cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23674521.v1
title: Microclimate and forest density drive plant population dynamics under climate change

description: R code and demographic data related the research article "Microclimate and forest density drive plant population dynamics under climate change" Macroclimatic changes are impacting ecosystems worldwide. The majority of terrestrial species, however, lives in the shade of trees where impacts of macroclimate change are buffered. Yet, how microclimate buffering can impact future below-canopy biodiversity redistributions at the continental scale is unknown. Here we assess the effects of changes in microclimate and forest density on plant population dynamics under macroclimate change. We built 25-m resolution mechanistic demographic distribution models at European extent based on plant demography responses to changes in the environment in a unique cross-continental climate change transplant experiment. We show that changes in microclimate and light due to canopy opening amplify macroclimate change impacts on forest biodiversity, while shady forest floors due to dense tree canopies mitigate severe warming impacts. The microclimate and forest density thus emerge as powerful tools for forest managers and policy makers to shelter forest biodiversity from climate change.
DOI: 10.5281/zenodo.7683786
title: All-Sky Imager Observations from Yellow River Station on 01 November 2007

description: This dataset contains the all-sky imager observations from a Chinese arctic station, the Yellow River Station, on 01 November 2007. The name of each compressed file corresponds to the observed waveband, i.e., 6300nm, 5577nm, and 4278nm. The observation data files are in img format.
DOI: 10.5281/zenodo.8141704
title: Fuentes de agua para beber

description: Localización de fuentes de agua para beber en la ciudad de Zaragoza
DOI: 10.5281/zenodo.8141703
title: Fuentes de agua para beber

description: Localización de fuentes de agua para beber en la ciudad de Zaragoza
DOI: 10.5281/zenodo.8137699
title: jaffe_all_cdrs_bb_only

description: Dataset of antibody CDR backbones used for foldseek clustering.
DOI: 10.5281/zenodo.8137698
title: jaffe_all_cdrs_bb_only

description: Dataset of antibody CDR backbones used for foldseek clustering.
DOI: 10.5281/zenodo.8141684
title: Dataset on Article: River ecological status is shaped by agricultural land use intensity across Europe: Establishing a typology of farming-driven freshwater impacts

description: This repository contains raw data from the article "River ecological status is shaped by agricultural land use intensity across Europe: Establishing a typology of farming-driven freshwater impacts" which is currently under review. It contains data to allocate the pressures (<strong>Data_pressure_allocation.csv</strong>) and calculate the Pressure Index (<strong>Data_pressure_index.csv</strong>) for Table 1, and for the Spearman correlations for Figure 2 (<strong>Data_Spearman_correlations.csv</strong>). Also available is the Shapefile used for the different agricultural maps (Figure 1 and Figure S1-S4): <strong>Shapefile Schürings_et_al._2023</strong> (Coordinate system: ETRS 1989 UTM Zone 32N) <strong>Attribute description</strong> Id - Identifier of polygons gridcode - Code of agricultural archetypes of Levers et al., (2018) M_ZHYD: Unique identifier of corresponding FEC mars_bt12: River types eco_stat_2: Ecological status Biogeoregi: Biogeographical Regions - AN = Northern and Highland, Temp = Temperate, Mediterranean = Mediterranean Cum_pressu: Agricultural pressure index Nitrogen: Agricultural nitrogen pressure Pesticides: Agricultural pesticide pressure Hydromorph: Agricultural hydromorphological pressure Water_abst: Agricultural water abstraction
DOI: 10.5281/zenodo.8141683
title: Dataset on Article: River ecological status is shaped by agricultural land use intensity across Europe: Establishing a typology of farming-driven freshwater impacts

