Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

About this dataset

Context

The dataset tabulates the White Earth 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 White Earth 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 White Earth was 93, a 0% decrease year-by-year from 2022. Previously, in 2022, White Earth population was 93, a decline of 4.12% compared to a population of 97 in 2021. Over the last 20 plus years, between 2000 and 2023, population of White Earth increased by 28. In this period, the peak population was 99 in the year 2020. 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 White Earth is shown in this column.
• Year on Year Change: This column displays the change in White Earth 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 White Earth Population by Year. You can refer the same here

This dataset contains estimates of the number of persons per square kilometer consistent with national censuses and population registers. There is one image for each modeled year. General Documentation The Gridded Population of World Version 4 (GPWv4), Revision 11 models the distribution of global human population for the years 2000, 2005, 2010, 2015, and 2020 on 30 arc-second (approximately 1 km) grid cells. Population is distributed to cells using proportional allocation of population from census and administrative units. Population input data are collected at the most detailed spatial resolution available from the results of the 2010 round of censuses, which occurred between 2005 and 2014. The input data are extrapolated to produce population estimates for each modeled year.

About this dataset

Context

The dataset tabulates the Blue Earth household income by gender. The dataset can be utilized to understand the gender-based income distribution of Blue Earth income.

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).

• Blue Earth, MN annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Blue Earth, MN annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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 Blue Earth income distribution by gender. You can refer the same here

About this dataset

Context

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

Key observations

With a zero Hispanic population, White Earth is 100% Non-Hispanic. Among the Non-Hispanic population, the largest racial group is White alone with a population of 76 (100% of the total Non-Hispanic population).

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

Tiny solid and liquid particles suspended in the atmosphere are called aerosols. Windblown dust, sea salts, volcanic ash, smoke from wildfires, and pollution from factories are all examples of aerosols. Depending upon their size, type, and location, aerosols can either cool the surface, or warm it. They can help clouds to form, or they can inhibit cloud formation. And if inhaled, some aerosols can be harmful to people’s health.

About this dataset

Context

The dataset tabulates the Black Earth town household income by gender. The dataset can be utilized to understand the gender-based income distribution of Black Earth town income.

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).

• Black Earth Town, Wisconsin annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Black Earth Town, Wisconsin annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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 Black Earth town income distribution by gender. You can refer the same here

Tiny solid and liquid particles suspended in the atmosphere are called aerosols. Windblown dust, sea salts, volcanic ash, smoke from wildfires, and pollution from factories are all examples of aerosols. Depending upon their size, type, and location, aerosols can either cool the surface, or warm it. They can help clouds to form, or they can inhibit cloud formation. And if inhaled, some aerosols can be harmful to people’s health.

