This data about nola.gov provides a window into how people are interacting with the the City of New Orleans online. The data comes from a unified Google Analytics account for New Orleans. We do not track individuals and we anonymize the IP addresses of all visitors.

Comprehensive dataset analyzing Amazon's daily website visits, traffic patterns, seasonal trends, and comparative analysis with other ecommerce platforms based on May 2025 data.

Context

This is a dataset created and curated by Demo Data - visit https://demodata.ai/digital to learn more and get the full dataset.

Content

Visits, sessions, ad events, and campaigns on an eCommerce website. Dataset details: - 1,000 ad campaigns - 10B events per year

Comprehensive dataset analyzing Walmart.com's daily website traffic, including 16.7 million daily visits, device distribution, geographic patterns, and competitive benchmarking data.

Young people's open spaces (play areas) in York. For further information please visit City of York Council's website. *Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.

Source:

Mariusz Šapczyński, Cracow University of Economics, Poland, lapczynm '@' uek.krakow.pl Sylwester Białowąs, Poznan University of Economics and Business, Poland, sylwester.bialowas '@' ue.poznan.pl

Data Set Information:

The dataset contains information on clickstream from online store offering clothing for pregnant women. Data are from five months of 2008 and include, among others, product category, location of the photo on the page, country of origin of the IP address and product price in US dollars.

Attribute Information:

The dataset contains 14 variables described in a separate file (See 'Data set description')

Relevant Papers:

N/A

Citation Request:

If you use this dataset, please cite:

Šapczyński M., Białowąs S. (2013) Discovering Patterns of Users' Behaviour in an E-shop - Comparison of Consumer Buying Behaviours in Poland and Other European Countries, “Studia Ekonomiczne†, nr 151, “La société de l'information : perspective européenne et globale : les usages et les risques d'Internet pour les citoyens et les consommateurs†, p. 144-153

Data description ìe-shop clothing 2008î

Variables:

1. YEAR (2008)

========================================================

2. MONTH -> from April (4) to August (8)

========================================================

3. DAY -> day number of the month

========================================================

4. ORDER -> sequence of clicks during one session

========================================================

5. COUNTRY -> variable indicating the country of origin of the IP address with the

following categories:

1-Australia 2-Austria 3-Belgium 4-British Virgin Islands 5-Cayman Islands 6-Christmas Island 7-Croatia 8-Cyprus 9-Czech Republic 10-Denmark 11-Estonia 12-unidentified 13-Faroe Islands 14-Finland 15-France 16-Germany 17-Greece 18-Hungary 19-Iceland 20-India 21-Ireland 22-Italy 23-Latvia 24-Lithuania 25-Luxembourg 26-Mexico 27-Netherlands 28-Norway 29-Poland 30-Portugal 31-Romania 32-Russia 33-San Marino 34-Slovakia 35-Slovenia 36-Spain 37-Sweden 38-Switzerland 39-Ukraine 40-United Arab Emirates 41-United Kingdom 42-USA 43-biz (.biz) 44-com (.com) 45-int (.int) 46-net (.net) 47-org (*.org)

========================================================

6. SESSION ID -> variable indicating session id (short record)

========================================================

7. PAGE 1 (MAIN CATEGORY) -> concerns the main product category:

1-trousers 2-skirts 3-blouses 4-sale

========================================================

8. PAGE 2 (CLOTHING MODEL) -> contains information about the code for each product

(217 products)

========================================================

9. COLOUR -> colour of product

1-beige 2-black 3-blue 4-brown 5-burgundy 6-gray 7-green 8-navy blue 9-of many colors 10-olive 11-pink 12-red 13-violet 14-white

========================================================

10. LOCATION -> photo location on the page, the screen has been divided into six parts:

1-top left 2-top in the middle 3-top right 4-bottom left 5-bottom in the middle 6-bottom right

========================================================

11. MODEL PHOTOGRAPHY -> variable with two categories:

1-en face 2-profile

========================================================

12. PRICE -> price in US dollars

========================================================

13. PRICE 2 -> variable informing whether the price of a particular product is higher than

the average price for the entire product category

1-yes 2-no

========================================================

14. PAGE -> page number within the e-store website (from 1 to 5)

++++++++++++++++++++++++++++++++++++++++++++++++++++++++

Children's open spaces (play areas) in York. For further information please visit City of York Council's website. *Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.

