In an effort to help combat COVID-19, we created a COVID-19 Public Datasets program to make data more accessible to researchers, data scientists and analysts. The program will host a repository of public datasets that relate to the COVID-19 crisis and make them free to access and analyze. These include datasets from the New York Times, European Centre for Disease Prevention and Control, Google, Global Health Data from the World Bank, and OpenStreetMap. Free hosting and queries of COVID datasets As with all data in the Google Cloud Public Datasets Program , Google pays for storage of datasets in the program. BigQuery also provides free queries over certain COVID-related datasets to support the response to COVID-19. Queries on COVID datasets will not count against the BigQuery sandbox free tier , where you can query up to 1TB free each month. Limitations and duration Queries of COVID data are free. If, during your analysis, you join COVID datasets with non-COVID datasets, the bytes processed in the non-COVID datasets will be counted against the free tier, then charged accordingly, to prevent abuse. Queries of COVID datasets will remain free until Sept 15, 2021. The contents of these datasets are provided to the public strictly for educational and research purposes only. We are not onboarding or managing PHI or PII data as part of the COVID-19 Public Dataset Program. Google has practices & policies in place to ensure that data is handled in accordance with widely recognized patient privacy and data security policies. See the list of all datasets included in the program

Point locations of the all Louisville Free public library branches and facilities. View detailed metadata.

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

Adapted from Wikipedia: OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. Created in 2004, it was inspired by the success of Wikipedia and more than two million registered users who can add data by manual survey, GPS devices, aerial photography, and other free sources. We've made available a number of tables (explained in detail below): history_* tables: full history of OSM objects planet_* tables: snapshot of current OSM objects as of Nov 2019 The history_* and planet_* table groups are composed of node, way, relation, and changeset tables. These contain the primary OSM data types and an additional changeset corresponding to OSM edits for convenient access. These objects are encoded using the BigQuery GEOGRAPHY data type so that they can be operated upon with the built-in geography functions to perform geometry and feature selection, additional processing. Example analyses are given below. This dataset is part of a larger effort to make data available in BigQuery through the Google Cloud Public Datasets program . OSM itself is produced as a public good by volunteers, and there are no guarantees about data quality. Interested in learning more about how these data were brought into BigQuery and how you can use them? Check out the sample queries below to get started. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

A computer program for accessing and visualization of thermodynamic and transport property data for chemical compounds and mixtures available at the TRC/NIST ThermoML archive https://data.nist.gov/od/id/mds2-2422. The data collection contains 2.2 million distinct property values (the whole archive can also be downloaded from that link, stored, and accessed from a local storage). The program has been compiled for Windows OS and tested under Windows 10. The operation procedures are described in the embedded Help.

This dataset is sourced from running the Synthetic Dataset Creation w/ InternVL2 script. The dataset was made in mind for compatibility for LLM Finetuning Script which finetunes Large Language Models (LLM) through the use of datasets. This is just an example of how a dataset is supposed to be structured for the LLM Finetuning Script. Feel free to make your own datasets with the help of the Synthetic Dataset Creation w/ InternVL2 script.

About this dataset

Context

The dataset tabulates the population of Gratis by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Gratis across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.0% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Gratis is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Gratis total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Gratis Population by Race & Ethnicity. You can refer the same here

About

Data from the State of California. From website:

Access raw State data files, databases, geographic data, and other data sources. Raw State data files can be reused by citizens and organizations for their own web applications and mashups.

Openness

Open. Effectively in the public domain. Terms of use page says:

In general, information presented on this web site, unless otherwise indicated, is considered in the public domain. It may be distributed or copied as permitted by law. However, the State does make use of copyrighted data (e.g., photographs) which may require additional permissions prior to your use. In order to use any information on this web site not owned or created by the State, you must seek permission directly from the owning (or holding) sources. The State shall have the unlimited right to use for any purpose, free of any charge, all information submitted via this site except those submissions made under separate legal contract. The State shall be free to use, for any purpose, any ideas, concepts, or techniques contained in information provided through this site.

Compiled school level data for the 2021 - 2022 school year sourced from the Virginia Department of Education.

• State Assessment (SOL) Pass Rates
• Chronic Absenteeism Rates
• Graduation Rates
• Free and Reduced Lunch Eligibility
• State and Federal Funding
• Teacher Quality, Education, and Licensure
• Demographic information

About this dataset

Context

The dataset tabulates the Free Soil population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of Free Soil.

Content

The dataset constitues the following two datasets across these two themes

• Free Soil, MI Population Breakdown by Gender
• Free Soil, MI Population Breakdown by Gender and Age

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

This dataset was originally collected for a data science and machine learning project that aimed at investigating the potential correlation between the amount of time an individual spends on social media and the impact it has on their mental health.

