PRISMA Checklist. (DOC)

The flow diagram of this meta-analysis. (DOC)

dataset and Octave/MatLab codes/scripts for data analysis Background: Methods for p-value correction are criticized for either increasing Type II error or improperly reducing Type I error. This problem is worse when dealing with thousands or even hundreds of paired comparisons between waves or images which are performed point-to-point. This text considers patterns in probability vectors resulting from multiple point-to-point comparisons between two event-related potentials (ERP) waves (mass univariate analysis) to correct p-values, where clusters of signiticant p-values may indicate true H0 rejection. New method: We used ERP data from normal subjects and other ones with attention deficit hyperactivity disorder (ADHD) under a cued forced two-choice test to study attention. The decimal logarithm of the p-vector (p') was convolved with a Gaussian window whose length was set as the shortest lag above which autocorrelation of each ERP wave may be assumed to have vanished. To verify the reliability of the present correction method, we realized Monte-Carlo simulations (MC) to (1) evaluate confidence intervals of rejected and non-rejected areas of our data, (2) to evaluate differences between corrected and uncorrected p-vectors or simulated ones in terms of distribution of significant p-values, and (3) to empirically verify rate of type-I error (comparing 10,000 pairs of mixed samples whit control and ADHD subjects). Results: the present method reduced the range of p'-values that did not show covariance with neighbors (type I and also type-II errors). The differences between simulation or raw p-vector and corrected p-vectors were, respectively, minimal and maximal for window length set by autocorrelation in p-vector convolution. Comparison with existing methods: Our method was less conservative while FDR methods rejected basically all significant p-values for Pz and O2 channels. The MC simulations, gold-standard method for error correction, presented 2.78±4.83% of difference (all 20 channels) from p-vector after correction, while difference between raw and corrected p-vector was 5,96±5.00% (p = 0.0003). Conclusion: As a cluster-based correction, the present new method seems to be biological and statistically suitable to correct p-values in mass univariate analysis of ERP waves, which adopts adaptive parameters to set correction.

Biology students’ understanding of statistics is incomplete due to poor integration of these two disciplines. In some cases, students fail to learn statistics at the undergraduate level due to poor student interest and cursory teaching of concepts, highlighting a need for new and unique approaches to the teaching of statistics in the undergraduate biology curriculum. The most effective method of teaching statistics is to provide opportunities for students to apply concepts, not just learn facts. Opportunities to learn statistics also need to be prevalent throughout a student’s education to reinforce learning. The purpose of developing and implementing curriculum that integrates a topic in biology with an emphasis on statistical analysis was to improve students’ quantitative thinking skills. Our lesson focuses on the change in the richness of native species for a specified area with the aid of iNaturalist and the capacity for analysis afforded by Google Sheets. We emphasized the skills of data entry, storage, organization, curation and analysis. Students then had to report their findings, as well as discuss biases and other confounding factors. Pre- and post-lesson assessment revealed students’ quantitative thinking skills, as measured by a paired-samples t test, improved. At the end of the lesson, students had an increased understanding of basic statistical concepts, such as bias in research and making data-based claims, within the framework of biology.

Primary Image: Website screenshot of an iNaturalist observation (Clasping Milkweed – Asclepias amplexicalis). This image is an example of a data entry on iNaturalist. The data students export from iNaturalist is made up of hundreds, or even thousands, of observations like this one. This image is licensed under Creative Commons Attribution - Share Alike 4.0 International license. Source: Observation by cassi saari, 2014.

