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317 results for “catch”
Global monthly catches from tuna fisheries by 5° grid (1950-2023) (FIRMS level 0)
We compiled a comprehensive dataset of geo-referenced catches from global tuna fisheries available on a spatial resolution of 5° grid areas. This dataset was created by harmonizing public domain data from the five tuna Regional Fisheries Management Organizations (t-RFMOs) for the period 1950-2023. Under the auspices of the Fisheries and Resources Monitoring System (FIRMS) of the United Nations Food and Agriculture Organization (FAO), we developed a systematic data flow process in collaboration with the t-RFMO Secretariats. This process involved the implementation of a data exchange format adhering to the standards of the FAO Coordinating Working Party on Fishery Statistics (CWP), facilitating the seamless integration of data into the dataset.<br><br>Geo-referenced catch data from tuna fisheries are reported in either the number of fish or live-weight equivalent (metric tonnes), with some strata providing catches in both units. The catches primarily represent the quantities of retained fish either landed or transhipped at sea and in ports. The data are stratified by year, month, fishing fleet, fishing gear, fishing mode, 5° grid area of longitude and latitude, and taxon.<br><br>The dataset encompasses 49 medium- and large-sized pelagic species found in both neritic and oceanic habitats of the world's oceans. This includes 15 species of tunas, 10 species of billfish, 7 species of Spanish mackerels, 2 species of bonitos, and wahoo. Despite uncertainties and incomplete data due to under-reporting, the dataset also includes reported catches for 14 species of pelagic sharks and rays that may be either targeted or incidentally caught in tuna and tuna-like fisheries.<br><br>The dataset serves as a benchmark for the monitoring and assessment of both artisanal and industrial fisheries from over 115 fishing fleets across 114 countries that have exploited tuna and tuna-like species for subsistence and commercial purposes over more than seven decades.
Deep learning to extract the meteorological by-catch of wildlife cameras: Supporting data, models and code
<p>This repository contains the data, models and code to train and deploy deep learning models related to the paper "Deep learning to extract the meteorological by-catch of wildlife cameras" published in the journal Global Change Biology (<a href="https://doi.org/10.1111/gcb.17078"><strong>https://doi.org/10.1111/gcb.17078</strong></a>).</p>
Trap catches of carrot fly (Psila rosae) and cutworm (Agrotis ipsilon) from 142 fields in Denmark and Southern Sweden 1997-2019
<p>The pests were caught in various vegetable crops in Denmark and Southern Sweden: Yellow sticky traps for carrot flies and pheromone traps for cutworm adults.</p> <p>The data is provided as both a tab-separated text file and a binary R data file. The R files provides code to read and plot the data. The two plots produced are also provided as PNG files.</p> <p>The data were collected by SEGES Innovation, Denmark.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Wave 1 Questionnaires: Greece
<p>File <strong>CatchEyoU_WP7_W1_Greece.por </strong>contains the Greek questionnaire data set for Wave 1 of Work Pachage 7 of Catch-EyoU project (Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions; funded by the European Commission under the Horizon 2020 Programme; GA number: 649538; 2015-2018; http://www.catcheyou.eu/). This dataset contains the data underlying the publications presented in the References section (see below). </p> <p>File <strong>CatchEyoU_WP7_W1_Greece.por </strong>corresponds to the responses of participants to the questionnaire study for the purposes of Catch-EyoU project, WP7, Wave 1, collected in December 2016. These were entered and coded as either numeric or alphanumeric variables. Detailed information on variable names and labels is included in the portable file itself and it is also provided in the accompanying README file.</p>
CATCH-EyoU Work Package 2 Dataset 2.2a - Aggregation of Literature
<p>The data is an aggregated dataset collected from all contributing partners, and includes a bibliography, concerning Europe, youth engagement and active citizenship across the disciplines represented in the Consortium (Political Science, Sociology, History, Media and Communications, Psychology and Education).</p> <p>The dataset contains reference to 770 individual texts.</p> <p>It is highly unlikely that a similar aggregated bibliography exists elsewhere.</p> <p>As this is new data, the reasons no suitable existing data are available is due to the breadth and scope of the aggregated dataset, which for the first time brings together literature from various disciplinary perspectives of young people and active citizenship across Europe, thus bridging a gap between previous studies that have mainly been limited to explorations of citizenship literatures within specific disciplines (and not across).</p> <p>The data sources are provided via csv document bibliography.</p> <p>The data set cleans and aggregates raw bibliographic data collected by consortium members during Months 1-3 of the research study.</p> <p>The aggregated literature dataset constitutes bibliographic data which can be reused by researchers who want to compare our data with similar data collected in different countries.</p>
