cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24769827
title: Protein sequences-alignments-treefile for 'Diversified molecular adaptations of inorganic nitrogen assimilation and signaling machineries in plants'.

description: Protein sequences-alignments-treefile for 'Diversified molecular adaptations of inorganic nitrogen assimilation and signaling machineries in plants'.
DOI: 10.5281/zenodo.10374530
title: Hydraulic Processes based on Managed Realignment

description: As a part of the primary requirement for the Data Science task, it has been written a document under name of Data Management Plan. For this purpose we used a tool provided by TU Wien - DMP Tool. The DMP has been build in a way to comply with Science Europe guidlines and hence following FAIR principles.

This is s Verison 1 of DMP and the updates will be performed through the research if that's required.
DOI: 10.5281/zenodo.10374529
title: Hydraulic Processes based on Managed Realignment

description: As a part of the primary requirement for the Data Science task, it has been written a document under name of Data Management Plan. For this purpose we used a tool provided by TU Wien - DMP Tool. The DMP has been build in a way to comply with Science Europe guidlines and hence following FAIR principles.

This is s Verison 1 of DMP and the updates will be performed through the research if that's required.
DOI: 10.25625/ckly7x
title: Canopy Height Models

description: Canopy Height Models derived from airborne LiDAR data acquired over the PTPN 6 estate, Batanghari regency, Harapan rainforest protected area, and Sarolangun regency, in Jambi, Sumatra, Indonesia. Pixel size: 1m. Geographic Coordinate System: EPSG 32748.
DOI: 10.5281/zenodo.10007382
title: Analysing state-backed propaganda websites: a new dataset and linguistic study (public dataset)

description: This is the dataset accompanying the EMNLP 2023 paper "Analysing state-backed propaganda websites: a new dataset and linguistic study".

For copyright and liability reasons, we do not publicly distribute the complete dataset. Instead, we provide the software used to create the dataset (DOI: 10.5281/zenodo.10008086) and a list containing the URLs of all the posts in the full dataset (this repository).

To reconstruct our dataset: use the software to extract the sites, then filter the posts to the corresponding URL list. Please note that some posts may no longer be available or may have been modified.

If you are researching disinformation, propaganda, or a relevant field: please contact the authors, we may be able to provide you with the original dataset.
DOI: 10.5281/zenodo.10007383
title: Analysing state-backed propaganda websites: a new dataset and linguistic study (public dataset)

description: This is the dataset accompanying the EMNLP 2023 paper "Analysing state-backed propaganda websites: a new dataset and linguistic study".

For copyright and liability reasons, we do not publicly distribute the complete dataset. Instead, we provide the software used to create the dataset (DOI: 10.5281/zenodo.10008086) and a list containing the URLs of all the posts in the full dataset (this repository).

To reconstruct our dataset: use the software to extract the sites, then filter the posts to the corresponding URL list. Please note that some posts may no longer be available or may have been modified.

If you are researching disinformation, propaganda, or a relevant field: please contact the authors, we may be able to provide you with the original dataset.
DOI: 10.6084/m9.figshare.24168492.v2
title: eBird preprocessed observer-oriented dataset

description: File information:ebird_oboriented.rda is the raw dataset we provided.Other R data were used to visualize. See https://github.com/XingJH-1998/eBird-Observer-oriented via Supplementary-dataset&visualization.RThe eBird dataset stands out as one of the premier citizen science data sources, greatly aiding numerous research endeavours. Although the existing eBird Application Programming Interface (API) provides useful access to various aspects of eBird dataset, it does not provide a dataset based on observers. In this study, we introduce a restructured eBird theme dataset which centers on the aggregation and summary of each observer’s monthly observation to give a profile, as well as categorize each observation into a binary concept based on the observer’s travelling status. Our dataset spans from 2018 to 2022 including 10,447 species, 576,228 observers from 250 countries and regions. This dataset offers a fresh perspective upon the eBird dataset, enabling insights into observer behaviour and preference, thereby facilitating further exploration of this valuable resource.
DOI: 10.5281/zenodo.10374874
title: who made that decision - dataset
DOI: 10.5281/zenodo.10374873
title: who made that decision - dataset
DOI: 10.12770/6e3935e2-6b5f-4449-9be9-c364bc4e67df
title: Valeurs d'évaluation 2024 de la concentration en contaminants dans les bivavles pour le D8C1 à la côte Manche/Atlantique

