cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15454/gsjsww/7jm1mo
title: 7_4_Chestnuts_Production_Estimates.tab

description: Fruit production estimation of trees from INRAE germplasm collection.
DOI: 10.15454/gsjsww/7pz6zn
title: 7_2_Male_Catkins-1.tab

description: Pollen production estimation of trees from INRAE germplasm collection.
DOI: 10.15454/gsjsww/8dlf9j
title: 6_1_Species_Identification_Genets-1.tab

description: Assignment of the 113 genets genotypes to chestnuts species.
DOI: 10.15454/gsjsww/sthy9b
title: 7_5_Seed_Set_2018-1.tab

description: 2018 Seed set of trees from INRAE germplasm collection.
DOI: 10.15454/gsjsww/amyowy
title: 1_2_List_INRAE_Chestnuts_Germplasm_Collection-1.tab

description: List of the 244 chestnuts from the INRAE germplasm collection.
DOI: 10.15454/gsjsww/cuc07t
title: 0_0_Read_Me-1.tab

description: Description of variables.
DOI: 10.15454/gsjsww/iha4vd
title: 7_1_Chestnuts_Phenotypes-1.tab

description: Phenotype of trees from INRAE germplasm collection.
DOI: 10.15454/gsjsww/p6oylm
title: 7_4_Chestnuts_Production_Estimates-1.tab

description: Fruit production estimation of trees from INRAE germplasm collection.
DOI: 10.15454/gsjsww/phsvhg
title: 7_3_Phenology-1.tab

description: Phenology of trees from INRAE germplasm collection.
DOI: 10.15454/gsjsww/xly7tu
title: 3_1_Clonal_Identification-1.tab

description: Identification of clones with Genalex.
DOI: 10.15454/gsjsww/06gc4d
title: 4_1_Consensus_genotypes_Genets-1.tab

description: Genotypes of the 113 genets characterized with 70 SNPs.
DOI: 10.15454/gsjsww/1y43hx
title: 2_2_Genotypes_Genalex_Input-1.tab

description: Genotypes input file for Genalex analysis.
DOI: 10.15454/gsjsww/geygqv
title: 8_1_Summary-1.tab

description: Summary of informations collected on trees from INRAE germplasm collection.
DOI: 10.15454/gsjsww/kdfxpa
title: 8_2_Map-1.tab

description: Map of trees from INRAE germplasm collection.
DOI: 10.15454/gsjsww/l4h1sk
title: 7_6_Seed_Set_2019-1.tab

description: 2019 Seed set of trees from INRAE germplasm collection.
DOI: 10.15454/gsjsww/uq2gtg
title: 1_1_List_Chestnuts-1.tab

description: List of 275 chestnuts identified inside and outside the INRAE intensive study plot.
DOI: 10.15454/gsjsww/gzeqnl
title: 7_6_Seed_Set_2019.tab

description: 2019 Seed set of trees from INRAE germplasm collection.
DOI: 10.15454/gsjsww/h7gddq
title: 8_1_Summary.tab

description: Summary of informations collected on trees from INRAE germplasm collection.
DOI: 10.15454/gsjsww/ngcamo
title: 3_1_Clonal_Identification.tab

description: Identification of clones with Genalex.
DOI: 10.15454/gsjsww/oalhw4
title: 1_1_List_Chestnuts.tab

description: List of 275 chestnuts identified inside and outside the INRAE intensive study plot.
DOI: 10.15454/gsjsww/sldlhb
title: 0_0_Read_Me.tab

description: Description of variables.
DOI: 10.15454/gsjsww/15eqym
title: 1_1_List_Chestnuts.tab

description: List of 275 chestnuts identified inside and outside the INRAE intensive study plot.
DOI: 10.15454/gsjsww/blcher
title: 2_1_Genotypes.tab

description: Genotypes of 273 chestnuts characterized with 105 SNPs.
DOI: 10.15454/gsjsww/doc9ka
title: 7_3_Phenology.tab

description: Phenology of trees from INRAE germplasm collection.
DOI: 10.15454/gsjsww/mgyf2t
title: 6_1_Species_Identification_Genets.tab

description: Assignment of the 113 genets genotypes to chestnuts species.
DOI: 10.15454/gsjsww/no6ooi
title: 7_4_Chestnuts_Production_Estimates.tab

