Dataset used in the article entitled 'Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools'. These datasets can be used to test several characteristics in machine learning and data processing algorithms.

The Data Center Backup Generator Market Report is Segmented by Product Type (Diesel, Natural Gas, and Other Product Types), Capacity (Less Than 1MW, 1-2MW, Greater Than 2MW), Tier (Tier I and II, Tier III, Tier IV), and Geography (North America, Europe, Asia-Pacific, Latin America, and Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

Dataset Card for test-data-generator

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/franciscoflorencio/test-data-generator/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/franciscoflorencio/test-data-generator.

This dataset compiles estimated generator unavailability for eight countries in Northwest Europe, plus Spain. The advantages and limitations of the data are described in detail in the paper submitted to the PMAPS 2022 (Manchester) conference, “Comparing Generator Unavailability Models with Empirical Distributions from Open Energy Datasets” (submitted); the code used to generate the csvs in this dataset are provided at https://github.com/deakinmt/entsoe_outage_models

The dataset consists of forced, planned and total outages, calculated by aggregating the unavailabilities reported in an individual balancing zone. An estimate of the uncertainty due to apparent inconsistencies in outage reports is also provided (also described in the paper).

Explore the Data Center Generator Global Market Report 2025 Market trends! Covers key players, growth rate 7.5% CAGR, market size $8.99 Billion, and forecasts to 2033. Get insights now!

The HazWaste database contains generator (companies and/or individuals) site and mailing address information, waste generation, the amount of waste generated etc. of all the hazardous waste generators in Vermont. Database was developed in early 1990's for program management and to meet EPA Authorization requirements. The database has been updated to more modern data systems periodically.�

The dataset contains information on utility or customer-owned dispersed generation (NOT grid-connected) such as the number, capacity and types of generators.

Data Center Generator Market is estimated to reach USD 15.3 billion By 2033, Riding on a Strong 7% CAGR throughout the forecast period.

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Generator Market In Data Centers Size 2024-2028

The generator market in data centers size is forecast to increase by USD 4.26 billion at a CAGR of 8.56% between 2023 and 2028. In the realm of data center operations, power reliability emerges as a critical factor, driving the market's growth. Next-generation power monitoring and management software are increasingly being adopted to ensure uninterrupted power supply and enhance overall efficiency. However, the data center industry's carbon footprint is a significant concern, leading to the exploration of renewable energy sources such as wind, solar, and hydroelectric power. Micro-economic factors, including the rising cost of fossil fuels and the growing popularity of nuclear energy, are also influencing market dynamics. Edge computing sites are gaining traction, necessitating the need for power solutions that cater to their unique requirements.

What will be the Size of the Market During the Forecast Period?

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The market play a pivotal role in the digital transformation of businesses, enabling the storage, processing, and dissemination of critical information. However, power interruptions and system downtime can lead to significant information loss and revenue damage. To mitigate these risks, data center operators are increasingly investing in power backup solutions. Power density, the amount of power used per unit area, is a critical factor in data center design. Edge data centers, which are smaller and closer to the source of data generation, require innovative power backup solutions due to their limited space.

Moreover, 5G technology and edge computing are driving the growth of edge data centers, necessitating the development of compact, efficient power backup systems. Power costs are a significant expense for data center operators. Fuel cells, solar-powered data parks, natural gas generators, and diesel generators are among the power backup solutions that offer cost-effective alternatives to traditional grid power. Li-ion batteries are gaining popularity as they provide high energy density and long cycle life. Colocation service providers offer customized capacity solutions to meet the unique power requirements of their clients. Power backup solutions, including backup power systems and power loss prediction technologies, are essential components of their offerings.

Furthermore, these solutions ensure uninterrupted power supply and enhance data center reliability. Electricity is the primary power source for data centers. Power backup solutions provide a safety net against power interruptions, ensuring business continuity. Power loss prediction technologies enable data center operators to anticipate power outages and take preventive measures. The generator market is witnessing significant growth due to the increasing demand for power backup solutions. Fuel cells, solar-powered data parks, natural gas generators, and diesel generators are among the generator types that cater to the power backup needs of data centers. In conclusion, power backup solutions are a critical component of data center infrastructure.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Diesel
  Gas


Capacity

  Less than 1MW
  1MW-2MW
  More than 2MW


Geography

  North America

    US


  Europe

    UK


  APAC

    China
    Japan


  South America



  Middle East and Africa

By Type Insights

The diesel segment is estimated to witness significant growth during the forecast period.In the data center industry, diesel generators play a significant role in providing power during fluctuating or transient scenarios. Their high-torque performance characteristic makes them an ideal choice for data centers with high power density requirements. Diesel generators come in various capacity ranges, making them a versatile option for data centers of all sizes. The diesel generator system consists of several components, including the diesel engine, generating unit, fuel storage/supply, and electrical switchgear. These generators are popular due to their reliability, safety, and minimal maintenance requirements. The output power capacity of diesel generators is greater than other types, making them suitable for large data center infrastructure.

