Distribution of doses of a volatile organic compound from inhalation of one consumer product, other near -field sources, far-field sources, and aggregate (total) exposure. In this instance, far-field scenarios account for several orders of magnitude of less of the predicted dose compared to near-field scenarios. This dataset is associated with the following publication: Vallero, D. Air Pollution Monitoring Changes to Accompany the Transition from a Control to a Systems Focus. Sustainability. MDPI AG, Basel, SWITZERLAND, 8(12): 1216, (2016).

Excel spreadsheets by species (4 letter code is abbreviation for genus and species used in study, year 2010 or 2011 is year data collected, SH indicates data for Science Hub, date is date of file preparation). The data in a file are described in a read me file which is the first worksheet in each file. Each row in a species spreadsheet is for one plot (plant). The data themselves are in the data worksheet. One file includes a read me description of the column in the date set for chemical analysis. In this file one row is an herbicide treatment and sample for chemical analysis (if taken). This dataset is associated with the following publication: Olszyk , D., T. Pfleeger, T. Shiroyama, M. Blakely-Smith, E. Lee , and M. Plocher. Plant reproduction is altered by simulated herbicide drift toconstructed plant communities. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 36(10): 2799-2813, (2017).

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

The Excel spreadsheet contains, in separate sheets, data on respondents’ characteristics, knowledge and practices, as well as data for Figures 2, 3 and 4.

The Geothermal Resource Portfolio Optimization and Reporting Tool (GeoRePORT) was developed as a way to distill large amounts of geothermal project data into an objective, reportable data set that can be used to communicate with experts and non-experts. GeoRePORT summarizes (1) resource grade and certainty and (2) project readiness. This Excel file allows users to easily navigate through the resource grade attributes, using drop-down menus to pick grades and project readiness, and then easily print and share the summary with others. This spreadsheet is the first draft, for which we are soliciting expert feedback. The spreadsheet will be updated based on this feedback to increase usability of the tool. If you have any comments, please feel free to contact us.

The South Florida Water Management District (SFWMD) and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 174 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in central and south Florida. The change factors were computed as the ratio of projected future to historical extreme precipitation depths fitted to extreme precipitation data from various downscaled climate datasets using a constrained maximum likelihood (CML) approach. The change factors correspond to the period 2050-2089 (centered in the year 2070) as compared to the 1966-2005 historical period.
A Microsoft Excel workbook is provided that tabulates best models for each downscaled climate dataset and for all downscaled climate datasets considered together. Best models were identified based on how well the models capture the climatology and interannual variability of four climate extreme indices using the Model Clima ...

Excel spreadsheet containing, in separate sheets, the underlying numerical data and statistical analysis for Figs panels 1B, 1C, 1F, 1G, 1H, 2C, 2D, 2E, 2F, 3A, 3B, 3D, 3E, 3G, 3H, 3J, 3L, 4C, 4D, 4F, 4G, 4J, 5C, 5D, 5E, 5F, 5G, 5J, 5K, 5L, 5M, 6A, 6B, 6C, 6G, 7A, 7B, 7C, 7H, S1C, S1D, S1E, S2D, S2E, S3A, S3B, S4C, S4E, S6A, S6B, S6D, S7A.

Dataset for post-survey data (after required LinkedIn assignment); provided in Excel spreadsheet (.xlsx).

(Fig 2 tab) Cell counts at designated times as measured by trypan blue exclusion. (Fig 3 tab) Normalized mRNA counts from NanoString array of T cells at day 29 of co-culture (top), surface phenotype of CAR+ T cells (middle), and multiparameter memory phenotype of T cells (bottom). (Fig 4 tab) MFI of IFNγ staining of CAR+ T cells following 6 hour co-culture with target cells. (Fig 5 tab) 4-hour chromium release assay of T cells co-cultured with target cells. (Fig 6 tab) BLI flux kinetics of Kasumi2-ffLuc-mKate cells following challenge with CAR+ T cells (top) and days of mouse euthanasia (bottom). (XLSX)

