cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18430/m37orw
title: X-ray diffraction data for the non-structural protein 10 (nsp10) from SARS CoV-2 in complex with fragment VT00265 (7ORW)
DOI: 10.15468/4gzqa7
title: A New Genus of Hover Wasps from Southeast Asia (Hymenoptera: Vespidae; Stenogastrinae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article CARPENTER, JAMES M., STARR, CHRISTOPHER K. (2000): A New Genus of Hover Wasps from Southeast Asia (Hymenoptera: Vespidae; Stenogastrinae). American Museum Novitates 3291 (1): 1-12, DOI: 10.1206/0003-0082(2000)291&lt;0001:ANGOHW&gt;2.0.CO;2, URL: http://www.bioone.org/perlserv/?request=get-abstract&doi=10.1206%2F0003-0082(2000)291%3C0001%3AANGOHW%3E2.0.CO%3B2
DOI: 10.6085/aa/srkx00_xxxitv2xlsr01_20190620.50.1
title: PISCO: Intertidal: site temperature data: Seal Rock, Oregon, USA (SRKX00)

description: This metadata record describes a mix of intertidal seawater and air temperature data collected at Seal Rock, Oregon, USA by PISCO. Measurements were collected using Onset Tidbit V2 Temp Data Logger (Onset Computer Corp. UTBI-001) beginning 2019-06-20. Site temperature loggers are bolted down in a wire cage at various locations within each site. Mussel growth temperature loggers are bolted down in a wire cage at high, mid, or low positions within a mussel bed. Temperature is recorded at 10.0 minute intervals.
DOI: 10.6085/aa/srkx00_xxxitv2xlsr01_20190620.40.1
title: (:unkn)
DOI: 10.6084/m9.figshare.16878490.v1
title: deepFlashTemplateT1.nii.gz

description: t1 template for deep flash<br>
DOI: 10.6084/m9.figshare.16878490
title: deepFlashTemplateT1.nii.gz

description: t1 template for deep flash<br>
DOI: 10.6084/m9.figshare.16878487
title: deepFlashTemplateT2.nii.gz

description: t2 template for deep flash<br>
DOI: 10.6084/m9.figshare.16878487.v1
title: deepFlashTemplateT2.nii.gz

description: t2 template for deep flash<br>
DOI: 10.15468/puzajj
title: First record of the genus Phoxoserphus Lin, 1988 (Hymenoptera: Proctotrupidae) from Japan

description: This dataset contains the digitized treatments in Plazi based on the original journal article Abe, Junta, Kolyada, Victor (2021): First record of the genus Phoxoserphus Lin, 1988 (Hymenoptera: Proctotrupidae) from Japan. Zootaxa 5057 (4): 597-600, DOI: https://doi.org/10.11646/zootaxa.5057.4.10
DOI: 10.25345/c5m84c
title: MassIVE MSV000088266 - GNPS Standard of Manzamine for Analysis
title: Standard of Manzamine for analysis of the sponge sample from Indonesia
DOI: 10.5281/zenodo.5600956
title: Bartramia longicauda decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5600955
title: Bartramia longicauda decontaminated FSCR

description: .
DOI: 10.25387/g3.14732073
title: Supplemental Material for Petrasch et al., 2021

