About this dataset

Context

The dataset tabulates the Excel population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Excel. The dataset can be utilized to understand the population distribution of Excel by age. For example, using this dataset, we can identify the largest age group in Excel.

Key observations

The largest age group in Excel, AL was for the group of age 5 to 9 years years with a population of 77 (15.28%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Excel, AL was the 85 years and over years with a population of 2 (0.40%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Excel is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Excel total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Excel Population by Age. You can refer the same here

Available on website, has all the reports published since 2009. Also provides bibliography and list in Excel format https://www.dol.gov/agencies/ilab/reports/child-labor/list-of-goods

The first row of the Excel spreadsheet describes the data - ID number, Metamorphic Relation Topic, Title of Comment, Type of Comment, Content of the Comment. Our original dataset contained names but these were removed from the dataset.

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About this dataset

Context

The dataset tabulates the Excel township population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Excel township across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Excel township was 300, a 0.99% decrease year-by-year from 2022. Previously, in 2022, Excel township population was 303, a decline of 0.98% compared to a population of 306 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Excel township increased by 17. In this period, the peak population was 308 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Excel township is shown in this column.
• Year on Year Change: This column displays the change in Excel township population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Excel township Population by Year. You can refer the same here

Last Version: 4

Authors: Carlota Balsa-Sánchez, Vanesa Loureiro

Date of data collection: 2022/12/15

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v4.xlsx: full list of 140 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v4.csv: full list of 140 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 4th version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Journal Citation Reports (JCR) and/or Scimago Journal and Country Rank (SJR), Scopus and Web of Science (WOS), Journal Master List.

Version: 3

Authors: Carlota Balsa-Sánchez, Vanesa Loureiro

Date of data collection: 2022/10/28

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v3.xlsx: full list of 124 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_3.csv: full list of 124 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 3rd version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Journal Citation Reports (JCR) and/or Scimago Journal and Country Rank (SJR).

Erratum - Data articles in journals Version 3:

Botanical Studies -- ISSN 1999-3110 -- JCR (JIF) Q2
Data -- ISSN 2306-5729 -- JCR (JIF) n/a
Data in Brief -- ISSN 2352-3409 -- JCR (JIF) n/a

Version: 2

Author: Francisco Rubio, Universitat Politècnia de València.

Date of data collection: 2020/06/23

General description: The publication of datasets according to the FAIR principles, could be reached publishing a data paper (or software paper) in data journals or in academic standard journals. The excel and CSV file contains a list of academic journals that publish data papers and software papers.
File list:

- data_articles_journal_list_v2.xlsx: full list of 56 academic journals in which data papers or/and software papers could be published
- data_articles_journal_list_v2.csv: full list of 56 academic journals in which data papers or/and software papers could be published

Relationship between files: both files have the same information. Two different formats are offered to improve reuse

Type of version of the dataset: final processed version

Versions of the files: 2nd version
- Information updated: number of journals, URL, document types associated to a specific journal, publishers normalization and simplification of document types
- Information added : listed in the Directory of Open Access Journals (DOAJ), indexed in Web of Science (WOS) and quartile in Scimago Journal and Country Rank (SJR)

Total size: 32 KB

Version 1: Description

This dataset contains a list of journals that publish data articles, code, software articles and database articles.

The search strategy in DOAJ and Ulrichsweb was the search for the word data in the title of the journals.
Acknowledgements:
Xaquín Lores Torres for his invaluable help in preparing this dataset.

IntroductionFollowing the identification of Local Area Energy Planning (LAEP) use cases, this dataset lists the data sources and/or information that could help facilitate this research. View our dedicated page to find out how we derived this list: Local Area Energy Plan — UK Power Networks (opendatasoft.com)

Methodological Approach Data upload: a list of datasets and ancillary details are uploaded into a static Excel file before uploaded onto the Open Data Portal.

Quality Control Statement

Quality Control Measures include: Manual review and correct of data inconsistencies Use of additional verification steps to ensure accuracy in the methodology

Assurance Statement The Open Data Team and Local Net Zero Team worked together to ensure data accuracy and consistency.