description: Les tableaux contiennent pour chaque contaminant de la liste de l'arrêté relatif à la définition du bon état écologique des eaux marines et aux normes méthodologiques d’évaluation:
- le seuil utilisé par substance
- la valeur d'évaluation pour les stations du réseau ROCCH-MV retenues
- le score d'évaluation (rapport entre valeur d'évaluation et seuil) pour chaque masse d'eau DCE (ME)
- le score de l'UMR côte pour chaque contaminant (moyenne quadratique des scores par ME)
DOI: 10.5281/zenodo.10374812
title: Files for MD simulation of the interaction between LCC-ICCG cutinase and PET polymer

description: 500 ns MD simulation of Cutinase adsorption onto the PET surface and cutinase in water.

MD simulations were done using Gromacs package

Force field:Charmm36
DOI: 10.5281/zenodo.10374813
title: Files for MD simulation of the interaction between LCC-ICCG cutinase and PET polymer

description: 500 ns MD simulation of Cutinase adsorption onto the PET surface and cutinase in water.

MD simulations were done using Gromacs package

Force field:Charmm36
DOI: 10.5281/zenodo.10362479
title: Results of "Curare and GenExVis: A versatile toolkit for analyzing and visualizing RNA-Seq data"

description: Even though high-throughput transcriptome sequencing is routinely performed in many laboratories, computational analysis of such data remains a cumbersome process often executed manually, hence error-prone and lacking reproducibility. For corresponding data processing, we introduce Curare, an easy-to-use yet versatile workflow builder for analyzing high-throughput RNA-Seq data focusing on differential gene expression experiments. Data analysis with Curare is customizable and subdivided into preprocessing, quality control, mapping, and downstream analysis stages, providing multiple options for each step while ensuring the reproducibility of the workflow. For a fast and straightforward exploration and visualization of differential gene expression results, we provide the gene expression visualizer software GenExVis. GenExVis can create various charts and tables from simple gene expression tables and DESeq2 results without the requirement to upload data or install software packages.
DOI: 10.5281/zenodo.10362480
title: Results of "Curare and GenExVis: A versatile toolkit for analyzing and visualizing RNA-Seq data"

description: Even though high-throughput transcriptome sequencing is routinely performed in many laboratories, computational analysis of such data remains a cumbersome process often executed manually, hence error-prone and lacking reproducibility. For corresponding data processing, we introduce Curare, an easy-to-use yet versatile workflow builder for analyzing high-throughput RNA-Seq data focusing on differential gene expression experiments. Data analysis with Curare is customizable and subdivided into preprocessing, quality control, mapping, and downstream analysis stages, providing multiple options for each step while ensuring the reproducibility of the workflow. For a fast and straightforward exploration and visualization of differential gene expression results, we provide the gene expression visualizer software GenExVis. GenExVis can create various charts and tables from simple gene expression tables and DESeq2 results without the requirement to upload data or install software packages.
DOI: 10.4121/94d7f868-5d2a-4a51-9f3d-11ab2041a127
title: Hamburg port demonstration litter map

description: A point cloud of a scene made up of the sea floor, a trash collection basket, two metal trays filled with example, preplaced litter and a preplaced example of a larger litter item - tire.A list of litter coordinates in above-mentioned scene
DOI: 10.4121/94d7f868-5d2a-4a51-9f3d-11ab2041a127.v1
title: Hamburg port demonstration litter map

description: A point cloud of a scene made up of the sea floor, a trash collection basket, two metal trays filled with example, preplaced litter and a preplaced example of a larger litter item - tire.A list of litter coordinates in above-mentioned scene
DOI: 10.5281/zenodo.10375302
title: OJ samples, Translation Results, and Evaluation

description: This dataset open source a small-scale OJ samples and its evaluation results. Please refer to README.md for details.
DOI: 10.5281/zenodo.10375303
title: OJ samples, Translation Results, and Evaluation

description: This dataset open source a small-scale OJ samples and its evaluation results. Please refer to README.md for details.
DOI: 10.5281/zenodo.10371762
title: CT-Scan der sog. Wartburg-Quinterne (ROI1: Wirbelkasten)

