cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5439/1668872
title: CSAPR2 CMAC 2.0 level c1 data.

description: Raw data from ARM precipitation radars must be corrected for atmospheric phenomena and instrument characteristics (e.g., attenuation, clutter) to retrieve precipitation properties. The Corrected Moments in Antenna Coordinates Version 2 (CMAC2) value-added product (VAP) is a set of algorithms and code that does such corrections, and it also retrieves precipitation quantities from the radar measurements. Similar to the X-SAPR radars at the ARM main site, C-SAPR2 radar data also needs corrections and improvements. CMAC 2.0 has been updated to work with the ARM C-SAPR 2 radar. Data and fields that have been processed to: correct for velocity aliasing, unfold and generate a cross-polarimetric phase difference that is monotonically increasing, removing impulses caused by non-uniform beam filling and phase shift on backscatter, recalculate specific differential phase using a 20-point Sobel filter on the aforementioned phase, correct for liquid path attenuation using the polarimetric signals, estimate rainfall rates at the gate using the specific attenuation. In addition, CMAC writes the data out into a community-standard format netCDF File using the CF/Radial conventions. The data are therefore compatible with new and existing National Center for Atmospheric Research (NCAR) tools such as RadXConvert for converting to a variety of popular file formats.
DOI: 10.6084/m9.figshare.13203394
title: Additional file 2 of Long-term experimental evolution reveals purifying selection on piRNA-mediated control of transposable element expression

description: Table showing change in median expression of individual transposable elements in MA lines. (CSV 8 kb)
DOI: 10.6084/m9.figshare.13203394.v1
title: Additional file 2 of Long-term experimental evolution reveals purifying selection on piRNA-mediated control of transposable element expression

description: Table showing change in median expression of individual transposable elements in MA lines. (CSV 8 kb)
DOI: 10.6084/m9.figshare.13203403
title: Additional file 3 of Natural food intake patterns have little synchronizing effect on peripheral circadian clocks

description: Additional file 3. PER2::LUC bioluminescence data. Tab Data_Expt1: PER2::LUC bioluminescence data in ZT and FT for Experiment 1. Tab Dictionary_Expt1: Data dictionary defining terms. Tab Data_Expt2: PER2::LUC bioluminescence data, locomotor activity period and phase, and image times in CT, FT, and ZT for Experiment 2. Tab Dictionary_Expt2: Data dictionary defining terms.
DOI: 10.6084/m9.figshare.13203409
title: Additional file 1 of Assessment of knowledge, practice and attitude towards prostate cancer screening among male patients aged 40 years and above at Kitwe Teaching Hospital, Zambia

description: Additional file 1: Dataset for AFJU-D-19-00041R2 manuscript.
DOI: 10.6084/m9.figshare.13203403.v1
title: Additional file 3 of Natural food intake patterns have little synchronizing effect on peripheral circadian clocks

description: Additional file 3. PER2::LUC bioluminescence data. Tab Data_Expt1: PER2::LUC bioluminescence data in ZT and FT for Experiment 1. Tab Dictionary_Expt1: Data dictionary defining terms. Tab Data_Expt2: PER2::LUC bioluminescence data, locomotor activity period and phase, and image times in CT, FT, and ZT for Experiment 2. Tab Dictionary_Expt2: Data dictionary defining terms.
DOI: 10.6084/m9.figshare.13203409.v1
title: Additional file 1 of Assessment of knowledge, practice and attitude towards prostate cancer screening among male patients aged 40 years and above at Kitwe Teaching Hospital, Zambia

description: Additional file 1: Dataset for AFJU-D-19-00041R2 manuscript.
DOI: 10.6084/m9.figshare.13203427.v1
title: Additional file 3 of New insights into the genetic diversity of the stone crayfish: taxonomic and conservation implications

