About this dataset

Context

The dataset tabulates the Sky Valley population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Sky Valley. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 290 (49.15% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Sky Valley population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Sky Valley is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Sky Valley is shown in the following column.

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 Sky Valley Population by Age. You can refer the same here

NASA's Wide-field Infrared Survey Explorer (WISE; Wright et al. 2010) mapped the sky at 3.4, 4.6, 12, and 22 μm (W1, W2, W3, W4) in 2010 with an angular resolution of 6.1", 6.4", 6.5", & 12.0" in the four bands. WISE achieved 5σ point source sensitivities better than 0.08, 0.11, 1 and 6 mJy in unconfused regions on the ecliptic in the four bands. Sensitivity improves toward the ecliptic poles due to denser coverage and lower zodiacal background.The All-Sky Release includes all data taken during the WISE full cryogenic mission phase, 7 January 2010 to 6 August 2010, that were processed with improved calibrations and reduction algorithms. Release data products include an Atlas of 18,240 match-filtered, calibrated and coadded image sets, a Source Catalog containing positional and photometric information for over 563 million objects detected on the WISE images, and an Explanatory Supplement that is a guide to the format, content, characteristics and cautionary notes for the WISE All-Sky Release products.The WISE All-Sky Data Release Single-exposure Source Working Database contains positions and brightness information, uncertainties, time of observation and assorted quality flags for 9,479,433,101 "sources" detected on the individual WISE 7.7s (W1 and W2) and 8.8s (W3 and W4) Single-exposure images. Because WISE scanned every point on the sky multiple times, the Single-exposure Database contains multiple, independent measurements of objects on the sky.Entries in the Single-exposure Source Table include detections of real astrophysical objects, as well as spurious detections of low SNR noise excursions, transient events such as hot pixels, charged particle strikes and satellite streaks, and image artifacts light from bright sources including the moon. Many of the unreliable detections are flagged in the Single-exposure Table, but they have not been filtered out as they were for the Source Catalog. Therefore, the Table must be used with caution. Users are strongly encouraged to read the Cautionary Notes before using the Table.

This database table contains the list German ROSAT All-Sky Survey observations which were obtained during the ROSAT All-Sky Survey phase (1990 July 30 to 1991 Jan 25) and which have become available to the public. These data were obtained in scanning mode and therefore an individual dataset covers a much larger area of the sky than do pointed moded observations. In addition all these data were obtained with PSPC-C, while all pointed mode observations after the end of the All-Sky Survey were obtained with PSPC-B. For each observation listed in this database table, the instrument used, processing site, and coordinates of the field center are given, as well as the ROSAT observation request number (ROR), actual exposure time, date the observation took place, and more. For details about the ROSAT instruments, consult the ROSAT Mission Description (NASA Research Announcement for ROSAT, Appendix F and its addendum) and the ROSAT GSFC GOF website at http://heasarc.gsfc.nasa.gov/docs/rosat/rosgof.html for more information. For more information about the ROSAT All Sky Survey, see the ROSAT All Sky Survey page at http://www.xray.mpe.mpg.de/cgi-bin/rosat/rosat-survey. This database table was created at the HEASARC in March 2002, based on information provided by Max-Planck-Institut fuer extraterrestrische Physik at http://wave.xray.mpe.mpg.de/. This is a service provided by NASA HEASARC .

About this dataset

Context

The dataset tabulates the Sky Valley 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 Sky Valley 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 2023, the population of Sky Valley was 487, a 0.83% increase year-by-year from 2022. Previously, in 2022, Sky Valley population was 483, a decline of 0.82% compared to a population of 487 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Sky Valley increased by 232. In this period, the peak population was 487 in the year 2021. 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 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Sky Valley is shown in this column.
• Year on Year Change: This column displays the change in Sky Valley 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 Sky Valley Population by Year. You can refer the same here

