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Deep Learning for Reaction-Diffusion Glioma Growth Modeling: Towards a Fully Personalized Model? — Supporting Data

Supporting data for Martens et al. Deep Learning for Reaction-Diffusion Glioma Growth Modelling: Towards a Fully Personalised Model? arXiv:2111.13404.

opencc-zeroMay 2022View details →

dryad40/100

Supporting data from: Characterizing changes in eastern U.S. pollution events in a warming world

Risk assessments of air pollution impacts on human health and ecosystems would ideally consider a broad set of climate and emission scenarios, as well as natural internal climate variability. We analyze initial condition chemistry-climate ensembles to gauge the significance of greenhouse-gas-induced air pollution changes relative to internal climate variability, and consider response differences in two models. To quantify the effects of climate change on the frequency and duration of summertime regional-scale pollution episodes over the Eastern United States (EUS), we apply an Empirical Orthogonal Function (EOF) analysis to a 3-member GFDL-CM3 ensemble with prognostic ozone and aerosols and a 12-member NCAR-CESM1 ensemble with prognostic aerosols under a 21st century RCP8.5 scenario with air pollutant emissions frozen in 2005. Correlations between GFDL-CM3 principal components for ozone, PM2.5 and temperature represent spatiotemporal relationships discerned previously from observational analysis. Over the Northeast region, both models simulate summertime surface temperature increases of over 4 C from 2006–2025 to 2081–2100 and PM2.5 of up to 1–4 μg m−3. The ensemble average decadal incidence of upper quartile Northeast PM2.5 events lasting at least three days doubles in GFDL-CM3 and increases by ∼50% in CESM1. In other EUS regions, inter-model differences in PM2.5 responses to climate change cannot be explained solely by internal climate variability. Our EOF-based approach anticipates future opportunities to data-mine initial condition chemistry-climate model ensembles for probabilistic assessments of changing regional-scale pollution and heat event frequency and duration, while obviating the need to bias-correct concentration-based thresholds separately in individual models.

Data supporting 'Controls on Greenland moulin geometry and evolution from the Moulin Shape model'

The data included here are MouSh model results generated as part of the following publication: Andrews, L. C., Poinar., K, Trunz, C. (2022). Controls on Greenland moulin geometry and evolution from the Moulin Shape model. The Cryosphere.  Nearly all meltwater from glaciers and ice sheets is routed englacially through moulins. Therefore, the geometry and evolution of moulins has the potential to influence subglacial water pressure variations, ice motion, and the runoff hydrograph delivered to the ocean. We develop the Moulin Shape (MouSh) model, a time-evolving model of moulin geometry. MouSh models ice deformation around a moulin using both viscous and elastic rheologies and melting within the moulin through heat dissipation from turbulent water flow, both above and below the water line. We force MouSh with idealized and realistic surface melt inputs. Our results show that variations in surface melt change the geometry of a moulin by approximately 10% daily and over 100% seasonally. These size variations cause observable differences in moulin water storage capacity and moulin water levels compared to a static, cylindrical moulin. Our results suggest that moulins are storage reservoirs for meltwater, with storage capacity and water levels varying over multiple timescales. Representing moulin geometry within subglacial hydrologic models may therefore improve the representation of subglacial pressures, especially over seasonal periods or in regions where overburden pressures are high. These data are also accessible at: https://ubir.buffalo.edu/xmlui/handle/10477/82587 The development of the MouSh model was funded by NASA Cryosphere grant 80NSSC19K0054 to Lauren Andrews and Kristin Poinar.

Data in support of "An exploration of linkage fine-mapping on sequences from case-control studies"

These data were simulated for an exploration of linkage fine-mapping on sequences from case-control studies. The code to generate and analyze the data is available on GitHub in the scripts at <a href="https://github.com/SFUStatgen/PBJ0">https://github.com/SFUStatgen/PBJ0</a>. Queries may be directed to Payman Nickchi at <a href="mailto:pnickchi@sfu.ca">pnickchi@sfu.ca</a> or Charith (Bhagya) Karunarathna at <a href="mailto:ch757276@dal.ca">ch757276@dal.ca</a>.

