cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc277zs8
title: CCDC 2063260: Experimental Crystal Structure Determination

description: Related Article: Di Wang, Shan-Ci Chen, Qingdong Zheng|2021|J.Mater.Chem.A|9|11778|doi:10.1039/D1TA01447A
DOI: 10.5281/zenodo.4732440
title: Data Modeling for Connected Data - SLR

description: A systematic literature review (SLR) was carried out to identify the known approaches for data modeling of connected data. The main contribution of this SLR is an analysis of sixteen works, from 2013 to 2020, in terms of three dimensions: type of contribution, bibliometrics, and data modeling characteristics. Some parameters of the review and results from data collection were converted into files. The entire set of works retrieved from DL is also available to allow reproductibility.
DOI: 10.5281/zenodo.4732439
title: Data Modeling for Connected Data - SLR

description: A systematic literature review (SLR) was carried out to identify the known approaches for data modeling of connected data. The main contribution of this SLR is an analysis of sixteen works, from 2013 to 2020, in terms of three dimensions: type of contribution, bibliometrics, and data modeling characteristics. Some parameters of the review and results from data collection were converted into files. The entire set of works retrieved from DL is also available to allow reproductibility.
DOI: 10.6084/m9.figshare.12423791.v3
title: Clinical phenotype information for Rheumatoid Arthritis Patients and Healthy controls

description: Clinical phenotype for RA-Map (Early RA) and Vaccine recipient samples.<br>Access to clinical data is regulated by the RA-Map access committee. Please email m.r.barnes@qmul.ac.uk
DOI: 10.6084/m9.figshare.14614824
title: Final Vaccine Study protocol v1.3.

description: Protocol for RA-MAP vaccine study
DOI: 10.6084/m9.figshare.14614824.v1
title: Final Vaccine Study protocol v1.3.

description: Protocol for RA-MAP vaccine study
DOI: 10.17909/t9-3z1c-bv23
title: Test Sarah 3

description: Swift DOIs
DOI: 10.6084/m9.figshare.14096912.v3
title: Supplementary Information for Current research on microbe-plastic interactions in the marine environment

description: Placeholder for the table of studies that are included in the figures of the review chapter
DOI: 10.17909/t9-kvsw-dy52
title: Test Swift 2
DOI: 10.26180/5f50935ad2aab
title: Ontogeny and ecological significance of metabolic rates in sea turtle hatchlings

description: Dataset for thesis chapter entitled "Ontogeny and ecological significance of metabolic rates in sea turtle hatchlings"
DOI: 10.17909/t9-kdaw-f362
title: Test Swift DOI 3
DOI: 10.15785/sbgrid/808
title: X-Ray Diffraction data from Chaetomium thermophilum FAD-dependent oxidoreductase in complex with 4-nitrocatechol, source of 6ZE6 structure

description: native dataset
DOI: 10.15785/sbgrid/810
title: X-Ray Diffraction data from FAD-dependent oxidoreductase from Chaetomium thermophilum in complex with ABTS, source of 7AA2 structure

description: native dataset
description: Regions 1.925-1.900A, 2.27-2.24A, and 3.69-3.65A on the detector were omitted due to ice rings.
DOI: 10.17632/2pscjykc4b.1
title: Data for: Online information adoption about public infrastructure projects in China

description: Code and data to reproduce the paper of Online information adoption about public infrastructure projects in China
DOI: 10.17632/z2kpcwrwfm.1
title: Data for: Can YouTube Videos on Colostomy or Ileostomy be Used as Learning Resources?

description: Data collected on YouTube videos on Colostomy/ileostomy showing group number (as indicated in the study), Useful and non-useful videos (based on scores), number of viewers, duration, number of likes, number of dislikes, days on YouTube, Comments, Scores for major criteria, scores for minor criteria, and total scores. Items of the criteria are explained in tables and the manuscript.
DOI: 10.15785/sbgrid/804
title: X-Ray Diffraction data from FAD-dependent oxidoreductase from Chaetomium thermophilum, source of 6ZE2 structure

description: native dataset
description: This dataset was indexed in space group P21 and last 100 images were excluded due to radiation damage. Regions 1.90-1.92A, 2.23-2.26A,and 3.63-3.68A on the detector were omitted due to ice rings.
DOI: 10.15785/sbgrid/834
title: X-Ray Diffraction data from Protein tyrosine phosphatase 1B, source of 7MM1 structure

