cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25403/upresearchdata.21456438.v1
title: Climate change adaptation: the threat of an Individual's emotions

description: As an literature based study, this study has focused on collecting and examining various literature found by way of a few selected keywords relevant to the topic and research question considered. Following the review of the preliminary and the selected literature, it was concluded that the debate is failing to incorporate a few key concepts such as ‘adaptation,’ the ‘security-dilemma,’ ‘threat perception,’ ‘the <em>individual’ </em>and<em> ‘</em>fear.’ Concepts which this study argues are important variables to be considered in understanding the CCN, climate security and national security.This study’s focus was on the qualitative integration and discussion of existing literature available in order to create or suggest new and additional pathways for better understanding what has already been said. <br> Dataset A - comprises of tables and charts which illustrate the literature focus areas, years of publication as well as favoured journals and publications. Note, however, that these datasets are not presented within the study. Thus, these numerical representations provided in Dataset A where simply used for personal reference and organisation. <br> <br> Dataset B - climate change adaptation and individual fear is then translated into a threat to global security by using an existing five factor qualitative expression. By applying this expression, climate security as a security issue, is operationalised through threats originating from adaptation, which is connected to human emotion and specifically fear and anxiety. <br> Formula below is applied to imagined scenarios which illustrate a hypothetical numerical value of future global security: <br> SI = RO x T T= PtS x d x Pwpl*px xPrbD=(SP)A <br> SI = T x RO where the SI = Climate Security, T = Climate Change adaptation and Individual fear, and RO = Global Security. <br>
DOI: 10.5281/zenodo.6628170
title: Conformational ensembles used in "Assessment of forward models for the hydrodynamic radius of intrinsically disordered proteins. Pesce et al. 2022"

description: Ensemble of intrinsically disordered proteins used in: <em>"Assessment of forward models for the hydrodynamic radius of intrinsically disordered proteins. Pesce et al. 2022"</em>. Ensembles are produced with Flexible-meccano and Langevin simulations with CALVADOS for: Hst5 RS DSS1 Sic1 ProTa NHE6cmdd A1 aSyn ANAC046 GHR-ICD Tau
DOI: 10.5281/zenodo.6628171
title: Conformational ensembles used in "Assessment of forward models for the hydrodynamic radius of intrinsically disordered proteins. Pesce et al. 2022"

description: Ensemble of intrinsically disordered proteins used in: <em>"Assessment of forward models for the hydrodynamic radius of intrinsically disordered proteins. Pesce et al. 2022"</em>. Ensembles are produced with Flexible-meccano and Langevin simulations with CALVADOS for: Hst5 RS DSS1 Sic1 ProTa NHE6cmdd aSyn ANAC046 GHR-ICD Tau
DOI: 10.5281/zenodo.7415039
title: Conformational ensembles used in "Assessment of forward models for the hydrodynamic radius of intrinsically disordered proteins. Pesce et al. 2022"

description: Ensemble of intrinsically disordered proteins used in: <em>"Assessment of forward models for the hydrodynamic radius of intrinsically disordered proteins. Pesce et al. 2022"</em>. Ensembles are produced with Flexible-meccano and Langevin simulations with CALVADOS for: Hst5 RS DSS1 Sic1 ProTa NHE6cmdd A1 aSyn ANAC046 GHR-ICD Tau
DOI: 10.6084/m9.figshare.21687692.v2
title: datasets

description: datasets
DOI: 10.6084/m9.figshare.21687692.v1
title: Mitochondrial phylogenomics of Mantodea

description: The recently reorganized classification of Mantodea has largely overcome past homoplasy problems, although many relationships among higher taxa remain ambiguous. We sequenced the mitochondrial genomes and three nuclear gene sequences of 23 mantid species, supported by published data from 53 mantises, to present familial phylogenomics of Mantodea.
DOI: 10.5281/zenodo.7415019
title: Plant macrofossil, peat geochemical and chronological data from sub-Arctic European peatlands

