cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12673256.v1
title: Clonal heterogeneity test

description: Heterogeneity in colony morphologies (colony phenotype switch) and frequency of petite mitochondrial mutants in packed products were assessed by plating samples directly after the first pre-culturing (as described above) onto YPD agar plates (for colony morphologies) and onto GlyYP (glycerol yeast extract peptone) + 0.1% glucose agar plates with cell densities of approx. 200/plate (after cell counting in a haemocytometer). Plates were incubated for 10 days at 30°C (with agar surface facing down) and were visually scored for various phenotypes on YPD (rough, wrinkled, sectored, stalk-like, and very small colonies) and for potential petite mutants on GlyYP. Presumed petites were transferred to YPD and after overnight culturing, were inoculated onto GlyYP plates without glucose. Subclones unable to grow on glucose-free GlyYP were scored as petites. Finally, YPD colonies were washed under tap water to determine the frequency of invasivitiy into agar. At least 1,000 subclone colonies were counted for each strain and for each assay.<br><br>Clonal heterogeneity ins tress tolerance was assessed by using the same stress conditions as in the spot plate assays, supplemented with assays on SD and YPD media. Freshly grown cells (as described for spot plates) were counted in a haemocytometer and spread to land about 200 cells/plate. Plates were incubated at 30°C for 2 days (YPD and SD media), or for 2, 3, 4, and 6 days (stress media) as colonies reached sizes that were visible but not yet close to each other. For each condition, three replicate plates were used for each sample. Photographs were taken 4 and 6 days after inoculation with a DSLR camera. Data on colony area was gathered by using the Fiji software package CountPHICS (Brzozowska et al., 2019) with circularity set to 0.8. Pixel to mm ratios were measured and area calculations were randomly verified by manual measurment in ImageJ for altogether ten colonies (Rueden et al., 2017). Plate photographs and all colony area values were uploaded to FigShare.
DOI: 10.6084/m9.figshare.12673241
title: Data on national minorities

description: Data on national minorities
DOI: 10.6084/m9.figshare.12673235
title: Lightning reconstructed Data

description: Lightning reconstructed Data calculated and used in the paper" Waveform Reconstruction of Lightning Current Based on Output-Error Model and Finite-Difference Time-Domain Method "
DOI: 10.6084/m9.figshare.12673241.v1
title: Data on national minorities

description: Data on national minorities
DOI: 10.6084/m9.figshare.12673229
title: Data on national minority issues

description: Data on national minority issues
DOI: 10.6084/m9.figshare.12673229.v1
title: Data on national minority issues

description: Data on national minority issues
DOI: 10.6084/m9.figshare.12673172
title: DATA on Queue.csv

description: These data resources used to analyze patient waiting time in the queue, which cleaned and finalized for the machine learning model.
DOI: 10.6084/m9.figshare.12673151
title: Full list of included studies .bib

description: Data obtained from Scopus<br>
DOI: 10.6084/m9.figshare.12673151.v1
title: Full list of included studies .bib

description: Data obtained from Scopus<br>
DOI: 10.6084/m9.figshare.12673157.v1
title: A dataset integrating water-related public health, social media, census, and administrative data in the United States

description: This project presents a dataset that is assembled from multiple sources between 2013 and 2017, including contaminants in drinking water, cancer incidence rates, public perception on Twitter of the relationship between water contaminants and cancer, and census data covering the population living in the United States. The units of analysis are 3,219 counties and 33,144 zip codes. The users of this dataset can address model-driven questions regarding water contaminants and cancer incidence rates in a geographic area, as well as how water contaminant levels and cancer incidence rates are directly and indirectly influenced by local population, economic, and social characteristics.
DOI: 10.6084/m9.figshare.12672950
title: Source data file for cardiomyocyte nucleus shape analysis

description: Size and shape values for each individual nucleus.
DOI: 10.6084/m9.figshare.12672929
title: Dados do artigo x As IP no STJ regulação, responsabilidade civil ou outra coisa.xlsx

description: Base de dados utilizada no artigo: As Instituições de Pagamento no Superior Tribunal de Justiça: regulação, responsabilidade civil ou outra coisa?
DOI: 10.6084/m9.figshare.12672929.v1
title: Dados do artigo x As IP no STJ regulação, responsabilidade civil ou outra coisa.xlsx

description: Base de dados utilizada no artigo: As Instituições de Pagamento no Superior Tribunal de Justiça: regulação, responsabilidade civil ou outra coisa?
DOI: 10.5522/04/12672899
title: Interview questionnaire about the selection criteria used by digital archives of historical newspapers

