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The global tool box market is experiencing robust growth, projected to reach a market size of $5 billion in 2025, with a Compound Annual Growth Rate (CAGR) of 6% from 2025 to 2033. This expansion is driven by several key factors. The increasing construction and manufacturing activities globally fuel demand for durable and efficient tool storage solutions. Furthermore, the rising adoption of advanced materials like lightweight aluminum and high-impact plastics in toolbox construction caters to the need for improved portability and protection. The market is segmented by type (plastic, aluminum, and other materials) and application (commercial and household). The preference for organized and secure tool storage is significantly impacting the demand for high-quality toolboxes across both commercial and residential sectors. Growth in emerging economies, particularly in Asia-Pacific, is also contributing to the market's expansion. However, fluctuating raw material prices and the presence of cheaper, less durable alternatives pose challenges to market growth. The competitive landscape is characterized by a mix of global giants like Apex Tool Group and STAHLWILLE, alongside regional players. These companies are focusing on product innovation, strategic partnerships, and expansion into new markets to maintain their competitive edge. The forecast period suggests continuous growth, fueled by rising infrastructure development and industrialization. The market's growth is expected to be particularly strong in regions with robust infrastructure projects and growing industrial sectors. North America and Europe currently hold significant market share, but the Asia-Pacific region is projected to witness the fastest growth due to rapid industrialization and urbanization. Product differentiation through innovative designs, enhanced durability, and smart features is becoming increasingly crucial for players to succeed in this competitive landscape. The industry is also witnessing a trend towards specialized toolboxes designed for specific trades and professions, further segmenting the market and creating niche opportunities for manufacturers. Sustainability considerations are also becoming increasingly important, with manufacturers exploring eco-friendly materials and production processes to meet growing consumer demand for environmentally conscious products. This report provides a detailed analysis of the global tool boxes market, a sector currently valued at approximately $2.5 billion and projected to reach $3.2 billion by 2028. We delve into key market segments, competitive landscapes, and emerging trends, offering actionable insights for businesses operating within this dynamic industry. High-search-volume keywords like "tool box market size," "plastic tool boxes," "aluminum tool boxes," and "professional tool boxes" are incorporated throughout for enhanced online visibility.

This upload is associated with the software, Gut Analysis Toolbox (GAT).

If you use it please cite:

Sorensen et al. Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons. J Cell Sci 2024; jcs.261950. doi: https://doi.org/10.1242/jcs.261950

The upload contains StarDist models for segmenting enteric neurons in 2D, enteric neuronal subtypes in 2D and UNet model for enteric ganglia in 2D in gut wholemount tissue. GAT is implemented in Fiji, but the models can be used in any software that supports StarDist and the use of 2D UNet models. The files here also consist of Python notebooks (Google Colab), training and test data as well as reports on model performance.

The model files are located in the respective folders as zip files. The folders have also been zipped:

• Neuron (Hu; StarDist model):
  • Main folder: 2D_enteric_neuron_model_QA.zip
  • Model File:2D_enteric_neuron_v4_1.zip
• Neuronal subtype (StarDist model):
  • Main folder: 2D_enteric_neuron_subtype_model_QA.zip
  • Model File: 2D_enteric_neuron_subtype_v4.zip
• Enteric ganglia (2D UNet model; Use in FIJI with deepImageJ)
  • Main folder: 2D_enteric_ganglia_model_QA.zip
  • Model File: 2D_Ganglia_RGB_v2.bioimage.io.model.zip (Compatible with deepimageJ v3)

For the all models, files included are:

1. Model for segmenting cells or ganglia in 2D FIJI. StarDist or 2D UNet.
2. Training and Test datasets used for training.
3. Google Colab notebooks used for training and quality assurance (ZeroCost DL4Mic notebooks).
4. Quality assurance reports generated from above notebooks.
5. StarDist model exported for use in QuPath.

The model files can be used within can be used within the software, StarDist. They are intended to be used within FIJI or QuPath, but can be used in any software that supports the implementation of StarDist in 2D.

