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830 results for “Industrialization”
Linked collectors and determiners for: Colección Herpetológica (Anfibios) del Museo de Historia Natural de la Universidad Industrial de Santander.
Natural history specimen data linked to collectors and determiners held within, "Colección Herpetológica (Anfibios) del Museo de Historia Natural de la Universidad Industrial de Santander". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/c1316d40-133e-485a-aa5b-e62091ebfdf5">https://bionomia.net/dataset/c1316d40-133e-485a-aa5b-e62091ebfdf5</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/c1316d40-133e-485a-aa5b-e62091ebfdf5">https://gbif.org/dataset/c1316d40-133e-485a-aa5b-e62091ebfdf5</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Colección Hidrobiológica del Museo de Historia Natural de la Universidad Industrial de Santander.
Natural history specimen data linked to collectors and determiners held within, "Colección Hidrobiológica del Museo de Historia Natural de la Universidad Industrial de Santander". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/c0717c48-e182-4bcf-b722-71c9af936058">https://bionomia.net/dataset/c0717c48-e182-4bcf-b722-71c9af936058</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/c0717c48-e182-4bcf-b722-71c9af936058">https://gbif.org/dataset/c0717c48-e182-4bcf-b722-71c9af936058</a>. Formatted as a Frictionless Data package.
TRANSFORMING CUSTOMER RETENTION IN FINTECH INDUSTRY THROUGH PREDICTIVE ANALYTICS AND MACHINE LEARNING
<p>In recent years, the fintech industry has experienced rapid growth, driven by technological advancements and evolving consumer expectations. Fintech companies offer innovative financial services, such as digital banking, investment platforms, and payment solutions, catering to the needs of a tech-savvy customer base. However, as competition intensifies, customer retention has emerged as a critical challenge for these companies. According to a study by Ransom (2021), acquiring a new customer can cost five times more than retaining an existing one, making it imperative for fintech organizations to focus on strategies that enhance customer loyalty. The financial technology (fintech) sector has experienced unprecedented growth in recent years, fundamentally transforming how individuals and businesses access and manage financial services. Characterized by the integration of technology with financial services, fintech encompasses a wide array of offerings, including digital banking, peer-to-peer lending, robo-advisory services, and payment processing. As of 2023, the global fintech market was valued at approximately $309 billion and is projected to reach around $1.5 trillion by 2030, according to a report by Fortune Business Insights. This remarkable growth is largely attributed to advancements in digital technology, increasing smartphone penetration, and a growing consumer preference for online financial solutions. Moreover, the COVID-19 pandemic accelerated the adoption of digital financial services, as consumers sought contactless transactions and remote banking options.</p>
Experiences Applying Lean R&D in Industry-Academia Collaboration Projects
<p>Supplementary materials of the paper Experiences Applying Lean R&D in Industry-Academia Collaboration Projects</p>
Normalized profile of industrial demand response
<p>Normalized profile of industrial demand response based on an industrial facility in Austria in 2019</p>
Dataset of the paper "Self-Admitted Technical Debt Practices: A Comparison Between Industry and Open-Source"
<p>This repository contains the dataset of the manuscript "Self-Admitted Technical Debt Practices: A Comparison Between Industry and Open-Source" accepted in the Empirical Software Engineering Journal, edited by Springer</p>
FIG. 6 in The versatility of bone, ivory and horn - their uses in the Sheffield cutlery industry
FIG. 6. – Top, horn pressing vice; middle, large cattle horn (courtesy of the Hawley Collection); bottom, 19th century open razor with pressed horn scales. Cutlers' Company collection. Scale in cm.
FIG. 5 in The versatility of bone, ivory and horn - their uses in the Sheffield cutlery industry
FIG. 5. – Metapodials sawn to length for knife handles, the centre one has filed decorated, excavated by ARCUS from the Sylvester Wheel, Sheffield, 2005. Scale in cm.
FIG. 4 in The versatility of bone, ivory and horn - their uses in the Sheffield cutlery industry
FIG. 4. – Bone found on the site of the Sheffield Assay Office, Portobello Street, Sheffield, excavated by Northamptonshire Ar- chaeology Unit, 2009. Top: bone sawn to length; bottom: dense bone sawn from round the central core leaving distinctive bone scrap. Scale in cm.
FIG. 1 in The versatility of bone, ivory and horn - their uses in the Sheffield cutlery industry
FIG. 1. – Silver plated knife and fork with carved ivory handles. Late 19th century. Cutlers' Company collection.
FIG. 2. – Two 19 in The versatility of bone, ivory and horn - their uses in the Sheffield cutlery industry
FIG. 2. – Two 19th century forks. The top specimen has bone scales riveted to the flat scale tang; the bottom fork has a round tang and would require a solid handle. Cutlers' Company collection.
