cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6991189
title: Data from the article "Plastome sequencing of South American Podocarpus species reveals low rearrangement rates despite ancient Gondwanan disjunctions"

description: Input data, intermediate and final analysis output files associated to the manuscript "Plastome sequencing of South American <em>Podocarpus </em>species reveals low rearrangement rates despite ancient Gondwanan disjunctions" We sequenced the plastomes of four South American species of <em>Podocarpus</em> from Patagonia, southern Yungas, and Brazilian subtropical forests: <em>P. nubigenus, P. parlatorei, P. salignus </em>and <em>P. selowii</em>. We compared their plastomes to those published from Brazil, Africa, New Zealand, and Southeast Asia, along with representatives from other genera within Podocarpaceae as outgroups. The four newly sequenced plastomes ranged in size between 133,791 bp and 133,991 bp. Gene content and order among chloroplasts from South American, African and Asian <em>Podocarpus</em> were conserved and different from the plastome of <em>P. totara</em>, from New Zealand. Most genes showed substitution patterns consistent with a conservative selective regime. Phylogenies inferred from either complete sequences or protein coding regions were mostly congruent with previous studies, but showed earlier branching of <em>P. salignus</em>, <em>P. totara</em> and <em>P. sellowii</em>.
description: "ARC_output_files.tar.gz" includes, for each of the four species: - ARC_config.txt: the ARC configuration file -contigs.fasta: the contigs assembled by ARC -log.txt: the log file of the ARC run -mapping_stats.tsv: the number of reads used by ARC for plastome assembly -target_summary_table.tsv: the summary of reference assembly, reads used, number of contigs and contig length "CODEML_analysis.tar.gz" includes - A "cds" folder including aligments for each of 83 coding genes in PHYLIP format, plus a file "all.cds" including all coding sequences for all species and a file "podocarpus.all.cds" including all coding sequences for Podocarpus species only. - A "family_level_analysis" containing all files generated by CODEML for the analysis including all 17 species from Podocarpaceae - A "genus_level_analysis" containing all files generated by CODEML for the analysis including the 9 species from Podocarpus "Geneious_assembly_files.tar.gz", for each of the four species: - A .fasta file with the final plastome assembly for the species in FASTA format. - A .gb file with the final plastome assembly for the species in GenBank format. - A .geneious file with the final plastome assembly including annotations for the species in Geneious 8.0 format. - A .tbl file with the annotations for the final plastome assembly for the species in GenBank Feature Table File format. "Mauve_Alignment.tar.gz" includes the output files for the progressiveMauve whole genome alignment, as implemented in Geneious 8.0
description: {"references": ["https://doi.org/10.21203/rs.3.rs-1547449/v1", "https://doi.org/10.1007/s11033-022-07969-y"]}
DOI: 10.5281/zenodo.6991188
title: Data from the article "Plastome sequencing of South American Podocarpus species reveals low rearrangement rates despite ancient Gondwanan disjunctions"

