Excel sheets in order: The sheet entitled “Hens Original Data” contains the results of an experiment conducted to study the response of laying hens during initial phase of egg production subjected to different intakes of dietary threonine. The sheet entitled “Simulated data & fitting values” contains the 10 simulated data sets that were generated using a standard procedure of random number generator. The predicted values obtained by the new three-parameter and conventional four-parameter logistic models were also appeared in this sheet. (XLSX)

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

The data collection has two purposes... one to create data for a examination of the efficiency and compatibility of Battery PV generators with power consumption requirements of outdoor festivals and events.

The other to create a data sonification based multimedia project to display this data in a multimedia artspace. Is there a data dictionary or any schema associated with the data? If so what is it?

See attached readme files in regards to interpreting the data.

Data collected via remote automated computer monitoring of electrical equipment. Please see Excel data readme file for more details.

Viewing instructions:

Excel and Word

Heterogenous Big dataset is presented in this proposed work: electrocardiogram (ECG) signal, blood pressure signal, oxygen saturation (SpO2) signal, and the text input. This work is an extension version for our relevant formulating of dataset that presented in [1] and a trustworthy and relevant medical dataset library (PhysioNet [2]) was used to acquire these signals. The dataset includes medical features from heterogenous sources (sensory data and non-sensory). Firstly, ECG sensor’s signals which contains QRS width, ST elevation, peak numbers, and cycle interval. Secondly: SpO2 level from SpO2 sensor’s signals. Third, blood pressure sensors’ signals which contain high (systolic) and low (diastolic) values and finally text input which consider non-sensory data. The text inputs were formulated based on doctors diagnosing procedures for heart chronic diseases. Python software environment was used, and the simulated big data is presented along with analyses.

Generator Sales Market size was valued at USD 21.6 billion in 2019 and is poised to grow from USD 22.85 billion in 2023 to USD 35.88 billion by 2031, growing at a CAGR of 5.8% in the forecast period (2024-2031).

Review citations used for picking reviews by random (random # generator produced by excel, and number listed on citations picked based on random number generated)

The data consist of 29 file. Nine data for the performances of the generator excited with circular permanent magnet poles. Nine data for the performances of the generator excited with rectangular permanent magnet poles. Nine data for the performances of the generator excited with trapezoidal permanent magnet poles. One cogging torque data for the most qualified generator. One data for all the generator combined in one excel sheet.

