This excel file will do a statistical tests of whether two ROC curves are different from each other based on the Area Under the Curve. You'll need the coefficient from the presented table in the following article to enter the correct AUC value for the comparison: Hanley JA, McNeil BJ (1983) A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148:839-843.

The Delta Neighborhood Physical Activity Study was an observational study designed to assess characteristics of neighborhood built environments associated with physical activity. It was an ancillary study to the Delta Healthy Sprouts Project and therefore included towns and neighborhoods in which Delta Healthy Sprouts participants resided. The 12 towns were located in the Lower Mississippi Delta region of Mississippi. Data were collected via electronic surveys between August 2016 and September 2017 using the Rural Active Living Assessment (RALA) tools and the Community Park Audit Tool (CPAT). Scale scores for the RALA Programs and Policies Assessment and the Town-Wide Assessment were computed using the scoring algorithms provided for these tools via SAS software programming. The Street Segment Assessment and CPAT do not have associated scoring algorithms and therefore no scores are provided for them. Because the towns were not randomly selected and the sample size is small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Dataset one contains data collected with the RALA Programs and Policies Assessment (PPA) tool. Dataset two contains data collected with the RALA Town-Wide Assessment (TWA) tool. Dataset three contains data collected with the RALA Street Segment Assessment (SSA) tool. Dataset four contains data collected with the Community Park Audit Tool (CPAT). [Note : title changed 9/4/2020 to reflect study name] Resources in this dataset:Resource Title: Dataset One RALA PPA Data Dictionary. File Name: RALA PPA Data Dictionary.csvResource Description: Data dictionary for dataset one collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA Data Dictionary. File Name: RALA TWA Data Dictionary.csvResource Description: Data dictionary for dataset two collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA Data Dictionary. File Name: RALA SSA Data Dictionary.csvResource Description: Data dictionary for dataset three collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT Data Dictionary. File Name: CPAT Data Dictionary.csvResource Description: Data dictionary for dataset four collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset One RALA PPA. File Name: RALA PPA Data.csvResource Description: Data collected using the RALA PPA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two RALA TWA. File Name: RALA TWA Data.csvResource Description: Data collected using the RALA TWA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three RALA SSA. File Name: RALA SSA Data.csvResource Description: Data collected using the RALA SSA tool.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Four CPAT. File Name: CPAT Data.csvResource Description: Data collected using the CPAT.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Data Dictionary. File Name: DataDictionary_RALA_PPA_SSA_TWA_CPAT.csvResource Description: This is a combined data dictionary from each of the 4 dataset files in this set.

Historical Dataset of Excel Academy - Englewood High School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2014-2020),Total Classroom Teachers Trends Over Years (2015-2023),Student-Teacher Ratio Comparison Over Years (2015-2018),Hispanic Student Percentage Comparison Over Years (2014-2020),Black Student Percentage Comparison Over Years (2014-2020),Two or More Races Student Percentage Comparison Over Years (2014-2015),Diversity Score Comparison Over Years (2014-2020),Free Lunch Eligibility Comparison Over Years (2014-2020),Reading and Language Arts Proficiency Comparison Over Years (2017-2021),Math Proficiency Comparison Over Years (2017-2021),Overall School Rank Trends Over Years (2017-2021),Graduation Rate Comparison Over Years (2017-2018)

two datasets in one excel file to analyse a regression model for distance calculation

About this dataset

Context

The dataset tabulates the Excel population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of Excel.

Content

The dataset constitues the following two datasets across these two themes

• Excel, AL Population Breakdown by Gender
• Excel, AL Population Breakdown by Gender and Age

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/.

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

About this dataset

Context

The dataset tabulates the Excel township population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of Excel township.

Content

The dataset constitues the following two datasets across these two themes

• Excel Township, Minnesota Population Breakdown by Gender
• Excel Township, Minnesota Population Breakdown by Gender and Age

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/.

