United States AHE: PW: Information: Motion Picture & Sound RecordingIndustries data was reported at 30.940 USD in Mar 2025. This records a decrease from the previous number of 31.320 USD for Feb 2025. United States AHE: PW: Information: Motion Picture & Sound RecordingIndustries data is updated monthly, averaging 21.580 USD from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 35.550 USD in Apr 2021 and a record low of 14.450 USD in Jul 1990. United States AHE: PW: Information: Motion Picture & Sound RecordingIndustries data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Employment Statistics: Average Hourly Earnings: Production Workers.

United States Avg Weekly Earnings: sa: IF: Motion Picture & Sound Recording Ind data was reported at 1,084.020 USD in May 2018. This records a decrease from the previous number of 1,096.780 USD for Apr 2018. United States Avg Weekly Earnings: sa: IF: Motion Picture & Sound Recording Ind data is updated monthly, averaging 850.590 USD from Mar 2006 (Median) to May 2018, with 147 observations. The data reached an all-time high of 1,132.740 USD in Feb 2018 and a record low of 716.800 USD in Nov 2007. United States Avg Weekly Earnings: sa: IF: Motion Picture & Sound Recording Ind data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G033: Current Employment Statistics Survey: Average Weekly and Hourly Earnings: Seasonally Adjusted.

About this dataset

Context

The dataset tabulates the population of Picture Rocks by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Picture Rocks. The dataset can be utilized to understand the population distribution of Picture Rocks by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Picture Rocks. 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 Picture Rocks.

Key observations

Largest age group (population): Male # 55-59 years (71) | Female # 65-69 years (67). 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 Picture Rocks population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Picture Rocks is shown in the following column.
• Population (Female): The female population in the Picture Rocks 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 Picture Rocks 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 Picture Rocks Population by Gender. You can refer the same here

Unsupervised exploratory data analysis (EDA) is often the first step in understanding complex data sets. While summary statistics are among the most efficient and convenient tools for exploring and describing sets of data, they are often overlooked in EDA. In this paper, we show multiple case studies that compare the performance, including clustering, of a series of summary statistics in EDA. The summary statistics considered here are pattern recognition entropy (PRE), the mean, standard deviation (STD), 1-norm, range, sum of squares (SSQ), and X4, which are compared with principal component analysis (PCA), multivariate curve resolution (MCR), and/or cluster analysis. PRE and the other summary statistics are direct methods for analyzing datathey are not factor-based approaches. To quantify the performance of summary statistics, we use the concept of the “critical pair,” which is employed in chromatography. The data analyzed here come from different analytical methods. Hyperspectral images, including one of a biological material, are also analyzed. In general, PRE outperforms the other summary statistics, especially in image analysis, although a suite of summary statistics is useful in exploring complex data sets. While PRE results were generally comparable to those from PCA and MCR, PRE is easier to apply. For example, there is no need to determine the number of factors that describe a data set. Finally, we introduce the concept of divided spectrum-PRE (DS-PRE) as a new EDA method. DS-PRE increases the discrimination power of PRE. We also show that DS-PRE can be used to provide the inputs for the k-nearest neighbor (kNN) algorithm. We recommend PRE and DS-PRE as rapid new tools for unsupervised EDA.

Background: Scientific field observation by members of the public is known as citizen science and has become popular all across the world. Citizen science is advantageous for collecting large amounts of scientific data and can be seen as a crowdsourcing approach to data collection. Information and communications technology is enhancing the availability of citizen science. Mobile devices, such as mobile phones, that have a digital camera with a global positioning system (GPS) are necessities of contemporary life and can be utilized as powerful observation tools in citizen science. New information: We developed a web-based system as a data collection tool for citizen science. Participants submit an e-mail with a photo taken by their mobile phones. The photos contain location information, which can be easily and automatically embedded if the mobile phone is equipped with GPS. We collaborated with regional event managers, such as museum curators, and held citizen science events in each region and for various target taxonomic groups. All photos were stored in our data server, and the organisms were taxonomically identified by citizen scientists, regional managers, and us. In total, 154 species and 843 data records were collected in this project conducted from 2011 to 2016.

