cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15454/pnqquq/oshlha
title: study_cross-mean_prediction.Rmd

description: This script takes as input predicted genotypic values for the prediction of cross mean (`S1a-1b-2_mean.obs.mean.pred-per-cross.tsv`, `pred_cross-mean_S1a-1b-2_parent-average-diallel-cross`), per-cross and per-trait best method for observed and predicted cross mean (`pred_cross-mean_S1a-1b-2_best-method-cross_parent-average-diallel-cross.tsv`, `pred_cross-mean_S1a-1b-2_best-method-cross_parent-average-diallel-trait.tsv`). It computes predictive ability for the prediction of cross mean, on cross and trait basis. Then, it analyzes variables affecting either per-trait or per-cross predictive ability of cross mean, with same parameters tested as in `compute_multiple-lin-reg_res_pred-ability.Rmd` script. Finally, this script analyzes cross mean prediction with optimized training sets with results generated in `genom-pred_inter-pop_S2_RR_optTRS.Rmd` and `genom-pred_inter-pop_S2_LASSO_optTRS.Rmd`. This script generates Figures 2, 5a, 6, S5, S10, S16, and Table S3.
DOI: 10.15454/pnqquq/pshlv8
title: analyze_marker-effects_diallel.Rmd

description: This script i) analyzes estimated marker effects in both populations and for both methods, ii) studies the proportion of homozygous loci within each cross of the half-diallel and links this to estimated BLUPs of genotypic values and predictive ability. It takes as input estimated marker effects (`GP_S1a/1b/2_RR/LASSO_mrk-effects.tsv`), `GP_S2_RR_per-cross-summary.tsv`, `geno-diallel_formated`, `BLUPs_diallel_formated-scaled.tsv` and `geno-p279_formated.tsv`. It generates a table of the proportion of non-segregating loci `diallel-cross_prop-nonsegreg-homo-loci.tsv` (Table S7).
DOI: 10.15454/pnqquq/uyz7ch
title: pheno_lme4_diallel_mal.ripe.Rmd

description: This script takes as input "pheno-diallel_with-outlier-columns.tsv". It performs mixed model selection (after raw data transformation when necessary), checks underluying assumptions, and estimates variance components, heritability values and BLUPs of genotypic values. This script generates two tables: one with mixed model fitting information and one with the BLUPs of genotypic values.
DOI: 10.15454/pnqquq/bkbtdt
title: diallel_fit-info_blups_chapitre.tab

description: Table S1 Information on mixed model selection and genotypic BLUP estimation for 15 traits in the half-diallel population.
DOI: 10.15454/pnqquq/gryjjv
title: compute_crosses_among-p279.Rmd

description: This script loads marker effects estimated in the whole diversity panel and computes parental average genotypes for all possible crosses among cultivars of this population. It generates predicted genotypic values of cross means for all possible crosses. Then, it performs a PCA on all these values on which half-diallel parents predicted values are projected. This script generates Figure S13.
DOI: 10.15454/pnqquq/gyuxns
title: genom-pred_inter-pop_S2_bestMeth_optTRS.Rmd

description: This script loads results from `genom-pred_inter-pop_S2_RR_optTRS.Rmd` and `genom-pred_inter-pop_S2_LASSO_optTRS.Rmd` and results of genomic prediction without training set optimization in `table_results_best-pred-ability_all-scenarios_per-cross.tsv`. It selects the best method in terms of predictive ability, both for Mendelian sampling genomic prediction and cross mean prediction. This script generates the Figure S11.
DOI: 10.15454/pnqquq/ldotyb
title: table_results_best-pred-ability_all-scenarios_per-cross.tab

description: Table of predictive ability for the best method between RR and LASSO, for the three scenarios and the 15 traits.
DOI: 10.15454/pnqquq/oldkhg
title: compute_multiple-lin-reg_res_pred-ability.Rmd

description: This script applies a multiple linear regression to several variables potentially affecting Mendelian sampling predictive ability for each cross and trait. It takes as inputs predictive abilities from RR, LASSO and the best method resulting from `compute_ensemble-genom-pred_diallel.Rmd` script; heritability and fitting information from `diallel_fit-info_blups_chapitre.tsv` and `diallel_H2-per-cross_all-traits.tsv`; the proportion of homozygous loci `diallel-cross_prop-nonsegreg-homo-loci.tsv`; measures of genetic distances generated in `study_p279_diallel_relationship.Rmd`.It generates Figure 5b.
DOI: 10.15454/pnqquq/usbkzc
title: pred_mean-cross_parental-average_diallel.Rmd

