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

Energy harvesting from flow induced vibrations (FIV) in flexible bodies offer opportunities for power generation in biomimicking robotic devices and is an active area of research. The focus of this study is on investigating the underlying physics and qualitatively analysing the energy extraction scenarios in similar structural systems, comprising of a flexible piezoelectric flapper in a low Reynolds number flow regime. A high-fidelity three-way fully coupled fluid-structure-electric energy solver is developed in-house to study the energy harvesting capabilities of such a flapper, its hydrodynamic characteristics and the associated unsteady flow-field. The results indicate that the flapper deformation profiles at the most efficient harvesting regimes, resemble the propulsion gaits of natural swimmers. Investigations on the effects of a sinusoidal heaving actuation reveal no significant impact on the harvested power at the high yield (high power output) regime, identified under the passiv..., This dataset includes the codes used for the simulation in the manuscript, as well as the data generated from it. A range of flexibility and inertia parameters were used in the same code to generate the data listed in the .dat, .txt files. These datasets were used for post-processing by post-processing codes (also included here) to generate the .csv files. Details are provided in the corresponding readme.md file. These datasets (.csv,.dat) were used to generate the figures (.fig->.eps files) with the post processing codes. The saved .eps files are then post-processed by using inkscape to generate a combined figure in .eps format. The schematics are also drawn in inkscape. Details of the data usage guidelines are mentioned in the readme file., The .dat, .txt files can be opened with any text reader (e.g. text editor) The .csv files can be opened with any excel file reader (Microsoft Excel/LibreOffice Calc) The .m codes can be opened and run by any version of Matlab (preferably >2021a). The .cpp files can be opened by gedit and run with pgc compilers. (details provided in the readme file for the code), # Energy harvesting in a flow-induced vibrating flapper with biomimetic gaits

Flow induced vibrating flappers with biomimetic gaits are energy efficient

This dataset contains all the raw data derived from the direct simulation for the range of parametric space. These datasets have been used for post-processing to get the derived datasets as well as to generate the figures. Description of the Data and file structure

Convergence studies

=11,51,101 $=01,004,002 *=001,0001 These numbers denote the level of fineness of element, grid and time-step size used in the folder numbering.

cd IJMS_submission/IJMS submission/Data/ Data\\\ folder/convergence_tests/element/n=*
                                    /mesh/grid=$
                                    /time/t=**

xf_total.dat: 1st column contains time stamps, 2-nd column contains X displacement location time history along the flapper arc length. yf_total...

The fatigue lives data corresponding to Figures 8, 11, and 12 are provided in an excel file. The reader can find the data stored in three separate sheets within the excel file attached.

An Excel spreadsheet of correlations between Mendeley readers and Scopus citations for 323 narrow Scopus fields for articles published in 2012, using reader count and citation count data from August/September 2017.Supporting material for the journal article, "Are Mendeley Reader Counts Useful Impact Indicators in all Fields?"

File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

This supplemental Excel file supports the article titled “A Better Explanation of Countercurrent Multiplication in the Formation of the Corticopapillary Osmotic Gradient in the Outer Medulla” in Advances in Physiology Education. This file serves the following three functions: First, it explains how the osmotic concentrations in the intra-thick ascending limb (TAL) and interstitium at multiple levels are simulated/determined in the two figures in the article. Second, it allows readers to generate alternative data set(s), i.e., alternative values of osmotic concentrations inside and outside the TAL at multiple levels. Finally, it points out the future direction of research. Instructions for how to use the supplemental Excel file are provided in each of the two tabs.

Global Data Center RFID Market size was valued at USD 1017.87 Million in 2022 and is poised to grow from USD 1,270.3 Million in 2023 to USD 9329.05 Million by 2031, growing at a CAGR of 24.8% in the forecast period (2024-2031).

The data contains 4 kinds of files. Files are organized in folders for easy interpretation:

1) An Excel file. This has all the data collected from the measurement. This file can be opened using Microsoft excel.

