Biases of antimicrobial use (AMU) reporting systems pose a challenge to monitoring of AMU. Our study aimed to cross-compare three data sources of AMU in Swedish dairy herds to provide an account of the validity of AMU reports. We studied AMU differences between two production systems, to investigate how the reporting system affected this comparison. On-farm quantification of AMU via a manual collection of empty drug containers (BIN) took place in organic (n = 30) and conventional (n = 30) dairy herds during two periods between February 2016 and March 2017. A data extract mirroring these periods was obtained from two linked datasets that contain AMU data as reported by the prescribing veterinarians. These included data from the Swedish Board of Agriculture system (SBA) and Växa milk recording system (VXA). Using the European Medicines Agency technical units, the total number of defined daily doses (DDDvet), and defined course doses (DCDvet) per animal/year were calculated for each herd/period/dataset. Descriptive statistics and Bland–Altman plots were used to evaluate the agreement and systematic bias between the datasets. Mixed models for repeated measures were used to assess AMU differences between production systems. We found consistent numerical differences for the calculated AMU metrics, with BIN presenting higher usage compared to the SBA and VXA. This was driven by a disparity in intramammary tubes (IMt) which appear to be underreported in the national datasets. A statistically significant interaction (BIN dataset) between the production system and drug administration form was found, where AMU for injectable and lactating cow IMt drug forms differed by the production system, but no difference was found for dry-cow IMt. We conclude that calculating AMU using DDDvet and DCDvet metrics at a herd level based on Swedish national datasets is useful, with the caveat of IMt potentially being misrepresented. The BIN method offers an alternative to monitoring AMU, but scaling up requires considerations. The lower disease caseload in organic herds partly explains the lower AMU in particular drug forms. The fact that organic and conventional herds' had equally low AMU for dry-cow IMt, coupled with mismatches in IMt report across herds indicated an area of further research.

Amazon Bin Image Dataset

The Amazon Bin Image Dataset contains 536,434 images and metadata from bins of a pod in an operating Amazon Fulfillment Center. The bin images in this dataset are captured as robot units carry pods as part of normal Amazon Fulfillment Center operations. This dataset has many images and the corresponding medadata.

The image files have three groups according to its naming scheme.

• A file name with 1~4 digits (1,200): 1.jpg ~ 1200.jpg
• A file name with 5 digits (99,999): 00001.jpg ~ 99999.jpg
• A file name with 6 digits (435,235): 100000.jpg ~ 535234.jpg

Amazon Bin Image Dataset File List dataset aims to provide a CSV file to contain all file locations and the quantity to help the analysis and distributed learning.

Documentation

• https://github.com/awslabs/open-data-docs/tree/main/docs/aft-vbi-pds

Download

• https://registry.opendata.aws/amazon-bin-imagery/
• https://github.com/awslabs/open-data-registry/blob/main/datasets/amazon-bin-imagery.yaml

Number of sources by bin number.

The Amazon Bin Image Dataset contains 50,000 images and metadata from bins of a pod in an operating Amazon Fulfillment Center. The bin images in this dataset are captured as robot units carry pods as part of normal Amazon Fulfillment Center operations. This dataset can be used for research in variety of areas like computer vision, counting genetic items and learning from weakly-tagged data.

For each image, there is a corresponding entry of its metadata in JSON format stored in metadata.sqlite i.e. for image 01290.jpg, there is a corresponding json object in the data field of the metadata file which can be retrieved with query SELECT data FROM metadata WHERE img_id = 01290;

Refer the Starter Notebook to see how to work with the dataset.

Amazon uses a random storage scheme where items are placed into accessible bins with available space, so the contents of each bin are random, rather than organized by specific product types. Thus, each bin image may show only one type of product or a diverse range of products. Occasionally, items are misplaced while being handled, so the contents of some bin images may not match the recorded inventory of that bin.

