Spreadsheet Version Control Market Outlook

According to our latest research, the global Spreadsheet Version Control market size reached USD 1.12 billion in 2024, reflecting the growing demand for robust data management and collaboration tools across industries. The market is expected to expand at a CAGR of 16.2% from 2025 to 2033, reaching a forecasted value of USD 4.02 billion by 2033. This remarkable growth is primarily fueled by the increasing adoption of cloud-based solutions, escalating data governance requirements, and the rise of remote and hybrid work environments that necessitate seamless version tracking and real-time collaboration.

One of the principal growth factors driving the Spreadsheet Version Control market is the rising complexity and volume of enterprise data. Organizations are increasingly reliant on spreadsheets for critical business operations, financial planning, and reporting. As data sets grow larger and more complex, the risks associated with manual versioning, accidental overwrites, and data loss have become significant concerns. This has led to a surge in demand for automated version control solutions that can ensure data integrity, facilitate audit trails, and enhance regulatory compliance. Furthermore, the proliferation of remote work has heightened the need for real-time collaboration, making version control an indispensable feature for modern enterprises.

Another key driver is the increasing emphasis on regulatory compliance and data governance across sectors such as BFSI, healthcare, and manufacturing. Regulatory frameworks like GDPR, SOX, and HIPAA require organizations to maintain accurate records of data changes, access logs, and audit trails. Spreadsheet version control solutions provide the necessary infrastructure to meet these requirements, thereby reducing the risk of non-compliance and associated penalties. Additionally, the growing integration of version control with other business intelligence and analytics platforms is enabling organizations to derive actionable insights from historical data, further amplifying the value proposition of these solutions.

Technological advancements and the advent of cloud computing have also played a pivotal role in shaping the growth trajectory of the Spreadsheet Version Control market. Cloud-based solutions offer unparalleled scalability, flexibility, and ease of deployment, allowing organizations of all sizes to implement robust version control mechanisms without significant upfront investments. The integration of artificial intelligence and machine learning capabilities is further enhancing the functionality of these solutions, enabling predictive analytics, anomaly detection, and automated error correction. As organizations continue to embrace digital transformation, the demand for advanced spreadsheet version control tools is expected to witness sustained growth.

From a regional perspective, North America currently dominates the Spreadsheet Version Control market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The regionÂ’s leadership can be attributed to the high concentration of technology-driven enterprises, early adoption of cloud-based solutions, and stringent regulatory frameworks. Meanwhile, Asia Pacific is emerging as the fastest-growing market, driven by rapid digitalization, increasing IT investments, and the proliferation of SMEs adopting advanced data management tools. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as organizations in these regions increasingly recognize the importance of data integrity and collaborative workflows.

The emergence of platforms like Worksheetplaces has revolutionized the way organizations approach spreadsheet version control. By offering a centralized hub for managing and sharing spreadsheets, Worksheetplaces facilitates seamless collaboration and enhances data integrity. This platform is particularly beneficial for teams working remotely, as it provides real-time access to the latest spreadsheet versions, reducing the risk of data discrepancies. Moreover, Worksheetplaces integrates with popular productivity tools, allowing users to streamline their workflows and improve efficiency. As more organizations adopt digital solutions, the role of platforms like Worksheetplaces in the spreadsheet version

The global Document Version Control Software market is poised for significant expansion, projected to reach an estimated market size of XXX million by 2025, with a robust Compound Annual Growth Rate (CAGR) of XX% during the forecast period of 2025-2033. This growth is primarily fueled by the increasing need for efficient document management across organizations of all sizes. Key drivers include the escalating volume of digital content, the demand for enhanced collaboration and streamlined workflows, and the critical requirement for audit trails and compliance with regulatory mandates. As businesses continue to embrace digital transformation and remote work models, the adoption of sophisticated version control solutions becomes indispensable for maintaining data integrity, reducing errors, and ensuring seamless project execution. Cloud-based solutions are expected to dominate the market, offering scalability, accessibility, and cost-effectiveness, while on-premise solutions will cater to organizations with stringent data security requirements. The market segmentation by enterprise size reveals a strong demand from large enterprises (1000+ users), where complex projects and large teams necessitate advanced version control capabilities. Medium-sized enterprises (499-1000 users) are also significant contributors, seeking to improve operational efficiency and collaboration. Small enterprises (1-499 users) are increasingly adopting these solutions to professionalize their document management processes and gain a competitive edge. Notable players like Hyland Software, Alfresco, and FileHold are at the forefront, offering innovative features and comprehensive solutions. Geographically, North America and Europe are expected to lead the market, driven by early adoption of technology and strong regulatory frameworks. However, the Asia Pacific region is anticipated to exhibit the fastest growth, propelled by the rapid digitization initiatives and expanding business landscape in countries like China and India. The market's trajectory is further supported by emerging trends such as integration with AI and machine learning for intelligent document analysis and the growing emphasis on cybersecurity within document management systems. Here is a unique report description on Document Version Control Software, incorporating the requested elements:

This comprehensive report offers an in-depth analysis of the global Document Version Control Software market, charting its trajectory from the historical period of 2019-2024 through to the projected growth up to 2033. With a base year of 2025, the report meticulously forecasts market dynamics, size, and key trends, estimating the market to reach a significant valuation in the millions of USD by the end of the study period. The report delves into the intricacies of this critical software category, examining its impact across various business functions and industries, and providing actionable insights for stakeholders.

File Manager Software Market Outlook

According to our latest research, the global file manager software market size reached USD 2.47 billion in 2024, reflecting a robust demand for digital file organization and management solutions across industries. The market is expected to grow at a CAGR of 9.1% from 2025 to 2033, reaching a projected value of USD 5.45 billion by 2033. This impressive growth trajectory is primarily driven by the escalating need for efficient data management, enhanced data security, and seamless collaboration tools in an increasingly digitalized business environment. The proliferation of remote work, cloud adoption, and regulatory compliance requirements are further propelling the widespread adoption of advanced file manager software solutions globally.

One of the key growth factors fueling the file manager software market is the exponential surge in digital data generation. Organizations across sectors are experiencing unprecedented data volumes, making traditional file management methods obsolete. As companies strive to maintain data integrity, ensure accessibility, and support collaborative workflows, the demand for sophisticated file manager software with advanced search, indexing, and permission control features has skyrocketed. Furthermore, the integration of artificial intelligence and machine learning into file management solutions is enhancing automation, enabling predictive file organization, and reducing manual intervention, which collectively boosts productivity and operational efficiency.

Another significant driver is the increasing emphasis on data security and regulatory compliance. With data breaches and cyber threats becoming more sophisticated, enterprises are prioritizing secure file management systems that offer robust encryption, audit trails, and access control mechanisms. Compliance with stringent data protection regulations such as GDPR, HIPAA, and CCPA necessitates the deployment of file manager software capable of ensuring data privacy and traceability. This regulatory landscape is compelling organizations, especially in highly regulated industries like BFSI and healthcare, to invest in advanced file management solutions to mitigate risks and avoid costly penalties.

The rapid shift towards remote and hybrid work models has also contributed substantially to the growth of the file manager software market. As businesses adapt to distributed teams and flexible work environments, the need for centralized, cloud-based file management platforms has intensified. These solutions enable seamless file sharing, real-time collaboration, and version control, regardless of geographical boundaries. The ability to access, organize, and secure files remotely is now a critical requirement for business continuity and agility, further driving the adoption of file manager software across small, medium, and large enterprises.

