Example data sets and computer code for the book chapter titled "Missing Data in the Analysis of Multilevel and Dependent Data" submitted for publication in the second edition of "Dependent Data in Social Science Research" (Stemmler et al., 2015). This repository includes the computer code (".R") and the data sets from both example analyses (Examples 1 and 2). The data sets are available in two file formats (binary ".rda" for use in R; plain-text ".dat").

The data sets contain simulated data from 23,376 (Example 1) and 23,072 (Example 2) individuals from 2,000 groups on four variables:

ID = group identifier (1-2000) x = numeric (Level 1) y = numeric (Level 1) w = binary (Level 2)

In all data sets, missing values are coded as "NA".

IntroductionThis study explores the stability of scores on psychometrically validated trait questionnaires over time. We illustrate potential pitfalls through a larger study that used the Ruminative Response Scale (RRS) to categorize participants prior to study inclusion into two groups based on their habitual tendency to ruminate. Surprisingly, when we re-administered the RRS at the start of an experimental session, significant score changes occurred, resulting in participants shifting between the two groups.MethodsTo address this, we modified our recruitment process, aiming to reduce careless responses, including an online RRS assessment a week before the lab appointment. We analyzed the different samples prior to and after changing the recruitment procedure, as well as the total sample regarding the psychometric properties of the RRS. We also explored various indices to identify and predict score changes due to careless responding; however, only a subgroup of participants was successfully identified.ResultsOur findings suggest that Mahalanobis distances are effective for identifying substantial score changes, with baseline state rumination emerging as a marginally significant predictor.DiscussionWe discuss the importance of conducting manipulation checks and offer practical implications for research involving psychometrically validated trait questionnaires.

Despite the wide application of longitudinal studies, they are often plagued by missing data and attrition. The majority of methodological approaches focus on participant retention or modern missing data analysis procedures. This paper, however, takes a new approach by examining how researchers may supplement the sample with additional participants. First, refreshment samples use the same selection criteria as the initial study. Second, replacement samples identify auxiliary variables that may help explain patterns of missingness and select new participants based on those characteristics. A simulation study compares these two strategies for a linear growth model with five measurement occasions. Overall, the results suggest that refreshment samples lead to less relative bias, greater relative efficiency, and more acceptable coverage rates than replacement samples or not supplementing the missing participants in any way. Refreshment samples also have high statistical power. The comparative strengths of the refreshment approach are further illustrated through a real data example. These findings have implications for assessing change over time when researching at-risk samples with high levels of permanent attrition.

ObjectiveThe German Health Data Lab is going to provide access to German statutory health insurance claims data ranging from 2009 to the present for research purposes. Due to evolving data formats within the German Health Data Lab, there is a need to standardize this data into a Common Data Model to facilitate collaborative health research and minimize the need for researchers to adapt to multiple data formats. For this purpose we selected transforming the data to the Observational Medical Outcomes Partnership Common Data Model.MethodsWe developed an Extract, Transform, and Load (ETL) pipeline for two distinct German Health Data Lab data formats: Format 1 (2009-2016) and Format 3 (2019 onwards). Due to the identical format structure of Format 1 and Format 2 (2017 -2018), the ETL pipeline of Format 1 can be applied on Format 2 as well. Our ETL process, supported by Observational Health Data Sciences and Informatics tools, includes specification development, SQL skeleton creation, and concept mapping. We detail the process characteristics and present a quality assessment that includes field coverage and concept mapping accuracy using example data.ResultsFor Format 1, we achieved a field coverage of 92.7%. The Data Quality Dashboard showed 100.0% conformance and 80.6% completeness, although plausibility checks were disabled. The mapping coverage for the Condition domain was low at 18.3% due to invalid codes and missing mappings in the provided example data. For Format 3, the field coverage was 86.2%, with Data Quality Dashboard reporting 99.3% conformance and 75.9% completeness. The Procedure domain had very low mapping coverage (2.2%) due to the use of mocked data and unmapped local concepts The Condition domain results with 99.8% of unique codes mapped. The absence of real data limits the comprehensive assessment of quality.ConclusionThe ETL process effectively transforms the data with high field coverage and conformance. It simplifies data utilization for German Health Data Lab users and enhances the use of OHDSI analysis tools. This initiative represents a significant step towards facilitating cross-border research in Europe by providing publicly available, standardized ETL processes (https://github.com/FraunhoferMEVIS/ETLfromHDLtoOMOP) and evaluations of their performance.

