cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25824/redu/xcr8xr
title: Supporting material referenced on the manuscript metabarcoding analysis reveals an interaction among distinct groups of bacteria associated with three different varietals of grapes used for wine production in Brazil

description: This repository contains all the additional information cited in the publication titled "Metabarcoding analysis reveals an interaction among distinct groups of bacteria associated with three different varietals of grapes used for wine production in Brazil."
DOI: 10.4121/d82ca2c2-cb60-45a2-9115-46fb4bac701b
title: Data underlying the PhD Thesis; Sustainable Energy Start-ups, Factors Influencing Fast Market Introduction and Survival of University Spin-offs in the Netherlands and Nordic Countries

description: The data pertains to 106 university spinoffs active in sustainable energy businesses from the Netherlands, Denmark, Finland, Norway, and Sweden, founded between 1999 and 2016. These firms were monitored from their establishment until the end of 2018, focusing on aspects such as product market introduction and firm survival.
DOI: 10.4121/d82ca2c2-cb60-45a2-9115-46fb4bac701b.v1
title: Data underlying the PhD Thesis; Sustainable Energy Start-ups, Factors Influencing Fast Market Introduction and Survival of University Spin-offs in the Netherlands and Nordic Countries

description: The data pertains to 106 university spinoffs active in sustainable energy businesses from the Netherlands, Denmark, Finland, Norway, and Sweden, founded between 1999 and 2016. These firms were monitored from their establishment until the end of 2018, focusing on aspects such as product market introduction and firm survival.
DOI: 10.25810/3hvn-ef66.39
title: Danish Newspaper Coverage of Climate Change or Global Warming, 2004-2023 - December 2023

description: The Media and Climate Change Observatory Data monitors 126 sources (across newspapers, radio and TV) in 58 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.5281/zenodo.10572811
title: VReqST - Fillable Meta-Model Template Files
DOI: 10.5281/zenodo.10572812
title: VReqST - Fillable Meta-Model Template Files
DOI: 10.5281/zenodo.10564804
title: Data for "Characteristics of dynamic thickness change across diverse outlet glacier geometries and basal conditions"
DOI: 10.5281/zenodo.10564805
title: Data for "Characteristics of dynamic thickness change across diverse outlet glacier geometries and basal conditions"
DOI: 10.15129/2325c010-dae5-4cca-95a1-48ff31259408
title: Data for: "Effect of cognitive tasks on forward lunge performance- A biomechanical analysis"

description: Data for the conference poster of the above named abstract

Includes raw data for participants involved in lunge study. This includes c3d and csv files exported from Vicon.
DOI: 10.25345/c5fj29q2x
title: MassIVE MSV000093929 - GNPS of Streptomyces sp ATCC samples 01.26.24 massive data file upload
title: Bactria culture was performed on MS2 for the analysis of GNPS.
DOI: 10.21227/74fk-an75
title: An RGB Image Dataset of Seed Germination Prediction and Seed Vigor 

description: Computer vision (CV) techniques help to perform non-destructive seed viability detection (SVD) for faster, more efficient and fairer results. However, the seed vigor dataset currently suffers from insufficient number of samples, data noise, and imbalance of positive and negative samples. In order to compensate for the shortcomings of the dataset, we created a maize seed germination dataset with multi-labeled classes and sufficient sample size, which helps in modeling seed germination prediction, seed viability classification, seed viability detection, and seed germination counting.
DOI: 10.5281/zenodo.10572723
title: Data from: Deep evolutionary roots of diet in seagrass bed fishes

description: Provided here are the datasets and R code for the study entitled "Deep evolutionary roots of diet in seagrass bed fishes". 

