The model contains a fixed effect for treatment and a random effect for pens (n = 24) nested within rooms (n = 2).

The model contains a fixed effect for period and a random effect for district (n = 15).

M2I3NVAER (or inst3_3d_aer_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of aerosol mixing ratio parameters at 72 model layers, such as dust, sulphur dioxide, sea salt, black carbon, and organic carbon. The data field is available every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov). Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME

The perfection ratio of a number is a concept that is related to perfect numbers and how closely a given number approximates the ideal perfection ratio, which is 2.0.

Perfect Numbers:

A perfect number is a positive integer that is equal to the sum of its proper divisors, excluding the number itself. For example: • 6 is a perfect number because its divisors are 1, 2, and 3, and 1 + 2 + 3 = 6 . • 28 is another perfect number because its divisors are 1, 2, 4, 7, and 14, and 1 + 2 + 4 + 7 + 14 = 28 .

Perfection Ratio:

The perfection ratio of a number n is a measure of how close the sum of its divisors (excluding the number itself) is to the number. It is defined as:

\text{Perfection Ratio} = \frac{\text{Sum of Proper Divisors of } n}{n}

•  If the perfection ratio is 2.0, the number is considered perfect.
•  If the perfection ratio is greater than 2.0, the number is abundant (i.e., the sum of its proper divisors exceeds the number itself).
•  If the perfection ratio is less than 2.0, the number is deficient (i.e., the sum of its proper divisors is less than the number itself).

Examples:

1. Perfect Number Example:
•  For n = 6 :
•  Proper divisors: 1, 2, 3 
•  Sum of proper divisors: 1 + 2 + 3 = 6 
•  Perfection ratio: \frac{6}{6} = 1.0 
•  Since the perfection ratio is 2.0 for a perfect number, we see the idea of perfect numbers where the sum of divisors divides evenly.

1. Near-Perfect Numbers

• Definition: A near-perfect number is a number for which the sum of its proper divisors is close to the number itself but not exactly equal.
• Example: Consider the number 24. Its proper divisors are 1, 2, 3, 4, 6, 8, and 12. The sum is 36, which is larger than 24, making it almost perfect in the sense that the sum of its divisors is significant but not equal to the number.

2. Almost-Perfect Numbers

• Definition: An almost-perfect number is a number where the sum of its proper divisors equals the number minus one.
• Example: The number 16 is an almost-perfect number. Its proper divisors are 1, 2, 4, and 8, which sum to 15 (16 - 1).

3. Abundant Numbers

• Definition: A number is abundant if the sum of its proper divisors is greater than the number itself.
• Example: The number 12 is abundant because its proper divisors (1, 2, 3, 4, and 6) sum to 16, which is greater than 12.

4. Deficient Numbers

• Definition: A number is deficient if the sum of its proper divisors is less than the number itself.
• Example: The number 8 is deficient because its proper divisors (1, 2, and 4) sum to 7, which is less than 8.

5. Semiperfect Numbers

• Definition: A semiperfect number is a number that is equal to the sum of some (or all) of its proper divisors.
• Example: The number 12 is semiperfect because 12 = 6 + 4 + 2 (some of its proper divisors).

Relevance to the Heat Map

• Density Analysis: By analyzing the heat map further, we might observe concentrations at other specific perfection ratios besides 2. These could indicate near-perfect, almost-perfect, abundant, deficient, or semiperfect numbers.
• Patterns and Trends: Identifying where these numbers cluster can help us understand the distribution and frequency of numbers with these properties within your dataset.

Graph and download economic data for Retailers: Inventories to Sales Ratio (RETAILIRSA) from Jan 1992 to Aug 2025 about ratio, inventories, sales, retail, and USA.

About this dataset

Context

The dataset tabulates the population of Red Level by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Red Level. The dataset can be utilized to understand the population distribution of Red Level by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Red Level. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Red Level.

Key observations

Largest age group (population): Male # 25-29 years (44) | Female # 20-24 years (45). 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Red Level population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Red Level is shown in the following column.
• Population (Female): The female population in the Red Level is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Red Level for each age group.

