cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21936399.v1
title: Supplemental Files — A genome-scale atlas reveals complex interplay of transcription and translation in an archaeon

description: Supplemental Files S1-S8. This is part of the supplemental material for the study entitled: "A genome-scale atlas reveals complex interplay of transcription and translation in an archaeon" by Alan P. R. Lorenzetti, Ulrike Kusebauch, Lívia S. Zaramela, Wei-Ju Wu, João P. P. de Almeida, Serdar Turkarslan, Adrián L. G. de Lomana, José V. Gomes-Filho, Ricardo Z. N. Vêncio, Robert L. Moritz, Tie Koide, and Nitin S. Baliga. File S1 | Atlas data. The non-redundant transcriptome locus tag dictionary, the normalized atlas data, and the non-normalized atlas data. File S2 | Differentially expressed genes in the absence of RNase_2099C. File S3 | Putative post-transcriptionally regulated genes (absolute abundance-based approach). Genes with patterns compatible with the post-transcriptional regulation hypothesis found by the abundance-based approach. File S4 | Gene set enrichment analysis and comparison of features. Comparison of quantitative variables and enrichment tests for putative post-transcriptionally regulated gene sets found by the absolute abundance- and by the relative abundance-based approaches. File S5 | Differential expression analysis of transcripts and proteins across the growth curve. File S6 | Putative post-transcriptionally regulated genes (relative abundance-based approach). Genes with patterns compatible with the post-transcriptional regulation hypothesis found by the relative abundance -based approach. File S7 | Atlas heatmap (expanded version). This file brings an expanded version of Figure 3. File S8 | Insertion sequence mobilization events detected by the long-read DNA-Seq experiment.
DOI: 10.3886/e184663v1-133160
title: Cancer warning label study

description: The objective of this research is to examine the role of different cancer warning labels in communicating alcohol-related cancer risks.
DOI: 10.5281/zenodo.7577670
title: Colossal optical anisotropy from atomic-scale modulations: manuscript data

description: Relevant data files for Main Text figures of "Colossal optical anisotropy from atomic-scale modulations" Relevant data files for Supplementary Information figures of "Colossal optical anisotropy from atomic-scale modulations"
DOI: 10.5281/zenodo.7577669
title: Colossal optical anisotropy from atomic-scale modulations: manuscript data

description: Relevant data files for Main Text figures of "Colossal optical anisotropy from atomic-scale modulations" Relevant data files for Supplementary Information figures of "Colossal optical anisotropy from atomic-scale modulations"
DOI: 10.6084/m9.figshare.21799793.v10
title: Targeting Cancer Cell Signaling Using Precision Oncology Towards a Holistic Approach to Cancer Therapeutics

description: The mutational changes occurring in the regulatory genes responsible for the maintenance of optimal cell growth and proliferation gradually lead to cancer progression and metastasis. Furthermore, patients with the same cancer types often respond differently to different cancer therapies, indicating the need for a patient-specific treatment regimen for the disease. Precision oncology is the new field of cancer research that relies on genetic profiling of individual tumors to identify targetable alterations for custom-tailored personalized treatment of the disease. It is to rely upon the genomic study of cancer cells to get a clear picture of the prognosis and pathways involved in disease progression and to look for the means to selectively target them for a cure. This article intends to briefly explain the foundations and frontiers of precision oncology in the context of technological advances being made in this direction to assess its scope and importance in the realization of a proper cure for cancer.
DOI: 10.21227/xxfh-qp91
title: GNSS processed interference features

description: Interference signals degrade the performance of a global navigation satellite system (GNSS) receiver. Detection and classification of these interference signals allow better situational awareness and facilitate appropriate countermeasures. However, classification is challenging and processing-intensive, especially in severe multipath environments. This dataset is the result of a proposal for a low-resource interference detection and classification approach that combines conventional statistical signal processing approaches with machine learning (ML). It leverages the processing efficiency of conventional statistical signal processing by summarizing, e.g., a short-time Fourier transform (STFT), with statistical measures. Furthermore, the ML design space is bounded as the signal is pre-processed. It results in fewer opportunities for ML but facilitates faster convergence and the use of more simple architectures. Therefore, this approach has lower ML training complexity and lower processing and memory requirements. The dataset contains the processed spectral features of 72 million randomly generated interference signals, for the types: single-tone, multi-tone, linear chirp, and band-limited noise, with and without a pulse mask, as well as the non-interfered case. The INR is varied between -35 dB and 25 dB and the duty cycle of the pulse mask ranges from 0.1% and 95%.
DOI: 10.6073/pasta/1b7cfe3fe957ec3f848d593cdbf655fd
title: UNDER REVIEW: MCR LTER: Coral Reef: Benthic Photosynthetically Active Radiation (PAR), ongoing since 2009

