cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/88nn-eb43
title: Ontology Module Suite for Electronics and Systems Engineering

description: This dataset desrcibes ontologies relevent for the domain of systems engineering and electronics. We developed various .owl ontologies that either desrcibe the domain and collected related ontologies that are relevant. This includes: hardware like processors, sensors, actuators, processor parts, adcs, software, systems like embedded systems, automobiles, driving assinstance systems. Further their properties and functions can be desribed. The base ontology is the GENIAL! Basic Ontology (GBO) which is based on BFO (GENIALOntBFO.owl). Further based on that ontology framework a hardware - software knowledge base is built. Further ontologies include sensor ontologies, car ontologies, innovation ontologies, roadmap ontologies, context ontolgoies and others.
DOI: 10.6084/m9.figshare.24082290
title: Nathaniel Palmer seismic data (2006)

description: Seismic data recorded by Nathaniel Palmer expedition in 2006 at Antarctic Peninsula
DOI: 10.6084/m9.figshare.24082290.v2
title: Nathaniel Palmer seismic data (2006)

description: Seismic data recorded by Nathaniel Palmer expedition in 2006 at Antarctic Peninsula
DOI: 10.6084/m9.figshare.24106140.v1
title: Raw Data_II-EBTSJKRT-240923-431_by Tran Minh Tung.xlsx

description: Marketing in higher education
DOI: 10.5517/ccdc.csd.cc2czhkf
title: CCDC 2204055: Experimental Crystal Structure Determination

description: Related Article: Anastasia D. Pournara, Dimitrios A. Evangelou, Christina Roukounaki, Evangelos K. Andreou, Gerasimos S. Armatas, Theodore Lazarides, Manolis J. Manos|2022|Dalton Trans.|51|17301|doi:10.1039/D2DT02848D
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.cc2czhlg
title: CCDC 2204056: Experimental Crystal Structure Determination

description: Related Article: Anastasia D. Pournara, Dimitrios A. Evangelou, Christina Roukounaki, Evangelos K. Andreou, Gerasimos S. Armatas, Theodore Lazarides, Manolis J. Manos|2022|Dalton Trans.|51|17301|doi:10.1039/D2DT02848D
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: 10.34740/kaggle/dsv/6435639
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.4121/21908796
title: Gas Chromatography-Mass Spectrometry Data used for "Identifying Palaeolithic birch tar production techniques: challenges from an experimental biomolecular approach"

description: This data set contains gas chromatography mass spectrometry (GC-MS) data of eight experimentally reproduced Palaeolithic birch bark tars. The intentional production of birch bark tar by European Neanderthals plays an important role in discussions about the technological and behavioural complexity of Pleistocene hominins. However, research is hampered because it is currently unknown how Neanderthals were producing birch tar. Organic biomarkers, identified with Gas Chromatograph-Mass Spectrometry (GC-MS), provide a promising way to identify possible production processes. Analysis of this data is presented and discussed in a research article written by Kozowyk et al. in Scientific Reports 2023. The attached GC-MS data provides additional information on the chemical composition of experimental birch bark tars. The dataset is made up of 2 types of files: Raw GC-MS data in .zip files for each experiment (containing .acaml, .dx, .mfx, .bin, .rx, .pmx, .amx) which can be used for further analysis and post-processing, and a summary (.xlsx) file documenting the key biomarkers and peak retention times used in the publication and information about the tar samples. The summary GC-MS .xlsx file contains peaks automatically assigned by the software from the NIST database and may contain errors. Relevant peaks used in the manuscript have been manually checked and renamed where appropriate. Finally, all data used and produced for PCA is available as .csv files.
DOI: 10.4121/21908796.v1
title: Gas Chromatography-Mass Spectrometry Data used for "Identifying Palaeolithic birch tar production techniques: challenges from an experimental biomolecular approach"

