Abstract

The intention is to collect data for the calendar year 2009 (or the nearest year for which each business keeps its accounts. The survey is considered a one-off survey, although for accurate NAs, such a survey should be conducted at least every five years to enable regular updating of the ratios, etc., needed to adjust the ongoing indicator data (mainly VAGST) to NA concepts. The questionnaire will be drafted by FSD, largely following the previous BAS, updated to current accounting terminology where necessary. The questionnaire will be pilot tested, using some accountants who are likely to complete a number of the forms on behalf of their business clients, and a small sample of businesses. Consultations will also include Ministry of Finance, Ministry of Commerce, Industry and Labour, Central Bank of Samoa (CBS), Samoa Tourism Authority, Chamber of Commerce, and other business associations (hotels, retail, etc.).

The questionnaire will collect a number of items of information about the business ownership, locations at which it operates and each establishment for which detailed data can be provided (in the case of complex businesses), contact information, and other general information needed to clearly identify each unique business. The main body of the questionnaire will collect data on income and expenses, to enable value added to be derived accurately. The questionnaire will also collect data on capital formation, and will contain supplementary pages for relevant industries to collect volume of production data for selected commodities and to collect information to enable an estimate of value added generated by key tourism activities.

The principal user of the data will be FSD which will incorporate the survey data into benchmarks for the NA, mainly on the current published production measure of GDP. The information on capital formation and other relevant data will also be incorporated into the experimental estimates of expenditure on GDP. The supplementary data on volumes of production will be used by FSD to redevelop the industrial production index which has recently been transferred under the SBS from the CBS. The general information about the business ownership, etc., will be used to update the Business Register.

Outputs will be produced in a number of formats, including a printed report containing descriptive information of the survey design, data tables, and analysis of the results. The report will also be made available on the SBS website in “.pdf” format, and the tables will be available on the SBS website in excel tables. Data by region may also be produced, although at a higher level of aggregation than the national data. All data will be fully confidentialised, to protect the anonymity of all respondents. Consideration may also be made to provide, for selected analytical users, confidentialised unit record files (CURFs).

A high level of accuracy is needed because the principal purpose of the survey is to develop revised benchmarks for the NA. The initial plan was that the survey will be conducted as a stratified sample survey, with full enumeration of large establishments and a sample of the remainder.

Geographic coverage

National Coverage

Analysis unit

The main statistical unit to be used for the survey is the establishment. For simple businesses that undertake a single activity at a single location there is a one-to-one relationship between the establishment and the enterprise. For large and complex enterprises, however, it is desirable to separate each activity of an enterprise into establishments to provide the most detailed information possible for industrial analysis. The business register will need to be developed in such a way that records the links between establishments and their parent enterprises. The business register will be created from administrative records and may not have enough information to recognize all establishments of complex enterprises. Large businesses will be contacted prior to the survey post-out to determine if they have separate establishments. If so, the extended structure of the enterprise will be recorded on the business register and a questionnaire will be sent to the enterprise to be completed for each establishment.

SBS has decided to follow the New Zealand simplified version of its statistical units model for the 2009 BAS. Future surveys may consider location units and enterprise groups if they are found to be useful for statistical collections.

It should be noted that while establishment data may enable the derivation of detailed benchmark accounts, it may be necessary to aggregate up to enterprise level data for the benchmarks if the ongoing data used to extrapolate the benchmark forward (mainly VAGST) are only available at the enterprise level.

Universe

The BAS's covered all employing units, and excluded small non-employing units such as the market sellers. The surveys also excluded central government agencies engaged in public administration (ministries, public education and health, etc.). It only covers businesses that pay the VAGST. (Threshold SAT$75,000 and upwards).

Kind of data

Sample survey data [ssd]

Sampling procedure

-Total Sample Size was 1240 -Out of the 1240, 902 successfully completed the questionnaire. -The other remaining 338 either never responded or were omitted (some businesses were ommitted from the sample as they do not meet the requirement to be surveyed) -Selection was all employing units paying VAGST (Threshold SAT $75,000 upwards)

WILL CONFIRM LATER!!

OSO LE MEA E LE FAASA...AEA :-)

Mode of data collection

Mail Questionnaire [mail]

Research instrument

1. General instructions, authority for the survey, etc;
2. Business demography information on ownership, contact details, structure, etc.;
3. Employment;
4. Income;
5. Expenses;
6. Inventories;
7. Profit or loss and reconciliation to business accounts' profit and loss;
8. Fixed assets - purchases, disposals, book values
9. Thank you and signature of respondent.

Supplementary Pages Additional pages have been prepared to collect data for a limited range of industries. 1.Production data. To rebase and redevelop the Industrial Production Index (IPI), it is intended to collect volume of production information from a selection of large manufacturing businesses. The selection of businesses and products is critical to the usefulness of the IPI. The products must be homogeneous, and be of enough importance to the economy to justify collecting the data. Significance criteria should be established for the selection of products to include in the IPI, and the 2009 BAS provides an opportunity to collect benchmark data for a range of products known to be significant (based on information in the existing IPI, CPI weights, export data, etc.) as well as open questions for respondents to provide information on other significant products. 2.Tourism. There is a strong demand for estimates of tourism value added. To estimate tourism value added using the international standard Tourism Satellite Account methodology requires the use of an input-output table, which is beyond the capacity of SBS at present. However, some indicative estimates of the main parts of the economy influenced by tourism can be derived if the necessary data are collected. Tourism is a demand concept, based on defining tourists (the international standard includes both international and domestic tourists), what products are characteristically purchased by tourists, and which industries supply those products. Some questions targeted at those industries that have significant involvement with tourists (hotels, restaurants, transport and tour operators, vehicle hire, etc.), on how much of their income is sourced from tourism would provide valuable indicators of the size of the direct impact of tourism.

