cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21995213.v5
title: Files for Main Figure 5

description: Files for generating main figure 5 1. intergenic_TE_gene_5kb: Correlation results between intergenic TEs and +/- 5kb genes 2. TCGA_COAD_four_endpoint_survival: Survival data in four endpoints for TCGA-COAD patients 3: TCGA_COAD_gene: Gene TPM expression for TCGA-COAD patients 4: trinucleotide_mutational_profile: Summary of mutational profile in trinucleotide context in TCGA-COAD patients 5. Sensing_gene_TE_cor_GSVA_lasTEq: Correlation results between DNA-RNA sensing genes and TE quantified by lasTEq 6. Sensing_gene_TE_cor_GSVA_telescope: Correlation results between DNA-RNA sensing genes and TE quantified by telescope 7. GSVA_TCGA_COAD_sensing_gene: GSVA scores calculated for DNA-RNA sensing genes 8. 3_UTR: locations of 3'UTR of genes 9. 5_UTR: locations of 5'UTR of genes 10: TCGA_COAD_TE_exp_lasTEq: TE TPM expression of full TCGA-COAD cohort using lasTEq
DOI: 10.6084/m9.figshare.22121018.v1
title: Data.7z

description: The attachments include: ObTask_UML.EAP (1826kb) ObTaskML.xml (13kb) Two xml instances: AfterUpdate.xml (7kb) and BeforUpdate.xml (5kb), respectively.
DOI: 10.6084/m9.figshare.21995213.v6
title: Files for Main Figure 5

description: Files for generating main figure 5 1. intergenic_TE_gene_5kb: Correlation results between intergenic TEs and +/- 5kb genes 2. TCGA_COAD_four_endpoint_survival: Survival data in four endpoints for TCGA-COAD patients 3: TCGA_COAD_gene: Gene TPM expression for TCGA-COAD patients 4: trinucleotide_mutational_profile: Summary of mutational profile in trinucleotide context in TCGA-COAD patients 5. Sensing_gene_TE_cor_GSVA_lasTEq: Correlation results between DNA-RNA sensing genes and TE quantified by lasTEq 6. Sensing_gene_TE_cor_GSVA_telescope: Correlation results between DNA-RNA sensing genes and TE quantified by telescope 7. GSVA_TCGA_COAD_sensing_gene: GSVA scores calculated for DNA-RNA sensing genes 8. 3_UTR: locations of 3'UTR of genes 9. 5_UTR: locations of 5'UTR of genes 10: TCGA_COAD_TE_exp_lasTEq: TE TPM expression of full TCGA-COAD cohort using lasTEq
DOI: 10.6084/m9.figshare.22121018.v2
title: Data.7z

description: The attachments include: ObTask_UML.EAP (1826kb) ObTaskML.xsd (13kb) Two xml instances: AfterUpdate.xml (7kb) and BeforUpdate.xml (5kb), respectively.
DOI: 10.6084/m9.figshare.22144898
title: Unbiased ratio of seats to population (2023)

description: This is a dataset containing the latest (Feb 15, 2023) population and seats of the parliament (total number if it is bicameral) with the biased and unbiased ratio of seats to population (usually used as an indicator for the comparative studies). The original population data was downloaded from the World Bank and the original parliament size data was downloaded from IPU (Inter-Parliamentary Union). See our paper for how to calculate the unbiased indicator.
DOI: 10.26186/147423
title: L213 Darling-Curnamona-Delamerian (DCD) 2D Seismic Survey