These are some typical images in the dataset. A bin contains multiple object categories and various number of instances. The corresponding metadata exist for each bin image and it includes the object category identification (ASIN - Amazon Standard Identification Number), quantity and dimensions of objects. The size of bins are various depending on the size of objects in it. The tapes in front of the bins are for preventing the items from falling out of the bins and sometimes it might make the objects unclear. Objects are sometimes heavily occluded by other objects or limited viewpoint of the images.

Image Credits: Unsplash - helloimnik

The advent of palaeontological occurrence databases has allowed for detailed reconstruction and analyses of species richness through deep time. While a substantial literature has evolved ensuring that taxa are fairly counted within and between different time periods, how time itself is divided has received less attention. Stage-level or equal-interval age bins have been frequently used for regional and global studies in vertebrate palaeontology. However, when assessing diversity at a regional scale, these resolutions can prove inappropriate with the available data. Herein, we propose a new method of binning geological time for regional studies that intrinsically incorporates the chronostratigraphic heterogeneity of different rock formations to generate unique stratigraphic bins. We use this method to investigate the diversity dynamics of dinosaurs from the Late Cretaceous of the Western Interior of North America prior to the Cretaceous–Palaeogene mass extinction. Increased resolution through formation binning pinpoints the Maastrichtian diversity decline to between 68–66 Ma, coinciding with the retreat of the Western Interior Seaway. Diversity curves are shown to exhibit volatile patterns using different binning methods, supporting claims that heterogeneous biases in this time-frame affect the pre-extinction palaeobiological record. We also show that apparent high endemicity of dinosaurs in the Campanian is a result of non-contemporaneous geological units within large time bins. This study helps to illustrate the utility of high-resolution, regional studies to supplement our understanding of factors governing global diversity in deep time and ultimately how geology is inherently tied to our understanding of past changes in species richness.

Methods Additional Data for this manuscript consists of three, zipped supplementary folders:

1. Supplementary Information 1. Folder contains all necessary R code and datasets to run Formation Binner, including a README guide. Data folder consists of a .csv file of dinosaur occurrences, downloaded from the PBDB (Occurrences_Final); a .csv file of dinosaur bearing formations, with information gathered from the available literature (Formations_Final); and a .csv file of references for the updated formation information (References). This dataset can also be found at https://github.com/ChristopherDavidDean/Formation_Binner.

2. Supplementary Information 2. Folder contains all results generated using Formation Binner and used for this project. Data is either produced using Score Grid 1 (folder SG1), Score Grid 2 (folder SG2), or using Standard Binning (folder Standard_Binned). For SG1 and SG2, data was produced at 3 different resolutions: 2, 3 or 4 million years. Within these folders, data is split into Images (plots of results) or Tables (raw results). Results generated using SG1 and SG2 at all resolutions were also carried out using three methods (M1, M2 and M3), which each include plots of diversity and collections (named Div, Colls, or div_colls), Goods' U (named Goodsu) and SQS (named SQS). Also available is the plot of suitability scores (named 0.001_res) and plots of Formations through time (named FormationGraph). Standard Bin results are instead split into either Stage or Substage resolutions (Stage_Bins and SubStage_bins respectively), and then produced using either Formation Ages or PBDB ages (FormAges and PBDB) and binned using every bin the occurrence appears within or using the midpoint of its age range (Allbins and Mid).

3. Figure_S1. Contains supplementary Figure S1 as well as the appropriate figure caption.

Agricultural Bins Market Outlook

The global agricultural bins market size was valued at USD 1.5 billion in 2023 and is projected to reach USD 3.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.7% during the forecast period. The steady expansion of the agricultural sector, coupled with the increasing need for efficient storage solutions, is a key factor driving the market growth.

One of the primary growth factors for the agricultural bins market is the increasing demand for efficient storage solutions that minimize post-harvest losses. As the global population continues to grow, the need for effective storage and management of agricultural produce becomes ever more critical. Agricultural bins provide a practical solution for farmers and agribusinesses to store and transport produce, thus reducing waste and improving food security. Additionally, the development of bins with advanced features such as climate control and pest resistance is further contributing to market growth.

