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12,632 results for “FISH”
Figure 19 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 19: Opposing relationships with total phosphorus for the biomass of the two most common fish taxa among the large and deep lakes (average depth> 50 m). Data are whole-lake average biomass (in grams) of fish per vertical net battery. Note that the horizontal axis is on a log scale. Regression statistics for Coregonus are p-value = 0.005, R2 = 0.57 and perch are p-value = 0.004, R2 = 0.58. Shaded regions show thresholds for total phosphorus of 10 μg / L and 5 μg / L.
Figure 22 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report
Figure 22: Whole-lake average number of European perch (Perca fluviatilis) per vertical net battery compared to total phosphorus concentration in large and deep lakes (average depth> 50 m). The left panel shows the relationship for all perch caught in the lake (p-value = 0.022, R2 = 0.42). The right panel shows the relationship for only perch larger than 20 cm (length from snout to the tip of the tail; p-value = 0.003, R2 = 0.6). Note that the horizontal axis is on a log scale. Dashed red lines indicate statistically significant relationships.
Data from: Intense upper ocean mixing due to large aggregations of spawning fish
<p>This dataset includes data collected during the cruise REMEDIOS-TL in the Ría de Pontevedra (NW Iberia) at station P2 (42.357°N, 8.773°W) from 29 June to 18 July 2018 onboard of the Research Vessel Ramón Margalef belonging to the Spanish Institude of Oceanography. The REMEDIOS project is funded by the Spanish Ministry of Economy and Inno-445vation under the research project REMEDIOS (CTM2016-75451-C2-1-R) and leaded by Beatriz Mouriño Carballido.</p> <p>The archived data are described in a manuscript entitled "Intense upper ocean mixing due to large aggregations of spawning fish" by Fernández Castro et al. published in Nature Geoscience:</p> <p>Fernández Castro, B., Peña, M., Nogueira, E. <em>et al.</em> Intense upper ocean mixing due to large aggregations of spawning fish. <em>Nat. Geosci.</em> <strong>15, </strong>287–292 (2022). https://doi.org/10.1038/s41561-022-00916-3</p> <p>The manuscript presents evidence that night-time aggregations of anchovies produce intense ocean turbulence and mixing. All the data needed to support the conclusions of the article are included in this dataset.</p> <p>The dataset includes:</p> <p>- Microstructure profiles collected with a MSS Sea&Sun profiler during the three intensive samplings of the cruise (I01, I02, I03)</p> <p>- Ocean currents measured with a bottom moored RD Instruments acoustic Doppler profiler (ADCP, 300Khz) for the duration of the cruise</p> <p>- Acoustic backscatter from a ship-borne echosounder Simrad EK80 for the frequencies 18, 38, 70, 120 and 200 KHz and the three intensive samplings of the cruise (I01, I02, I03)</p> <p>- European anchovy (Engraulis encrasicolus) egg counts from plankton hauls samplings.</p>
Montana State University Vertebrate Museum Fish Collection
<p>The Montana State University Vertebrate Museum Collection (MTVC) houses an expansive fish collection, containing historic and contemporary specimens that have contributed to decades of teaching and research. From 2018 to 2022, Montana State University, funded by the Council on Library and Information Resources “Hidden Collections” grant, digitized fish specimens housed in the MTVC. This produced over 2900 metadata records detailing sampling localities, collecting dates, and identifications of fish collected mainly from Montana, USA. The metadata records reveal the efforts of over 150 collectors, spanning seven decades of sampling. This dataset makes available the information associated with 48,000 individual fish specimens from 102 species stored in the MTVC. Digitization of this collection accompanies the previously digitized Montana Prairie Fish Collection at Montana State University. Together, they provide a comprehensive insight into the distribution of fishes in Montana over time and across habitats.</p>
A new ChEMBL dataset for FastTargetPred and target fishing for an exhaustive list of linear tetrapeptides