description: Geoscience Australia’s Exploring for the Future program provides precompetitive information to inform decision-making by government, community and industry on the sustainable development of Australia's mineral, energy and groundwater resources. By gathering, analysing and interpreting new and existing precompetitive geoscience data and knowledge, we are building a national picture of Australia’s geology and resource potential. This leads to a strong economy, resilient society and sustainable environment for the benefit of all Australians. This includes supporting Australia’s transition to a low emissions economy, strong resources and agriculture sectors, and economic opportunities and social benefits for Australia’s regional and remote communities. The Exploring for the Future program, which commenced in 2016, is an eight year, $225m investment by the Australian Government.<br>The Darling-Curnamona-Delamerian (DCD) 2D reflection seismic survey was acquired during May to August 2022 in the Delamerian Orogen, the Murray-Darling basin, the Curnamona Province, and the upper Darling River floodplain regions in South Australia, Victoria and New South Wales. This project is a collaboration between Geoscience Australia (GA), the Geological Survey of South Australia (GSSA), the Geological Survey of Victoria (GSV) and the Geological Survey of New South Wales (GSNSW) and was funded by the Australian Government’s Exploring for the Future (EFTF) program. The overall objective of the EFTF Darling-Curnamona-Delamerian project is to improve the understanding of mineral and groundwater resources of the Curnamona Province and Delamerian Orogen and overlying basin systems through acquisition and interpretation of new pre-competitive geoscience data sets. <br>The total length of acquisition was 1256 km distributed over five deep crustal 2D reflection seismic lines 22GA-DL1 (446 km), 22GA-DL2 (249 km), 22GA-CD1 (287 km), 22GA-CD2 (178 km), 22GA-CD3 (39.5 km) to image deep crustal structures, and a high resolution 2D reflection seismic line 22GA-UDF (56 km) to explore groundwater resources. The DL lines provide coverage of fundamental geophysical data over the Flinders Range, the Delamerian Province and the Murray-Darling basin region in eastern South Australia and Victoria. The CD lines extend through the Curnamona Province and into the Darling Basin. The UDF line will assist with refining the hydrogeological model, understanding groundwater dynamics, and locating areas better suited to groundwater bores for better quality groundwater in the upper Darling River floodplain area.<br><strong>Raw data for this survey are available on request from clientservices@ga.gov.au - Quote eCat# 147423</strong>
DOI: 10.34740/kaggle/dsv/5047275
title: 💥📰 Daily Breaking News Twitter Dataset
DOI: 10.34740/kaggle/dsv/5047253
title: Kata kerja aksara lontara / Verbs aksara script
DOI: 10.5281/zenodo.6656424
title: Biotic and abiotic drivers of coalescence asymmetry between soil and manure microbiomes

description: The dataset includes bacterial and fungal OTU tables for all soil and manure samples after 180 days incubation, bacterial and fungal OTU abundance tables, and data for variance partitioning analysis.
DOI: 10.5281/zenodo.6371329
title: Biotic and abiotic drivers of coalescence asymmetry between soil and manure microbiomes

description: The dataset includes bacterial and fungal OTU tables for all soil and manure samples after 180 days incubation, bacterial and fungal OTU abundance tables, the data for variance partitioning analysis, and the phylogenetic trees for calculating MNTD.
DOI: 10.5281/zenodo.7659861
title: Biotic and abiotic drivers of coalescence asymmetry between soil and manure microbiomes

description: The dataset includes bacterial and fungal OTU tables for all soil and manure samples after 180 days incubation, bacterial and fungal OTU abundance tables, the data for variance partitioning analysis, and the phylogenetic trees for calculating MNTD.
DOI: 10.5281/zenodo.7668528
title: Biotic and abiotic drivers of coalescence asymmetry between soil and manure microbiomes

description: The dataset includes bacterial and fungal OTU tables for all soil and manure samples after 180 days incubation, bacterial and fungal OTU abundance tables, the data for variance partitioning analysis, and the phylogenetic trees for calculating MNTD.
DOI: 10.34740/kaggle/dsv/5047338
title: 🇺🇦 Ukraine Conflict Twitter Dataset
DOI: 10.5517/ccdc.csd.cc2dfr0m
title: CCDC 2217740: Experimental Crystal Structure Determination

description: Related Article: Rajaghatta N. Suresh, Toreshettahally R. Swaroop, Darshini Gowda, Kempegowda Mantelingu, Kanchugarakoppal S. Rangappa|2023|RSC Advances|13|4910|doi:10.1039/D2RA08118K
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.cc2dg4mn
title: CCDC 2218131: Experimental Crystal Structure Determination

description: Related Article: Efraín Polo-Cuadrado, Karoll Ferrer, Edison Osorio, Iván Brito, Jonathan Cisterna, Luis Espinoza, Joel B. Alderete, Margarita Gutiérrez|2023|RSC Advances|13|5197|doi:10.1039/D2RA08101F
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.cc2dgdjt
title: CCDC 2218376: Experimental Crystal Structure Determination