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 Red Level Population by Gender. You can refer the same here

YouTube Video and Channel Analysis

YouTube Video and Channel Statistics

By VISHWANATH SESHAGIRI [source]

About this dataset

This dataset contains valuable information about YouTube videos and channels, including various metrics related to views, likes, dislikes, comments, and other related statistics. The dataset consists of 9 direct features and 13 indirect features. The direct features include the ratio of comments on a video to the number of views on the video (comments/views), the total number of subscribers of the channel (subscriberCount), the ratio of likes on a video to the number of subscribers of the channel (likes/subscriber), the total number of views on the channel (channelViewCount), and several other informative ratios such as views/elapsedtime, totalviews/channelelapsedtime, comments/subscriber, views/subscribers, dislikes/subscriber.

The dataset also includes indirect features that are derived from YouTube's API. These indirect features provide additional insights into videos and channels by considering factors such as dislikes/views ratio, channelCommentCount (total number of comments on the channel), likes/dislikes ratio, totviews/totsubs ratio (total views on a video to total subscribers of a channel), and more.

The objective behind analyzing this dataset is to establish statistical relationships between videos and channels within YouTube. Furthermore, this analysis aims to form a topic tree based on these statistical relations.

For further exploration or utilization purposes beyond this dataset description document itself, you can refer to relevant repositories such as the GitHub repository associated with this dataset where you might find useful resources that complement or expand upon what is available in this dataset.

Overall,this comprehensive collection provides diverse insights into YouTube video and channel metadata for conducting statistical analyses in order to better understand viewer engagement patterns varies parameters across different channels. With its range from basic counts like subscriber counts,counting no.of viewership per minute , timing vs viewership rate ,text related user responses etc.,this detailed Youtube Dataset will assist in making informed decisions regarding channel optimization,more effective targeting and creation of content that will appeal to the target audience

How to use the dataset

This dataset provides valuable information about YouTube videos and their corresponding channels. With this data, you can perform statistical analysis to gain insights into various aspects of YouTube video and channel performance. Here is a guide on how to effectively use this dataset for your analysis:

• Understanding the Columns:
  • totalviews/channelelapsedtime: The ratio of total views of a video to the elapsed time of the channel.
  • channelViewCount: The total number of views on the channel.
  • likes/subscriber: The ratio of likes on a video to the number of subscribers of the channel.
  • views/subscribers: The ratio of views on a video to the number of subscribers of the channel.
  • subscriberCount: The total number of subscribers of the channel.
  • dislikes/views: The ratio

Research Ideas

• Predicting the popularity of YouTube videos: By analyzing the various ratios and metrics in this dataset, such as comments/views, likes/subscriber, and views/subscribers, one can build predictive models to estimate the popularity or engagement level of YouTube videos. This can help content creators or businesses understand which types of videos are likely to be successful and tailor their content accordingly.
• Analyzing channel performance: The dataset provides information about the total number of views on a channel (channelViewCount), the number of subscribers (subscriberCount), and other related statistics. By examining metrics like views/elapsedtime and totalviews/channelelapsedtime, one can assess how well a channel is performing over time. This analysis can help content creators identify trends or patterns in their viewership and make informed decisions about their video strategies.
• Understanding audience engagement: Ratios like comments/subscriber, likes/dislikes, dislikes/subscriber provide insights into how engaged a channel's subscribers are with its content. By examining these ratios across multiple videos or channels, one can identify trends in audience behavior and preferences. For example, a high ratio of comments/subscriber may indicate strong community participation and active discussion around the videos posted by a particular YouTuber or channel

Acknowledgements

If you use this dataset in y...

Total herbaceous cover as measured by the fFractional non-overlapping absolute herbaceous cover, viewed vertically. Provides a first order measure of vegetation type when combined with parallel observations of tree and herbaceous cover. Data from the National Land Cover Database (NLCD) are used for training, and NLCD definitions for cover (for example, the distinction between tree vs shrub) are expected to be similar in the CECS data sets.

LTV/CAC Ratio Target: 3:1 at scale Example CAC: $1,500 (from $600,000 spend and 400 new customers)

Minimum Detectable Effect (MDE): 12%+ for activation example Allocation Ratio: 50/50 recommended for most tests False Positive Control: Alpha level required for statistical significance Statistical Power: Required to detect meaningful effects

The ratio of the heavy to light hydrogen isotopes (D/H) is used to understand the history of planetary atmospheres and the role of water in the formation of surface materials. For example, D/H measurements of clay minerals on Mars can be used to understand the hydrological setting at the time of formation. This IRAD proposes to develop a system to measure the D/H isotope ratio of hydrous minerals. The ratio of the heavy to light hydrogen isotopes (D/H) is used to understand the history of planetary atmospheres and the role of water in the formation of surface materials. For example, D/H measurements of clay minerals on Mars can be used to understand the hydrological setting at the time of formation. This IRAD proposes to develop a system to measure the D/H isotope ratio of hydrous minerals.

