cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48579/pro/vy7mh3/io9ujg
title: LSTN-2003-08.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/ue0kzq
title: NDVI-2007-12.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/1peteu
title: LSTD-2014-12.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/3aqzmq
title: LSTN-2006-01.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/7bxbaq
title: NDVI-2004-11.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/hesvqj
title: LSTD-2013-02.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/uh5azo
title: LSTD-2014-10.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/xlcogm
title: LSTN-2014-02.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/6lfwb3
title: LSTN-2010-04.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/73g1al
title: NDVI-2005-03.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/gxwvg6
title: NDVI-2017-02.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/mfhdfd
title: LSTN-2005-09.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/qsfwv3
title: LSTN-2002-08.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/2ebeid
title: LSTD-2003-10.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/2snljc
title: LSTN-2002-12.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/oyz2cu
title: NDVI-2009-10.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/rytqmb
title: NDVI-2020-07.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/slhqdn
title: LSTD-2019-02.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/w7n04y
title: LSTD-2005-07.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/bbtdrl
title: LSTN-2015-01.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/bftrdt
title: LSTN-2013-09.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/ribsfi
title: NDVI-2010-03.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/tvg83e
title: LSTD-2017-03.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/xyayp8
title: NDVI-2007-07.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/9jfmts
title: NDVI-2008-11.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/dmtvsq
title: NDVI-2011-02.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/nz7gmy
title: LSTD-2003-01.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/rlcf9n
title: NDVI-2016-07.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/slhqk6
title: LSTN-2019-03.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/7wvkz9
title: LSTD-2014-07.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/ehmcuw
title: LSTN-2018-01.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/gunrok
title: LSTN-2001-09.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/jrvkxl
title: NDVI-2003-07.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/npfqcl
title: NDVI-2017-07.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/odo1hy
title: LSTD-2017-12.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/j3v5hh
title: LSTD.json

description: json file describe the work on the LSTD rasters
DOI: 10.48579/pro/vy7mh3/l1wzc9
title: LSTD-2003-11.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/llvdvi
title: LSTN-2015-10.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/nsnbyr
title: LSTD-2020-01.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/rwditd
title: LSTD-2001-04.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/txcs5i
title: LSTD-2018-02.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/4zqwyv
title: LSTD-2020-07.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/9gutxk
title: LSTN-2016-03.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/dqzcxo
title: LSTN-2008-09.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/r6gfsw
title: LC06-2020-01.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/xi1bdf
title: LSTN-2009-03.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/18cdpl
title: LSTD-2011-12.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/3kxwhi
title: LSTD-2005-05.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/92y4xt
title: NDVI-2006-02.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/ilnbtz
title: LSTN-2014-12.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/sanjth
title: LSTN-2015-02.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/xnmoz4
title: LSTD-2006-02.asc

description: :unav
DOI: 10.48579/pro/vy7mh3/ftn1k2
title: thumbnail-1.png

description: :unav
DOI: 10.48579/pro/vy7mh3/ogjvjo
title: thumbnail.png

description: :unav
DOI: 10.34740/kaggle/dsv/7630549
title: 2021 World Population (updated daily)
DOI: 10.25381/cput.24658719
title: A virtual reality learning framework for improving student engagement at a selected tertiary institution, Cape Town, South Africa

description: Ethical clearance: FOBREC642use of virtual reality (VR) in education has attracted the interest of Higher Education Institutions (HEIs) and introduced the opportunity for a renewed method of teaching and learning. Furthermore, the advancement of VR has enabled academics to create immersive experiences themselves and observe how students become more engaged in their learning as a result thereof.The research problem states that due to a lack of using emerging technology such as VR in teaching and learning, a decrease in student engagement levels is noted. As a result, a drop in pass rates was also noted at a selected tertiary institution in Cape Town, South Africa (University X). The research questions focused on the extent to which VR technology can be incorporated into teaching and learning to improve the level of Human Resources (HR) student engagement and pass rates. Further research questions focused on the relevance of current teaching and learning methods used by University X and their academic staff’s preparedness to utilise VR.This study investigated the impact of a VR-learning framework on HR student engagement at University X. A VR-learning platform was deployed to facilitate immersive learning experiences for higher education students.
DOI: 10.25381/cput.24658719.v1
title: A VIRTUAL REALITY LEARNING FRAMEWORK FOR IMPROVING STUDENT ENGAGEMENT AT A SELECTED TERTIARY INSTITUTION, CAPE TOWN, SOUTH AFRICA.