Data in support of "An exploration of linkage fine-mapping on sequences from case-control studies"

These data were simulated for an exploration of linkage fine-mapping on sequences from case-control studies. The scripts to generate and analyze the data are available at <a href="https://github.com/SFUStatgen/PBJ0">https://github.com/SFUStatgen/PBJ0</a>. Queries may be directed to Payman Nickchi at <a href="mailto:pnickchi@sfu.ca">pnickchi@sfu.ca</a> or Charith (Bhagya) Karunarathna at <a href="mailto:ch757276@dal.ca">ch757276@dal.ca</a>. README file for All_data directory Directory structure The All_data directory consists of this README file and 500 sub-directories named DatasetX, for X=1 to 500. Within each DatasetX sub-directory are further sub-directories named alt and null containing files named pop_data.RData and sample_data.RData. alt versus null directories The files in the alt and null directories contain the same variant data but different phenotype data. In particular, under the null hypothesis, disease status is simulated at random according to a 5% prevalence in the population, whereas under the alternative hypothesis disease status is simulated according to a penetrance model that depends on causal SNVs. The R script to simulate data under the alternative hypothesis is in the file 1_SimulateData.R in the Github repository <a href="https://github.com/SFUStatgen/PBJ0">https://github.com/SFUStatgen/PBJ0</a>. pop_data.RData and sample_data.RData files The data structures contained in the pop_data.RData and sample_data.RData files are described below. The structure is the same under both the null and alternative hypothesis. pop_data.RData From R, load("pop_data.RData") loads a list named pop_data whose elements describe the population’s haplotype and phenotype data. The list elements are as follows. <ul> <li>Variants: a matrix of variants for the population of 6200 haplotypes <ul> <li>rows are SNVs,</li> <li>columns are sequences</li> </ul> </li> <li>Positions: a data frame of SNV positions <ul> <li>rows are SNVs,</li> <li>column 1 is the SNV name and column 2 is the SNV position in base pairs</li> </ul> </li> <li>Population.Mapping: a data frame telling us how the sequences are paired into individuals <ul> <li>rows are individuals</li> <li>First column 1 is an individual ID from 1,…,3100; columns 2 and 3 are the sequence IDs of the first and second sequence for that individual where the sequence IDs are the column names of the Variants matrix.</li> </ul> </li> <li>Genotype.Matrix: a matrix of genotypes (i.e. variant counts) for the 3100 individuals <ul> <li>rows are SNVs</li> <li>columns are the individuals</li> </ul> </li> <li>causal_region: a vector containing the lower- and upper-limit of the causal region in base pairs.</li> <li>cSNV: a vector containing the IDs of the causal SNVs, where the SNV IDs are the row names of the Variants matrix.</li> <li>DISCRETE: a list with the following elements. <ul> <li>CaseIndividuals: vector of IDs of the affected individuals in the population.</li> <li>ControlIndividuals: vector of IDs of the unaffected in the population.</li> <li>BinaryTrait: a vector of trait status (0=unaffected, 1=affected) for each individual.</li> </ul> </li> </ul> Note: Within the same DatasetX directory, the only difference between the pop_data data structures under the null and alternative hypothesis is the phenotype information contained in their respective DISCRETE list elements. Both the null and alternative pop_data data structure share list elements: Variants, Positions, Population.Mapping, Genotype.Matrix, causal_region and cSNV. sample_data.RData From R, load("sample_data.RData") loads a list whose elements describe the sequences and phenotypes of the sample of 50 affected individuals (cases) and 50 unaffected individuals (controls) from the population. <ul> <li>Haps: a list with two elements. <ul> <li>sample_haps: a matrix of 200 sequences for the 50 cases and 50 controls. Rows are SNVs and columns are sequences, with the sequences of sampled cases appearing first (i.e. first 100 columns), followed by the sequences of sampled controls (i.e. last 100 columns). Sequences include only those SNVs that are polymorphic in the sample.</li> <li>ccStatus: a vector indicating the case/control status of the individual to which the sequence belongs, with case=1 and control=0.</li> </ul> </li> <li>Genos: a list with two elements. <ul> <li>sample_genos: a matrix of 100 genotypes for the 50 cases and 50 controls. Rows are SNVs and columns are genotypes, with genotypes of cases appearing first, followed by genotypes of controls.</li> <li>ccStatus: a vector indicating the case/control status of each individual, with case=1 and control=0.</li> </ul> </li> <li>Posn: a data frame of SNV positions for each SNV that is polymorphic in the sample. The first column is the SNV name and the second is the SNV position in base pairs. Posn is a subset of pop_data$Positions.</li> <li>poly_cSNV: a vector of IDs for causal SNVs that are polymorphic in the sample.</li> <li>CaseIND: a vector of individual IDs for the case individuals (see pop_data$Population.Mapping).</li> <li>ControlIND: a vector of individual IDs for the control individuals (see pop_data$Population.Mapping).</li> <li>CaseHapID: a vector of IDs for the sequences that belong to cases (see the sequence IDs in the column names of the matrix pop_data$Variants).</li> <li>ControlHapID: a vector of IDs for the sequences that belong to controls (see the sequence IDs in the column names of the matrix pop_data$Variants).</li> </ul>

