cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23489014
title: Additional file 2 of Pan-cancer analysis of TASL: a novel immune infiltration-related biomarker for tumor prognosis and immunotherapy response prediction

description: Additional file 2: Supplementary Table 1. The NES values of KEGG pathways that significantly upregulated or downregulated in highly versus lowly TASL groups in more than five cancer types (|NES| ≥ 1.0, P&lt;0.05, FDR&lt;0.25). Supplementary Table 2. Correlation of TASL expression with immune infiltration signatures in 20 cancer types. Supplementary Table 3. ROC curves analysis of TASL, PDL1, PD1, CTLA4 expression, TMB, MSI, and glycolytic activity with immune score and TIL in 20 cancer types. Supplementary Table 4. Correlations of TASL expression with Immune checkpoints, TMB and MSI in 20 cancer types.Supplementary Table 5. Abbreviations, full names and sample sizes for 33 cancer types from the TCGA and GTEx databases. Supplementary Table 6. The original data (E_ normalized values) of TASL expression from GEO datasets used in Figure 6D. Supplementary Table 7. List of datasets from GEO, CGGA, and the article of Miao D, et al. involved in this study. Supplementary Table 8. List of gene sets.
DOI: 10.6084/m9.figshare.23489014.v1
title: Additional file 2 of Pan-cancer analysis of TASL: a novel immune infiltration-related biomarker for tumor prognosis and immunotherapy response prediction

description: Additional file 2: Supplementary Table 1. The NES values of KEGG pathways that significantly upregulated or downregulated in highly versus lowly TASL groups in more than five cancer types (|NES| ≥ 1.0, P&lt;0.05, FDR&lt;0.25). Supplementary Table 2. Correlation of TASL expression with immune infiltration signatures in 20 cancer types. Supplementary Table 3. ROC curves analysis of TASL, PDL1, PD1, CTLA4 expression, TMB, MSI, and glycolytic activity with immune score and TIL in 20 cancer types. Supplementary Table 4. Correlations of TASL expression with Immune checkpoints, TMB and MSI in 20 cancer types.Supplementary Table 5. Abbreviations, full names and sample sizes for 33 cancer types from the TCGA and GTEx databases. Supplementary Table 6. The original data (E_ normalized values) of TASL expression from GEO datasets used in Figure 6D. Supplementary Table 7. List of datasets from GEO, CGGA, and the article of Miao D, et al. involved in this study. Supplementary Table 8. List of gene sets.
DOI: 10.25824/redu/6kbvt8
title: Ramirez, Danilo. Entrevista concedida a Ian Marino_07-11-23.mp4

description: :unav
DOI: 10.25824/redu/ln7ftn
title: História oral sobre a coleta arquivística da Covid-19 na América Latina: COVID Guatemala

description: Atrelado ao projeto Coronarquivo, a iniciativa "História oral sobre a coleta arquivística da Covid-19 na América Latina" tem como principal objetivo entrevistar e registrar as narrativas daqueles que participaram/testemunharam a construção de arquivos digitais informais sobre a memória da COVID-19 na AMérica Latina. As entrevistas temáticas procuram investigar os agentes, os recursos, as formas de constituição dos acervos, as condições infraestruturais e os enfoques documentais dessas iniciativas. Esta entrevista foi cedida por Danilo Ramírez, acerca do projeto COVID Guatemala.
DOI: 10.6084/m9.figshare.11430426
title: MOESM2 of Full-length transcriptome sequencing reveals the low-temperature-tolerance mechanism of Medicago falcata roots

description: Additional file 2: Table S2. Statistics of AS. The QueryName and SubjectName are the IDs of the identified AS events. QhspStart1, QhspEnd1, QhspStart2, QhspEnd2, ShspStart, ShspEnd1, ShspStart2 and ShspEnd2 indicate the start and end positions of the 2 HSPs for these two AS events.
DOI: 10.6084/m9.figshare.11430426.v1
title: MOESM2 of Full-length transcriptome sequencing reveals the low-temperature-tolerance mechanism of Medicago falcata roots