description: Instrument: Quinterne Herkunftsland: Deutschland? Instrumentenzettel: "Hans Ot [zu?] / Nuremberg" Datierung: 15. Jahrhundert? Aufbewahrungsort: Eisenach, Wartburg Inventarnummer: KH50
Maße: Gesamtlänge: 689 mm Breite: 229 mm
Material: Korpus: Ahorn Decke: Fichte Rosette: Pergament Griffbrett: Birnbaum/Mooreiche Anhängestifte: Messing Wirbel: Ahorn Untersattel/Steg: Mahagoni mit Elfenbeineinlage
Mikro-CT-Scan: Fraunhofer-Entwicklungszentrum Röntgentechnik in Fürth (Fraunhofer-IIS/EZRT), Leitender Wissenschaftler: Dr. Theobald Fuchs, Produktionsmonitoring: Gabriele Scholz
Foto des Untersuchungs-Aufbaus: Fraunhofer-IIS/EZRT
Technische Angaben zu den CT-Scans: Röntgenröhre: Yxlon FXE-225.99 (Verwendung des Transmissionskopfs) Detektor: Thales Pixium RF4343 Größe: 42,39 cm x 42,39 cm Pixelanzahl: 2864 x 2864 Pixelpitch: 148 μm Aufnahmeparameter: 150 kV, 270 μA Aufnahmeverfahren für den Korpus: Helix-CT
Bis zum Erscheinen des Artikels zur Neuuntersuchung der Wartburg-Quinterne von Thilo Hirsch und weiteren Co-Autor*innen ist das Material nicht zugänglich (voraussichtlich bis Mitte 2024). Danach muss zuerst das Formular «request access» ausgefüllt und abgeschickt werden. Daraufhin bekommen Sie die Aufforderung, eine Genehmigung der Wartburg-Stiftung zur Verwendung der Daten einzuholen. Sobald diese vorliegt, wird der Download-Link verschickt.
DOI: 10.5281/zenodo.10371761
title: CT-Scan der sog. Wartburg-Quinterne (ROI1: Wirbelkasten)

description: Instrument: Quinterne Herkunftsland: Deutschland? Instrumentenzettel: "Hans Ot [zu?] / Nuremberg" Datierung: 15. Jahrhundert? Aufbewahrungsort: Eisenach, Wartburg Inventarnummer: KH50
Maße: Gesamtlänge: 689 mm Breite: 229 mm
Material: Korpus: Ahorn Decke: Fichte Rosette: Pergament Griffbrett: Birnbaum/Mooreiche Anhängestifte: Messing Wirbel: Ahorn Untersattel/Steg: Mahagoni mit Elfenbeineinlage
Mikro-CT-Scan: Fraunhofer-Entwicklungszentrum Röntgentechnik in Fürth (Fraunhofer-IIS/EZRT), Leitender Wissenschaftler: Dr. Theobald Fuchs, Produktionsmonitoring: Gabriele Scholz
Foto des Untersuchungs-Aufbaus: Fraunhofer-IIS/EZRT
Technische Angaben zu den CT-Scans: Röntgenröhre: Yxlon FXE-225.99 (Verwendung des Transmissionskopfs) Detektor: Thales Pixium RF4343 Größe: 42,39 cm x 42,39 cm Pixelanzahl: 2864 x 2864 Pixelpitch: 148 μm Aufnahmeparameter: 150 kV, 270 μA Aufnahmeverfahren für den Korpus: Helix-CT
Bis zum Erscheinen des Artikels zur Neuuntersuchung der Wartburg-Quinterne von Thilo Hirsch und weiteren Co-Autor*innen ist das Material nicht zugänglich (voraussichtlich bis Mitte 2024). Danach muss zuerst das Formular «request access» ausgefüllt und abgeschickt werden. Daraufhin bekommen Sie die Aufforderung, eine Genehmigung der Wartburg-Stiftung zur Verwendung der Daten einzuholen. Sobald diese vorliegt, wird der Download-Link verschickt.
DOI: 10.6084/m9.figshare.13042016
title: Annotation data set