opencc-by-4.0Jun 2022View details →

Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: Janus kinase inhibitors for the treatment of COVID-19

Risk of bias assessments and support for judgement with ROB 2 tool for the Cochrane Review: Janus kinase inhibitors for the treatment of COVID-19.

opencc-by-4.0Jun 2022View details →

Supporting Code for Icex

Supporting code to run the example provided in the main text for the R function "Icex"

opencc-by-4.0Jun 2022View details →

Supporting Data for "Decarbonization pathways for the residential sector in the United States"

This repository contains input and processed data for the analysis presented in "Decarbonization pathways for the residential sector in the United States" by Berrill et al. (2022), in Nature Climate Change.  Description of the files can be found in the 'Data_Descriptors.docx' file. Please direct any enquiries relating to this dataset to peter.berrill@aya.yale.edu

opencc-by-4.0Mar 2022View details →

Documentation and digital files in support of "Aftershock regions of Aleutian–Alaska megathrust earthquakes, 1938-2021" by Carl Tape and Anthony Lomax: Part A

These files support a paper entitled "Aftershock regions of Aleutian–Alaska megathrust earthquakes, 1938–2021," by Carl Tape and Anthony Lomax, published in Journal of Geophysical Research Solid Earth. This collection contains Part A. A separate collection contains Parts, B, C, and D. This research was supported by the U.S. Geological Survey (USGS), Department of the Interior, under USGS award number G19AP00050.

Factorial Survey: Decision Making for Extracorporeal life support (ECLS)

Extracorporeal life support (ECLS) provides support to patients with cardiopulmonary failure refractory to conventional therapy. While ECLS is potentially life-saving, it is associated with severe complications; decision making to initiate ECLS must, therefore, carefully consider which patients ECLS potentially benefits despite its consequences. Data of a factorial survey among 420 physicians from 111 hospitals in Switzerland and Germany.

Supporting data for the article "Charge-Induced Artifacts in Nonlocal Spin-Transport Measurements: How to Prevent Spurious Voltage Signals"

Supporting data for the article "Charge-Induced Artifacts in Nonlocal Spin-Transport Measurements: How to Prevent Spurious Voltage Signals" If the design files are used to reproduce the current source, we ask to cite the peer-reviewed publication of our work in any publication in which the adjustable virtual ground feature is used for measurements. We provide the following data and design files: 1.) Data used to create each figure both in the main manuscript and the supplementary material in the zip-folder "Data presented in figures". The data is provided in two formats: I.) Raw data in freely accessible file formats such as .dat, .txt, or .csv. II.) Graphically processed data (figures as shown in the publication) in the proprietary file format .opju. Used program: OriginPro 2019 2.) LTspice models in the zip-folder "LTspice models". The simulations were conducted with LTspice version XVII(x64). 3.) Altium Designer files of the current source in the zip-folder "Altium Designer files". 4.) Gerber X2 and NC drill files (dimensions in millimeters) for the manufacturing of the PCB that is used in our project in the zip-folder "Fabrication files". See the readme file in the directory for more information on the fabrication process. 5.) A bill of materials.

opencc-by-4.0Dec 2021View details →

Supporting information for Parametrized regionalization of paper recycling life-cycle assessment

This supporting information provides the numerical results for (1) the process parameters' regionalization (S4); (2) the regionalized LCA climate change results for three different paper grades (S7); (3) the destinations of the mixed paper bales exiting Quebec's sorting centers (S8); (4) the LCA results for the scenarios for Quebec's case study (S9) and (5) the sensitivity analysis results, performed on the most uncertain parameters from Quebec's case study (S10).