description: Additional file 2: Table S2. Statistics of AS. The QueryName and SubjectName are the IDs of the identified AS events. QhspStart1, QhspEnd1, QhspStart2, QhspEnd2, ShspStart, ShspEnd1, ShspStart2 and ShspEnd2 indicate the start and end positions of the 2 HSPs for these two AS events.
DOI: 10.6084/m9.figshare.11430432
title: MOESM3 of Full-length transcriptome sequencing reveals the low-temperature-tolerance mechanism of Medicago falcata roots

description: Additional file 3: Table S3. Statistics of SSRs.
DOI: 10.6084/m9.figshare.11430432.v1
title: MOESM3 of Full-length transcriptome sequencing reveals the low-temperature-tolerance mechanism of Medicago falcata roots

description: Additional file 3: Table S3. Statistics of SSRs.
DOI: 10.6084/m9.figshare.11430447
title: MOESM2 of The transcriptome of the rumen ciliate Entodinium caudatum reveals some of its metabolic features

description: Additional file 2: Table S2. Most highly expressed mRNAs in E. caudatum transcriptome.
DOI: 10.6084/m9.figshare.11430447.v1
title: MOESM2 of The transcriptome of the rumen ciliate Entodinium caudatum reveals some of its metabolic features

description: Additional file 2: Table S2. Most highly expressed mRNAs in E. caudatum transcriptome.
DOI: 10.6084/m9.figshare.11430456
title: MOESM3 of The transcriptome of the rumen ciliate Entodinium caudatum reveals some of its metabolic features

description: Additional file 3: Table S3. GO functional classification at subsystem_levels 1 to 3.
DOI: 10.6084/m9.figshare.11430456.v1
title: MOESM3 of The transcriptome of the rumen ciliate Entodinium caudatum reveals some of its metabolic features

description: Additional file 3: Table S3. GO functional classification at subsystem_levels 1 to 3.
DOI: 10.6084/m9.figshare.11430462
title: MOESM4 of The transcriptome of the rumen ciliate Entodinium caudatum reveals some of its metabolic features

description: Additional file 4: Table S4. Predicted metabolic pathways of E. caudatum based on KEGG.
DOI: 10.6084/m9.figshare.11430462.v1
title: MOESM4 of The transcriptome of the rumen ciliate Entodinium caudatum reveals some of its metabolic features

description: Additional file 4: Table S4. Predicted metabolic pathways of E. caudatum based on KEGG.
DOI: 10.6084/m9.figshare.11430465
title: MOESM8 of Full-length transcriptome sequencing reveals the low-temperature-tolerance mechanism of Medicago falcata roots

description: Additional file 8: Table S6. FPKM values and functional annotations of all transcripts.
DOI: 10.6084/m9.figshare.11430465.v1
title: MOESM8 of Full-length transcriptome sequencing reveals the low-temperature-tolerance mechanism of Medicago falcata roots

description: Additional file 8: Table S6. FPKM values and functional annotations of all transcripts.
DOI: 10.6084/m9.figshare.11430468
title: MOESM5 of The transcriptome of the rumen ciliate Entodinium caudatum reveals some of its metabolic features

description: Additional file 5: Table S5. The transcripts related to carbohydrate metabolism.
DOI: 10.6084/m9.figshare.11430468.v1
title: MOESM5 of The transcriptome of the rumen ciliate Entodinium caudatum reveals some of its metabolic features

description: Additional file 5: Table S5. The transcripts related to carbohydrate metabolism.
DOI: 10.6084/m9.figshare.11430471
title: MOESM6 of The transcriptome of the rumen ciliate Entodinium caudatum reveals some of its metabolic features

description: Additional file 6: Table S6. The CAZymes represented in the E. caudatum transcriptome.
DOI: 10.6084/m9.figshare.11430471.v1
title: MOESM6 of The transcriptome of the rumen ciliate Entodinium caudatum reveals some of its metabolic features

description: Additional file 6: Table S6. The CAZymes represented in the E. caudatum transcriptome.
DOI: 10.6084/m9.figshare.11430474
title: MOESM9 of Full-length transcriptome sequencing reveals the low-temperature-tolerance mechanism of Medicago falcata roots