description: Additional file 3: Estimates of evolutionary divergence over sequence pairs of Austropotamobius torrentium. Range values of within and between genetic distances of nine A. torrentium mtDNA phylogroups (ZV—Zeleni Vir; GK—Gorski Kotar; ŽPB—Žumberak, Plitvice and Bjelolasica; LD—Lika and Dalmatia; BAN—Banovina; SB—southern Balkans; CSE—central and southeastern Europe; APU—Apuseni) for COI, 16S rRNA and ITS2. Observed ranges of pairwise patristic distances for COI within and between the phylogroups measured on ML tree are also provided.
DOI: 10.6084/m9.figshare.13203421.v1
title: Additional file 1 of New insights into the genetic diversity of the stone crayfish: taxonomic and conservation implications

description: Additional file 1: List of Austropotamobius torrentium, as well as Austropotamobius pallipes, Astacus astacus and Pontastacus leptodactylus sequences used in the analyses. The information comprises the name of the country and sampling site, mtDNA phylogroup, COI haplotype ID and their GenBank Accession numbers, 16S haplotypte ID and their GenBank Accession numbers, concatenated sequences ID, ITS2 ID and their GenBank Accession numbers and bibliographic references.
DOI: 10.6084/m9.figshare.13203427
title: Additional file 3 of New insights into the genetic diversity of the stone crayfish: taxonomic and conservation implications

description: Additional file 3: Estimates of evolutionary divergence over sequence pairs of Austropotamobius torrentium. Range values of within and between genetic distances of nine A. torrentium mtDNA phylogroups (ZV—Zeleni Vir; GK—Gorski Kotar; ŽPB—Žumberak, Plitvice and Bjelolasica; LD—Lika and Dalmatia; BAN—Banovina; SB—southern Balkans; CSE—central and southeastern Europe; APU—Apuseni) for COI, 16S rRNA and ITS2. Observed ranges of pairwise patristic distances for COI within and between the phylogroups measured on ML tree are also provided.
DOI: 10.6084/m9.figshare.13203421
title: Additional file 1 of New insights into the genetic diversity of the stone crayfish: taxonomic and conservation implications

description: Additional file 1: List of Austropotamobius torrentium, as well as Austropotamobius pallipes, Astacus astacus and Pontastacus leptodactylus sequences used in the analyses. The information comprises the name of the country and sampling site, mtDNA phylogroup, COI haplotype ID and their GenBank Accession numbers, 16S haplotypte ID and their GenBank Accession numbers, concatenated sequences ID, ITS2 ID and their GenBank Accession numbers and bibliographic references.
DOI: 10.6084/m9.figshare.13147202.v1
title: Additional file 1 of Reproducibility and inter-observer agreement of Greulich-Pyle protocol to estimate skeletal age among female adolescent soccer players

description: Additional file 1.
DOI: 10.6084/m9.figshare.13147202
title: Additional file 1 of Reproducibility and inter-observer agreement of Greulich-Pyle protocol to estimate skeletal age among female adolescent soccer players

description: Additional file 1.
DOI: 10.6084/m9.figshare.13142069
title: Additional file 1 of Neutrophil count as a reliable marker for diabetic kidney disease in autoimmune diabetes

description: Additional file 1.
DOI: 10.6084/m9.figshare.13142069.v1
title: Additional file 1 of Neutrophil count as a reliable marker for diabetic kidney disease in autoimmune diabetes

description: Additional file 1.
DOI: 10.6084/m9.figshare.13120875
title: Additional file 1 of Unlocking the efficiency of genomics laboratories with robotic liquid-handling

description: Additional file 1.
DOI: 10.6084/m9.figshare.13203478.v1
title: Additional file 2 of Optimizing genomic reference populations to improve crossbred performance

description: Additional file 2. Programs and seeds to simulate data. This file contains the input file used for QMSim, the Fortran-programs to simulate phenotypes and create new generations and the seeds for the different programs in each of the replicates
DOI: 10.6084/m9.figshare.13203484.v1
title: Additional file 4 of Optimizing genomic reference populations to improve crossbred performance

