Top View Transplant

## Overview

Top View Transplant is a dataset for instance segmentation tasks - it contains Pass annotations for 320 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Developers using the DOL-wide API have access to a variety of queries providing usage metrics for their app's key.

John Ioannidis and co-authors [1] created a publicly available database of top-cited scientists in the world. This database, intended to address the misuse of citation metrics, has generated a lot of interest among the scientific community, institutions, and media. Many institutions used this as a yardstick to assess the quality of researchers. At the same time, some people look at this list with skepticism citing problems with the methodology used. Two separate databases are created based on career-long and, single recent year impact. This database is created using Scopus data from Elsevier[1-3]. The Scientists included in this database are classified into 22 scientific fields and 174 sub-fields. The parameters considered for this analysis are total citations from 1996 to 2022 (nc9622), h index in 2022 (h22), c-score, and world rank based on c-score (Rank ns). Citations without self-cites are considered in all cases (indicated as ns). In the case of a single-year case, citations during 2022 (nc2222) instead of Nc9622 are considered.

To evaluate the robustness of c-score-based ranking, I have done a detailed analysis of the matrix parameters of the last 25 years (1998-2022) of Nobel laureates of Physics, chemistry, and medicine, and compared them with the top 100 rank holders in the list. The latest career-long and single-year-based databases (2022) were used for this analysis. The details of the analysis are presented below: Though the article says the selection is based on the top 100,000 scientists by c-score (with and without self-citations) or a percentile rank of 2% or above in the sub-field, the actual career-based ranking list has 204644 names[1]. The single-year database contains 210199 names. So, the list published contains ~ the top 4% of scientists. In the career-based rank list, for the person with the lowest rank of 4809825, the nc9622, h22, and c-score were 41, 3, and 1.3632, respectively. Whereas for the person with the No.1 rank in the list, the nc9622, h22, and c-score were 345061, 264, and 5.5927, respectively. Three people on the list had less than 100 citations during 96-2022, 1155 people had an h22 less than 10, and 6 people had a C-score less than 2.
In the single year-based rank list, for the person with the lowest rank (6547764), the nc2222, h22, and c-score were 1, 1, and 0. 6, respectively. Whereas for the person with the No.1 rank, the nc9622, h22, and c-score were 34582, 68, and 5.3368, respectively. 4463 people on the list had less than 100 citations in 2022, 71512 people had an h22 less than 10, and 313 people had a C-score less than 2. The entry of many authors having single digit H index and a very meager total number of citations indicates serious shortcomings of the c-score-based ranking methodology. These results indicate shortcomings in the ranking methodology.

Annual Excel pivot tables display the top 25 MS-DRGs (Medicare Severity-Diagnosis Related Groups) per hospital. The ranking can be sorted by the number of discharges, average charge per stay, or average length of stay.

This dataset tracks annual distribution of students across grade levels in Round Top-carmine High School

This dataset tracks annual white student percentage from 1991 to 2023 for Round Top-carmine High School vs. Texas and Round Top-Carmine Independent School District

Top Street Images

## Overview

Top Street Images is a dataset for object detection tasks - it contains Stormwater Drains Sewers Manhole annotations for 580 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Top Piston

## Overview

Top Piston is a dataset for object detection tasks - it contains Piston annotations for 2,052 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Developers using the DOL-wide API have access to a variety of queries providing usage metrics for their app's key.

This dataset provides information about the number of properties, residents, and average property values for Tree Top Lane cross streets in Selbyville, DE.

Previous studies on supporting free-form keyword queries over RDBMSs provide users with linked-structures (e.g.,a set of joined tuples) that are relevant to a given keyword query. Most of them focus on ranking individual tuples from one table or joins of multiple tables containing a set of keywords. In this paper, we study the problem of keyword search in a data cube with text-rich dimension(s) (so-called text cube). The text cube is built on a multidimensional text database, where each row is associated with some text data (a document) and other structural dimensions (attributes). A cell in the text cube aggregates a set of documents with matching attribute values in a subset of dimensions. We define a keyword-based query language and an IR-style relevance model for coring/ranking cells in the text cube. Given a keyword query, our goal is to find the top-k most relevant cells. We propose four approaches, inverted-index one-scan, document sorted-scan, bottom-up dynamic programming, and search-space ordering. The search-space ordering algorithm explores only a small portion of the text cube for finding the top-k answers, and enables early termination. Extensive experimental studies are conducted to verify the effectiveness and efficiency of the proposed approaches. Citation: B. Ding, B. Zhao, C. X. Lin, J. Han, C. Zhai, A. N. Srivastava, and N. C. Oza, “Efficient Keyword-Based Search for Top-K Cells in Text Cube,” IEEE Transactions on Knowledge and Data Engineering, 2011.

This dataset contains historical price data for the top global cryptocurrencies, sourced from Yahoo Finance. The data spans the following time frames for each cryptocurrency:

BTC-USD (Bitcoin): From 2014 to December 2024 ETH-USD (Ethereum): From 2017 to December 2024 XRP-USD (Ripple): From 2017 to December 2024 USDT-USD (Tether): From 2017 to December 2024 SOL-USD (Solana): From 2020 to December 2024 BNB-USD (Binance Coin): From 2017 to December 2024 DOGE-USD (Dogecoin): From 2017 to December 2024 USDC-USD (USD Coin): From 2018 to December 2024 ADA-USD (Cardano): From 2017 to December 2024 STETH-USD (Staked Ethereum): From 2020 to December 2024

Key Features:

Date: The date of the record. Open: The opening price of the cryptocurrency on that day. High: The highest price during the day. Low: The lowest price during the day. Close: The closing price of the cryptocurrency on that day. Adj Close: The adjusted closing price, factoring in stock splits or dividends (for stablecoins like USDT and USDC, this value should be the same as the closing price). Volume: The trading volume for that day.