description: Fruit production estimation of trees from INRAE germplasm collection.
DOI: 10.15454/gsjsww/uybjbc
title: 7_2_Male_Catkins.tab

description: Pollen production estimation of trees from INRAE germplasm collection.
DOI: 10.15454/gsjsww/51iss9
title: 1_2_List_INRAE_Chestnuts_Germplasm_Collection.tab

description: List of the 244 chestnuts from the INRAE germplasm collection.
DOI: 10.15454/gsjsww/klxbsr
title: 4_1_Consensus_genotypes_Genets.tab

description: Genotypes of the 113 genets characterized with 70 SNPs.
DOI: 10.15454/gsjsww/qskwol
title: 7_5_Seed_Set_2018.tab

description: 2018 Seed set of trees from INRAE germplasm collection.
DOI: 10.15454/gsjsww/s6g1ub
title: 5_1_Genotypes_Ramets.tab

description: Genotypes of the ramets characterized with 70 SNPs.
DOI: 10.15454/gsjsww/ymgvad
title: 7_1_Chestnuts_Phenotypes.tab

description: Phenotype of trees from INRAE germplasm collection.
DOI: 10.15454/gsjsww/cyf5ay
title: 5_1_Genotypes_Ramets.tab

description: Genotypes of the ramets characterized with 70 SNPs.
DOI: 10.15454/gsjsww/jbylfc
title: 8_2_Map.tab

description: Map of trees from INRAE germplasm collection.
DOI: 10.15454/gsjsww/lossnn
title: 2_2_Genotypes_Genalex_Input.tab

description: Genotypes input file for Genalex analysis.
DOI: 10.15454/gsjsww/ubpgxq
title: 0_0_Read_Me.tab

description: Description of variables.
DOI: 10.57745/7g7xw3
title: 1_1_List_Chestnuts.tab

description: List of 275 chestnuts identified inside and outside the INRAE intensive study plot.
DOI: 10.5281/zenodo.7441180
title: Data resources for: "How career hubs shape the global corporate elite"

description: Here you find data resources for the article: "How career hubs shape the global corporate elite" published in Global Networks. The resources contain a merge list of IDs for firms in Boardex and links to WikiData. Data was collected in 2019. Furthermore there is R code for the variant of career networks used in the article.
DOI: 10.5281/zenodo.7441179
title: Data resources for: "How career hubs shape the global corporate elite"

description: Here you find data resources for the article: "How career hubs shape the global corporate elite" published in Global Networks. The resources contain a merge list of IDs for firms in Boardex and links to WikiData. Data was collected in 2019. Furthermore there is R code for the variant of career networks used in the article.
DOI: 10.34740/kaggle/dsv/4726695
title: Elon Musk Tweets
DOI: 10.5281/zenodo.7441332
title: Micromechanical high-Q trampoline resonators from strained crystalline InGaP for integrated free-space optomechanics

description: We share experimental data for Figs. 2,4,5,6,11,12,13 of arxiv manuscript arXiv:2211.12469 [physics.app-ph] entitled "Micromechanical high-Q trampoline resonators from strained crystalline InGaP for integrated free-space optomechanics".
description: We acknowledge funding by the QuantERA project C'MON-QSENS!, the Knut and Alice Wallenberg Foundation through a Wallenberg Academy Fellowship (W.W.), by the Wallenberg Center for Quantum Technology (WACQT, A.C.), by Chalmers Excellence Initiative Nano, and by the Swedish Research Council (Grant no. 2019-04946). Sample fabrication was performed in the Myfab Nanofabrication Laboratory at Chalmers. Simulations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at Tetralith, Link¨oping University, partially funded by the Swedish Research Council (Grant 2018-05973).
description: {"references": ["arXiv:2211.12469"]}
DOI: 10.5281/zenodo.7441331
title: Micromechanical high-Q trampoline resonators from strained crystalline InGaP for integrated free-space optomechanics

