cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5204501
title: MigrationsKB: A Knowledge Base of Migration related annotated Tweets

description: <strong>MigrationsKB(MGKB)</strong> is a public Knowledge Base of anonymized <strong>Migration</strong> related <strong>annotated</strong> tweets. The MGKB currently contains over <strong>200 thousand</strong> tweets, spanning over 9 years (January 2013 to July 2021), filtered with 11 European countries of <em>the United Kingdom, Germany, Spain, Poland, France, Sweden, Austria, Hungary, Switzerland, Netherlands and Italy</em>. <strong>Metadata</strong> information about the tweets, such as Geo information (<strong>place name</strong>, <strong>coordinates</strong>, <strong>country code</strong>). <strong>MGKB</strong> contains <strong>entities</strong>, <strong>sentiments</strong>, <strong>hate speeches</strong>, <strong>topics</strong>, <strong>hashtags</strong>, <em>encrypted user mentions</em> in RDF format. The schema of <strong>MGKB</strong> is an extension of TweetsKB for migrations related information. Moreover, to associate and represent the potential economic and social factors driving the migration flows such as <strong>eurostat</strong>, <strong>statista</strong>, etc. FIBO ontology was used. The extracted <strong>economic indicators</strong>, such as GDP Growth Rate, are connected with each Tweet in RDF using geographical and temporal dimensions. The user IDs and the tweet texts are encrypted for privacy purposes, while the tweet IDs are preserved. For this version, the <strong>MGKB</strong> is delivered as a whole and separately by year. The extracted entities and topic words are also published. Online SPARQL endpoint https://mgkb.fiz-karlsruhe.de/sparql/ More information please refer to the website https://migrationskb.github.io/MGKB/. Please contact Yiyi Chen (yiyi.chen@partner.kit.edu) for pretrained models (sentiment analysis/hate speech detection/ETM) if necessary.
DOI: 10.25573/data.19196240.v2
title: Panama Canal Watershed water quality monitoring program

description: Water quality monitoring data provided by the Panama Canal Authority (ACP). This program monitors WQ at around 40 stations located throughout the Panama Canal Watershed. Monitoring is carried out manually on a monthly basis.<br>The following variables are monitored:Total Alkalinity, Ca++, Cl, Conductivity, Hardness, K+, Mg++, Na+, Salinity, SO4, Total Dissolved Solids, Chlorophyll, Total Carbon, Biochemical Oxygen demand, E. coli, Microcystinas, Total Organic Carbon, NO2, NO3, PO4, Dissolved Oxygen, % Dissolved Oxygen, pH, Temperature, Total Suspended Solids, Visibility, Turbidity, Water Quality Index<br>Original reports (in Spanish) available at:https://micanaldepanama.com/nosotros/cuenca-hidrografica/<br>
DOI: 10.5517/ccdc.csd.cc291g0v
title: CCDC 2116556: Experimental Crystal Structure Determination

description: Related Article: Olga Lavinda, Collin H. Witt, K. A. Woerpel|2022|Angew.Chem.,Int.Ed.|61|e202114183|doi:10.1002/anie.202114183
DOI: 10.5517/ccdc.csd.cc291g1w
title: CCDC 2116557: Experimental Crystal Structure Determination

description: Related Article: Olga Lavinda, Collin H. Witt, K. A. Woerpel|2022|Angew.Chem.,Int.Ed.|61|e202114183|doi:10.1002/anie.202114183
DOI: 10.5517/ccdc.csd.cc291g2x
title: CCDC 2116558: Experimental Crystal Structure Determination

description: Related Article: Olga Lavinda, Collin H. Witt, K. A. Woerpel|2022|Angew.Chem.,Int.Ed.|61|e202114183|doi:10.1002/anie.202114183
DOI: 10.5517/ccdc.csd.cc291g3y
title: CCDC 2116559: Experimental Crystal Structure Determination

