cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2djwqk
title: CCDC 2220769: Experimental Crystal Structure Determination

description: Related Article: Hui-Xian Liang, Meng-Jiao Hao, Guang-Yu Zhang, Hou-Jin Li, Wen-Zhe Ma, Jun Xu, Wen-Jian Lan|2023|Fitoterapia|166|105433|doi:10.1016/j.fitote.2023.105433
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.cc2djwys
title: CCDC 2220776: Experimental Crystal Structure Determination

description: Related Article: Hui-Xian Liang, Meng-Jiao Hao, Guang-Yu Zhang, Hou-Jin Li, Wen-Zhe Ma, Jun Xu, Wen-Jian Lan|2023|Fitoterapia|166|105433|doi:10.1016/j.fitote.2023.105433
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.cc2dk505
title: CCDC 2221026: Experimental Crystal Structure Determination

description: Related Article: Hui-Xian Liang, Meng-Jiao Hao, Guang-Yu Zhang, Hou-Jin Li, Wen-Zhe Ma, Jun Xu, Wen-Jian Lan|2023|Fitoterapia|166|105433|doi:10.1016/j.fitote.2023.105433
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: 10.5281/zenodo.7792783
title: Linear breaking strength of porcine cystic ducts and distance to the gallbladder are not associated to allometric parameters

description: Raw data for the analyses in the manuscript with the same title.
description: The .csv-file is formated according to German nomenclature with a decimal comma and a semicolon as a separator.
DOI: 10.5281/zenodo.7792784
title: Linear breaking strength of porcine cystic ducts and distance to the gallbladder are not associated to allometric parameters

description: Raw data for the analyses in the manuscript with the same title.
description: The .csv-file is formated according to German nomenclature with a decimal comma and a semicolon as a separator.
DOI: 10.5281/zenodo.7792793
title: Linear breaking strength of porcine cystic ducts and distance to the gallbladder are not associated to allometric parameters

description: Raw data for the analyses in the manuscript with the same title.
description: The .csv-file is formated according to German nomenclature with a decimal comma and a semicolon as a separator.
DOI: 10.34740/kaggle/dsv/5294640
title: 2021 World Population (updated daily)
DOI: 10.6084/m9.figshare.22492468
title: Factors predicting adherent periadrenal fat in laparoscopic adrenalectomy

description: The spss data of the manuscript of "Factors predicting adherent periadrenal fat in laparoscopic adrenalectomy"
DOI: 10.6084/m9.figshare.22492468.v1
title: Factors predicting adherent periadrenal fat in laparoscopic adrenalectomy

description: The spss data of the manuscript of "Factors predicting adherent periadrenal fat in laparoscopic adrenalectomy"
DOI: 10.5281/zenodo.7384244
title: Internal Normal Mode Analysis applied to RNA flexibility and conformational changes