description: PTP1B in complex with TCS401 by Native S-SAD at Room Temperature
description: This data was collected with the Pilatus detector raised by two panels. This altered the beam center from that reported in the image headers and introduced a shadow on the detector. For data reduction, the beam center should be set to (290.5, 225.2), and the pixel mask provided in the "Processing Bundle" should be used. The experiment was conducted at 6.5 keV to improve the anomalous signal, but this introduced an additional harmonic in the incident X-ray beam. The resulting secondary diffraction pattern is only observed at low resolution, and was excluded from DIALS spot finding, indexing, and geometry refinement by setting d_max to 6 angstroms. The data was collected in two passes on the same crystal. Both passes were processed separately in DIALS and merged together in AIMLESS to yield the final merged intensities.
DOI: 10.5281/zenodo.4771196
title: German House Coordinates

description: coordinates of houses in Germany
DOI: 10.5281/zenodo.4771195
title: German House Coordinates

description: coordinates of houses in Germany
DOI: 10.15785/sbgrid/805
title: X-Ray Diffraction data from Chaetomium thermophilum FAD-dependent oxidoreductase in complex with methyl4-(aminomethyl)benzoate, source of 6ZE3 structure

description: native dataset
DOI: 10.5281/zenodo.4771204
title: CoVaxxy Tweet IDs data set

description: A collection of Tweet IDs related to Covid-19 Vaccines, gathered from Twitter since Jan 4, 2021. Please see https://arxiv.org/abs/2101.07694 for more information.
DOI: 10.15785/sbgrid/809
title: X-Ray Diffraction data from FAD-dependent oxidoreductase from Chaetomium thermophilum in complex with 4-nitrophenol, source of 6ZE7 structure

description: native dataset
description: First 1000 images were excluded.
DOI: 10.5281/zenodo.4771202
title: Highly Stable Preferential CO Oxidation by Dinuclear Heterogeneous Catalysts

description: Optimized geometries for the Ir2@CeO2 paper.
DOI: 10.5281/zenodo.4771201
title: Highly Stable Preferential CO Oxidation by Dinuclear Heterogeneous Catalysts

description: Optimized geometries for the Ir2@CeO2 paper.
DOI: 10.5281/zenodo.4771207
title: A Comparative Analysis of Agile Teamwork Quality Models.

description: Supplementary material for article A Comparative Analysis of Agile Teamwork Quality Models.
DOI: 10.15785/sbgrid/807
title: X-Ray Diffraction data from CtFDO in complex with 2-(1H-indol-3-yl)-N[(1-methyl-1H-pyrrol-2-yl)-methyl]ethan-amine, source of 6ZE5 structure

description: native dataset
DOI: 10.6084/m9.figshare.14614389
title: PN_components.tar.gz

description: Files related to the investigation of PN components and the construction of Figure 2.<br>
DOI: 10.6084/m9.figshare.14614389.v1
title: PN_components.tar.gz

description: Files related to the investigation of PN components and the construction of Figure 2.<br>
DOI: 10.6084/m9.figshare.14614317
title: background_MongoDB_database.tar.gz

description: The folder contains a MongoDB database, named 'background'. Its collections include the genomic annotation of species in order to perform the pathway analysis. Also, there are some collections related to the Gene Ontology - Biological Process domain, which are used for the semantic analysis and clustering on the respective ontological graph<br>
DOI: 10.6084/m9.figshare.14614317.v1
title: background_MongoDB_database.tar.gz

description: The folder contains a MongoDB database, named 'background'. Its collections include the genomic annotation of species in order to perform the pathway analysis. Also, there are some collections related to the Gene Ontology - Biological Process domain, which are used for the semantic analysis and clustering on the respective ontological graph<br>
DOI: 10.15785/sbgrid/806
title: X-Ray Diffraction data from CtFDO in complex with 4-oxo-N-[1-(3pyridinyl)ethyl]-2-thiophenebutanamide, source of 6ZE4 structure

description: native dataset
description: Regions 1.91-1.93A, 2.23-2.27A, 3.64-3.70A on the detector were omitted due to ice rings. Images 350-1000 and 2150-2200 were excluded.
DOI: 10.6084/m9.figshare.14614386
title: comparison_of_phylogenetic_trees.tar.gz