Cleaning operations

Partial imputation was done at the time of receipt of questionnaires, after follow-up procedures to obtain fully completed questionnaires have been followed. Imputation followed a process, i.e., apply ratios from responding units in the imputation cell to the partial data that was supplied. Procedures were established during the editing stage (a) to preserve the integrity of the questionnaires as supplied by respondents, and (b) to record all changes made to the questionnaires during editing. If SBS staff writes on the form, for example, this should only be done in red pen, to distinguish the alterations from the original information.

Additional edit checks were developed, including checking against external data at enterprise/establishment level. External data to be checked against include VAGST and SNPF for turnover and purchases, and salaries and wages and employment data respectively. Editing and imputation processes were undertaken by FSD using Excel.

Sampling error estimates

NOT APPLICABLE!!

SoilExcel workflow, a tool designed to optimize soil data analysis. It covers data preparation, statistical analysis methods, and result visualization. SoilExcel integrates various environmental data types and applies advanced techniques to enhance accuracy in soil studies. The results demonstrate its effectiveness in interpreting complex data, aiding decision-making in environmental management projects. Background Understanding the intricate relationships and patterns within soil samples is crucial for various environmental and agricultural applications. Principal Component Analysis (PCA) serves as a powerful tool in unraveling the complexity of multivariate soil datasets. Soil datasets often consist of numerous variables representing diverse physicochemical properties, making PCA an invaluable method for: ∙Dimensionality Reduction: Simplifying the analysis without compromising data integrity by reducing the dimensionality of large soil datasets. ∙Identification of Dominant Patterns: Revealing dominant patterns or trends within the data, providing insights into key factors contributing to overall variability. ∙Exploration of Variable Interactions: Enabling the exploration of complex interactions between different soil attributes, enhancing understanding of their relationships. ∙Interpretability of Data Variance: Clarifying how much variance is explained by each principal component, aiding in discerning the significance of different components and variables. ∙Visualization of Data Structure: Facilitating intuitive comprehension of data structure through plots such as scatter plots of principal components, helping identify clusters, trends, and outliers. ∙Decision Support for Subsequent Analyses: Providing a foundation for subsequent analyses by guiding decision-making, whether in identifying influential variables, understanding data patterns, or selecting components for further modeling. Introduction The motivation behind this workflow is rooted in the imperative need to conduct a thorough analysis of a diverse soil dataset, characterized by an array of physicochemical variables. Comprising multiple rows, each representing distinct soil samples, the dataset encompasses variables such as percentage of coarse sands, percentage of organic matter, hydrophobicity, and others. The intricacies of this dataset demand a strategic approach to preprocessing, analysis, and visualization. To lay the groundwork, the workflow begins with the transformation of an initial Excel file into a CSV format, ensuring improved compatibility and ease of use throughout subsequent analyses. Furthermore, the workflow is designed to empower users in the selection of relevant variables, a task facilitated by user-defined parameters. This flexibility allows for a focused and tailored dataset, essential for meaningful analysis. Acknowledging the inherent challenges of missing data, the workflow offers options for data quality improvement, including optional interpolation of missing values or the removal of rows containing such values. Standardizing the dataset and specifying the target variable are crucial, establishing a robust foundation for subsequent statistical analyses. Incorporating PCA offers a sophisticated approach, enabling users to explore inherent patterns and structures within the data. The adaptability of PCA allows users to customize the analysis by specifying the number of components or desired variance. The workflow concludes with practical graphical representations, including covariance and correlation matrices, a scree plot, and a scatter plot, offering users valuable visual insights into the complexities of the soil dataset. Aims The primary objectives of this workflow are tailored to address specific challenges and goals inherent in the analysis of diverse soil samples: ∙Data transformation: Efficiently convert the initial Excel file into a CSV format to enhance compatibility and ease of use. ∙Variable selection: Empower users to extract relevant variables based on user-defined parameters, facilitating a focused and tailored dataset. ∙Data quality improvement: Provide options for interpolation or removal of missing values to ensure dataset integrity for downstream analyses. ∙Standardization and target specification: Standardize the dataset values and designate the target variable, laying the groundwork for subsequent statistical analyses. ∙PCA: Conduct PCA with flexibility, allowing users to specify the number of components or desired variance for a comprehensive understanding of data variance and patterns. ∙Graphical representations: Generate visual outputs, including covariance and correlation matrices, a scree plot, and a scatter plot, enhancing the interpretability of the soil dataset. Scientific questions This workflow addresses critical scientific questions related to soil analysis: ∙Variable importance: Identify variables contributing significantly to principal components through the covariance matrix and PCA. ∙Data structure: Explore correlations between variables and gain insights from the correlation matrix. ∙Optimal component number: Determine the optimal number of principal components using the scree plot for effective representation of data variance. ∙Target-related patterns: Analyze how selected principal components correlate with the target variable in the scatter plot, revealing patterns based on target variable values.

