The Human Resource Strategic Assessment Program (HRSAP), located at Defense Manpower Data Center (DMDC), consists of both Web-based and paper-and-pencil surveys to support the personnel needs of the Under Secretary of Defense for Personnel and Readiness. These surveys assess the attitudes and opinions of the entire Department of Defense (DOD) community—active, reserve, civilian employees, and family members—on a wide range of personnel issues. The Web-based survey program, known as the Status of Forces Surveys (SOFS) provides timely data on active, reserve, and civilian employees. The paper-and-pencil surveys are used to obtain data on sensitive topics (e.g., sexual harassment) and from populations with limited Internet access (e.g., spouses of active duty and Reserve members).

Timeseries data from 'Southern Ocean Flux Station (SOFS) Historic CO2' (gov_ornl_cdiac_sofs_142w_46s) cdm_data_type=TimeSeries cdm_timeseries_variables=station,longitude,latitude contributor_email=imos@imos.org.au,None,feedback@axiomdatascience.com contributor_name=Australian Integrated Marine Observing System (IMOS),Ocean Carbon Data System (NOAA-NODC-OCADS),Axiom Data Science contributor_role=contributor,contributor,processor contributor_role_vocabulary=NERC contributor_url=https://imos.org.au/,https://www.nodc.noaa.gov/ocads/,https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.2 defaultDataQuery=sea_water_temperature,z,pco2_in_air,time,pco2_in_sea_water,sea_water_practical_salinity&time>=max(time)-3days Easternmost_Easting=142.0 featureType=TimeSeries geospatial_lat_max=-46.8 geospatial_lat_min=-46.8 geospatial_lat_units=degrees_north geospatial_lon_max=142.0 geospatial_lon_min=142.0 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from NOAA Pacific Marine Environmental Lab (PMEL) at https://www.ncei.noaa.gov/access/ocean-carbon-data-system/oceans/Moorings/SOFS.html id=49375 infoUrl=https://sensors.ioos.us/#metadata/49375/station institution=NOAA Pacific Marine Environmental Lab (PMEL) naming_authority=com.axiomdatascience Northernmost_Northing=-46.8 platform=buoy platform_name=Southern Ocean Flux Station (SOFS) Historic CO2 platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://www.pmel.noaa.gov/co2/story/SOFS,https://www.ncei.noaa.gov/access/ocean-carbon-data-system/oceans/Moorings/SOFS.html, sourceUrl=https://www.ncei.noaa.gov/access/ocean-carbon-data-system/oceans/Moorings/SOFS.html Southernmost_Northing=-46.8 standard_name_vocabulary=CF Standard Name Table v72 station_id=49375 time_coverage_end=2022-05-12T21:17:00Z time_coverage_start=2011-11-24T13:17:00Z Westernmost_Easting=142.0

Open access dataset (with identifying information removed) for a study conducted by the VetCompass team titled "Frequency and risk factors for tail injuries in UK dogs under primary veterinary care".

Gain valuable insights into music trends, artist popularity, and streaming analytics with our comprehensive Spotify Dataset. Designed for music analysts, marketers, and businesses, this dataset provides structured and reliable data from Spotify to enhance market research, content strategy, and audience engagement.

Dataset Features

Track Information: Access detailed data on songs, including track name, artist, album, genre, and release date. Streaming Popularity: Extract track popularity scores, listener engagement metrics, and ranking trends. Artist & Album Insights: Analyze artist performance, album releases, and genre trends over time. Related Searches & Recommendations: Track related search terms and suggested content for deeper audience insights. Historical & Real-Time Data: Retrieve historical streaming data or access continuously updated records for real-time trend analysis.

Customizable Subsets for Specific Needs Our Spotify Dataset is fully customizable, allowing you to filter data based on track popularity, artist, genre, release date, or listener engagement. Whether you need broad coverage for industry analysis or focused data for content optimization, we tailor the dataset to your needs.

