About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Excel: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 2(1.12%) households where the householder is under 25 years old, 72(40.45%) households with a householder aged between 25 and 44 years, 38(21.35%) households with a householder aged between 45 and 64 years, and 66(37.08%) households where the householder is over 65 years old.
• In Excel, the age group of 25 to 44 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 median household income by age. You can refer the same here

The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the periods 2020-59 (centered in the year 2040) and 2050-89 (centered in the year 2070) as compared to the 1966-2005 historical period. An R script (basin_boxplot.R) is provided as an example on how to create a wrapper function that will automate the generation of boxplots of change factors for all Florida HUC-8 basins. The wrapper script sources the file create_boxplot.R and calls the function create_boxplot() one Florida basin at a time to create a figure with boxplots of change factors for all durations (1, 3, and 7 days) and return periods (5, 10, 25, 50, 100, 200, and 500 years) evaluated as part of this project. An example is also provided in the code that shows how to generate a figure showing boxplots of change factors for a single duration and return period. A Microsoft Word file documenting code usage is also provided within this data release (Documentation_R_script_create_boxplot.docx). As described in the documentation, the R script relies on some of the Microsoft Excel spreadsheets published as part of this data release. The script uses HUC-8 basins defined in the "Florida Hydrologic Unit Code (HUC) Basins (areas)" from the Florida Department of Environmental Protection (FDEP; https://geodata.dep.state.fl.us/datasets/FDEP::florida-hydrologic-unit-code-huc-basins-areas/explore) and their names are listed in the file basins_list.txt provided with the script. County names are listed in the file counties_list.txt provided with the script. NOAA Atlas 14 stations located in each Florida basin or county are defined in the Microsoft Excel spreadsheet Datasets_station_information.xlsx which is part of this data release. Instructions are provided in code documentation (see highlighted text on page 7 of Documentation_R_script_create_boxplot.docx) so that users can modify the script to generate boxplots for basins different from the FDEP "Florida Hydrologic Unit Code (HUC) Basins (areas)."

About this dataset

Context

The dataset presents median household incomes for various household sizes in Excel, AL, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Excel did not include 2, 5, 6, or 7-person households. Across the different household sizes in Excel the mean income is $71,010, and the standard deviation is $39,365. The coefficient of variation (CV) is 55.44%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $25,559. It then further increased to $93,229 for 4-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/excel-al-median-household-income-by-household-size.jpeg" alt="Excel, AL median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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 median household income. You can refer the same here

Functional MRI data from a group of healthy females as control group for comparison with females with fibromyalgia syndrome (FMS) in a separate data set. The data and method are described in Warren et al. Medical Research Archives, 2024, https://doi.org/10.18103/mra.v12i3.5206 . Data are original (unprocessed) in NIfTI format, with multiple runs per participant, and are organized by participant. The data structure and corresponding behavioral data, and the stimulation paradigm, are defined in an Excel file. The structure of the database file (Excel file) are in the form used with the Pantheon analysis software. Pantheon is available on GitHub ( https://github.com/stromanp/pantheon-fMRI )

About this dataset

Context

The dataset presents median household incomes for various household sizes in Excel Township, Minnesota, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Excel township did not include 1, 6, or 7-person households. Across the different household sizes in Excel township the mean income is $113,608, and the standard deviation is $17,572. The coefficient of variation (CV) is 15.47%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 2-person households, with an income of $100,435. It then further increased to $139,167 for 5-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/excel-township-mn-median-household-income-by-household-size.jpeg" alt="Excel Township, Minnesota median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

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 median household income. You can refer the same here

This dataset from the VIRTA Publication Information Service consists of the metadata of 241,575 publications of Finnish universities (publication years 2016–2021) merged from yearly datasets downloaded from https://wiki.eduuni.fi/display/cscvirtajtp/Vuositasoiset+Excel-tiedostot.

The dataset contains following information:

Organisation: name of the university

Publication year: the year of publication

Subfield: one of 66 fields of science based on Statistics Finland field of science classification (in Finnish), see the classification in English: https://www2.stat.fi/en/luokitukset/tieteenala/

Peer-reviewed: 1=peer-reviewed publications, 0=not peer-reviewed publications

Science communication: 1=publications aimed at professional and general audiences, 0=peer-reviewed and not peer-reviewed publications aimed at academic audience.

Bibliodiversity: 1=peer-reviewed book publications (chapters, monographs and edited volumes) and conference articles, 0=peer-reviewed journal articles.

Multilingualism: share of peer-reviewed publications in languages other than English (Finnish, Swedish and other languages).

