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271 results for “Ontario”
Perry et al. (2025) Data Package: Effects of diluted bitumen and remediation methods on lower trophic levels within boreal lake enclosures. Data were collected during 2019 at the IISD Experimental Lakes Area in Northwestern Ontario.
This data package corresponds to a research study by Perry et al. (2025) titled "The effects of diluted bitumen, the shoreline cleaner Corexit EC9580A, and bio-stimulation on the lower food web of a boreal lake, with a focus on natural phytoplankton communities." The study examines the effect of controlled spills of diluted bitumen and two remediation methods on lower trophic levels (phytoplankton, periphyton, zooplankton). The study was undertaken within shoreline enclosures within Lake 260 at the IISD Experimental Lakes Area during 2019. In addition to primary oil recovery using sorbent pads, the two secondary remediation methods: 1) enhanced monitoring natural recovery (eMNR) that included the biostimulation of microbial communities via a slow release nutrient fertilizer, and 2) a shoreline washing agent (SWA or SCA; Corexit 9580) used to increase oil removal from affected shorelines. This data package includes the response of perphyton and zooplankton.
Year-round metabolism data from Lakes Simcoe (Ontario, 2010-2011), Diefenbaker, Blackstrap, and Broderick (Saskatchewan, 2013-2014), Canada.
This year-round limnology dataset is from four dimictic Canadian water bodies: three mesotrophic reservoirs in southern Saskatchewan (SK; Blackstrap, Broderick, Diefenbaker) and one oligo-mesotrophic large lake in southern Ontario (Lake Simcoe). Physical, chemical, and biological parameters were measured during the open-water and ice-covered seasons in 2010–2011 (Lake Simcoe) and the SK reservoirs in 2013–2014. Two stations were sampled on Blackstrap reservoir, one on Broderick reservoir, three on Diefenbaker reservoir, and seventeen on Lake Simcoe. Sampling was conducted from a boat during the open-water season and during winter, we accessed the same stations by snowmobile and sampled through holes in the ice. Epilimnetic or surface water was collected for parameters listed below, and δ18O-O2 (oxygen) stable isotope samples were collected from one to 4 depths per station, depending on water column depth and lake thermal structure that day. Parameters reported included photosynthetically active radiation, vertical attenuation coefficient, mean daily mixed layer irradiance, total phosphorus, total dissolved phosphorus, dissolved reactive phosphorus, total dissolved nitrogen, particulate nitrogen, ammonium, nitrate, chlorophyll a, particulate organic carbon, and phytoplankton biovolumes. Photosynthesis irradiance (P-E) parameters were measured including the light saturation parameter, maximum relative electron transport rate through PSII, and the light limited slope of the P-E curve. We also measured areal net productivity, areal gross productivity, and areal respiration via three different methods including fluorometry via a Water-PAM fluorometer, O2 concentrations and δ18O-O2 values, and light-dark bottle experiments measuring changes in O2 concentrations.
Sherbo et al. 2023 Data Package. Data associated with study assessing effects of dissolved organic matter on phytoplankton productivity in boreal lakes. The majority of data was collected in 2018 at the IISD Experimental Lakes Area in Northwestern Ontario
Allochthonous dissolved organic matter (DOM) structures many physical, chemical, and biological properties of lakes, including key variables that control productivity at the base of freshwater food webs. We examined phytoplankton biomass and productivity and their drivers, across eight pristine boreal lakes with DOM ranging from 3.5 to 9.5 mg DOC L-1. Increases in DOM were associated with significant increases in epilimnetic nitrogen, phosphorus and chlorophyll a (Chl a) concentrations suggesting that nutrients associated with DOM stimulate phytoplankton biomass and productivity. Such results were misleading; there was no significant relationship between Chl a and phytoplankton biomass measured via microscopy, and results did not incorporate the effects of DOM on thermocline and euphotic depth. Chl a:biomass and Chl a: carbon ratios indicated that increases in Chl a with DOM were driven by photo-acclimation to declining light availability. Increases. Further, increases in DOM led to large declines in thermocline (~50 %) and euphotic (~75 %) depths, and depth-integrated phytoplankton biomass and primary production (~70 %).