description: This dataset consists of raw data from peat records analysed for plant macrofossils, peat geochemical properties supplemented by chronological control data from high-latitude Sweden, Finland and European Russia. Altogether, 33 peat cores were analysed from 16 peatlands. Peat cores were collected from seasonally thawed active peat layer with a box corer or a so-called Russian corer. Peat records cover both currently intermediate (n= 25) and dry (n= 8) surfaces. Majority of the sites (n= 12) are permafrost peatlands either with sporadic or discontinuous permafrost. Changes in peatland vegetation and peat and carbon accumulation were studied to resolve how high-latitude peatlands react to past and recent changes in climate. Peat properties were examined for bulk density, carbon (C), nitrogen (N) and C/N ratio. C accumulation was calculated for the past two millennia. To establish chronological control, peat layers were dated with 210Pb and radiocarbon 14. To create age-depth models we used Plum and the ages retrieved form the models are found in this dataset. The data have been analysed between 2016 and 2020. More information about the methods can be found from Piilo et al. “Consistent centennial-scale change in European sub-Arctic peatland vegetation towards <em>Sphagnum</em> dominance – implications for carbon sink capacity”. Global Change Biology.
DOI: 10.17028/rd.lboro.21695972
title: Supplementary information files for Spatial and temporal scaling of sub-daily extreme rainfall for data sparse places

description: Supplementary information files for article Spatial and temporal scaling of sub-daily extreme rainfall for data sparse places <br> Global efforts to upgrade water, drainage, and sanitation services are hampered by hydrometeorological data-scarcity plus uncertainty about climate change. Intensity–duration–frequency (IDF) tables are used routinely to design water infrastructure so offer an entry point for adapting engineering standards. This paper begins with a novel procedure for guiding downscaling predictor variable selection for heavy rainfall simulation using media reports of pluvial flooding. We then present a three-step workflow to: (1) spatially downscale daily rainfall from grid-to-point resolutions; (2) temporally scale from daily series to sub-daily extreme rainfalls and; (3) test methods of temporal scaling of extreme rainfalls <em>within</em> Regional Climate Model (RCM) simulations under changed climate conditions. Critically, we compare the methods of moments and of parameters for temporal scaling annual maximum series of daily rainfall into sub-daily extreme rainfalls, whilst accounting for rainfall intermittency. The methods are applied to Kampala, Uganda and Kisumu, Kenya using the Statistical Downscaling Model (SDSM), two RCM simulations covering East Africa (CP4 and P25), and in hybrid form (RCM-SDSM). We demonstrate that Gumbel parameters (and IDF tables) can be reliably scaled to durations of 3 h within observations and RCMs. Our hybrid RCM-SDSM scaling reduces errors in IDF estimates for the present climate when compared with direct RCM output. Credible parameter scaling relationships are also found within RCM simulations under changed climate conditions. We then discuss the practical aspects of applying such workflows to other city-regions.
DOI: 10.17028/rd.lboro.21695972.v1
title: Supplementary information files for Spatial and temporal scaling of sub-daily extreme rainfall for data sparse places

description: Supplementary information files for article Spatial and temporal scaling of sub-daily extreme rainfall for data sparse places <br> Global efforts to upgrade water, drainage, and sanitation services are hampered by hydrometeorological data-scarcity plus uncertainty about climate change. Intensity–duration–frequency (IDF) tables are used routinely to design water infrastructure so offer an entry point for adapting engineering standards. This paper begins with a novel procedure for guiding downscaling predictor variable selection for heavy rainfall simulation using media reports of pluvial flooding. We then present a three-step workflow to: (1) spatially downscale daily rainfall from grid-to-point resolutions; (2) temporally scale from daily series to sub-daily extreme rainfalls and; (3) test methods of temporal scaling of extreme rainfalls <em>within</em> Regional Climate Model (RCM) simulations under changed climate conditions. Critically, we compare the methods of moments and of parameters for temporal scaling annual maximum series of daily rainfall into sub-daily extreme rainfalls, whilst accounting for rainfall intermittency. The methods are applied to Kampala, Uganda and Kisumu, Kenya using the Statistical Downscaling Model (SDSM), two RCM simulations covering East Africa (CP4 and P25), and in hybrid form (RCM-SDSM). We demonstrate that Gumbel parameters (and IDF tables) can be reliably scaled to durations of 3 h within observations and RCMs. Our hybrid RCM-SDSM scaling reduces errors in IDF estimates for the present climate when compared with direct RCM output. Credible parameter scaling relationships are also found within RCM simulations under changed climate conditions. We then discuss the practical aspects of applying such workflows to other city-regions.
DOI: 10.6084/m9.figshare.21195799
title: Selective exclusion of aromatic organic carbon during lake ice formation

