cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2022-10.2622603/2212732
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622636/2212733
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622634/2212734
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624252/2212735
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622611/2212736
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622594/2212737
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624306/2212738
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622595/2212739
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624232/2212740
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624229/2212741
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624285/2212742
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622614/2212743
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624163/2212744
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622599/2212745
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624261/2212746
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622616/2212747
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624284/2212748
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624175/2212749
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624217/2212750
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624313/2212751
title: Data for EMSL Project 51287 from October 2022
DOI: 10.6084/m9.figshare.24580675
title: RSMD

description: This finding comes from our study on single machine scheduling with due-date assignment and interval processing times.
DOI: 10.6084/m9.figshare.24580675.v1
title: RSMD

description: This finding comes from our study on single machine scheduling with due-date assignment and interval processing times.
DOI: 10.25582/data.2022-10.2622623/2212752
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624204/2212753
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622629/2212754
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624270/2212755
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622613/2212756
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622632/2212757
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624251/2212758
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624295/2212759
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622601/2212760
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624195/2212761
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622617/2212762
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622619/2212763
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624288/2212764
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622635/2212765
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622643/2212766
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624215/2212767
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622630/2212768
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624283/2212769
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622597/2212770
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622605/2212771
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624233/2212772
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624223/2212773
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622612/2212774
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622622/2212775
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624264/2212776
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624254/2212777
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624301/2212778
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622598/2212779
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622607/2212780
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624219/2212781
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624246/2212782
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624203/2212783
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624221/2212784
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622596/2212785
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622631/2212786
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624164/2212787
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2624205/2212788
title: Data for EMSL Project 51287 from October 2022
DOI: 10.25582/data.2022-10.2622620/2212789
title: Data for EMSL Project 51287 from October 2022
DOI: 10.5281/zenodo.10129659
title: Dataset: Very-small-aperture 3-D infrasonic array for volcanic jet observation at Stromboli Volcano, Geophysical Journal International, Volume 229, Issue 1, Pages 459–471, https://doi.org/10.1093/gji/ggab487

description: This is the dataset of the infrasound observation of Yamakawa et al. (GJI, 2022).
DOI: 10.5281/zenodo.10129658
title: Dataset: Very-small-aperture 3-D infrasonic array for volcanic jet observation at Stromboli Volcano, Geophysical Journal International, Volume 229, Issue 1, Pages 459–471, https://doi.org/10.1093/gji/ggab487

description: This is the dataset of the infrasound observation of Yamakawa et al. (GJI, 2022).
DOI: 10.5281/zenodo.5088490
title: Dataset for The Chosen Few — Variations in Common and Rare Soil Bacteria across Biomes

description: Rarefied relative abundance data of soil bacterial 16S rDNA amplicon sequence variants (ASVs) merged from three studies [1-3] as previously reported [4].The ASV abundance table provided here is based on the previously published data (https://doi.org/10.5281/zenodo.3366252) and was compiled to analyze patterns of bacterial rarity across terrestrial biomes [5].
This repository consists of the following files:- meta-samples.csv    Information on sample locations and additional variables (for units see Supplementary Information of reference [4], p.8)- meta-ASV.csv    Sequences and their taxonomic assignment with confidence for highest taxonomic level (see reference [4]).- abundance-table.csv    Table with relative abundance of rarefied counts (columns sum to one). The soil sample ids and ASV sequences are used as column and row indices, respectively.
Dataset citation should refer to [4] and method reference to [5].
References[1] Bahram, Mohammad, Falk Hildebrand, Sofia K. Forslund, Jennifer L. Anderson, Nadejda A. Soudzilovskaia, Peter M. Bodegom, Johan Bengtsson-Palme, et al. 2018. "Structure and Function of the Global Topsoil Microbiome." Nature 560 (7717): 233–37. https://doi.org/10.1038/s41586-018-0386-6.[2] Thompson, Luke R., Aaron R. Jex, Alexandra H. Campbell, Alexandra M. Linz, Allison Berry, Allison E. Williams, Alyssa Cochran, et al. 2017. "A Communal Catalogue Reveals Earth's Multiscale Microbial Diversity." Nature 551 (7681): 457–63. https://doi.org/10.1038/nature24621.[3] Zhou, Jizhong, Ye Deng, Lina Shen, Chongqing Wen, Qingyun Yan, Daliang Ning, Yujia Qin, et al. 2016. "Temperature Mediates Continental-Scale Diversity of Microbes in Forest Soils." Nature Communications 7 (July): 1–10. https://doi.org/10.1038/ncomms12083.[4] Bickel, Samuel, Xi Chen, Andreas Papritz, and Dani Or. 2019. "A Hierarchy of Environmental Covariates Control the Global Biogeography of Soil Bacterial Richness." Scientific Reports 9 (1): 1–10. https://doi.org/10.1038/s41598-019-48571-w.[5] Bickel, Samuel, and Dani Or. 2021. "The Chosen Few — Variations in Common and Rare Soil Bacteria across Biomes." The ISME Journal, May. https://doi.org/10.1038/s41396-021-00981-3.
DOI: 10.5281/zenodo.5088491
title: Dataset for The Chosen Few — Variations in Common and Rare Soil Bacteria across Biomes