description: We share experimental data for Figs. 2,4,5,6,11,12,13 of arxiv manuscript arXiv:2211.12469 [physics.app-ph] entitled "Micromechanical high-Q trampoline resonators from strained crystalline InGaP for integrated free-space optomechanics".
description: We acknowledge funding by the QuantERA project C'MON-QSENS!, the Knut and Alice Wallenberg Foundation through a Wallenberg Academy Fellowship (W.W.), by the Wallenberg Center for Quantum Technology (WACQT, A.C.), by Chalmers Excellence Initiative Nano, and by the Swedish Research Council (Grant no. 2019-04946). Sample fabrication was performed in the Myfab Nanofabrication Laboratory at Chalmers. Simulations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at Tetralith, Link¨oping University, partially funded by the Swedish Research Council (Grant 2018-05973).
description: {"references": ["arXiv:2211.12469"]}
DOI: 10.48670/moi-00143
title: GLOBAL OCEAN GRIDDED SEA LEVEL ANOMALIES NOISE NRT
DOI: 10.15454/pl3hwq/bpv5gs
title: Vivaldi_statistics.Rmd

description: Statistical analysis script (R format)
DOI: 10.15454/pl3hwq/87hvgq
title: Feuille2.png

description: Vine leaf image
DOI: 10.15454/pl3hwq/kagc5m
title: Vivaldi_statistics.html

description: Statistical analysis script (html format)
DOI: 10.15454/pl3hwq/oqljt3
title: ps_inoc_rar.RData

description: Dataset (rarefied ASV table and metadata) for inoculated leaf samples (phyloseq format)
DOI: 10.15454/pl3hwq/r9ci39
title: ps_control_rar.RData

description: Dataset (rarefied ASV table and metadata) for control leaf samples (phyloseq format)
DOI: 10.15454/pl3hwq/t5duyz
title: ps_no_patho_rar.RData

description: Dataset (rarefied ASV table and metadata) for inoculated leaf samples, after removal of the pathogen Erysiphe necator (phyloseq format)
DOI: 10.15454/pl3hwq/6fjwqz
title: ps_control_rar.RData

description: Dataset (rarefied ASV table and metadata) for control leaf samples (phyloseq format)
DOI: 10.15454/pl3hwq/mz9b4t
title: ps_inoc_rar.RData

description: Dataset (rarefied ASV table and metadata) for inoculated leaf samples (phyloseq format)
DOI: 10.15454/pl3hwq/sn1upt
title: Vivaldi_statistics_v2.html

description: Statistical analysis script (html format)
DOI: 10.15454/pl3hwq/uyxrdc
title: Vivaldi_statistics_v2.Rmd

description: Statistical analysis script (R format)
DOI: 10.15454/pl3hwq/wyhmzu
title: ps_no_patho_rar.RData

description: Dataset (rarefied ASV table and metadata) for inoculated leaf samples, after removal of the pathogen Erysiphe necator (phyloseq format)
DOI: 10.57745/c0nnwv
title: Vivaldi_statistics_v4.html

description: Statistical analysis script (html)
DOI: 10.57745/dl2dyr
title: ps_inoc_rar.RData

description: Rarefied ASV table for inoculated samples (whole community)
DOI: 10.57745/pypiyq
title: Vivaldi_statistics_v4.Rmd

description: Statistical analysis script (R)
DOI: 10.57745/srmiwl
title: ps_no_patho_rar.RData

description: Rarefied ASV table for inoculated samples (residential community)
DOI: 10.57745/zjlyhz
title: ps_control_rar.RData

description: Rarefied ASV table for control samples (whole community)
DOI: 10.57745/6bgcxc
title: Bergès_etal_FigS11ABDE.data.tab

description: :unav
DOI: 10.57745/yk9bhz
title: Bergès_etal_FigS7.data.tab

description: :unav
DOI: 10.57745/gb5brp
title: Bergès_etal_FigS9.data.tab

description: :unav
DOI: 10.57745/kzlc0u
title: Bergès_etal_Fig1.data.histogram.tab

