cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7374510
title: Reddmatter data

description: Reddmatter data
DOI: 10.5281/zenodo.7374509
title: Reddmatter data

description: Reddmatter data
DOI: 10.6084/m9.figshare.22250671
title: Metrics and AGB dataset

description: Fifteen height related vegetation metrics and AGB of the 30 sample locations.
DOI: 10.6084/m9.figshare.22250671.v1
title: Metrics and AGB dataset

description: Fifteen height related vegetation metrics and AGB of the 30 sample locations.
DOI: 10.48420/22012580.v1
title: The meiotic LINC complex component KASH5 is an activating adaptor for cytoplasmic dynein: source data files

description: These files contain the source data for immunofluorescence figures and graphs presented in the paper entitled "The meiotic LINC complex component KASH5 is an activating adaptor for cytoplasmic dynein", in J. Cell Biology. The images can be opened in ImageJ or FIJI (for the .dv files), and the raw counting data in excel or Prism.
DOI: 10.48420/22012580
title: The meiotic LINC complex component KASH5 is an activating adaptor for cytoplasmic dynein: source data files

description: These files contain the source data for immunofluorescence figures and graphs presented in the paper entitled "The meiotic LINC complex component KASH5 is an activating adaptor for cytoplasmic dynein", in J. Cell Biology. The images can be opened in ImageJ or FIJI (for the .dv files), and the raw counting data in excel or Prism.
DOI: 10.6084/m9.figshare.21948146
title: Supplemental Table S1.pdf

description: Supplementary Datas JCEM manuscript jc.2022-02330
DOI: 10.5281/zenodo.7716873
title: Enzyme inhibitors employ different mechanisms to stabilize broad-spectrum antiviral ACE2-Fc fusion proteins

description: Drugs effective for all variants of a virus are urgently needed to fight current and future pandemics. This can be achieved by creating fusion proteins between the extracellular domains of the virus docking sites on human cells and the Fc part from a human immunoglobulin. The angiotensin-converting enzyme 2 (ACE2) is a viral receptor used by sarbeco betacorona-viruses to infect cells. Fusion proteins comprising extracellular ACE2 domains exhibit high virus neutralization efficiency, but the structure and stability of these molecules are poorly understood. Here we analyzed the structure and stability of an ACE2-IgG4-Fc. We show that the hinge between the ACE2 region and the IgG4-FC is highly flexible, and the conformational dynamics of the fusion protein is restricted by the ACE2 domain[MS1] . Interestingly, chemical compounds inhibiting the enzymatic activity of ACE2 such as DX600 and MLN4760 employ different binding mechanisms to increase the thermal stability of the ACE2 by 8.8 and 14.3 °C, respectively. In particular, MLN4760 induced allosteric effects in the ACE2 domain consistent with structural rearrangements observed in the inhibitor-bound crystal structure. Thus, our findings reveal a general concept for stabilizing the labile receptor segment of therapeutic antiviral fusion proteins[MS2] .
DOI: 10.5281/zenodo.7716872
title: Enzyme inhibitors employ different mechanisms to stabilize broad-spectrum antiviral ACE2-Fc fusion proteins

description: Drugs effective for all variants of a virus are urgently needed to fight current and future pandemics. This can be achieved by creating fusion proteins between the extracellular domains of the virus docking sites on human cells and the Fc part from a human immunoglobulin. The angiotensin-converting enzyme 2 (ACE2) is a viral receptor used by sarbeco betacorona-viruses to infect cells. Fusion proteins comprising extracellular ACE2 domains exhibit high virus neutralization efficiency, but the structure and stability of these molecules are poorly understood. Here we analyzed the structure and stability of an ACE2-IgG4-Fc. We show that the hinge between the ACE2 region and the IgG4-FC is highly flexible, and the conformational dynamics of the fusion protein is restricted by the ACE2 domain[MS1] . Interestingly, chemical compounds inhibiting the enzymatic activity of ACE2 such as DX600 and MLN4760 employ different binding mechanisms to increase the thermal stability of the ACE2 by 8.8 and 14.3 °C, respectively. In particular, MLN4760 induced allosteric effects in the ACE2 domain consistent with structural rearrangements observed in the inhibitor-bound crystal structure. Thus, our findings reveal a general concept for stabilizing the labile receptor segment of therapeutic antiviral fusion proteins[MS2] .
DOI: 10.6084/m9.figshare.22144736
title: Nisin Lantibiotic Prevents Liver Steatosis and Mitochondrial Oxidative Stress following Periodontal Disease by Abrogating Oral, Gut, and Liver Dysbiosis

