Hule; åpning ca.0,7m i diam. i en fjellknaus. Hulens dybde 1,4m. Usikkert fornminne. Se skisse.

This paper presents infrared data obtained from observations carried out at the ESO 3.5m New Technology Telescope (NTT) of the Hubble Deep Field South (HDF-S) and the Chandra Deep Field South (CDF-S). These data were taken as part of the ESO Imaging Survey (EIS) program, a public survey conducted by ESO to promote follow-up observations with the VLT. In the HDF-S field the infrared observations cover an area of ~53 square arcmin, encompassing the HST WFPC2 and STIS fields, in the JHKs passbands. The seeing measured in the final stacked images ranges from 0.79" to 1.22" and the median limiting magnitudes (AB system, 2" aperture, 5sigma detection limit) are J_AB~23.0, H_AB~22.8 and K_AB~23.0mag. Less complete data are also available in JKs for the adjacent HST NICMOS field. For CDF-S, the infrared observations cover a total area of ~100 square arcmin, reaching median limiting magnitudes (as defined above) of J_AB~23.6 and K_AB~22.7mag. For one CDF-S field H-band data are also available. This paper describes the observations and presents the results of new reductions carried out entirely through the un-supervised, high-throughput EIS Data Reduction System and its associated EIS/MVM C++-based image processing library developed, over the past 5 years, by the EIS project and now publicly available. The paper also presents source catalogs extracted from the final co-added images which are used to evaluate the scientific quality of the survey products, and hence the performance of the software. This is done comparing the results obtained in the present work with those obtained by other authors from independent data and/or reductions carried out with different software packages and techniques. Cone search capability for table J/A+A/452/119/catalog (Selected values from complete catalog, for guidance)

Statistics on speeding rates and exceedance of speed limit along selected street segments throughout San Francisco. The dataset was prepared as part of the SF Indicators project and covers from 2004 through 2009. http://www.sfindicatorproject.org/indicators/view/49

Source: https://data.sfgov.org/d/mfjz-pnye
Last updated at https://datasf.org/opendata/ : 2019-09-06

This boundary file contains historic census tract boundaries for which the U.S. Census Bureau tabulated data and was produced by the Minnesota Population Center as part of the National Historical Geographic Information System (NHGIS) project. The NHGIS is an National Science Foundation-sponsored project (Grant No. BCS0094908) to create a digital spatial-temporal database of all available historical US aggregate census materials. The available shapefiles on the NHGIS site represent version 1.0 of historical US census tract boundary files for the 1910-2000 censuses. These electronic census tract boundary files were created by referencing publicly available, printed U.S. Census Bureau maps and considerable care was taken during their production. TIGER/Line spatial features that corresponded to boundaries on these maps were used to construct proper historic boundaries. When a TIGER/Line features was not available, we digitized the historic boundary from a geo-referenced, scanned census map. The boundary files have been checked against currently available historical census aggregate data.

The Digital Geologic-GIS Map of the Hasty Quadrangle, Arkansas is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (hsty_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (hsty_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (hsty_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (buff_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (buff_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (hsty_geology_metadata_faq.pdf). Please read the buff_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (hsty_geology_metadata.txt or hsty_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

About this dataset

Context

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

Key observations

In 2022, the population of Mount Pleasant was 4,184, a 0.90% decrease year-by-year from 2021. Previously, in 2021, Mount Pleasant population was 4,222, a decline of 0.49% compared to a population of 4,243 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Mount Pleasant decreased by 509. In this period, the peak population was 4,693 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

https://i.neilsberg.com/ch/population-of-mount-pleasant-pa-population-by-year-2000-2022.jpeg" alt="Mount Pleasant population by year">

Content

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

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

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

List of employees and associated meta data that will be used to generate agency, department, office, and section directories.

