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 data can be used to test the website

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

It 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_enteric_ganglia_v2.bioimage.io.model.zip

For the all models, files included are:

Model for segmenting cells or ganglia in 2D FIJI. StarDist or 2D UNet.

Training and Test datasets used for training.

Google Colab notebooks used for training and quality assurance (ZeroCost DL4Mic notebooks).

Quality assurance reports generated from above notebooks.

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.

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: https://github.com/pr4deepr/GutAnalysisToolbox/wiki

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.

tootsea (toolbox for time series exploration and analysis) is a matlab solftware, developped at lops (laboratoire d'océanographie physique et spatiale), ifremer. this tool is dedicated to analysing datasets from moored oceanographic instruments (currentmeter, ctd, thermistance, ...). tootsea allows the user to explore the data and metadata from various instruments file, to analyse them with multiple plots and stats available, to do some processing/corrections and qualify (automatically and manually) the data, and finally to export the work in a netcdf file.

This software archive is superseded by Hydrologic Toolbox v1.1.0, available at the following citation: Barlow, P.M., McHugh, A.R., Kiang, J.E., Zhai, T., Hummel, P., Duda, P., and Hinz, S., 2024, U.S. Geological Survey Hydrologic Toolbox version 1.1.0 software archive: U.S. Geological Survey software release, https://doi.org/10.5066/P13VDNAK. The U.S. Geological Survey Hydrologic Toolbox is a Windows-based desktop software program that provides a graphical and mapping interface for analysis of hydrologic time-series data with a set of widely used and standardized computational methods. The software combines the analytical and statistical functionality provided in the U.S. Geological Survey (USGS) Groundwater (Barlow and others, 2014) and Surface-Water (Kiang and others, 2018) Toolboxes and provides several enhancements to these programs. The main analysis methods are the computation of hydrologic-frequency statistics such as the 7-day minimum flow that occurs on average only once every 10 years (7Q10); the computation of design flows, including biologically based flows; the computation of flow-duration curves and duration hydrographs; eight computer-programming methods for hydrograph separation of a streamflow time series, including the BFI (Base-flow index), HYSEP, PART, and SWAT Bflow methods and Eckhardt’s two-parameter digital-filtering method; and the RORA recession-curve displacement method and associated RECESS program to estimate groundwater-recharge values from streamflow data. Several of the statistical methods provided in the Hydrologic Toolbox are used primarily for computation of critical low-flow statistics. The Hydrologic Toolbox also facilitates retrieval of streamflow and groundwater-level time-series data from the USGS National Water Information System and outputs text reports that describe their analyses. The Hydrologic Toolbox supersedes and replaces the Groundwater and Surface-Water Toolboxes. The Hydrologic Toolbox was developed by use of the DotSpatial geographic information system (GIS) programming library, which is part of the MapWindow project (MapWindow, 2021). DotSpatial is a nonproprietary, open-source program written for the .NET framework that includes a spatial data viewer and GIS capabilities. This software archive is designed to document different versions of the Hydrologic Toolbox. Details about version changes are provided in the “Release.txt” file with this software release. Instructions for installing the software are provided in files “Installation_instructions.pdf” and “Installation_instructions.txt.” The “Installation_instructions.pdf” file includes screen captures of some of the installation steps, whereas the “Installation_instructions.txt” file does not. Each version of the Hydrologic Toolbox is provided in a separate .zip file. Citations: Barlow, P.M., Cunningham, W.L., Zhai, T., and Gray, M., 2014, U.S. Geological Survey groundwater toolbox, a graphical and mapping interface for analysis of hydrologic data (version 1.0)—User guide for estimation of base flow, runoff, and groundwater recharge from streamflow data: U.S. Geological Survey Techniques and Methods 3–B10, 27 p., https://doi.org/10.3133/tm3B10. Kiang, J.E., Flynn, K.M., Zhai, T., Hummel, P., and Granato, G., 2018, SWToolbox: A surface-water toolbox for statistical analysis of streamflow time series: U.S. Geological Survey Techniques and Methods, book 4, chap. A–11, 33 p., https://doi.org/10.3133/tm4A11. MapWindow, 2021, MapWindow software, accessed January 9, 2021, at https://www.mapwindow.org/#home.