This dataset represents CLIGEN input parameters for locations in 68 countries. CLIGEN is a point-scale stochastic weather generator that produces long-term weather simulations with daily output. The input parameters are essentially monthly climate statistics that also serve as climate benchmarks. Three unique input parameter sets are differentiated by having been produced from 30-year, 20-year and 10-year minimum record lengths that correspond to 7673, 2336, and 2694 stations, respectively. The primary source of data is the NOAA GHCN-Daily dataset, and due to data gaps, records longer than the three minimum record lengths were often queried to produce the needed number of complete monthly records. The vast majority of stations used at least some data from the 2000's, and temporal coverages are shown in the Excel table for each station. CLIGEN has various applications including being used to force soil erosion models. This dataset may reduce the effort needed in preparing climate inputs for such applications. Revised input files added on 11/16/20. These files were revised from the original dataset. Fixed metadata issues with the headings of each file. Fixed inconsistencies with MX.5P and transition probability values for extremely dry climates and/or months. Second revision input files added on 2/12/20. A formatting error was fixed that affected transition probabilities for 238 stations with zero recorded precipitation for one or more months. The affected stations were predominantly in Australia and Mexico. Resources in this dataset:Resource Title: 30-year input files. File Name: 30-year.zipResource Description: CLIGEN .par input files based on 30-year minimum record lengths. May be viewed with text editor.Resource Software Recommended: CLIGEN v5.3,url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 20-year input files. File Name: 20-year.zipResource Description: CLIGEN .par input files based on 20-year minimum record lengths. May be viewed with text editor.Resource Software Recommended: CLIGEN v5.3,url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 10-year input files. File Name: 10-year.zipResource Description: CLIGEN .par input files based on 10-year minimum record lengths. May be viewed with text editor.Resource Software Recommended: CLIGEN v5.3,url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: Map Layer. File Name: MapLayer.kmzResource Description: Map Layer showing locations of the new CLIGEN stations. This layer may be imported into Google Earth and used to find the station closest to an area of interest.Resource Software Recommended: Google Earth,url: https://www.google.com/earth/ Resource Title: Temporal Ranges of Years Queried. File Name: GHCN-Daily Year Ranges.xlsxResource Description: Excel tables of the first and last years queried from GHCN-Daily when searching for complete monthly records (with no gaps in data). Any ranges in excess of 30 years, 20 years and 10 years, for respective datasets, are due to data gaps.Resource Title: 30-year input files (revised). File Name: 30-year revised.zipResource Description: CLIGEN .par input files based on 30-year minimum record lengths. May be viewed with text editor. Revised from the original dataset. Fixed metadata issues with the headings of each file. Fixed inconsistencies with MX.5P and transition probability values for extremely dry climates and/or months.Resource Software Recommended: CLIGEN v5.3,url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 20-year input files (revised). File Name: 20-year revised.zipResource Description: CLIGEN .par input files based on 20-year minimum record lengths. May be viewed with text editor. Revised from the original dataset. Fixed metadata issues with the headings of each file. Fixed inconsistencies with MX.5P and transition probability values for extremely dry climates and/or months.Resource Software Recommended: Cligen v5.3,url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 10-year input files (revised). File Name: 10-year revised.zipResource Description: CLIGEN .par input files based on 10-year minimum record lengths. May be viewed with text editor. Revised from the original dataset. Fixed metadata issues with the headings of each file. Fixed inconsistencies with MX.5P and transition probability values for extremely dry climates and/or months.Resource Software Recommended: Cligen v5.3,url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 30-year input files (revised 2). File Name: 30-year revised 2.zipResource Description: CLIGEN .par input files based on 30-year minimum record lengths. May be viewed with text editor. Fixed formatting issue for 238 stations that affected transition probabilities.Resource Software Recommended: Cligen v5.3,url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 20-year input files (revised 2). File Name: 20-year revised 2.zipResource Description: CLIGEN .par input files based on 20-year minimum record lengths. May be viewed with text editor. Fixed formatting issue for 238 stations that affected transition probabilities.Resource Software Recommended: Cligen v5.3,url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/ Resource Title: 10-year input files (revised 2). File Name: 10-year revised 2.zipResource Description: CLIGEN *.par input files based on 10-year minimum record lengths. May be viewed with text editor. Fixed formatting issue for 238 stations that affected transition probabilities.Resource Software Recommended: Cligen v5.3,url: https://www.ars.usda.gov/midwest-area/west-lafayette-in/national-soil-erosion-research/docs/wepp/cligen/

This is the replication package of the paper "MLSToolbox Code Generator: A tool for generating quality ML pipelines for ML systems".

This replication package contains three files:

1. MLSToolbox Code Generation_Evaluation protocol.pdf: Document containing the protocol for a preliminary evaluation of effectiviness and usability for the first launch of the MLSToolbox Code Generator.
2. MLSToolbox Code Generation_Usability results.xlsx: MS Excel file containing the data collected for the usability survey and the System usability scale (SUS) calculation based on the answers.
3. MLSToolbox Code Generation_Quality comparison.zip: Compressed file containing the pipelines that are compared with the code generated by the tool for the effectiviness. The file includes eleven folders:
   • folder "data" with the data source files for both examples used in the comparison (dibatetes and Big Mart Sales Prediction)
   • one folder for each pipeline (from P01 to P10) where you can find:
     • folder "src" including all the pipeline code modules and the data needed (in the “data” subfolder”) as input
     • foldr "quality metrics" including the output for the Radon and Pylint executions.

Pipelines P01 and P02 have been generated using MLSToolbox Code Generator tool.

Note: The licence is applicable to the protocol and usability results files. "MLSToolbox Code Generation_Quality comparison.zip" file contains source code repositories, some of them downloaded from GitHub, the license for these pipelines is defined in the corresponding GitHub repository by their authors.