Dissolved silicon isotope compositions have been analysed for the first time in pore waters (δ30SiPW) of three short sediment cores from the Peruvian margin upwelling region with distinctly different biogenic opal content in order to investigate silicon isotope fractionation behaviour during early diagenetic turnover of biogenic opal in marine sediments. The δ30SiPW varies between +1.1‰ and +1.9‰ with the highest values occurring in the uppermost part close to the sediment–water interface. These values are of the same order or higher than the δ30Si of the biogenic opal extracted from the same sediments (+0.3‰ to +1.2‰) and of the overlying bottom waters (+1.1‰ to +1.5‰). Together with dissolved silicic acid concentrations well below biogenic opal saturation, our collective observations are consistent with the formation of authigenic alumino-silicates from the dissolving biogenic opal. Using a numerical transport-reaction model we find that approximately 24% of the dissolving biogenic opal is re-precipitated in the sediments in the form of these authigenic phases at a relatively low precipitation rate of 56 μmol Si cm−2 yr−1. The fractionation factor between the precipitates and the pore waters is estimated at −2.0‰. Dissolved and solid cation concentrations further indicate that off Peru, where biogenic opal concentrations in the sediments are high, the availability of reactive terrigenous material is the limiting factor for the formation of authigenic alumino-silicate phases. M77/1-470-MUC29: The age model for core 470-MUC29 has been published and described in detail in Ehlert et al. (2015). Based on correlation with other cores from the region using 210Pb datings and sediment properties (opal concentrations, sediment density, etc.), the sedimentation rate varies between 1.8 mm yr-1 in the upper part and 0.6 mm yr-1 in the deeper part. Therefore, the core covers the past ca. 550 years, and therefore the Modern Warm Period (MWP, 1870AD - present), the Transition Period (TP, 1820 - 1870AD) and the Little Ice Age (LIA, 1400 - 1820AD). M77/1-449-MUC19: The age model of core 449-MUC19 was estimated using 210Pb datings. Assuming a constant sedimentation rate of 0.5 mm yr-1, the core covers the past ca. 1000 years and therefore reaches back to the Medieval Climatic Anomaly (MCA, ca. 800 - 1250AD) M77/1-549-MUC53: Core 549-MUC53 has an average sedimentation rate of 0.58 mm yr-1 (based on 210Pb dating), and covers the time period from the LIA until Present .

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Reynolds, ND, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 23,580, while the mean income for the highest quintile (20% of households with the highest income) is 163,888. This indicates that the top earners earn 7 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 216,658, which is 132.20% higher compared to the highest quintile, and 918.82% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

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 Reynolds median household income. You can refer the same here

The current dataset contains Tweet IDs for tweets mentioning "COVID" (e.g., COVID-19, COVID19) and shared between March and July of 2020.
Sampling Method: hourly requests sent to Twitter Search API using Social Feed Manager, an open source software that harvests social media data and related content from Twitter and other platforms.
NOTE: 1) In accordance with Twitter API Terms, only Tweet IDs are provided as part of this dataset.
2) To recollect tweets based on the list of Tweet IDs contained in these datasets, you will need to use tweet 'rehydration' programs like Hydrator (https://github.com/DocNow/hydrator) or Python library Twarc (https://github.com/DocNow/twarc).
3) This dataset, like most datasets collected via the Twitter Search API, is a sample of the available tweets on this topic and is not meant to be comprehensive. Some COVID-related tweets might not be included in the dataset either because the tweets were collected using a standardized but intermittent (hourly) sampling protocol or because tweets used hashtags/keywords other than COVID (e.g., Coronavirus or #nCoV).
4) To broaden this sample, consider comparing/merging this dataset with other COVID-19 related public datasets such as: https://github.com/thepanacealab/covid19_twitter https://ieee-dataport.org/open-access/corona-virus-covid-19-tweets-dataset https://github.com/echen102/COVID-19-TweetIDs