Technological advancements in the materials used for agricultural bins are also propelling market growth. Modern agricultural bins are made from durable and lightweight materials such as high-density polyethylene (HDPE) and galvanized steel, which offer superior strength and longevity compared to traditional wood. These materials are not only more resilient but also environmentally friendly, as they can be recycled and reused. Furthermore, the integration of smart technologies, such as sensors and IoT devices, enables real-time monitoring of storage conditions, improving overall efficiency and reducing operational costs.

Another significant factor driving the growth of the agricultural bins market is the increasing adoption of sustainable farming practices. Governments and regulatory bodies across the world are encouraging farmers to adopt sustainable methods to reduce environmental impact and enhance productivity. Agricultural bins, made from eco-friendly materials and designed to optimize storage, align with these sustainability goals. Incentives and subsidies provided by governments for the adoption of such practices are further boosting the market.

The introduction of innovative storage solutions such as the Portable Surge Bin is revolutionizing the agricultural storage landscape. These bins are designed to offer flexibility and mobility, allowing farmers to easily transport and store their produce across different locations. With the ability to handle varying quantities of agricultural products, Portable Surge Bins provide an efficient solution for managing surplus harvests. Their adaptability makes them particularly useful in regions with fluctuating production levels, ensuring that crops are stored safely and efficiently. Moreover, the ease of setup and dismantling enhances their appeal, especially for farmers who require temporary storage solutions during peak harvest seasons. By incorporating features such as weather resistance and durability, these bins are becoming an essential tool for modern agribusinesses looking to optimize their storage capabilities.

Regionally, the Asia Pacific region is expected to witness the highest growth in the agricultural bins market. The rapid expansion of the agricultural sector in countries like China and India, driven by increasing population and rising food demand, is a major factor. Additionally, initiatives by governments to improve agricultural infrastructure and storage facilities are fueling market growth. North America and Europe are also significant markets, driven by technological advancements and a well-established agricultural infrastructure. Latin America and the Middle East & Africa, although smaller markets, are expected to grow steadily due to increasing investments in agriculture.

Product Type Analysis

In the agricultural bins market, different product types cater to varied storage needs and preferences. Plastic bins dominate the market due to their lightweight nature, durability, and cost-effectiveness. Made from materials like HDPE, these bins are resistant to corrosion and can withstand harsh environmental conditions, making them ideal for outdoor storage. Additionally, plastic bins are easy to clean and maintain, further enhancing their appeal to farmers and agricultural businesses. The versatility of plastic bins, available in various sizes and shapes, also contributes to their widespread adoption.

Metal bins, primarily made from galvanized steel

Disparity-through-time analyses can be used to determine how morphological diversity changes in response to mass extinctions, and to investigate the drivers of morphological change. These analyses are routinely applied to palaeobiological datasets, yet although there is much discussion about how to best calculate disparity, there has been little consideration of how taxa should be sub-sampled through time. Standard practice is to group taxa into discrete time bins, often based on stratigraphic periods. However, this can introduce biases when bins are of unequal size, and implicitly assumes a punctuated model of evolution. In addition, many time bins may have few or no taxa, meaning that disparity cannot be calculated for the bin and making it harder to complete downstream analyses. Here we describe a different method to complement the disparity-through-time tool-kit: time-slicing. This method uses a time-calibrated phylogenetic tree to sample disparity-through-time at any fixed point in time rather than binning taxa. It uses all available data (tips, nodes and branches) to increase the power of the analyses, specifies the implied model of evolution (punctuated or gradual), and is implemented in R. We test the time-slicing method on four example datasets and compare its performance in common disparity-through-time analyses. We find that the way you time sub-sample your taxa can change your interpretations of the results of disparity-through-time analyses. We advise using multiple methods for time sub-sampling taxa, rather than just time binning, to gain a better understanding disparity-through-time.

"We believe that by accounting for the inherent uncertainty in the system during each measurement, the relationship between cause and effect can be assessed more accurately, potentially reducing the duration of research."