CATCH-EyoU: Exploiting European data and testing the integrated theory of youth active EU citizenship: PIDOP subset reanalysis
<p>This is a subset of the full PIDOP dataset. The derived subset contains cross-sectional survey results from the PIDOP questionnaire survey that were collected in 9 European countries (incl. Turkey) during a period of 16-26 year old in 2011. The data set includes 9060 individual cases. The questionnaire used in the survey is published in Barrett, M. & Zani, B. (Eds.) (2015). <em>Political and civic engagement: Multidisciplinary perspectives.</em> Hove: Routledge (p.519-534).</p>
CATCH-EyoU Work Package 2 Dataset 2.1a - Full Consortium Collection of Literature Matrix
<p>The dataset includes <strong>one master spreadsheet containing literatures searched, catalogued and summarised in the fields of Cultural Studies, Education, History, Media and Communication, Philosophy, Political Science, Psychology and Sociology,</strong> which contains <strong>770 selected texts.</strong></p> <p> </p> <p>The aims of the data collected are to produce an integrated theory that builds on the findings of different disciplines (Cultural Studies, Education, History, Media and Communication, Philosophy, Political Science, Psychology and Sociology) focused on the understanding of factors and processes (from the macro social level to the social and psychological level), within the different life contexts, that promote or hinder youth active citizenship in EU.</p> <p> </p> <p>It is possible that similar databases of literature around Europe, Young People and Active Citizenship across the fields of Cultural Studies, Education, History, Media and Communication, Philosophy, Political Science, Psychology and Sociology exist in other forms, perhaps collected for studies on one or more of the included disciplines, but we do not currently have access to a similar repository.</p> <p> </p> <p>With that said, it is highly unlikely that an exact dataset corresponding to the specifics of this study exist in any form elsewhere, thus justifying the creation of new data for this study in the absence of suitable existing data. Data collected here will bridge the gap between global aggregated literatures on youth and citizenship separated by discipline on the one hand, and a new dataset offering an integrated literature analysis of different fields of study.</p> <p> </p> <p>The data sources are available in bibliographic format and attached via csv document.</p> <p> </p> <p>The dataset relies on the following information taken from the data sources: specific identifying information about the text itself (title/author/year/publisher); and abstract or summarizing information either taken directly from the text or summarized by the researcher.</p> <p> </p> <p>Finally, the aggregated literature review spreadsheet constitutes raw data which can be reused by researchers who want to compare our data with similar data collected in different countries, or to perform textual analysis (content analysis and/or data mining) on our data.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Longitudinal Survey Data: Wave 1 & Wave 2: Estonia
<p>The data set was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538. The data set is a truncated version of the adolescents’ and young adults’ longitudinal survey that was carried out in Estonia from October 2016 to February 2018. It merges results of two polls (15-19 and 20-30 year olds). Survey was conducted by Univversity of Tartu (UT) within the WP7 research activity which aims at testing processes influencing societal and political engagement of young people.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Survey Data: Estonia: Wave 2
<p>The data set was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538. The data set is a truncated version of the adolescents’ and young adults’ survey that was carried out in Estonia from November 2017 to February 2018. It merges results of two polls (15-19 and 20-30 year olds). Survey was conducted by University of Taartu (UT) within the WP7 research activity which aims at testing processes influencing societal and political engagement of young people.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Pilot Questionnaires: Estonia
