Context

Blockchain technology, first implemented by Satoshi Nakamoto in 2009 as a core component of Bitcoin, is a distributed, public ledger recording transactions. Its usage allows secure peer-to-peer communication by linking blocks containing hash pointers to a previous block, a timestamp, and transaction data. Bitcoin is a decentralized digital currency (cryptocurrency) which leverages the Blockchain to store transactions in a distributed manner in order to mitigate against flaws in the financial industry.

Nearly ten years after its inception, Bitcoin and other cryptocurrencies experienced an explosion in popular awareness. The value of Bitcoin, on the other hand, has experienced more volatility. Meanwhile, as use cases of Bitcoin and Blockchain grow, mature, and expand, hype and controversy have swirled.

Content

In this dataset, you will have access to information about blockchain blocks and transactions. All historical data are in the bigquery-public-data:crypto_bitcoin dataset. It’s updated it every 10 minutes. The data can be joined with historical prices in kernels. See available similar datasets here: https://www.kaggle.com/datasets?search=bitcoin.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.crypto_bitcoin.[TABLENAME]. Fork this kernel to get started.

Method & Acknowledgements

Allen Day (Twitter | Medium), Google Cloud Developer Advocate & Colin Bookman, Google Cloud Customer Engineer retrieve data from the Bitcoin network using a custom client available on GitHub that they built with the bitcoinj Java library. Historical data from the origin block to 2018-01-31 were loaded in bulk to two BigQuery tables, blocks_raw and transactions. These tables contain fresh data, as they are now appended when new blocks are broadcast to the Bitcoin network. For additional information visit the Google Cloud Big Data and Machine Learning Blog post "Bitcoin in BigQuery: Blockchain analytics on public data".

Photo by Andre Francois on Unsplash.

Inspiration

• How many bitcoins are sent each day?
• How many addresses receive bitcoin each day?
• Compare transaction volume to historical prices by joining with other available data sources

As of the third quarter of 2024, internet users in South Africa spent more than nine hours and 37 minutes online per day, ranking first among the regions worldwide. Brazil followed, with roughly nine hours of daily online usage. As of the examined period, Japan registered the lowest number of daily hours spent online, with users in the country spending an average of over four hours per day using the internet. The data includes the daily time spent online on any device. Social media usage In recent years, social media has become integral to internet users' daily lives, with users spending an average of 143 minutes daily on social media activities. In April 2024, global social network penetration reached 62.2 percent, highlighting its widespread adoption. Among the various platforms, YouTube stands out, with over 2.5 billion monthly active users, making it one of the most popular social media platforms. YouTube’s global popularity In 2023, the keyword "YouTube" ranked among the most popular search queries on Google, highlighting the platform's immense popularity. YouTube generated most of its traffic through mobile devices, with about 98 billion visits. This popularity was particularly evident in the United Arab Emirates, where YouTube penetration reached approximately 94.2 percent, the highest in the world.

The dataset of this paper is collected based on Google, Blockchain, and the Bitcoin market. Generally, there is a total of 26 features, however, a feature whose correlation rate is lower than 0.3 between the variations of price and the variations of feature has been eliminated. Hence, a total of 21 practical features including Market capitalization, Trade-volume, Transaction-fees USD, Average confirmation time, Difficulty, High price, Low price, Total hash rate, Block-size, Miners-revenue, N-transactions-total, Google searches, Open price, N-payments-per Block, Total circulating Bitcoin, Cost-per-transaction percent, Fees-USD-per transaction, N-unique-addresses, N-transactions-per block, and Output-volume have been selected. In addition to the values of these features, for each feature, a new one is created that includes the difference between the previous day and the day before the previous day as a supportive feature. From the point of view of the number and history of the dataset used, a total of 1275 training data were used in the proposed model to extract patterns of Bitcoin price and they were collected from 12 Nov 2018 to 4 Jun 2021.

Context

This is the US Coronavirus data repository from The New York Times . This data includes COVID-19 cases and deaths reported by state and county. The New York Times compiled this data based on reports from state and local health agencies. More information on the data repository is available here . For additional reporting and data visualizations, see The New York Times’ U.S. coronavirus interactive site

Sample Queries

Query 1

Which US counties have the most confirmed cases per capita? This query determines which counties have the most cases per 100,000 residents. Note that this may differ from similar queries of other datasets because of differences in reporting lag, methodologies, or other dataset differences.

