A ranking of Office of Hearings Operations (OHO) hearing offices by the average number of hearings dispositions per administrative law judge (ALJ) per day. The average shown will be a combined average for all ALJs working in that hearing office.

A ranking of the Office of Hearings Operations (OHO) hearing offices by the average number of days until final disposition of the hearing request. The average shown will be a combined average for all cases completed in that hearing office.

DataForSEO Labs API offers three powerful keyword research algorithms and historical keyword data:

• Related Keywords from the “searches related to” element of Google SERP. • Keyword Suggestions that match the specified seed keyword with additional words before, after, or within the seed key phrase. • Keyword Ideas that fall into the same category as specified seed keywords. • Historical Search Volume with current cost-per-click, and competition values.

Based on in-market categories of Google Ads, you can get keyword ideas from the relevant Categories For Domain and discover relevant Keywords For Categories. You can also obtain Top Google Searches with AdWords and Bing Ads metrics, product categories, and Google SERP data.

You will find well-rounded ways to scout the competitors:

• Domain Whois Overview with ranking and traffic info from organic and paid search. • Ranked Keywords that any domain or URL has positions for in SERP. • SERP Competitors and the rankings they hold for the keywords you specify. • Competitors Domain with a full overview of its rankings and traffic from organic and paid search. • Domain Intersection keywords for which both specified domains rank within the same SERPs. • Subdomains for the target domain you specify along with the ranking distribution across organic and paid search. • Relevant Pages of the specified domain with rankings and traffic data. • Domain Rank Overview with ranking and traffic data from organic and paid search. • Historical Rank Overview with historical data on rankings and traffic of the specified domain from organic and paid search. • Page Intersection keywords for which the specified pages rank within the same SERP.

All DataForSEO Labs API endpoints function in the Live mode. This means you will be provided with the results in response right after sending the necessary parameters with a POST request.

The limit is 2000 API calls per minute, however, you can contact our support team if your project requires higher rates.

We offer well-rounded API documentation, GUI for API usage control, comprehensive client libraries for different programming languages, free sandbox API testing, ad hoc integration, and deployment support.

We have a pay-as-you-go pricing model. You simply add funds to your account and use them to get data. The account balance doesn't expire.

First of all, Amazon product datasets are indispensable for reverse engineering your rivals. For example, you can collect a list of keywords you already rank for or want to, and go through DataForSEO Amazon Products Database to find other sellers appearing as the top results for these terms.

Next, you can narrow down the scope of your contenders to those performing the best. To do so, you can filter out sellers who won the “Amazon’s Choice” and those whose products got listed multiple times on the first page.

Once you’ve compiled the final list of your challengers, Amazon Products Database will help you to quickly examine product titles, descriptions, prices, images, and other details that will let you grasp the main contributors to your competitors’ success. Once you’ve figured that out, you can start optimizing your product listings and pricing strategies to increase conversions.

However, the number of use cases for Amazon product data isn’t limited to competitor analysis. It can be applied to monitoring product rankings, running price comparisons, and more.

This dataset creates a collection of reports for the Office of Hearings Operations (OHO) offices based on the average number of days until final disposition of the hearing request. The average shown will be a combined average for all cases completed in that hearing office. Users will be able determine where a particular hearing office stands compared to other offices with respect to this workload category.

As of May 2023, Etsy collected around 15 unique data points from global iOS users, ranking as the least data-hungry app within the shopping and food delivery category. Finance and crypto app Binance collected a total of four unique data points from its global iOS users, while Khan Academy, an app used by children and students for homework and classes, collected a total of seven unique data points.

The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs

We have made it as simple as possible to collect data from websites

Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.

Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.

Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.

Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.

Walmart Product Details & Pricing Walmart Product Details & Pricing Get the product name, pricing, number of ratings, reviews, product images, URL other product-related data from Walmart.

Amazon Search Results Scraper Amazon Search Results Scraper Get product search rank, pricing, availability, best seller rank, and much more from Amazon.

Amazon Best Sellers Amazon Best Sellers Get the bestseller rank, product name, pricing, number of ratings, rating, product images, and more from any Amazon Bestseller List.

Google Search Scraper Google Search Scraper Scrape Google search results and get details like search rank, paid and organic results, knowledge graph, related search results, and more.

Walmart Product Reviews & Ratings Walmart Product Reviews & Ratings Get customer reviews for any product on Walmart.com and get details like product name, brand, reviews, and ratings.