Summary of the study: Diet is a fundamental ecological trait of species that defines their roles in food webs and in regulating the functions of natural ecosystems. How diet has been shaped over deep evolution remains poorly understood, especially for marine species. Using a series of phylogenetic comparative analyses, we investigated conservatism and divergences in the diet evolution of fishes associated with seagrass beds globally. We find strong conservatism in fish trophic level, with closely related fishes being more trophically similar. However, rare, but profound diet divergences took place following seagrass recolonization of the ocean and diverfication around 70-100 mya. Furthermore, phylogeny explains >60% of the variation in diet composition, even when morphological, ecological, and geographical covariates are accounted for. These results highlight the importance of evolutionary history in shaping diet and provide a conciliation for diet divergence versus conservatism from a group of globally distributed and ecologically and economically important species.

#####Files ordered by file name######

# all_covariates.xlsxThis file contains the data of ecological, geographical, and morphological covariates.

# diet.xlsx This file contains the data of fish diet compositions extracted from FishBase.

#final.R This file contains the R code (version 1) to conduct all the analyses in our study.

#final_tree.nexThis file contains the time-calibrated phylogeny we produced using BEAST.

#o_finaltree.phyThis file contains the reduced tree of the final_tree.nex for fish species that were reported in FishBase to be associated only with seagrass beds.

#regimes.xlsxThis file contains the assumed regimes to reconstruct a MOU model.

#seq_id.xslxThis file contains the sequence ID for the genetic sequences used to construct the phylogeny.

#species_list.xlsxThis file contains the scientific names and their taxonomy.

#tree_mr.csvThis file contains the matrix representing the phylogenetic relationship produced by compute.mr() in phytools.

#trophic_level.xlsxThis file contains the trophic level data of fish species estimated using dietr.

The metadata for interpreting the data in the Excel files are included in the Metadata sheet.
DOI: 10.5281/zenodo.10006037
title: MLPF results on the simulated CLIC dataset

description: Updates over the previous version:

updated plots for the cluster-based model
added Gaudi2 timing
split big zip to smaller splits, all .zip and .zXX files must be downloaded and unzipped together
Trained models and evaluation results for the upcoming paper "Scalable neural network models and terascale datasets for particle-flow reconstruction".
The archive contains the following subfolders:

clusters_best_tuned_gnn_clic_v130

MLPF GNN model after hypertuning
the inputs are reconstructed tracks and Pandora clusters
the outputs are reconstructed PF candidates
trained on tt and qq v1.3.0 (1M events each)
hits

MLPF GNN model
inputs are reconstructed tracks and calorimeter hits
outputs are reconstructed PF candidates
trained on tt, qq and gun samples (K0L, gamma, pi+-, pi0, neutron, ele, mu) v1.2.0
training was restarted several times from previous checkpoints
hypertuning

GNN and transformer model before and after hypertuning
timing

scaling study of baseline PF with number of gun particles on CPU
scaling study of GNN model with number of input elements on GPU
gpu_scaling

the scaling study of model training on multiple accelerator cards
 
The training dataset is available at

Pata, Joosep, Wulff, Eric, Duarte, Javier, Mokhtar, Farouk, Zhang, Mengke, Girone, Maria, & Southwick, David. (2023). Simulated datasets for detector and particle flow reconstruction: CLIC detector (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8260741
DOI: 10.5281/zenodo.10567397
title: MLPF results on the simulated CLIC dataset

description: Updates over the previous version:



updated validation outputs for the cluster-based model



fixed a bug with how the PF candidates were stored

added single particle gun samples to validation



added new timing runs



for the baseline algo, included memory information

run the GNN model up to ~10k inputs



added hypertuning summary tables


Trained models and evaluation results for the upcoming paper "Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors", https://doi.org/10.48550/arXiv.2309.06782.