The global plastic toolbox market is anticipated to attain a valuation of USD XXX million by 2033, expanding at a robust CAGR of XX% during the forecast period from 2025 to 2033. Factors such as increasing demand for portable storage solutions in commercial and household applications along with the rising popularity of DIY (Do-It-Yourself) projects drive market growth. Additionally, the availability of lightweight and durable plastic toolboxes caters to the evolving needs of professionals and hobbyists alike. Major industry players include Stanley Black & Decker, Snap-on Tools, Matco Tools, GreatStar Industrial, and Homak Manufacturing. These companies actively engage in strategic initiatives such as product innovation and geographic expansion to enhance their market presence. Regional analysis indicates that North America and Europe are prominent markets, driven by established construction and automotive industries. Asia Pacific is projected to witness substantial growth due to increasing industrialization and urbanization in countries like China and India. The report offers comprehensive market data, including detailed segmentation by application, type, and region, enabling stakeholders to identify growth opportunities and make informed business decisions.

The global toolbox market is experiencing robust growth, driven by increasing industrialization, expanding construction activities, and a rising demand for efficient storage and organization solutions across various sectors. The market, valued at approximately $5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of around 6% from 2025 to 2033, reaching an estimated value exceeding $8 billion by 2033. This growth is fueled by several key trends, including the adoption of lightweight yet durable materials like advanced plastics and aluminum alloys in toolbox manufacturing, a shift towards modular and customizable toolbox designs catering to specific professional and DIY needs, and the integration of smart features like integrated lighting and digital inventory management systems in higher-end models. The commercial sector, encompassing industries like automotive, aerospace, and manufacturing, currently dominates the market share, owing to the extensive use of toolboxes in maintenance, repair, and operations. However, the household segment is witnessing significant growth, driven by rising DIY and home improvement projects among consumers. Plastic toolboxes remain the most prevalent type, owing to their cost-effectiveness and versatility, although aluminum and other specialized material toolboxes are gaining traction due to their enhanced durability and protection capabilities. Geographic growth is particularly strong in Asia Pacific, fueled by rapid urbanization and industrial expansion in countries like China and India. While factors such as raw material price fluctuations and potential economic downturns pose challenges, the overall market outlook remains positive, driven by sustained demand across diverse applications and regions. The competitive landscape is characterized by a mix of established global players and regional manufacturers. Key players like Apex Tool Group, STAHLWILLE, and Peli Products are leveraging their brand reputation and technological expertise to maintain their market positions. However, several regional players are also emerging, particularly in Asia, offering competitive pricing and localized product adaptations. Successful strategies for market players include focusing on innovation in design and materials, expanding product lines to cater to diverse segments, and exploring strategic partnerships to enhance distribution networks. This includes developing sustainable and environmentally friendly toolbox options, leveraging e-commerce channels to reach a wider customer base, and focusing on personalized customer experiences to improve brand loyalty. The ongoing evolution of the construction and manufacturing sectors will significantly influence the future trajectory of the toolbox market, prompting ongoing innovation and adaptation.

Flutter is a potentially explosive phenomenon that results from the simultaneous interaction of aerodynamic, structural, and inertial forces. The nature of flutter mandates that flight testing be cautious and conservative.

The global plastic toolbox market is projected to witness a significant growth over the forecast period. The market size, valued at millions in 2023, is expected to grow at a CAGR of XX% over 2023-2033. This growth can be attributed to factors such as increasing demand for durable and portable storage solutions in various industries, including construction, automotive, and manufacturing. Key trends driving the market growth include rising awareness about safety and organization, growth in the DIY (do-it-yourself) and home improvement sector, and technological advancements leading to innovative designs and features. However, factors such as environmental concerns and the availability of alternative storage solutions may restrain the market growth to some extent. Major players in the market include Stanley Black & Decker, Snap-on Tools, and Matco Tools, among others. Regional analysis indicates that North America and Europe hold significant market shares due to the presence of established players and high demand for plastic toolboxes in these regions. This comprehensive report offers a deep dive into the global plastic tool box market, providing valuable insights into market dynamics, trends, and growth opportunities.