Jamaica number dataset makes your telemarketing more beneficial. Thus, this Jamaica number dataset has correct and up-to-date mobile numbers for direct marketing. As of 2024, there are about 3.27 Million mobile phone connections in Jamaica. This number is a bit higher than the total population, which is around 2.83 Million. Our List To Data website can assist in getting speedy replies from new clients for publicity. Besides, the Jamaica number dataset is effective for SMS marketing as well. As well as you have multiple chances to earn huge from other countries. So, using this contact number library is a perfect choice for reaching people in specific places. By using our library, you can enhance your marketing and find new B2C clients easily. Jamaica phone data is a great way to help your business grow. Also, this Jamaica phone data provides the most real and active phone numbers so you can easily reach people in Jamaica. Everybody can select who they want to contact based on their location, what their company does, or how big their company is. Further, the Jamaica phone data is very authentic and useful for finding new customers. At the same time, the sellers can deliver sales promotions and many offers to the consumers. Also, they can connect with the largest group of customers quickly in a selected area. List To Data includes contact leads for both businesses and individuals. Jamaica phone number list will make your business more profitable. Most importantly, a Jamaica phone number list plays a vital role in marketing and business, so take it now. Just visit our List To Data website today to get the most recent phone numbers for any business. With 95% precision, this contact book offers you contact numbers for many people who might want your services. So, the Jamaica phone number list is a great tool for reaching new customers through phone calls. In fact, you can pick from other packages on our website that fit your needs and budget. If your business is big or small, our mobile number data will help you in your entire journey. Ultimately, our team supplies this correct contact number cautiously as per your needs.

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

Analyze the marketing spending.

1- Overall ROMI 2- ROMI by campaigns 3- Performance of the campaign depending on the date - on which date did we spend the most money on advertising, when we got the biggest revenue when conversion rates were high and low? What were the average order values? 4- When buyers are more active? What is the average revenue on weekdays and weekends? 5- Which types of campaigns work best - social, banner, influencer, or a search? 6- Which geo locations are better for targeting - tier 1 or tier 2 cities?

Column. Description Date date of spending of the marketing budget Campaign name description of campaign Category type of marketing source Campaign id unique identifier Impressions number of times the ad has been shown Mark. budget money spent on this campaign on this day Clicks how many people clicked on a banner (=visited website) Leads how many people signed up and left their credentials Orders how many people paid for the product Revenue how much money we earned

Clicks, Leads, orders, and revenue are calculated for a specific marketing campaign on a specific date. E.g. For the “facebook_tier1” marketing campaign on the 1st of February, we spent INR 7,307.37, got 148,263 impressions that converted to 1,210 clicks that in turn converted to 13 leads and 1 order. We earned INR 4,981.

This data reflects some facts about what happened - how much we spent, how much we earned, how customers behaved (who clicked on the ad banner, who signed up, who paid). Now we need to calculate marketing metrics that would help us evaluate if we did a good job or not and also identify some parameters of the campaign that would be important for analysis. What are these metrics:

• Return on marketing investment (ROMI)
• Cost per click (CPC)
• Cost per lead (CPL)
• Customer acquisition cost (CAC)
• Average order value (AOV)
• Conversion rate 1
• Conversion rate 2

These metrics are actionable and allow us not only to analyze but to make decisions and act to improve the business result.

Let’s dive deeper.

ROMI return on marketing investments, how effective is marketing
campaign, one metric that shows effectiveness of every rupee spent. It is calculated ( Total earning (Revenue) - Marketing cost ) / Marketing cost )

Click-through rate(CTR). percentage of people who clicked at banner (Clicks/ Impressions)

Conversion 1 conversion from visitors to leads for this campaign (Leads/Click)

Conversion 2 conversion rate from leads to sales (Orders/Leads)

Average order value (AOV) Average order value for this campaign (Revenue/Number of Orders)

Cost per click (CPC) how much does it cost us to attract 1 click (on average) (Marketing spending/Clicks)

Cost per lead (CPL) how much does it cost us to attract 1 lead (on average) (Marketing spending/Leads)

Customer acquisition cost (CAC) -- how much does it cost us to attract 1 order (on average) (marketing spend/ orders) Gross profit Profit or loss after deducting marketing cost (Revenue-Marketing spending)

ROMI is the most important metric and it is used as the ultimate way to evaluate if the campaign is good or bad.

You can use this article to know more about marketing metrics. https://www.owox.com/blog/articles/digital-marketing-metrics-and-kpis/

Global C2C Fashion Store User Behaviour Analysis

Analyzing Buyer and Seller Profiles across Countries

By Jeffrey Mvutu Mabilama [source]

About this dataset

Welcome to an exciting exploration of global C2C fashion store user behaviour! This dataset seeks to serve as a benchmark by providing valuable insights into e-commerce users, enabling you to make informed decisions and effectively grow your business. Let's dive right into the data!

