cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5156961
title: Spike variant predictions

description: Spike variant glycosylation predictions. (spike_mut_idx.v3.xlsx) &gt; Glycoimpact*: likelihood the glycosylation of a proximal site is perturbed &gt; mannoseDist*: aggregate Fisher log-OR for any high-mannose (&gt;5mannose) structure (taken from IMRs). LIkelihood high-mannose structures, in general, will increase or decrease &gt; IMR - Expected Change: a combination of proximity information with mannoseDist to estimate joint effect &gt; IMR/InSaNNE prediction: predictions of differential glycosylation from IMRs or InSaNNE respectively *_seq refers to sequence-based predictions, _str refers to structure-based predictions Generating R code is also provided without raw-data
DOI: 10.5281/zenodo.5156936
title: Mouse actin dataset for microscopy image denoising benchmark as used in PPN2V paper

description: Mouse actin dataset for microscopy image denoising benchmark as used in PPN2V paper (https://arxiv.org/abs/1911.12291)
DOI: 10.6084/m9.figshare.15102123
title: Data Availability

description: Data Availability
DOI: 10.5281/zenodo.5156977
title: DSB (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)

description: DSB n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
DOI: 10.5281/zenodo.5156976
title: DSB (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)

description: DSB n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
DOI: 10.5281/zenodo.5156969
title: DSB (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)

description: DSB n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
DOI: 10.5281/zenodo.5156968
title: DSB (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)

description: DSB n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
DOI: 10.5281/zenodo.5156978
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.
DOI: 10.5281/zenodo.5156983
title: DSB (noise 20) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)

description: DSB n20 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
DOI: 10.5281/zenodo.5156982
title: DSB (noise 20) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)

description: DSB n20 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
DOI: 10.5517/ccdc.csd.cc27s5rz
title: CCDC 2078821: Experimental Crystal Structure Determination

description: Related Article: Kento Shimamoto, Yusuke Sunada|2021|Chem.Commun.|57|7649|doi:10.1039/D1CC02682H
DOI: 10.5517/ccdc.csd.cc27s5s0
title: CCDC 2078822: Experimental Crystal Structure Determination

description: Related Article: Kento Shimamoto, Yusuke Sunada|2021|Chem.Commun.|57|7649|doi:10.1039/D1CC02682H
DOI: 10.5517/ccdc.csd.cc27s5t1
title: CCDC 2078823: Experimental Crystal Structure Determination

description: Related Article: Kento Shimamoto, Yusuke Sunada|2021|Chem.Commun.|57|7649|doi:10.1039/D1CC02682H
DOI: 10.5517/ccdc.csd.cc27s5v2
title: CCDC 2078824: Experimental Crystal Structure Determination

description: Related Article: Kento Shimamoto, Yusuke Sunada|2021|Chem.Commun.|57|7649|doi:10.1039/D1CC02682H
DOI: 10.5281/zenodo.5156993
title: Flywing (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)

description: Flywing n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
DOI: 10.5281/zenodo.5156992
title: Flywing (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)

description: Flywing n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
DOI: 10.5281/zenodo.5156991
title: Flywing (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)

description: Flywing n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
DOI: 10.5281/zenodo.5156990
title: Flywing (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)

description: Flywing n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
DOI: 10.5281/zenodo.5156995
title: Flywing (noise 20) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)

description: Flywing n20 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
DOI: 10.5281/zenodo.5156994
title: Flywing (noise 20) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)

description: Flywing n20 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
DOI: 10.6084/m9.figshare.15102174
title: data1.xlsx

description: Impacts of coarse particles on ridges erosion were investigated
DOI: 10.6084/m9.figshare.15102174.v2
title: data1.xlsx

description: Impacts of coarse particles on ridges erosion were investigated
DOI: 10.6084/m9.figshare.15102174.v1
title: data1.xlsx

description: Impacts of coarse particles on ridges erosion were investigated
DOI: 10.5281/zenodo.5157003
title: Mouse (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)

description: Mouse n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
DOI: 10.5281/zenodo.5157002
title: Mouse (noise 10) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)

description: Mouse n10 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
DOI: 10.5281/zenodo.5157001
title: Mouse (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)