description: Additional file 9: Table S7. Information on the 11,369 genes differentially expressed under low-temperature stress.
DOI: 10.6084/m9.figshare.11430474.v1
title: MOESM9 of Full-length transcriptome sequencing reveals the low-temperature-tolerance mechanism of Medicago falcata roots

description: Additional file 9: Table S7. Information on the 11,369 genes differentially expressed under low-temperature stress.
DOI: 10.6084/m9.figshare.11430477
title: MOESM7 of The transcriptome of the rumen ciliate Entodinium caudatum reveals some of its metabolic features

description: Additional file 7: Table S7. Putative proteinase inhibitors identified in the E. caudatum transcriptome.
DOI: 10.6084/m9.figshare.11430477.v1
title: MOESM7 of The transcriptome of the rumen ciliate Entodinium caudatum reveals some of its metabolic features

description: Additional file 7: Table S7. Putative proteinase inhibitors identified in the E. caudatum transcriptome.
DOI: 10.6084/m9.figshare.11430480
title: MOESM8 of The transcriptome of the rumen ciliate Entodinium caudatum reveals some of its metabolic features

description: Additional file 8: Table S8. Putative signal transduction pathways predicted from the E. caudatum transcriptome.
DOI: 10.6084/m9.figshare.11430480.v1
title: MOESM8 of The transcriptome of the rumen ciliate Entodinium caudatum reveals some of its metabolic features

description: Additional file 8: Table S8. Putative signal transduction pathways predicted from the E. caudatum transcriptome.
DOI: 10.6084/m9.figshare.11430483
title: MOESM9 of The transcriptome of the rumen ciliate Entodinium caudatum reveals some of its metabolic features

description: Additional file 9: Table S9. Comparison of CAZymes and peptidases of E. caudatum transcriptome to the genomes of two model ciliates.
DOI: 10.6084/m9.figshare.11430483.v1
title: MOESM9 of The transcriptome of the rumen ciliate Entodinium caudatum reveals some of its metabolic features

description: Additional file 9: Table S9. Comparison of CAZymes and peptidases of E. caudatum transcriptome to the genomes of two model ciliates.
DOI: 10.34740/kaggle/dsv/7687806
title: Balochi Language to English Translation Dataset
DOI: 10.6084/m9.figshare.11954889
title: Data supporting figures, tables and supplementary information in the published article: Characterization of spike glycoprotein of SARS-CoV-2 on virus entry and its immune cross-reactivity with SARS-CoV

description: This dataset comprises a single Excel .xlsx spreadsheet with 29 tabs. The data are western blot images and quantitative data characterising spike glycoprotein of SARS-CoV-2 on virus entry and its immune cross-reactivity with SARS-CoV. The data underlie the figures, table and supplementary figures of the related manuscript.<br>The 29 tabs are as follows. (NOTE: Western blot images do not display in figshare viewer: please download the spreadsheet to view these)- Western blots for incorporation of SARS-CoV-2 S protein into pseudovirions.(<b>Figure 1B, </b><b>Figure 1C, </b><b>Figure 1D, </b><b>Figure 1E, </b><b>Figure 1F</b>, <b>Figure 1G, </b><b>Figure 1H, </b><b>Figure 1I, </b><b>Figure 1J</b>)- Entry and receptor of SARS-CoV-2 S pseudovirons.(<b>Figure 2A, </b><b>Figure 2B, Figure 2D</b>)- Endocytosis of SARS-CoV-2 S pseudovirions on 293/hACE2 cells.(<b>Figure 3A, </b><b>Figure 3B</b><b>, </b><b>Figure 3C</b><b>, </b><b>Figure 3D</b><b>, </b><b>Figure 3E</b><b>, </b><b>Figure 3F</b>)- Activation of SARS-CoV-2 S protein by cathepsin and trypsin.(<b>Figure 4A, </b><b>Figure 4C, </b><b>Figure 4D, </b><b>Figure 4E</b>)- Characterization of polyclonal rabbit anti-SARS S1 antibodies T62.(<b>Figure 5B, Figure 5E</b>)- Limited cross-neutralization of SARS-CoV and COVID-19 sera.(<b>Figure 6A, </b><b>Figure 6B</b>)- Neutralization activities of antisera from SARS-CoV and COVID-19 patients.(<b>Table 1</b>)- Treatment of cells with 130, a TRPML1 inhibitor.(<b>Supplementary Figure 1</b>)- Expressing type II membrane serine protease (TMPRSS) 2, 4, 11A, 11D, and 11E on 293/hACE2 cells.(<b>Supplementary Figure 3</b>).<b><br></b>In the related publication, the authors use a lentiviral pseudotype system and determine cell type susceptibility, virus receptor, entry pathway and protease priming for SARS-CoV-2. They also identify several potential drug targets for SARS-CoV-2.<br>
DOI: 10.6084/m9.figshare.11954889.v1
title: Data supporting figures, tables and supplementary information in the published article: Characterization of spike glycoprotein of SARS-CoV-2 on virus entry and its immune cross-reactivity with SARS-CoV