description: Data are from a study examining the preventive effect of nisin on periodontal disease-related gut dysbiosis and non-alcoholic fatty liver disease, including results from several DNA and RNA sequencing, biochemical, and histological analyses.
DOI: 10.6084/m9.figshare.22144736.v2
title: Nisin Lantibiotic Prevents Liver Steatosis and Mitochondrial Oxidative Stress following Periodontal Disease by Abrogating Oral, Gut, and Liver Dysbiosis

description: Data are from a study examining the preventive effect of nisin on periodontal disease-related gut dysbiosis and non-alcoholic fatty liver disease, including results from several DNA and RNA sequencing, biochemical, and histological analyses.
DOI: 10.6084/m9.figshare.22250593
title: Rational_Number_Data_Flores_2023

description: The current study investigated the effects of the concrete-representational-abstract integrated sequence (CRA-I) for teaching fifth grade students receiving tier two interventions within a multi-tiered system of support (MTSS). The researchers revised intervention materials from Flores et al. (2018) into 16 CRA-I lessons and used a quasi-experimental design using random assignment of matched pairs of fifth grade students. One group received CRA-I intervention and the second group did not. To address ethical concerns related to the use of a comparison group, the researchers provided intervention to the second group after the CRA-I group received intervention and reported the comparison group’s results with descriptive data. The researchers used the following questions to guide the study; one was answered using statistical analysis, and the other three were addressed using descriptive data. Do students receiving CRA-I intervention perform differently on a measure of rational number skills as compared to students who do not? To what extent do students receiving CRA-I intervention decrease their fraction estimation error as compared to students who do not? To what extent do students receiving CRA-I instruction and their teachers perceive it to be socially valid? To what extent do students who receive CRA-I intervention maintain their performance two months after instruction?
DOI: 10.6084/m9.figshare.22250593.v1
title: Rational_Number_Data_Flores_2023

description: The current study investigated the effects of the concrete-representational-abstract integrated sequence (CRA-I) for teaching fifth grade students receiving tier two interventions within a multi-tiered system of support (MTSS). The researchers revised intervention materials from Flores et al. (2018) into 16 CRA-I lessons and used a quasi-experimental design using random assignment of matched pairs of fifth grade students. One group received CRA-I intervention and the second group did not. To address ethical concerns related to the use of a comparison group, the researchers provided intervention to the second group after the CRA-I group received intervention and reported the comparison group’s results with descriptive data. The researchers used the following questions to guide the study; one was answered using statistical analysis, and the other three were addressed using descriptive data. Do students receiving CRA-I intervention perform differently on a measure of rational number skills as compared to students who do not? To what extent do students receiving CRA-I intervention decrease their fraction estimation error as compared to students who do not? To what extent do students receiving CRA-I instruction and their teachers perceive it to be socially valid? To what extent do students who receive CRA-I intervention maintain their performance two months after instruction?
DOI: 10.5281/zenodo.7716823
title: Supporting CESM output for "The effect of plant physiological responses to rising CO2 on global streamflow" (Fowler et al.; 2019)

description: Thirty years of CESM soil moisture, rain, and snow data is collected here based on each experiment contained within Fowler et al. (2019). These data were used to produce Figure 3 of that work. Other supporting data including daily runoff and river discharge is archived elsewhere, while analysis scripts and more information can be found here: https://github.com/megandevlan/Physiology-Streamflow.git.
DOI: 10.5281/zenodo.7716822
title: Supporting CESM output for "The effect of plant physiological responses to rising CO2 on global streamflow" (Fowler et al.; 2019)

description: Thirty years of CESM soil moisture, rain, and snow data is collected here based on each experiment contained within Fowler et al. (2019). These data were used to produce Figure 3 of that work. Other supporting data including daily runoff and river discharge is archived elsewhere, while analysis scripts and more information can be found here: https://github.com/megandevlan/Physiology-Streamflow.git.
DOI: 10.6084/m9.figshare.21546111
title: BinBench: Dataset for Binary Function Representations

description: Dataset associated to the article: <em><strong>"BinBench: a benchmark for x64 Portable Operating System Interface binary function representations"</strong></em>. <br> <br> The dataset includes all the files needed for evaluating a binary model over the tasks of BinBench. <br> <br> The dataset is composed as follows: <br> - <em>train</em> folder: contains binaries / jsons and datapoints for of the labeled dataset (used for training) <br> - <em>test</em> folder: contains jsons and datapoints for the blind dataset (used for testing and for the EvalAI challenge)
DOI: 10.5281/zenodo.7716969
title: Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning

description: File A: Datasets (.txt) A1: Webex A2: Zoom A3: Teams A4: Word A5: PowerPoint A6: Excel File B: Python Code (Both ours and baseline) B1: pert_class.ipynb B2: Baseline.ipynb File C: Trend of Adversarial Shifts (Graphs) C1: Webex Adversarial Shifts C2: Zoom Adversarial Shifts C3: Teams Adversarial Shifts C4: Word Adversarial Shifts C5: Powerpoint Adversarial Shifts C6: Excel Adversarial Shifts File D: Trend of Non-Adversarial Shifts (Graphs) D1: Webex Non-Adversarial Shifts D2: Zoom Non-Adversarial Shifts D3: Teams Non-Adversarial Shifts D4: Word Non-Adversarial Shifts D5: Powerpoint Non-Adversarial Shifts D6: Excel Non-Adversarial Shifts
DOI: 10.5281/zenodo.7716968
title: Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning

description: File A: Datasets (.txt) A1: Webex A2: Zoom A3: Teams A4: Word A5: PowerPoint A6: Excel File B: Python Code (Both ours and baseline) B1: pert_class.ipynb B2: Baseline.ipynb File C: Trend of Adversarial Shifts (Graphs) C1: Webex Adversarial Shifts C2: Zoom Adversarial Shifts C3: Teams Adversarial Shifts C4: Word Adversarial Shifts C5: Powerpoint Adversarial Shifts C6: Excel Adversarial Shifts File D: Trend of Non-Adversarial Shifts (Graphs) D1: Webex Non-Adversarial Shifts D2: Zoom Non-Adversarial Shifts D3: Teams Non-Adversarial Shifts D4: Word Non-Adversarial Shifts D5: Powerpoint Non-Adversarial Shifts D6: Excel Non-Adversarial Shifts
DOI: 10.7284/152280
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise LMG0301 using a SeaBird SBE-911+ instrument system onboard the platform RV Laurence M. Gould

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/151262
title: Splitbeam Echosounder data collected during research cruise RR2205 using a Kongsberg EK80 instrument system onboard the platform RV Roger Revelle

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/151303
title: Splitbeam Echosounder data collected during research cruise RR2207 using a Kongsberg EK80 instrument system onboard the platform RV Roger Revelle

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.5281/zenodo.7714145
title: Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning

description: File A: Datasets (.txt) A1: Webex A2: Zoom A3: Teams A4: Word A5: PowerPoint A6: Excel File B: Python Code (Both ours and baseline) B1: pert_class.ipynb B2: Baseline.ipynb FIle C: Result Tables (.xlsx) C1: Table of perturbed outputs in Webex C2: Table of perturbed outputs in Zoom C3: Table of perturbed outputs in Teams C4: Table of perturbed outputs in Word C5: Table of perturbed outputs in PowerPoint C6: Table of perturbed outputs in Excel File D: Trend of Adversarial Shifts (Graphs) D1: Webex Adversarial Shifts D2: Zoom Adversarial Shifts D3: Teams Adversarial Shifts D4: Word Adversarial Shifts D5: Powerpoint Adversarial Shifts D6: Excel Adversarial Shifts File E: Trend of Non-Adversarial Shifts (Graphs) E1: Webex Non-Adversarial Shifts E2: Zoom Non-Adversarial Shifts E3: Teams Non-Adversarial Shifts E4: Word Non-Adversarial Shifts E5: Powerpoint Non-Adversarial Shifts E6: Excel Non-Adversarial Shifts File F: Adversarial Examples (.pdf) F1: Adversarial vs original in Webex F2: Adversarial vs original in Zoom F3: Adversarial vs original in Teams F4: Adversarial vs original in Word F5: Adversarial vs original in PowerPoint F6: Adversarial vs original in Excel File G: Questionnaire<br> <br> <br> <br> <br>
DOI: 10.5281/zenodo.7714144
title: Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning

description: File A: Datasets (.txt) A1: Webex A2: Zoom A3: Teams A4: Word A5: PowerPoint A6: Excel File B: Python Code (Both ours and baseline) B1: pert_class.ipynb B2: Baseline.ipynb FIle C: Result Tables (.xlsx) C1: Table of perturbed outputs in Webex C2: Table of perturbed outputs in Zoom C3: Table of perturbed outputs in Teams C4: Table of perturbed outputs in Word C5: Table of perturbed outputs in PowerPoint C6: Table of perturbed outputs in Excel File D: Trend of Adversarial Shifts (Graphs) D1: Webex Adversarial Shifts D2: Zoom Adversarial Shifts D3: Teams Adversarial Shifts D4: Word Adversarial Shifts D5: Powerpoint Adversarial Shifts D6: Excel Adversarial Shifts File E: Trend of Non-Adversarial Shifts (Graphs) E1: Webex Non-Adversarial Shifts E2: Zoom Non-Adversarial Shifts E3: Teams Non-Adversarial Shifts E4: Word Non-Adversarial Shifts E5: Powerpoint Non-Adversarial Shifts E6: Excel Non-Adversarial Shifts File F: Adversarial Examples (.pdf) F1: Adversarial vs original in Webex F2: Adversarial vs original in Zoom F3: Adversarial vs original in Teams F4: Adversarial vs original in Word F5: Adversarial vs original in PowerPoint F6: Adversarial vs original in Excel File G: Questionnaire<br> <br> <br> <br> <br>
DOI: 10.34943/eb3651a2-6910-4efc-a664-9cd933af5935
title: ODP 1026 Broadband Seismometer Deployed 2010-10-06

description: The Guralp CMG-IT Broadband Seismometer 2533 was deployed on 2010-10-06 at ODP 1026. ODP 1026 is a borehole site used to pinpoint an area within Cascadia. This device is a Broadband Seismometer. Broadband Seismometers measure seismic waves over a broad frequency range depending on the device (e.g. 0.00278 Hz - 250 Hz). A broadband seismometer facilitates measurement of seismic events in the widest frequency band possible. It was deployed on a fixed platform. Data from this deployment were archived and made available through Ocean Networks Canada's Oceans 3.0 digital infrastructure, with quality assurance and derived data products following established practices.
DOI: 10.5281/zenodo.7716903
title: Data for Port-Hamiltonian Heat and Wave models

description: Data files for port-Hamiltonian heat and wave equation models
DOI: 10.5281/zenodo.7716954
title: Data for Port-Hamiltonian Heat and Wave models

description: Data files for port-Hamiltonian heat and wave equation models
DOI: 10.5281/zenodo.7716902
title: Data for Port-Hamiltonian Heat and Wave models

description: Data files for port-Hamiltonian heat and wave equation models
DOI: 10.5281/zenodo.7716965
title: Data for Port-Hamiltonian Heat and Wave models

description: Data files for port-Hamiltonian heat and wave equation models
DOI: 10.5281/zenodo.7716983
title: Data for Port-Hamiltonian Heat and Wave models

description: Data files for port-Hamiltonian heat and wave equation models
DOI: 10.34740/kaggle/dsv/5141627
title: Mahanagar Yatayat Dataset
DOI: 10.5281/zenodo.7600340
title: Species-level phylogenetic trees of all angiosperm species (100 trees)

description: One hundred species-level trees of all angiosperms (zip file) comprising all 329,798 species recognized by version 6 of the World Checklist of Vascular Plants. Details of how these trees were compiled is provided, as well as a spreadsheet listing the familial and ordinal assignation of each species in the trees.
DOI: 10.7284/152226
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise LMG0302 using a SeaBird SBE-911+ instrument system onboard the platform RV Laurence M. Gould

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/152227
title: Expendable Probe data collected during research cruise LMG0302 using a Sippican MK21 instrument system onboard the platform RV Laurence M. Gould

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.5517/ccdc.csd.cc2f0ry5
title: CCDC 2235067: Experimental Crystal Structure Determination

description: Related Article: Xiao Wang, Guo-Li Chai, Ya-Jing Hou, Ming-Qian Zhou, Junbiao Chang|2023|J.Org.Chem.|88|3254|doi:10.1021/acs.joc.3c00057
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7573534
title: Grapevine Bunch Detection

description: Grapevine Bunch Detection (optimal or damaged)
DOI: 10.5281/zenodo.7573798
title: Grapevine Bunch Condition Detection Dataset

description: Images of grapevine bunches with 720x540 pixels resolution and respective annotation under the YOLO format. Each annotation indicate a bounding box around each grape bunch representing its area, position, and class. The classes presents in this dataset indicate the condition of each grape bunch.
DOI: 10.5281/zenodo.7665510
title: Grapevine Bunch Condition Detection Dataset

description: Images of grapevine bunches with 720x540 pixels resolution and respective annotation under the YOLO format. Each annotation indicate a bounding box around each grape bunch representing its area, position, and class. The classes presents in this dataset indicate the condition of each grape bunch.
DOI: 10.5281/zenodo.7573533
title: Grapevine Bunch Condition Detection Dataset

