cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23792361.v1
title: Raw data files associated with Figure 1 in RBL2 represses the transcriptional activity of Multicilin to inhibit multiciliogenesis

description: <b>Immunoprecipitation, immunoblotting analysis, and silver staining</b>For identifying interacting proteins of FLAG-tagged Multicilin by MASS analysis, MEFs were infected by pAd/CMV/V5 vector encoding 3xFLAG-Multicilin, NLS-6xmyc-E2f4ΔCt-VP16-T2A-Multicilin, and NLS-6xmyc-E2f4WT-T2A-Multicilin. Non-infected MEFs were used as the negative control. Two days after infection, the non-infected MEFs or infected MEFs were lysed in lysis buffer (50mM HEPES-KOH [pH 7.5], 500NaCl, 5mM EDTA [pH 8.0]), 2% Triton, DNase I (10ug/ml)) by incubation on ice for 20min. Lysates were cleared by centrifugation at 12,000 rpm for 20 min at 4 °C, then precleared by incubation with Protein G agarose (Invitrogen #20397) agitating for 1hr at 4°C. Spun-down beads were incubated with FLAG M2 agarose (Sigma #A2220) agitating for 2.5 hours at 4°C. The beads were washed twice with wash buffer (1:1 mix of 50mM HEPES and Lysis buffer), washed with lysis buffer twice, then washed twice again with wash buffer. Spun-down beads were incubated with 40uL of FLAG peptide (500ng/mL) for bound protein elution overnight at 4°C. Elutes were subjected to protein sampling for further immunoblot analysis and silver staining. Eluted protein samples and experimental protein lysates were subjected to SDS-PAGE and transferred to a PVDF membrane that was then blocked with 0.5% casein blocker (#161-0783, Bio-Rad) in PBS for 30 min followed by incubation with the indicated primary antibodies overnight at 4 °C in 0.1% Tween-20 in blocking buffer. After extensive washing in 0.1% Tween-20 in PBS, the blots were incubated with Alexa 680 or 800-conjugated anti- mouse or rabbit secondary antibodies (Invitrogen, A21058, A21076, A32735) for 45 min at room temperature, washed with 0.1% Tween-20 in PBS, and imaged using Odyssey (LI-COR). Silver staining was performed with Pierce Silver Stain Kit (Thermo #24612) based on the manufacturer’s instructions.
DOI: 10.6084/m9.figshare.23792361
title: Raw data files associated with Figure 1 in RBL2 represses the transcriptional activity of Multicilin to inhibit multiciliogenesis

description: <b>Immunoprecipitation, immunoblotting analysis, and silver staining</b>For identifying interacting proteins of FLAG-tagged Multicilin by MASS analysis, MEFs were infected by pAd/CMV/V5 vector encoding 3xFLAG-Multicilin, NLS-6xmyc-E2f4ΔCt-VP16-T2A-Multicilin, and NLS-6xmyc-E2f4WT-T2A-Multicilin. Non-infected MEFs were used as the negative control. Two days after infection, the non-infected MEFs or infected MEFs were lysed in lysis buffer (50mM HEPES-KOH [pH 7.5], 500NaCl, 5mM EDTA [pH 8.0]), 2% Triton, DNase I (10ug/ml)) by incubation on ice for 20min. Lysates were cleared by centrifugation at 12,000 rpm for 20 min at 4 °C, then precleared by incubation with Protein G agarose (Invitrogen #20397) agitating for 1hr at 4°C. Spun-down beads were incubated with FLAG M2 agarose (Sigma #A2220) agitating for 2.5 hours at 4°C. The beads were washed twice with wash buffer (1:1 mix of 50mM HEPES and Lysis buffer), washed with lysis buffer twice, then washed twice again with wash buffer. Spun-down beads were incubated with 40uL of FLAG peptide (500ng/mL) for bound protein elution overnight at 4°C. Elutes were subjected to protein sampling for further immunoblot analysis and silver staining. Eluted protein samples and experimental protein lysates were subjected to SDS-PAGE and transferred to a PVDF membrane that was then blocked with 0.5% casein blocker (#161-0783, Bio-Rad) in PBS for 30 min followed by incubation with the indicated primary antibodies overnight at 4 °C in 0.1% Tween-20 in blocking buffer. After extensive washing in 0.1% Tween-20 in PBS, the blots were incubated with Alexa 680 or 800-conjugated anti- mouse or rabbit secondary antibodies (Invitrogen, A21058, A21076, A32735) for 45 min at room temperature, washed with 0.1% Tween-20 in PBS, and imaged using Odyssey (LI-COR). Silver staining was performed with Pierce Silver Stain Kit (Thermo #24612) based on the manufacturer’s instructions.
DOI: 10.5281/zenodo.6669964
title: Dataset for AIFix4SecCode