This all sky mosaic was created by Axel Mellinger and is used in SkyView with his permission. A fuller description is available at the survey website. Between October 2007 and August 2009 a digital all-sky mosaic was assembled from more than 3000 individual CCD frames. Using an SBIG STL-11000 camera, 70 fields (each covering 40x27 degrees) were imaged from dark-sky locations in South Africa, Texas and Michigan. In order to increase the dynamic range beyond the 16 bits of the camera's analog-to-digital converter (of which approximately 12 bits provide data above the noise leve) three different exposure times (240s, 15s and 0.5 s) were used. Five frames were taken for each exposure time and filter setting. The frames were photometrically calibrated using standard catalog stars and sky background data from the Pioneer 10 and 11 space probes. the panorama has an image scale of 36"/pixel and a limiting magnitude of approximately 14. The survey has an 18 bit dynamic range. The processing of these data used a custom data pipeline built using IRAF, Source Extractor and SWarp. The data used here were converted to three independent RGB color planes of 8 bits each and provided to SkyView as a single 36000x18000x3 Cartesian projection cube. To allow users to efficiently sample data in a region of the sky, this cube was broken up into 2100x2100 pixel regions with a 50 pixel overlap between adjacent images. Tiles at the poles were 2100x2050. In SkyView each color plane comprises a survey. The individual planes may be sampled as surveys independently as Mellinger-R, Mellinger-G and Mellinger-B. The color mosaics can be regenerated by creating an RGB image of all three surveys. Since SkyView may stretch the intensity values within each color, linear scaling and a minimum of 0 and maximum of 255 should be specified to keep the original intensity scalings. The full spatial resolution data is used for images of less than 30 degrees on a side. If a user requests a larger region, data are sampled from a lower resolution 3600x1800x3 data cube. Please contact the survey author if you need to use the higher resolution data for larger regions. The Mellinger survey is only available in SkyView through the website. SkyView-in-a-Jar cannot access the underlying data. Provenance: Axel Mellinger. This is a service of NASA HEASARC.

About this dataset

Context

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

Key observations

Largest age group (population): Male # 65-69 years (43) | Female # 30-34 years (124). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

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

Variables / Data Columns

• Age Group: This column displays the age group for the Sky Valley population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Sky Valley is shown in the following column.
• Population (Female): The female population in the Sky Valley 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 Sky Valley 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 Sky Valley Population by Gender. You can refer the same here

Large-scale integration of photovoltaics (PV) into electricity grids is challenged by the intermittent nature of solar power. Sky image-based solar forecasting has been recognized as a promising approach to predicting the short-term fluctuations. Here, we present a curated dataset from Stanford University in a format suitable for solar forecasting related research and applications. The dataset consists of high-resolution (2048x2048) sky images taken by a ground-based fish-eye camera and power output measurements from a 30-kW rooftop PV array approximately 125 meters away from the camera at Stanford Campus, both of which are logged in 1-min frequency. To support the flexibility of research, we also provide the source video footage recorded by the same camera. More details of the dataset can be found in our dataset GitHub repository with link shown in the “Related link” elsewhere on this page. We expect the users of this dataset to process the data based on their own needs, while some reference codes for data processing are provided in our dataset GitHub repository. We hope that the dataset will facilitate the research of image-based solar forecasting and we also encourage the users to explore on other related areas with this dataset, such as cloud classification, cloud image segmentation and cloud movement forecasting. This page includes data from March 2017 to December 2017. Data from 2018 and 2019 are also available. See links to related items elsewhere on this page.

Large-scale integration of photovoltaics (PV) into electricity grids is challenged by the intermittent nature of solar power. Sky image-based solar forecasting has been recognized as a promising approach to predicting the short-term fluctuations. Here, we present a curated dataset from Stanford University in a format suitable for solar forecasting related research and applications. The dataset consists of high-resolution (2048x2048) sky images taken by a ground-based fish-eye camera and power output measurements from a 30-kW rooftop PV array approximately 125 meters away from the camera at Stanford Campus, both of which are logged in 1-min frequency. To support the flexibility of research, we also provide the source video footage recorded by the same camera. More details of the dataset can be found in our dataset GitHub repository with link shown in the “Related link” elsewhere on this page. We expect the users of this dataset to process the data based on their own needs, while some reference codes for data processing are provided in our dataset GitHub repository. We hope that the dataset will facilitate the research of image-based solar forecasting and we also encourage the users to explore on other related areas with this dataset, such as cloud classification, cloud image segmentation and cloud movement forecasting. This page includes data from January 2019 to December 2019. Data from 2017 and 2018 are also available. See links to related items elsewhere on this page.

