This dataset presents data collect during research for my Ph.D. dissertation in Industrial and Systems Engineering at the University of Rhode Island in 2017-2018.

Research Purpose

Cost and schedule overruns have become increasingly common in large defense programs that attempt to build systems with improved performance and lifecycle characteristics, often using novel, untested, and complex product architectures. Based on the well documented relationship between product architecture and the structure of the product development organization, research examined the effectiveness of different organizational networks at designing complex engineered systems, comparing the performance or real-world organizations to ideal ones.

Method and Research Questions

Phase 1 examined information exchange models and implemented the model of information exchange proposed by Dodds, Watts and Sabel to confirm the model can be successfully implemented using agent-based models (ABM). Phase 2 examined artifact models and extended the information exchange model to include the processing artifacts. Phase 3 examined smart team models and phase 4 applied information exchange and artifact models to a real-world organization. Research questions:

1) How do random, multi-scale, military staff and matrix organizational networks perform in the information exchange and artifact task environments and how does increasing the degree of complexity affect performance?

2) How do military staff and matrix organizational networks (real organizations) perform compared to one another and to random and multi-scale networks (ideal organizations)? How does increasing degree of complexity affect performance and which structure is preferred for organizations that design complex engineered systems?

3) How can organizational networks be modified to improve performance?

Data Interpretation

Excel spreadsheets summarize and analyze data collected from MATLAB and NetLogo ABM experiments for each phase. In general, raw data was collected in a 'data' worksheet, and then additional worksheets and graphs were created to analyze data. Dataset includes link to associated dissertation, which provides further detail.

Notable Findings

1) All organizational networks perform well in the information exchange environment and in the artifact environment when complexity is low to moderate. 2) Military staff networks consistently out-perform matrix networks.
3) At high complexity, all networks are susceptible to congestion failure.
4) Military staff organizational networks exhibit performance comparable to multi-scale networks over a range of situations.

AI Spreadsheet Assistant Market Outlook

According to our latest research, the AI Spreadsheet Assistant market size reached USD 1.12 billion in 2024, registering a robust year-on-year growth. The market is poised for significant expansion, projected to achieve a value of USD 7.94 billion by 2033 at a compelling CAGR of 24.1% during the forecast period from 2025 to 2033. This surge is driven by increasing adoption across industries seeking to automate spreadsheet-based workflows, enhance data-driven decision-making, and reduce manual errors. The proliferation of AI-powered solutions, coupled with the rising complexity of business data, is fundamentally transforming the way organizations leverage spreadsheets for analytics, reporting, and operational efficiency.

One of the core growth factors fueling the AI Spreadsheet Assistant market is the accelerating digital transformation across enterprises of all sizes. As organizations grapple with vast volumes of structured and unstructured data, AI-powered spreadsheet assistants are emerging as essential tools for automating repetitive tasks, identifying trends, and generating actionable insights. These solutions are being integrated into traditional spreadsheet platforms, enabling users to perform advanced data analysis, predictive modeling, and workflow automation without requiring deep technical expertise. The demand for such intelligent assistants is further amplified by the need for real-time collaboration, data accuracy, and rapid decision-making in todayÂ’s fast-paced business environment.

Another significant driver is the increasing complexity of financial modeling and reporting requirements across sectors such as BFSI, healthcare, and manufacturing. AI Spreadsheet Assistants are revolutionizing financial operations by automating data consolidation, error detection, and scenario analysis, thereby reducing the risk of human error and improving compliance. Advanced AI algorithms can instantly flag inconsistencies, suggest formula corrections, and generate visual reports, making them invaluable for finance professionals and analysts. The growing emphasis on regulatory compliance and auditability is also pushing organizations to adopt AI-driven spreadsheet tools that ensure transparency, traceability, and data integrity throughout the reporting process.

Moreover, the rapid advancements in natural language processing (NLP) and machine learning are making AI Spreadsheet Assistants more intuitive and user-friendly. These technologies enable users to interact with spreadsheets using plain language queries, automate complex workflows, and receive intelligent recommendations based on historical data patterns. The integration of AI assistants with cloud-based platforms is further lowering the barrier to adoption, allowing even small and medium enterprises (SMEs) to access sophisticated analytics capabilities without significant upfront investment. As a result, the market is witnessing a democratization of advanced data analysis, empowering a broader range of users to derive value from their spreadsheet data.