description: Dataset for AIFix4SecCode (Work in progress) The dataset can be used for teaching machine-learning based fixing of detectable issues in AIFix4SecCode developed in the AssureMOSS project. It contains examples for vulnerable and fixed versions for: SonarQube: S1444 S2384 Spotbugs: EI_EXPOSE_REP EI_EXPOSE_REP2 FI_PUBLIC_SHOULD_BE_PROTECTED MS_EXPOSE_REP MS_MUTABLE_ARRAY MS_MUTABLE_COLLECTION MS_MUTABLE_COLLECTION_PKGPROTECT MS_SHOULD_BE_FINAL NP_NULL_ON_SOME_PATH NP_NULL_ON_SOME_PATH_EXCEPTION NP_NULL_PARAM_DEREF NP_NULL_PARAM_DEREF_ALL_TARGETS_DANGEROUS NP_NULL_PARAM_DEREF_NONVIRTUAL SQL_NONCONSTANT_STRING_PASSED_TO_EXECUTE XSS_REQUEST_PARAMETER_TO_SERVLET_WRITER
description: The Full paper using the dataset is available at https://doi.org/10.1109/SCAM55253.2022.00034
DOI: 10.5281/zenodo.6669965
title: Dataset for AIFix4SecCode

description: Dataset for AIFix4SecCode (Work in progress) The dataset can be used for teaching machine-learning based fixing of detectable issues in AIFix4SecCode developed in the AssureMOSS project. It contains examples for vulnerable and fixed versions for: SonarQube: S1444 S2384 Spotbugs: EI_EXPOSE_REP EI_EXPOSE_REP2 FI_PUBLIC_SHOULD_BE_PROTECTED MS_EXPOSE_REP MS_MUTABLE_ARRAY MS_MUTABLE_COLLECTION MS_MUTABLE_COLLECTION_PKGPROTECT MS_SHOULD_BE_FINAL NP_NULL_ON_SOME_PATH NP_NULL_ON_SOME_PATH_EXCEPTION NP_NULL_PARAM_DEREF NP_NULL_PARAM_DEREF_ALL_TARGETS_DANGEROUS NP_NULL_PARAM_DEREF_NONVIRTUAL SQL_NONCONSTANT_STRING_PASSED_TO_EXECUTE XSS_REQUEST_PARAMETER_TO_SERVLET_WRITER
description: The Full paper using the dataset is available at https://doi.org/10.1109/SCAM55253.2022.00034
DOI: 10.6084/m9.figshare.23523255.v1
title: Supplementary Material for: Assessment of spin in the abstracts of randomized controlled trials in dental caries with statistically nonsignificant results for primary outcomes: A methodological study

description: The study aimed to assess the prevalence of spin in the titles and abstracts of RCTs in dental caries with statistically nonsignificant primary outcomes and to assess the risk indicators which may be associated with spin. Any original publication reporting a two-arm RCT in dental caries with clearly identified statistically nonsignificant primary outcomes published from January 1, 2015 until October 28, 2022 were included. PubMed was searched electronically to identify the eligible publications. The prevalence of spin in titles and abstracts were assessed and categorized into spin patterns based on a pre-determined classification scheme. The association between spin and the potential risk indicators at study, author, journal, institutional, and national level was assessed. A total of 234 eligible RCT publications were included. The prevalence of spin in the titles and abstracts was 3% (95%CI: 2% to 6%) and 79% (95%CI: 74% to 84%), respectively. The most common spin patterns in the results and conclusion sections, respectively, were results focusing on statistically significant within-group comparisons (23%), and conclusions focusing only on statistically significant results without acknowledgment of statistically nonsignificant results for the primary outcomes (26%). The spin was significantly associated with number of study centers (single-center vs. multicenter) (OR=2.131; 95%CI: 1.092 to 4.158; P=0.03), trial designs (non-parallel designs vs. parallel designs) (OR=0.395; 95%CI: 0.193 to 0.810; P=0.01), and overall H index of institutions for last authors (OR=0.998; 95%CI: 0.996 to 0.999; P&lt;0.01), while it was not significantly associated with the other indicators. In the RCT publications with statistically nonsignificant results for primary outcomes in dental caries, the prevalence of spin may be low in the titles but high in the abstracts. Single-center studies with parallel designs and a lower overall H index of institutions for last authors may be more likely to have spin in the abstracts.
DOI: 10.6084/m9.figshare.23792208.v1
title: Underlying data: Total results