description: Mouse n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
DOI: 10.5281/zenodo.5157000
title: Mouse (noise 0) dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)

description: Mouse n0 dataset for microscopy image denoising and segmentation benchmark as used in DenoiSeg paper (https://arxiv.org/abs/2005.02987)
DOI: 10.5281/zenodo.5157014
title: Archive of aggregate data on work and parenthood

description: These are aggregate files of labor force participation for mothers, fathers, and women not living with own children. Read ReadMe.txt file first for definitions and codes. Generated using the Current Population Survey monthly files from ipums.org. These files are meant to encourage discussion and analysis of labor force participation of mothers in comparison to fathers and women without own children in their home.
DOI: 10.6084/m9.figshare.11846940
title: Corymbia hamersleyana cpDNA concatenated alignment

description: Concatenated cpDNA sequence alignment data for Corymbia hamersleyana (rpl16, ndhC-trnV, trnG)
DOI: 10.6084/m9.figshare.11846940.v1
title: Corymbia hamersleyana cpDNA concatenated alignment

description: Concatenated cpDNA sequence alignment data for Corymbia hamersleyana (rpl16, ndhC-trnV, trnG)
DOI: 10.6084/m9.figshare.11846937
title: Acacia pruinocarpa concatenated cpDNA sequences

description: concatenated sequence alignment of Acacia pruinocarpa (ndhF-rpl32, rpl32-trnL,trnS-trnG,psbD-trnT)
DOI: 10.6084/m9.figshare.11846937.v1
title: Acacia pruinocarpa concatenated cpDNA sequences

description: concatenated sequence alignment of Acacia pruinocarpa (ndhF-rpl32, rpl32-trnL,trnS-trnG,psbD-trnT)
DOI: 10.6084/m9.figshare.11846952
title: Acacia pruinocarpa genotype sheet

description: Genalex formatted microsatellite genotype dataset for Acacia pruinocarpa
DOI: 10.6084/m9.figshare.11846952.v1
title: Acacia pruinocarpa genotype sheet

description: Genalex formatted microsatellite genotype dataset for Acacia pruinocarpa
DOI: 10.6084/m9.figshare.11905626
title: Banksia sessilis genotype sheet

description: Genalex formatted microsatellite genotype data sheet for Banksia sessilis
DOI: 10.6084/m9.figshare.11905626.v1
title: Banksia sessilis genotype sheet

description: Genalex formatted microsatellite genotype data sheet for Banksia sessilis
DOI: 10.6084/m9.figshare.15102219
title: wild barley genome annotation

description: The annotation files of two wild barley genome.
DOI: 10.6084/m9.figshare.15102219.v1
title: wild barley genome annotation

description: The annotation files of two wild barley genome.
DOI: 10.6084/m9.figshare.12189639.v1
title: Accounting for Latent Covariates in Average Effects from Count Regressions

description: The effectiveness of a treatment on a count outcome can be assessed using a negative binomial regression, where treatment effects are defined as the difference between the expected outcome under treatment and under control. These treatment effects can to date only be estimated if all covariates are manifest (observed) variables. However, some covariates are latent variables that are measured by multiple fallible indicators. In such cases, it is important to control for measurement error of covariates in order to avoid attenuation bias and to get unbiased treatment effect estimates. In this paper, we propose a new approach to compute average and conditional treatment effects in regression models with a logarithmic link function involving multiple latent and manifest covariates. We extend the previously presented moment-based approach in several aspects: Building on a multigroup SEM framework for count variables instead of the generalized linear model, we allow for latent covariates and multiple covariates. We provide an illustrative example to explain the application and estimation in structural equation modeling software.
DOI: 10.6084/m9.figshare.12189639
title: Accounting for Latent Covariates in Average Effects from Count Regressions