Notes for the 13.10.2019 update- The months of January 2019 to July 2019 were added to the ERA5 reconstructions- The robustness of the ERA5 reconstruction was improved for a few Greenland and Antarctica mascons by better handling a special case occuring when air temperature is always lower than 0°C during the calibration period.- The updated ERA5 time series might differ from the previous version (especially individual ensemble members). With the exception of the special case mentioned above, these differences are not significant.List of all filesReadme file 00_readme.txtMonthly grids - ensemble means 01_monthly_grids_ensemble_means_allmodels.zipMonthly grids - ensembles, model 1 to 6 02_monthly_grids_ensemble_JPL_MSWEP_1979_2016.zip 02_monthly_grids_ensemble_JPL_GSWP3_1901_2014.zip 02_monthly_grids_ensemble_JPL_ERA5_1979_201907.zip 02_monthly_grids_ensemble_GSFC_MSWEP_1979_2016.zip 02_monthly_grids_ensemble_GSFC_GSWP3_1901_2014.zip 02_monthly_grids_ensemble_GSFC_ERA5_1979_201907.zipDaily grids - ensemble means, model 1 to 6 03_daily_grids_ensemble_means_JPL_MSWEP_1979_2016.zip 03_daily_grids_ensemble_means_JPL_GSWP3_1901_2014.zip 03_daily_grids_ensemble_means_JPL_ERA5_1979_201907.zip 03_daily_grids_ensemble_means_GSFC_MSWEP_1979_2016.zip 03_daily_grids_ensemble_means_GSFC_GSWP3_1901_2014.zip 03_daily_grids_ensemble_means_GSFC_ERA5_1979_201907.zipGlobal averages - daily and monthly time series 04_global_averages_allmodels.zipContent of readmeGRACE TWS Reconstruction (GRACE_REC_v03)The dataset contains reconstructed time series of daily and monthly anomalies of terrestrial water storage (TWS) based on two different GRACE solutions and three different meteorological forcing datasets. There is a total of 6 different models:JPL_MSWEP - trained with GRACE JPL mascons, forced with MSWEP forcing (1979-2016)JPL_GSWP3 - trained with GRACE JPL mascons, forced with GSWP3 forcing (1901-2014)JPL_ERA5 - trained with GRACE JPL mascons, forced with ERA5 forcing (1979-present)GSFC_MSWEP - trained with GRACE GSFC mascons, forced with MSWEP forcing (1979-2016)GSFC_GSWP3 - trained with GRACE GSFC mascons, forced with GSWP3 forcing (1901-2014)GSFC_ERA5 - trained with GRACE GSFC mascons, forced with ERA5 forcing (1979-present)The reconstruction aims at reproducing the sub-decadal climate-driven variability observed in the GRACE data. Seasonal cycle and human impacts on TWS are not reconstructed. A GRACE-based seasonal cycle is provided for convenience. Long-term signals (trends over a period >15 years) are removed during the model calibration procedure but are still present in the final dataset and mainly represent precipitation-driven trends. The interpretation of the reconstructed long-term trends should be done with the awareness that there can be some uncertainty in the reconstructed trends.For most applications, uncertainty ranges can be derived from the 100 ensemble members available for each model.The grids are stored in NetCDFv4 files in units of mm (kg m^-2). Although the data is provided on a 0.5 degrees grid, the effective spatial resolution should be considered to be 3 degrees, similar to the original resolution of the GRACE datasets. This might need to be taken into account when comparing this dataset against other sources.The global means are stored as csv files in units of Gt of water. To convert back to mm of water, use the land area values given in the reference paper below.When using this dataset, please cite:Humphrey, V., & Gudmundsson, L. (2019). GRACE-REC: a reconstruction of climate-driven water storage changes over the last century. Earth System Science Data, 11(3), 1153-1170.Vincent Humphrey, October 2019California Institute of TechnologyYour feedback is always welcome:vincent.humphrey[-a-t-]caltech.edu (vincent.humphrey[-a-t-]bluewin.ch) Abstract

The amount of water stored on continents is an important constraint for water mass and energy exchanges in the Earth system and exhibits large inter-annual variability at both local and continental scales. From 2002 to 2017, the satellites of the Gravity Recovery and Climate Experiment mission (GRACE) have observed changes in terrestrial water storage (TWS) with an unprecedented level of accuracy. In this paper, we use a statistical model trained with GRACE observations to reconstruct past climate-driven changes in TWS from historical and near real time meteorological datasets at daily and monthly scales. Unlike most hydrological models which represent water reservoirs individually (e.g. snow, soil moisture, etc.) and usually provide a single model run, the presented approach directly reconstructs total TWS changes and includes hundreds of ensemble members which can be used to quantify predictive uncertainty. We compare these data-driven TWS estimates with other independent evaluation datasets such as the sea level budget, large-scale water balance from atmospheric reanalysis and in-situ streamflow measurements. We find that the presented approach performs overall as well or better than a set of state-of-the-art global hydrological models (Water Resources Reanalysis version 2). We provide reconstructed TWS anomalies at a spatial resolution of 0.5°, at both daily and monthly scales over the period 1901 to present, based on two different GRACE products and three different meteorological forcing datasets, resulting in 6 reconstructed TWS datasets of 100 ensemble members each. Possible user groups and applications include hydrological modelling and model benchmarking, sea level budget studies, assessments of long-term changes in the frequency of droughts, the analysis of climate signals in geodetic time series and the interpretation of the data gap between the GRACE and the GRACE Follow-On mission.Check reference for additional details and caveats.ReferenceHumphrey, V., & Gudmundsson, L. (2019). GRACE-REC: a reconstruction of climate-driven water storage changes over the last century. Earth System Science Data, 11(3), 1153-1170.