The project involves conducting a survey to collect data, organizing the data, and using machine learning techniques to create a predictive model that can determine whether a person should seek professional help based on their answers to the survey questions.

This project was completed as part of a Statistics course at a university, and the team is currently in the process of writing a report and completing a paper that summarizes and discusses the findings in relation to other research on the topic.

The following is the Google Colab link to the project, done on Jupyter Notebook -

https://colab.research.google.com/drive/1p7P6lL1QUw1TtyUD1odNR4M6TVJK7IYN

The following is the GitHub Repository of the project -

https://github.com/daerkns/social-media-and-mental-health

Libraries used for the Project -

Pandas
Numpy
Matplotlib
Seaborn
Sci-kit Learn

Supplementary Material 5: S5. Prevalence of ectopic pregnancy overall and by subgroup. Time series of prevalence of ectopic pregnancy in CPRD Aurum from 2006-2021, overall and by subgroups of Index of Multiple Deprivation, region, ethnicity and age category.

AI Generated Summary: Data.gov.ro is Romania's national open data portal, established in 2013 to centralize open data published by Romanian institutions according to international standards. It serves as a central access point for open datasets from public authorities and institutions in Romania and acts as a liaison with the European Commission's European Data Portal, promoting the free use, reuse, and redistribution of data under the Open Government License - OGL ROU 1.0. About: The data.gov.ro portal was created in 2013 within the framework of open data efforts at the international level, with the aim of centralizing open data published by Romanian institutions in accordance with the principles and standards in the field. Currently, the General Secretariat of the Government ensures the coordination of the process of opening public data in Romania and manages the national portal data.gov.ro, the central access point for open datasets published by the authorities and institutions of the public administration in Romania and the point of contact in relation to the European Commission (europeandataportal.eu). Open data is data that can be freely used, reused, and redistributed by anyone, freely, without imposing restrictions such as copyright, patents, or other control mechanisms. In this sense, the portal provides users with the Open Government License - OGL ROU 1.0, issued in 2014 by the General Secretariat of the Government as an open license model. For data to be considered open, at least two conditions must be met: technical: the data is published online in file formats that can be automatically processed using computer programs (machine-readable), which are, as far as possible, available to anyone, free of charge (free and open source software). legal: at the time of publication, the data is attached to a license by which the data owner and publisher establishes the conditions for its reuse. In Romania, the legal framework for publishing open data was established by Law no. 109/2007 regarding the reuse of information from public institutions, amended and supplemented by Law no. 299/2015. More details can be found in the Methodology for publishing open data, developed by the General Secretariat of the Government. Open data visualization tool List of datasets assumed by public institutions through the OGP National Action Plan List 2016 Status 2017 Other useful resources DCAT application profile for data portals in Europe DCAT-AP: Information on the DCAT application profile for data portals in Europe Translated from Romanian Original Text: Portalul data.gov.ro a fost realizat în anul 2013 în marja demersurilor open data la nivel internațional, în scopul centralizării datelor deschise publicate de instituțiile din România conform principiilor și standardelor în domeniu. În prezent, Secretariatul General al Guvernului asigură coordonarea procesului de deschidere a datelor publice în România și administrează portalul național data.gov.ro, punctul central de acces pentru seturile de date deschise publicate de autoritățile și instituțiile administrației publice din România și punctul de legătură în relația cu Comisia Europeană (europeandataportal.eu). Datele deschise sunt date ce pot fi utilizate în mod liber, reutilizate și redistribuite de către oricine, în mod liber, fără a impune restricții de tipul drepturi de autor (copyright), patente sau alte mecanisme de control. În acest sens, portalul pune la dispoziția utilizatorilor Licența pentru o Guvernare Deschisă - OGL ROU 1.0, emisă în 2014 de Secretariatul General al Guvernului ca model de licență deschisă. Pentru ca datele să fie considerate deschise, trebuie îndeplinite minim două condiții: tehnic: datele sunt publicate online în formate de fișiere ce pot fi procesate în mod automat folosind programe de calculator (machine-readable), care sunt, pe cât posibil, disponibile oricui, în mod gratuit (free and open source software). legal: în momentul publicării, datelor li se atașează o licență prin care cel care deține și publică datele stabilește condițiile de reutilizare a acestora. În România, cadrul legal pentru publicarea datelor deschise a fost stabilit de Legea nr. 109/2007 privind reutilizarea informațiilor din instituții publice, modificată și completată de Legea nr. 299/ 2015. Mai multe detalii găsiți în Metodologia pentru publicarea datelor deschise, elaborată de Secretariatul General al Guvernului. Instrument de vizualizare a datelor deschise Lista seturilor de date asumate de instituțiile publice prin Planul Național de Acțiune OGP Listă 2016 Stadiu 2017 Alte resurse utile DCAT application profile for data portals in Europe DCAT-AP: Information on the DCAT application profile for data portals in Europe

The Buzsaki Lab is proud to present a large selection of experimental data available for public access: https://buzsakilab.com/wp/database/.

