Large white, Grade A chicken eggs, sold in a carton of a dozen. Includes organic, non-organic, cage free, free range, and traditional."

Indonesia Retail Price: Purebred Chicken Egg data was reported at 23,062.000 IDR/kg in Jul 2019. This records an increase from the previous number of 22,972.000 IDR/kg for Jun 2019. Indonesia Retail Price: Purebred Chicken Egg data is updated monthly, averaging 17,558.000 IDR/kg from Jan 2008 (Median) to Jul 2019, with 139 observations. The data reached an all-time high of 25,100.000 IDR/kg in Jul 2018 and a record low of 10,439.000 IDR/kg in Feb 2008. Indonesia Retail Price: Purebred Chicken Egg data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.PC001: Retail Price: By Major Commodities.

This repository contains Electrogastrography signals termed Electrogastrograms (EGG) recorded with surface Ag/AgCl electrodes placed over stomach and pre-processed in 20 healthy individuals (8 Females and 12 Males). The method for EGG recording and pre-processing together with subjects' data can be found in Popović et al. 2019.

For each subject, EGG was recorded from three locations before (fasting state) and after (postprandial state) a commercial oat meal (274 kcal). Two 20 minutes recordings (files) are obtained for each subject - fasting and postprandial.

Naming convention for files: subjects ID _ type of recording (fasting / postprandial).

Sample rate was set at 2 Hz and A/D card had 16 bits resolution. Gain of the amplifier was set at 1000. Overall, file size is 7200 samples (2400 samples for each channel). All signals were filtered with 3rd order band-pass Butterworth filter with cut-off frequencies of 0.03 Hz and 0.25 Hz. In order to avoid phase distortion, zero-phase digital filtering was performed in Matlab R2013a by filtfilt() function. GNU Octave code for analysis of EGG signals with statistical calculations presented in Popović et al. 2019 is also provided (eggAnalysis.m).

For convenient test download and appropriate preview, we provided all signals in .zip and sample signal for ID1 in .txt form.

Dataset contents

EGG-database.zip, data files, text format

eggAnalysis.m, GNU Octave code

README.txt, metadata for data files, text format

ID1_fasting.txt and ID1_postprandial.txt, sample data files for subject ID1, text format

Data files contain numerical values with decimal point according to the following structure

column - CH1* (recorded samples from channel 1)

column - CH2* (recorded samples from channel 2)

column - CH3* (recorded samples from channel 3)

• For exact anatomical locations for EGG channels CH1, CH2, and CH3, please refer to Popović et al. 2019.

If you find these signals useful for your own research or teaching class, please cite both relevant paper and dataset as:

Popović, N.B., Miljković, N. and Popović, M.B., 2019. Simple gastric motility assessment method with a single-channel electrogastrogram. Biomedical Engineering/Biomedizinische Technik, 64(2), pp.177-185, doi: 10.1515/bmt-2017-0218.

Popović, N.B., Miljković, N. and Popović, M.B., 2020. Three-channel surface electrogastrogram (EGG) dataset recorded during fasting and post-prandial states in 20 healthy individuals [Data set]. Zenodo, doi: 10.5281/zenodo.3730617.

DISCLAIMER: The GNU Octave code is provided without any guarantee and it is not intended for medical purposes.

Monthly average retail prices for selected products, for Canada and provinces. Prices are presented for the current month and the previous four months. Prices are based on transaction data from Canadian retailers, and are presented in Canadian current dollars.

This dataset contains concentrations of perfluoroalkyl substances (PFAS) measured in the eggs of brown pelicans nesting near Charleston, SC, USA, as well as movements of brown pelicans tracked with GPS-Platform Terminal transmitters. In total, 36 eggs were measured from three breeding colonies of brown pelicans for the presence of 24 PFAS analytes using LC-MS/MS. Concentrations were then analyzed for differences between colonies based on urban habitat use and distance from Charleston.