This dataset "Global hotspots of climate related disasters" shows the number of people impacted by climate-related disasters recorded in the EM-DAT database between 2000 and 2020. This dataset was used to prepare the maps and the analysis of the paper Donatti C.I., Nicholas K., Fedele G., Delforge D., Speybroeck N., Moraga P., Blatter J., Below R., Zvoleff A. 2024. Global hotspots of climate-related disasters. International Journal of Disaster Risk Reduction. https://doi.org/10.1016/j.ijdrr.2024.104488. This dataset includes information on people impacted by Drought, tropical cyclones, flash flood, riverine flood, forest fire, land fire, heat wave, landslide and mudslide. Data on coastal flood was not included because the database only had recordings until 2013. Data on disaster sub-types “landslides” and “mudslides” as presented in the EM-DAT were further combined as one single climate-related disaster (“land and mudslides”) for the analyses. Likewise, data on disaster sub-types “forest fire” and “land fire” were further combined as one climate-related disaster (“wildfire”). The data was accessed directly from the EM-DAT database and then summarized as show in the dataset. We used this database, downloaded on June 2nd 2021, to access data on “total affected” people and the “total deaths” per disaster event impacting a country (i.e., an entry in the EM-DAT), which were combined in this study to create the variable “total people impacted”. In the EM-DAT database, “total affected” represents the sum of people “injured,” “affected,” and “homeless” resulting from a particular event. “Injured” were considered those that have suffered from physical injuries, trauma, or an illness requiring immediate medical assistance, including people hospitalized, as a direct result of a disaster, “affected” were considered people requiring immediate assistance during an emergency and “homeless” were considered those whose homes were destroyed or heavily damaged and therefore needed shelter after an event. “Total deaths” include people that have died or were considered missing, those whose whereabouts since the disaster were unknown and presumed dead based on official figures. More details can be found under “documentation, data structure and content description” at emdat.be. In the dataset, "ADM-CODE" refers to the code used to identify each administrative area, which refers to the code of FAO's Global Administrative Unit Layer, GAUL.

DGGS Geologic Earth Resource Library of Alaska (GERILA) Database, Digital Data Series 22, is the enterprise database back-end to the Alaska Division of Geological & Geophysical Survey's (DGGS) website and enterprise data repository. GERILA serves as an index to geologic information that supports State of Alaska statutes relating to the potential of Alaskan land for the production of metals, minerals, fuels, and geothermal resources, the locations and supplies of groundwater and construction material (Sec. 41.08.010), and the potential geologic hazards to buildings, roads, bridges, and other installations and structures and systematic collection, recording, evaluation, and distribution of hydrological and seismic hazard data declared to be of public interest (Sec. 41.08.017). Much of the information stored in GERILA is viewable through DGGS's public website, which provides search interfaces for specific data modules, including our geologic publications catalog (https://dggs.alaska.gov/pubs). The database is actively updated as new information becomes available or published. Consequently, products developed from the database may change over time as information and data are updated. DGGS encourages public members to contact DGGS's Geologic Information Center staff (dggspubs@alaska.gov) to discuss potential changes to the data or resolve errors in our derivative products. See the DGGS citation page for the preferred citation and additional information (http://doi.org/10.14509/31119).

Open-access database of englacial temperature measurements compiled from data submissions and published literature. It is developed on GitHub and published to Zenodo. The dataset is described in the following publication:

Mylène Jacquemart, Ethan Welty, Marcus Gastaldello, and Guillem Carcanade (2025). glenglat: A database of global englacial temperatures. Earth System Science Data Discussions. https://doi.org/10.5194/essd-2024-249

Dataset structure

The dataset adheres to the Frictionless Data Tabular Data Package specification. The metadata in datapackage.json describes, in detail, the contents of the tabular data files in the data folder:

source.csv: Description of each data source (either a personal communication or the reference to a published study).

borehole.csv: Description of each borehole (location, elevation, etc), linked to source.csv via source_id and less formally via source identifiers in notes.

profile.csv: Description of each profile (date, etc), linked to borehole.csv via borehole_id and to source.csv via source_id and less formally via source identifiers in notes.

measurement.csv: Description of each measurement (depth and temperature), linked to profile.csv via borehole_id and profile_id.

For boreholes with many profiles (e.g. from automated loggers), pairs of profile.csv and measurement.csv are stored separately in subfolders of data named {source.id}-{glacier}, where glacier is a simplified and kebab-cased version of the glacier name (e.g. flowers2022-little-kluane).

