The hydrometeorological time series together with the catchment attributes constitute the CAMELS dataset: Catchment Attributes and MEteorology for Large-sample Studies.

TIME SERIES Data citation: A. Newman; K. Sampson; M. P. Clark; A. Bock; R. J. Viger; D. Blodgett, 2014. A large-sample watershed-scale hydrometeorological dataset for the contiguous USA. Boulder, CO: UCAR/NCAR. https://dx.doi.org/10.5065/D6MW2F4D

Associated paper: A. J. Newman, M. P. Clark, K. Sampson, A. Wood, L. E. Hay, A. Bock, R. J. Viger, D. Blodgett, L. Brekke, J. R. Arnold, T. Hopson, and Q. Duan: Development of a large-sample watershed-scale hydrometeorological dataset for the contiguous USA: dataset characteristics and assessment of regional variability in hydrologic model performance. Hydrol. Earth Syst. Sci., 19, 209-223, doi:10.5194/hess-19-209-2015, 2015.

We developed basin scale hydrometeorological forcing data for 671 basins in the United States Geological Survey’s Hydro-Climatic Data Network 2009 (HCDN-2009, Lins 2012) conterminous U.S. basin subset. Retrospective model forcings are derived from Daymet, NLDAS, and Maurer et al. (2002) Daymet and NLDAS forcing data run from 1 Jan 1980 to 31 Dec 2014, and Maurer run from 1 January 1980 to 31 December 2008. Model timeseries output is available for the same time periods as the forcing data. USGS streamflow data are also provided for all basins for all dates available in the 1 Jan to 31 Dec 2014 period. We then implemented the hydrologic model and calibration routine traditionally used by the NWS, the SNOW-17 and Sacramento soil moisture accounting (SAC-SMA) based hydrologic modeling system and the shuffled complex evolution (SCE) optimization approach (Duan et al. 1993).

To retrieve the entire time series dataset, all five *.zip files should be downloaded. The basin_timeseries_v1p2_metForcing_obsFlow.zip file contains all the basin forcing data for all three meteorology products, observed streamflow, basin metadata, readme files, and basin shapefiles. The three modelOutput.zip files contain all the model output for the various forcing datasets denoted in the link names. Finally, the basin_set_full_res.zip file is a full resolution basin shapefile containing the original basin boundaries from the geospatial fabric.

Note there are two versions of the basin shapefiles included in this dataset. The shapefile included with the basin forcing data was used to compute the basin forcing data and is a simplified representation of the basin boundaries which will include small holes in the interior of some basins where sub-basin HRU simplifications do not match. The full resolution shapefile does not have those discontinuities. The user can best determine which shapefile (or both) is appropriate for their needs.

CATCHMENT ATTRIBUTES Data citation: Addor, A. Newman, M. Mizukami, and M. P. Clark, 2017. Catchment attributes for large-sample studies. Boulder, CO: UCAR/NCAR. https://doi.org/10.5065/D6G73C3Q

Association paper: Addor, N., Newman, A. J., Mizukami, N. and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, doi:10.5194/hess-21-5293-2017, 2017.

This dataset covers the same 671 catchments as the Large-Sample Hydrometeorological Dataset introduced by Newman et al. (2015). For each catchment, we characterized a wide range of attributes that influence catchment behavior and hydrological processes. Datasets characterizing these attributes have been available separately for some time, but comprehensive multivariate catchment scale assessments have so far been difficult, because these datasets typically have different spatial configurations, are stored in different archives, or use different data formats. By creating catchment scale estimates of these attributes, our aim is to simplify the assessment of their interrelationships.

Topographic characteristics (e.g. elevation and slope) were retrieved from Newman et al. (2015). Climatic indices (e.g., aridity and frequency of dry days) and hydrological signatures (e.g., mean annual discharge and baseflow index) were computed using the time series provided by Newman et al. (2015). Soil characteristics (e.g., porosity and soil depth) were characterized using the STATSGO dataset and the Pelletier et al. (2016) dataset. Vegetation characteristics (e.g. the leaf area index and the rooting depth) were inferred using MODIS data. Geological characteristics (e.g., geologic class and the subsurface porosity) were computed using the GLiM and GLHYMPS datasets.

