9 datasets found
  1. T

    Japan Interest Rate

    • tradingeconomics.com
    • ru.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Aug 8, 2025
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    TRADING ECONOMICS (2025). Japan Interest Rate [Dataset]. https://tradingeconomics.com/japan/interest-rate
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    excel, xml, json, csvAvailable download formats
    Dataset updated
    Aug 8, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

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

    Time period covered
    Oct 2, 1972 - Jul 31, 2025
    Area covered
    Japan
    Description

    The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

  2. T

    Philippines Interest Rate

    • tradingeconomics.com
    • fa.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jun 19, 2025
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    TRADING ECONOMICS (2025). Philippines Interest Rate [Dataset]. https://tradingeconomics.com/philippines/interest-rate
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    csv, excel, json, xmlAvailable download formats
    Dataset updated
    Jun 19, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

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

    Time period covered
    Jan 31, 1985 - Jul 31, 2025
    Area covered
    Philippines
    Description

    The benchmark interest rate in Philippines was last recorded at 5.25 percent. This dataset provides the latest reported value for - Philippines Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

  3. t

    Data from: Multivariate time series dataset of milling 16mncr5 for anomaly...

    • service.tib.eu
    Updated Nov 28, 2024
    + more versions
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    (2024). Multivariate time series dataset of milling 16mncr5 for anomaly detection [Dataset]. https://service.tib.eu/ldmservice/dataset/rdr-doi-10-35097-1398
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    Dataset updated
    Nov 28, 2024
    License

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

    Description

    Abstract: The dataset was recorded during milling of 16MnCr5. Due to artificially introduced, though realistic anoma-lies in the workpiece the dataset can be applied for anomaly detection. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learning. TechnicalRemarks: The dataset consists of seven folders. Each folder represents one milling run. In each milling run the depth of cut was set to 3 mm. A folder contains a maximum of three json files. The number of files depends on the time needed for each run which is a function of milling tool diameter and feed rate. Files in each folder were numerated in sequence. For example, folder “run1” contains the files “run1_1” and “run1_2” with the last number indicating the order in which the files were generated. The frequency of recording datapoints was set to 500 Hz. During each milling run the milling tool moved along the longitudinal side and then was moved back alongside the workpiece. This way machining started always on the same side of the workpiece. Table 1 provides an overview of the milling runs. Run 1 to 4 were performed with a HSS tool with a diameter of 10 mm. The tool in use was an end mill (HSS-E-SPM HPC 10 mm) developed by Hoffmann Group. During the first three runs with this end mill no tool breakage occurred. However, in run 4 the tool broke. Runs 5 and 6 were performed by milling with an end mill of the same tool series (HSS-E-SPM HPC 8 mm) that just differs in tool diameter. In contrast to this run 7 was performed by using a solid carbid tool (Solid carbide roughing end mill HPC 8 mm). Cutting with SC tools provides much higher productivity with the downside being higher tool price. In our case the SC end mill performed cuts with a feed rate of 1150 mm/min compared to 191 mm/min achieved by a HSS end mill of the same diameter. Tool breakages were recorded on all runs with end mills of diameter 8 mm. Table 1. overview of the data folders folder name | number of json files | tool diameter | tool breakage | tool type run 1 2 10 mm No HSS run 2 2 10 mm No HSS run 3 2 10 mm No HSS run 4 2 10 mm Yes HSS run 5 2 8 mm Yes HSS run 6 3 8 mm Yes HSS run 7 1 8 mm Yes SC Each json file consists of a header and a payload. The header lists all parameters that were recorded such as position, motor torque and motor current of each of a maximum of five axes of a milling machine. However, the machine used in our experiments is a 3-axis machining center which leaves the payload of 2 possible additional axes to be empty. In the payload the sequential data for each parameter can be found. A list of recorded signals can be found in Table 2. Table 2. recorded signals during milling Signal index in payload | Signal name | Signal Address |Type 13-18 VelocityFeedForward VEL_FFW|1 double 19-24 Power POWER|1 string 25-30 CountourDeviation CONT_DEV|1 double 38-43 TorqueFeedForward TORQUE_FFW|1 double 44-49 Encoder1Position ENC1_POS|1 double 56-61 Load LOAD|1 double 68-73 Torque TORQUE|1 double 68-91 Current CURRENT|1 double 1 represents x-axis, 2 represents y-axis, 3 represents z-axis and 6 represents spindle-axis. Note that our milling center has 3 axis and therefore values for axes 4 and 5 are null.