<p>The data set was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538. The data set is a truncated version of the adolescents’ and young adults’ survey that was carried out in Estonia from May to June 2016. It merges results of two polls (16-18 and 20-26 year olds). Survey was conducted by University of Tartu (UT) within the WP7 research activity which aims at testing processes influencing societal and political engagement of young people.</p>
Detailed insight into gillnet catches: fish directivity and micro distribution
<p>This dataset contains data for gillnets that were deployed in Římov reservoir, South Bohemia, Czech Republic (48°50'55.0"N 14°29'14.0"E). The sampling dates were recorded from July 30 to August 2, 2019. This experiment was conducted to test the bias of gillnets in relation to fish direction capture. To determine if this is a random pattern or if it follows a directional pattern. The dataset includes various terms such as eventID, eventDate, country, countryCode, geodeticDatum, decimalLatitude, decimalLongitude, coordinateUncertaintyInMeters, habitat, waterBody, locality, DEIMS.iD, basisOfRecord, minimumDepthInMeters, maximumDepthInMeters, samplingEffort, samplingProtocol, dynamicProperties, occurrenceStatus, organismQuantity, organismQuantityType, measurementValue, measurementUnit, measurementType, measurementRemarks, organismRemarks, acceptedNameUsageID, scientificName, taxonRank, class, order, family.</p>
Binding site plasticity regulation of the FimH catch-bond mechanism: Molecular Dynamics dataset
<p>Dataset of Molecular Dynamics simulations and analysis scripts used in the article "Binding site plasticity regulation of the FimH catch-bond mechanism" [<a href="https://doi.org/10.1016/j.bpj.2023.05.029">paper</a>][<a href="https://doi.org/10.1101/2022.11.15.516604">bioRxiv</a>].</p> <p>Contains:</p> <ul> <li>Replica Exchange with Solute Scaling (REST2) simulations of the FimH protein lectin domain in its two main allosteric states (Associated and Separated), in presence and absence of its synthetic ligand heptyl α-ᴅ-mannose (input files and trajectories of the unscaled replicas)</li> <li>Replica Exchange Umbrella Sampling (REUS) simulations of the liganted systems along a collective variable (CV) describing binding site opening (input files and trajectories)</li> <li>REUS simulations in presence of a pulling force on the protein-ligand complex.</li> </ul> <p>See the article for more details.</p>
FIG. 3 in Catching tuna in the Aegean: biological background of tuna fisheries and the archaeological implications
FIG. 3. — Beach seine fishing scene on a Mycenean hydria (12th c. BC) from Naxos. The large fish in the net are probably tuna (reproduced from Hadjianastasiou 1991).
FIG. 2 in Catching tuna in the Aegean: biological background of tuna fisheries and the archaeological implications
FIG. 2. — Thynnus sp. bones from Late Classical strata (400-300 BC) at Kalaureia, Poros island. Scale bar: 4 cm. Credits: D. Mylona, Kalaureia excavations photographic archive.
FIG. 5 in Catching tuna in the Aegean: biological background of tuna fisheries and the archaeological implications
FIG. 5. — Sketch of a thynneion (stationary tuna trap) at Kandylio a few short distance north of Halieis (reproduced from Panagiotopoulos 1914).
FIG. 4 in Catching tuna in the Aegean: biological background of tuna fisheries and the archaeological implications
FIG. 4. — Wall painting of one of the two Little Fishermen from the so called West House at Akrotiri,Thera. The fish he holds are little tunny (Euthynnus allet- teratus Rafinesque, 1810) or bullet tunny (Auxis rochei Risso, 1810) (reproduced from Doumas 1992).
Data for Nemes et al. "Springing forward: Migrating songbirds catch up with the start of spring in North America"
<p>R scripts and data used to estimate bird migration rate and timing relative to spring onset rate and timing within North America.</p>
Figure 5 in Species Composition, Diversity and Length Frequency of By-Catch Sharks from the Syrian Coast
Figure 5. Geographic distribution of possible nursery areas for three species (H. griseus; +, C. plumbeus; ×, M. mustelus; *, black circles; main cities) in Syrian marine waters.
Figure1 in Species Composition, Diversity and Length Frequency of By-Catch Sharks from the Syrian Coast
Figure1. Changes of diversity indices seasonally for by-catch sharks: (a); diversity index H, (b); qualitative richness factor D, (c); evenness factor
Figure 3 in Species Composition, Diversity and Length Frequency of By-Catch Sharks from the Syrian Coast
Figure 3. Size (TL) variations of abundant and common of by-catch sharks per season off Syria coast. Males (gray), females (white): (a); H. griseus,(b); H. perlo, (c); C. plumbeus, (d); M. mustelus (e); G. melastomus, (f); S. blainvillei, (g); C. granulosus, (h); C. uyato
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
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DANDI Archive for NWB datasets
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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.