description: Input data, intermediate and final analysis output files associated to the manuscript "Plastome sequencing of South American <em>Podocarpus </em>species reveals low rearrangement rates despite ancient Gondwanan disjunctions" We sequenced the plastomes of four South American species of <em>Podocarpus</em> from Patagonia, southern Yungas, and Brazilian subtropical forests: <em>P. nubigenus, P. parlatorei, P. salignus </em>and <em>P. selowii</em>. We compared their plastomes to those published from Brazil, Africa, New Zealand, and Southeast Asia, along with representatives from other genera within Podocarpaceae as outgroups. The four newly sequenced plastomes ranged in size between 133,791 bp and 133,991 bp. Gene content and order among chloroplasts from South American, African and Asian <em>Podocarpus</em> were conserved and different from the plastome of <em>P. totara</em>, from New Zealand. Most genes showed substitution patterns consistent with a conservative selective regime. Phylogenies inferred from either complete sequences or protein coding regions were mostly congruent with previous studies, but showed earlier branching of <em>P. salignus</em>, <em>P. totara</em> and <em>P. sellowii</em>.
description: "ARC_output_files.tar.gz" includes, for each of the four species: - ARC_config.txt: the ARC configuration file -contigs.fasta: the contigs assembled by ARC -log.txt: the log file of the ARC run -mapping_stats.tsv: the number of reads used by ARC for plastome assembly -target_summary_table.tsv: the summary of reference assembly, reads used, number of contigs and contig length "CODEML_analysis.tar.gz" includes - A "cds" folder including aligments for each of 83 coding genes in PHYLIP format, plus a file "all.cds" including all coding sequences for all species and a file "podocarpus.all.cds" including all coding sequences for Podocarpus species only. - A "family_level_analysis" containing all files generated by CODEML for the analysis including all 17 species from Podocarpaceae - A "genus_level_analysis" containing all files generated by CODEML for the analysis including the 9 species from Podocarpus "Geneious_assembly_files.tar.gz", for each of the four species: - A .fasta file with the final plastome assembly for the species in FASTA format. - A .gb file with the final plastome assembly for the species in GenBank format. - A .geneious file with the final plastome assembly including annotations for the species in Geneious 8.0 format. - A .tbl file with the annotations for the final plastome assembly for the species in GenBank Feature Table File format. "Mauve_Alignment.tar.gz" includes the output files for the progressiveMauve whole genome alignment, as implemented in Geneious 8.0
description: {"references": ["https://doi.org/10.21203/rs.3.rs-1547449/v1", "https://doi.org/10.1007/s11033-022-07969-y"]}
DOI: 10.34740/kaggle/dsv/4460989
title: 💥📰 Daily Breaking News Twitter Dataset
DOI: 10.6084/m9.figshare.21152404.v2
title: Nucleotide sequences and metadata file of archaeal viruses

description: The database contained annotated nucleotide sequences (FASTA + GFF), accompanied with a metadata file (Table S6) describing the origin of each contig, taxonomy, including VC, host prediction information, completeness score etc.
DOI: 10.5517/ccdc.csd.cc25q2ss
title: CCDC 2017225: Experimental Crystal Structure Determination

description: Related Article: Harmeet Bhoday, Steven P. Kelley, Rainer Glaser|2020|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: 10.5519/qd.h15qllkt
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (6 records)
DOI: 10.34740/kaggle/dsv/4461054
title: 🇺🇦 Ukraine Conflict Twitter Dataset
DOI: 10.6084/m9.figshare.21508332.v1
title: bCFS_data

description: This is data for investigating the congruence effect of VWM and access to visual consciousness.
DOI: 10.6084/m9.figshare.21508332.v2
title: bCFS_data

description: This is data for investigating the congruence effect of VWM and access to visual consciousness.
DOI: 10.6084/m9.figshare.21508332.v3
title: bCFS_data

description: This is data for investigating the congruence effect of VWM and access to visual consciousness.
DOI: 10.5281/zenodo.7297158
title: Measured magnetic susceptibility data for different magnetite tracer stacking scenarios

description: Dataset includes measured data of the volume magnetic susceptibility of 36 artificial soil profiles with various distribution of magnetic tracer. The monitoring was done with Bartington MS2D field probe. The dataset was created for the fitting and calibration of the parameters of a MagHut model. The model and the procedure is described in a manuscript by Zumr D., Li T., Gómez J., Guzmán G., Modelling the response of a field probe for non-destructive measurements of the magnetic susceptibility of soils (to date of the data submission under review).
DOI: 10.5281/zenodo.7297157
title: Measured magnetic susceptibility data for different magnetite tracer stacking scenarios

description: Dataset includes measured data of the volume magnetic susceptibility of 36 artificial soil profiles with various distribution of magnetic tracer. The monitoring was done with Bartington MS2D field probe. The dataset was created for the fitting and calibration of the parameters of a MagHut model. The model and the procedure is described in a manuscript by Zumr D., Li T., Gómez J., Guzmán G., Modelling the response of a field probe for non-destructive measurements of the magnetic susceptibility of soils (to date of the data submission under review).
DOI: 10.17909/j98f-cs20
title: Data from paper "An Ap star catalogue based on LAMOST DR9"