The Sloan Digital Sky Survey (SDSS) is a comprehensive survey of the northern sky. This dataset contains a subset of this survey, namely the photometric measurements and spectroscopic labels of around 2.8 million objects. The dataset was generated by submitting an SQL query to the DR12 catalog on the SDSS CasJobs site.

Each row in the dataset corresponds to an object in the sky. There are 14 columns:

• The first two columns contain the right ascension (ra) and declination (dec) of an object. These two coordinates uniquely determine its position.
• The third column (class) is the spectroscopic class (Star, Galaxy, and Quasar) as determined by expert opinion. This can be the target vector in a classification model.
• There are 11 columns that we can use as feature vectors. These are the different PSF and Petrosian magnitude and colour measurements:
  • psfMag_r_w14: The PSF magnitude measurement in r-band, assuming the object is a point source.
  • psf_u_g_14: The difference between the PSF magnitude measurement in u-band and the g-band (i.e. the u-g colour), assuming the object is a point source.
  • psf_g_r_14: The difference between the PSF magnitude measurement in g-band and the r-band (i.e. the g-r colour), assuming the object is a point source.
  • psf_r_i_14: The difference between the PSF magnitude measurement in r-band and the i-band (i.e. the r-i colour), assuming the object is a point source.
  • psf_i_z_14: The difference between the PSF magnitude measurement in i-band and the z-band (i.e. the i-z colour), assuming the object is a point source.
  • petroMag_r_w14: The Petrosian magnitude measurement in r-band, assuming the object is an extended source.
  • petro_u_g_w14: The difference between the Petrosian magnitude measurement in the u-band and the g-band (i.e. the u-g colour), assuming the object is an extended source.
  • petro_g_r_w14: The difference between the Petrosian magnitude measurement in the g-band and the r-band (i.e. the g-r colour), assuming the object is an extended source.
  • petro_r_i_w14: The difference between the Petrosian magnitude measurement in the r-band and the i-band (i.e. the r-i colour), assuming the object is an extended source.
  • petro_i_z_w14: The difference between the Petrosian magnitude measurement in the i-band and the z-band (i.e. the i-z colour), assuming the object is an extended source.
  • petroRad_r: The size measurement of the object in r-band in arc seconds.

The measurements have been corrected for dust extinction (the scattering of light by the galactic dust) using the correction set provided by Wolf (2014). Each feature has also been standardised to have zero mean and unit variance.

For the code to generate this dataset, please go to the Github repo: https://github.com/chengsoonong/mclass-sky

If you use the SDSS data in your papers, please see here for instructions on how to cite: http://www.sdss.org/collaboration/citing-sdss/

Please also cite this upload if you have used this particular pre-processed dataset.

This datasets contains three maps of L-band (wavelength = 21 cm) brightness temperature of the celestial sky ("Galaxy") used in the processing of the NASA Aquarius instrument data. The maps report Sky brightness temperatures in Kelvin gridded on the Earth Centered Inertial (ECI) reference frame epoch J2000. They are sampled over 721 Declinations between -90 degrees and +90 degrees and 1441 Right Ascensions between 0 degrees and 360 degrees, all evenly spaced at 0.25 degrees intervals. The brightness temperatures are assumed temporally invariant and polarization has been neglected. They include microwave continuum and atomic hydrogen line (HI) emissions. The maps differ only in how the strong radio source Cassiopeia A has been included into the whole sky background surveys: 1/ TB_no_Cas_A does not include Cassiopeia A and reports only the whole Sky surveys. 2/ TB_Cas_A_1cell spread Cas A total flux homogeneously over 1 map grid cell (i.e. 9.8572E-6 sr). 3/ TB_Cas_A_beam spreads Cas A over surrounding grid cells using a convolution by a Gaussian beam with HPBW of 35 arcmin (equivalent to the instrument used for the Sky surveys). Cassiopeia A is a supernova remnant (SNR) in the constellation Cassiopeia and the brightest extra-solar radio source in the sky at frequencies above 1.