The Aerial Photography Single Frame Records collection is a large and diverse group of imagery acquired by Federal organizations from 1937 to the present. Over 6.4 million frames of photographic images are available for download as medium and high resolution digital products. The high resolution data provide access to photogrammetric quality scans of aerial photographs with sufficient resolution to reveal landscape detail and to facilitate the interpretability of landscape features. Coverage is predominantly over the United States and includes portions of Central America and Puerto Rico. Individual photographs vary in scale, size, film type, quality, and coverage.

United States Employment: NF: sa: IF: Motion Picture & Sound Recording Industries data was reported at 416.100 Person th in Jun 2018. This records a decrease from the previous number of 416.200 Person th for May 2018. United States Employment: NF: sa: IF: Motion Picture & Sound Recording Industries data is updated monthly, averaging 371.600 Person th from Jan 1990 (Median) to Jun 2018, with 342 observations. The data reached an all-time high of 442.200 Person th in Oct 2016 and a record low of 244.100 Person th in Jan 1990. United States Employment: NF: sa: IF: Motion Picture & Sound Recording Industries data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G026: Current Employment Statistics Survey: Employment: Non Farm: sa.

System of care statistics. In recent years, intensive consideration has been given to a coherent whole of statistics in the field of health(care) and welfare(care). Concepts previously developed for this field were used, such as the "System of health statistics", the "Conceptual framework for well-being and health" and the "Operational model for statistics in the field of health and well-being". Ultimately, these activities resulted in the creation of the Strategic Project Care. The main objective of this project is to develop a complete and coherent statistical picture of healthcare. This includes data on the health and welfare situation, (medical) consumption (use), the costs and financing of the healthcare system, the (human) resources deployed, production (services) and price and volume developments. . It is evident that for a description of all activities a functional perspective of the site is necessary. A functional perspective means that all activities in the field of health and well-being must be taken into consideration, regardless of whether these activities are the main or secondary activities of economic units (business units). In the CBS report 'Cohesive information about health care as a first step towards integrated health care statistics', this idea of ​​a coherent set of health care statistics has been given a more concrete (phased) interpretation. NB! The figures in this table do not yet describe the entire care area, as described in Description of complete care area. As a result, the figures in this table are not directly comparable with the figures for 2006 and 2007 in the table "Estimate of key figures on health care." By comparing the figure for 2005 in this table on the one hand and the table "Estimate of key figures on health care" on the other hand, it becomes clear that there is a difference of approximately 5 billion euros between the "old" demarcation of the care area and the full area coverage. . In the fourth quarter of 2008, the figures for the period 1998-2005 will be adjusted so that they describe the entire care area and are therefore comparable with the figures for 2006 and 2007. Changes compared to the previous version: This table has been discontinued and continued as Healthcare accounts; expenditure and financing. When will new figures be released? All figures are provisional when they are first published for a new reporting period. The figures for the previous reporting period are therefore more provisional. The figures for this period then become final. If there is a discrepancy between the corresponding provisional and final figures, it must be attributed to the availability of new or updated source material. Data available from 1998 Frequency: discontinued as of January 2009 Copyright (c) Statistics Netherlands, Voorburg 2007 Acknowledgment of the source is mandatory, reproduction for personal use or internal use is permitted.

Please note that the file msl-labeled-data-set-v2.1.zip below contains the latest images and labels associated with this data set.

Data Set Description

The data set consists of 6,820 images that were collected by the Mars Science Laboratory (MSL) Curiosity Rover by three instruments: (1) the Mast Camera (Mastcam) Left Eye; (2) the Mast Camera Right Eye; (3) the Mars Hand Lens Imager (MAHLI). With the help from Dr. Raymond Francis, a member of the MSL operations team, we identified 19 classes with science and engineering interests (see the "Classes" section for more information), and each image is assigned with 1 class label. We split the data set into training, validation, and test sets in order to train and evaluate machine learning algorithms. The training set contains 5,920 images (including augmented images; see the "Image Augmentation" section for more information); the validation set contains 300 images; the test set contains 600 images. The training set images were randomly sampled from sol (Martian day) range 1 - 948; validation set images were randomly sampled from sol range 949 - 1920; test set images were randomly sampled from sol range 1921 - 2224. All images are resized to 227 x 227 pixels without preserving the original height/width aspect ratio.