Scrape Emails and Contact Details Scrape Emails and Contact Details Get emails, addresses, contact numbers, social media links from any website.

Walmart Search Results Scraper Walmart Search Results Scraper Get Product details such as pricing, availability, reviews, ratings, and more from Walmart search results and categories.

Glassdoor Job Listings Glassdoor Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Glassdoor.

Indeed Job Listings Indeed Job Listings Scrape job details such as job title, salary, job description, location, company name, number of reviews, and ratings from Indeed.

LinkedIn Jobs Scraper Premium LinkedIn Jobs Scraper Scrape job listings on LinkedIn and extract job details such as job title, job description, location, company name, number of reviews, and more.

Redfin Scraper Premium Redfin Scraper Scrape real estate listings from Redfin. Extract property details such as address, price, mortgage, redfin estimate, broker name and more.

Yelp Business Details Scraper Yelp Business Details Scraper Scrape business details from Yelp such as phone number, address, website, and more from Yelp search and business details page.

Zillow Scraper Premium Zillow Scraper Scrape real estate listings from Zillow. Extract property details such as address, price, Broker, broker name and more.

Amazon product offers and third party sellers Amazon product offers and third party sellers Get product pricing, delivery details, FBA, seller details, and much more from the Amazon offer listing page.

Realtor Scraper Premium Realtor Scraper Scrape real estate listings from Realtor.com. Extract property details such as Address, Price, Area, Broker and more.

Target Product Details & Pricing Target Product Details & Pricing Get product details from search results and category pages such as pricing, availability, rating, reviews, and 20+ data points from Target.

Trulia Scraper Premium Trulia Scraper Scrape real estate listings from Trulia. Extract property details such as Address, Price, Area, Mortgage and more.

Amazon Customer FAQs Amazon Customer FAQs Get FAQs for any product on Amazon and get details like the question, answer, answered user name, and more.

Yellow Pages Scraper Yellow Pages Scraper Get details like business name, phone number, address, website, ratings, and more from Yellow Pages search results.

This is a test collection for passage and document retrieval, produced in the TREC 2023 Deep Learning track. The Deep Learning Track studies information retrieval in a large training data regime. This is the case where the number of training queries with at least one positive label is at least in the tens of thousands, if not hundreds of thousands or more. This corresponds to real-world scenarios such as training based on click logs and training based on labels from shallow pools (such as the pooling in the TREC Million Query Track or the evaluation of search engines based on early precision).Certain machine learning based methods, such as methods based on deep learning are known to require very large datasets for training. Lack of such large scale datasets has been a limitation for developing such methods for common information retrieval tasks, such as document ranking. The Deep Learning Track organized in the previous years aimed at providing large scale datasets to TREC, and create a focused research effort with a rigorous blind evaluation of ranker for the passage ranking and document ranking tasks.Similar to the previous years, one of the main goals of the track in 2022 is to study what methods work best when a large amount of training data is available. For example, do the same methods that work on small data also work on large data? How much do methods improve when given more training data? What external data and models can be brought in to bear in this scenario, and how useful is it to combine full supervision with other forms of supervision?The collection contains 12 million web pages, 138 million passages from those web pages, search queries, and relevance judgments for the queries.

This archive contains the simulated collections and the estimated correlation coefficients. For the full code and description, please refer to https://github.com/julian-urbano/sigir2016-correlation

As of March 2021, Facebook Messenger was the mobile messaging and video calls app found to collect the largest amount of data from global iOS users, with over 30 data points collected across 14 segments. Line ranked second with 26 data points, while WeChat collected a total number of 23 data points from iOS users. The most collected data segments for messaging and video call apps were users' contact information and user content.

DataForSEO will land you with accurate data for a SERP monitoring solution. In particular, our SERP API provides data from:

• Google Organic search, Maps, News, and Images tabs in vertical search
• Bing Organic and Local Pack search
• Yahoo, Yandex, Baidu, and Naver search

For each of the search engines, we support all possible locations. You can set any keyword, location, and language, as well as define additional parameters, e.g. time frame, category, number of results.

You can set the device and the OS that you want to obtain SERP results for. We support Android/iOS for mobile and Windows/macOS for desktop.

We can supply you with all organic, paid, and extra Google SERP elements, including featured snippet, answer box, knowledge graph, local pack, map, people also ask, people also search, and more.