The Fluvial Egg Drift Simulator (FluEgg) estimates bighead, silver, and grass carp egg and larval drift in rivers using species-specific egg developmental data combined with user-supplied hydraulic inputs (Garcia and others, 2013, Domanski, 2020). This data release contains results from 240 FluEgg 4.1.0 simulations of bighead carp eggs in the Illinois River under steady flow conditions. The data release also contains the hydraulic inputs used in the FluEgg simulations and a KML file of the centerline that represents the model domain. FluEgg simulations were run for all combinations of four spawning locations, six water temperatures, and ten steady flow conditions. Each simulation included 5,000 bighead carp eggs, which develop and eventually hatch into larvae. The simulations end when the larvae reach the gas bladder inflation stage. The four spawning locations were just downstream of the lock and dam structures at Marseilles, Starved Rock, Peoria, and LaGrange. For each of these spawning locations, the eggs were assumed to have been spawned at the water surface and at the midpoint of the channel. The six water temperatures were 18, 20, 22, 24, 26, and 28 degrees Celsius. The ten steady flow conditions ranged from half the annual mean flow to the 500-year peak flow and are discussed in more detail below. Note that in the streamwise coordinate system used by FluEgg, the streamwise coordinate of the Mississippi River confluence is 396,639 meters. Any drift distances greater than this value should be excluded from any further analysis of this data. The hydraulic inputs for the FluEgg simulations were generated using a one-dimensional steady Hydrologic Engineering Center-River Analysis System (HEC-RAS) 5.0.7 model for the Illinois River between Marseilles Lock and Dam and the Mississippi River confluence near Grafton, Illinois (HEC-RAS, 2019). The HEC-RAS model was developed by combining four individual HEC-RAS models obtained from the U.S. Army Corps of Engineers Rock Island District (U.S. Army Corps of Engineers Rock Island District, 2003). The model was run for the following ten flow profiles: half the annual mean flow, annual mean flow, annual mean flood, 2-year peak flow, 5-year peak flow, 10-year peak flow, 25-year peak flow, 50-year peak flow, 100-year peak flow, and 500-year peak flow. The flow rates for each of the profiles were obtained for the following U.S. Geological survey (USGS) streamgaging stations from USGS StreamStats: 5543500 Illinois River at Marseilles, Illinois, 5558300 Illinois River at Henry, Illinois, 5560000 Illinois River at Peoria, Illinois, 5568500 Illinois River at Kingston Mines, Illinois, 5570500 Illinois River near Havana, Illinois, 5585500 Illinois River at Meredosia, Illinois, 5586100 Illinois River at Valley City, Illinois (Soong and others, 2004; Granato and others, 2017). Garcia, T., Jackson, P.R., Murphy, E.A., Valocchi, A.J., Garcia, M.H., 2013. Development of a Fluvial Egg Drift Simulator to evaluate the transport and dispersion of Asian carp eggs in rivers. Ecol. Model. 263, 211–222, https://doi.org/10.1016/j.ecolmodel.2013.05.005. Granato G.E., Ries, K.G., III, and Steeves, P.A., 2017, Compilation of streamflow statistics calculated from daily mean streamflow data collected during water years 1901–2015 for selected U.S. Geological Survey streamgages: U.S. Geological Survey Open-File Report 2017–1108, 17 p., https://doi.org/10.3133/ofr20171108. Domanski, M.M., Berutti, M.C., 2020, FluEgg, U.S. Geological Survey software release, https://doi.org/10.5066/P93UCQR2. Hydrologic Engineering Center-River Analysis System (HEC-RAS), 2019, accessed August 20, 2020, at http://www.hec.usace.army.mil/software/hec-ras/. Soong, D.T., Ishii, A.L., Sharpe, J.B., and Avery, C.F., 2004, Estimating flood-peak discharge magnitudes and frequencies for rural streams in Illinois: U.S. Geological Survey Scientific Investigations Report 2004–5103, 147 p., https://doi.org/10.3133/sir20045103. U.S. Army Corps of Engineers Rock Island District, 2004, Upper Mississippi River System Flow Frequency Study, Hydrology and Hydraulics, Appendix C, Illinois River, accessed August 20, 2020, at https://www.mvr.usace.army.mil/Portals/48/docs/FRM/UpperMissFlowFreq/App.%20C%20Rock%20Island%20Dist.%20Illinois%20River%20Hydrology_Hydraulics.pdf.