We publicly share more than a thousand sessions (about 40TB of raw and spike- and LFP-processed data) via our public data repository. The datasets are from freely moving rodents and include sleep-task-sleep sessions (3 to 24 hrs continuous recording sessions) in various brain structures, including metadata. We are happy to assist you in using the data. Our goal is that by sharing these data, other users can provide new insights, extend, contradict, or clarify our conclusions.

The databank contains electrophysiological recordings performed in freely moving rats and mice collected by investigators in the Buzsaki Lab over several years (a subset from head-fixed mice). Sessions have been collected with extracellular electrodes using high-channel-count silicon probes, with spike sorted single units, and intracellular and juxtacellular combined with extracellular electrodes. Several sessions include physiologically and optogenetically identified units. The sessions have been collected from various brain region pairs: the hippocampus, thalamus, amygdala, post-subiculum, septal region, and the entorhinal cortex, and various neocortical regions. In most behavioral tasks, the animals performed spatial behaviors (linear mazes and open fields), preceded and followed by long sleep sessions. Brain state classification is provided.

Getting started

The top menu “Databank” serves as a navigational menu to the databank. The metadata describing the experiments is stored in a relational database which means that there are many entry points for exploring the data. The databank is organized by projects, animal subjects, and sessions.

Accessing and downloading the datasets

We share the data through two services: our public Globus.org endpoint and our webshare: buzsakilab.nyumc.org. A subset of the datasets is also available at CRCNS.org. If you have an interest in a dataset that is not listed or is lacking information, please contact us. We pledge to make our data available immediately after publication.

Support

For support, please use our Buzsaki Databank google group. If you have an interest in a dataset that is not listed or is lacking information, please send us a request. Feel free to contact us, if you need more details on a given dataset or if a dataset is missing.

This dataset is maintained by the European Centre for Disease Prevention and Control (ECDC) and reports on the geographic distribution of COVID-19 cases worldwide. This data includes COVID-19 reported cases and deaths broken out by country. This data can be visualized via ECDC’s Situation Dashboard . More information on ECDC’s response to COVID-19 is available here . This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery . This dataset is hosted in both the EU and US regions of BigQuery. See the links below for the appropriate dataset copy: US region EU region This dataset has significant public interest in light of the COVID-19 crisis. All bytes processed in queries against this dataset will be zeroed out, making this part of the query free. Data joined with the dataset will be billed at the normal rate to prevent abuse. After September 15, queries over these datasets will revert to the normal billing rate. Users of ECDC public-use data files must comply with data use restrictions to ensure that the information will be used solely for statistical analysis or reporting purposes.

This is a public school point data that be used for SCAG Active Transportation Program (ATP) in the SCAG region. The information includes enrollment by age, school type, and 2017-2018 academic year free and reduced priced meals (FRPM) eligibility.

Kendall correlation coefficient τ for different distance metrics within and between coronavirus genomes.

🗃️ Dataset

The dataset released is anonymized and not representative of the production characteristics.

The Multilingual Shopping Session Dataset is a collection of anonymized customer sessions containing products from six different locales: English, German, Japanese, French, Italian, and Spanish. It consists of two main components: user sessions and product attributes. User sessions are a list of products that a user has engaged with in chronological order, while product attributes include various details like product title, price in local currency, brand, colour, and description.

The dataset has been divided into three splits: train, phase-1 test, and phase-2 test. For Task 1 and Task 2, the proportions for each language are roughly 10:1:1. For Task 3, the number of samples in the phase-1 test and phase-2 test is fixed at 10,000. All three tasks share the same train set, while their test sets have been constructed according to their specific objectives. Task 1 uses English, German, and Japanese data, while Task 2 uses French, Italian, and Spanish data. Participants in Task 2 are encouraged to use transfer learning to improve their system's performance on the test set. For Task 3, the test set includes products that do not appear in the training set, and participants are asked to generate the title of the next product based on the user session.

Table 1 summarizes the dataset statistics, including the number of sessions, interactions, products, and average session length. The dataset will be made publicly available as part of the KDD Cup competition. Each product will be identified by a unique Amazon Standard Identification Number (ASIN), making extracting more information from the web easy. Participants are free to use external sources of information to train their systems, such as public datasets and pre-trained language models, but must declare them when describing their systems beyond the provided dataset.