About this dataset

Context

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

Key observations

The largest age group in Bad Axe, MI was for the group of age 60 to 64 years years with a population of 278 (9.19%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Bad Axe, MI was the 75 to 79 years years with a population of 59 (1.95%). 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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Bad Axe is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Bad Axe 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 Bad Axe Population by Age. You can refer the same here

This database was prepared using a combination of materials that include aerial photographs, topographic maps (1:24,000 and 1:250,000), field notes, and a sample catalog. Our goal was to translate sample collection site locations at Yellowstone National Park and surrounding areas into a GIS database. This was achieved by transferring site locations from aerial photographs and topographic maps into layers in ArcMap. Each field site is located based on field notes describing where a sample was collected. Locations were marked on the photograph or topographic map by a pinhole or dot, respectively, with the corresponding station or site numbers. Station and site numbers were then referenced in the notes to determine the appropriate prefix for the station. Each point on the aerial photograph or topographic map was relocated on the screen in ArcMap, on a digital topographic map, or an aerial photograph. Several samples are present in the field notes and in the catalog but do not correspond to an aerial photograph or could not be found on the topographic maps. These samples are marked with “No” under the LocationFound field and do not have a corresponding point in the SampleSites feature class. Each point represents a field station or collection site with information that was entered into an attributes table (explained in detail in the entity and attribute metadata sections). Tabular information on hand samples, thin sections, and mineral separates were entered by hand. The Samples table includes everything transferred from the paper records and relates to the other tables using the SampleID and to the SampleSites feature class using the SampleSite field.

We include Stata syntax (dummy_dataset_create.do) that creates a panel dataset for negative binomial time series regression analyses, as described in our paper "Examining methodology to identify patterns of consulting in primary care for different groups of patients before a diagnosis of cancer: an exemplar applied to oesophagogastric cancer". We also include a sample dataset for clarity (dummy_dataset.dta), and a sample of that data in a spreadsheet (Appendix 2).

The variables contained therein are defined as follows:

case: binary variable for case or control status (takes a value of 0 for controls and 1 for cases).

patid: a unique patient identifier.

time_period: A count variable denoting the time period. In this example, 0 denotes 10 months before diagnosis with cancer, and 9 denotes the month of diagnosis with cancer,

ncons: number of consultations per month.

period0 to period9: 10 unique inflection point variables (one for each month before diagnosis). These are used to test which aggregation period includes the inflection point.

burden: binary variable denoting membership of one of two multimorbidity burden groups.

We also include two Stata do-files for analysing the consultation rate, stratified by burden group, using the Maximum likelihood method (1_menbregpaper.do and 2_menbregpaper_bs.do).

Note: In this example, for demonstration purposes we create a dataset for 10 months leading up to diagnosis. In the paper, we analyse 24 months before diagnosis. Here, we study consultation rates over time, but the method could be used to study any countable event, such as number of prescriptions.

The Good/Bad data set is used for the image-quality research, containing unsuccessfully and successfully synthetic samples.

A dataset I generated to showcase a sample set of user data for a fictional streaming service. This data is great for practicing SQL, Excel, Tableau, or Power BI.

1000 rows and 25 columns of connected data.

See below for column descriptions.

Enjoy :)

This is an example of a public dataset on the AST Data Repository

Example of modeled customer behavioral data showing user sessions, engagement metrics, and conversion data across multiple platforms and devices

We propose a novel methodology for discovering the presence of relationships realized as binary time series between variables in high dimension. To make it visually intuitive, we regard the existence of a relationship as an edge connection, and call a collection of such edges a network. Our objective is thus rephrased as uncovering the network by selecting relevant edges, referred to as the network exploration. Our methodology is based on multiple testing for the presence or absence of each edge, designed to ensure statistical reproducibility via controlling the false discovery rate (FDR). In particular, we carefully construct p-variables, and apply the Benjamini-Hochberg (BH) procedure. We show that the BH with our p-variables controls the FDR under arbitrary dependence structure with any sample size and dimension, and has asymptotic power one under mild conditions. The validity is also confirmed by simulations and a real data example.