This dataset comprises the following components:

1. SHdataset: It contains 12,051 microscopic images taken from 103 urine samples, along with their corresponding segmentation masks manually annotated for Schistosoma haematobium eggs. The dataset is randomly partitioned into 80-20 train-test splits.

2. diagnosis_test_dataset: This dataset includes 65 clinical urine samples. Each sample consists of 117 Field-of-View (FoV) images required to capture the entire filter membrane. Additionally, the dataset includes the diagnosis results provided by an expert microscopist.

Samples were obtained from school-age children who had observed the presence of blood in their urine. These clinical urine samples were collected in 20 mL sterile universal containers as part of a field study conducted in the Federal Capital Territory (FCT), Abuja, Nigeria, in collaboration with the University of Lagos, Nigeria. The study received ethical approval from the Federal Capital Territory Health Research Ethics Committee (FCT-HREC) Nigeria (Reference No. FHREC/2019/01/73/18-07-19).

The standard urine filtration procedure was used to process the clinical urine samples. Specifically, 10 mL of urine was passed through a 13 mm diameter filter membrane with a pore size of 0.2 μm. After filtration, the membrane was placed on a microscopy glass slide and covered with a coverslip to enhance the flatness of the membrane for image capture. The images were acquired using a digital microscope called the Schistoscope and were saved in PNG format with a resolution of 2028 X 1520 pixels and a size of approximately 2 MB.

The annotation and microscopy analysis were performed by a team of two experts from the ANDI Centre of Excellence for Malaria Diagnosis, College of Medicine, University of Lagos, and Centre de Recherches Medicales des Lambaréné, CERMEL, Lambarene. The experts used the coco annotation tool to annotate the 12,051 images, creating polygons around the Schistosoma haematobium eggs. The output of the annotation process was a JSON file containing specific details about the image storage location, size, filename, and coordinates of all annotated regions.

The segmentation mask images were generated from the JSON file using a Python program. The SHdataset was used to develop an automated diagnosis framework for urogenital schistosomiasis, while the diagnosis_test_dataset was used to compare the performance of the developed framework with the results from the expert microscopist.

For further details about the dataset, more information can be found in the following articles:

1. Oyibo, P., Jujjavarapu, S., Meulah, B., Agbana, T., Braakman, I., van Diepen, A., Bengtson, M., van Lieshout, L., Oyibo, W., Vdovine, G., and Diehl, J.C. (2022). "Schistoscope: an automated microscope with artificial intelligence for detection of Schistosoma haematobium eggs in resource-limited settings." Micromachines, 13(5), p.643.

2. Oyibo, P., Meulah, B., Bengtson, M., van Lieshout, L., Oyibo, W., Diehl, J.C., Vdovine, G., and Agbana, T. (2023). "Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings." Journal of Medical Imaging. [Accepted Manuscript]

The Mackerel egg survey is undertaken every 3 years by the Fisheries Ecosystems Advisory Services (FEAS) department of the Marine Institute of Ireland as part of a series of international egg surveys co-ordinated by the International Council for the Exploration of the Seas (ICES). The MEGS provides a unique opportunity for surveillance of the summer distribution of cetaceans in both shelf water and deep water habitats along Ireland’s Atlantic margins which can be difficult to reach by other means. The Department of Arts, Heritage and the Gaeltacht (DAHG), through the Marine Institute, commissioned a cetacean survey from the MRV Corystes during the Mackerel Egg Survey (MEGS), running from 9th to 29th of June 2019. A standard, single platform line transect survey methodology was employed by the cetacean observer with additional visual point sampling at oceanographic sampling stations. Survey transects were undertaken at speeds of 5-10 knots, with fishing activity being conducted at speeds of 3-4 knots. The cetacean observer’s survey effort was maximized and optimized during periods of sea state less than or equal to sea state 6 and with visibility of greater than 1km. A total of 127 hours and 57 minutes of survey effort was conducted over the course of the MEGS 2019 survey. In total, 126 hours and 18 minutes of survey effort were conducted using a line transect methodology, while 1 hours and 38 minutes of effort were conducted using the point sampling methodology.