This data is usually updated quarterly by February 1st, May 1st, August 1st, and November 1st.The CEC Power Plant geospatial data layer contains point features representing power generating facilities in California, and power plants with imported electricity from Nevada, Arizona, Utah and Mexico.The transmission line, substation and power plant mapping database were started in 1990 by the CEC GIS staffs. The final project was completed in October 2010. The enterprise GIS system on CEC's critical infrastructure database was leaded by GIS Unit in November 2014 and was implemented in May 2016. The data was derived from CEC's Quarterly Fuel and Energy Report (QFER), Energy Facility Licensing (Siting), Wind Performance Reporting System (WPRS), and Renewable Energy Action Team (REAT). The sources for the power plant point digitizing are including sub-meter resolution of Digital Globe, Bing, Google, ESRI and NAIP aerial imageries, with scale at least 1:10,000. Occasionally, USGS Topographic map, Google Street View and Bing Bird's Eye are used to verify the precise location of a facility.Although a power plant may have multiple generators, or units, the power plant layer represents all units at a plant as one feature. Detailed attribute information associated with the power plant layer includes CEC Plant ID, Plant Label, Plant Capacity (MW), General Fuel, Plant Status, CEC Project Status, CEC Docket ID, REAT ID, Plant County, Plant State, Renewable Energy, Wind Resource Area, Local Reliability Area, Sub Area, Electric Service Area, Service Area Category, California Balancing Authorities, California Air District, California Air Basin, Quad Name, Senate District, Assembly District, Congressional District, Power Project Web Link, CEC Link, Aerial, QRERGEN Comment, WPRS Comment, Geoscience Comment, Carto Comment, QFERGEN Excel Link, WPRS Excel Link, Schedule 3 Excel Link, and CEC Data Source. For power plant layer which is joined with QFer database, additional fields are displayed: CEC Plant Name (full name), Plant Alias, EIA Plant ID, Plant City, Initial Start Date, Online Year, Retire Date, Generator or Turbine Count, RPS Eligible, RPS Number, Operator Company Name, and Prime Mover ID. In general, utility and non-utility operated power plant spatial data with at least 1 MW of demonstrated capacity and operating status are distributed. Special request is required on power plant spatial data with all capacities and all stages of status, including Cold Standby, Indefinite Shutdown, Maintenance, Non-Operational, Proposed, Retired, Standby, Terminated, and Unknown.For question on power generation or others, please contact Michael Nyberg at (916) 654-5968.California Energy Commission's Open Data Portal.

The dataset contains all image source data presented in the manuscript in CSV format, with a file size of 12.2MB. The data can be viewed using Excel software, and was generated using related data testing equipment such as Keithley and Fluke.

The EIA-906, EIA-920, EIA-923 and predecessor forms provide monthly and annual data on generation and fuel consumption at the power plant and prime mover level. A subset of plants, steam-electric plants 10 MW and above, also provides boiler level and generator level data. Data for utility plants are available from 1970, and for non-utility plants from 1999. Beginning with January 2004 data collection, the EIA-920 was used to collect data from the combined heat and power plant (cogeneration) segment of the non-utility sector; also as of 2004, nonutilities filed the annual data for nonutility source and disposition of electricity. Beginning in 2007, environmental data was collected on Schedules 8A – 8F of the Form 923 and includes by-product disposition, financial information, NOX control operations, cooling system operations and FGP and FGD unit operations. Beginning in 2008, the EIA-923 superseded the EIA-906, EIA-920, FERC 423, and the EIA-423. Schedule 2 of the EIA-923 collects the plant level fuel receipts and cost data previously collected on the FERC and EIA Forms 423. Data for fuel receipts and costs prior to 2010 are published at /cneaf/electricity/page/eia423.html.

Power plant data prior to 2001 are published as database (.DBF) files, with separate files for utility and non-utility plants. For 2001 data and subsequent years, the data are in Excel spreadsheet files that include data for all plants and make other changes to the presentation of the data.

Note that beginning with January 2001, the data for combined heat and power plants (i.e., the plants that provide data on the EIA-920 form) will only be posted in the combined Excel file.

The links will allow you to download the current Excel files, and will take you to the locations from which you can download the DBF-format utility and non-utility files for 2000 and earlier. The "Database Notes from EIA" link will take you to information on changes to the data and other points of interest to users.

