This compilation includes two isotope datasets for Yukon. The first dataset comprises whole-rock (Nd, Hf, Sr, Pb, S and O) and feldspar (Pb) isotope analyses; the second sulphide (S and Pb) isotope analyses.

This compilation includes published Nd, Hf, Sr and Pb (plus minor O, S and C) isotopic data from 1722 whole-rock powders and feldspar separates. About 1200 of these samples are located in Yukon and the remainder are located in British Columbia, Alaska or Northwest Territories. This compilation of whole-rock and feldspar isotopic data builds off of a compilation that Kirsten Rasmussen (2013) did as part of her doctoral thesis at the University of British Columbia.To allow easier integration into a user’s GIS platform, and for greater spatial querying of the data, the “database” is presented as a “flat file” (i.e., shapefile, geodatabase and text formats).This whole-rock and feldspar isotope database will be subject to periodic updates as new data are acquired through ongoing mapping, exploration and other research activities. Any errors, omissions or new data known to users should be reported to the Yukon Geological Survey. Your feedback contributes to improving the accuracy of the geoscience databases for Yukon.Contact: [Patrick.Sack@yukon.ca](mailto:Patrick.Sack@yukon.ca); [YGS-Bedrock@yukon.ca](YGS-Bedrock@yukon.ca)**Reference:**Rasmussen, K.L., 2013. The timing, composition and petrogenesis of syn- to post-accretionary magmatism in the northern Cordilleran miogeocline, eastern Yukon and southwest Northwest Territories. PhD thesis, University of British Columbia, 810 p.Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps).Discover more digital map data and interactive maps from Yukon’s digital map data collection. For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca).

This compilation includes S (1251 analyses) and Pb (1622 analyses) isotopic data from nearly 450 mineral occurrences. Approximately 300 of these occurrences are located in Yukon and the remainder are located in British Columbia, Alaska or Northwest Territories. To facilitate data interpretation, a hierarchical deposit model scheme and mineralization age are ascribed to each occurrence.The Pb isotope portion of this compilation builds upon the compilation by Collin Godwin (1988) that was subsequently maintained by the University of British Columbia. Data from Alaska are primarily from the Gaccetta and Church (1989) compilation.This dataset is designed to be used in conjunction with a GIS platform and as such is presented here as a "flat file" (i.e., shapefile, geodatabase and text formats). The simple data structure allows for easier integration into GIS platforms and greater spatial querying of the data.This sulphide isotope database will be subject to periodic updates as new data are acquired through ongoing mapping, exploration and other research activities. Any errors, omissions or new data known to users should be reported to the Yukon Geological Survey. Your feedback contributes to improving the accuracy of the geoscience databases for Yukon.Contact: [Patrick.Sack@yukon.ca](mailto:Patrick.Sack@yukon.ca) ; [YGS-Bedrock@yukon.ca](YGS-Bedrock@yukon.ca)References:Gaccetta, J.D. and Church, S.E., 1989. Lead isotope database for sulfide occurrences from Alaska. USGS Open-File 89-688, p. 60.Godwin, C.I., Gabites, J.E. and Andrew, A., 1988. Leadtable: A Galena lead isotope database for the Canadian Cordillera, with a guide to its use by explorationists. British Columbia Mineral Resources Division, Geological Survey Branch, Paper 1988-4, p. 1-24.Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) .Discover more digital map data and interactive maps from Yukon's digital map data collection. For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

Ferry Terminals is a point dataset identifying vehicle and passenger ferry terminals in British Columbia.