Furthermore, diesel fuel is the most commonly used fuel in generators installed in data centers. The cost-effectiveness of diesel generators is another reason for their popularity. However, electricity prices and taxes can impact the overall cost of operating a data center with diesel generators. Edge data centers and colocation service providers are increasingly adopting 5G technology, which may require even more power density

The Synthea generated data is provided here as a 1,000 person (1k), 100,000 person (100k), and 2,800,000 persom (2.8m) data sets in the OMOP Common Data Model format. SyntheaTM is a synthetic patient generator that models the medical history of synthetic patients. Our mission is to output high-quality synthetic, realistic but not real, patient data and associated health records covering every aspect of healthcare. The resulting data is free from cost, privacy, and security restrictions. It can be used without restriction for a variety of secondary uses in academia, research, industry, and government (although a citation would be appreciated). You can read our first academic paper here: https://doi.org/10.1093/jamia/ocx079

13032 Global import shipment records of Home Generator with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This database is composed of stator voltages and currents, field voltage and rotor position of a three-phase synchronous generator under resistive load. The data were acquired by means of two Tektronix MSO 2014B oscilloscopes with 4 channels each. Data were collected on 8 channels corresponding to phase voltages (Va,Vb,Vc), phase currents (Ia,Ib,Ic), field current of the generator (Ifd) and a pulse signal for angular position reference of the rotor (theta_m). For the simultaneous collection of the signals, a trip circuit was designed and implemented, the output of which was used as the triggering signal for the oscilloscopes. The synchronous generator was connected to a synchronous motor (Y-Y) and to a resistive circuit (18 units of 40W lamps) which served as loads. Voltage data were collected by means of a Keysight N2791 voltage probes; current data were collected using Tektronix A622 current tips. An PHCT203 optical key was used to collect rotor position pulse signal. The generator model is MOTROM M610-75-B-1K8-GS of 0.5 cv, 1800 rpm, 4 poles. The generator parameters obtained by means of physical bench tests were:

Rs = 32.5 ohms (stator winding resistance)

Rfd = 358.9 ohms (field winding resistance)

Ld = 0.803H (direct axis stator inductance)

Lq = 0.691H (quadrature axis stator inductance)

Lls = 0.12H (stator winding leakage inductance)

Lfd = 2.23H (field winding inductance)

Vf = 64V (field voltage applied during the experiment, supplied by an regulated DC source)

The database is composed by the following files of preprocessed data sampled at 10kHz in which the following variables are given, respectively, time, Va, Vb, Vc, Theta_r, Ia, Ib, Ic:

1) data0001.txt

2) data0002.txt

3) data0003.txt

4) data0001.csv

5) data0002.csv

6) data0003.csv

Further, the database contains the following files of raw data:

1) T0001A.txt

2) T0001B.txt

3) T0002A.txt

4) T0002B.txt

5) T0003A.txt

6) T0003B.txt

Each realization is contained in two files (suffixes A,B) and contain:

Suffix A

time

CH1 (Va)

CH1_peak (Va peak)

CH2 (Vb)

CH2_peak (Vb peak)

CH3 (Vc)

CH3_peak (Vc peak)

CH4 (Theta_r)

CH4_peak (Theta_r peak)

Suffix B

time

CH1 (Ia)

CH1_peak (Ia peak)

CH2 (Ib)

CH2_peak (Ib peak)

CH3 (Ic)

CH3_peak (Ic peak)

CH4 (EMPTY)

CH4_peak (EMPTY)

When using the raw data, it is important to consider that the values were acquired in the following conditions:

- Voltage probe scale: 100:1

- Current probe scale: 100mV/A

- Oscilloscope probe scale: 10x

- Oscilloscope configuration: check header of raw files.

Contact information: jose.grzybowski@uffs.edu.br

When run, the GFCM creates tabular and graphical data of the hourly operating status and market outcomes for the generators that make up the electric generating fleet.

China Generator & Generator Set: Sales Revenue: Year to Date data was reported at 337.522 RMB bn in Oct 2015. This records an increase from the previous number of 297.439 RMB bn for Sep 2015. China Generator & Generator Set: Sales Revenue: Year to Date data is updated monthly, averaging 82.796 RMB bn from Dec 2003 (Median) to Oct 2015, with 97 observations. The data reached an all-time high of 382.490 RMB bn in Dec 2014 and a record low of 4.744 RMB bn in Feb 2006. China Generator & Generator Set: Sales Revenue: Year to Date data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIA: Motor: Generator and Generator Set.

• Repository author: Tommaso Zanotti* email: tommaso.zanotti@unimore.it or francescomaria.puglisi@unimore.it * Version v1.0

This repository includes MATLAB files and datasets related to the IEEE IIRW 2023 conference proceeding:T. Zanotti et al., "Reliability Analysis of Random Telegraph Noisebased True Random Number Generators," 2023 IEEE International Integrated Reliability Workshop (IIRW), South Lake Tahoe, CA, USA, 2023, pp. 1-6, doi: 10.1109/IIRW59383.2023.10477697

The repository includes:

The data of the bitmaps reported in Fig. 4, i.e., the results of the simulation of the ideal RTN-based TRNG circuit for different reseeding strategies. To load and plot the data use the "plot_bitmaps.mat" file.