<p>A ChEMBL-v29 dataset was generated to be used with the ligand-based similarity search target prediction engine FastTargetPred (https://github.com/ludovicchaput/FastTargetPred). Using this new dataset, attempts to predict macromolecular targets for a published dataset of 160,000 tetrapeptides was performed.</p> <p>The dbchembl29 directory contains all the files for FastTargetPred. This command line tool compares using different types of fingerprints, a file containing small query molecules in SDF format (it can be 1 molecule or a collection) to molecules extracted from ChEMBL29. If a match is found, this suggests that your query molecule is similar to a ChEMBL compound and as the ChEMBL compound has bioactivity data against one or more macromolecular target, then this suggests that your query compound could bind to targets that interact with compounds that are similar to the query molecule. The so-called similarity principle in chemistry. Fingerprints are computed with http://www.mayachemtools.org/, a collection of Perl and Python scripts for Chemoinformatics and (Structural) Bioinformatics.</p> <p>To run FastTargetPred with the new ChEMBL 29 data, you just need to unzip the dbchembl29 directory into the FastTargetPred main directory.</p> <p>The default FastTargetPred commands (e.g., python3 FastTargetPred.py rivaroxaban.sdf, the default command uses ECFP4 fingerprints and a Tanimoto coef of 0.6, rivaroxaban here is the query compound, it is provided in the extra_data directory) will use the data present in the default db directory and thus a curated version of ChEMBL-25 release. It was the version of the ChEMBL database available when FastTargetPred was developed. Since then, many new molecules have been added and this is why we generated the ChEMBL-29 dataset (last ChEMBL version at the time of writing).</p> <p>To use the new ChEMBL-29 data, you can run the following command:</p> <p>python3 FastTargetPred.py rivaroxaban.sdf -fp MACCS -tc 0.9 -db dbchembl29/chembl29_active</p> <p>This applies a similarity search for the query compound (here rivaroxaban, you can for instance move this SDF file in the directory containing the file FastTargetPred.py) using MACCS fingerprints, a Tanimoto coefficient threshold of 0.9 and the -db option forces the system to look at the ChEMBL29 curated data and not the default ChEMBL-25 data. This 0.9 value means to focus on molecules very similar to rivaroxaban present in the ChEMBL data. If one is looking for more distantly related compounds, then a value of 0.7 can be used. Users can try different values or try consensus scoring...See FastTargetPred: a program enabling the fast prediction of putative protein targets for input chemical databases. Chaput et al., Bioinformatics. 2020 Aug 15;36(14):4225-4226</p> <p>The chembl29 directory also contains 714,780 compounds (canonical SMILES strings) extracted from ChEMBL29 that have bioactivity data. Fingerprints could not be computed for 19 molecules that have unusual chemistry. We selected the following thresholds (eg, binding assays, activity against targets less than 20 micro-molar, ChEMBL confidence_score = 6 or above, maximum = 9).</p> <p>With this new dataset, we attempted to predict potential targets with FastTargetPred for 160,000 input query peptides (4 amino acids, combination should be 20 x 20 x 20 x 20) previously reported by Dewi Prasasty and Perdana Istyastono, Data in brief 27 (2019) 104607. The peptides for which a putative target was predicted are shown in two DataWarrior files with the amino acid sequence of the query peptide, the compounds found to be similar in the ChEMBL29 dataset (fingerprints = ECFP4, Tanimoto 0.6) and thus the compound IDs, the target ChEMBL IDs, mapping to the UniProt database when available, information about disease involvements, Reactome pathway database identifiers. These two DataWarrior files are searchable, can be sorted and hyperlinks to the ChEMBL, UniProt and Reactome databases have been inserted.</p> <p> </p>
Data and custom codes from "Rapid evolution in salmon life-history induced by direct and indirect effects of fishing"
<p>Data and custom codes from Czorlich, Y., Aykanat, T., Erkinaro, J., Orell, P. & Primmer, C.R. (2021) <em>Rapid evolution in salmon life-history induced by direct and indirect effects of fishing. </em>Science.</p> <p><strong>Codes:</strong></p> <p>The R file "Fishing_effort_parallel.R" was used to estimate fishing effort/intensity (beta in equation 8) given the length distribution, the gear-specific catchability and harvest rate</p> <p>"Fishing_selection_estimate.R" was used to estimate fishery-induced selection at <em>vgll3.</em></p> <p><strong>Datasets:</strong></p> <p>Genetic_phenotypic_data.xlsx: Genetic and phenotypic data about salmon from the Teno mainstem population</p> <p>sonar_data.xlsx: Number of salmon per length class entering the river in 2018 and 2019. The length classes of salmon caught in those years by one of the fishing methods are also included</p> <p>annual_catch_data.xlsx: Total mass (kg) of salmon caught by each fishing method between 1975 to 2014.