Directory Contents

images - contains all 6,820 images

class_map.csv - string-integer class mappings

train-set-v2.1.txt - label file for the training set

val-set-v2.1.txt - label file for the validation set

test-set-v2.1.txt - label file for the test set

The label files are formatted as below:

"Image-file-name class_in_integer_representation"

Labeling Process

Each image was labeled with help from three different volunteers (see Contributor list). The final labels are determined using the following processes:

If all three labels agree with each other, then use the label as the final label.

If the three labels do not agree with each other, then we manually review the labels and decide the final label.

We also performed error analysis to correct labels as a post-processing step in order to remove noisy/incorrect labels in the data set.

Classes

There are 19 classes identified in this data set. In order to simplify our training and evaluation algorithms, we mapped the class names from string to integer representations. The names of classes, string-integer mappings, distributions are shown below:

Class name, counts (training set), counts (validation set), counts (test set), integer representation

Arm cover, 10, 1, 4, 0

Other rover part, 190, 11, 10, 1

Artifact, 680, 62, 132, 2

Nearby surface, 1554, 74, 187, 3

Close-up rock, 1422, 50, 84, 4

DRT, 8, 4, 6, 5

DRT spot, 214, 1, 7, 6

Distant landscape, 342, 14, 34, 7

Drill hole, 252, 5, 12, 8

Night sky, 40, 3, 4, 9

Float, 190, 5, 1, 10

Layers, 182, 21, 17, 11

Light-toned veins, 42, 4, 27, 12

Mastcam cal target, 122, 12, 29, 13

Sand, 228, 19, 16, 14

Sun, 182, 5, 19, 15

Wheel, 212, 5, 5, 16

Wheel joint, 62, 1, 5, 17

Wheel tracks, 26, 3, 1, 18

Image Augmentation

Only the training set contains augmented images. 3,920 of the 5,920 images in the training set are augmented versions of the remaining 2000 original training images. Images taken by different instruments were augmented differently. As shown below, we employed 5 different methods to augment images. Images taken by the Mastcam left and right eye cameras were augmented using a horizontal flipping method, and images taken by the MAHLI camera were augmented using all 5 methods. Note that one can filter based on the file names listed in the train-set.txt file to obtain a set of non-augmented images.

90 degrees clockwise rotation (file name ends with -r90.jpg)

180 degrees clockwise rotation (file name ends with -r180.jpg)

270 degrees clockwise rotation (file name ends with -r270.jpg)

Horizontal flip (file name ends with -fh.jpg)

Vertical flip (file name ends with -fv.jpg)

Acknowledgment

The authors would like to thank the volunteers (as in the Contributor list) who provided annotations for this data set. We would also like to thank the PDS Imaging Note for the continuous support of this work.

EOS-WEBSTER has agreed to serve satellite image subsets for the Forest Watch ("http://www.forestwatch.sr.unh.edu") program and other educational programs which make use of satellite imagery. Forest Watch is a New England-wide environmental education activity designed to introduce teachers and students to field, laboratory, and satellite data analysis methods for assessing the state-of-health of local forest stands. One of the activities in Forest Watch involves image processing and data analysis of Landsat Thematic Mapper data (TM/ETM+) for the area around a participant's school. The image processing of local Landsat data allows the students to use their ground truth data from field-based activities to better interpret the satellite data for their own back yard. Schools use a freely available image processing software, MultiSpec ("http://dynamo.ecn.purdue.edu/%7Ebiehl/MultiSpec/"), to analyze the imagery. Value-added Landsat data, typically in a 512 x 512 pixel subset, are supplied by this collection. The Forest Watch program has supplied the data subsets in this collection based on the schools involved with their activities.