We offer well-rounded API documentation, GUI for API usage control, comprehensive client libraries for different programming languages, free sandbox API testing, ad hoc integration, and deployment support.

We have a pay-as-you-go pricing model. You simply add funds to your account and use them to get data. The account balance doesn't expire.

We propose a moment inequality approach to test for the presence of overconfidence using data from ranking experiments where subjects rank themselves relative to other experimental participants. Although a ranking experiment is a typical way to collect data for the analysis of overconfidence, recent studies show that the resulting data may apparently indicate overconfidence even if participants are purely rational Bayesian updaters, in which case a set of inequalities hold. We apply state-of-the-art tests of moment inequalities to test such a set of inequalities. We examine the data from a traditional ranking experiment as well as those from more sophisticated designs.

description: The main objective of the study was to compile, in a format accessible to all researchers, all data collected on alligator numbers, biology, and ecology in south Florida. The data are required to set restoration success criteria, provide input to models being developed to evaluate effects of Everglades restoration on alligators, and to develop short and long-term monitoring protocols for assessing the success of Restoration efforts. This included: 1. Compile a list of studies and data sets relating to alligators in south Florida. 2. Determine the accessibility of data sets. Rank the data sets as to their importance and need for compilation (rankings will be made in cooperation with BRD modeling staff and crocodilian researchers and mangers). 3. Obtain and compile at least the 3 highest ranking data sets. 4. Develop a standardized format for collecting and managing data on alligators. 5. Develop a project plan for obtaining the remaining data sets and producing a digital library of historic reports. 6. Use the historical data assembled above to develop a method and to compare body condition among alligator populations in south Florida both spatially and temporally.; abstract: The main objective of the study was to compile, in a format accessible to all researchers, all data collected on alligator numbers, biology, and ecology in south Florida. The data are required to set restoration success criteria, provide input to models being developed to evaluate effects of Everglades restoration on alligators, and to develop short and long-term monitoring protocols for assessing the success of Restoration efforts. This included: 1. Compile a list of studies and data sets relating to alligators in south Florida. 2. Determine the accessibility of data sets. Rank the data sets as to their importance and need for compilation (rankings will be made in cooperation with BRD modeling staff and crocodilian researchers and mangers). 3. Obtain and compile at least the 3 highest ranking data sets. 4. Develop a standardized format for collecting and managing data on alligators. 5. Develop a project plan for obtaining the remaining data sets and producing a digital library of historic reports. 6. Use the historical data assembled above to develop a method and to compare body condition among alligator populations in south Florida both spatially and temporally.

Control of Corruption: Percentile Rank in Bangladesh was reported at 14.62 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Bangladesh - Control of Corruption: Percentile Rank - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

Abstract

To support national goals of educational access and equity, Senegal has launched PAQEEB 2013-2017 (Projet d’Amelioration de la Qualité et de l’Equité dans l’Education de Base), which is a comprehensive government strategic plan to improve school governance, as well as increase equity and access to formal education. This is a collaborative effort of the Ministry of Education (MoE) of Senegal, the World Bank (WB), and other International agencies to improve the quality and equity of basic education (World Bank, 2013). A sub-component of this wide initiative is the objective to reach children who do not typically access formal education and are enrolled in religious education in Koranic schools known as Daaras. With Muslims comprising around 95% of the Senegalese population, a vast majority of Senegalese males would have attended Daaras at one time or another, and it is estimated that between 800,000 and one million children and youth attend Daaras (D’Aoust, 2013; as cited in Goensch, 2016). In Senegal, a “Traditional Daara” is dedicated only to memorization of the Koran and advanced studies (Islamic law, etc.) and do not offer any additional instruction in science, math, French or other core courses under the official curriculum. “Modern Daaras”, on the other hand, train students not only in religious education like memorization of Koran but also in Math and French as per the official curriculum.

This subcomponent of the PAQEEB project aims to upgrade and improve Traditional Daaras to have language and math curricula like the Modern Daaras. This is an innovative intervention that provides pedagogical support through disbursement of “grants for results.” In return for this funding, project schools commit to perform the following activities: (1) to implement the specific “Modern Daaras” math and French curriculum; and (2) to ensure that students achieve learning results as reported through indicators measuring their levels of proficiency in reading and mathematics. The project stakeholders selected 100 Daaras in 20 counties based on the lowest gross enrollment ratios out of the 46 counties in Senegal (effectively, counties with gross enrollment ratios between 29 percent and 69 percent) to pilot the Daara modernization efforts (Bureau des Statistiques Scolaires et Universitaires, 2007).

