cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21539931.v3
title: DATA from: Jack pine of all trades: deciphering intraspecific variability of a key adaptive trait at the rear edge of a widespread fire-embracingr North American conifer

description: Data from: Jack pine of all trades: deciphering intraspecific variability of a key adaptive trait at the rear edge of a widespread fire-embracer North American conifer. American Journal of Botany. A MSExcel spreadsheet reporting results of 1) an experiment whereby closed jack pine (Pinus banksiana) cones were submitted to incrementing temperatures, and 2) a seed germination tests in order to investigate if and how various ecological factors (cone age, branch height, tree size, tree age) are related to cone dehiscence and seed viability in jack pines from the rear-edge (n = 17 sites) and the core (n = 7 sites) of the species’ range in eastern Canada. <br> <br> Sheet 1 - Metadata. Provides details about the study sites (Region, Site abbreviation, Site name, Sampling date, Longitude, Latitude, Altitude) <br> Sheet 2 - Stand-scale serotiny. At each site, up to 100 mature jack pine individuals were assigned to to one of six serotiny levels: class 0 (0% closed cones), class 1 (1−25% closed cones), class 2 (26−50% closed cones), class 3 (51−75% closed cones), class 4 (76−99% closed cones), or class 5 (100% closed cones). The diameter at breast height (DBH) of each tree sampled for serotiny is also provided in this sheet. <br> Sheet 3 - Experiments. At each site, 10 randomly-chosen mature jack pines were sampled. Three cone-bearing branches from different measured heights (lower, middle, and upper parts of the tree crown) were cut. For each tree, diameter at breast heigth (Tree DBH) was recorded and a core was sampled using a Pressler increment borer as close to the ground as possible. At the laboratory, three fully closed cones of contrasted ages were sampled from each branch (n = 1381 cones). Cone age and tree age was determined by counting growth rings. The cone opening temperatures correspond to the temperature required to reach each opening level, which refers to the relative abundance of opened scales on each individual cone (level 1: 1-25% opening, level 2: 26-50% opening, level 3: 51-75% opening and level 4: 76-100% opening). For each cone, the total number of seeds and the number of filled seeds were recorded. The filled seeds of each cone were batch-weighed. A total of 23,404 seeds were set to germinate. Every day (n = 33 days), germinated seeds (radicle &gt; 2 mm) were counted.
DOI: 10.5281/zenodo.7732867
title: The mitochondrial protease OMA1 acts as a metabolic safeguard upon nuclear DNA damage

description: The provided Dataset contains the liquid-chromatography mass spectrometry (LC-MS) raw files (3 zipped files containing the Thermo .raw files) and one zipped file containing three tables extracted containing the peak information (Compound ID, expected RT (min), Filename, Injection Volume (uL), peak height, peak area, actual RT (min), RT Delta, formula, adduct, m/z (Expected), m/z (Apex), m/z (Delta in ppm) and S/N (signa-to-noise)) of the steady state and the 13C6 glucose-traced metabolite measurements for the above mentioned (see title) publication in Cell Reports. The data was measured using either anion-chromatography coupled to high resolution mass spectrometry (for anionic compounds, including intermediates from the TCA cycle, glycolysis, pentose phosphate pathway and nucleotides) or ultra high performance liquid chromatography coupled to high resolution mass spectrometry (amine-containing compounds, including amino acids and polyamines). The methods used for extracting and analyzing the samples are described in detail in the above mentioned publication. The data deposited here is shown in figure 7a and b and figure S7a, b and c.
description: Summary of the paper: The metabolic plasticity of mitochondria ensures cell development, differentiation and survival. The peptidase OMA1 regulates mitochondrial morphology via OPA1 and stress signaling via DELE1 and orchestrates tumorigenesis and cell survival in a cell and tissue-specific manner. Here, we use unbiased systems-based approaches to demonstrate that OMA1-dependent cell survival depends on metabolic cues. A metabolism-focused CRISPR screen combined with an integrated analysis of human gene expression data unravels that OMA1 protects against DNA damage. Nucleotide deficiencies induced by chemotherapeutic agents promote p53-dependent apoptosis of cells lacking OMA1. The protective effect of OMA1 does not depend on OMA1 activation nor OMA1-mediated OPA1 and DELE1 processing. OMA1-deficient cells show reduced glycolysis and accumulate OXPHOS proteins upon DNA damage. OXPHOS inhibition restores glycolysis and confers resistance against DNA damage. Thus, OMA1 dictates the balance between cell death and survival through the control of glucose metabolism, shedding light on its role in cancerogenesis.
DOI: 10.6084/m9.figshare.21975017
title: Supplementary Material for: Folliculitis decalvans has a heterogeneous microbiological signature and impaired immunological response.