description: The effectiveness of a treatment on a count outcome can be assessed using a negative binomial regression, where treatment effects are defined as the difference between the expected outcome under treatment and under control. These treatment effects can to date only be estimated if all covariates are manifest (observed) variables. However, some covariates are latent variables that are measured by multiple fallible indicators. In such cases, it is important to control for measurement error of covariates in order to avoid attenuation bias and to get unbiased treatment effect estimates. In this paper, we propose a new approach to compute average and conditional treatment effects in regression models with a logarithmic link function involving multiple latent and manifest covariates. We extend the previously presented moment-based approach in several aspects: Building on a multigroup SEM framework for count variables instead of the generalized linear model, we allow for latent covariates and multiple covariates. We provide an illustrative example to explain the application and estimation in structural equation modeling software.
DOI: 10.5517/ccdc.csd.cc27txss
title: CCDC 2080496: Experimental Crystal Structure Determination

description: Related Article: Jonathan Pfeiffer, Clemens Trost, Anna Pachkovska, Frank Tambornino|2021|Inorg.Chem.|60|10722|doi:10.1021/acs.inorgchem.1c01421
DOI: 10.5517/ccdc.csd.cc27wt43
title: CCDC 2082305: Experimental Crystal Structure Determination

description: Related Article: Lilia Croitor, Maria Cocu, Ion Bulhac, Paulina N. Bourosh, Victor Ch. Kravtsov, Oleg Petuhov, Olga Danilescu|2021|Polyhedron|206|115329|doi:10.1016/j.poly.2021.115329
DOI: 10.15468/82w2zc
title: Phylogeny of Celastraceae subfamily Hippocrateoideae inferred from morphological characters and nuclear and plastid loci

description: This dataset contains the digitized treatments in Plazi based on the original journal article Coughenour, Jennifer M., Simmons, Mark P., Lombardi, Julio A., Yakobson, Kendra, Archer, Robert H. (2011): Phylogeny of Celastraceae subfamily Hippocrateoideae inferred from morphological characters and nuclear and plastid loci. Molecular Phylogenetics and Evolution 59 (2): 320-330, DOI: 10.1016/j.ympev.2011.02.017, URL: http://dx.doi.org/10.1016/j.ympev.2011.02.017
DOI: 10.5281/zenodo.5157097
title: Crustal and upper mantle structure beneath SE China from joint analysis of receiver functions and Rayleigh-wave dispersion

description: This file contains our new 3-D shear-wave velocity model for SE China.
DOI: 10.5281/zenodo.5157096
title: Crustal and upper mantle structure beneath SE China from joint analysis of receiver functions and Rayleigh-wave dispersion

description: This file contains our new 3-D shear-wave velocity model for SE China.
DOI: 10.14457/cu.the.1987.185
title: การใช้สิทธิเกินส่วนในกฎหมายลักษณะละเมิด
DOI: 10.14457/cu.the.1987.186
title: Exact propagator of a two dimensional system
DOI: 10.14457/cu.the.1987.187
title: ทัศนคติต่อวิชาศิลปศึกษาของนักเรียนชั้นมัธยมศึกษาตอนต้นในกรุงเทพมหานคร
DOI: 10.5281/zenodo.5157233
title: Paper English Halal

description: Code and Data set of Similarity Algorithm
DOI: 10.5281/zenodo.5090631
title: EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation

description: EMOPIA (pronounced ‘yee-mò-pi-uh’) dataset is a shared multi-modal (audio and MIDI) database focusing on perceived emotion in <strong>pop piano music</strong>, to facilitate research on various tasks related to music emotion. The dataset contains <strong>1,087</strong> music clips from 387 songs and <strong>clip-level</strong> emotion labels annotated by four dedicated annotators. For more detailed information about the dataset, please refer to our paper: <strong>EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation</strong>. <strong>File Description</strong> <em><strong>midis/</strong></em>: midi clips transcribed using GiantMIDI. Filename `Q1_xxxxxxx_2.mp3`: Q1 means this clip belongs to Q1 on the V-A space; xxxxxxx is the song ID on YouTube, and the `2` means this clip is the 2nd clip taken from the full song. <em><strong>metadata/</strong></em>: metadata from YouTube. (Got when crawling) <em><strong>songs_lists/</strong></em>: YouTube URLs of songs. <em><strong>tagging_lists/</strong></em>: raw tagging result for each sample. <em><strong>label.csv</strong></em>: metadata that records filename, clip timestamps, and annotator. <em><strong>metadata_by_song.csv</strong></em>: list all the clips by the song. Can be used to create the train/val/test splits to avoid the same song appear in both train and test. <em><strong>scripts/prepare_split.ipynb:</strong></em> the script to create train/val/test splits and save them to csv files. <strong>Cite this dataset</strong> <pre><code>@inproceedings{{EMOPIA}, author = {Hung, Hsiao-Tzu and Ching, Joann and Doh, Seungheon and Kim, Nabin and Nam, Juhan and Yang, Yi-Hsuan}, title = {{MOPIA}: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation}, booktitle = {Proc. Int. Society for Music Information Retrieval Conf.}, year = {2021} }</code></pre>
DOI: 10.5281/zenodo.5157246
title: Paper English Halal