description: :unav
DOI: 10.57745/plcmbv
title: Bergès_etal_Fig2A.data.tab

description: :unav
DOI: 10.57745/wmo9xd
title: Bergès_etal_FigS10.data.tab

description: :unav
DOI: 10.57745/zylegc
title: Bergès_etal_FigS6.data.tab

description: :unav
DOI: 10.57745/4at3ty
title: Bergès_etal_FigS2.data.tab

description: :unav
DOI: 10.57745/axl5hb
title: Bergès_etal_Fig3.data.tab

description: :unav
DOI: 10.57745/ibbsib
title: Bergès_etal_FigS12.data.tab

description: :unav
DOI: 10.57745/lyswar
title: Bergès_etal_FigS3.data.tab

description: :unav
DOI: 10.57745/p3g30u
title: Bergès_etal_FigS5.data.tab

description: :unav
DOI: 10.57745/wanqkm
title: Bergès_etal_Fig4A.data.tab

description: :unav
DOI: 10.57745/ahsjiv
title: Bergès_etal_FigS1.data.tab

description: :unav
DOI: 10.57745/ivyber
title: Bergès_etal_Fig5.data.tab

description: :unav
DOI: 10.57745/jmbxg9
title: Bergès_etal_FigS11CF.data.tab

description: :unav
DOI: 10.57745/llezco
title: Bergès_etal_FigS8.data.tab

description: :unav
DOI: 10.57745/vup4mh
title: Bergès_etal_Fig4B.data.tab

description: :unav
DOI: 10.57745/gipjdp
title: Bergès_etal_Fig1_2B_S4.data.cluster.tab

description: :unav
DOI: 10.57745/1fojzf
title: meanHRdifferences.tab

description: This file gives the mean heart rate differences between the last 3s and firts 3s of the trials.
DOI: 10.57745/8r86td
title: index.tab

description: This file gives for each horse included in the looking times analysis, the mean congruence index
DOI: 10.57745/h8cjbs
title: poneys_age.tab

description: this file gives the age of each horse included in the experiment
DOI: 10.5281/zenodo.7388037
title: Supplementary material for a usability evaluation of a semantic search for biological datasets

description: We conducted a usability evaluation for a semantic dataset search with 20 biodiversity scholars in June and July 2022 in Germany. Following the TREC guidelines (https://www-nlpir.nist.gov/projects/t9i/spec.html), we setup eight user tasks and surveys with questionnaires to guide users through the evaluation. The zip file provides questionnaires, survey templates and the original results. A Jupyter notebook for data analysis is provided in our GitHub repository: https://github.com/fusion-jena/semantic-search-usability-analysis
DOI: 10.5281/zenodo.7388038
title: Supplementary material for a usability evaluation of a semantic search for biological datasets

description: We conducted a usability evaluation for a semantic dataset search with 20 biodiversity scholars in June and July 2022 in Germany. Following the TREC guidelines (https://www-nlpir.nist.gov/projects/t9i/spec.html), we setup eight user tasks and surveys with questionnaires to guide users through the evaluation. The zip file provides survey templates and the original results. A Jupyter notebook for data analysis is provided in our GitHub repository: https://github.com/fusion-jena/semantic-search-usability-analysis
DOI: 10.5281/zenodo.7392043
title: Supplementary material for a usability evaluation of a semantic search for biological datasets

description: We conducted a usability evaluation for a semantic dataset search with 20 biodiversity scholars in June and July 2022 in Germany. Following the TREC guidelines (https://www-nlpir.nist.gov/projects/t9i/spec.html), we setup eight user tasks and surveys with questionnaires to guide users through the evaluation. The zip file provides questionnaires, survey templates and the original results. A Jupyter notebook for data analysis is provided in our GitHub repository: https://github.com/fusion-jena/semantic-search-usability-analysis
DOI: 10.34740/kaggle/dsv/4726845
title: YouTube Daily
DOI: 10.5281/zenodo.7441429
title: Dataset related to: "Relationship between perceived and neuromuscular fatigue in COPD patients with chronic respiratory failure with long‑term oxygen therapy: a cross‑sectional study"