SELECT covid19.county, covid19.state_name, total_pop AS county_population, confirmed_cases, ROUND(confirmed_cases/total_pop *100000,2) AS confirmed_cases_per_100000, deaths, ROUND(deaths/total_pop *100000,2) AS deaths_per_100000 FROM bigquery-public-data.covid19_nyt.us_counties covid19 JOIN bigquery-public-data.census_bureau_acs.county_2017_5yr acs ON covid19.county_fips_code = acs.geo_id WHERE date = DATE_SUB(CURRENT_DATE(),INTERVAL 1 day) AND covid19.county_fips_code != "00000" ORDER BY confirmed_cases_per_100000 desc

Query 2

How do I calculate the number of new COVID-19 cases per day? This query determines the total number of new cases in each state for each day available in the dataset SELECT b.state_name, b.date, MAX(b.confirmed_cases - a.confirmed_cases) AS daily_confirmed_cases FROM (SELECT state_name AS state, state_fips_code , confirmed_cases, DATE_ADD(date, INTERVAL 1 day) AS date_shift FROM bigquery-public-data.covid19_nyt.us_states WHERE confirmed_cases + deaths > 0) a JOIN bigquery-public-data.covid19_nyt.us_states b ON a.state_fips_code = b.state_fips_code AND a.date_shift = b.date GROUP BY b.state_name, date ORDER BY date desc

Google’s energy consumption has increased over the last few years, reaching 25.9 terawatt hours in 2023, up from 12.8 terawatt hours in 2019. The company has made efforts to make its data centers more efficient through customized high-performance servers, using smart temperature and lighting, advanced cooling techniques, and machine learning. Datacenters and energy Through its operations, Google pursues a more sustainable impact on the environment by creating efficient data centers that use less energy than the average, transitioning towards renewable energy, creating sustainable workplaces, and providing its users with the technological means towards a cleaner future for the future generations. Through its efficient data centers, Google has also managed to divert waste from its operations away from landfills. Reducing Google’s carbon footprint Google’s clean energy efforts is also related to their efforts to reduce their carbon footprint. Since their commitment to using 100 percent renewable energy, the company has met their targets largely through solar and wind energy power purchase agreements and buying renewable power from utilities. Google is one of the largest corporate purchasers of renewable energy in the world.

Update — December 7, 2014. – Evidence-based medicine (EBM) is not working for many reasons, for example: 1. Incorrect in their foundations (paradox): hierarchical levels of evidence are supported by opinions (i.e., lowest strength of evidence according to EBM) instead of real data collected from different types of study designs (i.e., evidence). http://dx.doi.org/10.6084/m9.figshare.1122534 2. The effect of criminal practices by pharmaceutical companies is only possible because of the complicity of others: healthcare systems, professional associations, governmental and academic institutions. Pharmaceutical companies also corrupt at the personal level, politicians and political parties are on their payroll, medical professionals seduced by different types of gifts in exchange of prescriptions (i.e., bribery) which very likely results in patients not receiving the proper treatment for their disease, many times there is no such thing: healthy persons not needing pharmacological treatments of any kind are constantly misdiagnosed and treated with unnecessary drugs. Some medical professionals are converted in K.O.L. which is only a puppet appearing on stage to spread lies to their peers, a person supposedly trained to improve the well-being of others, now deceits on behalf of pharmaceutical companies. Probably the saddest thing is that many honest doctors are being misled by these lies created by the rules of pharmaceutical marketing instead of scientific, medical, and ethical principles. Interpretation of EBM in this context was not anticipated by their creators. “The main reason we take so many drugs is that drug companies don’t sell drugs, they sell lies about drugs.” ―Peter C. Gøtzsche “doctors and their organisations should recognise that it is unethical to receive money that has been earned in part through crimes that have harmed those people whose interests doctors are expected to take care of. Many crimes would be impossible to carry out if doctors weren’t willing to participate in them.” —Peter C Gøtzsche, The BMJ, 2012, Big pharma often commits corporate crime, and this must be stopped. Pending (Colombia): Health Promoter Entities (In Spanish: EPS ―Empresas Promotoras de Salud).

