Enables detailed human body model reconstruction in clothing from a single monocular RGB video without requiring a pre scanned template or manually clicked points.

The first three annual SNAPSHOT USA surveys were coordinated by Roland Kays, Michael Cove, and William McShea. The 2019, 2020, and 2021 datasets are accessible for public use through the Supporting Information of their respective publications. Although the 2019 and 2020 surveys were originally processed and stored in eMammal (https://www.emammal.si.edu), all data are now housed in Wildlife Insights (WI) within the SNAPSHOT USA Initiative. The two most recent surveys, 2022 and 2023, were coordinated by the SNAPSHOT USA Survey Coordinator Brigit Rooney. This dataset represents the first publication of 2022 and 2023 SNAPSHOT USA data. The SNAPSHOT USA project developed a standard protocol in 2019 to survey mammals >100 g and large identifiable birds. Cameras are unbaited and set at approximately 50 cm height across an array of at least 7 cameras with a minimum distance of 200 m and a maximum of 5 km between them. The collection period for SNAPSHOT USA data is between September and ...

among others

Dataset Card for "arxiv-metadata-oai-snapshot"

More Information needed This is a mirror of the metadata portion of the arXiv dataset. The sync will take place weekly so may fall behind the original datasets slightly if there are more regular updates to the source dataset.

  Metadata

This dataset is a mirror of the original ArXiv data. This dataset contains an entry for each paper, containing:

id: ArXiv ID (can be used to access the paper, see below) submitter: Who… See the full description on the dataset page: https://huggingface.co/datasets/librarian-bots/arxiv-metadata-snapshot.

The Company Snapshot is a concise electronic record of company identification, size, commodity information, and safety record, including the safety rating (if any), a roadside out-of-service inspection summary, and crash information. The Company Snapshot is available via an ad-hoc query (one carrier at a time) free of charge.

This data set contains 4747 sequences of camera trap images, totaling 10072 images, from the Snapshot Kruger project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Kruger National Park, South Africa has been a refuge for wildlife since its establishment in 1898, and it houses one of the most diverse wildlife assemblages remaining in Africa. The Snapshot Safari grid was established in 2018 as part of a research project assessing the impacts of large mammals on plant life as boundary fences were removed and wildlife reoccupied areas of previous extirpation. Labels are provided for 46 categories, primarily at the species level (for example, the most common labels are impala, elephant, and buffalo). Approximately 61.60% of images are labeled as empty.

Year End SnapShots

To provide a snapshot of citywide student enrollment and demographic information across multiple years. Data is collected using multiple data sources, including DOE's Audited Register, biographic data from Automate The Schools (ATS) system and the Location Code Generation and Management System (LCGMS). Data can be used to view citywide demographic and enrollment trends over time. Enrollment counts are based on the October 31 Audited Register for each school year. Please note that October 31 enrollment is not audited for charter schools or Pre-K Early Education Centers(NYCEECs). Charter schools are required to submit enrollment as of BEDS Day the first Wednesday in October to the New York State Education Department of Education. Enrollment counts will exceed operational enrollment counts due the fact that long term absence (LTA) students are excluded for funding purposes.

Camera traps can be used to address large-scale questions in community ecology by providing systematic data on an array of wide-ranging species. We deployed 225 camera traps across 1,125 km2 in Serengeti National Park, Tanzania, to evaluate spatial and temporal inter-species dynamics. The cameras have operated continuously since 2010 and had accumulated 99,241 camera-trap days and produced 1.2 million sets of pictures by 2013. Members of the general public classified the images via the citizen-science website www.snapshotserengeti.org. Multiple users viewed each image and recorded the species, number of individuals, associated behaviours, and presence of young. Over 28,000 registered users contributed 10.8 million classifications. We applied a simple algorithm to aggregate these individual classifications into a final ‘consensus’ dataset, yielding a final classification for each image and a measure of agreement among individual answers. The consensus classifications and raw imagery provide an unparalleled opportunity to investigate multi-species dynamics in an intact ecosystem and a valuable resource for machine-learning and computer-vision research.