Other Download dataset information: Metadata (JSON)

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/

Please note that "number of records" in the top left corner is higher than the number of datasets available as many datasets are indexed against multiple use cases leading to them being counted as multiple records.

The dataset contains data from four Norwegian Red Lists. Data included are the Red List Categories, reasons for change, and threats. These data were used to evaluate metrics for quantifying the contributions of different threats to Red Lists, described by Sandvik & Pedersen (2023).

The dataset contains six files:

1. species.csv (semicolon-delimited plain-text file with Red Lists for species)
2. Species.pdf (explanations of species.csv)
3. Species.xlsx (microsoft excel spreadsheet workbook with Red Lists for species)
4. ecosyst.csv (semicolon-delimited plain-text file with the Red List for ecosystems)
5. Ecosyst.pdf (explanations of ecosyst.csv)
6. Ecosyst.xlsx (microsoft excel spreadsheet workbook with the Red List for ecosystems)

The excel workbooks contain the same information as the respective csv and pdf files combined.

Columns, abbreviations etc. are explained in the excel and pdf files.

Data were derived from the following sources, all published by the Norwegian Biodiversity Information Centre:

• 2010 Norwegian Red List for species
• 2015 Norwegian Red List for species
• 2018 Norwegian Red List for ecosystems and habitat types
• 2021 Norwegian Red List for species

R code to analyse the dataset and reproduce the results of the paper is available on Zenodo via doi:10.5281/zenodo.7843806.

BOLD5000: Brains, Objects, Landscapes Dataset

For details please refer to BOLD5000.org and our paper on arXiv (http://arxiv.org/abs/1809.01281)

Participant Directories Content 1) Four participants: CSI1, CSI2, CSI3, & CSI4 2) Functional task data acquisition sessions: sessions #1-15 Each functional session includes: -3 sets of fieldmaps (EPI opposite phase encoding; spin-echo opposite phase encoding pairs with partial & non-partial Fourier) -9 or 10 functional scans of slow event-related 5000 scene data (5000scenes) -1 or 0 functional localizer scans used to define scene selective regions (localizer) -each event.json file lists each stimulus, the onset time, and the participant’s response (participants performed a simple valence task) 3) Anatomical data acquisition session: #16 Anatomical Data: T1 weighted MPRAGE scan, a T2 weighted SPACE, diffusion spectrum imaging

Notes: -All MRI and fMRI data provided is with Siemens pre-scan normalization filter.
-CSI4 only participated in 10 MRI sessions: 1-9 were functional acquisition sessions, and 10 was the anatomical data acquisition session.

Derivatives Directory Content 1) fMRIprep: -Preprocessed data for all functional data of CSI1 through CSI4 (listed in folders for each participant: derivatives/fmriprep/sub-CSIX). Data was preprocessed both in T1w image space and on surface space. Functional data was motion corrected, susceptibility distortion corrected, and aligned to the anatomical data using bbregister. Please refer to the paper for the details on preprocessing. -Reports resulting from fMRI prep, which include the success of anatomical alignment and distortion correction, among other measures of preprocessing success are all listed in the sub-CSIX.html files.
2) Freesurfer: Freesurfer reconstructions as a result of fMRIprep preprocessing stream. 3) MRIQC: Image quality metrics (IQMs) of the dataset using MRIQC. -CSIX-func.csv files are text files with a list of all IQMs for each session, for each run. -CSIX-anat.csv files are text files with a list of all IQMs for the scans acquired in the anatomical session (e.g., MPRAGE). -CSIX_IQM.xls an excel workbook, each sheet of workbook lists the IQMs for a single run. This is the same data as CSIX-func.csv, except formatted differently. -sub-CSIX/derivatives: contain .json with the MRIQC/IQM results for each run. -sub-CSIX/reports: contains .html file with MRIQC/IQM results for each run along with mean signal and standard deviation maps. 4)spm: A directory that contains the masks used to define each region of interest (ROI) in each participant. There were 10 ROIs: early visual (EarlyVis), lateral occipital cortex (LOC), occipital place area (OPA), parahippocampal place area (PPA), retrosplenial complex (RSC) for the left hemisphere (LH) and right hemisphere (RH).

