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556 results for “infrastructure”
Bigger telcos are not necessarily better for infrastructure: A case study of EU versus US markets
<p>This is data and slides for an anticipated forthcoming publication.</p> <p>Telecommunications companies (telcos) provide infrastructure essential to the delivery of digital content. Further, investment in next-generation communication technologies is also seen as critical to overall competitiveness of a market. This dataset results from an examination of the case to be made for European telco consolidation, through comparison with both telcos in the more-concentrated US market, and with other corporations involved in the information or ``eye-ball'' value chain. We find that both profits and growth for EU and US telcos are already comparable before investment in infrastructure, and that in line with standard theory, more value is returned to customers in the form of infrastructure investment in the less-concentrated, EU market. Profits are also in line with other companies in the value chain, with the notable exception of the extremely-concentrated digital ad exchanges segment. </p> <p>The data for the charts was collected from Bloomberg, so we therefore have protected the primary datasheet, available on specific request.</p> <p>No discrepancies with information available from other public sources was identified in respect of the data on revenue. However, companies do not report operating profit (EBIT) and EBITDA systematically in the same manner. We based our calculation on the Bloomberg adjusted EBIT and EBITDA. We thank Benedikt Ströbl for comparing the Bloomberg revenue, EBIT and EBITDA figures with other available sources for all companies in the sample. In particular, the data from Bloomberg was compared to data from Alphaquery and 10-K and annual reports.</p> <p>The below is a non-exhaustive list of the data points for which the Bloomberg adjusted data displayed a delta compared to the data that could be collected from the public sources used for verification, where only some years displayed a delta in the data the year is specified in brackets: (i) in respect of EBIT: Publicis (2018, 2019), NYT (2017) and Axel Springer (2017 and 2019); (ii) in respect of EBITDA (additionally to EBIT list): Verizon, Bertelsmann (2018), Interpublic (2019).</p> <p>Finally, to avoid any confusion in respect of the segment data for Alphabet, the data is presented as retrieved from Bloomberg in full on the tab “Alphabet”, data from the SEC reports used on top of the Bloomberg data to estimate the EBITDA is also reproduced on this tab.</p>
Supplementary material 1 from: Pontoppidan M, Nachman G (2013) Changes in behavioural responses to infrastructure affect local and regional connectivity – a simulation study on pond breeding amphibians. Nature Conservation 5: 13-28. https://doi.org/10.3897/natureconservation.5.4611
Full model description following the ODD-template suggested by Grimm et al. (2006, 2010) and model parameterisation. (doi: 10.3897/natureconservation.5.4611.app). File format: Adobe PDF document (pdf).:
Risk Assessment of Extreme Precipitation on to Low- and Medium-Voltage Electrical Infrastructure Under the Influence of Climate Change
Open the record for dataset details and reuse information.
Data for: Characteristics of Selected Open Infrastructures, 2024 State of Open Infrastructure Report
<p>The State of Open Infrastructure report provides an annual snapshot of general characteristics for open infrastructures (OIs) listed in Invest in Open Infrastructure’s (IOI) open infrastructure selection tool, Infra Finder (https://infrafinder.investinopen.org/).</p> <p>The data were summarized and reported in the “2024 State of Open Infrastructure Report” section “Characteristics of selected open infrastructures.” The full report is available at https://doi.org/10.5281/zenodo.10934089.</p> <p>A readme, data dictionary, and additional metadata definition file are provided with the dataset with additional detail.</p>
Dataset: First Trust NASDAQ Clean Edge Smart Grid Infrastructure Index Fund (GRID) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Griid Infrastructure Inc. (GRDI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Griid Infrastructure Inc. (GRDIW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: FTAI Infrastructure Inc. (FIP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: iShares Emerging Markets Infrastructure ETF (EMIF) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: iShares Environmental Infrastructure and Industrials ETF (EFRA) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Global X Data Center & Digital Infrastructure ETF (DTCR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: JPMorgan Sustainable Infrastructure ETF (BLLD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Atlantica Sustainable Infrastructure plc (AY) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Xtrackers US Green Infrastructure Select Equity ETF (UPGR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Sterling Infrastructure, Inc. (STRL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: VanEck Green Infrastructure ETF (RNEW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Mawson Infrastructure Group Inc. (MIGI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: ClearBridge Sustainable Infrastructure ETF (INFR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: iShares Global Infrastructure ETF (IGF) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Advanced PySPAM: An Infrastructure to Constrain Underlying Interacting Galaxy Parameters Synthetic Results
<p>This database contains the results for the Chapter 3 of DOR's thesis. For a full description of these results and the way they were built, please see <em>Link to be added on publication</em>.</p> <p>The aim of this Chapter was to use MCMC methods with a fast, efficient simulation algorithm (APySPAM) to constrain the underyling parameters of observed interacting galaxy systems. This algorithm used a Chi-Squared distance minimisation between morphology distributions of observed and simulated images to constrain 13 underlying parameters of galaxy interaction. We applied our algorithm to to 50 of the 62 systems described in <a href="https://ui.adsabs.harvard.edu/abs/2016MNRAS.459..720H/abstract">Holincheck et al. (2016).</a></p> <p>We opted to use the Holincheck et al. sample as the underlying parameters of these systems had already been constrained using a Citizen Science project named <a href="https://mergers.galaxyzoo.org/">Galaxy Zoo: Mergers</a>. This gave us a ground truth to which compare our constraints to. We created synthetic observations of each image, and then ran our MCMC over them, achieving constraint across the sample and parameter space. However, when applied to observational data (we opted to use SDSS images of these systems) we are unable to constrain the full parameter space. This is particularily true of the orientations of the interacting system and their relative sizes.</p> <p>Exploring using velocity information in our constraints find that we improve almost all our constrains considerably. Therefore, adding in spectroscopic information to this method could drastically improve it. The main limitation of this approach, however, is computation time with each system taking approximately 20 hours on a well parallelised HPC to converge. Alternatives to improve performance lie in simulation based inference (SBI, an introduction can be found <a href="https://arxiv.org/pdf/2009.08459">here</a>) or including the use of GPUs (such as done by NVIDEA in fluid dynamics <a href="https://developer.nvidia.com/blog/ai-powered-simulation-tools-for-surrogate-modeling-engineering-workflows-with-siml-ai-and-nvidia-modulus/">here</a>)</p> <p>The results are portrayed as corner plots, with contour plots showing the distribution of likelihoods found in each MCMC run and the histograms on the side showing the marginalised posterior distributions.</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.