description: This data set contains gas chromatography mass spectrometry (GC-MS) data of eight experimentally reproduced Palaeolithic birch bark tars. The intentional production of birch bark tar by European Neanderthals plays an important role in discussions about the technological and behavioural complexity of Pleistocene hominins. However, research is hampered because it is currently unknown how Neanderthals were producing birch tar. Organic biomarkers, identified with Gas Chromatograph-Mass Spectrometry (GC-MS), provide a promising way to identify possible production processes. Analysis of this data is presented and discussed in a research article written by Kozowyk et al. in Scientific Reports 2023. The attached GC-MS data provides additional information on the chemical composition of experimental birch bark tars. The dataset is made up of 2 types of files: Raw GC-MS data in .zip files for each experiment (containing .acaml, .dx, .mfx, .bin, .rx, .pmx, .amx) which can be used for further analysis and post-processing, and a summary (.xlsx) file documenting the key biomarkers and peak retention times used in the publication and information about the tar samples. The summary GC-MS .xlsx file contains peaks automatically assigned by the software from the NIST database and may contain errors. Relevant peaks used in the manuscript have been manually checked and renamed where appropriate. Finally, all data used and produced for PCA is available as .csv files.
DOI: 10.5281/zenodo.8325706
title: Database of a study on long-term land cover changes in Thừa Thien Huế Province, Central Vietnam

description: Data provided here forms the basis of a study which is published in the journal Land Use Policy (year 2023) under the title 'The nature of a ‘forest transition’ in Thừa Thien Huế Province, Central Vietnam – A study of land cover changes over five decades'. The database contains the image files of the maps (and associated legends) shown in the publication. Further data (currently under use for other related research) will eventually be added to the repository.
DOI: 10.5281/zenodo.8325705
title: Database of a study on long-term land cover changes in Thừa Thien Huế Province, Central Vietnam

description: Data provided here forms the basis of a study which is published in the journal Land Use Policy (year 2023) under the title 'The nature of a ‘forest transition’ in Thừa Thien Huế Province, Central Vietnam – A study of land cover changes over five decades'. The database contains the image files of the maps (and associated legends) shown in the publication. Further data (currently under use for other related research) will eventually be added to the repository.
DOI: 10.5281/zenodo.7820796
title: Global distribution of predicted soil types at 1 km resolution based on the WRB 2022 classification

description: Global maps at 1 km spatial resolution of the predicted soil types (0–100% probabilities) at 1 km resolution based on the WRB 2022 (<strong>World Reference Base</strong> the international standard for soil classification) classification system. The training data comes from the following 3 main sources: WOSIS points available via: https://www.isric.org/explore/wosis; HWSD v2 (random draw of cca 20,000 points): https://iiasa.ac.at/models-tools-data/hwsd; Other national datasets / data from publications and projects. Predictions are based on using Rando Forest algorithm as implemented in the randomForestSRC package with cca 190 covariate layers representing soil forming factors (CHELSA Climate, Global Lithological DB GLiM, MODIS EVI and LST long-term derivatives, Digital Terrain model parameters and similar). All TIF files are provided as COGs, which means that you can open them directly in QGIS or similar. Publication explaining all modeling steps is pending. Update of the predictions takes about 4–5 hrs and will be regularly run provided that new training points are available. Disclaimer: These are initial results with limited accuracy and possible issues with quality of training points, location errors and harmonization issues. Use at own risk. Note: original list of soil types have been subset to classes that appear at least 10 times and at least in 2 countries. If you notice an error or artifact <strong>please report via the Github repository</strong>. Help us improve this dataset by contributing training points.
description: {"references": ["Batjes, N. H., Ribeiro, E., & Van Oostrum, A. (2020). Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019). Earth System Science Data, 12(1), 299-320. https://doi.org/10.5194/essd-12-299-2020", "FAO & IIASA. 2023. Harmonized World Soil Database version 2.0. Rome and Laxenburg. https://doi.org/10.4060/cc3823en", "Hengl, T., Mendes de Jesus, J., Heuvelink, G. B., Ruiperez Gonzalez, M., Kilibarda, M., Blagoti\u0107, A., ... & Kempen, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2), e0169748.", "Krasilnikov, P., J. Ib\u00e1\u00f1ez Mart\u00ed, R.W. Arnold, and S. Shoba. (2009). A Handbook of Soil Terminology, Correlation and Classification. Edited by P. Krasilnikov, J. Ib\u00e1\u00f1ez Mart\u00ed, R.W. Arnold, and S. Shoba. London: Earthscan. 25 https://www.researchgate.net/profile/Juan_Ibanez3/publication/285586468_Handbook_of_Soil_Terminology_Corre lation_and_Classificati/links/5660452908ae4988a7bf10e4.pdf"]}
DOI: 10.5281/zenodo.7820797
title: Global distribution of predicted soil types at 1 km resolution based on the WRB 2022 classification

