A single line street base map representing the city's streets and other linear geographic features, along with feature names and address ranges for each addressable street segment. This dataset includes the Nodes file. The Nodes file contains a point feature and unique NodeID for each node that exists in the LION file. The Node_StreetName.txt file lists the street names associated with those nodes. Most nodes, representing intersections, will have at least 2 street names associated in the Node_StreetName.txt file.

All previously released versions of this data are available at BYTES of the BIG APPLE - Archive.

SEE LION

## Overview

SEE LION is a dataset for object detection tasks - it contains Rescue Equipment annotations for 397 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).

California sea lions pup and breed at four of the nine Channel Islands in southern California. Since 1981, SWFSC MMTD has been conducting a diet study of sea lions at San Clemente Island (a small rookery) and San Nicolas Island (a large rookery). Information on the diet of sea lions is obtained from analyzing scats (i.e., fecal samples) and spewings (i.e., vomitus) collected at those two rookeries in January (winter), April (spring), July (summer), and October (autumn). Otoliths (a crystalline structure within the ear organ) from fish and beaks (mandibles composed of chitin) from cephalopods are recovered from the samples by washing each sample through sieves of varying mesh size. Otoliths and beaks, which are shaped and sized differently for each species of fish and cephalopod, respectively, are used to identify and enumerate fish, and cephalopods consumed by sea lions. Also, otoliths and beaks are measured for estimating size of prey being consumed by sea lions.

This data shows the location of 10 lions that were collared in the year 2016. This was generated during a predator monitoring project Kenya Wildlife Trust (KWT) were conducting in 2016. The animals being tracked were: 3 adult female Lions and 7 sub-adult male Lions. These animals traversed the greater Mara Ecosystem and a section of the Serengeti National Park.

This dataset contains images of lions and tigers sourced from the Open Images Dataset V6 and labeled specifically for object detection using the YOLO format. The dataset focuses on two classes: lion and tiger, with annotations provided for each image in a YOLO-compatible .txt file format. This dataset is ideal for training machine learning models for wildlife detection and classification tasks, particularly in distinguishing between these two majestic big cats. Key Features:

Classes: Lion and Tiger
Annotations: YOLO format, with bounding box coordinates and class labels provided in separate .txt files for each image.
Source: Images sourced from Open Images Dataset V6, which is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Application: Suitable for object detection models like YOLO, SSD, or Faster R-CNN.

Usage:

The dataset can be used for training, validating, or testing object detection models. Each image is accompanied by a corresponding YOLO annotation file, making it easy to integrate into any YOLO-based pipeline. Attribution:

This dataset is derived from the Open Images Dataset V6, and proper attribution must be given. Please credit the Open Images Dataset when using or sharing this dataset in any format.

Dataset Card for Dataset Name

UMIE (Unified Medical Imaging Ensemble) is currently the largest publicly available dataset of annotated radiological imaging, combining over 20 open-source datasets into a unified collection with standardized formatting and labeling based on the RadLex ontology.

  Dataset Details





  Dataset Description

UMIE datasets combine more than 20 open-source medical imaging datasets, containing over 1 million radiological images across… See the full description on the dataset page: https://huggingface.co/datasets/lion-ai/umie_datasets.

AAAa200955/Lion dataset hosted on Hugging Face and contributed by the HF Datasets community

Here are a few use cases for this project:

1. Wildlife Conservation: The Lion Baboon model can be used to monitor endangered lion and baboon populations in their natural habitat, providing vital information about population sizes, migration patterns, and behavioral trends to help inform conservation efforts.

2. Animal Behavior Research: Researchers can use this model to study the behavioral patterns of lions and baboons by automatically identifying and tracking individual animals in pictures or video footage, providing valuable insights into their social dynamics, hunting strategies, and other important aspects of their lives.

3. Safari Tours and Animal Tracking: Tour operators or park rangers can utilize the Lion Baboon model in combination with cameras mounted on safari vehicles or park facilities to automatically identify and track lions and baboons, allowing them to guide tourists to the best spots for viewing these animals.

4. Wildlife Photography Assistance: Wildlife photographers can leverage this model to set up motion-triggered camera traps that only activate when a baboon or lion is detected, increasing the chances of capturing breathtaking images of these animals without disturbing their natural behavior.

5. Educational Tools and Applications: Software and mobile app developers can integrate the Lion Baboon model into educational applications to teach users about wildlife, animal identification, or the ecosystems in which these animals live, using interactive quizzes or virtual field trips that leverage real-world images from the dataset.

Lion

## Overview

Lion is a dataset for object detection tasks - it contains Lion annotations for 565 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).

The LION Differences File (LDF) documents segment and node level changes that have occurred in the LION file between two subsequent releases. This file allows a user who "ties" organizational data to DCP's Segment ID and/or Node ID to migrate their data appropriately when these changes occur. All previously released versions of this data are available at BYTES of the BIG APPLE - Archive.

This database contains information about the distribution and abundance of Australian sea lions around the Australian coastline. It is derived from information held at the former CSIRO Division of Wildlife and Ecology, and provided by Dr. P. Shaughnessy.

Format: shapefile.

Quality: - Scope: Dataset. External accuracy: +/- one degree. Non Quantitative accuracy: Variable.

Data in the Fur Seal coverage contains:

LOCATION = The physical location of Fur Seal. DATE = Date and month when species recorded. Does not state the year. SEAL_NUMBER = Fur Seal unique identification number.

