cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc27yrqp
title: CCDC 2084183: Experimental Crystal Structure Determination

description: Related Article: Zhichao Lu, Tatsuya Kumon, Gerald B. Hammond, Teruo Umemoto|2021|Angew.Chem.,Int.Ed.|60|16171|doi:10.1002/anie.202104975
DOI: 10.25919/4vbs-e850
title: Parkes observations for project P885 semester 2021APRS_15

description: We propose to continue observing the magnetars 1E 1547.0-5408 and PSR J1622-4950 weekly, to track their rotation, flux density, and polarisation. The revolutionary UWL capabilities promise new opportunities to disentangle still mysterious radio emission mechanisms in magnetars. This promise is supported by the recent follow up of XTE J1810-197, again a radio magnetar after 10 years dormant, and of the hot-off-the-press magnetar Swift J1818.0-1607, for which our exciting recent results with UWL motivate an increased monitoring cadence to once weekly.
DOI: 10.25919/xzgs-x049
title: Parkes observations for project PX500 semester 2021APRS_11

description: This proposal relates to time purchased by the Chinese Academy of Sciences for Parkes follow-up confirmation and timing of FAST discoveries. As the observing time has been purchased this proposal (and the related PX501) does not require TAC grading. The primary reason for submitting a proposal is to ensure an outreach statement and that the proposal is entered into OPAL for the data archive to access. The Chinese team have two time requests. The time scheduled for the first, relating to this proposal, will be allocated in blocks of 24 hours and the data will become available in the data archive with an 18 month embargo period. The second type of time will be scheduled in shorter blocks and the data archived with a 10 year embargo.
DOI: 10.6084/m9.figshare.15001083
title: Dataset

description: Dataset
DOI: 10.6084/m9.figshare.15001083.v1
title: Dataset

description: Dataset
DOI: 10.6084/m9.figshare.15001056
title: Mourning on Twitter

description: Data used in paper "Emojis and Words to Contextualize Mourning on Twitter", and presented in the 32nd ACM Conference on Hypertext and Social Media.
DOI: 10.6084/m9.figshare.15001056.v2
title: Mourning on Twitter

description: Data used in paper "Emojis and Words to Contextualize Mourning on Twitter", and presented in the 32nd ACM Conference on Hypertext and Social Media.
DOI: 10.6084/m9.figshare.15001056.v1
title: Mourning on Twitter

description: Data used in paper "Emojis and Words to Contextualize Mourning on Twitter", and presented in the 32nd ACM Conference on Hypertext and Social Media.
DOI: 10.5281/zenodo.5112309
title: Experimental sloshing pressure data from "Improving stability of moving particle semi-implicit method by source terms based on time-scale correction of particle-level impulses"

description: 3D sloshing in a prismatic tank under translational coupled surge-sway (X and Y axis) motions. The main dimensions are height H<sub>T</sub> = 0.54m, width W<sub>T</sub> = 0.84m and length L<sub>T</sub> = 0.72m. The filling ratio of 50% (H<sub>F</sub> = 0.27m). The periods of surge and sway excitations are T<sub>s</sub> = 1.25s with amplitude motions of A<sub>x</sub> = 0.0144m (0.02 x length) and A<sub>y</sub> = 0.0168m (0.02 x width). Files: <strong>experimental_slosh_h50_20cycles_p1_dt0p000100.txt</strong>: Experimental pressure data at sensor P1 <strong>slosh_3d_exp_timer</strong>: Experimental movie <strong>sloshing_tank_dimensions.pdf</strong>: Tank main dimensions The experimental data was used in: Cheng, L.Y., Amaro Junior, R.A., Favero, E.H. (2021). Improving stability of moving particle semi-implicit method by source terms based on time-scale correction of particle-level impulses. Engineering Analysis with Boundary Elements, 131, 118-145. Available at. doi: https://doi.org/10.1016/j.enganabound.2021.06.018
DOI: 10.5281/zenodo.5085886
title: Experimental sloshing pressure data from "Improving stability of moving particle semi-implicit method by source terms based on time-scale correction of particle-level impulses"

