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86 results for “firm”
Global monthly catches from tuna surface fisheries by 1° grid (1958-2023) (FIRMS level 0)
<p>We compiled a comprehensive dataset of geo-referenced catches from global tuna fisheries that use fishing gears set at the water's surface. This dataset was created by harmonizing public domain data from the five tuna Regional Fisheries Management Organizations (t-RFMOs) for the period 1958-2023. 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.</p> <p>Geo-referenced catch data from tuna surface 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, 1° grid area of longitude and latitude, and taxon.</p> <p>The dataset encompasses 42 medium- and large-sized pelagic species found in both neritic and oceanic habitats of the world's oceans. This includes 14 species of tunas, 9 species of billfish, 4 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 12 species of pelagic sharks and rays that may be either targeted or incidentally caught in tuna and tuna-like fisheries.</p> <p>The dataset serves as a benchmark for the monitoring and assessment of both artisanal and industrial fisheries using surrounding nets, gillnets, entangling nets, and pole-and-lines from over 70 fishing fleets across 69 countries that have exploited tuna and tuna-like species for subsistence and commercial purposes over more than six decades.</p>
Global annual catches from tuna fisheries (1918-2023) (FIRMS level 0)
<p>We constructed the most comprehensive dataset of nominal catches from global tuna fisheries by compiling and harmonizing public domain data from the five tuna Regional Fisheries Management Organizations (t-RFMOs) for the period 1918-2023. 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>Nominal catch data are expressed in live-weight equivalent (metric tonnes) and primarily represent the quantities of retained fish either landed or transhipped at sea and in ports. In recent years,data from fisheries in the Atlantic and Western-Central Pacific Oceans have partially included amounts of fish discarded dead. The data are stratified by year,fishing fleet,fishing gear,large spatial area,and taxon.<br><br>The dataset encompasses 50 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,8 species of Spanish mackerels,2 species of bonitos,and wahoo. In 2023,the global catch for these species was estimated to exceed 6.4 million metric tonnes. 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. The total reported catch of these elasmobranch species was approximately 154,000 metric tonnes in 2023.<br><br>The dataset serves as a benchmark for the monitoring and assessment of both artisanal and industrial fisheries from over 161 fishing fleets across 159 countries that have exploited tuna and tuna-like species for subsistence and commercial purposes over more than seven decades.</p>
Data for "Competing for capitals: the great fragmentation of the firm and varieties of FDI attraction profiles in the European Union""
<p>Dataset for https://www.tandfonline.com/doi/full/10.1080/09692290.2020.1737564</p> <p> </p> <p>Code available at https://osf.io/q6x97/</p>
Global monthly catches from tuna fisheries by 1° and 5° grids (1950-2023) (FIRMS level 0)
<p>We compiled a comprehensive dataset of geo-referenced catches from global tuna fisheries available at a spatial resolution of 1° and 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, 1° or 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.</p>
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.
Board Leadership Database (U.S. Public Firms) + ML Script for Scaling Human Coded Data
<p>Files include: (1) an open sourced database of CEO duality and board chair orientations developed by scaling human coded data using supervised machine learning techniques (in both .dta and .csv formats), as well as (2) the accompanying training and scoring scripts to scale human coded data.</p> <p>Users may apply the scoring script to score the same variables from company proxy statements, or may adapt the training/scoring scripts and retrain models to scale human coded data of other constructs or measures. </p> <p>We note that early steps in the process to develop our database and script required web-scraping of company filings from SEC Edgar and text extraction from collected filings. We relied on other publicly available scripts to develop our own fetcher and extraction scripts. Users seeking to duplicate those parts of the process may benefit from the following resources from Kai Chen and pipy.org: </p> <p>For resources from Kai Chen: see <a href="https://urldefense.com/v3/__https:/www.kaichen.work/?p=681__;!!K6Z8K8YTIA!BizP9-ZnzgV0Pq7ck-UENJ1EBDrFkAkNoCaO34Ad1ezxH_okstsfxniagvxXdWudkDP44fPACbY9vaIG5A$">https://www.kaichen.work/?p=681</a> and <a href="https://urldefense.com/v3/__https:/www.kaichen.work/?p=946__;!!K6Z8K8YTIA!BizP9-ZnzgV0Pq7ck-UENJ1EBDrFkAkNoCaO34Ad1ezxH_okstsfxniagvxXdWudkDP44fPACbZhp5N_Tw$">https://www.kaichen.work/?p=946</a></p> <p>For resources from pipy.org, see <a href="https://urldefense.com/v3/__https:/pypi.org/project/sec-edgar-downloader/__;!!K6Z8K8YTIA!BizP9-ZnzgV0Pq7ck-UENJ1EBDrFkAkNoCaO34Ad1ezxH_okstsfxniagvxXdWudkDP44fPACbYxYSZGGw$">sec-edgar-downloader</a> and <a href="https://urldefense.com/v3/__https:/pypi.org/project/sec-api/__;!!K6Z8K8YTIA!BizP9-ZnzgV0Pq7ck-UENJ1EBDrFkAkNoCaO34Ad1ezxH_okstsfxniagvxXdWudkDP44fPACbZ_FwY6jw$">sec-api</a></p> <p> </p>
