Find research datasets worth reusing

opencc-by-nc-4.0Apr 2024View details →

India Flood Inventory-Impacts (IFI-Impacts) [1967-2023]: A multi-source national geospatial database to facilitate comprehensive flood research

This repository hosts the India Flood Inventory with Impacts (IFI-Impacts) database. It contains flood event data sourced from the Indian Meteorological Department from 1967-2023. It has undergone extensive manual digitization, cleaning, and includes new information to make it suitable for computational research in hydroclimate. v4.0: Development of District Flood Severity Index (DFSI) v3.0: India Flood Inventory (IFI) 1967-2023. Updated with local government codes (LGD) for state and district.  v1.0: India Flood Inventory (IFI) 1967-2016. v2.0: India Flood Inventory (IFI) 1967-2023. With impacts and district flooded area. REFERENCES Saharia, M., Jain, A., Baishya, R.R., Haobam, S., Sreejith, O.P., Pai, D.S., Rafieeinasab, A., 2021. India flood inventory: creation of a multi-source national geospatial database to facilitate comprehensive flood research. Nat Hazards. <a href="https://doi.org/10.1007/s11069-021-04698-6">https://doi.org/10.1007/s11069-021-04698-6</a> <div> <div>Saharia, M., Jain, S.K., Prakash, V., Malik, H., Sreejith, O.P., Joshi, D., 2025. A district-level flood severity index for flood management in India. Nat Hazards. <a href="https://doi.org/10.1007/s11069-025-07493-9">https://doi.org/10.1007/s11069-025-07493-9</a></div> </div>

opencc-by-4.0Nov 2023View details →

The first 500-meter, long-term winter wheat grain protein content dataset for China from multi-source data

In China, the demand for precise perception of wheat Grain Protein Content (GPC) has gained increased urgency, driven by the rising demands in the food consumption market and intensifying international market competition. However, due to the lack of extensive, prolonged high-resolution benchmark data, previous GPC studies have primarily focused on experimental fields, small geographic units, and limited temporal scopes. Additionally, the diversified geographical landscape in China introduces spatiotemporal heterogeneity and intricacy to the influence of wheat GPC, further amplifying the challenges of large-scale GPC estimation. To address this challenge and the data gap, the first 500-meter spatial resolution, long-term winter wheat dataset covering major planting regions in China (CNWheatGPC-500) was created by integrating multi-source data from ERA5 and MODIS.

openNov 2023View details →

A Global Multi-Source Tropical Cyclone Precipitation (MSTCP) Dataset

Tropical cyclone precipitation (TCP) is a key diagnostic in the context of atmospheric science, hazard, risk and flood research. This dataset provides estimates of TCP from global datasets. The various TCP metrics reported were estimated through the analysis of the global Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation product and the International Best Track Archive for Climate Stewardship (IBTrACS) version 4. There are two main files that comprise the dataset. The main dataset file includes information on the mean and maximum TCP found within 500 km of each storm centre as well as the rainfall area and radius of maximum rain. The second file includes the estimates of azimuthally averaged precipitation using a 10 km bin spacing which is useful for analyses of the storm-scale structure of precipitation.

opencc-by-4.0Dec 2021View details →

Mapping 10-m global impervious surface area (GISA-10m) using multi-source geospatial data

Artificial impervious surface area (ISA) documents human footprints. Accurate, timely, and detailed ISA datasets are therefore essential for global climate change and urban planning. However, due to the lack of sufficient training samples and operational mapping methods, global ISA mapping at 10-m resolution is still lacking. To this end, we proposed a global ISA mapping method leveraging multi-source geospatial data. Based on the existing satellite-derived ISA maps and the crowdsourcing OpenStreetMap (OSM), 58 million training samples were extracted via a series of temporal, spatial, spectral, and geometric rules. Combined with over 2.7 million Sentinel optical and radar images on the Google Earth Engine, we produced the 10 m global ISA dataset (GISA-10m). Based on the test samples that are independent to the training set, GISA-10m embraced an overall accuracy greater than 86%. In addition, the GISA-10m was comprehensively compared with the existing global ISA datasets, and the superiority of GISA-10m was demonstrated.