description: This repository includes genotypic (File S2), phenotypic (File S3), and other supplemental data files, figures, and tables for Petrasch et al. (2021). File S1 is a text file with a custom macro developed to process photographic images of strawberry fruit using ImageJ, a "public domain Java image processing program" (https://imagej.nih.gov/ij/index.html). File S2 stores 50K Axiom SNP array genotypic data for the multifamily and the Royal Royce x Tangi population. File S3 stores the phenotypic data from our study. Custom R and PERL scripts developed for genetic mapping analyses are stored in File S4. File S5 stores information on candidate genes linked to gray mold resistance QTL with physical addresses and genome annotations from the <i>F</i>. x <i>ananassa</i> 'Camarosa' v1.0 reference genome (Edger et al., 2019). The latter is available at the Genome Database for Rosaceae (https://www.rosaceae.org/species/fragaria_x_ananassa/genome_v1.0.a1). File S6 stores the estimated marginal means (EMMs) and genomic-estimated breeding values (GEBVs) for gray mold resistance and fruit quality phenotypes among 380 individuals in a multi-family training population and 233 individuals in the Royal Royce x Tangi population. Fig. S1 displays time-series photographs of fruit of the long shelf life summer-plant cultivars 'Finn' and 'Mojo' at 0, 7, and 14 days of postharvest storage. Fig. S2 displays the phenotypic distributions and parent EMMs for gray mold resistance phenotypes within full-sib families. Fig. S3 displays kernel densities for genomic prediction accuracy estimated by cross-validation using support vector machine.
DOI: 10.15468/pesh94
title: A new hip-pocket frog from mid-eastern Australia (Anura: Myobatrachidae: Assa)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Mahony, Michael J., Hines, Harry B., Mahony, Stephen V., Moses, Bede, Catalano, Sarah R., Myers, Steven, Donnellan, Stephen C. (2021): A new hip-pocket frog from mid-eastern Australia (Anura: Myobatrachidae: Assa). Zootaxa 5057 (4): 451-486, DOI: https://doi.org/10.11646/zootaxa.5057.4.1
DOI: 10.15468/59qdch
title: Identification key, checklist and new species of Loneura Navás (Psocodea: ‘ Psocoptera’: Ptiloneuridae) from Brazil

description: This dataset contains the digitized treatments in Plazi based on the original journal article Cutrim, Marcelo, Neto, Alberto Moreira Da Silva, García Aldrete, Alfonso N., Rafael, José Albertino (2021): Identification key, checklist and new species of Loneura Navás (Psocodea: ‘ Psocoptera’: Ptiloneuridae) from Brazil. Zootaxa 5057 (4): 487-502, DOI: https://doi.org/10.11646/zootaxa.5057.4.2
DOI: 10.6084/m9.figshare.14865870
title: DATA: Learning Words While Listening to Syllables: Electrophysiological Correlates of Statistical Learning in Children and Adults

description: Accuracy data and N1 and N400 ERP data from an auditory statitical learning paradigm. Participants were exposed to a continuous stream made up of triplets with high and low transitional probabilities. In a subsequent ecognition phase, learning was tested using a 2-AFC task. Participants performed firstly the implicit version of the task (i.e., without instructions of the content and structure of the stream of syllables); after that, they performed the same task with different stimuli, in this case under explicit instructions.
DOI: 10.6084/m9.figshare.14865870.v1
title: DATA: Developmental Differences in the Online Statistical Learning of Complex Speech Streams: An ERP Study with Children and Adults

description: Accuracy data and N1 and N400 ERP data from an auditory statitical learning paradigm. Participants were exposed to a continuous stream made up of triplets with high and low transitional probabilities. In a subsequent ecognition phase, learning was tested using a 2-AFC task. Participants performed firstly the implicit version of the task (i.e., without instructions of the content and structure of the stream of syllables); after that, they performed the same task with different stimuli, in this case under explicit instructions.
DOI: 10.6084/m9.figshare.16878889
title: The perceived impact of the COVID-19 pandemic on medical students’ future careers Survey Questions

description: Survey questions for manuscript assessing the perceived impact of the COVID-19 pandemic on medical students’ future careers
DOI: 10.6084/m9.figshare.16878889.v1
title: The perceived impact of the COVID-19 pandemic on medical students’ future careers Survey Questions

description: Survey questions for manuscript assessing the perceived impact of the COVID-19 pandemic on medical students’ future careers
DOI: 10.5281/zenodo.5601489
title: Generación, oferta y demanda energética

