57 datasets found
  1. N

    Germany Township, Pennsylvania Age Group Population Dataset: A Complete...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
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    Neilsberg Research (2025). Germany Township, Pennsylvania Age Group Population Dataset: A Complete Breakdown of Germany township Age Demographics from 0 to 85 Years and Over, Distributed Across 18 Age Groups // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/45255ff3-f122-11ef-8c1b-3860777c1fe6/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Feb 22, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Pennsylvania, Germany Township
    Variables measured
    Population Under 5 Years, Population over 85 years, Population Between 5 and 9 years, Population Between 10 and 14 years, Population Between 15 and 19 years, Population Between 20 and 24 years, Population Between 25 and 29 years, Population Between 30 and 34 years, Population Between 35 and 39 years, Population Between 40 and 44 years, and 9 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age groups. For age groups we divided it into roughly a 5 year bucket for ages between 0 and 85. For over 85, we aggregated data into a single group for all ages. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the Germany township population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Germany township. The dataset can be utilized to understand the population distribution of Germany township by age. For example, using this dataset, we can identify the largest age group in Germany township.

    Key observations

    The largest age group in Germany Township, Pennsylvania was for the group of age 50 to 54 years years with a population of 313 (10.95%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Germany Township, Pennsylvania was the 80 to 84 years years with a population of 60 (2.10%). 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

    Variables / Data Columns

    • Age Group: This column displays the age group in consideration
    • Population: The population for the specific age group in the Germany township is shown in this column.
    • % of Total Population: This column displays the population of each age group as a proportion of Germany township 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 Germany township Population by Age. You can refer the same here

  2. Urban Green Raster Germany 2018

    • zenodo.org
    • explore.openaire.eu
    • +1more
    zip
    Updated Feb 28, 2022
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    Tobias Krüger; Tobias Krüger; Lisa Eichler; Lisa Eichler; Gotthard Meinel; Gotthard Meinel; Julia Tenikl; Hannes Taubenböck; Hannes Taubenböck; Michael Wurm; Michael Wurm; Julia Tenikl (2022). Urban Green Raster Germany 2018 [Dataset]. http://doi.org/10.26084/ioerfdz-r10-urbgrn2018
    Explore at:
    zipAvailable download formats
    Dataset updated
    Feb 28, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Tobias Krüger; Tobias Krüger; Lisa Eichler; Lisa Eichler; Gotthard Meinel; Gotthard Meinel; Julia Tenikl; Hannes Taubenböck; Hannes Taubenböck; Michael Wurm; Michael Wurm; Julia Tenikl
    Area covered
    Germany
    Description

    Abstract

    The Urban Green Raster Germany is a land cover classification for Germany that addresses in particular the urban vegetation areas. The raster dataset covers the terrestrial national territory of Germany and has a spatial resolution of 10 meters. The dataset is based on a fully automated classification of Sentinel-2 satellite data from a full 2018 vegetation period using reference data from the European LUCAS land use and land cover point dataset.
    The dataset identifies eight land cover classes. These include Built-up, Built-up with significant green share, Coniferous wood, Deciduous wood, Herbaceous vegetation (low perennial vegetation), Water, Open soil, Arable land (low seasonal vegetation).
    The land cover dataset provided here is offered as an integer raster in GeoTiff format. The assignment of the number coding to the corresponding land cover class is explained in the legend file.

    Data acquisition

    The data acquisition comprises two main processing steps: (1) Collection, processing, and automated classification of the multispectral Sentinel 2 satellite data with the “Land Cover DE method”, resulting in the raw land cover classification dataset, NDVI layer, and RF assignment frequency vector raster. (2) GIS-based postprocessing including discrimination of (densely) built-up and loosely built-up pixels according NDVI threshold, and creating water-body and arable-land masks from geo-topographical base-data (ATKIS Basic DLM) and reclassification of water and arable land pixels based on the assignment frequency.

    Data collection

    Satellite data were searched and downloaded from the Copernicus Open Access Hub (https://scihub.copernicus.eu/).

    The LUCAS reference and validation points were loaded from the Eurostat platform (https://ec.europa.eu/eurostat/web/lucas/data/database).

    The processing of the satellite data was performed at the DLR data center in Oberpfaffenhofen.

    GIS-based post-processing of the automatic classification result was performed at IOER in Dresden.

    Value of the data

    The dataset can be used to quantify the amount of green areas within cities on a homogeneous data base [5].

    Thus it is possible to compare cities of different sizes regarding their greenery and with respect to their ratio of green and built-up areas [6].

    Built-up areas within cities can be discriminated regarding their built-up density (dense built-up vs. built-up with higher green share).

    Data description

    A Raster dataset in GeoTIFF format: The dataset is stored as an 8 bit integer raster with values ranging from 1 to 8 for the eight different land cover classes. The nomenclature of the coded values is as follows: 1 = Built-up, 2=open soil; 3=Coniferous wood, 4= Deciduous wood, 5=Arable land (low seasonal vegetation), 6=Herbaceous vegetation (low perennial vegetation), 7=Water, 8=Built-up with significant green share. Name of the file ugr2018_germany.tif. The dataset is zipped alongside with accompanying files: *.twf (geo-referencing world-file), *.ovr (Overlay file for quick data preview in GIS), *.clr (Color map file).

    A text file with the integer value assignment of the land cover classes. Name of the file: Legend_LC-classes.txt.

    Experimental design, materials and methods

    The first essential step to create the dataset is the automatic classification of a satellite image mosaic of all available Sentinel-2 images from May to September 2018 with a maximum cloud cover of 60 percent. Points from the 2018 LUCAS (Land use and land cover survey) dataset from Eurostat [1] were used as reference and validation data. Using Random Forest (RF) classifier [2], seven land use classes (Deciduous wood, Coniferous wood, Herbaceous vegetation (low perennial vegetation), Built-up, Open soil, Water, Arable land (low seasonal vegetation)) were first derived, which is methodologically in line with the procedure used to create the dataset "Land Cover DE - Sentinel-2 - Germany, 2015" [3]. The overall accuracy of the data is 93 % [4].