The global spreadsheet editor market is experiencing robust growth, driven by the increasing digitization of businesses and the rising demand for efficient data management solutions across various industries. The market, estimated at $50 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 10% from 2025 to 2033, reaching approximately $130 billion by 2033. This growth is fueled by several factors, including the expanding adoption of cloud-based spreadsheet editors offering enhanced collaboration and accessibility features, the increasing need for data analysis and visualization tools within organizations of all sizes (Large Enterprises and SMBs), and the integration of spreadsheet software with other business applications through APIs offered by companies like Zapier. The free segment holds a significant market share, particularly among individual users and small businesses, while the paid segment, which offers advanced features and support, contributes substantially to overall market revenue. Key players such as Microsoft, Google, and LibreOffice dominate the market, but emerging players are continually introducing innovative features and pricing models to gain a competitive edge. Significant regional variations exist. North America currently holds the largest market share due to high technology adoption and a well-established digital infrastructure, followed by Europe and Asia-Pacific. However, the Asia-Pacific region is anticipated to experience the fastest growth in the forecast period due to rapid technological advancements and increasing internet penetration across countries like India and China. Growth restraints include security concerns related to cloud storage, the cost of implementation and training for complex software, and the increasing competition from specialized data analysis tools. Despite these challenges, the consistent demand for streamlined data management across diverse sectors ensures the continued expansion of the spreadsheet editor market in the coming years. The market’s evolution reflects a shift towards user-friendly, feature-rich, and collaborative solutions that are seamlessly integrated into broader business ecosystems.

Big Data Technology Market Outlook

The global big data technology market size was valued at approximately $162 billion in 2023 and is projected to reach around $471 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 12.6% during the forecast period. The growth of this market is primarily driven by the increasing demand for data analytics and insights to enhance business operations, coupled with advancements in AI and machine learning technologies.

One of the principal growth factors of the big data technology market is the rapid digital transformation across various industries. Businesses are increasingly recognizing the value of data-driven decision-making processes, leading to the widespread adoption of big data analytics. Additionally, the proliferation of smart devices and the Internet of Things (IoT) has led to an exponential increase in data generation, necessitating robust big data solutions to analyze and extract meaningful insights. Organizations are leveraging big data to streamline operations, improve customer engagement, and gain a competitive edge.

Another significant growth driver is the advent of advanced technologies like artificial intelligence (AI) and machine learning (ML). These technologies are being integrated into big data platforms to enhance predictive analytics and real-time decision-making capabilities. AI and ML algorithms excel at identifying patterns within large datasets, which can be invaluable for predictive maintenance in manufacturing, fraud detection in banking, and personalized marketing in retail. The combination of big data with AI and ML is enabling organizations to unlock new revenue streams, optimize resource utilization, and improve operational efficiency.

Moreover, regulatory requirements and data privacy concerns are pushing organizations to adopt big data technologies. Governments worldwide are implementing stringent data protection regulations, like the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States. These regulations necessitate robust data management and analytics solutions to ensure compliance and avoid hefty fines. As a result, organizations are investing heavily in big data platforms that offer secure and compliant data handling capabilities.

As organizations continue to navigate the complexities of data management, the role of Big Data Professional Services becomes increasingly critical. These services offer specialized expertise in implementing and managing big data solutions, ensuring that businesses can effectively harness the power of their data. Professional services encompass a range of offerings, including consulting, system integration, and managed services, tailored to meet the unique needs of each organization. By leveraging the knowledge and experience of big data professionals, companies can optimize their data strategies, streamline operations, and achieve their business objectives more efficiently. The demand for these services is driven by the growing complexity of big data ecosystems and the need for seamless integration with existing IT infrastructure.

Regionally, North America holds a dominant position in the big data technology market, primarily due to the early adoption of advanced technologies and the presence of key market players. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by increasing digitalization, the rapid growth of industries such as e-commerce and telecommunications, and supportive government initiatives aimed at fostering technological innovation.

Component Analysis

The big data technology market is segmented into software, hardware, and services. The software segment encompasses data management software, analytics software, and data visualization tools, among others. This segment is expected to witness substantial growth due to the increasing demand for data analytics solutions that can handle vast amounts of data. Advanced analytics software, in particular, is gaining traction as organizations seek to gain deeper insights and make data-driven decisions. Companies are increasingly adopting sophisticated data visualization tools to present complex data in an easily understandable format, thereby enhancing decision-making processes.

Judging the significance and reproducibility of quantitative research requires a good understanding of relevant uncertainties, but it is often unclear how well these have been evaluated and what they imply. Reported scientific uncertainties were studied by analysing 41 000 measurements of 3200 quantities from medicine, nuclear and particle physics, and interlaboratory comparisons ranging from chemistry to toxicology. Outliers are common, with 5 σ disagreements up to five orders of magnitude more frequent than naively expected. Uncertainty-normalized differences between multiple measurements of the same quantity are consistent with heavy-tailed Student's t-distributions that are often almost Cauchy, far from a Gaussian Normal bell curve. Medical research uncertainties are generally as well evaluated as those in physics, but physics uncertainty improves more rapidly, making feasible simple significance criteria such as the 5 σ discovery convention in particle physics. Contributions to measurement uncertainty from mistakes and unknown problems are not completely unpredictable. Such errors appear to have power-law distributions consistent with how designed complex systems fail, and how unknown systematic errors are constrained by researchers. This better understanding may help improve analysis and meta-analysis of data, and help scientists and the public have more realistic expectations of what scientific results imply.

Spreadsheet Software Market Size And Forecast

Spreadsheet Software Market size was valued at USD 10.05 Billion in 2023 and is expected to reach USD 14.55 Billion by 2031, with a CAGR of 7.8% from 2024-2031.

Global Spreadsheet Software Market Drivers

The market drivers for the Spreadsheet Software Market can be influenced by various factors. These may include:

Increasing Data Volume: As organizations generate and collect more data, the need for efficient data analysis and management tools, such as spreadsheet software, grows. Rising Demand for Data Visualization: Users increasingly seek sophisticated tools to visualize data for better insights. Spreadsheet software can provide charts and graphs, making data interpretation easier.