Table 1: Dataset statistics

In addition, we list the column names and their meanings for product attribute data: - locale: the locale code of the product (e.g., DE) - id: a unique for the product. Also known as Amazon Standard Item Number (ASIN) (e.g., B07WSY3MG8) - title: title of the item (e.g., “Japanese Aesthetic Sakura Flowers Vaporwave Soft Grunge Gift T-Shirt”) - price: price of the item in local currency (e.g., 24.99) - brand: item brand name (e.g., “Japanese Aesthetic Flowers & Vaporwave Clothing”) - color: color of the item (e.g., “Black”) - size: size of the item (e.g., “xxl”) - model: model of the item (e.g., “iphone 13”) - material: material of the item (e.g., “cotton”) - author: author of the item (e.g., “J. K. Rowling”) - desc: description about a item’s key features and benefits called out via bullet points (e.g., “Solid colors: 100% Cotton; Heather Grey: 90% Cotton, 10% Polyester; All Other Heathers …”)

[Note: Integrated as part of FoodData Central, April 2019.] The database consists of several sets of data: food descriptions, nutrients, weights and measures, footnotes, and sources of data. The Nutrient Data file contains mean nutrient values per 100 g of the edible portion of food, along with fields to further describe the mean value. Information is provided on household measures for food items. Weights are given for edible material without refuse. Footnotes are provided for a few items where information about food description, weights and measures, or nutrient values could not be accommodated in existing fields. Data have been compiled from published and unpublished sources. Published data sources include the scientific literature. Unpublished data include those obtained from the food industry, other government agencies, and research conducted under contracts initiated by USDA’s Agricultural Research Service (ARS). Updated data have been published electronically on the USDA Nutrient Data Laboratory (NDL) web site since 1992. Standard Reference (SR) 28 includes composition data for all the food groups and nutrients published in the 21 volumes of "Agriculture Handbook 8" (US Department of Agriculture 1976-92), and its four supplements (US Department of Agriculture 1990-93), which superseded the 1963 edition (Watt and Merrill, 1963). SR28 supersedes all previous releases, including the printed versions, in the event of any differences. Attribution for photos: Photo 1: k7246-9 Copyright free, public domain photo by Scott Bauer Photo 2: k8234-2 Copyright free, public domain photo by Scott Bauer Resources in this dataset:Resource Title: READ ME - Documentation and User Guide - Composition of Foods Raw, Processed, Prepared - USDA National Nutrient Database for Standard Reference, Release 28. File Name: sr28_doc.pdfResource Software Recommended: Adobe Acrobat Reader,url: http://www.adobe.com/prodindex/acrobat/readstep.html Resource Title: ASCII (6.0Mb; ISO/IEC 8859-1). File Name: sr28asc.zipResource Description: Delimited file suitable for importing into many programs. The tables are organized in a relational format, and can be used with a relational database management system (RDBMS), which will allow you to form your own queries and generate custom reports.Resource Title: ACCESS (25.2Mb). File Name: sr28db.zipResource Description: This file contains the SR28 data imported into a Microsoft Access (2007 or later) database. It includes relationships between files and a few sample queries and reports.Resource Title: ASCII (Abbreviated; 1.1Mb; ISO/IEC 8859-1). File Name: sr28abbr.zipResource Description: Delimited file suitable for importing into many programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Title: Excel (Abbreviated; 2.9Mb). File Name: sr28abxl.zipResource Description: For use with Microsoft Excel (2007 or later), but can also be used by many other spreadsheet programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/ Resource Title: ASCII (Update Files; 1.1Mb; ISO/IEC 8859-1). File Name: sr28upd.zipResource Description: Update Files - Contains updates for those users who have loaded Release 27 into their own programs and wish to do their own updates. These files contain the updates between SR27 and SR28. Delimited file suitable for import into many programs.

The size of the Relational Database Market was valued at USD 19942.01 million in 2023 and is projected to reach USD 45481.69 million by 2032, with an expected CAGR of 12.50% during the forecast period. Recent developments include: October 2022: Oracle released latest advancements in database technology with the announcement of Oracle Database 23c Beta. It accommodates diverse data types, workloads, and development styles. The release incorporates numerous innovations across Oracle's database services and product portfolio., October 2023: Microsoft has launched a public preview of a new Azure SQL Database free offering, marking a significant addition to its cloud services. Users can access a 32 GB general purpose, serverless Azure SQL database with 100,000 vCore seconds of compute free monthly..