    Two downstream post-processing steps served to further qualify the product. The first step included the selective verification of pixels of the classes arable land and water. These are often misidentified by the classifier due to radiometric similarities with other land covers; in particular, radiometric signatures of water surfaces often resemble shadows or asphalt surfaces. Due to the heterogeneous inner-city structures, pixels are also frequently misclassified as cropland.

    To mitigate these errors, all pixels classified as water and arable land were matched with another data source. This consisted of binary land cover masks for these two land cover classes originating from the Monitor of Settlement and Open Space Development (IOER Monitor). For all water and cropland pixels that were outside of their respective masks, the frequencies of class assignments from the RF classifier were checked. If the assignment frequency to water or arable land was at least twice that to the subsequent class, the classification was preserved. Otherwise, the classification strength was considered too weak and the pixel was recoded to the land cover with the second largest assignment frequency.

    Furthermore, an additional land cover class "Built-up with significant vegetation share" was introduced. For this purpose, all pixels of the Built-up class were intersected with the NDVI of the satellite image mosaic and assigned to the new category if an NDVI threshold was exceeded in the pixel. The associated NDVI threshold was previously determined using highest resolution reference data of urban green structures in the cities of Dresden, Leipzig and Potsdam, which were first used to determine the true green fractions within the 10m Sentinel pixels, and based on this to determine an NDVI value that could be used as an indicator of a significant green fraction within the built-up pixel. However, due to the wide dispersion of green fraction values within the built-up areas, it is not possible to establish a universally valid green percentage value for the land cover class of Built-up with significant vegetation share. Thus, the class essentially serves to the visual differentiability of densely and loosely (i.e., vegetation-dominated) built-up areas.

    Acknowledgments

    This work was supported by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR) [10.06.03.18.101].The provided data has been developed and created in the framework of the research project “Wie grün sind bundesdeutsche Städte?- Fernerkundliche Erfassung und stadträumlich-funktionale Differenzierung der Grünausstattung von Städten in Deutschland (Erfassung der urbanen Grünausstattung)“ (How green are German cities?- Remote sensing and urban-functional differentiation of the green infrastructure of cities in Germany (Urban Green Infrastructure Inventory)). Further persons involved in the project were: Fabian Dosch (funding administrator at BBSR), Stefan Fina (research partner, group leader at ILS Dortmund), Annett Frick, Kathrin Wagner (research partners at LUP Potsdam).

    References

    [1] Eurostat (2021): Land cover / land use statistics database LUCAS. URL: https://ec.europa.eu/eurostat/web/lucas/data/database

    [2] L. Breiman (2001). Random forests, Mach. Learn., 45, pp. 5-32

    [3] M. Weigand, M. Wurm (2020). Land Cover DE - Sentinel-2—Germany, 2015 [Data set]. German Aerospace Center (DLR). doi: 10.15489/1CCMLAP3MN39

    [4] M. Weigand, J. Staab, M. Wurm, H. Taubenböck, (2020). Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data. Int J Appl Earth Obs, 88, 102065. doi: https://doi.org/10.1016/j.jag.2020.102065

    [5] L. Eichler., T. Krüger, G. Meinel, G. (2020). Wie grün sind deutsche Städte? Indikatorgestützte fernerkundliche Erfassung des Stadtgrüns. AGIT Symposium 2020, 6, 306–315. doi: 10.14627/537698030

    [6] H. Taubenböck, M. Reiter, F. Dosch, T. Leichtle, M. Weigand, M. Wurm (2021). Which city is the greenest? A multi-dimensional deconstruction of city rankings. Comput Environ Urban Syst, 89, 101687. doi: 10.1016/j.compenvurbsys.2021.101687

  3. Germany Data Center Rack Market Size & Share Analysis - Industry Research...

    • mordorintelligence.com
    pdf,excel,csv,ppt
    Updated Jun 21, 2025
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    Mordor Intelligence (2025). Germany Data Center Rack Market Size & Share Analysis - Industry Research Report - Growth Trends 2030 [Dataset]. https://www.mordorintelligence.com/industry-reports/germany-data-center-rack-market
    Explore at:
    pdf,excel,csv,pptAvailable download formats
    Dataset updated
    Jun 21, 2025
    Dataset authored and provided by
    Mordor Intelligence
    License

    https://www.mordorintelligence.com/privacy-policyhttps://www.mordorintelligence.com/privacy-policy

    Time period covered
    2019 - 2030
    Area covered
    Germany
    Description

    Germany's Data Center Rack Market is Segmented by Rack Size (Quarter Rack, Half Rack, Full Rack), by Rack Height (42U, 45U, 48U, Other Heights (≥52U and Custom), Rack Type (Cabinet (Closed) Racks, Open-Frame Racks, Wall-Mount Rack), Data Center Type (Colocation Facilities and More), Material (Steel and More). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Segments.

  4. German Sign Language (DGS) Alphabet

    • kaggle.com
    Updated Jan 20, 2022
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    Moritz Kronberger (2022). German Sign Language (DGS) Alphabet [Dataset]. https://www.kaggle.com/datasets/moritzkronberger/german-sign-language
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 20, 2022
    Dataset provided by
    Kaggle
    Authors
    Moritz Kronberger
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    This dataset was created to train a neural network for real time sign detection, which would be used as automated feedback for a learning application. The dataset ist based on the normalized hand landmark vectors provided by mediapipe's handpose in order to make the trained NN invariant to lighting situations or skin colors, which could not be represented in a diverse enough fashion in the dataset.

    The dataset is therefore designed to train a NN which categorizes the MULTI_HAND_LANDMARK output of the handpose solution.

    Content

    The dataset contains 64 columns with the first column being the sample's label. All static signs (meaning signs not involving movement) of the German Sign language alphabet are represented as 24 classes ('a'-'y', excluding 'j').

    All other columns represent the 21 linearized, three-dimensional hand landmarks provided by handpose in their normalized ([0.0, 1.0]) state.