description: This script takes as inputs `geno-diallel_formated.tsv`, `BLUPs_diallel_formated-scaled.tsv`, estimated marker effects in all scenarios and for both methods in `GP_S1a/1b/2_RR_mrk-effects.tsv`. It computes the mean predicted genotypic value per cross, based on realized or parental average genotypes and previously estimated marker effects (with RR and LASSO methods). It ouputs table for parental average genotypes (`diallel_parental-average-genotypes.tsv`), tables for mean observed and predicted genotypic value based on existing and parental average genotypes, for each cross for all scenarios (`S1a-1b-2_mean.obs.mean.pred-per-cross.tsv`, `pred_cross-mean_S1a-1b-2_parent-average-diallel-cross`), observed and predicted cross mean for the best method between RR and LASSO based on per-cross predictive ability (`pred_cross-mean_S1a-1b-2_best-method-cross_parent-average-diallel-cross.tsv`) or per-trait predictive ability `pred_cross-mean_S1a-1b-2_best-method-cross_parent-average-diallel-trait.tsv`.
DOI: 10.15454/pnqquq/8fwyer
title: 0_ReadMe.md

description: :unav
DOI: 10.15454/pnqquq/njf5ff
title: AllResults_genom-pred-3-scenarios_15-traits_10-crosses_correlated-variables.tab

description: Table which recapitulates results of Mendelian sampling prediction with 3 scenarios tested in 10 crosses and all associated co-variables. This table has been generated in "compute_multiple-lin-reg_res_pred-ability.Rmd"
DOI: 10.15454/pnqquq/ox289j
title: p279-diallel_explo-geno_SNPs_karyogram.Rmd

description: This script analyzes the set of SNP markers of this study. It takes as input a vcf file for diversity panel population, generated in Flutre et al. 2020, and "geno-p279_formated.tsv" for the list of SNP markers. It generates a plot of marker density along the genome, which is the Figure S15.
DOI: 10.15454/pnqquq/rwldco
title: 0_ReadMe.html

description: :unav
DOI: 10.15454/pnqquq/xafm1j
title: compute_multiple-lin-reg_res_pred-ability.Rmd

description: This script applies a multiple linear regression to several variables potentially affecting Mendelian sampling predictive ability for each cross and trait. It takes as inputs predictive abilities from RR, LASSO and the best method resulting from "compute_ensemble-genom-pred_diallel.Rmd" script; heritability and fitting information from "diallel_fit-info_blups_chapitre.tsv" and "diallel_H2-per-cross_all-traits.tsv"; the proportion of homozygous loci "diallel-cross_prop-nonsegreg-homo-loci.tsv"; measures of genetic distances generated in "study_p279_diallel_relationship.Rmd".It generates Figure 5b and table "AllResults_genom-pred-3-scenarios_15-traits_10-crosses_correlated-variables.tsv".
DOI: 10.15454/pnqquq/area12
title: 0_ReadMe.md

description: :unav
DOI: 10.15454/pnqquq/f0cyqm
title: 0_ReadMe.html

description: :unav
DOI: 10.15454/pnqquq/qnxujj
title: 0_ReadMe.html

description: :unav
DOI: 10.15454/pnqquq/sfboko
title: 0_ReadMe.md

description: :unav
DOI: 10.57745/r6j4oy
title: genotypes_popMtp3331

description: List of selected genotypes (selection made by Tello et al. 2018) for SxPN population
DOI: 10.57745/80vsrf
title: genotypes_popMtp3335

description: List of selected genotypes (selection made by Tello et al. 2018) for CSxS population
DOI: 10.57745/o94yqm
title: genotypes_popMtp3336

description: List of selected genotypes (selection made by Tello et al. 2018) for GxS population
DOI: 10.57745/sfkyrb
title: genotypes_popMtp3334

description: List of selected genotypes (selection made by Tello et al. 2018) for TNxPN population
DOI: 10.57745/vbcdys
title: genotypes_popMtp3337

description: List of selected genotypes (selection made by Tello et al. 2018) for TNxS population
DOI: 10.57745/zcfohu
title: genotypes_popMtp3340

description: List of selected genotypes (selection made by Tello et al. 2018) for TxG population
DOI: 10.57745/gwzcq5
title: genotypes_popMtp3339