description: We investigated the capability of internal normal modes to reproduce RNA flexibility and predict observed RNA conformational changes, and, notably, those induced by the formation of RNA-protein and RNA-ligand complexes. Here, we extended our iNMA approach developed for proteins to study RNA molecules using a simplified representation of RNA structure and its potential energy. In this study, we considered three main data sets to investigate different aspects : i) one based on single-stranded RNA molecules for which all-atom MD simulations were computed; ii) one based on the available structures belonged to a specific Rfam family; iii) one based on the transition from unbound to bound RNA. A new version with the application to the CrPV-IRES is also present in the new version2 (10.5281/zenodo.7672081) Open Access. <strong>In each folder</strong> <em>modes.dat</em>: results obtained by iNMA (frequency and normal modes) <em>das1.dat</em>: conversion from internal to cartesian normal modes Each file <em>name_enm.pdb</em> refers to a PDB structure with a CG representation (RNA three-bead model). <strong>Dataset 1</strong>: d1.zip For the first dataset, we provide MD simulations converted into CG representation (RNA three-bead model), PCA analysis, the results obtained by iNMA for different values of distance cut-off <em>R</em><sub><em>c</em> </sub> and some scripts. Matlab and python scripts: <em>analysis_pca.py</em>: to extract the different principal components <em>analysis_PCA.m</em>: to compute overlap and cumative overlap in each folder <em>analysis_complete_new.m</em>: to summarize the results <strong>Dataset 2</strong>: d2.zip For this dataset, we provide the structure ensemble for Rfam family and the results obtained by iNMA for different values of distance cut-off <em>R</em><sub><em>c</em> </sub>and some scripts. PDB files: <em>allensemble.pdb</em>: ensemble of PDB structures for a given Rfam family <em>allensemble_enm.pdb</em>: ensemble of PDB structures for a given Rfam family converted to CG representation (RNA three-bead model) <em>allensemble_enm_new.pdb</em>: ensemble of PDB structures for a given Rfam family with the same number of atoms for each model converted to CG representation (RNA three-bead model) <em>model.pdb</em>: reference PDB structure <em>model_enm.pdb</em>: reference PDB structure converted to CG representation (RNA three-bead model) Matlab script: <em>pca_xray_anal.m</em>: PCA analysis, overlap, cumulative overlap, rmsip and plots <strong>Dataset 3</strong>: d3.zip PDB structure: <em>bound.pdb</em>: bound structure <em>unbound.pdb</em>: unbound structure <em>diff.dat</em>: difference between bound and unbound structure after superimposition RMSD<em>n </em>with n a number: the first column represents \(\sqrt{\beta/2}\) Matlab script: <em>rmsd_anal.m</em>: analysis best mode based on RMSD
DOI: 10.34740/kaggle/dsv/5294690
title: Egypt Gold Prices | Daily Updated
DOI: 10.5281/zenodo.4785131
title: Pathologist's Annotated Image Tiles for Multi-Class Tissue Classification in Colorectal Cancer

description: <strong>Content</strong> The present dataset is related to a study aiming to identify the best method to perform multi-tissue classification from digital histological images. Histological images, completely anomized, come from formalin-fized paraffine-embedded sample of a patient affected by colorectal cancer. Two directories are available: “CRC_image_tiles.zip”: a zipped folder containing tiles (n=5984) annotated by a pathologist, grouped in 7 subdirectories, each of them representing a class ( 150 * 150 px). “Macenko_normalized_CRC_image_tiles.zip”: Macenko-normalized tiles (n=5984) annotated by a pathologist, grouped in 7 subdirectories, each of them representing a class ( 150 * 150 px). <strong>Ethical Statement</strong> The study has been funded by “Tecnopolo per la Medicina di Precisione (CUP B84I18000540002)”. The institutional Ethic Committee approved the study (Prot n. 780/CE). <strong>Info and Data Usage </strong> For further details concerning the aforementioned dataset, refer to the paper below. Please cite the following articles if you need this dataset for your research. Altini N. et al. (2021) Multi-class Tissue Classification in Colorectal Cancer with Handcrafted and Deep Features. In: Huang DS., Jo KH., Li J., Gribova V., Bevilacqua V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science, vol 12836. Springer, Cham. https://doi.org/10.1007/978-3-030-84522-3_42 Altini, N., Marvulli, T. M., Zito, F. A., Caputo, M., Tommasi, S., Azzariti, A., ... & Bevilacqua, V. (2023). The Role of Unpaired Image-to-Image Translation for Stain Color Normalization in Colorectal Cancer Histology Classification. <em>Computer Methods and Programs in Biomedicine</em>, 107511. <br> https://doi.org/10.1016/j.cmpb.2023.107511
description: {"references": ["M. Macenko et al., \"A method for normalizing histology slides for quantitative analysis,\" 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009, pp. 1107-1110, doi: 10.1109/ISBI.2009.5193250."]}
DOI: 10.5281/zenodo.4785130
title: Pathologist's Annotated Image Tiles for Multi-Class Tissue Classification in Colorectal Cancer

