cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/hz9dmhvjkr.2
title: Corpus : La discussion à visée philosophique et la littérature de jeunesse polynésienne. L’exemple du thème de l’enfance et de l’âge adulte

description: Le corpus a été réalisé dans le cadre d’une thèse doctorale intitulée Littérature de jeunesse polynésienne bilingue et dialogues à visée philosophique (DVP) au CP, réalisée par Simon Deprez, à l’Université de Polynésie française, dirigée par Rodica Ailincai et Alain Mougniotte. Ce corpus concerne la discussion à visée philosophique n°4 et l’entretien semi-directif n°4.
Ces transcriptions concernent une séance de discussion à visée philosophique en contexte scolaire et un entretien semi-directif réalisé à postériori avec un grand-père impliqué dans une expérimentation qui s’est déroulée à Moorea, une île située dans l’archipel de la Société, en Polynésie française.
DOI: 10.17632/gzv2rw3wv6.1
title: Supplemental Figure 1

description: Literature search using PubMed, Embase, Cochrane Library, Cumulative Index of Nursing and Allied Health Literature (CINAHL), and ClinicalTrials.gov databases from their inception to May 2020.
DOI: 10.17632/s2n7gbr4kg.1
title: The Impact of Fiscal Policy on Housing in the U.S. data

description: Data used in the paper "The Impact of Fiscal Policy on Housing in the U.S.
DOI: 10.6084/m9.figshare.16685488
title: Genetic diversity and evolutionary analyses of Medicago polymorpha in China

description: <b>Supplementary Materials</b>Table S1: <i>M. polymorpha</i> accessions used in this study.Table S2: Quality assessment of sequencing data.Table S3: Discovery results of SLAF markers.Table S4: Number and coverage of SLAFs for each accession.Table S5: Evaluation of SNP for each accession.Table S6: Distribution of minor allele counts.Table S7: Means value of morphological traits recorded for ten<i> M. polymorpha</i> accessions.Table S8: SLAF tags selected by purifying selection from group I to group III.
DOI: 10.17632/2ymx9m5jry.3
title: Height and diameter datasest for Pinus merkusii and P. michoacana in Zambia

description: Data presented here includes pairs of tree height and diameter collected during forest inventories in the Copperbelt province in Zambia. These data were collected from the four different plantation sites (i.e., Chati, Ichimpe, Lamba, and Ndola). We present datasets for 1,542 trees (i.e., 761 and 781 trees of P. merkusii and P. michoacana, respectively). The dataset zamemi_11.csv was used for model development and zamemi_16.csv for model validation.
DOI: 10.6084/m9.figshare.16685488.v1
title: Genetic diversity and evolutionary analyses of Medicago polymorpha in China

description: <b>Supplementary Materials</b>Table S1: <i>M. polymorpha</i> accessions used in this study.Table S2: Quality assessment of sequencing data.Table S3: Discovery results of SLAF markers.Table S4: Number and coverage of SLAFs for each accession.Table S5: Evaluation of SNP for each accession.Table S6: Distribution of minor allele counts.Table S7: Means value of morphological traits recorded for ten<i> M. polymorpha</i> accessions.Table S8: SLAF tags selected by purifying selection from group I to group III.
DOI: 10.17632/5ppw96s76x.2
title: Supplementary Material for "Clinical and microbiological characteristics of Reactive Infectious Mucocutaneous Eruption:  a case series of five patients."

description: Figure 3: Picture of genital lesions in a patient diagnosed with RIME
Figure 4: Picture of the skin lesions in a patient diagnosed with RIME
DOI: 10.17632/59mnsycp5b.3
title: Supporting data for [Temperature effects in the initial stages of heteroepitaxial film growth]

description: This data set contains the data for the article with the same title which will be submitted to the journal Surfaces (MDPI) in September 2021 by Tung B. T. To and Fabio D. A. Aarao Reis.

In the following data files, the first column is the thickness d and the second column is the roughness W as shown in Figure 3(a) and Figure 5.
w_mean[y].dat, where [y] is the name of the parameter set listed in Table 1.

