The reproductive success of birds is closely tied to the characteristics of their nests. It is crucial to understand the distribution of nest traits across phylogenetic and geographic dimensions to gain insight into bird evolution and adaptation. Despite the extensive historical documentation on breeding behavior, a structured dataset describing bird nest characteristics has been lacking. To address this gap, we have compiled a comprehensive dataset that characterizes three ecologically and evolutionarily significant nest traits—site, structure, and attachment—for 9,248 bird species, representing all 36 orders and 241 out of the 244 families. By defining seven sites, seven structures, and four attachment types, we have systematically classified the nests of each species using information from text descriptions, photos, and videos sourced from online databases and literature. This nest traits dataset serves as a valuable addition to the existing body of morphological and ecological trait data for bird species, providing a useful resource for a wide range of avian macroecological and macroevolutionary research.

This data set contains sensitive biological resource data for seabird and wading bird nesting colonies in coastal Louisiana. Vector points in this data set represent locations of seabird and wading bird colonies. Species-specific abundance, seasonality, status, life history, and source information are stored in relational data tables (described below) designed to be used in conjunction with this spatial data layer.This data set comprises a portion of the Environmental Sensitivity Index (ESI) data for Louisiana. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. See also the BIRDS (Bird Polygons) data layer, part of the larger Louisiana ESI database, for additional bird information.

Protein Tyrosine Phosphatase receptor type D (PTPRD) is a member of the protein tyrosine phosphatase family that mediates cell adhesion and synaptic specification. Genetic studies have linked Ptprd to several neuropsychiatric phenotypes, including Restless Leg Syndrome (RLS), opioid abuse disorder, and antipsychotic-induced weight gain. Genome-wide association studies (GWAS) of either pediatric obsessive-compulsive traits, or Obsessive-Compulsive Disorder (OCD), have identified loci near PTPRD as genome-wide significant, or strongly suggestive for this trait. We assessed Ptprd wild-type (WT), heterozygous (HT), and knockout (KO) mice for behavioral dimensions that are altered in OCD, including anxiety and exploration (open field test, dig test), perseverative behavior (splash-induced grooming, spatial d), sensorimotor gating (prepulse inhibition), and home cage goal-directed behavior (nest building). No effect of genotype was observed in any measure of the open field test, dig test, or splash test. However, Ptprd KO mice of both sexes showed impairments in nest building behavior. Finally, female, but not male, Ptprd KO mice showed deficits in prepulse inhibition, an operational measure of sensorimotor gating that is reduced in female, but not male, OCD patients. Our results indicate that constitutive lack of Ptprd may contribute to the development of certain domains that are altered OCD, including goal-directed behavior, and reduced sensorimotor gating specifically in females.

Nest data between and within regions.

Estimation of functional diversity in biological communities requires extensive and complete data on numerous functional traits of species or even individuals. When estimating functional diversity at large scales, this fact possesses an issue that may be hard to overcome: for many species, there might not be sufficient data on their functional traits. In such cases, even if there is missing information on functional trait value for one species in a community, this makes the trait impossible to use for the estimation of the functional diversity of a community. On the other hand, there are available datasets on the functional traits of all extant species within certain lineages across the world, but such datasets are often limited to very few functional traits, missing some dimensions of species' ecological niches. In this dataset, I compiled the available data from various sources that describe 23 functional traits of 703 bird species that occur in Canada, the United States, and Mexico. These functional traits include the following: diet type, diurnal and nocturnal feeding, diet items, feeding methods, feeding substrate, nest type, nest substrates, breeding system, chick development at hatching, nest aggregation, clutch size, first breeding age, number of clutches a year, breeding success, adult annual survival, mean biomass, maximum lifespan, hand-wing index, kleptoparasitism, nest parasitism, and the extent of dependency on other species for building a nest.

These data are part of the Gulf Watch Alaska (GWA) long-term monitoring program, nearshore monitoring component. The dataset is comprised of six comma separated values (.csv) file exported from a relational database. The data consist of: 1) transect summary, 2) nest details, 3) egg float and stage data, 4) chicks diets, 5) chick diet taxonomy, and 6) Gulf Watch Alaska contributors.

Unicorn Nest: Startup Fundraising Dataset

Our team has been active in fundraising for around 20 years. We’ve been thoroughly collecting all available data about deals on the market and we’ve aggregated all the essential data about funds: industries, average round sizes, exists, etc. in one of the most comprehensive datasets out there that consists of 500,000 cells.