As organizations continue to leverage AI technologies, the concept of Enterprise Spreadsheet Management is gaining traction. This approach involves the strategic oversight of spreadsheet usage across an organization, ensuring that spreadsheets are used efficiently and effectively as part of the broader data management strategy. By integrating AI Spreadsheet Assistants within an enterprise framework, businesses can enhance data governance, improve collaboration, and ensure consistency in data handling practices. This not only helps in reducing errors and redundancies but also aligns spreadsheet usage with organizational goals, thereby maximizing the value derived from data analytics initiatives.

From a regional perspective, North America currently dominates the AI Spreadsheet Assistant market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The high concentration of technology-driven enterprises, early adoption of AI solutions, and a robust ecosystem of software vendors are key factors contributing to North AmericaÂ’s leadership position. However, Asia Pacific is anticipated to exhibit the fastest growth rate over the forecast period, driven by rapid digitalization, increasing investments in AI research, a

Spreadsheet Automation Tools Market Outlook

According to our latest research, the global Spreadsheet Automation Tools market size reached USD 1.47 billion in 2024, with a robust year-over-year growth trajectory. The market is expected to expand at a CAGR of 13.8% during the forecast period, projecting a value of approximately USD 4.23 billion by 2033. This surge is attributed to the accelerating adoption of digital transformation initiatives, the need for error reduction, and increasing demand for process optimization across diverse industries. As per our comprehensive analysis, organizations are rapidly embracing spreadsheet automation tools to enhance productivity, minimize manual intervention, and drive data-driven decision-making processes.

One of the primary growth factors propelling the Spreadsheet Automation Tools market is the widespread digitization of business operations. Enterprises are increasingly seeking solutions that streamline repetitive tasks such as data entry, reconciliation, and reporting, which have traditionally been labor-intensive and prone to human error. Automation tools enable businesses to reduce operational costs, improve accuracy, and free up valuable human resources for more strategic activities. The proliferation of cloud computing and advancements in artificial intelligence further amplify the capabilities of these tools, making them indispensable assets for organizations aiming to remain competitive in a rapidly evolving digital landscape.

Another significant driver for the Spreadsheet Automation Tools market is the growing emphasis on data governance and compliance. As regulatory frameworks become more stringent, especially in sectors like BFSI and healthcare, organizations are compelled to ensure data integrity, traceability, and auditability. Spreadsheet automation solutions offer robust features such as version control, automated data validation, and secure collaboration, which help enterprises adhere to compliance standards while mitigating the risks associated with manual data handling. This has led to increased investments in automation technologies, particularly among large enterprises with complex data management requirements.

Additionally, the rise of remote and hybrid work models has further accelerated the adoption of Spreadsheet Automation Tools. With distributed teams collaborating across geographies, the need for seamless, real-time data sharing and workflow automation has become paramount. Automation tools facilitate efficient communication, task delegation, and centralized data management, enabling organizations to maintain productivity and agility despite physical barriers. This trend is expected to persist, driving sustained demand for advanced spreadsheet automation solutions that support flexible and scalable business operations.

From a regional perspective, North America continues to dominate the Spreadsheet Automation Tools market, accounting for the largest revenue share in 2024. This leadership is attributed to the region’s early adoption of cutting-edge technologies, presence of major market players, and a highly digitized business ecosystem. However, the Asia Pacific region is witnessing the fastest growth, fueled by rapid industrialization, expanding IT infrastructure, and increasing awareness of automation benefits among SMEs. Europe also maintains a significant share, driven by regulatory compliance needs and digital innovation initiatives across various sectors.

Component Analysis

The Component segment of the Spreadsheet Automation Tools market is primarily divided into software and services. Software solutions constitute the core of this segment, providing the essential functionalities required for automating spreadsheet-related tasks such as data processing, workflow management, and integration with other enterprise systems. These software offerings are continuously evolving, incorporating advanced technologies like artificial intelligence, machine learning, and natural language processing to enhance automation capabilities. The growing demand for feature-rich, user-friendly, and scalable automation tools has led to significant investments in software development, resulting in a diverse array of products tailored to different organizational needs.

On the other hand, the services segment plays a crucial role in facilitating the successful implementation and adoption of spre

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.

