The values in this raster are unit-less scores ranging from 0 to 1 that represent normalized dollars per acre damage claims from antelope on Wyoming lands. This raster is one of 9 inputs used to calculate the "Normalized Importance Index."

Security Data Normalization Platform Market Outlook

According to our latest research, the global Security Data Normalization Platform market size reached USD 1.87 billion in 2024, driven by the rapid escalation of cyber threats and the growing complexity of enterprise security infrastructures. The market is expected to grow at a robust CAGR of 12.5% during the forecast period, reaching an estimated USD 5.42 billion by 2033. Growth is primarily fueled by the increasing adoption of advanced threat intelligence solutions, regulatory compliance demands, and the proliferation of connected devices across various industries.

The primary growth factor for the Security Data Normalization Platform market is the exponential rise in cyberattacks and security breaches across all sectors. Organizations are increasingly realizing the importance of normalizing diverse security data sources to enable efficient threat detection, incident response, and compliance management. As security environments become more complex with the integration of cloud, IoT, and hybrid infrastructures, the need for platforms that can aggregate, standardize, and correlate data from disparate sources has become paramount. This trend is particularly pronounced in sectors such as BFSI, healthcare, and government, where data sensitivity and regulatory requirements are highest. The growing sophistication of cyber threats has compelled organizations to invest in robust security data normalization platforms to ensure comprehensive visibility and proactive risk mitigation.

Another significant driver is the evolving regulatory landscape, which mandates stringent data protection and reporting standards. Regulations such as the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and various national cybersecurity frameworks have compelled organizations to enhance their security postures. Security data normalization platforms play a crucial role in facilitating compliance by providing unified and actionable insights from heterogeneous data sources. These platforms enable organizations to automate compliance reporting, streamline audit processes, and reduce the risk of penalties associated with non-compliance. The increasing focus on regulatory alignment is pushing both large enterprises and SMEs to adopt advanced normalization solutions as part of their broader security strategies.

The proliferation of digital transformation initiatives and the accelerated adoption of cloud-based solutions are further propelling market growth. As organizations migrate critical workloads to the cloud and embrace remote work models, the volume and variety of security data have surged dramatically. This shift has created new challenges in terms of data integration, normalization, and real-time analysis. Security data normalization platforms equipped with advanced analytics and machine learning capabilities are becoming indispensable for managing the scale and complexity of modern security environments. Vendors are responding to this demand by offering scalable, cloud-native solutions that can seamlessly integrate with existing security information and event management (SIEM) systems, threat intelligence platforms, and incident response tools.

From a regional perspective, North America continues to dominate the Security Data Normalization Platform market, accounting for the largest revenue share in 2024. The region’s leadership is attributed to the high concentration of technology-driven enterprises, robust cybersecurity regulations, and significant investments in advanced security infrastructure. Europe and Asia Pacific are also witnessing strong growth, driven by increasing digitalization, rising threat landscapes, and the adoption of stringent data protection laws. Emerging markets in Latin America and the Middle East & Africa are gradually catching up, supported by growing awareness of cybersecurity challenges and the need for standardized security data management solutions.

Component Analysis

Corporate Registry Data Normalization Market Outlook

According to our latest research, the global corporate registry data normalization market size reached USD 1.42 billion in 2024, reflecting a robust expansion driven by digital transformation and regulatory compliance demands across industries. The market is forecasted to grow at a CAGR of 13.6% from 2025 to 2033, reaching a projected value of USD 4.23 billion by 2033. This impressive growth is primarily attributed to the increasing need for accurate, standardized, and accessible corporate data to support compliance, risk management, and digital business processes in a rapidly evolving regulatory landscape.

One of the primary growth factors fueling the corporate registry data normalization market is the escalating global regulatory pressure on organizations to maintain clean, consistent, and up-to-date business entity data. With the proliferation of anti-money laundering (AML), know-your-customer (KYC), and data privacy regulations, companies are under immense scrutiny to ensure that their corporate records are accurate and accessible for audits and compliance checks. This regulatory environment has led to a surge in adoption of data normalization solutions, especially in sectors such as banking, financial services, insurance (BFSI), and government agencies. As organizations strive to minimize compliance risks and avoid hefty penalties, the demand for advanced software and services that can seamlessly normalize and harmonize disparate registry data sources continues to rise.

Another significant driver is the exponential growth in data volumes, fueled by digitalization, mergers and acquisitions, and global expansion of enterprises. As organizations integrate data from multiple jurisdictions, subsidiaries, and business units, they face massive challenges in consolidating and reconciling heterogeneous registry data formats. Data normalization solutions play a critical role in enabling seamless data integration, providing a single source of truth for corporate identity, and powering advanced analytics and automation initiatives. The rise of cloud-based platforms and AI-powered data normalization tools is further accelerating market growth by making these solutions more scalable, accessible, and cost-effective for organizations of all sizes.

Technological advancements are also shaping the trajectory of the corporate registry data normalization market. The integration of artificial intelligence, machine learning, and natural language processing into normalization tools is revolutionizing the way organizations cleanse, match, and enrich corporate data. These technologies enhance the accuracy, speed, and scalability of data normalization processes, enabling real-time updates and proactive risk management. Furthermore, the proliferation of API-driven architectures and interoperability standards is facilitating seamless connectivity between corporate registry databases and downstream business applications, fueling broader adoption across industries such as legal, healthcare, and IT & telecom.

From a regional perspective, North America continues to dominate the corporate registry data normalization market, driven by stringent regulatory frameworks, early adoption of advanced technologies, and a high concentration of multinational corporations. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid digitalization, increasing cross-border business activities, and evolving regulatory requirements. Europe remains a key market due to GDPR and other data-centric regulations, while Latin America and the Middle East & Africa are witnessing steady growth as local governments and enterprises invest in digital infrastructure and compliance modernization.

Component Analysis

The corporate registry data normalization market is segmented by component into software and services, each playing a pivotal role in the ecosystem. Software solutions are designed to automate and streamline the normalization process, offering functionalities such as data cleansing, deduplication, matching, and enrichment. These platforms often leverage advanced algorithms and machine learning to handle large volumes of complex, unstructured, and multilingual data, making them indispensable for organizations with global operations. The software segment is witnessing substantial investment in research and development, with vendors focusing on enhancing

Flight Data Normalization Platform Market Outlook

According to our latest research, the global flight data normalization platform market size reached USD 1.12 billion in 2024, exhibiting robust industry momentum. The market is projected to grow at a CAGR of 10.3% from 2025 to 2033, reaching an estimated value of USD 2.74 billion by 2033. This growth is primarily driven by the increasing adoption of advanced analytics in aviation, the rising need for operational efficiency, and the growing emphasis on regulatory compliance and safety enhancements across the aviation sector.