opencc-by-4.0May 2024View details →

Forest segmentation of multi-source national forest inventory biomass rasters and canopy height model from 2021

The dataset is produced at Natural Resources Institute Finland (Luke) and the study is funded by the European Union's Horizon 2020 research and innovation programme (Holisoils, grant agreement No 101000289). Source data (multi-source National Forest Inventory, MS-NFI and peatland fertility map of Finland) of varying resolution (10m -16m) was reprojected to 10mx10m resolution from which stand polygons were formulated based on automatic segmentation and regional minimum size limit for a stand. The dataset is a file geodatabase with 5 regional layers, all including the polygons of stands with stand attributes based on MS-NFI 2021 information on the site type, fertility class, dominant height, basal area, diameter, age, volume as total and per tree species, total and aboveground biomass as total and per tree species. Coordinate system: ETRS-TM35FIN (EPSG:3067)

opencc-by-4.0Jun 2024View details →

FCH and FS Datasets for the paper "Integrating Multi-Source Remote Sensing Data for Mapping Boreal Forest Canopy Height and Species in interior Alaska in Support of Radar Modeling"

This dataset provides forest canopy height and forest species in Delta Junction, interior Alaska in 2017. This dataset was produced based on the multi-source remote sensing datasets (AirMOSS, UAVSAR, Sentinel-1, Sentinel-2, topography), using a XGBoost approach.

opencc-by-4.0Oct 2019View details →

Multi-Source Distributed System Data for AI-powered Analytics

Abstract: In recent years there has been an increased interest in Artificial Intelligence for IT Operations (AIOps). This field utilizes monitoring data from IT systems, big data platforms, and machine learning to automate various operations and maintenance (O&M) tasks for distributed systems. The major contributions have been materialized in the form of novel algorithms. Typically, researchers took the challenge of exploring one specific type of observability data sources, such as application logs, metrics, and distributed traces, to create new algorithms. Nonetheless, due to the low signal-to-noise ratio of monitoring data, there is a consensus that only the analysis of multi-source monitoring data will enable the development of useful algorithms that have better performance.   Unfortunately, existing datasets usually contain only a single source of data, often logs or metrics. This limits the possibilities for greater advances in AIOps research. Thus, we generated high-quality multi-source data composed of distributed traces, application logs, and metrics from a complex distributed system. This paper provides detailed descriptions of the experiment, statistics of the data, and identifies how such data can be analyzed to support O&M tasks such as anomaly detection, root cause analysis, and remediation. General Information: This repository contains the simple scripts for data statistics, and link to the multi-source distributed system dataset. You may find details of this dataset from the original paper: Sasho Nedelkoski, Jasmin Bogatinovski, Ajay Kumar Mandapati, Soeren Becker, Jorge Cardoso, Odej Kao, "Multi-Source Distributed System Data for AI-powered Analytics".  If you use the data, implementation, or any details of the paper, please cite!   BIBTEX: _________________________________________ <pre>@inproceedings{nedelkoski2020multi, title={Multi-source Distributed System Data for AI-Powered Analytics}, author={Nedelkoski, Sasho and Bogatinovski, Jasmin and Mandapati, Ajay Kumar and Becker, Soeren and Cardoso, Jorge and Kao, Odej}, booktitle={European Conference on Service-Oriented and Cloud Computing}, pages={161--176}, year={2020}, organization={Springer} } </pre> ___________________________ The multi-source/multimodal dataset is composed of distributed traces, application logs, and metrics produced from running a complex distributed system (Openstack). In addition, we also provide the workload and fault scripts together with the Rally report which can serve as ground truth. We provide two datasets, which differ on how the workload is executed. The sequential_data is generated via executing workload of sequential user requests. The concurrent_data is generated via executing workload of concurrent user requests. The raw logs in both datasets contain the same files. If the user wants the logs filetered by time with respect to the two datasets, should refer to the timestamps at the metrics (they provide the time window). In addition, we suggest to use the provided aggregated time ranged logs for both datasets in CSV format. Important: The logs and the metrics are synchronized with respect time and they are both recorded on CEST (central european standard time). The traces are on UTC (Coordinated Universal Time -2 hours). They should be synchronized if the user develops multimodal methods. Please read the IMPORTANT_experiment_start_end.txt file before working with the data. Our GitHub repository with the code for the workloads and scripts for basic analysis can be found at: <a href="https://github.com/SashoNedelkoski/multi-source-observability-dataset/">https://github.com/SashoNedelkoski/multi-source-observability-dataset/</a>

opencc-by-4.0Aug 2024View details →

SDUST2023BCO: a global seafloor model determined from multi-layer perceptron neural network using multi-source differential marine geodetic data

