cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21651434
title: Reference Tetranychus urticae genome annotation and variant data for inbred strains MR-VPi and ROS-ITi

description: An annotation of the genome of the two-spotted spider mite, <em>Tetranychus urticae</em>, is provided. Briefly, the annotation of Wybouw <em>et al.</em> (2019, GENETICS, 211:1409-1427; doi: 10.1534/genetics.118.301803) was updated with additional gene models present in the 2019 annotation of the <em>T. urticae</em> genome available from the Online Resource for Community Annotation of Eukaryotes (ORCAE: https://bioinformatics.psb.ugent.be/orcae/). This resulted in the Tetranychus_urticae_2022_12 annotation that is provided in the GTF format. Additionally, a variant call format (VCF) file is provided for single nucleotide polymorphisms (SNPs) detected in alignments of Illumina reads of <em>T. urticae</em> strains MR-VPi and ROS-ITi to the reference genome sequence reported by Wybouw et al. (2019). The VCF file was filtered to remove SNPs relative to the reference genome if they were fixed between the MR-VPi and ROS-ITi strains (variants were predicted with GATK). The resulting SNP data was used for selection of a maximally informative set of genetic markers (midpoints of genotype bins) for use in an eQTL mapping experiment (file “MR-VPi_vs_ROS-ITi_genetic_markers.txt").
DOI: 10.6084/m9.figshare.21779846
title: Substantial burial of terrestrial microplastics in the Three Gorges Reservoir, China

description: Raw data and source data for the figures in ‘Substantial burial of terrestrial microplastics in the Three Gorges Reservoir, China’
DOI: 10.6084/m9.figshare.21779786.v1
title: Young Hoon Soong & Eun Sung Chung Monthly potential evapotranspiration dataset based on CMIP6 GCM (IPSL-CM6A-LR-Future period)

description: Regridding Global CMIP6 GCMs datasets(0.5 degree) case of IPSL-CM6A-LR <br> Composition of datasets: Average temperature, Maximum temperature, Minimum temperature, Windspeed, Relative humidity, Solar radiation <br> SSP scenarios: SSP1-2.6; SSP2-4.5; SSP3-7.0; SSP5-8.5 <br>
DOI: 10.5519/qd.1v6wjjzw
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1001 records)
DOI: 10.5281/zenodo.7147882
title: Dataset and code for "Tiltable objective microscope visualizes selectivity for head motion direction and dynamics in zebrafish vestibular system", Nat Commun 13, 7622 (2022). https://doi.org/10.1038/s41467-022-35190-9

description: Dataset and code for "Tiltable objective microscope visualizes selectivity for head motion direction and dynamics in zebrafish vestibular system", Tanimoto, Watakabe, and Higashijima. The spreadsheet files contain source data for the figures. The file named "register_rotated_images_demo.zip" contains the MATLAB code, example image data, and instruction text data. To use the code, unzip the file and follow the instructions in the "readme.txt" file. The file named "register_rotated_images_demo_output.zip" contains expected output image data.
DOI: 10.5281/zenodo.7147881
title: Dataset and code for "Tiltable objective microscope visualizes selectivity for head motion direction and dynamics in zebrafish vestibular system", Nat Commun 13, 7622 (2022). https://doi.org/10.1038/s41467-022-35190-9

description: Dataset and code for "Tiltable objective microscope visualizes selectivity for head motion direction and dynamics in zebrafish vestibular system", Tanimoto, Watakabe, and Higashijima. The spreadsheet files contain source data for the figures. The file named "register_rotated_images_demo.zip" contains the MATLAB code, example image data, and instruction text data. To use the code, unzip the file and follow the instructions in the "readme.txt" file. The file named "register_rotated_images_demo_output.zip" contains expected output image data.
DOI: 10.5281/zenodo.7481960
title: ML-based magnetic sensing scan data

description: Experimental data sets of NV ODMR scans for training ML models.
DOI: 10.5281/zenodo.7481959
title: ML-based magnetic sensing scan data

description: Experimental data sets of NV ODMR scans for training ML models.
DOI: 10.5281/zenodo.7481986
title: Supplemental Datasets S1 to S4 for thesis of AE BACKHAUS 100039675

