cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23904219
title: Alignments of orchid-mycorrhizal fungi and ddPCR raw data of Neottia ovata

description: The folder 'alignments' contains alignments of OTUs of orchid mycorrhizal fungi detected from roots of the orchid <i>Neottia ovata using MiSeq </i>300-bp paired-end sequencing<i> (</i>Wang et al., 2023.<i> </i>https://doi.org/10.1111/1365-2435.14354<i>)</i>. Fungal OTUs were alignment within each of the eight fungal families including Serendipitaceae, Ceratobasidiaceae, Inocybaceae, Sebacinaceae, Thelephoraceae, Tulasnellaceae, Russulaceae and Atheliaceae.The folder 'alignments_GOM' contains family-specific alignments of sequences from the global dataset of orchid mycorrhizal fungi (GOM, Wang et al., 2021. https://doi.org/10.1111/nph.17414).The folder 'ddPCR' contains raw qlp files exported from Quansoft software and estimations of copy numbers by thresholding. These files contain the ddPCR mearuements of the eight fungal families including Serendipitaceae, Ceratobasidiaceae, Inocybaceae, Sebacinaceae, Thelephoraceae, Tulasnellaceae, Russulaceae and Atheliaceae in 30 DNA samples of N. ovata roots.<br>
DOI: 10.6084/m9.figshare.23904219.v1
title: Alignments of orchid-mycorrhizal OTUs of Neottia ovata

description: This is a dataset of alignments of OTUs of orchid mycorrhizal fungi detected from roots of the orchid <i>Neottia ovata</i>. The fungal OTUs were clustered based on 97% similarity of reads generated from MiSeq 300-bp paired-end sequencing. Fungal OTUs were alignment within each of the eight fungal families including Serendipitaceae, Ceratobasidiaceae, Inocybaceae, Sebacinaceae, Thelephoraceae, Tulasnellaceae, Russulaceae and Atheliaceae.
DOI: 10.5281/zenodo.8413761
title: TOOCAN cloud mask

description: This dataset comprises a high cloud mask derived from the TOOCAN dataset (Fiolleau et al., 2020) within the region spanning from 0N to 9.6N latitude and 140E to 149.6E longitude. The TOOCAN dataset offers calibrated infra-red brightness temperatures across the entire tropical band in the period 2012-2016.<br> The images have 0.04° spatial resolution and 30 minute temporal frequency. Fiolleau, T., R. Roca, S. Cloché, D. Bouniol, P. Raberanto, 2020: Homogenization of geostationary infrared imager channels for cold cloud studies using Megha-Tropiques/ScaRaB. IEEE Trans. Geosci. Remote Sens., vol 58, no. 9, pp. 6609-6622. doi: 10.1109/TGRS.2020.2978171
DOI: 10.5281/zenodo.8413762
title: TOOCAN cloud mask

description: This dataset comprises a high cloud mask derived from the TOOCAN dataset (Fiolleau et al., 2020) within the region spanning from 0N to 9.6N latitude and 140E to 149.6E longitude. The TOOCAN dataset offers calibrated infra-red brightness temperatures across the entire tropical band in the period 2012-2016.<br> The images have 0.04° spatial resolution and 30 minute temporal frequency. Fiolleau, T., R. Roca, S. Cloché, D. Bouniol, P. Raberanto, 2020: Homogenization of geostationary infrared imager channels for cold cloud studies using Megha-Tropiques/ScaRaB. IEEE Trans. Geosci. Remote Sens., vol 58, no. 9, pp. 6609-6622. doi: 10.1109/TGRS.2020.2978171
DOI: 10.5281/zenodo.8422191
title: TOOCAN cloud mask