H2020 Prime Fish Firm level competitiveness data Iceland Norway Newfoundland
<p>The data set contains survey data from three Norwegian, one Icelandic, one Newfoundland fish processing firm. Data are collected as part of the EU H2020 project PrimeFish (grant no 635761). The survey asks several questions concerning the firm’s evaluation of several aspects of competitiveness, following a Porter framework. The questions posed to the respondents are stated along with scoring help. All data are numeric, and on a 1-7 scale. Data were collected from the World Economic Forum 2017 competitiveness report and surveys and hard data collected in 2017.</p>
Shanghai zhizao changshang gailan 上海製造廠商概覽 (A Compendium of Shanghai Manufacturing Firms) dataset
<p>The 上海製造廠商概覽 dataset contains the data extracted from the survey of industrial establishments in Shanghai published in 1947 by Lianhe zhengxinsuo 聯合徵信所.</p> <p>The spreadsheet contains the original data ("Data-Main" tab), the metadata ("Metadata" tb) and a series of pre-calculated dataset based on the main data.</p> <p>The dataset was used as the basis for the papers published by Christian Henriot:</p> <p>1. “The impact of war on Shanghai’s industrial structure: A GIS-based analysis of the Shanghai industrial surveys (1935-1940)” (with Isabelle Durand), <a href="https://www.virtualshanghai.net/Data/Tables?ID=237#.Uweb8V5ySCU"><em>Annals of GIS</em></a>, Vol. 18, no. 1, 2012, 45-55</p> <p>2. “Regeneration and Mobility: The Spatial Dynamics of Industries in Wartime Shanghai”, <a href="http://www.journals.elsevier.com/journal-of-historical-geography/"><em>Journal of historical geography</em></a>, vol. 38, no. 2, 2012, pp. 167-180</p>
Figure 4. After merging, overview is more transparent. Tens of persons were merged together into clusters in order to clarify the visualization. Firms and persons are recognized based on their icons.-Browsing Semantic Data in Slovakia
<p>The usefulness of such visualization has its key points regarding connections. Thanks to SBR browsing module, we were able to get 22 firm records for “Váhostav” query. Between any 2 companies, connections may be (and often are) not bidirectional, so, in order to navigate through connections, we have refined all 22 records. Although, even being filtered, graph is still complex. And it is possible to further navigate and search for outgoing connections, for example firm “MERLIN TRADE, a.s.” on Fig.4 contains item on “Ján Kato”, which is already included in our graph and connected to “VÁHOSTAV&SK&DEVELOPEMENT” on bottom left side and “VÁHOSTAV&SK, a.s.” in the center. Edge coloring and drawing is helpful with overlapped edges. For methods of visualization, including coloring, we refer to studies of H. Omote and K. Sugiyama (2006), and I. Herman, G. Melanon, and M. S. Marshall (2000) or our study on graph clutter filtering and connectivity distance (Mojzis & Laclavik, 2014).</p>
Figure 2. Results for firm name "Váhostav" are in table. Each row defines a firm with its name, identification number and address. Then a connection is specified (whether it be a person or another firm).-Browsing Semantic Data in Slovakia
<p>We have searched for firm “Váhostav”, which is a rather big firm in Slovakia, with many press articles published about31. On Figure 2 there is a browsing window, for SBR data results, displaying tabular structure, which was refined from SBR dataset by continuous querying.</p>
Analysis of the survival of agricultural exporting firms in Peru, 2009-2019
