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782 results for “algae”
SBC LTER: Santa Cruz Island: Cover of Algae, Invertebrates and Benthic Substrate
These data describe the cover of benthic substrate as determined by a random point contact method. The presence of algae and invertebrate species as well as abiotic substrates are recorded at 80 randomly spaced points along 40 m x 2 m transects. Only one substrate type is recorded at a given point. Percent cover of a given substrate on a transect can be estimated from RPC observations as the fraction of total points at which that substrate was present x 100. These data are part of a long term investigation of temporal patterns in reef community composition. The sampling locations in this dataset include eleven sites along the north shore of Santa Cruz Island. Data collection began in 1982 and this dataset is updated annually.
SBC LTER: Nutrient concentrations and algae cover in the Ventura River catchment, California, 2008
Results from these data were reported in: Klose, K., Cooper, S. D., Leydecker, A. D. and Kreitler, J. 2012. Relationships among catchment land use and concentrations of nutrients, algae, and dissolved oxygen in a southern California river. Freshwater Science, 2012, 31(3):908-927 doi: 10.1899/11-155.1 Data not reported here: Chlorophyll-a, physicochemical and land use parameters (e.g., land-use type, water depth, substratum size, % open canopy, and water velocity) were used in the paper's analysis and so were also collected, but are not reported here. Nutrient diffusing substrata (NDS) were deployed at 12 sites to assess the nutrient(s) limiting algal growth; these data are also not reported here. Macroalgal cover and stream nutrients are reported during spring and summer 2008 at 15 stream and estuarine sites in the Ventura River catchment in southern California, USA. Data were collected within a mosaic of undeveloped, agricultural, and urban areas to examine relationships among land use, nutrients, algae, and dissolved oxygen (see paper, linked below). This dataset reports major dissolved nutrients (phosphate, nitrate, ammonium) and total dissolved nitrogen and phosphorus, from May to September 2008, and percent cover of macroalgae (benthic and floating) at the same sites at the beginning and end of this period.
Effects of DOM on benthic algae in northern lakes
<p><em>Differences in dissolved organic matter among lakes can impact the ecology of benthic primary producers by altering the availability of nutrients (positive relationship with DOM) and light (negative relationship with DOM). While these effects and their interaction has been explored for pelagic algae through field and modeling studies, the effects of DOM on production by benthic algae is less well understood. This dataset represents a field-based experiment to investigate the pattern in benthic algal growth and nutrient limitation along a regional DOM gradient among lakes in Boreal and Arctic Sweden. </em></p>
Latent infection of an active giant endogenous virus in a unicellular green alga
<p>Additional data for Latent infection of an active giant endogenous virus in a unicellular green alga.</p>
Dataset used in snow algae model
<p>This is a data set for the numerical simulation using the snow algae model (Onuma et al., 2018; 2020).<br> The content is as below.</p> <p>- data: algal cell concentration observed on the surface snow worldwide (CSV files). model input and output data (CSV files). output data simulated with Bio-MATSIRO (netCDF files).</p> <p>- python: programs for the visualization (python scripts)</p> <p>- figure: png files created by the python scripts</p> <p>The codes of the snow algae model can be downloaded below.<br> https://github.com/YukihikoOnuma/SnowAlgaeModel</p>
Data set used in glacier algae and filamentous cyanobacteria models
<p>This is a data set for using the glacier algae and filamentous cyanobacteria models (Onuma et al., 2022). The content is as below.</p> <p>- data: observed data (bio-volume, cell count, mineral weight, EC, pH and meteorological conditions) on the bare ice surface in Qaanaaq Ice Cap (CSV files). And, model input and output data (CSV files). About the detailed information on each file, please see the readme files.</p> <p>- python: programs for the visualization (python scripts)</p> <p>- figure: png files created by the python scripts</p> <p>The codes of the glacier algae model can be downloaded below.<br> https://github.com/YukihikoOnuma/SnowAlgaeModel<br> <br> The article regarding the models is as below.<br> https://doi.org/10.1017/jog.2022.76</p>
Supplementary data to "Changing microbial activities during low salinity acclimation in the brown alga Ectocarpus subulatus"
<p>This data set contains supplementary data related to the paper: “Insights into the potential for mutualistic and harmful host–microbe interactions affecting brown alga freshwater acclimation”: https://onlinelibrary.wiley.com/doi/10.1111/mec.16766</p> <p>Metagenome.zip:<br>This archive contains the reconstructed genomes of the different bacterial bins. The ".gbk" file was used for the reconstruction of metabolic networks. The ".fsa" and ".gff" files were used for "read mapping".</p> <p>Metabolic_networks.zip:<br>This archive contains all bacterial networks in the "padmet" format (see Aite et al. 2018). Furthermore, there is one file containing all gene-reaction associations (for all bins).</p> <p>Expression_data.zip:<br>This file contains algal gene expression data, bacterial gene expression data (number of reads mapping to each feature in each sample), and, lastly, the summarized bacterial expression per metabolic reaction. </p>
Harmfull algae bloom monitoring program dataset; ERDDAP, ERA5 and ONI datasets; and R script for multicriteria analisys in Santa Catarina coastal zone, Brazil.