Data:

All the images were collected from 4 different research labs and a public database (SPARC database) to account for variations in image acquisition, sample preparation and immunolabelling.

For enteric neurons the pan-neuronal marker, Hu has been used and the 2D wholemounts images from mouse, rat and human tissue.

For enteric neuronal subtypes, 2D images for nNOS, MOR, DOR, ChAT, Calretinin, Calbindin, Neurofilament, CGRP and SST from mouse tissue have been used..

25 images were used from the following entries in the SPARC database:

• Howard, M. (2021). 3D imaging of enteric neurons in mouse (Version 1) [Data set]. SPARC Consortium.
• Graham, K. D., Huerta-Lopez, S., Sengupta, R., Shenoy, A., Schneider, S., Wright, C. M., Feldman, M., Furth, E., Lemke, A., Wilkins, B. J., Naji, A., Doolin, E., Howard, M., & Heuckeroth, R. (2020). Robust 3-Dimensional visualization of human colon enteric nervous system without tissue sectioning (Version 1) [Data set]. SPARC Consortium.
• Wang, L., Yuan, P.-Q., Gould, T. and Tache, Y. (2021). Antibodies Tested in theColon – Mouse (Version 1) [Data set]. SPARC Consortium. doi:10.26275/i7dl-58h

The images have been acquired using a combination different microscopes. The images for the mouse tissue were acquired using:

• Leica TCS-SP8 confocal system (20x HC PL APO NA 1.33, 40 x HC PL APO NA 1.3)

• Leica TCS-SP8 lightning confocal system (20x HC PL APO NA 0.88)

• Zeiss Axio Imager M2 (20X HC PL APO NA 0.3)

• Zeiss Axio Imager Z1 (10X HC PL APO NA 0.45)

Human tissue images were acquired using:

• IX71 Olympus microscope (10X HC PL APO NA 0.3)

For more information, visit the Documentation website.

NOTE: The images for enteric neurons and neuronal subtypes have been rescaled to 0.568 µm/pixel for mouse and rat. For human neurons, it has been rescaled to 0.9 µm/pixel . This is to ensure the neuronal cell bodies have similar pixel area across images. The area of cells in pixels can vary based on resolution of image, magnification of objective used, animal species (larger animals -> larger neurons) and potentially how the tissue is stretched during wholemount preparation

Average neuron area for neuronal model: 701.2 ± 195.9 pixel² (Mean ± SD, 6267 cells)

Average neuron area for neuronal subtype model: 880.9 ± 316 pixel² (Mean ± SD, 924 cells)

Software References:

Stardist

Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018, September). Cell detection with star-convex polygons. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 265-273). Springer, Cham.

deepImageJ

Gómez-de-Mariscal, E., García-López-de-Haro, C., Ouyang, W., Donati, L., Lundberg, E., Unser, M., Muñoz-Barrutia, A. and Sage, D., 2021. DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. Nature Methods, 18(10), pp.1192-1195.

ZeroCost DL4Mic

von Chamier, L., Laine, R.F., Jukkala, J., Spahn, C., Krentzel, D., Nehme, E., Lerche, M., Hernández-Pérez, S., Mattila, P.K., Karinou, E. and Holden, S., 2021. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature communications, 12(1), pp.1-18.