In the following data files, the first column is the lattice distance s and the second column is the value of the autocorrelation function \Gamma as shown in Figure 3(b) and Figure 7.
gamma_meant[x][y].dat, where [x] is the film thickness and [y] is the name of the parameter set listed in Table 1.

The following data files show the height profiles of the film for one simulation realization of a given parameter set as shown in Figures 2, 4, 6.
fileht[x][y].dat, where [x] is the film thickness and [y] is the name of the parameter set listed in Table 1.
These files can be read as follows.
fileh = fopen("fileht[x][y].dat", "r");
for (i=0; i<1024; i++){
for (j=0; j<1024; j++){
fscanf(fileh, "%d\n", &htemp);
h[i][j]=htemp;
}
}
fclose(fileh);

The file coalescence_time.txt shows the mean film thickness in which the first layer (z=1) is occupied by 95% (d_c).
The first column shows the parameter set as listed in Table 1.
The second column shows the mean film thickness in which the first layer (z=1) is occupied by 95% as shown in Figures 3(a) and 5.

In the data file w_meanTp.dat, the first column is the thickness d and the second column is the roughness W as shown in Figure 8 for the dashed black curve.
DOI: 10.17632/cdhw4wn5sy.2
title: Extrema for Bessel Functions of the First Kind, J

description: Extrema for the zeroth Bessel function of the First Kind to the 600th Bessel function of the First kind.  10000 extrema are computed for each order of Bessel function.  The position of each extrema is calculated to a precision of 10^(-19).  Please see "Bessel function extrema to 600 orders and 10000 extrema" by N. A. Mecholsky, S. Akhbarifar, W. Lutze, M. Brandys, and I. L. Pegg, Data in Brief, (2021)
DOI: 10.17632/krkt9cwds9.4
title: Oxytocin and social preference data on young adult relationships to romantic partners, parents, friends, and strangers.

description: Forty Four young adults (91% female) in ongoing romantic relationships (M duration=21 months) completed a screening survey and two lab visits separated by 4-weeks. Results indicated that oxytocin administration decreased attachment to mothers, decreased attachment to subsidiary figures, and decreased attraction to strangers, but showed no effect on closeness to romantic partners or to the primary attachment relationship.
DOI: 10.17632/637d4s7vjh.1
title: Main Data File

description: The data set contains the Punjab schools data for year 2018. There are three files as described below. 1. The file with name [Census_Oct_2018.xlsx]  is the main file that contains multiple parameters. The second file with name [SSC RESULT 5 YEARS.xlsx] was used to get one parameter of Result against each school. The third file is actual file used for generating results which was prepared after applying preprocessing techniques named as [Final-File-For-Azure-72.csv]
DOI: 10.17632/dpj4dfbxj4.1
title: LINF_180015400

description: Outer arm dynein light chain 7 | LC7
Leishmania infantum (strain JPCM5)
DOI: 10.17632/tcs9wmdht7.3
title: Postmortem investigation of Onnagawa shale by X-ray computed tomography

description: These data were collected by deforming a sample in a high-pressure gas rig and performing a post mortem analysis in a commercially available X-ray CT scanner. An overview of the data is provided in the Repository Overview.pdf file.
DOI: 10.17632/prk43yv97n.1
title: Raw input and  Raw output

description: Basic structures to simulate three aptamers on the gold surface and structures to equilibrium
DOI: 10.17632/75vdm7phv9.1
title: INCAGI004

description: Randomized controlled trial comparing two neoadjuvant chemotherapy regimes for locally advanced rectal cancer. Stage II and III rectal cancer patients were randomized to receive neoCRT with either capecitabine (Group 1) or 5-Fu and leucovorin (Group 2) concomitant to long course radiotherapy. Clinical downstaging was accessed using MRI 6-8 weeks after treatment. EORTCs QLQ C30 and CR38 were applied before treatment (T0), after neoCRT (T1), after rectal resection (T2), early after adjuvant chemotherapy (T3), and one year after end of treatment or stoma closure (T4). Wexner scale was used for continence evaluation at T4. A C30SummaryScore (Geisinger et cols) was calculated to compare QOL results.
DOI: 10.17632/3v78kt3mgt.1
title: Collected LiDAR data in Changchun