2) Minitab project files (MPJ) . These files can be opened using the statistical software Minitab version 17. They include the data, analyses and plots used to interpret the results of the research.

3) A PDF document. This has all the plots related obtained through the research data to determine the optimal settings. This can be opened in any PDF reader.

4) Original TIF and BMP images obtained from the CT scan. Only one relevant image from each data-set is shown because it contains hundreds of images. These can be opened using most image viewing applications such as windows photo viewer.

This spreadsheet model calculates the net income for irrigated agricultural production. The model is designed to evaluate the economics of deficit irrigation (irrigation at less than the amount required to produce maximum yield). The spreadsheet first models the water production function for a crop, then uses that relationship along with crop price and production costs to calculate net income and the irrigation amount that maximizes net income. This spreadsheet is similar to another posted at Ag Data Commons: "Economic Model of Deficit Irrigation" (http://dx.doi.org/10.15482/USDA.ADC/1504421). That model was designed primarily to evaluate deficit irrigation as a means to compare revenue with reduced water consumption to income gained by transferring the saved water. The model includes two common scenarios: 1) irrigation water supply is adequate but expensive, and 2) irrigation water supply is inadequate to fully irrigate the available land. In the first scenario, net income is maximized when the marginal costs of production, including water, is equal to the marginal revenue. In the second scenario, net income is maximized when the value of the water is maximized by selecting the portion of the land that should be irrigated. In the second scenario, the value and costs of the un-irrigated land are included. The first worksheet of the spreadsheet describes the relationships used in each worksheet and the input parameters required. Additional worksheets calculate the water production function, the irrigation water production function, and the net income for each of the two scenarios. The worksheets allow the user to input the various biophysical and economic parameters relevant to their conditions and allows evaluating various parameter combinations. Each worksheet contains graphs to visualize the results. Resources in this dataset:Resource Title: Economic Model of Deficit Irrigation II (spreadsheet). File Name: WPF Econ Model V2 Mod.xlsxResource Description: Spreadsheet contains 5 worksheets. The first worksheet describes the relationships in the remaining worksheets and the parameters required by the model.Resource Software Recommended: Microsoft Excel 365 (may work on earlier versions),url: https://www.microsoft.com/en-us/microsoft-365/get-started-with-office-2019 Resource Title: Description of the Model. File Name: DataDictionary.pdfResource Description: Description of the model and input parameters.Resource Software Recommended: Adobe Reader,url: https://get.adobe.com/reader/otherversions/

These data represent temporal changes in the concentrations of individual oxylipins (OxL) and endocannabinoids (eCB) in adult women during a sub-maximal aerobic exercise bout, and at recovery (20 min. post-cessation of exercise) (Supplementary Materials 1). Results are depicted both before (pre-) and after (post-) a weight loss and fitness intervention lasting at least 14 wk. Participants were obese, sedentary, and insulin resistant in the pre-intervention phase. Also depicted are statistical groupings of metabolites, as an Excel file (Supplementary Materials 2). Resources in this dataset:Resource Title: Supporting Materials 1. File Name: oxy_timecourse, SUPPLEMENTAL MATERIALS 1, for submission.pdfResource Description: Blood plasma oxylipin and endocannabinoid concentrations over time during an acute sub-maximal aerobic exercise bout in women, both before and after a 14 wk+ fitness and weight loss intervention. Total exercise time = 30 min, followed by a recovery period of 20 min.Resource Software Recommended: Adobe Acrobat,url: https://acrobat.adobe.com/us/en/acrobat/pdf-reader.html Resource Title: Supporting Materials 2, oxylipin and endocannabinoid statistics & clustering. File Name: SUPPLEMENTAL MATERIALS 2, for submissionXLS.xlsResource Description: Excel sheet with statistics and statistical pattern clustering associated with blood plasma oxylipins and endocannabinoids in women performing 30 min. sub-maximal aerobic exercise, followed by a 20 min recovery period.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