description: Related Article: Olga Lavinda, Collin H. Witt, K. A. Woerpel|2022|Angew.Chem.,Int.Ed.|61|e202114183|doi:10.1002/anie.202114183
DOI: 10.5517/ccdc.csd.cc291g4z
title: CCDC 2116560: Experimental Crystal Structure Determination

description: Related Article: Olga Lavinda, Collin H. Witt, K. A. Woerpel|2022|Angew.Chem.,Int.Ed.|61|e202114183|doi:10.1002/anie.202114183
DOI: 10.5517/ccdc.csd.cc297csq
title: CCDC 2122253: Experimental Crystal Structure Determination

description: Related Article: Takuya Kodama, Mitsuya Aoba, Yasukazu Hirao, Samara Medina Rivero, Juan Casado, Takashi Kubo|2022|Angew.Chem.,Int.Ed.|61|e202200688|doi:10.1002/anie.202200688
DOI: 10.24400/802406/127n-tc5p
title: ULS-URAP RAR_BQT : Plasma Parameters (quasi-thermal noise method)

description: This data set contains daily files of ULYSSES/URAP radio data. These files contain plasma parameters (mainly the electron density and temperature) computed by the plasma quasi-thermal noise method from 128 s radio spectra measured on the 1.25-48.50 kHz receiver connected to the thin wire 2x35m electric dipole.
DOI: 10.24400/802406/p999-98h7
title: ULS-URAP RAR_192S : Flux Density - 192 second Averaged Data

description: This data set contains ULYSSES/URAP flux density values in the whole nominal range of 76 frequencies from 1.25 to 940 kHz. Experimental data have been processed and averaged over 192 second intervalls. Available data consist of mean, maximum and minimum values. They are expressed in SFU (solar flux units) : 1 SFU = 10-22 W/m2/Hz. The interval of 192 s was chosen to correspond to the largest cycle time of the radio receivers.
DOI: 10.24400/802406/pwg6-4byh
title: ULYSSES orbit data (GSE frame, 1 min resolution)

description: This data set contains ULYSSES GSE position and velocity at 1 min resolution.
DOI: 10.6084/m9.figshare.19733503
title: CLDN1 TNBC Study

description: This is a sample of TNBC tumors from a local community hospital analyzed for the expression of a tight junction protein. The data set includes demographical, clinical and pathological information.
DOI: 10.6084/m9.figshare.19733503.v1
title: CLDN1 TNBC Study

description: This is a sample of TNBC tumors from a local community hospital analyzed for the expression of a tight junction protein. The data set includes demographical, clinical and pathological information.
DOI: 10.5066/p9l6b4xe
title: Lumped Parameter Models of Groundwater Age, South Loup River, Nebraska

description: This data release describes one (1) Microsoft Excel table of lumped parameter models of groundwater age for groundwater discharging to the South Loup River, Nebraska. The table (LPMAgeResults) includes final models of groundwater age and metrics by calibration of lumped parameter models to tracer concentrations using TracerLPM software (Jurgens and others, 2012). Interpreted results presented here were used to guide hydrologic interpretations of groundwater sources and flow paths of groundwater discharging to the South Loup River, NE.
DOI: 10.5281/zenodo.4957647
title: A spatially-explicit harmonized global dataset of critical infrastructure

description: The spatially-explicit harmonized global dataset of critical infrastructure (CI): Amount_of_infrastructure.zip contains data on the amount of infrastructure per infrastructure type at a resolution of 0.10x0.10 and 0.25x0.25 degrees CISI.zip contains data on the Critical Infrastructure Spatial Index (CISI) at a resolution of 0.10x0.10 and 0.25x0.25 degrees Please consult the following publication for detailed information: Nirandjan, S., Koks, E.E., Ward, P.J. <em>et al.</em> A spatially-explicit harmonized global dataset of critical infrastructure. <em>Sci Data</em> <strong>9, </strong>150 (2022). https://doi.org/10.1038/s41597-022-01218-4<em> </em>
DOI: 10.5281/zenodo.4957646
title: A spatially-explicit harmonized global dataset of critical infrastructure