Short description

This dataset was created as part of a research project investigating the efficiency and learning mechanisms of a Bayesian adaptive search algorithm supported by the Imprecision Entropy Indicator (IEI) as a novel method. It includes detailed statistical results, posterior probability values, and the weighted averages of IEI across multiple simulations aimed at target localization within a defined spatial environment. Control experiments, including random search, random walk, and genetic algorithm-based approaches, were also performed to benchmark the system's performance and validate its reliability.

The task involved locating a target area centered at (100; 100) within a radius of 10 units (Research_area.png), inside a circular search space with a radius of 100 units. The search process continued until 1,000 successful target hits were achieved.

To benchmark the algorithm's performance and validate its reliability, control experiments were conducted using alternative search strategies, including random search, random walk, and genetic algorithm-based approaches. These control datasets serve as baselines, enabling comprehensive comparisons of efficiency, randomness, and convergence behavior across search methods, thereby demonstrating the effectiveness of our novel approach.

Uploaded files

The first dataset contains the average IEI values, generated by randomly simulating 300 x 1 hits for 10 bins per quadrant (4 quadrants in total) using the Python programming language, and calculating the corresponding IEI values. This resulted in a total of 4 x 10 x 300 x 1 = 12,000 data points. The summary of the IEI values by quadrant and bin is provided in the file results_1_300.csv. The calculation of IEI values for averages is based on likelihood, using an absolute difference-based approach for the likelihood probability computation. IEI_Likelihood_Based_Data.zip

The weighted IEI average values for likelihood calculation (Bayes formula) are provided in the file Weighted_IEI_Average_08_01_2025.xlsx

This dataset contains the results of a simulated target search experiment using Bayesian posterior updates and Imprecision Entropy Indicators (IEI). Each row represents a hit during the search process, including metrics such as Shannon entropy (H), Gini index (G), average distance, angular deviation, and calculated IEI values. The dataset also includes bin-specific posterior probability updates and likelihood calculations for each iteration. The simulation explores adaptive learning and posterior penalization strategies to optimize the search efficiency. Our Bayesian adaptive searching system source code (search algorithm, 1000 target searches): IEI_Self_Learning_08_01_2025.pyThis dataset contains the results of 1,000 iterations of a successful target search simulation. The simulation runs until the target is successfully located for each iteration. The dataset includes further three main outputs: a) Results files (results{iteration_number}.csv): Details of each hit during the search process, including entropy measures, Gini index, average distance and angle, Imprecision Entropy Indicators (IEI), coordinates, and the bin number of the hit. b) Posterior updates (Pbin_all_steps_{iter_number}.csv): Tracks the posterior probability updates for all bins during the search process acrosations multiple steps. c) Likelihoodanalysis(likelihood_analysis_{iteration_number}.csv): Contains the calculated likelihood values for each bin at every step, based on the difference between the measured IEI and pre-defined IE bin averages. IEI_Self_Learning_08_01_2025.py

Based on the mentioned Python source code (see point 3, Bayesian adaptive searching method with IEI values), we performed 1,000 successful target searches, and the outputs were saved in the:Self_learning_model_test_output.zip file.

Bayesian Search (IEI) from different quadrant. This dataset contains the results of Bayesian adaptive target search simulations, including various outputs that represent the performance and analysis of the search algorithm. The dataset includes: a) Heatmaps (Heatmap_I_Quadrant, Heatmap_II_Quadrant, Heatmap_III_Quadrant, Heatmap_IV_Quadrant): These heatmaps represent the search results and the paths taken from each quadrant during the simulations. They indicate how frequently the system selected each bin during the search process. b) Posterior Distributions (All_posteriors, Probability_distribution_posteriors_values, CDF_posteriors_values): Generated based on posterior values, these files track the posterior probability updates, including cumulative distribution functions (CDF) and probability distributions. c) Macro Summary (summary_csv_macro): This file aggregates metrics and key statistics from the simulation. It summarizes the results from the individual results.csv files. d) Heatmap Searching Method Documentation (Bayesian_Heatmap_Searching_Method_05_12_2024): This document visualizes the search algorithm's path, showing how frequently each bin was selected during the 1,000 successful target searches. e) One-Way ANOVA Analysis (Anova_analyze_dataset, One_way_Anova_analysis_results): This includes the database and SPSS calculations used to examine whether the starting quadrant influences the number of search steps required. The analysis was conducted at a 5% significance level, followed by a Games-Howell post hoc test [43] to identify which target-surrounding quadrants differed significantly in terms of the number of search steps. Results were saved in the Self_learning_model_test_results.zip

