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188 results for “Big data”
Field Data associated with study: Investigating the water movements around a shallow shipwreck in Big Tub Harbour of Lake Huron: implications for managing underwater shipwrecks.
<p>Field data (Water temperature, pressure, currents, summary of boat presence data) used in analysis of the study (and paper with the same name) titled: Investigating the water movements around a shallow shipwreck in Big Tub Harbour of Lake Huron: implications for managing underwater shipwrecks.</p>
A novel big data mining framework for reconstructing large-scale daily MAIAC AOD across China from 2000 to 2020
<p>This dataset contains the mean reconstructed MAIAC AOD and mean percentage of coverage of reconstructed MAIAC AOD across China from 2000 to 2020. </p>
The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics
<p>Vocalizations in animals, particularly birds, are critically important behaviors that influence their reproductive fitness. While recordings of bioacoustic data have been captured and stored in collections for decades, the automated extraction of data from these recordings has only recently been facilitated by artificial intelligence methods. These have yet to be evaluated with respect to accuracy of different automation strategies and features. Here, we use a recently published machine learning framework to extract syllables from ten bird species ranging in their phylogenetic relatedness from 1 to 85 million years, to compare how phylogenetic relatedness influences accuracy. We also evaluate the utility of applying trained models to novel species. Our results indicate that model performance is best on conspecifics, with accuracy progressively decreasing as phylogenetic distance increases between taxa. However, we also find that the application of models trained on multiple distantly related species can improve the overall accuracy to levels near that of training and analyzing a model on the same species. When planning big-data bioacoustics studies, care must be taken in sample design to maximize sample size and minimize human labor without sacrificing accuracy.</p>
A big data–model integration approach for predicting epizootics and population recovery in a keystone species
<p>Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex non-linear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a non-parametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, <em>Cynomys ludovicianus</em>) and sylvatic plague as an example. We synthesized colony data between 2001–2020 from eight USDA Forest Service National Grasslands across the range of BTPD in central North America. We then modeled extinctions due to plague and colony recovery of BTPD in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summer/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally > 0.80). Thus, these spatially-explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host-pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data–model integration approach provides a general framework for spatially-explicit forecasting of disease-induced population fluctuations, for use in natural resource management decision-making.</p>
C-MORE BiG-RAPA MV1015 Downcast Bottle Data
<p>This dataset contains the data from various water samples collected from Niskin bottles attached to a CTD rosette on the BiG-RAPA cruise that went from Chile to Easter Island in November-December 2010. Samples that were taken include: HPLC pigments, flow cytometry, and nutrients. “The South East Pacific (SEP) is characterized by very high nutrient concentrations in the waters adjacent to the Chilean coast, but very low nutrient concentrations (oligotrophic) in the mid- South Pacific Subtropical Gyre (SPSG), near Easter Island. The steep gradient in nutrient concentrations across the region affects the level of marine production, the composition of the microbial community, and the operation of major biogeochemical cycles in ways that are not fully understood. Despite the remarkable diversity of trophic conditions, strong gradients and even some unique singularities, the SEP is still the most sparsely sampled oceanic region of the global ocean from hydrodynamic, biological and biogeochemical points of view. The SPSG is also the most oligotrophic of all sub-tropical gyres. Previous expeditions and remote sensing studies have described the nutrient and chlorophyll field, but there have been few simultaneous measurements of chemical properties with microbial community structure and function. This expedition is designed to investigate the impact of elemental nutrient (nitrogen, phosphorus, iron, silicon, carbon) ratios on marine productivity and microbial community composition.”</p> <p><br> This description has been reproduced using the following source:<br> https://hahana.soest.hawaii.edu/cmorebigrapa/bigrapa.html</p>
Flux measurements at the CNRS big solar furnace in Odeillo with heliostat scan (L2 data)
<p><strong>Flux measurements at the big solar furnace in Odeillo.</strong></p> <p>Data levels description:</p> <ul> <li>Level 0 data: raw datasets from the different acquisition systems (cameras, radiometers, motion controllers…).</li> <li>Level 1 data: area of interest extracted from flux camera images and converted as HDF file (grayscale).</li> <li><strong>=> Level 2 data: All data synchronised, converted and supersampled at millisecond resolution in HDF files: DNI, radiometer, heliostat aiming location from ARGOS camera, image maps at focal plane on the receiver in suns normalised for DNI 1000 W/m2.</strong></li> <li>Level 3 data: data interpolated and smoothed for regular heliostat aiming positions.</li> <li>Level 4 data: idem, but spatially subsampled at various resolutions.</li> </ul> <p>One HDF file per heliostat.</p> <p>Heliostats chosen randomly among the 26 available.</p> <p>HDF file are compressed in zstandard. In python 3, requires the packages: pandas, hdf5plugin, tables, h5py, blosc, zstd.</p> <p>For each heliostat : the aiming position of the heliostats was moved in S patterns.</p> <p>Data from experiment started 2021-12-22 120618Z.</p> <p><strong>References:</strong></p> <ul> <li>Related to SolarPaces 2022 communication: <strong>Building a 5D Database of Heliostats Flux Distributions: toward High Flexibility High Accuracy Flux Control for Odeillo's Big Solar Furnace </strong></li> <li>Solar furnace description and more: <a href="https://doi.org/10.1063/1.5067052">https://doi.org/10.1063/1.5067052</a></li> <li>Cameras settings description and more: <a href="https://doi.org/10.1063/1.5067202">https://doi.org/10.1063/1.5067202</a></li> </ul>
Flux measurements at the CNRS big solar furnace in Odeillo with heliostat scan (L4 data)