AMAT is a Matlab-based, open source interface for searching fMRI coordinates together with a simple database of coordinates. The AMAT database is deliberately designed to be minimal. Effectively, the database reproduces the tables of XYZ coordinates which are common in fMRI papers. Each coordinate is associated with the anatomical label given by the authors of the original paper, a ag for Talaraich or MNI coordinates, a very brief description of the description of the functional task or contrast which activated this coordinate, and the PubMed ID of the published paper. The latter links directly to the abstract in PubMed and allows the user to retrieve the original publication. Anatomical information labeling a coordinate as a particular Brodmann area or functional region is optional, and is normally only included if the authors of the original paper included these labels. No other information is stored.

case data of 'Optimal Operation for Coupled Power-Transportation Systems with the Integration of Solar Roads and Electric Vehicles'

Bioinformatics analysis has become an integral part of research in biology. However, installation and use of scientific software can be difficult and often requires technical expert knowledge. Reasons are dependencies on certain operating systems or required third-party libraries, missing graphical user interfaces and documentation, or nonstandard input and output formats. In order to make bioinformatics software easily accessible to researchers, we here present a web-based platform. The Center for Bioinformatics Tuebingen (ZBIT) Bioinformatics Toolbox provides web-based access to a collection of bioinformatics tools developed for systems biology, protein sequence annotation, and expression data analysis. Currently, the collection encompasses software for conversion and processing of community standards SBML and BioPAX, transcription factor analysis, and analysis of microarray data from transcriptomics and proteomics studies. All tools are hosted on a customized Galaxy instance and run on a dedicated computation cluster. Users only need a web browser and an active internet connection in order to benefit from this service. The web platform is designed to facilitate the usage of the bioinformatics tools for researchers without advanced technical background. Users can combine tools for complex analyses or use predefined, customizable workflows. All results are stored persistently and reproducible. For each tool, we provide documentation, tutorials, and example data to maximize usability. The ZBIT Bioinformatics Toolbox is freely available at https://webservices.cs.uni-tuebingen.de/.

Locations of network hub nodes with betweenness centrality values over 1 or 2SD in the three groups.

Research data from the thesis "Computational Analysis of Audio Recordings and Music Scores for the Description and Discovery of Ottoman-Turkish Makam Music".

OXSA data and GUI classes.

The global plastic toolbox market is experiencing robust growth, driven by increasing demand from both commercial and household sectors. The market's expansion is fueled by several key factors. Firstly, the lightweight and durable nature of plastic makes it a cost-effective and practical material for tool storage compared to traditional metal alternatives. This advantage is particularly appealing to individual DIY enthusiasts and small businesses. Secondly, advancements in plastic manufacturing techniques have led to the development of high-impact, chemical-resistant toolboxes, catering to diverse needs across various industries. The rising popularity of DIY projects, home renovations, and professional trades, all contribute to the market's upward trajectory. Segmentation within the market shows strong growth in handheld toolboxes, favored for their portability, and box-type toolboxes, preferred for larger tool organization. While the market faces some restraints, such as concerns about plastic's environmental impact and competition from other materials, innovative designs incorporating recycled plastics and sustainable manufacturing practices are emerging to address these challenges. Geographic analysis indicates strong growth in regions like North America and Asia Pacific, driven by robust construction activities and a growing middle class with increased disposable income. We project a continued, albeit perhaps slightly moderated, growth rate in the coming years. The established players, alongside emerging regional brands, are continually innovating with smart storage solutions, enhanced durability, and modular designs to maintain their market share and attract new customers. The competitive landscape is characterized by a mix of large multinational corporations and smaller, regional manufacturers. Major players leverage their brand recognition, extensive distribution networks, and R&D capabilities to maintain a competitive edge. Smaller players, however, often gain traction through niche product specialization, cost-effectiveness, and agility in adapting to local market demands. The market is witnessing a surge in the introduction of specialized toolboxes catering to specific trades and applications, reflecting the growing demand for tailored solutions. Factors such as e-commerce expansion and effective marketing strategies also contribute to the market's overall growth. Future growth will likely be driven by a focus on sustainable manufacturing, smart storage solutions, and continued expansion into emerging markets. We anticipate steady growth, with the market segmenting further based on evolving consumer needs and technological advancements in materials and design.