1. Quantitative measurements of colour, pattern, and morphology are vital to a growing range of disciplines. Digital cameras are readily available and already widely used for making these measurements, having numerous advantages over other techniques, such as spectrometry. However, off-the-shelf consumer cameras are designed to produce images for human viewing, meaning that their uncalibrated photographs cannot be used for making reliable, quantitative measurements. Many studies still fail to appreciate this, and of those scientists who are aware of such issues, many are hindered by a lack usable tools for making objective measurements from photographs. 2. We have developed an image processing toolbox that generates images that are linear with respect to radiance from the RAW files of numerous camera brands, and can combine image channels from multispectral cameras, including additional ultraviolet photographs. Images are then normalised using one or more grey standards to control for lighting conditions. This enables objective measures of reflectance and colour using a wide range of consumer cameras. Furthermore, if the camera's spectral sensitivities are known, the software can convert images to correspond to the visual system (cone-catch values) of a wide range of animals, enabling human and non-human visual systems to be modelled. The toolbox also provides image analysis tools that can extract luminance (lightness), colour, and pattern information. Furthermore, all processing is performed on 32-bit floating point images rather than commonly used 8-bit images. This increases precision and reduces the likelihood of data loss through rounding error or saturation of pixels, while also facilitating the measurement of objects with shiny or fluorescent properties. 3. All cameras tested using this software were found to demonstrate a linear response within each image and across a range of exposure times. Cone-catch mapping functions were highly robust, converting images to several animal visual systems and yielding data that agreed closely with spectrometer-based estimates. 4. Our imaging toolbox is freely available as an addition to the open source ImageJ software. We believe that it will considerably enhance the appropriate use of digital cameras across multiple areas of biology, in particular researchers aiming to quantify animal and plant visual signals.

This dataset shows 2 m depth contours within the study area. The contours were created using the contour feature within the Spatial Analyst Toolbox in ArcMap (v 10.2.2). Contours were manually edited where necessary to clean up artifacts in the dataset. The input dataset was bathymetry data processed to 50 cm horizontal resolution that was collected June 11th-16th, 2015.

Unlike in its African range, very little information is available on the movement patterns of Lesser Flamingos in India. In one of the first satellite telemetry studies of Lesser Flamingos in India, we provide novel insights into the species’ movement patterns, which may further supplement the existing management of their key feeding and breeding sites. We investigated the daily movement patterns corresponding to the Lesser Flamingo’s feeding strategies, long-distance movements corresponding to potential nomadism, home range patterns and habitat use across important feeding sites in India. We deployed GPS-GSM satellite transmitters on four sub-adults and tracked their movements between September 2022 and July 2023. Their home ranges were calculated using kernel density estimators, and movement patterns were calculated using the Tracking Analyst toolbox in ArcGIS software. Habitat use was investigated by employing a robust machine-learning algorithm, Random Forest. The four Lesser Flamingos covered a mean ± SD distance of 2541.55 ± 1946.04 km per month, and an average daily distance of 83.45 ± 64.63 km. Long-distance movements were observed in two individuals. Overall, the mean home ranges (95% KDE) were calculated as 223.82 ± 337.48 km2 and core areas (50% KDE) as 39.14 ± 65.71 km2. The birds’ movements were positively associated with saltpans, mudflats, waterbodies and intertidal swamps. The long-distance movement pattern observed hints at the Lesser Flamingos’ nomadism, switching between key feeding sites across Gujarat and Maharashtra. This requires the conservation of their key feeding sites, in particular, and their breeding sites in general.

Discover the booming global toolbox market! This in-depth analysis reveals a $5 billion market projected to reach over $8 billion by 2033, driven by industrial growth and DIY trends. Explore market segments, key players (Apex Tool Group, STAHLWILLE, Peli Products), and regional insights.

Discover the booming plastic toolbox market! This comprehensive analysis reveals key trends, drivers, restraints, and regional growth projections from 2025-2033, covering leading brands like Stanley Black & Decker and Snap-on Tools. Explore market segmentation by type and application. Learn more about this dynamic industry!

The tool box market is continuously growing as industries and individuals consider efficient, durable, and organized storage solutions. Tool boxes, widely used in construction, automotive, and household sectors, are valued for their portability, customizability, and durability. By 2035, the global tool box market is estimated to be worth more than USD 7.4 billion and growing at a compound annual growth rate (CAGR) of 4.2%.

Global Market Share & Industry Share

The data and python code in jupyter notebooks are associated with the manuscript: Sorensen et al. Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons. J Cell Sci 2024; jcs.261950. doi: https://doi.org/10.1242/jcs.261950

FigS1_analysis.zip: Data files (csv) and jupyter notebooks (ipynb) pertaining to Fig. S1D,E.

