Report Filter Definitions and Guidance Please note that all filter options are present in the dataset. For example, if you are looking at a dataset and a state is missing, it means there is no data for the year selected in that state - it does not use a list of all US states. Also note that if the data table disappears, there is no data available for the filter selections made.

Summary

This dataset contains biographical information derived from articles on English Wikipedia as it stood in early June 2024. It was created as part of the Structured Contents initiative at Wikimedia Enterprise and is intended for evaluation and research use.

The beta sample dataset is a subset of the Structured Contents Snapshot focusing on people with infoboxes in EN wikipedia; outputted as json files (compressed in tar.gz).

We warmly welcome any feedback you have. Please share your thoughts, suggestions, and any issues you encounter on the discussion page for this dataset here on Kaggle.

Data Structure

• File name: wme_people_infobox.tar.gz
• Size of compressed file: 4.12 GB
• Size of uncompressed file: 21.28 GB

Noteworthy Included Fields: - name - title of the article. - identifier - ID of the article. - image - main image representing the article's subject. - description - one-sentence description of the article for quick reference. - abstract - lead section, summarizing what the article is about. - infoboxes - parsed information from the side panel (infobox) on the Wikipedia article. - sections - parsed sections of the article, including links. Note: excludes other media/images, lists, tables and references or similar non-prose sections.

The Wikimedia Enterprise Data Dictionary explains all of the fields in this dataset.

Stats

Infoboxes - Compressed: 2GB - Uncompressed: 11GB

Infoboxes + sections + short description - Size of compressed file: 4.12 GB - Size of uncompressed file: 21.28 GB

Article analysis and filtering breakdown: - total # of articles analyzed: 6,940,949 - # people found with QID: 1,778,226 - # people found with Category: 158,996 - people found with Biography Project: 76,150 - Total # of people articles found: 2,013,372 - Total # people articles with infoboxes: 1,559,985 End stats - Total number of people articles in this dataset: 1,559,985 - that have a short description: 1,416,701 - that have an infobox: 1,559,985 - that have article sections: 1,559,921

This dataset includes 235,146 people articles that exist on Wikipedia but aren't yet tagged on Wikidata as instance of:human.

Maintenance and Support

This dataset was originally extracted from the Wikimedia Enterprise APIs on June 5, 2024. The information in this dataset may therefore be out of date. This dataset isn't being actively updated or maintained, and has been shared for community use and feedback. If you'd like to retrieve up-to-date Wikipedia articles or data from other Wikiprojects, get started with Wikimedia Enterprise's APIs

Initial Data Collection and Normalization

The dataset is built from the Wikimedia Enterprise HTML “snapshots”: https://enterprise.wikimedia.com/docs/snapshot/ and focuses on the Wikipedia article namespace (namespace 0 (main)).

Who are the source language producers?

Wikipedia is a human generated corpus of free knowledge, written, edited, and curated by a global community of editors since 2001. It is the largest and most accessed educational resource in history, accessed over 20 billion times by half a billion people each month. Wikipedia represents almost 25 years of work by its community; the creation, curation, and maintenance of millions of articles on distinct topics. This dataset includes the biographical contents of English Wikipedia language editions: English https://en.wikipedia.org/, written by the community.

Attribution

Terms and conditions

Wikimedia Enterprise provides this dataset under the assumption that downstream users will adhere to the relevant free culture licenses when the data is reused. In situations where attribution is required, reusers should identify the Wikimedia project from which the content was retrieved as the source of the content. Any attribution should adhere to Wikimedia’s trademark policy (available at https://foundation.wikimedia.org/wiki/Trademark_policy) and visual identity guidelines (ava...

This dataset contains data collected during a study ("Towards High-Value Datasets determination for data-driven development: a systematic literature review") conducted by Anastasija Nikiforova (University of Tartu), Nina Rizun, Magdalena Ciesielska (Gdańsk University of Technology), Charalampos Alexopoulos (University of the Aegean) and Andrea Miletič (University of Zagreb) It being made public both to act as supplementary data for "Towards High-Value Datasets determination for data-driven development: a systematic literature review" paper (pre-print is available in Open Access here -> https://arxiv.org/abs/2305.10234) and in order for other researchers to use these data in their own work.

