cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13456379.v1
title: Table 2.S1

description: Distribution of 316 LOH tracts in the 100th bottleneck of S/Y hybrid. LOH tracts supported by two or more SNPs.
DOI: 10.21258/1714833
title: Swiss national forest inventory - Result table No. 902258
DOI: 10.6084/m9.figshare.13456379
title: Supplementary data for Chapter 2

description: Supplementary data for Chapter 2
DOI: 10.6084/m9.figshare.13456379.v3
title: Supplementary data for Chapter 2

description: Supplementary data for Chapter 2
DOI: 10.6084/m9.figshare.13456379.v2
title: Table 2.S2

description: Distribution of 942 LOH tracts in the 100th bottleneck of S/R hybrid. LOH tracts supported by two or more SNPs.
DOI: 10.6084/m9.figshare.13456388
title: Ureolytic bacteria.docx

description: Ureolytic bacteria biotechnology.
DOI: 10.6084/m9.figshare.13456388.v1
title: Ureolytic bacteria.docx

description: Ureolytic bacteria biotechnology.
DOI: 10.25919/sec3-dm14
title: Parkes observations for project P1052 semester 2020APRS_02

description: After five decades of study, the physics of radio wave emission and propagation in the pulsar magnetosphere remains poorly understood. This is largely due to the wide variety of dynamic phenomena observed in pulsar signals that are neither predicted nor explained by current theory, the limited window of the pulsar spectrum that previously could be studied simultaneously, and the biased sample of the pulsar population that has been studied to date. To address these limitations, we have developed pioneering statistical methods that provide new interpretive insights and enable us to study the dynamics of the radio pulsar signal on short time scales (e.g. drifting and quasi-periodic sub-pulse structure, transitions between orthogonally polarized modes of emission, nulling, etc.) even when individual pulses cannot be detected. Using our novel methods and the ultra-wide bandwidth receiver at Parkes, we will undertake a large-scale survey of pulsar radio emission statistics and explore two previously inaccessible regions where pulsar flux densities are typically much lower: the pulsar spectrum above 2 GHz, and the recycled pulsar population. Measuring the instantaneous bandwidth of impulsive emission events will yield new constraints on the physical conditions in the pulsar magnetosphere, such as the spatial scale of strong turbulence in the magnetospheric plasma. The breadth of our sample, from millisecond pulsars to magnetars, will enable us to study the evolution of emission and propagation physics over four orders of magnitude in magnetospheric size and seven orders of magnitude in magnetic field strength.
DOI: 10.25919/xf39-tg42
title: Parkes observations for project P1043 semester 2020APRS_03

description: We propose observations with the ultra-wide-bandwidth, low frequency receiver (UWL) installed at the Parkes telescope to carry out single pulse study of millisecond pulsars. We have selected two of the brightest pulsars that are regularly observed by the Parkes Pulsar Timing Array. Our observations will enable statistical properties study of single pulses with ultra-wide-bandwidth, measuring jitter noise level of the linear and circular polarization states as well as in the total intensity signal, measuring jitter bandwidth and how jitter noise decorrelates with frequency. The UWL receiver, with its much wider bandwidth, will provide unique opportunity for study of jitter noise evolution with frequency.
DOI: 10.6084/m9.figshare.13456373
title: NotebookInput

description: All required data inputs to run the python Jupiter Notebook for performing the analysis in the Integrative analysis paper
DOI: 10.21227/tf58-0r50
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.The paper associated with this dataset is available here: Design and analysis of a large-scale COVID-19 tweets dataset-------------------------------------Related datasets: (a) Coronavirus (COVID-19) Tweets Sentiment Trend (Global)(b) Tweets Originating from India During COVID-19 Lockdowns-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 283,369 tweets— Coverage : Global— Language : English (EN)— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time (and other notes) of each CSV file present in this dataset.
DOI: 10.5281/zenodo.4374309
title: Container Registry Benchmark experiments measurements and trace workload samples