Data in the Sea Lion coverage contains:

LOCATION = The physical location of a Sea Lion. DATE = Date, month and year when species has been recorded. SEALION_NUMBER = Seal Lion unique identification number.

Conceptual consistency: Coverages are topologically consistent. No particular tests conducted by ERIN. Completeness omission: Complete for the Australian continent. Lineage: Data were stored in VAX files, MS-DOS R-base files and as a microcomputer dataset accessible under the LUPIS (Land Use Planning Information System) land allocation package. CAMRIS was established using SPANS Geographic Information System (GIS) software running under a UNIX operating system on an IBM RS 6000 platform. A summary follows of processing completed by the CSIRO: 1. r-BASE: Information imported into r-BASE from a number of different sources (ie Digitised, scanned, CD-ROM, NOAA World Ocean Atlas, Atlas of Australian Soils, NOAA GEODAS archive and Complete book of Australian Weather). 2. From the information held in r-BASE a BASE Table was generated incorporating specific fields. 3. SPANS environment: Works on creating a UNIVERSE with a geographic projection - Equidistant Conic (Simple Conic) and Lambert Conformal Conic, Spheroid: International Astronomical Union 1965 (Australia/Sth America); the Lower left corner and the longitude and latitude of the centre point. 4. BASE Table imported into SPANS and a BASE Map generated. 5. Categorise Maps - created from the BASE map and table by selecting out specified fields, a desired window size (ie continental or continent and oceans) and resolution level (ie the quad tree level). 6. Rasterise maps specifying key parameters such as: number of bits, resolution (quad tree level 8 lowest - 16 highest) and the window size (usually 00 or cn). 7. Gifs produced using categorised maps with a title, legend, scale and long/lat grid. 8. Supplied to ERIN with .bil; .hdr; .gif; Arc export files .e00; and text files .asc and .txt formats. 9. The reference coastline for CAMRIS was the mean high water mark (AUSLIG 1;100 000 topographic map series).

lion-ai/UMIE-Visual-QA dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset tracks annual total students amount from 2003 to 2023 for The Lion Lane School

This dataset tracks annual total classroom teachers amount from 2003 to 2023 for The Lion Lane School

The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the ‘dismo''package (Hijmans et al. 2011). Model evaluation was carried out using the ‘PresenceAbsence''package in R (Freeman and Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband ‘tif''format with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

Historical Dataset of The Lion Lane School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2003-2023),Total Classroom Teachers Trends Over Years (2003-2023),Student-Teacher Ratio Comparison Over Years (2003-2023),Asian Student Percentage Comparison Over Years (2004-2023),Hispanic Student Percentage Comparison Over Years (2003-2023),Black Student Percentage Comparison Over Years (2003-2023),White Student Percentage Comparison Over Years (2003-2023),Two or More Races Student Percentage Comparison Over Years (2013-2022),Diversity Score Comparison Over Years (2003-2023),Free Lunch Eligibility Comparison Over Years (2003-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2002-2023)

Aim: Predict empirically the current and recent-historical (c. 1970) landscape connectivity and population size of the African lion as a baseline against which to assess conservation of the species. Location: Continental Africa. Methods: We compiled historical records of lion distribution to generate a recent-historical range for the species. Historical population size was predicted using a generalised additive model. Resistant kernel and factorial least-cost path analyses were used to predict recent-historical landscape connectivity and compare this with contemporary connectivity at continental, regional and country scales. Results: We estimate a baseline population of ~92,054 (83,017 – 101,094 95% CI) lions in c1970, suggesting Africa’s lion population has declined by ~75%, over the last five decades. Although greatly reduced from historical extents (c. 1500 AD), recent-historical lion habitat was substantially connected. However, in comparison, contemporary population connectivity ha...

Updated December, 2024HUMAN CONFLICT: Areas where Mountain Lions have been involved in incidents (conflict with humans that may have serious results), an attack on a human, predation on domestic pets, or depredation on livestock held in close proximity to human habitation. OVERALL RANGE: The area that encompasses all known activity areas within the observed range of a population of Mountain Lion. PERIPHERAL RANGE: An area of Mountain Lion overall range where habitat is limited and populations are isolated. Population density may be lower than in the central part of their range.This information was derived from Colorado Parks and Wildlife field personnel. Data was captured by digitizing through a SmartBoard Interactive Whiteboard using topographic maps and NAIP imagery at various scales (Cowardin, M., M. Flenner. March 2003. Maximizing Mapping Resources. GeoWorld 16(3):32-35). These data are updated on a four year rotation with one of the four Colorado Parks and Wildlife Regions updated each year. These data are not updated on a statewide level annually.

About this dataset

Context

The dataset tabulates the Red Lion population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Red Lion across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Red Lion was 6,487, a 0.06% increase year-by-year from 2022. Previously, in 2022, Red Lion population was 6,483, a decline of 0.28% compared to a population of 6,501 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Red Lion increased by 322. In this period, the peak population was 6,505 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Red Lion is shown in this column.
• Year on Year Change: This column displays the change in Red Lion population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Red Lion Population by Year. You can refer the same here

The latest closing stock price for Lion as of May 28, 2025 is 3.08. An investor who bought $1,000 worth of Lion stock at the IPO in 2019 would have $-1,000 today, roughly -1 times their original investment - a -77.62% compound annual growth rate over 6 years. The all-time high Lion stock closing price was 25740.00 on May 08, 2020. The Lion 52-week high stock price is 26.30, which is 753.9% above the current share price. The Lion 52-week low stock price is 2.13, which is 30.8% below the current share price. The average Lion stock price for the last 52 weeks is 10.07. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.