opencc-by-4.0Feb 2021View details →

Phylogenomic Analyses of 2,786 Genes in 158 Lineages Support a Root of The Eukaryotic Tree of Life Between Opisthokonts and All Other Lineages

Abstract Advances in phylogenetic methods and high-throughput sequencing have allowed the reconstruction of deep phylogenetic relationships in the evolutionary history of eukaryotes. Yet, the root of the eukaryotic tree of life remains elusive. The most ‘popular’ (i.e. in textbooks and reviews) hypothesis for the root is between Unikonta (Opisthokonta + Amoebozoa) and Bikonta (all other eukaryotes), which emerged from analyses of a single gene fusion and a limited sampling of eukaryotic lineages. Subsequent highly-cited studies based on concatenation of genes supported this hypothesis with some variations or proposed a root within the Excavata. However, concatenation of genes neither considers phylogenetically-informative events (i.e. gene duplications and losses), nor provides an estimate of the root. A more recent study using gene tree-species tree reconciliation methods suggested the root lies between Opisthokonta and all other eukaryotes, but only including 59 taxa and 20 genes. Here we apply a gene tree – species tree reconciliation approach to a gene-rich and taxon-rich dataset (i.e. 2,786 gene families from two sets of ~158 diverse eukaryotic lineages) to assess the root, and we iterate each analysis 100 times to quantify tree space uncertainty. Our results estimate a root between Fungi and all other eukaryotes, or between Opisthokonta and all other eukaryotes, and reject alternative popular roots from the literature. Based on further analysis of genome size we propose Opisthokonta + others as the most likely root. Finding the root of the eukaryotic tree of life is critical for the field of comparative biology as it allows us to understand the timing and mode of evolution of characters across the evolutionary history of eukaryotes. Methods Here we provide the alignments, gene trees, inputs, and outputs from our project entitled "Phylogenomic Analyses of 2,786 Genes in 158 Lineages Support a Root of The Eukaryotic Tree of Life Between Opisthokonts and All Other Lineages". Sequences and alignments were produced using the phylogenomic pipeline PhyloToL, which contains a taxon- and gene-rich database (including eukaryotes, archaea, and bacteria). These data were then used for 1) assessing the root of the eukaryotes and 2) for comparison with other previously published hypotheses. In both cases, we used the species tree - gene tree reconciliation tool iGTP. We also did a comparison of hypotheses using the likelihood-based tool SpeciesRax iGTP Input data These data are divided into four datasets based on taxa selection. For dataset SEL+, taxa were selected based on their taxonomy; for RAN+, taxa were selected randomly among the major eukaryotic clades Opisthokonta, Amoebozoa, Archaeplastida, Excavata, SAR, and some orphan lineages. Datasets SEL- and RAN- are the same as SEL+ and RAN+, but exclude microsporidians in order to account for and avoid long branch attraction due to microsporidians fast-evolutionary rates. We chose the gene families that contain at least 25 taxa representing at least four of the five major eukaryotic clades. Additionally, at least 2 of the major clades had to contain at least 2 minor clades (e.g. Glaucophytes and Rhodophyta are minor clades in the major clade Archaeplastida). In a pilot analysis, we produced an alignment and a phylogenetic tree for each gene family using the default settings of a previous version of PhyloToL (GUIDANCE V1.3.1 sequence cutoff = 0.3 and column cutoff = 0.4; RAxML quick tree with model PROTGAMMALG and no bootstraps). Then, we kept the gene families that are exclusive of eukaryotes or the ones in which eukaryotes were monophyletic. From a total of 