description: This dataset comprises a high cloud mask derived from the TOOCAN dataset (Fiolleau et al., 2020) within the region spanning from 0N to 9.6N latitude and 140E to 149.6E longitude. The TOOCAN dataset offers calibrated infra-red brightness temperatures across the entire tropical band in the period 2012-2016.<br> The images have 0.04° spatial resolution and 30 minute temporal frequency. Fiolleau, T., R. Roca, S. Cloché, D. Bouniol, P. Raberanto, 2020: Homogenization of geostationary infrared imager channels for cold cloud studies using Megha-Tropiques/ScaRaB. IEEE Trans. Geosci. Remote Sens., vol 58, no. 9, pp. 6609-6622. doi: 10.1109/TGRS.2020.2978171
DOI: 10.6084/m9.figshare.24089791
title: Network Estimation by Mixing: Adaptivity and More

description: Networks analysis has been commonly used to study the interactions between units of complex systems. One problem of particular interest is learning the network’s underlying connection pattern given a single and noisy instantiation. While many methods have been proposed to address this problem in recent years, they usually assume that the true model belongs to a known class, which is not verifiable in most real-world applications. Consequently, network modeling based on these methods either suffers from model misspecification or relies on additional model selection procedures that are not well understood in theory and can potentially be unstable in practice. To address this difficulty, we propose a mixing strategy that leverages available arbitrary models to improve their individual performances. The proposed method is computationally efficient and almost tuning-free for network modeling. We show that the proposed method performs equally well as the oracle estimate when the true model is included as individual candidates. More importantly, the method remains robust and outperforms all current estimates even when the models are misspecified. Extensive simulation examples are used to verify the advantage of the proposed mixing method. Evaluation of link prediction performance on more than 500 real-world networks from different domains also demonstrates the universal competitiveness of the mixing method across multiple domains. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24089791.v2
title: Network Estimation by Mixing: Adaptivity and More

description: Networks analysis has been commonly used to study the interactions between units of complex systems. One problem of particular interest is learning the network’s underlying connection pattern given a single and noisy instantiation. While many methods have been proposed to address this problem in recent years, they usually assume that the true model belongs to a known class, which is not verifiable in most real-world applications. Consequently, network modeling based on these methods either suffers from model misspecification or relies on additional model selection procedures that are not well understood in theory and can potentially be unstable in practice. To address this difficulty, we propose a mixing strategy that leverages available arbitrary models to improve their individual performances. The proposed method is computationally efficient and almost tuning-free for network modeling. We show that the proposed method performs equally well as the oracle estimate when the true model is included as individual candidates. More importantly, the method remains robust and outperforms all current estimates even when the models are misspecified. Extensive simulation examples are used to verify the advantage of the proposed mixing method. Evaluation of link prediction performance on more than 500 real-world networks from different domains also demonstrates the universal competitiveness of the mixing method across multiple domains. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24089791.v1
title: Network Estimation by Mixing: Adaptivity and More<sup>*</sup>

description: Networks analysis has been commonly used to study the interactions between units of complex systems. One problem of particular interest is learning the network’s underlying connection pattern given a single and noisy instantiation. While many methods have been proposed to address this problem in recent years, they usually assume that the true model belongs to a known class, which is not verifiable in most real-world applications. Consequently, network modeling based on these methods either suffers from model misspecification or relies on additional model selection procedures that are not well understood in theory and can potentially be unstable in practice. To address this difficulty, we propose a mixing strategy that leverages available arbitrary models to improve their individual performances. The proposed method is computationally efficient and almost tuning-free for network modeling. We show that the proposed method performs equally well as the oracle estimate when the true model is included as individual candidates. More importantly, the method remains robust and outperforms all current estimates even when the models are misspecified. Extensive simulation examples are used to verify the advantage of the proposed mixing method. Evaluation of link prediction performance on more than 500 real-world networks from different domains also demonstrates the universal competitiveness of the mixing method across multiple domains.
DOI: 10.5281/zenodo.8383558
title: All-atom MD trajectory TOCL:POPC (20%), 50mM KCl