This dataset contains randomly generated sequences of bin selections (1-40) from a control search algorithm (random search) used to benchmark the performance of Bayesian-based methods. The process iteratively generates random numbers until a stopping condition is met (reaching target bins 1, 11, 21, or 31). This dataset serves as a baseline for analyzing the efficiency, randomness, and convergence of non-adaptive search strategies. The dataset includes the following: a) The Python source code of the random search algorithm. b) A file (summary_random_search.csv) containing the results of 1000 successful target hits. c) A heatmap visualizing the frequency of search steps for each bin, providing insight into the distribution of steps across the bins. Random_search.zip

This dataset contains the results of a random walk search algorithm, designed as a control mechanism to benchmark adaptive search strategies (Bayesian-based methods). The random walk operates within a defined space of 40 bins, where each bin has a set of neighboring bins. The search begins from a randomly chosen starting bin and proceeds iteratively, moving to a randomly selected neighboring bin, until one of the stopping conditions is met (bins 1, 11, 21, or 31). The dataset provides detailed records of 1,000 random walk iterations, with the following key components: a) Individual Iteration Results: Each iteration's search path is saved in a separate CSV file (random_walk_results_.csv), listing the sequence of steps taken and the corresponding bin at each step. b) Summary File: A combined summary of all iterations is available in random_walk_results_summary.csv, which aggregates the step-by-step data for all 1,000 random walks. c) Heatmap Visualization: A heatmap file is included to illustrate the frequency distribution of steps across bins, highlighting the relative visit frequencies of each bin during the random walks. d) Python Source Code: The Python script used to generate the random walk dataset is provided, allowing reproducibility and customization for further experiments. Random_walk.zip

This dataset contains the results of a genetic search algorithm implemented as a control method to benchmark adaptive Bayesian-based search strategies. The algorithm operates in a 40-bin search space with predefined target bins (1, 11, 21, 31) and evolves solutions through random initialization, selection, crossover, and mutation over 1000 successful runs. Dataset Components: a) Run Results: Individual run data is stored in separate files (genetic_algorithm_run_.csv), detailing: Generation: The generation number. Fitness: The fitness score of the solution. Steps: The path length in bins. Solution: The sequence of bins visited. b) Summary File: summary.csv consolidates the best solutions from all runs, including their fitness scores, path lengths, and sequences. c) All Steps File: summary_all_steps.csv records all bins visited during the runs for distribution analysis. d) A heatmap was also generated for the genetic search algorithm, illustrating the frequency of bins chosen during the search process as a representation of the search pathways.Genetic_search_algorithm.zip

Technical Information

The dataset files have been compressed into a standard ZIP archive using Total Commander (version 9.50). The ZIP format ensures compatibility across various operating systems and tools.

The XLSX files were created using Microsoft Excel Standard 2019 (Version 1808, Build 10416.20027)

The Python program was developed using Visual Studio Code (Version 1.96.2, user setup), with the following environment details: Commit fabd6a6b30b49f79a7aba0f2ad9df9b399473380f, built on 2024-12-19. The Electron version is 32.6, and the runtime environment includes Chromium 128.0.6263.186, Node.js 20.18.1, and V8 12.8.374.38-electron.0. The operating system is Windows NT x64 10.0.19045.