An essential feature, that differentiates this dataset from similar ones, is that it both provides quantitative estimates of diverse catchment attributes, and involves assessments of the limitations of the data and methods used to compute those attributes (see Addor et al., 2017). The large number of catchments, combined with the diversity of their geophysical characteristics, makes these data well suited for large-sample studies and comparative hydrology.

Streamflow regime classes identified for the 671 stations in the CAMELS dataset (United States) using functional data analysis: (1) intermittent regime, (2) strong winter regime, (3) weak winter regime, (4) melt regime, and (5) New Year's regime. The textfile contains a table with the USGS gauge ID of each catchment in the CAMELS dataset and their regime class (1-5). More information on the CAMELS dataset can be found in Newman et al. (2015) and Addor et al. (2017). A detailed description on how the regime classes were derived can be found in Brunner et al. (2020).

Addor, N., A. J. Newman, N. Mizukami, and M. P. Clark (2017), The CAMELS data set: Catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21(10), 5293–5313, doi:10.5194/hess-21-5293-2017. Brunner, M. I., A. Newman, L. A. Melsen, and A. Wood (2020), Functional streamflow regime classes in the United States and their future changes, Hydrol. Earth Syst. Sci. Discuss., under review. Newman, A. J. et al. (2015), Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: Data set characteristics and assessment of regional variability in hydrologic model performance, Hydrol. Earth Syst. Sci., 19(1), 209–223, doi:10.5194/hess-19-209-2015.

The CAMELS datasets are not provided in an ideal format and takes a bit of data processing to convert them to useful and convenient forms for geospatial analyses. So, I decided to use the beloved netcdf and feather formats to make the dataset more accessible while taking care of some small annoyances! Three data sources are available from the CAMELS dataset: 1. Observed Flow: Streamflow observations for all 671 stations. 2. Basin Geometries: Polygons representing basins' boundaries for all 671 stations. 3. Basin Attributes: 60 Basin-level attributes for all 671 stations.

Two files are available: 1. camels_attributes_v2.0.feather: Includes basin geometries and 60 basin-level attributes that are available in CAMELS. 2. camels_attrs_v2_streamflow_v1p2.nc: Includes observed flows for all 671 stations, as well as the 60 basin-level attributes. It has two dimensions (station_id and time) and 60 data variables.

Additionally, some small annoyances in the original dataset are taken care of: 1. Station names didn't have a consistent format and there were some missing commas and extra periods! Now, the names have a consistent format (title) and there is comma before the states. 2. Station IDs and HUC 02 are strings with leading zeros if needed.

The code that was used to generate the dataset can be found at https://github.com/cheginit/camels_netcdf.

This dataset provides hydro-meteorological timeseries and landscape attributes for 671 catchments across Great Britain. It collates river flows, catchment attributes and catchment boundaries from the UK National River Flow Archive together with a suite of new meteorological timeseries and catchment attributes. Daily timeseries for the time period 1st October 1970 to the 30th September 2015 are provided for a range of hydro-meteorological data (including rainfall, potential evapotranspiration, temperature, radiation, humidity and flow). A comprehensive set of catchment attributes are quantified describing a range of catchment characteristics including topography, climate, hydrology, land cover, soils, hydrogeology, human influences and discharge uncertainty. This dataset is intended for the community as a freely available, easily accessible dataset to use in a wide range of environmental data and modelling analyses. A research paper (Coxon et al, CAMELS-GB: Hydrometeorological time series and landscape attributes for 671 catchments in Great Britain) describing the dataset in detail will be made available in Earth System Science Data (https://www.earth-system-science-data.net/).

There is a new version available (link here) with longer time series, extended gauge coverage and data from additional sources.

This is the CAMELS-BR dataset (Catchment Attributes and MEteorology for Large-sample Studies - Brazil) accompanying the paper: Chagas et al., Hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth System Science Data, 2020 (https://doi.org/10.5194/essd-12-2075-2020).

CAMELS-BR provides daily observed streamflow time series for 3679 stream gauges, daily meteorological time series and 65 attributes for 897 selected catchments in Brazil.