Abstract

The dataset is a relational dataset of 8,000 households households, representing a sample of the population of an imaginary middle-income country. The dataset contains two data files: one with variables at the household level, the other one with variables at the individual level. It includes variables that are typically collected in population censuses (demography, education, occupation, dwelling characteristics, fertility, mortality, and migration) and in household surveys (household expenditure, anthropometric data for children, assets ownership). The data only includes ordinary households (no community households). The dataset was created using REaLTabFormer, a model that leverages deep learning methods. The dataset was created for the purpose of training and simulation and is not intended to be representative of any specific country.

The full-population dataset (with about 10 million individuals) is also distributed as open data.

Geographic coverage

The dataset is a synthetic dataset for an imaginary country. It was created to represent the population of this country by province (equivalent to admin1) and by urban/rural areas of residence.

Analysis unit

Household, Individual

Universe

The dataset is a fully-synthetic dataset representative of the resident population of ordinary households for an imaginary middle-income country.

Kind of data

ssd

Sampling procedure

The sample size was set to 8,000 households. The fixed number of households to be selected from each enumeration area was set to 25. In a first stage, the number of enumeration areas to be selected in each stratum was calculated, proportional to the size of each stratum (stratification by geo_1 and urban/rural). Then 25 households were randomly selected within each enumeration area. The R script used to draw the sample is provided as an external resource.

Mode of data collection

other

Research instrument

The dataset is a synthetic dataset. Although the variables it contains are variables typically collected from sample surveys or population censuses, no questionnaire is available for this dataset. A "fake" questionnaire was however created for the sample dataset extracted from this dataset, to be used as training material.

Cleaning operations

The synthetic data generation process included a set of "validators" (consistency checks, based on which synthetic observation were assessed and rejected/replaced when needed). Also, some post-processing was applied to the data to result in the distributed data files.

Response rate

This is a synthetic dataset; the "response rate" is 100%.

GeoMAD is the Digital Earth Africa (DE Africa) surface reflectance geomedian and triple Median Absolute Deviation data service. It is a cloud-free composite of satellite data compiled over specific timeframes. This service is ideal for longer-term time series analysis, cloudless imagery and statistical accuracy.

GeoMAD has two main components: Geomedian and Median Absolute Deviations (MADs)

The geomedian component combines measurements collected over the specified timeframe to produce one representative, multispectral measurement for every pixel unit of the African continent. The end result is a comprehensive dataset that can be used to generate true-colour images for visual inspection of anthropogenic or natural landmarks. The full spectral dataset can be used to develop more complex algorithms.

For each pixel, invalid data is discarded, and remaining observations are mathematically summarised using the geomedian statistic. Flyover coverage provided by collecting data over a period of time also helps scope intermittently cloudy areas.

Variations between the geomedian and the individual measurements are captured by the three Median Absolute Deviation (MAD) layers. These are higher-order statistical measurements calculating variation relative to the geomedian. The MAD layers can be used on their own or together with geomedian to gain insights about the land surface and understand change over time.Key PropertiesGeographic Coverage: Continental Africa - approximately 37° North to 35° SouthTemporal Coverage: 2017 – 2022*Spatial Resolution: 10 x 10 meterUpdate Frequency: Annual from 2017 - 2022Product Type: Surface Reflectance (SR)Product Level: Analysis Ready (ARD)Number of Bands: 14 BandsParent Dataset: Sentinel-2 Level-2A Surface ReflectanceSource Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)*Time is enabled on this service using UTC – Coordinated Universal Time. To assure you are seeing the correct year for each annual slice of data, the time zone must be set specifically to UTC in the Map Viewer settings each time this layer is opened in a new map. More information on this setting can be found here: Set the map time zone.ApplicationsGeoMAD is the Digital Earth Africa (DE Africa) surface reflectance geomedian and triple Median Absolute Deviation data service. It is a cloud-free composite of satellite data compiled over specific timeframes. This service is ideal for:Longer-term time series analysisCloud-free imageryStatistical accuracyAvailable BandsBand IDDescriptionValue rangeData typeNo data valueB02Geomedian B02 (Blue)1 - 10000uint160B03Geomedian B03 (Green)1 - 10000uint160B04Geomedian B04 (Red)1 - 10000uint160B05Geomedian B05 (Red edge 1)1 - 10000uint160B06Geomedian B06 (Red edge 2)1 - 10000uint160B07Geomedian B07 (Red edge 3)1 - 10000uint160B08Geomedian B08 (Near infrared (NIR) 1)1 - 10000uint160B8AGeomedian B8A (NIR 2)1 - 10000uint160B11Geomedian B11 (Short-wave infrared (SWIR) 1)1 - 10000uint160B12Geomedian B12 (SWIR 2)1 - 10000uint160SMADSpectral Median Absolute Deviation0 - 1float32NaNEMADEuclidean Median Absolute Deviation0 - 31623float32NaNBCMADBray-Curtis Median Absolute Deviation0 - 1float32NaNCOUNTNumber of clear observations1 - 65535uint160Bands can be subdivided as follows:

Geomedian — 10 bands: The geomedian is calculated using the spectral bands of data collected during the specified time period. Surface reflectance values have been scaled between 1 and 10000 to allow for more efficient data storage as unsigned 16-bit integers (uint16). Note parent datasets often contain more bands, some of which are not used in GeoMAD. The geomedian band IDs correspond to bands in the parent Sentinel-2 Level-2A data. For example, the Annual GeoMAD band B02 contains the annual geomedian of the Sentinel-2 B02 band. Median Absolute Deviations (MADs) — 3 bands: Deviations from the geomedian are quantified through median absolute deviation calculations. The GeoMAD service utilises three MADs, each stored in a separate band: Euclidean MAD (EMAD), spectral MAD (SMAD), and Bray-Curtis MAD (BCMAD). Each MAD is calculated using the same ten bands as in the geomedian. SMAD and BCMAD are normalised ratios, therefore they are unitless and their values always fall between 0 and 1. EMAD is a function of surface reflectance but is neither a ratio nor normalised, therefore its valid value range depends on the number of bands used in the geomedian calculation.Count — 1 band: The number of clear satellite measurements of a pixel for that calendar year. This is around 60 annually, but doubles at areas of overlap between scenes. “Count” is not incorporated in either the geomedian or MADs calculations. It is intended for metadata analysis and data validation.ProcessingAll clear observations for the given time period are collated from the parent dataset. Cloudy pixels are identified and excluded. The geomedian and MADs calculations are then performed by the hdstats package. Annual GeoMAD datasets for the period use hdstats version 0.2.More details on this dataset can be found here.

Large go-around, also referred to as missed approach, data set. The data set is in support of the paper presented at the OpenSky Symposium on November the 10th.

If you use this data for a scientific publication, please consider citing our paper.

The data set contains landings from 176 (mostly) large airports from 44 different countries. The landings are labelled as performing a go-around (GA) or not. In total, the data set contains almost 9 million landings with more than 33000 GAs. The data was collected from OpenSky Network's historical data base for the year 2019. The published data set contains multiple files:

go_arounds_minimal.csv.gz

Compressed CSV containing the minimal data set. It contains a row for each landing and a minimal amount of information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight

The last two columns, n_approaches and n_rwy_approached, are useful to filter out training and calibration flight. These have usually a large number of n_approaches, so an easy way to exclude them is to filter by n_approaches > 2.

go_arounds_augmented.csv.gz

Compressed CSV containing the augmented data set. It contains a row for each landing and additional information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight


    registration
    string
    Aircraft registration


    typecode
    string
    Aircraft ICAO typecode


    icaoaircrafttype
    string
    ICAO aircraft type


    wtc
    string
    ICAO wake turbulence category


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection

string

    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometre


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)


    operator_country
    string
    ISO Alpha-3 country code of the operator


    operator_region
    string
    Geographical region of the operator of the aircraft (either Europe, North America, South America, Asia, Africa, or Oceania)


    wind_speed_knts
    integer
    METAR, surface wind speed in knots


    wind_dir_deg
    integer
    METAR, surface wind direction in degrees


    wind_gust_knts
    integer
    METAR, surface wind gust speed in knots


    visibility_m
    float
    METAR, visibility in m


    temperature_deg
    integer
    METAR, temperature in degrees Celsius


    press_sea_level_p
    float
    METAR, sea level pressure in hPa


    press_p
    float
    METAR, QNH in hPA


    weather_intensity
    list
    METAR, list of present weather codes: qualifier - intensity


    weather_precipitation
    list
    METAR, list of present weather codes: weather phenomena - precipitation


    weather_desc
    list
    METAR, list of present weather codes: qualifier - descriptor


    weather_obscuration
    list
    METAR, list of present weather codes: weather phenomena - obscuration


    weather_other
    list
    METAR, list of present weather codes: weather phenomena - other