<div> SDUST2023BCO.nc is the global marine bathymetric model covering 80°S~80°N and 0°~360°E on 1′×1′ grids. The dataset contains geospatial information (latitude, longitude), SDUST2023BCO bathymetric model and an attachment data. </div>

opencc-by-4.0Nov 2022View details →

Dataset presented in the recently submitted AGU manuscript "Constraining the crustal and mantle conductivity structures beneath islands by a joint inversion of multi-source magnetic transfer functions"

Dataset (observed tippers, solar quiet global-to-local transfer functions, and global Q responses) presented in the recently submitted AGU manuscript "Constraining the crustal and mantle conductivity structures beneath islands by a joint inversion of multi-source magnetic transfer functions".

opencc-by-4.0Sep 2023View details →

ASN Database - v3.2 - Database of Simulated Room Impulse Responses for Acoustic Sensor Networks Deployed in Complex Multi-Source Acoustic Environments

We present a large set of simulated room impulse responses for a multi-room apartment. The simulated apartment models a real vacation apartment for which a recorded set of audio data has already been made available in the context of the DCASE challenges. The impulse responses were rendered using a dense grid of sources and receivers by means of a hybrid auralization algorithm based on a low-order image-source method and deterministic cone tracing. The proposed data set can be used to generate a wide variety of acoustic scenes which, in turn, can benefit numerous data-demanding machine-learning algorithms. To obtain more information on the database, please visit <a href="https://github.com/Jearde/asn-database">the website</a>. Please read the license file (available in the GitHub repository) before using the database.

opencc-by-4.0Dec 2020View details →

Standard Bouguer anomaly model achieved by multi-source Bouguer gravity anomaly Bayesian data fusion algorithm in Sichuan-Yunnan region

* Method: Based on the equivalent source inversion and Bayesian uncertainty quantization theory, a new multi-source gravity data fusion algorithm is developed, which effectively solves the multi-source data fusion problem with different noise and datum. * Standard Bouguer anomaly is Fused from WGM2012 Bouguer gravity anomaly model and 394 gravity profile data measured in Sichuan-Yunnan region. Fusion anomaly results can eliminate datum draft between multi-source gravity and reduce incoherent noise. * Spatial resolution of the standard Bouguer anomaly is about 20 kilometers. * Correcting deviations means the difference between the fused standard Bouguer anomaly model and the WGM2012 Earth gravity model.

opencc-zeroOct 2021View details →

Data from: Long-term trends in the occupancy of ants revealed through use of multi-sourced datasets

We combined participatory science data and museum records to understand long-term changes in occupancy for 29 ant species in Denmark over 119 years. Bayesian occupancy modelling indicated change in occupancy for 15 species: five increased, four declined and six showed fluctuating trends. We consider how trends may have been influenced by life-history and habitat changes. Our results build on an emerging picture that biodiversity change in insects is more complex than implied by the simple insect decline narrative.

opencc-zeroJan 2022View details →

Machine learning identifies girls with central precocious puberty based on multi-source data

Objective: The study aimed to develop simplified diagnostic models for identifying girls with central precocious puberty (CPP), without the expensive and cumbersome gonadotropin-releasing hormone (GnRH) stimulation test, which is the gold standard for CPP diagnosis. Materials and Methods: Female patients who had secondary sexual characteristics before 8 years old and had taken a GnRH analog (GnRHa) stimulation test at a medical center in Guangzhou, China were enrolled. Data from clinical visiting, laboratory tests and medical image examinations were collected. We first extracted features from unstructured data such as clinical reports and medical images. Then, models based on each single-source data or multi-source data were developed with Extreme Gradient Boosting (XGBoost) classifier to classify patients as CPP or non-CPP. Results: The best performance achieved an AUC of 0.88 and Youden index of 0.64 in the model based on multi-source data. The performance of single-source models based on data from basal laboratory tests and the feature importance of each variable showed that the basal hormone test had the highest diagnostic value for a CPP diagnosis. Conclusion: We developed three simplified models that use easily accessed clinical data before the GnRH stimulation test to identify girls who are at high risk of CPP. These models are tailored to the needs of patients in different clinical settings. Machine learning technologies and multi-source data fusion can help to make a better diagnosis than traditional methods.