Please see associated README file for information on what this data is and how this data was collected and processed.

About this dataset

Context

The dataset illustrates the median household income in Little Egg Harbor township, spanning the years from 2010 to 2023, with all figures adjusted to 2023 inflation-adjusted dollars. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2023, the median household income for Little Egg Harbor township increased by $6,037 (7.24%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $5,602 (7.68%) between 2010 and 2023.

Analyzing the trend in median household income between the years 2010 and 2023, spanning 13 annual cycles, we observed that median household income, when adjusted for 2023 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 6 years and declined for 7 years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 0223

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2023
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2023 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

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 Little Egg Harbor township median household income. You can refer the same here

The number of fertilized and unfertilized eggs produced by M. beryllina individuals collected in Suisun Bay, California in spawning experiments. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv acquisition_description=Fish survey data\u00a0were collected by beach seine in the Suisun Bay region of the San Francisco Bay-Delta by Susanne Brander and Bryan Cole. Sampling methodology is fully described in Brander et al. (2013).

Laboratory spawning trials were used to determine the relationship between sex ratio and egg production. Adult inland silversides were placed together in 95 liter circular tanks and allowed to spawn on an artificial spawning substrate (polyester yarn clumps). Substrate was removed daily and inspected for eggs; fertilization was determined by coloration. Full details are provided in White et al. (2017). awards_0_award_nid=542383 awards_0_award_number=OCE-1435473 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward?AWD_ID=1435473 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=Eggs produced per trial J. W. White and S. Brander, PIs Version 4 August 2017 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.713302.1 infoUrl=https://www.bco-dmo.org/dataset/713302 institution=BCO-DMO instruments_0_acronym=Purse-seine instruments_0_dataset_instrument_description=Used to collect samples instruments_0_dataset_instrument_nid=713310 instruments_0_description=A purse seine is a large wall of netting deployed in a circle around an entire school of fish. The seine has floats along the top line with a lead line of chain along the bottom. Once a school of fish is located, a skiff pulls the seine into the water as the vessel encircles the school with the net. A cable running along the bottom is then pulled in, "pursing" the net closed on the bottom, preventing fish from escaping by swimming downward. The catch is harvested by bringing the net alongside the vessel and brailing the fish aboard. instruments_0_instrument_name=Purse-seine Fishing Gear instruments_0_instrument_nid=675173 instruments_0_supplied_name=Beach seine metadata_source=https://www.bco-dmo.org/api/dataset/713302 param_mapping={'713302': {}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/713302/parameters people_0_affiliation=University of North Carolina - Wilmington people_0_affiliation_acronym=UNC-Wilmington people_0_person_name=J Wilson White people_0_person_nid=516429 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=University of North Carolina - Wilmington people_1_affiliation_acronym=UNC-Wilmington people_1_person_name=Dr Susanne Brander people_1_person_nid=712930 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=University of North Carolina - Wilmington people_2_affiliation_acronym=UNC-Wilmington people_2_person_name=J Wilson White people_2_person_nid=516429 people_2_role=Contact people_2_role_type=related people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Hannah Ake people_3_person_nid=650173 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=Goby size-selection projects_0_acronym=Goby size-selection projects_0_description=Description from NSF award abstract: Many marine fish species change sex during their lifetimes, and many of them are targets of commercial and recreational fishing. The timing of sex change in these animals is often related to body size, so populations typically consist of many small fish of the initial sex (usually female) and few large fish of the other sex (usually male). In nature, smaller fish are at a greater risk of mortality due to predation, but fishermen tend to seek larger fish. Thus fishing that targets larger individuals may skew sex ratios, removing enough of the larger sex to hinder reproduction. However, the extent to which size-selective mortality affects sex-changing fishes is poorly understood. This research will explore the effects of size-selective mortality on the population dynamics of sex-changing species using an integrated set of field experiments and mathematical models. It will provide the first experimental exploration of the sensitivity of different sex-change patterns and reproductive strategies to selective mortality. The results will advance our knowledge of the susceptibility and resilience of sex-changing organisms to different types of size-selective mortality and will reveal how sex-changing species can recover after size-selection ceases, as in populations within marine reserves where fishing is suddenly prohibited. The findings will inform fisheries management policies, which do not currently consider the ability of a species to change sex in setting fisheries regulations. This project will consist of a three-year study of the effects of size-specific mortality on sex-changing fishes. Field experiments will use three closely related rocky-reef fishes that differ in sex-change pattern and are amenable to field manipulation and direct measurement of reproductive output. The species include a protogynous hermaphrodite (a female-to-male sex-change pattern common among harvested species) and two simultaneous hermaphrodites that differ in their ability to switch between male and female. Two types of experiments will be conducted on populations established on replicate patch reefs at Santa Catalina Island, California: (1) sex ratios will be manipulated to determine when the scarcity of males limits population-level reproductive output; and (2) experiments cross-factoring the intensity of mortality with the form of size-selection (i.e., higher mortality of large or small individuals) will test the demographic consequences of size-selective mortality. In concert with the field experiments, size- and sex-structured population models (integral projection models) will be developed for use in three ways: (1) to evaluate how different types of selective mortality should affect population dynamics; (2) to predict outcomes of the field experiments, testing/validating the model and allowing direct prediction of the ecological significance of short-term selection; and (3) to fit to existing survey data for a fourth species, a widely fished, sex-changing fish, inside and outside of marine reserves. Part (3) will evaluate whether and how quickly the mating system and reproductive output of that species (not directly measurable in the field) is recovering inside reserves. This integrated set of field experiments and models will yield novel insight into the effects of size-selective mortality on the population dynamics of sex-changing marine species. projects_0_end_date=2018-02 projects_0_geolocation=Southern California, Santa Catalina Island projects_0_name=Impacts of size-selective mortality on sex-changing fishes projects_0_project_nid=516431 projects_0_start_date=2015-03 sourceUrl=(local files) standard_name_vocabulary=CF Standard Name Table v55 version=1 xml_source=osprey2erddap.update_xml() v1.3