From a regional perspective, North America continues to dominate the file manager software market, accounting for the largest revenue share in 2024. This leadership is attributed to the region's advanced IT infrastructure, high digital adoption rates, and the presence of major technology providers. However, Asia Pacific is emerging as the fastest-growing market, driven by rapid digital transformation in countries like China, India, and Japan. The region's expanding SME sector, increasing cloud adoption, and government initiatives promoting digitalization are contributing to the surging demand for file manager software. Europe also maintains a significant market share, bolstered by strict data protection regulations and a strong focus on enterprise data governance.

In the context of managing digital assets, File Lifecycle Management has emerged as a critical component of file manager software solutions. This approach encompasses the entire lifespan of a file, from its creation and active use to its archival and eventual deletion. By implementing effective file lifecycle management strategies, organizations can optimize storage resources, ensure compliance with data retention policies, and enhance data security. This is particularly important in industries with stringent regulatory requirements, where maintaining an audit trail and ensuring data integrity are paramount. As businesses generate increasing volumes of data, the ability to automate and streaml

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Managed File Transfer Software Market Size 2025-2029

The managed file transfer software market size is forecast to increase by USD 542.7 million at a CAGR of 9.5% between 2024 and 2029.

The market is driven by the increasing demand for secure and efficient data exchange solutions. With the digital transformation of businesses and the rise of big data, managing and transferring large files securely has become a critical challenge. Integration with smart applications is another key driver, enabling automation and streamlining of file transfer processes. However, the transfer of large files continues to pose difficulties, necessitating robust solutions that can handle high volumes and complex data formats. Despite these opportunities, the market faces significant challenges.
Security remains a top priority, with the need to protect sensitive data during transfer and ensure compliance with various regulations. Additionally, the complexity of managing multiple transfer protocols and integrations can be a barrier to adoption. Companies must navigate these challenges to effectively capitalize on the market's potential and stay competitive in the evolving digital landscape. Business continuity, data security, and advanced encryption standards are vital for maintaining data confidentiality.

What will be the Size of the Managed File Transfer Software Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market is witnessing significant activity and trends, with a focus on enhancing security and streamlining operations. The Cloud Security Alliance highlights the importance of secure file transfer solutions, driving demand for features like multi-factor authentication, penetration testing, and vulnerability management. Technical support and customer service are crucial, with centralized management and real-time monitoring enabling efficient problem resolution. Distributed architecture and deployment automation are key trends, allowing for seamless integration with software defined networking and virtual private networks. Automated testing, version control, and change management ensure compliance and reduce errors.

Service level agreements, incident management, and single sign-on provide a superior user experience. Data analytics and data visualization offer valuable insights, while threat intelligence and professional services help organizations stay ahead of potential risks. Maintenance contracts, software licensing, and application programming interfaces facilitate easy integration and customization. Extensible markup language and XML-based configurations enable flexibility and scalability. With the digital transformation of businesses and the rise of big data, managing and transferring large files securely has become a critical challenge.

How is this Managed File Transfer Software Industry segmented?

The managed file transfer software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Sector

  Large enterprises
  SMEs


Component

  Software
  Services


Deployment

  Cloud-based
  On-premises
  Hybrid


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Sector Insights

The Large enterprises segment is estimated to witness significant growth during the forecast period. In today's data-driven business landscape, managing file transfers is a crucial aspect for enterprises. Merely transferring files from one place to another is no longer sufficient. Advanced technologies are essential for businesses to automate and secure file transfers while gaining insights into employee interactions with data. The evolution of file transfer protocols began with FTP, which lacked security features. Subsequently, secure protocols like SFTP and FTPS emerged, providing end-to-end encryption and safeguarding data during transfer over public networks. Enterprise-level file transfer involves several critical considerations. Automation and workflow are essential to manage complex business operations, ensuring seamless file access and transfer.

Data integrity is paramount, with validation, error handling, and file integrity checks in place. On-premise deployment and high availability ensure business continuity. Cloud integration and hybrid deployments enable flexibility and scalability. Threat modeling, PCI DSS, intrusion detection systems, and network security protect against potential threats. Data transformation, masking, and encryption standards secure sensitive data. Disaster recovery, audit trails, dig

File Version Cleanup Tools Market Outlook

According to our latest research, the global File Version Cleanup Tools market size reached USD 1.47 billion in 2024, reflecting a robust demand for efficient data management solutions across diverse industries. The market is projected to grow at a CAGR of 12.3% from 2025 to 2033, reaching an estimated value of USD 4.17 billion by 2033. This sustained growth is primarily driven by the exponential increase in digital data volumes, the proliferation of remote work environments, and the rising need for optimized storage management and data security protocols.

The primary growth factor for the File Version Cleanup Tools market is the ongoing surge in unstructured data generated by enterprises worldwide. As businesses increasingly digitize their operations, the accumulation of redundant, obsolete, and trivial (ROT) files has become a significant challenge. Organizations are realizing the critical importance of automating file version management to avoid storage inefficiencies, reduce operational costs, and enhance data governance. File version cleanup tools, leveraging advanced algorithms and artificial intelligence, enable enterprises to streamline data repositories, minimize storage bloat, and ensure that only the most recent and relevant file versions are retained. This not only boosts productivity but also supports compliance with regulatory requirements regarding data retention and deletion.

Another key driver fueling market expansion is the accelerated adoption of cloud-based solutions. With the migration of enterprise workloads to cloud infrastructures, the complexity of file version management has increased dramatically. Cloud environments, while scalable, often lead to version sprawl due to collaborative workflows and frequent document updates. File version cleanup tools specifically designed for cloud ecosystems are witnessing heightened demand as they help organizations maintain storage hygiene, optimize resource allocation, and control associated costs. Furthermore, the integration of these tools with leading cloud storage platforms such as Microsoft OneDrive, Google Drive, and Amazon S3 has made their deployment seamless and highly effective for both large enterprises and small to medium-sized businesses.

The increasing emphasis on cybersecurity and data privacy is also shaping the File Version Cleanup Tools market. As data breaches and ransomware attacks become more sophisticated, organizations are prioritizing the elimination of unnecessary file versions that could potentially serve as entry points for malicious actors. Automated cleanup tools not only help enforce strict access controls but also ensure that outdated or vulnerable files are systematically purged from the system. This proactive approach to data hygiene is especially crucial for sectors with stringent compliance mandates, such as BFSI, healthcare, and government, where the risks associated with data leaks and regulatory penalties are particularly high.

From a regional perspective, North America currently dominates the File Version Cleanup Tools market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The strong presence of technologically advanced enterprises, early adoption of cloud technologies, and robust regulatory frameworks in these regions have contributed significantly to market growth. Meanwhile, Asia Pacific is emerging as the fastest-growing market, driven by rapid digital transformation initiatives, expanding IT infrastructure, and increasing awareness about the benefits of effective file management solutions among businesses of all sizes.

Component Analysis

The File Version Cleanup Tools market by component is segmented into Software and Services. The software segment holds the lion’s share of the market, primarily due to the widespread adoption of standalone and integrated solutions that automate the identification and deletion of redundant file versions. These software tools are increasingly leveraging artificial intelligence and machine learning to enhance their accuracy and efficiency, making them indispensable for organizations managing large volumes of digital assets. The growing complexity of file systems, both on-premises and in the cloud, has further fueled demand for advanced software solutions capable of handling multi-format data and supporting diverse operating environments.