Fig3_analysis.zip: Data files (csv) and jupyter notebooks (ipynb) pertaining to Fig. 3D-N.

The images and analysis files associated with analysis in GAT are also uploaded: CalR_CalB_GAT_analysis.zip

The images used in this analysis are from EXP174 in this dataset: https://zenodo.org/records/7236748

This interactive map shows 1,731 UCSB research publications (theses and dissertations) available through eScholarship and the UCSB Library's Alexandria Digital Research Library. Publications are spatially configured according to their text descriptions from their titles and abstracts. Text was processed with a topic modeling tool, MALLET, from which 71 topic classes were derived. The number of classes also correspond to the number of graduate departments. Each publication is assigned a topic likelihood distribution. Based on topic assignment, the publications are placed into a map space. Publications that are closer together are more topically similar, while those that are dissimilar are spaced further apart. The self-organizing map was generated using a SOM Analyst toolbox for ArcGIS 9.x written by Martin Lacayo-Emery (SDSU).

The global home toolbox market is experiencing robust growth, driven by the increasing popularity of DIY home improvement projects and a rising demand for organized storage solutions. The market, valued at approximately $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 6% from 2025 to 2033, reaching an estimated market value of $4 billion by 2033. This growth is fueled by several key factors, including the rising disposable incomes in developing economies, increased urbanization leading to smaller living spaces and a greater need for efficient storage, and a growing trend toward personalization and customization of home workshops. The expanding e-commerce sector also plays a significant role, providing consumers with convenient access to a wide variety of toolboxes from various brands. However, certain restraints are also at play. Fluctuations in raw material prices, particularly metals used in toolbox manufacturing, can impact profitability and pricing. Furthermore, competition among established players and the emergence of new entrants are creating a dynamic and challenging market landscape. Segmentation analysis reveals a strong preference for mobile toolboxes due to their portability and versatility, while online sales channels are gaining traction, reflecting the growing preference for convenience and online shopping. Key players like Kistenberg, Dewalt, and SHUTER are leveraging their brand reputation and product innovation to maintain market share, while smaller players are focusing on niche markets and innovative designs to compete effectively. The geographic distribution shows significant market potential in North America and Asia-Pacific regions, driven by strong economic growth and a thriving DIY culture.

This dataset is about books. It has 1 row and is filtered where the book is Foreign policy analysis : a toolbox. It features 7 columns including author, publication date, language, and book publisher.

The solar radiation layers are simulations of solar radiation based on the Digital Surface Model. The simulation considers the topographic situation (surrounding, slope, exposition) as well as time-based variation of the sun radiation for a specific geographic location. The result is a raster visualization of the sun duration per pixel (with 1 m ground resolution). The simulation is configured to return the sun hours per pixel for a given day. Currently 3 days were calculated: 15/02 (winter), 15/05 (spring) and 15/08 (summer).

The solar radiation analysis is based on the solar radiation toolset of the ESRI ArcMap toolbox. A detailed documentation can be found in the corresponding documentation by ESRI: http://desktop.arcgis.com/en/arcmap/10.6/tools/spatial-analyst-toolbox/area-solar-radiation.htm

The analysis used the following parameters:

- Input raster: Digital Surface model provided by the Administration de la navigation aérienne (ANA) based on a LiDAR flight from 2017. (DSM available here : https://data.public.lu/fr/datasets/digital-surface-model-high-dem-resolution/ )

- Latitude : 49.46 °

- Time configuration : Time Within a day (for 3 dates: 15/02 winter, 15/05 spring and 15/08 summer)

- Hour interval: 0.5 – The solar radiation was calculated in 30 min. intervals and summed up per day.

- Slope and aspect input : The slope and aspect rasters are calculated from the input digital surface model

- Calculation directions: 32, which is adequate for a complex topography.

- Diffuse proportion : 0.3 for a generally clear sky conditions.