Global Spreadsheet Software Market Restraints

Several factors can act as restraints or challenges for the Spreadsheet Software Market, These may include:

Market Saturation: Many organizations already use established spreadsheet software such as Microsoft Excel or Google Sheets. The reliance on these platforms can make it difficult for new entrants or alternative solutions to capture market share. High Competition: The market is highly competitive, with numerous players offering similar features and functionalities. This can lead to price wars and reduced profit margins for software providers.

According to Cognitive Market Research, the global Graph Analytics market size will be USD 2522 million in 2024 and will expand at a compound annual growth rate (CAGR) of 34.0% from 2024 to 2031. Market Dynamics of Graph Analytics Market

Key Drivers for Graph Analytics Market

Increasing Recognition of the Advantages of Graph Databases- One of the main reasons for the Graph Analytics market is the increasing recognition of the advantages of graph databases. Unlike traditional relational databases, graph databases excel at handling complex relationships and interconnected data, making them ideal for use cases such as fraud detection, recommendation engines, and social network analysis. Businesses are leveraging these capabilities to uncover insights and patterns that were previously difficult to detect. The rise of big data and the need for real-time analytics are further driving the adoption of graph databases, as they offer enhanced performance and scalability for large-scale data sets. Additionally, advancements in artificial intelligence and machine learning are amplifying the value of graph databases, enabling more sophisticated data modeling and predictive analytics.
Growing Uptake of Big Data Tools to Drive the Graph Analytics Market's Expansion in the Years Ahead.

Key Restraints for Graph Analytics Market

Limited Awareness and Understanding pose a serious threat to the Graph Analytics industry.
The market also faces significant difficulties related to data security and privacy.

Introduction of the Graph Analytics Market

The Graph Analytics Market is rapidly expanding, driven by the growing need for advanced data analysis techniques in various sectors. Graph analytics leverages graph structures to represent and analyze relationships and dependencies, providing deeper insights than traditional data analysis methods. Key factors propelling this market include the rise of big data, the increasing adoption of artificial intelligence and machine learning, and the demand for real-time data processing. Industries such as finance, healthcare, telecommunications, and retail are major contributors, utilizing graph analytics for fraud detection, personalized recommendations, network optimization, and more. Leading vendors are continually innovating to offer scalable, efficient solutions, incorporating advanced features like graph databases and visualization tools.

Excel workbook containing example data extractions for different analyses and types of data as described in the DECiMAL guide. The workbook contains the following worksheets - One study per row (arm), One study per row (relative), Rate data, Diagnostic test accuracy, Codebook. (XLSX 26Â kb)

I found this Interesting Dataset on Maven Analytics about Space Missions and decided to work on it. The Dataset comes with the Data of Space Missions from 1957 to 2022. It consist of Date, Location, Rocket Name, Rocket Status, Mission Name, Mission Status, and the Company Launch the Mission. 🚀

Firstly, I ensure Data quality by meticulously Cleaning and Preparing it for Analysis. Then, I create Pivot Tables to Summarize and Analyze the Data from different angles. Next, I dive into Visualization, leveraging Tools to Transform complex Datasets into Clear, Actionable Insights. After Creating the Visuals, I Delve Deeper to Uncover Valuable Trends and Patterns, Empowering informed Decision-Making Insights. Every step, from Cleaning the Data to Visualization to Extracting Insights, is essential in Unlocking the True Power of Data-Driven Strategies. 📊 📈

ACTIONABLE DATA-DRIVEN INSIGHTS FROM THIS DASHBOARD:

1. THE NUMBER OF SPACE MISSIONS BY YEAR IS INCREASING. This suggests that there is a Growing Interest in Space Exploration. Businesses and Organizations Involved in Space Exploration could take Advantage of this Trend by Developing New Products and Services.
2. THE OVERALL SUCCESS RATE OF SPACE MISSIONS IS INCREASING. This could be due to a Number of Factors, such as Improvements in Technology and Engineering. Companies Involved in Space Exploration can Leverage this Information to Market their Services to Potential Customers.
3. (RVSN USSR) IS THE COMPANY WITH THE MOST TOTAL MISSIONS. As of 2022, they have Launched 1777 Missions. This suggests that they are a Leader in the Space Exploration Industry. Other Companies Looking to Enter the Space Exploration Industry may want to Study (RVSN USSR)'s Business Model.
4. ARIANESPACE HAS THE HIGHEST SUCCESS RATE OF ANY COMPANY LISTED ON THE DATASET AT 96.25%. This suggests that they are a Reliable Provider of Space Launch Services. Companies Looking to Launch Satellites or other Spacecraft into Orbit may want to consider Using Arianespace's Services.
5. THE MAJORITY OF SPACE MISSIONS (4162) HAVE BEEN SUCCESSFUL. This is a Positive Sign for the Future of Space Exploration. It suggests that Space Missions are Becoming more Routine and Less Risky. This could lead to an Increase in the Number of Private Companies and Organizations Involved in Space Exploration.

Overall, the Data in this Dashboard suggests that Space Exploration is a Growing Industry with a Bright Future. Companies and Organizations that are Involved in Space Exploration can take Advantage of this Trend by Developing New Products and Services. 🚀 📊

TOOL USED: Microsoft Excel

DataAnalytics #DataScience #DataAnalyst #DataVisualization #BusinessIntelligence #DataAnalysis #DataStorytelling #DataDrivenDecisions #DataDriven

