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Anatoxin concentrations, algal assemblages, and water quality data for the South Fork Eel, Salmon, and Russian Rivers in northern California, 2022-2023
We collected this data to better understand the timing of peak benthic cyanobacterial mat occurrence (specifically taxa associated with anatoxin production, Microcoleus and Anabaena) and mat anatoxin concentrations in rivers. We sampled in northern California on the South Fork Eel, Salmon, and Russian Rivers biweekly in 2022, and the Salmon River biweekly and South Fork Eel weekly in 2023. During each sampling event, we conducted benthic cover surveys, measured in-situ water quality parameters (temperature, pH, dissolved oxygen, conductivity), and collected surface water samples and targeted cyanobacteria samples. In 2022 on all rivers and in 2023 at the Salmon River, we also collected distributed non-targeted periphyton samples to characterize full-reach community compositions. All sampling was completed in 150-m reaches upstream of sensors recording continuous dissolved oxygen, conductivity, and temperature data. We analyzed surface water samples for nitrate, ammonium, soluble reactive phosphate, total dissolved carbon, and dissolved organic carbon. We also analyzed surface water samples from 2022 for major anions (Cl, SO4, Br) and cations (Na, K, Mg, Ca). Targeted-cyanobacteria and non-target periphyton samples were analyzed for anatoxins (and two other classes of toxins, microcystins and cylindrospermopsins), relative abundance of algal taxa (via microscopy), ash-free dry mass, and chlorophyll-a. To estimate mean river depth within the dissolved oxygen footprint upstream of sensors, we kayaked portions of the river and collected river depth measurements. We also measured discharge at each river excluding the Salmon River (due to high discharge) and completed pebble counts at the South Fork Eel River to obtain sediment grain size distributions. Lastly, we estimated daily reach-scale river metabolism (gross primary productivity and ecosystem respiration) using data from dissolved oxygen sensors with the "streamMetabolizer" package in R at all sensor placements.
Generalised Oscillator Strengths for the simulation of EELS spectra, with a broader coverage of high energy and minor edges
<p>This deposit contains a tabulated set of generalised oscillator strengths, which are required to compute the double differential cross sections for the inelastic scattering of fast electrons by atoms, i.e. for the simulation of EELS spectra.</p> <p>These tabulated values are calculated self-consistently within the local density approximation using the exchange correlation potential after Perdew [1]. For this a modified version of a program by Hamann is used [2]. Using this atomic potential the wave function of the ejected free electron is calculated, which is normalised by matching it to spherical Bessel and Neumann functions at large distances from the core [3]. The remaining integral constitutes a spherical Bessel transform. Using the convolution theorem this integral is solved with the fast Fourier transformation routine as done in [4]. A further discussion is available along with the code (see below), or more in-depth (but in German) in the <a href="https://www.uni-muenster.de/imperia/md/content/physik_pi/kohl/abschlussarbeiten/lsegger-bsc-arbeit.pdf">Thesis of L. Segger</a>.</p> <p><strong>This updated version offered here greatly expands the number of available edges</strong>, but is otherwise identical to the earlier version uploaded at <a href="https://zenodo.org/record/6599071">https://zenodo.org/record/6599071</a>.</p> <p> </p> <p>The data offered here is in the GOSH file format, a file format developed for the distribution of such datasets. A description of the file format as used here is included in the file `gosh.md`, while an up to date version can be found at:</p> <p><a href="https://gitlab.com/gguzzina/gosh">https://gitlab.com/gguzzina/gosh</a></p> <p>The code used to compute the GOS is publicly available, along with a discussion of the approach and methods, at:</p> <p><a href="https://github.com/Br0Fi/goscalc">https://github.com/Br0Fi/goscalc</a></p>
Electrofishing data for eel in the Iberian Peninsula (SUDOANG project)