description: Background: Folliculitis decalvans (FD) is a rare primary neutrophilic scarring alopecia whose etiology has not been completely elucidated yet. Objective: To determine if the follicular microbiota residing in FD-affected hair follicles had a distinct microbiological signature and if an aberrant immune response was present in the pathogenesis of FD. Methods: We conducted a cross-sectional study of ten patients affected by FD. Trichoscopy-guided follicular biopsies were taken from affected and healthy scalp to identify the follicular microbiome using next-generation sequencing. We searched for microbiological biomarkers of FD-affected follicles using the linear discriminant analysis (LDA) effect size (LEfSe) tool. Additionally, peripheral blood mononuclear cells were obtained, and their cytokine production was quantified after incubation with pathogen-associated molecular patterns isolated from patients’ biopsies and compared with healthy controls. Results: β-diversity analysis showed statistically significant differences regarding bacteria comparing follicular microbiota of healthy and FD-affected hairs. Ruminococcaceae, Agathobacter sp., Tyzzerella sp. and Bacteriodales vadin HA21 family were good predictors of disease status. IL-10, TNF-α and IL-6 levels were significantly decreased in patients after incubation with various strains of bacteria compared with controls. Conclusion: FD hair follicles have a specific heterogenous follicular bacterial microbiota signature. Additionally, these patients seem to have an impaired immunological response.
DOI: 10.6084/m9.figshare.21975017.v1
title: Supplementary Material for: Folliculitis decalvans has a heterogeneous microbiological signature and impaired immunological response.

description: Background: Folliculitis decalvans (FD) is a rare primary neutrophilic scarring alopecia whose etiology has not been completely elucidated yet. Objective: To determine if the follicular microbiota residing in FD-affected hair follicles had a distinct microbiological signature and if an aberrant immune response was present in the pathogenesis of FD. Methods: We conducted a cross-sectional study of ten patients affected by FD. Trichoscopy-guided follicular biopsies were taken from affected and healthy scalp to identify the follicular microbiome using next-generation sequencing. We searched for microbiological biomarkers of FD-affected follicles using the linear discriminant analysis (LDA) effect size (LEfSe) tool. Additionally, peripheral blood mononuclear cells were obtained, and their cytokine production was quantified after incubation with pathogen-associated molecular patterns isolated from patients’ biopsies and compared with healthy controls. Results: β-diversity analysis showed statistically significant differences regarding bacteria comparing follicular microbiota of healthy and FD-affected hairs. Ruminococcaceae, Agathobacter sp., Tyzzerella sp. and Bacteriodales vadin HA21 family were good predictors of disease status. IL-10, TNF-α and IL-6 levels were significantly decreased in patients after incubation with various strains of bacteria compared with controls. Conclusion: FD hair follicles have a specific heterogenous follicular bacterial microbiota signature. Additionally, these patients seem to have an impaired immunological response.
DOI: 10.5281/zenodo.7732866
title: The mitochondrial protease OMA1 acts as a metabolic safeguard upon nuclear DNA damage

description: The provided Dataset contains the liquid-chromatography mass spectrometry (LC-MS) raw files (3 zipped files containing the Thermo .raw files) and one zipped file containing three tables extracted containing the peak information (Compound ID, expected RT (min), Filename, Injection Volume (uL), peak height, peak area, actual RT (min), RT Delta, formula, adduct, m/z (Expected), m/z (Apex), m/z (Delta in ppm) and S/N (signa-to-noise)) of the steady state and the 13C6 glucose-traced metabolite measurements for the above mentioned (see title) publication in Cell Reports. The data was measured using either anion-chromatography coupled to high resolution mass spectrometry (for anionic compounds, including intermediates from the TCA cycle, glycolysis, pentose phosphate pathway and nucleotides) or ultra high performance liquid chromatography coupled to high resolution mass spectrometry (amine-containing compounds, including amino acids and polyamines). The methods used for extracting and analyzing the samples are described in detail in the above mentioned publication. The data deposited here is shown in figure 7a and b and figure S7a, b and c.
description: Summary of the paper: The metabolic plasticity of mitochondria ensures cell development, differentiation and survival. The peptidase OMA1 regulates mitochondrial morphology via OPA1 and stress signaling via DELE1 and orchestrates tumorigenesis and cell survival in a cell and tissue-specific manner. Here, we use unbiased systems-based approaches to demonstrate that OMA1-dependent cell survival depends on metabolic cues. A metabolism-focused CRISPR screen combined with an integrated analysis of human gene expression data unravels that OMA1 protects against DNA damage. Nucleotide deficiencies induced by chemotherapeutic agents promote p53-dependent apoptosis of cells lacking OMA1. The protective effect of OMA1 does not depend on OMA1 activation nor OMA1-mediated OPA1 and DELE1 processing. OMA1-deficient cells show reduced glycolysis and accumulate OXPHOS proteins upon DNA damage. OXPHOS inhibition restores glycolysis and confers resistance against DNA damage. Thus, OMA1 dictates the balance between cell death and survival through the control of glucose metabolism, shedding light on its role in cancerogenesis.
DOI: 10.5281/zenodo.7736695
title: Energy Profiler Raw Data Results