The datasets in the .pdf and .zip attached to this record are in support of Intelligent Transportation Systems Joint Program Office (ITS JPO) report FHWA-JPO-15-222, "Impacts Assessment of Dynamic Speed Harmonization with Queue Warning: Task 3, Impacts Assessment Report". The files in these zip files are specifically related to the US-101 Testbed, near San Mateo, CA. The uncompressed and compressed files total 2.0265 GB in size. The files have been uploaded as-is; no further documentation was supplied by NTL. All located .docx files were converted to .pdf document files which are an open, archival format. These .pdfs were then added to the zip file alongside the original .docx files. The attached zip files can be unzipped using any zip compression/decompression software. These zip file contains files in the following formats: .pdf document files which can be read using any pdf reader; .xlsxm macro-enabled spreadsheet files which can be read in Microsoft Excel and some Tech Report spreadsheet programs; .accdb database files which may be opened with Microsoft Access Database software and Tech Report open database software applications ; as well as .db generic database files, often associated with thumbnail images in the Windows operating environment. [software requirements] These files were last accessed in 2017. File and .zip file names include: FHWA_JPO_15_222_INFLO_Performance_Measure_METADATA.pdf ; FHWA_JPO_15_222_INFLO_Performance_Measure_METADATA.docx ; FHWA_JPO_15_222_INFLO_VISSIM_Output_and_Analysis_Spreadsheets.zip ; FHWA_JPO_15_222_INFLO_Spreadsheet_PDFs.zip ; FHWA_JPO_15_222_DATA_CV50.zip ; and, FHWA_JPO_15_222_DATA_CV25.zip

Please see the PDF named arabidopsis-conditional-expression-manuscript-methods-only.pdf in the Zenodo supplemental material attached to this submission for the full methods and equations used for data processing, including citations to relevant publications.

Excel file that contains pre-labeled cells for a 96-well plate or 384-well plate that will calculate the number of cells per milliliter in a starting culture. User will input the OD values from the plate. (XLSX)

The report shows how the uncertainty should be evaluated for a measurement from an orifice meter where the flowrate is evaluated using a discharge-coefficient equation based on data from a population of orifice meters: the orifice meter in service is not itself calibrated in a flowing fluid, but is similar (with permitted variability) to those on which the discharge-coefficient equation is based.

The data on which the discharge-coefficient equation in ISO 5167-2:2003 (the Reader-Harris/Gallagher (1998) Equation) is based are given in the uploaded dataset together with the analysis required by the report (to follow).

Files contained in the dataset are:

• D4-2_Report.pdf: Report “The uncertainty of the orifice-plate discharge coefficient”
• D4-2 LaTeX Source Files.zip: LaTeX files to be compiled in order to produce D4-2_Report.pdf.
• D4-2_Figures.zip: figures/graphs from the report, including data points.
• EMUE-D4-2-OrificePlate.xlsm: Excel spreadsheet containing all data associated with the example.

This theme presents the location of samples of contaminants of emerging interest (CIE) carried out by the Directorate-General for Monitoring the State of the Environment (DGSEE) as part of its work since 2000. Each station is accompanied by a link in the attribute table allowing the reader to access an Excel file presenting the concentrations of CIE detected, the list of CIE analyzed at least once at the station, the list of reports produced from the samples taken at the station, as well as information on surface water quality criteria for the protection of aquatic life. The dataset on contaminants of emerging interest also includes a layer of polygons presenting the drainage areas of some of the stations and a data table including the compilation of land use by category for the last year available at the time the data was generated. The drainage areas and the land use table are linked to the sampling stations based on the BQMA station number.This third party metadata element was translated using an automated translation tool (Amazon Translate).