    In total the dataset contains ca. 7300 samples with at least 250 samples per class, recorded by 7 different non-native signers.

    The dataset is purely made up of recorded samples and does not make use of data augmentation.

    Acknowledgements

    This dataset was inspired by the desire to create a German version of the Sign Language MNIST dataset with a stronger focus on practical applicability.

    Inspiration

    Our team is interested in providing a foundation for all kinds of practical applications involving sign language recognition. As with our own work, we appreciate a focus on applications challenging non-signers to engage with sign language in a way that promotes inclusion.

    Ethical considerations

    We are aware of the ethical implications of such a dataset and encourage developers to seriously consider research on the ethics of machine learning and sign language to avoid harmful outcomes of well intended projects. For more information on this topic we recommend Bragg, D., Caselli, N., Hochgesang, J. A., Huenerfauth, M., Katz-Hernandez, L., Koller, O., Kushalnagar, R., Vogler, C., & Ladner, R. E. (2021). The FATE Landscape of Sign Language AI Datasets: An Interdisciplinary Perspective. In ACM Transactions on Accessible Computing (14th ed., Vol. 2, pp. 1-45). Association for Computing Machinery. 10.1145/3436996 as a starting point.

  5. h

    Gucci.Product.prices.Germany

    • huggingface.co
    Updated Nov 17, 2023
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    Data Boutique (2023). Gucci.Product.prices.Germany [Dataset]. https://huggingface.co/datasets/DBQ/Gucci.Product.prices.Germany
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 17, 2023
    Dataset authored and provided by
    Data Boutique
    License

    https://choosealicense.com/licenses/unknown/https://choosealicense.com/licenses/unknown/

    Area covered
    Germany
    Description

    Gucci web scraped data

      About the website
    

    The luxury fashion industry in the EMEA region, especially in Germany, is significantly advanced and sophisticated, thanks to the embracing of digital technologies. Gucci, a prominent brand in this industry, has successfully penetrated the German market. The recently observed dataset is a collection of Ecommerce product-list page (PLP) data specifically on Guccis operations in Germany. The initiative is to study and understand the… See the full description on the dataset page: https://huggingface.co/datasets/DBQ/Gucci.Product.prices.Germany.

  6. N

    North Germany Township, Minnesota Age Group Population Dataset: A Complete...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
    + more versions
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    Neilsberg Research (2025). North Germany Township, Minnesota Age Group Population Dataset: A Complete Breakdown of North Germany township Age Demographics from 0 to 85 Years and Over, Distributed Across 18 Age Groups // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/453b0b56-f122-11ef-8c1b-3860777c1fe6/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 22, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Minnesota, North Germany Township
    Variables measured
    Population Under 5 Years, Population over 85 years, Population Between 5 and 9 years, Population Between 10 and 14 years, Population Between 15 and 19 years, Population Between 20 and 24 years, Population Between 25 and 29 years, Population Between 30 and 34 years, Population Between 35 and 39 years, Population Between 40 and 44 years, and 9 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age groups. For age groups we divided it into roughly a 5 year bucket for ages between 0 and 85. For over 85, we aggregated data into a single group for all ages. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the North Germany township population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for North Germany township. The dataset can be utilized to understand the population distribution of North Germany township by age. For example, using this dataset, we can identify the largest age group in North Germany township.

    Key observations

    The largest age group in North Germany Township, Minnesota was for the group of age 10 to 14 years years with a population of 45 (15.46%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in North Germany Township, Minnesota was the 35 to 39 years years with a population of 3 (1.03%). 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

    Variables / Data Columns

    • Age Group: This column displays the age group in consideration
    • Population: The population for the specific age group in the North Germany township is shown in this column.
    • % of Total Population: This column displays the population of each age group as a proportion of North Germany township 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 North Germany township Population by Age. You can refer the same here

  7. p

    Lockdown data-V6.0.csv

    • psycharchives.org
    Updated Jun 4, 2020
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    (2020). Lockdown data-V6.0.csv [Dataset]. https://www.psycharchives.org/en/item/8a0c3db3-d4bf-46dd-8ffc-557430d45ddd
    Explore at:
    Dataset updated
    Jun 4, 2020
    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Description

    The outbreak of the COVID-19 pandemic has prompted the German government and the 16 German federal states to announce a variety of public health measures in order to suppress the spread of the coronavirus. These non-pharmaceutical measures intended to curb transmission rates by increasing social distancing (i.e., diminishing interpersonal contacts) which restricts a range of individual behaviors. These measures span moderate recommendations such as physical distancing, up to the closures of shops and bans of gatherings and demonstrations. The implementation of these measures are not only a research goal for themselves but have implications for behavioral research conducted in this time (e.g., in form of potential confounder biases). Hence, longitudinal data that represent the measures can be a fruitful data source. The presented data set contains data on 14 governmental measures across the 16 German federal states. In comparison to existing datasets, the data set at hand is a fine-grained daily time series tracking the effective calendar date, introduction, extension, or phase-out of each respective measure. Based on self-regulation theory, measures were coded whether they did not restrict, partially restricted or fully restricted the respective behavioral pattern. The time frame comprises March 08, 2020 until May 15, 2020. The project is an open-source, ongoing project with planned continued updates in regular (approximately monthly) intervals. New variables include restrictions on travel and gastronomy. The variable trvl (travel) comprises the following categories: fully restricted (=2) reflecting a potential general ban to travel within Germany (except for sound reasons like health or business); partially restricted (=1): travels are allowed but may be restricted through prohibition of accommodation or entry ban for certain groups (e.g. people from risk areas); free (=0): no travel and accommodation restrictions in place). The variable gastr (gastronomy) comprises: fully restricted (=2): closure of restaurants or bars; partially restricted (=1): Only take-away or food delivery services are allowed; free (=0): restaurants are allowed to open without restrictions). Further, the variables msk (recommendations to wear a mask) and zoo (restrictions of zoo visits) have been adjusted.:

  8. Remote sensing derived onshore wind turbine locations for Germany

    • zenodo.org
    bin, pdf, txt
    Updated Jul 10, 2025
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    Claudius Wehner; Johannes Albert; Johannes Albert; Jan Siegismund; Johannes Zschache; Sebastian Röhl; Philipp Gärtner; Philipp Gärtner; Claudius Wehner; Jan Siegismund; Johannes Zschache; Sebastian Röhl (2025). Remote sensing derived onshore wind turbine locations for Germany [Dataset]. http://doi.org/10.5281/zenodo.15835057
    Explore at:
    bin, pdf, txtAvailable download formats
    Dataset updated
    Jul 10, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Claudius Wehner; Johannes Albert; Johannes Albert; Jan Siegismund; Johannes Zschache; Sebastian Röhl; Philipp Gärtner; Philipp Gärtner; Claudius Wehner; Jan Siegismund; Johannes Zschache; Sebastian Röhl
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Germany
    Description
    The rapid expansion of renewable energy sources poses significant challenges in reconciling energy development with competing interests. This underscores the necessity for precise spatial data to facilitate effective balancing, management, or evaluation of compliance with regulatory frameworks.


    In Germany, a widely used data source for location and plant specific data is the Marktstammdatenregister operated by the Federal Network Agency. This dataset builds upon the Marktstammdatenregister and enhances its spatial accuracy of onshore wind power plant locations using high resolution remote sensing data and object detection deep learning.

    The dataset consists of point geometries for onshore wind turbines in Germany. The wind turbines were detected using YOLO an object detection algorithm, on high-resolution PlanetScope satellite image time series. The inference on PlanetScope time series, in particular Global Monthly basemaps, include basemaps for the months of April to October for the years 2018 to 2024 with 45 German wide inferences in total. The training dataset was build using wind turbine sites from Manske & Schmiedt (2023) from the UFZ, which semi-manually corrected the site information of the Marktstammdatenregister for the years 2021, 2022 and 2023. Hereby the training dataset was curated by evenly sampling wind power plants based on geographic location. Additionally negative samples were added to the dataset both picked randomly from all over Germany, excluding wind power plant sites by a certain distance, as well as from specific objects prone to be detected as false positives such as power towers, cell phone towers or similar based on Open Street Map.

    The dataset was refined in multiple iterations by filtering bad initial labels which could not be re-detected by a trained model over multiple time steps, as well as by adding more samples of scenes where the model had difficulties.
    The resulting monthly detections of onshore wind turbines over Germany were aggregated throughout time by merging all detections to a single detection time series if it would occur in a certain buffer throughout time. Hereby, we added the detected time stamp time series as an addition attribute to the resulting aggregate points. In order to filter detection noise, the dataset can individually be filtered for points which exceed a certain detection count.
    In future works we will update this dataset to recent month, an enhanced post-processing to filter false detections, as well as links to IDs of the German Marktstammdatenregister per detection.

  9. g

    Germany mosaic from Sentinel-2 data (2021) | gimi9.com

    • gimi9.com
    Updated Dec 22, 2024
    + more versions
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    (2024). Germany mosaic from Sentinel-2 data (2021) | gimi9.com [Dataset]. https://gimi9.com/dataset/eu_c2b935fb-5cbe-45cf-970b-577d4f5680e2/
    Explore at:
    Dataset updated
    Dec 22, 2024
    Area covered
    Germany
    Description

    The mosaic allows demand carriers to incorporate remote sensing information from the Earth observation programme Copernicus (Sentinel-2, L1C and L2A) for the Federal Republic of Germany into existing technical procedures. Image data of the year 2020 will be merged into a mosaic that has a floor resolution of 10 m. Three composites of five bands (Sentinel-2 bands: 2, 3, 4, 5 and 8 (R, G, B, Red Edge and NIR)) as well as an overview layer of the input image data are offered. The product Deutschlandmosaik is currently available as a WMS service. Possible uses for the mosaic are: Visualisations for own services and cartographic applications, mapping and updating support for land cover, 3D flight simulations in conjunction with altitude information (DGM, DOM).

  10. Ger-RAG-eval

    • huggingface.co
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    Deutsche Telekom AG, Ger-RAG-eval [Dataset]. https://huggingface.co/datasets/deutsche-telekom/Ger-RAG-eval
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset provided by
    Deutsche Telekomhttp://www.telekom.de/
    Authors
    Deutsche Telekom AG
    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Description

    German RAG LLM Evaluation Dataset

    This dataset is intended for the evaluation of German RAG (retrieval augmented generation) capabilities of LLM models. It is based on the test set of the deutsche-telekom/wikipedia-22-12-de-dpr data set (also see wikipedia-22-12-de-dpr on GitHub) and consists of 4 subsets or tasks.

      Task Description
    

    The dataset consists of 4 subsets for the following 4 tasks (each task with 1000 prompts):

      choose_context_by_question (subset… See the full description on the dataset page: https://huggingface.co/datasets/deutsche-telekom/Ger-RAG-eval.
    
  11. z

    OpenRefine Training Dataset based on a subset of the BGBM Herbarium

    • zenodo.org
    csv
    Updated Mar 11, 2025
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    Botanic Garden and Botanical Museum, Berlin (2025). OpenRefine Training Dataset based on a subset of the BGBM Herbarium [Dataset]. http://doi.org/10.5281/zenodo.14918375
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    csvAvailable download formats
    Dataset updated
    Mar 11, 2025
    Dataset provided by
    Botanic Garden and Botanical Museum, Berlin
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset was created for a training workshop for the OpenRefine software. It is a subset of an old snapshot of the herbarium database of the Botanic Garden and Botanical Museum (BGBM) Berlin, Germany.

    This is NOT a complete, accurate or up-to-date dataset.

    DO NOT USE FOR SCIENTIFIC PURPOSES.

