cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5039355
title: MicroED datasets of hemozoin

description: Nineteen diffraction datasets (tilt_04.tar.xz contains a repeat tilt series) were collected using a Talos Arctica operated at 200 kV with a stage temperature of ~80 K. The gun lens was reduced to the minimum (8), the smallest spot size (11) with a C2 aperture of 50 μm was used, and the beam condensed to be 1.78 μm in diameter. The diffraction images were recorded by a Ceta-D camera in MRC format, using the EPU-D data collection software. The diffraction patterns were collected at a nominal camera length of 1.05 m. The dose rate under these conditions was 0.04 e<sup>-</sup> Å<sup>-2</sup> s<sup>-1</sup>. A data processing script is provided to index, integrate and scale the data using DIALS 3.5. An associated manuscript is in preparation.
DOI: 10.6084/m9.figshare.12380606.v1
title: KarimAF et al. RNA seq normalized counts.xlsx

description: Formation of pathological anti-FVIII antibodies, or “inhibitors”, is the most serious complication of therapeutic FVIII infusions, affecting up to 1/3 of severe Hemophilia A (HA) patients. Inhibitor formation is a classical T-cell dependent adaptive immune response. As such, it requires help from the innate immune system. However, the roles of innate immune cells and mechanisms of inhibitor development versus immune tolerance, achieved with or without Immune Tolerance Induction (ITI) therapy, are not well understood. To address these questions, temporal transcriptomics profiling of FVIII-stimulated peripheral blood mononuclear cells (PBMCs) was carried out for HA subjects with and without a current or historic inhibitor using RNA-Seq. PBMCs were isolated from 40 subjects in the following groups: HA with an inhibitor that resolved either following ITI or spontaneously; HA with a current inhibitor; HA with no inhibitor history and non-HA controls. PBMCs were stimulated with 5 nM FVIII and RNA was isolated 4, 16, 24, and 48 hours following stimulation. Time-series differential expression analysis was performed and distinct transcriptional signatures were identified for each group, providing clues as to cellular mechanisms leading to or accompanying their disparate anti-FVIII antibody responses. Subjects with a current inhibitor showed differential expression of 56 genes and a clustering analysis identified three major temporal profiles. Interestingly, gene ontology enrichments featured innate immune modulators, including <i>NLRP3, TLR8, IL32, CLEC10A</i> and <i>COLEC12. NLRP3</i> and <i>TLR8</i> are associated with enhanced secretion of the pro-inflammatory cytokines IL-1b and TNFa, while IL32, which has several isoforms, has been associated with both inflammatory and regulatory immune processes. RNA-Seq results were validated by RT-qPCR, ELISAs, multiplex cytokine analysis and flow cytometry. The inflammatory status of HA patients suffering from an ongoing inhibitor includes up-regulated innate immune modulators, which may act as ongoing danger signals that influence the responses to, and eventual outcome of, ITI therapy.
DOI: 10.25934/00004958.0
title: Available IPD datapackage for study 'A Single-Arm, Open-Label Study to Evaluate the Efficacy and Safety of ABT-493/ABT-530 in Adults With Chronic Hepatitis C Virus Genotype 4, 5, or 6 Infection (ENDURANCE-4)'

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.25934/00004958.1
title: IPD datapackage for study 'A Single-Arm, Open-Label Study to Evaluate the Efficacy and Safety of ABT-493/ABT-530 in Adults With Chronic Hepatitis C Virus Genotype 4, 5, or 6 Infection (ENDURANCE-4)' and research request: 'Data-sharing and re-analysis for main studies assessed by the European Medicines Agency - a cross-sectional study on European Public Assessment Reports '

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.6084/m9.figshare.12380606
title: KarimAF et al. RNA seq normalized counts.xlsx