description: rotational variables and pulsators in TESS.
DOI: 10.6084/m9.figshare.21508317
title: MicroTOM_annotation

description: we established a comprehensive database and website with genomic, transcriptomic, and metadata on microTom, which should be of great value to those who wants to adopt microTom as a model plant for research. Here we present some functional annotation information about microTom.
DOI: 10.6084/m9.figshare.21407937
title: Chloroplast Genome

description: <strong>It's other Chloroplast Genomes data for "Molecular and morphological evidence for two new interspecific </strong><em><strong>Isoetes</strong></em><strong> (Isoetaceae) hybrids from southeastern China."</strong>
DOI: 10.6084/m9.figshare.21407937.v1
title: Chloroplast Genome

description: <strong>It's other Chloroplast Genomes data for "Molecular and morphological evidence for two new interspecific </strong><em><strong>Isoetes</strong></em><strong> (Isoetaceae) hybrids from southeastern China."</strong>
DOI: 10.6084/m9.figshare.21407937.v2
title: Chloroplast Genome

description: <strong>It's other Chloroplast Genomes data for "Molecular and morphological evidence for two new interspecific </strong><em><strong>Isoetes</strong></em><strong> (Isoetaceae) hybrids from southeastern China."</strong>
DOI: 10.6084/m9.figshare.14604087.v8
title: hypothyroidism during pregnancy and SIBO

description: 妊娠期甲状腺功能低下和SIBO
DOI: 10.6084/m9.figshare.14604087.v7
title: hypothyroidism during pregnancy and SIBO

description: 妊娠期甲状腺功能低下和SIBO
DOI: 10.6084/m9.figshare.20390535
title: Temporal Evolution of the Internal Wave Spectra in the Northeastern South China Sea and Its Origins

description: The data used to construct the figures in the article "Temporal Evolution of the Internal Wave Spectra in the Northeastern South China Sea and Its Origins." <br> <br>
DOI: 10.6084/m9.figshare.20390535.v2
title: Temporal Evolution of the Internal Wave Spectra in the Northeastern South China Sea and Its Origins

description: The data used to construct the figures in the article "Temporal Evolution of the Internal Wave Spectra in the Northeastern South China Sea and Its Origins." <br> <br>
DOI: 10.6084/m9.figshare.20390535.v1
title: Temporal Evolution of the Internal Wave Spectra in the Northeastern South China Sea and Its Origins

description: The data used to construct the figures in the article "Temporal Evolution of the Internal Wave Spectra in the Northeastern South China Sea and Its Origins." <br> <br>
DOI: 10.5281/zenodo.7297755
title: Band Excitation Piezoresponse Force Microscopy of PbZr<sub>0.2</sub>Ti<sub>0.8</sub>O<sub>3</sub>

description: Film grown and measurements conducted by Joshua C. Agar at Oak Ridge National Laboratory This dataset has been the subject of 4 manuscripts: 1. Agar, J., Damodaran, A. R., Pandya, S., C, Cao, Y., Vasudevan, R. K., Xu, R., Saremi, S., Li, Q., Kim, J., McCarter, M. R., Dedon, L. R., Angsten, T., Balke, N., Jesse, S., Asta, M., Kalinin, S. V. & Martin, L. W. Three-State Ferroelastic Switching and Large Electromechanical Responses in PbTiO&lt;sub&gt;3 Thin Films. Adv. Mater. 29, 1702069 (2017). [doi:10.1002/adma.201702069](https://onlinelibrary.wiley.com/doi/10.1002/adma.201702069) 2. Agar, J. C., Cao, Y., Naul, B., Pandya, S., van der Walt, S., Luo, A. I., Maher, J. T., Balke, N., Jesse, S., Kalinin, S. V., Vasudevan, R. K. & Martin, L. W. Machine detection of enhanced electromechanical energy conversion in PbZr&lt;sub&gt;0.2&lt;/sub&gt;Ti&lt;sub&gt;0.8&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt; thin films. Adv. Mater. 30, e1800701 (2018). [doi:10.1002/adma.201800701](https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.201800701) 3. Griffin, L. A., Gaponenko, I. & Bassiri-Gharb, N. Better, Faster, and Less Biased Machine Learning: Electromechanical Switching in Ferroelectric Thin Films. Adv. Mater. e2002425 (2020). [doi:10.1002/adma.202002425](https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202002425) 4. Qin, S., Guo, Y., Kaliyev, A. T. & Agar, J. C. Why it is Unfortunate that Linear Machine Learning ‘Works’ so well in Electromechanical Switching of Ferroelectric Thin Films. Adv. Mater. e2202814 (2022). [doi:10.1002/adma.202202814](https://onlinelibrary.wiley.com/doi/10.1002/adma.202202814)
DOI: 10.5281/zenodo.7297754
title: Band Excitation Piezoresponse Force Microscopy of PbZr<sub>0.2</sub>Ti<sub>0.8</sub>O<sub>3</sub>

