cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.15051825.v1
title: Identification of long non-coding RNA biomarkers and signature scoring, with competing endogenous RNA networks- targeted drug candidates for recurrent implantation failure

description: Recurrent implantation failure (RIF) remains a source of frustration and presents challenges to clinicians in the practice of assisted reproductive technology (ART). Long non-coding RNAs (lncRNAs) are increasingly recognised as potential biomarkers in various diseases. In this study, eight differentially expressed lncRNAs (LINC00645, LINC00844, LINC02349, AC010975.1, AC022034.1, AC096719.1, AC104072.1 and DLGAP1-AS3) to distinguish RIF from fertile women were identified by RobustRankAggreg (RRA). A two-lncRNA signature for predicting RIF was established by least absolute shrinkage and selection operator (LASSO) regression, with accuracy confirmed by receiver operating characteristic (ROC) curves. After lncRNA-microRNA-mRNA regulatory networks were established by Cytoscape 3.7.2, Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analyses were performed, suggesting that the lncRNA-miRNA-mRNA regulatory networks were associated with biological processes involved in endometrial receptivity. Finally, three putative drugs (miconazole, terfenadine and STOCK1N-35215) for RIF were predicted by a Connectivity Map. In conclusion, we identified eight lncRNA biomarkers and a two-lncRNA signature for predicting RIF, as well as proposing three candidate drugs against RIF by targeting the ceRNA networks.
DOI: 10.25403/upresearchdata.22226776
title: Professional development framework (PD4PCT) for teachers to teach computational thinking

description: This is dataset for an interpretive study where data was collected through a literature review, pre- and postquestionnaires, semi-structured interviews as well as self-reporting journals. The study is aimed at giving an overview framework of the components to be considered when designing effective professional development for teachers to teach computational thinking.
DOI: 10.25403/upresearchdata.22226776.v1
title: Professional development framework (PD4PCT) for teachers to teach computational thinking

description: This is dataset for an interpretive study where data was collected through a literature review, pre- and postquestionnaires, semi-structured interviews as well as self-reporting journals. The study is aimed at giving an overview framework of the components to be considered when designing effective professional development for teachers to teach computational thinking.
DOI: 10.6084/m9.figshare.22262221
title: Raw data.7z

description: Raw Data for Clear Cell Renal Carcinoma
DOI: 10.6084/m9.figshare.22262221.v1
title: Raw data.7z

description: Raw Data for Clear Cell Renal Carcinoma
DOI: 10.5519/qd.5avxzg02
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.7670360
title: Machine Learning Models for Surface Wave Dispersion Curve Inversion using Mixture Density Networks

description: Machine learning (ML) approach for dispersion curve inversion using mixture density networks (MDN) based on Keil and Wassermann (2023). The ML approach presented here allows the simultaneous estimation of layer numbers, layer depth and a complete probability distribution of the S-wave velocity structure in the upper 100 m. This is achieved by a two-step ML approach, where 1) a regular NN classifies the number of layers within the upper 100 m of the subsurface and 2) individual trained mixture density networks output the depth estimates together with a fully probabilistic solution of the S-wave velocity structure. We trained the model to distinguish structures with 2 - 7 subsurface layers. The trained classification NN and the individual MDNs are located in the folder ./trained_models.<br> With the jupyter notebook Prediction.ipynb the dispersion curve inversion can be performed using the already trained ML models.<br> With the jupyter notebooks Training-MDN.ipynb and Training-classification.ipynb the models can be trained on new data.<br> The code for the set-up of the MDN is based on Earp et al. (2020). More details and updates on the code can be found on: https://github.com/SabrinaKeil/MDN_Inversion
description: {"references": ["Earp, S., Curtis, A., Zhang, X., & Hansteen, F. (2020). Probabilistic neural network tomography across Grane field (North Sea) from surface wave dispersion data. Geophysical Journal International, 223(3), 1741-1757.", "Keil, S. and Wassermann, J. (2023). Dispersion curve inversion using mixture density networks. Geophysical Journal International.(Submitted)"]}
DOI: 10.5281/zenodo.7670359
title: Machine Learning Models for Surface Wave Dispersion Curve Inversion using Mixture Density Networks