  4. Explainable AI (XAI) Drilling Dataset

    • kaggle.com
    Updated Aug 24, 2023
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    Raphael Wallsberger (2023). Explainable AI (XAI) Drilling Dataset [Dataset]. https://www.kaggle.com/datasets/raphaelwallsberger/xai-drilling-dataset
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 24, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Raphael Wallsberger
    License

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

    Description

    This dataset is part of the following publication at the TransAI 2023 conference: R. Wallsberger, R. Knauer, S. Matzka; "Explainable Artificial Intelligence in Mechanical Engineering: A Synthetic Dataset for Comprehensive Failure Mode Analysis" DOI: http://dx.doi.org/10.1109/TransAI60598.2023.00032

    This is the original XAI Drilling dataset optimized for XAI purposes and it can be used to evaluate explanations of such algortihms. The dataset comprises 20,000 data points, i.e., drilling operations, stored as rows, 10 features, one binary main failure label, and 4 binary subgroup failure modes, stored in columns. The main failure rate is about 5.0 % for the whole dataset. The features that constitute this dataset are as follows:

    • ID: Every data point in the dataset is uniquely identifiable, thanks to the ID feature. This ensures traceability and easy referencing, especially when analyzing specific drilling scenarios or anomalies.
    • Cutting speed vc (m/min): The cutting speed is a pivotal parameter in drilling, influencing the efficiency and quality of the drilling process. It represents the speed at which the drill bit's cutting edge moves through the material.
    • Spindle speed n (1/min): This feature captures the rotational speed of the spindle or drill bit, respectively.
    • Feed f (mm/rev): Feed denotes the depth the drill bit penetrates into the material with each revolution. There is a balance between speed and precision, with higher feeds leading to faster drilling but potentially compromising hole quality.
    • Feed rate vf (mm/min): The feed rate is a measure of how quickly the material is fed to the drill bit. It is a determinant of the overall drilling time and influences the heat generated during the process.
    • Power Pc (kW): The power consumption during drilling can be indicative of the efficiency of the process and the wear state of the drill bit.
    • Cooling (%): Effective cooling is paramount in drilling, preventing overheating and reducing wear. This ordinal feature captures the cooling level applied, with four distinct states representing no cooling (0%), partial cooling (25% and 50%), and high to full cooling (75% and 100%).
    • Material: The type of material being drilled can significantly influence the drilling parameters and outcomes. This dataset encompasses three primary materials: C45K hot-rolled heat-treatable steel (EN 1.0503), cast iron GJL (EN GJL-250), and aluminum-silicon (AlSi) alloy (EN AC-42000), each presenting its unique challenges and considerations. The three materials are represented as “P (Steel)” for C45K, “K (Cast Iron)” for cast iron GJL and “N (Non-ferrous metal)” for AlSi alloy.
    • Drill Bit Type: Different materials often require specialized drill bits. This feature categorizes the type of drill bit used, ensuring compatibility with the material and optimizing the drilling process. It consists of three categories, which are based on the DIN 1836: “N” for C45K, “H” for cast iron and “W” for AlSi alloy [5].
    • Process time t (s): This feature captures the full duration of each drilling operation, providing insights into efficiency and potential bottlenecks.

    • Main failure: This binary feature indicates if any significant failure on the drill bit occurred during the drilling process. A value of 1 flags a drilling process that encountered issues, which in this case is true when any of the subgroup failure modes are 1, while 0 indicates a successful drilling operation without any major failures.