Supporting information

The folder sources, available on GitHub but omitted from dataset releases on Zenodo, contains subfolders (with names matching column source.id) with files that document how and from where the data was extracted.

Tables

Jump to: source · borehole · profile · measurement

source

Sources of information considered in the compilation of this database. Column names and categorical values closely follow the Citation Style Language (CSL) 1.0.2 specification. Names of people in non-Latin scripts are followed by a latinization in square brackets (e.g. В. С. Загороднов [V. S. Zagorodnov]) and non-English titles are followed by a translation in square brackets. The family name of Latin-script names is wrapped in curly braces when it is not the last word of the name (e.g. Emmanuel {Le Meur}, e.g. {Duan} Keqin) or the name ends in two or more unabbreviated words (e.g. Jon Ove {Hagen}). The family name of a Chinese name (and of the latinization) is wrapped in curly braces when it is not the first character.

name type description

id (required) string Unique identifier constructed from the first author's lowercase, latinized, family name and the publication year, followed as needed by a lowercase letter to ensure uniqueness (e.g. Загороднов 1981 → zagorodnov1981a).

author string Author names (optionally followed by their ORCID or contact email in parentheses) as a pipe-delimited list.

year (required) year Year of publication.

type (required) string Item type.- article-journal: Journal article- book: Book (if the entire book is relevant)- chapter: Book section- document: Document not fitting into any other category- dataset: Collection of data- map: Geographic map- paper-conference: Paper published in conference proceedings- personal-communication: Personal communication between individuals- speech: Presentation (talk, poster) at a conference- report: Report distributed by an institution- thesis-phd: Doctor of Philosophy (PhD) thesis- thesis-msc: Master of Science (MSc) thesis- webpage: Website or page on a website

title (required) string Item title.

url string URL (DOI if available).

language (required) string Language as ISO 639-1 two-letter language code.- da: Danish- de: German- en: English- es: Spanish- fr: French- ja: Japanese- ko: Korean- ru: Russian- sv: Swedish- zh: Chinese

container_title string Title of the container (e.g. journal, book).

volume integer Volume number of the item or container.

issue string Issue number (e.g. 1) or range (e.g. 1-2) of the item or container, with an optional letter prefix (e.g. F1) or part number (e.g. 75pt2).

page string Page number (e.g. 1) or range (e.g. 1-2) of the item in the container, with an optional letter prefix (e.g. S1).

version string Version number (e.g. 1.0) of the item.

editor string Editor names (e.g. of the containing book) as a pipe-delimited list.

collection_title string Title of the collection (e.g. book series).

collection_number string Number (e.g. 1) or range (e.g. 1-2) in the collection (e.g. book series volume).

publisher string Publisher name.

borehole

Metadata about each borehole.

name type description

id (required) integer Unique identifier.

source_id (required) string Identifier of the source of the earliest temperature measurements. This is also the source of the borehole attributes unless otherwise stated in notes.

glacier_name (required) string Glacier or ice cap name (as reported).

glims_id string Global Land Ice Measurements from Space (GLIMS) glacier identifier.

location_origin (required) string Origin of location (latitude, longitude).- submitted: Provided in data submission- published: Reported as coordinates in original publication- digitized: Digitized from published map with complete axes- estimated: Estimated from published plot by comparing to a map (e.g. Google Maps, CalTopo)- guessed: Estimated with difficulty, for example by comparing elevation to a map (e.g. Google Maps, CalTopo)

latitude (required) number [degree] Latitude (EPSG 4326).

longitude (required) number [degree] Longitude (EPSG 4326).

elevation_origin (required) string Origin of elevation (elevation).- submitted: Provided in data submission- published: Reported as number in original publication- digitized: Digitized from published plot with complete axes- estimated: Estimated from elevation contours in published map- guessed: Estimated with difficulty, for example by comparing location (latitude, longitude) to a map of contemporary elevations (e.g. CalTopo, Google Maps)

elevation (required) number [m] Elevation above sea level.

mass_balance_area string Mass balance area.- ablation: Ablation area- equilibrium: Near the equilibrium line- accumulation: Accumulation area

label string Borehole name (e.g. as labeled on a plot).

date_min date (%Y-%m-%d) Begin date of drilling, or if not known precisely, the first possible date (e.g. 2019 → 2019-01-01).