File List hb-migration.r (MD5: 1904c1692a02d984890e4575d0eeb4e6) R script that imports the eBird, map, and equal-area icosahedron data, summarizes the population-level migration patterns, runs the statistical analyses, and outputs figures. migration-fxns.r (MD5: a2ae2a47c066a253f18cad5b13cddcf6) R script that holds the relevant functions for executing the hb-migration.R script. BBL-Appendix.r (MD5: 370c701d6afb07851907922dcab51de4) R script that imports the Breeding Bird Laboratory data and outputs the figures for the Appendix. output-data.zip (MD5: 36e3a92a7d35e84b299d82c8bd746950) Folder containing the partially-processed text files (15 .txt files, 3 per species for centroids, migration dates, and migration speed) for the main analyses and figures in the paper. These text files can be used in part II of hb-migration.r and contain output data on the daily population-level centroids, migration dates, and migration speed. Part I of hb-migration.r relies on raw eBird data, which was queried from the eBird server directly. The raw eBird data can be requested through their online portal after making a user account (http://help.ebird.org/customer/portal/articles/1010524-can-i-download-raw-data-from-ebird-). The equal-area icosahedron maps are available at (http://discreteglobalgrids.org/). The BBL data, used in BBL-Appendix.R, can be requested from the USGS Bird Banding Laboratory (http://www.pwrc.usgs.gov/BBL/homepage/datarequest.cfm). Description The code and data in this supplement allow for the analyses and figures in the paper to be fully replicated using a data set of manipulated communities collected from the literature. Requirements: R 3.x, and the following packages: chron, fields, knitr, gamm4, geosphere, ggplot2, ggmap, maps, maptools, mapdata, mgcv, plyr, raster, reshape2, rgdal, Rmisc, SDMTools, sp, spaa, and files containing functions specific to this code (listed above). The analyses can then be replicated by changing the working directory at the top of the file hb-migration.R to the location on your computer where you have stored the .R and .csv files and running the code. Note that to fully replicate the analyses, the data will need to be requested from the sources listed above. Starting at Part II in hb-migration.R, it should take approximately 30 minutes to run all the code from start to finish. Figures should output as pdfs in your working directory. If you download the raw data and run the analyses starting at Part I, you will need a workstation with large memory to run the analyses in a reasonable amount of time since the raw eBird datafiles are very large. Version Control Repository: The full version control repository for this project (including post- publication improvements) is publicly available https://github.com/sarahsupp/hb-migration. If you would like to use the code in this Supplement for your own analyses it is strongly suggested that you use the equivalent code in the repositories as this is the code that is being actively maintained and developed. Data use: Partially-processed data is provided in this supplement for the purposes of replication. If you wish to use the raw data for additional research, they should be obtained from the original data providers listed above.