description: Additional file 4. Benefit of crossbred over purebred reference population. R-script to make a contour plot of the benefit of a crossbred over a purebred reference population as a function of $${r}_{pc}$$ r pc and reference population size.
DOI: 10.6084/m9.figshare.13120875.v1
title: Additional file 1 of Unlocking the efficiency of genomics laboratories with robotic liquid-handling

description: Additional file 1.
DOI: 10.6084/m9.figshare.13203484
title: Additional file 4 of Optimizing genomic reference populations to improve crossbred performance

description: Additional file 4. Benefit of crossbred over purebred reference population. R-script to make a contour plot of the benefit of a crossbred over a purebred reference population as a function of $${r}_{pc}$$ r pc and reference population size.
DOI: 10.6084/m9.figshare.13203478
title: Additional file 2 of Optimizing genomic reference populations to improve crossbred performance

description: Additional file 2. Programs and seeds to simulate data. This file contains the input file used for QMSim, the Fortran-programs to simulate phenotypes and create new generations and the seeds for the different programs in each of the replicates
DOI: 10.6084/m9.figshare.13203492
title: Additional file 2 of Neuronal surface P antigen (NSPA) modulates postsynaptic NMDAR stability through ubiquitination of tyrosine phosphatase PTPMEG

description: Additional file 2. Excel sheet containing source data file for Fig. 1e, 2c, d, 5a, b, 6a, c, d and Additional file 1: Fig. S4.
DOI: 10.6084/m9.figshare.13203492.v1
title: Additional file 2 of Neuronal surface P antigen (NSPA) modulates postsynaptic NMDAR stability through ubiquitination of tyrosine phosphatase PTPMEG

description: Additional file 2. Excel sheet containing source data file for Fig. 1e, 2c, d, 5a, b, 6a, c, d and Additional file 1: Fig. S4.
DOI: 10.6084/m9.figshare.13115576
title: Additional file 2 of Divergent genes in gerbils: prevalence, relation to GC-biased substitution, and phenotypic relevance

description: Additional file 2 Supplementary Data. Accession IDs for nucleotide sequences and protein sequences used in analysis. Raw Sneath values and Epstein values for all orthologues. The ranking of 9771 1-to-1 orthologues containing a sand rat gene calculated using the Sneath Index and Epstein’s Coefficient are provided in separate sheets. The ranking of 10,069 1-to-1 orthologues containing a Mongolian jird gene calculated using the Sneath Index and Epstein’s Coefficient are also provided.
DOI: 10.6084/m9.figshare.13115576.v1
title: Additional file 2 of Divergent genes in gerbils: prevalence, relation to GC-biased substitution, and phenotypic relevance

description: Additional file 2 Supplementary Data. Accession IDs for nucleotide sequences and protein sequences used in analysis. Raw Sneath values and Epstein values for all orthologues. The ranking of 9771 1-to-1 orthologues containing a sand rat gene calculated using the Sneath Index and Epstein’s Coefficient are provided in separate sheets. The ranking of 10,069 1-to-1 orthologues containing a Mongolian jird gene calculated using the Sneath Index and Epstein’s Coefficient are also provided.
DOI: 10.6084/m9.figshare.13115579
title: Additional file 3 of Divergent genes in gerbils: prevalence, relation to GC-biased substitution, and phenotypic relevance

description: Additional file 3 Supplementary Data. DNA alignments for 10,554 1-to-1 orthologues used in analysis. Each alignment file is named using its orthogroup ID.
DOI: 10.6084/m9.figshare.13115579.v1
title: Additional file 3 of Divergent genes in gerbils: prevalence, relation to GC-biased substitution, and phenotypic relevance

description: Additional file 3 Supplementary Data. DNA alignments for 10,554 1-to-1 orthologues used in analysis. Each alignment file is named using its orthogroup ID.
DOI: 10.6084/m9.figshare.13201145
title: R0235_20181120a_videos

description: Rat 0235, skilled reaching data collected in Leventhal laboratory by Alexandra Bova, alternating stimulation session
DOI: 10.6084/m9.figshare.13201145.v1
title: R0235_20181120a_videos