The daily hydrometeorological time series include (i) observed streamflow accompanied by quality control information, (ii) precipitation extracted from three global products, (iii) actual evapotranspiration, (iv) potential evapotranspiration, and (v) minimum, average, and maximum temperature.

The 65 catchment attributes cover properties such as (i) topography, (ii) climate, (iii) hydrology, (iv) land cover, (v) geology, (vi) soil, and (vii) human intervention.

The data follow the same standards from the other CAMELS datasets for the United States (https://doi.org/10.5194/hess-21-5293-2017), Chile (https://doi.org/10.5194/hess-22-5817-2018), and Great Britain (https://doi.org/10.5194/essd-2020-49).

This is the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS comprises data for 222 unregulated catchments, combining hydrometeorological timeseries (streamflow and 18 climatic variables) with 134 attributes related to geology, soil, topography, land cover, anthropogenic influence, and hydroclimatology. The CAMELS-AUS catchments have been monitored for decades (more than 85% have streamflow records longer than 40 years) and are relatively free of large scale changes, such as significant changes in landuse. Rating curve uncertainty estimates are provided for most (75%) of the catchments and multiple atmospheric datasets are included, offering insights into forcing uncertainty. This dataset allows users globally to freely access catchment data drawn from Australia's unique hydroclimatology, particularly notable for its large interannual variability. Combined with arid catchment data from the CAMELS datasets for USA and Chile, CAMELS-AUS constitutes an unprecedented resource for the study of arid-zone hydrology. […]

This is the accompanying dataset to the following paper https://www.nature.com/articles/s41597-023-01975-w

Caravan is an open community dataset of meteorological forcing data, catchment attributes, and discharge daat for catchments around the world. Additionally, Caravan provides code to derive meteorological forcing data and catchment attributes from the same data sources in the cloud, making it easy for anyone to extend Caravan to new catchments. The vision of Caravan is to provide the foundation for a truly global open source community resource that will grow over time.

If you use Caravan in your research, it would be appreciated to not only cite Caravan itself, but also the source datasets, to pay respect to the amount of work that was put into the creation of these datasets and that made Caravan possible in the first place.

All current development and additional community extensions can be found at https://github.com/kratzert/Caravan

IMPORTANT: Due to size limitations for individual repositories, the netCDF version and the CSV version of Caravan (since Version 1.6) are split into two different repositories. You can find the netCDF version at https://zenodo.org/records/14673536

Channel Log:

• 23 May 2022: Version 0.2 - Resolved a bug when renaming the LamaH gauge ids from the LamaH ids to the official gauge ids provided as "govnr" in the LamaH dataset attribute files.
• 24 May 2022: Version 0.3 - Fixed gaps in forcing data in some "camels" (US) basins.
• 15 June 2022: Version 0.4 - Fixed replacing negative CAMELS US values with NaN (-999 in CAMELS indicates missing observation).
• 1 December 2022: Version 0.4 - Added 4298 basins in the US, Canada and Mexico (part of HYSETS), now totalling to 6830 basins. Fixed a bug in the computation of catchment attributes that are defined as pour point properties, where sometimes the wrong HydroATLAS polygon was picked. Restructured the attribute files and added some more meta data (station name and country).
• 16 January 2023: Version 1.0 - Version of the official paper release. No changes in the data but added a static copy of the accompanying code of the paper. For the most up to date version, please check https://github.com/kratzert/Caravan
• 10 May 2023: Version 1.1 - No data change, just update data description.
• 17 May 2023: Version 1.2 - Updated a handful of attribute values that were affected by a bug in their derivation. See https://github.com/kratzert/Caravan/issues/22 for details.
• 16 April 2024: Version 1.4 - Added 9130 gauges from the original source dataset that were initially not included because of the area thresholds (i.e. basins smaller than 100sqkm or larger than 2000sqkm). Also extended the forcing period for all gauges (including the original ones) to 1950-2023. Added two different download options that include timeseries data only as either csv files (Caravan-csv.tar.xz) or netcdf files (Caravan-nc.tar.xz). Including the large basins also required an update in the earth engine code
• 16 Jan 2025: Version 1.5 - Added FAO Penman-Monteith PET (potential_evaporation_sum_FAO_PENMAN_MONTEITH) and renamed the ERA5-LAND potential_evaporation band to potential_evaporation_sum_ERA5_LAND. Also added all PET-related climated indices derived with the Penman-Monteith PET band (suffix "_FAO_PM") and renamed the old PET-related indices accordingly (suffix "_ERA5_LAND").
• 27 May 2025: Version 1.6
  