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset contains species lists and cover values for the Barrow, Atqasuk, Oumalik, and Ivotuk grids on the Arctic Slope, Alaska. The data were collected from marked study plots in 1998 and 1999 for the Arctic Transitions in the Land-Atmosphere System (ATLAS) project and are in Excel format. See the README for additional information.

This dataset is associated with the forthcoming publication entitled, "Microbial volatile organic compounds mediate attraction by a primary but not secondary stored product insect pest in wheat", and includes data on grain damage from near infrared spectroscopy, behavioral data from wind tunnel and release-recapture experiments, as well as volatile characterization of headspace from moldy grain. For all files, incubation intervals 9, 18, and 27 d represent how long grain was incubated after being tempered to a grain moisture of 12, 15, or 19% or left untempered (ctrl; 10.8% grain moisture). TSO = Trece storgard oil; empty = negative control (no stimulus), LGB = lesser grain borer (Rhzyopertha dominica), and RFB = red flour beetle (Tribolium castaneum). Note: The resource 'GC/MS Grain MVOC Headspace Data' was added 2021-08-04 with the deletion of some compounds as unlikely natural compounds and potential contaminants. This is the dataset that undergirds the non-metric multidimensional scaling analysis. See the included file list for more information about methods and results of each file in this dataset. Resources in this dataset:Resource Title: GC-MS/Headspace Data. File Name: tvw_final_gc_ms_data.csvResource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Microbial damage on wheat evaluated with near-infrared spectroscopy. File Name: tvw_nearinfrared_sorting_damaged_grain_fungal_exp.csvResource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Release-Recapture Datasets with LGB & RFB. File Name: tvw_rr_lgb_rfb_microbial_cues.csvResource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: Wind tunnel response by RGB & LGB. File Name: tvw_wt_lgb_rfb_data_microbial_cues.csvResource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: GC/MS Grain MVOC Headspace Data. File Name: taylor_headspace_final_data_peer_reviewed_ag_commons.csvResource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: README file list. File Name: file_list_MVOCwheat.txt

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple datasets. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. The dataset consists of an excel spreadsheet and shapefile representing the locations of simulation nodes used in the AWRA-R model. Some of the nodes correspond to gauging station locations or dam locations whereas other locations represent river confluences or catchment outlets which have no gauging. These are marked as "Dummy". ## Purpose Locations are used as pour points in oder to define reach areas for river system modelling. ## Dataset History Subset of data for the Hunter that was extracted from the Bureau of Meteorology's hydstra system and includes all gauges where data has been received from the lead water agency of each jurisdiction. Simulation nodes were added in locations in which the model will provide simulated streamflow. There are 3 files that have been extracted from the Hydstra database to aid in identifying sites in each bioregion and the type of data collected from each on. These data were used to determine the simulation node locations where model outputs were generated. The 3 files contained within the source dataset used for this determination are: Site - lists all sites available in Hydstra from data providers. The data provider is listed in the #Station as _xxx. For example, sites in NSW are _77, QLD are _66. Some sites do not have locational information and will not be able to be plotted. Period - the period table lists all the variables that are recorded at each site and the period of record. Variable - the variable table shows variable codes and names which can be linked to the period table. Relevant location information and other data were extracted to construct the spreadsheet and shapefile within this dataset. ## Dataset Citation Bioregional Assessment Programme (XXXX) HUN AWRA-R simulation nodes v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/fda20928-d486-49d2-b362-e860c1918b06. ## Dataset Ancestors * Derived From National Surface Water sites Hydstra