    To get the complete and current version of the Herbarium dataset from the BGBM, please use: https://www.gbif.org/dataset/85714c48-f762-11e1-a439-00145eb45e9a" target="_blank" rel="noopener">https://www.gbif.org/dataset/85714c48-f762-11e1-a439-00145eb45e9a

    This dataset contains 1136 records with 9 different columns: Barcode (string), Family (string), FullScientificName (string), KindOfUnit (string, 4 different values), CollectionDate (string), CollectorName (string), LocationDetails (string), Country (string, 127 different values), CountryCode (string, 127 different values).

  12. e

    Characteristics of ChatGPT users from Germany: implications for the digital...

    • b2find.eudat.eu
    Updated Jul 23, 2024
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    (2024). Characteristics of ChatGPT users from Germany: implications for the digital divide from web tracking data - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/bcfed45e-617e-5a91-b174-6d676aa137e5
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    Dataset updated
    Jul 23, 2024
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Area covered
    Germany
    Description

    A major challenge of our time is reducing disparities in access to and effective use of digital technologies, with recent discussions highlighting the role of AI in exacerbating the digital divide. We examine user characteristics that predict usage of the AI-powered conversational agent ChatGPT. We combine behavioral and survey data in a web tracked sample of N=1376 German citizens to investigate differences in ChatGPT activity (usage, visits, and adoption) during the first 11 months from the launch of the service (November 30, 2022). Guided by a model of technology acceptance (UTAUT-2), we examine the role of socio-demographics commonly associated with the digital divide in ChatGPT activity and explore further socio-political attributes identified via stability selection in Lasso regressions. We confirm that lower age and higher education affect ChatGPT usage, but neither gender nor income do. We find full-time employment and more children to be barriers to ChatGPT activity. Using a variety of social media was positively associated with ChatGPT activity. In terms of political variables, political knowledge and political self-efficacy as well as some political behaviors such as voting, debating political issues online and offline and political action online were all associated with ChatGPT activity, with online political debating and political self-efficacy negatively so. Finally, need for cognition and communication skills such as writing, attending meetings, or giving presentations, were also associated with ChatGPT engagement, though chairing/organizing meetings was negatively associated. Our research informs efforts to address digital disparities and promote digital literacy among underserved populations by presenting implications, recommendations, and discussions on ethical and social issues of our findings.

  13. N

    New Germany, MN Age Group Population Dataset: A Complete Breakdown of New...

    • neilsberg.com
    csv, json
    Updated Feb 22, 2025
    + more versions
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    Neilsberg Research (2025). New Germany, MN Age Group Population Dataset: A Complete Breakdown of New Germany Age Demographics from 0 to 85 Years and Over, Distributed Across 18 Age Groups // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/4539bf7b-f122-11ef-8c1b-3860777c1fe6/
    Explore at:
    json, csvAvailable download formats
    Dataset updated
    Feb 22, 2025
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    New Germany, Minnesota
    Variables measured
    Population Under 5 Years, Population over 85 years, Population Between 5 and 9 years, Population Between 10 and 14 years, Population Between 15 and 19 years, Population Between 20 and 24 years, Population Between 25 and 29 years, Population Between 30 and 34 years, Population Between 35 and 39 years, Population Between 40 and 44 years, and 9 more
    Measurement technique
    The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age groups. For age groups we divided it into roughly a 5 year bucket for ages between 0 and 85. For over 85, we aggregated data into a single group for all ages. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the New Germany population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for New Germany. The dataset can be utilized to understand the population distribution of New Germany by age. For example, using this dataset, we can identify the largest age group in New Germany.

    Key observations

    The largest age group in New Germany, MN was for the group of age 25 to 29 years years with a population of 88 (15.15%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in New Germany, MN was the 85 years and over years with a population of 0 (0%). 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

    Variables / Data Columns

    • Age Group: This column displays the age group in consideration
    • Population: The population for the specific age group in the New Germany is shown in this column.
    • % of Total Population: This column displays the population of each age group as a proportion of New Germany 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 New Germany Population by Age. You can refer the same here

  14. Magnetic raw data (SENSYS MXV3 system entire dataset) during campaign...

    • doi.pangaea.de
    html, tsv
    Updated Mar 5, 2024
    + more versions
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    Moritz Mennenga; Anja Behrens (2024). Magnetic raw data (SENSYS MXV3 system entire dataset) during campaign AFM-NIhK-2020-25, Ahlen-Falkenberger Moor (area 45), Germany [Dataset]. http://doi.org/10.1594/PANGAEA.963780
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    tsv, htmlAvailable download formats
    Dataset updated
    Mar 5, 2024
    Dataset provided by
    PANGAEA
    Authors
    Moritz Mennenga; Anja Behrens
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jul 8, 2020 - Jul 9, 2020
    Area covered
    Variables measured
    LATITUDE, Track ID, DATE/TIME, LONGITUDE, Sensor, number, Latitude (EPSG), Longitude (EPSG), Magnetic field, Z-gradient
    Description

    This is the measurement data from the geomagnetic prospection of the Ahlen-Falkenberger Moor (district of Cuxhaven, Lower Saxony, Germany) as part of the research project Preserved in the bog - relics of prehistoric settlement landscapes in the Elbe-Weser triangle funded by Niedersachsen Vorab. A total of about 800 ha were measured. The aim was to locate archaeological sites as well as landscape features. The measurements were carried out with a MXV3 system from Sensys with 6 FGM650/3 probes with a distance of 0.5m. Each probe consists of 2 sensors with 650mm basedistance and gives the gradient of the vertical component of the magnetic field (Z). The location was measured using a Stonex S10 GPS with Sapos HEPS correction data, resulting in a horizontal position accuracy of 0.01 – 0.02m and an elevation accuracy of 0.02 – 0.03m. The data were exported using DLMGPS (Sensys), whereby the coordinates of the individual probes are automatically determined from the central GPS position on the device. The data were exported without automatic track compensation. Due to the system, the position data is in UTM32/ETRS84 (EPSG 4647) and for conformity with PANGAEA also in WGS84 (EPSG 4326) (conversion is done with spTransform in R).