description: Machine learning (ML) approach for dispersion curve inversion using mixture density networks (MDN) based on Keil and Wassermann (2023). The ML approach presented here allows the simultaneous estimation of layer numbers, layer depth and a complete probability distribution of the S-wave velocity structure in the upper 100 m. This is achieved by a two-step ML approach, where 1) a regular NN classifies the number of layers within the upper 100 m of the subsurface and 2) individual trained mixture density networks output the depth estimates together with a fully probabilistic solution of the S-wave velocity structure. We trained the model to distinguish structures with 2 - 7 subsurface layers. The trained classification NN and the individual MDNs are located in the folder ./trained_models.<br> With the jupyter notebook Prediction.ipynb the dispersion curve inversion can be performed using the already trained ML models.<br> With the jupyter notebooks Training-MDN.ipynb and Training-classification.ipynb the models can be trained on new data.<br> The code for the set-up of the MDN is based on Earp et al. (2020). More details and updates on the code can be found on: https://github.com/SabrinaKeil/MDN_Inversion
description: {"references": ["Earp, S., Curtis, A., Zhang, X., & Hansteen, F. (2020). Probabilistic neural network tomography across Grane field (North Sea) from surface wave dispersion data. Geophysical Journal International, 223(3), 1741-1757.", "Keil, S. and Wassermann, J. (2023). Dispersion curve inversion using mixture density networks. Geophysical Journal International.(Submitted)"]}
DOI: 10.12751/g-node.vz6mr1
title: Effects of heterozygous deletion of autism-related gene Cullin-3 in mice

description: Cul3 heterozygous deletion impairs spatial object recognition memory, alters cytoskeletal organization proteins, but does not cause major hippocampal neuronal morphology, functional, or behavioral abnormalities in adult global Cul3 heterozygous mice
description: IsSupplementTo: Qiang-qiang Xia, ... , Craig M. Powell: Effects of Heterozygous Deletion of Autism-related Gene Cullin-3 in Mice. PLOS ONE.
DOI: 10.17632/md7f7c5pzn.1
title: ENRICH: multi-purposE dataset for beNchmaRking In Computer vision and pHotogrammetry

description: A new synthetic, multi-purpose dataset - called ENRICH - for testing photogrammetric and computer vision algorithms. Compared to existing datasets, ENRICH offers higher resolution images also rendered with different lighting conditions, camera orientation, scales, and field of view. Specifically, ENRICH is composed of three sub-datasets: ENRICH-Aerial, ENRICH-Square, and ENRICH-Statue, each exhibiting different characteristics. The proposed dataset is useful for several photogrammetry and computer vision-related tasks, such as the evaluation of hand-crafted and deep learning-based local features, effects of ground control points (GCPs) configuration on the 3D accuracy, and monocular depth estimation.


Each zip file in the root is relative to a specific dataset:

- ENRICH-Aerial, is an aerial image block of the city of Launceston, Australia. The acquisition is performed by simulating a typical oblique aerial camera with five views (nadir and four oblique views).
- ENRICH-Square, is a ground-level dataset of a square captured by four cameras, each one moving on a different path with different focal length, orientation, and lighting conditions.
- ENRICH-Statue, is a ground-level dataset portraying a statue (placed in the center of the ENRICH-Square scene), acquired using four cameras moving on different paths with different focal lengths, orientations, and lighting conditions.

Be sure to check the README file in the dataset root for information on folder structure and file contents. Please refer to the related paper (https://doi.org/10.1016/j.isprsjprs.2023.03.002) for information about the generation method and the purpose of ENRICH.
DOI: 10.6084/m9.figshare.22262338
title: Bibliographic Dataset e-goverment from Scopus

description: Search query: TITLE-ABS-KEY ( <em>"e-government"</em> OR <em>"electronic government"</em> AND <em>"public administration"</em> OR <em>"public management"</em> OR <em>"public policy"</em> OR <em>"information technology"</em> OR <em>"digital government"</em> OR <em>"open government"</em> OR <em>"smart government"</em> OR <em>"government innovation"</em> OR <em>"government transformation"</em> ) AND ( LIMIT-TO ( LANGUAGE , <em>"English"</em> ) ) AND ( LIMIT-TO ( SRCTYPE , <em>"j"</em> ) )
DOI: 10.6084/m9.figshare.22262338.v1
title: Bibliographic Dataset e-goverment from Scopus