description: List of selected genotypes (selection made by Tello et al. 2018) for TxCS population
DOI: 10.57745/k3zaon
title: genotypes_popMtp3338

description: List of selected genotypes (selection made by Tello et al. 2018) for GxCS population
DOI: 10.57745/pbx9pn
title: genotypes_popMtp3333

description: List of selected genotypes (selection made by Tello et al. 2018) for GxPN population
DOI: 10.57745/ycqio8
title: genotypes_popMtp3332

description: List of selected genotypes (selection made by Tello et al. 2018) for CSxPN population
DOI: 10.34740/kaggle/dsv/5057476
title: Mahanagar Yatayat Dataset
DOI: 10.6084/m9.figshare.16771195
title: Replication data for How localised are knowledge spillovers? Evidence from microgeographic data on UK patent citations

description: These files contain the data needed to replicate "How localised are knowledge spillovers? Evidence from microgeographic data on UK patent citations" in <em>Economics of Innovation and New Technology</em> at: https://doi.org/10.1080/10438599.2023.2184809 <br> The data are described in the file README.txt
DOI: 10.5281/zenodo.7674732
title: Data from: Specialization patterns in symbiotic associations: a community perspective over spatial scales.

description: <strong>Nostoc_rbcLX_alignment: </strong>Alignment of Nostoc rbcLX sequences in FASTA format. <strong>Name_equivalences: </strong>Excel cointaining mycociont species names, abbreviations, alignment code for each sample used in the aligment, forest and Nostoc phylogroup. <strong>Abstract:</strong> Specialization, contextualized in a resource axis of an organism niche, is a core concept in ecology. In biotic interactions, specialization can be determined by the range of interacting partners. Evolutionary and ecological factors, in combination with the surveyed scale (spatial, temporal, biological and/or taxonomic) influence the conception of specialization. This study aimed to assess the specialization patterns and drivers in the lichen symbiosis, considering the interaction between the principal fungus (mycobiont) and the associated <em>Nostoc</em> (cyanobiont), from a community perspective considering different spatial scales. Thus, we determined <em>Nostoc</em> phylogroup richness and composition of lichen communities in eleven <em>Nothofagus pumilio</em> forests across a wide latitudinal gradient in Chile. To measure specialization, cyanobiont richness, Simpson’s, and d’ indices were estimated for 37 mycobiont species in these communities. Potential drivers that might shape <em>Nostoc</em> composition and specialization measures along the environmental gradient were analysed. Limitations in lichen distributional ranges due to the availability of their cyanobionts were studied. Turnover patterns of cyanobionts were identified at multiple spatial scales. The results showed that environmental factors shaped the <em>Nostoc</em> composition of these communities, thus limiting cyanobiont availability to establish the symbiotic association. Besides, specialization changed with the spatial scale and with the metric considered. Cyanolichens were more specialized than cephalolichens when considering partner richness and Simpson’s index, whereas the d’ index was mostly explained by mycobiont identity. Little evidence of lichen distributional ranges due to the distribution of their cyanobionts was found. Thus, lichens with broad distributional ranges either associated with several cyanobionts or with widely distributed cyanobionts. Comparisons between local vs. regional scales showed a decreasing degree of specialization at larger scales due to an increase in cyanobiont richness. <em>Synthesis</em>. The results support the context dependency of specialization and how its consideration changes with the metric and the spatial scale considered. Subsequently, we suggest considering the entire community, and widening the spatial scale studied as it is crucial to understand factors determining specialization.
DOI: 10.5281/zenodo.7674731
title: Data from: Specialization patterns in symbiotic associations: a community perspective over spatial scales.