description: Interview questionnaire about the selection criteria used by digital archives of historical newspapers. The corresponding article where the results of the survey are summarised and analysed is:<br>Hauswedell, T., Nyhan, J., Beals, M.H. et al. Of global reach yet of situated contexts: an examination of the implicit and explicit selection criteria that shape digital archives of historical newspapers. Arch Sci 20, 139–165 (2020). https://doi.org/10.1007/s10502-020-09332-1<br>
DOI: 10.17608/k6.auckland.12672884
title: Occurrence records of marine species

description: These are the occurrence records of nearly 50,000 marine species (data downloaded from Ocean Biodiversity Information System), which are cleaned, and defined as pelagic and benthic in the column "our class" for further research (Chaudhary et al., 2017; Chaudhary, 2019). <br>
DOI: 10.5522/04/12672899.v1
title: Interview questionnaire about the selection criteria used by digital archives of historical newspapers

description: Interview questionnaire about the selection criteria used by digital archives of historical newspapers. The corresponding article where the results of the survey are summarised and analysed is:<br>Hauswedell, T., Nyhan, J., Beals, M.H. et al. Of global reach yet of situated contexts: an examination of the implicit and explicit selection criteria that shape digital archives of historical newspapers. Arch Sci 20, 139–165 (2020). https://doi.org/10.1007/s10502-020-09332-1<br>
DOI: 10.6084/m9.figshare.12672863
title: Our World in Data COVID-19 Testing Database

description: This new dataset brings together official data on the extent of PCR testing over time for 92 countries. We provide a time series for the daily number of tests performed, or people tested, together with metadata describing data quality and comparability issues needed for the interpretation of the time series. The dataset is updated regularly through a combination of automated scraping and manual collection and verification, and is entirely replicable, with sources provided for each observation. In providing accessible cross-country data on testing output, it aims to facilitate the incorporation of this crucial information into epidemiological studies, as well as track a key component of countries’ responses to COVID-19.
DOI: 10.6084/m9.figshare.12672821
title: S1 Appendix Probabilistic projection of the sex ratio at birth and missing female births by State and Union Territory in India

description: This web appendix is for: Probabilistic projection of the sex ratio at birth and missing female births by State and Union Territory in India.
DOI: 10.6084/m9.figshare.12672854
title: COVID-19 Testing Dataset

description: This new dataset brings together official data on the extent of PCR testing over time for 92 countries. We provide a time series for the daily number of tests performed, or people tested, together with metadata describing data quality and comparability issues needed for the interpretation of the time series.<br><br>
DOI: 10.6084/m9.figshare.12672854.v1
title: COVID-19 Testing Dataset

description: This new dataset brings together official data on the extent of PCR testing over time for 92 countries. We provide a time series for the daily number of tests performed, or people tested, together with metadata describing data quality and comparability issues needed for the interpretation of the time series.<br><br>
DOI: 10.6084/m9.figshare.12672821.v1
title: S1 Appendix Probabilistic projection of the sex ratio at birth and missing female births by State and Union Territory in India