</p> <p>Environmental_data.xlsx: Data about Barents Sea temperature, biomass of key species, fishing data</p> <p>individual_weight_salmon_catches.xlsx: Individual weight of salmon caught with different fishing gears in the last decades</p> <p><strong>Data sources:</strong></p> <p>- Genetic data (Tenojoki population, random sampling): From Czorlich et al. 2018, https://datadryad.org/stash/dataset/doi:10.5061/dryad.7hm4708</p> <p>- Data about krill biomass (1980 – 2013) were taken from (<em>1</em>, <em>2</em>).</p> <p>- Capelin biomass estimated from acoustic survey and the landed capelin catches were derived from (<em>3</em>) for 1973 – 2013.</p> <p>- Herring biomass data were retrieved from (<em>4</em>) for the 1973-1998 period. Herring biomass was calculated from the number of 1-2 year old herring and the mean weight per age as reported in (<em>3</em>) for 1988 – 2013.</p> <p>- The annual biomass of cod (a predator of forage fish) was derived from VPA analyses ((<em>5</em>), table 3.24). Landed cod biomass was also taken from (<em>5</em>).</p> <p>- An index for mesozooplankton (a forage fish food source) corresponding to the sum of <em>Calanus</em> biomass indices from different parts of the Barents Sea was used (<em>6</em>).</p> <p>- The annual sea temperature in the Kola section of the Barents Sea measured in the upper 200 meters was from <a href="http://www.pinro.vniro.ru/">pinro.vniro.ru</a></p> <p>- The total number of nets used to catch salmon in the Finnmark coastal region was calculated for each year using data from (7)</p> <p>- Other data were generated for this study, please check the Material and Methods. </p> <p><em>References:</em></p> <p>1. E. Eriksen, P. Dalpadado, Long-term changes in Krill biomass and distribution in the Barents Sea: Are the changes mainly related to capelin stock size and temperature conditions? <em>Polar Biology</em>. <strong>34</strong>, 1399–1409 (2011).</p> <p>2. ICES, “Report of the Working Group on the Integrated Assessments of the Barents Sea. ICES CM 2017/SSGIEA:04. 186 pp.” (2017).</p> <p>3. ICES, “Report of the Arctic Fisheries Working Group (AFWG). ICES CM 2015/ACOM:05. 639 pp.” (2015).</p> <p>4. R. Toresen, O. J. Østvedt, Variation in abundance of Norwegian spring-spawning herring (Clupea harengus, Clupeidae) throughout the 20th century and the influence of climatic fluctuations. <em>Fish and Fisheries</em>. <strong>85</strong>, 385–391 (2000).</p> <p>5. ICES, “Report of the Arctic Fisheries Working Group (AFWG). ICES CM 2016/ACOM:06. 621 pp.” (2016).</p> <p>6. L. C. Stige et al., Spatiotemporal statistical analyses reveal predator-driven zooplankton fluctuations in the Barents Sea. <em>Progress in Oceanography</em>. <strong>120</strong>, 243–253 (2014).</p> <p>7. E. Niemelä, T. Kalske, E. Hassinen, “Numbers of fishing gears used in Kolarctic salmon project area, numbers of allowed sites for salmon fishing and numbers of salmon fishermen in Finnmark; development until the year 2013” (2013).</p>
Data for "Examining functional impact and trophic morphology of small, sand-sifting fishes on coral reefs"
<p>This data is the product of the study published as "<strong>Examining functional impact and trophic morphology of small, sand-sifting fishes on coral reefs </strong>"</p> <p>It contains:<br> Feeding depth count of the two fish species used</p> <p>Granulometry on the experimental sediment</p> <p>Gut content analysis of the 8 fish used in the experiment. Measurements of maximum and minimum size of each individual prey item noted.</p> <p>Feeding experiment count data. ID and count data of meiobenthos (benthic meiofauna) found during the feeding experiment. The benthic community was assessed in 3 replicates for each fish individual at each timepoint. See the methods in publications for details or contact the Ole Brodnicke or Camilla Hansen for details. </p>
Two fish in a pod. Data from a self-sampling pilot program to separate between black hake species in W-Africa
<p>Data from self-sampling pilot trial in Senegal and Mauritanian waters where two species of black hake were separated manually onboard fishing vessels and the results then validated by genetic analysis. </p>
Fig. 2 in Bony Fishes From The Late Miocene And Pliocene Strata Of Popovo Locality (Ukraine): Taxonomic Changes And Their Palaeoecological Explanation
Fig. 2. Dynamics of taxonomic changes in bony fish communities from Popovo during the Late Miocene and Pliocene: 1 — species level; 2 — genus level; 3 — family level Рис. 2. Динамика таксономических изменений в сообществах костистых рыб из местонахождения Попово на протяжении позднего миоцена и плиоцена: 1 — уровень вида; 2 — уровень рода; 3 — уровень семейства.