description: <strong>Content</strong> The present dataset is related to a study aiming to identify the best method to perform multi-tissue classification from digital histological images. Histological images, completely anomized, come from formalin-fized paraffine-embedded sample of a patient affected by colorectal cancer. Two directories are available: “CRC_image_tiles.zip”: a zipped folder containing tiles (n=5984) annotated by a pathologist, grouped in 7 subdirectories, each of them representing a class ( 150 * 150 px). “Macenko_normalized_CRC_image_tiles.zip”: Macenko-normalized tiles (n=5984) annotated by a pathologist, grouped in 7 subdirectories, each of them representing a class ( 150 * 150 px). <strong>Ethical Statement</strong> The study has been funded by “Tecnopolo per la Medicina di Precisione (CUP B84I18000540002)”. The institutional Ethic Committee approved the study (Prot n. 780/CE). <strong>Info and Data Usage </strong> For further details concerning the aforementioned dataset, refer to the paper below. Please cite the following articles if you need this dataset for your research. Altini N. et al. (2021) Multi-class Tissue Classification in Colorectal Cancer with Handcrafted and Deep Features. In: Huang DS., Jo KH., Li J., Gribova V., Bevilacqua V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science, vol 12836. Springer, Cham. https://doi.org/10.1007/978-3-030-84522-3_42 Altini, N., Marvulli, T. M., Zito, F. A., Caputo, M., Tommasi, S., Azzariti, A., ... & Bevilacqua, V. (2023). The Role of Unpaired Image-to-Image Translation for Stain Color Normalization in Colorectal Cancer Histology Classification. <em>Computer Methods and Programs in Biomedicine</em>, 107511. <br> https://doi.org/10.1016/j.cmpb.2023.107511
description: {"references": ["M. Macenko et al., \"A method for normalizing histology slides for quantitative analysis,\" 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009, pp. 1107-1110, doi: 10.1109/ISBI.2009.5193250."]}
DOI: 10.5281/zenodo.7109754
title: Colorectal Cancer Histology Image Tiles for Tissue Multi-class Classification

description: <strong>Content</strong> The present dataset is linked to a research aimed at discovering the best normalization pipeline and classification model for colorectal cancer multi-class tissue classification.<br> The 15,856 histological image tiles are completely anomized and are extracted from 10 formalin-fized paraffine-embedded samples of patients affected by colorectal cancer. The materials are inside the following zip file: “CRC_Tiles_IRCCS_ISTITUTO_TUMORI_BARI.zip”: a zipped folder containing tiles (n=15,856) annotated by a pathologist, grouped in 6 subdirectories, each of them representing a class. Tiles are of size 224 x 224 px, taken at a resolution of 0.5 μm/px.<br> <br> <strong>Ethical Statement</strong> The study has been funded by “Tecnopolo per la Medicina di Precisione (CUP B84I18000540002)”. The institutional Ethic Committee approved the study (Prot n. 780/CE). <br> <strong>Related Datasets and Works</strong><br> <br> For further details concerning the aforementioned dataset, refer to the papers below. <br> Please cite the following articles if you need this dataset for your research. Altini N. et al. (2021) Multi-class Tissue Classification in Colorectal Cancer with Handcrafted and Deep Features. In: Huang DS., Jo KH., Li J., Gribova V., Bevilacqua V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science, vol 12836. Springer, Cham. <br> https://doi.org/10.1007/978-3-030-84522-3_42 Altini, N., Marvulli, T. M., Zito, F. A., Caputo, M., Tommasi, S., Azzariti, A., ... & Bevilacqua, V. (2023). The Role of Unpaired Image-to-Image Translation for Stain Color Normalization in Colorectal Cancer Histology Classification. <em>Computer Methods and Programs in Biomedicine</em>, 107511. <br> https://doi.org/10.1016/j.cmpb.2023.107511 Please also consider the dataset offered in our previous work: Altini N. et al. (2021). Pathologist's Annotated Image Tiles for Multi-Class Tissue Classification in Colorectal Cancer (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4785131
DOI: 10.5281/zenodo.5729264
title: Colorectal Cancer Histology Image Tiles for Tissue Multi-class Classification