<p><strong><span>Background</span></strong><strong><span>: </span></strong><span>At the international level, the survival of exporting companies represents a critical issue in a context of heightened uncertainty and intensified competition within the framework of the country's commercial opening. This is a context in which different companies are born and die as a result of the interaction between the market and other factors. The objective of this research was to analysis of the survival of exporting agricultural companies in Peru, 2009-2019 . To this end, data from the Commission for the Promotion of Peru for Exports and Tourism (Promperu) was utilised.</span></p> <p><strong><span>Methods: </span></strong><span>The methodological contribution of the research is based on the quantitative approach, of basic type with a descriptive elk; being the population that involves a total of data of the agricultural exporting companies of Peru in the period 2009-2019 and the sample is census and the non-parametric statistical technique used was the Kaplan Meier estimate for the estimation of the survival rate. </span></p> <p><strong><span>Results: </span></strong><span>Exports of Peru's non-traditional agricultural sector in FOB value have had an average annual growth of 12% in terms of FOB value and 9% in terms of volume exported</span><strong><span>; </span></strong><span>the entry rate of new agro-exporting agricultural companies reached an average growth of 2.3% and the exit rate reached an average of 2.2% in the period 2009-2019.</span></p> <p><strong><span>Conclusions: </span></strong><span>The survival of exporting companies in the non-traditional agricultural sector is critical, where 89% of them survive only one year, while in the second year only 75% survive and in the sixth year only 33% survive.</span></p> <p><strong><span>Keywords: </span></strong><span>Agricultural exporting firms, Export resilience, Economic sustainability, Peru's agricultural exports, Business survival analysis</span></p>
Text-fig. 36. Scanning electron microscope (SEM) images of flower of Eckhartianthus lusitanicus gen. et sp. nov.; Torres Vedras locality, Portugal. a) Holotype; oblique lateral view of hypogynous flower showing perianth surrounding the stamens and protruding gynoecium; b–d) Pollen from the flower in (a) showing single distal aperture and reticulate exine with thick muri, small lumina and short, densely spaced columellae; e) Reticulum showing thick, smooth muri supported by numerous short columellae firmly attached to the smooth surface of the foot layer. Specimen, TV39-S101306 (holotype). Scale bars 300 Μm (a), 6 Μm (b–d), 3 Μm (e). in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 36. Scanning electron microscope (SEM) images of flower of Eckhartianthus lusitanicus gen. et sp. nov.; Torres Vedras locality, Portugal. a) Holotype; oblique lateral view of hypogynous flower showing perianth surrounding the stamens and protruding gynoecium; b–d) Pollen from the flower in (a) showing single distal aperture and reticulate exine with thick muri, small lumina and short, densely spaced columellae; e) Reticulum showing thick, smooth muri supported by numerous short columellae firmly attached to the smooth surface of the foot layer. Specimen, TV39-S101306 (holotype). Scale bars 300 Μm (a), 6 Μm (b–d), 3 Μm (e).
Text-fig. 39. Scanning electron microscope (SEM) images of monocolpate pollen of Eckhartia intermedia sp. nov. from a pollen clump, probably fragment of a stamen (a–e) and anthers and pollen of Eckhartia sp. (f–k); Torres Vedras locality, Portugal. a) Pollen clump that yielded the pollen in (b–e); b–d) Pollen grains viewed from distal (d) and proximal surfaces (b, c) showing the long colpus and well-developed reticulum with large and small luminae; e) Reticulum showing smooth muri supported by short columellae firmly attached to the smooth surface of the foot layer; note the occasional small luminae; f, g) Fragments of narrow elongate anthers with same kind of pollen as in (i); h) Inner surface of anther wall showing small spherical orbicules; in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 39. Scanning electron microscope (SEM) images of monocolpate pollen of Eckhartia intermedia sp. nov. from a pollen clump, probably fragment of a stamen (a–e) and anthers and pollen of Eckhartia sp. (f–k); Torres Vedras locality, Portugal. a) Pollen clump that yielded the pollen in (b–e); b–d) Pollen grains viewed from distal (d) and proximal surfaces (b, c) showing the long colpus and well-developed reticulum with large and small luminae; e) Reticulum showing smooth muri supported by short columellae firmly attached to the smooth surface of the foot layer; note the occasional small luminae; f, g) Fragments of narrow elongate anthers with same kind of pollen as in (i); h) Inner surface of anther wall showing small spherical orbicules;
Fig. 3 in Scientific Note Feeding ecology of the leaf fish Monocirrhus polyacanthus (Perciformes: Polycentridae) in a terra firme stream in the Brazilian Amazon
Fig. 3. Prey size (total length, in mm) in relation to predator's size (standard length, in mm) for specimens of the leaf fish Monocirrhus polyacanthus (r2 = 0.44, F = 13.24, p = 0.002, Number of measured preys = 19).