description: Comparison of phylogenetic criteria:1. Ribosomal sequences2. Proteostasis Network3. HSP404. HSP70<br>
DOI: 10.6084/m9.figshare.14614386.v1
title: comparison_of_phylogenetic_trees.tar.gz

description: Comparison of phylogenetic criteria:1. Ribosomal sequences2. Proteostasis Network3. HSP404. HSP70<br>
DOI: 10.6084/m9.figshare.14614383
title: HSPs_analysis.tar.gz

description: Results from the phylogenetic analysis with HSPs<br>
DOI: 10.6084/m9.figshare.14614383.v1
title: HSPs_analysis.tar.gz

description: Results from the phylogenetic analysis with HSPs<br>
DOI: 10.6084/m9.figshare.14614374.v1
title: PN_analysis.tar.gz

description: Results for the phylogenetic analysis based on proteostasis network<br>
DOI: 10.6084/m9.figshare.14614374
title: PN_analysis.tar.gz

description: Results for the phylogenetic analysis based on proteostasis network<br>
DOI: 10.34740/kaggle/dsv/2247659
title: 2021 World Population (updated daily)
DOI: 10.6084/m9.figshare.14614368
title: rRNAs_analysis.tar.gz

description: Results for the phylogenetic analysis based on ribosomal sequences (16S and 18S)<br>
DOI: 10.6084/m9.figshare.14614368.v1
title: rRNAs_analysis.tar.gz

description: Results for the phylogenetic analysis based on ribosomal sequences (16S and 18S)<br>
DOI: 10.21227/xp2x-he14
title: Communication of Smart Devices

description: Communication of selected devices captured on LAN. The communication is stored in pcapng format. Files captures device's communication such as: connection to a network, configuration and other activities specific to each device.Selected devices are:- TP-LINK LB110- TP-LINK HS100- NETATMO smart radiators valves- BML smart IP camera
DOI: 10.34740/kaggle/dsv/2247681
title: COVID Vaccination in World (updated daily)
DOI: 10.6084/m9.figshare.14608002.v2
title: Homozygous MTAP deletion in primary human glioblastoma is not associated with elevation of methylthioadenosine

description: Dataset supporting Barekatain et al. Nature Communications. Article titled: Homozygous <i>MTAP</i> deletion in primary human glioblastoma is not associated with elevation of methylthioadenosine.
DOI: 10.5281/zenodo.4771228
title: DATOS DE EVALUACIÓN DE LA CONCENTRACIÓN DE INMISIONES DE PM10 Y PM2.5 EN LAS ESTACIONES EL TUNAL Y LAS FERIAS

description: Actualmente, se ha presentando un cambio brusco en el material particulado en la estación Las Ferias y el Tunal a causa del confinamiento provocado por la pandemia global COVID-19. Con este articulo buscamos realizar una comparación en la concentración de las inmisiones de estos contaminantes, y de como seria su evolución en el tiempo.
DOI: 10.6084/m9.figshare.14615007
title: Supplementary_File1_moDC_E7_ActiveGeneEnh_5Feb2021.bed from Logical modelling of <i>in vitro</i> differentiation of human monocytes into dendritic cells unravels novel transcriptional regulatory interactions

description: Dendritic cells (DCs) are the major specialized antigen-presenting cells, thereby connecting innate and adaptive immunity. Because of their role in establishing adaptive immunity, they constitute promising targets for immunotherapy. Monocytes can differentiate into DCs <i>in vitro</i> in the presence of colony-stimulating factor 2 (CSF2) and interleukin 4 (IL4), activating four signalling pathways (MAPK, JAK/STAT, NFKB and PI3 K). However, the downstream transcriptional program responsible for DC differentiation from monocytes (moDC) remains unknown. By analysing the scientific literature on moDC differentiation, we established a preliminary logical model that helped us identify missing information regarding the activation of genes responsible for this differentiation, including missing targets for key transcription factors (TFs). Using ChIP-seq and RNA-seq data from the Blueprint consortium, we defined active, and inactive promoters, together with differentially expressed genes in monocytes, moDCs and macrophages, which correspond to an alternative cell fate. We then used this functional genomic information to predict novel targets for previously identified TFs. By integrating this information, we refined our model and recapitulated the main established facts regarding moDC differentiation. Prospectively, the resulting model should be useful to develop novel immunotherapies targeting moDCs.
DOI: 10.6084/m9.figshare.14615007.v1
title: Supplementary_File1_moDC_E7_ActiveGeneEnh_5Feb2021.bed from Logical modelling of <i>in vitro</i> differentiation of human monocytes into dendritic cells unravels novel transcriptional regulatory interactions

