cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4834876
title: JetNet

description: Gluon (g), Top Quark (t), Light Quark (q) jets saved in .csv format. The shape is [N, 30 * 4], where N is the total number of jets, and 30 is the number of particles per jet, and 4 is the number of particle features (in order): [eta_rel, phi_rel, pt_rel, mask]. N = 177252, 177945, 170679 for g, t, q jets respectively.
DOI: 10.5281/zenodo.5502543
title: JetNet

description: Gluon (g), Top Quark (t), Light Quark (q) jets saved in .csv format. The shape is [N, 30 * 4], where N is the total number of jets, 30 is the number of particles per jet, and 4 is the number of particle features (in order): [eta_rel, phi_rel, pt_rel, mask]. N = 177252, 177945, 170679 for g, t, q jets respectively.
DOI: 10.5281/zenodo.6302454
title: JetNet

description: Gluon (g), Top Quark (t), Light Quark (q) jets saved in .csv and .hdf5 formats. The shape is [N, 30 * 4] for .csv and [N, 30, 4] for .hdf5, where N is the total number of jets, 30 is the number of particles per jet, and 4 is the number of particle features (in order): [eta_rel, phi_rel, pt_rel, mask]. N = 177252, 177945, 170679 for g, t, q jets respectively.
DOI: 10.5281/zenodo.6975118
title: JetNet

description: Gluon (<code>g</code>), Top Quark (<code>t</code>), Light Quark (<code>q</code>), W boson (<code>w</code>), and Z boson (<code>z</code>) jets of ~1 TeV transverse momentum (\(p_T\)), as introduced in Ref. [1]. Each file has <code>particle_features</code>; and <code>jet_features</code>; arrays, containing the list of particles' features per jet and the corresponding jet's features, respectively. <code>particle_features</code> is of shape [N, 30, 4], where N is the total number of jets, 30 is the max number of particles per jet, and 4 is the number of particle features, in order: [\(\eta_{\mathrm{rel}}\), \(\varphi_{\mathrm{rel}}\), \(p_{T\mathrm{rel}}\), mask]. See [1, Sec. 2] for definitions of these. <code>jet_features</code> is of shape [N, 4], where 4 is the number of jet features, in order: [\(p_T\), \(\eta\), mass, # of particles]. N = 177252, 177945, 170679, 177172, 176952 for g, t, q, w, z jets respectively. Finally, the JetNet package is recommended for easy access and use of this dataset. [1] Kansal et. al., <em>Particle Cloud Generation with Message Passing Generative Adversarial Networks</em>, NeurIPS 2021 <code>arXiv:2106.11535</code>
DOI: 10.5281/zenodo.7288829
title: Nonsmooth simulations of 3D Drucker-Prager granular flows and validation against experimental column collapses

description: # About This archive aims to reproduce the results of the article entitled: "***Nonsmooth simulations of 3D Drucker-Prager granular flows and validation against experimental column collapses***" *by Gauthier Rousseau, Thibaut Métivet, Hugo Rousseau, Gilles Daviet, and Florence Bertails-Descoubes* ## Contents - The experimental data: raw videos, experimental information, velocity and profiles - The `sand6py` code which is a fork of [Daviet PhD][1] `sand6` code including [diphasic feature][2] and other new features developed for the paper (collapses scenarios with frictional door, hysteresis, python binding with `pybind11`) - Python scripts: - for performing velocimetry measurements - for running the `sand6` 3D simulations of granular column collapses - for plotting paper figures <br> The git repository corresponding to `sand6py` is available on [https://gitlab.com/groussea/sand6py](https://gitlab.com/groussea/sand6py) A maintained version of sand6 is available on [https://gitlab.inria.fr/elan-public-code/sand6](https://gitlab.inria.fr/elan-public-code/sand6) ## Figures the figures of the article are reproducible from the python scripts in the `python/figurejfm` path python dependencies are `matplotlib opencv jupyter vtk h5py tqdm scikit-image opyf` You may obtain all the required dependencies in a convenient conda environment using the following command line in your terminal: ```shell conda create --channel conda-forge -n sand6env python=3.9 matplotlib opencv jupyter vtk h5py tqdm scikit-image conda activate sand6env ``` Corresponding e-mail: gauthier.rousseau@gmail.com [1]: tel.archives-ouvertes.fr/tel-01684673 "*Modeling and simulating complex materials subject to frictional contact: Application to fibrous and granular media*. Diss. Ph. D. Dissertation. Université Grenoble Alpes. , 2016" [2]: https://hal.inria.fr/hal-01458951 "Simulation of Drucker–Prager granular flows inside Newtonian fluids"
DOI: 10.5281/zenodo.7288828
title: Nonsmooth simulations of 3D Drucker-Prager granular flows and validation against experimental column collapses

description: # About This archive aims to reproduce the results of the article entitled: "***Nonsmooth simulations of 3D Drucker-Prager granular flows and validation against experimental column collapses***" *by Gauthier Rousseau, Thibaut Métivet, Hugo Rousseau, Gilles Daviet, and Florence Bertails-Descoubes* ## Contents - The experimental data: raw videos, experimental information, velocity and profiles - The `sand6py` code which is a fork of [Daviet PhD][1] `sand6` code including [diphasic feature][2] and other new features developed for the paper (collapses scenarios with frictional door, hysteresis, python binding with `pybind11`) - Python scripts: - for performing velocimetry measurements - for running the `sand6` 3D simulations of granular column collapses - for plotting paper figures <br> The git repository corresponding to `sand6py` is available on [https://gitlab.com/groussea/sand6py](https://gitlab.com/groussea/sand6py) A maintained version of sand6 is available on [https://gitlab.inria.fr/elan-public-code/sand6](https://gitlab.inria.fr/elan-public-code/sand6) ## Figures the figures of the article are reproducible from the python scripts in the `python/figurejfm` path python dependencies are `matplotlib opencv jupyter vtk h5py tqdm scikit-image opyf` You may obtain all the required dependencies in a convenient conda environment using the following command line in your terminal: ```shell conda create --channel conda-forge -n sand6env python=3.9 matplotlib opencv jupyter vtk h5py tqdm scikit-image conda activate sand6env ``` Corresponding e-mail: gauthier.rousseau@gmail.com [1]: tel.archives-ouvertes.fr/tel-01684673 "*Modeling and simulating complex materials subject to frictional contact: Application to fibrous and granular media*. Diss. Ph. D. Dissertation. Université Grenoble Alpes. , 2016" [2]: https://hal.inria.fr/hal-01458951 "Simulation of Drucker–Prager granular flows inside Newtonian fluids"
DOI: 10.23645/epacomptox.21502758.v1
title: High Throughput Transcriptomics (HTTr)

description: Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25 <br>
DOI: 10.5281/zenodo.6808784
title: Taylor Valley meteorological input data for ICEMELT model

description: These input files were too large to be included in ICEMELT-Cross GitHub repository (https://doi.org/10.5281/zenodo.6808770). To run the ICEMELT model, copy and paste the contents of this repository into the 'input' directory.
DOI: 10.5066/p945muc0
title: Whiskey Island Caillou Lake Headlands restoration area 2016?2019 shorelines, Louisiana Barrier Island Comprehensive Monitoring Program

description: This data package includes a high-water shoreline position map and a structures map for each year from 2016?2019 for the Natural Resource Damage Assessment (NRDA) Caillou Lake Headlands project area (TE-0100), also known as Whiskey Island. The project created 170 acres of marsh habitat and 917 acres of dune and beach habitat on Whiskey Island in Terrebonne Parish using material dredged from Ship Shoal. For more information on this restoration project, see the project page on Louisiana Coastal Protection and Restoration Authority?s (CPRA) Coastal Information Management System (http://cims.coastal.la.gov/outreach/projects/ProjectView?projID=TE-0100). Together with previous shoreline products developed for CPRA's Barrier Island Comprehensive Monitoring (BICM) Program Phase 2, this suite of Whiskey Island shoreline position maps spans the time period before, during, and after the restoration project was conducted. The shorelines were delineated primarily using linework from existing habitat classifications of Whiskey Island. Please see the Processing Steps sections of the individual metadata files for information on methodology for map development. The BICM Program, for which these maps were developed, uses both historical data and contemporary data collections to assess and monitor changes to barrier islands, and develops data products, including shoreline position maps. For more information about the BICM Program, see Kindinger and others (2013).
DOI: 10.5066/p9a4rekp
title: Caminada Headland restoration area 2016-2019 shorelines, Louisiana Barrier Island Comprehensive Monitoring Program

description: This data package includes a high-water shoreline position map and a structures map for each year from 2016?2019 for the National Fish and Wildlife Foundation (NFWF) Caminada Headland Beach and Dune Restoration Incr2 project area (BA-0143). The project restored 489 acres of beach and dune habitat along more than seven miles of Caminada Headland in Jefferson and Lafourche Parishes through the direct placement of about 5.4 million cubic yards of sandy substrate from Ship Shoal. For more information on this restoration project, see the project page on Louisiana Coastal Protection and Restoration Authority?s (CPRA) Coastal Information Management System (http://cims.coastal.la.gov/outreach/projects/ProjectView?projID=BA-0143). Together with previous shoreline products developed for CPRA's Barrier Island Comprehensive Monitoring (BICM) Program Phase 2, this suite of Caminada Headland shoreline position maps spans the time period before, during, and after the restoration project was conducted. The shorelines were delineated primarily using linework from existing habitat classifications of Caminada Headland. Please see the Processing Steps sections of the individual metadata files for information on methodology for map development. The BICM Program, for which these maps were developed, uses both historical data and contemporary data collections to assess and monitor changes to barrier islands, and develops data products, including shoreline position maps. For more information about the BICM Program, see Kindinger and others (2013).
DOI: 10.5517/ccsffxj
title: CCDC 727908: Experimental Crystal Structure Determination

description: Related Article: Zhong-Feng Shi, Jing Jin, Lei Li, Yong-Heng Xing, Shu-Yun Niu|2009|Wuli Huaxue Xuebao|25|2011|
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: 10.21957/1n36-qg55
title: IFS nextGEMS cycle 2 4km 1-year simulation
DOI: 10.3886/e114087v1-130405
title: Replication data for: The Simple Economics of Commodity Price Speculation

description: The price of crude oil never exceeded $40 per barrel until mid-2004. By July 2008 it peaked at $145 and by late 2008 it fell to $30 before increasing to $110 in 2011. Are speculators partly to blame for these price changes? Using a simple model of supply and demand in the cash and storage markets, we determine whether speculation is consistent with data on production, inventory changes, and convenience yields. We focus on crude oil, but our approach can be applied to other commodities. We show speculation had little, if any, effect on oil prices. (JEL G13, G18, G23, G31, Q35, Q38)
DOI: 10.5281/zenodo.6720296
title: ArXiV-AIKG dataset

description: This dataset is a collection of abstracts from the CS section of ArXiV, each paired with triples from the Artificial Intelligence Knowledge Graph (AIKG) https://scholkg.kmi.open.ac.uk/ The pairing is determined by the fact that one or more entities of the triples are in the abstract. The dataset can be used to train triple extractors or to cluster triples (in the Computer Science and AI domains).
DOI: 10.6084/m9.figshare.21498453
title: BLUP values for 2016-2021 Voles

description: All the BLUP values for each individual vole caught from 2016 through 2021 for each personality trait. This was generated using the R script provided for calculating BLUPs.
DOI: 10.6084/m9.figshare.21498453.v2
title: BLUP values for 2016-2021 Voles

description: All the BLUP values for each individual vole caught from 2016 through 2021 for each personality trait. This was generated using the R script provided for calculating BLUPs.
DOI: 10.6084/m9.figshare.21498453.v1
title: BLUP values for 2016-2021 Voles

description: All the BLUP values for each individual vole caught from 2016 through 2021 for each personality trait. This was generated using the R script provided for calculating BLUPs.
DOI: 10.5281/zenodo.7291834
title: ArXiV-Entity/Relation annotated dataset

description: This dataset is a collection of abstracts from the CS section of ArXiV, each annotated with DyGIE++ (SciERC model) The dataset can be used to train triple extractors or to cluster triples (in the Computer Science and AI domains). Supersedes the ArXiV-AIKG dataset as these triples are unconstrained (so they don't forcibly appear in AIKG)
DOI: 10.5281/zenodo.6720295
title: ArXiV-Entity/Relation annotated dataset

description: This dataset is a collection of abstracts from the CS section of ArXiV, each annotated with DyGIE++ (SciERC model) The dataset can be used to train triple extractors or to cluster triples (in the Computer Science and AI domains). Supersedes the ArXiV-AIKG dataset as these triples are unconstrained (so they don't forcibly appear in AIKG)
DOI: 10.6084/m9.figshare.21498450.v1
title: 2016-2021 Capture and Personality Data

description: All the capture data and raw data from behavioral assays for all mice, voles, and shrews from 2016 through 2021.
DOI: 10.5281/zenodo.7291989
title: A dataset of the landslide thickness estimated from InSAR-derived 2D deformation

description: This dataset provides supporting to the research of the landslide thickness estimated based on the InSAR deformation. And it mainly includes the ascending and descending orbital tracks InSAR deformation, the inferred two-dimensional deformation, and the estimated landslide thickness.
DOI: 10.5281/zenodo.7291988
title: A dataset of the landslide thickness estimated from InSAR-derived 2D deformation

description: This dataset provides supporting to the research of the landslide thickness estimated based on the InSAR deformation. And it mainly includes the ascending and descending orbital tracks InSAR deformation, the inferred two-dimensional deformation, and the estimated landslide thickness.
DOI: 10.5281/zenodo.5570278
title: Dataset for Pre-referral rectal artesunate and referral completion among children with suspected severe malaria in the Democratic Republic of the Congo, Nigeria and Uganda

description: Dataset for the publication <strong>"Pre-referral rectal artesunate and referral completion among children with suspected severe malaria in the Democratic Republic of the Congo, Nigeria and Uganda".</strong> Data from the Community Access to Rectal Artesunate for Malaria (CARAMAL) Project, 2018-2021 Analysis of the effect of the roll-out and administration of pre-referral rectal artesunate (RAS) on referral completion to designated referral health facilities in the Democratic Republic of the Congo, Nigeria and Uganda.
description: {"references": ["Brunner et al. Prereferral rectal artesunate and referral completion among children with suspected severe malaria in the Democratic Republic of the Congo, Nigeria and Uganda. BMJ Glob Health. 2022 May;7(5):e008346. doi: 10.1136/bmjgh-2021-008346.", "Brunner et al.: Pre-referral rectal artesunate and referral completion among children with suspected severe malaria in the Democratic Republic of the Congo, Nigeria and Uganda. Added to medRxiv:2021.09.27.21264073"]}
DOI: 10.5281/zenodo.5570277
title: Dataset for Pre-referral rectal artesunate and referral completion among children with suspected severe malaria in the Democratic Republic of the Congo, Nigeria and Uganda

description: Dataset for the publication <strong>"Pre-referral rectal artesunate and referral completion among children with suspected severe malaria in the Democratic Republic of the Congo, Nigeria and Uganda".</strong> Data from the Community Access to Rectal Artesunate for Malaria (CARAMAL) Project, 2018-2021 Analysis of the effect of the roll-out and administration of pre-referral rectal artesunate (RAS) on referral completion to designated referral health facilities in the Democratic Republic of the Congo, Nigeria and Uganda.
description: {"references": ["Brunner et al. Prereferral rectal artesunate and referral completion among children with suspected severe malaria in the Democratic Republic of the Congo, Nigeria and Uganda. BMJ Glob Health. 2022 May;7(5):e008346. doi: 10.1136/bmjgh-2021-008346.", "Brunner et al.: Pre-referral rectal artesunate and referral completion among children with suspected severe malaria in the Democratic Republic of the Congo, Nigeria and Uganda. Added to medRxiv:2021.09.27.21264073"]}
DOI: 10.6084/m9.figshare.6741422.v17
title: COCI CSV dataset of all the citation data

description: This dataset contains all the citation data (in CSV format) included in COCI, released on 31 August 2022. In particular, each line of the CSV file defines a citation, and includes the following information: <strong>[field "oci"]</strong> the Open Citation Identifier (OCI) for the citation; <strong>[field "citing"]</strong> the DOI of the citing entity; <strong>[field "cited"]</strong> the DOI of the cited entity; <strong>[field "creation"]</strong> the creation date of the citation (i.e. the publication date of the citing entity); <strong>[field "timespan"]</strong> the time span of the citation (i.e. the interval between the publication date of the cited entity and the publication date of the citing entity); <strong>[field "journal_sc"]</strong> it records whether the citation is a journal self-citations (i.e. the citing and the cited entities are published in the same journal); <strong>[field "author_sc"]</strong> it records whether the citation is an author self-citation (i.e. the citing and the cited entities have at least one author in common).<br> This version of the dataset contains: 1,363,718,366 citations; 75,030,924 bibliographic resources. The size of the zipped archive is 34.81 GB, while the size of the unzipped CSV file is 221.8 GB. <br> Additional information about COCI can be retrieved in the <sub>official webpage</sub>.<br>
DOI: 10.6084/m9.figshare.6741425.v17
title: COCI N-Triples dataset of all the citation data

description: This dataset contains all the citation data (in N-Triples format) included in COCI, released on 31 August 2022. In particular, any citation in the dataset, defined as an individual of the class <em>cito:Citation</em>, includes the following information: <strong>[citation IRI]</strong> the Open Citation Identifier (OCI) for the citation, defined in the final part of the URL identifying the citation (https://w3id.org/oc/index/coci/ci/[OCI]); <strong>[property "cito:hasCitingEntity"]</strong> the citing entity identified by its DOI URL (http://dx.doi.org/[DOI]); <strong>[property "cito:hasCitedEntity"]</strong> the cited entity identified by its DOI URL (http://dx.doi.org/[DOI]); <strong>[property "cito:hasCitationCreationDate"]</strong> the creation date of the citation (i.e. the publication date of the citing entity); <strong>[property "cito:hasCitationTimeSpan"]</strong> the time span of the citation (i.e. the interval between the publication date of the cited entity and the publication date of the citing entity); <strong>[type "cito:JournalSelfCitation"]</strong> it records whether the citation is a journal self-citations (i.e. the citing and the cited entities are published in the same journal); <strong>[type "cito:AuthorSelfCitation"]</strong> it records whether the citation is an author self-citation (i.e. the citing and the cited entities have at least one author in common). This version of the dataset contains: 1,363,718,366 citations; 75,030,924 bibliographic resources. The size of the zipped archive is 67.64 GB, while the size of the unzipped N-Triples file is 1.46 TB. <br> Additional information about COCI can be retrieved in the <sub>official webpage</sub>.<br>
DOI: 10.6084/m9.figshare.11663574.v14
title: COCI Scholix dataset of all the citation data

description: This dataset contains all the citation data (in Scholix format) included in COCI, released on 31 August 2022. <br> This version of the dataset contains: 1,363,718,366 citations; 75,030,924 bibliographic resources. The size of the zipped archive is 36.13 GB, while the size of the unzipped Scholix file is 1.21 TB. Additional information about COCI can be retrieved in the <sub>official webpage</sub>.<br>
DOI: 10.6084/m9.figshare.21502878
title: Metadata

description: metadata file with bacterial densities
DOI: 10.6084/m9.figshare.21502878.v1
title: Metadata

description: metadata file with bacterial densities
DOI: 10.6084/m9.figshare.20588385.v2
title: Table A1. Lightning data detected by lightning location array

description: Table A1: Lightning data detected by lightning location array Note: IC: intracloud lightning. CG: cloud-to-ground lightning. <br>
DOI: 10.25411/aru.21501531
title: Ilg et al PLOSOne full dataset

description: Dataset of data underlying results in PLOSOne study
DOI: 10.25411/aru.21501531.v1
title: Ilg et al PLOSOne full dataset

description: Dataset of data underlying results in PLOSOne study
DOI: 10.6073/pasta/3f497bb31bd1fcb99cf2ef64ed87806f
title: Jornada Basin LTER: Wireless meteorological station at NPP C-CALI site: 5-minute summary wind and air temperature data: 2013 - ongoing

description: 5-minute summary data at NPP C-CALI met station. Average air temperature, wind speed and wind direction are measured and calculated based on 1-second scan rate of all sensors located at an automated meteorological station installed at Jornada LTER NPP C-CALI site. Wind speed is measured at 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature at approximate 2.5m. This climate station is operated by the Jornada LTER Program. This is an ONGOING dataset.
DOI: 10.5281/zenodo.7284138
title: Measurement report: Springtime reactive Volatile Organic Compounds (VOCs) and impacts on ozone in urban areas of Yunnan-Guizhou Plateau, China: a PTR-TOF-MS study

description: Time-varying sequence and day-to-day concentration after treatment. It includes 18 observed major VOCs [acetaldehyde, 2-acrolein, acetone, methyl ethyl ketone (MEK), methyl vinyl ketone (MVK), methacrolein (MACR), methyl isobutyl ketone (MIK), 2-pentanone, ethyl acetate, isoprene, α-pinene, benzene, toluene, styrene, C8 aromatic hydrocarbons, C9 aromatic hydrocarbons, 1,3-dichlorobenzene, naphthalene, and acetonitrile].
DOI: 10.5281/zenodo.7284240
title: Measurement report: Springtime reactive Volatile Organic Compounds (VOCs) and impacts on ozone in urban areas of Yunnan-Guizhou Plateau, China: a PTR-TOF-MS study

description: Time-varying sequence and day-to-day concentration after treatment. It includes 18 observed major VOCs [acetaldehyde, 2-acrolein, acetone, methyl ethyl ketone (MEK), methyl vinyl ketone (MVK), methacrolein (MACR), methyl isobutyl ketone (MIK), 2-pentanone, ethyl acetate, isoprene, α-pinene, benzene, toluene, styrene, C8 aromatic hydrocarbons, C9 aromatic hydrocarbons, 1,3-dichlorobenzene, naphthalene, and acetonitrile].