1. Misinterpretations New technologies or concepts are difficult to understand in the beginning, it doesn’t matter their simplicity, we need to get used to new tools aimed to improve our professional practice. Probably the best explanation is here in these videos (credits to Antonio Villafaina for sharing these videos with me). English https://www.youtube.com/watch?v=pQHX-SjgQvQ&w=420&h=315 Spanish https://www.youtube.com/watch?v=DApozQBrlhU&w=420&h=315 ----------------------- Hypothesis: hierarchical levels of evidence based medicine are wrong Dear Editor, I have data to support the hypothesis described in the title of this letter. Before rejecting the null hypothesis I would like to ask the following open question:Could you support with data that hierarchical levels of evidence based medicine are correct? (1,2) Additional explanation to this question: – Only respond to this question attaching publicly available raw data.– Be aware that more than a question this is a challenge: I have data (i.e., evidence) which is contrary to classic (i.e., McMaster) or current (i.e., Oxford) hierarchical levels of evidence based medicine. An important part of this data (but not all) is publicly available. References
2. Ramirez, Jorge H (2014): The EBM challenge. figshare. http://dx.doi.org/10.6084/m9.figshare.1135873
3. The EBM Challenge Day 1: No Answers. Competing interests: I endorse the principles of open data in human biomedical research Read this letter on The BMJ – August 13, 2014.http://www.bmj.com/content/348/bmj.g3725/rr/762595Re: Greenhalgh T, et al. Evidence based medicine: a movement in crisis? BMJ 2014; 348: g3725. _ Fileset contents Raw data: Excel archive: Raw data, interactive figures, and PubMed search terms. Google Spreadsheet is also available (URL below the article description). Figure 1. Unadjusted (Fig 1A) and adjusted (Fig 1B) PubMed publication trends (01/01/1992 to 30/06/2014). Figure 2. Adjusted PubMed publication trends (07/01/2008 to 29/06/2014) Figure 3. Google search trends: Jan 2004 to Jun 2014 / 1-week periods. Figure 4. PubMed publication trends (1962-2013) systematic reviews and meta-analysis, clinical trials, and observational studies.
   Figure 5. Ramirez, Jorge H (2014): Infographics: Unpublished US phase 3 clinical trials (2002-2014) completed before Jan 2011 = 50.8%. figshare.http://dx.doi.org/10.6084/m9.figshare.1121675 Raw data: "13377 studies found for: Completed | Interventional Studies | Phase 3 | received from 01/01/2002 to 01/01/2014 | Worldwide". This database complies with the terms and conditions of ClinicalTrials.gov: http://clinicaltrials.gov/ct2/about-site/terms-conditions Supplementary Figures (S1-S6). PubMed publication delay in the indexation processes does not explain the descending trends in the scientific output of evidence-based medicine. Acknowledgments I would like to acknowledge the following persons for providing valuable concepts in data visualization and infographics:
4. Maria Fernanda Ramírez. Professor of graphic design. Universidad del Valle. Cali, Colombia.
5. Lorena Franco. Graphic design student. Universidad del Valle. Cali, Colombia. Related articles by this author (Jorge H. Ramírez)
6. Ramirez JH. Lack of transparency in clinical trials: a call for action. Colomb Med (Cali) 2013;44(4):243-6. URL: http://www.ncbi.nlm.nih.gov/pubmed/24892242
7. Ramirez JH. Re: Evidence based medicine is broken (17 June 2014). http://www.bmj.com/node/759181
8. Ramirez JH. Re: Global rules for global health: why we need an independent, impartial WHO (19 June 2014). http://www.bmj.com/node/759151
9. Ramirez JH. PubMed publication trends (1992 to 2014): evidence based medicine and clinical practice guidelines (04 July 2014). http://www.bmj.com/content/348/bmj.g3725/rr/759895 Recommended articles
10. Greenhalgh Trisha, Howick Jeremy,Maskrey Neal. Evidence based medicine: a movement in crisis? BMJ 2014;348:g3725
11. Spence Des. Evidence based medicine is broken BMJ 2014; 348:g22
12. Schünemann Holger J, Oxman Andrew D,Brozek Jan, Glasziou Paul, JaeschkeRoman, Vist Gunn E et al. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies BMJ 2008; 336:1106
13. Lau Joseph, Ioannidis John P A, TerrinNorma, Schmid Christopher H, OlkinIngram. The case of the misleading funnel plot BMJ 2006; 333:597
14. Moynihan R, Henry D, Moons KGM (2014) Using Evidence to Combat Overdiagnosis and Overtreatment: Evaluating Treatments, Tests, and Disease Definitions in the Time of Too Much. PLoS Med 11(7): e1001655. doi:10.1371/journal.pmed.1001655
15. Katz D. A-holistic view of evidence based medicinehttp://thehealthcareblog.com/blog/2014/05/02/a-holistic-view-of-evidence-based-medicine/ ---