The protocol is intended for the Systematic Literature review on the topic of High-value Datasets with the aim to gather information on how the topic of High-value datasets (HVD) and their determination has been reflected in the literature over the years and what has been found by these studies to date, incl. the indicators used in them, involved stakeholders, data-related aspects, and frameworks. The data in this dataset were collected in the result of the SLR over Scopus, Web of Science, and Digital Government Research library (DGRL) in 2023.

Methodology

To understand how HVD determination has been reflected in the literature over the years and what has been found by these studies to date, all relevant literature covering this topic has been studied. To this end, the SLR was carried out to by searching digital libraries covered by Scopus, Web of Science (WoS), Digital Government Research library (DGRL).

These databases were queried for keywords ("open data" OR "open government data") AND ("high-value data*" OR "high value data*"), which were applied to the article title, keywords, and abstract to limit the number of papers to those, where these objects were primary research objects rather than mentioned in the body, e.g., as a future work. After deduplication, 11 articles were found unique and were further checked for relevance. As a result, a total of 9 articles were further examined. Each study was independently examined by at least two authors.

To attain the objective of our study, we developed the protocol, where the information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information.

Test procedure Each study was independently examined by at least two authors, where after the in-depth examination of the full-text of the article, the structured protocol has been filled for each study. The structure of the survey is available in the supplementary file available (see Protocol_HVD_SLR.odt, Protocol_HVD_SLR.docx) The data collected for each study by two researchers were then synthesized in one final version by the third researcher.

Description of the data in this data set

Protocol_HVD_SLR provides the structure of the protocol Spreadsheets #1 provides the filled protocol for relevant studies. Spreadsheet#2 provides the list of results after the search over three indexing databases, i.e. before filtering out irrelevant studies

The information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information

Descriptive information
1) Article number - a study number, corresponding to the study number assigned in an Excel worksheet 2) Complete reference - the complete source information to refer to the study 3) Year of publication - the year in which the study was published 4) Journal article / conference paper / book chapter - the type of the paper -{journal article, conference paper, book chapter} 5) DOI / Website- a link to the website where the study can be found 6) Number of citations - the number of citations of the article in Google Scholar, Scopus, Web of Science 7) Availability in OA - availability of an article in the Open Access 8) Keywords - keywords of the paper as indicated by the authors 9) Relevance for this study - what is the relevance level of the article for this study? {high / medium / low}

Approach- and research design-related information 10) Objective / RQ - the research objective / aim, established research questions 11) Research method (including unit of analysis) - the methods used to collect data, including the unit of analy-sis (country, organisation, specific unit that has been ana-lysed, e.g., the number of use-cases, scope of the SLR etc.) 12) Contributions - the contributions of the study 13) Method - whether the study uses a qualitative, quantitative, or mixed methods approach? 14) Availability of the underlying research data- whether there is a reference to the publicly available underly-ing research data e.g., transcriptions of interviews, collected data, or explanation why these data are not shared? 15) Period under investigation - period (or moment) in which the study was conducted 16) Use of theory / theoretical concepts / approaches - does the study mention any theory / theoretical concepts / approaches? If any theory is mentioned, how is theory used in the study?

Quality- and relevance- related information
17) Quality concerns - whether there are any quality concerns (e.g., limited infor-mation about the research methods used)? 18) Primary research object - is the HVD a primary research object in the study? (primary - the paper is focused around the HVD determination, sec-ondary - mentioned but not studied (e.g., as part of discus-sion, future work etc.))

HVD determination-related information
19) HVD definition and type of value - how is the HVD defined in the article and / or any other equivalent term? 20) HVD indicators - what are the indicators to identify HVD? How were they identified? (components & relationships, “input -> output") 21) A framework for HVD determination - is there a framework presented for HVD identification? What components does it consist of and what are the rela-tionships between these components? (detailed description) 22) Stakeholders and their roles - what stakeholders or actors does HVD determination in-volve? What are their roles? 23) Data - what data do HVD cover? 24) Level (if relevant) - what is the level of the HVD determination covered in the article? (e.g., city, regional, national, international)

Format of the file .xls, .csv (for the first spreadsheet only), .odt, .docx

Licenses or restrictions CC-BY

For more info, see README.txt

Dataset of topology optimization problems in 50x140 2D meshes. It was used to train machine learning models to accelerate topology optimization. Each sample contains:

Binary definition of supports

Sparse definitions of position and components of two point loads

Volume fraction

Von Mises and strain energy density fields of initial FEA solution

Final topology

Other than the dataset, contains files related to data filtering, and checkpoints of the trained generative adversarial networks.