Historical database (.dbf) files for utility (1970-2000) and non-utility (1999-2000)

Utility Database	Legacy (.DBF) Format
Non-Utility Database	Legacy (.DBF) Format
Database Notes from EIA	Updated 4/21/10
Comments or Questions?	E-Mail EIA-923@eia.doe.gov

Additional Links:
Monthly Generation and Fuel Consumption by State
Electric Power Monthly
Form EIA-923, Power Plant Operations Report, form and instructions, (http://www.eia.doe.gov/oiaf/aeo/images/pdf.gif" alt="pdf file" height="16" width="16">) pdf format
Form EIA-923, Power Plant Operations Report, form and instructions, MS Word format

    <b>Contact:</b> <span class="bodypara"><div align="left"> Channele Wirman<br> Phone: 202-586-5356<br> Email: <a href="mailto:channele.wirman@eia.doe.gov">Channele Wirman</a></div></span>

The Single-Family Portfolio Snapshot consists of a monthly data table and a report generator (Excel pivot table) that can be used to quickly create new reports of interest to the user from the data records. The data records themselves are loan level records using all of the categorical variables highlighted on the report generator table. Users may download and save the Excel file that contains the data records and the pivot table.The report generator sheet consists of an Excel pivot table that gives individual users some ability to analyze monthly trends on dimensions of interest to them. There are six choice dimensions: property state, property county, loan purpose, loan type, property product type, and downpayment source.Each report generator selection variable has an associated drop-down menu that is accessed by clicking once on the associated arrows. Only single selections can be made from each menu. For example, users must choose one state or all states, one county or all counties. If a county is chosen that does not correspond with the selected state, the result will be null values.The data records include each report generator choice variable plus the property zip code, originating mortgagee (lender) number, sponsor-lender name, sponsor number, nonprofit gift provider tax identification number, interest rate, and FHA insurance endorsement year and month. The report generator only provides output for the dollar amount of loans. Users who desire to analyze other data that are available on the data table, for example, interest rates or sponsor number, must first download the Excel file. See the data definitions (PDF in top folder) for details on each data element.Files switch from .zip to excel in August 2017.

The data collection has two purposes... one to create data for a statistical examination of the efficiency and compatibility of Battery PV generators as measured against the off grid AC power requirements of outdoor festivals and events.

The other purpose is to create a database to be used as the foundation controller inputs in a sonification based multimedia creative project that displays specific coherent digital data sets in a multimedia artspace.

A set of Electrical Power Generation (Solar PV and connected Lithium (LiPo)battery Storage)) and Electrical Supply Performance Data gathered during Installation of Solar Sunflower Prototype Mobile (offgrid/UPS) Solar generator at Island Vibes Music Festival , Point Lookout Stradbroke Island 2016.

All data collected by Barry Hill via remote automated computer monitoring of electrical equipment. Please see Excel data readme file for more details.

This data set is a collection of historic data of wind turbine installations in the whole of Denmark from the Danish Energy Agency (Energistyrelsen). The data was used in a power system flexibility study by Karen Olsen in 2018-19 leading to a paper that is to appear in the proceedings of the ICAE19 conference and is entitled: "Data-driven flexibility requirements for current and future scenarios with high penetration of renewables". A journal paper has also been submitted using the same data.The data has been extracted from the website of Energistyrelsen at the following link where historic data is publicly available (called "Stamdataregister"):http://ens.dk/service/statistik-data-noegletal-og-kort/data-oversigt-over-energisektoren The present version was extracted in September 2019 and contains installation and production data until and including June 2019. The data is in the originally downloaded excel file, ready to be parsed by the python code written by Karen Olsen (see reference to Fanfare code).Data used for analysis:- turbine ID number (column: "Turbine identifier (GSRN)" in the excel spreadsheet)- date of installation (column: "Date of original connection to grid" in the excel spreadsheet)- turbine capacity (column: "Capacity (kW)" in the excel spreadsheet)- turbine location commune (column: "Local authority no" in the excel spreadsheet)- turbine placing sea/land (column: "Type of location" in the excel spreadsheet)- yearly production (columns starting at: "Historic production figures (kWh):" in the excel spreadsheet)Further information and code for analysis can be found under:https://kpolsen.github.io/FANFARE/