  15. e

    Helgoland Roads - Germany - Dataset - B2FIND

    • b2find.eudat.eu
    Updated Sep 3, 2022
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    (2022). Helgoland Roads - Germany - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/3807eb9b-dee4-590f-8209-072a28f69b28
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    Dataset updated
    Sep 3, 2022
    Area covered
    Heligoland, Germany
    Description

    Helgoland Roads summary The Helgoland Roads time-series, located at the island of Helgoland in the German Bight, approximately 60 km off the German mainland (54°11'N 7°54'E), is one of the richest temporal marine datasets available. The time-series was initiated in 1962 at the Helgoland Roads site, which is located between the main island of Helgoland and a small sandy outcrop, the so-called 'dune'. The location near Helgoland is of particular interest because the site is essentially in a transitional zone between coastal and oceanic conditions, which is seen most clearly in the salinity patterns at Helgoland Roads. Initially, the sampling frequency was thrice weekly, but this was increased to daily in the early 1970s. Since then, the high sampling frequency has provided a unique opportunity to study long-term trends in abiotic and biotic parameters, but also ecological phenomena, such as seasonal interactions between different foodweb components, niche properties, and the dynamics and timing of the spring bloom (Grüner et al. 2011; Mieruch et al. 2010; Tian et al. 2008; Wiltshire et al. 2015; Wiltshire et al. 2010). The measured parameters comprise phytoplankton, temperature, salinity, and nutrient analyses. Inorganic nutrients. The taxon list now contains over 350 entities (with 230 distinct species). Both the phytoplankton and chemical dataseries are fully quality-controlled, based on original data sheets and metadata (Wiltshire and Dürselen, 2004; Raabe and Wiltshire, 2009). The phytoplankton time-series is augmented by the biological parameters zooplankton, rocky shore macroalgae, macro-zoobenthos, and bacteria, providing a unique opportunity to investigate longterm changes at an ecosystem scale. Some historic data sets are also available and have been archived in the online repository Pangaea, alongside all core phytoplankton and environmental data sets for Helgoland Roads (Kraberg et al. 2015). Analyses by Wiltshire et al. (2010) have demonstrated the statistical significance of these changes, with temperature since 1962 amounting to 1.7°C (Wiltshire et al., 2010). In tandem with the increases in temperature and salinity, nutrient dynamics at Helgoland Roads have also changed considerably, with phosphate concentrations having declined significantly since 1962. Long-term trends are also seen in the biota, with Diatoms in particular having exhibited an increase in abundance, with a concomitant increase in positive trend for total Dinoflagellates (see also (Wiltshire et al. 2008)). This was not a gradual change, but a rapid shift from negative to positive anomalies around 1998. The exact causes for this are still under investigation. Breaking this down to monthly trends, the swing seems to be largely driven by shifts in autumn and winter. There was also a significant shift in seasonal densities of individual Diatom species (Guinardia delicatula, Paralia sulcata) and in the numbers of large Diatoms (e.g. Cocinodiscus wailesii), which are difficult for copepods to graze. The large Diatom Mediopyxis helysia has recently been observed for the first time and now occurs almost throughout the year, with an intensive bloom in spring 2010 (Kraberg et al. 2012). Generally speaking, the spring Diatom bloom now appears to start later, if the preceding autumn was very warm (Wiltshire and Manly, 2004). It is worth noting that species introductions are also occurring in the zooplankton, with the ctenophore Mnemiopsis leidyi being the most obvious new species (Boersma et al. 2007). References Boersma M, Malzahn AM, Greve W, Javidpour J (2007) The first occurrence of the ctenophore Mnemiopsis leidyi in the North Sea Helgoland Marine Research Grüner N, Gebühr C, Boersma M, Feudel U, Wiltshire KH, Freund JA (2011) Reconstructin g the realized niche of phytopankton species from environmental data: fitness versus abundance approach Limnology and Oceanography methods 9:432-442 Kraberg A, Carstens K, Tilly K, Wiltshire KH (2012) The diatom Mediopyxis helysia at Helgoland Roads: a success story? Helgoland Marine Research 66:463-468 Kraberg AC, Rodriguez N, Salewski CR (2015) Historical phytoplankton data from Helgoland Roads: Can they be linked to modern time series data? Journal of Sea Research 101:51-58 Mieruch S, Freund JA, Feudel U, Boersma M, Janisch S, Wiltshire KH (2010) A new method for describing phytoplankton blooms: Examples from Helgoland Journal of Marine Systems 79:36-43 Tian Y, Kidokoro H, Watanabe T, Iguchi N (2008) The late 1980s regime shift in the ecosystem of Tsushima warm current in the Japan/ East Sea: Evidence from historical data and possible mechanisms Progress in Oceanography 77:127-145 Wiltshire KH, Boersma M, Carstens K, Kraberg AC, Peters S, Scharfe M (2015) Control of phytoplankton in a shelf sea: Determination of the main drivers based on the Helgoland Roads Time Series Journal of Sea Research 105:42-52 Wiltshire KH et al. (2010) Helgoland Roads: 45 years of change in the North Sea Estuaries and Coasts DOI 10.1007/s12237-009-9228-y Wiltshire KH et al. (2008) Resilience of North Sea phytoplankton spring bloom dynamics: An analysis of long-term data at Helgoland Roads Limnology and Oceanography 53:1294-1302

  16. e

    Magnetic raw data (SENSYS MXV3 system entire dataset) during campaign...