This survey was used to evaluate this sub-component of the larger PAQEEB project that provides “grants for results” to selected Daaras. The survey consists of three distinct instruments that collected relevant data from teachers, caretakers of students, and students.

Geographic coverage

Rural and peri-urban areas only.

Analysis unit

Individuals, households, and schools.

Universe

Primary school and Daara teachers, students, and caretakers of students.

Kind of data

Sample survey data [ssd]

Sampling procedure

Identifying Eligible Treatment and Control Schools The first step in the sampling implementation process was to identify a list of eligible treatment and control schools for the sample. To this effect, The World Bank and the Inspections des Daara committee provided IMPAQ with lists of Daaras that participated in the selection process for the Daara sub-component of the PAQEEB program within each of the 20 included districts. These lists included details on the ranking assigned to each candidate Daara and which Daaras were selected into the program (treatment Daaras) based on those rankings. Using this data on school rankings and characteristics, as well as information gathered during an initial visit to candidate Daaras, IMPAQ began the sample selection process by disqualifying schools from the sample that have previously been deemed ineligible for program allocation based on PAQEEB guidelines.

Selecting from Eligible Treatment and Comparison Schools In order to decrease spillover effects between individuals in treatment school communities and those in comparison school communities, IMPAQ used GPS data to apply a set of minimum distance criteria to all eligible comparison schools and remove any that were too close to treatment schools. More specifically, comparison Daaras were removed from the sample if they were less than 2 kilometers from a treatment school. This decision was based on Theunynck (2009), who shows that distance to school is inversely related to the probability of being enrolled in school in Senegal. Additionally, Theunynck explains that evidence from multiple countries in Africa shows that enrollment and retention decline significantly when students must walk more that 1 to 2 kilometers to get to school. This trend is particularly strong among younger children. Thus, at a distance of two kilometers, we should see minimal interference between treatment and comparison Daaras.

Additionally, in order to be able to distinguish the communities around comparison schools, comparison Daaras were removed from the sample if they were less than ½ kilometer away from other comparison school. In these cases, one school out of the two was randomly chosen to remain as eligible for selection. The radius around comparison schools is smaller because there is no concern of spillover effects between these Daaras. Rather, this radius ensured the research team that they were not measuring the outcomes of two comparison Daaras within the same community. The concern that children from comparison communities may enroll in other nearby comparison Daaras is not considered a major source of bias in the ITT estimate, as the comparison Daaras are generally considered to be of similar quality, making it less likely for a child in a comparison community to commute to a Daara in a different comparison community.

Remaining eligible Daaras were selected for inclusion in the sample based on their ranking in the PAQEEB program selection process. Specifically, Daaras included in the PAQEEB program that were ranked closest to (just above) the program selection threshold were identified as treatment Daaras. Daaras not included in the PAQEEB program that were ranked closest to (just below) the program selection threshold were identified as comparison Daaras. In this way, IMPAQ ensured that treatment and comparison Daaras were as similar as possible concerning the key criteria used for program selection. In the event that multiple comparison schools received equivalent rankings, a random number generator was used to select among them for inclusion into the sample. If an appropriate comparison school could not be identified within a given IEF, all schools from that IEF were dropped from the sample. In most IEFs, IMPAQ selected 3 treatment Daaras and 3 comparison Daaras into the sample.

Selecting Eligible Secondary Comparison Schools In addition to the comparison Daaras, IMPAQ included a second comparison group consisting of formal government schools. These schools were selected based on proximity to treatment Daaras, while still meeting the minimum distance criteria outlined above for comparison Daaras (i.e. 2-kilometer distance).

Household and Child Selection IMPAQ performed a house-listing census of all households with children under the age 16 within a 1-kilometer perimeter (school catchment areas) of each Daara and formal school selected into the sample. For details on this house listing please see section 6.3.3 below. Once all households within the established perimeter of a selected school that had at least one child aged 7-10 were identified, IMPAQ randomly selected 15 households with at least one girl aged 7-10 and 15 households with at least one boy aged 7-10 for inclusion in the study. Only one child of each gender was selected from a given household in order to minimize the influence of larger households on the study outcome. Lastly, if a selected household had more than one child aged 7-10 of a single gender, IMPAQ randomly selected which of those children would be included in the sample, in order to prevent any bias in the selection of children within households.