Generation of multiple true-false questions

This project provides a Natural Language Pipeline for processing German Textbook sections as an input generating Multiple True-False Questions using GPT2.

Assessments are an important part of the learning cycle and enable the development and promotion of competencies. However, the manual creation of assessments is very time-consuming. Therefore, the number of tasks in learning systems is often limited. In this repository, we provide an algorithm that can automatically generate an arbitrary number of German True False statements from a textbook using the GPT-2 model. The algorithm was evaluated with a selection of textbook chapters from four academic disciplines (see `data` folder) and rated by individual domain experts. One-third of the generated MTF Questions are suitable for learning. The algorithm provides instructors with an easier way to create assessments on chapters of textbooks to test factual knowledge.

As a type of Multiple-Choice question, Multiple True False (MTF) Questions are, among other question types, a simple and efficient way to objectively test factual knowledge. The learner is challenged to distinguish between true and false statements. MTF questions can be presented differently, e.g. by locating a true statement from a series of false statements, identifying false statements among a list of true statements, or separately evaluating each statement as either true or false. Learners must evaluate each statement individually because a question stem can contain both incorrect and correct statements. Thus, MTF Questions as a machine-gradable format have the potential to identify learners’ misconceptions and knowledge gaps.

Example MTF question:

Check the correct statements:

[ ] All trees have green leafs.

[ ] Trees grow towards the sky.

[ ] Leafes can fall from a tree.

Features

- generation of false statements

- automatic selection of true statements

- selection of an arbitrary similarity for true and false statements as well as the number of false statements

- generating false statements by adding or deleting negations as well as using a german gpt2

Setup

Installation

1. Create a new environment: `conda create -n mtfenv python=3.9`

2. Activate the environment: `conda activate mtfenv`

3. Install dependencies using anaconda:

```

conda install -y -c conda-forge pdfplumber

conda install -y -c conda-forge nltk

conda install -y -c conda-forge pypdf2

conda install -y -c conda-forge pylatexenc

conda install -y -c conda-forge packaging

conda install -y -c conda-forge transformers

conda install -y -c conda-forge essential_generators

conda install -y -c conda-forge xlsxwriter

```

3. Download spacy: `python3.9 -m spacy download de_core_news_lg`

Getting started

After installation, you can execute the bash script `bash run.sh` in the terminal to compile MTF questions for the provided textbook chapters.

To create MTF questions for your own texts use the following command:

`python3 main.py --answers 1 --similarity 0.66 --input ./

The parameter `answers` indicates how many false answers should be generated.

By configuring the parameter `similarity` you can determine what portion of a sentence should remain the same. The remaining portion will be extracted and used to generate a false part of the sentence.

## History and roadmap

* Outlook third iteration: Automatic augmentation of text chapters with generated questions

* Second iteration: Generation of multiple true-false questions with improved text summarizer and German GPT2 sentence generator

* First iteration: Generation of multiple true false questions in the Bachelor thesis of Mirjam Wiemeler

Publications, citations, license

Publications

• Kasakowskij, R., Kasakowskij, T. & Seidel, N., (2022). Generation of Multiple True False Questions. In: Henning, P. A., Striewe, M. & Wölfel, M. (Hrsg.), 20. Fachtagung Bildungstechnologien (DELFI). Bonn: Gesellschaft für Informatik e.V.. (S. 147-152). DOI: [10.18420/delfi2022-026](https://dl.gi.de/handle/20.500.12116/38826)

Citation of the Dataset

• Kasakowskij, R., Kasakowskij, T., & Seidel, N. (2022). Dataset for Generation of multiple true false questions. Zenodo. https://doi.org/10.5281/zenodo.7303300