description: Rat 0235, skilled reaching data collected in Leventhal laboratory by Alexandra Bova, alternating stimulation session
DOI: 10.21227/e0mx-qj25
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.The paper associated with this dataset is available here: Design and analysis of a large-scale COVID-19 tweets dataset-------------------------------------Related: Coronavirus (COVID-19) Tweets Sentiment Trend (Global), Coronavirus (COVID-19) Tweets Dataset and Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 257,358 tweets— Coverage : Global— Language : English (EN)— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Usage policy : As per Twitter's Developer Policy— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time (and other notes) of each CSV file present in this dataset.
DOI: 10.21227/cewe-f535
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.The paper associated with this dataset is available here: Design and analysis of a large-scale COVID-19 tweets dataset-------------------------------------Related: Coronavirus (COVID-19) Tweets Sentiment Trend (Global), Coronavirus (COVID-19) Tweets Dataset and Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 257,358 tweets— Coverage : Global— Language : English (EN)— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time (and other notes) of each CSV file present in this dataset.
DOI: 10.21227/qyy5-nd03
title: Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4

description: This India-specific COVID-19 tweets dataset has been developed using the large-scale Coronavirus (COVID-19) Tweets Dataset, which currently contains more than 600 million COVID-19 specific English language tweets. This dataset contains tweets originating from India during the first week of each four phases of nationwide lockdowns initiated by the Government of India. For more information on filtering keywords, please visit the primary dataset page.— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z-------------------------------------Related: Coronavirus (COVID-19) Tweets Dataset, Coronavirus (COVID-19) Geo-tagged Tweets Dataset and Coronavirus (COVID-19) Tweets Sentiment Trend (Global)-------------------------------------What's inside the dataset? The files in the dataset contain IDs of the tweets present in the Coronavirus (COVID-19) Tweets Dataset. Note: Below, (all files) means that all the files mentioned and in-between have been considered to develop the ID file, while (only even-numbered files) suggests that only the even-numbered files have been considered.Lockdown period tweets: (all files)Lockdown1.zip: March 25, 2020 - April 02, 2020; corona_tweets_08.csv to corona_tweets_14.csvLockdown2.zip: April 14, 2020 - April 21, 2020; corona_tweets_27.csv to corona_tweets_33.csvLockdown3.zip: May 01, 2020 - May 07, 2020; corona_tweets_44.csv to corona_tweets_49.csvLockdown4.zip: May 18, 2020 - May 23, 2020; corona_tweets_61.csv to corona_tweets_66.csvExtras: (all files)extras_june1_june7.zip: corona_tweets_75.csv to corona_tweets_80.csvExtras: (only even-numbered files)extras_june24_july1.zip: corona_tweets_96.csv to corona_tweets_104.csvextras_july2_july15.zip: corona_tweets_106.csv to corona_tweets_118.csvextras_july16_august4.zip: corona_tweets_120.csv to corona_tweets_138.csvextras_august5_august18.zip: corona_tweets_140.csv to corona_tweets_152.csvextras_august19_september1.zip: corona_tweets_154.csv to corona_tweets_166.csvextras_september2_september15.zip: corona_tweets_168.csv to corona_tweets_180.csv
DOI: 10.6084/m9.figshare.13140047
title: WRF-CHEM reanalysis data output

description: This dataset contains all the data from WRF-Chem reanalysis papers.<br>
DOI: 10.6084/m9.figshare.13140047.v2
title: WRF-CHEM reanalysis data output

description: This dataset contains all the data from WRF-Chem reanalysis papers.<br>
DOI: 10.6084/m9.figshare.13140047.v1
title: WRF-CHEM reanalysis data output

description: This dataset contains all the data from WRF-Chem reanalysis papers.<br>
DOI: 10.6084/m9.figshare.12311198
title: Quasiperiodic music