description: Energy profiler data collected for the Masters' thesis of Linus Jungemann.
DOI: 10.5281/zenodo.7736694
title: Energy Profiler Raw Data Results

description: Energy profiler data collected for the Masters' thesis of Linus Jungemann.
DOI: 10.5281/zenodo.5771936
title: Complement activation induces excessive T cell cytotoxicity in severe COVID-19: Analysis of single cell data cohort 1 (Berlin).

description: This repository contains the R Markdown files with the analysis of CyTOF and scRNA-seq data corresponding to cohort 1 (Berlin) analysed in Georg et al. 2021 "Complement activation induces excessive T cell cytotoxicity in severe COVID-19". Additionally, here we include the necessary CyTOF data to reproduce this analysis. CyTOF data: The debarcoded fcs files (before batch-correction) can be found in https://flowrepository.org/id/FR-FCM-Z4P5. \ Here you can find the necessary data to reproduce the analysis (cytof_analysis.Rmd, cytof_analysis.html): data_norm_all.csv: single-cell protein expression data (after batch-normalization and in linear scale). data_Tcells_annotated.csv: single-cell protein expression of gated T cells with cluster annotation. phenograph_CD4_k30.csv, phenograph_CD8_k30.csv, phenograph_TCRgd_k30.csv: output from Louvain Clustering computed with PhenoGraph (https://github.com/jacoblevine/PhenoGraph) per T cell compartment. clusterannotation.csv: annotation for each cluster and metacluster scRNA-seq data: The raw data can be found in https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450 Other files to reproduce the analysis (scRNAseq_analysis_1preprocessing.Rmd, scRNAseq_analysis_2clustering.Rmd, scRNAseq_analysis_3convalescent.Rmd): scRNAseq_Sawitzki_RECAST_09_2021.xlsx: Single-cell metadata. scRNAseq_samples.tsv: Samples metadata. scRNAseq_genelist_annotation.xlsx: Gene list for the annotation of T cells (Also in Mendeley, see Data and Code Availability). scRNAseq_GO_RESPONSE_TO_TYPE_I_INTERFERON.txt, scRNAseq_GO_DEFENSE_RESPONSE_TO_VIRUS.txt, , scRNAseq_GO_T_CELL_MEDIATED_CYTOTOXICITY.txt: Gene lists for the signatures “Response to Type I Interferon” , “Defense Response to virus” and “Cytotoxicity” used for GSEA. (Also in Table S2). scRNAseq_traj18_trav10.txt,scRNAseq_trbv25.txt: sequences to determine the proportion of TRAV10-TRAJ18-TRBV25 pairing T cell clones across all T cell clusters.
DOI: 10.5281/zenodo.5771937
title: Complement activation induces excessive T cell cytotoxicity in severe COVID-19: Analysis of single cell data cohort 1 (Berlin).