<h2><strong>1. SUDOANG PROJECT</strong></h2><p>The <a href="https://sudoang.eu/en/">SUDOANG</a> project has provided common tools to managers to support eel conservation in the SUDOE area (Spain, France and Portugal). One of the goals of the project was to develop an eel abundance and distribution <a href="https://zenodo.org/record/7546419">atlas</a> in the three countries, based on the results of the implementation of Eel Density Analysis (<a href="http://chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://sudoang.eu/wp-content/uploads/2022/02/E411_Briand_et_al_2022_EDA_report_opt-1.pdf">EDA</a>). This model extrapolates eel abundance from a range of river segments sampled by electrofishing, to the whole river and lake network, by considering how eel abundance, size and sex vary according to different parameters related to eel habitat.</p><h2><strong>2. SUDOANG DATABASE</strong></h2><p>Electrofishing data for Spain and Portugal were imported in the SUDOANG database (<a href="https://sudoang.eu/wp-content/uploads/2020/06/E121_import_tool-2.html#2_source_of_data">deliverable 1.2.1</a>), whose structure is inherited from the DataBase for EEl (DBEEL), developed during a European research project (POSE - Pilot projects to estimate potential and actual escapement of silver eel, Walker et al., 2011). This database is designed to contain all data relative to eel biology and anthropogenic pressures applying to eel. During the course of SUDOANG, this database was used and ameliorated. </p><p>The data providers (mainly SUDOE water managers, SUDOANG researchers and pilot basins from <a href="https://sudoang.eu/en/task-groups/">GT6: Task Group on eel stock monitoring transnational network</a>) are listed below:</p><ul><li><strong>SPAIN</strong><ul><li>Ministry for ecological transition and the demographic challenge (MITECO)</li><li>Spanish Fsih Chart (SIBIC): Data from different sources (indicated in the data)</li><li>Basque Water Agency (URA) - Basque Country</li><li>Gipuzkoa Council - Gipuzkoa (Basque Country)</li><li>Navarra Council (GAN-NIK) - Navarra</li><li>Asturias Council (DGPM) - Asturias</li><li>Xunta de Galicia, Consellería de Medio Ambiente, Territorio e Vivenda - Galicia</li><li>University of Córdoba (UCO) - Andalucía</li><li>Valencian Regional Hunting and Fishing Service (GVA) - Valencia</li><li>Catalan Water Agency (ACA) - Catalonia: <i>ACUERDO GOV/139/2013, de 15 de octubre, por el que se aprueba el Programa de seguimiento y control del Distrito de cuenca fluvial de Catalunya para el período 2013-2018</i></li></ul></li><li><strong>PORTUGAL</strong><ul><li>University of Porto (UP), CIIMAR</li><li>University of Lisbon, MARE: Data from different sources (indicated in the data)</li></ul></li></ul><h2><strong>3. DATA DESCRIPTION</strong></h2><p>Electrofishing data is mainly based on fishing stations, operations and eel biometry.</p><p>The <strong>station</strong> level corresponds to a location, identified by coordinates (Spatial Reference System 4326). The attributes associated with stations are:</p><ul><li><i>op_id</i>: identifier [data type: UUID]</li><li><i>institution</i>: data provider [data type: character]</li><li><i>ref_article</i>: reference to the article from which the data originated (only for SIBIC data source) [data type: character]</li><li><i>articletitle</i>: reference linked with data (only for SIBIC data source) [data type: character]</li><li><i>op_placename</i>: station name [data type: character]</li><li><i>x_espg_4326</i>: longitude (EPSG: 4326) [data type: numeric]</li><li><i>y_espg_4326</i>: latitude (EPSG: 4326) [data type: numeric]</li><li><i>country</i>: country [data type: character]</li></ul><p>The <strong>operation</strong> level corresponds to an event occurring at a specific date. At this level, a few more details such as the method or the material used, the wetted area, and electrofished length and width are added. The total number of eels, the numbers collected at each pass, and the number of measured eels are also included. The type of sampling used could not be specified but it is mostly single or several pass surveys. Therefore, the type was set to an unknown type of fishing. All electrofishing reporting eels in the second pass were considered as full electrofishing. The