Replication data for the manuscript.. Visit https://dataone.org/datasets/sha256%3A34a1590ea6fb0dc5ce0582d8721f8e9b0199871a67766bc75e5fab0f88bfe9ea for complete metadata about this dataset.

This dataset contains the replication material (data, R code) for two experiments related to the analyses described in the paper "Tasting and re-labeling meat substitute products can affect consumers’ product evaluations and dietary preferences"

This chipped training dataset is over Shanghai and includes 30cm high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 or smaller pixel tile/label pairs. This dataset is a ramp Tier 1 dataset, meaning it has been thoroughly reviewed and improved. This dataset was used in developing the ramp baseline model and contains 3,574 tiles and 7,118 buildings. The original dataset was sourced from the SpaceNet 2 Dataset before the imagery was tiled down from 650x650 pixel chips and labels were revised to be consistent with the ramp datasets notion of rooftop as the building footprint. Dataset keywords: Urban, Dense.

Training, development and test sets for supervised named entity recognition for materials science. The data is labelled using the IOB annotation scheme. There exist 7 entity tags: material (MAT), sample descriptor (DSC), symmetry/phase label (SPL), property (PRO), application (APL), synthesis method (SMT), and characterization method (CMT), along with the outside tag (O).The data consists of 800 hand-labelled materials science abstracts. The data has an 80-10-10 split, giving 640 abstracts in the training set, 80 in the development set, and 80 in the test set.

This dataset contains high-resolution aerial imagery from the USDA NAIP program [1], high-resolution land cover labels from the Chesapeake Conservancy, low-resolution land cover labels from the USGS NLCD 2011 dataset, low-resolution multi-spectral imagery from Landsat 8, and high-resolution building footprint masks from Microsoft Bing, formatted to accelerate machine learning research into land cover mapping. The Chesapeake Conservancy spent over 10 months and $1.3 million creating a consistent six-class land cover dataset covering the Chesapeake Bay watershed. While the purpose of the mapping effort by the Chesapeake Conservancy was to create land cover data to be used in conservation efforts, the same data can be used to train machine learning models that can be applied over even wider areas. The organization of this dataset (detailed below) will allow users to easily test questions related to this problem of geographic generalization, i.e. how to train machine learning models that can be applied over even wider areas. For example, this dataset can be used to directly estimate how well a model trained on data from Maryland can generalize over the remainder of the Chesapeake Bay.

The global pre-printed self-laminating label market is experiencing robust growth, driven by increasing demand across diverse sectors. While the exact market size for 2025 isn't provided, considering typical growth rates in the labeling industry and a conservative estimate based on available data, we can reasonably assume a 2025 market value of approximately $2.5 billion. This market is projected to exhibit a Compound Annual Growth Rate (CAGR) of 6% from 2025 to 2033. Key drivers include the rising adoption of these labels in various applications like electronics, household products, and the chemical industry, where they offer durable, weather-resistant, and tamper-evident labeling solutions. The increasing focus on product traceability and enhanced brand protection also contributes significantly to market expansion. Technological advancements in label printing and materials science, leading to improved adhesion, durability, and printing quality, further fuel the market's growth trajectory. Market segmentation reveals strong demand across diverse applications, with electronics and household segments leading the charge, owing to their broad usage in product identification and information dissemination. Though challenges exist, such as the rising costs of raw materials and potential environmental concerns associated with label waste, these are anticipated to be offset by the consistent demand and ongoing innovation within the industry. The competitive landscape is characterized by a mix of established global players and regional manufacturers. Companies like Avery Products, Brady, and Honeywell International are key players leveraging their established brand recognition and extensive distribution networks. However, the market also sees participation from smaller, specialized manufacturers catering to niche segments and regional demands. Geographic expansion continues, with North America and Europe currently dominating the market share. However, the Asia-Pacific region, particularly China and India, is witnessing rapid growth due to increasing industrialization and manufacturing activities, making it a significant focus area for future market expansion. The forecast period of 2025-2033 promises continued growth, driven by the underlying trends and factors already discussed, solidifying the pre-printed self-laminating label market as a significant and expanding segment within the broader labeling industry. This in-depth report provides a comprehensive overview of the global pre-printed self-laminating labels market, projecting a value exceeding $2.5 billion by 2028. It delves into market dynamics, competitive landscapes, and future growth trajectories, incorporating insights from key players like Avery Dennison, Honeywell International, and Brady Corporation. The report is meticulously crafted for strategic decision-making by industry stakeholders, investors, and researchers. High-search-volume keywords like "self-adhesive labels," "custom labels," "durable labels," "waterproof labels," and "industrial labels" are integrated throughout the report to enhance its online visibility.