description: We chose three sites in Changchun for LiDAR data collecting.
DOI: 10.5281/zenodo.5530146
title: Climate induced economic shocks using CLIMRISK

description: Various risk measures of climate induced economic shocks using CLIMRISK. Metrics include year of exceeding 1 billion in climate damages, year of exceeding 5% annual GDP lost and a multivariate risk index (1 billion, 5% GDP and 3 degrees temperature exceedance).
DOI: 10.5281/zenodo.5530237
title: Time of emergence of climate change impacts

description: Expected year in which climate impacts would exceed an extreme past economic shock value (95th percentile). Model used: CLIMRISK Scale: 0.5 degrees * 0.5 degrees Shock database consists of changes in annual GDP between 1950 - 2016. Citation: Ignjacevic, Predrag, Francisco Estrada Porrua, and Willem Jan Wouter Botzen. "Time of emergence of economic impacts of climate change." <em>Environmental Research Letters</em> (2021).
DOI: 10.5281/zenodo.5530145
title: Climate induced economic shocks using CLIMRISK

description: Various risk measures of climate induced economic shocks using CLIMRISK. Metrics include year of exceeding 1 billion in climate damages, year of exceeding 5% annual GDP lost and a multivariate risk index (1 billion, 5% GDP and 3 degrees temperature exceedance).
DOI: 10.5281/zenodo.5530236
title: Time of emergence of climate change impacts

description: Expected year in which climate impacts would exceed an extreme past economic shock value (95th percentile). Model used: CLIMRISK Scale: 0.5 degrees * 0.5 degrees Shock database consists of changes in annual GDP between 1950 - 2016. Citation: Ignjacevic, Predrag, Francisco Estrada Porrua, and Willem Jan Wouter Botzen. "Time of emergence of economic impacts of climate change." <em>Environmental Research Letters</em> (2021).
DOI: 10.6084/m9.figshare.16685497.v1
title: ANUVUD, calculation of unit geometry and NBO analysis

description: Gaussian computational archive
DOI: 10.5281/zenodo.5235705
title: Dataset and Code for Manuscript "Multi-angle pulse shape detection of scattered light in flow cytometry for label-free cell cycle classification"

description: Dataset of measurements for cell cycle analysis with description: ReadMe file with explanations on the data set and analysis exemplary Matlab script file for analysis binary data files conatining the pulse shapes in all channels FCS data files containing common flow cytometry parameters in each channel Data on unsorted HEK cells, HEK cells sorted for cell cycle phases, and unsorted Jurkat cell are included.
DOI: 10.5281/zenodo.5113375
title: Radar Precipitation Estimates (Radolan RW product) interpolated onto LfULG Stations Saxony

description: Radar Precipitation Estimates interpolated onto LfULG Air Quality Measurement / Monitoring Stations <strong>Processing Steps</strong> Radolan RW data were retrieved from DWD Climate Data Center: https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/historical/bin/ Radolan RW data were interpolated onto a subset of LfULG Stations using the Nearest Neighbor method statistics (average, maximum & standard deviation) are provided for a 5 x 5 km**2 cutout <strong>Data Description</strong> data are packed into TAR Archives for each station individual data are stored as ASCII tables for each month and station (CSV with space as separator) variable meaning is described in the header section of each file <strong>Example for Data Input with Python</strong> <pre><code class="language-python">import numpy as np import xarray as xr import datetime def read_rado_dat( filename ): ''' Reads Radolan RW time series from ASCII files and returns `xarray` Dataset. Parameters ---------- filename : str input filename Returns ------- rr : xr.Dataset time series data (rain rates in mm/h) ''' print(f'.. open {filename}') dat = np.genfromtxt( filename ) ndat = len(dat) print(ndat) time = [] for i in range( ndat ): d = dat[i] t = datetime.datetime(int( d[0] ), int( d[1] ), int( d[2] ), int( d[3] ), int( d[4] )) time += [t,] rr = xr.Dataset() rr['time'] = time rr['rr'] = xr.DataArray( data = dat[:, 7], dims = 'time', coords = {'time':time}) rr['rr_mean'] = xr.DataArray( data = dat[:, 8], dims = 'time', coords = {'time':time}) rr['rr_max'] = xr.DataArray( data = dat[:, 9], dims = 'time', coords = {'time':time}) rr['rr_std'] = xr.DataArray( data = dat[:, 10], dims = 'time', coords = {'time':time}) m = (rr != -999) return rr.where( m )</code></pre>
DOI: 10.5281/zenodo.5113374
title: Radar Precipitation Estimates (Radolan RW product) interpolated onto LfULG Stations Saxony