Note: To visualize the data in the viewer, zoom into the area of interest.The National Air Photo Library (NAPL) of Natural Resources Canada archives over 6 million aerial photographs covering all of Canada, some of which date back to the 1920s.This collection includes Time Series of aerial orthophoto mosaics over a selection of major cities or targeted areas that allow the observation of various changes that occur over time in those selected regions.These mosaics are disseminated through the Data Cube Platform implemented by NRCan using geospatial big data management technologies. These technologies enable the rapid and efficient visualization of high-resolution geospatial data and allow for the rapid generation of dynamically derived products. The data is available as Cloud Optimized GeoTIFF (COG) files for direct access and as Web Map Services (WMS) or Web Coverage Services (WCS) with a temporal dimension for consumption in Web or GIS applications. The NAPL mosaics are made from the best spatial resolution available for each time period, which means that the orthophotos composing a NAPL Time Series are not necessarily coregistered. For this dataset, the spatial resolutions vary from 10 cm to 50 cm.The NAPL indexes and stores federal aerial photography for Canada, and maintains a comprehensive historical archive and public reference centre. The Earth Observation Data Management System (EODMS) online application allows clients to search and retrieve metadata for over 3 million out of 6 million air photos.The EODMS online application enables public and government users to search and order raw Government of Canada Earth Observation images and archived products managed by NRCan such as aerial photos and satellite imagery. To access air photos, you can visit the EODMS web site: https://eodms-sgdot.nrcan-rncan.gc.ca/index-en.html

DFO Maritimes Region has conducted a cumulative human impact mapping analysis for the Scotian Shelf-Bay of Fundy management area to support ongoing Marine Spatial Planning initiatives (Murphy et al. 2024). Cumulative human impact mapping (CIM) combines spatial information on human activities and habitats with a matrix of vulnerability weights, into an intuitive relative ‘cumulative impact score’ that shows where cumulative human impacts are greatest and least. To map cumulative impacts in DFO’s Maritimes Region, a recently developed ecosystem vulnerability assessment for Atlantic Canadian waters (Murray et al. 2022) was combined with spatial information on 21 different habitat types and 45 human activities across five different sectors (climate change, land-based, marine-based, coastal, commercial fishing) following the methodology from Halpern et al. (2008). An uncertainty analysis of the cumulative impact map was conducted to assess the robustness of results and identify hot and cold spots of cumulative impacts. This dataset provides: 1) cumulative impact maps for the DFO Maritimes Region at 1 km2 resolution: a total cumulative impact map (i.e. including all 45 human activities), as well as cumulative impact maps for each of the five sectors, 2) a layer that identifies which grid cells are considered hot and cold spots of cumulative human impacts, and 3) the habitat layers included in the CIM.For further information concerning specifics of the maps and methods see Murphy et al. (2024) or contact the data provider. References:Halpern, B.S., Walbridge, S., Selkoe, K.A., Kappel, C.V., Micheli, F., D'Agrosa, C., Bruno, J.F., Casey, K.S., Ebert, C., Fox, H.E., Fujita, R., Heinemann, D., Lenihan, H.S., Madin, E.M.P., Perry, M.T., Selig, E.R., Spalding, M., Steneck, R., and Watson, R. 2008. A Global Map of Human Impact on Marine Ecosystems. Science. 319(5865): 948-952. doi:10.1126/science.1149345.Murray, C.C., Kelly, N.E., Nelson, J.C., Murphy, G.E.P., and Agbayani, S. 2022. Cumulative impact mapping and vulnerability of Canadian marine ecosystems to anthropogenic activities and stressors. DFO Can. Sci. Advis. Sec. Res. Doc. 2022/XXX. vi. + 52 p.Murphy, G.E.P., Stock, A., and Kelly, N.E. 2024 (in press). From land to deep sea: A continuum of cumulative human impacts on marine habitats in Atlantic Canada. Ecosphere.Cite this data as: Murphy, Grace; Kelly, Noreen (2023) Cumulative human impact maps for the Bay of Fundy and Scotian Shelf. Published September 2023. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/37b59b8b-1c1c-4869-802f-c09571cc984b

To identify the overall reach and configuration of the Capital Pathway, show the location of existing pathways (as of 2020) and proposed pathway segments, and distinguish between discovery routes and feeder links.Capital Pathway Strategic Plan, October 2020 https://ncc-website-2.s3.amazonaws.com/documents/Pathways-BOOK-10-15-2020-EN_Final_Compressed.pdf