description: <strong>Nostoc_rbcLX_alignment: </strong>Alignment of Nostoc rbcLX sequences in FASTA format. <strong>Name_equivalences: </strong>Excel cointaining mycociont species names, abbreviations, alignment code for each sample used in the aligment, forest and Nostoc phylogroup. <strong>Abstract:</strong> Specialization, contextualized in a resource axis of an organism niche, is a core concept in ecology. In biotic interactions, specialization can be determined by the range of interacting partners. Evolutionary and ecological factors, in combination with the surveyed scale (spatial, temporal, biological and/or taxonomic) influence the conception of specialization. This study aimed to assess the specialization patterns and drivers in the lichen symbiosis, considering the interaction between the principal fungus (mycobiont) and the associated <em>Nostoc</em> (cyanobiont), from a community perspective considering different spatial scales. Thus, we determined <em>Nostoc</em> phylogroup richness and composition of lichen communities in eleven <em>Nothofagus pumilio</em> forests across a wide latitudinal gradient in Chile. To measure specialization, cyanobiont richness, Simpson’s, and d’ indices were estimated for 37 mycobiont species in these communities. Potential drivers that might shape <em>Nostoc</em> composition and specialization measures along the environmental gradient were analysed. Limitations in lichen distributional ranges due to the availability of their cyanobionts were studied. Turnover patterns of cyanobionts were identified at multiple spatial scales. The results showed that environmental factors shaped the <em>Nostoc</em> composition of these communities, thus limiting cyanobiont availability to establish the symbiotic association. Besides, specialization changed with the spatial scale and with the metric considered. Cyanolichens were more specialized than cephalolichens when considering partner richness and Simpson’s index, whereas the d’ index was mostly explained by mycobiont identity. Little evidence of lichen distributional ranges due to the distribution of their cyanobionts was found. Thus, lichens with broad distributional ranges either associated with several cyanobionts or with widely distributed cyanobionts. Comparisons between local vs. regional scales showed a decreasing degree of specialization at larger scales due to an increase in cyanobiont richness. <em>Synthesis</em>. The results support the context dependency of specialization and how its consideration changes with the metric and the spatial scale considered. Subsequently, we suggest considering the entire community, and widening the spatial scale studied as it is crucial to understand factors determining specialization.
DOI: 10.5281/zenodo.7674836
title: Suplement

description: Supplement Materials Article: Unproven Scientific Drugs (Repurposed Drugs: Antiparasitic Ivermectin, Antimalarial Hydroxychloroquine/Chloroquine, and Antimicrobial Azithromycin) to Treat and to Manage Coronavirus Disease (COVID)-19: a Systematic Review of the Clinical Trials Journal: International Journal of Environmental Research and Public Health
DOI: 10.5281/zenodo.7674835
title: Suplement

description: Supplement Materials Article: Unproven Scientific Drugs (Repurposed Drugs: Antiparasitic Ivermectin, Antimalarial Hydroxychloroquine/Chloroquine, and Antimicrobial Azithromycin) to Treat and to Manage Coronavirus Disease (COVID)-19: a Systematic Review of the Clinical Trials Journal: International Journal of Environmental Research and Public Health
DOI: 10.6084/m9.figshare.20741410
title: A methodology to quantify the neighborhood decay effect of urban cellular automata models

description: Spatial heterogeneity is an essential feature of land use change. It is important for cellular automata (CA)-based urban expansion modeling to consider the spatial heterogeneity of neighborhood effect. According to the first law of geography, there exists a distance decay effect in the neighborhood space of the urban CA models. But no research has ever been able to accurately quantify the neighborhood decay effect and apply it to models. In this study, we propose a methodology to accurately quantify the neighborhood decay effect of CA models with large neighborhood size, including the design of neighborhood configuration and the selection and optimization of decay strategies. <br> <br>
DOI: 10.2906/112117098108/22
title: Web analytics of user searches on the Publications Office websites

description: This dataset contains statistics related to searches performed in websites of the Publications Office. The data included corresponds exclusively to searches performed in the websites that created a click on a search result (e.g., publication, legal document).
DOI: 10.5281/zenodo.7674857
title: CBE validation and market share

description: Dataset of CBE validation and market share
DOI: 10.5281/zenodo.7674856
title: CBE validation and market share

description: Dataset of CBE validation and market share
DOI: 10.6084/m9.figshare.22154942
title: test upload

description: This is a description
DOI: 10.6084/m9.figshare.22154942.v1
title: test upload

description: This is a description
DOI: 10.6084/m9.figshare.22154939
title: Packebush et al Data.zip

description: Data associated with Packebush et al. 2023
DOI: 10.5517/ccdc.csd.cc297nbk
title: CCDC 2122518: Experimental Crystal Structure Determination

description: Related Article: Daniel Bindl, Pradeep K. Mandal, Lars Allmendinger, Ivan Huc|2022|Angew.Chem.,Int.Ed.|61|e202116509|doi:10.1002/anie.202116509
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5066/p92u1d63
title: Peak-flow frequency analyses for selected streamgages in Dawson and Richland Counties, and the Powder River Basin, Montana, based on data through water year 2022