Spreadsheets targeted at the analysis of GHS safety fingerprints.AbstractOver a 20-year period, the UN developed the Globally Harmonized System (GHS) to address international variation in chemical safety information standards. By 2014, the GHS became widely accepted internationally and has become the cornerstone of OSHA’s Hazard Communication Standard. Despite this progress, today we observe that there are inconsistent results when different sources apply the GHS to specific chemicals, in terms of the GHS pictograms, hazard statements, precautionary statements, and signal words assigned to those chemicals. In order to assess the magnitude of this problem, this research uses an extension of the “chemical fingerprints” used in 2D chemical structure similarity analysis to GHS classifications. By generating a chemical safety fingerprint, the consistency of the GHS information for specific chemicals can be assessed. The problem is the sources for GHS information can differ. For example, the SDS for sodium hydroxide pellets found on Fisher Scientific’s website displays two pictograms, while the GHS information for sodium hydroxide pellets on Sigma Aldrich’s website has only one pictogram. A chemical information tool, which identifies such discrepancies within a specific chemical inventory, can assist in maintaining the quality of the safety information needed to support safe work in the laboratory. The tools for this analysis will be scaled to the size of a moderate large research lab or small chemistry department as a whole (between 1000 and 3000 chemical entities) so that labelling expectations within these universes can be established as consistently as possible.Most chemists are familiar with programs such as excel and google sheets which are spreadsheet programs that are used by many chemists daily. Though a monadal programming approach with these tools, the analysis of GHS information can be made possible for non-programmers. This monadal approach employs single spreadsheet functions to analyze the data collected rather than long programs, which can be difficult to debug and maintain. Another advantage of this approach is that the single monadal functions can be mixed and matched to meet new goals as information needs about the chemical inventory evolve over time. These monadal functions will be used to converts GHS information into binary strings of data called “bitstrings”. This approach is also used when comparing chemical structures. The binary approach make data analysis more manageable, as GHS information comes in a variety of formats such as pictures or alphanumeric strings which are difficult to compare on their face. Bitstrings generated using the GHS information can be compared using an operator such as the tanimoto coefficent to yield values from 0 for strings that have no similarity to 1 for strings that are the same. Once a particular set of information is analyzed the hope is the same techniques could be extended to more information. For example, if GHS hazard statements are analyzed through a spreadsheet approach the same techniques with minor modifications could be used to tackle more GHS information such as pictograms.Intellectual Merit. This research indicates that the use of the cheminformatic technique of structural fingerprints can be used to create safety fingerprints. Structural fingerprints are binary bit strings that are obtained from the non-numeric entity of 2D structure. This structural fingerprint allows comparison of 2D structure through the use of the tanimoto coefficient. The use of this structural fingerprint can be extended to safety fingerprints, which can be created by converting a non-numeric entity such as GHS information into a binary bit string and comparing data through the use of the tanimoto coefficient.Broader Impact. Extension of this research can be applied to many aspects of GHS information. This research focused on comparing GHS hazard statements, but could be further applied to other bits of GHS information such as pictograms and GHS precautionary statements. Another facet of this research is allowing the chemist who uses the data to be able to compare large dataset using spreadsheet programs such as excel and not need a large programming background. Development of this technique will also benefit the Chemical Health and Safety community and Chemical Information communities by better defining the quality of GHS information available and providing a scalable and transferable tool to manipulate this information to meet a variety of other organizational needs.

Energy Consumption of United States Over Time

Building Energy Data Book

By Department of Energy [source]

About this dataset

The Building Energy Data Book (2011) is an invaluable resource for gaining insight into the current state of energy consumption in the buildings sector. This dataset provides comprehensive data on residential, commercial and industrial building energy consumption, construction techniques, building technologies and characteristics. With this resource, you can get an in-depth understanding of how energy is used in various types of buildings - from single family homes to large office complexes - as well as its impact on the environment. The BTO within the U.S Department of Energy's Office of Energy Efficiency and Renewable Energy developed this dataset to provide a wealth of knowledge for researchers, policy makers, engineers and even everyday observers who are interested in learning more about our built environment and its energy usage patterns

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How to use the dataset

This dataset provides comprehensive information regarding energy consumption in the buildings sector of the United States. It contains a number of key variables which can be used to analyze and explore the relations between energy consumption and building characteristics, technologies, and construction. The data is provided in both CSV format as well as tabular format which can make it helpful for those who prefer to use programs like Excel or other statistical modeling software.

In order to get started with this dataset we've developed a guide outlining how to effectively use it for your research or project needs.

• Understand what's included: Before you start analyzing the data, you should read through the provided documentation so that you fully understand what is included in the datasets. You'll want to be aware of any potential limitations or requirements associated with each type of data point so that your results are valid and reliable when drawing conclusions from them.

• Clean up any outliers: You may need to take some time upfront investigating suspicious outliers within your dataset before using it in any further analyses — otherwise, they can skew results down the road if not dealt with first-hand! Furthermore, they could also make complex statistical modeling more difficult as well since they artificially inflate values depending on their magnitude within each example data point (i.e., one outlier could affect an entire model’s prior distributions). Missing values should also be accounted for too since these may not always appear obvious at first glance when reviewing a table or graphical representation - but accurate statistics must still be obtained either way no matter how messy things seem!

• Exploratory data analysis: After cleaning up your dataset you'll want to do some basic exploring by visualizing different types of summaries like boxplots, histograms and scatter plots etc.. This will give you an initial case into what trends might exist within certain demographic/geographic/etc.. regions & variables which can then help inform future predictive models when needed! Additionally this step will highlight any clear discontinuous changes over time due over-generalization (if applicable), making sure predictors themselves don’t become part noise instead contributing meaningful signals towards overall effect predictions accuracy etc…

• Analyze key metrics & observations: Once exploratory analyses have been carried out on rawsamples post-processing steps are next such as analyzing metrics such ascorrelations amongst explanatory functions; performing significance testing regression models; imputing missing/outlier values and much more depending upon specific project needs at hand… Additionally – interpretation efforts based

Research Ideas

• Creating an energy efficiency rating system for buildings - Using the dataset, an organization can develop a metric to rate the energy efficiency of commercial and residential buildings in a standardized way.
• Developing targeted campaigns to raise awareness about energy conservation - Analyzing data from this dataset can help organizations identify areas of high energy consumption and create targeted campaigns and incentives to encourage people to conserve energy in those areas.
• Estimating costs associated with upgrading building technologies - By evaluating various trends in building technologies and their associated costs, decision-makers can determine the most cost-effective option when it comes time to upgrade their structures' energy efficiency...