description: <strong>Content</strong> The present dataset is linked to a research aimed at discovering the best normalization pipeline and classification model for colorectal cancer multi-class tissue classification.<br> The 15,856 histological image tiles are completely anomized and are extracted from 10 formalin-fized paraffine-embedded samples of patients affected by colorectal cancer. The materials are inside the following zip file: “CRC_Tiles_IRCCS_ISTITUTO_TUMORI_BARI.zip”: a zipped folder containing tiles (n=15,856) annotated by a pathologist, grouped in 6 subdirectories, each of them representing a class. Tiles are of size 224 x 224 px, taken at a resolution of 0.5 μm/px.<br> <br> <strong>Ethical Statement</strong> The study has been funded by “Tecnopolo per la Medicina di Precisione (CUP B84I18000540002)”. The institutional Ethic Committee approved the study (Prot n. 780/CE). <br> <strong>Related Datasets and Works</strong><br> <br> For further details concerning the aforementioned dataset, refer to the papers below. <br> Please cite the following articles if you need this dataset for your research. Altini N. et al. (2021) Multi-class Tissue Classification in Colorectal Cancer with Handcrafted and Deep Features. In: Huang DS., Jo KH., Li J., Gribova V., Bevilacqua V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science, vol 12836. Springer, Cham. <br> https://doi.org/10.1007/978-3-030-84522-3_42 Altini, N., Marvulli, T. M., Zito, F. A., Caputo, M., Tommasi, S., Azzariti, A., ... & Bevilacqua, V. (2023). The Role of Unpaired Image-to-Image Translation for Stain Color Normalization in Colorectal Cancer Histology Classification. <em>Computer Methods and Programs in Biomedicine</em>, 107511. <br> https://doi.org/10.1016/j.cmpb.2023.107511 Please also consider the dataset offered in our previous work: Altini N. et al. (2021). Pathologist's Annotated Image Tiles for Multi-Class Tissue Classification in Colorectal Cancer (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4785131
DOI: 10.34740/kaggle/dsv/5294780
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.25345/c5n58cw68
title: MassIVE MSV000091608 - GNPS6-1-23fungle simm no gungle jinyang china
title: fungle simm no gungle jinyang china fungle simm no gungle jinyang china fungle simm no gungle jinyang china fungle simm no gungle jinyang china fungle simm no gungle jinyang china
DOI: 10.6084/m9.figshare.21711491
title: TableS4.xlsx

description: <strong>Table S4.</strong> Description of the 6 genes involved in the degradation of L-cysteine or D-cysteine into pyruvate, ammonia and hydrogen sulfide
DOI: 10.6084/m9.figshare.21711491.v1
title: TableS4.xlsx

description: <strong>Table S4.</strong> Description of the 6 genes involved in the degradation of L-cysteine or D-cysteine into pyruvate, ammonia and hydrogen sulfide
DOI: 10.6084/m9.figshare.21711536
title: TableS8.xlsx

description: <strong>Table S8. </strong>HMMsearch table output of 16S rRNA amplicons of the 28 isolates that produce H2S from Figure S1 against the metagenome-assembled-genomes from the LMTS.
DOI: 10.6084/m9.figshare.21711536.v1
title: TableS8.xlsx

description: <strong>Table S8. </strong>HMMsearch table output of 16S rRNA amplicons of the 28 isolates that produce H2S from Figure S1 against the metagenome-assembled-genomes from the LMTS.
DOI: 10.6084/m9.figshare.21711494
title: TableS5.xlsx

description: <strong>Table S5.</strong> Detailed list of all potential enzymatic activities of genes in the cysteine degradation and biosynthesis pathways.
DOI: 10.6084/m9.figshare.21711494.v1
title: TableS5.xlsx