description: Este dataset contiene información sobre la demanda y consumo energético en dos vertientes: - Nivel masivo: * Información extraída de la AEMET con indicadores metereológicos de 16 estaciones repartidas en 16 ciudades de España. * Información extraída de REE (Red Eléctrica de España) con la oferta y la demanda de energía desglosada por fuente de generación. - Nivel individual: * Hábito de consumo energético por horas de uno de los autores del dataset. Cada registro representa un día comprendido entre 1 de enero de 2020 y 30 de septiembre de 2021.
DOI: 10.5281/zenodo.5601488
title: Generación, oferta y demanda energética

description: Este dataset contiene información sobre la demanda y consumo energético en dos vertientes: - Nivel masivo: * Información extraída de la AEMET con indicadores metereológicos de 16 estaciones repartidas en 16 ciudades de España. * Información extraída de REE (Red Eléctrica de España) con la oferta y la demanda de energía desglosada por fuente de generación. - Nivel individual: * Hábito de consumo energético por horas de uno de los autores del dataset. Cada registro representa un día comprendido entre 1 de enero de 2020 y 30 de septiembre de 2021.
DOI: 10.5281/zenodo.5599993
title: Comau Fjord CTD dataset (January 2014)

description: CTD and water quality profiles from Comau Fjord (January 2014), presented in: Sepúlveda Steiner, O. 2015. Efecto de la circulación baroclínica inducida por el viento en la mezcla vertical del fiordo Comau. Master thesis, University of Chile, Chile (in spanish) / <em>Effect of wind-induced baroclinic circulation on the vertical mixing in Comau Fjord (Chile)</em> URI: http://repositorio.uchile.cl/handle/2250/137528
DOI: 10.5281/zenodo.5599992
title: Comau Fjord CTD dataset (January 2014)

description: CTD and water quality profiles from Comau Fjord (January 2014), presented in: Sepúlveda Steiner, O. 2015. Efecto de la circulación baroclínica inducida por el viento en la mezcla vertical del fiordo Comau. Master thesis, University of Chile, Chile (in spanish) / <em>Effect of wind-induced baroclinic circulation on the vertical mixing in Comau Fjord (Chile)</em> URI: http://repositorio.uchile.cl/handle/2250/137528
DOI: 10.5517/ccdc.csd.cc282llk
title: CCDC 2087868: Experimental Crystal Structure Determination

description: Related Article: Jugal Kishore Rai Deka, Debajit Kalita, Biswajit Sahariah, Bani Kanta Sarma|2021|Chem.Commun.|57|11236|doi:10.1039/D1CC04533D
DOI: 10.5517/ccdc.csd.cc282lml
title: CCDC 2087869: Experimental Crystal Structure Determination

description: Related Article: Jugal Kishore Rai Deka, Debajit Kalita, Biswajit Sahariah, Bani Kanta Sarma|2021|Chem.Commun.|57|11236|doi:10.1039/D1CC04533D
DOI: 10.5517/ccdc.csd.cc2835j3
title: CCDC 2088424: Experimental Crystal Structure Determination

description: Related Article: Jugal Kishore Rai Deka, Debajit Kalita, Biswajit Sahariah, Bani Kanta Sarma|2021|Chem.Commun.|57|11236|doi:10.1039/D1CC04533D
DOI: 10.5517/ccdc.csd.cc288rq1
title: CCDC 2093793: Experimental Crystal Structure Determination

description: Related Article: Jugal Kishore Rai Deka, Debajit Kalita, Biswajit Sahariah, Bani Kanta Sarma|2021|Chem.Commun.|57|11236|doi:10.1039/D1CC04533D
DOI: 10.5517/ccdc.csd.cc28chms
title: CCDC 2096425: Experimental Crystal Structure Determination

description: Related Article: Jessica E. Waters, Schirin Hanf, Marina Rincón-Nocito, Andrew D. Bond, Raul García-Rodríguez, Dominic S. Wright, Annie L. Colebatch|2021|Dalton Trans.|50|14551|doi:10.1039/D1DT02438H
DOI: 10.5517/ccdc.csd.cc28dvm5
title: CCDC 2097727: Experimental Crystal Structure Determination