Domestic publishing: 1=peer-reviewed publications in journals and books published in Finland, 0=peer-reviewed publications in journals and books published outside Finland.

Domestic collaboration: 1=peer-reviewed publications with co-authors from more than one Finnish university, 0=peer-reviewed publications without co-authors from more than one Finnish university.

International collaboration: 1=share of peer-reviewed publications with co-authors affiliated with foreign institutions, 0=share of peer-reviewed publications without co-authors affiliated with foreign institutions.

Research performance: 1=peer-reviewed outputs in JUFO levels 2 (“leading”) and 3 (“top”) publication channels, 0=peer-reviewed outputs in JUFO levels 1 (“basic”) and 0 (“other”) publication channels.

Open access: 1=peer-reviewed open access publications, including gold, hybrid and green OA, 0=peer-reviewed closed publications.

The Survey of Household Spending provides detailed information on household expenditures, dwelling characteristics, and ownership of household equipment such as appliances, audio and video equipment, and vehicles. Expenditure categories include: shelter expenses, furnishings and equipment, cost of running the home, communications, child care, food, alcohol and tobacco products, clothing, gifts, medical and health care, transportation and travel, recreation, reading materials, education , taxes, insurance payments and pension contributions. Dwelling characteristics include: type of dwelling, repairs needed (major, minor, none), tenure, year of move, period of constr uction, number of rooms, number of bathrooms, principal heating equipment and fuel, age of principal heating equipment, principal heating fuel for hot water, and principal cooking fuel. Household equipment includes: washing machines, dryers, dishwashers, refrigerators, freezers, air conditioners, telephones, cellular phones, compact disc players, cablevision, video cassette recorders, computers, modems, internet use from home, televisions, and vehicles. Characteristics of the household, reference person, and spouse of reference person are also provided. The annual Survey of Household Spending replaces the Family Expenditure (FAMEX) Survey which was conducted approximately every four years. The last FAMEX survey was for the reference year 1996. Content from the former annual Household Facilities and Equipment (HFE) Survey is also included in the Survey of Household Spending. The last HFE survey was for the reference year 1998. Please note that when comparing data to the HIFE files, HIFE Reference Year refers to the year in which the data was collected - based on previous year's income and spending. Therefore HIFE Reference Year 1998 collected data based on the 1997 income year. Conversly, the SHS (Survey of Household Spending) uses the term Reference Year to indicate the year of the income and spending rather than the year the data was collected. Therefore, in SHS, the 1999 Reference Year refers to 1999 income and spending, not the year (2000) in which the data was collected.

Data for meta-analysis derived from studies comparing timber harvest or tree size to nest site occupancy or productivity in northern goshawks (Accipiter gentilis)These are data derived from published literature for a meta-analysis assessing the degree to which timber harvest and tree size explain productivity and site occupancy in northern goshawks (Accipiter gentilis)

We present these data in two alternative formats. (A) An Excel spreadsheet with multiple tabs. This spread sheet includes many comments linked to individual cells explaining the derivation of individual values. (B) A series of .csv files, each corresponding to a different tab in the Excel file. These are the same data as in the Excel sheet, but without the comments linked to individual cells.

The Excel tabs / CSV files are as follows: (1) repro_raw These are the studies that compared productivity (mostly number of fledged young per pair or per nest) to either timber harvest or tree size. We report the individual effe...

The Survey of Household Spending provides detailed information on household expenditures, dwelling characteristics, and ownership of household equipment such as appliances, audio and video equipment, and vehicles. Expenditure categories include: shelter expenses, furnishings and equipment, cost of running the home, communications, child care, food, alcohol and tobacco products, clothing, gifts, medical and health care, transportation and travel, recreation, reading materials, education , taxes, insurance payments and pension contributions. Dwelling characteristics include: type of dwelling, repairs needed (major, minor, none), tenure, year of move, period of construction, number of rooms, number of bathrooms, principal heating equipment and fuel, age of principal heating equipment, principal heating fuel for hot water, and principal cooking fuel. Household equipment includes: washing machines, dryers, dishwashers, refrigerators, freezers, air conditioners, telephones, cellular phones, compact disc players, cablevision, video cassette recorders, computers, modems, internet use from home, televisions, and vehicles. Characteristics of the household, reference person, and spouse of reference person are also provided. The annual Survey of Household Spending replaces the Family Expenditure (FAMEX) Survey which was conducted approximately every four years. The last FAMEX survey was for the reference year 1996. Content from the former annual Household Facilities and Equipment (HFE) Survey is also included in the Survey of Household Spending. The last HFE survey was for the reference year 1998. Please note that when comparing data to the HIFE files, HIFE Reference Year refers to the year in which the data was collected - based on previous year's income and spending. Therefore HIFE Reference Year 1998 collected data based on the 1997 income year. Conversly, the SHS (Survey of Household Spending) uses the term Reference Year to indicate the year of the income and spending rather than the year the data was collected. Therefore, in SHS, the 1997 Reference Year refers to 1997 income and spending, not the year (1998) in which the data was collected.