Spreadsheet Automation Tools Market Outlook

According to our latest research, the global spreadsheet automation tools market size in 2024 stands at USD 2.5 billion, reflecting growing enterprise demand for digital transformation and operational efficiency. The market is expected to grow at a robust CAGR of 12.8% from 2025 to 2033, reaching a forecasted market size of USD 7.4 billion by 2033. This rapid expansion is primarily fueled by increasing adoption of automation technologies to minimize manual errors, enhance productivity, and streamline workflows across diverse industries.

One of the key growth factors driving the spreadsheet automation tools market is the rising need for businesses to eliminate repetitive, error-prone manual processes. Organizations across the globe are under immense pressure to increase efficiency while reducing operational costs, and spreadsheet automation tools offer a compelling solution. These tools enable seamless data entry, automatic report generation, and integration with other enterprise systems, thereby reducing the time spent on routine tasks. The growing complexity of business data and the necessity for real-time analytics further propel the adoption of these solutions, as they empower organizations to make faster, data-driven decisions. Additionally, the proliferation of cloud-based spreadsheet automation platforms has made advanced automation capabilities accessible to businesses of all sizes, thereby broadening the market's reach and potential.

Another significant driver is the increasing integration of artificial intelligence (AI) and machine learning (ML) capabilities within spreadsheet automation tools. AI-powered automation not only boosts accuracy and consistency but also enables predictive analytics and intelligent data processing. As organizations continue to generate vast volumes of structured and unstructured data, the demand for AI-enhanced automation tools that can intelligently categorize, analyze, and visualize this data is surging. Furthermore, the rise in remote and hybrid work models has necessitated robust collaborative tools that can automate workflows and support distributed teams, further accelerating the adoption of spreadsheet automation across sectors such as BFSI, healthcare, IT, and retail.

Regulatory compliance and data security requirements are also contributing to the market's growth. Industries like finance and healthcare are subject to stringent regulations that mandate accurate record-keeping and secure data management. Spreadsheet automation tools offer built-in compliance features, audit trails, and secure access controls, making them indispensable for organizations navigating complex regulatory landscapes. The ability to automate compliance reporting and ensure data integrity not only minimizes risks but also enhances organizational reputation and trust. This has led to increased investments in automation technologies, particularly in highly regulated sectors, further bolstering market expansion.

Scripting Automation Tools are becoming increasingly vital in the realm of spreadsheet automation. These tools allow users to create scripts that can automate repetitive tasks, reducing the need for manual intervention and minimizing the risk of human error. By leveraging scripting capabilities, organizations can customize their automation processes to fit specific business needs, enhancing flexibility and efficiency. This is particularly beneficial in scenarios where standard automation features may not fully address unique workflow requirements. As businesses continue to seek ways to optimize their operations, the integration of scripting automation tools within spreadsheet platforms offers a powerful means to achieve greater control and precision in data management and processing.

From a regional perspective, North America currently dominates the spreadsheet automation tools market, driven by early technology adoption, significant investments in digital transformation, and a mature IT infrastructure. Europe follows closely, with strong demand from industries focused on operational excellence and regulatory compliance. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by rapid industrialization, expanding IT and telecommunications sectors, and increasing awareness of automation benefits among small and medium enterprises. Latin America and the Middle East &

Spreadsheet Risk Management Market Outlook

According to our latest research, the global spreadsheet risk management market size in 2024 stands at USD 2.13 billion, reflecting a robust demand for solutions that mitigate risks associated with spreadsheet use across enterprises. The market is experiencing a significant growth trajectory, with a CAGR of 11.8% projected from 2025 to 2033. By the end of 2033, the spreadsheet risk management market size is forecasted to reach USD 5.83 billion. This remarkable growth is primarily driven by the increasing reliance on spreadsheets for critical business operations, the growing complexity of regulatory compliance requirements, and the heightened focus on data integrity and security across industries.

One of the primary growth factors for the spreadsheet risk management market is the pervasive use of spreadsheets in financial reporting, budgeting, forecasting, and data analysis. Organizations of all sizes, from small and medium enterprises to large corporations, heavily depend on spreadsheets for their daily operations. However, this reliance introduces significant risks, such as data errors, version control issues, and unauthorized access. The increasing recognition of these risks, coupled with high-profile incidents of financial loss and compliance breaches due to spreadsheet errors, is compelling organizations to invest in robust spreadsheet risk management solutions. These solutions offer advanced capabilities such as automated error detection, access control, audit trails, and real-time monitoring, thereby enhancing data accuracy and regulatory compliance.