description: These data in this data package are currently under review for further quality assurance and should not be utilized at this time. These data contain a time-series from sequential deployments of an ALW-CMP PAR sensor recording quanta of photosynthetically active radiation (PAR) underwater between wavelengths 400 nm to 700 nm every two seconds. The sensor window is wiped free of algae periodically to prevent fouling. Deployments are three to six months in length. The sensor was mounted at 17 m depth on the fore reef, and at 2 m depth on the Backreef. PAR irradiance is measured as quanta in micromoles of photons per square meter per second. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
DOI: 10.6084/m9.figshare.21823455.v24
title: The ReptileEvolution.com Project

description: Taxon exclusion is the number one problem currently vexing paleontology. The search for pterosaur, turtle, bat, snake, whale, reptile and tetrapod ancestors continues to unnecessarily befuddle paleontologists who continue to work from cherry-picked, textbook and traditional taxon lists that overlook and omit pertinent taxa. That’s why the large reptile tree (LRT), the nearly completely resolved cladogram at the heart of www.ReptileEvolution.com, includes 2212 taxa. Larger taxon numbers minimize taxon exclusion issues. Results indicate the simple act of adding taxa resolves all phylogenetic issues.
DOI: 10.34740/kaggle/dsv/5083431
title: Nigerian Presidential Election 2023 Tweets
DOI: 10.25345/c5bv7b546
title: MassIVE MSV000091379 - 111in flow rate using a binary buffer system (buffer A
title: separated by an analytical column (PepMap C18, 2.0 um; 15 cm x 75mm I.D.; Thermo Scientific) at 300 nl/min flow rate using a binary buffer system (buffer A, 0.1% formic acid in water; buffer B, 0.1% formic acid in acetonitrile) with a 165-min gradient (1% to 10% in 8 min; then to 25% buffer B over 117 min; 25% to 32% buffer B in 10 min, then to 95% buffer B over 3 min; back to 1% B in 5 min, and stay equilibration at 1% B for 20 min). Multiple CVs (-40, -55 and -70) were applied for FAIMS separation. For all experiments, the survey scans (MS1) were acquired over a mass range of 375-1500 m/z at a resolution of 60,000 in the Orbitrap. The maximum injection time was set to Dynamic, and AGC target was set to Standard. Monoisotopic peak selection was set to Peptides, and the charge state filter was set to 2-7. For MS/MS acquisition, precursors were isolated with a width of 1.6 m/z, fragmented with HCD using 30% collision energy with a maximum injection time of 100 ms, and collected in Orbitrap at 15,000 resolution. The dynamic exclusion was set to 60 s, and can be shared across different FAIMS experiments. Protein identification and quantitation were performed using the MaxQuant-Andromeda software suite (version 1.6.3.4) with most of the default parameters. An UniProt human database (20,402 Reviewed sequences) was used for the protein identification. Other parameters include: trypsin as an enzyme with maximally two missed cleavage sites; protein N-terminal acetylation and methionine oxidation as variable modifications; cysteine carbamidomethylation as a fixed modification; peptide length must be at least 7 amino acids. False discovery rate was set at 1% for both proteins and peptides. [doi:10.25345/C5C53F57S] [dataset license: CC0 1.0 Universal (CC0 1.0)]
DOI: 10.6084/m9.figshare.22190140
title: GO enrichment beta-glucan treatment

description: GO enrichment beta-glucan treatment
DOI: 10.6084/m9.figshare.22190140.v1
title: GO enrichment beta-glucan treatment

description: GO enrichment beta-glucan treatment
DOI: 10.6084/m9.figshare.22190920
title: GO enrichment chitosan treatment

description: GO enrichment chitosan treatment
DOI: 10.6084/m9.figshare.22190920.v1
title: GO enrichment chitosan treatment

description: GO enrichment chitosan treatment
DOI: 10.6084/m9.figshare.22191157
title: GO enrichment inulin treatment

description: GO enrichment inulin treatment
DOI: 10.6084/m9.figshare.22191157.v1
title: GO enrichment inulin treatment

description: GO enrichment inulin treatment
DOI: 10.6084/m9.figshare.22190878
title: Parameters selection in the bulding of ecological niche models