description: Formation of pathological anti-FVIII antibodies, or “inhibitors”, is the most serious complication of therapeutic FVIII infusions, affecting up to 1/3 of severe Hemophilia A (HA) patients. Inhibitor formation is a classical T-cell dependent adaptive immune response. As such, it requires help from the innate immune system. However, the roles of innate immune cells and mechanisms of inhibitor development versus immune tolerance, achieved with or without Immune Tolerance Induction (ITI) therapy, are not well understood. To address these questions, temporal transcriptomics profiling of FVIII-stimulated peripheral blood mononuclear cells (PBMCs) was carried out for HA subjects with and without a current or historic inhibitor using RNA-Seq. PBMCs were isolated from 40 subjects in the following groups: HA with an inhibitor that resolved either following ITI or spontaneously; HA with a current inhibitor; HA with no inhibitor history and non-HA controls. PBMCs were stimulated with 5 nM FVIII and RNA was isolated 4, 16, 24, and 48 hours following stimulation. Time-series differential expression analysis was performed and distinct transcriptional signatures were identified for each group, providing clues as to cellular mechanisms leading to or accompanying their disparate anti-FVIII antibody responses. Subjects with a current inhibitor showed differential expression of 56 genes and a clustering analysis identified three major temporal profiles. Interestingly, gene ontology enrichments featured innate immune modulators, including <i>NLRP3, TLR8, IL32, CLEC10A</i> and <i>COLEC12. NLRP3</i> and <i>TLR8</i> are associated with enhanced secretion of the pro-inflammatory cytokines IL-1b and TNFa, while IL32, which has several isoforms, has been associated with both inflammatory and regulatory immune processes. RNA-Seq results were validated by RT-qPCR, ELISAs, multiplex cytokine analysis and flow cytometry. The inflammatory status of HA patients suffering from an ongoing inhibitor includes up-regulated innate immune modulators, which may act as ongoing danger signals that influence the responses to, and eventual outcome of, ITI therapy.
DOI: 10.6084/m9.figshare.12380606.v3
title: KarimAF et al. RNA seq normalized counts.xlsx

description: Formation of pathological anti-FVIII antibodies, or “inhibitors”, is the most serious complication of therapeutic FVIII infusions, affecting up to 1/3 of severe Hemophilia A (HA) patients. Inhibitor formation is a classical T-cell dependent adaptive immune response. As such, it requires help from the innate immune system. However, the roles of innate immune cells and mechanisms of inhibitor development versus immune tolerance, achieved with or without Immune Tolerance Induction (ITI) therapy, are not well understood. To address these questions, temporal transcriptomics profiling of FVIII-stimulated peripheral blood mononuclear cells (PBMCs) was carried out for HA subjects with and without a current or historic inhibitor using RNA-Seq. PBMCs were isolated from 40 subjects in the following groups: HA with an inhibitor that resolved either following ITI or spontaneously; HA with a current inhibitor; HA with no inhibitor history and non-HA controls. PBMCs were stimulated with 5 nM FVIII and RNA was isolated 4, 16, 24, and 48 hours following stimulation. Time-series differential expression analysis was performed and distinct transcriptional signatures were identified for each group, providing clues as to cellular mechanisms leading to or accompanying their disparate anti-FVIII antibody responses. Subjects with a current inhibitor showed differential expression of 56 genes and a clustering analysis identified three major temporal profiles. Interestingly, gene ontology enrichments featured innate immune modulators, including <i>NLRP3, TLR8, IL32, CLEC10A</i> and <i>COLEC12. NLRP3</i> and <i>TLR8</i> are associated with enhanced secretion of the pro-inflammatory cytokines IL-1b and TNFa, while IL32, which has several isoforms, has been associated with both inflammatory and regulatory immune processes. RNA-Seq results were validated by RT-qPCR, ELISAs, multiplex cytokine analysis and flow cytometry. The inflammatory status of HA patients suffering from an ongoing inhibitor includes up-regulated innate immune modulators, which may act as ongoing danger signals that influence the responses to, and eventual outcome of, ITI therapy.
DOI: 10.6084/m9.figshare.12380606.v2
title: KarimAF et al. RNA seq normalized counts.xlsx

description: Formation of pathological anti-FVIII antibodies, or “inhibitors”, is the most serious complication of therapeutic FVIII infusions, affecting up to 1/3 of severe Hemophilia A (HA) patients. Inhibitor formation is a classical T-cell dependent adaptive immune response. As such, it requires help from the innate immune system. However, the roles of innate immune cells and mechanisms of inhibitor development versus immune tolerance, achieved with or without Immune Tolerance Induction (ITI) therapy, are not well understood. To address these questions, temporal transcriptomics profiling of FVIII-stimulated peripheral blood mononuclear cells (PBMCs) was carried out for HA subjects with and without a current or historic inhibitor using RNA-Seq. PBMCs were isolated from 40 subjects in the following groups: HA with an inhibitor that resolved either following ITI or spontaneously; HA with a current inhibitor; HA with no inhibitor history and non-HA controls. PBMCs were stimulated with 5 nM FVIII and RNA was isolated 4, 16, 24, and 48 hours following stimulation. Time-series differential expression analysis was performed and distinct transcriptional signatures were identified for each group, providing clues as to cellular mechanisms leading to or accompanying their disparate anti-FVIII antibody responses. Subjects with a current inhibitor showed differential expression of 56 genes and a clustering analysis identified three major temporal profiles. Interestingly, gene ontology enrichments featured innate immune modulators, including <i>NLRP3, TLR8, IL32, CLEC10A</i> and <i>COLEC12. NLRP3</i> and <i>TLR8</i> are associated with enhanced secretion of the pro-inflammatory cytokines IL-1b and TNFa, while IL32, which has several isoforms, has been associated with both inflammatory and regulatory immune processes. RNA-Seq results were validated by RT-qPCR, ELISAs, multiplex cytokine analysis and flow cytometry. The inflammatory status of HA patients suffering from an ongoing inhibitor includes up-regulated innate immune modulators, which may act as ongoing danger signals that influence the responses to, and eventual outcome of, ITI therapy.
DOI: 10.5281/zenodo.4792318
title: Data and Code for Shriver et al. 2021, Quantifying the demographic vulnerabilities of dry woodlands to climate and competition using range-wide monitoring data