Definition Extract of Ordnance Survey's Code-Point® Open product filtered for London Borough of Barnet coverage. Data has been processed from the .csv schema filtering on the administrative district code, with administrative district and ward names appended for ease of use Postcodes do not have an exact match to administrative boundaries, see the Ordnance Survey product support page for full definition. Purpose These datasets have been created as general resource for the council. The information is sourced from Ordnance Survey Open Data products and may be used more widely subject to the Open Government Licence (v3). Disclaimer This dataset is not the primary source and may not reflect the latest version or scope of the original product. You should assess whether using the original product directly is more appropriate for your purpose. Acknowledgements Contains OS data © Crown copyright and database right 2025 Contains Royal Mail data © Royal Mail copyright and Database right 2025 Contains National Statistics data © Crown copyright and database right 2025

Italy number dataset includes phone numbers that businesses can trust. The dataset comes from reliable sources, ensuring accuracy. These sources collect numbers from various places, such as public records and directories. You can also find source URLs, which help you verify where the data came from. This adds another layer of credibility to the information. Additionally, this data provides 24/7 support. This is important for businesses that need quick answers. Furthermore, this Italy number dataset follows an opt-in process. This means every person whose number appears in the list agreed to have their number shared. They understand how we will use their information, making it safe to contact them. With this number dataset, businesses gain access to trustworthy and reliable information. List to Data is a website that helps you quickly find important phone numbers. Italy phone data is a valuable database that allows businesses to filter information based on specific needs. This means you can filter the data by gender, age, and relationship status. For example, businesses can easily find numbers for younger people to reach that age group. This ability to filter information makes communication more effective. You can focus on the audience that matters most to you. Moreover, you can remove invalid Italy phone data from the list. That means if any number becomes inactive, you can take it out. Keeping only active numbers helps ensure that your contacts are always up-to-date. This process makes it easy to get up-to-date info regularly. The ability to filter, remove invalid data, and stay GDPR compliant makes this data powerful for organizations. Italy phone number list is a collection of phone numbers from people living in Italy. This list is very useful for businesses and organizations that want to reach out to these individuals. The numbers in this list are 100% correct and valid. This means that every number works, so businesses can call confidently. If any number does not work, you receive a replacement guarantee. Furthermore, every number in the Italy phone number list comes from a customer permission basis. This means that people on the list agreed to have their phone numbers shared. By using this list, businesses can effectively connect with the right people while keeping everything legal and safe. The valid numbers and replacement guarantee make this list an excellent tool for outreach.

This folder contains the formation energy of BDE-db, QM9, PC9, QMugs, and QMugs1.1 datasets by filtering (The training, test, and validation sets were randomly split in a ratio of 0.8, 0.1, and 0.1, respectively). The filtered process is described in the article "Graph-based deep learning models for thermodynamic property prediction: The interplay between target definition, data distribution, featurization, and model architecture" and the code can be found at https://github.com/chimie-paristech-CTM/thermo_GNN.After application of the filter procedure described in the article, final versions of the QM9 (127,007 data points), BDE-db (289,639 data points), PC9 (96,634 data points), QMugs (636,821 data points) and QMugs1.1 (70,546 data points) were obtained and used throughout this study.

The ckanext-custom-dataset-metadata extension for CKAN enhances dataset organization by introducing custom dataset types and a corresponding facet for filtering. This allows users to categorize datasets beyond the standard offerings and refine their searches based on these custom classifications. The extension aims to improve dataset discoverability and management within a CKAN instance by providing a flexible typing system. It appears this extension is somewhat outdated and should be considered alongside the newer ckanext-crc1153 extension. Key Features: Custom Dataset Types: Enables the definition of custom dataset types tailored to specific organizational needs, extending the default CKAN dataset classification options. Dataset Type Facet: Adds a new facet section to the CKAN interface, allowing users to filter datasets based on the defined custom dataset types for easier information retrieval. CKAN Integration: Seamlessly integrates with CKAN's existing framework, appearing as a plugin that enhances the dataset metadata schema without significantly altering core functionality. Use Cases: Research Data Repositories: Institutions can use custom dataset types to categorize research datasets based on methodologies (e.g., surveys, experiments, simulations), enabling researchers to locate data relevant to their specific field. Government Open Data Portals: Government agencies can classify datasets based on sector (e.g., health, education, transportation), improving the accessibility of data for citizens and businesses. Corporate Data Catalogs: Companies can leverage custom dataset types to categorize internal datasets according to department or project, facilitating data sharing and reuse across different teams. Technical Integration: The extension integrates with CKAN by registering itself as a plugin, adding custom dataset type fields to the dataset schema, and inserting a new faceted search component. Activation involves adding custom_dataset_type to the ckan.plugins configuration setting and restarting CKAN to apply the changes. The requirements.txt file would likely specify any additional python packages required for the extension to function correctly. Benefits & Impact: By implementing the ckanext-custom-dataset-metadata extension, CKAN administrators can provide a more tailored and user-friendly experience for accessing datasets. This ultimately enhances the value of the data catalog by improving dataset discoverability and promoting data reuse within the organization. The ability to filter datasets based on custom types empowers users to quickly locate relevant information, streamlining their workflows and fostering data-driven decision-making. Keep in mind that the extension may have limited compatability beyond CKAN 2.9.