This data set is augmented with data from various public data sources. Aircraft related data is mostly from the OpenSky Network's aircraft data base, the METAR information is from the Iowa State University, and the rest is mostly scraped from different web sites. If you need help with the METAR information, you can consult the WMO's Aerodrom Reports and Forecasts handbook.

go_arounds_agg.csv.gz

Compressed CSV containing the aggregated data set. It contains a row for each airport-runway, i.e. every runway at every airport for which data is available. The data is structured in the following way:

    Column name
    Type
    Description




    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    n_landings
    integer
    Total number of landings observed on this runway in 2019


    ga_rate
    float
    Go-around rate, per 1000 landings


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection
    string
    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometres


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)

This aggregated data set is used in the paper for the generalized linear regression model.

Downloading the trajectories

Users of this data set with access to OpenSky Network's Impala shell can download the historical trajectories from the historical data base with a few lines of Python code. For example, you want to get all the go-arounds of the 4th of January 2019 at London City Airport (EGLC). You can use the Traffic library for easy access to the database:

import datetime from tqdm.auto import tqdm import pandas as pd from traffic.data import opensky from traffic.core import Traffic

load minimum data set

df = pd.read_csv("go_arounds_minimal.csv.gz", low_memory=False) df["time"] = pd.to_datetime(df["time"])

select London City Airport, go-arounds, and 2019-01-04

airport = "EGLC" start = datetime.datetime(year=2019, month=1, day=4).replace( tzinfo=datetime.timezone.utc ) stop = datetime.datetime(year=2019, month=1, day=5).replace( tzinfo=datetime.timezone.utc )

df_selection = df.query("airport==@airport & has_ga & (@start <= time <= @stop)")

iterate over flights and pull the data from OpenSky Network

flights = [] delta_time = pd.Timedelta(minutes=10) for _, row in tqdm(df_selection.iterrows(), total=df_selection.shape[0]): # take at most 10 minutes before and 10 minutes after the landing or go-around start_time = row["time"] - delta_time stop_time = row["time"] + delta_time

# fetch the data from OpenSky Network
flights.append(
  opensky.history(
    start=start_time.strftime("%Y-%m-%d %H:%M:%S"),
    stop=stop_time.strftime("%Y-%m-%d %H:%M:%S"),
    callsign=row["callsign"],
    return_flight=True,
  )
)

The flights can be converted into a Traffic object

Traffic.from_flights(flights)

Additional files

Additional files are available to check the quality of the classification into GA/not GA and the selection of the landing runway. These are:

validation_table.xlsx: This Excel sheet was manually completed during the review of the samples for each runway in the data set. It provides an estimate of the false positive and false negative rate of the go-around classification. It also provides an estimate of the runway misclassification rate when the airport has two or more parallel runways. The columns with the headers highlighted in red were filled in manually, the rest is generated automatically.

validation_sample.zip: For each runway, 8 batches of 500 randomly selected trajectories (or as many as available, if fewer than 4000) classified as not having a GA and up to 8 batches of 10 random landings, classified as GA, are plotted. This allows the interested user to visually inspect a random sample of the landings and go-arounds easily.