description: Global maps at 1 km spatial resolution of the predicted soil types (0–100% probabilities) at 1 km resolution based on the WRB 2022 (<strong>World Reference Base</strong> the international standard for soil classification) classification system. The training data comes from the following 3 main sources: WOSIS points available via: https://www.isric.org/explore/wosis; HWSD v2 (random draw of cca 20,000 points): https://iiasa.ac.at/models-tools-data/hwsd; Other national datasets / data from publications and projects. Predictions are based on using Rando Forest algorithm as implemented in the randomForestSRC package with cca 190 covariate layers representing soil forming factors (CHELSA Climate, Global Lithological DB GLiM, MODIS EVI and LST long-term derivatives, Digital Terrain model parameters and similar). All TIF files are provided as COGs, which means that you can open them directly in QGIS or similar. Publication explaining all modeling steps is pending. Update of the predictions takes about 4–5 hrs and will be regularly run provided that new training points are available. Disclaimer: These are initial results with limited accuracy and possible issues with quality of training points, location errors and harmonization issues. Use at own risk. Note: original list of soil types have been subset to classes that appear at least 10 times and at least in 2 countries. If you notice an error or artifact <strong>please report via the Github repository</strong>. Help us improve this dataset by contributing training points.
description: {"references": ["Batjes, N. H., Ribeiro, E., & Van Oostrum, A. (2020). Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019). Earth System Science Data, 12(1), 299-320. https://doi.org/10.5194/essd-12-299-2020", "FAO & IIASA. 2023. Harmonized World Soil Database version 2.0. Rome and Laxenburg. https://doi.org/10.4060/cc3823en", "Hengl, T., Mendes de Jesus, J., Heuvelink, G. B., Ruiperez Gonzalez, M., Kilibarda, M., Blagoti\u0107, A., ... & Kempen, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2), e0169748.", "Krasilnikov, P., J. Ib\u00e1\u00f1ez Mart\u00ed, R.W. Arnold, and S. Shoba. (2009). A Handbook of Soil Terminology, Correlation and Classification. Edited by P. Krasilnikov, J. Ib\u00e1\u00f1ez Mart\u00ed, R.W. Arnold, and S. Shoba. London: Earthscan. 25 https://www.researchgate.net/profile/Juan_Ibanez3/publication/285586468_Handbook_of_Soil_Terminology_Corre lation_and_Classificati/links/5660452908ae4988a7bf10e4.pdf"]}
DOI: 10.5281/zenodo.3903164
title: PaleoMAP PaleoCoastlines data

description: ESRI vector shapefiles of continental margins and coastlines are included for every 5 myr of the Phanerozoic. Continental margins are -1400m isobaths of the PaleoMAP PaleoDEMs (https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/). Coastlines are adjusted from the PaleoDEMs to represent maximum transgressive surfaces, as indicated by the stage-level fossil occurences of the Paleobiology Database (https://paleobiodb.org). The dataset is intended to supplement the journal article describing it, which is in preparation. This is version v6.
DOI: 10.5281/zenodo.4017820
title: PaleoMAP PaleoCoastlines data

description: ESRI vector shapefiles of continental margins and coastlines are included for every 5 myr of the Phanerozoic. Continental margins are -1400m isobaths of the PaleoMAP PaleoDEMs (https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/). Coastlines are adjusted from the PaleoDEMs to represent maximum transgressive surfaces, as indicated by the stage-level fossil occurences of the Paleobiology Database (https://paleobiodb.org). The dataset is intended to supplement the journal article describing it, which is in preparation. This is version v7.
DOI: 10.5281/zenodo.7828387
title: PaleoMAP PaleoCoastlines data