3,002 gene families that met our criteria, 2786 passed the initial steps of PhyloToL when including only the data from the dataset SEL+. These 2,786 gene families were used for further analyses with all datasets. MSAs were produced with PhyloToL (GUIDANCE V2.02 sequence cutoff = 0.3, column cutoff = 0.4, number of iterations = 5; Sela, et al. 2015). The default parameters of PhyloToL include up to five iterations of GUIDANCE V2.02 with 10 bootstraps and MAFFT V7 with algorithm E-INS-i for less than 200 sequences or “auto” option if more than 200 sequences, and maxiterate = 1000. Instead, here we run up to five iterations of GUIDANCE with 20 bootstraps and the simple MAFFT algorithm FFT-NS-2. Then, we perform an additional GUIDANCE run with 100 bootstraps and the default MAFFT parameters for PhyloToL. Gene trees were inferred with RAxML v.8.2.4 with 10 ML searches for best-ML tree (option "-# 10"), using the rapid hill-climbing algorithm (option "-f d") and no bootstrap replicates. The protein evolution model used was evaluated during the gene tree inference (option "-m PROTCATAUTO") by testing all models available in RAxML (e.g. JTT, LG, WAG, etc) with optimization of substitution rates and of site-specific evolutionary rates which were categorized into four distinct rate categories for greater computational efficiency. We ran 100 repetitions of iGTP analyses per dataset. But, given the complexity of the datasets and the heuristic nature of some key steps of the iGTP algorithm (e.g. gene tree rooting and initial starting species tree generation), in a preliminary analysis, we faced two systematic challenges with iGTP as the inferred species tree was affected by: 1) the order of the leaves in the input unrooted gene tree Newick strings (i.e. the input trees were treated as rooted even though we specified that they were not); and 2) the input gene order in the 100 replicates. Therefore, we randomly shuffled the order of the leaves in the unrooted gene trees (keeping the same topology), and randomly shuffled the order of the input gene trees in each of the 100 replicates per dataset. Here we provide the 100 input files generated for those iGTP analyses.  Output data Here we also share the data generated after two analyses: 1) root assessment and 2) hypothesis testing. For the former, we allowed iGTP to calculate the more parsimonious root given our input files. For the latter, we allowed iGTP to calculate the reconciliation cost of the gene trees given the input files and constraints in the species trees to reflect previously published root hypotheses. The constraints are explained in the README file. SpeciesRax Since we removed LGT and contamination from our dataset using a series of filters, we applied the model UndatedDL instead of UndatedDTL, which implies that we only took into consideration duplications and losses and ignored the transferences. Then, the command used for SpeciesRax was... ./generax --families forGeneRax/famFile --species-tree forGeneRax/spsTreeAn.newick --strategy SKIP --rec-model UndatedDL --per-family-rates --prefix An_DS1 --si-strategy EVAL  input  Here, we are sharing all the necessary files to run SpeciesRax, including the mapping file (famFile), the species trees (spsTrees; the best iGTP constrained species trees per hypothesis), and their underlying gene trees (trees_r) output Output folder from SpeciesRax, which includes log files, events (duplications, losses) counts, and statistics (i.e., reconciliation likelihood values) Note:  * As in the iGTP analyses, for the SpeciesRax files, the words Op, Fu, Di, Un, An, refer to the five root hypotheses compared: Opisthokonta-others, Fungi-others, Discoba-others, Unikonta-Bikonta, and (Ancyromonadida + Metamonada)-others, respectively.  * For the SpeciesRax analyses, the Un word refers to Ut (Cavalier-Smith 2003)