<p>Project Harmful Algae Bloom (HAB) Monitoring Network in Santa Catarina, Brazil - Database and R script with data analysis. This project was funded by the Foundation for Research Support of the State of Santa Catarina – FAPESC and generated a database combining a HAB monitoring dataset with oceanographic (from ERDDAP) and climatic (from ERA5 and ONI) data which was submitted to multicriteria analysis using R. The HAB monitoring dataset was obtained from Cidasc/SC State Government (http://www.cidasc.sc.gov.br/defesasanitariaanimal/monitoramento-de-algas-nocivas/) and contains results of phytoplankton counts in water samples and toxin levels in shellfish samples obtained from 39 points located in shellfish farms distributed along the SC coastline. Oceanographic data were obtained from the ERDDAP/NOAA website (https://coastwatch.pfeg.noaa.gov/erddap/index.html), including the variables mean chlorophyll concentration (mg.m-3) and mean sea surface temperature (ºC); Climate data were obtained from Copernicus/ERA5 (https://cds.climate.copernicus.eu/) including the variables mean air temperature (ºC), mean pressure (Pasc.), mean cloud cover (%), mean precipitation (kg.m-2), radiation (Einsteins.m-2.day-1), mean U wind (m.s-1), and mean V wind (m.s-1).; Oceanic Niño Index (ONI) data were obtained from the NOAA website (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php); The R script involves a pre-processing routine aimed at summarizing and integrating all datasets and the subsequent data analyses carried out to evidence temporal patterns related to different type of algal blooms. Detailed methods will be provided in a scientific article.</p>
Optimising multispectral active fluorescence to distinguish the photosynthetic variability of cyanobacteria and algae
<p>Dataset underlying the following paper:</p> <p>Courtecuisse, E.; Marchetti, E.; Oxborough, K.; Hunter, P.D.; Spyrakos, E.; Tilstone, G.H.; Simis, S.G.H. Optimising Multispectral <br> Active Fluorescence to Distinguish the Photosynthetic Variability of Cyanobacteria and Algae. Sensors 2023, 23</p> <p>This study assesses the ability of a new active fluorometer, the LabSTAF, to diagnostically assess the physiology of freshwater cyanobacteria in a reservoir exhibiting annual blooms. Specifically, we analyse the correlation of relative cyanobacteria abundance with photosynthetic parameters derived from fluorescence light curves (FLCs) obtained using several combinations of excitation wavebands, photosystem II (PSII) excitation spectra and the emission ratio of 730 over 685 nm (Fo(730/685)) using obtained with excitation protocols with varying degrees of sensitivity to cyanobacteria and algae. FLCs captured obtained with blue excitation (B) and green–orange–red (GOR) excitation wavebands capture physiology parameters of algae and cyanobacteria, respectively. The green–orange (GO) protocol, expected to have the best diagnostic properties for cyanobacteria, did not guarantee PSII saturation. PSII excitation spectra showed distinct response from cyanobacteria and algae, depending on spectral optimisation of the light dose. Fo(730/685), obtained using a combination of GOR excitation wavebands, Fo(GOR, 730/685), showed a significant correlation with the relative abundance of cyanobacteria (linear regression, p-value < 0.01, adjusted R2 = 0.42). We recommend using, in parallel, Fo(GOR, 730/685), PSII excitation spectra (appropriately optimised for cyanobacteria versus algae), and physiological parameters derived from the FLCs obtained with GOR and B protocols to assess the physiology of cyanobacteria and to ultimately predict their growth. Higher intensity LEDs (G and O) should be considered to reach PSII saturation to further increase diagnostic sensitivity to the cyanobacteria component of the community.</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)</p>
Dataset from University of Idaho 2004, master's thesis [Littoral ecology of epilithic algae in the Rocky Reach Pool, Mid-Columbia River (Washington State) - The effects of reservoir fluctuations.]