Data Source:

The dataset is sourced from Yahoo Finance and spans daily data from 2014 to December 2024, offering a rich set of data points for cryptocurrency analysis.

Use Cases:

Market Analysis: Analyze price trends and historical market behavior of leading cryptocurrencies. Price Prediction: Use the data to build predictive models, such as time-series forecasting for future price movements. Backtesting: Test trading strategies and financial models on historical data. Volatility Analysis: Assess the volatility of top cryptocurrencies to gauge market risk. Overview of the Cryptocurrencies in the Dataset: Bitcoin (BTC): The pioneer cryptocurrency, often referred to as digital gold and used as a store of value. Ethereum (ETH): A decentralized platform for building smart contracts and decentralized applications (DApps). Ripple (XRP): A payment protocol focused on enabling fast and low-cost international transfers. Tether (USDT): A popular stablecoin pegged to the US Dollar, providing price stability for trading and transactions. Solana (SOL): A high-speed blockchain known for low transaction fees and scalability, often seen as a competitor to Ethereum. Binance Coin (BNB): The native token of Binance, the world's largest cryptocurrency exchange, used for various purposes within the Binance ecosystem. Dogecoin (DOGE): Initially a meme-inspired coin, Dogecoin has gained a strong community and mainstream popularity. USD Coin (USDC): A fully-backed stablecoin pegged to the US Dollar, commonly used in decentralized finance (DeFi) applications. Cardano (ADA): A proof-of-stake blockchain focused on scalability, sustainability, and security. Staked Ethereum (STETH): A token representing Ethereum staked in the Ethereum 2.0 network, earning staking rewards.

This dataset provides a comprehensive overview of key cryptocurrencies that have shaped and continue to influence the digital asset market. Whether you're conducting research, building prediction models, or analyzing trends, this dataset is an essential resource for understanding the evolution of cryptocurrencies from 2014 to December 2024.

This dataset tracks annual asian student percentage from 1997 to 2023 for Red Top Middle School vs. Georgia and Bartow County School District

This is looking at 5 years of the top 25 expenditures with rankings for each year.

This statistic depicts a ranking of the top 10 U.S. nonprofit hospital operators based on number of hospitals as of December 2015. At this point, Ascension Health, based in St. Louis, Missouri, was ranked first in the United States, with a total of 76 hospitals.

This dataset shows how Treasury offsets federal payments, such as tax refunds, to pay off delinquent debts such as unpaid child support.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset is the average clay content of the top 2m of soil over the Sydney Basin. It is derived from the TERN digital soil mapping (http://www.tern.org.au/Soil-and-Landscape-Grid-of-Australia-pg17731.html).

Purpose

This dataset was created to estimate groundwater recharge in the alluvial areas of the Sydney Basin using the empirical method of Wohling et al (2012).

Wohling DL, Leaney FW and Crosbie RS (2012) Deep drainage estimates using multiple linear regression with percent clay content and rainfall. Hydrol. Earth Syst. Sci. 16(2), 563-572. DOI: 10.5194/hess-16-563-2012.

Dataset History

This is a depth-weighted average of the clay content from 6 soil layers in the TERN digital soil mapping (http://data.bioregionalassessments.gov.au/dataset/f8640540-4bb7-42ee-995a-219881e67705).

It has then been re-projected to MGA94 Zone 56 and re-sampled to a 500m raster.

Dataset Citation

Bioregional Assessment Programme (XXXX) SYD Soil - Average Clay Content Top 2m v01. Bioregional Assessment Derived Dataset. Viewed 22 June 2018, http://data.bioregionalassessments.gov.au/dataset/95fc3145-5249-4bd4-bfd2-a812c39d68f9.

Dataset Ancestors

• Derived From Bioregional Assessment areas v02

• Derived From Gippsland Project boundary

• Derived From Bioregional Assessment areas v04

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From Bioregional Assessment areas v05

• Derived From GEODATA TOPO 250K Series 3

• Derived From Soil and Landscape Grid National Soil Attribute Maps - Clay 3 resolution - Release 1

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v06

• Derived From Victoria - Seamless Geology 2014

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

This dataset is about book subjects. It has 1 row and is filtered where the books is Top pubs in the South. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

About this dataset

Context

The dataset tabulates the Broad Top City population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Broad Top City. The dataset can be utilized to understand the population distribution of Broad Top City by age. For example, using this dataset, we can identify the largest age group in Broad Top City.

Key observations

The largest age group in Broad Top City, PA was for the group of age 20 to 24 years years with a population of 60 (12.99%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Broad Top City, PA was the 30 to 34 years years with a population of 9 (1.95%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Broad Top City is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Broad Top City 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 Broad Top City Population by Age. You can refer the same here

linkedin-top-voices-market-alpaca Dataset

Dataset containing 300 records for fine-tuning language models.

  Columns

instruction input output input_tokens output_tokens input_cost output_cost total_cost