Supplementary material 3 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
Supplementary material 3 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
Using Hydroclimate Modeling and Social Science to Enhance Flood Resilience on Lake Ontario through the Climate Smart Communities Program
<p>This repository contains several data products associated with the New York Sea Grant project R/CHD-15 entitled <em>Using Hydroclimate Modeling and Social Science to Enhance Flood Resilience on Lake Ontario through the Climate Smart Communities Program.</em><strong><em> </em></strong>These products include:</p> <p>1. Estimates of the 25-year, 50-year, and 100-year flood across the New York coastline of Lake Ontario. These design events (reported in feet) are for still water levels that take into account both average water levels across the lake as well as local variations in water level due to storm surge. Wave setup and wave run-up are not considered in these design events. The design events incorporate the effects of water level regulation and the potential impacts of climate change on water supplies to Lake Ontario, and they are tailored for 79 unique locations along the shoreline (identified based on longitude and latitude). These flood levels are presented in an online flood risk assessment tool at: https://kts48.users.earthengine.app/view/lake-ontario-water-level-scenarios</p> <p>2. Protocols and summary of results for a series of focus groups and structured telephone interviews with local officials from communities along the Lake Ontario shoreline to assess barriers to participation in the New York State Climate Smart Communities Program.</p> <p>3. A Crosswalk between activities and administrative requirements of the New York State Climate Smart Communities Program and other federal and state flood resiliency programs. </p> <p>4. A final report summarizing the products above. </p>
Environmental and taxonomic data along the Thames River - Lake St. Clair continuum in Ontario, August 2020 to September 2023.
Data for this research were collected from water samples obtained along the Thames River- Lake St. Clair continuum to determine differences in water quality and biodiversity between river and lake sites. Sampling began in Lake St. Clair in August of 2020 in Stoney Point Ontario. Samples were taken from the intake well of the water treatment plant. The intake pipe is located approximately 1km off-shore, at a depth of 1.9m. To address spatial differences, a second site was added in Lake St. Clair in Belle River, Ontario and a river site was added in Chatham, Ontario. Belle River samples were collected from the intake well at the water treatment plant, with the intake pipe located approximately 1km off-shore and at a depth of 3.1m. Chatham samples were collected from the surface of the Thames River as grab samples. Samples from both locations were collected weekly to monthly throughout June to October, 2021. To further increase spatial resolution, samples were collected from the mouth of the Thames River in Lighthouse Cove, Ontario, from June to October 2022, and from this site and another river site in Prairie Siding, Ontario, from June to October 2023. They were collected as grab samples from the surface of the water. Each site was sampled weekly to monthly.
Higgins et al. 2024 study on dissolved organic matter controls and effects at the IISD Experimental Lakes Area, Northwestern Ontario, Canada, 1970-2019
This dataset represents long-term ecological reserch data collected at the IISD Experimental Lakes Area, Northwestern Ontario, Canada (1970 - 2019) used to support a research study reported in (Higgins et al. 2024). The study examines the relationships between long-term changes in precipitation, dissolved organic matter (DOM) loading and lake concentrations, and various physical, chemical and biological indicators.
Supplementary material 1: Reviewer Comments from: Widening the circle of care: An arts-based, participatory dialogue with stakeholders on cancer care for First Nations, Inuit, and Métis peoples in Ontario, Canada - Research Ideas and Outcomes 2: e9115 (25 May 2016) https://doi.org/10.3897/rio.2.e9115
The attached file includes the evaluation of the postdoctoral fellowship application from three reviewers. Guidelines for reviewers are available online for more inforamtion (http://www.cihr-irsc.gc.ca/e/33043.html), including the rating scale that is used to score each section of the evaluation.