description: This dataset comprises a single Excel .xlsx spreadsheet with 29 tabs. The data are western blot images and quantitative data characterising spike glycoprotein of SARS-CoV-2 on virus entry and its immune cross-reactivity with SARS-CoV. The data underlie the figures, table and supplementary figures of the related manuscript.<br>The 29 tabs are as follows. (NOTE: Western blot images do not display in figshare viewer: please download the spreadsheet to view these)- Western blots for incorporation of SARS-CoV-2 S protein into pseudovirions.(<b>Figure 1B, </b><b>Figure 1C, </b><b>Figure 1D, </b><b>Figure 1E, </b><b>Figure 1F</b>, <b>Figure 1G, </b><b>Figure 1H, </b><b>Figure 1I, </b><b>Figure 1J</b>)- Entry and receptor of SARS-CoV-2 S pseudovirons.(<b>Figure 2A, </b><b>Figure 2B, Figure 2D</b>)- Endocytosis of SARS-CoV-2 S pseudovirions on 293/hACE2 cells.(<b>Figure 3A, </b><b>Figure 3B</b><b>, </b><b>Figure 3C</b><b>, </b><b>Figure 3D</b><b>, </b><b>Figure 3E</b><b>, </b><b>Figure 3F</b>)- Activation of SARS-CoV-2 S protein by cathepsin and trypsin.(<b>Figure 4A, </b><b>Figure 4C, </b><b>Figure 4D, </b><b>Figure 4E</b>)- Characterization of polyclonal rabbit anti-SARS S1 antibodies T62.(<b>Figure 5B, Figure 5E</b>)- Limited cross-neutralization of SARS-CoV and COVID-19 sera.(<b>Figure 6A, </b><b>Figure 6B</b>)- Neutralization activities of antisera from SARS-CoV and COVID-19 patients.(<b>Table 1</b>)- Treatment of cells with 130, a TRPML1 inhibitor.(<b>Supplementary Figure 1</b>)- Expressing type II membrane serine protease (TMPRSS) 2, 4, 11A, 11D, and 11E on 293/hACE2 cells.(<b>Supplementary Figure 3</b>).<b><br></b>In the related publication, the authors use a lentiviral pseudotype system and determine cell type susceptibility, virus receptor, entry pathway and protease priming for SARS-CoV-2. They also identify several potential drug targets for SARS-CoV-2.<br>
DOI: 10.5281/zenodo.10668480
title: Enhancing Combinatorial Optimization with Classical and Quantum Generative Models
DOI: 10.5281/zenodo.7636721
title: Neural Encoding Models of Olfaction (NEMO) dataset

description: This dataset constitutes files from a precision fMRI imaging experiment in which 18 hours of fMRI data/subject were collected from 3 human subjects (S1-3). In this experiment, 160 monomolecular odors were presented in 4 sets of 40 odors. Each set was presented in 3 different scanning sessions, and the dataset was collected over 12 separate sessions. Each session was further conducted over four runs. Each odor was presented 27-30 for each subject. Ratings of odors to perceptual ratings were also acquired during the scanning sessions. For S2 and S3, additional perceptual ratings were acquired outside the scanner.

The dataset consists of zip files of .nii images, behavioral ratings, breathing/respiratory data, and uncompressed and defaced T1 images for each subject.