Groundwater samples were collected and analyzed from 1,015 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water-Quality Program and the water-quality data and quality-control data are included in this data release. The samples were collected from three types of well networks: principal aquifer study networks, which are used to assess the quality of groundwater used for public water supply; land-use study networks, which are used to assess land-use effects on shallow groundwater quality; and major aquifer study networks, which are used to assess the quality of groundwater used for domestic supply. Groundwater samples were analyzed for a large number of water-quality indicators and constituents, including nutrients, major ions, trace elements, volatile organic compounds (VOCs), pesticides, radionuclides, and microbial indicators. Data from samples collected between 2012 and 2019 are associated with networks described in a collection of data series reports and associated data releases (Arnold and others, 2016a,b, 2017a,b, 2018a,b, 2020a,b; Kingsbury and others, 2020 and 2021). This data release includes data from networks sampled in 2019 through 2023. For some networks, certain constituent group data were not completely reviewed and released by the analyzing laboratory for all network sites in time for publication of this data release. For networks with incomplete data, no data were published for the incomplete constituent group(s). Datasets excluded from this data release because of incomplete results will be included in the earliest data release published after the dataset is complete. NOTE: While previous versions are available from the author, all the records in previous versions can be found in version 4.0. First posted - December 12, 2021 (available from author) Revised - January 27, 2023 (version 2.0: available from author) Revised - November 2, 2023 (version 3.0: available from author) Revised - April 18, 2025 (version 4.0) The compressed file (NWQP_GW_QW_DataRelease_v4.zip) contains 24 files: 23 files of groundwater-quality, quality-control data, and general information in ASCII text tab-delimited format, and one corresponding metadata file in xml format that includes descriptions of all the tables and attributes. A shapefile containing study areas for each of the sampled groundwater networks also is provided in folder NWQP_GW_QW_Network_Boundaries_v4 of this data release and is described in the metadata (Network_Boundaries_v4.zip). The 23 data files are as follows: Description_of_Data_Fields_v4.txt: Information for all constituents and ancillary information found in Tables 3 through 21. Network_Reference_List_v4.txt: References used for the description of the networks sampled by the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Project. Table_1_site_list_v4.txt: Information about wells that have environmental data. Table_2_parameters_v4.txt: Constituent primary uses and sources; laboratory analytical schedules and sampling period; USGS parameter codes (pcodes); comparison thresholds; and reporting levels. Table_3_qw_indicators_v4.txt: Water-quality indicators in groundwater samples collected by the USGS NAWQA Project. Table_4_nutrients_v4.txt: Nutrients and dissolved organic carbon in groundwater samples collected by the USGS NAWQA Project. Table_5_major_ions_v4.txt: Major and minor ions in groundwater samples collected by the USGS NAWQA Project. Table_6_trace_elements_v4.txt: Trace elements in groundwater samples collected by the USGS NAWQA Project. Table_7_vocs_v4.txt: Volatile organic compounds (VOCs) in groundwater samples collected by the USGS NAWQA Project. Table_8_pesticides_v4.txt: Pesticides in groundwater samples collected by the USGS NAWQA Project. Table_9_radchem_v4.txt: Radionuclides in groundwater samples collected by the USGS NAWQA Project. Table_10_micro_v4.txt: Microbiological indicators in groundwater samples collected by the USGS NAWQA Project. Table_11_qw_ind_QC_v4.txt: Water-quality indicators in groundwater replicate samples collected by the USGS NAWQA Project. Table_12_nuts_QC_v4.txt: Nutrients and dissolved organic carbon in groundwater blank and replicate samples collected by the USGS NAWQA Project. Table_13_majors_QC_v4.txt: Major and minor ions in groundwater blank and replicate samples collected by the USGS NAWQA Project. Table_14_trace_element_QC_v4.txt: Trace elements in groundwater blank and replicate samples collected by the USGS NAWQA Project. Table_15_vocs_QC_v4.txt: Volatile organic compounds (VOCs) in groundwater blank, replicate, and spike samples collected by the USGS NAWQA Project. Table_16_pesticides_QC_v4.txt: Pesticide compounds in groundwater blank, replicate, and spike samples collected by the USGS NAWQA Project. Table_17_radchem_QC_v4.txt: Radionuclides in groundwater replicate samples collected by the USGS NAWQA Project. Table_18_micro_QC_v4.txt: Microbiological indicators in groundwater blank, replicate, and spike samples collected by the USGS NAWQA Project. Table_19_TE_SpikeStats_v4.txt: Statistics for trace elements in groundwater spike samples collected by the USGS NAWQA Project. Table_20_VOCLabSpikeStats_v4.txt: Statistics for volatile organic compounds (VOCs) in groundwater spike samples collected by the USGS NAWQA Project. Table_21_PestFieldSpikeStats_v4.txt: Statistics for pesticide compounds in groundwater spike samples collected by the USGS NAWQA Project. References Arnold, T.L., Bexfield, L.M., Musgrove, MaryLynn, Lindsey, B.D., Stackelberg, P.E., Barlow, J.R., DeSimone, L.A., Kulongoski, J.T., Kingsbury, J.A., Ayotte, J.D., Fleming, B.J., and Belitz, Kenneth, 2017a, Groundwater-quality data from the National Water-Quality Assessment Project, January through December 2014 and select quality-control data from May 2012 through December 2014: U.S. Geological Survey Data Series 1063, 83 p., https://doi.org/10.3133/ds1063. Arnold, T.L., Bexfield, L.M., Musgrove, MaryLynn, Lindsey, B.D., Stackelberg, P.E., Barlow, J.R., DeSimone, L.A., Kulongoski, J.T., Kingsbury, J.A., Ayotte, J.D., Fleming, B.J., and Belitz, Kenneth, 2017b, Datasets from Groundwater quality data from the National Water Quality Assessment Project, January through December 2014 and select quality-control data from May 2012 through December 2014: U.S. Geological Survey data release, https://doi.org/10.5066/F7W0942N. Arnold, T.L., Bexfield, L.M., Musgrove, M., Erickson, M.L., Kingsbury, J.A., Degnan, J.R., Tesoriero, A.J., Kulongoski, J.T., and Belitz, K., 2020a, Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2016, and previously unpublished data from 2013 to 2015: U.S. Geological Survey Data Series 1124, 135 p., https://doi.org/10.3133/ds1124. Arnold, T.L., Bexfield, L.M., Musgrove, M., Lindsey, B.D., Stackelberg, P.E., Lindsey, B.D., Barlow, J.R., Kulongoski, J.T., and Belitz, K., 2018b, Datasets from Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, January through December 2015 and Previously Unpublished Data from 2013-2014, U.S. Geological Survey data release, https://doi.org/10.5066/F7XK8DHK. Arnold, T.L., Bexfield, L.M., Musgrove, M., Stackelberg, P.E., Lindsey, B.D., Kingsbury, J.A., Kulongoski, J.T., and Belitz, K., 2018a, Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2015, and previously unpublished data from 2013 to 2014: U.S. Geological Survey Data Series 1087, 68 p., https://doi.org/10.3133/ds1087. Arnold, T.L., DeSimone, L.A., Bexfield, L.M., Lindsey, B.D., Barlow, J.R., Kulongoski, J.T., Musgrove, MaryLynn, Kingsbury, J.A., and Belitz, Kenneth, 2016a, Groundwater quality data from the National Water-Quality Assessment Project, May 2012 through December 2013 (ver. 1.1, November 2016): U.S. Geological Survey Data Series 997, 56 p., https://doi.org/10.3133/ds997. Arnold, T.L., DeSimone, L.A., Bexfield, L.M., Lindsey, B.D., Barlow, J.R., Kulongoski, J.T., Musgrove, MaryLynn, Kingsbury, J.A., and Belitz, Kenneth, 2016b, Groundwater quality data from the National Water Quality Assessment Project, May 2012 through December 2014 and select quality-control data from May 2012 through December 2013: U.S. Geological Survey data release, https://doi.org/10.5066/F7HQ3X18. Arnold, T.L., Sharpe, J.B., Bexfield, L.M., Musgrove, M., Erickson, M.L., Kingsbury, J.A., Degnan, J.R., Tesoriero, A.J., Kulongoski, J.T., and Belitz, K., 2020b, Datasets from groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2016, and previously unpublished data from 2013 to 2015: U.S. Geological Survey data release, https://doi.org/10.5066/P9W4RR74. Kingsbury, J.A., Sharpe, J.B., Bexfield, L.M., Arnold, T.L., Musgrove, M., Erickson, M.L., Degnan, J.R., Kulongoski, J.T., Lindsey, B.D., and Belitz, K., 2020, Datasets from Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, January 2017 through December 2019 (ver. 1.1, January 2021): U.S. Geological Survey data release, https://doi.org/10.5066/P9XATXV1. Kingsbury, J.A., Bexfield, L.M., Arnold, T.L., Musgrove, M., Erickson, M.L., Degnan, J.R., Tesoriero, A.J., Lindsey B.D., and Belitz, K., 2021, Groundwater-Quality and Select Quality-Control Data from the National Water-Quality Assessment Project, January 2017 through December 2019: U.S. Geological Survey Data Series 1136, 97 p., https://doi.org/10.3133/ds1136.

File Version Cleanup Tools Market Outlook

According to our latest research, the global File Version Cleanup Tools market size reached USD 1.21 billion in 2024, driven by the increasing necessity for efficient data management and storage optimization across various industries. The market is experiencing robust momentum with a CAGR of 14.3% during the forecast period, and it is projected to attain a value of USD 3.76 billion by 2033. The primary growth factor fueling this expansion is the exponential rise in digital data volumes and the corresponding demand for automated solutions that streamline file version management, reduce storage costs, and enhance organizational productivity.

The surge in unstructured data across enterprises, particularly with the proliferation of collaborative tools and cloud-based platforms, is a significant catalyst for the growth of the File Version Cleanup Tools market. Organizations are increasingly recognizing the operational and financial implications of redundant, obsolete, and trivial (ROT) files cluttering their storage environments. This awareness is compelling IT departments to adopt advanced cleanup tools that can automatically identify, archive, or delete unnecessary file versions, thereby optimizing storage utilization and improving data retrieval efficiency. Furthermore, stringent data compliance and governance regulations are amplifying the need for systematic file version management, as organizations strive to mitigate risks associated with data sprawl and unauthorized access.

Another critical driver is the growing trend of digital transformation initiatives across sectors such as BFSI, healthcare, and manufacturing. As enterprises migrate legacy systems to modern digital infrastructures, the complexity of managing multiple file versions escalates. File Version Cleanup Tools equipped with AI-driven analytics and automation capabilities are becoming indispensable for ensuring data integrity, minimizing manual intervention, and reducing the likelihood of human error. The integration of these tools with enterprise content management (ECM) and cloud storage solutions further enhances their value proposition by providing seamless and scalable version control mechanisms.

The rapid adoption of hybrid and remote work models post-pandemic has also contributed to market expansion. With employees collaborating across geographies and devices, organizations are witnessing a surge in file duplication and versioning issues. File Version Cleanup Tools play a pivotal role in maintaining a clean and organized digital workspace, enhancing user productivity, and ensuring compliance with internal data management policies. Additionally, the rising focus on cost optimization, particularly in large enterprises managing petabytes of data, is accelerating investments in comprehensive cleanup solutions that deliver measurable ROI through reduced storage expenses and improved system performance.

From a regional perspective, North America continues to dominate the File Version Cleanup Tools market due to its advanced IT infrastructure, high digital adoption rates, and the presence of leading technology vendors. However, the Asia Pacific region is emerging as a lucrative market, fueled by rapid enterprise digitization, increasing cloud adoption, and supportive government initiatives for data management and cybersecurity. Europe, with its stringent data protection regulations such as GDPR, is also witnessing significant uptake of file version cleanup solutions, particularly among BFSI and healthcare organizations. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, driven by growing awareness of data governance and rising investments in digital transformation projects.