description: Dendritic cells (DCs) are the major specialized antigen-presenting cells, thereby connecting innate and adaptive immunity. Because of their role in establishing adaptive immunity, they constitute promising targets for immunotherapy. Monocytes can differentiate into DCs <i>in vitro</i> in the presence of colony-stimulating factor 2 (CSF2) and interleukin 4 (IL4), activating four signalling pathways (MAPK, JAK/STAT, NFKB and PI3 K). However, the downstream transcriptional program responsible for DC differentiation from monocytes (moDC) remains unknown. By analysing the scientific literature on moDC differentiation, we established a preliminary logical model that helped us identify missing information regarding the activation of genes responsible for this differentiation, including missing targets for key transcription factors (TFs). Using ChIP-seq and RNA-seq data from the Blueprint consortium, we defined active, and inactive promoters, together with differentially expressed genes in monocytes, moDCs and macrophages, which correspond to an alternative cell fate. We then used this functional genomic information to predict novel targets for previously identified TFs. By integrating this information, we refined our model and recapitulated the main established facts regarding moDC differentiation. Prospectively, the resulting model should be useful to develop novel immunotherapies targeting moDCs.
DOI: 10.6084/m9.figshare.14615010.v1
title: Jupyter Notebook of the Logical model of the monocyte to moDCs differentiation from Logical modelling of <i>in vitro</i> differentiation of human monocytes into dendritic cells unravels novel transcriptional regulatory interactions

description: Dendritic cells (DCs) are the major specialized antigen-presenting cells, thereby connecting innate and adaptive immunity. Because of their role in establishing adaptive immunity, they constitute promising targets for immunotherapy. Monocytes can differentiate into DCs <i>in vitro</i> in the presence of colony-stimulating factor 2 (CSF2) and interleukin 4 (IL4), activating four signalling pathways (MAPK, JAK/STAT, NFKB and PI3 K). However, the downstream transcriptional program responsible for DC differentiation from monocytes (moDC) remains unknown. By analysing the scientific literature on moDC differentiation, we established a preliminary logical model that helped us identify missing information regarding the activation of genes responsible for this differentiation, including missing targets for key transcription factors (TFs). Using ChIP-seq and RNA-seq data from the Blueprint consortium, we defined active, and inactive promoters, together with differentially expressed genes in monocytes, moDCs and macrophages, which correspond to an alternative cell fate. We then used this functional genomic information to predict novel targets for previously identified TFs. By integrating this information, we refined our model and recapitulated the main established facts regarding moDC differentiation. Prospectively, the resulting model should be useful to develop novel immunotherapies targeting moDCs.
DOI: 10.6084/m9.figshare.14615010
title: Jupyter Notebook of the Logical model of the monocyte to moDCs differentiation from Logical modelling of <i>in vitro</i> differentiation of human monocytes into dendritic cells unravels novel transcriptional regulatory interactions

description: Dendritic cells (DCs) are the major specialized antigen-presenting cells, thereby connecting innate and adaptive immunity. Because of their role in establishing adaptive immunity, they constitute promising targets for immunotherapy. Monocytes can differentiate into DCs <i>in vitro</i> in the presence of colony-stimulating factor 2 (CSF2) and interleukin 4 (IL4), activating four signalling pathways (MAPK, JAK/STAT, NFKB and PI3 K). However, the downstream transcriptional program responsible for DC differentiation from monocytes (moDC) remains unknown. By analysing the scientific literature on moDC differentiation, we established a preliminary logical model that helped us identify missing information regarding the activation of genes responsible for this differentiation, including missing targets for key transcription factors (TFs). Using ChIP-seq and RNA-seq data from the Blueprint consortium, we defined active, and inactive promoters, together with differentially expressed genes in monocytes, moDCs and macrophages, which correspond to an alternative cell fate. We then used this functional genomic information to predict novel targets for previously identified TFs. By integrating this information, we refined our model and recapitulated the main established facts regarding moDC differentiation. Prospectively, the resulting model should be useful to develop novel immunotherapies targeting moDCs.
DOI: 10.6084/m9.figshare.14615013
title: Logical model of the monocyte to moDCs differentiation from Logical modelling of <i>in vitro</i> differentiation of human monocytes into dendritic cells unravels novel transcriptional regulatory interactions