The (unheralded) first step in many applications of automated text analysis involves selecting keywords to choose documents from a large text corpus for further study. Although all substantive results depend on this choice, researchers usually pick keywords in ad hoc ways that are far from optimal and usually biased. Most seem to think that keyword selection is easy, since they do Google searches every day, but we demonstrate that humans perform exceedingly poorly at this basic task. We offer a better approach, one that also can help with following conversations where participants rapidly innovate language to evade authorities, seek political advantage, or express creativity; generic web searching; eDiscovery; look-alike modeling; industry and intelligence analysis; and sentiment and topic analysis. We develop a computer-assisted (as opposed to fully automated or human-only) statistical approach that suggests keywords from available text without needing structured data as inputs. This framing poses the statistical problem in a new way, which leads to a widely applicable algorithm. Our specific approach is based on training classifiers, extracting information from (rather than correcting) their mistakes, and summarizing results with easy-to-understand Boolean search strings. We illustrate how the technique works with analyses of English texts about the Boston Marathon Bombings, Chinese social media posts designed to evade censorship, and others.

Monster MIDI Dataset

  Giant searchable raw MIDI dataset for MIR and Music AI purposes







  Monster MIDI Dataset GPU Search and Filter






  Search, filter and explore Monster MIDI Dataset :)





  PLEASE NOTE: Google Colab Pro or Pro+ subscription/A100 GPU is required to use the provided colab/code because of the size of the dataset and its data files






  Monster MIDI Dataset Sample Search Results





  Here are the Monster MIDI Dataset Sample… See the full description on the dataset page: https://huggingface.co/datasets/projectlosangeles/Monster-MIDI-Dataset.

A dataset of fashion keywords, including their definitions, synonyms, antonyms, search volume and costs.

Several urban landscape planning solutions have been introduced around the world to find a balance between developing urban spaces, maintaining and restoring biodiversity, and enhancing quality of human life. Our global mini-review, combined with analysis of big data collected from Google Trends at global scale, reveals the importance of enjoying day-to-day contact with nature and engaging in such activities as nature observation and identification and gardening for the mental well-being of humans during the COVID-19 pandemic. Home-based activities, such as watching birds from one’s window, identifying species of plants and animals, backyard gardening, and collecting information about nature for citizen science projects, were popular during the first lockdown in spring 2020, when people could not easily venture out of their homes. In our mini-review, we found 37 articles from 28 countries with a total sample of 114,466 people. These papers suggest that home-based engagement with nature was an entertaining and pleasant distraction that helped preserve mental well-being during a challenging time. According to Google Trends, interest in such activities increased during lockdown compared to the previous five years. Millions of people worldwide are chronically or temporarily confined to their homes and neighborhoods because of illness, childcare chores, or elderly care responsibility, which makes it difficult for them to travel far to visit such places as national parks, created through land sparing, where people go to enjoy nature and relieve stress. This article posits that for such people, living in an urban landscape designed to facilitate effortless contact with small natural areas is a more effective way to receive the mental health benefits of contact with nature than visiting a sprawling nature park on rare occasions. Methods 1. Identifying the most common types of activities related to nature observation, gardening, and taxa identification during the first lockdown based on scientific articles and non-scientific press For scientific articles, in March 2023 we searched Scopus and Google Scholar. For countries where Google is restricted, such as China, similar results will be available from other scientific browsers, with the highest number of results from our database being available from Scopus. We used the Google Search browser to search for globally published non-scientific press articles. Some selection criteria were applied during article review. Specifically, we excluded articles that were not about the first lockdown; did not study activities at a local scale (from balcony, window, backyard) but rather in areas far away from home (e.g., visiting forests); studied the mental health effect of observing indoor potted plants and pet animals; or transiently mentioned the topic or keyword without going into any scientific detail. We included all papers that met our criteria, that is, studies that analyzed our chosen topic with experiments or planned observations. We included all research papers, but not letters that made claims without any data. Google Scholar automatically screened the title, abstract, keywords, and the whole text of each article for the keywords we entered. All articles that met our criteria were read and double-checked for keywords and content related to the keywords (e.g., synonyms or if they presented content about the relevant topic without using the specific keywords). We identified, from both types of articles, the major nature-based activities that people engaged in during the first lockdown in the spring of 2020. Keywords used in this study were grouped into six main topics: (1) COVID-19 pandemic; (2) nature-oriented activity focused on nature observation, identification of different taxa, or gardening; (3) mental well-being; (4) activities performed from a balcony, window, or in gardens; (5) entertainment; and (6) citizen science (see Table 1 for all keywords). 2. Increase in global trends in interest in nature observation, gardening, and taxa identification during the first lockdown We used the categorical cluster method, which was combined with big data from Google Trends (downloaded on 1 September 2020) and anomaly detection to identify trend anomalies globally in peoples’ interests. We used this combination of methods to examine whether interest in nature-based activities that were mentioned in scientific and nonscientific press articles increased during the first lockdown. Keywords linked with the main types of nature-oriented activities, as identified from press and scientific articles, and used according to the categorical clustering method were classified into the following six main categories: (1) global interest in bird-watching and bird identification combined with citizen science; (2) global interest in plant identification and gardening combined with citizen science; (3) global interest in butterfly watching, (4) local interest in early-spring (lockdown time), summer, or autumn flowering species that usually can be found in Central European (country: Poland) backyards; (5) global interest in traveling and social activities; and (6) global interest in nature areas and activities typically enjoyed during holidays and thus requiring traveling to land-spared nature reserves. The six categories were divided into 15 subcategories so that we could attach relevant words or phrases belonging to the same cluster and typically related to the activity (according to Google Trends and Google browser’s automatic suggestions; e.g., people who searched for “bird-watching” typically also searched for “binoculars,” “bird feeder,” “bird nest,” and “birdhouse”). The subcategories and keywords used for data collection about trends in society’s interest in the studied topic from Google Trends are as follows.

Bird-watching: “binoculars,” “bird feeder,” “bird nest,” “birdhouse,” “bird-watching”; Bird identification: “bird app,” “bird identification,” “bird identification app,” “bird identifier,” “bird song app”; Bird-watching combined with citizen science: “bird guide,” “bird identification,” “eBird,” “feeding birds,” “iNaturalist”; Citizen science and bird-watching apps: “BirdNET,” “BirdSong ID,” “eBird,” “iNaturalist,” “Merlin Bird ID”; Gardening: “gardening,” “planting,” “seedling,” “seeds,” “soil”; Shopping for gardening: “garden shop,” “plant buy,” “plant ebay,” “plant sell,” “plant shop”; Plant identification apps: “FlowerChecker,” “LeafSnap,” “NatureGate,” “Plantifier,” “PlantSnap”; Citizen science and plant identification: “iNaturalist,” “plant app,” “plant check,” “plant identification app,” “plant identifier”; Flowers that were flowering in gardens during lockdown in Poland: “fiołek” (viola), “koniczyna” (shamrock), “mlecz” (dandelion), “pierwiosnek” (primose), “stokrotka” (daisy). They are typical early-spring flowers growing in the gardens in Central Europe. We had to be more specific in this search because there are no plant species blooming across the world at the same time. These plant species have well-known biology; thus, we could easily interpret these results; Flowers that were not flowering during lockdown in Poland: “chaber” (cornflower), “mak” (poppy), “nawłoć” (goldenrod), “róża” (rose), “rumianek” (chamomile). They are typical mid-summer flowering plants often planted in gardens; Interest in traveling long distances and in social activities that involve many people: “airport,” “bus,” “café,” “driving,” “pub”; Single or mass commuting, and traveling: “bike,” “boat,” “car,” “flight,” “train”; Interest in distant places and activities for visiting natural areas: “forest,” “nature park,” “safari,” “trekking,” “trip”; Places and activities for holidays (typically located far away): “coral reef,” “rainforest,” “safari,” “savanna,” “snorkeling”; Butterfly watching: “butterfly watching,” “butterfly identification,” “butterfly app,” “butterfly net,” “butterfly guide”;

In Google Trends, we set the following filters: global search, dates: July 2016–July 2020; language: English.

A dataset of keywords that are relevant to lawyers, including their definitions, synonyms, antonyms, search volume and costs.

The dataset contains the query suggestions for 5 major German parties (terms: "afd", "csu", "dielinke", "fdp", "grüne", "spd") and ten popular politicians and party leaders (terms: "Alexander Gauland", "Alice Weidel", "Angela Merkel", "Cem Özdemir", "Christian Lindner", "Dietmar Bartsch", "Katrin Göring-Eckardt", "Martin Schulz", "Sahra Wagenknecht").

The data was crawled on (mostly) two times per day from Tue Aug 04, 2017 to Tue Oct 31, 2017. The dataset contains 20001 suggestions from Bing search (http://api.bing.net/osjson.aspx), 11935 suggestions from Duck-Duck-Go (https://duckduckgo.com/ac/) and 33521 suggestions from Google search (http://clients1.google.de/complete/search). Note, that for some terms and dates no suggestions were returned by some of the APIs.

German language settings were used for Google and Bing, English language setting was used for Duck-Duck-Go. The API requests were sent with an IP address from Cologne, Germany.

The UTF-8 encoded comma separated text file contains the following columns:

: google, bing or ddg