description: This is the underlying data for the article entitled "L2 Acquisition of <i>Wh-</i>interrogatives at the Syntax-discourse Interface: Interface Hypothesis again", by Mohammed Shormani
DOI: 10.5281/zenodo.8190026
title: TCGA Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC) Clinical Data

description: <strong>Abstract:</strong> The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled "An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics". The paper highlights four types of carefully curated survival endpoints, and recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type. The dataset also includes phenotypic information about CESC. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset. <strong>Inspiration:</strong> This dataset was uploaded to UBRITE for GTKB project. <strong>Instruction:</strong> The survival and phenotype data were merged into one file. <strong>Acknowledgments:</strong> Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8 Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. https://doi.org/10.1016/j.cell.2018.02.052 The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764 <strong>U-BRITE last update: </strong>07/13/2023
description: UBRITE location: /data/project/ubrite/gtkb/TCGA/Clinical
description: {"references": ["Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11.\u00a0https://doi.org/10.1016/j.cell.2018.02.052", "Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8", "The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113\u20131120 (2013). https://doi.org/10.1038/ng.2764"]}
DOI: 10.5281/zenodo.8190025
title: TCGA Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC) Clinical Data

description: <strong>Abstract:</strong> The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled "An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics". The paper highlights four types of carefully curated survival endpoints, and recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type. The dataset also includes phenotypic information about CESC. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset. <strong>Inspiration:</strong> This dataset was uploaded to UBRITE for GTKB project. <strong>Instruction:</strong> The survival and phenotype data were merged into one file. <strong>Acknowledgments:</strong> Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8 Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. https://doi.org/10.1016/j.cell.2018.02.052 The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764 <strong>U-BRITE last update: </strong>07/13/2023
description: UBRITE location: /data/project/ubrite/gtkb/TCGA/Clinical
description: {"references": ["Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11.\u00a0https://doi.org/10.1016/j.cell.2018.02.052", "Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8", "The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113\u20131120 (2013). https://doi.org/10.1038/ng.2764"]}
DOI: 10.6084/m9.figshare.23792208.v2
title: Underlying data

description: This is the underlying data for the article entitled "L2 Acquisition of <i>Wh-</i>interrogatives at the Syntax-discourse Interface: Interface Hypothesis again", by Mohammed Shormani
DOI: 10.5281/zenodo.8190043
title: agave_americana_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.8190042
title: agave_americana_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.8190041
title: TCGA Glioblastoma Multiforme (GBM) Clinical Data

description: <strong>Abstract:</strong> The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled "An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics". The paper highlights four types of carefully curated survival endpoints, and recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type. The dataset also includes phenotypic information about GBM. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset. <strong>Inspiration:</strong> This dataset was uploaded to UBRITE for GTKB project. <strong>Instruction:</strong> The survival and phenotype data were merged into one file. Empty columns were removed. Columns with the same value for every sample were also removed. <strong>Acknowledgments:</strong> Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8 Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. https://doi.org/10.1016/j.cell.2018.02.052 The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764 <strong>U-BRITE last update: </strong>07/13/2023
description: UBRITE location: /data/project/ubrite/gtkb/TCGA/Clinical
description: {"references": ["Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8", "Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11.\u00a0https://doi.org/10.1016/j.cell.2018.02.052", "The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113\u20131120 (2013). https://doi.org/10.1038/ng.2764"]}
DOI: 10.5281/zenodo.8190040
title: TCGA Glioblastoma Multiforme (GBM) Clinical Data