Historical Dataset of Excel Academy Pcs is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2009-2023),Total Classroom Teachers Trends Over Years (2011-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2011-2023),Hispanic Student Percentage Comparison Over Years (2013-2023),Black Student Percentage Comparison Over Years (2009-2023),Two or More Races Student Percentage Comparison Over Years (2015-2023),Diversity Score Comparison Over Years (2009-2023),Free Lunch Eligibility Comparison Over Years (2011-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2009-2023),Reading and Language Arts Proficiency Comparison Over Years (2012-2018),Math Proficiency Comparison Over Years (2012-2018),Overall School Rank Trends Over Years (2012-2018)

The Excel file contains the model input-out data sets that where used to evaluate the two-layer soil moisture and flux dynamics model. The model is original and was developed by Dr. Hantush by integrating the well-known Richards equation over the root layer and the lower vadose zone. The input-output data are used for: 1) the numerical scheme verification by comparison against HYDRUS model as a benchmark; 2) model validation by comparison against real site data; and 3) for the estimation of model predictive uncertainty and sources of modeling errors. This dataset is associated with the following publication: He, J., M.M. Hantush, L. Kalin, and S. Isik. Two-Layer numerical model of soil moisture dynamics: Model assessment and Bayesian uncertainty estimation. JOURNAL OF HYDROLOGY. Elsevier Science Ltd, New York, NY, USA, 613 part A: 128327, (2022).

The South Florida Water Management District (SFWMD) and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 174 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in central and south Florida. The change factors were computed as the ratio of projected future to historical extreme precipitation depths fitted to extreme precipitation data from various downscaled climate datasets using a constrained maximum likelihood (CML) approach. The change factors correspond to the period 2050-2089 (centered in the year 2070) as compared to the 1966-2005 historical period. A Microsoft Excel workbook is provided that tabulates best models for each downscaled climate dataset and for all downscaled climate datasets considered together. Best models were identified based on how well the models capture the climatology and interannual variability of four climate extreme indices using the Model Climatology Index (MCI) and the Model Variability Index (MVI) of Srivastava and others (2020). The four indices consist of annual maxima consecutive precipitation for durations of 1, 3, 5, and 7 days compared against the same indices computed based on the PRISM and SFWMD gridded precipitation datasets for two climate regions: climate region 4 in South Central Florida, and climate region 5 in South Florida. The PRISM dataset is based on the Parameter-elevation Relationships on Independent Slopes Model interpolation method of Daly and others (2008). The South Florida Water Management District’s (SFWMD) precipitation super-grid is a gridded precipitation dataset developed by modelers at the agency for use in hydrologic modeling (SFWMD, 2005). This dataset is considered by the SFWMD as the best available gridded rainfall dataset for south Florida. Best models were selected based on MCI and MVI evaluated within each individual downscaled dataset. In addition, best models were selected by comparison across datasets and referred to as "ALL DATASETS" hereafter. Due to the small sample size, all models in the using the Weather Research and Forecasting Model (JupiterWRF) dataset were considered as best models.

On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data on services capacity, inpatient/outpatient utilization, patients, revenues and expenses by type and payer, balance sheet and income statement.

Due to the large size of the complete dataset, a selected set of data representing a wide range of commonly used data items, has been created that can be easily managed and downloaded. The selected data file includes general hospital information, utilization data by payer, revenue data by payer, expense data by natural expense category, financial ratios, and labor information.

There are two groups of data contained in this dataset: 1) Selected Data - Calendar Year: To make it easier to compare hospitals by year, hospital reports with report periods ending within a given calendar year are grouped together. The Pivot Tables for a specific calendar year are also found here. 2) Selected Data - Fiscal Year: Hospital reports with report periods ending within a given fiscal year (July-June) are grouped together.

Our analyses are based on 148×148 time- and frequency-domain correlation matrices. A correlation matrix covers all the possible use cases of every activity metric listed in the article. With these activity metrics and different preprocessing methods, we were able to calculate 148 different activity signals from multiple datasets of a single measurement. Each cell of a correlation matrix contains the mean and standard deviation of the calculated Pearson’s correlation coefficients between two types of activity signals based on 42 different subjects’ 10-days-long motion. The small correlation matrices presented both in the article and in the appendixes are derived from these 148 × 148 correlation matrices. This published Excel workbook contains multiple sheets labelled according to their content. The mean and standard deviation values for both time- and frequency-domain correlations can be found on their own separate sheet. Moreover, we reproduced the correlation matrix with an alternatively parametrized digital filter, which doubled the number of sheets to 8. In the Excel workbook, we used the same notation for both the datasets and activity metrics as presented in this article with an extension to the PIM metric: PIMs denotes the PIM metric where we used Simpson’s 3/8 rule integration method, PIMr indicates the PIM metric where we calculated the integral by simple numerical integration (Riemann sum). (XLSX)

