cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7660173
title: Research Mapping of Trauma Experiences in Autism Spectrum Disorders: A Bibliometric Analysis. Bibliometrix file

description: This is the Bibliometrix file of the paper entitled: Research Mapping of Trauma Experiences in Autism Spectrum Disorders: A Bibliometric Analysis.
DOI: 10.5281/zenodo.7660174
title: Research Mapping of Trauma Experiences in Autism Spectrum Disorders: A Bibliometric Analysis. Bibliometrix file

description: This is the Bibliometrix file of the paper entitled: Research Mapping of Trauma Experiences in Autism Spectrum Disorders: A Bibliometric Analysis.
DOI: 10.5281/zenodo.7661199
title: Epistasis decreases with increasing antibiotic pressure

description: <strong>Data and code for 'Epistasis decreases with increasing antibiotic pressure but not temperature'</strong> All of the competition data and code used to run the analysis presented in the manuscript "Epistasis decreases with increasing antibiotic pressure but not temperature" is available here. The bioRxiv pre-print is found here: https://doi.org/10.1101/2022.09.01.506172. The publication will appear in <em>Philosophical Transactions of the Royal Society B: Biological Sciences</em> on 03 April 2023 at the following permanent link: https://dx.doi.org/10.1098/rstb.2022.0058. All flow cytometry and estimated competitive fitness data is found in the 'data' folder. The data files include raw data files as well as intermediate data files that were created along the course of the analysis. Running the `.Rmd` files locally may over-write these intermediate data files (if you're trying to re-create these intermediate data files but it's not working, remember to check whether `eval=FALSE` in the header of the chunk). Analyses are found in the 'analysis' folder. For R notebook files, the code is available in the `.Rmd` files and is best run using RStudio (but the `.Rmd` files can also be opened for viewing with any text editor). Intermediate data files in the 'analysis' folder have the extension `.RData` or `.dat`; running R and python files locally may over-write these intermediate data files. The output of each R notebook file has also been saved to `.html` files. It is therefore easiest to start by viewing the `.html` file for each R notebook and then opening `.Rmd` files only if you wish to alter the code. The python code calculates gamma epistasis and parametric bootstrapping of the gamma epistasis values.
description: additional funding grant: Human Frontier Science Program Young Investigator grant no. RGY0081/2020 awarded to Claudia Bank
DOI: 10.5281/zenodo.7661198
title: Epistasis decreases with increasing antibiotic pressure

description: <strong>Data and code for 'Epistasis decreases with increasing antibiotic pressure but not temperature'</strong> All of the competition data and code used to run the analysis presented in the manuscript "Epistasis decreases with increasing antibiotic pressure but not temperature" is available here. The bioRxiv pre-print is found here: https://doi.org/10.1101/2022.09.01.506172. The publication will appear in <em>Philosophical Transactions of the Royal Society B: Biological Sciences</em> on 03 April 2023 at the following permanent link: https://dx.doi.org/10.1098/rstb.2022.0058. All flow cytometry and estimated competitive fitness data is found in the 'data' folder. The data files include raw data files as well as intermediate data files that were created along the course of the analysis. Running the `.Rmd` files locally may over-write these intermediate data files (if you're trying to re-create these intermediate data files but it's not working, remember to check whether `eval=FALSE` in the header of the chunk). Analyses are found in the 'analysis' folder. For R notebook files, the code is available in the `.Rmd` files and is best run using RStudio (but the `.Rmd` files can also be opened for viewing with any text editor). Intermediate data files in the 'analysis' folder have the extension `.RData` or `.dat`; running R and python files locally may over-write these intermediate data files. The output of each R notebook file has also been saved to `.html` files. It is therefore easiest to start by viewing the `.html` file for each R notebook and then opening `.Rmd` files only if you wish to alter the code. The python code calculates gamma epistasis and parametric bootstrapping of the gamma epistasis values.
description: additional funding grant: Human Frontier Science Program Young Investigator grant no. RGY0081/2020 awarded to Claudia Bank
DOI: 10.5281/zenodo.7660169
title: Satellite soil and vegetation water content data capturing main terrestrial ecosystem changes