A key growth factor for the flight data normalization platform market is the rapid digital transformation within the aviation industry. Airlines, airports, and maintenance organizations are increasingly relying on digital platforms to aggregate, process, and normalize vast volumes of flight data generated by modern aircraft systems. The transition from legacy systems to integrated digital solutions is enabling real-time data analysis, predictive maintenance, and enhanced situational awareness. This shift is not only improving operational efficiency but also reducing downtime and maintenance costs, making it an essential strategy for airlines and operators aiming to remain competitive in a highly regulated environment.

Another significant driver fueling the expansion of the flight data normalization platform market is the stringent regulatory landscape governing aviation safety and compliance. Aviation authorities worldwide, such as the Federal Aviation Administration (FAA) and the European Union Aviation Safety Agency (EASA), are mandating the adoption of advanced flight data monitoring and normalization solutions to ensure adherence to safety protocols and to facilitate incident investigation. These regulatory requirements are compelling aviation stakeholders to invest in platforms that can seamlessly normalize and analyze data from diverse sources, thereby supporting proactive risk management and compliance reporting.

Additionally, the growing complexity of aircraft systems and the proliferation of connected devices in aviation have led to an exponential increase in the volume and variety of flight data. The need to harmonize disparate data formats and sources into a unified, actionable format is driving demand for sophisticated flight data normalization platforms. These platforms enable stakeholders to extract actionable insights from raw flight data, optimize flight operations, and support advanced analytics use cases such as fuel efficiency optimization, fleet management, and predictive maintenance. As the aviation industry continues to embrace data-driven decision-making, the demand for robust normalization solutions is expected to intensify.

Regionally, North America continues to dominate the flight data normalization platform market owing to the presence of major airlines, advanced aviation infrastructure, and early adoption of digital technologies. Europe is also witnessing significant growth, driven by stringent safety regulations and increasing investments in aviation digitization. Meanwhile, the Asia Pacific region is emerging as a lucrative market, fueled by rapid growth in air travel, expanding airline fleets, and government initiatives to modernize aviation infrastructure. Latin America and the Middle East & Africa are gradually embracing these platforms, supported by ongoing efforts to enhance aviation safety and operational efficiency.

Component Analysis

The component segment of the flight data normalization platform market is broadly categorized into software, hardware, and services. The software segment accounts for the largest share, driven by the increasing adoption of advanced analytics, machine learning, and artificial intelligence technologies for data processing and normalization. Software solutions are essential for aggregating raw flight data from multiple sources, standardizing formats, and providing actionable insights for decision-makers. With the rise of clou

Cloud EHR Data Normalization Platforms Market Outlook

According to our latest research, the global Cloud EHR Data Normalization Platforms market size in 2024 reached USD 1.2 billion, reflecting robust adoption across healthcare sectors worldwide. The market is experiencing a strong growth trajectory, with a compound annual growth rate (CAGR) of 16.5% projected from 2025 to 2033. By the end of 2033, the market is expected to attain a value of approximately USD 4.3 billion. This expansion is primarily fueled by the rising demand for integrated healthcare data systems, the proliferation of electronic health records (EHRs), and the critical need for seamless interoperability between disparate healthcare IT systems.

One of the principal growth factors driving the Cloud EHR Data Normalization Platforms market is the global healthcare sector's increasing focus on digitization and interoperability. As healthcare organizations strive to improve patient outcomes and operational efficiencies, the adoption of cloud-based EHR data normalization solutions has become essential. These platforms enable the harmonization of heterogeneous data sources, ensuring that clinical, administrative, and financial data are standardized across multiple systems. This standardization is critical for supporting advanced analytics, clinical decision support, and population health management initiatives. Moreover, the growing adoption of value-based care models is compelling healthcare providers to invest in technologies that facilitate accurate data aggregation and reporting, further propelling market growth.

Another significant growth catalyst is the rapid advancement in cloud computing technologies and the increasing availability of scalable, secure cloud infrastructure. Cloud EHR data normalization platforms leverage these technological advancements to offer healthcare organizations flexible deployment options, robust data security, and real-time access to normalized datasets. The scalability of cloud platforms allows healthcare providers to efficiently manage large volumes of data generated from diverse sources, including EHRs, laboratory systems, imaging centers, and wearable devices. Additionally, the integration of artificial intelligence and machine learning algorithms into these platforms enhances their ability to map, clean, and standardize data with greater accuracy and speed, resulting in improved clinical and operational insights.

Regulatory and compliance requirements are also playing a pivotal role in shaping the growth trajectory of the Cloud EHR Data Normalization Platforms market. Governments and regulatory bodies across major regions are mandating the adoption of interoperable health IT systems to improve patient safety, data privacy, and care coordination. Initiatives such as the 21st Century Cures Act in the United States and similar regulations in Europe and Asia Pacific are driving healthcare organizations to implement advanced data normalization solutions. These platforms help ensure compliance with data standards such as HL7, FHIR, and SNOMED CT, thereby reducing the risk of data silos and enhancing the continuity of care. As a result, the market is witnessing increased investments from both public and private stakeholders aiming to modernize healthcare IT infrastructure.

From a regional perspective, North America holds the largest share of the Cloud EHR Data Normalization Platforms market, driven by the presence of advanced healthcare infrastructure, high EHR adoption rates, and supportive regulatory frameworks. Europe follows closely, with significant investments in health IT modernization and interoperability initiatives. The Asia Pacific region is emerging as a high-growth market due to rising healthcare expenditures, expanding digital health initiatives, and increasing awareness about the benefits of data normalization. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by ongoing healthcare reforms and investments in digital health technologies. Collectively, these regional dynamics underscore the global momentum toward interoperable, cloud-based healthcare data ecosystems.

Component Analysis

The Cloud EHR Data Normalization Platforms market is segmented by component into software and services, each playing a distinct and critical role in driving the market's growth. Software solutions form the technological backbone of the market, enabling healthcare organizations to autom

Building Telemetry Normalization Market Outlook

According to our latest research, the global Building Telemetry Normalization market size reached USD 2.59 billion in 2024, reflecting the growing adoption of intelligent building management solutions worldwide. The market is experiencing robust expansion with a recorded CAGR of 13.2% from 2025 through 2033, and is forecasted to reach an impressive USD 7.93 billion by 2033. This strong growth trajectory is driven by increasing demand for energy-efficient infrastructure, the proliferation of smart city initiatives, and the need for seamless integration of building systems to enhance operational efficiency and sustainability.

One of the primary growth factors for the Building Telemetry Normalization market is the accelerating shift towards smart building ecosystems. As commercial, industrial, and residential structures become more interconnected, the volume and diversity of telemetry data generated by various building systems—such as HVAC, lighting, security, and energy management—have surged. Organizations are recognizing the value of normalizing this data to enable unified analytics, real-time monitoring, and automated decision-making. The need for interoperability among heterogeneous devices and platforms is compelling property owners and facility managers to invest in advanced telemetry normalization solutions, which streamline data collection, enhance system compatibility, and support predictive maintenance strategies.