attributes associated with operation are:</p><ul><li><i>id</i>: identifier [data type: UUID]</li><li><i>op_id</i>: station identifier [data type: UUID]</li><li><i>op_gis_layername</i>: data provider [data type: character]</li><li><i>data_provider</i>: data provider [data type: character]</li><li><i>op_placename</i>: station name [data type: character]</li><li><i>ob_id</i>: observation identifier [data type: UUID]</li><li><i>ob_starting_date</i>: period starting date</li><li><i>ef_wetted_area</i>: wetted area of the station, in m2 [data type: numeric]</li><li><i>ef_nbpas</i>: the number of electrofishing pass during the observation [data type: numeric]</li><li><i>ef_fished_length</i>: the electrofished river length, in m [data type: numeric]</li><li><i>ef_fished_width</i>: the electrofished river width, in m [data type: numeric]</li><li><i>ob_origin</i>: origin of the observation, raw data [data type: character]</li><li><i>ob_type</i>: type of observation, electro-fishing [data type: character]</li><li><i>ob_period</i>: time step used for observation period, daily [data type: character]</li><li><i>ef_fishingmethod</i>: type of method used during the scientific sampling [data type: character]</li><li><i>ef_electrofishing_mean</i>: mean used to realize the scientific sampling, by foot [data type: character]</li><li><i>density</i>: density of eels collected, in nb/m2 [data type: numeric]</li><li><i>totalnumber</i>: total number of eels collected [data type: numeric]</li><li><i>nbp1</i>: number of eels collected during the 1st pass [data type: numeric]</li><li><i>nbp2</i>: number of eels collected during the 2nd pass [data type: numeric]</li><li><i>nbp3</i>: number of eels collected during the 3rd pass [data type: numeric]</li><li><i>nb_size_measured</i>: number of measured eels [data type: numeric]</li></ul><p>The <strong>individual </strong>level corresponds to the biological characteristics (length and weight) of the measured eels. The attributes associated with indivial are:</p><ul><li><i>dp_name</i>: name of data provider [data type: character]</li><li><i>ob_id</i>: observation identifier [data type: UUID]</li><li><i>bc_id</i>: batch (eels sampled during the observation) identifier [data type: UUID]</li><li><i>bc_ba_id</i>: sub-batch identifier [data type: UUID]</li><li><i>size</i>: total length of eel, in mm [data type: numeric]</li><li><i>fish_id</i>: individual identifier [data type: UUID]</li><li><i>weight</i>: body weight of eel, in g [data type: numeric]</li></ul><p>The three tables can be related to each other through the identifiers, meaning that the eels measured in the <strong>individual</strong> table can be identified with the <strong>operations</strong> through the <i>ob_id</i> identifier, and these operations can be linked to the <strong>stations </strong>through the <i>op_id</i> identifier, allowing for a comprehensive view of the electrofishing sampling collected for Spain and Portugal.</p><h2><strong>4. VERSIONS</strong></h2><ul><li><a href="https://doi.org/10.5281/zenodo.8009823">10.5281/zenodo.8009823 </a>1.0.0 - 2023-06-06 - Initial upload (closed access)</li><li><a href="https://doi.org/10.5281/zenodo.8207785">10.5281/zenodo.8207785</a> 1.0.1 - 2023-08-02 - Fixed data provider in station and operation tables (closed access)</li><li><a href="https://doi.org/10.5281/zenodo.8348353">10.5281/zenodo.8348353</a> 1.0.2 - 2023-15-09 - Fixed stations (providers) (closed access)</li><li><a href="https://doi.org/10.5281/zenodo.8348353">10.5281/zenodo.8348353</a> 1.0.2 - 2023-11-08 - Final version (open access)</li></ul><h2><strong>5. READ MORE</strong></h2><ul><li>Atlas of European Eel Distribution (<i>Anguilla anguilla</i>) in Portugal, Spain and France (<a href="https://doi.org/10.5281/zenodo.7546419">10.5281/zenodo.7546419</a>)</li><li>Eel data (<i>Anguilla anguilla</i>) and associated environment variables used to fit the EDA model in the SUDOE area (SUDOANG project) (<a href="https://doi.org/10.5281/zenodo.6397009">10.5281/zenodo.6397009</a>)</li><li>Cumulated dam impact in France and the Iberian Peninsula (SUDOANG project) (<a href="https://doi.org/10.5281/zenodo.8348374">10.5281/zenodo.8348374</a>)</li></ul><h2><strong>6. FUNDING</strong></h2><p>Project co-financed by the INTERREG SUDOE Programme through the European Regional Development Fund (ERDF).</p>