Global Car Tool Box Market Report 2022 comes with the extensive industry analysis of development components, patterns, flows and sizes. The report also calculates present and past market values to forecast potential market management through the forecast period between 2022-2028. The report may be the best of what is a geographic area which expands the competitive landscape and industry perspective of the market.

The research focus in the field of remotely sensed imagery has shifted from collection and warehousing of data ' tasks for which a mature technology already exists, to auto-extraction of information and knowledge discovery from this valuable resource ' tasks for which technology is still under active development. In particular, intelligent algorithms for analysis of very large rasters, either high resolutions images or medium resolution global datasets, that are becoming more and more prevalent, are lacking. We propose to develop the Geospatial Pattern Analysis Toolbox (GeoPAT) a computationally efficient, scalable, and robust suite of algorithms that supports GIS processes such as segmentation, unsupervised/supervised classification of segments, query and retrieval, and change detection in giga-pixel and larger rasters. At the core of the technology that underpins GeoPAT is the novel concept of pattern-based image analysis. Unlike pixel-based or object-based (OBIA) image analysis, GeoPAT partitions an image into overlapping square scenes containing 1,000'100,000 pixels and performs further processing on those scenes using pattern signature and pattern similarity ' concepts first developed in the field of Content-Based Image Retrieval. This fusion of methods from two different areas of research results in orders of magnitude performance boost in application to very large images without sacrificing quality of the output.

GeoPAT v.1.0 already exists as the GRASS GIS add-on that has been developed and tested on medium resolution continental-scale datasets including the National Land Cover Dataset and the National Elevation Dataset. Proposed project will develop GeoPAT v.2.0 ' much improved and extended version of the present software. We estimate an overall entry TRL for GeoPAT v.1.0 to be 3-4 and the planned exit TRL for GeoPAT v.2.0 to be 5-6. Moreover, several new important functionalities will be added. Proposed improvements includes conversion of GeoPAT from being the GRASS add-on to stand-alone software capable of being integrated with other systems, full implementation of web-based interface, writing new modules to extent it applicability to high resolution images/rasters and medium resolution climate data, extension to spatio-temporal domain, enabling hierarchical search and segmentation, development of improved pattern signature and their similarity measures, parallelization of the code, implementation of divide and conquer strategy to speed up selected modules.

The proposed technology will contribute to a wide range of Earth Science investigations and missions through enabling extraction of information from diverse types of very large datasets. Analyzing the entire dataset without the need of sub-dividing it due to software limitations offers important advantage of uniformity and consistency. We propose to demonstrate the utilization of GeoPAT technology on two specific applications. The first application is a web-based, real time, visual search engine for local physiography utilizing query-by-example on the entire, global-extent SRTM 90 m resolution dataset. User selects region where process of interest is known to occur and the search engine identifies other areas around the world with similar physiographic character and thus potential for similar process. The second application is monitoring urban areas in their entirety at the high resolution including mapping of impervious surface and identifying settlements for improved disaggregation of census data.

This dataset presents the output of the application of the Jarkus Analysis Toolbox (JAT) to the Jarkus dataset. The Jarkus dataset is one of the most elaborate coastal datasets in the world and consists of coastal profiles of the entire Dutch coast, spaced about 250-500 m apart, which have been measured yearly since 1965. Different available definitions for extracting characteristic parameters from coastal profiles were collected and implemented in the JAT. The characteristic parameters allow stakeholders (e.g. scientists, engineers and coastal managers) to study the spatial and temporal variations in parameters like dune height, dune volume, dune foot, beach width and closure depth. This dataset includes a netcdf file (on the opendap server, see data link) that contains all characteristic parameters through space and time, and a distribution plot that shows the overview of each characteristic parameters. The Jarkus Analysis Toolbox and all scripts that were used to extract the characteristic parameters and create the distribution plots are available through Github (https://github.com/christavanijzendoorn/JAT). Example 5 that is included in the JAT provides a python script that shows how to load and work with the netcdf file.Documentation: https://jarkus-analysis-toolbox.readthedocs.io/.