Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Germany's main stock market index, the DE40, fell to 23787 points on July 4, 2025, losing 0.61% from the previous session. Over the past month, the index has declined 2.20%, though it remains 28.75% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Germany. Germany Stock Market Index (DE40) - values, historical data, forecasts and news - updated on July of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Germany's main stock market index, the DE40, rose to 23668 points on June 24, 2025, gaining 1.71% from the previous session. Over the past month, the index has declined 1.50%, though it remains 30.20% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Germany. Germany Stock Market Index (DE40) - values, historical data, forecasts and news - updated on June of 2025.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
Australia
-> Aus200
Brazil
-> Bra50 and MinDol
Spain
-> Esp35
France
-> Fra40
Germany
-> Ger40
Hong Kong
-> HkInd
Italy
-> Ita40
Netherlands
-> Neth25
Switzerland
-> Swi20
United Kingdom
-> UK100
United States
-> Usa500, UsaTec and UsaRus
Note: the MinDol, Swi20 and Neth25 data were taken by it's monthly contract, because MetaTrader5 don't have their historical series (like S&P 500, that has the 'Usa500' and 'Usa500Mar24'):
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F17272056%2Fefa5c9f6d7841c496d20d467d4a1c874%2Ffutures_dailycontract.png?generation=1704756245532483&alt=media" alt="">
import MetaTrader5 as mt5
import pandas as pd
import numpy as np
import pytz
from datetime import datetime
if not mt5.initialize(login= , server= "server", password=""):
# you can use your login and password if you have an account on a broker to use mt5
print("initialize() failed, error code =", mt5.last_error())
quit()
symbols = mt5.symbols_get()
list_symbols = []
for num in range(0, len(symbols)):
list_symbols.append(symbols[num].name)
print(list_symbols)
list_futures = ['Aus200', 'Bra50', 'Esp35', 'Fra40', 'Ger40', 'HKInd', 'Ita40Mar24', 'Jp225', 'MinDolFeb24', 'Neth25Jan24', 'UK100', 'Usa500', 'UsaRus', 'UsaTec', 'Swi20Mar24']
time_frame = mt5.TIMEFRAME_D1
dynamic_vars = {}
time_zone = pytz.timezone('Etc/UTC')
time_start = datetime(2017, 1, 1, tzinfo= time_zone)
time_end = datetime(2023, 12, 31, tzinfo= time_zone)
for sym in list_futures:
var = f'{sym}'
rates = mt5.copy_rates_range(sym, time_frame, time_start, time_end)
rates_frame = pd.DataFrame(rates)
rates_frame['time'] = pd.to_datetime(rates_frame['time'], unit='s')
rates_frame = rates_frame[['time', 'close']]
rates_frame.rename(columns = {'close': var}, inplace = True)
dynamic_vars[var] = rates_frame
dynamic_vars[sym].to_csv(f'{sym}.csv', index = False)
Comprehensive dataset of 40 Roller skating clubs in Germany as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.
Comprehensive dataset of 40 Mining consultants in Germany as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of German Valley by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for German Valley. The dataset can be utilized to understand the population distribution of German Valley by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in German Valley. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for German Valley.
Key observations
Largest age group (population): Male # 0-4 years (44) | Female # 40-44 years (21). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
Scope of gender :
Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for German Valley Population by Gender. You can refer the same here
This dataset provides information on 40 in Germany as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset is about books. It has 1 row and is filtered where the book is The Forty-eighters : political refugees of the German Revolution of 1848. It features 7 columns including author, publication date, language, and book publisher.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of German town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for German town. The dataset can be utilized to understand the population distribution of German town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in German town. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for German town.
Key observations
Largest age group (population): Male # 50-54 years (23) | Female # 40-44 years (12). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.
Age groups:
Scope of gender :
Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for German town Population by Gender. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Used-car market monitoring was started in 2018, 03 January, when Federal Administrative Court in Leipzig (Germany) allowed cities and communes in Germany to impose bans for diesel cars in order to reduce the level of nitric oxide in the air. This data-set represent 5th year of market monitoring, 2022. First year monitoring (2018) data-set is available together with data article: Skribans, V. Used-car market dataset for Latvia 2018, (2019) Data in Brief, 22, pp. 859-862. doi: 10.1016/j.dib.2018.12.075. Methodology of data mining is available in research paper: SKRIBANS, V. and HEGERTY, S.W., 2019. Data mining application for used-car market analysis, IMCIC 2019 - 10th International Multi-Conference on Complexity, Informatics and Cybernetics, Proceedings 2019, pp. 35-40. Second year monitoring (2019) data-set is available there: Skribans, Valerijs (2019), “Latvian Used-car Market Announcements Monitoring in 2019”, Mendeley Data, V1, doi: 10.17632/kyf948x63n.1 Third year year monitoring (2020) data-set is available there: Skribans, Valerijs; Gulbis, Aivars; Krastins, Aivars (2020), “Latvian Used-car Market Announcements Monitoring in 2020”, Mendeley Data, V1, doi: 10.17632/hg38nn6c45.1 . First quarter of the fourth year of market monitoring, 2021. set is available there: Skribans, Valerijs; Gulbis, Aivars; Cevers, Aldis; Rudzitis, Normunds; Krastins, Aivars (2021), “Latvian Used-car Market Announcements Monitoring in 2021 Q1”, Mendeley Data, V1, doi: 10.17632/rh9zh9ncnk.1
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Imports from Samoa in Germany decreased to 35 EUR Thousand in January from 40 EUR Thousand in December of 2023. This dataset includes a chart with historical data for Germany Imports from Samoa.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the German Valley 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 German Valley. The dataset can be utilized to understand the population distribution of German Valley by age. For example, using this dataset, we can identify the largest age group in German Valley.
Key observations
The largest age group in German Valley, IL was for the group of age 40 to 44 years years with a population of 52 (10.46%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in German Valley, IL was the 80 to 84 years years with a population of 4 (0.80%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for German Valley Population by Age. You can refer the same here
https://ega-archive.org/dacs/EGAC00001003403https://ega-archive.org/dacs/EGAC00001003403
The whole study comprises of two patient cohorts. Screening cohort: 40 patients of Germany; validation cohort: 40 patients from Asia. Further, bile duct and CCA cell lines have been analyzed. This dataset contains whole exome sequencing data of 37 tumor/normal pairs from the screening cohort plus an additional relapse tumor of one of those 37 patients. Data was generated on Illumina HiSeq 2000 device in paired-end mode and is stored in BAM file format.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Les prix de gros en Allemagne ont augmenté à 117,40 points en janvier contre 116,30 points en décembre 2024. Cette dataset fournit - Prix de gros en Allemagne - valeurs réelles, données historiques, prévisions, graphique, statistiques, calendrier économique et actualités.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Les prix de gros sur un an en Allemagne ont diminué à 0,40 % en mai contre 0,80 % en avril 2025. Cette dataset présente un graphique avec des données historiques sur l'Indice des prix de gros en Allemagne sur un an.
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Germany's main stock market index, the DE40, fell to 23787 points on July 4, 2025, losing 0.61% from the previous session. Over the past month, the index has declined 2.20%, though it remains 28.75% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Germany. Germany Stock Market Index (DE40) - values, historical data, forecasts and news - updated on July of 2025.