Supplementary data for "Deep learning for industrial processes: Forecasting amine emissions from a carbon capture plant"
<p>A preliminary analysis of the data already has been discussed in <a href="https://dx.doi.org/10.2139/ssrn.3812299">10.2139/ssrn.3812299</a>.</p> <p><strong>Raw data</strong></p> <p>Raw measurement data is in the Excel files `day*_raw.xlsx`.</p> <p><strong>Model</strong></p> <p>Covariate and label scaler objects are serialized in joblib format in the following files:</p> <ul> <li>20210812_y_transformer_co2_ammonia_reduced_feature_set</li> <li>20210812_y_transformer__reduced_feature_set</li> <li>20210812_x_scaler_reduced_feature_set</li> </ul> <p>Checkpoints of the models are in the `*.pth.tar` files. An example for loading the models is:</p> <pre><code class="language-python">from pyprocessta.model.tcn import TCNModelDropout model_cov = TCNModelDropout( input_chunk_length=8, output_chunk_length=1, num_layers=5, num_filters=16, kernel_size=6, dropout=0.3, weight_norm=True, batch_size=32, n_epochs=100, log_tensorboard=True, optimizer_kwargs={"lr": 2e-4}, ) model_cov.load_from_checkpoint('20210814_2amp_pip_model_reduced_feature_set_darts')</code></pre> <p>which assumes that the checkpoints are placed as `model_best.pth.tar` in a folder called `20210812_2amp_pip_model_reduced_feature_set_darts`.</p> <p> </p>
Fig. 1 in Effects of the proximity from an industrial plant on fish assemblages in the rio Paraíba do Sul, southeastern Brazil
Fig. 1. Study area, rio Paraíba do Sul reaches. Indication of the six sampling sites and three zones - Z I (sites 1 and 2); Z II (sites: 3 and 4); and Z III (sites 5 and 6). Buffers marked in black indicate main sources of urban and industrial pollution. Dams indicated by black line marks.
Fig. 6 in Effects of the proximity from an industrial plant on fish assemblages in the rio Paraíba do Sul, southeastern Brazil
Fig. 6. Cluster analysis of fish abundance on mode Q, showing the three zones in the Paraíba do Sul river, in 1998/99. In x-axis = 1: Z I; 2: Z II; 3: Z III.
Fig. 5 in Effects of the proximity from an industrial plant on fish assemblages in the rio Paraíba do Sul, southeastern Brazil
Fig. 5. Cluster analysis of fish abundance on mode Q, showing the three zones in the Paraíba do Sul river, in 1997/98. In x-axis = 1: Z I; 2: Z II; 3: Z III.
Fig. 4 in Effects of the proximity from an industrial plant on fish assemblages in the rio Paraíba do Sul, southeastern Brazil
Fig. 4. Number of species per geometric classes (x 2) in the three zones of the rio Paraíba do Sul, in 1997/99.
Fig. 2 in Effects of the proximity from an industrial plant on fish assemblages in the rio Paraíba do Sul, southeastern Brazil
Fig. 2. ABC curves for fish species in the three zones of the rio Paraíba do Sul, in 1997/99. Round marks - abundance; triangle marks - biomass. ABC-indexes indicated for each zone.
Fig. 3. K in Effects of the proximity from an industrial plant on fish assemblages in the rio Paraíba do Sul, southeastern Brazil
Fig. 3. K-dominance curves for the fish species in the three zones of the rio Paraíba do Sul, in 1997/99.
A Collection of Industry-Developed Blockchain-based Applications
<p>A set of blockchain-based applications (DApps) consists of 400 public, private, and hybrid DApps. They are manually selected between September 2019 and February 2020 from nine blockchain platforms: Blockstack, Corda, Ethereum, EOS, Hive, Hyperledger Fabric, Klaytn, POA, and Steem.</p>
Green taxes as ecosystem conservation: an analysis of the industrial sector's view in Peru
<p><strong>Background:</strong> Environmental problems are becoming more and more recurrent nowadays, which is why many countries have acted on the matter, through different forms, laws and taxes that can contribute and reduce the polluting impact of companies at the time of manufacturing their products. One of the reforms has been environmental taxes, which are not only aimed at raising money, but also at taking corrective action on the behavior of companies that damage both the environment and the health of the population.</p> <p><strong>Objective:</strong> The research is interested in finding out the opinion and interest of managers of different companies in the industrial sector on environmental taxes, also known as green taxes, and whether they consider it necessary to add green taxes to the current tax system.</p> <p><strong>Method:</strong> For the data collection of the research, 120 managers of small and medium-sized enterprises in the industrial sector were questioned about whether green taxes could have an influence on ecosystem conservation and several other questions.</p> <p><strong>Results: </strong>63.3% of the managers surveyed agree that the application of an environmental tax is necessary.</p>
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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.