Popular Use Cases

Market Analysis & Trend Forecasting: Identify emerging music trends, genre popularity, and listener preferences. Artist & Label Performance Tracking: Monitor artist rankings, album success, and audience engagement. Competitive Intelligence: Analyze competitor music strategies, playlist placements, and streaming performance. AI & Machine Learning Applications: Use structured music data to train AI models for recommendation engines, playlist curation, and predictive analytics. Advertising & Sponsorship Insights: Identify high-performing tracks and artists for targeted advertising and sponsorship opportunities.

Whether you're optimizing music marketing, analyzing streaming trends, or enhancing content strategies, our Spotify Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

In 2023, a total of around *** thousand dogs were registered in the entire country, down from the prior year. While registrations were introduced in 2008, it became obligatory in 2014. Registrations peaked notably in 2021 at around half a million dogs.

The datasets in this collection support the manuscript "Multiplatform calibration and validation of CFOSat ocean surface waves" which presents a comprehensive comparison of satellite-derived wind and wave data products from the Surface Waves Investigation and Monitoring (SWIM) instrument onboard the China France Oceanography Satellite (CFOSat) against multiple in-situ wave buoy datasets. Specifically, the NetCDFs contain the matchups or collocated observations (in time and space) between SWIM and wave buoys. Lineage: Original datasets used in the matchups 1.\tSatellite (SWIM) data Level-2 data from the CFOSat SWIM sensor processing version 6.0 were downloaded for the period from April 2019 to December 2023 from AVISO (https://aviso.altimetry.fr). The use of CFOSat wind or wave products is subject to AVISO Standard license (http://www.aviso.altimetry.fr/fileadmin/documents/data/License_Aviso.pdf), which allows free public access to the data for any purpose, including scientific applications, operational, and commercial.

2.\tWave buoy observations were accessed from the following sources: a.\tNational Data Buoy Centre (NDBC) wave buoy archive The NDBC wave buoy data archive was accessed from the U.S. Army Corps of Engineers (USACE) Coastal and Hydraulic Laboratory (CHL) Data server (https://chldata.erdc.dren.mil/). This archive contains a consistent collection of Northern Hemisphere focused meteorological and wave buoy measurements fully described in Hall C, Jensen RE (2022) USACE Coastal and Hydraulics Laboratory Quality Controlled, Consistent Measurement Archive. 9:248 (https://doi.org/10.1038/s41597-022-01344-z). The NDBC data archive are open with no restrictions on their use.

b.\tAustralian Ocean Data Network (AODN) Portal The Southern Ocean Flux Station (SOFS) moored wave buoy data from four deployments (SOFS-8 to SOFS-11) from March 2019 to May 2023 were downloaded from the https://portal.aodn.org.au. SOFS wave buoy is a large surface buoy moored in the Subantarctic zone (near 140°E and 47°S) in ~4,500 m water depth southwest of Tasmania providing an important source of wave data in this remote oceanic region in the Southern Ocean.

Matchup approach Collocation data for each satellite-buoy pair contain matched observations in space and time. Collocations are obtained for each satellite observation that is within 50 km and 30 minutes of wave buoy observation.

Matchups were carried between four SWIM satellite products (6° beam, 8° beam, 10° beam, and combined beam) and the SOFS wave buoy observations OR the NDBC wave buoys observation.

Directory structure •\tList of NetCDFs containing satellite-buoy matchups between SWIM and NDBC observations /matchups_data/ndbc: --ds_buoy_ndbc_efthsmoothed.nc --ds_swim_ndbc_efth_beam06.nc --ds_swim_ndbc_efth_beam08.nc --ds_swim_ndbc_efth_beam10.nc --ds_swim_ndbc_efth_combined.nc

•\tList of NetCDFs containing satellite-buoy matchups between SWIM and SOFS observations /matchups_data/sofs: --ds_buoys_sofs_8-11_lp.nc --ds_buoys_sofs_8-11_swh.nc --ds_buoys_sofs_8-11_tm02.nc --ds_swim_sofs_8_to_11_b06_1Dspec.nc --ds_swim_sofs_8_to_11_b08_1Dspec.nc --ds_swim_sofs_8_to_11_b10_1Dspec.nc --ds_swim_sofs_8_to_11_cmb_1Dspec.nc