<p><strong>Flux measurements at the big solar furnace in Odeillo.</strong></p> <p>Data levels description:</p> <ul> <li>Level 0 data: raw datasets from the different acquisition systems (cameras, radiometers, motion controllers…).</li> <li>Level 1 data: area of interest extracted from flux camera images and converted as HDF file (grayscale).</li> <li>Level 2 data: All data synchronised, converted and supersampled at millisecond resolution in HDF files: DNI, radiometer, heliostat aiming location from ARGOS camera, image maps at focal plane on the receiver in suns normalised for DNI 1000 W/m2.</li> <li>Level 3 data: data interpolated and smoothed for regular heliostat aiming positions.</li> <li><strong>=> Level 4 data: idem, but spatially subsampled at various resolutions. Provided here: 10mm resolution.</strong></li> </ul> <p>One HDF file per heliostat.</p> <p>Heliostats chosen randomly among the 26 available.</p> <p>HDF file are compressed in zstandard. In python 3, requires the packages: pandas, hdf5plugin, tables, h5py, blosc, zstd.</p> <p>For each heliostat : the aiming position of the heliostats was moved in S patterns.</p> <p>Data from experiment started 2021-12-22 120618 GMT.</p> <p>Data processing completed 2022-03-02 1459 GMT.</p> <p><strong>References:</strong></p> <ul> <li>Related to SolarPaces 2022 communication: <strong>Building a 5D Database of Heliostats Flux Distributions: toward High Flexibility High Accuracy Flux Control for Odeillo's Big Solar Furnace </strong></li> <li>Solar furnace description and more: <a href="https://doi.org/10.1063/1.5067052">https://doi.org/10.1063/1.5067052</a></li> <li>Cameras settings description and more: <a href="https://doi.org/10.1063/1.5067202">https://doi.org/10.1063/1.5067202</a></li> </ul>
BIG DATA ANALYTICS IN DIGITAL HUMAN RESOURCES MANAGEMENT: IMPACT ON THE RECRUITMENT PROCESS
<p>This study investigates how HR employees experience the big data phenomenon in the recruitment function of HRM and how their perceptions of the phenomenon have evolved. This study also examines how BD will affect organizational and HRM and how it can be improved in other functions of HR. In this exploratory study, which comprehensively addresses the BD phenomenon in HRM, the phenomenological design approach, one of the qualitative research methods, was applied to test the research questions and a semi-structured interview form was used for research data. Using the snowball sampling method, in-depth interviews were conducted with 10 HR employees working in large and semi-structured organizations in Turkey and the interviews were analyzed with MAXQDA 20. The findings show that HRM employees are aware of BD. On the other hand, it is understood that BD technologies provide easy accessibility in recruitment, offer a strategic competitive advantage, and enable more effective management of information management, which saves HR employees' work in a facilitating way. They benefit from technology as a decision support assistant. Finally, the research results provide theoretical and practical implications for future researchers and practitioners for the development and effective use of BD technology in the field of HRM.</p>
Supplemental networks of cowords of the paper Measuring the impact of Big Data in the scientific research in Agriculture and allied fields
<p>Supplemental networks of cowords of the paper Measuring the impact of Big Data in the scientific research in Agriculture and allied fields.</p>
Lista de Referências - Ciência da Informação e Big Data: interrelações a partir de revisão de literatura
<p><strong>Lista de Referências - Ciência da Informação e Big Data: interrelações a partir de revisão de literatura</strong></p> <p><strong>Resumo</strong><br> Trata-se de revisão de literatura, que tem por objetivo descrever as interrelações entre Big Data e Ciência da informação (CI), identificadas nas comunicações científicas indexadas nas bases de dados Scopus e Web of Sciences - WoS. Metodologicamente, a expressão genérica de busca foi ("big data" AND "information science"), e foram recuperados 233 registros. Após filtragens criteriosas, o objeto de estudo foi definido por uma seleção de 52 artigos. Mediante análise temática, foram identificados os elementos mais relevantes a serem classificados, identificando-se 10 temas principais. Como resultados qualitativos, foram verificados aportes metodológicos da Ciência da Informação em pesquisas com Big Data em diversos campos, tais como: métodos e ferramentas da CI, medicina e saúde pública, pesquisa na CI, formação profissional, planejamento urbano e políticas públicas, além de discussões éticas e pesquisas teóricas e aplicações práticas. Conclui-se que a CI possui muitas interrelações com Big Data e tem muito a contribuir na pesquisa e uso desse tipo de análise, além de fortalecer a interdisciplinaridade entre a Ciência da Informação e a Ciência de Dados.<br> <strong>Palavras-chave</strong>: Ciência da informação; Big data; Ciência de dados; Interdisciplinaridade.</p>
Data from: Tiny patch, big value: A small dry forest patch supports wildlife conservation in Guanacaste, Costa Rica
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A big data–model integration approach for predicting epizootics and population recovery in a keystone species
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Data and code from: Big trees burning: Divergent wildfire effects on large trees in open- vs. closed-canopy forests
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Data from: The invisible species: Big data unveil coverage gaps in the Atlantic forest hotspot
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The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics
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Data from: Convergent evolution of the army ant syndrome and congruence in big-data phylogenetics
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Clinical trial generalizability assessment in the big data era: a review
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Data from: Horses in the Cloud: big data exploration and mining of fossil and extant Equus (Mammalia: Equidae)
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Data for: Modeling of spatial pattern and influencing factors of cultivated land quality based on spatial-temporal big data (PONE-D-21-21084R1)
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Supporting data for: A big chill: Examining an extremely sharp cold front from three perspectives
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ScienceDex guides
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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.