description: We provide the raw data used for the following article: Paneroni M, Vitacca M, Comini L, Salvi B, Saleri M, Schena F, Venturelli M. <strong>Relationship between perceived and neuromuscular fatigue in COPD patients with chronic respiratory failure with long-term oxygen therapy: a cross-sectional study</strong>. Eur J Appl Physiol. 2022 Nov;122(11):2403-2416. doi: 10.1007/s00421-022-05021-2. Abstract <strong>Purpose: </strong>To evaluate perceived fatigue (PF) and neuromuscular fatigue (NMF) in patients with COPD and chronic respiratory failure (CRF) on long-term oxygen therapy (CRF-COPD group), and the relationships between PF, NMF, patient's characteristics, comparing severe patients with COPD to patients without CRF (COPD group). <strong>Methods: </strong>This cross-sectional study compared 19 CRF-COPD patients with 10 COPD patients attending a rehabilitation program. PF was determined by Fatigue Severity Scale (FSS), while dyspnea by the Barthel Dyspnea Index (BDI). We assessed quadriceps NMF via electrical nerve stimulation during and following a Maximal Voluntary Contraction (MVC) detecting changes after a Constant Workload Cycling Test (CWCT) at 80% of the peak power output at exhaustion. <strong>Results: </strong>CRF-COPD patients showed higher PF (+ 1.79 of FSS score, p = 0.0052) and dyspnea (+ 21.03 of BDI score, p = 0.0023) than COPD patients. After the fatiguing task and normalization for the total work, there was a similar decrease in the MVC (CRF-COPD -1.5 ± 2.4 vs COPD -1.1 ± 1.2% baseline kJ<sup>-1</sup>, p = 0.5819), in the potentiated resting twitch force (CRF-COPD -2.8 ± 4.7 vs COPD -2.0 ± 3.3% baseline kJ<sup>-1</sup>, p = 0.7481) and in the maximal voluntary activation (CRF-COPD -0.1 ± 3.9 vs COPD -0.9 ± 1.2 -2.0 ± 3.3% baseline kJ<sup>-1</sup>, p = 0.4354). FSS and BDI were closely related (R = 0.5735, p = 0.0011), while no correlation between PF and NMF was found. <strong>Conclusion: </strong>Patients with CRF-COPD develop higher levels of perceived fatigue and dyspnea than patients with COPD; while neuromuscular fatigue is similar, suggesting a mismatch between symptoms and neuromuscular dysfunction.
DOI: 10.5281/zenodo.7441430
title: Dataset related to: "Relationship between perceived and neuromuscular fatigue in COPD patients with chronic respiratory failure with long‑term oxygen therapy: a cross‑sectional study"

description: We provide the raw data used for the following article: Paneroni M, Vitacca M, Comini L, Salvi B, Saleri M, Schena F, Venturelli M. <strong>Relationship between perceived and neuromuscular fatigue in COPD patients with chronic respiratory failure with long-term oxygen therapy: a cross-sectional study</strong>. Eur J Appl Physiol. 2022 Nov;122(11):2403-2416. doi: 10.1007/s00421-022-05021-2. Abstract <strong>Purpose: </strong>To evaluate perceived fatigue (PF) and neuromuscular fatigue (NMF) in patients with COPD and chronic respiratory failure (CRF) on long-term oxygen therapy (CRF-COPD group), and the relationships between PF, NMF, patient's characteristics, comparing severe patients with COPD to patients without CRF (COPD group). <strong>Methods: </strong>This cross-sectional study compared 19 CRF-COPD patients with 10 COPD patients attending a rehabilitation program. PF was determined by Fatigue Severity Scale (FSS), while dyspnea by the Barthel Dyspnea Index (BDI). We assessed quadriceps NMF via electrical nerve stimulation during and following a Maximal Voluntary Contraction (MVC) detecting changes after a Constant Workload Cycling Test (CWCT) at 80% of the peak power output at exhaustion. <strong>Results: </strong>CRF-COPD patients showed higher PF (+ 1.79 of FSS score, p = 0.0052) and dyspnea (+ 21.03 of BDI score, p = 0.0023) than COPD patients. After the fatiguing task and normalization for the total work, there was a similar decrease in the MVC (CRF-COPD -1.5 ± 2.4 vs COPD -1.1 ± 1.2% baseline kJ<sup>-1</sup>, p = 0.5819), in the potentiated resting twitch force (CRF-COPD -2.8 ± 4.7 vs COPD -2.0 ± 3.3% baseline kJ<sup>-1</sup>, p = 0.7481) and in the maximal voluntary activation (CRF-COPD -0.1 ± 3.9 vs COPD -0.9 ± 1.2 -2.0 ± 3.3% baseline kJ<sup>-1</sup>, p = 0.4354). FSS and BDI were closely related (R = 0.5735, p = 0.0011), while no correlation between PF and NMF was found. <strong>Conclusion: </strong>Patients with CRF-COPD develop higher levels of perceived fatigue and dyspnea than patients with COPD; while neuromuscular fatigue is similar, suggesting a mismatch between symptoms and neuromuscular dysfunction.
DOI: 10.34740/kaggle/dsv/2685927
title: CottonWeedID15
DOI: 10.34740/kaggle/dsv/4726861
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.6084/m9.figshare.21674255
title: Data used in GLM model