Another significant driver is the increasing emphasis on sustainability and regulatory compliance. Governments and industry bodies worldwide are introducing stringent mandates for energy efficiency, carbon emission reduction, and occupant safety in built environments. Building telemetry normalization plays a crucial role in helping stakeholders aggregate, standardize, and analyze data from disparate sources, thereby enabling them to monitor compliance, optimize resource consumption, and generate actionable insights for green building certifications. The trend towards net-zero energy buildings and the integration of renewable energy sources is further propelling the adoption of telemetry normalization platforms, as they facilitate seamless data exchange and holistic performance benchmarking.

The rapid advancement of digital technologies, including IoT, edge computing, and artificial intelligence, is also transforming the landscape of the Building Telemetry Normalization market. Modern buildings are increasingly equipped with a multitude of connected sensors, controllers, and actuators, generating vast amounts of telemetry data. The normalization of this data is essential for unlocking its full potential, enabling advanced analytics, anomaly detection, and automated system optimization. The proliferation of cloud-based solutions and scalable architectures is making telemetry normalization more accessible and cost-effective, even for small and medium-sized enterprises. As a result, the market is witnessing heightened competition and innovation, with vendors focusing on user-friendly interfaces, robust security features, and seamless integration capabilities.

From a regional perspective, North America currently leads the Building Telemetry Normalization market, driven by widespread adoption of smart building technologies, substantial investments in infrastructure modernization, and a strong focus on sustainability. Europe follows closely, benefiting from progressive energy efficiency regulations and a mature building automation ecosystem. The Asia Pacific region is emerging as the fastest-growing market, fueled by rapid urbanization, government-led smart city projects, and increasing awareness of the benefits of intelligent building management. Latin America and the Middle East & Africa are also witnessing steady growth, supported by ongoing infrastructure development and rising demand for efficient facility operations.

Component Analysis

The Component segment of the Building Telemetry Normalization market is categorized into software, hard

Although the basic structure of logit-mixture models is well understood, important identification and normalization issues often get overlooked. This paper addresses issues related to the identification of parameters in logit-mixture models containing normally distributed error components associated with alternatives or nests of alternatives (normal error component logit mixture, or NECLM, models). NECLM models include special cases such as unrestricted, fixed covariance matrices; alternative-specific variances; nesting and cross-nesting structures; and some applications to panel data. A general framework is presented for determining which parameters are identified as well as what normalization to impose when specifying NECLM models. It is generally necessary to specify and estimate NECLM models at the levels, or structural, form. This precludes working with utility differences, which would otherwise greatly simplify the identification and normalization process. Our results show that identification is not always intuitive; for example, normalization issues present in logit-mixture models are not present in analogous probit models. To identify and properly normalize the NECLM, we introduce the equality condition, an addition to the standard order and rank conditions. The identifying conditions are worked through for a number of special cases, and our findings are demonstrated with empirical examples using both synthetic and real data.

Dataset Description

This data repository provides the underlying data and neural network training scripts associated with the manuscript titled "A Transformer Network for High-Throughput Materials Characterization with X-ray Photoelectron Spectroscopy" by Simperl and Werner published in the Journal of Applied Physics (https://doi.org/10.1063/5.0296600) (2025)

All data files are released under the Creative Commons Attribution 4.0 International (CC-BY) license, while all code files are distributed under the MIT license.

The repository contains simulated X-ray photoelectron spectroscopy (XPS) spectra stored as hdf5 files in the zipped (h5_files.zip) folder, which was generated using the software developed by the authors. The NIST Standard Reference Database 100 – Simulation of Electron Spectra for Surface Analysis (SESSA) is freely available at https://www.nist.gov/srd/nist-standard-reference-database-100.

The neural network architecture is implemented using the PyTorch Lightning framework and is fully available within the attached materials as Transformer_SimulatedSpectra.py contained in the python_scripts.zip.

The trained model and the list of materials for the train, test and validation sets are contained in the models.zip folder.

The repository contains all the data necessary to replot the figures from the manuscript. These data are available in the form of .csv files or .h5 files for the spectra. In addition, the repository also contains a Python script (Plot_Data_Manuscript.ipynb) which is contained in the python_scripts.zip file.

Context and methodology

The dataset and accompanying Python code files included in this repository were used to train a transformer-based neural network capable of directly inferring chemical concentrations from simulated survey X-ray photoelectron spectroscopy (XPS) spectra of bulk compounds.

The spectral dataset provided here represents the raw output from the SESSA software (version 2.2.2), prior to the normalization procedure described in the associated manuscript. This step of normalisation is of paramount importance for the effective training of the neural network.

The repository contains the Python scripts utilised to execute the spectral simulations and the neural network training on the Vienna Scientific Cluster (VSC5) which is part of the Austrian Scientific Computing Infrastructure (ASC). In order to obtain guidance on the proper configuration of the Command Line Interface (CLI) tools required for SESSA, users are advised to consult the official SESSA manual, which is available at the following address: https://nvlpubs.nist.gov/nistpubs/NSRDS/NIST.NSRDS.100-2024.pdf.

To run the neural network training we provided the requirements_nn_training.txt file that contains all the necessary python packages and version numbers. All other python scripts can be run locally with the python libraries listed in requirements_data_analysis.txt.

Data details

HDF5 (in zip folder): As described in the manuscript, we simulate X-ray photoelectron spectra for each of the 7,587 inorganic [1] and organic [2] materials in our dataset. To reflect realistic experimental conditions, each simulated spectrum was augmented by systematically varying parameters such as peak width, peak shift, and peak type—all configurable within the SESSA software—as well as by applying statistical Poisson noise to simulate varying signal-to-noise ratios. These modifications account for experimentally observed and material-specific spectral broadening, peak shifts, and detector-induced noise. Each material is represented by an individual HDF5 (.h5) file, named according to its chemical formula and mass density (in g/cm³). For example, the file for SiO2 with a density of 2.196 gcm-3 is named SiO2_2.196.h5. For more complex chemical formulas, such as Co(ClO4)2 with a density of 3.33 gcm-3, the file is named Co_ClO4_2_3.33.h5. Within each HDF5 file, the metadata for each spectrum is stored alongside a fixed energy axis and the corresponding intensity values. The spectral data are organized hierarchically by augmentation parameters in the following directory structure, e.g. for Ac_10.0.h5 we have SNR_0/WIDTH_0.3/SHIFT_-3.0/PEAK_gauss/Ac_10.0/. These files can be easily inspected with H5Web in Visual Studio Code or using h5py in Python or any other h5 interpretable program.

Session Files: The .ses files are SESSA specific input files that can be directly loaded into SESSA to specify certain input parameters for the initilization (ini), the geometry (geo) and the simulation parameters (sim_para) and are required by the python script Simulation_Script_VSC_json.py to run the simulation on the cluster.