Excel spreadsheet containing the numerical data and details of statistical analysis for Figs 1D, 1E, 1F, 1G, 2C, 2D, 2F, 2G, 2H, 3B–3D, 3F, 3G, 4B, 4C, 4D, 4E, 4G, 4H, 5C, 5D, 5E, 5F, 6C, 6D–6F, 7A, 7C, 7D, 7E, 7F, 7G, 7H, 7I, 7J, 7K, S1C, S1D, S1F, S1G, S2B, S2C, S2G, S2H, S2I, S2J, S2K, S3A, S3C, S3D, S3F, S3G, S3I, S4B, S5C, S5D, S5E, S5F, S5G and S5H.

Document Databases Market Outlook

The global document databases market size was valued at approximately USD 3.5 billion in 2023 and is projected to reach around USD 8.2 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 9.7% over the forecast period. This impressive growth can be attributed to the increasing demand for more flexible and scalable database solutions that can handle diverse data types and structures.

One of the primary growth factors for the document databases market is the rising adoption of NoSQL databases. Traditional relational databases often struggle with the unstructured data generated by modern applications, social media, and IoT devices. NoSQL databases, such as document databases, offer a more flexible and scalable solution to handle this data, which has led to their increased adoption across various industry verticals. Additionally, the growing popularity of microservices architecture in application development also drives the need for document databases, as they provide the necessary agility and performance.

Another significant growth factor is the increasing volume of data generated globally. With the exponential growth of data, organizations require robust and efficient database management systems to store, process, and analyze vast amounts of information. Document databases excel in managing large volumes of semi-structured and unstructured data, making them an ideal choice for enterprises looking to harness the power of big data analytics. Furthermore, advancements in cloud computing have made it easier for organizations to deploy and scale document databases, further driving their adoption.

The rise of artificial intelligence (AI) and machine learning (ML) technologies is also propelling the growth of the document databases market. AI and ML applications require databases that can handle complex data structures and provide quick access to large datasets for training and inference purposes. Document databases, with their schema-less design and ability to store diverse data types, are well-suited for these applications. As more organizations incorporate AI and ML into their operations, the demand for document databases is expected to grow significantly.

Regionally, North America holds the largest market share for document databases, driven by the presence of major technology companies and a high adoption rate of advanced database solutions. Europe is also a significant market, with growing investments in digital transformation initiatives. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, fueled by rapid technological advancements and increasing adoption of cloud-based solutions in countries like China, India, and Japan. Latin America and the Middle East & Africa are also experiencing growth, albeit at a slower pace, due to increasing digitalization efforts and the need for efficient data management solutions.

NoSQL Databases Analysis

NoSQL databases, a subset of document databases, have gained significant traction over the past decade. They are designed to handle unstructured and semi-structured data, making them highly versatile and suitable for a wide range of applications. Unlike traditional relational databases, NoSQL databases do not require a predefined schema, allowing for greater flexibility and scalability. This has led to their adoption in industries such as retail, e-commerce, and social media, where the volume and variety of data are constantly changing.

The key advantage of NoSQL databases is their ability to scale horizontally. Traditional relational databases often face challenges when scaling up, as they require more powerful hardware and complex configurations. In contrast, NoSQL databases can easily scale out by adding more servers to the database cluster. This makes them an ideal choice for applications that experience high traffic and require real-time data processing. Companies like Amazon, Facebook, and Google have already adopted NoSQL databases to manage their massive data workloads, setting a precedent for other organizations to follow.

Another driving factor for the adoption of NoSQL databases is their performance in handling large datasets. NoSQL databases are optimized for read and write operations, making them faster and more efficient than traditional relational databases. This is particularly important for applications that require real-time analytics and immediate data access. For instance, e-commerce platforms use NoSQL databases to provide personalized recommendations to users based on th

According to Cognitive Market Research, the global GPU Database market size will be USD 455 million in 2024 and will expand at a compound annual growth rate (CAGR) of 20.7% from 2024 to 2031. Market Dynamics of GPU Database Market Key Drivers for GPU Database Market Growing Demand for High-Performance Computing in Various Data-Intensive Industries- One of the main reasons the GPU Database market is growing demand for high-performance computing (HPC) across various data-intensive industries. These industries, including finance, healthcare, and telecommunications, require rapid data processing and real-time analytics, which GPU databases excel at providing. Unlike traditional CPU databases, GPU databases leverage the parallel processing power of GPUs to handle complex queries and large datasets more efficiently. This capability is crucial for applications such as machine learning, artificial intelligence, and big data analytics. The expansion of data and the increasing need for speed and scalability in processing are pushing enterprises to adopt GPU databases. Consequently, the market is poised for robust growth as organizations continue to seek solutions that offer enhanced performance, reduced latency, and greater computational power to meet their evolving data management needs. The increasing demand for gaining insights from large volumes of data generated across verticals to drive the GPU Database market's expansion in the years ahead. Key Restraints for GPU Database Market Lack of efficient training professionals poses a serious threat to the GPU Database industry. The market also faces significant difficulties related to insufficient security options. Introduction of the GPU Database Market The GPU database market is experiencing rapid growth due to the increasing demand for high-performance data processing and analytics. GPUs, or Graphics Processing Units, excel in parallel processing, making them ideal for handling large-scale, complex data sets with unprecedented speed and efficiency. This market is driven by the proliferation of big data, advancements in AI and machine learning, and the need for real-time analytics across industries such as finance, healthcare, and retail. Companies are increasingly adopting GPU-accelerated databases to enhance data visualization, predictive analytics, and computational workloads. Key players in this market include established tech giants and specialized startups, all contributing to a competitive landscape marked by innovation and strategic partnerships. As organizations continue to seek faster and more efficient ways to harness their data, the GPU database market is poised for substantial growth, reshaping the future of data management and analytics.< /p>