Another critical driver is the evolving regulatory landscape across industries such as BFSI, healthcare, and government. Regulatory bodies are imposing stricter guidelines to ensure transparency, data integrity, and accountability in business processes. For instance, regulations like SOX, GDPR, and HIPAA require organizations to maintain accurate records and demonstrate effective controls over financial and personal data. Spreadsheet risk management tools play a pivotal role in helping organizations meet these regulatory requirements by providing comprehensive audit and control features, automated compliance checks, and detailed reporting functionalities. As compliance becomes increasingly complex and costly, the demand for sophisticated spreadsheet risk management solutions continues to surge.

The rapid adoption of cloud-based technologies and digital transformation initiatives further accelerates the growth of the spreadsheet risk management market. Cloud deployment enables organizations to manage spreadsheet risks across geographically dispersed teams, ensuring real-time collaboration, centralized control, and seamless integration with existing enterprise systems. Additionally, the increasing frequency of cyber threats and data breaches has heightened the need for advanced security features in spreadsheet management solutions. Organizations are seeking platforms that offer robust encryption, multi-factor authentication, and continuous monitoring to protect sensitive business data. The convergence of these factors is fostering a dynamic market environment, characterized by continuous innovation and the emergence of next-generation risk management tools.

Spreadsheet Software plays a pivotal role in the modern business landscape, serving as the backbone for a multitude of organizational tasks ranging from financial analysis to project management. With the increasing complexity of data and the need for precise calculations, spreadsheet software has evolved to offer enhanced functionalities that support decision-making processes. Organizations are leveraging these tools not only for basic data entry but also for advanced data modeling and forecasting. As businesses strive to maintain competitiveness, the integration of spreadsheet software with other enterprise systems has become crucial, enabling seamless data flow and improved operational efficiency. The versatility and adaptability of spreadsheet software make it an indispensable asset in the toolkit of any organization aiming to optimize its data management strategies.

From a regional perspective, North America currently dominates the spreadsheet risk management market, accounting for the largest revenue share in 2024. This leadership position is attributed to the early adoption of advanced technologies, stringent