•\tList of NetCDFs containing satellite-buoy matchups between SWIM and SOFS-11 2D spectra observations /matchups_data/sofs: --sofs11-swim_2Dspectra_collocation.nc --swim-sofs11_2Dspectra_collocation.nc

•\tList of NetCDFs containing matchups between a mean wave period (MWP) algorithm and buoy (NDBC or SOFS) observations /matchups_data/jiang-mwp: --ds_jiang-ndbc_Tm02.nc --ds_ndbc-jiang_Tm02.nc --ds_jiang-sofs_Tm02.nc --ds_sofs-jiang_Tm02.nc

Despite extensive research into the Theory of Mind abilities in nonhuman animals, it remains controversial whether they can attribute mental states to other individuals or whether they merely predict future behaviour based on previous behavioural cues. In the present study, we tested pet dogs (in total, N=92) on adaptations of the “goggles test” previously used with human infants and great apes. In both a cooperative and a competitive task, dogs were given direct experience with the properties of novel screens (one opaque, the other transparent) inserted into identical, but differently coloured, tunnels. Dogs learned and remembered the properties of the screens even when, later on, these were no longer directly visible to them. Nevertheless, they were not more likely to follow the experimenter’s gaze to a target object when the experimenter could see it through the transparent screen. Further, they did not prefer to steal a forbidden treat first in a location obstructed from the experimenter’s view by the opaque screen. Therefore, dogs did not show perspective-taking abilities in this study in which the only available cue to infer others’ visual access consisted of the subjects’ own previous experience with novel visual barriers. We conclude that the behaviour of our dogs, unlike that of infants and apes in previous studies, does not show evidence of experience projection abilities.

Overview
Established pests and weeds represent a high ongoing cost to Australian agriculture. The Australian Government has invested $50 million through the Australian Competitiveness White Paper to improve the way pest animals and weeds are managed and to increase the capacity of farmers to deal with these threats. This is through investment in projects that: develop new and improved control tools and technologies; improve land manager awareness and access to control tools and technologies; improve land manager knowledge of best practice pest animal and weed management; and increase land manager and community participation in pest animal and weed management activities.

To enable the Department of Agriculture and Water Resources to evaluate outcomes of this investment, baseline data is required to provide a national overview of the status and management of established pest animals and weeds. Currently, there is insufficient data available to establish the baseline. Therefore a national survey of agricultural land managers was undertaken to collect data on pest animal and weed status, impacts and management actions at the property and local area levels.

This report presents the results of responses from 6470 agricultural land managers, representing broadacre, dairy, horticulture and other livestock industries across all natural resource management regions in Australia.

Key Issues
Nearly 80 per cent of respondents were actively managing a pest animal, and 85 per cent were actively managing weeds on their property in the last 12 months.

Shooting and ground baiting continue to be widely used tools for vertebrate pest management. Herbicide is used by 90 per cent of landholders in the management of weeds and reported as effective by the vast majority.

The majority of respondents reported that pest animal and weed control methods they were using were at least moderately effective.

Agricultural businesses spend a significant amount on managing pest animals and weeds. An average of $19,620 was spent per agricultural business on undertaking pest animal and weed management activities. This include pest animal and weed management related expenditure on traps, baits, herbicides, pesticides/insecticides, fuel, fencing materials, and labour (including the cost of contractors) on the property in the last 12 months. • For those managing pest animals they spend an average of $7,023 per year (pest animals including feral, natives, insects and other pests (for example, parasites and mites)). • Agricultural businesses spend an average of $17,917 per year on weed management activities, this includes all activities undertaken as part of the businesses’ usual production cycle activities, for example spraying weeds before planting a crop. Pest animal and weed control activities took an average of 77 person days per agricultural business by owners/operators (including family members) and 39 person days by contractors, employees and other people (for example, volunteers) in the last 12 months.