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. The PAD-US 3.0 Combined Fee, Designation, Easement feature class (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to remove overlaps, avoiding overestimation in protected area statistics and to support user needs. A Python scripted process ("PADUS3_0_CreateVectorAnalysisFileScript.zip") associated with this data release prioritized overlapping designations (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation status (e.g. GAP Status Code 1 over 2), public access values (in the order of Closed, Restricted, Open, Unknown), and geodatabase load order (records are deliberately organized in the PAD-US full inventory with fee owned lands loaded before overlapping management designations, and easements). The Vector Analysis File ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") associated item of PAD-US 3.0 Spatial Analysis and Statistics ( https://doi.org/10.5066/P9KLBB5D ) was clipped to the Census state boundary file to define the extent and serve as a common denominator for statistical summaries. Boundaries of interest to stakeholders (State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative) were incorporated into separate geodatabase feature classes to support various data summaries ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip") and Comma-separated Value (CSV) tables ("PADUS3_0SummaryStatistics_TabularData_CSV.zip") summarizing "PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip" are provided as an alternative format and enable users to explore and download summary statistics of interest (Comma-separated Table [CSV], Microsoft Excel Workbook [.XLSX], Portable Document Format [.PDF] Report) from the PAD-US Lands and Inland Water Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). In addition, a "flattened" version of the PAD-US 3.0 combined file without other extent boundaries ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") allow for other applications that require a representation of overall protection status without overlapping designation boundaries. The "PADUS3_0VectorAnalysis_State_Clip_CENSUS2020" feature class ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.gdb") is the source of the PAD-US 3.0 raster files (associated item of PAD-US 3.0 Spatial Analysis and Statistics, https://doi.org/10.5066/P9KLBB5D ). Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types represented in some manner, while work continues to maintain updates and improve data quality (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, changes in protected area status between versions of the PAD-US may be attributed to improving the completeness and accuracy of the spatial data more than actual management actions or new acquisitions. USGS provides no legal warranty for the use of this data. While PAD-US is the official aggregation of protected areas ( https://www.fgdc.gov/ngda-reports/NGDA_Datasets.html ), agencies are the best source of their lands data.

The Biostatistical Consulting Services market has emerged as a vital component in the fields of healthcare, pharmaceuticals, and biotechnology, providing essential expertise in statistical methods tailored to biological and clinical research. These consulting services offer valuable solutions including study design,

This release presents experimental statistics from the Mental Health Services Data Set (MHSDS), using final submissions for March 2016. This is the fourth monthly release from the dataset, which replaces the Mental Health and Learning Disabilities Dataset (MHLDDS). As well as analysis of waiting times, first published in March 2016, this release includes elements of the reports that were previously included in monthly reports produced from final MHLDDS submissions. It also includes some new measures. Because of the scope of the changes to the dataset (resulting in the name change to MHSDS and the new name for these monthly reports) it will take time to re-introduce all possible measures that were previously part of the MHLDS Monthly Reports. Additional measures will be added to this report in the coming months. Further details about these changes and the consultation that informed were announced in November. From January 2016 the release includes information on people in children and young people's mental health services, including CAMHS, for the first time. Learning disabilities services have been included since September 2014. The expansion in the scope of the dataset means that many of the basic measures in this release now cover a wider set of services. We have introduced service level breakdowns for some measures to provide new information to users, but also, importantly, to provide comparability with key measures that were part of the previous monthly release. This release of final data for March 2016 comprises: - An Executive Summary, which presents national-level analysis across the whole dataset and also for some specific service areas and age groups - Data tables about access and waiting times in mental health services for the based on final data for the period 1 January 2016 to 31 March 2016. In addition to National and Provider level, Clinical Commissioning Group (CCG) level statistics are included in this release for the first time. - A monthly data file which presents 90 measures at National, Provider and Clinical Commissioning Group (CCG) level - A Currency and Payments (CAP) data file, containing three measures relating to people assigned to Adult Mental Health Care Clusters. Further measures will be added in future releases. - Exploratory analysis of the coverage and completeness of information regarding people in contact with perinatal mental health services, and of the use of SNOMED CT within MHSDS. - A set of provider level data quality measures. - A metadata file, which provide contextual information for each measure, including a full description, current uses, method used for analysis and some notes on usage. We will release the reports as experimental statistics until the characteristics of data flowed using the new data standard are understood. A correction has been made to this publication on 10 September 2018. This amendment relates to statistics in the monthly CSV data file; the specific measures effected are listed in the “Corrected Measures” CSV. All listed measures have now been corrected. NHS Digital apologises for any inconvenience caused.

Simulated datasets used in the supplement with large group size of 30, log-scale noise levels and zero proportions.The settings are reflected in the title of the data:(1) large: group size = 30; (2) 0.5, 1, 1.5: the log-scale noise level(3) 0.3, 0.4, 0.5: the extra zero proportion(4) The last number is the index of replicates. There are 50 replicates for each setting

These tables will be updated monthly. Data were previously published in the Supplement to the Federal Reserve Bulletin, which ceased publication in December 2008.

About this dataset

Context

The dataset tabulates the Gate household income by age. The dataset can be utilized to understand the age-based income distribution of Gate income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Gate, OK annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Gate, OK household incomes: Comparative analysis across 16 income brackets

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Gate income distribution by age. You can refer the same here

Interactive Summary Health Statistics for Adults provide annual estimates of selected health topics for adults aged 18 years and over based on final data from the National Health Interview Survey.