description: Measurements for experiments using Container Registry Benchmark, CReB. 4 experiments: Long running, small experiment stress mode, small experiment delay mode, and large workload experiment. Structure: <strong>full-measurements-long-running-pull.csv : </strong>measurements for long running pull experiment <strong>full-measurements-long-running-push.csv: </strong>measurements for long running push experiment <strong>result-bug-analysis.zip: </strong>results from bug analysis of trace replayer <strong>results-1hr-experiment.zip: </strong>measurements for the large experiment (4 registries) <strong>results-small-delay.zip: </strong>measurements for the delay mode, small experiment with real workload <strong>results-small-stress.zip: </strong>measurements for the stress mode, small experiment with real workload <strong>traces.zip: </strong>traces used for pen-and-paper experiment, 1 hour sample, and the trace used for small experiment (selected are first 405 requests)
DOI: 10.5281/zenodo.4374308
title: Container Registry Benchmark experiments measurements and trace workload samples

description: Measurements for experiments using Container Registry Benchmark, CReB. 4 experiments: Long running, small experiment stress mode, small experiment delay mode, and large workload experiment. Structure: <strong>full-measurements-long-running-pull.csv : </strong>measurements for long running pull experiment <strong>full-measurements-long-running-push.csv: </strong>measurements for long running push experiment <strong>result-bug-analysis.zip: </strong>results from bug analysis of trace replayer <strong>results-1hr-experiment.zip: </strong>measurements for the large experiment (4 registries) <strong>results-small-delay.zip: </strong>measurements for the delay mode, small experiment with real workload <strong>results-small-stress.zip: </strong>measurements for the stress mode, small experiment with real workload <strong>traces.zip: </strong>traces used for pen-and-paper experiment, 1 hour sample, and the trace used for small experiment (selected are first 405 requests)
DOI: 10.5281/zenodo.4374644
title: Dataset for: Searching for Imaging Biomarkers of Psychotic Dysconnectivity

description: This dataset contains features used in analyses for the following manuscript: Rodrigue, AL, et al. (2021). Searching for Imaging Biomarkers of Psychotic Dysconnectivity. <em>Biological Psychiatry: Cognitive Neuroscience and Neuroimaging,</em> in press. Contents: <br> - 4 Demographic csv files. Each dataset has a csv for covariates of interest- Age, Sex, and Site (BSNIP1 only)<br> site coding: 1=Hartford,CT, 2=Baltimore,MD<br> DIAG coding: SZ=Schizophrenia, SAD=Schizoaffective Disorder, BPP=Bipolar Disorder I with Psychosis, MDD=Major Depressive Disorder with Psychosis,OTH=Other Psychotic Disorder - 16 feature csv files. Each dataset has a .csv for raw and residualized DTI and rsfMRI features<br> ISMMS_DTI_raw_Features.csv<br> Olin_DTI_raw_Features.csv<br> BSNIP1_DTI_raw_Features.csv<br> BSNIP2_DTI_raw_Features.csv<br> ISMMS_rsfMRI_raw_Features.csv<br> Olin_rsfMRI_raw_Features.csv<br> BSNIP1_rsfMRI_raw_Features.csv<br> BSNIP2_rsfMRI_raw_Features.csv Residualized (age, sex, site (BNIP1 only))<br> ISMMS_DTI_res_Features.csv<br> Olin_DTI_res_Features.csv<br> BSNIP1_DTI_res_Features.csv<br> BSNIP2_DTI_res_Features.csv<br> ISMMS_rsfMRI_res_Features.csv<br> Olin_rsfMRI_res_Features.csv<br> BSNIP1_rsfMRI_res_Features.csv<br> BSNIP2_rsfMRI_res_Features.csv
DOI: 10.5281/zenodo.4374643
title: Dataset for: Searching for Imaging Biomarkers of Psychotic Dysconnectivity

description: This dataset contains features used in analyses for the following manuscript: Rodrigue, AL, et al. (2021). Searching for Imaging Biomarkers of Psychotic Dysconnectivity. <em>Biological Psychiatry: Cognitive Neuroscience and Neuroimaging,</em> in press. Contents: <br> - 4 Demographic csv files. Each dataset has a csv for covariates of interest- Age, Sex, and Site (BSNIP1 only)<br> site coding: 1=Hartford,CT, 2=Baltimore,MD<br> DIAG coding: SZ=Schizophrenia, SAD=Schizoaffective Disorder, BPP=Bipolar Disorder I with Psychosis, MDD=Major Depressive Disorder with Psychosis,OTH=Other Psychotic Disorder - 16 feature csv files. Each dataset has a .csv for raw and residualized DTI and rsfMRI features<br> ISMMS_DTI_raw_Features.csv<br> Olin_DTI_raw_Features.csv<br> BSNIP1_DTI_raw_Features.csv<br> BSNIP2_DTI_raw_Features.csv<br> ISMMS_rsfMRI_raw_Features.csv<br> Olin_rsfMRI_raw_Features.csv<br> BSNIP1_rsfMRI_raw_Features.csv<br> BSNIP2_rsfMRI_raw_Features.csv Residualized (age, sex, site (BNIP1 only))<br> ISMMS_DTI_res_Features.csv<br> Olin_DTI_res_Features.csv<br> BSNIP1_DTI_res_Features.csv<br> BSNIP2_DTI_res_Features.csv<br> ISMMS_rsfMRI_res_Features.csv<br> Olin_rsfMRI_res_Features.csv<br> BSNIP1_rsfMRI_res_Features.csv<br> BSNIP2_rsfMRI_res_Features.csv
DOI: 10.34740/kaggle/dsv/1765033
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.4375312
title: Digitális bölcsészet: A szövegtől az adatig (Bibliográfia)