Waterloo, Ontario Federal and Provincial Voting Intention and Vaccine Hesitancy
<p>This it the initial release of federal and provincial voting intention in Waterloo Region in the spring of 2022, commissioned by the Laurier Institute for the Study of Public Opinion and Policy.</p>
Ontario Lake-River Routing Product version 1.0
<p>Thank you for your interest in our lake-river routing product. Please go to this <a href="https://lake-river-routing-products-uwaterloo.hub.arcgis.com/">website </a>to download and learn more about the Routing product and BasinMaker. </p> <p>In your publication using the version v1.0 of the routing product, please cite the following paper:</p> <p>BasinMaker: a GIS toolbox for distributed watershed delineation of complex lake and river routing networks. Han, M., H. Shen, B. A. Tolson, J. R. Craig, J. Mai, S. Lin, N. Basu, F. Awol, submitted April 2021 to Environmental Modelling and Software.</p> <p>(But please also check BasinMaker website where you downloaded this for the most up to date citation)</p> <p>Note that version 1.0 of this product covered only Canada and used a different DEM and is described in the following paper:</p> <p>Han, M., J. Mai, B. A. Tolson, J. R. Craig, E. Gaborit, H. Liu, K. Lee, Subwatershed-based lake and river routing products for hydrologic and land surface models applied over Canada, Canadian Water Resources Journal, 45(3), doi.org/10.1080/07011784.2020.1772116.</p> <p> </p> <p>The lake-river routing product provides a routing structure (which here refers to both the topology of the stream network and the contributing areas to individual lakes and stream reaches), to correctly represent lakes and be easily customized based on various user requirements.</p> <p>BasinMaker, which is a GIS toolbox to delineate watersheds with lakes, was used to develop this routing product. In this routing product, each lake is represented by a lake catchment. A lake catchment is defined by the following rules:1) The extent of the lake catchment will fully cover the lake; 2) the outlet of the lake catchment is the same as the outlet of the lake; 3) each lake’s inlets are treated as a catchment outlet. In this way, both inflow and outflow of each lake can be explicitly simulated by hydrologic routing models.</p> <p>Support for BasinMaker and the Ontario lake-river routing product development came from the Ontario Ministry of Northern Development, Mines, Natural Resources and Forestry.</p>
Data and Software Archive for "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada"
<p>This is the Zenodo archive for the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada" (Mucaki EJ, Shirley BC and Rogan PK. <em>F1000Research</em> 2021, <strong>10</strong>:1312, DOI: <a href="http://dx.doi.org/10.12688/f1000research.75891.1">10.12688/f1000research.75891.1</a>). This study aimed to produce community-level geo-spatial mapping of patterns and clusters of symptoms, and of confirmed COVID-19 cases, in near real-time in order to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. This archive will contain data and image files from this study, which were too numerous to be included in the manuscript for this study. It also provides all program files pertaining to the <em>Geostatistical Epidemiology Toolbox </em>(Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript and other software developed (cluster, outlier, streak identification and pairing)..</p> <p>We also provide a guide which provides a general description of the contents of the four sections in this archive (<em>Documentation_for_Sections_of_Zenodo_Archive.docx</em>). If you have any intent to utilize the data provided in Section 3, we greatly advise you to review this document as it describes the output of all geostatistical analyses performed in this study in detail.</p> <p><strong>Data Files:</strong></p> <p><strong>Section 1. "Section_1.Tables_S1_S7.Figures_S1_S11.zip"</strong></p> <p>This section contains all additional tables and figures described in the manuscript "Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada". Additional tables S1 to S7 are presented in an Excel document. These 7 tables provide summary statistics of various geostatistical tests described in the study (“Section 1 – Tables S1-S4”) and lists all identified single and paired high-case cluster streaks (“Section 1 – Tables S5-S7”). This section also contains 11 additional figures referred to in the manuscript (“Section 1 – Figures S1-S11”) both individually and within a Word document which describes them.</p> <p><strong>Section 2. "Section_2.Localized_Hotspot_Lists.zip"</strong></p> <p>All localized hotspots (identified through kriging analysis) were catalogued for each municipality evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex). These files indicate the FSA in which the hotspot was identified, the date in which it was identified (utilizing 3-day case data at the postal code level), the amount of cases which occurred within the FSA within these 3 dates, the range of cases interpolated by kriging analysis (between 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-50, >50), and whether or not the FSA was deemed a hotspot by Gi* relative to the rest of Ontario on any of the three dates evaluated. Please see Section 4 for map images of these localized hotspots.