Macroalgae are multicellular, aquatic autotrophs that play vital roles in global climate maintenance and have diverse applications in biotechnology and eco-engineering, which are directly linked to their multicellularity phenotypes. However, their genomic diversity and the evolutionary mechanisms underlying multicellularity in these organisms remain uncharacterized. Here, we sequenced 112 macroalgal genomes from diverse climates and phyla, identifying key genomic features that distinguish them from their microalgal relatives. We found that macroalgae have expanded gene families related to cellular adhesion, extracellular matrix formation, cytoskeletal organization and signaling pathways. We discovered that many of these genes have viral origins and are lineage-specific or conserved among the three major macroalgal phyla: Rhodophyta (red algae), Chlorophyta (green algae) and Ochrophyta (brown algae). Our work reveals genetic determinants of convergent and divergent evolutionary trajectories that have shaped morphological diversity in macroalgae and provides genome-wide frameworks to understand photosynthetic multicellular evolution in marine environments.

Table S1. Functional annotation and metadata for macroalgal species. This is a multi-sheet Excel workbook containing the PFAM count matrix for decontaminated assemblies and their strain metadata, contamination estimates, assembly metrics including BUSCO and N50 scores, and source data for the ternary analysis. Related to Figs. 2 and 3.

Table S2. GO enrichment in macroalgal-specific genes. This is a multi-sheet Excel workbook containing enriched GO terms in macroalgal-specific PFAMs conserved in Rhodophyta, Ochrophyta, and Chlorophyta and response screening results comparing means in PFAM counts between divisions. Related to Fig. 3.

Table S3. Comparative genomics of micro- and macroalgae. This is a multi-sheet Excel workbook containing response screening tables comparing PFAM and GO variation in micro- compared to macroalgae. Related to Fig. 4.

Table S4. Unraveling the macroalgal adhesome. This is a multi-sheet Excel workbook containing macroalgal adhesome atlas and response screens among macroalgal phyla and between micro- and macroalgae. Related to Fig. 4.

Table S5. Endogenous viral elements in macroalgae. This is a multi-sheet Excel workbook containing VFAM count matrix and response screening results for comparisons of macroalgal VFAM counts by climate and habitat. This table also includes the EVOP matrix and response screening results comparing EVOPs found in the macroalgal genomes among climates and macroalgal tORFs with EsV-1-7 domains and their codomains. Related to Fig. 5.

The MAR Web Geocoder is a web browser-based tool for geocoding locations, typically addresses, in Washington, DC. It is developed by the Office of Chief Technology Officer (OCTO) and can input Excel or CSV files to output an Excel file. Geocoding is the process of assigning a location in the form of geographic coordinates (often expressed as latitude and longitude) to spreadsheet data. This is done by comparing the descriptive geographic data to known geographic locations such as addresses, blocks, intersections, or place names.

The Survey of Household Spending provides detailed information on household expenditures, dwelling characteristics, and ownership of household equipment. Conducted since 1997, the Survey of Household Spending integrates most of the content found in the Family Expenditure Survey (FAMEX) (1969-1996) and the Household Facilities and Equipment Survey (apart of the Survey of Consumer Finances) (1973-1998). Many data from these two surveys are comparable to the Survey of Household Spending d ata. However, some differences related to methodology, to data quality and to definitions must be considered before comparing these data. Detailed information was collected about expenditures for consumer goods and services, changes in assets, mortgages and other loans, and annual income. Information was also collected about dwelling characteristics (e.g., type and age of heating equipment) and household equipment (e.g., appliances, communications equipment, and vehicles).

