Search

Saint Mary's University Dataverse Logo
Fish Mercury Datalayer for Canada (FIDMAC) Restricted content
Depew, David C.; Campbell, Linda M.; Burgess, Neil M. — 2021-05-13 Interested researchers should submit the online Data Request Form. It is important to understand the nature of this datalayer that is based on NDMMF output, the limitations of this data and conditions for using this data For more information on the data and to request access: www.smu.ca/research/fish-mercury-datalayer.html The FIMDAC represents a model-derived output of Hg concentrations in a common indicator species (12-cm whole-yellow perch), established from the application of the United States Geological Survey's (USGS) National Descriptive Model of Mercury in Fish (NDMMF, Wente 2004) to the Canadian Fish Mercury Database (CFMD, Depew et al. 2013b). The geographical distribution of yellow perch is wide-ranging, and they represent an important prey species for piscivorous fish, birds, and mammals. Parameters estimated by way of NDMMF were unbiased, and strong spatial biases in prediction error were not evident. The FIMDAC records represent the estimated Hg burden (ug.g, wet weight) for a standard length (12 cm) whole-yellow perch at 1936 unique freshwater sites across Canada, collected between 1990 and 2010. Further details regarding the development of the FIMDAC can be found in Depew et al. (2013a).
Saint Mary's University Dataverse Logo
Functional Trait Database for Nova Scotian Coastal Barren, Green Roof, and Ruderal Flora
Heim, Amy; Lundholm, Jeremy — 2022-02-28 This is the first plant functional trait database for Nova Scotia, Canada. The data contained here were collected between 2016 and 2019 from locations around Halifax, Nova Scotia. The species selected for trait collection were chosen based on species inventories taken across Nova Scotian coastal barrens and from green roofs at Saint Mary’s University. The purpose of the coastal barren trait data was to understand community assembly in this understudied ecosystem. The green roof inventory was included as coastal barren species are known to succeed on green roofs in Nova Scotia. The green roof trait data was used to answer questions surrounding coexistence and trait divergence, and community assembly and spatial heterogeneity. In total, this database contains 14,341 trait values from 203 species comprising 130 genera and 53 families. The majority of species are commonly found on coastal barrens (84 species), disturbed sites (48 species), and forests (27 species). Additionally, this database contains trait data for 30 species that have been successfully established (survival for >1 year) on green roofs in Nova Scotia and ruderal species that commonly colonize both green roofs and coastal barens. This database contains 12 plant functional traits: leaf thickness (203 species), leaf area (203 species), specific leaf area (203 species), leaf dry matter content (203 species), plant height (203 species), canopy width (203 species), seed weight (79 species), seed shape (61 species), root radius (22 species), leaf phosphorus content (3 species), leaf nitrogen content (30 species), and leaf carbon content (30 species). The species in this database can be subdivided into 10 growth forms, with the majority of species characterized as forbs (75 species), shrubs (56 species), or graminoids (33 species). See NS Flora Guide for details on data
Saint Mary's University Dataverse Logo
Land- and water-only Level 3 products from MOPITT TIR-NIR Version 8 CO retrievals
Ashpole, Ian; Wiacek, Aldona — 2022-11-22 <p>This dataset contains alternative products to the official Level 3 (L3) product from Measurements of Pollution in the Troposphere (MOPITT) joint thermal infrared (TIR) – near infrared (NIR) Version 8 Carbon Monoxide (CO) retrievals (available here: <a>https://doi.org/10.5067/TERRA/MOPITT/MOP03J_L3.008</a>). The products are described and analysed in a paper in the journal Atmospheric Measurement Techniques by Ian Ashpole and Aldona Wiacek (2022, <a>https://doi.org/10.5194/amt-2022-90</a>). </p> <p>In short, whereas the official MOPITT L3 product is based on retrievals performed over both land AND water surface types, the products here are created separately from retrievals performed ONLY over land (“L3L”) OR water (“L3W”). The code for creating L3L and L3W is available here: <a>https://github.com/ianashpole/MOPITT_L3L_L3W</a> </p> <p>The version naming is consistent with the official MOPITT product version, although note that version 8 is the first version that these alternatives are produced for (i.e. although MOPITT product versions 1-7 exist, L3L and L3W do not). However, it is intended that L3L and L3W are created for MOPITT product versions after version 8.</p> <p>The dataset stored here consists of two main .zip archives:<br> <i>“MOPITT_v8.L3L.20010901_20190228.zip”</i><br> <i>“MOPITT_v8.L3W.20010901_20190228.zip”</i><br> When unzipped, each archive contains 6057 individual NetCDF (".nc") files that correspond to the daily L3L and L3W data products for the period 2001-09-01 to 2019-02-28, inclusive. Daily files represent the satellite instrument measurements for a single day. Users are referred to the <b>"README.txt"</b> file for a full description of the individual file contents. Note that when unzipped, the products require ~22.5 GB of data storage each (45 GB total for both L3L and L3W). Because of this, a single file from each product has been uploaded separately (file date = “20020801”; see below for naming convention) to facilitate user experimentation before unpacking the full L3L/L3W products. Individual L3L/L3W NetCDF files are ~3.4 MB in size.</p> <p>The individual NetCDF files are named as follows: <i>MOPITT_v8.L3L.from_MOPO2J.selected_variables.YYYYMMDD.nc</i> (replace “L3L” with “L3W” in the filename for the corresponding L3W product.) The date corresponds to the YYYYMMDD that the retrievals were made. E.g. the file <i>“MOPITT_v8.L3L.from_MOPO2J.selected_variables.20020801.nc”</i> corresponds to the L3L product for MOPITT retrievals made on August 1st 2002. Variables contained within the file are described in detail in the <b>"README.txt"</b> file.