</p> <p><strong>Section 3. "Section_3.All-Data_Files.Kriging_GiStar_Local_and_GlobalMorans.2020_2021"</strong></p> <p>Section 3 – All output files from the geostatistical tests performed in this study are provided in this section. This includes the output from Ontario-wide FSA-level Gi* and Cluster and Outlier analyses, and PC-level Cluster and Outlier, Spatial Autocorrelation, and kriging analysis of 6 municipal regions. It also includes kriging analysis of 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan). This section also provides data files from our analyses of stratified case data (by age, gender, and at-risk condition). All coordinates presented in these data files are given in “PCS_Lambert_Conformal_Conic” format. Case values between 1-5 were masked (appear as “NA”).</p> <p><strong>Section 4. "Section_4.All_Map_Images_of_Geostat_Analyses.zip"</strong></p> <p>Sets of image files which map the results of our geostatistical analyses onto a map of Ontario or within the municipalities evaluated (Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, Windsor/Essex) are provided. This includes: Kriging analysis (PC-level), Local Moran's I cluster and outlier analysis (FSA and PC-level), normal and space-time Gi* analysis, and all images for all analyses performed on stratified data (by age, gender and at-risk condition). Kriging contour maps are also included for 7 other municipal regions adjacent to Toronto (Ajax, Brampton, Markham, Mississauga, Pickering, Richmond Hill and Vaughan). </p> <p><strong>Software:</strong></p> <p>This Zenodo archive also provides all program files pertaining to the <em>Geostatistical Epidemiology Toolbox </em>(Geostatistical analysis software package to be used in ArcGIS), as well as all other scripts described in this manuscript. This geostatistical toolbox was developed by CytoGnomix Inc., London ON, Canada and is distributed freely under the terms of the GNU General Public License v3.0. It can be easily modified to accommodate other Canadian provinces and, with some additional effort, other countries. </p> <p>This distribution of the <em>Geostatistical Epidemiology Toolbox </em>does not include postal code (PC) boundary files (which are required for some of the tools included in the toolbox). The PC boundary shapefiles used to test the toolbox were obtained from <a href="https://www.dmtispatial.com/">DMTI</a> (<a href="https://www.google.com/url?q=https://www.dmtispatial.com/canmap/&sa=D&source=hangouts&ust=1637875735980000&usg=AOvVaw2wG3iVnyGyrkTIkN5FQ4NS">https://www.dmtispatial.com/canmap/</a>) through the Scholar's Geoportal at the University of Western Ontario (<a href="http://geo2.scholarsportal.info/">http://geo2.scholarsportal.info/</a>). The distribution of these files (through sharing, sale, donation, transfer, or exchange) is strictly prohibited. However, any equivalent PC boundary shape file should suffice, provided it contains polygon boundaries representing postal code regions (see guide for more details).</p> <p><strong>Software File 1. "Software.GeostatisticalEpidemiologyToolbox.zip"</strong></p> <p>The Geostatistical Epidemiology Toolbox is a set of custom Python-based geoprocessing tools which function as any built-in tool in the ArcGIS system. This toolbox implements data preprocessing, geostatistical analysis and post-processing software developed to evaluate the distribution and progression of COVID-19 cases in Canada. The purpose of developing this toolbox is to allow external users without programming knowledge to utilize the software scripts which generated our analyses and was intended to be used to evaluate Canadian datasets. While the toolbox was developed for evaluating the distribution of COVID-19, it could be utilized for other purposes. </p> <p>The toolbox was developed to evaluate statistically significant distributions of COVID-19 case data at Canadian Forward Sortation Area (FSA) and Postal Code-level in the province of Ontario utilizing geostatistical tools available through the ArcGIS system. These tools include: 1) Standard Gi* analysis (finds areas where cases are significantly spatially clustered), 2) spacetime based Gi* analysis (finds areas where cases are both spatially and temporally clustered), 3) cluster and outlier analysis (determines if high case regions are an regional outlier or part of a case cluster), 4) spatial autocorrelation (determines the cases in a region are clustered overall) and, 5) Empirical Bayesian Kriging analysis (creates contour maps which define the interpolation of COVID-19 cases in measured and unmeasured areas). Post-processing tools are included that import these all of the preceding results into the ArcGIS system and automatically generate PNG images. </p> <p>This archive also includes a guide ("UserManual_GeostatisticalEpidemiologyToolbox_CytoGnomix.pdf") which describes in detail how to set up the toolbox, how to format input case data, and how to use each tool (describing both the relevant input parameters and the structure of the resultant output files).