description: <strong>Supplemental Dataset S1: </strong>All raw field data collected in 2021 and 2022 for mature spikes (post-harvest). <strong>Supplemental Dataset S2: </strong>All developmental scores taken for floret development in the basal six and central two spikelets from spike collected in CF 2022. <strong>Supplemental Dataset S3: </strong>Raw normalized sugar concentration (µg/mg tissue wt) for samples collected in CF 2021 and 2022. <strong>Supplemental Dataset S4: </strong>Number of grains predicted for each spikelet using CDF, with and without optimisation.
DOI: 10.5281/zenodo.7481985
title: Supplemental Datasets S1 to S4 for thesis of AE BACKHAUS 100039675

description: <strong>Supplemental Dataset S1: </strong>All raw field data collected in 2021 and 2022 for mature spikes (post-harvest). <strong>Supplemental Dataset S2: </strong>All developmental scores taken for floret development in the basal six and central two spikelets from spike collected in CF 2022. <strong>Supplemental Dataset S3: </strong>Raw normalized sugar concentration (µg/mg tissue wt) for samples collected in CF 2021 and 2022. <strong>Supplemental Dataset S4: </strong>Number of grains predicted for each spikelet using CDF, with and without optimisation.
DOI: 10.3886/e118648v2-131460
title: Data and Code for Aid, China, and Growth: Evidence from a New Global Development Finance Dataset

description: <b>Abstract:</b> This article introduces a new dataset of official financing from China to 138 developing countries between 2000 and 2014. It investigates whether Chinese development finance affects economic growth in recipient countries. The results demonstrate that Chinese development finance boosts short-term economic growth. An additional project increases growth by between 0.41 and 1.49 percentage points two years after commitment, on average. While this study does not find that significant financial support from China impairs the overall effectiveness of aid from Western donors, aid from the United States tends to be more effective in countries that receive no substantial support from China.
DOI: 10.5517/ccdc.csd.cc1p1tdy
title: CCDC 1550880: Experimental Crystal Structure Determination

description: Related Article: CheongCheon Lee, Ju Hee Lim, A Young Cho, Woojin Yoon, Hoseop Yun, Jeong Won Kang, Jonghwi Lee|2022|J.Ind.Eng.Chem.(Seoul, Repub.Korea)|118|309|doi:10.1016/j.jiec.2022.11.015
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.cc1p1tfz
title: CCDC 1550881: Experimental Crystal Structure Determination

description: Related Article: CheongCheon Lee, Ju Hee Lim, A Young Cho, Woojin Yoon, Hoseop Yun, Jeong Won Kang, Jonghwi Lee|2022|J.Ind.Eng.Chem.(Seoul, Repub.Korea)|118|309|doi:10.1016/j.jiec.2022.11.015
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.cc2bqmwl
title: CCDC 2166710: Experimental Crystal Structure Determination

description: Related Article: Ramiro Barraza, Alexander G. Sertage, Aravind B. Kajjam, Cassandra L. Ward, Jacob C. Lutter, H. Bernhard Schlegel, Matthew J. Allen|2022|Inorg.Chem.|61|19649|doi:10.1021/acs.inorgchem.2c02911
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.cc2bsq1x
title: CCDC 2168699: Experimental Crystal Structure Determination

description: Related Article: Ben Zhang, Xuefeng Guo, Pengfei Tan, Wei Lv, Xiaoye Bai, Yang Zhou, Aihua Yuan, Lei Chen, Dan Liu, Hui-Hui Cui, Ruosong Wang, Xue-Tai Chen|2022|Inorg.Chem.|61|19726|doi:10.1021/acs.inorgchem.2c02476
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.cc2cb3sm
title: CCDC 2184470: Experimental Crystal Structure Determination

description: Related Article: Dandan Lu, Weidong Lin, Feng Su, Kongchuan Wu, Ting-Bin Wen, Hui-Jun Zhang|2022|Organometallics|41|3499|doi:10.1021/acs.organomet.2c00478
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.cc2cnx21
title: CCDC 2194833: Experimental Crystal Structure Determination