Satellite data subsets may be searched by state or other category, and by spectral type. These images may be previewed through this system, ordered, and downloaded. Some historic Landsat 5 data subsets, which were acquired for this program, are also provided through this system. Landsat 5 subsets are multispectral data with 5 bands of data (TM bands 1-5). Landsat 7 subsets contain all bands of data and each subset has three spectral file types: 1) multispectral (ETM+ bands 1-5 and 7), 2) panchromatic (ETM+ band 8), and 3) Thermal (ETM+ band 6 high and low gain channels). Each spectral type must be ordered separately; this can be accomplished by choosing more than one spectral file type in your search parameters.

These image subsets are served in the ERDAS Imagine (.img) format, which can be opened by newer versions of the MultiSpec program (versions greater than Nov. 1999). The MultiSpec program can be downloaded via the Internet at: "http://dynamo.ecn.purdue.edu/%7Ebiehl/MultiSpec/"

A header file is provided with most Landsat 7 subsets giving the specifics of the image.

Please refer to the references to learn more about Forest Watch, Landsat, and the data this satellite acquires.

In the near future we hope to release a new Satellite Interface, which would allow a user to search for satellite data from a number of platforms based on user-selected search parameters and then sub-set the data and choose an appropriate output format.

If you have any other questions regarding our Forest Watch Satellite data holdings, please contact our User Services Personnel (support@eos-webster.sr.unh.edu).

Available Data Sets:

Many New England subsets are available, based on the location of participating schools in the Forest Watch program. Additional scenes are also included based on historical use within the Forest Watch program. Other scenes may be added in the future. If you don't see a scene of the location you are interested in, and that location is within New England, then please contact User Services (support@eos-webster.sr.unh.edu) to see if we can custom-create a subset for you.

Data Format

The data are currently held in EOS-WEBSTER in ERDAS Imagine (.img) format. This format is used by new versions of the MultiSpec program, and other image processing programs. Most of the subset scenes provided through this system have been projected to a Lambert Projection so that MultiSpec can display Latitude and Longitude values for each image cell (see "http://www.forestwatch.sr.unh.edu/online/" Using Mac MultiSpec to display Lat./Lon. Coordinates).

Data can be ordered by spectral type. For Landsat 7, three spectral types are available: 1) Multispectral (bands 1-5 & 7), 2) Panchromatic (pan), and 3) Thermal (bands 6 a&b) (see Table 2). The multispectral (ms) files contain six bands of data, the panchromatic (pan) files contains one band of data, and the thermal (therm) files contain two bands of data representing a high and low sensor gain.

A header file is provided for most Landsat 7 subsets which have been projected in the Lambert projection. This header file provides the necessary information for importing the data into MultiSpec for Latitude/Longitude display.

Police-recorded crash data has improved over time, but still fails to report all aspects of crashes that are important to developing a full understanding of crash mechanisms, injury burdens, pre-crash conditions, and ultimately total health and cost outcomes. Traditionally, safety and injury analysis has occurred in siloed fields, with road safety researchers relying predominately on police-recorded crash reports, and public health researchers relying on hospitalization records. Depending on the context of the study and the database used, findings vary. This is the case for the micro-level (e.g., injury severity of an individual) to the macro-level (e.g., injury rate) scale. This project begins to map disparate data sets to inform questions surrounding crashes. The data-mapping process will aim to build linkages between police-crash datasets and other datasets (i.e., incident-oriented data, spatial data, emerging datasets) and scale it up to larger geographic areas. Efforts to augment crash data are not new. A notable health-oriented example which sought to link health and police records was the Crash Outcome Data Evaluation System (CODES). Although this federal program ended in 2013, some states, including California, North Carolina, and Tennessee, have continued this effort. Added data and analytics resulted in a more “complete picture” of crashes and injuries. This complete picture enables researchers to improve their modeling, assist policy makers, and contribute to visualization that helps tell compelling safety stories that guide safety improvements.

How many people use Instagram?