(Abstract from thesis) Epilithic algae, water column physical/chemical properties, and sediments were examined in the impounded Mid-Columbia River including the Rocky Reach Reservoir. Primary objectives included determination of the effects reservoir drawdown has on epilithic algae and potential nutrient enrichment via sediment. Epilithic algae were analyzed by pigment concentration, gravimetrically, and species composition. Reservoir elevation fluctuated at higher rates at tailrace sites (0.41-0.25 m/hr) compared to the forebay site (0.06-0.08 m/hr). Littoral exposure times were also greater at tailrace sites (mean of 8 hrs compared to 0 hr at the forebay site). Mean epilithic algae monochromatic chlorophyll a over all sampling periods at mainstem sites was 76.7 ± 4.8 mg/m2 (95 % C.I.). Epilithic algae monochromatic chlorophyll a in the zone of water fluctuation (0-1 m) was less at Wells tailrace (38.8 mg/m2) compared to Rocky Reach forebay (141.3 mg/m2) during summer, 2000 and 2001. Mean epilithic biofilm ash-free oven-dry weight over all sampling periods at mainstem sites was 25.6 ± 1.5 g/m2 (95 % C.I.). Mean autotrophic index across all mainstem locations was 439 indicating a large heterotrophic component within the epilithic biofilms. Epilithic algae communities were dominated by diatoms (50.2 %) and cyanobacteria (35.9 %), with some green algae (13.8 %). Canonical correlation analysis indicated that temperature, depth, site, and the water elevation change rate were important controllers of epilithic algae chlorophyll pigments. Mean textural characteristics of dredged sediment were 51.1 % sand, 43.2 % silt, and 5.7 % clay. Mean organic matter content in this sediment was 4.1 %. The mean seston sedimentation rate across mainstem locations was 11.3 g m-2 d-1 and organic matter comprised 14.7 % of the material collected from the water column.
UCSB SONGS Mitigation Monitoring: Reef Survey - Benthic Algae and Invertebrate Abundance
These data describe annual estimates of the density of benthic algae and macroinvertebrates at three subtidal reefs collected as part of the San Onofre Nuclear Generating Station (SONGS) Mitigation Monitoring Program. Data collection began in 2009 at an artificial reef (Wheeler North Reef in Orange County, CA) and two natural reference reefs (San Mateo Kelp in Orange County, CA and Barn Kelp in San Diego County, CA). In the summer of each year, divers identified and counted species of benthic algae and macroinvertebrates in quadrats of varying size that were uniformly distributed along fixed transects at each reef.
UCSB SONGS Mitigation Monitoring: Reef Survey - Benthic Algae, Invertebrate, and Substrate Cover
These data describe annual estimates of the percent cover of benthic macroalgae, sessile macroinvertebrates, and hard and soft substrates at three subtidal reefs collected as part of the San Onofre Nuclear Generating Station (SONGS) Mitigation Monitoring Program. Data collection began in 2009 at an artificial reef (Wheeler North Reef in Orange County, CA) and two natural reference reefs (San Mateo Kelp in Orange County, CA and Barn Kelp in San Diego County, CA). In the summer of each year, divers identified and recorded species of sessile algae and macroinvertebrates, and substrate types under twenty uniformly placed points within five 1 m2 quadrats that were uniformly distributed along semi-permanent transects at each reef.