description: Data and code for demographic analyses in Shriver et al. 2021. See paper and ReadMe for analysis description and further details. Shriver, R.K., C.B. Yackulic, D.M. Bell, J.B. Bradford. (2021)Quantifying the demographic vulnerabilities of dry woodlands to climate and competition using range-wide demographic models. Ecology. https://doi.org/10.1002/ecy.3425
DOI: 10.5281/zenodo.4792317
title: Data and Code for Shriver et al. 2021, Quantifying the demographic vulnerabilities of dry woodlands to climate and competition using range-wide monitoring data

description: Data and code for demographic analyses in Shriver et al. 2021. See paper and ReadMe for analysis description and further details. Shriver, R.K., C.B. Yackulic, D.M. Bell, J.B. Bradford. (2021)Quantifying the demographic vulnerabilities of dry woodlands to climate and competition using range-wide demographic models. Ecology. https://doi.org/10.1002/ecy.3425
DOI: 10.6084/m9.figshare.14156369
title: Dataset from: "Prenatal hypoxia affects scaling of hemodynamics and arterial wall mechanics in the common snapping turtle, Chelydra serpentina"

description: <b>File:</b><br><i>Organ mass.txt</i><br> Data from the organ mass of common snapping turtles, <i>Chelydra serpentina</i>, reared either under normoxia or 10% hypoxia. The headings are as follows: bm = individual body mass, in kilograms (kg) exp = experimental group, where “n” refers to turtles reared under normoxia, and “h” are turtles reared under 10% hypoxia hm = heart mass, grams (g) liver = liver mass, in grams (g) lung = lung mass, in grams (g) stom = stomach mass, in grams (g) s_int = small intestine mass, in grams (g) l_int = large intestine mass, in grams (g) kid = kidneys mass, in grams (g) <i> </i> <i>Hemodynamics.txt</i> Data from the heart rate and arterial pressures of the systemic and pulmonary circuits before and after injection of adrenaline in anesthetized common snapping turtles, <i>Chelydra serpentina</i>. The missing values are represented as “NA”. The headings are as follows: bm = individual body mass, in kilograms (kg) exp = experimental group, where “n” refers to turtles reared under normoxia, and “h” are turtles reared under 10% hypoxia c/a = indicates if the data corresponds to the control baseline “c” or after adrenaline injection “a” hr = heart rate, in beats per minute (bpm) pss = systemic systolic pressure, in kilopascal (kPa) psp = pulmonary systolic pressure, in kilopascal (kPa) pms = mean systemic pressure, in kilopascal (kPa) pmp = mean pulmonary pressure, in kilopascal (kPa) <i>Arterial mechanics.txt</i> Data from the arterial wall mechanics and morphology of common snapping turtles, <i>Chelydra serpentina</i>, reared either under normoxia or 10% hypoxia. The missing values are represented as “NA”. The headings are as follows: bm = individual body mass, in kilograms (kg) exp = experimental group, where “n” refers to turtles reared under normoxia, and “h” are turtles reared under 10% hypoxia art = corresponds to the artery studied, where “lao” is the left aortic arch, “pap” is the proximal pulmonary artery, and “pad” refers to the distal pulmonary artery ri = internal luminal radius, in millimeters (mm) w = arterial wall thickness, in millimeters (mm) t = arterial wall tension, in newtons per meter (N/m) s = arterial wall stress, on kilopascal (kPa) col = relative collagen content, unitless. Using the birefringent properties of the arteries stained with Picrosirius red, we compared the ratio of pixel density of collagen with the total amount of pixels from the total arterial cross sectional area using the image analysis program ImageJ.
DOI: 10.6084/m9.figshare.14888283.v1
title: yelp

