The excelforms extension for CKAN provides a mechanism for users to input data into Table Designer tables using Excel-based forms, enhancing data entry efficiency. This extension focuses on streamlining the process of adding data rows to tables within CKAN's Table Designer. A key component of the functionality is the ability to import multiple rows in a single operation, which significant reduces overhead associated with entering multiple data points. Key Features: Excel-Based Forms: Users can enter data using familiar Excel spreadsheets, leveraging their existing skills and software. Table Designer Integration: Designed to work seamlessly with CKAN's Table Designer, extending its functionality to include Excel-based data entry. Multiple Row Import: Supports importing multiple rows of data at once, improving data entry efficiency, especially when dealing with large datasets. Data mapping: Simplifies the process of aligning excel column headers to their corresponding data fields in tables. Improved Data Entry Speed: Provides an alternative to manual data entry, resulting in faster population and easier updates. Technical Integration: The excelforms extension integrates with CKAN by introducing new functionalities and workflows around the Table Designer plugin. The installation instructions specify that this plugin to be added before the tabledesigner plugin. Benefits & Impact: By enabling Excel-based data entry, the excelforms extension improves the user experience for those familiar with spreadsheet software. The ability to import multiple rows simultaneously significantly reduces the time and effort required to populate tables, particularly when dealing with large amounts of data. The impact is better data accessibility through the streamlining of data population workflows.

Data used in the paper

StopWords dataset: Integration of a set of stopwords in English and Portuguese - rev. 1

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StopWords dataset - rev. 1 (two MS-Excel files)

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StopWords Integrated
Basic integration of a set of stopwords (English and Portuguese) for use in Text Mining tasks.

File name 1: StopWords_Integrated_Favaretto.xlsx

Tab 1 of MS-Excel: pt_accent (215 words)
Column Label: stopwords_pt

Tab 2 of MS-Excel: pt_noaccent (208 words)
Column Label: stopwords_pt_na

Tab 3 of MS-Excel: en (213 words)
Column Label: stopwords_en

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StopWords Extended
Extension of a set of stopwords (English and Portuguese) for use in Text Mining tasks.

File name 2: StopWords_Extended_Favaretto.xlsx

Tab 1 of MS-Excel: pt_extend (614 words)
Column Label: stopwords_pt_extend

Tab 2 of MS-Excel: en_extended (483 words)
Column Label: stopwords_en_extend

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Warning: Some words in this set of stopwords may even be misspelled intentionally, as they may occur in practice in texts that are not written correctly.

Aviso: Algumas palavras deste conjunto de stopwords podem até mesmo ter grafia errada de forma intencional, pois podem ocorrer na prática em textos não escritos corretamente.

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Source: elaborated by Prof. Dr. José Eduardo Ricciardi Favaretto based on a mix of several different sources

https://orcid.org/0000-0002-0143-0809
https://lattes.cnpq.br/3790103269421610
https://linkedin.com/in/favaretto

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At CompanyData.com (BoldData), we provide direct access to comprehensive, verified retail company data from around the world—available in easy-to-use Excel files. With a curated list of 38 million retail companies, our database is built on official trade registers, ensuring accuracy, compliance, and depth. Whether you're targeting retailers globally or analyzing markets, our dataset is a reliable foundation for your business strategies.

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Aim of this project is to provide the Diederich Method for calculating the lift distribution of a wing in a Microsoft Excel spreadsheet based on didactic considerations. The Diederich Method is described based on primary and secondary literature. Diagrams are digitized so that the method can run automatically. To optimize the lift distribution of the wing, the elliptical and triangular lift distribution as well as Mason's lift distribution are offered for comparison. A method for calculating the maximum lift coefficient of the wing is integrated into the Diederich Method. To do this, the maximum lift coefficients of the airfoils at the wing root and at the wing tip must be entered in the program. The calculation assumes a trapezoidal wing. Both wing sweep and linear wing twist can be taken into account. The aspect ratio must not assume values that are too small. Subsonic flow and unseparated flow are assumed. Since only the wing is described, all other influences such as from the fuselage or from the engines are not taken into account. The Excel workbook was created for teaching in aircraft preliminary design. At the moment, the Diederich Method is apparently nowhere offered as a spreadsheet. With this work, this gap can be closed. The Excel file is set up in English. The project report with background information is written in German. A "User Guide for the Diederich Method Excel-File" is written in English and is available in this upload and as Appendix C of the project report.