Mode of data collection

Face-to-face [f2f]

Research instrument

All instruments were originally developed in French, but have been translated to English as well.

Instruments The baseline survey consisted of three unique instruments: A caretaker survey, a child survey and academic assessment, and a teacher survey.

Caretakers’ instrument (Enquête sur les personnes qui s’occupent des enfants) The caretaker survey was designed to learn about the decisions and opinions within each household in the sample. A caretaker was defined as “the person who takes care of the child and makes decisions about what he/she eats and how he/she spends his/her time.” The survey instrument was divided into a schooling section and a household information section. Within the schooling section, caretakers were asked about schools and Daaras in their community, last year’s schooling choices, this year’s schooling choices, their opinions about education, and the child’s school participation/attendance. The household information section briefly captured some basic household characteristics, such as household size, number of children, education levels, and household assets.

Children’s instrument (Enquête sur les enfants) The children’s survey begins with a few questions for the child’s caretaker, which are used to confirm the child’s name, age, and the school he or she attends. The rest of the survey is addressed to the child. First, the enumerator spent 3 to 5 minutes speaking with the child and setting him/her at ease. Next, the child answers questions about the school/Daara they attend. There are different sets of questions depending on whether he/she attends

Data Source: California Office of Traffic Safety

This data biography shares the how, who, what, where, when, and why about this dataset. We, the epidemiology team at Napa County Health and Human Services Agency, Public Health Division, created it to help you understand where the data we analyze and share comes from. If you have any further questions, we can be reached at epidemiology@countyofnapa.org.

Data dashboard featuring this data: https://data.countyofnapa.org/stories/s/abqu-wcty

Why was the data collected?  California Office of Traffic Safety (OTS) ranking metric is a tool used to compare similarly sized cities on traffic safety statistics. A smaller the assigned number means that the city is ranked higher, and a higher ranking means the city has worse traffic safety compared to similar locations.

How was the data collected? Crash data comes from Statewide Traffic Records System (SWITRS). This system collects and processes data gathered from a collision scene. Population estimates come from California Department of Finance (DoF), which are based on changes in births, deaths, domestic migration, and international migration. Estimates are developed using aggregate data from a variety of sources, including birth and death counts provided by the Department of Public Health, driver's license data from the Department of Motor Vehicles, housing unit data from local governments, school enrollment data from the Department of Education, and federal income tax return data from the U.S. Internal Revenue Service. Daily Vehicle Miles Traveled (DVMT) come from California Department of Transportation (Caltrans). The Traffic Data Branch at Caltrans estimates the number of vehicle miles that motorists traveled on California State Highways using a sampling of up to 20 traffic monitoring sites and reports on that data. Crash rankings are based on a ranking method that assigns statistical weights to categories including observed crash counts, population, and vehicle miles traveled. Counties are assigned statewide rankings, while cities are assigned population group rankings. DUI arrests data comes from the Department of Justice.

Who was included and excluded from the data & Where was the data collected? Data for the rankings is taken from Incorporated cities only. This includes local streets and state highways within city limits that share jurisdiction with the CHP. DUI arrest data is only available for cities that report it to the Department of Justice. Data from the OTS crash was sources specifically for Napa County, the City of Napa, American Canyon, Calistoga, St. Helena and Yountville.

When was the data collected? 2017-2022

Where can I learn more about this data? Office of traffic safety: https://www.ots.ca.gov/media-and-research/crash-rankings/ Methodology: https://rosap.ntl.bts.gov/view/dot/24410

This dataset contains data collected during a study "Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries" conducted by Martin Lnenicka (University of Pardubice, Pardubice, Czech Republic), Anastasija Nikiforova (University of Tartu, Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Kosovska Mitrovica, Serbia), Daniel Rudmark (University of Gothenburg and RISE Research Institutes of Sweden, Gothenburg, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Caterina Santoro (KU Leuven, Leuven, Belgium), Cesar Casiano Flores (University of Twente, Twente, the Netherlands), Marijn Janssen (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain).

It is being made public both to act as supplementary data for "Identifying patterns and recommendations of and for sustainable open data initiatives: a benchmarking-driven analysis of open government data initiatives among European countries", Government Information Quarterly*, and in order for other researchers to use these data in their own work.

Methodology

The paper focuses on benchmarking of open data initiatives over the years and attempts to identify patterns observed among European countries that could lead to disparities in the development, growth, and sustainability of open data ecosystems.