Slug additions are often the most accurate method for determining discharge when traditional current meter or acoustic measurements are unreliable because of high turbulence, rocky streambed, shallow or sheet flow, or the stream is physically inaccessible (e.g., under ice or canyon walls) or unsafe to wade (Zellweger et al., 1989, Kilpatrick and Cobb 1984, Ferranti 2015). The slug addition method for determining discharge requires an injection of a known amount of a single salt and high-frequency downstream measurement of solute concentration to capture the response curve (Kilpatrick and Cobb 1984). A new slug method was developed to determine stream discharge utilizing specific conductance and ionic molal conductivities to quantify the downstream salt concentration. The new method adopts an approach that accurately calculates the specific conductance of natural waters (McCleskey, et al., 2012). The main advantage of the new method is high-frequency measurements of specific conductance are easily obtained and the method does not require collection or analyses of discrete samples, allowing for more rapid and less expensive measurements. Data from twenty-nine slug additions are presented. The data were used to evaluate the performance of the new discharge method by comparing with discharge estimates obtained by other means (Manning et al. 2022; McCleskey et al., 2021; U.S. Geological Survey, 2022). File information: SlugAdditions.csv is a tab separated file containing details of each slug addition including stream location, date and time, type and mass of salt added, and discharge determined by an alternative method are presented. SlugAdditions_SC.csv is a tab separated file containing high-frequency specific conductance data collected from twenty-nine slug addition tests. FourmileWQ.csv is a tab separated file containing water quality data from eight different slug addition tests in Fourmile Creek, CO. Specific conductance was calculated and compared to field measurements to demonstrate the validity of the approach. Discharge.xlsx is an Excel spreadsheet that calculates discharge using high-frequency specific conductance data collected downstream from a slug addition. SaltMass.xlsx is an Excel spreadsheet that calculates the salt mass used in the slug addition. References Cited Ferranti, F., 2015. Validation Of Salt Dilution Method For Discharge Measurements In The Upper Valley Of Aniene River (Central Italy). Recent Advances in Environment, Ecosystems and Development. Kilpatrick, F.A. and Cobb, E.D., 1984. Measurement of discharge using tracers. U. S. Geological Survey Open-File Report 84-136. McCleskey, R.B., Nordstrom, D.K., Ryan, J.N. and Ball, J.W., 2012. A new method of calculating electrical conductivity with applications to natural waters. Geochimica et Cosmochimica Acta, 77(0): 369-382. McCleskey, R.B., Antweiler, R.C., Andrews, E.D., Roth, D.A. and Runkel, R.L., 2021. Streamflow and water chemistry in the Tenaya Lake Basin, Yosemite National Park, California. U.S. Geological Survey data release, https://doi.org/10.5066/P9X3WI80. U.S. Geological Survey, 2022. USGS water data for the Nation: U.S. Geological Survey National Water Information System database. accessed July 28, 2022, at https://doi.org/10.5066/F7P55KJN. Zellweger, G.W., Avanzino, R.J. and Bencala, K.E., 1989. Comparison of tracer-dilution and current-meter discharge measurements in a small gravel-bed stream, Little Lost Man Creek, California. U.S. Geological Survey Water-Resources Investigations Report 89-4150.

Referenced DatasetExcel file (.xlsx) containing the dataset of Orthopteran life history traits ("Data" tab), and numbered references ("References" tab).Dataset_Refs.xlsxOrthoptera dataset (plain text)Plain text version of the Orthoptera dataset, used for statistical analyses. The references are listed in the Excel spreadsheet.orthoptera_dataset.txtMammal DatasetMammal life history data used in the analyses across taxonomic groups. Collated from: Capellini et al, 2015; Jeschke and Kokko, 2009; Myhrvold et al., 2015 [see text].Mammal_FINAL.csvBird DatasetBird life history data used in the analyses across taxonomic groups. Collated from: de Magalhães and Costa, 2009; Jeschke and Kokko, 2009, Lislevand et al., 2007; Myhrvold et al., 2015 [see text].Bird_FINAL.csvReptile DatasetReptile life history data used in the analyses across taxonomic groups. Collated from: Allen et al., 2017; Myhrvold et al., 2015 [see text].Reptile_FINAL.csvOrthoptera SupertreeOrthoptera supertree used to impute miss...

# Go Linter Evaluation Dataset

This is a publicly available dataset for 'An empirical evaluation of Golang static code analysis tools for real-world issues.' Please refer to the data according to the names of the spreadsheets.

Authors: Jianwei Wu, James Clause

## Collected Survey Data:
- This Excel file contains the collected survey data for the empirical study in details.

## R Scripts and Raw Data:
- These scripts are used for data analysis and processing.
- This is the initial data collected from surveys or other sources before any processing or analysis.

## Surveys for External Participants:
- This Excel file contains survey data collected for the evaluation of Go linters.
- This folder contains the surveys sent to external participants for collecting their feedback or data.