The archive contains the following subfolders:



clusters_best_tuned_gnn_clic_v130



MLPF GNN model configs and weight files after hypertuning

the inputs are reconstructed tracks and Pandora clusters

the outputs are reconstructed PF candidates

trained on tt and qq v1.3.0 (1M events each)



hits



MLPF GNN model configs and weight files

inputs are reconstructed tracks and calorimeter hits

outputs are reconstructed PF candidates

trained on tt, qq and gun samples (K0L, gamma, pi+-, pi0, neutron, ele, mu) v1.2.0

training was restarted several times from previous checkpoints



hypertuning



GNN and transformer model before and after hypertuning

summary tables of the hypertuning runs



timing



scaling study of baseline PF with number of gun particles on CPU

scaling study of GNN model with number of input elements on GPU



gpu_scaling



the scaling study of model training on multiple accelerator cards




 

The training dataset is available at



Pata, Joosep, Wulff, Eric, Duarte, Javier, Mokhtar, Farouk, Zhang, Mengke, Girone, Maria, & Southwick, David. (2023). Simulated datasets for detector and particle flow reconstruction: CLIC detector (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8260741
DOI: 10.5281/zenodo.8328682
title: MLPF results on the simulated CLIC dataset

description: Updates over the previous version:



updated validation outputs for the cluster-based model



fixed a bug with how the PF candidates were stored

added single particle gun samples to validation



added new timing runs



for the baseline algo, included memory information

run the GNN model up to ~10k inputs



added hypertuning summary tables


Trained models and evaluation results for the upcoming paper "Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors", https://doi.org/10.48550/arXiv.2309.06782.

The archive contains the following subfolders:



clusters_best_tuned_gnn_clic_v130



MLPF GNN model configs and weight files after hypertuning

the inputs are reconstructed tracks and Pandora clusters

the outputs are reconstructed PF candidates

trained on tt and qq v1.3.0 (1M events each)



hits



MLPF GNN model configs and weight files

inputs are reconstructed tracks and calorimeter hits

outputs are reconstructed PF candidates

trained on tt, qq and gun samples (K0L, gamma, pi+-, pi0, neutron, ele, mu) v1.2.0

training was restarted several times from previous checkpoints



hypertuning



GNN and transformer model before and after hypertuning

summary tables of the hypertuning runs



timing



scaling study of baseline PF with number of gun particles on CPU

scaling study of GNN model with number of input elements on GPU



gpu_scaling



the scaling study of model training on multiple accelerator cards




 

The training dataset is available at



Pata, Joosep, Wulff, Eric, Duarte, Javier, Mokhtar, Farouk, Zhang, Mengke, Girone, Maria, & Southwick, David. (2023). Simulated datasets for detector and particle flow reconstruction: CLIC detector (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8260741
DOI: 10.5281/zenodo.8328683
title: MLPF results on the simulated CLIC dataset

description: Trained models and evaluation results for the upcoming paper "Scalable neural network models and terascale
datasets for particle-flow reconstruction".


The archive contains the following subfolders:



	
clusters_best_tuned_gnn_clic_v130

	

		
MLPF GNN model after hypertuning
		
the inputs are reconstructed tracks and Pandora clusters
		
the outputs are reconstructed PF candidates
		
trained on tt and qq v1.3.0 (1M events each)
	
	
	
  hits
	

		
MLPF GNN model
		
inputs are reconstructed tracks and calorimeter hits
		
outputs are reconstructed PF candidates
		
trained on tt, qq and gun samples (K0L, gamma, pi+-, pi0, neutron, ele, mu) v1.2.0
		
training was restarted several times from previous checkpoints
	
	
	
hypertuning
	

		
GNN and transformer model before and after hypertuning
	
	
	
timing
	

		
scaling study of baseline PF with number of gun particles on CPU
		
scaling study of GNN model with number of input elements on GPU
	
	
	
gpu_scaling
	

		
the scaling study of model training on multiple accelerator cards
	
	



 


The training dataset is available at



	
Pata, Joosep, Wulff, Eric, Duarte, Javier, Mokhtar, Farouk, Zhang, Mengke, Girone, Maria, & Southwick, David. (2023). Simulated datasets for detector and particle flow reconstruction: CLIC detector (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8260741
DOI: 10.25810/fx6e-r462.57
title: European Newspaper Coverage of Climate Change or Global Warming, 2004-2023 - December 2023

description: The Media and Climate Change Observatory Data monitors 126 sources (across newspapers, radio and TV) in 58 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.5281/zenodo.10572394
title: Transcription of Bayer AG Film "Die Tür des Minaretts blickt nicht mehr nach Mekka" (Producer: Ferdinand Khittl KG München, 1970)

description: Manual transcription of approx. 20 min lengthy voiceovers in German from the digitized version of the documentary film "Die Tür des Minaretts blickt nicht mehr nach Mekka" from 1970 (producer: Ferdinand Khittl KG München, commissioner: Bayer AG Leverkusen) using the program f4transcript for time coding.