Supporting material for "Impact of gender on the formation and outcome of formal mentoring relationships in the life sciences"

This repository contains data and analysis code associated with the manuscript: L.P. Schwartz, J. Liénard, S. V. David. (2022) "Impact of gender on formation and outcome of formal mentoring relationships in the life sciences." Figures and tables in the manuscript can be produced by running the make_figures.ipynb notebook. Figures have been marked with headings indicating their position in the manuscript (Figure 1, Figure S1, etc.). In addition, the notebook contains code to reproduce regression analyses that are cited in the text but not directly associated with a figure. Data on mentoring relationships derives from Academic Family Tree (AFT, www.academictree.org) and public data sources on funding, publications, and awards. Inclusion criteria, public data sources, and procedures for linking across sources are described in the manuscript.  Personal identifiers for researchers have been anonymized, but remain consistent across all data in the repository. In other words, the personal identifier "1" refers to the same person in all dataframes in the repository. But, that person is *not* the same researcher identified as "1" on the public AFT website. Installation Requires Python 3.x. and Pandas. To load required libraries using Anaconda, run: `conda create --name aft -c conda-forge pandas numpy scipy ipython jupyterlab scipy scikit-learn pandas matplotlib numpy statsmodels seaborn pytables` Dataframes Data is stored as a series of Pandas dataframes within HDF5 or CSV files: * cng_tc: The primary dataset used in the analysis. The name is an acronym for "connections" (i.e. training relationships, "cn"), "gender" ("g"), and "trainee count" ("tc"). Each row contains data on the mentor and trainee in one training relationship. See manuscript for inclusion criteria. * mentors: Data on mentors. Each row contains data on one mentor. See manunscript for inclusion criteria. * mentors_grants, mentors_hindex, mentors_locs_ranked: Subset of mentors with data available for funding (mentors_grants), citation (mentors_hindex), and institution rank (mentors_locs_ranked). * mentors_nobel, mentors_hhmi, mentors_nas: Subsets of mentors that received a Nobel (mentors_nobel), Howard Hughes Medical Institute grants (mentors_hhmi), or membership in the National Academy of Sciences (mentors_nas). See manuscript for details of data sources and linking procedures. * cn, cng, first_names, gn, gn_all, locs: Partial data (connections only, inferred gender only, connections and gender only, location only, first names and inferred gender only) for more inclusive sets of researchers in AFT. They are generally not used used for analysis, but have been included here to calculate statistics on the total amount of data included and to screen for data from U.S. locations. * nsf_gender_phds, nsf_gender_pds: National Science Foundation survey data on gender and fraction PhDs conferred per year (nsf_gender_phds) or fraction postdocs employed per year (nsf_gender_pds). See manuscript for details of data source. * photo: Data for validation of gender inference method. Dataframe columns * amount: Mentor's total funding * amount_adj: Mentor's total funding (adjusted to 2020 dollars) * broad_field: Mentor's general research area (e.g., life sciences, engineering, based on National Science Foundation classifications) * continue: Whether trainee went on to become a mentor (i.e., has trainees listed in AFT) * country: Country in which mentor's current institution is located * firstname: First name of researcher (table of first names is not aligned with tables containing anonymized personal identifiers) * first_grant_year: Year of mentor's first grant * funding_rate: Mentor's annual funding rate (since first grant) * funding_rate_adj: Mentor's annual funding rate (since first grant) adjusted to 2020 dollars * hhmi: Whether mentor was granted HHMI funding * hindex: Mentor's hindex * location: Name of mentor's current institution * locid: Identifier for mentor's institution * locid_rank: Postion of mentor's institution in 2015 Quacquarelli-Symonds rankings (lower numbers are better) * locid_rank_rev: Reversed version of "locid_rank" (i.e., higher numbers are better) * majorarea: Mentor's specific research area (e.g, neuroscience) * male_mentor, male trainee: Whether the probability that a researcher's first name is used by a person identifying as a man meets threshold (see manuscript for details on gender inference using first names) * match_score: Score for string match between institution or name of awardee and researcher * mentor_career_start: The date at which the mentor's academic career began * mentor_continue_rate: Fraction of mentor's trainees that become mentors * mentor_continue_rate_ft: Fraction of mentor's woman trainees that become mentors * mentor_continue_rate_mt: Fraction of mentor's man trainees that become mentors * mentor_t_p_male0: Fraction of mentor's trainees that are men * mentor_t_p_male0_gs: Fraction of mentor's trainees that are men (graduate students only) * mentor_t_p_male0_pd: Fraction of mentor's trainees that are men (postdocs only) * mentor_tcount0: Mentor's total number of trainees * nas: Whether mentor is a member of the National Academy of Sciences * nobel: Whether mentor is a Nobel laureate * p_male_mentor, p_male_trainee: Probability that a researcher's first name is used by a person identifying as a man * pid: Anonymized identifier of researcher * pid_mentor: Anonymized identifier of mentor in training relationship * pid_trainee: Anonymized identifier of trainee in training relationship * pq: "1" if data on training relationship is drawn from ProQuest database and has not been manually edited a human AFT user * relation: Type of training relationship (1: graduate student, 2: postdoc) * scorer1, scorer2, scorer3: Results of photo validation of gender inference for each scorer * start: Training start year * stop: Training end year * trainee_tcount: Total people that the trainee has trained * triad: Whether trainee has participated in both a graduate-level and postdoctoral training relationship The cn dataframe follows slightly different naming conventions, but is not generally used in the analysis (pid1 = pid_trainee, pid2 = pid_mentor, startdate = start, stopdate = stop).