description: Images of grapevine bunches with 720x540 pixels resolution and respective annotation under the YOLO format. Each annotation indicates a bounding box around each grape bunch representing its area, position, and class. The classes present in this dataset indicate the condition of each grape bunch The images and their annotations are stored in an archive with the following structure: <em>GrapevineBunchConditionDetection/</em> label_map.pbtx <em>test/</em> <em>images/</em> <em>labels/</em> <em>train/</em> <em>images/</em> <em>labels/</em> <em>val/</em> <em>images/</em> <em>labels/</em> <br> Each annotation file links to its image by the file name, so if an image is named <strong>"IMG_20220721_101616.jpg</strong><em><strong>"</strong></em>, its annotation files are named <strong>"IMG_20220721_101616.txt"</strong> in YOLO format. The file label_map.pbtx defines the class id and the name of each class.
DOI: 10.5281/zenodo.7717014
title: Grapevine Bunch Condition Detection Dataset

description: Images of grapevine bunches with 720x540 pixels resolution and respective annotation under the YOLO format. Each annotation indicates a bounding box around each grape bunch representing its area, position, and class. The classes present in this dataset indicate the condition of each grape bunch The images and their annotations are stored in an archive with the following structure: <em>GrapevineBunchConditionDetection/</em> label_map.pbtx <em>test/</em> <em>images/</em> <em>labels/</em> <em>train/</em> <em>images/</em> <em>labels/</em> <em>val/</em> <em>images/</em> <em>labels/</em> <br> Each annotation file links to its image by the file name, so if an image is named <strong>"IMG_20220721_101616.jpg</strong><em><strong>"</strong></em>, its annotation files are named <strong>"IMG_20220721_101616.txt"</strong> in YOLO format. The file label_map.pbtx defines the class id and the name of each class.
DOI: 10.7284/152281
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise LMG0303 using a SeaBird SBE-911+ instrument system onboard the platform RV Laurence M. Gould

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.5519/qd.7kqw915x
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.48544/f0a51a54-cc4b-4960-8d24-f6c9245edc8f
title: This is a test
DOI: 10.5281/zenodo.7665519
title: Grapevine Bunch Detection Dataset

description: Images of grapevine bunches with 720x540 pixels resolution and respective annotation under the YOLO format. Each annotation indicates a bounding box around each grape bunch representing its area and position. The images and their annotations are stored in an archive with the following structure: <em>GrapevineBunchDetection/</em> label_map.pbtx <em>test/</em> <em>images/</em> <em>labels/</em> <em>train/</em> <em>images/</em> <em>labels/</em> <em>val/</em> <em>images/</em> <em>labels/</em> <br> Each annotation file links to its image by the file name, so if an image is named <strong>"IMG_20220721_101616.jpg</strong><em><strong>"</strong></em>, its annotation files are named <strong>"IMG_20220721_101616.txt"</strong> in YOLO format. The file label_map.pbtx defines the class id and the name of each class.
DOI: 10.5281/zenodo.7665520
title: Grapevine Bunch Detection Dataset

description: Images of grapevine bunches with 720x540 pixels resolution and respective annotation under the YOLO format. Each annotation indicates a bounding box around each grape bunch representing its area, position.
DOI: 10.5281/zenodo.7717055
title: Grapevine Bunch Detection Dataset

description: Images of grapevine bunches with 720x540 pixels resolution and respective annotation under the YOLO format. Each annotation indicates a bounding box around each grape bunch representing its area and position. The images and their annotations are stored in an archive with the following structure: <em>GrapevineBunchDetection/</em> label_map.pbtx <em>test/</em> <em>images/</em> <em>labels/</em> <em>train/</em> <em>images/</em> <em>labels/</em> <em>val/</em> <em>images/</em> <em>labels/</em> <br> Each annotation file links to its image by the file name, so if an image is named <strong>"IMG_20220721_101616.jpg</strong><em><strong>"</strong></em>, its annotation files are named <strong>"IMG_20220721_101616.txt"</strong> in YOLO format. The file label_map.pbtx defines the class id and the name of each class.
DOI: 10.25345/c5057d293
title: MassIVE MSV000091467 - Evaluation of Orbitrap Ascend Tribrid Mass Spectrometer for Shotgun Proteomics
title: The Orbitrap Ascend Tribrid mass spectrometer was systematically evaluated in the context of shotgun proteomics, and its performance was compared to that of the previous generation of Tribrid instrument - Orbitrap Eclipse