This dataset is the databased part of algae from the botanical collections of the Musée des Confluences (Lyon). It comprises 73 specimens belonging to E. David, Lelièvre de la Morinière & Prouhet and P. Koubbi collections. The specimens have not been revised but the nomenclature has been updated.

Dataset for lakebed plant species detection at Lake George

The Algae Products Market Report is Segmented by Source (Brown Algae, Red Algae, Green Algae, Blue-Green Algae), Product Type (Hydrocolloids, Algal Protein, Carotenoids, Lipids, and Other Product Types), Application (Personal Care and Cosmetics, Food and Beverage, and More), and Geography (North America, Europe, Asia-Pacific, South America, and Middle East and Africa). The Market Forecasts are Provided in Terms of Value (USD).

This database is supplementary material of the article "Update of the Brazilian floristic list of Algae and Cyanobacteria" published in the Journal Rodriguésia in 2015 (DOI: 10.1590 / 2175-7860201566408). Attention! In this database the genus Characium A.Braun is under Charophyceae. However, it should be considered as belonging to Chlorophyceae.

Algae Detection

## Overview

Algae Detection is a dataset for object detection tasks - it contains Algae SWfx annotations for 1,062 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

#Raw Data, Source, More Information :: https://www.kaggle.com/datasets/huseyingunduz/diatom-dataset?select=images Citation @article{gunduz2022, title={Segmentation of diatoms using edge detection and deep learning}, volume={30}, DOI={10.55730/1300-0632.3938}, number={6}, journal={Turkish Journal of Electrical Engineering & Computer Sciences}, author={Gunduz, Huseyin and Solak, Cuneyt Nadir and Gunal, Serkan}, year={2022}, pages={ 2268–2285}} Diatoms are a group of algae found in oceans, freshwater, moist soils, and surfaces. They are one of the most common phytoplankton species found in nature. There are more than 200 genera of diatoms, as well as about 200,000 species. They produce approximately 20-25% of the oxygen on the planet.

Accurate detection, segmentation and classification of diatoms is very important, especially in terms of determining water quality and ecological change. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F25409507%2F0b140a77bdc1e8b3955453f9eb60a294%2F1049_10.jpg?generation=1748347264264824&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F25409507%2F5dfebe496555e0d44f88a323020b5c29%2F1435_11.jpg?generation=1748347286426827&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F25409507%2F52b9bac2244c46778d4e7a5680d5db9b%2F1057_8.jpg?generation=1748347333663011&alt=media" alt=""> Colorized Data Processing Techniques for Medical Imaging

Medical images like CT scans and X-rays are typically grayscale, making subtle anatomical or pathological differences harder to distinguish. The following image processing and enhancement techniques are used to colorize and improve visual interpretation for diagnostics, training, or AI preprocessing.

🔷 1. 3D_Rendering Renders medical image volumes into three-dimensional visualizations. Though often grayscale, color can be applied to different tissue types or densities to enhance spatial understanding. Useful in surgical planning or tumor visualization.

🔷 2. 3D_Volume_Rendering An advanced visualization technique that projects 3D image volumes with transparency and color blending, simulating how light passes through tissue. Color helps distinguish internal structures like organs, vessels, or tumors.

🔷 3. Adaptive Histogram Equalization (AHE) Enhances contrast locally within the image, especially in low-contrast regions. When colorized, different intensities are mapped to distinct hues, improving visibility of fine-grained details like soft tissues or lesions.

🔷 4. Alpha Blending A layering technique that combines multiple images (e.g., CT + annotation masks) with transparency. Colors represent different modalities or regions of interest, providing composite visual cues for diagnosis.

🔷 5. Basic Color Map Applies a standard color palette (like Jet or Viridis) to grayscale data. Different intensities are mapped to different colors, enhancing the visual discrimination of anatomical or pathological regions in the image.

🔷 6. Contrast Stretching Expands the grayscale range to improve brightness and contrast. When combined with color mapping, tissues with similar intensities become visually distinct, aiding in tasks like bone vs. soft tissue separation.

🔷 7. Edge Detection Extracts and overlays object boundaries (e.g., organ or lesion outlines) on the original scan. Edge maps are typically colorized (e.g., green or red) to highlight anatomical structures or abnormalities clearly.

