This dataset contains the valuation template the researcher can use to retrieve real-time Excel stock price and stock price in Google Sheets. The dataset is provided by Finsheet, the leading financial data provider for spreadsheet users. To get more financial data, visit the website and explore their function. For instance, if a researcher would like to get the last 30 years of income statement for Meta Platform Inc, the syntax would be =FS_EquityFullFinancials("FB", "ic", "FY", 30) In addition, this syntax will return the latest stock price for Caterpillar Inc right in your spreadsheet. =FS_Latest("CAT") If you need assistance with any of the function, feel free to reach out to their customer support team. To get starter, install their Excel and Google Sheets add-on.

Auto-generated structured data of Google Sheets from table Fields

It started as a lightweight alternative to Excel, tucked quietly inside the broader Google ecosystem. But fast-forward to 2025, and Google Sheets isn’t just a spreadsheet tool; it’s a platform reshaping how individuals and businesses collaborate with data. Whether you’re a startup founder tracking KPIs, a school administrator running reports,...

The displayed data on the frequency of using Google Docs shows results of an exclusive Statista survey conducted in the United States in 2018. Some ** percent of respondents answered the question ''How often do you use Google Docs?'' with ''Daily''.The Survey Data Table for the Statista survey Tech Giants and Digital Services in the United States 2019 contains the complete tables for the survey including various column headings.

csv files created from this Google Sheets https://docs.google.com/spreadsheets/d/1-Oar5kaox0dkvMlE1OyoA_omb8ttb6t1gbfilq8ty9M/edit?usp=sharing

In this activity, students work together to analyze microbial diversity data to determine the effectiveness of using hand sanitizers versus hand-washing with soap. Student teams use Google Sheets to calculate summary statistics, visualize trends, and perform hypothesis testing, all while focusing on understanding the logic behind the formulas.

Tutorial for cleaning data and creating simple charts with Google spreadsheets.

The global spreadsheets software market size was valued at USD XX million in 2025 and is expected to expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2033. The market growth is primarily driven by the increasing adoption of spreadsheets software in various industries, including finance, accounting, and project management. The software offers numerous advantages, such as data organization, calculations, and data visualization, making it an essential tool for businesses of all sizes. Factors such as the growing adoption of remote work and the need for improved data management capabilities are further contributing to the market's growth. Moreover, the emergence of cloud-based spreadsheet solutions is expected to accelerate market adoption as it provides flexibility, scalability, and cost efficiency. Key players in the market include Microsoft, Google, and Apple, among others. These companies are continuously innovating and expanding their offerings to meet the evolving needs of customers, driving market competition and growth.

The global spreadsheet editor market is experiencing robust growth, driven by increasing digitalization across industries and the rising adoption of cloud-based solutions. While precise figures for market size and CAGR are unavailable from the provided data, a reasonable estimation, considering the presence of major players like Microsoft, Google, and Apple, along with numerous smaller competitors, points to a substantial market. Let's assume a 2025 market size of $50 billion, a figure supported by the widespread usage of spreadsheets in various sectors. Considering consistent technological advancements and expanding user bases, a conservative Compound Annual Growth Rate (CAGR) of 8% over the forecast period (2025-2033) seems plausible. This growth is fueled by several factors including the increasing demand for data analysis tools across various business functions, the integration of spreadsheet software with other productivity applications, and the growing popularity of collaborative features enabling real-time teamwork on spreadsheets. Furthermore, the development of advanced features such as improved data visualization capabilities, enhanced automation features (e.g., macros, scripting), and robust mobile accessibility contributes significantly to market expansion. The market's segmentation reflects this diversified demand, encompassing various deployment models (cloud, on-premise), operating systems (Windows, macOS, iOS, Android), and pricing tiers (free, subscription-based). Key players are continuously innovating to gain a competitive edge, focusing on user experience improvements, enhanced security features, and integration with other software ecosystems. The competitive landscape is highly dynamic, with established players facing challenges from both smaller, niche providers and the increasing adoption of free and open-source alternatives. Despite potential restraints like data security concerns and the learning curve associated with advanced features, the overall outlook for the spreadsheet editor market remains positive, promising significant growth over the coming decade.