description: Rarefied relative abundance data of soil bacterial 16S rDNA amplicon sequence variants (ASVs) merged from three studies [1-3] as previously reported [4].The ASV abundance table provided here is based on the previously published data (https://doi.org/10.5281/zenodo.3366252) and was compiled to analyze patterns of bacterial rarity across terrestrial biomes [5].
This repository consists of the following files:- meta-samples.csv    Information on sample locations and additional variables (for units see Supplementary Information of reference [4], p.8)- meta-ASV.csv    Sequences and their taxonomic assignment with confidence for highest taxonomic level (see reference [4]).- abundance-table.csv    Table with relative abundance of rarefied counts (columns sum to one). The soil sample ids and ASV sequences are used as column and row indices, respectively.
Dataset citation should refer to [4] and method reference to [5].
References[1] Bahram, Mohammad, Falk Hildebrand, Sofia K. Forslund, Jennifer L. Anderson, Nadejda A. Soudzilovskaia, Peter M. Bodegom, Johan Bengtsson-Palme, et al. 2018. "Structure and Function of the Global Topsoil Microbiome." Nature 560 (7717): 233–37. https://doi.org/10.1038/s41586-018-0386-6.[2] Thompson, Luke R., Aaron R. Jex, Alexandra H. Campbell, Alexandra M. Linz, Allison Berry, Allison E. Williams, Alyssa Cochran, et al. 2017. "A Communal Catalogue Reveals Earth's Multiscale Microbial Diversity." Nature 551 (7681): 457–63. https://doi.org/10.1038/nature24621.[3] Zhou, Jizhong, Ye Deng, Lina Shen, Chongqing Wen, Qingyun Yan, Daliang Ning, Yujia Qin, et al. 2016. "Temperature Mediates Continental-Scale Diversity of Microbes in Forest Soils." Nature Communications 7 (July): 1–10. https://doi.org/10.1038/ncomms12083.[4] Bickel, Samuel, Xi Chen, Andreas Papritz, and Dani Or. 2019. "A Hierarchy of Environmental Covariates Control the Global Biogeography of Soil Bacterial Richness." Scientific Reports 9 (1): 1–10. https://doi.org/10.1038/s41598-019-48571-w.[5] Bickel, Samuel, and Dani Or. 2021. "The Chosen Few — Variations in Common and Rare Soil Bacteria across Biomes." The ISME Journal, May. https://doi.org/10.1038/s41396-021-00981-3.
DOI: 10.6084/m9.figshare.24580753
title: Targeted metabolomics of Rapeseeds lateral roots

description: JA,SA etc content in rapeseeds lateral roots.
DOI: 10.5281/zenodo.10145137
title: Machine Learning-assisted immunophenotyping of peripheral blood identifies innate immune cells as best predictor of response to induction chemo-immunotherapy in head and neck squamous cell carcinoma – knowledge obtained from the CheckRad-CD8 trial

description: Raw cell counts from peripheral blood immune phenotyping across individual patients.
DOI: 10.5281/zenodo.10145138
title: Machine Learning-assisted immunophenotyping of peripheral blood identifies innate immune cells as best predictor of response to induction chemo-immunotherapy in head and neck squamous cell carcinoma – knowledge obtained from the CheckRad-CD8 trial

description: Raw cell counts from peripheral blood immune phenotyping across individual patients.
DOI: 10.5281/zenodo.8027250
title: De novo transcriptome assembly of hyperaccumulating Noccaea praecox