The author has calculated and plotted all epidemic curves in Excel for the paper "A Simple, SIR-like but Individual-Based Epidemic Model: Application in Comparison of COVID-19 in New York City and Wuhan". All these calculated curves are shown in Figures 2-11, which are separately placed in different sheets in the Excel file. The values of parameters l and c are separately placed in two cells marked in yellow. The two cells are located in top one or two row on the left. After the two parameters are changed, the Excel file will calculate the 4 variables An, In, Rn and Tn from n=1 to N. The calculated values are listed in 4 different columns of cells below the column labels An, In, Rn and Tn, respectively.

About this dataset

Context

The dataset tabulates the population of Excel by race. It includes the population of Excel across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Excel across relevant racial categories.

Key observations

The percent distribution of Excel population by race (across all racial categories recognized by the U.S. Census Bureau): 84.33% are white, 6.75% are Black or African American, 0.99% are American Indian and Alaska Native, 2.18% are Asian and 5.75% are multiracial.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Excel
• Population: The population of the racial category (excluding ethnicity) in the Excel is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race 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 Race & Ethnicity. You can refer the same here

Historical Dataset of Chicago Excel Academy High School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2013-2023),Total Classroom Teachers Trends Over Years (2015-2018),Distribution of Students By Grade Trends,Hispanic Student Percentage Comparison Over Years (2013-2023),Black Student Percentage Comparison Over Years (2013-2023),Two or More Races Student Percentage Comparison Over Years (2019-2023),Diversity Score Comparison Over Years (2013-2023),Free Lunch Eligibility Comparison Over Years (2013-2023),Reading and Language Arts Proficiency Comparison Over Years (2013-2022),Math Proficiency Comparison Over Years (2013-2021),Overall School Rank Trends Over Years (2013-2022),Graduation Rate Comparison Over Years (2013-2022)

This dataset was used by the NCI's Quantitative Imaging Network (QIN) PET-CT Subgroup for their project titled: Multi-center Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Datasets. The purpose of this project was to assess the agreement among radiomic features when computed by several groups by using different software packages under very tightly controlled conditions, which included common image data sets and standardized feature definitions. The image datasets (and Volumes of Interest – VOIs) provided here are the same ones used in that project and reported in the publication listed below (ISSN 2379-1381 https://doi.org/10.18383/j.tom.2019.00031). In addition, we have provided detailed information about the software packages used (Table 1 in that publication) as well as the individual feature value results for each image dataset and each software package that was used to create the summary tables (Tables 2, 3 and 4) in that publication. For that project, nine common quantitative imaging features were selected for comparison including features that describe morphology, intensity, shape, and texture and that are described in detail in the International Biomarker Standardisation Initiative (IBSI, https://arxiv.org/abs/1612.07003 and publication (Zwanenburg A. Vallières M, et al, The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020 May;295(2):328-338. doi: https://doi.org/10.1148/radiol.2020191145). There are three datasets provided – two image datasets and one dataset consisting of four excel spreadsheets containing feature values.