description: <strong>Table S5.</strong> Detailed list of all potential enzymatic activities of genes in the cysteine degradation and biosynthesis pathways.
DOI: 10.6084/m9.figshare.21711509
title: TableS2.xlsx

description: <strong>Table S2.</strong> Quantitative measurement of H2S over time in the 3 isolates
DOI: 10.6084/m9.figshare.21711509.v1
title: TableS2.xlsx

description: <strong>Table S2.</strong> Quantitative measurement of H2S over time in the 3 isolates
DOI: 10.6084/m9.figshare.21711551
title: TableS12.xlsx

description: <strong>Table S12.</strong> MAG characteristics binned from Lake Mendota Time Seriese.
DOI: 10.6084/m9.figshare.21711551.v1
title: TableS12.xlsx

description: <strong>Table S12.</strong> MAG characteristics binned from Lake Mendota Time Seriese.
DOI: 10.6084/m9.figshare.21711527
title: TableS10.xlsx

description: <strong>Table S10. </strong>Cysteine hits the metagenomic assemblies.
DOI: 10.6084/m9.figshare.21711527.v1
title: TableS10.xlsx

description: <strong>Table S10. </strong>Cysteine hits the metagenomic assemblies.
DOI: 10.34740/kaggle/dsv/5295142
title: COVID Vaccination in World (updated daily)
DOI: 10.6084/m9.figshare.21711488
title: TableS1.xls

description: <strong>Table S1.</strong> Description of all initial isolates used in screening
DOI: 10.6084/m9.figshare.21711488.v1
title: TableS1.xls

description: <strong>Table S1.</strong> Description of all initial isolates used in screening
DOI: 10.6084/m9.figshare.21711548
title: TableS9.xlsx

description: <strong>Table S9.</strong> HMMsearch table output of 16S rRNA amplicons of the 28 isolates that produce H2S from Figure 1 against the SSU (16S small ribosomal subunit) of the metagenome-assembled-genomes from the Lake Mendota Time Series.
DOI: 10.6084/m9.figshare.21711548.v1
title: TableS9.xlsx

description: <strong>Table S9.</strong> HMMsearch table output of 16S rRNA amplicons of the 28 isolates that produce H2S from Figure 1 against the SSU (16S small ribosomal subunit) of the metagenome-assembled-genomes from the Lake Mendota Time Series.
DOI: 10.6084/m9.figshare.21711500
title: TableS6.xlsx

description: <strong>Table S6.</strong> METABOLIC table: Full genomic content of the 3 isolates
DOI: 10.6084/m9.figshare.21711497
title: TableS3.xlsx

description: <strong>Table S3. </strong>Quantitative measurement of cystine over time in the 3 isolates.
DOI: 10.6084/m9.figshare.21711497.v1
title: TableS3.xlsx

description: <strong>Table S3. </strong>Quantitative measurement of cystine over time in the 3 isolates.
DOI: 10.6084/m9.figshare.21711545
title: TableS11.xlsx

description: <strong>Table S11.</strong> Cysteine hits to metagenome-assembled-genomes (MAGs)
DOI: 10.6084/m9.figshare.21711545.v1
title: TableS11.xlsx

description: <strong>Table S11.</strong> Cysteine hits to metagenome-assembled-genomes (MAGs)
DOI: 10.6084/m9.figshare.21711530
title: TableS7.xlsx

description: <strong>Table S7.</strong> HMMsearch table output of 16S rRNA amplicons of the 28 isolates that produce H2S from Figure S1 against the Lake Mendota Time Series assembly.
DOI: 10.6084/m9.figshare.21711530.v1
title: TableS7.xlsx

description: <strong>Table S7.</strong> HMMsearch table output of 16S rRNA amplicons of the 28 isolates that produce H2S from Figure S1 against the Lake Mendota Time Series assembly.
DOI: 10.21942/uva.22492519
title: Data Choh and Janssen Funct Ecol 2023