The source code and data are maintained at GitHub: https://github.com/D2L2/multiple-true-false-question-generation

Contact

• Regina Kasakowskij (M.A.) - regina.kasakowskij@fernuni-hagen.de
• Dr. Niels Seidel - niels.seidel@fernuni-hagen.de

License Distributed under the MIT License. See [LICENSE.txt](https://gitlab.pi6.fernuni-hagen.de/la-diva/adaptive-assessment/generationofmultipletruefalsequestions/-/blob/master/LICENSE.txt) for more information.

Acknowledgments This research was supported by CATALPA - Center of Advanced Technology for Assisted Learning and Predictive Analytics of the FernUniversität in Hagen, Germany.

This project was carried out as part of research in the CATALPA project [LA DIVA](https://www.fernuni-hagen.de/forschung/schwerpunkte/catalpa/forschung/projekte/la-diva.shtml)

NASA's Wide-field Infrared Survey Explorer (WISE; Wright et al. 2010) mapped the sky at 3.4, 4.6, 12, and 22 μm (W1, W2, W3, W4) in 2010 with an angular resolution of 6.1", 6.4", 6.5", & 12.0" in the four bands. WISE achieved 5σ point source sensitivities better than 0.08, 0.11, 1 and 6 mJy in unconfused regions on the ecliptic in the four bands. Sensitivity improves toward the ecliptic poles due to denser coverage and lower zodiacal background.The All-Sky Release includes all data taken during the WISE full cryogenic mission phase, 7 January 2010 to 6 August 2010, that were processed with improved calibrations and reduction algorithms. Release data products include an Atlas of 18,240 match-filtered, calibrated and coadded image sets, a Source Catalog containing positional and photometric information for over 563 million objects detected on the WISE images, and an Explanatory Supplement that is a guide to the format, content, characteristics and cautionary notes for the WISE All-Sky Release products.The Known Solar System Object Possible Associations List is a compendium of asteroids, comets, planets or planetary satellites, with orbits known at the time of WISE second-pass data processing, that were predicted to be within the field-of-view at the time of individual WISE exposures. Individual objects were observed multiple times, so may have multiple entries in the list. When the predicted position of a solar system object is in proximity to a detection in the WISE single-exposures, the WISE source position and brightness information are also provided. The WISE All-Sky Data Release Single-exposure Source Working Database contains positions and brightness information, uncertainties, time of observation and assorted quality flags for 9,479,433,101 "sources" detected on the individual WISE 7.7s (W1 and W2) and 8.8s (W3 and W4) Single-exposure images. Because WISE scanned every point on the sky multiple times, the Single-exposure Database contains multiple, independent measurements of objects on the sky.Entries in the Single-exposure Source Table include detections of real astrophysical objects, as well as spurious detections of low SNR noise excursions, transient events such as hot pixels, charged particle strikes and satellite streaks, and image artifacts light from bright sources including the moon. Many of the unreliable detections are flagged in the Single-exposure Table, but they have not been filtered out as they were for the Source Catalog. Therefore, the Table must be used with caution. Users are strongly encouraged to read the Cautionary Notes before using the Table.

About this dataset

Context

The dataset tabulates the population of Sky Valley by race. It includes the population of Sky Valley across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Sky Valley across relevant racial categories.

Key observations

The percent distribution of Sky Valley population by race (across all racial categories recognized by the U.S. Census Bureau): 72.95% are white, 25.71% are Asian, 0.17% are Native Hawaiian and other Pacific Islander and 1.17% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Sky Valley
• Population: The population of the racial category (excluding ethnicity) in the Sky Valley is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Sky Valley 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 Sky Valley Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Sky Valley Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of Sky Valley, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of Sky Valley.