This dataset contains records on over 9 million registered users from a successful online C2C fashion store launched in Europe around 2009 and later expanded worldwide. It includes metrics such as country, gender, active users, top buyers/sellers/ratio*, products bought/sold/listed* and social network features (likes/follows). Furthermore this is just a preview of much larger data set which contains more detailed information including product listings, comments from listed products etc.

E-commerce has become an essential part of our lives - people are now accustomed to buying anything with a few clicks online. With so many unknown elements that come with not only selling but also providing good customer service - understanding user behavior is key for success in this domain. By utilizing this dataset you can answer questions such as 'how many customers are likely to drop off after years of using my service?,' 'are my users active enough compared to those in this dataset?,” or “how likely are people from other countries signing up in a C2C website?' In addition, if you think this kind odf dataset may be useful don't forget do show your support or appreciation by leaving an upvote or comment on the page!

My Telegram bot will answer any queries regarding the datasets as well allow you see contact me directly if necessary; also please don't forget check out the *[data.world page](https://data.world/jfreex/e-commerce-users-of-a-french-c2c

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides a useful overview of global users' behavior in an online C2C fashion store. The data includes metrics such as buyers, top buyers, top buyer ratio, female buyers and their respective ratios, etc., per country. This dataset can be used to gain insights into how global audiences interact with the store and draw conclusions from comparison between different countries.

In order to make use of this dataset, one must first familiarize themselves with the various metrics included in it. These include: country; number of overall buyers; number of top buyers; ratio(s) of them (top buyer to total buyer); female-related data (buyers, top female buyers); bought-to-wish/like ration (top and non-top separately); overall products bought/wished/liked; total products sold by tops sellers in the same country versus what they sold outside the country; mean value for product stats (sold/listed/etc...) from looking at the whole population or just users that make those actions multiple times; average days for user offline /lurking around on the site without posting anything or buying anything etc.; mean follower(s) count(s).

Using this data one could generate reports about user behavior within particular countries either manually by computing all statistics or by using libraries like Pandas or SQL with queries made toward this datasets which consists of columns representing individual countries with all values necessary to answer any questions you might have regarding how many people buy something out there per region and what type they are –– Are they Top Buyer? Female? Etc.

Further potential work could involve utilising machine learning tools such as clustering algorithms to group similar customers together based on certain traits like age group, profession etc., so that personalised marketing promotions can be targetted at these customer clusters rather than aiming more generic ads at everyone!

Finally combined with other related product datasets which is available upon request via JfreexDatasets_bot provided by Jfreex team , this dataset can become another powerful tool providing you actionable insights into customers today — allowing you build better strategies towards improving customer experience tomorrow!

Research Ideas

• Analyzing the conversion rate of users on a website - Comparing user metrics like the overall number of buyers, female buyers, top buyers ratio and top buyer gender can help determine if users in certain countries are more or less likely to convert into customers. Additionally, comparing average metrics like products bought or offl...

Likes and image data from the community art website Behance. This is a small, anonymized, version of a larger proprietary dataset.

Metadata includes

• appreciates (likes)

• timestamps

• extracted image features

Basic Statistics:

• Users: 63,497

• Items: 178,788

• Appreciates (likes): 1,000,000

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

How much time do people spend on social media?

              As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in
              the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively.
              People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general.
              During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

Finland number dataset makes your SNS marketing more profitable. Thus, this Finland number dataset has correct and up-to-date mobile numbers for direct marketing. As of 2024, there are about 9.21 Million mobile phone connections in Finland. This number is a bit higher than the total population, which is around 5.55 Million. This List To Data can assist in getting speedy replies from new clients for advertising. Besides, the Finland number dataset is effective for SMS marketing as well. In addition, you have multiple chances to earn huge from other countries. Thus, using this contact number library is an ideal selection for reaching people in specific areas. By using this phone book, you can enhance your marketing and find new B2C clients easily. Finland phone data is a wonderful way to help your business grow. Also, this Finland phone data gives the most real and active phone numbers so you can easily reach people in Finland. Anyone can decide who they like to contact based on their location, what their company does, or how big their company is. Further, the Finland phone data is very faithful and useful for finding new customers. In other words, the sellers can give sales promotions and many offers to the consumers. Hence, they can connect with the largest group of customers quickly in a fixed area. Through the List To Data, both businesses and individuals can earn a better rerun on investment [ROI]. Finland phone number list will make your business more profitable. Even, it plays a vital role in marketing and business, so take the Finland phone number list now. So, visit our List To Data website today to obtain the most recent mobile numbers for your business. This phone book offers you 95% accurate contact numbers for many people who might want your services. Also, the Finland phone number list is a great tool for reaching new customers through phone calls. Moreover, you can pick from different packages on this website that fit your needs and budget. If buy it at a reasonable price, our mobile database will help you in your entire journey. Yet, our team supplies the correct contact number cautiously as per your needs.