🔷 8. Gamma Correction Adjusts image brightness non-linearly. Color can be used to highlight underexposed or overexposed regions, often revealing soft tissue structures otherwise hidden in raw grayscale CT/X-ray images.

🔷 9. Gaussian Blur Smooths image noise and details. When visualized with color overlays (e.g., before vs. after), it helps assess denoising effectiveness. It is also used in segmentation preprocessing to reduce edge artifacts.

🔷 10. Heatmap Visualization Encodes intensity or prediction confidence into a heatmap overlay (e.g., red for high activity). Common in AI-assisted diagnosis to localize tumors, fractures, or infections, layered over the original grayscale image.

🔷 11. Interactive Segmentation A semi-automated method to extract regions of interest with user input. Segmented areas are color-coded (e.g., tumor = red, background = blue) for immediate visual confirmation and further analysis.

🔷 12. LUT (Lookup Table) Color Map Maps grayscale values to custom color palettes using a lookup table. This enhances contrast and emphasizes certain intensity ranges (e.g., blood vessels vs. bone), improving interpretability for radiologists.

🔷 13. Random Color Palette Applies random but consistent colors to segmented regions or labels. Common in datasets with multiple classes (e.g., liver, spleen, kidneys), it helps in v...

This dataset is a collection of hyperspectral imagery profiles of algae, many associated with Harmful Algae Blooms (HABs). Data were collected using a microscope-based hyperspectral imaging system with the cooperation of the National Institute of Standards and Technology. Samples were collected from U.S. Geological Survey (USGS) water quality sampling efforts, to include water quality parameters and algal biomass. Data are shown in basic hyperspectral imagery form, normalized to 1.

The Culture Collection of Algae at The University of Texas at Austin (UTEX) is a successor to a collection of algal cultures begun in the 1920s by E.G. Pringsheim. Richard C. Starr studied with Pringsheim in 1953 at Cambridge, U.K, where he was provided nearly 400 strains of green algae to serve as the basis for the Indiana University Culture Collection of Algae (IUCC). This Collection of living algae was expanded, diversified, and then moved to its present site as UTEX in 1976. R. Starr served as Director of UTEX from its inception until 1998, after which Jerry Brand has served as Director. The IUCC was first funded by Indiana University and the Eli Lilly Corporation. Cultures of living algae were provided to requesters free of charge for pure research and educational purposes, at the discretion of the Director. NSF support was begun in approximately 1963, when the Collection became a public repository and a modest charge for cultures was implemented. The majority of funding for UTEX operational costs is obtained through the sales of products and services, but the Collection is also supported by the National Science Foundation and The University of Texas at Austin. UTEX houses approximately 3,000 distinct strains of living algae. Most strains are microscopic unicells, filaments, or colonial algae isolated from soil and freshwater. However, UTEX also maintains marine and macrophytic algal strains. The majority of strains are obligate photoautotrophs, but the Collection also houses a number of facultative and obligate heterotrophs. The green algae are disproportionately represented. However, all major taxa of algae are represented in the Collection. Over 85% of UTEX strains are unique to this collection. UTEX currently maintains 470 type species, approximately half of which are also maintained in one or more other publicly-accessible collections. All UTEX strains are unialgal. Approximately 20% are currently maintained axenically on agar slants. Others are cultured in liquid media, typically carrying small amounts of bacteria. All strains of algae maintained at UTEX are from natural sources. UTEX does not currently include patented or proprietary stock. This allows UTEX to send cultures to users with a minimum of legal restrictions. The UTEX web site (URL www.utex.org), updated weekly, includes a complete listing of available UTEX stock cultures, organized alphabetically by genus and numerically by accession number.

Freshwater harmful algal bloom (HAB) data from the Freshwater Harmful Algal Bloom (FHAB) data system. The FHAB data system is the California State Water Resources Control Board's data system for data and information voluntarily reported to the agency. Bloom reports are voluntary reports submitted by the public or organization to identify a POTENTIAL HAB for evaluation. Bloom Reports may or may not include a report that is confirmed to be a HAB, regardless, all bloom reports are published. Due to the voluntary basis of information and data included in the database, data and information may include: waterbody name and location, potential algal bloom location and observed characteristics, observed field observations and/or analytical sampling results, waterbody and/or land management, general information, recommended advisory status (if any), and updates regarding bloom status. Refer to Data Dictionary and Data Disclaimer for additional information about this dataset. Please visit the Water Boards FHABS web site for more information and data visualizations https://mywaterquality.ca.gov/habs/index.html.

