Zenodo
Kilic, Oz;
Taşkaya Temizel, Tuğba
—
2024-01-23
https://creativecommons.org/licenses/by/4.0/legalcode
Résultats de recherche
Zenodo
Pontone, Nicholas;
Millard, Koreen;
Thompson, Dan K.;
Guindon, Luc;
Beaudoin, André
—
2024-02-07
Authors: Pontone, N., Millard, K., Thompson, D. K., Guindon, L., Beaudoin A. (2024)
Contact:NicholasPontone@cmail.carleton.ca
Description:A map of peatland sub-classes (bog, poor fen, rich fen and permafrost peat complex) for the Canadian Boreal Forest circa 2020 created using a three-stage hierarchical classification framework. Training and validation data consisted of peatland locations derived from various sources (field data, aerial photo interpretation, measurements documented in literature). A combination of multispectral data, L-band SAR and C-Band interferometric SAR coherence, forest structure, and ancillary variables were used as model predictors. Ancillary data were used to mask agricultural areas and urban regions, and account for regions that may exhibit permafrost
Pixel Values:
1: Bog2: Rich Fen3: Poor Fen4: Peatland Permafrost Complex5: Mineral Wetlands6: Water7: Upands8: Agriculture9: Urban
Recommended Colours
1: 4C00732: FFFF003: E64C004: 7272725: F4C2C26: 0070FF7: 4C73008: 6231319: 000000
Please cite as:
Pontone, N., Millard, K., Thompson, D.K., Guindon, L. and Beaudoin, A. (2024), A hierarchical, multi-sensor framework for peatland sub-class and vegetation mapping throughout the Canadian boreal forest. Remote Sens Ecol Conserv. https://doi.org/10.1002/rse2.384
This data was released in combination with PALSAR-2 L-band dual-polarized radar backscatter summer composites (circa 2020).
Beaudoin, A., Villemaire, P., Gignac, C., Tolszczuk, S., Guindon, L., Pontone, N., Millard, C. (2024). Canada’s PALSAR-2 dual-polarized L-band radar summer backscatter composite, circa 2020. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. https://doi.org/10.23687/8ec4ee78-9240-4bd0-9c97-d3a27829e209
The peatland map is also available as a Google Earth Engine asset (projects/ee-peatlandthesis/assets/PeatlandMap8b_2023_07_17).
https://creativecommons.org/licenses/by/4.0/legalcode
Zenodo
Maltagliati, Silvio;
Boisgontier, Matthieu;
Cheval, Boris
—
2020-11-09
Data set used for analysis.
Sheet 1 : Data
Sheet 2 : Description of the variables.
https://creativecommons.org/licenses/by/4.0/legalcode
Zenodo
Saine, Sonja;
Penttilä, Reijo;
Fukami, Tadashi;
Furneaux, Brendan;
Hytönen, Tuija;
Miettinen, Otto;
Monkhouse, Norman;
Mäkipää, Raisa;
Pennanen, Jorma;
Zakharov, Evgeny V.;
Ovaskainen, Otso;
Abrego, Nerea
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2024-05-06
These files include the data, the scripts, and the pipeline for bioinformatic analyses for reproducing the results presented in the manuscript "Idiosyncratic responses to biotic and environmental filters in wood-inhabiting fungal communities".
Description of the files can be found from the README.docx file.
https://creativecommons.org/licenses/by/4.0/legalcode
Zenodo
Chen, Liwei;
Shiokawa, Kazuo;
Connors, Martin;
Kato, Yuto;
Tsuboi, Takuma
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2024-01-09
These images show the Strong Thermal Emission Velocity Enhancement (STEVE) observed on Sept 3rd, 2022, at Athabasca, Canada, using two Nikon D610 cameras with an all-sky fish-eye lens. The two cameras were located at AUGO-I (54.71°N, 246.69°E, C002) and AUGO-II (54.60°N, 246.36°E, C001), respectively. The format of the filename is C00X_YYDDMMhhmmss_0030.jpg.
https://creativecommons.org/licenses/by/4.0/legalcode
Zenodo
Securo, Andrea;
Del Gobbo, Costanza;
Citterio, Michele;
Machguth, Horst;
Marcer, Marco;
Korsgaard, Niels J.;
Colucci, R. Renato
—
2024-10-04
This dataset refers to:
Area, volume and ELA changes of West Greenland local glaciers and ice caps over the last 35 yearsSecuro Andrea, Del Gobbo Costanza, Citterio Michele, Machguth Horst, Marcer Marco, Korsgaard Niels J., Colucci Renato R.
This study analyses the cumulative area, ice mass and Equilibrium Line Altitude changes that occurred on more than 4000 glaciers and ice caps in West Greenland (outside of the Greenland Ice Sheet) from 1985 to 2020, using remotely sensed data and including glaciers smaller than 1 km2 in the calculations.