Description:Night-time sea surface temperature (SST) was acquired from the AVHRR Pathfinder project, with data distributed by NOAA, and averaged into monthly climatological composites. The data span late 1981 through 2010 at 4 km pixel resolution.Methods:AVHRR Pathfinder version 5.3 Level 3C night Sea Surface Temperature (SST) was acquired from NOAA at 4 km spatial resolution. The monthly mean value at all pixels was calculated for individual years, then all years were combined to produce final maps of monthly mean and monthly standard deviation of SST, and the number of occurrences of valid data at each pixel over the period of observation. The quality level of all satellite observations was also acquired with this dataset, and used to remove any pixels with a quality level lower than 4. Further, pixels with fewer than two occurrences over the period 1981-2010 were removed from these maps, and set to a NaN value in the tif files. All resulting rasters were cropped to the Canadian Exclusive Economic Zone and assigned to the NAD83 geographic coordinate reference system (EPSG:4269), and have a final pixel resolution of approximately 0.0417 degrees. The monthly mean, monthly standard deviation, and number of occurrences for all pixels are provided.Uncertainties:Satellite values have been evaluated against global datasets, and datasets of samples in the Pacific region (see references). However, uncertainties are introduced when averaging together images over time as each pixel has a differing number of observations. Short-lived or spatially limited events may be missed.

High resolution forest change for Canada (Change Year) 1985-2011The forest change data included in this product is national in scope (entire forested ecosystem) and represents the first wall-to-wall characterization of wildfire and harvest in Canada at a spatial resolution commensurate with human impacts. The information outcomes represent 27 years of stand replacing change in Canada’s forests, derived from a single, consistent spatially-explicit data source, derived in a fully automated manner. This demonstrated capacity to characterize forests at a resolution that captures human impacts is key to establishing a baseline for detailed monitoring of forested ecosystems from management and science perspectives. Time series of Landsat data were used to characterize national trends in stand replacing forest disturbances caused by wildfire and harvest for the period 1985–2011 for Canada's 650 million hectare forested ecosystems (https://authors.elsevier.com/sd/article/S0034425717301360 ). Landsat data has a 30m spatial resolution, so the change information is highly detailed and is commensurate with that of human impacts. These data represent annual stand replacing forest changes. The stand replacing disturbances types labeled are wildfire and harvest, with lower confidence wildfire and harvest, also shared. The distinction and sharing of lower class membership likelihoods is to indicate to users that some change events were more difficult to allocate to a change type, but are generally found to be in the correct category. For an overview on the data, image processing, and time series change detection methods applied, as well as information on independent accuracy assessment of the data, see Hermosilla et al. (2016; http://www.tandfonline.com/doi/full/10.1080/17538947.2016.1187673).The data available is, 1. a binary change/no-change; 2. Change year; and, 3. Change type. When using this data, please cite as: White, J.C., M.A. Wulder, T. Hermosilla, N.C. Coops, and G. Hobart. (2017). A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sensing of Environment. 192: 303-321. DOI: 10.1016/j.rse.2017.03.035. https://authors.elsevier.com/sd/article/S0034425717301360Geographic extent: Canada's forested ecosystems (~ 650 Mha)Time period: 1985–2011

Description:Night-time sea surface temperature (SST) was acquired from the AVHRR Pathfinder project, with data distributed by NOAA, and averaged into monthly climatological composites. The data span 1990 through 2020 at 4 km pixel resolution.Methods:AVHRR Pathfinder version 5.3 Level 3C night Sea Surface Temperature (SST) was acquired from NOAA at 4 km spatial resolution. The monthly mean value at all pixels was calculated for individual years, then all years were combined to produce final maps of monthly mean and monthly standard deviation of SST, and the number of occurrences of each pixel over the period of observation. The quality level of all satellite observations was also acquired with this dataset, and used to remove any pixels with a quality level lower than 4. Further, pixels with fewer than two occurrences over the period 1990-2020 were removed from these maps, and set to a NaN value in the tif files. All resulting rasters were cropped to the Canadian Exclusive Economic Zone and assigned to the NAD83 geographic coordinate reference system (EPSG:4269), and have a final pixel resolution of approximately 0.0417 degrees. The monthly mean, monthly standard deviation, and number of occurrences for all pixels are provided.Uncertainties:Satellite values have been evaluated against global datasets, and datasets of samples in the Pacific region (see references). However, uncertainties are introduced when averaging together images over time as each pixel has a differing number of observations. Short-lived or spatially limited events may be missed.