description: Ethical clearance: FOBREC642use of virtual reality (VR) in education has attracted the interest of Higher Education Institutions (HEIs) and introduced the opportunity for a renewed method of teaching and learning. Furthermore, the advancement of VR has enabled academics to create immersive experiences themselves and observe how students become more engaged in their learning as a result thereof.The research problem states that due to a lack of using emerging technology such as VR in teaching and learning, a decrease in student engagement levels is noted. As a result, a drop in pass rates was also noted at a selected tertiary institution in Cape Town, South Africa (University X). The research questions focused on the extent to which VR technology can be incorporated into teaching and learning to improve the level of Human Resources (HR) student engagement and pass rates. Further research questions focused on the relevance of current teaching and learning methods used by University X and their academic staff’s preparedness to utilise VR.This study investigated the impact of a VR-learning framework on HR student engagement at University X. A VR-learning platform was deployed to facilitate immersive learning experiences for higher education students.
DOI: 10.25381/cput.24658719.v2
title: A virtual reality learning framework for improving student engagement at a selected tertiary institution, Cape Town, South Africa

description: Ethical clearance: FOBREC642use of virtual reality (VR) in education has attracted the interest of Higher Education Institutions (HEIs) and introduced the opportunity for a renewed method of teaching and learning. Furthermore, the advancement of VR has enabled academics to create immersive experiences themselves and observe how students become more engaged in their learning as a result thereof.The research problem states that due to a lack of using emerging technology such as VR in teaching and learning, a decrease in student engagement levels is noted. As a result, a drop in pass rates was also noted at a selected tertiary institution in Cape Town, South Africa (University X). The research questions focused on the extent to which VR technology can be incorporated into teaching and learning to improve the level of Human Resources (HR) student engagement and pass rates. Further research questions focused on the relevance of current teaching and learning methods used by University X and their academic staff’s preparedness to utilise VR.This study investigated the impact of a VR-learning framework on HR student engagement at University X. A VR-learning platform was deployed to facilitate immersive learning experiences for higher education students.
DOI: 10.34740/kaggle/dsv/7630554
title: ⏳ endoflife.date database (duckdb)
DOI: 10.34740/kaggle/dsv/7630562
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.6084/m9.figshare.25225154
title: Fig1A

description: statistical test of tumor weight.
DOI: 10.6084/m9.figshare.25225154.v1
title: Fig1A

description: statistical test of tumor weight.
DOI: 10.17632/dvh5jcd63m.1
title: Tensile mechanical properties of MS AHSS grade 1700

description: Tensile curves obtained at room temperature on a martensitic Advanced High Strength Steel grade 1700.
The angular direction is measured with respect to the sheet rolling direction.


Financed by the European Union - NextGenerationEU (National Sustainable Mobility Center CN00000023, Italian Ministry of University and Research Decree n. 1033 - 17/06/2022, Spoke 11 - Innovative Materials & Lightweighting). The opinions expressed are those of the authors only and should not be considered as representative of the European Union or the European Commission’s official position. Neither the European Union nor the European Commission can be held responsible for them. 

Keywords: Spoke11 - CNMS MOST, Ferrous alloys
DOI: 10.17632/6vjwfx5yyk.3
title: Dermbase SOC supplements

description: Supplemental Tables for Dermbase SOC
DOI: 10.17632/6vjwfx5yyk.4
title: Dermbase SOC supplements

description: Supplemental Tables for Dermbase SOC
DOI: 10.17632/6vjwfx5yyk.5
title: Outcomes of Cutaneous Surgery for Nonmelanoma Skin Cancer in Patients with Different Fitzpatrick Skin Types: A Nationwide, Multicenter, Prospective Study

description: Supplemental Tables for Dermbase SOC
DOI: 10.17632/6vjwfx5yyk.1
title: Dermbase SOC supplements

description: Supplemental Tables for Dermbase SOC
DOI: 10.17632/6vjwfx5yyk.2
title: Dermbase SOC supplements

description: Supplemental Tables for Dermbase SOC
DOI: 10.17632/6vjwfx5yyk.6
title: Outcomes of Cutaneous Surgery for Nonmelanoma Skin Cancer in Patients with Different Fitzpatrick Skin Types: A Nationwide, Multicenter, Prospective Study