The statistical analysis included in this dataset was partially conducted using IBM SPSS Statistics, Version 29.0.1.0

The CSV files in this dataset were created following European standards, using a semicolon (;) as the delimiter instead of a comma, encoded in UTF-8 to ensure compatibility with a wide

This dataset provides advice and restrictions on the methods available to dispose of common waste items by Brisbane City Council residents.

Descriptions for the disposal options listed below are available in the Attachments and Dataset schema sections of this dataset.

Disposal options:

Council Drop Off Points/Public Drop Off Points

Household Hazardous Waste

Kerbside Green Waste

Kerbside Large Item Collection.

Kerbside Recycle

Kerbside Waste

Must follow proper procedure

Other

Public Drop Off Points

Resource Recovery Centre

The Attachments and Dataset schema sections of this dataset contain further information for this dataset.

The global classified waste bin market was valued at USD 2 billion in 2019 and is estimated to reach USD 7 billion by 2026, expanding at a CAGR of 18% during the forecast period, 2020 – 2026. The growth of the market is attributed to the growing economy, changing lifestyles, and rapid urbanization.



In most of the industrialized regions, the traditional method to gather waste has been to dispose of it as inexpensively as possible, giving little concern about waste management methods. This scenario is altering as augmented environmental awareness is redirected in stringent waste organization regulations and a devoted approach on the part of the market for a better environmental presentation and meet customer needs and expectations. The changing environmental awareness and concern have led to the use of classified bin in every place such as schools, hospitals, restaurants, and shopping malls. These classified waste bins are secret on the basis of dry waste bin and wet waste bin, recyclable waste bin and non-recyclable waste bin, and paper, glass, and plastic waste bin.

Drivers, Restraints, Opportunities, and Market Trends:

• Growth of the global classified waste bin market is driven by active government measures to decrease illegal dumping. Additionally, surge in population and rise in globalization has led to an upsurge in the overall waste volume across the world. Urban population produced round 1 billion tons of urban solid waste in the year 2012, which is projected to grow to 2 billion tones by 2026. Furthermore, upsurge in industrialization in the developing region has led to growth of oil and gas, chemical, medical industries, and automobile, which generates huge amount of waste and in turn causes pollution. These factors are anticipated to expressively contribute toward growth of the global market during the forecast period.
• The increase in global population, improved living standards, and availability of better infrastructure are the major factors expected to drive the global classified waste bin market during the forecast period. The requirement for basic services such as residential infrastructure upsurges with the growth in urban population.
• The high cost of classified waste bin solutions is projected to hamper the global classified waste bin market growth during the forecast period.
• Major factors such as high up front cost, requirement of more global buy in, and lesser quality of bin is projected to hamper the global classified waste bin market growth during the forecast period.
• Increasing in awareness about classified waste bin among government agencies and public have increase its need to manage waste, which in turn is projected to provide lucrative growth opportunities for major market players during the forecast period. Several classified waste bin companies have accepted strategies such as acquisitions, business expansion, agreement, and partnership to provide better services in the market, owing to the opportunities.
• The trend of fighting against global warming is expected to drive the global market during the forecast period.

Scope of the Report:

Smart Trash Bin Market Outlook

According to our latest research, the global smart trash bin market size reached USD 1.27 billion in 2024. The market is experiencing robust expansion, supported by a CAGR of 13.4% from 2025 to 2033. By the end of the forecast period, the smart trash bin market is projected to achieve a valuation of USD 3.76 billion in 2033. This growth is primarily driven by the increasing adoption of smart home technologies, rising urbanization, and escalating concerns over hygiene and efficient waste management.

One of the most significant growth factors for the smart trash bin market is the rapid integration of Internet of Things (IoT) technologies into everyday household and commercial products. Smart trash bins, equipped with sensors, connectivity modules, and automation features, are revolutionizing waste disposal by providing touchless operation, real-time monitoring, and optimized waste collection. As urban populations expand and cities pursue smart infrastructure initiatives, the demand for connected waste management solutions has soared. These advanced bins help reduce manual intervention, minimize the risk of contamination, and promote sustainability through efficient waste segregation and timely collection. Additionally, the proliferation of smart city projects worldwide is further accelerating the adoption of smart trash bins in public spaces, commercial complexes, and industrial facilities.