Component Analysis

The File Version Cleanup Tools market is segmented by component into software and services, each playing a vital role in addressing the diverse needs of organizations. The software segment dominates the market, accounting for a significant share in 2024, a

A collection of 22 data set of 50+ requirements each, expressed as user stories.

The dataset has been created by gathering data from web sources and we are not aware of license agreements or intellectual property rights on the requirements / user stories. The curator took utmost diligence in minimizing the risks of copyright infringement by using non-recent data that is less likely to be critical, by sampling a subset of the original requirements collection, and by qualitatively analyzing the requirements. In case of copyright infringement, please contact the dataset curator (Fabiano Dalpiaz, f.dalpiaz@uu.nl) to discuss the possibility of removal of that dataset [see Zenodo's policies]

The data sets have been originally used to conduct experiments about ambiguity detection with the REVV-Light tool: https://github.com/RELabUU/revv-light

This collection has been originally published in Mendeley data: https://data.mendeley.com/datasets/7zbk8zsd8y/1

Overview of the datasets [data and links added in December 2024]

The following text provides a description of the datasets, including links to the systems and websites, when available. The datasets are organized by macro-category and then by identifier.

Public administration and transparency

g02-federalspending.txt (2018) originates from early data in the Federal Spending Transparency project, which pertain to the website that is used to share publicly the spending data for the U.S. government. The website was created because of the Digital Accountability and Transparency Act of 2014 (DATA Act). The specific dataset pertains a system called DAIMS or Data Broker, which stands for DATA Act Information Model Schema. The sample that was gathered refers to a sub-project related to allowing the government to act as a data broker, thereby providing data to third parties. The data for the Data Broker project is currently not available online, although the backend seems to be hosted in GitHub under a CC0 1.0 Universal license. Current and recent snapshots of federal spending related websites, including many more projects than the one described in the shared collection, can be found here.

g03-loudoun.txt (2018) is a set of extracted requirements from a document, by the Loudoun County Virginia, that describes the to-be user stories and use cases about a system for land management readiness assessment called Loudoun County LandMARC. The source document can be found here and it is part of the Electronic Land Management System and EPlan Review Project - RFP RFQ issued in March 2018. More information about the overall LandMARC system and services can be found here.

g04-recycling.txt(2017) concerns a web application where recycling and waste disposal facilities can be searched and located. The application operates through the visualization of a map that the user can interact with. The dataset has obtained from a GitHub website and it is at the basis of a students' project on web site design; the code is available (no license).

g05-openspending.txt (2018) is about the OpenSpending project (www), a project of the Open Knowledge foundation which aims at transparency about how local governments spend money. At the time of the collection, the data was retrieved from a Trello board that is currently unavailable. The sample focuses on publishing, importing and editing datasets, and how the data should be presented. Currently, OpenSpending is managed via a GitHub repository which contains multiple sub-projects with unknown license.

g11-nsf.txt (2018) refers to a collection of user stories referring to the NSF Site Redesign & Content Discovery project, which originates from a publicly accessible GitHub repository (GPL 2.0 license). In particular, the user stories refer to an early version of the NSF's website. The user stories can be found as closed Issues.

(Research) data and meta-data management

g08-frictionless.txt (2016) regards the Frictionless Data project, which offers an open source dataset for building data infrastructures, to be used by researchers, data scientists, and data engineers. Links to the many projects within the Frictionless Data project are on GitHub (with a mix of Unlicense and MIT license) and web. The specific set of user stories has been collected in 2016 by GitHub user @danfowler and are stored in a Trello board.

g14-datahub.txt (2013) concerns the open source project DataHub, which is currently developed via a GitHub repository (the code has Apache License 2.0). DataHub is a data discovery platform which has been developed over multiple years. The specific data set is an initial set of user stories, which we can date back to 2013 thanks to a comment therein.

g16-mis.txt (2015) is a collection of user stories that pertains a repository for researchers and archivists. The source of the dataset is a public Trello repository. Although the user stories do not have explicit links to projects, it can be inferred that the stories originate from some project related to the library of Duke University.

g17-cask.txt (2016) refers to the Cask Data Application Platform (CDAP). CDAP is an open source application platform (GitHub, under Apache License 2.0) that can be used to develop applications within the Apache Hadoop ecosystem, an open-source framework which can be used for distributed processing of large datasets. The user stories are extracted from a document that includes requirements regarding dataset management for Cask 4.0, which includes the scenarios, user stories and a design for the implementation of these user stories. The raw data is available in the following environment.

g18-neurohub.txt (2012) is concerned with the NeuroHub platform, a neuroscience data management, analysis and collaboration platform for researchers in neuroscience to collect, store, and share data with colleagues or with the research community. The user stories were collected at a time NeuroHub was still a research project sponsored by the UK Joint Information Systems Committee (JISC). For information about the research project from which the requirements were collected, see the following record.

g22-rdadmp.txt (2018) is a collection of user stories from the Research Data Alliance's working group on DMP Common Standards. Their GitHub repository contains a collection of user stories that were created by asking the community to suggest functionality that should part of a website that manages data management plans. Each user story is stored as an issue on the GitHub's page.

g23-archivesspace.txt (2012-2013) refers to ArchivesSpace: an open source, web application for managing archives information. The application is designed to support core functions in archives administration such as accessioning; description and arrangement of processed materials including analog, hybrid, and
born digital content; management of authorities and rights; and reference service. The application supports collection management through collection management records, tracking of events, and a growing number of administrative reports. ArchivesSpace is open source and its

File List rodent_wrapper.r (MD5: 2c73de19e83585b1f4c37ebb9ee9ab1f)R script that imports the eBird, map, and equal-area icosahedron data, summarizes the population-level migration patterns, runs the statistical analyses, and outputs figures.

    movement_fxns.r (MD5: 4417176e0bfed18b3c2188eb26a5908e)R script that holds the relevant functions for executing the hb-migration.R script.

    MARK_analyses.r (MD5: 0a59e029a076e1bec8b4fb529af4c361)R script that imports the Breeding Bird Laboratory data and outputs the figures for the Appendix.

