cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2dzzv7
title: CCDC 2234320: Experimental Crystal Structure Determination

description: Related Article: Han-Ying Liu, Samuel E. Neale, Michael S. Hill, Mary F. Mahon, Claire L. McMullin|2023|Chemical Science|14|2866|doi:10.1039/D3SC00240C
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.cc2dzzw8
title: CCDC 2234321: Experimental Crystal Structure Determination

description: Related Article: Han-Ying Liu, Samuel E. Neale, Michael S. Hill, Mary F. Mahon, Claire L. McMullin|2023|Chemical Science|14|2866|doi:10.1039/D3SC00240C
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.cc2dzzx9
title: CCDC 2234322: Experimental Crystal Structure Determination

description: Related Article: Han-Ying Liu, Samuel E. Neale, Michael S. Hill, Mary F. Mahon, Claire L. McMullin|2023|Chemical Science|14|2866|doi:10.1039/D3SC00240C
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.cc2dzzyb
title: CCDC 2234323: Experimental Crystal Structure Determination

description: Related Article: Han-Ying Liu, Samuel E. Neale, Michael S. Hill, Mary F. Mahon, Claire L. McMullin|2023|Chemical Science|14|2866|doi:10.1039/D3SC00240C
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.cc2dzzzc
title: CCDC 2234324: Experimental Crystal Structure Determination

description: Related Article: Han-Ying Liu, Samuel E. Neale, Michael S. Hill, Mary F. Mahon, Claire L. McMullin|2023|Chemical Science|14|2866|doi:10.1039/D3SC00240C
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.cc2f000h
title: CCDC 2234325: Experimental Crystal Structure Determination

description: Related Article: Han-Ying Liu, Samuel E. Neale, Michael S. Hill, Mary F. Mahon, Claire L. McMullin|2023|Chemical Science|14|2866|doi:10.1039/D3SC00240C
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: 10.25919/s2xx-ky16
title: Two-monthly Maximum Flood Water Depth Spatial Timeseries for the MDB

description: The two-monthly spatial layers of flooding was developed to represent maximum surface water extent and water depth within each two-month period across the Murray-Darling Basin (MDB). This work is based on the multi-index method (MIM) surface water mapping and Digital Elevation Model (DEM) using the Floodwater Depth Estimation Tool (FwDET). More than thirty years of two-monthly images of surface water depth across the whole MDB have been produced were produced as part of the Murray-Darling Basin Ecosystem Function (MDB-EF) to reveal historical lateral connectivity throughout the MDB. The MDB-EF project is a collaborative research project between the Commonwealth Scientific and Industrial Research Organisation and Commonwealth agencies – the Murray-Darling Basin Authority; the Department of Agriculture, Water and the Environment and the Commonwealth Environmental Water Office. Funding was provided by the Australian Government Department of Agriculture, Water and the Environment. A newer version of the dataset is being developed in collaboration with the Murray-Darling Water and Environment Research Program (MD-WERP) project. The MD-WERP is a collaboration between Commonwealth partners (Department of Agriculture, Water and the Environment, Commonwealth Environmental Water Holder, and Murray–Darling Basin Authority) and a research consortium. Funding was provided by the Australian Government. Note: The data type for the files is uint16. The unit of the data is in millimetres. The maximum depth value is constrained to 65535 mm. The minimum water depth is 1 mm. Value 0 represent either dry land or no-data. The file format is Cloud Optimised Geotiff (COG) and the projection used is EPSG:4326.
DOI: 10.6084/m9.figshare.22280830
title: Raw data used for the statistical analyses for the article: Complementary roles of hummingbirds and bees: pollen pick-up, pollen deposition and fruit production in the distylous Palicourea rigida Kunth

description: Raw data used for the statistical analyses for the article: Complementary roles of hummingbirds and bees: pollen pick-up, pollen deposition and fruit production in the distylous Palicourea rigida Kunth
DOI: 10.6084/m9.figshare.22280830.v1
title: Raw data used for the statistical analyses for the article: Complementary roles of hummingbirds and bees: pollen pick-up, pollen deposition and fruit production in the distylous Palicourea rigida Kunth