description: Related Article: Rintaro Matsuo, Ayumu Watanabe, Shogo Kamo, Akinobu Matsuzawa, Kazuyuki Sugita|2021|Org.Chem.Front.|8|6063|doi:10.1039/D1QO01251G
DOI: 10.6084/m9.figshare.16879039.v1
title: Untitled Item

description: <b>Student comments regarding step exams and other concerns</b>
DOI: 10.6084/m9.figshare.16879039
title: Untitled Item

description: <b>Student comments regarding step exams and other concerns</b>
DOI: 10.6084/m9.figshare.16879062
title: Physiological models to study the effect of molecular crowding on multi-drug bound proteins: insights from SARS-CoV-2 main protease

description: Molecular Dynamics simulations are often used in drug design. However, such simulations do not account for the physiological environment of the receptor; hence overlook its impact on biomolecular interactions. To address this lacuna, we identified three objectives to pursue – develop models of physiological environment, study a drug-receptor complex in such environments, and identify methods to analyze these complicated simulations. Two novel physiological models were developed and studied. The first, called ‘m10’, comprises of 10 of the most abundant cytoplasmic metabolites at physiological concentrations. The second, called ‘phy’, supplements m10 with an additional crowder protein to elicit macromolecular crowding effect. The main protease (M<i><sup>pro</sup></i>) of SARS-CoV-2, being essential for viral replication, is an attractive drug target for COVID-19. Hence, we chose M<i><sup>pro</sup></i> docked with multiple drugs as our model drug-receptor system. With a plethora of compounds, physiological systems can be exceedingly large and complex. A novel Spark-based software (SparkTraj) was developed to rapidly analyze non-specific contacts and water interactions. Our study shows that crowding enhances the difference in the dynamics of apo- vs drug-bound complexes. Metabolites, at times as a cluster, were seen interacting with the protease, drugs, and binding sites in drug-free receptor. Except one that <i>crawled</i> to an adjacent pocket in phy, the drugs remained in their respective pockets in all simulations. Given these observations, we hope that the models and approach presented here would help the optimization, evaluation, and selection of potential drugs. Generic biomolecular dynamics could also benefit from such models and tools. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.16879062.v1
title: Physiological models to study the effect of molecular crowding on multi-drug bound proteins: insights from SARS-CoV-2 main protease

description: Molecular Dynamics simulations are often used in drug design. However, such simulations do not account for the physiological environment of the receptor; hence overlook its impact on biomolecular interactions. To address this lacuna, we identified three objectives to pursue – develop models of physiological environment, study a drug-receptor complex in such environments, and identify methods to analyze these complicated simulations. Two novel physiological models were developed and studied. The first, called ‘m10’, comprises of 10 of the most abundant cytoplasmic metabolites at physiological concentrations. The second, called ‘phy’, supplements m10 with an additional crowder protein to elicit macromolecular crowding effect. The main protease (M<i><sup>pro</sup></i>) of SARS-CoV-2, being essential for viral replication, is an attractive drug target for COVID-19. Hence, we chose M<i><sup>pro</sup></i> docked with multiple drugs as our model drug-receptor system. With a plethora of compounds, physiological systems can be exceedingly large and complex. A novel Spark-based software (SparkTraj) was developed to rapidly analyze non-specific contacts and water interactions. Our study shows that crowding enhances the difference in the dynamics of apo- vs drug-bound complexes. Metabolites, at times as a cluster, were seen interacting with the protease, drugs, and binding sites in drug-free receptor. Except one that <i>crawled</i> to an adjacent pocket in phy, the drugs remained in their respective pockets in all simulations. Given these observations, we hope that the models and approach presented here would help the optimization, evaluation, and selection of potential drugs. Generic biomolecular dynamics could also benefit from such models and tools. Communicated by Ramaswamy H. Sarma
DOI: 10.5517/ccdc.csd.cc28g9jl
title: CCDC 2099119: Experimental Crystal Structure Determination