description: The Emissions & Generation Resource Integrated Database (eGRID) is a comprehensive source of data on the environmental characteristics of almost all electric power generated in the United States. These environmental characteristics include air emissions for nitrogen oxides, sulfur dioxide, carbon dioxide, methane, and nitrous oxide; emissions rates; net generation; resource mix; and many other attributes. eGRID2012 Version 1.0 is the eighth edition of eGRID, which contains the complete release of year 2009 data, as well as year 2007, 2005, and 2004 data. For year 2009 data, all the data are contained in a single Microsoft Excel workbook, which contains boiler, generator, plant, state, power control area, eGRID subregion, NERC region, U.S. total and grid gross loss factor tabs. Full documentation, summary data, eGRID subregion and NERC region representational maps, and GHG emission factors are also released in this edition. The fourth edition of eGRID, eGRID2002 Version 2.01, containing year 1996 through 2000 data is located on the eGRID Archive page (http://www.epa.gov/cleanenergy/energy-resources/egrid/archive.html). The current edition of eGRID and the archived edition of eGRID contain the following years of data: 1996 - 2000, 2004, 2005, and 2007. eGRID has no other years of data.; abstract: The Emissions & Generation Resource Integrated Database (eGRID) is a comprehensive source of data on the environmental characteristics of almost all electric power generated in the United States. These environmental characteristics include air emissions for nitrogen oxides, sulfur dioxide, carbon dioxide, methane, and nitrous oxide; emissions rates; net generation; resource mix; and many other attributes. eGRID2012 Version 1.0 is the eighth edition of eGRID, which contains the complete release of year 2009 data, as well as year 2007, 2005, and 2004 data. For year 2009 data, all the data are contained in a single Microsoft Excel workbook, which contains boiler, generator, plant, state, power control area, eGRID subregion, NERC region, U.S. total and grid gross loss factor tabs. Full documentation, summary data, eGRID subregion and NERC region representational maps, and GHG emission factors are also released in this edition. The fourth edition of eGRID, eGRID2002 Version 2.01, containing year 1996 through 2000 data is located on the eGRID Archive page (http://www.epa.gov/cleanenergy/energy-resources/egrid/archive.html). The current edition of eGRID and the archived edition of eGRID contain the following years of data: 1996 - 2000, 2004, 2005, and 2007. eGRID has no other years of data.

The proportions of K+ and Na+ were varied while the total amount was kept constant at 23.2 mM, the level in MS medium. For example, treatment point #1 would include 17.4 mM Na+ and 5.8 mM K+.

This upload contains the Supplementary Information file and the underlying data as Excel-file for the referenced Journal article ("Hourly marginal electricity mixes and their relevance for assessing the environmental performance of installations with variable load or power")

More specifically, it provides

• time series of the Spanish electricity generation mix for the year 2021 for energy system analysis (as used as basis for the underlying publication), including imports and environmental impacts of the hourly generation mix for all EF3.0 impact categories (Marginal_Electricity_Results_Analysis_2021_V17.xls)
• a spreadshhet calculator for determining the average and marginal hourly benefits of a generator with variable load (a PV installation, but the generation profile can be substituted by any other) Load-Gen_Balancing_V15.xlsm
• life cycle inventory data for import into openLCA and re-use in combination with the ecoinvent databse (Version 3.7.1).

Further details are available on request to the main author.

The data collection has two purposes:

One to create data for an examination of the efficiency and compatibility of Battery PV generators with power consumption requirements of outdoor festivals and events.

The other to create a data sonification based multimedia project to display this data in a multimedia artspace.

Dataset contains multiple graphs and tables and raw data files in digital form.

Data collected via remote automated computer monitoring of electrical equipment. Please see Excel data readme file for more details.

Viewing Instructions:

Photovoltaic Solar Array technologies. Open source computer software. Lithium phosphate battery technology. Prototype designed mobile variable deploy folding solar panel array.

The coefficients for K+ and Na+ under the linear mixture are estimates of the response at each vertex, not estimates of the effects of these two ions. The K+ x Na+ term is not an interaction term, though it looks like one, but a quadratic blending term unique to mixture models. This term is used to determine if the mixture components exhibit nonlinear blending and if that blending is synergistic or antagonistic.