The goal of this project is to develop a system to measure the D/H ratio of hydrous phases of planetary analog samples and martian (SNC) meteorites. To do this, we will couple a state-of-the art Commercial Water Isotope Analyzer (for D/H and ¹⁸O/¹⁶O of water, here after presented in del notation as δD and δ¹⁸O) with a custom front end to thermally evolve water from samples under a carrier gas stream. The front end will be capable of stepped heating, in which water evolved at different temperatures is collected and then injected in bulk to the analyzer, and continuous heating, in which the δD of water is analyzed continuously as it is evolved from a sample.

Stepped heating is a conventional method used to study the isotopic composition of volatiles in meteorites because the mass spectrometer must receive discrete pulse of gas, as isotope ratios are measured by integrating the areas under peaks of different masses of interest. Optical isotope analyzers, only recently commercially available, can continuously measure high precision isotope ratios of trace amounts of water, which could enable continuous δD measurement of waters thermally evolved from samples. This may be advantageous over batch methods, especially for samples that have multiple mineral phases releasing waters with different isotopic compositions over a narrow temperature range.

Total tree cover as measured by the fractional non-overlapping absolute tree cover, viewed vertically. Provides a first order measure of vegetation type when combined with parallel observations of shrub and herbaceous cover. Data from the National Land Cover Database (NLCD) are used for training, and NLCD definitions for cover (for example, the distinction between tree vs shrub) are expected to be similar in the CECS data sets.

A simulated dataset that includes variables (log odds ratios, standard errors, and area-level indicators of poverty) for use in the R code in Additional file 2. The variables and the data in this example dataset are simulated, but are representative (similar in magnitude and structure) to outputs from the ZIP Code Tabluation Area (ZCTA)-specific case-crossover models described in Stage 1 models in O'Lenick et al., 2017 [1]. (XLSX 49Â kb)

CO and CO2 mixing ratio and flux measurements from the Amazon tropical rainforest

This dataset belongs to the manuscript 'The emission of CO from tropical rain forest soils', submitted to the journal Biogeosciences in December 2023.

More details on this dataset can be found in this manuscript:

https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2746/egusphere-2023-2746.pdf

For questions, please reach out to Hella van Asperen: hasperen@bgc.mpg-jena.de

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Plateau tower CO and CO2 mixing ratio measurements

Plateau tower CO and CO2 mixing ratio measurements took place in a dry season campaign (28 Sep- 7 Oct 2020) and a wet season campaign (11-18 May 2021) at the K34 tower at field site ZF2 in the Amazon rain forest (-2.60898, -60.209106). Due to a problem in the beginning of the dry season campaign, the measurements at the tower were continued until outside the campaign period, until 18 October 2020. Measurements were performed by a Spectronus FTIR analyzer. Concentrations were measured at 3 heights (5,15 and 36m) every half hour. Canopy height is ~28m.

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Valley CO and CO2 mixing ratio measurements

Valley CO and CO2 mixing ratio measurements took place in a dry season campaign (28 Sep- 7 Oct 2020) and a wet season campaign (11-18 May 2021) at a valley close to the K34 tower at field site ZF2 (-2.600026, -60.217079). Since no electricity was available, automatic battery-driven bag sampling was performed during the night at 3h time intervals, with 4 measurements per night from a 1m height inlet (~18:00, ~21:00, ~0:00, ~3:00). Bag samples were measured the following morning by a Spectronus FTIR-analyzer.

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Plateau and valley chamber CO and CO2 fluxes

Flux chamber measurements were performed over soil and litter together on the plateau and in the valley at field site ZF2, in a dry season campaign (28 Sep- 7 Oct 2020) and a wet season campaign (11-18 May 2021). Five soil collars were installed in the valley, and five on the plateau. Each collar was measured 3 times during each campaign week (on different days). Measurements from the same collar are indicated as (for example) V1A, V1B, V1C. After each flux chamber measurement, soil moisture and soil temperature was measured.