The Ontario government, generates and maintains thousands of datasets. Since 2012, we have shared data with Ontarians via a data catalogue. Open data is data that is shared with the public. Click here to learn more about open data and why Ontario releases it. Ontario’s Open Data Directive states that all data must be open, unless there is good reason for it to remain confidential. Ontario’s Chief Digital and Data Officer also has the authority to make certain datasets available publicly. Datasets listed in the catalogue that are not open will have one of the following labels: If you want to use data you find in the catalogue, that data must have a licence – a set of rules that describes how you can use it. A licence: Most of the data available in the catalogue is released under Ontario’s Open Government Licence. However, each dataset may be shared with the public under other kinds of licences or no licence at all. If a dataset doesn’t have a licence, you don’t have the right to use the data. If you have questions about how you can use a specific dataset, please contact us. The Ontario Data Catalogue endeavors to publish open data in a machine readable format. For machine readable datasets, you can simply retrieve the file you need using the file URL. The Ontario Data Catalogue is built on CKAN, which means the catalogue has the following features you can use when building applications. APIs (Application programming interfaces) let software applications communicate directly with each other. If you are using the catalogue in a software application, you might want to extract data from the catalogue through the catalogue API. Note: All Datastore API requests to the Ontario Data Catalogue must be made server-side. The catalogue's collection of dataset metadata (and dataset files) is searchable through the CKAN API. The Ontario Data Catalogue has more than just CKAN's documented search fields. You can also search these custom fields. You can also use the CKAN API to retrieve metadata about a particular dataset and check for updated files. Read the complete documentation for CKAN's API. Some of the open data in the Ontario Data Catalogue is available through the Datastore API. You can also search and access the machine-readable open data that is available in the catalogue. How to use the API feature: Read the complete documentation for CKAN's Datastore API. The Ontario Data Catalogue contains a record for each dataset that the Government of Ontario possesses. Some of these datasets will be available to you as open data. Others will not be available to you. This is because the Government of Ontario is unable to share data that would break the law or put someone's safety at risk. You can search for a dataset with a word that might describe a dataset or topic. Use words like “taxes” or “hospital locations” to discover what datasets the catalogue contains. You can search for a dataset from 3 spots on the catalogue: the homepage, the dataset search page, or the menu bar available across the catalogue. On the dataset search page, you can also filter your search results. You can select filters on the left hand side of the page to limit your search for datasets with your favourite file format, datasets that are updated weekly, datasets released by a particular organization, or datasets that are released under a specific licence. Go to the dataset search page to see the filters that are available to make your search easier. You can also do a quick search by selecting one of the catalogue’s categories on the homepage. These categories can help you see the types of data we have on key topic areas. When you find the dataset you are looking for, click on it to go to the dataset record. Each dataset record will tell you whether the data is available, and, if so, tell you about the data available. An open dataset might contain several data files. These files might represent different periods of time, different sub-sets of the dataset, different regions, language translations, or other breakdowns. You can select a file and either download it or preview it. Make sure to read the licence agreement to make sure you have permission to use it the way you want. Read more about previewing data. A non-open dataset may be not available for many reasons. Read more about non-open data. Read more about restricted data. Data that is non-open may still be subject to freedom of information requests. The catalogue has tools that enable all users to visualize the data in the catalogue without leaving the catalogue – no additional software needed. Have a look at our walk-through of how to make a chart in the catalogue. Get automatic notifications when datasets are updated. You can choose to get notifications for individual datasets, an organization’s datasets or the full catalogue. You don’t have to provide and personal information – just subscribe to our feeds using any feed reader you like using the corresponding notification web addresses. Copy those addresses and paste them into your reader. Your feed reader will let you know when the catalogue has been updated. The catalogue provides open data in several file formats (e.g., spreadsheets, geospatial data, etc). Learn about each format and how you can access and use the data each file contains. A file that has a list of items and values separated by commas without formatting (e.g. colours, italics, etc.) or extra visual features. This format provides just the data that you would display in a table. XLSX (Excel) files may be converted to CSV so they can be opened in a text editor. How to access the data: Open with any spreadsheet software application (e.g., Open Office Calc, Microsoft Excel) or text editor. Note: This format is considered machine-readable, it can be easily processed and used by a computer. Files that have visual formatting (e.g. bolded headers and colour-coded rows) can be hard for machines to understand, these elements make a file more human-readable and less machine-readable. A file that provides information without formatted text or extra visual features that may not follow a pattern of separated values like a CSV. How to access the data: Open with any word processor or text editor available on your device (e.g., Microsoft Word, Notepad). A spreadsheet file that may also include charts, graphs, and formatting. How to access the data: Open with a spreadsheet software application that supports this format (e.g., Open Office Calc, Microsoft Excel). Data can be converted to a CSV for a non-proprietary format of the same data without formatted text or extra visual features. A shapefile provides geographic information that can be used to create a map or perform geospatial analysis based on location, points/lines and other data about the shape and features of the area. It includes required files (.shp, .shx, .dbt) and might include corresponding files (e.g., .prj). How to access the data: Open with a geographic information system (GIS) software program (e.g., QGIS). A package of files and folders. The package can contain any number of different file types. How to access the data: Open with an unzipping software application (e.g., WinZIP, 7Zip). Note: If a ZIP file contains .shp, .shx, and .dbt file types, it is an ArcGIS ZIP: a package of shapefiles which provide information to create maps or perform geospatial analysis that can be opened with ArcGIS (a geographic information system software program). A file that provides information related to a geographic area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open using a GIS software application to create a map or do geospatial analysis. It can also be opened with a text editor to view raw information. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format for sharing data in a machine-readable way that can store data with more unconventional structures such as complex lists. How to access the data: Open with any text editor (e.g., Notepad) or access through a browser. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format to store and organize data in a machine-readable way that can store data with more unconventional structures (not just data organized in tables). How to access the data: Open with any text editor (e.g., Notepad). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A file that provides information related to an area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open with a geospatial software application that supports the KML format (e.g., Google Earth). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. This format contains files with data from tables used for statistical analysis and data visualization of Statistics Canada census data. How to access the data: Open with the Beyond 20/20 application. A database which links and combines data from different files or applications (including HTML, XML, Excel, etc.). The database file can be converted to a CSV/TXT to make the data machine-readable, but human-readable formatting will be lost. How to access the data: Open with Microsoft Office Access (a database management system used to develop application software). A file that keeps the original layout and