description: This repository contains raw data from the article "River ecological status is shaped by agricultural land use intensity across Europe: Establishing a typology of farming-driven freshwater impacts" which is currently under review. It contains data to allocate the pressures (<strong>Data_pressure_allocation.csv</strong>) and calculate the Pressure Index (<strong>Data_pressure_index.csv</strong>) for Table 1, and for the Spearman correlations for Figure 2 (<strong>Data_Spearman_correlations.csv</strong>). Also available is the Shapefile used for the different agricultural maps (Figure 1 and Figure S1-S4): <strong>Shapefile Schürings_et_al._2023</strong> (Coordinate system: ETRS 1989 UTM Zone 32N) <strong>Attribute description</strong> Id - Identifier of polygons gridcode - Code of agricultural archetypes of Levers et al., (2018) M_ZHYD: Unique identifier of corresponding FEC mars_bt12: River types eco_stat_2: Ecological status Biogeoregi: Biogeographical Regions - AN = Northern and Highland, Temp = Temperate, Mediterranean = Mediterranean Cum_pressu: Agricultural pressure index Nitrogen: Agricultural nitrogen pressure Pesticides: Agricultural pesticide pressure Hydromorph: Agricultural hydromorphological pressure Water_abst: Agricultural water abstraction
DOI: 10.5281/zenodo.8141917
title: Test dataset for MoBIE Viewer

description: 2 wells out of a 384-well plate, in ome-zarr format. 3 channels, 2 segmentations sharing the same table source.
DOI: 10.5281/zenodo.8141916
title: Test dataset for MoBIE Viewer

description: 2 wells out of a 384-well plate, in ome-zarr format. 3 channels, 2 segmentations sharing the same table source.
DOI: 10.34740/kaggle/dsv/6127697
title: AutoXGB
DOI: 10.5281/zenodo.7624080
title: Data from: Geometric morphometrics and paleoproteomics in tandem enlighten the paleodiversity of Pongo

description: This folder contains the data used for the creation of the phylogenetic trees for the manuscript 'Geometric morphometrics and paleoproteomics in tandem enlighten the paleodiversity of Pongo'. Ancient_Sequences.fa is a fasta file containing all palaeoproteomic sequences reconstructed in the context of the above study. (15 fosil pongo samples) 'Raw_Reference_Dataset' is a folder containing 9 fasta files. Each fasta contains the reference sequences of multiple samples (6 H.sapiens,27 Pongo, 30 Gorilla, 38 Pan, 2 Macaca, 1 Nomascus, 1 Microcebus, 1 Papio ) for one protein. 'Alignments' is a folder containing 9 fasta files with the name of a protein. Each fasta file contains the aligned sequences of the reference dataset and the ancient samples for that protein. Additionally, the fasta file 'CONCATENATED.fa' contains all proteins concatenated, aligned and I/L fixed.
DOI: 10.5281/zenodo.7624081
title: Geometric morphometrics and paleoproteomics in tandem enlighten the paleodiversity of Pongo - Data for Phylogenies

description: This folder contains the data used for the creation of the phylogenetic trees for the manuscript 'Geometric morphometrics and paleoproteomics in tandem enlighten the paleodiversity of Pongo'. Ancient_Sequences.fa is a fasta file containing all palaeoproteomic sequences reconstructed in the context of the above study. (15 fosil pongo samples) 'Raw_Reference_Dataset' is a folder containing 9 fasta files. Each fasta contains the reference sequences of multiple samples (6 H.sapiens,27 Pongo, 30 Gorilla, 38 Pan, 2 Macaca, 1 Nomascus, 1 Microcebus, 1 Papio ) for one protein. 'Alignments' is a folder containing 9 fasta files with the name of a protein. Each fasta file contains the aligned sequences of the reference dataset and the ancient samples for that protein. Additionally, the fasta file 'CONCATENATED.fa' contains all proteins concatenated, aligned and I/L fixed.
DOI: 10.5281/zenodo.7628016
title: Data from: Geometric morphometrics and paleoproteomics in tandem enlighten the paleodiversity of Pongo

description: This folder contains the data used for the creation of the phylogenetic trees for the manuscript 'Geometric morphometrics and paleoproteomics in tandem enlighten the paleodiversity of Pongo'. Ancient_Sequences.fa is a fasta file containing all palaeoproteomic sequences reconstructed in the context of the above study. (15 fosil pongo samples) 'Raw_Reference_Dataset' is a folder containing 9 fasta files. Each fasta contains the reference sequences of multiple samples (6 H.sapiens,27 Pongo, 30 Gorilla, 38 Pan, 2 Macaca, 1 Nomascus, 1 Microcebus, 1 Papio ) for one protein. 'Alignments' is a folder containing 9 fasta files with the name of a protein. Each fasta file contains the aligned sequences of the reference dataset and the ancient samples for that protein. Additionally, the fasta file 'CONCATENATED.fa' contains all proteins concatenated, aligned and I/L fixed.
DOI: 10.5281/zenodo.8141962
title: Data from: Geometric morphometrics and paleoproteomics in tandem enlighten the paleodiversity of Pongo