</p> <p><strong>Software File 2: “Software.Additional_Programs_for_Cluster_Outlier_Streak_Idendification_and_Pairing.zip"</strong></p> <p>In the manuscript associated with this archive, Perl scripts were utilized to evaluate postal code-level Cluster and Outlier analysis to identify significantly, highly clustered postal codes over consecutive periods (i.e., high-case cluster “streaks”). The identified streaks are then paired to those in close proximity, based on the neighbors of each postal code from PC centroid data ("paired streaks"). Multinomial logistic regression models were then derived in the R programming language to measure the correlation between the number of cases reported in each paired streak, the interval of time separating each streak, and the physical distance between the two postal codes. Here, we provide the 3 Perl scripts and the R markdown file which perform these tasks:</p> <p><em>“Ontario_City_Closest_Postal_Code_Identification.pl”</em></p> <p>Using an input file with postal code coordinates (by centroid), this program identifies the nearest neighbors to all postal codes for a given municipal region (the name of this region is entered on the command line). Postal code centroids were calculated in ArcGIS using the “Calculate Geometry” function against DMTI postal code boundary files (not provided). Input from other sources could be used, however, as long as the input includes a list of coordinates with a unique label associated with a particular municipality.</p> <p>The output of this program (for the same municipal region being evaluated) is required for the following two Perl scripts:</p> <p><em>“Local_Morans_Analysis.Recurrent_Clustered_PC_Identifier.pl”</em></p> <p>This program uses the output of postal code-level Cluster and Outlier analysis for a municipality (these files are available in a second Zenodo archive: <a href="http://doi.org/10.5281/zenodo.5585812">doi.org/10.5281/zenodo.5585812</a>) and the output from <em>“Ontario_City_Closest_Postal_Code_Identification.pl” </em>(for the same municipal region) as input to identify high-case clustered postal codes that occur consecutively over a course of several dates (referred to as high-case cluster “streaks”). The script allows for a single day in which the PC was either not clustered or did not meet the minimum case count threshold of ≥ 6 cases within the 3-day sliding window (i.e. if clustered for 3 days, then not significant for one, then clustered for 3 more days, it will considered a 7 day streak). This script also lists any neighbors that are also identified to have streaks during these same dates.</p> <p><em>“Local_Morans_Analysis.Clustered_Streak_Pairing_Program.pl”</em></p> <p>This program uses the output from “<em>Local_Morans_Analysis.Recurrent_Clustered_PC_Identifier.pl</em>” to pair streaks that were identified in two closely situated postal codes spatially (requires output from <em>“Ontario_City_Closest_Postal_Code_Identification.pl”)</em>. The output of this script provides the postal codes of the streaks which are paired, describe the interval of each streak (and whether they occur concurrently), the number of cases which occurred during these streaks, and how these streaks are separated (both distance [in meters] and temporally [in days]).</p> <p>"<em>Streak_Analysis_using_Multinomial_Logistic_Regression_Models.Rmd</em>"</p> <p>This R Markdown file contains the code which derived multinomial logistic regression models to describe the relation between the number of COVID-19 case counts, physical distance (in meters), and the time interval between paired streaks (in days). The script then performs a Wald two-tailed z-test to identify which factors are significantly correlated (relative to total case counts between streaks [i.e., the response variable]). The p-values computed from the Wald test are then reported. This script requires the 'multinom' function of the 'nnet' package in R.</p> <p>Two data files in which these models were derived (a list of all consecutive Toronto paired streaks for COVID-19 wave 2 and wave 3) are also included. </p>
Supplementary material 1 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
Supplementary material 1 from: Bongard C, Butler K, Fulthorpe R (2013) Investigation of fungal root colonizers of the invasive plant Vincetoxicum rossicum and co-occurring local native plants in a field and woodland area in Southern Ontario. Nature Conservation 4: 55-76. https://doi.org/10.3897/natureconservation.4.3578
Linked collectors and determiners for: Royal Ontario Museum: Entomology.
Natural history specimen data linked to collectors and determiners held within, "Royal Ontario Museum: Entomology". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="http://bionomia.net/dataset/8464c76c-f762-11e1-a439-00145eb45e9a">https://bionomia.net/dataset/8464c76c-f762-11e1-a439-00145eb45e9a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/8464c76c-f762-11e1-a439-00145eb45e9a">https://gbif.org/dataset/8464c76c-f762-11e1-a439-00145eb45e9a</a>. Formatted as a Frictionless Data package.
Fig. 2 in The earliest known Kinnella, an orthide brachiopod from the Upper Ordovician of Manitoulin Island, Ontario, Canada
Fig. 2. Stratigraphic position of Kinnella laurentiana sp. nov. British graptolite zonation follows Webby, Cooper et al. (2004). North American graptolite and Atlantic faunal region conodont zonations and correlations are based on Bergström and Mitchell (1986), McCracken and Nowlan (1988), Melchin et al. (1991) and Goldman and Bergström (1997). Chitinozoan zonation from Achab (1989) and Asselin et al. (2004). Manitoulin Island graptolite species ranges from Goldman and Bergström (1997). Correlation of Pusgillian–Cautleyan and equivalent graptolite zones is based on the assumption that the Amorphognathus superbus–A. ordovicicus zonal boundary is not significantly diachronous globally. NA St., North American Stage; Se., Series; St., Stage. Graptolite genera: A., Amplexograptus; C., Climacograptus; G., Geniculograptus; O., Orthograptus; R., Rectograptus.