The ANSP Algae Image Database contains light micrograph images of diatom taxa from rivers throughout the USA. Many taxa are represented. There are multiple images of several to help represent within-taxon variability. The images were made primarily by ANSP Patrick Center Phycology Section staff as part of their routine analysis of algal samples. Purposes of the database are to: provide a set of reference images for ANSP algal analysts and collaborators to help them maintain consistency in taxonomic identifications help document names of taxa used in papers and reports by providing easy access to representative images make images available to other phycologists as a resource to help with identifications The set of images available here is not meant to be comprehensive or to be taxonomically definitive, but only to show representative specimens that can be used to supplement image resources in the published taxonomic literature. Read the documentation to learn more about the database and how to search for and view images. More images will be added on a regular basis. More information on this dataset can be found in the Freshwater Metadatabase - BFE_71 (http://www.freshwatermetadata.eu/metadb/bf_mdb_view.php?entryID=BFE_71).

The dataset folder contains essential files required for the execution and testing of the iCulture pipeline. This folder includes:

1. Reference Databases:

• Pfam-A.hmm and associated index files (*.h3f, *.h3i, *.h3m, *.h3p):

• These files constitute the Pfam database used for HMMER annotation of protein sequences.

• The database enables the identification of protein domains and families within query sequences.

2. Input FASTA Files:

• brown-algae_dataset.fa:

• This FASTA file is generated from the brown algae dataset downloaded from The Phaeoexplorer Project.

• It contains protein sequences from brown algae, formatted for compatibility with the pipeline.

• This file is used as an input for clustering and annotation during the pipeline execution.

3. Sample Input Files:

• These files are provided to facilitate testing and ensure reproducibility of the pipeline results.

Purpose:

This directory serves as a centralized location for storing datasets and databases necessary for running the iCulture pipeline, particularly in reproducibility-focused workflows.

Note: If you wish to use the pipeline with custom datasets, replace the example files in this folder with your own, following the required format.

This database contains information on the algae specimens registered so far in the herbarium of the Swedish Museum of Natural History.

The UCSB botanical collections, housed at the Cheadle Center for Biodiversity and Ecological Restoration, include over 250,000 taxa of terrestrial and marine species. The vascular plant herbarium includes approximately 100,000 vascular plant specimens, lichens curated by Dr. Shirley Tucker, and the C.H. Muller Oak collection. The algal herbarium houses approximately 8000 specimens dating from the 1880s.

FRC 2025 Algae And Coral

## Overview

FRC 2025  Algae And Coral is a dataset for object detection tasks - it contains Algae And Coral annotations for 892 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Oregon State University Herbarium (OSC) houses approximately 700,000 vascular plant, bryophyte, algal, and fungal specimens. The collections are worldwide in scope, with a focus on the state of Oregon and the Pacific Northwest of the United States and Canada.Our algae collection is approximately 18,000 specimens. The majority were collected in Oregon by Harry K. Phinney and Grace S. Phinney, and their students, between 1947-1980. A collection of ~1800 marine algae specimens from Portland State University (HPSU) was transferred to OSC in 2015. That collection was built primarily by Ed Lippert at HPSU, and includes material from his students, Ellen Benedict, and others, dating ca. 1956-1990. It is mostly from the PNW, with some from CA, east coast, and Oceania. David Bilderback donated 1600 of his specimens in 2018. There are a number of Bob Scagel's exsiccati from UBC (1960s).

This statistic shows the compound annual growth rate (CAGR) of the algae market worldwide between 2018 and 2025, by segment. The green algae segment is expected to grow by a CAGR of *** percent between 2018 and 2025.