date_max date (%Y-%m-%d) End date of drilling, or if not known precisely, the last possible date (e.g. 2019 → 2019-12-31).

drill_method string Drilling method.- mechanical: Push, percussion, rotary- thermal: Hot point, electrothermal, steam- combined: Mechanical and thermal

ice_depth number [m] Starting depth of continuous ice. Infinity (INF) indicates that only snow, firn, or intermittent ice was reached.

depth number [m] Total borehole depth (not including drilling in the underlying bed).

to_bed boolean Whether the borehole reached the glacier bed.

temperature_uncertainty number [°C] Estimated temperature uncertainty (as reported).

notes string Additional remarks about the study site, the borehole, or the measurements therein as a pipe-delimited list. Sources are referenced by source.id. Quality concerns are prefixed with '[flag]'.

curator string Names of people who added the data to the database, as a pipe-delimited list.

investigators string Names of people and/or agencies who performed the work, as a pipe-delimited list. Each entry is in the format 'person (agency; ...) {notes}', where only person or one agency is required. Person and agency may contain a latinized form in square brackets.

funding string Funding sources as a pipe-delimited list. Each entry is in the format 'funder [rorid] > award [number] url', where only funder is required and rorid is the funder's ROR (https://ror.org) ID (e.g. 01jtrvx49).

profile

Date and time of each measurement profile.

name type description

borehole_id (required) integer Borehole identifier.

id (required) integer Borehole profile identifier (starting from 1 for each borehole).

source_id (required) string Source identifier.

measurement_origin (required) string Origin of measurements (measurement.depth, measurement.temperature).- submitted: Provided as numbers in data submission- published: Numbers read from original publication- digitized-discrete: Digitized with Plot Digitizer from discrete points of depth versus temperature- digitized-continuous: Digitized with Plot Digitizer from a continuous data source (e.g. line plot of depth versus temperature)

date_min date (%Y-%m-%d) Measurement date, or if not known precisely, the first possible date (e.g. 2019 → 2019-01-01).

date_max (required) date (%Y-%m-%d) Measurement date, or if not known precisely, the last possible date (e.g. 2019 → 2019-12-31).

time time (%H:%M:%S) Measurement time.

utc_offset number [h] Time offset relative to Coordinated Universal Time (UTC).

equilibrium string Whether and how reported temperatures equilibrated following drilling.- true: Equilibrium was measured- estimated: Equilibrium was estimated (typically by extrapolation)- false: Equilibrium was not reached

notes string Additional remarks about the profile or the measurements therein as a pipe-delimited list. Sources are referenced by source.id. Quality concerns are prefixed with '[flag]'.

measurement

Temperature measurements with depth.

name type description

borehole_id (required) integer Borehole identifier.

profile_id (required) integer Borehole profile identifier.

depth (required) number [m] Depth below the glacier surface.

temperature (required) number [°C] Temperature.

Harsh environmental conditions including microgravity and radiation during prolonged spaceflights are known to alter hepatic metabolism. Our studies have focused on the analysis of possible changes in metabolic pathways in livers of mice which experienced 30 days of spaceflight with and without an additional re-adaption period of 7 days compared to control mice on Earth. Utilizing shotgun mass spectrometry and label-free quantification we performed proteomic profiling to investigate mice livers from the spaceflight project xe2 x80 x9cBion-M 1 xe2 x80 x9d. No significant alterations in protein levels were observed between control mice liver and spaceflight mice which is possibly caused by insufficient fold change detection combined with high variances within the groups. In contrast our results show that more than a third of the quantified protein levels are altered comparing the liver proteome of mice with and without re-adaption time after their spaceflight. Proteins related to amino acid metabolism showed higher levels after re-adaption which may indicate higher rates of gluconeogenesis. Members of the peroxisome proliferator-activated receptor pathway reconstitute their level after 7 days due to a decrease in fold change which indicates decreased signs of non-alcoholic fatty liver disease. Moreover bile acid secretion regenerates on Earth due to reconstitution of related transmembrane proteins and elevated levels of the drug-metabolising enzymes belonging to the CYP superfamily decrease 7 days after the spaceflight. Thus our study demonstrates reconstitution of pharmacological response and early signs of non-alcoholic fatty liver disease recover within 7 days whereas glucose uptake should be monitored due to alterations in gluconeogenesis.