    Subgroup failures: - Build-up edge failure (215x): Represented as a binary feature, a build-up edge failure indicates the occurrence of material accumulation on the cutting edge of the drill bit due to a combination of low cutting speeds and insufficient cooling. A value of 1 signifies the presence of this failure mode, while 0 denotes its absence. - Compression chips failure (344x): This binary feature captures the formation of compressed chips during drilling, resulting from the factors high feed rate, inadequate cooling and using an incompatible drill bit. A value of 1 indicates the occurrence of at least two of the three factors above, while 0 suggests a smooth drilling operation without compression chips. - Flank wear failure (278x): A binary feature representing the wear of the drill bit's flank due to a combination of high feed rates and low cutting speeds. A value of 1 indicates significant flank wear, affecting the drilling operation's accuracy and efficiency, while 0 denotes a wear-free operation. - Wrong drill bit failure (300x): As a binary feature, it indicates the use of an inappropriate drill bit for the material being drilled. A value of 1 signifies a mismatch, leading to potential drilling issues, while 0 indicates the correct drill bit usage.

  5. f

    Datasets information.

    • plos.figshare.com
    xls
    Updated Oct 8, 2024
    + more versions
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    Xiaotong Bai; Yuefeng Zheng; Yang Lu; Yongtao Shi (2024). Datasets information. [Dataset]. http://doi.org/10.1371/journal.pone.0311602.t001
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    xlsAvailable download formats
    Dataset updated
    Oct 8, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Xiaotong Bai; Yuefeng Zheng; Yang Lu; Yongtao Shi
    License

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

    Description

    Hybrid feature selection algorithm is a strategy that combines different feature selection methods aiming to overcome the limitations of a single feature selection method and improve the effectiveness and performance of feature selection. In this paper, we propose a new hybrid feature selection algorithm, to be named as Tandem Maximum Kendall Minimum Chi-Square and ReliefF Improved Grey Wolf Optimization algorithm (TMKMCRIGWO). The algorithm consists of two stages: First, the original features are filtered and ranked using the bivariate filter algorithm Maximum Kendall Minimum Chi-Square (MKMC) to form a subset of candidate features S1; Subsequently, S1 features are filtered and sorted to form a candidate feature subset S2 by using ReliefF in tandem, and finally S2 is used in the wrapper algorithm to select the optimal subset. In particular, the wrapper algorithm is an improved Grey Wolf Optimization (IGWO) algorithm based on random disturbance factors, while the parameters are adjusted to vary randomly to make the population variations rich in diversity. Hybrid algorithms formed by combining filter algorithms with wrapper algorithms in tandem show better performance and results than single algorithms in solving complex problems. Three sets of comparison experiments were conducted to demonstrate the superiority of this algorithm over the others. The experimental results show that the average classification accuracy of the TMKMCRIGWO algorithm is at least 0.1% higher than the other algorithms on 20 datasets, and the average value of the dimension reduction rate (DRR) reaches 24.76%. The DRR reached 41.04% for 12 low-dimensional datasets and 0.33% for 8 high-dimensional datasets. It also shows that the algorithm improves the generalization ability and performance of the model.

  6. R

    Cdd Dataset

    • universe.roboflow.com
    zip
    Updated Sep 5, 2023
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    hakuna matata (2023). Cdd Dataset [Dataset]. https://universe.roboflow.com/hakuna-matata/cdd-g8a6g/3
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    zipAvailable download formats
    Dataset updated
    Sep 5, 2023
    Dataset authored and provided by
    hakuna matata
    License

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

    Variables measured
    Cumcumber Diease Detection Bounding Boxes
    Description

    Project Documentation: Cucumber Disease Detection

    1. Title and Introduction Title: Cucumber Disease Detection

    Introduction: A machine learning model for the automatic detection of diseases in cucumber plants is to be developed as part of the "Cucumber Disease Detection" project. This research is crucial because it tackles the issue of early disease identification in agriculture, which can increase crop yield and cut down on financial losses. To train and test the model, we use a dataset of pictures of cucumber plants.

    1. Problem Statement Problem Definition: The research uses image analysis methods to address the issue of automating the identification of diseases, including Downy Mildew, in cucumber plants. Effective disease management in agriculture depends on early illness identification.