description: Extended bibliography of the book: Maróthy, Szilvia. Digitális bölcsészet: A szövegtől az adatig [Digital Humanities: From text to data]. Budapest: Akadémiai Kiadó, 2021. ISBN: 978 963 454 578 1
DOI: 10.5281/zenodo.4375311
title: Digitális bölcsészet: A szövegtől az adatig (Bibliográfia)

description: Extended bibliography of the book: Maróthy, Szilvia. Digitális bölcsészet: A szövegtől az adatig [Digital Humanities: From text to data]. Budapest: Akadémiai Kiadó, 2021. ISBN: 978 963 454 578 1
DOI: 10.5880/sfb806.59
title: Geodata of northern timberline in Europe during the last glacial maximum

description: This geodataset includes the northern timberline in Europe during the last glacial maximum as published in Lehmkuhl et al. (accepted). It is based on Grichuk, 1992, but was modified. A layer package file can be used to obtain the same visual appearance as published. See further details in the publication. References Grichuk, V.P., 1992. Main types of vegetation (ecosystems) for the maximum cooling of the last glaciation., in: In: Frenzel, B., Pecsi, B. Velichko, A.A. (Eds.): Atlas of Palaeoclimates and Palaeoenvironments of the Northern Hemisphere. NQUA/Hungarian Academy of Sciences, Budapest, pp. 123–124. Lehmkuhl, F., Nett, J.J., Pötter, S., Schulte, P., Sprafke, T., Jary, Z., Antoine, P., Wacha, L., Wolf, D., Zerboni, A., Hošek, J., Marković, S.B., Obreht, I., Sümegi, P., Veres, D., Zeeden, C., Boemke, B.J., Schaubert, V., Viehweger, J., Hambach, U., accepted. Loess landscapes of Europe - mapping, geomorphology, and zonal differentiation. Earth-Science Reviews.
DOI: 10.5880/sfb806.57
title: Geodata of alluvial fill and fluvial deposits in Europe during the last glacial maximum

description: For the investigation of potential source areas of loess in Europe, alluvial fill and fluvial deposits were mapped. The mapping is based on different national and pan-European datasets. The methodological approach is reported in Lehmkuhl et al. (accepted). This geodataset contains a shapefiles of "alluvial fill and fluvial deposits" according to the European loess map published by Lehmkuhl et al. (accepted). A layer package file can be used to obtain the same visual appearance as published. References Lehmkuhl, F., Nett, J.J., Pötter, S., Schulte, P., Sprafke, T., Jary, Z., Antoine, P., Wacha, L., Wolf, D., Zerboni, A., Hošek, J., Marković, S.B., Obreht, I., Sümegi, P., Veres, D., Zeeden, C., Boemke, B.J., Schaubert, V., Viehweger, J., Hambach, U., accepted. Loess landscapes of Europe - mapping, geomorphology, and zonal differentiation. Earth-Science Reviews.
DOI: 10.5880/sfb806.60
title: Geodata of paleochannels on dry continental shelves in Europe during the last glacial maximum