Weather and climate variability strongly influence the people, infrastructure and economy of Alaska. However, the sparse observational network in Alaska limits understanding of meteorological variability, particularly precipitation processes that influence the hydrologic cycle. Here a new 4-km resolution dataset for Alaska is made available to the community for investigating recent historical climate variability, and potential changes in the future. The dataset, generated with the Weather Research and Forecasting model, is useful for gaining insight into meteorological and hydrologic processes, and provides a baseline against which to measure future environmental change. The dataset may be particularly useful for applications that require high temporal frequency weather fields, such as driving hydrologic or glacier models. The historical fields, which span 14 years (September 2002-August 2016), are available to the community at hourly resolution for near surface (2-dimensional) fields and 6 hourly for the 3-dimensional atmosphere.

Geospatial Services Land management within the US Forest Service and on the 900,000+ acre Monongahela National Forest (NF) is driven by a wide mix of resource and societal demands that prove a challenge in fulfilling the Forest Service’s mission of “Caring for the Land and Serving the People.” Programmatically, the 2006 Land and Resource Management Plan guide natural resource management activities on lands administered by the Monongahela National Forest. The Forest Plan describes management direction and practices, resource protection methods and monitoring, desired resource conditions, and the availability and suitability of lands for resource management. Technology enables staff to address these land management issues and Forest Plan direction by using a science-based approach to facilitate effective decisions. Monongahela NF geospatial services, using enabling-technologies, incorporate key tools such as Environmental Systems Research Institute’s ArcGIS desktop suite and Trimble’s global positioning system (GPS) units to meet program and Forest needs. Geospatial Datasets The Forest has a broad set of geospatial datasets that capture geographic features across the eastern West Virginia landscape. Many of these datasets are available to the public through our download site. Selected geospatial data that encompass the Monongahela National Forest are available for download from this page. A link to the FGDC-compliant metadata is provided for each dataset. All data are in zipped format (or available from the specified source), in one of two spatial data formats, and in the following coordinate system: Coordinate System: Universal Transverse Mercator Zone: 17 Units: Meters Datum: NAD 1983 Spheroid: GRS 1980 Map files – All map files are in pdf format. These maps illustrate the correlated geospatial data. All maps are under 1 MB unless otherwise noted. Metadata file – This FGDC-compliant metadata file contains information pertaining to the specific geospatial dataset. Shapefile – This downloadable zipped file is in ESRI’s shapefile format. KML file – This downloadable zipped file is in Google Earth’s KML format. Resources in this dataset:Resource Title: Monongahela National Forest Geospatial Data. File Name: Web Page, url: https://www.fs.usda.gov/detail/mnf/landmanagement/gis/?cid=stelprdb5108081 Selected geospatial data that encompass the Monongahela National Forest are available for download from this page.

TROPIS is the acronym for the Tree Growth and Permanent Plot Information System sponsored by CIFOR to promote more effective use of existing data and knowledge about tree growth.

    TROPIS is concerned primarily with information about permanent plots and tree
    growth in both planted and natural forests throughout the world. It has five
    components:

    - a network of people willing to share permanent plot data and tree
    growth information;
    - an index to people and institutions with permanent plots;
    - a database management system to promote more efficient data management;
    - a method to find comparable sites elsewhere, so that observations
    can be supplemented or contrasted with other data; and
    - an inference system to allow growth estimates to be made in the
    absence of empirical data.
    - TROPIS is about people and information. The core of TROPIS is an
    index to people and their plots maintained in a relational
    database. The database is designed to fulfil two primary needs:
    - to provide for efficient cross-checking, error-checking and
    updating; and to facilitate searches for plots matching a wide range
    of specified criteria, including (but not limited to) location, forest
    type, taxa, plot area, measurement history.

    The database is essentially hierarchical: the key element of the
    database is the informant. Each informant may contribute information
    on many plot series, each of which has consistent objectives. In turn,
    each series may comprise many plots, each of which may have a
    different location or different size. Each plot may contain many
    species. A series may be a thinning or spacing experiment, some
    species or provenance trials, a continuous forest inventory system, or
    any other aggregation of plots convenient to the informant. Plots need
    not be current. Abandoned plots may be included provided that the
    location is known and the plot data remain accessible. In addition to
    details of the informant, we try to record details of additional
    contact people associated with plots, to maintain continuity when
    people transfer or retire. Thus the relational structure may appear
    complex, but ensures data integrity.