description: Related Article: Dandan Lu, Weidong Lin, Feng Su, Kongchuan Wu, Ting-Bin Wen, Hui-Jun Zhang|2022|Organometallics|41|3499|doi:10.1021/acs.organomet.2c00478
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.cc2cnx54
title: CCDC 2194836: Experimental Crystal Structure Determination

description: Related Article: Dandan Lu, Weidong Lin, Feng Su, Kongchuan Wu, Ting-Bin Wen, Hui-Jun Zhang|2022|Organometallics|41|3499|doi:10.1021/acs.organomet.2c00478
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.cc2bsq2y
title: CCDC 2168700: Experimental Crystal Structure Determination

description: Related Article: Ben Zhang, Xuefeng Guo, Pengfei Tan, Wei Lv, Xiaoye Bai, Yang Zhou, Aihua Yuan, Lei Chen, Dan Liu, Hui-Hui Cui, Ruosong Wang, Xue-Tai Chen|2022|Inorg.Chem.|61|19726|doi:10.1021/acs.inorgchem.2c02476
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.cc2d5klr
title: CCDC 2209884: Experimental Crystal Structure Determination

description: Related Article: Ramiro Barraza, Alexander G. Sertage, Aravind B. Kajjam, Cassandra L. Ward, Jacob C. Lutter, H. Bernhard Schlegel, Matthew J. Allen|2022|Inorg.Chem.|61|19649|doi:10.1021/acs.inorgchem.2c02911
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.cc2d62l9
title: CCDC 2210380: Experimental Crystal Structure Determination

description: Related Article: De-Gang Ke, Sheng-Li Huang, Guo-Yu Yang|2022|Inorg.Chem.|61|20080|doi:10.1021/acs.inorgchem.2c03504
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.cc2d62mb
title: CCDC 2210381: Experimental Crystal Structure Determination

description: Related Article: De-Gang Ke, Sheng-Li Huang, Guo-Yu Yang|2022|Inorg.Chem.|61|20080|doi:10.1021/acs.inorgchem.2c03504
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.cc2d62nc
title: CCDC 2210382: Experimental Crystal Structure Determination

description: Related Article: De-Gang Ke, Sheng-Li Huang, Guo-Yu Yang|2022|Inorg.Chem.|61|20080|doi:10.1021/acs.inorgchem.2c03504
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.21779864
title: Dataset for drug users servey in the Philippines

description: This is a data set collected in the Phippines to study the relationship between demographic profiles, other psychological variables and relapse risk.
DOI: 10.5281/zenodo.7482058
title: Data snapshot from the Protein Ligand Binding Database (PLBD)