    Importance: Early disease diagnosis helps minimize crop losses, stop the spread of diseases, and better allocate resources in farming. Agriculture is a real-world application of this concept.

    Goals and Objectives: Develop a machine learning model to classify cucumber plant images into healthy and diseased categories. Achieve a high level of accuracy in disease detection. Provide a tool for farmers to detect diseases early and take appropriate action.

    1. Data Collection and Preprocessing Data Sources: The dataset comprises of pictures of cucumber plants from various sources, including both healthy and damaged specimens.

    Data Collection: Using cameras and smartphones, images from agricultural areas were gathered.

    Data Preprocessing: Data cleaning to remove irrelevant or corrupted images. Handling missing values, if any, in the dataset. Removing outliers that may negatively impact model training. Data augmentation techniques applied to increase dataset diversity.

    1. Exploratory Data Analysis (EDA) The dataset was examined using visuals like scatter plots and histograms. The data was examined for patterns, trends, and correlations. Understanding the distribution of photos of healthy and ill plants was made easier by EDA.

    2. Methodology Machine Learning Algorithms:

    Convolutional Neural Networks (CNNs) were chosen for image classification due to their effectiveness in handling image data. Transfer learning using pre-trained models such as ResNet or MobileNet may be considered. Train-Test Split:

    The dataset was split into training and testing sets with a suitable ratio. Cross-validation may be used to assess model performance robustly.

    1. Model Development The CNN model's architecture consists of layers, units, and activation operations. On the basis of experimentation, hyperparameters including learning rate, batch size, and optimizer were chosen. To avoid overfitting, regularization methods like dropout and L2 regularization were used.

    2. Model Training During training, the model was fed the prepared dataset across a number of epochs. The loss function was minimized using an optimization method. To ensure convergence, early halting and model checkpoints were used.

    3. Model Evaluation Evaluation Metrics:

    Accuracy, precision, recall, F1-score, and confusion matrix were used to assess model performance. Results were computed for both training and test datasets. Performance Discussion:

    The model's performance was analyzed in the context of disease detection in cucumber plants. Strengths and weaknesses of the model were identified.

    1. Results and Discussion Key project findings include model performance and disease detection precision. a comparison of the many models employed, showing the benefits and drawbacks of each. challenges that were faced throughout the project and the methods used to solve them.

    2. Conclusion recap of the project's key learnings. the project's importance to early disease detection in agriculture should be highlighted. Future enhancements and potential research directions are suggested.

    3. References Library: Pillow,Roboflow,YELO,Sklearn,matplotlib Datasets:https://data.mendeley.com/datasets/y6d3z6f8z9/1

    4. Code Repository https://universe.roboflow.com/hakuna-matata/cdd-g8a6g

    Rafiur Rahman Rafit EWU 2018-3-60-111

  7. e

    CFRS II: 0000-00 and 1000+25 fields - Dataset - B2FIND

    • b2find.eudat.eu
    Updated May 9, 2023
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    (2023). CFRS II: 0000-00 and 1000+25 fields - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/1a1212ea-3c49-5055-bb16-6d6f78efc237
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    Dataset updated
    May 9, 2023
    Description

    This paper describes the methods used to obtain the spectroscopic data and construct redshift catalogs for the Canada-France Deep Redshift Survey (CFRS). The full data set consists of more than 1000 spectra, of objects with 17.5=<I_AB_=<22.5, obtained from deep multislit data with the MARLIN and MOS-SIS spectrographs at the Canada-France-Hawaii Telescope (CFHT). The final spectroscopic catalog contains 200 stars, 591 galaxies with secure redshifts in the range 0=<z=<1.3, six QSOs, and 146 objects with very uncertain or unknown redshifts, leading to an overall success rate of identification of 85%. In addition, 67 objects affected by observational problems have been placed in a supplemental list. We describe here the instrumental setup and the observing procedures used to gather this large data set efficiently. New optimal ways of packing spectra on the detector to increase significantly the multiplexing gain offered by multislit spectroscopy are described. Dedicated data reduction procedures have been developed under the IRAF environment to allow for fast and accurate processing. Very strict procedures have been followed to establish a reliable list of final spectroscopic measurements. Fully independent processing of the data has been carried out by three members of the team for each data set associated with a multislit mask, and final redshifts were assigned only after the careful comparison of the three independent measurements. A confidence class scheme was established. We strongly emphasize the benefits of such procedures. Finally, we present the spectroscopic data obtained for 303 objects in the 0000-00 and 1000+25 fields. The success rate in spectroscopic identification is 83% for the 0000-00 field and 84% for the 1000+25 field.