    At present, searches are possible only via mail, fax or email requests
    to the TROPIS co-ordinator at CIFOR. Self-service on-line searching
    will also be available in 1997. Clients may search for plots with
    specified taxa, locations, silvicultural treatment, or other specified
    criteria and combinations. TROPIS currently contains references to
    over 10,000 plots with over 2,000 species contributed by 100
    individuals world-wide.

    This database will help CIFOR as well as other users to make more
    efficient use of existing information, and to develop appropriate and
    effective techniques and policies for sustainable forest management
    world-wide.

    TROPIS is supported by the Government of Japan.

    This information is from the CIFOR web site.

About this dataset

Context

The dataset tabulates the Lincoln township household income by gender. The dataset can be utilized to understand the gender-based income distribution of Lincoln township income.

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).

• Lincoln township, Blue Earth County, Minnesota annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Lincoln township, Blue Earth County, Minnesota annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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 Lincoln township income distribution by gender. You can refer the same here

These data include all datasets published for 'CMIP6.CMIP.EC-Earth-Consortium.EC-Earth3-CC.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The EC-Earth3-CC climate model, released in 2019, includes the following components: atmos: IFS cy36r4 (TL255, linearly reduced Gaussian grid equivalent to 512 x 256 longitude/latitude; 91 levels; top level 0.01 hPa), atmosChem: TM5 (3 x 2 degrees; 120 x 90 longitude/latitude; 34 levels; top level: 0.1 hPa), land: HTESSEL (land surface scheme built in IFS) and LPJ-GUESS v4, ocean: NEMO3.6 (ORCA1 tripolar primarily 1 degree with meridional refinement down to 1/3 degree in the tropics; 362 x 292 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: PISCES v2, seaIce: LIM3. The model was run by the AEMET, Spain; BSC, Spain; CNR-ISAC, Italy; DMI, Denmark; ENEA, Italy; FMI, Finland; Geomar, Germany; ICHEC, Ireland; ICTP, Italy; IDL, Portugal; IMAU, The Netherlands; IPMA, Portugal; KIT, Karlsruhe, Germany; KNMI, The Netherlands; Lund University, Sweden; Met Eireann, Ireland; NLeSC, The Netherlands; NTNU, Norway; Oxford University, UK; surfSARA, The Netherlands; SMHI, Sweden; Stockholm University, Sweden; Unite ASTR, Belgium; University College Dublin, Ireland; University of Bergen, Norway; University of Copenhagen, Denmark; University of Helsinki, Finland; University of Santiago de Compostela, Spain; Uppsala University, Sweden; Utrecht University, The Netherlands; Vrije Universiteit Amsterdam, the Netherlands; Wageningen University, The Netherlands. Mailing address: EC-Earth consortium, Rossby Center, Swedish Meteorological and Hydrological Institute/SMHI, SE-601 76 Norrkoping, Sweden (EC-Earth-Consortium) in native nominal resolutions: atmos: 100 km, atmosChem: 250 km, land: 100 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

Individuals using the data must abide by terms of use for CMIP6 data (https://pcmdi.llnl.gov/CMIP6/TermsOfUse). The original license restrictions on these datasets were recorded as global attributes in the data files, but these may have been subsequently updated.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Black Earth. The dataset can be utilized to gain insights into gender-based income distribution within the Black Earth population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Black Earth, among individuals aged 15 years and older with income, there were 677 men and 591 women in the workforce. Among them, 290 men were engaged in full-time, year-round employment, while 233 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 5.52% fell within the income range of under $24,999, while 3.86% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 15.86% of men in full-time roles earned incomes exceeding $100,000, while 13.30% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 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 $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Black Earth median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Earth. The dataset can be utilized to gain insights into gender-based income distribution within the Earth population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Earth, among individuals aged 15 years and older with income, there were 380 men and 236 women in the workforce. Among them, 217 men were engaged in full-time, year-round employment, while 79 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 8.29% fell within the income range of under $24,999, while 34.18% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 23.96% of men in full-time roles earned incomes exceeding $100,000, while none of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 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 $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Earth median household income by race. You can refer the same here