description: This repository contains the R Markdown files with the analysis of CyTOF and scRNA-seq data corresponding to cohort 1 (Berlin) analysed in Georg et al. 2021 "Complement activation induces excessive T cell cytotoxicity in severe COVID-19". Additionally, here we include the necessary CyTOF data to reproduce this analysis. CyTOF data: The debarcoded fcs files (before batch-correction) can be found in https://flowrepository.org/id/FR-FCM-Z4P5. \ Here you can find the necessary data to reproduce the analysis (cytof_analysis.Rmd, cytof_analysis.html): data_norm_all.csv: single-cell protein expression data (after batch-normalization and in linear scale). data_Tcells_annotated.csv: single-cell protein expression of gated T cells with cluster annotation. phenograph_CD4_k30.csv, phenograph_CD8_k30.csv, phenograph_TCRgd_k30.csv: output from Louvain Clustering computed with PhenoGraph (https://github.com/jacoblevine/PhenoGraph) per T cell compartment. clusterannotation.csv: annotation for each cluster and metacluster scRNA-seq data: The raw data can be found in https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450 Other files to reproduce the analysis (scRNAseq_analysis_1preprocessing.Rmd, scRNAseq_analysis_2clustering.Rmd, scRNAseq_analysis_3convalescent.Rmd): scRNAseq_Sawitzki_RECAST_09_2021.xlsx: Metadata scRNAseq_genelist_annotation.xlsx: Gene list for the annotation of T cells (Also in Mendeley, see Data and Code Availability). scRNAseq_GO_RESPONSE_TO_TYPE_I_INTERFERON.txt, scRNAseq_GO_DEFENSE_RESPONSE_TO_VIRUS.txt, , scRNAseq_GO_T_CELL_MEDIATED_CYTOTOXICITY.txt: Gene lists for the signatures “Response to Type I Interferon” , “Defense Response to virus” and “Cytotoxicity” used for GSEA. (Also in Table S2). scRNAseq_traj18_trav10.txt,scRNAseq_trbv25.txt: sequences to determine the proportion of TRAV10-TRAJ18-TRBV25 pairing T cell clones across all T cell clusters.
DOI: 10.5281/zenodo.7737653
title: Complement activation induces excessive T cell cytotoxicity in severe COVID-19: Analysis of single cell data cohort 1 (Berlin).

description: This repository contains the R Markdown files with the analysis of CyTOF and scRNA-seq data corresponding to cohort 1 (Berlin) analysed in Georg et al. 2021 "Complement activation induces excessive T cell cytotoxicity in severe COVID-19". Additionally, here we include the necessary CyTOF data to reproduce this analysis. CyTOF data: The debarcoded fcs files (before batch-correction) can be found in https://flowrepository.org/id/FR-FCM-Z4P5. \ Here you can find the necessary data to reproduce the analysis (cytof_analysis.Rmd, cytof_analysis.html): data_norm_all.csv: single-cell protein expression data (after batch-normalization and in linear scale). data_Tcells_annotated.csv: single-cell protein expression of gated T cells with cluster annotation. phenograph_CD4_k30.csv, phenograph_CD8_k30.csv, phenograph_TCRgd_k30.csv: output from Louvain Clustering computed with PhenoGraph (https://github.com/jacoblevine/PhenoGraph) per T cell compartment. clusterannotation.csv: annotation for each cluster and metacluster scRNA-seq data: The raw data can be found in https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450 Other files to reproduce the analysis (scRNAseq_analysis_1preprocessing.Rmd, scRNAseq_analysis_2clustering.Rmd, scRNAseq_analysis_3convalescent.Rmd): scRNAseq_Sawitzki_RECAST_09_2021.xlsx: Single-cell metadata. scRNAseq_samples.tsv: Samples metadata. scRNAseq_genelist_annotation.xlsx: Gene list for the annotation of T cells (Also in Mendeley, see Data and Code Availability). scRNAseq_GO_RESPONSE_TO_TYPE_I_INTERFERON.txt, scRNAseq_GO_DEFENSE_RESPONSE_TO_VIRUS.txt, , scRNAseq_GO_T_CELL_MEDIATED_CYTOTOXICITY.txt: Gene lists for the signatures “Response to Type I Interferon” , “Defense Response to virus” and “Cytotoxicity” used for GSEA. (Also in Table S2). scRNAseq_traj18_trav10.txt,scRNAseq_trbv25.txt: sequences to determine the proportion of TRAV10-TRAJ18-TRBV25 pairing T cell clones across all T cell clusters.
DOI: 10.5281/zenodo.7457182
title: The agrofood systems in France, Spain and Portugal at a NUT2 resolution for the 2014-2019 period (GRAFS Data set)

description: The GRAFS approach (Generalized Representation of Agro-food systems) describes the agro-food system by considering four main components exchanging nutrient flows: cropland, grassland, livestock systems, and local population. The agro-food system is documented here for the period 2014–2019, at the territorial level of the EU administrative units NUTS2, in terms of N for (i) nutrient inputs to the soil (exogenous fertilization such as synthetic and/or organic fertilization and atmospheric deposition, as well as symbiotic fixation); (ii) the feed required for the existing livestock; (iii) the size of the human population, its dietary preferences, and its excreta; and (iv) food and feed imports/exports (Billen et al., 2018; 2021). These N flows link grassland and cropland productivity (from annual and perennial crops) to livestock feeding, and, finally, to human food. Detailed figures for these different components are presented in the joint Table. Using the GRAFS approach to our study area, we also expand upon the approach used by Le Noë et al. (2018), which established different typologies of the agro-food systems in France. This approach intends to describe the degree of coupling between crop and livestock farming, local production/consumption to shows regional differences (see Table 1SM for a detailed description of the different typologies defined). The GRAFS approach also allows to calculates, NH3 volatisization and N2O emission, as well as leaching concentration (the net soil surplus as a proxy).
description: {"references": ["How much can changes in the agro-food system reduce agricultural nitrogen losses to the environment? Example of France and the Iberian Peninsula"]}
DOI: 10.5281/zenodo.7457183
title: The agrofood systems in France, Spain and Portugal at a NUT2 resolution for the 2014-2019 period (GRAFS Data set)