description: This folder contains the data used for the creation of the phylogenetic trees for the manuscript 'Geometric morphometrics and paleoproteomics in tandem enlighten the paleodiversity of Pongo'. Ancient_Sequences.fa is a fasta file containing all palaeoproteomic sequences reconstructed in the context of the above study. (15 fosil pongo samples) 'Raw_Reference_Dataset' is a folder containing 9 fasta files. Each fasta contains the reference sequences of multiple samples (6 H.sapiens,27 Pongo, 30 Gorilla, 38 Pan, 2 Macaca, 1 Nomascus, 1 Microcebus, 1 Papio ) for one protein. 'Alignments' is a folder containing 9 fasta files with the name of a protein. Each fasta file contains the aligned sequences of the reference dataset and the ancient samples for that protein. Additionally, the fasta file 'CONCATENATED.fa' contains all proteins concatenated, aligned and I/L fixed.
DOI: 10.5281/zenodo.8033162
title: Towards a real-world technical test battery for remote microphone systems used with hearing prostheses

description: Wav format audio files of both source and response for 5 different wireless remote microphone systems under various test conditions, as reported in the paper : Stone M.A., Lough M., Whiston H., Wilbraham K., Dillon H. (2023) Towards a real-world technical test battery for remote microphone systems used with hearing prostheses. Trends in Hearing DOI: 10.1177/23312165231182518 Also includes the MATLAB script used to analyse the recordings.
DOI: 10.5281/zenodo.8033161
title: Towards a real-world technical test battery for remote microphone systems used with hearing prostheses

description: Wav format audio files of both source and response for 5 different wireless remote microphone systems under various test conditions, as reported in the paper : Stone M.A., Lough M., Whiston H., Wilbraham K., Dillon H. (2023) Towards a real-world technical test battery for remote microphone systems used with hearing prostheses. Trends in Hearing DOI: 10.1177/23312165231182518 Also includes the MATLAB script used to analyse the recordings.
DOI: 10.6084/m9.figshare.23635446
title: Increased hydraulic risk in assemblages of woody plant species predicts spatial patterns of drought-induced mortality

description: Minimum dataset required to replicate analyses in the paper titled: Increased hydraulic risk in assemblages of woody plant species predicts spatial patterns of drought-induced mortality.
DOI: 10.18712/nsd-nsd3123-v1
title: Democratic Urban Development in the Digital Age (DEMUDIG)

description: The data was collected as part of the DEMUDIG project, which took place from July 1, 2018, to March 31, 2022. The project compared citizen participation in urban development in Oslo, Madrid, and Melbourne. It focused on participation and engagement, studying how residents and other stakeholders get involved and what authorities do to gather input from the population. It specifically looked at participation through websites, apps, and social media. The project also explored what happens to the inputs once the participation process is completed, and how policies are implemented.

Data collection took place in Oslo, Madrid, and Melbourne. Questions were asked regarding the extent to which digital platforms strengthen citizen participation and influence, and whether they contribute to greater representativeness in urban politics and improved problem-solving. The project also examined how participation and influence through digital platforms are influenced by activist cultures, the design of different channels and platforms, and the organizational and political characteristics of the municipality. The project aimed to identify best practices in terms of participation methods, the design of digital platforms, and governance practices.
DOI: 10.6084/m9.figshare.23674692
title: Traj-Learn-Data

description: In this paper, we have three model potentials. Each potential has three trajectories. In addition, we have performed 1 μs MD simulations for alanine dipeptide for three independent runs.
DOI: 10.6084/m9.figshare.23674692.v1
title: Traj-Learn-Data

description: In this paper, we have three model potentials. Each potential has three trajectories. In addition, we have performed 1 μs MD simulations for alanine dipeptide for three independent runs.
DOI: 10.5517/ccdc.csd.cc1n2yrf
title: CCDC 1522185: Experimental Crystal Structure Determination

description: Related Article: Xinxin Liu, Biao Guo, Le Zhang, Sisi Zhao, Yuwei Dong, Zhen Zhao|2022|New J.Chem.|46|16352|doi:10.1039/D2NJ03124H
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.7659210
title: Genome-wide screen reveals Rab12 GTPase as a critical activator of pathogenic LRRK2 kinase