This dataset contains the data dictionary in one worksheet, describing the fields of analytical data and descriptive data relating to each of the grab samples taken for the project (second worksheet). The data dictionary also describes caveats and limitations of the data. This dataset is associated with the following publication: Bosscher, V., D. Lytle, M. Schock, A. Porter, and M. Deltoral. POU Water Filters Effectively Reduce Lead in Drinking Water: A Demonstration Field Study in Flint, Michigan. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 54(5): 484-493, (2019).

Japan number dataset allows you to filter phone numbers based on different criteria. You can pick contacts by gender, age, and whether they are single or taken. This feature makes it easy for you to find the right contacts for your needs. We define this title so you can access the most relevant information. Additionally, we regularly remove invalid data to keep the list accurate and reliable. Also, using the Japan number dataset makes your search much simpler. You can easily find contacts that fit your specific needs. Following GDPR rules helps us respect everyone’s privacy while providing useful information. Moreover, we always remove invalid data to keep the list correct. This way, you get the most reliable contact numbers. Japan Phone Data contains contact numbers collected from trusted sources. We define this title to make sure you have reliable and correct information. You can check the source URLs to see where we got the data. Moreover, we provide support 24/7 to help you with any questions. We are always available to support you. Additionally, we only collect opt-in data. This means that everyone on the list has agreed to share their contact details. With Japan Phone Data, you can feel confident that you have the right information. We gather data from trusted sources to ensure every number is correct. If you have any questions, you can reach out for help anytime. We want to help you connect with others easily. The List to Data helps you to find contact information for businesses. Japan phone number list helps you find the right phone numbers easily. You can filter this list by gender, age, and relationship status. This feature helps narrow your search and find exactly what you need. We define this list to provide the best data. Additionally, we remove invalid data regularly to keep the list fresh. Using the Japan phone number list is simple and quick. You can find contacts that match your needs without any hassle. Furthermore, we work hard to remove invalid data so you only see valid numbers. This effort helps keep your searches accurate and efficient. Overall, this list is a great tool for connecting with people in Japan while respecting their privacy.

The coronavirus disease 2019 pandemic has impacted and changed consumer behavior because of a prolonged quarantine and lockdown. This study proposed a theoretical framework to explore and define the influencing factors of online consumer purchasing behavior (OCPB) based on electronic word-of-mouth (e-WOM) data mining and analysis. Data pertaining to e-WOM were crawled from smartphone product reviews from the two most popular online shopping platforms in China, Jingdong.com and Taobao.com. Data processing aimed to filter noise and translate unstructured data from complex text reviews into structured data. The machine learning based K-means clustering method was utilized to cluster the influencing factors of OCPB. Comparing the clustering results and Kotler’s five products level, the influencing factors of OCPB were clustered around four categories: perceived emergency context, product, innovation, and function attributes. This study contributes to OCPB research by data mining and analysis that can adequately identify the influencing factors based on e-WOM. The definition and explanation of these categories may have important implications for both OCPB and e-commerce.