description: This file is supplementally data set for Maeda et al. 2021Annotations of the genes used in the paper are collected in a separate directory for each species.Contents-annotation_file.xlsxAnnotation information created in the paper-EVM.gff3 (only for sea slug data)A gff3 file containing gene model information.-gene_description.listA list of gene names used for registration with DDBJ.-Trinity.cdhit0.95.fa (Algal transcriptomic data only)De novo transcriptomic data that were assembled by Tirniy and compiled by CD-Hit.-Trinity.cdhit0.95.fa.transdecoder.gff3 (Algal transcriptomic data only)Information on the position of the genes on the assembled sequences, created by Transdecoder.<br><br>
DOI: 10.5281/zenodo.10375649
title: killing assay TLRagonists

description: Impact of TLRagonists on killing capacity of CD8 T cells
DOI: 10.5281/zenodo.10375650
title: killing assay TLRagonists

description: Impact of TLRagonists on killing capacity of CD8 T cells
DOI: 10.6084/m9.figshare.24805773
title: Supplementary Material for: Clinical characteristics of pathogenic ACAN variants and 3-year response to growth hormone treatment: real-world data

description: Introduction 
Heterozygous variants in the ACAN gene may underlie disproportionate short stature with characteristically accelerated bone age (BA) maturation and/or early-onset osteoarthritis (OA).
Methods
To describe phenotype, analyze genotype-phenotype correlations and assess the response of growth hormone (GH) treatment in children with a heterozygous ACAN variant. Thirty-six subjects (23 boys, 13 girls) with ACAN-deficiency and treated for ≥1 year with GH were identified in the Dutch National Registry of GH treatment in children.
Results
We identified 25 different heterozygous ACAN variants in 36 subjects. Median (IQR) height SDS at start of GH was -2.6 SDS (-3.2 to -2.2). Characteristic features such as disproportion, advanced BA, early-onset OA and dysmorphic features like midface hypoplasia and brachydactyly were present in the majority of children, but in ~20% no specific features were reported. Subjects with a truncating ACAN variant had a shorter height SDS compared to subjects with a non-truncating variant (-2.8 SDS and -2.1 SDS respectively, P=0.002). After 3 years of GH, height gain SDS in prepubertal children was 1.0 SDS (0.9 to 1.4). In pubertal children, height SDS remained relatively stable. 
Conclusion
The phenotype of subjects with pathogenic heterozygous ACAN variants is highly variable and genetic testing for ACAN-deficiency should be considered in any child with significant short stature, even in the absence of disproportion, specific dysmorphic features or bone-age advancement. Furthermore, children with ACAN-deficiency may benefit from GH with a modest but significant response, which is sustained during 3 years of treatment.
DOI: 10.5281/zenodo.10276480
title: Bathymetry of Bossons Lake of 26 June 2023

description: Bathymetry of the Bossons lake done the 26 June 2023.
DOI: 10.5281/zenodo.10276481
title: Bathymetry of Bossons Lake of 26 June 2023

description: Bathymetry of the Bossons lake done the 26 June 2023.
DOI: 10.5281/zenodo.10013763
title: Training and test datasets for the PredictONCO tool

description: This dataset was used for training and validating the PredictONCO web tool, supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning. The dataset consists of 1073 single-point mutants of 42 proteins, whose effect was classified as Oncogenic (509 data points) and Benign (564 data points). All mutations were annotated with a clinically verified effect and were compiled from the ClinVar and OncoKB databases. The dataset was manually curated based on the available information in other precision oncology databases (The Clinical Knowledgebase by The Jackson Laboratory, Personalized Cancer Therapy Knowledge Base by MD Anderson Cancer Center, cBioPortal, DoCM database) or in the primary literature. To create the dataset, we also removed any possible overlaps with the data points used in the PredictSNP consensus predictor and its constituents. This was implemented to avoid any test set data leakage due to using the PredictSNP score as one of the features (see below).

The entire dataset (SEQ) was further annotated by the pipeline of PredictONCO. Briefly, the following six features were calculated regardless of the structural information available: essentiality of the mutated residue (yes/no), the conservation of the position (the conservation grade and score), the domain where the mutation is located (cytoplasmic, extracellular, transmembrane, other), the PredictSNP score, and the number of essential residues in the protein. For approximately half of the data (STR: 377 and 76 oncogenic and benign data points, respectively), the structural information was available, and six more features were calculated: FoldX and Rosetta ddg_monomer scores, whether the residue is in the catalytic pocket (identification of residues forming the ligand-binding pocket was obtained from P2Rank), and the pKa changes (the minimum and maximum changes as well as the number of essential residues whose pKa was changed – all values obtained from PROPKA3). For both STR and SEQ datasets, 20% of the data was held out for testing. The data split was implemented at the position level to ensure that no position from the test data subset appears in the training data subset. 