description: ~400-ns MD simulation of a lipid bilayer, which monolayer was composed of 44 POPC (1-Palmitoyl-2-oleoylphosphatidylcholine) molecules and 6 TLCL (tetra-linoeoyl-cardiolipin) molecules. The background electrolyte is KCl; these ions were added in that amount, which imitates 50-mM ionic force in the bulk. The water amount is large (more than 140 water molecules per lipid), allowing it to equilibrate the electric double layer at a relatively low ionic force. TIP3P water model and CHARMM36 force field were used; the double-deprotonated form of cardiolipin was used (TLCL2). Production simulation was performed with MD integrator (2-fs step, bonds to hydrogen atoms were constrained), Nose-Hoover thermostat (303K, 1-ps time constant) and Parinello-Rahman semi-isotropic barostat (5-ps time constant, water compressibility). PME was used for electrostatics; cut-off radii were set to 1.2 nm. VDW switch radius was set to 1.0 nm. Resulting trajectories deposited: <strong>COMBINED-50ps.trr</strong> [373 ns length, 50-ps step, with velocities] <strong>COMBINED-10ps-100ns.trr</strong> [last 100 ns, 10-ps step, with velocities] <strong>COMBINED.edr</strong> [373 ns length, 2-ps step] The additional ZIP archive contains intermediate TPR and MDP files and a force field folder.
description: The study was supported in 2019-2021 partly by the Russian Foundation for Basic Research (No. 19-04-00242 to Yu. A. Ermakov) and by the Ministry of Science and Higher Education of Russia (theme #AAAA-A20-120101090002-4). Simulations were performed on SMP machines at the Biophysics Department of the School of Biology, MSU.
DOI: 10.5281/zenodo.8383557
title: All-atom MD trajectory TOCL:POPC (20%), 50mM KCl

description: ~400-ns MD simulation of a lipid bilayer, which monolayer was composed of 44 POPC (1-Palmitoyl-2-oleoylphosphatidylcholine) molecules and 6 TLCL (tetra-linoeoyl-cardiolipin) molecules. The background electrolyte is KCl; these ions were added in that amount, which imitates 50-mM ionic force in the bulk. The water amount is large (more than 140 water molecules per lipid), allowing it to equilibrate the electric double layer at a relatively low ionic force. TIP3P water model and CHARMM36 force field were used; the double-deprotonated form of cardiolipin was used (TLCL2). Production simulation was performed with MD integrator (2-fs step, bonds to hydrogen atoms were constrained), Nose-Hoover thermostat (303K, 1-ps time constant) and Parinello-Rahman semi-isotropic barostat (5-ps time constant, water compressibility). PME was used for electrostatics; cut-off radii were set to 1.2 nm. VDW switch radius was set to 1.0 nm. Resulting trajectories deposited: <strong>COMBINED-50ps.trr</strong> [373 ns length, 50-ps step, with velocities] <strong>COMBINED-10ps-100ns.trr</strong> [last 100 ns, 10-ps step, with velocities] <strong>COMBINED.edr</strong> [373 ns length, 2-ps step] The additional ZIP archive contains intermediate TPR and MDP files and a force field folder.
description: The study was supported in 2019-2021 partly by the Russian Foundation for Basic Research (No. 19-04-00242 to Yu. A. Ermakov) and by the Ministry of Science and Higher Education of Russia (theme #AAAA-A20-120101090002-4). Simulations were performed on SMP machines at the Biophysics Department of the School of Biology, MSU.
DOI: 10.34740/kaggle/dsv/6647952
title: MoviesDatasets
DOI: 10.34740/kaggle/dsv/6647948
title: Mahanagar Yatayat Transport Dataset
DOI: 10.5281/zenodo.8013207
title: 3D volumes of suspended particulate matter in the Belgian part of the North Sea