  Description
    The code in this supplement allows for the analyses and figures in the paper to be fully replicated using a subset of the published Portal data set which includes individual-level rodent data from 1989–2009. Species evaluated include granivores, folivores, and insectivores: Peromyscus eremicus (PE), Peromyscus maniculatus (PM), Peromyscus leucopus (PL), Onychomys torridus (OT), Onychomys leucogaster (OL), Dipodomys merriami (DM), Dipodomys ordii (DO), Dipodomys spectabilis (DS), Chaetodipus baileyi (PB), Chaetodipus penicillatus (PP), Perognathus flavus (PF), Chaetodipus intermedius (PI), Chaetodipus hispidus (PH), Sigmodon hispidus (SH), Sigmodon fulviventer (SF), Sigmodon ochrognathus (SO), Neotoma albigula (NAO), Baiomys taylori (BA), Reithrodontomys megalotis (RM), Reithrodontomys fulvescens (RF), and Reithrodontomys montanus (RM).
    Requirements: R 2.x, Program MARK (http://www.phidot.org/software/mark), the files containing data and functions specific to this code and the following packages: ape, calibrate, fields, geiger, ggbiplot, ggmap, ggplot2, gridExtra, picante, PhyloOrchard,plyr, reshape2, and RMark.
    The analyses can then be replicated by changing the working directory at the top of the file rodent_wrapper.R to the location on your computer where you have stored the .R and .csv files and running the code.
    Code for Part I of rodent_wrapper.R should take approximately 30 minutes to run, but depending on the capabilities of the computer used to run the code, it may take many hours to run the code in MARK_analyses.R. Figures should output as pdf, png, or eps files in your working directory. Part II of rodent_wrapper.R continues the anaylsis using the MARK results. If you download the raw data and run the start to finish, you will need a workstation with large memory to run the program in a reasonable amount of time since the files are large and the analyses require a lot of memory.
   Version Control Repository: The full version control repository for this project (including post-publication improvements) is publicly available at https://github.com/weecology/portal-rodent-dispersal. If you would like to use the code in this Supplement for your own analyses it is strongly suggested that you use the equivalent code in the repositories as this is the code that is being actively maintained and developed. 
    Data use: Partially-processed data is provided in the GitHub repository for the purposes of replication. The raw data should be obtained from the original data providers (Ernest et al. 2009) and can be downloaded from Ecological Archives (http://www.esajournals.org/doi/abs/10.1890/08-1222.1).

This dataset is the large training data files for the NeonTreeEvaluation Benchmark for individual tree detection from airborne imagery. For each geographic site, given by the NEON four letter code (e.g HARV -> Harvard Forest), there are up to 4 files: a RGB image, a LiDAR tile, and a 426 band hyperpspectral file, and a 1m canopy height file. For more information on the benchmark, and the corresponding R package, see https://github.com/weecology/NeonTreeEvaluation_package Annotations for the tiles, made by looking at the RGB are under version control here: https://github.com/weecology/NeonTreeEvaluation/tree/master/annotations

FragPipe is recognized as one of the fastest computational platforms in proteomics, making it a practical solution for the rapid quality control of high-throughput sample analyses. Starting with version 23.0, FragPipe introduced the “Generate Summary Report” feature, offering .pdf reports with essential quality control metrics to address the challenge of intuitively assessing large-scale proteomics data. While traditional spreadsheet formats (e.g., tsv files) are accessible, the complexity of the data often limits user-friendly interpretation. To further enhance accessibility, PSManalyst, a Shiny-based R application, was developed to process FragPipe output files (psm.tsv, protein.tsv, and combined_protein.tsv) and provide interactive, code-free data visualization. Users can filter peptide-spectrum matches (PSMs) by quality scores, visualize protease cleavage fingerprints as heatmaps and SeqLogos, and access a range of quality control metrics and representations such as peptide length distributions, ion densities, mass errors, and wordclouds for overrepresented peptides. The tool facilitates seamless switching between PSM and protein data visualization, offering insights into protein abundance discrepancies, samplewise similarity metrics, protein coverage, and contaminants evaluation. PSManalyst leverages several R libraries (lsa, vegan, ggfortify, ggseqlogo, wordcloud2, tidyverse, ggpointdensity, and plotly) and runs on Windows, MacOS, and Linux, requiring only a local R setup and an IDE. The app is available at (https://github.com/41ison/PSManalyst.

Received through direct exchange notice and OCC announcements – Processed and validated via Symbol Master

CorpAct

The SMI corporate actions (CorpAct) family of files track changes to the option and strike level details that impact Position and Risk Management due to a corporate action event’s. This would include any adjustments that affect either the option symbol or the strike prices of an option. Symbol Master provides standard and custom-tailored delivery of Corporate Actions content available through industry standard means. We currently support multiple versions of the Corp Act files based on the influence and data models of large data vendors within the Market Data industry.

Corporate Actions (Corp Act) Pending

The Corp Act Pending file tracks all confirmed but not yet effective corporate actions that involve changes to the option and strike level details. The pending corporate actions have been confirmed by either direct exchange or OCC memos.

Symbol ChangeOp

The Symbol Change Op file logs changes to an option symbol most commonly due to a corporate action.

Symbol ChangeEq

The Symbol Change Eq file logs changes to an options underlying symbol most commonly due to a corporate action.

Intraday

This is an Intraday update service that allows consumers to ensure they capture intraday Strike additions

The Intraday files supplement the nightly series master files (SeriesExt) with intraday strike additions. They are created every hour from 9am to 4pm Eastern. There are multiple versions of the Intraday files, and this is a value-added service to ensure what is being traded in the market is reflected in the source security master and OMS/EMS Trading Platform. Beginning in September 2023 Intraday additions are also available for FLEX options (see FLEX section for more details)

This project will provide monitoring of key fish species and habitat features within the planned Ocean Wind site and a nearby control site off the coast of southern New Jersey before, during, and after wind farm construction. This glider is equipped with an Acoustic Zooplankton Fish Profiler (AZFP), an autonomous, low-power echo sounder that possesses the capabilities to study the abundance, distribution, and behavior of various sizes of fishes and large zooplankton throughout the water column over large spatial scales by measuring acoustic backscatter returns with ultrasonic frequencies. This 3-4 week deployment will test the use of gliders for conducting fisheries and ecosystem surveys in and around the Ocean Wind study site. Delayed mode dataset. _NCProperties=version=1|netcdflibversion=4.6.1|hdf5libversion=1.10.3 acknowledgement=This deployment is supported by funding from Ocean Wind LLC cdm_data_type=Trajectory cdm_trajectory_variables=trajectory comment=Deployed by David Aragon, Kaycee Coleman, and Brian Buckhingham aboard R/V Petrel with Capt. Steve Evert out of Port Republic, NJ. contributor_name=Josh Kohut, Grace Saba, Thomas Grothues, Kaycee Coleman, Nicole Waite, David Aragon, Brian Buckingham, Chip Haldeman, John Kerfoot, Laura Nazzaro, Lori Garzio contributor_role=Principal Investigator, Principal Investigator, Principal Investigator, Project Manager, Pilot, Pilot, Pilot, Pilot, Data Management, Data Management, Data Management Conventions=CF-1.6, COARDS, ACDD-1.3 defaultGraphQuery=longitude,latitude,time&.draw=markers&.marker=6%7C3&.color=0xFFFFFF&.colorBar=Rainbow2%7C%7C%7C%7C%7C&.bgColor=0xffccccff deployment=ru28-20220520T1425 Easternmost_Easting=-74.06687333333333 featureType=Trajectory geospatial_bounds=POLYGON ((38.90845166666666 -74.41338666666667, 38.90845166666666 -74.41338666666667, 38.90845166666666 -74.41338666666667, 38.90845166666666 -74.41338666666667, 38.90845166666666 -74.41338666666667)) geospatial_bounds_crs=EPSG:4326 geospatial_bounds_vertical_crs=EPSG:5831 geospatial_lat_max=39.32609333333333 geospatial_lat_min=38.80777333333333 geospatial_lat_resolution=0.00001 degree geospatial_lat_units=degrees_north geospatial_lon_max=-74.06687333333333 geospatial_lon_min=-74.61127833333333 geospatial_lon_resolution=0.00001 degree geospatial_lon_units=degrees_east geospatial_verical_resolution=0 geospatial_vertical_max=39.20708 geospatial_vertical_min=-0.1091665 geospatial_vertical_positive=down geospatial_vertical_units=m gts_ingest=True history=2022-07-13T15:24:27Z: /tmp/tmp7mrow0wv/TrajectoryNetCDFWriter.py6hxrdh0x.nc created 2022-07-13T15:24:27Z: /home/glideradm/code/glider-proc/scripts/proc_deployment_trajectories_to_nc.py /home/coolgroup/slocum/deployments/2022/ru28-20220520T1425/data/in/ascii/dbd/ru28_2022_155_0_9_dbd.dat