description: Related Article: Meng Wang, Hong Wei Gao, Jin Xiao Li, Feng Ying Bai, Yong Heng Xing, Zhan Shi|2021|Dalton Trans.|50|14618|doi:10.1039/D1DT02560K
DOI: 10.5517/ccdc.csd.cc28g9km
title: CCDC 2099120: Experimental Crystal Structure Determination

description: Related Article: Meng Wang, Hong Wei Gao, Jin Xiao Li, Feng Ying Bai, Yong Heng Xing, Zhan Shi|2021|Dalton Trans.|50|14618|doi:10.1039/D1DT02560K
DOI: 10.5517/ccdc.csd.cc28g9ln
title: CCDC 2099121: Experimental Crystal Structure Determination

description: Related Article: Meng Wang, Hong Wei Gao, Jin Xiao Li, Feng Ying Bai, Yong Heng Xing, Zhan Shi|2021|Dalton Trans.|50|14618|doi:10.1039/D1DT02560K
DOI: 10.5517/ccdc.csd.cc28gc8d
title: CCDC 2099173: Experimental Crystal Structure Determination

description: Related Article: Arimasa Matsumoto, Ayame Tanaka, Yoshiyasu Kaimori, Natsuki Hara, Yuji Mikata, Kenso Soai|2021|Chem.Commun.|57|11209|doi:10.1039/D1CC04206H
DOI: 10.6084/m9.figshare.16879087
title: Students Comments CC0

description: Qualitative comments from students regarding impact of COVID-19 pandemic on medical career
DOI: 10.6084/m9.figshare.16879087.v1
title: Students Comments CC0

description: Qualitative comments from students regarding impact of COVID-19 pandemic on medical career
DOI: 10.6084/m9.figshare.16879090
title: Supplemental Table 2 Survey Questions.docx

description: Survey questionnaire for impact of COVID-19 pandemic on medical student career planning
DOI: 10.6084/m9.figshare.16879090.v1
title: Supplemental Table 2 Survey Questions.docx

description: Survey questionnaire for impact of COVID-19 pandemic on medical student career planning
DOI: 10.25934/pr00007494
title: Vivli Data Request 3369 - Request to re-access Vivli Research Environment

description: N/A
DOI: 10.3886/e147225
title: Data and Code for: "Forecasting in the Presence of Instabilities"

description: These are the data and codes to replicate the figures in: "Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them". <br><br>This paper provides guidance on how to evaluate and improve the forecasting ability of models in the presence of instabilities, which are widespread in economic time series. Empirically relevant examples include predicting the financial crisis of 2007-2008, as well as, more broadly, fluctuations in asset prices, exchange rates, output growth and inflation. In the context of unstable environments, I discuss how to assess models' forecasting ability; how to robustify models' estimation; and how to correctly report measures of forecast uncertainty. Importantly, and perhaps surprisingly, breaks in models' parameters are neither necessary nor sufficient to generate time variation in models' forecasting performance: thus, one should not test for breaks in models' parameters, but rather evaluate their forecasting ability in a robust way. In addition, local measures of models' forecasting performance are more appropriate than traditional, average measures.<br>
DOI: 10.3886/e147225v1
title: Data and Code for: "Forecasting in the Presence of Instabilities"

description: These are the data and codes to replicate the figures in: "Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them". <br><br>This paper provides guidance on how to evaluate and improve the forecasting ability of models in the presence of instabilities, which are widespread in economic time series. Empirically relevant examples include predicting the financial crisis of 2007-2008, as well as, more broadly, fluctuations in asset prices, exchange rates, output growth and inflation. In the context of unstable environments, I discuss how to assess models' forecasting ability; how to robustify models' estimation; and how to correctly report measures of forecast uncertainty. Importantly, and perhaps surprisingly, breaks in models' parameters are neither necessary nor sufficient to generate time variation in models' forecasting performance: thus, one should not test for breaks in models' parameters, but rather evaluate their forecasting ability in a robust way. In addition, local measures of models' forecasting performance are more appropriate than traditional, average measures.<br>
DOI: 10.6084/m9.figshare.16574438
title: Coadministration of silymarin with iron chelators in transfusion-dependent β-thalassemia patients: a systematic review and meta-analysis for effect on iron overload

