a description

Dataset Card for PANDA

  Dataset Summary

PANDA (Perturbation Augmentation NLP DAtaset) consists of approximately 100K pairs of crowdsourced human-perturbed text snippets (original, perturbed). Annotators were given selected terms and target demographic attributes, and instructed to rewrite text snippets along three demographic axes: gender, race and age, while preserving semantic meaning. Text snippets were sourced from a range of text corpora (BookCorpus, Wikipedia, ANLI… See the full description on the dataset page: https://huggingface.co/datasets/facebook/panda.

Red Pandas 100

## Overview

Red Pandas 100 is a dataset for object detection tasks - it contains Red Pandas 5VrQ annotations for 328 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).

PANDA is the first gigaPixel-level humAN-centric viDeo dAtaset, for large-scale, long-term, and multi-object visual analysis. The videos in PANDA were captured by a gigapixel camera and cover real-world scenes with both wide field-of-view (~1 square kilometer area) and high-resolution details (~gigapixel-level/frame). The scenes may contain 4k head counts with over 100x scale variation. PANDA provides enriched and hierarchical ground-truth annotations, including 15,974.6k bounding boxes, 111.8k fine-grained attribute labels, 12.7k trajectories, 2.2k groups and 2.9k interactions.

Analysis of ‘Datasets for Pandas’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rajacsp/datasets-for-pandas on 28 January 2022.

--- No further description of dataset provided by original source ---

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

Analysis of ‘Pandas practices’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/melihkanbay/police on 28 January 2022.

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

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3718520%2Fccd96a32c92d21640b67c1aa74a685c6%2Findir%20(1).jpg?generation=1581067964496524&alt=media" alt="">

Context

vehicles stopped and search by the police

Content

Age, reason....

Acknowledgements

thx for stanford

Inspiration

do practice

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

Financial overview and grant giving statistics of Pandas Resource Network Inc

Please feel free to share it with others and consider supporting me if you find it helpful ⭐️.

Overview:

• Chapter 1: Getting started with pandas
• Chapter 2: Analysis: Bringing it all together and making decisions
• Chapter 3: Appending to DataFrame
• Chapter 4: Boolean indexing of dataframes
• Chapter 5: Categorical data
• Chapter 6: Computational Tools
• Chapter 7: Creating DataFrames
• Chapter 8: Cross sections of different axes with MultiIndex
• Chapter 9: Data Types
• Chapter 10: Dealing with categorical variables
• Chapter 11: Duplicated data
• Chapter 12: Getting information about DataFrames
• Chapter 13: Gotchas of pandas
• Chapter 14: Graphs and Visualizations
• Chapter 15: Grouping Data
• Chapter 16: Grouping Time Series Data
• Chapter 17: Holiday Calendars
• Chapter 18: Indexing and selecting data
• Chapter 19: IO for Google BigQuery
• Chapter 20: JSON
• Chapter 21: Making Pandas Play Nice With Native Python Datatypes
• Chapter 22: Map Values
• Chapter 23: Merge, join, and concatenate
• Chapter 24: Meta: Documentation Guidelines
• Chapter 25: Missing Data
• Chapter 26: MultiIndex
• Chapter 27: Pandas Datareader
• Chapter 28: Pandas IO tools (reading and saving data sets)
• Chapter 29: pd.DataFrame.apply
• Chapter 30: Read MySQL to DataFrame
• Chapter 31: Read SQL Server to Dataframe
• Chapter 32: Reading files into pandas DataFrame
• Chapter 33: Resampling
• Chapter 34: Reshaping and pivoting
• Chapter 35: Save pandas dataframe to a csv file
• Chapter 36: Series
• Chapter 37: Shifting and Lagging Data
• Chapter 38: Simple manipulation of DataFrames
• Chapter 39: String manipulation
• Chapter 40: Using .ix, .iloc, .loc, .at and .iat to access a DataFrame
• Chapter 41: Working with Time Series

This dataset provides an overview of companies listed in the Y Combinator directory, scraped on 13 July 2023. It offers a valuable resource for analysing the startup ecosystem, allowing users to explore companies by industry, geographic location, company size, and more. Y Combinator is a prominent startup accelerator that has funded over 4,000 companies, collectively valued at over $600 billion, with the primary aim of supporting new ventures in their growth.