id=ru28-20220520T1425 infoUrl=https://rucool.marine.rutgers.edu institution=Rutgers University instrument=In Situ/Laboratory Instruments > Profilers/Sounders > CTD instrument_vocabulary=NASA/GCMD Instrument Keywords Version 8.5 keywords_vocabulary=NASA/GCMD Earth Sciences Keywords Version 8.5 naming_authority=edu.rutgers.rucool ncei_template_version=NCEI_NetCDF_Trajectory_Template_v2.0 Northernmost_Northing=39.32609333333333 platform=In Situ Ocean-based Platforms > AUVS > Autonomous Underwater Vehicles platform_type=Slocum Glider platform_vocabulary=NASA/GCMD Platforms Keywords Version 8.5 processing_level=Raw Slocum glider time-series dataset from the native data file format. Additional quality control variables provided where applicable. Thresholds used for quality control flags are under development. Delayed mode dataset. program=Fisheries Monitoring Plan Development and Execution for the Ocean Wind Offshore Wind Farm project=Rutgers Offshore Wind Living Resource Studies references=https://rucool.marine.rutgers.edu sea_name=Mid-Atlantic Bight source=Observational Slocum glider data from source dba file ru28-2022-155-0-9-dbd(05680009) sourceUrl=(local files) Southernmost_Northing=38.80777333333333 standard_name_vocabulary=CF Standard Name Table v27 subsetVariables=source_file time_coverage_duration=PT01M42.12317S time_coverage_end=2022-06-10T14:46:58Z time_coverage_resolution=PT01S time_coverage_start=2022-05-20T14:25:02Z uuid=de7101a8-8c7b-4768-88fb-29242a28b556 Westernmost_Easting=-74.61127833333333 wmo_id=4801925 wmo_platform_code=4801925

Groundwater-quality data were collected from 648 wells as part of the National Water-Quality Assessment Project of the U.S. Geological Survey National Water-Quality Program and are included in this report. Most of the wells (514) were sampled from January through December 2016 and 60 of them were sampled in 2013 and 74 in 2014. The data were collected from seven types of well networks: principal aquifer study networks, which are used to assess the quality of groundwater used for public water supply; land-use study networks, which are used to assess land-use effects on shallow groundwater quality; major aquifer study networks, which are used to assess the quality of groundwater used for domestic supply; enhanced trends networks, which are used to evaluate the time scales during which groundwater quality changes; vertical flow-path study networks, which are used to evaluate changes in groundwater quality from shallow to deeper depths; flow path study networks, which are used to evaluate changes in groundwater quality from shallow to deeper depths over a horizontal distance; and modeling support studies, which are used to provide data to support groundwater modeling. Groundwater samples were analyzed for a large number of water-quality indicators and constituents, including major ions, nutrients, trace elements, volatile organic compounds, pesticides, radionuclides, and some special interest constituents (arsenic speciation, chromium [VI] and perchlorate). These groundwater quality data are tabulated in a U.S. Geological Survey Data Series Report DS-1124 which is available at https://dx.doi.org/XXXXXX and in this data release. Some data from environmental samples collected in 2013-14 and quality-control samples collected in 2012-15 also are included in the associated data release. Data from samples collected in 2016 are associated with networks described in this report and have not been published previously; data from samples collected between 2012 and 2015 are associated with networks described in previous reports in this data series (Arnold and others, 2016a,b, 2017a,b, 2018a,b). There are 23 data tables included in this data release and they are referenced as tables 1-13 and appendix tables 4.10-4.19 in the larger work citation. There are 36 tables that are part of the larger work citation; the 13 tables not included in the data release are summary tables derived from some of the other tables (tables 1.1, 2.2-2.3, 3.1, 4.1-4.9). A version of table 1 is included in both the text and data release. This compressed file contains 23 files of groundwater-quality data in ASCII text tab-delimited format and one corresponding metadata in xml format that describes all the tables and attributes.

Building a bone fracture detection system using computer vision involves several steps. Here's a general outline to get you started:

1. Dataset Collection: Gather a dataset of X-ray images with labeled fractures. You can explore datasets like MURA, NIH Chest X-ray Dataset, or create your own dataset with proper ethical considerations.

2. Data Preprocessing: Clean and preprocess the X-ray images. This may involve resizing, normalization, and data augmentation to increase the diversity of your dataset.

3. Model Selection: Choose a suitable pre-trained deep learning model for image classification. Models like ResNet, DenseNet, or custom architectures have shown good performance in medical image analysis tasks.

4. Transfer Learning: Fine-tune the selected model on your X-ray dataset using transfer learning. This helps leverage the knowledge gained from pre-training on a large dataset.

5. Model Training: Split your dataset into training, validation, and test sets. Train your model on the training set and validate its performance on the validation set to fine-tune hyperparameters.

6. Evaluation Metrics: Choose appropriate evaluation metrics such as accuracy, precision, recall, F1-score, or area under the ROC curve (AUC) to assess the model's performance.

7. Post-processing: Implement any necessary post-processing steps, such as non-maximum suppression, to refine the model's output and reduce false positives.

8. Deployment: Deploy the trained model as part of a computer vision application. This could be a web-based application, mobile app, or integrated into a healthcare system.

9. Continuous Improvement: Regularly update and improve your model based on new data or advancements in the field. Monitoring its performance in real-world scenarios is crucial.

10. Ethical Considerations: Ensure that your project follows ethical guidelines and regulations for handling medical data. Implement privacy measures and obtain necessary approvals if you are using patient data.

Tools and Libraries: Python, TensorFlow, PyTorch, Keras for deep learning implementation. OpenCV for image processing. Flask/Django for building a web application. Docker for containerization. GitHub for version control.

Context

The data presented here was obtained in a Kali Machine from University of Cincinnati,Cincinnati,OHIO by carrying out packet captures for 1 hour during the evening on Oct 9th,2023 using Wireshark.This dataset consists of 394137 instances were obtained and stored in a CSV (Comma Separated Values) file.This large dataset could be used utilised for different machine learning applications for instance classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

The dataset can be used for a variety of machine learning tasks, such as network intrusion detection, traffic classification, and anomaly detection.

Content :

This network traffic dataset consists of 7 features.Each instance contains the information of source and destination IP addresses, The majority of the properties are numeric in nature, however there are also nominal and date kinds due to the Timestamp.

The network traffic flow statistics (No. Time Source Destination Protocol Length Info) were obtained using Wireshark (https://www.wireshark.org/).

Dataset Columns:

No : Number of Instance. Timestamp : Timestamp of instance of network traffic Source IP: IP address of Source Destination IP: IP address of Destination Portocol: Protocol used by the instance Length: Length of Instance Info: Information of Traffic Instance

Acknowledgements :

I would like thank University of Cincinnati for giving the infrastructure for generation of network traffic data set.