description: Raw data used in this manuscript including also Orbitrap dataset.
DOI: 10.6084/m9.figshare.21195799.v3
title: Selective exclusion of aromatic organic carbon during lake ice formation

description: Raw data used in this manuscript including also Orbitrap dataset.
DOI: 10.6084/m9.figshare.21195799.v2
title: Selective exclusion of aromatic organic carbon during lake ice formation

description: Raw data used in this manuscript including also Orbitrap dataset.
DOI: 10.21942/uva.21696047
title: XXX

description: xXXXXX
DOI: 10.21942/uva.21696047.v2
title: XXX

description: xXXXXX
DOI: 10.21942/uva.21696047.v1
title: Scheme S1

description: Scheme S1.cdxml shows the immobilization scheme of the RhGly on coverslips. One can find the scheme in the chemdraw file.
DOI: 10.5281/zenodo.7340038
title: Dataset for Numerical constraints on the one-stage and two-stage Greater India collision models

description: This is the dataset for the article Numerical constraints on the one-stage and two-stage Greater India collision models. It is mainly about the experimental data and visualization from Figure 3 to Figure 8.
DOI: 10.5281/zenodo.7340037
title: Dataset for Numerical constraints on the one-stage and two-stage Greater India collision models

description: This is the dataset for the article Numerical constraints on the one-stage and two-stage Greater India collision models. It is mainly about the experimental data and visualization from Figure 3 to Figure 8.
DOI: 10.5281/zenodo.7414984
title: Dataset for Numerical constraints on the one-stage and two-stage Greater India collision models

description: This is the dataset for the article Numerical constraints on the one-stage and two-stage Greater India collision models. It is mainly about the experimental data and visualization from Figure 3 to Figure 8.
DOI: 10.5281/zenodo.7415109
title: Dataset for Numerical constraints on the one-stage and two-stage Greater India collision models

description: This is the dataset for the article Numerical constraints on the one-stage and two-stage Greater India collision models. It is mainly about the experimental data and visualization from Figure 3 to Figure 8.
DOI: 10.5066/p9rbxv53
title: Quality-Assurance and Quality-Control Data for Discrete Water-Quality Samples Collected in McHenry County, Illinois, 2020

description: In June and July of 2020, 45 groundwater wells in McHenry County, Illinois, were sampled for water quality (field properties, major ions, nutrients, and trace metals) and 12 wells were sampled for contaminants of emerging concern (pharmaceuticals, pesticides, and wastewater indicator compounds). Quality-assurance and quality-control samples were collected during the June and July 2020 sampling that included equipment blanks, field blanks, and replicates. The results of these samples were used to understand the sources of bias and variability associated with sample collection, processing, storage, and shipping. This data release contains one comma separated values files containing the results of the quality-control sample collection for general water quality (metals, nutrients, and major ions) and contaminants of emerging concern (wastewater indicator compounds and pharmaceuticals). Water-quality data from the associated groundwater monitoring well data are available at the National Water Information System (NWIS) web database (https://doi.org/10.5066/F7P55KJN). Results and discussion of the water quality and contaminants of emerging concern can also be found in the associated scientific investigation report referenced.
DOI: 10.5281/zenodo.7414499
title: Orthomam species body mass annotation

description: Supplementary data for our article "Phylogenetic detection of protein sites associated with<br> continuous traits". These data were used to perform simulations under a mutation-selection model to compare a model of genotype associations to a continuous trait against discrete models, on their ability to detect protein sites associated to the phenotype. Includes : a phylogeny of 116 mammalian species, reconstructed from the Orthomam database, using IQ-TREE; a table of body mass trait for each of the species.
DOI: 10.5281/zenodo.7414498
title: Orthomam species body mass annotation