File Description:



s<>_imaging.zip: *nii files containing unprocessed BOLD data while odors were presented. <> Corresponds to the 3 subjects S01, S02, S03. It also contains whole brain images useful in coregistering the BOLD data to anatomical T1.

s<>_behavior.zip:



Unprocessed Matlab (.mat) datasets of odors delivered during the scans. Each file also contains perceptual ratings in response to odors. The time series for the raw behavioral data is not in alignment with BOLD data.

For S2 and S3, additional ratings acquired outside the scanner are in the folder <behavior_task>.



s<>_breathing.zip: Unprocessed breathing/sniffing data in .mat format. It also contains information to align behavioral and scanning time series.

s<>_defaced.nii: Defaced anatomical T1 images for each subject.


For a detailed description of data acquisition, scanning parameters, and the experimental design, please refer to the publication. For scripts to analyze this dataset and further documentation, please refer to the GitHub repository. To download this dataset, please submit a guest access request below.To see how  to file a guest access please see: Guest Access Help

Github: https://github.com/viveksgr/NEMO_scriptsAll queries must be directed to:Vivek Sagar at sgr.vivek@gmail.com
DOI: 10.5281/zenodo.7636722
title: Neural Encoding Models of Olfaction (NEMO) dataset

description: This dataset constitutes files from a precision fMRI imaging experiment in which 18 hours of fMRI data/subject were collected from 3 human subjects (S1-3). In this experiment, 160 monomolecular odors were presented in 4 sets of 40 odors. Each set was presented in 3 different scanning sessions, and the dataset was collected over 12 separate sessions. Each session was further conducted over four runs. Each odor was presented 27-30 for each subject. Ratings of odors to perceptual ratings were also acquired during the scanning sessions. For S2 and S3, additional perceptual ratings were acquired outside the scanner.

The dataset consists of zip files of .nii images, behavioral ratings, breathing/respiratory data, and uncompressed and defaced T1 images for each subject.

File Description:



s<>_imaging.zip: *nii files containing unprocessed BOLD data while odors were presented. <> Corresponds to the 3 subjects S01, S02, S03. It also contains whole brain images useful in coregistering the BOLD data to anatomical T1.

s<>_behavior.zip:



Unprocessed Matlab (.mat) datasets of odors delivered during the scans. Each file also contains perceptual ratings in response to odors. The time series for the raw behavioral data is not in alignment with BOLD data.

For S2 and S3, additional ratings acquired outside the scanner are in the folder <behavior_task>.



s<>_breathing.zip: Unprocessed breathing/sniffing data in .mat format. It also contains information to align behavioral and scanning time series.

s<>_defaced.nii: Defaced anatomical T1 images for each subject.


For a detailed description of data acquisition, scanning parameters, and the experimental design, please refer to the publication. For scripts to analyze this dataset and further documentation, please refer to the GitHub repository. To download this dataset, please submit a guest access request below.To see how  to file a guest access please see: Guest Access Help

Github: https://github.com/viveksgr/NEMO_scriptsAll queries must be directed to:Vivek Sagar at sgr.vivek@gmail.com
DOI: 10.6084/m9.figshare.11430699
title: MOESM1 of Mepiquat chloride promotes cotton lateral root formation by modulating plant hormone homeostasis

description: Additional file 1: Table S1. Summary of sequence assembly after Illumina sequencing.
DOI: 10.6084/m9.figshare.11430699.v1
title: MOESM1 of Mepiquat chloride promotes cotton lateral root formation by modulating plant hormone homeostasis

description: Additional file 1: Table S1. Summary of sequence assembly after Illumina sequencing.
DOI: 10.6084/m9.figshare.11430705
title: MOESM2 of Mepiquat chloride promotes cotton lateral root formation by modulating plant hormone homeostasis

description: Additional file 2: Table S2. Statistics of genes in different expression-level interval.
DOI: 10.6084/m9.figshare.11430705.v1
title: MOESM2 of Mepiquat chloride promotes cotton lateral root formation by modulating plant hormone homeostasis

description: Additional file 2: Table S2. Statistics of genes in different expression-level interval.
DOI: 10.6084/m9.figshare.14052146
title: Vegetation Carbon Accumulation Driven by Stand Characteristics and Climatic Factors in Subtropical Forests of Southeastern China