This data set contains data from the MEGABITESS 2019 cohort including ovitrap locations and characteristics, egg count data, and demographic data.Below are definitions and descriptions for the columns of dataNo - Unique IDSite ID Alpha - yy-site## where yy is the two digit year, site is a 4 digit school/teacher ID, ## is the number 1-10 for each siteSite ID - yy-site-## where yy is the two digit year, site is a 4 digit school/teacher ID, ## is the number 1-10 for each siteInformal Name - school name, teacher name, site number (1-10)School - school nameEducator - Teacher nameTrap Number - Each school has 10 traps, this is the number of the trap at each schoolElevation - Elevation where the trap was located in feetTotalPopulation - the total number of people in that census tractChildUnder18 - number of children in that census tract that are under 18DateStaratedISO - date in the format yyyymmddDate Started - The day egg traps were set out for the weekDateCollectedISO - date in the format yyyymmddDate Collected - The day the egg traps were collected during the weekEggs_Counted_by - the initials of the person at UTK who counted the eggsDataEntry - Initials of the person at UTK who entered the dataInformal_ID - school name, teacher name, site number (1-10)Calendar Week - The calendar week that the egg traps were set out (1-52)Study Week - Each school set up traps for 10 weeks; this number is a number 1-10Aedes hatched - the number of eggs on the germination paper that had hatchedAedes Embryonating - the number of eggs on the germination paper that had not hatchedOther - the number of eggs of a different speciesTotal - the total number of eggs and embryonating eggsComments - comments about the egg countingAdults_Identified_by - Initials of the person at UTK who coutned the adultsData_Entered_by - Initials of the person at UTK who entered the dataAdult Female mosquitoes - number of adult female mosquitoes that hatchedAdult Male mosquitoes - number of adult male mosquitoes that hatchedIdentification NotesLand Cover -primary land cover where the ovitrap was locatedOther - Land Cover - primary land cover if other was selectedShade Covered - how much shade was at the ovitrap locationNotes Or Comments - notes or comments about the trap locationShade Type - what is causing the shadeOther-Shade Type - cause of shade if otherWater Adjacency - is the ovitrap adjacent to a water sourceLatitude - latitude in decimal degreesLongitude - longitude in decimal degrees-9999 = no data; be sure to filter out the no data values when running any statistics