description: To investigate potential source areas of loess during the last glacial maximum, dry continental shelfs and their paleochannels were mapped for Europe (Lehmkuhl et al., accepted). The shapefile of dry continental shelf after Willmes (2015) was modified, as it did not include areas covered by ice. The shapefile was expanded using the LGM glaciation (Ehlers et al., 2011). Additionally, it was aligned to fit the used country shapefile (www.naturalearthdata.com) of the loess map. A layer package file can be used to obtain the same visual appearance as published. Furthere information on methodology is given in Lehmkuhl et al. (accepted). References Ehlers, J., Gibbard, P.L., Hughes, P.D., 2011. Quaternary Glaciations - Extent and Chronology, 1st Edition. ed. Elsevier. Lehmkuhl, F., Nett, J.J., Pötter, S., Schulte, P., Sprafke, T., Jary, Z., Antoine, P., Wacha, L., Wolf, D., Zerboni, A., Hošek, J., Marković, S.B., Obreht, I., Sümegi, P., Veres, D., Zeeden, C., Boemke, B.J., Schaubert, V., Viehweger, J., Hambach, U., accepted. Loess landscapes of Europe - mapping, geomorphology, and zonal differentiation. Earth-Science Reviews. Willmes, C., 2015. LGM sealevel change (HiRes), CRC806 database, Collaborative Research Centre 806.
DOI: 10.5880/sfb806.52
title: Preparation of a comprehensive palynological norwegian dataset for digital re-analysis and publication as Open Data

description: The data management aspects to prepare a comprehensive palynological dataset, digitally collected and curated by Dr. Helge Høeg during his career as Palynologist in Norway since the 1970ies, for reuse with modern analysis methods and technologies and publication as Open Data, are discussed in this presentation. As presented on last years CAA, this dataset has distinguished potential to increase the knowledge of the norwegian paleoenvironment since the last ice age, for example by application of new machine learning based methods to this dataset. The palynological data are given in a proprietary format as .til and .tgx files originating from the MS Windows based palynological analysis software Tilia. There are around 1000 of these files for the &gt;300 sites constituting this dataset. The process of data transformation to an open format, as well as the alignment with metadata schemas and taxonomies from the palynology domain will be discussed. An overview of the current Open Data and data management practice in the palynology domain, including some Open Data base projects will be given. The technical upload process into some of these systems were tested and will be discussed, to align and compare our approach with existing best practice in this field.
DOI: 10.5281/zenodo.4104280
title: Project GeRDI collection in RDF

description: This collection contains the metadata harvested in the context of Project GeRDI (https://www.gerdi-project.eu/). The dataset records are organized based on the institutions that provide them. This allows to download and use any provider of interest. In addition to the 11 dataset providers, the dataset also contains provenance information (neatly stored in the "provenance" named graph) for the conversion process, which is not part of the original dataset collectoin and was generated at the end of the conversion process.
description: This work was partly supported by the DFG (German Research Foundation) with the GeRDI project (Grant No. BO818/16-1).
DOI: 10.5281/zenodo.4008461
title: Project GeRDI collection in RDF

description: This collection contains the metadata harvested in the context of Project GeRDI (https://www.gerdi-project.eu/). The dataset records are organized based on the institutions that provide them. This allows to download and use any provider of interest. In addition to the 11 dataset providers, the dataset also contains provenance information (neatly stored in the "provenance" named graph) for the conversion process, which is not part of the original dataset collectoin and was generated at the end of the conversion process.
description: This work was partly supported by the DFG (German Research Foundation) with the GeRDI project (Grant No. BO818/16-1).
DOI: 10.13140/rg.2.2.24148.22409
title: Book1COVERJeffrey20190209WINENGL
DOI: 10.5281/zenodo.4375860
title: STIS 1d and 2d optical/near-UV spectra of Orion targets from ULLYSES

description: STIS 1d and 2d optical/near-UV spectra of Orion targets from ULLYSES in November-December 2020. Low-resolution spectra from 1700-10000 A. All reductions and spectral extractions were automated. Data downloaded from the MAST archive and renamed for the source name. CVSO 165: close binary, so extraction uncertain. Third star also in image.<br> CVSO 104: binary in image, not sure which component extracted<br> CVSO 109: close-ish faint companion in image
description: All data publicly available
DOI: 10.5281/zenodo.4375859
title: STIS 1d and 2d optical/near-UV spectra of Orion targets from ULLYSES

description: STIS 1d and 2d optical/near-UV spectra of Orion targets from ULLYSES in November-December 2020. Low-resolution spectra from 1700-10000 A. All reductions and spectral extractions were automated. Data downloaded from the MAST archive and renamed for the source name. CVSO 165: close binary, so extraction uncertain. Third star also in image.<br> CVSO 104: binary in image, not sure which component extracted<br> CVSO 109: close-ish faint companion in image
description: All data publicly available
DOI: 10.5281/zenodo.3843094
title: Labeling and tracking of immune cells in ex vivo human skin