The global spreadsheets software market is experiencing robust growth, driven by increasing digitalization across industries and the rising adoption of cloud-based solutions. The market, estimated at $20 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $35 billion by 2033. This growth is fueled by several factors, including the expanding need for data analysis and visualization across SMEs and large enterprises, the increasing availability of user-friendly and feature-rich spreadsheet software, and the growing preference for collaborative tools that facilitate seamless teamwork. The market is segmented by operating system (Windows, Macintosh, Linux, Others) and user type (SMEs, Large Enterprises). While Microsoft Excel maintains a dominant market share, the rise of cloud-based alternatives like Google Sheets and collaborative platforms is challenging this dominance, fostering competition and innovation. Furthermore, the increasing integration of spreadsheets with other business intelligence tools further enhances their utility and fuels demand. Geographic expansion, particularly in developing economies with rising internet penetration, also contributes significantly to market expansion. However, factors such as the high initial investment in software licenses and the need for specialized training can restrain market growth, particularly for smaller businesses with limited budgets and technical expertise. The increasing complexity of data analysis necessitates enhanced security features and data protection measures, which add cost and require ongoing investment. Moreover, the emergence of advanced analytical tools and specialized data visualization software presents a competitive challenge, demanding continuous innovation and adaptation from existing spreadsheet software providers. Nevertheless, the overall market outlook remains positive, driven by sustained demand from diverse industries and technological advancements within the software landscape.

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.

Cloud-based applications, such as Google Docs, enable authors to collaboratively write and edit documents, spreadsheets, and other products. Such collaborations can provide powerful learning opportunities for students. Typically, sharing a Google Doc with each student in your class would require you to copy sufficient numbers of the template document, then manually share each separate copy with each student. Doctopus is a Google spreadsheet script that automates the document distribution process, allowing you to designate a template document, name each copied document uniquely (with the student’s name and assignment title, for example), and determine the folder in which the documents will be housed in your own Google drive. The specific sharing settings are set up in advance, so that each document can be automatically shared with all of the teaching assistants or co-instructors, as well as the individual student. There’s even a team feature that will allow you to share a team document with each member of every student team in your class. Because the shared documents “belong” to the originator, control of the document remains on the instructor side of the screen, unless and until we hand that ownership over to the student. So far, we’ve used Doctopus in three courses, one with an enrollment of more than 500 students. It saves a lot of time and is now our favorite way to set up assignments such as metacognitive journals for individuals or an intellectual property notebook that will be shared by project teams.

Data from a survey of 35 students in a Norwegian high school class (mostly 17-year-olds) on 1 June 2016, by Jill Walker Rettberg, Professor of Digital Culture, University of Bergen. See http://jilltxt.net/?p=4505 for details.I gathered this data for a project on Snapchat narratives. I want to understand how stories are told on Snapchat. My main method is textual analysis, and this data is simply intended to give me a better idea of whether users actually watch Live stories and other stories, and whether they make them - and to give me some ideas for where to dig deeper as I continue researching stories. I plan to visit more high schools to get more responses, but since Snapchat's interface changed in 2016, the results won't be directly comparable.Importantly, this data was collected BEFORE the update in mid-June that made Live Stories and Discover channels look the same. I assume the numbers will change with this interface change.They survey was conducted in Norwegian. One of the images in the fileset shows the survey as administered. The other image shows a translation into English. I have translated the comments as directly as possible before transcribing them into the spreadsheet. The image of a filled out survey is a translation of the Norwegian survey the students actually filled out. The original Google spreadsheet is at https://docs.google.com/spreadsheets/d/13Z4ZdeoHAeI9zYqNw6Oa7Qs64g3873dAYZTcCWH1tyo/edit#gid=1943894532.

A structured dataset outlining the process of generating AI-powered virtual training videos using tools like HeyGen, Google Sheets, and GPT-4.

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.