description: 3D sloshing in a prismatic tank under translational coupled surge-sway (X and Y axis) motions. The main dimensions are height H<sub>T</sub> = 0.54m, width W<sub>T</sub> = 0.84m and length L<sub>T</sub> = 0.72m. The filling ratio of 50% (H<sub>F</sub> = 0.27m). The periods of surge and sway excitations are T<sub>s</sub> = 1.25s with amplitude motions of A<sub>x</sub> = 0.0144m (0.02 x length) and A<sub>y</sub> = 0.0168m (0.02 x width). Files: <strong>experimental_slosh_h50_20cycles_p1_dt0p000100.txt</strong>: Experimental pressure data at sensor P1 <strong>slosh_3d_exp_timer</strong>: Experimental movie <strong>sloshing_tank_dimensions.pdf</strong>: Tank main dimensions The experimental data was used in: Cheng, L.Y., Amaro Junior, R.A., Favero, E.H. (2021). Improving stability of moving particle semi-implicit method by source terms based on time-scale correction of particle-level impulses. Engineering Analysis with Boundary Elements, 131, 118-145. Available at. doi: https://doi.org/10.1016/j.enganabound.2021.06.018
DOI: 10.5281/zenodo.4759103
title: Data related to: Hippocampal ripples and their coordinated dialogue with the default mode network during recent and remote recollection, Norman et al. (2021)

description: This dataset contains intra-cranial EEG recordings and analysis code related to the paper: "Hippocampal ripples and their coordinated dialogue with the default mode network during recent and remote recollection" by Norman et al. (https://doi.org/10.1016/j.neuron.2021.06.020)<br> The study investigates the role of hippocampal ripples in the human brain during retrieval of recent and remote autobiographical memories and semantic facts. The intracranial recordings underwent standard preprocessing as described in the paper and were stored in EEGLAB datasets. The analysis code that accompanies the dataset implements the main analyses described in the paper. The dataset includes the following zip files: iEEG data and main analysis code: “Norman_et_al_2021_iEEG_data_and_code_1.zip" “Norman_et_al_2021_iEEG_data_and_code_2.zip" Patients' anatomical data: “Norman_et_al_2021_Freesurfer.zip” Additional toolboxes (developed by others): “MATLAB_toolboxes.zip” The code is written primarily in Matlab (version R2018b) and runs on a desktop computer with a 3.4Ghz Intel Core i7-6700 CPU with 64GB RAM. Matlab's Signal Processing Toolbox is required, as well as EEGLAB, Unfold toolbox, and several other open-source toolboxes.
description: see "data and code readme.pdf" for further information
DOI: 10.5281/zenodo.4759102
title: Data related to: Hippocampal ripples and their coordinated dialogue with the default mode network during recent and remote recollection, Norman et al. (2021)

description: This dataset contains intra-cranial EEG recordings and analysis code related to the paper: "Hippocampal ripples and their coordinated dialogue with the default mode network during recent and remote recollection" by Norman et al. (https://doi.org/10.1016/j.neuron.2021.06.020)<br> The study investigates the role of hippocampal ripples in the human brain during retrieval of recent and remote autobiographical memories and semantic facts. The intracranial recordings underwent standard preprocessing as described in the paper and were stored in EEGLAB datasets. The analysis code that accompanies the dataset implements the main analyses described in the paper. The dataset includes the following zip files: iEEG data and main analysis code: “Norman_et_al_2021_iEEG_data_and_code_1.zip" “Norman_et_al_2021_iEEG_data_and_code_2.zip" Patients' anatomical data: “Norman_et_al_2021_Freesurfer.zip” Additional toolboxes (developed by others): “MATLAB_toolboxes.zip” The code is written primarily in Matlab (version R2018b) and runs on a desktop computer with a 3.4Ghz Intel Core i7-6700 CPU with 64GB RAM. Matlab's Signal Processing Toolbox is required, as well as EEGLAB, Unfold toolbox, and several other open-source toolboxes.
description: see "data and code readme.pdf" for further information
DOI: 10.6084/m9.figshare.14751747
title: 18 years of otolith increment widths of barramundi (Lates calcarifer) in three catchments in the Gulf of Carpentaria