opencc-by-4.0Jun 2024View details →

Multi-Source Precipitation Data Fusion Across Continental United States

Dataset Description: This dataset supports our research presented in the paper "A deep learning-based framework for multi-source precipitation fusion" by Gavahi et al., (2023) published in Remote Sensing of Environment. The study introduces a novel deep learning architecture for merging and downscaling multiple precipitation products, aiming to enhance quantitative precipitation estimation (QPE) accuracy. The developed model, the Precipitation Data Fusion Network (PDFN), integrates 3D-CNN and ConvLSTM layers to capture the inherent spatiotemporal dependencies of precipitation data. The results indicate significant improvements in error statistics. The dataset includes merged daily precipitation estimations using the PDFN model. The data cover the Continental United States (CONUS) and are provided at a spatial resolution of 0.05 degrees. Temporal coverage spans from January 1, 2015, to April 30, 2024. The coordinate reference system used is WGS1984. Note: Since the PERSIANN-CDR dataset is only available until the end of 2023, in this dataset, we used PDIR-Now instead to ensure the dataset's continuity and completeness. In the original paper, we used PERSIANN-CDR, but here we used PDIR-Now to extend the dataset to cover the period until April 30, 2024. Usage Notes: This dataset is intended for use in applications such as land surface modeling, flood forecasting, drought monitoring and prediction. Users are requested to cite the associated paper when utilizing the dataset for academic or research purposes.  Related Publications: For further details on the methodology and applications of this dataset, refer to the paper "Gavahi, K., E. Foroumandi, and H. Moradkhani (2023), A deep learning-based framework for multi-source precipitation fusion, Remote Sensing of Environment, doi:10.1016/j.rse.2023.113723"