Labelling-based proteomics is a powerful method for detection of differentially expressed proteins (DEPs) between biological samples. The current data analysis platform relies on protein-level ratios, where peptide-level ratios are averaged to yield a single summary ratio for each protein. In shotgun proteomics, however, some proteins are quantified with more peptides than others, and this reproducibility information is incorporated into the differential expression (DE) analysis. Here we propose a novel probabilistic framework EBprot that directly models the peptide-to-protein hierarchy and rewards the proteins with reproducible quantification over multiple peptides. To evaluate its performance with known DE states, we first verified that the peptide-level analysis of EBprot provides more accurate estimation of the false discovery rates and better receiver-operating characteristic than other protein ratio analyses using simulation datasets, and confirmed the superior classification performance in a UPS1 mixture spike-in dataset. To illustrate the performance of EBprot in realistic applications, we applied EBprot to a SILAC dataset for lung cancer subtype analysis and an iTRAQ dataset for time course phosphoproteome analysis of EGF-stimulated HeLa cells, each featuring a different experimental design. Through these various examples, we show that the peptide-level analysis of EBprot provides a competitive advantage over alternative methods for the DE analysis of labelling-based quantitative datasets.

About this dataset

Context

The dataset tabulates the population of Red Level by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Red Level. The dataset can be utilized to understand the population distribution of Red Level by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Red Level. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Red Level.

Key observations

Largest age group (population): Male # 25-29 years (33) | Female # 20-24 years (45). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Red Level population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Red Level is shown in the following column.
• Population (Female): The female population in the Red Level is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Red Level for each age group.

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 Red Level Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Green Level by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Green Level. The dataset can be utilized to understand the population distribution of Green Level by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Green Level. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Green Level.

Key observations

Largest age group (population): Male # 10-14 years (219) | Female # 15-19 years (227). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Green Level population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Green Level is shown in the following column.
• Population (Female): The female population in the Green Level is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Green Level for each age group.

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 Green Level Population by Gender. You can refer the same here

Additional file 1: Supplementary text. Supplementary Figure S1. Possible values of cellular features used in QMP. Supplementary Figure S2. Probability models for CalMorph measures. Supplementary Figure S3. Conversion of the CV to noise values. Supplementary Figure S4. Flowchart of the methodology for checking the modality of the CalMorph parameters. Supplementary Figure S5. An example of the effect of confounding factors on modality. Supplementary Figure S6. An example of the effect of outliers on modality. Supplementary Figure S7. Comparison of this study with our previous results. Supplementary Figure S8. Comparison of results between UNIMO and Box-Cox transformed methods. Supplementary Figure S9. Canonical correlation analysis used for extraction of 32 pairs of canonical variables. Supplementary Figure S10. Phenotypic similarity network of non-essential genes. Supplementary Figure S11. Enrichment of KEGG categories. Supplementary Figure S12. Morphological defects of autophagy mutants. Supplementary Figure S13. Multimodal CalMorph parameters. Supplementary Figure S14. Outlines of generalization of UNIMO. Supplementary Figure S15. An example of versatility of UNIMO. Supplementary Figure S16. Defining characteristics of the 130 functional groups. Supplementary Table S1. Selection of the probability distribution for each morphological parameter; non-negative (A), ratio (B), noise (C), and proportion (D) measures. Supplementary Table S2. Population level modality check for each morphological parameter; non-negative (A), ratio (B), noise (C), and proportion (D) measures. Supplementary Table S3. The best probability distribution and final modality for each morphological parameter; non-negative (A), ratio (B), noise (C), and proportion (D) measures. Supplementary Table S4. List of non-essential mutants. Supplementary Table S5. List of the representative GO terms enriched in each functional group. Supplementary Table S6. KEGG pathway enrichment (FDR = 0.05). Supplementary Table S7. Data types and probability distributions employed for a typical quantitative morphological phenotyping (QMP) experiment. Supplementary Table S8. List of measurements extracted by CellProfiler. Supplementary Table S9. An example of generalization and versatility of UNIMO using morphological data presented in Mattiazzi Usaj et al. (2020).