The data are organized into separate sheets corresponding to the following figure panels: 1C, 1G, 2B, 2D, 2F, 2H, 4C, 4D, 4F, 5B, 5C, S3B, S5C, S5E, S7B, S8B, S10B, S12A, S12B, and S21B. (XLSX)

Explanatory notes on the NELA Data: - Timeframe – Data was collected from December 2015 to November 2016 - Please follow this link to see the Inclusion/Exclusion criteria - http://www.nela.org.uk/NELADocs - For an explanation as to why these indicators were selected and reported on please see the Second Patient Report of the National Emergency Laparotomy Audit – http://www.nela.org.uk/reports - Grey rows indicate Hospitals submitting less than ten cases in the Second year of data collection - Cells with N/A mean that Data was not available / Not able to calculate

Data to reproduce figures.Data required to reproduce figures.

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

In order to test hypotheses about groundwater flow under and into estuaries and the Atlantic Ocean, geophysical surveys, geophysical probing, submarine groundwater sampling, and sediment coring were conducted by U.S. Geological Survey (USGS) scientists at Cape Cod National Seashore (CCNS) from 2004 through 2006. Coastal resource managers at CCNS and elsewhere are concerned about nutrients that are entering coastal waters via submarine groundwater discharge, which are contributing to eutrophication and harmful algal blooms. The research carried out as part of the study described here was designed, in part, to help refine assumptions required by earlier versions of models about the nature of submarine groundwater flow and discharge at CCNS. This study was conducted in four phases, with a variety of field techniques and equipment employed in each phase. Phase 1 consisted of continuous resistivity profiling (CRP) surveys of the entire study area conducted in 2004. Phase 2 consisted of CRP ground-truthing via resistivity probe measurements and submarine groundwater sampling from hydraulically-drive piezometers using a barge in the Salt Pond/Nauset Marsh area in 2005. Phase 3 consisted of supplemental detailed CRP surveys in the Salt Pond/Nauset Marsh area in 2006. Finally, Phase 4 consisted of sediment coring and porewater extraction in the Salt Pond/Nauset Marsh area later in 2006 to supplement the 2005 sampling.

Data to reproduce figures.Data required to reproduce figures.

Spreadsheet template for Habitat data for fungi

Excel spreadsheet containing, in separate sheets, the underlying numerical data and statistical analysis for Figs 1B, 1C, 1E, 1F, 1G, 2A, 2B, 3, 4B, 4C, 4D, 4E, 4F, 4G, 5A, 5B, 5C, 5D, 5E, 6A, 6B, 6C, 6D, 8A, 8B, 8C, 8D, 9A, 9B, 9C, 9D, 10A, 10B, 10C, 10D, 10E, 10F, 10G, 10H, 11A, 11B, 11C, S1A, S1B, S1C, S1D, S2A, S2B, S2C, and S2D.

In order to test hypotheses about groundwater flow under and into estuaries and the Atlantic Ocean, geophysical surveys, geophysical probing, submarine groundwater sampling, and sediment coring were conducted by U.S. Geological Survey (USGS) scientists at Cape Cod National Seashore (CCNS) from 2004 through 2006. Coastal resource managers at CCNS and elsewhere are concerned about nutrients that are entering coastal waters via submarine groundwater discharge, which are contributing to eutrophication and harmful algal blooms. The research carried out as part of the study described here was designed, in part, to help refine assumptions required by earlier versions of models about the nature of submarine groundwater flow and discharge at CCNS. This study was conducted in four phases, with a variety of field techniques and equipment employed in each phase. Phase 1 consisted of continuous resistivity profiling (CRP) surveys of the entire study area conducted in 2004. Phase 2 consisted of CRP ground-truthing via resistivity probe measurements and submarine groundwater sampling from hydraulically-drive piezometers using a barge in the Salt Pond/Nauset Marsh area in 2005. Phase 3 consisted of supplemental detailed CRP surveys in the Salt Pond/Nauset Marsh area in 2006. Finally, Phase 4 consisted of sediment coring and porewater extraction in the Salt Pond/Nauset Marsh area later in 2006 to supplement the 2005 sampling.