</p> <p>NetCDF is a common format for gridded geoscientific data, easily readable by all widely used scientific programming languages (e.g. Python, R, Matlab, IDL…), as well as dedicated command line tools (e.g. cdo, gdal). Panoply (<a>https://www.giss.nasa.gov/tools/panoply/</a>) is an alternative application for quickly plotting these data without the requirement of coding experience. Most GIS packages can also handle NetCDF data.</p> <p>An example python code for reading and plotting data from a single L3L file is available here: <a>https://github.com/ianashpole/MOPITT_L3L_L3W/blob/main/example_read_and_plot_MOPITT_L3L.ipynb</a></p> <p>The L3L and L3W products are available for public use, and are citable as follows:</p> <p><i>"Ashpole, Ian; Wiacek, Aldona, 2022, "Land- and water-only Level 3 products from MOPITT TIR-NIR Version 8 CO retrievals", <a>https://doi.org/10.5683/SP3/ERCG2H</a>, Borealis, V1"</i></p> <p>We request that in addition to citing the dataset, the following text is included in any data acknowledgement section:</p> <i>"The L3L/L3W products are described in detail in Ashpole and Wiacek, 2022 (<a>https://doi.org/10.5194/amt-2022-90<a/>). These were created from original MOPITT Level 2 (MOP02J: <a>https://doi.org/10.5067/TERRA/MOPITT/MOP02J_L2.008</a>) and Level 3 (MOP03J: <a>https://doi.org/10.5067/TERRA/MOPITT/MOP03J_L3.008</a>) Version 8 products. The MOPITT Version 8 algorithm is described in Deeter et al., 2019 (<a>https://doi.org/10.5194/amt-12-4561-2019</a>)."</i></p> <p>The creators of this dataset received funding from the Canadian Space Agency through the Earth System Science Data Analyses program (grant no. 16SUASMPTN), the Canadian National Science and Engineering Research Council through the Discovery Grants Program, and Saint Mary’s University.</p>
Saint Mary's University Dataverse Logo
Forest Edge Research Network
Harper, Karen — 2022-10-25 Many studies have focused on vegetation across forest edges to study impacts of edges created by human activities on forest structure and composition, or patterns of vegetation at inherent natural edges. Our objective was to create a database of plant-related variables across different types of edges from various studies (mainly from across Canada, but also in Brazil and Belize) to facilitate edge research. We compiled data on vegetation along more than 300 transects perpendicular to forest edges adjacent to clear-cuts, burned areas, bogs, lakes, barrens, insect disturbances and riparian areas from 24 studies conducted over the past three decades. Data were compiled for more than 400 plant species and forest structure variables (e.g., trees, logs, canopy cover). All data were collected with a similar sampling design of quadrats along transects perpendicular to forest edges, but with varying numbers of transects and quadrats, and distances from the edge. The purpose for most of the studies was either to determine the distance of edge influence (edge width) or to explore the pattern of vegetation along the edge to interior gradient. We provide data tables for the cover of plant species and functional groups, the species and size of live and dead trees, the density of saplings, maximum height of functional groups and shrub species, and the cover of functional groups at different heights (vertical distribution of vegetation). The Forest Edge Research Network (FERN) database provides extensive data on many variables that can be used for further study including meta-analyses and can assist in answering questions important to conservation efforts (e.g., how is distance of edge influence from created edges affected by different factors?). We plan to expand this database with subsequent studies from the authors and we invite others to contribute to make this a more global database. When using these data, we ask that you cite this data paper and any relevant publications listed in our metadata file. We also encourage you to contact the first author if you are planning to use or contribute to this database.
Saint Mary's University Dataverse Logo
Port Hood Tree Survival - 2019
Vessey, J Kevin — 2021-06-24 This dataset contains the survival data for two biomass tree crops (PO = Hybrid Poplar; WW = willow) at the end of the growing season in the year the crop were established (2019). Each crop was treated with one of four soil amendment treatments (CT = untreated control; DG = liquid anaerobic digestate; PS = pulp-mill sludge; SE = seaweed extract). Hybrid Poplar and Willow cuttings were planted at 16,250 plants/ha. Measurement units: % of trees planted in summer still surviving in the fall.
Saint Mary's University Dataverse Logo
Port Hood Dry Weight - 2019
Vessey, J Kevin — 2021-06-24 This dataset contains the aboveground biomass data for four biomass crops (MS = Miscanthus; PO = Hybrid Poplar; WW = willow; SG = Switchgrass) at the end of the growing season in the year the crop were established (2019). Each crop was treated with one of four soil amendment treatments (CT = untreated control; DG = liquid anaerobic digestate; PS = pulp-mill sludge; SE = seaweed extract). Miscanthus plantlets were planted at 22,000 plants/ha; multiply g/plant by 22 to calculate kg/ha. Hybrid Poplar and Willow cuttings were planted at 16,250 plants/ha; multiply g/plant by 16.25 then by survival rate to calculate kg/ha. Switchgrass seed was planted at 8 kg/ha in 40,000 patches/ha (0.2 g seed/patch); 1 sample = DW/patch; multiply g/sample by 40 to calculate kg/ ha.

Map search instructions

1.Turn on the map filter by clicking the “Limit by map area” toggle.
2.Move the map to display your area of interest. Holding the shift key and clicking to draw a box allows for zooming in on a specific area. Search results change as the map moves.
3.Access a record by clicking on an item in the search results or by clicking on a location pin and the linked record title.
Note: Clusters are intended to provide a visual preview of data location. Because there is a maximum of 1000 records displayed on the map, they may not be a completely accurate reflection of the total number of search results.

(Source Repository: Saint Mary's University Dataverse)

Limit by:

Clear all

to