Files

• capture_quantity_joined.csv -> Joined and cleaned dataset created from: https://www.kaggle.com/tcashion/fao-fisheries-data-cleaning
• Capture_Quantity.csv -> Table with capture production (amount) of aquatic animals and plants from 1950 to present by country
• CL_FI_COUNTRY_GROUPS.csv -> Table with countries' UN Codes, ISO codes, and names in various languages
• CL_FI_SPECIES_GROUPS.csv -> Table with species' internal codes, scientific names, and common names in various languages
• CL_FI_SYMBOL.csv
• CL_FI_WATERAREA_GROUPS.csv -> Table with water areas' internal codes, and names in various languages
• CL_History.txt
• CL_Index.txt
• Capture_E.html - Dataset description in English
• Capture_F.html - Dataset description in French
• Capture_S.html - Dataset description in Spanish
• DSD_FI_CAPTURE.xlsx
• FSJ_UNIT.csv
• ISSCAAP.pdf -> International Standard Statistical Classification of Aquatic Animals and Plants Description (hierarchical codes used)
• MAP.jpg -> Map of FAO Water Areas
• NOTES_CAPTURE_COUNTRY_En.html -> Additional data notes in English
• NOTES_CAPTURE_COUNTRY_Es.html -> Additional data notes in Spanish
• NOTES_CAPTURE_COUNTRY_Fr.html -> Additional data notes in French
• ReadMe.txt

Important Notes/Acronyms: - NEI -> Not Elsewhere Included - Q_tlw -> Quantity Tonnes Live Weight (Metric tonnes). - Q_no_1 -> Quantity Number (i.e., count)

Other versions of the dataset that are incomplete or not up to date: - https://www.kaggle.com/datasets/mpwolke/cusersmarildownloadscapturecsv - https://www.kaggle.com/datasets/tbhamidipati/aquaculture

Dataset

This dataset was created by Liny Chandran

Contents

Each line in the file “Lake Michigan fish acoustic data from 2011 to 2016.csv” represents the acoustic data and estimated fish density for a single depth layer of water. Surveys are conducted along transects, transects are divided horizontally into successive intervals, and then within an interval there are multiple successive depth layers. Area backscattering (ABC), mean acoustic size (sigma), and fish density are reported for each unique transect-interval – layer from Lake Michigan in the years 2011-2016. Area backscattering (PRC_ABC), mean acoustic size (sigma), and fish density in the intervals and layers of acoustic survey transects of Lake Michigan in the years 2011-2016. The survey is carried out using a stratified, systematic design with transect locations randomized within each stratum. As a result, transect location varies each year.

The Biodiversity Science Database is a compilation of fish community data from DFO Science Surveys. Data includes: sampling site, date, fish counts, fish species, and associated habitat information.

Image and biometric data were collected for 22 species of fish from Great Lakes Tributaries in Michigan and Ohio, and the Illinois River for the purpose of developing a fish identification classifier. Data consists of a comma delimited spreadsheet that identifies image file names and associated fish identification number, common name, species code, family name, genus, and species, date collected, river from which each fish was collected, location of sampling, fish fork length in millimeters, girth in millimeters, weight in kilograms, and personnel involved with image collection. Biometric data are saved as .csv comma delimited format and image files are saved as .png file type.

Fish Tracking - Object Detection dataset

The collection of video frames, capturing various types of fish swimming in the water. The dataset includes fish of different colors, sizes and with different swimming speeds.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on our website to buy the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F05e0a6bd3bdaf28d534777ac1dee8b42%2Fout1.gif?generation=1692029879238359&alt=media" alt="">

Data Format

Each video frame from images folder is paired with an annotations.xml file that meticulously defines the tracking of each fish using bouunding boxes.

The data labeling is visualized in the boxes folder.

Example of the XML-file

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Object tracking might be made in accordance with your requirements.

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keywords: animal recognition, animal detection, farming, fish recognition, fish detection, image-based recognition, fish images dataset, object detection, object tracking, deep learning, computer vision, animal contacts, images dataset, agriculture, fish species annotations, fish pond, pond water, sea, fish farms, environment, aquaculture production, bounding boxes

Image bank of fish trays collected in the wholesale fish market in El Campello (Alicante, Spain) by artisanal fishing belonging to the DeepFish project.

The original fish tray images are provided in the "fish_tray_images_2021_MM_DD.zip" files. MM and DD stand for the month and initial day (e.g. 04_01 stands for the first of April and subsequent days, and 05_17 stands for the 17th of May, and subsequent days until the end of the month). The last zip file of this kind, 2021_06-09, contains all images from June to September.