Supporting Information for Article "MarINvaders: A web toolkit of marine species for use in environmental assessments"

An Excel file with the overview of number of species per ecoregion and classes of alien species is available online. The code for querying and harmonizing the databases was published as a separate package (see Lonka et al. (2021))and available with an open source (GPL v3) license at <a href="https://gitlab.com/marinvaders/marinvaders">https://gitlab.com/marinvaders/marinvaders</a>.

opencc-by-4.0Nov 2016View details →

Supporting data for "On the maximum dual volume of a canonical Fano polytope"

Supporting data for: <a href="https://arxiv.org/abs/1611.02455">On the maximum dual volume of a canonical Fano polytope</a>, Gabriele Balletti, Alexander M. Kasprzyk, Benjamin Nill. This data supports the results of Section 6.3.

Datasets supporting the original submission of Harris et al., "A Global Survey of Rotating Convective Updrafts in the GFDL X-SHiELD 2021 Global Storm Resolving Model"

Datafiles used in the analyses described by Harris et al, "A Global Survey of Rotating Convective Updrafts in the GFDL X-SHiELD 2021 Global Storm Resolving Model", to be submitted to the Journal of Geophysical Research. Model output was created by X-SHiELD 2021 <a href="http://doi.org/10.5281/zenodo.6941034">https://doi.org/10.5281/zenodo.6941034</a> described in the paper: Harris, L., Zhou, L., Lin, S.-J., Chen, J.-H., Chen, X., Gao, K., et al. (2020). GFDL SHiELD: A unified system for weather-to-seasonal prediction. Journal of Advances in Modeling Earth Systems, 12, e2020MS002223.<a href="https://doi.org/10.1029/2020MS002223"> https://doi.org/10.1029/2020MS002223</a> GPM data used for Figure 6b is derived from Huffman, G.J., E.F. Stocker, D.T. Bolvin, E.J. Nelkin, Jackson Tan (2019), GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed:  4 August 2021,<a href="https://doi.org/10.5067/GPM/IMERG/3B-HH/06"> 10.5067/GPM/IMERG/3B-HH/06</a>

opencc-by-4.0Mar 2022View details →

Constraining Andean Propagation of Exhumation at the Limit of the Eastern Cordillera, NW Argentina, using Low-Temperature Thermochronology in a Structural Context - Supporting Information

Supporting information accompanying the publication "Constraining Andean Propagation at the Limit of the Eastern Cordillera, NW Argentina, using Low-Temperature Thermochronology in a Structural Context" published in Tectonics. The dataset contains apatite and zircon (U-Th-Sm)/He and apatite fission track data from the Tilcara Range and San Lucas block, Jujuy, Argentina, as well as additional QTQt thermal models that are discussed in the paper. Table S1 contains full single-grain results from apatite fission track, apatite (AHe) (U-Th-Sm)/He and zircon (ZHe) (U-Th-Sm)/He analyses. Outliers are marked in grey and are not included in the weighted mean age. Figure S1 supports (U-Th-Sm)/He data graphically. Apatite fission track (AFT) data is supported by radial plots in Figure S2. Figure S3 shows QTQt thermal models using either AHe, AFT or ZHe single-grain ages. All of the models results are explained in the main text.

opencc-by-4.0Aug 2022View details →

Supplementary figures supporting the corrigendum on the PARASO Geoscientific Model Development paper

Figures supporting the corrigendum to the PARASO GMD paper. In the original version of the code, the COSMO wind stress were not properly rotated from the COSMO grid to NEMO's. figure_windstress_fix.png provides an illustration of the bug, and the other figures are a selection of diagnostics performed with a fixed version of PARASO, sharing the same experimental design and numbering as the original paper. Original paper: Pelletier, C., Fichefet, T., Goosse, H., Haubner, K., Helsen, S., Huot, P.-V., Kittel, C., Klein, F., Le clec'h, S., van Lipzig, N. P. M., Marchi, S., Massonnet, F., Mathiot, P., Moravveji, E., Moreno-Chamarro, E., Ortega, P., Pattyn, F., Souverijns, N., Van Achter, G., Vanden Broucke, S., Vanhulle, A., Verfaillie, D., and Zipf, L.: PARASO, a circum-Antarctic fully coupled ice-sheet–ocean–sea-ice–atmosphere–land model involving f.ETISh1.7, NEMO3.6, LIM3.6, COSMO5.0 and CLM4.5, Geosci. Model Dev., 15, 553–594, <a href="https://doi.org/10.5194/gmd-15-553-2022">10.5194/gmd-15-553-2022</a>, 2022.