ul> <li>01_Physical_model_description: Full description of the urban drainage installation.</li> <li>02_Geometries: Geometry of streets, roofs, and drainage network ready for import into numerical.</li> <li>03_Rain_intensity_maps: Rainfall maps of the intensities generated with the rainfall simulator in the experiments.</li> <li>04_Digital_Elevation_Models: Digital Terrain Models of the street surface and roofs.</li> <li>05_Experimental_procedure: Full description of the experimental procedure, equipment, measuring points, results, and structure of the database.</li> <li>06_ Hydraulic_tests_database: Database with post-processed experimental results ready for use. Results of flow rates of the 4 roofs, the 4 inlets, the 4 manholes, the grate and the outfall and the depths of the 9 surface points are presented. A file with additional data on the measuring points is also included.</li> <li>07_Supplementary_material: QGIS project with the products obtained.</li> <li>08_Multimedia: Photos and videos of the platform and experiments.</li> </ul> <p>The roofs results have been used in Sañudo et al. (2022) to assess three different numerical implementations to model rainfall-runoff transformation on roofs [DOI: <a href="https://doi.org/10.1002/hyp.14588">https://doi.org/10.1002/hyp.14588</a>]</p> <p>This study was received financial support from the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033) within the project “SATURNO: Early warning against pluvial flooding in urban areas” (PID2020-118368RB-I00).</p>
Exact large-scale fluctuations of the phase field in the sine-Gordon model
<p>Raw data and Mathematica Notebooks for the thermodynamics and phase correlations in the sine-Gordon model</p> <p>It can be found:</p> <ol> <li>A transfer matrix code for equilibrium correlation functions.</li> <li>A solver for the classical Thermodynamic Bethe Ansatz and the correlation functions of the phase field.</li> <li>A solver of the Thermodynamic Bethe Ansatz of the quantum sine-Gordon at the reflectionless points, and the computation of cumulants of the phase field.</li> <li>Monte Carlo data presented in the paper: cumulants of the phase field at unequal times on homogeneous thermal background and phase correlation on the inhomogeneous background mimicking the tunnel-coupled experiments.</li> <li>Mathematica notebooks that read the data and produce the figures shown in the paper.</li> </ol>
Agroforestry-based community forestry as a large-scale strategy to reforest agricultural encroachment areas in Myanmar: ambition vs. local reality
<p>Abstract: <br> Context: <br> The high rate of deforestation in Myanmar is mainly due to agricultural expansion. One task of the Forest Department is to increase tree cover in the encroaching farmland by establishing large-scale agroforestry-based community forests (ACFs).<br> Aim: <br> The objectives of this study were to analyze the adoption and performance of the ACFs in the agricultural encroachment areas in the Bago-Yoma region, Myanmar; and to provide recommendations to enhance the adoption of ACFs by farmers.<br> Methods: <br> We inventoried 42 sample plots and surveyed 291 farmers. Survey responses were analyzed by binary logistic regression, one-way ANOVA, and non-parametric correlation tests to evaluate factors influencing the adoption of ACFs. Stand characteristics were calculated from the inventory data to evaluate the performance of ACFs.<br> Results: <br> Our results show that farmer participation in ACFs was lower than stated in the registry of the Forest Department. Farmers practiced four different agroforestry designs in ACFs with different outcomes. The Forest Department strongly determined tree species and planting designs, farmers’ perception and participation in ACFs. Farmland size, unclear and insufficient information on ACFs, and a negative perception of raising trees in crop fields were the major factors limiting the adoption rates of ACFs. <br> Conclusion: <br> We recommend capacity building for farmers and Forest Department staff and raising awareness about the benefits of planting designs and trees on farmland. A stronger consideration of farmers’ preferences for design and species selection could increase their motivation to adopt ACFs and improve the long-term sustainability of ACFs.</p> <p> </p>
Throughfall exclusion and fertilization effects on tropical dry forest tree plantations, a large-scale experiment
<p>Across tropical ecosystems, global environmental change is causing drier climatic conditions and increased nutrient deposition. Such changes represent large uncertainties due to unknown interactions between drought and nutrient availability in controlling ecosystem net primary productivity (NPP). Using a large-scale manipulative experiment, we studied for 4 years whether nutrient availability affects the individual and integrated responses of aboveground and belowground ecosystem processes to throughfall exclusion in 30-year-old mixed plantations of tropical dry forest tree species in Guanacaste, Costa Rica. We used a factorial design with four treatments: control, fertilization (F), drought (D), and drought + fertilization (D + F). While we found that a 13 %–15 % reduction in soil moisture only led to weak effects in the studied ecosystem processes, NPP increased as a function of F and D + F. The relative contribution of each biomass flux to NPP varied depending on the treatment, with woody biomass being more important for F and root biomass for D + F and D. Moreover, the F treatment showed modest increases in maximum canopy cover. Plant functional type (i.e., N fixation or deciduousness) and not the experimental manipulations was the main source of variation in tree growth. Belowground processes also responded to experimental treatments, as we found a decrease in nodulation for F plots and an increase in microbial carbon use efficiency 25 for F and D plots. Our results emphasize that nutrient availability, more so than modest reductions in soil moisture, limits ecosystem processes in tropical dry forests and that soil fertility interactions with other aspects of drought intensity (e.g., vapor pressure deficit) are yet to be explored.</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.