JSON files (in fish_tray_json_labels.zip) are prepared to be used with the "Django Labeller" software, but can be converted to any format, e.g. "COCO" which is also provided in the "coco_format_fish_data.json" file.

Each of these JSON files is composed by an object containing the name of the image and the labels appearing in it. Inside each label, the following information is provided:

• Type of label. It can be a size (total, diameter of the eye...), tray or fish specie.
• Class of the label. It means the concrete specie, measurement or tray depending on the type of label.
• Semantic segmentation represented by one or multiple regions in case of occlusions. Represented by an array of coordinates in the image (x and y).
• Object_id: Identifier of the label, unique in the entire dataset.
• Father_object_id: In case this is not the main object (The label with the segmentation of the species). It will point to the identifier (ID) of that main species to which it belongs. It means, if this is the total size, it will point to the fish sized like that.

Furthermore, estimated fish sizes are also provided in the "size_estimation_homography_DeepFish.csv" file. These size estimations are calculated using homography of the known tray size, to convert from pixel units to centimetres.

This data release presents part of historical fisheries-dependent data for Cisco (Coregonus artedi) in Green Bay of Lake Michigan and Saginaw Bay of Lake Huron collected by scientists from U.S. Geological Survey's Great Lakes Science Center, including three tables for monthly Cisco-catch and fishing-effort data and two tables for biological data of Cisco individuals. The monthly Cisco-catch and fishing-effort data tables are for small-mesh gill-net fisheries in Green Bay and Saginaw Bay (GBSB_GN.csv), pound-net fisheries in Green Bay and Saginaw Bay (GBSB_PD.csv), and shallow trap-net fishery in Saginaw Bay (SB_ST.csv) in the period 1929–1970.

The biological data of Cisco individuals are from historical Saginaw Bay Cisco scale collections of 1942,1945, 1946, 1948, and 1953. These Ciscoes were caught by commercial trap-net fishing carried out by Bay Port Fish Company, located in Bay Port, Michigan. The table “SB_Scale_Collection.csv” includes data associate ...

The data we provide here have been assembled to categorise fish into feeding guilds and determine change in populations of fish with different feeding traits relevant to food web status assessment advocated by OSPAR. We provide four datasets: the first is a csv file titled ‘stomach data observations’ contains observations from fish stomach contents of individual prey weight, prey functional group (i.e., zooplankton, benthos, fish, nekton and other), predator taxonomy, predator size, and the region and year the samples were collected (Table 1; see Column_headers_readme.txt); the second is a csv file titled ‘modelled stomach data’ provides predictions from a linear mixed effects model of individual prey weight based on those stomach contents observations, alongside modelled estimates of prey counts and biomass which enable the full collation of stomach contents information to be used in our feeding guild classification (Table 2; feeding guilds are predatory categories assigned using cluster analysis on stomach content data; see Thompson et al., 2023); the third dataset is a shapefile titled ‘feeding guild responses in survey data’ and provides haul-level estimates of feeding guild species richness, numbers of fish and their biomass based on scientific trawl surveys from the Northeast Atlantic (Tables 3-4); the fourth is a shapefile titled ‘temporal changes in feeding guilds’ which contains correlation coefficients and p values following Kendall’s τ trend analysis between mean haul-level values of feeding guild biomass and species richness for each assessment strata and year. Kendall’s τ scores of –1 to +1 represent a 100% probability of a decreasing or increasing trend, respectively (Table 5). Stomach contents data

We draw together stomach contents data primarily collected from the North Atlantic shelf seas, with important contributions from the Baltic, Barents and Norwegian Seas in the Arctic Ocean. These data were sourced from a combination of previously published and unpublished data including DAPSTOM (Pinnegar, 2019), ICES Year of the Stomach (Daan, 1981; ICES, 1997), the Northeast US continental shelf (Smith & Link, 2010), Northern Spanish shelf (Arroyo et al., 2017), Gulf of Cadiz (Torres et al., 2013), Swedish-, Icelandic-, Norwegian-, French- (Cachera et al., 2017; Timmerman et al., 2020; Travers-Trolet, 2017; Verin, 2018) and German-led surveys (e.g., FishNet, https://www.nationalpark-wattenmeer.de/wissensbeitrag/fishnet/; BioConsult, 2023). The observations used to model prey weight contain information from 621,040 fish stomachs (Table 1), from 146 predator taxa collected between 1963 – 2020 and includes larvae (<1 g) to adults (up to 351 kg). The full dataset contains predictions of prey weights and counts based on predator size, predator taxa and prey functional group information from 944,129 fish stomachs (Table 2), 227 predator taxa collected between 1836 – 2020 and includes larvae (<1 g) to adults (up to 351 kg).