Columns

• company_id: Unique identifier for each company, provided by Y Combinator.
• company_name: The name of the company.
• short_description: A concise, one-line summary of the company.
• long_description: A more detailed description of the company.
• batch: The specific Y Combinator batch the company belongs to.
• status: The current operational status of the company.
• tags: Industry-specific tags associated with the company.
• location: The physical location of the company.
• country: The country where the company is located.
• year_founded: The year the company was established.
• num_founders: The number of founders associated with the company.
• founders_names: The full names of the company's founders.
• team_size: The number of employees in the company.
• website: The official website URL for the company.
• cb_url: The Crunchbase URL for the company.
• linkedin_url: The LinkedIn profile URL for the company.

Distribution

The dataset is supplied as a CSV file, based on data scraped on 27 February 2023. While specific total row or record counts are not available, various distributions of column values have been noted.

Usage

This dataset is ideal for market research, competitive intelligence, and startup ecosystem analysis. It can be used to identify industry trends, study company demographics, or explore investment opportunities within the Y Combinator portfolio.

Coverage

The dataset covers companies globally, with locations and countries explicitly noted for each entry. The time range for company founding years spans from 2005 to 2023. The data was collected as of 13 July 2023.

License

CCO

Who Can Use It

• Researchers: For academic studies on startup accelerators, entrepreneurship, and tech industry trends.
• Business Analysts: To gain insights into market segments, competitor landscapes, and potential partnership opportunities.
• Investors: For identifying promising startups and understanding investment patterns.
• Aspiring Entrepreneurs: To learn about successful startup profiles and their development paths.

Dataset Name Suggestions

• Y Combinator Company Directory
• YC Startup Data
• Global Startup Accelerator Dataset
• Y Combinator Investment Portfolio

Attributes

Original Data Source: Y Combinator Directory

PandasPlotBench

PandasPlotBench is a benchmark to assess the capability of models in writing the code for visualizations given the description of the Pandas DataFrame. 🛠️ Task. Given the plotting task and the description of a Pandas DataFrame, write the code to build a plot. The dataset is based on the MatPlotLib gallery. The paper can be found in arXiv: https://arxiv.org/abs/2412.02764v1. To score your model on this dataset, you can use the our GitHub repository. 📩 If you have… See the full description on the dataset page: https://huggingface.co/datasets/JetBrains-Research/PandasPlotBench.

The giant panda is an example of a species that has faced extensive historical habitat fragmentation and anthropogenic disturbance, and is assumed to be isolated in numerous subpopulations with limited gene flow between them. To investigate the population size, health and connectivity of pandas in a key habitat area, we noninvasively collected a total of 539 fresh wild giant panda fecal samples for DNA extraction within Wolong Nature Reserve, Sichuan, China. Seven validated tetra-microsatellite markers were used to analyze each sample, and a total of 142 unique genotypes were identified. Non-spatial and spatial capture-recapture models estimated the population size of the reserve at 164 and 137 individuals (95% confidence intervals 153-175 and 115-163), respectively. Relatively high levels of genetic variation and low levels of inbreeding were estimated, indicating adequate genetic diversity. Surprisingly, no significant genetic boundaries were found within the population despite the national road G350 that bisects the reserve, which is also bordered with patches of development and agricultural land. We attribute this to high rates of migration, with 4 giant panda road-crossing events confirmed within a year based on repeated captures of individuals. This likely means that giant panda populations within mountain ranges are better connected than previously thought. Increased development and tourism traffic in the area and throughout the current panda distribution poses a threat of increasing population isolation, however. Maintaining and restoring adequate habitat corridors for dispersal is thus a vital step for preserving the levels of gene flow seen in our analysis and the continued conservation of the giant panda meta-population in both Wolong and throughout their current range.