The documentary film shows the joint humanitarian relief operation of Bayer AG Leverkusen with the German Red Cross (DRK) in the 1970 earthquake disaster zone of Gediz in Turkey.

The original and digitized film is preserved and licensed by Bayer Corporate Archives Leverkusen, Germany.
DOI: 10.5281/zenodo.10572395
title: Transcription of Bayer AG Film "Die Tür des Minaretts blickt nicht mehr nach Mekka" (Producer: Ferdinand Khittl KG München, 1970)

description: Manual transcription of approx. 20 min lengthy voiceovers in German from the digitized version of the documentary film "Die Tür des Minaretts blickt nicht mehr nach Mekka" from 1970 (producer: Ferdinand Khittl KG München, commissioner: Bayer AG Leverkusen) using the program f4transcript for time coding.

The documentary film shows the joint humanitarian relief operation of Bayer AG Leverkusen with the German Red Cross (DRK) in the 1970 earthquake disaster zone of Gediz in Turkey.

The original and digitized film is preserved and licensed by Bayer Corporate Archives Leverkusen, Germany.
DOI: 10.5517/cc3qp2k
title: CCDC 111168: Experimental Crystal Structure Determination

description: Related Article: H. Wamhoff, W. Wambach, M. Jansen, B. Brühne|1996|CSD Communication|||
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.ccxx5hg
title: CCDC 861226: Experimental Crystal Structure Determination

description: Related Article: Anitha Sankaraperumal, Jayabalan Karthikeyan |2022|CSD Communication|||
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.ccz4bm1
title: CCDC 897903: Experimental Crystal Structure Determination

description: Related Article: Aleksandra Bocian, Violetta Patroniak, Adam Gorczynski, Maciej Kubicki|2023|CSD Communication|||
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: 10.15472/5w8sxt
title: Listado de las especies de Lutrochidae (Coleoptera: Byrrhoidea) de Colombia

description: Los Lutrochidae son un grupo pequeño y poco conocido de escarabajos acuáticos, endémico del hemisferio occidental. Este recurso presenta el listado de los taxones de la familia Lutrochidae (Coleoptera: Byrrhoidea) con ocurrencia conocida para el territorio colombiano. Se lista un género. Hasta el momento no se han reportado especies para Colombia. El listado ha sido compilado a partir de información disponible en los principales catálogos que tratan el grupo y en literatura reciente. A pesar de que se conocen especies en países de centro y suramérica, y el caribe, la fauna colombiana aún no ha sido estudiada.
DOI: 10.25810/n3xc-9577.33
title: Finnish Newspaper Coverage of Climate Change or Global Warming, 2000-2023 - December 2023

description: The Media and Climate Change Observatory Data monitors 126 sources (across newspapers, radio and TV) in 58 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.5281/zenodo.10572745
title: QuantSeq 3' mRNA-Seq testing data set based on samples from NCBI Bioproject PRJNA509074

description: As described in the contained README.md file, this is a QuantSeq 3’ mRNA-Seq testing dataset that has been derived from data published in this study:Corley, S.M., Troy, N.M., Bosco, A. et al. QuantSeq. 3′ Sequencing combined with Salmon provides a fast, reliable approach for high throughput RNA expression analysis. Sci Rep 9, 18895 (2019). https://doi.org/10.1038/s41598-019-55434-x