Scientific trawl survey data

Scientific trawl data were obtained from Lynam and Ribeiro (2022), a data product derived from Northeast Atlantic groundfish data from surveys undertaken between 1983-2020 with observations for the biomass and numbers of species size classes standardised to the area swept for each haul. We make use of data collected using otter trawls between 1997 – 2020. We process the data by categorising fish into feeding guilds using taxonomic and size information and sum estimates of biomass, numbers, and species richness by feeding guild for each haul observation. We provide the unique assessment units used in the pilot assessment to determine change in feeding guild responses (Lynam et al., 2022; Lynam & Piet, 2022; Thompson et al., 2023). We provide details of the columns used in Table 3, a summary of the specific surveys used in Table 4. Details of the columns in the data containing correlations and p values based following Kendall’s τ trend analysis are provided in Table 5.

Associated scientific study

These data were processed for the study "Fish functional groups of the North Atlantic and Arctic Oceans ", by Murray S.A. Thompson, Izaskun Preciado, Federico Maioli, Valerio Bartolino, Andrea Belgrano, Michele Casini, Pierre Cresson, Elena Eriksen, Gema Hernandez-Milian, Ingibjörg G. Jónsdóttir, Stefan Neuenfeldt, John K. Pinnegar, Stefán Ragnarsson, Sabine Schückel, Ulrike Schückel, Brian E. Smith, María Ángeles Torres, Thomas J. Webb, and Christopher P. Lynam (in prep; see also Thompson et al., 2023).

File Descriptions

b0.summary.csv - Parameter value for the intercept in the size bias correction model.

beta.summary.csv - Posterior distribution of the parameter value for the "size" predictor variable in the size bias correction model. The median (50%) value is used in the model in the associated R script.

samplefishdata.csv - Example dataset of single-pass electrofishing fish assemblage data. Each row represents an individual fish that was captured at a specific site. The variables included in the dataset consist of: "SiteId" - Descriptor of the unique site(s) in the dataset; Length - Fork or total length of the individual fish (mm); and Weight - Total weight of the individual fish (g).

sizespectrum.model.jags - The model that is used by the JAGS program to implement the Bayesian sampler. This file is generated by the R script and can be edited through R.

sizespectrum.R - The R script that can be used to parameterize the Bayesian size spectrum model usin...

This volume's release consists of 41933 media files captured by autonomous wildlife monitoring devices under the project, Vermont Fish and Wildlife Department. The attached files listed below include several CSV files that provide information about the data release. The file, "media.csv" provides the metadata about the media, such as filename and date/time of capture. The actual media files are housed within folders under the volume's "child items" as compressed files. A critical CSV file is "dictionary.csv", which describes each CSV file, including field names, data types, descriptions, and the relationship of each field to fields in other CSV files. Some of the media files may have been "tagged" or "annotated" by either humans or by machine learning models, identifying wildlife targets within the media. If so, this information is stored in "annotations.csv" and "modeloutputs.csv", respectively. To protect privacy, all personally identifiable information (PII) have been removed, locations have been "blurred" by bounding boxes, and media featuring sensitive taxa or humans have been omitted. To enhance data reuse, the sbRehydrate() function in the AMMonitor R package will download files and re-create the original AMMonitor project (database + media files). See source code at https://code.usgs.gov/vtcfwru/ammonitor.

Global production of fish and seafood has quadrupled over the past 50 years. Not only has the world population more than doubled over this period, the average person now eats almost twice as much seafood as half a century ago.

This has increased pressure on fish stocks across the world. Globally, the share of fish stocks which are overexploited – meaning we catch them faster than they can reproduce to sustain population levels – has more than doubled since the 1980s and this means that current levels of wild fish catch are unsustainable.

One innovation has helped to alleviate some of the pressure on wild fish catch: aquaculture, the practice of fish and seafood farming. The distinction between farmed fish and wild catch is similar to the difference between raising livestock rather than hunting wild animals. Except that for land-based animals, farming is many thousand years old while it was very uncommon for seafood until just over 50 years ago.