description: The dataset is intended to supplement the journal article describing it (Kocsis and Scotese, Mapping paleocoastlines and continental flooding during the Phanerozoic. Earth-Science Reviews, https://doi.org/10.1016/j.earscirev.2020.103463). Please cite this study if you use the dataset. ESRI vector shapefiles of continental margins and coastlines are included. Continental margins are -1400m isobaths of the PaleoMAP PaleoDEMs (https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/). Coastlines from the PaleoDEMs are adjusted to represent maximum transgressive surfaces, as indicated by the stage-level fossil occurrences of the Paleobiology Database (https://paleobiodb.org). This is version 7.2 (deposited for efficient reuse). <strong>Contents</strong> paleocoastlines_v7_shapefiles.zip - The paleocoastlines in ESRI shapefiles. Directory CM includes continental margins, CS includes the coastlines. paleomap_global_plate_model_v19o_r1c.zip - The "v19o_r1c" version of the paleomap rotation file, with the corresponding partitioning polygons. paleomap_global_plate_model_v3.zip - The earlier, "v3" version of the paleomap rotation file with the corresponding partitioning polygons. (Scotese, 2016) paleomap_political_boundaries_v3.zip - The earlier, "v3" version of the paleomap poltical/administrative boundaries (GPlates markup and ESRI Shapefiles, Scotese, 2016) paleocoastlines_png_equirect.zip - Equirectangular projection of the plotted paleocoastlines with present-day coastlines. (3600x1800) paleocoastlines_png_mollweide.zip - Mollweide projection of the plotted paleocoastlines with present-day coastlines. (3600x1800) equirectangular.pdf - Equirectangular projection of the plotted paleocoastlines (vector) mollweide.pdf - Mollweide projection of the plotted paleocoastlines (vector) readme.md - Readme and change log
DOI: 10.5281/zenodo.3903163
title: PaleoMAP PaleoCoastlines data

description: The dataset is intended to supplement the journal article describing it (Kocsis and Scotese, Mapping paleocoastlines and continental flooding during the Phanerozoic. Earth-Science Reviews, https://doi.org/10.1016/j.earscirev.2020.103463). Please cite this study if you use the dataset. ESRI vector shapefiles of continental margins and coastlines are included. Continental margins are -1400m isobaths of the PaleoMAP PaleoDEMs (https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/). Coastlines from the PaleoDEMs are adjusted to represent maximum transgressive surfaces, as indicated by the stage-level fossil occurrences of the Paleobiology Database (https://paleobiodb.org). This is version 7.3 (added calculated areas from Fig. 12 of Kocsis and Scotese). <strong>Contents</strong> paleocoastlines_v7_shapefiles.zip - The paleocoastlines in ESRI shapefiles. Directory CM includes continental margins, CS includes the coastlines. paleomap_global_plate_model_v19o_r1c.zip - The "v19o_r1c" version of the paleomap rotation file, with the corresponding partitioning polygons. paleomap_global_plate_model_v3.zip - The earlier, "v3" version of the paleomap rotation file with the corresponding partitioning polygons. (Scotese, 2016) paleomap_political_boundaries_v3.zip - The earlier, "v3" version of the paleomap poltical/administrative boundaries (GPlates markup and ESRI Shapefiles, Scotese, 2016) paleocoastlines_png_equirect.zip - Equirectangular projection of the plotted paleocoastlines with present-day coastlines. (3600x1800) paleocoastlines_png_mollweide.zip - Mollweide projection of the plotted paleocoastlines with present-day coastlines. (3600x1800) equirectangular.pdf - Equirectangular projection of the plotted paleocoastlines (vector) mollweide.pdf - Mollweide projection of the plotted paleocoastlines (vector) areas.csv - Calcuated areas for the landmasses and flooded shelves. Includes values separately for the climatic belts separately and cumulated northwards from the south polar area. readme.md - Readme and change log
DOI: 10.5281/zenodo.7994000
title: PaleoMAP PaleoCoastlines data