description: Related Article: Anna L. Poptic, Ying-Pin Chen, Tieyan Chang, Yu-Sheng Chen, Curtis E. Moore, Shiyu Zhang|2023|J.Am.Chem.Soc.|145|3491|doi:10.1021/jacs.2c11930
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.cc2dgdkv
title: CCDC 2218377: Experimental Crystal Structure Determination

description: Related Article: Anna L. Poptic, Ying-Pin Chen, Tieyan Chang, Yu-Sheng Chen, Curtis E. Moore, Shiyu Zhang|2023|J.Am.Chem.Soc.|145|3491|doi:10.1021/jacs.2c11930
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.cc2dgdlw
title: CCDC 2218378: Experimental Crystal Structure Determination

description: Related Article: Anna L. Poptic, Ying-Pin Chen, Tieyan Chang, Yu-Sheng Chen, Curtis E. Moore, Shiyu Zhang|2023|J.Am.Chem.Soc.|145|3491|doi:10.1021/jacs.2c11930
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: 10.6084/m9.figshare.20134481.v10
title: The effect of zooplankton on the distributions of dimethyl sulfide and dimethylsulfoniopropionate in the Bohai and Yellow Seas

description: We aim to study the effect of zooplankton on the distributions of dimethyl sulfide and dimethylsulfoniopropionate in the Bohai and Yellow Seas. Horizontal and vertical distributions of DMSP, DMSP lyase activity, DMS, and the abundances of bacteria, DMSP-consuming bacteria (DCB), DMSO-consuming bacteria and picophytoplankton in the Bohai Sea (BS) and Yellow Sea (YS) were investigated in autumn 2020. Bacterial community composition was conducted in February of 2021.
DOI: 10.6084/m9.figshare.22132691.v5
title: High-Resolution Earthquake Catalog and Velocity Model Reveal Detailed Seismogenic Structures and Earthquake Mechanisms in Changning Area, Southwestern Sichuan Basin, China

description: Event waveforms data that detected in Changning area, China from July 6, 2019 to August 21, 2019. Folders in all_event compressed files are all named by event occurrence time. The earthquake catalog (tomoDD_mag.dat) and Velocity models are also included. Each column in the tomoDD_mag.dat represents longitude, latitude, year, month, day, magnitude, depth, hour, minute, and second.
DOI: 10.6084/m9.figshare.20134481.v11
title: The effect of zooplankton on the distributions of dimethyl sulfide and dimethylsulfoniopropionate in the Bohai and Yellow Seas

description: We aim to study the effect of zooplankton on the distributions of dimethyl sulfide and dimethylsulfoniopropionate in the Bohai and Yellow Seas. Horizontal and vertical distributions of DMSP, DMSP lyase activity, DMS, and the abundances of bacteria, DMSP-consuming bacteria (DCB), DMSO-consuming bacteria and picophytoplankton in the Bohai Sea (BS) and Yellow Sea (YS) were investigated in autumn 2020. Bacterial community composition was conducted in February of 2021.
DOI: 10.6084/m9.figshare.21345177.v4
title: Arctic surface air temperatures from 1979-2021: A newly reconstructed data with deep learning method

description: The dataset provides 1°x1° monthly temperature anomalies (relative to 1981–2010 average) from 30°N to 90°N from 1979 to 2021.
DOI: 10.5281/zenodo.7668537
title: SVAD Age Data: Brazil