description: Film grown and measurements conducted by Joshua C. Agar at Oak Ridge National Laboratory This dataset has been the subject of 4 manuscripts: 1. Agar, J., Damodaran, A. R., Pandya, S., C, Cao, Y., Vasudevan, R. K., Xu, R., Saremi, S., Li, Q., Kim, J., McCarter, M. R., Dedon, L. R., Angsten, T., Balke, N., Jesse, S., Asta, M., Kalinin, S. V. & Martin, L. W. Three-State Ferroelastic Switching and Large Electromechanical Responses in PbTiO&lt;sub&gt;3 Thin Films. Adv. Mater. 29, 1702069 (2017). [doi:10.1002/adma.201702069](https://onlinelibrary.wiley.com/doi/10.1002/adma.201702069) 2. Agar, J. C., Cao, Y., Naul, B., Pandya, S., van der Walt, S., Luo, A. I., Maher, J. T., Balke, N., Jesse, S., Kalinin, S. V., Vasudevan, R. K. & Martin, L. W. Machine detection of enhanced electromechanical energy conversion in PbZr&lt;sub&gt;0.2&lt;/sub&gt;Ti&lt;sub&gt;0.8&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt; thin films. Adv. Mater. 30, e1800701 (2018). [doi:10.1002/adma.201800701](https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.201800701) 3. Griffin, L. A., Gaponenko, I. & Bassiri-Gharb, N. Better, Faster, and Less Biased Machine Learning: Electromechanical Switching in Ferroelectric Thin Films. Adv. Mater. e2002425 (2020). [doi:10.1002/adma.202002425](https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202002425) 4. Qin, S., Guo, Y., Kaliyev, A. T. & Agar, J. C. Why it is Unfortunate that Linear Machine Learning ‘Works’ so well in Electromechanical Switching of Ferroelectric Thin Films. Adv. Mater. e2202814 (2022). [doi:10.1002/adma.202202814](https://onlinelibrary.wiley.com/doi/10.1002/adma.202202814)
DOI: 10.6084/m9.figshare.20417526
title: Bristol et al. Meta-data Figshare.xlsx

description: Data used in the meta-analysis Bristol et al. 2022 which includes; Author, citation, experimental type, ecosystem type, annual MAP, annual mean temp, lattitude and longitude, experimental time, replicates, treatment effect %, trophic groups and treatment data. Raw nematode count data from 'Bristol et al. Unpublished' that was included in the analysis is included. Furthermore, packages and codes used in the meta-analysis and data visualization is included.
DOI: 10.5281/zenodo.7297779
title: PERTEMUAN 9_07 NOVEMBER 2022_Penjualan Konsinyasi Untuk Pengamanat_AKL1_JAM 18.00-20.00

description: <strong>Capaian Pembelajaran Mata Kuliah (CPMK)</strong> Mahasiswa Mampu mengetahui dan menghitung Akuntansi Penjualan Konsinyasi Untuk Pengamanat (Consignor) <strong>Indikator Pembelajaran</strong> 1. Pendahuluan 2. Akuntansi Konsinyasi Pengamanat Dengan Menggunakan Metode Laba Terpisah 3. Akuntansi Konsinyasi Untuk Pengamanat Dengan Menggunakan Metode Laba Tak Terpisah 4. Latihan kasus
description: PERTEMUAN 9_07 NOVEMBER 2022_Penjualan Konsinyasi Untuk Pengamanat_AKL1_JAM 18.00-20.00
DOI: 10.5281/zenodo.7297778
title: PERTEMUAN 9_07 NOVEMBER 2022_Penjualan Konsinyasi Untuk Pengamanat_AKL1_JAM 18.00-20.00