  • Updated the CAMELS-AUS data to source from CAMELS-AUS v2. This means more basins (561 compared to 222) and more recent streamflow data (2022 compared to 2014). Note that the gauge id for four basins changed between the original CAMELS-AUS version and v2. Those gauges are ['camelsaus_224213A', 'camelsaus_224214A', 'camelsaus_227225A', 'camelsaus_403213A'] that all lost their trailing "A". To stay synced with CAMELS-AUS (v2), we also adapted the new naming.
  • Added VERSION file to the root directory that contains the current version number.
  • Updated the code to the most recent GitHub snapshot (commit 6eab036).
  • Due to the 50GB repository limit, we had to split the netCDF version and the CSV version into two separate repositories. The CSV version can be found under https://zenodo.org/records/15530021

The CAMELS-Chem dataset is a comprehensive collection of stream water chemistry data, atmospheric deposition data, and catchment attribute data for 516 minimally impacted headwater catchments across the continental United States. The dataset spans a period of 39 years, from 1980 through 2018, and includes 18 common stream water chemistry constituents, such as Al, Ca, Cl, Dissolved Organic Carbon, Total Organic Carbon, HCO3, K, Mg, Na, Total Dissolved Nitrogen, NO3, Dissolved Oxygen, pH, Si, SO4, and water temperature. Additionally, the dataset provides annual wet deposition loads for several key components. The dataset is based on the existing CAMELS dataset, which provides catchment attribute data such as topography, climate, land cover, soil, and geology. In CAMELS-Chem, this catchment attribute data is paired with atmospheric deposition data from the National Atmospheric Deposition Program and water chemistry data and instantaneous discharge from the US Geological Survey. The dataset also includes paired instantaneous and discharge measurements for all chemistry samples. The catchment attribute data files used in the CAMELS-Chem dataset were downloaded from the CAMELS website (https://ral.ucar.edu/solutions/products/camels

Yellow Red Camels

## Overview

Yellow Red Camels is a dataset for object detection tasks - it contains Yellowish Camel annotations for 270 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This is the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS comprises data for 222 unregulated catchments, combining hydrometeorological timeseries (streamflow and 18 climatic variables) with 134 attributes related to geology, soil, topography, land cover, anthropogenic influence, and hydroclimatology. The CAMELS-AUS catchments have been monitored for decades (more than 85% have streamflow records longer than 40 years) and are relatively free of large scale changes, such as significant changes in landuse. Rating curve uncertainty estimates are provided for most (75%) of the catchments and multiple atmospheric datasets are included, offering insights into forcing uncertainty. This dataset allows users globally to freely access catchment data drawn from Australia's unique hydroclimatology, particularly notable for its large interannual variability. Combined with arid catchment data from the CAMELS datasets for USA and Chile, CAMELS-AUS constitutes an unprecedented resource for the study of arid-zone hydrology.---To download the dataset, please click the link below "View dataset as HTML".

Download our curated dataset of 400 high-quality images for binary classification of horses and camels. Perfect for testing and enhancing your machine learning models.