Fig. 2 in Scientific Note Feeding ecology of the leaf fish Monocirrhus polyacanthus (Perciformes: Polycentridae) in a terra firme stream in the Brazilian Amazon
Fig. 2. Proportion of preys (fish and invertebrates) recorded in the stomach of the leaf fish Monocirrhus polyacanthus, by predator size classes (Number of stomachs with food = 19).
Fig. 1 in Sampling effort and fish species richness in small terra firme forest streams of central Amazonia, Brazil
Fig. 1. Fish species accumulation curves estimated from samples obtained in 1st, 2nd, and 3rd order streams reaches located in the study areas of Biological Dynamics of Forest Fragments Project, Manaus, Amazonas State. The curves represent extrapolations from five reaches sampled in each stream segment.
Dataset for Manually Coded Firm Founding Dates, CEO Duality, and CEO Photograph Prominence in Annual Reports
<p>In this dataset, we wish to make available some measures which we manually coded as part of our PhDs. It contains manually coded information on S&P1500 firms and their CEOs, specifically, the firm’s founding year, whether the CEO is simultaneously chairperson of the board (CEO duality), and the prominence of the CEO in photographs in the firm’s annual report. More information can be found in the attached documentation. Both authors contributed equally to the creation of this dataset.</p> <p>Since this is the first version of this dataset, we are happy about any feedback, corrections, or additions. Please reach out to us in case you spot any irregularities or have questions via mappels@mail.uni-mannheim.de or mkowalzi@staff.mail.uni-mannheim.de. </p> <p> </p> <p>The creation of this dataset was supported by the <a href="https://de.wikiversity.org/wiki/Wikiversity:Fellow-Programm_Freies_Wissen">Fellowship Freies Wissen</a> of the Wikimedia Foundation. More information on the project can be found <a href="https://de.wikiversity.org/wiki/Wikiversity:Fellow-Programm_Freies_Wissen/Einreichungen/Lowering_the_Barriers_for_Young_Strategy_Scholars:_An_Open-Access_Database_for_Manually_Computed_Variables_of_Strategy_Research">here</a>.</p>
Replication Package for: Mapping Firms' Locations in Technological Space: A Topological Analysis of Patent Statistics
<p>This replication package contains the data and the code to generate the paper’s main results, as well as the Online Appendix, for “Mapping Firms’ Locations in Technological Space: A Topological Analysis of Patent Statistics” by Emerson G. Escolar, Yasuaki Hiraoka, Mitsuru Igami, and Yasin Ozcan (published in <em>Research Policy</em>, volume 52, issue 8, October 2023; full text available online at https://doi.org/10.1016/j.respol.2023.104821).</p>
Data set on firm characteristic and cash holding in Nigeria
<p>This data set is for research on firm characteristics and cash holding. The data comprise panel data for variables such as return on asset, asset tangibility, leverage, capital expenditure, dividend, growth, cash, firm size, firm age and foreign ownership, for 600 firm-year. </p>
Large Firm Dominance on Country's Entrepreneurship Potentials
<p>It is the data set that I used to study how large firm dominance affect country's entrepreneurship potentials. Data sourced from Global Entrepreneurship Monitor (GEM) and World Bank-International Finance Corporation's MSME Country Indicators.</p>
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.