- Transmittitivity : 0.5 for a generally clear sky.

- Output raster: The result is an output raster representing the duration of direct incoming solar radiation.

The global aluminum toolbox market is booming, reaching $21.58 billion in 2025 and projected to grow at a 3.6% CAGR through 2033. Discover key market trends, leading companies, and regional insights in this comprehensive analysis. Explore the impact of lightweight materials, durability, and cost-effectiveness on market growth.

This dataset contains 50-ft contours for the Hot Springs shallowest unit of the Ouachita Mountains aquifer system potentiometric-surface map. The potentiometric-surface shows altitude at which the water level would have risen in tightly-cased wells and represents synoptic conditions during the summer of 2017. Contours were constructed from 59 water-level measurements measured in selected wells (locations in the well point dataset). Major streams and creeks were selected in the study area from the USGS National Hydrography Dataset (U.S. Geological Survey, 2017), and the spring point dataset with 18 spring altitudes calculated from 10-meter digital elevation model (DEM) data (U.S. Geological Survey, 2015; U.S. Geological Survey, 2016). After collecting, processing, and plotting the data, a potentiometric surface was generated using the interpolation method Topo to Raster in ArcMap 10.5 (Esri, 2017a). This tool is specifically designed for the creation of digital elevation models and imposes constraints that ensure a connected drainage structure and a correct representation of the surface from the provided contour data (Esri, 2017a). Once the raster surface was created, 50-ft contour interval were generated using Contour (Spatial Analyst), a spatial analyst tool (available through ArcGIS 3D Analyst toolbox) that creates a line-feature class of contours (isolines) from the raster surface (Esri, 2017b). The Topo to Raster and contouring done by ArcMap 10.5 is a rapid way to interpolate data, but computer programs do not account for hydrologic connections between groundwater and surface water. For this reason, some contours were manually adjusted based on topographical influence, a comparison with the potentiometric surface of Kresse and Hays (2009), and data-point water-level altitudes to more accurately represent the potentiometric surface. Select References: Esri, 2017a, How Topo to Raster works—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/how-topo-to-raster-works.htm. Esri, 2017b, Contour—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro Raster Surface toolset at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/contour.htm. Kresse, T.M., and Hays, P.D., 2009, Geochemistry, Comparative Analysis, and Physical and Chemical Characteristics of the Thermal Waters East of Hot Springs National Park, Arkansas, 2006-09: U.S. Geological Survey 2009–5263, 48 p., accessed November 28, 2017, at https://pubs.usgs.gov/sir/2009/5263/. U.S. Geological Survey, 2015, USGS NED 1 arc-second n35w094 1 x 1 degree ArcGrid 2015, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html. U.S. Geological Survey, 2016, USGS NED 1 arc-second n35w093 1 x 1 degree ArcGrid 2016, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html.

This is a zip file containing the example data for nSTAT matlab toolbox (http://doi.org/10.1016/j.jneumeth.2012.08.009) . The data directory should be un-zipped into the main nSTAT directory. Updated 7-2-2017

Discover the booming steel toolbox market! This in-depth analysis reveals a $1720 million market (2025) growing at a 3.6% CAGR, driven by construction, automotive, and DIY trends. Explore regional breakdowns, key players, and future forecasts for 2025-2033.

Report summarising the contents of the seismic analysis toolbox produced during the DiSECCS project. The toolbox comprises an online library of seismic software developed and utilised in the project, and presented in a form that other practitioners can utilise and tailor to their own specific needs. The toolbox include software for the measurement and characterisation of thin CO2 layers by spectral and attenuation analysis, fracture characterisation via wavelet coda analysis, novel rock physics algorithms and a summary of new laboratory analyses.

Discover the booming global tool boxes market! This comprehensive analysis reveals a $5 billion market in 2025, projecting 6% CAGR growth to 2033. Explore key trends, regional insights, leading companies, and future projections for plastic, aluminum, and other tool box types across commercial and household applications.