description: Preprocessing the yelp datset<br>The data set contains 44,023 restaurants, and for each one we added a name (based on their description found in reviews), a real photo of that location and country of origin. The final version I uploaded will be useful to programmers in the development complex applications that include recommendation systems.<br>
DOI: 10.6084/m9.figshare.14888283
title: yelp

description: Preprocessing the yelp datset<br>The data set contains 44,023 restaurants, and for each one we added a name (based on their description found in reviews), a real photo of that location and country of origin. The final version I uploaded will be useful to programmers in the development complex applications that include recommendation systems.<br>
DOI: 10.5281/zenodo.5048287
title: Meditables-IIIT

description: MediTables, is a dataset of 200 multi-class annotated medical document images. The dataset contains a wide range of medical document images with variety in capture quality, layouts, skew, occlusion and illumination. They are a good representation of prevalent healthcare, medical images such as pathology, diagnostic and hospital-related reports.
DOI: 10.5281/zenodo.5048286
title: Meditables-IIIT

description: MediTables, is a dataset of 200 multi-class annotated medical document images. The dataset contains a wide range of medical document images with variety in capture quality, layouts, skew, occlusion and illumination. They are a good representation of prevalent healthcare, medical images such as pathology, diagnostic and hospital-related reports.
DOI: 10.5281/zenodo.5047020
title: Protein language model embeddings and predictions of the human proteome

description: Residue and sequence embeddings of the human proteome (SwissProt for organism Human, downloaded on 2021.06.09) computed using bio_embeddings (bioembeddings.com) using the ProtT5 embedder at full precision (https://www.biorxiv.org/content/10.1101/2020.07.12.199554v3). Additionally: - Sequence-level predictions of subcellular localization in 10 classes using LA (https://www.biorxiv.org/content/10.1101/2021.04.25.441334v1) - Residue-level three state secondary structure prediction (alpha, sheet or other) using models reported in the ProtTrans paper (https://www.biorxiv.org/content/10.1101/2020.07.12.199554v3) Files included: - human.fasta --&gt; FASTA-formatted sequences of human from SwissProt - DSSP3_human_ProtT5Sec.fasta --&gt; Secondary structure predictions in three states for each residue of each protein in human.fasta. "H" stands for Helix; "E" stands for Sheet; "C" stands for Other. - subcell_human_LA_ProtT5.csv --&gt; Subcellular location (10 states) and memrane-boundness (2 states) for each protein in human.fasta - embeddings_file.h5 --&gt; per-residue embeddings of sequences in human.fasta. Each dataset in the .h5 file represents a protein sequence and contains a matrix of length Lx1024, with L being the length of the protein sequence. Datasets are indexed using integers. The original sequence identifier (from the FASTA header) can be accessed through the "original_id" attribute. See https://docs.bioembeddings.com/v0.2.0/notebooks/open_embedding_file.html for information on how to open the file - reduced_embeddings_file.h5 --&gt; per-sequence embeddings of sequences in human.fasta (obtained by mean-pooling the residue-embeddings along the length dimension of the protein sequence). Each dataset in the .h5 file represents a protein sequence and contains a vector of size 1024 (meaning, each sequence has the same dimension).
DOI: 10.5281/zenodo.5047019
title: Protein language model embeddings and predictions of the human proteome

description: Residue and sequence embeddings of the human proteome (SwissProt for organism Human, downloaded on 2021.06.09) computed using bio_embeddings (bioembeddings.com) using the ProtT5 embedder at full precision (https://www.biorxiv.org/content/10.1101/2020.07.12.199554v3). Additionally: - Sequence-level predictions of subcellular localization in 10 classes using LA (https://www.biorxiv.org/content/10.1101/2021.04.25.441334v1) - Residue-level three state secondary structure prediction (alpha, sheet or other) using models reported in the ProtTrans paper (https://www.biorxiv.org/content/10.1101/2020.07.12.199554v3) Files included: - human.fasta --&gt; FASTA-formatted sequences of human from SwissProt - DSSP3_human_ProtT5Sec.fasta --&gt; Secondary structure predictions in three states for each residue of each protein in human.fasta. "H" stands for Helix; "E" stands for Sheet; "C" stands for Other. - subcell_human_LA_ProtT5.csv --&gt; Subcellular location (10 states) and memrane-boundness (2 states) for each protein in human.fasta - embeddings_file.h5 --&gt; per-residue embeddings of sequences in human.fasta. Each dataset in the .h5 file represents a protein sequence and contains a matrix of length Lx1024, with L being the length of the protein sequence. Datasets are indexed using integers. The original sequence identifier (from the FASTA header) can be accessed through the "original_id" attribute. See https://docs.bioembeddings.com/v0.2.0/notebooks/open_embedding_file.html for information on how to open the file - reduced_embeddings_file.h5 --&gt; per-sequence embeddings of sequences in human.fasta (obtained by mean-pooling the residue-embeddings along the length dimension of the protein sequence). Each dataset in the .h5 file represents a protein sequence and contains a vector of size 1024 (meaning, each sequence has the same dimension).
DOI: 10.21942/uva.14838486
title: Full dataset