The data used in the study titled "A Study on Giant Panda Recognition Based on Images of a Large Proportion of Captive Pandas".

Panda Detection

## Overview

Panda Detection is a dataset for object detection tasks - it contains Pandas annotations for 449 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).

Aim: Understanding and predicting how species will respond to global environmental change (i.e., climate and land use change) is essential to efficiently inform conservation and management strategies for authorities and managers. Here, we assessed the combined effect of future climate and land use change on the potential range shifts of the giant pandas (Ailuropoda melanoleuca). Location: Sichuan Province, China. Methods: We used ensemble species distribution models (SDMs) to forecast range shifts of the giant pandas by the 2050s and 2070s under four combined climate and land use change scenarios. We also compared the differences in distributional changes of giant pandas among the five mountains in the study area. Results: Our ensemble SDMs exhibited good model performance in terms of both AUC (0.931) and TSS (0.747), and suggested that precipitation seasonality, annual mean temperature, the proportion of forest cover and total annual precipitation are the most important factors in sh...

Understanding the patterns and drivers of species range shifts is essential to disentangle mechanisms driving species’ responses to global change. Here, we quantified local extinction and colonization dynamics of giant pandas (Ailuropoda melanoleuca) using occurrence data collected by harnessing the labor of >1,000 workers and >60,000 worker days for each of the three periods (TP1: 1985-1988, TP2: 1998-2002, and TP3: 2011-2014), and evaluated how these patterns were associated with (1) protected area, (2) local rarity/abundance, and (3) abiotic factors (i.e., climate, land-use and topography). We documented a decreased rate (from 0.433 during TP1-TP2 to 0.317 during TP2-TP3) of local extinction and a relatively stable rate (from 0.060 during TP1-TP2 to 0.056 during TP2-TP3) of local colonization through time. Furthermore, the occupancy gains have exceeded losses by a ratio of approximately 1.5 to 1, illustrating an expanding of panda’s range at a rate of 1408.3 km2/decade. We also..., , , # Data from: Ecological and anthropogenic drivers of local extinction and colonization of giant pandas over the past 30 years

https://doi.org/10.5061/dryad.2280gb60d

Description of the data and file structure

Data from: Ecological and anthropogenic drivers of local extinction and colonization of giant pandas over the past 30 years

Datasets used to identify ecological and anthropogenic drivers of local extinction and colonization of giant pandas over the past 30 years

Files and variables:

File:

R script—Script to run spatial generalized additive models in the programming language R

TP12_5km_ext.csv — local extinction (loss [1] and persistence [0]), local rarity, local abundance, protected area status, 19 future bioclimatic variables and 10 land use variables during TP1-TP2 at 5 km X 5 km grid cell

TP12_5km_col.csv — local coloniz...

Dataset

This dataset was created by npscul

Contents

Please refer to the materials and methods section of the article for the details.

Comprehending the population trend and understanding the distribution range dynamics of species is necessary for global species protection. Recognizing what causes dynamic distribution change is crucial for identifying species’ environmental preferences and formulating protection policies. Here, we studied the rear-edge population of the flagship species, giant pandas (Ailuropoda melanoleuca), to 1) assess their population trend using their distribution patterns, 2) evaluate their distribution dynamics change from the 2nd (1988) to the 3rd (2001) surveys (2–3 Interval) and 3rd to the 4th (2013) survey (3–4 Interval) using a machine learning algorithm (The Extremely Gradient Boosting), and 3) decode model results to identify driver factors in the first known use of SHapley Additive exPlanations. Our results showed that the population trends in Liangshan Mountains were worst in the 2nd survey (k = 1.050), improved by the 3rd survey (k = 0.97), but got worse by the 4th survey (k = 0.996), ...

A dataset about design corridors for giant pandas in national park

This is the result collected for the red pandas informed consent survey.