This dataset contains:
1_Area_1985_2020.csv - Comma Separated Value file with the following data for all Glaciers and ice caps involved in the study:Area Loss from 1985 to 2020 (km2), Total Area of 1985 (km2), Relative Area Loss from 1985 to 2020 (%), Longitude, Latitude
2_Area_Loss_1985_2020.gpkg - Geopackage file contanining the same information as (1.) but including the centroids positions. EPSG 4326 WGS84
3_Volume_and_ELA_1985_2020.csv - Comma Separated Value file with the following data for all Glaciers and ice caps involved in the study: GLIMS Glacier ID, minimum elevation (m a.s.l.), mean elevation (m a.s.l.), maximum elevation (m a.s.l.), Surface elevation change 1985-Present (m), Glacier Area from RGI (km2), Ice Mass Loss (Gt), 1985 mean ice thickness from Millan and others (m), Relative volume loss from 1985 to Present (%), Longitude, Latitude, Equilibrium Line Altitude* (m)
4_Volume_and_ELA_1985_2020.gpkg - Geopackage file contanining the same information as (3.) but including the centroids positions. EPSG 4326 WGS84
* note that not all glaciers and ice caps have ELA value for present.
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Zenodo
Bell, Isaac;
Katz-Rosene, Ryan
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2024-06-26
This is the Supplementary Data Table for "The Contested Futures of Just Transition in Canada's Energy Sector," by Isaac Bell and Ryan Katz-Rosene, published in Studies In Political Economy.
https://creativecommons.org/licenses/by/4.0/legalcode
Zenodo
Naranjo-Orrico, Domenica;
Ovaskainen, Otso;
Furneaux, Brendan;
Purhonen, Jenna;
Arancibia, Paulina A.;
Burg, Skylar;
Moser, Niklas;
Niku, Jenni;
Tikhonov, Gleb;
Zakharov, Evgeny;
Monkhouse, Norman;
Abrego, Nerea
—
2024-06-10
Data and scripts for reproducing the analyses of Naranjo-Orrico et al Wind is a primary driver of fungal dispersal across a mainland-island system.
The file allData.RData contains the data in R format. The data include three matrices: the metadata (md), the sample x OTU table (otu.table) and the taxonomic information of the identified OTUs (taxonomy). These three files need to be loaded using the function load in R for reproducing the statistical analyses performed in the study.
The statistical analyses consist of joint species distribution modelling with the package Hmsc. To perform the analyses the three scripts need to be run consecutively from S1 to S3. S1 defines and fits the 12 models fitted in the study (which include both presence-absence and abundance models, with different variants with a different set of explanatory variables). S2 shows the parameter estimates from the fitted models, in particular the beta parameters and the variance partitioning across environmental covariates. S3 builds the predictions on species richness and community weighted spore traits in relation to the focal environmental predictors.
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Zenodo
Gharaee, Zahra;
Lowe, Scott C.;
Gong, ZeMing;
Millan Arias, Pablo;
Pellegrino, Nicholas;
Wang, Austin T.;
Bruslund Haurum, Joakim;
Zarubiieva, Iuliia;
Kari, Lila;
Steinke, Dirk;
Taylor, Graham W.;
Fieguth, Paul;
Chang, Angel X.
—
2024-06-14
Overview
As part of an ongoing worldwide effort to comprehend and monitor insect biodiversity, we present the BIOSCAN-5M Insect dataset to the machine learning community. BIOSCAN-5M is a comprehensive dataset containing multi-modal information for over 5 million insect specimens, and it significantly expands existing image-based biological datasets by including taxonomic labels, raw nucleotide barcode sequences, assigned barcode index numbers, geographical information, and specimen size.
Every record has both image and DNA data. Each record of the BIOSCAN-5M dataset contains six primary attributes:
RGB image
DNA barcode sequence
Barcode Index Number (BIN)
Biological taxonomic classification
Geographical information
Specimen size
Additional BIOSCAN-5M dataset-related packages are accessible through the GoogleDrive folder including:
BIOSCAN_5M_original_full: The raw images of the dataset.
BIOSCAN_5M_cropped: Images after cropping with our cropping tool introduced in BIOSCAN-1M.
BIOSCAN_5M_original_256: Original images resized to 256 on their shorter side.
https://creativecommons.org/licenses/by/3.0/legalcode
Zenodo
Gutgesell, Marie
—
2024-07-09
Historical pollen records from Europe used to detect evidence of agricultural development on plant pollen dynamics.
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Zenodo
Roy, Raymond;
D'Angiulli, Amedeo
—
2024-02-16
https://creativecommons.org/licenses/by/4.0/legalcode
Zenodo
Khan, Sameer;
Forrester, T.J.B.;
Kimber, M.S.;
Seah, S.Y.K.