description: Dataset with all variables that belong to the paper. It contains potentially identifiable information so the data cannot be shared publicly. <br>
DOI: 10.21942/uva.14838486.v1
title: Full dataset

description: Dataset with all variables that belong to the paper. It contains potentially identifiable information so the data cannot be shared publicly. <br>
DOI: 10.5281/zenodo.5048312
title: Detecting Code Smells in React-based Web Systems

description: Dataset - Detecting Code Smells in React-based Web Systems
DOI: 10.5517/ccdc.csd.cc26lhq1
title: CCDC 2043573: Experimental Crystal Structure Determination

description: Related Article: S. Pawlędzio, M. Malinska, M. Woińska, J. Wojciechowski, L. Andrade Malaspina, F. Kleemiss, S. Grabowsky, K. Woźniak|2021|IUCrJ|8|608|doi:10.1107/S2052252521004541
DOI: 10.5517/ccdc.csd.cc26lhr2
title: CCDC 2043574: Experimental Crystal Structure Determination

description: Related Article: S. Pawlędzio, M. Malinska, M. Woińska, J. Wojciechowski, L. Andrade Malaspina, F. Kleemiss, S. Grabowsky, K. Woźniak|2021|IUCrJ|8|608|doi:10.1107/S2052252521004541
DOI: 10.5517/ccdc.csd.cc26lhs3
title: CCDC 2043575: Experimental Crystal Structure Determination

description: Related Article: S. Pawlędzio, M. Malinska, M. Woińska, J. Wojciechowski, L. Andrade Malaspina, F. Kleemiss, S. Grabowsky, K. Woźniak|2021|IUCrJ|8|608|doi:10.1107/S2052252521004541
DOI: 10.5517/ccdc.csd.cc26lht4
title: CCDC 2043576: Experimental Crystal Structure Determination

description: Related Article: S. Pawlędzio, M. Malinska, M. Woińska, J. Wojciechowski, L. Andrade Malaspina, F. Kleemiss, S. Grabowsky, K. Woźniak|2021|IUCrJ|8|608|doi:10.1107/S2052252521004541
DOI: 10.5517/ccdc.csd.cc26lhv5
title: CCDC 2043577: Experimental Crystal Structure Determination

description: Related Article: S. Pawlędzio, M. Malinska, M. Woińska, J. Wojciechowski, L. Andrade Malaspina, F. Kleemiss, S. Grabowsky, K. Woźniak|2021|IUCrJ|8|608|doi:10.1107/S2052252521004541
DOI: 10.5517/ccdc.csd.cc26lhw6
title: CCDC 2043578: Experimental Crystal Structure Determination

description: Related Article: S. Pawlędzio, M. Malinska, M. Woińska, J. Wojciechowski, L. Andrade Malaspina, F. Kleemiss, S. Grabowsky, K. Woźniak|2021|IUCrJ|8|608|doi:10.1107/S2052252521004541
DOI: 10.5517/ccdc.csd.cc26lhx7
title: CCDC 2043579: Experimental Crystal Structure Determination

description: Related Article: S. Pawlędzio, M. Malinska, M. Woińska, J. Wojciechowski, L. Andrade Malaspina, F. Kleemiss, S. Grabowsky, K. Woźniak|2021|IUCrJ|8|608|doi:10.1107/S2052252521004541
DOI: 10.5517/ccdc.csd.cc26lhy8
title: CCDC 2043580: Experimental Crystal Structure Determination

description: Related Article: S. Pawlędzio, M. Malinska, M. Woińska, J. Wojciechowski, L. Andrade Malaspina, F. Kleemiss, S. Grabowsky, K. Woźniak|2021|IUCrJ|8|608|doi:10.1107/S2052252521004541
DOI: 10.5517/ccdc.csd.cc26lhz9
title: CCDC 2043581: Experimental Crystal Structure Determination

description: Related Article: S. Pawlędzio, M. Malinska, M. Woińska, J. Wojciechowski, L. Andrade Malaspina, F. Kleemiss, S. Grabowsky, K. Woźniak|2021|IUCrJ|8|608|doi:10.1107/S2052252521004541