description: Parameters of 50 ecological niche models, obtained in Wallace (Kass et al. 2018) for <em>Reithrodontomys mexicanus </em>species complex (Rodentia: Cricetidae).
DOI: 10.6084/m9.figshare.22190878.v1
title: Parameters selection in the bulding of ecological niche models

description: Parameters of 50 ecological niche models, obtained in Wallace (Kass et al. 2018) for <em>Reithrodontomys mexicanus </em>species complex (Rodentia: Cricetidae).
DOI: 10.6084/m9.figshare.13611385
title: A comparison of methods for clustering longitudinal data with slowly changing trends

description: Longitudinal clustering provides a detailed yet comprehensible description of time profiles among subjects. With several approaches that are commonly used for this purpose, it remains unclear under which conditions a method is preferred over another method. We investigated the performance of five methods using Monte Carlo simulations on synthetic datasets, representing various scenarios involving polynomial time profiles. The performance was evaluated on two aspects: The agreement of the group assignment to the simulated reference, as measured by the split-join distance, and the trend estimation error, as measured by a weighted minimum of the mean squared error (WMMSE). Growth mixture modeling (GMM) was found to achieve the best overall performance, followed closely by a two-step approach using growth curve modeling and <i>k</i>-means (GCKM). Considering the model similarities between GMM and GCKM, the latter is preferred for large datasets for its computational efficiency. Longitudinal <i>k</i>-means (KML) and group-based trajectory modeling were found to have practically identical solutions in the case that the group trajectory model of the latter method is correctly specified. Both methods performed less than GMM and GCKM in most settings.
DOI: 10.6084/m9.figshare.13480065.v1
title: Bayesian estimator of multiple Poisson means assuming two different priors

description: This paper describes an empirical Bayesian estimator of multiple Poisson means based on a novel concept. The idea is to assume two different priors; Jeffreys’ prior for determining the hyperparameter and the uniform prior for the canonical parameter for estimating multiple Poisson means. The validity of this idea is discussed in detail. Furthermore, the empirical Bayesian estimator is constructed using the posterior mean of the canonical parameters instead of that of the mean parameters. The proposed estimator is evaluated using three familiar losses, and the results are found to be very promising. Finally, an actual dataset is analyzed to demonstrate the practical use of the proposed estimator.
DOI: 10.6084/m9.figshare.13547418
title: Median bias reduction in cumulative link models

description: This paper presents a novel estimation approach for cumulative link models, based on median bias reduction. The median bias reduced estimator is obtained as solution of an estimating equation based on an adjustment of the score. It allows to obtain higher-order median centering of maximum likelihood estimates without requiring their finiteness. The estimator is equivariant under componentwise monotone reparameterizations and the method is effective in preventing boundary estimates. Through simulation studies and an application, we compare the median bias reduced estimator with the two main competitors, the maximum likelihood and the mean bias reduced estimators. The method is seen to be highly successful in achieving median centering and shows remarkable properties under reparameterizations related to effect measure.
DOI: 10.6084/m9.figshare.13547418.v1
title: Median bias reduction in cumulative link models

description: This paper presents a novel estimation approach for cumulative link models, based on median bias reduction. The median bias reduced estimator is obtained as solution of an estimating equation based on an adjustment of the score. It allows to obtain higher-order median centering of maximum likelihood estimates without requiring their finiteness. The estimator is equivariant under componentwise monotone reparameterizations and the method is effective in preventing boundary estimates. Through simulation studies and an application, we compare the median bias reduced estimator with the two main competitors, the maximum likelihood and the mean bias reduced estimators. The method is seen to be highly successful in achieving median centering and shows remarkable properties under reparameterizations related to effect measure.
DOI: 10.5517/ccdc.csd.cc2ckdn0
title: CCDC 2191472: Experimental Crystal Structure Determination

description: Related Article: Hiroaki Shimomoto, Yuto Miyano, Kaito Kinoshita, Tomomichi Itoh, Eiji Ihara|2023|Polym.Chem.|14|1007|doi:10.1039/D2PY01548J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d30h1
title: CCDC 2207432: Experimental Crystal Structure Determination

description: Related Article: Hiroaki Shimomoto, Yuto Miyano, Kaito Kinoshita, Tomomichi Itoh, Eiji Ihara|2023|Polym.Chem.|14|1007|doi:10.1039/D2PY01548J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d42j5
title: CCDC 2208456: Experimental Crystal Structure Determination