    • b2find.eudat.eu
    Updated Apr 30, 2024
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    (2024). Magnetic raw data (SENSYS MXV3 system entire dataset) during campaign AFM-NIhK-2019-02, Ahlen-Falkenberger Moor (area 4), Germany - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/e4dc0735-ff98-5bb1-8f60-a4b45c787407
    Explore at:
    Dataset updated
    Apr 30, 2024
    Description

    This is the measurement data from the geomagnetic prospection of the Ahlen-Falkenberger Moor (district of Cuxhaven, Lower Saxony, Germany) as part of the research project Preserved in the bog - relics of prehistoric settlement landscapes in the Elbe-Weser triangle funded by Niedersachsen Vorab. A total of about 800 ha were measured. The aim was to locate archaeological sites as well as landscape features. The measurements were carried out with a MXV3 system from Sensys with 6 FGM650/3 probes with a distance of 0.5m. Each probe consists of 2 sensors with 650mm basedistance and gives the gradient of the vertical component of the magnetic field (Z). The location was measured using a Stonex S10 GPS with Sapos HEPS correction data, resulting in a horizontal position accuracy of 0.01 – 0.02m and an elevation accuracy of 0.02 – 0.03m. The data were exported using DLMGPS (Sensys), whereby the coordinates of the individual probes are automatically determined from the central GPS position on the device. The data were exported without automatic track compensation. Due to the system, the position data is in UTM32/ETRS84 (EPSG 4647) and for conformity with PANGAEA also in WGS84 (EPSG 4326) (conversion is done with spTransform in R). Measurement carried out by D. Dallaserra (NIhK) and J. Lühmann (NIhK)

  17. e

    Tree-ring width of Quercus species from historical object sample 6691B/04A,...

    • b2find.eudat.eu
    Updated Oct 21, 2023
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    (2023). Tree-ring width of Quercus species from historical object sample 6691B/04A, Germany, Bohmte - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/a32514d9-d8c0-58d5-b265-67eeb40b2758
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    Dataset updated
    Oct 21, 2023
    Description

    Species: Quercus species; No of rings: 45; first year: 1679; tree-death year: 1724; Pith: not applicable; Heartwood: complete; Sapwood: complete; Number of sapwood rings: 10; Last ring under bark: present

  18. EC-Earth-Consortium EC-Earth3P-VHR model output prepared for CMIP6 CMIP

    • wdc-climate.de
    Updated 2020
    + more versions
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    EC-Earth Consortium (EC-Earth) (2020). EC-Earth-Consortium EC-Earth3P-VHR model output prepared for CMIP6 CMIP [Dataset]. http://doi.org/10.22033/ESGF/CMIP6.2326
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    Dataset updated
    2020
    Dataset provided by
    Earth System Grid
    World Data Center for Climate (WDCC) at DKRZ
    Authors
    EC-Earth Consortium (EC-Earth)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets. These data include all datasets published for 'CMIP6.CMIP.EC-Earth-Consortium.EC-Earth3P-VHR' 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-Earth3P-VHR in PRIMAVERA climate model, released in 2017, includes the following components: atmos: IFS cy36r4 (TL1279, linearly reduced Gaussian grid equivalent to 2560 x 1280 longitude/latitude; 91 levels; top level 0.01 hPa), land: HTESSEL (land surface scheme built in IFS), ocean: NEMO3.6 (ORCA012 tripolar primarily 0.08 degrees; 4322 x 3059 longitude/latitude; 75 levels; top grid cell 0-1 m), 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: 25 km, land: 25 km, ocean: 10 km, seaIce: 10 km.

    Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6).

    CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ).

    The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.

  19. Z

    Dataset for the IntoValue 1 + 2 studies on results dissemination from...

    • data.niaid.nih.gov
    • explore.openaire.eu
    • +1more
    Updated Mar 1, 2023
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    Strech, Daniel (2023). Dataset for the IntoValue 1 + 2 studies on results dissemination from clinical trials conducted at German university medical centers completed between 2009 and 2017 [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_5141342
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    Dataset updated
    Mar 1, 2023
    Dataset provided by
    Salholz-Hillel, Maia
    Riedel, Nico
    Kahrass, Hannes
    Holst, Martin R.
    Wieschowski, Susanne
    Bruckner, Till
    Meerpohl, Joerg J.
    Nury, Edris
    Strech, Daniel
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Germany
    Description

    The IntoValue dataset contains clinical trials conducted at one of 35 German UMCs and registered on ClinicalTrials.gov or the German Clinical Trials Registry (DRKS). All trials were reported as complete between 2009 and 2017 on the trial registry at the time of data collection. The dataset also includes a results publication found via manual searches; if multiple results publications were found, the earliest was included.

    Trials were associated with a German UMC by searching for trials with a UMC listed as responsible party or lead sponsor, or with a principle investigator (PI) from a UMC ('lead_city'). Version 1 additionally includes trials with a UMC only as a facility (facility_city). A lookup table of regular expressions used to identify German UMCs is available at https://github.com/quest-bih/IntoValue2/blob/master/data/1_sample_generation/city_search_terms.csv.

    Trials include all interventional studies and are not limited to investigational medical product trials, as regulated by the EU's Clinical Trials Directive or Germany's Arzneimittelgesetz (AMG) or Novelle des Medizinproduktegesetzes (MPG).

    DRKS data were searched (pre-filtered for completion years and study status as well as Germany as 'Country of recruitment') and downloaded as CSVs from the DRKS website (https://www.drks.de/). ClinicalTrials.gov data were downloaded downloaded as pipe files from Clinical Trials Transformation Initiative (CTTI) Aggregate Content of ClinicalTrials.gov (AACT) (https://aact.ctti-clinicaltrials.org/pipe_files). DRKS and ClinicalTrials.gov use different terminology for various trial aspects, such as phase and masking; these different levels are captured in the data dictionary as levels_drks and levels_ctgov. For later analyses requiring parity across registries, levels for some variables were collapsed and a lookup table is provided in iv_data_lookup_registries.csv.

    These data were generated and used for two publications (Wieschowski et al., 2019; Riedel et al. 2021) and therefore comprises two versions (indicated as iv_version).

    For version 1, registry data was collected on April 17, 2017 from ClinicalTrials.gov and on July 27, 2017 for DRKS and was limited to trials with a completion date on DRKS and primary completion date on ClinicalTrials.gov between 2009 and 2013. Version 1 manual searches for results publications were conducted from 2017-07-01 to 2017-12-01. For version 2, registry data was collected on June 3, 2020 and was limited to trials with a completion date on DRKS and ClinicalTrials.gov between 2014 and 2017. Version 2 manual searches for results publications were conducted from 2020-07-01 to 2020-09-01.