description: Data of Choh and JanssenFunct Ecol 2023
DOI: 10.21942/uva.22492519.v1
title: Data Choh and Janssen Funct Ecol 2023

description: Data of Choh and JanssenFunct Ecol 2023
DOI: 10.6084/m9.figshare.16989109.v5
title: Data & code

description: Data
DOI: 10.5281/zenodo.7792932
title: poicephalus_flavifrons_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.7792931
title: poicephalus_flavifrons_zanfona_1.fasta.gz

description: .
DOI: 10.6084/m9.figshare.22492534
title: vocabulary

description: Participants engaged in Comprehensible Output (CO) and Language Production (LP) tasks, and their word recognition and recall scores were assessed. A General Linear Model (GLM) analysis was utilized, taking into account both within-subjects factor scores and between-subjects task types.
DOI: 10.6084/m9.figshare.22492534.v1
title: vocabulary

description: Participants engaged in Comprehensible Output (CO) and Language Production (LP) tasks, and their word recognition and recall scores were assessed. A General Linear Model (GLM) analysis was utilized, taking into account both within-subjects factor scores and between-subjects task types.
DOI: 10.5519/qd.9u9is1lw
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (272 records)
DOI: 10.5281/zenodo.7792948
title: Referências da Primeira Versão da OntoTurnover

description: Base conceitual usada na revisão bibliográfica utilizada na primeira versão da OntoTurnover
DOI: 10.5281/zenodo.7792947
title: Referências da Primeira Versão da OntoTurnover

description: Base conceitual usada na revisão bibliográfica utilizada na primeira versão da OntoTurnover
DOI: 10.6084/m9.figshare.22492525
title: Data Set Therapeutic Hypothermia Using HBN-1

description: <strong>Therapeutic Hypothermia Using HBN-1 </strong>
DOI: 10.6084/m9.figshare.22492525.v1
title: Data Set Therapeutic Hypothermia Using HBN-1

description: <strong>Therapeutic Hypothermia Using HBN-1 </strong>
DOI: 10.6084/m9.figshare.17005681
title: ALIGNN models on JARVIS-DFT dataset

description: ALIGNN models on JARVIS-DFT datasethttps://github.com/usnistgov/alignn<br>
DOI: 10.6084/m9.figshare.17005681.v5
title: ALIGNN models on JARVIS-DFT dataset

description: ALIGNN models on JARVIS-DFT datasethttps://github.com/usnistgov/alignn<br>
DOI: 10.5281/zenodo.7792981
title: archaeomysis_grebnitzkii_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.7792980
title: archaeomysis_grebnitzkii_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.7791582
title: TuReV Corpus

description: <strong>Tu</strong>nisian <strong>Rev</strong>olution: Corpus of online news on the Tunisian Revolution extracted from France 24.com
DOI: 10.5281/zenodo.7791581
title: TuReV Corpus

description: <strong>Tu</strong>nisian <strong>Rev</strong>olution: Corpus of online news on the Tunisian Revolution extracted from France 24.com
DOI: 10.21227/5q6q-c107
title: LORA_RFFI_DATASET_DIFFERENT_SPREADING_FACTORS

description: This project builds a length-versatile and noise-robust LoRa radio frequency fingerprint identification (RFFI) system. The LoRa signals are collected from 10 commercial-off-the-shelf LoRa devices, with the spreading factor (SF) set to 7, 8, 9, respectively. The packet preamble part and device labels are provided. More details can be found at https://github.com/gxhen/TIFS_2023. Please cite the paper 'Towards length-versatile and noise-robust radio frequency fingerprint identification', IEEE Trans. Inf. Forensics Security (TIFS), 2023.
DOI: 10.5281/zenodo.7793007
title: archaeomysis_grebnitzkii_decontaminated_gx.fasta.gx

description: .
DOI: 10.5281/zenodo.7793006
title: archaeomysis_grebnitzkii_decontaminated_gx.fasta.gx

description: .