description: Measurements of the otolith increment widths of barramundi in three regions in the Gulf of Carpentaria. Measurements were taken during the course of NESP Northern Australia Environmental Resources Hub projects 1.3.1 and 1.4. Otoliths were predominantly sourced from Fisheries Queensland’s historical otolith collection, supplemented with samples collected during FRDC project 2007/002. The comma separated values (csv) file contains the following fields:<br>- Region from which the fish was collected<br>- Unique reference ID for each fish<br>- The age at capture of each fish, used as a covariate in growth models<br>- The year class of each fish<br>- The year of growth represented by the increment being measured (range: 1997 to 2014)<br>- The otolith increment being measured (one, two, or three in most instances, unless the fish was less than 3 years old)<br>- The measured width of the increment, in mm
DOI: 10.6084/m9.figshare.14751747.v1
title: 18 years of otolith increment widths of barramundi (Lates calcarifer) in three catchments in the Gulf of Carpentaria

description: Measurements of the otolith increment widths of barramundi in three regions in the Gulf of Carpentaria. Measurements were taken during the course of NESP Northern Australia Environmental Resources Hub projects 1.3.1 and 1.4. Otoliths were predominantly sourced from Fisheries Queensland’s historical otolith collection, supplemented with samples collected during FRDC project 2007/002. The comma separated values (csv) file contains the following fields:<br>- Region from which the fish was collected<br>- Unique reference ID for each fish<br>- The age at capture of each fish, used as a covariate in growth models<br>- The year class of each fish<br>- The year of growth represented by the increment being measured (range: 1997 to 2014)<br>- The otolith increment being measured (one, two, or three in most instances, unless the fish was less than 3 years old)<br>- The measured width of the increment, in mm
DOI: 10.6084/m9.figshare.15001131
title: Raw_Data_PrehospTrachRup_210718.xlsx

description: Raw dataset <br>
DOI: 10.6084/m9.figshare.15001131.v1
title: Raw_Data_PrehospTrachRup_210718.xlsx

description: Raw dataset <br>
DOI: 10.5281/zenodo.5112543
title: GAP_workshop_3_materials

description: This archive contains the data needed to run the analysis for the GAP_workshop 3 in the ASBS 2021 conference workshop series. It contains the raw data needed to run all analyses and pre-prepared output files for anyone to follow the workshop in case their analysis runs into problems or takes too long.
DOI: 10.25949/14852925
title: Survival risk ratios for ICD-10-AM injury diagnosis classifications for all ages

description: The survival risk ratios (SRRs) were calculated using linked hospitalisation and mortality data from New South Wales (NSW), Australia. Hospital admissions was obtained from the NSW Ministry of Health and included all injury-related admissions identified using a principal diagnosis of injury (ICD-10-AM: S00-T89) during 1 January 2010 to 30 June 2014. Mortality data was obtained from the NSW Registry of Births, Deaths and Marriages from 1 January 2010 to 31 March 2015. Hospitalisation and mortality data were probabilistic linked by the Centre for Health Record Linkage (CHeReL). NSW covers an area of 800,628km<sup>2</sup> with a population of around 7.7 million. The SRRs were calculated for each injury diagnosis. A SRR represents the ratio of the number of individuals with each injury diagnosis who did not die to the total number of individuals with the injury diagnosis. The SRRs can be used to estimate injury severity (i.e. the International Classification of Injury Severity Score: ICISS). The ICISS is calculated by applying the SRRs to each injury diagnosis code in your data. There are two methods commonly used to then estimate ICISS values: (i) multiplicative-injury ICISS where ICISS is the product of all SRRs for each of the individual’s injuries; and (ii) single worst-injury, where ICISS only includes the worst-injury (i.e. the injury diagnosis with the lowest SRR) as the single worst-injury.
DOI: 10.25949/14852949
title: Survival risk ratios for ICD-10-AM injury diagnosis classifications for children

description: The survival risk ratios (SRRs) were calculated using linked hospitalisation and mortality data from Australia. Hospital admissions was obtained from each Health Department or the Australian Institute of Health and Welfare and included all injury-related admissions identified using a principal diagnosis of injury (ICD-10-AM: S00-T89) of children aged ≤16 years during 1 July 2002 to 30 June 2012. In the Australian Capital Territory (ACT) data were only available from 1 July 2004. Mortality data was obtained from the National Death Index. Hospitalisation and mortality data were probabilistic linked by the Australian Institute of Health and Welfare Data Linkage Unit. There were an estimated population of 4.5 million children aged ≤16 years in Australia. The SRRs were calculated for each injury diagnosis. A SRR represents the ratio of the number of individuals with each injury diagnosis who did not die to the total number of individuals with the injury diagnosis. The SRRs can be used to estimate injury severity (i.e. the International Classification of Injury Severity Score: ICISS). The ICISS is calculated by applying the SRRs to each injury diagnosis code in your data. A SRR represents the ratio of the number of children with each injury diagnosis who did not die to the total number of children with the injury diagnosis. There are two methods commonly used to estimate ICISS values: (i) multiplicative-injury ICISS where ICISS is the product of all SRRs for each of the child’s injuries; and (ii) single worst-injury, where ICISS only includes the worst-injury (i.e. the injury diagnosis with the lowest SRR) as the single worst-injury.
DOI: 10.25949/14852949.v1
title: Survival risk ratios for ICD-10-AM injury diagnosis classifications for children