description: <strong>Abstract:</strong> The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled "An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics". The paper highlights four types of carefully curated survival endpoints, and recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type. The dataset also includes phenotypic information about GBM. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset. <strong>Inspiration:</strong> This dataset was uploaded to UBRITE for GTKB project. <strong>Instruction:</strong> The survival and phenotype data were merged into one file. Empty columns were removed. Columns with the same value for every sample were also removed. <strong>Acknowledgments:</strong> Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8 Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. https://doi.org/10.1016/j.cell.2018.02.052 The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764 <strong>U-BRITE last update: </strong>07/13/2023
description: UBRITE location: /data/project/ubrite/gtkb/TCGA/Clinical
description: {"references": ["Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8", "Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11.\u00a0https://doi.org/10.1016/j.cell.2018.02.052", "The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113\u20131120 (2013). https://doi.org/10.1038/ng.2764"]}
DOI: 10.6084/m9.figshare.20502948
title: Supplemental Raw NPX protein data

description: The dataset contains the raw NPX data for all patients included in the sudy (Olink PEA dataset)
DOI: 10.5281/zenodo.8190063
title: agave_fourcroydes_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.8190062
title: agave_fourcroydes_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.7540444
title: Immersive haptic simulation for training nurses in emergency medical procedures - Data collected and statistical analysis

description: Data collected during the evaluation presented in "Haptic simulation for emergency procedures in nursing training" paper. HR ALL Measure 1 Measure 2 t df p Mann pre HR - Mann post HR 2.857 29 0.008 VR pre HR - VR post HR -8.089 29 &lt; .001 Mann pre HR - VR pre HR 7.567 29 &lt; .001 Mann post HR - VR post HR -2.962 29 0.006 <em>Note.</em> Paired samples student's t-test. HR FIRST MANN Measure 1 Measure 2 t df p Mann pre HR - Mann post HR 1.665 14 0.118 VR pre HR - VR post HR -7.104 14 &lt; .001 Mann pre HR - VR pre HR 6.498 14 &lt; .001 Mann post HR - VR post HR -1.461 14 0.166 <em>Note.</em> Paired samples student's t-test. HR FIRST VR Measure 1 Measure 2 t df p Mann pre HR - Mann post HR 2.341 14 0.035 VR pre HR - VR post HR -4.612 14 &lt; .001 Mann pre HR - VR pre HR 4.482 14 &lt; .001 Mann post HR - VR post HR -2.688 14 0.018 <em>Note.</em> Paired samples student's t-test. HR BETWEEN GROUPS t df p Mann pre HR -1.958 28 0.060 Mann post HR -1.902 28 0.068 VR pre HR -4.013 28 &lt; .001 VR post HR -2.344 28 0.026 <em>Note.</em> Independent samples student's t-test. Physiological T-Test results for the participants who started the experiment performing the procedure in the mannequin. First variable μ σ Second variable μ σ t df p SBP pre-mannequin 128.333 10.715 SBP pre-simulator 134.533 11.819 -1.870 14 0.083 SBP post-mannequin 125.600 11.648 SBP post-simulator 131.467 14.643 -2.094 14 0.055 DBP pre-mannequin 80.133 5.527 DBP pre-simulator 81.533 9.039 -0.623 14 0.544 DBP post-mannequin 78.667 6.956 DBP post-simulator 81.400 8.475 -2.073 14 0.057 HR pre-mannequin 92.133 14.837 HR pre-simulator 75.733 9.9625 6.498 29 &lt; .001 HR post-mannequin 87.400 9.132 HR post-simulator 91.400 14.217 -1.461 29 0.166 SBP = Systolic blood pressure. DBP = Diastolic blood pressure. HR = Heart Rate. Physiological T-Test results for the participants who started the experiment performing the procedure in the ParaVR simulator. First variable μ σ Second variable μ σ t df p SBP pre-mannequin 119.067 12.898 SBP pre-simulator 130.600 12.188 -3.799 14 0.002 SBP post-mannequin 117.533 13.410 SBP post-simulator 128.200 13.385 -4.022 14 0.001 DBP pre-mannequin 76.533 8.943 DBP pre-simulator 80.200 6.899 -1.815 14 0.091 DBP post-mannequin 74.333 8.541 DBP post-simulator 79.133 7.864 -2.003 14 0.065 HR pre-mannequin 102.067 12.876 HR pre-simulator 91.533 11.825 4.482 29 &lt; .001 HR post-mannequin 95.867 14.623 HR post-simulator 103.667 14.450 -2.688 29 0.018