In the visualizations and tables we see: - Captured and farmed (production) levels per year and per country or region - Consumption levels throughout the world for the past 50 years - Levels of sustainable vs overexploited fish - Global fishery types and their production levels - Types of fish produced per country

This USGS data release documents presence and absences of 419 fish species in the conterminous United States for 35,918 stream reaches of the National Hydrography Dataset Plus Version 2.1 (NHDPlusV2.1). Sample dates for this dataset span 1990-2019. Fish samples were aligned to the Integrated Taxonomic Information System (ITIS), where each species record was assigned a Taxonomic Serial Number (TSN). The dataset is structured with records representing a stream reach (i.e. comid), sample description (i.e. source, date) and a series of 419 columns representing species where each species column is named as the TSN. Data can be visualized on the NHDPlusV2.1 after a tabular join using the field 'comid' or can be projected and visualized as point data using the latitude and longitude fields (using coordinate reference system NAD83) that represent the midpoint of the stream reach that they were associated with. Data are provided in comma separated value (CSV) format.

This USGS data release documents coarse ranges for 257 fish species in the conterminous United States for level 8 hydrologic units from the Watershed Boundary Dataset (WBD). These range maps were derived by combining known fish occurrence information from four data sources: point occurrences from the Aquatic Gap Analysis Project (AGAP) fish database, stream segment (i.e., NHDPlusV2.1 COMID) occurrences from the IchthyMaps dataset, point occurrences from the Global Biodiversity Information Facility (GBIF), and HUC-8 level range maps developed by NatureServe. Data can be linked to geospatial units of the WBD using the HUC8 field. Data are provided in comma separated value (CSV) and zipped Parquet file formats. Parquet file format is provided to help facilitate faster download and read capabilities when using compatible packages in coding languages such as R and Python. Source data from GBIF are also included in range_source_data_gbif.csv and are further documented at https://doi.org/10.15468/dd.qctv4s.

The AmazonFISH - database of freshwater fish species of the Amazon Basin is organised in two sub-datasets and one shapefile. Updated Version of the Database, Version 2 (2022).GeneralDistribution2022_v2.csvCompleteDatabase2022_v2GeneralDistribution.csv: Table containing the species list by sub-drainage with the taxonomic reference names and the species status. CompleteDatabase: Table containing the geographic coordinates of the georeferenced records and the information source.SubDrainageShapefile: Shapefile delineating the 144 sub-drainages, along with the corresponding geographic information (main river name, country, geographic coordinates and surface area)

This dataset is the species and species group predictions of the density of 37 commercially important fish and invertebrates that are known to extensively use mangroves. All methods are provided in detail in the accompanying bioRxiv preprint, zu Ermgassen et al. (2024) The global fish and invertebrate abundance value of mangroves

Description of files

• Mangrove_commercial_fauna_density_data_references.csv: this is the raw data used to create the linear model using generalized least squares relating the fish density values to the covariate data

R Scripts & Data

• This folder contain several R scripts and data files to used calculate the values in zu Ermgassen et al. (2024) The global fish and invertebrate abundance value of mangroves.

Species Predictions*

• all_sp_fit_fn.csv: the mean predicted species density for 37 commercially important fish and invertebrates for a grid with a spatial resolution of 1 km2.
• all_sp_fit_fn_lower.csv: the lower (1.96 * standard error of the model fit) predicted species density for 37 commercially important fish and invertebrates for a grid with a spatial resolution of 1 km2.
• all_sp_fit_fn_upper.csv: the upper (1.96 * standard error of the model fit) predicted species density for 37 commercially important fish and invertebrates for a grid with a spatial resolution of 1 km2.
• Species Name Contractions.csv: file with key to name contractions in above datasets
• Shapfiles: spatial representations of the above datasets based on the 1km2 grid

Species Group Predictions

• all_sp_fit_fn_total.csv: the mean, lower and upper (1.96 * standard error of the model fit) predicted species density for 37 commercially important fish and invertebrates for a grid with a spatial resolution of 1 km2, with species summed into finfishes (n = 29), crabs (n = 4), bivalves (n = 1), and prawns (n = 3).
• Shapfiles: spatial representations of the above dataset based on the 1km2 grid

* N.B. data labeled Neosarmatium meinerti in the above files has been corrected to Neosarmatium africanum