Another key driver propelling the smart trash bin market is the heightened awareness of hygiene and public health, particularly in the wake of global health crises such as the COVID-19 pandemic. Consumers and organizations are increasingly prioritizing contactless solutions to mitigate the spread of germs and bacteria. Smart trash bins, with their sensor-based and voice-activated features, offer a hygienic alternative to traditional waste disposal methods. This trend is especially pronounced in healthcare facilities, educational institutions, restaurants, and office buildings, where maintaining high standards of cleanliness is paramount. Furthermore, governments and municipal bodies are implementing regulations and incentives to encourage the adoption of smart waste management systems, further boosting market growth.

Technological advancements and product innovation are also playing a pivotal role in shaping the smart trash bin market landscape. Manufacturers are continuously introducing new models with enhanced features such as Wi-Fi connectivity, integration with smart home ecosystems, automatic bag replacement, and real-time fill-level alerts. These innovations not only improve user convenience but also contribute to sustainability by enabling data-driven waste collection and reducing operational costs for waste management companies. The increasing availability of smart trash bins across various price points and capacities is making them accessible to a broader consumer base, from individual households to large-scale commercial and industrial users.

From a regional perspective, North America currently dominates the smart trash bin market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The high adoption rate of smart home devices, strong presence of leading market players, and ongoing investments in smart city infrastructure are key factors contributing to North America's leadership. Meanwhile, Asia Pacific is expected to witness the fastest growth during the forecast period, driven by rapid urbanization, rising disposable incomes, and government initiatives to modernize waste management systems. Latin America and the Middle East & Africa are also emerging as promising markets, supported by increasing awareness and gradual infrastructure development.

Product Type Analysis

The smart trash bin market is segmented by product type into sensor-based, Wi-Fi enabled, voice-activated, and others. Sensor-based smart trash bins represent the most widely adopted category, owing to their convenience and affordability. These bi

Eurobodalla Council has installed full level sensors in selected rubbish bins to gain valuable insights into the waste collection process and optimize their operations. These sensors provide real-time data on the fill-level of the bins, allowing the council to efficiently plan routes for waste collection and reduce unnecessary pickups. Additionally, the insights gained from these sensors can help the council identify trends in waste generation and make informed decisions about waste management policies and practices.

Number of sources by data provider.

The global ESD (Electrostatic Discharge) Waste Bin market is experiencing robust growth, driven by the increasing adoption of ESD-protective measures in electronics manufacturing, cleanrooms, and data centers. The expanding electronics industry, particularly in Asia-Pacific and North America, fuels significant demand for these specialized waste bins, which are crucial for preventing electrostatic damage to sensitive components. The market is segmented by application (electronics manufacturing holding the largest share, followed by cleanrooms and data centers) and bin capacity (with the "less than 50L" segment currently dominating due to its cost-effectiveness and suitability for numerous applications). Growth is further propelled by stringent industry regulations concerning electrostatic discharge control and heightened awareness among manufacturers about the financial and reputational consequences of component damage due to static electricity. While a specific CAGR wasn't provided, a conservative estimate based on industry growth trends places it at around 7-8% for the forecast period (2025-2033). This growth, however, might face some constraints from the relatively mature nature of the market in certain regions and the presence of substitute waste disposal methods. The competitive landscape is moderately fragmented with several key players such as Bondline Electronics, Protektive Pak, and Desco vying for market share through product innovation and strategic partnerships. The market is projected to witness a shift towards larger capacity bins (50-100L and above) in the coming years, driven by increasing production volumes and the need for more efficient waste management solutions in larger facilities. Furthermore, innovations in material science and manufacturing processes are leading to more durable and cost-effective ESD waste bins. The adoption of smart bin technologies, enabling real-time monitoring of waste levels and optimized disposal practices, represents a promising future growth area. Regional expansion, particularly in developing economies with burgeoning electronics industries, will play a crucial role in market expansion. Despite potential restraints, the overall outlook for the ESD Waste Bin market remains positive, driven by continuous technological advancements and a growing need for effective ESD control in various sensitive industries.