description: I)SUMMARY This repository contains a harmonized database for the study of terrestrial ecosystem changes published in [Bueso et al., 2021]. It covers the period June 2010 - July 2020 and includes the following variables, which were harmonized to a common spatial scale of 25km and monthly temporal resolution and clustered as detailed in [Bueso et al., 2021]: - SM: soil moisture from SMOS-IC v2<br> - VOD: vegetation optical depth from SMOS-IC v2<br> - NDVI: Normalized Vegetation Difference Index from MODIS, product MOD13Q1 v6<br> - PREC: Rainfall from PERSIANN-CDR v2.2. Additionally, land cover information from MODIS MCD12Q1 collection 6 for years 2011 and 2019 is provided for each cluster. II) CONTACT For questions, please e-mail Diego Bueso at diego.bueso@uv.es III) DATABASE We provide the maps of identified clusters by quantile of SM and VOD and the code to generate Figs 1 and 2 of supplementary material in [Bueso et al., 2023]. We then provide for each identified cluster .mat files containing the variables described above. Further details are in the readme.txt file IV) CITE To properly acknowledge the dataset we kindly encourage users to (1) cite the DOI as an in-text citation and/or in the data acknowledgements in any publication and (2) reference the following publication: D. Bueso, M. Piles, P. Ciais, J-P. Wigneron, Á. Moreno-Martínez, G. Camps-Valls, "Soil and vegetation water content identify the main terrestrial ecosystem changes", National Science Review, 2023, https://doi.org/10.1093/nsr/nwad026
description: Code for further analysis is available in GitHub: https://github.com/IPL-UV/SM-VOD-Trends
description: {"references": ["D. Bueso, M. Piles, P. Ciais, J-P. Wigneron, \u00c1. Moreno-Mart\u00ednez, G. Camps-Valls, \"Soil and vegetation water content identify the main terrestrial ecosystem changes\", National Science Review, 2023, https://doi.org/10.1093/nsr/nwad026"]}
DOI: 10.5281/zenodo.7660170
title: Satellite soil and vegetation water content data capturing main terrestrial ecosystem changes

description: I)SUMMARY This repository contains a harmonized database for the study of terrestrial ecosystem changes published in [Bueso et al., 2021]. It covers the period June 2010 - July 2020 and includes the following variables, which were harmonized to a common spatial scale of 25km and monthly temporal resolution and clustered as detailed in [Bueso et al., 2021]: - SM: soil moisture from SMOS-IC v2<br> - VOD: vegetation optical depth from SMOS-IC v2<br> - NDVI: Normalized Vegetation Difference Index from MODIS, product MOD13Q1 v6<br> - PREC: Rainfall from PERSIANN-CDR v2.2. Additionally, land cover information from MODIS MCD12Q1 collection 6 for years 2011 and 2019 is provided for each cluster. II) CONTACT For questions, please e-mail Diego Bueso at diego.bueso@uv.es III) DATABASE We provide the maps of identified clusters by quantile of SM and VOD and the code to generate Figs 1 and 2 of supplementary material in [Bueso et al., 2023]. We then provide for each identified cluster .mat files containing the variables described above. Further details are in the readme.txt file IV) CITE To properly acknowledge the dataset we kindly encourage users to (1) cite the DOI as an in-text citation and/or in the data acknowledgements in any publication and (2) reference the following publication: D. Bueso, M. Piles, P. Ciais, J-P. Wigneron, Á. Moreno-Martínez, G. Camps-Valls, "Soil and vegetation water content identify the main terrestrial ecosystem changes", National Science Review, 2023, https://doi.org/10.1093/nsr/nwad026
description: Code for further analysis is available in GitHub: https://github.com/IPL-UV/SM-VOD-Trends
description: {"references": ["D. Bueso, M. Piles, P. Ciais, J-P. Wigneron, \u00c1. Moreno-Mart\u00ednez, G. Camps-Valls, \"Soil and vegetation water content identify the main terrestrial ecosystem changes\", National Science Review, 2023, https://doi.org/10.1093/nsr/nwad026"]}
DOI: 10.5281/zenodo.7661221
title: Data Set to doi.org/10.3390/app13042747 "Robot Assisted THz Imaging with a Time Domain Spectrometer"

description: Data set to article "Robot Assisted THz Imaging with a Time Domain Spectrometer" in Applied Sciences 2023, 13, 2747.
DOI: 10.5281/zenodo.7661220
title: Data Set to doi.org/10.3390/app13042747 "Robot Assisted THz Imaging with a Time Domain Spectrometer"

description: Data set to article "Robot Assisted THz Imaging with a Time Domain Spectrometer" in Applied Sciences 2023, 13, 2747.
DOI: 10.5287/ora-xqkqzwkex
title: WatchAuth dataset (extended)