description: Trinity de novo assembly for hyperaccumulating plant species Noccaea praecox (syn. Thlaspi praecox), Brassicaceae. The dataset includes annotations from SwissProt, Pfam, Rfam databases and information on transmembrane regions and signal peptide cleavage sites (annotated using BLAST, HMMER, infernal, tmHMM and signalP through Trinotate). Detailed information on the preprocessing, assembly, post-processing and annotations are described in the ReadMe file.
description: Slovenian Research Agency by core founding P1-0212
DOI: 10.25345/c5028pq4v
title: MassIVE MSV000093414 - Calorie restriction and rapamycin distinctly restore non-canonical ORF translation in the muscles of aging mice
title: Loss of protein homeostasis is one of the hallmarks of aging. As such, interventions that restore proteostasis should slow down the aging process and improve healthspan. Two of the most broadly used anti-aging interventions that are effective in organisms from yeast to mammals are calorie restriction (CR) and rapamycin (RM) treatment. To identify the regulatory mechanisms by which these interventions improve the protein homeostasis in the muscle, we carried out ribosome footprinting in the muscle from mice aged under standard conditions, or under long-term treatment with CR or RM. We found that RM primarily remodels the translation of upstream, and CR the translation of downstream, open reading frames (ORFs). Mass spectrometry analysis reveals the expression of numerous non-canonical ORFs at the protein level. These novel peptides may represent new targets for therapies aiming to maintain muscle function and extend healthspan.
DOI: 10.6084/m9.figshare.24579538
title: Error analysis and optimization of phase inversion in the DASH technique.zip

description: This code can be used to analyze and compare the error sources of phase inversion in the DASH technique, as well as to analyze the optimization scheme。
DOI: 10.6084/m9.figshare.24579538.v1
title: Error analysis and optimization of phase inversion in the DASH technique.zip

description: This code can be used to analyze and compare the error sources of phase inversion in the DASH technique, as well as to analyze the optimization scheme。
DOI: 10.5517/ccdc.csd.cc2gxqrw
title: CCDC 2291729: Experimental Crystal Structure Determination

description: Related Article: Jianmei Guo, Chenwei Hu, Jiaqi Liu, Yongtao Wang, Lei Ma|2024|Dyes Pigm.|221|111760|doi:10.1016/j.dyepig.2023.111760
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2gxqsx
title: CCDC 2291730: Experimental Crystal Structure Determination

description: Related Article: Jianmei Guo, Chenwei Hu, Jiaqi Liu, Yongtao Wang, Lei Ma|2024|Dyes Pigm.|221|111760|doi:10.1016/j.dyepig.2023.111760
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2g6k08
title: CCDC 2270409: Experimental Crystal Structure Determination

description: Related Article: Sophie A. Fitzgerald, Ellie N. Payce, Peter N. Horton, Simon J. Coles, Simon J. A. Pope|2023|Dalton Trans.|52|16480|doi:10.1039/D3DT02193A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.24580777
title: EMNLP2022-MCD.csv

description: The dataset contains Amazon reviews of different products divided into five categories.
DOI: 10.6084/m9.figshare.24580777.v1
title: EMNLP2022-MCD.csv

description: The dataset contains Amazon reviews of different products divided into five categories.
DOI: 10.14278/rodare.2554
title: Data publication: Fluid Transport in Ordinary Portland Cement and Slag Cement from in-situ Positron Emission Tomography

description: Supplemental Primary PET Data to Fluid Transport in Ordinary Portland Cement and Slag Cement from in-situ Positron Emission Tomography Reiss, A.; Kulenkampff, J.; Bar-Nes, G.; Fischer, C.; Emmanuel, S. Submitted to Cement and Concrete Research 02.11.24 Material and procedure are characterized in the paper. PET data are supplied in Interfile format (Original: Cradduck T.D., Bailey D.L., Hutton BF, Deconinck F., Busemann Sokole E., Bergmann H., Noelpp U.: “A standard protocol for the exchange of nuclear medicine image files. Nucl Med Commun; 10:703-713 (1989), used version: https://stir.sourceforge.net/links/petinterfile03.pdf). The interfile format includes an ASCII header file (.hv) and a binary file containing the volume data (.v). Import filters exist for many visualization frameworks (e.g. Matlab, Avizo); otherwise the binary data files can be imported as raw data, taking into account the format given in the header file. The header tags were extended for relevant experimental parameters of non-medical PET experiments and in this way serve as experimental protocol. List of data files: cem1_F-18.7z: 17 PET frames from the 18F intrusion experiment cem1_Cu-64.7z: 31 PET frames from the 64Cu intrusion experiment cem1_I-124.7z: 34 PET frames from the 124I intrusion experiment The PET data sets (LMFs) were acquired with a tilted ClearPET-scanner (Elysia-Raytest) with a vertical axis of the cylindrical FOV at HZDR. The “trues”-projections were corrected for attenuation and scatter with a procedure based on the STIR-library (https://stir.sourceforge.net, version 3.0, Kris Thielemans, Charalampos Tsoumpas, Sanida Mustafovic, Tobias Beisel, Pablo Aguiar, Nikolaos Dikaios, and Matthew W Jacobson, STIR: Software for Tomographic Image Reconstruction Release 2, Physics in Medicine and Biology, 57 (4), 2012 pp.867-883).
DOI: 10.14278/rodare.2555
title: Data publication: Fluid Transport in Ordinary Portland Cement and Slag Cement from in-situ Positron Emission Tomography