These are the most recent Data Dictionary (pop-ups) and Panarctic Species List (PASL) zip files for all the vegetation plot data entered into Turboveg for the Alaska AVA. These files are necessary to correctly use the Turboveg data with regards to coded data. The Data Dictionary file will be updated when new datasets are entered into Turboveg which result in additions to coded data such as references, author code, habitat type, surficial geology, etc. Updates to the PASL will occur less frequently. Check the dates in the file names to be certain that you are using the most current files. Our data model is a set of tables that comprise our relational database. The Excel spreadsheet included in the resources below provides information about each field in our database, such as data type, description, if it is a required field, whether the information within the field is selected from a pop-up list, and whether the field is a standard within Turboveg or is specific to the AVA. Using Turboveg: 1) Download the installation file available through the link at Alaska Arctic Geoecological Atlas portal from the official Turboveg webpage (general installation file for worldwide users, however, some adjustments will be needed when using data from AAVA after installation of this program). 2) Open the Turboveg program and restore the most recent Data Dictionary and PASL zipped files into the Turboveg program by using the function 'Database-Backup/Restore-Restore.' All the previous versions of data dictionary files and PASL that are already in program will be overwritten. 3) Use the Alaska-AVA following the manual for Turboveg for Windows which is available at http://www.synbiosys.alterra.nl/turboveg/tvwin.pdf

ABulgaria number dataset can be a great element for direct marketing nationwide right now. Also, this Bulgaria number dataset has thousands of active mobile numbers that help to grow sales in the company. In fact, you can develop your business by getting many trustworthy B2C customers. Again, clients can send you a fast answer if they need it or not Similarly, this Bulgaria number dataset is a very essential tool for telemarketing. In other words, you get all these 95% accurate number leads at a very cheap price from us. In addition, our List To Data website always follows the full GDPR laws strictly. As such, the return on investment (ROI) will provide you satisfaction from the business. Bulgaria phone data is a very strong contact database that you can get in your budget. Moreover, the Bulgaria phone data is very beneficial for fast business growth through direct marketing. Besides, our List To Data assures you that we give verified numbers at an affordable cost. Most importantly, you can say that it brings you more profit than your expense. Additionally, the Bulgaria phone data has all the details like name, age, gender, location, and business. Anyway, people can join with the most extensive group of customers quickly through it. Yet, people can use these numbers directory without any worry. So, buy it from us as our experts are ready to present the most satisfactory service. Bulgaria phone number list is very helpful for any business and marketing. People can use this Bulgaria phone number list to develop their telemarketing. They can efficiently contact consumers through direct calls or SMS. In other words, we collect it from authentic sites, so you should purchase our packages right now. Furthermore, you can believe this proper directory to maximize your company’s growth rapidly. Also, we deliver the Bulgaria phone number list in an Excel and CSV file. Actually, the country’s mobile number data will help you in obtaining more profit than investment. Likewise, the List To Data expert team is ready to help you 24 hours with any necessary details that can help any business. Indeed, buy this telemarketing lead at a very reasonable price to expand sales through B2C customers.

Abstract

The dataset was derived by the Bioregional Assessment Programme from HUN_GW_Model_v01l. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

The dataset includes text and excel version of two datafiles pertaining to the groundwater monitoring bores and the surface water gauging stations where the model predicts water levels and baseflow estimates respectively. Also included is an excel file which lists the extraction rates used in the modellling for production bores.

probe_points_plus_extras.xyz GW model output points

no_repeats_with_elevation.txt - points where the groundwater model provides baseflow estimates that are then fed into the river model.

Purpose

Used to generate shapefiles for the two datasets

Dataset History

The dataset was created by exporting text files from the groundwater model after calibration and simulation were complete. Text files were converted to excel spreadsheets.

Dataset Citation

Bioregional Assessment Programme (2016) HUN GW model output points v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/63573849-6e91-45b4-a97c-ad59e48eeb9f.

Dataset Ancestors

• Derived From HUN GW Model Mines raw data v01

• Derived From HUN GW Model v01

• Derived From HUN GW Model code v01

Download Employee Vehicle Personal Use Excel SheetThis dataset lists the employee name and taxable benefit for personal use of City of Greater Sudbury Vehicle as travel expenses for the year 2020. Expenses are broken down in separate tabs by Quarter (Q1, Q2, Q3 and Q4). Data for other years is available in separate datasets. Updated quarterly when expenses are prepared.