description: Dendritic cells (DCs) are the major specialized antigen-presenting cells, thereby connecting innate and adaptive immunity. Because of their role in establishing adaptive immunity, they constitute promising targets for immunotherapy. Monocytes can differentiate into DCs <i>in vitro</i> in the presence of colony-stimulating factor 2 (CSF2) and interleukin 4 (IL4), activating four signalling pathways (MAPK, JAK/STAT, NFKB and PI3 K). However, the downstream transcriptional program responsible for DC differentiation from monocytes (moDC) remains unknown. By analysing the scientific literature on moDC differentiation, we established a preliminary logical model that helped us identify missing information regarding the activation of genes responsible for this differentiation, including missing targets for key transcription factors (TFs). Using ChIP-seq and RNA-seq data from the Blueprint consortium, we defined active, and inactive promoters, together with differentially expressed genes in monocytes, moDCs and macrophages, which correspond to an alternative cell fate. We then used this functional genomic information to predict novel targets for previously identified TFs. By integrating this information, we refined our model and recapitulated the main established facts regarding moDC differentiation. Prospectively, the resulting model should be useful to develop novel immunotherapies targeting moDCs.
DOI: 10.6084/m9.figshare.14615016.v1
title: Supplementary Material from Logical modelling of <i>in vitro</i> differentiation of human monocytes into dendritic cells unravels novel transcriptional regulatory interactions

description: Dendritic cells (DCs) are the major specialized antigen-presenting cells, thereby connecting innate and adaptive immunity. Because of their role in establishing adaptive immunity, they constitute promising targets for immunotherapy. Monocytes can differentiate into DCs <i>in vitro</i> in the presence of colony-stimulating factor 2 (CSF2) and interleukin 4 (IL4), activating four signalling pathways (MAPK, JAK/STAT, NFKB and PI3 K). However, the downstream transcriptional program responsible for DC differentiation from monocytes (moDC) remains unknown. By analysing the scientific literature on moDC differentiation, we established a preliminary logical model that helped us identify missing information regarding the activation of genes responsible for this differentiation, including missing targets for key transcription factors (TFs). Using ChIP-seq and RNA-seq data from the Blueprint consortium, we defined active, and inactive promoters, together with differentially expressed genes in monocytes, moDCs and macrophages, which correspond to an alternative cell fate. We then used this functional genomic information to predict novel targets for previously identified TFs. By integrating this information, we refined our model and recapitulated the main established facts regarding moDC differentiation. Prospectively, the resulting model should be useful to develop novel immunotherapies targeting moDCs.
DOI: 10.6084/m9.figshare.14615013.v1
title: Logical model of the monocyte to moDCs differentiation from Logical modelling of <i>in vitro</i> differentiation of human monocytes into dendritic cells unravels novel transcriptional regulatory interactions

description: Dendritic cells (DCs) are the major specialized antigen-presenting cells, thereby connecting innate and adaptive immunity. Because of their role in establishing adaptive immunity, they constitute promising targets for immunotherapy. Monocytes can differentiate into DCs <i>in vitro</i> in the presence of colony-stimulating factor 2 (CSF2) and interleukin 4 (IL4), activating four signalling pathways (MAPK, JAK/STAT, NFKB and PI3 K). However, the downstream transcriptional program responsible for DC differentiation from monocytes (moDC) remains unknown. By analysing the scientific literature on moDC differentiation, we established a preliminary logical model that helped us identify missing information regarding the activation of genes responsible for this differentiation, including missing targets for key transcription factors (TFs). Using ChIP-seq and RNA-seq data from the Blueprint consortium, we defined active, and inactive promoters, together with differentially expressed genes in monocytes, moDCs and macrophages, which correspond to an alternative cell fate. We then used this functional genomic information to predict novel targets for previously identified TFs. By integrating this information, we refined our model and recapitulated the main established facts regarding moDC differentiation. Prospectively, the resulting model should be useful to develop novel immunotherapies targeting moDCs.