description: <b>Background and aim</b>: We conducted a systematic review to apprise the efficacy of silymarin in conjunction with standard iron chelators on iron overload for transfusion-dependent β-thalassemia (TDT) patients. <b>Methods</b>: We searched PubMed, Web of Science, Scopus, Sciencedirect, the Cochrane Library (the Cochrane Database of Systematic Reviews, and the Cochrane Central Register of Controlled Trials (CENTRAL) to 1 May 2020. All randomized controlled trials (RCTs) studies comparing the effect of iron chelators alone versus silymarin plus standard routine treatment on iron burden amid TDT were included in this review. Primary outcomes comprised serum ferritin level (ng/mL), liver iron concentration (LIC Fe/kg dry weight), and total iron binding capacity (TIBC mcg/dL) <b>Results</b>: Combination therapy of silymarin and iron chelators showed a significant improvement in serum ferritin level in TDT patients, compared to nonsilymarin users [eight studies, n = 477]; weighted mean difference (WMD) −1.79, 95% confidence interval [CI] −2.86 to −0.72, I<sup>2</sup> 96.1%; P = 0.001. Concurrent treatment with silymarin failed to significantly decrease LIC in TDT patients [two studies, n = 106]; WMD 0.74, 95% CI −1.62 to 3.10, I<sup>2</sup> 96.6%; P = 0.54. <b>Conclusion</b>: There is no evidence of the effectiveness of adding silymarin to standard iron chelators to reduce iron load in TDT.
DOI: 10.15468/nmnxhr
title: Two new species of Megophrys Kuhl and Van Hasselt (Amphibia: Megophryidae) from Sumatra, Indonesia

description: This dataset contains the digitized treatments in Plazi based on the original journal article Munir, Misbahul, Nishikawa, Kanto, Hamidy, Amir, Smith, Eric N. (2021): Two new species of Megophrys Kuhl and Van Hasselt (Amphibia: Megophryidae) from Sumatra, Indonesia. Zootaxa 5057 (4): 503-529, DOI: https://doi.org/10.11646/zootaxa.5057.4.3
DOI: 10.6084/m9.figshare.16574438.v2
title: Coadministration of silymarin with iron chelators in transfusion-dependent β-thalassemia patients: a systematic review and meta-analysis for effect on iron overload

description: <b>Background and aim</b>: We conducted a systematic review to apprise the efficacy of silymarin in conjunction with standard iron chelators on iron overload for transfusion-dependent β-thalassemia (TDT) patients. <b>Methods</b>: We searched PubMed, Web of Science, Scopus, Sciencedirect, the Cochrane Library (the Cochrane Database of Systematic Reviews, and the Cochrane Central Register of Controlled Trials (CENTRAL) to 1 May 2020. All randomized controlled trials (RCTs) studies comparing the effect of iron chelators alone versus silymarin plus standard routine treatment on iron burden amid TDT were included in this review. Primary outcomes comprised serum ferritin level (ng/mL), liver iron concentration (LIC Fe/kg dry weight), and total iron binding capacity (TIBC mcg/dL) <b>Results</b>: Combination therapy of silymarin and iron chelators showed a significant improvement in serum ferritin level in TDT patients, compared to nonsilymarin users [eight studies, n = 477]; weighted mean difference (WMD) −1.79, 95% confidence interval [CI] −2.86 to −0.72, I<sup>2</sup> 96.1%; P = 0.001. Concurrent treatment with silymarin failed to significantly decrease LIC in TDT patients [two studies, n = 106]; WMD 0.74, 95% CI −1.62 to 3.10, I<sup>2</sup> 96.6%; P = 0.54. <b>Conclusion</b>: There is no evidence of the effectiveness of adding silymarin to standard iron chelators to reduce iron load in TDT.