description: The spatially-explicit harmonized global dataset of critical infrastructure (CI): Amount_of_infrastructure.zip contains data on the amount of infrastructure per infrastructure type at a resolution of 0.10x0.10 and 0.25x0.25 degrees CISI.zip contains data on the Critical Infrastructure Spatial Index (CISI) at a resolution of 0.10x0.10 and 0.25x0.25 degrees Please consult the following publication for detailed information: Nirandjan, S., Koks, E.E., Ward, P.J. <em>et al.</em> A spatially-explicit harmonized global dataset of critical infrastructure. <em>Sci Data</em> <strong>9, </strong>150 (2022). https://doi.org/10.1038/s41597-022-01218-4<em> </em>
DOI: 10.6084/m9.figshare.19722121.v3
title: Telemedicine for Clinical Preceptorship During the COVID-19 Pandemic: A Developmental evaluation

description: The raw data transcriptions from the focus group dscussions and the guide questions for these are included as appendices. The thematic analysis and representative quotations are in the results section.
DOI: 10.17036/researchdata.aston.ac.uk.00000538
title: Mode division multiplexing free space optical transmission
DOI: 10.17036/researchdata.aston.ac.uk.00000540
title: Data underpinning article "Single-wavelength transmission at 1.1-Tbit/s net data rate over a multi-modal free-space optical link using commercial devices"
DOI: 10.17637/rh.13207973
title: Molecular mechanisms of Beta vulgaris cold-induced secondary dormancy raw data

description: Here we show that prolonged incubation of sugar beet fruits at low temperature can induce secondary nondeep physiological dormancy of apparently nondormant crop species. The physiological and biophysical mechanisms underpinning this cold-induced secondary dormancy include the chilling-induced accumulation of abscisic acid in the seeds, a reduction in the embryo growth potential and a block in weakening of the endosperm covering the embryonic root.<br>
DOI: 10.17637/rh.13207973.v1
title: Molecular mechanisms of Beta vulgaris cold-induced secondary dormancy raw data

description: Here we show that prolonged incubation of sugar beet fruits at low temperature can induce secondary nondeep physiological dormancy of apparently nondormant crop species. The physiological and biophysical mechanisms underpinning this cold-induced secondary dormancy include the chilling-induced accumulation of abscisic acid in the seeds, a reduction in the embryo growth potential and a block in weakening of the endosperm covering the embryonic root.<br>
DOI: 10.6084/m9.figshare.19367933.v4
title: Visual Near-Duplicates Detection in the Context of Social Media (Dataset)

description: The dataset of the paper: ``Dataset and Case Studies for Visual Near-Duplicates Detection in the Context of Social Media'', by Hana Matatov, Mor Naaman, and Ofra Amir.<br>See the Github repository for details:https://github.com/sTechLab/Visual-Near-Duplicates-Detection-in-the-Context-of-Social-Media<br><br>See the associated paper on arXiv:https://arxiv.org/abs/2203.07167<br>
DOI: 10.6084/m9.figshare.19722121
title: Telemedicine for Clinical Preceptorship During the COVID-19 Pandemic: A Developmental evaluation

description: The raw data transcriptions from the focus group dscussions and the guide questions and informed consent forms, and Standard Operating Procedures for Telemedicine are included as appendices.
DOI: 10.6084/m9.figshare.19722121.v5
title: Telemedicine for Clinical Preceptorship During the COVID-19 Pandemic: A Developmental evaluation

description: The raw data transcriptions from the focus group dscussions and the guide questions and informed consent forms, and Standard Operating Procedures for Telemedicine are included as appendices.
DOI: 10.34740/kaggle/dsv/3605860
title: Mahanagar Yatayat Dataset
DOI: 10.6084/m9.figshare.19722121.v4
title: Telemedicine for Clinical Preceptorship During the COVID-19 Pandemic: A Developmental evaluation