Remote cameras (“trail cameras”) are a popular tool for non-invasive, continuous wildlife monitoring, and as they become more prevalent in wildlife research, machine learning (ML) is increasingly used to automate or accelerate the labor-intensive process of labelling (i.e., tagging) photos. Human-machine hybrid tagging approaches have been shown to greatly increase tagging efficiency (i.e., time to tag a single image). However, those potential increases hinge on the extent to which an ML model makes correct vs. incorrect predictions. We performed an experiment using a ML model that produces bounding boxes around animals, people, and vehicles in remote camera imagery (MegaDetector), to consider the impact of a ML model’s performance on its ability to accelerate human labeling. Six participants tagged trail camera images collected from 12 sites in Vermont and Maine, USA (January-September 2022) using three tagging methods (one with ML bounding box assistance and two without assistan ...

This dataset is the annotation associated with the hydrologic features used as a background for the Geological Map of the Tongariro National Park area. The dataset was developed from the LINZ Topo50 placenames dataset and was produced by GNS Science. The dataset is stored in an ESRI vector geodatabase and exported to ArcGIS Server.

The C2S-MS Floods Dataset is a dataset of global flood events with labeled Sentinel-1 & Sentinel-2 pairs. There are 900 sets (1800 total) of near-coincident Sentinel-1 and Sentinel-2 chips (512 x 512 pixels) from 18 global flood events. Each chip contains a water label for both Sentinel-1 and Sentinel-2, as well as a cloud/cloud shadow mask for Sentinel-2. The dataset was constructed by Cloud to Street in collaboration with and funded by the Microsoft Planetary Computer team.

This dataset was built for training and validating terrain classification models for Mars, which may be useful in future autonomous rover efforts. It consists of ~326K semantic segmentation full image labels on 35K images from Curiosity, Opportunity, and Spirit rovers, collected through crowdsourcing. Each image was labeled by 10 people to ensure greater quality and agreement of the crowdsourced labels. It also includes ~1.5K validation labels annotated by the rover planners and scientists from NASA’s MSL (Mars Science Laboratory) mission, which operates the Curiosity rover, and MER (Mars Exploration Rovers) mission, which operated the Spirit and Opportunity rovers.

The study of protein function and dynamics in their native cellular environment is essential for progressing fundamental science. To overcome the requirement of genetic modification of the protein or the limitations of dissociable fluorescent ligands, ligand-directed (LD) chemistry has most recently emerged as a complementary, bioorthogonal approach for labeling native proteins. Here, we describe the rational design, development, and application of the first ligand-directed chemistry approach for labeling the A1AR in living cells. We pharmacologically demonstrate covalent labeling of A1AR expressed in living cells while the orthosteric binding site remains available. The probes were imaged using confocal microscopy and fluorescence correlation spectroscopy to study A1AR localization and dynamics in living cells. Additionally, the probes allowed visualization of the specific localization of A1ARs endogenously expressed in dorsal root ganglion (DRG) neurons. LD probes developed here hold promise for illuminating ligand-binding, receptor signaling, and trafficking of the A1AR in more physiologically relevant environments.