description: This dataset comprises 3D volumes that display the converted mean mass concentrations of suspended particulate matter (SPMC) for different size ranges (1-500 µm, 1-3 µm, 3-20 µm, 20-200 µm, 200-500 µm). These 3D grids (2 m resolution) were made for five campaigns, which were conducted during 2020-2021 in the Belgian part of the North Sea. This dataset was used in the research article in Remote Sensing titled "The potential of multibeam sonars as 3D turbidity and SPM monitoring tool in the North Sea" by Praet et al. (2023).
description: Five campaigns were conducted with the RV Simon Stevin in the Kwinte (KW) and Westdiep (WD) areas during fall/winter (October 2020, February 2021) and spring/summer (March 2021, May 2021, July 2021). During each campaign, 3D multibeam water column (EM2040) and in-situ optical sensor (LISST-200X) datasets were collected to yield an empirical relation using linear regression modelling. This relationship was then used to predict SPM volume concentrations from the 3D acoustic measurements, which were further converted to SPM mass concentrations using calculated densities. The raw and processed LISST-200X datasets can be consulted on the Marine Data Archive (DOI: doi.org/10.14284/572). This research was conducted in the framework of the TIMBERS project (grant number SR/00/381), the STURMAPSS (dissemination) project (grant number SR/L9/221) and the TURBEAMS project (grant number RV/21/TURBEAMS). All projects were funded by the Belgian Science Policy Office BELSPO.
DOI: 10.5281/zenodo.8013206
title: 3D volumes of suspended particulate matter in the Belgian part of the North Sea

description: This dataset comprises 3D volumes that display the converted mean mass concentrations of suspended particulate matter (SPMC) for different size ranges (1-500 µm, 1-3 µm, 3-20 µm, 20-200 µm, 200-500 µm). These 3D grids (2 m resolution) were made for five campaigns, which were conducted during 2020-2021 in the Belgian part of the North Sea. This dataset was used in the research article in Remote Sensing titled "The potential of multibeam sonars as 3D turbidity and SPM monitoring tool in the North Sea" by Praet et al. (2023).
description: Five campaigns were conducted with the RV Simon Stevin in the Kwinte (KW) and Westdiep (WD) areas during fall/winter (October 2020, February 2021) and spring/summer (March 2021, May 2021, July 2021). During each campaign, 3D multibeam water column (EM2040) and in-situ optical sensor (LISST-200X) datasets were collected to yield an empirical relation using linear regression modelling. This relationship was then used to predict SPM volume concentrations from the 3D acoustic measurements, which were further converted to SPM mass concentrations using calculated densities. The raw and processed LISST-200X datasets can be consulted on the Marine Data Archive (DOI: doi.org/10.14284/572). This research was conducted in the framework of the TIMBERS project (grant number SR/00/381), the STURMAPSS (dissemination) project (grant number SR/L9/221) and the TURBEAMS project (grant number RV/21/TURBEAMS). All projects were funded by the Belgian Science Policy Office BELSPO.
DOI: 10.6084/m9.figshare.24272203
title: 20231010_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.24272203.v1
title: 20231010_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.24272110
title: Automatic Identification of Illegal Construction and Demolition Waste Landfills: A Computer Vision Approach.

description: This link contains the raw data, which includes 35 environmental reports for construction and demolition waste landfills, data information for 231 landfills, and 360 raw images of construction and demolition waste landfills used for the experiments and the corresponding labels.
DOI: 10.6084/m9.figshare.24271210
title: Figure 3. Alignment and newick tree of new MAGs in the global lucinid symbiont tree

description: Concatenated alignment of 120 conserved bacterial marker genes from the MAGs was obtained with the GTDB-Tk v2.1.1 (Genome Taxonomy Database Toolkit) classify workflow, including MAGs published in this work and in Osvatic et al. 2023 ISMEJ. Maximum likelihood phylogenomic tree inferred from GTDB’s multiple sequence alignment with IQ-Tree v2.2.2.1 (Minh et al. 2020; Hoang et al. 2018; Kalyaanamoorthy et al. 2017) including MAGs published in this work and in Osvatic et al. 2023 ISMEJ. The tree was inferred with auto substitution model detection, 1,000 ultrafast bootstrap (UFB) replicates and 1,000 samples for SH-aLRT branch testing. Nodes with values of UFB greater or equal to 95% and of SH-aLRT greater or equal to 80% were considered to be strongly supported. The best-fit model was Q.plant+F+I+G4.
DOI: 10.6084/m9.figshare.24271210.v5
title: Figure 3. Alignment and newick tree of new MAGs in the global lucinid symbiont tree