—
2024-05-31
X-ray diffraction data used for the structrual determination of steroid hydratase from Comamonas testosteroni, deposited in the PDB with the ID 8VWR.
https://creativecommons.org/licenses/by/4.0/legalcode
Zenodo
Ricke, Konrad;
Rousseaux, Maxime;
Parmasad, Jean-Louis
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2024-02-13
This is the excel file containing all quantative data for the manuscript "Genetic and pharmacological reduction of CDK14 mitigates synucleinopathy" by the Rousseaux lab.
https://creativecommons.org/licenses/by/4.0/legalcode
Zenodo
Hanna, Dalal
—
2024-12-02
These are the data and R code that accompany the Forest Ecology and Management publication titled "An evidence map of research assessing the effects of timber harvesting on water quality, biotic and biodiversity indicators in running waters".
https://creativecommons.org/licenses/by/4.0/legalcode
Zenodo
Khan, Sameer
—
2024-05-04
A comprehensive dataset detailing potential interactions of the 20 Arabidopsis thaliana MPKs.
https://creativecommons.org/licenses/by/4.0/legalcode
Zenodo
D'Angiulli, Amedeo;
Wymark, Dana;
Telfer, Andre;
Bahrami, Sahar;
Temi, Santina
—
2024-02-04
https://creativecommons.org/licenses/by/4.0/legalcode
Zenodo
Danzmann, Roy;
Turner, Leah;
Ferguson, Moira;
Easton, Anne
—
2025-01-08
The attached supplementary files complement the publication: Turner LA, Easton AA, Ferguson MM, Danzmann RG (2025). Differences in gene expression between high and low tolerance rainbow trout (Oncorhynchus mykiss) to acute thermal stress. PLoS ONE 20(1):e0312694 (https://doi.org/10.1371/journal.pone0312694). The supplementary files contain information on the transcriptomic responses (liver tissue) of juvenile rainbow trout (~ 9 months of age) derived from two different hatchery strains. The fish were subjected to an acute thermal stress trial (~ 16 hours in duration) and gene expression profiles of Low tolerance fish, High tolerance fish, and Control fish were compared in pairwise fashion.
https://creativecommons.org/licenses/by/4.0/legalcode
Zenodo
Yeung, Jonas;
DeYoung, Taylor;
Spring, Shoshana;
de Guzman, A. Elizabeth;
Elder, Madeline W.;
Wong, Shun C.;
Palmert, Mark R.;
P. Lerch, Jason;
Nieman, Brian J.
—
2024-02-13
Description
Biological sex influences prevalence of developmental disorders through sex hormones and sex chromosomes. However, our understanding of their impacts in neurodevelopment and response to injury remains limited. In this project, we use high resolution magnetic resonance imaging (MRI) to investigate the four core genotype mouse model (FCG) that separates the influences of sex hormones and sex chromosomes during normal brain development and after cranial radiation therapy.
Sex differences are attributed to either sex hormones or sex chromosomes. This can be distinguished by the FCG model which decouples the sex determining region (SRY) from the Y chromosome by moving SRY onto an autosome. This gives us four core sex genotypes: XX NULL, XY NULL, XX SRY, and XY SRY.
This dataset represents the most comprehensive mouse brain imaging study employing the FCG model to date with 5 timepoints (P14, P23, P42, P63, P98), Ccl2 wildtype (+/+) and knockouts (-/-), irradiation (7Gy) and sham (0Gy) mice. All in all, a total of 1071 images! The results presented here is published in PNAS.
In vivo MRI scans were obtained using a 7-T MRI scanner (Bruker BioSpin, Ettlingen, Germany) equipped with four cryocoils for simultaneous imaging of four mice. The scans were performed with the following settings: T1-weighted, 3D-gradient echo sequence, 75μm isotropic resolution, TR=26ms, TE=8.25ms, flip angle=26°, field of view=25×22×22mm, and matrix size=334×294×294.
All structural MR images are stored in images.tar.gz. Images were segmented and registered using an automated pipeline which are stored in labels.tar.gz. The consensus average and labels are final_average.mnc and final_labels.mnc, respectively. Extracted structure volumes alongside the metadata are included in df_micevolumes.csv. Structural MRIs are in MINC format and the readme.txt provides further information on this dataset.
The authors express their sincere gratitude for the research funding recieved from the Canadian Institutes of Health Research (158622, 168037) and the Ontario Institute for Cancer Research (IA-024) with funding from the Government of Ontario and Restracomp from the SIckKids Research Training Centre.
Publication: https://www.pnas.org/doi/10.1073/pnas.2404042121
Code/Software
MINChttps://www.bic.mni.mcgill.ca/ServicesSoftware/MINC
RMINChttps://github.com/Mouse-Imaging-Centre/RMINC
PydPiperhttps://github.com/Mouse-Imaging-Centre/pydpiper/tree/v2.0.19.1
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Zenodo
Chlumsky, Robert;
Craig, James R.;
Tolson, Bryan A.
—
2025-01-22
Supporting data, results and source code for the initial Blackbird benchmarking study.
Blackbird is a software which implements the Geospatially Augmented Standard Step (GASS) method, designed for efficient flood mapping with low computational cost.
Software and available data is provided under the MIT license, though terrain data and land cover data included in the data set for the Waldemar case study are provided partially by the Grand River Conservation Authority and the Government of Ontario, which are governed by their respective licenses.
https://opensource.org/licenses/MIT
Zenodo
Soltani, Sepideh
—
2024-06-01
https://creativecommons.org/licenses/by/4.0/legalcode
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