Consensus QSAR models estimating acute aquatic toxicity for three trophic levels organisms: Algae, Daphnia and Fish
<p>We report new consensus models estimating acute toxicity for algae, daphnia and fish endpoints. We assembled a large collection of 3680 public unique compounds annotated by, at least, one experimental value for the given endpoint. Support Vector Machine models were internally and externally validated following the OECD principles. Reasonable predictive performances were achieved (RMSE<sub>ext</sub> = 0.56 – 0.78) which are in line with those of state-of-the-art models. The known structural alerts are compared with analysis of the atomic contributions to these models obtained using the ISIDA/<em>ColorAtom</em> utility. A benchmarking against existing tools has been carried out on a set of compounds considered more representative and relevant for the chemical space of the current chemical industry. Our model scored one of the best accuracies and data coverage.</p> <p>Nevertheless, industrial data performances were noticeably lower than those on public data, indicating that existing models fail to meet the industrial needs. Thus, final models were updated with the inclusion of new industrial compounds, extending applicability domain and relevance for application in an industrial context. Generate models and collected public data are made freely available.</p> <p><strong>Available fields in the SDF file:</strong></p> <ul> <li>SMILES_Canonical: canonical SMILES code</li> <li>DB: source of the data, "Litterature set" means that the data is originated from an article (see the companion article of the dataset for details).</li> <li>endpoint: organism for which endpoint is available</li> <li>CASRN: CAS registration number</li> <li>98-81-7</li> <li>pEC50 - DAPHNIA: Daphnia, mortality, which is evaluated by the immobilization of the invertebrate is recorded at 48 hours and expressed as the log median effective concentration (pEC50)</li> <li>mg/L - DAPHNIA: Daphnia, mortality, which is evaluated by the immobilization of the invertebrate is recorded at 48 hours and expressed as the median effective concentration (EC50)</li> <li>pLC50 - FISH: Fish, the log median lethal concentration measured at 96 hours is considered (pLC50)</li> <li>mg/L - FISH: Fish, the log median lethal concentration measured at 96 hours is considered (LC50)</li> <li>pEC50 - ALGA: Algae, the purpose is to determine the substance’s growth inhibition effect, expressed as the log median effective concentration (pEC50) measured at 72 hours</li> <li>mg/L - ALGA: Algae, the purpose is to determine the substance’s growth inhibition effect, expressed as the median effective concentration (EC50) measured at 72 hours</li> </ul>
Biological soil covers: data on lichen, bryophyte and algae coverage in soils gathered by SoilSkin citizen science program using eBryoSoil app for smartphones
<p>Biological soil covers (BSC) are small-sized topsoil communities composed mainly by lichens, bryophytes and algae that cover the terrestrial surface and play an essential role in maintaining the quality of the soil. However, little is known about their distribution, conservation, and ecosystem functions. The SoilSkin citizen science project aims to expand the scientific knowledge about the distribution of biological soil covers as an important step to evaluate the vulnerability of soil ecosystems of the Iberian Peninsula in the face of global change.</p> <p>The project has a dedicated free of charge app for smartphones (eBryoSoil, available at Google Play <a href="https://play.google.com/store/apps/details?id=com.omarfiz.ebryosoil&hl=ca&gl=US">https://play.google.com/store/apps/details?id=com.omarfiz.ebryosoil&hl=ca&gl=US</a>) that is designed to obtain information about the coverage of the BSC communities. To use this app, users must select a sampling location and capture the three soil pictures required to complete a transect. These photographs are taken at a 27 cm distance from the soil, in a straight line with 15 meters of distance between each picture. After the acquisition of each image, users can quantify the coverage percentage of biological soil covers and select the type of habitat where the transect took place. The transect is complete when all three pictures and their respective information are uploaded.</p> <p>The data presented here contains the records from SoilSkin participants, which mainly include a characterization of the cover patterns of biological soil covers, the type of habitat and the coordinates where each record was taken. The data set is composed by 279 unique records taken by 37 unique users from 28/11/2019 to 12/12/2020, across the Iberian Peninsula. These records specifically detail the percentage of cover occupied by three types of lichen growth forms (crustose, foliose and fruticose); liverworts; two types of moss growth forms (acrocarpous and pleurocarpous); algae; and soil. Moreover, each record also contains a description of the main type of habitat where the transect took place, that was selected from a list contained in the app with the following habitats:</p> <ul> <li>Dense forest - Habitat characterized by trees of more than 2 meters tall and canopy over 60%.