London Borough level tourism trip estimates (thousands). The ‘top-down’ nature of the Local Area Tourism Impact (LATI) model (starting with London data) means it is best suited to disaggregate expenditure. However, tourism trips were also disaggregated for comparative purposes using the estimated proportions of spending by overseas, domestic and day visitors in the boroughs. Since the trip estimates are derived from data on trips to London they do not account for trips to different boroughs by visitors whilst in London. Indicative borough level day visitor/tourist estimates for 2007 were derived from the LDA’s own experimental London level day visitor estimates. As such the borough level day visitor estimates should be treated with caution and the 2007 day visitor estimates are not comparable with those from previous years. They are intended only to give a best estimate of the scale of day visitor tourism in each borough from the currently available data. Further tourism data for UK regions covering trends in visits, nights, and spend to London by visitors from overseas is available on the Visit Britain website. Analyse data by age, purpose, duration, and quarter. This dataset is no longer updated.

[1] The Progress by Population Group analysis is a component of the Healthy People 2020 (HP2020) Final Review. The analysis included subsets of the 1,111 measurable HP2020 objectives that have data available for any of six broad population characteristics: sex, race and ethnicity, educational attainment, family income, disability status, and geographic location. Progress toward meeting HP2020 targets is presented for up to 24 population groups within these characteristics, based on objective data aggregated across HP2020 topic areas. The Progress by Population Group data are also available at the individual objective level in the downloadable data set. [2] The final value was generally based on data available on the HP2020 website as of January 2020. For objectives that are continuing into HP2030, more recent data will be included on the HP2030 website as it becomes available: https://health.gov/healthypeople. [3] For more information on the HP2020 methodology for measuring progress toward target attainment and the elimination of health disparities, see: Healthy People Statistical Notes, no 27; available from: https://www.cdc.gov/nchs/data/statnt/statnt27.pdf. [4] Status for objectives included in the HP2020 Progress by Population Group analysis was determined using the baseline, final, and target value. The progress status categories used in HP2020 were: a. Target met or exceeded—One of the following applies: (i) At baseline, the target was not met or exceeded, and the most recent value was equal to or exceeded the target (the percentage of targeted change achieved was equal to or greater than 100%); (ii) The baseline and most recent values were equal to or exceeded the target (the percentage of targeted change achieved was not assessed). b. Improved—One of the following applies: (i) Movement was toward the target, standard errors were available, and the percentage of targeted change achieved was statistically significant; (ii) Movement was toward the target, standard errors were not available, and the objective had achieved 10% or more of the targeted change. c. Little or no detectable change—One of the following applies: (i) Movement was toward the target, standard errors were available, and the percentage of targeted change achieved was not statistically significant; (ii) Movement was toward the target, standard errors were not available, and the objective had achieved less than 10% of the targeted change; (iii) Movement was away from the baseline and target, standard errors were available, and the percent change relative to the baseline was not statistically significant; (iv) Movement was away from the baseline and target, standard errors were not available, and the objective had moved less than 10% relative to the baseline; (v) No change was observed between the baseline and the final data point. d. Got worse—One of the following applies: (i) Movement was away from the baseline and target, standard errors were available, and the percent change relative to the baseline was statistically significant; (ii) Movement was away from the baseline and target, standard errors were not available, and the objective had moved 10% or more relative to the baseline. NOTE: Measurable objectives had baseline data. SOURCE: National Center for Health Statistics, Healthy People 2020 Progress by Population Group database.

First Impressions Dataset

The dataset contains 20,000 images of people. For each person, a first impression of them was created. The first impression is a text consisting of several sentences.

  💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on our website to buy the dataset






  Content

The dataset includes a folder with images of 20,000 people. The .csv file consists of columns:

image_id - the… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/first-impressions-dataset.

Statistics of how many adults access the internet and use different types of technology covering:

home internet access

how people connect to the web

how often people use the web/computers

whether people use mobile devices

whether people buy goods over the web

whether people carried out specified activities over the internet

For more information see the ONS website and the UKDS website.

This dataset contains the results of running the automatic audio annotation algorithms for pitch, tempo and key used for the evaluation of algorithms developed during the AudioCommons H2020 EU project and which are part of the Audio Commons Audio Extractor tool. It also includes estimation results information for the single-eventness audio descriptor also developed for the same tool.