Core-loss EELS dataset and neural networks for element identification
<p>We present a large dataset containing simulated core-loss electron energy loss spectroscopy (EELS) spectra with the elemental content as ground-truth labels. Additionally we present some neural networks trained on this data for element identification. </p> <p>The simulated dataset contains zero padded core-loss spectra from 0 to 3072 eV, which represents 107 core-loss edges through all 80 elements from Be up to Bi. The core-loss edges are calculated from the generalised oscillator strength (GOS) database presented by Zhang et al.[1] Generic fine structures using lifetime broadened peaks are used to imitate fine structure due to solid-state effects in experimental spectra. Generic low-loss regions are used to imitate the effect of multiple scattering. Each spectrum contains at least one edge of a given query element and possibly additional edges depending on samples drawn from The Materials Project [2]. The dataset contains for each of the 80 elements: 7000 training spectra, 1500 test spectra, 600 validation spectra and 100 spectra representing only the query element. This results in a total 736 000 labeled spectra.</p> <p>Code on how to <br> - read the simulated data<br> - transform HDF5 format to TFRecord format<br> - train and evaluate neural networks using the simulated data<br> - use the trained networks for automated element identification<br> is available on GitHub at arnoannys/EELS_ID</p> <p>A full report on the simulation of the dataset and the training and evaluation of the neural networks can be found at: Annys, A., Jannis, D. & Verbeeck, J. Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy. <em>Sci Rep</em> 13, 13724 (2023). https://doi.org/10.1038/s41598-023-40943-7</p> <p>[1] Zezhong Zhang, Ivan Lobato, Daen Jannis, Johan Verbeeck, Sandra Van Aert, & Peter Nellist. (2023). Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7729585<br> [2] Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, Kristin A. Persson; Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. __APL Mater__ 1 July 2013; 1 (1): 011002. [https://doi.org/10.1063/1.4812323](https://doi.org/10.1063/1.4812323)</p>
PDST sensor files European eel (Anguilla anguilla) Belgium
<p><strong>Brief data description</strong></p> <p>This data consists of the raw sensor data from a tagging study on European eel (<em>Anguilla anguilla</em> L.) caught and released in Nieuwpoort Belgium. The applied tags were pop-off G5 data storage tags (CEFAS Technology, Lowestoft, UK). Temperature was measured every 10 seconds and pressure (i.e. depth) every 2 seconds. The sensor data contains the raw data per eel with the tag ID as an individual eel. The tags were externally attached to eels and came off before or at a preprogrammed time, drifted to the surface, washed ashore and when found, the data could be downloaded when the tag was retrieved. Note that some recovered tags were reused hence a tag ID can occur more than once.</p> <p>This data is part of the eel-pdst-analysis GitHub repository: https://github.com/PieterjanVerhelst/eel-pdst-analysis. The sensor files have the following destination: eel-pdst-analysis\data\interim\sensorlogs</p> <p> </p> <p><strong>Files</strong></p> <p>Files are structured as a <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package</a>. You can access all data in R via <code>https://zenodo.org/record/8398240/files/datapackage.json</code> using <a href="https://frictionlessdata.github.io/frictionless-r/">frictionless</a>.</p> <p> </p> <p><strong>More Info</strong></p> <p>For more information on the data collection and analysis, see the following two research papers:</p> <p>Scientific Reports: https://doi.org/10.1038/s41598-021-04052-7</p> <p>Science of The Total Environment: <a href="https://doi.org/10.1016/j.scitotenv.2023.167341">https://doi.org/10.1016/j.scitotenv.2023.167341</a></p>
Fig. 5 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator
Fig. 5 Lateral view of Electrophorus electricus. National Museum of Natural History, NMNH 225670, 520 mm TL. Corantijn River, Suriname
Fig. 4 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator
Fig. 4 Ecological Niche Model and electric organ discharges for species of Electrophorus. Species niche models generated by MaxEnt for Greater Amazonia: a Electrophorus electricus (red); b E. varii (yellow); and c E. voltai (blue). d Measurements of voltage of high-voltage EODs, low-voltage EODs waveforms from Sach's organ, and posterior one-third of Hunter's organ (grey lines = individually recorded fish, black lines = averaged waveform for each species). e Nearest-neighbor hierarchical clustering of prominent time-frequency features of the low-voltage Sach's organ EOD from seven individuals of Electrophorus
Fig. 3 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator
Fig. 3 Electrophorus tree of life and time of species diversification. Time-calibrated genealogy of Electrophorus based on a maximum clade credibility (MCC) species tree derived from *BEAST2.4 analyses of 10 genes (colored lines) and 94 specimens of Electrophorus (relaxed molecular clock and uncorrelated lognormal model implemented). Purple bars represent 95% highest posterior density distributions for the estimated divergence time of each major node. Voltage measurements made by us are reported below E. electricus (National Museum of Natural History, NMNH 225670, 520 mm TL, Corantijn River, Suriname), E. voltai (Museu Paraense Emílio Goeldi, MPEG 15529; holotype, 1290 mm TL), and E. varii (MPEG 25422; holotype, 1000 mm TL)
Fig. 1 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator
Fig. 1 Sampling localities and gene trees for the three species of Electrophorus. a Map of northern South America showing distributions of sampled records and type localities (indicated by numbers) for three electric eel species: Electrophorus electricus (red dots, 1 = Suriname River, Suriname); E. voltai (blue dots, 2 = Rio Ipitinga, Brazil); and E. varii (yellow dots, 3 = Rio Goiapi, Brazil). Bicolor dots (blue/yellow) indicate sympatric co-occurrence of E. voltai and E. varii. The map was created in ArcGIS (https://www.arcgis.com) with images available at Shuttle Radar Topography Mission, Global Multi-resolution Terrain Elevation Data, and HydroSHEDS database. b *BEAST2.4 species tree (top cladogram; 94 specimens: 15 E. electricus, 41 E. voltai, 38 E. varii) based on 5 mitochondrial (trees 1–5; 107 specimens: 19 E. electricus, 43 E. voltai, 45 E. varii) and 5 nuclear genes (6–10; 94 specimens). Higher shading densities represent areas where the majority of trees agree in topology and branch lengths (posterior probabilities>0.99), while lower densities represent areas of uncertainty (Supplementary Data 1)
Fig. 2 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator
Fig. 2 Key morphological features to recognize the three species of Electrophorus. Top, radiographs of lateral view of the anterior portion of body (skull and pectoral girdle highlighted red). The cleithrum lies between the fifth and sixth vertebrae (v) in Electrophorus electricus (a) and E. voltai (b) versus first and second vertebrae in E. varii (c). Bottom, illustrations of ventral view of the head, showing key features listed in Diagnoses. a top: National Museum of Natural History, NMNH 403765, 300 mm TL, Cuyuni River, Guyana; bottom: NMNH 225576, 1000 mm TL, Corantijn River, Suriname. b top: Instituto Nacional de Pesquisas de Amazônia, INPA 39009, 450 mm TL, Teles Pires River, Brazil; bottom: Academy of Natural Sciences of Drexel University, ANSP 197583 (t3539), 1280 mm TL, Xingu River, Brazil. c top: NMNH 306677, 450 mm TL, Lago Janauari, Amazon River, Brazil; bottom: NMNH 196634, 1220 mm TL, Amazon River, Brazil
Fig. 7 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator
Fig. 7 Lateral view of Electrophorus voltai sp. nov. Holotype, Museu Paraense Emílio Goeldi MPEG 15529, 1290 mm TL. Ipitinga River, Brazil
Fig. 6 in Unexpected species diversity in electric eels with a description of the strongest living bioelectricity generator
Fig. 6 Lateral view of Electrophorus varii sp. nov. Holotype, Museu Paraense Emílio Goeldi MPEG 25422, 1000 mm TL. Goiapi River, Brazil
FIGURE 1 in Gymnothorax hansi, a new species of moray eel (Teleostei: Anguilliformes: Muraenidae) from the Comoro Islands, Western Indian Ocean
FIGURE 1. Gymnothorax hansi sp. nov. holotype, 101 cm TL, male, photographed shortly after death and before preservation.