description: Related Article: Evan P. Van Orman, Steven M. Wolf, Zheng Yung, Ethan D. Holt, Kimberly de La Harpe, Matthias Zeller, Tod A. Grusenmeyer, Thomas G. Gray|2023|Inorg.Chem.Front.|10|1623|doi:10.1039/D2QI02078E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d42k6
title: CCDC 2208457: Experimental Crystal Structure Determination

description: Related Article: Evan P. Van Orman, Steven M. Wolf, Zheng Yung, Ethan D. Holt, Kimberly de La Harpe, Matthias Zeller, Tod A. Grusenmeyer, Thomas G. Gray|2023|Inorg.Chem.Front.|10|1623|doi:10.1039/D2QI02078E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d434t
title: CCDC 2208475: Experimental Crystal Structure Determination

description: Related Article: Tian-Tian Wang, Zi-Wei Che, Ji-Tun Chen, Han Yan, Teng-Da Zhou, Yi-Quan Zhang, Wen-Bin Sun|2023|Inorg.Chem.Front.|10|1501|doi:10.1039/D2QI02202H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d435v
title: CCDC 2208476: Experimental Crystal Structure Determination

description: Related Article: Tian-Tian Wang, Zi-Wei Che, Ji-Tun Chen, Han Yan, Teng-Da Zhou, Yi-Quan Zhang, Wen-Bin Sun|2023|Inorg.Chem.Front.|10|1501|doi:10.1039/D2QI02202H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d64tl
title: CCDC 2210449: Experimental Crystal Structure Determination

description: Related Article: Daisuke Sato, Akira Tsubouchi, Yuichiro Watanabe, Keiichi Noguchi, Kazunori Miyamoto, Masanobu Uchiyama, Akio Saito|2023|Org.Chem.Front.|10|1112|doi:10.1039/D2QO01845D
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d64wn
title: CCDC 2210451: Experimental Crystal Structure Determination

description: Related Article: Daisuke Sato, Akira Tsubouchi, Yuichiro Watanabe, Keiichi Noguchi, Kazunori Miyamoto, Masanobu Uchiyama, Akio Saito|2023|Org.Chem.Front.|10|1112|doi:10.1039/D2QO01845D
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d6sbr
title: CCDC 2211054: Experimental Crystal Structure Determination

description: Related Article: Mikhail A. Kinzhalov, Daniil M. Ivanov, Anastasia V. Shishkina, Anna A. Melekhova, Vitalii V. Suslonov, Antonio Frontera, Vadim Yu. Kukushkin, Nadezhda A. Bokach|2023|Inorg.Chem.Front.|10|1522|doi:10.1039/D2QI02634A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d6scs
title: CCDC 2211055: Experimental Crystal Structure Determination

description: Related Article: Mikhail A. Kinzhalov, Daniil M. Ivanov, Anastasia V. Shishkina, Anna A. Melekhova, Vitalii V. Suslonov, Antonio Frontera, Vadim Yu. Kukushkin, Nadezhda A. Bokach|2023|Inorg.Chem.Front.|10|1522|doi:10.1039/D2QI02634A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d6sdt
title: CCDC 2211056: Experimental Crystal Structure Determination

description: Related Article: Mikhail A. Kinzhalov, Daniil M. Ivanov, Anastasia V. Shishkina, Anna A. Melekhova, Vitalii V. Suslonov, Antonio Frontera, Vadim Yu. Kukushkin, Nadezhda A. Bokach|2023|Inorg.Chem.Front.|10|1522|doi:10.1039/D2QI02634A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d6s9q
title: CCDC 2211053: Experimental Crystal Structure Determination

description: Related Article: Mikhail A. Kinzhalov, Daniil M. Ivanov, Anastasia V. Shishkina, Anna A. Melekhova, Vitalii V. Suslonov, Antonio Frontera, Vadim Yu. Kukushkin, Nadezhda A. Bokach|2023|Inorg.Chem.Front.|10|1522|doi:10.1039/D2QI02634A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d7zl7
title: CCDC 2212209: Experimental Crystal Structure Determination

description: Related Article: Manabu Yamada, Ruka Yoshizaki, Fumiya Uemura, Hiroshi Katagiri, Sumio Kato, Kazuhiko Akimoto, Fumio Hamada|2023|Chem.Commun.|59|2604|doi:10.1039/D2CC05647J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d8q1g
title: CCDC 2212905: Experimental Crystal Structure Determination

description: Related Article: Yanhong Liu, Wenyan Dan, Bing Yan|2023|Inorg.Chem.Front.|10|1660|doi:10.1039/D2QI02701A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.