The HR analytics tools market is experiencing robust growth, driven by the increasing need for data-driven decision-making in human resource management. The market, estimated at $15 billion in 2025, is projected to achieve a compound annual growth rate (CAGR) of 12% from 2025 to 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key factors. Firstly, organizations are increasingly leveraging data to optimize recruitment processes, improve employee engagement, and enhance workforce planning. Secondly, advancements in artificial intelligence (AI) and machine learning (ML) are enabling more sophisticated analytics capabilities, providing actionable insights into employee behavior, performance, and attrition. Thirdly, the rising adoption of cloud-based HR solutions is facilitating easier access to data and enhanced collaboration across HR teams. The market is segmented by various tools, including Python, RStudio, Tableau, KNIME, Power BI, Microsoft Excel, Orange, and Apache Hadoop, each catering to different analytical needs and organizational scale. Despite the significant growth potential, the market faces certain challenges. Data privacy and security concerns remain a major hurdle, especially given the sensitive nature of employee data. The lack of skilled professionals proficient in data analytics and HR practices also presents a limitation. Furthermore, the integration of disparate HR data sources can be complex and time-consuming. However, these challenges are being addressed through the development of robust data security protocols, specialized training programs, and integrated HR software solutions. The North American region currently holds the largest market share, but Asia-Pacific is anticipated to show the fastest growth in the coming years due to the increasing adoption of HR analytics tools in rapidly growing economies.