description: Concatenated alignment of 120 conserved bacterial marker genes from the MAGs was obtained with the GTDB-Tk v2.1.1 (Genome Taxonomy Database Toolkit) classify workflow, including MAGs published in this work and in Osvatic et al. 2023 ISMEJ. Maximum likelihood phylogenomic tree inferred from GTDB’s multiple sequence alignment with IQ-Tree v2.2.2.1 (Minh et al. 2020; Hoang et al. 2018; Kalyaanamoorthy et al. 2017) including MAGs published in this work and in Osvatic et al. 2023 ISMEJ. The tree was inferred with auto substitution model detection, 1,000 ultrafast bootstrap (UFB) replicates and 1,000 samples for SH-aLRT branch testing. Nodes with values of UFB greater or equal to 95% and of SH-aLRT greater or equal to 80% were considered to be strongly supported. The best-fit model was Q.plant+F+I+G4.
DOI: 10.6084/m9.figshare.24271210.v4
title: Figure 3B. Placement of new MAGs in the global lucinid symbiont tree reveals patchiness in the distribution of nitrogen fixation genes, even within species clades - Alignment and newick tree

description: Concatenated alignment of 120 conserved bacterial marker genes from the MAGs was obtained with the GTDB-Tk v2.1.1 (Genome Taxonomy Database Toolkit) classify workflow, including MAGs published in this work and in Osvatic et al. 2023 ISMEJ. Maximum likelihood phylogenomic tree inferred from GTDB’s multiple sequence alignment with IQ-Tree v2.2.2.1 (Minh et al. 2020; Hoang et al. 2018; Kalyaanamoorthy et al. 2017) including MAGs published in this work and in Osvatic et al. 2023 ISMEJ. The tree was inferred with auto substitution model detection, 1,000 ultrafast bootstrap (UFB) replicates and 1,000 samples for SH-aLRT branch testing. Nodes with values of UFB greater or equal to 95% and of SH-aLRT greater or equal to 80% were considered to be strongly supported. The best-fit model was Q.plant+F+I+G4.
DOI: 10.18453/rosdok_id00002986
title: Measurement of a Binaural Room Impulse Response with High Peak-to-Noise Ratio

description: During their measurement, binaural room impulse responses (BRIRs) are impaired by equipment and background noise. The resulting noise floor might become perceptible when BRIRs are used for auralization purposes, i.e. by convolution with a dry source. To avoid the audibility of noise, high peak-to-noise ratio BRIRs are desired. A high peak-to-noise ratio (PNR) can be computed by averaging of multiple measured BRIRs. With this measurement at least a 90 dB PNR-BRIR is aspired. The published data set contains the sweep signal as excitation signal, the captured room responses as raw-data which are measured from the frontal and the lateral side in two sessions each and the resulting binaural room impulse responses. Additionally, the computed averaged high-PNR-BRIRs and the high-PNR-Raw-Data are included.
DOI: 10.5517/ccdc.csd.ccnd5t0
title: CCDC 607532: Experimental Crystal Structure Determination

description: Related Article: T.J.Emge,B.P.Regler,L.S.Romsted|2023|CSD Communication|||
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.ccnd5sz
title: CCDC 607531: Experimental Crystal Structure Determination

description: Related Article: T.J.Emge,B.P.Regler,L.S.Romsted|2023|CSD Communication|||
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.ccw18w1
title: CCDC 805593: Experimental Crystal Structure Determination

description: Related Article: Radoslaw Starosta|2023|CSD Communication|||
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.cczkmgl
title: CCDC 910670: Experimental Crystal Structure Determination

description: Related Article: Radoslaw Starosta|2023|CSD Communication|||
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.cc103m6y
title: CCDC 926999: Experimental Crystal Structure Determination

description: Related Article: Radoslaw Starosta|2023|CSD Communication|||
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.cc103m5x
title: CCDC 926998: Experimental Crystal Structure Determination

description: Related Article: Radoslaw Starosta|2023|CSD Communication|||
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.cc10n33w
title: CCDC 942837: Experimental Crystal Structure Determination

description: Related Article: T.J.Emge, Long Pan|2023|CSD Communication|||
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.