The 2010 Annual Parole Survey provides a count of the total number of persons supervised in the community on January 1 and December 31, 2010, and a count of the number entering and leaving supervision during the year. The survey also provides counts of th

This dataset describes the value of various sectors of the Economy by economic activity in order to quantify the national gross domestic product for the years 2001 - 2009 (Value in Kenya Schilling in Millions)

Sensitivity map made by the ODONAT Grand Est network in 2018-2019. The distribution of the species is represented from recent occurrence data (1999-2018 or 2009-2018 by species). These are the 10 x 10 km Lambert 93 meshes in which at least one observation of the species has been made in the recent period. Any observations shall be taken into account: they can be implanted populations, but also erratic individuals. This layer represents the state of knowledge at the time of its realisation, it should not be considered exhaustive. The presence of the species outside the identified areas is possible.
Refer to the card reading instructions as well as PDF cards for more information.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Ila: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 0 households where the householder is under 25 years old, 46(32.86%) households with a householder aged between 25 and 44 years, 53(37.86%) households with a householder aged between 45 and 64 years, and 41(29.29%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the city of Ila, showcasing varying income levels among different age demographics.

https://i.neilsberg.com/ch/ila-ga-household-income-by-age-groups.jpeg" alt="Ila, GA household income distribution across age groups">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Ila median household income by age. You can refer the same here

About this dataset

Context

The dataset tabulates the median household income in Litchfield. It can be utilized to understand the trend in median household income and to analyze the income distribution in Litchfield by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Litchfield, MN Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Litchfield, MN: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Litchfield, MN
• Litchfield, MN households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Litchfield median household income. You can refer the same here

The Mars Global Surveyor spacecraft included a laser altimeter instrument. The primary objective of the Mars Orbiter Laser Altimeter (MOLA) is to determine globally the topography of Mars at a level suitable for addressing problems in geology and geophysics.

This is one dataset of users consuming live content on Twitch. We retrieved all streamers, and all users connected in their specific chats, every 10 minutes during 43 days.

Auguries of BERT on Winogender prior and after several different types of interventions. This is extra physical to support this publication 'The MultiBERTs, BOB Reproductions for Robustness Analysis', ICLR'22 (Section 4: 'Application: Gender Bias inbound Coreference Systems').

Analysis of ‘Počet prednostných hlasov pre kandidátov politických subjektov za SR’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/e6ee6068-1f21-4d96-a699-545bea92da01 on 16 January 2022.

--- Dataset description provided by original source is as follows ---

Výsledky volieb do Európskeho parlamentu 2019

--- Original source retains full ownership of the source dataset ---

NODC Accession 0113549 includes biological, chemical, discrete sample, physical and profile data collected from HESPERIDES in the South Atlantic Ocean from 1995-12-03 to 1996-01-05 and retrieved during cruise CARINA/29HE19951203. These data include ALKALINITY, AMMONIUM, CHLOROPHYLL A, DISSOLVED ORGANIC CARBON, DISSOLVED OXYGEN, HYDROSTATIC PRESSURE, NITRATE, NITRITE, PARTICULATE ORGANIC CARBON, PARTICULATE ORGANIC NITROGEN, PHOSPHATE, Potential temperature (theta), SALINITY, SILICATE, WATER TEMPERATURE and pH. The instruments used to collect these data include CTD and bottle. These data were collected by Aida F. RÃos of Bermuda Biological Station for Research and R. Anadon and M. Estrada [affiliation unknown] as part of the CARINA/29HE19951203 data set.

The CARINA (CARbon dioxide IN the Atlantic Ocean) data synthesis project is an international collaborative effort of the EU IP CARBOOCEAN, and U.S. partners. It has produced a merged internally consistent data set of open ocean subsurface measurements for biogeochemical investigations, in particular, studies involving the carbon system. The original focus area was the North Atlantic Ocean, but over time the geographic extent expanded and CARINA now includes data from the entire Atlantic, the Arctic Ocean, and the Southern Ocean.