description: Radar Precipitation Estimates interpolated onto LfULG Air Quality Measurement / Monitoring Stations <strong>Processing Steps</strong> Radolan RW data were retrieved from DWD Climate Data Center: https://opendata.dwd.de/climate_environment/CDC/grids_germany/hourly/radolan/historical/bin/ Radolan RW data were interpolated onto a subset of LfULG Stations using the Nearest Neighbor method statistics (average, maximum & standard deviation) are provided for a 5 x 5 km**2 cutout <strong>Data Description</strong> data are packed into TAR Archives for each station individual data are stored as ASCII tables for each month and station (CSV with space as separator) variable meaning is described in the header section of each file <strong>Example for Data Input with Python</strong> <pre><code class="language-python">import numpy as np import xarray as xr import datetime def read_rado_dat( filename ): ''' Reads Radolan RW time series from ASCII files and returns `xarray` Dataset. Parameters ---------- filename : str input filename Returns ------- rr : xr.Dataset time series data (rain rates in mm/h) ''' print(f'.. open {filename}') dat = np.genfromtxt( filename ) ndat = len(dat) print(ndat) time = [] for i in range( ndat ): d = dat[i] t = datetime.datetime(int( d[0] ), int( d[1] ), int( d[2] ), int( d[3] ), int( d[4] )) time += [t,] rr = xr.Dataset() rr['time'] = time rr['rr'] = xr.DataArray( data = dat[:, 7], dims = 'time', coords = {'time':time}) rr['rr_mean'] = xr.DataArray( data = dat[:, 8], dims = 'time', coords = {'time':time}) rr['rr_max'] = xr.DataArray( data = dat[:, 9], dims = 'time', coords = {'time':time}) rr['rr_std'] = xr.DataArray( data = dat[:, 10], dims = 'time', coords = {'time':time}) m = (rr != -999) return rr.where( m )</code></pre>
DOI: 10.5281/zenodo.5531084
title: COVID-19++: A Citation-Aware Covid-19 Dataset for the Analysis of Research Dynamics

description: COVID-19++ is a citation-aware COVID-19 dataset for the analysis of research dynamics. In addition to primary COVID-19 related articles and preprints from 2020, it includes citations and the metadata of first-order cited work. All publications are annotated with MeSH terms, either from the ground truth, or via ConceptMapper, if no ground truth was available. The data is organized in CSV files - Paper metadata (paper_id, publdate, title, data_source): paper.csv - Annotation data, mapping paper_id to MeSH terms: annotation.csv - Authorship data, mapping paper_id to author, optionally with ORCID: authorship.csv<br> - Paired DOIs of citing and cited papers: references.csv The column data source within the paper metadata has the value KE (for metadata from ZB MED KE), PP (for preprints) or CR (for cited resources from CrossRef)<br> This work was supported by BMBF within the programme ``Quantitative Wissenschaftsforschung'' under grant numbers 01PU17013A, 01PU17013B, 01PU17013C.<br>
DOI: 10.5281/zenodo.5531083
title: COVID-19++: A Citation-Aware Covid-19 Dataset for the Analysis of Research Dynamics