description: Data snapshot from the Protein Ligand Binding Database (PLBD)<br> ============================================================= This data set contains tabular data snapshots from the PLBD. Data are provided in the time-stamped JSON [1] files. The time stamp in all files (the first line of each data file) records the moment when the data tables were retrieved from the PLBD server. Since the PLBD is a constantly evolving database, it is recommended that the latest data from the PLBD server [2] are downloaded and used, unless historic data are needed. The provided Makefile illustrates a possible download procedure. The tables here are provided solely to comply with the editorial policies of the "Scientific Data" journal. The ".json" files in the "outputs/main" directory contain the data; the ".nrec" files contain the number of records in the PLBD tables at the moment of download (a single decimal integer recorded on the second line of each ".nrec" file) and were used to request the the appropriate number of rows when downloading the "outputs/main/*.json" files. The meaning and the data type descriptions of the JSON file attributes are provided in the database description file "main-database-description.xml". This file is deposited as a separate digital object in Zenodo [3]. The layout of the JSON files follows ideas described in the JSON:API schema [4]. Layout of the archived file tree<br> -------------------------------- .<br> ├── Makefile<br> ├── README<br> ├── README.pdf<br> ├── inputs<br> │ ├── tables.lst<br> │ ├── tables.lst.history<br> │ └── tables.lst.log<br> ├── makefiles<br> │ ├── available<br> │ │ ├── Makelocal-download-json<br> │ │ └── Makelocal-download-nrec<br> │ └── enabled<br> │ ├── Makelocal-download-json<br> │ └── Makelocal-download-nrec<br> └── outputs<br> └── main [52 entries exceeds filelimit, not opening dir] References: 1. ECMA Standard. The JSON data interchange syntax. 2017, 1-16, URL: https://www.ecma-international.org/publications/files/ECMA-ST/ECMA-404.pdf 2. The Protein Ligand Binding Database (PLBD) server. URL: https://plbd.org/db/main [accessed: 2022-12-21T09:29+02:00] 3. Lingė, D.; Gedgaudas, M.; Merkys, A.; Petrauskas, V.; Vaitkus, A.; Grybauskas, A.; Paketurytė, V.; Zubrienė, A.; Zakšauskas, A.; Mickevičiūtė, A.; Smirnovienė, J.; Baranauskienė, L.; Čapkauskaitė, E.; Dudutienė, V.; Urniežius, E.; Konovalovas, A.; Kazlauskas, E.; Gražulis, S. & Matulis, D. PLBD (Protein Ligand Binding Database) table description XML file. Zenodo, 2022, DOI: https://doi.org/10.5281/ZENODO.7482008 4. Katz, Y.; Gebhardt, D.; Sullice, G.; Hanschke, J.; Kellen, T.; Klabnik, S. & Resnick, E. JSON:API version 1.1. 2022 URL: https://jsonapi.org/format/1.1/ [accessed: 2022-12-25T16:04+02:00]
DOI: 10.5281/zenodo.7482057
title: Data snapshot from the Protein Ligand Binding Database (PLBD)

description: Data snapshot from the Protein Ligand Binding Database (PLBD)<br> ============================================================= This data set contains tabular data snapshots from the PLBD. Data are provided in the time-stamped JSON [1] files. The time stamp in all files (the first line of each data file) records the moment when the data tables were retrieved from the PLBD server. Since the PLBD is a constantly evolving database, it is recommended that the latest data from the PLBD server [2] are downloaded and used, unless historic data are needed. The provided Makefile illustrates a possible download procedure. The tables here are provided solely to comply with the editorial policies of the "Scientific Data" journal. The ".json" files in the "outputs/main" directory contain the data; the ".nrec" files contain the number of records in the PLBD tables at the moment of download (a single decimal integer recorded on the second line of each ".nrec" file) and were used to request the the appropriate number of rows when downloading the "outputs/main/*.json" files. The meaning and the data type descriptions of the JSON file attributes are provided in the database description file "main-database-description.xml". This file is deposited as a separate digital object in Zenodo [3]. The layout of the JSON files follows ideas described in the JSON:API schema [4]. Layout of the archived file tree<br> -------------------------------- .<br> ├── Makefile<br> ├── README<br> ├── README.pdf<br> ├── inputs<br> │ ├── tables.lst<br> │ ├── tables.lst.history<br> │ └── tables.lst.log<br> ├── makefiles<br> │ ├── available<br> │ │ ├── Makelocal-download-json<br> │ │ └── Makelocal-download-nrec<br> │ └── enabled<br> │ ├── Makelocal-download-json<br> │ └── Makelocal-download-nrec<br> └── outputs<br> └── main [52 entries exceeds filelimit, not opening dir] References: 1. ECMA Standard. The JSON data interchange syntax. 2017, 1-16, URL: https://www.ecma-international.org/publications/files/ECMA-ST/ECMA-404.pdf 2. The Protein Ligand Binding Database (PLBD) server. URL: https://plbd.org/db/main [accessed: 2022-12-21T09:29+02:00] 3. Lingė, D.; Gedgaudas, M.; Merkys, A.; Petrauskas, V.; Vaitkus, A.; Grybauskas, A.; Paketurytė, V.; Zubrienė, A.; Zakšauskas, A.; Mickevičiūtė, A.; Smirnovienė, J.; Baranauskienė, L.; Čapkauskaitė, E.; Dudutienė, V.; Urniežius, E.; Konovalovas, A.; Kazlauskas, E.; Gražulis, S. & Matulis, D. PLBD (Protein Ligand Binding Database) table description XML file. Zenodo, 2022, DOI: https://doi.org/10.5281/ZENODO.7482008 4. Katz, Y.; Gebhardt, D.; Sullice, G.; Hanschke, J.; Kellen, T.; Klabnik, S. & Resnick, E. JSON:API version 1.1. 2022 URL: https://jsonapi.org/format/1.1/ [accessed: 2022-12-25T16:04+02:00]
DOI: 10.34740/kaggle/dsv/4770014
title: Mahanagar Yatayat Dataset
DOI: 10.5281/zenodo.7071865
title: Data and code for figures of SelectBCM tool