Fig. 3 in The earliest known Kinnella, an orthide brachiopod from the Upper Ordovician of Manitoulin Island, Ontario, Canada
Fig. 3. Orthide brachiopod Kinnella laurentiana sp. nov.; Kagawong Submember, upper Georgian Bay Formation, Richmondian (mid−Ashgill), Manitoulin Island. A. GSC 117898, paratype, dorsal (A1), ventral (A2), lateral (A3), posterior (A4), anterior (A5), and enlarged (A6) costae. B. GSC 117899, holotype, dorsal (B1), ventral (B2), lateral (B3), posterior (B4), anterior (B5), and enlarged (B6) delthyrium. C. GSC 117900, paratype, dorsal (C1), ventral (C2), lateral (C3), posterior (C4), and anterior (C5) views. D. GSC 117901, paratype, dorsal (D1), lateral (D2), and posterior (D3) views of immature shell. E. GSC 117902, paratype, dorsal (E1) and lateral (E2) views of immature shell, showing nearly catacline ventral interea.
Fig. 1 in The earliest known Kinnella, an orthide brachiopod from the Upper Ordovician of Manitoulin Island, Ontario, Canada
Fig. 1. Map of Manitoulin Island showing the localities of Kinnella laurentiana sp. nov. in the lower Kagawong Submember, upper Georgian Bay Formation. Dark shaded region corresponds to the outcrop belt of the Kagawong Submember.
Fig. 6 in The earliest known Kinnella, an orthide brachiopod from the Upper Ordovician of Manitoulin Island, Ontario, Canada
Fig. 6. Cluster analysis of Kinnella−bearing brachiopod faunas worldwide. Software: PAST (Hammer et al. 2001; Hammer and Harper 2005); algorithm: unweighted pair−group; Raup−Crick similarity coefficient. Refer to Appendix 1 for identification of assemblage localities, published sources and taxa employed in the analysis.
Fig. 4 in The earliest known Kinnella, an orthide brachiopod from the Upper Ordovician of Manitoulin Island, Ontario, Canada
Fig. 4. Plot of measurements of 50 conjoined shells of Kinnella laurentiana sp. nov.; Kagawong Submember, upper Georgian Bay Formation, Richmondian (mid−Ashgill), Manitoulin Island. Note the largely isometric shell outline (consistent length/width ratio) and convexity (thickness/width ratio) with ontogeny.
Fig. 5 in The earliest known Kinnella, an orthide brachiopod from the Upper Ordovician of Manitoulin Island, Ontario, Canada
Fig. 5. Orthide brachiopod Kinnella laurentiana sp. nov.; Kagawong Submember, upper Georgian Bay Formation, Richmondian (mid−Ashgill), Manitoulin Island. A. GSC 117903, paratype, various views of interior of ventral valve (A1) showing dental plates (A2) and large interarea (A3). B. GSC 117904, paratype, interior of ventral valve. C. GSC 117905, paratype, interior of ventral valve (C1) showing dental plates and muscle field (C2). D. GSC 117906, paratype, interior of dorsal valve (D1), with details of cardinalia and adductor muscle scars (D2 and D3). E. GSC 117907, paratype, interior of dorsal valve, with relatively strong median ridge. F. GSC 117908, paratype, interior of dorsal valve (F1), with crenulated, anteriorly swollen cardinal process (F2).
Linked collectors and determiners for: Ornithology Collection Passeriformes - Royal Ontario Museum.
Natural history specimen data linked to collectors and determiners held within, "Ornithology Collection Passeriformes - Royal Ontario Museum". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/f6e21753-5129-498f-92bd-73710b0087e3">https://bionomia.net/dataset/f6e21753-5129-498f-92bd-73710b0087e3</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/f6e21753-5129-498f-92bd-73710b0087e3">https://gbif.org/dataset/f6e21753-5129-498f-92bd-73710b0087e3</a>. Formatted as a Frictionless Data package.
ScienceDex guides
Understand access before you commit
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.