description: Supplementary data for our article "Phylogenetic detection of protein sites associated with<br> continuous traits". These data were used to perform simulations under a mutation-selection model to compare a model of genotype associations to a continuous trait against discrete models, on their ability to detect protein sites associated to the phenotype. Includes : a phylogeny of 116 mammalian species, reconstructed from the Orthomam database, using IQ-TREE; a table of body mass trait for each of the species.
DOI: 10.17604/jnvv-nm74
title: High latitude sea surface skin temperature from the Saildrone uncrewed surface vehicles deployed in 2019

description: Saildrone, as one of the most capable uncrewed surface vehicles, carries a suite of meteorological and oceanographic instruments for long-duration cruises. From 15th May to 11th October 2019, two NASA-funded Saildrones, SD-1036 and SD-1037, made a 150-day round-trip cruise from Dutch Harbor, Alaska, into the Arctic Ocean. Both were equipped with a pair of IR pyrometers on the deck at 0.8 m height for the sea surface skin temperature (SSTskin) retrieval, viewing the sea surface and the sky at the same nadir angle and zenith angles. The pyrometers were manufactured by Heitronics Inc., the sea-viewing sensor was a CT15.10 while the sky-viewing sensor was a CT09.10. After the deployments, the CT15 calibration was tested in the University of Miami Rosenstiel School laboratory using SI-traceable blackbody calibrator, and also by Wintronics, the US agent of Heitronics Inc. The performance of CT15 was much better than the stated accuracy in the manufacturer's specifications. The uncertainty of Saildrone-derived SSTskin is ~0.12 K and the systematic error is insignificant. Therefore, the SSTskin data from SD-1036 and SD-1037 are sufficiently accurate to be used in the validation of satellite SSTskin retrievals and other studies of sea surface and air-sea interaction at high latitudes.
DOI: 10.5281/zenodo.4434363
title: Immunofluorescence staining of a human kidney (#1, peri-tumor area) obtained by MELC

description: 19 marker MELC run in a human peri-tumor kidney sample (#1). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm. All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -&gt; 2<sup>16</sup>).
DOI: 10.5281/zenodo.4434362
title: Immunofluorescence staining of a human kidney (#1, peri-tumor area) obtained by MELC

description: 19 marker MELC run in a human peri-tumor kidney sample (#1). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm. All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -&gt; 2<sup>16</sup>).
DOI: 10.5281/zenodo.7396289
title: Databases for MyCodentifier: A tool for routine identification of nontuberculous mycobacteria using MGIT enriched shotgun metagenomics.

description: Databases used for MyCodentifier a Nextflow pipeline to identify Mycobacterium tuberculosis complex (MTBC) and Nontuberculous mycobacteria (NTM) species from Next-generation sequencing (NGS) data.<br> <br> <strong>Short description:</strong><br> The pipeline is constructed using nextflow as workflow manager running in a docker container. It is able to identify species of MTBC/NTM from positive Mycobacterial Growth Indicator Tube (MGIT) cultures. To do so it uses an hsp65 database for fast identification coupled with a Metagenomic method using centrifuge to identify on genome level. For TB it also is able to identify subspecies. Results are presented in automated pdf and html reports. <strong>Databases</strong> <strong>Name</strong> <strong>Short Description</strong> 20220726_ref.tar.gz 7 major mycobacterial genomes as centrifuge classification database, used for reference-based mapping and genotype resistance prediction 20220726_wgs_centrifuge_db_Radboudumc_MB.tar.gz centrifuge classification database using Tortoli <em>et al</em> 2017 Mycobacterium strains + additional strains genomes.tar.gz 7 major mycobacterial genomes, annotation and Genbank files. Files are paired with 20220726_ref.tar.gz snpEff.tar.gz 7 major mycobacterial genomes annotation models for snpEff. Tortoli_etal_hsp65.tar.gz KMA database of hsp65 gene extractions of the Tortoli <em>et al</em> 2017 Mycobacterium strains. Used in the study:<br> p_compressed+h+v.tar.gz (12/06/2016) Databases available via ftp://ftp.ccb.jhu.edu/pub/infphilo/centrifuge/data or https://ccb.jhu.edu/software/centrifuge/manual.shtml#custom-database <strong>MyCodentifier Github:</strong> https://jordycoolen.github.io/MyCodentifier/
DOI: 10.5281/zenodo.7396288
title: Databases for MyCodentifier: A tool for routine identification of nontuberculous mycobacteria using MGIT enriched shotgun metagenomics.