description: Raw data used for the statistical analyses for the article: Complementary roles of hummingbirds and bees: pollen pick-up, pollen deposition and fruit production in the distylous Palicourea rigida Kunth
DOI: 10.5281/zenodo.7274651
title: Coral reefs and coastal tourism in Hawaii

description: Coral reefs are popular for their vibrant biodiversity. By combining Web-scraped Instagram data from tourists and high-resolution live coral cover maps in Hawaii, we find that, regionally, coral reefs both attract and suffer from coastal tourism. Higher live coral cover attracts reef visitors, but that visitation contributes to subsequent reef degradation. Such feedback loops threaten the highest-quality reefs, highlighting both their economic value and the need for effective conservation management. This repository contains the raw Instagram post data used to run these analyses as well as the Python script used to generate this dataset. The base Python script was adapted from code written by Zoe Volenec.
description: Meta reached out to the authors after publication and asked that the original web-scraping script uploaded in this accompanying Zenodo repository be removed from public access to limit user data exposure and its risk of misuse.
description: {"references": ["Volenec, Z. M., Abraham, J. O., Becker, A. D., & Dobson, A. P. (2021). Public parks and the pandemic: How park usage has been affected by COVID-19 policies. PLOS ONE, 16(5), e0251799. https://doi.org/10.1371/journal.pone.0251799"]}
DOI: 10.5281/zenodo.7274650
title: Coral reefs and coastal tourism in Hawaii

description: Coral reefs are popular for their vibrant biodiversity. By combining Web-scraped Instagram data from tourists and high-resolution live coral cover maps in Hawaii, we find that, regionally, coral reefs both attract and suffer from coastal tourism. Higher live coral cover attracts reef visitors, but that visitation contributes to subsequent reef degradation. Such feedback loops threaten the highest-quality reefs, highlighting both their economic value and the need for effective conservation management. This repository contains the raw Instagram post data used to run these analyses as well as the Python script used to generate this dataset. The base Python script was adapted from code written by Zoe Volenec.
description: Meta reached out to the authors after publication and asked that the original web-scraping script uploaded in this accompanying Zenodo repository be removed from public access to limit user data exposure and its risk of misuse.
description: {"references": ["Volenec, Z. M., Abraham, J. O., Becker, A. D., & Dobson, A. P. (2021). Public parks and the pandemic: How park usage has been affected by COVID-19 policies. PLOS ONE, 16(5), e0251799. https://doi.org/10.1371/journal.pone.0251799"]}
DOI: 10.7298/rpe5-wa81
title: Bibliography of relevant literature from: 'Disease threats to tigers and their prey'
DOI: 10.26093/cds/vizier.18990123
title: Spitzer follow up of 95 brown dwarfs

description: VizieR online Data Catalogue associated with article published in journal Astronomical Journal (AAS) with title 'Spitzer Follow-up of Extremely Cold Brown Dwarfs Discovered by the Backyard Worlds: Planet 9 Citizen Science Project.' (bibcode: 2020ApJ...899..123M)
DOI: 10.34740/kaggle/dsv/5173815
title: AdultoMayor_34_fc
DOI: 10.6084/m9.figshare.21432804
title: EPOS-based Plans for Drones

description: The data is used for training and testing in EPOS-based coordination of drones in scale to complete spatio-temporal sensing tasks. The data is generated via the proposed plan generation strategy.
DOI: 10.6084/m9.figshare.21432804.v16
title: EPOS-based Plans for Drones

description: The data is used for training and testing in EPOS-based coordination of drones in scale to complete spatio-temporal sensing tasks. The data is generated via the proposed plan generation strategy.
DOI: 10.34740/kaggle/dsv/5173847
title: AdultoMayor_34_fc
DOI: 10.18112/openneuro.ds004532.v1.0.0
title: EEG: Probabilistic Selection Task (PST) + PST with Cabergoline Challenge
DOI: 10.34740/kaggle/dsv/5173743
title: Paraguay Supermarket Products Dataset
DOI: 10.5281/zenodo.7739038
title: AlphaFold-Multimer (v3) model of a complex of Protein Phosphatase 2A catalytic subunit (PP2A/C), PP2A scaffold subunit (PP2A/A), the B55alpha substrate binding subunit, and FAM122A, a PP2A inhibitor protein.