description: Search query: TITLE-ABS-KEY ( <em>"e-government"</em> OR <em>"electronic government"</em> AND <em>"public administration"</em> OR <em>"public management"</em> OR <em>"public policy"</em> OR <em>"information technology"</em> OR <em>"digital government"</em> OR <em>"open government"</em> OR <em>"smart government"</em> OR <em>"government innovation"</em> OR <em>"government transformation"</em> ) AND ( LIMIT-TO ( LANGUAGE , <em>"English"</em> ) ) AND ( LIMIT-TO ( SRCTYPE , <em>"j"</em> ) )
DOI: 10.25403/upresearchdata.22212130
title: Manager's perspective of service delivery in nuclear medicine

description: This research instrument contains the interview questions that were used during data collection, the analysed data and the different figures and tables in the research paper. From figure 5 there are analysis of the study. The researchers outlined the age and geographical distribution of participants.Other figures include the different themes and categories that were outlined in the study. The table represents the different themes, categories, subcategories and codes.
DOI: 10.25403/upresearchdata.22212130.v1
title: Manager's perspective of service delivery in nuclear medicine

description: This research instrument contains the interview questions that were used during data collection, the analysed data and the different figures and tables in the research paper. From figure 5 there are analysis of the study. The researchers outlined the age and geographical distribution of participants.Other figures include the different themes and categories that were outlined in the study. The table represents the different themes, categories, subcategories and codes.
DOI: 10.6084/m9.figshare.20326272
title: Neogen® GGP Porcine 80K SNP genotype data for Suhuai pigs

description: Neogen® GGP Porcine 80K array data for 517 Suhaui pigs
DOI: 10.5258/soton/d2517
title: Mathematica Notebooks supporting the publication "The Aharonov-Anandan phase and geometric double-quantum excitation in strongly-coupled nuclear spin pairs".

description: The data set consists of a collection of Mathematica supporting some of the calculations found in "The Aharonov-Anandan phase and geometric double-quantum excitation in strongly-coupled nuclear spin pairs". published in the Journal of Chemical Physics
DOI: 10.5281/zenodo.7569104
title: Data: More than 1000 genotypes are required to derive robust relationships between yield, yield stability and physiological parameters: a computational study on wheat crop

description: APSIM-Wheat <strong>(</strong>www.apsim.info<strong>)</strong> was used to simulate a data set (for details, see Casadebaig<em> et al.</em>, 2016) with 9100 virtual genotypes (<em>N</em><sub>gen</sub>= 9100) grown under 9000 environments (<em>N</em><sub>env</sub>=9000). In short, virtual genotypes were created by varying the value of 90 independent physiological parameters in a range of ±20% from the reference cultivar <em>Hartog</em>. Environments in the dataset contain historical climate data of 125 years (1889-2013) in four locations (Emerald, Narrabri, Yanco and Merredin) in Australia, in combination with two CO<sub><sup>2</sup></sub> levels (380 and 555 ppm), three nitrogen levels (low: 50%, control: 100% and high fertilization: 100% plus 50 kg‧ha<sup>-1</sup>) and three sowing dates (early, control and late).
description: Links to publications that cite or use the data: 1. Casadebaig et al., 2016 https://doi.org/10.1371/journal.pone.0146385 2. Wang et al., 2023 https://doi.org/10.1007/s00122-023-04264-7 Information about funding sources that supported the simulation/analysis of the data: * French National Research Agency (ANR) - Projectnumber ANR-11-BTBR-0005 ; Granted to Casadebaig * German Research Foundation (DFG) - Projectnumber 419973621 ; Granted to Chen
DOI: 10.11583/dtu.21757241
title: Raw data for the paper "Generation of robust bispecific antibodies through fusion of single-domain antibodies on IgG scaffolds: a comprehensive comparison of formats"

description: The data contains raw files for the paper " Generation of robust bispecific antibodies through fusion of single-domain antibodies on IgG scaffolds: a comprehensive comparison of formats". The types of files are excel files that contain the actual data and then .txt log files for guiding the data analysis using the python script available through https://github.com/andreasvisbech/Plotly_data_analysis/releases/tag/3.3.0
DOI: 10.5281/zenodo.7333227
title: Hominid Palaeoproteomic Reference Dataset