Json Files: The two json files (MaterialsListVSC_gauss.json, MaterialsListVSC_lorentz.json) are used as the input files to the Python script Simulation_Script_VSC_json.py. These files contain all the material specific information for the SESSA simulation.

csv files: The csv files are used to generate the plots from the manuscript described in the section "Plotting Scripts".

npz files: The two .npz files (element_counts.npz, single_elements.npz) are python arrays that are needed by the Transformer_SimulatedSpectra.py script and contain the number of each single element in the dataset and an array of each single element present, respectively.

SESSA Simulation Script

There is one python file that sets the communication with SESSA:

• Simulation_Script_VSC_json.py: This script is the heart of the simulation as it controls the communication through the CLI with SESSA using the specified input paramters in the .json and .ses files together with external functions specified in VSC_function.py

Technical Details

Simulation_Script_VSC_json.py: This script uses the functions of the VSC_function.py script (therefore needs to be placed in the same directory as this script) and can be called with the following command:

python3 Simulation_Script_VSC_json.py MaterialsListVSC_gauss.json 0

It simulates the spectrum for the material at index 0 in the .json file and with the corresponding parameters specified in the .json file.

It is important that before running this script the following paths need to be specified:

• sessa_path: The path to their SESSA installation in sessa_path and the path to their session files in
• folder_path: The path to their .ses files. In this directory an output folder will be generated where all the output files, including the simulated spectra, are written to.

To run SESSA on a computing cluster it is important to have a working Xvfb (virtual frame buffer) or a similar tool available to which any graphical output from SESSA can be written to.

Neural Network Training Script

Before running the training script it is important to normalize the data such that the squared integral of the spectrum is 1 (as described in the manuscript) and shown in the code: normalize_spectra.py

For the neural network training we use the Transformer_SimulatedSpectra.py where the external functions used are specified in external_functions.py. This script contains the full description of the neural network architecture, the hyperparameter tuning and the Wandb logging.

In the models.zip folder the fully trained network final_trained_model.ckpt presented in the manuscript is available as well as the list of training, validation and testing materials (test_materials_list.pt, train_materials_list.pt, val_materials_list.pt) where the corresponding spectra are extracted from the hdf5 files. The file types .ckpt and .pt can be read in by using the pytorch specific load functions in Python, e.g.

torch.load(train_materials_list)

Technical Details

normalize_spectra.py: To run this script properly it is important to set up a python environment with the necessary libraries specified in the requirements_data_analysis.txt file. Then it can be called with

python3 normalize_spectra.py

where it is important to specify the path to the .h5 files containing the unnormalized spectra.

Transformer_SimulatedSpectra.py: To run this script properly on the cluster it is important to set up a python environment with the necessary libraries specified in the requirements_nn_training.txt file. This script also relies on external_functions.py, single_elements.npz and element_counts.npz (that should be placed in the same directory as the python script) file. This is important for creating the datasets for training, validation and testing and ensures that all the single elements appear in the testing set. You can call this script (on the cluster) within a slurm script to start the GPU training.

python3 Transformer_SimulatedSpectra.py

It is important that before running this script the following paths need to be specified:

• data_path: General path where all the data is stored
• neural_network_data: The location where you keep your normalized hdf5 files
• wandb_api_key: The api key to use

Telemetry Normalization for NetOps Market Outlook

According to our latest research, the global Telemetry Normalization for NetOps market size was valued at USD 1.87 billion in 2024 and is projected to reach USD 6.92 billion by 2033, expanding at a robust CAGR of 15.5% during the forecast period of 2025 to 2033. The primary growth factors for this market include the rising complexity of network environments, increased adoption of cloud-based services, and the escalating demand for real-time network visibility and performance analytics. Organizations across all sectors are increasingly recognizing the critical importance of telemetry normalization to ensure seamless NetOps processes, enhance network security, and support digital transformation initiatives.

One of the most significant growth drivers for the Telemetry Normalization for NetOps market is the exponential rise in network data volume and diversity. As enterprises deploy a mix of on-premises, cloud, and hybrid infrastructures, the variety and volume of telemetry data generated have surged. This data, originating from disparate network devices, applications, and endpoints, often comes in multiple formats and protocols, making it challenging to analyze and act upon in real-time. Telemetry normalization tools play a vital role in standardizing this data, enabling organizations to derive actionable insights, streamline network operations, and facilitate proactive issue detection. The increased adoption of IoT devices and the proliferation of edge computing further amplify the necessity for robust telemetry normalization solutions, as they introduce additional data sources and complexity into the network ecosystem.

Another key factor propelling the growth of the Telemetry Normalization for NetOps market is the heightened emphasis on network security and regulatory compliance. Cybersecurity threats are becoming more sophisticated, and organizations must ensure comprehensive visibility across their network infrastructure to detect anomalies and potential breaches. Telemetry normalization solutions enable security teams to aggregate and standardize data from various sources, enhancing the effectiveness of security analytics and incident response. Additionally, stringent regulatory requirements in sectors such as BFSI, healthcare, and government are driving the adoption of telemetry normalization to maintain compliance with data protection and privacy mandates. These solutions not only help in meeting compliance obligations but also in building a resilient and secure network environment.

The ongoing digital transformation and migration to cloud-native architectures are also significant contributors to market expansion. As organizations transition to cloud-based and hybrid environments, the need for unified network monitoring and management becomes paramount. Telemetry normalization tools facilitate seamless integration and correlation of data across diverse environments, supporting advanced use cases such as automated network performance optimization, predictive maintenance, and intelligent traffic management. The integration of artificial intelligence and machine learning with telemetry normalization is further enhancing the capabilities of NetOps teams, enabling predictive analytics and automated remediation. These advancements are expected to continue driving demand for telemetry normalization solutions across all industry verticals.

From a regional perspective, North America currently leads the Telemetry Normalization for NetOps market, driven by the presence of major technology providers, early adoption of advanced network management solutions, and a strong focus on digital innovation. Europe and Asia Pacific are also witnessing significant growth, fueled by increasing investments in IT infrastructure, rising cybersecurity concerns, and the rapid digitalization of enterprises. Latin America and the Middle East & Africa are emerging markets, with growing awareness of the benefits of telemetry normalization and increasing adoption in sectors such as BFSI, government, and telecommunications. The global market is expected to witness robust growth across all regions, with Asia Pacific projected to register the highest CAGR during the forecast period, owing to the rapid expansion of digital infrastructure and the proliferation of connected devices.

Security Data Normalization Platform Market Outlook

According to our latest research, the global Security Data Normalization Platform market size reached USD 1.48 billion in 2024, reflecting robust demand across industries for advanced security data management solutions. The market is registering a compound annual growth rate (CAGR) of 18.7% and is projected to achieve a value of USD 7.18 billion by 2033. The ongoing surge in sophisticated cyber threats and the increasing complexity of enterprise IT environments are among the primary growth factors driving the adoption of security data normalization platforms worldwide.