These estimation results data has been used to generate the following documents:

• Deliverable D4.4: Evaluation report on the first prototype tool for the automatic semantic description of music samples
• Deliverable D4.10: Evaluation report on the second prototype tool for the automatic semantic description of music samples
• Deliverable D4.12: Release of tool for the automatic semantic description of music samples

All these documents are available in the materials section of the AudioCommons website.

All data in this repository is provided in the form of CSV files. Each CSV file corresponds to the analysis results of one musical task and one of the individual datasets used in the aforementioned deliverables. This repository does not include the audio files of each individual dataset, but includes references to the audio files. The following paragraphs describe the structure of the CSV files and give some notes about how to obtain the audio files in case these would be needed.

Structure of the CSV files

All the CSV files in this repository (with the sole exception of SINGLE EVENT - Estimation Results Truth.csv) are named according to the following convention: "DATASET_NAME - ESTIMATION_TASK Estimation Results.csv". Therefore, estimation results for pitch, tempo and tonality music tasks are separated in different files. All these files share the same structure for the first 2 CSV columns:

1. Audio reference: reference to the corresponding audio file. This will either be a string withe the filename, or the Freesound ID (for one dataset based on Freesound content). See below for details about how to obtain those files.
2. Audio reference type: will be one of Filename or Freesound ID, and specifies how the previous column should be interpreted.

The rest of the columns include the estimation results for each one of the algorithms included in the evaluation of each music facet. For each algorithms two columns are reserved, the first one containing the actual estimation and the second one the confidence of this estimation (see CSV file previews below). The format of actual estimations depends on the musical task, check the description of the corresponding ground truth dataset for more information on that. The confidence value is a float number, typically in the range from 0.0 to 1.0. It can happen that one or both columns are empty for a given analysis algorithm and CSV row. This will be the case if the algorithm could not successfully produce an estimation for the audio file row corresponding to the CSV row.

The remaining CSV file, SINGLE EVENT - Estimation Results.csv, has the following 4 columns:

• Freesound ID: sound ID used in Freesound to identify the audio clip.
• ACExtractorV2: single-eventness estimation of the algorithm included in the second version of the Audio Commons Audio Extractor tool (bool).
• ACExtractorV2-opt: single-eventness estimation of the algorithm included in the second version of the Audio Commons Audio Extractor tool with optimized parameters (bool).
• ACExtractorV3: single-eventness estimation of the algorithm included in the third version of the Audio Commons Audio Extractor tool (bool).

How to get the audio data

In this section we provide some notes about how to obtain the audio files corresponding to the estimation results provided here. Note that due to licensing restrictions we are not allowed to re-distribute the audio data corresponding to most of these automatic annotations.

• Apple Loops (APPL): This dataset includes some of the music loops included in Apple's music software such as Logic or GarageBand. Access to these loops requires owning a license for the software. Detailed instructions about how to set up this dataset are provided here.
• Carlos Vaquero Instruments Dataset (CVAQ): This dataset includes single instrument recordings carried out by Carlos Vaqueroas part of this master thesis. Sounds are available as Freesound packs and can be downloaded at this page: https://freesound.org/people/Carlos_Vaquero/packs
• Freesound Loops 4k (FSL4): This dataset set includes a selection of music loops taken from Freesound. Detailed instructions about how to set up this dataset are provided here.
• Giant Steps Key Dataset (GSKY): This dataset includes a selection of previews from Beatport annotated by key. Audio and original annotations available here.
• Good-sounds Dataset (GSND): This dataset contains monophonic recordings of instrument samples. Full description, original annotations and audio are available here.
• University of IOWA Musical Instrument Samples (IOWA): This dataset was created by the Electronic Music Studios of the University of IOWA and contains recordings of instrument samples. The dataset is available upon request by visiting this website.
• Mixcraft Loops (MIXL): This dataset includes some of the music loops included in Acoustica's Mixcraft music software. Access to these loops requires owning a license for the software. Detailed instructions about how to set up this dataset are provided here.
• NSynth Dataset Test and Validation sets (NSYT and NSYV): NSynth is a large-scale and high-quality dataset of annotated musical notes built with synthesized sounds by Google's Magenta team. Full dataset description including original annotations and audio files is available here.
• Philarmonia Orchestra Sound Samples Dataset (PHIL): This includes thousands of free, downloadable sound samples specially recorded by Philharmonia Orchestra players. Audio files are freely downloadable from the philarmonia orchestra website.
• Freesound Single Events Dataset (SINGLE EVENT): This includes a selection of Freesound audio clips representing audio signals containing either a single audio eventor multiple ones. Original audio files can be retrieved by downloading individual audio clips from Freesound using the ID identifier provided in the CSV file. A similar procedure to that described here could be followed.