Ravikumar Gattu , Susmitha Choppadandi

Inspiration : This dataset goes beyond the majority of network traffic classification datasets, which only identify the type of application (WWW, DNS, ICMP,ARP,RARP) that an IP flow contains. Instead, it generates machine learning models that can identify specific applications (like Tiktok,Wikipedia,Instagram,Youtube,Websites,Blogs etc.) from IP flow statistics (there are currently 25 applications in total).

**Dataset License: ** CC0: Public Domain

Dataset Usages : This dataset can be used for different machine learning applications in the field of cybersecurity such as classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

ML techniques benefits from this Dataset :

This dataset is highly useful because it consists of 394137 instances of network traffic data obtained by using the 25 applications on a public,private and Enterprise networks.Also,the dataset consists of very important features that can be used for most of the applications of Machine learning in cybersecurity.Here are few of the potential machine learning applications that could be benefited from this dataset are :

1. Network Performance Monitoring : This large network traffic data set can be utilised for analysing the network traffic to identifying the network patterns in the network .This help in designing the network security algorithms for minimise the network probelms.

2. Anamoly Detection : Large network traffic dataset can be utilised training the machine learning models for finding the irregularitues in the traffic which could help identify the cyber attacks.

3.Network Intrusion Detection : This large dataset could be utilised for machine algorithms training and designing the models for detection of the traffic issues,Malicious traffic network attacks and DOS attacks as well.

This repository contains wireless link quality estimation data for the FlockLab testbed [1,2]. The rationale and description of this dataset is described in a the following abstract (pdf is included in this repository -- see below).

Dataset: Wireless Link Quality Estimationon FlockLab – and Beyond Romain Jacob, Reto Da Forno, Roman Trüb, Andreas Biri, Lothar Thiele DATA '19 Proceedings of the 2nd Workshop on Data Acquisition To Analysis, 2019

Data collection scenario

The data collection scenario is simple. Each FlockLab node is assigned one dedicated time slot. In this slot, a node sends 100 packets, called strobes. All strobes have the same payload size and use a given radio frequency channel and transmit power. All other nodes listen for the strobes and log packet reception events (i.e., success or failed).

The test scenario is ran every two hours on two different platforms: the TelosB [3] and DPP-cc430 [4] platforms. We used all nodes currently available at test time (between 27 and 29).

Final dataset status

3 months of data with about 12 tests per day per platform

5 month of data with about 4 tests per day per platform

Data collection firmware

We are happy to share the link quality data we collected for the FlockLab testbed, but we also wanted to make it easier for others to collect similar datasets for other wireless networks. To achieve this, we include in this repository the data collection firmware we design. The entire data collection scheduling and control is done entirely in software, in order to make the firmware usable in a large variety on wireless networks. We implemented our data collection software using Baloo [5], a flexible network stack design framework based on Synchronous Transmission. Baloo efficiently handles network time synchronization and offers a flexible interface to schedule communication rounds. The firmware source code is available in the Baloo repository [6].

A set of experiment parameters can be patched directly in the firmware, which let the user tune the data collection without having to recompile the source code. This improves usability and facilitates automation. An example patching script is included in this repository. Currently, the following parameters can be patched:

rf_channel,

payload,

host_id, and

rand_seed

Current supported platforms

TelosB [3]

DPP-cc430 [4]

Repository versions

v1.4.1 Updated visualizations in the notebook

v1.4.0 Addition of data from November 2019 to March 2020. Data collection is discontinued (the new FlockLab testbed is being setup).

v1.3.1 Update abstract and notebook

v1.3.0 Addition of October 2019 data. The frequency of tests has been reduced to 4 per day, executing at (approximately) 1:00, 7:00, 13:00, and 19:00. From October 28 onward, time shifted by one hour (2:00, 8:00, 14:00, 20:00).

v1.2.0 Addition of September 2019 data. Many missing tests on the 12, 13, 19, and 20 of September (due to construction works in the building).

v1.1.4 Update of the abstract to have hyperlinks to the plots. Corrected typos.

v1.1.0 Initial version. Add the data collected in August 2019. Data collected was disturbed at the beginning of the month and resumed normally on the August 13. Data from previous days are incomplete.

v1.0.0 Initial version. Contain collected data in July 2019, from the 10th to 30th of July. No data were collected on the 31st of July (technical issue).

List of files

yyyy-mm_raw_platform.zip Archive containing all FlockLab test result files (one .zip file per month and per platform).

yyyy-mm_preprocessed_all.zip Archive containing preprocessed csv files, one per month and per platform.

firmware.zip Archive containing the firmware for all supported platform.

firmware_patch.sh Example bash script illustrating the firmware patching.

parse_flocklab_results.ipynb [open in nbviewer] Jupyter notebook used to create the pre-process data files. Also includes some example of data visualization.

parse_flocklab_results.html HTML rendering of the notebook (static).

plots.zip Archive containing high resolution visualization of the dataset, generated by the parse_flocklab_results notebook, and presented in the abstract.

abstract.pdf A 3 page abstract presenting the dataset.

CRediT.pdf The list of contributions from the authors.

References

[1] R. Lim, F. Ferrari, M. Zimmerling, C. Walser, P. Sommer, and J. Beutel, “FlockLab: A Testbed for Distributed, Synchronized Tracing and Profiling of Wireless Embedded Systems,” in Proceedings of the 12th International Conference on Information Processing in Sensor Networks, New York, NY, USA, 2013, pp. 153–166.

[2] “FlockLab,” GitLab. [Online]. Available: https://gitlab.ethz.ch/tec/public/flocklab/wikis/home. [Accessed: 24-Jul-2019].

[3] Advanticsys, “MTM-CM5000-MSP 802.15.4 TelosB mote Module.” [Online]. Available: https://www.advanticsys.com/shop/mtmcm5000msp-p-14.html. [Accessed: 21-Sep-2018].

[4] Texas Instruments, “CC430F6137 16-Bit Ultra-Low-Power MCU.” [Online]. Available: http://www.ti.com/product/CC430F6137. [Accessed: 21-Sep-2018].

[5] R. Jacob, J. Bächli, R. Da Forno, and L. Thiele, “Synchronous Transmissions Made Easy: Design Your Network Stack with Baloo,” in Proceedings of the 2019 International Conference on Embedded Wireless Systems and Networks, 2019.

[6] “Baloo,” Dec-2018. [Online]. Available: http://www.romainjacob.net/research/baloo/.

EA-MD-QD is a collection of large monthly and quarterly EA and EA member countries datasets for macroeconomic analysis.The EA member countries covered are: AT, BE, DE, EL, ES, FR, IE, IT, NL, PT.

The formal reference to this dataset is:

Barigozzi, M. and Lissona, C. (2024) "EA-MD-QD: Large Euro Area and Euro Member Countries Datasets for Macroeconomic Research". Zenodo.

Please refer to it when using the data.

Each zip file contains:- Excel files for the EA and the countries covered, each containing an unbalanced panel of raw de-seasonalized data.- A Matlab code taking as input the raw data and allowing to perform various operations such as:choose the frequency, fill-in missing values, transform data to stationarity, and control for covid outliers.- A pdf file with all informations about the series names, sources, and transformation codes.

This version (03.2025):

Updated data as of 28-March-2025. We improved the matlab code and included a ReadME file containing details on the parameters' choice from the user, which before were only briefly commented in the code.