description: COVID-19++ is a citation-aware COVID-19 dataset for the analysis of research dynamics. In addition to primary COVID-19 related articles and preprints from 2020, it includes citations and the metadata of first-order cited work. All publications are annotated with MeSH terms, either from the ground truth, or via ConceptMapper, if no ground truth was available. The data is organized in CSV files - Paper metadata (paper_id, publdate, title, data_source): paper.csv - Annotation data, mapping paper_id to MeSH terms: annotation.csv - Authorship data, mapping paper_id to author, optionally with ORCID: authorship.csv<br> - Paired DOIs of citing and cited papers: references.csv The column data source within the paper metadata has the value KE (for metadata from ZB MED KE), PP (for preprints) or CR (for cited resources from CrossRef)<br> This work was supported by BMBF within the programme ``Quantitative Wissenschaftsforschung'' under grant numbers 01PU17013A, 01PU17013B, 01PU17013C.<br>
DOI: 10.6084/m9.figshare.16685524.v1
title: Figure4

description: Figure4: ATAC-5c versus RNA-5c. ATACseq regions are associated with genes; counts associated with each gene indicate the number of reads detected by ATACseq within the genomic coordinates of each gene. A) Cells were represented on the basis of the number of genes supported by at least 3 UMI and plotted with respect to the total number of UMI in each cell. B) Seurat clustering (resolution: 0.6) of the cells’ ATACseq counts. C) SCA pseudo-bulk Pearson similarity among ATACseq clusters (a1-6) and scRNAseq (X1-5, Figure 2A). Square boxes indicate the highest similarity among clusters: a1/X1, a4/X3 and a6/X5.<br>Figure 4Asomewhere_in_your_computer/fig4/atac_seq3/2_Atac_Genes/umiPlotGSM4224433.pdf<br>Figure4B<br>somewhere_in_your_computer/fig4/atac_seq3/3_Atac_Genes_filtered/Clustering_&_PseudoBulk/annotated_matrix/6/annotated_matrix_Stability_Plot.pdf<br>Figure4Csomewhere_in_your_computer/fig4/atac_seq3/3_Atac_Genes_filtered/Clustering_&_PseudoBulk/pseudoBulk/permutation/log2_reformatedCPM.psblkAE-rna5c_mean-centered.png
DOI: 10.6084/m9.figshare.16685524
title: Figure4

description: Figure4: ATAC-5c versus RNA-5c. ATACseq regions are associated with genes; counts associated with each gene indicate the number of reads detected by ATACseq within the genomic coordinates of each gene. A) Cells were represented on the basis of the number of genes supported by at least 3 UMI and plotted with respect to the total number of UMI in each cell. B) Seurat clustering (resolution: 0.6) of the cells’ ATACseq counts. C) SCA pseudo-bulk Pearson similarity among ATACseq clusters (a1-6) and scRNAseq (X1-5, Figure 2A). Square boxes indicate the highest similarity among clusters: a1/X1, a4/X3 and a6/X5.<br>Figure 4Asomewhere_in_your_computer/fig4/atac_seq3/2_Atac_Genes/umiPlotGSM4224433.pdf<br>Figure4B<br>somewhere_in_your_computer/fig4/atac_seq3/3_Atac_Genes_filtered/Clustering_&_PseudoBulk/annotated_matrix/6/annotated_matrix_Stability_Plot.pdf<br>Figure4Csomewhere_in_your_computer/fig4/atac_seq3/3_Atac_Genes_filtered/Clustering_&_PseudoBulk/pseudoBulk/permutation/log2_reformatedCPM.psblkAE-rna5c_mean-centered.png
DOI: 10.34740/kaggle/dsv/2655677
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.18430/m37rzc
title: X-ray diffraction data for the papain-Like Protease of SARS CoV-2 in complex with Jun9-84-3 inhibitor (7RZC)
DOI: 10.6084/m9.figshare.16685365
title: EMET: Embeddings from Multilingual-Encoder Transformer for Fake News Detection