description: The raw data transcriptions from the focus group dscussions and the guide questions and informed consent forms, and Standard Operating Procedures for Telemedicine are included as appendices.
DOI: 10.5517/ccdc.csd.cc295jxy
title: CCDC 2120490: Experimental Crystal Structure Determination

description: Related Article: Qinghao Xu|2021|CSD Communication|||
description: Related Article: Qing-Hao Xu, Li-Pu Wei, Bin Xiao|2022|Angew.Chem.,Int.Ed.|61|e202115592|doi:10.1002/anie.202115592
DOI: 10.5517/ccdc.csd.cc295kcf
title: CCDC 2120504: Experimental Crystal Structure Determination

description: Related Article: Qinghao Xu|2021|CSD Communication|||
description: Related Article: Qing-Hao Xu, Li-Pu Wei, Bin Xiao|2022|Angew.Chem.,Int.Ed.|61|e202115592|doi:10.1002/anie.202115592
DOI: 10.5519/qd.2zsu8nzw
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (19 records)
DOI: 10.18112/openneuro.ds002761.v1.1.1
title: memoryreplay
DOI: 10.5281/zenodo.6477646
title: GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Data Quality Products for GW Searches

description: This material is part of several data products associated with GWTC-2.1, the deep extended catalog of compact binary coalescences observed by the LIGO Scientific Collaboration and the Virgo Collaboration during the first half of the third observing run. For further information, see the paper (dcc.ligo.org/LIGO-P2100063/public), the related material linked from this page, and the GWTC-2.1 data release documentation (www.gw-openscience.org/GWTC-2.1/). This release contains data quality products that are used by search analyses to help mitigate non-Gaussian noise in the detector data. <strong>Renormalized iDQ timeseries</strong> This release contains the renormalized iDQ timeseries data quality product used within the GstLAL search to generate results for GWTC-2.1 as described in Goodwin <em>et al</em>. 2020. This data product was found to be statistically helpful in improving data quality within the GstLAL search. For further information about iDQ see Essick <em>et al</em>. 2020. The file H1L1-IDQ_TIMESERIES-1238166018-15843600.h5 contains a time series for each LIGO detector related to the probability of a glitch in the strain data given the behavior in the analyzed auxiliary channels which monitor the behavior of the detectors and their environment. The HDF5-formatted file contains two groups, H1 and L1, corresponding to LIGO Hanford and LIGO Livingston, respectively. Each group contains several datasets; the data dataset corresponds to the renormalized iDQ log-likelihoods, as described in Godwin <em>et al</em>. 2020, and the time dataset corresponds to the times associated with the renormalized iDQ log-likelihoods in the data dataset. For more general background on gravitational-wave data quality, try the materials from a GW Open Data Workshop or the guide to LIGO-Virgo data analysis.
description: LIGO Laboratory and Advanced LIGO are funded by the United States National Science Foundation (NSF) as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain.
description: {"references": ["https://doi.org/10.7935/nfnt-hm34"]}
DOI: 10.5281/zenodo.6477645
title: GWTC-2.1: Deep Extended Catalog of Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run - Data Quality Products for GW Searches