Our analyses are based on 148×148 time- and frequency-domain correlation matrices. A correlation matrix covers all the possible use cases of every activity metric listed in the article. With these activity metrics and different preprocessing methods, we were able to calculate 148 different activity signals from multiple datasets of a single measurement. Each cell of a correlation matrix contains the mean and standard deviation of the calculated Pearson’s correlation coefficients between two types of activity signals based on 42 different subjects’ 10-days-long motion. The small correlation matrices presented both in the article and in the appendixes are derived from these 148 × 148 correlation matrices. This published Excel workbook contains multiple sheets labelled according to their content. The mean and standard deviation values for both time- and frequency-domain correlations can be found on their own separate sheet. Moreover, we reproduced the correlation matrix with an alternatively parametrized digital filter, which doubled the number of sheets to 8. In the Excel workbook, we used the same notation for both the datasets and activity metrics as presented in this article with an extension to the PIM metric: PIMs denotes the PIM metric where we used Simpson’s 3/8 rule integration method, PIMr indicates the PIM metric where we calculated the integral by simple numerical integration (Riemann sum). (XLSX)

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.

This data release consists of a ZIP file that includes: two Excel workbooks, detailed metadata files, and data directories. The Hydraulic Properties Database (HPD; Table-of-AQtests.xlsm) is an interactive Excel workbook that catalogues single-well, aquifer-test results at Pahute Mesa and vicinity. Results from 1,459 single-well, aquifer-test analyses are uniquely identified to 360 tested wells. The Integrated Borehole Workbook (Borehole_Index_PM.xlsm) is an interactive Excel workbook that presents integrated results from slug tests and pumping aquifer tests that took place in 17 boreholes in Pahute Mesa and vicinity from 1962 to 2013. The Metadata folder has detailed metadata.xml files for both Excel workbooks. Directories labeled USGS, NavarroPDF, or NavarroAQT are accompanying data that both Excel workbooks access through hyperlinks and are required to be downloaded for the Excel workbooks to properly function.

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.

Global Hybrid Integration Platform Market size was valued at USD 31.69 Billion in 2022 and is poised to grow from USD 35.49 Billion in 2023 to USD 87.88 Billion by 2031, growing at a CAGR of 12% in the forecast period (2024-2031).

Final EFO-CLO alignment result. The 874 EFO-CLO mapped cell lines aligned and merged into CLO (Tab. 1 in the excel file) and 344 EFO unique immortalized permanent cell lines added to CLO (Tab. 2 in the excel file). File is stored in Microsoft Excel spreadsheet (xlsx) format. (XLSX 54Â kb)

This is the raw SO2 data. Each sheet (tab) is formatted to be exported as a .csv for use with the R-code (AQ-June20.R). In order for this code to work properly, it is important that this file remain intact. Do not change the column names or codes for data, for example. And to be safe, don’t even sort. One simple change in the excel file could make the code full of bugs.

This is the raw H2S data- concentration of H2S in parts per million in the biogas. Each sheet (tab) is formatted to be exported as a .csv for use with the R-code (AQ-June20.R). In order for this code to work properly, it is important that this file remain intact. Do not change the column names or codes for data, for example. And to be safe, don’t even sort. One simple change in the excel file could make the code full of bugs.

The Hybrid Data Integration market is experiencing robust growth, driven by the increasing need for organizations to seamlessly integrate data from diverse sources – cloud, on-premise, and big data platforms. The market's expansion is fueled by the rising adoption of hybrid cloud strategies, the exponential growth of data volume and velocity, and the imperative to leverage data for improved decision-making across various business functions. This necessitates solutions that offer flexibility, scalability, and security, which hybrid data integration services excel at providing. The market is segmented by deployment (cloud, on-premise), organization size (small, medium, large), industry (BFSI, healthcare, retail, manufacturing), and integration type (ETL, EAI, data virtualization). While precise figures for market size and CAGR are unavailable, leveraging similar technology markets suggests a 2025 market size in the range of $15 billion, exhibiting a CAGR of approximately 15% between 2025 and 2033. This growth is expected to continue as businesses strive to gain a competitive edge through data-driven insights. Leading vendors like Informatica, IBM, and MuleSoft are aggressively innovating and expanding their offerings to capture market share, while smaller players are focusing on niche segments and specialized capabilities. The competitive landscape is dynamic, with established players facing challenges from agile startups and open-source solutions. The major restraints on market growth include the complexity of implementing hybrid integration solutions, the need for skilled professionals, and concerns about data security and governance. However, the advantages of enhanced data visibility, improved operational efficiency, and accelerated digital transformation are overcoming these obstacles, ensuring sustained market expansion. Future growth will be further propelled by the increasing adoption of AI and machine learning within hybrid data integration platforms, enabling automated data processing and improved data quality. The integration of advanced analytics capabilities will also significantly contribute to the market's expansion in the coming years.

Standards support synthetic biology research by enabling the exchange of component information. However, using formal representations, such as the Synthetic Biology Open Language (SBOL), typically requires either a thorough understanding of these standards or a suite of tools developed in concurrence with the ontologies. Since these tools may be a barrier for use by many practitioners, the Excel–SBOL Converter was developed to facilitate the use of SBOL and integration into existing workflows. The converter consists of two Python libraries: one that converts Excel templates to SBOL and another that converts SBOL to an Excel workbook. Both libraries can be used either directly or via a SynBioHub plugin.

The Alaska Geochemical Database Version 2.0 (AGDB2) contains new geochemical data compilations in which each geologic material sample has one "best value" determination for each analyzed species, greatly improving speed and efficiency of use. Like the Alaska Geochemical Database (AGDB) before it, the AGDB2 was created and designed to compile and integrate geochemical data from Alaska in order to facilitate geologic mapping, petrologic studies, mineral resource assessments, definition of geochemical baseline values and statistics, environmental impact assessments, and studies in medical geology. This relational database, created from the Alaska Geochemical Database (AGDB) that was released in 2011, serves as a data archive in support of present and future Alaskan geologic and geochemical projects, and contains data tables in several different formats describing historical and new quantitative and qualitative geochemical analyses. The analytical results were determined by 85 laboratory and field analytical methods on 264,095 rock, sediment, soil, mineral and heavy-mineral concentrate samples. Most samples were collected by U.S. Geological Survey (USGS) personnel and analyzed in USGS laboratories or, under contracts, in commercial analytical laboratories. These data represent analyses of samples collected as part of various USGS programs and projects from 1962 through 2009. In addition, mineralogical data from 18,138 nonmagnetic heavy mineral concentrate samples are included in this database. The AGDB2 includes historical geochemical data originally archived in the USGS Rock Analysis Storage System (RASS) database, used from the mid-1960s through the late 1980s and the USGS PLUTO database used from the mid-1970s through the mid-1990s. All of these data are currently maintained in the National Geochemical Database (NGDB). Retrievals from the NGDB were used to generate most of the AGDB data set. These data were checked for accuracy regarding sample location, sample media type, and analytical methods used. This arduous process of reviewing, verifying and, where necessary, editing all USGS geochemical data resulted in a significantly improved Alaska geochemical dataset. USGS data that were not previously in the NGDB because the data predate the earliest USGS geochemical databases, or were once excluded for programmatic reasons, are included here in the AGDB2 and will be added to the NGDB. The AGDB2 data provided here are the most accurate and complete to date, and should be useful for a wide variety of geochemical studies. The AGDB2 data provided in the linked database may be updated or changed periodically.

Presentation The objective of the programme is to mobilise and structure all actors in the educational community (State, local authorities, parents, associations, extracurricular workers, social workers, schools and colleges...) around shared educational issues to consolidate the care of children and young people in priority urban policy areas, from early childhood to their professional integration, at all times and in all living areas. Educational Cities are located in large neighbourhoods of social housing, with major urban dysfunctions and school mix issues. #### Data The dataset contains: — the list of educational cities | Name | Description | |---|---| | id_cite| ID of the Educational City | | lib_cite | Text of the Educational City | — the list of municipalities covered by the programme | Name | Description | |---|---| | insee_com| code insee de la commune | | lib_com | town label | | id_cite| ID of the Educational City | | lib_cite | Text of the Educational City | — the list of priority areas covered by the programme | Name | Description | |---|---| | yearnee| year of initiation of the device in the territory | | id_cite| ID of the Educational City | | quote_sister| Sister Educational City | | lib_cite| language of the Educational City | | qp_intercom | intercommunal priority neighbourhood | | com_cite | membership of the commune of qp in the perimeter of the educational city | #### Useful Links — crossing with other ANCT devices (data.gouv) — detailed presentation of the programme (ANCT) — Opening the data file If you are using the Microsoft Excel spreadsheet, a particular operation is required to open the data file: 1. Create a new Excel workbook 2. Click on the **Data tab located in the ribbon and then click from the text 3. Choose the location of the csv file and click Importer 4. In the window that opens, choose the option Delimited and in File Origin, choose 65001: Unicode UTF8. Click on Next 5. Select only the Separator Virgule. Click on Next 6. Choose the right column data format by referring to the dataset documentation. Click Finish.

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 Excel spreadsheet contains the raw data for the research project “Integrating patients with intermittent claudication into an established cardiac rehabilitation programme: a feasibility study with embedded pilot.”This study aimed to assess the feasibility of integrating patients with intermittent claudication (IC) into an established Cardiac Rehabilitation Programme (CRP), and to evaluate trial procedures, and to collect pilot data to guide the methodology for a future randomised control trial (RCT).A prospective, non-randomised controlled trial was conducted using two NHS hospitals. Feasibility was evaluated using a mixed methods approach, with quantitative measures including eligibility, consent, adherence, and adverse event rates, and qualitative interviews and focus groups assessed the acceptability among patients and service providers. Descriptive statistics, and thematic analysis was used to analyse the data.People with symptomatic peripheral artery disease (PAD) were considered for inclusion if they were aged 18 or over, diagnosed with PAD within the past 12 months, and had not had previous treatment for PAD. People with coronary artery disease (CAD) were considered for inclusion if they were aged 18 or over, diagnosed with CAD in the past 12 months, and had no previous diagnosis of PAD. Of eligible IC patients referred to the integrated CRP, 24% (n=17) consented to participate in the trial. A total of 10 IC and CAD patients from the integrated CRP, and 10 CRP staff members took part in the qualitative component of the study.Participants diagnosed with PAD were referred to either an IC only rehabilitation programme or a novel integrated cardiovascular rehabilitation programme (CRP). Both programmes consisted of once-a-week session for twelve weeks incorporating exercise and educations.The spreadsheet includes (on separate tabs):Participant demographicsParticipant anthropometricsParticipant activity dataExercise test resultsQuality of life questionnairesThere is a separate Word document which provides an overview of the individual tabs and details about abbreviations used.This project did not recieve any funding and was part of a Professional Doctorate.

The Joint Army Navy NASA Air Force Modeling and Simulation Subcommittee's Integrated Health Management panel was started about 6 years ago to help foster communication and collaboration in health management related issues for liquid and solid rocket propulsion systems. The panel is co-chaired by Mr. Scott Hyde (ATK) and Ashok N. Srivastava, Ph.D. (NASA). In order to have a common langauge for health management, we need to have a common set of definitions. We have attached a MS Excel spreadsheet that covers the many terms that are of interest to us in the field. Please take a look at the definitions and provide comments and additional terms (with or without definitions) using the feedback box below. We will compile all the definitions into a master list for submittal to the Modeling and Simulation Subcommittee.