description: We collected inertial sensor data on a smartwatch as users performed activities. The data contains the tap gestures (n=31) as users performed tap gestures against payment terminals and activity data (n=9) as users performed out-of-lab activities (walking, commuting, in-store, or a combination of them). Each user folder contains 1 nfctimstamper.csv file, 1+ --watch-sensors.csv file(s), and 0+ --.csv files. These types of files are described as follows: - nfctimstamper.csv This file contains the in-lab timestamps collected for the purpose of segmenting the watch sensor data. The first column is the human-readable timestamp, the second is the UNIX timestamp, and the third contains the data read from the NFC tag that triggered the entry. The NFC tag is either 'userWTC' which was attached to the smartwatch or something containing 'TAP' which shows which terminal is being interacted with -- that is, the 'TAP' tag tells the system which terminal is being used and then each subsequent 'userWTC' is the timestamp of the NFC connection established during a tap gesture as the user interacts with that terminal. The third type of tag is of the form 'ATK--', which timestamps an impersonation attempt made by the user having watched a video clip of performing . - --watch-sensors.csv These files contain the in-lab inertial sensor data collected on the smartwatch. Use the nfctimestamper.csv file to segment this data into tap gestures. - --.csv These files contain the out-of-lab inertial sensor data collected on the smartwatch when the user was performing the . The following users participated in both the first and second runs of the experiment, roughly 18 months apart. These datasets can be used to investigate the time stability component of the tap gesture biometric. In the first run, they had the userIDs in the first column below; in the second, the second. 001 -&gt; 020 008 -&gt; 025 009 -&gt; 027 010 -&gt; 033 014 -&gt; 037 018 -&gt; 022
DOI: 10.5285/e8926a1a-305a-441d-a0d8-7f6c96d83c3b
title: Tsunami trimlines identified across different islands of the Tonga archipelago

description: Tsunami trimlines identified across different islands of the Tonga archipelago. Trimlines have been used as a reference land feature following the January 2022 Tonga tsunami event that ripped off vegetation and built-up areas. Trimlines are distinctive limits between an area with sand coverage, vegetation destruction, and soil erosion on the one hand, and the unaffected natural vegetation on the other. This distinction provides a good landmark to map the inundation width and the landward extension of tsunami runup.

In this case, the trimlines have been manually delineated by BGS - Earth Observation team using different high-resolution satellite datasets both optical (KompSat, Planet, Pleiades, WorldView) and radar (TerraSAR-X).
Trimlines are well known from task-force publications documenting recent tsunami detection efforts and provide key information to support tsunami triggering mechanism models.

For more info, see https://www.usgs.gov/media/images/tsunami-terms and Scheffers et al. (2012), https://doi.org/10.1007/s11069-010-9691-6
DOI: 10.5285/1a7715dd-9adb-4a44-ad87-75ca5202f859
title: Waterlines delimiting the edge of the Hunga Tonga - Hunga Ha'apai island between April 2017 and April 2022

description: Waterlines have been extracted to delimit the edge of the Hunga Tonga - Hunga Ha'apai island between April 2017 and April 2022. Waterline is defined as the instantaneous land - water boundary at the time of the imaging process.
Waterlines have been generated by BGS - Earth Observation team through a thresholding-based classification based on Sentinel-2 multispectral imagery and developed on Google Earth Engine. Specifically, the thresholding has been applied to the Normalized Difference Water Index (NDWI) has been derived as a basis to discriminate between the land and sea based on their spectral characteristics.
Changes in waterlines over volcanic islands can provide key information to understand volcanic processes.
For more info on the methodology, see Novellino et al. (2020) https://doi.org/10.3390/app10020536
DOI: 10.5281/zenodo.7661254
title: vnl_silicon

description: The non-local part of the pseudo-potential of Silicon
DOI: 10.5281/zenodo.7661253
title: vnl_silicon

description: The non-local part of the pseudo-potential of Silicon
DOI: 10.5517/ccdc.csd.cc297rx7
title: CCDC 2122629: Experimental Crystal Structure Determination

description: Related Article: Tao He, Xiang-Jing Kong, Zhen-Xing Bian, Yong-Zheng Zhang, Guang-Rui Si, Lin-Hua Xie, Xue-Qian Wu, Hongliang Huang, Ze Chang, Xian-He Bu, Michael J. Zaworotko, Zuo-Ren Nie, Jian-Rong Li|2022|Nat.Mater|21|689|doi:10.1038/s41563-022-01237-x
description: Related Article: Tao He|2022|CSD Communication|||
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: 10.6084/m9.figshare.22134443
title: Supplementary files: The comparison of knowledge score between lecture – based learning and the combination of problem – based learning and lecture – based learning among internal medicine students: a meta – analysis