description: The survival risk ratios (SRRs) were calculated using linked hospitalisation and mortality data from Australia. Hospital admissions was obtained from each Health Department or the Australian Institute of Health and Welfare and included all injury-related admissions identified using a principal diagnosis of injury (ICD-10-AM: S00-T89) of children aged ≤16 years during 1 July 2002 to 30 June 2012. In the Australian Capital Territory (ACT) data were only available from 1 July 2004. Mortality data was obtained from the National Death Index. Hospitalisation and mortality data were probabilistic linked by the Australian Institute of Health and Welfare Data Linkage Unit. There were an estimated population of 4.5 million children aged ≤16 years in Australia. The SRRs were calculated for each injury diagnosis. A SRR represents the ratio of the number of individuals with each injury diagnosis who did not die to the total number of individuals with the injury diagnosis. The SRRs can be used to estimate injury severity (i.e. the International Classification of Injury Severity Score: ICISS). The ICISS is calculated by applying the SRRs to each injury diagnosis code in your data. A SRR represents the ratio of the number of children with each injury diagnosis who did not die to the total number of children with the injury diagnosis. There are two methods commonly used to estimate ICISS values: (i) multiplicative-injury ICISS where ICISS is the product of all SRRs for each of the child’s injuries; and (ii) single worst-injury, where ICISS only includes the worst-injury (i.e. the injury diagnosis with the lowest SRR) as the single worst-injury.
DOI: 10.6084/m9.figshare.15001134
title: human_chr2_HEK

description: human chr2 chip-seq data
DOI: 10.6084/m9.figshare.15001134.v1
title: human_chr2_HEK

description: human chr2 chip-seq data
DOI: 10.6084/m9.figshare.5119804.v1
title: ridge directed ring detector

description: ridge-directed-ring-detector demo movie and data :<br><b>Robust and highly performant ring detection algorithm for 3d particle tracking using 2d microscope imaging<br></b><br>Related Papers:<br><br> Afik, Eldad. 2015. “Robust and Highly Performant Ring Detection Algorithm for 3d Particle Tracking Using 2d Microscope Imaging.” Scientific Reports 5 (September): 13584. https://doi.org/10.1038/srep13584.<br><br> Afik, Eldad, and Victor Steinberg. 2017. “On the Role of Initial Velocities in Pair Dispersion in a Microfluidic Chaotic Flow.” Nature Communications 8 (1). https://doi.org/10.1038/s41467-017-00389-8.<br><br> https://github.com/eldad-a/ridge-directed-ring-detector<br><br>Related Data:<br>E. Afik and V. Steinberg. A Lagrangian approach to elastic turbulence in a curvilinear microfluidic channel. figshare doi: 10.6084/m9.figshare.5112991 (2017).<br><br><br><br>
DOI: 10.6084/m9.figshare.5119804
title: ridge directed ring detector

description: ridge-directed-ring-detector demo movie and data :<br><b>Robust and highly performant ring detection algorithm for 3d particle tracking using 2d microscope imaging<br></b><br>Related Papers:<br><br> Afik, Eldad. 2015. “Robust and Highly Performant Ring Detection Algorithm for 3d Particle Tracking Using 2d Microscope Imaging.” Scientific Reports 5 (September): 13584. https://doi.org/10.1038/srep13584.<br><br> Afik, Eldad, and Victor Steinberg. 2017. “On the Role of Initial Velocities in Pair Dispersion in a Microfluidic Chaotic Flow.” Nature Communications 8 (1). https://doi.org/10.1038/s41467-017-00389-8.<br><br> https://github.com/eldad-a/ridge-directed-ring-detector<br><br>Related Data:<br>E. Afik and V. Steinberg. A Lagrangian approach to elastic turbulence in a curvilinear microfluidic channel. figshare doi: 10.6084/m9.figshare.5112991 (2017).<br><br><br><br>
DOI: 10.6084/m9.figshare.5119804.v2
title: ridge directed ring detector