description: The dataset is intended to supplement the journal article describing it (Kocsis and Scotese, Mapping paleocoastlines and continental flooding during the Phanerozoic. Earth-Science Reviews, https://doi.org/10.1016/j.earscirev.2020.103463). Please cite this study if you use the dataset. ESRI vector shapefiles of continental margins and coastlines are included. Continental margins are -1400m isobaths of the PaleoMAP PaleoDEMs (https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/). Coastlines from the PaleoDEMs are adjusted to represent maximum transgressive surfaces, as indicated by the stage-level fossil occurrences of the Paleobiology Database (https://paleobiodb.org). This is version 7.3 (added calculated areas from Fig. 12 of Kocsis and Scotese). <strong>Contents</strong> paleocoastlines_v7_shapefiles.zip - The paleocoastlines in ESRI shapefiles. Directory CM includes continental margins, CS includes the coastlines. paleomap_global_plate_model_v19o_r1c.zip - The "v19o_r1c" version of the paleomap rotation file, with the corresponding partitioning polygons. paleomap_global_plate_model_v3.zip - The earlier, "v3" version of the paleomap rotation file with the corresponding partitioning polygons. (Scotese, 2016) paleomap_political_boundaries_v3.zip - The earlier, "v3" version of the paleomap poltical/administrative boundaries (GPlates markup and ESRI Shapefiles, Scotese, 2016) paleocoastlines_png_equirect.zip - Equirectangular projection of the plotted paleocoastlines with present-day coastlines. (3600x1800) paleocoastlines_png_mollweide.zip - Mollweide projection of the plotted paleocoastlines with present-day coastlines. (3600x1800) equirectangular.pdf - Equirectangular projection of the plotted paleocoastlines (vector) mollweide.pdf - Mollweide projection of the plotted paleocoastlines (vector) areas.csv - Calcuated areas for the landmasses and flooded shelves. Includes values separately for the climatic belts separately and cumulated northwards from the south polar area. readme.md - Readme and change log
DOI: 10.6084/m9.figshare.24105444
title: BDD 数据集

description: BDD licenseCopyright ©2018. The Regents of the University of California (Regents). All Rights Reserved.<br><br>THIS SOFTWARE AND/OR DATA WAS DEPOSITED IN THE BAIR OPEN RESEARCH COMMONS REPOSITORY ON 1/1/2021<br><br>Permission to use, copy, modify, and distribute this software and its documentation for educational, research, and not-for-profit purposes, without fee and without a signed licensing agreement; and permission to use, copy, modify and distribute this software for commercial purposes (such rights not subject to transfer) to BDD and BAIR Commons members and their affiliates, is hereby granted, provided that the above copyright notice, this paragraph and the following two paragraphs appear in all copies, modifications, and distributions. Contact The Office of Technology Licensing, UC Berkeley, 2150 Shattuck Avenue, Suite 510, Berkeley, CA 94720-1620, (510) 643-7201, otl@berkeley.edu, http://ipira.berkeley.edu/industry-info for commercial licensing opportunities.<br><br>IN NO EVENT SHALL REGENTS BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF REGENTS HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.<br><br>REGENTS SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE SOFTWARE AND ACCOMPANYING DOCUMENTATION, IF ANY, PROVIDED HEREUNDER IS PROVIDED "AS IS". REGENTS HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
DOI: 10.6084/m9.figshare.22892834
title: <b>Haplotype-resolved chromosomal-level assembly of Buzhaye (Microcos paniculata</b><b>)</b><b>, a medicinal plant in China</b>

description: The genome annotations of<i> </i><b><i>Microcos paniculata</i></b>
DOI: 10.34740/kaggle/dsv/6435756
title: 2021 World Population (updated daily)
DOI: 10.34740/kaggle/dsv/6435753
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5519/qd.9ztaa0zn
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (712 records)
DOI: 10.57768/87hj-yf44
title: ppp

description: aaa
description: bbb
DOI: 10.6084/m9.figshare.24083538
title: Supplementary Material for: Xia-Gibbs syndrome with pes cavus, conjunctival melanosis and eye asymmetry due to a de novo AHDC1 gene variant: a case report and a brief review of the literature

description: Introduction: Xia-Gibbs syndrome (OMIM 615829) is a rare developmental disorder, caused by heterozygous de novo variants in the AHDC1 gene. Hallmark features include global developmental delay, facial dysmorphisms and behavioral problems. To date, around 270 individuals have been diagnosed worldwide. Case report: we report a 13-year-old female who, in association with typical features of Xia-Gibbs syndrome, presented with macrocrania, pes cavus and conjunctival melanosis. Whole exome sequencing identified a de novo frameshift variant, which had not been reported in the literature before. Discussion/conclusion: we summarized the main clinical and phenotypic features of patients described in the literature and in addition, we discuss another feature found in our patient and observed in other cases described, eye asymmetry, that has never been highlighted, and suggest that it could be part of the typical clinical presentation of this condition.