Graph Database Market Outlook

The global graph database market size was valued at USD 1.5 billion in 2023 and is projected to reach USD 8.5 billion by 2032, growing at a CAGR of 21.2% from 2024 to 2032. The substantial growth of this market is driven primarily by increasing data complexity, advancements in data analytics technologies, and the rising need for more efficient database management systems.

One of the primary growth factors for the graph database market is the exponential increase in data generation. As organizations generate vast amounts of data from various sources such as social media, e-commerce platforms, and IoT devices, the need for sophisticated data management and analysis tools becomes paramount. Traditional relational databases struggle to handle the complexity and interconnectivity of this data, leading to a shift towards graph databases which excel in managing such intricate relationships.

Another significant driver is the growing adoption of artificial intelligence (AI) and machine learning (ML) technologies. These technologies rely heavily on connected data for predictive analytics and decision-making processes. Graph databases, with their inherent ability to model relationships between data points effectively, provide a robust foundation for AI and ML applications. This synergy between AI/ML and graph databases further accelerates market growth.

Additionally, the increasing prevalence of personalized customer experiences across industries like retail, finance, and healthcare is fueling demand for graph databases. Businesses are leveraging graph databases to analyze customer behaviors, preferences, and interactions in real-time, enabling them to offer tailored recommendations and services. This enhanced customer experience translates to higher customer satisfaction and retention, driving further adoption of graph databases.

From a regional perspective, North America currently holds the largest market share due to early adoption of advanced technologies and the presence of key market players. However, significant growth is also anticipated in the Asia-Pacific region, driven by rapid digital transformation, increasing investments in IT infrastructure, and growing awareness of the benefits of graph databases. Europe is also expected to witness steady growth, supported by stringent data management regulations and a strong focus on data privacy and security.

Component Analysis

The graph database market can be segmented into two primary components: software and services. The software segment holds the largest market share, driven by extensive adoption across various industries. Graph database software is designed to create, manage, and query graph databases, offering features such as scalability, high performance, and efficient handling of complex data relationships. The growth in this segment is propelled by continuous advancements and innovations in graph database technologies. Companies are increasingly investing in research and development to enhance the capabilities of their graph database software products, catering to the evolving needs of their customers.

On the other hand, the services segment is also witnessing substantial growth. This segment includes consulting, implementation, and support services provided by vendors to help organizations effectively deploy and manage graph databases. As businesses recognize the benefits of graph databases, the demand for expert services to ensure successful implementation and integration into existing systems is rising. Additionally, ongoing support and maintenance services are crucial for the smooth operation of graph databases, driving further growth in this segment.

The increasing complexity of data and the need for specialized expertise to manage and analyze it effectively are key factors contributing to the growth of the services segment. Organizations often lack the in-house skills required to harness the full potential of graph databases, prompting them to seek external assistance. This trend is particularly evident in large enterprises, where the scale and complexity of data necessitate robust support services.

Moreover, the services segment is benefiting from the growing trend of outsourcing IT functions. Many organizations are opting to outsource their database management needs to specialized service providers, allowing them to focus on their core business activities. This shift towards outsourcing is further bolstering the demand for graph database services, driving market growth.

Heart failure (HF) following myocardial infarction (MI) is associated with high incidence of cardiac arrhythmias. Development of therapeutic strategy requires detailed understanding of electrophysiological remodeling. However, changes of ionic currents in ischemic HF remain incompletely understood, especially in translational large animal models. Here, we systematically measure the major ionic currents in ventricular myocytes from the infarct border and remote zones in a porcine model of post-MI HF. We recorded eight ionic currents during the cell’s action potential (AP) under physiologically relevant conditions using selfAP-clamp Sequential Dissection. Compared to healthy controls, HF-remote zone myocytes exhibited increased late Na+ current, Ca2+-activated K+ current, Ca2+-activated Cl- current, decreased rapid delayed rectifier K+ current, and altered Na+/Ca2+ exchange current profile. In HF-border zone myocytes, the above changes also occurred but with additional decrease of L-type Ca2+ current, decrease of inward rectifier K+ current, and Ca2+ release-dependent delayed afterdepolarizations. Our data reveal that the changes in any individual current are relatively small, but the integrated impacts shift the balance between the inward and outward currents to shorten AP in the border zone but prolong AP in the remote zone. This differential remodeling in post-MI HF increases the inhomogeneity of AP repolarization which may enhance the arrhythmogenic substrate. Our comprehensive findings provide a new mechanistic framework for understanding why single channel blockers may fail to suppress arrhythmias, and highlight the need to consider the rich tableau and integration of many ionic currents in designing therapeutic strategies for treating arrhythmias in HF.

Methods Electrophysiology data were collected using pClamp10 (Molecular Devices) software, then Clampfit 10 (Molecular Devices), Excel 2016 (Microsoft) and Origin 2016 (OriginLab) softwares were used for data processing and analysis.

Calcium and contraction data were collected using IonWizard (IonOptix) software, then IonWizard, Excel and Origin softwares were used for data processing and analysis.