FIGURE 2 in Gymnothorax hansi, a new species of moray eel (Teleostei: Anguilliformes: Muraenidae) from the Comoro Islands, Western Indian Ocean
FIGURE 2. Gymnothorax hansi sp. nov. paratype, 101 cm TL, female, photographed shortly after death and before preservation.
STEM-EELS hyperspectral data: Nanowires, Nanoparticles, Interface
<p>STEM-EELS hyperspectral datasets. These 3D datasets accompany the manuscript titled "Discovering Invariant Spatial Features in Electron Energy Loss Spectroscopy Images on the Mesoscopic and Atomic Levels" by K. Roccapriore et. al. These data are used to demonstrate correlations between pixels in space, not just energy, using what are known as multichannel rVAE models.</p> <p> </p> <p>See the following url for Jupyter Notebook walkthrough</p> <p>https://github.com/kevinroccapriore/Multichannel-rVAE</p>
Figure 3 in Diet of the European eel Anguilla anguilla (Linnaeus, 1758) in two transitional waters of Southwestern Mediterranean
Figure 3. – Comparison of the IRI of major items ingested by Anguilla anguilla from Mellah lagoon (in black) and Wadi El Kebir (in gray).
Figure 1 in Diet of the European eel Anguilla anguilla (Linnaeus, 1758) in two transitional waters of Southwestern Mediterranean
Figure 1. – Geographical location of sampling sites (*) of Anguilla anguilla in Mellah lagoon (ML) and Wadi El Kebir (WEK).
F I G U R E 1 0 in Three-dimensional migratory behaviour of European silver eels (Anguilla anguilla) approaching a hydropower plant
F I G U R E 1 0 Heatmap of search time for eels within 3 m of the bar rack for 53 attempts by 34 individual eels. Successful passes are shown on the left, and unsuccessful passes on the right. The hydraulic gate is located on the right side. The colours represent the proportion of search time in each zone, averaged over all attempts
F I G U R E 8 in Three-dimensional migratory behaviour of European silver eels (Anguilla anguilla) approaching a hydropower plant
F I G U R E 8 Upper: eel observations along four transects (a–d, with simulated relative water velocities). Observations are coloured by swimming direction. Lower: the density distribution of relative velocities at eel locations for upstream and downstream swimming, with the cross-section relative water velocity distribution in grey., downstream;, upstream;, cross-section
F I G U R E 7 in Three-dimensional migratory behaviour of European silver eels (Anguilla anguilla) approaching a hydropower plant
F I G U R E 7 Vertical and lateral eel distribution in river cross-sections in three parts of the river: the upstream river reach (river), the ponded forebay area (ponded) and the intake channel (channel) (see Figure 2a) for combinations of swimming directions and time of day. The observations are weighted by eel ground velocity and the colours represent scaled density
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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)
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