The growth of the Security Data Normalization Platform market is primarily fuelled by the exponential rise in cyberattacks and the proliferation of digital transformation initiatives across various sectors. As organizations accumulate vast amounts of security data from disparate sources, the need for platforms that can aggregate, normalize, and analyze this data has become critical. Enterprises are increasingly recognizing that traditional security information and event management (SIEM) systems fall short in handling the volume, velocity, and variety of data generated by modern IT infrastructures. Security data normalization platforms address this challenge by transforming heterogeneous data into a standardized format, enabling more effective threat detection, investigation, and response. This capability is particularly vital as organizations move toward zero trust architectures and require real-time insights to secure their digital assets.

Another significant growth driver for the Security Data Normalization Platform market is the evolving regulatory landscape. Governments and regulatory bodies worldwide are introducing stringent data protection and cybersecurity regulations, compelling organizations to enhance their security postures. Compliance requirements such as GDPR, HIPAA, and CCPA demand that organizations not only secure their data but also maintain comprehensive audit trails and reporting mechanisms. Security data normalization platforms facilitate compliance by providing unified, normalized logs and reports that simplify audit processes and ensure regulatory adherence. The market is also witnessing increased adoption in sectors such as BFSI, healthcare, and government, where data integrity and compliance are paramount.

Technological advancements are further accelerating the adoption of security data normalization platforms. The integration of artificial intelligence (AI) and machine learning (ML) capabilities into these platforms is enabling automated threat detection, anomaly identification, and predictive analytics. Cloud-based deployment models are gaining traction, offering scalability, flexibility, and cost-effectiveness to organizations of all sizes. As the threat landscape becomes more dynamic and sophisticated, organizations are prioritizing investments in advanced security data normalization solutions that can adapt to evolving risks and support proactive security strategies. The growing ecosystem of managed security service providers (MSSPs) is also contributing to market expansion by delivering normalization as a service to organizations with limited in-house expertise.

From a regional perspective, North America continues to dominate the Security Data Normalization Platform market, accounting for the largest share in 2024 due to the presence of major technology vendors, high cybersecurity awareness, and significant investments in digital infrastructure. Europe follows closely, driven by strict regulatory mandates and increasing cyber threats targeting critical sectors. The Asia Pacific region is emerging as a high-growth market, propelled by rapid digitization, expanding IT ecosystems, and rising cybercrime incidents. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as organizations in these regions accelerate their cybersecurity modernization efforts. The global outlook for the Security Data Normalization Platform market remains positive, with sustained demand expected across all major regions through 2033.

Component Analysis

The Security Data Normalization Platform market is segmented by component into software and services. Software solutions form the core of this market, providing the essential functionalities for data aggregation, normalization, enrichment, and integration with downs

Threat Exposure Score Normalization Market Outlook

According to our latest research, the Global Threat Exposure Score Normalization market size was valued at $1.2 billion in 2024 and is projected to reach $6.7 billion by 2033, expanding at a robust CAGR of 21.8% during the forecast period of 2025–2033. A primary factor propelling the growth of this market globally is the accelerated adoption of advanced cybersecurity frameworks across enterprises, driven by the increasing complexity and frequency of cyber threats. As organizations strive to quantify, normalize, and prioritize threat exposures across diverse digital environments, the demand for comprehensive threat exposure score normalization solutions is surging. This trend is further amplified by the regulatory mandates for risk management and compliance, compelling businesses to deploy sophisticated tools that can consolidate threat data, standardize risk metrics, and deliver actionable insights for security teams.

Regional Outlook

North America currently dominates the Threat Exposure Score Normalization market, accounting for the largest share of global revenue, estimated at approximately 38% in 2024. This leadership is attributed to the region's mature cybersecurity ecosystem, high digital adoption rates, and stringent regulatory frameworks such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). The presence of major technology vendors and a robust base of large enterprises, especially in sectors like BFSI, healthcare, and IT, further fuels the demand for threat exposure normalization solutions. Additionally, North America benefits from a well-established culture of security awareness and continuous investments in advanced threat intelligence platforms, making it the most lucrative market for both established players and innovative startups.

Asia Pacific is poised to be the fastest-growing region in the Threat Exposure Score Normalization market, expected to register an impressive CAGR of 24.5% from 2025 to 2033. This growth trajectory is underpinned by rapid digital transformation, expanding cloud adoption, and a surge in cyberattacks targeting critical infrastructure and financial institutions. Governments across countries such as China, India, and Japan are rolling out ambitious cybersecurity strategies and investing heavily in next-generation security technologies. The region's burgeoning SME sector is also increasingly prioritizing threat exposure management to safeguard digital assets, creating significant opportunities for solution providers to expand their footprint through localized offerings and strategic partnerships.

Emerging economies in Latin America and the Middle East & Africa are gradually recognizing the importance of threat exposure score normalization, driven by the growing sophistication of cyber threats and evolving regulatory landscapes. However, these regions face unique adoption challenges, including limited cybersecurity budgets, a shortage of skilled professionals, and fragmented IT infrastructures. Despite these hurdles, localized demand is rising as organizations in sectors such as government, healthcare, and retail seek to modernize their security operations and comply with new regulatory requirements. Policy reforms, coupled with capacity-building initiatives and international collaborations, are expected to accelerate market penetration in these regions over the coming years.

Report Scope

Dataset Title: Global Climate Change Indicators: A Comprehensive Dataset (2000-2024)

Subtitle: Tracking Temperature, Emissions, Sea Level Rise, and Environmental Trends Across Countries

Description: This dataset provides a comprehensive overview of key climate change indicators collected across different countries from the year 2000 to 2024. It includes 1000 data points capturing various environmental and socio-economic factors that reflect the global impact of climate change. The dataset focuses on average temperature, CO2 emissions, sea-level rise, rainfall patterns, and more, enabling users to analyze trends, correlations, and anomalies.

Fields Explanation: Year: The year in which the data was recorded, ranging from 2000 to 2024. It helps track historical trends in climate change and related variables over time.

Country: The country or region where the climate data was collected. The dataset includes a diverse set of countries from across the globe, representing different geographic regions and climates.

Average Temperature (°C): The average annual temperature recorded in each country, measured in degrees Celsius. This field allows for comparisons of temperature changes across regions and time.

CO2 Emissions (Metric Tons per Capita): The average amount of CO2 emissions per capita in metric tons, reflecting the country's contribution to greenhouse gases. This field is useful for analyzing the link between human activity and environmental changes.

Sea Level Rise (mm): The recorded annual sea-level rise in millimeters for coastal regions. This indicator reflects the global warming effect on melting glaciers and thermal expansion of seawater, critical for studying impacts on coastal populations.

Rainfall (mm): The total annual rainfall recorded in millimeters. This field highlights changing precipitation patterns, essential for understanding droughts, floods, and water resource management.

Population: The population of the country in the given year. Population data is important to normalize emissions or other per-capita analyses and understand human impact on the environment.