Iran number dataset allows you to filter phone numbers by gender, age, and relationship status. You can easily find the contacts you need with this helpful feature. We create this list to give you the best data for your search. Additionally, we remove invalid data regularly to keep the list fresh and accurate. This method keeps your info fresh, giving you the latest details each time. Moreover, using the Iran number dataset helps you find the exact contacts you need. You can filter numbers based on gender, age, or relationship status, making it simple to target your audience. We follow GDPR rules to respect everyone’s privacy. Plus, we remove invalid data and provide updates. Therefore, you always have access to the latest, reliable contact details. Iran phone data contains 100% correct and valid phone numbers. We define this list to ensure all numbers are checked. Thus, they can be used easily. If a number doesn’t work, we offer a replacement guarantee. This means we will replace any invalid numbers with correct ones at no extra cost. List to Data is a helpful website for finding important phone numbers quickly. Additionally, the Iran phone data gives you reliable contact information. Every number is verified to ensure it’s valid. If you find any invalid numbers, our replacement guarantee covers them. We make sure you only get the correct data. By collecting numbers with customer permission, we respect privacy and ensure fair information sharing. Iran phone number list helps you find contact numbers easily. This list includes phone numbers collected from trusted sources. We define this list to ensure you have accurate and reliable data. You can see the source websites to find out where we got the info. This transparency builds trust, so you feel confident using the list. Also, we offer 24/7 support to help you whenever you need it. Also, the data comes from opt-in sources, meaning people agreed to be contacted. Moreover, the Iran phone number list makes it easy to connect with people. You can trust the data because we collect it from reliable sources and verify it using the source URLs provided. Our customer support makes sure you can get answers anytime you need help.

The data include atmospheric-loading frequency response functions (table 1) and filtered detrended and reconstructed (trends restored) groundwater levels (tables 2–4) computed for selected, parsed time series for three USGS monitoring wells [BR–1 (USGS site 410233104093203); LN–1 (USGS site 410233104093202); and FH–1 (USGS site 410233104093201)], and the associated hourly resampled water-level and barometric-pressure time-series "pieces" (tables 2–4) used to create the parsed series. Table headings are defined in the Data Dictionary. Digital filters were developed based on the computed water-level response to Earth tides and barometric pressure in all three wells, and these filters were used to compute the filtered water-level time series in tables 2, 3 and 4 for wells BR–1, LN–1, and FH–1, respectively. The content of tables (1–4) and the development of the digital filters and filtering techniques are described in the associated publication, https://doi.org/10.3133/sir20215020.

The University of Wyoming (UW) King Air atmospheric boundary layer measurement missions were flown in 1987 during IFCs 3 and 4. This Raw Boundary Layer Fluxes data set contains parameters that describe the environment in which the flux data were collected and the flux data itself. The fluctuations in all variables were calculated with three different methods (the arithmetic means removed, the linear trends removed, or filtered with a high-pass recursive filter) prior to the eddy correlation calculations. This data set contains the data with the arithmetic means removed (i.e., RAW). All the flux measurements were obtained with the eddy-correlation method, wherein the aircraft is equipped with an inertial platform, accelerometers, and a gust probe for measurement of earth-relative gusts in the x, y, and z directions. Gusts in these dimensions are then correlated with each other for momentum fluxes and with fluctuations in other variables to obtain the various scalar fluxes, such as temperature (for sensible heat flux) and water vapor mixing ratio (for latent heat flux). The summary of data calculated from each aircraft pass includes various statistics, correlations, and fluxes calculated after the time series for each variable with the arithmetic means removed.