description: Supplemental Primary PET Data to Fluid Transport in Ordinary Portland Cement and Slag Cement from in-situ Positron Emission Tomography Reiss, A.; Kulenkampff, J.; Bar-Nes, G.; Fischer, C.; Emmanuel, S. Submitted to Cement and Concrete Research 02.11.24 Material and procedure are characterized in the paper. PET data are supplied in Interfile format (Original: Cradduck T.D., Bailey D.L., Hutton BF, Deconinck F., Busemann Sokole E., Bergmann H., Noelpp U.: “A standard protocol for the exchange of nuclear medicine image files. Nucl Med Commun; 10:703-713 (1989), used version: https://stir.sourceforge.net/links/petinterfile03.pdf). The interfile format includes an ASCII header file (.hv) and a binary file containing the volume data (.v). Import filters exist for many visualization frameworks (e.g. Matlab, Avizo); otherwise the binary data files can be imported as raw data, taking into account the format given in the header file. The header tags were extended for relevant experimental parameters of non-medical PET experiments and in this way serve as experimental protocol. List of data files: cem1_F-18.7z: 17 PET frames from the 18F intrusion experiment cem1_Cu-64.7z: 31 PET frames from the 64Cu intrusion experiment cem1_I-124.7z: 34 PET frames from the 124I intrusion experiment The PET data sets (LMFs) were acquired with a tilted ClearPET-scanner (Elysia-Raytest) with a vertical axis of the cylindrical FOV at HZDR. The “trues”-projections were corrected for attenuation and scatter with a procedure based on the STIR-library (https://stir.sourceforge.net, version 3.0, Kris Thielemans, Charalampos Tsoumpas, Sanida Mustafovic, Tobias Beisel, Pablo Aguiar, Nikolaos Dikaios, and Matthew W Jacobson, STIR: Software for Tomographic Image Reconstruction Release 2, Physics in Medicine and Biology, 57 (4), 2012 pp.867-883).
DOI: 10.4121/19936487.v1
title: Data and results for the bachelor thesis: Reasons to continue or stop using a virtual coach for quitting smoking and increasing physical activity: A mixed-methods analysis.

description: This dataset contains data and results from the analysis conducted for the bachelor thesis, "Reasons to continue or stop using a virtual coach for quitting smoking and increasing physical activity: A mixed-methods analysis.". In this study, a mixed-methods analysis (thematic analysis, quantitative analysis, and a literature study) was carried out to find out what the reasons are for wanting to stop or continue using a virtual coach. By using triangulation, themes were found. Based on these themes, recommendations have been given on how to improve the virtual coach.
DOI: 10.4121/20066186.v1
title: Acceptance of a Virtual Coach as Guided Intervention for Smoking Cessation: Dataset

description: This dataset contains data and analysis that was used during a study into the acceptance of a virtual coach as guided intervention for smoking cessation. This study aimed to discover why people might or might not accept a virtual coach for smoking cessation. This study was conducted as a bachelor thesis and made use of the original dataset https://doi.org/10.4121/19934783.v1. The study is part of the Perfect Fit project which is funded by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO), with project number 628.011.211.
DOI: 10.4121/20099102.v1
title: Data and code underlying the bachelor thesis: Traits for a virtual coach to be a ”friend”

description: This dataset contains data and code snippets from the analysis performed during the thesis, "Traits for a virtual coach to be a friend". Here, an investigation has taken place to what characteristics the virtual coach must possess to establish this friendly relationship. Thus, the main research question is: <strong>What are the reasons for seeing the virtual coach as a stranger or friend?</strong> This research has been based on a retrospective study performed by Albers and Brinkman. Here, five hundred participants interacted with the text-based virtual coach Sam - developed for the Perfect Fit project to convince smokers to quit by performing small activities - in five separate sessions. Afterwards, each participant rated the relationship with the virtual coach, followed by an explanatory free-text response to which thematic analysis was applied. The quality of the thematic analysis has been ensured by researcher and method triangulation. Researcher triangulation, where multiple researchers were involved, determined the reliability of the coding scheme generated during thematic analysis. Method triangulation, which supported the findings, was executed with the free-text responses, literature, and quantitative data, containing demographic and smoke-related characteristics.
DOI: 10.6084/m9.figshare.24580801
title: Computer network learning achievements of higher education students using Advanced Virtual Learning Environments (Advanced VLEs)

description: The research aims to find the effect of VLEs on learning achievement using one-way ANCOVA and the differences between beginner, intermediate, and advanced VLEs.