Renewable Energy (%): The percentage of total energy consumption in a country that comes from renewable energy sources (solar, wind, hydro, etc.). This metric is vital for assessing the progress made toward sustainable energy and reducing reliance on fossil fuels.

Extreme Weather Events: The number of extreme weather events recorded in each country, such as hurricanes, floods, wildfires, and droughts. Tracking these events helps correlate the increase in climate change with the frequency of natural disasters.

Forest Area (%): The percentage of the total land area of a country covered by forests. Forest cover is a critical indicator of biodiversity and carbon sequestration, with reductions often linked to deforestation and habitat loss.

Applications: Climate Research: This dataset is invaluable for researchers and analysts studying global climate change trends. By focusing on multiple indicators, users can assess the relationships between temperature changes, emissions, deforestation, and extreme weather patterns.

Environmental Policy Making: Governments and policy analysts can use this dataset to develop more effective climate policies based on historical and regional data. For example, countries can use emissions data to set realistic reduction goals in line with international agreements.

Renewable Energy Studies: Renewable energy data provides insights into how different regions are transitioning toward greener energy sources, offering a comparison between high-emission and low-emission countries.

Predictive Modeling: The data can be used for machine learning models to predict future climate scenarios, especially in relation to global temperature rise, sea-level changes, and extreme weather events.

Public Awareness & Education: This dataset is a useful educational tool for raising awareness about the impacts of climate change. Students and the general public can use it to explore real-world data and learn about the importance of sustainable development.

Room Type Normalization Engine Market Outlook

According to our latest research, the global Room Type Normalization Engine market size reached USD 1.17 billion in 2024, reflecting robust expansion in the hospitality and travel technology sectors. The market is anticipated to grow at a CAGR of 11.7% from 2025 to 2033, projecting a significant increase to USD 3.19 billion by 2033. This growth is primarily driven by the increasing adoption of digital solutions in the hospitality industry, the rising complexity of room inventory across distribution channels, and the demand for seamless guest experiences. As per our latest research, the Room Type Normalization Engine market is witnessing substantial traction as hotels, OTAs, and travel agencies seek to streamline room categorization and enhance booking accuracy.

One of the key growth factors propelling the Room Type Normalization Engine market is the rapid digital transformation within the hospitality and travel industries. The proliferation of online travel agencies (OTAs), meta-search engines, and direct booking platforms has resulted in a highly fragmented room inventory ecosystem. Each platform often uses its own nomenclature and classification for room types, which can lead to confusion, booking errors, and suboptimal user experiences. Room Type Normalization Engines address these challenges by leveraging advanced algorithms and machine learning to standardize room descriptions and categories across platforms. This not only ensures consistency and accuracy but also enhances operational efficiency for hotels, travel agencies, and technology providers, fueling market growth.

Another significant driver is the increasing focus on personalized guest experiences and the need for real-time data synchronization. As travelers demand more tailored options and transparent information, hotels and OTAs are compelled to present clear, accurate, and comparable room data. Room Type Normalization Engines play a critical role in aggregating and normalizing disparate data from multiple sources, enabling seamless integration with property management systems (PMS), booking engines, and channel managers. This integration empowers businesses to offer dynamic pricing, upselling opportunities, and improved inventory management, all of which contribute to higher revenue and guest satisfaction. The shift towards cloud-based solutions and the integration of artificial intelligence further amplify the market’s growth trajectory.

Furthermore, the growing complexity of global distribution systems (GDS) and the expansion of alternative accommodation providers, such as vacation rentals and serviced apartments, are intensifying the need for robust normalization solutions. With the rise of multi-property portfolios and cross-border travel, maintaining consistency in room categorization has become increasingly challenging. Room Type Normalization Engines enable stakeholders to overcome these hurdles by providing scalable, automated solutions that reduce manual intervention and minimize the risk of overbooking or miscommunication. This trend is particularly pronounced among large hotel chains and online travel platforms that operate across multiple regions, underscoring the strategic importance of normalization technologies in sustaining competitive advantage.

From a regional perspective, North America and Europe are leading the adoption of Room Type Normalization Engines, driven by the presence of major hospitality brands, advanced technology infrastructure, and a high concentration of OTAs. However, the Asia Pacific region is emerging as a high-growth market, fueled by rapid urbanization, increasing travel demand, and the proliferation of online booking platforms. Countries such as China, India, and Southeast Asian nations are witnessing a surge in hotel construction and digital transformation initiatives, creating ample opportunities for normalization engine providers. Meanwhile, the Middle East & Africa and Latin America are gradually embracing these solutions, propelled by tourism development and investments in smart hospitality technologies. The global market outlook remains highly positive, with sustained growth expected across all major regions through 2033.

Component Analysis

The Room Type Normalization Engine market is segmented by component into software and services, each playing a pivotal role in the overall ecosystem. The software segment comprises the core normalization engines, which utiliz

IMPORTANT! PLEASE READ DISCLAIMER BEFORE USING DATA. This dataset backcasts estimated modeled savings for a subset of 2007-2012 completed projects in the Home Performance with ENERGY STAR® Program against normalized savings calculated by an open source energy efficiency meter available at https://www.openee.io/. Open source code uses utility-grade metered consumption to weather-normalize the pre- and post-consumption data using standard methods with no discretionary independent variables. The open source energy efficiency meter allows private companies, utilities, and regulators to calculate energy savings from energy efficiency retrofits with increased confidence and replicability of results. This dataset is intended to lay a foundation for future innovation and deployment of the open source energy efficiency meter across the residential energy sector, and to help inform stakeholders interested in pay for performance programs, where providers are paid for realizing measurable weather-normalized results. To download the open source code, please visit the website at https://github.com/openeemeter/eemeter/releases

D I S C L A I M E R: Normalized Savings using open source OEE meter. Several data elements, including, Evaluated Annual Elecric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), and Post-retrofit Usage Gas (MMBtu) are direct outputs from the open source OEE meter.

Home Performance with ENERGY STAR® Estimated Savings. Several data elements, including, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, and Estimated First Year Energy Savings represent contractor-reported savings derived from energy modeling software calculations and not actual realized energy savings. The accuracy of the Estimated Annual kWh Savings and Estimated Annual MMBtu Savings for projects has been evaluated by an independent third party. The results of the Home Performance with ENERGY STAR impact analysis indicate that, on average, actual savings amount to 35 percent of the Estimated Annual kWh Savings and 65 percent of the Estimated Annual MMBtu Savings. For more information, please refer to the Evaluation Report published on NYSERDA’s website at: http://www.nyserda.ny.gov/-/media/Files/Publications/PPSER/Program-Evaluation/2012ContractorReports/2012-HPwES-Impact-Report-with-Appendices.pdf.