description: Two publicly available datasets that were collected from Twitter, and are part of two social media verification challenges held in 2015 and 2016. The only difference between them is that the first one was created considering two categories (fake and real), while the other also has the unknown class. These datasets consist of tweets related to 35 distinct events covering six languages. For more details take a look at EMET paper 10.1109/ICASSP40776.2020.9054673
DOI: 10.15131/shef.data.16438716
title: Retrospective reflective vignettes from paper "In-betweenness in ICT4D research: critically examining the role of the researcher"

description: These three retrospective vignettes go along with the study "In-betweenness in ICT4D research: critically examining the role of the researcher." These observations are based on our personal ICT4D research experiences. They are presented in three vignettes, each of which is organised around the common themes that emerged from analysis which is described in the paper.
DOI: 10.15131/shef.data.16438716.v1
title: Retrospective reflective vignettes from paper "In-betweenness in ICT4D research: critically examining the role of the researcher"

description: These three retrospective vignettes go along with the study "In-betweenness in ICT4D research: critically examining the role of the researcher." These observations are based on our personal ICT4D research experiences. They are presented in three vignettes, each of which is organised around the common themes that emerged from analysis which is described in the paper.
DOI: 10.6084/m9.figshare.8852084
title: 10 Years Bug-Fix Dataset (PROMISE'19)

description: Replication Package of the paper "From Reports to Bug-Fix Commits: A 10 Years Dataset of Bug-Fixing Activity from 55 Apache's Open Source Projects"<br><br><b>ABSTRACT:</b><br>Bugs appear in almost any software development. Solving all or at least a large part of them requires a great deal of time, effort, and budget. Software projects typically use issue tracking systems as a way to report and monitor bug-fixing tasks. In recent years, several researchers have been conducting bug tracking analysis to better understand the problem and thus provide means to reduce costs and improve the efficiency of the bug-fixing task. In this paper, we introduce a new dataset composed of more than 70,000 bug-fix reports from 10 years of bug-fixing activity of 55 projects from the Apache Software Foundation, distributed in 9 categories. We have mined this information from Jira issue track system concerning two different perspectives of reports with closed/resolved status: static (the latest version of reports) and dynamic (the changes that have occurred in reports over time). We also extract information from the commits (if they exist) that fix such bugs from their respective version-control system (Git).We also provide a change analysis that occurs in the reports as a way of illustrating and characterizing the proposed dataset. Once the data extraction process is an error-prone nontrivial task, we believe such initiatives like this could be useful to support researchers in further more detailed investigations.<br><br>You can find the full paper at: https://doi.org/10.1145/3345629.3345639<br>If you use this dataset for your research, please reference the following paper:<pre><br>@inproceedings{Vieira:2019:RBC:3345629.3345639, author = {Vieira, Renan and da Silva, Ant\^{o}nio and Rocha, Lincoln and Gomes, Jo\~{a}o Paulo}, title = {From Reports to Bug-Fix Commits: A 10 Years Dataset of Bug-Fixing Activity from 55 Apache's Open Source Projects}, booktitle = {Proceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering}, series = {PROMISE'19}, year = {2019}, isbn = {978-1-4503-7233-6}, location = {Recife, Brazil}, pages = {80--89}, numpages = {10}, url = {http://doi.acm.org/10.1145/3345629.3345639}, doi = {10.1145/3345629.3345639}, acmid = {3345639}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {Bug-Fix Dataset, Mining Software Repositories, Software Traceability}, } <br><br>P.S: We added a new dataset version (v1.0.1). In this version, we fix the git commit features that track the src and test files. More info can be found in the fix-script.py file. </pre>
DOI: 10.5281/zenodo.5526055
title: Extended data for "TeenCovidLife: A resource to understand the impact of the Covid-19 pandemic on adolescents in Scotland"

description: Extended data for "TeenCovidLife: A resource to understand the impact of the Covid-19 pandemic on adolescents in Scotland" Wellcome Open Research submission