Abstract

This dataset provides secondary literature data on STEM Education collected from peer-reviewed databases such as Web of Science, Scopus, and ACM. The collection contains significant information about the scientific literature, including intervention studies conducted between 2020 and 2024. The data fields' key characteristics include Domain, Education Level, TEL Practices, Learning Technologies, LA Practices, Learning Analytics Techniques, Study Designs, Theories, Countries, Populations, Learning Strategies, Learning Measures, Impact Areas, Learning Outcomes, Challenges and Limitations. This dataset aims to provide a thorough trajectory of STEM educational research works as well as an overview of numerous technologies and analytics methods that have contributed to the expansion of this growing discipline.

The rank-1 identification rate and EER by different methods using the CASIA database.

The global medical trash bin market is anticipated to reach a market size of 153.1 million value units by 2033, expanding at a CAGR of 4.6% from 2025 to 2033. The market growth is attributed to the increasing demand for sterile and hygienic waste management solutions in the healthcare industry, stringent government regulations mandating the proper disposal of medical waste, and the rising prevalence of infectious diseases. Factors driving the market include technological advancements, such as the introduction of touchless trash bins and automated waste disposal systems, which enhance convenience and safety. Additionally, the growing emphasis on healthcare waste segregation and efficient disposal methods contributes to market growth. However, the presence of stringent regulations and the high cost of waste disposal can restrain market expansion. The increasing focus on sustainability and environmental protection also presents new challenges for medical waste management. North America holds a dominant share in the market, followed by Europe and Asia Pacific. Key players in the market include BOYUE, Raqwani Medicals, Daniels Health, GAMC, Star Hygiene, ALDA SA, Calco Industrial Products, AGS-Medical, Medicus Health, Erpa Medikal, and Gient.

The global RFID Smart Bin Tags market is experiencing robust growth, driven by increasing urbanization, the need for efficient waste management solutions, and the rising adoption of smart city initiatives. The market, currently valued at approximately $800 million in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% between 2025 and 2033, reaching an estimated market value of $2.5 billion by 2033. This growth is fueled by several key factors. Firstly, passive RFID smart bin tags, offering a cost-effective solution for waste management optimization, are gaining significant traction across various applications, including household, commercial, and public utility sectors. Secondly, the rising demand for real-time waste monitoring and improved route optimization for waste collection vehicles is bolstering market expansion. Furthermore, government initiatives promoting sustainable waste management practices and technological advancements in RFID technology, such as improved battery life and enhanced data analytics capabilities, are contributing to the market's expansion. The active RFID smart bin tags segment is also showing promise due to its longer read range and suitability for larger-scale deployments. However, the market's growth is not without its challenges. High initial investment costs associated with implementing RFID smart bin systems can be a barrier to entry for smaller municipalities and businesses. Concerns about data security and privacy related to the collection and use of waste data also need to be addressed. Despite these restraints, the long-term prospects for the RFID Smart Bin Tags market remain positive, with continued innovation and increasing awareness of the benefits of efficient waste management driving market expansion across diverse geographic regions, particularly in North America, Europe, and the Asia-Pacific region which are expected to dominate the market share due to advanced waste management infrastructure and increasing adoption of smart city technologies. Key players in the market are constantly innovating to offer improved products and services, fostering competition and driving further growth.

Contains scans of a bin filled with different parts ( screws, nuts, rods, spheres, sprockets). For each part type, RGB image and organized 3D point cloud obtained with structured light sensor are provided. In addition, unorganized 3D point cloud representing an empty bin and a small Matlab script to read the files is also provided. 3D data contain a lot of outliers and the data were used to demonstrate a new filtering technique.