1. The first image dataset is a set of three Digital Reference Objects (DROs) used in the project, which are: (a) a sphere with uniform intensity, (b) a sphere with intensity variation (c) a nonspherical (but mathematically defined) object with uniform intensity. These DROs were created by the team at Stanford University and are described in (Jaggi A, Mattonen SA, McNitt-Gray M, Napel S. Stanford DRO Toolkit: digital reference objects for standardization of radiomic features. Tomography. 2019;6:–.) and are a subset of the DROs described in DRO Toolkit. Each DRO is represented in both DICOM and NIfTI format and the VOI was provided in each format as well (DICOM Segmentation Object (DSO) as well as NIfTI segmentation boundary).
2. The second image dataset is the set of 10 patient CT scans, originating from the LIDC-IDRI dataset, that were used in the QIN multi-site collection of Lung CT data with Nodule Segmentations project ( https://doi.org/10.7937/K9/TCIA.2015.1BUVFJR7 ). In that QIN study, a single lesion from each case was identified for analysis and then nine VOIs were generated using three repeat runs of three segmentation algorithms (one from each of three academic institutions) on each lesion. To eliminate one source of variability in our project, only one of the VOIs previously created for each lesion was identified and all sites used that same VOI definition. The specific VOI chosen for each lesion was the first run of the first algorithm (algorithm 1, run 1). DICOM images were provided for each dataset and the VOI was provided in both DICOM Segmentation Object (DSO) and NIfTI segmentation formats.
3. The third dataset is a collection of four excel spreadsheets, each of which contains detailed information corresponding to each of the four tables in the publication. For example, the raw feature values and the summary tables for Tables 2,3 and 4 reported in the publication cited (https://doi.org/10.18383/j.tom.2019.00031). These tables are:

Software Package details : This table contains detailed information about the software packages used in the study (and listed in Table 1 in the publication) including version number and any parameters specified in the calculation of the features reported. DRO results : This contains the original feature values obtained for each software package for each DRO as well as the table summarizing results across software packages (Table 2 in the publication) . Patient Dataset results: This contains the original feature values for each software package for each patient dataset (1 lesion per case) as well as the table summarizing results across software packages and patient datasets (Table 3 in the publication). Harmonized GLCM Entropy Results : This contains the values for the “Harmonized” GLCM Entropy feature for each patient dataset and each software package as well as the summary across software packages (Table 4 in the publication).

Spreadsheets targeted at the analysis of GHS safety fingerprints.AbstractOver a 20-year period, the UN developed the Globally Harmonized System (GHS) to address international variation in chemical safety information standards. By 2014, the GHS became widely accepted internationally and has become the cornerstone of OSHA’s Hazard Communication Standard. Despite this progress, today we observe that there are inconsistent results when different sources apply the GHS to specific chemicals, in terms of the GHS pictograms, hazard statements, precautionary statements, and signal words assigned to those chemicals. In order to assess the magnitude of this problem, this research uses an extension of the “chemical fingerprints” used in 2D chemical structure similarity analysis to GHS classifications. By generating a chemical safety fingerprint, the consistency of the GHS information for specific chemicals can be assessed. The problem is the sources for GHS information can differ. For example, the SDS for sodium hydroxide pellets found on Fisher Scientific’s website displays two pictograms, while the GHS information for sodium hydroxide pellets on Sigma Aldrich’s website has only one pictogram. A chemical information tool, which identifies such discrepancies within a specific chemical inventory, can assist in maintaining the quality of the safety information needed to support safe work in the laboratory. The tools for this analysis will be scaled to the size of a moderate large research lab or small chemistry department as a whole (between 1000 and 3000 chemical entities) so that labelling expectations within these universes can be established as consistently as possible.Most chemists are familiar with programs such as excel and google sheets which are spreadsheet programs that are used by many chemists daily. Though a monadal programming approach with these tools, the analysis of GHS information can be made possible for non-programmers. This monadal approach employs single spreadsheet functions to analyze the data collected rather than long programs, which can be difficult to debug and maintain. Another advantage of this approach is that the single monadal functions can be mixed and matched to meet new goals as information needs about the chemical inventory evolve over time. These monadal functions will be used to converts GHS information into binary strings of data called “bitstrings”. This approach is also used when comparing chemical structures. The binary approach make data analysis more manageable, as GHS information comes in a variety of formats such as pictures or alphanumeric strings which are difficult to compare on their face. Bitstrings generated using the GHS information can be compared using an operator such as the tanimoto coefficent to yield values from 0 for strings that have no similarity to 1 for strings that are the same. Once a particular set of information is analyzed the hope is the same techniques could be extended to more information. For example, if GHS hazard statements are analyzed through a spreadsheet approach the same techniques with minor modifications could be used to tackle more GHS information such as pictograms.Intellectual Merit. This research indicates that the use of the cheminformatic technique of structural fingerprints can be used to create safety fingerprints. Structural fingerprints are binary bit strings that are obtained from the non-numeric entity of 2D structure. This structural fingerprint allows comparison of 2D structure through the use of the tanimoto coefficient. The use of this structural fingerprint can be extended to safety fingerprints, which can be created by converting a non-numeric entity such as GHS information into a binary bit string and comparing data through the use of the tanimoto coefficient.Broader Impact. Extension of this research can be applied to many aspects of GHS information. This research focused on comparing GHS hazard statements, but could be further applied to other bits of GHS information such as pictograms and GHS precautionary statements. Another facet of this research is allowing the chemist who uses the data to be able to compare large dataset using spreadsheet programs such as excel and not need a large programming background. Development of this technique will also benefit the Chemical Health and Safety community and Chemical Information communities by better defining the quality of GHS information available and providing a scalable and transferable tool to manipulate this information to meet a variety of other organizational needs.

The dataset includes a PDF file containing the results and an Excel file with the following tables:

Table S1 Results of comparing the performance of MetaFetcheR to MetaboAnalystR using Diamanti et al. Table S2 Results of comparing the performance of MetaFetcheR to MetaboAnalystR for Priolo et al. Table S3 Results of comparing the performance of MetaFetcheR to MetaboAnalyst 5.0 webtool using Diamanti et al. Table S4 Results of comparing the performance of MetaFetcheR to MetaboAnalyst 5.0 webtool for Priolo et al. Table S5 Data quality test results for running 100 iterations on HMDB database. Table S6 Data quality test results for running 100 iterations on KEGG database. Table S7 Data quality test results for running 100 iterations on ChEBI database. Table S8 Data quality test results for running 100 iterations on PubChem database. Table S9 Data quality test results for running 100 iterations on LIPID MAPS database. Table S10 The list of metabolites that were not mapped by MetaboAnalystR for Diamanti et al. Table S11 An example of an input matrix for MetaFetcheR. Table S12 Results of comparing the performance of MetaFetcheR to MS_targeted using Diamanti et al. Table S13 Data set from Diamanti et al. Table S14 Data set from Priolo et al. Table S15 Results of comparing the performance of MetaFetcheR to CTS using KEGG identifiers available in Diamanti et al. Table S16 Results of comparing the performance of MetaFetcheR to CTS using LIPID MAPS identifiers available in Diamanti et al. Table S17 Results of comparing the performance of MetaFetcheR to CTS using KEGG identifiers available in Priolo et al. Table S18 Results of comparing the performance of MetaFetcheR to CTS using KEGG identifiers available in Priolo et al. (See the "index" tab in the Excel file for more information)

Small-compound databases contain a large amount of information for metabolites and metabolic pathways. However, the plethora of such databases and the redundancy of their information lead to major issues with analysis and standardization. Lack of preventive establishment of means of data access at the infant stages of a project might lead to mislabelled compounds, reduced statistical power and large delays in delivery of results.

We developed MetaFetcheR, an open-source R package that links metabolite data from several small-compound databases, resolves inconsistencies and covers a variety of use-cases of data fetching. We showed that the performance of MetaFetcheR was superior to existing approaches and databases by benchmarking the performance of the algorithm in three independent case studies based on two published datasets.

The dataset was originally published in DiVA and moved to SND in 2024.

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 …Show full descriptionAbstract 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

This study consists of data files that code the data availability policies of top-20 academic journals in the fields of Business & Finance, Economics, International Relations, Political Science, and Sociology. Journals that were ranked as top-20 titles based on 2003-vintage ISI Impact Factor scores were coded on their data policies in 2003 and on their data policies in 2015. In addition, journals that were ranked as top-20 titles based on most recent ISI Impact Factor scores were likewise coded on their data polices in 2015. The included Stata .do file imports the contents of each of the Excel files, cleans and labels the data, and produces two tables: one comparing the data policies of 2003-vintage top-20 journals in 2003 those journals' policies in 2015, and one comparing the data policies of 2003-vintage top-20 journals in 2003 to the data policies of current top-20 journals in 2015.