</li> <li>Open forest – Habitat characterized by trees with more than 2 meters tall and a canopy below 60%.</li> <li>Shrubland – Habitat characterized by woody vegetation with less than 2 meters tall.</li> <li>Grassland – Habitat characterized by herbaceous plants.</li> <li>Agricultural land – Habitat characterized by temporary or woody crops.</li> <li>Coastal habitat – Habitat characterized by a landscape where land is in contact with the sea, creating a visibly different landscape from inner terrestrial one’s.</li> <li>Urban green spaces – Habitat characterized by a landscape in which man-made structures are present.</li> </ul> <p>The database was revised to correct any possible mistakes (e.g., miscalculation of total percentages; habitat missing in some registers; removal of invalid registers).</p> <p>The data file contains the following columns:</p> <ul> <li>Date: numerical variable indicating the “day”/”month”/”year” when the register was generated.</li> <li>User_ID: categorical variable with the identification number of the user who gathered the record.</li> <li>Transect: categorical variable with the identification of the number of the transect.</li> <li>Photo_number: numeric variable that takes values of 1, 2 or 3 and corresponds with the identification of the photographs within each transect.</li> <li>Photo_label: character string with the identification of the photograph from each record.</li> <li>Register_localization: categorical variable with the identification of the geographic area where the record was done.</li> <li>Latitude: integer, variable indicating the latitude of the sampling location in decimal degrees.</li> <li>Longitude: integer, variable indicating the longitude of the sampling location in decimal degrees.</li> <li>Accuracy: integer, variable indicating the accuracy of the coordinates given by the GPS.</li> <li>Habitat_type: categorical variable with the description of the main type of habitat of the sampling location.</li> <li>Lichen_Crustose: integer, variable indicating the percentage of crustose lichen cover quantified in the record.</li> <li>Lichen_Foliose: integer, variable indicating the percentage of foliose lichen cover quantified in the record.</li> <li>Lichen_Fruticose: integer, variable indicating the percentage of fruticose lichen cover quantified in the record.</li> <li>Total_lichen: integer, variable indicating the sum of all lichen coverage quantified in the record.</li> <li>Liverwort: integer, variable indicating the percentage of liverwort cover quantified in the record.</li> <li>Moss_Acrocarpous: integer, variable indicating the percentage of acrocarpous moss cover quantified in the record.</li> <li>Moss_Pleurocarpous: integer, variable indicating the percentage of pleurocarpous moss cover quantified in the record.</li> <li>Total_ moss: integer, variable indicating the sum of all moss coverage quantified in the record.</li> <li>Algae: integer, variable indicating the percentage of algae cover quantified in the record.</li> <li>Soil: integer, variable indicating the percentage of soil visible in the record.</li> </ul> <p> </p>
Fig. 11 in A new polyphysacean alga from the Miocene of Romania and its biomineralization
Fig. 11. Scheme of intracellular mineralization in polyphysaceans (gametophores are sketched in cross section). A. Gametangia and intergametangial space not mineralized. B. Gametangia unmineralized, intergametangial space mineralized. C. Gametangia mineralized, intergametangial space not mineralized. D. Gametangia and intergametangial space mineralized.
Algae-Bacteria Community Analysis for Drinking Water Taste and Odour Risk Management
<p>The datasets and accompanying R script included in this upload are provided to complement the manuscript titled <em>"Algae-Bacteria Community Analysis for Drinking Water Taste and Odour Risk Management."</em> These resources are intended to facilitate the replication and verification of the analyses presented in the paper.</p> <p> </p> <p> </p>
FIG. 2 in Molecular data and culture observations show that the microfilamentous marine alga Uronema marinum Womersley is a member of the genus Okellya Leliaert & Rueness (Cladophorales, Chlorophyta)
FIG. 2. — Maximum likelihood phylogenetic tree of selected Cladophorales Haeckel, showing the position of the species Okellya marina (Womersley) Wetherbee, comb. nov. as sister to Okellya curvata (Printz) Leliaert & Rueness in the Okellyaceae Leliaert & Rueness family. Numbers shown at nodes represent RAxML rapid bootstrap values. The scale is in estimated substitutions per site in the concatenated 18S and 28S alignment.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.