description: Subtropical forests act as a carbon sink and play an important role in mitigating global warming. However, the understanding of the effects of stand characteristics and climatic factors on forest carbon sequestration capacity remains limited. Based on multiple regression analyses using structural equation modeling and plot-level data of coniferous and broadleaf forests from the national forest resource inventories, our study quantified vegetation carbon accumulation rate (VCAR) and the underlying influence pattern in Zhejiang Province, Southeastern China. The mean VCAR of coniferous and broadleaf forests was 2.30 Mg C ha<sup>−1</sup> yr<sup>−1</sup> and 4.53 Mg C ha<sup>−1</sup> yr<sup>−1</sup> during 2004–2009, and 1.56 Mg C ha<sup>−1</sup> yr<sup>−1</sup> and 1.98 Mg C ha<sup>−1</sup> yr<sup>−1</sup> during 2009–2014, respectively. Stand characteristics (i.e., change in tree density and mean diameter at breast height) and climatic factors (i.e., mean annual precipitation (MAP), temperature, sunshine hours, and monthly precipitation fluctuation) were the major driving factors on the change patterns of VCAR. All factors, except precipitation fluctuation, had positive effects on VCAR. The positive effects of MAP decreased with the increase in MAP, but the negative effects of precipitation fluctuation increased with the increase in precipitation fluctuation. Precipitation increased with increasing elevation, while the VCAR was lower at lower elevations. Additionally, we found that broadleaf forests could be able to cope better with the negative effects of precipitation fluctuation. Overall, this study provides new insights into the sensitivity and potential of vegetation carbon accumulation for structural development and climate change response in Zhejiang Province to better promote sustainable forestry development.
DOI: 10.6084/m9.figshare.14052146.v1
title: Vegetation Carbon Accumulation Driven by Stand Characteristics and Climatic Factors in Subtropical Forests of Southeastern China

description: Subtropical forests act as a carbon sink and play an important role in mitigating global warming. However, the understanding of the effects of stand characteristics and climatic factors on forest carbon sequestration capacity remains limited. Based on multiple regression analyses using structural equation modeling and plot-level data of coniferous and broadleaf forests from the national forest resource inventories, our study quantified vegetation carbon accumulation rate (VCAR) and the underlying influence pattern in Zhejiang Province, Southeastern China. The mean VCAR of coniferous and broadleaf forests was 2.30 Mg C ha<sup>−1</sup> yr<sup>−1</sup> and 4.53 Mg C ha<sup>−1</sup> yr<sup>−1</sup> during 2004–2009, and 1.56 Mg C ha<sup>−1</sup> yr<sup>−1</sup> and 1.98 Mg C ha<sup>−1</sup> yr<sup>−1</sup> during 2009–2014, respectively. Stand characteristics (i.e., change in tree density and mean diameter at breast height) and climatic factors (i.e., mean annual precipitation (MAP), temperature, sunshine hours, and monthly precipitation fluctuation) were the major driving factors on the change patterns of VCAR. All factors, except precipitation fluctuation, had positive effects on VCAR. The positive effects of MAP decreased with the increase in MAP, but the negative effects of precipitation fluctuation increased with the increase in precipitation fluctuation. Precipitation increased with increasing elevation, while the VCAR was lower at lower elevations. Additionally, we found that broadleaf forests could be able to cope better with the negative effects of precipitation fluctuation. Overall, this study provides new insights into the sensitivity and potential of vegetation carbon accumulation for structural development and climate change response in Zhejiang Province to better promote sustainable forestry development.
DOI: 10.6084/m9.figshare.11430717
title: MOESM3 of Mepiquat chloride promotes cotton lateral root formation by modulating plant hormone homeostasis

description: Additional file 3: Table S3. GO annotation enrichment analysis for differentially expressed genes.
DOI: 10.6084/m9.figshare.11430717.v1
title: MOESM3 of Mepiquat chloride promotes cotton lateral root formation by modulating plant hormone homeostasis

description: Additional file 3: Table S3. GO annotation enrichment analysis for differentially expressed genes.