Files are compressed to keep the folder structure and to ease the navigation between the uploaded documents.

This dataset belongs to the article: "Nanopores with an Engineered Selective Entropic Gate DetectProteins at Nanomolar Concentration in Complex Biological Sample" published in JACS (DOI: 10.1021/jacs.4c17147) and contains the raw electrophysiology data of protein capture by engineered YaxAB, analysis MATLAB scripts and analysis Excel files.

For YaxAB_JACS_2025.zip:

- Each electrophysiology file contains measurement info as follows:
[Date of measurement]_[Buffer conditions]_[Pore type]_[added analyte(s)]_[operator initials]

- Each electrophys trace is accompanied by Excel file with Clampfit analysis in Sheet1 (named [voltage], with the columns corresponding to: Trace; Search; Level; State; Event Start Time (ms); Event End Time (ms); Amplitude (pA); Amp S.D. (pA); Dwell Time (ms); Inst. Freq. (Hz); Interevent Interval (ms); [empy]; open pore current (pA)

- This Excel sheet was analyzed with inhouse MATLAB script; and event data (Iex, dwell time, Amp S.D.) is placed in second sheet of Excel file.

- A summary of all the event data (mean +/- S.D.) for all replicates is in a separate Excel file in the corresponding folder.

This dataset contains one replicate per experiment. Raw data for all replicates can be shared upon reasonable request.

The intention is to collect data for the calendar year 2009 (or the nearest year for which each business keeps its accounts. The survey is considered a one-off survey, although for accurate NAs, such a survey should be conducted at least every five years to enable regular updating of the ratios, etc., needed to adjust the ongoing indicator data (mainly VAGST) to NA concepts. The questionnaire will be drafted by FSD, largely following the previous BAS, updated to current accounting terminology where necessary. The questionnaire will be pilot tested, using some accountants who are likely to complete a number of the forms on behalf of their business clients, and a small sample of businesses. Consultations will also include Ministry of Finance, Ministry of Commerce, Industry and Labour, Central Bank of Samoa (CBS), Samoa Tourism Authority, Chamber of Commerce, and other business associations (hotels, retail, etc.).

The questionnaire will collect a number of items of information about the business ownership, locations at which it operates and each establishment for which detailed data can be provided (in the case of complex businesses), contact information, and other general information needed to clearly identify each unique business. The main body of the questionnaire will collect data on income and expenses, to enable value added to be derived accurately. The questionnaire will also collect data on capital formation, and will contain supplementary pages for relevant industries to collect volume of production data for selected commodities and to collect information to enable an estimate of value added generated by key tourism activities.

The principal user of the data will be FSD which will incorporate the survey data into benchmarks for the NA, mainly on the current published production measure of GDP. The information on capital formation and other relevant data will also be incorporated into the experimental estimates of expenditure on GDP. The supplementary data on volumes of production will be used by FSD to redevelop the industrial production index which has recently been transferred under the SBS from the CBS. The general information about the business ownership, etc., will be used to update the Business Register.

Outputs will be produced in a number of formats, including a printed report containing descriptive information of the survey design, data tables, and analysis of the results. The report will also be made available on the SBS website in “.pdf” format, and the tables will be available on the SBS website in excel tables. Data by region may also be produced, although at a higher level of aggregation than the national data. All data will be fully confidentialised, to protect the anonymity of all respondents. Consideration may also be made to provide, for selected analytical users, confidentialised unit record files (CURFs).

A high level of accuracy is needed because the principal purpose of the survey is to develop revised benchmarks for the NA. The initial plan was that the survey will be conducted as a stratified sample survey, with full enumeration of large establishments and a sample of the remainder.

v01: This is the first version of the documentation. Basic raw data, obtained from data entry.

The scope of the 2009 BAS is all employing businesses in the private sector other than those involved in agricultural activities.

Included are:
· Non-governmental organizations (NGOs, not-for profit organizations, etc.);
· Government Public Bodies

Excluded are:
· Non-employing units (e.g., market sellers);
· Government ministries, constitutional offices and those public bodies involved in public administration and included in the Central Government Budget Sector;
· Agricultural units (unless large scale/commercial - if the Agriculture census only covers household activities);
· “Non-resident” bodies such as international agencies, diplomatic missions (e.g., high commissions and embassies, UNDP, FAO, WHO);

The survey coverage is of all businesses in scope as defined above. Statistical units relevant to the survey are the enterprise and the establishment. The enterprise is an institutional unit and generally corresponds to legal entities such as a company, cooperative, partnership or sole proprietorship. The establishment is an institutional unit or part of an institutional unit, which engages in one, or predominantly one, type of economic activity. Sufficient data must be available to derive or meaningfully estimate value added in order to recognize an establishment. The main statistical unit from which data will be collected in the survey is the establishment. For most businesses there will be a one-to-one relationship between the enterprise and the establishment, i.e., simple enterprises will comprise only one establishment. The purpose of collecting data from establishments (rather than from enterprises) is to enable the most accurate industry estimates of value added possible.

• Collection start: 2009
• Collection end: 2009

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Excel spreadsheet containing, in separate sheets, the underlying numerical data for Figs 2J, 2K, 2L, 2N, 2O, 2P, 2Q, 3B, 3C, 4B, 4C, 4D, 4E, 4G, 4H, 5A, 5C, 5D, 6C, 6D, 6Q, 6R, 7C, 7D, 7E, 7H, 7I, 7J, 7K, 7L, 7M, 7N, S2G, S2H, S2I, S2L, S2M, S2N, S3C, S3D, S3E, S3F, S3G, S3H, S5E, S5F, S5G, S5J, S5K, S5L, S6E, S6G, S8H, S8I, S8J, S9B and S9C.