description: Primary data associated with the figure 1 of the manuscript "<strong>Genome wide screen reveals Rab12 GTPase as a critical activator of pathogenic LRRK2 kinase" </strong>(Herschel S. Dhekne, Francesca Tonelli, Wondwossen M. Yeshaw, Claire Y. Chiang, Charles Limouse, Ebsy Jaimon, Elena Purlyte, Dario Alessi, and Suzanne Pfeffer). These include - data (annotated .tiff exports) from Metamorph acquired spinning disk confocal microscope - sequencing data as fastq files .gz files from Hiseq or Miseq next generation sequencing, - graphs made using GraphPad Prism (.pzf files). - flow cytometry .fcs files and workspace files - Figure 1- file names legend.xlsx excel sheet explaining the details
description: All description of file names in the Figure 1- file names legend.xlsx
DOI: 10.5281/zenodo.7659209
title: Genome-wide screen reveals Rab12 GTPase as a critical activator of pathogenic LRRK2 kinase

description: Primary data associated with the figure 1 of the manuscript "<strong>Genome wide screen reveals Rab12 GTPase as a critical activator of pathogenic LRRK2 kinase" </strong>(Herschel S. Dhekne, Francesca Tonelli, Wondwossen M. Yeshaw, Claire Y. Chiang, Charles Limouse, Ebsy Jaimon, Elena Purlyte, Dario Alessi, and Suzanne Pfeffer). These include - data (annotated .tiff exports) from Metamorph acquired spinning disk confocal microscope - sequencing data as fastq files .gz files from Hiseq or Miseq next generation sequencing, - graphs made using GraphPad Prism (.pzf files). - flow cytometry .fcs files and workspace files - Figure 1- file names legend.xlsx excel sheet explaining the details
description: All description of file names in the Figure 1- file names legend.xlsx
DOI: 10.5281/zenodo.8141955
title: DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space

description: We present <em>Deep learning for Collective Variables</em> (DeepCV), a computer code that provides an efficient and customizable implementation of the deep autoencoder neural network (DAENN) algorithm that has been developed in our group for computing collective variables (CVs) and can be used with enhanced sampling methods to reconstruct free energy surfaces of chemical reactions. DeepCV can be used to conveniently calculate molecular features, train models, generate CVs, validate rare events from sampling, and analyze a trajectory for chemical reactions of interest. We use DeepCV in an example study of the conformational transition of cyclohexene, where metadynamics simulations are performed using DAENN-generated CVs. The results show that the adopted CVs give free energies in line with those obtained by previously developed CVs and experimental results. DeepCV is open-source software written in Python/C++ object-oriented languages, based on the TensorFlow framework and distributed free of charge for noncommercial purposes, which can be incorporated into general molecular dynamics software. DeepCV also comes with several additional tools, i.e., an application program interface (API), documentation, and tutorials.
DOI: 10.5281/zenodo.8141954
title: DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space

description: We present <em>Deep learning for Collective Variables</em> (DeepCV), a computer code that provides an efficient and customizable implementation of the deep autoencoder neural network (DAENN) algorithm that has been developed in our group for computing collective variables (CVs) and can be used with enhanced sampling methods to reconstruct free energy surfaces of chemical reactions. DeepCV can be used to conveniently calculate molecular features, train models, generate CVs, validate rare events from sampling, and analyze a trajectory for chemical reactions of interest. We use DeepCV in an example study of the conformational transition of cyclohexene, where metadynamics simulations are performed using DAENN-generated CVs. The results show that the adopted CVs give free energies in line with those obtained by previously developed CVs and experimental results. DeepCV is open-source software written in Python/C++ object-oriented languages, based on the TensorFlow framework and distributed free of charge for noncommercial purposes, which can be incorporated into general molecular dynamics software. DeepCV also comes with several additional tools, i.e., an application program interface (API), documentation, and tutorials.
DOI: 10.5281/zenodo.8141921
title: Machine-learning based lightning nowcasting data archive