The first version of the Swift UVOT Serendipitous Source Catalog (UVOTSSC) provides positions and magnitudes, as well as errors and upper limits of confirmed sources, for observations taken from the start of operations in 2005 until October 1st of 2010. The first version of the UVOTSSC has been produced by processing the image data obtained from the Swift Ultraviolet and Optical Telescope (UVOT). The data processing was performed at the Mullard Space Science Laboratory (MSSL, University College London, U.K.) using Swift FTOOLS from NASA's High Energy Astrophysics Software package (HEASoft-6.11), with some customizing of the UVOT packages in order to get more complete source detection and to properly apply quality flags to those sources that were detected within the UVOT image artifacts. The total number of observations with 17'x17' images used for version 1 of the catalog is 23,059, giving 6,200,016 sources in total, of which 2,027,265 have multiple entries in the source table because they have been detected in more than one observation. Some sources were only observed in one filter. The total number of entries in the source table is 13,860,568. The S/N ratio for all sources exceeds 5 in at least one UVOT filter, the rest of the filters having a S/N greater than 3. One Swift ObsID can consist of one or more images, which for this catalog have been summed, yielding the quoted total exposure times. The original UVOT images can be found in the on-line archives at MAST, and in the Swift archives at http://swift.ac.uk/ and at the HEASARC (http://heasarc.gsfc.nasa.gov/W3Browse/all/swiftmastr.html), using the ObsID as the search key. For higher temporal resolution, the original images need to be used because the catalog data herein are summed over all of the individual images within an ObsID. The upper limits per filter for the summed images are constructed for each ObsID because the sensitivity hardly varies over the detector. Usually the images within one ObsID share the same pointing, however, whereas the quoted upper limits always apply for sources near the pointing direction given, if the images had small offsets in pointing they may not apply to sources near the edge of the summed image, which is typically about 8 arcminutes from the quoted pointing direction. U, B, V, UVW2, UVM2 and UVW1 refer to the filter bandpasses defined in the UVOT Filterwheel section of the MSSL documentation at http://www.mssl.ucl.ac.uk/www_astro/uvot/uvot_instrument/filterwheel/filterwheel.html. This HEASARC table contains version 1.1 of the Swift UVOT table of observations in which the sources in the source table were detected and contains the details of 23,059 Swift UVOT observations. The HEASARC has changed the names of many of the parameters from those given in the original table. In such cases, we have listed the original names in parentheses at the end of the parameter descriptions given below. There is a related table which lists the 13,860,568 source detections that is available at the HEASARC as the SWUVOTSSC table. This table was created by the HEASARC in May 2017 based upon the CDS Catalog II/339 file summary.dat. This is a service provided by NASA HEASARC .

Intending to cover the existing gap regarding behavioral datasets modelling interactions of users with individual a multiple devices in Smart Office to later authenticate them continuously, we publish the following collection of datasets, which has been generated after having five users interacting for 60 days with their personal computer and mobile devices. Below you can find a brief description of each dataset.Dataset 1 (2.3 GB). This dataset contains 92975 vectors of features (8096 per vector) that model the interactions of the five users with their personal computers. Each vector contains aggregated data about keyboard and mouse activity, as well as application usage statistics. More info about features meaning can be found in the readme file. Originally, the number of features of this dataset was 24 065 but after filtering the constant features, this number was reduced to 8096. There was a high number of constant features to 0 since each possible digraph (two keys combination) was considered when collecting the data. However, there are many unusual digraphs that the users never introduced in their computers, so these features were deleted in the uploaded dataset.Dataset 2 (8.9 MB). This dataset contains 61918 vectors of features (15 per vector)that model the interactions of the five users with their mobile devices. Each vector contains aggregated data about application usage statistics. More info about features meaning can be found in the readme file.Dataset 3 (28.9 MB). This dataset contains 133590vectors of features (42 per vector)that model the interactions of the five users with their mobile devices. Each vector contains aggregated data about the gyroscope and Accelerometer sensors.More info about features meaning can be found in the readme file.Dataset 4 (162.4 MB). This dataset contains 145465vectors of features (241 per vector)that model the interactions of the five users with both personal computers and mobile devices. Each vector contains the aggregation of the most relevant features of both devices. More info about features meaning can be found in the readme file.Dataset 5 (878.7 KB). This dataset is composed of 7 datasets. Each one of them contains an aggregation of feature vectors generated from the active/inactive intervals of personal computers and mobile devices by considering different time windows ranging from 1h to 24h.1h: 4074 vectors2h: 2149 vectors3h: 1470 vectors4h: 1133 vectors6h: 770 vectors12h: 440 vectors24h: 229 vectors

Bangladesh number dataset provides contact information from trusted sources. We only collect phone numbers from reliable sources and define this information. To ensure transparency, we also provide the source URL to show where the information was collected from. In addition, we offer 24/7 support. If you have a question or need help, we’re always here. However, we care about accuracy, so we carefully collect the Bangladesh number dataset from trusted sources. You may rely on this data for business or personal use. With customer support, you’ll never have to wait when you need help or more information. We use opt-in data to respect privacy. This way, we contact only people who want to hear from you. Bangladesh phone data gives you access to contacts in Bangladesh. Here you can filter information by gender, age, and relationship status. This makes it easy to find exactly the people you want to connect with. We define this data by ensuring it follows all GDPR rules to keep it safe and legal. Our system works hard to remove any invalid data so you get only accurate and valid numbers. List to Data is a helpful website for finding important phone numbers quickly. Also, our Bangladesh phone data is suitable for doing business targeting specific groups. You can easily filter your list to focus on specific types of customers. Since we remove invalid data regularly, you don’t have to deal with old or useless numbers. We assure you that all data follows strict GDPR rules, so you can use it without any problems. Bangladesh phone number list is a collection of phone numbers from people in Bangladesh. We define this list by providing 100% correct and valid phone numbers that are ready to use. Also, we offer a replacement guarantee if you ever receive an invalid number. This means you will always have accurate data. We collect phone numbers that we provide based on customer’s permission. Moreover, we work hard to provide the best Bangladesh phone number list for businesses and personal use. We gather data correctly, so you won’t have to worry about getting outdated or incorrect information. Our replacement guarantee means you’ll always have valid numbers, so you can relax and feel confident.