opencc-by-4.0Feb 2024View details →

SeverusPT - A multi-source burn severity dataset for mainland Portugal

<h3>Abstract (EN)</h3> The SeverusPT project aims to periodically and timely provide relevant and standardized information on burn severity supported by satellite and field observations. Key objectives include developing a spatially explicit framework for assessing, mapping, and predicting burn severity and delivering a co-designed product/service to enhance institutional and operational capacity for fire hazard management and post-fire ecosystem restoration. The project currently provides standardized satellite-based datasets on mainland Portugal’s observed/historical burn severity, leveraging multiple satellite missions (Sentinel-2, Landsat, MODIS), spectral indices (e.g., Normalized Burn Ratio – NBR, Tasseled Cap Transformation – TCT), and burn severity indicators. The datasets are derived from pre-calculated severity products through algorithms that integrate satellite image time series (SITS) in two main approaches: (i) a delta-based pipeline, employing “classical” severity measurements (e.g., delta NBR) and focused primarily on high spatial resolution satellites; and (ii) a trajectory-based pipeline supported by SITS and the analysis of post-fire trajectories for multiple dimensions of ecosystem functioning and primarily focusing on high-temporal/moderate spatial resolution satellites. Field assessments, critical for validating satellite products and obtaining nuanced results regarding post-fire effects, were used to provide information on burn severity across different structural components of vegetation. The project used a purposive stratified approach for field surveys, focusing on the 2022 fire season across mainland Portugal. Selection criteria based on fire size, location, main vegetation type, and other ancillary layers enabled comprehensive coverage and diversity in post-fire conditions. Approximately 111 sites in 28 burned areas were surveyed in north and centre Portugal (the wildfire foci in the country) using the Geometrically Structured Composite Burn Index (GeoCBI) protocol. Two methods were used to validate the delta-based products by comparing in situ GeoCBI and satellite burn severity estimates: (i) non-parametric linear correlation (Spearman method) and nonlinear correlation; and (ii) a nonlinear exponential model adapted from pre-existing studies. Delta-based SeverusPT products agreed well with GeoCBI field measures of burn severity. The best linear correlation results were bounded between 0.64 and 0.71. Sentinel-2 and the NBR spectral index with RBR generally ranked higher when compared to Landsat-8. For nonlinear correlation, results were between 0.65 and 0.76, with the best results for Landsat-8 TCTG, closely followed by Sentinel-2 NBR spectral index with RDT or RBR indicators. The results for the nonlinear model validation were similar, with the best marks attained by the RdNBR, RBR, and dNBR indicators (R2= 0.64, 0.62, and 0.60, respectively). The project’s data portal is a centralized gateway for accessing and downloading project data and metadata. It offers two primary levels of data access: Level 1 includes the products, and Level 2 comprises image data files along with metadata. The trajectory-based pipeline and products are still under active development and will be added to the SeverusPT Data Portal. SeverusPT builds on a comprehensive approach combining satellite data with rigorous field validation, yielding significant insights into wildfire severity. The project’s innovative methodologies and the data portal’s accessibility contribute to the field of wildfire severity assessment, offering valuable data and tools for fire management and prioritizing post-fire mitigation and recovery strategies. SeverusPT Data Products Manual: <a href="https://doi.org/10.5281/zenodo.10640961" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10640961</a> ___ <h3>Resumo (PT)</h3> O projecto SeverusPT tem como objetivo disponibilizar, de forma regular e atempada, informação relevante e pradonizada da severidade da área ardida, baseada em dados de satélite e observações no terreno. Os principais objetivos incluem o desenvolvimento de uma moldura de análise espacialmente explícita para avaliar, mapear e prever a severidade da área ardida, fornecendo um produto/serviço resultante de co-desenho para melhorar a capacidade institucional e operacional para a gestão do risco de incêndio e o restauro dos ecossistemas pós-incêndio. Atualmente este projeto disponibiliza conjuntos de dados derivados de imagens de satélite para Portugal Continental acerca da severidade histórica/observada da área ardida. Estes conjuntos de dados fazem uso de informação proveniente de múltiplas missões espaciais (Sentinel-2, Landsat, MODIS), índices espetrais (p.ex. “Normalized Burn Ratio” – NBR, “Tasseled Cap Transformation” – TCT), e indicadores da severidade da área ardida. Os conjuntos de dados derivam de produtos pré-calculados de severidade através de algoritmos que integram séries temporais de imagens de satélite (SITS) em duas abordagens principais: (i) a cadeia de processamento (“pipeline”) baseado em deltas, que implementa medidas “clássicas” de severidade (p.ex. delta-NBR) e que se foca principalmente em satélites de alta resolução espacial; e (ii) a cadeia de processamento baseado em trajetórias, que se baseia na análise de trajetórias pós-incêndio para múltiplas dimensões do funcionamento dos ecossistemas e que se foca em dados de satélite de alta resolução temporal e resolução espacial moderada. No sentido de fornecer informação acerca da severidade da área ardida em vários componentes estruturais da vegetação, foram utilizados dados recolhidos no terreno, os quais são cruciais para validar produtos derivados de imagens de satélite e obter resultados pormenorizados relativamente a efeitos pós-incêndio. No âmbito do projeto foi usada uma abordagem estratificada para efetuar os levantamentos no terreno, focada na época de incêndios de 2022 em Portugal Continental. Critérios de seleção baseados no tamanho da área ardida, localização, tipo de vegetação principal e outras camadas auxiliares de informação permitiram cobrir uma maior diversidade de condições pós-incêndio. Foram visitados aproximadamente 111 locais pertencentes a um total de 28 áreas ardidas no Norte e Centro de Portugal Continental (as zonas do país mais afetadas por incêndios), usando o protocolo “Geometrically Structured Composite Burn Index” (GeoCBI). Foram utilizados dois métodos para validar os produtos baseados em deltas, através da comparação do GeoCBI in situ com estimativas obtidas por satélite: (i) correlações lineares não paramétricas (método de Spearman) e correlações não-lineares; e (ii) um modelo exponencial não-linear adaptado de estudo pré-existentes. Os produtos SeverusPT baseados em deltas apresentaram elevada concordância com as medidas de severidade da área ardida recolhidas no terreno através do GeoCBI. Os valores resultantes mais elevados para a correlação linear situaram-se entre 0,64 e 0,71. Foram obtidos valores geralmente mais elevados para Sentinel-2 e para o índice espetral NBR e o indicador de severidade RBR, em comparação com os resultados obtidos para Landsat-8. Relativamente às correlações não-lineares, foram obtidos valores entre 0,65 e 0,76, tendo os valores mais elevados sido obtidos para o índice espetral TCTG derivado de Landsat-8, seguido do índice espetral NBR derivado de Sentinel-2 com os indicadores de severidade RDT ou RBR. Foram obtidos resultados semelhantes para a validação através de modelo não-linear, com os valores mais elevados correspondendo aos indicadores de severidade RdNBR, RBR e dNBR (R2 = 0,64, 0,62 e 0,60, respetivamente). O portal de dados do projeto constitui uma via de acesso centralizada para a visualização e descarregamento de dados e metadados do projeto, oferecendo dois níveis primários de acesso: o Nível 1 inclui os produtos e o Nível 2 é constituído por ficheiros de imagens e metadados. Os produtos provenientes da cadeia de processamento baseada em trajetórias estão ainda em desenvolvimento activo e serão adicionados posteriormente no Portal de Dados do SeverusPT. O SeverusPT assenta numa abordagem abrangente que combina dados provenientes de satélites com uma rigorosa validação baseada em dados recolhidos no terreno, oferecendo uma melhor compreensão da severidade dos incêndios. As suas metodologias inovadoras e a acessibilidade do seu portal de dados contribuem para o campo da avaliação da severidade dos incêndios, providenciando dados e ferramentas valiosos para a gestão do fogo e para a priorização de estratégias de mitigação e recuperação pós-incêndio.