description: Using the definition of quasiperiodic function as a motivation, we introduce the idea of quasiperiodic music and detail the composition process of a quasiperiodic music piece, <i>Raindrops in A minor</i>. We also discuss connections between quasiperiodic music and other works of music theory and composition that make use of aperiodic order or periodic order with large periods, such as Lindenmayer systems, Vuza canons, Messiaen's <i>Quatuor pour la Fin du Temps</i>, and the phase music of Steve Reich.
DOI: 10.6084/m9.figshare.12311198.v1
title: Quasiperiodic music

description: Using the definition of quasiperiodic function as a motivation, we introduce the idea of quasiperiodic music and detail the composition process of a quasiperiodic music piece, <i>Raindrops in A minor</i>. We also discuss connections between quasiperiodic music and other works of music theory and composition that make use of aperiodic order or periodic order with large periods, such as Lindenmayer systems, Vuza canons, Messiaen's <i>Quatuor pour la Fin du Temps</i>, and the phase music of Steve Reich.
DOI: 10.17632/7bkbb3vv7n.1
title: Data for: Uncertainty in critical source area predictions from watershed-scale hydrologic models

description: Simulation results for each model in the ensemble, summarized by HUC12 subwatershed for flow (Q), total phosphorus (TP), dissolved reactive phosphorus (DRP), total nitrogen (TN), and total suspended solids (TSS).
DOI: 10.5281/zenodo.4255185
title: Data from ROMS-CoSiNE-Fe model outputs at Station S1

description: ROMS-CoSiNE-Fe model outputs at Station S1. Standard ROMS averaged files.
DOI: 10.5281/zenodo.4255184
title: Data from ROMS-CoSiNE-Fe model outputs at Station S1

description: ROMS-CoSiNE-Fe model outputs at Station S1. Standard ROMS averaged files.
DOI: 10.17632/mbxxpjwjhf.1
title: Data for: Reliability evaluation method for pipes buried in fault areas based on the probabilistic fault displacement hazard analysis
DOI: 10.5281/zenodo.4255798
title: Dataset from 1709.10429

description: Dataset from 1709.10429.
DOI: 10.5281/zenodo.4255797
title: Dataset from 1709.10429

description: Dataset from 1709.10429.
DOI: 10.5281/zenodo.4255542
title: Survei Pengguna Media Sosial dan Pemilik Toko Online dalam Promosi di Kolom Komentar Influencer

description: Dataset survei pengguna media sosial dan pemilik toko online pada promosi melalui kolom komentar influencer
DOI: 10.5281/zenodo.4255802
title: Datasets of ASONAM-2015 paper "Tweet sentiment: From classification to quantification"

description: Datasets used for the following ASONAM 2015 paper:<br> ---------------------------------------------------------------------------------------------------<br> Title: Tweet Sentiment: From Classification to Quantification<br> Authors: Wei Gao and Fabrizio Sebastiani<br> Organization: Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar<br> --------------------------------------------------------------------------------------------------- [Content] * SemEval2013, SemEval2014, SemEval2015 datasets:<br> - semeval.train.feature.txt: Training set for learning sentiment models at development stage<br> - semeval.dev.feature.txt: Held-out set for tuning parameters<br> - semeval.train+dev.feature.txt: Training set for learning the final sentiment model<br> - semeval13.test.feature.txt: SemEval2013 test set<br> - semeval14.test.feature.txt: SemEval2014 test set<br> - semeval15.test.feature.txt: SemEval2015 test set<br> <br> * Other datasets: sanders, sst, omd, hcr, gasp<br> - X.train.feature.txt: Training set for learning sentiment models at development stage<br> - X.dev.feature.txt: Held-out set for tuning parameters<br> - X.train+dev.feature.txt: Traing set for learning the final sentiment model<br> - X.test.feature.txt: Test set<br> where X is one of sanders, sst, omd, hcr and gasp. For more details, please refer to the paper. <br> [Citation]<br> You can cite the folowing paper when referring to the dataset: @inproceedings{gao2015tweet,<br> title={Tweet sentiment: From classification to quantification},<br> author={Gao, Wei and Sebastiani, Fabrizio},<br> booktitle={2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},<br> pages={97--104},<br> year={2015},<br> organization={IEEE}<br> }