Nearly eight per cent of landholders were members of a pest animal or weed management group. The majority of the groups had developed a plan for coordinating management activities or collaborating with other stakeholders.

A majority of land managers reported: new and improved control methods; access to information on control options and methods; more management activities by other land managers (private and public); and better access to biological controls as important actions that could improve pest animal and weed management.

Since The Eras Tour Film was just released, this time we're exploring Taylor Swift song data!

Are you Ready for It?

The taylor R package from W. Jake Thompson is a curated data set of Taylor Swift songs, including lyrics and audio characteristics. The data comes from Genius and the Spotify API.

There are three main datasets.

The first is taylor_album_songs, which includes lyrics and audio features from the Spotify API for all songs on Taylor’s official studio albums. Notably this excludes singles released separately from an album (e.g., Only the Young, Christmas Tree Farm, etc.), and non-Taylor-owned albums that have a Taylor-owned alternative (e.g., Fearless is excluded in favor of Fearless (Taylor’s Version)). We stan artists owning their own songs.

You can access Taylor’s entire discography with taylor_all_songs. This includes all of the songs in taylor_album_songs plus EPs, individual singles, and the original versions of albums that have been re-released as Taylor’s Version.

Finally, there is a small data set, taylor_albums, summarizing Taylor’s album release history.

Information on the audio features in the dataset from Spotify are included in their API documentation.

For your visualizations, the {taylor} package comes with it’s own class of color palettes, inspired by the work of Josiah Parry in the {cpcinema} package.

You might also be interested in the tayoRswift package by Alex Stephenson, a ggplot2 color palette based on Taylor Swift album covers. "For when your colors absolutely should not be excluded from the narrative."

Data Dictionary

taylor_album_songs.csv

Unlock powerful insights with our custom music datasets, offering access to millions of records from popular music platforms like Spotify, SoundCloud, Amazon Music, YouTube Music, and more. These datasets provide comprehensive data points such as track titles, artists, albums, genres, release dates, play counts, playlist details, popularity scores, user-generated tags, and much more, allowing you to analyze music trends, listener behavior, and industry patterns with precision. Use these datasets to optimize your music strategies by identifying trending tracks, analyzing artist performance, understanding playlist dynamics, and tracking audience preferences across platforms. Gain valuable insights into streaming habits, regional popularity, and emerging genres to make data-driven decisions that enhance your marketing campaigns, content creation, and audience engagement. Whether you’re a music producer, marketer, data analyst, or researcher, our music datasets empower you with the data needed to stay ahead in the ever-evolving music industry. Available in various formats such as JSON, CSV, and Parquet, and delivered via flexible options like API, S3, or email, these datasets ensure seamless integration into your workflows.

In order to request access to this data please complete the data request form.*

The Service Dogs Act complements the Blind Persons’ Rights Act by providing Albertans with disabilities who use qualified service dogs the right of access to public places. Individuals with disabilities who are accompanied by qualified service dogs must be allowed access to any location where the general public is allowed.

MusicOSet is an open and enhanced dataset of musical elements (artists, songs and albums) based on musical popularity classification. Provides a directly accessible collection of data suitable for numerous tasks in music data mining (e.g., data visualization, classification, clustering, similarity search, MIR, HSS and so forth). To create MusicOSet, the potential information sources were divided into three main categories: music popularity sources, metadata sources, and acoustic and lyrical features sources. Data from all three categories were initially collected between January and May 2019. Nevertheless, the update and enhancement of the data happened in June 2019.

The attractive features of MusicOSet include:

• Integration and centralization of different musical data sources
• Calculation of popularity scores and classification of hits and non-hits musical elements, varying from 1962 to 2018
• Enriched metadata for music, artists, and albums from the US popular music industry
• Availability of acoustic and lyrical resources
• Unrestricted access in two formats: SQL database and compressed .csv files

|    Data    | # Records |
|:-----------------:|:---------:|
| Songs       | 20,405  |
| Artists      | 11,518  |
| Albums      | 26,522  |
| Lyrics      | 19,664  |
| Acoustic Features | 20,405  |
| Genres      | 1,561   |