description: AlphaFold-Multimer (v3) model structure of a complex of Protein Phosphatase 2A catalytic subunit (PP2A/C), PP2A scaffold subunit (PP2A/A), the B55alpha substrate binding subunit, and FAM122A, a PP2A inhibitor protein. The protein sequences were obtained from UniProt: P67775 · PP2AA_HUMAN P30153 · 2AAA_HUMAN P63151 · 2ABA_HUMAN Q96E09 · PBIR1_HUMAN Coordinates are in mmCIF format. A PyMOL session file is included. Modeling was performed with AlphaFold-Multimer v3, downloaded from DeepMind's github. Structure prediction was performed without templates. The model was relaxed with Amber. FAM122A binds using a short linear motif (SLIM) (residues 84-89) first identified in RBL1 (p107) (Fowle et al., eLife, https://doi.org/10.7554/eLife.63181 in the form of a short alpha helix (residues 84-92). This is followed by a long alpha helix (residues 96-122) which blocks access to the active site of the catalytic subunit. There are further contacts of FAM122A (residues 150-170) with one of the beta sheets of the B55alpha beta propeller domain. FAM122A regulates PP2A activity during the cell cycle.
DOI: 10.5281/zenodo.7739037
title: AlphaFold-Multimer (v3) model of a complex of Protein Phosphatase 2A catalytic subunit (PP2A/C), PP2A scaffold subunit (PP2A/A), the B55alpha substrate binding subunit, and FAM122A, a PP2A inhibitor protein.

description: AlphaFold-Multimer (v3) model structure of a complex of Protein Phosphatase 2A catalytic subunit (PP2A/C), PP2A scaffold subunit (PP2A/A), the B55alpha substrate binding subunit, and FAM122A, a PP2A inhibitor protein. The protein sequences were obtained from UniProt: P67775 · PP2AA_HUMAN P30153 · 2AAA_HUMAN P63151 · 2ABA_HUMAN Q96E09 · PBIR1_HUMAN Coordinates are in mmCIF format. A PyMOL session file is included. Modeling was performed with AlphaFold-Multimer v3, downloaded from DeepMind's github. Structure prediction was performed without templates. The model was relaxed with Amber. FAM122A binds using a short linear motif (SLIM) (residues 84-89) first identified in RBL1 (p107) (Fowle et al., eLife, https://doi.org/10.7554/eLife.63181 in the form of a short alpha helix (residues 84-92). This is followed by a long alpha helix (residues 96-122) which blocks access to the active site of the catalytic subunit. There are further contacts of FAM122A (residues 150-170) with one of the beta sheets of the B55alpha beta propeller domain. FAM122A regulates PP2A activity during the cell cycle.
DOI: 10.5281/zenodo.7739032
title: Crosswalk between Source Classification Codes (SCCs) and 14 sectors of economy

description: This dataset is a crosswalk between US EPA Source Classification Codes (SCCs) and sectors of economy. The 14 sectors include all anthropogenic emission sources, and exclude biogenic, wildfire, and international sources. The 14 sectors are: 1) agriculture (“Ag”); 2) coal electricity utility (“Coal Elec.”); 3) noncoal electricity utility (“Non-coal Elec.”); 4) commercial cooking (“Cooking”); 5) construction (“Const.”); 6) diesel heavy-duty vehicle (“Diesel HD Veh.”); 7) gasoline light-duty vehicle (“Gas LD Veh.”); 8) industrial; 9) road dust (“Road Dst”); 10) residential gas combustion (“Res. Gas”); 11) residential wood combustion (“Res. Wood”); 12) residential others (“Res. Other”); 13) off-highway vehicle and equipment (“Offroad”); and 14) miscellaneous (“Misc.”). The SCC list are downloaded from https://ofmpub.epa.gov/sccwebservices/sccsearch/ [Accessed Sep 22, 2022]. In the data table, the first column is the SCC; the second column is the classification of the 14 sectors; and the reset of the columns includes detailed description of each SCC from EPA.
DOI: 10.5281/zenodo.7739031
title: Crosswalk between Source Classification Codes (SCCs) and 14 sectors of economy