description: Supplemental Tables for "Outcomes of Cutaneous Surgery for Nonmelanoma Skin Cancer in Patients with Different Fitzpatrick Skin Types: A Nationwide, Multicenter, Prospective Study"
DOI: 10.17632/m7zgksh4vd.1
title: DIFFERENTIAL PRIVACY TECHNIQUES IN MACHINE LEARNING FOR ENHANCED PRIVACY PRESERVATION

description: The increasing reliance on machine learning models has prompted growing concerns regarding the privacy of sensitive information used in the training process. As a result, using differential privacy techniques has become a viable paradigm for attaining strong privacy preservation without sacrificing the models' usefulness. This study investigates and summarises important approaches in the field of machine learning differential privacy. Adding controlled noise at various points in the machine learning pipeline is the first class of approaches. To avoid unintentionally revealing private information, Laplace and Gaussian noise are deliberately added to training data, predictions, and model parameters. By employing strategies like randomised response mechanisms, data perturbation can enhance privacy for each individual without compromising the model's quality. Collaborative model training is made easier by privacy-preserving aggregation techniques like Secure Multi-Party Computation (SMPC), which protects raw data. By adding noise to gradients during training, Differential Privacy Stochastic Gradient Descent (DP-SGD) provides privacy guarantees during the optimization stage. When differential privacy is combined with federated learning, it allows for decentralized model training across devices while maintaining the security and localization of sensitive data. By allowing computations on encrypted data or safely aggregating model updates, advanced cryptographic approaches like homomorphic encryption and secure aggregation protocols give another degree of privacy. When taken as a whole, these methods add to a thorough framework for machine learning differential privacy that strikes a balance between the need to protect individual privacy and the drive to create accurate models
DOI: 10.17632/ksg9csk8f9.1
title: Apalachicola-Pilot'sCove-SoilSubfractions

description: Bulk soil organic carbon (OC), Particulate organic carbon (POC), Mineral-associated organic carbon (MAOC), and reactive iron-associated organic carbon (FeR-MAOC) stocks and d13C composition
DOI: 10.17632/ksg9csk8f9.2
title: Apalachicola-Pilot'sCove-SoilSubfractions

description: Content and d13C composition of bulk soil organic carbon (SOC) and its sub-fractions; including particulate organic carbon (POC), mineral-associated organic carbon (MAOC), and reactive iron-associated organic carbon (FeR-MAOC). The data in this data set was achieved from 12 soil cores (down to ~45 cm depth) collected from Avicennia germinans, Rhizophora mangle, Spartina alterniflora, and Juncus roemerianus plant habitats in Pilot's Cove region, Little St. George island, Apalachicola estuary. The updated dataset (14 February 2024) includes the new data of soil organic sub-fractions, the concentration of reactive irons extracted from soil samples via Citrate-Bicarbonate-Dithionite (CBD) method, and the definitions of all abbreviations.
DOI: 10.17632/4yv8zg4k2p.1
title: Supplementary Data

description: Supplementary data for the manuscript "Integration of a deep learning basal cell carcinoma detection and tumor mapping algorithm into the Mohs Micrographic Surgery workflow and effects on clinical staffing: a simulated, retrospective study" accepted to JAAD International.
DOI: 10.17632/grxygf2szg.1
title: Horizontal Flow Tubular Flocculator

description: Data collected included measurement of turbidity and color of a horizontal flow tubular flocculator.
DOI: 10.17632/3g7bdj422p.2
title: Energy Balance Drives Diurnal and Nocturnal Brain Transcriptome Rhythms

description: Plasticity in the daily timing of activity has been observed in a wide variety of species, yet the underlying mechanisms driving nocturnality and diurnality remain to be discovered. By regulating how much wheel-running activity will be rewarded with a food pellet, we can manipulate energy balance, and switch mice to be nocturnal or diurnal. Here we present the rhythmic transcriptome of 21 tissues, including 17 brain regions (hypothalamic, thalamic, cortical), sampled every 4 hours over a 24-hour period from nocturnal and diurnal male CBA/CaJ mice. Rhythmic gene expression across tissues comprised a different set of genes with minimal overlap between nocturnal and diurnal mice. We show that genes other than clock genes in the suprachiasmatic nucleus (SCN) of nocturnal and diurnal mice change, and the habenula was the most affected tissue. Our results indicate that adaptive flexibility in daily timing of behavior is supported by gene expression dynamics in many tissues and brain regions, especially in the habenula, which suggests a crucial role for the observed nocturnal-diurnal switch.
DOI: 10.17632/wzf3tyvfk2.1
title: Compression testing of lattice structures

description: Compression tests of lattice structure cubic specimens.