Here we provide the data and R scripts to complete the analyses and create the figures presented in the manuscript titled, “Solute export patterns across the contiguous United States” by Kincaid et al. 2024 at Hydrological Processes. Importantly, this resource contains paired solute concentration (C) and discharge (Q) data for 11 solutes from CAMELS-Chem (Sterle et al. 2024; https://doi.org/10.5194/hess-28-611-2024). This relational database was built upon the CAMELS dataset (https://doi.org/10.5194/hess-21-5293-2017), an existing dataset of catchment and hydroclimatic attributes from relatively undisturbed catchments across the contiguous United States. The version of CAMELS-Chem provided here has US Geological Survey (USGS) National Water Information System (NWIS) C and Q data for 506 catchments. C and Q measurements span from 1898 to 2020 with the first paired C-Q sample occurring in 1924. Solutes include aluminum (Al), calcium (Ca), chloride (Cl), dissolved organic C and N (DOC, DON), magnesium (Mg), nitrate (NO3), potassium (K), silica (Si), sodium (Na), and sulfate (SO4). Of note, a shorter version of the CAMELS-Chem database that spans from 1980 to 2018, but includes data for more stream water quality constituents and atmospheric deposition data is described in CAMELS-Chem (Sterle et al. 2024; https://doi.org/10.5194/hess-28-611-2024) and available for download via Hydroshare (http://www.hydroshare.org/resource/841f5e85085c423f889ac809c1bed4ac).

The R scripts and data files provided in this resource are intended to allow users to replicate the tables and figures in the Kincaid et al. manuscript. Specifically, we provide all files to complete the analyses coded in in the R script 9_analyses_figures_for_manuscript.R. However, other R scripts and data files provided should allow users to replicate intermediate steps in the analyses as well. See the README file for more details, but analyses provided in the R scripts include: modeling C-Q relationships with the power-law function using data-driven Bayesian segmented regression; conducting hierarchical clustering to group catchments based on catchment attributes; building random forest models to select catchment attribute correlates of C-Q metrics; conducting flow-duration exceedance probability analyses; and general code for figures, tables, and other statistics presented in the Kincaid et al. manuscript.

The metadata for the CAMELS-Chem dataset (camels_chem_all_2022-02-25.csv) is available in camels_chem_metadata.csv

Camel Detiction

## Overview

Camel Detiction is a dataset for object detection tasks - it contains Camels annotations for 397 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Camel Object Detection MAIN

## Overview

Camel Object Detection MAIN is a dataset for object detection tasks - it contains Camels annotations for 1,502 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Camels Tracking And Position

## Overview

Camels Tracking And Position is a dataset for object detection tasks - it contains Sleeping annotations for 1,255 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

Camel

## Overview

Camel is a dataset for object detection tasks - it contains Camel annotations for 206 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Version 1.0 - This version is the final revised one.

This is the LamaH-CE dataset accompanying the paper: Klingler et al., LamaH-CE | LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, published at Earth System Science Data (ESSD), 2021 (https://doi.org/10.5194/essd-13-4529-2021).

LamaH-CE contains a collection of runoff and meteorological time series as well as various (catchment) attributes for 859 gauged basins. The hydrometeorological time series are provided with daily and hourly time resolution including quality flags. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses with high temporal resolution. LamaH is in its basics quite sililar to the well-known CAMELS datasets for the contiguous United States (https://doi.org/10.5194/hess-21-5293-2017), Chile (https://doi.org/10.5194/hess-22-5817-2018), Brazil (https://doi.org/10.5194/essd-12-2075-2020), Great Britain (https://doi.org/10.5194/essd-12-2459-2020) and Australia (https://doi.org/10.5194/essd-13-3847-2021), but new features like additional basin delineations (intermediate catchments) and attributes allow to consider the hydrological network and river topology in further applications.

We provide two different files to download: 1) Hydrometeorological time series with daily and hourly resolution, which requires decompressed about 70 GB of free disk space. 2) Hydrometeorological time series only with daily resolution, which requires 5 GB. Beyond the temporal resolution of the time series, there are no differences.

Note: It is recommended to read the supplementary info file before using the dataset. For example, it clarifies the time conventions and that NAs are indicated by the number -999 in the runoff time series.

Disclaimer: We have created LamaH with care and checked the outputs for plausibility. By downloading the dataset, you agree that we nor the provider of the used source datasets (e.g. runoff time series) cannot be liable for the data provided. The runoff time series of the German federal states Bavaria and Baden-Württemberg are retrospective checked and updated by the hydrographic services. Therefore, it might be appropriate to obtain more up-to-date runoff data from Bavaria (https://www.gkd.bayern.de/en/rivers/discharge/tables) and Baden-Württemberg (https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer). Runoff data from the Czech Republic may not be used to set up operational warning systems (https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf).