  8. e

    Economic Survey, February 1975 - Dataset - B2FIND

    • b2find.eudat.eu
    Updated Apr 29, 2023
    + more versions
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    (2023). Economic Survey, February 1975 - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/35d567a6-b99c-5e77-857a-91169fe6cac6
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    Dataset updated
    Apr 29, 2023
    Description

    Abstract copyright UK Data Service and data collection copyright owner.This study aims to assess the electorate's attitudes towards Britain's economic situation. Main Topics: Attitudinal/Behavioural Questions Satisfaction/dissatisfaction with way Government is running country/Mr. Wilson as Prime Minister/Mr. Heath as Leader of Opposition. Intended vote if there was a General Election, party most inclined to support. Degree of seriousness of Britain's economic problems and who is most to blame (Government/Trade Unions/employers), whether Government is taking the right action. Whether wage rises kept pace with price rises previous year, whether unemployment is likely to increase or decrease in near future, whether would prefer a cut in everyone's living standards or a slight rise in unemployment in order to solve Britain's economic problems. Whether family income is same/more/less than a year ago, whether family is better off/worse off/about the same financially as a year ago, whether it is easier/more difficult/same to make ends meet.Whether the Government should introduce laws to control prices/profits and dividends/wages and salaries, maximum percentage increase in wages that should be allowed during the current year. Knowledge of the Social Contract between the Government and the Trade Unions, and whether it is helping control wage claims/rate of inflation. Main causes of inflation and rising prices (for eg. joining the Common Market, metrication, trade union wage demands, etc.), current rate of inflation. Whether current levels of unemployment are acceptable. Full time employees only: when last had a pay rise, whether feels self as deserving of a rise, minimum percentage rise that would be acceptable to meet needs, whether would be prepared to accept a cut in pay or go without a rise that year. Background Variables Sex, marital status, whether head of household, household composition, age, cohort, social class, television area, age finished full-time education, employment status, registered elector, income, number of electors/ non-electors, occupation of head of household, trade union membership.

  9. T

    Chinese Yuan Data

    • tradingeconomics.com
    • jp.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jan 4, 2017
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    TRADING ECONOMICS (2017). Chinese Yuan Data [Dataset]. https://tradingeconomics.com/china/currency
    Explore at:
    xml, csv, excel, jsonAvailable download formats
    Dataset updated
    Jan 4, 2017
    Dataset authored and provided by
    TRADING ECONOMICS
    License

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

    Time period covered
    Jan 2, 1981 - Aug 22, 2025
    Area covered
    China
    Description

    The USD/CNY exchange rate fell to 7.1718 on August 22, 2025, down 0.12% from the previous session. Over the past month, the Chinese Yuan has weakened 0.29%, and is down by 0.80% over the last 12 months. Chinese Yuan - values, historical data, forecasts and news - updated on August of 2025.

  10. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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TRADING ECONOMICS (2025). Japan Interest Rate [Dataset]. https://tradingeconomics.com/japan/interest-rate

Japan Interest Rate

Japan Interest Rate - Historical Dataset (1972-10-02/2025-07-31)

Explore at:
284 scholarly articles cite this dataset (View in Google Scholar)
excel, xml, json, csvAvailable download formats
Dataset updated
Aug 8, 2025
Dataset authored and provided by
TRADING ECONOMICS
License

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

Time period covered
Oct 2, 1972 - Jul 31, 2025
Area covered
Japan
Description

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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