AI Generated Summary: The Ontario Data Catalogue is a data portal providing access to open datasets generated and maintained by the Ontario government. It allows users to search, access, visualize, and download data in various machine-readable formats, often through APIs, while also indicating licensing terms and data update frequencies. The catalogue also provides tools for data visualization and notifications for dataset updates. About: The Ontario government generates and maintains thousands of datasets. Since 2012, we have shared data with Ontarians via a data catalogue. Open data is data that is shared with the public. Click here to learn more about open data and why Ontario releases it. Ontario’s Digital and Data Directive states that all data must be open, unless there is good reason for it to remain confidential. Ontario’s Chief Digital and Data Officer also has the authority to make certain datasets available publicly. Datasets listed in the catalogue that are not open will have one of the following labels: If you want to use data you find in the catalogue, that data must have a licence – a set of rules that describes how you can use it. A licence: Most of the data available in the catalogue is released under Ontario’s Open Government Licence. However, each dataset may be shared with the public under other kinds of licences or no licence at all. If a dataset doesn’t have a licence, you don’t have the right to use the data. If you have questions about how you can use a specific dataset, please contact us. The Ontario Data Catalogue endeavors to publish open data in a machine readable format. For machine readable datasets, you can simply retrieve the file you need using the file URL. The Ontario Data Catalogue is built on CKAN, which means the catalogue has the following features you can use when building applications. APIs (Application programming interfaces) let software applications communicate directly with each other. If you are using the catalogue in a software application, you might want to extract data from the catalogue through the catalogue API. Note: All Datastore API requests to the Ontario Data Catalogue must be made server-side. The catalogue's collection of dataset metadata (and dataset files) is searchable through the CKAN API. The Ontario Data Catalogue has more than just CKAN's documented search fields. You can also search these custom fields. You can also use the CKAN API to retrieve metadata about a particular dataset and check for updated files. Read the complete documentation for CKAN's API. Some of the open data in the Ontario Data Catalogue is available through the Datastore API. You can also search and access the machine-readable open data that is available in the catalogue. How to use the API feature: Read the complete documentation for CKAN's Datastore API. The Ontario Data Catalogue contains a record for each dataset that the Government of Ontario possesses. Some of these datasets will be available to you as open data. Others will not be available to you. This is because the Government of Ontario is unable to share data that would break the law or put someone's safety at risk. You can search for a dataset with a word that might describe a dataset or topic. Use words like “taxes” or “hospital locations” to discover what datasets the catalogue contains. You can search for a dataset from 3 spots on the catalogue: the homepage, the dataset search page, or the menu bar available across the catalogue. On the dataset search page, you can also filter your search results. You can select filters on the left hand side of the page to limit your search for datasets with your favourite file format, datasets that are updated weekly, datasets released by a particular ministry, or datasets that are released under a specific licence. Go to the dataset search page to see the filters that are available to make your search easier. You can also do a quick search by selecting one of the catalogue’s categories on the homepage. These categories can help you see the types of data we have on key topic areas. When you find the dataset you are looking for, click on it to go to the dataset record. Each dataset record will tell you whether the data is available, and, if so, tell you about the data available. An open dataset might contain several data files. These files might represent different periods of time, different sub-sets of the dataset, different regions, language translations, or other breakdowns. You can select a file and either download it or preview it. Make sure to read the licence agreement to make sure you have permission to use it the way you want. A non-open dataset may be not available for many reasons. Read more about non-open data. Read more about restricted data. Data that is non-open may still be subject to freedom of information requests. The catalogue has tools that enable all users to visualize the data in the catalogue without leaving the catalogue – no additional software needed. Get automatic notifications when datasets are updated. You can choose to get notifications for individual datasets, an organization’s datasets or the full catalogue. You don’t have to provide and personal information – just subscribe to our feeds using any feed reader you like using the corresponding notification web addresses. Copy those addresses and paste them into your reader. Your feed reader will let you know when the catalogue has been updated. The catalogue provides open data in several file formats (e.g., spreadsheets, geospatial data, etc). Learn about each format and how you can access and use the data each file contains. A file that has a list of items and values separated by commas without formatting (e.g. colours, italics, etc.) or extra visual features. This format provides just the data that you would display in a table. XLSX (Excel) files may be converted to CSV so they can be opened in a text editor. How to access the data: Open with any spreadsheet software application (e.g., Open Office Calc, Microsoft Excel) or text editor. Note: This format is considered machine-readable, it can be easily processed and used by a computer. Files that have visual formatting (e.g. bolded headers and colour-coded rows) can be hard for machines to understand, these elements make a file more human-readable and less machine-readable. A file that provides information without formatted text or extra visual features that may not follow a pattern of separated values like a CSV. How to access the data: Open with any word processor or text editor available on your device (e.g., Microsoft Word, Notepad). A spreadsheet file that may also include charts, graphs, and formatting. How to access the data: Open with a spreadsheet software application that supports this format (e.g., Open Office Calc, Microsoft Excel). Data can be converted to a CSV for a non-proprietary format of the same data without formatted text or extra visual features. A shapefile provides geographic information that can be used to create a map or perform geospatial analysis based on location, points/lines and other data about the shape and features of the area. It includes required files (.shp, .shx, .dbt) and might include corresponding files (e.g., .prj). How to access the data: Open with a geographic information system (GIS) software program (e.g., QGIS). A package of files and folders. The package can contain any number of different file types. How to access the data: Open with an unzipping software application (e.g., WinZIP, 7Zip). Note: If a ZIP file contains .shp, .shx, and .dbt file types, it is an ArcGIS ZIP: a package of shapefiles which provide information to create maps or perform geospatial analysis that can be opened with ArcGIS (a geographic information system software program). A file that provides information related to a geographic area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open using a GIS software application to create a map or do geospatial analysis. It can also be opened with a text editor to view raw information. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format for sharing data in a machine-readable way that can store data with more unconventional structures such as complex lists. How to access the data: Open with any text editor (e.g., Notepad) or access through a browser. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format to store and organize data in a machine-readable way that can store data with more unconventional structures (not just data organized in tables). How to access the data: Open with any text editor (e.g., Notepad). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A file that provides information related to an area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open with a geospatial software application that supports the KML format (e.g., Google Earth). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. This format contains files with data from tables used for statistical analysis and data visualization of Statistics Canada census data. How to access the data: Open with the Beyond 20/20 application. A database which links and combines data from different files or

File contains multiple tabs, linking to spreadsheets that correspond to data in specific figures. Each spreadsheet contains the data used to calculate averages or plotted on graphs in the indicated figures. (XLSX)

The data used for each figure is included on a separate tab, organized by figure number. (XLSX)