The global fluorescent protein labeling market is experiencing robust growth, driven by advancements in biotechnology, increasing research activities in life sciences, and the rising demand for advanced diagnostic tools. The market, valued at approximately $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This growth is fueled by several key factors, including the increasing adoption of fluorescent protein labeling techniques in various applications such as drug discovery, disease research, and diagnostics. The development of novel fluorescent proteins with improved properties, such as brightness and photostability, is further boosting market expansion. Furthermore, the rising prevalence of chronic diseases and the increasing demand for personalized medicine are contributing to the market’s growth trajectory. Segments like protein-based fluorophores and applications within biopharmaceutical manufacturing and research institutions are expected to demonstrate particularly strong growth due to their crucial role in advanced research methodologies. The market's growth is not without challenges. High costs associated with advanced fluorescent protein labeling technologies and reagents can pose a barrier to entry for some researchers and companies, particularly smaller entities. Regulatory hurdles and stringent quality control requirements for fluorescent labeling reagents in the pharmaceutical and diagnostic industries can also influence market expansion. Despite these constraints, the long-term outlook for the fluorescent protein labeling market remains positive, primarily driven by continuous innovation in the field and the ever-increasing need for advanced analytical and diagnostic techniques in life science research and clinical applications. Geographical expansion, particularly in emerging economies, also presents significant opportunities for growth in the coming years. This comprehensive report provides a detailed analysis of the global fluorescent protein labeling market, projected to exceed $2 billion by 2028. We delve into market concentration, key trends, dominant regions, and leading companies, offering invaluable insights for stakeholders across the life sciences industry. This report utilizes extensive data analysis and market research to provide a robust overview of this dynamic sector. Search terms like "fluorescent protein labeling techniques," "fluorescence microscopy," "fluorescent dyes," and "bioconjugation" are frequently used, and have been strategically incorporated throughout this report description to maximize its visibility online.

A set of 3 datasets for Hierarchical Text Classification (HTC), with samples divided into training and testing splits. The hierarchies of labels within all datasets have depth 2.

• The Amazon5x5 dataset contains 500,000 user reviews tagged with the reviewed product's categories. There are 5 product categories with 100,000 examples each, and each category has 5 sub-categories.
• The Bugs dataset contains 30,050 bugs of the Linux kernel, labeled with exactly two categories identifying the affected component.
• Finally, the Web Of Science dataset contains 46,960 abstracts of scientific papers, labeled the article's domain (see original repo for more details).

Datasets are published in JSONL format, where each line is a string formatted as a JSON, like in the example below.

{ "text": , "labels": [

The hierarchical structure of labels in each dataset is documented in this repository.

These datasets have been presented in this paper:

• "Hierarchical Text Classification and its Foundations: a Review of Current Research" - DOI: 10.3390/electronics13071199

Some of these datasets have also been used in:

• "Ticket Automation: an Insight into Current Research with Applications to Multi-level Classification Scenarios" - DOI: 10.1016/j.eswa.2023.119984
• "A multi-level approach for hierarchical Ticket Classification", accepted at WNUT 2022 - link

These datasets are partially derived from previous work, namely:

• [Amazon] J. Ni, J. Li, J. McAuley, "Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects", EMNLP 2019, doi: 10.18653/v1/D19-1018
• [WOS] K. Kowsari, D. E. Brown, M. Heidarysafa, K. Jafari Meimandi, M. S. Gerber and L. E. Barnes, "HDLTex: Hierarchical Deep Learning for Text Classification," 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017, pp. 364-371, doi: 10.1109/ICMLA.2017.0-134
• [Linux Bugs] V. Lyubinets, T. Boiko and D. Nicholas, "Automated Labeling of Bugs and Tickets Using Attention-Based Mechanisms in Recurrent Neural Networks," 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), 2018, pp. 271-275, doi: 10.1109/DSMP.2018.8478511

This dataset has been superseded by a new edition (2nd edition, 2020) available here: https://data.gns.cri.nz/metadata/srv/eng/catalog.search#/metadata/79DFDE2D-14C3-4E1A-9BAA-EE4AD2B545AB.

This dataset is the annotation associated with the hydrologic features used as a background for the Geological Map of the Tongariro National Park area. The dataset was developed from the LINZ Topo50 placenames dataset and was produced by GNS Science. The dataset is stored in an ESRI vector geodatabase and exported to ArcGIS Server.

This dataset has been superseded by a new edition (2nd edition, 2020) available here: https://data.gns.cri.nz/metadata/srv/eng/catalog.search#/metadata/79DFDE2D-14C3-4E1A-9BAA-EE4AD2B545AB.

This dataset is the annotation associated with the cultural features used as a background for the Geological Map of the Tongariro National Park area. The dataset was developed from the LINZ Topo50 placenames dataset and was produced by GNS Science. The dataset is stored in an ESRI vector geodatabase and exported to ArcGIS Server.

Triple random ensemble method for multi-label classification