description: This web appendix is for: Probabilistic projection of the sex ratio at birth and missing female births by State and Union Territory in India.
DOI: 10.6084/m9.figshare.12672588
title: Additional file 27 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 27:. Supplementary File S7. GO term enrichment analysis between MKL1 ΔN200 and MCF7 control cells. The GO analysis was performed with differentially transcribed, differentially translated and differentially transcribed and translated genes separately. Selected GO terms used in the figures are highlighted in gray.
DOI: 10.6084/m9.figshare.12672591
title: Additional file 28 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 28:. Supplementary File S8. Results of both total RNA and RPF of the differential expression analysis for the genes encoding components of the translation machinery. Genes that are significantly regulated are shown in red.
DOI: 10.6084/m9.figshare.12672582
title: Additional file 25 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 25:. Supplementary File S5. GO term enrichment analysis between MKL1 ΔN200 and MKL1 ΔC301. The GO analysis was performed with differentially transcribed, differentially translated and differentially transcribed and translated genes separately. Selected GO terms used in the figures are highlighted in gray.
DOI: 10.6084/m9.figshare.12672585
title: Additional file 26 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 26:. Supplementary File S6. GO term enrichment analysis between MKL1 ΔC301 and MCF7 control cells. The GO analysis was performed with differentially transcribed, differentially translated and differentially transcribed and translated genes separately. Selected GO terms used in the figures are highlighted in gray.
DOI: 10.6084/m9.figshare.12672585.v1
title: Additional file 26 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 26:. Supplementary File S6. GO term enrichment analysis between MKL1 ΔC301 and MCF7 control cells. The GO analysis was performed with differentially transcribed, differentially translated and differentially transcribed and translated genes separately. Selected GO terms used in the figures are highlighted in gray.
DOI: 10.6084/m9.figshare.12672588.v1
title: Additional file 27 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 27:. Supplementary File S7. GO term enrichment analysis between MKL1 ΔN200 and MCF7 control cells. The GO analysis was performed with differentially transcribed, differentially translated and differentially transcribed and translated genes separately. Selected GO terms used in the figures are highlighted in gray.
DOI: 10.6084/m9.figshare.12672576
title: Additional file 23 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 23:. Supplementary File S3. Differential RPF expression analysis.
DOI: 10.6084/m9.figshare.12672579
title: Additional file 24 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 24:. Supplementary File S4. Differential translation efficiency analysis.
DOI: 10.6084/m9.figshare.12672582.v1
title: Additional file 25 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 25:. Supplementary File S5. GO term enrichment analysis between MKL1 ΔN200 and MKL1 ΔC301. The GO analysis was performed with differentially transcribed, differentially translated and differentially transcribed and translated genes separately. Selected GO terms used in the figures are highlighted in gray.
DOI: 10.6084/m9.figshare.12672591.v1
title: Additional file 28 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 28:. Supplementary File S8. Results of both total RNA and RPF of the differential expression analysis for the genes encoding components of the translation machinery. Genes that are significantly regulated are shown in red.
DOI: 10.6084/m9.figshare.12672570
title: Additional file 21 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 21:. Supplementary File S1. Lists of genes with particular expression. Genes with high transcription and low translation comprise the group of red genes; genes with high transcription and high translation are designated blue; genes with high transcription and high but saturated translation are shown in green. In each list, genes that fulfill the condition in a particular sample are identified in gray. ND: not detected in the particular sample.
DOI: 10.6084/m9.figshare.12672573
title: Additional file 22 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 22:. Supplementary File S2. Differential mRNA expression analysis.
DOI: 10.6084/m9.figshare.12672576.v1
title: Additional file 23 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 23:. Supplementary File S3. Differential RPF expression analysis.
DOI: 10.6084/m9.figshare.12672579.v1
title: Additional file 24 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 24:. Supplementary File S4. Differential translation efficiency analysis.
DOI: 10.6084/m9.figshare.12672567
title: Additional file 20 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 20: Supplementary Table T6. Metabolomic measurements of various metabolites. Metabolites were analyzed by liquid chromatography (LC)- mass spectrometry (MS) (LC-MS/MS) as described [29, 30]. Only significant average fold change values (p &lt; 0,05) from seven technical replicates of three biological replicates are shown. NS: not significant. (2) Only two biological replicates were measured for Glucose 6-P and Oxaloacetate. (1) Only one biological replicate was measured for DHAP and ɑ-Ketoglutarate.
DOI: 10.6084/m9.figshare.12672570.v1
title: Additional file 21 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 21:. Supplementary File S1. Lists of genes with particular expression. Genes with high transcription and low translation comprise the group of red genes; genes with high transcription and high translation are designated blue; genes with high transcription and high but saturated translation are shown in green. In each list, genes that fulfill the condition in a particular sample are identified in gray. ND: not detected in the particular sample.
DOI: 10.6084/m9.figshare.12672573.v1
