Data organization for the figures in the document: Figure 3A LineOutWithSun_SSAzi_135to225_green_Correct_ROI5_INFO.xls Figure 3b LineOutWithSun_SSAzi_m45to45_green_Correct_ROI5_INFO.xls Figure 4 fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Correct_ROI5_INFO.xls fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Sim_Correct_ROI5_INFO.xls Figure 5a LineOut_Camera_Elevation_SqAzi_m180to0_green_Sim_Correct_ROI5_INFO.xls LineOut_Camera_Elevation_SqAzi_m180to0_green_Correct_ROI5_INFO.xls Figure 5b LineOut_Camera_Elevation_SqAzi_0to180_green_Correct_ROI5_INFO.xls LineOut_Camera_Elevation_SqAzi_0to180_green_Sim_Correct_ROI5_INFO.xls Figure 6a LineOutColor_SqAzi_m180to0_CP_20to50_Correct_ROI5_INFO.xls Figure 6b LineOutROI_SqAzi_m180to0_CP_20to50_green_Correct_INFO.xls Figure 7 fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Correct_ROI5_INFO.xls LineOut_MeshAoPDif_Camera_Elevation_SqAzi_0to180_green_Correct_ROI5_INFO.xls LineOut_MeshAoPDif_Camera_Elevation_SqAzi_m180to0_green_Correct_ROI5_INFO.xls

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

With a step-by-step approach, learn to prepare Excel files, data worksheets, and individual data columns for data analysis; practice conditional formatting and creating pivot tables/charts; go over basic principles of Research Data Management as they might apply to an Excel project. Avec une approche étape par étape, apprenez à préparer pour l’analyse des données des fichiers Excel, des feuilles de calcul de données et des colonnes de données individuelles; pratiquez la mise en forme conditionnelle et la création de tableaux croisés dynamiques ou de graphiques; passez en revue les principes de base de la gestion des données de recherche tels qu’ils pourraient s’appliquer à un projet Excel.

Students use small mammal data from the National Ecological Observatory Network to understand necessary steps of data management from data collection to data analysis by re-organising excel sheets in an R-compatible format and doing basic analysis in R

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.

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. The PAD-US 3.0 Combined Fee, Designation, Easement feature class (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to remove overlaps, avoiding overestimation in protected area statistics and to support user needs. A Python scripted process ("PADUS3_0_CreateVectorAnalysisFileScript.zip") associated with this data release prioritized overlapping designations (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation status (e.g. GAP Status Code 1 over 2), public access values (in the order of Closed, Restricted, Open, Unknown), and geodatabase load order (records are deliberately organized in the PAD-US full inventory with fee owned lands loaded before overlapping management designations, and easements). The Vector Analysis File ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") associated item of PAD-US 3.0 Spatial Analysis and Statistics ( https://doi.org/10.5066/P9KLBB5D ) was clipped to the Census state boundary file to define the extent and serve as a common denominator for statistical summaries. Boundaries of interest to stakeholders (State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative) were incorporated into separate geodatabase feature classes to support various data summaries ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip") and Comma-separated Value (CSV) tables ("PADUS3_0SummaryStatistics_TabularData_CSV.zip") summarizing "PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip" are provided as an alternative format and enable users to explore and download summary statistics of interest (Comma-separated Table [CSV], Microsoft Excel Workbook [.XLSX], Portable Document Format [.PDF] Report) from the PAD-US Lands and Inland Water Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). In addition, a "flattened" version of the PAD-US 3.0 combined file without other extent boundaries ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") allow for other applications that require a representation of overall protection status without overlapping designation boundaries. The "PADUS3_0VectorAnalysis_State_Clip_CENSUS2020" feature class ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.gdb") is the source of the PAD-US 3.0 raster files (associated item of PAD-US 3.0 Spatial Analysis and Statistics, https://doi.org/10.5066/P9KLBB5D ). Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types represented in some manner, while work continues to maintain updates and improve data quality (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, changes in protected area status between versions of the PAD-US may be attributed to improving the completeness and accuracy of the spatial data more than actual management actions or new acquisitions. USGS provides no legal warranty for the use of this data. While PAD-US is the official aggregation of protected areas ( https://www.fgdc.gov/ngda-reports/NGDA_Datasets.html ), agencies are the best source of their lands data.

GSA, the nation's largest public real estate organization, provides workspace for over one million federal workers. These employees, along with government property, are housed in space owned by the federal government and in leased properties including buildings, land, antenna sites, etc. across the country.