The result of the circuit simulations considering the EvolvingRTN from the HfO2 device shown in Fig. 7, for two Rgain values. Specifically, the data is contained in the following csv files:

"Sim_TRNG_Circuit_HfO2_3_20s_Vth_210m_no_Noise_Ibias_11n.csv" (lower Rgain)

"Sim_TRNG_Circuit_HfO2_3_20s_Vth_210m_no_Noise_Ibias_4_8n.csv" (higher Rgain)

The result of the circuit simulations considering the temporary RTN from the SiO2 device shown in Fig. 8. Specifically, the data is contained in the following csv files:

"Sim_TRNG_Circuit_SiO2_1c_300s_Vth_180m_Noise_Ibias_1.5n.csv" (ref. Rgain)

"Sim_TRNG_Circuit_SiO2_1c_100s_200s_Vth_180m_Noise_Ibias_1.575n.csv" (lower Rgain)

"Sim_TRNG_Circuit_SiO2_1c_100s_200s_Vth_180m_Noise_Ibias_1.425n.csv" (higher Rgain)

China Generator & Generator Set: Total Asset data was reported at 458.934 RMB bn in Oct 2015. This records an increase from the previous number of 451.458 RMB bn for Sep 2015. China Generator & Generator Set: Total Asset data is updated monthly, averaging 299.527 RMB bn from Dec 2003 (Median) to Oct 2015, with 97 observations. The data reached an all-time high of 458.934 RMB bn in Oct 2015 and a record low of 28.965 RMB bn in Dec 2003. China Generator & Generator Set: Total Asset data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIA: Motor: Generator and Generator Set.

NOTES:
- Please use the following link to leave the data view and view the full description: https://data.ct.gov/Environment-and-Natural-Resources/Hazardous-Waste-Manifest-Data-CT-1984-2008-Generat/72mi-3f82

-Please use ALL CAPS when searching using the "Filter" function on text such as: LITCHFIELD. But not needed for the upper right corner "Find in this Dataset" search where for example "Litchfield" can be used.

Dataset Description: We know there are errors in the data although we strive to minimize them. Examples include: • Manifests completed incorrectly by the generator or the transporter - data was entered based on the incorrect information. We can only enter the information we receive. • Data entry errors – we now have QA/QC procedures in place to prevent or catch and fix a lot of these. • Historically there are multiple records of the same generator. Each variation in spelling in name or address generated a separate handler record. We have worked to minimize these but many remain. The good news is that as long as they all have the same EPA ID they will all show up in your search results. • Handlers provide erroneous data to obtain an EPA ID - data entry was based on erroneous information. Examples include incorrect or bogus addresses and names. There are also a lot of MISSPELLED NAMES AND ADDRESSES! • Missing manifests – Not every required manifest gets submitted to DEEP. Also, of the more than 100,000 paper manifests we receive each year, some were incorrectly handled and never entered. • Missing data – we know that the records for approximately 25 boxes of manifests, mostly prior to 1985 were lost from the database in the 1980’s. • Translation errors – the data has been migrated to newer data platforms numerous times, and each time there have been errors and data losses. • Wastes incorrectly entered – mostly due to complex names that were difficult to spell, or typos in quantities or units of measure.

Since Summer 2019, scanned images of manifest hardcopies may be viewed at the DEEP Document Online Search Portal: https://filings.deep.ct.gov/DEEPDocumentSearchPortal/

This submission contains cleaned and filtered data from the Environmental Protection Agency Clean Air Markets CAM database of thermal power plant operation and performance.

China Generator & Generator Set: Product Inventory data was reported at 22.855 RMB bn in Oct 2015. This records a decrease from the previous number of 22.964 RMB bn for Sep 2015. China Generator & Generator Set: Product Inventory data is updated monthly, averaging 14.001 RMB bn from Dec 2003 (Median) to Oct 2015, with 97 observations. The data reached an all-time high of 22.964 RMB bn in Sep 2015 and a record low of 1.672 RMB bn in Dec 2003. China Generator & Generator Set: Product Inventory data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIA: Motor: Generator and Generator Set.

China Generator & Generator Set: YoY: Account Receivable data was reported at 12.139 % in Oct 2015. This records an increase from the previous number of 11.472 % for Sep 2015. China Generator & Generator Set: YoY: Account Receivable data is updated monthly, averaging 27.840 % from Jan 2006 (Median) to Oct 2015, with 89 observations. The data reached an all-time high of 87.380 % in Mar 2011 and a record low of -7.849 % in May 2013. China Generator & Generator Set: YoY: Account Receivable data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIA: Motor: Generator and Generator Set.