description: The GRAFS approach (Generalized Representation of Agro-food systems) describes the agro-food system by considering four main components exchanging nutrient flows: cropland, grassland, livestock systems, and local population. The agro-food system is documented here for the period 2014–2019, at the territorial level of the EU administrative units NUTS2, in terms of N for (i) nutrient inputs to the soil (exogenous fertilization such as synthetic and/or organic fertilization and atmospheric deposition, as well as symbiotic fixation); (ii) the feed required for the existing livestock; (iii) the size of the human population, its dietary preferences, and its excreta; and (iv) food and feed imports/exports (Billen et al., 2018; 2021). These N flows link grassland and cropland productivity (from annual and perennial crops) to livestock feeding, and, finally, to human food. Detailed figures for these different components are presented in the joint Table. Using the GRAFS approach to our study area, we also expand upon the approach used by Le Noë et al. (2018), which established different typologies of the agro-food systems in France. This approach intends to describe the degree of coupling between crop and livestock farming, local production/consumption to shows regional differences (see Table 1SM for a detailed description of the different typologies defined). The GRAFS approach also allows to calculates, NH3 volatisization and N2O emission, as well as leaching concentration (the net soil surplus as a proxy).
description: {"references": ["How much can changes in the agro-food system reduce agricultural nitrogen losses to the environment? Example of France and the Iberian Peninsula"]}
DOI: 10.5255/ukda-sn-856315
title: The Impact of Social Anxiety and Instruction on Avoidance, 2021-2022

description: This experiment examined the impact of social anxiety and instruction to not avoid on avoidance behaviour within a Pavlovian Conditioning paradigm. We measured skin conductance responses, expectancy ratings and avoidant behaviour during an associative threat learning and avoidance task. To assess social anxiety, intolerance of uncertainty and trait anxiety, we administered the Social Phobia Inventory (SPIN) (Connor, Davidson, Churchill, Sherwood, Foa, & Weisler), The Intolerance of Uncertainty Scale (IUS) (Freeton, Rhéaume, Letarte, Dugas, & Ladouceur, 1994) and the trait version of the State-Trait Anxiety Inventory (Spielberger, Gorsuch, Lushene, Vagg & Jacobs, 1968). The experimental task included four phases; threat acquisition, avoidance conditioning, avoidance test, and threat extinction. All participants completed all four phases. The study used a between subjects design and participants were allocated to one of two conditions: “instruction” and “control”. Participants in the instruction condition received the following instruction between the avoidance conditioning and avoidance test phases of the task: “Please press the non-avoidance button to test your expectancy whether an electrical stimulus and statement will occur”. At the same timepoint, the control group saw the following message “you may take a short break and continue when you are ready”. There were no other differences between the “instruction” and “control” conditions.
DOI: 10.34740/kaggle/dsv/5171966
title: AdultoMayor_32_fc
DOI: 10.57906/kgvn-0a84
title: London Business Survey - England
DOI: 10.6084/m9.figshare.22277158
title: All dataset

description: Dataset of "The role of folate receptor α in the partial rejuvenation of dentate gyrus cells. Improvement of cognitive function in elderly mice". Data from experiments where intracerebral injection of small compounds, such as folate or an FRalpha binding peptide on old mice has been performed.
DOI: 10.6084/m9.figshare.22277158.v1
title: All dataset

description: Dataset of "The role of folate receptor α in the partial rejuvenation of dentate gyrus cells. Improvement of cognitive function in elderly mice". Data from experiments where intracerebral injection of small compounds, such as folate or an FRalpha binding peptide on old mice has been performed.
DOI: 10.5281/zenodo.7737587
title: Multimodala Dataset for multimodal contrastive learning for crop classification

description: We developed this dataset using an existing dataset name DENETHOR developed by TUM https://openreview.net/forum?id=uUa4jNMLjrL to conduct our multi-modal contrastive learning experiments.
DOI: 10.5281/zenodo.7737586
title: Multimodala Dataset for multimodal contrastive learning for crop classification

description: We developed this dataset using an existing dataset name DENETHOR developed by TUM https://openreview.net/forum?id=uUa4jNMLjrL to conduct our multi-modal contrastive learning experiments.