description: <strong>Capaian Pembelajaran Mata Kuliah (CPMK)</strong> Mahasiswa Mampu mengetahui dan menghitung Akuntansi Penjualan Konsinyasi Untuk Pengamanat (Consignor) <strong>Indikator Pembelajaran</strong> 1. Pendahuluan 2. Akuntansi Konsinyasi Pengamanat Dengan Menggunakan Metode Laba Terpisah 3. Akuntansi Konsinyasi Untuk Pengamanat Dengan Menggunakan Metode Laba Tak Terpisah 4. Latihan kasus
description: PERTEMUAN 9_07 NOVEMBER 2022_Penjualan Konsinyasi Untuk Pengamanat_AKL1_JAM 18.00-20.00
DOI: 10.5281/zenodo.7295661
title: Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation (Unlabeled Data Part II)

description: Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. The paper can be found at https://arxiv.org/pdf/2206.08023.pdf In addition to providing the labeled 600 CT and MRI scans, we expect to provide 2000 CT and 1200 MRI scans without labels to support more learning tasks (semi-supervised, un-supervised, domain adaption, ...). The link can be found in: labeled data (500CT+100MRI) unlabeled data Part I (900CT) unlabeled data Part II (1100CT) (Now there are 1000CT, we will replenish to 1100CT) unlabeled data Part III (1200MRI) if you found this dataset useful for your research, please cite: <pre>@article{ji2022amos, title={AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation}, author={Ji, Yuanfeng and Bai, Haotian and Yang, Jie and Ge, Chongjian and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhang, Lingyan and Ma, Wanling and Wan, Xiang and others}, journal={arXiv preprint arXiv:2206.08023}, year={2022} </pre>
DOI: 10.5281/zenodo.7265969
title: Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation (Unlabeled Data Part II)

description: Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. The paper can be found at https://arxiv.org/pdf/2206.08023.pdf In addition to providing the labeled 600 CT and MRI scans, we expect to provide 2000 CT and 1200 MRI scans without labels to support more learning tasks (semi-supervised, un-supervised, domain adaption, ...). The link can be found in: labeled data (500CT+100MRI) unlabeled data Part I (900CT) unlabeled data Part II (1100CT) (Now there are 1000CT, we will replenish to 1100CT) unlabeled data Part III (1200MRI) if you found this dataset useful for your research, please cite: <pre>@article{ji2022amos, title={AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation}, author={Ji, Yuanfeng and Bai, Haotian and Yang, Jie and Ge, Chongjian and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhang, Lingyan and Ma, Wanling and Wan, Xiang and others}, journal={arXiv preprint arXiv:2206.08023}, year={2022} </pre>
DOI: 10.5281/zenodo.7295816
title: Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation (Unlabeled Data Part III)

description: Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. The paper can be found at https://arxiv.org/pdf/2206.08023.pdf In addition to providing the labeled 600 CT and MRI scans, we expect to provide 2000 CT and 1200 MRI scans without labels to support more learning tasks (semi-supervised, un-supervised, domain adaption, ...). The link can be found in: labeled data (500CT+100MRI) unlabeled data Part I (900CT) unlabeled data Part II (1100CT) (Now there are 1000CT, we will replenish to 1100CT) unlabeled data Part III (1200MRI) if you found this dataset useful for your research, please cite: <pre>@article{ji2022amos, title={AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation}, author={Ji, Yuanfeng and Bai, Haotian and Yang, Jie and Ge, Chongjian and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhang, Lingyan and Ma, Wanling and Wan, Xiang and others}, journal={arXiv preprint arXiv:2206.08023}, year={2022} }</pre>