About this dataset

Context

The dataset tabulates the Excel population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Excel across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Excel was 539, a 1.46% decrease year-by-year from 2021. Previously, in 2021, Excel population was 547, a decline of 1.08% compared to a population of 553 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Excel decreased by 36. In this period, the peak population was 713 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Excel is shown in this column.
• Year on Year Change: This column displays the change in Excel population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Excel Population by Year. You can refer the same here

This parent dataset (collection of datasets) describes the general organization of data in the datasets for the 2009 and 2011 growing seasons (year) when sunflower (Helianthus annuus L.) was grown for seed grain at the USDA-ARS Conservation and Production Laboratory (CPRL), Soil and Water Management Research Unit (SWMRU), Bushland, Texas (Lat. 35.186714°, Long. -102.094189°, elevation 1170 m above MSL). Sunflower was grown for seed grain on two large, precision weighing lysimeters, each in the center of a 4.44 ha square field. The two fields were contiguous, arranged along a north-south axis, and were labeled northeast (NE), and southeast (SE). See the resource titled "Geographic Coordinates, USDA, ARS, Bushland, Texas" for UTM geographic coordinates for field and lysimeter locations. The fields were irrigated by a linear move sprinkler system equipped with spray applicators. Irrigations were managed to replenish soil water used by the crop on a weekly or more frequent basis as determined by soil profile water content readings made with a neutron probe from 0.10- to 2.4-m depth in the field. The number and spacing of neutron probe reading locations changed through the years (additional sites were added), which is one reason why subsidiary datasets and data dictionaries are needed. The lysimeters and fields were planted to the same plant density, row spacing, tillage depth (by hand on the lysimeters and by machine in the fields), and fertilizer and pesticide applications. The weighing lysimeters were used to measure relative soil water storage to 0.05 mm accuracy at 5-minute intervals, and the 5-minute change in soil water storage was used along with precipitation, dew and frost accumulation, and irrigation amounts to calculate crop evapotranspiration (ET), which is reported at 15-minute intervals. Each lysimeter was equipped with a suite of instruments to sense wind speed, air temperature and humidity, radiant energy (incoming and reflected, typically both shortwave and longwave), surface temperature, soil heat flux, and soil temperature, all of which are reported at 15-minute intervals. Instruments used changed from season to season, which is another reason that subsidiary datasets and data dictionaries for each season are required.
Important conventions concerning the data-time correspondence, sign conventions, and terminology specific to the USDA ARS, Bushland, TX, field operations are given in the resource titled "Conventions for Bushland, TX, Weighing Lysimeter Datasets". There are six datasets in this collection. Common symbols and abbreviations used in the datasets are defined in the resource titled, "Symbols and Abbreviations for Bushland, TX, Weighing Lysimeter Datasets". Datasets consist of Excel (xlsx) files. Each xlsx file contains an Introductory tab that explains the other tabs, lists the authors, describes conventions and symbols used and lists any instruments used. The remaining tabs in a file consist of dictionary and data tabs. There is a dictionary tab for every data tab. The name of the dictionary tab contains the name of the corresponding data tab. Tab names are unique so that if individual tabs were saved to CSV files, each CSV file in the entire collection would have a different name. The six datasets, according to their titles, are as follows:

Agronomic Calendars for the Bushland, Texas Sunflower Datasets Growth and Yield Data for the Bushland, Texas Sunflower Datasets Weighing Lysimeter Data for The Bushland, Texas Sunflower Datasets Soil Water Content Data for The Bushland, Texas, Large Weighing Lysimeter Experiments Evapotranspiration, Irrigation, Dew/frost - Water Balance Data for The Bushland, Texas Sunflower Datasets Standard Quality Controlled Research Weather Data – USDA-ARS, Bushland, Texas

See the README for descriptions of each dataset. The land slope is

The dataset is an Excel data table, which contains all the place names in the first and second batches of Nanjing Historical Place Names List. There are 232 records of historical place names, each of which contains 13 kinds of information.