description: This dataset contains the 'Hominid Palaeoproteomic Reference Dataset'. We used PaleoProPhyler ( https://github.com/johnpatramanis/Proteomic_Pipeline ) to generate a palaeoproteomic reference dataset of protein sequences from ancient and present-day hominids. Using the first two modules of PaleoProPhyler, we translated 195 publicly available whole genomes from extant hominid groups. Details on the processing of the sequences can be found in the supplementary materials of PaleoProPhyler (https://github.com/johnpatramanis/Proteomic_Pipeline/blob/main/GitHub_Tutorial/Supplementary.pdf). We also translated 8 ancient hominin genomes from VCF files, including those of several Neanderthals and one Denisovan. Since the dataset is tailored for palaeoproteomic analyses, we chose to translate proteins that have previously been reported as present in either teeth or bone tissue. We compiled a list of 1,696 proteins from previous works and successfully translated 1,543 of them. For each protein, both the canonical and all alternative protein coding isoforms were translated, leading to a total of 10,058 protein sequences for each individual in the dataset.
DOI: 10.5281/zenodo.7404727
title: Hominid Palaeoproteomic Reference Dataset

description: This dataset contains the 'Hominid Palaeoproteomic Reference Dataset'. We used PaleoProPhyler ( https://github.com/johnpatramanis/Proteomic_Pipeline ) to generate a palaeoproteomic reference dataset of protein sequences from ancient and present-day hominids. Using the first two modules of PaleoProPhyler, we translated 195 publicly available whole genomes from extant hominid groups. Details on the processing of the sequences can be found in the supplementary materials of PaleoProPhyler (https://github.com/johnpatramanis/Proteomic_Pipeline/blob/main/GitHub_Tutorial/Supplementary.pdf). We also translated 8 ancient hominin genomes from VCF files, including those of several Neanderthals and one Denisovan. Since the dataset is tailored for palaeoproteomic analyses, we chose to translate proteins that have previously been reported as present in either teeth or bone tissue. We compiled a list of 1,696 proteins from previous works and successfully translated 1,543 of them. For each protein, both the canonical and all alternative protein coding isoforms were translated, leading to a total of 10,058 protein sequences for each individual in the dataset.
DOI: 10.5281/zenodo.7404731
title: Hominid Palaeoproteomic Reference Dataset

description: This dataset contains the 'Hominid Palaeoproteomic Reference Dataset'. We used PaleoProPhyler ( https://github.com/johnpatramanis/Proteomic_Pipeline ) to generate a palaeoproteomic reference dataset of protein sequences from ancient and present-day hominids. Using the first two modules of PaleoProPhyler, we translated 195 publicly available whole genomes from extant hominid groups. Details on the processing of the sequences can be found in the supplementary materials of PaleoProPhyler (https://github.com/johnpatramanis/Proteomic_Pipeline/blob/main/GitHub_Tutorial/Supplementary.pdf). We also translated 8 ancient hominin genomes from VCF files, including those of several Neanderthals and one Denisovan. Since the dataset is tailored for palaeoproteomic analyses, we chose to translate proteins that have previously been reported as present in either teeth or bone tissue. We compiled a list of 1,696 proteins from previous works and successfully translated 1,543 of them. For each protein, both the canonical and all alternative protein coding isoforms were translated, leading to a total of 10,058 protein sequences for each individual in the dataset.
DOI: 10.5281/zenodo.7404802
title: Hominid Palaeoproteomic Reference Dataset

description: This dataset contains the 'Hominid Palaeoproteomic Reference Dataset'. We used PaleoProPhyler ( https://github.com/johnpatramanis/Proteomic_Pipeline ) to generate a palaeoproteomic reference dataset of protein sequences from ancient and present-day hominids. Using the first two modules of PaleoProPhyler, we translated 195 publicly available whole genomes from extant hominid groups. Details on the processing of the sequences can be found in the supplementary materials of PaleoProPhyler ( https://github.com/johnpatramanis/Proteomic_Pipeline/blob/main/GitHub_Tutorial/Supplementary.pdf ). We also translated 8 ancient hominin genomes from VCF files, including those of several Neanderthals and one Denisovan. Since the dataset is tailored for palaeoproteomic analyses, we chose to translate proteins that have previously been reported as present in either teeth or bone tissue. We compiled a list of 1,696 proteins from previous works and successfully translated 1,543 of them. For each protein, both the canonical and all alternative protein coding isoforms were translated, leading to a total of 10,058 protein sequences for each individual in the dataset. Content: The zipped file contains 4 files, two fasta files as well as two additional folders: - PalaeoProPhyler_Publication_Data_for_Tree.fa contains all of the sequences used to generate the phylogenetic tree presented at PalaeoProPhylers manuscript - ALL_PROT_REFERENCE.fa contains all of the sequences generated as part of the Hominid Palaeoproteomic Reference Dataset described above - PER_PROTEIN is a folder containing one fasta file for each protein within the Hominid Palaeoproteomic Reference Dataset, each protein fasta file has the sequences of all individuals for that particular protein - PER_SAMPLE is a folder containing one fasta file for each sample/individual within the Hominid Palaeoproteomic Reference Dataset, each sample fasta file has the sequences of all proteins for that particular sample.
DOI: 10.5281/zenodo.7405283
title: Hominid Palaeoproteomic Reference Dataset