description: Supplementary files: The comparison of knowledge score between lecture – based learning and the combination of problem – based learning and lecture – based learning among internal medicine students: a meta – analysis
DOI: 10.5281/zenodo.7661309
title: Data: Promoting informed decision making about Maternal Pertussis Vaccination in the Netherlands

description: We used Intervention Mapping to develop and test two interventions promoting informed decision making about Maternal Pertussis Vaccination. Anonymised focus-group/interview data, trial data in which one of the interventions is tested, and data from an experiment testing strategies to reduce negative affect in the decision making process about MPV are included. Data descriptions can be found in the accompanied files. Results are yet to be published by the authors.
description: This project was funded by ZonMW (Netherlands), grant number 522004003
DOI: 10.5281/zenodo.7661308
title: Data: Promoting informed decision making about Maternal Pertussis Vaccination in the Netherlands

description: We used Intervention Mapping to develop and test two interventions promoting informed decision making about Maternal Pertussis Vaccination. Anonymised focus-group/interview data, trial data in which one of the interventions is tested, and data from an experiment testing strategies to reduce negative affect in the decision making process about MPV are included. Data descriptions can be found in the accompanied files. Results are yet to be published by the authors.
description: This project was funded by ZonMW (Netherlands), grant number 522004003
DOI: 10.17638/datacat.liverpool.ac.uk/2157
title: Program code and data for 'Elongation enhances migration through hydrodynamic shear'

description: MATLAB R020A code & simulated data for paper 'Elongation enhances migration through hydrodynamic shear'
DOI: 10.14469/hpc/12257
title: Dissolution of bubbles in a micromodel (Cases 1-8)

description: The data was collected as part of the PhD project of Nerine Joewondo in the Complex Porous Media Lab at the Department of Chemical Engineering at Imperial College London. The experiments consist of a (CO2-saturated) water-filled microfluidic chip in which CO2 bubbles were generated. An inverted optical microscope (Zeiss Axio Observer A.1m, Carl Zeiss AG, Oberkochen, Germany) was used to monitor the dissolution of CO2 bubbles. The microscope was used in combination with a programmable stage (MAC 6000, Ludl Electronic Products, Ltd., New York, United States), a CMOS (DCC1545M, Thorlabs, New Jersey, United States) camera, and a 5X objective lens to capture monochrome images of the microfluidic chip with the size of 1280x1024 pixels resulting in the resolution of 1.62 micron/px. Images of bubbles in the micromodel were compiled into an avi format with frame per second indicated by the filename ('Case_#_#_fps.avi'). A background image of the water-filled channel ('background_case_#.tiff') was obtained at the end of each experiment. The temperature ('temperature_case_#.csv') was logged using a type K thermocouple tip connected to a data logger (TC-08, Pico Technology Ltd., United Kingdom) placed on the top glass of the microfluidic chip during the image acquisition. The data sets can be opened and processed with, for instance, the MATLAB® environment. Upon decompressing the zip file, eight folders will be generated. Each folder ('Case_#') contains: - One avi file named 'Case_#_#_fps' consists of the (main) images of bubbles in the water-filled channels acquired during the experiment. The first # corresponds to the Case number, while the second # corresponds to the frame per second (fps) used to compile images into the avi format. -One tiff file named 'background_case_#' is the background image (water-filled channels) acquired at the end of the experiment. # is a number corresponding to the Case number. The image processing routine includes subtracting the background image from the main images to identify bubbles in the channels. - One a csv file name 'temperature_case_#' provides the temperature log during the experiments (T2) and the measured water temperature (T1) for each case.
DOI: 10.6084/m9.figshare.19885941
title: Genome-wide association studies on Northern Italy isolated populations provide further support concerning genetic susceptibility for major depressive disorder