Key observations

Among the Hispanic population in Sky Valley, regardless of the race, the largest group is of Other Hispanic or Latino origin, with a population of 13 (72.22% of the total Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Origin for Hispanic or Latino population include:

• Mexican
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the Sky Valley
• Population: The population of the specific origin for Hispanic or Latino population in the Sky Valley is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of Sky Valley total Hispanic or Latino 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 Sky Valley Population by Race & Ethnicity. You can refer the same here

The IRAS data include all data distributed as part of the IRAS Sky Survey Atlas. Data from the four IRAS bands are shown as individual surveys in SkyView. Users should be aware that IPAC does not encourage the use of data near the ecliptic plane as they feel that contribution from local cirrus emission is significant. The data are distributed in sets of 430 maps. Each map covers approximately 12.5x12.5 degrees, and the map centers are offset by 5 degrees so that there is a 2.5 degree overlap. IPAC has processed to a uniform standard so that excellent mosaics of the maps can be made. Users should be cautious of data in saturated regions. Known problems in the analysis mean that data values are unlikely to be correct. Note that IPAC has optimized the processing of these data for features of 5' or more although the resolution of the data is closer to the 1.5' pixel size. There are occasional pixels in the IRAS maps which are given as NULL values. Unless these are explicitly trapped by user software, these data will appear as large negative values. SkyView ignores these pixels when determining the color scale to display an image. Essentially the entire sky is covered by the survey. However there are a few regions not surveyed and the data values in these regions are suspect. These are given to users as delivered from IPAC. Provenance: NASA IPAC/Jet Propulsion Laboratory. This is a service of NASA HEASARC.

This BAT Hard X-ray Survey data is the 70-month survey product of the BAT instrument on the Swift observatory. Swift/BAT is a wide field-of-view (70x100 degrees) hard X-ray imager consisting of a coded mask and a large array of CdZnTe detectors (with an effective area of ~ 5000 cm2). BAT is sensitive in the energy range 14-195 keV. The data are divided into 8 energy bands BandEnergy (keV)Frequency (EHz) 1 14-20 3.38-4.84 2 20-24 4.84-5.80 3 24-35 5.80-8.46 4 35-50 8.46-12.1 5 50-75 12.1-18.1 6 75-100 18.1-24.2 7 100-150 24.2-36.3 8 150-195 36.3-47.2 Sum (SNR only)14-195 3.38-47.2 Each band is represented as as two separate surveys, a signal-to-noise (SNR) map and a flux map. (e.g., BAT-snr-1 or BAT SNR 1 or BAT SNR 14-20, or BAT-Flux-1, ...). An additional summed survey, BAT-SNR-SUM or BAT SNR SUM or BAT SNR 14-195, is also available, but there is no summed flux survey. In our Web interface only the SNR surveys are shown in the Web form. Users can get flux maps corresponding to a given SNR image from the results pages. The batch interfaces may directly query any of the surveys since the user chooses the names explicitly rather than from a selection box. The values displayed in the significance maps are the local signal to noise ratio in each pixel. The noise in these coded-mask images follows a Gaussian distribution with center at zero and a characteristic width (sigma) of 1.0. The noise is calculated locally for each pixel by measuring the RMS value of all pixel values in an annulus around each pixel and hence includs both statistical and systematic components. Known sources are excluded from the annuli. The signal in each pixel is taken from the flux maps. The flux values are in the native BAT survey units of counts/sec/detector. The detector is an individual piece of CZT in the BAT array with an area of 1.6 x 10-7m2. While the Swift mission is primarily designed to follow gamma-ray bursts, the random distribution of bursts in the sky means that these survey's sky coverage is relatively uniform with the exposure at any point varying between about 6 to 16 megaseconds. The survey limits for source detection are about 10-11 ergs/s/cm2 over about half the sky and 1.3x10-11 ergs/s/cm2 over 90%. These data replace the 9-month BAT datasets which we have retired. If you wish access to the older data please let us know. Note that for the 9-month data we provided access through the web page to the flux data and gave links to the signal-to-noise maps. Since the existence of sources is most easily seen in the SNR maps, we decided to invert that for this release. For the 8 band data, the source data were provided by the BAT team as 6 FITS files. Each of these contained the 8 bands in separate image extensions for a region centered at l=0,b=+/-90 or l=0,90,180,270,b=0, the centers of 6 cubic facets. However these data are not the classical cube-faced projections, e.g., as used in COBE data. The data on the facets overlap, so that this is just a convenient way to tile the sky. SkyView separated each of the FITS image extensions into a separate file, but no other modifications were made to the data. The summed image was provided as six separate files. Provenance: NASA BAT Team. This is a service of NASA HEASARC.