The study of modularity is paramount for understanding trends of phenotypic evolution, and for determining the extent to which covariation patterns are conserved across taxa and levels of biological organization. However, biologists currently lack quantitative methods for statistically comparing the strength of modular signal across datasets, and a robust approach for evaluating alternative modular hypotheses for the same dataset. As a solution to these challenges, we propose an effect size measure (Z_CR) derived from the covariance ratio, and develop hypothesis-testing procedures for their comparison. Computer simulations demonstrate that Z_CR displays appropriate statistical properties and low levels of misspecification, implying that it correctly identifies modular signal, when present. By contrast, alternative methods based on likelihood (EMMLi) and goodness of fit (MINT) suffer from high false positive rates and high model misspecification rates. An empirical example in sigmodontin...

In October and November of 2016 and 2017, the U.S. Geological Survey collected horizontal-to-vertical spectral ratio (HVSR) data at 104 sites in the Genesee Valley, Livingston County, New York as part of a saline-groundwater investigation in cooperation with the New York State Department of Environmental Resources. The HVSR technique, commonly referred to as the passive-seismic method, is used to estimate the thickness of unconsolidated sediments and the depth to bedrock (Lane and others, 2008). The passive-seismic method uses a single, broad-band three-component (two horizontal and one vertical) seismometer to record ambient seismic noise. In areas that have a strong acoustic contrast between the bedrock and overlying sediments, the seismic noise induces resonance at frequencies that range from about 0.3 to 40 hertz (Hz). The ratio of the average horizontal-to-vertical spectrums produces a spectral-ratio curve with peaks at fundamental and higher-order resonance frequencies. The spectral ratio curve (the ratio of the averaged horizontal-to-vertical component spectrums) is used to determine the fundamental resonance frequency that can be used along with an average shear-wave velocity or a power-law regression equation to estimate sediment thickness and depth to bedrock (Lane and others, 2008; Brown and others, 2013; Chandler and others, 2014; and Johnson and Lane, 2016). The HVSR data presented in this data release were collected at each site for 30 minutes using a Tromino Model TEP-3C three-component seismometer (1). The data were processed with Grilla 2011 version. 6.1 software1 to 1) remove anthropogenic noise, 2) convert the time-domain data to frequency domain, 3) compute and plot the spectral ratio curve, and 4) determine the resonance frequency. This data release presents the resonance frequency peaks identified from the HVSR measurements. Also presented are reported depth-to-bedrock data for wells located at or near HVSR data-collection sites in the Genesee Valley for use in the development of a local regression equation that relates the resonance frequency peak to the depth to bedrock. Raw HVSR data for each HVSR measurement are presented in the attached. The HVSR data-collection sites are designated by a county sequential numbering system (LVHVSR1, LVHVSR2, etc. where LV indicates Livingston County). Additional HVSR measurements at a HVSR data-collection site are indicated by a sequential number extension (LVHVSR27.01, LVHVSR27.02, etc.). (1) Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References Brown, C.J., Voytek, E.B., Lane, J.W., Jr., and Stone, J.R., 2013, Mapping bedrock surface contours using the horizontal-to-vertical spectral ratio (HVSR) method near the middle quarter area, Woodbury, Connecticut: U.S. Geological Survey Open-File Report 2013–1028, 4 p., available at http://pubs.usgs.gov/of/2013/1028. Chandler, V. W., and Lively, R. S., 2014, Evaluation of the horizontal-to-vertical spectral ratio (HVSR) passive seismic method for estimating the thickness of Quaternary deposits in Minnesota and adjacent parts of Wisconsin: Minnesota Geological Survey Open File Report 14-01, 52 p. Johnson, C. D. and Lane, J. W., 2016, Statistical comparison of methods for estimating sediment thickness from horizontal-to-vertical spectral ratio (HVSR) seismic methods: An example from Tylerville, Connecticut, USA, in Symposium on the Application of Geophysics to Engineering and Environmental Problems Proceedings: Denver, Colorado, Environmental and Engineering Geophysical Society, pp. 317-323. https://doi.org/10.4133/SAGEEP.29-057 Lane, J.W., Jr., White, E.A., Steele, G.V., and Cannia, J.C., 2008, Estimation of bedrock depth using the horizontal-to-vertical (H/V) ambient-noise seismic method, in Symposium on the Application of Geophysics to Engineering and Environmental Problems Proceedings: Denver, Colorado, Environmental and Engineering Geophysical Society, pp. 490-502. https://doi.org/10.4133/1.2963289