TopicNavi/Wikipedia-example-data dataset hosted on Hugging Face and contributed by the HF Datasets community

An increasing number of studies are using landscape genomics to investigate local adaptation in wild and domestic populations. The implementation of this approach requires the sampling phase to consider the complexity of environmental settings and the burden of logistic constraints. These important aspects are often underestimated in the literature dedicated to sampling strategies. In this study, we computed simulated genomic datasets to run against actual environmental data in order to trial landscape genomics experiments under distinct sampling strategies. These strategies differed by design approach (to enhance environmental and/or geographic representativeness at study sites), number of sampling locations and sample sizes. We then evaluated how these elements affected statistical performances (power and false discoveries) under two antithetical demographic scenarios. Our results highlight the importance of selecting an appropriate sample size, which should be modified based on the demographic characteristics of the studied population. For species with limited dispersal, sample sizes above 200 units are generally sufficient to detect most adaptive signals, while in random mating populations this threshold should be increased to 400 units. Furthermore, we describe a design approach that maximizes both environmental and geographical representativeness of sampling sites and show how it systematically outperforms random or regular sampling schemes. Finally, we show that although having more sampling locations (between 40 and 50 sites) increase statistical power and reduce false discovery rate, similar results can be achieved with a moderate number of sites (20 sites). Overall, this study provides valuable guidelines for optimizing sampling strategies for landscape genomics experiments.

The TMS-EEG signal analyser (TESA) is an open source extension for EEGLAB that includes functions necessary for cleaning and analysing TMS-EEG data. Both EEGLAB and TESA run in Matlab (r2015b or later). The attached files are example data files which can be used with TESA.

To download TESA, visit here:

http://nigelrogasch.github.io/TESA/

To read the TESA user manual, visit here:

https://www.gitbook.com/book/nigelrogasch/tesa-user-manual/details

File info:

example_data.set

WARNING: file size = 1.1 GB. A raw data set for trialling TESA. Load the data file in to EEGLAB using the existing EEGLAB data set functions. Note that both the .fdt and .set files are required.

example_data_epoch_demean.set

File size = 340 MB. A partially processed data file of smaller size corresponding to step 8 of the analysis pipeline in the TESA user manual. Channel locations were loaded, unused electrodes removed, bad electrodes removed, epoched (-1000 to 1000 ms) and demeaned (baseline correct -1000 to 1000). Load the data file in to EEGLAB using the existing EEGLAB data set functions. Note that both the .fdt and .set files are required.

example_data_epoch_demean_cut_int_ds.set

File size = 69 MB. A further processed data file even smaller in size corresponding to step 11 of the analysis pipeline in the TESA user manual. In addition to the above steps, data around the TMS pulse artifact was removed (-2 to 10 ms), replaced using linear interpolation, and downsampled to 1,000 Hz. Load the data file in to EEGLAB using the existing EEGLAB data set functions. Note that both the .fdt and .set files are required.

Example data info:

Monophasic TMS pulses (current flow = posterior-anterior in brain) were given through a figure-of-eight coil (external diameter = 90 mm) connected to a Magstim 2002 unit (Magstim company, UK). 150 TMS pulses were delivered over the left superior parietal cortex (MNI coordinates: -20, -65, 65) at a rate of 0.2 Hz ± 25% jitter. TMS coil position was determined using frameless stereotaxic neuronavigation (Localite TMS Navigator, Localite, Germany) and intensity was set at resting motor threshold of the first dorsal interosseous muscle (68% maximum stimulator output). EEG was recorded from 62 TMS-specialised, c-ring slit electrodes (EASYCAP, Germany) using a TMS-compatible EEG amplifier (BrainAmp DC, BrainProducts GmbH, Germany). Data from all channels were referenced to the FCz electrode online with the AFz electrode serving as the common ground. EEG signals were digitised at 5 kHz (filtering: DC-1000 Hz) and EEG electrode impedance was kept below 5 kΩ.

About this dataset

Context

The dataset tabulates the Bad Axe 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 Bad Axe across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Bad Axe was 3,006, a 0.43% decrease year-by-year from 2021. Previously, in 2021, Bad Axe population was 3,019, a decline of 0.07% compared to a population of 3,021 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Bad Axe decreased by 426. In this period, the peak population was 3,432 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

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

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Bad Axe is shown in this column.
• Year on Year Change: This column displays the change in Bad Axe 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 Bad Axe Population by Year. You can refer the same here

Tick surveillance performed using tick drags and flags. The tick drags/flags report three life stages independently (adult, larvae, and nymph). Larvae are only identified to the genus, while adults and nymphs are identified to the species. Observations of different life stages and sexes are preferably documented in separate records. A Sample Name is used to help link these records (but would not be necessary.)