description: Code and Data set of Similarity Algorithm
DOI: 10.5281/zenodo.5157232
title: Paper English Halal

description: Code and Data set of Similarity Algorithm
DOI: 10.5281/zenodo.5151045
title: EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation

description: EMOPIA (pronounced ‘yee-mò-pi-uh’) dataset is a shared multi-modal (audio and MIDI) database focusing on perceived emotion in <strong>pop piano music</strong>, to facilitate research on various tasks related to music emotion. The dataset contains <strong>1,087</strong> music clips from 387 songs and <strong>clip-level</strong> emotion labels annotated by four dedicated annotators. For more detailed information about the dataset, please refer to our paper: <strong>EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation</strong>. <strong>File Description</strong> <em><strong>midis/</strong></em>: midi clips transcribed using GiantMIDI. Filename `Q1_xxxxxxx_2.mp3`: Q1 means this clip belongs to Q1 on the V-A space; xxxxxxx is the song ID on YouTube, and the `2` means this clip is the 2nd clip taken from the full song. <em><strong>metadata/</strong></em>: metadata from YouTube. (Got when crawling) <em><strong>songs_lists/</strong></em>: YouTube URLs of songs. <em><strong>tagging_lists/</strong></em>: raw tagging result for each sample. <em><strong>label.csv</strong></em>: metadata that records filename, clip timestamps, and annotator. <em><strong>metadata_by_song.csv</strong></em>: list all the clips by the song. Can be used to create the train/val/test splits to avoid the same song appear in both train and test. <em><strong>scripts/prepare_split.ipynb:</strong></em> the script to create train/val/test splits and save them to csv files. ------ <strong>2.1 Update</strong> Add one file and one folder: <em><strong>key_mode_tempo.csv</strong></em>: key, mode, and tempo information extracted from files. <strong><em>CP_events/</em></strong>: CP events used in our paper. Extracted using this script, and add the emotion event to the front. Modify one folder: The <strong><em>REMI_events/</em></strong> files in version 2.0 contain some information that is not related to the paper, so remove it. <strong>2.0 Update</strong> Add two new folders: <strong><em>corpus/</em></strong>: processed data that following the preprocessing flow. (Please notice that although we have <code>1078</code> clips in our dataset, we lost some clips during steps 1~4 of the flow, so the final number of clips in this <strong><code>corpus</code></strong> is <code>1052</code>, and that's the number we used for training the generative model.) <strong><em>REMI_events/</em></strong>: REMI event for each midi file. They are generated using this script. -------- <strong>Cite this dataset</strong> <pre><code>@inproceedings{{EMOPIA}, author = {Hung, Hsiao-Tzu and Ching, Joann and Doh, Seungheon and Kim, Nabin and Nam, Juhan and Yang, Yi-Hsuan}, title = {{MOPIA}: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation}, booktitle = {Proc. Int. Society for Music Information Retrieval Conf.}, year = {2021} }</code></pre>
DOI: 10.18120/ftcb-sh15
title: t
DOI: 10.12755/trf.res.2015.1
title: การยื้อแย่งที่ดินในบริบทความขัดแย้งของ ‘การพัฒนาทางเศรษฐกิจ’ และ ‘การอนุรักษ์ธรรมชาติ’ : กรณีศึกษาชุมชนชายแดนไทย-ลาว จังหวัดอุบลราชธานี
DOI: 10.12755/trf.res.2012.2
title: การทดสอบการใช้งานของแผ่นทดสอบการตั้งทองของโคนมในฟาร์ม
DOI: 10.15468/ge2thb
title: A new species of the genus Dorytomus Germar, 1817 (Coleoptera, Curculionidae) from Rovno amber