description: <strong>Description</strong> The dental data were collected from anonymized radiographic images generated at the Universidade de São Paulo (FOUSP). Individuals are between 6 years and 15 years of age. As part of the SVAD age indicator template, there are 64 age variables (columns) in the dataset, which include diaphyseal lengths and breadths, epiphyseal fusion/appearance of ossification centers, and dental development. However, because this data is collected from two dimensional panoramic radiographs, only dental data are included; all other variables have a 'NA'. The data collection methodology can be found in Stull and Corron (2022) and Corron et al. (2021). <strong>Basic Key Code:</strong> *DL_L/R == diaphyseal lengths *DB_L/R == diapyseal lengths *EF_L/R == epiphyseal fusion *Oss == ossification *man_ and max_ == mandibular and maxillary dental development <strong>References</strong> Stull, K.E. and Corron, L.K. (<em>2022</em>) The Subadult Virtual Anthropology Database (SVAD): An Accessible Repository of Contemporary Subadult Reference Data. Forensic. Sci. 2022, 2, 20–36. https://doi.org/10.3390/forensicsci2010003 Corron, LK, <em>et al. (2021)</em> Standardizing ordinal subadult age indicators: Testing for observer agreement and consistency across modalities. <em>Forensic Science International</em> <strong>320</strong>, 110687.
description: Funded by the National Institute of Justice Award 2017-DN-BX-0144
DOI: 10.5281/zenodo.7668536
title: SVAD Age Data: Brazil

description: <strong>Description</strong> The dental data were collected from anonymized radiographic images generated at the Universidade de São Paulo (FOUSP). Individuals are between 6 years and 15 years of age. As part of the SVAD age indicator template, there are 64 age variables (columns) in the dataset, which include diaphyseal lengths and breadths, epiphyseal fusion/appearance of ossification centers, and dental development. However, because this data is collected from two dimensional panoramic radiographs, only dental data are included; all other variables have a 'NA'. The data collection methodology can be found in Stull and Corron (2022) and Corron et al. (2021). <strong>Basic Key Code:</strong> *DL_L/R == diaphyseal lengths *DB_L/R == diapyseal lengths *EF_L/R == epiphyseal fusion *Oss == ossification *man_ and max_ == mandibular and maxillary dental development <strong>References</strong> Stull, K.E. and Corron, L.K. (<em>2022</em>) The Subadult Virtual Anthropology Database (SVAD): An Accessible Repository of Contemporary Subadult Reference Data. Forensic. Sci. 2022, 2, 20–36. https://doi.org/10.3390/forensicsci2010003 Corron, LK, <em>et al. (2021)</em> Standardizing ordinal subadult age indicators: Testing for observer agreement and consistency across modalities. <em>Forensic Science International</em> <strong>320</strong>, 110687.
description: Funded by the National Institute of Justice Award 2017-DN-BX-0144
DOI: 10.22002/d1.1613
title: Synthetic mammalian signaling circuits for robust cell population control

description: Synthetic signaling circuits could allow engineered cells to sense and control their own population density. The ideal circuit would operate independently of endogenous pathways and be robust to selection pressure for mutations that allow cells to evade population size limits. Here, we show that the plant hormone auxin can be repurposed as an orthogonal communication channel and linked to pathways that control cell survival and death to create synthetic mammalian population control circuits. We identified enzymes, transporters, and other components that allow sending and receiving of two auxin variants. Using these components, we constructed a synthetic quorum sensing system and coupled it to regulation of antibiotic resistance to limit cell population size. Because this population control circuit was susceptible to mutations in signal sensing, we then designed a paradoxical population control circuit, in which auxin both stimulates and inhibits net cell growth at different concentrations. This design provides evolutionarily robust control in a 43 day continuous culture experiment. These results demonstrate robust synthetic population control in mammalian cells and establish a foundation for future cell therapies that can respond to and control their own population sizes within multicellular organisms.
DOI: 10.6084/m9.figshare.22106153.v1
title: LeakPair: An automated memory leak repair tool for Single Page Applications

description: Our tool LeakPair is now publicly available on npm's official site: https://www.npmjs.com/package/leakpair
DOI: 10.6084/m9.figshare.20134481
title: The effect of zooplankton on the distributions of dimethyl sulfide and dimethylsulfoniopropionate in the Bohai and Yellow Seas

description: We aim to study the effect of zooplankton on the distributions of dimethyl sulfide and dimethylsulfoniopropionate in the Bohai and Yellow Seas. Horizontal and vertical distributions of DMSP, DMSP lyase activity, DMS, and the abundances of bacteria, DMSP-consuming bacteria (DCB), DMSO-consuming bacteria and picophytoplankton in the Bohai Sea (BS) and Yellow Sea (YS) were investigated in autumn 2020. Bacterial community composition was conducted in February of 2021.
DOI: 10.6084/m9.figshare.20134481.v12
title: The effect of zooplankton on the distributions of dimethyl sulfide and dimethylsulfoniopropionate in the Bohai and Yellow Seas