Financed by the European Union - NextGenerationEU (National Sustainable Mobility Center CN00000023, Italian Ministry of University and Research Decree n. 1033 - 17/06/2022, Spoke 11 - Innovative Materials & Lightweighting). The opinions expressed are those of the authors only and should not be considered as representative of the European Union or the European Commission’s official position. Neither the European Union nor the European Commission can be held responsible for them. 

Keywords: Spoke11 - CNMS MOST, Ferrous alloys
DOI: 10.17632/hfcwkmcmn4.1
title: the turbidites of the Jiayuguan complex 

description: S table 1. U–Pb ages of the zircons of sedimentary rocks from the Jiayuguan turbidite in the Hexi Corridor, North Qilian. Highly discordant analyses (con. % < 90% or > 110%) are considered unusable, displayed in strikethrough text, and not included in the Concordia diagrams. Concord % = 100*(207Pb/206Pb) age/(206Pb/238U) age for age >1500 Ma, or 100*(207Pb/235U) age/(206Pb/238U) age for age <1500 Ma.
S Figure 1 Concordia U-Pb diagram and CL images of the zircons of sedimentary rocks from the Jiayuguan turbidite in the Hexi Corridor, North Qilian
S analytical methods
DOI: 10.17632/hfcwkmcmn4.2
title: Tectonic evolution of the Proto-Tethys in the early Devonian: insights from the turbidite in the North Qilian belt, NW China

description: Supplemetary table 1. U–Pb ages of the zircons of sedimentary rocks from the Jiayuguan turbidite in the Hexi Corridor, North Qilian. Highly discordant analyses (con. % &lt; 90% or &gt; 110%) are considered unusable, displayed in strikethrough text, and not included in the Concordia diagrams. Concord % = 100*(207Pb/206Pb) age/(206Pb/238U) age for age &gt;1500 Ma, or 100*(207Pb/235U) age/(206Pb/238U) age for age &lt;1500 Ma. Supplemetary Figure 1 Concordia U-Pb diagram and CL images of the zircons of sedimentary rocks from the Jiayuguan turbidite in the Hexi Corridor, North Qilian Supplemetary analytical methods
DOI: 10.17632/hfcwkmcmn4.3
title: Tectonic evolution of the Proto-Tethys in the early Devonian: insights from the turbidite in the North Qilian belt, NW China

description: The data contains： Supporting table 1. U–Pb ages of the zircons of sedimentary rocks from the Jiayuguan turbidite in the Hexi Corridor, North Qilian. Highly discordant analyses (con. % &lt; 90% or &gt; 110%) are considered unusable, displayed in strikethrough text, and not included in the Concordia diagrams. Concord % = 100*(207Pb/206Pb) age/(206Pb/238U) age for age &gt;1500 Ma, or 100*(207Pb/235U) age/(206Pb/238U) age for age &lt;1500 Ma. Supporting Figure 1 Concordia U-Pb diagram and CL images of the zircons of sedimentary rocks from the Jiayuguan turbidite in the Hexi Corridor, North Qilian Supplemetary analytical methods
DOI: 10.17632/hfcwkmcmn4.4
title: Tectonic evolution of the Proto-Tethys in the early Devonian: insights from the turbidite in the North Qilian belt, NW China

description: S1 analytical method Whole‒rock major and trace element and U‒Pb dating analytical methods.
S2 Table 1 U‒Pb ages of zircons from the Jiayuguan mélange turbiditic sedimentary rocks in the Hexi Corridor, North Qilian belt. Highly discordant analyses (&lt; 90% or &gt; 110%) are considered unusable and are displayed in strikethrough text; they are not included in the concordia diagrams. Concordant % = 100*(207Pb/206Pb) age/(206Pb/238U) age for age &gt;1500 Ma, or 100*(207Pb/235U) age/(206Pb/238U) age for age &lt;1500 Ma.
S3 Figure 1 Concordia U‒Pb diagram and CL images of zircons from the Jiayuguan mélange sedimentary rocks in the Hexi Corridor, North Qilian belt
DOI: 10.17632/fznc3j52hp.1
title: Replication kit for "Demand-and-Supply Imbalance Risk and Long-Term Swap Spreads" (2024) by Samuel G. Hanson, Aytek Malkhozov, Gyuri Venter