description: This dataset is a crosswalk between US EPA Source Classification Codes (SCCs) and sectors of economy. The 14 sectors include all anthropogenic emission sources, and exclude biogenic, wildfire, and international sources. The 14 sectors are: 1) agriculture (“Ag”); 2) coal electricity utility (“Coal Elec.”); 3) noncoal electricity utility (“Non-coal Elec.”); 4) commercial cooking (“Cooking”); 5) construction (“Const.”); 6) diesel heavy-duty vehicle (“Diesel HD Veh.”); 7) gasoline light-duty vehicle (“Gas LD Veh.”); 8) industrial; 9) road dust (“Road Dst”); 10) residential gas combustion (“Res. Gas”); 11) residential wood combustion (“Res. Wood”); 12) residential others (“Res. Other”); 13) off-highway vehicle and equipment (“Offroad”); and 14) miscellaneous (“Misc.”). The SCC list are downloaded from https://ofmpub.epa.gov/sccwebservices/sccsearch/ [Accessed Sep 22, 2022]. In the data table, the first column is the SCC; the second column is the classification of the 14 sectors; and the reset of the columns includes detailed description of each SCC from EPA.
DOI: 10.5281/zenodo.7688973
title: FIRO_synthetic-ensemble-forecasts dataset v0

description: Dataset to support initial submission of WRR manuscript 'Synthetic forecast ensembles for evaluating Forecast Informed Reservoir Operations (FIRO)' Code and workflow description are in public GitHub repository: https://github.com/zpb4/FIRO_synthetic-ensemble-forecasts
DOI: 10.6084/m9.figshare.22280980
title: Part 1-data_extraction.xls

description: It contains the result of data extraction for RQ 1 for the paper entitled " A survey on Association Rule Mining for Enterprise Architecture Model Discovery"
DOI: 10.6084/m9.figshare.22280980.v1
title: Part 1-data_extraction.xls

description: It contains the result of data extraction for RQ 1 for the paper entitled " A survey on Association Rule Mining for Enterprise Architecture Model Discovery"
DOI: 10.6084/m9.figshare.22257268
title: BLUPF 90.rar

description: Data analyzed with BLUPF90
DOI: 10.6084/m9.figshare.22257268.v1
title: BLUPF 90.rar

description: Data analyzed with BLUPF90
DOI: 10.6084/m9.figshare.22280983
title: Part 2-articles export.xls

description: It contains the list of papers returned in the search bases, and the result of the selection for RQ 2 for the paper entitled " A survey on Association Rule Mining for Enterprise Architecture Model Discovery"
DOI: 10.6084/m9.figshare.22280920.v1
title: geno

description: beef cattle simulation blupf90
DOI: 10.6084/m9.figshare.22280920.v2
title: blupf90

description: beef cattle simulation blupf90
DOI: 10.6084/m9.figshare.22280986
title: Part 2-data_extraction.xls

description: It contains the result of data extraction for RQ 2 for the paper entitled " A survey on Association Rule Mining for Enterprise Architecture Model Discovery"
DOI: 10.6084/m9.figshare.22280986.v1
title: Part 2-data_extraction.xls

description: It contains the result of data extraction for RQ 2 for the paper entitled " A survey on Association Rule Mining for Enterprise Architecture Model Discovery"
DOI: 10.25345/c5c824q5x
title: MassIVE MSV000091495 - Chapter 5: Inclusion of Porous Graphitic Carbon Chromatography Yields Greater Protein Identification, Compartment and Process Coverage, and Enables More Reflective Protein-Level Label-Free Quantitation
title: The ubiquity of mass spectrometry-based bottom-up proteomic analyses as a component of biological investigation mandates the validation of methodologies that increase acquisition efficiency, improve sample coverage, and enhance profiling depth. Chromatographic separation is often ignored as an area of potential improvement with most analyses relying on traditional reversed-phase liquid chromatography (RPLC); this consistent reliance on a single chromatographic paradigm fundamentally limits our view of the observable proteome. Within, we build upon early reports and validate porous graphitic carbon chromatography (PGC) as a facile means to substantially enhance proteomic coverage without changes to sample preparation, instrument configuration, or acquisition method. Analysis of offline fractionated cell line digests using both separations revealed increase peptide and protein identifications by 43% and 24%, respectively. Increased identifications provided more comprehensive coverage of cellular components and biological processes independent of protein abundance, highlighting the substantial quantity of proteomic information that may go undetected in standard analyses. We further utilize these data to reveal that label-free quantitative analyses using RPLC separations alone may not be reflective of actual protein constituency. Together, these data highlight the value and comprehension offered through PGC-MS proteomic analyses.
DOI: 10.5281/zenodo.6930826
title: Dataset for "Application of dissipative particle dynamics to interfacial systems: Parameterization and scaling"

description: Dataset for figures of the upcoming article "Application of dissipative particle dynamics to interfacial systems: Parameterization and scaling" submitted to "AIP Advances".
DOI: 10.5281/zenodo.7739096
title: MICOM model databases version 1

description: This repository includes pre-built model databases for the microbial community modeling tool MICOM (https://micom-dev.github.io/micom). All model databases will work with the MICOM python package or the MICOM Qiime 2 plugin (http://github.com/micom-dev/q2-micom).<br><br> The Zenodo release is built and versioned automatically from the the source repository at http://github.com/micom-dev/databases. Thus, all bug reports, requests, and comments should be made there.<br> Model databases are named with the following scheme:<br> SOURCE_SVER_TAXONOMY_RANK_VERSION.qza<br> where:<br> SOURCE = source database for the models SVER = version of the source database TAXONOMY = the taxonomy naming scheme used RANK = the taxonomic rank models were collapsed on VERSION = the version of the built database For all practical purposes the TAXONOMY should coincide with the taxonomic classification of your amplicons or genomes. For instance, if you used kraken2 and bracken2 you should use a model database with TAXONOMY = ncbi. Note that some taxonomy databases (GTDB, GreenGenes) prefix taxonomy identifiers with a rank indentifier like `s__Species`. Those are usually maintained in the databases here except for the NCBI Taxonomy which usually does not use those. You can verify taxon names using the manifests in the `release` section of http://github.com/micom-dev/databases . <br><br> For growth media that can be used with the model databases here please see the MICOM media repository at https://github.com/micom-dev/media.
description: {"references": ["Diener C, Gibbons SM, Resendis-Antonio O. MICOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota. mSystems. 2020 Jan 21;5(1):e00606-19. doi: 10.1128/mSystems.00606-19. PMID: 31964767; PMCID: PMC6977071", "Machado D, Andrejev S, Tramontano M, Patil KR. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 2018 Sep 6;46(15):7542-7553. doi: 10.1093/nar/gky537. PMID: 30192979; PMCID: PMC6125623", "Magn\u00fasd\u00f3ttir S, Heinken A, Kutt L, Ravcheev DA, Bauer E, Noronha A, Greenhalgh K, J\u00e4ger C, Baginska J, Wilmes P, Fleming RM, Thiele I. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat Biotechnol. 2017 Jan;35(1):81-89. doi: 10.1038/nbt.3703. Epub 2016 Nov 28. PMID: 27893703", "Zimmermann, J., Kaleta, C. & Waschina, S. gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biol 22, 81 (2021). doi: 10.1186/s13059-021-02295-1", "Heinken, A., Hertel, J., Acharya, G. et al. Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine. Nat Biotechnol (2023). doi: 10.1038/s41587-022-01628-0", "Almeida, A., Nayfach, S., Boland, M. et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nat Biotechnol 39, 105\u2013114 (2021). doi: 10.1038/s41587-020-0603-3"]}