Datasettet inneholder forenklet data om cruiseanløpet i Stavanger for en periode. Ting som antall passasjerer per skip, dato skipet kommer og går, størrelse/lengde på skipet, hvor det er registert, hvor det kommer ifra og hvor det skal, med mer. Ser du etter spesifikke data eller ønsker enklere datasett? For å finne enkle datasett kan du følge denne LENKEN:https://opencom.no/organization/stavangerregionen-havn For nærmere forkalring av datatyper og beskrivelse av felter se LENKE:https://developers.griegconnect.com/apis/explorer/port Dataformater: JSON: Allsidig format for datautveksling, brukt for enkel og effektiv representasjon av strukturerte data, med bred støtte i ulike programmeringsmiljøer. XML: Utbredt for strukturert datautveksling, med egendefinerte tags og hierarki for dataene. YAML: Menneskevennlig form av datastrukturering, spesielt egnet for komplekse datamodeller. CSV: Vanlig filtype for import og analyse av data i ulike verktøy. XLSX: Regnearkformat, nyttig for videre bearbeiding og analyse av dataene. EN: The dataset contains simplified data on cruise calls in Stavanger for a period. Information such as the number of passengers per ship, the date the ship arrives and departs, the size/length of the ship, where it is registered, where it is coming from and where it is headed, and more. Are you looking for specific data or simpler datasets? To find simpler datasets, you can follow this LINK:https://opencom.no/organization/stavangerregionen-havn For a detailed explanation of data types and field descriptions, see LINK:https://developers.griegconnect.com/apis/explorer/port Data Formats: JSON: Versatile data exchange format, used for simple and efficient representation of structured data, with broad support in various programming environments. XML: Widely used for structured data exchange, with custom tags and hierarchy for the data. YAML: Human-friendly form of data structuring, especially suitable for complex data models. CSV: Common file type for importing and analyzing data in various tools. XLSX: Spreadsheet format, useful for further processing and analysis of the data.

Context

Animals are multicellular, eukaryotic organisms in the biological kingdom Animalia. With few exceptions, animals consume organic material, breathe oxygen, have myocytes and are able to move, can reproduce sexually, and grow from a hollow sphere of cells, the blastula, during embryonic development. As of 2022, 2.16 million living animal species have been described—of which around 1.05 million are insects, over 85,000 are mollusks, and around 65,000 are vertebrates. It has been estimated there are around 7.77 million animal species. Animals range in length from 8.5 micrometers (0.00033 in) to 33.6 meters (110 ft). They have complex interactions with each other and their environments, forming intricate food webs. The scientific study of animals is known as zoology.

Content

This dataset encompasses a diverse array of attributes pertaining to various animal species worldwide. The dataset prominently includes fields such as Animal, Height (cm), Weight (kg), Color, Lifespan (years), Diet, Habitat, Predators, Average Speed (km/h), Countries Found, Conservation Status, Family, Gestation Period (days), Top Speed (km/h), Social Structure, and Offspring per Birth. These columns collectively offer a comprehensive understanding of animal characteristics, habitats, behaviors, and conservation statuses. Researchers and enthusiasts can utilize this dataset to analyze animal traits, study their habitats, explore dietary patterns, assess conservation needs, and conduct a wide range of ecological research and wildlife studies.

Dataset Glossary (Column-wise)

• Animal - Name of the animal.
• Height (cm) - Height range in centimeters for the animal.
• Weight (kg) - Weight range in kilograms for the animal.
• Color - Common colors associated with the animal's appearance.
• Lifespan (years) - Average lifespan of the animal in years.
• Diet - Type of diet the animal primarily follows (e.g., Carnivore, Herbivore).
• Habitat - Typical habitat or environment where the animal is found.
• Predators - Natural enemies or organisms that prey on the animal.
• Average Speed (km/h) - The average speed range the animal can achieve in kilometers per hour.
• Countries Found - Countries or regions where the animal is commonly found.
• Conservation Status - The conservation status of the animal as per relevant conservation organizations.
• Family - Taxonomic family the animal belongs to.
• Gestation Period (days) - Range of days representing the gestation or pregnancy period of the animal.
• Top Speed (km/h) - The maximum speed the animal can achieve in kilometers per hour.
• Social Structure - Information about the social behavior or structure of the animal (e.g., Solitary, Group-based).
• Offspring per Birth - The typical number of offspring born per birth or reproduction event for the animal.

Structure of the Dataset

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Acknowledgement

This dataset was generated using information from: https://www.wikipedia.org/. If you wish to delve deeper, you can explore the website.

Cover Photo by: Image by brgfx on Freepik

Thumbnail by: Dog icons created by Flat Icons - Flaticon