Early rearing of steelhead (Oncorhynchus mykiss) in Oregon hatcheries is often problematic; fry can become emaciated and die during the period between hatch and first feed. Thiamine (vitamin B1) deficiency has caused early mortality in salmonids; however, thiamine status of Oregon’s anadromous steelhead populations is currently unknown. We sampled eggs of 7-10 females during spawning from three Oregon hatcheries in 2019. In 2022, females were injected with buffered thiamine HCl 50 mg/kg prior to spawning; additionally, a subset of eggs were supplemented via bath treatment with thiamine mononitrate (1000 ppm) at spawning. We then compared egg thiamine from control and injected females, as well as measured mortality and growth in fry for 8-weeks post-hatch from control, egg baths of thiamine, injection-only, and both injection and baths. These data identify thiamine deficiency in Oregon steelhead and suggest supplementation with thiamine can mitigate impacts to survival during early rearing.

This page contains results from 304 Fluvial Egg Drift Simulator (FluEgg; version 4.1.1) simulations of invasive carp eggs and larvae in the Maumee River, Ohio, under unsteady flow conditions. FluEgg models the drift and dispersion of eggs and larvae in fluvial environments. The eggs develop, changing in size and density, and eventually hatch into larvae. The simulations end when the larvae reach the gas bladder inflation stage or when the set duration of the simulation is exceeded (whichever comes first). FluEgg requires the user to provide hydraulic data to drive the drift model. The hydraulic inputs for these FluEgg simulations were generated using a one-dimensional unsteady hydraulic model of the Maumee River (see the other child items of this data release for more information about the hydraulic model) for four unsteady flow periods in which grass carp eggs or larvae were collected on the Maumee River: July 11-14, 2017, June 11-14, 2018, June 22-27, 2018, and May 28-30, 2019. The upstream end of the model domain (0.0 river kilometers) is located 280 meters downstream from Independence Dam near Defiance, Ohio, and the downstream end of the model domain is the mouth of the Maumee River at Lake Erie near NOAA tidal gage 9063085 (95.6 river kilometers). In FluEgg, the hydraulic conditions at the downstream end of the model domain extend infinitely downstream to allow eggs and larvae to drift beyond the model domain. Therefore, any drift distances greater than 95.6 kilometers should be excluded from further analysis of these data. FluEgg simulations were first run in reverse using the reverse time particle tracking algorithm (RTPT) in FluEgg using the time, location, and developmental stage of 73 captured grass carp eggs and larvae as input. Accounting for replicates, a total of 28 FluEgg simulations were run in reverse for a single invasive carp species (grass carp). Because RTPT simulations result in distributions of potential spawning areas, a series of 276 iterative forward FluEgg simulations were run to further refine the likely grass carp spawning area for the 28 groups of eggs/larvae. Each simulation included 10,000 grass carp eggs, which were assumed to have been spawned at the water surface and at the midpoint of the channel. This page includes: --MaumeeRiver_unsteady_fluegg_reverse_sim_list.csv: comma-separated values (csv) file listing the simulation parameters used for 28 unsteady FluEgg RTPT simulations (reverse) --MaumeeRiver_unsteady_fluegg_forward_sim_list.csv: comma-separated values (csv) file listing the simulation parameters used for 276 unsteady FluEgg simulations (forward) --MaumeeRiver_centerline.KML: KML file of the Maumee River centerline that represents the model domain --MaumeeRiver_unsteady_fluegg_reverse_output.zip: ZIP file containing Hierarchical Data Format 5 (HDF5) results files from 28 reverse FluEgg simulations with the naming convention Maumee_RTPT_RunX_10Kgc_TIMESTEPs.h5, where RunX is the run number (1 to 28) and TIMESTEPs is the simulation timestep in seconds. Each HDF5 file has a corresponding set of simulation parameters given in MaumeeRiver_unsteady_fluegg_reverse_sim_list.csv. --MaumeeRiver_unsteady_fluegg_forward_output.zip: ZIP file containing Hierarchical Data Format 5 (HDF5) results files from 276 forward FluEgg simulations with the naming convention Maumee_FRunIteration_10Kgc_TIMESTEPs.h5, where FRunIteration is the forward simulation identifier (run number and iteration; F1a, F1b, F1c) and TIMESTEPs is the simulation timestep in seconds. Each HDF5 file has a corresponding set of simulation parameters given in MaumeeRiver_unsteady_fluegg_forward_sim_list.csv.