description: We aim to study the effect of zooplankton on the distributions of dimethyl sulfide and dimethylsulfoniopropionate in the Bohai and Yellow Seas. Horizontal and vertical distributions of DMSP, DMSP lyase activity, DMS, and the abundances of bacteria, DMSP-consuming bacteria (DCB), DMSO-consuming bacteria and picophytoplankton in the Bohai Sea (BS) and Yellow Sea (YS) were investigated in autumn 2020. Bacterial community composition was conducted in February of 2021.
DOI: 10.6084/m9.figshare.22145033
title: MS_identified_information（serum）

description: MS_identified_information（srum）
DOI: 10.6084/m9.figshare.22145033.v1
title: MS_identified_information（serum）

description: MS_identified_information（srum）
DOI: 10.5281/zenodo.7661012
title: Data for Computer Vison final project

description: dataset.zip contains images used for our colorization project. We collected those images by filtering the places dataset. Statistically, we obtain 36500 images for training, 18250 images for validation and 18250 images for testing. colorized_images.zip contains the test images inferred by the SOTA approaches we tried.
DOI: 10.5281/zenodo.7661013
title: Data for Computer Vison final project

description: dataset.zip contains images used for our colorization project. We collected those images by filtering the places dataset. Statistically, we obtain 36500 images for training, 18250 images for validation and 18250 images for testing. colorized_images.zip contains the test images inferred by the SOTA approaches we tried.
DOI: 10.6084/m9.figshare.22144985
title: MS_identified_information(tissue).xlsx

description: MS_identified_information(tissue).
DOI: 10.6084/m9.figshare.22144985.v1
title: MS_identified_information(tissue).xlsx

description: MS_identified_information(tissue).
DOI: 10.21227/bevs-9q73
title: Decapterus Macarellus Rott and Fresh Model

description: Pelagic fish such as mackerel are a source of protein in Indonesia. However, there is no decapterus macarellus as an open dataset for image processing using various classification algorithms. Where its use includes the sensor-assisted sorting process in checking fresh fish and rotten fish. For this reason, this study aims to provide a classification model for pelagic fish and their primary datasets which is available for free on the IEEE data port. Artificial intelligence is used in the process of guided classification with the help of ground truth for the preparation of fish classes. The dataset used is a primary dataset consisting of fish images, arranged in two classes, namely 71 fresh fish and 96 rotten fish. The methods used are k-NN classifiers, naive bayes and ridge regression. Experiments were drawn up to classify rotten fish and fresh fish. The preprocessing was assisted by InceptionV3 as a feature extraction method. Furthermore, the image data is trained in a ratio of 60:40 for training and testing data. Validation was performed using 2-fold cross validation with the results obtained being 99.4%, 94% and 100% accuracy using the classifiers namely k-NN, naive bayes and ridge regression, respectively.
DOI: 10.21227/5xrx-q398
title: Decapterus Macarellus Rott and Fresh Model

description: Pelagic fish such as mackerel are a source of protein in Indonesia. However, there is no decapterus macarellus as an open dataset for image processing using various classification algorithms. Where its use includes the sensor-assisted sorting process in checking fresh fish and rotten fish. For this reason, this study aims to provide a classification model for pelagic fish and their primary datasets which is available for free on the IEEE data port. Artificial intelligence is used in the process of guided classification with the help of ground truth for the preparation of fish classes. The dataset used is a primary dataset consisting of fish images, arranged in two classes, namely 71 fresh fish and 96 rotten fish. The methods used are k-NN classifiers, naive bayes and ridge regression. Experiments were drawn up to classify rotten fish and fresh fish. The preprocessing was assisted by InceptionV3 as a feature extraction method. Furthermore, the image data is trained in a ratio of 60:40 for training and testing data. Validation was performed using 2-fold cross validation with the results obtained being 99.4%, 94% and 100% accuracy using the classifiers namely k-NN, naive bayes and ridge regression, respectively.