This repository contains the data supporting the manuscript "A Generic Scenario Analysis of End-of-Life Plastic Management: Chemical Additives" (to be) submitted to the Energy and Environmental Science Journal https://pubs.rsc.org/en/journals/journalissues/ee#!recentarticles&adv This repository contains Excel spreadsheets used to calculate material flow throughout the plastics life cycle, with a strong emphasis on chemical additives in the end-of-life stages. Three major scenarios were presented in the manuscript: 1) mechanical recycling (existing recycling infrastructure), 2) implementing chemical recycling to the existing plastics recycling, and 3) extracting chemical additives before the manufacturing stage. Users would primarily modify values on the yellow tab "US 2018 Facts - Sensitivity". Values highlighted in yellow may be changed for sensitivity analysis purposes. Please note that the values shown for MSW generated, recycled, incinerated, landfilled, composted, imported, exported, re-exported, and other categories in this tab were based on 2018 data. Analysis for other years can be made possible with a replicate version of this spreadsheet and the necessary data to replace those of 2018. Most of the tabs, especially those that contain "Stream # - Description", do not require user interaction. They are intermediate calculations that change according to the user inputs. It is available for the user to see so that the calculation/method is transparent. The major results of these individual stream tabs are ultimately compiled into one summary tab. All streams throughout the plastics life cycle, for each respective scenario (1, 2, and 3), are shown in the "US Mat Flow Analysis 2018" tab. For each stream, we accounted the approximate mass of plastics found in MSW, additives that may be present, and non-plastics. Each spreadsheet contains a representative diagram that matches the stream label. This illustration is placed to aid the user with understanding the connection between each stage in the plastics' life cycle. For example, the Scenario 1 spreadsheet uniquely contains Material Flow Analysis Summary, in addition to the LCI. In the "Material Flow Analysis Summary" tab, we represented the input, output, releases, exposures, and greenhouse gas emissions based on the amount of materials inputted into a specific stage in the plastics life cycle. The "Life Cycle Inventory" tab contributes additional calculations to estimate land, air, and water releases. Figures and Data - A gs analysis on eol plastic management This word document contains the raw data used to create all the figures in the main manuscript. The major references used to obtain the data are also included where appropriate.

The Data Crunch handout series, developed at the Research Data Service Center at the University of Bonn, concisely describes various aspects of research data management (RDM) and is aimed at all researchers and interested parties who want to expand their knowledge of RDM.In the "DIY: FAIR Spreadsheet" handout a summary of best practices, the do's and dont's when working with spreadsheets and examples of helpful resources are provided. ---------------------- The Data Crunch "DIY: FAIR Spreadsheet" handout was created by Ewa E. Bres from the Research Data Service Center.

Excel spreadsheet containing raw data, organized by figure.

This Excel file contains a comprehensive list of fields in the DIEM dataset along with detailed descriptions for each. The information is organized across multiple sheets within the workbook. You can navigate between sheets by selecting them from the list at the bottom of the Excel window. The first sheet contains a readme text. The second sheet provides the list of fields and detailed descriptions for the DIEM Microdata, which includes data at the household level. The subsequent four sheets contain the list of fields and their descriptions for the four DIEM aggregated datasets, each corresponding to a specific DIEM thematic area:

Income, Shocks and Needs thematic area Crop Production thematic area Livestock Production thematic area Food Security thematic area

The datasets in this zip file are in support of Intelligent Transportation Systems Joint Program Office (ITS JPO) report FHWA-JPO-16-385, "Analysis, Modeling, and Simulation (AMS) Testbed Development and Evaluation to Support Dynamic Mobility Applications (DMA) and Active Transportation and Demand Management (ATDM) Programs — Evaluation Report for ATDM Program," https://rosap.ntl.bts.gov/view/dot/32520 and FHWA-JPO-16-373, "Analysis, modeling, and simulation (AMS) testbed development and evaluation to support dynamic mobility applications (DMA) and active transportation and demand management (ATDM) programs : Dallas testbed analysis plan," https://rosap.ntl.bts.gov/view/dot/32106 The files in this zip file are specifically related to the Dallas Testbed. The compressed zip files total 2.2 GB in size. The files have been uploaded as-is; no further documentation was supplied by NTL. All located .docx files were converted to .pdf document files which are an open, archival format. These pdfs were then added to the zip file alongside the original .docx files. These files can be unzipped using any zip compression/decompression software. This zip file contains files in the following formats: .pdf document files which can be read using any pdf reader; .cvs text files which can be read using any text editor; .txt text files which can be read using any text editor; .docx document files which can be read in Microsoft Word and some other word processing programs; . xlsx spreadsheet files which can be read in Microsoft Excel and some other spreadsheet programs; .dat data files which may be text or multimedia; as well as GIS or mapping files in the fowlling formats: .mxd, .dbf, .prj, .sbn, .shp., .shp.xml; which may be opened in ArcGIS or other GIS software. [software requirements] These files were last accessed in 2017.

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.

Data files for F1000Research manuscript submission “DataUp: A tool to help researchers describe and share tabular data”. Authors: C Strasser, J Kunze, S Abrams, P Cruse. Submitted December 2013. readme.txt has description of all files in this fileset.

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.

This dataset resulted from conducting focus groups with scientists from five disciplines (atmospheric and earth science, chemistry, computer science, ecology, and neuroscience) about data management to lead into a discussion of what features they think are necessary to include in data repository systems and services to help them implement the data sharing and preservation parts of their data management plans. Participants identified metadata quality control and training as problem areas in data management. Participants discussed several desired repository features, including: metadata control, data traceability, security, stable infrastructure, and data use restrictions. Our dataset includes five anonymized focus group transcripts in .pdf file format (one for each focus group with scientists from each discipline), our codebook as a spreadsheet in excel file format (.xlsx), and coded segments of our transcript text to visualize our data analysis in an excel spreadsheet in excel file format (.xlsx).

Associated with all model outputs and options

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.

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.

Raw data used in analysis of determinants of dividend policy - a case of banking sector in Serbia.