description: Contain dataset and code for generating figures of SelectBCM tool manuscript.
DOI: 10.5281/zenodo.7482141
title: Data and code for figures of SelectBCM tool

description: Contain dataset and code for generating figures of SelectBCM tool manuscript.
DOI: 10.5281/zenodo.7071864
title: Data and code for figures of SelectBCM tool

description: Contain dataset and code for generating figures of SelectBCM tool manuscript.
DOI: 10.5519/qd.4sif18wz
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1069794 records)
DOI: 10.5519/qd.vv1eddw0
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.34740/kaggle/dsv/4770493
title: ECB speeches etc. 1997 to 2022-12-11
DOI: 10.5281/zenodo.7482308
title: Conformation Database for Publication: Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction

description: <strong>Conformation database</strong> for 2022 Publication "Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction" DOI of Physica A publication: https://doi.org/10.1016/j.physa.2022.128395 GitHub source code: https://github.com/CompSoftMatterBiophysics-CityU-HK/Applying-DRL-to-HP-Model-for-Protein-Structure-Prediction This conformation database shows the distinct conformations of best-known and next best energies: ├── <strong>20merA</strong><br> │ ├── <strong>20merA_E8_set</strong><br> │ ├── <strong>20merA_E9_set</strong><br> │ ├── confs_20merA_E8.txt<br> │ └── confs_20merA_E9.txt<br> ├── <strong>20merB</strong><br> │ ├── <strong>20merB_E10_set</strong><br> │ ├── <strong>20merB_E9_set</strong><br> │ ├── confs_20merB_E10.txt<br> │ └── confs_20merB_E9.txt<br> ├── <strong>24mer</strong><br> │ ├── <strong>24mer_E8_set</strong><br> │ ├── <strong>24mer_E9_set</strong><br> │ ├── confs_24mer_E8.txt<br> │ └── confs_24mer_E9.txt<br> ├── <strong>25mer</strong><br> │ ├── <strong>25mer_E7_set</strong><br> │ ├── <strong>25mer_E8_set</strong><br> │ ├── confs_25mer_E7.txt<br> │ └── confs_25mer_E8.txt<br> ├── <strong>36mer</strong><br> │ ├── <strong>36mer_E13_set</strong><br> │ ├── <strong>36mer_E14_set</strong><br> │ ├── confs_36mer_E13.txt<br> │ └── confs_36mer_E14.txt<br> ├── <strong>48mer</strong><br> │ ├── <strong>48mer_E22_set</strong><br> │ ├── <strong>48mer_E23_set</strong><br> │ ├── confs_48mer_E22.txt<br> │ └── confs_48mer_E23.txt<br> └── <strong>50mer</strong><br> ├── <strong>50mer_E20_set</strong><br> ├── <strong>50mer_E21_set</strong><br> ├── confs_50mer_E20.txt<br> └── confs_50mer_E21.txt
description: Please cite the Physica A journal publication: @article{DRL-HP-model-DQN-YANG-PhysicaA, title = {Applying deep reinforcement learning to the HP model for protein structure prediction}, journal = {Physica A: Statistical Mechanics and its Applications}, volume = {609}, pages = {128395}, year = {2023}, issn = {0378-4371}, doi = {https://doi.org/10.1016/j.physa.2022.128395}, url = {https://www.sciencedirect.com/science/article/pii/S0378437122009530}, author = {Kaiyuan Yang and Houjing Huang and Olafs Vandans and Adithya Murali and Fujia Tian and Roland H.C. Yap and Liang Dai}, }