description: ridge-directed-ring-detector demo movie and data :<br><b>Robust and highly performant ring detection algorithm for 3d particle tracking using 2d microscope imaging<br></b><br>Related Papers:<br><br> Afik, Eldad. 2015. “Robust and Highly Performant Ring Detection Algorithm for 3d Particle Tracking Using 2d Microscope Imaging.” Scientific Reports 5 (September): 13584. https://doi.org/10.1038/srep13584.<br><br> Afik, Eldad, and Victor Steinberg. 2017. “On the Role of Initial Velocities in Pair Dispersion in a Microfluidic Chaotic Flow.” Nature Communications 8 (1). https://doi.org/10.1038/s41467-017-00389-8.<br><br> https://github.com/eldad-a/ridge-directed-ring-detector<br><br>Related Data:<br>E. Afik and V. Steinberg. A Lagrangian approach to elastic turbulence in a curvilinear microfluidic channel. figshare doi: 10.6084/m9.figshare.5112991 (2017).<br><br><br><br>
DOI: 10.6084/m9.figshare.14822370.v3
title: Reusable data is the new oil

description: <b>Status</b><br>This preprint is submitted to ICENIS 2021 organized by Universitas Diponegoro<br><br><b>Abstract</b><br> Data is the new oil is the jargon of the 21st century. The movement has not been widely echoed in Indonesia. Although some initiatives should have been acknowledged and appreciated, but the status of reusable data availability in most countries, especially in Indonesia remains low. Data published in the open access Indonesian journals are mostly in form of a non-reusable pdf files. We suggest Indonesian scientific journal editors to consider adopting F.A.I.R data sharing by encouraging authors to share their data as supplementary files in machine readable formats, eg: csv or xls. This effort would also contribute to the principle of transparency and continuous development in Indonesia research ecosystem. @font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:3 0 0 0 1 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0cm; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman";}p.MsoCommentText, li.MsoCommentText, div.MsoCommentText {mso-style-unhide:no; mso-style-link:"Comment Text Char"; margin:0cm; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman";}span.MsoCommentReference {mso-style-unhide:no; mso-ansi-font-size:8.0pt; mso-bidi-font-size:8.0pt;}span.CommentTextChar {mso-style-name:"Comment Text Char"; mso-style-unhide:no; mso-style-locked:yes; mso-style-link:"Comment Text"; mso-ansi-language:EN-ID; mso-fareast-language:EN-US;}.MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-size:10.0pt; mso-ansi-font-size:10.0pt; mso-bidi-font-size:10.0pt; mso-ansi-language:FR; mso-fareast-language:FR;}div.WordSection1 {page:WordSection1;}
DOI: 10.5281/zenodo.5112417
title: This is a test repository!

description: This is a test repository, with a test file.
description: These are additional notes.
DOI: 10.5281/zenodo.5112416
title: This is a test repository!

description: This is a test repository, with a test file.
description: These are additional notes.
DOI: 10.5281/zenodo.5034524
title: Shadow-wall lithography of ballistic superconductor-semiconductor quantum devices

description: This repository contains data sets that support the conclusions and figures in the manuscript "Shadow-wall lithography of ballistic superconductor-semiconductor quantum devices". The Jupyter notebook "Manuscript figures.ipynb" contains all the necessary scripts to generate the figures. The measurement data is contained in the "Data" folder.
DOI: 10.5281/zenodo.3954464
title: Shadow-wall lithography of ballistic superconductor-semiconductor quantum devices

description: This repository contains data sets that support the conclusions and figures in the manuscript "Shadow-wall lithography of ballistic superconductor-semiconductor quantum devices". The Jupyter notebook "Manuscript figures.ipynb" contains all the necessary scripts to generate the figures. The measurement data is contained in the "Data" folder.
DOI: 10.5281/zenodo.5112562
title: CoVaxxy Tweet IDs data set

description: A collection of Tweet IDs related to Covid-19 Vaccines, gathered from Twitter since Jan 4, 2021. Please see https://arxiv.org/abs/2101.07694 for more information.