The ROSAT PSPC surveys were generated by SkyView as mosaics from publically available PSPC observations. The surveys include all data available through March 1, 1997. This includes the vast majority of ROSAT PSPC observations. Filter observations and observations taken during the verification phase in 1991 were not included in either set. The details of the generation of the surveys are discussed in a companion document. Basically the counts and exposure from all observations were added and then an intensity map was generated as the ratio of the two. The smaller cut-offs allow users to distinguish point sources in fields where a bright source may have been towards the edge of one observation and near the center of another. In these cases the source appears fuzzy due to the poor resolution of ROSAT near the edge of the field of view. This comes at the cost of a substantial reduction in the fraction of the sky covered. Counts and exposure maps are included for users who may need this information (e.g., to do statistical analysis). The global organization of the surveys is similar to the IRAS survey. Each map covers an area of 2.5°;x2.5°; with a minimum overlap of 0.25°;. To cover the entire sky would require over 10,000 maps. However due to lack of coverage only approximately 4000-6000 maps are actually populated. Users asking for reqions where there is no ROSAT coverage may get a blank region returned. Detailed information regarding the creation of the ROSAT suveys can be found in the ROSAT PSPC Generation Document. Provenance: Observational data from NASA Goddard Space Flight Center, mosaicking of images done by SkyView.. This is a service of NASA HEASARC.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Sky Valley by race. It includes the distribution of the Non-Hispanic population of Sky Valley across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Sky Valley across relevant racial categories.

Key observations

Of the Non-Hispanic population in Sky Valley, the largest racial group is White alone with a population of 438 (76.57% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Sky Valley
• Population: The population of the racial category (for Non-Hispanic) in the Sky Valley is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Sky Valley total Non-Hispanic 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 Sky Valley Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the data for the Sky Valley, GA population pyramid, which represents the Sky Valley population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Sky Valley, GA, is 2.4.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Sky Valley, GA, is 99.0.
• Total dependency ratio for Sky Valley, GA is 101.4.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Sky Valley, GA is 1.0.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Sky Valley population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Sky Valley for the selected age group is shown in the following column.
• Population (Female): The female population in the Sky Valley for the selected age group is shown in the following column.
• Total Population: The total population of the Sky Valley for the selected age group is shown in the following column.

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 Sky Valley Population by Age. You can refer the same here

This database table contains the list of all Röntgen Satellite (ROSAT) X-Ray Telescope (XRT) pointing-mode observations for which data sets are available, i.e., it excludes the ROSAT All-Sky Survey observations. Users should consult the RASSMASTER database table for those XRT observations which were made in scanning mode during the ROSAT All-Sky Survey (RASS) phase (30 July 1990 to 25 January 1991, and 3 August 1991 to 13 August 1991). For each observation listed in this table, parameters such as the focal-plane instrument used, the data processing site, and the target name and coordinates are given, as well as the ROSAT Observation Request (ROR) number, the actual and requested exposure times, the date(s) on which the observation took place, etc. For details about the ROSAT instruments, consult the ROSAT Guest Observer Facility (GOF) website at https://heasarc.gsfc.nasa.gov/docs/rosat/. A list of the available online ROSAT documentation can be found at https://heasarc.gsfc.nasa.gov/docs/rosat/rosdocs.html. This table was created by the HEASARC in July 2004 by combining the data from two long-standing HEASARC Browse tables into one master table. It was updated by the HEASARC in March 2022 to add start and end times for the 157 sequence IDs which did not already have start and end times. This is a service provided by NASA HEASARC .