In times of the COVID-19 crisis, we decided to share some elements of our dataset with the startup community for free. We suppose that file will save you dozens of hours that you’d previously spend on searching and collecting information about potential investors.

If you enjoy the dataset, please don't forget to upvote it at https://www.producthunt.com/posts/unicorn-nest-dataset

The dataset consists of two .csv files:

• "funds.csv" provides information about funds, where you can learn which funds invested before, after, or together with your selected fund, see the rankings based on the number of unicorns, exits, average round sizes, sort funds by country
• "key persons.csv" gives details about key persons at each of the funds.

This dataset includes over 500,000 cells with data about 26500 investment funds.

Please keep in mind that all the data in this dataset is relevant as of 4/1/2020. You can check the relevance of each of the indicators on our website.
Also, for ease of use, some indicators from UNICORN NEST database have been simplified, and some are missing in this dataset.
For your convenience, a link was placed on each of the indicators. By following it you can check the relevance of a particular indicator or see more detailed information about it.

Feel free to provide your feedback at info@unicorn-nest.com with the subject line ”UN dataset feedback”

By using this dataset you agree that the data in the file:

• is for startup and information use only
• is provided on “as is” basis with no warranties
• Shall not be used or allowed to be used by you for any science or academic research, in commerce, for analytical purpose, for or any mailout or information distribution purposes, as well as for any illegal purpose
• Is the property of Unicorn Nest, all rights reserved as may be applicable by law, hence you shall not perform or attempt to register the database or any its derivative object in your or any third person’s name.

You need to place attribution to Unicorn Nest if using the dataset for any type of paper.

Comprehensive dataset containing 51 verified Nest locations in United States with complete contact information, ratings, reviews, and location data.

A compilation of published data on mean depths of reptile nests

This data set contains sensitive biological resource data for raptors in Maryland. Vector points in this data set represent bird nesting sites. Species-specific abundance, seasonality, status, life history, and source information are stored in relational data tables (described below) designed to be used in conjunction with this spatial data layer.This data set comprises a portion of the Environmental Sensitivity Index (ESI) data for Maryland. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. See also the BIRDS (Bird Polygons) data layer, part of the larger Maryland ESI database, for additional bird information.

This data set contains sensitive biological resource data for nesting birds in Western Alaska. Vector points in this data set represent locations of nesting birds. Species-specific abundance, seasonality, status, life history, and source information are stored in relational data tables (described below) designed to be used in conjunction with this spatial data layer. This data set comprises a portion of the Environmental Sensitivity Index (ESI) data for Western Alaska. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. See also the BIRDS (Bird Polygons) data layer, part of the larger Western Alaska ESI database, for additional bird information.

16 Global import shipment records of Bird Nest with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

204 Global import shipment records of Wooden Nest with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This database compiled information from a variety of sources on 23 traits for all breeding birds, including 157 resident and 14 summer visiting species, in Taiwan and surrounding islands. The 23 traits include those related to the movement patterns, morphology, geographic distribution, activity patterns, feeding behaviors, habitat use, and breeding behaviors and strategies of the species. The trait information was obtained not only from published literatures and datasets, but also from unpublished banding records and specimen measurements. The database also contains derived traits, such as elevation and temperature boundaries of species distribution ranges in Taiwan. In addition, structured information on nest characters, which is seldom complied in trait databases, has be made available, for the first time, for the breeding birds in Taiwan. Therefore, so far the most comprehensive trait information provided by this database will allow trait-based research and applications in diverse topics and thus enhance our understanding of the patterns and dynamics of breeding bird diversity and its functions in Taiwan.

