cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/7hd4-cq25
title: Appendix of Optimizing systemic redundancy of traffic sensor networks while maintaining resilience: New evidence from using graph learning

description: Appendix of Optimizing systemic redundancy of traffic sensor networks while maintaining resilience: New evidence from using graph learning
DOI: 10.25919/szav-b038
title: Parkes observations for project P1173 semester 2022OCTS_03

description: Spider pulsar systems are compact binary pulsars with low-mass companions in tight orbits, where the primary pulsar is ablating the secondary through the output of high-energy radiation and pulsar wind due to proximity. These systems present an interesting opportunity to probe the mechanisms related to the destruction of these low-mass bodies in the radio regime, as debris surrounding the ablated secondary companion causes the pulsar emission to propagate and scatter as it moves through it, along our line of sight - revealing details of the complex, turbulent structure of this region. Recent studies have shown that the eclipsing region can cause pulsar signals to scatter, revealing small-scale structure of the turbulent field, as well as a strong variation in the pulsar’s polarsiation, rotation measure and dispersion measure. Under this proposal, we wish to study two known spider pulsars for their full orbit with the Parkes UWL capability – which has not been achieved by any other telescope. This will give us the opportunity to analyse he eclipsing effects of these systems.
DOI: 10.5281/zenodo.4650795
title: CCG Starter Data Kit: Liberia

description: A starter data kit for Liberia
DOI: 10.5281/zenodo.4756708
title: CCG Starter Data Kit: Liberia

description: A starter data kit for Liberia
DOI: 10.5281/zenodo.7539162
title: CCG Starter Data Kit: Liberia

description: A starter data kit for Liberia
DOI: 10.5281/zenodo.4650794
title: CCG Starter Data Kit: Liberia

description: A starter data kit for Liberia
DOI: 10.5281/zenodo.4650920
title: CCG Starter Data Kit: Libya

description: A starter data kit for Libya
DOI: 10.5281/zenodo.4650921
title: CCG Starter Data Kit: Libya

description: A starter data kit for Libya
DOI: 10.5281/zenodo.4756702
title: CCG Starter Data Kit: Libya

description: A starter data kit for Libya
DOI: 10.5281/zenodo.5733323
title: CCG Starter Data Kit: Libya

description: A starter data kit for Libya
DOI: 10.5281/zenodo.5751699
title: CCG Starter Data Kit: Libya

description: A starter data kit for Libya
DOI: 10.5281/zenodo.7539255
title: CCG Starter Data Kit: Libya

description: A starter data kit for Libya
DOI: 10.5281/zenodo.6609583
title: Data for "Lifecycle of Major Sudden Stratospheric Warmings in the Southern Hemisphere from a Multi-Millennial GCM Simulation" by J. Jucker and T. Reichler

description: Contains all data needed to produce all figures for Jucker & Reichler <em>Journal of Climate</em> https://doi.org/10.1175/JCLI-D-22-0425.1. To produce figures: Untar all archives, create new directory called "figures", and open jupyter notebook file. The file will specify any needed additional python packages. Plotting also includes computation of statistical computations.
DOI: 10.5281/zenodo.6609584
title: Data for "Lifecycle of Major Sudden Stratospheric Warmings in the Southern Hemisphere from a Multi-Millennial GCM Simulation" by J. Jucker and T. Reichler

description: Contains all data needed to produce all figures for Jucker & Reichler <em>Journal of Climate</em> https://doi.org/10.1175/JCLI-D-22-0425.1. To produce figures: Untar all archives, create new directory called "figures", and open jupyter notebook file. The file will specify any needed additional python packages. Plotting also includes computation of statistical computations.
DOI: 10.5281/zenodo.7539243
title: Nitrogen fixation responds to soil nitrogen at low but not high light in two invasive understory species

description: Light and soil nitrogen availability can be strong controls of plant nitrogen (N) fixation, but data on how understory N-fixing plants respond to these drivers are limited despite their important role in ecosystem N cycling. Furthermore, ecosystem N cycling can be altered by the introduction of species with nutrient use patterns that differ from natives. We assessed how N fixation of two exotic, understory species responded to varying light and soil N environments. We sampled leaf tissue from <em>Mimosa pudica </em>L., <em>Desmodium triflorum</em> (L.) DC., and a non-fixing reference plant (<em>Axonopus</em>) growing in control and two N fertilization treatments under either N-fixing or non-N-fixing trees, which may alter local soil nutrient cycling, across a range of light conditions. We measured N fixation with <sup>15</sup>N isotope dilution, and ensured that N-fixing neighbor trees were in fact fixing N. All understory plants were wild-growing species not native to the study location. <em>Desmodium </em>and <em>Mimosa </em>acquired 82.6% and 71.6% of their nitrogen from fixation (%N<sub>dfa</sub>) in the control, compared to 66.8% and 58.1% in the +10 g N m<sup>−2</sup> y<sup>−1</sup> treatment and 73.1% and 64.7% in the +15 g N m<sup>−2</sup> y<sup>−1</sup> treatment. These subtle %N<sub>dfa</sub> differences across fertilization treatments were more apparent at low light availability and disappeared at high light availability. The amount of N fixed by neighboring trees did not influence %N<sub>dfa</sub> in the understory species. Our study shows some differences in N fixation across different nutrient environments at low light for two N-fixing species, though the changes were small, and both species derived most of their N from fixation. These findings imply that introduced N-fixing species could exacerbate ecosystem N enrichment, particularly under high soil N conditions
DOI: 10.5281/zenodo.4652799
title: CCG Starter Data Kit: Malawi

description: A starter data kit for Malawi
DOI: 10.5281/zenodo.4756699
title: CCG Starter Data Kit: Malawi

description: A starter data kit for Malawi
DOI: 10.5281/zenodo.4652798
title: CCG Starter Data Kit: Malawi

description: A starter data kit for Malawi
DOI: 10.5281/zenodo.6478260
title: CCG Starter Data Kit: Malawi

description: A starter data kit for Malawi
DOI: 10.5281/zenodo.7539262
title: CCG Starter Data Kit: Malawi

description: A starter data kit for Malawi
DOI: 10.34740/kaggle/dsv/4856746
title: US counties COVID 19 dataset
DOI: 10.6084/m9.figshare.21901974
title: Primer criterio Moriyama

description: cuestionario correspo9nsabilida
DOI: 10.25919/9shp-qv05
title: Parkes observations for project P1165 semester 2022OCTS_03

description: We propose to confirm and time five pulsar candidates recently discovered by the Five-hundred-meter Aperture Spherical Telescope (FAST). These candidates were discovered during a pilot survey of an intermediate Galactic latitude region with six-minute pointings. One candidate, J1826-0049, has already been confirmed to be a 4.59 ms pulsar in a binary system with an orbital period of ~6.3 hr. The superior sensitivity of FAST has the potential to discover pulsars in compact binary systems and we propose to use Parkes to confirm the nature of these candidates. We also propose to time two of the brightest candidates with Parkes and measure their spin and orbital parameters. These observations have the potential to open new opportunities to probe exciting physics with these newly discovered pulsars.
DOI: 10.5517/ccdc.csd.cc29knl4
title: CCDC 2132136: Experimental Crystal Structure Determination

description: Related Article: Subramaniyam Kalaivanan, Ganesan Prabusankar|2022|Catal.Lett|153|167|doi:10.1007/s10562-022-03971-y
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.cc29knm5
title: CCDC 2132137: Experimental Crystal Structure Determination

description: Related Article: Subramaniyam Kalaivanan, Ganesan Prabusankar|2022|Catal.Lett|153|167|doi:10.1007/s10562-022-03971-y
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.cc29knn6
title: CCDC 2132138: Experimental Crystal Structure Determination

description: Related Article: Subramaniyam Kalaivanan, Ganesan Prabusankar|2022|Catal.Lett|153|167|doi:10.1007/s10562-022-03971-y
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: 10.5281/zenodo.5517235
title: The Nova Scotia Disease Knowledge Graph

description: Nova Scotia's government has an abundance of resources in terms of data and information. All this data has been collected and stored on the NSOD portal (https://data.novascotia.ca) in the form of datasets. The Nova Scotia Open Data Portal was built and managed by Socrata API (https://dev.socrata.com). We transformed the disease-related datasets of Nova Scotia Open Data into RDF, enriched them by disease ontology, and it is available to be used under the MIT licence. - Added DBpedia linking
DOI: 10.5281/zenodo.5517236
title: The Nova Scotia Disease Knowledge Graph

description: Nova Scotia's government has an abundance of resources in terms of data and information. All this data has been collected and stored on the NSOD portal (https://data.novascotia.ca) in the form of datasets. The Nova Scotia Open Data Portal was built and managed by Socrata API (https://dev.socrata.com). We transformed the disease-related datasets of Nova Scotia Open Data into RDF, enriched them by disease ontology, and it is available to be used under the MIT licence.
DOI: 10.5281/zenodo.7539374
title: The Nova Scotia Disease Knowledge Graph

description: Nova Scotia's government has an abundance of resources in terms of data and information. All this data has been collected and stored on the NSOD portal (https://data.novascotia.ca) in the form of datasets. The Nova Scotia Open Data Portal was built and managed by Socrata API (https://dev.socrata.com). We transformed the disease-related datasets of Nova Scotia Open Data into RDF, enriched them by disease ontology, and it is available to be used under the MIT licence. - Added DBpedia linking
DOI: 10.6084/m9.figshare.21901977
title: Meta data _ Team Pub.xlsx

description: To assess team composition, we used an existing dataset of survey responses collected from NSF science teams (Settles et al. 2019; https://doi.org/10.3886/E105622V1). Teams and participants were identified using the NSF database of awards (National Science Foundation 2020). We identified a total of 1,727 individuals from 229 interdisciplinary research teams in this database and invited them to participate in an online survey regarding perceptions of team composition (Settles et al. 2019). We included only teams in which two or more individuals participated in the survey in this current dataset (for a total of 50 teams comprised of 182 individuals). To protect the identities of research participants, only team-level variables are shared here. To determine team bibliometrics, we collected publication information from the NSF online database, including all peer-reviewed scientific journal articles published by the teams and reported on NSF annual reports through 2019. For publication productivity, we used the number of years that each team received grant funding from NSF to calculate an average rate of publications per funding year (i.e., mean number of publications per funding year). For publication impact, we used the InCites Journal Citation Reports database to obtain journal impact factors for each article in the year of publication (Clarivate 2020) and calculated an average impact factor for each team (i.e., mean journal impact factor).
DOI: 10.5281/zenodo.7539392
title: HealthE

description: # HealthE Dataset HealthE contains 3,400 pieces of health advice gathered 1) from public health websites (i.e. WebMD.com, MedlinePlus.gov, CDC.gov, and MayoClinic.org) 2) from the publicly available [Preclude dataset]([https://userpages.umbc.edu/~nroy/courses/shhasp18/papers/p286-preum.pdf](https://userpages.umbc.edu/~nroy/courses/shhasp18/papers/p286-preum.pdf)). Each sample was hand-labeled for health entity recognition by a team of 14 annotators at the author's institution. Automatic recognition of health entities will enable further research in large-scale modeling of texts from online health communities. The data is provided in two parts. Both are formatted using the popular, free python `pickle` library and require use of the popular, free `pandas` library. `healthe.pkl` is a `pandas.DataFrame` object containing the 3,400 health-advice statement with hand-labeled health entities. `non_advice.pkl` is a `pandas.DataFrame` object containing the 2,256 pieces of non-advice statements. To load the files in python, use the following code block.<br> ```<br> import pickle<br> import pandas as pd<br> healthe_df = pd.read_pickle('healthe.pkl')<br> non_advice_df = pd.read_pickle('non_advice_df.pkl')<br> ``` `healthe_df` has four columns.<br> * `text` contains the health advice statement text<br> * `entities` contains a python list of (entity, class) tuples<br> * `tokenized_text` contains a list of tokens obtained by tokenizing the health advice statement text <br> * `labels` contains a list of the same length as `tokenized_text`, where each token is mapped to a class label. `non_advice_df` has one column, `text`, referring to each non-health-advice-statement.
DOI: 10.5281/zenodo.7539391
title: HealthE

description: # HealthE Dataset HealthE contains 3,400 pieces of health advice gathered 1) from public health websites (i.e. WebMD.com, MedlinePlus.gov, CDC.gov, and MayoClinic.org) 2) from the publicly available [Preclude dataset]([https://userpages.umbc.edu/~nroy/courses/shhasp18/papers/p286-preum.pdf](https://userpages.umbc.edu/~nroy/courses/shhasp18/papers/p286-preum.pdf)). Each sample was hand-labeled for health entity recognition by a team of 14 annotators at the author's institution. Automatic recognition of health entities will enable further research in large-scale modeling of texts from online health communities. The data is provided in two parts. Both are formatted using the popular, free python `pickle` library and require use of the popular, free `pandas` library. `healthe.pkl` is a `pandas.DataFrame` object containing the 3,400 health-advice statement with hand-labeled health entities. `non_advice.pkl` is a `pandas.DataFrame` object containing the 2,256 pieces of non-advice statements. To load the files in python, use the following code block.<br> ```<br> import pickle<br> import pandas as pd<br> healthe_df = pd.read_pickle('healthe.pkl')<br> non_advice_df = pd.read_pickle('non_advice_df.pkl')<br> ``` `healthe_df` has four columns.<br> * `text` contains the health advice statement text<br> * `entities` contains a python list of (entity, class) tuples<br> * `tokenized_text` contains a list of tokens obtained by tokenizing the health advice statement text <br> * `labels` contains a list of the same length as `tokenized_text`, where each token is mapped to a class label. `non_advice_df` has one column, `text`, referring to each non-health-advice-statement.
DOI: 10.5281/zenodo.5139480
title: CCG Starter Data Kit: Malaysia

description: A starter data kit for Malaysia
DOI: 10.5281/zenodo.5139481
title: CCG Starter Data Kit: Malaysia

description: A starter data kit for Malaysia
DOI: 10.5281/zenodo.6542363
title: CCG Starter Data Kit: Malaysia

description: A starter data kit for Malaysia
DOI: 10.5281/zenodo.7539356
title: CCG Starter Data Kit: Malaysia

description: A starter data kit for Malaysia
DOI: 10.5281/zenodo.4725448
title: CCG Starter Data Kit: Mali

description: A starter data kit for Mali
DOI: 10.5281/zenodo.4728156
title: CCG Starter Data Kit: Mali

description: A starter data kit for Mali
DOI: 10.5281/zenodo.4756807
title: CCG Starter Data Kit: Mali

description: A starter data kit for Mali
DOI: 10.5281/zenodo.4725447
title: CCG Starter Data Kit: Mali

description: A starter data kit for Mali
DOI: 10.5281/zenodo.7539414
title: CCG Starter Data Kit: Mali

description: A starter data kit for Mali
DOI: 10.5519/qd.7lqy1ljo
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5422398 records)
DOI: 10.21227/a70k-v376
title: Data files for "Investigating Thermionic Emission Properties of Polycrystalline Perovskite BaMoO3"

description: This is the dataset containing the original data for article "Investigating Thermionic Emission Properties of Polycrystalline Perovskite BaMoO3" submitted to IEEE TED.
DOI: 10.26180/21902043
title: Fiji Manual Resources (v6.5)

description: Demo Images and plugins for use with the Fiji Training Manual v6.5 created by Cameron J Nowell
DOI: 10.26180/21902043.v1
title: Fiji Manual Resources (v6.5)

description: Demo Images and plugins for use with the Fiji Training Manual v6.5 created by Cameron J Nowell
DOI: 10.5281/zenodo.7537838
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2009 (001–177)

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2009 (001–177). <em>Please <strong><em>click</em> here</strong> to download the MUSES FVC product <strong>in 2008 (185–361)</strong></em>, and <em><strong>click here</strong> to download the MUSES FVC product <strong>in 2009 (185–361</strong></em><em><strong>)</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2009 (001–177) Spatial Resolution: 250m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 100 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7537837
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2009 (001–177)

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2009 (001–177). <em>Please <strong><em>click</em> here</strong> to download the MUSES FVC product <strong>in 2008 (185–361)</strong></em>, and <em><strong>click here</strong> to download the MUSES FVC product <strong>in 2009 (185–361</strong></em><em><strong>)</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2009 (001–177) Spatial Resolution: 250m Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 100 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.4650915
title: CCG Starter Data Kit: Mauritania

description: A starter data kit for Mauritania
DOI: 10.5281/zenodo.4756693
title: CCG Starter Data Kit: Mauritania

description: A starter data kit for Mauritania
DOI: 10.5281/zenodo.4650914
title: CCG Starter Data Kit: Mauritania

description: A starter data kit for Mauritania
DOI: 10.5281/zenodo.7539448
title: CCG Starter Data Kit: Mauritania

description: A starter data kit for Mauritania
DOI: 10.25574/27520
title: Chandra X-ray Observatory ObsId 27520

description: Measuring nuclear activity and extended emission in a strongly-lensed, super-starburst galaxy at z~2
DOI: 10.5281/zenodo.4725483
title: CCG Starter Data Kit: Morocco

description: A starter data kit for Morocco
DOI: 10.5281/zenodo.4725482
title: CCG Starter Data Kit: Morocco

description: A starter data kit for Morocco
DOI: 10.5281/zenodo.4729508
title: CCG Starter Data Kit: Morocco

description: A starter data kit for Morocco
DOI: 10.5281/zenodo.4756788
title: CCG Starter Data Kit: Morocco

description: A starter data kit for Morocco
DOI: 10.5281/zenodo.5733324
title: CCG Starter Data Kit: Morocco

description: A starter data kit for Morocco
DOI: 10.25919/qxh0-x079
title: Parkes observations for project P971 semester 2022OCTS_03

description: We propose further timing observations of the relativistic pulsar - white-dwarf binary, PSR J1141$-$6545. This unique system offers access to a wide range of physics, from relativistic phenomena through pulsar emission cone studies, to the structure and distribution of interstellar plasma along our line-of-sight. The analysis of existing data has already revealed the first detection of Lense-Thirring precession in a binary star system, and hinted at complex emission beam structure probed via geodetic precession. Further timing of this system for the next few years is needed for studies of these effects at higher precision.