License: This work is licensed with CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/). This means that you may freely use and modify the data (even for commercial purposes). But you have to give appropriate credit (associated ESSD paper, version of dataset and all sources which are declared in the folder "Info"), indicate if and what changes were made and distribute your work under the same public license as the original.

Additional references: We ask kindly for compliance in citing the following references when using LamaH, as an agreement to cite was usually a condition of sharing the data: BAFU (2020), CHMI (2020), GKD (2020), HZB (2020), LUBW (2020), BMLFUW (2013), Broxton et al. (2014), CORINE (2012), EEA (2019), ESDB (2004), Farr et al. (2007), Friedl and Sulla-Menashe (2019), Gleeson et al. (2014), HAO (2007), Hartmann and Moosdorf (2012), Hiederer (2013a, b), Linke et al. (2019), Muñoz Sabater et al. (2021), Muñoz Sabater (2019a), Myneni et al. (2015), Pelletier et al. (2016), Toth et al. (2017), Trabucco and Zomer (2019), and Vermote (2015). These references are listed in detail in the accompanying paper.

Supplements: We have created additional files after publication (therefore non peer-reviewed): 1) Shapefiles for reservoirs (points) and cross-basin water transfers (lines) including several attributes as well as tables with information about the accumulated storage volume and effective catchment area (considerung artificial in- and outflows) for every runoff gauge. 2) Water quality data (e.g. dissolved oxygen, water temperature, conductivity, NO3-N), which are suitable to the gauges. The data for water quality may not be used for commercial purposes. If you are interessted, just send us an email with your name, affiliation and the intended purpose for the requested files to the address listed below. If you find any errors in the dataset, feel free to send us an email to: christoph.klingler@boku.ac.at

Camel Skin Marks

## Overview

Camel Skin Marks is a dataset for object detection tasks - it contains Skin Disease Blood Spot annotations for 427 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Camel Detection

## Overview

Camel Detection is a dataset for object detection tasks - it contains Camel annotations for 546 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

CAMEL Die Kursverlaufsverfolgung ermöglicht es Krypto-Investoren, die Wertentwicklung ihrer Anlagen auf einfache Weise zu überwachen. Sie können den Eröffnungswert, den Höchst- und den Schlusskurs von CAMEL sowie das Handelsvolumen im Zeitverlauf bequem verfolgen. Darüber hinaus können Sie die tägliche Veränderung sofort als Prozentsatz anzeigen, sodass Sie Tage mit erheblichen Schwankungen mühelos identifizieren können. Laut unseren Kursverlaufsdaten auf CAMEL stieg sein Wert auf einen beispiellosen Höchststand von 2025-03-26 und übertraf damit $0.003044 USD. Andererseits trat der tiefste Punkt in der Kursentwicklung von CAMEL, der gemeinhin als „CAMEL Allzeittief“ bezeichnet wird, am 2025-05-29 ein. Hätte man in dieser Zeit CAMEL gekauft, würde man derzeit einen beachtlichen Gewinn von 48% erzielen. Planmäßig wird 1,000,000,000 CAMEL erstellt. Gegenwärtig beträgt das zirkulierende Angebot an CAMEL ungefähr 0. Alle auf dieser Seite aufgeführten Kurse stammen von Bitget, einer zuverlässigen Quelle. Es ist entscheidend, sich bei der Überprüfung Ihrer Investitionen auf eine einzige Quelle zu stützen, da die Werte bei verschiedenen Anbietern variieren können. Unser historischer CAMEL-Kursdaten-Satz umfasst Daten in Intervallen von 1 Minute, 1 Tag, 1 Woche und 1 Monat (Eröffnungs-/Höchst-/Tiefst-/Schlusskurs sowie Volumen). Diese Datensätze wurden strengen Tests unterzogen, um Konsistenz, Vollständigkeit und Genauigkeit sicherzustellen. Sie wurden speziell für die Handelssimulation und das Backtesting entwickelt, stehen kostenlos zum Download bereit und werden in Echtzeit aktualisiert.