## Recruitment Letter.pdf:
- This PDF contains an example of the recruitment letter sent to potential survey participants, inviting them to take part in the study.

## Outputs from Existing Go Linters and Summarized Categories.xlsx:
- This Excel file contains outputs from various Go linters and categorized summaries of these outputs. It helps in comparing the performance and features of different linters.

## Selection of Go Linters.xlsx:
- This Excel file lists the Go linters selected for evaluation, along with criteria or reasons for their selection.

## UD IRB Exempt Letter.pdf:
- This PDF contains the Institutional Review Board (IRB) exemption letter from the University of Delaware (UD), indicating that the study involving human participants was exempt from full review.

## Survey Template.pdf:
- This PDF contains an example of the survey sent to the participants.

## govet issues.pdf:
- This PDF contains a list of reported issues about govet.

This dataset is a Bangla dataset. There are total of 4 columns. The data is collected from different newspaper and more importantly, the dataset is raw. Total number of data is 6150. There are 3 classes in the Title Category. They are: National, International, and Sports—number of data from different categories, National-2089, Sports-2008, International-2053. We aim to find the title category from the title using ML, DL, and transformer-based models. As we said the data is in Bangla, after downloading the data might look corrupted. If the data is uploaded on the drive the data will look okay or the Microsoft Excel is up to date, the data will look OK.

This data set was generated during an RCT comparing efficacy, safety, maternal acceptability of using membrane sweep, cervical massage and controls as adjuncts to induction of labour

Additional file 6: Table S5. This Excel sheet presents a comparative table of Pearson’s correlation coefficients between different binding-signal metrics and DNA-binding motif occurrences under two conditions: (1) when looking at just the motif presence/absence and (2) when looking at the actual motif counts

This dataset contains one Excel sheet and five Word documents. In this dataset, Simulation.xlsx describes the parameter values used for the numerical analysis based on empirical data. In this Excel sheet, we calculated the values of each capped call-option model parameter. Computation of Table 2.docx and other documents show the results of the comparative statistics.

description: This report presents the results of over 1100 geochemical analyses of samples of soil and sediment from the Coeur d'Al ne (CdA) drainage basin in northern Idaho. The location (in 3 dimensions) and a lithological description of each sample is included with the laboratory analytical data. Methods of sample location, collection, preparation, digestion and geochemical analysis are described. Five different laboratories contributed geochemical data for this report and the quality control procedures used by each laboratory are described. Comparison of the analytical accuracy and precision of each laboratory is given by comparing analyses of standard reference materials and of splits of CdA samples. These geochemical data are presented in seven MS Excel tables and seven dBase4 tables. The seven dBase4 files allow users to more easily import these geochemical data into a GIS. Only one of these seven tables includes geospatial data AppendixB. However, in AppendixB there is a Site ID column that will allow users to link or join the matching Site Id columns in the six associated lithologic and geochemical tables. Due to format constraints of dBase4, the column names (headers) had to be modified to a maximum of only ten ASCII characters. As a result, some of the dBase4 column header names can be rather cryptic. To assist dBase files users, this ten digit dBase4 column name is also found directly under the more descriptive column names found in the MS Excel tables packaged with this report. Additional formatting requirements such as changing the “below detection limit” symbol (<) to a negative symbol (-) were used to accurately display the data the dBase4 format.; abstract: This report presents the results of over 1100 geochemical analyses of samples of soil and sediment from the Coeur d'Al ne (CdA) drainage basin in northern Idaho. The location (in 3 dimensions) and a lithological description of each sample is included with the laboratory analytical data. Methods of sample location, collection, preparation, digestion and geochemical analysis are described. Five different laboratories contributed geochemical data for this report and the quality control procedures used by each laboratory are described. Comparison of the analytical accuracy and precision of each laboratory is given by comparing analyses of standard reference materials and of splits of CdA samples. These geochemical data are presented in seven MS Excel tables and seven dBase4 tables. The seven dBase4 files allow users to more easily import these geochemical data into a GIS. Only one of these seven tables includes geospatial data AppendixB. However, in AppendixB there is a Site ID column that will allow users to link or join the matching Site Id columns in the six associated lithologic and geochemical tables. Due to format constraints of dBase4, the column names (headers) had to be modified to a maximum of only ten ASCII characters. As a result, some of the dBase4 column header names can be rather cryptic. To assist dBase files users, this ten digit dBase4 column name is also found directly under the more descriptive column names found in the MS Excel tables packaged with this report. Additional formatting requirements such as changing the “below detection limit” symbol (<) to a negative symbol (-) were used to accurately display the data the dBase4 format.