In short, the respective dataset has been downloaded, mapped against the human transcriptome, and reduced to the reads that map to two smaller gene sets. One of the gene sets was found as enriched for differentially expressed genes in the original publication, the other gene set wasn't. This leads to a reasonably small testing dataset, that nevertheless has useful expected results. For details of the data generation, see the contained README.md file and the self-contained workflow used for generating it:



https://doi.org/10.5281/zenodo.10572324The MSigDB gene sets are used according to their Creative Commons Attribution 4.0 International License, which is given here:https://www.gsea-msigdb.org/gsea/msigdb_license_terms.jsp
DOI: 10.5281/zenodo.10572746
title: QuantSeq 3' mRNA-Seq testing data set based on samples from NCBI Bioproject PRJNA509074

description: As described in the contained README.md file, this is a QuantSeq 3’ mRNA-Seq testing dataset that has been derived from data published in this study:Corley, S.M., Troy, N.M., Bosco, A. et al. QuantSeq. 3′ Sequencing combined with Salmon provides a fast, reliable approach for high throughput RNA expression analysis. Sci Rep 9, 18895 (2019). https://doi.org/10.1038/s41598-019-55434-x

In short, the respective dataset has been downloaded, mapped against the human transcriptome, and reduced to the reads that map to two smaller gene sets. One of the gene sets was found as enriched for differentially expressed genes in the original publication, the other gene set wasn't. This leads to a reasonably small testing dataset, that nevertheless has useful expected results. For details of the data generation, see the contained README.md file and the self-contained workflow used for generating it:



https://doi.org/10.5281/zenodo.10572324The MSigDB gene sets are used according to their Creative Commons Attribution 4.0 International License, which is given here:https://www.gsea-msigdb.org/gsea/msigdb_license_terms.jsp
DOI: 10.6084/m9.figshare.22110011
title: SRGAN_Data

description: This repository contains data associated with the manuscript titled "<b>Downscaling and reconstruction of high-resolution gridded rainfall data over India using deep learning-based generative adversarial network</b>"<br>Authors: Midhun Murukesh, Sreevathsa Golla, Pankaj Kumar
DOI: 10.6084/m9.figshare.25075493
title: 20240127_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.25075493.v1
title: 20240127_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.5281/zenodo.10572920
title: Borghorst Schwanen-Apotheke

description: Historical questionnaire/s 1924/1948 and index cards, partly selected enclosures regarding the history of aGerman pharmacy, catalogued via Kalliope portal (Historischer Fragebogen 1924/1948 und Karteikarten, ggf.gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient das Nachlassportal Kalliope):https://kalliope-verbund.info/DE-611-BF-70963[Funktion: Im Findbuch anzeigen]

Please note: The Kalliope catalogue entry might indicate related material in the archival folder which cannotbe published due to copyright or other legal restrictions (NB: Das Katalogisat bei Kalliope kann auch aufMaterialien - teils erheblichen Umfangs - verweisen, die aus archiv- oder urheberrechtlichen Gründen nichtveröffentlicht werden dürfen).
DOI: 10.21227/ybr7-1728
title: Hypergraph-based Modelling for Coexistence-Aware Channel Allocation

description: In this paper a novel technique for modelling a radio frequency (RF) environment based on hypergraph theory is investigated for solving coexistence management of heterogeneous networks and efficient channel allocation for spectrum sharing. Conventionally, traditional graph theory is used to model interference relationships and exclusive channel allocation. The demand for wireless services is increasing, hence the need for efficient spectrum management techniques, such as spectrum sharing among coexistent networks. However, coexistence-aware channel allocation would require representation of both interference and spectral coexistence relationships in the RF environment model. A graph cannot be used to represent multiple and multifaceted relationships without violating consistency with graph theory. This paper therefore proposes representing the RF environment using a hypergraph. The network coexistence method used is an implementation of the IEEE 802.19.1 method for co-sharing via network geometry classification. The simulation results show that the hypergraph-based model allocated channels, on average, up to 8% more networks than the graph-based model. The results also show that, for the same RF environment, the hypergraph model requires up to 36% fewer channels than the graph model to achieve an average of 100% operational networks. The rate of growth of the running time of the hypergraph-based algorithm with respect to the input size is quadratic, like the graph-based algorithm.