This theme presents the location of samples of contaminants of emerging interest (CIE) carried out by the Directorate-General for Monitoring the State of the Environment (DGSEE) as part of its work since 2000. Each station is accompanied by a link in the attribute table allowing the reader to access an Excel file presenting the concentrations of CIE detected, the list of CIE analyzed at least once at the station, the list of reports produced from the samples taken at the station, as well as information on surface water quality criteria for the protection of aquatic life. The dataset on contaminants of emerging interest also includes a layer of polygons presenting the drainage areas of some of the stations and a data table including the compilation of land use by category for the last year available at the time the data was generated. The drainage areas and the land use table are linked to the sampling stations based on the BQMA station number.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Excel file that contains the plate layouts and all optical density readings for the organisms used in this protocol. (XLSX)

In this study, we raised crickets on four diets that differed in macronutrient availability: high lipid, high carb, high protein, and a control diet. For the first two weeks of our experiment, we collected weights, number of eggs laid, and survivorship. After two weeks, half of the crickets received an immune challenge while the others received a sham challenge. For the next week, we measured survivorship and egg-laying. Finally, we measured their lytic activity to examine the effect of diet and immune challenges.This is our full dataset, including the raw data as it was collected and any/all transformations that we completed prior to analysis. The attached Excel file has several sheets, which are briefly explained below:Weight: Shows the weight for each individual in the experiment, at the start of our experiment (roughly 1 week post-adult molt), week 1 of our experiment, week 2 of our experiment, and week 3 of our experiment (after the immune challenge). The individual's diet group is noted in the "Individual ID."EggCount: Shows the egg count for each individual in the experiment, at week 1 of our experiment, week 2 of our experiment, and week 3 of our experiment (after the immune challenge). The individual's diet group is noted in the "Individual ID."Survivorship: In columns A-C, shows the total number of days that each individual lived during our experiment (started approximately one week post-adult molt) and the number of days they lived after the immune challenge; in columns E-I, the data is converted to show what proportion of crickets in each diet treatment were alive on each day of the experiment (this converted data was used to produce Figure 5).Lytic Activity: Shows the individual ID (including diet group), immune challenge status, and calculated lytic activity for each individualAssay Wells: Shows the data from the plate reader, including absorbance readings for each cell at each minute of the lytic assay; below (in rows 73-92), it shows the layout of each plateDiet Macronutrient: Shows the compiled nutrition information for each component of the diets (high lipid, high protein, high carbohydrate, and control), as well as the calculations that we used to compile Tables 1 and 2

This page lists ad-hoc statistics released during the period October to December 2020. These are additional analyses not included in any of the Department for Digital, Culture, Media and Sport’s standard publications.

If you would like any further information please contact evidence@dcms.gov.uk.

October 2020 - Taking Part: Lotteries request

This piece of analysis covers:

1. The proportion of adults who had played a National Lottery Game, who also had played any society lotteries in the last 12 months
2. The proportion of adults who had played a Society Lottery Game, who also had played any National Lottery game in the last 12 months.

Here is a link to the lotteries and gambling page for the annual Taking Part survey.

https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/924428/Lottery_data_table.xlsx">https://www.gov.uk/assets/whitehall/pub-cover-spreadsheet-471052e0d03e940bbc62528a05ac204a884b553e4943e63c8bffa6b8baef8967.png">

National Lottery and Society Lottery Participation

MS Excel Spreadsheet, 70.2KB

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October 2020 - Community Life Survey: Loneliness request

This piece of analysis covers how often people feel they lack companionship, feel left out and feel isolated. This analysis also provides demographic breakdowns of the loneliness indicators.

Here is a link to the wellbeing and loneliness page for the annual Community Life survey.

https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/924431/Loneliness_Demographic_Tables.xlsx">https://www.gov.uk/assets/whitehall/pub-cover-spreadsheet-471052e0d03e940bbc62528a05ac204a884b553e4943e63c8bffa6b8baef8967.png">

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