This dataset includes the following data points for a subset of projects completed in 2007-2012: Contractor ID, Project County, Project City, Project ZIP, Climate Zone, Weather Station, Weather Station-Normalization, Project Completion Date, Customer Type, Size of Home, Volume of Home, Number of Units, Year Home Built, Total Project Cost, Contractor Incentive, Total Incentives, Amount Financed through Program, Estimated Annual kWh Savings, Estimated Annual MMBtu Savings, Estimated First Year Energy Savings, Evaluated Annual Electric Savings (kWh), Evaluated Annual Gas Savings (MMBtu), Pre-retrofit Baseline Electric (kWh), Pre-retrofit Baseline Gas (MMBtu), Post-retrofit Usage Electric (kWh), Post-retrofit Usage Gas (MMBtu), Central Hudson, Consolidated Edison, LIPA, National Grid, National Fuel Gas, New York State Electric and Gas, Orange and Rockland, Rochester Gas and Electric.

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

Content Normalization for Travel Market Outlook

As per our latest research, the global Content Normalization for Travel market size in 2024 stood at USD 2.93 billion, driven by rapid digital transformation and the increasing need for consistent, high-quality content across travel platforms. The market is poised to expand at a robust CAGR of 15.2% from 2025 to 2033, reaching a projected value of USD 10.41 billion by the end of the forecast period. The surge in online travel bookings, the proliferation of user-generated content, and the imperative to deliver seamless omnichannel experiences are among the primary growth drivers fueling this market’s momentum.

One of the most significant growth factors for the Content Normalization for Travel market is the exponential rise in digital content generated by both travel service providers and consumers. As travelers increasingly rely on online platforms to research, compare, and book travel services, the need for accurate, uniform, and easily digestible content has become paramount. Content normalization ensures that data from myriad sources—such as airlines, hotels, car rentals, and tour operators—is standardized, eliminating inconsistencies and redundancies. This enables travel companies to present cohesive information across various channels, enhancing user trust and satisfaction. Moreover, the integration of artificial intelligence and machine learning technologies is further streamlining the normalization process, allowing for real-time updates and superior content quality, which directly impacts conversion rates and customer loyalty.

Another key factor driving market growth is the increasing adoption of cloud-based solutions by travel enterprises. Cloud deployment not only provides scalability and cost efficiency but also facilitates seamless integration with other digital tools and platforms. As travel businesses expand their digital footprints to cater to a global audience, the ability to quickly adapt content for different markets, languages, and regulatory requirements becomes essential. Content normalization platforms equipped with advanced localization and translation features are witnessing heightened demand, especially among multinational travel companies. Additionally, the rise of personalized travel experiences, powered by big data and analytics, underscores the need for normalized content that can be dynamically tailored to individual preferences, further fueling market expansion.

The evolving regulatory landscape and growing focus on data governance are also contributing to the growth of the Content Normalization for Travel market. With stricter regulations around data privacy and consumer protection, travel companies are under pressure to ensure that the content they display is not only accurate but also compliant with regional and international standards. Content normalization platforms help organizations maintain compliance by systematically verifying and updating information, reducing the risk of misinformation and legal repercussions. This is particularly relevant in the context of cross-border travel, where discrepancies in content can lead to confusion, operational inefficiencies, and reputational damage.

From a regional perspective, North America continues to dominate the Content Normalization for Travel market, driven by the presence of major technology providers, high internet penetration, and a mature travel ecosystem. However, Asia Pacific is emerging as the fastest-growing region, propelled by the rapid digitalization of the travel industry, increasing disposable incomes, and a burgeoning middle-class population eager to explore new destinations. European markets, characterized by a diverse linguistic landscape and a strong emphasis on compliance, are also witnessing substantial investments in content normalization technologies. Latin America and the Middle East & Africa, while still nascent, present significant untapped potential as travel infrastructure and digital adoption improve.

Component Analysis

The Content Normalization for Travel market is segmented by component into Software and Services, each playing a pivotal role in addressing the unique challenges faced by the travel industry. Software solutions are at the core of content normalization, providing the algorithms and platforms necessary to aggregate, cleanse, and standardize data from multiple sources. These software solutions are increas

The intestinal mucosal development of piglets (Sus scrofa) during the weaning stage is important to their disease susceptibility and later growth. Quantitative real-time PCR (RT-qPCR) is commonly used to screen for differentially expressed genes and, for accurate results, proper reference housekeeping genes are essential. Here we assessed the mRNA expression of 18 well-known candidate reference genes at different parts of the gastrointestinal tract (GIT) of piglets during the weaning process by RT-qPCR assay. GeNorm analysis revealed that B2M/HMBS/HPRT1 were the three most stable reference genes and GAPDH was the least stable gene in the duodenum, jejunum, ileum, colon, and whole GIT. BestKeeper analysis found that B2M/HMBS/PGK11, HMBS/B2M/HPRT1, B2M/HMBS/HSPCB, B2M/HPRT1/HMBS, and B2M/HMBS/HPRT1 were the most stable genes in the duodenum, jejunum, ileum, colon, and whole GIT, respectively, whereas GAPDH, B-actin, and 18S rRNA were the least stable genes at different parts of the GIT. To confirm the crucial role of appropriate housekeeping genes in obtaining reliable results, we analyzed the expression of ALP using each of the 18 reference genes to normalize the RT-qPCR data. We found that the expression levels of ALP normalized using the most stable reference genes (B2M/HMBS/HPRT1) differed greatly from the expression levels obtained when the data were normalized using the least stable genes (GAPDH, B-actin, and 18S). We concluded that B2M/HMBS/HPRT1 were the optimal reference genes for gene expression analysis by RT-qPCR in the intestinal mucosal development stages of piglets at weaning. Our findings provide a set of porcine housekeeping reference genes for studies of mRNA expression in different parts of the pig intestine.

Title:

Amazon Financial Dataset: R&D, Marketing, Campaigns, and Profit

Description:

This dataset provides fictional yet insightful financial data of Amazon's business activities across all 50 states of the USA. It is specifically designed to help students, researchers, and practitioners perform various data analysis tasks such as log normalization, Gaussian distribution visualization, and financial performance comparisons.

Each row represents a state and contains the following columns:
- R&D Amount (in $): The investment made in research and development.
- Marketing Amount (in $): The expenditure on marketing activities.
- Campaign Amount (in $): The costs associated with promotional campaigns.
- State: The state in which the data is recorded.
- Profit (in $): The net profit generated from the state.

Additional features include log-normalized and Z-score transformations for advanced analysis.

Use Cases:

This dataset is ideal for practicing:
1. Log Transformation: Normalize skewed data for better modeling and analysis.
2. Statistical Analysis: Explore relationships between financial investments and profit.
3. Visualization: Create compelling graphs such as Gaussian distributions and standard normal distributions.
4. Machine Learning Projects: Build regression models to predict profits based on R&D and marketing spend.

File Information:

• File Format: Excel (.xlsx)
• Number of Records: 50 (one for each state of the USA)
• Columns: 5 primary financial columns and additional preprocessed columns for normalization and Z-scores.

Important Note:

This dataset is synthetically generated and is not based on actual Amazon financial records. It is created solely for educational and practice purposes.