For more details about the tool, please visit the help page or get in touch with us.

14-Dec-2023 update: the file with features PredictONCO-features.txt now includes UniProt IDs, transcripts, PDB codes, and mutations.
DOI: 10.5281/zenodo.10013764
title: Training and test datasets for the PredictONCO tool

description: This dataset was used for training and validating the PredictONCO web tool, supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning. The dataset consists of 1073 single-point mutants of 42 proteins, whose effect was classified as Oncogenic (509 data points) and Benign (564 data points). All mutations were annotated with a clinically verified effect and were compiled from the ClinVar and OncoKB databases. The dataset was manually curated based on the available information in other precision oncology databases (The Clinical Knowledgebase by The Jackson Laboratory, Personalized Cancer Therapy Knowledge Base by MD Anderson Cancer Center, cBioPortal, DoCM database) or in the primary literature. To create the dataset, we also removed any possible overlaps with the data points used in the PredictSNP consensus predictor and its constituents. This was implemented to avoid any test set data leakage due to using the PredictSNP score as one of the features (see below).
The entire dataset (SEQ) was further annotated by the pipeline of PredictONCO. Briefly, the following six features were calculated regardless of the structural information available: essentiality of the mutated residue (yes/no), the conservation of the position (the conservation grade and score), the domain where the mutation is located (cytoplasmic, extracellular, transmembrane, other), the PredictSNP score, and the number of essential residues in the protein. For approximately half of the data (STR: 377 and 76 oncogenic and benign data points, respectively), the structural information was available, and six more features were calculated: FoldX and Rosetta ddg_monomer scores, whether the residue is in the catalytic pocket (identification of residues forming the ligand-binding pocket was obtained from P2Rank), and the pKa changes (the minimum and maximum changes as well as the number of essential residues whose pKa was changed – all values obtained from PROPKA3). For both STR and SEQ datasets, 20% of the data was held out for testing. The data split was implemented at the position level to ensure that no position from the test data subset appears in the training data subset. 
For more details about the tool, please visit the help page or get in touch with us.
DOI: 10.5281/zenodo.10374835
title: Training and test datasets for the PredictONCO tool

description: This dataset was used for training and validating the PredictONCO web tool, supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning. The dataset consists of 1073 single-point mutants of 42 proteins, whose effect was classified as Oncogenic (509 data points) and Benign (564 data points). All mutations were annotated with a clinically verified effect and were compiled from the ClinVar and OncoKB databases. The dataset was manually curated based on the available information in other precision oncology databases (The Clinical Knowledgebase by The Jackson Laboratory, Personalized Cancer Therapy Knowledge Base by MD Anderson Cancer Center, cBioPortal, DoCM database) or in the primary literature. To create the dataset, we also removed any possible overlaps with the data points used in the PredictSNP consensus predictor and its constituents. This was implemented to avoid any test set data leakage due to using the PredictSNP score as one of the features (see below).

The entire dataset (SEQ) was further annotated by the pipeline of PredictONCO. Briefly, the following six features were calculated regardless of the structural information available: essentiality of the mutated residue (yes/no), the conservation of the position (the conservation grade and score), the domain where the mutation is located (cytoplasmic, extracellular, transmembrane, other), the PredictSNP score, and the number of essential residues in the protein. For approximately half of the data (STR: 377 and 76 oncogenic and benign data points, respectively), the structural information was available, and six more features were calculated: FoldX and Rosetta ddg_monomer scores, whether the residue is in the catalytic pocket (identification of residues forming the ligand-binding pocket was obtained from P2Rank), and the pKa changes (the minimum and maximum changes as well as the number of essential residues whose pKa was changed – all values obtained from PROPKA3). For both STR and SEQ datasets, 20% of the data was held out for testing. The data split was implemented at the position level to ensure that no position from the test data subset appears in the training data subset. 

For more details about the tool, please visit the help page or get in touch with us.

14-Dec-2023 update: the file with features PredictONCO-features.txt now includes UniProt IDs, transcripts, PDB codes, and mutations.