description: The USGS Wyoming-Montana Water Science Center (WY?MT WSC) completed a report (Sando and McCarthy, 2018) documenting methods for peak-flow frequency analysis following implementation of the Bulletin 17C guidelines. The methods are used to provide estimates of peak-flow quantiles for 66.7-, 50-, 42.9-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (AEPs) for selected USGS streamgages. This data release presents peak-flow frequency analyses based on methods described by Sando and McCarthy (2018), for selected streamgages in Dawson and Richland Counties, and the Powder River Basin, based on data through water year 2022.
DOI: 10.5281/zenodo.7674883
title: cacatua_tenuirostris_decontaminated_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.7674882
title: cacatua_tenuirostris_decontaminated_gx.fasta.gz

description: .
DOI: 10.34740/kaggle/dsv/5054885
title: Landshapes-4041
DOI: 10.5281/zenodo.7636350
title: Catálogo de teses e dissertações da área de Comunicação e Informação - CAPES - 1987 a 2020

description: Os dados disponíveis foram coletados a partir dos conjuntos de dados “Catálogo de Teses e Dissertações – Brasil” disponibilizados no Portal Dados Abertos CAPES &lt; https://dadosabertos.capes.gov.br/dataset?groups=catalogo-de-teses-e-dissertacoes-brasil &gt;. Em seguida, com os arquivos em formato de planilha “*xlsx”, e com o auxílio do <em>Microsoft Excel</em>, filtramos os registros da área de conhecimento "Biblioteconomia", "Ciência da Informação", "Comunicação", "Museologia" e "Teoria da Comunicação", . Considerando que haviam diferenças no uso de metadados entre os conjuntos de dados disponíveis no Catálogo de Teses e Dissertações – Brasil, selecionamos apenas os dados relacionados a: Ano, Região, UF, Sigla IES, Nome do Programa, Grande Área CAPES, Área do conhecimento, Autor, Título, Nível, Palavras-Chaves, Linha de Pesquisa, URL do registro no Catálogo de Teses e Dissertações e Orientador. Legenda das colunas: cc_AnoBase = Ano de defesa<br> cc_Regiao = Região do Programa de Pós-Graduação<br> cc_Uf = Unidade Federativa do Programa de Pós-Graduação<br> cc_SiglaIes = Sigla da Instituição de Ensino Superior do Programa de Pós-Graduação<br> cc_NomeIes = Nome da Instituição de Ensino Superior do Programa de Pós-Graduação<br> cc_NomePrograma = Nome do Programa de Pós-Graduação<br> cc_GrandeAreaDescricao = Grande Área de Descrição - CAPES<br> cc_AreaConhecimento = Área de Conhecimento - CAPES<br> cc_Autor = Autor da tese ou dissertação<br> cc_TituloTese = Título da tese ou dissertação<br> cc_Nivel = Indicação se Mestrado / Doutorado / Mestrado Profissional / Profissionalizante<br> cc_PalavrasChave = Palavras-Chaves da tese ou dissertação<br> cc_Idioma = Idioma<br> cc_ResumoTese = Texto registrado no Resumo da tese ou dissertação<br> cc_LinhaPesquisa - Linha de Pesquisa do Autor<br> cc_URLTextoCompleto = Link para a página do Catálogo de Teses e Dissertações – Brasil”<br> cc_Orientador = Nome do orientador Observações: Os dados foram preenchidos pelos coordenadores dos Programas de Pós-Graduação, convém para melhor utilização dos dados, revisar os dados em repositórios e bibliotecas digitais de teses e dissertações das Universidades. Acesse o Plano de Gestão de Dados e: https://dmptool.org/plans/94834/export.pdf?export%5Bquestion_headings%5D=true Havendo dúvidas. Entre em contato com o autor. Prof. Dr. Pedro Andretta. E-mail: pedro.andretta @unir.br
description: Painel interativo, disponível em: &lt; https://lookerstudio.google.com/u/0/reporting/7340b3ab-3ff4-47ae-ba77-9735b39dfbff/page/p_piejlr8yvc &gt;.
description: {"references": ["Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior. Dados Abertos CAPES. Cat\u00e1logo de Teses e Disserta\u00e7\u00f5es - Brasil. 2022. https://dadosabertos.capes.gov.br/dataset?groups=catalogo-de-teses-e-dissertacoes-brasil"]}