description: This dataset contains the 'Hominid Palaeoproteomic Reference Dataset'. We used PaleoProPhyler ( https://github.com/johnpatramanis/Proteomic_Pipeline ) to generate a palaeoproteomic reference dataset of protein sequences from ancient and present-day hominids. Using the first two modules of PaleoProPhyler, we translated 195 publicly available whole genomes from extant hominid groups. Details on the processing of the sequences can be found in the supplementary materials of PaleoProPhyler ( https://github.com/johnpatramanis/Proteomic_Pipeline/blob/main/GitHub_Tutorial/Supplementary.pdf ). We also translated 8 ancient hominin genomes from VCF files, including those of several Neanderthals and one Denisovan. Since the dataset is tailored for palaeoproteomic analyses, we chose to translate proteins that have previously been reported as present in either teeth or bone tissue. We compiled a list of 1,696 proteins from previous works and successfully translated 1,543 of them. For each protein, both the canonical and all alternative protein coding isoforms were translated, leading to a total of 10,058 protein sequences for each individual in the dataset. Content: The zipped file contains 4 files, two fasta files as well as two additional folders: - PalaeoProPhyler_Publication_Data_for_Tree.fa contains all of the sequences used to generate the phylogenetic tree presented at PalaeoProPhylers manuscript - ALL_PROT_REFERENCE.fa contains all of the sequences generated as part of the Hominid Palaeoproteomic Reference Dataset described above - PER_PROTEIN is a folder containing one fasta file for each protein within the Hominid Palaeoproteomic Reference Dataset, each protein fasta file has the sequences of all individuals for that particular protein - PER_SAMPLE is a folder containing one fasta file for each sample/individual within the Hominid Palaeoproteomic Reference Dataset, each sample fasta file has the sequences of all proteins for that particular sample.
DOI: 10.5281/zenodo.7553844
title: Hominid Palaeoproteomic Reference Dataset

description: This dataset contains the 'Hominid Palaeoproteomic Reference Dataset'. We used PaleoProPhyler ( https://github.com/johnpatramanis/Proteomic_Pipeline ) to generate a palaeoproteomic reference dataset of protein sequences from ancient and present-day hominids. Using the first two modules of PaleoProPhyler, we translated 195 publicly available whole genomes from extant hominid groups. Details on the processing of the sequences can be found in the supplementary materials of PaleoProPhyler ( https://github.com/johnpatramanis/Proteomic_Pipeline/blob/main/GitHub_Tutorial/Supplementary.pdf ). We also translated 8 ancient hominin genomes from VCF files, including those of several Neanderthals and one Denisovan. Since the dataset is tailored for palaeoproteomic analyses, we chose to translate proteins that have previously been reported as present in either teeth or bone tissue. We compiled a list of 1,696 proteins from previous works and successfully translated 1,543 of them. For each protein, both the canonical and all alternative protein coding isoforms were translated, leading to a total of 10,058 protein sequences for each individual in the dataset. Content: The zipped file contains 4 files, two fasta files as well as two additional folders: - PalaeoProPhyler_Publication_Data_for_Tree.fa contains all of the sequences used to generate the phylogenetic tree presented at PalaeoProPhylers manuscript - ALL_PROT_REFERENCE.fa contains all of the sequences generated as part of the Hominid Palaeoproteomic Reference Dataset described above - PER_PROTEIN is a folder containing one fasta file for each protein within the Hominid Palaeoproteomic Reference Dataset, each protein fasta file has the sequences of all individuals for that particular protein - PER_SAMPLE is a folder containing one fasta file for each sample/individual within the Hominid Palaeoproteomic Reference Dataset, each sample fasta file has the sequences of all proteins for that particular sample. Important Note: The dataset is still not fully generated! However around half of the total number of samples are ready to go and in their appropriate folder (~/PER_SAMPLE /).