Most mimicry systems involve imperfect mimicry, whereas perfect and high-fidelity mimicry are rare. When the fidelity of mimicry is high, mimics might be expected to have the upper hand against their antagonists. However, in coevolving systems, diversification of model phenotypes may provide an evolutionary escape, because mimics cannot simultaneously match all model individuals in the population. Here we investigate high-fidelity mimicry in a highly specialised, Afrotropical brood parasite-host system: the African cuckoo and fork-tailed drongo. Specifically, we test whether host egg polymorphisms are an effective defence against such mimicry. We show, using a combination of image analysis, field experiments, and simulations, that 1) egg colour and pattern mimicry of fork-tailed drongo eggs by African cuckoos is near-perfect on average; 2) drongos show fine-tuned rejection of foreign eggs, exploiting unpredictable pattern differences between parasitic eggs and their own; and 3) the high degree of interclutch variation (polymorphic egg ‘signatures’) exhibited by drongos gives them the upper hand in the arms race, with 93.7% of cuckoo eggs predicted to be rejected, despite cuckoos mimicking the full range of drongo egg phenotypes. These results demonstrate the effectiveness of model diversification as a defence against mimics, even when mimicry is highly accurate. Field work took place in September–November 2009, 2010, 2011, and 2019 within a ~3,500 hectare area centred at 16°45' S, 26°54' E in the Choma district, southern Zambia. Fork-tailed drongo and African cuckoo egg phenotypes were measured from nests found at this study site. Eggshell pattern was quantified from photographs of eggs. Eggshell background colour (i.e. avoiding pattern) was measured using spectrophotometry. Egg length and width were measured with digital calipers. Egg rejection experiments on fork-tailed drongos were performed in 2009, 2010, 2011, and 2019 (n = 14, 19, 19, and 65 respectively). To simulate a parasitism event, we replaced a host egg with a conspecific egg from another female (hereafter ‘experimental egg’; a surrogate for a cuckoo egg). We monitored experiments daily in 2019 and daily when possible in 2009–2011, for four days to determine whether the experimental egg was rejected or accepted. We took the disappearance of the experimental egg to be evidence of host rejection. Code is implemented in R version 4.2.1.

About this dataset

Context

The dataset illustrates the median household income in Egg Harbor township, spanning the years from 2010 to 2023, with all figures adjusted to 2023 inflation-adjusted dollars. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2023, the median household income for Egg Harbor township decreased by $1,932 (1.97%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $5,602 (7.68%) between 2010 and 2023.