description: Data used in GLM model in manuscript called "Innate preferences of <em>Eristalis tenax</em> L. (Syrphidae) for flower colour, size and symmetry" <br> <br> Variables: id = id of the trial (1 trial = 1 row) combination = the flower combination of the two flowers entering the trial trial = same as id date = the date when the trial was conducted. We conducted all of the trials between 12.-24.1.2021. We simulted the combinations on 7th of the july 2022. clutch = The id of the clutch. We compared two cluteches #3 and #4. NZ is the clutch label for the simulated data. flower1 = the morph oíf flower which was asignes as flower 1 count1 = the sum of visits on flower1 flower2 =the morph oíf flower which was asignes as flower 2 count2 = the sum of visits on flower2 sum = the overall sum of visits for particular trial time = The time of the start of the trial in seconds (every trial lasted for 30 minutes) diff_size = the difference in size between flower1 and flower2 (0 - no difference, 1 - they differ) diff_col = the difference in colour between flower1 and flower2 (0 - no difference, 1 - they differ) diff_sym = the difference in symetry between flower1 and flower2 (0 - no difference, 1 - they differ) size1 = size of flower1 (levels L/S) col1 = colour of flower1 (levels Y/P) sym1 = symmetry of flower1 (levels R/B) size2 = size of flower2 (levels L/S) col2 = colour of flower2 (levels Y/P) sym2 = symmetry of flower2 (levels R/B)
DOI: 10.6084/m9.figshare.21674255.v1
title: Daat used in GLM model

description: Data used in GLM model in manuscript called "Innate preferences of <em>Eristalis tenax</em> L. (Syrphidae) for flower colour, size and symmetry"
DOI: 10.34740/kaggle/dsv/4726868
title: UNET Lung Segmentation Weights for Chest X Rays
DOI: 10.6084/m9.figshare.21731783
title: Description of variables in GLM dataset

description: Description of variables in GLM dataset
DOI: 10.24378/exe.4404
title: Navarasa Sadhana 50th Anniversary: An embodied exploration of sringara

description: Video (mp4) created and presented by the author in occasion of the 50th anniversary of Navarasa Sadhana, Navarasa Suvarna Mudra, a conference webinar, which took place at Natana Kairali on the 1st March 2021.
description: It is possible to access the video through both QuickTime Player and VLC.
description: The thesis associated with this dataset is located in ORE at: http://hdl.handle.net/10871/132017
DOI: 10.6084/m9.figshare.21731702
title: Community composition exceeds area as a predictor of long-term conservation value - Simulation Data

description: Included in this repository are the simulation output required to reproduce a subset of the results shown in "Community composition exceeds area as a predictor of long-term conservation value". Specifically, these are the model objects that best fit the empirical datasets presented in Benito et al. (2018) and da Silva Caceres et al. (2017). Model output for the 'random metacommunities' exceeds 200GB and therefore will be made available upon request. <br> The software required to reproduce all results is publicly available (https://github.com/jacobosullivan/LVMCM_src).
DOI: 10.6084/m9.figshare.21731702.v1
title: Community composition exceeds area as a predictor of long-term conservation value - Simulation Data

description: Included in this repository are the simulation output required to reproduce a subset of the results shown in "Community composition exceeds area as a predictor of long-term conservation value". Specifically, these are the model objects that best fit the empirical datasets presented in Benito et al. (2018) and da Silva Caceres et al. (2017). Model output for the 'random metacommunities' exceeds 200GB and therefore will be made available upon request. <br> The software required to reproduce all results is publicly available (https://github.com/jacobosullivan/LVMCM_src).
DOI: 10.21227/vmjw-aq60
title: T40

description: 40 real-world underwater images from the Internet which is tough to enhance.