title: Additional file 22 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 22:. Supplementary File S2. Differential mRNA expression analysis.
DOI: 10.6084/m9.figshare.12672558
title: Additional file 18 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 18:. Supplementary Table T4. Translation efficiencies. Polysomal fractionation was done as described (15) with some modifications. Polysomal fractions were not pooled but instead fractions 7-14 for all samples were used individually for study gene expression analysis by qRT-PCRs (3 replicates per fraction, 24 points per sample). Expression was normalized to the pGEMEX-1 RNA spike-in control. Values were calculated from the quantitation cycle (Cq) using the formula relative quantity = 2 Cq(min)-Cq(sample). The inverse of corrected Cq values (corrected Cq-1) for specific monosomes and polysomes fractions are shown on the left. Translation efficiencies with different criterias were determined as the fold change of the polysomes fractions vs the monosomes fractions.
DOI: 10.6084/m9.figshare.12672561
title: Additional file 19 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 19: Supplementary Table T5. Specific rates of glucose consumption and lactate production. Data are mean ± SD from three biological replicates. *, p &lt; 0,05.
DOI: 10.6084/m9.figshare.12672555
title: Additional file 17 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 17:. Supplementary Table T3. Comparison of expression fold changes between RNAseq and microarray data for selected genes. Microarray data was obtained from Jehanno and Fernandez-Calero et al. (29).
DOI: 10.6084/m9.figshare.12672561.v1
title: Additional file 19 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 19: Supplementary Table T5. Specific rates of glucose consumption and lactate production. Data are mean ± SD from three biological replicates. *, p &lt; 0,05.
DOI: 10.6084/m9.figshare.12672549
title: Additional file 15 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 15:. Supplementary Table T1. Quality and alignment statistics. Each row represents one biological replicate. Under the quality statistics section, columns list the numbers and percentages of reads that do not pass the quality filters (i.e., reads that are too short, adaptor only reads or reads without adaptor) and the filtered ones used for subsequent steps of the analysis. Under the alignment statistics section, columns list the numbers and percentages of reads that align to the different kinds of features to which the read alignment was performed.
DOI: 10.6084/m9.figshare.12672552
title: Additional file 16 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 16:. Supplementary Table T2. Summary of the expressed genes. Each row represents one biological sample. Columns list the number of genes expressed at the transcription level (in total RNA samples), at the translation level (in RPF samples) and at both levels. A gene is considered present in a sample if rpkm &gt;1. A gene is considered expressed in one condition if rpkm &gt;1 in the 3 replicates simultaneously.
DOI: 10.6084/m9.figshare.12672558.v1
title: Additional file 18 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 18:. Supplementary Table T4. Translation efficiencies. Polysomal fractionation was done as described (15) with some modifications. Polysomal fractions were not pooled but instead fractions 7-14 for all samples were used individually for study gene expression analysis by qRT-PCRs (3 replicates per fraction, 24 points per sample). Expression was normalized to the pGEMEX-1 RNA spike-in control. Values were calculated from the quantitation cycle (Cq) using the formula relative quantity = 2 Cq(min)-Cq(sample). The inverse of corrected Cq values (corrected Cq-1) for specific monosomes and polysomes fractions are shown on the left. Translation efficiencies with different criterias were determined as the fold change of the polysomes fractions vs the monosomes fractions.
DOI: 10.6084/m9.figshare.12672567.v1
title: Additional file 20 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 20: Supplementary Table T6. Metabolomic measurements of various metabolites. Metabolites were analyzed by liquid chromatography (LC)- mass spectrometry (MS) (LC-MS/MS) as described [29, 30]. Only significant average fold change values (p &lt; 0,05) from seven technical replicates of three biological replicates are shown. NS: not significant. (2) Only two biological replicates were measured for Glucose 6-P and Oxaloacetate. (1) Only one biological replicate was measured for DHAP and ɑ-Ketoglutarate.
DOI: 10.6084/m9.figshare.12672543
title: Additional file 13 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 13:. Supplementary Figure S13. Schematic of glycolysis, TCA and PPP showing expression changes at the translational level of enzymes involved in the different steps. Changes in MCF7 MKL1 ΔN200 (a) and MKL1 ΔC301 (b) compared to MCF7 control cells. Fold-change (FC) values were taken from Supplementary File S3; only FC values higher than 0.5 (FDR &lt; 0.01) were considered as significant. Blue: glycolysis; orange: PPP; pink: TCA. Arrow colors indicate the following: increase (blue: FC&gt;1.0, light blue: 1.0&gt;FC&gt;0.5); decrease (red: FC&gt;1.0, light red: 1.0&gt;FC&gt;0.5); grey: no significant changes; orange: variable changes within an enzyme complex. Values shown inside the small boxes indicate the fold change in the amount of each metabolite between both cell lines (Supplementary Table T6). Stars depict changes in the prevailing isoform. Steps associated with NADP reduction are indicated.
DOI: 10.6084/m9.figshare.12672555.v1
title: Additional file 17 of Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells

description: Additional file 17:. Supplementary Table T3. Comparison of expression fold changes between RNAseq and microarray data for selected genes. Microarray data was obtained from Jehanno and Fernandez-Calero et al. (29).
DOI: 10.13145/bacdive108317.20180622.3
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.