Analyzing the trend in median household income between the years 2010 and 2023, spanning 13 annual cycles, we observed that median household income, when adjusted for 2023 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 6 years and declined for 7 years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 0223

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2023
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2023 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

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 Egg Harbor township median household income. You can refer the same here

Despite constituting an essential component of fitness, reproductive success can vary remarkably between individuals and the causes of such variation are not well understood across taxa. In the zebra finch – a model songbird, almost all the variation in sperm morphology and swimming speed is maintained by a large polymorphic inversion (commonly known as a supergene) on the Z chromosome. The relationship between this polymorphism and reproductive success is not fully understood, particularly for females. Here, we explore the effects of female haplotype, and the combination of male and female genotype, on several primary reproductive traits in a captive population of zebra finches. Despite the inversion polymorphism’s known effects on sperm traits, we find no evidence that inversion haplotype influences egg production by females or survival of embryos through to hatching. However, our findings do reinforce existing evidence that the inversion polymorphism is maintained by a heterozygote a..., The dataset has been curated primarily from SNP genotyping data collected as part of this study in 2019 and 2022, with some additional SNP data obtained from a separate study (see Kim et al., 2017). Breeding data originates from a breeding database for a population of domesticated zebra finches maintained at The University of Sheffield between 1985 and 2016. Birds from this population were bred in single pairs without access to other individuals, where no natural mate choice was permitted, and where the paternity was conclusively known for every egg. Data was collected on parental identity (ring number), parental age, number of breeding attempts per pair, number of eggs laid per breeding attempt (clutch), the fertility/development status of the eggs at 3 days of incubation (by candling), the outcome of every egg (hatching success), and offspring sex. Genotyping data included the SNP Z chromosome supergene genotypes for males and females, determined by KASP-genotyping on an LGC SNPLine s..., Code created in R (v 4.2.1). Data files are all in .csv format. Packages required: brms, Matrix, tidyverse, dplyr, tidybayes, bayestestR, bayesplot, posterior, ggpubr, cowplot, gghalves, modelr, # Code and datasets associated with: A sex-linked supergene with large effects on sperm traits has little impact on reproductive traits in female zebra finches

Dataset structure

Five separate sheets of data are provided corresponding to the five primary analyses: 1) Egg Production, 2) Egg fertility and early embryo development, 3) Hatching success of developing eggs, 4) Offspring sex ratio and 5) Offspring genotype. An additional data sheet (6) is provided for the diagnostic SNP information, and raw cluster analysis output.

Datasets

1) EggProductionBCBoth: Dataset for the egg production analysis, formatted by clutch (each row is a separate clutch).

2) DevelopedBoth: Dataset for the fertility/early development analysis, formatted by pair (each row is a separate pair).

3) HatchedBoth: Dataset for the hatching success of developed eggs analysis, formatted by pair (each row is a separate pair).

4) Sex_data: Dataset for the offspring sex ratio analysis, formatte...

Notes

Electroglottography (EGG) and audio are included in the EEG files themselves, rather than in sidecar files, as they were converted from analog to digital on the same hardware. The audio is the audio the subject heard, i.e. their delayed auditory feedback. If you want the speech waveform aligned to the time the subject produced it, you can shift the audio back by the timestamps recorded (for each trial) in the delay field of the events sidecar file.

EGG has already been minimally preprocessed to correct for phase delays induced by the built-in hardware filter of the EGG amplifier by applying an equivalent software filter in the opposite temporal direction. (This is the same strategy employed by "zero phase shift" filters in MATLAB and scipy.)

Data was organized according the the BIDS standard for EEG data using the MNE-BIDS software (Appelhoff et al., 2019; Pernet et al., 2019).

References

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896

Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Egg Harbor town. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Egg Harbor town. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Egg Harbor town, the median household income stands at $97,500 for householders within the 45 to 64 years age group, followed by $70,000 for the 65 years and over age group. Notably, householders within the 25 to 44 years age group, had the lowest median household income at $63,333.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific 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 Egg Harbor town median household income by age. You can refer the same here

About this dataset

Context

The dataset illustrates the median household income in Egg Harbor town, spanning the years from 2010 to 2023, with all figures adjusted to 2023 inflation-adjusted dollars. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2023, the median household income for Egg Harbor town increased by $8,036 (11.35%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $5,602 (7.68%) between 2010 and 2023.

Analyzing the trend in median household income between the years 2010 and 2023, spanning 13 annual cycles, we observed that median household income, when adjusted for 2023 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 8 years and declined for 5 years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 0223

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2023
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2023 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

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 Egg Harbor town median household income. You can refer the same here