description: Major depressive disorder (MDD) is a psychiatric disorder with pathogenesis influenced by both genetic and environmental factors. To date, the molecular-level understanding of its aetiology remains unclear. Thus, we aimed to identify genetic variants and susceptibility genes for MDD with a genome-wide association study (GWAS) approach. We performed a meta-analysis of GWASs and a gene-based analysis on two Northern Italy isolated populations (cases/controls <i>n</i> = 166/472 and 33/320), followed by replication and polygenic risk score (PRS) analyses in Italian independent samples (cases <i>n</i> = 464, controls <i>n</i> = 339). We identified two novel MDD-associated genes, <i>KCNQ5</i> (lead SNP rs867262, <i>p</i> = 3.82 × 10<sup>−9</sup>) and <i>CTNNA2</i> (rs6729523, <i>p</i> = 1.25 × 10<sup>−8</sup>). The gene-based analysis revealed another six genes (<i>p</i> &lt; 2.703 × 10<sup>−6</sup>): <i>GRM7</i>, <i>CTNT4</i>, <i>SNRK</i>, <i>SRGAP3</i>, <i>TRAPPC9</i>, and <i>FHIT</i>. No replication of the genome-wide significant SNPs was found in the independent cohort, even if 14 SNPs around <i>CTNNA2</i> showed association with MDD and related phenotypes at the nominal level of <i>p</i> (&lt;0.05). Furthermore, the PRS model developed in the discovery cohort discriminated cases and controls in the replication cohort. Our work suggests new possible genes associated with MDD, and the PRS analysis confirms the polygenic nature of this disorder. Future studies are required to better understand the role of these findings in MDD.
DOI: 10.6084/m9.figshare.19885941.v1
title: Genome-wide association studies on Northern Italy isolated populations provide further support concerning genetic susceptibility for major depressive disorder.

description: Major depressive disorder (MDD) is a psychiatric disorder with pathogenesis influenced by both genetic and environmental factors. To date, the molecular-level understanding of its etiology remains unclear. Thus, we aimed to identify genetic variants and susceptibility genes for MDD with a genome-wide study (GWAS) approach. We performed a meta-analysis of GWASs and a gene-based analysis on two Northern Italy isolated populations (cases/controls n = 166/472 and 33/320), followed by replication and polygenic risk score (PRS) analyses in Italian independent samples (cases n = 464, controls n = 339). We identified two novel MDD associated genes, <i>KCNQ5</i> (lead SNP rs867262, p = 3.82 × 10<sup>−9</sup>) and <i>CTNNA2</i> (rs6729523, p = 1.25 × 10<sup>−8</sup>). The gene-based analysis revealed another six genes (p &lt; 2.703 × 10<sup>−6</sup>): <i>GRM7</i>, <i>CTNT4</i>, <i>SNRK</i>, <i>SRGAP3</i>, <i>TRAPPC9</i> and <i>FHIT</i>. No replication of the genome-wide significant SNPs was found in the independent cohort, even if 14 SNPs around <i>CTNNA2</i> showed association with MDD and related phenotypes at the nominal level of p (&lt;0.05). Furthermore, the PRS model developed on discovery cohort discriminated cases and controls in the replication cohort. Our work suggests new possible genes associated with MDD, and the PRS analysis confirms the polygenic nature of this disorder. Future studies are required to better understand the role of these findings in MDD.
DOI: 10.6084/m9.figshare.19885941.v2
title: Genome-wide association studies on Northern Italy isolated populations provide further support concerning genetic susceptibility for major depressive disorder

description: Major depressive disorder (MDD) is a psychiatric disorder with pathogenesis influenced by both genetic and environmental factors. To date, the molecular-level understanding of its aetiology remains unclear. Thus, we aimed to identify genetic variants and susceptibility genes for MDD with a genome-wide association study (GWAS) approach. We performed a meta-analysis of GWASs and a gene-based analysis on two Northern Italy isolated populations (cases/controls <i>n</i> = 166/472 and 33/320), followed by replication and polygenic risk score (PRS) analyses in Italian independent samples (cases <i>n</i> = 464, controls <i>n</i> = 339). We identified two novel MDD-associated genes, <i>KCNQ5</i> (lead SNP rs867262, <i>p</i> = 3.82 × 10<sup>−9</sup>) and <i>CTNNA2</i> (rs6729523, <i>p</i> = 1.25 × 10<sup>−8</sup>). The gene-based analysis revealed another six genes (<i>p</i> &lt; 2.703 × 10<sup>−6</sup>): <i>GRM7</i>, <i>CTNT4</i>, <i>SNRK</i>, <i>SRGAP3</i>, <i>TRAPPC9</i>, and <i>FHIT</i>. No replication of the genome-wide significant SNPs was found in the independent cohort, even if 14 SNPs around <i>CTNNA2</i> showed association with MDD and related phenotypes at the nominal level of <i>p</i> (&lt;0.05). Furthermore, the PRS model developed in the discovery cohort discriminated cases and controls in the replication cohort. Our work suggests new possible genes associated with MDD, and the PRS analysis confirms the polygenic nature of this disorder. Future studies are required to better understand the role of these findings in MDD.