description: Example imaging datasets linked to the publication 'Labeling and tracking of immune cells in ex vivo human skin' (doi: 10.1038/s41596-020-00435-8). Additional information filenames:<br> - m = ex vivo murine skin<br> - h = ex vivo human skin<br> - mCD8= anti-murine CD8-AF594 nanobody staining (red)<br> - hCD8= anti-human CD8-AF594 nanobody staining (red)<br> - Hoechst= Hoechst 33342 staining (grey)<br> - CD1a= anti-hCD1a-AF488 staining (green)<br> - CD103= anti-hCD103-AF488 staining (green)<br> - SHG = second harmonic generation (cyan) Imaris x64 v9.2.0.
description: This work was supported by ERC AdG Life-His-T (to T.N.S.) and an EADV Research Fellowship (to T.R.M.).
DOI: 10.5281/zenodo.3843093
title: Labeling and tracking of immune cells in ex vivo human skin

description: Example imaging datasets linked to the publication 'Labeling and tracking of immune cells in ex vivo human skin' (doi: 10.1038/s41596-020-00435-8). Additional information filenames:<br> - m = ex vivo murine skin<br> - h = ex vivo human skin<br> - mCD8= anti-murine CD8-AF594 nanobody staining (red)<br> - hCD8= anti-human CD8-AF594 nanobody staining (red)<br> - Hoechst= Hoechst 33342 staining (grey)<br> - CD1a= anti-hCD1a-AF488 staining (green)<br> - CD103= anti-hCD103-AF488 staining (green)<br> - SHG = second harmonic generation (cyan) Imaris x64 v9.2.0.
description: This work was supported by ERC AdG Life-His-T (to T.N.S.) and an EADV Research Fellowship (to T.R.M.).
DOI: 10.5281/zenodo.4376665
title: Topological properties of multiterminal superconducting nanostructures: Effect of a continuous spectrum

description: Data and code for Figures in the manuscript
DOI: 10.5281/zenodo.4376664
title: Topological properties of multiterminal superconducting nanostructures: Effect of a continuous spectrum

description: Data and code for Figures in the manuscript
DOI: 10.5281/zenodo.4377127
title: Topological numbers of quantum superpositions of topologically non-trivial bands

description: Code to obtain a Figure
DOI: 10.5281/zenodo.4377126
title: Topological numbers of quantum superpositions of topologically non-trivial bands

description: Code to obtain a Figure
DOI: 10.5281/zenodo.4377165
title: Braiding and all quantum operations with Majorana modes in 1D

description: Code to obtain a Figure
DOI: 10.5281/zenodo.4377164
title: Braiding and all quantum operations with Majorana modes in 1D

description: Code to obtain a Figure
DOI: 10.5281/zenodo.4377239
title: Weyl points in the multi-terminal Hybrid Superconductor-Semiconductor Nanowire devices

description: Code and data points to obtain Figures in the manuscript
DOI: 10.5281/zenodo.4377238
title: Weyl points in the multi-terminal Hybrid Superconductor-Semiconductor Nanowire devices

description: Code and data points to obtain Figures in the manuscript
DOI: 10.25345/c5dz2n
title: MassIVE MSV000086621 - GNPS Marine Specimens Capon Lab
title: A gig GNPS molecular networking was generated with MSMS data of around one thousand marine specimens from Capon group which were collected from Southern Australia and Antarctica. Around one hundred pure compounds of fourteen different classes of marine sponge metabolites from the Capon lab pure compound library were also included in GNPS analysis for dereplication purpose.
DOI: 10.21227/4xkr-0f88
title: Student-Performance-and-Engagement-Prediction-eLearning-datasets

description: Description:
DOI: 10.21227/6v77-jr02
title: Student-Performance-and-Engagement-Prediction-eLearning-datasets

description: Description:
DOI: 10.21227/cke6-cx22
title: Student-Performance-and-Engagement-Prediction-eLearning-datasets

description: Description:
DOI: 10.21227/j4fh-pb22
title: Student Performance and Engagement Prediction in eLearning datasets

description: Description:
DOI: 10.21227/1xzc-5f48
title: Student Performance and Engagement Prediction in eLearning datasets

description: Description: