cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2022-11.2644441/2221657
title: Data for EMSL Project 60171 from November 2022
DOI: 10.25582/data.2022-11.2644460/2221658
title: Data for EMSL Project 60171 from November 2022
DOI: 10.25582/data.2022-11.2644465/2221659
title: Data for EMSL Project 60171 from November 2022
DOI: 10.25582/data.2022-11.2644423/2221660
title: Data for EMSL Project 60171 from November 2022
DOI: 10.25582/data.2022-11.2642537/2221661
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2644456/2221662
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2645920/2221663
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2646778/2221664
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2645526/2221665
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2643149/2221666
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2641591/2221667
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2646005/2221668
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2646115/2221669
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2645784/2221670
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2646779/2221671
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2645887/2221672
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2645544/2221673
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2645785/2221674
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2646054/2221675
title: Data for EMSL Project 51241 from November 2022
DOI: 10.6084/m9.figshare.24631164.v1
title: Litter addition CO<sub>2</sub>.xlsx

description: Partitioning of root, litter and microbial respiration by plant input manipulation in forests.
DOI: 10.25582/data.2022-11.2641587/2221676
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2644540/2221677
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2645469/2221678
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2644557/2221679
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2646031/2221680
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2646123/2221681
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2645125/2221682
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2645169/2221683
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2646051/2221684
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2644651/2221685
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2644569/2221686
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2645164/2221687
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2644552/2221688
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2646082/2221689
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2644478/2221690
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2644543/2221691
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2645699/2221692
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2645747/2221693
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2643434/2221694
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2645205/2221695
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2645501/2221696
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2644580/2221697
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2644578/2221698
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2644638/2221699
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2644300/2221700
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2645476/2221701
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2645633/2221702
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2646143/2221703
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2644400/2221704
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2645151/2221705
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2646091/2221706
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2646022/2221707
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2645147/2221708
title: Data for EMSL Project 51241 from November 2022
DOI: 10.25582/data.2022-11.2647820/2221709
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2648283/2221710
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2647588/2221711
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2648664/2221712
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2649310/2221713
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2647861/2221714
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2647741/2221715
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2647628/2221716
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2647824/2221717
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2647536/2221718
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2647513/2221719
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2649325/2221720
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2649144/2221721
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2647754/2221722
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2649116/2221723
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2649146/2221724
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2649174/2221725
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2649172/2221726
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2648727/2221727
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2649159/2221728
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2648219/2221729
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2649123/2221730
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2647858/2221731
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2649160/2221732
title: Data for EMSL Project 51514 from November 2022
DOI: 10.25582/data.2022-11.2635703/2221733
title: Data for EMSL Project 60460 from November 2022
DOI: 10.25582/data.2022-11.2635542/2221734
title: Data for EMSL Project 60460 from November 2022
DOI: 10.25582/data.2022-05.2516155/2221735
title: Data for EMSL Project 60460 from May 2022
DOI: 10.25582/data.2022-04.2472714/2221736
title: Data for EMSL Project 60284 from April 2022
DOI: 10.25582/data.2022-04.2473141/2221737
title: Data for EMSL Project 60284 from April 2022
DOI: 10.25582/data.2022-04.2473159/2221738
title: Data for EMSL Project 60284 from April 2022
DOI: 10.25582/data.2022-04.2473158/2221739
title: Data for EMSL Project 60284 from April 2022
DOI: 10.25582/data.2022-04.2472720/2221740
title: Data for EMSL Project 60284 from April 2022
DOI: 10.25582/data.2022-04.2473142/2221741
title: Data for EMSL Project 60284 from April 2022
DOI: 10.25582/data.2022-10.2616955/2221742
title: Data for EMSL Project 60519 from October 2022
DOI: 10.15151/esrf-es-1318529268
title: Structural Biology of macromolecules and macromolecular complexes from India

description: Structural biology research groups located in India constitute the INDIA-BAG and will utilize MX, SAXS and CryoEM to study different biological macromolecules. The non-proprietary agreement signed between ESRF and the Regional Centre for Biotechnology (RCB), on behalf of the Department of Biotechnology (DBT) enables studies that provide fundamental insight regarding different biological processes . About 50 groups are part of this BAG and the allocated beamtime will be available for over 500 young researchers. A brief description of all the research groups is provided in the global summary. The ESRF-India partnership is supported by a grant provided by the DBT. Please note that a comprehensive annual report has to be sent to the DBT to obtain release of funds to pay ESRF membership dues. Consequently, all the structural biologists from India are grouped in a single BAG to enable smooth allocation of beamtime and monitoring of progress to collect the required statistics.
DOI: 10.15151/esrf-es-1397353032
title: Joint-Structural Biology Group (JSBG) BAG proposal for ID29 beamline

description: The aim of the JSBG BAG proposal for ID29 beamline is to develop new methods for time-resolved serial crystallography experiments at 4th generation source and to study time dependent structural conformation of biological macromolecules that are studies at the ESRF-EMBL-GR Joint Structural Biology Group. Method developments include the study of radiation damage at high flux densities and with partially polychromatic beam (1% and 0.3%), the implementation and development of various sample delivery methods - high viscosity, GDVN, mix-inject injectors, microfluidics and fixed targets and the integration of software for large amount of SSX data reduction, data-acquisition software for JungFrau 4M detector, and various data collection strategies for SSX experiments. Biological systems include the study of photoactivatable proteins, dynamics/inhibitor mechanisms of non-photoactivatable proteins by using photo-cage compounds and enzymatic systems.
DOI: 10.15151/esrf-es-1351192163
title: Characterisation of the P-T phase diagram and thermal EoS of Ir via RH-DAC and X-ray diffraction

description: Iridium is one of the most incompressible element within the family of 5d metals and it is the only refractory metal with a face-centred cubic structure. It has a number of exceptional properties, such as: high shear modulus, chemical inertness, high thermal and phase stability. For these reasons it is a very good candidate as gasket material or as pressure standard for high temperature and high pressure diamond-anvil cell experiments. However, the stability of its fcc structure under pressure has been debated for years and so far there is not experimental evidences of its melting curve. Therefore, we propose to perform a thorough investigation of the P-T phase diagram of iridium up to 180 GPa and 1300 K combining resistively-heated DAC (RH-DAC) and x-ray diffraction techniques. In this way we will be able to point out any possible solid-solid phase transition or anomalies linked to a predicted core-level crossing electronic transition and,extract its PVT equation of state.
DOI: 10.3886/e116501v1-143303
title: Replication data for: The Behavioralist Goes to School: Leveraging Behavioral Economics to Improve Educational Performance

description: We explore the power of behavioral economics to influence the level of effort exerted by students in a low stakes testing environment. We find a substantial impact on test scores from incentives when the rewards are delivered immediately. There is suggestive evidence that rewards framed as losses outperform those framed as gains. Nonfinancial incentives can be considerably more cost-effective than financial incentives for younger students, but are less effective with older students. All motivating power of incentives vanishes when rewards are handed out with a delay. Our results suggest that the current set of incentives may lead to underinvestment.
DOI: 10.5281/zenodo.7768533
title: A high-resolution global land daily drought index dataset during 1979–2022

description: A global daily drought index dataset named as daily evapotranspiration deficit index (DEDI) is constructed using daily actual evapotranspiration and potential evapotranspiration data provided by European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). The DEDI dataset has a spatial grid resolution of 0.25°×0.25° and covers global land areas for the period 1979 to 2022. The DEDI dataset can be a good index for assessing the dry and wet severity in terms of spatial patterns and temporal evolutions when compared to other available daily drought indices. Moreover, the DEDI dataset is also demonstrated to have advantages in detecting ecological or agricultural droughts. The DEDI dataset also appears reasonable and promising in facilitating drought monitoring and early warning from a daily perspective.
This dataset accompanies the following publication: Zhang, X., Duan, J., Cherubini, F. et al. A global daily evapotranspiration deficit index dataset for quantifying drought severity from 1979 to 2022. Sci Data 10, 824 (2023). https://doi.org/10.1038/s41597-023-02756-1
DOI: 10.5281/zenodo.7768534
title: A high-resolution global land daily drought index dataset during 1979–2022

description: A global daily drought index dataset named as daily evapotranspiration deficit index (DEDI) is constructed using daily actual evapotranspiration and potential evapotranspiration data provided by European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). The DEDI dataset has a spatial grid resolution of 0.25°×0.25° and covers global land areas for the period 1979 to 2022. The DEDI dataset can be a good index for assessing the dry and wet severity in terms of spatial patterns and temporal evolutions when compared to other available daily drought indices. Moreover, the DEDI dataset is also demonstrated to have advantages in detecting ecological or agricultural droughts. The DEDI dataset also appears reasonable and promising in facilitating drought monitoring and early warning from a daily perspective.
This dataset accompanies the following publication: Zhang, X., Duan, J., Cherubini, F. et al. A global daily evapotranspiration deficit index dataset for quantifying drought severity from 1979 to 2022. Sci Data 10, 824 (2023). https://doi.org/10.1038/s41597-023-02756-1
DOI: 10.6084/m9.figshare.24634722
title: Research on precast beam field production scheduling

description: This study aims to provide a quantitative approach to precast beam production scheduling that can replace the current empirical decision-making mode.
DOI: 10.6084/m9.figshare.24634722.v1
title: Research on precast beam field production scheduling

description: This study aims to provide a quantitative approach to precast beam production scheduling that can replace the current empirical decision-making mode.
DOI: 10.6084/m9.figshare.24155394
title: Data.zip

description: The study aims to provide a quantitative approach for location and size planning of high-speed rail precast beam fields.
DOI: 10.6084/m9.figshare.24155394.v1
title: Data.zip

description: The study aims to provide a quantitative approach for location and size planning of high-speed rail precast beam fields.
DOI: 10.5281/zenodo.10207132
title: Core collapse supernova yield from the post-processing of a long-term 3D simulation

description: This dataset accompanies the publication "Production of 44Ti and Iron-group Nuclei in the Ejecta of 3D Neutrino-driven Supernovae" published in the Astrophysical Journal Letters Volume 957, Issue 2, id.L25.
The dataset consists of an ACII text file that contains the isotopic yields from the post-processing of a 3D long-term supernova simulation for a 18.88 solar mass progenitor model. The yields are given in units of solar masses. 
Important: The dataset does not include the full stellar yield. It only represents the inner 0.142 solar masses. The total ejecta mass is expected to be larger. 
The dataset is also available on the websites of the Max-Planck Institute for Astrophysics in Garching, Germany: https://wwwmpa.mpa-garching.mpg.de/ccsnarchive/data/Sieverding2023/
The results have been obtained using the open source nuclear reaction network code XNet.
Calculations have been performed on the supercomputing cluster Cobra the Max-Planck Computing and Data Facility (MPCDF) in Garching, Germany.
DOI: 10.5281/zenodo.10207133
title: Core collapse supernova yield from the post-processing of a long-term 3D simulation

description: This dataset accompanies the publication "Production of 44Ti and Iron-group Nuclei in the Ejecta of 3D Neutrino-driven Supernovae" published in the Astrophysical Journal Letters Volume 957, Issue 2, id.L25.
The dataset consists of an ACII text file that contains the isotopic yields from the post-processing of a 3D long-term supernova simulation for a 18.88 solar mass progenitor model. The yields are given in units of solar masses. 
Important: The dataset does not include the full stellar yield. It only represents the inner 0.142 solar masses. The total ejecta mass is expected to be larger. 
The dataset is also available on the websites of the Max-Planck Institute for Astrophysics in Garching, Germany: https://wwwmpa.mpa-garching.mpg.de/ccsnarchive/data/Sieverding2023/
The results have been obtained using the open source nuclear reaction network code XNet.
Calculations have been performed on the supercomputing cluster Cobra the Max-Planck Computing and Data Facility (MPCDF) in Garching, Germany.
DOI: 10.25919/gngv-w117
title: Parkes observations for project P971 semester 2023OCTS_01

description: We propose 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 Parkes Ultra Wide Band Low Frequency (UWL) receiver has opened up the possibility of performing the first-ever studies of its orbital dynamics at high temporal resolution. Our analysis of data obtained to date suggests that the pulsar will precess away from our line-of-sight before 2023. Hence, regular observations are crucial to maximize science outcomes with this source.
DOI: 10.6084/m9.figshare.24418144
title: UAV DOM image; 2021-05-22

description: UAV DOM image; 2021-05-22
DOI: 10.6084/m9.figshare.24418144.v1
title: UAV DOM image; 2021-05-22

description: UAV DOM image; 2021-05-22
DOI: 10.34740/kaggle/dsv/7055559
title: Egypt Gold Prices | Daily Updated
DOI: 10.6084/m9.figshare.24550741
title: UHPLC-Q-Orbitrap HRMS of<i>Duhaldea nervosa</i>

description: the UHPLC-Q-Orbitrap HRMS of <i>Duhaldea nervosa</i>
DOI: 10.6084/m9.figshare.24550741.v1
title: UHPLC-Q-Orbitrap HRMS of<i>Duhaldea nervosa</i>

description: the UHPLC-Q-Orbitrap HRMS of <i>Duhaldea nervosa</i>
DOI: 10.34740/kaggle/dsv/7055553
title: Multi-Platform Online Courses Dataset
DOI: 10.13016/dspace/vijt-jshg
title: Supplementary materials for Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data

description: Our research objective is to identify potential vaccine antigen candidates targeting the parasite P. vivax, the second most prevalent cause of malaria. To improve the performance of the autologous model for P. vivax, which has constraints in protein size and a small set of labeled antigens, we leverage heterologous data from P. falciparum. We utilized multiple models trained on various combinations of heterologous and autologous data using the positive-unlabeled random forest (PURF) algorithm. The research notebook contains both data and code generated in the study titled "Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data." Further, the notebook provides guidance on extracting protein variables and assembling machine learning input from the database, along with code for conducting experimental analyses and creating plots.
description: Intensive malaria control and elimination efforts have led to substantial reductions in malaria incidence over the past two decades. However, the reduction in Plasmodium falciparum malaria cases has led to a species shift in some geographic areas, with P. vivax predominating in many areas outside of Africa. Despite its wide geographic distribution, P. vivax vaccine development has lagged far behind that for P. falciparum, in part due to the inability to cultivate P. vivax in vitro, hindering traditional approaches for antigen identification. In a prior study, we have used a positive-unlabeled random forest (PURF) machine learning approach to identify P. falciparum antigens for consideration in vaccine development efforts. Here we integrate systems data from P. falciparum (the better-studied species) to improve PURF models to predict potential P. vivax vaccine antigen candidates. We further show that inclusion of known antigens from the other species is critical for model performance, but the inclusion of unlabeled proteins the other species can result in misdirection of the model toward predictors of species classification, rather than antigen identification. Beyond malaria, incorporating antigens from a closely related species may aid in vaccine development for emerging pathogens having few or no known antigens.
DOI: 10.25919/cz12-7m65
title: Parkes observations for project P1178 semester 2023OCTS_01

description: We are actively developing a new version of the ATNF pulsar catalogue. In the paper describing the new catalogue we wish to take the opportunity to publish important pulsar parameters that have, to-date, not been published. The fundamental property of a pulsar is its pulse period and yet we have identified a large number of pulsars that do not having a measured spin-down rate and hence we do not know their evolutionary state, nor have good positions or knowledge of whether they are in a binary system. We have therefore selected 53 pulsars with the goal of measuring their spin-down rates. During this project we will also develop an automated pulsar-fold-mode online processing pipeline that includes online RFI mitigation. This pipeline will be made available as an ATNF service for other users.
DOI: 10.5281/zenodo.8015759
title: C-axis data from 3 depths at EGRIP

description: C-axis data as azimuth and latitude in degrees, from 3 depths at EGRIP: 145.93m, 1048.3m, 1449.07m
DOI: 10.17632/zjj3hxs4gv.1
title: Water content and hydrogen isotopes of olivine-hosted melt inclusions in the Dali picrites, SW China: Reassessing the role of water in the formation of the Emeishan large igneous province

description: S1. Compositions of olivine phenocrysts from the Dali picrites, Emeishan LIP (wt.%)
S2. Calculated compositions of melt inclusions and forsterite contents of their host olivines from the Dali picrites, Emeishan LIP (wt.%)
S3. Trace element compositions of the melt inclusions from the Dali picrite (ppm)
S4. SIMS water content (wt.%) and δD (‰) of melt inclusions from the Dali picrite
S5. SIMS standard results for δD value
DOI: 10.25914/5fc4975c7dda8
title: Ocean-Pol C-band polarimetric research radar