Fig. 3 in Bony Fishes From The Late Miocene And Pliocene Strata Of Popovo Locality (Ukraine): Taxonomic Changes And Their Palaeoecological Explanation
Fig. 3. Connection between heterochronous bony fish communities from the Popovo locality: 1, 2 — minimal connection; 3, 5 — similar level of the taxonomic richness; 4, 6, 7 — substantial similarity of faunistic lists.
Fig. 2 in Distribution Of Trematodes Cryptokotyle (Trematoda, Heterophyidae), In Fish Of The Family Gobiidae In The Estuary Waters And The Black Sea In Southern Ukraine
Fig. 2. Metacercariae of trematodes of Heterophyidae familyon the body surface and fins of N. fluviatialis.
Fig. 3 in Distribution Of Trematodes Cryptokotyle (Trematoda, Heterophyidae), In Fish Of The Family Gobiidae In The Estuary Waters And The Black Sea In Southern Ukraine
Fig. 3. Part of small intestines of duckling at autopsy. Visible trematodes C. jejunain mucus and on mucosal surfaces.
Fig. 1 in Late Neogene And Pleistocene Porgy Fishes (Teleostei, Sparidae) Of The Eastern Paratethys, With Comments On Their Palaeoecology
Fig. 1. Localities with fossil remains of sparid fishes from Ukraine and their stratigraphic sequence.
Fig. 3 in New Extinct Carp Fish Species (Teleostei, Cyprinidae) From The Late Neogene Of Southeastern Europe
Fig. 3. Scardinius ponticus sp. n.: 1 — isolated pharyngeal tooth, holotype (NMNH–P 41/2358, Odesa Pontian Lectostratotype); 2 — paratype (NMNH–P 41/2359). Scardinius erythrophthalmus, recent (used for comparison). Рис. 3. Scardinius ponticus sp. n.: 1 — изолированный глоточный зуб, голотип (NMNH–P 41/2358, лектостратотип понта); 2 — паратип (NMNH–P 41/2359). Scardinius erythrophthalmus, современный (использован для сравнения).
Fig. 2 in New Extinct Carp Fish Species (Teleostei, Cyprinidae) From The Late Neogene Of Southeastern Europe
Fig. 2. Pharyngeal bones: 1 — Rutilus robustus sp. n., holotype (Prz 10–1/12, Priozernoe); 2 — Rutilus robustus sp. n., fragment of ceratobranchiale (NMNH–P 41/2342, Odesa Pontian Lectostratotype); 3 — Rutilus frisii, subfossil (NMNH–P 53/4108, Vinohradnyi Sad); 4 — Rutilus frisii, recent. CS — cavernous surface; DS — dentiferous surface.
Fig. 3 in The Relationship Between Fish Length And Otolith Size And Weight Of The Australian Anchovy, Engraulis Australis (Clupeiformes, Engraulidae), Retrieved From The Food Of The Australasian Gannet, Morus Serrator (Suliformes, Sulidae), Hauraki Gulf, New Zealand
Fig. 3. Fish total length relationship with: A — otolith length; B — otolith width; C — otolith weight.
Fig. 4 in Helminth Diversity In Teleost Fishes From The Area Of The Ukrainian Antarctic Station "Akademik Vernadsky", Argentine Islands, West Antarctica
Fig. 4. Cluster analysis of the similarity between the helminth communities in five teleost fish species off the area of the UAS "Akademik Vernadsky", Argentine Islands, and West Antarctica.
Fig. 3 in Helminth Diversity In Teleost Fishes From The Area Of The Ukrainian Antarctic Station "Akademik Vernadsky", Argentine Islands, West Antarctica
Fig. 3. Proportion (in %) of helminth species parasitize five Antarctic teleost fishes off the area of the UAS "Akademik Vernadsky" on larval and adult stages.
Fig. 2. A in The Relationship Between Fish Length And Otolith Size And Weight Of The Australian Anchovy, Engraulis Australis (Clupeiformes, Engraulidae), Retrieved From The Food Of The Australasian Gannet, Morus Serrator (Suliformes, Sulidae), Hauraki Gulf, New Zealand
Fig. 2. A, Engraulis australis, 138 mm TL; B, Otolith of Engraulis australis, 135 mm TL showing otolith sizes, length (OL) and width (OW).
Fig. 2 in Helminth Diversity In Teleost Fishes From The Area Of The Ukrainian Antarctic Station "Akademik Vernadsky", Argentine Islands, West Antarctica
Fig. 2. Intensity of teleost fish infection off the area of the UAS "Akademik Vernadsky" by five parasite taxa (proportion of different parasite taxa is in %).
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.