    Raw registry data for versions 1 and 2 is available in raw-registries.zip.

    Publication identifiers (DOI, PMID, URL) were manually entered during the publication search and then further enhanced using the API of Internet Archive's open-source Fatcat catalog of research publications, to add PMIDs based on DOIs, and vice versa.

    Manual search steps differed slightly in the two versions and are indicated and described in identification_step. Version 1 includes trials with a German UMC as either a lead_city or a facility_city, whereas version 2 is limited to trials a German UMC as a lead_city.

    Each row indicates a single trial registration. Due to changes in completion dates, some trials are duplicated between versions as indicated in is_dupe. Cross-registered trials were manually deduplicated, and some cross-registered duplicates remain (e.g., DRKS00004156 and NCT00215683) and are not indicated in the dataset.

    All dates are provided as yyyy-mm-dd.

    Additional documentation on each variable (type, description, levels) is provided in iv_data_dictionary.csv.

    Additional information on the project and methods for generating the dataset is available in associated publications and at the project's OSF page (https://osf.io/98j7u/). Code for the project is available at https://github.com/quest-bih/IntoValue2.

    References:

    Wieschowski, S., Riedel, N., Wollmann, K., Kahrass, H., Müller-Ohlraun, S., Schürmann, C., Kelley, S., Kszuk, U., Siegerink, B., Dirnagl, U., Meerpohl, J., & Strech, D. (2019). Result dissemination from clinical trials conducted at German university medical centers was delayed and incomplete. Journal of Clinical Epidemiology, 115, 37–45. https://doi.org/10.1016/j.jclinepi.2019.06.002

    Riedel, N., Wieschowski, S., Bruckner, T., Holst, M. R., Kahrass, H., Nury, E., Meerpohl, J. J., Salholz-Hillel, M., & Strech, D. (2021). Results dissemination from completed clinical trials conducted at German university medical centers remained delayed and incomplete. The 2014-2017 cohort. Journal of Clinical Epidemiology, 0(0). https://doi.org/10.1016/j.jclinepi.2021.12.012

  20. t

    Data from: High-resolution sensor data for pCO2, O2 and temperature/salinity...

    • service.tib.eu
    • doi.pangaea.de
    Updated Nov 30, 2024
    + more versions
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    (2024). High-resolution sensor data for pCO2, O2 and temperature/salinity from METEOR M133: Cape Town to the Falklands [Dataset]. https://service.tib.eu/ldmservice/dataset/png-doi-10-1594-pangaea-925069
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    Dataset updated
    Nov 30, 2024
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Falkland Islands (Islas Malvinas), Cape Town
    Description

    Flow-through pCO2, Temp/Sal, and O2 measurements across the Atlantic (Meteor M133 cruise 2016/17) from Cape Town, SA to Stanley, The Falkland Islands. Surface water flow-through system set up on R.V. Meteor M133 (15.12.2016 - 13.01.2017), across the Atlantic. All sensors ran on the same water in a complete flow-through sensor set-up. Depth of pumps: 5.7m from the moon pool Flow rate: ~ 5-6 L/min All data was processed following Canning et al., 2020 (In Review). Sensor data in separate files. Sensors: pCO2: CONTROS HydroC CO2 FT - formerly Kongsberg Maritime Contros GmbH, Kiel, Germany; now -4H-JENA engineering GmbH, Jena, Germany O2: CONTROS HydroFlash O2 - formerly Kongsberg Maritime Contros GmbH, Kiel, Germany SBE 45 Micro Thermosalinograph - Sea-Bird Electronics, Bellevue, USA D-SHIP data from the ships SBE 38 and 21 for sea surface temperature and salinity. Latitude and longitude also from the D-SHIP. All sensors combined together in the same flow-through set-up.

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Neilsberg Research (2025). Germany Township, Pennsylvania Age Group Population Dataset: A Complete Breakdown of Germany township Age Demographics from 0 to 85 Years and Over, Distributed Across 18 Age Groups // 2025 Edition [Dataset]. https://www.neilsberg.com/research/datasets/45255ff3-f122-11ef-8c1b-3860777c1fe6/

Germany Township, Pennsylvania Age Group Population Dataset: A Complete Breakdown of Germany township Age Demographics from 0 to 85 Years and Over, Distributed Across 18 Age Groups // 2025 Edition

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csv, jsonAvailable download formats
Dataset updated
Feb 22, 2025
Dataset authored and provided by
Neilsberg Research
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
Pennsylvania, Germany Township
Variables measured
Population Under 5 Years, Population over 85 years, Population Between 5 and 9 years, Population Between 10 and 14 years, Population Between 15 and 19 years, Population Between 20 and 24 years, Population Between 25 and 29 years, Population Between 30 and 34 years, Population Between 35 and 39 years, Population Between 40 and 44 years, and 9 more
Measurement technique
The data presented in this dataset is derived from the latest U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. To measure the two variables, namely (a) population and (b) population as a percentage of the total population, we initially analyzed and categorized the data for each of the age groups. For age groups we divided it into roughly a 5 year bucket for ages between 0 and 85. For over 85, we aggregated data into a single group for all ages. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
Dataset funded by
Neilsberg Research
Description
About this dataset

Context

The dataset tabulates the Germany township population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Germany township. The dataset can be utilized to understand the population distribution of Germany township by age. For example, using this dataset, we can identify the largest age group in Germany township.

Key observations

The largest age group in Germany Township, Pennsylvania was for the group of age 50 to 54 years years with a population of 313 (10.95%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Germany Township, Pennsylvania was the 80 to 84 years years with a population of 60 (2.10%). 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

Variables / Data Columns

  • Age Group: This column displays the age group in consideration
  • Population: The population for the specific age group in the Germany township is shown in this column.
  • % of Total Population: This column displays the population of each age group as a proportion of Germany township 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 Germany township Population by Age. You can refer the same here

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