Tags:

• Financial Analysis
• Data Visualization
• Machine Learning
• Statistical Analysis
• Educational Dataset

Residential Home Energy Efficiency

Evaluated Meter Project Data 2007-2012

By State of New York [source]

About this dataset

This dataset provides energy efficiency meter evaluated data from 2007-2012 for residential existing homes (one to four units) in New York State. It includes the following data points: Project County, Project City, Project ZIP, Climate Zone, Weather Station, Weather Station-Normalization, Project Completion Date, Customer Type, Size of Home, Volume of Home, Number of Units .Year Home Built , Total Project Cost , Contractor Incentive , Total Incentives , Amount Financed through Program , Estimated Annual Electric Savings (kWh), Estimated Annual Gas Savings (MMBtu), Estimated First Year Energy Bill Savings ($) Baseline Electric (kWh), Baseline Gas (MMBtu), Reporting Electric (kWh), Reporting Gas (MMBtu ),Evaluated Annual Electric Savings( kWh ), Evaluated Annual gas Savings( MMBTU )Central Hudson LIPA National Fuel gas NYSEG Orange and Rockland Rochester Gas and electric Location 1. This dataset backcasts estimated modeled savings for a subset of 2007 -2012 completed projects in the Home Performance with ENERGY STARprogram against normalized savings calculated by an open source energy efficiency meter. The open source code uses utility grade metered consumption to weather normalize the pre -and post consumption data using standard methods with no discretionary independent variables. It is intended to lay a foundation for future innovation and deployment of the open source energy efficiency meter across the residential energy sector and help inform stakeholders interested in Pay For Performance programs where providers are paid for realizing measurable weather normalized results. Please make sure you read the Disclaimer included before using this data; it contains important information about evaluating savings from contractor reported modeling estimates as well as evaluating Normalized Savigns using Open Source OEE meter

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How to use the dataset

• Last updated information: The last update for this dataset was 2019-11-15

• Data Elements Overview:This dataset includes a variety of data points that provide valuable insights into residential energy efficiency projects undertaken between 2007 TO 2012 in New York State; including project ID, county, city zip code, climate zone, weather station used for normalization methods, completion date customer type size and volume of home number of units year home was built total project cost contractor incentive total incentives amount financed through program etc.

• Definitions Overview: There are several acronyms included in this datasets such as Central Hudson (a utility company), LIPA (the Long Island Power Authority), National Fuel Gas (National Fuel Gas Utility Company), NYSEG (New York State Electric & Gas Utility Company) and Rochester Gas & Electric (Rochester Gas & Electric Utility Company). Additionally “Climate Zone” are numbered 1 through 5 representing regions from coolest north/northwest regions to warmest south/southeast regions across New York; these correspond with Warm-Humid, Marine VBZc&De2VBladium Marine Subtropical HotSummer ColdWinter ColdSummer Moderate Winter regions respectively. A Weather Station is used for normalizing Savings Data which a location like described Niagara Falls International Airport that obtains historical average temperature values from various temperatures sources . Weather Stations Normalization compares day-of vs seasonal temperature difference outside homes against model prediction retrofit reduction predictions inside home without weather normalizing watt reduction products can be over or under estimated depending on current season vs expected seasons which this model accounts The estimated annual electric savings are calculated using factors such as pre-retrofit baseline electric kWh post-retrofit usage electric kWh evaluated annual electric savings calculated by open source library software installed by customers neighborhood ? measured GHG emission reductions determined with assumptions provided input device SDK so on life cycle greenhouse gas emission reductions also tracked documented impact studies have been conducted verify conclusion accuracy projected values reported nyserda industry rebate programs benchmarking standardized meter data allowing future compare patterns? measurements document capture utilities grid management initiated demand response events companies target focus market . Moving forward Total Project Cost is figure analyzed depending estimates provided

Research Ideas

• Developing an in...

Reverse transcription quantitative real-time PCR (RT-qPCR) is a common way to study gene regulation at the transcriptional level due to its sensibility and specificity, but it needs appropriate reference genes to normalize data. Ananas comosus var. bracteatus, with white-green chimeric leaves, is an important pantropical ornamental plant. Up to date, no reference genes have been evaluated in Ananas comosus var. bracteatus. In this work, we used five common statistics tools (geNorm, NormFinder, BestKeeper, ΔCt method, RefFinder) to evaluate 10 candidate reference genes. The results showed that Unigene.16454 and Unigene.16459 were the optimal reference genes for different tissues, Unigene.16454 and zinc finger ran-binding domain-containing protein 2 (ZRANB2) for chimeric leaf at different developmental stages, isocitrate dehydrogenase NADP and triacylglycerol lipase SDP1-like (SDP) for seedlings under different hormone treatments. The comprehensive results showed IDH, pentatricopeptide repeat-containing protein (PPRC), Unigene.16454, and caffeoyl-CoA O methyltransferase 5-like (CCOAOMT) are the top-ranked stable genes across all the samples. The stability of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was the least during all experiments. Furthermore, the reliability of recommended reference gene was validated by the detection of porphobilinogen deaminase (HEMC) expression levels in chimeric leaves. Overall, this study provides appropriate reference genes under three specific experimental conditions and will be useful for future research on spatial and temporal regulation of gene expression and multiple hormone regulation pathways in Ananas comosus var. bracteatus.

BackgroundQuantitative RT-PCR is the method of choice for studying, with both sensitivity and accuracy, the expression of genes. A reliable normalization of the data, using several reference genes, is critical for an accurate quantification of gene expression. Here, we propose a set of reference genes, of the phytopathogenic bacteria Dickeya dadantii and Pectobacterium atrosepticum, which are stable in a wide range of growth conditions. ResultsWe extracted, from a D. dadantii micro-array transcript profile dataset comprising thirty-two different growth conditions, an initial set of 49 expressed genes with very low variation in gene expression. Out of these, we retained 10 genes representing different functional categories, different levels of expression (low, medium, and high) and with no systematic variation in expression correlating with growth conditions. We measured the expression of these reference gene candidates using quantitative RT-PCR in 50 different experimental conditions, mimicking the environment encountered by the bacteria in their host and directly during the infection process in planta. The two most stable genes (ABF-0017965 (lpxC) and ABF-0020529 (yafS) were successfully used for normalization of RT-qPCR data. Finally, we demonstrated that the ortholog of lpxC and yafS in Pectobacterium atrosepticum also showed stable expression in diverse growth conditions. ConclusionsWe have identified at least two genes, lpxC (ABF-0017965) and yafS (ABF-0020509), whose expressions are stable in a wide range of growth conditions and during infection. Thus, these genes are considered suitable for use as reference genes for the normalization of real-time RT-qPCR data of the two main pectinolytic phytopathogenic bacteria D. dadantii and P. atrosepticum and, probably, of other Enterobacteriaceae. Moreover, we defined general criteria to select good reference genes in bacteria.