Nest predation is typically the main cause of nest failure in forest understory birds thus identification of primary nest predators is key to understanding nest predation patterns. Furthermore, responses of predators are likely affected by vegetation structure, but predator responses to micro-scale habitat characteristics are largely unknown, especially in tropical forests. We used a long-term study with one of the largest datasets of its kind to investigate the extent to which micro-habitat structure (5-m radius surrounding a nest) can predict the likelihood of predation and by which predator. In a secondary evergreen forest in northeastern Thailand 2013–2021, we found 1,016 active nests of 13 species and 24-hour video-monitored 500 of them. We recorded 336 predation events from 16 nest predator species. From this and previous studies at our site, we identified the top four predator species/species-groups accounting for ~83% of predation events: northern pig-tailed macaque Macaca leonina (36% of predation events), cat snakes (Boiga cyanea and Boiga siamensis) (20%), Blandford’s bridle snake Lycodon cf. davisonii (18%), and accipiters (A. trivirgatus and A. badius) (9%). These four differed in their responses to vegetation structure likely reflecting differences in foraging behaviors. Macaque and accipiters, both diurnal and visually oriented, tended to depredate more visible/open nests, but macaques depredated nests surrounded by more trees and short woody stems (< 3 m tall) compared to raptors. For snakes, both nocturnal, cat snakes depredated nests with higher numbers of both short woody stems and woody climbers, while bridle snakes depredated nests with more trees and fewer climbers. As noted previously, nest predator identity is critical to understanding habitat-predation patterns. Our data suggest that nest site vegetation characteristics influence the likelihood of a given species of predator locating a nest and that even small changes in vegetation structure could significantly alter predation patterns.

This data set contains sensitive biological resource data for seabird nesting colonies in coastal Hawaii. Vector points in this data set represent locations of seabird colonies. Species-specific abundance, seasonality, status, life history, and source information are stored in relational data tables (described below) designed to be used in conjunction with this spatial data layer. This data set comprises a portion of the Environmental Sensitivity Index (ESI) data for Hawaii. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. See also the BIRDS data layer, part of the larger Hawaii ESI database, for additional bird information.

19 Global export shipment records of Bird Nest with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This data set contains sensitive biological resource data for nesting birds in coastal Rhode Island, Connecticut, New York, and New Jersey. Vector points in this data set represent locations of nesting birds. Species-specific abundance, seasonality, status, life history, and source information are stored in relational data tables (described below) designed to be used in conjunction with this spatial data layer.This data set comprises a portion of the Environmental Sensitivity Index (ESI) data for Rhode Island, Connecticut, New York, and New Jersey. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. See also the BIRDS (Bird Polygons) data layer, part of the larger Rhode Island, Connecticut, New York, and New Jersey ESI database, for additional bird information.

This data set contains biological resource data for alcids, shorebirds, waterfowl, diving birds, pelagic birds, gulls, and terns in Southeast Alaska. Points in this data set represent locations of bird nesting sites. Species-specific abundance, seasonality, status, life history, and source information are stored in relational data tables (described below) designed to be used in conjunction with this spatial data layer. See also the BIRDS (Bird Polygons) data layer, part of the larger Southeast Alaska ESI database, for additional bird information.This data set comprises a portion of the Environmental Sensitivity Index (ESI) data for Southeast Alaska. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. See also the BIRDS (Bird Polygons) data layer, part of the larger Southeast Alaska ESI database, for additional bird information.

Traditional bird deterrent methods, such as scarecrows, loud noise emitters, and netting, can become less effective over time due to bird habituation. This study presents an AI-driven avian monitoring system, integrating advanced deep learning models and real-time environmental sensing as a baseline for potential adaptive deterrent mechanisms to manage bird populations in aquaculture environments. The proposed system leverages high-resolution imaging, motion tracking, and environmental sensors to identify species and analyze behavioral patterns. The AI-powered classification is driven by the developed Avian Eye Net, a specialized neural network optimized for avian species detection and classification, ensuring high precision in real-time monitoring. The AI framework utilizes a multi-stage image processing pipeline, starting with region of interest (ROI) extraction using adaptive image segmentation. Image metadata is then processed through AI-based feature extraction and context-aware metadata parsing, which fuses structured data with neural network outputs. The final dataset is compiled into structured formats, exporting key parameters—including image filename, date, time, location, detected species, and population count—to a ready to analyze data file for further analysis. This structured approach enhances system efficiency, providing real-time, high-fidelity bird population monitoring. Experimental results demonstrate a classification accuracy of 97.2%, with a precision rate of 95.8% and a recall rate of 96.4% for avian species identification for three important species associated with aquaculture farms. In the future, the system’s integration with Internet of Things (IoT) devices may enable the deployment of non-invasive deterrent measures—such as LED lighting, ultrasonic sound waves, and airflow manipulation—to mitigate avian interference with aquaculture operations. This IoT with AI-powered approach enhances sustainable aquaculture management by ensuring minimal disruption to avian species while optimizing fish farm productivity.The dataset contains a subset of images used to build the species identification and quantification models, highlighting the main predatory birds in the area: great blue heron, egret, and Canada goose. Also included in classification are humans. Meta-data annotation from camera trap images is also included in the code and extraction from the images.