Algae Market by Type (Macroalgae/Seaweed {Red, Brown}, Microalgae {Spirulina, Chlorella, D. Salina}), Distribution Channel (B2B, B2C), Form (Dry, Liquid), Application (Nutraceuticals, Food & Beverages, Animal Feed, Cosmetics) - Global Forecast to 2032

Water quality and ecosystem health data are collected in the nearshore zone of the Great Lakes to address the problem of nuisance benthic algae. Monitoring data include physical and chemical water quality data as well as biological data, primarily from Cladophora and dreissenid mussels on the lakebed. Monitoring is conducted (i) to improve understanding of the factors impacting nearshore water quality, algae growth, and ecosystem health; (ii) to develop ecosystem health indicators for the nearshore; (iii) to provide validation and calibration data for modelling; (iv) to support the development of a binational nearshore assessment and management framework; and, (v) to measure the success of ongoing and future phosphorus reduction targets to support a healthy ecosystem.

Blue-green Algae Sensors Market Outlook

The global Blue-green Algae Sensors market size was estimated at USD 150 million in 2023 and is projected to reach USD 350 million by 2032, growing at a compound annual growth rate (CAGR) of 9.5% from 2024 to 2032. This impressive growth is driven by the increasing need for monitoring water quality and the rising awareness about the detrimental impacts of blue-green algae, also known as cyanobacteria, on aquatic ecosystems and human health.

One of the primary growth factors for the Blue-green Algae Sensors market is the surging demand for water quality monitoring systems. With the global population on the rise and the subsequent increase in water consumption, there is a heightened focus on maintaining and ensuring the safety of water resources. Blue-green algae pose significant risks, including the production of harmful toxins that can lead to severe health issues and ecological imbalances. Consequently, the increasing initiatives by governments and environmental agencies to monitor and manage water quality are bolstering the demand for advanced sensor technologies capable of detecting blue-green algae.

Another pivotal factor contributing to the market's growth is the rapid advancements in sensor technology. The development of more accurate, reliable, and cost-effective sensors has made it feasible for various industries, including aquaculture and water treatment plants, to adopt these technologies. The integration of IoT (Internet of Things) and AI (Artificial Intelligence) with blue-green algae sensors has further augmented their functionality, offering real-time monitoring and predictive analytics, thereby enabling proactive measures to mitigate the adverse effects of algal blooms.

Moreover, the increasing cases of algal blooms, driven by climate change and nutrient pollution, have necessitated the adoption of blue-green algae sensors. Factors such as rising temperatures, increased carbon dioxide levels, and nutrient runoff from agricultural activities contribute to the proliferation of blue-green algae in water bodies. These blooms not only affect water quality but also have significant economic repercussions, impacting fisheries, tourism, and water supply. Consequently, there is an urgent need for effective monitoring solutions, propelling the demand for blue-green algae sensors across various sectors.

From a regional perspective, North America holds a significant share in the Blue-green Algae Sensors market, primarily attributed to the stringent regulations and policies aimed at ensuring water quality and the presence of key market players in the region. Europe is also witnessing substantial growth due to increasing environmental concerns and proactive measures by regulatory bodies. Meanwhile, the Asia Pacific region is expected to exhibit the highest growth rate, driven by rapid industrialization, urbanization, and the growing awareness about water pollution issues. These regional dynamics are shaping the global market landscape, offering lucrative opportunities for industry players.

Type Analysis

Optical sensors represent a significant segment within the Blue-green Algae Sensors market. These sensors are favored for their accuracy and reliability in detecting specific wavelengths of light associated with blue-green algae. Optical sensors operate by measuring the fluorescence or absorbance characteristics of algae, providing real-time data on their presence and concentration in water bodies. The advancements in optical sensor technology, such as increased sensitivity and reduced interference from other substances, have enhanced their efficacy, making them a preferred choice for continuous monitoring applications in diverse settings, including lakes, rivers, and reservoirs.

Electrochemical sensors, another vital segment, are widely used for their ability to detect various parameters related to blue-green algae, such as dissolved oxygen, pH levels, and specific toxins. These sensors function through electrochemical reactions that occur at the sensor’s surface, providing valuable insights into the algal activity and water quality. The integration of nanotechnology in electrochemical sensors has significantly improved their performance, enabling the detection of low concentrations of algae and toxins with high accuracy. This segment is expected to witness steady growth due to ongoing research and developments aimed at enhancing sensor precision and durability.

Biosensors, which utilize biological elements like enzymes or antibodies to detect blue-green algae, represent a pr