Instagram is a social media app which focuses on photo and video sharing. It is owned by Facebook, which recently rebranded as Meta, in a move towards the metaverse. As of December 2021, Instagram reached a new milestone of 2 billion active users worldwide. The platform previously reached 1 billion users in June 2018.

Users aged 25 to 34 years made up the biggest Instagram user group by age worldwide, followed by 18 to 24 year olds. Overall, men accounted for 51.6 percent of global Instagram users.

Most popular Instagram accounts and content

Footballer Cristiano Ronaldo was the most followed person on Instagram as of 2021, with approximately 315 million followers. Ranked in second and third position were the Rock and Ariana Grande, both having over 250 million followers each.

It’s not only athletes and actors who drive the views on Instagram though. Instagram has long since become a medium for people and communities to spread their messages, and in the past two years, social activists and educators have used Instagram for their movements. Political and educational accounts generate high engagement rates with their content. Posts from political accounts receive an average engagement rate of 2.74 percent and educational posts receive an average of 2.56 percent.

Social media usage worldwide

There are 3.4 billion social media users worldwide and this is expected to increase to 4.41 billion by 2025. Instagram is most popular in India with a total of 201.1 million users, followed by the U.S. with 157.1 million and Brazil with 114.9 million active users. Instagram is the fourth most popular social media site in the world, after Facebook, YouTube and WhatsApp, respectively.

As of October 2021, Brunei was the country with the highest Instagram audience reach with 92 percent of the population using the platform. This was followed by Guam with 79.2 percent and the Cayman Islands with 78.8 percent audience reach.

As of October 2021, Facebook had almost 3 billion users worldwide. The penetration rate for Facebook in the United States in 2021 was 70.59 percent and it is expected to reach 76.87 percent by 2026.

A comparison of image statistics of peacock jumping spider colour patterns and natural scenes

• Author: Marie-Christin Hardenbicker
• Other contributors: Joseph Schubert, Cynthia Tedore
• Date created: 2025-02-19

Data and File Overview

Table of Contents

File format: .txt

• spectralSlopesMaratus.txt
• spectralSlopesNaturalScenes.txt
• In Maratus.zip:
  • Data for Further Analysis contains 403 .txt files
• In Natural Scenes.zip:
  • Data for Further Analysis contains 123 .txt files

File format: .mat (Matlab structure/table)

• imageSets.mat
• salticidVirtualFilters_530nm_5realFilters.mat
• salticidVirtualFilters_530nm_6realFilters.mat

File format: .m (Matlab script) In Functions.zip:

• circularWindow.m
• prepMatFileNoMask.m
• remGam.m
• rotateAndCenter.m
• round2even.m
• squareCrop.m
• developImgsAndFourierTransform.m

File format: .png In Maratus.zip:

• Images for Viewing on Digital Display contains 145 .png files
• Images Edited for Fourier Transform ...

These datasets contain the total number of photo card transactions and statuses. The data includes:

• The count (number) of Photo Card transactions per month for a given year.
• The count of active Photo Cards at the end of each month for a given year.

Data will be updated to include the backdated transactions in the previous 2 months.

Interactive analytics from 1 July 2022 is available on Registration and Licensing Statistics Portal

https://www.transport.nsw.gov.au/registration-and-licensing-statistics-p...

Information prior to 1 July 2022 is available on Webstats

https://roads-waterways.transport.nsw.gov.au/cgi-bin/index.cgi?action=st...

Disclaimer:

Transport for NSW (TfNSW) publishes statistical licensing and registration data to disseminate information and provide transparency for the benefit of the public.

TfNSW has taken care to ensure that data published is correct and accurate as possible. The data has been consistently produced and is updated regularly. However, TfNSW does not warrant, represent or guarantee the accuracy, reliability, quality or suitability for any purpose of the information or data published at this website nor accept any responsibility arising in connection with any errors in, or omissions from, the information or data provided.

TfNSW does not accept any legal liability for any loss or damage arising directly or indirectly from the use of information or data published at this website.

TfNSW recommends that users exercise their own skill, care and judgement with respect to their use of information or data published including by evaluating the relevance of the material on this website for their purpose.