This dataset article describes the curation and preprocessing of the 2024 Eco-Innovation Index (EII) dataset, published by the European Commission. The raw dataset (in .xlsx format) was filtered to focus on the 2024 report, and missing values in the "Water Productivity" indicator were addressed via two imputation methods: (1) EU27 mean substitution and (2) cluster-based mean imputation using K-means, an unsupervised machine learning algorithm.

Macedonia number dataset is a collection of phone numbers from people living in Macedonia. You can filter the data by gender, age, and relationship status. This flexibility helps you connect with the right audience. If you want to reach young adults or families, you can quickly find the right numbers. This makes your communication more effective and targeted. List to Data helps find phone numbers for your business. Additionally, the Macedonia number dataset follows GDPR rules. These rules protect people’s privacy and ensure that all data usage is legal. You can remove invalid data, keeping only active, accurate numbers. This helps update your list as numbers change. With this database, you have access to information that is not only reliable but also respectful of privacy. Macedonia phone data refers to a database of phone numbers that is 100% correct and valid. We carefully check every number in this database to ensure it works. This means businesses can call these numbers confidently, knowing they will reach real people. If you find a number that doesn’t work, you have a replacement guarantee. This means the company will give you a new number for free. Therefore, your contact list stays fresh and reliable. Furthermore, all phone numbers in this Macedonia phone data are based on a customer permission basis. This means each person included their number in the database. They know they use their information safely and ethically. You can trust this data for marketing or outreach efforts. Overall, phone data from Macedonia provides a strong foundation for any outreach campaign. Macedonia phone number list is a valuable tool that allows you to filter information based on specific needs. This list is helpful for businesses and organizations that want to reach out to people in this country. The phone numbers come from trusted sources, meaning companies gather data from reliable sources. You can also check the source URLs to see where the information comes from. Moreover, the Macedonia phone number list follows an opt-in process. This means that everyone on the list of Macedonia agreed to share their phone number. They understand that they will use their information and permit it. This ensures the data is legal and respectful of people’s privacy. Businesses can use the list without worrying about breaking any rules.

This upload contains dataset splits of SoundDesc [1] and other supporting material for our paper:

Data leakage in cross-modal retrieval training: A case study [arXiv] [ieeexplore]

In our paper, we demonstrated that a data leakage problem in the previously published splits of SoundDesc leads to overly optimistic retrieval results.
Using an off-the-shelf audio fingerprinting software, we identified that the data leakage stems from duplicates in the dataset.
We define two new splits for the dataset: a cleaned split to remove the leakage and a group-filtered to avoid other kinds of weak contamination of the test data.

SoundDesc is a dataset which was automatically sourced from the BBC Sound Effects web page [2]. The results from our paper can be reproduced using clean_split01 and group_filtered_split01.

If you use the splits, please cite our work:

Benno Weck, Xavier Serra, "Data Leakage in Cross-Modal Retrieval Training: A Case Study," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10094617.

@INPROCEEDINGS{10094617,
 author={Weck, Benno and Serra, Xavier},
 booktitle={ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
 title={Data Leakage in Cross-Modal Retrieval Training: A Case Study}, 
 year={2023},
 volume={},
 number={},
 pages={1-5},
 doi={10.1109/ICASSP49357.2023.10094617}}

References:

[1] A. S. Koepke, A. -M. Oncescu, J. Henriques, Z. Akata and S. Albanie, "Audio Retrieval with Natural Language Queries: A Benchmark Study," in IEEE Transactions on Multimedia, doi: 10.1109/TMM.2022.3149712.

[2] https://sound-effects.bbcrewind.co.uk/