description: This data archive contains the derived data supporting the findings of article "Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset". The paper is currently in the preprint version: https://doi.org/10.21203/rs.3.rs-2616886/v1 The prediction results in this data archive are generated by various models: 1. Current model. The model involves data input of aerosol observations together with meteorological variables and auxiliary datasets, as well as data enrichment by Geostationary Lightning Mapper (GLM). In the demo of the dataset, the year of 2020 is trained and predicted on a cross-validation scheme. 2. LMA model. The model acts as the baseline model considering only data label obtained from the ground-based Lightning Mapping Array (LMA), which observes accurate lightning occurrence in limited spatial range. 3. No-AOD model. The model acts as the baseline model considering no aerosol observation is utilized during the machine learning process. The model results are demonstrated in a continuous value in 0-1. Trade-offs between Probability of Detection (POD) and False Alarm Ratio (FAR) can be optimized by selection of different thresholds. Other datasets: 1. Dataset for training. It is for the public use of machine learning training for the current model and no-AOD model (training input features vary). 2. PM2.5 dataset. The real-time spatially continuous and hourly-level PM<sub>2.5</sub> dataset is obtained following a published method by Zeng et al.. In this method, the fundamental in-situ measurements are obtained from Air Quality System (AQS) monitoring network operated by United States Environmental Protection Agency. Reference: Siwei Li, Ge Song, Jia Xing et al. Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset, 14 March 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-2616886/v1] Zeng, Z. et al. Estimating hourly surface PM2. 5 concentrations across China from high-density meteorological observations by machine learning. Atmospheric Research 254, 105516 (2021).
description: {"references": ["Siwei Li, Ge Song, Jia Xing et al. Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset, 14 March 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-2616886/v1]", "Zeng, Z.\u00a0et al.\u00a0Estimating hourly surface PM2. 5 concentrations across China from high-density meteorological observations by machine learning. Atmospheric Research\u00a0254, 105516 (2021)."]}
DOI: 10.5281/zenodo.8141920
title: Machine-learning based lightning nowcasting data archive

description: This data archive contains the derived data supporting the findings of article "Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset". The paper is currently in the preprint version: https://doi.org/10.21203/rs.3.rs-2616886/v1 The prediction results in this data archive are generated by various models: 1. Current model. The model involves data input of aerosol observations together with meteorological variables and auxiliary datasets, as well as data enrichment by Geostationary Lightning Mapper (GLM). In the demo of the dataset, the year of 2020 is trained and predicted on a cross-validation scheme. 2. LMA model. The model acts as the baseline model considering only data label obtained from the ground-based Lightning Mapping Array (LMA), which observes accurate lightning occurrence in limited spatial range. 3. No-AOD model. The model acts as the baseline model considering no aerosol observation is utilized during the machine learning process. The model results are demonstrated in a continuous value in 0-1. Trade-offs between Probability of Detection (POD) and False Alarm Ratio (FAR) can be optimized by selection of different thresholds. Other datasets: 1. Dataset for training. It is for the public use of machine learning training for the current model and no-AOD model (training input features vary). 2. PM2.5 dataset. The real-time spatially continuous and hourly-level PM<sub>2.5</sub> dataset is obtained following a published method by Zeng et al.. In this method, the fundamental in-situ measurements are obtained from Air Quality System (AQS) monitoring network operated by United States Environmental Protection Agency. Reference: Siwei Li, Ge Song, Jia Xing et al. Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset, 14 March 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-2616886/v1] Zeng, Z. et al. Estimating hourly surface PM2. 5 concentrations across China from high-density meteorological observations by machine learning. Atmospheric Research 254, 105516 (2021).
description: {"references": ["Siwei Li, Ge Song, Jia Xing et al. Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset, 14 March 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-2616886/v1]", "Zeng, Z.\u00a0et al.\u00a0Estimating hourly surface PM2. 5 concentrations across China from high-density meteorological observations by machine learning. Atmospheric Research\u00a0254, 105516 (2021)."]}
DOI: 10.5281/zenodo.8027553
title: The dataset of "Deep Learning-enabled 3D Multimodal Fusion of Cone-Beam CT and Intraoral Mesh Scans for Clinically Applicable Tooth-bone Reconstruction"

description: The dataset used in the study "Deep Learning-enabled 3D Multimodal Fusion of Cone-Beam CT and Intraoral Mesh Scans for Clinically Applicable Tooth-bone Reconstruction" is available upon request. Please contact the authors of the study or the responsible institution for access to the dataset.