description: This material is part of several data products associated with GWTC-2.1, the deep extended catalog of compact binary coalescences observed by the LIGO Scientific Collaboration and the Virgo Collaboration during the first half of the third observing run. For further information, see the paper (dcc.ligo.org/LIGO-P2100063/public), the related material linked from this page, and the GWTC-2.1 data release documentation (www.gw-openscience.org/GWTC-2.1/). This release contains data quality products that are used by search analyses to help mitigate non-Gaussian noise in the detector data. <strong>Renormalized iDQ timeseries</strong> This release contains the renormalized iDQ timeseries data quality product used within the GstLAL search to generate results for GWTC-2.1 as described in Goodwin <em>et al</em>. 2020. This data product was found to be statistically helpful in improving data quality within the GstLAL search. For further information about iDQ see Essick <em>et al</em>. 2020. The file H1L1-IDQ_TIMESERIES-1238166018-15843600.h5 contains a time series for each LIGO detector related to the probability of a glitch in the strain data given the behavior in the analyzed auxiliary channels which monitor the behavior of the detectors and their environment. The HDF5-formatted file contains two groups, H1 and L1, corresponding to LIGO Hanford and LIGO Livingston, respectively. Each group contains several datasets; the data dataset corresponds to the renormalized iDQ log-likelihoods, as described in Godwin <em>et al</em>. 2020, and the time dataset corresponds to the times associated with the renormalized iDQ log-likelihoods in the data dataset. For more general background on gravitational-wave data quality, try the materials from a GW Open Data Workshop or the guide to LIGO-Virgo data analysis.
description: LIGO Laboratory and Advanced LIGO are funded by the United States National Science Foundation (NSF) as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain.
description: {"references": ["https://doi.org/10.7935/nfnt-hm34"]}
DOI: 10.6084/m9.figshare.19733656
title: 10 Ganoderma genomes

description: 10 Genome assemblies for 10 Ganoderma species.
DOI: 10.25739/kat4-na03
title: Public Data Release of the Event Horizon Telescope 2017 Observations

description: The Event Horizon Telescope Collaboration (EHTC) and Joint ALMA Observatory (JAO) announce the public data release of the Very Long Baseline Interferometry (VLBI) 1-mm 2017 observations by the Event Horizon Telescope (EHT). The overall goal of the observations is to image the supermassive black holes M87* and Sagittarius A* at event horizon scales and to image the AGNs OJ287, 3C279, CENA, and NGC1052 at high resolution. This data delivery contains what is defined by the EHTC as L1 data for the 2017 observing campaign. The 2017 campaign was the first where the Atacama Large Millimeter Array (ALMA) participated as a phased array, a capability that was developed by the ALMA Phasing Project. The EHT L1 data encompasses the VLBI correlator output, converted into circular polarization basis, as well as the calibration information from the participating telescopes. For ALMA this includes their quality assurance (QA2) output that is used for the polarization basis conversion. The primary reference for the data from the EHT 2017 April observation campaign is Event Horizon Telescope Collaboration et al. 2019, ApJL, 875, L3 (doi: 10.3847/2041-8213/ab0c57) which in detail discusses the processing carried out for the current L1 data delivery. For scientific results derived from the EHT 2017 April Observation Campaign see eventhorizontelescope.org/publications. Additional data products derived from this L1 data can be found at eventhorizontelescope.org/for-astronomers/data. This data delivery includes observations from the following facilities: ALMA, the Atacama Pathfinder Experiment (APEX), the Submillimeter Telescope (SMT), the James Clerk Maxwell Telescope (JCMT), the Large Millimeter Telescope Alfonso Serrano (LMT), the IRAM 30m telescope, the Submillimeter Array (SMA), and the South Pole Telescope (SPT). More information about these facilities can be found in Event Horizon Telescope Collaboration et al. 2019, ApJL, 875, L2 (doi: 10.3847/2041-8213/ab0c96).
DOI: 10.5517/ccdc.csd.cc29338t
title: CCDC 2118145: Experimental Crystal Structure Determination

description: Related Article: Oleksandra Veselska, Shefali Vaidya, Chinmoy Das, Nathalie Guillou, Pierre Bordet, Alexandra Fateeva, François Toche, Rodica Chiriac, Gilles Ledoux, Stefan Wuttke, Satoshi Horike, Aude Demessence|2022|Angew.Chem.,Int.Ed.|61|e202117261|doi:10.1002/anie.202117261
DOI: 10.5517/ccdc.csd.cc2933fz
title: CCDC 2118150: Experimental Crystal Structure Determination