Summary statistics from GWAMA of PCSK9 levels. These data are intended for research purposes only, and available upon publication. For any enquiries about the datasets, please contact Janne Pott (janne.pott@imise.uni-leipzig.de) or Markus Scholz (markus.scholz@imise.uni-leipzig.de).

Citation: tba

When using this data acknowledge the source as follows: 'Data on PCSK9 has been downloaded from https://www.health-atlas.de/data_files/551'

We make three data sets available:

1. Containing statistics regarding the analyses of PCSK9 level in Europeans adjusted for statin treatment.
2. Containing statistics regarding the analyses of PCSK9 level in Europeans not on statin treatment.
3. Containing statistics regarding the analyses of PCSK9 level in African-American not on statin treatment (only the PCSK9 locus).

Files are in text delimited format and include:

• markername
• chr
• bp_hg19 (base position according to hg19)
• ea (effect allele)
• oa (other allele)
• eaf (weighted effect allele frequency)
• info (minimal info score across all used studies)
• nSamples
• nStudies
• beta
• se
• p
• I² (if applicable)
• phenotype

Density estimation for statistics and data analysis is a book. It was written by B. W. Silverman and published by Routledge in 2018.

Analysis of ‘Access statistics from moers.de for May 2015 ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/8175bf5f-0be1-488b-8d1a-d602b075694d on 14 January 2022.

--- Dataset description provided by original source is as follows ---

The Zip file contains the following CSV files:

• Visitors per year_1.csv
• Visitors per month — individual period_1.csv
• Visitors per month_1.csv
• Visitors per hour_1.csv
• Visitors per day_1.csv
• Visitors_1.csv
• Browser main versions by operating system_1.csv
• Browser by operating system subversions_1.csv
• Browser subversions_1.csv
• Access pages_1.csv
• Statement of origin_1.csv
• Use per domain_1.csv
• PAGE Impression per page_1.csv
• Search word statistics Phrasen by search engine_1.csv
• Search word statistics Phrasen_1.csv
• Search word statistics words_1.csv

--- Original source retains full ownership of the source dataset ---

Summary statistics from GWAS of CPA_max for - all samples (adjusted for age and sex) - men only (adjusted for age) - women only (adjusted for age)

These data are intended for research purposes only.

Citation: Pott et al. (2020) Genome-wide analysis of carotid plaque burden suggests a role of IL5 in men. PLOS ONE. PubMed ID: 32469969

When using this data acknowledge the source as follows: 'Data on area of the largest plaque detected has been contributed by LIFE-Adult investigators and has been downloaded from https://www.health-atlas.de/assays/31'

For any enquiries about the datasets, please contact Janne Pott (janne.pott@imise.uni-leipzig.de) or Markus Scholz (markus.scholz@imise.uni-leipzig.de)

Files are in text delimited format and include: Markername, chr, bp_hg19, effect_allele, other_allele, effect_allele_freq, info, n, beta, se, p

This is the latest statistical publication of linked HES (Hospital Episode Statistics) and DID (Diagnostic Imaging Data set) data held by the Health and Social Care Information Centre. The HES-DID linkage provides the ability to undertake national (within England) analysis along acute patient pathways to understand typical imaging requirements for given procedures, and/or the outcomes after particular imaging has been undertaken, thereby enabling a much deeper understanding of outcomes of imaging and to allow assessment of variation in practice. This publication aims to highlight to users the availability of this updated linkage and provide users of the data with some standard information to assess their analysis approach against. The two data sets have been linked using specific patient identifiers collected in HES and DID. The linkage allows the data sets to be linked from April 2012 when the DID data was first collected; however this report focuses on patients who were present in the either data set in the period 1 April 2015 to 30 September 2015 only. This is provisional 2015-16 data. The linkage used for this publication was created on 7 January 2016 and released together with this publication on 4 February 2016.

Analysis of ‘Port of Los Angeles - Historical TEU Statistics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2b70c27d-54b3-4447-8b74-835c1e594285 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Port of Los Angeles - Historical TEU Statistics: A "TEU" is a "twenty-foot equivalent unit," which is a standard measurement of shipping cargo based on a twenty-foot long shipping container.

--- Original source retains full ownership of the source dataset ---