description: This is the replication kit for Hanson, Malkhozov, and Venter "Demand-and-Supply Imbalance Risk and Long-Term Swap Spreads". The corresponding author is Samuel Hanson, shanson@hbs.edu.
DOI: 10.11583/dtu.19703731
title: Input files for "Evaluation of the Fitch wind-farm parameterization with large-eddy simulations of wakes using the Weather Research and Forecasting model"

description: Namelists, input, and sounding files necessary to run the idealized simulations in the Weather Research and Forecasting model (version 3.7.1) and analyzed in the manuscript "Evaluation of the Fitch wind-farm wake parameterization with large-eddy simulations of wakes using the Weather Research and Forecasting model".
The files are required as input for atmospheric simulations using the Weather Research and Forecasting model version 3.7.1, which is available at <sub>https://www2.mmm.ucar.edu/wrf/users/download/get_source.html</sub> 
The manuscript has been accepted by Monthly Weather Review journal.
DOI: 10.11583/dtu.19703731.v1
title: Input files for "Evaluation of the Fitch wind-farm parameterization with large-eddy simulations of wakes using the Weather Research and Forecasting model"

description: Namelists, input, and sounding files necessary to run the idealized simulations in the Weather Research and Forecasting model (version 3.7.1) and analyzed in the manuscript "Evaluation of the Fitch wind-farm wake parameterization with large-eddy simulations of wakes using the Weather Research and Forecasting model".
The files are required as input for atmospheric simulations using the Weather Research and Forecasting model version 3.7.1, which is available at <sub>https://www2.mmm.ucar.edu/wrf/users/download/get_source.html</sub> 
The manuscript has been accepted by Monthly Weather Review journal.
DOI: 10.17632/h6snst5hm8.1
title: Effect of High NEFA Concentration on Lipid Metabolism Disorders in Hepatocytes Based on lipidomics

description: Fatty liver is a common nutritional metabolic disease in periparturient dairy cows, which seriously affects their reproductive performance. During periparturient period, dairy cows are in a state of negative energy balance because they cannot meet the nutritional requirements for maintenance and lactation. As a result, non-esterified fatty acids (NEFA) are mobilized from adipose tissue, leading to an increase in NEFA concentration in the blood. When the rate of NEFA esterification into triglycerides (TAG) exceeds the rate of NEFA processing, fatty liver will form. Existing studies suggest that NEFA is closely associated with lipid metabolism disorders in hepatocytes, but it is not clear through which metabolites or metabolic pathways NEFA affects lipid metabolism disorders in hepatocytes. Therefore, the aim of this study was to investigate the effect of high concentration of NEFA on lipid metabolism in hepatocytes through the lipidomic approach and molecular biology techniques. Here, we found that NEFA (0.6-2.4 mM) significantly reduced the cell viability in a concentration-dependent manner, indicating that high concentrations of NEFA have lipotoxicity on hepatocytes. In addition, NEFA promoted TAG accumulation, increased the mRNA expression of the lipogenic molecules SREBP and FASN, and decreased the mRNA expression of lipolytic molecules CPT1A and HSL in hepatocytes. Mechanistically, the results of lipidomics analysis further showed that NEFA induced lipid metabolism disorders in hepatocytes by regulating metabolic pathways such as glycerol phospholipid metabolism, glycosyl phosphatidylinositol anchored biosynthesis, triglyceride metabolism, sphingolipid metabolism, and inositol phosphate metabolism.
DOI: 10.17632/jvf9jdgkt2.1
title: Modularity of PRC1 Composition and Chromatin Interaction define Condensate Properties; Niekamp et al.; part 2 of 5

description: This project is described in: Modularity of PRC1 Composition and Chromatin Interaction define Condensate Properties (2023) and can be found on BioRxiv: https://www.biorxiv.org/content/10.1101/2023.10.26.564217v2

Here, we provide the experimental data together with the custom python code and ImageJ macros used for data analysis. Details on how the code works are given in the script itself.

This is part 2 of 5.

Part 1: Data for Figure 1, 2, and 3
Part 2: Data for Figure 4, 5, 6, and 7
Part 3: Data for Figure S1, S2, S3, S4, and S5
Part 4: Data for Figure S6
Part 5: Data for Figure S7