This data set contains 12132 sequences of camera trap images, totaling 30227 images, from the Snapshot Camdeboo project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Camdeboo National Park, South Africa is crucial habitat for many birds on a global scale, with greater than fifty endemic and near-endemic species and many migratory species. Labels are provided for 43 categories, primarily at the species level (for example, the most common labels are kudu, springbok, and ostrich).

This dataset contains data for the Healthcare Payments Data (HPD) Snapshot visualization. The Enrollment data file contains counts of claims and encounter data collected for California's statewide HPD Program. It includes counts of enrollment records, service records from medical and pharmacy claims, and the number of individuals represented across these records. Aggregate counts are grouped by payer type (Commercial, Medi-Cal, or Medicare), product type, and year. The Medical data file contains counts of medical procedures from medical claims and encounter data in HPD. Procedures are categorized using claim line procedure codes and grouped by year, type of setting (e.g., outpatient, laboratory, ambulance), and payer type. The Pharmacy data file contains counts of drug prescriptions from pharmacy claims and encounter data in HPD. Prescriptions are categorized by name and drug class using the reported National Drug Code (NDC) and grouped by year, payer type, and whether the drug dispensed is branded or a generic.

The World Ocean Database (WOD) is world's largest collection of uniformly formatted, quality controlled, publicly available ocean profile data. This dataset is a snapshot of the XBT observations which have been preprocessed for use in a machine learning pipeline.

The data is organised by year in CSV files, covering 1966-2015. This dataset does not include the actual temperature and depth profiles, as this dataset was focused on a project to improve the metadata.

Links World Ocean Database

This is a database snapshot of the iCite web service (provided here as a single zipped CSV file, or compressed, tarred JSON files). In addition, citation links in the NIH Open Citation Collection are provided as a two-column CSV table in open_citation_collection.zip. iCite provides bibliometrics and metadata on publications indexed in PubMed, organized into three modules:Influence: Delivers metrics of scientific influence, field-adjusted and benchmarked to NIH publications as the baseline.Translation: Measures how Human, Animal, or Molecular/Cellular Biology-oriented each paper is; tracks and predicts citation by clinical articlesOpen Cites: Disseminates link-level, public-domain citation data from the NIH Open Citation CollectionDefinitions for individual data fields:pmid: PubMed Identifier, an article ID as assigned in PubMed by the National Library of Medicinedoi: Digital Object Identifier, if availableyear: Year the article was publishedtitle: Title of the articleauthors: List of author namesjournal: Journal name (ISO abbreviation)is_research_article: Flag indicating whether the Publication Type tags for this article are consistent with that of a primary research articlerelative_citation_ratio: Relative Citation Ratio (RCR)--OPA's metric of scientific influence. Field-adjusted, time-adjusted and benchmarked against NIH-funded papers. The median RCR for NIH funded papers in any field is 1.0. An RCR of 2.0 means a paper is receiving twice as many citations per year than the median NIH funded paper in its field and year, while an RCR of 0.5 means that it is receiving half as many citations per year. Calculation details are documented in Hutchins et al., PLoS Biol. 2016;14(9):e1002541.provisional: RCRs for papers published in the previous two years are flagged as "provisional", to reflect that citation metrics for newer articles are not necessarily as stable as they are for older articles. Provisional RCRs are provided for papers published previous year, if they have received with 5 citations or more, despite being, in many cases, less than a year old. All papers published the year before the previous year receive provisional RCRs. The current year is considered to be the NIH Fiscal Year which starts in October. For example, in July 2019 (NIH Fiscal Year 2019), papers from 2018 receive provisional RCRs if they have 5 citations or more, and all papers from 2017 receive provisional RCRs. In October 2019, at the start of NIH Fiscal Year 2020, papers from 2019 receive provisional RCRs if they have 5 citations or more and all papers from 2018 receive provisional RCRs.citation_count: Number of unique articles that have cited this onecitations_per_year: Citations per year that this article has received since its publication. If this appeared as a preprint and a published article, the year from the published version is used as the primary publication date. This is the numerator for the Relative Citation Ratio.field_citation_rate: Measure of the intrinsic citation rate of this paper's field, estimated using its co-citation network.expected_citations_per_year: Citations per year that NIH-funded articles, with the same Field Citation Rate and published in the same year as this paper, receive. This is the denominator for the Relative Citation Ratio.nih_percentile: Percentile rank of this paper's RCR compared to all NIH publications. For example, 95% indicates that this paper's RCR is higher than 95% of all NIH funded publications.human: Fraction of MeSH terms that are in the Human category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)animal: Fraction of MeSH terms that are in the Animal category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)molecular_cellular: Fraction of MeSH terms that are in the Molecular/Cellular Biology category (out of this article's MeSH terms that fall into the Human, Animal, or Molecular/Cellular Biology categories)x_coord: X coordinate of the article on the Triangle of Biomediciney_coord: Y Coordinate of the article on the Triangle of Biomedicineis_clinical: Flag indicating that this paper meets the definition of a clinical article.cited_by_clin: PMIDs of clinical articles that this article has been cited by.apt: Approximate Potential to Translate is a machine learning-based estimate of the likelihood that this publication will be cited in later clinical trials or guidelines. Calculation details are documented in Hutchins et al., PLoS Biol. 2019;17(10):e3000416.cited_by: PMIDs of articles that have cited this one.references: PMIDs of articles in this article's reference list.Large CSV files are zipped using zip version 4.5, which is more recent than the default unzip command line utility in some common Linux distributions. These files can be unzipped with tools that support version 4.5 or later such as 7zip.Comments and questions can be addressed to iCite@mail.nih.gov