description: Related Article: Oleksandra Veselska, Shefali Vaidya, Chinmoy Das, Nathalie Guillou, Pierre Bordet, Alexandra Fateeva, François Toche, Rodica Chiriac, Gilles Ledoux, Stefan Wuttke, Satoshi Horike, Aude Demessence|2022|Angew.Chem.,Int.Ed.|61|e202117261|doi:10.1002/anie.202117261
DOI: 10.5517/ccdc.csd.cc2933dy
title: CCDC 2118149: Experimental Crystal Structure Determination

description: Related Article: Oleksandra Veselska, Shefali Vaidya, Chinmoy Das, Nathalie Guillou, Pierre Bordet, Alexandra Fateeva, François Toche, Rodica Chiriac, Gilles Ledoux, Stefan Wuttke, Satoshi Horike, Aude Demessence|2022|Angew.Chem.,Int.Ed.|61|e202117261|doi:10.1002/anie.202117261
DOI: 10.5281/zenodo.6532809
title: Campbell

description: Campbell.rds
DOI: 10.5281/zenodo.6532808
title: Campbell

description: Campbell.rds
DOI: 10.7265/0k6h-yn09
title: Largest Glaciers and Glacier Complexes in the World, Version 1
DOI: 10.5517/ccdc.csd.cc29l1hg
title: CCDC 2132505: Experimental Crystal Structure Determination

description: Related Article: Xiaofei Shao, Chang Zheng, Peng Xu, Taro Shiraishi, Tomohisa Kuzuyama, Antonio Molinaro, Alba Silipo, Biao Yu|2022|Angew.Chem.,Int.Ed.|61|e202200818|doi:10.1002/anie.202200818
DOI: 10.5517/ccdc.csd.cc29l1nm
title: CCDC 2132510: Experimental Crystal Structure Determination

description: Related Article: Xiaofei Shao, Chang Zheng, Peng Xu, Taro Shiraishi, Tomohisa Kuzuyama, Antonio Molinaro, Alba Silipo, Biao Yu|2022|Angew.Chem.,Int.Ed.|61|e202200818|doi:10.1002/anie.202200818
DOI: 10.25345/c58w38622
title: MassIVE MSV000089446 - Proteomic Analysis of Arylamine N-Acetyltransferase 1 Knockout Breast Cancer Cells
title: A phase II metabolic enzyme, NAT1 is frequently upregulated in breast cancers. Previous studies have shown that inhibition or depletion of NAT1 in breast cancer cell lines leads to growth retardation both in vitro and in vivo, suggesting that NAT1 contributes to rapid growth of breast cancer cells. A 2DLC-MS approach using TMT 10-plex labeling performed comparing three three groups (n=3 per group) plus a pooled internal standard/normalization channel. These groups include a parental MDA-MB-231 breast cancer cells and two separate NAT1 knockout (KO) cell lines differing by proliferation rates. Relative protein abundances were compared by Scaffold Q+S v4.4.5 at the 99.9% CI at the peptide and protein levels to estimate protein group identification and differential abundances by Kruskal-Wallis and log fold change differences. MetaCore was used to explore pathway analyses and to identify Gene Ontology biological processes, molecular functions, and cellular components that were enriched in each data set.
DOI: 10.6084/m9.figshare.19733509
title: watson_and_mattock_v1.tar.gz

description: Data in support of "A comparison of single-coverage and multi-coverage metagenomic binning reveals extensive hidden contamination", Mick Watson and Jennifer Mattock, 2022
DOI: 10.6084/m9.figshare.19733704
title: Exploration of the functional properties of red cuticle patch in redclaw crayfish Cherax quadricarinatus by histological and transcriptomic analysis

description: Functional annotation of unigenes in GO database of "Exploration of the functional properties of red cuticle patch in redclaw crayfish<em> Cherax quadricarinatus</em> by histological and transcriptomic analysis"