This study examines existing benchmarks, indices, and rankings of open (government) data initiatives to find the contexts by which these initiatives are shaped, both of which then outline a protocol to determine the patterns. The composite benchmarks-driven analytical protocol is used as an instrument to examine the understanding, effects, and expert opinions concerning the development patterns and current state of open data ecosystems implemented in eight European countries - Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. 3-round Delphi method is applied to identify, reach a consensus, and validate the observed development patterns and their effects that could lead to disparities and divides. Specifically, this study conducts a comparative analysis of different patterns of open (government) data initiatives and their effects in the eight selected countries using six open data benchmarks, two e-government reports (57 editions in total), and other relevant resources, covering the period of 2013–2022.

Description of the data in this data set

The file "OpenDataIndex_2013_2022" collects an overview of 27 editions of 6 open data indices - for all countries they cover, providing respective ranks and values for these countries. These indices are:

1) Global Open Data Index (GODI) (4 editions)

2) Open Data Maturity Report (ODMR) (8 editions)

3) Open Data Inventory (ODIN) (6 editions)

4) Open Data Barometer (ODB) (5 editions)

5) Open, Useful and Re-usable data (OURdata) Index (3 editions)

6) Open Government Development Index (OGDI) (2 editions)

These data shapes the third context - open data indices and rankings. The second sheet of this file covers countries covered by this study, namely, Austria, Belgium, Czech Republic, Italy, Latvia, Poland, Serbia, Sweden. It serves the basis for Section 4.2 of the paper.

Based on the analysis of selected countries, incl. the analysis of their specifics and performance over the years in the indices and benchmarks, covering 57 editions of OGD-oriented reports and indices and e-government-related reports (2013-2022) that shaped a protocol (see paper, Annex 1), 102 patterns that may lead to disparities and divides in the development and benchmarking of ODEs were identified, which after the assessment by expert panel were reduced to a final number of 94 patterns representing four contexts, from which the recommendations defined in the paper were obtained. These patterns are available in the file "OGDdevelopmentPatterns". The first sheet contains the list of patterns, while the second sheet - the list of patterns and their effect as assessed by expert panel.

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

Licenses or restrictionsCC-BY

For more info, see README.txt

Ranking COVID-19 response indicators (criteria) sorted in to four scenarios.

Analysis of ‘Board Games’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/andrewmvd/board-games on 14 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This dataset contains data collected on board games from the BoardGameGeek (BGG) website in February 2021. BGG is the largest online collection of board game data which consists of data on more than 100,000 total games (ranked and unranked).

The voluntary online community contributes to the site with reviews, ratings, images, videos, session reports and live discussion forums on the expanding database of board games.

This data set contains all ranked games (~20,000) as of the date of collection from the BGG database. Unranked games are ignored as they have not been rated by enough BGG users (a game should receive at least 30 votes to be eligible for ranking).

How to use this dataset

• Predict board game rating based on its mechanics and features.
• Explore the landscape of board games

Highlighted Notebooks

• Board Game Analysis by Karnika Kapoor
• More datasets

Acknowledgements

If you use this dataset in your research, please credit the authors

Citation

Dilini Samarasinghe, July 5, 2021, "BoardGameGeek Dataset on Board Games", IEEE Dataport, doi: https://dx.doi.org/10.21227/9g61-bs59.

License

CC BY 4.0

Splash banner

Icon by Freepik on FlatIcon.

--- Original source retains full ownership of the source dataset ---

Doing archives offers empirical data relevant to archival practice, science and management from 2013 to the present. (global) see all the data for all repositories: rankings for topics, indicator values, and detailed information like the steps required to process a collection; (subnational) all the data for a city or region: rankings for topics, indicator values, and detailed information; (topics) in-depth, cross-repository view of the data, 2013.001 Getting administration, 2013.002 Employing workers, 2013.003 Educating workers, 2013.004 Getting processing, 2013.005 Dealing with appraisal, 2013.006 Registering intellectual property, 2013.007 Enforcing contracts, 2013.008 Cooperation across repositories, 2013.009 Enforcing policies, 2013.010 Dealing with acquisitions, 2013.011 Sustainability; (distance to frontier); (good practices); (transparency); (rankings) the Ease of Doing archives index ranks all repositories at the national level. Where does your repository rank? Rank repositories within their region, budget, or size; (historical)