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We have found 691 datasets for the keyword " collaborative research". You can continue exploring the search results in the list below.
Datasets: 106,087
Contributors: 42
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691 Datasets, Page 1 of 70
Dominant Genus - Common Attribute Schema for Forest Resource Inventories
The Common Attribute Schema for Forest Resource Inventories (CASFRI) is a Canadian forest resource inventory data repository. Forest resource inventory datasets in CASFRI are harmonized to a common data model so that data collected by different agencies following different standards can be used together. Participating provincial, territorial and federal government departments and agencies share current and historical map-based forest resource inventory datasets through CASFRI so that their data are available to users who’s areas of interest span multiple jurisdictions. CASFRI was originally developed by academic researchers (Cumming et al., https://doi.org/10.1139/cjfr-2014-0102). This flavour of CASFRI (CASFRIv5) was developed anew in collaboration with academic researchers at the University of Laval to provide a government version of CASFRI that is findable, accessible, interoperable, and reusable. It uses the most up-to-date forest inventory data provided by participating provincial, territorial, and federal government departments and agencies. CASFRIv5 is hosted on the Canadian Council of Forest Ministers’ data portal, the National Forest Information System (http://nfis.org).
Tree Type - Common Attribute Schema for Forest Resource Inventories
The Common Attribute Schema for Forest Resource Inventories (CASFRI) is a Canadian forest resource inventory data repository. Forest resource inventory datasets in CASFRI are harmonized to a common data model so that data collected by different agencies following different standards can be used together. Participating provincial, territorial and federal government departments and agencies share current and historical map-based forest resource inventory datasets through CASFRI so that their data are available to users who’s areas of interest span multiple jurisdictions. CASFRI was originally developed by academic researchers (Cumming et al., https://doi.org/10.1139/cjfr-2014-0102). This flavour of CASFRI (CASFRIv5) was developed anew in collaboration with academic researchers at the University of Laval to provide a government version of CASFRI that is findable, accessible, interoperable, and reusable. It uses the most up-to-date forest inventory data provided by participating provincial, territorial, and federal government departments and agencies. CASFRIv5 is hosted on the Canadian Council of Forest Ministers’ data portal, the National Forest Information System (http://nfis.org).
Kernel Density Analyses of Coral and Sponge Catches from Research Vessel Survey Data (2016)
Kernel density estimation (KDE) utilizes spatially explicit data to model the distribution of a variable of interest. It is a simple non-parametric neighbour-based smoothing function that relies on few assumptions about the structure of the observed data. It has been used in ecology to identify hotspots, that is, areas of relatively high biomass/abundance, and in 2010 was used by Fisheries and Oceans Canada to delineate significant concentrations of corals and sponges. The same approach has been used successfully in the Northwest Atlantic Fisheries Organization (NAFO) Regulatory Area. Here, we update the previous analyses with the catch records from up to 5 additional years of trawl survey data from Eastern Canada, including the Gulf of Saint Lawrence. We applied kernel density estimation to create a modelled biomass surface for each of sponges, small and large gorgonian corals, and sea pens, and applied an aerial expansion method to identify significant concentrations of these taxa. We compared our results to those obtained previously and provided maps of significant concentrations as well as point data co-ordinates for catches above the threshold values used to construct the significant area polygons. The borders of the polygons can be refined using knowledge of null catches and species distribution models of species presence/absence and/or biomass.
Agriculture and Agri-Food Canada Research Centres
This dataset series highlights the locations of research centres where scientists, technicians and staff work to create better opportunities for farmers and all Canadians through agricultural research and innovation.
2019-20 Grants and Contributions
Data provided shows grants and contributions provided to Canadian firms by National Research Council (NRC) and its Industrial Research Assistance Program (IRAP) between April 1, 2019 and March 31, 2020.
2020-21 Grants and Contributions
Data provided shows grants and contributions provided to Canadian firms by National Research Council (NRC) and its Industrial Research Assistance Program (IRAP) between April 1, 2020 and March 31, 2021.
2023-24 Grants and Contributions
Data provided shows grants and contributions provided to Canadian firms by National Research Council (NRC) and its Industrial Research Assistance Program (IRAP) between April 1, 2023 and March 31, 2024.
2021-22 Grants and Contributions
Data provided shows grants and contributions provided to Canadian firms by National Research Council (NRC) and its Industrial Research Assistance Program (IRAP) between April 1, 2021 and March 31, 2022.
2022-23 Grants and Contributions
Data provided shows grants and contributions provided to Canadian firms by National Research Council (NRC) and its Industrial Research Assistance Program (IRAP) between April 1, 2022 and March 31, 2023.
2024-25 Grants and Contributions
Data provided shows grants and contributions provided to Canadian firms by National Research Council (NRC) and its Industrial Research Assistance Program (IRAP) between April 1, 2024 and March 31, 2025.
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