description: This dataset contains the digitized treatments in Plazi based on the original journal article Legalov, Andrei A., Nazarenko, Vitaliy Y., Perkovsky, Evgeny E. (2021): A new species of the genus Dorytomus Germar, 1817 (Coleoptera, Curculionidae) from Rovno amber. Zootaxa 5006 (1): 95-100, DOI: https://doi.org/10.11646/zootaxa.5006.1.12
DOI: 10.15468/q4msgn
title: A new species of Plectrocnemia (Trichoptera: Polycentropodidae) from Rovno amber

description: This dataset contains the digitized treatments in Plazi based on the original journal article Melnitsky, Stanislav I., Ivanov, Vladimir D., Perkovsky, Evgeny E. (2021): A new species of Plectrocnemia (Trichoptera: Polycentropodidae) from Rovno amber. Zootaxa 5006 (1): 106-109, DOI: https://doi.org/10.11646/zootaxa.5006.1.14
DOI: 10.6084/m9.figshare.15102481
title: Additional file 10 of Progesterone receptor antagonists reverse stem cell expansion and the paracrine effectors of progesterone action in the mouse mammary gland

description: Additional file 10. Supplementary Table 4: List of differentially expressed genes, LM
DOI: 10.6084/m9.figshare.15102481.v1
title: Additional file 10 of Progesterone receptor antagonists reverse stem cell expansion and the paracrine effectors of progesterone action in the mouse mammary gland

description: Additional file 10. Supplementary Table 4: List of differentially expressed genes, LM
DOI: 10.6084/m9.figshare.15102484
title: Additional file 11 of Progesterone receptor antagonists reverse stem cell expansion and the paracrine effectors of progesterone action in the mouse mammary gland

description: Additional file 11. Supplementary Table 5: Alternative splicing, EP, MSC
DOI: 10.6084/m9.figshare.15102484.v1
title: Additional file 11 of Progesterone receptor antagonists reverse stem cell expansion and the paracrine effectors of progesterone action in the mouse mammary gland

description: Additional file 11. Supplementary Table 5: Alternative splicing, EP, MSC
DOI: 10.6084/m9.figshare.15102487
title: Additional file 12 of Progesterone receptor antagonists reverse stem cell expansion and the paracrine effectors of progesterone action in the mouse mammary gland

description: Additional file 12. Supplementary Table 6: Alternative splicing, EP, LP
DOI: 10.6084/m9.figshare.15102487.v1
title: Additional file 12 of Progesterone receptor antagonists reverse stem cell expansion and the paracrine effectors of progesterone action in the mouse mammary gland

description: Additional file 12. Supplementary Table 6: Alternative splicing, EP, LP
DOI: 10.6084/m9.figshare.15102490
title: Additional file 13 of Progesterone receptor antagonists reverse stem cell expansion and the paracrine effectors of progesterone action in the mouse mammary gland

description: Additional file 13. Supplementary Table 7: Alternative splicing, EP, LM
DOI: 10.6084/m9.figshare.15102490.v1
title: Additional file 13 of Progesterone receptor antagonists reverse stem cell expansion and the paracrine effectors of progesterone action in the mouse mammary gland

description: Additional file 13. Supplementary Table 7: Alternative splicing, EP, LM
DOI: 10.6084/m9.figshare.15102493.v1
title: Additional file 14 of Progesterone receptor antagonists reverse stem cell expansion and the paracrine effectors of progesterone action in the mouse mammary gland

description: Additional file 14. Supplementary Table 8: Alternative splicing, EP + PR inhibitors, MSC
DOI: 10.6084/m9.figshare.15102496.v1
title: Additional file 15 of Progesterone receptor antagonists reverse stem cell expansion and the paracrine effectors of progesterone action in the mouse mammary gland

description: Additional file 15. Supplementary Table 9: Alternative splicing, EP + PR inhibitors, LP