Enrollment counts are based on the October 31st Audited Register for each school year. * Please note that October 31st enrollment is not audited for charter schools or Pre-K Early Education Centers (NYCEECs). Charter schools are required to submit enrollment as of BEDS Day, the first Wednesday in October, to the New York State Department of Education.

This data set contains 13301 sequences of camera trap images, totaling 28544 images, from the Snapshot Enonkishu project, part of the Snapshot Safari network. Using the same camera trapping protocols at every site, Snapshot Safari members are collecting standardized data from many protected areas in Africa, which allows for cross-site comparisons to assess the efficacy of conservation and restoration programs. Enonkishu Conservancy is located on the northern boundary of the Mara-Serengeti ecosystem in Kenya, and is managed by a consortium of stakeholders and land-owning Maasai families. Their aim is to promote coexistence between wildlife and livestock in order to encourage regenerative grazing and build stability in the Mara conservancies. Labels are provided for 39 categories, primarily at the species level (for example, the most common labels are impala, warthog, and zebra).

Year End SnapShots

We present the first dataset from a collaborative camera trap survey using the Snapshot protocol in Japan conducted in 2023. We collected data at 90 locations across 9 arrays for a total of 6162 trap-nights of survey effort. The total number of sequences with mammals and birds was 7967, including 20 mammal species and 23 avian species. Apart from humans, wild boar, sika deer, and rodents were the most commonly observed taxa on the camera traps, covering 57.9% of all the animal individuals. We provide the dataset Camtrap DP 1.0 format. Our dataset can be used for a part of global dataset for comparing relative abundances of wildlife and for a baseline of wildlife population trends in Japan.

Whether you're building an eCommerce dashboard, researching market trends, or prototyping beauty intelligence tools — this dataset is a perfect place to start.

Interested in Live Tracking or API?

This dataset represents just a snapshot of what we track in real time at https://beautyfeeds.io/" target="_new" rel="noopener" data-start="2004" data-end="2041">BeautyFeeds:

• Monitor price & stock changes daily or weekly

• Track products from major retailers like Sephora, Ulta, Nykaa, Amazon, and more

• Access via export or live API

• Filter by brand, country, or category

• Assign custom URLs for targeted scraping

👉 Learn more and get 500 free credits at https://beautyfeeds.io/" target="_new" rel="noopener" data-start="2342" data-end="2382">BeautyFeeds.io

Dataset

This dataset was created by ionafinasnf sdsoifso

Contents

To contribute to the terrific work done by the Snapshot Serengeti community to provide bounding box coordinates for the Gold Standard Snapshot Serengeti dataset for the purpose of training deep learning object detectors to detect, localize, and classify species from camera trap images.