opencc-by-4.0Sep 2019View details →

Drought impacts on Australian vegetation during the Millennium Drought measured with multi-source spaceborne remote sensing

During the period from 1997 to 2009, Australia experienced a severe and persistent drought known as the Millennium Drought (MD). Major water shortages were reported across the continent as were various field accounts of tree mortality and dieback, but large-area assessment has been lacking. Given uncertain projections of future drought conditions in South-East Australia, analysis of the MD presents a valuable opportunity to assess possible impacts of these future trends. In this study, we analyzed the magnitude and sensitivity of vegetation responses to the MD with satellite-derived information including the fraction of photosynthetically absorbed radiation (FPAR), photosynthetic vegetation cover (PVC), canopy density derived from vegetation optical depth (VOD) and aboveground biomass carbon (ABC). Bioclimatic diferences in drought impacts and sensitivity were examined as well. Here are generated datasets and related codes for processing. Drought impcats and sensitivities for FPAR and PVC are too large to be uploaded but users could calculate their own results with the codes provided here. See Metadata.txt for details.

opencc-by-4.0Oct 2019View details →

Development of a global 30-m impervious surface map using multi-source and multi-temporal remote sensing datasets with the Google Earth Engine platform

An accurate global impervious surface map at a resolution of 30-m for 2015 by combining Landsat-8 OLI optical images, Sentinel-1 SAR images and VIIRS NTL images based on the Google Earth Engine (GEE) platform.

opencc-zeroNov 2022View details →

A STP-HSI index method for urban built-up area extraction based on multi-source remote sensing data

The changes of urban built-up areas can reflect the process of urbanization, and it can reflect the population, economy, and cultural development of the city. Therefore, accurate and timely extraction of urban built-up areas plays an important role in the dynamic management of the city. In the existing research, single-source remote sensing data is used to extract urban built-up areas, and there is a problem that the spectrum of urban areas and non-urban areas is easily confused. Multi-source remote sensing data, including luojia-1 remote sensing data, Landsat 8 OLI remote sensing data, etc., can make up for the spectrum confusing issues. We fuse the time series information of night light remote sensing data, neighborhood information and point of interest (POI) data in spatial dimension, and propose a built-up area extraction method that integrates night light time and space information and POI information.