description: Databases used for MyCodentifier a Nextflow pipeline to identify Mycobacterium tuberculosis complex (MTBC) and Nontuberculous mycobacteria (NTM) species from Next-generation sequencing (NGS) data.<br> <br> <strong>Short description:</strong><br> The pipeline is constructed using nextflow as workflow manager running in a docker container. It is able to identify species of MTBC/NTM from positive Mycobacterial Growth Indicator Tube (MGIT) cultures. To do so it uses an hsp65 database for fast identification coupled with a Metagenomic method using centrifuge to identify on genome level. For TB it also is able to identify subspecies. Results are presented in automated pdf and html reports. <strong>Databases</strong> <strong>Name</strong> <strong>Short Description</strong> 20220726_ref.tar.gz 7 major mycobacterial genomes as centrifuge classification database, used for reference-based mapping and genotype resistance prediction 20220726_wgs_centrifuge_db_Radboudumc_MB.tar.gz centrifuge classification database using Tortoli <em>et al</em> 2017 Mycobacterium strains + additional strains genomes.tar.gz 7 major mycobacterial genomes, annotation and Genbank files. Files are paired with 20220726_ref.tar.gz snpEff.tar.gz 7 major mycobacterial genomes annotation models for snpEff. Tortoli_etal_hsp65.tar.gz KMA database of hsp65 gene extractions of the Tortoli <em>et al</em> 2017 Mycobacterium strains. Used in the study:<br> p_compressed+h+v.tar.gz (12/06/2016) Databases available via ftp://ftp.ccb.jhu.edu/pub/infphilo/centrifuge/data or https://ccb.jhu.edu/software/centrifuge/manual.shtml#custom-database <strong>MyCodentifier Github:</strong> https://jordycoolen.github.io/MyCodentifier/
DOI: 10.21227/zcj1-v517
title: CRAWDAD umkc/networkslicing5g

description: We have created a Deep Learning model for 5G and Network Slicing. (eMBB, URLLC, IoT).I encourage developers and researchers working on the 4G/LTE, 5G, 6G and similar interest to use and provide feedback:Our research can be found at1. IEEE paper "DeepSlice: A Deep Learning Approach towards an Efficient and Reliable Network Slicing in 5G Networks" (https://ieeexplore.ieee.org/document/8993066)2. IEEE paper "Secure5G: A Deep Learning Framework Towards a Secure Network Slicing in 5G and Beyond" (https://ieeexplore.ieee.org/document/9031158)date/time of measurement start: 2019-05-01date/time of measurement end: 2019-10-30measurement purpose: Computer Malware (Worms) Investigation, Energy-Efficient Wireless Network, Network Diagnosis, Network Performance Analysis, Network Security, Routing Protocol file: 5G_Dataset_Network_Slicing_CRAWDAD_Shared.zip
DOI: 10.6084/m9.figshare.21282600.v1
title: Without Immersion media.zmx

description: For Fig. S8c
DOI: 10.6084/m9.figshare.21282609.v1
title: magnification is 5over2.zos

description: For Fig. S9
DOI: 10.6084/m9.figshare.21282612.v1
title: magnification is 1.zos

description: For Fig. S9
DOI: 10.6084/m9.figshare.21282615.v1
title: magnification is 1over4.zos

description: For Fig. S9
DOI: 10.21227/8cj7-q373
title: Berlin V2X

description: The Berlin V2X dataset offers high-resolution GPS-located wireless measurements across diverse urban environments in the city of Berlin for both cellular and sidelink radio access technologies, acquired with up to 4 cars over 3 days. The data enables thus a variety of different ML studies towards vehicle-to-anything (V2X) communication.The data includes information onphysical layer parameters (such as signal strength and signal quality)cellular radio resource management like cell identity, carrier aggregation and assigned resource blockswireless Quality of Service (QoS) like delay and throughput (for cellular) or packet error rate (for sidelink)positioning information.The datasets are labelled and pre-filtered for a fast on-boarding and applicability. The measurement methodology pursues an application to Machine Learning (ML) for tasks such as QoS prediction, transfer learning, proactive radio resource allocation or link selection, among others.