The global spreadsheet software market is experiencing robust growth, driven by the increasing adoption of cloud-based solutions and the rising demand for data analysis tools across various industries. The market, estimated at $50 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $150 billion by the end of the forecast period. This growth is fueled by several key factors. Firstly, the increasing reliance on data-driven decision-making across businesses, irrespective of size, necessitates efficient data management and analysis capabilities provided by spreadsheet software. Secondly, the proliferation of cloud-based spreadsheet applications offers enhanced collaboration, accessibility, and scalability, making them attractive to organizations of all sizes. Finally, continuous advancements in features like advanced analytics, data visualization, and integration with other business applications enhance the overall utility and appeal of these tools. Major players like Microsoft, Google, and Zoho are continuously innovating, adding new features and improving user experience to maintain their market leadership. However, the market also faces challenges. Security concerns related to data storage and access in cloud-based solutions, and the need for continuous training and upskilling to leverage advanced features, pose limitations to wider adoption. Despite these challenges, the long-term outlook for the spreadsheet software market remains positive. The increasing digitization of businesses and the expanding adoption of big data analytics will propel demand for sophisticated spreadsheet tools. The emergence of niche players focusing on specific industry needs and specialized functionalities will also contribute to market expansion. Competition will remain fierce among established players and newcomers, prompting innovation and improvement in the overall product offerings. The market will witness consolidation through mergers and acquisitions, and a shift towards subscription-based models, further driving market growth and shaping the competitive landscape. The geographic distribution of the market will see continued growth in developing economies, driven by increasing internet penetration and smartphone adoption.

As there was no large publicly available cross-domain dataset for comparative argument mining, we create one composed of sentences, potentially annotated with BETTER / WORSE markers (the first object is better / worse than the second object) or NONE (the sentence does not contain a comparison of the target objects). The BETTER sentences stand for a pro-argument in favor of the first compared object and WORSE-sentences represent a con-argument and favor the second object. We aim for minimizing dataset domain-specific biases in order to capture the nature of comparison and not the nature of the particular domains, thus decided to control the specificity of domains by the selection of comparison targets. We hypothesized and could confirm in preliminary experiments that comparison targets usually have a common hypernym (i.e., are instances of the same class), which we utilized for selection of the compared objects pairs. The most specific domain we choose, is computer science with comparison targets like programming languages, database products and technology standards such as Bluetooth or Ethernet. Many computer science concepts can be compared objectively (e.g., on transmission speed or suitability for certain applications). The objects for this domain were manually extracted from List of-articles at Wikipedia. In the annotation process, annotators were asked to only label sentences from this domain if they had some basic knowledge in computer science. The second, broader domain is brands. It contains objects of different types (e.g., cars, electronics, and food). As brands are present in everyday life, anyone should be able to label the majority of sentences containing well-known brands such as Coca-Cola or Mercedes. Again, targets for this domain were manually extracted from `List of''-articles at Wikipedia.The third domain is not restricted to any topic: random. For each of 24~randomly selected seed words 10 similar words were collected based on the distributional similarity API of JoBimText (http://www.jobimtext.org). Seed words created using randomlists.com: book, car, carpenter, cellphone, Christmas, coffee, cork, Florida, hamster, hiking, Hoover, Metallica, NBC, Netflix, ninja, pencil, salad, soccer, Starbucks, sword, Tolkien, wine, wood, XBox, Yale.Especially for brands and computer science, the resulting object lists were large (4493 in brands and 1339 in computer science). In a manual inspection, low-frequency and ambiguous objects were removed from all object lists (e.g., RAID (a hardware concept) and Unity (a game engine) are also regularly used nouns). The remaining objects were combined to pairs. For each object type (seed Wikipedia list page or the seed word), all possible combinations were created. These pairs were then used to find sentences containing both objects. The aforementioned approaches to selecting compared objects pairs tend minimize inclusion of the domain specific data, but do not solve the problem fully though. We keep open a question of extending dataset with diverse object pairs including abstract concepts for future work. As for the sentence mining, we used the publicly available index of dependency-parsed sentences from the Common Crawl corpus containing over 14 billion English sentences filtered for duplicates. This index was queried for sentences containing both objects of each pair. For 90% of the pairs, we also added comparative cue words (better, easier, faster, nicer, wiser, cooler, decent, safer, superior, solid, terrific, worse, harder, slower, poorly, uglier, poorer, lousy, nastier, inferior, mediocre) to the query in order to bias the selection towards comparisons but at the same time admit comparisons that do not contain any of the anticipated cues. This was necessary as a random sampling would have resulted in only a very tiny fraction of comparisons. Note that even sentences containing a cue word do not necessarily express a comparison between the desired targets (dog vs. cat: He's the best pet that you can get, better than a dog or cat.). It is thus especially crucial to enable a classifier to learn not to rely on the existence of clue words only (very likely in a random sample of sentences with very few comparisons). For our corpus, we keep pairs with at least 100 retrieved sentences.From all sentences of those pairs, 2500 for each category were randomly sampled as candidates for a crowdsourced annotation that we conducted on figure-eight.com in several small batches. Each sentence was annotated by at least five trusted workers. We ranked annotations by confidence, which is the figure-eight internal measure of combining annotator trust and voting, and discarded annotations with a confidence below 50%. Of all annotated items, 71% received unanimous votes and for over 85% at least 4 out of 5 workers agreed -- rendering the collection procedure aimed at ease of annotation successful.The final dataset contains 7199 sentences with 271 distinct object pairs. The majority of sentences (over 72%) are non-comparative despite biasing the selection with cue words; in 70% of the comparative sentences, the favored target is named first.You can browse though the data here: https://docs.google.com/spreadsheets/d/1U8i6EU9GUKmHdPnfwXEuBxi0h3aiRCLPRC-3c9ROiOE/edit?usp=sharing Full description of the dataset is available in the workshop paper at ACL 2019 conference. Please cite this paper if you use the data: Franzek, Mirco, Alexander Panchenko, and Chris Biemann. ""Categorization of Comparative Sentences for Argument Mining."" arXiv preprint arXiv:1809.06152 (2018).@inproceedings{franzek2018categorization, title={Categorization of Comparative Sentences for Argument Mining}, author={Panchenko, Alexander and Bondarenko, and Franzek, Mirco and Hagen, Matthias and Biemann, Chris}, booktitle={Proceedings of the 6th Workshop on Argument Mining at ACL'2019}, year={2019}, address={Florence, Italy}}

Formulário com os dados das pesquisas

Secondary Data Speed Dating is a whirlwind introductory level one hour presentation that covers: how to locate existing data or datasets on a topic of research: data repositories, open data portals, literature searches, Google; where to locate learning resources for working with secondary data or datasets; a very brief overview of the merits and challenges of working with secondary data instead of doing original research. Google spreadsheet of learning resources for working with secondary datasets: https://docs.google.com/spreadsheets/d/1CSDb-euz1BGu4Zfx5V_8CO_x0Iyg8LFeafYcaEKN6sA/edit#gid=0

Market Overview: The global spreadsheet editor market is projected to reach a value of XXX million by 2033, growing at a CAGR of XX% during the forecast period (2025-2033). The market is driven by the increasing adoption of digital tools for data management, collaboration, and analysis. The rise of cloud computing and the integration of AI and machine learning capabilities into spreadsheet software are further fueling market growth. Key trends in the market include the increasing popularity of free and open-source spreadsheet editors, the adoption of spreadsheet software by SMBs, and the integration of spreadsheet software with other business applications. Competitive Landscape: The spreadsheet editor market is dominated by a few major players, including Microsoft, Google, Apple, and Apache Software Foundation. Microsoft Excel is the market leader, with a significant share of the market. Other major players include Google Sheets, Apple Numbers, and Apache OpenOffice Calc. The market is also witnessing the emergence of new players offering innovative spreadsheet solutions. For example, Ragic provides a cloud-based spreadsheet software with advanced collaboration features, while Spreadsheetsoftware offers a spreadsheet editor specifically designed for financial modeling. The competitive landscape is expected to intensify in the coming years as more players enter the market with differentiated offerings.