Effective management of aquatic resources, wild and farmed, has implications for the livelihoods of dependent communities, food security, and ecosystem health. Good management requires information on the status of harvested species, yet many gaps remain in our understanding of these species and systems, in particular the lack of taxonomic resolution of harvested species. To assess these gaps we compared the occurrence of landed species (freshwater and marine) from the United Nations Food and Agriculture Organization (FAO) global fisheries production database to those in the International Union for Conservation of Nature (IUCN) Red List and the RAM Legacy Stock Assessment Database, some of the largest and most comprehensive global datasets of consumed aquatic species. We also quantified the level of resolution and trends in taxonomic reporting for all landed taxa in the FAO database. Of the 1,695 consumed aquatic species or groups in the FAO database considered in this analysis, a large portion (35%) are missing from both of the other two global datasets, either IUCN or RAM, used to monitor, manage, and protect aquatic resources. Only a small number of all fished taxa reported in FAO data (150 out of 1,695; 9%) have both a stock assessment in RAM and a conservation assessment in IUCN. Furthermore, 40% of wild caught landings are not reported to the species level, limiting our ability to effectively account for the environmental impacts of wild harvest. Landings of invertebrates (44%) and landings in Asia (>75%) accounted for the majority of harvest without species specific information in 2018. Assessing the overlap of species which are both farmed and fished to broadly map possible interactions – which can help or hinder wild populations - we found 296 species, accounting for 12% of total wild landings globally, and 103 countries and territories that have overlap in the species caught in the wild and produced through aquaculture. In all, our work highlights that while fisheries management is improving in many areas there remain key gaps in data resolution that are critical for fisheries assessments and conservation of aquatic systems into the future.

This volume's release consists of 463615 media files captured by autonomous wildlife monitoring devices under the project, New Hampshire Fish and Game Department. The attached files listed below include several CSV files that provide information about the data release. The file, "media.csv" provides the metadata about the media, such as filename and date/time of capture. The actual media files are housed within folders under the volume's "child items" as compressed files. A critical CSV file is "dictionary.csv", which describes each CSV file, including field names, data types, descriptions, and the relationship of each field to fields in other CSV files. Some of the media files may have been "tagged" or "annotated" by either humans or by machine learning models, identifying wildlife targets within the media. If so, this information is stored in "annotations.csv" and "modeloutputs.csv", respectively. To protect privacy, all personally identifiable information (PII) have been removed, locations have been "blurred" by bounding boxes, and media featuring sensitive taxa or humans have been omitted. To enhance data reuse, the sbRehydrate() function in the AMMonitor R package will download files and re-create the original AMMonitor project (database + media files). See source code at https://code.usgs.gov/vtcfwru/ammonitor.

In recent years considerable emphasis has been placed on finding 'ecosystem-based' approaches to fisheries management and multispecies models are seen as crucial for addressing this new agenda. However, there are currently, very few long-term datasets within the European context available for parameterising such models. DAPSTOM (Integrated Database and Portal for Fish Stomach Records) is an ongoing initiative (supported by Defra and the EU) to digitize and make available fish stomach content records spanning the past 100 years. The online database contains information (over 225,000 records) on 188 predator species (most of those occurring in northern European groundfish surveys) and can be searched by predator name or by prey name for given sea areas and years. CSV data files can be outputted containing all records from a particular query. The current database contains information spanning 1893 - 2012.

Age, sex and length data provide population dynamics information that can indicate how populations trends occur and may be changing. These data can help researchers estimate population growth rates, age-class distribution and population demographics. Knowing population demographics, growth rates and trends is particularly valuable to fisheries managers who must perform population assessments to inform management decisions. These data are therefore particularly important in valuable fisheries like the salmon fisheries of Alaska. This dataset includes age, sex and length data compiled from annual sampling of commercial and subsistence salmon harvests and research projects in westward and southeast Kodiak. It includes data on five salmon species: chinook, chum, coho, pink and sockeye. Age estimates were made by examining scales or bony structures (e.g. otoliths - ear bones). Scales were removed from the side of the fish; usually the left side above the lateral line. Scales or bony structures were then mounted on gummed cards and pressed on acetate to make an impression. The number of freshwater and saltwater annuli (i.e. rings) was counted to estimate age in years. Age is recorded in European Notation, which is a method of recording both fresh and saltwater annuli. For example, for a fish that spent one year in freshwater and 3 years in saltwater, its age is recorded as 1.3. The total fish age is the sum of the first and second numbers, plus one to account for the time between deposition and emergence. Therefore the fish in this example is 5 years old. Fish sex was determined by either examining external morphology (eg. head and belly shape) or internal sex organ. Length was measured in millimeters, generally from mid-eye to the fork of the tail. This data package includes the original data file (ASL DATA EXPORT.csv), a reformatting script that reformats the original data file into a consistent format (ASL_Formatting_SoutheastKodiak.R), and the reformatted dataset as a .csv file (ASL_formatted_SoutheastKodiak.csv).